Add support for collating and analysing rejected trades in backtest
This commit is contained in:
froggleston
parent
f28b314266
commit
5a4e99b413
@ -29,7 +29,7 @@ If all goes well, you should now see a `backtest-result-{timestamp}_signals.pkl`
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`user_data/backtest_results` folder.
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To analyze the entry/exit tags, we now need to use the `freqtrade backtesting-analysis` command
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with `--analysis-groups` option provided with space-separated arguments (default `0 1 2`):
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with `--analysis-groups` option provided with space-separated arguments:
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``` bash
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freqtrade backtesting-analysis -c <config.json> --analysis-groups 0 1 2 3 4
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@ -39,6 +39,7 @@ This command will read from the last backtesting results. The `--analysis-groups
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used to specify the various tabular outputs showing the profit fo each group or trade,
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ranging from the simplest (0) to the most detailed per pair, per buy and per sell tag (4):
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* 0: overall winrate and profit summary by enter_tag
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* 1: profit summaries grouped by enter_tag
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* 2: profit summaries grouped by enter_tag and exit_tag
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* 3: profit summaries grouped by pair and enter_tag
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@ -114,3 +115,37 @@ For example, if your backtest timerange was `20220101-20221231` but you only wan
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```bash
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freqtrade backtesting-analysis -c <config.json> --timerange 20220101-20220201
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```
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### Printing out rejected trades
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Use the `--rejected` option to print out rejected trades.
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```bash
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freqtrade backtesting-analysis -c <config.json> --rejected
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```
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### Writing tables to CSV
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Some of the tabular outputs can become large, so printing them out to the terminal is not preferable.
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Use the `--analysis-to-csv` option to disable printing out of tables to standard out and write them to CSV files.
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```bash
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freqtrade backtesting-analysis -c <config.json> --analysis-to-csv
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```
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By default this will write one file per output table you specified in the `backtesting-analysis` command, e.g.
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```bash
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freqtrade backtesting-analysis -c <config.json> --analysis-to-csv --rejected --analysis-groups 0 1
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```
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This will write to `user_data/backtest_results`:
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* rejected.csv
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* group_0.csv
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* group_1.csv
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To override where the files will be written, also specify the `--analysis-csv-path` option.
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```bash
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freqtrade backtesting-analysis -c <config.json> --analysis-to-csv --analysis-csv-path another/data/path/
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```
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@ -723,6 +723,9 @@ usage: freqtrade backtesting-analysis [-h] [-v] [--logfile FILE] [-V]
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[--exit-reason-list EXIT_REASON_LIST [EXIT_REASON_LIST ...]]
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[--indicator-list INDICATOR_LIST [INDICATOR_LIST ...]]
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[--timerange YYYYMMDD-[YYYYMMDD]]
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[--rejected]
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[--analysis-to-csv]
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[--analysis-csv-path PATH]
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optional arguments:
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-h, --help show this help message and exit
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@ -736,19 +739,27 @@ optional arguments:
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pair and enter_tag, 4: by pair, enter_ and exit_tag
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(this can get quite large)
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--enter-reason-list ENTER_REASON_LIST [ENTER_REASON_LIST ...]
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Comma separated list of entry signals to analyse.
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Default: all. e.g. 'entry_tag_a,entry_tag_b'
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Space separated list of entry signals to analyse.
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Default: all. e.g. 'entry_tag_a entry_tag_b'
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--exit-reason-list EXIT_REASON_LIST [EXIT_REASON_LIST ...]
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Comma separated list of exit signals to analyse.
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Space separated list of exit signals to analyse.
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Default: all. e.g.
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'exit_tag_a,roi,stop_loss,trailing_stop_loss'
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'exit_tag_a roi stop_loss trailing_stop_loss'
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--indicator-list INDICATOR_LIST [INDICATOR_LIST ...]
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Comma separated list of indicators to analyse. e.g.
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'close,rsi,bb_lowerband,profit_abs'
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Space separated list of indicators to analyse. e.g.
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'close rsi bb_lowerband profit_abs'
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--timerange YYYYMMDD-[YYYYMMDD]
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Timerange to filter trades for analysis,
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start inclusive, end exclusive. e.g.
