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
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from argparse import Namespace
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from copy import deepcopy
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from datetime import datetime, timedelta
from pathlib import Path
from typing import Any, Dict, List, NamedTuple, Optional
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from pandas import DataFrame
from tabulate import tabulate
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from freqtrade import optimize
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from freqtrade import DependencyException, constants
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from freqtrade.arguments import Arguments
from freqtrade.configuration import Configuration
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from freqtrade.data import history
from freqtrade.data.dataprovider import DataProvider
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.misc import file_dump_json
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from freqtrade.persistence import Trade
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.state import RunMode
from freqtrade.strategy.interface import SellType, IStrategy
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logger = logging.getLogger(__name__)
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class BacktestResult(NamedTuple):
"""
NamedTuple Defining BacktestResults inputs.
"""
pair: str
profit_percent: float
profit_abs: float
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open_time: datetime
close_time: datetime
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open_index: int
close_index: int
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trade_duration: float
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open_at_end: bool
open_rate: float
close_rate: float
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sell_reason: SellType
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class Backtesting(object):
"""
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:
self.config = config
# Reset keys for backtesting
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self.config['exchange']['key'] = ''
self.config['exchange']['secret'] = ''
self.config['exchange']['password'] = ''
self.config['exchange']['uid'] = ''
self.config['dry_run'] = True
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self.strategylist: List[IStrategy] = []
exchange_name = self.config.get('exchange', {}).get('name', 'bittrex').title()
self.exchange = ExchangeResolver(exchange_name, self.config).exchange
self.fee = self.exchange.get_fee()
if self.config.get('runmode') != RunMode.HYPEROPT:
self.dataprovider = DataProvider(self.config, self.exchange)
IStrategy.dp = self.dataprovider
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if self.config.get('strategy_list', None):
# Force one interval
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self.ticker_interval = str(self.config.get('ticker_interval'))
self.ticker_interval_mins = timeframe_to_minutes(self.ticker_interval)
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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(stratconf).strategy)
else:
# only one strategy
self.strategylist.append(StrategyResolver(self.config).strategy)
# Load one strategy
self._set_strategy(self.strategylist[0])
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def _set_strategy(self, strategy):
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"""
Load strategy into backtesting
"""
self.strategy = strategy
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self.ticker_interval = self.config.get('ticker_interval')
self.ticker_interval_mins = timeframe_to_minutes(self.ticker_interval)
self.tickerdata_to_dataframe = strategy.tickerdata_to_dataframe
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self.advise_buy = strategy.advise_buy
self.advise_sell = strategy.advise_sell
# 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 _generate_text_table(self, data: Dict[str, Dict], results: DataFrame,
skip_nan: bool = False) -> str:
"""
Generates and returns a text table for the given backtest data and the results dataframe
:return: pretty printed table with tabulate as str
"""
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stake_currency = str(self.config.get('stake_currency'))
max_open_trades = self.config.get('max_open_trades')
floatfmt = ('s', 'd', '.2f', '.2f', '.8f', '.2f', 'd', '.1f', '.1f')
tabular_data = []
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headers = ['pair', 'buy count', 'avg profit %', 'cum profit %',
'tot profit ' + stake_currency, 'tot profit %', 'avg duration',
'profit', 'loss']
for pair in data:
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result = results[results.pair == pair]
