complexity science & the art of trading
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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 - PowerPoint PPT PresentationTRANSCRIPT
ByPaul Cottrell, BSc, MBA, ABD
Complexity Science &
The Art of Trading
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
Introduction
The study of complex systems Using simple rules for agentsSelf organizing behavior Interactions that have a magnifying effect
What is Complexity Science?
Agents are the atoms of the complex systemCan be programmed to interact with
External environmentInternal environment
Complex behavior can emergeWith simple interaction rule
Agents should be able to morph their behavior (DNA)Exhibits evolutionary pathways and allows for
diversity
Agents
Simple AutomataIs a cybernetic systems
Does not evolve and communicate with environment
Complex AutomataIs an evolving system
Communicates with internal and external environment
Automata
Simple Automata & Complex Automata
Simple Automata
Complex Automata
How do we optimize trading strategies?Local optimum Global optimum
Current strategies Compare trading strategies with P/L
performanceMACD vs. RSI, MA vs. Fibonacci Problem with this optimization method
The selection set is limitedNot very efficient to evaluate
For all possible parameter options
The Optimization Problem
Ant Algorithms A programming method were an agent crawls
the landscape to find a solutionStores 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.
Simulation Methods
Stochastic SimulationRandom 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 AlgorithmsTakes a solution population and ranks them
Combines the top 10% to produce possible better solutions
Other Simulation Methods
ANN vs. GA
Genetic AlgorithmArtificial Neural Network
But strategies can combine both methods.
The problemHow to pick the best trading strategy?
Use complexity scienceLet 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 dataBut verification should be conducted with out-of-sample testing.
Strategy Filtering
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
Conclusion