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ensure proper integer type casting for timestamps. Add check test for backtesting subdaily time periods

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This commit is contained in:
Robert Caulk 2022-07-25 15:07:09 +02:00
parent 7b105532d1
commit 897f18a8c8
3 changed files with 32 additions and 5 deletions
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4
freqtrade/freqai/data_kitchen.py
View File

@ -909,10 +909,10 @@ class FreqaiDataKitchen:
coin, _ = pair.split("/") coin, _ = pair.split("/")
self.data_path = Path( self.data_path = Path(
self.full_path self.full_path
/ str("sub-train" + "-" + pair.split("/")[0] + "_" + str(int(trained_timerange.stopts))) / f"sub-train-{pair.split('/')[0]}_{int(trained_timerange.stopts)}"
) )
self.model_filename = "cb_" + coin.lower() + "_" + str(int(trained_timerange.stopts)) self.model_filename = f"cb_{coin.lower()}_{int(trained_timerange.stopts)}"
def download_all_data_for_training(self, timerange: TimeRange) -> None: def download_all_data_for_training(self, timerange: TimeRange) -> None:
""" """

5
freqtrade/freqai/freqai_interface.py
View File

@ -210,11 +210,12 @@ class IFreqaiModel(ABC):
) )
) )
if not self.model_exists( if not self.model_exists(
metadata["pair"], dk, trained_timestamp=trained_timestamp.stopts metadata["pair"], dk, trained_timestamp=int(trained_timestamp.stopts)
): ):
dk.find_features(dataframe_train) dk.find_features(dataframe_train)
self.model = self.train(dataframe_train, metadata["pair"], dk) self.model = self.train(dataframe_train, metadata["pair"], dk)
self.dd.pair_dict[metadata["pair"]]["trained_timestamp"] = trained_timestamp.stopts self.dd.pair_dict[metadata["pair"]]["trained_timestamp"] = int(
trained_timestamp.stopts)
dk.set_new_model_names(metadata["pair"], trained_timestamp) dk.set_new_model_names(metadata["pair"], trained_timestamp)
dk.save_data(self.model, metadata["pair"]) dk.save_data(self.model, metadata["pair"])
else: else:

28
tests/freqai/test_freqai_interface.py
View File

@ -90,7 +90,7 @@ def test_start_backtesting(mocker, freqai_conf):
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC") df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
metadata = {"pair": "ADA/BTC"} metadata = {"pair": "LTC/BTC"}
freqai.start_backtesting(df, metadata, freqai.dk) freqai.start_backtesting(df, metadata, freqai.dk)
model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()] model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
@ -99,6 +99,32 @@ def test_start_backtesting(mocker, freqai_conf):
shutil.rmtree(Path(freqai.dk.full_path)) shutil.rmtree(Path(freqai.dk.full_path))
def test_start_backtesting_subdaily_backtest_period(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180120-20180124"})
freqai_conf.get("freqai", {}).update({"backtest_period_days": 0.5})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = False
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dk.load_all_pair_histories(timerange)
sub_timerange = TimeRange.parse_timerange("20180110-20180130")
corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC")
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
metadata = {"pair": "LTC/BTC"}
freqai.start_backtesting(df, metadata, freqai.dk)
model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
pytest.set_trace()
assert len(model_folders) == 8
shutil.rmtree(Path(freqai.dk.full_path))
def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog): def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
freqai_conf.update({"timerange": "20180120-20180130"}) freqai_conf.update({"timerange": "20180120-20180130"})
strategy = get_patched_freqai_strategy(mocker, freqai_conf) strategy = get_patched_freqai_strategy(mocker, freqai_conf)