226 lines
9.4 KiB
Python
226 lines
9.4 KiB
Python
"""
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Helpers when analyzing backtest data
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"""
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import logging
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from pathlib import Path
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from typing import Dict, Union, Tuple
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import numpy as np
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import pandas as pd
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from datetime import timezone
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from freqtrade import persistence
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from freqtrade.misc import json_load
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from freqtrade.persistence import Trade
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logger = logging.getLogger(__name__)
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# must align with columns in backtest.py
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BT_DATA_COLUMNS = ["pair", "profitperc", "open_time", "close_time", "index", "duration",
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"open_rate", "close_rate", "open_at_end", "sell_reason"]
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def load_backtest_data(filename: Union[Path, str]) -> pd.DataFrame:
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"""
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Load backtest data file.
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:param filename: pathlib.Path object, or string pointing to the file.
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:return: a dataframe with the analysis results
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"""
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if isinstance(filename, str):
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filename = Path(filename)
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if not filename.is_file():
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raise ValueError(f"File {filename} does not exist.")
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with filename.open() as file:
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data = json_load(file)
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df = pd.DataFrame(data, columns=BT_DATA_COLUMNS)
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df['open_time'] = pd.to_datetime(df['open_time'],
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unit='s',
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utc=True,
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infer_datetime_format=True
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)
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df['close_time'] = pd.to_datetime(df['close_time'],
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unit='s',
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utc=True,
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infer_datetime_format=True
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)
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df['profit'] = df['close_rate'] - df['open_rate']
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df = df.sort_values("open_time").reset_index(drop=True)
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return df
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def analyze_trade_parallelism(results: pd.DataFrame, timeframe: str) -> pd.DataFrame:
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"""
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Find overlapping trades by expanding each trade once per period it was open
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and then counting overlaps.
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:param results: Results Dataframe - can be loaded
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:param timeframe: Timeframe used for backtest
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:return: dataframe with open-counts per time-period in timeframe
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"""
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from freqtrade.exchange import timeframe_to_minutes
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timeframe_min = timeframe_to_minutes(timeframe)
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dates = [pd.Series(pd.date_range(row[1].open_time, row[1].close_time,
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freq=f"{timeframe_min}min"))
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for row in results[['open_time', 'close_time']].iterrows()]
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deltas = [len(x) for x in dates]
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dates = pd.Series(pd.concat(dates).values, name='date')
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df2 = pd.DataFrame(np.repeat(results.values, deltas, axis=0), columns=results.columns)
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df2 = pd.concat([dates, df2], axis=1)
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df2 = df2.set_index('date')
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df_final = df2.resample(f"{timeframe_min}min")[['pair']].count()
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df_final = df_final.rename({'pair': 'open_trades'}, axis=1)
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return df_final
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def evaluate_result_multi(results: pd.DataFrame, timeframe: str,
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max_open_trades: int) -> pd.DataFrame:
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"""
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Find overlapping trades by expanding each trade once per period it was open
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and then counting overlaps
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:param results: Results Dataframe - can be loaded
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:param timeframe: Frequency used for the backtest
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:param max_open_trades: parameter max_open_trades used during backtest run
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:return: dataframe with open-counts per time-period in freq
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"""
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df_final = analyze_trade_parallelism(results, timeframe)
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return df_final[df_final['open_trades'] > max_open_trades]
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def load_trades_from_db(db_url: str) -> pd.DataFrame:
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"""
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Load trades from a DB (using dburl)
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:param db_url: Sqlite url (default format sqlite:///tradesv3.dry-run.sqlite)
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:return: Dataframe containing Trades
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"""
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trades: pd.DataFrame = pd.DataFrame([], columns=BT_DATA_COLUMNS)
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persistence.init(db_url, clean_open_orders=False)
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columns = ["pair", "open_time", "close_time", "profit", "profitperc",
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"open_rate", "close_rate", "amount", "duration", "sell_reason",
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"fee_open", "fee_close", "open_rate_requested", "close_rate_requested",
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"stake_amount", "max_rate", "min_rate", "id", "exchange",
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"stop_loss", "initial_stop_loss", "strategy", "ticker_interval"]
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trades = pd.DataFrame([(t.pair,
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t.open_date.replace(tzinfo=timezone.utc),
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t.close_date.replace(tzinfo=timezone.utc) if t.close_date else None,
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t.calc_profit(), t.calc_profit_ratio(),
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t.open_rate, t.close_rate, t.amount,
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(round((t.close_date.timestamp() - t.open_date.timestamp()) / 60, 2)
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if t.close_date else None),
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t.sell_reason,
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t.fee_open, t.fee_close,