if skip_nan and result.profit_abs.isnull().all():
continue
tabular_data.append([
pair,
len(result.index),
result.profit_percent.mean() * 100.0,
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result.profit_percent.sum() * 100.0,
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result.profit_abs.sum(),
result.profit_percent.sum() * 100.0 / max_open_trades,
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str(timedelta(
minutes=round(result.trade_duration.mean()))) if not result.empty else '0:00',
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len(result[result.profit_abs > 0]),
len(result[result.profit_abs < 0])
])
# Append Total
tabular_data.append([
'TOTAL',
len(results.index),
results.profit_percent.mean() * 100.0,
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results.profit_percent.sum() * 100.0,
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results.profit_abs.sum(),
results.profit_percent.sum() * 100.0 / max_open_trades,
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str(timedelta(
minutes=round(results.trade_duration.mean()))) if not results.empty else '0:00',
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len(results[results.profit_abs > 0]),
len(results[results.profit_abs < 0])
])
# Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(tabular_data, headers=headers, # type: ignore
floatfmt=floatfmt, tablefmt="pipe")
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def _generate_text_table_sell_reason(self, data: Dict[str, Dict], results: DataFrame) -> str:
"""
Generate small table outlining Backtest results
"""
tabular_data = []
headers = ['Sell Reason', 'Count']
for reason, count in results['sell_reason'].value_counts().iteritems():
tabular_data.append([reason.value, count])
return tabulate(tabular_data, headers=headers, tablefmt="pipe")
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def _generate_text_table_strategy(self, all_results: dict) -> str:
"""
Generate summary table per strategy
"""
stake_currency = str(self.config.get('stake_currency'))
max_open_trades = self.config.get('max_open_trades')
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floatfmt = ('s', 'd', '.2f', '.2f', '.8f', '.2f', 'd', '.1f', '.1f')
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tabular_data = []
headers = ['Strategy', 'buy count', 'avg profit %', 'cum profit %',
'tot profit ' + stake_currency, 'tot profit %', 'avg duration',
'profit', 'loss']
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for strategy, results in all_results.items():
tabular_data.append([
strategy,
len(results.index),
results.profit_percent.mean() * 100.0,
results.profit_percent.sum() * 100.0,
results.profit_abs.sum(),
results.profit_percent.sum() * 100.0 / max_open_trades,
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str(timedelta(
minutes=round(results.trade_duration.mean()))) if not results.empty else '0:00',
len(results[results.profit_abs > 0]),
len(results[results.profit_abs < 0])
])
# Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(tabular_data, headers=headers, # type: ignore
floatfmt=floatfmt, tablefmt="pipe")
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def _store_backtest_result(self, recordfilename: str, results: DataFrame,
strategyname: Optional[str] = None) -> None:
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records = [(t.pair, t.profit_percent, t.open_time.timestamp(),
t.close_time.timestamp(), t.open_index - 1, t.trade_duration,
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t.open_rate, t.close_rate, t.open_at_end, t.sell_reason.value)
for index, t in results.iterrows()]
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if records:
if strategyname:
# Inject strategyname to filename
recname = Path(recordfilename)
recordfilename = str(Path.joinpath(
recname.parent, f'{recname.stem}-{strategyname}').with_suffix(recname.suffix))
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logger.info('Dumping backtest results to %s', recordfilename)
file_dump_json(recordfilename, records)
def _get_ticker_list(self, processed) -> Dict[str, DataFrame]:
"""
Helper function to convert a processed tickerlist into a list for performance reasons.
Used by backtest() - so keep this optimized for performance.
"""
headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high']
ticker: Dict = {}
# Create ticker dict
for pair, pair_data in processed.items():
pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run
ticker_data = self.advise_sell(
self.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
# to avoid using data from future, we buy/sell with signal from previous candle
ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1)
ticker_data.loc[:, 'sell'] = ticker_data['sell'].shift(1)
ticker_data.drop(ticker_data.head(1).index, inplace=True)
