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

• An Interactive Brokers account with TWS/API gateway
• IbPy @ https://github.com/blampe/IbPy
• Pandas
• NumPy

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:

• MIT - Developing high-frequency equities trading model @ http://dspace.mit.edu/handle/1721.1/59122
• SMU - Profiting from mean-reverting yield-curve trading strategies @ http://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=3488&context=lkcsb_research

And book:

• Introduction to High-Frequency Finance @ http://www.amazon.com/Introduction-High-Frequency-Finance-Ramazan-Gen%C3%A7ay/dp/0122796713

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]