(feat) isolate candles_graph and panel metrics

hummingbot · Sep 20, 2023 · 1a12e72 · 1a12e72
1 parent 75b9dc7
commit 1a12e72
Showing 1 changed file with 30 additions and 13 deletions.
diff --git a/pages/strategy_performance/app.py b/pages/strategy_performance/app.py
@@ -261,6 +261,15 @@ def returns_histogram(df: pd.DataFrame):
     return fig
 
 
+def candles_graph(candles: pd.DataFrame, strat_data, show_volume=False, extra_rows=2):
+    cg = CandlesGraph(candles, show_volume=show_volume, extra_rows=extra_rows)
+    cg.add_buy_trades(strat_data.buys)
+    cg.add_sell_trades(strat_data.sells)
+    cg.add_pnl(strategy_data, row=2)
+    cg.add_quote_inventory_change(strat_data, row=3)
+    return cg.figure()
+
+
 style_metric_cards()
 st.subheader("🔫 Data source")
 with st.expander("⬆️ Upload"):
@@ -344,12 +353,15 @@ def returns_histogram(df: pd.DataFrame):
 
                     st.subheader("💱 Market activity")
                     if "Error" not in selected_db.status["market_data"] and strategy_data_filtered.market_data is not None:
-                        col1, col2, col3 = st.columns([1, 1, 2])
+                        col1, col2, col3, col4 = st.columns(4)
                         with col1:
                             interval = st.selectbox("Candles Interval:", intervals.keys(), index=2)
                         with col2:
                             rows_per_page = st.number_input("Candles per Page", value=1500, min_value=1, max_value=5000)
                         with col3:
+                            st.markdown("##")
+                            show_panel_metrics = st.checkbox("Show panel metrics", value=True)
+                        with col4:
                             total_rows = len(
                                 strategy_data_filtered.get_market_data_resampled(interval=f"{intervals[interval]}S"))
                             total_pages = math.ceil(total_rows / rows_per_page)
@@ -367,18 +379,23 @@ def returns_histogram(df: pd.DataFrame):
                             end_time_page = candles_df.index.max()
                             page_data_filtered = single_market_strategy_data.get_filtered_strategy_data(start_time_page,
                                                                                                         end_time_page)
-                        col1, col2 = st.columns([5.5, 1.5])
-                        with col1:
-                            cg = CandlesGraph(candles_df, show_volume=False, extra_rows=2)
-                            cg.add_buy_trades(page_data_filtered.buys)
-                            cg.add_sell_trades(page_data_filtered.sells)
-                            cg.add_pnl(page_data_filtered, row=2)
-                            cg.add_quote_inventory_change(page_data_filtered, row=3)
-                            fig = cg.figure()
-                            st.plotly_chart(fig, use_container_width=True)
-                        with col2:
-                            st.plotly_chart(intraday_performance(page_data_filtered.trade_fill), use_container_width=True)
-                            st.plotly_chart(returns_histogram(page_data_filtered.trade_fill), use_container_width=True)
+                        if show_panel_metrics:
+                            col1, col2 = st.columns([2, 1])
+                            with col1:
+                                candles_chart = candles_graph(candles_df, page_data_filtered)
+                                st.plotly_chart(candles_chart, use_container_width=True)
+                            with col2:
+                                chart_tab, table_tab = st.tabs(["Chart", "Table"])
+                                with chart_tab:
+                                    st.plotly_chart(intraday_performance(page_data_filtered.trade_fill), use_container_width=True)
+                                    st.plotly_chart(returns_histogram(page_data_filtered.trade_fill), use_container_width=True)
+                                with table_tab:
+                                    st.dataframe(page_data_filtered.trade_fill[["timestamp", "realized_pnl"]].dropna(subset="realized_pnl"),
+                                                 use_container_width=True,
+                                                 hide_index=True,
+                                                 height=candles_chart.layout.height - 180)
+                        else:
+                            st.plotly_chart(candles_graph(candles_df, page_data_filtered), use_container_width=True)
                     else:
                         st.warning("Market data is not available so the candles graph is not going to be rendered. "
                                    "Make sure that you are using the latest version of Hummingbot and market data recorder activated.")