From the Web
1. A High Frequency Trader’s Apology, Pt 1 and Pt 2: a decent insider’s explanation for non-HFT peeps
2. John Nolan on the State of Hardware Acceleration with GPUs/FPGAs, Parallel Algorithm Design: an industry veteran discusses recent trends the hardware side of accelerating trading
3. [Disclosure: Self-plug on ] how the existence of HFT undermines the efficient markets hypothesis
How Tos and Examples
4. A Primer on algorithmic game theory by Tim Roughgarden of Stanford
5. Ziptrader: an academic example of an agent-based system
6. Systematic Investor on Regime Detection: “Regime Detection comes handy when you are trying to decide which strategy to deploy. For example there are periods (regimes) when Trend Following strategies work better and there are periods when Mean Reversion strategies work better.”
7. A bestiary of algorithmic trading strategies
8. How to build strategies via OpenQuant, but actually a good general discussion
9. Black-Scholes in Hardware: Describes how to migrate the design and implementation of the Black-Scholes model from software to hardware (FPGA).
10. Quantivity on how to learn algorithmic trading Pt1, Pt2, Pt3
11. Jesse Spauld on How I made 500k with machine learning and HFT
Academic Courses
12. A Financial econometrics with intro to R course
13. Intro to algorithmic trading strategies: a course on the topic and a earlier version here
14. A number of courses on coursera.org
From Regulators and Market Commentators
15. The Future of Computer Trading in Financial Markets: A recent UK regulator working paper on HFT and financial stability
16. Anything by Nanex, a high frequency data vendor who actually looks for patterns in the data, to find out what is going on
Important Articles From Academia
17. High Frequency Trading and Price Discovery: “examine[s] the role of high-frequency traders (HFT) in price discovery and price efficiency. Overall HFT facilitate price efficiency by trading in the direction of permanent price changes and in the opposite direction of transitory pricing errors on average days and the highest volatility days. This is done through their marketable orders. In contrast, HFT liquidity-supplying non-marketable orders are adversely selected in terms of the permanent and transitory components as these trades are in the direction opposite to permanent price changes and in the same direction as transitory pricing errors.”
18. Trading Networks: “network metrics can be used to characterize the interaction between latent order submission strategies and transaction prices, trading volume, intertrade duration, and liquidity.”
19. Common Mistakes when Applying Computational Intelligence and Machine Learning to Stock Market modelling: “examines some of the more common mistakes, namely dataset insufficiency; inappropriate scaling; time-series tracking; inappropriate target quantification and inappropriate measures of performance. The rationale that leads to each of these mistakes is examined, as well as the nature of the errors they introduce to the analysis / design. Alternative ways of performing each task are also recommended in order to avoid perpetuating these mistakes, and hopefully to aid in clearing the way for the use of these powerful techniques in industry.”
20. A Theory for Market Impact: How Order Flow Affects Stock Price
21. The Almgren-Chriss Framework: “Optimal Execution of Portfolio Transactions”
22. Evidence-based studies of unregulated market making and high frequency trading: an academic bibliography from Themis Trading, HFT naysayers
Legitimately interesting news coverage
23. Bond Trading Loses Some Swagger Amid Upheaval: More bond traders are actually programmers than ever before
24. Execution services more important than algos
25. Why are investors fleeing equities: Hint: It’s not the computers
And a special bonus:
26. High-Speed Trading No Longer Hurtling Forward: The NYT discloses that HFT is now cooling down?!?
Filed under: Software Tagged: HFT, High-frequency trading, Investing, John Nolan, Nanex
