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starting backtest live models

This commit is contained in:
Wagner Costa Santos 2022-09-24 12:28:52 -03:00
parent 2cc00a1a2c
commit 3ee7eb63f7
2 changed files with 34 additions and 0 deletions
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33
freqtrade/freqai/data_kitchen.py
View File

@ -1283,3 +1283,36 @@ class FreqaiDataKitchen:
f"Could not find backtesting prediction file at {path_to_predictionfile}"
)
return file_exists
def get_timerange_from_ready_models(self):
return self.gen_get_timerange_from_ready_models(self.full_path)
def gen_get_timerange_from_ready_models(self, models_path: Path):
all_models_end_dates = []
pairs_end_dates: Dict[str, Any] = {}
for model_dir in models_path.iterdir():
if str(model_dir.name).startswith("sub-train"):
model_end_date = model_dir.name.split("_")[1]
pair = model_dir.name.split("_")[0].replace("sub-train-", "")
model_file_name = f"cb\
_{str(model_dir.name).replace('sub-train-', '').lower()}_model.joblib"
model_path_file = Path(model_dir / model_file_name)
if model_path_file.is_file():
if pair not in pairs_end_dates:
pairs_end_dates[pair] = []
pairs_end_dates[pair].append({
"model_end_date": int(model_end_date),
"model_path_file": model_path_file,
"model_dir": model_dir
})
if model_end_date not in all_models_end_dates:
all_models_end_dates.append(int(model_end_date))
start = datetime.fromtimestamp(min(all_models_end_dates), tz=timezone.utc)
stop = datetime.fromtimestamp(max(all_models_end_dates), tz=timezone.utc)
backtesting_string_timerange = f"{start.strftime('%Y%m%d')}-{stop.strftime('%Y%m%d')}"
backtesting_timerange = TimeRange('date', 'date', min(all_models_end_dates),
max(all_models_end_dates))
return backtesting_timerange, backtesting_string_timerange, pairs_end_dates

1
freqtrade/freqai/freqai_interface.py
View File

@ -134,6 +134,7 @@ class IFreqaiModel(ABC):
# the concatenated results for the full backtesting period back to the strategy.
elif not self.follow_mode:
self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
self.dk.get_timerange_from_ready_models()
logger.info(f"Training {len(self.dk.training_timeranges)} timeranges")
dataframe = self.dk.use_strategy_to_populate_indicators(
strategy, prediction_dataframe=dataframe, pair=metadata["pair"]