stable/freqtrade/optimize/backtesting.py

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# pragma pylint: disable=missing-docstring, W0212, too-many-arguments
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"""
This module contains the backtesting logic
"""
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import logging
from collections import defaultdict
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from copy import deepcopy
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from datetime import datetime, timedelta, timezone
from typing import Any, Dict, List, Optional, Tuple
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from pandas import DataFrame
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from freqtrade.configuration import TimeRange, remove_credentials, validate_config_consistency
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from freqtrade.constants import DATETIME_PRINT_FORMAT
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from freqtrade.data import history
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from freqtrade.data.btanalysis import trade_list_to_dataframe
from freqtrade.data.converter import trim_dataframe
from freqtrade.data.dataprovider import DataProvider
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from freqtrade.exceptions import DependencyException, OperationalException
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from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
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from freqtrade.mixins import LoggingMixin
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from freqtrade.optimize.optimize_reports import (generate_backtest_stats, show_backtest_results,
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store_backtest_stats)
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from freqtrade.persistence import LocalTrade, PairLocks, Trade
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from freqtrade.plugins.pairlistmanager import PairListManager
from freqtrade.plugins.protectionmanager import ProtectionManager
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
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from freqtrade.strategy.interface import IStrategy, SellCheckTuple, SellType
from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
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from freqtrade.wallets import Wallets
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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
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class Backtesting:
"""
Backtesting class, this class contains all the logic to run a backtest
To run a backtest:
backtesting = Backtesting(config)
backtesting.start()
"""
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def __init__(self, config: Dict[str, Any]) -> None:
LoggingMixin.show_output = False
self.config = config
# Reset keys for backtesting
remove_credentials(self.config)
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self.strategylist: List[IStrategy] = []
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self.all_results: Dict[str, Dict] = {}
self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
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dataprovider = DataProvider(self.config, self.exchange)
IStrategy.dp = dataprovider
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if self.config.get('strategy_list', None):
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for strat in list(self.config['strategy_list']):
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stratconf = deepcopy(self.config)
stratconf['strategy'] = strat
self.strategylist.append(StrategyResolver.load_strategy(stratconf))
validate_config_consistency(stratconf)
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else:
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# No strategy list specified, only one strategy
self.strategylist.append(StrategyResolver.load_strategy(self.config))
validate_config_consistency(self.config)
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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'))
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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 'PerformanceFilter' in self.pairlists.name_list:
raise OperationalException("PerformanceFilter 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])
Trade.use_db = False
Trade.reset_trades()
PairLocks.timeframe = self.config['timeframe']
PairLocks.use_db = False
PairLocks.reset_locks()
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if self.config.get('enable_protections', False):
self.protections = ProtectionManager(self.config)
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self.wallets = Wallets(self.config, self.exchange, log=False)
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# Get maximum required startup period
self.required_startup = max([strat.startup_candle_count for strat in self.strategylist])
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# Load one (first) strategy
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self._set_strategy(self.strategylist[0])
def __del__(self):
LoggingMixin.show_output = True
PairLocks.use_db = True
Trade.use_db = True
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def _set_strategy(self, strategy: IStrategy):
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"""
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
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def load_bt_data(self) -> Tuple[Dict[str, DataFrame], TimeRange]:
"""
Loads backtest data and returns the data combined with the timerange
as tuple.
"""
timerange = TimeRange.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
data = history.load_data(
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datadir=self.config['datadir'],
pairs=self.pairlists.whitelist,
timeframe=self.timeframe,
timerange=timerange,
startup_candles=self.required_startup,
fail_without_data=True,
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data_format=self.config.get('dataformat_ohlcv', 'json'),
)
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min_date, max_date = history.get_timerange(data)
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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
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timerange.adjust_start_if_necessary(timeframe_to_seconds(self.timeframe),
self.required_startup, min_date)
return data, timerange
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def prepare_backtest(self, enable_protections):
"""
Backtesting setup method - called once for every call to "backtest()".
