The Ultimate Guide to Quantitative Trading: How to Replace Emotion with Evidence
Stop guessing. Learn the 5 pillars of quantitative trading: Tactical Asset Allocation, Momentum, Relative Strength, and Risk Management. Evidence-based strategies for superior returns.
The financial media industry is built on a single, flawed premise: that the future can be predicted.
Every day, pundits shout forecasts about interest rates, earnings reports, and geopolitical events. Retail investors consume this noise, try to connect the dots, and make decisions based on gut feelings, fear, or Fear of Missing Out (FOMO).
The result is predictable. The average investor consistently underperforms the market, buying high when euphoria peaks and selling low when panic sets in.
Quantitative trading offers a different path. It is the antidote to emotional investing.
At TradeRounds, we believe that successful investing isn’t about being smarter than the market or having a crystal ball. It’s about having a system. Quantitative (or “systematic”) trading removes guesswork by using mathematical models, historical data, and strict rules to make trading decisions.
This guide outlines the five foundational pillars of a robust quantitative trading framework. In future articles, we will dive deep into the specific mechanics of each, but below we review the core concepts and the academic evidence that supports them.
Pillar 1: Tactical Asset Allocation (TAA)
The Engine of Adaptability
Most investors are familiar with Strategic Asset Allocation—the classic “60/40” portfolio. While better than nothing, this static approach ignores the current market environment, forcing you to hold falling assets during a bear market.
Tactical Asset Allocation (TAA) is a dynamic approach. It uses quantitative signals to shift the portfolio’s weightings based on what is actually happening in the market right now. If the data indicates a high-risk environment for stocks, a TAA strategy might mechanically reduce equity exposure, moving the difference into cash or defensive assets.
The Evidence: Data in Action
The power of TAA was popularized by Meb Faber in his seminal paper, A Quantitative Approach to Tactical Asset Allocation. Faber tested a simple timing model (buying when price > 10-month moving average, selling when < 10-month MA) across five asset classes from 1973 to 2012.
The results were statistically significant:
Returns: The TAA model generated equity-like returns (approx. 10.4% annualized) comparable to the S&P 500.
Risk: Crucially, it did so with bond-like volatility. The maximum drawdown (peak-to-trough decline) was approximately 50% lower than a buy-and-hold strategy (Faber, 2007).
The Takeaway: TAA allows investors to participate in bull markets while sidestepping the catastrophic losses of bear markets.
Pillar 2: Momentum Investing
The Premier Market Anomaly
If you learn only one quantitative concept, make it Momentum.
Momentum is the empirically validated tendency for assets that have performed well in the recent past (3 to 12 months) to continue performing well in the near future. It challenges the Efficient Market Hypothesis, yet it persists due to deep-seated human behavioral biases like anchoring and herding.
The Evidence: Data in Action
In 1993, researchers Narasimhan Jegadeesh and Sheridan Titman published Returns to Buying Winners and Selling Losers, the study that put momentum on the academic map. Analyzing U.S. stock data from 1965 to 1989, they found that a strategy of buying the top 10% of past performers (”winners”) and selling the bottom 10% (”losers”) generated an average excess return of roughly 12% per year (Jegadeesh & Titman, 1993).
This “anomaly” has since been validated across nearly every asset class and geography, spanning over 200 years of financial history.
The Takeaway: Momentum is not a fad; it is a pervasive market force driven by human psychology.
Pillar 3: Relative Strength
Finding the Market Leaders
While momentum often looks at an asset’s own past performance, Relative Strength (RS) looks at its performance compared to everything else.
In any market environment, capital is flowing somewhere. Even in a flat stock market, certain sectors (like Technology or Healthcare) may be booming while others lag. A quantitative system uses RS to identify where that capital is flowing and rotates the portfolio into leadership.
The Evidence: Data in Action
Gary Antonacci refined this concept in his development of “Dual Momentum,” which combines absolute momentum (is the trend up?) with relative momentum (is it outperforming others?).
