freqtrade/optimize/backtesting.py
# 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, timezone
from typing import Any, Dict, List, Optional, Tuple
from numpy import nan
from pandas import DataFrame
from freqtrade import constants
from freqtrade.configuration import TimeRange, validate_config_consistency
from freqtrade.constants import DATETIME_PRINT_FORMAT, Config, IntOrInf, LongShort
from freqtrade.data import history
from freqtrade.data.btanalysis import find_existing_backtest_stats, trade_list_to_dataframe
from freqtrade.data.converter import trim_dataframe, trim_dataframes
from freqtrade.data.dataprovider import DataProvider
from freqtrade.data.metrics import combined_dataframes_with_rel_mean
from freqtrade.enums import (BacktestState, CandleType, ExitCheckTuple, ExitType, RunMode,
TradingMode)
from freqtrade.exceptions import DependencyException, OperationalException
from freqtrade.exchange import (amount_to_contract_precision, price_to_precision,
timeframe_to_seconds)
from freqtrade.exchange.exchange import Exchange
from freqtrade.mixins import LoggingMixin
from freqtrade.optimize.backtest_caching import get_strategy_run_id
from freqtrade.optimize.bt_progress import BTProgress
from freqtrade.optimize.optimize_reports import (generate_backtest_stats, generate_rejected_signals,
generate_trade_signal_candles,
show_backtest_results,
store_backtest_analysis_results,
store_backtest_stats)
from freqtrade.persistence import (CustomDataWrapper, LocalTrade, Order, PairLocks, Trade,
disable_database_use, enable_database_use)
from freqtrade.plugins.pairlistmanager import PairListManager
from freqtrade.plugins.protectionmanager import ProtectionManager
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
from freqtrade.types import BacktestResultType, get_BacktestResultType_default
from freqtrade.util.migrations import migrate_data
from freqtrade.wallets import Wallets
logger = logging.getLogger(__name__)
# Indexes for backtest tuples
DATE_IDX = 0
OPEN_IDX = 1
HIGH_IDX = 2
LOW_IDX = 3
CLOSE_IDX = 4
LONG_IDX = 5
ELONG_IDX = 6 # Exit long
SHORT_IDX = 7
ESHORT_IDX = 8 # Exit short
ENTER_TAG_IDX = 9
EXIT_TAG_IDX = 10
# 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', 'enter_tag', 'exit_tag']
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: Config, exchange: Optional[Exchange] = None) -> None:
LoggingMixin.show_output = False
self.config = config
self.results: BacktestResultType = get_BacktestResultType_default()
self.trade_id_counter: int = 0
self.order_id_counter: int = 0
config['dry_run'] = True
self.run_ids: Dict[str, str] = {}
self.strategylist: List[IStrategy] = []
self.all_results: Dict[str, Dict] = {}
self.processed_dfs: Dict[str, Dict] = {}
self.rejected_dict: Dict[str, List] = {}
self.rejected_df: Dict[str, Dict] = {}
self._exchange_name = self.config['exchange']['name']
if not exchange:
exchange = ExchangeResolver.load_exchange(self.config, load_leverage_tiers=True)
self.exchange = exchange
self.dataprovider = DataProvider(self.config, self.exchange)
if self.config.get('strategy_list'):
if self.config.get('freqai', {}).get('enabled', False):
logger.warning("Using --strategy-list with FreqAI REQUIRES all strategies "
"to have identical feature_engineering_* functions.")
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 needs to be set in either "
"configuration or as cli argument `--timeframe 5m`")
self.timeframe = str(self.config.get('timeframe'))
self.timeframe_secs = timeframe_to_seconds(self.timeframe)
self.timeframe_min = self.timeframe_secs // 60
self.timeframe_td = timedelta(seconds=self.timeframe_secs)
self.disable_database_use()
self.init_backtest_detail()
self.pairlists = PairListManager(self.exchange, self.config, self.dataprovider)
self._validate_pairlists_for_backtesting()
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])
self.precision_mode = self.exchange.precisionMode
if self.config.get('freqai_backtest_live_models', False):
from freqtrade.freqai.utils import get_timerange_backtest_live_models
self.config['timerange'] = get_timerange_backtest_live_models(self.config)
self.timerange = TimeRange.parse_timerange(
None if self.config.get('timerange') is None else str(self.config.get('timerange')))
# Get maximum required startup period
self.required_startup = max([strat.startup_candle_count for strat in self.strategylist])
self.exchange.validate_required_startup_candles(self.required_startup, self.timeframe)
# Add maximum startup candle count to configuration for informative pairs support
self.config['startup_candle_count'] = self.required_startup
if self.config.get('freqai', {}).get('enabled', False):
# For FreqAI, increase the required_startup to includes the training data
# This value should NOT be written to startup_candle_count
self.required_startup = self.dataprovider.get_required_startup(self.timeframe)
self.trading_mode: TradingMode = config.get('trading_mode', TradingMode.SPOT)
# strategies which define "can_short=True" will fail to load in Spot mode.
self._can_short = self.trading_mode != TradingMode.SPOT
self._position_stacking: bool = self.config.get('position_stacking', False)
self.enable_protections: bool = self.config.get('enable_protections', False)
migrate_data(config, self.exchange)
self.init_backtest()
def _validate_pairlists_for_backtesting(self):
if 'VolumePairList' in self.pairlists.name_list:
raise OperationalException("VolumePairList not allowed for backtesting. "
"Please use StaticPairList instead.")
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."
