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ERMD_Strategy_No_Exits.py
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ERMD_Strategy_No_Exits.py
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# --- Do not remove these libs ---
from datetime import datetime
from functools import reduce
from typing import Optional, Union
from freqtrade.persistence import Trade
from freqtrade.strategy import BooleanParameter, CategoricalParameter, DecimalParameter, IntParameter, IStrategy
import pandas as pd
from pandas import DataFrame
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import warnings
warnings.simplefilter(action='ignore', category=pd.errors.PerformanceWarning)
DEBUG = False
class ERMD_Strategy_No_Exits(IStrategy):
INTERFACE_VERSION: int = 3
can_short = True # enable short direction
timeframe = "1d"
startup_candle_count = 100 # how many candles we need to skip to start receiving robust signals
risk = 0.01
min_pos_pct = 0.01 # minimum position size, percent of current portfolio value
max_pos_pct = DecimalParameter(0.01, 0.5, default=0.48, decimals=2, optimize=True, space="buy") # maximum position size, percent of current portfolio value
minimal_roi = {"0": 0.8380000000000001} # optimized tp
# Parameters: signal
buy_ema_enabled = BooleanParameter(default=False, optimize=True)
buy_ema1_length = IntParameter(3, 100, default=5, optimize=True)
buy_ema2_length = IntParameter(3, 100, default=10, optimize=True)
buy_ema3_length = IntParameter(3, 100, default=20, optimize=True)
buy_rsi_enabled = BooleanParameter(default=False, optimize=True)
buy_rsi_length = IntParameter(3, 100, default=14, optimize=True)
buy_rsi_threshold = IntParameter(50, 100, default=60, optimize=True)
buy_macd_enabled = BooleanParameter(default=True, optimize=True)
buy_macd_fast = IntParameter(3, 50, default=12, optimize=True)
buy_macd_slow = IntParameter(20, 200, default=26, optimize=True)
buy_macd_smooth = IntParameter(1, 30, default=9, optimize=True)
buy_candlestick_enabled = BooleanParameter(default=False, optimize=True)
sell_ema_enabled = BooleanParameter(default=True, optimize=True)
sell_ema1_length = IntParameter(3, 100, default=5, optimize=True)
sell_ema2_length = IntParameter(3, 100, default=10, optimize=True)
sell_ema3_length = IntParameter(3, 100, default=20, optimize=True)
sell_rsi_enabled = BooleanParameter(default=True, optimize=True)
sell_rsi_length = IntParameter(3, 100, default=14, optimize=True)
sell_rsi_threshold = IntParameter(0, 50, default=40, optimize=True)
sell_macd_enabled = BooleanParameter(default=True, optimize=True)
sell_macd_fast = IntParameter(3, 50, default=12, optimize=True)
sell_macd_slow = IntParameter(20, 200, default=26, optimize=True)
sell_macd_smooth = IntParameter(1, 30, default=9, optimize=True)
sell_candlestick_enabled = BooleanParameter(default=False, optimize=True)
# signals
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
""" adds indicators to dataframe """
# BUY
# EMAs
for val in self.buy_ema1_length.range:
dataframe[f"buy_ema1_{val}"] = ta.EMA(dataframe, val)
for val in self.buy_ema2_length.range:
dataframe[f"buy_ema2_{val}"] = ta.EMA(dataframe, val)
for val in self.buy_ema3_length.range:
dataframe[f"buy_ema3_{val}"] = ta.EMA(dataframe, val)
# RSI
for val in self.buy_rsi_length.range:
dataframe[f"buy_rsi_{val}"] = ta.RSI(dataframe, val)
# MACD
for fast_val in self.buy_macd_fast.range:
for slow_val in self.buy_macd_slow.range:
