Notebook
In [5]:
from quantopian.pipeline.data import Fundamentals
from quantopian.pipeline import Pipeline
from quantopian.research import run_pipeline
from quantopian.pipeline.factors import SimpleMovingAverage,Latest,RSI, MACDSignal, SimpleBeta
from quantopian.pipeline.filters import Q1500US, QTradableStocksUS, Q500US, Q3000US
from quantopian.research import get_pricing
from quantopian.pipeline.data import USEquityPricing
from quantopian.pipeline.factors import CustomFactor
from alphalens.utils import get_clean_factor_and_forward_returns
from alphalens.tears import create_full_tear_sheet
from quantopian.pipeline import CustomFilter
import pandas as pd
import numpy as np

import talib
In [23]:
#This class is to create the price action pattern of engulging, 
class engulf(CustomFilter):
    
    inputs = [USEquityPricing.close, USEquityPricing.low, USEquityPricing.open, USEquityPricing.high]  

    window_length = 2
    
    def compute(self, today, assets, out, close, low, open1, high): 
        
        condition_1 = (high[-1] > high[-2]) & (low[-1]<low[-2])
        
        condition_2 = (close[-1] > close[-2]) & (open1[-1]<open1[-2])
        
        condition_3 = close[-1] > open1[-1]
        
        condition_4 = (close[-1] > open1[-2]) & (open1[-1]<close[-2])
        
        
        result = condition_1 & condition_2 & condition_3 & condition_4 
        
        out[:] = result   
        
        
class volume(CustomFilter):
    
    inputs = [USEquityPricing.volume]
    window_length = 6
    def compute(self, today, assets, out, vol):     
                
        low_condition_1 = vol[-1] / np.mean(vol, axis = 0)>0.29
        
        low_condition_2 = (vol[-1] / np.mean(vol, axis = 0))<1.54
                                
        out[:] =  low_condition_1 

        
        


def filtro():
    sector = Fundamentals.morningstar_sector_code.latest
    industry = Fundamentals.morningstar_industry_code.latest
    #universe= Q3000US() & sector.element_of([206]) 
    universe = Q3000US() & QTradableStocksUS()   
    sma20 = SimpleMovingAverage(inputs = [USEquityPricing.close], window_length=20 )
    sma50 = SimpleMovingAverage(inputs = [USEquityPricing.close], window_length=50, mask = universe )
    sma200 = SimpleMovingAverage(inputs = [USEquityPricing.close], window_length=200 )
    price = SimpleMovingAverage(inputs = [USEquityPricing.close], window_length=1, mask = universe )
    high = SimpleMovingAverage(inputs = [USEquityPricing.high], window_length=1, mask = universe )
    low = SimpleMovingAverage(inputs = [USEquityPricing.low], window_length=1, mask = universe )
    close = SimpleMovingAverage(inputs = [USEquityPricing.close], window_length=1, mask = universe )
    open1 = SimpleMovingAverage(inputs = [USEquityPricing.open], window_length=1, mask = universe )
    betaytd = SimpleBeta(target=symbols(8554)  , regression_length=100)
    betayty = SimpleBeta(target=symbols(8554),regression_length = 253) 
    rsi = RSI()
    macd = MACDSignal()
    
    
    condition_1 = rsi < 40
    condition_2 = macd < -1.5
    
    technical_condition =  condition_2 & condition_1
    
       
    condition_1 = price > sma20
    condition_2 = price < sma50
    condition_3 = price < sma200 
    condition_4 = 5 < price <120
    sma_condition = condition_1 & condition_4 & condition_3 & condition_2 

    mid_price = (high+low)/2
    box = (close-open1).abs()
    limit_box  = (high-low)*0.25
    condition_5 = close > mid_price
    condition_6 = open1 > mid_price
    condition_7 = box <= limit_box
    
    hammer = condition_5 & condition_6 & condition_7 
        
    price_action =    (hammer & volume()) | engulf()
                  
    filter1= technical_condition | sma_condition
                                
    return Pipeline(columns={"filter":filter1} ,screen = universe & price_action )
 
factor = run_pipeline(filtro(), start_date="2019-05-5", end_date="2020-06-11")











    






Pipeline Execution Time: 21.30 Seconds


















In [12]:



    


asset_list = factor.index.levels[1].unique()
pricing = get_pricing(asset_list,start_date="2019-4-5", end_date="2020-6-10", fields= "close_price" )
merged_data = get_clean_factor_and_forward_returns(factor, pricing, periods=[1,2,3,4,5], quantiles=None, bins=[-1, 0.5, 2])



    















    






Dropped 1.8% entries from factor data: 1.8% 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!



















In [15]:



    


merged_data[merged_data["factor_quantile"]==2].mean()



    















    
Out[15]:








1D                -0.003141
2D                -0.005695
3D                -0.006788
4D                -0.001240
5D                -0.003629
factor             1.000000
factor_quantile    2.000000
dtype: float64



















In [13]:



    


create_full_tear_sheet(merged_data)



    















    






Quantiles Statistics










    










  
    

      
      min
      max
      mean
      std
      count
      count %
    

    

      factor_quantile
      
      
      
      
      
      
    

  
  
    

      1
      False
      False
      False
      0.0
      58471
      84.600804
    

    

      2
      True
      True
      True
      0.0
      10643
      15.399196
    

  













    






Returns Analysis










    










  
    

      
      1D
      2D
      3D
      4D
      5D
    

  
  
    

      Ann. alpha
      0.071
      0.074
      0.065
      0.065
      0.066
    

    

      beta
      0.088
      0.116
      0.084
      0.079
      0.068
    

    

      Mean Period Wise Return Top Quantile (bps)
      3.485
      3.549
      2.894
      3.386
      3.685
    

    

      Mean Period Wise Return Bottom Quantile (bps)
      -1.719
      -1.677
      -2.132
      -1.853
      -1.657
    

    

      Mean Period Wise Spread (bps)
      5.203
      5.061
      4.874
      5.075
      5.150
    

  













    








<matplotlib.figure.Figure at 0x7f6e4d1ace80>










    

















    






Information Analysis










    










  
    

      
      1D
      2D
      3D
      4D
      5D
    

  
  
    

      IC Mean
      0.001
      0.001
      0.002
      0.006
      0.007
    

    

      IC Std.
      0.116
      0.123
      0.121
      0.118
      0.118
    

    

      Risk-Adjusted IC
      0.011
      0.007
      0.019
      0.048
      0.055
    

    

      t-stat(IC)
      0.178
      0.113
      0.310
      0.795
      0.916
    

    

      p-value(IC)
      0.859
      0.910
      0.757
      0.428
      0.361
    

    

      IC Skew
      0.377
      -0.057
      0.332
      0.239
      0.167
    

    

      IC Kurtosis
      1.784
      2.165
      1.745
      1.739
      1.607
    

  













    






/venvs/py35/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j










    

















    






/venvs/py35/lib/python3.5/site-packages/alphalens/utils.py:912: UserWarning: Skipping return periods that aren't exact multiples of days.
  + " of days."










    






Turnover Analysis










    










  
    

      
      1D
      2D
      3D
      4D
      5D
    

  
  
    

      Quantile 1 Mean Turnover
      0.870
      0.876
      0.876
      0.878
      0.881
    

    

      Quantile 2 Mean Turnover
      0.887
      0.910
      0.922
      0.927
      0.940
    

  













    










  
    

      
      1D
      2D
      3D
      4D
      5D
    

  
  
    

      Mean Factor Rank Autocorrelation
      0.844
      0.683
      0.556
      0.489
      0.429
    

  













    


























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