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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


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
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 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
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.
  • 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