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20220101-20220201
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--rejected
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Print out rejected trades table
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--analysis-to-csv
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Write out tables to individual CSVs, by default to
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'user_data/backtest_results' unless '--analysis-csv-path' is given.
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--analysis-csv-path [PATH]
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Optional path where individual CSVs will be written. If not used,
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CSVs will be written to 'user_data/backtest_results'.
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Common arguments:
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-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
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@ -106,7 +106,8 @@ ARGS_HYPEROPT_SHOW = ["hyperopt_list_best", "hyperopt_list_profitable", "hyperop
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"disableparamexport", "backtest_breakdown"]
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ARGS_ANALYZE_ENTRIES_EXITS = ["exportfilename", "analysis_groups", "enter_reason_list",
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"exit_reason_list", "indicator_list", "timerange"]
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"exit_reason_list", "indicator_list", "timerange",
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"analysis_rejected", "analysis_to_csv", "analysis_csv_path"]
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NO_CONF_REQURIED = ["convert-data", "convert-trade-data", "download-data", "list-timeframes",
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"list-markets", "list-pairs", "list-strategies", "list-freqaimodels",
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@ -634,7 +634,7 @@ AVAILABLE_CLI_OPTIONS = {
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"3: by pair and enter_tag, "
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"4: by pair, enter_ and exit_tag (this can get quite large)"),
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nargs='+',
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default=['0', '1', '2'],
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default=[],
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choices=['0', '1', '2', '3', '4'],
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),
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"enter_reason_list": Arg(
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@ -658,6 +658,21 @@ AVAILABLE_CLI_OPTIONS = {
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nargs='+',
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default=[],
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),
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"analysis_rejected": Arg(
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'--rejected',
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help='Analyse rejected trades',
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action='store_true',
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),
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"analysis_to_csv": Arg(
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'--analysis-to-csv',
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help='Save selected analysis tables to individual CSVs',
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action='store_true',
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),
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"analysis_csv_path": Arg(
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'--analysis-csv-path',
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help=("Specify a path to save the analysis CSVs "
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"if --analysis-to-csv is enabled. Default: user_data/basktesting_results/"),
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),
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"freqaimodel": Arg(
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'--freqaimodel',
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help='Specify a custom freqaimodels.',
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@ -465,6 +465,15 @@ class Configuration:
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self._args_to_config(config, argname='timerange',
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logstring='Filter trades by timerange: {}')
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self._args_to_config(config, argname='analysis_rejected',
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logstring='Analyse rejected trades: {}')
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self._args_to_config(config, argname='analysis_to_csv',
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logstring='Store analysis tables to CSV: {}')
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self._args_to_config(config, argname='analysis_csv_path',
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logstring='Path to store analysis CSVs: {}')
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def _process_runmode(self, config: Config) -> None:
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self._args_to_config(config, argname='dry_run',
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@ -15,22 +15,30 @@ from freqtrade.exceptions import OperationalException
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logger = logging.getLogger(__name__)
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def _load_signal_candles(backtest_dir: Path):
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def _load_backtest_analysis_data(backtest_dir: Path, name: str):
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if backtest_dir.is_dir():
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scpf = Path(backtest_dir,
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Path(get_latest_backtest_filename(backtest_dir)).stem + "_signals.pkl"
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Path(get_latest_backtest_filename(backtest_dir)).stem + "_" + name + ".pkl"
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)
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else:
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scpf = Path(backtest_dir.parent / f"{backtest_dir.stem}_signals.pkl")
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scpf = Path(backtest_dir.parent / f"{backtest_dir.stem}_{name}.pkl")
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try:
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scp = open(scpf, "rb")
