源链接：https://github.com/Jackal08/Quan ... atistical-Arbitrage

文字浅显易懂，并附有R代码以及回测信息，需要的坛友可以自行去作者的GitHub查看。欢迎交流、讨论。


History of Statistical Arbitrage:

• First developed and used in the mid 1980s by Nunzio Tartaglia’s quantitative group at Morgan Stanly
• Pair Trading is a “contrarian strategy” designed to harness mean-reverting behavior of the pair ratio
• David Shaw, founder of D.E Shaw & Co, left Morgan Stanley and started his own “Quant” trading firm in the late 1980s dealing mainly in pair trading
  

What is Pair Trading:

Statistical arbitrage trading or pairs trading as it is commonly known is defined as trading one financial instrument or a basket of financial instruments – in most cases to create a value neutral basket.

It is the idea that a co-integrated pair is mean reverting in nature. There is a spread between the instruments and the further it deviates from its mean, the greater the probability of a reversal.

Note however that statistical arbitrage is not a risk free strategy. Say for example that you have entered positions for a pair and then the spread picks up a trend rather than mean reverting.

The Concept:

• Step 1: Find 2 related securities Find two securities that are in the same sector / industry, they should have similar market capitalization and average volume traded. An example of this is Anglo Gold and Harmony Gold.

• Step 2: Calculate the spread In the code to follow I used the pair ratio to indicate the spread. It is simply the price of asset A / price asset B.

• Step 3: Calculate the mean, standard deviation, and z-score of the pair ratio / spread.

• Step 4: Test for co-integration In the code to follow I use the Augmented Dicky Fuller Test (ADF Test) to test for co-integration. I set up three tests, each with a different number of observations (120, 90, 60), all three tests have to reject the null hypothesis that the pair is not co-integrated.

• Step 5: Generate trading signals Trading signals are based on the z-score, given they pass the test for co-integration. In my project I used a z-score of 1 as I noticed that other algorithms that I was competing with were using very low parameters. (I would have preferred a z-score of 2, as it better matches the literature, however it is less profitable)

• Step 6: Process transactions based on signals

• Step 7: Reporting