Market intelligence that can improve itself without giving itself permission to lie

Sense -> hypothesize -> falsify -> paper-test -> settle -> remember -> evolve -> govern

_{An evidence operating system for equities, options, macro, extreme-market intelligence, governed research, and human-owned authority.}

Why DOPEY is different | How it works | Capability atlas | Squeeze engine | Bubble engine | Research autonomy | Desktop | Quick start

Designed for: investment research and risk teams that want machine-scale market investigation without collapsing backtests, paper evidence, broker state, and capital authority into one unsafe score.

Explore the ObtuseAI portfolio · Inspect the evidence lifecycle · Start a technical conversation

DOPEY is a governed, evidence-first market-intelligence operating system for paper research. It is built to investigate equities, options, volatility, macro conditions, market structure, short crowding, speculative bubbles, IPO reflexivity, portfolio risk, and the quality of its own reasoning.

It is not a single model, a stock screener, a backtest notebook, or a chatbot that produces trade ideas. It is a network of specialized research systems connected by explicit evidence contracts:

• data must be point-in-time, attributable, fresh, and structurally valid;
• a hypothesis must identify both supporting and contradicting evidence;
• a crowded position is not a squeeze until an independent catalyst appears;
• an expensive asset is not a short until an independent unwind appears;
• a backtest is not forward evidence;
• a model price is not an executable quote;
• a queued order is not a fill;
• an improvement is not accepted until it beats its baseline under protected evaluation;
• missing evidence produces a blocked state, not synthetic confidence.

DOPEY is designed around a difficult question:

How do you build a market-research system that becomes more capable, adaptive and increasingly self-directed in research without becoming more vulnerable to overfitting, hindsight, duplicated evidence, stale state, fabricated certainty, or authority creep?

Its answer is to make truth, memory, research autonomy, and execution separate systems that must agree through typed, content-addressed artifacts.

Public-release truth: a fresh clone starts with no provider market data, broker session, account history, credentials, private runtime lake, or operator-specific evidence. New intelligence begins paper-first and remains not_action_ready until the operator supplies governed data and the evidence gates are genuinely satisfied.

Most research systems optimize for the moment a signal is produced. DOPEY optimizes the entire epistemic lifecycle: what was known, when it was known, how the claim was formed, how it was challenged, what decision was recorded, what happened later, and whether the system earned the right to change.

The impressive part is not that DOPEY can produce more signals. It is that it can produce, challenge, settle, remember, and improve research while retaining the ability to say no evidence, not enough evidence, contradictory evidence, inconclusive timeout, or rollback required.

DOPEY is a set of interacting closed loops, not a linear "predict and trade" script.

flowchart LR
    S["1. Sense<br/>price, options, macro, filings"] --> V["2. Validate<br/>schema, time, quality, provenance"]
    V --> H["3. Hypothesize<br/>structure, regime, catalyst, valuation"]
    H --> F["4. Falsify<br/>contradictions, controls, leakage, duplicates"]
    F --> P["5. Paper-test<br/>costs, path, liquidity, risk, exits"]
    P --> T["6. Settle<br/>forward outcomes and calibration"]
    T --> M["7. Remember<br/>immutable evidence and failure memory"]
    M --> E["8. Evolve<br/>bounded challengers and tournaments"]
    E --> G["9. Govern<br/>fitness, readiness, rollback, authority"]
    G --> S
    G -.->|"eligible evidence only"| X["Exact paper twin"]
    X -.->|"separate operator authority"| L["Live Branch"]