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signal_candles = joblib.load(scp)
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logger.info(f"Loaded signal candles: {str(scpf)}")
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rejected_trades = joblib.load(scp)
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logger.info(f"Loaded {name} data: {str(scpf)}")
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except Exception as e:
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logger.error("Cannot load signal candles from pickled results: ", e)
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logger.error(f"Cannot load {name} data from pickled results: ", e)
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return signal_candles
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return rejected_trades
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def _load_rejected_trades(backtest_dir: Path):
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return _load_backtest_analysis_data(backtest_dir, "rejected")
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def _load_signal_candles(backtest_dir: Path):
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return _load_backtest_analysis_data(backtest_dir, "signals")
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def _process_candles_and_indicators(pairlist, strategy_name, trades, signal_candles):
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@ -43,9 +51,9 @@ def _process_candles_and_indicators(pairlist, strategy_name, trades, signal_cand
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for pair in pairlist:
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if pair in signal_candles[strategy_name]:
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analysed_trades_dict[strategy_name][pair] = _analyze_candles_and_indicators(
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pair,
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trades,
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signal_candles[strategy_name][pair])
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pair,
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trades,
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signal_candles[strategy_name][pair])
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except Exception as e:
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print(f"Cannot process entry/exit reasons for {strategy_name}: ", e)
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@ -85,7 +93,7 @@ def _analyze_candles_and_indicators(pair, trades, signal_candles):
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return pd.DataFrame()
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def _do_group_table_output(bigdf, glist):
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def _do_group_table_output(bigdf, glist, to_csv=False, csv_path=None):
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for g in glist:
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# 0: summary wins/losses grouped by enter tag
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if g == "0":
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@ -116,7 +124,8 @@ def _do_group_table_output(bigdf, glist):
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sortcols = ['total_num_buys']
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_print_table(new, sortcols, show_index=True)
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_print_table(new, sortcols, show_index=True, name="Group 0:",
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to_csv=to_csv, csv_path=csv_path)
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else:
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agg_mask = {'profit_abs': ['count', 'sum', 'median', 'mean'],
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@ -148,11 +157,23 @@ def _do_group_table_output(bigdf, glist):
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new['mean_profit_pct'] = new['mean_profit_pct'] * 100
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new['total_profit_pct'] = new['total_profit_pct'] * 100
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_print_table(new, sortcols)
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_print_table(new, sortcols, name=f"Group {g}:",
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to_csv=to_csv, csv_path=csv_path)
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else:
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logger.warning("Invalid group mask specified.")
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def _do_rejected_trades_output(rejected_trades_df, to_csv=False, csv_path=None):
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cols = ['pair', 'date', 'enter_tag']
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sortcols = ['date', 'pair', 'enter_tag']
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_print_table(rejected_trades_df[cols],
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sortcols,
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show_index=False,
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name="Rejected Trades:",
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to_csv=to_csv,
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csv_path=csv_path)
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def _select_rows_within_dates(df, timerange=None, df_date_col: str = 'date'):
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if timerange:
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if timerange.starttype == 'date':
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@ -186,38 +207,65 @@ def prepare_results(analysed_trades, stratname,
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return res_df
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def print_results(res_df, analysis_groups, indicator_list):
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def print_results(res_df, analysis_groups, indicator_list,
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rejected_trades=None, to_csv=False, csv_path=None):
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if res_df.shape[0] > 0:
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if analysis_groups:
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_do_group_table_output(res_df, analysis_groups)
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_do_group_table_output(res_df, analysis_groups, to_csv=to_csv, csv_path=csv_path)
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if rejected_trades is not None and not rejected_trades.empty:
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_do_rejected_trades_output(rejected_trades, to_csv=to_csv, csv_path=csv_path)
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# NB this can be large for big dataframes!
