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A high-frequency trading model using Interactive Brokers API with pairs and mean-reversion in Python

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kevinlawler/High-Frequency-Trading-Model-with-IB

 
 

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Purpose

These files are intended for recruiters, headhunters and hiring managers in evaluating my proficiency in Python and looking to fill entry-level roles in automated trading strategy development, quantitative trading/developer/analyst/researcher, portfolio management/analyst, high-frequency trading, data analyst/visualization, and model validation.

Requirements

Key Concepts

At the present moment, this model utilizes statistical arbitrage incorporating these methodologies:

  • Cash-neutral strategy with long-short position
  • Bootstrap the model with historical data to derive usable strategy parameters
  • Bootstrapping takes some time, we need to bridge historical data with recent tick data
  • Transforming inhomogenous to homogeneous time series of 1 second intervals
  • Selection of highly-correlated stock pairs
  • Using volatility ratio to detect trending, mean-reversion or Brownian motion
  • Fair valuation by using beta of average 5 minute look-back price window
  • Fair valuation of stock A against more than 1 security (stock B, C...) is possible
  • Trade decisions based on mean-reversion, volatility ratio and deviation from fair prices

Other functionalities:

  • Generate trade signals and place buy/sell orders based on every incoming tick data
  • Re-evaluating beta every 30 seconds to account for small regime shifts

And greatly inspired by these papers:

And book:

Future Enhancements

I would love to extend this model in the unforeseeable future:

  • Extending to more than 2 securities and trade on optimum prices
  • Generate trade signals based on correlation and co-integration
  • Using PCA for next-period evaluation
  • Back-testing with zipline
  • Maybe include vector auto-regressions
  • Account for regime shifts (trending or mean-reverting states)
  • Account for structural breaks
  • Using EMA kernels instead of a rectangular one
  • Use and store rolling betas and standard deviations
  • Add in alphas(P/E, B/P ratios) and Kalman filter prediction
  • Storing of tick data in MongoDb for future back-tests.

What It Can Do

  • Establish connection to broker and request for tick data
  • Generate trade signals on each incoming tick
  • Open position with buy/sell orders, and reverse position
  • Display and update chart in real-time of stock A's last prices and fair prices using stock B

Disclaimer

  • Any securities listed is not a solicitation to trade
  • This model has not been proven to make money, and I will not be responsible for any outcome of your trading account

Is this HFT?

Sure, I had some questions "how is this high-frequency" or "not for UHFT" or "this is not front-running". Let's take a closer look at these definitions:

  • High-frequency finance: the studying of incoming tick data arriving at high frequencies, say hundreds of ticks per second. High frequency finance aims to derive stylized facts from high frequency signals.

  • High-frequency trading: the turnover of positions at high frequencies; positions are typically held at most in seconds, which amounts to hundreds of trades per second.

This models aims to incorporate the above two functions and present a simplistic view to traders who wish to automate their trades, get started in Python trading or use a free trading platform.

Final Notes

  • I haven't come across any complete high-frequency trading model lying around, so here's one to get started off the ground and running.
  • This model has never been used with a real account. All testing was done in demo account only.
  • The included strategy parameters are theoretical ideal conditions, which have not been adjusted for back-tested results.
  • This project is still a work in progress. A good model could take months or even years!
  • I possess a valid US EAD.

Email stuff here: [email protected]

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A high-frequency trading model using Interactive Brokers API with pairs and mean-reversion in Python

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