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Merge pull request #1959 from freqtrade/split_btanalysis_load_trades

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Split btanalysis load trades
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Matthias
2019-06-24 19:41:56 +02:00
committed by GitHub
parent 158569f5e8 e83f8941a1
commit 31a2aac627
11 changed files with 86 additions and 60 deletions
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18
docs/backtesting.md
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@@ -221,24 +221,8 @@ strategies, your configuration, and the crypto-currency you have set up.
### Further backtest-result analysis
To further analyze your backtest results, you can [export the trades](#exporting-trades-to-file).
You can then load the trades to perform further analysis.
You can then load the trades to perform further analysis as shown in our [data analysis](data-analysis.md#backtesting) backtesting section.
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.
``` python
from freqtrade.data.btanalysis import load_backtest_data
df = load_backtest_data("user_data/backtest-result.json")
# Show value-counts per pair
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.
If you have some ideas for interesting / helpful backtest data analysis ideas, please submit a PR so the community can benefit from it.
## Backtesting multiple strategies

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docs/data-analysis.md Normal file
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# Analyzing bot data
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.
The following helpers will help you loading the data into Pandas DataFrames, and may also give you some starting points in analyzing the results.
## Backtesting
To analyze your backtest results, you can [export the trades](#exporting-trades-to-file).
You can then load the trades to perform further analysis.
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
df = load_backtest_data("user_data/backtest-result.json")
# Show value-counts per pair
df.groupby("pair")["sell_reason"].value_counts()
```
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.
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 follows:
``` python
from freqtrade.data.btanalysis import load_trades_from_db
df = load_trades_from_db("sqlite:///tradesv3.sqlite")
df.groupby("pair")["sell_reason"].value_counts()
```
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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docs/plotting.md
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@@ -58,7 +58,7 @@ Timerange doesn't work with live data.
To plot trades stored in a database use `--db-url` argument:
``` bash
python3 scripts/plot_dataframe.py --db-url sqlite:///tradesv3.dry_run.sqlite -p BTC/ETH
python3 scripts/plot_dataframe.py --db-url sqlite:///tradesv3.dry_run.sqlite -p BTC/ETH --trade-source DB
```
To plot trades from a backtesting result, use `--export-filename <filename>`