Not supported · Exits · 2026-10-01; timeframe gap fill 2026-10-11
Would bigger fixed targets (3R, 5R) beat 2R?
What this means
Letting winners run to fixed 3R or 5R targets did not beat random entries; with a swing-low stop all three fixed targets did worse than random (−0.05 to −0.09R). Fixed-R targets worked in 2016-20 and failed in 2021-26 (swing low to 5R: +0.59R then −0.60R). On this evidence, larger fixed targets do not add an edge. The same is true on 1h and 4h: letting the trade run to 3R or 5R does worse than random entries with the same stop and target, and the gap widens as the target grows.
The explanation is our best reading; the data shows the result but not the cause directly.
By timeframe
| Timeframe | Verdict | n | Result |
|---|---|---|---|
| 15-minute | Not tested: not tested yet: no 15-minute price data; needs 15m prices or a TradingView run (see tradingview_runs.md) | ||
| 1-hour | Not supported | 3,414 | vs random: 3R -0.025 (q=0.60), 5R -0.087 (q=0.07); 2R reproduces +0.01 gap fill G13, 2026-10-11 (price simulation; 1h/4h prices cover Nov 2023-Sep 2026 only) |
| 4-hour | Not supported | 735 | vs random: 3R -0.13 (q=0.12), 5R -0.21 (q=0.04); 2R -0.11 gap fill G13, 2026-10-11 (price simulation; 1h/4h prices cover Nov 2023-Sep 2026 only) |
| Daily | Not supported | 2,006 | 3R -0.04, 5R -0.06 vs random |
- Hypothesis
- Letting winners run to 3R or 5R improves the edge over random entries.
- Method
- Same random-entry design as exit-swing-high, daily 2016-26.
- Result
- Logged stop -> 3R / 5R: -0.04 / -0.06 vs random. Swing-low -> 2R / 3R / 5R: -0.05 / -0.07 / -0.09 (CIs exclude 0). Fixed-R targets were positive in 2016-20 and negative in 2021-26 (e.g. swing low -> 5R +0.59 / -0.60). 1h/4h gap fill (2026-10-11), same random-entry design (Nov 2023-Sep 2026; 2R arm reproduces +0.01 / -0.11): 3R -0.025 (1h) / -0.130 (4h); 5R -0.087 (-0.161, -0.010) / -0.212 (-0.378, -0.046), q = 0.07 / 0.04.
- How much to trust it
- Medium: Python re-sim of TV setups; reproduced on re-run
- Did it change the live rule?
- No.
Scripts and data for this study will be linked here when the research code is published on GitHub.
Education and research only. Not financial advice. Backtests use today's index members (survivorship bias).