Merge branch 'develop' into fix/multioutput-bug
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commit
888ba65367
@ -79,8 +79,7 @@
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"test_size": 0.33,
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"random_state": 1
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},
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"model_training_parameters": {
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}
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"model_training_parameters": {}
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},
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"bot_name": "",
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"force_entry_enable": true,
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@ -26,10 +26,7 @@ FreqAI is configured through the typical [Freqtrade config file](configuration.m
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},
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"data_split_parameters" : {
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"test_size": 0.25
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},
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"model_training_parameters" : {
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"n_estimators": 100
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},
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}
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}
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```
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@ -355,6 +355,13 @@ def _validate_freqai_include_timeframes(conf: Dict[str, Any]) -> None:
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f"Main timeframe of {main_tf} must be smaller or equal to FreqAI "
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f"`include_timeframes`.Offending include-timeframes: {', '.join(offending_lines)}")
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# Ensure that the base timeframe is included in the include_timeframes list
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if main_tf not in freqai_include_timeframes:
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feature_parameters = conf.get('freqai', {}).get('feature_parameters', {})
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include_timeframes = [main_tf] + freqai_include_timeframes
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conf.get('freqai', {}).get('feature_parameters', {}) \
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.update({**feature_parameters, 'include_timeframes': include_timeframes})
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def _validate_freqai_backtest(conf: Dict[str, Any]) -> None:
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if conf.get('runmode', RunMode.OTHER) == RunMode.BACKTEST:
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@ -608,8 +608,7 @@ CONF_SCHEMA = {
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"backtest_period_days",
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"identifier",
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"feature_parameters",
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"data_split_parameters",
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"model_training_parameters"
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"data_split_parameters"
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]
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},
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},
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@ -61,7 +61,7 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
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model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
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tensorboard_log=Path(
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dk.full_path / "tensorboard" / dk.pair.split('/')[0]),
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**self.freqai_info['model_training_parameters']
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**self.freqai_info.get('model_training_parameters', {})
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)
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else:
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logger.info('Continual training activated - starting training from previously '
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@ -1046,8 +1046,13 @@ def test__validate_freqai_include_timeframes(default_conf, caplog) -> None:
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# Validation pass
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conf.update({'timeframe': '1m'})
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validate_config_consistency(conf)
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conf.update({'analyze_per_epoch': True})
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# Ensure base timeframe is in include_timeframes
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conf['freqai']['feature_parameters']['include_timeframes'] = ["5m", "15m"]
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validate_config_consistency(conf)
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assert conf['freqai']['feature_parameters']['include_timeframes'] == ["1m", "5m", "15m"]
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conf.update({'analyze_per_epoch': True})
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with pytest.raises(OperationalException,
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match=r"Using analyze-per-epoch .* not supported with a FreqAI strategy."):
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validate_config_consistency(conf)
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