)
@staticmethod
def cleanup():
LoggingMixin.show_output = True
enable_database_use()
def init_backtest_detail(self) -> None:
# Load detail timeframe if specified
self.timeframe_detail = str(self.config.get('timeframe_detail', ''))
if self.timeframe_detail:
timeframe_detail_secs = timeframe_to_seconds(self.timeframe_detail)
self.timeframe_detail_td = timedelta(seconds=timeframe_detail_secs)
if self.timeframe_secs <= timeframe_detail_secs:
raise OperationalException(
"Detail timeframe must be smaller than strategy timeframe.")
else:
self.timeframe_detail_td = timedelta(seconds=0)
self.detail_data: Dict[str, DataFrame] = {}
self.futures_data: Dict[str, DataFrame] = {}
def init_backtest(self):
self.prepare_backtest(False)
self.wallets = Wallets(self.config, self.exchange, is_backtest=True)
self.progress = BTProgress()
self.abort = False
def _set_strategy(self, strategy: IStrategy):
"""
Load strategy into backtesting
"""
self.strategy: IStrategy = strategy
strategy.dp = self.dataprovider
# Attach Wallets to Strategy baseclass
strategy.wallets = self.wallets
# Set stoploss_on_exchange to false for backtesting,
# since a "perfect" stoploss-exit is assumed anyway
# And the regular "stoploss" function would not apply to that case
self.strategy.order_types['stoploss_on_exchange'] = False
# Update can_short flag
self._can_short = self.trading_mode != TradingMode.SPOT and strategy.can_short
self.strategy.ft_bot_start()
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)
def load_bt_data(self) -> Tuple[Dict[str, DataFrame], TimeRange]:
"""
Loads backtest data and returns the data combined with the timerange
as tuple.
"""
self.progress.init_step(BacktestState.DATALOAD, 1)
data = history.load_data(
datadir=self.config['datadir'],
pairs=self.pairlists.whitelist,
timeframe=self.timeframe,
timerange=self.timerange,
startup_candles=self.required_startup,
fail_without_data=True,
data_format=self.config['dataformat_ohlcv'],
candle_type=self.config.get('candle_type_def', CandleType.SPOT)
)
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
self.timerange.adjust_start_if_necessary(timeframe_to_seconds(self.timeframe),
self.required_startup, min_date)
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['dataformat_ohlcv'],
candle_type=self.config.get('candle_type_def', CandleType.SPOT)
)
else:
self.detail_data = {}
if self.trading_mode == TradingMode.FUTURES:
self.funding_fee_timeframe: str = self.exchange.get_option('funding_fee_timeframe')
self.funding_fee_timeframe_secs: int = timeframe_to_seconds(self.funding_fee_timeframe)
mark_timeframe: str = self.exchange.get_option('mark_ohlcv_timeframe')
# Load additional futures data.
funding_rates_dict = history.load_data(
datadir=self.config['datadir'],
pairs=self.pairlists.whitelist,
timeframe=self.funding_fee_timeframe,
timerange=self.timerange,
startup_candles=0,
fail_without_data=True,
data_format=self.config['dataformat_ohlcv'],
candle_type=CandleType.FUNDING_RATE
)
# For simplicity, assign to CandleType.Mark (might contain index candles!)
mark_rates_dict = history.load_data(
datadir=self.config['datadir'],
pairs=self.pairlists.whitelist,
timeframe=mark_timeframe,
timerange=self.timerange,
startup_candles=0,
fail_without_data=True,
data_format=self.config['dataformat_ohlcv'],
candle_type=CandleType.from_string(self.exchange.get_option("mark_ohlcv_price"))
)
# Combine data to avoid combining the data per trade.
unavailable_pairs = []
for pair in self.pairlists.whitelist:
if pair not in self.exchange._leverage_tiers:
unavailable_pairs.append(pair)
continue
self.futures_data[pair] = self.exchange.combine_funding_and_mark(
funding_rates=funding_rates_dict[pair],
mark_rates=mark_rates_dict[pair],
futures_funding_rate=self.config.get('futures_funding_rate', None),
)
if unavailable_pairs:
raise OperationalException(
f"Pairs {', '.join(unavailable_pairs)} got no leverage tiers available. "
"It is therefore impossible to backtest with this pair at the moment.")
else:
self.futures_data = {}
def disable_database_use(self):
disable_database_use(self.timeframe)
def prepare_backtest(self, enable_protections):
"""
Backtesting setup method - called once for every call to "backtest()".
"""
self.disable_database_use()
PairLocks.reset_locks()
Trade.reset_trades()
CustomDataWrapper.reset_custom_data()
self.rejected_trades = 0
self.timedout_entry_orders = 0
self.timedout_exit_orders = 0
self.canceled_trade_entries = 0
self.canceled_entry_orders = 0
self.replaced_entry_orders = 0
self.dataprovider.clear_cache()
if enable_protections:
self._load_protections(self.strategy)
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.
:param processed: a processed dictionary with format {pair, data}, which gets cleared to
optimize memory usage!
"""
data: Dict = {}
self.progress.init_step(BacktestState.CONVERT, len(processed))
# Create dict with data
for pair in processed.keys():
pair_data = processed[pair]
self.check_abort()
self.progress.increment()
if not pair_data.empty:
# Cleanup from prior runs
pair_data.drop(HEADERS[5:] + ['buy', 'sell'], axis=1, errors='ignore')
df_analyzed = self.strategy.ft_advise_signals(pair_data, {'pair': pair})
# Update dataprovider cache
self.dataprovider._set_cached_df(
pair, self.timeframe, df_analyzed, self.config['candle_type_def'])
# Trim startup period from analyzed dataframe
df_analyzed = processed[pair] = pair_data = trim_dataframe(
df_analyzed, self.timerange, startup_candles=self.required_startup)
# Create a copy of the dataframe before shifting, that way the entry signal/tag
# remains on the correct candle for callbacks.
df_analyzed = df_analyzed.copy()
# To avoid using data from future, we use entry/exit signals shifted
# from the previous candle
for col in HEADERS[5:]:
tag_col = col in ('enter_tag', 'exit_tag')
if col in df_analyzed.columns:
df_analyzed[col] = df_analyzed.loc[:, col].replace(
[nan], [0 if not tag_col else None]).shift(1)
elif not df_analyzed.empty:
df_analyzed[col] = 0 if not tag_col else None
df_analyzed = df_analyzed.drop(df_analyzed.head(1).index)
# Convert from Pandas to list for performance reasons
# (Looping Pandas is slow.)
data[pair] = df_analyzed[HEADERS].values.tolist() if not df_analyzed.empty else []
return data
def _get_close_rate(self, row: Tuple, trade: LocalTrade, exit: ExitCheckTuple,
trade_dur: int) -> float:
"""
Get close rate for backtesting result
"""
# Special handling if high or low hit STOP_LOSS or ROI
if exit.exit_type in (
ExitType.STOP_LOSS, ExitType.TRAILING_STOP_LOSS, ExitType.LIQUIDATION):
return self._get_close_rate_for_stoploss(row, trade, exit, trade_dur)
elif exit.exit_type == (ExitType.ROI):
return self._get_close_rate_for_roi(row, trade, exit, trade_dur)
else:
return row[OPEN_IDX]
def _get_close_rate_for_stoploss(self, row: Tuple, trade: LocalTrade, exit: ExitCheckTuple,
trade_dur: int) -> float:
