Independent strategy research
Do filters make a breakout system better? We tested a dozen.
Honest answer: zero of them beat doing nothing. We took a working, cost-aware breakout system on liquid futures — one that made money every year across a 13-year, 600,000-plus-trade backtest — and bolted on 10+ classic sizing and regime filters to see if any could improve it. None beat the plain flat baseline. The most convincing one was an artefact.
The question people never actually test
Every retail trader with a working setup asks the same next question: how do I make it better? Add a volatility filter. Size up in the good regime. Skip the choppy days. It feels like free money — you already have an edge, you're just steering it. We had the rare thing that makes this testable: a breakout system that already works after real costs. So we ran the experiment properly instead of eyeballing an equity curve.
The base system is boring on purpose. Pick the best-decorrelated legs, size them flat, hold. No regime cleverness. It made money in every one of 13 years. Then we tried, one at a time, to beat it with the filters everyone reaches for. Each overlay had to clear the same three gates the base system passes: a best-of-N placebo shuffle, a look-ahead audit, and a nested, rolling out-of-sample check. Same bar, no exceptions.
What we tried, and how each one died
Name-free by rule — this is a proprietary system, so no strategy, no basket, no tickers. What follows is the filter type and the exact failure mode. That's the useful part anyway.
| Overlay / knob (what was tested) | Placebo | Verdict | Why it failed |
|---|---|---|---|
| Volatility-regime position sizer | P100 | ARTEFACT | circular — returns divided by an auto-correlated volatility term, so the "regime" was roughly 1/its own denominator; tail-fragile on FX |
| Range-width sizer (size up on narrow ranges) | — | FAILED | real on one instrument class alone, but on the basket it concentrated whipsaw risk and roughly doubled drawdown |
| Whipsaw / second-break down-weighter | — | FAILED | improved the worst year slightly, lowered risk-adjusted return and net; a real structure that doesn't monetize as a filter |
| Two overlays stacked | — | WORST | cumulated variance — risk-adjusted return fell by roughly two-thirds and drawdown ballooned |
| Selection-config knobs (lookback, decorrelation, cadence, breadth) | — | NO GAIN | current settings are a genuine interior optimum; a short lookback inflated drawdown +44% to +109% over 13 years |
| Options-gamma regime (same-day) | — | LOOK-AHEAD | the day's realized regime leaks into an overnight-started book; the honest prior-day version predicts nothing |
| Options-gamma regime (intraday, at-entry) | P100 | WEAK / OPEN | look-ahead-safe and robust, but small (~+14% relative per-trade); portfolio benefit still being quantified |
Placebo percentile is where the overlay ranks against thousands of shuffled-label copies of itself. P100 means it topped every shuffle — which, as the vol-regime sizer shows, is not the same as an edge.
The one that looked like a winner
The volatility-regime sizer was the seductive one. It looked significant in every single year and scored at the 100th placebo percentile — it beat every one of its own shuffled copies. On a normal day that's a green light. Here it was a red flag, because the effect was too big. A headline lift north of nine times should never come from a sizing tweak, and when a number is that good you go looking for the leak.
The leak was arithmetic. The sizer divided returns by a volatility term that was itself auto-correlated with those returns, so the "regime signal" was roughly one-over-its-own-denominator. It was measuring itself. Clean on metals, noise on energy, and on FX it was tail-fragile — dropping the top 5% of days flipped the sign. That's not a regime edge. That's a divide-by-volatility circularity artefact wearing a perfect placebo score.
The real lesson: filters mostly add variance
Here's the pattern across all 10+ overlays. The base system's edge doesn't live in cleverness about when to trade. It lives in leg selection and decorrelation — picking positions that don't all lose on the same day. Every filter we added was a bet on top of that bet. And a bet on top of a bet doesn't compound the edge. It compounds the noise.
Stacking made this brutally visible. Combine the two least-bad overlays and you don't get the average of two mild tilts. You get their variance stacked: risk-adjusted return fell by roughly two-thirds and drawdown ballooned. Double-tilting is double-jeopardy. The range-width sizer told the same story more quietly — dazzling on one instrument class in isolation, a drawdown-doubler once it had to survive at the basket level, where its "narrow is better" mirage ignored the correlation it was quietly concentrating.
