mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-11-10 18:23:55 +00:00
221 lines
8.5 KiB
Python
221 lines
8.5 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.exchange.exchange import market_is_active
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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 = [p for p, m in dp._exchange.markets.items() if market_is_active(m)
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or config.get('include_inactive')]
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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", "json"),
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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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# Keep below for when we wish to download heterogeneously lengthed data for FreqAI.
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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 a FreqAI 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 not None:
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# markets = [p for p, m in dp._exchange.markets.items() if market_is_active(m)
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# or config.get('include_inactive')]
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# else:
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# # This should not occur:
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# raise OperationalException('No exchange object found.')
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# all_pairs = dynamic_expand_pairlist(config, markets)
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# if not dp._exchange:
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# # Not realistic - this is only called in live mode.
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# raise OperationalException("Dataprovider did not have an exchange attached.")
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# time = datetime.now(tz=timezone.utc).timestamp()
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# for tf in config["freqai"]["feature_parameters"].get("include_timeframes"):
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# timerange = TimeRange()
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# timerange.startts = int(time)
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# timerange.stopts = int(time)
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# startup_candles = dp.get_required_startup(str(tf))
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# tf_seconds = timeframe_to_seconds(str(tf))
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# timerange.subtract_start(tf_seconds * startup_candles)
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# new_pairs_days = int((timerange.stopts - timerange.startts) / 86400)
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# # FIXME: now that we are looping on `refresh_backtest_ohlcv_data`, the function
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# # redownloads the funding rate for each pair.
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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=[tf],
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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", "json"),
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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 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" or "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 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 open(params_record_path, "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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