# our stoploss was already lower than candle high,
# possibly due to a cancelled trade exit.
# exit at open price.
is_short = trade.is_short or False
leverage = trade.leverage or 1.0
side_1 = -1 if is_short else 1
if exit.exit_type == ExitType.LIQUIDATION and trade.liquidation_price:
stoploss_value = trade.liquidation_price
else:
stoploss_value = trade.stop_loss
if is_short:
if stoploss_value < row[LOW_IDX]:
return row[OPEN_IDX]
else:
if stoploss_value > row[HIGH_IDX]:
return 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 exit.exit_type == ExitType.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 = (row[OPEN_IDX] *
(1 + side_1 * abs(self.strategy.trailing_stop_positive_offset) -
side_1 * abs(self.strategy.trailing_stop_positive / leverage)))
else:
# Worst case: price ticks tiny bit above open and dives down.
stop_rate = row[OPEN_IDX] * (1 - side_1 * abs(
(trade.stop_loss_pct or 0.0) / leverage))
# Limit lower-end to candle low to avoid exits below the low.
# This still remains "worst case" - but "worst realistic case".
if is_short:
return min(row[HIGH_IDX], stop_rate)
else:
return max(row[LOW_IDX], stop_rate)
# Set close_rate to stoploss
return stoploss_value
def _get_close_rate_for_roi(self, row: Tuple, trade: LocalTrade, exit: ExitCheckTuple,
trade_dur: int) -> float:
is_short = trade.is_short or False
leverage = trade.leverage or 1.0
side_1 = -1 if is_short else 1
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 force_exiting 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 row[OPEN_IDX]
# - (Expected abs profit - open_rate - open_fee) / (fee_close -1)
roi_rate = trade.open_rate * roi / leverage
open_fee_rate = side_1 * trade.open_rate * (1 + side_1 * trade.fee_open)
close_rate = -(roi_rate + open_fee_rate) / ((trade.fee_close or 0.0) - side_1 * 1)
if is_short:
is_new_roi = row[OPEN_IDX] < close_rate
else:
is_new_roi = row[OPEN_IDX] > close_rate
if (trade_dur > 0 and trade_dur == roi_entry
and roi_entry % self.timeframe_min == 0
and is_new_roi):
# new ROI entry came into effect.
# use Open rate if open_rate > calculated exit rate
return row[OPEN_IDX]
if (trade_dur == 0 and (
(
is_short
# Red candle (for longs)
and row[OPEN_IDX] < row[CLOSE_IDX] # Red candle
and trade.open_rate > row[OPEN_IDX] # trade-open above open_rate
and close_rate < row[CLOSE_IDX] # closes below close
)
or
(
not is_short
# green candle (for shorts)
and row[OPEN_IDX] > row[CLOSE_IDX] # green candle
and trade.open_rate < row[OPEN_IDX] # trade-open below open_rate
and close_rate > row[CLOSE_IDX] # closes above close
)
)):
# ROI on opening candles with custom pricing can only
# trigger if the entry was at Open or lower wick.
# details: https: // github.com/freqtrade/freqtrade/issues/6261
# If open_rate is < open, only allow exits below the close on red candles.
raise ValueError("Opening candle ROI on red candles.")
# Use the maximum between close_rate and low as we
# cannot exit outside of a candle.
# Applies when a new ROI setting comes in place and the whole candle is above that.
return min(max(close_rate, row[LOW_IDX]), row[HIGH_IDX])
else:
# This should not be reached...
return row[OPEN_IDX]
def _get_adjust_trade_entry_for_candle(
self, trade: LocalTrade, row: Tuple, current_time: datetime
) -> LocalTrade:
current_rate: float = row[OPEN_IDX]
current_profit = trade.calc_profit_ratio(current_rate)
min_stake = self.exchange.get_min_pair_stake_amount(trade.pair, current_rate, -0.1)
max_stake = self.exchange.get_max_pair_stake_amount(trade.pair, current_rate)
stake_available = self.wallets.get_available_stake_amount()
stake_amount, order_tag = self.strategy._adjust_trade_position_internal(
trade=trade, # type: ignore[arg-type]
current_time=current_time, current_rate=current_rate,
current_profit=current_profit, min_stake=min_stake,
max_stake=min(max_stake, stake_available),
current_entry_rate=current_rate, current_exit_rate=current_rate,
current_entry_profit=current_profit, current_exit_profit=current_profit
)
# Check if we should increase our position
if stake_amount is not None and stake_amount > 0.0:
check_adjust_entry = True
if self.strategy.max_entry_position_adjustment > -1:
entry_count = trade.nr_of_successful_entries
check_adjust_entry = (entry_count <= self.strategy.max_entry_position_adjustment)
if check_adjust_entry:
pos_trade = self._enter_trade(
trade.pair, row, 'short' if trade.is_short else 'long', stake_amount, trade,
entry_tag1=order_tag)
if pos_trade is not None:
self.wallets.update()
return pos_trade
if stake_amount is not None and stake_amount < 0.0:
amount = amount_to_contract_precision(
abs(stake_amount * trade.amount / trade.stake_amount),
trade.amount_precision,
self.precision_mode, trade.contract_size)
if amount == 0.0:
return trade
remaining = (trade.amount - amount) * current_rate
if min_stake and remaining != 0 and remaining < min_stake:
# Remaining stake is too low to be sold.
return trade
exit_ = ExitCheckTuple(ExitType.PARTIAL_EXIT, order_tag)
pos_trade = self._get_exit_for_signal(trade, row, exit_, current_time, amount)
if pos_trade is not None:
order = pos_trade.orders[-1]
if self._try_close_open_order(order, trade, current_time, row):
trade.recalc_trade_from_orders()
self.wallets.update()
return pos_trade
return trade
def _get_order_filled(self, rate: float, row: Tuple) -> bool:
""" Rate is within candle, therefore filled"""
return row[LOW_IDX] <= rate <= row[HIGH_IDX]
def _call_adjust_stop(self, current_date: datetime, trade: LocalTrade, current_rate: float):
profit = trade.calc_profit_ratio(current_rate)
self.strategy.ft_stoploss_adjust(current_rate, trade, # type: ignore
current_date, profit, 0, after_fill=True)
def _try_close_open_order(
self, order: Optional[Order], trade: LocalTrade, current_date: datetime,
row: Tuple) -> bool:
"""
Check if an order is open and if it should've filled.
