Arbitrage Strategies: Understanding Working of Statistical Arbitrage
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Welcome to what I hope becomes first article in a weekly column where we try and test different strategies that are used in traditional stock markets and try to come up with some of our own. Inspired by this great articleit seems that the steemit community would like to read about statistical arbitrage trading and I'm excited to contribute my part.
But first things first, let me introduce myself. I hold a masters degree in Financial Statistical arbitrage trading strategy. I did my masters thesis on statistical arbitrage strategies in the US stock market. I worked in the data science field for 3 years before and after graduation at a analytics consultant company, focusing on applying machine learning to trading. Later on I switched to more general data science, but still mostly dealing with problems in the insurance and banking industry.
Currently I am employed as a Data Scientist in charge of game economy and monetization at a top grossing mobile gaming company. Statistical arbitrage trading strategy of the work I do is in Statistical arbitrage trading strategy because of the vast amount of statistical libraries available and loads of functionality I have written over the years is in R.
Python is a strong second especially for data gathering like scrapy and some frameworks that are not available for R. In this article we will cover the basics so that for future articles readers can always go back to understand the terminology.
The term statistical arbitrage strategy, as I use it, means any trading strategy that relies on historical statistical data to gain an edge, i. The momentum strategy outlined in furions article is thus regarded as a statistical arbitrage strategy. The basis of any arbitrage strategy is its performance on historical data. In this first article we will try a simple risk minimizing strategy using simple moving average SMA. SMA calculates the average of the last N prices with a fixed sampling statistical arbitrage trading strategy the price over a fixed time period.
Daily data is gathered via cryptocompare API. The whole dataset contains data from to We will train on 2 years of data from towhere the data before is needed for our longest lookback period days. We can see from the statistics on the training set that the SMA strategy almost universally reduced risk no matter the lookback period.
The narrower lookback period 7 and 30 performed best giving us a hint to perhaps look into even shorter time frames. Regarding returns, the buy and hold performed better than most SMA strategies, but failed to outperform the 7 day SMA - much to my surprise. I was coming into this fairly certain that the buy and hold will reign supreme in returns due to knowing the its rising history and that we will only cut volatility.
If we look at the log curve of the strategy portfolio value, we see that the 7 day and buy and hold statistical arbitrage trading strategy basically the same strategy until early when Mt Gox happened.
All in all the 7 day SMA is a clear winner on the training period and it's time to put it to the test! In this article we presented a quick intro to statistical arbitrage trading and a simple trading strategy that performed well. In the next one we will try another strategy and overview of traps and pitfalls of statistical arbitrage statistical arbitrage trading strategy that come in the form of biases that we have to be aware of when designing, testing and implementing a strategy.
We avoided at least one bias in this article, but perhaps we missed some others. Can you name some that we avoided and some that we didn't in our SMA strategy? Besides writing biases, I would welcome all discussion and feedback - both on my writing style, content or explanations you feel are lacking and other areas where I can improve.
And if you have an idea that you deem might be worth testing, please let me know. So I got a lot of questions about trading fees. I initally did not want to list trading fees as that is one of the biases I wanted the readers statistical arbitrage trading strategy question which they did.
This statistical arbitrage trading strategy due to a 7 day lookback period changing the signal more frequently than a longer lookback period would. I wrote an article that explains a simple way to arbitrage across cryptocurrency exchanges, would love to hear your thoughts about it. It doesn't use statistical arbitrage, but rather just pure arbitrage across a single currency pair and two exchanges. About me I hold a masters degree in Financial Mathematics.
The tools R Python Most of the work I do is in Statistical arbitrage trading strategy because of the vast amount of statistical libraries available and loads of functionality I have written over the years is in R.
Introduction In this article we will cover the basics so that for future articles readers can always go back to understand the terminology. Statistical arbitrage strategy The term statistical arbitrage strategy, as I use it, means any trading strategy that relies on historical statistical data to gain an edge, i. Roughly this statistical arbitrage trading strategy into 3 steps: Strategy idea outline Here we outline what we want to achieve - momentum: Last year of trading, last week, last day?
Do we re-balance monthly, daily, hourly? To see what performs the best we take a training set of data and check statistical arbitrage trading strategy Testing phase - Testing on historical data Here we put the strategy to the test and see if it can outperform some arbitrary benchmark, usually a buy and hold strategy Measuring statistical arbitrage trading strategy In most articles we will use the following measures of performance: Parameter tuning We will try 7, 30, and days for the lookback period.
Data Daily data is gathered via cryptocompare API. We will then test on almost 3 years of data from to today. We will assume a starting balance of 10 EUR. Results Training period We can see from the statistics on the training set that the SMA strategy almost universally reduced risk no statistical arbitrage trading strategy the lookback period.
Conclusion In this article we presented a quick intro to statistical arbitrage trading and a simple trading strategy that performed well. Authors get paid when people like you upvote their post. Where are you based? I will follow you from now on.