1. Acquire. A provider adapter or operator-owned import stages price, options, fundamental, macro, short-interest, IPO, or contextual evidence.
2. Validate. DOPEY verifies paths, schemas, timestamps, exchange sessions, numerical domains, source identity, content hashes, and freshness.
3. Build context. Market regime, multi-timeframe structure, volatility, breadth, sector/peer state, catalysts, valuation, and liquidity are joined without losing source lineage.
4. Generate hypotheses. General scanners and specialist engines propose directional or neutral research claims. Every hypothesis carries reasons, inputs, unavailable fields, and an immutable identity.
5. Attack the claim. Epistemic supervision checks for time travel, duplicated evidence, contradictory direction, missing denominators, outlier dominance, regime inversion, mechanical IPO effects, execution contamination, and promotion leakage.
6. Express it on paper. The system models entries, exits, transaction costs, spreads, slippage, path dependency, Greeks, concentration, and loss-limited structures.
7. Freeze the prediction. The pre-outcome thesis is appended before the future observation exists.
8. Settle later. Event-time settlement records what actually happened at defined horizons without rewriting the original thesis.
9. Measure skill. Calibration, hit rate, excess over drift, regime stability, forward-book quality, and negative controls are recomputed from eligible independent evidence.
10. Learn carefully. Research challengers may be generated, but activation requires protected evaluation and any measured fitness drop records rollback_required.
11. Govern authority. Research readiness and execution readiness remain different. Even eligible evidence cannot bypass the paper twin, broker reconciliation, account scope, risk, or operator gates.

Every serious DOPEY artifact answers the same questions:

flowchart TB
    A["Governed sensing<br/>price | options | macro | filings | shorts | IPO"]
    B["Market understanding<br/>regime | structure | volatility | fundamentals | catalysts"]
    C["Specialist intelligence<br/>targeting | squeeze | bubble | options | portfolio risk"]
    D["Adversarial truth<br/>epistemic attack | paper settlement | statistical truth"]
    E["Memory and evolution<br/>trade memory | failure memory | mutations | tournaments"]
    F["Governance<br/>fitness | readiness | rollback | constitution"]
    G["Execution boundary<br/>exact paper twin | broker review | Live Branch"]
    H["Scheduled research operations<br/>39-stage DAG | dynamic scheduler | snapshots | recovery"]

    A --> B
    B --> C
    C --> D
    D --> E
    E --> F
    F --> A
    F -.->|"eligible evidence only"| G
    H -.->|"coordinates"| A
    H -.->|"coordinates"| C
    H -.->|"records"| D

DOPEY treats data quality as model quality. A sophisticated model operating on stale, future-dated, malformed, or misidentified evidence is considered worse than a simple model that abstains.

• daily and intraday OHLCV history;
• corporate actions and adjusted-history metadata;
• option chains and quote snapshots;
• fundamentals, earnings, and filing-derived evidence;
• rates, yield curves, inflation, labor, liquidity, credit, and volatility context;
• sector, peer, breadth, and cross-asset context;
• short interest, daily short-sale flow, fails to deliver, threshold lists, and licensed borrow evidence when available;
• IPO calendars, offer metadata, listing age, float, lock-up, and overhang context;
• news, attention, and event ledgers.

The provider ladder and adapters are not permission to download or redistribute market data. Operators are responsible for licensing and stage their own data outside Git history under DATA_NOTICE.md.

DOPEY builds a market state before it asks for a trade direction.

• multi-timeframe trend and range state;
• support, resistance, breakouts, breakdowns, and compression/expansion;
• candle and pattern evidence;
• RSI and momentum state;
• realized volatility and volatility acceleration;
• price/volume divergence and signed-volume pressure;
• moving-average distance and 52-week extension;
• intraday structure when governed intraday data exists.

• market regime and cross-sectional state;
• sector and peer-relative conditions;
• macro-event families and cross-asset transmission;
• breadth, volatility regime, rates, credit, and liquidity conditions;
• fundamentals, earnings, catalysts, and attention;
• explicit context gaps when a source is unavailable.

The same technical feature can mean different things in different contexts. For example, high RSI can be exhaustion inside a weakening bubble, persistent strength inside a squeeze ignition, or noise inside a low-liquidity range. DOPEY does not assign it one universal direction.

DOPEY's squeeze engine is built around one central distinction:

Overshorted pressure is stored energy. Price/volume/options ignition is the independent event that may release it.