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if "all" in indicator_list:
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print(res_df)
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elif indicator_list is not None:
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_print_table(res_df,
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show_index=False,
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name="Indicators:",
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to_csv=to_csv,
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csv_path=csv_path)
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elif indicator_list is not None and indicator_list:
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available_inds = []
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for ind in indicator_list:
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if ind in res_df:
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available_inds.append(ind)
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ilist = ["pair", "enter_reason", "exit_reason"] + available_inds
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_print_table(res_df[ilist], sortcols=['exit_reason'], show_index=False)
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_print_table(res_df[ilist],
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sortcols=['exit_reason'],
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show_index=False,
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name="Indicators:",
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to_csv=to_csv,
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csv_path=csv_path)
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else:
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print("\\No trades to show")
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def _print_table(df, sortcols=None, show_index=False):
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def _print_table(df, sortcols=None, show_index=False, name=None, to_csv=False, csv_path=None):
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if (sortcols is not None):
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data = df.sort_values(sortcols)
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else:
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data = df
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print(
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tabulate(
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data,
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headers='keys',
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tablefmt='psql',
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showindex=show_index
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if to_csv:
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if csv_path is not None:
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safe_name = Path(csv_path,
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name.lower().replace(" ", "_").replace(":", ""))
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else:
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safe_name = Path("user_data",
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"backtest_results",
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name.lower().replace(" ", "_").replace(":", ""))
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data.to_csv(f"{str(safe_name)}.csv")
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else:
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if name is not None:
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print(name)
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print(
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tabulate(
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data,
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headers='keys',
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tablefmt='psql',
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showindex=show_index
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)
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)
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)
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def process_entry_exit_reasons(config: Config):
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@ -226,6 +274,9 @@ def process_entry_exit_reasons(config: Config):
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enter_reason_list = config.get('enter_reason_list', ["all"])
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exit_reason_list = config.get('exit_reason_list', ["all"])
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indicator_list = config.get('indicator_list', [])
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do_rejected = config.get('analysis_rejected', False)
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to_csv = config.get('analysis_to_csv', False)
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csv_path = config.get('analysis_csv_path', config['exportfilename'])
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timerange = TimeRange.parse_timerange(None if config.get(
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'timerange') is None else str(config.get('timerange')))
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@ -235,8 +286,16 @@ def process_entry_exit_reasons(config: Config):
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for strategy_name, results in backtest_stats['strategy'].items():
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trades = load_backtest_data(config['exportfilename'], strategy_name)
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if not trades.empty:
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if trades is not None and not trades.empty:
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signal_candles = _load_signal_candles(config['exportfilename'])
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rej_df = None
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if do_rejected:
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rejected_trades_dict = _load_rejected_trades(config['exportfilename'])
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rej_df = prepare_results(rejected_trades_dict, strategy_name,
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enter_reason_list, exit_reason_list,
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timerange=timerange)
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analysed_trades_dict = _process_candles_and_indicators(
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config['exchange']['pair_whitelist'], strategy_name,
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trades, signal_candles)
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@ -247,7 +306,10 @@ def process_entry_exit_reasons(config: Config):
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print_results(res_df,
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analysis_groups,
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indicator_list)
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indicator_list,
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rejected_trades=rej_df,
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to_csv=to_csv,
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csv_path=csv_path)
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except ValueError as e:
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raise OperationalException(e) from e
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@ -29,6 +29,7 @@ from freqtrade.mixins import LoggingMixin
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from freqtrade.optimize.backtest_caching import get_strategy_run_id
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from freqtrade.optimize.bt_progress import BTProgress
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from freqtrade.optimize.optimize_reports import (generate_backtest_stats, show_backtest_results,
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store_backtest_rejected_trades,
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store_backtest_signal_candles,
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store_backtest_stats)
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from freqtrade.persistence import LocalTrade, Order, PairLocks, Trade
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@ -83,6 +84,8 @@ class Backtesting:
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self.strategylist: List[IStrategy] = []
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self.all_results: Dict[str, Dict] = {}
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self.processed_dfs: Dict[str, Dict] = {}
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self.rejected_dict: Dict[str, List] = {}
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self.rejected_df: Dict[str, Dict] = {}
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self._exchange_name = self.config['exchange']['name']
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self.exchange = ExchangeResolver.load_exchange(
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@ -1048,6 +1051,18 @@ class Backtesting:
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return None
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return row
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def _collate_rejected(self, pair, row):
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"""
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Temporarily store rejected trade information for downstream use in backtesting_analysis
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"""
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# It could be fun to enable hyperopt mode to write
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# a loss function to reduce rejected signals
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if (self.config.get('export', 'none') == 'signals' and
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self.dataprovider.runmode == RunMode.BACKTEST):
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if pair not in self.rejected_dict:
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self.rejected_dict[pair] = []
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self.rejected_dict[pair].append([row[DATE_IDX], row[ENTER_TAG_IDX]])
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def backtest_loop(
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self, row: Tuple, pair: str, current_time: datetime, end_date: datetime,
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max_open_trades: int, open_trade_count_start: int, is_first: bool = True) -> int:
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@ -1073,20 +1088,22 @@ class Backtesting:
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if (
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(self._position_stacking or len(LocalTrade.bt_trades_open_pp[pair]) == 0)
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and is_first
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and self.trade_slot_available(max_open_trades, open_trade_count_start)
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and current_time != end_date
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and trade_dir is not None
|
||||
and not PairLocks.is_pair_locked(pair, row[DATE_IDX], trade_dir)
|
||||
):
|
||||
trade = self._enter_trade(pair, row, trade_dir)
|
||||
if trade:
