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add self-retraining functionality for live/dry

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This commit is contained in:
robcaulk
2022-05-09 15:25:00 +02:00
parent 178c2014b0
commit 22bd5556ed
7 changed files with 218 additions and 44 deletions
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58
freqtrade/freqai/freqai_interface.py
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@@ -8,9 +8,9 @@ import numpy.typing as npt
import pandas as pd
from pandas import DataFrame
from freqtrade.data.dataprovider import DataProvider
from freqtrade.enums import RunMode
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.strategy.interface import IStrategy
pd.options.mode.chained_assignment = None
@@ -33,15 +33,14 @@ 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.backtest_timerange = config["timerange"]
# self.backtest_timerange = config["timerange"]
self.time_last_trained = None
self.current_time = None
self.model = None
self.predictions = None
self.live_trained_timerange = None
def start(self, dataframe: DataFrame, metadata: dict, dp: DataProvider) -> DataFrame:
def start(self, dataframe: DataFrame, metadata: dict, strategy: IStrategy) -> DataFrame:
"""
Entry point to the FreqaiModel, it will train a new model if
necesssary before making the prediction.
@@ -57,11 +56,18 @@ class IFreqaiModel(ABC):
the model.
:metadata: pair metadataa coming from strategy.
"""
self.pair = metadata["pair"]
self.dh = FreqaiDataKitchen(self.config, dataframe)
if dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
logger.info('testing live')
live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
self.pair = metadata["pair"]
self.dh = FreqaiDataKitchen(self.config, dataframe, live)
if live:
# logger.info('testing live')
self.start_live(dataframe, metadata, strategy)
return (self.dh.full_predictions, self.dh.full_do_predict,
self.dh.full_target_mean, self.dh.full_target_std)
logger.info("going to train %s timeranges", len(self.dh.training_timeranges))
@@ -98,6 +104,42 @@ class IFreqaiModel(ABC):
return (self.dh.full_predictions, self.dh.full_do_predict,
self.dh.full_target_mean, self.dh.full_target_std)
def start_live(self, dataframe: DataFrame, metadata: dict, strategy: IStrategy) -> None:
self.dh.set_paths()
file_exists = self.model_exists(metadata['pair'],
training_timerange=self.freqai_info[
'live_trained_timerange'])
(retrain,
new_trained_timerange) = self.dh.check_if_new_training_required(self.freqai_info[
'live_trained_timerange'],
metadata)
if retrain or not file_exists:
self.dh.download_new_data_for_retraining(new_trained_timerange, metadata)
# dataframe = download-data
corr_dataframes, pair_dataframes = self.dh.load_pairs_histories(new_trained_timerange,
metadata)
unfiltered_dataframe = self.dh.use_strategy_to_populate_indicators(strategy,
metadata,
corr_dataframes,
pair_dataframes)
self.model = self.train(unfiltered_dataframe, metadata)
self.dh.save_data(self.model)
self.freqai_info
self.model = self.dh.load_data()
preds, do_preds = self.predict(dataframe)
self.dh.append_predictions(preds, do_preds, len(dataframe))
# dataframe should have len 1 here
return
def make_labels(self, dataframe: DataFrame) -> DataFrame:
"""
User defines the labels here (target values).