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Pairs Trading Algorithm

A general algorithm to trade pairs based on the concept of cointegration. I tried three different pairs and don't think I would trade on any of them.

Clone Algorithm
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Backtest from to with initial capital
Total Returns
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Alpha
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Beta
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Sharpe
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Sortino
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Max Drawdown
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Benchmark Returns
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Volatility
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Returns 1 Month 3 Month 6 Month 12 Month
Alpha 1 Month 3 Month 6 Month 12 Month
Beta 1 Month 3 Month 6 Month 12 Month
Sharpe 1 Month 3 Month 6 Month 12 Month
Sortino 1 Month 3 Month 6 Month 12 Month
Volatility 1 Month 3 Month 6 Month 12 Month
Max Drawdown 1 Month 3 Month 6 Month 12 Month
# Backtest ID: 552eaca8f87a682cdd33a46d
There was a runtime error.
6 responses

Thanks for sharing Aaron! This is a good template. Pair trading investigations will be interesting to explore in IPython notebooks using the new research environment. There you can visualize different pair combinations and watch the effect of swapping different securities.

If you don't have research yet, you can submit an algo to the contest and get your access:
https://www.quantopian.com/posts/enter-the-quantopian-open-to-get-early-access-to-the-research-platform

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Would the same pairs work with OLS based pair trading?

Not sure I understand your question. This algorithm uses OLS to model the relationship between the chosen pairs. The rest of the logic is centered around the decision to enter into the trade. Here I'm using cointegration as a test condition and then waiting for a specified # of standard deviation from the mean before I take a position.

Aaron, A very neat and good job.

Hey Aaron,
Nice work on this algo, I like is that you consider whether the previous spread has reverted before estimating new parameters, sometimes new estimations cause a position to be closed before the original spread was profitable. Tracking the pair's pnl helps avoid that, but it's good to keep the original parameters around anyway.

The drift in the hedge ratio tends to be the killer in pairs trading In my experience, you could try adding a maximum time you'll wait for a reversion, or track how far the original parameters have deviated from updated ones and change them when the model has gone sufficiently out of whack. I added a simple max holding period as an example. I also switched it to use log prices since the returns of the spread are,

(log(A,t1) - log(A,t2)) + beta*(log(B, t2) - log(B, t1)).

It's heading in the right direction though, you'll just need risk stuff and some decent pairs.

Thanks for sharing,
David

Clone Algorithm
68
Loading...
Backtest from to with initial capital
Total Returns
--
Alpha
--
Beta
--
Sharpe
--
Sortino
--
Max Drawdown
--
Benchmark Returns
--
Volatility
--
Returns 1 Month 3 Month 6 Month 12 Month
Alpha 1 Month 3 Month 6 Month 12 Month
Beta 1 Month 3 Month 6 Month 12 Month
Sharpe 1 Month 3 Month 6 Month 12 Month
Sortino 1 Month 3 Month 6 Month 12 Month
Volatility 1 Month 3 Month 6 Month 12 Month
Max Drawdown 1 Month 3 Month 6 Month 12 Month
# Backtest ID: 555bcffbb55b901049e946ae
There was a runtime error.

Is there anyone who could help me modify this algorithm for a much simpler trend-following strategy? I want to test the spread between DIA and SPY as a simple crossover strategy on an hourly chart. I would like the strategy to start with an equivalent dollar amount of each stock. I believe the pair ratio is around .86 for these ETFs. I would like it to trade a simple cross moving average cross over strategy. So, if the price of the spread goes above the moving average, the strategy would buy an addition 100 or so shares of one ETF, and then go short once it crosses back below the moving average, trading around the core equivalent holding. I am using this strategy as one part of a larger strategy, so I don't expect the results to be spectacular, but there should be reasonably low volatility. If someone could help me out, that would be great. It seems like this is a good starting algorithm, but I know they are generally correlated and I only want to trade the one pair. Any help would be appreciated. Thanks.