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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
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
### Example config file
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:
self.data = json.load(fp)
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.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]:
"""
Filter the training data and train a model to it. Train makes heavy use of the datahandler
for storing, saving, loading, and managed.
Filter the training data and train a model to it. Train makes heavy use of the datahkitchen
for storing, saving, loading, and analyzing the data.
:params:
:unfiltered_dataframe: Full dataframe for the current training period
:metadata: pair metadata from strategy.