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 _get_sell_trade_entry(
self, pair: str, buy_row: DataFrame,
partial_ticker: List, trade_count_lock: Dict, args: Dict) -> Optional[Tuple]:
stake_amount = args['stake_amount']
max_open_trades = args.get('max_open_trades', 0)
trade = Trade(
open_rate=buy_row.close + self.slippage, #implement slippage 0.01 for buy_row
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
#implement slippage 0.01 for sell_row
if self.analyze.should_sell(trade, sell_row.close - self.slippage, 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(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 create_trade(stake_amount: float) -> Optional[Trade]:
"""
Checks the implemented trading indicator(s) for a randomly picked pair,
if one pair triggers the buy_signal a new trade record gets created
:param stake_amount: amount of btc to spend
"""
logger.info(
'Checking buy signals to create a new trade with stake_amount: %f ...',
stake_amount)
whitelist = copy.deepcopy(_CONF['exchange']['pair_whitelist'])
# Check if stake_amount is fulfilled
if exchange.get_balance(_CONF['stake_currency']) < stake_amount:
raise ValueError('stake amount is not fulfilled (currency={})'.format(
_CONF['stake_currency']))
# Remove currently opened and latest pairs from whitelist
for trade in Trade.query.filter(Trade.is_open.is_(True)).all():
if trade.pair in whitelist:
whitelist.remove(trade.pair)
logger.debug('Ignoring %s in pair whitelist', trade.pair)
if not whitelist:
raise ValueError('No pair in whitelist')
# Pick pair based on StochRSI buy signals
for _pair in whitelist:
if get_buy_signal(_pair):
pair = _pair
break
else:
return None
# Calculate amount and subtract fee
fee = exchange.get_fee()
buy_limit = get_target_bid(exchange.get_ticker(pair))
amount = (1 - fee) * stake_amount / buy_limit
order_id = exchange.buy(pair, buy_limit, amount)
# Create trade entity and return
message = '*{}:* Buying [{}]({}) with limit `{:.8f}`'.format(
exchange.get_name().upper(), pair.replace('_', '/'),
exchange.get_pair_detail_url(pair), buy_limit)
logger.info(message)
telegram.send_msg(message)
# Fee is applied twice because we make a LIMIT_BUY and LIMIT_SELL
return Trade(pair=pair,
stake_amount=stake_amount,
amount=amount,
fee=fee * 2,
open_rate=buy_limit,
open_date=datetime.utcnow(),
exchange=exchange.get_name().upper(),
open_order_id=order_id)
def test_exchange_misc(mocker):
api_mock = MagicMock()
mocker.patch('freqtrade.exchange._API', api_mock)
exchange.get_markets()
assert api_mock.get_markets.call_count == 1
exchange.get_market_summaries()
assert api_mock.get_market_summaries.call_count == 1
api_mock.name = 123
assert exchange.get_name() == 123
api_mock.fee = 456
assert exchange.get_fee() == 456
exchange.get_wallet_health()
assert api_mock.get_wallet_health.call_count == 1
def create_trade(self) -> bool:
"""
Checks the implemented trading indicator(s) for a randomly picked pair,
if one pair triggers the buy_signal a new trade record gets created
:param stake_amount: amount of btc to spend
:param interval: Ticker interval used for Analyze
:return: True if a trade object has been created and persisted, False otherwise
"""
stake_amount = self.config['stake_amount']
interval = self.analyze.get_ticker_interval()
logger.info(
'Checking buy signals to create a new trade with stake_amount: %f ...',
stake_amount)
whitelist = copy.deepcopy(self.config['exchange']['pair_whitelist'])
# Check if stake_amount is fulfilled
if exchange.get_balance(self.config['stake_currency']) < stake_amount:
raise DependencyException(
'stake amount is not fulfilled (currency={})'.format(
self.config['stake_currency']))
# Remove currently opened and latest pairs from whitelist
for trade in Trade.query.filter(Trade.is_open.is_(True)).all():
if trade.pair in whitelist:
whitelist.remove(trade.pair)
logger.debug('Ignoring %s in pair whitelist', trade.pair)
if not whitelist:
raise DependencyException('No currency pairs in whitelist')
# Pick pair based on StochRSI buy signals
for _pair in whitelist:
(buy, sell) = self.analyze.get_signal(_pair, interval)
if buy and not sell:
pair = _pair
break
else:
return False
# Calculate amount
buy_limit = self.get_target_bid(exchange.get_ticker(pair))
amount = stake_amount / buy_limit
order_id = exchange.buy(pair, buy_limit, amount)
stake_amount_fiat = self.fiat_converter.convert_amount(
stake_amount, self.config['stake_currency'],
self.config['fiat_display_currency'])
# Create trade entity and return
self.rpc.send_msg(
'*{}:* Buying [{}]({}) with limit `{:.8f} ({:.6f} {}, {:.3f} {})` '
.format(exchange.get_name().upper(), pair.replace('_', '/'),
exchange.get_pair_detail_url(pair), buy_limit,
stake_amount, self.config['stake_currency'],
stake_amount_fiat, self.config['fiat_display_currency']))
# Fee is applied twice because we make a LIMIT_BUY and LIMIT_SELL
trade = Trade(pair=pair,
stake_amount=stake_amount,
amount=amount,
fee=exchange.get_fee(),
open_rate=buy_limit,
open_date=datetime.utcnow(),
exchange=exchange.get_name().upper(),
open_order_id=order_id)
Trade.session.add(trade)
Trade.session.flush()
return True
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 test_get_fee(default_conf, mocker):
mocker.patch('freqtrade.exchange.validate_pairs',
