From e30c3b1348178820e068cbab416fcad72c064eee Mon Sep 17 00:00:00 2001
From: Vincenzo <vincenzo.ventriglia@outlook.com>
Date: Sat, 16 Nov 2024 12:49:54 +0100
Subject: [PATCH] invested capital line on wealth history plot

---
 src/aggregation.py | 17 ++++++++++-------
 src/plot.py        | 26 +++++++++++++++++++++-----
 2 files changed, 31 insertions(+), 12 deletions(-)

diff --git a/src/aggregation.py b/src/aggregation.py
index 6a1b1cd..bf069b8 100644
--- a/src/aggregation.py
+++ b/src/aggregation.py
@@ -42,7 +42,7 @@ def aggregate_by_ticker(df: pd.DataFrame, in_pf_only: bool = False) -> pd.DataFr
 @st.cache_data(ttl=10 * CACHE_EXPIRE_SECONDS, show_spinner=False)
 def get_wealth_history(
     df_transactions: pd.DataFrame, ticker_list: list[str]
-) -> pd.Series:
+) -> pd.DataFrame:
     begin_date = df_transactions["transaction_date"].min()
     today = datetime.now().date()
     date_range = pd.date_range(start=begin_date, end=today, freq="D")
@@ -62,18 +62,19 @@ def get_wealth_history(
 
     df_transactions = df_transactions[df_transactions["ticker_yf"].isin(ticker_list)]
 
-    df_asset_allocation = pd.DataFrame(
-        index=date_range,
-        columns=ticker_list,
-        data=0,
-    )
+    df_asset_allocation = pd.DataFrame(index=date_range, columns=ticker_list, data=0)
+    df_cumulative_spent = pd.Series(index=date_range, data=0)
 
     for (data, ticker), group in df_transactions.groupby(
         ["transaction_date", "ticker_yf"]
     ):
         total_shares = group["shares"].sum()
+        total_spent = group["ap_amount"].sum()
         df_asset_allocation.loc[data, ticker] += total_shares
+        df_cumulative_spent.loc[data] += total_spent
+
     df_asset_allocation = df_asset_allocation.cumsum()
+    df_cumulative_spent = df_cumulative_spent.cumsum()
 
     df_wealth = pd.DataFrame(
         df_asset_allocation.multiply(df_prices.loc[begin_date:])
@@ -81,7 +82,9 @@ def get_wealth_history(
         .sum(axis=1)
         .rename("ap_daily_value")
     )
-    df_wealth["diff_previous_day"] = df_wealth.diff()
+    df_wealth["diff_previous_day"] = df_wealth["ap_daily_value"].diff()
+    df_wealth["ap_cum_spent"] = df_cumulative_spent
+    df_wealth["ap_cum_pnl"] = df_wealth["ap_daily_value"] - df_wealth["ap_cum_spent"]
 
     return df_wealth
 
diff --git a/src/plot.py b/src/plot.py
index b993465..81bfc70 100644
--- a/src/plot.py
+++ b/src/plot.py
@@ -17,7 +17,7 @@ def plot_sunburst(df: pd.DataFrame) -> go.Figure:
     fig.update_traces(hovertemplate="<b>%{value:.1f}%")
     fig.update_layout(
         autosize=False,
-        height=600,
+        height=620,
         margin=dict(l=0, r=0, t=20, b=20),
         hoverlabel_font_size=PLT_FONT_SIZE,
     )
@@ -56,17 +56,33 @@ def plot_wealth(df: pd.DataFrame) -> go.Figure:
         data_frame=df, x=df.index, y="ap_daily_value", custom_data=["diff_previous_day"]
     )
     fig.update_traces(
-        hovertemplate="%{x}: <b>%{y:,.0f}€</b> <extra>Gain/Loss on previous day: %{customdata:,.0f}€</extra>"
+        hovertemplate=(
+            "Portfolio value: <b>%{y:,.0f}€</b>"
+            "<extra>P&L vs previous day: %{customdata:,.0f}€</extra>"
+        )
+    )
+    fig.add_trace(
+        go.Scatter(
+            x=df.index,
+            y=df["ap_cum_spent"],
+            mode="lines",
+            name="Cumulative Spent",
+            line=dict(dash="6px", width=1.5, color="bisque"),
+            customdata=df["ap_cum_pnl"],
+            hovertemplate=(
+                "Invested capital: <b>%{y:,.0f}€</b>"
+                "<extra>Ap-/De-preciation: %{customdata:,.0f}€</extra>"
+            ),
+        )
     )
     fig.update_layout(
         autosize=False,
         height=550,
         hoverlabel_font_size=PLT_FONT_SIZE,
+        hovermode="x",
         margin=dict(l=0, r=0, t=20, b=20),
         xaxis=dict(title=""),
-        yaxis=dict(
-            title="Daily value", showgrid=False, tickformat=",.0f", ticksuffix=" €"
-        ),
+        yaxis=dict(title="", showgrid=False, tickformat=",.0f", ticksuffix=" €"),
         showlegend=False,
     )
     return fig