Human judgment × machine intelligence
DTRM Research Lab tests a precise proposition: in uncertain, open systems, computational power creates more value when human judgment retains control of the objective, constraints and final decision.
Narrative protocol
Five connected chapters explain the hypothesis without claiming victories the research has not yet demonstrated.
DeepBlue, AlphaGo and AlphaStar demonstrated extraordinary search and optimization inside bounded environments. Their victories begin after humans have already specified what the game is and what winning means.
The machine solves the game. The human defines it.
A normalized comparison between human-controlled selection, machine recommendations, AI-managed instruments and the S&P 500 benchmark.
In a competitive market, the objective is not to maximize a forecast in isolation. Game theory frames the problem as a sequence of decisions made against other adaptive players, where payoff must be evaluated together with the risk required to obtain it.
The Adjusted Score is the search layer: it ranks opportunities after the model signal has been conditioned by the research methodology. Portfolio selection is the allocation layer: the human chooses how those ranked opportunities interact inside a finite portfolio. CAGR / volatility and maximum drawdown are ex-post tests of that decision process — efficiency per unit of realized risk and the depth of the worst peak-to-trough loss.
The current sample is consistent with a stronger result than return alone suggests: DTRM converts realized volatility into growth more efficiently while containing downside. It does not prove causality, but it is precisely the behavior the ranking-plus-selection protocol is designed to test.
CAGR / volatility = annualized compound growth divided by annualized realized volatility. Max drawdown = largest peak-to-trough decline. Metrics are recalculated from the same daily published series on every snapshot refresh.
| Rank | Movement | Asset | Adjusted | Trump score | Signal |
|---|---|---|---|---|---|
| 01 | WMWM | Adjusted score: 92.5 | Trump Score: 95.9 | strong | |
| 02 | TMOTMO | Adjusted score: 89.2 | Trump Score: 94.1 | strong | |
| 03 | MCKMCK | Adjusted score: 88.0 | Trump Score: 92.4 | strong | |
| 04 | JCIJCI | Adjusted score: 87.8 | Trump Score: 95.3 | strong | |
| 05 | MRSHMRSH | Adjusted score: 86.6 | Trump Score: 89.5 | strong | |
| 06 | PMPhilip Morris International Inc. | Adjusted score: 85.6 | Trump Score: 90.0 | strong | |
| 07 | TTTT | Adjusted score: 85.3 | Trump Score: 93.0 | strong | |
| 08 | BKNGBKNG | Adjusted score: 84.9 | Trump Score: 91.8 | strong | |
| 09 | NVDANVIDIA Corporation | Adjusted score: 84.2 | Trump Score: 99.4 | watch | |
| 10 | ADPADP | Adjusted score: 82.9 | Trump Score: 87.7 | watch | |
| 11 | DHRDHR | Adjusted score: 82.6 | Trump Score: 93.6 | watch | |
| 12 | MOMO | Adjusted score: 81.3 | Trump Score: 90.6 | watch | |
| 13 | HCAHCA | Adjusted score: 80.7 | Trump Score: 91.2 | watch | |
| 14 | MCDMcDonald's Corporation | Adjusted score: 79.8 | Trump Score: 82.4 | watch | |
| 15 | VZVZ | Adjusted score: 78.3 | Trump Score: 84.8 | watch | |
| 16 | DUKDUK | Adjusted score: 78.3 | Trump Score: 79.5 | watch | |
| 17 | AMTAMT | Adjusted score: 77.5 | Trump Score: 87.1 | watch | |
| 18 | COFCOF | Adjusted score: 76.6 | Trump Score: 80.1 | defensive | |
| 19 | TMUST-Mobile US, Inc. | Adjusted score: 75.9 | Trump Score: 85.9 | defensive | |
| 20 | AAPLApple Inc. | Adjusted score: 75.8 | Trump Score: 83.0 | defensive | |
| 21 | MARMAR | Adjusted score: 75.3 | Trump Score: 81.8 | defensive | |
| 22 | PHPH | Adjusted score: 74.9 | Trump Score: 77.7 | defensive | |
| 23 | FTNTFTNT | Adjusted score: 74.7 | Trump Score: 85.4 | defensive | |
| 24 | SBUXSBUX | Adjusted score: 74.5 | Trump Score: 78.3 | defensive | |
| 25 | ABNBABNB | Adjusted score: 71.9 | Trump Score: 80.7 | defensive |
The rebalance date, 60-day cadence, eleven-asset output and 100% allocation are controlled. The selection mechanism is the experimental variable.
