import alphalens
Z = W[:-1].copy() # W are the factors
Z /= np.tile(np.sum(np.abs(Z), axis=1), (Z.shape[1], 1)).T #normalize factors
PnL = Z * ret[K+1, :] # multiply by next day returns
PnL = np.sum(PnL, axis=1) # sum P/L across stocks each day
plt.plot(PnL.cumsum()) # cumsum P/L
factors = pd.DataFrame(Z, columns=prices.columns, index=prices.index[K+2:]).stack()

factor_data = alphalens.utils.get_clean_factor_and_forward_returns(
    factors,
    prices,
    quantiles=2,
    periods=(1,2,5), 
    filter_zscore=None)

Dropped 0.4% entries from factor data: 0.4% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).
max_loss is 35.0%, not exceeded: OK!

alphalens.tears.create_full_tear_sheet(factor_data, long_short=True)

Quantiles Statistics

Returns Analysis

<matplotlib.figure.Figure at 0x7ff45f5d9590>

Information Analysis

Turnover Analysis

	min	max	mean	std	count	count %
factor_quantile
1	-0.140107	0.018651	-0.021577	0.015081	32799	50.0
2	-0.030220	0.064397	0.000956	0.014170	32799	50.0

	1D	2D	5D
Ann. alpha	0.518	0.426	0.250
beta	0.113	0.103	0.070
Mean Period Wise Return Top Quantile (bps)	8.112	6.205	3.233
Mean Period Wise Return Bottom Quantile (bps)	-8.112	-6.209	-3.237
Mean Period Wise Spread (bps)	16.225	12.415	6.470

	1D	2D	5D
IC Mean	0.044	0.055	0.051
IC Std.	0.213	0.210	0.199
Risk-Adjusted IC	0.208	0.262	0.256
t-stat(IC)	6.983	8.815	8.617
p-value(IC)	0.000	0.000	0.000
IC Skew	0.083	-0.053	-0.297
IC Kurtosis	-0.114	0.062	0.352

	1D	2D	5D
Quantile 1 Mean Turnover	0.352	0.386	0.414
Quantile 2 Mean Turnover	0.352	0.386	0.414