  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.
