# Learning the dynamics of technical trading strategies

## TL;DR

This paper employs an adversarial online learning algorithm to optimize and analyze technical trading strategies on the Johannesburg Stock Exchange, testing their arbitrage potential and understanding their dynamics through advanced statistical and machine learning methods.

## Contribution

It introduces a novel application of adversarial online learning to optimize trading strategies and uses unsupervised learning to analyze their population dynamics and arbitrage potential.

## Key findings

- Intraday strategies are not falsified as arbitrages after costs.
- Daily strategies fail the arbitrage test after costs.
- The approach estimates overfitting risk and trading costs effectively.

## Abstract

We use an adversarial expert based online learning algorithm to learn the optimal parameters required to maximise wealth trading zero-cost portfolio strategies. The learning algorithm is used to determine the relative population dynamics of technical trading strategies that can survive historical back-testing as well as form an overall aggregated portfolio trading strategy from the set of underlying trading strategies implemented on daily and intraday Johannesburg Stock Exchange data. The resulting population time-series are investigated using unsupervised learning for dimensionality reduction and visualisation. A key contribution is that the overall aggregated trading strategies are tested for statistical arbitrage using a novel hypothesis test proposed by Jarrow et al. (2012) on both daily sampled and intraday time-scales. The (low frequency) daily sampled strategies fail the arbitrage tests after costs, while the (high frequency) intraday sampled strategies are not falsified as statistical arbitrages after costs. The estimates of trading strategy success, cost of trading and slippage are considered along with an online benchmark portfolio algorithm for performance comparison. In addition, the algorithms generalisation error is analysed by recovering a probability of back-test overfitting estimate using a nonparametric procedure introduced by Bailey et al. (2016). The work aims to explore and better understand the interplay between different technical trading strategies from a data-informed perspective.