adelorenzo
/
stable
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

uncomplex backtest

This commit is contained in:
kryofly 2018-01-11 17:45:41 +01:00
parent feb5da0c35
commit 27769f0301
3 changed files with 83 additions and 60 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

116
freqtrade/optimize/backtesting.py
View File

@ -68,17 +68,59 @@ def generate_text_table(
return tabulate(tabular_data, headers=headers, floatfmt=floatfmt)
def backtest(stake_amount: float, processed: Dict[str, DataFrame],
max_open_trades: int = 0, realistic: bool = True, sell_profit_only: bool = False,
stoploss: int = -1.00, use_sell_signal: bool = False) -> DataFrame:
def get_trade_entry(pair, row, ticker, trade_count_lock, args):
stake_amount = args['stake_amount']
max_open_trades = args.get('max_open_trades', 0)
sell_profit_only = args.get('sell_profit_only', False)
stoploss = args.get('stoploss', -1)
use_sell_signal = args.get('use_sell_signal', False)
trade = Trade(open_rate=row.close,
open_date=row.date,
stake_amount=stake_amount,
amount=stake_amount / row.open,
fee=exchange.get_fee()
)
# calculate win/lose forwards from buy point
sell_subset = ticker[row.Index + 1:][['close', 'date', 'sell']]
for row2 in sell_subset.itertuples(index=True):
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
current_profit_percent = trade.calc_profit_percent(rate=row2.close)
if (sell_profit_only and current_profit_percent < 0):
continue
if min_roi_reached(trade, row2.close, row2.date) or \
(row2.sell == 1 and use_sell_signal) or \
current_profit_percent <= stoploss:
current_profit_btc = trade.calc_profit(rate=row2.close)
return row2.Index, (pair,
current_profit_percent,
current_profit_btc,
row2.Index - row.Index,
current_profit_btc > 0,
current_profit_btc < 0
)
def backtest(args) -> DataFrame:
"""
Implements backtesting functionality
:param stake_amount: btc amount to use for each trade
:param processed: a processed dictionary with format {pair, data}
:param max_open_trades: maximum number of concurrent trades (default: 0, disabled)
:param realistic: do we try to simulate realistic trades? (default: True)
: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
"""
processed = args['processed']
max_open_trades = args.get('max_open_trades', 0)
realistic = args.get('realistic', True)
trades = []
trade_count_lock: dict = {}
exchange._API = Bittrex({'key': '', 'secret': ''})
@ -101,41 +143,11 @@ def backtest(stake_amount: float, processed: Dict[str, DataFrame],
# 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,
stake_amount=stake_amount,
amount=stake_amount / row.open,
fee=exchange.get_fee()
)
# calculate win/lose forwards from buy point
sell_subset = ticker[row.Index + 1:][['close', 'date', 'sell']]
for row2 in sell_subset.itertuples(index=True):
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
current_profit_percent = trade.calc_profit_percent(rate=row2.close)
if (sell_profit_only and current_profit_percent < 0):
continue
if min_roi_reached(trade, row2.close, row2.date) or \
(row2.sell == 1 and use_sell_signal) or \
current_profit_percent <= stoploss:
current_profit_btc = trade.calc_profit(rate=row2.close)
lock_pair_until = row2.Index
trades.append(
(
pair,
current_profit_percent,
current_profit_btc,
row2.Index - row.Index,
current_profit_btc > 0,
current_profit_btc < 0
)
)
break
ret = get_trade_entry(pair, row, ticker,
trade_count_lock, args)
if ret:
lock_pair_until, trade_entry = ret
trades.append(trade_entry)
labels = ['currency', 'profit_percent', 'profit_BTC', 'duration', 'profit', 'loss']
return DataFrame.from_records(trades, columns=labels)
@ -181,17 +193,17 @@ def start(args):
# Print timeframe
min_date, max_date = get_timeframe(preprocessed)
logger.info('Measuring data from %s up to %s ...', min_date.isoformat(), max_date.isoformat())
# Execute backtest and print results
results = backtest(
stake_amount=config['stake_amount'],
processed=preprocessed,
max_open_trades=max_open_trades,
realistic=args.realistic_simulation,
sell_profit_only=config.get('experimental', {}).get('sell_profit_only', False),
stoploss=config.get('stoploss'),
use_sell_signal=config.get('experimental', {}).get('use_sell_signal', False)
)
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': config.get('stoploss')
})
logger.info(
'\n==================================== BACKTESTING REPORT ====================================\n%s', # noqa
generate_text_table(data, results, config['stake_currency'], args.ticker_interval)

4
freqtrade/optimize/hyperopt.py
View File

@ -128,7 +128,9 @@ def optimizer(params):
from freqtrade.optimize import backtesting
backtesting.populate_buy_trend = buy_strategy_generator(params)
results = backtest(OPTIMIZE_CONFIG['stake_amount'], PROCESSED, stoploss=params['stoploss'])
results = backtest({'stake_amount': OPTIMIZE_CONFIG['stake_amount'],
'processed': PROCESSED,
'stoploss': params['stoploss']})
result_explanation = format_results(results)
total_profit = results.profit_percent.sum()

23
freqtrade/tests/optimize/test_backtesting.py
View File

@ -43,8 +43,10 @@ def test_backtest(default_conf, mocker):
exchange._API = Bittrex({'key': '', 'secret': ''})
data = optimize.load_data(None, ticker_interval=5, pairs=['BTC_ETH'])
results = backtest(default_conf['stake_amount'],
optimize.preprocess(data), 10, True)
results = backtest({'stake_amount': default_conf['stake_amount'],
'processed': optimize.preprocess(data),
'max_open_trades': 10,
'realistic': True})
assert not results.empty
@ -54,8 +56,10 @@ def test_backtest_1min_ticker_interval(default_conf, mocker):
# Run a backtesting for an exiting 5min ticker_interval
data = optimize.load_data(None, ticker_interval=1, pairs=['BTC_UNITEST'])
results = backtest(default_conf['stake_amount'],
optimize.preprocess(data), 1, True)
results = backtest({'stake_amount': default_conf['stake_amount'],
'processed': optimize.preprocess(data),
'max_open_trades': 1,
'realistic': True})
assert not results.empty
@ -113,7 +117,10 @@ def simple_backtest(config, contour, num_results):
data = load_data_test(contour)
processed = optimize.preprocess(data)
assert isinstance(processed, dict)
results = backtest(config['stake_amount'], processed, 1, True)
results = backtest({'stake_amount': config['stake_amount'],
'processed': processed,
'max_open_trades': 1,
'realistic': True})
# results :: <class 'pandas.core.frame.DataFrame'>
assert len(results) == num_results
@ -125,8 +132,10 @@ def simple_backtest(config, contour, num_results):
def test_backtest2(default_conf, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
data = optimize.load_data(None, ticker_interval=5, pairs=['BTC_ETH'])
results = backtest(default_conf['stake_amount'],
optimize.preprocess(data), 10, True)
results = backtest({'stake_amount': default_conf['stake_amount'],
'processed': optimize.preprocess(data),
'max_open_trades': 10,
'realistic': True})
assert not results.empty