def backtest(backtest_conf, backdata, mocker):
trades = []
exchange._API = Bittrex({'key': '', 'secret': ''})
mocked_history = mocker.patch('freqtrade.analyze.get_ticker_history')
mocker.patch.dict('freqtrade.main._CONF', backtest_conf)
mocker.patch('arrow.utcnow', return_value=arrow.get('2017-08-20T14:50:00'))
for pair, pair_data in backdata.items():
mocked_history.return_value = pair_data
ticker = analyze_ticker(pair)[['close', 'date', 'buy']].copy()
# for each buy point
for row in ticker[ticker.buy == 1].itertuples(index=True):
trade = Trade(open_rate=row.close,
open_date=row.date,
amount=1,
fee=exchange.get_fee() * 2)
# calculate win/lose forwards from buy point
for row2 in ticker[row.Index:].itertuples(index=True):
if should_sell(trade, row2.close, row2.date):
current_profit = trade.calc_profit(row2.close)
trades.append(
(pair, current_profit, row2.Index - row.Index))
break
labels = ['currency', 'profit', 'duration']
results = DataFrame.from_records(trades, columns=labels)
return results
def backtest(backtest_conf, processed, mocker):
trades = []
exchange._API = Bittrex({'key': '', 'secret': ''})
mocker.patch.dict('freqtrade.main._CONF', backtest_conf)
for pair, pair_data in processed.items():
pair_data['buy'] = 0
pair_data['sell'] = 0
ticker = populate_sell_trend(populate_buy_trend(pair_data))
# for each buy point
for row in ticker[ticker.buy == 1].itertuples(index=True):
trade = Trade(open_rate=row.close,
open_date=row.date,
amount=backtest_conf['stake_amount'],
fee=exchange.get_fee() * 2)
# calculate win/lose forwards from buy point
for row2 in ticker[row.Index:].itertuples(index=True):
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']
return DataFrame.from_records(trades, columns=labels)
def test_backtest(default_conf, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
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)
assert not results.empty
def test_backtest_1min_ticker_interval(default_conf, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
exchange._API = Bittrex({'key': '', 'secret': ''})
# 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)
assert not results.empty
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
exchange._API = Bittrex({'key': '', 'secret': ''})
def test_backtest(backtest_conf, mocker):
print('')
exchange._API = Bittrex({'key': '', 'secret': ''})
# Load configuration file based on env variable
conf_path = os.environ.get('BACKTEST_CONFIG')
if conf_path:
print('Using config: {} ...'.format(conf_path))
config = load_config(conf_path)
else:
config = backtest_conf
# Parse ticker interval
ticker_interval = int(os.environ.get('BACKTEST_TICKER_INTERVAL') or 5)
print('Using ticker_interval: {} ...'.format(ticker_interval))
data = {}
if os.environ.get('BACKTEST_LIVE'):
print('Downloading data for all pairs in whitelist ...')
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)
print('Using stake_currency: {} ...\nUsing stake_amount: {} ...'.format(
config['stake_currency'], config['stake_amount']))
# Print timeframe
min_date, max_date = get_timeframe(data)
print('Measuring data from {} up to {} ...'.format(min_date.isoformat(),
max_date.isoformat()))
# Execute backtest and print results
results = backtest(config, preprocess(data), mocker)
print(
'====================== BACKTESTING REPORT ======================================\n\n'
'NOTE: This Report doesn\'t respect the limits of max_open_trades, \n'
' so the projected values should be taken with a grain of salt.\n'
)
print(generate_text_table(data, results, config['stake_currency']))
ax1.plot(dataframe.index.values, dataframe['blower'], '-.', label='BB low')
ax1.plot(dataframe.index.values, dataframe['buy_price'], 'bo', label='buy')
ax1.legend()
ax2.plot(dataframe.index.values, dataframe['adx'], label='ADX')
ax2.plot(dataframe.index.values, dataframe['mfi'], label='MFI')
# ax2.plot(dataframe.index.values, [25] * len(dataframe.index.values))
ax2.legend()
ax3.plot(dataframe.index.values, dataframe['fastk'], label='k')
ax3.plot(dataframe.index.values, dataframe['fastd'], label='d')
ax3.plot(dataframe.index.values, [20] * len(dataframe.index.values))
ax3.legend()