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t.open_rate_requested,
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t.close_rate_requested,
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t.stake_amount,
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t.max_rate,
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t.min_rate,
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t.id, t.exchange,
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t.stop_loss, t.initial_stop_loss,
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t.strategy, t.ticker_interval
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)
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for t in Trade.get_trades().all()],
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columns=columns)
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return trades
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def load_trades(source: str, db_url: str, exportfilename: Path,
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no_trades: bool = False) -> pd.DataFrame:
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"""
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Based on configuration option "trade_source":
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* loads data from DB (using `db_url`)
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* loads data from backtestfile (using `exportfilename`)
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:param source: "DB" or "file" - specify source to load from
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:param db_url: sqlalchemy formatted url to a database
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:param exportfilename: Json file generated by backtesting
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:param no_trades: Skip using trades, only return backtesting data columns
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:return: DataFrame containing trades
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"""
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if no_trades:
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df = pd.DataFrame(columns=BT_DATA_COLUMNS)
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return df
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if source == "DB":
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return load_trades_from_db(db_url)
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elif source == "file":
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return load_backtest_data(exportfilename)
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def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame) -> pd.DataFrame:
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"""
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Compare trades and backtested pair DataFrames to get trades performed on backtested period
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:return: the DataFrame of a trades of period
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"""
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trades = trades.loc[(trades['open_time'] >= dataframe.iloc[0]['date']) &
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(trades['close_time'] <= dataframe.iloc[-1]['date'])]
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return trades
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def combine_dataframes_with_mean(data: Dict[str, pd.DataFrame],
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column: str = "close") -> pd.DataFrame:
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"""
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Combine multiple dataframes "column"
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:param data: Dict of Dataframes, dict key should be pair.
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:param column: Column in the original dataframes to use
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:return: DataFrame with the column renamed to the dict key, and a column
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named mean, containing the mean of all pairs.
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"""
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df_comb = pd.concat([data[pair].set_index('date').rename(
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{column: pair}, axis=1)[pair] for pair in data], axis=1)
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df_comb['mean'] = df_comb.mean(axis=1)
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return df_comb
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def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
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timeframe: str) -> pd.DataFrame:
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"""
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Adds a column `col_name` with the cumulative profit for the given trades array.
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:param df: DataFrame with date index
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:param trades: DataFrame containing trades (requires columns close_time and profitperc)
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:param col_name: Column name that will be assigned the results
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:param timeframe: Timeframe used during the operations
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:return: Returns df with one additional column, col_name, containing the cumulative profit.
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"""
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from freqtrade.exchange import timeframe_to_minutes
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timeframe_minutes = timeframe_to_minutes(timeframe)
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# Resample to timeframe to make sure trades match candles
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_trades_sum = trades.resample(f'{timeframe_minutes}min', on='close_time')[['profitperc']].sum()
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df.loc[:, col_name] = _trades_sum.cumsum()
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# Set first value to 0
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df.loc[df.iloc[0].name, col_name] = 0
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# FFill to get continuous
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df[col_name] = df[col_name].ffill()
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return df
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def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_time',
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value_col: str = 'profitperc'
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) -> Tuple[float, pd.Timestamp, pd.Timestamp]:
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"""
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Calculate max drawdown and the corresponding close dates
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:param trades: DataFrame containing trades (requires columns close_time and profitperc)
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:param date_col: Column in DataFrame to use for dates (defaults to 'close_time')
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:param value_col: Column in DataFrame to use for values (defaults to 'profitperc')
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:return: Tuple (float, highdate, lowdate) with absolute max drawdown, high and low time
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:raise: ValueError if trade-dataframe was found empty.
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"""
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if len(trades) == 0:
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raise ValueError("Trade dataframe empty.")
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profit_results = trades.sort_values(date_col)
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max_drawdown_df = pd.DataFrame()
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max_drawdown_df['cumulative'] = profit_results[value_col].cumsum()
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max_drawdown_df['high_value'] = max_drawdown_df['cumulative'].cummax()
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max_drawdown_df['drawdown'] = max_drawdown_df['cumulative'] - max_drawdown_df['high_value']
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high_date = profit_results.loc[max_drawdown_df['high_value'].idxmax(), date_col]
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low_date = profit_results.loc[max_drawdown_df['drawdown'].idxmin(), date_col]
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return abs(min(max_drawdown_df['drawdown'])), high_date, low_date
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