side_effect=lambda s: True)
init(default_conf)
assert get_fee() == 0.0025
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 create_trade(stake_amount: float) -> bool:
"""
Checks the implemented trading indicator(s) for a randomly picked pair,
if one pair triggers the buy_signal a new trade record gets created
:param stake_amount: amount of btc to spend
:return: True if a trade object has been created and persisted, False otherwise
"""
logger.info(
'Checking buy signals to create a new trade with stake_amount: %f ...',
stake_amount
)
whitelist = copy.deepcopy(_CONF['exchange']['pair_whitelist'])
# Check if stake_amount is fulfilled
if exchange.get_balance(_CONF['stake_currency']) < stake_amount:
raise DependencyException(
'stake amount is not fulfilled (currency={})'.format(_CONF['stake_currency'])
)
# Remove currently opened and latest pairs from whitelist
for trade in Trade.query.filter(Trade.is_open.is_(True)).all():
if trade.pair in whitelist:
whitelist.remove(trade.pair)
logger.debug('Ignoring %s in pair whitelist', trade.pair)
if not whitelist:
raise DependencyException('No pair in whitelist')
# Pick pair based on StochRSI buy signals
for _pair in whitelist:
if get_signal(_pair, SignalType.BUY):
pair = _pair
break
else:
return False
# Calculate amount
buy_limit = get_target_bid(exchange.get_ticker(pair))
amount = stake_amount / buy_limit
order_id = exchange.buy(pair, buy_limit, amount)
fiat_converter = CryptoToFiatConverter()
stake_amount_fiat = fiat_converter.convert_amount(
stake_amount,
_CONF['stake_currency'],
_CONF['fiat_display_currency']
)
# Create trade entity and return
rpc.send_msg('*{}:* Buying [{}]({}) with limit `{:.8f} ({:.6f} {}, {:.3f} {})` '.format(
exchange.get_name().upper(),
pair.replace('_', '/'),
exchange.get_pair_detail_url(pair),
buy_limit, stake_amount, _CONF['stake_currency'],
stake_amount_fiat, _CONF['fiat_display_currency']
))
# Fee is applied twice because we make a LIMIT_BUY and LIMIT_SELL
trade = Trade(
pair=pair,
stake_amount=stake_amount,
amount=amount,
fee=exchange.get_fee(),
open_rate=buy_limit,
open_date=datetime.utcnow(),
exchange=exchange.get_name().upper(),
open_order_id=order_id
)
Trade.session.add(trade)
Trade.session.flush()
return True
def test_get_fee(default_conf, mocker):
mocker.patch('freqtrade.exchange.validate_pairs',
side_effect=lambda s: True)
init(default_conf)
assert get_fee() == 0.0025
def create_trade(stake_amount: float) -> Optional[Trade]:
"""
Checks the implemented trading indicator(s) for a randomly picked pair,
if one pair triggers the buy_signal a new trade record gets created
:param stake_amount: amount of btc to spend
"""
logger.info('Creating new trade with stake_amount: %f ...', stake_amount)
whitelist = copy.deepcopy(_CONF['exchange']['pair_whitelist'])
# Check if stake_amount is fulfilled
if exchange.get_balance(_CONF['stake_currency']) < stake_amount:
raise ValueError('stake amount is not fulfilled (currency={})'.format(
_CONF['stake_currency']))
# Remove currently opened and latest pairs from whitelist
for trade in Trade.query.filter(Trade.is_open.is_(True)).all():
if trade.pair in whitelist:
whitelist.remove(trade.pair)
logger.debug('Ignoring %s in pair whitelist', trade.pair)
# if not whitelist:
# raise ValueError('No pair in whitelist')
# Pick pair based on StochRSI buy signals
if analyzer.name == 'danml':
for _pair in whitelist:
if get_buy_signal(_pair, exchange.get_name(), analyzer):
pair = _pair
break
else:
return None
elif analyzer.name == 'cryptoml':
update = False
if datetime.utcnow().minute % 5 == 0:
update = True
pair = analyzer.get_buy_signal(whitelist,
update=update,
threshold=0.01,
repeats=3)
if pair is None:
return None
# Calculate amount and subtract fee
fee = exchange.get_fee()
buy_limit = get_target_bid(exchange.get_ticker(pair))
amount = (1 - fee) * stake_amount / buy_limit
health = exchange.get_wallet_health()
if exchange.get_name() == 'HitBTC':
token = pair
else:
token = get_quote_token(pair)
token_healthy = False
for status in health:
if status['Currency'] == token:
token_healthy = status['IsActive']
if token_healthy:
order_id = exchange.buy(pair, buy_limit, amount)
# Create trade entity and return
message = '*{}:* Buying [{}]({}) with limit `{:.8f}`'.format(
exchange.get_name().upper(), pair.replace('_', '/'),
exchange.get_pair_detail_url(pair), buy_limit)
logger.info(message)
telegram.send_msg(message)
# Fee is applied twice because we make a LIMIT_BUY and LIMIT_SELL
return Trade(
pair=pair,
stake_amount=stake_amount,
amount=amount,
fee=fee * 2.,
open_rate=buy_limit,
open_date=datetime.utcnow(),
exchange=exchange.get_name().upper(),
open_order_id=order_id,
# open_order_type='buy'
)
# ensure directory exists
base_path = os.path.join(os.path.expanduser('~'), 'freqtrade')
# get configuration
with open(os.path.join(base_path, 'config.json')) as file:
_CONF = json.load(file)
# initialize the exchange
exchange.init(_CONF)
# get ticker
data = exchange.get_ticker(pair='ETH_BTC')
print(data)
# get ticker history
df = exchange.get_ticker_history(pair='ETH_BTC', tick_interval=1)
print(pd.DataFrame(df))
# get markets
data = exchange.get_markets()
print(data)
# get name
print(exchange.get_name())
print(exchange.get_sleep_time())
print(exchange.get_fee())