|
||||
# TODO: hacky workaround to avoid opening > max_open_trades
|
||||
# This emulates previous behavior - not sure if this is correct
|
||||
# Prevents entering if the trade-slot was freed in this candle
|
||||
open_trade_count_start += 1
|
||||
# logger.debug(f"{pair} - Emulate creation of new trade: {trade}.")
|
||||
LocalTrade.add_bt_trade(trade)
|
||||
self.wallets.update()
|
||||
if (self.trade_slot_available(max_open_trades, open_trade_count_start)):
|
||||
trade = self._enter_trade(pair, row, trade_dir)
|
||||
if trade:
|
||||
# TODO: hacky workaround to avoid opening > max_open_trades
|
||||
# This emulates previous behavior - not sure if this is correct
|
||||
# Prevents entering if the trade-slot was freed in this candle
|
||||
open_trade_count_start += 1
|
||||
# logger.debug(f"{pair} - Emulate creation of new trade: {trade}.")
|
||||
LocalTrade.add_bt_trade(trade)
|
||||
self.wallets.update()
|
||||
else:
|
||||
self._collate_rejected(pair, row)
|
||||
|
||||
for trade in list(LocalTrade.bt_trades_open_pp[pair]):
|
||||
# 3. Process entry orders.
|
||||
@ -1266,6 +1283,7 @@ class Backtesting:
|
||||
if (self.config.get('export', 'none') == 'signals' and
|
||||
self.dataprovider.runmode == RunMode.BACKTEST):
|
||||
self._generate_trade_signal_candles(preprocessed_tmp, results)
|
||||
self._generate_rejected_trades(preprocessed_tmp, self.rejected_dict)
|
||||
|
||||
return min_date, max_date
|
||||
|
||||
@ -1282,12 +1300,33 @@ class Backtesting:
|
||||
for t, v in pairresults.open_date.items():
|
||||
allinds = pairdf.loc[(pairdf['date'] < v)]
|
||||
signal_inds = allinds.iloc[[-1]]
|
||||
signal_candles_only_df = pd.concat([signal_candles_only_df, signal_inds])
|
||||
signal_candles_only_df = pd.concat([
|
||||
signal_candles_only_df.infer_objects(),
|
||||
signal_inds.infer_objects()])
|
||||
|
||||
signal_candles_only[pair] = signal_candles_only_df
|
||||
|
||||
self.processed_dfs[self.strategy.get_strategy_name()] = signal_candles_only
|
||||
|
||||
def _generate_rejected_trades(self, preprocessed_df, rejected_dict):
|
||||
rejected_candles_only = {}
|
||||
for pair, trades in rejected_dict.items():
|
||||
rejected_trades_only_df = DataFrame()
|
||||
pairdf = preprocessed_df[pair]
|
||||
|
||||
for t in trades:
|
||||
data_df_row = pairdf.loc[(pairdf['date'] == t[0])].copy()
|
||||
data_df_row['pair'] = pair
|
||||
data_df_row['enter_tag'] = t[1]
|
||||
|
||||
rejected_trades_only_df = pd.concat([
|
||||
rejected_trades_only_df.infer_objects(),
|
||||
data_df_row.infer_objects()])
|
||||
|
||||
rejected_candles_only[pair] = rejected_trades_only_df
|
||||
|
||||
self.rejected_df[self.strategy.get_strategy_name()] = rejected_candles_only
|
||||
|
||||
def _get_min_cached_backtest_date(self):
|
||||
min_backtest_date = None
|
||||
backtest_cache_age = self.config.get('backtest_cache', constants.BACKTEST_CACHE_DEFAULT)
|
||||
@ -1353,6 +1392,9 @@ class Backtesting:
|
||||
store_backtest_signal_candles(
|
||||
self.config['exportfilename'], self.processed_dfs, dt_appendix)
|
||||
|
||||
store_backtest_rejected_trades(
|
||||
self.config['exportfilename'], self.rejected_df, dt_appendix)
