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give beta testers more information in the doc

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This commit is contained in:
robcaulk 2022-05-15 14:01:53 +02:00
parent a7029e35b5
commit a8022c104a
3 changed files with 19 additions and 4 deletions
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17
docs/freqai.md
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@ -41,6 +41,23 @@ in the model.
intermediate performance of the model during training. This data does not intermediate performance of the model during training. This data does not
directly influence nodal weights within the model. directly influence nodal weights within the model.
## Install prerequisites
Use `pip` to install the prerequisities with:
`pip install -r requirements-freqai.txt`
## Running from the example files
An example strategy, example prediction model, and example config can all be found in
`freqtrade/templates/ExampleFreqaiStrategy.py`, `freqtrade/templates/ExamplePredictionModel.py`,
`config_examples/config_freqai.example.json`, respectively. Assuming the user has downloaded
the necessary data, Freqai can be executed from these templates with:
`freqtrade backtesting --config config_examples/config_freqai.example.json--strategy
ExampleFreqaiStrategy --freqaimodel ExamplePredictionModel
--freqaimodel-path freqtrade/templates --strategy-path freqtrade/templates`
## Configuring the bot ## Configuring the bot
### Example config file ### Example config file
The user interface is isolated to the typical config file. A typical Freqai The user interface is isolated to the typical config file. A typical Freqai

2
freqtrade/freqai/data_kitchen.py
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@ -113,8 +113,6 @@ class FreqaiDataKitchen:
with open(self.model_path / str(self.model_filename + "_metadata.json"), "r") as fp: with open(self.model_path / str(self.model_filename + "_metadata.json"), "r") as fp:
self.data = json.load(fp) self.data = json.load(fp)
self.training_features_list = self.data["training_features_list"] self.training_features_list = self.data["training_features_list"]
# if self.data.get("training_features_list"):
# self.training_features_list = [*self.data.get("training_features_list")]
self.data_dictionary["train_features"] = pd.read_pickle( self.data_dictionary["train_features"] = pd.read_pickle(
self.model_path / str(self.model_filename + "_trained_df.pkl") self.model_path / str(self.model_filename + "_trained_df.pkl")

4
freqtrade/templates/ExamplePredictionModel.py
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@ -42,8 +42,8 @@ class ExamplePredictionModel(IFreqaiModel):
def train(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Tuple[DataFrame, DataFrame]: def train(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Tuple[DataFrame, DataFrame]:
""" """
Filter the training data and train a model to it. Train makes heavy use of the datahandler Filter the training data and train a model to it. Train makes heavy use of the datahkitchen
for storing, saving, loading, and managed. for storing, saving, loading, and analyzing the data.
:params: :params:
:unfiltered_dataframe: Full dataframe for the current training period :unfiltered_dataframe: Full dataframe for the current training period
:metadata: pair metadata from strategy. :metadata: pair metadata from strategy.