Merge pull request #28 from viventriglia/develop

v0.2.3

src/0_🏠_Home.py

@@ -3,7 +3,14 @@
 import streamlit as st
 
 from input_output import load_data, write_disclaimer
-from var import GLOBAL_STREAMLIT_STYLE, DATA_PATH, FAVICON, APP_VERSION, COVER
+from var import (
+    GLOBAL_STREAMLIT_STYLE,
+    DATA_PATH,
+    FILE_UPLOADER_CSS,
+    FAVICON,
+    APP_VERSION,
+    COVER,
+)
 
 st.set_page_config(
     page_title="PFN",
@@ -56,17 +63,21 @@
     """,
     unsafe_allow_html=True,
 )
-col_l, col_r = st.columns([0.8, 1], gap="small")
+col_l, col_r = st.columns([0.2, 0.95], gap="small")
 
-uploaded_file = col_l.file_uploader(
-    label="Upload your Data", label_visibility="collapsed", accept_multiple_files=False
+uploaded_file = col_r.file_uploader(
+    label="Upload your Data",
+    type=["xls", "xlsx", "xlsm", "xlsb", "odf", "ods"],
+    label_visibility="collapsed",
+    accept_multiple_files=False,
 )
+st.markdown(FILE_UPLOADER_CSS, unsafe_allow_html=True)
 if uploaded_file is not None:
     try:
         df_storico, df_anagrafica = load_data(uploaded_file)
         st.session_state["data"] = df_storico
         st.session_state["dimensions"] = df_anagrafica
-    except:
+    except ValueError:
         st.error("Please check your file format and make sure it matches the template")
 
 with open(DATA_PATH / Path("template.xlsx"), "rb") as f:
@@ -76,15 +87,20 @@
         data=f,
         file_name="template.xlsx",
         mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
+        help="Fill in this template and upload it using the adjacent button",
     )
 
-st.markdown("<br>", unsafe_allow_html=True)
 st.markdown(
-    "If you wish to explore the app first, load some **demo** data instead.",
+    "<br>If you wish to explore the app first, load some **demo** data instead.",
     unsafe_allow_html=True,
 )
 
-if st.button(label="Load Mock Data", icon="📊", key="load_mock_df"):
+if st.button(
+    label="Load Mock Data",
+    icon="📊",
+    key="load_mock_df",
+    help="Load data to start a demo",
+):
     df_storico, df_anagrafica = load_data(DATA_PATH / Path("demo.xlsx"))
     st.session_state["data"] = df_storico
     st.session_state["dimensions"] = df_anagrafica
@@ -106,10 +122,23 @@
 )
 col_l_l, col_l_m, col_l_r = st.columns([1, 1, 1], gap="large")
 col_l_l.page_link(
-    "pages/1_🎯_Asset_Allocation_&_PnL.py", label="Asset Allocation & PnL", icon="🎯"
+    "pages/1_🎯_Asset_Allocation_&_PnL.py",
+    label="Asset Allocation & PnL",
+    icon="🎯",
+    help="Portfolio composition, PnL, wealth evolution",
+)
+col_l_m.page_link(
+    "pages/2_📈_Return_Analysis.py",
+    label="Return Analysis",
+    icon="📈",
+    help="Distribution and correlation of returns, rolling returns",
+)
+col_l_r.page_link(
+    "pages/3_⚠️_Risk_Analysis.py",
+    label="Risk Analysis",
+    icon="⚠️",
+    help="Portfolio drawdowns",
 )
-col_l_m.page_link("pages/2_📈_Return_Analysis.py", label="Return Analysis", icon="📈")
-col_l_r.page_link("pages/3_⚠️_Risk_Analysis.py", label="Risk Analysis", icon="⚠️")
 
 st.markdown(
     """

src/input_output.py

@@ -36,6 +36,7 @@ def write_load_message(df_data: pd.DataFrame, df_dimensions: pd.DataFrame) -> No
         st.error(
             f"There are null values: {n_data_na} among transactions, {n_dimensions_na} among tickers' descriptions"
         )
+        st.stop()
 
     if set_data_tickers != set_dimensions_tickers:
         st.warning(
@@ -152,7 +153,7 @@ def get_last_closing_price_from_api(ticker: str, days_of_delay: int = 5) -> List
 def get_full_price_history(ticker_list: List[str]) -> Dict:
     df_history = dict()
 
-    for i, ticker_ in zip(range(len(ticker_list)), ticker_list):
+    for ticker_ in ticker_list:
         ticker_data = yf.Ticker(ticker_)
         df_history[ticker_] = ticker_data.history(
             period="max",

src/pages/1_🎯_Asset_Allocation_&_PnL.py

@@ -1,3 +1,5 @@
+from datetime import datetime
+
 import streamlit as st
 
 from var import (
@@ -44,25 +46,47 @@
 expense = (df_j["shares"] * df_j["dca"]).sum()
 fees = df_storico["fees"].sum().round(2)
 
-st.markdown("## Profit and Loss")
+st.markdown("## Profit & Loss")
 