|
||||
|
||||
# Results may be mixed up now. Sort them so they follow --strategy-list order.
|
||||
if 'strategy_list' in self.config and len(self.results) > 0:
|
||||
self.results['strategy_comparison'] = sorted(
|
||||
|
||||
@ -45,29 +45,41 @@ def store_backtest_stats(
|
||||
file_dump_json(latest_filename, {'latest_backtest': str(filename.name)})
|
||||
|
||||
|
||||
def store_backtest_signal_candles(
|
||||
recordfilename: Path, candles: Dict[str, Dict], dtappendix: str) -> Path:
|
||||
def _store_backtest_analysis_data(
|
||||
recordfilename: Path, data: Dict[str, Dict],
|
||||
dtappendix: str, name: str) -> Path:
|
||||
"""
|
||||
Stores backtest trade signal candles
|
||||
Stores backtest trade candles for analysis
|
||||
:param recordfilename: Path object, which can either be a filename or a directory.
|
||||
Filenames will be appended with a timestamp right before the suffix
|
||||
while for directories, <directory>/backtest-result-<datetime>_signals.pkl will be used
|
||||
while for directories, <directory>/backtest-result-<datetime>_<name>.pkl will be used
|
||||
as filename
|
||||
:param stats: Dict containing the backtesting signal candles
|
||||
:param candles: Dict containing the backtesting data for analysis
|
||||
:param dtappendix: Datetime to use for the filename
|
||||
:param name: Name to use for the file, e.g. signals, rejected
|
||||
"""
|
||||
if recordfilename.is_dir():
|
||||
filename = (recordfilename / f'backtest-result-{dtappendix}_signals.pkl')
|
||||
filename = (recordfilename / f'backtest-result-{dtappendix}_{name}.pkl')
|
||||
else:
|
||||
filename = Path.joinpath(
|
||||
recordfilename.parent, f'{recordfilename.stem}-{dtappendix}_signals.pkl'
|
||||
recordfilename.parent, f'{recordfilename.stem}-{dtappendix}_{name}.pkl'
|
||||
)
|
||||
|
||||
file_dump_joblib(filename, candles)
|
||||
file_dump_joblib(filename, data)
|
||||
|
||||
return filename
|
||||
|
||||
|
||||
def store_backtest_signal_candles(
|
||||
recordfilename: Path, candles: Dict[str, Dict], dtappendix: str) -> Path:
|
||||
return _store_backtest_analysis_data(recordfilename, candles, dtappendix, "signals")
|
||||
|
||||
|
||||
def store_backtest_rejected_trades(
|
||||
recordfilename: Path, trades: Dict[str, Dict], dtappendix: str) -> Path:
|
||||
return _store_backtest_analysis_data(recordfilename, trades, dtappendix, "rejected")
|
||||
|
||||
|
||||
def _get_line_floatfmt(stake_currency: str) -> List[str]:
|
||||
"""
|
||||
Generate floatformat (goes in line with _generate_result_line())
|
||||
|
||||
@ -191,8 +191,18 @@ def test_backtest_analysis_nomock(default_conf, mocker, caplog, testdatadir, tmp
|
||||
assert '2.5' in captured.out
|
||||
|
||||
# test date filtering
|
||||
args = get_args(base_args + ['--timerange', "20180129-20180130"])
|
||||
args = get_args(base_args +
|
||||
['--analysis-groups', "0", "1", "2",
|
||||
'--timerange', "20180129-20180130"]
|
||||
)
|
||||
start_analysis_entries_exits(args)
|
||||
captured = capsys.readouterr()
|
||||
assert 'enter_tag_long_a' in captured.out
|
||||
assert 'enter_tag_long_b' not in captured.out
|
||||
|
||||
# test rejected - how to mock this?
|
||||
# args = get_args(base_args + ['--rejected'])
|
||||
# start_analysis_entries_exits(args)
|
||||
# captured = capsys.readouterr()
|
||||
# assert 'Rejected Trades:' in captured.out
|
||||
# assert False
|
||||
|
||||
Loading…
Reference in New Issue
Block a user