mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-11-11 02:33:55 +00:00
482 lines
19 KiB
Python
482 lines
19 KiB
Python
# pragma pylint: disable=missing-docstring, W0212, too-many-arguments
|
|
|
|
"""
|
|
This module contains the backtesting logic
|
|
"""
|
|
import logging
|
|
import operator
|
|
from argparse import Namespace
|
|
from datetime import datetime
|
|
from typing import Any, Dict, List, NamedTuple, Optional, Tuple
|
|
|
|
import arrow
|
|
from pandas import DataFrame, to_datetime
|
|
from tabulate import tabulate
|
|
|
|
import freqtrade.optimize as optimize
|
|
from freqtrade import DependencyException, constants
|
|
from freqtrade.analyze import Analyze
|
|
from freqtrade.arguments import Arguments
|
|
from freqtrade.configuration import Configuration
|
|
from freqtrade.exchange import Exchange
|
|
from freqtrade.misc import file_dump_json
|
|
from freqtrade.optimize.backslapping import Backslapping
|
|
from freqtrade.persistence import Trade
|
|
from profilehooks import profile
|
|
from collections import OrderedDict
|
|
import timeit
|
|
from time import sleep
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class BacktestResult(NamedTuple):
|
|
"""
|
|
NamedTuple Defining BacktestResults inputs.
|
|
"""
|
|
pair: str
|
|
profit_percent: float
|
|
profit_abs: float
|
|
open_time: datetime
|
|
close_time: datetime
|
|
open_index: int
|
|
close_index: int
|
|
trade_duration: float
|
|
open_at_end: bool
|
|
open_rate: float
|
|
close_rate: float
|
|
|
|
|
|
class Backtesting(object):
|
|
"""
|
|
Backtesting class, this class contains all the logic to run a backtest
|
|
|
|
To run a backtest:
|
|
backtesting = Backtesting(config)
|
|
backtesting.start()
|
|
"""
|
|
|
|
def __init__(self, config: Dict[str, Any]) -> None:
|
|
self.config = config
|
|
self.analyze = Analyze(self.config)
|
|
self.ticker_interval = self.analyze.strategy.ticker_interval
|
|
self.tickerdata_to_dataframe = self.analyze.tickerdata_to_dataframe
|
|
self.populate_buy_trend = self.analyze.populate_buy_trend
|
|
self.populate_sell_trend = self.analyze.populate_sell_trend
|
|
|
|
# Reset keys for backtesting
|
|
self.config['exchange']['key'] = ''
|
|
self.config['exchange']['secret'] = ''
|
|
self.config['exchange']['password'] = ''
|
|
self.config['exchange']['uid'] = ''
|
|
self.config['dry_run'] = True
|
|
self.exchange = Exchange(self.config)
|
|
self.fee = self.exchange.get_fee()
|
|
|
|
self.stop_loss_value = self.analyze.strategy.stoploss
|
|
|
|
#### backslap config
|
|
'''
|
|
Numpy arrays are used for 100x speed up
|
|
We requires setting Int values for
|
|
buy stop triggers and stop calculated on
|
|
# buy 0 - open 1 - close 2 - sell 3 - high 4 - low 5 - stop 6
|
|
'''
|
|
self.np_buy: int = 0
|
|
self.np_open: int = 1
|
|
self.np_close: int = 2
|
|
self.np_sell: int = 3
|
|
self.np_high: int = 4
|
|
self.np_low: int = 5
|
|
self.np_stop: int = 6
|
|
self.np_bto: int = self.np_close # buys_triggered_on - should be close
|
|
self.np_bco: int = self.np_open # buys calculated on - open of the next candle.
|
|
self.np_sto: int = self.np_low # stops_triggered_on - Should be low, FT uses close
|
|
self.np_sco: int = self.np_stop # stops_calculated_on - Should be stop, FT uses close
|
|
# self.np_sto: int = self.np_close # stops_triggered_on - Should be low, FT uses close
|
|
# self.np_sco: int = self.np_close # stops_calculated_on - Should be stop, FT uses close
|
|
|
|
self.use_backslap = True # Enable backslap - if false Orginal code is executed.
