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Forecasting Structural Breaks with Application to Algorithmic Trading

King Wong
King Wong

47th PyData London meetup lighting talk slides.

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Forecasting Structural Breaks in
Application to Algorithmic Trading
Results and Improvements
King Fung Wong
Hidden Markov Model
• Initial Transition Matrix
• Emission Matrix
• Transition Matrix, e.g.
𝑃00 𝑃01
𝑃10 𝑃11
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
Forecasting Structural Breaks with Application to Algorithmic Trading
Forecasting Structural Breaks with Application to Algorithmic Trading
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

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Forecasting Structural Breaks with Application to Algorithmic Trading

  • 1. Forecasting Structural Breaks in Application to Algorithmic Trading Results and Improvements King Fung Wong
  • 2. Hidden Markov Model • Initial Transition Matrix • Emission Matrix • Transition Matrix, e.g. 𝑃00 𝑃01 𝑃10 𝑃11
  • 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
  • 6. 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

Editor's Notes

  1. Features: bid-ask spread, implied cross rate arbitrage Assuming features n.i.i.d., emitted by hidden states Features have predictive on market dynamics Transition matrix based on fitted time series Change in transition probabilities imply structural breaks in market/ shift of market regime Long if the transition probability goes up, short vice versa