119 lines
4.4 KiB
Python
119 lines
4.4 KiB
Python
"""
|
|
Helpers when analyzing backtest data
|
|
"""
|
|
import logging
|
|
from pathlib import Path
|
|
|
|
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("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 = df2.astype(dtype={"open_time": "datetime64", "close_time": "datetime64"})
|
|
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(db_url: str = None, exportfilename: str = None) -> pd.DataFrame:
|
|
"""
|
|
Load trades, either from a DB (using dburl) or via a backtest export file.
|
|
:param db_url: Sqlite url (default format sqlite:///tradesv3.dry-run.sqlite)
|
|
:param exportfilename: Path to a file exported from backtesting
|
|
:return: Dataframe containing Trades
|
|
"""
|
|
timeZone = pytz.UTC
|
|
|
|
trades: pd.DataFrame = pd.DataFrame([], columns=BT_DATA_COLUMNS)
|
|
|
|
if db_url:
|
|
persistence.init(db_url, clean_open_orders=False)
|
|
columns = ["pair", "profit", "open_time", "close_time",
|
|
"open_rate", "close_rate", "duration"]
|
|
|
|
for x in Trade.query.all():
|
|
logger.info("date: {}".format(x.open_date))
|
|
|
|
trades = pd.DataFrame([(t.pair, t.calc_profit(),
|
|
t.open_date.replace(tzinfo=timeZone),
|
|
t.close_date.replace(tzinfo=timeZone) if t.close_date else None,
|
|
t.open_rate, t.close_rate,
|
|
t.close_date.timestamp() - t.open_date.timestamp()
|
|
if t.close_date else None)
|
|
for t in Trade.query.all()],
|
|
columns=columns)
|
|
|
|
elif exportfilename:
|
|
|
|
trades = load_backtest_data(Path(exportfilename))
|
|
|
|
return trades
|
|
|
|
|
|
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
|