From 3becf41ad7edaba51cdc047c957ed2ca62b67250 Mon Sep 17 00:00:00 2001
From: AurumnPegasus <subramanian.shivansh@gmail.com>
Date: Sun, 3 Oct 2021 15:23:19 +0530
Subject: [PATCH 01/11] Preliminary done

---
 src/mplfinance/_arg_validators.py | 11 ++++
 src/mplfinance/_utils.py          | 86 ++++++++++++++++++++++++-------
 src/mplfinance/plotting.py        | 19 +++++--
 3 files changed, 95 insertions(+), 21 deletions(-)

diff --git a/src/mplfinance/_arg_validators.py b/src/mplfinance/_arg_validators.py
index ef91eb05..4ad370e1 100644
--- a/src/mplfinance/_arg_validators.py
+++ b/src/mplfinance/_arg_validators.py
@@ -142,6 +142,17 @@ def _valid_mav(value, is_period=True):
             return True
         return False
 
+def _colors_validator(value):
+    if not isinstance(value, list):
+        return False
+
+    for v in value:
+        if v:
+            if not (isinstance(v, dict) or isinstance(v, str)):
+                return False
+
+    return True
+
 
 def _hlines_validator(value):
     if isinstance(value,dict):
diff --git a/src/mplfinance/_utils.py b/src/mplfinance/_utils.py
index e39b6d05..f640917f 100644
--- a/src/mplfinance/_utils.py
+++ b/src/mplfinance/_utils.py
@@ -20,7 +20,7 @@
 
 from six.moves import zip
 
-def _check_input(opens, closes, highs, lows):
+def _check_input(opens, closes, highs, lows, colors=None):
     """Checks that *opens*, *highs*, *lows* and *closes* have the same length.
     NOTE: this code assumes if any value open, high, low, close is
     missing (*-1*) they all are missing
@@ -46,6 +46,10 @@ def _check_input(opens, closes, highs, lows):
     if not same_length:
         raise ValueError('O,H,L,C must have the same length!')
 
+    if colors:
+        if len(opens) != len(colors):
+            raise ValueError('O,H,L,C and Colors must have the same length!')
+
     o = np.where(np.isnan(opens))[0]
     h = np.where(np.isnan(highs))[0]
     l = np.where(np.isnan(lows))[0]
@@ -85,11 +89,11 @@ def _check_and_convert_xlim_configuration(data, config):
     return xlim
 
 
-def _construct_mpf_collections(ptype,dates,xdates,opens,highs,lows,closes,volumes,config,style):
+def _construct_mpf_collections(ptype,dates,xdates,opens,highs,lows,closes,volumes,config,style,colors):
     collections = None
     if ptype == 'candle' or ptype == 'candlestick':
         collections = _construct_candlestick_collections(xdates, opens, highs, lows, closes,
-                                                         marketcolors=style['marketcolors'],config=config )
+                                                         marketcolors=style['marketcolors'],config=config, colors=colors )
 
     elif ptype =='hollow_and_filled':
             collections = _construct_hollow_candlestick_collections(xdates, opens, highs, lows, closes,
@@ -176,16 +180,45 @@ def coalesce_volume_dates(in_volumes, in_dates, indexes):
     return volumes, dates
 
 
-def _updown_colors(upcolor,downcolor,opens,closes,use_prev_close=False):
-    if upcolor == downcolor:
-        return upcolor
-    cmap = {True : upcolor, False : downcolor}
-    if not use_prev_close:
-        return [ cmap[opn < cls] for opn,cls in zip(opens,closes) ]
+def _updown_colors(upcolor,downcolor,opens,closes,use_prev_close=False,colors=None):
+    if not colors:
+        if upcolor == downcolor:
+            return upcolor
+        cmap = {True : upcolor, False : downcolor}
+        if not use_prev_close:
+            return [ cmap[opn < cls] for opn,cls in zip(opens,closes) ]
+        else:
+            first = cmap[opens[0] < closes[0]] 
+            _list = [ cmap[pre < cls] for cls,pre in zip(closes[1:], closes) ]
+            return [first] + _list
     else:
-        first = cmap[opens[0] < closes[0]] 
-        _list = [ cmap[pre < cls] for cls,pre in zip(closes[1:], closes) ]
-        return [first] + _list
+        cmap = {True: 'up', False: 'down'}
+        default = {'up': upcolor, 'down': downcolor}
+        custom = []
+        if not use_prev_close:
+            for i in range(len(opens)):
+                opn = opens[i]
+                cls = closes[i]
+                if colors[i]:
+                    custom.append(colors[i][cmap[opn < cls]])
+                else:
+                    custom.append(default[cmap[opn < cls]])
+        else:
+            if color[0]:
+                custom.append(colors[0][cmap[opens[0] < closes[0]]])
+            else:
+                custom.append(default[cmap[opens[0] < closes[0]]])
+                
+            for i in range(len(closes) - 1):
+                pre = closes[1:][i]
+                cls = closes[i]
+                if colors[i]:
+                    custom.append(colors[i][cmap[pre < cls]])
+                else:
+                    custom.append(default[cmap[pre < cls]])
+        
+        return custom
+            
 
 
 def _updownhollow_colors(upcolor,downcolor,hollowcolor,opens,closes):
@@ -525,7 +558,7 @@ def _construct_ohlc_collections(dates, opens, highs, lows, closes, marketcolors=
     return [rangeCollection, openCollection, closeCollection]
 
 
-def _construct_candlestick_collections(dates, opens, highs, lows, closes, marketcolors=None, config=None):
+def _construct_candlestick_collections(dates, opens, highs, lows, closes, marketcolors=None, config=None, colors=None):
     """Represent the open, close as a bar line and high low range as a
     vertical line.
 
@@ -552,8 +585,8 @@ def _construct_candlestick_collections(dates, opens, highs, lows, closes, market
     ret : list
         (lineCollection, barCollection)
     """
-    
-    _check_input(opens, highs, lows, closes)
+
+    _check_input(opens, highs, lows, closes, colors)
 
     if marketcolors is None:
         marketcolors = _get_mpfstyle('classic')['marketcolors']
@@ -581,17 +614,34 @@ def _construct_candlestick_collections(dates, opens, highs, lows, closes, market
 
     alpha  = marketcolors['alpha']
 
+    candle_c = None
+    wick_c = None
+    edge_c = None
+    if colors:
+        candle_c = []
+        wick_c = []
+        edge_c = []
+        for color in colors:
+            if color:
+                candle_c.append({'up': mcolors.to_rgba(color['candle']['up'], alpha), 'down': mcolors.to_rgba(color['candle']['down'], alpha)})
+                wick_c.append({'up': mcolors.to_rgba(color['wick']['up'], 1), 'down': mcolors.to_rgba(color['wick']['down'], 1)})
+                edge_c.append({'up': mcolors.to_rgba(color['edge']['up'], 1), 'down': mcolors.to_rgba(color['edge']['down'], 1)})
+            else:
+                candle_c.append(None)
+                wick_c.append(None)
+                edge_c.append(None)
+
     uc     = mcolors.to_rgba(marketcolors['candle'][ 'up' ], alpha)
     dc     = mcolors.to_rgba(marketcolors['candle']['down'], alpha)
-    colors = _updown_colors(uc, dc, opens, closes)
+    colors = _updown_colors(uc, dc, opens, closes, colors=candle_c)
 
     uc     = mcolors.to_rgba(marketcolors['edge'][ 'up' ], 1.0)
     dc     = mcolors.to_rgba(marketcolors['edge']['down'], 1.0)
-    edgecolor = _updown_colors(uc, dc, opens, closes)
+    edgecolor = _updown_colors(uc, dc, opens, closes, colors=edge_c)
     
     uc     = mcolors.to_rgba(marketcolors['wick'][ 'up' ], 1.0)
     dc     = mcolors.to_rgba(marketcolors['wick']['down'], 1.0)
-    wickcolor = _updown_colors(uc, dc, opens, closes)
+    wickcolor = _updown_colors(uc, dc, opens, closes, colors=wick_c)
 
     lw = config['_width_config']['candle_linewidth']
 
diff --git a/src/mplfinance/plotting.py b/src/mplfinance/plotting.py
index c6cccc0b..6867aa6d 100644
--- a/src/mplfinance/plotting.py
+++ b/src/mplfinance/plotting.py
@@ -40,6 +40,7 @@
 from mplfinance._arg_validators import _scale_padding_validator, _yscale_validator
 from mplfinance._arg_validators import _valid_panel_id, _check_for_external_axes
 from mplfinance._arg_validators import _xlim_validator
+from mplfinance._arg_validators import _colors_validator
 
 from mplfinance._panels import _build_panels
 from mplfinance._panels import _set_ticks_on_bottom_panel_only
@@ -49,6 +50,8 @@
 from mplfinance._helpers import _num_or_seq_of_num
 from mplfinance._helpers import _adjust_color_brightness
 
+from mplfinance._styles import make_marketcolors
+
 VALID_PMOVE_TYPES = ['renko', 'pnf']
 
 DEFAULT_FIGRATIO = (8.00,5.75)
@@ -125,6 +128,9 @@ def _valid_plot_kwargs():
  
         'marketcolors'              : { 'Default'     : None, # use 'style' for default, instead.
                                         'Validator'   : lambda value: isinstance(value,dict) },
+
+        'colors'                    : { 'Default'     : None, # use default style instead.
+                                        'Validator'   : lambda value: _colors_validator(value) },
  
         'no_xgaps'                  : { 'Default'     : True,  # None means follow default logic below:
                                         'Validator'   : lambda value: _warn_no_xgaps_deprecated(value) },
@@ -391,14 +397,21 @@ def plot( data, **kwargs ):
     rwc = config['return_width_config']
     if isinstance(rwc,dict) and len(rwc)==0:
         config['return_width_config'].update(config['_width_config'])
- 
+
+    if config['colors']:
+        colors = config['colors']
+        for c in range(len(colors)):
+            if isinstance(colors[c], str):
+                config['colors'][c] = make_marketcolors(up=colors[c], down=colors[c], edge=colors[c], wick=colors[c])
+    else:
+        config['colors'] = None
 
     collections = None
     if ptype == 'line':
         lw = config['_width_config']['line_width']
         axA1.plot(xdates, closes, color=config['linecolor'], linewidth=lw)
     else:
-        collections =_construct_mpf_collections(ptype,dates,xdates,opens,highs,lows,closes,volumes,config,style)
+        collections =_construct_mpf_collections(ptype,dates,xdates,opens,highs,lows,closes,volumes,config,style,config['colors'])
 
     if ptype in VALID_PMOVE_TYPES:
         collections, calculated_values = collections
@@ -858,7 +871,7 @@ def _addplot_collections(panid,panels,apdict,xdates,config):
     if not isinstance(apdata,pd.DataFrame):
         raise TypeError('addplot type "'+aptype+'" MUST be accompanied by addplot data of type `pd.DataFrame`')
     d,o,h,l,c,v = _check_and_prepare_data(apdata,config)
-    collections = _construct_mpf_collections(aptype,d,xdates,o,h,l,c,v,config,config['style'])
+    collections = _construct_mpf_collections(aptype,d,xdates,o,h,l,c,v,config,config['style'],config['colors'])
 
     if not external_axes_mode:
         lo = math.log(max(math.fabs(np.nanmin(l)),1e-7),10) - 0.5

From 774a7f1ec5300590d6a7a004003c0c6ad95ab115 Mon Sep 17 00:00:00 2001
From: AurumnPegasus <subramanian.shivansh@gmail.com>
Date: Sun, 3 Oct 2021 16:25:34 +0530
Subject: [PATCH 02/11] Testing done

---
 src/mplfinance/_utils.py   | 64 ++++++++++++++++++++++++++++----------
 src/mplfinance/plotting.py |  2 +-
 2 files changed, 49 insertions(+), 17 deletions(-)

diff --git a/src/mplfinance/_utils.py b/src/mplfinance/_utils.py
index f640917f..b742c9ef 100644
--- a/src/mplfinance/_utils.py
+++ b/src/mplfinance/_utils.py
@@ -97,11 +97,11 @@ def _construct_mpf_collections(ptype,dates,xdates,opens,highs,lows,closes,volume
 
     elif ptype =='hollow_and_filled':
             collections = _construct_hollow_candlestick_collections(xdates, opens, highs, lows, closes,
-                                                         marketcolors=style['marketcolors'],config=config )
+                                                         marketcolors=style['marketcolors'],config=config, colors=colors )
 
     elif ptype == 'ohlc' or ptype == 'bars' or ptype == 'ohlc_bars':
         collections = _construct_ohlc_collections(xdates, opens, highs, lows, closes,
-                                                  marketcolors=style['marketcolors'],config=config )
+                                                  marketcolors=style['marketcolors'],config=config, colors=colors )
     elif ptype == 'renko':
         collections = _construct_renko_collections(
             dates, highs, lows, volumes, config['renko_params'], closes, marketcolors=style['marketcolors'])
@@ -480,7 +480,7 @@ def _valid_lines_kwargs():
     return vkwargs
 
 
-def _construct_ohlc_collections(dates, opens, highs, lows, closes, marketcolors=None, config=None):
+def _construct_ohlc_collections(dates, opens, highs, lows, closes, marketcolors=None, config=None, colors=None):
     """Represent the time, open, high, low, close as a vertical line
     ranging from low to high.  The left tick is the open and the right
     tick is the close.
@@ -505,8 +505,8 @@ def _construct_ohlc_collections(dates, opens, highs, lows, closes, marketcolors=
     ret : list 
         a list or tuple of matplotlib collections to be added to the axes
     """
-
-    _check_input(opens, highs, lows, closes)
+    
+    _check_input(opens, highs, lows, closes, colors)
 
     if marketcolors is None:
         mktcolors = _get_mpfstyle('classic')['marketcolors']['ohlc']
@@ -530,13 +530,25 @@ def _construct_ohlc_collections(dates, opens, highs, lows, closes, marketcolors=
     # we'll translate these to the date, close location
     closeSegments = [((dt, close), (dt+ticksize, close)) for dt, close in zip(dates, closes)]
 
-    if mktcolors['up'] == mktcolors['down']:
-        colors = mktcolors['up']
-    else:
-        colorup = mcolors.to_rgba(mktcolors['up'])
-        colordown = mcolors.to_rgba(mktcolors['down'])
-        colord = {True: colorup, False: colordown}
-        colors = [colord[open < close] for open, close in zip(opens, closes)]
+    bar_c = None
+    if colors:
+        bar_c = []
+        for color in colors:
+            if color:
+                bar_up = color['ohlc']['up']
+                bar_down = color['ohlc']['down']
+                if bar_up == 'k':
+                    bar_up = mktcolors['up']
+                if bar_down == 'k':
+                    bar_down = mktcolors['down']
+
+                bar_c.append({'up': mcolors.to_rgba(bar_up, 1), 'down': mcolors.to_rgba(bar_down, 1)})
+            else:
+                bar_c.append(None)
+
+    uc = mcolors.to_rgba(mktcolors['up'])
+    dc = mcolors.to_rgba(mktcolors['down'])
+    colors = _updown_colors(uc, dc, opens, closes, colors=bar_c)
 
     lw = config['_width_config']['ohlc_linewidth']
 
@@ -623,9 +635,29 @@ def _construct_candlestick_collections(dates, opens, highs, lows, closes, market
         edge_c = []
         for color in colors:
             if color:
-                candle_c.append({'up': mcolors.to_rgba(color['candle']['up'], alpha), 'down': mcolors.to_rgba(color['candle']['down'], alpha)})
-                wick_c.append({'up': mcolors.to_rgba(color['wick']['up'], 1), 'down': mcolors.to_rgba(color['wick']['down'], 1)})
-                edge_c.append({'up': mcolors.to_rgba(color['edge']['up'], 1), 'down': mcolors.to_rgba(color['edge']['down'], 1)})
+                candle_up = color['candle']['up']
+                candle_down = color['candle']['down']
+                edge_up = color['edge']['up']
+                edge_down = color['edge']['down']
+                wick_up = color['wick']['up']
+                wick_down = color['wick']['down']
+                if candle_up == 'w':
+                    candle_up = marketcolors['candle']['up']
+                if candle_down == 'k':
+                    candle_down = marketcolors['candle']['down']
+                if edge_up == 'k':
+                    edge_up = candle_up
+                if edge_down == 'k':
+                    edge_down = candle_down
+                if wick_up == 'k':
+                    wick_up = candle_up
+                if wick_down == 'k':
+                    wick_down = candle_down
+
+                candle_c.append({'up': mcolors.to_rgba(candle_up, alpha), 'down': mcolors.to_rgba(candle_down, alpha)})
+                edge_c.append({'up': mcolors.to_rgba(edge_up, 1), 'down': mcolors.to_rgba(edge_down, 1)})
+                wick_c.append({'up': mcolors.to_rgba(wick_up, 1), 'down': mcolors.to_rgba(wick_down, 1)})
+                
             else:
                 candle_c.append(None)
                 wick_c.append(None)
@@ -659,7 +691,7 @@ def _construct_candlestick_collections(dates, opens, highs, lows, closes, market
     return [rangeCollection, barCollection]
 
 
-def _construct_hollow_candlestick_collections(dates, opens, highs, lows, closes, marketcolors=None, config=None):
+def _construct_hollow_candlestick_collections(dates, opens, highs, lows, closes, marketcolors=None, config=None, colors=None):
     """Represent today's open to close as a "bar" line (candle body)
     and high low range as a vertical line (candle wick)
      
diff --git a/src/mplfinance/plotting.py b/src/mplfinance/plotting.py
index 6867aa6d..7dfc5156 100644
--- a/src/mplfinance/plotting.py
+++ b/src/mplfinance/plotting.py
@@ -402,7 +402,7 @@ def plot( data, **kwargs ):
         colors = config['colors']
         for c in range(len(colors)):
             if isinstance(colors[c], str):
-                config['colors'][c] = make_marketcolors(up=colors[c], down=colors[c], edge=colors[c], wick=colors[c])
+                config['colors'][c] = make_marketcolors(up=colors[c], down=colors[c], edge=colors[c], wick=colors[c], ohlc=colors[c], volume=colors[c])
     else:
         config['colors'] = None
 

From de86b62ea8aeb69aece120efeedd407d7c53b80e Mon Sep 17 00:00:00 2001
From: AurumnPegasus <subramanian.shivansh@gmail.com>
Date: Sun, 3 Oct 2021 16:30:05 +0530
Subject: [PATCH 03/11] Final

---
 src/trial.py | 45 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 45 insertions(+)
 create mode 100644 src/trial.py

diff --git a/src/trial.py b/src/trial.py
new file mode 100644
index 00000000..57c5bce4
--- /dev/null
+++ b/src/trial.py
@@ -0,0 +1,45 @@
+import pandas as pd
+import random
+from datetime import date, timedelta
+from mplfinance.plotting import plot
+from mplfinance._styles import make_marketcolors
+
+dict_data = []
+start_date = date(2019, 1, 1)
+end_date = date(2020, 1, 1)
+delta = timedelta(days=1)
+start = 20
+end = 30
+while start_date <= end_date:
+    openval = random.randint(start, end)
+    closeval = random.randint(start, end)
+    high = random.randint(max(openval, closeval), end)
+    low = random.randint(start, min(openval, closeval))
+    change = random.randint(-5, 5)
+    volume = random.randint(10000, 20000)
+    dict_data.append({
+        "Open": openval,
+        "Close": closeval,
+        "High": high,
+        "Low": low,
+        "Date": start_date,
+        "Volume": volume
+    })
+    start += change
+    end += change
+    start_date += delta
+
+df = pd.DataFrame(dict_data)
+df.index = pd.to_datetime(df['Date'])
+
+# custom_colors = []
+# for i in range(len(df)):
+#     if i % 3 == 0:
+#         custom_colors.append(make_marketcolors(up='#29c9ff', down='#f3b5ff', edge='#29c9ff', wick='#29c9ff', ohlc='#32a852', volume='#a89132'))
+#     elif i%5 == 0:
+#         custom_colors.append("#000000")
+#     else:
+#         custom_colors.append(None)
+
+# plot(df, type='candle', style='yahoo', colors=custom_colors, volume=True)
+plot(df, type='candle', style='yahoo', volume=True)
\ No newline at end of file

From 71ca40365436fc7753c4f757cdfa55b89cbab8ca Mon Sep 17 00:00:00 2001
From: Shivansh Subramanian <54315149+AurumnPegasus@users.noreply.github.com>
Date: Sun, 3 Oct 2021 16:31:09 +0530
Subject: [PATCH 04/11] Delete trial.py

---
 src/trial.py | 45 ---------------------------------------------
 1 file changed, 45 deletions(-)
 delete mode 100644 src/trial.py

diff --git a/src/trial.py b/src/trial.py
deleted file mode 100644
index 57c5bce4..00000000
--- a/src/trial.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import pandas as pd
-import random
-from datetime import date, timedelta
-from mplfinance.plotting import plot
-from mplfinance._styles import make_marketcolors
-
-dict_data = []
-start_date = date(2019, 1, 1)
-end_date = date(2020, 1, 1)
-delta = timedelta(days=1)
-start = 20
-end = 30
-while start_date <= end_date:
-    openval = random.randint(start, end)
-    closeval = random.randint(start, end)
-    high = random.randint(max(openval, closeval), end)
-    low = random.randint(start, min(openval, closeval))
-    change = random.randint(-5, 5)
-    volume = random.randint(10000, 20000)
-    dict_data.append({
-        "Open": openval,
-        "Close": closeval,
-        "High": high,
-        "Low": low,
-        "Date": start_date,
-        "Volume": volume
-    })
-    start += change
-    end += change
-    start_date += delta
-
-df = pd.DataFrame(dict_data)
-df.index = pd.to_datetime(df['Date'])
-
-# custom_colors = []
-# for i in range(len(df)):
-#     if i % 3 == 0:
-#         custom_colors.append(make_marketcolors(up='#29c9ff', down='#f3b5ff', edge='#29c9ff', wick='#29c9ff', ohlc='#32a852', volume='#a89132'))
-#     elif i%5 == 0:
-#         custom_colors.append("#000000")
-#     else:
-#         custom_colors.append(None)
-
-# plot(df, type='candle', style='yahoo', colors=custom_colors, volume=True)
-plot(df, type='candle', style='yahoo', volume=True)
\ No newline at end of file

From 15e9f98928dce5b9655e4819415ee254d5233e40 Mon Sep 17 00:00:00 2001
From: Shivansh Subramanian <subramanian.shivansh@gmail.com>
Date: Sat, 4 Dec 2021 21:42:36 +0530
Subject: [PATCH 05/11] made changes according to pr

---
 src/mplfinance/_arg_validators.py |  30 ++---
 src/mplfinance/_utils.py          | 196 +++++++++++++++---------------
 src/mplfinance/plotting.py        | 162 ++++++++++++------------
 src/trial.py                      |  45 +++++++
 4 files changed, 239 insertions(+), 194 deletions(-)
 create mode 100644 src/trial.py

diff --git a/src/mplfinance/_arg_validators.py b/src/mplfinance/_arg_validators.py
index 4ad370e1..ceb8072b 100644
--- a/src/mplfinance/_arg_validators.py
+++ b/src/mplfinance/_arg_validators.py
@@ -52,7 +52,7 @@ def _check_and_prepare_data(data, config):
         columns =  ('Open', 'High', 'Low', 'Close', 'Volume')
         if all([c.lower() in data for c in columns[0:4]]):
             columns =  ('open', 'high', 'low', 'close', 'volume')
-        
+
     o, h, l, c, v = columns
     cols = [o, h, l, c]
 
@@ -100,7 +100,7 @@ def _get_valid_plot_types(plottype=None):
         return _alias_types[plottype]
     else:
         return plottype
-        
+
 
 def _mav_validator(mav_value):
     '''
@@ -143,12 +143,12 @@ def _valid_mav(value, is_period=True):
         return False
 
 def _colors_validator(value):
-    if not isinstance(value, list):
+    if not isinstance(value, (list, tuple, np.ndarray)):
         return False
 
     for v in value:
         if v:
-            if not (isinstance(v, dict) or isinstance(v, str)):
+            if v is not None and not isinstance(v, (dict, str)):
                 return False
 
     return True
@@ -204,11 +204,11 @@ def _alines_validator(value, returnStandardizedValue=False):
         A sequence of (line0, line1, line2), where:
 
         linen = (x0, y0), (x1, y1), ... (xm, ym)
-       
+
         or the equivalent numpy array with two columns. Each line can be a different length.
 
     The above is from the matplotlib LineCollection documentation.
-    It basically says that the "segments" passed into the LineCollection constructor 
+    It basically says that the "segments" passed into the LineCollection constructor
     must be a Sequence of Sequences of 2 or more xy Pairs.  However here in `mplfinance`
     we want to allow that (seq of seq of xy pairs) _as well as_ just a sequence of pairs.
     Therefore here in the validator we will allow both:
@@ -270,8 +270,8 @@ def _tlines_subvalidator(value):
 def _bypass_kwarg_validation(value):
     ''' For some kwargs, we either don't know enough, or
         the validation is too complex to make it worth while,
-        so we bypass kwarg validation.  If the kwarg is 
-        invalid, then eventually an exception will be 
+        so we bypass kwarg validation.  If the kwarg is
+        invalid, then eventually an exception will be
         raised at the time the kwarg value is actually used.
     '''
     return True
@@ -300,7 +300,7 @@ def _process_kwargs(kwargs, vkwargs):
     Given a "valid kwargs table" and some kwargs, verify that each key-word
     is valid per the kwargs table, and that the value of the kwarg is the
     correct type.  Fill a configuration dictionary with the default value
-    for each kwarg, and then substitute in any values that were provided 
+    for each kwarg, and then substitute in any values that were provided
     as kwargs and return the configuration dictionary.
     '''
     # initialize configuration from valid_kwargs_table:
@@ -327,7 +327,7 @@ def _process_kwargs(kwargs, vkwargs):
 
        # ---------------------------------------------------------------
        #  At this point in the loop, if we have not raised an exception,
-       #      then kwarg is valid as far as we can tell, therefore, 
+       #      then kwarg is valid as far as we can tell, therefore,
        #      go ahead and replace the appropriate value in config:
 
         config[key] = value
@@ -346,7 +346,7 @@ def _scale_padding_validator(value):
             if key not in valid_keys:
                 raise ValueError('Invalid key "'+str(key)+'" found in `scale_padding` dict.')
             if not isinstance(value[key],(int,float)):
-                raise ValueError('`scale_padding` dict contains non-number at key "'+str(key)+'"') 
+                raise ValueError('`scale_padding` dict contains non-number at key "'+str(key)+'"')
         return True
     else:
         raise ValueError('`scale_padding` kwarg must be a number, or dict of (left,right,top,bottom) numbers.')
@@ -372,9 +372,9 @@ def _yscale_validator(value):
 
 def _check_for_external_axes(config):
     '''
-    Check that all `fig` and `ax` kwargs are either ALL None, 
+    Check that all `fig` and `ax` kwargs are either ALL None,
     or ALL are valid instances of Figures/Axes:
- 
+
     An external Axes object can be passed in three places:
         - mpf.plot() `ax=` kwarg
         - mpf.plot() `volume=` kwarg
@@ -391,7 +391,7 @@ def _check_for_external_axes(config):
             raise TypeError('addplot must be `dict`, or `list of dict`, NOT '+str(type(addplot)))
         for apd in addplot:
             ap_axlist.append(apd['ax'])
- 
+
     if len(ap_axlist) > 0:
         if config['ax'] is None:
             if not all([ax is None for ax in ap_axlist]):
@@ -416,6 +416,6 @@ def _check_for_external_axes(config):
             raise ValueError('`volume` must be of type `matplotlib.axis.Axes`')
         #if not isinstance(config['fig'],mpl.figure.Figure):
         #    raise ValueError('`fig` kwarg must be of type `matplotlib.figure.Figure`')
-    
+
     external_axes_mode = True if isinstance(config['ax'],mpl.axes.Axes) else False
     return external_axes_mode
diff --git a/src/mplfinance/_utils.py b/src/mplfinance/_utils.py
index b742c9ef..e3109aff 100644
--- a/src/mplfinance/_utils.py
+++ b/src/mplfinance/_utils.py
@@ -85,7 +85,7 @@ def _check_and_convert_xlim_configuration(data, config):
             xlim = [ _date_to_mdate(dt) for dt in xlim]
         else:
             xlim = [ _date_to_iloc_extrapolate(data.index.to_series(),dt) for dt in xlim]
-        
+
     return xlim
 
 
@@ -111,7 +111,7 @@ def _construct_mpf_collections(ptype,dates,xdates,opens,highs,lows,closes,volume
             dates, highs, lows, volumes, config['pnf_params'], closes, marketcolors=style['marketcolors'])
     else:
         raise TypeError('Unknown ptype="',str(ptype),'"')
-     
+
     return collections
 
 
@@ -142,7 +142,7 @@ def combine_adjacent(arr):
     Returns
     -------
     output: new summed array
-    indexes: indexes indicating the first 
+    indexes: indexes indicating the first
              element summed for each group in arr
     """
     output, indexes = [], []
@@ -155,7 +155,7 @@ def combine_adjacent(arr):
         output.append(sum(arr[:index]))
         indexes.append(curr_i)
         curr_i += index
-        
+
         for _ in range(index):
             arr.pop(0)
     return output, indexes
@@ -188,7 +188,7 @@ def _updown_colors(upcolor,downcolor,opens,closes,use_prev_close=False,colors=No
         if not use_prev_close:
             return [ cmap[opn < cls] for opn,cls in zip(opens,closes) ]
         else:
-            first = cmap[opens[0] < closes[0]] 
+            first = cmap[opens[0] < closes[0]]
             _list = [ cmap[pre < cls] for cls,pre in zip(closes[1:], closes) ]
             return [first] + _list
     else:
@@ -208,7 +208,7 @@ def _updown_colors(upcolor,downcolor,opens,closes,use_prev_close=False,colors=No
                 custom.append(colors[0][cmap[opens[0] < closes[0]]])
             else:
                 custom.append(default[cmap[opens[0] < closes[0]]])
-                
+
             for i in range(len(closes) - 1):
                 pre = closes[1:][i]
                 cls = closes[i]
@@ -216,9 +216,9 @@ def _updown_colors(upcolor,downcolor,opens,closes,use_prev_close=False,colors=No
                     custom.append(colors[i][cmap[pre < cls]])
                 else:
                     custom.append(default[cmap[pre < cls]])
-        
+
         return custom
-            
+
 
 
 def _updownhollow_colors(upcolor,downcolor,hollowcolor,opens,closes):
@@ -232,7 +232,7 @@ def _updownhollow_colors(upcolor,downcolor,hollowcolor,opens,closes):
 
 
 def _date_to_iloc(dtseries,date):
-    '''Convert a `date` to a location, given a date series w/a datetime index. 
+    '''Convert a `date` to a location, given a date series w/a datetime index.
        If `date` does not exactly match a date in the series then interpolate between two dates.
        If `date` is outside the range of dates in the series, then raise an exception
       .
@@ -269,7 +269,7 @@ def _date_to_iloc_linear(dtseries,date,trace=False):
     i1 = 0.0
     i2 = len(dtseries) - 1.0
     if trace: print('i1,i2=',i1,i2)
-    
+
     slope   = (i2 - i1) / (d2 - d1)
     yitrcpt1 = i1 - (slope*d1)
     if trace: print('slope,yitrcpt=',slope,yitrcpt1)
@@ -279,7 +279,7 @@ def _date_to_iloc_linear(dtseries,date,trace=False):
         print('WARNING: yintercepts NOT equal!!!(',yitrcpt1,yitrcpt2,')')
         yitrcpt = (yitrcpt1 + yitrcpt2) / 2.0
     else:
-        yitrcpt = yitrcpt1 
+        yitrcpt = yitrcpt1
     return (slope * _date_to_mdate(date)) + yitrcpt
 
 def _date_to_iloc_5_7ths(dtseries,date,direction,trace=False):
@@ -299,14 +299,14 @@ def _date_to_iloc_5_7ths(dtseries,date,direction,trace=False):
         return loc_5_7ths
 
 def _date_to_iloc_extrapolate(dtseries,date):
-    '''Convert a `date` to a location, given a date series w/a datetime index. 
+    '''Convert a `date` to a location, given a date series w/a datetime index.
        If `date` does not exactly match a date in the series then interpolate between two dates.
        If `date` is outside the range of dates in the series, then extrapolate:
        Extrapolation results in increased error as the distance of the extrapolation increases.
        We have two methods to extrapolate:
        (1) Determine a linear equation based on the data provided in `dtseries`,
            and use that equation to calculate the location for the date.
-       (2) Multiply by 5/7 the number of days between the edge date of dtseries and the 
+       (2) Multiply by 5/7 the number of days between the edge date of dtseries and the
            date for which we are requesting a location.
            THIS ASSUMES DAILY data AND a 5 DAY TRADING WEEK.
        Empirical observation (scratch_pad/date_to_iloc_extrapolation.ipynb) shows that
@@ -359,7 +359,7 @@ def _date_to_mdate(date):
 
 def _convert_segment_dates(segments,dtindex):
     '''
-    Convert line segment dates to matplotlib dates 
+    Convert line segment dates to matplotlib dates
     Inputted segment dates may be: pandas-parseable date-time string, pandas timestamp,
                                    or a python datetime or date, or (if dtindex is not None) integer index
     A "segment" is a "sequence of lines",
@@ -374,7 +374,7 @@ def _convert_segment_dates(segments,dtindex):
         new_line = []
         for dt,value in line:
             if dtindex is not None:
-                date = _date_to_iloc(dtseries,dt)  
+                date = _date_to_iloc(dtseries,dt)
             else:
                 date = _date_to_mdate(dt)
             if date is None:
@@ -385,13 +385,13 @@ def _convert_segment_dates(segments,dtindex):
 
 def _valid_renko_kwargs():
     '''
-    Construct and return the "valid renko kwargs table" for the mplfinance.plot(type='renko') 
-    function. A valid kwargs table is a `dict` of `dict`s. The keys of the outer dict are 
-    the valid key-words for the function.  The value for each key is a dict containing 2 
+    Construct and return the "valid renko kwargs table" for the mplfinance.plot(type='renko')
+    function. A valid kwargs table is a `dict` of `dict`s. The keys of the outer dict are
+    the valid key-words for the function.  The value for each key is a dict containing 2
     specific keys: "Default", and "Validator" with the following values:
         "Default"      - The default value for the kwarg if none is specified.
         "Validator"    - A function that takes the caller specified value for the kwarg,
-                         and validates that it is the correct type, and (for kwargs with 
+                         and validates that it is the correct type, and (for kwargs with
                          a limited set of allowed values) may also validate that the
                          kwarg value is one of the allowed values.
     '''
@@ -399,7 +399,7 @@ def _valid_renko_kwargs():
         'brick_size'  : { 'Default'     : 'atr',
                           'Validator'   : lambda value: isinstance(value,(float,int)) or value == 'atr' },
         'atr_length'  : { 'Default'     : 14,
-                          'Validator'   : lambda value: isinstance(value,int) or value == 'total' },               
+                          'Validator'   : lambda value: isinstance(value,int) or value == 'total' },
     }
 
     _validate_vkwargs_dict(vkwargs)
@@ -408,13 +408,13 @@ def _valid_renko_kwargs():
 
 def _valid_pnf_kwargs():
     '''
-    Construct and return the "valid pnf kwargs table" for the mplfinance.plot(type='pnf') 
-    function. A valid kwargs table is a `dict` of `dict`s. The keys of the outer dict are 
-    the valid key-words for the function.  The value for each key is a dict containing 2 
+    Construct and return the "valid pnf kwargs table" for the mplfinance.plot(type='pnf')
+    function. A valid kwargs table is a `dict` of `dict`s. The keys of the outer dict are
+    the valid key-words for the function.  The value for each key is a dict containing 2
     specific keys: "Default", and "Validator" with the following values:
         "Default"      - The default value for the kwarg if none is specified.
         "Validator"    - A function that takes the caller specified value for the kwarg,
-                         and validates that it is the correct type, and (for kwargs with 
+                         and validates that it is the correct type, and (for kwargs with
                          a limited set of allowed values) may also validate that the
                          kwarg value is one of the allowed values.
     '''
@@ -424,7 +424,7 @@ def _valid_pnf_kwargs():
         'atr_length'  : { 'Default'     : 14,
                           'Validator'   : lambda value: isinstance(value,int) or value == 'total' },
         'reversal'    : { 'Default'     : 1,
-                          'Validator'   : lambda value: isinstance(value,int) }               
+                          'Validator'   : lambda value: isinstance(value,int) }
     }
 
     _validate_vkwargs_dict(vkwargs)
@@ -433,14 +433,14 @@ def _valid_pnf_kwargs():
 
 def _valid_lines_kwargs():
     '''
-    Construct and return the "valid lines (hlines,vlines,alines,tlines) kwargs table" 
+    Construct and return the "valid lines (hlines,vlines,alines,tlines) kwargs table"
     for the mplfinance.plot() `[h|v|a|t]lines=` kwarg functions.
-    A valid kwargs table is a `dict` of `dict`s. The keys of the outer dict are 
-    the valid key-words for the function.  The value for each key is a dict containing 2 
+    A valid kwargs table is a `dict` of `dict`s. The keys of the outer dict are
+    the valid key-words for the function.  The value for each key is a dict containing 2
     specific keys: "Default", and "Validator" with the following values:
         "Default"      - The default value for the kwarg if none is specified.
         "Validator"    - A function that takes the caller specified value for the kwarg,
-                         and validates that it is the correct type, and (for kwargs with 
+                         and validates that it is the correct type, and (for kwargs with
                          a limited set of allowed values) may also validate that the
                          kwarg value is one of the allowed values.
     '''
@@ -463,12 +463,12 @@ def _valid_lines_kwargs():
                         'Validator'   : lambda value: value is None or value in valid_linestyles },
         'linewidths': { 'Default'     : None,
                         'Validator'   : lambda value: value is None or
-                                            isinstance(value,(float,int)) or 
+                                            isinstance(value,(float,int)) or
                                             all([isinstance(v,(float,int)) for v in value]) },
         'alpha'     : { 'Default'     : 1.0,
                         'Validator'   : lambda value: isinstance(value,(float,int)) },
 
-        'tline_use' : { 'Default'     : 'close', 
+        'tline_use' : { 'Default'     : 'close',
                         'Validator'   : lambda value: isinstance(value,str) or (isinstance(value,(list,tuple)) and
                                                                       all([isinstance(v,str) for v in value]) ) },
         'tline_method': { 'Default'   : 'point-to-point',
@@ -502,10 +502,10 @@ def _construct_ohlc_collections(dates, opens, highs, lows, closes, marketcolors=
 
     Returns
     -------
-    ret : list 
+    ret : list
         a list or tuple of matplotlib collections to be added to the axes
     """
-    
+
     _check_input(opens, highs, lows, closes, colors)
 
     if marketcolors is None:
@@ -618,10 +618,10 @@ def _construct_candlestick_collections(dates, opens, highs, lows, closes, market
 
     rangeSegLow   = [((date, low), (date, min(open,close)))
                      for date, low, open, close in zip(dates, lows, opens, closes)]
-    
+
     rangeSegHigh  = [((date, high), (date, max(open,close)))
                      for date, high, open, close in zip(dates, highs, opens, closes)]
-                      
+
     rangeSegments = rangeSegLow + rangeSegHigh
 
     alpha  = marketcolors['alpha']
@@ -657,7 +657,7 @@ def _construct_candlestick_collections(dates, opens, highs, lows, closes, market
                 candle_c.append({'up': mcolors.to_rgba(candle_up, alpha), 'down': mcolors.to_rgba(candle_down, alpha)})
                 edge_c.append({'up': mcolors.to_rgba(edge_up, 1), 'down': mcolors.to_rgba(edge_down, 1)})
                 wick_c.append({'up': mcolors.to_rgba(wick_up, 1), 'down': mcolors.to_rgba(wick_down, 1)})
-                
+
             else:
                 candle_c.append(None)
                 wick_c.append(None)
@@ -670,7 +670,7 @@ def _construct_candlestick_collections(dates, opens, highs, lows, closes, market
     uc     = mcolors.to_rgba(marketcolors['edge'][ 'up' ], 1.0)
     dc     = mcolors.to_rgba(marketcolors['edge']['down'], 1.0)
     edgecolor = _updown_colors(uc, dc, opens, closes, colors=edge_c)
-    
+
     uc     = mcolors.to_rgba(marketcolors['wick'][ 'up' ], 1.0)
     dc     = mcolors.to_rgba(marketcolors['wick']['down'], 1.0)
     wickcolor = _updown_colors(uc, dc, opens, closes, colors=wick_c)
@@ -694,7 +694,7 @@ def _construct_candlestick_collections(dates, opens, highs, lows, closes, market
 def _construct_hollow_candlestick_collections(dates, opens, highs, lows, closes, marketcolors=None, config=None, colors=None):
     """Represent today's open to close as a "bar" line (candle body)
     and high low range as a vertical line (candle wick)
-     
+
     If config['type']=='hollow_and_filled' (hollow and filled candles) then candle edge and
     wick color depend on PREVIOUS close to today's close (up or down), and the center of the
     candle body (hollow or filled) depends on the today's open to close (up or down).
@@ -721,7 +721,7 @@ def _construct_hollow_candlestick_collections(dates, opens, highs, lows, closes,
     ret : list
         (lineCollection, barCollection)
     """
-    
+
     _check_input(opens, highs, lows, closes)
 
     if marketcolors is None:
@@ -742,23 +742,23 @@ def _construct_hollow_candlestick_collections(dates, opens, highs, lows, closes,
 
     rangeSegLow   = [((date, low), (date, min(open,close)))
                      for date, low, open, close in zip(dates, lows, opens, closes)]
-    
+
     rangeSegHigh  = [((date, high), (date, max(open,close)))
                      for date, high, open, close in zip(dates, highs, opens, closes)]
-                      
+
     rangeSegments = rangeSegLow + rangeSegHigh
 
     alpha  = marketcolors['alpha']
 
     uc     = mcolors.to_rgba(marketcolors['candle'][ 'up' ], alpha)
     dc     = mcolors.to_rgba(marketcolors['candle']['down'], alpha)
-   
+
     hc = mcolors.to_rgba(marketcolors['hollow']) if 'hollow' in marketcolors else (0,0,0,0)
-    
+
     colors = _updownhollow_colors(uc, dc, hc, opens, closes)  # for candle body.
 
     edgecolor = _updown_colors(uc, dc, opens, closes, use_prev_close=True)
-    
+
     wickcolor = _updown_colors(uc, dc, opens, closes, use_prev_close=True)
 
     # For hollow candles, we scale the candle linewidth up a little:
@@ -788,19 +788,19 @@ def _construct_renko_collections(dates, highs, lows, volumes, config_renko_param
     ---------------------
     In the first part of the algorithm, we populate the cdiff array
     along with adjusting the dates and volumes arrays into the new_dates and
-    new_volumes arrays. A single date includes a range from no bricks to many 
-    bricks, if a date has no bricks it shall not be included in new_dates, 
-    and if it has n bricks then it will be included n times. Volumes use a 
+    new_volumes arrays. A single date includes a range from no bricks to many
+    bricks, if a date has no bricks it shall not be included in new_dates,
+    and if it has n bricks then it will be included n times. Volumes use a
     volume cache to save volume amounts for dates that do not have any bricks
     before adding the cache to the next date that has at least one brick.
-    We populate the cdiff array with each close values difference from the 
+    We populate the cdiff array with each close values difference from the
     previously created brick divided by the brick size.
 