:return: True if the order filled.
"""
if order and self._get_order_filled(order.ft_price, row):
order.close_bt_order(current_date, trade)
self._run_funding_fees(trade, current_date, force=True)
strategy_safe_wrapper(
self.strategy.order_filled,
default_retval=None)(
pair=trade.pair, trade=trade, # type: ignore[arg-type]
order=order, current_time=current_date)
if not (order.ft_order_side == trade.exit_side and order.safe_amount == trade.amount):
# trade is still open
trade.set_liquidation_price(self.exchange.get_liquidation_price(
pair=trade.pair,
open_rate=trade.open_rate,
is_short=trade.is_short,
amount=trade.amount,
stake_amount=trade.stake_amount,
leverage=trade.leverage,
wallet_balance=trade.stake_amount,
))
self._call_adjust_stop(current_date, trade, order.ft_price)
# pass
return True
return False
def _get_exit_for_signal(
self, trade: LocalTrade, row: Tuple, exit_: ExitCheckTuple,
current_time: datetime,
amount: Optional[float] = None) -> Optional[LocalTrade]:
if exit_.exit_flag:
trade.close_date = current_time
exit_reason = exit_.exit_reason
amount_ = amount if amount is not None else trade.amount
trade_dur = int((trade.close_date_utc - trade.open_date_utc).total_seconds() // 60)
try:
close_rate = self._get_close_rate(row, trade, exit_, trade_dur)
except ValueError:
return None
# call the custom exit price,with default value as previous close_rate
current_profit = trade.calc_profit_ratio(close_rate)
order_type = self.strategy.order_types['exit']
if exit_.exit_type in (ExitType.EXIT_SIGNAL, ExitType.CUSTOM_EXIT,
ExitType.PARTIAL_EXIT):
# Checks and adds an exit tag, after checking that the length of the
# row has the length for an exit tag column
if (
len(row) > EXIT_TAG_IDX
and row[EXIT_TAG_IDX] is not None
and len(row[EXIT_TAG_IDX]) > 0
and exit_.exit_type in (ExitType.EXIT_SIGNAL,)
):
exit_reason = row[EXIT_TAG_IDX]
# Custom exit pricing only for exit-signals
if order_type == 'limit':
rate = strategy_safe_wrapper(self.strategy.custom_exit_price,
default_retval=close_rate)(
pair=trade.pair,
trade=trade, # type: ignore[arg-type]
current_time=current_time,
proposed_rate=close_rate, current_profit=current_profit,
exit_tag=exit_reason)
if rate is not None and rate != close_rate:
close_rate = price_to_precision(rate, trade.price_precision,
self.precision_mode)
# We can't place orders lower than current low.
# freqtrade does not support this in live, and the order would fill immediately
if trade.is_short:
close_rate = min(close_rate, row[HIGH_IDX])
else:
close_rate = max(close_rate, row[LOW_IDX])
# Confirm trade exit:
time_in_force = self.strategy.order_time_in_force['exit']
if (exit_.exit_type not in (ExitType.LIQUIDATION, ExitType.PARTIAL_EXIT)
and not strategy_safe_wrapper(
self.strategy.confirm_trade_exit, default_retval=True)(
pair=trade.pair,
trade=trade, # type: ignore[arg-type]
order_type=order_type,
amount=amount_,
rate=close_rate,
time_in_force=time_in_force,
sell_reason=exit_reason, # deprecated
exit_reason=exit_reason,
current_time=current_time)):
return None
trade.exit_reason = exit_reason
return self._exit_trade(trade, row, close_rate, amount_, exit_reason)
return None
def _exit_trade(self, trade: LocalTrade, sell_row: Tuple, close_rate: float,
amount: float, exit_reason: Optional[str]) -> Optional[LocalTrade]:
self.order_id_counter += 1
exit_candle_time = sell_row[DATE_IDX].to_pydatetime()
order_type = self.strategy.order_types['exit']
# amount = amount or trade.amount
amount = amount_to_contract_precision(amount or trade.amount, trade.amount_precision,
self.precision_mode, trade.contract_size)
order = Order(
id=self.order_id_counter,
ft_trade_id=trade.id,
order_date=exit_candle_time,
order_update_date=exit_candle_time,
ft_is_open=True,
ft_pair=trade.pair,
order_id=str(self.order_id_counter),
symbol=trade.pair,
ft_order_side=trade.exit_side,
side=trade.exit_side,
order_type=order_type,
status="open",
ft_price=close_rate,
price=close_rate,
average=close_rate,
amount=amount,
filled=0,
remaining=amount,
cost=amount * close_rate,
ft_order_tag=exit_reason,
)
order._trade_bt = trade
trade.orders.append(order)
return trade
def _check_trade_exit(
self, trade: LocalTrade, row: Tuple, current_time: datetime
) -> Optional[LocalTrade]:
self._run_funding_fees(trade, current_time)
# Check if we need to adjust our current positions
if self.strategy.position_adjustment_enable:
trade = self._get_adjust_trade_entry_for_candle(trade, row, current_time)
enter = row[SHORT_IDX] if trade.is_short else row[LONG_IDX]
exit_sig = row[ESHORT_IDX] if trade.is_short else row[ELONG_IDX]
exits = self.strategy.should_exit(
trade, row[OPEN_IDX], row[DATE_IDX].to_pydatetime(), # type: ignore
enter=enter, exit_=exit_sig,
low=row[LOW_IDX], high=row[HIGH_IDX]
)
for exit_ in exits:
t = self._get_exit_for_signal(trade, row, exit_, current_time)
if t:
return t
return None
def _run_funding_fees(self, trade: LocalTrade, current_time: datetime, force: bool = False):
"""
Calculate funding fees if necessary and add them to the trade.
