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An online learning algorithm for technical trading
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 ba...
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| Format: | Thesis |
| Language: | English |
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Department of Statistical Sciences
2020
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| _version_ | 1867613159448117248 |
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| access_status_str | Open Access |
| author | Murphy, Nicholas John |
| author2 | Gebbie, Tim |
| author_browse | Gebbie, Tim Murphy, Nicholas John |
| author_facet | Gebbie, Tim Murphy, Nicholas John |
| author_sort | Murphy, Nicholas John |
| collection | Thesis |
| description | 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. [31] 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 offline 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. [19]. The work aims to explore and better understand the interplay between different technical trading strategies from a data-informed perspective. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/31048 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:31:43.046Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/31048 An online learning algorithm for technical trading Murphy, Nicholas John Gebbie, Tim online learning technical analysis portfolio selection back-testing statistical arbitrage overfitting Johannesburg Stock Exchange 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. [31] 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 offline 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. [19]. The work aims to explore and better understand the interplay between different technical trading strategies from a data-informed perspective. 2020-02-12T13:03:35Z 2020-02-12T13:03:35Z 2019 2020-02-12T12:15:09Z Master Thesis Masters MSc http://hdl.handle.net/11427/31048 eng application/pdf Department of Statistical Sciences Faculty of Science |
| spellingShingle | online learning technical analysis portfolio selection back-testing statistical arbitrage overfitting Johannesburg Stock Exchange Murphy, Nicholas John An online learning algorithm for technical trading |
| thesis_degree_str | Master's |
| title | An online learning algorithm for technical trading |
| title_full | An online learning algorithm for technical trading |
| title_fullStr | An online learning algorithm for technical trading |
| title_full_unstemmed | An online learning algorithm for technical trading |
| title_short | An online learning algorithm for technical trading |
| title_sort | online learning algorithm for technical trading |
| topic | online learning technical analysis portfolio selection back-testing statistical arbitrage overfitting Johannesburg Stock Exchange |
| url | http://hdl.handle.net/11427/31048 |
| work_keys_str_mv | AT murphynicholasjohn anonlinelearningalgorithmfortechnicaltrading AT murphynicholasjohn onlinelearningalgorithmfortechnicaltrading |