523 lines
23 KiB
Python
523 lines
23 KiB
Python
# pragma pylint: disable=missing-docstring, W0212, too-many-arguments
|
|
|
|
"""
|
|
This module contains the backtesting logic
|
|
"""
|
|
import logging
|
|
from copy import deepcopy
|
|
from datetime import datetime, timedelta
|
|
from pathlib import Path
|
|
from typing import Any, Dict, List, NamedTuple, Optional
|
|
|
|
from pandas import DataFrame
|
|
from tabulate import tabulate
|
|
|
|
from freqtrade import OperationalException
|
|
from freqtrade.configuration import (TimeRange, remove_credentials,
|
|
validate_config_consistency)
|
|
from freqtrade.data import history
|
|
from freqtrade.data.dataprovider import DataProvider
|
|
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
|
|
from freqtrade.misc import file_dump_json
|
|
from freqtrade.persistence import Trade
|
|
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
|
|
from freqtrade.state import RunMode
|
|
from freqtrade.strategy.interface import IStrategy, SellType
|
|
|
|
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
|
|
sell_reason: SellType
|
|
|
|
|
|
class Backtesting:
|
|
"""
|
|
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
|
|
|
|
# Reset keys for backtesting
|
|
remove_credentials(self.config)
|
|
self.strategylist: List[IStrategy] = []
|
|
self.exchange = ExchangeResolver(self.config['exchange']['name'], self.config).exchange
|
|
|
|
if config.get('fee'):
|
|
self.fee = config['fee']
|
|
else:
|
|
self.fee = self.exchange.get_fee(symbol=self.config['exchange']['pair_whitelist'][0])
|
|
|
|
if self.config.get('runmode') != RunMode.HYPEROPT:
|
|
self.dataprovider = DataProvider(self.config, self.exchange)
|
|
IStrategy.dp = self.dataprovider
|
|
|
|
if self.config.get('strategy_list', None):
|
|
for strat in list(self.config['strategy_list']):
|
|
stratconf = deepcopy(self.config)
|
|
stratconf['strategy'] = strat
|
|
self.strategylist.append(StrategyResolver(stratconf).strategy)
|
|
validate_config_consistency(stratconf)
|
|
|
|
else:
|
|
# No strategy list specified, only one strategy
|
|
self.strategylist.append(StrategyResolver(self.config).strategy)
|
|
validate_config_consistency(self.config)
|
|
|
|
if "ticker_interval" not in self.config:
|
|
raise OperationalException("Ticker-interval needs to be set in either configuration "
|
|
"or as cli argument `--ticker-interval 5m`")
|
|
self.timeframe = str(self.config.get('ticker_interval'))
|
|
self.timeframe_min = timeframe_to_minutes(self.timeframe)
|
|
|
|
# Get maximum required startup period
|
|
self.required_startup = max([strat.startup_candle_count for strat in self.strategylist])
|
|
# Load one (first) strategy
|
|
self._set_strategy(self.strategylist[0])
|
|
|
|
def _set_strategy(self, strategy):
|
|
"""
|
|
Load strategy into backtesting
|
|
"""
|
|
self.strategy = strategy
|
|
# Set stoploss_on_exchange to false for backtesting,
|
|
# since a "perfect" stoploss-sell is assumed anyway
|
|
# And the regular "stoploss" function would not apply to that case
|
|
self.strategy.order_types['stoploss_on_exchange'] = False
|
|
|
|
def load_bt_data(self):
|
|
timerange = TimeRange.parse_timerange(None if self.config.get(
|
|
'timerange') is None else str(self.config.get('timerange')))
|
|
|
|
data = history.load_data(
|
|
datadir=Path(self.config['datadir']),
|
|
pairs=self.config['exchange']['pair_whitelist'],
|
|
timeframe=self.timeframe,
|
|
timerange=timerange,
|
|
startup_candles=self.required_startup,
|
|
fail_without_data=True,
|
|
)
|
|
|
|
min_date, max_date = history.get_timeframe(data)
|
|
|
|
logger.info(
|
|
'Loading data from %s up to %s (%s days)..',
|
|
min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
|
|
)
|
|
# Adjust startts forward if not enough data is available
|
|
timerange.adjust_start_if_necessary(timeframe_to_seconds(self.timeframe),
|
