stable/freqtrade/data/btanalysis.py
2019-06-23 23:10:37 +03:00

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