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Learning the dynamics of technical trading strategies

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  • Nicholas Murphy
  • Tim Gebbie

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.

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  • Nicholas Murphy & Tim Gebbie, 2019. "Learning the dynamics of technical trading strategies," Papers 1903.02228, arXiv.org, revised Dec 2019.
    • Handle: RePEc:arx:papers:1903.02228
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    Cited by:

    1. Joel da Costa & Tim Gebbie, 2020. "Learning low-frequency temporal patterns for quantitative trading," Papers 2008.09481, arXiv.org.
    2. Ivan Jericevich & Patrick Chang & Tim Gebbie, 2020. "Comparing the market microstructure between two South African exchanges," Papers 2011.04367, arXiv.org.
    3. Andrew Paskaramoorthy & Terence van Zyl & Tim Gebbie, 2020. "A Framework for Online Investment Algorithms," Papers 2003.13360, arXiv.org.

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