Modeling at Scale in Systematic Trading

1000..1100 Modeling at Scale in Systematic Trading

• Scott Clark, CEO, SigOpt
• Nick Payton(organizer)
• MOE, Metric Optimization Engine
• from Cornell Univ
• optimization
• Bayesian global optimization

Asset Management

• optimization
• The Quants Run Wall Street Now => WSJ
• $300B+ Assets under managements
• sigopt
• two sigma(asset manager) <=> sigopt

Lessons

1. Invest in a reproducible process
2. Balance flexibility with standardization
3. Dividde labor between humans & machines
4. Maximize resource utilization
5. Prioritize performance (broadly)

1. Invest a reproducible process

• 5 pillars: data, modeling, simulation, optimization, execution
• Data
  • historical stock prices
  • company data
  • comapny news
  • social data
  • location data
  • satellite data (what shows in the parking lot)
• modeling
  • picking the right tool for the job
• simulation
  • backtest must avoid:
    • overfitting bias
    • look ahread bias
    • survivorship bias
    • p-hacking bias
    • metric bias
  • defining the methods you trust
• optimization
  • how to tune the hyper permimter
  • academic: grid search, random search
  • particle based methods
• execution
  • once you have a model you trust
  • high frequency trading
  • market making
  • statistical arbitrage
  • rebalancing
  • portfolio optimization

Balance flexibility with standardization

• how to continue the advance
• framework:
  • solutions: standard or properietary per firm?
  • innovation: incremental or existential for firm?
  • status: still evolving or fully established?
• modeling:
  • sklearn, pytorch, tensorflow

3. Divide labor between humans & machines

• what humans are bad at:
  • high dimentional optimization
  • tuning the knobs on the airplane
• Hyperparameter Optimization
• Optimizing Optimizor problem
• focus on your model, leave hyperparameter optimization to us
• pic 8, pros and cons with different approach
• random search -> nvidia
• evolutionary algo -> google
• bayesian optimization
• you focus on data, model and backtest
• what is sent: learning rate, number of hidden layers

4. Maximize resource utilization

• build or buy
• well maint product:
  • apache/spark, 19443 stars
  • sheffieldml/gpyopt => 369 stars (sigopt competitor)
• 2 sigma
  • asynchronous parallelization is critical for resource utilization

5. Prioritize performance (broadly defined)

• performance (table stakes)
• better, faster, cheaper
• pic 11. two sigma better result 8x faster
• paper: https://sigopt.com/research
  • A stratified analysis of bayseian optimization methods
  • helping real world problems
  • car classification problem
    • stanford dataset
    • tuning the hyperpa
• pic 12, performance table
• entirely new capabilities

Thank you

• https://sigopt.com/company/careers
• https://sigopt.com/blog
• https://sigopt.com/research
• https://sigopt.com/try-it
• blackbox optimization problem