     In the second part of the algorithm, we iterate through the values in cdiff
-    and add 1s or -1s to the bricks array depending on whether the value is 
+    and add 1s or -1s to the bricks array depending on whether the value is
     positive or negative. Every time there is a trend change (ex. previous brick is
     an upbrick, current brick is a down brick) we draw one less brick to account
-    for the price having to move the previous bricks amount before creating a 
+    for the price having to move the previous bricks amount before creating a
     brick in the opposite direction.
 
     In the final part of the algorithm, we enumerate through the bricks array and
@@ -811,7 +811,7 @@ def _construct_renko_collections(dates, highs, lows, volumes, config_renko_param
     Useful sources:
     https://avilpage.com/2018/01/how-to-plot-renko-charts-with-python.html
     https://school.stockcharts.com/doku.php?id=chart_analysis:renko
-    
+
     Parameters
     ----------
     dates : sequence
@@ -836,10 +836,10 @@ def _construct_renko_collections(dates, highs, lows, volumes, config_renko_param
     if marketcolors is None:
         marketcolors = _get_mpfstyle('classic')['marketcolors']
         #print('default market colors:',marketcolors)
-    
+
     brick_size = renko_params['brick_size']
     atr_length = renko_params['atr_length']
-    
+
 
     if brick_size == 'atr':
         if atr_length == 'total':
@@ -860,7 +860,7 @@ def _construct_renko_collections(dates, highs, lows, volumes, config_renko_param
     dc     = mcolors.to_rgba(marketcolors['candle']['down'], alpha)
     euc    = mcolors.to_rgba(marketcolors['edge'][ 'up' ], 1.0)
     edc    = mcolors.to_rgba(marketcolors['edge']['down'], 1.0)
-    
+
     cdiff = [] # holds the differences between each close and the previously created brick / the brick size
     prev_close_brick = closes[0]
     volume_cache = 0 # holds the volumes for the dates that were skipped
@@ -887,7 +887,7 @@ def _construct_renko_collections(dates, highs, lows, volumes, config_renko_param
     last_diff_sign = 0 # direction the bricks were last going in -1 -> down, 1 -> up
     dates_volumes_index = 0 # keeps track of the index of the current date/volume
     for diff in cdiff:
-        
+
         curr_diff_sign = diff/abs(diff)
         if last_diff_sign != 0 and curr_diff_sign != last_diff_sign:
             last_diff_sign = curr_diff_sign
@@ -900,7 +900,7 @@ def _construct_renko_collections(dates, highs, lows, volumes, config_renko_param
                 new_volumes.pop(dates_volumes_index)
             continue
         last_diff_sign = curr_diff_sign
-    
+
         if diff > 0:
             bricks.extend([1]*abs(diff))
         else:
@@ -922,7 +922,7 @@ def _construct_renko_collections(dates, highs, lows, volumes, config_renko_param
 
         curr_price += (brick_size * number)
         brick_values.append(curr_price)
-        
+
         x, y = index, curr_price
 
         verts.append((
@@ -954,41 +954,41 @@ def _construct_pointnfig_collections(dates, highs, lows, volumes, config_pointnf
     ---------------------
     In the first part of the algorithm, we populate the boxes array
     along with adjusting the dates and volumes arrays into the new_dates and
-    new_volumes arrays. A single date includes a range from no boxes to many 
-    boxes, if a date has no boxes it shall not be included in new_dates, 
-    and if it has n boxes then it will be included n times. Volumes use a 
+    new_volumes arrays. A single date includes a range from no boxes to many
+    boxes, if a date has no boxes it shall not be included in new_dates,
+    and if it has n boxes then it will be included n times. Volumes use a
     volume cache to save volume amounts for dates that do not have any boxes
     before adding the cache to the next date that has at least one box.
-    We populate the boxes array with each close values difference from the 
+    We populate the boxes array with each close values difference from the
     previously created brick divided by the box size.
 
     The second part of the algorithm has a series of step. First we combine the
     adjacent like signed values in the boxes array (ex. [-1, -2, 3, -4] -> [-3, 3, -4]).
-    Next we subtract 1 from the absolute value of each element in boxes except the 
+    Next we subtract 1 from the absolute value of each element in boxes except the
     first to ensure every time there is a trend change (ex. previous box is
-    an X, current brick is a O) we draw one less box to account for the price 
-    having to move the previous box's amount before creating a box in the 
-    opposite direction. During this same step we also combine like signed elements 
-    and associated volume/date data ignoring any zero values that are created by 
-    subtracting 1 from the box value. Next we recreate the box array utilizing a 
-    rolling_change and volume_cache to store and sum the changes that don't break 
+    an X, current brick is a O) we draw one less box to account for the price
+    having to move the previous box's amount before creating a box in the
+    opposite direction. During this same step we also combine like signed elements
+    and associated volume/date data ignoring any zero values that are created by
+    subtracting 1 from the box value. Next we recreate the box array utilizing a
+    rolling_change and volume_cache to store and sum the changes that don't break
     the reversal threshold.
 
     Lastly, we enumerate through the boxes to populate the line_seg and circle_patches
-    arrays. line_seg holds the / and \ line segments that make up an X and 
+    arrays. line_seg holds the / and \ line segments that make up an X and
     circle_patches holds matplotlib.patches Ellipse objects for each O. We start
-    by filling an x and y array each iteration which contain the x and y 
+    by filling an x and y array each iteration which contain the x and y
     coordinates for each box in the column. Then for each coordinate pair in
-    x, y we add to either the line_seg array or the circle_patches array 
-    depending on the value of sign for the current column (1 indicates 
-    line_seg, -1 indicates circle_patches). The height of the boxes take 
-    into account padding which separates each box by a small margin in 
+    x, y we add to either the line_seg array or the circle_patches array
+    depending on the value of sign for the current column (1 indicates
+    line_seg, -1 indicates circle_patches). The height of the boxes take
+    into account padding which separates each box by a small margin in
     order to increase readability.
 
     Useful sources:
     https://stackoverflow.com/questions/8750648/point-and-figure-chart-with-matplotlib
     https://www.investopedia.com/articles/technical/03/081303.asp
-    
+
     Parameters
     ----------
     dates : sequence
@@ -1013,7 +1013,7 @@ def _construct_pointnfig_collections(dates, highs, lows, volumes, config_pointnf
     if marketcolors is None:
         marketcolors = _get_mpfstyle('classic')['marketcolors']
         #print('default market colors:',marketcolors)
-    
+
     box_size = pointnfig_params['box_size']
     atr_length = pointnfig_params['atr_length']
     reversal = pointnfig_params['reversal']
@@ -1033,7 +1033,7 @@ def _construct_pointnfig_collections(dates, highs, lows, volumes, config_pointnf
 
     if reversal < 1 or reversal > 9:
         raise ValueError("Specified reversal must be an integer in the range [1,9]")
-    
+
     alpha  = marketcolors['alpha']
 
     uc     = mcolors.to_rgba(marketcolors['ohlc'][ 'up' ], alpha)
@@ -1044,7 +1044,7 @@ def _construct_pointnfig_collections(dates, highs, lows, volumes, config_pointnf
     prev_close_box = closes[0] # represents the value of the last box in the previous column
     volume_cache = 0 # holds the volumes for the dates that were skipped
     temp_volumes, temp_dates = [], [] # holds the temp adjusted volumes and dates respectively
-    
+
     for i in range(len(closes)-1):
         box_diff = int((closes[i+1] - prev_close_box) / box_size)
         if box_diff == 0:
@@ -1062,7 +1062,7 @@ def _construct_pointnfig_collections(dates, highs, lows, volumes, config_pointnf
     # combine adjacent similarly signed differences
     boxes, indexes = combine_adjacent(boxes)
     new_volumes, new_dates = coalesce_volume_dates(temp_volumes, temp_dates, indexes)
-    
+
     adjusted_boxes = [boxes[0]]
     temp_volumes, temp_dates = [new_volumes[0]], [new_dates[0]]
     volume_cache = 0
@@ -1098,7 +1098,7 @@ def _construct_pointnfig_collections(dates, highs, lows, volumes, config_pointnf
     boxes = [adjusted_boxes[0]]
     new_volumes = [temp_volumes[0]]
     new_dates = [temp_dates[0]]
-    
+
     rolling_change = 0
     volume_cache = 0
     biggest_difference = 0 # only used for the last column
@@ -1106,11 +1106,11 @@ def _construct_pointnfig_collections(dates, highs, lows, volumes, config_pointnf
     #Clean data to account for reversal size (added to allow overriding the default reversal of 1)
     for i in range(1, len(adjusted_boxes)):
 
-        # Add to rolling_change and volume_cache which stores the box and volume values 
+        # Add to rolling_change and volume_cache which stores the box and volume values
         rolling_change += adjusted_boxes[i]
         volume_cache += temp_volumes[i]
 
-        # if rolling_change is the same sign as the previous box and the abs value is bigger than the 
+        # if rolling_change is the same sign as the previous box and the abs value is bigger than the
         # abs value of biggest_difference then we should replace biggest_difference with rolling_change
         if rolling_change*boxes[-1] > 0 and abs(rolling_change) > abs(biggest_difference):
             biggest_difference = rolling_change
@@ -1128,14 +1128,14 @@ def _construct_pointnfig_collections(dates, highs, lows, volumes, config_pointnf
                 boxes.append(rolling_change)
                 new_volumes.append(volume_cache)
                 new_dates.append(temp_dates[i])
-            
+
             # reset rolling_change and volume_cache once we've used them
             rolling_change = 0
             volume_cache = 0
-            
+
             # reset biggest_difference as we start from the beginning every time there is a reversal
             biggest_difference = 0
-    
+
     # Adjust the last box column if the left over rolling_change is the same sign as the column
     boxes[-1] += biggest_difference
     new_volumes[-1] += volume_cache
@@ -1150,33 +1150,33 @@ def _construct_pointnfig_collections(dates, highs, lows, volumes, config_pointnf
 
         sign = (difference / abs(difference)) # -1 or 1
         start_iteration = 0 if sign > 0 else 1
-        
+
         x = [index] * (diff)
         y = [curr_price + (i * box_size * sign) for i in range(start_iteration, diff+start_iteration)]
 
         curr_price += (box_size * sign * (diff))
         box_values.append( y )
-        
+
         for i in range(len(x)): # x and y have the same length
             height = box_size * 0.85
             width = 0.6
             if height < 0.5:
                 width = height
-            
+
             padding = (box_size * 0.075)
             if sign == 1: # X
                 line_seg.append([(x[i]-width/2, y[i] + padding), (x[i]+width/2, y[i]+height + padding)]) # create / part of the X
                 line_seg.append([(x[i]-width/2, y[i]+height+padding), (x[i]+width/2, y[i]+padding)]) # create \ part of the X
             else: # O
                 circle_patches.append(Ellipse((x[i], y[i]-(height/2) - padding), width, height))
-    
+
     useAA = 0,    # use tuple here
-    lw = 0.5        
+    lw = 0.5
 
     cirCollection = PatchCollection(circle_patches)
     cirCollection.set_facecolor([tfc] * len(circle_patches))
     cirCollection.set_edgecolor([dc] * len(circle_patches))
-    
+
     xCollection = LineCollection(line_seg,
                                  colors=[uc] * len(line_seg),
                                  linewidths=lw,
@@ -1194,7 +1194,7 @@ def _construct_aline_collections(alines, dtix=None):
     ----------
     alines : sequence
         sequences of segments, which are sequences of lines,
-        which are sequences of two or more points ( date[time], price ) or (x,y) 
+        which are sequences of two or more points ( date[time], price ) or (x,y)
 
         date[time] may be (a) pandas.to_datetime parseable string,
                           (b) pandas Timestamp, or
@@ -1284,7 +1284,7 @@ def _construct_hline_collections(hlines,minx,maxx):
 
     #print('hconfig=',hconfig)
     #print('hlines=',hlines)
-    
+
     lines = []
     if not isinstance(hlines,(list,tuple)):
         hlines = [hlines,] # may be a single price value
@@ -1347,7 +1347,7 @@ def _construct_vline_collections(vlines,dtix,miny,maxy):
 
     #print('vconfig=',vconfig)
     #print('vlines=',vlines)
-    
+
     if not isinstance(vlines,(list,tuple)):
         vlines = [vlines,]
 
@@ -1430,7 +1430,7 @@ def _tline_lsq(dfslice,tline_use):
         https://mmas.github.io/least-squares-fitting-numpy-scipy
         '''
         si = dfslice[tline_use].mean(axis=1)
-        s  = si.dropna() 
+        s  = si.dropna()
         if len(s) < 2:
             err = 'NOT enough data for Least Squares'
             if (len(si) > 2):
@@ -1467,7 +1467,7 @@ def _tline_lsq(dfslice,tline_use):
         alines.append((p1,p2))
 
     del tconfig['alines']
-    alines = dict(alines=alines,**tconfig) 
+    alines = dict(alines=alines,**tconfig)
     alines['tlines'] = None
 
     return _construct_aline_collections(alines, dtix)
@@ -1485,7 +1485,7 @@ class IntegerIndexDateTimeFormatter(Formatter):
     you would otherwise plot on that axis.  Construct this formatter
     by providing the arrange of datetimes (as matplotlib floats). When
     the formatter receives an integer in the range, it will look up the
-    datetime and format it.  
+    datetime and format it.
 
     """
     def __init__(self, dates, fmt='%b %d, %H:%M'):
@@ -1499,7 +1499,7 @@ def __call__(self, x, pos=0):
         # not sure what 'pos' is for: see
         # https://matplotlib.org/gallery/ticks_and_spines/date_index_formatter.html
         ix = int(np.round(x))
-         
+
         if ix >= self.len or ix < 0:
             date = None
             dateformat = ''
diff --git a/src/mplfinance/plotting.py b/src/mplfinance/plotting.py
index 7dfc5156..0478e70f 100644
--- a/src/mplfinance/plotting.py
+++ b/src/mplfinance/plotting.py
@@ -95,7 +95,7 @@ def _valid_plot_kwargs():
     2 specific keys: "Default", and "Validator" with the following values:
         "Default"      - The default value for the kwarg if none is specified.
         "Validator"    - A function that takes the caller specified value for the kwarg,
-                         and validates that it is the correct type, and (for kwargs with 
+                         and validates that it is the correct type, and (for kwargs with
                          a limited set of allowed values) may also validate that the
                          kwarg value is one of the allowed values.
     '''
@@ -107,46 +107,46 @@ def _valid_plot_kwargs():
                                                                    and all(isinstance(c, str) for c in value) },
         'type'                      : { 'Default'     : 'ohlc',
                                         'Validator'   : lambda value: value in _get_valid_plot_types() },
- 
+
         'style'                     : { 'Default'     : None,
                                         'Validator'   : _styles._valid_mpf_style },
- 
+
         'volume'                    : { 'Default'     : False,
                                         'Validator'   : lambda value: isinstance(value,bool) or isinstance(value,mpl_axes.Axes) },
- 
+
         'mav'                       : { 'Default'     : None,
                                         'Validator'   : _mav_validator },
-        
+
         'renko_params'              : { 'Default'     : dict(),
                                         'Validator'   : lambda value: isinstance(value,dict) },
 
         'pnf_params'                : { 'Default'     : dict(),
                                         'Validator'   : lambda value: isinstance(value,dict) },
- 
+
         'study'                     : { 'Default'     : None,
-                                        'Validator'   : lambda value: _kwarg_not_implemented(value) }, 
- 
+                                        'Validator'   : lambda value: _kwarg_not_implemented(value) },
+
         'marketcolors'              : { 'Default'     : None, # use 'style' for default, instead.
                                         'Validator'   : lambda value: isinstance(value,dict) },
 
-        'colors'                    : { 'Default'     : None, # use default style instead.
+        'override_marketcolors'     : { 'Default'     : None, # use default style instead.
                                         'Validator'   : lambda value: _colors_validator(value) },
- 
+
         'no_xgaps'                  : { 'Default'     : True,  # None means follow default logic below:
                                         'Validator'   : lambda value: _warn_no_xgaps_deprecated(value) },
- 
-        'show_nontrading'           : { 'Default'     : False, 
+
+        'show_nontrading'           : { 'Default'     : False,
                                         'Validator'   : lambda value: isinstance(value,bool) },
- 
+
         'figscale'                  : { 'Default'     : None, # scale base figure size up or down.
                                         'Validator'   : lambda value: isinstance(value,float) or isinstance(value,int) },
- 
+
         'figratio'                  : { 'Default'     : None, # aspect ratio; scaled to 8.0 height
                                         'Validator'   : lambda value: isinstance(value,(tuple,list))
                                                                       and len(value) == 2
                                                                       and isinstance(value[0],(float,int))
                                                                       and isinstance(value[1],(float,int)) },
- 
+
         'figsize'                   : { 'Default'     : None,  # figure size; overrides figratio and figscale
                                         'Validator'   : lambda value: isinstance(value,(tuple,list))
                                                                       and len(value) == 2
@@ -155,32 +155,32 @@ def _valid_plot_kwargs():
 
         'fontscale'                 : { 'Default'     : None, # scale all fonts up or down
                                         'Validator'   : lambda value: isinstance(value,float) or isinstance(value,int) },
- 
+
         'linecolor'                 : { 'Default'     : None, # line color in line plot
                                         'Validator'   : lambda value: mcolors.is_color_like(value) },
 
         'title'                     : { 'Default'     : None, # Figure Title
                                         'Validator'   : lambda value: isinstance(value,(str,dict)) },
- 
+
         'axtitle'                   : { 'Default'     : None, # Axes Title (subplot title)
                                         'Validator'   : lambda value: isinstance(value,(str,dict)) },
- 
+
         'ylabel'                    : { 'Default'     : 'Price', # y-axis label
                                         'Validator'   : lambda value: isinstance(value,str) },
- 
+
         'ylabel_lower'              : { 'Default'     : None, # y-axis label default logic below
                                         'Validator'   : lambda value: isinstance(value,str) },
- 
-        'addplot'                   : { 'Default'     : None, 
+
+        'addplot'                   : { 'Default'     : None,
                                         'Validator'   : lambda value: isinstance(value,dict) or (isinstance(value,list) and all([isinstance(d,dict) for d in value])) },
- 
-        'savefig'                   : { 'Default'     : None, 
+
+        'savefig'                   : { 'Default'     : None,
                                         'Validator'   : lambda value: isinstance(value,dict) or isinstance(value,str) or isinstance(value, io.BytesIO) or isinstance(value, os.PathLike) },
- 
-        'block'                     : { 'Default'     : None, 
+
+        'block'                     : { 'Default'     : None,
                                         'Validator'   : lambda value: isinstance(value,bool) },
- 
-        'returnfig'                 : { 'Default'     : False, 
+
+        'returnfig'                 : { 'Default'     : False,
                                         'Validator'   : lambda value: isinstance(value,bool) },
 
         'return_calculated_values'  : {'Default'      : None,
@@ -188,29 +188,29 @@ def _valid_plot_kwargs():
 
         'set_ylim'                  : {'Default'      : None,
                                        'Validator'    : lambda value: _warn_set_ylim_deprecated(value) },
- 
+
         'ylim'                      : {'Default'      : None,
-                                       'Validator'    : lambda value: isinstance(value, (list,tuple)) and len(value) == 2 
+                                       'Validator'    : lambda value: isinstance(value, (list,tuple)) and len(value) == 2
                                                                       and all([isinstance(v,(int,float)) for v in value])},
- 
+
         'xlim'                      : {'Default'      : None,
                                        'Validator'    : lambda value: _xlim_validator(value) },
- 
+
         'set_ylim_panelB'           : {'Default'      : None,
                                        'Validator'    : lambda value: _warn_set_ylim_deprecated(value) },
- 
-        'hlines'                    : { 'Default'     : None, 
+
+        'hlines'                    : { 'Default'     : None,
                                         'Validator'   : lambda value: _hlines_validator(value) },
- 
-        'vlines'                    : { 'Default'     : None, 
+
+        'vlines'                    : { 'Default'     : None,
                                         'Validator'   : lambda value: _vlines_validator(value) },
 
-        'alines'                    : { 'Default'     : None, 
+        'alines'                    : { 'Default'     : None,
                                         'Validator'   : lambda value: _alines_validator(value) },
- 
-        'tlines'                    : { 'Default'     : None, 
+
+        'tlines'                    : { 'Default'     : None,
                                         'Validator'   : lambda value: _tlines_validator(value) },
-       
+
         'panel_ratios'              : { 'Default'     : None,
                                         'Validator'   : lambda value: isinstance(value,(tuple,list)) and len(value) <= 10 and
                                                                       all([isinstance(v,(int,float)) for v in value]) },
@@ -237,7 +237,7 @@ def _valid_plot_kwargs():
                                         'Validator'   : lambda value: isinstance(value,bool) },
 
         'fill_between'              : { 'Default'     : None,
-                                        'Validator'   : lambda value: _num_or_seq_of_num(value) or 
+                                        'Validator'   : lambda value: _num_or_seq_of_num(value) or
                                                                      (isinstance(value,dict) and 'y1' in value and
                                                                        _num_or_seq_of_num(value['y1'])) },
 
@@ -258,8 +258,8 @@ def _valid_plot_kwargs():
 
         'saxbelow'                  : { 'Default'     : True,  # Issue#115 Comment#639446764
                                         'Validator'   : lambda value: isinstance(value,bool) },
-        
-        'scale_padding'             : { 'Default'     : 1.0,   # Issue#193 
+
+        'scale_padding'             : { 'Default'     : 1.0,   # Issue#193
                                         'Validator'   : lambda value: _scale_padding_validator(value) },
 
         'ax'                        : { 'Default'     : None,
@@ -300,7 +300,7 @@ def plot( data, **kwargs ):
 
     # translate alias types:
     config['type'] = _get_valid_plot_types(config['type'])
-    
+
     dates,opens,highs,lows,closes,volumes = _check_and_prepare_data(data, config)
 
     config['xlim'] = _check_and_convert_xlim_configuration(data, config)
@@ -375,7 +375,7 @@ def plot( data, **kwargs ):
 
     fmtstring = _determine_format_string(dates, config['datetime_format'])
 
-    ptype = config['type'] 
+    ptype = config['type']
 
     if config['show_nontrading']:
         formatter = mdates.DateFormatter(fmtstring)
@@ -398,20 +398,20 @@ def plot( data, **kwargs ):
     if isinstance(rwc,dict) and len(rwc)==0:
         config['return_width_config'].update(config['_width_config'])
 
-    if config['colors']:
-        colors = config['colors']
-        for c in range(len(colors)):
-            if isinstance(colors[c], str):
-                config['colors'][c] = make_marketcolors(up=colors[c], down=colors[c], edge=colors[c], wick=colors[c], ohlc=colors[c], volume=colors[c])
+    if config['override_marketcolors']:
+        override_marketcolors = config['override_marketcolors']
+        for c in range(len(override_marketcolors)):
+            if isinstance(override_marketcolors[c], str):
+                config['override_marketcolors'][c] = make_marketcolors(up=override_marketcolors[c], down=override_marketcolors[c], edge=override_marketcolors[c], wick=override_marketcolors[c], ohlc=override_marketcolors[c], volume=override_marketcolors[c])
     else:
-        config['colors'] = None
+        config['override_marketcolors'] = None
 
     collections = None
     if ptype == 'line':
         lw = config['_width_config']['line_width']
         axA1.plot(xdates, closes, color=config['linecolor'], linewidth=lw)
     else:
-        collections =_construct_mpf_collections(ptype,dates,xdates,opens,highs,lows,closes,volumes,config,style,config['colors'])
+        collections =_construct_mpf_collections(ptype,dates,xdates,opens,highs,lows,closes,volumes,config,style,config['override_marketcolors'])
 
     if ptype in VALID_PMOVE_TYPES:
         collections, calculated_values = collections
@@ -498,7 +498,7 @@ def plot( data, **kwargs ):
             if len(mav) != len(mavprices):
                 warnings.warn('len(mav)='+str(len(mav))+' BUT len(mavprices)='+str(len(mavprices)))
             else:
-                for jj in range(0,len(mav)):     
+                for jj in range(0,len(mav)):
                     retdict['mav' + str(mav[jj])] = mavprices[jj]
         retdict['minx'] = minx
         retdict['maxx'] = maxx
@@ -525,7 +525,7 @@ def plot( data, **kwargs ):
         tlines = [tlines,]
     for tline_item in tlines:
         line_collections.append(_construct_tline_collections(tline_item, dtix, dates, opens, highs, lows, closes))
-     
+
     for collection in line_collections:
         if collection is not None:
             axA1.add_collection(collection)
@@ -561,7 +561,7 @@ def plot( data, **kwargs ):
         axA1.set_yscale(yscale,**ysd)
     elif isinstance(ysd,str):
         axA1.set_yscale(ysd)
- 
+
 
     addplot = config['addplot']
     if addplot is not None and ptype not in VALID_PMOVE_TYPES:
@@ -595,7 +595,7 @@ def plot( data, **kwargs ):
 
         for apdict in addplot:
 
-            panid = apdict['panel'] 
+            panid = apdict['panel']
             if not external_axes_mode:
                 if   panid == 'main' : panid = 0  # for backwards compatibility
                 elif panid == 'lower': panid = 1  # for backwards compatibility
@@ -606,11 +606,11 @@ def plot( data, **kwargs ):
             if aptype == 'ohlc' or aptype == 'candle':
                 ax = _addplot_collections(panid,panels,apdict,xdates,config)
                 _addplot_apply_supplements(ax,apdict)
-            else:         
+            else:
                 apdata = apdict['data']
                 if isinstance(apdata,list) and not isinstance(apdata[0],(float,int)):
                     raise TypeError('apdata is list but NOT of float or int')
-                if isinstance(apdata,pd.DataFrame): 
+                if isinstance(apdata,pd.DataFrame):
                     havedf = True
                 else:
                     havedf = False      # must be a single series or array
@@ -636,7 +636,7 @@ def plot( data, **kwargs ):
         fb['x'] = xdates
         ax = panels.at[panid,'axes'][0]
         ax.fill_between(**fb)
-            
+
     # put the primary axis on one side,
     # and the twinx() on the "other" side:
     if not external_axes_mode:
@@ -653,14 +653,14 @@ def plot( data, **kwargs ):
     # TODO:  ===========
     # TODO:  It appears to me that there may be some or significant overlap
     # TODO:  between what the following functions actually do:
-    # TODO:  At the very least, all four of them appear to communicate 
+    # TODO:  At the very least, all four of them appear to communicate
     # TODO:  to matplotlib that the xaxis should be treated as dates:
     # TODO:   ->  'ax.autoscale_view()'
     # TODO:   ->  'ax.xaxis_dates()'
     # TODO:   ->  'plt.autofmt_xdates()'
     # TODO:   ->  'fig.autofmt_xdate()'
     # TODO: ================================================================
-    
+
 
     #if config['autofmt_xdate']:
         #print('CALLING fig.autofmt_xdate()')
@@ -671,7 +671,7 @@ def plot( data, **kwargs ):
                            # for `addplot`, that this IS necessary when the only thing done to the
                            # the axes is .add_collection().  (However, if ax.plot() .scatter() or
                            # .bar() was called, then possibly this is not necessary; not entirely
-                           # sure, but it definitely was necessary to get 'ohlc' and 'candle' 
+                           # sure, but it definitely was necessary to get 'ohlc' and 'candle'
                            # working in `addplot`).
 
     axA1.set_ylabel(config['ylabel'])
@@ -727,7 +727,7 @@ def plot( data, **kwargs ):
                 offset = '\n'+offset
             vol_label = config['ylabel_lower'] + offset
         volumeAxes.set_ylabel(vol_label)
-    
+
     if config['title'] is not None:
         if config['tight_layout']:
             # IMPORTANT: `y=0.89` is based on the top of the top panel
@@ -747,8 +747,8 @@ def plot( data, **kwargs ):
         else:
             title = config['title']      # config['title'] is a string
         fig.suptitle(title,**title_kwargs)
-    
-    
+
+
     if config['axtitle'] is not None:
         axA1.set_title(config['axtitle'])
 
@@ -784,10 +784,10 @@ def plot( data, **kwargs ):
         if config['closefig']: # True or 'auto'
             plt.close(fig)
     elif not config['returnfig']:
-        plt.show(block=config['block']) # https://stackoverflow.com/a/13361748/1639359 
+        plt.show(block=config['block']) # https://stackoverflow.com/a/13361748/1639359
         if config['closefig'] == True or (config['block'] and config['closefig']):
             plt.close(fig)
-    
+
     if config['returnfig']:
         if config['closefig'] == True: plt.close(fig)
         return (fig, axlist)
@@ -852,7 +852,7 @@ def _addplot_collections(panid,panels,apdict,xdates,config):
     apdata = apdict['data']
     aptype = apdict['type']
     external_axes_mode = apdict['ax'] is not None
-   
+
     #--------------------------------------------------------------#
     # Note: _auto_secondary_y() sets the 'magnitude' column in the
     #       `panels` dataframe, which is needed for automatically
@@ -871,18 +871,18 @@ def _addplot_collections(panid,panels,apdict,xdates,config):
     if not isinstance(apdata,pd.DataFrame):
         raise TypeError('addplot type "'+aptype+'" MUST be accompanied by addplot data of type `pd.DataFrame`')
     d,o,h,l,c,v = _check_and_prepare_data(apdata,config)
-    collections = _construct_mpf_collections(aptype,d,xdates,o,h,l,c,v,config,config['style'],config['colors'])
+    collections = _construct_mpf_collections(aptype,d,xdates,o,h,l,c,v,config,config['style'],config['override_marketcolors'])
 
     if not external_axes_mode:
         lo = math.log(max(math.fabs(np.nanmin(l)),1e-7),10) - 0.5
         hi = math.log(max(math.fabs(np.nanmax(h)),1e-7),10) + 0.5
         secondary_y = _auto_secondary_y( panels, panid, lo, hi )
         if 'auto' != apdict['secondary_y']:
-            secondary_y = apdict['secondary_y'] 
+            secondary_y = apdict['secondary_y']
         if secondary_y:
-            ax = panels.at[panid,'axes'][1] 
+            ax = panels.at[panid,'axes'][1]
             panels.at[panid,'used2nd'] = True
-        else: 
+        else:
             ax = panels.at[panid,'axes'][0]
     else:
         ax = apdict['ax']
@@ -908,9 +908,9 @@ def _addplot_columns(panid,panels,ydata,apdict,xdates,config):
             #print("apdict['secondary_y'] says secondary_y is",secondary_y)
 
         if secondary_y:
-            ax = panels.at[panid,'axes'][1] 
+            ax = panels.at[panid,'axes'][1]
             panels.at[panid,'used2nd'] = True
-        else: 
+        else:
             ax = panels.at[panid,'axes'][0]
     else:
         ax = apdict['ax']
@@ -999,7 +999,7 @@ def _plot_mav(ax,config,xdates,prices,apmav=None,apwidth=None):
             mavgs = mavgs,      # convert to tuple
         if len(mavgs) > 7:
             mavgs = mavgs[0:7]  # take at most 7
-     
+
         if style['mavcolors'] is not None:
             mavc = cycle(style['mavcolors'])
         else:
@@ -1049,8 +1049,8 @@ def _valid_addplot_kwargs():
 
         'mav'         : { 'Default'     : None,
                           'Validator'   : _mav_validator },
-        
-        'panel'       : { 'Default'     : 0, 
+
+        'panel'       : { 'Default'     : 0,
                           'Validator'   : lambda value: _valid_panel_id(value) },
 
         'marker'      : { 'Default'     : 'o',
@@ -1066,7 +1066,7 @@ def _valid_addplot_kwargs():
         'linestyle'   : { 'Default'     : None,
                           'Validator'   : lambda value: value in valid_linestyles },
 
-        'width'       : { 'Default'     : None, # width of `bar` or `line` 
+        'width'       : { 'Default'     : None, # width of `bar` or `line`
                           'Validator'   : lambda value: isinstance(value,(int,float)) or
                                                         all([isinstance(v,(int,float)) for v in value]) },
 
@@ -1082,12 +1082,12 @@ def _valid_addplot_kwargs():
 
         'y_on_right'  : { 'Default'     : None,
                           'Validator'   : lambda value: isinstance(value,bool) },
-        
+
         'ylabel'      : { 'Default'     : None,
                           'Validator'   : lambda value: isinstance(value,str) },
 
         'ylim'        : {'Default'      : None,
-                         'Validator'    : lambda value: isinstance(value, (list,tuple)) and len(value) == 2 
+                         'Validator'    : lambda value: isinstance(value, (list,tuple)) and len(value) == 2
                                                                       and all([isinstance(v,(int,float)) for v in value])},
 
         'title'       : { 'Default'     : None,
@@ -1100,7 +1100,7 @@ def _valid_addplot_kwargs():
                           'Validator'   : lambda value: _yscale_validator(value) },
 
         'stepwhere'      : { 'Default'     : 'pre',
-                          'Validator'   : lambda value : value in valid_stepwheres },                  
+                          'Validator'   : lambda value : value in valid_stepwheres },
     }
 
     _validate_vkwargs_dict(vkwargs)
@@ -1112,7 +1112,7 @@ def make_addplot(data, **kwargs):
     '''
     Take data (pd.Series, pd.DataFrame, np.ndarray of floats, list of floats), and
     kwargs (see valid_addplot_kwargs_table) and construct a correctly structured dict
-    to be passed into plot() using kwarg `addplot`.  
+    to be passed into plot() using kwarg `addplot`.
     NOTE WELL: len(data) here must match the len(data) passed into plot()
     '''
     if not isinstance(data, (pd.Series, pd.DataFrame, np.ndarray, list)):
diff --git a/src/trial.py b/src/trial.py
new file mode 100644
index 00000000..d75885ec
--- /dev/null
+++ b/src/trial.py
@@ -0,0 +1,45 @@
+import pandas as pd
+import random
+from datetime import date, timedelta
+from mplfinance.plotting import plot
+from mplfinance._styles import make_marketcolors
+
+dict_data = []
+start_date = date(2019, 1, 1)
+end_date = date(2020, 1, 1)
+delta = timedelta(days=1)
+start = 20
+end = 30
+while start_date <= end_date:
+    openval = random.randint(start, end)
+    closeval = random.randint(start, end)
+    high = random.randint(max(openval, closeval), end)
+    low = random.randint(start, min(openval, closeval))
+    change = random.randint(-5, 5)
+    volume = random.randint(10000, 20000)
+    dict_data.append({
+        "Open": openval,
+        "Close": closeval,
+        "High": high,
+        "Low": low,
+        "Date": start_date,
+        "Volume": volume
+    })
+    start += change
+    end += change
+    start_date += delta
+
+df = pd.DataFrame(dict_data)
+df.index = pd.to_datetime(df['Date'])
+
+custom_colors = []
+for i in range(len(df)):
+    if i % 3 == 0:
+        custom_colors.append(make_marketcolors(up='#29c9ff', down='#f3b5ff', edge='#29c9ff', wick='#29c9ff', ohlc='#32a852', volume='#a89132'))
+    elif i%5 == 0:
+        custom_colors.append("#000000")
+    else:
+        custom_colors.append(None)
+
+plot(df, type='candle', style='yahoo', override_marketcolors=custom_colors, volume=True)
+# plot(df, type='candle', style='yahoo', volume=True)

From d653acdb6c95eb53555e358f68a20b2acf531108 Mon Sep 17 00:00:00 2001
From: Shivansh Subramanian <subramanian.shivansh@gmail.com>
Date: Sat, 4 Dec 2021 21:44:04 +0530
Subject: [PATCH 06/11] example

---
 src/Figure_1.png | Bin 0 -> 59649 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 src/Figure_1.png

diff --git a/src/Figure_1.png b/src/Figure_1.png
new file mode 100644
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HcmV?d00001


From f06db4b080c6cd2e100a8f14e2cc32570b3b2adb Mon Sep 17 00:00:00 2001
From: Daniel Goldfarb <dgoldfarb.github@gmail.com>
Date: Thu, 9 Dec 2021 12:59:22 -0500
Subject: [PATCH 07/11] simplify marketcolor overrides

---
 src/mplfinance/_arg_validators.py |  56 +++--
 src/mplfinance/_utils.py          | 357 ++++++++++++------------------
 src/mplfinance/plotting.py        | 172 +++++++-------
 3 files changed, 258 insertions(+), 327 deletions(-)

diff --git a/src/mplfinance/_arg_validators.py b/src/mplfinance/_arg_validators.py
index ceb8072b..945f117f 100644
--- a/src/mplfinance/_arg_validators.py
+++ b/src/mplfinance/_arg_validators.py
@@ -1,4 +1,5 @@
 import matplotlib.dates  as mdates
+import matplotlib.colors as mcolors
 import pandas   as pd
 import numpy    as np
 import datetime
@@ -52,7 +53,7 @@ def _check_and_prepare_data(data, config):
         columns =  ('Open', 'High', 'Low', 'Close', 'Volume')
         if all([c.lower() in data for c in columns[0:4]]):
             columns =  ('open', 'high', 'low', 'close', 'volume')
-
+        
     o, h, l, c, v = columns
     cols = [o, h, l, c]
 
@@ -100,7 +101,7 @@ def _get_valid_plot_types(plottype=None):
         return _alias_types[plottype]
     else:
         return plottype
-
+        
 
 def _mav_validator(mav_value):
     '''
@@ -142,17 +143,6 @@ def _valid_mav(value, is_period=True):
             return True
         return False
 
-def _colors_validator(value):
-    if not isinstance(value, (list, tuple, np.ndarray)):
-        return False
-
-    for v in value:
-        if v:
-            if v is not None and not isinstance(v, (dict, str)):
-                return False
-
-    return True
-
 
 def _hlines_validator(value):
     if isinstance(value,dict):
@@ -204,11 +194,11 @@ def _alines_validator(value, returnStandardizedValue=False):
         A sequence of (line0, line1, line2), where:
 
         linen = (x0, y0), (x1, y1), ... (xm, ym)
-
+       
         or the equivalent numpy array with two columns. Each line can be a different length.
 
