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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Fix exception when few pairs with no data do not result in aborting backtest. Exception is triggered by backtesting 20210301-20210501 range with BAKE/USDT pair (binance). Pair data starts on 2021-04-30 12:00:00 and after adjusting for startup candles pair dataframe is empty. Solution: Since there are other pairs with enough data - skip pairs with no data and issue a warning. Exception: ``` Traceback (most recent call last): File "/home/rk/src/freqtrade/freqtrade/main.py", line 37, in main return_code = args['func'](args) File "/home/rk/src/freqtrade/freqtrade/commands/optimize_commands.py", line 53, in start_backtesting backtesting.start() File "/home/rk/src/freqtrade/freqtrade/optimize/backtesting.py", line 502, in start min_date, max_date = self.backtest_one_strategy(strat, data, timerange) File "/home/rk/src/freqtrade/freqtrade/optimize/backtesting.py", line 474, in backtest_one_strategy results = self.backtest( File "/home/rk/src/freqtrade/freqtrade/optimize/backtesting.py", line 365, in backtest data: Dict = self._get_ohlcv_as_lists(processed) File "/home/rk/src/freqtrade/freqtrade/optimize/backtesting.py", line 199, in _get_ohlcv_as_lists pair_data.loc[:, 'buy'] = 0 # cleanup from previous run File "/home/rk/src/freqtrade/venv/lib/python3.9/site-packages/pandas/core/indexing.py", line 692, in __setitem__ iloc._setitem_with_indexer(indexer, value, self.name) File "/home/rk/src/freqtrade/venv/lib/python3.9/site-packages/pandas/core/indexing.py", line 1587, in _setitem_with_indexer raise ValueError( ValueError: cannot set a frame with no defined index and a scalar ```
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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, trim_dataframes
from freqtrade.data.dataprovider import DataProvider
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from freqtrade.enums import BacktestState, SellType
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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.bt_progress import BTProgress
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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
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
OPEN_IDX = 1
HIGH_IDX = 2
LOW_IDX = 3
CLOSE_IDX = 4
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LONG_IDX = 5
ELONG_IDX = 6 # Exit long
SHORT_IDX = 7
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ESHORT_IDX = 8 # Exit short
BUY_TAG_IDX = 9
SHORT_TAG_IDX = 10
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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
self.results: Optional[Dict[str, Any]] = None
# 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_name = self.config['exchange']['name']
self.exchange = ExchangeResolver.load_exchange(self._exchange_name, self.config)
self.dataprovider = DataProvider(self.config, None)
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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)
# Load detail timeframe if specified
self.timeframe_detail = str(self.config.get('timeframe_detail', ''))
if self.timeframe_detail:
self.timeframe_detail_min = timeframe_to_minutes(self.timeframe_detail)
if self.timeframe_min <= self.timeframe_detail_min:
raise OperationalException(
"Detail timeframe must be smaller than strategy timeframe.")
else:
self.timeframe_detail_min = 0
self.detail_data: Dict[str, DataFrame] = {}
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."
)
self.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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self.wallets = Wallets(self.config, self.exchange, log=False)
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self.timerange = TimeRange.parse_timerange(
None if self.config.get('timerange') is None else str(self.config.get('timerange')))
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# Get maximum required startup period
self.required_startup = max([strat.startup_candle_count for strat in self.strategylist])
# Add maximum startup candle count to configuration for informative pairs support
self.config['startup_candle_count'] = self.required_startup
self.exchange.validate_required_startup_candles(self.required_startup, self.timeframe)
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self.progress = BTProgress()
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self.abort = False
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def __del__(self):
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self.cleanup()
def cleanup(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
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strategy.dp = self.dataprovider
# Attach Wallets to Strategy baseclass
IStrategy.wallets = self.wallets
# 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_protections(self, strategy: IStrategy):
if self.config.get('enable_protections', False):
conf = self.config
if hasattr(strategy, 'protections'):
conf = deepcopy(conf)
conf['protections'] = strategy.protections
self.protections = ProtectionManager(self.config, strategy.protections)
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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.
"""
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self.progress.init_step(BacktestState.DATALOAD, 1)
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data = history.load_data(
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datadir=self.config['datadir'],
pairs=self.pairlists.whitelist,
timeframe=self.timeframe,
timerange=self.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)} '
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f'({(max_date - min_date).days} days).')
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# Adjust startts forward if not enough data is available
self.timerange.adjust_start_if_necessary(timeframe_to_seconds(self.timeframe),
self.required_startup, min_date)
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self.progress.set_new_value(1)
return data, self.timerange
def load_bt_data_detail(self) -> None:
"""
Loads backtest detail data (smaller timeframe) if necessary.