-col_l, col_r = st.columns([1, 1], gap="small")
+col_l, col_m, col_r = st.columns([1, 1, 1], gap="small")
 
 consider_fees = st.checkbox("Take fees into account")
 
-pf_actual_value = (df_j["shares"] * df_j["price"]).sum()
+df_j["position_value"] = df_j["shares"] * df_j["price"]
+pf_actual_value = df_j["position_value"].sum()
 total_expense = expense + fees if consider_fees else expense
 pnl = pf_actual_value - total_expense
-pnl_perc = 100 * pnl / total_expense
+pnl_perc = pnl / total_expense
 sign = "+" if pnl >= 0 else ""
 
 col_l.metric(
-    label="Actual Portfolio Value",
+    label="Actual portfolio value",
     value=f"{pf_actual_value: ,.1f} €",
-    delta=f"{pnl: ,.1f} € ({sign}{pnl_perc: .1f}%)",
+    delta=f"{sign}{pnl: ,.1f} €",
+)
+
+col_m.metric(
+    label="Return On Investment (ROI)",
+    value=f"{sign}{pnl_perc: .1%}",
+    help="""
+    ROI shows the total gain or loss as a percentage of the original amount
+    invested, regardless of how long the investment was held
+    """,
+)
+
+first_transaction = df_storico["transaction_date"].sort_values()[0]
+n_years = (datetime.now() - first_transaction).days / 365.25
+annualised_ret = ((pf_actual_value / total_expense) ** (1 / n_years)) - 1
+
+col_r.metric(
+    label="Annualised return",
+    value=f"{sign}{annualised_ret: .1%}",
+    help="""
+    This measures the average yearly return, helping compare investments
+    held for different periods by standardising the return on an annual basis
+    """,
 )
 
-df_j["position_value"] = df_j["shares"] * df_j["price"]
 df_pivot = get_portfolio_pivot(
     df=df_j,
     df_dimensions=df_anagrafica,
@@ -72,7 +96,7 @@
 
 st.markdown("***")
 
-st.markdown("## Current Portfolio Asset Allocation")
+st.markdown("## Current portfolio asset allocation")
 fig = plot_sunburst(df=df_pivot)
 st.plotly_chart(fig, use_container_width=True, config=PLT_CONFIG_NO_LOGO)
 
@@ -111,7 +135,7 @@
 
 st.markdown("***")
 
-st.markdown("## Profit and Loss by asset class")
+st.markdown("## Profit & Loss by asset class")
 
 group_by = st.radio(
     label="Evaluate PnL with respect to:",
@@ -135,7 +159,7 @@
 
 st.markdown("***")
 
-st.markdown("## Daily value of the Accumulation Plan")
+st.markdown("## Wealth history")
 
 df_wealth = get_wealth_history(df_transactions=df_storico, ticker_list=ticker_list)
 

src/pages/2_📈_Return_Analysis.py

@@ -46,6 +46,7 @@
     horizontal=True,
     index=2,
     key="level",
+    help="Choose how to aggregate data for analysis: Tickers provides the most granular view, while Macro Asset Classes aggregates at the highest level",
 )
 freq = col_r_up.radio(
     label="Frequency of returns:",
@@ -75,7 +76,7 @@
 st.markdown(f"## Correlation of {freq.lower().replace('day','dai')}ly returns")
 
 enhance_corr = st.radio(
-    "Kind of correlation to enhance:",
+    "Kind of correlation to highlight:",
     options=["Positive", "Null", "Negative"],
     index=1,
     horizontal=True,

src/pages/3_⚠️_Risk_Analysis.py

@@ -47,6 +47,7 @@
     horizontal=True,
     index=2,
     key="level",
+    help="Choose how to aggregate data for analysis: Tickers provides the most granular view, while Macro Asset Classes aggregates at the highest level",
 )
 freq = col_r_up.radio(
     label="Frequency of returns:",

src/var.py

@@ -9,7 +9,7 @@
     os.path.join(*split_script_running_path[0 : len(split_script_running_path) - 1])
 )
 
-APP_VERSION = "0.2.2"
+APP_VERSION = "0.2.3"
 
 # Data/images
 
@@ -20,12 +20,25 @@
 # Streamlit/Plotly vars
 
 GLOBAL_STREAMLIT_STYLE = """
-            <style>
-            #MainMenu {visibility: hidden;}
-            footer {visibility: hidden;}
-            .css-15zrgzn {display: none}
-            </style>
-            """
+    <style>
+        #MainMenu {visibility: hidden;}
+        footer {visibility: hidden;}
+        .css-15zrgzn {display: none}
+    </style>
+    """
+
+FILE_UPLOADER_CSS = """
+    <style>
+        [data-testid='stFileUploader'] section {
+            padding: 0;
+            float: left;
+        }
+        [data-testid='stFileUploader'] section > input + div {
+            display: none;
+        }
+    </style>
+    """
+
 
 PLT_CONFIG = {
     "displaylogo": False,