Notebook
In [9]:
# Imports
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from quantopian.pipeline import Pipeline
from quantopian.research import run_pipeline
from quantopian.pipeline.data.builtin import USEquityPricing
from quantopian.pipeline.data import morningstar
from quantopian.pipeline.filters import QTradableStocksUS
from quantopian.pipeline.data import Fundamentals
In [10]:
def tech_sector_pipeline():
    #UNIVERSE
    tradable_stocks = QTradableStocksUS()
    sectors = morningstar.asset_classification.morningstar_sector_code.latest
    tech_sector = sectors.eq(311)
    price_filter = USEquityPricing.close.latest > 250
    
    my_universe = tradable_stocks & tech_sector & price_filter
    
    #Data Columns
    prices = USEquityPricing.close.latest
    market_cap = Fundamentals.market_cap.latest
    
    # Return Pipeline
    return Pipeline(columns={
        'price':prices,
        'market_cap':market_cap
    },screen=my_universe)
In [11]:
tech_stocks = run_pipeline(tech_sector_pipeline(),'2018-09-12','2018-10-12')
In [12]:
tech_stocks
Out[12]:
market_cap price
2018-09-12 00:00:00+00:00 Equity(114 [ADBE]) 1.307399e+11 267.010
Equity(19008 [ULTI]) 1.001985e+10 321.220
Equity(19725 [NVDA]) 1.658624e+11 272.800
Equity(26578 [GOOG_L]) 8.234306e+11 1189.990
Equity(47063 [ANET]) 1.999364e+10 267.210
2018-09-13 00:00:00+00:00 Equity(114 [ADBE]) 1.311268e+11 267.780
Equity(7681 [TYL]) 9.674055e+09 250.320
Equity(19008 [ULTI]) 1.014215e+10 325.170
Equity(19725 [NVDA]) 1.630656e+11 268.200
Equity(26578 [GOOG_L]) 8.119841e+11 1171.670
Equity(47063 [ANET]) 2.004153e+10 267.860
2018-09-14 00:00:00+00:00 Equity(114 [ADBE]) 1.314842e+11 268.490
Equity(19008 [ULTI]) 1.023014e+10 328.210
Equity(19725 [NVDA]) 1.649747e+11 271.340
Equity(26578 [GOOG_L]) 8.200058e+11 1182.170
Equity(47063 [ANET]) 2.020689e+10 270.100
2018-09-17 00:00:00+00:00 Equity(114 [ADBE]) 1.345054e+11 274.690
Equity(19008 [ULTI]) 1.031874e+10 330.910
Equity(19725 [NVDA]) 1.680694e+11 276.450
Equity(26578 [GOOG_L]) 8.175876e+11 1177.960
Equity(47063 [ANET]) 2.029593e+10 271.310
2018-09-18 00:00:00+00:00 Equity(114 [ADBE]) 1.313520e+11 268.220
Equity(19008 [ULTI]) 9.996760e+09 320.410
Equity(19725 [NVDA]) 1.665494e+11 273.930
Equity(26578 [GOOG_L]) 8.055454e+11 1159.830
Equity(47063 [ANET]) 1.983426e+10 265.130
2018-09-19 00:00:00+00:00 Equity(114 [ADBE]) 1.325958e+11 270.790
Equity(19008 [ULTI]) 1.001142e+10 320.995
Equity(19725 [NVDA]) 1.647802e+11 271.010
Equity(26578 [GOOG_L]) 8.098717e+11 1167.160
... ... ... ...
2018-10-03 00:00:00+00:00 Equity(19725 [NVDA]) 1.741798e+11 286.520
Equity(26578 [GOOG_L]) 8.374937e+11 1209.210
Equity(47063 [ANET]) 1.916609e+10 256.149
2018-10-04 00:00:00+00:00 Equity(114 [ADBE]) 1.320450e+11 270.440
Equity(19008 [ULTI]) 9.988960e+09 320.160
Equity(19725 [NVDA]) 1.743318e+11 286.730
Equity(26578 [GOOG_L]) 8.398325e+11 1212.020
Equity(47063 [ANET]) 1.956116e+10 261.430
2018-10-05 00:00:00+00:00 Equity(114 [ADBE]) 1.287257e+11 263.760
Equity(19008 [ULTI]) 9.626105e+09 308.580
Equity(19725 [NVDA]) 1.698083e+11 279.280
Equity(26578 [GOOG_L]) 8.157545e+11 1177.070
Equity(47063 [ANET]) 1.928506e+10 257.740
2018-10-08 00:00:00+00:00 Equity(114 [ADBE]) 1.284865e+11 263.210
Equity(19008 [ULTI]) 9.488202e+09 304.040
Equity(19725 [NVDA]) 1.640749e+11 269.860
Equity(26578 [GOOG_L]) 8.087666e+11 1167.830
Equity(47063 [ANET]) 1.880095e+10 251.260
2018-10-09 00:00:00+00:00 Equity(114 [ADBE]) 1.243227e+11 254.650
Equity(19008 [ULTI]) 9.086035e+09 291.220
Equity(19725 [NVDA]) 1.615882e+11 265.750
Equity(26578 [GOOG_L]) 8.017162e+11 1155.610
2018-10-10 00:00:00+00:00 Equity(114 [ADBE]) 1.240640e+11 254.160
Equity(19008 [ULTI]) 9.181195e+09 294.400
Equity(19725 [NVDA]) 1.614483e+11 265.530
Equity(26578 [GOOG_L]) 7.944476e+11 1145.170
2018-10-11 00:00:00+00:00 Equity(19008 [ULTI]) 8.905076e+09 285.480
Equity(26578 [GOOG_L]) 7.559637e+11 1091.510
2018-10-12 00:00:00+00:00 Equity(19008 [ULTI]) 8.844548e+09 283.480
Equity(26578 [GOOG_L]) 7.559637e+11 1090.910

108 rows × 2 columns