|
self.required_startup, min_date)
|
|
|
|
return data, timerange
|
|
|
|
def _generate_text_table(self, data: Dict[str, Dict], results: DataFrame,
|
|
skip_nan: bool = False) -> 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'))
|
|
max_open_trades = self.config.get('max_open_trades')
|
|
|
|
floatfmt = ('s', 'd', '.2f', '.2f', '.8f', '.2f', 'd', '.1f', '.1f')
|
|
tabular_data = []
|
|
headers = ['pair', 'buy count', 'avg profit %', 'cum profit %',
|
|
'tot profit ' + stake_currency, 'tot profit %', 'avg duration',
|
|
'profit', 'loss']
|
|
for pair in data:
|
|
result = results[results.pair == pair]
|
|
if skip_nan and result.profit_abs.isnull().all():
|
|
continue
|
|
|
|
tabular_data.append([
|
|
pair,
|
|
len(result.index),
|
|
result.profit_percent.mean() * 100.0,
|
|
result.profit_percent.sum() * 100.0,
|
|
result.profit_abs.sum(),
|
|
result.profit_percent.sum() * 100.0 / max_open_trades,
|
|
str(timedelta(
|
|
minutes=round(result.trade_duration.mean()))) if not result.empty else '0:00',
|
|
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.profit_percent.sum() * 100.0 / max_open_trades,
|
|
str(timedelta(
|
|
minutes=round(results.trade_duration.mean()))) if not results.empty else '0:00',
|
|
len(results[results.profit_abs > 0]),
|
|
len(results[results.profit_abs < 0])
|
|
])
|
|
# Ignore type as floatfmt does allow tuples but mypy does not know that
|
|
return tabulate(tabular_data, headers=headers,
|
|
floatfmt=floatfmt, tablefmt="pipe") # type: ignore
|
|
|
|
def _generate_text_table_sell_reason(self, data: Dict[str, Dict], results: DataFrame) -> str:
|
|
"""
|
|
Generate small table outlining Backtest results
|
|
"""
|
|
tabular_data = []
|
|
headers = ['Sell Reason', 'Count']
|
|
for reason, count in results['sell_reason'].value_counts().iteritems():
|
|
tabular_data.append([reason.value, count])
|
|
return tabulate(tabular_data, headers=headers, tablefmt="pipe")
|
|
|
|
def _generate_text_table_strategy(self, all_results: dict) -> str:
|
|
"""
|
|
Generate summary table per strategy
|
|
"""
|
|
stake_currency = str(self.config.get('stake_currency'))
|
|
max_open_trades = self.config.get('max_open_trades')
|
|
|
|
floatfmt = ('s', 'd', '.2f', '.2f', '.8f', '.2f', 'd', '.1f', '.1f')
|
|
tabular_data = []
|
|
headers = ['Strategy', 'buy count', 'avg profit %', 'cum profit %',
|
|
'tot profit ' + stake_currency, 'tot profit %', 'avg duration',
|
|
'profit', 'loss']
|
|
for strategy, results in all_results.items():
|
|
tabular_data.append([
|
|
strategy,
|
|
len(results.index),
|
|
results.profit_percent.mean() * 100.0,
|
|
results.profit_percent.sum() * 100.0,
|
|
results.profit_abs.sum(),
|
|
results.profit_percent.sum() * 100.0 / max_open_trades,
|
|
str(timedelta(
|
|
minutes=round(results.trade_duration.mean()))) if not results.empty else '0:00',
|
|
len(results[results.profit_abs > 0]),
|
|
len(results[results.profit_abs < 0])
|
|
])
|
|
# Ignore type as floatfmt does allow tuples but mypy does not know that
|
|
return tabulate(tabular_data, headers=headers,
|
|
floatfmt=floatfmt, tablefmt="pipe") # type: ignore
|
|
|
|
def _store_backtest_result(self, recordfilename: Path, results: DataFrame,
|
|
strategyname: Optional[str] = None) -> 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, t.sell_reason.value)
|
|
for index, t in results.iterrows()]
|
|
|
|
if records:
|
|
if strategyname:
|
|
# Inject strategyname to filename
|
|
recordfilename = Path.joinpath(
|
|
recordfilename.parent,
|
|
f'{recordfilename.stem}-{strategyname}').with_suffix(recordfilename.suffix)
|
|
logger.info(f'Dumping backtest results to {recordfilename}')
|
|
file_dump_json(recordfilename, records)
|
|
|
|
def _get_ticker_list(self, processed) -> Dict[str, DataFrame]:
|
|
"""
|
|
Helper function to convert a processed tickerlist into a list for performance reasons.