"""
if self.trading_mode == TradingMode.FUTURES:
if (
force
or (current_time.timestamp() % self.funding_fee_timeframe_secs) == 0
):
# Funding fee interval.
trade.set_funding_fees(
self.exchange.calculate_funding_fees(
self.futures_data[trade.pair],
amount=trade.amount,
is_short=trade.is_short,
open_date=trade.date_last_filled_utc,
close_date=current_time
)
)
def get_valid_price_and_stake(
self, pair: str, row: Tuple, propose_rate: float, stake_amount: float,
direction: LongShort, current_time: datetime, entry_tag: Optional[str],
trade: Optional[LocalTrade], order_type: str, price_precision: Optional[float]
) -> Tuple[float, float, float, float]:
if order_type == 'limit':
new_rate = strategy_safe_wrapper(self.strategy.custom_entry_price,
default_retval=propose_rate)(
pair=pair,
trade=trade, # type: ignore[arg-type]
current_time=current_time,
proposed_rate=propose_rate, entry_tag=entry_tag,
side=direction,
) # default value is the open rate
# We can't place orders higher than current high (otherwise it'd be a stop limit entry)
# which freqtrade does not support in live.
if new_rate is not None and new_rate != propose_rate:
propose_rate = price_to_precision(new_rate, price_precision,
self.precision_mode)
if direction == "short":
propose_rate = max(propose_rate, row[LOW_IDX])
else:
propose_rate = min(propose_rate, row[HIGH_IDX])
pos_adjust = trade is not None
leverage = trade.leverage if trade else 1.0
if not pos_adjust:
try:
stake_amount = self.wallets.get_trade_stake_amount(
pair, self.strategy.max_open_trades, update=False)
except DependencyException:
return 0, 0, 0, 0
max_leverage = self.exchange.get_max_leverage(pair, stake_amount)
leverage = strategy_safe_wrapper(self.strategy.leverage, default_retval=1.0)(
pair=pair,
current_time=current_time,
current_rate=row[OPEN_IDX],
proposed_leverage=1.0,
max_leverage=max_leverage,
side=direction, entry_tag=entry_tag,
) if self.trading_mode != TradingMode.SPOT else 1.0
# Cap leverage between 1.0 and max_leverage.
leverage = min(max(leverage, 1.0), max_leverage)
min_stake_amount = self.exchange.get_min_pair_stake_amount(
pair, propose_rate, -0.05 if not pos_adjust else 0.0, leverage=leverage) or 0
max_stake_amount = self.exchange.get_max_pair_stake_amount(
pair, propose_rate, leverage=leverage)
stake_available = self.wallets.get_available_stake_amount()
if not pos_adjust:
stake_amount = strategy_safe_wrapper(self.strategy.custom_stake_amount,
default_retval=stake_amount)(
pair=pair, current_time=current_time, current_rate=propose_rate,
proposed_stake=stake_amount, min_stake=min_stake_amount,
max_stake=min(stake_available, max_stake_amount),
leverage=leverage, entry_tag=entry_tag, side=direction)
stake_amount_val = self.wallets.validate_stake_amount(
pair=pair,
stake_amount=stake_amount,
min_stake_amount=min_stake_amount,
max_stake_amount=max_stake_amount,
trade_amount=trade.stake_amount if trade else None
)
return propose_rate, stake_amount_val, leverage, min_stake_amount
def _enter_trade(self, pair: str, row: Tuple, direction: LongShort,
stake_amount: Optional[float] = None,
trade: Optional[LocalTrade] = None,
requested_rate: Optional[float] = None,
requested_stake: Optional[float] = None,
entry_tag1: Optional[str] = None
) -> Optional[LocalTrade]:
"""
:param trade: Trade to adjust - initial entry if None
:param requested_rate: Adjusted entry rate
:param requested_stake: Stake amount for adjusted orders (`adjust_entry_price`).
"""
current_time = row[DATE_IDX].to_pydatetime()
entry_tag = entry_tag1 or (row[ENTER_TAG_IDX] if len(row) >= ENTER_TAG_IDX + 1 else None)
# let's call the custom entry price, using the open price as default price
order_type = self.strategy.order_types['entry']
pos_adjust = trade is not None and requested_rate is None
stake_amount_ = stake_amount or (trade.stake_amount if trade else 0.0)
precision_price = self.exchange.get_precision_price(pair)
propose_rate, stake_amount, leverage, min_stake_amount = self.get_valid_price_and_stake(
pair, row, row[OPEN_IDX], stake_amount_, direction, current_time, entry_tag, trade,
order_type, precision_price,
)
# replace proposed rate if another rate was requested
propose_rate = requested_rate if requested_rate else propose_rate
stake_amount = requested_stake if requested_stake else stake_amount
if not stake_amount:
# In case of pos adjust, still return the original trade
# If not pos adjust, trade is None
return trade
time_in_force = self.strategy.order_time_in_force['entry']
if stake_amount and (not min_stake_amount or stake_amount >= min_stake_amount):
self.order_id_counter += 1
base_currency = self.exchange.get_pair_base_currency(pair)
amount_p = (stake_amount / propose_rate) * leverage
contract_size = self.exchange.get_contract_size(pair)
precision_amount = self.exchange.get_precision_amount(pair)
amount = amount_to_contract_precision(amount_p, precision_amount, self.precision_mode,
contract_size)
if not amount:
# No amount left after truncating to precision.
return trade
# Backcalculate actual stake amount.
stake_amount = amount * propose_rate / leverage
if not pos_adjust:
# Confirm trade entry:
if not strategy_safe_wrapper(
self.strategy.confirm_trade_entry, default_retval=True)(
pair=pair, order_type=order_type, amount=amount, rate=propose_rate,
time_in_force=time_in_force, current_time=current_time,
entry_tag=entry_tag, side=direction):
return trade
is_short = (direction == 'short')
