# pragma pylint: disable=missing-docstring,W0212
import logging
from typing import Dict, Tuple
import arrow
from pandas import DataFrame, Series
from tabulate import tabulate
import freqtrade.misc as misc
import freqtrade.optimize as optimize
from freqtrade import exchange
from freqtrade.analyze import populate_buy_trend, populate_sell_trend
from freqtrade.exchange import Bittrex
from freqtrade.main import should_sell
from freqtrade.persistence import Trade
from freqtrade.strategy.strategy import Strategy
logger = logging.getLogger(__name__)
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(
data: Dict[str, Dict], results: DataFrame, stake_currency) -> str:
"""
Generates and returns a text table for the given backtest data and the results dataframe
:return: pretty printed table with tabulate as str
"""
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(pair, buy_row, partial_ticker, trade_count_lock, args):
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 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(args) -> DataFrame:
"""
Implements backtesting functionality
: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
stoploss: use stoploss
:return: DataFrame
"""
headers = ['date', 'buy', 'open', 'close', 'sell']
processed = args['processed']
max_open_trades = args.get('max_open_trades', 0)
realistic = args.get('realistic', True)
record = args.get('record', None)
records = []
trades = []
trade_count_lock: dict = {}
exchange._API = Bittrex({'key': '', 'secret': ''})
for pair, pair_data in processed.items():
pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run
ticker_data = populate_sell_trend(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 = 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.strftime('%s'),
row2.date.strftime('%s'),
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')
misc.file_dump_json('backtest-result.json', records)
labels = ['currency', 'profit_percent', 'profit_BTC', 'duration']
return DataFrame.from_records(trades, columns=labels)
def start(args):
# Initialize logger
logging.basicConfig(
level=args.loglevel,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
)
exchange._API = Bittrex({'key': '', 'secret': ''})
logger.info('Using config: %s ...', args.config)
config = misc.load_config(args.config)
# If -i/--ticker-interval is use we override the configuration parameter
# (that will override the strategy configuration)
if args.ticker_interval:
config.update({'ticker_interval': args.ticker_interval})
# init the strategy to use
config.update({'strategy': args.strategy})
strategy = Strategy()
strategy.init(config)
logger.info('Using ticker_interval: %d ...', strategy.ticker_interval)
data = {}
pairs = config['exchange']['pair_whitelist']
logger.info('Using stake_currency: %s ...', config['stake_currency'])
logger.info('Using stake_amount: %s ...', config['stake_amount'])
if args.live:
logger.info('Downloading data for all pairs in whitelist ...')
for pair in pairs:
data[pair] = exchange.get_ticker_history(pair, strategy.ticker_interval)
else:
logger.info('Using local backtesting data (using whitelist in given config) ...')
timerange = misc.parse_timerange(args.timerange)
data = optimize.load_data(args.datadir,
pairs=pairs,
ticker_interval=strategy.ticker_interval,
refresh_pairs=args.refresh_pairs,
timerange=timerange)
max_open_trades = 0
if args.realistic_simulation:
logger.info('Using max_open_trades: %s ...', config['max_open_trades'])
max_open_trades = config['max_open_trades']
# Monkey patch config
from freqtrade import main
main._CONF = config
preprocessed = optimize.tickerdata_to_dataframe(data)
# Print timeframe
min_date, max_date = 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 = config.get('experimental', {}).get('sell_profit_only', False)
use_sell_signal = config.get('experimental', {}).get('use_sell_signal', False)
results = backtest({'stake_amount': config['stake_amount'],
'processed': preprocessed,
'max_open_trades': max_open_trades,
'realistic': args.realistic_simulation,
'sell_profit_only': sell_profit_only,
'use_sell_signal': use_sell_signal,
'stoploss': strategy.stoploss,
'record': args.export
})
logger.info(
'\n==================================== BACKTESTING REPORT ====================================\n%s', # noqa
generate_text_table(data, results, config['stake_currency'])
)