A systematic comparison of 78 trade-management configurations, evaluated on a 100,000-trade stratified sample and validated on the full 2,803,855-trade backtest. This report explains the three choices that govern our trade exits — the trailing stop, the breakeven trigger, and whether to take partial profits — and the evidence behind the configuration we selected.
Every trade-management decision in a breakout system reduces to three axes: which moving average to trail, when to move the stop to breakeven, and whether to take a partial profit. Our prior rules chose the tightest valid EMA (8→13→21→50 cascade), moved to breakeven at 1R, and scaled half at 1R. In backtest they produced a mean R of +0.070 per trade. This report asks whether those three choices are individually and jointly correct, and quantifies the change from the alternatives we tested.
tightest — cascade 8→13→21→50, exit on close below the tightest EMA the stock is still holding.
ema50 — fixed 50 EMA; exit only on close below it.
0.5 → 5.0R in 0.25R / 0.5R steps, plus never (trail only, no BE). Breakeven only engages after the trigger is tagged.
none — full position held until trail fires. half_1r — sell 50% at +1R, trail remainder. half_2r — sell 50% at +2R, trail remainder.
Pair every trigger/partial cell across the two trail choices. The 50 EMA row has a higher mean R than the tightest-EMA row in all 39 matchups. The gap widens with the trigger: at a 5R breakeven with no partial, the 50 EMA trail produces +0.4145R vs +0.3766R for the tightest-EMA cascade — a 10% relative edge on the best cell.
Hold trail and trigger constant. Compare no partial vs half at 1R vs half at 2R across all 26 cells. No partial produces a higher mean R in every single pairing. Taking half the position off at 1R or 2R caps upside in a distribution where roughly 10% of trades produce the majority of the total R.
Hold the trail (50 EMA) and partial (none) constant. Sweep the breakeven trigger from 0.5R up to 5.0R, plus a never option where no breakeven stop is applied. Mean R rises monotonically from +0.091 at 0.5R to +0.414 at 5R, then drops to +0.111 when no breakeven trigger is used. That drop is why a breakeven rule matters at all: without one, the 50 EMA trail catches weak trades that never developed and gives back the edge through the left tail.
Every cell tested. Green row is the selected configuration; red row is the prior configuration used by the system before this review. Every step from red to green — widening the trail, delaying the breakeven, removing the partial — moves mean R in the same direction.
| Trail | BE Trigger | None | Half @ 1R | Half @ 2R |
|---|
The sweep used a 100,000-trade stratified sample to make 78 configurations tractable. The selected configuration was then rebuilt against the full backtest: 2,803,855 simulated trades. The change raised mean R from +0.070 (prior rules) to +0.385 (selected rules). Almost all of the improvement comes from the right tail of the winner distribution — trades exited by the 50 EMA trail after reaching 5R average +5.25R and a median hold of 150 days.
| Metric | Prior | Selected | Δ |
|---|---|---|---|
| Mean R per trade | +0.070 | +0.385 | +0.315 |
| Median R | −0.16 | −1.00 | heavier tails |
| Trades reaching 2R | 12.8% | 21.7% | +8.9pp |
| Winner mean R | +1.66 | +4.15 | +2.49R |
| Winner median hold | ~50d | 169d | +119d |
| Loser median hold | ~14d | 12d | ≈ |
| Trail-exit frequency | 40.2% | 15.4% | fewer, larger |
| Outcome | % | Mean R | Med Days |
|---|---|---|---|
| Initial stop hit | 51.7% | −1.05 | 28 |
| Failed breakout | 23.1% | −0.37 | 1 |
| 50 EMA trail exit | 15.4% | +5.25 | 150 |
| Position still open | 9.8% | +2.09 | 234 |
| Breakeven stop hit | 0.1% | −0.26 | 30 |
This is not a comfortable equity curve. In the backtest, 75.6% of trades end in a loss, median R is −1.00, and most trades cluster at the full-stop outcome. Positive expectancy comes from the right tail: roughly the top 10% of trades produce about 90% of total R. That concentration is the mathematical reason partial profit-taking is costly — cutting the winners down to size directly reduces the tail that produces the edge.