# Necessary for Margin trading. Disabled until support is enabled.
# interest_rate = self.exchange.get_interest_rate()
if trade is None:
# Enter trade
self.trade_id_counter += 1
trade = LocalTrade(
id=self.trade_id_counter,
pair=pair,
base_currency=base_currency,
stake_currency=self.config['stake_currency'],
open_rate=propose_rate,
open_rate_requested=propose_rate,
open_date=current_time,
stake_amount=stake_amount,
amount=amount,
amount_requested=amount,
fee_open=self.fee,
fee_close=self.fee,
is_open=True,
enter_tag=entry_tag,
exchange=self._exchange_name,
is_short=is_short,
trading_mode=self.trading_mode,
leverage=leverage,
# interest_rate=interest_rate,
amount_precision=precision_amount,
price_precision=precision_price,
precision_mode=self.precision_mode,
contract_size=contract_size,
orders=[],
)
trade.adjust_stop_loss(trade.open_rate, self.strategy.stoploss, initial=True)
order = Order(
id=self.order_id_counter,
ft_trade_id=trade.id,
ft_is_open=True,
ft_pair=trade.pair,
order_id=str(self.order_id_counter),
symbol=trade.pair,
ft_order_side=trade.entry_side,
side=trade.entry_side,
order_type=order_type,
status="open",
order_date=current_time,
order_filled_date=current_time,
order_update_date=current_time,
ft_price=propose_rate,
price=propose_rate,
average=propose_rate,
amount=amount,
filled=0,
remaining=amount,
cost=amount * propose_rate + trade.fee_open,
ft_order_tag=entry_tag,
)
order._trade_bt = trade
trade.orders.append(order)
self._try_close_open_order(order, trade, current_time, row)
trade.recalc_trade_from_orders()
return trade
def handle_left_open(self, open_trades: Dict[str, List[LocalTrade]],
data: Dict[str, List[Tuple]]) -> None:
"""
Handling of left open trades at the end of backtesting
"""
for pair in open_trades.keys():
for trade in list(open_trades[pair]):
if trade.has_open_orders and trade.nr_of_successful_entries == 0:
# Ignore trade if entry-order did not fill yet
continue
exit_row = data[pair][-1]
self._exit_trade(trade, exit_row, exit_row[OPEN_IDX], trade.amount,
ExitType.FORCE_EXIT.value)
trade.orders[-1].close_bt_order(exit_row[DATE_IDX].to_pydatetime(), trade)
trade.close_date = exit_row[DATE_IDX].to_pydatetime()
trade.exit_reason = ExitType.FORCE_EXIT.value
trade.close(exit_row[OPEN_IDX], show_msg=False)
LocalTrade.close_bt_trade(trade)
def trade_slot_available(self, open_trade_count: int) -> bool:
# Always allow trades when max_open_trades is enabled.
max_open_trades: IntOrInf = self.strategy.max_open_trades
if max_open_trades <= 0 or open_trade_count < max_open_trades:
return True
# Rejected trade
self.rejected_trades += 1
return False
def check_for_trade_entry(self, row) -> Optional[LongShort]:
enter_long = row[LONG_IDX] == 1
exit_long = row[ELONG_IDX] == 1
enter_short = self._can_short and row[SHORT_IDX] == 1
exit_short = self._can_short and 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
def run_protections(self, pair: str, current_time: datetime, side: LongShort):
if self.enable_protections:
self.protections.stop_per_pair(pair, current_time, side)
self.protections.global_stop(current_time, side)
def manage_open_orders(self, trade: LocalTrade, current_time: datetime, row: Tuple) -> bool:
"""
Check if any open order needs to be cancelled or replaced.
Returns True if the trade should be deleted.
"""
for order in [o for o in trade.orders if o.ft_is_open]:
oc = self.check_order_cancel(trade, order, current_time)
if oc:
# delete trade due to order timeout
return True
elif oc is None and self.check_order_replace(trade, order, current_time, row):
# delete trade due to user request
self.canceled_trade_entries += 1
return True
# default maintain trade
return False
def check_order_cancel(
self, trade: LocalTrade, order: Order, current_time: datetime) -> Optional[bool]:
"""
Check if current analyzed order has to be canceled.
Returns True if the trade should be Deleted (initial order was canceled),
False if it's Canceled
None if the order is still active.
"""
timedout = self.strategy.ft_check_timed_out(
trade, # type: ignore[arg-type]
order, current_time)
if timedout:
if order.side == trade.entry_side:
self.timedout_entry_orders += 1
if trade.nr_of_successful_entries == 0:
# Remove trade due to entry timeout expiration.
return True
else:
# Close additional entry order
del trade.orders[trade.orders.index(order)]
return False
if order.side == trade.exit_side:
self.timedout_exit_orders += 1
# Close exit order and retry exiting on next signal.
del trade.orders[trade.orders.index(order)]
return False
return None
def check_order_replace(self, trade: LocalTrade, order: Order, current_time,
row: Tuple) -> bool:
"""
Check if current analyzed entry order has to be replaced and do so.
If user requested cancellation and there are no filled orders in the trade will
instruct caller to delete the trade.
Returns True if the trade should be deleted.
"""
# only check on new candles for open entry orders
if order.side == trade.entry_side and current_time > order.order_date_utc:
requested_rate = strategy_safe_wrapper(self.strategy.adjust_entry_price,
default_retval=order.ft_price)(
trade=trade, # type: ignore[arg-type]
order=order, pair=trade.pair, current_time=current_time,
proposed_rate=row[OPEN_IDX], current_order_rate=order.ft_price,
entry_tag=trade.enter_tag, side=trade.trade_direction
) # default value is current order price
# cancel existing order whenever a new rate is requested (or None)
if requested_rate == order.ft_price:
# assumption: there can't be multiple open entry orders at any given time
return False
else:
del trade.orders[trade.orders.index(order)]
self.canceled_entry_orders += 1
# place new order if result was not None
if requested_rate:
self._enter_trade(pair=trade.pair, row=row, trade=trade,
requested_rate=requested_rate,
requested_stake=(
order.safe_remaining * order.ft_price / trade.leverage),
direction='short' if trade.is_short else 'long')
# Delete trade if no successful entries happened (if placing the new order failed)
if not trade.has_open_orders and trade.nr_of_successful_entries == 0:
return True
self.replaced_entry_orders += 1
else:
# assumption: there can't be multiple open entry orders at any given time
return (trade.nr_of_successful_entries == 0)
return False
def validate_row(
self, data: Dict, pair: str, row_index: int, current_time: datetime) -> Optional[Tuple]:
try:
