Quantocracy’s Daily Wrap for 03/02/2024

This is the final article in our recent stat arb series. The previous articles are linked below: A short take on stat arb trading in the real world A general approach for exploiting stat arb alphas Ideas for crypto stat arb features Quantifying and combining crypto alphas A simple and effective way to manage turnover and not get killed by costs How to model features as expected returns Building

Let me present our recent research paper with Parviz Rakhmonov on the stochastic volatility model for Factor Heath-Jarrow-Morton (HJM) interest rate framework (available on SSRN: Stochastic Volatility for Factor Heath-Jarrow-Morton Framework). Factor Heath-Jarrow-Morton (HJM) model Under the risk-neutral measure, the interest rate curve can be conveniently modeled using the forward curve f_t(tau)

In a previous article I made a detailed comparison of Mathematica and Python and tried to identify areas where the former excels. Despite the many advantages of the Python technology stack, I was able to pinpoint a few areas in which I think Mathematica holds the upper hand. Whether those are sufficient to warrant the investment of time and money required to master the Wolfram Language is another

Multi-timeframe (MTF) analysis lets traders build powerful intraday trading strategies. It does this by analyzing asset prices during different timeframes throughout the trading day. The problem is most people get MTF wrong. It requires a vector-based backtest to speed up the operations making it easy to introduce look-ahead bias. When a backtest introduces look-ahead bias, it will overstate

Conventional wisdom can be defined as ideas that are so accepted that they go unquestioned. Unfortunately, conventional wisdom is often wrong. Two examples are that millions of people once believed the conventional wisdom that the Earth is flat, and millions also believed that the Earth is the center of the universe. An example of conventional wisdom in investing is: Dont just stand