43 lines
1.7 KiB
Markdown
43 lines
1.7 KiB
Markdown
# Analyzing bot data
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After performing backtests, or after running the bot for some time, it will be interesting to analyze the results your bot generated.
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A good way for this is using Jupyter (notebook or lab) - which provides an interactive environment to analyze the data.
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The following helpers will help you loading the data into Pandas DataFrames, and may also give you some starting points in analyzing the results.
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## Backtesting
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To analyze your backtest results, you can [export the trades](#exporting-trades-to-file).
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You can then load the trades to perform further analysis.
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Freqtrade provides the `load_backtest_data()` helper function to easily load the backtest results, which takes the path to the the backtest-results file as parameter.
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``` python
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from freqtrade.data.btanalysis import load_backtest_data
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df = load_backtest_data("user_data/backtest_results/backtest-result.json")
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# Show value-counts per pair
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df.groupby("pair")["sell_reason"].value_counts()
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```
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This will allow you to drill deeper into your backtest results, and perform analysis which otherwise would make the regular backtest-output very difficult to digest due to information overload.
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If you have some ideas for interesting / helpful backtest data analysis ideas, please submit a Pull Request so the community can benefit from it.
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## Live data
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To analyze the trades your bot generated, you can load them to a DataFrame as follows:
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``` python
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from freqtrade.data.btanalysis import load_trades_from_db
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df = load_trades_from_db("sqlite:///tradesv3.sqlite")
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df.groupby("pair")["sell_reason"].value_counts()
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```
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Feel free to submit an issue or Pull Request enhancing this document if you would like to share ideas on how to best analyze the data.
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