# Row is treated as "current incomplete candle".
# entry / exit 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
return None
# Waits until the time-counter reaches the start of the data for this pair.
if row[DATE_IDX] > current_time:
return None
return row
def _collate_rejected(self, pair, row):
"""
Temporarily store rejected signal information for downstream use in backtesting_analysis
"""
# It could be fun to enable hyperopt mode to write
# a loss function to reduce rejected signals
if (self.config.get('export', 'none') == 'signals' and
self.dataprovider.runmode == RunMode.BACKTEST):
if pair not in self.rejected_dict:
self.rejected_dict[pair] = []
self.rejected_dict[pair].append([row[DATE_IDX], row[ENTER_TAG_IDX]])
def backtest_loop(
self, row: Tuple, pair: str, current_time: datetime, end_date: datetime,
open_trade_count_start: int, trade_dir: Optional[LongShort],
is_first: bool = True) -> int:
"""
NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
Backtesting processing for one candle/pair.
"""
for t in list(LocalTrade.bt_trades_open_pp[pair]):
# 1. Manage currently open orders of active trades
if self.manage_open_orders(t, current_time, row):
# Close trade
open_trade_count_start -= 1
LocalTrade.remove_bt_trade(t)
self.wallets.update()
# 2. Process entries.
# without positionstacking, we can only have one open trade per pair.
# max_open_trades must be respected
# don't open on the last row
# We only open trades on the main candle, not on detail candles
if (
(self._position_stacking or len(LocalTrade.bt_trades_open_pp[pair]) == 0)
and is_first
and current_time != end_date
and trade_dir is not None
and not PairLocks.is_pair_locked(pair, row[DATE_IDX], trade_dir)
):
if (self.trade_slot_available(open_trade_count_start)):
trade = self._enter_trade(pair, row, trade_dir)
if trade:
# TODO: hacky workaround to avoid opening > max_open_trades
# This emulates previous behavior - not sure if this is correct
# Prevents entering if the trade-slot was freed in this candle
open_trade_count_start += 1
# logger.debug(f"{pair} - Emulate creation of new trade: {trade}.")
LocalTrade.add_bt_trade(trade)
self.wallets.update()
else:
self._collate_rejected(pair, row)
for trade in list(LocalTrade.bt_trades_open_pp[pair]):
# 3. Process entry orders.
order = trade.select_order(trade.entry_side, is_open=True)
if self._try_close_open_order(order, trade, current_time, row):
self.wallets.update()
# 4. Create exit orders (if any)
if not trade.has_open_orders:
self._check_trade_exit(trade, row, current_time) # Place exit order if necessary
# 5. Process exit orders.
order = trade.select_order(trade.exit_side, is_open=True)
if order and self._try_close_open_order(order, trade, current_time, row):
sub_trade = order.safe_amount_after_fee != trade.amount
if sub_trade:
trade.recalc_trade_from_orders()
else:
trade.close_date = current_time
trade.close(order.ft_price, show_msg=False)
# logger.debug(f"{pair} - Backtesting exit {trade}")
LocalTrade.close_bt_trade(trade)
self.wallets.update()
self.run_protections(pair, current_time, trade.trade_direction)
return open_trade_count_start
def backtest(self, processed: Dict,
start_date: datetime, end_date: datetime) -> Dict[str, Any]:
"""
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}, which gets cleared to
optimize memory usage!
:param start_date: backtesting timerange start datetime
:param end_date: backtesting timerange end datetime
:return: DataFrame with trades (results of backtesting)
"""
self.prepare_backtest(self.enable_protections)
# Ensure wallets are up-to-date (important for --strategy-list)
self.wallets.update()
# 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 = defaultdict(int)
current_time = start_date + self.timeframe_td
self.progress.init_step(BacktestState.BACKTEST, int(
(end_date - start_date) / self.timeframe_td))
# Loop timerange and get candle for each pair at that point in time
while current_time <= end_date:
open_trade_count_start = LocalTrade.bt_open_open_trade_count
self.check_abort()
strategy_safe_wrapper(self.strategy.bot_loop_start, supress_error=True)(
current_time=current_time)
for i, pair in enumerate(data):
row_index = indexes[pair]
row = self.validate_row(data, pair, row_index, current_time)
if not row:
continue
row_index += 1
indexes[pair] = row_index
self.dataprovider._set_dataframe_max_index(self.required_startup + row_index)
self.dataprovider._set_dataframe_max_date(current_time)
current_detail_time: datetime = row[DATE_IDX].to_pydatetime()
trade_dir: Optional[LongShort] = self.check_for_trade_entry(row)
if (
(trade_dir is not None or len(LocalTrade.bt_trades_open_pp[pair]) > 0)
and self.timeframe_detail and pair in self.detail_data
):
# Spread out into detail timeframe.
# Should only happen when we are either in a trade for this pair
# or when we got the signal for a new trade.
exit_candle_end = current_detail_time + self.timeframe_td
detail_data = self.detail_data[pair]
detail_data = detail_data.loc[
(detail_data['date'] >= current_detail_time) &
(detail_data['date'] < exit_candle_end)
].copy()
if len(detail_data) == 0:
# Fall back to "regular" data if no detail data was found for this candle
open_trade_count_start = self.backtest_loop(
row, pair, current_time, end_date,
open_trade_count_start, trade_dir)
continue
detail_data.loc[:, 'enter_long'] = row[LONG_IDX]
detail_data.loc[:, 'exit_long'] = row[ELONG_IDX]
detail_data.loc[:, 'enter_short'] = row[SHORT_IDX]
detail_data.loc[:, 'exit_short'] = row[ESHORT_IDX]
detail_data.loc[:, 'enter_tag'] = row[ENTER_TAG_IDX]
detail_data.loc[:, 'exit_tag'] = row[EXIT_TAG_IDX]
is_first = True
current_time_det = current_time
for det_row in detail_data[HEADERS].values.tolist():
self.dataprovider._set_dataframe_max_date(current_time_det)
open_trade_count_start = self.backtest_loop(
det_row, pair, current_time_det, end_date,
open_trade_count_start, trade_dir, is_first)
current_time_det += self.timeframe_detail_td
is_first = False
else:
self.dataprovider._set_dataframe_max_date(current_time)
open_trade_count_start = self.backtest_loop(
row, pair, current_time, end_date,
open_trade_count_start, trade_dir)