     The above is from the matplotlib LineCollection documentation.
-    It basically says that the "segments" passed into the LineCollection constructor
+    It basically says that the "segments" passed into the LineCollection constructor 
     must be a Sequence of Sequences of 2 or more xy Pairs.  However here in `mplfinance`
     we want to allow that (seq of seq of xy pairs) _as well as_ just a sequence of pairs.
     Therefore here in the validator we will allow both:
@@ -270,8 +260,8 @@ def _tlines_subvalidator(value):
 def _bypass_kwarg_validation(value):
     ''' For some kwargs, we either don't know enough, or
         the validation is too complex to make it worth while,
-        so we bypass kwarg validation.  If the kwarg is
-        invalid, then eventually an exception will be
+        so we bypass kwarg validation.  If the kwarg is 
+        invalid, then eventually an exception will be 
         raised at the time the kwarg value is actually used.
     '''
     return True
@@ -300,7 +290,7 @@ def _process_kwargs(kwargs, vkwargs):
     Given a "valid kwargs table" and some kwargs, verify that each key-word
     is valid per the kwargs table, and that the value of the kwarg is the
     correct type.  Fill a configuration dictionary with the default value
-    for each kwarg, and then substitute in any values that were provided
+    for each kwarg, and then substitute in any values that were provided 
     as kwargs and return the configuration dictionary.
     '''
     # initialize configuration from valid_kwargs_table:
@@ -327,7 +317,7 @@ def _process_kwargs(kwargs, vkwargs):
 
        # ---------------------------------------------------------------
        #  At this point in the loop, if we have not raised an exception,
-       #      then kwarg is valid as far as we can tell, therefore,
+       #      then kwarg is valid as far as we can tell, therefore, 
        #      go ahead and replace the appropriate value in config:
 
         config[key] = value
@@ -346,7 +336,7 @@ def _scale_padding_validator(value):
             if key not in valid_keys:
                 raise ValueError('Invalid key "'+str(key)+'" found in `scale_padding` dict.')
             if not isinstance(value[key],(int,float)):
-                raise ValueError('`scale_padding` dict contains non-number at key "'+str(key)+'"')
+                raise ValueError('`scale_padding` dict contains non-number at key "'+str(key)+'"') 
         return True
     else:
         raise ValueError('`scale_padding` kwarg must be a number, or dict of (left,right,top,bottom) numbers.')
@@ -370,11 +360,29 @@ def _yscale_validator(value):
     return True
 
 
+def _is_marketcolor_object(obj):
+    if not isinstance(obj,dict): return False
+    market_colors_keys = ('candle','edge','wick','ohlc')
+    return all([k in obj for k in market_colors_keys])
+    
+
+def _mco_validator(value):  # marketcolor overrides validator
+    if isinstance(value,dict):     # not yet supported, but maybe we will have other
+        if 'colors' not in value:  # kwargs related to mktcolor overrides (ex: `mco_faceonly`)
+            raise ValueError('`marketcolor_overrides` as dict must contain `colors` key.')
+        colors = value['colors']
+    else:
+        colors = value
+    if not isinstance(colors,(list,tuple,np.ndarray)):
+        return False
+    return all([(c is None or mcolors.is_color_like(c) or _is_marketcolor_object(c)) for c in colors])
+
+
 def _check_for_external_axes(config):
     '''
-    Check that all `fig` and `ax` kwargs are either ALL None,
+    Check that all `fig` and `ax` kwargs are either ALL None, 
     or ALL are valid instances of Figures/Axes:
-
+ 
     An external Axes object can be passed in three places:
         - mpf.plot() `ax=` kwarg
         - mpf.plot() `volume=` kwarg
@@ -391,7 +399,7 @@ def _check_for_external_axes(config):
             raise TypeError('addplot must be `dict`, or `list of dict`, NOT '+str(type(addplot)))
         for apd in addplot:
             ap_axlist.append(apd['ax'])
-
+ 
     if len(ap_axlist) > 0:
         if config['ax'] is None:
             if not all([ax is None for ax in ap_axlist]):
@@ -416,6 +424,6 @@ def _check_for_external_axes(config):
             raise ValueError('`volume` must be of type `matplotlib.axis.Axes`')
         #if not isinstance(config['fig'],mpl.figure.Figure):
         #    raise ValueError('`fig` kwarg must be of type `matplotlib.figure.Figure`')
-
+    
     external_axes_mode = True if isinstance(config['ax'],mpl.axes.Axes) else False
     return external_axes_mode
diff --git a/src/mplfinance/_utils.py b/src/mplfinance/_utils.py
index e3109aff..9f519010 100644
--- a/src/mplfinance/_utils.py
+++ b/src/mplfinance/_utils.py
@@ -20,7 +20,7 @@
 
 from six.moves import zip
 
-def _check_input(opens, closes, highs, lows, colors=None):
+def _check_input(opens, closes, highs, lows):
     """Checks that *opens*, *highs*, *lows* and *closes* have the same length.
     NOTE: this code assumes if any value open, high, low, close is
     missing (*-1*) they all are missing
@@ -46,10 +46,6 @@ def _check_input(opens, closes, highs, lows, colors=None):
     if not same_length:
         raise ValueError('O,H,L,C must have the same length!')
 
-    if colors:
-        if len(opens) != len(colors):
-            raise ValueError('O,H,L,C and Colors must have the same length!')
-
     o = np.where(np.isnan(opens))[0]
     h = np.where(np.isnan(highs))[0]
     l = np.where(np.isnan(lows))[0]
@@ -85,23 +81,23 @@ def _check_and_convert_xlim_configuration(data, config):
             xlim = [ _date_to_mdate(dt) for dt in xlim]
         else:
             xlim = [ _date_to_iloc_extrapolate(data.index.to_series(),dt) for dt in xlim]
-
+        
     return xlim
 
 
-def _construct_mpf_collections(ptype,dates,xdates,opens,highs,lows,closes,volumes,config,style,colors):
+def _construct_mpf_collections(ptype,dates,xdates,opens,highs,lows,closes,volumes,config,style):
     collections = None
     if ptype == 'candle' or ptype == 'candlestick':
         collections = _construct_candlestick_collections(xdates, opens, highs, lows, closes,
-                                                         marketcolors=style['marketcolors'],config=config, colors=colors )
+                                                         marketcolors=style['marketcolors'],config=config )
 
     elif ptype =='hollow_and_filled':
             collections = _construct_hollow_candlestick_collections(xdates, opens, highs, lows, closes,
-                                                         marketcolors=style['marketcolors'],config=config, colors=colors )
+                                                         marketcolors=style['marketcolors'],config=config )
 
     elif ptype == 'ohlc' or ptype == 'bars' or ptype == 'ohlc_bars':
         collections = _construct_ohlc_collections(xdates, opens, highs, lows, closes,
-                                                  marketcolors=style['marketcolors'],config=config, colors=colors )
+                                                  marketcolors=style['marketcolors'],config=config )
     elif ptype == 'renko':
         collections = _construct_renko_collections(
             dates, highs, lows, volumes, config['renko_params'], closes, marketcolors=style['marketcolors'])
@@ -111,7 +107,7 @@ def _construct_mpf_collections(ptype,dates,xdates,opens,highs,lows,closes,volume
             dates, highs, lows, volumes, config['pnf_params'], closes, marketcolors=style['marketcolors'])
     else:
         raise TypeError('Unknown ptype="',str(ptype),'"')
-
+     
     return collections
 
 
@@ -142,7 +138,7 @@ def combine_adjacent(arr):
     Returns
     -------
     output: new summed array
-    indexes: indexes indicating the first
+    indexes: indexes indicating the first 
              element summed for each group in arr
     """
     output, indexes = [], []
@@ -155,7 +151,7 @@ def combine_adjacent(arr):
         output.append(sum(arr[:index]))
         indexes.append(curr_i)
         curr_i += index
-
+        
         for _ in range(index):
             arr.pop(0)
     return output, indexes
@@ -180,47 +176,38 @@ def coalesce_volume_dates(in_volumes, in_dates, indexes):
     return volumes, dates
 
 
-def _updown_colors(upcolor,downcolor,opens,closes,use_prev_close=False,colors=None):
-    if not colors:
-        if upcolor == downcolor:
-            return upcolor
-        cmap = {True : upcolor, False : downcolor}
-        if not use_prev_close:
-            return [ cmap[opn < cls] for opn,cls in zip(opens,closes) ]
-        else:
-            first = cmap[opens[0] < closes[0]]
-            _list = [ cmap[pre < cls] for cls,pre in zip(closes[1:], closes) ]
-            return [first] + _list
+def _updown_colors(upcolor,downcolor,opens,closes,use_prev_close=False):
+    # -----------------------------------------------------
+    # Note that `nan` values result in `opn < cls` == False
+    # In other words, nans don't get plotted by collections
+    # but this function will choose DOWN COLOR for nans.
+    # -----------------------------------------------------
+    if upcolor == downcolor:
+        return [upcolor]*len(opens)
+    cmap = {True : upcolor, False : downcolor}
+    if not use_prev_close:
+        return [ cmap[opn < cls] for opn,cls in zip(opens,closes) ]
     else:
-        cmap = {True: 'up', False: 'down'}
-        default = {'up': upcolor, 'down': downcolor}
-        custom = []
-        if not use_prev_close:
-            for i in range(len(opens)):
-                opn = opens[i]
-                cls = closes[i]
-                if colors[i]:
-                    custom.append(colors[i][cmap[opn < cls]])
-                else:
-                    custom.append(default[cmap[opn < cls]])
-        else:
-            if color[0]:
-                custom.append(colors[0][cmap[opens[0] < closes[0]]])
-            else:
-                custom.append(default[cmap[opens[0] < closes[0]]])
-
-            for i in range(len(closes) - 1):
-                pre = closes[1:][i]
-                cls = closes[i]
-                if colors[i]:
-                    custom.append(colors[i][cmap[pre < cls]])
-                else:
-                    custom.append(default[cmap[pre < cls]])
-
-        return custom
-
-
-
+        first = cmap[opens[0] < closes[0]] 
+        _list = [ cmap[pre < cls] for cls,pre in zip(closes[1:], closes) ]
+        return [first] + _list
+
+def _make_updown_color_list(key,marketcolors,opens,closes,overrides=None):
+    length = len(opens)
+    ups    = [marketcolors[key][ 'up' ]]*length
+    downs  = [marketcolors[key]['down']]*length
+    if overrides is not None:
+        for ix,mco in enumerate(overrides):
+            if mco is None: continue
+            if mcolors.is_color_like(mco):
+                ups[ix]   = mco
+                downs[ix] = mco
+            else: # assume it is correctly a marketcolors object (dict)
+                ups[ix]   = mco[key][ 'up' ]
+                downs[ix] = mco[key]['down']
+    return [ups[ix] if opens[ix] < closes[ix] else downs[ix] for ix in range(length)]
+       
+                
 def _updownhollow_colors(upcolor,downcolor,hollowcolor,opens,closes):
     if upcolor == downcolor:
         return upcolor
@@ -232,7 +219,7 @@ def _updownhollow_colors(upcolor,downcolor,hollowcolor,opens,closes):
 
 
 def _date_to_iloc(dtseries,date):
-    '''Convert a `date` to a location, given a date series w/a datetime index.
+    '''Convert a `date` to a location, given a date series w/a datetime index. 
        If `date` does not exactly match a date in the series then interpolate between two dates.
        If `date` is outside the range of dates in the series, then raise an exception
       .
@@ -269,7 +256,7 @@ def _date_to_iloc_linear(dtseries,date,trace=False):
     i1 = 0.0
     i2 = len(dtseries) - 1.0
     if trace: print('i1,i2=',i1,i2)
-
+    
     slope   = (i2 - i1) / (d2 - d1)
     yitrcpt1 = i1 - (slope*d1)
     if trace: print('slope,yitrcpt=',slope,yitrcpt1)
@@ -279,7 +266,7 @@ def _date_to_iloc_linear(dtseries,date,trace=False):
         print('WARNING: yintercepts NOT equal!!!(',yitrcpt1,yitrcpt2,')')
         yitrcpt = (yitrcpt1 + yitrcpt2) / 2.0
     else:
-        yitrcpt = yitrcpt1
+        yitrcpt = yitrcpt1 
     return (slope * _date_to_mdate(date)) + yitrcpt
 
 def _date_to_iloc_5_7ths(dtseries,date,direction,trace=False):
@@ -299,14 +286,14 @@ def _date_to_iloc_5_7ths(dtseries,date,direction,trace=False):
         return loc_5_7ths
 
 def _date_to_iloc_extrapolate(dtseries,date):
-    '''Convert a `date` to a location, given a date series w/a datetime index.
+    '''Convert a `date` to a location, given a date series w/a datetime index. 
        If `date` does not exactly match a date in the series then interpolate between two dates.
        If `date` is outside the range of dates in the series, then extrapolate:
        Extrapolation results in increased error as the distance of the extrapolation increases.
        We have two methods to extrapolate:
        (1) Determine a linear equation based on the data provided in `dtseries`,
            and use that equation to calculate the location for the date.
-       (2) Multiply by 5/7 the number of days between the edge date of dtseries and the
+       (2) Multiply by 5/7 the number of days between the edge date of dtseries and the 
            date for which we are requesting a location.
            THIS ASSUMES DAILY data AND a 5 DAY TRADING WEEK.
        Empirical observation (scratch_pad/date_to_iloc_extrapolation.ipynb) shows that
@@ -359,7 +346,7 @@ def _date_to_mdate(date):
 
 def _convert_segment_dates(segments,dtindex):
     '''
-    Convert line segment dates to matplotlib dates
+    Convert line segment dates to matplotlib dates 
     Inputted segment dates may be: pandas-parseable date-time string, pandas timestamp,
                                    or a python datetime or date, or (if dtindex is not None) integer index
     A "segment" is a "sequence of lines",
@@ -374,7 +361,7 @@ def _convert_segment_dates(segments,dtindex):
         new_line = []
         for dt,value in line:
             if dtindex is not None:
-                date = _date_to_iloc(dtseries,dt)
+                date = _date_to_iloc(dtseries,dt)  
             else:
                 date = _date_to_mdate(dt)
             if date is None:
@@ -385,13 +372,13 @@ def _convert_segment_dates(segments,dtindex):
 
 def _valid_renko_kwargs():
     '''
-    Construct and return the "valid renko kwargs table" for the mplfinance.plot(type='renko')
-    function. A valid kwargs table is a `dict` of `dict`s. The keys of the outer dict are
-    the valid key-words for the function.  The value for each key is a dict containing 2
+    Construct and return the "valid renko kwargs table" for the mplfinance.plot(type='renko') 
+    function. A valid kwargs table is a `dict` of `dict`s. The keys of the outer dict are 
+    the valid key-words for the function.  The value for each key is a dict containing 2 
     specific keys: "Default", and "Validator" with the following values:
         "Default"      - The default value for the kwarg if none is specified.
         "Validator"    - A function that takes the caller specified value for the kwarg,
-                         and validates that it is the correct type, and (for kwargs with
+                         and validates that it is the correct type, and (for kwargs with 
                          a limited set of allowed values) may also validate that the
                          kwarg value is one of the allowed values.
     '''
@@ -399,7 +386,7 @@ def _valid_renko_kwargs():
         'brick_size'  : { 'Default'     : 'atr',
                           'Validator'   : lambda value: isinstance(value,(float,int)) or value == 'atr' },
         'atr_length'  : { 'Default'     : 14,
-                          'Validator'   : lambda value: isinstance(value,int) or value == 'total' },
+                          'Validator'   : lambda value: isinstance(value,int) or value == 'total' },               
     }
 
     _validate_vkwargs_dict(vkwargs)
@@ -408,13 +395,13 @@ def _valid_renko_kwargs():
 
 def _valid_pnf_kwargs():
     '''
-    Construct and return the "valid pnf kwargs table" for the mplfinance.plot(type='pnf')
-    function. A valid kwargs table is a `dict` of `dict`s. The keys of the outer dict are
-    the valid key-words for the function.  The value for each key is a dict containing 2
+    Construct and return the "valid pnf kwargs table" for the mplfinance.plot(type='pnf') 
+    function. A valid kwargs table is a `dict` of `dict`s. The keys of the outer dict are 
+    the valid key-words for the function.  The value for each key is a dict containing 2 
     specific keys: "Default", and "Validator" with the following values:
         "Default"      - The default value for the kwarg if none is specified.
         "Validator"    - A function that takes the caller specified value for the kwarg,
-                         and validates that it is the correct type, and (for kwargs with
+                         and validates that it is the correct type, and (for kwargs with 
                          a limited set of allowed values) may also validate that the
                          kwarg value is one of the allowed values.
     '''
@@ -424,7 +411,7 @@ def _valid_pnf_kwargs():
         'atr_length'  : { 'Default'     : 14,
                           'Validator'   : lambda value: isinstance(value,int) or value == 'total' },
         'reversal'    : { 'Default'     : 1,
-                          'Validator'   : lambda value: isinstance(value,int) }
+                          'Validator'   : lambda value: isinstance(value,int) }               
     }
 
     _validate_vkwargs_dict(vkwargs)
@@ -433,14 +420,14 @@ def _valid_pnf_kwargs():
 
 def _valid_lines_kwargs():
     '''
-    Construct and return the "valid lines (hlines,vlines,alines,tlines) kwargs table"
+    Construct and return the "valid lines (hlines,vlines,alines,tlines) kwargs table" 
     for the mplfinance.plot() `[h|v|a|t]lines=` kwarg functions.
-    A valid kwargs table is a `dict` of `dict`s. The keys of the outer dict are
-    the valid key-words for the function.  The value for each key is a dict containing 2
+    A valid kwargs table is a `dict` of `dict`s. The keys of the outer dict are 
+    the valid key-words for the function.  The value for each key is a dict containing 2 
     specific keys: "Default", and "Validator" with the following values:
         "Default"      - The default value for the kwarg if none is specified.
         "Validator"    - A function that takes the caller specified value for the kwarg,
-                         and validates that it is the correct type, and (for kwargs with
+                         and validates that it is the correct type, and (for kwargs with 
                          a limited set of allowed values) may also validate that the
                          kwarg value is one of the allowed values.
     '''
@@ -463,12 +450,12 @@ def _valid_lines_kwargs():
                         'Validator'   : lambda value: value is None or value in valid_linestyles },
         'linewidths': { 'Default'     : None,
                         'Validator'   : lambda value: value is None or
-                                            isinstance(value,(float,int)) or
+                                            isinstance(value,(float,int)) or 
                                             all([isinstance(v,(float,int)) for v in value]) },
         'alpha'     : { 'Default'     : 1.0,
                         'Validator'   : lambda value: isinstance(value,(float,int)) },
 
-        'tline_use' : { 'Default'     : 'close',
+        'tline_use' : { 'Default'     : 'close', 
                         'Validator'   : lambda value: isinstance(value,str) or (isinstance(value,(list,tuple)) and
                                                                       all([isinstance(v,str) for v in value]) ) },
         'tline_method': { 'Default'   : 'point-to-point',
@@ -480,7 +467,7 @@ def _valid_lines_kwargs():
     return vkwargs
 
 
-def _construct_ohlc_collections(dates, opens, highs, lows, closes, marketcolors=None, config=None, colors=None):
+def _construct_ohlc_collections(dates, opens, highs, lows, closes, marketcolors=None, config=None):
     """Represent the time, open, high, low, close as a vertical line
     ranging from low to high.  The left tick is the open and the right
     tick is the close.
@@ -502,11 +489,11 @@ def _construct_ohlc_collections(dates, opens, highs, lows, closes, marketcolors=
 
     Returns
     -------
-    ret : list
+    ret : list 
         a list or tuple of matplotlib collections to be added to the axes
     """
 
-    _check_input(opens, highs, lows, closes, colors)
+    _check_input(opens, highs, lows, closes)
 
     if marketcolors is None:
         mktcolors = _get_mpfstyle('classic')['marketcolors']['ohlc']
@@ -530,25 +517,11 @@ def _construct_ohlc_collections(dates, opens, highs, lows, closes, marketcolors=
     # we'll translate these to the date, close location
     closeSegments = [((dt, close), (dt+ticksize, close)) for dt, close in zip(dates, closes)]
 
-    bar_c = None
-    if colors:
-        bar_c = []
-        for color in colors:
-            if color:
-                bar_up = color['ohlc']['up']
-                bar_down = color['ohlc']['down']
-                if bar_up == 'k':
-                    bar_up = mktcolors['up']
-                if bar_down == 'k':
-                    bar_down = mktcolors['down']
-
-                bar_c.append({'up': mcolors.to_rgba(bar_up, 1), 'down': mcolors.to_rgba(bar_down, 1)})
-            else:
-                bar_c.append(None)
-
-    uc = mcolors.to_rgba(mktcolors['up'])
-    dc = mcolors.to_rgba(mktcolors['down'])
-    colors = _updown_colors(uc, dc, opens, closes, colors=bar_c)
+    if mktcolors['up'] == mktcolors['down'] and config['marketcolor_overrides'] is None:
+        colors = mktcolors['up']
+    else:
+        overrides = config['marketcolor_overrides']
+        colors    = _make_updown_color_list('ohlc',marketcolors,opens,closes,overrides)
 
     lw = config['_width_config']['ohlc_linewidth']
 
@@ -570,7 +543,7 @@ def _construct_ohlc_collections(dates, opens, highs, lows, closes, marketcolors=
     return [rangeCollection, openCollection, closeCollection]
 
 
-def _construct_candlestick_collections(dates, opens, highs, lows, closes, marketcolors=None, config=None, colors=None):
+def _construct_candlestick_collections(dates, opens, highs, lows, closes, marketcolors=None, config=None):
     """Represent the open, close as a bar line and high low range as a
     vertical line.
 
@@ -597,8 +570,8 @@ def _construct_candlestick_collections(dates, opens, highs, lows, closes, market
     ret : list
         (lineCollection, barCollection)
     """
-
-    _check_input(opens, highs, lows, closes, colors)
+    
+    _check_input(opens, highs, lows, closes)
 
     if marketcolors is None:
         marketcolors = _get_mpfstyle('classic')['marketcolors']
@@ -618,62 +591,22 @@ def _construct_candlestick_collections(dates, opens, highs, lows, closes, market
 
     rangeSegLow   = [((date, low), (date, min(open,close)))
                      for date, low, open, close in zip(dates, lows, opens, closes)]
-
+    
     rangeSegHigh  = [((date, high), (date, max(open,close)))
                      for date, high, open, close in zip(dates, highs, opens, closes)]
-
+                      
     rangeSegments = rangeSegLow + rangeSegHigh
 
     alpha  = marketcolors['alpha']
 
-    candle_c = None
-    wick_c = None
-    edge_c = None
-    if colors:
-        candle_c = []
-        wick_c = []
-        edge_c = []
-        for color in colors:
-            if color:
-                candle_up = color['candle']['up']
-                candle_down = color['candle']['down']
-                edge_up = color['edge']['up']
-                edge_down = color['edge']['down']
-                wick_up = color['wick']['up']
-                wick_down = color['wick']['down']
-                if candle_up == 'w':
-                    candle_up = marketcolors['candle']['up']
-                if candle_down == 'k':
-                    candle_down = marketcolors['candle']['down']
-                if edge_up == 'k':
-                    edge_up = candle_up
-                if edge_down == 'k':
-                    edge_down = candle_down
-                if wick_up == 'k':
-                    wick_up = candle_up
-                if wick_down == 'k':
-                    wick_down = candle_down
-
-                candle_c.append({'up': mcolors.to_rgba(candle_up, alpha), 'down': mcolors.to_rgba(candle_down, alpha)})
-                edge_c.append({'up': mcolors.to_rgba(edge_up, 1), 'down': mcolors.to_rgba(edge_down, 1)})
-                wick_c.append({'up': mcolors.to_rgba(wick_up, 1), 'down': mcolors.to_rgba(wick_down, 1)})
-
-            else:
-                candle_c.append(None)
-                wick_c.append(None)
-                edge_c.append(None)
-
-    uc     = mcolors.to_rgba(marketcolors['candle'][ 'up' ], alpha)
-    dc     = mcolors.to_rgba(marketcolors['candle']['down'], alpha)
-    colors = _updown_colors(uc, dc, opens, closes, colors=candle_c)
-
-    uc     = mcolors.to_rgba(marketcolors['edge'][ 'up' ], 1.0)
-    dc     = mcolors.to_rgba(marketcolors['edge']['down'], 1.0)
-    edgecolor = _updown_colors(uc, dc, opens, closes, colors=edge_c)
+    overrides = config['marketcolor_overrides']
+    faceonly  = config['mco_faceonly']
 
-    uc     = mcolors.to_rgba(marketcolors['wick'][ 'up' ], 1.0)
-    dc     = mcolors.to_rgba(marketcolors['wick']['down'], 1.0)
-    wickcolor = _updown_colors(uc, dc, opens, closes, colors=wick_c)
+    colors    = _make_updown_color_list('candle',marketcolors,opens,closes,overrides)
+    colors    = [mcolors.to_rgba(c,alpha) for c in colors] # include alpha
+    if faceonly: overrides = None
+    edgecolor = _make_updown_color_list('edge',marketcolors,opens,closes,overrides)
+    wickcolor = _make_updown_color_list('wick',marketcolors,opens,closes,overrides)
 
     lw = config['_width_config']['candle_linewidth']
 
@@ -691,10 +624,10 @@ def _construct_candlestick_collections(dates, opens, highs, lows, closes, market
     return [rangeCollection, barCollection]
 
 
-def _construct_hollow_candlestick_collections(dates, opens, highs, lows, closes, marketcolors=None, config=None, colors=None):
+def _construct_hollow_candlestick_collections(dates, opens, highs, lows, closes, marketcolors=None, config=None):
     """Represent today's open to close as a "bar" line (candle body)
     and high low range as a vertical line (candle wick)
-
+     
     If config['type']=='hollow_and_filled' (hollow and filled candles) then candle edge and
     wick color depend on PREVIOUS close to today's close (up or down), and the center of the
     candle body (hollow or filled) depends on the today's open to close (up or down).
@@ -721,7 +654,7 @@ def _construct_hollow_candlestick_collections(dates, opens, highs, lows, closes,
     ret : list
         (lineCollection, barCollection)
     """
-
+    
     _check_input(opens, highs, lows, closes)
 
     if marketcolors is None:
@@ -742,23 +675,23 @@ def _construct_hollow_candlestick_collections(dates, opens, highs, lows, closes,
 
     rangeSegLow   = [((date, low), (date, min(open,close)))
                      for date, low, open, close in zip(dates, lows, opens, closes)]
-
+    
     rangeSegHigh  = [((date, high), (date, max(open,close)))
                      for date, high, open, close in zip(dates, highs, opens, closes)]
-
+                      
     rangeSegments = rangeSegLow + rangeSegHigh
 
     alpha  = marketcolors['alpha']
 
     uc     = mcolors.to_rgba(marketcolors['candle'][ 'up' ], alpha)
     dc     = mcolors.to_rgba(marketcolors['candle']['down'], alpha)
-
+   
     hc = mcolors.to_rgba(marketcolors['hollow']) if 'hollow' in marketcolors else (0,0,0,0)
-
+    
     colors = _updownhollow_colors(uc, dc, hc, opens, closes)  # for candle body.
 
     edgecolor = _updown_colors(uc, dc, opens, closes, use_prev_close=True)
-
+    
     wickcolor = _updown_colors(uc, dc, opens, closes, use_prev_close=True)
 
     # For hollow candles, we scale the candle linewidth up a little:
@@ -788,19 +721,19 @@ def _construct_renko_collections(dates, highs, lows, volumes, config_renko_param
     ---------------------
     In the first part of the algorithm, we populate the cdiff array
     along with adjusting the dates and volumes arrays into the new_dates and
-    new_volumes arrays. A single date includes a range from no bricks to many
-    bricks, if a date has no bricks it shall not be included in new_dates,
-    and if it has n bricks then it will be included n times. Volumes use a
+    new_volumes arrays. A single date includes a range from no bricks to many 
+    bricks, if a date has no bricks it shall not be included in new_dates, 
+    and if it has n bricks then it will be included n times. Volumes use a 
     volume cache to save volume amounts for dates that do not have any bricks
     before adding the cache to the next date that has at least one brick.
-    We populate the cdiff array with each close values difference from the
+    We populate the cdiff array with each close values difference from the 
     previously created brick divided by the brick size.
 
     In the second part of the algorithm, we iterate through the values in cdiff
-    and add 1s or -1s to the bricks array depending on whether the value is
+    and add 1s or -1s to the bricks array depending on whether the value is 
     positive or negative. Every time there is a trend change (ex. previous brick is
     an upbrick, current brick is a down brick) we draw one less brick to account
-    for the price having to move the previous bricks amount before creating a
+    for the price having to move the previous bricks amount before creating a 
     brick in the opposite direction.
 
     In the final part of the algorithm, we enumerate through the bricks array and
@@ -811,7 +744,7 @@ def _construct_renko_collections(dates, highs, lows, volumes, config_renko_param
     Useful sources:
     https://avilpage.com/2018/01/how-to-plot-renko-charts-with-python.html
     https://school.stockcharts.com/doku.php?id=chart_analysis:renko
-
+    
     Parameters
     ----------
     dates : sequence
@@ -836,10 +769,10 @@ def _construct_renko_collections(dates, highs, lows, volumes, config_renko_param
     if marketcolors is None:
         marketcolors = _get_mpfstyle('classic')['marketcolors']
         #print('default market colors:',marketcolors)
-
+    
     brick_size = renko_params['brick_size']
     atr_length = renko_params['atr_length']
-
+    
 
     if brick_size == 'atr':
         if atr_length == 'total':
@@ -860,7 +793,7 @@ def _construct_renko_collections(dates, highs, lows, volumes, config_renko_param
     dc     = mcolors.to_rgba(marketcolors['candle']['down'], alpha)
     euc    = mcolors.to_rgba(marketcolors['edge'][ 'up' ], 1.0)
     edc    = mcolors.to_rgba(marketcolors['edge']['down'], 1.0)
-
+    
     cdiff = [] # holds the differences between each close and the previously created brick / the brick size
     prev_close_brick = closes[0]
     volume_cache = 0 # holds the volumes for the dates that were skipped
@@ -887,7 +820,7 @@ def _construct_renko_collections(dates, highs, lows, volumes, config_renko_param
     last_diff_sign = 0 # direction the bricks were last going in -1 -> down, 1 -> up
     dates_volumes_index = 0 # keeps track of the index of the current date/volume
     for diff in cdiff:
-
+        
         curr_diff_sign = diff/abs(diff)
         if last_diff_sign != 0 and curr_diff_sign != last_diff_sign:
             last_diff_sign = curr_diff_sign
@@ -900,7 +833,7 @@ def _construct_renko_collections(dates, highs, lows, volumes, config_renko_param
                 new_volumes.pop(dates_volumes_index)
             continue
         last_diff_sign = curr_diff_sign
-
+    
         if diff > 0:
             bricks.extend([1]*abs(diff))
         else:
@@ -922,7 +855,7 @@ def _construct_renko_collections(dates, highs, lows, volumes, config_renko_param
 
         curr_price += (brick_size * number)
         brick_values.append(curr_price)
-
+        
         x, y = index, curr_price
 
         verts.append((
@@ -954,41 +887,41 @@ def _construct_pointnfig_collections(dates, highs, lows, volumes, config_pointnf
     ---------------------
     In the first part of the algorithm, we populate the boxes array
     along with adjusting the dates and volumes arrays into the new_dates and
-    new_volumes arrays. A single date includes a range from no boxes to many
-    boxes, if a date has no boxes it shall not be included in new_dates,
-    and if it has n boxes then it will be included n times. Volumes use a
+    new_volumes arrays. A single date includes a range from no boxes to many 
+    boxes, if a date has no boxes it shall not be included in new_dates, 
+    and if it has n boxes then it will be included n times. Volumes use a 
     volume cache to save volume amounts for dates that do not have any boxes
     before adding the cache to the next date that has at least one box.
-    We populate the boxes array with each close values difference from the
+    We populate the boxes array with each close values difference from the 
     previously created brick divided by the box size.
 
     The second part of the algorithm has a series of step. First we combine the
     adjacent like signed values in the boxes array (ex. [-1, -2, 3, -4] -> [-3, 3, -4]).
-    Next we subtract 1 from the absolute value of each element in boxes except the
+    Next we subtract 1 from the absolute value of each element in boxes except the 
     first to ensure every time there is a trend change (ex. previous box is
-    an X, current brick is a O) we draw one less box to account for the price
-    having to move the previous box's amount before creating a box in the
-    opposite direction. During this same step we also combine like signed elements
-    and associated volume/date data ignoring any zero values that are created by
-    subtracting 1 from the box value. Next we recreate the box array utilizing a
-    rolling_change and volume_cache to store and sum the changes that don't break
+    an X, current brick is a O) we draw one less box to account for the price 
+    having to move the previous box's amount before creating a box in the 
+    opposite direction. During this same step we also combine like signed elements 
+    and associated volume/date data ignoring any zero values that are created by 
+    subtracting 1 from the box value. Next we recreate the box array utilizing a 
+    rolling_change and volume_cache to store and sum the changes that don't break 
     the reversal threshold.
 
     Lastly, we enumerate through the boxes to populate the line_seg and circle_patches
-    arrays. line_seg holds the / and \ line segments that make up an X and
+    arrays. line_seg holds the / and \ line segments that make up an X and 
     circle_patches holds matplotlib.patches Ellipse objects for each O. We start
-    by filling an x and y array each iteration which contain the x and y
+    by filling an x and y array each iteration which contain the x and y 
     coordinates for each box in the column. Then for each coordinate pair in
-    x, y we add to either the line_seg array or the circle_patches array
-    depending on the value of sign for the current column (1 indicates
-    line_seg, -1 indicates circle_patches). The height of the boxes take
-    into account padding which separates each box by a small margin in
+    x, y we add to either the line_seg array or the circle_patches array 
+    depending on the value of sign for the current column (1 indicates 
+    line_seg, -1 indicates circle_patches). The height of the boxes take 
+    into account padding which separates each box by a small margin in 
     order to increase readability.
 
     Useful sources:
     https://stackoverflow.com/questions/8750648/point-and-figure-chart-with-matplotlib
     https://www.investopedia.com/articles/technical/03/081303.asp
-
+    
     Parameters
     ----------
     dates : sequence
@@ -1013,7 +946,7 @@ def _construct_pointnfig_collections(dates, highs, lows, volumes, config_pointnf
     if marketcolors is None:
         marketcolors = _get_mpfstyle('classic')['marketcolors']
         #print('default market colors:',marketcolors)
-
+    
     box_size = pointnfig_params['box_size']
     atr_length = pointnfig_params['atr_length']
     reversal = pointnfig_params['reversal']
@@ -1033,7 +966,7 @@ def _construct_pointnfig_collections(dates, highs, lows, volumes, config_pointnf
 
     if reversal < 1 or reversal > 9:
         raise ValueError("Specified reversal must be an integer in the range [1,9]")
-
+    
     alpha  = marketcolors['alpha']
 
     uc     = mcolors.to_rgba(marketcolors['ohlc'][ 'up' ], alpha)
@@ -1044,7 +977,7 @@ def _construct_pointnfig_collections(dates, highs, lows, volumes, config_pointnf
     prev_close_box = closes[0] # represents the value of the last box in the previous column
     volume_cache = 0 # holds the volumes for the dates that were skipped
     temp_volumes, temp_dates = [], [] # holds the temp adjusted volumes and dates respectively
-
+    
     for i in range(len(closes)-1):
         box_diff = int((closes[i+1] - prev_close_box) / box_size)
         if box_diff == 0:
@@ -1062,7 +995,7 @@ def _construct_pointnfig_collections(dates, highs, lows, volumes, config_pointnf
     # combine adjacent similarly signed differences
     boxes, indexes = combine_adjacent(boxes)
     new_volumes, new_dates = coalesce_volume_dates(temp_volumes, temp_dates, indexes)
-
+    
     adjusted_boxes = [boxes[0]]
     temp_volumes, temp_dates = [new_volumes[0]], [new_dates[0]]
     volume_cache = 0
@@ -1098,7 +1031,7 @@ def _construct_pointnfig_collections(dates, highs, lows, volumes, config_pointnf
     boxes = [adjusted_boxes[0]]
     new_volumes = [temp_volumes[0]]
     new_dates = [temp_dates[0]]
-
+    
     rolling_change = 0
     volume_cache = 0
     biggest_difference = 0 # only used for the last column
@@ -1106,11 +1039,11 @@ def _construct_pointnfig_collections(dates, highs, lows, volumes, config_pointnf
     #Clean data to account for reversal size (added to allow overriding the default reversal of 1)
     for i in range(1, len(adjusted_boxes)):
 
-        # Add to rolling_change and volume_cache which stores the box and volume values
+        # Add to rolling_change and volume_cache which stores the box and volume values 
         rolling_change += adjusted_boxes[i]
         volume_cache += temp_volumes[i]
 
-        # if rolling_change is the same sign as the previous box and the abs value is bigger than the
+        # if rolling_change is the same sign as the previous box and the abs value is bigger than the 
         # abs value of biggest_difference then we should replace biggest_difference with rolling_change
         if rolling_change*boxes[-1] > 0 and abs(rolling_change) > abs(biggest_difference):
             biggest_difference = rolling_change
@@ -1128,14 +1061,14 @@ def _construct_pointnfig_collections(dates, highs, lows, volumes, config_pointnf
                 boxes.append(rolling_change)
                 new_volumes.append(volume_cache)
                 new_dates.append(temp_dates[i])
-
+            
             # reset rolling_change and volume_cache once we've used them
             rolling_change = 0
             volume_cache = 0
-
+            
             # reset biggest_difference as we start from the beginning every time there is a reversal
             biggest_difference = 0
-
+    
     # Adjust the last box column if the left over rolling_change is the same sign as the column
     boxes[-1] += biggest_difference
     new_volumes[-1] += volume_cache
@@ -1150,33 +1083,33 @@ def _construct_pointnfig_collections(dates, highs, lows, volumes, config_pointnf
 
         sign = (difference / abs(difference)) # -1 or 1
         start_iteration = 0 if sign > 0 else 1
-
+        
         x = [index] * (diff)
         y = [curr_price + (i * box_size * sign) for i in range(start_iteration, diff+start_iteration)]
 
         curr_price += (box_size * sign * (diff))
         box_values.append( y )
-
+        
         for i in range(len(x)): # x and y have the same length
             height = box_size * 0.85
             width = 0.6
             if height < 0.5:
                 width = height
-
+            
             padding = (box_size * 0.075)
             if sign == 1: # X
                 line_seg.append([(x[i]-width/2, y[i] + padding), (x[i]+width/2, y[i]+height + padding)]) # create / part of the X
                 line_seg.append([(x[i]-width/2, y[i]+height+padding), (x[i]+width/2, y[i]+padding)]) # create \ part of the X
             else: # O
                 circle_patches.append(Ellipse((x[i], y[i]-(height/2) - padding), width, height))
-
+    
     useAA = 0,    # use tuple here
-    lw = 0.5
+    lw = 0.5        
 
     cirCollection = PatchCollection(circle_patches)
     cirCollection.set_facecolor([tfc] * len(circle_patches))
     cirCollection.set_edgecolor([dc] * len(circle_patches))
-
+    
     xCollection = LineCollection(line_seg,
                                  colors=[uc] * len(line_seg),
                                  linewidths=lw,
@@ -1194,7 +1127,7 @@ def _construct_aline_collections(alines, dtix=None):
     ----------
     alines : sequence
         sequences of segments, which are sequences of lines,
-        which are sequences of two or more points ( date[time], price ) or (x,y)
+        which are sequences of two or more points ( date[time], price ) or (x,y) 
 
         date[time] may be (a) pandas.to_datetime parseable string,
                           (b) pandas Timestamp, or
@@ -1284,7 +1217,7 @@ def _construct_hline_collections(hlines,minx,maxx):
 
     #print('hconfig=',hconfig)
     #print('hlines=',hlines)
-
+    
     lines = []
     if not isinstance(hlines,(list,tuple)):
         hlines = [hlines,] # may be a single price value
@@ -1347,7 +1280,7 @@ def _construct_vline_collections(vlines,dtix,miny,maxy):
 
     #print('vconfig=',vconfig)
     #print('vlines=',vlines)
-
+    
     if not isinstance(vlines,(list,tuple)):
         vlines = [vlines,]
 
@@ -1430,7 +1363,7 @@ def _tline_lsq(dfslice,tline_use):
         https://mmas.github.io/least-squares-fitting-numpy-scipy
         '''
         si = dfslice[tline_use].mean(axis=1)
-        s  = si.dropna()
+        s  = si.dropna() 
         if len(s) < 2:
             err = 'NOT enough data for Least Squares'
             if (len(si) > 2):
@@ -1467,7 +1400,7 @@ def _tline_lsq(dfslice,tline_use):
         alines.append((p1,p2))
 
     del tconfig['alines']
-    alines = dict(alines=alines,**tconfig)
+    alines = dict(alines=alines,**tconfig) 
     alines['tlines'] = None
 
     return _construct_aline_collections(alines, dtix)
@@ -1485,7 +1418,7 @@ class IntegerIndexDateTimeFormatter(Formatter):
     you would otherwise plot on that axis.  Construct this formatter
     by providing the arrange of datetimes (as matplotlib floats). When
     the formatter receives an integer in the range, it will look up the
-    datetime and format it.
+    datetime and format it.  
 
