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The 1st of 4 newsletters supporting your ArbMaker demo
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First things first...

Post-install checklist:
  • Select your default currency: Parameters=>Currencies=>[select currency by clicking on the currency's row]=>Set Default.
  • Unzip & load the sample strategy files: Utilities=>Import Data=>[select the files, Open].
  • Using IQFeed? Configure it using client version 4.7.2. Full guidance in Appendix B here.
  • Using Bloomberg? Follow the configuration guidance in Appendix B.
  • Now read the Frequently Asked Questions eBook!


Quick start: getting the first trade

Where’s the best place to start?

ArbMaker is designed to do this: scan for cointegrated pairs, assess their profitability, track and trade them. So start by mastering the scan functionality!
Accelerate up the ArbMaker learning curve!
Go to the video to zoom up the ArbMaker learning curve!
Note that you should dig into every pair of interest with an individual back test before deciding that it is tradable. This will break out each trade in the aggregate numbers and reveal outliers or oddities that may be reasons to reject the pair.

Is it normal...?

ArbMaker breaks its functions into important segments. First, it looks for cointegration. Then it assesses potential profitability.

Profitability leans heavily on being able to get the timing right. Using the idea of a normal (or near normal) distribution is a key element for this. Normality means predictability.

The Q-Q chart, below is an example, is used in the software to assess normality...

...and here’s a great presentation on distributions showing how to interpret it (pages 23-26).

Cointegration vs correlation

An explanation in one slide:

See the whole presentation this came from here.
What research tells us
We talk about a “proven” methodology when it comes to ArbMaker and cointegration. Here are a couple of research papers to back that up. We’ll feature research work every week.

Thomakos, Wang, Schizas: Pairs Trading on International ETFs
Abstract:
Pairs trading is a popular market-neutral trading strategy among finance practitioners that has been recently evaluated for U.S. stocks (Gatev, Goetzmann, and Rouwenhorst 2006). In this paper, we examine the pairs trading performance as well as the source of the profitability using international exchange traded funds (ETFs). Our results suggest that there are returns from pairs trading on international ETFs. Those returns could partially explained by economic factors.



Caldeira, Moura: Selection of a Portfolio of Pairs Based on Cointegration: A Statistical Arbitrage Strategy
Abstract:
Statistical arbitrage strategies, such as pairs trading and its generalizations, rely on the construction of mean- reverting spreads with a certain degree of predictability. This paper applies cointegration tests to identify stocks to be used in pairs trading strategies. In addition to estimating long-term equilibrium and to model the resulting residuals, we select stock pairs to compose a pairs trading portfolio based on an indicator of profitability evaluated in-sample. The profitability of the strategy is assessed with data from the São Paulo stock exchange ranging from January 2005 to October 2012. Empirical analysis shows that the proposed strategy exhibit excess returns of 16.38% per year, Sharpe Ratio of 1.34 and low correlation with the market.
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