Update docs, add test
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@ -22,23 +22,19 @@ DataFrame of the candles that resulted in buy signals. Depending on how many buy
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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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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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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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To analyze the entry/exit tags, we now need to use the `freqtrade analysis` command:
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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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freqtrade analysis -c <config.json> -s <strategy_name> --analysis_groups 0,1,2,3,4
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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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The `--analysis_groups` 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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@ -54,18 +50,18 @@ To show only certain buy and sell tags in the displayed output, use the followin
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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 --enter_reason_list "enter_tag_a,enter_tag_b" --exit_reason_list "roi,custom_exit_tag_a,stop_loss"
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freqtrade analysis -c <config.json> -s <strategy_name> --analysis_groups 0,1,2,3,4 --enter_reason_list "enter_tag_a,enter_tag_b" --exit_reason_list "roi,custom_exit_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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The real power of `freqtrade analysis` 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 --enter_reason_list "enter_tag_a,enter_tag_b" --exit_reason_list "roi,custom_exit_tag_a,stop_loss" --indicator_list "rsi,rsi_1h,bb_lowerband,ema_9,macd,macdsignal"
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freqtrade analysis -c <config.json> -s <strategy_name> --analysis_groups 0,1,2,3,4 --enter_reason_list "enter_tag_a,enter_tag_b" --exit_reason_list "roi,custom_exit_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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@ -52,8 +52,6 @@ def start_analysis_entries_exits(args: Dict[str, Any]) -> None:
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# Initialize configuration
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config = setup_analyze_configuration(args, RunMode.BACKTEST)
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print(config)
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logger.info('Starting freqtrade in analysis mode')
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process_entry_exit_reasons(Path(config['user_data_dir'], 'backtest_results'),
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@ -15,10 +15,18 @@ logger = logging.getLogger(__name__)
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def _load_signal_candles(backtest_dir: Path):
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if backtest_dir.is_dir():
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scpf = Path(backtest_dir,
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os.path.splitext(
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get_latest_backtest_filename(backtest_dir))[0] + "_signals.pkl"
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)
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else:
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scpf = Path(os.path.splitext(
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get_latest_backtest_filename(backtest_dir))[0] + "_signals.pkl"
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)
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print(scpf)
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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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@ -246,7 +254,6 @@ def process_entry_exit_reasons(backtest_dir: Path,
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signal_candles = _load_signal_candles(backtest_dir)
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analysed_trades_dict = _process_candles_and_indicators(pairlist, strategy_name,
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trades, signal_candles)
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_print_results(analysed_trades_dict,
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strategy_name,
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analysis_groups,
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94
tests/data/test_entryexitanalysis.py
Executable file
94
tests/data/test_entryexitanalysis.py
Executable file
@ -0,0 +1,94 @@
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from pathlib import Path
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from unittest.mock import MagicMock, PropertyMock
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import pandas as pd
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from freqtrade.commands.analyze_commands import start_analysis_entries_exits
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from freqtrade.commands.optimize_commands import start_backtesting
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from freqtrade.enums import ExitType
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from tests.conftest import get_args, patch_exchange, patched_configuration_load_config_file
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def test_backtest_analysis_nomock(default_conf, mocker, caplog, testdatadir, capsys):
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default_conf.update({
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"use_exit_signal": True,
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"exit_profit_only": False,
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"exit_profit_offset": 0.0,
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"ignore_roi_if_entry_signal": False,
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'analysis_groups': "0",
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'enter_reason_list': "all",
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'exit_reason_list': "all",
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'indicator_list': "bb_upperband,ema_10"
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})
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patch_exchange(mocker)
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result1 = pd.DataFrame({'pair': ['ETH/BTC', 'LTC/BTC'],
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'profit_ratio': [0.0, 0.0],
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'profit_abs': [0.0, 0.0],
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'open_date': pd.to_datetime(['2018-01-29 18:40:00',
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'2018-01-30 03:30:00', ], utc=True
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),
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'close_date': pd.to_datetime(['2018-01-29 20:45:00',
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'2018-01-30 05:35:00', ], utc=True),
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'trade_duration': [235, 40],
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'is_open': [False, False],
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'stake_amount': [0.01, 0.01],
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'open_rate': [0.104445, 0.10302485],
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'close_rate': [0.104969, 0.103541],
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"is_short": [False, False],
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'enter_tag': ["enter_tag_long", "enter_tag_long"],
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'exit_reason': [ExitType.ROI, ExitType.ROI]
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})
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backtestmock = MagicMock(side_effect=[
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{
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'results': result1,
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'config': default_conf,
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'locks': [],
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'rejected_signals': 20,
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'timedout_entry_orders': 0,
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'timedout_exit_orders': 0,
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'canceled_trade_entries': 0,
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'canceled_entry_orders': 0,
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'replaced_entry_orders': 0,
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'final_balance': 1000,
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}
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])
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mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist',
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PropertyMock(return_value=['ETH/BTC', 'LTC/BTC', 'DASH/BTC']))
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mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest', backtestmock)
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patched_configuration_load_config_file(mocker, default_conf)
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args = [
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'backtesting',
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'--config', 'config.json',
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'--datadir', str(testdatadir),
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'--strategy-path', str(Path(__file__).parents[1] / 'strategy/strats'),
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'--timeframe', '5m',
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'--timerange', '1515560100-1517287800',
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'--export', 'signals',
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'--cache', 'none',
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'--strategy-list',
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'StrategyTestV3',
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]
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args = get_args(args)
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start_backtesting(args)
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captured = capsys.readouterr()
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assert 'BACKTESTING REPORT' in captured.out
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assert 'EXIT REASON STATS' in captured.out
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assert 'LEFT OPEN TRADES REPORT' in captured.out
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args = [
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'analysis',
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'--config', 'config.json',
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'--datadir', str(testdatadir),
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'--analysis_groups', '0',
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'--strategy',
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'StrategyTestV3',
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]
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args = get_args(args)
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start_analysis_entries_exits(args)
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captured = capsys.readouterr()
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assert 'enter_tag_long' in captured.out
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@ -143,12 +143,13 @@ class StrategyTestV3(IStrategy):
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(dataframe['adx'] > 65) &
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(dataframe['plus_di'] > self.buy_plusdi.value)
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),
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'enter_long'] = 1
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['enter_long', 'enter_tag']] = 1, 'enter_tag_long'
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dataframe.loc[
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(
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qtpylib.crossed_below(dataframe['rsi'], self.sell_rsi.value)
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),
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'enter_short'] = 1
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['enter_short', 'enter_tag']] = 1, 'enter_tag_short'
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return dataframe
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@ -166,13 +167,13 @@ class StrategyTestV3(IStrategy):
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(dataframe['adx'] > 70) &
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(dataframe['minus_di'] > self.sell_minusdi.value)
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),
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'exit_long'] = 1
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['exit_long', 'exit_tag']] = 1, 'exit_tag_long'
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dataframe.loc[
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(
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qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value)
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),
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'exit_short'] = 1
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['exit_long', 'exit_tag']] = 1, 'exit_tag_short'
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return dataframe
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