mirror of
https://github.com/freqtrade/freqtrade.git
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ad0f88796b
Fix logical error in the conditional checks for model classes. The `elif` statement that looks for "lightgbm.sklearn" or "xgb" in the model class string is now broken into two separate conditions because the old condition would always evaluate to `True` due to the non-empty string "xgb".
201 lines
7.3 KiB
Python
201 lines
7.3 KiB
Python
import logging
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Any, Dict
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import numpy as np
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import pandas as pd
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import rapidjson
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from freqtrade.configuration import TimeRange
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from freqtrade.constants import Config
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from freqtrade.data.dataprovider import DataProvider
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from freqtrade.data.history.history_utils import refresh_backtest_ohlcv_data
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from freqtrade.exceptions import OperationalException
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from freqtrade.exchange import timeframe_to_seconds
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from freqtrade.freqai.data_drawer import FreqaiDataDrawer
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist
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logger = logging.getLogger(__name__)
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def download_all_data_for_training(dp: DataProvider, config: Config) -> None:
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"""
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Called only once upon start of bot to download the necessary data for
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populating indicators and training the model.
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:param timerange: TimeRange = The full data timerange for populating the indicators
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and training the model.
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:param dp: DataProvider instance attached to the strategy
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"""
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if dp._exchange is None:
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raise OperationalException('No exchange object found.')
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markets = [
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p for p in dp._exchange.get_markets(
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tradable_only=True, active_only=not config.get('include_inactive')
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).keys()
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]
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all_pairs = dynamic_expand_pairlist(config, markets)
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timerange = get_required_data_timerange(config)
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new_pairs_days = int((timerange.stopts - timerange.startts) / 86400)
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refresh_backtest_ohlcv_data(
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dp._exchange,
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pairs=all_pairs,
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timeframes=config["freqai"]["feature_parameters"].get("include_timeframes"),
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datadir=config["datadir"],
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timerange=timerange,
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new_pairs_days=new_pairs_days,
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erase=False,
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data_format=config.get("dataformat_ohlcv", "feather"),
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trading_mode=config.get("trading_mode", "spot"),
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prepend=config.get("prepend_data", False),
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)
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def get_required_data_timerange(config: Config) -> TimeRange:
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"""
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Used to compute the required data download time range
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for auto data-download in FreqAI
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"""
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time = datetime.now(tz=timezone.utc).timestamp()
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timeframes = config["freqai"]["feature_parameters"].get("include_timeframes")
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max_tf_seconds = 0
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for tf in timeframes:
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secs = timeframe_to_seconds(tf)
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if secs > max_tf_seconds:
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max_tf_seconds = secs
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startup_candles = config.get('startup_candle_count', 0)
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indicator_periods = config["freqai"]["feature_parameters"]["indicator_periods_candles"]
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# factor the max_period as a factor of safety.
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max_period = int(max(startup_candles, max(indicator_periods)) * 1.5)
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config['startup_candle_count'] = max_period
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logger.info(f'FreqAI auto-downloader using {max_period} startup candles.')
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additional_seconds = max_period * max_tf_seconds
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startts = int(
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time
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- config["freqai"].get("train_period_days", 0) * 86400
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- additional_seconds
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)
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stopts = int(time)
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data_load_timerange = TimeRange('date', 'date', startts, stopts)
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return data_load_timerange
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def plot_feature_importance(model: Any, pair: str, dk: FreqaiDataKitchen,
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count_max: int = 25) -> None:
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"""
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Plot Best and worst features by importance for a single sub-train.
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:param model: Any = A model which was `fit` using a common library
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such as catboost or lightgbm
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:param pair: str = pair e.g. BTC/USD
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:param dk: FreqaiDataKitchen = non-persistent data container for current coin/loop
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:param count_max: int = the amount of features to be loaded per column
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"""
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from freqtrade.plot.plotting import go, make_subplots, store_plot_file
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# Extract feature importance from model
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models = {}
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if 'FreqaiMultiOutputRegressor' in str(model.__class__):
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for estimator, label in zip(model.estimators_, dk.label_list):
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models[label] = estimator
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else:
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models[dk.label_list[0]] = model
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for label in models:
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mdl = models[label]
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if "catboost.core" in str(mdl.__class__):
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feature_importance = mdl.get_feature_importance()
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elif "lightgbm.sklearn" in str(mdl.__class__):
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feature_importance = mdl.feature_importances_
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elif "xgb" in str(mdl.__class__):
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feature_importance = mdl.feature_importances_
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else:
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logger.info('Model type does not support for generating feature importances.')
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return
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# Data preparation
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fi_df = pd.DataFrame({
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"feature_names": np.array(dk.data_dictionary['train_features'].columns),
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"feature_importance": np.array(feature_importance)
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})
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fi_df_top = fi_df.nlargest(count_max, "feature_importance")[::-1]
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fi_df_worst = fi_df.nsmallest(count_max, "feature_importance")[::-1]
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# Plotting
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def add_feature_trace(fig, fi_df, col):
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return fig.add_trace(
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go.Bar(
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x=fi_df["feature_importance"],
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y=fi_df["feature_names"],
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orientation='h', showlegend=False
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), row=1, col=col
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)
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fig = make_subplots(rows=1, cols=2, horizontal_spacing=0.5)
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fig = add_feature_trace(fig, fi_df_top, 1)
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fig = add_feature_trace(fig, fi_df_worst, 2)
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fig.update_layout(title_text=f"Best and worst features by importance {pair}")
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label = label.replace('&', '').replace('%', '') # escape two FreqAI specific characters
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store_plot_file(fig, f"{dk.model_filename}-{label}.html", dk.data_path)
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def record_params(config: Dict[str, Any], full_path: Path) -> None:
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"""
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Records run params in the full path for reproducibility
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"""
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params_record_path = full_path / "run_params.json"
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run_params = {
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"freqai": config.get('freqai', {}),
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"timeframe": config.get('timeframe'),
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"stake_amount": config.get('stake_amount'),
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"stake_currency": config.get('stake_currency'),
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"max_open_trades": config.get('max_open_trades'),
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"pairs": config.get('exchange', {}).get('pair_whitelist')
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}
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with params_record_path.open("w") as handle:
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rapidjson.dump(
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run_params,
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handle,
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indent=4,
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default=str,
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number_mode=rapidjson.NM_NATIVE | rapidjson.NM_NAN
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)
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def get_timerange_backtest_live_models(config: Config) -> str:
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"""
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Returns a formated timerange for backtest live/ready models
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:param config: Configuration dictionary
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:return: a string timerange (format example: '20220801-20220822')
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"""
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dk = FreqaiDataKitchen(config)
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models_path = dk.get_full_models_path(config)
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dd = FreqaiDataDrawer(models_path, config)
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timerange = dd.get_timerange_from_live_historic_predictions()
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return timerange.timerange_str
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def get_tb_logger(model_type: str, path: Path, activate: bool) -> Any:
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if model_type == "pytorch" and activate:
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from freqtrade.freqai.tensorboard import TBLogger
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return TBLogger(path, activate)
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else:
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from freqtrade.freqai.tensorboard.base_tensorboard import BaseTensorboardLogger
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return BaseTensorboardLogger(path, activate)
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