add config asserts, use .get method with default values for optional functionality, move data_cleaning_* to freqai_interface (away from user custom pred model) since it is controlled by config params.
This commit is contained in:
robcaulk
parent
dede128648
commit
e1c068ca66
@ -43,6 +43,7 @@ class FreqaiDataKitchen:
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self.data: Dict[Any, Any] = {}
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self.data_dictionary: Dict[Any, Any] = {}
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self.config = config
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self.assert_config(self.config, live)
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self.freqai_config = config["freqai"]
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self.predictions: npt.ArrayLike = np.array([])
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self.do_predict: npt.ArrayLike = np.array([])
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@ -59,7 +60,7 @@ class FreqaiDataKitchen:
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self.svm_model: linear_model.SGDOneClassSVM = None
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if not self.live:
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self.full_timerange = self.create_fulltimerange(self.config["timerange"],
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self.freqai_config["train_period"]
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self.freqai_config.get("train_period")
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)
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(self.training_timeranges, self.backtesting_timeranges) = self.split_timerange(
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@ -68,14 +69,33 @@ class FreqaiDataKitchen:
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config["freqai"]["backtest_period"],
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)
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def assert_config(self, config: Dict[str, Any], live: bool) -> None:
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assert config.get('freqai'), "No Freqai parameters found in config file."
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assert config.get('freqai', {}).get('train_period'), ("No Freqai train_period found in"
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"config file.")
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assert type(config.get('freqai', {})
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.get('train_period')) is int, ('Can only train on full day period.'
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'No fractional days permitted.')
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assert config.get('freqai', {}).get('backtest_period'), ("No Freqai backtest_period found"
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"in config file.")
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if not live:
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assert type(config.get('freqai', {})
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.get('backtest_period')) is int, ('Can only backtest on full day'
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'backtest_period. Only live/dry mode'
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'allows fractions of days')
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assert config.get('freqai', {}).get('identifier'), ("No Freqai identifier found in config"
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"file.")
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assert config.get('freqai', {}).get('feature_parameters'), ("No Freqai feature_parameters"
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"found in config file.")
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def set_paths(self) -> None:
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self.full_path = Path(self.config['user_data_dir'] /
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"models" /
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str(self.freqai_config['live_full_backtestrange'] +
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self.freqai_config['identifier']))
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str(self.freqai_config.get('live_full_backtestrange') +
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self.freqai_config.get('identifier')))
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self.model_path = Path(self.full_path / str("sub-train" + "-" +
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str(self.freqai_config['live_trained_timerange'])))
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str(self.freqai_config.get('live_trained_timerange'))))
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return
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@ -117,7 +137,7 @@ class FreqaiDataKitchen:
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# do not want them having to edit the default save/load methods here. Below is an example
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# of what we do NOT want.
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# if self.freqai_config['feature_parameters']['determine_statistical_distributions']:
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# if self.freqai_config.get('feature_parameters','determine_statistical_distributions'):
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# self.data_dictionary["upper_quantiles"].to_pickle(
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# save_path / str(self.model_filename + "_upper_quantiles.pkl")
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# )
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@ -147,7 +167,7 @@ class FreqaiDataKitchen:
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# do not want them having to edit the default save/load methods here. Below is an example
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# of what we do NOT want.