# Fine-tune figure; make subplots close to each other and hide x ticks for
# all but bottom plot.
fig.subplots_adjust(hspace=0)
plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)
plt.show()
if __name__ == '__main__':
# Install PYQT5==5.9 manually if you want to test this helper function
while True:
exchange.EXCHANGE = Bittrex({'key': '', 'secret': ''})
test_pair = 'BTC_ETH'
# for pair in ['BTC_ANT', 'BTC_ETH', 'BTC_GNT', 'BTC_ETC']:
# get_buy_signal(pair)
plot_dataframe(analyze_ticker(test_pair), test_pair)
time.sleep(60)
def test_hyperopt(backtest_conf, mocker):
mocked_buy_trend = mocker.patch('freqtrade.tests.test_backtesting.populate_buy_trend')
backdata = load_backtesting_data()
processed = preprocess(backdata)
exchange._API = Bittrex({'key': '', 'secret': ''})
def optimizer(params):
mocked_buy_trend.side_effect = buy_strategy_generator(params)
results = backtest(backtest_conf, processed, mocker)
result = format_results(results)
total_profit = results.profit.sum() * 1000
trade_count = len(results.index)
trade_loss = 1 - 0.35 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.2)
profit_loss = max(0, 1 - total_profit / 10000) # max profit 10000
# pylint: disable=W0603
global current_tries
current_tries += 1
print('{:5d}/{}: {}'.format(current_tries, TOTAL_TRIES, result))
return {
'loss': trade_loss + profit_loss,
'status': STATUS_OK,
'result': result
}
space = {
'mfi': hp.choice('mfi', [
{'enabled': False},
{'enabled': True, 'value': hp.quniform('mfi-value', 5, 25, 1)}
]),
'fastd': hp.choice('fastd', [
{'enabled': False},
{'enabled': True, 'value': hp.quniform('fastd-value', 10, 50, 1)}
]),
'adx': hp.choice('adx', [
{'enabled': False},
{'enabled': True, 'value': hp.quniform('adx-value', 15, 50, 1)}
]),
'rsi': hp.choice('rsi', [
{'enabled': False},
{'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 1)}
]),
'uptrend_long_ema': hp.choice('uptrend_long_ema', [
{'enabled': False},
{'enabled': True}
]),
'uptrend_short_ema': hp.choice('uptrend_short_ema', [
{'enabled': False},
{'enabled': True}
]),
'over_sar': hp.choice('over_sar', [
{'enabled': False},
{'enabled': True}
]),
'green_candle': hp.choice('green_candle', [
{'enabled': False},
{'enabled': True}
]),
'uptrend_sma': hp.choice('uptrend_sma', [
{'enabled': False},
{'enabled': True}
]),
'trigger': hp.choice('trigger', [
{'type': 'lower_bb'},
{'type': 'faststoch10'},
{'type': 'ao_cross_zero'},
{'type': 'ema5_cross_ema10'},
{'type': 'macd_cross_signal'},
{'type': 'sar_reversal'},
{'type': 'stochf_cross'},
{'type': 'ht_sine'},
]),
}
trials = Trials()
best = fmin(fn=optimizer, space=space, algo=tpe.suggest, max_evals=TOTAL_TRIES, trials=trials)
print('\n\n\n\n==================== HYPEROPT BACKTESTING REPORT ==============================')
print('Best parameters {}'.format(best))
newlist = sorted(trials.results, key=itemgetter('loss'))
print('Result: {}'.format(newlist[0]['result']))
#!/usr/bin/env python3
"""This script generate json data from bittrex"""
import json
from os import path
from freqtrade import exchange
from freqtrade.exchange import Bittrex
PAIRS = [
'BTC_BCC', 'BTC_ETH', 'BTC_MER', 'BTC_POWR', 'BTC_ETC', 'BTC_OK',
'BTC_NEO', 'BTC_EMC2', 'BTC_DASH', 'BTC_LSK', 'BTC_LTC', 'BTC_XZC',
'BTC_OMG', 'BTC_STRAT', 'BTC_XRP', 'BTC_QTUM', 'BTC_WAVES', 'BTC_VTC',
'BTC_XLM', 'BTC_MCO'
]
TICKER_INTERVAL = 5 # ticker interval in minutes (currently implemented: 1 and 5)
OUTPUT_DIR = path.dirname(path.realpath(__file__))
# Init Bittrex exchange
exchange._API = Bittrex({'key': '', 'secret': ''})
for pair in PAIRS:
data = exchange.get_ticker_history(pair, TICKER_INTERVAL)
filename = path.join(OUTPUT_DIR, '{}-{}.json'.format(
pair,
TICKER_INTERVAL,
))
with open(filename, 'w') as fp:
json.dump(data, fp)
def start(args):
global TOTAL_TRIES, PROCESSED, SPACE, TRIALS, _CURRENT_TRIES
TOTAL_TRIES = args.epochs
exchange._API = Bittrex({'key': '', 'secret': ''})
# Initialize logger
logging.basicConfig(
level=args.loglevel,
format='\n%(message)s',
)
logger.info('Using config: %s ...', args.config)
config = load_config(args.config)
pairs = config['exchange']['pair_whitelist']
PROCESSED = optimize.preprocess(
optimize.load_data(args.datadir,
pairs=pairs,
ticker_interval=args.ticker_interval))
if args.mongodb:
logger.info('Using mongodb ...')
