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Fetcher 2.0

Fetcher - Load any CSV file

Quantopian provides a 11-year history of US equity market data in minute and daily bars. The US market data provides a backbone for financial analysis, but some of the most promising areas of research are finding signals in non-market data. Fetcher provides your algorithm with access to external time series data. Any time series that can be retrieved as a csv file via http or https can be incorporated into a Quantopian algorithm.

Fetcher lets you load csv files over http. To use it, invoke fetch_csv(url) in your initialize method. fetch_csv will retrieve the text of the csv file using the (wonderful) requests module. The csv is parsed into a pandas dataframe using pandas.io.parsers.read_csv. You may then specify your own methods to modify the entire dataframe prior to the start of the simulation. During simulation, the rows of the csv/dataframe are streamed to your algorithm's handle_data method as additional properties of the data parameter.

Best of all, your F

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#!/usr/bin/perl

################

use strict;
use warnings;

use CGI ':standard';
use CGI::Carp 'fatalsToBrowser';
use Data::Dumper;

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"""
Generator-style DataSource that loads from CSV.
"""

import pytz
import csv
import mmap
import os.path

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#!/bin/bash

##########IMPORTANT###########################################################################
# This script requires rvm: http://beginrescueend.com/ 
# Before running this script you must install rvm. This script will install ruby 1.9.3-p0 if it 
# is not already. Ruby can take 10 minutes to compile.

# Load RVM into a shell session *as a function*
if [[ -s "$HOME/.rvm/scripts/rvm" ]] ; then
  # First try to load from a user install

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class SecurityState():
    """An example class to track persistent state for a 
    specific security. This trivial example just tracks the max
    price for the security, as well as shares ordered. 
    Modify this class to track individual securities."""
    
    
    def __init__(self, tick):
        self.sid = tick.sid
        self.max_price = tick.price

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vwapMap = {}
         
class DailyVWAP:
    """A class that tracks the volume weighted average price
       based on tick updates."""
    def __init__(self, sid, period_length):
        self.sid = sid
        self.ticks = []
        self.value = 0.0
        self.period_length = period_length