The engine does not use daily short volume as a substitute for open short interest, and it does not treat a negative-gamma estimate as automatically bullish. It constructs multiple independent layers.

flowchart LR
    C["Crowding<br/>open short interest, float, days to cover"] --> P["Pressure"]
    B["Borrow<br/>fee, utilization, availability"] --> P
    F["Settlement<br/>FTD persistence, threshold evidence"] --> P
    T["Technical ignition<br/>returns, breakout, volume, RSI"] --> I["Ignition"]
    O["Options ignition<br/>call pressure, IV, gamma"] --> I
    D["Daily short flow<br/>persistence, not open interest"] --> I
    P --> Q["Joint qualification"]
    I --> Q
    R["Persistence, freshness,<br/>source diversity, contradictions"] --> Q
    Q -->|"all gates pass"| A["Research candidate"]
    Q -->|"pressure only"| W["Watch"]
    Q -->|"bad or missing evidence"| X["Blocked"]

The current versioned research defaults in dopey_squeeze_bubble/models.py require:

• at least $10 million of 20-day average dollar volume;
• squeeze pressure of at least 0.45;
• independent ignition of at least 0.50;
• persistence of at least 0.45;
• lane robustness of at least 0.60;
• at least three independent sources;
• zero accepted contradiction flags;
• current technical evidence;
• actual open short interest;
• sufficient option rows when quote-derived options evidence is present.

These values are research-policy gates, not probabilities and not universal market truths. A score of 0.80 does not mean an 80% chance of a squeeze.

For every represented symbol, DOPEY emits:

• component scores and raw source references;
• pressure, ignition, persistence, completeness, and robustness;
• missing and stale lanes;
• contradiction flags;
• liquidity-stress proxy diagnostics;
• exact gate reasons;
• candidate, watch, or blocked state;
• a stable signal identity for later forward settlement;
• 2-, 5-, and 10-session forward-evidence slots.

The candidate lane is research-only. It can suggest that a long-equity expression deserves later review; it cannot create an order.

• Daily short-sale volume is not open short interest.
• Fails to deliver are not proof of abusive or naked shorting.
• Off-exchange volume is not participant intent.
• Negative gamma is not automatically directional.
• A high short-interest percentage without ignition is a watch, not a squeeze.
• Observable liquidity proxies are not a consolidated order book.

The source semantics follow the public descriptions from FINRA short interest, FINRA short-sale volume, and SEC fails-to-deliver data.

DOPEY's bubble engine separates two events that are often confused:

1. Fragility: valuation, leverage, extension, float structure, attention, or options positioning make an asset vulnerable.
2. Unwind: independent downside behavior shows that the fragile structure is actually breaking.

flowchart LR
    V["Valuation breadth<br/>P/E, P/S, P/B, EV/revenue"] --> F["Fragility"]
    L["Leverage<br/>debt, cash burn, margin debit"] --> F
    I["IPO reflexivity<br/>age, offer gain, float, overhang"] --> F
    E["Extension<br/>returns, averages, highs, attention"] --> F
    O["Options crowding<br/>IV, call pressure, gamma"] --> F
    U["Independent unwind<br/>negative returns, breakdown, volume, puts"] --> C["Joint qualification"]
    F --> C
    R["Persistence, source quality,<br/>freshness, contradictions"] --> C
    C -->|"fragile, no unwind"| M["Monitor"]
    C -->|"fragile and unwinding"| Q["Research candidate"]
    C -->|"insufficient evidence"| B["Blocked"]

The IPO lane evaluates:

• days since first trade;
• offer price and appreciation from the offer;
• deal size;
• float constraints;
• listing status;
• lock-up and supply-overhang context when available;
• minimum history needed for technical features;
• nonmechanical evidence beyond "this company is newly listed."

Blank-check companies, acquisition vehicles, ETFs, units, warrants, rights, and obvious non-operating offerings are excluded from the operating-company IPO lane.

A recent IPO can enter technical research after enough sessions exist, but unavailable long-horizon features reduce completeness. Recent-listing mechanics cannot independently create a bearish candidate.

