import numpy as np
import pandas as pd
from quantopian.research import run_pipeline
from quantopian.pipeline import Pipeline
from quantopian.pipeline.factors import CustomFactor
from quantopian.pipeline.filters import QTradableStocksUS
from quantopian.pipeline.data.quandl import cboe_vix as vix

from time import time

start = '2004-01-01'
end = '2004-05-03'

import alphalens as al
universe = QTradableStocksUS()

class VIX(CustomFactor):  
    inputs = [vix.vix_close]  
    window_length = 1  
    def compute(self, today, assets, out, v_close):  
        out[:] = v_close


pipe = Pipeline(  
    columns={  
        'Vix' : VIX(),
        'Close': USEquityPricing.close.latest,
    },
    screen=universe)  

factor_data = run_pipeline(pipe, start, end) 
factor_data_clean = factor_data['Vix'].dropna()

asset_list = factor_data_clean.index.levels[1]
prices = get_pricing(asset_list, start_date=start, end_date=end, fields='close_price')
prices.head()

periods = (1, 2, 5)
factor_data = al.utils.get_clean_factor_and_forward_returns(factor=factor_data_clean,
                                                            prices=prices,
                                                            periods=periods, max_loss=0)


ValueErrorTraceback (most recent call last)
<ipython-input-81-4c3b7a9e2a3c> in <module>()
      2 factor_data = al.utils.get_clean_factor_and_forward_returns(factor=factor_data_clean,
      3                                                             prices=prices,
----> 4                                                             periods=periods, max_loss=0)

/usr/local/lib/python2.7/dist-packages/alphalens/utils.pyc in get_clean_factor_and_forward_returns(factor, prices, groupby, binning_by_group, quantiles, bins, periods, filter_zscore, groupby_labels, max_loss, zero_aware, cumulative_returns)
    797                                    quantiles=quantiles, bins=bins,
    798                                    binning_by_group=binning_by_group,
--> 799                                    max_loss=max_loss, zero_aware=zero_aware)
    800 
    801     return factor_data

/usr/local/lib/python2.7/dist-packages/alphalens/utils.pyc in get_clean_factor(factor, forward_returns, groupby, binning_by_group, quantiles, bins, groupby_labels, max_loss, zero_aware)
    604         binning_by_group,
    605         no_raise,
--> 606         zero_aware
    607     )
    608 

/usr/local/lib/python2.7/dist-packages/alphalens/utils.pyc in dec(*args, **kwargs)
     76         except ValueError as e:
     77             if 'Bin edges must be unique' in str(e):
---> 78                 rethrow(e, message)
     79             raise
     80     return dec

/usr/local/lib/python2.7/dist-packages/alphalens/utils.pyc in rethrow(exception, additional_message)
     43     else:
     44         e.args = (e.args[0] + m,) + e.args[1:]
---> 45     raise e
     46 
     47 

ValueError: Bin edges must be unique: array([ 18.22,  18.22,  18.22,  18.22,  18.22,  18.22])

    An error occurred while computing bins/quantiles on the input provided.
    This usually happens when the input contains too many identical
    values and they span more than one quantile. The quantiles are choosen
    to have the same number of records each, but the same value cannot span
    multiple quantiles. Possible workarounds are:
    1 - Decrease the number of quantiles
    2 - Specify a custom quantiles range, e.g. [0, .50, .75, 1.] to get unequal
        number of records per quantile
    3 - Use 'bins' option instead of 'quantiles', 'bins' chooses the
        buckets to be evenly spaced according to the values themselves, while
        'quantiles' forces the buckets to have the same number of records.
    4 - for factors with discrete values use the 'bins' option with custom
        ranges and create a range for each discrete value
    Please see utils.get_clean_factor_and_forward_returns documentation for
    full documentation of 'bins' and 'quantiles' options.

start_timer = time()
al.tears.create_returns_tear_sheet(factor_data, by_group=False);
end_timer = time()
print "Time to run tearsheet %.2f secs" % (end_timer - start_timer)

	Equity(2 [ARNC])	Equity(24 [AAPL])	Equity(31 [ABAX])	Equity(41 [ARCB])	Equity(60 [ABS])	Equity(62 [ABT])	Equity(64 [GOLD])	Equity(67 [ADSK])	Equity(76 [TAP])	Equity(85 [ACF])	...	Equity(25920 [MAR])	Equity(26169 [SHLD])	Equity(26204 [FHN])	Equity(26259 [HNI])	Equity(26307 [LAUR])	Equity(26350 [PRX])	Equity(26434 [MFE])	Equity(26437 [PDCO])	Equity(26439 [VVI])	Equity(26470 [IAC])
2004-01-02 00:00:00+00:00	37.335	21.18	16.66	31.619	22.356	43.184	23.00	24.45	55.538	15.90	...	46.028	23.00	43.017	43.146	29.63	64.129	14.95	63.05	25.368	33.31
2004-01-05 00:00:00+00:00	38.530	22.17	15.59	31.908	22.838	43.231	23.35	24.72	56.086	16.05	...	46.208	29.10	42.967	43.246	30.29	66.210	15.99	61.27	25.797	34.00
2004-01-06 00:00:00+00:00	38.281	22.10	17.13	31.430	22.808	42.935	22.94	24.58	55.408	16.16	...	46.088	30.00	43.037	42.977	30.23	65.489	16.16	60.33	25.767	33.22
2004-01-07 00:00:00+00:00	38.102	22.59	18.10	31.728	23.093	43.258	22.71	24.74	54.421	16.20	...	46.058	29.15	43.116	42.837	31.42	64.360	15.84	60.76	25.657	33.01
2004-01-08 00:00:00+00:00	38.540	23.41	18.25	31.818	23.192	42.020	22.63	25.05	53.753	16.72	...	46.537	30.26	42.561	43.027	31.59	63.840	16.05	60.35	25.697	32.68