One Introduction to Quantitative Finance, via Algorithmic Trading
Before we begin, you’ll notice this post deals with finance and investing, both of which are highly risky. This site and this post is strictly educational. Nothing in this post or on this website is intended as, nor should be construed as investment advice. Kindly first visit the Disclaimers page before you proceed, taking careful note of CFTC Rule 4.41 regarding hypothetical performance. Further, investing and trading is extremely risky, to the point that 90% of participants rapidly fail. As such, your participation in investing and trading could lead to loss of principal, and even further losses beyond that in some cases. Neither this site in its entirety, nor this post, nor any portion thereof, nor any resulting discussion or correspondence related to this site and post, is intended to be a recommendation to invest or trade mutual funds, stocks, commodities, options, or any other financial instrument. I will not accept any responsibility for any losses which might result from applications of ideas expressed on this site or in this post from the techniques or systems mentioned on this site or in this post. Nothing shown is the result of an actual trade. Neither past actual performance, not simulations of performance assure future results, profitable or otherwise.
Why Am I Writing This?
Similar to my Absolute Beginning Steps for Learning SQL, I’m embarking on a number of education journeys in 2017. Another journey, seen here, is algorithmic trading. Loosely known by several monikers (below), this is an intensive field that may be both challenging and rewarding. As self-accountability and sharing, I’m posting what I learn as I progress.
What is Algorithmic Trading? (AKA Automated, Quantitative or Systematic Trading)
Put simply, algorithmic trading (quantitative trading) is trading financial instruments via computers, through logic, programs and preset conditions.
Types of Algorithmic Trading
For the purposes of this post, there are two types of algorithmic (algo) trading. 1) High frequency trading (HFT), and, 2) Low frequency trading.
High Frequency Trading (HFT): Speed is the absolute and expensive edge. Co-located servers in New York and Chicago run proprietary code on custom computer hardware, relying on fiber and microwave communications for trading movements that happen in less than a second. Having tried a variant of HFT in 2016, saying HFT is difficult is probably the understatement of the decade.
Low Frequency Algorithmic Trading: What my educational journey is going to focus on. For low frequency algorithmic trading, the trader is the edge, further demonstrated by the trading model(s) and strategy or strategies he/she designs, executes and manages.
Taking a step back, if algorithmic trading is computerized via programs and preset conditions, what main kinds of algo trading logic are out there?
Types of Algorithmic Trading Logic
- Macroeconomic news (Examples: Non-Farm payroll reports, FOMC policy minutes, Federal Reserve Beige Book releases)
- Fundamental analysis of a financial instrument (Revenues, Earnings releases, Inventory reports, Supply/demand reports, Free cash flow)
- Statistical Functions (Correlation, Cointegration, Mean reversion)
- Technical analysis of a financial instrument (Moving averages, Stochastics, Relative Strength Index (RSI))
- Market Microstructure (Order flow, volume profiling)
3 Core Values for Developing Algorithmic Trading Robots
- Market-Prudency (Ideas are fundamentally sound from a market and economic perspective.)
- Mathematical Models (Models and strategies are based on sound statistical methods, to maintain positive expectancy and reduce risk of drawdown and loss of capital as able.)
- Low Frequency (Trading frequency of the models and strategies are less than one trade per minute. Thus, the strategies do not depend on the speed or computing capacity of computer hardware and/or software.)
Goal: design and execute algorithmic trading models (robots/strategies) using basic software and hardware that average retail traders can afford.
3 Crucial Tranches of Knowledge for Successful Algorithmic Trading
- Tools (Trading software platforms, Brokers and execution infrastructure, Coding languages and skills, Performance analysis)
- Data (How to access sources, manage, cleanse and use data for algorithmic trading strategies.)
- Design Theory (Combining statistics, finance into focused tasks of product development and management.)
As I start out on this educational journey, I’ll definitely be using the tools below, of which I have no commercial interest in. There may be more tools down the road, but that’s it for now.
- MetaTrader 4
- MQL4 language
- Microsoft Excel