Complexity Science & The Art of Trading

1. Complexity Science & The Art of Trading By Paul Cottrell, BSc, MBA, ABD

2. Introduction • Author • Complexity Science, Behavioral Finance, Dynamic Hedging, Financial Statistics, Chaos Theory • Proprietary Trader • Energy and Currency • Dissertation • Dynamically Hedging Oil and Currency Futures Using Receding Horizontal Control and Stochastic Programming

3. What is Complexity Science? • The study of complex systems • Using simple rules for agents • Self organizing behavior • Interactions that have a magnifying effect

4. Agents • Agents are the atoms of the complex system • Can be programmed to interact with • External environment • Internal environment • Complex behavior can emerge • With simple interaction rule • Agents should be able to morph their behavior (DNA) • Exhibits evolutionary pathways and allows for diversity

5. Automata • Simple Automata • Is a cybernetic systems • Does not evolve and communicate with environment • Complex Automata • Is an evolving system • Communicates with internal and external environment

6. Simple Automata & Complex Automata Complex Automata Simple Automata

7. The Optimization Problem • How do we optimize trading strategies? • Local optimum • Global optimum • Current strategies • Compare trading strategies with P/L performance • MACD vs. RSI, MA vs. Fibonacci • Problem with this optimization method • The selection set is limited • Not very efficient to evaluate • For all possible parameter options

8. Simulation Methods • Ant Algorithms • A programming method were an agent crawls the landscape to find a solution • Stores the location of the solution with a pheromone trail. • Strongest pheromone scent is considered the most optimized. • Does have a local optimum issue in certain cases • Need to run simulation multiple times to get optimum convergence.

9. Other Simulation Methods • Stochastic Simulation • Random select parameters and add a stochastic process to evaluate P/L change. • Artificial Neural Network • Used to determine optimum weights for inputs to produce best trading signal • Genetic Algorithms • Takes a solution population and ranks them • Combines the top 10% to produce possible better solutions

10. ANN vs. GA Artificial Neural Network Genetic Algorithm But strategies can combine both methods.

11. Strategy Filtering • The problem • How to pick the best trading strategy? • Use complexity science • Let the agents provide a solution. • Program simple trading rules for the automata • Random selection of risk taking personality • Start with equal equity in account • Let agents select a particular strategy from defined strategy landscape • Let agents learn which strategies work and which do not • Store working strategies in a data array with parameters used in “winning strategy” • Need many simulations to develop a global optimum. • Can implement ANT, ANN, and GA methods. • Price action can be a stochastic simulation or historic data • But verification should be conducted with out-of-sample testing.

12. Conclusion • Complexity Science canhelp with optimization • Brute force with determining best strategy is not computationally efficient • Agents can be programmed with certain personalities and can evolve through time • Can gain unexpected knowledge about optimized parameters for certain trading strategies. • Allows for machine learning