In his backtests of the “Global Equities Momentum” (GEM) model from 1974 to 2013, the strategy of switching between US stocks, International stocks, and Bonds based on relative strength returned 17.4% annually, compared to just 10.4% for the S&P 500, with significantly lower volatility (Antonacci, 2012).
The Takeaway: Don’t just own “the market.” Use Relative Strength to mathematically determine which parts of the market are leading, and own those.
Pillar 4: Moving Average Tactics
The Essential Trend Filters
Quantitative trading requires binary signals: Buy or Sell. Risk-On or Risk-Off. The cleanest tools for generating these signals are Moving Averages (MAs), specifically the long-term averages like the 200-day Simple Moving Average (SMA).
The Evidence: Data in Action
Professor Jeremy Siegel, in his book Stocks for the Long Run, analyzed the Dow Jones Industrial Average from 1886 to 2006. He tested a strategy of buying the Dow only when it was above its 200-day moving average and moving to cash when it fell below. The result was a dramatic improvement in risk-adjusted returns, preserving capital when it mattered most (Siegel, 2014).
This protection has held true during the major crises of the modern era:
The Great Financial Crisis (2008-2009): The S&P 500 broke below its 200-day average in late 2007/early 2008. A quantitative trend filter would have moved an investor to safety (cash or bonds) long before the market collapsed 50%, avoiding the devastating losses of late 2008.
COVID-19 (Winter 2020): While the crash was rapid, the index broke its long-term trend in late February 2020. Trend followers were signaled to exit weeks before the market bottomed in March, bypassing the most volatile days of the panic.
The 2022 Bear Market: As inflation soared, the market entered a slow, grinding downtrend. The 200-day average acted as a reliable ceiling, keeping quantitative investors in a “Risk-Off” posture for the majority of the year, while buy-and-hold investors suffered a drawdown of nearly 20% (and significantly more in the tech sector).
The Takeaway: Moving averages act as an objective “circuit breaker” for your portfolio. They may not catch the exact top, but they are highly effective at preventing catastrophic drawdowns during sustained market failures.
Pillar 5: Portfolio Risk Management
The Math of Survival
Novice traders obsess over returns. Professional quantitative traders obsess over risk. This is due to the punishing mathematics of loss recovery: a 50% loss requires a 100% gain just to break even.
The Evidence: Data in Action
Modern quantitative funds often use “Volatility Targeting”—adjusting position sizes based on the current volatility (VIX) of the market.
Research by the Man Group and Research Affiliates has shown that volatility targeting significantly improves Sharpe Ratios (return per unit of risk). For instance, during the COVID-19 crash of early 2020, a standard S&P 500 portfolio suffered drawdowns of roughly -34%. A volatility-targeted portfolio, which would have systematically reduced leverage as volatility spiked in late February, saw drawdowns of less than -10% in many models (Harvey et al., 2018).
The Takeaway: Survival is the prerequisite for success. By mathematically managing position size and volatility, you ensure you are always around to play the next hand.
Conclusion
Quantitative trading is not a magic bullet. It requires the discipline to follow the rules when your gut tells you otherwise. However, as the academic literature demonstrates, a systematic approach based on Tactical Allocation, Momentum, Relative Strength, Moving Averages, and Risk Management offers a proven path to superior risk-adjusted returns.
In the coming weeks, we will be publishing detailed “practitioner guides” for each of these five pillars, showing you exactly how to implement them in your own portfolio.
References
Antonacci, G. (2012). Risk Premia Harvesting Through Dual Momentum. Portfolio Management Consultants.
Faber, M. T. (2007). A Quantitative Approach to Tactical Asset Allocation. The Journal of Wealth Management.
Harvey, C. R., et al. (2018). The Impact of Volatility Targeting. Journal of Portfolio Management.
Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance.
Siegel, J. J. (2014). Stocks for the Long Run: The Definitive Guide to Financial Market Returns & Long-Term Investment Strategies. McGraw-Hill Education.