Hey Eray,

You may want to have a look at Quandl for data on futures prices on different exchanges. However, it looks like most of this data is daily resolution. Tick data in general is expensive and may be difficult to obtain for free. Caltech has a list of resources for obtaining higher-resolution data, but most are not free.

Ryan

Hi Eray,

Like Ryan mentioned, Quandl provides data at the daily level in this instance which you can load into your algorithm by using the 'fetch_csv' method. Here's an example of how to use it:

'''
This algorithm uses the new universe_func callback inside of fetch_csv to define an investible  
universe of sids based on an externally sourced list of index constituents. 

In this example we are using a csv file containing the list of SP500 constituents and GICS  
sector codes from Quandl. This file is a static snapshot -- but you can point to a csv file  
with varying universe composition over time and fetcher will correctly update the universe  
with the current constituents.

'''


import datetime  
import pandas as pd  
import numpy as np

def preview(df):  
    log.info(' \n %s ' % df.head())  
    df['date'] = '2011-01-04'  
    df = df.rename(columns={'Ticker': 'symbol'})  
    return df

# Function for returning a set of SIDs from fetcher_data  
def my_universe(context, fetcher_data):  
    # Grab just the SIDs for the Financials sector w/in the SP500:  
    financials = fetcher_data[fetcher_data['GICS Sector'] == 'Financials']  
    sids = set(financials['sid'])  
    symbols = [s.symbol for s in sids]  
    context.count = len(symbols)  
    print "total universe size: {c}".format(c=context.count)  
    # Compute target equal-weight for each stock in the SP500 Financials universe  
    context.target_weight = 1.0/context.count  
    return sids

def initialize(context):  
    # Fetch the SP500 constituents -- sourced from Quandl  
    # https://s3.amazonaws.com/quandl-static-content/Ticker+CSV%27s/Indicies/SP500.csv  
    # static snapshot of the SP500 constituents as of 10/2013, along with GICS sectors  
    # I'm grabbing the data from a file on my dropbox folder which I modified by adding  
    # a date column, alternatively you could add the date inside of a more complicated  
    # pre_func and use the csv file as is.  
    fetch_csv(  
       "https://s3.amazonaws.com/quandl-static-content/Ticker+CSV%27s/Indicies/SP500.csv",  
        pre_func=preview,  
        date_column='date',  
        universe_func=(my_universe))

    context.target_weight = 0.01  
def handle_data(context,data):  
    # Loop over every stock in the Financials sector and make sure we have an equal-weight  
    # exposure using the order_target_percent() method.  
    for stock in data:  
        # Guard for missing stock data  
        if 'price' in data[stock]:  
            order_target_percent(stock,context.target_weight)  
        else:  
            log.warn("No price for {s}".format(s=stock))  

Seong

Hi Eray.

I'm interested in using wavelets in trading. How did your thesis go?

Vic

Hi Vic,

I completed my thesis and obtained significant results.
It is useful for capturing cointegration and causality.