|
|
|
|
Used by backtest() - so keep this optimized for performance.
|
|
"""
|
|
headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high']
|
|
ticker: Dict = {}
|
|
# Create ticker dict
|
|
for pair, pair_data in processed.items():
|
|
pair_data.loc[:, 'buy'] = 0 # cleanup from previous run
|
|
pair_data.loc[:, 'sell'] = 0 # cleanup from previous run
|
|
|
|
ticker_data = self.strategy.advise_sell(
|
|
self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[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)
|
|
|
|
# Convert from Pandas to list for performance reasons
|
|
# (Looping Pandas is slow.)
|
|
ticker[pair] = [x for x in ticker_data.itertuples()]
|
|
return ticker
|
|
|
|
def _get_close_rate(self, sell_row, trade: Trade, sell, trade_dur) -> float:
|
|
"""
|
|
Get close rate for backtesting result
|
|
"""
|
|
# Special handling if high or low hit STOP_LOSS or ROI
|
|
if sell.sell_type in (SellType.STOP_LOSS, SellType.TRAILING_STOP_LOSS):
|
|
# Set close_rate to stoploss
|
|
return trade.stop_loss
|
|
elif sell.sell_type == (SellType.ROI):
|
|
roi_entry, roi = self.strategy.min_roi_reached_entry(trade_dur)
|
|
if roi is not None:
|
|
if roi == -1 and roi_entry % self.timeframe_min == 0:
|
|
# When forceselling with ROI=-1, the roi time will always be equal to trade_dur.
|
|
# If that entry is a multiple of the timeframe (so on candle open)
|
|
# - we'll use open instead of close
|
|
return sell_row.open
|
|
|
|
# - (Expected abs profit + open_rate + open_fee) / (fee_close -1)
|
|
close_rate = - (trade.open_rate * roi + trade.open_rate *
|
|
(1 + trade.fee_open)) / (trade.fee_close - 1)
|
|
|
|
if (trade_dur > 0 and trade_dur == roi_entry
|
|
and roi_entry % self.timeframe_min == 0
|
|
and sell_row.open > close_rate):
|
|
# new ROI entry came into effect.
|
|
# use Open rate if open_rate > calculated sell rate
|
|
return sell_row.open
|
|
|
|
# Use the maximum between close_rate and low as we
|
|
# cannot sell outside of a candle.
|
|
# Applies when a new ROI setting comes in place and the whole candle is above that.
|
|
return max(close_rate, sell_row.low)
|
|
|
|
else:
|
|
# This should not be reached...
|
|
return sell_row.open
|
|
else:
|
|
return sell_row.open
|
|
|
|
def _get_sell_trade_entry(
|
|
self, pair: str, buy_row: DataFrame,
|
|
partial_ticker: List, trade_count_lock: Dict,
|
|
stake_amount: float, max_open_trades: int) -> Optional[BacktestResult]:
|
|
|
|
trade = Trade(
|
|
pair=pair,
|
|
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,
|
|
is_open=True,
|
|
)
|
|
logger.debug(f"{pair} - Backtesting emulates creation of new trade: {trade}.")
