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Forecasting Structural Breaks with Application to Algorithmic Trading

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47th PyData London meetup lighting talk slides.

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Forecasting Structural Breaks with Application to Algorithmic Trading

  1. 1. Forecasting Structural Breaks in Application to Algorithmic Trading Results and Improvements King Fung Wong
  2. 2. Hidden Markov Model • Initial Transition Matrix • Emission Matrix • Transition Matrix, e.g. 𝑃00 𝑃01 𝑃10 𝑃11
  3. 3. Transition matrices as alpha signals • Assuming features n.i.i.d., emitted by the hidden states • Features have predictive power on market dynamics • Change in transition probabilities imply structural breaks in shift of market regime • Long if the transition probability goes up, short vice versa
  4. 4. Further Improvements • Hyperparameters tuning (length of rolling window, baseline setting, trading rules etc.) • Features do not behave n. i. i. d. • No bootstrapping, stress period to backtest (doubling trade size, avoid overfitting etc.) • Does it create/ contribute to “a disorderly market”? MiFID ii
  5. 5. Thank you • king.wong@cass.city.ac.uk

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