# Trading algorithms with learning in latent alpha models

arXiv: https://arxiv.org/abs/1806.04472 · HTML: https://mlsanity.com/paper/1806.04472/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2018.

## Built on by (most cited first, 4 of 4)

- [Universal Trading for Order Execution with Oracle Policy Distillation](https://mlsanity.com/paper/2103.10860/) (2021)
- [Deviations from the Nash equilibrium in a two-player optimal execution game with reinforcement learning](https://mlsanity.com/paper/2408.11773/) (2024)
- [Deep reinforcement learning for optimal trading with partial information](https://mlsanity.com/paper/2511.00190/) (2025)
- [When a Correct Reward Is Not Enough: Diagnosing and Guiding PPO in an Analytically Solved Broker-Trader Game](https://mlsanity.com/paper/2610.03598/) (2026)

## Abstract (arXiv)

Alpha signals for statistical arbitrage strategies are often driven by latent factors. This paper analyses how to optimally trade with latent factors that cause prices to jump and diffuse. Moreover, we account for the effect of the trader's actions on quoted prices and the prices they receive from trading. Under fairly general assumptions, we demonstrate how the trader can learn the posterior distribution over the latent states, and explicitly solve the latent optimal trading problem. We provide a verification theorem, and a methodology for calibrating the model by deriving a variation of the expectation-maximization algorithm. To illustrate the efficacy of the optimal strategy, we demonstrate its performance through simulations and compare it to strategies which ignore learning in the latent factors. We also provide calibration results for a particular model using Intel Corporation stock as an example.