adelorenzo/stable
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

automatically detect maximum required data based on user fed indicators (to avoid NaNs in dataset for rolling indicators), add new config parameter for backtesting to let users increase their startup_candles to accommodate high timeframe indicators, add docs to explain all. Add new feature for automatic indicator duplication according to user defined intervals (exhibited in example strat and configs now).

Browse Source
This commit is contained in:
robcaulk
2022-05-31 18:42:27 +02:00
parent 9b3b08a2bb
commit 7523ed825e
7 changed files with 141 additions and 71 deletions
Download Patch File Download Diff File Expand all files Collapse all files

37
freqtrade/freqai/freqai_interface.py
View File

@@ -1,4 +1,5 @@
# import contextlib
import datetime
import gc
import logging
# import sys
@@ -149,8 +150,15 @@ class IFreqaiModel(ABC):
# self.training_timerange_timerange = tr_train
dataframe_train = dh.slice_dataframe(tr_train, dataframe)
dataframe_backtest = dh.slice_dataframe(tr_backtest, dataframe)
logger.info("training %s for %s", metadata["pair"], tr_train)
trained_timestamp = tr_train # TimeRange.parse_timerange(tr_train)
tr_train_startts_str = datetime.datetime.utcfromtimestamp(
tr_train.startts).strftime('%Y-%m-%d %H:%M:%S')
tr_train_stopts_str = datetime.datetime.utcfromtimestamp(
tr_train.stopts).strftime('%Y-%m-%d %H:%M:%S')
logger.info("Training %s", metadata["pair"])
logger.info(f'Training {tr_train_startts_str} to {tr_train_stopts_str}')
dh.data_path = Path(dh.full_path /
str("sub-train" + "-" + metadata['pair'].split("/")[0] +
str(int(trained_timestamp.stopts))))
@@ -218,16 +226,19 @@ class IFreqaiModel(ABC):
model_filename=model_filename)
(self.retrain,
new_trained_timerange) = dh.check_if_new_training_required(trained_timestamp)
new_trained_timerange,
data_load_timerange) = dh.check_if_new_training_required(trained_timestamp)
dh.set_paths(metadata, new_trained_timerange.stopts)
if self.retrain or not file_exists:
if coin_first:
self.train_model_in_series(new_trained_timerange, metadata, strategy, dh)
self.train_model_in_series(new_trained_timerange, metadata,
strategy, dh, data_load_timerange)
else:
self.training_on_separate_thread = True # acts like a lock
self.retrain_model_on_separate_thread(new_trained_timerange,
metadata, strategy, dh)
metadata, strategy,
dh, data_load_timerange)
elif self.training_on_separate_thread and not self.follow_mode:
logger.info("FreqAI training a new model on background thread.")
@@ -342,11 +353,12 @@ class IFreqaiModel(ABC):
@threaded
def retrain_model_on_separate_thread(self, new_trained_timerange: TimeRange, metadata: dict,
strategy: IStrategy, dh: FreqaiDataKitchen):
strategy: IStrategy, dh: FreqaiDataKitchen,
data_load_timerange: TimeRange):
# with nostdout():
dh.download_new_data_for_retraining(new_trained_timerange, metadata, strategy)
corr_dataframes, base_dataframes = dh.load_pairs_histories(new_trained_timerange,
dh.download_new_data_for_retraining(data_load_timerange, metadata, strategy)
corr_dataframes, base_dataframes = dh.load_pairs_histories(data_load_timerange,
metadata)
# protecting from common benign errors associated with grabbing new data from exchange:
@@ -355,6 +367,8 @@ class IFreqaiModel(ABC):
corr_dataframes,
base_dataframes,
metadata)
unfiltered_dataframe = dh.slice_dataframe(new_trained_timerange, unfiltered_dataframe)
except Exception:
logger.warning('Mismatched sizes encountered in strategy')
# self.data_drawer.pair_to_end_of_training_queue(metadata['pair'])
@@ -390,10 +404,11 @@ class IFreqaiModel(ABC):
return
def train_model_in_series(self, new_trained_timerange: TimeRange, metadata: dict,
strategy: IStrategy, dh: FreqaiDataKitchen):
strategy: IStrategy, dh: FreqaiDataKitchen,
data_load_timerange: TimeRange):
dh.download_new_data_for_retraining(new_trained_timerange, metadata, strategy)
corr_dataframes, base_dataframes = dh.load_pairs_histories(new_trained_timerange,
dh.download_new_data_for_retraining(data_load_timerange, metadata, strategy)
corr_dataframes, base_dataframes = dh.load_pairs_histories(data_load_timerange,
metadata)
unfiltered_dataframe = dh.use_strategy_to_populate_indicators(strategy,
@@ -401,6 +416,8 @@ class IFreqaiModel(ABC):
base_dataframes,
metadata)
unfiltered_dataframe = dh.slice_dataframe(new_trained_timerange, unfiltered_dataframe)
model = self.train(unfiltered_dataframe, metadata, dh)
self.data_drawer.pair_dict[metadata['pair']][