     """
     def __init__(self, dates, fmt='%b %d, %H:%M'):
@@ -1499,7 +1432,7 @@ def __call__(self, x, pos=0):
         # not sure what 'pos' is for: see
         # https://matplotlib.org/gallery/ticks_and_spines/date_index_formatter.html
         ix = int(np.round(x))
-
+         
         if ix >= self.len or ix < 0:
             date = None
             dateformat = ''
diff --git a/src/mplfinance/plotting.py b/src/mplfinance/plotting.py
index 0478e70f..14ef37dd 100644
--- a/src/mplfinance/plotting.py
+++ b/src/mplfinance/plotting.py
@@ -39,8 +39,7 @@
 from mplfinance._arg_validators import _alines_validator, _tlines_validator
 from mplfinance._arg_validators import _scale_padding_validator, _yscale_validator
 from mplfinance._arg_validators import _valid_panel_id, _check_for_external_axes
-from mplfinance._arg_validators import _xlim_validator
-from mplfinance._arg_validators import _colors_validator
+from mplfinance._arg_validators import _xlim_validator, _mco_validator
 
 from mplfinance._panels import _build_panels
 from mplfinance._panels import _set_ticks_on_bottom_panel_only
@@ -50,8 +49,6 @@
 from mplfinance._helpers import _num_or_seq_of_num
 from mplfinance._helpers import _adjust_color_brightness
 
-from mplfinance._styles import make_marketcolors
-
 VALID_PMOVE_TYPES = ['renko', 'pnf']
 
 DEFAULT_FIGRATIO = (8.00,5.75)
@@ -95,7 +92,7 @@ def _valid_plot_kwargs():
     2 specific keys: "Default", and "Validator" with the following values:
         "Default"      - The default value for the kwarg if none is specified.
         "Validator"    - A function that takes the caller specified value for the kwarg,
-                         and validates that it is the correct type, and (for kwargs with
+                         and validates that it is the correct type, and (for kwargs with 
                          a limited set of allowed values) may also validate that the
                          kwarg value is one of the allowed values.
     '''
@@ -107,46 +104,46 @@ def _valid_plot_kwargs():
                                                                    and all(isinstance(c, str) for c in value) },
         'type'                      : { 'Default'     : 'ohlc',
                                         'Validator'   : lambda value: value in _get_valid_plot_types() },
-
+ 
         'style'                     : { 'Default'     : None,
                                         'Validator'   : _styles._valid_mpf_style },
-
+ 
         'volume'                    : { 'Default'     : False,
                                         'Validator'   : lambda value: isinstance(value,bool) or isinstance(value,mpl_axes.Axes) },
-
+ 
         'mav'                       : { 'Default'     : None,
                                         'Validator'   : _mav_validator },
-
+        
         'renko_params'              : { 'Default'     : dict(),
                                         'Validator'   : lambda value: isinstance(value,dict) },
 
         'pnf_params'                : { 'Default'     : dict(),
                                         'Validator'   : lambda value: isinstance(value,dict) },
-
+ 
         'study'                     : { 'Default'     : None,
-                                        'Validator'   : lambda value: _kwarg_not_implemented(value) },
-
-        'marketcolors'              : { 'Default'     : None, # use 'style' for default, instead.
-                                        'Validator'   : lambda value: isinstance(value,dict) },
-
-        'override_marketcolors'     : { 'Default'     : None, # use default style instead.
-                                        'Validator'   : lambda value: _colors_validator(value) },
-
+                                        'Validator'   : lambda value: _kwarg_not_implemented(value) }, 
+ 
+        'marketcolor_overrides'     : { 'Default'     : None, 
+                                        'Validator'   : _mco_validator },
+ 
+        'mco_faceonly'              : { 'Default'     : False, # If True: Override only the face of the candle
+                                        'Validator'   : lambda value: isinstance(value,bool) },
+ 
         'no_xgaps'                  : { 'Default'     : True,  # None means follow default logic below:
                                         'Validator'   : lambda value: _warn_no_xgaps_deprecated(value) },
-
-        'show_nontrading'           : { 'Default'     : False,
+ 
+        'show_nontrading'           : { 'Default'     : False, 
                                         'Validator'   : lambda value: isinstance(value,bool) },
-
+ 
         'figscale'                  : { 'Default'     : None, # scale base figure size up or down.
                                         'Validator'   : lambda value: isinstance(value,float) or isinstance(value,int) },
-
+ 
         'figratio'                  : { 'Default'     : None, # aspect ratio; scaled to 8.0 height
                                         'Validator'   : lambda value: isinstance(value,(tuple,list))
                                                                       and len(value) == 2
                                                                       and isinstance(value[0],(float,int))
                                                                       and isinstance(value[1],(float,int)) },
-
+ 
         'figsize'                   : { 'Default'     : None,  # figure size; overrides figratio and figscale
                                         'Validator'   : lambda value: isinstance(value,(tuple,list))
                                                                       and len(value) == 2
@@ -155,32 +152,32 @@ def _valid_plot_kwargs():
 
         'fontscale'                 : { 'Default'     : None, # scale all fonts up or down
                                         'Validator'   : lambda value: isinstance(value,float) or isinstance(value,int) },
-
+ 
         'linecolor'                 : { 'Default'     : None, # line color in line plot
                                         'Validator'   : lambda value: mcolors.is_color_like(value) },
 
         'title'                     : { 'Default'     : None, # Figure Title
                                         'Validator'   : lambda value: isinstance(value,(str,dict)) },
-
+ 
         'axtitle'                   : { 'Default'     : None, # Axes Title (subplot title)
                                         'Validator'   : lambda value: isinstance(value,(str,dict)) },
-
+ 
         'ylabel'                    : { 'Default'     : 'Price', # y-axis label
                                         'Validator'   : lambda value: isinstance(value,str) },
-
+ 
         'ylabel_lower'              : { 'Default'     : None, # y-axis label default logic below
                                         'Validator'   : lambda value: isinstance(value,str) },
-
-        'addplot'                   : { 'Default'     : None,
+ 
+        'addplot'                   : { 'Default'     : None, 
                                         'Validator'   : lambda value: isinstance(value,dict) or (isinstance(value,list) and all([isinstance(d,dict) for d in value])) },
-
-        'savefig'                   : { 'Default'     : None,
+ 
+        'savefig'                   : { 'Default'     : None, 
                                         'Validator'   : lambda value: isinstance(value,dict) or isinstance(value,str) or isinstance(value, io.BytesIO) or isinstance(value, os.PathLike) },
-
-        'block'                     : { 'Default'     : None,
+ 
+        'block'                     : { 'Default'     : None, 
                                         'Validator'   : lambda value: isinstance(value,bool) },
-
-        'returnfig'                 : { 'Default'     : False,
+ 
+        'returnfig'                 : { 'Default'     : False, 
                                         'Validator'   : lambda value: isinstance(value,bool) },
 
         'return_calculated_values'  : {'Default'      : None,
@@ -188,29 +185,29 @@ def _valid_plot_kwargs():
 
         'set_ylim'                  : {'Default'      : None,
                                        'Validator'    : lambda value: _warn_set_ylim_deprecated(value) },
-
+ 
         'ylim'                      : {'Default'      : None,
-                                       'Validator'    : lambda value: isinstance(value, (list,tuple)) and len(value) == 2
+                                       'Validator'    : lambda value: isinstance(value, (list,tuple)) and len(value) == 2 
                                                                       and all([isinstance(v,(int,float)) for v in value])},
-
+ 
         'xlim'                      : {'Default'      : None,
                                        'Validator'    : lambda value: _xlim_validator(value) },
-
+ 
         'set_ylim_panelB'           : {'Default'      : None,
                                        'Validator'    : lambda value: _warn_set_ylim_deprecated(value) },
-
-        'hlines'                    : { 'Default'     : None,
+ 
+        'hlines'                    : { 'Default'     : None, 
                                         'Validator'   : lambda value: _hlines_validator(value) },
-
-        'vlines'                    : { 'Default'     : None,
+ 
+        'vlines'                    : { 'Default'     : None, 
                                         'Validator'   : lambda value: _vlines_validator(value) },
 
-        'alines'                    : { 'Default'     : None,
+        'alines'                    : { 'Default'     : None, 
                                         'Validator'   : lambda value: _alines_validator(value) },
-
-        'tlines'                    : { 'Default'     : None,
+ 
+        'tlines'                    : { 'Default'     : None, 
                                         'Validator'   : lambda value: _tlines_validator(value) },
-
+       
         'panel_ratios'              : { 'Default'     : None,
                                         'Validator'   : lambda value: isinstance(value,(tuple,list)) and len(value) <= 10 and
                                                                       all([isinstance(v,(int,float)) for v in value]) },
@@ -237,7 +234,7 @@ def _valid_plot_kwargs():
                                         'Validator'   : lambda value: isinstance(value,bool) },
 
         'fill_between'              : { 'Default'     : None,
-                                        'Validator'   : lambda value: _num_or_seq_of_num(value) or
+                                        'Validator'   : lambda value: _num_or_seq_of_num(value) or 
                                                                      (isinstance(value,dict) and 'y1' in value and
                                                                        _num_or_seq_of_num(value['y1'])) },
 
@@ -258,8 +255,8 @@ def _valid_plot_kwargs():
 
         'saxbelow'                  : { 'Default'     : True,  # Issue#115 Comment#639446764
                                         'Validator'   : lambda value: isinstance(value,bool) },
-
-        'scale_padding'             : { 'Default'     : 1.0,   # Issue#193
+        
+        'scale_padding'             : { 'Default'     : 1.0,   # Issue#193 
                                         'Validator'   : lambda value: _scale_padding_validator(value) },
 
         'ax'                        : { 'Default'     : None,
@@ -300,7 +297,7 @@ def plot( data, **kwargs ):
 
     # translate alias types:
     config['type'] = _get_valid_plot_types(config['type'])
-
+    
     dates,opens,highs,lows,closes,volumes = _check_and_prepare_data(data, config)
 
     config['xlim'] = _check_and_convert_xlim_configuration(data, config)
@@ -375,7 +372,7 @@ def plot( data, **kwargs ):
 
     fmtstring = _determine_format_string(dates, config['datetime_format'])
 
-    ptype = config['type']
+    ptype = config['type'] 
 
     if config['show_nontrading']:
         formatter = mdates.DateFormatter(fmtstring)
@@ -397,21 +394,14 @@ def plot( data, **kwargs ):
     rwc = config['return_width_config']
     if isinstance(rwc,dict) and len(rwc)==0:
         config['return_width_config'].update(config['_width_config'])
-
-    if config['override_marketcolors']:
-        override_marketcolors = config['override_marketcolors']
-        for c in range(len(override_marketcolors)):
-            if isinstance(override_marketcolors[c], str):
-                config['override_marketcolors'][c] = make_marketcolors(up=override_marketcolors[c], down=override_marketcolors[c], edge=override_marketcolors[c], wick=override_marketcolors[c], ohlc=override_marketcolors[c], volume=override_marketcolors[c])
-    else:
-        config['override_marketcolors'] = None
+ 
 
     collections = None
     if ptype == 'line':
         lw = config['_width_config']['line_width']
         axA1.plot(xdates, closes, color=config['linecolor'], linewidth=lw)
     else:
-        collections =_construct_mpf_collections(ptype,dates,xdates,opens,highs,lows,closes,volumes,config,style,config['override_marketcolors'])
+        collections =_construct_mpf_collections(ptype,dates,xdates,opens,highs,lows,closes,volumes,config,style)
 
     if ptype in VALID_PMOVE_TYPES:
         collections, calculated_values = collections
@@ -498,7 +488,7 @@ def plot( data, **kwargs ):
             if len(mav) != len(mavprices):
                 warnings.warn('len(mav)='+str(len(mav))+' BUT len(mavprices)='+str(len(mavprices)))
             else:
-                for jj in range(0,len(mav)):
+                for jj in range(0,len(mav)):     
                     retdict['mav' + str(mav[jj])] = mavprices[jj]
         retdict['minx'] = minx
         retdict['maxx'] = maxx
@@ -525,7 +515,7 @@ def plot( data, **kwargs ):
         tlines = [tlines,]
     for tline_item in tlines:
         line_collections.append(_construct_tline_collections(tline_item, dtix, dates, opens, highs, lows, closes))
-
+     
     for collection in line_collections:
         if collection is not None:
             axA1.add_collection(collection)
@@ -561,7 +551,7 @@ def plot( data, **kwargs ):
         axA1.set_yscale(yscale,**ysd)
     elif isinstance(ysd,str):
         axA1.set_yscale(ysd)
-
+ 
 
     addplot = config['addplot']
     if addplot is not None and ptype not in VALID_PMOVE_TYPES:
@@ -595,7 +585,7 @@ def plot( data, **kwargs ):
 
         for apdict in addplot:
 
-            panid = apdict['panel']
+            panid = apdict['panel'] 
             if not external_axes_mode:
                 if   panid == 'main' : panid = 0  # for backwards compatibility
                 elif panid == 'lower': panid = 1  # for backwards compatibility
@@ -606,11 +596,11 @@ def plot( data, **kwargs ):
             if aptype == 'ohlc' or aptype == 'candle':
                 ax = _addplot_collections(panid,panels,apdict,xdates,config)
                 _addplot_apply_supplements(ax,apdict)
-            else:
+            else:         
                 apdata = apdict['data']
                 if isinstance(apdata,list) and not isinstance(apdata[0],(float,int)):
                     raise TypeError('apdata is list but NOT of float or int')
-                if isinstance(apdata,pd.DataFrame):
+                if isinstance(apdata,pd.DataFrame): 
                     havedf = True
                 else:
                     havedf = False      # must be a single series or array
@@ -636,7 +626,7 @@ def plot( data, **kwargs ):
         fb['x'] = xdates
         ax = panels.at[panid,'axes'][0]
         ax.fill_between(**fb)
-
+            
     # put the primary axis on one side,
     # and the twinx() on the "other" side:
     if not external_axes_mode:
@@ -653,14 +643,14 @@ def plot( data, **kwargs ):
     # TODO:  ===========
     # TODO:  It appears to me that there may be some or significant overlap
     # TODO:  between what the following functions actually do:
-    # TODO:  At the very least, all four of them appear to communicate
+    # TODO:  At the very least, all four of them appear to communicate 
     # TODO:  to matplotlib that the xaxis should be treated as dates:
     # TODO:   ->  'ax.autoscale_view()'
     # TODO:   ->  'ax.xaxis_dates()'
     # TODO:   ->  'plt.autofmt_xdates()'
     # TODO:   ->  'fig.autofmt_xdate()'
     # TODO: ================================================================
-
+    
 
     #if config['autofmt_xdate']:
         #print('CALLING fig.autofmt_xdate()')
@@ -671,7 +661,7 @@ def plot( data, **kwargs ):
                            # for `addplot`, that this IS necessary when the only thing done to the
                            # the axes is .add_collection().  (However, if ax.plot() .scatter() or
                            # .bar() was called, then possibly this is not necessary; not entirely
-                           # sure, but it definitely was necessary to get 'ohlc' and 'candle'
+                           # sure, but it definitely was necessary to get 'ohlc' and 'candle' 
                            # working in `addplot`).
 
     axA1.set_ylabel(config['ylabel'])
@@ -727,7 +717,7 @@ def plot( data, **kwargs ):
                 offset = '\n'+offset
             vol_label = config['ylabel_lower'] + offset
         volumeAxes.set_ylabel(vol_label)
-
+    
     if config['title'] is not None:
         if config['tight_layout']:
             # IMPORTANT: `y=0.89` is based on the top of the top panel
@@ -747,8 +737,8 @@ def plot( data, **kwargs ):
         else:
             title = config['title']      # config['title'] is a string
         fig.suptitle(title,**title_kwargs)
-
-
+    
+    
     if config['axtitle'] is not None:
         axA1.set_title(config['axtitle'])
 
@@ -784,10 +774,10 @@ def plot( data, **kwargs ):
         if config['closefig']: # True or 'auto'
             plt.close(fig)
     elif not config['returnfig']:
-        plt.show(block=config['block']) # https://stackoverflow.com/a/13361748/1639359
+        plt.show(block=config['block']) # https://stackoverflow.com/a/13361748/1639359 
         if config['closefig'] == True or (config['block'] and config['closefig']):
             plt.close(fig)
-
+    
     if config['returnfig']:
         if config['closefig'] == True: plt.close(fig)
         return (fig, axlist)
@@ -852,7 +842,7 @@ def _addplot_collections(panid,panels,apdict,xdates,config):
     apdata = apdict['data']
     aptype = apdict['type']
     external_axes_mode = apdict['ax'] is not None
-
+   
     #--------------------------------------------------------------#
     # Note: _auto_secondary_y() sets the 'magnitude' column in the
     #       `panels` dataframe, which is needed for automatically
@@ -871,18 +861,18 @@ def _addplot_collections(panid,panels,apdict,xdates,config):
     if not isinstance(apdata,pd.DataFrame):
         raise TypeError('addplot type "'+aptype+'" MUST be accompanied by addplot data of type `pd.DataFrame`')
     d,o,h,l,c,v = _check_and_prepare_data(apdata,config)
-    collections = _construct_mpf_collections(aptype,d,xdates,o,h,l,c,v,config,config['style'],config['override_marketcolors'])
+    collections = _construct_mpf_collections(aptype,d,xdates,o,h,l,c,v,config,config['style'])
 
     if not external_axes_mode:
         lo = math.log(max(math.fabs(np.nanmin(l)),1e-7),10) - 0.5
         hi = math.log(max(math.fabs(np.nanmax(h)),1e-7),10) + 0.5
         secondary_y = _auto_secondary_y( panels, panid, lo, hi )
         if 'auto' != apdict['secondary_y']:
-            secondary_y = apdict['secondary_y']
+            secondary_y = apdict['secondary_y'] 
         if secondary_y:
-            ax = panels.at[panid,'axes'][1]
+            ax = panels.at[panid,'axes'][1] 
             panels.at[panid,'used2nd'] = True
-        else:
+        else: 
             ax = panels.at[panid,'axes'][0]
     else:
         ax = apdict['ax']
@@ -908,9 +898,9 @@ def _addplot_columns(panid,panels,ydata,apdict,xdates,config):
             #print("apdict['secondary_y'] says secondary_y is",secondary_y)
 
         if secondary_y:
-            ax = panels.at[panid,'axes'][1]
+            ax = panels.at[panid,'axes'][1] 
             panels.at[panid,'used2nd'] = True
-        else:
+        else: 
             ax = panels.at[panid,'axes'][0]
     else:
         ax = apdict['ax']
@@ -999,7 +989,7 @@ def _plot_mav(ax,config,xdates,prices,apmav=None,apwidth=None):
             mavgs = mavgs,      # convert to tuple
         if len(mavgs) > 7:
             mavgs = mavgs[0:7]  # take at most 7
-
+     
         if style['mavcolors'] is not None:
             mavc = cycle(style['mavcolors'])
         else:
@@ -1049,8 +1039,8 @@ def _valid_addplot_kwargs():
 
         'mav'         : { 'Default'     : None,
                           'Validator'   : _mav_validator },
-
-        'panel'       : { 'Default'     : 0,
+        
+        'panel'       : { 'Default'     : 0, 
                           'Validator'   : lambda value: _valid_panel_id(value) },
 
         'marker'      : { 'Default'     : 'o',
@@ -1066,7 +1056,7 @@ def _valid_addplot_kwargs():
         'linestyle'   : { 'Default'     : None,
                           'Validator'   : lambda value: value in valid_linestyles },
 
-        'width'       : { 'Default'     : None, # width of `bar` or `line`
+        'width'       : { 'Default'     : None, # width of `bar` or `line` 
                           'Validator'   : lambda value: isinstance(value,(int,float)) or
                                                         all([isinstance(v,(int,float)) for v in value]) },
 
@@ -1082,12 +1072,12 @@ def _valid_addplot_kwargs():
 
         'y_on_right'  : { 'Default'     : None,
                           'Validator'   : lambda value: isinstance(value,bool) },
-
+        
         'ylabel'      : { 'Default'     : None,
                           'Validator'   : lambda value: isinstance(value,str) },
 
         'ylim'        : {'Default'      : None,
-                         'Validator'    : lambda value: isinstance(value, (list,tuple)) and len(value) == 2
+                         'Validator'    : lambda value: isinstance(value, (list,tuple)) and len(value) == 2 
                                                                       and all([isinstance(v,(int,float)) for v in value])},
 
         'title'       : { 'Default'     : None,
@@ -1100,7 +1090,7 @@ def _valid_addplot_kwargs():
                           'Validator'   : lambda value: _yscale_validator(value) },
 
         'stepwhere'      : { 'Default'     : 'pre',
-                          'Validator'   : lambda value : value in valid_stepwheres },
+                          'Validator'   : lambda value : value in valid_stepwheres },                  
     }
 
     _validate_vkwargs_dict(vkwargs)
@@ -1112,7 +1102,7 @@ def make_addplot(data, **kwargs):
     '''
     Take data (pd.Series, pd.DataFrame, np.ndarray of floats, list of floats), and
     kwargs (see valid_addplot_kwargs_table) and construct a correctly structured dict
-    to be passed into plot() using kwarg `addplot`.
+    to be passed into plot() using kwarg `addplot`.  
     NOTE WELL: len(data) here must match the len(data) passed into plot()
     '''
     if not isinstance(data, (pd.Series, pd.DataFrame, np.ndarray, list)):

From def4e86e5452ed27908d2c68449319ba9be68caf Mon Sep 17 00:00:00 2001
From: Daniel Goldfarb <dgoldfarb.github@gmail.com>
Date: Thu, 9 Dec 2021 13:05:19 -0500
Subject: [PATCH 08/11] clean up test code

---
 examples/scratch_pad/pr451_test.py       |  29 ++++
 examples/scratch_pad/pr451_testing.ipynb | 180 +++++++++++++++++++++++
 examples/scratch_pad/pr451data.csv       | 124 ++++++++++++++++
 src/Figure_1.png                         | Bin 59649 -> 0 bytes
 src/mplfinance/_version.py               |   2 +-
 src/trial.py                             |  45 ------
 6 files changed, 334 insertions(+), 46 deletions(-)
 create mode 100644 examples/scratch_pad/pr451_test.py
 create mode 100644 examples/scratch_pad/pr451_testing.ipynb
 create mode 100644 examples/scratch_pad/pr451data.csv
 delete mode 100644 src/Figure_1.png
 delete mode 100644 src/trial.py

diff --git a/examples/scratch_pad/pr451_test.py b/examples/scratch_pad/pr451_test.py
new file mode 100644
index 00000000..40b60e6c
--- /dev/null
+++ b/examples/scratch_pad/pr451_test.py
@@ -0,0 +1,29 @@
+import pandas as pd
+import mplfinance as mpf
+import ast 
+
+df = pd.read_csv('pr451data.csv',index_col=0,parse_dates=True)
+
+print(df.head(3))
+
+custom_colors = []
+for i in range(len(df)):
+    if i % 3 == 0:
+        #custom_colors.append(mpf.make_marketcolors(up='#29c9ff', down='#f3b5ff', edge='#29c9ff', wick='#29c9ff', ohlc='#32a852', volume='#a89132'))
+        custom_colors.append(mpf.make_marketcolors(up='#29c9ff',down='#f3b5ff',edge='#29c9ff',wick='#29c9ff',
+                                                   ohlc={'up':'lime','down':'blue'}, volume='#a89132'))
+    elif i%5 == 0:
+        custom_colors.append("#000000")
+    else:
+        custom_colors.append(None)
+
+STYLE = 'binance'
+STYLE = 'yahoo'
+
+#mpf.plot(df, type='candle',style=STYLE,volume=True,block=False,figscale=1.25,savefig='pr451t2no.jpg')
+mpf.plot(df, type='ohlc',style=STYLE,volume=True,block=False,figscale=1.25)
+#mpf.plot(df, type='hollow',style=STYLE,volume=True,block=False,figscale=1.25)
+
+#mpf.plot(df, type='candle',style=STYLE,marketcolor_overrides=custom_colors,volume=True,figscale=1.25,savefig='pr451t2ye.jpg')
+mpf.plot(df, type='ohlc',style=STYLE,marketcolor_overrides=custom_colors,volume=True,figscale=1.25)
+#mpf.plot(df, type='hollow',style=STYLE,marketcolor_overrides=custom_colors,volume=True,figscale=1.25)
diff --git a/examples/scratch_pad/pr451_testing.ipynb b/examples/scratch_pad/pr451_testing.ipynb
new file mode 100644
index 00000000..6e517a9d
--- /dev/null
+++ b/examples/scratch_pad/pr451_testing.ipynb
@@ -0,0 +1,180 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "\n",
+    "## PR451 Testing\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This allows multiple outputs from a single jupyter notebook cell:\n",
+    "from IPython.core.interactiveshell import InteractiveShell\n",
+    "InteractiveShell.ast_node_interactivity = \"all\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import pandas     as pd\n",
+    "import mplfinance as mpf\n",
+    "#import datetime   as datetime\n",
+    "#import numpy      as np\n",
+    "#import matplotlib.dates as mdates\n",
+    "#import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            Open  Close  High  Low      Date.1  Volume\n",
+      "Date                                                  \n",
+      "2019-09-01    29     20    29   20  2019-09-01   10787\n",
+      "2019-09-02    29     31    33   23  2019-09-02   17215\n",
+      "2019-09-03    29     20    29   20  2019-09-03   16697\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x575 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x575 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x575 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# %load pr451_test.py\n",
+    "import pandas as pd\n",
+    "import mplfinance as mpf\n",
+    "import ast \n",
+    "\n",
+    "df = pd.read_csv('pr451data.csv',index_col=0,parse_dates=True)\n",
+    "\n",
+    "df = df.iloc[0:30]\n",
+    "print(df.head(3))\n",
+    "\n",
+    "\n",
+    "custom_colors = []\n",
+    "for i in range(len(df)):\n",
+    "    if i % 3 == 0:\n",
+    "        custom_colors.append(mpf.make_marketcolors(up='#29c9ff', down='#f3b5ff', edge='#29c9ff',\n",
+    "                                                   wick='#29c9ff', ohlc='#32a852', volume='#a89132'))\n",
+    "    elif i%5 == 0:\n",
+    "        custom_colors.append(\"#000000\")\n",
+    "    else:\n",
+    "        custom_colors.append(None)\n",
+    "\n",
+    "#STYLE = 'binance'\n",
+    "STYLE = 'yahoo'\n",
+    "\n",
+    "#mpf.plot(df, type='candle',style=STYLE,volume=True,block=False,figscale=3.0,savefig='pr451t2no.jpg')\n",
+    "mpf.plot(df, type='candle',style=STYLE,volume=True,block=False)\n",
+    "\n",
+    "mpf.plot(df, type='candle',style=STYLE,volume=True,block=False,marketcolor_overrides=custom_colors)\n",
+    "\n",
+    "mpf.plot(df, type='candle',style=STYLE,volume=True,block=False,marketcolor_overrides=custom_colors,mco_faceonly=True)\n",
+    "\n",
+    "#nans = [float('nan')]*len(df.columns)\n",
+    "#for row in [8,9,10,11]:\n",
+    "#    df.loc[df.index[row]] = nans\n",
+    "#mpf.plot(df, type='candle',style=STYLE,volume=True,block=False,figscale=1.5)\n",
+    "#mpf.plot(df, type='candle',style=STYLE,override_marketcolors=custom_colors,volume=True,figscale=3.0,savefig='pr451t2ye.jpg')\n",
+    "#mpf.plot(df, type='candle',style=STYLE,override_marketcolors=custom_colors,volume=True,figscale=1.5)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/scratch_pad/pr451data.csv b/examples/scratch_pad/pr451data.csv
new file mode 100644
index 00000000..fe22c178
--- /dev/null
+++ b/examples/scratch_pad/pr451data.csv
@@ -0,0 +1,124 @@
+Date,Open,Close,High,Low,Date,Volume
+2019-09-01,29,20,29,20,2019-09-01,10787
+2019-09-02,29,31,33,23,2019-09-02,17215
+2019-09-03,29,20,29,20,2019-09-03,16697
+2019-09-04,24,16,24,15,2019-09-04,12104
+2019-09-05,23,20,25,15,2019-09-05,12159
+2019-09-06,22,24,24,20,2019-09-06,13618
+2019-09-07,19,15,22,14,2019-09-07,13645
+2019-09-08,13,13,15,13,2019-09-08,13472
+2019-09-09,20,16,20,14,2019-09-09,17861
+2019-09-10,17,25,25,16,2019-09-10,18565
+2019-09-11,30,24,30,23,2019-09-11,10283
+2019-09-12,24,25,26,19,2019-09-12,10102
+2019-09-13,22,22,26,21,2019-09-13,17100
+2019-09-14,27,28,28,21,2019-09-14,13079
+2019-09-15,26,29,29,26,2019-09-15,10800
+2019-09-16,30,23,30,21,2019-09-16,17927
+2019-09-17,30,27,30,26,2019-09-17,16355
+2019-09-18,22,27,29,22,2019-09-18,12066
+2019-09-19,19,23,23,18,2019-09-19,19733
+2019-09-20,21,14,24,14,2019-09-20,14761
+2019-09-21,24,23,24,19,2019-09-21,19410
+2019-09-22,15,25,25,15,2019-09-22,17238
+2019-09-23,23,20,26,18,2019-09-23,10840
+2019-09-24,18,25,26,17,2019-09-24,17668
+2019-09-25,23,28,29,23,2019-09-25,19267
+2019-09-26,30,26,31,26,2019-09-26,18340
+2019-09-27,33,28,37,27,2019-09-27,18905
+2019-09-28,26,25,34,25,2019-09-28,16682
+2019-09-29,31,27,32,24,2019-09-29,12605
+2019-09-30,30,31,32,23,2019-09-30,12702
+2019-10-01,37,31,38,29,2019-10-01,17485
+2019-10-02,41,39,42,39,2019-10-02,12198
+2019-10-03,39,45,46,37,2019-10-03,14923
+2019-10-04,41,48,50,41,2019-10-04,10326
+2019-10-05,41,44,47,40,2019-10-05,17738
+2019-10-06,47,49,52,46,2019-10-06,13330
+2019-10-07,47,45,48,38,2019-10-07,19102
+2019-10-08,43,44,45,39,2019-10-08,16682
+2019-10-09,40,32,41,32,2019-10-09,12813
+2019-10-10,46,38,46,36,2019-10-10,12306
+2019-10-11,44,42,45,38,2019-10-11,17205
+2019-10-12,44,44,44,35,2019-10-12,14736
+2019-10-13,39,41,42,34,2019-10-13,19296
+2019-10-14,34,31,34,31,2019-10-14,12587
+2019-10-15,32,31,33,31,2019-10-15,14709
+2019-10-16,42,36,43,36,2019-10-16,18555
+2019-10-17,43,40,43,40,2019-10-17,18850
+2019-10-18,37,41,41,37,2019-10-18,14650
+2019-10-19,31,37,38,31,2019-10-19,10405
+2019-10-20,26,34,35,26,2019-10-20,12074
+2019-10-21,34,30,34,26,2019-10-21,17407
+2019-10-22,23,32,32,22,2019-10-22,18649
+2019-10-23,20,23,27,18,2019-10-23,16161
+2019-10-24,20,18,20,18,2019-10-24,14008
+2019-10-25,20,21,25,19,2019-10-25,16251
+2019-10-26,19,17,24,17,2019-10-26,14594
+2019-10-27,29,20,29,19,2019-10-27,10994
+2019-10-28,28,26,30,23,2019-10-28,16978
+2019-10-29,30,22,30,22,2019-10-29,11373
+2019-10-30,18,26,28,18,2019-10-30,11589
+2019-10-31,26,21,31,21,2019-10-31,14787
+2019-11-01,19,25,26,18,2019-11-01,15088
+2019-11-02,25,26,27,22,2019-11-02,10827
+2019-11-03,24,28,28,22,2019-11-03,12281
+2019-11-04,24,15,24,15,2019-11-04,19768
+2019-11-05,22,17,23,15,2019-11-05,11398
+2019-11-06,20,23,25,18,2019-11-06,15819
+2019-11-07,20,26,26,17,2019-11-07,14057
+2019-11-08,15,14,24,14,2019-11-08,15366
+2019-11-09,11,12,18,11,2019-11-09,10403
+2019-11-10,16,13,20,13,2019-11-10,15611
+2019-11-11,18,15,18,10,2019-11-11,14544
+2019-11-12,18,11,19,11,2019-11-12,12009
+2019-11-13,16,16,22,14,2019-11-13,18481
+2019-11-14,14,11,20,11,2019-11-14,11876
+2019-11-15,21,16,23,15,2019-11-15,16481
+2019-11-16,23,23,23,15,2019-11-16,18562
+2019-11-17,26,18,26,17,2019-11-17,16095
+2019-11-18,24,24,26,21,2019-11-18,14052
+2019-11-19,30,23,31,22,2019-11-19,12698
+2019-11-20,27,25,32,22,2019-11-20,10097
+2019-11-21,24,28,30,22,2019-11-21,12405
+2019-11-22,18,22,23,18,2019-11-22,11883
+2019-11-23,18,21,22,17,2019-11-23,15834
+2019-11-24,21,18,24,18,2019-11-24,17172
+2019-11-25,24,15,25,15,2019-11-25,11327
+2019-11-26,27,25,27,17,2019-11-26,11876
+2019-11-27,30,24,32,23,2019-11-27,17715
+2019-11-28,28,22,28,19,2019-11-28,14520
+2019-11-29,14,16,19,13,2019-11-29,18113
+2019-11-30,22,19,23,19,2019-11-30,16670
+2019-12-01,31,26,32,25,2019-12-01,15978
+2019-12-02,29,26,30,26,2019-12-02,15367
+2019-12-03,28,28,28,28,2019-12-03,18364
+2019-12-04,27,27,28,27,2019-12-04,17550
+2019-12-05,37,41,42,34,2019-12-05,16636
+2019-12-06,31,28,36,28,2019-12-06,15326
+2019-12-07,33,28,36,28,2019-12-07,15007
+2019-12-08,37,38,38,35,2019-12-08,16018
+2019-12-09,44,43,44,43,2019-12-09,17392
+2019-12-10,36,40,40,33,2019-12-10,17550
+2019-12-11,27,35,36,26,2019-12-11,13408
+2019-12-12,32,24,32,23,2019-12-12,19170
+2019-12-13,29,34,34,29,2019-12-13,12548
+2019-12-14,34,31,36,28,2019-12-14,19605
+2019-12-15,33,31,37,31,2019-12-15,14594
+2019-12-16,43,43,43,39,2019-12-16,16895
+2019-12-17,50,40,50,40,2019-12-17,18880
+2019-12-18,42,48,48,39,2019-12-18,18207
+2019-12-19,44,39,49,39,2019-12-19,17279
+2019-12-20,53,46,53,45,2019-12-20,11028
+2019-12-21,51,44,51,43,2019-12-21,19840
+2019-12-22,44,49,49,41,2019-12-22,10863
+2019-12-23,43,40,45,38,2019-12-23,13153
+2019-12-24,50,42,50,40,2019-12-24,16929
+2019-12-25,51,42,52,42,2019-12-25,17419
+2019-12-26,49,56,56,49,2019-12-26,13232
+2019-12-27,47,48,50,45,2019-12-27,19252
+2019-12-28,54,53,54,53,2019-12-28,11631
+2019-12-29,46,53,53,46,2019-12-29,14527
+2019-12-30,46,46,55,46,2019-12-30,10600
+2019-12-31,54,58,59,53,2019-12-31,17911
+2020-01-01,55,48,56,47,2020-01-01,17827
diff --git a/src/Figure_1.png b/src/Figure_1.png
deleted file mode 100644
index 062eb929699fec7bc2f16bf363069783a7f67305..0000000000000000000000000000000000000000
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diff --git a/src/mplfinance/_version.py b/src/mplfinance/_version.py
index ac383efb..8a17c325 100644
--- a/src/mplfinance/_version.py
+++ b/src/mplfinance/_version.py
@@ -1,5 +1,5 @@
 
-version_info = (0, 12, 7, 'alpha', 18)
+version_info = (0, 12, 8, 'beta', 4)
 
 _specifier_ = {'alpha': 'a','beta': 'b','candidate': 'rc','final': ''}
 
diff --git a/src/trial.py b/src/trial.py
deleted file mode 100644
index d75885ec..00000000
--- a/src/trial.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import pandas as pd
-import random
-from datetime import date, timedelta
-from mplfinance.plotting import plot
-from mplfinance._styles import make_marketcolors
-
-dict_data = []
-start_date = date(2019, 1, 1)
-end_date = date(2020, 1, 1)
-delta = timedelta(days=1)
-start = 20
-end = 30
-while start_date <= end_date:
-    openval = random.randint(start, end)
-    closeval = random.randint(start, end)
-    high = random.randint(max(openval, closeval), end)
-    low = random.randint(start, min(openval, closeval))
-    change = random.randint(-5, 5)
-    volume = random.randint(10000, 20000)
-    dict_data.append({
-        "Open": openval,
-        "Close": closeval,
-        "High": high,
-        "Low": low,
-        "Date": start_date,
-        "Volume": volume
-    })
-    start += change
-    end += change
-    start_date += delta
-
-df = pd.DataFrame(dict_data)
-df.index = pd.to_datetime(df['Date'])
-
-custom_colors = []
-for i in range(len(df)):
-    if i % 3 == 0:
-        custom_colors.append(make_marketcolors(up='#29c9ff', down='#f3b5ff', edge='#29c9ff', wick='#29c9ff', ohlc='#32a852', volume='#a89132'))
-    elif i%5 == 0:
-        custom_colors.append("#000000")
-    else:
-        custom_colors.append(None)
-
-plot(df, type='candle', style='yahoo', override_marketcolors=custom_colors, volume=True)
-# plot(df, type='candle', style='yahoo', volume=True)

From fb9e6ff56690720602c1307c440a35c0f080e22f Mon Sep 17 00:00:00 2001
From: Daniel Goldfarb <dgoldfarb.github@gmail.com>
Date: Thu, 9 Dec 2021 18:24:40 -0500
Subject: [PATCH 09/11] add marketcolor_overrides tutorial