# Convert from Pandas to list for performance reasons
# (Looping Pandas is slow.)
ticker[pair] = [x for x in ticker_data.itertuples()]
return ticker
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def _get_sell_trade_entry(
self, pair: str, buy_row: DataFrame,
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partial_ticker: List, trade_count_lock: Dict, args: Dict) -> Optional[BacktestResult]:
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stake_amount = args['stake_amount']
max_open_trades = args.get('max_open_trades', 0)
trade = Trade(
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open_rate=buy_row.open,
open_date=buy_row.date,
stake_amount=stake_amount,
amount=stake_amount / buy_row.open,
fee_open=self.fee,
fee_close=self.fee
)
# calculate win/lose forwards from buy point
for sell_row in partial_ticker:
if max_open_trades > 0:
# Increase trade_count_lock for every iteration
trade_count_lock[sell_row.date] = trade_count_lock.get(sell_row.date, 0) + 1
buy_signal = sell_row.buy
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sell = self.strategy.should_sell(trade, sell_row.open, sell_row.date, buy_signal,
sell_row.sell, low=sell_row.low, high=sell_row.high)
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if sell.sell_flag:
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trade_dur = int((sell_row.date - buy_row.date).total_seconds() // 60)
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# 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
closerate = trade.stop_loss
elif sell.sell_type == (SellType.ROI):
# get next entry in min_roi > to trade duration
# Interface.py skips on trade_duration <= duration
roi_entry = max(list(filter(lambda x: trade_dur >= x,
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self.strategy.minimal_roi.keys())))
roi = self.strategy.minimal_roi[roi_entry]
# - (Expected abs profit + open_rate + open_fee) / (fee_close -1)
closerate = - (trade.open_rate * roi + trade.open_rate *
(1 + trade.fee_open)) / (trade.fee_close - 1)
else:
closerate = sell_row.open
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return BacktestResult(pair=pair,
profit_percent=trade.calc_profit_percent(rate=closerate),
profit_abs=trade.calc_profit(rate=closerate),
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open_time=buy_row.date,
close_time=sell_row.date,
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trade_duration=trade_dur,
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open_index=buy_row.Index,
close_index=sell_row.Index,
open_at_end=False,
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open_rate=buy_row.open,
close_rate=closerate,
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sell_reason=sell.sell_type
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)
if partial_ticker:
# no sell condition found - trade stil open at end of backtest period
sell_row = partial_ticker[-1]
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btr = BacktestResult(pair=pair,
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profit_percent=trade.calc_profit_percent(rate=sell_row.open),
profit_abs=trade.calc_profit(rate=sell_row.open),
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open_time=buy_row.date,
close_time=sell_row.date,
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trade_duration=int((
sell_row.date - buy_row.date).total_seconds() // 60),
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open_index=buy_row.Index,
close_index=sell_row.Index,
open_at_end=True,
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open_rate=buy_row.open,
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close_rate=sell_row.open,
sell_reason=SellType.FORCE_SELL
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)
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logger.debug('Force_selling still open trade %s with %s perc - %s', btr.pair,
btr.profit_percent, btr.profit_abs)
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return btr
return None
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def backtest(self, args: Dict) -> DataFrame:
"""
Implements backtesting functionality
NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
Of course try to not have ugly code. By some accessor are sometime slower than functions.
Avoid, logging on this method
:param args: a dict containing:
stake_amount: btc amount to use for each trade
processed: a processed dictionary with format {pair, data}
max_open_trades: maximum number of concurrent trades (default: 0, disabled)
position_stacking: do we allow position stacking? (default: False)
:return: DataFrame
"""
processed = args['processed']
max_open_trades = args.get('max_open_trades', 0)
position_stacking = args.get('position_stacking', False)
start_date = args['start_date']
end_date = args['end_date']
trades = []
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trade_count_lock: Dict = {}
# Dict of ticker-lists for performance (looping lists is a lot faster than dataframes)
ticker: Dict = self._get_ticker_list(processed)
lock_pair_until: Dict = {}
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# Indexes per pair, so some pairs are allowed to have a missing start.
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indexes: Dict = {}
tmp = start_date + timedelta(minutes=self.ticker_interval_mins)
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# Loop timerange and get candle for each pair at that point in time
while tmp < end_date:
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for i, pair in enumerate(ticker):
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if pair not in indexes:
indexes[pair] = 0
try:
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row = ticker[pair][indexes[pair]]
except IndexError:
# missing Data for one pair at the end.
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# Warnings for this are shown by `validate_backtest_data`
continue
# Waits until the time-counter reaches the start of the data for this pair.
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if row.date > tmp.datetime:
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continue
indexes[pair] += 1
if row.buy == 0 or row.sell == 1:
continue # skip rows where no buy signal or that would immediately sell off
if (not position_stacking and pair in lock_pair_until
and row.date <= lock_pair_until[pair]):
# without positionstacking, we can only have one open trade per pair.
continue
if max_open_trades > 0:
# Check if max_open_trades has already been reached for the given date
if not trade_count_lock.get(row.date, 0) < max_open_trades:
continue
trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1
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trade_entry = self._get_sell_trade_entry(pair, row, ticker[pair][indexes[pair]:],
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trade_count_lock, args)
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if trade_entry:
lock_pair_until[pair] = trade_entry.close_time
trades.append(trade_entry)
else:
# Set lock_pair_until to end of testing period if trade could not be closed
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lock_pair_until[pair] = end_date.datetime
