Trading Baskets Instead of Individual Instruments: The Evolution of My Approach to Risk and Profitability

At the beginning of my trading journey, like many aspiring traders, I focused on trading one or a few individual instruments—stocks, futures, currency pairs. I chased trends, played bounces off local extremes, applied classic “buy low — sell high” strategies and mean-reversion techniques. But over time, I discovered that directional trading in a single asset hides non-obvious pitfalls, often leading to persistent losses and emotional burnout.

The Problems of Trading a Single Asset

• High volatility and sudden breakouts
  Any asset can experience a sharp price spike due to major news, hitting stop-losses and wiping out weeks or months of profit.

• Correlation risks
  If you trade only tech stocks or oil futures, changes in macroeconomic conditions or geopolitics can hit all your positions at once.

• Psychological pressure
  When a single asset holds a large share of your portfolio, every loss feels especially painful, leading to wider stop-losses or premature exits.

• Overreliance on historical patterns
  A strategy that worked in the past may break down due to changing market regimes, requiring constant retesting and adaptation.

An Attempt at Diversification… in Reverse

• More trades, but no increase in returns
  New instruments brought no synergy since they all followed the same logic.

• Higher transaction costs
  The more instruments in the portfolio, the higher the commissions and spreads.

• Management chaos
  Different markets have different trading hours and risks, making it hard to monitor a dozen charts at once.

Classic diversification—“knowing little about each market, but holding more instruments”—did not solve the core issue: the lack of cross-asset risk control and relationship management.

Breakthrough: Edward Thorp’s “The Horse Hedge Method”

One day I came across an article about Edward O. Thorp and his famous “Horse Hedge Method”—a mathematical idea initially developed for betting in horse racing, later adapted for financial markets. The core of the method is that different assets are treated like “horses” in a race: each with its own probability of winning and correlations with others. Properly combining bets (positions) allows one to almost completely neutralize systemic risk and profit from relative value changes.

This concept changed the way I saw trading: instead of isolated bets on the trend or correction of a single asset, I began to view a basket of assets as a unified playing field—where I could manage money allocation among the “horses” based on their relationships and expected returns.

Basket Trading Methods

• Mean–variance optimization (MVO)
  The classic Harry Markowitz approach: minimize portfolio variance for a target return. It finds the optimal asset mix based on average returns and the covariance matrix.

• Risk parity
  Allocates capital so that each asset contributes equally to the total portfolio risk. Highly effective in divergent markets and increases robustness to black swan events.

• Cointegration trading
  Finds pairs or groups of assets that move in sync over time. Opens opposing positions when they diverge, expecting mean reversion.

• PCA method (Principal Component Analysis)
  Extracts “hidden factors” driving the overall trend in a basket. Enables portfolio trading with minimized exposure to major risk components.

• Machine learning and modern models
  Advanced algorithms (graph neural networks, gradient boosting) can detect complex nonlinear dependencies and adapt weights in real-time.

PCA ARBITRAGE 3X EA                                                                    PCA PAIRS HEDGER EA

Advantages Over Single-Instrument Trading and Classic Diversification

• Reduced systemic risk
  Proper asset selection and weighting offset negative moves in individual assets.

• Stable returns
  Baskets tend to show a smoother “yield curve” without sharp drawdowns.

• More efficient capital use
  No need to fear drawdowns in a single name—risk diversification allows for more leverage.

• Correlation management
  Modern math models account for not only historical but also forecasted relationships.

• Adaptability
  Automated algorithms adjust weights as market conditions change.

Conclusion
Today, by using methods for trading baskets of correlated assets—from mean–variance optimization to PCA and machine learning—retail traders gain access to institutional-grade tools. This means:

• Improved strategy performance
  Fewer “empty” trades and more accurate entry/exit selection.

• Strict risk control
  Knowing each asset’s contribution to the portfolio allows for planned maximum drawdown.

• Lower emotional stress
  A basket of 5–10 instruments with different drivers makes trading calmer and more consistent.

• Flexibility and scalability
  Adding new assets or changing weights takes minutes and doesn’t break the overall strategy structure.

Ultimately, basket trading methods allow retail traders to move beyond the traditional “tunnel vision” of single-instrument focus and build a truly balanced, mathematically grounded portfolio that can perform in any market condition.