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Matthias
2019-06-24 17:20:41 +02:00
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docs/data-analysis.md
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# Analyzing bot data
After performing backtests, or after running the bot for some time, it will be interresting to analyze the results your bot generated.
After performing backtests, or after running the bot for some time, it will be interesting to analyze the results your bot generated.
A good way for this is using Jupyter (notebook or lab) - which provides an interactive environment to analyze the data.
@@ -11,9 +11,7 @@ The following helpers will help you loading the data into Pandas DataFrames, and
To analyze your backtest results, you can [export the trades](#exporting-trades-to-file).
You can then load the trades to perform further analysis.
A good way for this is using Jupyter (notebook or lab) - which provides an interactive environment to analyze the data.
Freqtrade provides an easy to load the backtest results, which is `load_backtest_data` - and takes a path to the backtest-results file.
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.
``` python
from freqtrade.data.btanalysis import load_backtest_data
@@ -24,13 +22,13 @@ df.groupby("pair")["sell_reason"].value_counts()
```
This will allow you to drill deeper into your backtest results, and perform analysis which would make the regular backtest-output unreadable.
This will allow you to drill deeper into your backtest results, and perform analysis which would make the regular backtest-output very difficult to digest due to information overload.
If you have some ideas for interesting / helpful backtest data analysis ideas, please submit a PR so the community can benefit from it.
If you have some ideas for interesting / helpful backtest data analysis ideas, please submit a Pull Request so the community can benefit from it.
## Live data
To analyze the trades your bot generated, you can load them to a DataFrame as follwos:
To analyze the trades your bot generated, you can load them to a DataFrame as follows:
``` python
from freqtrade.data.btanalysis import load_trades_from_db
@@ -41,4 +39,4 @@ df.groupby("pair")["sell_reason"].value_counts()
```
Feel free to submit an issue or Pull Request if you would like to share ideas on how to best analyze the data.
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.