|
|
self.debug = False # Main debug enable, very print heavy, enable 2 loops recommended
|
|
self.debug_timing = False # Stages within Backslap
|
|
self.debug_2loops = False # Limit each pair to two loops, useful when debugging
|
|
self.debug_vector = False # Debug vector calcs
|
|
self.debug_timing_main_loop = False # print overall timing per pair - works in Backtest and Backslap
|
|
|
|
self.backslap_show_trades = False # prints trades in addition to summary report
|
|
self.backslap_save_trades = True # saves trades as a pretty table to backslap.txt
|
|
|
|
self.stop_stops: int = 9999 # stop back testing any pair with this many stops, set to 999999 to not hit
|
|
|
|
self.backslap = Backslapping(config)
|
|
|
|
@staticmethod
|
|
def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
|
|
"""
|
|
Get the maximum timeframe for the given backtest data
|
|
:param data: dictionary with preprocessed backtesting data
|
|
:return: tuple containing min_date, max_date
|
|
"""
|
|
timeframe = [
|
|
(arrow.get(min(frame.date)), arrow.get(max(frame.date)))
|
|
for frame in data.values()
|
|
]
|
|
return min(timeframe, key=operator.itemgetter(0))[0], \
|
|
max(timeframe, key=operator.itemgetter(1))[1]
|
|
|
|
def _generate_text_table(self, data: Dict[str, Dict], results: DataFrame) -> str:
|
|
"""
|
|
Generates and returns a text table for the given backtest data and the results dataframe
|
|
:return: pretty printed table with tabulate as str
|
|
"""
|
|
stake_currency = str(self.config.get('stake_currency'))
|
|
|
|
floatfmt = ('s', 'd', '.2f', '.2f', '.8f', '.1f')
|
|
tabular_data = []
|
|
headers = ['pair', 'buy count', 'avg profit %', 'cum profit %',
|
|
'total profit ' + stake_currency, 'avg duration', 'profit', 'loss']
|
|
for pair in data:
|
|
result = results[results.pair == pair]
|
|
tabular_data.append([
|
|
pair,
|
|
len(result.index),
|
|
result.profit_percent.mean() * 100.0,
|
|
result.profit_percent.sum() * 100.0,
|
|
result.profit_abs.sum(),
|
|
result.trade_duration.mean(),
|
|
len(result[result.profit_abs > 0]),
|
|
len(result[result.profit_abs < 0])
|
|
])
|
|
|
|
# Append Total
|
|
tabular_data.append([
|
|
'TOTAL',
|
|
len(results.index),
|
|
results.profit_percent.mean() * 100.0,
|
|
results.profit_percent.sum() * 100.0,
|
|
results.profit_abs.sum(),
|
|
results.trade_duration.mean(),
|
|
len(results[results.profit_abs > 0]),
|
|
len(results[results.profit_abs < 0])
|
|
])
|
|
return tabulate(tabular_data, headers=headers, floatfmt=floatfmt, tablefmt="pipe")
|
|
|
|
def _store_backtest_result(self, recordfilename: Optional[str], results: DataFrame) -> None:
|
|
|
|
records = [(t.pair, t.profit_percent, t.open_time.timestamp(),
|
|
t.close_time.timestamp(), t.open_index - 1, t.trade_duration,
|
|
t.open_rate, t.close_rate, t.open_at_end)
|
|
for index, t in results.iterrows()]
|
|
|
|
if records:
|
|
logger.info('Dumping backtest results to %s', recordfilename)
|
|
file_dump_json(recordfilename, records)
|
|
|
|
def _get_sell_trade_entry(
|
|
self, pair: str, buy_row: DataFrame,
|
|
partial_ticker: List, trade_count_lock: Dict, args: Dict) -> Optional[BacktestResult]:
|
|
|
|
stake_amount = args['stake_amount']
|
|
max_open_trades = args.get('max_open_trades', 0)
|
|
trade = Trade(
|
|
open_rate=buy_row.open,
|
|
open_date=buy_row.date,
|
|
stake_amount=stake_amount,
|
|
amount=stake_amount / buy_row.open,
|
|
fee_open=self.fee,
|
|
fee_close=self.fee
|
|
)
|
|
|
|