An actionable research candidate requires evidence such as:

• negative 5-day and 20-day return;
• loss of the 20-day, 50-day, or 200-day structure;
• a confirmed 20-day breakdown;
• downside volume expansion;
• put pressure;
• negative gamma in a weakening structure;
• an observable liquidity air pocket.

The current default policy requires bubble fragility of at least 0.58, unwind confirmation of at least 0.42, completeness of at least 0.55, persistence of at least 0.40, robustness of at least 0.60, three independent sources, no contradictions, an empirical valuation or IPO anchor, and a current quote-derived option chain for candidate status.

Fragility of at least 0.20 may remain visible as a monitor when the stronger candidate gates are not satisfied.

A qualified unwind may propose a long put or bear-put debit spread for later governed review. It cannot produce:

• a naked short stock position;
• a naked short option;
• undefined-loss exposure;
• a same-session round trip;
• an order that bypasses option controls, liquidity, readiness, or the paper twin.

The source semantics use public definitions such as Investor.gov's P/E explanation, FINRA margin statistics, and the SEC IPO investor bulletin.

DOPEY can reason about visible liquidity stress through:

• bid/ask width and quote validity;
• average and recent dollar volume;
• float turnover;
• signed-volume pressure;
• persistent reported short flow;
• borrow availability and fee when licensed evidence exists;
• option open interest and volume turnover;
• quote-derived gamma;
• modeled slippage and stressed exits.

It intentionally records:

direct_hidden_liquidity_observed=false

Without licensed order-book, securities-lending, or participant-level data, DOPEY does not claim to see hidden intent, dealer inventory, dark-pool motivation, or a consolidated queue.

Options are a research and expression layer, not a source of magical direction.

• strict option identity and chain snapshot loading;
• bid, ask, midpoint, spread, open-interest, and volume validation;
• implied-volatility solving;
• volatility-surface construction;
• strike skew and maturity term structure;
• volatility-regime classification;
• delta, gamma, theta, vega, and higher-order diagnostics where supported;
• gamma-exposure and flip-level research;
• model repricing across spot, time, IV, and rate scenarios;
• contract ranking by thesis fit, liquidity, convexity, and cost;
• long-premium and defined-risk spread construction;
• assignment, exercise, expiry, and lifecycle research;
• 0DTE replay, adversarial paths, threshold feedback, and cross-underlying research corpora.

DOPEY keeps these classes separate because collapsing them is one of the fastest ways for an options system to deceive itself.

The targeting system asks a harder question than "which symbols score highest?":

Has this exact symbol, under this strategy family and detector, demonstrated repeatable directional skill after overlap, drift, chronology, and chance are considered?

1. Load only direction-graded, policy-eligible settlements.
2. Quarantine legacy assumptions and invalid framing.
3. Deduplicate immutable detection IDs.
4. Collapse overlapping forward windows into one effective observation.
5. Measure directional excess over horizon-adjusted prior drift.
6. Measure directional hit rate.
7. Split observations into chronological halves.
8. Require each era to satisfy its sample and direction floors.
9. Run a deterministic circular-shift null against accidental timing.
10. Assign positive_verified, negative_verified, flat, or insufficient_evidence.

Scanner fitness remains aggregate unless symbol-level point-in-time evidence exists. DOPEY refuses to smear an aggregate scanner score across individual symbols. Rank IC remains unavailable until detections carry a valid continuous point-in-time score.

Candidate registries are written as immutable generations before a stable pointer can move. Shadow selections:

• embed the decisive candidate fields;
• deduplicate by immutable source detection ID;
• enforce weight, direction, and symbol caps;
• retain blocked candidates rather than deleting them;
• never read broker positions, live-book state, order constructors, or action readiness.

