443 lines
19 KiB
Python
443 lines
19 KiB
Python
# pragma pylint: disable=missing-docstring, W0212, too-many-arguments
|
|
|
|
"""
|
|
This module contains the backtesting logic
|
|
"""
|
|
import logging
|
|
from collections import defaultdict
|
|
from copy import deepcopy
|
|
from datetime import datetime, timedelta
|
|
from typing import Any, Dict, List, NamedTuple, Optional, Tuple
|
|
|
|
from pandas import DataFrame
|
|
|
|
from freqtrade.configuration import TimeRange, remove_credentials, validate_config_consistency
|
|
from freqtrade.constants import DATETIME_PRINT_FORMAT
|
|
from freqtrade.data import history
|
|
from freqtrade.data.converter import trim_dataframe
|
|
from freqtrade.data.dataprovider import DataProvider
|
|
from freqtrade.exceptions import OperationalException
|
|
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
|
|
from freqtrade.optimize.optimize_reports import (generate_backtest_stats, show_backtest_results,
|
|
store_backtest_stats)
|
|
from freqtrade.pairlist.pairlistmanager import PairListManager
|
|
from freqtrade.persistence import Trade
|
|
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
|
|
from freqtrade.strategy.interface import IStrategy, SellCheckTuple, SellType
|
|
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
# Indexes for backtest tuples
|
|
DATE_IDX = 0
|
|
BUY_IDX = 1
|
|
OPEN_IDX = 2
|
|
CLOSE_IDX = 3
|
|
SELL_IDX = 4
|
|
LOW_IDX = 5
|
|
HIGH_IDX = 6
|
|
|
|
|
|
class BacktestResult(NamedTuple):
|
|
"""
|
|
NamedTuple Defining BacktestResults inputs.
|
|
"""
|
|
pair: str
|
|
profit_percent: float
|
|
profit_abs: float
|
|
open_date: datetime
|
|
open_rate: float
|
|
open_fee: float
|
|
close_date: datetime
|
|
close_rate: float
|
|
close_fee: float
|
|
amount: float
|
|
trade_duration: float
|
|
open_at_end: bool
|
|
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.load_exchange(self.config['exchange']['name'], self.config)
|
|
|
|
dataprovider = DataProvider(self.config, self.exchange)
|
|
IStrategy.dp = 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.load_strategy(stratconf))
|
|
validate_config_consistency(stratconf)
|
|
|
|
else:
|
|
# No strategy list specified, only one strategy
|
|
self.strategylist.append(StrategyResolver.load_strategy(self.config))
|
|
validate_config_consistency(self.config)
|
|
|
|
if "timeframe" not in self.config:
|
|
raise OperationalException("Timeframe (ticker interval) needs to be set in either "
|
|
"configuration or as cli argument `--timeframe 5m`")
|
|
self.timeframe = str(self.config.get('timeframe'))
|
|
self.timeframe_min = timeframe_to_minutes(self.timeframe)
|
|
|
|
self.pairlists = PairListManager(self.exchange, self.config)
|
|
if 'VolumePairList' in self.pairlists.name_list:
|
|
raise OperationalException("VolumePairList not allowed for backtesting.")
|
|
|
|
if len(self.strategylist) > 1 and 'PrecisionFilter' in self.pairlists.name_list:
|
|
raise OperationalException(
|
|
"PrecisionFilter not allowed for backtesting multiple strategies."
|
|
)
|
|
|
|
dataprovider.add_pairlisthandler(self.pairlists)
|
|
self.pairlists.refresh_pairlist()
|
|
|
|
if len(self.pairlists.whitelist) == 0:
|
|
raise OperationalException("No pair in whitelist.")
|
|
|
|
if config.get('fee', None) is not None:
|
|
self.fee = config['fee']
|
|
else:
|
|
self.fee = self.exchange.get_fee(symbol=self.pairlists.whitelist[0])
|
|
|
|
# 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: IStrategy = 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) -> Tuple[Dict[str, DataFrame], TimeRange]:
|
|
timerange = TimeRange.parse_timerange(None if self.config.get(
|
|
'timerange') is None else str(self.config.get('timerange')))
|
|
|
|
data = history.load_data(
|
|
datadir=self.config['datadir'],
|
|
pairs=self.pairlists.whitelist,
|
|
timeframe=self.timeframe,
|
|
timerange=timerange,
|
|
startup_candles=self.required_startup,
|
|
fail_without_data=True,
|
|
data_format=self.config.get('dataformat_ohlcv', 'json'),
|
|
)
|
|
|
|
min_date, max_date = history.get_timerange(data)
|
|
|
|
logger.info(f'Loading data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
|
|
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
|
|
f'({(max_date - min_date).days} 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 _get_ohlcv_as_lists(self, processed: Dict[str, DataFrame]) -> Dict[str, Tuple]:
|
|
"""
|
|
Helper function to convert a processed dataframes into lists for performance reasons.
|
|
|
|
Used by backtest() - so keep this optimized for performance.