# calculate win/lose forwards from buy point
|
|
for sell_row in partial_ticker:
|
|
if max_open_trades > 0:
|
|
# Increase trade_count_lock for every iteration
|
|
trade_count_lock[sell_row.date] = trade_count_lock.get(sell_row.date, 0) + 1
|
|
|
|
buy_signal = sell_row.buy
|
|
if self.analyze.should_sell(trade, sell_row.open, sell_row.date, buy_signal,
|
|
sell_row.sell):
|
|
return BacktestResult(pair=pair,
|
|
profit_percent=trade.calc_profit_percent(rate=sell_row.open),
|
|
profit_abs=trade.calc_profit(rate=sell_row.open),
|
|
open_time=buy_row.date,
|
|
close_time=sell_row.date,
|
|
trade_duration=(sell_row.date - buy_row.date).seconds // 60,
|
|
open_index=buy_row.Index,
|
|
close_index=sell_row.Index,
|
|
open_at_end=False,
|
|
open_rate=buy_row.open,
|
|
close_rate=sell_row.open
|
|
)
|
|
if partial_ticker:
|
|
# no sell condition found - trade stil open at end of backtest period
|
|
sell_row = partial_ticker[-1]
|
|
btr = BacktestResult(pair=pair,
|
|
profit_percent=trade.calc_profit_percent(rate=sell_row.open),
|
|
profit_abs=trade.calc_profit(rate=sell_row.open),
|
|
open_time=buy_row.date,
|
|
close_time=sell_row.date,
|
|
trade_duration=(sell_row.date - buy_row.date).seconds // 60,
|
|
open_index=buy_row.Index,
|
|
close_index=sell_row.Index,
|
|
open_at_end=True,
|
|
open_rate=buy_row.open,
|
|
close_rate=sell_row.open
|
|
)
|
|
logger.debug('Force_selling still open trade %s with %s perc - %s', btr.pair,
|
|
btr.profit_percent, btr.profit_abs)
|
|
return btr
|
|
return None
|
|
|
|
def s(self):
|
|
st = timeit.default_timer()
|
|
return st
|
|
|
|
def f(self, st):
|
|
return (timeit.default_timer() - st)
|
|
|
|
def backtest(self, args: Dict) -> DataFrame:
|
|
"""
|
|
Implements backtesting functionality
|
|
|
|
NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
|
|
Of course try to not have ugly code. By some accessor are sometime slower than functions.
|
|
Avoid, logging on this method
|
|
|
|
:param args: a dict containing:
|
|
stake_amount: btc amount to use for each trade
|
|
processed: a processed dictionary with format {pair, data}
|
|
max_open_trades: maximum number of concurrent trades (default: 0, disabled)
|
|
realistic: do we try to simulate realistic trades? (default: True)
|
|
:return: DataFrame
|
|
"""
|
|
|
|
use_backslap = self.use_backslap
|
|
debug_timing = self.debug_timing_main_loop
|
|
|
|
if use_backslap: # Use Back Slap code
|
|
return self.backslap.run(args)
|
|
else: # use Original Back test code
|
|
########################## Original BT loop
|
|
|
|
headers = ['date', 'buy', 'open', 'close', 'sell']
|
|
processed = args['processed']
|
|
max_open_trades = args.get('max_open_trades', 0)
|
|
realistic = args.get('realistic', False)
|
|
trades = []
|
|
trade_count_lock: Dict = {}
|
|
|
|
for pair, pair_data in processed.items():
|
|
if debug_timing: # Start timer
|
|
fl = self.s()
|
|
|
|
pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run
|
|
|
|
ticker_data = self.populate_sell_trend(
|
|
self.populate_buy_trend(pair_data))[headers].copy()
|
|
|
|
# to avoid using data from future, we buy/sell with signal from previous candle
|
|
ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1)
|
|
ticker_data.loc[:, 'sell'] = ticker_data['sell'].shift(1)
|
|
|
|
ticker_data.drop(ticker_data.head(1).index, inplace=True)
|
|
|
|
if debug_timing: # print time taken
|
|
flt = self.f(fl)
|
|
# print("populate_buy_trend:", pair, round(flt, 10))
|
|
st = self.s()