| p05 | −1.06 |
| p25 | −1.00 |
| p50 (median) | −1.00 |
| p75 | −0.06 |
| p90 | +4.14 |
| p95 | +5.42 |
| p99 | +9.92 |
| Winners (R>0) | 24.4% · N=683K |
| Mean R | +4.15 |
| Median hold | 169 days |
| Losers (R≤0) | 75.6% · N=2.12M |
| Mean R | −0.83 |
| Median hold | 12 days |
We train a gradient-boosted model to predict which simulated trades will reach 5R based on setup quality, fundamentals, and market context. The model produces a conviction score from 0 to 1, which we bucket into letter grades. Apply these grades to out-of-sample trades only (N = 466,895, dates on or after 2025-03-05). Grade performance is monotonic: every higher conviction bucket has a higher mean R, higher positive-R rate, and higher 5R-reach rate than the bucket below. A+ isolates a tier with +3.19R mean and 54% positive-R on roughly 0.24% of the out-of-sample population.
| Bucket | N | Mean R | R>0 | Hit 5R | Winner Med Days |
|---|---|---|---|---|---|
| A+ (≥0.40) | 1,111 | +3.19 | 54.0% | 38.3% | 130 |
| A (0.25–0.40) | 23,060 | +1.61 | 41.5% | 23.3% | 110 |
| B (0.20–0.25) | 44,572 | +0.95 | 35.1% | 18.7% | 104 |
| C (0.15–0.20) | 98,946 | +0.71 | 33.3% | 16.3% | 102 |
| D (<0.15) | 299,206 | +0.28 | 31.3% | 9.8% | 103 |
| Flagged (≥0.20) | 68,743 | +1.21 | 37.6% | 20.6% | 107 |
| All | 466,895 | +0.51 | 32.6% | 12.8% | 104 |
These exit rules are designed around infrequent, large winners rather than frequent, small ones. That shape has specific characteristics anyone using or following the system should understand before drawing conclusions from short-run results.
Roughly three out of every four simulated trades end at or near the initial stop. The feedback loop is sparse: statistical confirmation that the system is working only arrives when the occasional fat-tail winner catches a long move. Short-term samples look discouraging even when the long-run math is sound.
The median winner holds 169 days. The 75th-percentile winner holds 295 days. During that hold, price will regularly pull back 10–15% without triggering the 50 EMA stop. The system requires tolerance for substantial unrealized-gain volatility between entry and eventual exit.
Without a partial, open gains are given back on the exit day when the 50 EMA is breached — a trade that ran to +7R can close at +2.5R. The prior half-at-1R rule locked in a more comfortable median exit; the current rule gives up that comfort for higher mean R per trade in backtest.
Three statistical findings, each independently verified on the 78-configuration sweep and jointly verified on the 2,803,855-trade backtest rebuild:
The selected configuration — 50 EMA trail, 5R breakeven trigger, no partial — is therefore the strongest in our testing along each axis independently and jointly. It is not a single local optimum but the combination of three individually dominant choices.
In backtest, the change raises mean R per trade from +0.070 under the prior configuration to +0.385 under the selected one. Restricting to trades the model flags (conviction ≥ 0.20) raises the out-of-sample mean R further, to +1.21. These are backtest results on simulated trades; live performance has not been established and actual results will differ.
Important disclosures. The figures in this document are hypothetical results derived from a backtest simulation applied to historical price data. They do not represent the performance of any real trading account, and no client funds were managed in producing them. The simulation uses assumptions about entry price, slippage, and execution that may not match real-market conditions.
Past performance, whether hypothetical or actual, is not a reliable indicator of future results. Backtest performance in particular is subject to overfitting, look-ahead bias, selection of the historical period, and the specific design choices documented here; forward performance will differ, often materially.
This document is methodology documentation for a personal trading system operated by the author. It is not investment advice, a recommendation to buy or sell any security, an offer to manage money, or a solicitation of any kind. Nothing here should be relied upon to make investment decisions. Readers should consult a qualified financial professional regarding their own circumstances.
Data sources: U.S. equity OHLCV and fundamentals via Financial Modeling Prep (FMP). Backtest and model training code maintained by the author.