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alifanovalifanov
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test bracket buy
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‎bt_with_prediction_lstm_bracket_order.py

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from datetime import datetime, timedelta
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import backtrader as bt
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import numpy as np
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from keras.models import model_from_json
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class SmaCross(bt.Strategy):
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params = dict(
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limit=0.005,
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limdays=2,
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limdays2=1000,
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)
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def __init__(self):
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self.dataclose = self.datas[0].close
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self.order = None
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self.orefs = list()
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# self.signal_add(bt.SIGNAL_LONG, bt.ind.CrossOver(sma1, sma2))
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# load json and create model
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json_file = open('model.json', 'r')
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loaded_model_json = json_file.read()
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json_file.close()
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self.model = model_from_json(loaded_model_json)
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# load weights into new model
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self.model.load_weights("model.h5")
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print("Loaded model from disk")
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# evaluate loaded model on test data
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self.model.compile(loss='mean_squared_error', optimizer='adam')
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def log(self, txt, dt=None):
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''' Logging function fot this strategy'''
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dt = dt or self.datas[0].datetime.date(0)
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print('%s, %s' % (dt.isoformat(), txt))
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def notify_order(self, order):
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print('{}: Order ref: {} / Type {} / Status {}'.format(
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self.data.datetime.date(0),
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order.ref, 'Buy' * order.isbuy() or 'Sell',
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order.getstatusname()))
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if order.status == order.Completed:
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self.holdstart = len(self)
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if not order.alive() and order.ref in self.orefs:
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self.orefs.remove(order.ref)
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def notify_trade(self, trade):
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if not trade.isclosed:
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return
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self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' %
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(trade.pnl, trade.pnlcomm))
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def next(self):
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predicted_close = self.model.predict(np.array([[[self.dataclose[0]]]]))
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predicted_close = predicted_close[0][0]
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prev_predicted_close = self.model.predict(np.array([[[self.dataclose[-1]]]]))
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prev_predicted_close = prev_predicted_close[0][0]
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if self.orefs:
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return # pending orders do nothing
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if not self.position:
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if predicted_close > prev_predicted_close:
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close = self.data.close[0]
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p1 = close * (1.0 - self.p.limit)
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p2 = p1 - 0.02 * close
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p3 = p1 + 0.06 * close
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print('p1: {:.4f}, p2: {:.4f}, p3: {:.4f}'.format(
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p1,
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p2,
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p3
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))
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valid1 = timedelta(self.p.limdays)
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valid2 = valid3 = timedelta(self.p.limdays2)
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os = self.buy_bracket(
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price=p1, valid=valid1,
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stopprice=p2, stopargs=dict(valid=valid2),
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limitprice=p3, limitargs=dict(valid=valid3), )
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self.orefs = [o.ref for o in os]
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# if not self.position:
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# if predicted_close > prev_predicted_close:
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# self.order = self.buy()
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# else:
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# if predicted_close < prev_predicted_close:
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# self.order = self.sell()
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cerebro = bt.Cerebro()
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data = bt.feeds.GenericCSVData(
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dataname='eur_usd_1d.csv',
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separator=',',
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dtformat=('%Y%m%d'),
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tmformat=('%H%M%S'),
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datetime=0,
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time=-1,
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open=2,
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high=3,
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low=4,
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close=5,
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volume=6,
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openinterest=-1
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)
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# data = bt.feeds.YahooFinanceData(dataname='YHOO', fromdate=datetime(2011, 1, 1),
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# todate=datetime(2012, 12, 31))
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cerebro.adddata(data)
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cerebro.broker.setcash(1000.0)
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cerebro.addsizer(bt.sizers.FixedSize, stake=100)
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cerebro.addstrategy(SmaCross)
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# Print out the starting conditions
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print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
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# Run over everything
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cerebro.run()
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# Print out the final result
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print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
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# cerebro.plot()

