Reexport File to docs to have this available as documentation too
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@ -130,6 +130,39 @@ trades = load_backtest_data(backtest_dir)
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trades.groupby("pair")["sell_reason"].value_counts()
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```
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## Plotting daily profit / equity line
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```python
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# Plotting equity line (starting with 0 on day 1 and adding daily profit for each backtested day)
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from freqtrade.configuration import Configuration
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from freqtrade.data.btanalysis import load_backtest_data, load_backtest_stats
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import plotly.express as px
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import pandas as pd
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# strategy = 'SampleStrategy'
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# config = Configuration.from_files(["user_data/config.json"])
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# backtest_dir = config["user_data_dir"] / "backtest_results"
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stats = load_backtest_stats(backtest_dir)
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strategy_stats = stats['strategy'][strategy]
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equity = 0
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equity_daily = []
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for dp in strategy_stats['daily_profit']:
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equity_daily.append(equity)
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equity += float(dp)
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dates = pd.date_range(strategy_stats['backtest_start'], strategy_stats['backtest_end'])
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df = pd.DataFrame({'dates': dates,'equity_daily': equity_daily})
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fig = px.line(df, x="dates", y="equity_daily")
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fig.show()
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```
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### Load live trading results into a pandas dataframe
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In case you did already some trading and want to analyze your performance
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@ -197,7 +197,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -223,7 +223,7 @@
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"\n",
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"dates = pd.date_range(strategy_stats['backtest_start'], strategy_stats['backtest_end'])\n",
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"\n",
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"df = pd.DataFrame({'dates':dates,'equity_daily':equity_daily})\n",
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"df = pd.DataFrame({'dates': dates,'equity_daily': equity_daily})\n",
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"\n",
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"fig = px.line(df, x=\"dates\", y=\"equity_daily\")\n",
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"fig.show()\n"
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