def test_can_buy_side(self):
now = datetime.datetime.now()
test_petterns = [
('BUY', False),
('SELL', True)
]
for side, expect_result in test_petterns:
with self.subTest(side=side):
s = SignalEvents([SignalEvent(now - datetime.timedelta(days=1), self.product_code, side, 1, 1)])
self.assertEqual(s.can_buy(now), expect_result)
def test_can_buy_time(self):
now = datetime.datetime.now()
test_petterns = [
([], now, True), # not signals
([now-datetime.timedelta(days=1)], now, True), # last signal time < now
([now+datetime.timedelta(days=1)], now, False), # last signal time > now
]
for times, t, expect_result in test_petterns:
with self.subTest(times=times, t=t):
s = SignalEvents([SignalEvent(time, self.product_code, 'SELL', 1, 1) for time in times])
self.assertEqual(s.can_buy(t), expect_result)
class AI(object):
def __init__(self, product_code, use_percent, duration, past_period,
stop_limit_percent, back_test):
self.API = APIClient(settings.access_token, settings.account_id)
if back_test:
self.signal_events = SignalEvents()
else:
self.signal_events = SignalEvents.get_signal_events_by_count(1)
self.product_code = product_code
self.use_percent = use_percent
self.duration = duration
self.past_period = past_period
self.optimized_trade_params = None
self.stop_limit = 0
self.stop_limit_percent = stop_limit_percent
self.back_test = back_test
self.start_trade = datetime.datetime.utcnow()
self.candle_cls = factory_candle_class(self.product_code,
self.duration)
self.update_optimize_params(False)
def update_optimize_params(self, is_continue: bool):
logger.info('action=update_optimize_params status=run')
df = DataFrameCandle(self.product_code, self.duration)
df.set_all_candles(self.past_period)
if df.candles:
self.optimized_trade_params = df.optimize_params()
if self.optimized_trade_params is not None:
logger.info(
f'action=update_optimize_params params={self.optimized_trade_params.__dict__}'
)
if is_continue and self.optimized_trade_params is None:
time.sleep(10 * duration_seconds(self.duration))
self.update_optimize_params(is_continue)
def buy(self, candle):
if self.back_test:
could_buy = self.signal_events.buy(self.product_code,
candle.time,
candle.close,
1.0,
save=False)
return could_buy
if self.start_trade > candle.time:
logger.warning('action=buy status=false error=old_time')
return False
if not self.signal_events.can_buy(candle.time):
logger.warning('action=buy status=false error=previous_was_buy')
return False
balance = self.API.get_balance()
units = int(balance.available * self.use_percent)
order = Order(self.product_code, constants.BUY, units)
trade = self.API.send_order(order)
could_buy = self.signal_events.buy(self.product_code,
candle.time,
trade.price,
trade.units,
save=True)
return could_buy
class AI(object):
def __init__(self, product_code, use_percent, duration, past_period, stop_limit_percent, back_test):
self.API = APIClient(settings.access_token, settings.account_id)
if back_test:
self.signal_events = SignalEvents()
else:
self.signal_events = SignalEvents.get_signal_events_by_count(1)
self.product_code = product_code
self.use_percent = use_percent
self.duration = duration
self.past_period = past_period
self.optimized_trade_params = None
self.stop_limit = 0
self.stop_limit_percent = stop_limit_percent
self.back_test = back_test
self.start_trade = datetime.datetime.utcnow()
self.candle_cls = factory_candle_class(self.product_code, self.duration)
self.update_optimize_params(False)
def update_optimize_params(self, is_continue: bool):
logger.info('action=update_optimize_params status=run')
df = DataFrameCandle(self.product_code, self.duration)
df.set_all_candles(self.past_period)
if df.candles:
self.optimized_trade_params = df.optimize_params()
if self.optimized_trade_params is not None:
logger.info(f'action=update_optimize_params params={self.optimized_trade_params.__dict__}')
if is_continue and self.optimized_trade_params is None:
time.sleep(10 * duration_seconds(self.duration))
self.update_optimize_params(is_continue)
def buy(self, candle):
if self.back_test:
could_buy = self.signal_events.buy(self.product_code, candle.time, candle.close, 1.0, save=False)
return could_buy
if self.start_trade > candle.time:
logger.warning('action=buy status=false error=old_time')
return False
if not self.signal_events.can_buy(candle.time):
logger.warning('action=buy status=false error=previous_was_buy')
return False
balance = self.API.get_balance()
units = int(balance.available * self.use_percent)
order = Order(self.product_code, constants.BUY, units)
trade = self.API.send_order(order)
could_buy = self.signal_events.buy(self.product_code, candle.time, trade.price, trade.units, save=True)
return could_buy
def sell(self, candle):
if self.back_test:
could_sell = self.signal_events.sell(self.product_code, candle.time, candle.close, 1.0, save=False)
return could_sell
if self.start_trade > candle.time:
logger.warning('action=sell status=false error=old_time')
return False
if not self.signal_events.can_sell(candle.time):
logger.warning('action=sell status=false error=previous_was_sell')
return False
trades = self.API.get_open_trade()
sum_price = 0
units = 0
for trade in trades:
closed_trade = self.API.trade_close(trade.trade_id)
sum_price += closed_trade.price * abs(closed_trade.units)
units += abs(closed_trade.units)
could_sell = self.signal_events.sell(self.product_code, candle.time, sum_price/units, units, save=True)
return could_sell
def trade(self):
logger.info('action=trade status=run')
params = self.optimized_trade_params
if params is None:
return
df = DataFrameCandle(self.product_code, self.duration)
df.set_all_candles(self.past_period)
if params.ema_enable:
ema_values_1 = talib.EMA(np.array(df.closes), params.ema_period_1)
ema_values_2 = talib.EMA(np.array(df.closes), params.ema_period_2)
if params.bb_enable:
bb_up, _, bb_down = talib.BBANDS(np.array(df.closes), params.bb_n, params.bb_k, params.bb_k, 0)
if params.ichimoku_enable:
tenkan, kijun, senkou_a, senkou_b, chikou = ichimoku_cloud(df.closes)
if params.rsi_enable:
rsi_values = talib.RSI(np.array(df.closes), params.rsi_period)
if params.macd_enable:
macd, macd_signal, _ = talib.MACD(np.array(df.closes), params.macd_fast_period, params.macd_slow_period, params.macd_signal_period)
for i in range(1, len(df.candles)):
buy_point, sell_point = 0, 0
if params.ema_enable and params.ema_period_1 <= i and params.ema_period_2 <= i:
if ema_values_1[i - 1] < ema_values_2[i - 1] and ema_values_1[i] >= ema_values_2[i]:
buy_point += 1
if ema_values_1[i - 1] > ema_values_2[i - 1] and ema_values_1[i] <= ema_values_2[i]:
sell_point += 1
if params.bb_enable and params.bb_n <= i:
if bb_down[i - 1] > df.candles[i - 1].close and bb_down[i] <= df.candles[i].close:
buy_point += 1
if bb_up[i - 1] < df.candles[i - 1].close and bb_up[i] >= df.candles[i].close:
sell_point += 1
if params.ichimoku_enable:
if (chikou[i-1] < df.candles[i-1].high and
chikou[i] >= df.candles[i].high and
senkou_a[i] < df.candles[i].low and
senkou_b[i] < df.candles[i].low and
tenkan[i] > kijun[i]):
buy_point += 1
if (chikou[i - 1] > df.candles[i - 1].low and
chikou[i] <= df.candles[i].low and
senkou_a[i] > df.candles[i].high and
senkou_b[i] > df.candles[i].high and
tenkan[i] < kijun[i]):
sell_point += 1
if params.macd_enable:
if macd[i] < 0 and macd_signal[i] < 0 and macd[i - 1] < macd_signal[i - 1] and macd[i] >= macd_signal[i]:
buy_point += 1
if macd[i] > 0 and macd_signal[i] > 0 and macd[i-1] > macd_signal[i - 1] and macd[i] <= macd_signal[i]:
sell_point += 1
if params.rsi_enable and rsi_values[i-1] != 0 and rsi_values[i-1] != 100:
if rsi_values[i-1] < params.rsi_buy_thread and rsi_values[i] >= params.rsi_buy_thread:
buy_point += 1
if rsi_values[i-1] > params.rsi_sell_thread and rsi_values[i] <= params.rsi_sell_thread:
sell_point += 1
if buy_point > 0:
if not self.buy(df.candles[i]):
continue
self.stop_limit = df.candles[i].close * self.stop_limit_percent
if sell_point > 0 or self.stop_limit > df.candles[i].close:
if not self.sell(df.candles[i]):
continue
self.stop_limit = 0.0
self.update_optimize_params(is_continue=True)