112 lines
4.2 KiB
Python
112 lines
4.2 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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import numpy as np
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import pandas as pd
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import pytz
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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) -> 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("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['profitabs'] = 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 evaluate_result_multi(results: pd.DataFrame, freq: str, 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 freq: 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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dates = [pd.Series(pd.date_range(row[1].open_time, row[1].close_time, freq=freq))
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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 = df2.astype(dtype={"open_time": "datetime64", "close_time": "datetime64"})
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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(freq)[['pair']].count()
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return df_final[df_final['pair'] > 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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:returns: 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", "profit", "open_time", "close_time",
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"open_rate", "close_rate", "duration", "sell_reason",
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"max_rate", "min_rate"]
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trades = pd.DataFrame([(t.pair, t.calc_profit(),
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t.open_date.replace(tzinfo=pytz.UTC),
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t.close_date.replace(tzinfo=pytz.UTC) if t.close_date else None,
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t.open_rate, t.close_rate,
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t.close_date.timestamp() - t.open_date.timestamp()
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if t.close_date else None,
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t.sell_reason,
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t.max_rate,
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t.min_rate,
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)
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for t in Trade.query.all()],
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columns=columns)
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return trades
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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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