stable/freqtrade/data/btanalysis.py

"""
Helpers when analyzing backtest data
"""
import logging
from pathlib import Path
from typing import Dict

import numpy as np
import pandas as pd
import pytz

from freqtrade import persistence
from freqtrade.misc import json_load
from freqtrade.persistence import Trade

logger = logging.getLogger(__name__)

# must align with columns in backtest.py
BT_DATA_COLUMNS = ["pair", "profitperc", "open_time", "close_time", "index", "duration",
                   "open_rate", "close_rate", "open_at_end", "sell_reason"]


def load_backtest_data(filename) -> pd.DataFrame:
    """
    Load backtest data file.
    :param filename: pathlib.Path object, or string pointing to the file.
    :return: a dataframe with the analysis results
    """
    if isinstance(filename, str):
        filename = Path(filename)

    if not filename.is_file():
        raise ValueError(f"File {filename} does not exist.")

    with filename.open() as file:
        data = json_load(file)

    df = pd.DataFrame(data, columns=BT_DATA_COLUMNS)

    df['open_time'] = pd.to_datetime(df['open_time'],
                                     unit='s',
                                     utc=True,
                                     infer_datetime_format=True
                                     )
    df['close_time'] = pd.to_datetime(df['close_time'],
                                      unit='s',
                                      utc=True,
                                      infer_datetime_format=True
                                      )
    df['profitabs'] = df['close_rate'] - df['open_rate']
    df = df.sort_values("open_time").reset_index(drop=True)
    return df


def evaluate_result_multi(results: pd.DataFrame, freq: str, max_open_trades: int) -> pd.DataFrame:
    """
    Find overlapping trades by expanding each trade once per period it was open
    and then counting overlaps
    :param results: Results Dataframe - can be loaded
    :param freq: Frequency used for the backtest
    :param max_open_trades: parameter max_open_trades used during backtest run
    :return: dataframe with open-counts per time-period in freq
    """
    dates = [pd.Series(pd.date_range(row[1].open_time, row[1].close_time, freq=freq))
             for row in results[['open_time', 'close_time']].iterrows()]
    deltas = [len(x) for x in dates]
    dates = pd.Series(pd.concat(dates).values, name='date')
    df2 = pd.DataFrame(np.repeat(results.values, deltas, axis=0), columns=results.columns)

    df2 = pd.concat([dates, df2], axis=1)
    df2 = df2.set_index('date')
    df_final = df2.resample(freq)[['pair']].count()
    return df_final[df_final['pair'] > max_open_trades]


def load_trades_from_db(db_url: str) -> pd.DataFrame:
    """
    Load trades from a DB (using dburl)
    :param db_url: Sqlite url (default format sqlite:///tradesv3.dry-run.sqlite)
    :return: Dataframe containing Trades
    """
    trades: pd.DataFrame = pd.DataFrame([], columns=BT_DATA_COLUMNS)
    persistence.init(db_url, clean_open_orders=False)

    columns = ["pair", "open_time", "close_time", "profit", "profitperc",
               "open_rate", "close_rate", "amount", "duration", "sell_reason",
               "fee_open", "fee_close", "open_rate_requested", "close_rate_requested",
               "stake_amount", "max_rate", "min_rate", "id", "exchange",
               "stop_loss", "initial_stop_loss", "strategy", "ticker_interval"]

    trades = pd.DataFrame([(t.pair,
                            t.open_date.replace(tzinfo=pytz.UTC),
                            t.close_date.replace(tzinfo=pytz.UTC) if t.close_date else None,
                            t.calc_profit(), t.calc_profit_percent(),
                            t.open_rate, t.close_rate, t.amount,
                            (t.close_date.timestamp() - t.open_date.timestamp()
                                if t.close_date else None),
                            t.sell_reason,
                            t.fee_open, t.fee_close,
                            t.open_rate_requested,
                            t.close_rate_requested,
                            t.stake_amount,
                            t.max_rate,
                            t.min_rate,
                            t.id, t.exchange,
                            t.stop_loss, t.initial_stop_loss,
                            t.strategy, t.ticker_interval
                            )
                           for t in Trade.query.all()],
                          columns=columns)

    return trades


def load_trades(source: str, db_url: str, exportfilename: str) -> pd.DataFrame:
    """
    Based on configuration option "trade_source":
    * loads data from DB (using `db_url`)
    * loads data from backtestfile (using `exportfilename`)
    """
    if source == "DB":
        return load_trades_from_db(db_url)
    elif source == "file":
        return load_backtest_data(Path(exportfilename))


def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame) -> pd.DataFrame:
    """
    Compare trades and backtested pair DataFrames to get trades performed on backtested period
    :return: the DataFrame of a trades of period
    """
    trades = trades.loc[(trades['open_time'] >= dataframe.iloc[0]['date']) &
                        (trades['close_time'] <= dataframe.iloc[-1]['date'])]
    return trades


def combine_tickers_with_mean(tickers: Dict[str, pd.DataFrame], column: str = "close"):
    """
    Combine multiple dataframes "column"
    :param tickers: Dict of Dataframes, dict key should be pair.
    :param column: Column in the original dataframes to use
    :return: DataFrame with the column renamed to the dict key, and a column
        named mean, containing the mean of all pairs.
    """
    df_comb = pd.concat([tickers[pair].set_index('date').rename(
        {column: pair}, axis=1)[pair] for pair in tickers], axis=1)

    df_comb['mean'] = df_comb.mean(axis=1)

    return df_comb


def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str) -> pd.DataFrame:
    """
    Adds a column `col_name` with the cumulative profit for the given trades array.
    :param df: DataFrame with date index
    :param trades: DataFrame containing trades (requires columns close_time and profitperc)
    :return: Returns df with one additional column, col_name, containing the cumulative profit.
    """
    # Use groupby/sum().cumsum() to avoid errors when multiple trades sold at the same candle.
    df[col_name] = trades.groupby('close_time')['profitperc'].sum().cumsum()
    # Set first value to 0
    df.loc[df.iloc[0].name, col_name] = 0
    # FFill to get continuous
    df[col_name] = df[col_name].ffill()
    return df