---
 examples/marketcolor_overrides.ipynb | 795 +++++++++++++++++++++++++++
 examples/scratch_pad/pr451_test.py   |   8 +-
 2 files changed, 800 insertions(+), 3 deletions(-)
 create mode 100644 examples/marketcolor_overrides.ipynb

diff --git a/examples/marketcolor_overrides.ipynb b/examples/marketcolor_overrides.ipynb
new file mode 100644
index 00000000..6e436e51
--- /dev/null
+++ b/examples/marketcolor_overrides.ipynb
@@ -0,0 +1,795 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This allows multiple outputs from a single jupyter notebook cell:\n",
+    "from IPython.core.interactiveshell import InteractiveShell\n",
+    "InteractiveShell.ast_node_interactivity = \"all\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "<a name=\"mcoverride\"></a>\n",
+    "# Coloring Individual Candlesticks\n",
+    "\n",
+    "- Users can **color one or more specific candlesticks (or ohlc bars) differently than others**.\n",
+    "  - Users may choose to do this in order to highlight a specific pattern or technical signal.\n",
+    "\n",
+    "\n",
+    "- The **`marketcolor_overrides` kwarg** is used to specify individual candle colors.\n",
+    "\n",
+    "\n",
+    "- **`marketcolor_overrides`** must be set it to an interable (`list`,`tuple`,`ndarray`) **that is the *same length as the dataframe*** being plotted.\n",
+    "  - The simplest way to do this is to create a \"marketcolor overrides\" column in the dataframe.\n",
+    "\n",
+    "\n",
+    "- **Rows where the user wants to override the candle color must contain a \"color-like\" object:**\n",
+    "  - Examples of color-like objects include:\n",
+    "    - a **string** such as `'yellow'` or `'#ffff00'`\n",
+    "    - an **rgb or rgba tuple** such as `(255, 255, 0)` or `(255, 255, 0, 0.75)`\n",
+    "    - an **mplfinance marketcolor object**, created with `mpf.make_marketcolors()`\n",
+    "\n",
+    "\n",
+    "- **Rows where the user does *NOT* want to override** the candle color ***MUST*** contain **`None`** values.\n",
+    "\n",
+    "---\n",
+    "---\n",
+    "\n",
+    "### To illustrate, we will first give simple examples using just a few candles.\n",
+    "### This is followed by examples using more realistic, larger, data sets:\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pandas version= 1.1.2\n",
+      "mplfinance version= 0.12.8b4\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import pandas as pd\n",
+    "print('pandas version=',pd.__version__)\n",
+    "\n",
+    "import mplfinance as mpf\n",
+    "print('mplfinance version=',mpf.__version__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Start with a simple, 5-row DataFrame:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2021-10-11</th>\n",
+       "      <td>131.4</td>\n",
+       "      <td>133.2</td>\n",
+       "      <td>131.3</td>\n",
+       "      <td>132.1</td>\n",
+       "      <td>19591</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-10-12</th>\n",
+       "      <td>131.9</td>\n",
+       "      <td>132.7</td>\n",
+       "      <td>131.3</td>\n",
+       "      <td>131.4</td>\n",
+       "      <td>21467</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-10-13</th>\n",
+       "      <td>132.0</td>\n",
+       "      <td>133.2</td>\n",
+       "      <td>131.5</td>\n",
+       "      <td>131.8</td>\n",
+       "      <td>20406</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-10-14</th>\n",
+       "      <td>130.9</td>\n",
+       "      <td>132.7</td>\n",
+       "      <td>130.6</td>\n",
+       "      <td>132.1</td>\n",
+       "      <td>22611</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-10-15</th>\n",
+       "      <td>131.6</td>\n",
+       "      <td>131.8</td>\n",
+       "      <td>130.7</td>\n",
+       "      <td>131.0</td>\n",
+       "      <td>22001</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Open   High    Low  Close  Volume\n",
+       "2021-10-11  131.4  133.2  131.3  132.1   19591\n",
+       "2021-10-12  131.9  132.7  131.3  131.4   21467\n",
+       "2021-10-13  132.0  133.2  131.5  131.8   20406\n",
+       "2021-10-14  130.9  132.7  130.6  132.1   22611\n",
+       "2021-10-15  131.6  131.8  130.7  131.0   22001"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x575 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ix = pd.DatetimeIndex(['2021-10-11','2021-10-12','2021-10-13','2021-10-14','2021-10-15'])\n",
+    "\n",
+    "df = pd.DataFrame(dict(  Open=[131.4, 131.9, 132.0, 130.9, 131.6],\n",
+    "                         High=[133.2, 132.7, 133.2, 132.7, 131.8],\n",
+    "                          Low=[131.3, 131.3, 131.5, 130.6, 130.7],\n",
+    "                        Close=[132.1, 131.4, 131.8, 132.1, 131.0],\n",
+    "                       Volume=[19591, 21467, 20406, 22611, 22001]),\n",
+    "                  index=ix)\n",
+    "df\n",
+    "mpf.plot(df,volume=True,style='yahoo',type='candle')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "--- \n",
+    "\n",
+    "#### Suppose we want to color the third candle yellow, and the fourth candle blue.\n",
+    "#### Then we create a list of overrides as follows: `mco = [None,None,'yellow','blue',None]`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x575 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mco = [None,None,'yellow','blue',None]\n",
+    "mpf.plot(df,volume=True,style='yahoo',type='candle',marketcolor_overrides=mco)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "#### We can change only the \"face\" (body) of the candle, with the `mco_faceonly` kwarg:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x575 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mco = [None,None,'yellow','blue',None]\n",
+    "mpf.plot(df,volume=True,style='yahoo',type='candle',marketcolor_overrides=mco,mco_faceonly=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "#### marketcolor overrides work also with ohlc plots\n",
+    "- Presently only `type='candle'` and `type='ohlc'` are supported.\n",
+    "- If there is enough demand, we will consider also supporting types 'hollow_and_filled', 'renko', and 'point_and_figure'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x575 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mco = [None,None,'yellow','blue',None]\n",
+    "mpf.plot(df,volume=True,style='yahoo',type='ohlc',marketcolor_overrides=mco)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "#### We can also fully customize indivual candles using a marketcolors object.\n",
+    "#### Notice that with the approach, we do not have to know whether a candle is up or down;<br> a single marketcolors object can specify different colors for up candles and down candles:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AAIDpCJ0AAAAwHaETAADAB7Zs2aLhw4crLi5OISEhWrduXZn5M2fOVGJiotq0aaOoqCglJSVp27Zt3vkHDhzQ+PHj1alTJ7Vu3VqdO3fW888/L5fLVWm/J0+e1IQJExQdHa1LLrlEo0ePVnZ2tinreD4InQAAAD5QWFio+Ph4vfjiixXOv/TSS/XCCy9oy5Yt+uijjxQREaHBgwfr6NGjkqT//Oc/8ng8eumll/T111/rueee0xtvvKHp06dX2u/UqVO1fv16vfnmm1q7dq0OHz6sUaNG1WgdXC6X0tLSVFxcXKOfrwxXrwMAAPhAv3791K9fv7POHzZsWJnnzz77rJKTk7Vz50716tVL1113na677jrv/KioKKWnp+v1118/a/DMy8vT22+/rcWLF+uaa66RJL366qvq2rWrtm7dqoSEhCqNvbCwUJMnT9by5cslSdu2bVNUVJQmTZqk1q1b66GHHqrScipTrdDpdrvlcDjKTTv9sb5xe0ruIWT8/M8yhuF9NBzW3c6kdB3dHk+9e809P7/Wnnq47qXS09P1008W37rnyG4FStq9e7ccRy3tWs2aNVP79u2t7fQ0bnfJ77bx83+WMn55NBzW9V26nm63R263xetss/r+eWqXyupu/PxZm5+fXyb/BAYGKjAw8Lz6dblceuuttxQUFKT4+PiztsvPz1dISMhZ53/33XcqKipS7969vdMuu+wyXXLJJdUKnc8884x27NihNWvWlAnHvXv31syZM60PnWlpad4X4Ezp6ennPZjaKONUyTkTbnex/DzWbyCt3ji5jZL+MvZnqHHgCUv7tlvp+TEHDx7UqVOnbB6N9TIyMjRo0CDL+23RSBrSQXov+X+UY8Nb7m9/+5siIyOt71hSRkYjSZfJ7XabcqirKord1vZbuk3LyMhQ48b1axtTqr5+ntqtoro7HA5FRkYqPj5eBad9Y8HkyZM1ZcqUGvWzfv16jRs3ToWFhQoPD9fq1avVokWLCtvu3btXr732WqWH17OystSgQQMFBweXmd6qVStlZWVVeVzr1q3T66+/roSEhDIB+/LLL9f+/furvJzKVCt0xsTEVLinMz09Xe3bt5fTad3N0S8UhfmNpEzJ6fSX08Kbw8sw5Ha7S2pu4Z5Oj8cheaTIqEjFBkVY1u+FoPSv2rZt2yoqKsrewdigsLBQkpQcnKw4/zjL+jVkyPNfj+5s7CdHY+ve67uLd2tU3ii1bNlSsbGxlvV7usLCkvV1Op3y97f4bCijJHD6O/1l5b3hSz9HIiMjFRtb//Z01ufPU7tUVnfDMORyubRjx45yezprqmfPntq8ebNycnK0dOlSjR07Vhs2bFDLli3LtDt06JCGDh2qpKQkjRkzpsb9VVVOTk65MUjS8eO++za0am3FnE5nudB5+rz6+Evi9Cu5FsshS7fLvxxSd1j7XSGlfTn9/Ord6+3382vtVw/XXfolDMQFxOlXAb+yrmNDKvazPvyU9mXntq20W8fP/1nJe0jdIUv7Lu3L6fRTPfw1k1R/P0/tVlHdS4/uBgUFnTX/VFeTJk0UHR2t6OhoJSQkqEuXLkpOTtbDDz/sbZOZmamBAwcqMTFRL7/8cqXLCwsLk8vlUl5eXpm9ndnZ2QoLC6vyuK666ip98sknuuuuuyTJu77JyclKTEysxhqeHRcSAQAA2MTj8ZS5JdKhQ4c0cOBAderUSfPnz/fu8DibTp06KSAgQJs2bdLAgQMllZwO+cMPP1T5fE5Jevzxx3XzzTfr+++/V3Fxsf785z9rz549+vbbb7V27dqardwZuGUSAACADxQUFCg1NVWpqamSSs5NTk1N1cGDB3X8+HE988wz2rp1qw4cOKCUlBTdd999yszM9J4vf+jQIQ0YMECXXHKJpk+frqNHjyorK6vMuZmHDh1SYmKitm/fLkkKDg7WyJEjNW3aNP39739XSkqK/vjHPyohIaFaobNbt27avHmz3G63OnTooI0bN+qiiy7Sxx9/rKuuuson9WFPJwAAgA+kpKRowIAB3ufTpk2TJI0YMUJz585VWlqaVqxYoZycHIWGhqpz58768MMPFRdXcp78F198ob1792rv3r3q2LFjmWXn5uZKkoqLi5WWlqYTJ3650G7GjBny8/PT6NGj5XK51KdPH82ePbva42/Xrp3mzZtX7Z+rKkInAACAD/To0cMbDiuSnJxc6c/feuutuvXWWyttExERUa6Phg0bavbs2TUKmqU++eQTOZ1O9e3bt8z0zz77TB6Pp9L7j1YVh9cBAADquaeffvqs9yp9+umnfdIHoRMAAKCe27t3ry6//PJy0y+77DLt27fPJ31weB2104ED0s/3jbSK4/DhkseMDMmOm8M3bixF1K97owIArBEUFKT9+/cr4ozPmb1796px48Y+6YPQidrnwAH5D73Z8m4v8vfXwIta6KKpj8nfrm+HWfkOwRMA4HM33HCDHn30Ub399ttq166dpJLA+dhjj+mGG27wSR+ETtQ+P+/hNJo2lSz8lpZgGbqp4Lj8mzaRYfGNulVcLEdBgeV7dwEA9cPTTz+tYcOGKTExURdffLGkktszdevWrdKv4awOQidqL39/S0OnZPzSr9WhEwAAEwUHB+vjjz/Wxo0btWPHDjVs2FAdO3bU1Vdf7bM+CJ0AAACQw+FQnz591KdPH1OWT+gEAACohxYtWqQxY8aoYcOGWrRoUaVt77777vPuj9AJAABQDy1YsEDDhg1Tw4YNtWDBgrO2czgchE4AAADUzHfffVfh/5uFm8MDAADUY0VFRercubP27Nljaj+ETgAAgHosICBApyz40hMOrwOolt3Fu63t0JDcbrecHqeld6qyfD0BwEb/8z//o3nz5umVV16Rv0m3IyR0AqiSpk2bSpJG/TjK0n5bNJKGdJDe2yXlnLC0a0m/rDdgtry8PH399dcKDw9XaGio3cNBPfOvf/1Lmzdv1saNG9WhQ4dyX32ZnJx83n0QOgFUSUxMjHbu3KmCggJL+/Vk71LAmrEa+9Qb8mvVwdK+mzZtqpiYGEv7RP2Vl5enb775Rn369CF0wnLBwcEaMGCAqX0QOgFUmR0BrGC/W3vXSLGxsWoa1dny/gGgLvN4PHrllVeUnp6uoqIi9ezZU1OmTFGjRo183hcXEgEAANRTc+bM0fTp09W0aVO1bt1ar732miZOnGhKX4ROAACAemrFihWaPXu23nvvPf3lL3/R8uXL9e6778rj8fi8L0InAABAPfXDDz+oX79+3ue9e/eWw+FQZmamz/sidAIAANRTxcXFatiwYZlpAQEBKi4u9nlfXEgEAABQTxmGoXvvvVeBgYHeaSdPntTDDz9c5rZJ3DIJAAAANTZixIhy026++WZT+iJ0AgAA1FPz58+3rC/O6QQAAIDpCJ0AAAAwHaETAAAApiN0AgAAwHSETgAAAJiO0AkAAADTEToBAABgOkInAAAATEfoBAAAgOn4RiIAAM504IBUWGhpl47Dh0seMzKkU6cs7VuNG0sREdb2iXqH0AkAwOkOHJD/UHO+e7oyF/n7a+BFLXTR1MfkX1xsef/FK98heMJUhE4AAE738x5Oo2lTyd+6j8lgGbqp4Lj8mzaRIYdl/aq4WI6CAsv37KL+IXQCAFARf39LQ6dk/NKvlaETsAgXEgEAAMB0hE4AAACYjtAJAAAA0xE6AQAAfGDLli0aPny44uLiFBISonXr1pWZP3PmTCUmJqpNmzaKiopSUlKStm3bVqbN7Nmzdf311+viiy9WZGRklfq99957FRISUubf0KFDfbZevkLoBAAA8IHCwkLFx8frxRdfrHD+pZdeqhdeeEFbtmzRRx99pIiICA0ePFhHjx71tikqKlJSUpLuuOOOavXdt29fff/9995/S5YsOa91MQNXrwMAAPhAv3791K9fv7POHzZsWJnnzz77rJKTk7Vz50716tVLkvToo49KkpYtW1atvgMDAxUWFlbNEVurWqHT7XbL4XCUm3b6Y33j9ngkldzowrCyY8PwPhoO626tUbqObo/Httfc8XPNra76aSWXw2Hpq63S9fR4PDLq2e+a5+fX22Pje84ubnfJ77bx83+WMn55NCx8v5eup9vtkdtt9e9Zifq3jam/2xep8hxj/Pyi5Ofnl8k/gYGBCgwMPK9+XS6X3nrrLQUFBSk+Pv68liVJX375pWJiYtS8eXP17NlTjz32mEJDQ897ub5UrdCZlpbmfQHOlJ6e7pMB1TYZp7IlSW53sfw81m8grf4Qdhsl/WXsz1DjwBOW9l2q0f4MxUgqLrZn4+h2W/9NISp2K0Aldben6vYxjp+Q49d362DOCTlO7rF7OJbKyGgk6TK53W4V2/ANNZJUbPH7vXSblpGRocaN2cZYoh5vX05XUY5xOByKjIxUfHy8CgoKvNMnT56sKVOm1Kif9evXa9y4cSosLFR4eLhWr16tFi1a1HjcUsmh9f79+ysyMlL79+/X9OnTNWzYMH3yySdyOp3ntWxfqlbojImJqXBPZ3p6utq3b39BrZhVCvMbSZmS0+kvp5+F628YcrvdJTW3cE+nx+OQPFJkVKRig+z5urTStfX3d1p642bDKPkwcDr9rSx5GZFRkTJiY+3p3CZud3ulN2lZL7cxhYUlbzSn0yl/S29SLskoCZz+Tn9L71Ne+hpHRkYqNtamPZ0/P9a3bUx93L5IlecYwzDkcrm0Y8eOcns6a6pnz57avHmzcnJytHTpUo0dO1YbNmxQy5Yta7zMIUOGeP+/Y8eO6tixozp37qwvv/zSe9j+QlCt3yan01kudJ4+r759IEiS06/kWiyHrP3+CO8hdYfD0n5L+3L6+dn3evuVXv9mbdVLD3eVlN7qT4SS/vz8/KR6+Hsm1c9tTOnqOn7+z0reQ+oOWdp3aV9Op599b/V6t41h+yJVvI0pPbobFBR01vxTXU2aNFF0dLSio6OVkJCgLl26KDk5WQ8//LBPli9JUVFRatGihfbu3XtBhU6uXgcAALCJx+ORy+Xy6TL/7//+T8eOHbvgLiwidAIAAPhAQUGBUlNTlZqaKqnk3OTU1FQdPHhQx48f1zPPPKOtW7fqwIEDSklJ0X333afMzEwNGjTIu4yDBw8qNTVVP/zwgzwej3d5p59TmpiYqLVr13r7fPzxx73L3bRpk2677TZFR0erb9++1hbgHLhlEgAAgA+kpKRowIAB3ufTpk2TJI0YMUJz585VWlqaVqxYoZycHIWGhqpz58768MMPFRcX5/2Z559/XsuXL/c+v+aaayRJa9asUY8ePSSVXNidn58vqeS0gF27dmnFihXKy8tTeHi4+vTpo6lTp573Ffa+RugEAADwgR49eig3N/es85OTk8+5jAULFmjBggWVtjm9j0aNGum9996r+iBtxOF1AAAAmI7QCQAAANNxeN1Hig235Dl3O18xVHKjdo/H2hupFBsX0LdVWH6zbEPy3iza4lsm2XRjcAAAfIXQeZ6a+jeUJP1YbO33ODQ6KcXuk/a0k040tLRrSb+sty0aN5YkOU67ks8KP/r764uLWqj30Rw1tysE/rzuAADUNoTO8xTTJEw7e01XQfFJS/vNOnhIq9a/oWd+O1ZhbS+2tO+m/g0V08TGe39FRKh45TtSYaGl3R49fFgfLFum+IcfUtPwcEv7llQSOCPs+RYoAADOF6HTB+wIYPuOlRzLj20SrnbBkZb3bzsbwpfx860njMhIqV07y/sHAKA240IiAAAAmI7QCQAAANMROgEAAGA6QicAAABMR+gEAACA6QidAAAAMB2hEwAAAKYjdNZSwcHB6tq1q4KDg+0eSr1BzQEAqDlCZy0VHBysbt26EYAsRM0BAKg5QicAAABMR+gEAACA6QidAAAAMB2hEwAAAKYjdAIAAMB0hE4AAACYjtAJAAAA0xE6AQAAYDpCJwAAAExH6AQAAIDpCJ0AAAAwHaETAAAApiN0AgAAwHSETgAAAJiO0AkAAADTEToBAABgOkInAAAATEfoBAAAgOkInQAAADAdoRMAAACmI3QCAADAdIROAAAAmI7QCQAAANMROgEAAGA6QicAAABMR+gEAADwgS1btmj48OGKi4tTSEiI1q1bV2b+zJkzlZiYqDZt2igqKkpJSUnatm1bmTazZ8/W9ddfr4svvliRkZFV6tcwDM2YMUOXX365WrduraSkJP33v//12Xr5CqETAADABwoLCxUfH68XX3yxwvmXXnqpXnjhBW3ZskUfffSRIiIiNHjwYB09etTbpqioSElJSbrjjjuq3O+8efO0aNEizZ07V59++qkaN26sIUOG6OTJk+e9Tr7kX53GbrdbDoej3LTTH2EN6m49am6P+lx3t7tke2v8/J+ljF8eDYd1fZeup9vtkdtt8Tr/zOHxlPxPcbFkYd0NQ5LbLcOQzvioNVdxye+Wx+ORUS9/z86+jTGMktc/Pz+/TP4JDAxUYGBgufb9+vVTv379ztrXsGHDyjx/9tlnlZycrJ07d6pXr16SpEcffVSStGzZsiqN3zAM/fnPf9aECRN04403SpIWLlyo2NhYrVu3TkOGDKnScqxQrdCZlpbmfQHOlJ6e7pMBoXqou/WouT3qY90zMhpJukxut1vFxcW2jKHYbW2/pR/8GRkZatz4hKV9l2pwOEuXS3IUFFjab56/v764qIV6H81Rcxte772Hs+SyvNcLR0XbGIfDocjISMXHx6vgtPfD5MmTNWXKlPPqz+Vy6a233lJQUJDi4+NrvJyMjAxlZWWpd+/e3mnBwcHq0qWLtm7dWntDZ0xMTIV7OtPT09W+fXs5nU6fDg5nR92tR83tUZ/rXlhYsr11Op3y96/W5vr8GSWB09/pL1m41630NY6MjFRsrD17OhUbK9c7K6TCQku7zcrM1Ad//aviHrhfjVu3trRvNW6sdm3bWtvnBaKybYxhGHK5XNqxY0e5PZ01tX79eo0bN06FhYUKDw/X6tWr1aJFixovLysrS5LUsmXLMtNbtWql7OzsGi/XDNXaijmdznKh8/R59e0D4UJA3a1Hze1RH+teurqOn/+zkveQukOW9l3al9PpJ1tf7qgoy7v0a9So5DE6Wn7t2lnef31X0Tam9OhuUFDQWfNPdfXs2VObN29WTk6Oli5dqrFjx2rDhg3lQmNdxIVEAAAAFmnSpImio6OVkJCgP/3pT/L391dycnKNlxcWFiZJOnLkSJnp2dnZatWq1XmN1dcInQAAADbxeDxyuWp+Nm1kZKTCwsK0adMm77T8/Hxt375dCQkJvhiiz1h8khAAAEDdVFBQoH379nmfZ2RkKDU1Vc2bN1doaKjmzJmjG264QWFhYTp27JiWLFmizMxMDRo0yPszBw8e1I8//qgffvhBHo9HqampkqR27dqpadOmkqTExEQ98cQT6t+/vxwOh+655x7Nnj1b0dHRioyM1IwZMxQeHq6bbrrJ2gKcA6ETAADAB1JSUjRgwADv82nTpkmSRowYoblz5yotLU0rVqxQTk6OQkND1blzZ3344YeKi4vz/szzzz+v5cuXe59fc801kqQ1a9aoR48ekkruJpSfn+9t88ADD6iwsFAPPfSQ8vLy9Jvf/EYrV65Uw4YNTV3f6iJ0AgAA+ECPHj2Um5t71vlVOXdzwYIFWrBgQaVtzuzD4XBo6tSpmjp1atUGahPO6QQAAIDpCJ0AAAAwHYfXAeACd2B3E8v7NGTI7XaX3J/Zwvt02rGuAKxB6ASAC9TPF6pq1qgrLO+7RaNMDemwSO/tuls5Jyz+dhz9su4A6g5CJwBcoGJipJ07i2TxV4BLkjzZGQpY87TGPvU7+bW6yNK+mzYtWXcAdQuhEwAuYHaFr4L9hvaukWJjDTWNsmcMAOoWLiQCAACA6QidAAAAMB2hEwAAAKYjdAIAAMB0hE4AAACYjtAJAAAA0xE6AQAAYDpCJwAAAExH6AQAAIDpCJ0AAAAwHaETAAAApiN0AgAAwHSETgAAAJiO0AkAAADTEToBAABgOkInAAAATEfoBAAAgOkInQAAXACCg4PVtWtXBQcH2z0UwBSETgAALgDBwcHq1q0boRN1FqETAAAApiN0AgAAwHSETgAAAJiO0AkAAADTEToBAABgOkInAAAATEfoBAAAgOkInQAAADAdoRMAUI5/ULgcv75b/kHhdg8FQB1B6AQAlBMQ3FrOxD8oILi13UMBUEcQOgEAAGA6QicAAABMR+gEAACA6QidAAAAMB2hEwAAwAe2bNmi4cOHKy4uTiEhIVq3bl2Z+TNnzlRiYqLatGmjqKgoJSUladu2bWXa5Obm6s4771RERIQiIyM1fvx4FRQUVNpv//79FRISUubfQw895PP1O1+ETgAAAB8oLCxUfHy8XnzxxQrnX3rppXrhhRe0ZcsWffTRR4qIiNDgwYN19OhRb5s777xT33//vVatWqUVK1boq6++0oMPPnjOvseMGaPvv//e++/pp5/21Wr5jL/dAwAAAKgL+vXrp379+p11/rBhw8o8f/bZZ5WcnKydO3eqV69e2rNnjz777DN9/vnn6ty5syRp1qxZuvnmmzV9+nS1bn32W5g1atRIYWFhvlkRk1QrdLrdbjkcjnLTTn+ENai79ai5Pai7Pai79ai5PSqru2EYkqT8/Pwy+ScwMFCBgYHn1a/L5dJbb72loKAgxcfHS5K2bt2q4OBgb+CUpN69e8vPz0/bt29X//79z7q8d999V++8845atWql3/3ud5o4caIaN258XmP0tWqFzrS0NO8LcKb09HSfDAjVQ92tR83tQd3tQd2tR83tUVHdHQ6HIiMjFR8fX+a8ysmTJ2vKlCk16mf9+vUaN26cCgsLFR4ertWrV6tFixaSpKysLLVs2bJMe39/f4WEhCgrK+usyxw6dKjatm2r8PBw7dy5U08//bTS09OVnJxcozGapVqhMyYmpsI9nenp6Wrfvr2cTqdPB4ezo+7Wo+b2oO72oO7Wo+b2qKzuhmHI5XJpx44d5fZ01lTPnj21efNm5eTkaOnSpRo7dqw2bNhQLmxWx+233+79/44dOyo8PFyDBg3Svn371K5duxov19eqFDpP3+V8Zuh0OBzy8/OTw+EoNw/moe7Wo+b2oO72oO7Wo+b2qKzupUd3mzRpIn9/31wG06RJE0VHRys6OloJCQnq0qWLkpOT9fDDDyssLExHjhwp0764uFi5ubnVOl+zS5cukqS9e/fWvtBZyuVyVTg9IiJCRUVFKioq8smgUDXU3XrU3B7U3R7U3XrU3B521t3j8XjzVUJCgvLy8pSSkqKrrrpKkrR582Z5PB5vkKyK1NRUSbrgLiyqVuhs2LBhhYfX09LSFBMTw+EAC1F361Fze1B3e1B361Fze1RWd8MwdPLkySovq6CgQPv27fM+z8jIUGpqqpo3b67Q0FDNmTNHN9xwg8LCwnTs2DEtWbJEmZmZGjRokCQpNjZWffv21QMPPKC5c+eqqKhIkyZN0uDBg71Xrh86dEhJSUlauHChunTpon379mnlypXq16+fQkNDtWPHDk2bNk3du3f3XqB0oahS6Cx9ESra9exwOGQYBocDLEbdrUfN7UHd7UHdrUfN7VGVulf1j4CUlBQNGDDA+3zatGmSpBEjRmju3LlKS0vTihUrlJOTo9DQUHXu3Fkffvih4uLivD+zePFiTZw4UUlJSXI4HBo4cKBmzpzpnV9cXKy0tDSdOHFCkhQQEKAvvvhCCxcuVGFhodq0aaMBAwZowoQJ1a6F2bhPJwAAgA/06NFDubm5Z51flavJQ0JCtGTJkrPOj4iIKNPHJZdcUu6bjy5UfCMRAAAATEfoBAAAgOkInQAAADAdoRMAAACmI3QCAADAdIROAAAAmI7QCQAAANMROgEAAGA6QicAAABMxzcSAYDJRm17TTmu43YPo5oMeTyG/L5ZL6n2fCVjiwZNlPzru+weBoAKEDoBwGQ5ruNqFBho9zDqhZxTtS3cA/UHh9cBAABgOkInAAAATMfhdQBAneN3z73SsWN2D6Na/CR1MDzyc9TC/UGhofL8eYHdo8AFjtAJAKh7jh2To83Fdo+i2px2D6CGjP87ZPcQUAvUwj+nAAAAUNuwpxO2qW2Hvzj0BQBAzRE6YZ9aePiLQ18AANRMLdxlAwAAgNqG0AkAAADTEToBAABgOkInAACAD8ydO1d9+vRR27ZtFRMTo9tuu01paWll2pw8eVITJkxQdHS0LrnkEo0ePVrZ2dll2hw8eFA333yzLr74YsXExOjxxx9XcXFxmTZffvmlevXqpbCwMP3qV7/SsmXLyo1n8eLFuvLKKxUeHq7rrrtO27dv9/1KVwOhEwAAwAe++uorjRs3Tp988olWrVqloqIiDR48WMePH/e2mTp1qtavX68333xTa9eu1eHDhzVq1CjvfLfbrVtuuUVFRUX6+OOPtWDBAi1fvlwzZszwtsnIyNAtt9yinj17avPmzbrnnnt0//3367PPPvO2WbVqlR577DFNnjxZX3zxheLj4zVkyBAdOXLEmmJUgKvXgXpk1LbXlOM6fu6GFxRDHo8hv2/WS3LYPZhqadGgiZJ/fZfdwwBgkZUrV5Z5vmDBAsXExCglJUVXX3218vLy9Pbbb2vx4sW65pprJEmvvvqqunbtqq1btyohIUGff/659uzZo/fff1+tWrXSFVdcoalTp+qpp57SlClT1KBBA73++uuKiIjQs88+K0mKjY3VP/7xDy1cuFB9+/b19j169Gjddtttkkr2wn7yySd6++239dBDD1lYlV9UK3S63W45HI5y005/hDXqQt3ZzW4tt9utHFeBGgU2tHso9UbOqYKff0cNu4dSjxhyu91sX2xQWz+PKvs8NYyS3938/Pwy+ScwMFCBgYHnXHZ+fr4kKSQkRJL03XffqaioSL179/a2ueyyy3TJJZd4Q+fWrVvVoUMHtWrVytumb9++euSRR/T999/ryiuv1NatW8sso7TNo48+KklyuVxKSUkpEy79/PzUq1cvbd269ZzjNku1QmdaWpr3BThTenq6TwaE6qnNde9geGrtfS9rG4/h0Z49e+TxEH6s5PEY1N1ipTVn+2Kt0m1MbVbR56nD4VBkZKTi4+NVUFDgnT558mRNmTKl0uV5PB49+uij6tq1qzp06CBJysrKUoMGDRQcHFymbatWrZSVlSVJys7OLhM4Jally5beny9tUzrt9DY//fSTTpw4oR9//FFut7vCNmeeY2qlaoXOmJiYCvd0pqenq3379nI6+RW3Sl2oe638Zp9ays/hp9jY2J8PUcMqfn4O6m4xb83ZvliqdBtTG1X2eWoYhlwul3bs2FFuT+e5TJgwQbt379ZHH33k8zHXVtUKnU6ns1zoPH1ebQ0/tRl1R1WVvE9q1zmRtZ+DulvOwTbRJrW97hV9npYe3Q0KCjpr/qnIxIkT9fHHH+vDDz9UmzZtvNPDwsLkcrmUl5dXZm9ndna2wsLCJJXs9TzzKvPSi39Ob3PmBUFHjhxRs2bN1KhRI++6VNTmzL2oVuJCInFxhdW4uAIAUBcZhqFJkyZp3bp1WrNmjSIjI8vM79SpkwICArRp0yYNHDhQUsmpiz/88IMSEhIkSQkJCZozZ46OHDniPTy+ceNGNWvWzLs3OSEhQZ9++mmZZW/cuFGJiYmSpAYNGuiqq67Spk2bdNNNN0kqOdy/efNmjRs3zrwCnAOhU1KO67gaVWFXOXwj51RtC/gAAJzbhAkTtHLlSi1btkxNmzb1noMZFBSkRo0aKTg4WCNHjtS0adMUEhKiZs2aadKkSUpISPCGzj59+ig2Nlb33HOPnnrqKWVnZ+u5557TuHHjvIf177jjDi1ZskRPPPGERo4cqc2bN+v999/XX//6V+9Y7r33Xt17773q3LmzfvWrX2nhwoU6fvy492p2OxA6AQAAfOD111+XJPXv37/M9Pnz5+vWW2+VJM2YMUN+fn4aPXq0XC6X+vTpo9mzZ3vbOp1OrVixQo888oh++9vfqnHjxhoxYoSmTp3qbRMZGam//vWvmjp1qhYtWqSLL75Yr7zyivd2SZI0ePBgHT16VDNmzFB2drauuOIKrVy5ksPrAAAAtV1ubu452zRs2FCzZ88uEzTPFBERoXfffbfS5fTo0UObN2+utM1dd92lu+6q2elsX331ld58803t379fb775pi6++GKtWLFCkZGR6tatW42WyeV9AAAA8Prggw80dOhQNWrUSP/+97/lcrkkldx3dO7cuTVeLqETAAAAXrNnz9bcuXM1b948BQQEeKf/5je/0b///e8aL5fQCQAAAK/09HR179693PSgoCDl5eXVeLmETgAAAHi1atVKe/fuLTf9H//4h6Kiomq8XEInAAAAvEaPHq1HH31U27Ztk8PhUGZmpt555x09/vjjuuOOO2q8XK5eBwAAgNdDDz0kj8ejpKQkFRYW6qabblJgYKDuu+++Gl8NLxE6AQAAcBqHw6EJEybo/vvv1969e3X8+HHFxsaqadOm57VcQicAAADKadCggS6//HKfLY/QCQAAAK+TJ0/qtdde09///ncdPXpUHo+nzPxNmzbVaLmETgAAAHiNHz9eGzdu1MCBA9WlSxc5HA6fLJfQCQAAAK+PP/5Y77zzjn7zm9/4dLncMgkAAABeF1988XlfNFQRQicAAAC8pk+frqeeekoHDhzw6XI5vA4AAACvzp0769SpU+rcubMaN24sf/+ycXHfvn01Wi6hEwAAAF7jxo1TZmamHn/8cbVq1YoLiQAAAOB73377rT7++GNdccUVPl0u53QCAADAKyYmRidPnvT5cgmdAAAA8HryySf12GOP6csvv9SxY8eUn59f5l9NcXgdAAAAXkOHDpUkDRo0qMx0wzDkcDiUk5NTo+USOgEAAOC1Zs0aU5bL4XUAAAAf2LJli4YPH664uDiFhIRo3bp1Zebfe++9CgkJKfOvdK9iqdzcXN15552KiIhQZGSkxo8fr4KCgjJtduzYoRtuuEHh4eHq2LGj5s2bV24s77//vhITExUeHq7u3bvrk08+qfJ6XH311ZX+qyn2dAIAAPhAYWGh4uPjNXLkSI0aNarCNn379tX8+fO9zwMDA8vMv/POO5WVlaVVq1apqKhI9913nx588EEtWbJEkpSfn68hQ4aoV69emjt3rnbt2qXx48crODhYt99+uyTpm2++0bhx4/TEE0/ot7/9rVauXKmRI0fqiy++UIcOHc65Hlu2bKl0fk2DJ6ETAADAB/r166d+/fpV2iYwMFBhYWEVztuzZ48+++wzff755+rcubMkadasWbr55ps1ffp0tW7dWu+++65cLpdeffVVNWjQQHFxcUpNTdWCBQu8oXPRokXq27ev7r//fknStGnT9MUXX2jx4sV66aWXzrkeAwYMKDft9Ht1WnJOp9vtLneDULfbXeaxdjLsHkA9Y8jtdnNuh8VKfkd5r1vLoO6WY/til9qaAyrLMYZR8rubn59fJv8EBgaW20NZVV9++aViYmLUvHlz9ezZU4899phCQ0MlSVu3blVwcLA3cEpS79695efnp+3bt6t///7aunWrunfvrgYNGnjb9O3bV/PmzdOPP/6o5s2b69tvv9Uf//jHMv326dOn3OH+sznzG4eKi4v173//WzNmzNBjjz1Wo/WWqhk609LSvC/AmdLT02s8CLt5PHwgWMnjMbRnzx51MDxy2j2YesJjeLRnzx7e6xYrfa9Td+uwfbFH6TamNqsoxzgcDkVGRio+Pr7MeZWTJ0/WlClTqt1H37591b9/f0VGRmr//v2aPn26hg0bpk8++UROp1NZWVlq2bJlmZ/x9/dXSEiIsrKyJEnZ2dmKiIgo06b0Z7KystS8eXNlZ2eXW07Lli2VnZ1dpXEGBweXm3bttdeqQYMG3r2mNVGt0BkTE1Phns709HS1b99eTmft/BX3+2a93UOoV/z8HIqNjZWfg30RVvFz+JXUnPe6pbzvdepuGbYv9ijdxtRGleUYwzDkcrm0Y8eOcns6a2LIkCHe/+/YsaM6duyozp0768svv1SvXr1qtgIWatmy5XntZKxW6HQ6nWf9/k2n01lrQ6fkm+8URVU5avF7pfYqqTnvdWs5qLvl2L7YpbbXvaIcU3p0NygoyGffP366qKgotWjRQnv37lWvXr0UFhamI0eOlGlTXFys3Nxc73mgrVq1Ktem9Pm52rRq1apK49qxY0eZ54ZhKCsrSy+//LLi4+OrvoJn4EIiAAAAG/zf//2fjh075g2LCQkJysvLU0pKiq666ipJ0ubNm+XxeNSlSxdvm2effVZFRUUKCAiQJG3cuNF7nqgkJSYmatOmTfrDH/7g7Wvjxo1KSEio0riuueYaORyOcqdU/vrXv9arr75a4/UldAIAAPhAQUFBmYtwMjIylJqaqubNmyskJESzZs3SwIEDFRYWpn379unJJ59UdHS0+vbtK0mKjY1V37599cADD2ju3LkqKirSpEmTNHjwYLVu3VpSybcFvfDCCxo/frweeOAB7d69W4sWLdJzzz3n7ffuu+9W//799eqrr+r666/XqlWrlJKSopdffrlK65GSklLmuZ+fny666CI1bNjwvOpD6AQAAPCBlJSUMrcbmjZtmiRpxIgRmjNnjnbt2qUVK1YoLy9P4eHh6tOnj6ZOnVrmHNHFixdr4sSJSkpKksPh0MCBAzVz5kzv/ODgYL333nuaOHGirr32WrVo0UITJ0703i5Jkrp27arFixfrueee0/Tp0xUdHa233367SvfolFTuQiVfIXQCAAD4QI8ePZSbm3vW+e+99945lxESEuK9EfzZxMfH66OPPqq0TVJSkpKSks7ZX6lFixZVue3dd99d5banI3QCAADUcwsWLKhSO4fDQegEAABAzXz33Xem98GNzAAAAFAhwzDO+sVA1UXoBAAAQBkrVqxQ9+7d1bp1a7Vu3VpXX321VqxYcV7L5PA6AAAAvObPn68ZM2Zo3Lhx6tq1qyTpH//4hx555BEdO3ZM9957b42WS+gEAACA12uvvaY5c+Zo+PDh3mk33nij4uLiNHPmzBqHTg6vAwAAwCsrK0uJiYnlpicmJiorK6vGyyV0AgAAwKtdu3ZavXp1uemrV69WdHR0jZfL4XUAAABo165d6tChg6ZOnaqxY8fq66+/9p7T+c0332jTpk164403arx89nQCAABAPXr00HXXXaecnBz97W9/U2hoqNatW6d169YpNDRUn332mfr371/j5bOnEwAAAFq7dq2WLVumJ554Qh6PRwMGDNBzzz2nq6++2ifLZ08nAAAA1L17d7366qvavXu3Zs2apQMHDmjgwIH69a9/rZdffvm8LiKSCJ0AAAA4TZMmTXTbbbdp3bp12rp1qwYNGqQlS5boiiuu0IgRI2q8XEInAAAAKhQdHa2HH35YEyZMUNOmTfXJJ5/UeFmc0wkAAIBytmzZor/85S9as2aNHA6Hfv/732vkyJE1Xh6hEwAAAJKkzMxMLVu2TMuXL9fevXuVmJiomTNnKikpSU2aNDmvZRM6AQAAoKFDh2rTpk1q0aKFbrnlFo0cOVIxMTE+Wz6hEwAAAAoICNBbb72l3/72t3I6nT5fPqETAAAAWr58uanL5+p1AAAAH9iyZYuGDx+uuLg4hYSEaN26dWXmG4ahGTNm6PLLL1fr1q2VlJSk//73v2Xa5Obm6s4771RERIQiIyM1fvx4FRQUlGmzY8cO3XDDDQoPD1fHjh01b968cmN5//33lZiYqPDwcHXv3v28rjr3FUInAACADxQWFio+Pl4vvvhihfPnzZunRYsWae7cufr000/VuHFjDRkyRCdPnvS2ufPOO/X9999r1apVWrFihb766is9+OCD3vn5+fkaMmSI2rZtq40bN+qZZ57RrFmz9Oabb3rbfPPNNxo3bpxGjhypTZs26aabbtLIkSO1a9cus1a9SgidAAAAPtCvXz899thjFX4/uWEY+vOf/6wJEyboxhtvVHx8vBYuXKjDhw9794ju2bNHn332mV555RX9+te/Vrdu3TRr1iytWrVKmZmZkqR3331XLpdLr776quLi4jRkyBDdddddWrBggbevRYsWqW/fvrr//vsVGxuradOmqVOnTlq8eLE1hTiLap3T6Xa75XA4yk07/bF2MuweQD1jyO128xePxUp+R3mvW8ug7pZj+2KX2poDKssxhlHyu5ufn18m/wQGBiowMLBa/WRkZCgrK0u9e/f2TgsODlaXLl20detWDRkyRFu3blVwcLA6d+7sbdO7d2/5+flp+/bt6t+/v7Zu3aru3burQYMG3jZ9+/bVvHnz9OOPP6p58+b69ttv9cc//rFM/3369Cl3uN9q1QqdaWlp3hfgTOnp6T4ZkB08Hj4QrOTxGNqzZ486GB75/to4VMRjeLRnzx7e6xYrfa9Td+uwfbFH6TamNqsoxzgcDkVGRio+Pr7MeZWTJ0/WlClTqrX80u8tb9myZZnprVq1UnZ2trfNmfP9/f0VEhLi/fns7GxFRESUaVP6M1lZWWrevLmys7PLLadly5befuxSrdAZExNT4Z7O9PR0tW/f3pTL663g9816u4dQr/j5ORQbGys/B/sirOLn8CupOe91S3nf69TdMmxf7FG6jamNKssxhmHI5XJpx44d5fZ0ovqqFTqdTme50Hn6vNoaOqWK1wlmcdTi90rtVVJz3uvWclB3y7F9sUttr3tFOab06G5QUNBZ809VhYWFSZKOHDmi8PBw7/Ts7GxdccUV3jZHjhwp83PFxcXKzc31/nyrVq3KtSl9fq42rVq1Oq91OF/8KQgAAGCyyMhIhYWFadOmTd5p+fn52r59uxISEiRJCQkJysvLU0pKirfN5s2b5fF41KVLF2+br776SkVFRd42GzduVExMjJo3by5JSkxMLNNPaZvSfuxC6AQAAPCBgoICpaamKjU1VVLJxUOpqak6ePCgHA6H7rnnHs2ePVsffvihdu7cqT/84Q8KDw/XTTfdJEmKjY1V37599cADD2j79u36xz/+oUmTJmnw4MFq3bq1pJKvqmzQoIHGjx+v3bt3a9WqVVq0aJHuvfde7zjuvvtuffbZZ3r11Vf1n//8RzNnzlRKSoruvPNO64tyGr6RCAAAwAdSUlI0YMAA7/Np06ZJkkaMGKEFCxbogQceUGFhoR566CHl5eXpN7/5jVauXKmGDRt6f2bx4sWaOHGikpKS5HA4NHDgQM2cOdM7Pzg4WO+9954mTpyoa6+9Vi1atNDEiRN1++23e9t07dpVixcv1nPPPafp06crOjpab7/9tjp06GB+ESpB6AQAAPCBHj16KDc396zzHQ6Hpk6dqqlTp561TUhIiJYsWVJpP/Hx8froo48qbZOUlKSkpKRK21iNw+sAAAAwHaETAAAApiN0AgAAwHSETgAAAJiO0AkAAADTEToBAABgOkInAAAATEfoBAAAgOkInQAAADAdoRMAAACmI3QCAADAdIROAAAAmI7QCQAAANMROgEAAGA6QicAAABMR+gEAACA6QidAAAAMJ1/VRoZhlHm8cx5DodDhmFUOL82aOQXoIZ+AXYPo/7w85S8Xxo2lAID7R5NvWA0bCjDMHivW+3n9zp1txDbF1uUbmNqo8pyzOn5x+Fw2DG8OsVhVOFd4vF49NNPP1kxHgAAgAtKs2bN5OfHweHzVeXQ6f2BM5J+fn6+4uPjtWPHDgUFBfl+hKgQdbceNbcHdbcHdbceNbdHZXU/PSIROs9flQ6vV1Zoh8OhgoICORwOdj1biLpbj5rbg7rbg7pbj5rbo7K68zr4FrEdAAAApiN0AgAAwHTnHToDAwM1efJkBXKVoKWou/WouT2ouz2ou/WouT2ou3WqdCERAAAAcD44vA4AAADTEToBAABgOkInAAAATEfoBAAAgOkInQAAADAdoRMAcN7cbrfdQ6hXTv96aqC2IHTWcUVFRXYPod4pKChQdna28vLy+CC2UE5Ojv71r38pNTVVx44ds3s49cKePXs0d+5cSZLT6eT9bpF//etfGjlypE6dOmX3UIBqqdJ3r6N2Sk9P1xtvvKGRI0cqLi7O7uHUC7t379bEiROVk5Mjl8ulMWPGaPTo0WrevLndQ6vTdu7cqbvvvlvFxcUqKChQ37599dRTTykkJMTuodVZJ0+e1JAhQ3To0CEdOXJEzz//vDd4Op1Ou4dXZ6WmpmrAgAEaMWIENzNHrcOezjpq3759uummm7R06VItWbJEe/bssXtIdd6ePXs0cOBAXXHFFXrmmWd0/fXXa+nSpdq9e7fdQ6vTvv/+ew0cOFB9+/bVsmXLdN9992njxo3s7TRZgwYN1KZNG91222369ttvNWnSJEklezw59GuO1NRU3XDDDbrjjjv04osvSpKKi4vlcrnE97ygNuAbieqgwsJCPfLII3K5XLriiiu0evVqde7cWX/4wx8UGxtr9/DqpLy8PI0bN05t27b1Hm6UpN/97ndq166dFi5caOPo6q7c3Fzdeuut6tSpk2bOnOmd/vvf/1533323QkJC1Lp1a0VERNg4yrrrwQcfVExMjJxOp9544w1dd911eu6557R582ZdddVVCgoKsnuIdUZOTo46deqka6+9VsnJySoqKtKjjz6qffv2KTMzU3379tXo0aMVExNj91CBs+Lweh3UsGFDXXPNNXI4HBo+fLguuugiLV68WAsXLiR4miQrK0vNmjXT73//e0mSy+VSgwYN1LNnT+3bt8/m0dVdJ0+e1LBhw9S1a1fvtNmzZ2vTpk06fPiw/P39lZubq2XLlunKK6+0caR1S+kh9MDAQJ04cUIPP/ywJOkvf/mLOnXqJMMw9PXXX8vj8cjPjwNqvuB2u3XTTTdpw4YN2rJli15++WUVFhbq2muvVXZ2trZv365//etfmj9/viIjI+0eLlAhtgZ1kJ+fn4YMGaJbbrlFkjRy5Ej9z//8j1JSUrRw4ULvofaioiIdPnzYzqHWGZdddpmGDh2qnj17SpL8/Uv+nrvooot08uTJMm1/+ukny8dXV7Vu3VqDBg1Sx44dJUnLly/XrFmz9Oabb2rdunV67bXXdPnll2vBggU6deoUhyB9pDRI9unTR7t375afn5/uuece+fv7Kzs7W126dFGTJk3k5+fHoXYfadWqlZ5//nldf/31GjBggAzDUHJysiZMmKAXXnhB9913n3JycvT111/bPVTgrAiddUzp1aMNGjSQw+HwPh89erTGjh3rDZ47duzQE088oWHDhnEF5HkqvUPAjTfeKEkyDMP7oVx6JXtp2HnppZc0ffp0FRcX2zPYOqT0vd2iRQvvtBtvvFHr16/XwIEDFRoaqri4OAUFBSkvL0+BgYFyOBx2DbdOKA2QpXUMDAzUrl27JEn333+/Dh06pLFjx2rv3r267777JIk9nT5Q+l4PDQ3V008/raeeekp33HGHQkNDva/JjTfeKJfLpe+++87OoQKV4vB6HXDgwAF99913GjBggPck/tIN/enPx4wZI4fDoTfffFO33nqrcnNztWbNGq6ArIHTax4QEFCm5g6HQ4ZhyOFwqEmTJgoKCpLD4dCMGTO8h35L94Sieip7r7vdbgUHB6tLly6SSsK/YRhq1KiRoqOj5fF45HA4CJ7VdHrNS/dclta8c+fOioiI0NChQ7Vr1y6tWbNGF198sRYuXKgNGzYoKytLYWFhNq9B7XTme730lIZWrVppzJgxaty4saSSUO92u1VYWKjIyEhOI8EFjU++Wi49PV2/+93v1KxZMx0/flzDhw8v98Fw+vPRo0dr+fLl+umnn7R+/XrvYUlUXVVqXqphw4Zq0aKFZs2apT/96U/6/PPPdcUVV9g08trtXHU/8zY9Ho9Hs2bN0saNG/XBBx+wx60GzlXz4OBgHT16VAcOHNB7772nyy67TJJ01113ady4cdyyqoYqqvvpwTM4OLhMez8/P82fP1///e9/1b17d5tGDZwbV6/XYkePHtXdd98th8Oh4OBg/fDDDxozZoxuvfVWSSoXgoqLizVhwgQtXbpUf//73wmcNVDdms+fP1+PP/64GjdurLVr1+qqq66yaeS1W3Xr/sUXX2jt2rV6//33tWrVKvb+1MC5al5UVKSAgAAVFhYqKytL7dq1kyTvXn7UTHXf65999pnWrFmjv/3tb/rggw/4oxYXNPZ01mKnTp1S06ZNdfvtt6t169Z6+eWX9dZbb0mSbr311nJ73/z9/dWtWzeNGTOGwFlDVan56R+6LVq0UGRkpFasWMFdA85Ddet+9OhROZ1OrVu3jrrX0LlqHhAQoOLiYjVu3NgbOCUROM9Tdd/rpeeMf/TRR7r88svtHDpwTuzprOUyMzPVunVrSdKuXbs0b9487d+/X6NHj9Ztt90mqWQPJ+cQ+k5Val66F0gS57X5SFXqXnqrKqnkfrWl572hZti+2KO67/Xjx4+rSZMmto0XqCpOcqrlSjdMbrdbHTp00EMPPaSoqCgtXbpUy5YtkyQ98sgjeuONN+wcZp1SlZpPmDBBixcvliQCp49Upe4TJ07U//7v/0oSgdMH2L7Yo7rvdQInagv2dNZB33//vV566SUdPHhQDodDX3/9tTZs2KBf/epXdg+tzqLm9qDu1qPm9qDuqAsInbXUuU7WT01N1S233KLCwkKtXbtW8fHxFo6ubqLm9qDu1qPm9qDuqOs4vF5LlN4cOCcnR1LlJ+u7XC7vbZE+/PBDNkw1RM3tQd2tR83tQd1R3xA6L3Dp6elau3atnE6n3n//fd1zzz06cuRIpT/z008/aevWrfrggw/UoUMHi0Zad1Bze1B361Fze1B31FsGLlhut9t4/vnnjZCQEOOxxx4zQkJCjOXLl1f6Mx6PxzAMwzh16pQVQ6xzqLk9qLv1qLk9qDvqM87prAWGDRumzz//XHfccYdefPHFCr/55kwGN2g+L9TcHtTdetTcHtQd9RGH1y9QpX8LFBUVKTQ0VN27d9frr7+u9957z3tz4DP/Xjj9ORum6qPm9qDu1qPm9qDuqPcs2qOKaig9lPLPf/7T+PTTT42ffvrJMAzDePLJJ43Q0FDj3XffLdMuIyPDnoHWIdTcHtTdetTcHtQdMAz2dF5gjJ8Pn3zwwQcaNmyYtm/frkOHDkkquQnz+PHjdc8992jlypVyOByaM2eOJk2apOPHj9s88tqLmtuDuluPmtuDugM/szXyokJff/21ERERYbzxxhvGiRMnyszLz883nn32WSMkJMS4/vrrjdatWxspKSk2jbTuoOb2oO7Wo+b2oO4AFxJdUIyf/xqeMWOGdu7cqb/85S/eeW63W06n0/v8008/1d69e3X99derXbt2dgy3TqDm9qDu1qPm9qDuwC/87R4AftkolZ4kfvjwYe9Ng0uvaCzdMH333XeKjY1Vv379uJLxPFBze1B361Fze1B3oDzO6bTRiRMndOrUKf3www86deqUd/rFF1+sb775Rjk5Od4rGqWSmwOvWrVK33zzjSSuZKwJam4P6m49am4P6g5Uwspj+fjF999/b4waNcro1q2bcdFFFxk9e/Y0Hn/8ccMwDKOgoMC45pprjG7duhlZWVmGx+MxioqKjKefftro2LGjcfDgQZtHXztRc3tQd+tRc3tQd6BynNNpg507d+rGG2/UzTffrCuvvFIhISFavny5NmzYoGuvvVZvvvmm0tLS9NBDDyk9PV2XX365AgICtHv3bq1atUpXXnml3atQ61Bze1B361Fze1B34NwInRY7evSohgwZoj59+ujJJ58sM3316tV68sknNWDAAC1atEiGYWjx4sU6evSomjZtqgEDBnByeQ1Qc3tQd+tRc3tQd6CKbNvHWk999913Rrdu3YydO3caxcXFhmGUfBevYRjGjz/+aLz44otG69atjTVr1tg5zDqFmtuDuluPmtuDugNVw4VEFtuxY4f27dunDh06yOl0yjAM7/ftBgcHa9iwYQoICNC+ffvK/JzBDukao+b2oO7Wo+b2oO5A1RA6LVZ6GOWDDz6QVP5KxcjISEVFRSkzM7PMdK5orDlqbg/qbj1qbg/qDlQNodNiERERatasmVasWKEDBw54p3s8HknSjz/+qIYNG6pTp052DbHOoeb2oO7Wo+b2oO5A1RA6LdamTRvNmTNHn332mWbMmKHdu3dLkvdQzPz583X48GF169bNzmHWKdTcHtTdetTcHtQdqBquXreB2+3W0qVLNWnSJLVr105du3ZVWFiYMjIytGHDBv3tb3/j9hk+Rs3tQd2tR83tQd2BcyN02mjbtm165ZVXlJaWpuDgYMXHx+uuu+7SZZddZvfQ6ixqbg/qbj1qbg/qDpwdodNmbrdbfn5+cjgc3u/jhbmouT2ou/WouT2oO1AxfhNsVrphkriS0SrU3B7U3XrU3B7UHagYezoBAABgOvZ0AgAAwHSETgAAAJiO0AkAAADTEToBAABgOkInAAAATEfoBAAAgOkInQAAADAdoRMAAACmI3QCAADAdIROAAAAmO7/AQ51/yYjNyW4AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 800x575 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mc = mpf.make_marketcolors(base_mpf_style='yahoo',up=(0.7,1.0,0.7,0.4),down='fuchsia',\n",
+    "                           edge={'up':'blue','down':'#000000'},wick='#cc6600')\n",
+    "#mc = mpf.make_marketcolors(base_mpf_style='yahoo',up=(0.7,1.0,0.7,0.4),down=(1,0.25,1),\n",
+    "#                           edge={'up':'blue','down':'#000000'},wick='#cc6600')\n",
+    "\n",
+    "mco = [None,None,mc,mc,None]\n",
+    "mpf.plot(df,volume=True,style='yahoo',type='candle',marketcolor_overrides=mco)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "#### When dealing with larger data sets we recommend setting up your marketcolor overrides as a COLUMN in your DataFrame\n",
+    "#### This has two advantages:\n",
+    "1. It helps ensure that your marketcolor overrides are the same length as your dataframe, and\n",
+    "2. It allows you to use the dataframe's DatetimeIndex to set the override values.\n",
+    "#### Note that you will still have to pass the `marketcolor_overrides` as an separate iterable (apart from the dataframe)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(50, 5)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2011-08-29</th>\n",
+       "      <td>119.559998</td>\n",
+       "      <td>121.430000</td>\n",
+       "      <td>118.059998</td>\n",
+       "      <td>121.360001</td>\n",
+       "      <td>190977200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2011-08-30</th>\n",
+       "      <td>120.830002</td>\n",
+       "      <td>122.430000</td>\n",
+       "      <td>119.260002</td>\n",
+       "      <td>121.680000</td>\n",
+       "      <td>241315700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2011-08-31</th>\n",
+       "      <td>122.459999</td>\n",
+       "      <td>123.510002</td>\n",
+       "      <td>121.300003</td>\n",
+       "      <td>122.220001</td>\n",
+       "      <td>301828400</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  Open        High         Low       Close     Volume\n",
+       "Date                                                                 \n",
+       "2011-08-29  119.559998  121.430000  118.059998  121.360001  190977200\n",
+       "2011-08-30  120.830002  122.430000  119.260002  121.680000  241315700\n",
+       "2011-08-31  122.459999  123.510002  121.300003  122.220001  301828400"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('data/SPY_20110701_20120630_Bollinger.csv',index_col=0,parse_dates=True)\n",
+    "df = df[['Open','High','Low','Close','Volume']].iloc[40:90] # Arbitrarily choose a 50 row subset of the data\n",
+    "df.shape\n",
+    "df.head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "#### Create a new column for the overrides:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Volume</th>\n",
+       "      <th>MCOverrides</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2011-08-29</th>\n",
+       "      <td>119.559998</td>\n",
+       "      <td>121.430000</td>\n",
+       "      <td>118.059998</td>\n",
+       "      <td>121.360001</td>\n",
+       "      <td>190977200</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2011-08-30</th>\n",
+       "      <td>120.830002</td>\n",
+       "      <td>122.430000</td>\n",
+       "      <td>119.260002</td>\n",
+       "      <td>121.680000</td>\n",
+       "      <td>241315700</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2011-08-31</th>\n",
+       "      <td>122.459999</td>\n",
+       "      <td>123.510002</td>\n",
+       "      <td>121.300003</td>\n",
+       "      <td>122.220001</td>\n",
+       "      <td>301828400</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  Open        High         Low       Close     Volume  \\\n",
+       "Date                                                                    \n",
+       "2011-08-29  119.559998  121.430000  118.059998  121.360001  190977200   \n",
+       "2011-08-30  120.830002  122.430000  119.260002  121.680000  241315700   \n",
+       "2011-08-31  122.459999  123.510002  121.300003  122.220001  301828400   \n",
+       "\n",
+       "           MCOverrides  \n",
+       "Date                    \n",
+       "2011-08-29        None  \n",
+       "2011-08-30        None  \n",
+       "2011-08-31        None  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['MCOverrides'] = [None]*len(df)\n",
+    "df.head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "#### Use the DatetimeIndex to set the Overrides:\n",
+    "#### For demonstration purposes, let's override every Monday as black, and every Tuesday as \"blueskies\" marketcolors:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Volume</th>\n",
+       "      <th>MCOverrides</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2011-08-29</th>\n",
+       "      <td>119.559998</td>\n",
+       "      <td>121.430000</td>\n",
+       "      <td>118.059998</td>\n",
+       "      <td>121.360001</td>\n",
+       "      <td>190977200</td>\n",
+       "      <td>black</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2011-08-30</th>\n",
+       "      <td>120.830002</td>\n",
+       "      <td>122.430000</td>\n",
+       "      <td>119.260002</td>\n",
+       "      <td>121.680000</td>\n",
+       "      <td>241315700</td>\n",
+       "      <td>{'candle': {'up': 'w', 'down': '#0095ff'}, 'ed...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2011-08-31</th>\n",
+       "      <td>122.459999</td>\n",
+       "      <td>123.510002</td>\n",
+       "      <td>121.300003</td>\n",
+       "      <td>122.220001</td>\n",
+       "      <td>301828400</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2011-09-01</th>\n",
+       "      <td>122.290001</td>\n",
+       "      <td>123.400002</td>\n",
+       "      <td>120.779999</td>\n",
+       "      <td>120.940002</td>\n",
+       "      <td>254585900</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2011-09-02</th>\n",
+       "      <td>118.419998</td>\n",
+       "      <td>120.870003</td>\n",
+       "      <td>117.430000</td>\n",
+       "      <td>117.849998</td>\n",
+       "      <td>255517200</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  Open        High         Low       Close     Volume  \\\n",
+       "Date                                                                    \n",
+       "2011-08-29  119.559998  121.430000  118.059998  121.360001  190977200   \n",
+       "2011-08-30  120.830002  122.430000  119.260002  121.680000  241315700   \n",
+       "2011-08-31  122.459999  123.510002  121.300003  122.220001  301828400   \n",
+       "2011-09-01  122.290001  123.400002  120.779999  120.940002  254585900   \n",
+       "2011-09-02  118.419998  120.870003  117.430000  117.849998  255517200   \n",
+       "\n",
+       "                                                  MCOverrides  \n",
+       "Date                                                           \n",
+       "2011-08-29                                              black  \n",
+       "2011-08-30  {'candle': {'up': 'w', 'down': '#0095ff'}, 'ed...  \n",
+       "2011-08-31                                               None  \n",
+       "2011-09-01                                               None  \n",
+       "2011-09-02                                               None  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mc = mpf.make_marketcolors(base_mpf_style='blueskies')\n",
+    "for ts in df.index:\n",
+    "    if 0 == ts.weekday():\n",
+    "        df.loc[ts,'MCOverrides'] = 'black'\n",
+    "    elif 1 == ts.weekday():\n",
+    "        df.loc[ts,'MCOverrides'] = [mc]\n",
+    "#mc\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "=== without marketcolor overrides: ===\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1120x805 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "=== WITH MARKETCOLOR OVERRIDES: ===\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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R7sLE//rWFdwbc79Q95Ui5/3Hf0EjOTm5UXOJP9Tt3LlTK1asqNJaV5MzzzxTZWVlys3NVY8ePRqtHKGInE6hAAAACJm7TDJlyJFgymZIpin5vGWKscXKMCSvKblKDLnLwl1SIDL4Q90PP/yglStXyuFwHHOfzZs3KyYmpsEtp02JYAcAANAC2QwpNqY82Hl9ki1GMgxJvvofw+12a+3atcrKypLdbm+ysgJN6cCBA9qxY0fgd6fTqc2bN6tt27ZKT0/XlVdeqU2bNum1116T1+vV7t27JZVPeh8fH6///ve/+uKLLzR48GC1adNG//3vfzV37lxddtllatu2bZieVXUEOwAAANTI7XYrJydHAwcOJNjBsjZu3KjRo0cHfp87d64kafz48ZozZ47efvttSdKQIUOq7Ldy5UoNHjxYCQkJWr58uR588EF5PB5lZmZq6tSpVe67iwQEOwAAAAAt1uDBg1VYWFjr+rrWSVLfvn313nvvNXaxGl1MuAsAAAAAAAgNwQ4AAAAALI5gBwAAAAAWxz12AAAAKJdfIB0+dOR3l6v8e16e5PGU/5yYJLVv1/xlizCMGIpIQ7ADAACAlF8g2wWjZFRMAC1JyTabxnRor+TrpirW65UkmYYh76qcqA93jBiKSENXTAAAAACwOFrsAAAAIpS3opWsoeuC0r5deUtcpa6YxS6XVixerAHz71Oiw1G+kK6YlpSamqo77rhDqamp4S4KmgjBDgAAIMLY7XYZhiGX/x63WhiG0bjdAI8ObPHx5d/T06W0tMZ7HDQ7h8OhefPmhbsYaEIEOwAAgGZS3wE30tPT5XQ65Xa7A8tyc3OVnZ2t1atXKyMjQ1J5AExPT2/ycgOIfAQ7AACAJpRXUiR3WYkkqTC/QDk5OTqubw+l6kjrmD02QekJKVX2Ozqw+bvS9e3bVw5/t0g0qyqv5aECSVLuoQLtP5K/a3wtG4vdbteoUaMYrAU1ItgBAAA0kbySImWunSn/OJPxHql3T2nohkXyxB/ZzpDkzFpQZyCgK114NeZrGSy73a4xY8Y0+nHRMhDsAAAAmoi7rESmJEecXTbDkOKln/pKlU/5vaYpV6m7vCUoIVwlxbH4X8uU2KTy1zJOyj1Nal1pG69pqqjsEK8lwoJgBwAA0MRshqFYw1bL2kYe3RJNwh6bIENSUVn5qKHxHqn3D9I33VWtxc4eS6pD8yPYAQAAAMeQnpAiZ9aCKvdLPvPWIi25ZLpS29d9vyTQHAh2AAAAQD2kJ6QEuljuqRgwJSOpndLsTAWB8IsJdwEAAAAAAKEh2AEAAADNiGkL0BToigkAAAA0I6YtQFOgxa4ZuN1urVixQm63+9gbAwCABnG5XLr33nvlcrnCXRRYkOuwdO8XMXIdDndJgNAQ7JqB2+1WTk4OwQ4AgCZQWFio+fPnq7CwMNxFgQUVeqT5X9pU6Al3SYDQEOwiGC19AAAAAOqDYBfBaOkDAAAAUB8EOwAAAACwOIIdAAAAAFgc0x0AAAA0Ma9pSvLWsa4pHlOSTzJNyWdKpk8yjIrlAFocgh0AAIhabrdba9euVVZWVpNMFm2PTZAhyVVafr98vEfq/YP0TXfJE39kO6Ni28Z5TMmQKVeJUWlpnFRW+fFM2TkLBFoU/qUBAEDU8g9UNnDgwCYJdukJKXJmLZC7rESSVJhfoGfeWqQll0xXavt2ge3ssQlKT0hpnMdsJTknlsldEeR+LPJp5DsJevs3JTohJabi8cq3A9ByEOwAAACaUHpCilTRGLenYqDrjKR2SrOnNd1jVgptvooeoBmtpe7JTfaQAMKMwVMAAAAAwOIIdgAAAABgcQQ7AACAFqxtgqnrjs9T2wSGw2xMdrtdo0aNapJ7M4FgcI8dAABAMwlHGHAkSFMzdsuR0LbZHjMa2O12jRkzJtzFAAIIdgAAICR5JUWBUR9r05ijPkpSXl6e3O7ykUhyc3OrfA88pt2u9PT0RnvMxkAYANBUCHYAACBoeSVFylw7U8fq5GdIcmYtaJRwl5eXp8zMTJlHTeydnZ1d9TENQ06nM+LCHQA0BYJdU8gvkA4fOvK7y1X+PS9P8njKf05MkirNXwMAgBW5y0pkSnLE2WUzjBq38ZqmXKXu8la9OubgdrlcWrx4sW666SY5HI7aH9PtlmmaWrNmjTIzM2vcxul0avjw4YFWPQBo6Qh2jS2/QLYLRsmodBUx2WbTmA7tlXzdVMV6yyeTMQ1D3lU5hDsAQItgMwzFGrZa1nrrdYzCwkLNnz9fEydOrDPY+WVmZqp79+4NKCUAtFwEu3qq71VEHT4kwzRlJidLtvIPOLukC0s8UnJyeVcVr1dGcXHVVj0AAND06FUDoIUi2NVTQ68iymYLBLsGqfyBw4cNAACNh141AFowgl1T8dbR7aS2dUd94PBhAwBAI6JXDYAWjGDX2BKTZBpG+YdChQM2m9Z0aK/he/PVulJAU2JS1X2P+sDhwwYAgCYQbK+aKMQk3IB1EOwaW/t25S1qlcJXsculFYsXa8D8+5To78ZZV5dKPnAAAEAEYN49wDoIdk3h6MAWH1/+PT1dSktr/vIAAKKW2+3W2rVrlZWVRatLI+Lvam15ByV3WfnPuQeqfpcke6yU3qr5ywWEgmAHAEAL5na7lZOTo4EDB0ZkAMnLywvMNZebm1vlu1TeYhSJE4xH+t8Vtcs7KGW+HCtTVeddzF4VF/jZkCnnxDLCHSyFYAe0UOG4muw6LC3+OkY3neKTI7FZHhJAhPCapmqbr85baRTKyvLy8pSZmSnzqPXZ2dmBnw3DkNPpjMhwBwCRhGAHtFDhuJpc6JHmf2nTxB4EOyBa2GMTZEhylZa3usV7pN4/SN90lzzxR7YzKratzO12yzRNORwO2Wq4t9zr9crlcgVa9IDGkN5Kck4sq9IVM3tVnFZfUKqM1uXL6IoJKyLYAQCAoKUnpMiZtUDushJJUmF+gZ55a5GWXDJdqZXuObfHJig9IaXGY9hsNsXGckqC5lNTaMtoLXVPbv6yAI2Fd1EAABCS9IQUqaIxbk9F41pGUjul2RkwDACaC8GuDla9oRsAAABAdCHY1YIbugEAQGV5JUVHupweKpAk5R4q0P6KVsq6upsCQFMj2NXCf0P3mjVrlJmZWW290+nU8OHDuaEbAIAokFdSpMy1M+W/3BvvkXr3lIZuWBQYJMaQ5MxaQLgDEBYEu2PIzMxU9+7dw10M4JgqX0mWuJoMAI3JXVYiU5Ijzi6bYUjx0k99Jf87qtc05Sp1l78PJ9R1JABoGgQ7IMIcHdBqcnRAO/pKssTVZAAA6iM1XrrjDK9S44+9LRDJoi7YuVwuPf3005o3b546dOgQ7uIAVdQU0GpydECrdiVZ4moyADSi+szXV9NcfYh8jkRp3pm+cBcDCFmLD3aVR7aUpB9//FFLly7VRRddpBNOOEESo1sictQY0I5SV0CzGYZijeqT/Fbs2ahlBYBoUp/5+ujuDiCcWnSwq21kS0kaOXJk4GdGt0SkCTageU2z1vXeGv4PAAD1x3x9ACJZiw52/pEtHQ6HbLbyk2TTNOXz+RQTEyPDMOT1euVyuZp0dEu73a5Ro0bJbrc32WMguh3dRUiimxAAAEA0adHBzs9msyk2tvypmqYpr9crm80mo5aubo3NbrdrzJgxzfJYiE5HdxGS6CYEIDwscTHTW0fX9LrWAUAEi4pgB0SDyl2EJLoJAQiPiL6YmZgk0zBkFBcHFh2w2bSmQ3sN35uv1hWhzjQMKTEpXKUEgKAQ7AAAQHRo307eVTnS4UOBRcUul1YsXqwB8+9TosNRvjAxSaro6QAAVkGwAwAA0ePowBZfcRNyerqURu8GANZFsItEtfXvp98/AAAAgBoQ7I7B6XQ2aHlIjur7Hw39/t1ut9auXausrKzIvtE+irhcLi1evFg33XSTHP5uSQAAAIhoBLta2O12GYah4cOH17qNYRiNG0aO6vsfbL9/K4Ult9utnJwcDRw4MOLLGi0KCws1f/58TZw48ZjBLu+g5C478nvugarfJckeK6W3aoKCAmhSVvosAQAQ7GqVnp4up9MZmN8uNzdX2dnZWr16tTIyMiSVh79Gn9S8Umizp6aWDxndvbvUgA9VwlLkaYknSHkHpcyXY2Wq+rQh2aviAj8bMuWcWEa4A5pJXklR1alPDhVIknIPFWh/xWi59Zn6hM8SALAWgl0dagptGRkZ6t69e7M8fkQPGY0GaegJktc0JdV8T2X5uvBzl0mmDDkSTNlqmRLSa0quEqNKqx6AppNXUqTMtTNV+V0i3iP17ikN3bBInopxQgxJzqwFzGspi8y7BwD1QLADIog9NkGGJFdp+WX1eI/U+wfpm+4KnJBJ5Sdl9tiEGo/R3GyGFBtTy0pfsxYFiHrushKZklJik2QzKq64xEm5p0mtK7bxmqaKyg6Vt+pFxttIWHERFUBLQbADIkh6QoqcWQsC3agK8wv0zFuLtOSS6Uqt3E23Ht2orKQldlUFwsF/caio7Mg8bTVdIIqki0MAgMZBsAMiTHpCSuAq+p6K+2Eyktopzd5y51fiXh6gcRx9cUiq+QJRbReHKt+fF+y9eQCA8CDYAQDQglS+OCTV/wLR0ffncW8eAFgLwQ4AAATuz3PE2cvvz4uXfuor+SOc1zTlKnVzbx4ARCiCHYJ29JDaR6PLjnXk5eVVmdqj8nepiab2ABCRbIahWMNWw5qaR+oFAEQGgh2CUtOQ2kejy0541XcI77y8PGVmZso8ahqF7OzswM+GYcjpdDZuuMsvkA5XDPDgcvkLI3k85T8nJlWZ1xFAcKwwnL/XW8v0LrUsBwBUR7BDUKp12TkKXXbCr75DeLvdbpmmKYfDIZut+lV6r9crl8sVaNGrtt5UrdMaeGtL/vkFsl0wSkZFmEy22TSmQ3slXzdVsRUncqZhyLsqh3AHhCiSh/O32+0yDEMu/8WdGhiGEdGhFAAiBcEOIaHLTsths9kUG1v/twR7rGTIlKukltnJKxgyZT/6sIcPyTBNmcnJks0mu6QLSzxScnJ5K7DXK6O4+EiLHoAWKT09XU6ns0pX8OzsbK1evVoZGRmS6u4K7nQ6az12XesARJePPvpITz75pDZt2qS8vDwtW7ZMF1xwgSSptLRU8+fP13vvvSen06nk5GSde+65uuuuu9SpU6fAMQoLCzVr1iytXr1ahmFozJgx+tOf/qTWrVvX9rDNjmAHICjprSTnxDK5y44syz0gZa+K0+oLSpVR8T5njy3ftkY2W/kXgKhVU2jLyMhQ9+7da93H39I3fPjwOo9Nax8ASTp48KD69OmjSZMmafLkydXWffXVV5o5c6b69Omjffv26bbbbtOECRP0/vvvB7a79tprtXv3bi1fvlylpaW66aabdMstt+i5555r7qdTqwYFO6/XK6OGbneRyucr7x9mmma1+4eOXu7z+ersy+8/1rG2iwTNUdbA37bi62hmpe1ayt9VOnK/R0PLGuzzbNbXso7/k9rK0CGh/CtwLG/5+0OXJJ9OsB85VrWie70Vbz511yCv11vDztYUbN0BpOapP/73gjLTlFlDrwtvPT8vG6MMx3qMDh06aPv27Tp48KCk8pa+kSNH6u233w609ElSq1at1KFDhyYpr5U+v3j/QSgirf74z0uKi4ur5JKEhAQlJNR8/8+IESM0YsSIGtelpKTon//8Z5VlDz/8sLKysrRz504df/zx+v7777V27VqtW7dOp59+uiTpoYce0mWXXab77ruvSsteODUo2G3durXGE79ItXPnTkk1v+n6f/e/MW/fvl2lpaXHPNaxtosE+/btk1Re1rruWwjFztLyx/CVeeU1qtcJn1np7xpXexmao6xNYdu2bQ3aPtjn2SyvZR3/J/7l/jIcq+4Xldp03fHtlZ+br9K42j8A4vfsUS9JZWVlUk3vKV6v4iTt2LFDnlru7bOqhtYdQJKKior0yiuvaMKECUpJaZoBqfLL3DIkFZaW/8/Fe6TeP0jfdFeVeex+2bFTpbFN+37U0M/asrKywPfK+xUVFamoqKhxC1nBip9fvP8gFJFSfwzDUGZmpvr06aMDBw4Els+ePVtz5sxplMfwh0b/++1nn32mlJSUQKiTpKFDhyomJkZffPGFRo0a1SiPG6oGBbsePXpYqsUuLi5OkhQTE1NlUAiv1xv43R9Uu3Xrpm7duh3zWMfaLhLs3btXUnlZO3To0CSPEXdwr/STFBNrk62Ge+xM0yt5Kv5erWovQ3OUtTF5vV5t27ZNJ554Yo0DjdQm2OfZLK9lLf8nfvX9H/Eb0EeS2ta9UUXXqNjY2Jq7Yla8z3Tt2lXq0uWYj2kFwdYdQCq/sLp06VLdeOON6tGjR5M8Rk9J2098UAe95SPTFua79Nxbj2nx2FuU2t4hSWpli2/SkY47dOiguXPn6owzzpDD4aj3fuH4jLbS5xfvPwhFpNUf0zTl8Xi0ZcuWai12jeHw4cO6++67dfHFFys5OVmStHv37mr/57GxsUpNTdXu3bsb5XEbQ4OCnc1ms1Swi4mJkVSe7P3lrtziWHl5bSe1Rx/rWNtFguYoa+BvW/F1NKPSdi3l71qZzWZrUHmDfZ7N+lpW+n+orL7/Iw0SOE7dNcjWAu/Ba2jdASQF6kxT15/OrY6EqT2Hyt8bTrC3V1qbtCZ7zMo6dOigu+66q8H7heOzxIqfX7z/IBSRUn/85/LJycmNnktKS0t19dVXyzRNLVy4sFGP3RwYPAUAAKCBrDA/IID684e6nTt3asWKFYHWOknq2LFjoJXer6ysTIWFherYsWNzF7VWBDsAACJQXl5elWkA/N/9LUVS3VMBoGlF8vyAABrGH+p++OEHrVy5slpX8P79+6uoqEgbN25Uv379JEnr16+Xz+fTmWeeGYYS14xgBwBAhMnLy1NmZma1ActGjhxZ5XfDMOR0Ogl3AFCHAwcOaMeOHYHfnU6nNm/erLZt2yo9PV1XXnmlNm3apNdee01erzdw31xqaqri4+PVs2dPZWVl6Q9/+IMeffRRlZaWatasWRo7dmzEjIgpEewQDvkFRyae9o8klpcneTxHtklMktq3a/6yAUAjq9zytm/fPi1btkyTJk1S27ZtJdXc6uZ2u2WaptasWaPMzExJ5d1+YmOPfGw7nU4NHz48cGwAQM02btyo0aNHB36fO3euJGn8+PGaM2eO3n77bUnSkCFDquy3cuVKDR48WJL07LPPaubMmbrooosCE5Q/+OCDzfQM6icqgl3lIdxN05TP55NpmjIMI2Lm5Iga+QWyXTBKhv/GV5tNYzq0V/J1UxVb+XUyDHlX5RDumlFt/wv8jwDBq63l7amnngr8XFerW2ZmZmCi7qODHQCgfgYPHqzCwsJa19e1zi81NTWiJiOvSYv+hLDb7TIM45jzyxiGwc3PzeXwIRmmKTM5WbLZZJd0YYlHSk4+Mk211yujuPhIqx6aVH3+T/gfCZ2rRHo6t6PmnSB1aBXu0qC5+FveHA5HjaPJeb1euVwuWt0AACFr0cEuPT1dTqezygfmjz/+qJEjR+rtt9/WCSecIImbz8OiBQ5jb1VH/5/k5uYqOztbq1evVkZGhiT+RxrDvhJDS3em6+aSEoJdFLLZbLS2AQCaVIv/lDn6ZNTn80mSMjIyAt1bgKaQV1Ikd1mJJKnwUIEkKfdQgfZXXGewxyYcc6Lf5hpOu6bQxv8IAACAdbT4YAeEQ15JkTLXzgx0L433SL17SkM3LJInvnyZIcmZtaDOcMdw2gAAAKgPgl09paam6o477lBqamq4iwILcJeVyJTkiLPLZhhSvPRTX8kf4bymKVepu7xFLyGcJQUAAEBLQLCrJ4fDoXnz5oW7GLAYm2Eo1qjpXkJGmgSAxsLFVwBW4vF45HQ61bVr10a9/zqm0Y4EAAAQBv6Lrw6HI9xFAYBaHTx4UNOmTdNxxx2nQYMGadeuXZKkWbNmadGiRSEfn2CH8PB66/4CAIRVcw3eBADR4t5779WWLVu0cuVKJSYmBpYPHTpU//znP0M+PsEOzSsxSaZhyCgullFYKHdxsf6dEC93xe9GYaGM4mKZhiElJtV5KLfbrRUrVjD/EwA0Af/gTQQ7AGgcq1at0sMPP6xBgwbJMIzA8l69eunHH38M+fjcY4fm1b6dvKtyApOPF7tcWrF4sQbMv0+JlbvQJCZJ7dvVeSi3262cnBwNHDiQEw8AAABEtIKCAnXo0KHa8sZqpCDYoflVDmzxFWP/p6dLaWnhKQ8ARCin0xn4uaysrMpN9pXXAQAiX79+/fTuu+9qypQpkhRotXvppZc0YMCAkI9PsAMAIMLY7XYZhqHhw4fXuZ1hGPRYAACLuPPOO3XZZZfpu+++U1lZmf785z/r+++/13//+1/l5OSEfHyCHULiNU3VNHR/+XIAQDDS09PldDoD3XN+/PFHjRw5Um+//bZOOOGEwHZ2u13p6elhKiUAoCEGDRqk9evX67HHHlPv3r31/vvv67TTTtPq1at1yimnhHx8gh2CYo9NkCHJVVp+0hHvkXr/IH3TXfJU9K40Krar8ziMuhZxmA8KiAyVA1tycrKuu+46nXbaaTXenwEAsIauXbvq8ccfb5JjE+wQlPSEFDmzFshdViJJKswv0DNvLdKSS6YrteIeOntsgtITUuo8jn/UNUQO/3xQACKHw+HQ1KlTmacNACzs3Xfflc1mU1ZWVpXla9eulc/n04gRI0I6PtMdIGjpCSnqbk9Td3uaMpLKw1xGUrvAsmOFOgAAACBa3HPPPfLWMF+zaZq65557Qj4+LXYA0ETyDkrusvKfcw8c+R5jO7KNPVZKb9X8ZQMAAM1r+/bt6tWrV7XlJ510knbs2BHy8Ql2LUV+QWBuOLlc5d/z8iSP58g29ZgbDkDjyDsoZb4cK1NGleUj36l636khU86JZXWGO7fbrbVr1yorK4v7UQEAsKjk5GT9+OOPysjIqLJ8+/btatUq9Ku8BLuWIL9AtgtGyagYiTLZZtOYDu2VfN1UxVZq7jUNo3xycMId0OTcZZIpQ44EUzZDMk3J5y1TjC1WFdPWyGtKrhIj0KpX67HcbuXk5GjgwIEEOwAALGrkyJG67bbbtGzZMnXt2lVSeai74447NHLkyJCPT7BrCQ4fkmGaMpOTJZtNdkkXlnik5GQFJh3wemUUFx9p1WtkjG5ZM6aDgM2QYmPKg53XJ9liFAh28oW1aAAAoBndc889uvTSSzVgwAAdd9xxkqSff/5ZgwYN0n333Rfy8Ql2LYnNVv4VBoxuWVVjTQcBAACAliElJUWrV6/W+++/ry1btigxMVGnnHKKzj777EY5PsEOaAKNNR0EAAAAWg7DMDRs2DANGzas0Y9NsAMawOVy6emnn9a8efOOOUlwekKKVNEgt6e84U4ZSe2UZk9r4lICAAAgEixdulRXXnmlEhMTtXTp0jq3ve6660J6LIId0AD79u3T0qVLdfPNNx8z2AEAACC6LVmyRJdeeqkSExO1ZMmSWrczDINg11Bt27bVddddp7Zt24a7KAAAAABasE2bNtX4c1OIadKjRyCHw6GpU6fK4XCEuygAAAAAokBpaalOP/10ff/99032GFEX7AAAAACgOcXFxamkpKRJH4Ng15J4vXV/AQAAAAiLa665Ro8//rjKysqa5PhRd49di5SYJNMwyicgl3TAZtOaDu01fG++WlcKdKZhSIlJ4SolgIbIL5AOHyr/2eUq/56XJ3k85T8nJkkVU2cAAIDIt2HDBq1fv17vv/++evfurVatWlVZ/9JLL4V0fIJdS9C+nbyrcgIngcUul1YsXqwB8+9TYuV7CTkRbLC8vDy53e7A77m5uYHvMTHlDd52u13p6elhKR9aqPwC2S4YJcM0JUnJNpvGdGiv5OumKrbiYo1pGOX/9/xPAwBgCSkpKRo9enSTHZ9g11JUPrmLjy//np4upbWwOdOasRUjLy9PmZmZMitOrisbOXJk4GfDMOR0Ogl3aDyHD8kwTZnJyZLNJrukC0s8UnKyTEnyestb6P3/C4h43lq6w9e2HADQcvh8Pj3xxBPatm2bSktLdc4552jOnDlKSmrcnnQEO1hHM7diuN1umaapNWvWKDMzM7C8rKxMsbHl/zpOp1PDhw+v0qoHNBqbrfwLlmW322UYhlz+C1E1MAxDdru9GUsFAGhOCxcu1IMPPqihQ4cqMTFRzzzzjAoKCrR48eJGfRyCHawjTK0YmZmZ6t69e+D3ysEOOBavKcknmabkMyXTJxlGpXVNyO12a+3atcrKyiI4hEl6erqcTmedF3/ozg0ALdtrr72mRx55RFdffbUk6T//+Y8uv/xyPfHEE4FbexoDZ6ewHloxYAH2WMmQKVeJUWlpnHTUQFiGTNmb6J3Y7XYrJydHAwcOJNiFEaENAKLbrl27NGLEiMDvQ4cOlWEY+uWXX9S5c+dGexyCHQA0gfRWknNimdwVQe7HIp9GvpOgt39TohNSjlyds8eWbwsAAFqmsrIyJSYmVlkWFxfX6NMeEOxaILvdrlGjRnGFHgizyoHNVzFGRkZrqXtyeMoDAACan2mauuGGG5SQkBBYdvjwYf3xj3+sMuUB0x2gGrvdrjFjxoS7GAAAAEDUGz9+fLVll112WaM/DsEOAAAAAJrIU0891SyPQ7ADAOBYKs+hKcl96JDWfvqpsn79a9n98xA14jyaAAA0FMEOVbhcLi1evFg33XSTHA5HuIsDAOF31ByakmSz2RTTob1sTz3dJPNoAgDQUAQ7VFFYWKj58+dr4sSJBLsKTqezyu9HT1AORJTKLUv+SbHz8iSP58g2tCw1zFFzaEpqtnk0AQCoL4JdlMvLy6sycW5ubm6V71L0Tp5rt9tlGIaGDx9e53aGYTACKSLDUS1LyTabxnRor+TrpgZalSRaloLGHJoAgAhGsItieXl5yszMlFmpe5FfdnZ24GfDMOR0OqMu3KWnp8vpdFYJvj/++KNGjhypt99+WyeccIKk6A2+iEBHtSxVa1WSaFkCAKCFIthFMbfbLdM0tWbNGmVmZta4jdPp1PDhw6uEm2hydGDz+XySpIyMDHXv3j0cRQKOjZYlAACiDsEOyszMJKQAAAAAFhYT7gIAQDRom2DquuPz1DahetdnAACAUBHsEHVcLpfuvfdeufwjBgLNwJEgTc3YLUdCuEsCAABaIrpiIipUHv0zNzdX8+fP1znnnKOMjIzANk05CIrdbteoUaMYPfNolUZqrNdyAAAA1IhghxavttE/K4/8KTXt6J92u11jxoxp9ONaVmKSTMMoH51R0gGbTWs6tNfwvflqXWmyZyUmhbOUaOHcbrfWrl2rrKwsLroAACyPYIcWzz/6p8PhkK2WkQK9Xq9cLlfUjv7Z7Nq3K59HrWLI/WKXSysWL9aA+fcp0eEo34ZJtNHE3G63cnJyNHDgQIIdAMDyCHaIGjabTbGxVPmIUTm0xceXf09Pl9LSwlMeRIW8kiK5y0okSYWHCiRJuYcKtL/imo49NkHpCSnhKh4AAEHjLBdA2HEPIppDXkmRMtfODEzWHu+ReveUhm5YJE/FtQVDkjNrAeEOAGA5jIoJIOz89yBaIdgxqqp1uctKZEpyxNnVIb61Ulq31k99y793iG8tR5xdZsV2AABYDcEOABqgsLBQ8+fPV2FhYbiLgiDZDEOxhq3al80wwl00AACCRldMADiGo6fLqPxdatqpMgAAAOqDYAc0QNu2bXXdddepbdu24S4Kmkl9pstoyqkyAAAA6oNgBzSAw+HQ1KlT5fAPyY8W71jTZTBVBgAAiAQEOwCoB0tNl1ExyXuD1wEAAMuyyFkKAOCYEpNkGoaM4mJJ0gGbTWs6tNfwvflqXSnQmYZRPgE8AABoMQh2sJ7aWhxoiUC0a99O3lU50uFDkqRil0srFi/WgPn3KbFy9+HEpKoTxAMAAMsj2ME66tEaQUsEol7lwBZfMet2erqUlhae8gAAgGZBsIN11Kc1gpYIAAAARCGCHayF1ggAAACgGoIdAAD1wWijAIAIRrADAKAuR93fK3GPLwAg8hDsAACoy1H390rc4wsAiDwEOwAAjuXowMY9vgCACBMT7gIAAAAAAEJDsAMAVON2u7VixQq53e5wFwUAANQDwQ4AUI3b7VZOTg7BDgAAiyDYAQAAAIDFEexgWXa7XaNGjZLdbg93UQAAAICwItjBsux2u8aMGUOwQ8vl9db+BQAA6uWjjz7SuHHjdPLJJys1NVWrVq2qsn7lypUaO3asunXrptTUVG3evLnaMUaNGqXU1NQqX9OnT2+up1AvBDsAiDSVJsQ2CgvlLi7WvxPi5a743SguZjJsAADq6eDBg+rTp48WLFhQ43q3262BAwfq7rvvrvM4V155pb777rvA1z333NMEpQ1eg+ax83q9MgyjqcrSLLwVV7q9XPGWz+