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README.md

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+# pairs_trading
+<p>
+This repository contains Python code and a Jupyter notebook that examines the statistics of pairs trading.
+</p>
+
+</p>
+<blockquote>
+<p>
+Pairs trading is an approach that takes advantage of the
+mispricing between two (or more) co-moving assets, by
+taking a long position in one (many) and shorting the
+other(s), betting that the relationship will hold and that
+prices will converge back to an equilibrium level.
+</p>
+<p>
+<i>Definitive Guide to Pairs Trading</i> availabel from <a href="https://hudsonthames.org/">Hudson and Thames</a>
+</p>
+</blockquote>
+<p>
+Pairs trading is sometimes referred to as a statistical arbitrage trading strategy.
+</p>
+<blockquote>
+<p>
+Statistical arbitrage and pairs trading tries to solve this problem using price relativity. If two assets share the same
+characteristics and risk exposures, then we can assume that their behavior would be similar as well. This has
+the benefit of not having to estimate the intrinsic value of an asset but rather just if it is under or overvalued
+relative to a peer(s). We only have to focus on the relationship between the two, and if the spread happens
+to widen, it could be that one of the securities is overpriced, the other is underpriced, or the mispricing is a
+combination of both.
+</p>
+<p>
+<i>Definitive Guide to Pairs Trading</i> availabel from <a href="https://hudsonthames.org/">Hudson and Thames</a>
+</p>
+</blockquote>
+<p>
+Pairs trading algorithms have been reported to yield portfolios with Sharpe ratios in excess of 1.0 and returns of 10% or
+higher. Pairs trading takes both long and short positions, so the portfolio tends to be market neutral. A pairs trading portfolio
+can have drawdowns, but the drawdowns should be less than a benchmark like the S&P 500 because of the market neutral nature of the
+portfolio.
+</p>
+<p>
+Markets tend toward efficiency and many quantitative approaches fade over time as they are adopted by hedge funds. Pairs trading
+goes back to the mid-1980s. Surprisingly, the approach still seems to be profitable. One reason for this could be that there are a vast
+number of possible pairs and the pairs portfolio is a faction of the pairs universe. This could
+leave unexploited pairs in the market. Pairs trading may also be difficult to scale to a level that would be attractive to institutional
+traders, like hedge funds, so the strategy has not been arbitraged out of the market.
+</p>
+<p>
+Mathematical finance often uses models that are based on normal distributions, constant means and standard deviations. Actual market
+data is often not normally distributed and changes constantly. The statistics used to select stocks for pairs trading makes an assumption
+that the pair distribution has a constant mean and standard deviation (e.g., the pairs spread is a stationary time series). This
+assumption holds, at best, over a window of time. This notebook explores the statistics of the cointegrated pairs that are candidates
+for pairs trading.
+</p>
+<p>
+The statistics that predict a successful pair will not be accurate in all time periods. For the strategy to be successful, the predicition
+must be right more often than not. To minimize the risk in any particular trade, this suggests that trading a larger portfolio will
+be more successful than trading a small portfolio.
+</p>