432 lines
18 KiB
Python
432 lines
18 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 Any, Dict, List, Optional, Tuple, Union
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import numpy as np
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import pandas as pd
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from freqtrade.constants import LAST_BT_RESULT_FN
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from freqtrade.exceptions import OperationalException
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from freqtrade.misc import json_load
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from freqtrade.persistence import LocalTrade, Trade, init_db
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logger = logging.getLogger(__name__)
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# Newest format
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BT_DATA_COLUMNS = ['pair', 'stake_amount', 'amount', 'open_date', 'close_date',
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'open_rate', 'close_rate',
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'fee_open', 'fee_close', 'trade_duration',
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'profit_ratio', 'profit_abs', 'sell_reason',
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'initial_stop_loss_abs', 'initial_stop_loss_ratio', 'stop_loss_abs',
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'stop_loss_ratio', 'min_rate', 'max_rate', 'is_open', 'buy_tag']
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def get_latest_optimize_filename(directory: Union[Path, str], variant: str) -> str:
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"""
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Get latest backtest export based on '.last_result.json'.
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:param directory: Directory to search for last result
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:param variant: 'backtest' or 'hyperopt' - the method to return
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:return: string containing the filename of the latest backtest result
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:raises: ValueError in the following cases:
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* Directory does not exist
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* `directory/.last_result.json` does not exist
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* `directory/.last_result.json` has the wrong content
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"""
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if isinstance(directory, str):
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directory = Path(directory)
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if not directory.is_dir():
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raise ValueError(f"Directory '{directory}' does not exist.")
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filename = directory / LAST_BT_RESULT_FN
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if not filename.is_file():
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raise ValueError(
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f"Directory '{directory}' does not seem to contain backtest statistics yet.")
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with filename.open() as file:
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data = json_load(file)
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if f'latest_{variant}' not in data:
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raise ValueError(f"Invalid '{LAST_BT_RESULT_FN}' format.")
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return data[f'latest_{variant}']
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def get_latest_backtest_filename(directory: Union[Path, str]) -> str:
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"""
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Get latest backtest export based on '.last_result.json'.
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:param directory: Directory to search for last result
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:return: string containing the filename of the latest backtest result
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:raises: ValueError in the following cases:
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* Directory does not exist
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* `directory/.last_result.json` does not exist
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* `directory/.last_result.json` has the wrong content
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"""
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return get_latest_optimize_filename(directory, 'backtest')
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def get_latest_hyperopt_filename(directory: Union[Path, str]) -> str:
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"""
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Get latest hyperopt export based on '.last_result.json'.
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:param directory: Directory to search for last result
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:return: string containing the filename of the latest hyperopt result
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:raises: ValueError in the following cases:
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* Directory does not exist
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* `directory/.last_result.json` does not exist
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* `directory/.last_result.json` has the wrong content
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"""
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try:
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return get_latest_optimize_filename(directory, 'hyperopt')
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except ValueError:
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# Return default (legacy) pickle filename
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return 'hyperopt_results.pickle'
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def get_latest_hyperopt_file(directory: Union[Path, str], predef_filename: str = None) -> Path:
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"""
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Get latest hyperopt export based on '.last_result.json'.
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:param directory: Directory to search for last result
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:return: string containing the filename of the latest hyperopt result
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:raises: ValueError in the following cases:
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* Directory does not exist
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* `directory/.last_result.json` does not exist
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* `directory/.last_result.json` has the wrong content
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"""
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if isinstance(directory, str):
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directory = Path(directory)
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if predef_filename:
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return directory / predef_filename
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return directory / get_latest_hyperopt_filename(directory)
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def load_backtest_stats(filename: Union[Path, str]) -> Dict[str, Any]:
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"""
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Load backtest statistics file.
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:param filename: pathlib.Path object, or string pointing to the file.
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:return: a dictionary containing the resulting file.
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"""
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if isinstance(filename, str):
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filename = Path(filename)
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if filename.is_dir():
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filename = filename / get_latest_backtest_filename(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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logger.info(f"Loading backtest result from {filename}")
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with filename.open() as file:
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data = json_load(file)
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return data
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def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = None) -> 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 a file or directory
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:param strategy: Strategy to load - mainly relevant for multi-strategy backtests
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Can also serve as protection to load the correct result.
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:return: a dataframe with the analysis results
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:raise: ValueError if loading goes wrong.
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"""
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data = load_backtest_stats(filename)
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if not isinstance(data, list):
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# new, nested format
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if 'strategy' not in data:
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raise ValueError("Unknown dataformat.")
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if not strategy:
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if len(data['strategy']) == 1:
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strategy = list(data['strategy'].keys())[0]
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else:
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raise ValueError("Detected backtest result with more than one strategy. "
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"Please specify a strategy.")
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if strategy not in data['strategy']:
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raise ValueError(f"Strategy {strategy} not available in the backtest result.")
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data = data['strategy'][strategy]['trades']
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df = pd.DataFrame(data)
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if not df.empty:
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df['open_date'] = pd.to_datetime(df['open_date'],
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utc=True,
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infer_datetime_format=True
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)
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df['close_date'] = pd.to_datetime(df['close_date'],
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utc=True,
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infer_datetime_format=True
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)
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else:
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# old format - only with lists.
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raise OperationalException(
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"Backtest-results with only trades data are no longer supported.")