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# if self.freqai_config['feature_parameters']['determine_statistical_distributions']:
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# if self.freqai_config.get('feature_parameters','determine_statistical_distributions'):
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# self.data_dictionary["upper_quantiles"] = pd.read_pickle(
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# self.model_path / str(self.model_filename + "_upper_quantiles.pkl")
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# )
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@ -193,15 +213,15 @@ class FreqaiDataKitchen:
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"""
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weights: npt.ArrayLike
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if self.config["freqai"]["feature_parameters"]["weight_factor"] > 0:
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if self.freqai_config["feature_parameters"].get("weight_factor", 0) > 0:
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weights = self.set_weights_higher_recent(len(filtered_dataframe))
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else:
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weights = np.ones(len(filtered_dataframe))
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if self.config["freqai"]["feature_parameters"]["stratify"] > 0:
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if self.freqai_config["feature_parameters"].get("stratify", 0) > 0:
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stratification = np.zeros(len(filtered_dataframe))
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for i in range(1, len(stratification)):
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if i % self.config["freqai"]["feature_parameters"]["stratify"] == 0:
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if i % self.freqai_config.get("feature_parameters", {}).get("stratify", 0) == 0:
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stratification[i] = 1
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(
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@ -525,6 +545,14 @@ class FreqaiDataKitchen:
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return None
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def pca_transform(self, filtered_dataframe: DataFrame) -> None:
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pca_components = self.pca.transform(filtered_dataframe)
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self.data_dictionary["prediction_features"] = pd.DataFrame(
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data=pca_components,
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columns=["PC" + str(i) for i in range(0, self.data["n_kept_components"])],
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index=filtered_dataframe.index,
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)
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def compute_distances(self) -> float:
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logger.info("computing average mean distance for all training points")
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pairwise = pairwise_distances(self.data_dictionary["train_features"], n_jobs=-1)
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@ -675,7 +703,7 @@ class FreqaiDataKitchen:
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self.full_path = Path(
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self.config["user_data_dir"]
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/ "models"
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/ str(full_timerange + self.freqai_config["identifier"])
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/ str(full_timerange + self.freqai_config.get("identifier"))
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)
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config_path = Path(self.config["config_files"][0])
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@ -696,13 +724,15 @@ class FreqaiDataKitchen:
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if trained_timerange.startts != 0:
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elapsed_time = (time - trained_timerange.stopts) / SECONDS_IN_DAY
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retrain = elapsed_time > self.freqai_config['backtest_period']
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retrain = elapsed_time > self.freqai_config.get('backtest_period')
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if retrain:
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trained_timerange.startts += self.freqai_config['backtest_period'] * SECONDS_IN_DAY
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trained_timerange.stopts += self.freqai_config['backtest_period'] * SECONDS_IN_DAY
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trained_timerange.startts += self.freqai_config.get(
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'backtest_period', 0) * SECONDS_IN_DAY
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trained_timerange.stopts += self.freqai_config.get(
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'backtest_period', 0) * SECONDS_IN_DAY
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else: # user passed no live_trained_timerange in config
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trained_timerange = TimeRange()
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trained_timerange.startts = int(time - self.freqai_config['train_period'] *
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trained_timerange.startts = int(time - self.freqai_config.get('train_period') *
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SECONDS_IN_DAY)
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trained_timerange.stopts = int(time)
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retrain = True
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@ -725,13 +755,13 @@ class FreqaiDataKitchen:
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exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'],
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self.config, validate=False)
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pairs = self.freqai_config['corr_pairlist']
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pairs = self.freqai_config.get('corr_pairlist', [])
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if metadata['pair'] not in pairs:
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pairs += metadata['pair'] # dont include pair twice
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# timerange = TimeRange.parse_timerange(new_timerange)
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refresh_backtest_ohlcv_data(
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exchange, pairs=pairs, timeframes=self.freqai_config['timeframes'],
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exchange, pairs=pairs, timeframes=self.freqai_config.get('timeframes'),
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datadir=self.config['datadir'], timerange=timerange,
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new_pairs_days=self.config['new_pairs_days'],
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erase=False, data_format=self.config['dataformat_ohlcv'],
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@ -743,13 +773,14 @@ class FreqaiDataKitchen:
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DataFrame]:
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corr_dataframes: Dict[Any, Any] = {}
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base_dataframes: Dict[Any, Any] = {}
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pairs = self.freqai_config['corr_pairlist'] # + [metadata['pair']]
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pairs = self.freqai_config.get('corr_pairlist', []) # + [metadata['pair']]
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# timerange = TimeRange.parse_timerange(new_timerange)
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for tf in self.freqai_config['timeframes']:
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for tf in self.freqai_config.get('timeframes'):
