Overall Statistics
Total Trades
0
Average Win
0%
Average Loss
0%
Compounding Annual Return
0%
Drawdown
0%
Expectancy
0
Net Profit
0%
Sharpe Ratio
0
Probabilistic Sharpe Ratio
0%
Loss Rate
0%
Win Rate
0%
Profit-Loss Ratio
0
Alpha
0
Beta
0
Annual Standard Deviation
0
Annual Variance
0
Information Ratio
0
Tracking Error
0
Treynor Ratio
0
Total Fees
$0.00
Estimated Strategy Capacity
$0
 
import clr
clr.AddReference("System")
clr.AddReference("QuantConnect.Algorithm")
clr.AddReference("QuantConnect.Common")

from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
import datetime
from datetime import timedelta

import numpy as np
from sklearn.linear_model import LinearRegression

import pandas as pd
import statsmodels.api as sm

class ScikitLearnLinearRegressionAlgorithm(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2021, 2, 6)  # Set Start Date # BTC Future Start Date 2009, 1, 1
        self.SetEndDate(2021, 3, 6) # Set End Date
        
         #1. Request BTC futures and save the BTC security
        self.BTC = self.AddFuture(Futures.Currencies.BTC, Resolution.Minute) 
        self.BTC.SetFilter(lambda x: x.FrontMonth())
        
        
    def OnData(self, data):
        for chain in data.FutureChains.Values:
            contracts = chain.Contracts
            for contract in contracts.Values:
                history = self.History(contract.Symbol, 30, Resolution.Minute)
        
                self.Log(history.to_string())
                self.Quit()
                return