HOW-TO: Combine Four Strategies Into a Lower-Risk PortfolioE-mini S&P 500 FuturesCME_MINI_DL:ES1!EdgeToolsYour Strategy Works? Fine but Four of Them Together Work Better. This Guide Shows You How and Why. This guide explains how to combine our four S&P 500 strategies (CCI, RAI, VTM, BBI) into a single equal-weight portfolio. It walks through the theory behind the combination, shows the measured correlation between the strategies, and presents the resulting improvement in risk-adjusted performance. No new signal is needed. No optimization. The improvement comes from a structural property of the strategies: near-zero daily return correlation. 1. Why combining strategies matters more than improving them Every trader wants a better strategy. A sharper signal. A faster entry. A more reliable exit. The entire retail trading industry is built around the idea that the next indicator, the next parameter tweak will be the one that finally works. Meanwhile, the institutional world is solving a completely different problem. They stopped looking for the perfect strategy decades ago. Instead, they found something more powerful: a way to make imperfect strategies work together better than any single strategy could work alone. The concept is not new. It is not complicated. And it is the single most underappreciated idea in retail trading. Correlation. 2. The problem with combining price-based indicators Picture this. SPY drops 10 percent over two weeks. Your RSI crosses below 30. The close falls below the lower Bollinger Band. The MACD histogram shows a divergence. Three indicators light up simultaneously. Three buy signals. You might think you have three independent confirmations. You do not. You have one event measured with three rulers. RSI, MACD, and Bollinger Bands are all transformations of the same input: the closing price of the asset. RSI rescales recent momentum into a 0 to 100 range. MACD smooths the difference between two moving averages. Bollinger Bands place a volatility envelope around a moving average. All three respond to the same thing: the magnitude and direction of recent price changes. This is the core problem with how most traders approach strategy combination. Adding a second indicator to a chart does not add a second source of information. It adds a second view of the same information. The resulting strategies will be highly correlated, and correlated strategies do not diversify. 3. Why the data source matters more than the indicator 09_information_domains.png The left side of Figure 2 shows the fundamental problem. RSI, Bollinger Bands, MACD, and moving average crossovers are all functions of the same closing price series. They dress the same information in different mathematical clothing. You can combine them however you want, but the underlying data overlap guarantees high correlation. The right side shows a different architecture. Each of our four strategies draws from a separate data domain. CCI reads credit market conditions. RAI measures risk appetite across multiple asset classes. VTM monitors volatility term structure in the options market. BBI aggregates market breadth from thousands of individual stocks. These four data sources respond to different economic forces on different timescales. There is no shared input, which is the structural reason their correlations are near zero. 4. What each strategy measures and how it thinks Before combining these models, it helps to understand what each one does and how it generates signals. They do not just measure different data. They also apply different signal philosophies. CCI (Credit Cycle Index) Data domain: credit spreads and financial conditions. Signal philosophy: regime-based. CCI stays invested when credit markets indicate healthy conditions and goes defensive when credit conditions deteriorate. It does not try to catch bottoms. It follows the credit cycle, recognizing that favorable credit environments tend to persist and support equity returns. Credit conditions typically lead equity markets by weeks to months. Best suited for: patient, risk-aware investors who want to avoid major drawdowns by monitoring the financial plumbing of the economy. RAI (Risk Appetite Index) Data domain: multi-factor risk appetite across equities, rates, credit, and volatility. Signal philosophy: regime-based. RAI combines information from multiple asset classes to measure how willing market participants are to take risk. High risk appetite supports equity exposure. Declining risk appetite triggers defensive positioning. Because it synthesizes signals from several markets, it captures dynamics that single-asset analysis misses. Best suited for: investors who want a cross-asset perspective on market conditions rather than relying on equity data alone. VTM (Volatility Term Model) Data domain: volatility term structure from the options market. Signal philosophy: regime identification. VTM analyzes the relationship between short-term and long-term implied volatility. In calm markets, the term structure slopes upward (contango). During stress, it inverts (backwardation). This structural information reflects the positioning of sophisticated derivatives market participants. VTM holds equity exposure during calm regimes and reduces it when volatility conditions deteriorate. Best suited for: traders who respect the informational content of the options market and want a volatility-aware overlay for their equity positions. BBI (Bull Bear Index) Data domain: market breadth and