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base_dataframes[tf] = load_pair_history(datadir=self.config['datadir'],
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timeframe=tf,
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pair=metadata['pair'], timerange=timerange)
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if pairs:
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for p in pairs:
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if metadata['pair'] in p:
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continue # dont repeat anything from whitelist
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@ -767,15 +798,17 @@ class FreqaiDataKitchen:
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metadata: dict) -> DataFrame:
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dataframe = base_dataframes[self.config['timeframe']]
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pairs = self.freqai_config.get("corr_pairlist", [])
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for tf in self.freqai_config["timeframes"]:
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for tf in self.freqai_config.get("timeframes"):
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dataframe = strategy.populate_any_indicators(metadata['pair'],
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dataframe.copy(),
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tf,
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base_dataframes[tf],
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coin=metadata['pair'].split("/")[0] + "-"
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)
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for i in self.freqai_config["corr_pairlist"]:
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if pairs:
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for i in pairs:
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if metadata['pair'] in i:
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continue # dont repeat anything from whitelist
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dataframe = strategy.populate_any_indicators(i,
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@ -20,7 +20,7 @@ from freqtrade.strategy.interface import IStrategy
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pd.options.mode.chained_assignment = None
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logger = logging.getLogger(__name__)
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# FIXME: suppress stdout for background training
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# FIXME: suppress stdout for background training?
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# class DummyFile(object):
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# def write(self, x): pass
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@ -51,6 +51,7 @@ class IFreqaiModel(ABC):
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def __init__(self, config: Dict[str, Any]) -> None:
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self.config = config
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self.assert_config(self.config)
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self.freqai_info = config["freqai"]
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self.data_split_parameters = config["freqai"]["data_split_parameters"]
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self.model_training_parameters = config["freqai"]["model_training_parameters"]
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@ -64,12 +65,25 @@ class IFreqaiModel(ABC):
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self.training_on_separate_thread = False
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self.retrain = False
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self.first = True
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if self.freqai_info['live_trained_timerange']:
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if self.freqai_info.get('live_trained_timerange'):
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self.new_trained_timerange = TimeRange.parse_timerange(
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self.freqai_info['live_trained_timerange'])
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else:
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self.new_trained_timerange = TimeRange()
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def assert_config(self, config: Dict[str, Any]) -> None:
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assert config.get('freqai'), "No Freqai parameters found in config file."
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assert config.get('freqai', {}).get('data_split_parameters'), ("No Freqai"
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"data_split_parameters"
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"in config file.")
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assert config.get('freqai', {}).get('model_training_parameters'), ("No Freqai"
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"modeltrainingparameters"
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"found in config file.")
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assert config.get('freqai', {}).get('feature_parameters'), ("No Freqai"
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"feature_parameters found in"
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"config file.")
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def start(self, dataframe: DataFrame, metadata: dict, strategy: IStrategy) -> DataFrame:
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"""
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Entry point to the FreqaiModel, it will train a new model if
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@ -192,55 +206,30 @@ class IFreqaiModel(ABC):
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return
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@abstractmethod
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def train(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Any:
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"""
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Filter the training data and train a model to it. Train makes heavy use of the datahandler
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for storing, saving, loading, and analyzing the data.
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:params:
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:unfiltered_dataframe: Full dataframe for the current training period
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:metadata: pair metadata from strategy.
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:returns:
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:model: Trained model which can be used to inference (self.predict)
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"""
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@abstractmethod
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def fit(self) -> Any:
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"""
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Most regressors use the same function names and arguments e.g. user
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can drop in LGBMRegressor in place of CatBoostRegressor and all data
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management will be properly handled by Freqai.
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:params:
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:data_dictionary: the dictionary constructed by DataHandler to hold
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all the training and test data/labels.
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"""
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return
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@abstractmethod
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def predict(self, dataframe: DataFrame, metadata: dict) -> Tuple[npt.ArrayLike, npt.ArrayLike]:
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"""
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Filter the prediction features data and predict with it.
