Hi,

I'm new to QC platform.

I want to incorporate my ML model saved as pkl file in backtesting and later live trading on quantconnect.

My first ptoblem is that I don't know how to calculate ta_lib indicators. I create simple example that does not work:

import pandas as pd
import numpy as np
from talib.abstract import (
DEMA, EMA, MIDPRICE, SMA, T3, TEMA, TRIMA, WMA,
ADX, ADXR, AROONOSC, BOP, CMO, DX, MFI, MINUS_DM, MOM, ROC, RSI,
TRIX , WILLR, ATR, NATR, BBANDS, AROON, STOCHRSI,
HT_TRENDLINE, AD, OBV, HT_DCPERIOD, HT_DCPHASE, HT_TRENDMODE,
TRANGE, AVGPRICE, MEDPRICE, TYPPRICE, WCLPRICE, ULTOSC,
MAMA, SAR, SAREXT, APO, MACD, ADOSC,
HT_PHASOR, HT_SINE, STOCHF, STOCH
)

# GLOBALS
periods = [5, 30, 60, 300, 480, 2400, 12000, 96000]

class CalibratedResistanceAtmosphericScrubbers(QCAlgorithm):

def Initialize(self):
self.SetStartDate(2019, 12, 31) # Set Start Date
self.SetCash(100000) # Set Strategy Cash
self.AddEquity("SPY", Resolution.Minute)

model = self.Download("https://github.com/MislavSag/trademl/blob/master/trademl/modeling/rf_model.pkl")


def OnData(self, data):
'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
Arguments:
data: Slice object keyed by symbol containing the stock data
'''

open_ = self.Securities["SPY"].Open
high_ = self.Securities["SPY"].High
low_ = self.Securities["SPY"].Low
close_ = self.Securities["SPY"].Close
volume_ = self.Securities["SPY"].Volume

dema = DEMA(np.array(close_), 30)

self.Debug(f'DEMA value is equal to: {dema}')

If I ran this, it returns an error:

Runtime Error: TypeError : Argument 'real' has incorrect type (expected numpy.ndarray, got NoneType) at OnData in main.py:line 81 TypeError : Argument 'real' has incorrect type (expected numpy.ndarray, got NoneType)