sentiment aggregation. Signal philosophy: contrarian. This is the only contrarian model of the four. BBI accumulates positions when collective fear dominates and reduces them when greed takes over. Conceptually inspired by institutional sentiment gauges, it buys when the crowd panics and sells when the crowd celebrates. This means BBI will often enter positions during declining markets, which requires psychological resilience. Best suited for: long-term investors with genuine contrarian temperament who can tolerate interim drawdowns while waiting for mean reversion. Three of the four models are regime-based: they position with the prevailing conditions. One is contrarian: it positions against the crowd. This difference in signal philosophy is another reason their daily returns show near-zero correlation. During the same market event, the regime models and the contrarian model will often act at different times and in different directions. 5. What correlation does to portfolio risk Markowitz (1952) formalized something that is obvious in hindsight but remains widely ignored in practice: the risk of a portfolio is not the average risk of its components. It is the average risk minus a term that depends on the correlation between the components. When correlation is high, that term is small and risk barely decreases. When correlation is zero, the term dominates and risk drops substantially. Two strategies with zero correlation and equal volatility produce a portfolio with 29 percent less risk than either strategy alone. That is 29 percent less risk for free. No trade-off. No cost. Just the mathematics of combining independent return streams. Markowitz called it the only free lunch in finance. The catch is the word "independent." Two RSI strategies on SPY have a correlation around 0.6 or higher. The diversification benefit is minimal. The orange square in Figure 3 shows where most traders sit when they think they are diversifying. The green circle shows what zero correlation achieves. 6. How to measure correlation between CCI, RAI, VTM, and BBI Step 1: Export the strategy performance data from each of the four strategies on TradingView (Settings > Strategy Tester > Export). Step 2: Align the equity curves to a common start date. Step 3: Compute daily returns for each strategy. Step 4: Calculate the Pearson correlation matrix of those daily returns. Here is what the result looks like over the common operating period, February 1994 through December 2025: CCI to RAI: 0.002. CCI to VTM: 0.034. CCI to BBI: -0.001. RAI to VTM: 0.009. RAI to BBI: -0.000. VTM to BBI: -0.000. Every pairwise correlation is below 0.04 in absolute value. These strategies are not "low correlation." They are effectively uncorrelated. Zero. Over 31 years of daily data. 7. How they behave during real crises Theory is one thing. Watching the strategies react to actual market stress is another. Here is how each model behaved during three major market events: During the 2008 Financial Crisis, credit conditions deteriorated early. CCI began shifting defensive before the worst of the selloff. VTM responded to volatility term structure inversion. BBI, as a contrarian model, accumulated positions during the panic. Each model reacted on its own timeline. During COVID in early 2020, the crash was fast and the recovery faster. The strategies diverged in timing and magnitude. Some captured the rebound quickly, others were slower to re-engage. The composite absorbed the differences and delivered a smoother path than any individual curve. During the 2022 rate-hike bear market, conditions were less dramatic but more prolonged. The strategies again spread their responses across different timelines and different magnitudes. The composite avoided the worst individual outcomes in each case. This is what decorrelation looks like in practice. Not identical reactions offset by magnitude, but genuinely different reactions at different times. 8. How to construct the equal-weight composite The construction is straightforward. Normalize each strategy's equity curve to a common starting point (we use 100). Average all four normalized curves on each trading day. That is the composite. No leverage. No optimization. No dynamic weighting. Equal allocation across all four, rebalanced implicitly through the normalization. The individual strategies span a wide range. Their risk-adjusted returns differ by more than 4x between the best and worst. Their volatilities differ by a factor of 10x. Their maximum drawdowns range from single digits to nearly 30 percent. They are genuinely different strategies that happen to be uncorrelated. 9. What the combination produces The equal-weight composite improved risk-adjusted returns by 28 percent relative to the individual average. Return landed near the top of the individual range. Maximum drawdown came in below the worst individual drawdown. Volatility dropped to roughly half the simple average of the individual volatilities. The drawdown chart makes the mechanism visible. The individual strategies experience deep drawdowns at different times. When one strategy is in a drawdown, the others are often near their highs, absorbing the loss. The composite never experiences the full depth of any individual strategy's worst period. 