315 lines
12 KiB
Python
315 lines
12 KiB
Python
# pragma pylint: disable=missing-docstring, W0212, too-many-arguments
|
|
|
|
"""
|
|
This module contains the backtesting logic
|
|
"""
|
|
import logging
|
|
from argparse import Namespace
|
|
from typing import Dict, Tuple, Any, List, Optional
|
|
|
|
import arrow
|
|
from pandas import DataFrame, Series
|
|
from tabulate import tabulate
|
|
|
|
import freqtrade.optimize as optimize
|
|
from freqtrade import exchange
|
|
from freqtrade.analyze import Analyze
|
|
from freqtrade.arguments import Arguments
|
|
from freqtrade.configuration import Configuration
|
|
from freqtrade.misc import file_dump_json
|
|
from freqtrade.persistence import Trade
|
|
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class Backtesting(object):
|
|
"""
|
|
Backtesting class, this class contains all the logic to run a backtest
|
|
|
|
To run a backtest:
|
|
backtesting = Backtesting(config)
|
|
backtesting.start()
|
|
"""
|
|
def __init__(self, config: Dict[str, Any]) -> None:
|
|
self.config = config
|
|
self.analyze = None
|
|
self.ticker_interval = None
|
|
self.tickerdata_to_dataframe = None
|
|
self.populate_buy_trend = None
|
|
self.populate_sell_trend = None
|
|
self._init()
|
|
|
|
def _init(self) -> None:
|
|
"""
|
|
Init objects required for backtesting
|
|
:return: None
|
|
"""
|
|
self.analyze = Analyze(self.config)
|
|
self.ticker_interval = self.analyze.strategy.ticker_interval
|
|
self.tickerdata_to_dataframe = self.analyze.tickerdata_to_dataframe
|
|
self.populate_buy_trend = self.analyze.populate_buy_trend
|
|
self.populate_sell_trend = self.analyze.populate_sell_trend
|
|
|
|
# Reset keys for backtesting
|
|
self.config['exchange']['key'] = ''
|
|
self.config['exchange']['secret'] = ''
|
|
self.config['exchange']['password'] = ''
|
|
self.config['exchange']['uid'] = ''
|
|
self.config['dry_run'] = True
|
|
exchange.init(self.config)
|
|
|
|
@staticmethod
|
|
def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
|
|
"""
|
|
Get the maximum timeframe for the given backtest data
|
|
:param data: dictionary with preprocessed backtesting data
|
|
:return: tuple containing min_date, max_date
|
|
"""
|
|
all_dates = Series([])
|
|
for pair_data in data.values():
|
|
all_dates = all_dates.append(pair_data['date'])
|
|
all_dates.sort_values(inplace=True)
|
|
return arrow.get(all_dates.iloc[0]), arrow.get(all_dates.iloc[-1])
|
|
|
|
def _generate_text_table(self, data: Dict[str, Dict], results: DataFrame) -> str:
|
|
"""
|
|
Generates and returns a text table for the given backtest data and the results dataframe
|
|
:return: pretty printed table with tabulate as str
|
|
"""
|
|
stake_currency = self.config.get('stake_currency')
|
|
|
|
floatfmt = ('s', 'd', '.2f', '.8f', '.1f')
|
|
tabular_data = []
|
|
headers = ['pair', 'buy count', 'avg profit %',
|
|
'total profit ' + stake_currency, 'avg duration', 'profit', 'loss']
|
|
for pair in data:
|
|
result = results[results.currency == pair]
|
|
tabular_data.append([
|
|
pair,
|
|
len(result.index),
|
|
result.profit_percent.mean() * 100.0,
|
|
result.profit_BTC.sum(),
|
|
result.duration.mean(),
|
|
len(result[result.profit_BTC > 0]),
|
|
len(result[result.profit_BTC < 0])
|
|
])
|
|
|
|
# Append Total
|
|
tabular_data.append([
|
|
'TOTAL',
|
|
len(results.index),
|
|
results.profit_percent.mean() * 100.0,
|
|
results.profit_BTC.sum(),
|
|
results.duration.mean(),
|
|
len(results[results.profit_BTC > 0]),
|
|
len(results[results.profit_BTC < 0])
|
|
])
|
|
return tabulate(tabular_data, headers=headers, floatfmt=floatfmt)
|
|
|
|
def _get_sell_trade_entry(
|
|
self, pair: str, buy_row: DataFrame,
|
|
partial_ticker: List, trade_count_lock: Dict, args: Dict) -> Optional[Tuple]:
|
|
|
|
stake_amount = args['stake_amount']
|
|
max_open_trades = args.get('max_open_trades', 0)
|
|
trade = Trade(
|
|
open_rate=buy_row.close,
|
|
open_date=buy_row.date,
|
|
stake_amount=stake_amount,
|
|
amount=stake_amount / buy_row.open,
|
|
fee=exchange.get_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
|
|
if self.analyze.should_sell(trade, sell_row.close, sell_row.date, buy_signal,
|
|
sell_row.sell):
|
|
return \
|
|
sell_row, \
|
|
(
|
|
pair,
|
|
trade.calc_profit_percent(rate=sell_row.close),
|
|
trade.calc_profit(rate=sell_row.close),
|
|
(sell_row.date - buy_row.date).seconds // 60
|
|
), \
|
|
sell_row.date
|
|
return None
|
|
|
|
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)
|
|
realistic: do we try to simulate realistic trades? (default: True)
|
|
sell_profit_only: sell if profit only
|
|
use_sell_signal: act on sell-signal
|
|
:return: DataFrame
|
|
"""
|
|
headers = ['date', 'buy', 'open', 'close', 'sell']
|
|