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:param: unfiltered_dataframe: Full dataframe for the current backtest period.
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:return:
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:predictions: np.array of predictions
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:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
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data (NaNs) or felt uncertain about data (PCA and DI index)
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"""
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@abstractmethod
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def data_cleaning_train(self) -> None:
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"""
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User can add data analysis and cleaning here.
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Base data cleaning method for train
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Any function inside this method should drop training data points from the filtered_dataframe
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based on user decided logic. See FreqaiDataKitchen::remove_outliers() for an example
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of how outlier data points are dropped from the dataframe used for training.
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"""
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if self.freqai_info.get('feature_parameters', {}).get('principal_component_analysis'):
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self.dh.principal_component_analysis()
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@abstractmethod
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def data_cleaning_predict(self) -> None:
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# if self.feature_parameters["determine_statistical_distributions"]:
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# self.dh.determine_statistical_distributions()
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# if self.feature_parameters["remove_outliers"]:
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# self.dh.remove_outliers(predict=False)
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if self.freqai_info.get('feature_parameters', {}).get('use_SVM_to_remove_outliers'):
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self.dh.use_SVM_to_remove_outliers(predict=False)
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if self.freqai_info.get('feature_parameters', {}).get('DI_threshold'):
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self.dh.data["avg_mean_dist"] = self.dh.compute_distances()
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def data_cleaning_predict(self, filtered_dataframe: DataFrame) -> None:
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"""
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User can add data analysis and cleaning here.
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Base data cleaning method for predict.
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These functions each modify self.dh.do_predict, which is a dataframe with equal length
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to the number of candles coming from and returning to the strategy. Inside do_predict,
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1 allows prediction and < 0 signals to the strategy that the model is not confident in
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@ -249,6 +238,19 @@ class IFreqaiModel(ABC):
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of how the do_predict vector is modified. do_predict is ultimately passed back to strategy
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for buy signals.
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"""
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if self.freqai_info.get('feature_parameters', {}).get('principal_component_analysis'):
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self.dh.pca_transform()
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# if self.feature_parameters["determine_statistical_distributions"]:
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# self.dh.determine_statistical_distributions()
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# if self.feature_parameters["remove_outliers"]:
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# self.dh.remove_outliers(predict=True) # creates dropped index
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if self.freqai_info.get('feature_parameters', {}).get('use_SVM_to_remove_outliers'):
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self.dh.use_SVM_to_remove_outliers(predict=True)
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if self.freqai_info.get('feature_parameters', {}).get('DI_threshold'):
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self.dh.check_if_pred_in_training_spaces() # sets do_predict
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def model_exists(self, pair: str, training_timerange: str) -> bool:
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"""
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@ -303,3 +305,42 @@ class IFreqaiModel(ABC):
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self.model = self.train(unfiltered_dataframe, metadata)
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self.dh.save_data(self.model)
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self.retrain = False
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# Methods which are overridden by user made prediction models.
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# See freqai/prediction_models/CatboostPredictionModlel.py for an example.
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@abstractmethod
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def train(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Any:
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"""
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Filter the training data and train a model to it. Train makes heavy use of the datahandler
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for storing, saving, loading, and analyzing the data.
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:params:
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:unfiltered_dataframe: Full dataframe for the current training period
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:metadata: pair metadata from strategy.
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:returns:
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:model: Trained model which can be used to inference (self.predict)
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"""
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@abstractmethod
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def fit(self) -> Any:
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"""
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Most regressors use the same function names and arguments e.g. user
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can drop in LGBMRegressor in place of CatBoostRegressor and all data
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management will be properly handled by Freqai.
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:params:
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:data_dictionary: the dictionary constructed by DataHandler to hold
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all the training and test data/labels.
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"""
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return
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@abstractmethod
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def predict(self, dataframe: DataFrame, metadata: dict) -> Tuple[npt.ArrayLike, npt.ArrayLike]:
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"""
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Filter the prediction features data and predict with it.
