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Having issues using sklearn

Hi all,

I am looking to use the strategy from the tutorial section. In my approach, i was using sklearn to calculate the intercept as opposed to numpy as in the example. For some reason, I cant get it to work. I can though get it to use in the research notebook. Attaching the code to seek you help.

Thanks a lot.

Update Backtest







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The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.


Having issues attaching the backtest. Here is the code.

import numpy as np
from sklearn.linear_model import LinearRegression


class BetaAlgorithm(QCAlgorithm):

def Initialize(self):
self.SetStartDate(2016, 1, 1) # Set Start Date
self.SetEndDate(2017, 1, 1) # Set End Date
self.SetCash(10000) # Set Strategy Cash

# Dow 30 companies.
self.symbols = [self.AddEquity(ticker).Symbol
for ticker in ['AAPL', 'AXP', 'BA', 'CAT', 'CSCO', 'CVX', 'DD',
'DIS', 'GE', 'GS', 'HD', 'IBM', 'INTC', 'JPM',
'KO', 'MCD', 'MMM', 'MRK', 'MSFT', 'NKE', 'PFE',
'PG', 'TRV', 'UNH', 'UTX', 'V', 'VZ', 'WMT', 'XOM'] ]

# Benchmark
self.benchmark = Symbol.Create('SPY', SecurityType.Equity, Market.USA)

# Set number days to trace back
self.lookback = 21

# Schedule Event: trigger the event at the begining of each month.
self.Schedule.On(self.DateRules.MonthStart(self.symbols[0]),
self.TimeRules.AfterMarketOpen(self.symbols[0]),
self.Rebalance)


def Rebalance(self):

# Fetch the historical data to perform the linear regression
history = self.History(
self.symbols + [self.benchmark],
self.lookback,
Resolution.Daily).close.unstack(level=0)

symbols = self.SelectSymbols(history)

# Liquidate positions that are not held by selected symbols
for holdings in self.Portfolio.Values:
symbol = holdings.Symbol
if symbol not in symbols and holdings.Invested:
self.Liquidate(symbol)

# Invest 100% in the selected symbols
for symbol in symbols:
self.SetHoldings(symbol, 1)


def SelectSymbols(self, history):
'''Select symbols with the highest intercept/alpha to the benchmark
'''
alphas = dict()

# Get the benchmark returns
benchmark = history[self.benchmark].pct_change().dropna()

# Conducts linear regression for each symbol and save the intercept/alpha
for symbol in self.symbols:

# Get the security returns
returns = history[symbol].pct_change().dropna()
benchmark = benchmark.values.reshape(-1,1)
returns = returns.values

lr = LinearRegression()
lr.fit(benchmark, returns)

intercept = lr.intercept_
alphas[symbol] = intercept

# Select symbols with the highest intercept/alpha to the benchmark
selected = sorted(alphas.items(), key=lambda x: x[1], reverse=True)[:2]
return [x[0] for x in selected]

 

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Hi Mohamed,

The issues are caused by numpy syntax errors and the parameters being passed out of order to the LinearRegression function. See the attached backtest for a working solution. Also, consider reviewing our Introduction to Financial Python tutorial series as it covers numpy and pandas.

Best,
Derek Melchin

1

The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.


Update Backtest





0

The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.


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