for smooth_val in self.buy_macd_smooth.range:
macd = ta.MACD(dataframe, fast_val, slow_val, smooth_val)
dataframe[f'buy_macd_{fast_val}_{slow_val}_{smooth_val}'] = macd['macd']
dataframe[f'buy_macds_{fast_val}_{slow_val}_{smooth_val}'] = macd['macdsignal']
# SELL
# EMAs
for val in self.sell_ema1_length.range:
dataframe[f"sell_ema1_{val}"] = ta.EMA(dataframe, val)
for val in self.sell_ema2_length.range:
dataframe[f"sell_ema2_{val}"] = ta.EMA(dataframe, val)
for val in self.sell_ema3_length.range:
dataframe[f"sell_ema3_{val}"] = ta.EMA(dataframe, val)
# RSI
for val in self.sell_rsi_length.range:
dataframe[f"sell_rsi_{val}"] = ta.RSI(dataframe, val)
# MACD
for fast_val in self.sell_macd_fast.range:
for slow_val in self.sell_macd_slow.range:
for smooth_val in self.sell_macd_smooth.range:
macd = ta.MACD(dataframe, fast_val, slow_val, smooth_val)
dataframe[f'sell_macd_{fast_val}_{slow_val}_{smooth_val}'] = macd['macd']
dataframe[f'sell_macds_{fast_val}_{slow_val}_{smooth_val}'] = macd['macdsignal']
# DOJI candlestick pattern
doji = ta.CDLDOJI(dataframe)
doji_dir = (dataframe["high"] - dataframe["close"]) / (dataframe["close"] - dataframe["low"])
dataframe.loc[((doji == 100) & (doji_dir > 1), "doji")] = dataframe["high"]
dataframe.loc[((doji == 100) & (doji_dir < 1), "doji")] = dataframe["low"]
# MARUBOZU candlestick pattern
body = (dataframe["close"] - dataframe["open"]).abs()
wick_top = dataframe["high"] - dataframe[["open", "close"]].max(axis=1)
wick_bottom = dataframe[["open", "close"]].min(axis=1) - dataframe["low"]
marubozu = (wick_top/body < 0.25) & (wick_bottom/body < 0.25)
dataframe.loc[(marubozu & (body > 0), 'marubozu')] = dataframe["high"]
dataframe.loc[(marubozu & (body < 0), 'marubozu')] = dataframe["low"]
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
""" generate signals """
# BUY signal
# only trade if there is volume
conditions = [(dataframe['volume'] > 0)]
# Close > EMA1 > EMA2 > EMA3
if self.buy_ema_enabled.value:
conditions.append((dataframe["close"] > dataframe[f"buy_ema1_{self.buy_ema1_length.value}"]) &
(dataframe[f"buy_ema1_{self.buy_ema1_length.value}"] > dataframe[f"buy_ema2_{self.buy_ema2_length.value}"]) &
(dataframe[f"buy_ema2_{self.buy_ema2_length.value}"] > dataframe[f"buy_ema3_{self.buy_ema3_length.value}"]))
# RSI > RSI_Threshold_Long
if self.buy_rsi_enabled:
conditions.append(dataframe[f"buy_rsi_{self.buy_rsi_length.value}"] > self.buy_rsi_threshold.value)
# MACD cross MACDs above or MACD > 0
if self.buy_macd_enabled.value:
macd = dataframe[f'buy_macd_{self.buy_macd_fast.value}_{self.buy_macd_slow.value}_{self.buy_macd_smooth.value}']
macds = dataframe[f'buy_macds_{self.buy_macd_fast.value}_{self.buy_macd_slow.value}_{self.buy_macd_smooth.value}']
conditions.append(qtpylib.crossed_above(macd, macds) | (macd > 0))
# Candlestick patterns: any DOJI and green MARUBOZU
if self.buy_candlestick_enabled.value:
doji = dataframe['doji'] == dataframe["high"]
marubozu = dataframe['marubozu'] == dataframe["high"]
conditions.append(doji | marubozu)
# combine conditions and return
buy_conditions = reduce(lambda x, y: x & y, conditions)
dataframe.loc[buy_conditions, 'enter_long'] = 1
# SELL signal
# only trade if there is volume
conditions = [(dataframe['volume'] > 0)]
# Close < EMA1 < EMA2 < EMA3
if self.sell_ema_enabled.value:
conditions.append((dataframe["close"] < dataframe[f"sell_ema1_{self.sell_ema1_length.value}"]) &
