SMOKIN' ACES·Research

Research

Original research from our own data: what we tested, what we measured, and what we refused to ship.

It returned 616% with a Sharpe of 1.16. We did not deploy it.

2026-08-14

A Weinstein Stage-2 trend sleeve backtested at 616% total return, Sharpe 1.16, profit factor 3.30 and a deflated Sharpe of 0.9997. Every number is real. We shelved it, and the three reasons are more useful than the headline.

Our backtest says 1.37. Live says 0.96. The comparison is a trap.

2026-08-14

Our Wyckoff signal tracker backtests at profit factor 1.37 across 95,319 trades and returns 0.96 live. The obvious conclusion is that the backtest overstated by 43%. We checked, and the two numbers are not measuring the same thing.

Elliott Wave counts are subjective. We ship the invalidation level instead.

2026-08-14

Wave counting is irreducibly subjective: the ZigZag threshold determines the count, multiple valid counts coexist, and labels are provisional until hindsight. So our engine never emits one count. It emits ranked candidates with the price that breaks each one.

201 Gann rules produced zero testable results. We kept one construct and threw the rest away.

2026-08-14

We extracted 201 Gann rules from trading books. None ever reached twenty trades and 116 were skipped as unevaluable. Gann angles need an arbitrary scaling constant, which makes them unfalsifiable. We shipped only the Square-of-9 price ladder, and only as confluence.

We extracted 1,275 rules from seven technical frameworks. Thirteen were testable.

2026-08-14

Wyckoff, Elliott Wave, Gann, Fibonacci, Ichimoku, RSI and MACD. We turned 1,275 book rules from these frameworks into machine-evaluable form and backtested them. Thirteen ever accumulated twenty trades. Gann produced none at all.

511 SMC and Dow Theory rules from books. Two were testable. The engines are a different story.

2026-08-14

We extracted 358 Smart Money Concepts rules and 153 Dow Theory rules from trading books. Two ever reached twenty trades. Meanwhile the deterministic engines built from the same methods raise our buy list from +4.66% to +5.87%. Both facts are real.

The random-entry baseline everyone assumes is wrong by up to 24 points

2026-08-13

If a trade has a fixed target, stop and time limit, the usual 1/(1+RR) baseline is not the right null. We measured it directly: for our equity setups the true random-entry rate is 27.9%, not 33.3%, and the error grows with barrier width.

We tested 21,191 trading rules from 656 books. Zero survived.

2026-08-13

We extracted 21,191 mechanical rules from 656 trading books and backtested every one. After correcting for the number of trials, none showed a statistically significant edge. Here is the method, the result, and what it changed.

Our signals win 64.9% of the time and lose money

2026-08-13

Across 11,615 resolved futures signals we win 64.9% of the time and average -0.0208 R per trade. Here is the arithmetic that makes those two facts compatible, and why a published win rate tells you almost nothing on its own.

Our Wyckoff-gated buy list is up 2.71% per position. Here is the number we don't lead with.

2026-08-13

Sixty positions of real money over two months. The closed trades show 91.8% wins and +3.63%. Including the eleven still open, the honest figure is +2.71% and 85%. Here is why we publish the second number, and what Wyckoff actually contributes.