"""
if self.timeframe_detail:
self.detail_data = history.load_data(
datadir=self.config['datadir'],
pairs=self.pairlists.whitelist,
timeframe=self.timeframe_detail,
timerange=self.timerange,
startup_candles=0,
fail_without_data=True,
data_format=self.config.get('dataformat_ohlcv', 'json'),
)
else:
self.detail_data = {}
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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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self.rejected_trades = 0
self.dataprovider.clear_cache()
self._load_protections(self.strategy)
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def check_abort(self):
"""
Check if abort was requested, raise DependencyException if that's the case
Only applies to Interactive backtest mode (webserver mode)
"""
if self.abort:
self.abort = False
raise DependencyException("Stop requested")
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', 'open', 'high', 'low', 'close', 'enter_long', 'exit_long',
'enter_short', 'exit_short', 'long_tag', 'short_tag']
data: Dict = {}
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self.progress.init_step(BacktestState.CONVERT, len(processed))
# Create dict with data
for pair, pair_data in processed.items():
self.check_abort()
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self.progress.increment()
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if not pair_data.empty:
# Cleanup from prior runs
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# TODO-lev: The below is not 100% compatible with the interface compatibility layer
if 'enter_long' in pair_data.columns:
pair_data.loc[:, 'enter_long'] = 0
pair_data.loc[:, 'enter_short'] = 0
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if 'exit_long' in pair_data.columns:
pair_data.loc[:, 'exit_long'] = 0
pair_data.loc[:, 'exit_short'] = 0
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pair_data.loc[:, 'long_tag'] = None
pair_data.loc[:, 'short_tag'] = None
df_analyzed = self.strategy.advise_sell(
self.strategy.advise_buy(pair_data, {'pair': pair}),
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{'pair': pair}
).copy()
# Trim startup period from analyzed dataframe
df_analyzed = trim_dataframe(df_analyzed, self.timerange,
startup_candles=self.required_startup)
# To avoid using data from future, we use buy/sell signals shifted
# from the previous candle
df_analyzed.loc[:, 'enter_long'] = df_analyzed.loc[:, 'enter_long'].shift(1)
df_analyzed.loc[:, 'enter_short'] = df_analyzed.loc[:, 'enter_short'].shift(1)
df_analyzed.loc[:, 'exit_long'] = df_analyzed.loc[:, 'exit_long'].shift(1)
df_analyzed.loc[:, 'exit_short'] = df_analyzed.loc[:, 'exit_short'].shift(1)
df_analyzed.loc[:, 'long_tag'] = df_analyzed.loc[:, 'long_tag'].shift(1)
# Update dataprovider cache
self.dataprovider._set_cached_df(pair, self.timeframe, df_analyzed)
df_analyzed = df_analyzed.drop(df_analyzed.head(1).index)
# Convert from Pandas to list for performance reasons
# (Looping Pandas is slow.)
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data[pair] = df_analyzed[headers].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):
if trade.stop_loss > sell_row[HIGH_IDX]:
# our stoploss was already higher than candle high,
# possibly due to a cancelled trade exit.
# sell at open price.
return sell_row[OPEN_IDX]
# Special case: trailing triggers within same candle as trade opened. Assume most
# pessimistic price movement, which is moving just enough to arm stoploss and
# immediately going down to stop price.
if sell.sell_type == SellType.TRAILING_STOP_LOSS and trade_dur == 0:
if (
not self.strategy.use_custom_stoploss and self.strategy.trailing_stop
and self.strategy.trailing_only_offset_is_reached
and self.strategy.trailing_stop_positive_offset is not None
and self.strategy.trailing_stop_positive
):
# Worst case: price reaches stop_positive_offset and dives down.
stop_rate = (sell_row[OPEN_IDX] *
(1 + abs(self.strategy.trailing_stop_positive_offset) -
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abs(self.strategy.trailing_stop_positive)))
else:
