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auto build full_timerange and self manage training_timerange

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robcaulk
2022-05-05 15:35:51 +02:00
parent 764f9449b4
commit def71a0afe
4 changed files with 38 additions and 27 deletions
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19
freqtrade/freqai/freqai_interface.py
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@@ -1,6 +1,5 @@
import gc
import logging
import shutil
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, Tuple
@@ -32,24 +31,13 @@ class IFreqaiModel(ABC):
self.data_split_parameters = config["freqai"]["data_split_parameters"]
self.model_training_parameters = config["freqai"]["model_training_parameters"]
self.feature_parameters = config["freqai"]["feature_parameters"]
self.full_path = Path(
config["user_data_dir"]
/ "models"
/ str(self.freqai_info["full_timerange"] + self.freqai_info["identifier"])
)
self.backtest_timerange = config["timerange"]
self.time_last_trained = None
self.current_time = None
self.model = None
self.predictions = None
if not self.full_path.is_dir():
self.full_path.mkdir(parents=True, exist_ok=True)
shutil.copy(
self.config["config_files"][0],
Path(self.full_path / self.config["config_files"][0]),
)
def start(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Entry point to the FreqaiModel, it will train a new model if
@@ -82,12 +70,11 @@ class IFreqaiModel(ABC):
gc.collect()
# self.config['timerange'] = tr_train
self.dh.data = {} # clean the pair specific data between models
self.freqai_info["training_timerange"] = tr_train
self.training_timerange = tr_train
dataframe_train = self.dh.slice_dataframe(tr_train, dataframe)
dataframe_backtest = self.dh.slice_dataframe(tr_backtest, dataframe)
logger.info("training %s for %s", self.pair, tr_train)
# self.dh.model_path = self.full_path + "/" + "sub-train" + "-" + str(tr_train) + "/"
self.dh.model_path = Path(self.full_path / str("sub-train" + "-" + str(tr_train)))
self.dh.model_path = Path(self.dh.full_path / str("sub-train" + "-" + str(tr_train)))
if not self.model_exists(self.pair, training_timerange=tr_train):
self.model = self.train(dataframe_train, metadata)
self.dh.save_data(self.model)