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Merge pull request #297 from jblestang/add_stoploss_and_use_sell_profit_only_to_hyperopt

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Add stoploss, sell_only_profit and use_sell_signal conf parameters to backtest function
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Janne Sinivirta 2018-01-04 13:33:01 +02:00 committed by GitHub
commit c60ef181dc
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2 changed files with 55 additions and 35 deletions
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25
freqtrade/optimize/backtesting.py
View File

@ -41,7 +41,7 @@ def generate_text_table(
floatfmt = ('s', 'd', '.2f', '.8f', '.1f') floatfmt = ('s', 'd', '.2f', '.8f', '.1f')
tabular_data = [] tabular_data = []
headers = ['pair', 'buy count', 'avg profit %', headers = ['pair', 'buy count', 'avg profit %',
'total profit ' + stake_currency, 'avg duration'] 'total profit ' + stake_currency, 'avg duration', 'profit', 'loss']
for pair in data: for pair in data:
result = results[results.currency == pair] result = results[results.currency == pair]
tabular_data.append([ tabular_data.append([
@ -50,6 +50,8 @@ def generate_text_table(
result.profit_percent.mean() * 100.0, result.profit_percent.mean() * 100.0,
result.profit_BTC.sum(), result.profit_BTC.sum(),
result.duration.mean() * ticker_interval, result.duration.mean() * ticker_interval,
result.profit.sum(),
result.loss.sum()
]) ])
# Append Total # Append Total
@ -59,12 +61,15 @@ def generate_text_table(
results.profit_percent.mean() * 100.0, results.profit_percent.mean() * 100.0,
results.profit_BTC.sum(), results.profit_BTC.sum(),
results.duration.mean() * ticker_interval, results.duration.mean() * ticker_interval,
results.profit.sum(),
results.loss.sum()
]) ])
return tabulate(tabular_data, headers=headers, floatfmt=floatfmt) return tabulate(tabular_data, headers=headers, floatfmt=floatfmt)
def backtest(stake_amount: float, processed: Dict[str, DataFrame], def backtest(stake_amount: float, processed: Dict[str, DataFrame],
max_open_trades: int = 0, realistic: bool = True) -> DataFrame: max_open_trades: int = 0, realistic: bool = True, sell_profit_only: bool = False,
stoploss: int = -1.00, use_sell_signal: bool = False) -> DataFrame:
""" """
Implements backtesting functionality Implements backtesting functionality
:param stake_amount: btc amount to use for each trade :param stake_amount: btc amount to use for each trade
@ -110,8 +115,12 @@ def backtest(stake_amount: float, processed: Dict[str, DataFrame],
# Increase trade_count_lock for every iteration # Increase trade_count_lock for every iteration
trade_count_lock[row2.date] = trade_count_lock.get(row2.date, 0) + 1 trade_count_lock[row2.date] = trade_count_lock.get(row2.date, 0) + 1
if min_roi_reached(trade, row2.close, row2.date) or row2.sell == 1:
current_profit_percent = trade.calc_profit_percent(rate=row2.close) 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) current_profit_btc = trade.calc_profit(rate=row2.close)
lock_pair_until = row2.Index lock_pair_until = row2.Index
@ -120,11 +129,13 @@ def backtest(stake_amount: float, processed: Dict[str, DataFrame],
pair, pair,
current_profit_percent, current_profit_percent,
current_profit_btc, current_profit_btc,
row2.Index - row.Index row2.Index - row.Index,
current_profit_btc > 0,
current_profit_btc < 0
) )
) )
break break
labels = ['currency', 'profit_percent', 'profit_BTC', 'duration'] labels = ['currency', 'profit_percent', 'profit_BTC', 'duration', 'profit', 'loss']
return DataFrame.from_records(trades, columns=labels) return DataFrame.from_records(trades, columns=labels)
@ -172,7 +183,9 @@ def start(args):
# Execute backtest and print results # Execute backtest and print results
results = backtest( results = backtest(
config['stake_amount'], preprocessed, max_open_trades, args.realistic_simulation config['stake_amount'], preprocessed, max_open_trades, args.realistic_simulation,
config.get('experimental', {}).get('sell_profit_only', False), config.get('stoploss'),
config.get('experimental', {}).get('use_sell_signal', False)
) )
logger.info( logger.info(
'\n====================== BACKTESTING REPORT ================================\n%s', '\n====================== BACKTESTING REPORT ================================\n%s',