‎bt_with_prediction_lstm_with_sp_tp.py

+148
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from datetime import datetime
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import backtrader as bt
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import numpy as np
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from keras.models import model_from_json
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class SmaCross(bt.Strategy):
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params = (
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('pfast', 100), ('pslow', 300),
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('stoploss', 0.01),
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('profit_mult', 3),
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('prdata', False),
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('prtrade', False),
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('prorder', False),
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)
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def __init__(self):
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self.dataclose = self.datas[0].close
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self.order = None
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self.order_dict = {}
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# self.signal_add(bt.SIGNAL_LONG, bt.ind.CrossOver(sma1, sma2))
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# load json and create model
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json_file = open('model.json', 'r')
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loaded_model_json = json_file.read()
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json_file.close()
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self.model = model_from_json(loaded_model_json)
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# load weights into new model
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self.model.load_weights("model.h5")
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print("Loaded model from disk")
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# evaluate loaded model on test data
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self.model.compile(loss='mean_squared_error', optimizer='adam')
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def log(self, txt, dt=None):
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''' Logging function fot this strategy'''
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dt = dt or self.datas[0].datetime.date(0)
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print('%s, %s' % (dt.isoformat(), txt))
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def notify_order(self, order):
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if order.status in [order.Margin, order.Rejected]:
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return
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elif order.status == order.Completed:
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if 'name' in order.info:
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self.broker.cancel(self.order_dict[order.ref])
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self.order = None
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else:
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if order.isbuy():
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stop_loss = order.executed.price * (1.0 - (self.p.stoploss))
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take_profit = order.executed.price * (1.0 + self.p.profit_mult * (self.p.stoploss))
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sl_ord = self.sell(exectype=bt.Order.Stop,
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price=stop_loss)
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sl_ord.addinfo(name="Stop")
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tkp_ord = self.sell(exectype=bt.Order.Limit,
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price=take_profit)
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tkp_ord.addinfo(name="Prof")
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self.order_dict[sl_ord.ref] = tkp_ord
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self.order_dict[tkp_ord.ref] = sl_ord
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elif order.issell():
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stop_loss = order.executed.price * (1.0 + (self.p.stoploss))
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take_profit = order.executed.price * (1.0 - 3 * (self.p.stoploss))
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sl_ord = self.buy(exectype=bt.Order.Stop,
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price=stop_loss)
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sl_ord.addinfo(name="Stop")
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tkp_ord = self.buy(exectype=bt.Order.Limit,
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price=take_profit)
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tkp_ord.addinfo(name="Prof")
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self.order_dict[sl_ord.ref] = tkp_ord
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self.order_dict[tkp_ord.ref] = sl_ord
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if self.p.prorder:
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print("Open: %s %s %.2f %.2f %.2f" %
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(order.ref,
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self.data.num2date(order.executed.dt).date().isoformat(),
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order.executed.price,
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order.executed.size,
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order.executed.comm))
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def notify_trade(self, trade):
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if not trade.isclosed:
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return
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self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' %
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(trade.pnl, trade.pnlcomm))
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def next(self):
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# print('Date: ', self.datas[0].datetime.datetime(0))
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predicted_close = self.model.predict(np.array([[[self.dataclose[0]]]]))
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# predicted_close = scaler.inverse_transform(predicted_close)[0][0]
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predicted_close = predicted_close[0][0]
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prev_predicted_close = self.model.predict(np.array([[[self.dataclose[-1]]]]))
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prev_predicted_close = prev_predicted_close[0][0]
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# print('Current close price: ', self.dataclose[0])
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# print('Predicted next close price: ', predicted_close)
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if self.order:
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return
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if not self.position:
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if predicted_close > prev_predicted_close:
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self.order = self.buy()
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else:
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if predicted_close < prev_predicted_close:
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self.order = self.sell()
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cerebro = bt.Cerebro()
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data = bt.feeds.GenericCSVData(
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dataname='eur_usd_1d.csv',
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separator=',',
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dtformat=('%Y%m%d'),
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tmformat=('%H%M%S'),
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datetime=0,
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time=-1,
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open=2,
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high=3,
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low=4,
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close=5,
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volume=6,
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openinterest=-1
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)
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# data = bt.feeds.YahooFinanceData(dataname='YHOO', fromdate=datetime(2011, 1, 1),
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# todate=datetime(2012, 12, 31))
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cerebro.adddata(data)
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cerebro.broker.setcash(1000.0)
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cerebro.addsizer(bt.sizers.FixedSize, stake=10)
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cerebro.addstrategy(SmaCross)
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# Print out the starting conditions
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print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
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# Run over everything
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cerebro.run()
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# Print out the final result
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print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
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# cerebro.plot()

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