|
|
|
|
# Convert from Pandas to list for performance reasons
|
|
# (Looping Pandas is slow.)
|
|
ticker = [x for x in ticker_data.itertuples()]
|
|
|
|
lock_pair_until = None
|
|
for index, row in enumerate(ticker):
|
|
if row.buy == 0 or row.sell == 1:
|
|
continue # skip rows where no buy signal or that would immediately sell off
|
|
|
|
if realistic:
|
|
if lock_pair_until is not None and row.date <= lock_pair_until:
|
|
continue
|
|
if max_open_trades > 0:
|
|
# Check if max_open_trades has already been reached for the given date
|
|
if not trade_count_lock.get(row.date, 0) < max_open_trades:
|
|
continue
|
|
|
|
trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1
|
|
|
|
trade_entry = self._get_sell_trade_entry(pair, row, ticker[index + 1:],
|
|
trade_count_lock, args)
|
|
|
|
if trade_entry:
|
|
lock_pair_until = trade_entry.close_time
|
|
trades.append(trade_entry)
|
|
else:
|
|
# Set lock_pair_until to end of testing period if trade could not be closed
|
|
# This happens only if the buy-signal was with the last candle
|
|
lock_pair_until = ticker_data.iloc[-1].date
|
|
|
|
if debug_timing: # print time taken
|
|
tt = self.f(st)
|
|
print("Time to BackTest :", pair, round(tt, 10))
|
|
print("-----------------------")
|
|
|
|
return DataFrame.from_records(trades, columns=BacktestResult._fields)
|
|
####################### Original BT loop end
|
|
|
|
def start(self) -> None:
|
|
"""
|
|
Run a backtesting end-to-end
|
|
:return: None
|
|
"""
|
|
data = {}
|
|
pairs = self.config['exchange']['pair_whitelist']
|
|
logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
|
|
logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
|
|
|
|
if self.config.get('live'):
|
|
logger.info('Downloading data for all pairs in whitelist ...')
|
|
for pair in pairs:
|
|
data[pair] = self.exchange.get_ticker_history(pair, self.ticker_interval)
|
|
else:
|
|
logger.info('Using local backtesting data (using whitelist in given config) ...')
|
|
|
|
timerange = Arguments.parse_timerange(None if self.config.get(
|
|
'timerange') is None else str(self.config.get('timerange')))
|
|
|
|
ld_files = self.s()
|
|
data = optimize.load_data(
|
|
self.config['datadir'],
|
|
pairs=pairs,
|
|
ticker_interval=self.ticker_interval,
|
|
refresh_pairs=self.config.get('refresh_pairs', False),
|
|
exchange=self.exchange,
|
|
timerange=timerange
|
|
)
|
|
|
|
if not data:
|
|
logger.critical("No data found. Terminating.")
|
|
return
|
|
# Ignore max_open_trades in backtesting, except realistic flag was passed
|
|
if self.config.get('realistic_simulation', False):
|
|
max_open_trades = self.config['max_open_trades']
|
|
else:
|
|
logger.info('Ignoring max_open_trades (realistic_simulation not set) ...')
