Trading on Mulham Fetna

Our track record can't cheat. Here's how we froze it

contact@mulhamfetna.com (Mulham Fetna) — Mon, 07 Sep 2026 00:00:00 +0000

Every trading track record you’ve ever seen asks you to trust its author about one thing: that the rules weren’t adjusted along the way. Strategies quietly swapped after a bad month, windows chosen after the fact, losers left out of the tally. You can’t audit any of it, so the record is worth exactly as much as the author’s word.

For the final act of this project, we built a track record where the author’s word doesn’t matter.

Out-of-sample by construction
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On 2026-08-31 we signed a protocol — a public document in the repository — that froze everything an author could later fudge:

The universe: exactly 9 of our 54 configurations, admitted by five pre-registered criteria applied to the forward books (the selection beat a random-set control — claim LIVE-ALLOWLIST-FROZEN). Not one slot was hand-picked.
The parameters: the deployed set, pinned by cryptographic hash into the signed document. The replay tooling refuses to run if the universe file differs by one byte from the signed hash.
The rules: one contract always, the engine’s stated fill conventions, mechanical kill rules, and a “never-list” (no manual overrides, no window cherry-picking, no pausing to wait out a drawdown) — violations void the record’s claims from that point, by the protocol’s own text.

flowchart LR
    P["✍️ signed protocol
2026-08-31, public in the repo"] --> U["THE UNIVERSE
9 of 54 configurations,
admitted by five pre-registered
criteria (beat a random-set control)"]
    P --> PAR["THE PARAMETERS
pinned by cryptographic hash —
replay refuses to run on a
one-byte difference"]
    P --> RU["THE RULES
1 contract · stated fills ·
mechanical kills · never-list
(violations void the record)"]

Because the parameters and universe were published before any future data existed, every recorded window is out-of-sample by construction. When new data arrives, it is first audited against the previous delivery for retroactive changes (a repaint check), then replayed with the frozen set. The result becomes a new claim in the machine-verified ledger — a losing window under exactly the same rules and prominence as a winning one. The protocol says so in writing: a negative outcome is a publishable result of the protocol, not a failure of it. No verdict is allowed before a pre-registered power threshold; interim windows are descriptive only.

We forward-tested our own strategies on data they'd never seen. They kept 17.6%

contact@mulhamfetna.com (Mulham Fetna) — Mon, 07 Sep 2026 00:00:00 +0000

Everyone forward-tests other people’s strategies. The uncomfortable experiment is doing it to your own — the 54 optimizer-selected configurations you actually believe in — on a window of the future that arrived after every parameter was fixed, with a falsifier written before the run.

We did it twice (round two after a data correction — more on that below). Here’s what honest out-of-sample performance looks like.

The headline table
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3,733 entirely new trades across all 54 configurations, nine futures markets:

Fresh window, all 54	trades	raw	at $10/round-trip	at $25/round-trip
total	3,733	+$29,807	−$7,523	−$63,518

The raw positive is statistically indistinguishable from zero (t = 0.88). Against what the calibration window promised, the fleet kept 17.6% of its per-trade rate — a decay that is itself statistically significant (t = −2.53). The survivors of $25 costs are few and nameable: the 4-hour timeframe (+$10,106 net) and ES as an instrument (+$17,119). Fifteen of 54 configurations stayed positive at $25; most of the rest are small-timeframe cells whose $4–7 gross per trade is a commission illusion.

We swept the entire economic calendar since 2010. Exactly one place pays

contact@mulhamfetna.com (Mulham Fetna) — Mon, 07 Sep 2026 00:00:00 +0000

Scheduled economic news — inflation prints, jobs reports, rate decisions, oil inventories — moves futures markets violently. Every trading course says so, and it’s true. We wanted the question nobody answers with a complete sample: which of these moves can you actually get paid for, after real costs?

So we tested all of it. Not the promising ones — all of it.

The sweep
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Three waves, each pre-registered before running: first 612 market×news pairs on nine futures markets against the full calendar since 2010; then the secondary series (11,822 additional news moments); finally a literal closure census of all 661 remaining series×instrument cells, so that every combination in our registry has a recorded verdict — positive, negative-with-power, or honestly “underpowered, no verdict”. Costs stressed on every table.

