In quantitative trading, the most dangerous illusion is mistaking luck for edge. Many strategies with “perfect” backtests fall apart in live markets because they are built on correlation, not cause-and-effect.

A backtest is a replay of the past. If the market environment changes, the backtest result can stop applying.

1) The Rate Cut Trap (Correlation ≠ Cause)

A classic myth: “Rate cuts always lead to stock market rallies.”

Sometimes the data supports this. But in 2008, the Fed cut rates aggressively and stocks still collapsed.

Why? Because rate cuts were not the real driver. The real driver was the economic recession.

• Recession → forces rate cuts

• Recession → pushes stocks down
  
  

If you only look at correlation, you confuse the “medicine” (rate cuts) with the “disease” (recession). In plain terms: correlation tells you two things moved together; causation tells you one thing drove the other. If that difference is fuzzy, read Correlation vs Causation.

Key point: You must identify the “environment driver,” not just the visible event.

2) Three Ways Trading Models Think (and Where They Break)

Statistical Regression

• What it does: measures correlation (beta-style relationships)

• Weakness: treats the environment as noise

• Failure mode: relationships drift when conditions change
  
  

Black-Box ML (DNN / XGB)

• What it does: learns patterns from lots of features

• Weakness: can learn the wrong reason without telling you

• Failure mode: sudden crashes when the regime shifts
  
  

Causal Diagram (DAG)

• What it does: explicitly labels the environment as a driver

• Strength: makes the “why chain” visible

• Benefit: when performance changes, you can see which link broke
  
  

Causal diagrams are designed to make confounding and hidden drivers explicit.

3) The Math of Deception: One Win Rate Can Hide the Truth

Breakout strategy:

• Z = Market regime (Trending vs Choppy)

• T = Breakout signal

• Y = Win/Loss
  
  

A 100-day backtest

Trending days (70 days):

• 50 wins, 10 losses → 83% win rate
  
  

Choppy days (30 days):

• 2 wins, 8 losses → 20% win rate
  
  

Traditional backtest result (averages everything)

Total wins / total trades

= (50 + 2) / (50 + 10 + 2 + 8)

= 52 / 70

= 74.2%

The illusion: “This strategy wins 74% of the time.”

Causal result (separates environment from strategy)

• Win rate | Trending = 83%

• Win rate | Choppy = 20%
  
  

Real conclusion: the environment may be the “edge,” not the strategy.

This is a close cousin of backtest overfitting: great results can appear when you “benefit from the sample.”

4) Why Causal Thinking Handles Regime Shifts Better

Regime shifts are what kill strategies: the market stops behaving like the backtest period.

Why black boxes fail

When the market flips from trending to choppy, the learned patterns stop holding. Many traders respond by returning and shortening windows often turning into curve-fitting.

Why causal diagrams stay stable

Causal thinking is simpler:

• monitor whether Z (the regime) changed

• if conditions for success vanished, stop trading

• don’t confuse “model broke” with “world changed”
  
  

5) The Practical Fix (Simple Checklist)

1. Name your regime (Z): trend/chop, vol level, liquidity, macro risk

2. Report results by regime: don’t trust one average number

3. Use out-of-sample / walk-forward tests: to see if it survives different periods

4. Monitor the regime live: so you know when your edge no longer applies

5. Ask “what-if” questions: what if vol spikes, spreads widen, liquidity disappears?
   
   

Final Thought

Statistical models chase patterns in the past. Causal models try to explain what is driving outcomes right now.

Stop looking for a holy grail backtest in a vacuum. Build a simple causal diagram around your key drivers, volatility, liquidity, macro stress and evaluate strategies conditional on the environment, not averaged across it.