"""
PairLocks.use_db = False
PairLocks.timeframe = self.config['timeframe']
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Trade.use_db = False
PairLocks.reset_locks()
Trade.reset_trades()
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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():
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pair_data.loc[:, 'buy'] = 0 # cleanup from previous run
pair_data.loc[:, 'sell'] = 0 # cleanup from previous run
df_analyzed = self.strategy.advise_sell(
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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
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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] = df_analyzed.values.tolist()
return data
def _get_close_rate(self, sell_row: Tuple, trade: LocalTrade, sell: SellCheckTuple,
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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):
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roi_entry, roi = self.strategy.min_roi_reached_entry(trade_dur)
if roi is not None and roi_entry is not None:
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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
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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.
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# Applies when a new ROI setting comes in place and the whole candle is above that.
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return min(max(close_rate, sell_row[LOW_IDX]), sell_row[HIGH_IDX])
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else:
# This should not be reached...
return sell_row[OPEN_IDX]
else:
return sell_row[OPEN_IDX]
def _get_sell_trade_entry(self, dataframe: DataFrame, trade: LocalTrade,
sell_row: Tuple) -> Optional[LocalTrade]:
sell = self.strategy.should_sell(dataframe, trade, sell_row[OPEN_IDX], # type: ignore
sell_row[DATE_IDX].to_pydatetime(), sell_row[BUY_IDX],
sell_row[SELL_IDX],
low=sell_row[LOW_IDX], high=sell_row[HIGH_IDX])
if sell.sell_flag:
trade.close_date = sell_row[DATE_IDX].to_pydatetime()
trade.sell_reason = sell.sell_reason
trade_dur = int((trade.close_date_utc - trade.open_date_utc).total_seconds() // 60)
closerate = self._get_close_rate(sell_row, trade, sell, trade_dur)
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# Confirm trade exit:
time_in_force = self.strategy.order_time_in_force['sell']
if not strategy_safe_wrapper(self.strategy.confirm_trade_exit, default_retval=True)(
pair=trade.pair, trade=trade, order_type='limit', amount=trade.amount,
rate=closerate,
time_in_force=time_in_force,
sell_reason=sell.sell_reason):
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return None
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trade.close(closerate, show_msg=False)
return trade
return None
def _enter_trade(self, pair: str, row: List) -> Optional[LocalTrade]:
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try:
stake_amount = self.wallets.get_trade_stake_amount(pair, None)
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except DependencyException:
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return None
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min_stake_amount = self.exchange.get_min_pair_stake_amount(pair, row[OPEN_IDX], -0.05)
order_type = self.strategy.order_types['buy']
time_in_force = self.strategy.order_time_in_force['sell']
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# Confirm trade entry:
if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)(
pair=pair, order_type=order_type, amount=stake_amount, rate=row[OPEN_IDX],
time_in_force=time_in_force):
return None
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if stake_amount and (not min_stake_amount or stake_amount > min_stake_amount):
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# Enter trade
trade = LocalTrade(
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pair=pair,
open_rate=row[OPEN_IDX],
open_date=row[DATE_IDX].to_pydatetime(),
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stake_amount=stake_amount,
amount=round(stake_amount / row[OPEN_IDX], 8),
fee_open=self.fee,
fee_close=self.fee,
is_open=True,
exchange='backtesting',
)
return trade
return None
def handle_left_open(self, open_trades: Dict[str, List[LocalTrade]],
data: Dict[str, List[Tuple]]) -> List[LocalTrade]:
"""
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.close_date = sell_row[DATE_IDX].to_pydatetime()
trade.sell_reason = SellType.FORCE_SELL.value
trade.close(sell_row[OPEN_IDX], show_msg=False)
LocalTrade.close_bt_trade(trade)
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# Deepcopy object to have wallets update correctly
trade1 = deepcopy(trade)
trade1.is_open = True
trades.append(trade1)
return trades
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def backtest(self, processed: Dict,
start_date: datetime, end_date: datetime,
max_open_trades: int = 0, position_stacking: bool = False,
enable_protections: bool = False) -> Dict[str, Any]:
"""
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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.