# Move time one configured time_interval ahead.
tmp += timedelta(minutes=self.ticker_interval_mins)
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return DataFrame.from_records(trades, columns=BacktestResult._fields)
def start(self) -> None:
"""
Run a backtesting end-to-end
:return: None
"""
data: Dict[str, Any] = {}
pairs = self.config['exchange']['pair_whitelist']
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logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
if self.config.get('live'):
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logger.info('Downloading data for all pairs in whitelist ...')
self.exchange.refresh_latest_ohlcv([(pair, self.ticker_interval) for pair in pairs])
data = {key[0]: value for key, value in self.exchange._klines.items()}
else:
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logger.info('Using local backtesting data (using whitelist in given config) ...')
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timerange = Arguments.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
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data = history.load_data(
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datadir=Path(self.config['datadir']) if self.config.get('datadir') else None,
pairs=pairs,
ticker_interval=self.ticker_interval,
refresh_pairs=self.config.get('refresh_pairs', False),
exchange=self.exchange,
timerange=timerange
)
if not data:
logger.critical("No data found. Terminating.")
return
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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):
max_open_trades = self.config['max_open_trades']
else:
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logger.info('Ignoring max_open_trades (--disable-max-market-positions was used) ...')
max_open_trades = 0
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all_results = {}
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for strat in self.strategylist:
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logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
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self._set_strategy(strat)
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min_date, max_date = optimize.get_timeframe(data)
# Validate dataframe for missing values (mainly at start and end, as fillup is called)
optimize.validate_backtest_data(data, min_date, max_date,
timeframe_to_minutes(self.ticker_interval))
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logger.info(
'Measuring data from %s up to %s (%s days)..',
min_date.isoformat(),
max_date.isoformat(),
(max_date - min_date).days
)
# need to reprocess data every time to populate signals
preprocessed = self.strategy.tickerdata_to_dataframe(data)
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# Execute backtest and print results
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all_results[self.strategy.get_strategy_name()] = self.backtest(
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{
'stake_amount': self.config.get('stake_amount'),
'processed': preprocessed,
'max_open_trades': max_open_trades,
'position_stacking': self.config.get('position_stacking', False),
'start_date': min_date,
'end_date': max_date,
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}
)
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for strategy, results in all_results.items():
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if self.config.get('export', False):
self._store_backtest_result(self.config['exportfilename'], results,
strategy if len(self.strategylist) > 1 else None)
print(f"Result for strategy {strategy}")
print(' BACKTESTING REPORT '.center(133, '='))
print(self._generate_text_table(data, results))
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print(' SELL REASON STATS '.center(133, '='))
print(self._generate_text_table_sell_reason(data, results))
print(' LEFT OPEN TRADES REPORT '.center(133, '='))
print(self._generate_text_table(data, results.loc[results.open_at_end], True))
print()
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if len(all_results) > 1:
# Print Strategy summary table
print(' Strategy Summary '.center(133, '='))
print(self._generate_text_table_strategy(all_results))
print('\nFor more details, please look at the detail tables above')
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def setup_configuration(args: Namespace) -> Dict[str, Any]:
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"""
Prepare the configuration for the backtesting
:param args: Cli args from Arguments()
:return: Configuration
"""
configuration = Configuration(args, RunMode.BACKTEST)
config = configuration.get_config()
# Ensure we do not use Exchange credentials
config['exchange']['key'] = ''
config['exchange']['secret'] = ''
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if config['stake_amount'] == constants.UNLIMITED_STAKE_AMOUNT:
raise DependencyException('stake amount could not be "%s" for backtesting' %
constants.UNLIMITED_STAKE_AMOUNT)
return config
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def start(args: Namespace) -> None:
"""
Start Backtesting script
:param args: Cli args from Arguments()
:return: None
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"""
# Initialize configuration
config = setup_configuration(args)
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logger.info('Starting freqtrade in Backtesting mode')
# Initialize backtesting object
backtesting = Backtesting(config)
backtesting.start()