Even the config knobs said it. Sweep the selection lookback and a short, recency-weighted setting looks brilliant on the last two years — then inflates drawdown +44% to +109% across the full 13. That's a textbook recency overfit: the market's most recent character sold you a parameter that the other eleven years reject. The middle lookback beat both the shorter and longer ones on every metric. The current settings aren't a lucky best-of-many pick; they're an interior optimum that holds up when you actually roll it forward.
The one clean thing we did learn
The audit wasn't empty. Its cleanest result was diagnostic, not a filter: the system's losses concentrate in whipsaw ranges that break both directions. One-sided breaks were solidly positive. The first break that then reverses was the primary loss source. A genuine second break was roughly flat. This held across every instrument class and all 13 years — a real structural signature that explains why the system loses when it loses.
You'd think that's a filter waiting to happen: just down-weight the whipsaw. We tried. It improved the worst year a touch and lowered risk-adjusted return and net everywhere else. A real structure that still doesn't beat doing nothing once you price the variance it adds. Knowing your loss mechanism is worth a lot. It is not the same as having a knob that pays you to avoid it.
Why believe a null this flat
A null is only worth reading if the test could have found something. Ours could — the base system passes these exact gates, so the machinery clearly distinguishes signal from noise. Every overlay was judged on risk-adjusted return (net over max drawdown), its placebo percentile, and its worst year, with the tail checked separately so a fragile winner couldn't hide behind a good average. The look-ahead audit is what caught the same-day gamma regime leaking tomorrow's information into a book opened last night — the sort of look-ahead that turns a backtest into fiction.
- A "failed" overlay means not a robust improvement on this 13-year, 600,000-plus-trade book — not that the idea is impossible everywhere.
- Several overlays weren't cost-fatal so much as never a real edge — they didn't beat their own placebo before costs even entered.
- We publish the audit, not the base system's absolute numbers, config, or composition. The filters not helping is the finding. The edge itself is the paid product.
How we test
Same pipeline we run on everything. Systems are ported to Python and run against real costs — spreads and commissions modelled from tick data, not a flat guess. Futures come from 13 years of CME data; FX from tick-level bid/ask; crypto as spot and perps. A fast model does the bulk porting, then the strongest model tries to break every apparent winner, hunting look-ahead and impossible fills. It is the same process that rejects roughly 78% of the retail strategies we test — and here, turned inward, it rejected every filter we tried to bolt onto a system that already worked. The uncomfortable honesty cuts both ways.
Research and education, not financial advice. No signals, no return promises. Independent, and not affiliated with TradingView.
Want the named verdicts?
This page gives you the method: which filters die on a working system, and exactly how. What it doesn't give you is the roster — every strategy and indicator we audited, named, with its verdict and the exact reason it lived or died. That is The No List.
Get The No List → or join the Discord first →FAQ
Can adding a filter make a working breakout system better?
In our testing, no. We stress-tested 10+ sizing and regime filters on a breakout system that already worked after costs. Zero beat the plain flat baseline once each had to clear a placebo shuffle, a look-ahead audit and rolling out-of-sample. On an already-honest system, filters mostly add variance.
The best filter scored at the 100th placebo percentile — wasn't that a real edge?
No, and that's the lesson. The top-scoring overlay beat every shuffled copy of itself, but the effect was a divide-by-volatility circularity artefact — it divided returns by a term auto-correlated with those returns, so it was measuring itself. A headline lift over nine times is a red flag, not a green light.
What about stacking two filters together?
Worst of all. Combining the two least-bad overlays didn't average their tilts, it stacked their variance: risk-adjusted return fell by roughly two-thirds and drawdown ballooned. Double-tilting is double-jeopardy.
If filters don't help, where does the edge come from?
Leg selection and decorrelation — holding positions that don't all lose on the same day — not from timing filters. The base system sizes flat and picks the best-decorrelated legs. Every filter added on top was a bet on a bet, which compounds noise rather than edge.
Does a "failed" filter mean the idea never works anywhere?
No. It means not a robust improvement on this 13-year, 600,000-plus-trade book under our gates. Some overlays weren't cost-fatal so much as never a real edge — they didn't beat their own placebo before costs entered. A null on this data and window is not a proof of impossibility.