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(feat) add metrics hint texts + rename returns tab

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drupman
2023-11-19 22:22:26 -03:00
parent 814d409389
commit d2fc254bb8
1 changed files with 43 additions and 35 deletions
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78
pages/strategy_performance/app.py
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@@ -100,31 +100,39 @@ time_filtered_performance_charts = PerformanceGraphs(time_filtered_strategy_data
col1, col2, col3, col4, col5, col6, col7, col8 = st.columns(8)
with col1:
st.metric(label=f'Net PNL {time_filtered_strategy_data.quote_asset}',
value=round(time_filtered_strategy_data.net_pnl_quote, 2))
value=round(time_filtered_strategy_data.net_pnl_quote, 2),
help="The overall profit or loss achieved in quote asset.")
with col2:
st.metric(label='Total Trades', value=time_filtered_strategy_data.total_orders)
st.metric(label='Total Trades', value=time_filtered_strategy_data.total_orders,
help="The total number of closed trades, winning and losing.")
with col3:
st.metric(label='Accuracy',
value=f"{100 * time_filtered_strategy_data.accuracy:.2f} %")
value=f"{100 * time_filtered_strategy_data.accuracy:.2f} %",
help="The percentage of winning trades, the number of winning trades divided by the total number of closed trades.")
with col4:
st.metric(label="Profit Factor",
value=round(time_filtered_strategy_data.profit_factor, 2))
value=round(time_filtered_strategy_data.profit_factor, 2),
help="The amount of money the strategy made for every unit of money it lost, net profits divided by gross losses.")
with col5:
st.metric(label='Duration (Days)',
value=round(time_filtered_strategy_data.duration_seconds / (60 * 60 * 24), 2))
value=round(time_filtered_strategy_data.duration_seconds / (60 * 60 * 24), 2),
help="The number of days the strategy was running.")
with col6:
st.metric(label='Price change',
value=f"{round(time_filtered_strategy_data.price_change * 100, 2)} %")
value=f"{round(time_filtered_strategy_data.price_change * 100, 2)} %",
help="The percentage change in price from the start to the end of the strategy.")
with col7:
buy_trades_amount = round(time_filtered_strategy_data.total_buy_amount, 2)
avg_buy_price = round(time_filtered_strategy_data.average_buy_price, 4)
st.metric(label="Total Buy Volume",
value=round(buy_trades_amount * avg_buy_price, 2))
value=round(buy_trades_amount * avg_buy_price, 2),
help="The total amount of quote asset bought.")
with col8:
sell_trades_amount = round(time_filtered_strategy_data.total_sell_amount, 2)
avg_sell_price = round(time_filtered_strategy_data.average_sell_price, 4)
st.metric(label="Total Sell Volume",
value=round(sell_trades_amount * avg_sell_price, 2))
value=round(sell_trades_amount * avg_sell_price, 2),
help="The total amount of quote asset sold.")
# Cummulative pnl chart
st.plotly_chart(time_filtered_performance_charts.pnl_over_time(), use_container_width=True)
@@ -160,12 +168,12 @@ else:
candles_chart = page_performance_charts.candles_graph(candles_df)
# Show auxiliary charts
intraday_tab, returns_tab, raw_tab, positions_tab = st.tabs(["Intraday", "Returns", "Raw", "Positions"])
intraday_tab, returns_tab, returns_data_tab, positions_tab, other_metrics_tab = st.tabs(["Intraday", "Returns", "Returns Data", "Positions", "Other Metrics"])
with intraday_tab:
st.plotly_chart(time_filtered_performance_charts.intraday_performance(), use_container_width=True)
with returns_tab:
st.plotly_chart(time_filtered_performance_charts.returns_histogram(), use_container_width=True)
with raw_tab:
with returns_data_tab:
raw_returns_data = time_filtered_strategy_data.trade_fill[["timestamp", "gross_pnl", "trade_fee", "realized_pnl"]].dropna(subset="realized_pnl")
st.dataframe(raw_returns_data,
use_container_width=True,
@@ -174,34 +182,34 @@ else:
download_csv_button(raw_returns_data, "raw_returns_data", "download-raw-returns")
with positions_tab:
st.plotly_chart(page_performance_charts.position_executor_summary_sunburst(), use_container_width=True)
with other_metrics_tab:
col3, col4 = st.columns(2)
with col3:
st.metric(label=f'Trade PNL {time_filtered_strategy_data.quote_asset}',
value=round(time_filtered_strategy_data.trade_pnl_quote, 2),
help="The overall profit or loss achieved in quote asset, without fees.")
st.metric(label='Total Buy Trades', value=time_filtered_strategy_data.total_buy_trades,
help="The total number of buy trades.")
st.metric(label='Total Buy Trades Amount',
value=round(time_filtered_strategy_data.total_buy_amount, 2),
help="The total amount of base asset bought.")
st.metric(label='Average Buy Price', value=round(time_filtered_strategy_data.average_buy_price, 4),
help="The average price of the base asset bought.")
with col4:
st.metric(label=f'Fees {time_filtered_strategy_data.quote_asset}',
value=round(time_filtered_strategy_data.cum_fees_in_quote, 2),
help="The overall fees paid in quote asset.")
st.metric(label='Total Sell Trades', value=time_filtered_strategy_data.total_sell_trades,
help="The total number of sell trades.")
st.metric(label='Total Sell Trades Amount',
value=round(time_filtered_strategy_data.total_sell_amount, 2),
help="The total amount of base asset sold.")
st.metric(label='Average Sell Price', value=round(time_filtered_strategy_data.average_sell_price, 4),
help="The average price of the base asset sold.")
with col1:
st.plotly_chart(candles_chart, use_container_width=True)
# Community metrics section
st.divider()
st.subheader("👥 Community Metrics")
with st.container():
col1, col2, col3, col4, col5 = st.columns(5)
with col1:
st.metric(label=f'Trade PNL {time_filtered_strategy_data.quote_asset}',
value=round(time_filtered_strategy_data.trade_pnl_quote, 2))
st.metric(label=f'Fees {time_filtered_strategy_data.quote_asset}',
value=round(time_filtered_strategy_data.cum_fees_in_quote, 2))
with col2:
st.metric(label='Total Buy Trades', value=time_filtered_strategy_data.total_buy_trades)
st.metric(label='Total Sell Trades', value=time_filtered_strategy_data.total_sell_trades)
with col3:
st.metric(label='Total Buy Trades Amount',
value=round(time_filtered_strategy_data.total_buy_amount, 2))
st.metric(label='Total Sell Trades Amount',
value=round(time_filtered_strategy_data.total_sell_amount, 2))
with col4:
st.metric(label='Average Buy Price', value=round(time_filtered_strategy_data.average_buy_price, 4))
st.metric(label='Average Sell Price', value=round(time_filtered_strategy_data.average_sell_price, 4))
with col5:
st.metric(label='Inventory change in Base asset',
value=round(time_filtered_strategy_data.inventory_change_base_asset, 4))
# Tables section
st.divider()
st.subheader("Tables")