|
|
max_open_trades = 0
|
|
|
|
preprocessed = self.tickerdata_to_dataframe(data)
|
|
t_t = self.f(ld_files)
|
|
print("Load from json to file to df in mem took", t_t)
|
|
|
|
# Print timeframe
|
|
min_date, max_date = self.get_timeframe(preprocessed)
|
|
logger.info(
|
|
'Measuring data from %s up to %s (%s days)..',
|
|
min_date.isoformat(),
|
|
max_date.isoformat(),
|
|
(max_date - min_date).days
|
|
)
|
|
|
|
# Execute backtest and print results
|
|
results = self.backtest(
|
|
{
|
|
'stake_amount': self.config.get('stake_amount'),
|
|
'processed': preprocessed,
|
|
'max_open_trades': max_open_trades,
|
|
'realistic': self.config.get('realistic_simulation', False),
|
|
}
|
|
)
|
|
|
|
if self.config.get('export', False):
|
|
self._store_backtest_result(self.config.get('exportfilename'), results)
|
|
|
|
if self.use_backslap:
|
|
logger.info(
|
|
'\n====================================================== '
|
|
'BackSLAP REPORT'
|
|
' =======================================================\n'
|
|
'%s',
|
|
self._generate_text_table(
|
|
data,
|
|
results
|
|
)
|
|
)
|
|
# optional print trades
|
|
if self.backslap_show_trades:
|
|
TradesFrame = results.filter(['open_time', 'pair', 'exit_type', 'profit_percent', 'profit_abs',
|
|
'buy_spend', 'sell_take', 'trade_duration', 'close_time'], axis=1)
|
|
|
|
def to_fwf(df, fname):
|
|
content = tabulate(df.values.tolist(), list(df.columns), floatfmt=".8f", tablefmt='psql')
|
|
print(content)
|
|
|
|
DataFrame.to_fwf = to_fwf(TradesFrame, "backslap.txt")
|
|
|
|
# optional save trades
|
|
if self.backslap_save_trades:
|
|
TradesFrame = results.filter(['open_time', 'pair', 'exit_type', 'profit_percent', 'profit_abs',
|
|
'buy_spend', 'sell_take', 'trade_duration', 'close_time'], axis=1)
|
|
|
|
def to_fwf(df, fname):
|
|
content = tabulate(df.values.tolist(), list(df.columns), floatfmt=".8f", tablefmt='psql')
|
|
open(fname, "w").write(content)
|
|
|
|
DataFrame.to_fwf = to_fwf(TradesFrame, "backslap.txt")
|
|
|
|
else:
|
|
logger.info(
|
|
'\n================================================= '
|
|
'BACKTEST REPORT'
|
|
' ==================================================\n'
|
|
'%s',
|
|
self._generate_text_table(
|
|
data,
|
|
results
|
|
)
|
|
)
|
|
logger.info(
|
|
'\n=============================================== '
|
|
'LEFT OPEN TRADES REPORT'
|
|
' ===============================================\n'
|
|
'%s',
|
|
self._generate_text_table(
|
|
data,
|
|
results.loc[results.open_at_end]
|
|
)
|
|
)
|
|
|
|
|
|
def setup_configuration(args: Namespace) -> Dict[str, Any]:
|
|
"""
|
|
Prepare the configuration for the backtesting
|
|
:param args: Cli args from Arguments()
|
|
:return: Configuration
|
|
"""
|
|
configuration = Configuration(args)
|
|
config = configuration.get_config()
|
|
|
|
# Ensure we do not use Exchange credentials
|
|
config['exchange']['key'] = ''
|
|
config['exchange']['secret'] = ''
|
|
|
|
if config['stake_amount'] == constants.UNLIMITED_STAKE_AMOUNT:
|
|
raise DependencyException('stake amount could not be "%s" for backtesting' %
|
|
constants.UNLIMITED_STAKE_AMOUNT)
|
|
|
|
return config
|
|
|
|
|
|
def start(args: Namespace) -> None:
|
|
"""
|
|
Start Backtesting script
|
|
:param args: Cli args from Arguments()
|
|
:return: None
|
|
"""
|
|
# Initialize configuration
|
|
config = setup_configuration(args)
|
|
logger.info('Starting freqtrade in Backtesting mode')
|
|
|
|
# Initialize backtesting object
|
|
backtesting = Backtesting(config)
|
|
backtesting.start()
|