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Advanced Backtesting Analysis

Analyze the buy/entry and sell/exit tags

It can be helpful to understand how a strategy behaves according to the buy/entry tags used to mark up different buy conditions. You might want to see more complex statistics about each buy and sell condition above those provided by the default backtesting output. You may also want to determine indicator values on the signal candle that resulted in a trade opening.

!!! Note The following buy reason analysis is only available for backtesting, not hyperopt.

We need to run backtesting with the --export option set to signals to enable the exporting of signals and trades:

freqtrade backtesting -c <config.json> --timeframe <tf> --strategy <strategy_name> --timerange=<timerange> --export=signals

To analyze the buy tags, we need to use the buy_reasons.py script from froggleston's repo. Follow the instructions in their README to copy the script into your freqtrade/scripts/ folder.

This will tell freqtrade to output a pickled dictionary of strategy, pairs and corresponding DataFrame of the candles that resulted in buy signals. Depending on how many buys your strategy makes, this file may get quite large, so periodically check your user_data/backtest_results folder to delete old exports.

Before running your next backtest, make sure you either delete your old backtest results or run backtesting with the --cache none option to make sure no cached results are used.

If all goes well, you should now see a backtest-result-{timestamp}_signals.pkl file in the user_data/backtest_results folder.

Now run the buy_reasons.py script, supplying a few options:

python3 scripts/buy_reasons.py -c <config.json> -s <strategy_name> -t <timerange> -g0,1,2,3,4

The -g option is used to specify the various tabular outputs, ranging from the simplest (0) to the most detailed per pair, per buy and per sell tag (4). More options are available by running with the -h option.

Tuning the buy tags and sell tags to display

To show only certain buy and sell tags in the displayed output, use the following two options:

--buy_reason_list : Comma separated list of buy signals to analyse. Default: "all"
--sell_reason_list : Comma separated list of sell signals to analyse. Default: "stop_loss,trailing_stop_loss"

For example:

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"

Outputting signal candle indicators

The real power of the buy_reasons.py script comes from the ability to print out the indicator values present on signal candles to allow fine-grained investigation and tuning of buy signal indicators. To print out a column for a given set of indicators, use the --indicator-list option:

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"

The indicators have to be present in your strategy's main DataFrame (either for your main timeframe or for informative timeframes) otherwise they will simply be ignored in the script output.