# Move time one configured time_interval ahead.
self.progress.increment()
current_time += self.timeframe_td
self.handle_left_open(LocalTrade.bt_trades_open_pp, data=data)
self.wallets.update()
results = trade_list_to_dataframe(LocalTrade.trades)
return {
'results': results,
'config': self.strategy.config,
'locks': PairLocks.get_all_locks(),
'rejected_signals': self.rejected_trades,
'timedout_entry_orders': self.timedout_entry_orders,
'timedout_exit_orders': self.timedout_exit_orders,
'canceled_trade_entries': self.canceled_trade_entries,
'canceled_entry_orders': self.canceled_entry_orders,
'replaced_entry_orders': self.replaced_entry_orders,
'final_balance': self.wallets.get_total(self.strategy.config['stake_currency']),
}
def backtest_one_strategy(self, strat: IStrategy, data: Dict[str, DataFrame],
timerange: TimeRange):
self.progress.init_step(BacktestState.ANALYZE, 0)
strategy_name = strat.get_strategy_name()
logger.info(f"Running backtesting for Strategy {strategy_name}")
backtest_start_time = datetime.now(timezone.utc)
self._set_strategy(strat)
# Use max_open_trades in backtesting, except --disable-max-market-positions is set
if not self.config.get('use_max_market_positions', True):
logger.info(
'Ignoring max_open_trades (--disable-max-market-positions was used) ...')
self.strategy.max_open_trades = float('inf')
self.config.update({'max_open_trades': self.strategy.max_open_trades})
# need to reprocess data every time to populate signals
preprocessed = self.strategy.advise_all_indicators(data)
# Trim startup period from analyzed dataframe
# This only used to determine if trimming would result in an empty dataframe
preprocessed_tmp = trim_dataframes(preprocessed, timerange, self.required_startup)
if not preprocessed_tmp:
raise OperationalException(
"No data left after adjusting for startup candles.")
# Use preprocessed_tmp for date generation (the trimmed dataframe).
# Backtesting will re-trim the dataframes after entry/exit signal generation.
min_date, max_date = history.get_timerange(preprocessed_tmp)
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,
end_date=max_date,
)
backtest_end_time = datetime.now(timezone.utc)
results.update({
'run_id': self.run_ids.get(strategy_name, ''),
'backtest_start_time': int(backtest_start_time.timestamp()),
'backtest_end_time': int(backtest_end_time.timestamp()),
})
self.all_results[strategy_name] = results
if (self.config.get('export', 'none') == 'signals' and
self.dataprovider.runmode == RunMode.BACKTEST):
self.processed_dfs[strategy_name] = generate_trade_signal_candles(
preprocessed_tmp, results)
self.rejected_df[strategy_name] = generate_rejected_signals(
preprocessed_tmp, self.rejected_dict)
return min_date, max_date
def _get_min_cached_backtest_date(self):
min_backtest_date = None
backtest_cache_age = self.config.get('backtest_cache', constants.BACKTEST_CACHE_DEFAULT)
if self.timerange.stopts == 0 or self.timerange.stopdt > datetime.now(tz=timezone.utc):
logger.warning('Backtest result caching disabled due to use of open-ended timerange.')
elif backtest_cache_age == 'day':
min_backtest_date = datetime.now(tz=timezone.utc) - timedelta(days=1)
elif backtest_cache_age == 'week':
min_backtest_date = datetime.now(tz=timezone.utc) - timedelta(weeks=1)
elif backtest_cache_age == 'month':
min_backtest_date = datetime.now(tz=timezone.utc) - timedelta(weeks=4)
return min_backtest_date
def load_prior_backtest(self):
self.run_ids = {
strategy.get_strategy_name(): get_strategy_run_id(strategy)
for strategy in self.strategylist
}
# Load previous result that will be updated incrementally.
# This can be circumvented in certain instances in combination with downloading more data
min_backtest_date = self._get_min_cached_backtest_date()
if min_backtest_date is not None:
self.results = find_existing_backtest_stats(
self.config['user_data_dir'] / 'backtest_results', self.run_ids, min_backtest_date)
def start(self) -> None:
"""
Run backtesting end-to-end
"""
data: Dict[str, DataFrame] = {}
data, timerange = self.load_bt_data()
self.load_bt_data_detail()
logger.info("Dataload complete. Calculating indicators")
self.load_prior_backtest()
for strat in self.strategylist:
if self.results and strat.get_strategy_name() in self.results['strategy']:
# When previous result hash matches - reuse that result and skip backtesting.
logger.info(f'Reusing result of previous backtest for {strat.get_strategy_name()}')
continue
min_date, max_date = self.backtest_one_strategy(strat, data, timerange)
# Update old results with new ones.
if len(self.all_results) > 0:
results = generate_backtest_stats(
data, self.all_results, min_date=min_date, max_date=max_date)
if self.results:
self.results['metadata'].update(results['metadata'])
self.results['strategy'].update(results['strategy'])
self.results['strategy_comparison'].extend(results['strategy_comparison'])
else:
self.results = results
dt_appendix = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
if self.config.get('export', 'none') in ('trades', 'signals'):
combined_res = combined_dataframes_with_rel_mean(data, min_date, max_date)
store_backtest_stats(self.config['exportfilename'], self.results, dt_appendix,
market_change_data=combined_res)
if (self.config.get('export', 'none') == 'signals' and
self.dataprovider.runmode == RunMode.BACKTEST):
store_backtest_analysis_results(
self.config['exportfilename'], self.processed_dfs, self.rejected_df,
dt_appendix)
# Results may be mixed up now. Sort them so they follow --strategy-list order.
if 'strategy_list' in self.config and len(self.results) > 0:
self.results['strategy_comparison'] = sorted(
self.results['strategy_comparison'],
key=lambda c: self.config['strategy_list'].index(c['key']))
self.results['strategy'] = dict(
sorted(self.results['strategy'].items(),
key=lambda kv: self.config['strategy_list'].index(kv[0])))
if len(self.strategylist) > 0:
# Show backtest results
show_backtest_results(self.config, self.results)