Scaling out does not add profit. It only defends

Scaling Out vs. Pyramiding: Stress Testing the v1.4.1 Core

I recently put my core trading model (v1.4.1, running on H1 timeframe) through a stress test to see if adding scale-out (partial profit taking) or pyramiding (adding to a winning position) could improve performance. I evaluated these using both Out-of-Sample (OOS) data and M1-based intraday stress testing to see how they handle daily equity swings.

Comparison of Strategy Adjustments

Why Pyramiding is a Non-Starter

Pyramiding consistently degrades risk-adjusted performance. As shown in the table, increasing the position size as a trade goes in your favor significantly expanded the drawdown from -17% to -22% and cratered the r/DD ratio. Even with a high-timeframe core, this approach fails because it concentrates risk on winning streaks that eventually reverse. I have confirmed this in previous research; it simply doesn’t work for this system.

The Scale-Out Tradeoff

Scaling out by taking half the profit at a target and letting the rest run looks attractive on the surface. It boosted the win rate (up to 57%) and significantly reduced drawdown. However, it came at the cost of a lower PF and lower overall returns. It effectively “harvests” the big wins that drive the system’s growth, making the strategy more dependent on specific market regimes. The real benefit appeared in the M1 intraday test. By trimming positions early, the worst single-day loss dropped from 3.65% to 2.48%. In other words, it creates a safety buffer against the dreaded -5% daily loss limit. Despite this, I have decided not to adopt it in the current v1.4.1 build. My system is already passing M1 stress tests comfortably (worst day 2.74%), so the extra buffer isn’t currently required. Furthermore, scale-out increases the total trade count by 2.4 times, which adds unnecessary transaction costs.

Can We Re-Leverage for More Profit?

A common question I receive is whether reducing intraday risk allows for higher leverage to boost returns. I ran a simulation (study_scaleout_relever.py) to see if scale-out could “unlock” higher leverage.

• Baseline: The M1 intraday limit is the primary constraint. At a 0.5% risk level, it hits the 5.67% M1 limit. Scaling back to 0.43% risk yields a monthly return of 0.25%.
• Scale-out: By lowering M1 risk, the system hits the DD limit instead of the M1 limit. We can increase risk to 0.51%, resulting in a monthly return of 0.267%. While this does provide a 5 to 7% increase in monthly returns, the gain is marginal. Because the scale-out reduces M1 swings but does almost nothing to lower the total drawdown, you quickly run into the DD “wall” again. The takeaway here is fundamental: to increase the returns of my full system, I need to reduce the maximum drawdown, not the M1 intraday volatility. Since I am already using volatility targeting and filters, I am likely at the efficiency frontier. Scaling out remains a valuable tool to keep in my back pocket as a “safety valve” if I ever run a high-leverage sub-account where M1 constraints are the primary bottleneck. However, for the main system, it stays off.

How this connects

This verification builds on earlier ones (what failed before and what I tried this time, comparisons between approaches).

• Pyramiding into winners is a hazard in a prop account
• Why a 0.85% monthly backtest becomes 0.7% in a live account,…