# Worst case: price ticks tiny bit above open and dives down.
stop_rate = sell_row[OPEN_IDX] * (1 - abs(trade.stop_loss_pct))
assert stop_rate < sell_row[HIGH_IDX]
return stop_rate
# 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_for_candle(self, trade: LocalTrade,
sell_row: Tuple) -> Optional[LocalTrade]:
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sell_candle_time = sell_row[DATE_IDX].to_pydatetime()
enter = sell_row[SHORT_IDX] if trade.is_short else sell_row[LONG_IDX]
exit_ = sell_row[ESHORT_IDX] if trade.is_short else sell_row[ELONG_IDX]
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sell = self.strategy.should_exit(
trade, sell_row[OPEN_IDX], sell_candle_time, # type: ignore
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enter=enter, exit_=exit_,
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low=sell_row[LOW_IDX], high=sell_row[HIGH_IDX]
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)
if sell.sell_flag:
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trade.close_date = sell_candle_time
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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current_time=sell_candle_time):
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return None
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trade.close(closerate, show_msg=False)
return trade
return None
def _get_sell_trade_entry(self, trade: LocalTrade, sell_row: Tuple) -> Optional[LocalTrade]:
if self.timeframe_detail and trade.pair in self.detail_data:
sell_candle_time = sell_row[DATE_IDX].to_pydatetime()
sell_candle_end = sell_candle_time + timedelta(minutes=self.timeframe_min)
detail_data = self.detail_data[trade.pair]
detail_data = detail_data.loc[
(detail_data['date'] >= sell_candle_time) &
(detail_data['date'] < sell_candle_end)
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]
if len(detail_data) == 0:
# Fall back to "regular" data if no detail data was found for this candle
return self._get_sell_trade_entry_for_candle(trade, sell_row)
detail_data['enter_long'] = sell_row[LONG_IDX]
detail_data['exit_long'] = sell_row[ELONG_IDX]
detail_data['enter_short'] = sell_row[SHORT_IDX]
detail_data['exit_short'] = sell_row[ESHORT_IDX]
headers = ['date', 'open', 'high', 'low', 'close', 'enter_long', 'exit_long',
'enter_short', 'exit_short']
for det_row in detail_data[headers].values.tolist():
res = self._get_sell_trade_entry_for_candle(trade, det_row)
if res:
return res
return None
else:
return self._get_sell_trade_entry_for_candle(trade, sell_row)
def _enter_trade(self, pair: str, row: List, direction: str) -> 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
min_stake_amount = self.exchange.get_min_pair_stake_amount(pair, row[OPEN_IDX], -0.05) or 0
max_stake_amount = self.wallets.get_available_stake_amount()
stake_amount = strategy_safe_wrapper(self.strategy.custom_stake_amount,
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default_retval=stake_amount)(
pair=pair, current_time=row[DATE_IDX].to_pydatetime(), current_rate=row[OPEN_IDX],
proposed_stake=stake_amount, min_stake=min_stake_amount, max_stake=max_stake_amount)
stake_amount = self.wallets._validate_stake_amount(pair, stake_amount, min_stake_amount)
if not stake_amount:
return None
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, current_time=row[DATE_IDX].to_pydatetime()):
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
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has_buy_tag = len(row) >= BUY_TAG_IDX + 1
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,
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buy_tag=row[BUY_TAG_IDX] if has_buy_tag else None,
exchange=self._exchange_name,
is_short=(direction == 'short'),
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)
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 trade_slot_available(self, max_open_trades: int, open_trade_count: int) -> bool:
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# Always allow trades when max_open_trades is enabled.
if max_open_trades <= 0 or open_trade_count < max_open_trades:
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return True
# Rejected trade
self.rejected_trades += 1
return False
def check_for_trade_entry(self, row) -> Optional[str]:
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enter_long = row[LONG_IDX] == 1
exit_long = row[ELONG_IDX] == 1
enter_short = row[SHORT_IDX] == 1
exit_short = row[ESHORT_IDX] == 1
if enter_long == 1 and not any([exit_long, enter_short]):
# Long
return 'long'
if enter_short == 1 and not any([exit_short, enter_long]):
# Short
return 'short'
return None
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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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self.progress.init_step(BacktestState.BACKTEST, int(
(end_date - start_date) / timedelta(minutes=self.timeframe_min)))
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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
self.check_abort()
for i, pair in enumerate(data):
row_index = indexes[pair]
try:
# Row is treated as "current incomplete candle".
# Buy / sell signals are shifted by 1 to compensate for this.
row = data[pair][row_index]
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
row_index += 1
indexes[pair] = row_index
self.dataprovider._set_dataframe_max_index(row_index)
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# without positionstacking, we can only have one open trade per pair.
# max_open_trades must be respected
# don't open on the last row
trade_dir = self.check_for_trade_entry(row)
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if (
(position_stacking or len(open_trades[pair]) == 0)
and self.trade_slot_available(max_open_trades, open_trade_count_start)
and tmp != end_date
and trade_dir is not None
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and not PairLocks.is_pair_locked(pair, row[DATE_IDX])
):
trade = self._enter_trade(pair, row, trade_dir)
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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)
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for trade in list(open_trades[pair]):
# also check the buying candle for sell conditions.
trade_entry = self._get_sell_trade_entry(trade, row)
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# Sell occurred
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.