(dataframe[f"sell_ema1_{self.sell_ema1_length.value}"] < dataframe[f"sell_ema2_{self.sell_ema2_length.value}"]) &
(dataframe[f"sell_ema2_{self.sell_ema2_length.value}"] < dataframe[f"sell_ema3_{self.sell_ema3_length.value}"]))
# RSI < RSI_Threshold_Long
if self.sell_rsi_enabled:
conditions.append(dataframe[f"sell_rsi_{self.sell_rsi_length.value}"] < self.sell_rsi_threshold.value)
# MACD cross MACDs below or MACD < 0
if self.sell_macd_enabled.value:
macd = dataframe[f'sell_macd_{self.sell_macd_fast.value}_{self.sell_macd_slow.value}_{self.sell_macd_smooth.value}']
macds = dataframe[f'sell_macds_{self.sell_macd_fast.value}_{self.sell_macd_slow.value}_{self.sell_macd_smooth.value}']
conditions.append(qtpylib.crossed_below(macd, macds) | (macd < 0))
# Candlestick patterns, any DOJI and red MARUBOZU
if self.sell_candlestick_enabled.value:
doji = dataframe['doji'] == dataframe['low']
marubozu = dataframe['marubozu'] == dataframe['low']
conditions.append(doji | marubozu)
# combine conditions and return
sell_conditions = reduce(lambda x, y: x & y, conditions)
dataframe.loc[sell_conditions, 'enter_short'] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
return dataframe
stop_info = {}
# Stop Loss
stoploss = -1.00 # disable built-in stop-loss with 100% level
# Position Size
def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
proposed_stake: float, min_stake: Optional[float], max_stake: float,
leverage: float, entry_tag: Optional[str], side: str,
**kwargs) -> float:
""" custom position size """
if self.wallets:
total = self.wallets.get_total("USDT")
else:
if DEBUG:
print("Wallets are not available, using max_stake instead of portfolio value to calculate position size!")
total = max_stake
dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
last_candle = dataframe.iloc[-1].squeeze()
if side == "long":
level = last_candle["low"]
stop_distance = current_rate / level - 1
self.stop_info[pair] = level
else:
level = last_candle["high"]
stop_distance = level / current_rate - 1
self.stop_info[pair] = level
result = total * self.risk / stop_distance
# apply maximum/minimum position size
min_pos = self.min_pos_pct * total
max_pos = self.max_pos_pct.value * total
if result < min_pos:
if DEBUG:
print(f"Calculated position ({round(result, 2)}) is less than minimum position size ({round(min_pos)})! Skipping...")
return 0
if result > max_pos:
if DEBUG:
print(f"Calculated position ({round(result, 2)}) is greater than maximum position size ({round(min_pos)})! Reduced!")
result = max_pos
# check min/max stake
if result < min_stake:
if DEBUG:
print(f"Calculated stake ({round(result,2)}) is less than minimum stake ({min_stake})! Skipping trade...")
return 0
elif result > max_stake:
if DEBUG:
print(f"Calculated stake ({round(result,2)}) is greater than maximum stake ({max_stake})! Reducing to maximum!")
result = max_stake
return result
# set up plotting
@property
def plot_config(self):
plot_config = {
'main_plot': {
f"buy_ema1_{self.buy_ema1_length.value}": {},
f"buy_ema2_{self.buy_ema2_length.value}": {},
f"buy_ema3_{self.buy_ema3_length.value}": {},
f"doji": {"type": "scatter"},
f"marubozu": {"type": "scatter"}
},
'subplots': {
"MACD": {
f'buy_macd_{self.buy_macd_fast.value}_{self.buy_macd_slow.value}_{self.buy_macd_smooth.value}': {},
f'buy_macds_{self.buy_macd_fast.value}_{self.buy_macd_slow.value}_{self.buy_macd_smooth.value}': {},
},
"RSI": {
f'buy_rsi_{self.buy_rsi_length.value}': {}
}
}}
return plot_config