The Craziest Backtest I’ve Ever Seen | OVTLYR University Lesson 9
Backtesting, Expectancy, and Trusting the Data
This lesson is about learning to trust data instead of emotions. Losing streaks feel personal, but markets do not care how a trader feels. Backtesting exists to answer one question: Is the plan actually working, even when it doesn’t feel like it is?
1. Losing Trades Do Not Mean the Plan Is Broken
Recent trades in the energy sector produced losses, while financials performed well.
This led to emotional reactions and claims that the sector was “broken.”
The reality:
● All trades followed the same rules
● All entries were valid
● Exits were taken exactly as planned
● Losses were expected
A plan doing what it is designed to do is not failure.
2. Why Small Sample Sizes Are Dangerous
Judging a strategy based on a handful of trades is misleading.
● Six trades do not tell you anything
● Fifty trades start to tell a story
● Hundreds of trades reveal expectancy
Emotion reacts to short-term results.
Data requires a large sample.
3. The Role of Backtesting
Backtesting is used to:
● Measure expectancy
● Understand win/loss distribution
● Build confidence during losing streaks
● Prevent emotional plan changes
Backtesting is not about proving you are right.
It is about discovering what is actually true.
4. Energy Sector Backtesting Example
The hypothesis tested:
If oil controls energy stocks, maybe energy trades should depend on oil’s trend.
Three oil conditions were tested:
● Oil trending up
● Oil trending sideways
● Oil trending down
All three showed positive expectancy.
The surprise:
● Energy stocks had higher expectancy when oil was trending down
● Losing streaks occurred inside a profitable system
● Expectancy only appeared when all data was included
5. What Expectancy Really Means
Expectancy is simply the average return per trade.
● Add all trade results
● Divide by number of trades
● The average is expectancy
High expectancy does not mean:
● No losses
● Smooth equity curves
● Emotional comfort
It means the math works over time.
6. Why Losing Streaks Are Normal
Trades cluster.
● Wins often come in groups
● Losses often come in groups
● This is normal behavior in probabilistic systems
Confidence comes from knowing:
● Losses were planned
● Risk was controlled
● Winners are statistically larger than losers
7. Overfitting Is a Trap
Adjusting rules only to avoid recent losses leads to fragile systems.
● Systems that only work in one condition are not robust
● Robust systems work across many environments
● One variable should be tested at a time
The goal is not perfection.
The goal is durability.
8. Capital Efficiency Matters
A strategy does not need constant exposure to work.
● Being in the market less can still outperform
● Capital can be deployed elsewhere during downtime
● Lower exposure reduces emotional stress
Efficiency matters more than activity.
9. When to Change a Plan
Plans should only change when:
● Data proves a rule is inferior
● A tested replacement improves expectancy
● Changes are measured, not emotional
Plans should not change because of:
● One bad week
● A losing streak
● Frustration or fear
Core Lesson Takeaways
● Data beats emotion
● Small samples lie
● Expectancy explains losing streaks
● Backtesting builds confidence
● Robust systems survive different markets
● Discipline means trusting the plan when it feels wrong
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