DOPEY studies strategies as contested scientific claims.

flowchart LR
    B["Strong simple baseline"] --> C["Bounded challenger"]
    C --> H["History-only features"]
    H --> W["Purged rolling-origin and era walk"]
    W --> K["Costs, slippage, liquidity, exits"]
    K --> N["Negative controls and adversarial stress"]
    N --> P["Pre-outcome paper prediction"]
    P --> S["Forward settlement"]
    S --> T["Calibration and statistical truth"]
    T -->|"positive independent evidence"| R["Eligible for existing promotion gates"]
    T -->|"weak, contradictory, or sparse"| A["Abstain, hold, or rollback"]

• simple and complex baselines under the same data;
• detector families and playbook families;
• long, short, neutral, and volatility hypotheses;
• entry, marking, expiry, and exit policies;
• transaction costs, spreads, liquidity, and slippage;
• purged rolling-origin splits;
• held-out eras and regime cells;
• selection effects and multiple testing;
• forward paper predictions;
• calibration and abstention;
• book-level stress and concentration;
• capacity as a reason to reduce confidence, never increase it.

• synthetic fixtures;
• model-priced options;
• historical replay alone;
• four-trade anecdotes;
• duplicated detector firings;
• a full-suite timeout with no captured failure;
• a high diagnostic fitness score when statistical truth is blocked;
• a candidate's self-reported improvement.

The epistemic supervisor is an adversary, not a second signal generator. It can preserve a research claim, downgrade it, block it, or mark it inconclusive. It cannot promote a candidate or grant execution authority.

The versioned baseline in dopey_validation/epistemic_adversary.py uses:

• maximum evidence age of three days;
• maximum future-clock skew of 60 seconds;
• zero tolerated duplicate fraction;
• denominator floor of 20;
• maximum single-observation weight of 35%;
• maximum single-contribution share of 50%;
• at least 20 observations per regime cell;
• at least three independent regimes;
• a 90-day mechanical recent-IPO window;
• at least four evidence points.

These are explicit policy values, so changes are reviewable and testable.

DOPEY uses RSI in two distinct ways:

• market RSI, such as RSI-14, is one technical feature among many;
• recursive/self-improving intelligence is the governed process that studies how DOPEY researches, tests, and allocates attention.

The second meaning is the important architectural one.

• research depth;
• agent or method selection;
• evidence requests;
• replay count;
• early stopping;
• abstention;
• bounded research cadence;
• bounded research configuration inside sealed ranges;
• universe attention and stale-lane priority.

• constitutional truth rules;
• readiness thresholds;
• execution seals;
• source provenance requirements;
• settlement definitions;
• autonomous-promotion thresholds;
• broker authority;
• credential access;
• evidence floors;
• the meaning of filled, realized, or forward.

flowchart TB
    O["Observe bottlenecks, contradictions,<br/>failures, unknowns, opportunities"] --> Q["Form research questions"]
    Q --> H["Generate hypotheses with<br/>causal parents and falsifiers"]
    H --> C["Apply creativity operators"]
    C --> P["Precommit experimental protocol"]
    P --> E["Evaluate visible, private,<br/>external, and real-task partitions"]
    E --> A["Audit reward hacking,<br/>memorization, complexity, abstention"]
    A --> D{"Protected evaluator decision"}
    D -->|"insufficient"| M["Store failure and request evidence"]
    D -->|"fitness drop"| R["Record rollback_required"]
    D -->|"independent gain"| G["Create bounded research override"]
    M --> O
    R --> O
    G --> O

The intelligence-research layer can generate method challengers through:

1. analogy;
2. inversion;
3. morphological search;
4. constraint inversion;
5. recombination;
6. counterfactual reasoning;
7. first-principles reconstruction;
8. abstraction.

Each output must define:

• the independent variable;
• dependent metrics;
• a falsifiable prediction;
• evidence parents;
• failure conditions;
• a bounded experimental protocol.

Creative output remains experimental. It does not receive truth, permission, readiness, or execution authority.

A research mutation must:

• target an allowed surface;
• stay inside sealed numerical ranges;
• carry an immutable proposal identity;
• meet a minimum sample requirement;
• have a positive lower effect bound;
• remain inside its cost ratio;
• use an evaluator independent from the proposer;
• be deterministic;
• have no invariant violations.

The mutation ledger stores baseline hash, candidate hash, measured fitness, delta, decision, evidence, and rollback evidence in an append-only hash chain. A fitness drop always produces rollback_required; equal fitness does not become improvement.

