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Algorithmic Trading: Strategies to Avoiding Market Pitfalls
Markets are now almost always electronic, and as a result of decimalization, algorithmic trading is booming. Algorithmic trading (or "algo trading" as it is referred) is the use of computer programs (such as Excel or MatLab) for entering and executing trading orders with the computer algorithm deciding on certain aspects of the order, such as the timing, price, or even the final quantity of the order. Algorithmic trading may be used in any investment strategy, including market-making, inter-market spreading, arbitrage, or pure speculation (including trend following). The investment decision and implementation may be augmented at any stage with algorithmic support or may operate completely automatically. "Algorithmic Trading" provides an understanding of the core concepts in quantitative trading, an understanding of the process of using mathematics and statistics to analyze the profitability of a trading model, a "hands on" experience of how backtesting is done, and an understanding of pair trading in stocks, ETFs, futures and currencies. Chan shows investors how to use Excel and MatLab to build their own algo trading tools, as well as FX1, an FX trading platform growing in popularity.He also shows investors how to conduct quantitative research and analysis, and turn quantitative trading strategies into profits using stocks, ETFs, futures, and other financial instruments.
224 pages, Kindle Edition
First published January 1, 2013
175 people are currently reading
1212 people want to read
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Displaying 1 - 10 of 10 reviews
November 28, 2024
Algorithmic Trading a true pleasure to read and my new go-to recommendation for someone interested in mid-frequency quant.
Chan’s blog is well known amongst practitioner quants (it is often one of the early resources one encounters). But I had mixed feelings about the first book – Quantitative Trading – it was too focused on nuts-and-bolts practicality, with a lot of ink spent describing different data vendors (many of which were obsolete at the time of reading).
Algorithmic Trading instead focuses more on the actual trading strategies: Chan walks through several real strategies, outlining the critical theory but also providing the implementation in Matlab.
While many of the tools he introduces may not be entirely new to people familiar with basic statarb ideas, Chan has a knack for identifying subtleties that other texts often gloss over (for example using log prices vs prices to construct mean-reverting spreads) and lucidly explaining the implications of various design choices. He contrasts multiple tools against each other both theoretically and experimentally – taking a step back, it is very helpful to see these examples of experiment design, which apply far beyond the specific strategies presented in the book.
Chan’s blog is well known amongst practitioner quants (it is often one of the early resources one encounters). But I had mixed feelings about the first book – Quantitative Trading – it was too focused on nuts-and-bolts practicality, with a lot of ink spent describing different data vendors (many of which were obsolete at the time of reading).
Algorithmic Trading instead focuses more on the actual trading strategies: Chan walks through several real strategies, outlining the critical theory but also providing the implementation in Matlab.
While many of the tools he introduces may not be entirely new to people familiar with basic statarb ideas, Chan has a knack for identifying subtleties that other texts often gloss over (for example using log prices vs prices to construct mean-reverting spreads) and lucidly explaining the implications of various design choices. He contrasts multiple tools against each other both theoretically and experimentally – taking a step back, it is very helpful to see these examples of experiment design, which apply far beyond the specific strategies presented in the book.
October 4, 2022
This is a really good book, which the author teaches the basics of algo trading.
Author focuses most on mean reversion strategies, teaching about cointegration tests, Hurst exponent, etc. There is a point that I don't agree with him, he likes mean reversion more than momentum because he thinks momentum might break up suddenly. Well, there are studies showing that for centuries momentum strategies works, and mean reversion strategies might with a single rare loss wipe your account. At least the author says we must indeed be aware of this. Showing that correlations/cointegrations can be broken.
He advocates more ETF than stocks for mean reversion, due to the possibility that single events on a stock may disrupt the cointegration. Anyway, it has some interesting results on simple mean-reversion models on relative returns. The problem, I think, is that probably the strategies here might even be unprofitable due to slippage and commissions. Teaches how to use the Kalman filter to dynamically update the expected price of an instrument.
Author have some ideas for using an instrument with cointegrates with spot price/return of a commodity, to extract the roll return of the commodity future by shorting it during backwardation or longing it during contango, showing also that momentum models thrive on black swan events (unlike mean-reversion).
He says that intraday momentum strategies has many advantages, pointing to some possible strategies for doing it, like order flow, stop hunting, HFT imbalance, rebalancing of ETFs/indexes, breakouts and opening gap strategies.
Finally, on risk indicators, he likes half-Kelly formula (more explained on his other book), but says that Monte Carlo simulations are better if strategy returns are fat-tailed (trend following strategies). There is a special topic on "Constant proportion portfolio insurance" method
Great book, I highly recommend it.
Average reading time: 6h30m
Author focuses most on mean reversion strategies, teaching about cointegration tests, Hurst exponent, etc. There is a point that I don't agree with him, he likes mean reversion more than momentum because he thinks momentum might break up suddenly. Well, there are studies showing that for centuries momentum strategies works, and mean reversion strategies might with a single rare loss wipe your account. At least the author says we must indeed be aware of this. Showing that correlations/cointegrations can be broken.
He advocates more ETF than stocks for mean reversion, due to the possibility that single events on a stock may disrupt the cointegration. Anyway, it has some interesting results on simple mean-reversion models on relative returns. The problem, I think, is that probably the strategies here might even be unprofitable due to slippage and commissions. Teaches how to use the Kalman filter to dynamically update the expected price of an instrument.
Author have some ideas for using an instrument with cointegrates with spot price/return of a commodity, to extract the roll return of the commodity future by shorting it during backwardation or longing it during contango, showing also that momentum models thrive on black swan events (unlike mean-reversion).
He says that intraday momentum strategies has many advantages, pointing to some possible strategies for doing it, like order flow, stop hunting, HFT imbalance, rebalancing of ETFs/indexes, breakouts and opening gap strategies.
Finally, on risk indicators, he likes half-Kelly formula (more explained on his other book), but says that Monte Carlo simulations are better if strategy returns are fat-tailed (trend following strategies). There is a special topic on "Constant proportion portfolio insurance" method
Great book, I highly recommend it.
Average reading time: 6h30m
August 13, 2025
Good introduction; would be easier to digest if it wasn't in MATLAB
November 3, 2013
Doesn't spend a lot of time thinking about the economics of the strategies constructed and therefore what risk is actually being taken on, but otherwise a very interesting and clearly-written book. Covers a lot of ground and has solid description of the design process and rationale for the strategies described.
January 9, 2018
Not so great book. May be a book with python examples would be helpful
March 15, 2024
I thought that this book did a good job of presenting difficult concepts in a more approachable way, but I have a hard time getting over how bad the code is --maybe that's just MATLAB, but I think rewriting the code samples in this book would go a long way.
May 1, 2018
A bit shallow.
January 7, 2019
Very nice. Both scientific and practical.
April 4, 2022
3.75/5
July 2, 2024
my first quant finance book and one of the few books on how to actually create trading strategies using the actual statistics to backup your findings
Displaying 1 - 10 of 10 reviews