logger.info(
'Start scripts/start-mongodb.sh and start-hyperopt-worker.sh manually!'
)
db_name = 'freqtrade_hyperopt'
TRIALS = MongoTrials('mongo://127.0.0.1:1234/{}/jobs'.format(db_name),
exp_key='exp1')
else:
logger.info('Preparing Trials..')
signal.signal(signal.SIGINT, signal_handler)
# read trials file if we have one
if os.path.exists(TRIALS_FILE):
TRIALS = read_trials()
_CURRENT_TRIES = len(TRIALS.results)
TOTAL_TRIES = TOTAL_TRIES + _CURRENT_TRIES
logger.info(
'Continuing with trials. Current: {}, Total: {}'.format(
_CURRENT_TRIES, TOTAL_TRIES))
try:
best_parameters = fmin(fn=optimizer,
space=SPACE,
algo=tpe.suggest,
max_evals=TOTAL_TRIES,
trials=TRIALS)
results = sorted(TRIALS.results, key=itemgetter('loss'))
best_result = results[0]['result']
except ValueError:
best_parameters = {}
best_result = 'Sorry, Hyperopt was not able to find good parameters. Please ' \
'try with more epochs (param: -e).'
# Improve best parameter logging display
if best_parameters:
best_parameters = space_eval(SPACE, best_parameters)
logger.info('Best parameters:\n%s', json.dumps(best_parameters, indent=4))
logger.info('Best Result:\n%s', best_result)
# Store trials result to file to resume next time
save_trials(TRIALS)
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:
"""
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)
:return: DataFrame
"""
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
ticker = populate_sell_trend(populate_buy_trend(pair_data))
# for each buy point
lock_pair_until = None
buy_subset = ticker[ticker.buy == 1][[
'buy', 'open', 'close', 'date', 'sell'
]]
for row in buy_subset.itertuples(index=True):
if realistic:
if lock_pair_until is not None and row.Index <= 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
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,
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
labels = [
'currency', 'profit_percent', 'profit_BTC', 'duration', 'profit',
'loss'
]
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)
logger.info('Using ticker_interval: %s ...', args.ticker_interval)
data = {}
pairs = config['exchange']['pair_whitelist']
if args.live:
logger.info('Downloading data for all pairs in whitelist ...')
for pair in pairs:
data[pair] = exchange.get_ticker_history(pair,
args.ticker_interval)
else:
logger.info(
'Using local backtesting data (using whitelist in given config) ...'
)
data = optimize.load_data(args.datadir,
pairs=pairs,
ticker_interval=args.ticker_interval,
refresh_pairs=args.refresh_pairs)
logger.info('Using stake_currency: %s ...', config['stake_currency'])
logger.info('Using stake_amount: %s ...', config['stake_amount'])
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 = preprocess(data)
# 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))
logger.info(
'\n==================================== BACKTESTING REPORT ====================================\n%s', # noqa
generate_text_table(data, results, config['stake_currency'],
args.ticker_interval))