stable/freqtrade/tests/test_backtesting.py

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# pragma pylint: disable=missing-docstring,W0212
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import logging
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import os
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from typing import Tuple, Dict
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import arrow
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import pytest
from pandas import DataFrame
from tabulate import tabulate
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from freqtrade import exchange
from freqtrade.analyze import parse_ticker_dataframe, populate_indicators, \
populate_buy_trend, populate_sell_trend
from freqtrade.exchange import Bittrex
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from freqtrade.main import min_roi_reached
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from freqtrade.misc import load_config
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from freqtrade.persistence import Trade
from freqtrade.tests import load_backtesting_data
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logger = logging.getLogger(__name__)
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def format_results(results: DataFrame):
return ('Made {:6d} buys. Average profit {: 5.2f}%. '
'Total profit was {: 7.3f}. Average duration {:5.1f} mins.').format(
len(results.index),
results.profit.mean() * 100.0,
results.profit.sum(),
results.duration.mean() * 5,
)
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def preprocess(backdata) -> Dict[str, DataFrame]:
processed = {}
for pair, pair_data in backdata.items():
processed[pair] = populate_indicators(parse_ticker_dataframe(pair_data))
return processed
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def get_timeframe(data: Dict[str, Dict]) -> Tuple[arrow.Arrow, arrow.Arrow]:
"""
Get the maximum timeframe for the given backtest data
:param data: dictionary with backtesting data
:return: tuple containing min_date, max_date
"""
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min_date, max_date = None, None
for values in data.values():
sorted_values = sorted(values, key=lambda d: arrow.get(d['T']))
if not min_date or sorted_values[0]['T'] < min_date:
min_date = sorted_values[0]['T']
if not max_date or sorted_values[-1]['T'] > max_date:
max_date = sorted_values[-1]['T']
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return arrow.get(min_date), arrow.get(max_date)
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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
"""
tabular_data = []
headers = ['pair', 'buy count', 'avg profit', 'total profit', 'avg duration']
for pair in data:
result = results[results.currency == pair]
tabular_data.append([
pair,
len(result.index),
'{:.2f}%'.format(result.profit.mean() * 100.0),
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'{:.08f} {}'.format(result.profit.sum(), stake_currency),
'{:.2f}'.format(result.duration.mean() * 5),
])
# Append Total
tabular_data.append([
'TOTAL',
len(results.index),
'{:.2f}%'.format(results.profit.mean() * 100.0),
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'{:.08f} {}'.format(results.profit.sum(), stake_currency),
'{:.2f}'.format(results.duration.mean() * 5),
])
return tabulate(tabular_data, headers=headers)
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def backtest(config: Dict, processed, mocker, max_open_trades=0):
"""
Implements backtesting functionality
:param config: config to use
:param processed: a processed dictionary with format {pair, data}
:param mocker: mocker instance
:param max_open_trades: maximum number of concurrent trades (default: 0, disabled)
:return: DataFrame
"""
trades = []
trade_count_lock = {}
exchange._API = Bittrex({'key': '', 'secret': ''})
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mocker.patch.dict('freqtrade.main._CONF', config)
for pair, pair_data in processed.items():
pair_data['buy'], pair_data['sell'] = 0, 0
ticker = populate_sell_trend(populate_buy_trend(pair_data))
# for each buy point
for row in ticker[ticker.buy == 1].itertuples(index=True):
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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
if max_open_trades > 0:
# Increase lock
trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1
trade = Trade(
open_rate=row.close,
open_date=row.date,
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amount=config['stake_amount'],
fee=exchange.get_fee() * 2
)
# calculate win/lose forwards from buy point
for row2 in ticker[row.Index + 1:].itertuples(index=True):
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if max_open_trades > 0:
# Increase trade_count_lock for every iteration
trade_count_lock[row2.date] = trade_count_lock.get(row2.date, 0) + 1
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if min_roi_reached(trade, row2.close, row2.date) or row2.sell == 1:
current_profit = trade.calc_profit(row2.close)
trades.append((pair, current_profit, row2.Index - row.Index))
break
labels = ['currency', 'profit', 'duration']
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return DataFrame.from_records(trades, columns=labels)
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def get_max_open_trades(config):
if not os.environ.get('BACKTEST_LIMIT_MAX_TRADES'):
return 0
print('Using max_open_trades: {} ...'.format(config['max_open_trades']))
return config['max_open_trades']
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@pytest.mark.skipif(not os.environ.get('BACKTEST'), reason="BACKTEST not set")
def test_backtest(backtest_conf, mocker):
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print('')
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exchange._API = Bittrex({'key': '', 'secret': ''})
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# Load configuration file based on env variable
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conf_path = os.environ.get('BACKTEST_CONFIG')
if conf_path:
print('Using config: {} ...'.format(conf_path))
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config = load_config(conf_path)
else:
config = backtest_conf
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# Parse ticker interval
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ticker_interval = int(os.environ.get('BACKTEST_TICKER_INTERVAL') or 5)
print('Using ticker_interval: {} ...'.format(ticker_interval))
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data = {}
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if os.environ.get('BACKTEST_LIVE'):
print('Downloading data for all pairs in whitelist ...')
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for pair in config['exchange']['pair_whitelist']:
data[pair] = exchange.get_ticker_history(pair, ticker_interval)
else:
print('Using local backtesting data (ignoring whitelist in given config)...')
data = load_backtesting_data(ticker_interval)
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print('Using stake_currency: {} ...\nUsing stake_amount: {} ...'.format(
config['stake_currency'], config['stake_amount']
))
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# Print timeframe
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min_date, max_date = get_timeframe(data)
print('Measuring data from {} up to {} ...'.format(
min_date.isoformat(), max_date.isoformat()
))
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# Execute backtest and print results
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results = backtest(config, preprocess(data), mocker, get_max_open_trades(config))
print('====================== BACKTESTING REPORT ======================================\n\n')
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print(generate_text_table(data, results, config['stake_currency']))