This is the code I used for the calculations in this story http://qz.com/157404/how-you-could-have-turned-1000-into-billions-of-dollars-by-perfectly-trading-the-sp-500-this-year/

perfectSP500.py

#!/usr/bin/env python
# encoding: utf-8

import sys
import os
import csv
import datetime

#define market holidays
marketHolidays = ["2013-01-21","2013-02-18","2013-03-29","2013-05-27","2013-07-04","2013-09-02","2013-11-28","2013-12-25"]

def main():
	data = []
	#this is a spreadsheet with all of the data
	#the first column is dates, every subsiquent column is the closing price of a stock (the ticker is the header)
	#i.e. every row is a day of data
	with open("perfectsp500raw.csv","rb") as f:
		#open the file turn every row into a dict with the tickers as the keys
		reader = csv.DictReader(f)

		#cache the tickers to make things easier
		headers = reader.fieldnames

		rawdata = []

		#put the data into a list to make it easier to work with
		for row in reader:
			rawdata.append(row)

	for i in range(1,len(rawdata)):
		#loop through all of the data
		thenrow = rawdata[i]
		nowrow = rawdata[i-1]

		#convert the date column into a native date object
		nowdate = datetime.datetime.strptime(nowrow["date"],"%Y-%m-%d")

		#skip the day if it's a weekday or a market holiday
		if nowdate.weekday() > 4 or nowrow["date"] in marketHolidays:
			continue

		#initialize a dummy row of data to fill in later
		data_row = {"date":nowrow["date"],"max": DayInfo("NADA",float("-inf"),float("-inf"))}

		#loop through every ticker (the first one is the date)
		for j in range(1,len(headers)):
			ticker = headers[j]
			#some days don't have data (they weren't listed then or didn't trade that day or otherwise)
			#set the price on that day to 0 (0's are ignored)
			try:
				now = float(nowrow[ticker])
			except:
				now = 0
			try:
				then = float(thenrow[ticker])
			except:
				then = 0

			#create a DayInfo object using the ticker and the one day close to close change
			data_row[ticker] = DayInfo(ticker,now,pctChange(now,then))

			#if the the current company has the largest pct change set it as the max for the day
			data_row["max"] = max(data_row["max"],data_row[ticker])

		#add the row to the cleaned data list
		data.append(data_row)

	#set a amount of money to start with
	val = 1000

	#set a trading fee
	fee = 0
	data = reversed(data)
	for row in data:
		#convert the pct change into a factor to multiply the running total by
		factor = row["max"].pctChng + 1

		#calculate the effect of the trade on the running total
		val = val*factor - (fee * 2) #this isn't exactly right, on the first day there is only the buy fee and on the last day there is only the sell fee
		
		#print out the date, a the running value as formatted number, the change, and the Day info object for the max (ticker + price)
		print "%s\t%s\t%s\t%s" % (row["date"], "{:,}".format(val/1000), float(round(row["max"].pctChng*10000))/100, row["max"])

	pass


def pctChange(now,then):

	if now == 0 or then == 0:
		#this effectively ignores 0 prices
		return float("-inf")

	return (now-then)/then

class DayInfo():
	"""dayInfo is a custom class i created so I could use the max function and not have to keep track separately of the ticker"""
	
	def __init__(self, ticker, val,pctChng):
		self.ticker = ticker
		self.val = val
		self.pctChng = pctChng

	def __cmp__(self, other):
		#on comparison, use pctChng to compare
		if self.pctChng == other.pctChng:
			return 0
		elif self.pctChng > other.pctChng:
			return 1
		return -1

	def __repr__(self):
		#on print use ticker {tab} price
		return "%s\t%s" % (self.ticker,self.val)
		

if __name__ == '__main__':
	main()