Sign in

A student of life, risks, and markets. I write short essays and tutorials on life, games of chance, and trading.
Photo by Brett Jordan on Unsplash


Every trading system requires at least two principal components, whether explicitly separated or not: an alpha-seeking, signal generation component that is concerned about the direction (long or short) of a trade, and an execution component that actually interacts with the market to fulfill those signals by submitting actual orders.

We can therefore decompose every trade into two components:

True PnL of trade = Gross PnL from trade + Cost of executing trade

Photo by Michael Dziedzic on Unsplash


To predict prices, we build models that attempt prescience. Typically, our models focus on one central theme (factor) that may influence prices, such as momentum, value, mean-reversion, etc. We then make predictions based on those models.

Due to the Sisyphean task of model-building, most traders specialize in the understanding of one theme (factor) and their strategies correspondingly make bets only from the predictions of one model revolving around that factor.

The purpose of this article is to first introduce a novel way of thinking about prices and price changes and then argue against trading with a single model with a…

Photo by Maksym Tymchyk on Unsplash


Python is a relatively popular choice of language for the implementation of trading algorithms on several popular algorithmic trading platforms.

In trading algorithms, speed is a crucial factor, and hence computational efficiency is a much sought-after area of optimization. In the spirit of maximizing our computational efficiency, we will identify the type of bottlenecks where we can utilize concurrency and parallelism to reduce the time required for these tasks to be completed.

This article will explore concurrency and parallelism in Python, and provide snippets of code for the application of these topics in quantitative trading. Please note that the examples…

Photo by Cassie Matias on Unsplash


In pairs trading, we are expecting a pair of securities to share some relationships between them. These relationships can be economical, statistical, or behavioral in nature.

Due to these relationships, there are some long-term behaviors that we can expect from them. However, in the short run, these relationships can diverge from their expected long-term behavior, however fleeting or temporary. When they do diverge, they represent an opportunity for us to trade in the direction of their expected long-term behavior.

One such long-term behavior that a pair might share, is that their price spread has some mean that it reverts to…

Photo by Tim Johnson on Unsplash


If you are a portfolio manager, an investor in a portfolio, or a capital provider, it is of great interest to you to determine if the fees you are paying for the strategy or portfolio is worth your buck. Fees can take on many forms, the active fee you pay for a hedge fund manager to implement his fund’s portfolio with your capital, the salary you pay for traders to come up with trading strategies for your portfolio, or even just the passive fees you pay for an investing vehicle (think robo-advisors and the like).

Portfolio Performance, Returns, and Risks

For simplicity and consistency, we…

Photo by Bonneval Sebastien on Unsplash


The purpose of this article is to share how institutions achieve high-complexity strategies through thoughtful design — modularising common models to promote reusability and scalable complexity. My hopes are that readers will find modularisation to be broadly helpful in the design and implementation of trading strategies, especially in teams but even among individuals.

Gall’s Law And Trading Systems

Borrowing from process design, Gall’s Law states:

“A complex system that works is invariably found to have evolved from a simple system that worked. The inverse proposition also appears to be true: A complex system designed from scratch never works and cannot be made to work. You…

Photo by Clem Onojeghuo on Unsplash

My Experience

I’ve been in a large public corporation of tens of thousands of employees, where I felt insignificant and felt like it was where ideas went to die. I left to build my own tech start-up, where, like many others before me, I thought I had a radical technology-driven idea to change the world.

Alas, on the cusp of significant funding and hiring plans that would have kickstarted it all, Covid19 had better plans for me. VCs that were previously warm and receptive went silent in the economic uncertainty. …

Photo by Clay Banks on Unsplash


Do not construe anything in this article as investment advice. It is far easier to lose money in the markets than it is to make money. Mistakes made in quantitative trading are also often compounded because of the algorithmic nature of things. Carry a healthy amount of skepticism with everything you read on the subject of markets.


My Intentions For This Article

Quantitative trading is the act of finding inefficiencies in the market that are persistent and from which we can profit from. The purpose of this article is to present a tutorial for which one can find such persistent inefficiencies and act on them.

Photo by Dan Dennis on Unsplash


My Intentions For This Article

Inaction in any field will result in an impossibility of success, yet the haphazard application of theories, indicators, and tools in this field will also lead to an improbable chance of success. The purpose of this article is to first discuss market inefficiency, which naturally leads us to identify where the sources of profitability in quantitative trading are, and then argue for their existence and persistence. Finally, I will suggest that the best way to beat the markets is through applications of data science.

In Economic Departments, Dropped Bills Remain

Economic Theory on Market Efficiency

There still remains a notion of market efficiency in economic theory. Market efficiency centers around the…

Photo by Nicholas Cappello on Unsplash


My Intentions For This Article

The purpose of this article is to first agree on the very subject we are going to have a long discussion about and then present an argument as to why this field is perceived to be so impenetrable. Finally, why this impenetrability should result in healthy skepticism towards everything you read in this field.

On the Subject

The Myth Of The Kingdom Of Geniuses

There is something mystical about quantitative trading, perhaps because it is an amalgamation of many respected disciplines into a single endeavor; or that it conjures the image of unbridled geniuses producing an algorithm that is so wise and sibylline that its PnL values unfailingly increase in…

Oscar Lee Feng Qi

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store