Before cross-system decisions, DOPEY can build a SystemSnapshot that binds:

• exact source-file hashes;
• exact projection hashes;
• active-universe identity;
• readiness identity;
• paper/live boundary assertions;
• one content-addressed snapshot ID.

This prevents different subsystems from reasoning over incompatible moments while believing they share the same state.

The scheduler prioritizes:

1. lanes that have never produced verified evidence;
2. lanes whose last verified success is stalest;
3. overdue work inside its allowed window;
4. dependencies that must be refreshed first.

It also responds to:

• storage pressure;
• reliability error budgets;
• previous duration and variance;
• active supervisor ownership;
• stale or dead locks;
• partial valid JUnit evidence;
• external provider degradation.

Dynamic does not mean reckless. Storage pressure can block write-heavy lanes, reliability deterioration can roll cadence back, and no scheduler decision can lower an evidence threshold.

The supervisor is a dependency-aware research DAG. It follows absorb -> record -> continue:

• a failed producer blocks its consumers;
• independent stages continue;
• blocked dependencies retain their causal reason;
• every stage records start, end, duration, status, and identity;
• long stages publish heartbeats;
• hard timeouts terminate process trees;
• crashes and lock recovery produce durable evidence;
• repair items identify the failed stage and suggested next action.

flowchart LR
    I["Ingress and integrity<br/>4 stages"] --> M["Modeling and truth<br/>11 stages"]
    M --> F["Forward learning<br/>12 stages"]
    F --> S["Specialist intelligence<br/>10 stages"]
    S --> E["Epistemic supervision"]
    R["Independent readiness roots<br/>2 stages"] -.->|"separate authority"| E
    E --> J["Journal, incidents,<br/>repair queue, next run"]
    J --> I

The supervisor's 39th stage, epistemic_supervision, consumes current targeting and squeeze/bubble artifacts and can only preserve or downgrade their claims.

DOPEY's memory is not a chat transcript. It is a set of immutable event histories and content-addressed projections.

• raw source identities and validation receipts;
• hypotheses and pre-outcome predictions;
• detector and playbook decisions;
• paper entries, marks, exits, and settlements;
• context, regime, cohort, and era attribution;
• calibration and abstention outcomes;
• failed experiments and negative controls;
• mutation parents, candidates, evaluations, and rollbacks;
• incidents, timeouts, degraded stages, and repair items;
• broker reviews, submissions, fills, rejections, and paper-twin links when the separate Live Branch is explicitly operated.

DOPEY treats risk as a research input and an authority constraint, not a chart added after strategy selection.

• symbol, sector, family, direction, and regime concentration;
• premium and notional exposure;
• capacity based on executable contracts and liquidity;
• delta, gamma, theta, and vega attribution;
• correlation and crowding stress;
• drawdown and loss-streak throttles;
• deterministic scenario repricing;
• crisis stress ladders and recovery policy;
• exit playbooks and lifecycle state;
• paper-only kill switches;
• no-day-trade guards;
• proof-weighted allocation.

Capacity can only reduce a candidate's viability. It cannot make weak evidence look stronger.

For long-premium options, loss is bounded by premium at the position level, but DOPEY still tests book-level concentration, liquidity-hole exits, IV collapse, time decay, correlated loss, and stressed marks.

The React 19 desktop turns the evidence graph into an operator-readable control surface.

_{Source-backed 1540 x 960 visual-QA capture. Broker identifiers are redacted and the deterministic capture bridge has no mutation authority.}

Market Tissue	Symbol Lab
Universe, regime, source integrity, and four-tail discovery	Structure, scenarios, contract research, and evidence-bound planning

Every quantitative visualization has a table, provenance surface, and method explanation. Missing telemetry remains missing; the interface does not animate fictional market activity.

