74 lines
3.3 KiB
Markdown
74 lines
3.3 KiB
Markdown
# Advanced Backtesting Analysis
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## Analyze the buy/entry and sell/exit tags
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It can be helpful to understand how a strategy behaves according to the buy/entry tags used to
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mark up different buy conditions. You might want to see more complex statistics about each buy and
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sell condition above those provided by the default backtesting output. You may also want to
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determine indicator values on the signal candle that resulted in a trade opening.
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!!! Note
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The following buy reason analysis is only available for backtesting, *not hyperopt*.
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We need to run backtesting with the `--export` option set to `signals` to enable the exporting of
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signals **and** trades:
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``` bash
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freqtrade backtesting -c <config.json> --timeframe <tf> --strategy <strategy_name> --timerange=<timerange> --export=signals
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```
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To analyze the buy tags, we need to use the `buy_reasons.py` script from
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[froggleston's repo](https://github.com/froggleston/freqtrade-buyreasons). Follow the instructions
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in their README to copy the script into your `freqtrade/scripts/` folder.
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This will tell freqtrade to output a pickled dictionary of strategy, pairs and corresponding
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DataFrame of the candles that resulted in buy signals. Depending on how many buys your strategy
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makes, this file may get quite large, so periodically check your `user_data/backtest_results`
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folder to delete old exports.
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Before running your next backtest, make sure you either delete your old backtest results or run
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backtesting with the `--cache none` option to make sure no cached results are used.
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If all goes well, you should now see a `backtest-result-{timestamp}_signals.pkl` file in the
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`user_data/backtest_results` folder.
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Now run the `buy_reasons.py` script, supplying a few options:
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``` bash
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python3 scripts/buy_reasons.py -c <config.json> -s <strategy_name> -t <timerange> -g0,1,2,3,4
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```
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The `-g` option is used to specify the various tabular outputs, ranging from the simplest (0)
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to the most detailed per pair, per buy and per sell tag (4). More options are available by
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running with the `-h` option.
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### Tuning the buy tags and sell tags to display
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To show only certain buy and sell tags in the displayed output, use the following two options:
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```
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--buy_reason_list : Comma separated list of buy signals to analyse. Default: "all"
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--sell_reason_list : Comma separated list of sell signals to analyse. Default: "stop_loss,trailing_stop_loss"
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```
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For example:
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```bash
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python3 scripts/buy_reasons.py -c <config.json> -s <strategy_name> -t <timerange> -g0,1,2,3,4 --buy_reason_list "buy_tag_a,buy_tag_b" --sell_reason_list "roi,custom_sell_tag_a,stop_loss"
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```
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### Outputting signal candle indicators
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The real power of the buy_reasons.py script comes from the ability to print out the indicator
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values present on signal candles to allow fine-grained investigation and tuning of buy signal
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indicators. To print out a column for a given set of indicators, use the `--indicator-list`
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option:
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```bash
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python3 scripts/buy_reasons.py -c <config.json> -s <strategy_name> -t <timerange> -g0,1,2,3,4 --buy_reason_list "buy_tag_a,buy_tag_b" --sell_reason_list "roi,custom_sell_tag_a,stop_loss" --indicator_list "rsi,rsi_1h,bb_lowerband,ema_9,macd,macdsignal"
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```
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The indicators have to be present in your strategy's main DataFrame (either for your main
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timeframe or for informative timeframes) otherwise they will simply be ignored in the script
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output.
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