|
|
# 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
|
|
|
|
sell = self.strategy.should_sell(trade, sell_row.open, sell_row.date, sell_row.buy,
|
|
sell_row.sell, low=sell_row.low, high=sell_row.high)
|
|
if sell.sell_flag:
|
|
trade_dur = int((sell_row.date - buy_row.date).total_seconds() // 60)
|
|
closerate = self._get_close_rate(sell_row, trade, sell, trade_dur)
|
|
|
|
return BacktestResult(pair=pair,
|
|
profit_percent=trade.calc_profit_ratio(rate=closerate),
|
|
profit_abs=trade.calc_profit(rate=closerate),
|
|
open_time=buy_row.date,
|
|
close_time=sell_row.date,
|
|
trade_duration=trade_dur,
|
|
open_index=buy_row.Index,
|
|
close_index=sell_row.Index,
|
|
open_at_end=False,
|
|
open_rate=buy_row.open,
|
|
close_rate=closerate,
|
|
sell_reason=sell.sell_type
|
|
)
|
|
if partial_ticker:
|
|
# no sell condition found - trade stil open at end of backtest period
|
|
sell_row = partial_ticker[-1]
|
|
bt_res = BacktestResult(pair=pair,
|
|
profit_percent=trade.calc_profit_ratio(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=int((
|
|
sell_row.date - buy_row.date).total_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,
|
|
sell_reason=SellType.FORCE_SELL
|
|
)
|
|
logger.debug(f"{pair} - Force selling still open trade, "
|
|
f"profit percent: {bt_res.profit_percent}, "
|
|
f"profit abs: {bt_res.profit_abs}")
|
|
|
|
return bt_res
|
|
return None
|
|
|
|
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)
|
|
position_stacking: do we allow position stacking? (default: False)
|
|
:return: DataFrame
|
|
"""
|
|
# Arguments are long and noisy, so this is commented out.
|
|
# Uncomment if you need to debug the backtest() method.
|
|
# logger.debug(f"Start backtest, args: {args}")
|
|
processed = args['processed']
|
|
stake_amount = args['stake_amount']
|
|
max_open_trades = args.get('max_open_trades', 0)
|
|
position_stacking = args.get('position_stacking', False)
|
|
start_date = args['start_date']
|
|
end_date = args['end_date']
|
|
trades = []
|
|
trade_count_lock: Dict = {}
|
|
|
|
# Dict of ticker-lists for performance (looping lists is a lot faster than dataframes)
|
|
ticker: Dict = self._get_ticker_list(processed)
|
|
|
|
lock_pair_until: Dict = {}
|
|
# Indexes per pair, so some pairs are allowed to have a missing start.
|
|
indexes: Dict = {}
|
|
tmp = start_date + timedelta(minutes=self.timeframe_min)
|
|
|
|
# Loop timerange and get candle for each pair at that point in time
|
|
while tmp < end_date:
|
|
|
|
for i, pair in enumerate(ticker):
|
|
if pair not in indexes:
|
|
indexes[pair] = 0
|
|
|
|
try:
|
|
row = ticker[pair][indexes[pair]]
|
|
except IndexError:
|
|
# missing Data for one pair at the end.
|
|
# Warnings for this are shown during data loading
|
|
continue
|
|
|
|
# Waits until the time-counter reaches the start of the data for this pair.
|
|
if row.date > tmp.datetime:
|
|
continue
|
|
|
|
indexes[pair] += 1
|
|
|
|
if row.buy == 0 or row.sell == 1:
|
|
continue # skip rows where no buy signal or that would immediately sell off
|
|
|
|
if (not position_stacking and pair in lock_pair_until
|
|
and row.date <= lock_pair_until[pair]):
