Read about history on the Quantopian help page, and the DataFrame class and its pct_change method on the Pandas documentation page.

• https://www.quantopian.com/help
  
• http://pandas.pydata.org/pandas-docs/stable/computation.html
  

Hi Travis,

Welcome to Quantopian! This sounds like a job for the Pipeline API. With Pipeline, you can create custom factors such as percent returns and then rank and filter securities as you like. Take a look at this example that filters based on market cap and simple moving average.

There's also a Getting Started tutorial series to help you get familiarized with the API. The tutorials don't yet cover Pipeline but they should help you get the hang of coding an algo on Quantopian. The history function that André mentioned is covered in one of the videos!

Much appreciated André and Jamie I've been looking over those answers all day. I'm starting to get a grasp on the pipeline API but don't have the functionality that I want quite yet. I have all weekend to work on my algo so ill keep everyone update with snippets and progress.

I got the screener set up to calculate percent change of the current price and yesterday's close. Problem is that I want to use pipeline to load 8000+ equities but I'm not sure how to do it. Can anybody help?

from quantopian.pipeline.data.builtin import USEquityPricing  
import pandas as pd  
import datetime

def initialize(context):  
    context.stock = sid(7408)  
    context.yesterday_high = None  


def handle_data(context, data):  
    # print yesterday high if set (non None)  
    #if context.yesterday_high:  
        #log.info("Yesterday high: %f" %  context.yesterday_high)  
    yesterdays_close = history(bar_count=2, frequency='1d', field='close_price')  
    log.info(yesterdays_close)  
    # print today high  
    todays_high = data[context.stock].open_price  
    log.info("current price: %f" % todays_high)

    # Save today high in context for tomorrow's use  
    context.yesterday_high = todays_high  
    pct_change = (todays_high - yesterdays_close) / todays_high  
    log.info(pct_change*100)  

Travis - Read the Pipeline manual. Start at the address Jamie posted, https://www.quantopian.com/help#pipeline-title .

Figured out how to sort through the entire universe and get the previous day close for every stock.
This code isn't perfect and it sorts through the stock alphabetically but it does what it's supposed to do. :D

from quantopian.algorithm import attach_pipeline, pipeline_output  
from quantopian.pipeline import Pipeline  
from quantopian.pipeline.data.builtin import USEquityPricing  
from quantopian.pipeline.factors import SimpleMovingAverage  
from quantopian.pipeline import CustomFactor  
import numpy as np  
import pandas as pd  
import datetime  
class PriceRange(CustomFactor):  
    # define inputs for the compute method  
    inputs = [USEquityPricing.close]  
    def compute(self, today, assets, out, close):  
        out[:] = close[-1]  
def initialize(context):  
    stock_prices = PriceRange(window_length=10)  
    #attaches a name to the pipeline  
    pipe = attach_pipeline(Pipeline(), name = 'pipeline')  
    # adds the stock_prices variable to the stock_prices pipeline  
    pipe.add(stock_prices, 'pipeline')

def before_trading_start(context, data):  
    # Access results using the name passed to `attach_pipeline`.  
    results = pipeline_output('pipeline')  
    print results.head(30)

    # Define a universe with the results of a Pipeline.  
    update_universe(results.sort('pipeline').index[:10])  
def handle_data(context, data):  
    # placeholder variable  
    variable = int