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Complexity Science & The Art of Trading. By Paul Cottrell, BSc, MBA, ABD. Introduction. Author Complexity Science, Behavioral Finance, Dynamic Hedging, Financial Statistics, Chaos Theory Proprietary Trader Energy and Currency Dissertation

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Complexity science the art of trading

Complexity Science & The Art of Trading

By

Paul Cottrell, BSc, MBA, ABD


Introduction
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


What is complexity science
What is Complexity Science?

  • The study of complex systems

    • Using simple rules for agents

    • Self organizing behavior

    • Interactions that have a magnifying effect


Agents
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


Automata
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


Simple automata complex automata
Simple Automata & Complex Automata

Complex Automata

Simple Automata


The optimization problem
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


Simulation methods
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.


Other simulation methods
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


Ann vs ga
ANN vs. GA

Artificial Neural Network

Genetic Algorithm

But strategies can combine both methods.


Strategy filtering
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.


Conclusion
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