Intermarket Correlation Trading

Directional trading has higher reward potential but also higher risk. Spreads offer: 1) Reduced directional risk - a market crash affects both legs. 2) More consistent returns - spread behaviour is more predictable than absolute returns. 3) Statistical edge - spread mean reversion is a documented phenomenon. 4) Lower drawdowns - spread volatility is typically lower. Trade-off: you sacrifice maximum upside for more consistent, lower-risk returns.

Several methods: 1) Some trading platforms and charting tools have correlation features. 2) Spreadsheet: download daily closes, calculate a 30-day rolling correlation using the CORREL function. 3) Python/R scripts for real-time calculation. 4) Free tools like TradingView can overlay charts (e.g., FTSE 100 vs FTSE 250, or Shell vs BP) for visual correlation. Start with daily monitoring; advance to intraday as you develop. Track correlation in a journal alongside your spread trades.

This happens when correlation breaks down - both instruments move against you simultaneously. Example: Long FTSE 100 (falls), Short FTSE 250 (rises) - lose on both. Protection: 1) Always use stop losses on spread positions. 2) Reduce size during high VFTSE (breakdown risk). 3) Monitor correlation daily - exit if correlation drops significantly. 4) Accept this as a risk of the strategy - proper sizing ensures no single breakdown is catastrophic.

More than single-instrument trading due to multiple positions. Minimum practical with futures: roughly £30,000-£50,000 - enough for one FTSE 100 future plus a beta-adjusted FTSE 250 hedge, plus a buffer for drawdowns. Better: £80,000-£100,000 for proper position sizing and diversification across 2-3 spreads. Spread betting lowers the capital barrier because you can stake a small £ per point on each leg. The capital requirement is a barrier but also a safety feature - undercapitalised correlation trading is very risky.

Yes, correlations can shift due to structural changes. Examples: regulatory changes affecting one sector, new index composition, economic shifts (for instance, a lasting change in the FTSE 100's overseas-earnings tilt versus the domestic FTSE 250). Signs of permanent change: 1) Correlation stays at a new level for 3+ months. 2) A fundamental reason exists. 3) The spread doesn't revert despite extreme readings. Adaptation: periodically recalculate normal correlation levels. If the relationship has structurally changed, update your spread models accordingly.

Use correlation for: shorter-term trades (intraday to a few days), simpler implementation, when instruments have a stable correlation. Use cointegration for: longer-term trades (weeks), when you need statistical rigour, when correlation alone seems insufficient. Practical approach: start with correlation-based spreads for simplicity. Graduate to cointegration analysis as you develop skills. For most retail traders, correlation-based spreads are sufficient and easier to manage - and for index pairs like the FTSE 100-FTSE 250, correlation is usually more appropriate than strict cointegration because the relationship drifts.

It depends on the holding period and market conditions. Guidelines: intraday spreads - calculate at the start of the day, no intraday adjustment. Multi-day spreads - recalculate weekly or when beta changes >5%. High volatility periods - recalculate more frequently (every 2-3 days). Stable periods - weekly is sufficient. Balance: too frequent adjustment = high costs; too infrequent = tracking error. Monitor the spread residual - if it's drifting, the ratio may need adjustment.

Options from best to acceptable: 1) API-based execution: send both legs simultaneously once you have decided to enter - fastest, most reliable for avoiding leg risk. 2) Bracket/multi-leg orders: some platforms allow multi-leg orders. 3) Two screens: have both legs ready, execute within seconds of each other - manual but workable. 4) Execute the less liquid leg first, then immediately execute the liquid leg. Slippage budget: expect some slippage on each leg, especially on the thinner FTSE 250 or single-stock leg. Factor this into trade expectancy calculations.

