control, independence from regime, and compounding reliability
If you can consistently extract small pieces of daily variance with asymmetric risk, the math compounds faster than passive directional expectation.
You don’t need the market to trend 10%.
You need structure.
You need:
Controlled losses
Small but repeatable wins
Statistical discipline
If the edge holds — even imperfectly — the arithmetic does the heavy lifting.
Beating the benchmark isn’t about predicting where the index will end.
It’s about how efficiently you can harvest what it does every day.
Every year, someone projects the S&P 500 will gain ~10%.
If the index sits at 7,000, that’s roughly +700 points.
That’s the benchmark expectation.
But what if instead of forecasting direction, you focus on daily variance extraction?
Assume:
3 trades per day
Minimum +3 points per winning trade
90% win rate
10% losing trades
Average losing trade: −8 points
The 3 points is conservative. It’s the floor.
Now let’s calculate expected value per trade.
Expected Value Per Trade
EV = (Win% × Avg Win) − (Loss% × Avg Loss)
EV = (0.90 × 3) − (0.10 × 8)
EV = 2.7 − 0.8
EV = +1.9 points per trade
At 3 trades per day:
1.9 × 3 = 5.7 points per day
Annualized Impact
Assume ~252 trading days.
5.7 × 252 = 1,436 points per year
At a 7,000 index level:
1,436 / 7,000 ≈ 20.5%
That’s not a forecast.
That’s math.
Compare That to “10%”
Projected benchmark gain:
+700 points (~10%)
Modeled variance aggregation:
+1,436 points (~20%)
Difference:
+736 points
More than double the benchmark projection.
Now this is where it gets interesting:
≈ 20.5% of index level
≈ 20.5% return on notional exposure
≈ upto 600% return on posted margin on futures
(if fully utilized)