|
|
"""
|
|
# Every change to this headers list must evaluate further usages of the resulting tuple
|
|
# and eventually change the constants for indexes at the top
|
|
headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high']
|
|
data: Dict = {}
|
|
# Create dict with data
|
|
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
|
|
|
|
df_analyzed = self.strategy.advise_sell(
|
|
self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
|
|
|
|
# To avoid using data from future, we use buy/sell signals shifted
|
|
# from the previous candle
|
|
df_analyzed.loc[:, 'buy'] = df_analyzed.loc[:, 'buy'].shift(1)
|
|
df_analyzed.loc[:, 'sell'] = df_analyzed.loc[:, 'sell'].shift(1)
|
|
|
|
df_analyzed.drop(df_analyzed.head(1).index, inplace=True)
|
|
|
|
# Convert from Pandas to list for performance reasons
|
|
# (Looping Pandas is slow.)
|
|
data[pair] = [x for x in df_analyzed.itertuples(index=False, name=None)]
|
|
return data
|
|
|
|
def _get_close_rate(self, sell_row: Tuple, trade: Trade, sell: SellCheckTuple,
|
|
trade_dur: int) -> 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 and roi_entry 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_IDX]
|
|
|
|
# - (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_IDX] > close_rate):
|
|
# new ROI entry came into effect.
|
|
# use Open rate if open_rate > calculated sell rate
|
|
return sell_row[OPEN_IDX]
|
|
|
|
# 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_IDX])
|
|
|
|
else:
|
|
# This should not be reached...
|
|
return sell_row[OPEN_IDX]
|
|
else:
|
|
return sell_row[OPEN_IDX]
|
|
|
|
def _get_sell_trade_entry(self, trade: Trade, sell_row: Tuple) -> Optional[BacktestResult]:
|
|
|
|
sell = self.strategy.should_sell(trade, sell_row[OPEN_IDX], sell_row[DATE_IDX],
|
|
sell_row[BUY_IDX], sell_row[SELL_IDX],
|
|
low=sell_row[LOW_IDX], high=sell_row[HIGH_IDX])
|
|
if sell.sell_flag:
|
|
trade_dur = int((sell_row[DATE_IDX] - trade.open_date).total_seconds() // 60)
|
|
closerate = self._get_close_rate(sell_row, trade, sell, trade_dur)
|
|
|
|
return BacktestResult(pair=trade.pair,
|
|
profit_percent=trade.calc_profit_ratio(rate=closerate),
|
|
profit_abs=trade.calc_profit(rate=closerate),
|
|
open_date=trade.open_date,
|
|
open_rate=trade.open_rate,
|
|
open_fee=self.fee,
|
|
close_date=sell_row[DATE_IDX],
|
|
close_rate=closerate,
|
|
close_fee=self.fee,
|
|
amount=trade.amount,
|
|
trade_duration=trade_dur,
|
|
open_at_end=False,
|
|
sell_reason=sell.sell_type
|
|
)
|
|
return None
|
|
|
|
def handle_left_open(self, open_trades: Dict[str, List[Trade]],
|
|
data: Dict[str, List[Tuple]]) -> List[BacktestResult]:
|
|
"""
|
|
Handling of left open trades at the end of backtesting
|
|
"""
|
|
trades = []
|
|
for pair in open_trades.keys():
|
|
if len(open_trades[pair]) > 0:
|
|
for trade in open_trades[pair]:
|
|
sell_row = data[pair][-1]
|
|
trade_entry = BacktestResult(pair=trade.pair,
|
|
profit_percent=trade.calc_profit_ratio(
|
|
rate=sell_row[OPEN_IDX]),
|
|
profit_abs=trade.calc_profit(sell_row[OPEN_IDX]),
|
|
open_date=trade.open_date,
|
|
open_rate=trade.open_rate,
|
|
open_fee=self.fee,
|
|
close_date=sell_row[DATE_IDX],
|
|
close_rate=sell_row[OPEN_IDX],
|
|
close_fee=self.fee,
|
|
amount=trade.amount,
|
|
trade_duration=int((
|
|
sell_row[DATE_IDX] - trade.open_date
|
|
).total_seconds() // 60),
|
|
open_at_end=True,
|
|
sell_reason=SellType.FORCE_SELL
|
|
)
|
|
trades.append(trade_entry)
|
|
return trades
|
|
|
|
def backtest(self, processed: Dict, stake_amount: float,
|
|
start_date: datetime, end_date: datetime,
|
|
max_open_trades: int = 0, position_stacking: bool = False) -> DataFrame:
|
|
"""
|
|
Implement 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 extensive logging in this method and functions it calls.
|
|
|
|
:param processed: a processed dictionary with format {pair, data}
|
|
:param stake_amount: amount to use for each trade
|
|
:param start_date: backtesting timerange start datetime
|
|
:param end_date: backtesting timerange end datetime
|
|
:param max_open_trades: maximum number of concurrent trades, <= 0 means unlimited
|
|
:param position_stacking: do we allow position stacking?
