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Make model_training_parameters optional

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Emre 2022-12-05 14:54:42 +03:00
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commit e734b39929
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4 changed files with 8 additions and 14 deletions
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4
config_examples/config_freqai.example.json
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@ -79,9 +79,7 @@
"test_size": 0.33,
"random_state": 1
},
"model_training_parameters": {
"n_estimators": 1000
}
"model_training_parameters": {}
},
"bot_name": "",
"force_entry_enable": true,

11
docs/freqai-configuration.md
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@ -26,10 +26,7 @@ FreqAI is configured through the typical [Freqtrade config file](configuration.m
},
"data_split_parameters" : {
"test_size": 0.25
},
"model_training_parameters" : {
"n_estimators": 100
},
}
}
```
@ -118,7 +115,7 @@ The FreqAI strategy requires including the following lines of code in the standa
```
Notice how the `populate_any_indicators()` is where [features](freqai-feature-engineering.md#feature-engineering) and labels/targets are added. A full example strategy is available in `templates/FreqaiExampleStrategy.py`.
Notice how the `populate_any_indicators()` is where [features](freqai-feature-engineering.md#feature-engineering) and labels/targets are added. A full example strategy is available in `templates/FreqaiExampleStrategy.py`.
Notice also the location of the labels under `if set_generalized_indicators:` at the bottom of the example. This is where single features and labels/targets should be added to the feature set to avoid duplication of them from various configuration parameters that multiply the feature set, such as `include_timeframes`.
@ -182,7 +179,7 @@ The `startup_candle_count` in the FreqAI strategy needs to be set up in the same
## Creating a dynamic target threshold
Deciding when to enter or exit a trade can be done in a dynamic way to reflect current market conditions. FreqAI allows you to return additional information from the training of a model (more info [here](freqai-feature-engineering.md#returning-additional-info-from-training)). For example, the `&*_std/mean` return values describe the statistical distribution of the target/label *during the most recent training*. Comparing a given prediction to these values allows you to know the rarity of the prediction. In `templates/FreqaiExampleStrategy.py`, the `target_roi` and `sell_roi` are defined to be 1.25 z-scores away from the mean which causes predictions that are closer to the mean to be filtered out.
Deciding when to enter or exit a trade can be done in a dynamic way to reflect current market conditions. FreqAI allows you to return additional information from the training of a model (more info [here](freqai-feature-engineering.md#returning-additional-info-from-training)). For example, the `&*_std/mean` return values describe the statistical distribution of the target/label *during the most recent training*. Comparing a given prediction to these values allows you to know the rarity of the prediction. In `templates/FreqaiExampleStrategy.py`, the `target_roi` and `sell_roi` are defined to be 1.25 z-scores away from the mean which causes predictions that are closer to the mean to be filtered out.
```python
dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25
@ -230,7 +227,7 @@ If you want to predict multiple targets, you need to define multiple labels usin
#### Classifiers
If you are using a classifier, you need to specify a target that has discrete values. FreqAI includes a variety of classifiers, such as the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. If you elects to use a classifier, the classes need to be set using strings. For example, if you want to predict if the price 100 candles into the future goes up or down you would set
If you are using a classifier, you need to specify a target that has discrete values. FreqAI includes a variety of classifiers, such as the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. If you elects to use a classifier, the classes need to be set using strings. For example, if you want to predict if the price 100 candles into the future goes up or down you would set
```python
df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down')

5
freqtrade/constants.py
View File

@ -608,9 +608,8 @@ CONF_SCHEMA = {
"backtest_period_days",
"identifier",
"feature_parameters",
"data_split_parameters",
"model_training_parameters"
]
"data_split_parameters"
]
},
},
}

2
freqtrade/freqai/prediction_models/ReinforcementLearner.py
View File

@ -61,7 +61,7 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
tensorboard_log=Path(
dk.full_path / "tensorboard" / dk.pair.split('/')[0]),
**self.freqai_info['model_training_parameters']
**self.freqai_info.get('model_training_parameters', {})
)
else:
logger.info('Continual training activated - starting training from previously '