10. The math behind the free lunch For readers who want the mechanics, the portfolio variance of an equal-weight combination of n strategies is: Portfolio Variance = (1/n) * Average Variance + (1 - 1/n) * Average Covariance When the average covariance is zero, the second term vanishes. For n=4, portfolio volatility is approximately half the average individual volatility, since sqrt(1/4) = 0.5. Lower volatility also reduces the drag from compounding. A strategy with 20 percent annual volatility loses approximately 2 percentage points of geometric return relative to its arithmetic mean. At 10 percent volatility, that drag is only 0.5 percentage points. The composite captures this second-order benefit automatically. 11. Two paths to better performance Suppose you have one strategy with a Sharpe ratio of 0.4. You want to double it to 0.8. Path A: improve the signal. Under Grinold and Kahn's (1999) Fundamental Law, doubling the Sharpe requires doubling the information coefficient, which means finding a signal that is twice as good at predicting returns. That could take years of research with no guarantee of success. Path B: add three more uncorrelated strategies at the same Sharpe of 0.4. The portfolio Sharpe becomes 0.4 * sqrt(4) = 0.8. Same result. No signal improvement required. Just four independent return streams. 12. Practical setup: how to use all four on TradingView If you have access to all four strategies, here is how to set them up for combined use. Chart layout: Use TradingView's multi-chart layout (2x2 grid or tabbed layout). Place each strategy on its own chart panel with SP:SPX on the daily timeframe. Alternatively, stack them in separate indicator panels on a single chart. Alerts: Configure alerts for each strategy independently. The key signals to watch are regime transitions: when a model shifts from risk-on to risk-off or vice versa. Set alerts for these threshold crossings in each model. Reading the consensus: On any given day, each model produces a directional view (risk-on or risk-off). A simple consensus framework: - 4 of 4 risk-on: full equity allocation. All four data domains agree that conditions are favorable. - 3 of 4 risk-on: maintain equity exposure. Broad agreement with one dissenter, which is normal. - 2 of 2 split: reduce to half position or hold current allocation. Conditions are mixed. - 3 or 4 risk-off: reduce equity exposure significantly. Multiple independent data domains are flagging deterioration simultaneously. This is rare, and when it happens, it deserves attention. When signals conflict: BBI will frequently disagree with the other three because it is the only contrarian model. This is expected and desirable. During a selloff, CCI, RAI, and VTM may go defensive (regime deterioration) while BBI starts accumulating (extreme fear). This divergence is exactly what produces the low correlation and the diversification benefit. The consensus framework handles this naturally: if only BBI is risk-on, the portfolio reduces overall exposure while BBI captures the contrarian opportunity. What if you only use one or two models? The diversification benefit scales with the number of uncorrelated strategies. Using two uncorrelated strategies still delivers a 29 percent volatility reduction. If you are choosing a single model, select the one whose data domain and signal philosophy best match your investment temperament: - Risk-aware, drawdown-sensitive: CCI or VTM (regime-based, defensive during stress) - Cross-asset perspective: RAI (broadest data inputs) - Contrarian temperament with long time horizon: BBI 13. Limitations you should understand The performance data comes from TradingView strategy backtests on the S&P 500 with percentage-based position sizing. These are hypothetical results. They do not include slippage, real execution costs, or the behavioral reality of following four strategies simultaneously. Past performance is not indicative of future results. The composite is constructed with hindsight: we know all four strategies produced positive returns over this period. Past decorrelation does not guarantee future decorrelation. A structural change in one strategy's data domain could introduce correlation where none previously existed. All four models trade the same underlying asset and are exposed to the equity risk premium. The correlation between daily returns is zero. The correlation between extreme tail events is unknown and likely higher. Longin and Solnik (2001) documented that correlations increase during market stress. A true systemic crisis that synchronizes all risk signals would hit the composite harder than the daily correlation numbers suggest. 14. Summary Combining CCI, RAI, VTM, and BBI into an equal-weight portfolio improved risk-adjusted returns by 28 percent over the individual average. No new signal. No optimization. The improvement came from the near-total absence of correlation between their daily returns. This works because the four strategies measure genuinely independent dimensions of market state: credit conditions, cross-asset risk appetite, volatility term structure, and market breadth. If you are using one of these strategies and want to reduce risk without reducing return, the simplest step is adding uncorrelated strategies from different information domains. References CFA Institute (2024) CFA Program Curriculum Level I: Portfolio Management. Charlottesville: CFA Institute. Grinold, R.C. and Kahn, R.N. (1999) Active Portfolio Management. 2nd edn. New York: McGraw-Hill. Longin, F. and Solnik, B. (2001) 'Extreme correlation of international equity markets', Journal of Finance, 56(2), pp. 649-676. Markowitz, H. (1952) 'Portfolio selection', Journal of Finance, 7(1), pp. 77-91.