processed = args['processed']
|
|
max_open_trades = args.get('max_open_trades', 0)
|
|
realistic = args.get('realistic', False)
|
|
record = args.get('record', None)
|
|
records = []
|
|
trades = []
|
|
trade_count_lock = {}
|
|
for pair, pair_data in processed.items():
|
|
pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run
|
|
|
|
ticker_data = self.populate_sell_trend(self.populate_buy_trend(pair_data))[headers]
|
|
ticker = [x for x in ticker_data.itertuples()]
|
|
|
|
lock_pair_until = None
|
|
for index, row in enumerate(ticker):
|
|
if row.buy == 0 or row.sell == 1:
|
|
continue # skip rows where no buy signal or that would immediately sell off
|
|
|
|
if realistic:
|
|
if lock_pair_until is not None and row.date <= lock_pair_until:
|
|
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
|
|
|
|
ret = self._get_sell_trade_entry(pair, row, ticker[index + 1:],
|
|
trade_count_lock, args)
|
|
|
|
if ret:
|
|
row2, trade_entry, next_date = ret
|
|
lock_pair_until = next_date
|
|
trades.append(trade_entry)
|
|
if record:
|
|
# Note, need to be json.dump friendly
|
|
# record a tuple of pair, current_profit_percent,
|
|
# entry-date, duration
|
|
records.append((pair, trade_entry[1],
|
|
row.date.timestamp(),
|
|
row2.date.timestamp(),
|
|
row.date, trade_entry[3]))
|
|
# For now export inside backtest(), maybe change so that backtest()
|
|
# returns a tuple like: (dataframe, records, logs, etc)
|
|
if record and record.find('trades') >= 0:
|
|
logger.info('Dumping backtest results')
|
|
file_dump_json('backtest-result.json', records)
|
|
labels = ['currency', 'profit_percent', 'profit_BTC', 'duration']
|
|
return DataFrame.from_records(trades, columns=labels)
|
|
|
|
def start(self) -> None:
|
|
"""
|
|
Run a backtesting end-to-end
|
|
:return: None
|
|
"""
|
|
data = {}
|
|
pairs = self.config['exchange']['pair_whitelist']
|
|
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'):
|
|
logger.info('Downloading data for all pairs in whitelist ...')
|
|
for pair in pairs:
|
|
data[pair] = exchange.get_ticker_history(pair, self.ticker_interval)
|
|
else:
|
|
logger.info('Using local backtesting data (using whitelist in given config) ...')
|
|
|
|
timerange = Arguments.parse_timerange(self.config.get('timerange'))
|
|
data = optimize.load_data(
|
|
self.config['datadir'],
|
|
pairs=pairs,
|
|
ticker_interval=self.ticker_interval,
|
|
refresh_pairs=self.config.get('refresh_pairs', False),
|
|
timerange=timerange
|
|
)
|
|
|
|
# Ignore max_open_trades in backtesting, except realistic flag was passed
|
|
if self.config.get('realistic_simulation', False):
|
|
max_open_trades = self.config['max_open_trades']
|
|
else:
|
|
logger.info('Ignoring max_open_trades (realistic_simulation not set) ...')
|
|
max_open_trades = 0
|
|
|
|
preprocessed = self.tickerdata_to_dataframe(data)
|
|
|
|
# Print timeframe
|
|
min_date, max_date = self.get_timeframe(preprocessed)
|
|
logger.info(
|
|
'Measuring data from %s up to %s (%s days)..',
|
|
min_date.isoformat(),
|
|
max_date.isoformat(),
|
|
(max_date - min_date).days
|
|
)
|
|
|
|
# Execute backtest and print results
|
|
sell_profit_only = self.config.get('experimental', {}).get('sell_profit_only', False)
|
|
use_sell_signal = self.config.get('experimental', {}).get('use_sell_signal', False)
|
|
results = self.backtest(
|
|
{
|
|
'stake_amount': self.config.get('stake_amount'),
|
|
'processed': preprocessed,
|
|
'max_open_trades': max_open_trades,
|
|
'realistic': self.config.get('realistic_simulation', False),
|
|
'sell_profit_only': sell_profit_only,
|
|
'use_sell_signal': use_sell_signal,
|
|
'record': self.config.get('export')
|
|
}
|
|
)
|
|
logger.info(
|
|
'\n==================================== '
|
|
'BACKTESTING REPORT'
|
|
' ====================================\n'
|
|
'%s',
|
|
self._generate_text_table(
|
|
data,
|
|
results
|
|
)
|
|
)
|
|
|
|
|
|
def setup_configuration(args: Namespace) -> Dict[str, Any]:
|
|
"""
|
|
Prepare the configuration for the backtesting
|
|
:param args: Cli args from Arguments()
|
|
:return: Configuration
|
|
"""
|
|
configuration = Configuration(args)
|
|
config = configuration.get_config()
|
|
|
|
# Ensure we do not use Exchange credentials
|
|
config['exchange']['key'] = ''
|
|
config['exchange']['secret'] = ''
|
|
|
|
return config
|
|
|
|
|
|
def start(args: Namespace) -> None:
|
|
"""
|
|
Start Backtesting script
|
|
:param args: Cli args from Arguments()
|
|
:return: None
|
|
"""
|
|
|
|
# Initialize logger
|
|
logger.info('Starting freqtrade in Backtesting mode')
|
|
|
|
# Initialize configuration
|
|
config = setup_configuration(args)
|
|
|
|
# Initialize backtesting object
|
|
backtesting = Backtesting(config)
|
|
backtesting.start()
|