|
|
:return: DataFrame with trades (results of backtesting)
|
|
"""
|
|
logger.debug(f"Run backtest, stake_amount: {stake_amount}, "
|
|
f"start_date: {start_date}, end_date: {end_date}, "
|
|
f"max_open_trades: {max_open_trades}, position_stacking: {position_stacking}"
|
|
)
|
|
trades = []
|
|
|
|
# Use dict of lists with data for performance
|
|
# (looping lists is a lot faster than pandas DataFrames)
|
|
data: Dict = self._get_ohlcv_as_lists(processed)
|
|
|
|
# Indexes per pair, so some pairs are allowed to have a missing start.
|
|
indexes: Dict = {}
|
|
tmp = start_date + timedelta(minutes=self.timeframe_min)
|
|
|
|
open_trades: Dict[str, List] = defaultdict(list)
|
|
open_trade_count = 0
|
|
|
|
# Loop timerange and get candle for each pair at that point in time
|
|
while tmp <= end_date:
|
|
open_trade_count_start = open_trade_count
|
|
|
|
for i, pair in enumerate(data):
|
|
if pair not in indexes:
|
|
indexes[pair] = 0
|
|
|
|
try:
|
|
row = data[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_IDX] > tmp:
|
|
continue
|
|
indexes[pair] += 1
|
|
|
|
# without positionstacking, we can only have one open trade per pair.
|
|
# max_open_trades must be respected
|
|
# don't open on the last row
|
|
if ((position_stacking or len(open_trades[pair]) == 0)
|
|
and (max_open_trades <= 0 or open_trade_count_start < max_open_trades)
|
|
and tmp != end_date
|
|
and row[BUY_IDX] == 1 and row[SELL_IDX] != 1):
|
|
# Enter trade
|
|
trade = Trade(
|
|
pair=pair,
|
|
open_rate=row[OPEN_IDX],
|
|
open_date=row[DATE_IDX],
|
|
stake_amount=stake_amount,
|
|
amount=round(stake_amount / row[OPEN_IDX], 8),
|
|
fee_open=self.fee,
|
|
fee_close=self.fee,
|
|
is_open=True,
|
|
)
|
|
# TODO: hacky workaround to avoid opening > max_open_trades
|
|
# This emulates previous behaviour - not sure if this is correct
|
|
# Prevents buying if the trade-slot was freed in this candle
|
|
open_trade_count_start += 1
|
|
open_trade_count += 1
|
|
# logger.debug(f"{pair} - Backtesting emulates creation of new trade: {trade}.")
|
|
open_trades[pair].append(trade)
|
|
|
|
for trade in open_trades[pair]:
|
|
# 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(trade, row)
|
|
# Sell occured
|
|
if trade_entry:
|
|
# logger.debug(f"{pair} - Backtesting sell {trade}")
|
|
open_trade_count -= 1
|
|
open_trades[pair].remove(trade)
|
|
trades.append(trade_entry)
|
|
|
|
# Move time one configured time_interval ahead.
|
|
tmp += timedelta(minutes=self.timeframe_min)
|
|
|
|
trades += self.handle_left_open(open_trades, data=data)
|
|
|
|
return DataFrame.from_records(trades, columns=BacktestResult._fields)
|
|
|
|
def start(self) -> None:
|
|
"""
|
|
Run 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'])
|
|
|
|
position_stacking = self.config.get('position_stacking', False)
|
|
|
|
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)
|
|
|
|
# Use max_open_trades in backtesting, except --disable-max-market-positions is set
|
|
if self.config.get('use_max_market_positions', True):
|
|
# Must come from strategy config, as the strategy may modify this setting.
|
|
max_open_trades = self.strategy.config['max_open_trades']
|
|
else:
|
|
logger.info(
|
|
'Ignoring max_open_trades (--disable-max-market-positions was used) ...')
|
|
max_open_trades = 0
|
|
|
|
# need to reprocess data every time to populate signals
|
|
preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
|
|
|
|
# Trim startup period from analyzed dataframe
|
|
for pair, df in preprocessed.items():
|
|
preprocessed[pair] = trim_dataframe(df, timerange)
|
|
min_date, max_date = history.get_timerange(preprocessed)
|
|
|
|
logger.info(f'Backtesting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
|
|
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
|
|
f'({(max_date - min_date).days} days)..')
|
|
# Execute backtest and print results
|
|
results = self.backtest(
|
|
processed=preprocessed,
|
|
stake_amount=self.config['stake_amount'],
|
|
start_date=min_date.datetime,
|
|
end_date=max_date.datetime,
|
|
max_open_trades=max_open_trades,
|
|
position_stacking=position_stacking,
|
|
)
|
|
all_results[self.strategy.get_strategy_name()] = {
|
|
'results': results,
|
|
'config': self.strategy.config,
|
|
}
|
|
|
|
stats = generate_backtest_stats(data, all_results, min_date=min_date, max_date=max_date)
|
|
|
|
if self.config.get('export', False):
|
|
store_backtest_stats(self.config['exportfilename'], stats)
|
|
|
|
# Show backtest results
|
|
show_backtest_results(self.config, stats)
|