The main issue with the above custom factor is the
smx.OLS method only calculates regressions for one asset at a time. One needs to iterate over each asset to then output all the values. Take a look at this post for a little more explanation https://www.quantopian.com/posts/fixing-linear-regression-custom-factor.
However, a second issue will be passing the
SimpleMovingAverage as an input to another factor (ie the slope factor). This isn't 'window_safe' which means the values will change depending upon the timeframe (ie 'window') one looks at it. For example, if there is a 2:1 split, the value of the SMA will be halved. Getting the slope of these values will be problematic. Unless there is a big need to find the slope of the averages, I'd go with just finding the slope of the actual prices. That can be done like this. It uses
stats.linregress rather than
smx.OLS but pretty much the same.
inputs = [USEquityPricing.close]
window_length = 200
def compute(self, today, assets, out, close_prices):
# Get the log prices
log_close_prices = np.log(close_prices)
# Create 1D array like [1, 2, 3,...] to regress against
# Length is the number of days (ie window_length)
x = np.arange(close_prices.shape)
slope, intercept, r_value, p_value, std_err = stats.linregress(x, column)
return_value = (np.power(np.exp(slope), 252)-1) * 100
# Iterate over the columns of data (ie the assets)
# Use the numpy apply_along_axis method in place of a for loop
out[:] = np.apply_along_axis(annualized_slope, axis=0, arr=log_close_prices)
Attached is notebook with this custom factor in action.
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