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huba-v1
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Pairs Trading using Statistical Arbitrage
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huba-v1
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# What is this? This repo holds the very first algo trading strategy I have developed and live traded from 2012 to 2016. It was built using [PyAlgoTrade](https://github.com/gbeced/pyalgotrade) with the Pair Trading / Stat Arb guidelines posted in [Ernie Chan's Quantitative Trading book](https://amzn.to/3arFQNz). # Why are you releasing it? It created lower-than-expected live trading returns, so I moved on. I think it might be useful for others as learning material. Even though it made [some profits live](https://github.com/tibkiss/huba-v1/blob/master/logs/trades-real.csv), it wasn't up to my expectation: The initial [capital](https://github.com/deltaray-io/huba-v1/blob/master/logs/equities-real.csv) trading this strategy was $40k. While continuously adding savings to the account, it ended up making $6k over the 3-year period. Even if we ignore the fact that additional was capital was deployed to the account the CAGR would be around 5%. I made two other iterations of this strategy: [huba-v2](https://github.com/tibkiss/huba-v2) and [huba-v3](https://github.com/tibkiss/huba-v3/) will be released shortly, with similar commentary. # What was the approach? Finding cointegrated pairs and trading them long/short when they drift too far apart from their expected fair price. You can find all the details in Ernie's [first](https://amzn.to/3arFQNz) and [second](https://amzn.to/3OLJf8W) book on the subject. # What did u do step by step? 1) Purchased minute data from iqfeed, ingested it 2) Implemented Statistical Arbitrage as discussed in [the book](https://amzn.to/3arFQNz) 3) Created a liquidity and price filter to filter out non-tradable stocks 4) Created pairs for each equity sector 5) Did a bruteforce on half of my data 6) Picked the best looking pairs, which *seemed* to make sense 7) Validated them on my other half of the data 8) Picked the ones which still remained reasonable and [put it](https://github.com/tibkiss/huba-v1/blob/main/config.py) into paper trading 9) After gaining confidence put the best pairs to live trading 10) Explored AD-Fuller tests, Hurst exponents, Earnings filters and bunch of other stup 11) Profit of 6k after 4 years 12) Moved on to zipline based implementation (huba-v2) # What have you learned from this? 0) Max time in trade is a *must*. huba-v1 didn't have this implemented, but [huba-v2](https://github.com/tibkiss/huba-v3) has this feature. 1) Running this strategy for 3 years had a serious opportunity cost. 2) Getting intra-day data was challenging and expensive at the time. 3) Finding pairs by grid-search (even in the same industries) is expensive and often results from spurious discoveries. # What others can learn from this? Some pairs I [traded](https://github.com/deltaray-io/huba-v1/blob/master/config.py) might still show correlation. # Why is the code not pythonic? Coming from C/C++ background this was my first serious Python project. # How did you search for pairs? The whole solution was migrated to PyPy so that we have reasonable speed. Then machines were crunching data for weeks. The pair-scan results are located [here](https://github.com/deltaray-io/huba-v1/tree/master/pairscan). # What's next? Over time I have created two additional iterations of the StatArb strategy: - [huba-v2](https://github.com/tibkiss/huba-v2) : Using Zipline - [huba-v3](https://github.com/tibkiss/huba-v3) : Using QuantConnect's Lean After these iterations I gave up and moved to Mean Reversion and Momentum Strategies. Most recently I learned Options Trading and created a [backtester service](https://deltaray.io) for income strategies. # I made this work, what shall I do now? If you don't mind sharing with me, that is awesome. Drop me a mail at tibor (d0t) kiss (at-sign) gmail (d0t) com. If you don't feel like sharing, that's also fine. Enjoy the ride! :) # I have something to say publicly about this strategy. Please use the [Discussion Board](https://github.com/tibkiss/huba-v1/discussions/). # Can I buy you a coffee? [Sure](https://ko-fi.com/tiborkiss), thank you for your consideration! # What is HUBA? It is a joke delivered as an acronym: Highly Unorthodox Broker Agent. I must admit: nothing is unorthodox about this approach (it is well studied). When I started developing this strategy Hungary's Minister of Economy [Huba Gyorgy, Matolcsy](https://hu.wikipedia.org/wiki/Matolcsy_Gy%C3%B6rgy_(k%C3%B6zgazd%C3%A1sz)) started campaigning with his 'Unorthodox' approaches. I thought this project will be as qualified as his decisions, hence the name.
