Add documentation sample for parallel_trade_analysis
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@ -107,6 +107,22 @@ trades = load_trades_from_db("sqlite:///tradesv3.sqlite")
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trades.groupby("pair")["sell_reason"].value_counts()
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```
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## Analyze the loaded trades for trade parallelism
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This can be useful to find the best `max_open_trades` parameter, when used with backtesting in conjunction with `--disable-max-market-positions`.
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`parallel_trade_analysis()` returns a timeseries dataframe with an "open_trades" column, specifying the number of open trades for each candle.
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```python
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from freqtrade.data.btanalysis import parallel_trade_analysis
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# Analyze the above
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parallel_trades = parallel_trade_analysis(trades, '5m')
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parallel_trades.plot()
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```
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## Plot results
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Freqtrade offers interactive plotting capabilities based on plotly.
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@ -68,9 +68,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load strategy using values set above\n",
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@ -169,6 +167,31 @@
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"trades.groupby(\"pair\")[\"sell_reason\"].value_counts()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Analyze the loaded trades for trade parallelism\n",
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"This can be useful to find the best `max_open_trades` parameter, when used with backtesting in conjunction with `--disable-max-market-positions`.\n",
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"\n",
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"`parallel_trade_analysis()` returns a timeseries dataframe with an \"open_trades\" column, specifying the number of open trades for each candle."
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]
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},
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{
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"cell_type": "code",
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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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"from freqtrade.data.btanalysis import parallel_trade_analysis\n",
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"\n",
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"# Analyze the above\n",
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"parallel_trades = parallel_trade_analysis(trades, '5m')\n",
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"\n",
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"\n",
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"parallel_trades.plot()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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