|
|
# without positionstacking, we can only have one open trade per pair.
|
|
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
|
|
|
|
# since indexes has been incremented before, we need to go one step back to
|
|
# also check the buying candle for sell conditions.
|
|
trade_entry = self._get_sell_trade_entry(pair, row, ticker[pair][indexes[pair]-1:],
|
|
trade_count_lock, stake_amount,
|
|
max_open_trades)
|
|
|
|
if trade_entry:
|
|
logger.debug(f"{pair} - Locking pair till "
|
|
f"close_time={trade_entry.close_time}")
|
|
lock_pair_until[pair] = trade_entry.close_time
|
|
trades.append(trade_entry)
|
|
else:
|
|
# Set lock_pair_until to end of testing period if trade could not be closed
|
|
lock_pair_until[pair] = end_date.datetime
|
|
|
|
# Move time one configured time_interval ahead.
|
|
tmp += timedelta(minutes=self.timeframe_min)
|
|
return DataFrame.from_records(trades, columns=BacktestResult._fields)
|
|
|
|
def start(self) -> None:
|
|
"""
|
|
Run a backtesting end-to-end
|
|
:return: None
|
|
"""
|
|
data: Dict[str, Any] = {}
|
|
logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
|
|
logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
|
|
# Use max_open_trades in backtesting, except --disable-max-market-positions is set
|
|
if self.config.get('use_max_market_positions', True):
|
|
max_open_trades = self.config['max_open_trades']
|
|
else:
|
|
logger.info('Ignoring max_open_trades (--disable-max-market-positions was used) ...')
|
|
max_open_trades = 0
|
|
|
|
data, timerange = self.load_bt_data()
|
|
|
|
all_results = {}
|
|
for strat in self.strategylist:
|
|
logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
|
|
self._set_strategy(strat)
|
|
|
|
# need to reprocess data every time to populate signals
|
|
preprocessed = self.strategy.tickerdata_to_dataframe(data)
|
|
|
|
# Trim startup period from analyzed dataframe
|
|
for pair, df in preprocessed.items():
|
|
preprocessed[pair] = history.trim_dataframe(df, timerange)
|
|
min_date, max_date = history.get_timeframe(preprocessed)
|
|
|
|
logger.info(
|
|
'Backtesting with data from %s up to %s (%s days)..',
|
|
min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
|
|
)
|
|
# Execute backtest and print results
|
|
all_results[self.strategy.get_strategy_name()] = self.backtest(
|
|
{
|
|
'stake_amount': self.config.get('stake_amount'),
|
|
'processed': preprocessed,
|
|
'max_open_trades': max_open_trades,
|
|
'position_stacking': self.config.get('position_stacking', False),
|
|
'start_date': min_date,
|
|
'end_date': max_date,
|
|
}
|
|
)
|
|
|
|
for strategy, results in all_results.items():
|
|
|
|
if self.config.get('export', False):
|
|
self._store_backtest_result(Path(self.config['exportfilename']), results,
|
|
strategy if len(self.strategylist) > 1 else None)
|
|
|
|
print(f"Result for strategy {strategy}")
|
|
print(' BACKTESTING REPORT '.center(133, '='))
|
|
print(self._generate_text_table(data, results))
|
|
|
|
print(' SELL REASON STATS '.center(133, '='))
|
|
print(self._generate_text_table_sell_reason(data, results))
|
|
|
|
print(' LEFT OPEN TRADES REPORT '.center(133, '='))
|
|
print(self._generate_text_table(data, results.loc[results.open_at_end], True))
|
|
print()
|
|
if len(all_results) > 1:
|
|
# Print Strategy summary table
|
|
print(' Strategy Summary '.center(133, '='))
|
|
print(self._generate_text_table_strategy(all_results))
|
|
print('\nFor more details, please look at the detail tables above')
|