221 lines
8.5 KiB
Python
221 lines
8.5 KiB
Python
import logging
|
|
from datetime import datetime, timezone
|
|
from pathlib import Path
|
|
from typing import Any, Dict
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
import rapidjson
|
|
|
|
from freqtrade.configuration import TimeRange
|
|
from freqtrade.constants import Config
|
|
from freqtrade.data.dataprovider import DataProvider
|
|
from freqtrade.data.history.history_utils import refresh_backtest_ohlcv_data
|
|
from freqtrade.exceptions import OperationalException
|
|
from freqtrade.exchange import timeframe_to_seconds
|
|
from freqtrade.exchange.exchange import market_is_active
|
|
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
|
from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist
|
|
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
def download_all_data_for_training(dp: DataProvider, config: Config) -> None:
|
|
"""
|
|
Called only once upon start of bot to download the necessary data for
|
|
populating indicators and training the model.
|
|
:param timerange: TimeRange = The full data timerange for populating the indicators
|
|
and training the model.
|
|
:param dp: DataProvider instance attached to the strategy
|
|
"""
|
|
|
|
if dp._exchange is None:
|
|
raise OperationalException('No exchange object found.')
|
|
markets = [p for p, m in dp._exchange.markets.items() if market_is_active(m)
|
|
or config.get('include_inactive')]
|
|
|
|
all_pairs = dynamic_expand_pairlist(config, markets)
|
|
|
|
timerange = get_required_data_timerange(config)
|
|
|
|
new_pairs_days = int((timerange.stopts - timerange.startts) / 86400)
|
|
|
|
refresh_backtest_ohlcv_data(
|
|
dp._exchange,
|
|
pairs=all_pairs,
|
|
timeframes=config["freqai"]["feature_parameters"].get("include_timeframes"),
|
|
datadir=config["datadir"],
|
|
timerange=timerange,
|
|
new_pairs_days=new_pairs_days,
|
|
erase=False,
|
|
data_format=config.get("dataformat_ohlcv", "json"),
|
|
trading_mode=config.get("trading_mode", "spot"),
|
|
prepend=config.get("prepend_data", False),
|
|
)
|
|
|
|
|
|
def get_required_data_timerange(config: Config) -> TimeRange:
|
|
"""
|
|
Used to compute the required data download time range
|
|
for auto data-download in FreqAI
|
|
"""
|
|
time = datetime.now(tz=timezone.utc).timestamp()
|
|
|
|
timeframes = config["freqai"]["feature_parameters"].get("include_timeframes")
|
|
|
|
max_tf_seconds = 0
|
|
for tf in timeframes:
|
|
secs = timeframe_to_seconds(tf)
|
|
if secs > max_tf_seconds:
|
|
max_tf_seconds = secs
|
|
|
|
startup_candles = config.get('startup_candle_count', 0)
|
|
indicator_periods = config["freqai"]["feature_parameters"]["indicator_periods_candles"]
|
|
|
|
# factor the max_period as a factor of safety.
|
|
max_period = int(max(startup_candles, max(indicator_periods)) * 1.5)
|
|
config['startup_candle_count'] = max_period
|
|
logger.info(f'FreqAI auto-downloader using {max_period} startup candles.')
|
|
|
|
additional_seconds = max_period * max_tf_seconds
|
|
|
|
startts = int(
|
|
time
|
|
- config["freqai"].get("train_period_days", 0) * 86400
|
|
- additional_seconds
|
|
)
|
|
stopts = int(time)
|
|
data_load_timerange = TimeRange('date', 'date', startts, stopts)
