I cannot seem to figure out the correct way to go about creating a custom factor that takes the price of the assets and divides them by the price of the SPY.
Some built-in factors such as linear_regression() seem to be able to take an argument specifying a separate target symbol and do a regression analysis on all symbols vs. that target. This is basically what I am trying to do, but instead of performing a regression, I just want to do a simple division operation.
Is this possible?
Hi,
from quantopian.pipeline import Pipeline, CustomFactor
from quantopian.research import run_pipeline
from quantopian.pipeline.data.builtin import USEquityPricing
import pandas as pd
import numpy as np
class betta(CustomFactor):
inputs = [USEquityPricing.close]
window_length = 1
def compute(self, today, assets, out, price):
df = pd.DataFrame(price, columns=assets)
spy=df[8554].values
out[:]=price/spy
def make_pipeline():
price= USEquityPricing.close.latest
price_div_betta = betta()
return Pipeline(columns={'price_div_betta': price_div_betta ,
'close_price':price})
my_pipe=make_pipeline()
results=run_pipeline(my_pipe, '2015-01-01', '2015-01-01')
results
basically you create pandas data frame which contains stock and the price, then you can use this data frame to search for SPY which i believe is 8554 and finally your out[:] is all the price decided by spy =)
good luck
well this is alternative version, maybe more faster without creating pandas data frame
from quantopian.pipeline import Pipeline, CustomFactor
from quantopian.research import run_pipeline
from quantopian.pipeline.data.builtin import USEquityPricing
import pandas as pd
import numpy as np
class betta(CustomFactor):
inputs = [USEquityPricing.close]
window_length = 1
def compute(self, today, assets, out, price):
out[:]=price/price[0][np.where(assets==8554)]
def make_pipeline():
price= USEquityPricing.close.latest
price_div_betta = betta()
return Pipeline(columns={'price_div_betta': price_div_betta ,
'close_price':price})
my_pipe=make_pipeline()
results=run_pipeline(my_pipe, '2015-01-01', '2015-01-01')
results
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