Pairs trading is a market-neutral strategy that simultaneously takes a long position in one asset and a short position in a correlated asset, profiting from the convergence of their relative price spread. By trading the relationship between two assets rather than their absolute direction, pairs trading hedges market risk and generates returns independent of the overall market trend. This approach is widely used by quantitative hedge funds and institutional traders.
Pairs are selected based on statistical cointegration — a long-term equilibrium relationship between two assets. The spread between the pair is calculated and monitored for deviations from its historical mean. Entry occurs when the spread reaches an extreme standard deviation from the mean, anticipating reversion. The trade is market-neutral: long the underperformer and short the outperformer. Correlation versus cointegration: high correlation alone is insufficient — cointegration implies a stable long-term relationship. Risk arises when the fundamental relationship between the pair breaks down permanently.
Enter when the spread between the pair reaches two standard deviations from its historical mean. Go long the underperforming asset and short the outperforming asset simultaneously. Confirm cointegration using statistical tests such as the Augmented Dickey-Fuller test. Use a z-score of the spread to standardise entry and exit thresholds.
Exit when the spread reverts to its historical mean (z-score returns to zero). Stop loss when the spread reaches three or more standard deviations, indicating a potential structural break. Close both legs simultaneously to maintain the hedge. Exit if statistical tests indicate the cointegration relationship has broken down.
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Pairs trading requires careful selection of genuinely cointegrated pairs — not just correlated ones. Regularly re-test the statistical relationship as cointegration can deteriorate over time due to fundamental changes. In crypto, common pairs include Bitcoin versus Ethereum, or different exchange tokens within the same sector. Always maintain equal dollar exposure on both legs to preserve market neutrality.
News and sentiment trading incorporates breaking news, economic data releases, social media sentiment, and market psychology into trading decisions. This approach recognises that markets are driven by narratives and information flow as much as by technicals, and that the speed and accuracy of interpreting news events creates tradable edge. Sentiment analysis tools aggregate data from social media, news sources, and options markets to quantify crowd psychology.
Mean reversion trading is based on the principle that prices tend to return to a statistical average over time. When price deviates significantly from its mean (typically represented by a moving average or VWAP), mean reversion traders take positions expecting a snapback toward that average. This approach systematically exploits overextended moves and excesses in market sentiment.
Range trading exploits markets that are moving sideways between clearly defined support and resistance levels. Traders buy near support and sell near resistance, capitalising on the predictable oscillation. This style thrives in non-trending conditions where many trend-following strategies struggle, making it a valuable complement to a trader's toolkit.
Swing trading captures price movements that unfold over several days to several weeks by riding the natural 'swings' between support and resistance levels. Swing traders combine technical analysis with patience, entering on pullbacks within a trend or at reversal points and holding until the next significant swing target is reached. This style balances active trading with the flexibility of not needing to monitor screens all day.