An Introduction to Stock Market Data Analysis with Python

Introduction

Advanced mathematics and statistics has been present in finance for some time. Prior to the 1980s, banking and finance were well known for being “boring”; investment banking was distinct from commercial banking and the primary role of the industry was handling “simple” (at least in comparison to today) financial instruments, such as loans. Deregulation under the Reagan administration, coupled with an influx of mathematical talent, transformed the industry from the “boring” business of banking to what it is today, and since then, finance has joined the other sciences as a motivation for mathematical research and advancement. For example one of the biggest recent achievements of mathematics was the derivation of the Black-Scholes formula, which facilitated the pricing of stock options (a contract giving the holder the right to purchase or sell a stock at a particular price to the issuer of the option). That said, bad statistical models, including the Black-Scholes formula, hold part of the blame for the 2008 financial crisis.

In recent years, computer science has joined advanced mathematics in revolutionizing finance and trading, the practice of buying and selling of financial assets for the purpose of making a profit. In recent years, trading has become dominated by computers; algorithms are responsible for making rapid split-second trading decisions faster than humans could make (so rapidly, the speed at which light travels is a limitation when designing systems). Additionally, machine learning and data mining techniques are growing in popularity in the financial sector, and likely will continue to do so. In fact, a large part of algorithmic trading is high-frequency trading (HFT). While algorithms may outperform humans, the technology is still new and playing in a famously turbulent, high-stakes arena. HFT was responsible for phenomena such as the 2010 flash crash and a 2013 flash crash prompted by a hacked Associated Press tweet about an attack on the White House.

This lecture, however, will not be about how to crash the stock market with bad mathematical models or trading algorithms. Instead, I intend to provide you with basic tools for handling and analyzing stock market data with Python. I will also discuss moving averages, how to construct trading strategies using moving averages, how to formulate exit strategies upon entering a position, and how to evaluate a strategy with backtesting.

DISCLAIMER: THIS IS NOT FINANCIAL ADVICE!!! Furthermore, I have ZERO experience as a trader (a lot of this knowledge comes from a one-semester course on stock trading I took at Salt Lake Community College)! This is purely introductory knowledge, not enough to make a living trading stocks. People can and do lose money trading stocks, and you do so at your own risk!

https://ntguardian.wordpress.com/2016/09/19/introduction-stock-market-data-python-1/

Trading Strategy

Call an open position a trade that will be terminated in the future when a condition is met. A long position is one in which a profit is made if the financial instrument traded increases in value, and a short position is on in which a profit is made if the financial asset being traded decreases in value. When trading stocks directly, all long positions are bullish and all short position are bearish. That said, a bullish attitude need not be accompanied by a long position, and a bearish attitude need not be accompanied by a short position (this is particularly true when trading stock options).

Here is an example. Let’s say you buy a stock with the expectation that the stock will increase in value, with a plan to sell the stock at a higher price. This is a long position: you are holding a financial asset for which you will profit if the asset increases in value. Your potential profit is unlimited, and your potential losses are limited by the price of the stock since stock prices never go below zero. On the other hand, if you expect a stock to decrease in value, you may borrow the stock from a brokerage firm and sell it, with the expectation of buying the stock back later at a lower price, thus earning you a profit. This is called shorting a stock, and is a short position, since you will earn a profit if the stock drops in value. The potential profit from shorting a stock is limited by the price of the stock (the best you can do is have the stock become worth nothing; you buy it back for free), while the losses are unlimited, since you could potentially spend an arbitrarily large amount of money to buy the stock back. Thus, a broker will expect an investor to be in a very good financial position before allowing the investor to short a stock.

Any trader must have a set of rules that determine how much of her money she is willing to bet on any single trade. For example, a trader may decide that under no circumstances will she risk more than 10% of her portfolio on a trade. Additionally, in any trade, a trader must have an exit strategy, a set of conditions determining when she will exit the position, for either profit or loss. A trader may set a target, which is the minimum profit that will induce the trader to leave the position. Likewise, a trader must have a maximum loss she is willing to tolerate; if potential losses go beyond this amount, the trader will exit the position in order to prevent any further loss (this is usually done by setting a stop-loss order, an order that is triggered to prevent further losses)…

https://ntguardian.wordpress.com/2016/09/26/introduction-stock-market-data-python-2

 

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