“There are very few things which we know, which are not capable of being reduced to a mathematical reasoning. And when they cannot, it’s a sign our knowledge of them is very small and confused. Where a mathematical reasoning can be had, it’s as great a folly to make use of any other, as to grope for a thing in the dark, when you have a candle standing by you.” —Of the Laws of Chance, Preface (1692) John Arbuthnot (1667–1735)

Sisyphus Paradigm:

Sisyphus paradigm was a king who was punished by the gods for his deceitfulness by being forced to push a boulder up a hill for eternity. I like the analogy because thats exactly how I currently feel.

Meta-Strategy Paradigm

Book is a research manual for teams, not individuals. Proposes setting up a research factor as well as various stations of the assembly line. The role of each quant is to specialize in a task and be the best there is it at it and to have a holistic view of the entire process.

Makes analogy to gold and silver extraction where things aren’t as they used to and that the true alpha thats left nowadays is microscopic but much more abundant than macroscopic alpha. The methods now require much more capital intensive industrial methods and heavy ML tools.

Production Line Participants

• Data Curators: collecting, cleaning, indexing, storing, adjusting and delivering all data to the production chain
• Feature Analysts: transforming raw data into informative signals which should have predictive power over financial variables. Feature analysts don’t develop strategies, they collect and catelogue libraries of finds that can be useful to a multiplicity of stations. Team members are experts in information theory, signal extraction and processing, visualiztion, labeling, weighting, classifiers and feature importance techniques.
• Strategists: data scientists with deep knowledge of financial markets take informative features and transform them into actual investment algos. Strategist will parse through the libraries of features looking for ideas to develop a strategy.
• Backtesters: data scientists with deep understanding of empirical and experimental techniques assess the profitability of a strategy under various scenarios.
• Deployment Team: The deployment team is tasked with integrating the strategy code into the production line. Team members are algorithm specialists and hardcore mathematical programmers. As production calculations often are time sensitive, this team will rely heavily on process schedulers, automation servers (Jenkins), vectorization, multithreading, multiprocessing, graphics processing unit (GPU-NVIDIA), distributed computing (Hadoop), high-performance computing (Slurm), and parallel computing techniques in general.

Portfolio Oversight

1. Embargo: strategy is run on data observed after the end date of the backtest. If embargoes performance is consistent with the backtest results, the strategy is promoted to the next stage.
2. Paper Trading: Strategy run on live, real time feed until we have enough evidence that the strategy performs as expected
3. Graduation: manages a real position. Performance is evaulated precisely (risk, returns, costs)
4. Reallocation: based on performance based on the production performance, the allocation to graduated strategies is re-assessed frequently and automatically in the context of a diversified portfolio. In general, a strategy’s allocation follows a concave function. The initial allocation (at graduation) is small. As time passes, and the strategy performs as expected, the allocation is increased. Over time, performance decays, and allocations become gradually smaller.
5. 5. Decommission: Eventually, all strategies are discontinued. This happens when they perform below expectations for a sufficiently extended period of time to conclude that the supporting theory is no longer backed by empirical evidence.