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https://github.com/aljazceru/hummingbot-dashboard.git
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64 lines
3.1 KiB
Python
64 lines
3.1 KiB
Python
import streamlit as st
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def render_backtesting_metrics(summary_results):
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net_pnl = summary_results.get('net_pnl', 0)
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net_pnl_quote = summary_results.get('net_pnl_quote', 0)
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total_volume = summary_results.get('total_volume', 0)
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total_executors_with_position = summary_results.get('total_executors_with_position', 0)
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max_drawdown_usd = summary_results.get('max_drawdown_usd', 0)
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max_drawdown_pct = summary_results.get('max_drawdown_pct', 0)
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sharpe_ratio = summary_results.get('sharpe_ratio', 0)
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profit_factor = summary_results.get('profit_factor', 0)
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# Displaying KPIs in Streamlit
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st.write("### Backtesting Metrics")
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col1, col2, col3, col4, col5, col6 = st.columns(6)
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col1.metric(label="Net PNL (Quote)", value=f"{net_pnl_quote:.2f}", delta=f"{net_pnl:.2%}")
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col2.metric(label="Max Drawdown (USD)", value=f"{max_drawdown_usd:.2f}", delta=f"{max_drawdown_pct:.2%}")
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col3.metric(label="Total Volume (Quote)", value=f"{total_volume:.2f}")
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col4.metric(label="Sharpe Ratio", value=f"{sharpe_ratio:.2f}")
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col5.metric(label="Profit Factor", value=f"{profit_factor:.2f}")
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col6.metric(label="Total Executors with Position", value=total_executors_with_position)
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def render_accuracy_metrics(summary_results):
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accuracy = summary_results.get('accuracy', 0)
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total_long = summary_results.get('total_long', 0)
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total_short = summary_results.get('total_short', 0)
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accuracy_long = summary_results.get('accuracy_long', 0)
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accuracy_short = summary_results.get('accuracy_short', 0)
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st.write("#### Accuracy Metrics")
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# col1, col2, col3, col4, col5 = st.columns(5)
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st.metric(label="Global Accuracy", value=f"{accuracy:.2%}")
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st.metric(label="Total Long", value=total_long)
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st.metric(label="Total Short", value=total_short)
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st.metric(label="Accuracy Long", value=f"{accuracy_long:.2%}")
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st.metric(label="Accuracy Short", value=f"{accuracy_short:.2%}")
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def render_accuracy_metrics2(summary_results):
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accuracy = summary_results.get('accuracy', 0)
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total_long = summary_results.get('total_long', 0)
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total_short = summary_results.get('total_short', 0)
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accuracy_long = summary_results.get('accuracy_long', 0)
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accuracy_short = summary_results.get('accuracy_short', 0)
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st.write("#### Accuracy Metrics")
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col1, col2, col3, col4, col5 = st.columns(5)
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col1.metric(label="Global Accuracy", value=f"{accuracy}:.2%")
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col2.metric(label="Total Long", value=total_long)
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col3.metric(label="Total Short", value=total_short)
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col4.metric(label="Accuracy Long", value=f"{accuracy_long:.2%}")
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col5.metric(label="Accuracy Short", value=f"{accuracy_short:.2%}")
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def render_close_types(summary_results):
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st.write("#### Close Types")
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close_types = summary_results.get('close_types', {})
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st.metric(label="TAKE PROFIT", value=f"{close_types.get('TAKE_PROFIT', 0)}")
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st.metric(label="STOP LOSS", value=f"{close_types.get('STOP_LOSS', 0)}")
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st.metric(label="TIME LIMIT", value=f"{close_types.get('TIME_LIMIT', 0)}")
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st.metric(label="EARLY STOP", value=f"{close_types.get('EARLY_STOP', 0)}")
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