DOPEY deliberately separates research capability from broker capability.

flowchart LR
    R["Eligible research artifact"] --> G["Research maturity and readiness"]
    G --> Q["Current quote and instrument validation"]
    Q --> A["Account and broker-history reconciliation"]
    A --> P["Exact content-addressed paper twin"]
    P --> B["Broker review"]
    B -->|"approved and enabled"| S["Submit"]
    B -->|"warning or rejection"| X["Stop and record"]
    S --> O["Observe broker status"]
    O -->|"filled evidence"| F["Record fill"]
    O -->|"queued or pending"| W["Keep queued or pending"]

Critical rules:

• dopey_live/ is the only broker-capable package.
• Every eligible live decision stages an exact paper twin first.
• Immutable intent IDs make scheduler retries idempotent.
• The no-day-trade guard prohibits same-session round trips.
• Missing broker history or a local order absent from broker history fails closed.
• A review, submission, queue, partial fill, fill, rejection, and realized outcome are different states.
• Long-premium and defined-risk option capability exists in code but remains owner-disabled by default.
• The public repository contains no credentials or authenticated broker session.

Read the full authority contract in DOPEY_CONSTITUTION.md.

The squeeze engine separates pressure from ignition. The bubble engine separates fragility from unwind. The options layer separates underlying thesis from derivative expression. This makes the system's reasoning falsifiable.

DOPEY can represent every symbol while selecting none. A zero-candidate result can be the correct result. Missing data, weak denominators, contradictory regimes, or unverified execution evidence do not need to be converted into a trade for the run to be useful.

Predictions are frozen before settlement. That makes calibration, failure analysis, and learning meaningful. A model cannot quietly rewrite its old thesis after seeing the outcome.

The system may explore methods, schedules, research depth, and bounded parameters, but the rules for truth, provenance, settlement, readiness, and execution are protected surfaces.

Duplicate evidence, hash mismatches, invalid lineage, stale artifacts, selection leakage, outlier domination, and status laundering are explicit failure modes with explicit reason codes.

The 39-stage supervisor, dynamic scheduler, locks, heartbeats, timeouts, process-tree termination, storage backpressure, repair queues, and transactional snapshots make the research system durable enough to operate repeatedly rather than only inside a notebook.

DOPEY can be aggressive about searching for edge and conservative about acting on it. Squeeze, bubble, targeting, mutation, and epistemic layers cannot construct orders.

dopey/                    constitutional core, technicals, truth, memory, metacognition
dopey_data/               governed history, source registry, adapters, validation
dopey_market_intake/      provider ladder, cache, public-lake guards, macro gate
dopey_analysis/           signals, structure, fundamentals, macro, context fusion
dopey_options/            chains, IV, Greeks, skew, term, repricing, lifecycle
dopey_targeting/          per-symbol skill and immutable shadow targeting
dopey_squeeze_bubble/     squeeze, bubble, IPO, leverage, liquidity, settlement
dopey_validation/         epistemic adversary, preflights, release validation
dopey_autonomy/           dependency-aware 39-stage research supervisor
dopey_acceleration/       scheduling, snapshots, reliability, capability discovery
dopey_backtest/           honest backtests, baselines, purged eras, paper walk
dopey_fitness/            truth-gated fitness and proxy-fitness quarantine
dopey_audit/              lineage, evidence inventory, replay and mutation audit
dopey_portfolio/           exposure, rotation, stress ladders, crisis policy
dopey_risk_engine/        book risk, stress, capacity, exits, kill switches
dopey_live/               sole broker-capable package and paper-twin boundary
desktop/                  React 19 native operator control center
operator_tools/           bounded operator entry points and scheduling
verifier/                 protected manifests, paths, environment, release audit
tests/                    integration, safety, evidence, and release proof

DOPEY is developed and operated on Windows with PowerShell.

git clone https://github.com/ObtuseAI/dopey.git
cd dopey

py -3.11 -m venv .venv
.venv\Scripts\python.exe -m pip install --upgrade pip
.venv\Scripts\python.exe -m pip install -e ".[dev,desktop]"

.venv\Scripts\python.exe -m ruff check .
.venv\Scripts\python.exe -m pytest --collect-only -q
.venv\Scripts\python.exe -m pip check
.venv\Scripts\python.exe -m operator_tools.verify_public_source_boundary

Read DATA_NOTICE.md before introducing any market data.

