Literature Review on the Black Box

Rishi K. Narang’s book Inside the Black Box: A Simple Guide to Quantitative and High Frequency Trading (Hoboken, NJ: John Wiley & Sons, 2013) proposes a generic model of a black box trading system.


Narang’s generic model features: (1) an alpha module for alpha generation; (2) a risk module for risk management; and (3) a transaction cost module for costs. These feed into (4) a portfolio management module, which then feeds into (5) an execution module.


High frequency trading’s innovation was to alter the alpha /risk / portfolio equation through changes to transaction cost and execution strategies.


As an exercise I used Narang’s categories from his generic module to organise some Amazon Kindle trading books:


  • Alpha module: 83 books.
  • Risk module: 65 books.
  • Transaction Cost module: 22 books
  • Portfolio Construction module: 38 books
  • Execution module: 85 books


There is some overlap in books between each of the categories. I also used some additional categories:


  • Algorithmic trading: 80 books
  • Trading strategies: 229 books
  • Trading psychology (including therapeutic manuals): 174 books
  • Funds: 76 books


A couple of observations from this initial cumulative literature review of black box trading systems:


  • Most of the public trading literature deals in an unstructured way with alpha strategies or with trading strategies – the overwhelming emphasis is on momentum and trend-following strategies that high-frequency trading has now disrupted. Some of these books are still variants on trading strategies from the pre-dotcom 1990s. Some publishers recycle themes using art design, new authors, and small, cumulative information. In contrast, some of the most interesting information comes from outlier authors. I have screened out most of the cheap Kindle books that now add noise to new retail traders.
  • The real sources of institutional or proprietary alpha are only hinted at in the publicly available trading literature – and is more often glimpsed in investigative journalism accounts. Many trading books are written by pseudo-retail traders who have developed white box trading systems using basic technical analysis, risk, and money management rules. Jack Schwager’s Market Wizards series remains influential and significant in part because it offers a glimpse of how professional traders and successful money managers actually think.
  • The literature on trading psychology developed in part as a way to deal with the methodological limitations of the Edwards and Magee school of technical analysis that focused on signals and indicators.
  • The portfolio construction literature emerged from Harry Markowitz’s work in corporate finance, and later, David Swensen’s development of the Yale endowment model of foundation investment.
  • The risk literature covers either traditional corporate finance models, value at risk models, post-Taleb extreme value models on tail risk, or recent applications of Bayesian probabilities to portfolio models.
  • The funds literature covers white box versions of hedge fund, mutual fund, and sovereign wealth fund strategies.
  • The algorithmic trading literature covers general overviews, order types, white box strategies, and some computer science / programming manuals on algorithms. There is very little publication of actual trading algorithms or code. A computer science / programming background is helpful for quantitative finance.
  • The rise of high frequency trading has led to a greater focus on transaction costs and execution as sources of competitive edge. This emphasis differs from the Edwards and Magee focus on signals and indicators that provide set-ups for possible trades. It’s also what is missing from much of the publicly available literature on trading systems (which itself is very fractured). Thus, most trading books suffer from transaction / execution cost decays.


This initial literature review suggests the following strategies for future systems development:


  • Continue to find potential sources of alpha whilst noting the patterns of alpha decay (i.e. how alpha ends).
  • Decompose the alpha-risk-portfolio literature into checklists, an expert system, portfolio screens, or rules with an awareness of Bayesian probabilities (= edge / positive expectancy as ‘go / no go’ criteria: if there is no real edge then don’t trade – and this also involves understanding other traders and known trading algorithms). Eventually, this ‘explicit’ codification may be integrated with an off-the-shelf machine learning system such as David Aronson and Timothy Masters’ TSSB software.
  • Screen out the trading strategies that are now unsuccessful in the current market environment (strategy decay): focusing on transaction / execution costs will be helpful.
  • Continue to do developmental / therapeutic work for cultivating expertise and improving decision heuristics / judgment.
  • Search for new opportunities that involve more competitive transaction / competitive costs (although this is difficult as an Australian-based retail trader due to broker / exchange / platform  limitations).