end_date = datetime(2018, 5, 1)
timeframe = 1440
alg = TNG(name, regime, start_date, end_date)
alg.addInstrument("btcusd")
alg.addTimeframe("btcusd", timeframe)
positionId = None
def onBar(instrument):
global positionId
if positionId is None:
if instrument.macd().histogram[2] > instrument.macd().histogram[1]:
positionId = alg.openLong(1)
if instrument.macd().histogram[2] < instrument.macd().histogram[1]:
positionId = alg.openShort(1)
else:
if instrument.macd().histogram[2] > instrument.macd().histogram[1] and\
alg.getPositionSide(positionId) <=0:
alg.closePosition(positionId)
positionId = alg.openLong(1)
if instrument.macd().histogram[2] < instrument.macd().histogram[1] and \
alg.getPositionSide(positionId) >= 0:
alg.closePosition(positionId)
positionId = alg.openShort(1)
alg.run_backtest(onBar)
stat = bs.BacktestStatistics(alg)
stat.backtest_results()
return 1
elif data['close'][lookforward - 1] * 1.01 < data['open'][0]:
return -1
else:
return 0
def onBar(instrument):
inp = calculate_input(
instrument.rates[1:lookback + 1]) # Calculating inputs
prediction = model.predict([inp])[0] # Making prediction
if prediction > 0:
alg.buy()
elif prediction < 0:
alg.sell()
# end of onBar()
model = prepare_model() # Creating an ML-model.
alg = TNG(end_train_date, end_test_date) # Creating an instance of environment to run algorithm in.
alg.addInstrument(ticker) # Adding an instrument.
alg.addTimeframe(ticker, timeframe) # Adding a time frame.
alg.run_backtest(onBar) # Backtesting...
stat = bs.BacktestStatistics(alg) # Retrieving statistics of the backtest
pnl = stat.calculate_PnL()
num_positions = stat.calculate_number_of_trades()
print("pnl=%f, num_positions=%d" % (pnl, num_positions))
stat.backtest_results() # Displaying the backtest statistics