By Paul Cottrell, BSc, MBA, ABD

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

The study of complex systems Using simple rules for agents Self organizing behavior Interactions that have a magnifying effect

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

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

Simple Automata Complex Automata

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

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.

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

Genetic Algorithm Artificial Neural Network But strategies can combine both methods.

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.

Complexity Science can help 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