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Avoid extensive logging in this method and functions it calls.
:param processed: a processed dictionary with format {pair, data}
: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?
:param enable_protections: Should protections be enabled?
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:return: DataFrame with trades (results of backtesting)
"""
trades: List[LocalTrade] = []
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self.prepare_backtest(enable_protections)
# 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)
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# Indexes per pair, so some pairs are allowed to have a missing start.
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indexes: Dict = defaultdict(int)
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tmp = start_date + timedelta(minutes=self.timeframe_min)
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open_trades: Dict[str, List[LocalTrade]] = defaultdict(list)
open_trade_count = 0
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# Loop timerange and get candle for each pair at that point in time
while tmp <= end_date:
open_trade_count_start = open_trade_count
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for i, pair in enumerate(data):
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:
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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)
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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
and not PairLocks.is_pair_locked(pair, row[DATE_IDX])):
trade = self._enter_trade(pair, row)
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if trade:
# 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
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# logger.debug(f"{pair} - Emulate creation of new trade: {trade}.")
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open_trades[pair].append(trade)
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LocalTrade.add_bt_trade(trade)
for trade in open_trades[pair]:
# also check the buying candle for sell conditions.
trade_entry = self._get_sell_trade_entry(processed[pair], trade, row)
# Sell occured
if trade_entry:
# logger.debug(f"{pair} - Backtesting sell {trade}")
open_trade_count -= 1
open_trades[pair].remove(trade)
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LocalTrade.close_bt_trade(trade)
trades.append(trade_entry)
if enable_protections:
self.protections.stop_per_pair(pair, row[DATE_IDX])
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self.protections.global_stop(tmp)
# Move time one configured time_interval ahead.
tmp += timedelta(minutes=self.timeframe_min)
trades += self.handle_left_open(open_trades, data=data)
self.wallets.update()
results = trade_list_to_dataframe(trades)
return {
'results': results,
'config': self.strategy.config,
'locks': PairLocks.get_all_locks(),
'final_balance': self.wallets.get_total(self.strategy.config['stake_currency']),
}
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def backtest_one_strategy(self, strat: IStrategy, data: Dict[str, Any], timerange: TimeRange):
logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
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backtest_start_time = datetime.now(timezone.utc)
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self._set_strategy(strat)
strategy_safe_wrapper(self.strategy.bot_loop_start, supress_error=True)()
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# 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,
startup_candles=self.required_startup)
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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 store results
results = self.backtest(
processed=preprocessed,
start_date=min_date.datetime,
end_date=max_date.datetime,
max_open_trades=max_open_trades,
position_stacking=self.config.get('position_stacking', False),
enable_protections=self.config.get('enable_protections', False),
)
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backtest_end_time = datetime.now(timezone.utc)
results.update({
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'backtest_start_time': int(backtest_start_time.timestamp()),
'backtest_end_time': int(backtest_end_time.timestamp()),
})
self.all_results[self.strategy.get_strategy_name()] = results
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return min_date, max_date
def start(self) -> None:
"""
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Run backtesting end-to-end
:return: None
"""
data: Dict[str, Any] = {}
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data, timerange = self.load_bt_data()
logger.info("Dataload complete. Calculating indicators")
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for strat in self.strategylist:
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min_date, max_date = self.backtest_one_strategy(strat, data, timerange)
if len(self.strategylist) > 0:
stats = generate_backtest_stats(data, self.all_results,
min_date=min_date, max_date=max_date)
if self.config.get('export', False):
store_backtest_stats(self.config['exportfilename'], stats)
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# Show backtest results
show_backtest_results(self.config, stats)