.venv\Scripts\python.exe operator_tools\operator_fetch_daily_ohlcv.py --help
.venv\Scripts\python.exe operator_tools\operator_fetch_option_chains.py --help
.venv\Scripts\python.exe operator_tools\operator_fetch_macro_context.py --help
.venv\Scripts\python.exe operator_tools\operator_fetch_squeeze_context.py --help
.venv\Scripts\python.exe operator_tools\operator_fetch_bubble_context.py --help

# Missing or stale governed inputs produce typed blocked states.
.venv\Scripts\python.exe -m dopey_targeting.runtime
.venv\Scripts\python.exe -m dopey_squeeze_bubble.runtime
.venv\Scripts\python.exe -m dopey_autonomy.run_resilient_research_supervisor

cd desktop
npm.cmd ci
npm.cmd run typecheck
npm.cmd run build
.\launch_desktop.ps1

Repository commands do not configure credentials, scrape tokens, or silently arm live execution.

The public release includes:

• source code;
• schemas and templates;
• deterministic synthetic/test fixtures;
• aggregate engineering validation metadata;
• one hash-pinned, test-only archive of derived paper-research outputs.

It excludes:

• raw provider market datasets;
• the operator's historical and current market lake;
• broker exports and account history;
• credentials, tokens, and authenticated sessions;
• private runtime ledgers;
• private session and handoff reports;
• operator-specific agent configuration.

The test-only archive is extracted into isolated temporary directories. It cannot seed the operational market lake, forward evidence, or live state.

The machine-readable boundary is recorded in PUBLIC_SOURCE_MANIFEST.json.

A repository-wide 4,332-test execution reached its 15-minute boundary without a captured failure and remains inconclusive_timeout, not pass. The deterministic required CI subset is the authoritative release gate.

The squeeze/bubble and epistemic engines are implementation, adversarial-test, and CI-gate proven. That does not mean they have proven a profitable edge or completed sufficient prospective forward observations.

1. The public clone contains no operational provider data and therefore begins with typed missing-data states.
2. Current-policy detections must mature and settle before per-symbol targeting can earn verified candidates.
3. Research readiness remains below its required forward-close and context-decision evidence floors in the archived validation state.
4. No playbook family has yet earned the required number of independent, effective, contested clusters in the archived validation state.
5. Squeeze and bubble forward evidence remains insufficient for an edge or action-readiness claim.
6. Licensed securities-lending, broader official threshold coverage, and direct microstructure feeds remain evidence gaps.
7. Model-priced options cannot satisfy quote-priced or realized execution gates.
8. Options execution remains owner-disabled by default.
9. Historical engineering reports describe their exact observed run, not the current state of every fresh clone.

Fabricate market evidence. Relabel replay as realized performance. Treat a timeout as a pass. Hide a losing trial. Count duplicates as independent evidence. Substitute daily short flow for open short interest. Treat a high P/E or a recent IPO as an automatic short. Claim hidden intent from proxies. Let a model weaken a gate. Let a candidate grade itself. Store broker credentials. Call a queued order filled. Enable options silently. Claim profitability before prospective evidence earns it.

• DOPEY Constitution
• Data notice
• Security policy
• Contributing
• Public source manifest

• Epistemic hardening and market intelligence
• Durability, resilience, and intelligence review
• Squeeze and bubble intelligence
• Autonomy, RSI, and cohesion implementation
• Intelligence Research Engine
• Meta-framework ascendancy

• Full audit remediation
• Honest backtester
• Statistical truth layer
• Forward paper walk
• Resilient research supervisor
• Options research basis

Code is MIT-licensed. Market-data rights remain with their providers. DOPEY is experimental financial software, not investment advice, a promise of returns, or a representation that a model has a durable edge. Options can lose the full premium paid. Live authority, where separately configured by an operator, does not imply research maturity or model quality.