27
freqtrade/tests/optimize/test_backtesting.py
View File

@ -16,19 +16,22 @@ def test_generate_text_table():
'currency': ['BTC_ETH', 'BTC_ETH'], 'currency': ['BTC_ETH', 'BTC_ETH'],
'profit_percent': [0.1, 0.2], 'profit_percent': [0.1, 0.2],
'profit_BTC': [0.2, 0.4], 'profit_BTC': [0.2, 0.4],
'duration': [10, 30] 'duration': [10, 30],
'profit': [2, 0],
'loss': [0, 0]
} }
) )
print(generate_text_table({'BTC_ETH': {}}, results, 'BTC', 5)) print(generate_text_table({'BTC_ETH': {}}, results, 'BTC', 5))
assert generate_text_table({'BTC_ETH': {}}, results, 'BTC', 5) == ( assert generate_text_table({'BTC_ETH': {}}, results, 'BTC', 5) == (
'pair buy count avg profit % total profit BTC avg duration\n' 'pair buy count avg profit % total profit BTC avg duration profit loss\n' # noqa
'------- ----------- -------------- ------------------ --------------\n' '------- ----------- -------------- ------------------ -------------- -------- ------\n' # noqa
'BTC_ETH 2 15.00 0.60000000 100.0\n' 'BTC_ETH 2 15.00 0.60000000 100.0 2 0\n' # noqa
'TOTAL 2 15.00 0.60000000 100.0') 'TOTAL 2 15.00 0.60000000 100.0 2 0') # noqa
def test_get_timeframe(): def test_get_timeframe():
data = preprocess(optimize.load_data(ticker_interval=1, pairs=['BTC_UNITEST'])) data = preprocess(optimize.load_data(
ticker_interval=1, pairs=['BTC_UNITEST']))
min_date, max_date = get_timeframe(data) min_date, max_date = get_timeframe(data)
assert min_date.isoformat() == '2017-11-04T23:02:00+00:00' assert min_date.isoformat() == '2017-11-04T23:02:00+00:00'
assert max_date.isoformat() == '2017-11-14T22:59:00+00:00' assert max_date.isoformat() == '2017-11-14T22:59:00+00:00'
@ -39,7 +42,8 @@ def test_backtest(default_conf, mocker):
exchange._API = Bittrex({'key': '', 'secret': ''}) exchange._API = Bittrex({'key': '', 'secret': ''})
data = optimize.load_data(ticker_interval=5, pairs=['BTC_ETH']) data = optimize.load_data(ticker_interval=5, pairs=['BTC_ETH'])
results = backtest(default_conf['stake_amount'], optimize.preprocess(data), 10, True) results = backtest(default_conf['stake_amount'],
optimize.preprocess(data), 10, True)
assert not results.empty assert not results.empty
@ -49,7 +53,8 @@ def test_backtest_1min_ticker_interval(default_conf, mocker):
# Run a backtesting for an exiting 5min ticker_interval # Run a backtesting for an exiting 5min ticker_interval
data = optimize.load_data(ticker_interval=1, pairs=['BTC_UNITEST']) data = optimize.load_data(ticker_interval=1, pairs=['BTC_UNITEST'])
results = backtest(default_conf['stake_amount'], optimize.preprocess(data), 1, True) results = backtest(default_conf['stake_amount'],
optimize.preprocess(data), 1, True)
assert not results.empty assert not results.empty
@ -96,7 +101,8 @@ def load_data_test(what):
[{'T': pair[x]['T'], # Keep old dates [{'T': pair[x]['T'], # Keep old dates
'V': pair[x]['V'], # Keep old volume 'V': pair[x]['V'], # Keep old volume
'BV': pair[x]['BV'], # keep too 'BV': pair[x]['BV'], # keep too
'O': math.sin(x*hz) / 1000 + base, # But replace O,H,L,C # But replace O,H,L,C
'O': math.sin(x * hz) / 1000 + base,
'H': math.sin(x * hz) / 1000 + base + 0.0001, 'H': math.sin(x * hz) / 1000 + base + 0.0001,
'L': math.sin(x * hz) / 1000 + base - 0.0001, 'L': math.sin(x * hz) / 1000 + base - 0.0001,
'C': math.sin(x * hz) / 1000 + base} for x in range(0, datalen)]} 'C': math.sin(x * hz) / 1000 + base} for x in range(0, datalen)]}
@ -119,7 +125,8 @@ def simple_backtest(config, contour, num_results):
def test_backtest2(default_conf, mocker): def test_backtest2(default_conf, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf) mocker.patch.dict('freqtrade.main._CONF', default_conf)
data = optimize.load_data(ticker_interval=5, pairs=['BTC_ETH']) data = optimize.load_data(ticker_interval=5, pairs=['BTC_ETH'])
results = backtest(default_conf['stake_amount'], optimize.preprocess(data), 10, True) results = backtest(default_conf['stake_amount'],
optimize.preprocess(data), 10, True)
assert not results.empty assert not results.empty