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:param: unfiltered_dataframe: Full dataframe for the current backtest period.
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:return:
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:predictions: np.array of predictions
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:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
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data (NaNs) or felt uncertain about data (PCA and DI index)
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"""
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@ -1,7 +1,6 @@
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import logging
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from typing import Any, Dict, Tuple
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import pandas as pd
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from catboost import CatBoostRegressor, Pool
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from pandas import DataFrame
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@ -149,7 +148,7 @@ class CatboostPredictionModel(IFreqaiModel):
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based on user decided logic. See FreqaiDataKitchen::remove_outliers() for an example
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of how outlier data points are dropped from the dataframe used for training.
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"""
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if self.feature_parameters["principal_component_analysis"]:
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if self.freqai_info.get('feature_parameters', {}).get('principal_component_analysis'):
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self.dh.principal_component_analysis()
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# if self.feature_parameters["determine_statistical_distributions"]:
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@ -157,9 +156,10 @@ class CatboostPredictionModel(IFreqaiModel):
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# if self.feature_parameters["remove_outliers"]:
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# self.dh.remove_outliers(predict=False)
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if self.feature_parameters["use_SVM_to_remove_outliers"]:
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if self.freqai_info.get('feature_parameters', {}).get('use_SVM_to_remove_outliers'):
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self.dh.use_SVM_to_remove_outliers(predict=False)
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if self.feature_parameters["DI_threshold"]:
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if self.freqai_info.get('feature_parameters', {}).get('DI_threshold'):
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self.dh.data["avg_mean_dist"] = self.dh.compute_distances()
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def data_cleaning_predict(self, filtered_dataframe: DataFrame) -> None:
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@ -173,21 +173,16 @@ class CatboostPredictionModel(IFreqaiModel):
|
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of how the do_predict vector is modified. do_predict is ultimately passed back to strategy
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for buy signals.
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"""
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if self.feature_parameters["principal_component_analysis"]:
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pca_components = self.dh.pca.transform(filtered_dataframe)
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self.dh.data_dictionary["prediction_features"] = pd.DataFrame(
|
||||
data=pca_components,
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columns=["PC" + str(i) for i in range(0, self.dh.data["n_kept_components"])],
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||||
index=filtered_dataframe.index,
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||||
)
|
||||
if self.freqai_info.get('feature_parameters', {}).get('principal_component_analysis'):
|
||||
self.dh.pca_transform()
|
||||
|
||||
# if self.feature_parameters["determine_statistical_distributions"]:
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||||
# self.dh.determine_statistical_distributions()
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||||
# if self.feature_parameters["remove_outliers"]:
|
||||
# self.dh.remove_outliers(predict=True) # creates dropped index
|
||||
|
||||
if self.feature_parameters["use_SVM_to_remove_outliers"]:
|
||||
if self.freqai_info.get('feature_parameters', {}).get('use_SVM_to_remove_outliers'):
|
||||
self.dh.use_SVM_to_remove_outliers(predict=True)
|
||||
|
||||
if self.feature_parameters["DI_threshold"]:
|
||||
if self.freqai_info.get('feature_parameters', {}).get('DI_threshold'):
|
||||
self.dh.check_if_pred_in_training_spaces() # sets do_predict
|
||||
|
||||
@ -207,7 +207,7 @@ class Backtesting:
|
||||
if self.config.get('freqai') is not None:
|
||||
self.required_startup += int((self.config.get('freqai', {}).get('train_period') *
|
||||
86400) / timeframe_to_seconds(self.config['timeframe']))
|
||||
logger.info("Increasing startup_candle_count for freqai to %s", self.required_startup)
|
||||
logger.info(f'Increasing startup_candle_count for freqai to {self.required_startup}')
|
||||
self.config['startup_candle_count'] = self.required_startup
|
||||
|
||||
data = history.load_data(
|
||||
|
||||
Loading…
Reference in New Issue
Block a user