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Improve FreqAI documentation (#7072)

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Improve doc + some other small fixes

Co-authored-by: robcaulk <rob.caulk@gmail.com>
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lolong 2022-07-18 11:57:52 +02:00 committed by GitHub
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13
docs/freqai.md
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@ -408,12 +408,9 @@ It is common to want constant retraining, in whichcase, user should set `live_re
### Controlling the model learning process ### Controlling the model learning process
The user can define model settings for the data split `data_split_parameters` and learning parameters Model training parameters are unqiue to the library employed by the user. FreqAI allows users to set any parameter for any library using the `model_training_parameters` dictionary in the user configuration file. The example configuration files show some of the example parameters associated with `Catboost` and `LightGBM`, but users can add any parameters available in those libraries.
`model_training_parameters`. Users are encouraged to visit the Catboost documentation
for more information on how to select these values. `n_estimators` increases the Data split parameters are defined in `data_split_parameters` which can be any parameters associated with `Sklearn`'s `train_test_split()` function. Meanwhile, FreqAI includes some additional parameters such `weight_factor` which allows the user to weight more recent data more strongly
computational effort and the fit to the training data. If a user has a GPU
installed in their system, they may benefit from changing `task_type` to `GPU`.
The `weight_factor` allows the user to weight more recent data more strongly
than past data via an exponential function: than past data via an exponential function:
$$ W_i = \exp(\frac{-i}{\alpha*n}) $$ $$ W_i = \exp(\frac{-i}{\alpha*n}) $$
@ -422,7 +419,9 @@ where $W_i$ is the weight of data point $i$ in a total set of $n$ data points._
![weight-factor](assets/weights_factor.png) ![weight-factor](assets/weights_factor.png)
Finally, `period` defines the offset used for the `labels`. In the present example, `train_test_split()` has a parameters called `shuffle`, which users also have access to in FreqAI, that allows them to keep the data unshuffled. This is particularly useful to avoid biasing training with temporally autocorrelated data.
Finally, `label_period_candles` defines the offset used for the `labels`. In the present example,
the user is asking for `labels` that are 24 candles in the future. the user is asking for `labels` that are 24 candles in the future.
### Removing outliers with the Dissimilarity Index ### Removing outliers with the Dissimilarity Index

2
freqtrade/freqai/data_kitchen.py
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@ -799,7 +799,7 @@ class FreqaiDataKitchen:
max_timeframe_chars = self.freqai_config.get( max_timeframe_chars = self.freqai_config.get(
"feature_parameters", {}).get("include_timeframes")[-1] "feature_parameters", {}).get("include_timeframes")[-1]
max_period = self.freqai_config.get("feature_parameters", {}).get( max_period = self.freqai_config.get("feature_parameters", {}).get(
"indicator_max_period", 50 "indicator_max_period_candles", 50
) )
additional_seconds = 0 additional_seconds = 0
if max_timeframe_chars[-1] == "d": if max_timeframe_chars[-1] == "d":

6
freqtrade/freqai/prediction_models/CatboostPredictionMultiModel.py
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@ -35,10 +35,12 @@ class CatboostPredictionMultiModel(BaseRegressionModel):
X = data_dictionary["train_features"] X = data_dictionary["train_features"]
y = data_dictionary["train_labels"] y = data_dictionary["train_labels"]
# eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"]) eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"])
sample_weight = data_dictionary["train_weights"] sample_weight = data_dictionary["train_weights"]
model = MultiOutputRegressor(estimator=cbr) model = MultiOutputRegressor(estimator=cbr)
model.fit(X=X, y=y, sample_weight=sample_weight) # , eval_set=eval_set) model.fit(X=X, y=y, sample_weight=sample_weight) # , eval_set=eval_set)
train_score = model.score(X, y)
test_score = model.score(*eval_set)
logger.info(f"Train score {train_score}, Test score {test_score}")
return model return model