|
|
|
|
return data_load_timerange
|
|
|
|
|
|
# Keep below for when we wish to download heterogeneously lengthed data for FreqAI.
|
|
# def download_all_data_for_training(dp: DataProvider, config: Config) -> None:
|
|
# """
|
|
# Called only once upon start of bot to download the necessary data for
|
|
# populating indicators and training a FreqAI model.
|
|
# :param timerange: TimeRange = The full data timerange for populating the indicators
|
|
# and training the model.
|
|
# :param dp: DataProvider instance attached to the strategy
|
|
# """
|
|
|
|
# if dp._exchange is not None:
|
|
# markets = [p for p, m in dp._exchange.markets.items() if market_is_active(m)
|
|
# or config.get('include_inactive')]
|
|
# else:
|
|
# # This should not occur:
|
|
# raise OperationalException('No exchange object found.')
|
|
|
|
# all_pairs = dynamic_expand_pairlist(config, markets)
|
|
|
|
# if not dp._exchange:
|
|
# # Not realistic - this is only called in live mode.
|
|
# raise OperationalException("Dataprovider did not have an exchange attached.")
|
|
|
|
# time = datetime.now(tz=timezone.utc).timestamp()
|
|
|
|
# for tf in config["freqai"]["feature_parameters"].get("include_timeframes"):
|
|
# timerange = TimeRange()
|
|
# timerange.startts = int(time)
|
|
# timerange.stopts = int(time)
|
|
# startup_candles = dp.get_required_startup(str(tf))
|
|
# tf_seconds = timeframe_to_seconds(str(tf))
|
|
# timerange.subtract_start(tf_seconds * startup_candles)
|
|
# new_pairs_days = int((timerange.stopts - timerange.startts) / 86400)
|
|
# # FIXME: now that we are looping on `refresh_backtest_ohlcv_data`, the function
|
|
# # redownloads the funding rate for each pair.
|
|
# refresh_backtest_ohlcv_data(
|
|
# dp._exchange,
|
|
# pairs=all_pairs,
|
|
# timeframes=[tf],
|
|
# datadir=config["datadir"],
|
|
# timerange=timerange,
|
|
# new_pairs_days=new_pairs_days,
|
|
# erase=False,
|
|
# data_format=config.get("dataformat_ohlcv", "json"),
|
|
# trading_mode=config.get("trading_mode", "spot"),
|
|
# prepend=config.get("prepend_data", False),
|
|
# )
|
|
|
|
|
|
def plot_feature_importance(model: Any, pair: str, dk: FreqaiDataKitchen,
|
|
count_max: int = 25) -> None:
|
|
"""
|
|
Plot Best and worst features by importance for a single sub-train.
|
|
:param model: Any = A model which was `fit` using a common library
|
|
such as catboost or lightgbm
|
|
:param pair: str = pair e.g. BTC/USD
|
|
:param dk: FreqaiDataKitchen = non-persistent data container for current coin/loop
|
|
:param count_max: int = the amount of features to be loaded per column
|
|
"""
|
|
from freqtrade.plot.plotting import go, make_subplots, store_plot_file
|
|
|
|
# Extract feature importance from model
|
|
models = {}
|
|
if 'FreqaiMultiOutputRegressor' in str(model.__class__):
|
|
for estimator, label in zip(model.estimators_, dk.label_list):
|
|
models[label] = estimator
|
|
else:
|
|
models[dk.label_list[0]] = model
|
|
|
|
for label in models:
|
|
mdl = models[label]
|
|
if "catboost.core" in str(mdl.__class__):
|
|
feature_importance = mdl.get_feature_importance()
|
|
elif "lightgbm.sklearn" or "xgb" in str(mdl.__class__):
|
|
feature_importance = mdl.feature_importances_
|
|
else:
|
|
logger.info('Model type not support for generating feature importances.')
|
|
return
|
|
|
|
# Data preparation
|
|
fi_df = pd.DataFrame({
|
|
"feature_names": np.array(dk.data_dictionary['train_features'].columns),
|
|
"feature_importance": np.array(feature_importance)
|
|
})
|
|
fi_df_top = fi_df.nlargest(count_max, "feature_importance")[::-1]
|
|
fi_df_worst = fi_df.nsmallest(count_max, "feature_importance")[::-1]
|
|
|
|
# Plotting
|
|
def add_feature_trace(fig, fi_df, col):
|
|
return fig.add_trace(
|
|
go.Bar(
|
|
x=fi_df["feature_importance"],
|
|
y=fi_df["feature_names"],
|
|
orientation='h', showlegend=False
|
|
), row=1, col=col
|
|
)
|
|
fig = make_subplots(rows=1, cols=2, horizontal_spacing=0.5)
|
|
fig = add_feature_trace(fig, fi_df_top, 1)
|
|
fig = add_feature_trace(fig, fi_df_worst, 2)
|
|
fig.update_layout(title_text=f"Best and worst features by importance {pair}")
|
|
label = label.replace('&', '').replace('%', '') # escape two FreqAI specific characters
|
|
store_plot_file(fig, f"{dk.model_filename}-{label}.html", dk.data_path)
|
|
|
|
|
|
def record_params(config: Dict[str, Any], full_path: Path) -> None:
|
|
"""
|
|
Records run params in the full path for reproducibility
|
|
"""
|
|
params_record_path = full_path / "run_params.json"
|
|
|
|
run_params = {
|
|
"freqai": config.get('freqai', {}),
|
|
"timeframe": config.get('timeframe'),
|
|
"stake_amount": config.get('stake_amount'),
|
|
"stake_currency": config.get('stake_currency'),
|
|
"max_open_trades": config.get('max_open_trades'),
|
|
"pairs": config.get('exchange', {}).get('pair_whitelist')
|
|
}
|
|
|
|
with open(params_record_path, "w") as handle:
|
|
rapidjson.dump(
|
|
run_params,
|
|
handle,
|
|
indent=4,
|
|
default=str,
|
|
number_mode=rapidjson.NM_NATIVE | rapidjson.NM_NAN
|
|
)
|