stable/freqtrade/freqai/utils.py

import logging
from datetime import datetime, timezone
from pathlib import Path
from typing import Any

import numpy as np
import pandas as pd

from freqtrade.configuration import TimeRange
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: dict) -> 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: dict
) -> 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: dict) -> 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
    if "catboost.core" in str(model.__class__):
        feature_importance = model.get_feature_importance()
    elif "lightgbm.sklearn" in str(model.__class__):
        feature_importance = model.feature_importances_
    else:
        # TODO: Add support for more libraries
        raise NotImplementedError(f"Cannot extract feature importance from {model.__class__}")

    # Data preparation
    fi_df = pd.DataFrame({
        "feature_names": np.array(dk.training_features_list),
        "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}")

    # Store plot file
    model_dir, train_name = str(dk.data_path).rsplit("/", 1)
    fi_dir = Path(f"{model_dir}/feature_importance/{pair.split('/')[0]}")
    store_plot_file(fig, f"{train_name}.html", fi_dir)