Tag: data

How to Get (Almost) Free Tick Data

Access to high quality, cost effective market data is a continuing problem for retail traders. I was recently told about the ongoing efforts of the startup brokerage “Alpaca”. The gentleman I spoke with said the API gave access to the tick data of thousands of stocks everyday and without cost. I thought it was too good to be true but recently I took a little bit of time to investigate. In this article I will describe the basic process to accessing the tick data, and some basic code I was exper

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Synthetic ETF Data Generation (Part-2) - Gaussian Mixture Models

This post is a summary of a more detailed Jupyter (IPython) notebook where I demonstrate a method of using Python, Scikit-Learn and Gaussian Mixture Models to generate realistic looking return series. In this post we will compare real ETF returns versus synthetic realizations. To evaluate the similarity of the real and synthetic returns we will compare the following: * visual inspection * histogram comparisons * descriptive statistics * correlations * autocorrelations The data set we will

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Download Intraday Stock Data with IEX and Parquet

Post Outline * Why IEX? * Why Parquet? * System Outline * Code * Links WHY IEX? IEX is a relatively new exchange (founded in 2012). For our purposes, what makes them different from other exchanges is they provide a robust FREE API to query their stock exchange data. As a result we can leverage the pandas-datareader framework to query IEX data quite simply. WHY PARQUET? I don't use Hadoop, however Parquet is a great storage format within the pandas ecosystem as well. It is fast, s

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Exploring Our Scraped Options Data Bid-Ask Spreads (Part-2)

Post Outline * Notes on Part-2 * The Data * Bid-Ask Spread Analysis * How Do Aggregate Bid-Ask Spreads Vary with Days To Expiration? * How Do Bid-Ask Spreads Vary with Volume? * How Do Bid-Ask Spreads Vary with Volatility? * Summary Conclusions Notes on Part-2 Some astute readers in the comments noted that analysis based on the absolute difference in bid-ask price is not robust when considering the price of the underlying option and can lead to spurious conclusions. They recomme

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Exploring Our Scraped Options Data Bid-Ask Spreads

Post Outline * The Objective * The Data * Basic Data Analysis * Bid-Ask Spread Analysis * How Do Aggregate Bid-Ask Spreads Vary with Days To Expiration? * How Do Bid-Ask Spreads Vary with Volume? * How Do Bid-Ask Spreads Vary with Volatility? * Summary Conclusions The Objective Compared to the equity market, the options market is a level up in complexity. For each symbol there are multiple expiration dates, strike prices for each expiration date, implied volatilities, and that'

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Get Free Financial Data w/ Python (Fundamental Ratios-From Finviz.com)

A simple script to scrape fundamental ratios from Finviz.com. This basic code can be tailored to suit your application. The function should produce the following: Very simple adaptable code, allowing you to spend more time analyzing the data and less time aggregating it.

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How to get Free Intraday Stock Data from Netfonds

Daily stock data is everywhere for free. Yahoo, Google, and Quandl all provide useful daily stock prices for basic number crunching. However computational analysis for intraday stock data is much harder to find. In fact, Intraday stock data can be very expensive. So what is a cost conscious quant supposed to do? The Norwegian website Netfonds.no provides free intraday data on stocks and ETF's on the NYSE, Nasdaq, and Amex exchanges. They provide up to 5 days of trade/bid/offer data. I wrote som

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Project Update_iVC Reporting Engine

Still working industriously behind the scenes I thought to take some time and give a progress report. Good news is the iVC Reporting Engine is almost fully operational. I've been able to automate the following processes: Data Collection: I have two methods to obtain public company filings from the SEC via Python scripts. * The primary method I use leverages the excellent services of the free (for now) data provider, Quandl.com. They aggregate and distribute data from several primary sources i

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