dr0LZW/Zv5n6dpmjJNs8Zt/MuP9TypP9HnWPWnSepOalt5V/5bOlQ+Ifa+wkKtWLJEv7r3HsWlppZvk5Qkpbats/XOX/Zg/n9D2TdSBV7Liq+jmZW2O9ZzTkxM1AUXXKDExMRm+fvw3oNQUH8QikirP/7P3eLi4iq5JCEhQQkJCTXuM2LECI0YMaLWY44bN06SlJubW+djJyUlqWPHjg0tcrNpULDbunVrrSfGVrNt27ZwFyHsdu7cKUkqKytTWVlZjdv4l2/fvl2lpaXNVrbG5H+edZ2s+U/46vs8qT/R41j1pznqzr6DB8sf4+BBufwTY7vdUn5+3fvt2xcom8vlathjhrBvpNpZuk+S5CvzymtU/yzzmZVey7hjP+cePXpo165djVrGY+G9B6Gg/iAUkVJ/DMNQZmam+vTpowMHDgSWz549W3PmzGnSx/7HP/6h119/XWlpafrNb36jmTNnqlWrVk36mA3RoGDXo0ePFtFit23bNp144omy2WzhLk5YxcXFSZJiY2MVG1tzVfAv79atm7p169ZsZWtM/ucZExNT62vuv2BxrOdJ/Yk+x6o/zVF33G63LrjgAvXp06dB95Tu3bs3ULYOHTo06DFD2TdSxR3cK/0kxcTaZDNqei29kqfitWwVWc+Z9x6EgvqDUERa/TFNUx6PR1u2bKnWYteULrnkEh1//PFKT0/X119/rXvuuUfbtm3TSy+91KSP2xANCnY2m83ywc7PZrNFROUMp5iY+t9iWVcoinT+52kYRq3117+8vs+T+hM9jlV/mqPuJCcn68ILL2zQPv4yNaRsjbVvpAq8lhVfRzMqbRepz5n3HoSC+oNQREr98V9QTU5ObtZcctVVVwV+PuWUU5Senq4LL7xQO3bsUNeuXZutHHVh8BQAAAAAaIAzzzxTUnn3/UhBsAMAAACABvBPiRBJg6k0qCsmAACRxu12a+3atcrKymJeSwBANQcOHNCOHTsCvzudTm3evFlt27bV8ccfr8LCQu3atUu//PKLpPIBIyUpLS1NHTt21I4dO/TGG29oxIgRcjgc2rJli+bOnauzzjpLffr0CctzqgnBDgDqobYRVSNl+Odo5na7lZOTo4EDBxLsAADVbNy4UaNHjw78PnfuXEnS+PHjtWTJEr399tu68cYbA+uvueYaSUdG2oyLi9N//vMfPf300zp48KA6d+6s0aNH69Zbb23eJ3IMBDsAqIPdbpdhGHUO+W8YBoECAIAINXjwYBUWFta6fsKECZowYUKt67t06aJVq1Y1RdEaFcEOUaM+k0cDR0tPT5fT6ZTb7ZZUPnlpdna2Vq9erYyMDEnl4S89PT2cxQQAAFGOYIcWrz4tLhKtLqhdTaEtIyND3bt3D0Npmkh+gXT40JHf/f8veXmSx1P+c2KS1L5d85ftKHklRXKXlQR+LzxUIEnKPVSg/eX5W/bYBKUnpISjeAAAhAXBDnI6nUGts4r6tLhItLogiuUXyHbBKBkVcwNJUrLNpjEd2iv5uqmKrWjRNg1D3lU5YQ13eSVFylw7U2alZfEeqXdPaeiGRfLEly8zJDmzFtQY7rymKal6K73XNKstAwDAKgh2UczfkjV8+PA6t2sJLVlR0eICBOvwIRmmKTM5WaqYfNYu6cISj5ScXB6ivF4ZxcVVW/XCwF1WIlOSI84um39i2njpp76SP8J5TVOuUnd5q17CkX3tsQkyJLlKyy/yxHuk3j9I33RXlUBoj620EwAAFkGwi2JHt2TVhpYsIErYbIFgF+lshqFYo7ay1nzPbHpCipxZCwLdOAvzC/TMW4u05JLpSq1ohaQLJwDAqgh2UY7ABiCapCekBFrx9lRc08pIaqc0e1r4CgUAQCOICXcBACBYbrdbK1asOGarc2PvCwAAEGkIdgAsyz8xdbDBLth9AQAAIg3BDlEnNTVVd9xxh1JTU8NdFAAAAKBRcI8doo7D4dC8efPCXQwAAACg0dBiBwAtlN1u16hRo5p1uhKXy6V7771XLv8E5wAAoFkQ7ACghbLb7RozZkyzBrvCwkLNnz9fhYWFzfaYAACAYAcAAAAAlkewAwAAAACLI9gBAAAAgMUR7AAAAADA4pjuAEBUyCspkrusJPB74aECSVLuoQLtr5ij3B6boPSElHAUDwAAICQEOwAtXl5JkTLXzpRZaVm8R+rdUxq6YZE88eXLDEnOrAWEuygRjukgAABoKgQ7AC2eu6xEpiRHnF02wyhfGC/91FfyRzivacpV6i5v1UsIV0nRnPzTQQAA0BIQ7ABEDZthKNaw1bLW26xlAQAAaEwMngIAAAAAFkeLHQBryS+QDh8q/9nlKv+elyd5PEe2SUyS2rdr/rIBAACECcEOgHXkF8h2wSgZZvkwKMk2m8Z0aK/k66Yq1nukK6VpGPKuyiHcNZS3ju6oda0DAABhR7ADYB2HD8kwTZnJyZLNJrukC0s8UnLykREvvV4ZxcVHWvVwbIlJMg2j/O9W4YDNpjUd2mv43ny1rgh1pmGUt4YCAICIQ7ADYD02W/kXGkf7duUtnJXCsPfQIfk+/VTeX/9aZUkVYY4urgAARCyCHQCgWmCzSxrTo0d4ygIAABqMUTEBoAFSU1N1xx13KDU1NdxFAQAACKDFDoD1hHGQD4fDoXnz5jXpYwAAADQUwQ6AdRw1yEdNA3xIDPIBAACiD8EOgHUcNchHsculFYsXa8D8+5TocBzZjkE+AABAlCHYAbCWyoEtPr78e3q6lJYWnvIAAABEAIIdAMByvKYpqeb7Kb2mWeNyAABaMoIdAMAy7LEJMiS5St2BZfEeqfcP0jfdJU9FI65RsS0AANGCYAcACEleXp7c7vKglZubW+W7JNntdqWnp9d5DJfLpcWLF+umm26So/L9kkdJT0iRM2uB3GUlgWWF+QV65q1FWnLJdKVWdNW1xyYoPSEl6OcEAIDVEOwAAEHLy8tTZmamzKO6P2ZnZwd+NgxDTqezznBXWFio+fPna+LEiXUGO6k83KlSY9yeisa7jKR2SrNzryUAIDoR7AAAQXO73TJNUw6HQzabrdp6r9crl8sVaNEDAABNg2AHAAiZzWZTbGzDPlKO1YVTql83TgAAQLADAIRBfbpwSvXrxgkAAAh2AIAw8HfhXLNmjTIzM2vcxul0avjw4XTjBACgHgh2AKIGc59FnszMTHXv3j3cxQAAwPIIdgBaPOY+AwAALR3BDkCLx9xnAACgpSPYAYgKR8995pZdo0aN0sntM2S328NXMAAAgEZAsAMQlex2u8aMGRPuYgAAADSKmHAXAAAAAAAQGoIdAAAAAFgcwQ4AAAAALI5gBwAAAAAWR7ADAAAAAIsj2AEALM1uL5+6gmkrAADRjOkOAACWxtQVAADQYgcAAAAAlkewAwAAAACLI9gBAAAAgMUR7ABYFoNmAAAAlGPwFACWxaAZAAAA5WixAwAAAACLI9gBAAAAgMUR7AAAAADA4gh2AAAAAGBxBDsAAAAAsDiCHQAAAABYHMEOAAAAACyOYAcAAAAAFkewAwAAAACLI9gBAAAAgMUR7AAAAADA4gh2AAAAAGBxBDsAAAAAsDiCHQAAAABYHMEOAAAAACyOYAcAAAAAFkewAwAAANBiffTRRxo3bpxOPvlkpaamatWqVVXWr1y5UmPHjlW3bt2UmpqqzZs3VzvG4cOHdeutt6pbt27q0qWLrrjiCu3Zs6e5nkK9EOwAAAAAtFgHDx5Unz59tGDBghrXu91uDRw4UHfffXetx7j99tv1zjvv6IUXXlBOTo7y8vI0efLkJipxcGLDXQAAAAAAaCojRozQiBEjal0/btw4SVJubm6N64uKirRs2TI9++yzGjJkiCRp8eLF+vWvf63PPvtM/fv3b/xCB6FBwc7r9cowjKYqS7Pwer1VvgMNQf1BsFpq3fH5fJIk0zRlmma19f5lPp+vynP371ffx2hpf7eGaqn1B82D+oNQRFr98X+uFBcXV8klCQkJSkhIaJLH3LRpk0pLSzV06NDAspNOOkldunSxbrDbunVrjR/cVrRt27ZwFwEWRv1BsFpa3dm5c6ckqbS0tMaw5j8R2L59u0pLS6vtV1ZWprKyshqP7V9+9L7RrKXVHzQv6g9CESn1xzAMZWZmqk+fPjpw4EBg+ezZszVnzpwmeczdu3crPj5eKSkpVZanpaVp9+7dTfKYwWhQsOvRo0eLaLHbtm2bTjzxRNlstnAXBxZD/UGwWmrdSUlJkWEYKioqqnUbwzDUp08fpaenB5bFxcVJkmJjYxUbW/NHkX95t27d1K1bt0YstfW01PqD5kH9QSgirf6YpimPx6MtW7ZUa7GLdg0KdjabzfLBzs9ms0VE5YQ1UX8QrJZWdzp37iyn0ym32y2p/P6E7OxsrV69WhkZGZIku91eJdRJUkxM/cfuiomJaVF/s1C0tPqD5kX9QSgipf74ew8mJyc3Wy7p2LGjPB6PioqKqrTa7dmzRx07dmyWMtQHg6cAAEJSObSlpqbqjjvuUN++feVwOMJYKgAAGkffvn0VFxenDz74QGPGjJFUfovarl27Iub+OolgBwBoRA6HQ/PmzQt3MQAACDhw4IB27NgR+N3pdGrz5s1q27atjj/+eBUWFmrXrl365ZdfJJWHNqn8HrqOHTsqJSVFkyZN0ty5c5Wamqo2bdpo1qxZ6t+/P8EOAAAAAJrDxo0bNXr06MDvc+fOlSSNHz9eS5Ys0dtvv60bb7wxsP6aa66RVHVAlgceeEAxMTG64oor5PF4NGzYMD3yyCPN+CyOjWAHAAAAoMUaPHiwCgsLa10/YcIETZgwoc5jJCYm6pFHHom4MFdZ/e9eBwAAAABEJIIdAAAAAFgcwQ4AAAAALI5gBwAAAAAWR7ADAAAAAIsj2AEAAACAxRHsAAAAAMDiCHYAAAAAYHEEOwAAAACwOIIdAAAAAFgcwQ4AAAAALI5gBwAAAAAWR7ADAAAAAIsj2AEAAACAxRHsAAAAAMDiCHYAAAAAYHEEOwAAAACwOIIdAAAAAFgcwQ4AAAAALC423AUAAEQvp9MZ1DoAAFAVwQ4A0OzsdrsMw9Dw4cPr3M4wDNnt9mYqFQAA1kWwAwA0u/T0dDmdTrndbklSbm6usrOztXr1amVkZAS2s9vtSk9PD1cxAQCwDIIdACAsagpsGRkZ6t69exhKAwCAtTF4CgAAAABYHMEOAAAAACyOYAcAAAAAFkewAwAAAACLI9gBAAAAgMUR7AAAAADA4gh2AAAAAGBxBDsAAAAAsDiCHQAAAABYHMEOAAAAACyOYAcAAAAAFkewAwAAAACLI9gBAMIuNTVVd9xxh1JTU8NdFAAALCk23AUAAMDhcGjevHnhLgYAAJZFix0AAAAAWBzBDgAAAAAsjmAHAAAAABZHsAMAAAAAiyPYAQAAAIDFEewAAAAAwOIIdgAAAABgcQQ7AAAAALA4gh0AAAAAWBzBDgAAAAAsjmAHAAAAABZHsAMAAAAAiyPYAQAAAIDFEewAAAAAwOIIdgAAAABgcQQ7AAAAALC42Pps5PV6A99jYqydBX0+n2JiYuTz+WQYRriLA4uh/iBY1B2EgvqDUFB/EIpIqz8+n09SeS6Jja1XlIkahmma5rE2Kisrk9vtbo7yAAAAAECd7HY7we4oDfprJCYmRkRSD4XX69XWrVvVo0cP2Wy2cBcHFkP9QbCoOwgF9QehoP4gFJFWf0zT1OHDh8NdjIhUr2DnfxENw7B8sDMMQ6ZptojnguZH/UGwqDsIBfUHoaD+IBSRWn8iIWRGGmvfMAcAAAAAINgBAAAAgNUR7AAAAADA4gh2AAAAAGBxBDsAAAAAsDiCHQAAAABYHMEOAAAAACyOYAcAAAAAFkewAwAAAACLI9gBAAAAgMUR7AAAAADA4gh2AAAAAGBxBDsAAAAAsLjYcBcAAI4l5vobJJer4Ts6HPL9eUnjFwgAACDCEOwARD6XS0bn4xq8m/nTz01QGAAAgMhDV0wAAAAAsDiCHQAAAABYHMEOAAAAACyOYAcAAAAAFkewAwAAAACLI9gBAAAAgMUR7AAAAADA4gh2AAAAAGBxBDsAAAAAsDiCHQAAAABYHMEOAAAAACyOYAcAAAAAFkewAwAAAACLI9gBAAAAgMUR7AAAAADA4mLDXQAAQPSY/PkzKvC4G7xfu3i7XvrVlCYoEQAALQPBDgDQbAo8biUlJDR8v5KGh0EAAKIJXTEBAAAAwOIIdgAAAABgcQQ7AAAAALA4gh0AAAAAWByDpwAAAABAEzh06JASEhIUE1PenvbBBx9o8+bNOumkk3Teeec16mPRYgcAAAAATWDEiBHav3+/JOnRRx/VwoULZZqm/vrXv+rOO+9s1Mci2AEAAABAE/B6vUpJSZEkrVy5Um+88YamTZuml19+WevWrWvUxyLYAQAAAEAT6NChgz799FNJUnp6ugoKCiQp0IrXmLjHDgAAAACawOLFi3XDDTcoPj5erVq10jnnnKN+/fopPz9f9957b6M+FsEOAAAAAJpARkaGcnJy9N1332n79u267LLL1KlTJ51xxhmy2WyN+lgEOwAAAABoQr169VKvXr2a9DEIdgAAAADQDAoKCrRs2TJ99tln2rNnjyQpLS1NAwYM0IQJE9S+ffugj02wAwAAQIsXc/0NksvV8B0dDvn+vKTxC4So8+WXX+riiy9Wq1atdO6556p79+6SpL1792rp0qV67LHH9Oabb+r0008P6vgEOwAAALR8LpeMzsc1eDfzp5+boDAIl0WLFunee+/V9ddfrz/96U+SpMOHD+uOO+7Q8uXL5fF4NGzYMD3yyCNKS0sL7Ldz507NmDFDH374oex2u8aNG6e77rpLsbH1j1OzZ8/WhRdeqEWLFskwjCrrTNPU9OnTNXv2bL377rtBPTemOwAAAADQ4n355Zd64YUXdMopp1RZfvvtt+udd97RCy+8oJycHOXl5Wny5MmB9V6vV5dffrlKS0u1evVqLVmyRK+++qoeeOCBBj3+li1bdMMNN1QLdZJkGIZuuOEGbd68ObgnJ4IdAAAAgBbuwIEDmjJlih5//HG1bds2sLyoqEjLli3T/fffryFDhqhfv35avHix/vvf/+qzzz6TJK1bt07ff/+9li5dqlNPPVUjRozQ7bffrueee04ej6feZUhLS9OXX35Z6/ovv/xSHTp0CPo5NqgrptfrrTFhWonX663yHWgI6k94hHIFKlJeK+qOnxn0ftH8t6P+IBTUn3It4bMkHCKt/phm+edIcXFxlVySkJCghISEWvebOXOmzjvvPA0dOlSPPPJIYPmmTZtUWlqqoUOHBpaddNJJ6tKliz777DP1799fn332mXr37l2la2ZWVpZmzJih7777Tqeddlq9yn7TTTfplltu0caNGzVkyJDA8fbs2aP169frxRdfDGluuwYFu61btwb+mFa3bdu2cBcBFkb9aV69TZ+CmenFZ/r0/fffN3p5QhHtdcfnC+4zxOczI+61DIdorz8ITbTXn5b0WRIOkVJ/DMNQZmam+vTpowMHDgSWz549W3PmzKlxnzfffFObNm3SunXrqq3bvXu34uPjlZKSUmV5Wlqadu/eLak8eFUOdZICLWv+berj2muvVbt27fT000/rL3/5SyAs22w29evXT0899ZR++9vf1vt4R2tQsOvRo0eLaLHbtm2bTjzxxEafFBAtH/UnPGKM4K6zxhgx6tmzZyOXJjjUnXIxn74T3H4xRsS8luFA/UEoqD/lWsJnSThEWv0xTVMej0dbtmyp1mJXk127dum2227T8uXLlZiY2FzFrNXYsWM1duxYlZaWqqCgQJLUrl07xcXFhXzsBgU7m81m+WDnZ7PZIqJywpqoP9YRaa8TdSfYzxAjyv9u5ag/CEVT1J/Jnz+jAo+7wfu1i7frpV9NadSyNCX+7yLn/cffezA5ObleuWTTpk3au3dvla6WXq9XH3/8sZ599lm9+eab8ng8KioqqtJqt2fPHnXs2FFSeevdF198UeW4e/fulaTANg0VFxen9PT0oPatDYOnAAAAICgFHreSEhIa/BVMGASCMWTIEH300Udav3594Ov000/XpZdeqvXr16tfv36Ki4vTBx98ENhn69at2rVrl/r37y9J6t+/v7755ptAmJOk999/X23atGnU1txdu3bppptuCnp/5rEDAAAA0CK1adNGvXv3rrKsVatWcjgcgeWTJk3S3LlzlZqaqjZt2mjWrFnq379/INgNGzZMPXv21PXXX6+7775be/bs0f3336/f//73dQ7Y0lCFhYV69dVXtXjx4qD2J9gBAAAAiFoPPPCAYmJidMUVV1SZoNzPZrPptdde04wZM5Sdna1WrVpp/Pjxuv322xv0OG+99Vad651OZ1Dl9yPYIeLFXH+D5HI1fEeHQ74/L2n8AgEAAMCycnJyqvyemJioRx55pEqYO1pGRob+8Y9/hPS4kyZNkmEYdc4yEMp4JgQ7RD6XS0bn4xq8m/nTz01QGAAAAKDh0tPT9cgjj+j888+vcf3mzZurDPLSUAyeAgAAAABNrG/fvtq4cWOd24QyZzgtdgAAAADQxKZNm6aDBw/Wur5bt25auXJl0Mcn2AEAAABAEzvrrLPqXG+323X22WcHfXyCHQCgwYIe1GhqP6kRh4YGAADlCHYAgIYLclAjhXDvAAAAqB2DpwAAAACAxRHsAAAAAMDiCHYAAAAAYHEEOwAAAACwOIIdAAAAAFgcwQ4AAAAALI5gBwAAAAAWR7ADAAAAgGb08ccfa8qUKTrvvPP0888/S5Jee+01/d///V/QxyTYAQAAAEAzWbFihS655BIlJSXpq6++ksfjkSQVFxfr0UcfDfq4BDsAAAAAaCaPPPKIHn30UT3++OOKi4sLLB84cKC++uqroI8b2xiFAxCayZ8/owKPu8H7tYu366VfTWmCEgEAAKApbNu2TWeddVa15cnJySoqKgr6uAQ7IAIUeNxKSkho+H4lDQ+DAAAACJ+0tDRt375dGRkZVZZ/8sknOuGEE4I+Ll0xAQAAAKCZXHHFFbrtttv0+eefyzAM/fLLL3r99dd155136ne/+13Qx6XFDmhEwXapLPGWKkkNb7EDgGhCt3UALcH06dPl8/l00UUX6eDBg7rggguUkJCgm266SVOmBP9eRbADGlGwXSoPH/Q0QWkiT8z1N0guV8N3LClp/MIAsBy6rQNoCQzD0K233qqbb75Z27dvl9vtVs+ePdW6deuQjkuwA9B8XC4ZnY9r8G7m9h1NUJimU3Orgimfz1TMp+9IMqrtQ4tCZAr6YoTDId+flzR+gQAALUZ8fLx69erVaMcj2AFo0cLRdSuYVgVaFCJUsBcjfvq5CQoDAGgpDh8+rK+//lp79+6Vz+ersu78888P6pgEO7RYkyb3Vv7Hixq8Hy0nLQtdtwAAQCRZs2aNpk6dqoKCgmrrDMOocXl9EOwaUTAtA4SIppNvj+OEHgAAABFl9uzZuvDCCzVr1iylpaU12nEJdo2I7lcAwoGRAgEAsI69e/fqxhtvbNRQJxHsAMDy6G4KAIB1jBkzRh9++KG6du3aqMcl2AEAAABAM3n44Yd11VVX6f/+7//Uu3dvxcXFVVl/3XXXBXVcgh0ARDHmFgQgBd+lu8RbqiQ1vMdAtKCrPGry5ptv6v3331diYqI+/PBDGcaRaZAMwyDYoWGYmwmIPEH/X07tJwXRFVNS1MwtCKBuwXbpPnzQ0wSlaTnoKo+azJ8/X3PmzNH06dMVExPTaMcl2EUr5mZqMbga2IIE+X8p02z8skQg6joAoCXweDwaO3Zso4Y6iWAHWB5XAxEtqOt1I/hGFl4PALUZP368li9frhkzZjTqcQl2AAC0AATfyMLrAaA2Xq9XTzzxhNatW6dTTjml2uAp999/f1DHJdgBNaj9Sqspn89UzKfvSDKqreUmcgAAANTlm2++0WmnnSZJ+vbbb6usqzyQSkMR7IAacBM5AAAAmsLKlSub5LgEOwAAgBagtpF1YyT1Nn2KMWoZqMHhkK7o0bSFA9DkCHYAAAAtQR0j69rq2I0Rr4Hm9fDDD9e5ftasWUEdl2AHADUo8Zbq/I8XNXi/dvH2JigNgHBh4m4AjS0nJ6fK76WlpcrNzZXNZlPXrl0JdgDQmEyZjGjXQoQS0l9ugvLAWrjnGkBjW79+fbVlxcXFuvHGG3XBBRcEfVyCXQSorU/8MZWVSbFBvIQOR8P3AQCLIqQDACJdcnKy5syZo/Hjx2vcuHFBHYNgFwnq6BNfF3P7DhmZGQ3fLwx96YMOr4RQAAAARIHi4mIVFxcHvT/BDs0j2PDKDd0AACDK/OWZ/yrmsSBabRwO+f68pPELhEa1dOnSKr+bpqm8vDy9/vrrGj58eNDHJdgBAAAAdQhmEJ1QBtNqd8Aj4/jMBu/HBXFrWLKkaviOiYlR+/btNW7cOE2fPj3o4xLsjhLs6FeMhAer4WogAAD1E8wgOtyni9ps2rSpSY5LsDtKsKNf8c8Lq+FqIAAAQMtBsAMAAACAJjR37tx6b3v//fcH9RgEOwAAAABoQl999VW9tjMMI+jHINgBANDIJk3urfwgJ0V/6VdTmqBEQMsR9BRKJSWNXxignlauXNnkj0GwAwCgkeXb47hfO8IEHQam9pOCeC1Rt1AG8Apl/l8gUhQVFemll17S//73P0lSr169NHHiRKWkpAR9TIIdAABo+YIMAzLNxi8LGMALUW3Dhg26+OKLlZSUpDPOOEOS9NRTT2nhwoVavny5+vbtG9RxCXYAGiyoK98OR9MUBgAAwEJuv/12jRw5Uo8//rhiY8vjWFlZmW6++Wbddttteuutt4I6LsEOQMMFceWbq6wAAADSxo0bq4Q6SYqNjdXNN9+sYcOGBX1cgh0AAFEslHudfH9e0vgFAoAWrk2bNtq1a5dOOumkKst/+ukntW7dOujjEuzQIMGO9PZvX5kSm6A8AIDQhHKv0+TPn1GBp2EDvjDyJ4Dm9OijjyonJ0dbt25VYmKiBgwYoLvvvls9evQIbHP48GHdcccdWr58uTwej4YNG6ZHHnlEaWlpgW127typGTNm6MMPP5Tdbte4ceN01113VWl1q6/f/va3mjZtmu677z4NGDBAkvTpp59q3rx5uvjii4N+rgQ7NEiwI72Z4uZzADiWEm+pzq/x4pkpn89UzKfvSKo+x1G7eHuTl60mBR53gz8TGPkTQHP6+OOP9fvf/16nn366ysrKdN9992ns2LH65JNPZLeXv3fefvvtevfdd/XCCy8oOTlZs2bN0uTJk7V69WpJktfr1eWXX66OHTtq9erVysvL09SpUxUXF6d58+Y1uEz33XefDMPQ1KlTVVZWJkmKi4vT7373O911111BP9cGBTuv1xvSpHmRwOv1VvleXbABpLn3Q9Ox0mtipbKGS3P/jaz0XmCl+hMdfx9TZpDTJBxogtLURzB/I7OOz+CmeLxQ9gsHU5M/e0YFpQ1/TUPrHWOlv1E4BFfXwyH4/6+6j9fYxw2WWTFSbXFxcZVckpCQoIQa3j/feOONKr8vWbJEPXr00MaNG3X22WerqKhIy5Yt07PPPqshQ4ZIkhYvXqxf//rX+uyzz9S/f3+tW7dO33//vf71r38pLS1Np556qm6//XbdfffdmjNnjuLj4+tV9m+++Ua9e/dWfHy8HnzwQc2bN087dpRPw9G1a1e1atUqqL+JX4OC3datWwN/TKvbtm1bjct9vuCeXyj7+UyfbEHtHeRjmj5JCvIxm1c4yhrsaxkO4ShrsK9JaK9l8HW9uf9G4XkvCE443n+CFZa6HobXJFjhei8I5nF9PlPmtdcpdn9xwx906ulSQsOji9Vey70l+2VPSmrwvsH2jgnHe2WwrPRZEq7/y++//75Jjl3buXNzMwxDmZmZ6tOnjw4cOHIBZPbs2ZozZ84x9y8uLn/vSU1NlSRt2rRJpaWlGjp0aGCbk046SV26dAkEu88++0y9e/eu0jUzKytLM2bM0HfffafTTjutXmUfPHiwzjjjDE2ePFljx45VmzZtdMopp9Rr3/poULDr0aNHi2ix27Ztm0488UTZbNXfFsq7uTRcTExwf5eYGEMxRkxQ+9bUHadejxn04zW/cJQ12NcyHMJR1mBfk9Bey+DrenP/jcLzXhCccLz/BCssdT0Mr0mwwvVeEMzjxsQYij9wQEaXLk1Qqtofk9eyjscMw3tlsKz0WRKu1/JP7g+DavFtF9daL5xxTbXlxzp3bm6macrj8WjLli3VWuyOxefz6bbbbtOvf/1r9e7dW5K0e/duxcfHV5sYPC0tTbt375Yk7dmzp0qok6QOHToE9q+vnJwcvfLKK7rzzjs1d+5cjR49WpMnT9ZZZ51V72PUpUHBzmazWT7Y+dlstloqZ7DPr7n3Q9Ox0mtipbKGS3P/jaz0XmCl+sPfp27hKmswj8trWTcrvZbRxCp1XSoodSspiJbtghJ3ncGt9nPn5uXvPZicnNzgXHLrrbfq22+/1dtvv90URTums846S2eddZYeeugh/etf/9Irr7yiUaNGqVu3bpo0aZLGjx+vjh07Bn38Fjt4Su0jddV9A3qJt1RJavj9DQAAAAAi08yZM7V69Wq99dZb6ty5c2B5x44d5fF4VFRUVKXVbs+ePYGQlZaWpi+++KLK8fbu3RvYv6HsdrsmTpyoiRMnavv27Xr55Zf13HPP6YEHHlBWVpZeffXVYJ6irNEvIQj+kbqqfyXKnpSkpITEGtczeiMAAADQMpimqZkzZ2rVqlVasWKFMjOrTu/St29fxcXF6YMPPggs27p1q3bt2qX+/ftLkvr3769vvvkmEOYk6f3331ebNm3Us2fPkMrXrVs3/fGPf9Stt96q1q1b69133w36WC22xQ4AAABAdLv11lv1xhtv6JVXXlHr1q0D98QlJycrKSlJKSkpmjRpkubOnavU1FS1adNGs2bNUv/+/QPBbtiwYerZs6euv/563X333dqzZ4/uv/9+/f73v6/XvX21+eijj/Tyyy9r5cqVMgxDv/3tbzVp0qSgj0ewAwAAANAiPf/885KkUaNGVVn+1FNPacKECZKkBx54QDExMbriiiuqTFDuZ7PZ9Nprr2nGjBnKzs5Wq1atNH78eN1+++0NLs8vv/yiV155Ra+++qq2b9+uAQMG6MEHH9RFF10UmFcvWAQ7AAAAAC1SYWHhMbdJTEzUI488UiXMHS0jI0P/+Mc/QirLJZdcog8++EDt2rXT5ZdfrkmTJqlHjx4hHbMygh0AAAAANLG4uDj97W9/U3Z2dpOMMEqwAwAAAIAmFuxol/XVYkfFBAAAAIBoQbADAAAAAIsj2AEAAACAxRHsAAAAAMDiCHYAAAAAYHEEOwAAAACwOIIdAAAAAFgcwQ4AAAAALI5gBwAAAAAWR7ADAAAAAIsj2AEAAACAxRHsAAAAAMDiCHYAAAAAYHEEOwAAAACwOIIdAAAAAFgcwQ4AAAAALI5gBwAAAAAWR7ADAAAAAIsj2AEAAACAxRHsAAAAAMDiCHYAAAAAYHEEOwAAAACwOIIdAAAAAFgcwQ4AAAAALI5gBwAAAAAWR7ADAAAAAIsj2AEAAACAxRHsAAAAAMDiCHYAAAAAYHEEOwAAAACwOIIdAAAAAFgcwQ4AAAAALI5gBwAAAAAWR7ADAAAAAIsj2AEAAACAxcXWZyPTNKt8t4KkmDglxsQ1236K8UlSw/eN8clMTJQSEhr8kGZSUnD7JSaW/xDEvkm2+KD+PmZiokyLlDXo11IWqndq/tcklNcylLpumdckhPeCUOp6c7//WOn/MhyvidXeC4Iqr8XqOq9l3YLdLxznBc3+WWKx11IxvhrP9U3TlGEYMk0zIrJA5VxiGEaYSxNZDLMer5DP59P+/fubozwAAAAAUKc2bdooJobOh5XVO9gFdrB4Mi4uLlafPn20ZcsWJScnh7s4sBjqD4JF3UEoqD8IBfUHoYi0+lM5uhDsqqpXV8yW9EczDEMHDhyQYRiWD6loftQfBIu6g1BQfxAK6g9CEWn1JxLKEKlaTmIDAAAAgChFsAMAAAAAi4u6YJeQkKDZs2crIYiRhgDqD4JF3UEoqD8IBfUHoaD+WEe9Bk8BAAAAAESuqGuxAwAAAICWhmAHAAAAABZHsAMAAAAAiyPYAQAAAIDFEeyAJlJ5XCLGKEJDlZaWhrsIAKIYn1uA9RDsgCZy+PBhSVJZWZkMw5DP5wtziWAVb775psaPH6+DBw+GuygAosjGjRt1/fXXS5IMwwhzaQA0VGy4CxCpNmzYoF27dikxMVGdO3dW7969w10kWMjKlSu1fPlyFRQUqEePHrrzzjvVtm3bcBcLFvDXv/5VM2bMUOvWreV0OnXyySfLNE1OslAvpaWliouLC3cxYEGbN2/WBRdcoEmTJlVZzvsPYB202NXgpZde0uWXX66nn35ac+fO1eTJk/XMM8+Eu1iwiGXLlumGG25Qr169dOKJJ2rTpk166aWXAuvp3oLavPDCC5o1a5b++te/qkuXLlqyZIkkrpyjfrZt26a7775b3377bbiLAovZvHmzRo4cqd/97nd66KGHqqzj/QewDlrsjvLxxx/r3nvv1UMPPaSLLrpIP/zwgx5//HHNmTNHBw4c0B//+MdwFxER7K233tL8+fO1ZMkSjR49WpI0efJkJSQkSJI8Ho/i4+Pl8/kUE8N1FRzxl7/8JRDqxowZo927d+u5557Txo0b1a9fv3AXDxFux44duuCCC3Tw4EEdPnxYU6ZMUc+ePcNdLFjA7t27df755+v888/XfffdJ4/Ho/nz5+vHH39Ufn6+xo4dq9/85jfq0qVLuIsK4Bg4szzKli1b9Ktf/Upjx46VYRjq0aOHBg8erLZt22r+/Pl68sknw11ERKjS0lJ9//33mjhxorKzswPLCwsL9fe//11ZWVm68MILlZubq5iYGO65Q8CmTZv05JNP6oUXXtCYMWMkSYMGDVJhYaE++eQTSbT0onYHDx7Uww8/rMGDB2vGjBn6/PPP9fTTT+v7778Pd9FgAXv27FH//v21ceNGffPNN5o8ebI+//xzderUSR06dNCLL76oP/3pT9qzZ0+4iwrgGAh2RykpKZHT6dQvv/wS6H5w8OBBDRs2TDNnztSf//xnffXVV2EuJSJRXFycJkyYoKuuukrx8fGSpKuuukpOp1PXXXedrr32WiUlJWns2LEqKiqixQ4BJ510kl5//XWNHj06EOBOPfVUXXHFFXryySe1a9cuukOhVomJiRoyZIhGjBihW265Rddee602bNhAuEO9nHrqqbrnnnvUrVs3DR48WF6vV8uWLdNDDz2kv/3tb5o8ebI+/PBDbd26NdxFBXAMdMU8ykknnSSfz6fnnntOAwYM0J49ezRnzhy9+OKLOvPMM/XGG28oPz8/3MVEhPDfVO7vWtmxY8fAury8PCUlJSknJ0eZmZmSpHbt2umGG25Qbm6uTj311HAVGxEmKSlJJ510UqA++b8PGzZMy5cv1+eff64uXbrI6/XKZrOFu7iIMDExMbr44osDg6ZMmjRJPp9Pzz//vJ5++mlNnTpVPXv2VGlpqQoKCpSenh7mEiPc/O8l/veaU089VbNmzdLJJ5+sIUOGyOFwBLa59tpr9dBDD+njjz/W2WefHe6iA6hD1Ae7vLw8eTweSVJGRoays7P1v//9TytWrNBrr70mSVqyZIl+85vfSJJ8Pp9++OEHDRs2LGxlRuTIy8tTp06dAl0r/a1wpmkqPT1dixcvls1mC6yz2WzKzMxUampqmEuOcPvqq6+0fft22Ww29e7dW927dw+0yvm/n3322erevbsWL16siy66iFCHavwn3/5eAv7fr7jiCpmmqb/+9a96+umn9fvf/14vv/yyPvzwQ61ZsyZw3y+iz9atW/WXv/xF3377rX71q19p2LBhOvvss3XGGWeoQ4cOgQuUNptNXq9XLpdL3bp10ymnnBLmkgM4lqgOdq+99pqef/557dixQ127dlVWVpZmz56tadOm6cILLwycXB1//PGSJKfTqeTkZJ144onhLDYixD/+8Q/dfvvtmj9/vi6//HLFxMQErn76644/6MXExKikpETPPPOMjj/+eHXu3DmcRUeY+bs5paSkaP/+/erUqZMefvhhnXbaaYFt/Cfo06dP10033aR///vfuvDCC8NYakSK3Nxcbdq0SaNHj65y4UhSld+vvPJKGYahF154QRMmTFBhYaFWrlxJqIti33zzjUaPHq3s7Gx16tRJn3zyif73v/+pW7du6tSpU+B8x89ms+kvf/mLCgsL6WUCWEDUBrvXX39dt956qx588EE5HA59+eWXWrNmjbKysvSrX/1Kxx9/fODk3OPxaNeuXbr99tsVHx+vIUOGhLn0CLf//Oc/mjdvnjp37qzHH39chmHosssuq9KNTipveSkpKdE333yj+++/X3l5eXr//ferdN9EdMnJydEdd9yhhQsX6rzzztOmTZu0aNEirV+/Xqeddlqg/vhb504++WS53W5t3ryZYAdt27ZNv/nNb9SmTRu53W6NGzeuWo+Byr9fccUVevXVV7V//3698847tLpEsby8PN1www2aNGmS7rnnHknln2XXXnutcnNz1alTpyrbv/fee3rvvff097//XTk5OdVCH4DIE5Vnld99952eeuopPfDAA5o0aZLOP/98XXnllfrpp5+0efNmSVXnbdm2bZtuv/127d69W2+99VagewKik9vt1vLly5Wdna358+dr4MCBWrhwoV5//XVJCoQ2v08//VQvvPCCDMPQ+++/r7i4OJWVlRHqotDevXv16quv6vrrr9fFF1+sNm3aaPDgwcrMzNSqVaskVX3vMU1TDodDf//73zVnzpxwFRsRIj8/X7Nnz1a/fv10xhln6K9//ateeeUVSao20m5MTIzKysp0yy236NNPP1VOTg6hLsp99dVX6tKli8aNGxcYpGno0KE68cQTtWPHDklVR9/dunWrfv75Z7399tu01gEWEZUtdm63W7169dIZZ5whqfyN7Pjjj9egQYNUUFAgSVUGKcjIyNCMGTN0xhlnyGazqaysTLGxUfmngyS73a5rrrlGe/fu1eDBg9W+fXuZpqmFCxdKki677LIq3TIHDhwoh8Oh3r17B062qD/RKSYmRr169dKgQYMkKdCqMmDAAH399deSVKXF17/N6aefLknUnShXUlKi1q1b66qrrlKnTp302GOP6W9/+5skacKECdVa7mJjYzVo0CBdeeWVhDooLS1N5513nk4++WRJR0JcSUmJ9u7dK6nqhSV/615ycnLzFxZAUKLyDCEjI0PXXnut+vTpU2W5aZrav3+/JFUZpKB169bq37+/pPLAx4kV+vbtG/i5V69emjJliiTpkUcekVQe7vLz8/X5559r5MiRgbrm8/moP1GsXbt2uvLKK5WRkSHpyEmUw+GQVDW4ffvttzr55JOrnGhRd6Jb586d9eCDDwa6zN188816/PHH9be//U2maWrixInVLh5dfvnl4SwyIki/fv3Ur18/SVUvIKWkpFQ553n22WcDg8kR6gBricqzhA4dOqhDhw6SqnY78Hg8KikpCfx+6aWXasiQIZo2bVpgGaPSoTL/h+PJJ5+sa6+9VoZh6NFHH9X+/fv1j3/8QwUFBfrNb35TbTAVRC9/qKt8YrVv3z4VFxcHTsYvvvhiHT58ONA9E/Dzhzqv16vevXtr+vTpWrRokV588UUZhqEJEyZoxowZ6tevn66++uowlxaRqvIFo9atWwdGVb333nv15z//WevWrQtX0QCEIOrPMiuPYNi2bVu1atVKUvmJ1Y8//qjrr78+nMVDhKv84di7d29dd911OvPMMzVz5kyVlJTo448/DgyoAlR29ITj/pEKL7vsMu3cuVP/+te/wlAqWIX/ImOvXr00ffp0nXDCCVq2bJkuuOACvfTSS1V6FQB1fQbt27dPPp9PixYt0tNPP6233npLvXr1asbSAWgsUdliV5u4uDiVlJRo8uTJ+vHHH/XJJ58EBrqgCxTqo3PnztqwYYNOP/10rV69WrGxsdQfHFNaWpri4+N1wQUX6JdfftGnn37Kew+qOPrey8p69eqlm266SZdffrkOHjyo9evXV7vVANHFP05AQUGB2rVrV2vdkcpb7B588EF5PB699dZbge6aAKynRbfYNbSVpKioSE8//bScTiehDoH6U7keVR517milpaW6++67ZZqm3nnnHUJdFGto3SksLNTnn38ut9tNqIMkBUZe9g/oVdeJucfjCUxp8NZbbxHqoti2bduUk5Mjm82mf/3rX7r++usDA6PUJjU1VXFxcVq9enVgoCYA1tRig13lq5svvvii1q5de8x9fvWrXyk7O1vr1q3jxCrK+Xy+QP3Jz8/XgQMHZJpmnffIxcXF6ZprrtGHH35I/YliwdSdE088UTfccIPWrFlD3YlywZyY79+/X5999plWrFih3r17N1NJEWl8Pp/eeOMNXXHFFbrzzjv1u9/9ThdffHFgTIHajBs3Tu+++y4jpwItgGG2wJt/Kg/3vGnTJt1555365Zdf9NRTT2nAgAG17rd792516NCBIekR8Kc//UkrVqxQTEyM2rdvrz/96U/VRiqsSeXpMhCdgq07vPdEL5/Pp4cfflgPP/ywbrzxRj311FNasmSJxo0bV+s+/ouYHo8nMAAGotull16qdevW6Xe/+50WLFhQ5ZwIQMvWIv/T/W9gf/rTn/Twww/r8OHD2rVrl/7whz/o//2//1frfh07dgzMP8aJVXSq3F3ulVde0dKlS3X99dfrqquuUkxMjEaNGqV3331XUt1dfQl10SfYunN0PeK9J3rFxMRozpw5ysrK0pIlS3TNNddo3LhxdXbj9V8oiIuLa65iIgL530dKS0vlcDh01lln6fnnn9ebb74ZOK85+r2mBV7XB6Jei2yxk6Tnn39e8+bN09///nf17NlTn3zyiZYtW6adO3fq4Ycf1tlnnx3uIiKCvfPOO/ryyy/VtWtXjR8/PrB86tSpevfdd/XRRx8pPT09jCVEpKLuIBj+lrfS0lLddNNN+vnnn/Xxxx/rmWee0cUXXxw4Ca/c4lvXgCqIHv56sGHDBhUUFGjgwIFq3bq17r77bj355JNaunSpLrnkksB2ubm5gWlXALQsLbLFTpI2bNig888/X2effbbat2+vUaNGadq0aWrTpo1uvfVWffrpp+EuIiLUhg0bdOedd2rx4sWBkyaPxyNJevrpp9W5c2c9+eST4SwiIhR1B8GofGL+wQcfaOHChVq5cqWmTZumKVOm6I033qgS4HJzcyXVPaAKooO/7qxYsUKXXnqpvvjiC/3888+SpBkzZmjatGm6/vrrA3Vo4cKFmjVrltxud5hLDqAptIhgV1M3ldTUVDmdThUXFweWnX322Ro1apS+//57zZw5Ux999FFzFhMR6uhG627duumaa65RamqqXn/9dUlSfHy8ysrK5PV6ddxxx+nw4cPhKCoiDHUHoeLEHKEwDEOffPKJpk2bpjvuuEN/+MMfdNJJJ0mS2rRpoxkzZmj69OmaMmWKsrOztXDhQt12222y2+1hLjmAptAigp3/nro1a9YElvXu3Vu7d+/WO++8U+UDsGvXrho1apROPvlkPffccyoqKmr28iJyVB7BUJLcbrdSUlJ01VVXacaMGfrxxx81ZcoUSeX3PtlsNu3duzcwmTSiF3UHjYETcwTLf2Fp3bp1Gjx4sK666iolJiZKOjJdRps2bTR37lz9/e9/19ixY/XRRx8xeT3QgrWYe+y+/fZbnX322Zo8ebIef/xxSdL111+v9evX69Zbbw10ybzpppt0xhlnqFOnTrr99tu1bt06nXjiiWEuPcKh8khhixcv1saNG/XVV19p8uTJGjFihHr06KHnn39ejz32mNq2bauTTjpJMTEx2rhxoz799FMGuYhi1B00Bn9r3QMPPKCvv/5aL7/8cmDd0SPrvvfee9q+fbvOO+88de3aNRzFRYQ4+t7Km2++WXv27NFrr71WbQTMTZs2qWfPnkpMTOSeTCAKtIgWO0k64YQTtGTJEv3zn//ULbfcIkn685//rPPOO0/PP/+8RowYod/85jf64YcfNHPmTP36179WWlpanaONoWXzf/jdc889evLJJ3XGGWfohhtu0MKFC/XAAw/o0KFDGj9+vKZPny6Px6PvvvtOkydP1hdffBGYfBzRibqDUBw9EEpeXl6ghcX/meQPdZs2bdLhw4c1YsQITZkyhVAXxQ4dOqSSkhLt2rVLJSUlgeXHHXecPv30UxUUFARGwJTK5zdcvnx5YEwBQh3Q8rWYy8ZJSUm66KKLFBMTo1tuuUWmaerxxx/XY489pg0bNuiXX36RYRg677zzJEnPPfeckpKSjjlxJ1q2DRs2KCcnR8uWLVP//v21YcMGHThwQOeff75at24tSZo4caJ8Pp9ef/11LV++XEOHDpXEh2S0o+6goQ4dOqSYmBjt2bNHaWlpgW65xx13nFauXKmCggK1a9cu0LLiPzEfNmyYzj33XOpNFPv+++91//33a9u2bdq6datOPvlkDR06VPfee6+mTZumt99+W6NHj9a//vUvdejQQWVlZVq0aJHefPNNXXvtteEuPoDmYlrYk08+ad5zzz1Vlh06dMh87bXXzA4dOpizZ8+uts/HH39s3njjjWa3bt3Mr776qrmKighx+PDhKr9/8cUX5rBhw0zTNM3ly5ebXbp0MZ977jnTNE1z//795rp160zTNM3i4mJz6dKl5tChQ82rr766eQuNiEDdQSi+++47c/LkyeagQYPM9u3bm+ecc4555513mqZpmgcOHDCHDBliDho0yNy9e7fp8/nM0tJS85577jFPOeUUc+fOnWEuPcJpy5YtZkZGhnnrrbeaL774orly5UpzwoQJZlpamnn55Zebhw4dMr/66iszKyvLzMzMNLOzs81Ro0aZ3bt3Nzdt2hTu4gNoRpZtsTt06JAOHjyopUuXym63a8aMGZKkxMREjRkzRh988IGeeeYZ7d+/X0899VRgv9jYWBUUFGjlypXq3bt3uIqPMFi3bp22bNmis88+W2eeeaak8qHo8/Ly9OKLL+rOO+/UXXfdpWuuuUaS9MUXX+j5559X586dddJJJ2nChAk6dOiQVq9erby8POYiiyLUHYTi66+/1vnnn6/LLrtMI0aMUGpqql599VUtXbpU//vf//TCCy9o8eLFmj59ugYMGKBevXopLi5O3377rZYvX64uXbqE+ykgTPLz83XDDTfod7/7ne66667A8oEDB+qf//yn7rrrLv3hD3/Q0qVL9d577+nZZ59Vfn6+WrdurSeeeIKuu0C0CXeyrC+v11vlu2maZn5+vvnkk0+aGRkZ5oIFC6psv2DBAvPSSy81L7744ir7mGZ5qx6iy7Jly8zevXubf/zjH80vvviiyrprrrnGTE1NNR966KHAssOHD5uXX365ecUVV1SpP/v37zcLCwubq9iIANQdhGLv3r3mkCFDzLvvvrva8meeecbs1KmTOWXKFNM0TdPn85lLly4177//fvPxxx83t2/fHo4iI4Js2rTJHDRokPn111+bZWVlpmkeOQ/at2+fuWDBArNTp07mypUrw1lMABHCEi12b775pt5//33dcsst6tSpU2CY53bt2mncuHHy+Xx69NFHJUm33nqrDhw4oG+++UYXXnihJk6cKKnqKHb+4YARHd58803NmjVLixcvVlZWlpKTk6usnzJlivbs2aNXXnlFGRkZ2rdvn9577z39/PPPWr9+vWJiYgJD2/vvnUJ0oO4gVD///LNKS0t16aWXBka69Pl8at++vS677DIVFRXp0Ucf1ejRozVq1KjAFBmAJG3ZskU7duwI9DAyTTNwLpOSkqJLL71UTz75pHbs2FFlP5MRMIGoFPHTHRQXF2vo0KHav3+/0tLSdOaZZ2rQoEEaP358YJs9e/Zo+fLlmjdvnjIzM2UYhuLi4vTBBx8oNjaWN7golp+fr6uvvlpjxoypcgP5gQMH9N133yk2Nlb9+vXTtm3b9Nhjj+k///mPunXrphNOOEGPPvpoYARDhqePPtQdNIZXXnlFM2bM0C+//CKp+gm30+nUkCFDdOutt2ratGmB5XxuQZL+7//+T2PHjtXSpUs1ZsyYGrc599xzdfbZZ+uBBx5o5tIBiDQRf8Zht9t10UUX6fjjj9cZZ5yh9evX67bbbtO6devUq1cv3XzzzUpLS9P111+vs846S6tWrVLbtm117bXXKjY2ttpcQIg++fn5Ou644wK//+Uvf9H/+3//TytWrFBaWpp69uypf//731q8eHFgVDo/TsyjG3UHofLf47RixQqNGTOmWljLzMzUCSecEAh+foQ6SFJGRobatGmj1157Tf369VNGRoakI72Q9u3bp8TERCYdByDJAvPY2Ww2DRo0SHfddZdsNpumTZum7777Tl27dtX999+vrKwsPf744/rqq6902mmn6bbbbtPUqVMDV8sJdSguLta7776r9evX68orr9Tzzz+vdu3a6c0339RDDz0kp9OpBQsWSCrv2uJnmiYn5lGOuoNQVT4xz83NDSz3z1fHiTnq0rlzZy1cuFBr167VAw88oG+//VbSkbk0n3rqKeXl5WnQoEHhLCaACBHxXTH9br31VknSI488IkkaNGiQunfvrq5du+qbb77R+++/ryeeeEKTJk0KZzERgT744ANdccUVcjgcat26te6//3716dNHDodD+/bt05gxY3T++edrzpw54S4qIgx1B41h5cqV+v3vf6/f/va3+sMf/qCTTz45sO7+++/X66+/rpUrVwZaY4DKvF6vXnzxRc2aNUtdu3bVr3/9a3Xs2FFOp1Nr1qzRv//9b5122mnhLiaACGCZS8p9+/bVyy+/rH379unCCy9USkqKlixZouTkZP3888/65JNPau1/juh27rnn6osvvpDb7VZmZma19a1bt2b4edSIuoPGcP755+vBBx/UrFmz9OWXX9Z4Yk6oQ21sNpuuvvpqnXrqqXriiSf0+eefKyUlRX369NHq1at10kknhbuIACKEZVrsJCkrK0sbNmzQWWedpZdeekmpqanVtuG+FtRXfn6+brzxRrlcLr3zzjt020W9UXcQjM8//1xPPPGEtm7dGjgxnzJlCifmqDev16uYmBgZhlFltG8AkCwS7Pyjg/3973/X448/riVLlqhfv36MGoagFBQU6MUXX9Qnn3yi/Px8vfPOO4qLi2OgHRwTdQeh4sQcoah83sM5EICjWeITxf/Gdc4556iwsFDvv/9+leVAQ/z888/69NNP1a1bN61evVpxcXEMtIN6oe4gVP5QJ/EZhoarXGeoPwCOZokWu8qeeeYZPfTQQ1q1apV69eoV7uLAooqKipScnCzDMGhtQYNQdwAAQCSy3M1oI0aM0IYNG7gnASHxD01vmiYn5mgQ6g4AAIhElmuxk470K+dqOQAAAABYNNgBAAAAAI6wxOApAAAAAIDaEewAAAAAwOIIdgAAAABgcQQ7AAAAALA4gh0AAAAAWBzBDgAAAAAsjmAHAAAAABZHsAMAAAAAiyPYAQAAAIDF/X+95fssMgCURwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1120x805 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print('\\n=== without marketcolor overrides: ===')\n",
+    "mpf.plot(df,volume=True,type='candle',style='yahoo',figscale=1.4)\n",
+    "\n",
+    "print('\\n=== WITH MARKETCOLOR OVERRIDES: ===')\n",
+    "mco = df['MCOverrides'].values\n",
+    "mpf.plot(df,volume=True,type='candle',style='yahoo',marketcolor_overrides=mco,figscale=1.4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### TO DO:\n",
+    "1. `marketcolor_overrides` should affect volume *when the override is a marketcolor object* that includes a 'volume' key.\n",
+    "2. support \"hollow and filled\" candles.\n",
+    "3. support renko and point-and-figure.  This may be tricky (since one \"box\" may cover more and/or less than one date).  Also, is support for marketcolor overrides even needed for renko and pnf? (find out from those who commonly use them)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/scratch_pad/pr451_test.py b/examples/scratch_pad/pr451_test.py
index 40b60e6c..da439c37 100644
--- a/examples/scratch_pad/pr451_test.py
+++ b/examples/scratch_pad/pr451_test.py
@@ -17,13 +17,15 @@
     else:
         custom_colors.append(None)
 
