AI For Trading:Ranking Exercise (64)

Ranking

Install packages

import sys

!{sys.executable} -m pip install -r requirements.txt

import cvxpy as cvx
import numpy as np
import pandas as pd
import time
import os
import quiz_helper
import matplotlib.pyplot as plt

%matplotlib inline
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (14, 8)

data bundle

import os
import quiz_helper
from zipline.data import bundles

os.environ['ZIPLINE_ROOT'] = os.path.join(os.getcwd(), '..', '..','data','module_4_quizzes_eod')
ingest_func = bundles.csvdir.csvdir_equities(['daily'], quiz_helper.EOD_BUNDLE_NAME)
bundles.register(quiz_helper.EOD_BUNDLE_NAME, ingest_func)
print('Data Registered')

Build pipeline engine

from zipline.pipeline import Pipeline
from zipline.pipeline.factors import AverageDollarVolume
from zipline.utils.calendars import get_calendar

universe = AverageDollarVolume(window_length=120).top(500) 
trading_calendar = get_calendar('NYSE') 
bundle_data = bundles.load(quiz_helper.EOD_BUNDLE_NAME)
engine = quiz_helper.build_pipeline_engine(bundle_data, trading_calendar)

View Data露

With the pipeline engine built, let's get the stocks at the end of the period in the universe we're using. We'll use these tickers to generate the returns data for the our risk model.

universe_end_date = pd.Timestamp('2016-01-05', tz='UTC')

universe_tickers = engine\
    .run_pipeline(
        Pipeline(screen=universe),
        universe_end_date,
        universe_end_date)\
    .index.get_level_values(1)\
    .values.tolist()

universe_tickers

Get Returns data

from zipline.data.data_portal import DataPortal

data_portal = DataPortal(
    bundle_data.asset_finder,
    trading_calendar=trading_calendar,
    first_trading_day=bundle_data.equity_daily_bar_reader.first_trading_day,
    equity_minute_reader=None,
    equity_daily_reader=bundle_data.equity_daily_bar_reader,
    adjustment_reader=bundle_data.adjustment_reader)

Get pricing data helper function

def get_pricing(data_portal, trading_calendar, assets, start_date, end_date, field='close'):
    end_dt = pd.Timestamp(end_date.strftime('%Y-%m-%d'), tz='UTC', offset='C')
    start_dt = pd.Timestamp(start_date.strftime('%Y-%m-%d'), tz='UTC', offset='C')

    end_loc = trading_calendar.closes.index.get_loc(end_dt)
    start_loc = trading_calendar.closes.index.get_loc(start_dt)

    return data_portal.get_history_window(
        assets=assets,
        end_dt=end_dt,
        bar_count=end_loc - start_loc,
        frequency='1d',
        field=field,
        data_frequency='daily')

get pricing data into a dataframe

returns_df = \
    get_pricing(
        data_portal,
        trading_calendar,
        universe_tickers,
        universe_end_date - pd.DateOffset(years=5),
        universe_end_date)\
    .pct_change()[1:].fillna(0) #convert prices into returns

returns_df

Sector data helper function

We'll create an object for you, which defines a sector for each stock. The sectors are represented by integers. We inherit from the Classifier class. Documentation for Classifier, and the source code for Classifier

from zipline.pipeline.classifiers import Classifier
from zipline.utils.numpy_utils import int64_dtype
class Sector(Classifier):
    dtype = int64_dtype
    window_length = 0
    inputs = ()
    missing_value = -1

    def __init__(self):
        self.data = np.load('../../data/project_4_sector/data.npy')

    def _compute(self, arrays, dates, assets, mask):
        return np.where(
            mask,
            self.data[assets],
            self.missing_value,
        )

sector = Sector()

We'll use 2 years of data to calculate the factor

Note: Going back 2 years falls on a day when the market is closed. Pipeline package doesn't handle start or end dates that don't fall on days when the market is open. To fix this, we went back 2 extra days to fall on the next day when the market is open.

factor_start_date = universe_end_date - pd.DateOffset(years=2, days=2)
factor_start_date

Quiz 1

Create a factor of one year returns, demeaned, and ranked

Answer 1

from zipline.pipeline.factors import Returns
#TODO
# create a pipeline called p
p = Pipeline(screen=universe)
# create a factor of one year returns, deman by sector, then rank
factor = (
    Returns(window_length=252, mask=universe).
    demean(groupby=Sector()). #we use the custom Sector class that we reviewed earlier
    rank()
)
# add the factor to the pipeline
p.add(factor, 'Momentum_1YR_demean_by_sector_ranked')

visualize the pipeline

p.show_graph(format='png')

run pipeline and view the factor data

df = engine.run_pipeline(p, factor_start_date, universe_end_date)

df.head()

Quiz 2

What do you notice about the factor values?

Answer 2

The factor values are now integers from 1 to N, where N is the number of stocks in the universe.

为者常成，行者常至