Key differences: 1) Single-stock correlations are generally lower and less stable than index correlations. 2) Stock-specific events (earnings, news) can break correlations temporarily. 3) Liquidity varies more for stocks - execution risk is higher, and UK single-stock leverage is accessed via CFDs/spread bets since exchange-traded single-stock futures are illiquid. 4) Fundamental divergences are more common (one company outperforms another). Approach: use sector pairs (Shell-BP, Lloyds-NatWest) rather than cross-sector. Monitor upcoming events for both legs. Require a higher correlation threshold (0.8+ vs 0.75 for indices). Use wider stops to accommodate higher variability.

VFTSE is a crucial correlation-regime indicator: Low VFTSE (<13): stable correlations, spread strategies work well, normal sizing appropriate. Medium VFTSE (13-18): monitor more closely, correlations may weaken, consider slightly reduced size. High VFTSE (>18): correlations unstable, can spike or break down, reduce spread exposure significantly. VFTSE spike (>25): high correlation-breakdown risk, consider pausing spread strategies. Rule of thumb: for every 5 points VFTSE above 15, reduce spread position size by 20%.

Kalman filter implementation: 1) State equation: the hedge ratio follows a random walk (beta_t = beta_{t-1} + noise). 2) Observation equation: spread = y - beta x x + error. 3) Update: as each new price pair arrives, update the beta estimate and its uncertainty. 4) Libraries: pykalman in Python, or implement from scratch using the standard Kalman equations. Benefits: smoother adaptation than rolling regression, weights recent data appropriately. Challenges: you need to tune the noise parameters, and it can be unstable if poorly calibrated. Validate against simple rolling regression before production use.

Primary risks: 1) Systemic correlation breakdown: during crises, all spreads may blow out simultaneously despite individual diversification. 2) Hidden correlations: spreads you thought were independent may be correlated through hidden factors. 3) Liquidity clustering: multiple legs may become illiquid simultaneously. 4) Model risk: if cointegration relationships change, the entire portfolio strategy fails. Mitigation: stress test the portfolio under historical crises (e.g., March 2020), monitor inter-spread correlations, maintain a cash buffer for margin calls, and have a hard portfolio-level stop loss.

Components: 1) Data pipeline: real-time tick data to aggregated bars to database storage. 2) Calculation engine: parallel processing of multiple correlation/spread calculations. 3) Alert system: threshold triggers to notification (SMS, email, push) for the trader to review - the decision to trade stays with the human. 4) Dashboard: real-time visualisation of correlations, spreads, Z-scores. 5) Logging: all calculations and alerts logged for analysis. 6) Backtesting integration: ability to replay historical data. Tech stack: Python/pandas for calculations, Redis/Kafka for real-time data, PostgreSQL for storage, Grafana for visualisation. Start simple, add complexity as needed. Keep order execution manual or confirmation-gated rather than fully automated.

Testing hierarchy: 1) Augmented Dickey-Fuller (ADF): the standard stationarity test, p < 0.05 preferred. 2) KPSS test: complementary to ADF (its null hypothesis is stationarity), confirms the ADF result. 3) Phillips-Perron: robust to serial correlation and heteroskedasticity. 4) Hurst exponent: H < 0.5 indicates mean reversion, H > 0.5 indicates trending. Best practice: require ADF p < 0.05 AND KPSS p > 0.05 AND Hurst < 0.45 for high-confidence stationarity. Re-test monthly as relationships can change.

Portfolio allocation framework: 1) Correlation/spread strategies: 20-40% of trading capital (their lower volatility allows a larger allocation). 2) Within the correlation allocation: no single spread > 30% of correlation capital. 3) Correlation with the rest of the portfolio: ensure correlation strategies don't simply replicate directional bets elsewhere. 4) Margining: correlation trades may get a spread-margin benefit on the exchange - factor this into capital efficiency (note retail CFD/spread-bet legs are margined separately). 5) Drawdown budget: allocate a specific drawdown budget to correlation strategies (e.g., max 5% portfolio drawdown from spread trades). 6) Rebalancing: monthly rebalance between correlation and other strategies based on performance and regime.