The answer: one place
#

The US CPI (inflation) release, on equity-index futures. That’s it. The market jumps on many things; it pays on this one:

Long NQ (Nasdaq futures), entering 300 seconds before the release, fixed stop and target: +$133 per event net of stressed costs, t = 4.13 — and it survives a Bonferroni correction across all 54 cells of that study design.
ES (S&P futures) riding CPI alone: 116 events, +$151.37 per event at $52.50 of stressed costs, p = 0.0027, the release jump 20.5× the quiet baseline, both sample halves positive, control floor and a 1,000-placebo noise check green. Recent regime (2024–2026): +$529 per event.
The premium is ordered exactly by index sensitivity: Nasdaq > S&P > Dow > Russell. It is an equity-index phenomenon — the same event on gold or oil pays nothing.

flowchart LR
    NQ["NQ · Nasdaq
most index-sensitive
largest premium"] --> ES["ES · S&P 500"]
    ES --> YM["YM · Dow"]
    YM --> RTY["RTY · Russell
smallest premium"]
    RTY -.-> X["GC · CL · NG …
same event,
pays nothing"]

The graveyard (equally important)
#

Of the 661-cell closing census, exactly one cell came out exploratory-positive. 179 cells are significant negatives — and here’s the instructive part: 41% of those lose purely to costs (their gross edge is within $5 of zero), and 29% are actually gross-positive before friction. The market really does move; the move really is bigger than quiet times; and the spread eats it. Violence is not premium.

We tested the most famous day-trading strategy on 16 years of data. Zero of 225 versions survived real costs.

contact@mulhamfetna.com (Mulham Fetna) — Mon, 07 Sep 2026 00:00:00 +0000

If you’ve ever watched a trading tutorial, you’ve met the opening-range breakout: mark the high and low of the first minutes of the session, and trade the breakout when price escapes that range. It’s simple, mechanical, and everywhere — and recent academic papers report spectacular returns for it on stocks.

We wanted to know: does it actually work on futures — the markets we research — once you pay real-world trading costs?

How we made it impossible to fool ourselves
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The problem with backtests is that the person running them controls everything: which variants get tried, which costs get assumed, which results get shown. Try enough variants and something will always look great by accident.

So before computing a single profit number, we pre-registered the entire experiment in our public repository: nine futures markets (Nasdaq, S&P, Russell, Dow, gold, silver, copper, crude oil, natural gas), two session anchors, four range lengths (5/15/30/60 minutes), three exit rules taken verbatim from the literature, plus a published comparator rule — 225 configurations, zero tunable parameters, verdict thresholds and controls fixed in advance. Sixteen years of one-minute data (2010–2026). One contract per trade. Costs stressed at $25 per round trip. No configuration could be added, removed, or “fixed” after seeing results.

What 16 years of data and 3.5 months of honest testing taught us about trading strategies

contact@mulhamfetna.com (Mulham Fetna) — Mon, 07 Sep 2026 00:00:00 +0000

This is the story of a research project my team at BeInMedia ran from May to September 2026: a systematic attempt to find out what actually survives in futures trading once you stop fooling yourself. We built the machinery, paid for the answers, and published everything — including the failures. The code, the evidence, and every number below are public and machine-verifiable: github.com/mulhamfetna/trading-strategy-finder.

Here is the whole arc, honestly told.

flowchart LR
    A["May 2026
machinery built:
backtest engine · optimizer ·
claims ledger"] --> B["Jun–Aug 2026
the studies:
economic-calendar sweep ·
225-cell ORB grid ·
forward test of our own fleet"]
    B --> C["Aug 31, 2026
track record frozen
under a signed protocol"]
    C --> D["Sep 2026
two preprints filed ·
everything published,
including the failures"]

The rule that shaped everything
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Backtests lie — not because the math is wrong, but because the person running them chooses what to try, what costs to assume, and what to show you. Our defense was structural: every study was pre-registered before it ran (design, thresholds, controls frozen in writing first), every negative result had to prove it had the statistical power to see an effect, every positive had to beat a dumb control, and every published number lives in a machine-verified claims ledger — re-derived from committed evidence files by our CI on every change, 79 claims and counting. If a number in this series doesn’t re-derive, our own build fails.

Trading on Mulham Fetna

Our track record can't cheat. Here's how we froze it

Out-of-sample by construction #

We forward-tested our own strategies on data they'd never seen. They kept 17.6%

The headline table #

We swept the entire economic calendar since 2010. Exactly one place pays

The sweep #

The answer: one place #

The graveyard (equally important) #

We tested the most famous day-trading strategy on 16 years of data. Zero of 225 versions survived real costs.

How we made it impossible to fool ourselves #

What 16 years of data and 3.5 months of honest testing taught us about trading strategies

The rule that shaped everything #

Out-of-sample by construction
#

The headline table
#

The sweep
#

The answer: one place
#

The graveyard (equally important)
#

How we made it impossible to fool ourselves
#

The rule that shaped everything
#