The ranking narrows the 174-asset universe to a Top 25 candidate squad. Human judgment then combines Trump Score, adjusted score and market context to define the final eleven. Higher adjusted scores generally inform the offensive, higher-weight positions.
ChatGPT selects eleven assets directly from the validated 174-asset universe and independently determines their proportional weights. The private advisory protocol is repeated every 60 days on the same date as DTRM, with no user memory or retrieval context.
The operational prompt remains in the private agent. This public snapshot exposes only the approved protocol identity and integrity digest.
SHA-256 · a853eb5d6ccd881d…One private daily batch curates the same FMP evidence set used by the model. DTRM agent claims describe possible impact on published positions; they never alter the locked 60-day alignments.
28 Aug, 10:06 UTCFinancial Modeling Prep (opens in a new tab)
HP’s emphasis on an AI PC strategy could modestly support demand expectations for AI chips and related infrastructure, which may be supportive for current positions in NVDA and AVGO.
28 Aug, 00:00 UTCFinancial Modeling Prep (opens in a new tab)
Elastic’s strong earnings and AI search growth narrative could reinforce broader enterprise AI adoption trends that may support cloud and AI platform demand relevant to MSFT and GOOGL.
27 Aug, 13:00 UTCFinancial Modeling Prep (opens in a new tab)
Marvell’s pending earnings amid AI chip market scrutiny could spill over to sentiment on AI semiconductor suppliers, which may affect NVDA and AVGO holdings.
27 Aug, 12:16 UTCFinancial Modeling Prep (opens in a new tab)
Okta’s strong performance tied to rising AI security demand could be read as supportive of continued enterprise AI deployment, indirectly benefiting MSFT and GOOGL as major cloud and AI ecosystem providers.
28 Aug, 08:01 UTCFinancial Modeling Prep (opens in a new tab)
Marvell’s share drop on timing of a Google deal could signal delayed progress on a Google-related partnership, which may slightly weigh on sentiment around GOOGL’s near-term infrastructure initiatives.
27 Aug, 17:00 UTCFinancial Modeling Prep (opens in a new tab)
The IREN earnings preview highlighting AI cloud performance could influence sentiment around AI compute demand and infrastructure buildout, with potential read-through to GOOGL and AVGO.
27 Aug, 11:04 UTCFinancial Modeling Prep (opens in a new tab)
Veeva’s strong earnings and AI-driven growth narrative may support expectations for continued enterprise software and cloud usage, indirectly supportive for MSFT and GOOGL.
27 Aug, 08:13 UTCFinancial Modeling Prep (opens in a new tab)
The article’s framing of NVIDIA’s AI dominance and strong growth outlook may support positive investor sentiment toward NVDA and, by association, the broader AI hardware stack including AVGO.
LLM-assisted research annotation · Probabilistic claims · Not financial advice
Checked-in fallback snapshot for builds without a backend publication bundle. Set DTRM_PUBLICATION_BUNDLE to consume a verified daily_publication output. Not financial advice.
Human checkpoint
The Resistance was never meant to be a solo project. It is not a movement against artificial intelligence. It is a refusal to stop thinking simply because our machines have become extraordinarily capable.
The people leading today's AI revolution deserve admiration for what they have built. But even extraordinary products can become dangerous ideas when we fall so deeply in love with them that we confuse automation with progress and remove humanity from the decision.
Humanity should not fear machines that think, but men and women who stop doing it.
The future we want gives people better machines, better evidence and a wider search space — while keeping purpose, judgment and responsibility human. The machine can search. The human must still define the game.
We should never build a future that looks back at its own ruins and can only say:“People lived here.”