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if not df.empty:
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if 'profit_ratio' not in df.columns:
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df['profit_ratio'] = df['profit_percent']
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df = df.sort_values("open_date").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_date'], row[1]['close_date'],
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freq=f"{timeframe_min}min"))
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for row in results[['open_date', 'close_date']].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 trade_list_to_dataframe(trades: List[LocalTrade]) -> pd.DataFrame:
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"""
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Convert list of Trade objects to pandas Dataframe
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:param trades: List of trade objects
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:return: Dataframe with BT_DATA_COLUMNS
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"""
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df = pd.DataFrame.from_records([t.to_json() for t in trades], columns=BT_DATA_COLUMNS)
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if len(df) > 0:
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df.loc[:, 'close_date'] = pd.to_datetime(df['close_date'], utc=True)
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df.loc[:, 'open_date'] = pd.to_datetime(df['open_date'], utc=True)
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df.loc[:, 'close_rate'] = df['close_rate'].astype('float64')
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return df
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def load_trades_from_db(db_url: str, strategy: Optional[str] = None) -> 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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:param strategy: Strategy to load - mainly relevant for multi-strategy backtests
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Can also serve as protection to load the correct result.
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:return: Dataframe containing Trades
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"""
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init_db(db_url, clean_open_orders=False)
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filters = []
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if strategy:
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filters.append(Trade.strategy == strategy)
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trades = trade_list_to_dataframe(Trade.get_trades(filters).all())
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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, strategy: Optional[str] = None) -> 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, strategy)
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def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame,
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date_index=False) -> 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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if date_index:
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trades_start = dataframe.index[0]
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trades_stop = dataframe.index[-1]
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else:
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trades_start = dataframe.iloc[0]['date']
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trades_stop = dataframe.iloc[-1]['date']
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trades = trades.loc[(trades['open_date'] >= trades_start) &
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(trades['close_date'] <= trades_stop)]
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return trades
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def calculate_market_change(data: Dict[str, pd.DataFrame], column: str = "close") -> float:
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"""
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Calculate market change based on "column".
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Calculation is done by taking the first non-null and the last non-null element of each column
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and calculating the pctchange as "(last - first) / first".
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Then the results per pair are combined as mean.
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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:
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"""
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tmp_means = []
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for pair, df in data.items():
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start = df[column].dropna().iloc[0]
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end = df[column].dropna().iloc[-1]
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tmp_means.append((end - start) / start)
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return float(np.mean(tmp_means))
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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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:raise: ValueError if no data is provided.
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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_date and profit_abs)
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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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: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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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_date'
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)[['profit_abs']].sum()
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df.loc[:, col_name] = _trades_sum['profit_abs'].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 _calc_drawdown_series(profit_results: pd.DataFrame, *, date_col: str, value_col: str
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) -> pd.DataFrame:
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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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max_drawdown_df['date'] = profit_results.loc[:, date_col]
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return max_drawdown_df
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def calculate_underwater(trades: pd.DataFrame, *, date_col: str = 'close_date',
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value_col: str = 'profit_ratio'
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):
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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_date and profit_ratio)
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:param date_col: Column in DataFrame to use for dates (defaults to 'close_date')
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:param value_col: Column in DataFrame to use for values (defaults to 'profit_ratio')
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:return: Tuple (float, highdate, lowdate, highvalue, lowvalue) with absolute max drawdown,
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high and low time and high and low value.
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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).reset_index(drop=True)
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max_drawdown_df = _calc_drawdown_series(profit_results, date_col=date_col, value_col=value_col)
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return max_drawdown_df
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def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date',
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value_col: str = 'profit_abs', starting_balance: float = 0
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) -> Tuple[float, pd.Timestamp, pd.Timestamp, float, float, float]:
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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_date and profit_ratio)
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:param date_col: Column in DataFrame to use for dates (defaults to 'close_date')
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:param value_col: Column in DataFrame to use for values (defaults to 'profit_abs')
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:param starting_balance: Portfolio starting balance - properly calculate relative drawdown.
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:return: Tuple (float, highdate, lowdate, highvalue, lowvalue, relative_drawdown)
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with absolute max drawdown, high and low time and high and low value,
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and the relative account drawdown
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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).reset_index(drop=True)
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max_drawdown_df = _calc_drawdown_series(profit_results, date_col=date_col, value_col=value_col)
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idxmin = max_drawdown_df['drawdown'].idxmin()
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if idxmin == 0:
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raise ValueError("No losing trade, therefore no drawdown.")
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high_date = profit_results.loc[max_drawdown_df.iloc[:idxmin]['high_value'].idxmax(), date_col]
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low_date = profit_results.loc[idxmin, date_col]
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high_val = max_drawdown_df.loc[max_drawdown_df.iloc[:idxmin]
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['high_value'].idxmax(), 'cumulative']
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low_val = max_drawdown_df.loc[idxmin, 'cumulative']
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max_drawdown_rel = 0.0
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if high_val + starting_balance != 0:
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max_drawdown_rel = (high_val - low_val) / (high_val + starting_balance)
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return (
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abs(min(max_drawdown_df['drawdown'])),
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high_date,
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low_date,
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high_val,
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low_val,
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max_drawdown_rel
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)
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def calculate_csum(trades: pd.DataFrame, starting_balance: float = 0) -> Tuple[float, float]:
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"""
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Calculate min/max cumsum of trades, to show if the wallet/stake amount ratio is sane
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:param trades: DataFrame containing trades (requires columns close_date and profit_percent)
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:param starting_balance: Add starting balance to results, to show the wallets high / low points
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:return: Tuple (float, float) with cumsum of profit_abs
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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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csum_df = pd.DataFrame()
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csum_df['sum'] = trades['profit_abs'].cumsum()
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csum_min = csum_df['sum'].min() + starting_balance
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csum_max = csum_df['sum'].max() + starting_balance
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return csum_min, csum_max
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