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self.progress.increment()
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(),
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'rejected_signals': self.rejected_trades,
'final_balance': self.wallets.get_total(self.strategy.config['stake_currency']),
}
def backtest_one_strategy(self, strat: IStrategy, data: Dict[str, DataFrame],
timerange: TimeRange):
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self.progress.init_step(BacktestState.ANALYZE, 0)
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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.advise_all_indicators(data)
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# Trim startup period from analyzed dataframe
preprocessed_tmp = trim_dataframes(preprocessed, timerange, self.required_startup)
Fix exception when few pairs with no data do not result in aborting backtest. Exception is triggered by backtesting 20210301-20210501 range with BAKE/USDT pair (binance). Pair data starts on 2021-04-30 12:00:00 and after adjusting for startup candles pair dataframe is empty. Solution: Since there are other pairs with enough data - skip pairs with no data and issue a warning. Exception: ``` Traceback (most recent call last): File "/home/rk/src/freqtrade/freqtrade/main.py", line 37, in main return_code = args['func'](args) File "/home/rk/src/freqtrade/freqtrade/commands/optimize_commands.py", line 53, in start_backtesting backtesting.start() File "/home/rk/src/freqtrade/freqtrade/optimize/backtesting.py", line 502, in start min_date, max_date = self.backtest_one_strategy(strat, data, timerange) File "/home/rk/src/freqtrade/freqtrade/optimize/backtesting.py", line 474, in backtest_one_strategy results = self.backtest( File "/home/rk/src/freqtrade/freqtrade/optimize/backtesting.py", line 365, in backtest data: Dict = self._get_ohlcv_as_lists(processed) File "/home/rk/src/freqtrade/freqtrade/optimize/backtesting.py", line 199, in _get_ohlcv_as_lists pair_data.loc[:, 'buy'] = 0 # cleanup from previous run File "/home/rk/src/freqtrade/venv/lib/python3.9/site-packages/pandas/core/indexing.py", line 692, in __setitem__ iloc._setitem_with_indexer(indexer, value, self.name) File "/home/rk/src/freqtrade/venv/lib/python3.9/site-packages/pandas/core/indexing.py", line 1587, in _setitem_with_indexer raise ValueError( ValueError: cannot set a frame with no defined index and a scalar ```
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if not preprocessed_tmp:
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raise OperationalException(
Fix exception when few pairs with no data do not result in aborting backtest. Exception is triggered by backtesting 20210301-20210501 range with BAKE/USDT pair (binance). Pair data starts on 2021-04-30 12:00:00 and after adjusting for startup candles pair dataframe is empty. Solution: Since there are other pairs with enough data - skip pairs with no data and issue a warning. Exception: ``` Traceback (most recent call last): File "/home/rk/src/freqtrade/freqtrade/main.py", line 37, in main return_code = args['func'](args) File "/home/rk/src/freqtrade/freqtrade/commands/optimize_commands.py", line 53, in start_backtesting backtesting.start() File "/home/rk/src/freqtrade/freqtrade/optimize/backtesting.py", line 502, in start min_date, max_date = self.backtest_one_strategy(strat, data, timerange) File "/home/rk/src/freqtrade/freqtrade/optimize/backtesting.py", line 474, in backtest_one_strategy results = self.backtest( File "/home/rk/src/freqtrade/freqtrade/optimize/backtesting.py", line 365, in backtest data: Dict = self._get_ohlcv_as_lists(processed) File "/home/rk/src/freqtrade/freqtrade/optimize/backtesting.py", line 199, in _get_ohlcv_as_lists pair_data.loc[:, 'buy'] = 0 # cleanup from previous run File "/home/rk/src/freqtrade/venv/lib/python3.9/site-packages/pandas/core/indexing.py", line 692, in __setitem__ iloc._setitem_with_indexer(indexer, value, self.name) File "/home/rk/src/freqtrade/venv/lib/python3.9/site-packages/pandas/core/indexing.py", line 1587, in _setitem_with_indexer raise ValueError( ValueError: cannot set a frame with no defined index and a scalar ```
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"No data left after adjusting for startup candles.")
# Use preprocessed_tmp for date generation (the trimmed dataframe).
# Backtesting will re-trim the dataframes after buy/sell signal generation.
min_date, max_date = history.get_timerange(preprocessed_tmp)
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logger.info(f'Backtesting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
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f'({(max_date - min_date).days} days).')
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# Execute backtest and store results
results = self.backtest(
processed=preprocessed,
start_date=min_date,
end_date=max_date,
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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()
self.load_bt_data_detail()
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:
self.results = generate_backtest_stats(data, self.all_results,
min_date=min_date, max_date=max_date)
if self.config.get('export', 'none') == 'trades':
store_backtest_stats(self.config['exportfilename'], self.results)
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# Show backtest results
show_backtest_results(self.config, self.results)