-STYLE = 'binance'
+#STYLE = 'binance'
 STYLE = 'yahoo'
 
 #mpf.plot(df, type='candle',style=STYLE,volume=True,block=False,figscale=1.25,savefig='pr451t2no.jpg')
-mpf.plot(df, type='ohlc',style=STYLE,volume=True,block=False,figscale=1.25)
+#mpf.plot(df, type='ohlc',style=STYLE,volume=True,block=False,figscale=1.25)
+mpf.plot(df, type='candle',style=STYLE,volume=True,block=False,figscale=1.25)
 #mpf.plot(df, type='hollow',style=STYLE,volume=True,block=False,figscale=1.25)
 
 #mpf.plot(df, type='candle',style=STYLE,marketcolor_overrides=custom_colors,volume=True,figscale=1.25,savefig='pr451t2ye.jpg')
-mpf.plot(df, type='ohlc',style=STYLE,marketcolor_overrides=custom_colors,volume=True,figscale=1.25)
+#mpf.plot(df, type='ohlc',style=STYLE,marketcolor_overrides=custom_colors,volume=True,figscale=1.25)
+mpf.plot(df, type='candle',style=STYLE,marketcolor_overrides=custom_colors,volume=True,figscale=1.25)
 #mpf.plot(df, type='hollow',style=STYLE,marketcolor_overrides=custom_colors,volume=True,figscale=1.25)

From 328cf512f0f7623d47cc273836dea2f63276ddd9 Mon Sep 17 00:00:00 2001
From: Daniel Goldfarb <dgoldfarb.github@gmail.com>
Date: Fri, 10 Dec 2021 15:03:49 -0500
Subject: [PATCH 10/11] check length of overrides; allow (0-255) format for rgb

---
 examples/marketcolor_overrides.ipynb | 47 ++++++++++++++++++++++++----
 src/mplfinance/_arg_validators.py    | 16 +++++-----
 src/mplfinance/_helpers.py           | 39 ++++++++++++++++++++++-
 src/mplfinance/_styles.py            | 22 ++++++-------
 src/mplfinance/_utils.py             |  3 +-
 src/mplfinance/plotting.py           |  4 +++
 6 files changed, 104 insertions(+), 27 deletions(-)

diff --git a/examples/marketcolor_overrides.ipynb b/examples/marketcolor_overrides.ipynb
index 6e436e51..b58596d8 100644
--- a/examples/marketcolor_overrides.ipynb
+++ b/examples/marketcolor_overrides.ipynb
@@ -33,7 +33,7 @@
     "- **Rows where the user wants to override the candle color must contain a \"color-like\" object:**\n",
     "  - Examples of color-like objects include:\n",
     "    - a **string** such as `'yellow'` or `'#ffff00'`\n",
-    "    - an **rgb or rgba tuple** such as `(255, 255, 0)` or `(255, 255, 0, 0.75)`\n",
+    "    - an **rgb or rgba tuple** such as `(1.0, 1.0, 0)` or `(1.0, 1.0, 0, 0.75)`, or `(255, 255, 0)` or `(255, 255, 0, 0.75)`\n",
     "    - an **mplfinance marketcolor object**, created with `mpf.make_marketcolors()`\n",
     "\n",
     "\n",
@@ -309,13 +309,31 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x575 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
+    "# ===============================================================================================\n",
+    "# Here we specify `up` as rgba using the matplotlib convention: rgb are floats from 0.0 and 1.0 :\n",
     "mc = mpf.make_marketcolors(base_mpf_style='yahoo',up=(0.7,1.0,0.7,0.4),down='fuchsia',\n",
     "                           edge={'up':'blue','down':'#000000'},wick='#cc6600')\n",
-    "#mc = mpf.make_marketcolors(base_mpf_style='yahoo',up=(0.7,1.0,0.7,0.4),down=(1,0.25,1),\n",
-    "#                           edge={'up':'blue','down':'#000000'},wick='#cc6600')\n",
+    "\n",
+    "mco = [None,None,mc,mc,None]\n",
+    "mpf.plot(df,volume=True,style='yahoo',type='candle',marketcolor_overrides=mco)\n",
+    "\n",
+    "# ====================================================================================\n",
+    "# Here we specify `up` as rgba using the convention that rgb are ints from 0 and 255 :\n",
+    "mc = mpf.make_marketcolors(base_mpf_style='yahoo',up=(178,255,178,0.4),down='fuchsia',\n",
+    "                           edge={'up':'blue','down':'#000000'},wick='#cc6600')\n",
     "\n",
     "mco = [None,None,mc,mc,None]\n",
     "mpf.plot(df,volume=True,style='yahoo',type='candle',marketcolor_overrides=mco)"
@@ -337,10 +355,27 @@
     "#### When dealing with larger data sets we recommend setting up your marketcolor overrides as a COLUMN in your DataFrame\n",
     "#### This has two advantages:\n",
     "1. It helps ensure that your marketcolor overrides are the same length as your dataframe, and\n",
-    "2. It allows you to use the dataframe's DatetimeIndex to set the override values.\n",
+    "2. It allows you to use the dataframe's `DatetimeIndex` in order to position the override values.\n",
+    "\n",
     "#### Note that you will still have to pass the `marketcolor_overrides` as an separate iterable (apart from the dataframe)."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "#### Step 1: read in the data:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 8,
@@ -447,7 +482,7 @@
    "source": [
     "---\n",
     "\n",
-    "#### Create a new column for the overrides:"
+    "#### Step 2: Create a new column for the overrides:"
    ]
   },
   {
@@ -555,7 +590,7 @@
    "source": [
     "---\n",
     "\n",
-    "#### Use the DatetimeIndex to set the Overrides:\n",
+    "#### Step 3: Use the DatetimeIndex to position the Overrides:\n",
     "#### For demonstration purposes, let's override every Monday as black, and every Tuesday as \"blueskies\" marketcolors:"
    ]
   },
diff --git a/src/mplfinance/_arg_validators.py b/src/mplfinance/_arg_validators.py
index 945f117f..e38bbeac 100644
--- a/src/mplfinance/_arg_validators.py
+++ b/src/mplfinance/_arg_validators.py
@@ -1,9 +1,8 @@
 import matplotlib.dates  as mdates
-import matplotlib.colors as mcolors
 import pandas   as pd
 import numpy    as np
 import datetime
-from   mplfinance._helpers import _list_of_dict
+from   mplfinance._helpers import _list_of_dict, _mpf_is_color_like
 import matplotlib as mpl
 import warnings
 
@@ -364,19 +363,20 @@ def _is_marketcolor_object(obj):
     if not isinstance(obj,dict): return False
     market_colors_keys = ('candle','edge','wick','ohlc')
     return all([k in obj for k in market_colors_keys])
-    
 
-def _mco_validator(value):  # marketcolor overrides validator
-    if isinstance(value,dict):     # not yet supported, but maybe we will have other
-        if 'colors' not in value:  # kwargs related to mktcolor overrides (ex: `mco_faceonly`)
+
+def _mco_validator(value):        # marketcolor overrides validator
+    if isinstance(value,dict):    # not yet supported, but maybe we will have other
+        if 'colors' not in value: # kwargs related to mktcolor overrides (ex: `mco_faceonly`)
             raise ValueError('`marketcolor_overrides` as dict must contain `colors` key.')
         colors = value['colors']
     else:
         colors = value
     if not isinstance(colors,(list,tuple,np.ndarray)):
         return False
-    return all([(c is None or mcolors.is_color_like(c) or _is_marketcolor_object(c)) for c in colors])
-
+    return all([(c is None or 
+                 _mpf_is_color_like(c) or
+                 _is_marketcolor_object(c) ) for c in colors])
 
 def _check_for_external_axes(config):
     '''
diff --git a/src/mplfinance/_helpers.py b/src/mplfinance/_helpers.py
index ece35913..e8a38359 100644
--- a/src/mplfinance/_helpers.py
+++ b/src/mplfinance/_helpers.py
@@ -1,9 +1,13 @@
 """
 Some helper functions for mplfinance.
+NOTE: This is the lowest level in mplfinance:
+      This file should have NO dependencies on
+      any other mplfinance files.
 """
 
 import datetime
-import matplotlib.dates as mdates
+import matplotlib.dates  as mdates
+import matplotlib.colors as mcolors
 import numpy as np
 
 def _adjust_color_brightness(color,amount=0.5):
@@ -82,3 +86,36 @@ def roundTime(dt=None, roundTo=60):
    seconds = (dt.replace(tzinfo=None) - dt.min).seconds
    rounding = (seconds+roundTo/2) // roundTo * roundTo
    return dt + datetime.timedelta(0,rounding-seconds,-dt.microsecond)
+
+
+def _is_uint8_rgb_or_rgba(tup):
+    """ Deterine if rgb or rgba is in (0-255) format:
+    Matplotlib expects rgb (and rgba) tuples to contain
+    three (or four) floats between 0.0 and 1.0 
+    
+    Some people express rgb as tuples of three integers
+    between 0 and 255.
+    (In rgba, alpha is still a float from 0.0 to 1.0)
+    """
+    if isinstance(tup,str):  return False
+    if not np.iterable(tup): return False
+    L = len(tup)
+    if L < 3 or L > 4: return False
+    if L == 4 and (tup[3] < 0 or tup[3] > 1): return False
+    return not any([not isinstance(v,(int,np.unsignedinteger)) or v<0 or v>255 for v in tup[0:3]])
+
+def _mpf_is_color_like(c):
+    """Determine if an object is a color.
+    
+    Identical to `matplotlib.colors.is_color_like()`
+    BUT ALSO considers int (0-255) rgb and rgba colors.
+    """
+    if mcolors.is_color_like(c): return True
+    return _is_uint8_rgb_or_rgba(c)
+    
+def _mpf_to_rgba(c, alpha=None):
+    cnew = c
+    if _is_uint8_rgb_or_rgba(c) and any(e>1 for e in c[:3]):
+        cnew = tuple([e/255. for e in c[:3]])
+        if len(c) == 4: cnew += c[3:]
+    return mcolors.to_rgba(cnew, alpha)
diff --git a/src/mplfinance/_styles.py b/src/mplfinance/_styles.py
index fde40ae8..a8cb08dc 100644
--- a/src/mplfinance/_styles.py
+++ b/src/mplfinance/_styles.py
@@ -1,11 +1,11 @@
 import matplotlib.pyplot as plt
-import matplotlib.colors as mcolors
 import copy
 import pprint
 import os.path as path
 
 from   mplfinance._arg_validators import _process_kwargs, _validate_vkwargs_dict
 from   mplfinance._styledata      import _styles
+from   mplfinance._helpers        import _mpf_is_color_like
 
 
 def _get_mpfstyle(style):
@@ -70,7 +70,7 @@ def _valid_make_mpf_style_kwargs():
                             'Validator'   : lambda value: isinstance(value,dict)  },
 
         'mavcolors'     : { 'Default'     : None,
-                            'Validator'   : lambda value: isinstance(value,list) },  # TODO: all([mcolors.is_color_like(v) for v in value.values()])
+                            'Validator'   : lambda value: isinstance(value,list) },  # TODO: all([_mpf_is_color_like(v) for v in value.values()])
 
         'facecolor'     : { 'Default'     : None,
                             'Validator'   : lambda value: isinstance(value,str) },
@@ -153,10 +153,10 @@ def make_mpf_style( **kwargs ):
 
 def _valid_mpf_color_spec(value):
     'value must be a color, "inherit"-like, or dict of colors'
-    return ( mcolors.is_color_like(value) or 
+    return ( _mpf_is_color_like(value) or 
              ( isinstance(value,str) and value == 'inherit'[0:len(value)]) or
              ( isinstance(value,dict) and
-               all([mcolors.is_color_like(v) for v in value.values()])
+               all([_mpf_is_color_like(v) for v in value.values()])
              )
            )
 
@@ -190,13 +190,13 @@ def _valid_mpf_style(value):
 def _valid_make_marketcolors_kwargs():
     vkwargs = {
         'up'         : { 'Default'     : None,
-                         'Validator'   : lambda value: mcolors.is_color_like(value) },
+                         'Validator'   : lambda value: _mpf_is_color_like(value) },
 
         'down'       : { 'Default'     : None,
-                         'Validator'   : lambda value: mcolors.is_color_like(value) },
+                         'Validator'   : lambda value: _mpf_is_color_like(value) },
 
         'hollow'     : { 'Default'     : None,
-                         'Validator'   : lambda value: mcolors.is_color_like(value) },
+                         'Validator'   : lambda value: _mpf_is_color_like(value) },
 
         'alpha'      : { 'Default'     : None,
                          'Validator'   : lambda value: ( isinstance(value,float) and
@@ -208,17 +208,17 @@ def _valid_make_marketcolors_kwargs():
         'wick'       : { 'Default'     : None,
                          'Validator'   : lambda value: isinstance(value,dict)
                                                        or isinstance(value,str) 
-                                                       or mcolors.is_color_like(value) },
+                                                       or _mpf_is_color_like(value) },
 
         'ohlc'       : { 'Default'     : None,
                          'Validator'   : lambda value: isinstance(value,dict)
                                                        or isinstance(value,str) 
-                                                       or mcolors.is_color_like(value) },
+                                                       or _mpf_is_color_like(value) },
 
         'volume'     : { 'Default'   : None,
                          'Validator'   : lambda value: isinstance(value,dict)
                                                        or isinstance(value,str) 
-                                                       or mcolors.is_color_like(value) },
+                                                       or _mpf_is_color_like(value) },
 
         'vcdopcod'   : { 'Default'     : False,
                          'Validator'   : lambda value: isinstance(value,bool) },
@@ -282,7 +282,7 @@ def _check_and_set_mktcolor(candle,**kwarg):
         else:
             colors = dict(up=value, down=value)
         for updown in ['up','down']:
-            if not mcolors.is_color_like(colors[updown]):
+            if not _mpf_is_color_like(colors[updown]):
                 err = f'NOT is_color_like() for {key}[\'{updown}\'] = {colors[updown]}'
                 raise ValueError(err)
         return colors
diff --git a/src/mplfinance/_utils.py b/src/mplfinance/_utils.py
index 9f519010..6dd7d366 100644
--- a/src/mplfinance/_utils.py
+++ b/src/mplfinance/_utils.py
@@ -17,6 +17,7 @@
 from mplfinance._arg_validators import _alines_validator, _bypass_kwarg_validation
 from mplfinance._arg_validators import _xlim_validator, _is_datelike
 from mplfinance._styles         import _get_mpfstyle
+from mplfinance._helpers        import _mpf_to_rgba
 
 from six.moves import zip
 
@@ -603,7 +604,7 @@ def _construct_candlestick_collections(dates, opens, highs, lows, closes, market
     faceonly  = config['mco_faceonly']
 
     colors    = _make_updown_color_list('candle',marketcolors,opens,closes,overrides)
-    colors    = [mcolors.to_rgba(c,alpha) for c in colors] # include alpha
+    colors    = [ _mpf_to_rgba(c,alpha) for c in colors ] # include alpha
     if faceonly: overrides = None
     edgecolor = _make_updown_color_list('edge',marketcolors,opens,closes,overrides)
     wickcolor = _make_updown_color_list('wick',marketcolors,opens,closes,overrides)
diff --git a/src/mplfinance/plotting.py b/src/mplfinance/plotting.py
index 14ef37dd..0bd0b987 100644
--- a/src/mplfinance/plotting.py
+++ b/src/mplfinance/plotting.py
@@ -306,6 +306,10 @@ def plot( data, **kwargs ):
         err = "`addplot` is not supported for `type='" + config['type'] +"'`"
         raise ValueError(err)
 
+    if config['marketcolor_overrides'] is not None:
+        if len(config['marketcolor_overrides']) != len(dates):
+            raise ValueError('`marketcolor_overrides` must be same length as dataframe.')
+
     external_axes_mode = _check_for_external_axes(config)
 
     if external_axes_mode:

From 28b05ca247f5557298f0b07e850796dfcdc826b1 Mon Sep 17 00:00:00 2001
From: Daniel Goldfarb <dgoldfarb.github@gmail.com>
Date: Sat, 11 Dec 2021 18:33:10 -0500
Subject: [PATCH 11/11] fix grammer; and add **`make_addplot()`** to TODO list

---
 examples/marketcolor_overrides.ipynb | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/examples/marketcolor_overrides.ipynb b/examples/marketcolor_overrides.ipynb
index b58596d8..e4968366 100644
--- a/examples/marketcolor_overrides.ipynb
+++ b/examples/marketcolor_overrides.ipynb
@@ -357,7 +357,7 @@
     "1. It helps ensure that your marketcolor overrides are the same length as your dataframe, and\n",
     "2. It allows you to use the dataframe's `DatetimeIndex` in order to position the override values.\n",
     "\n",
-    "#### Note that you will still have to pass the `marketcolor_overrides` as an separate iterable (apart from the dataframe)."
+    "#### Note that you will still have to pass the `marketcolor_overrides` as a separate iterable (apart from the dataframe)."
    ]
   },
   {
@@ -787,8 +787,9 @@
    "source": [
     "### TO DO:\n",
     "1. `marketcolor_overrides` should affect volume *when the override is a marketcolor object* that includes a 'volume' key.\n",
-    "2. support \"hollow and filled\" candles.\n",
-    "3. support renko and point-and-figure.  This may be tricky (since one \"box\" may cover more and/or less than one date).  Also, is support for marketcolor overrides even needed for renko and pnf? (find out from those who commonly use them)."
+    "2. support `marketcolor_overrides` in **`mpf.make_addplot()`**\n",
+    "3. support \"hollow and filled\" candles (both `mpf.plot()` and `mpf.make_addplot()`).\n",
+    "4. support renko and point-and-figure.  This may be tricky (since one \"box\" may cover more and/or less than one date).  Also, is support for marketcolor overrides even needed for renko and pnf? (find out from those who commonly use them)."
    ]
   },
   {