Halcyon Capital — a 3-day hedge-fund simulation, audited
Scenario infinite-bench-fund-and-research v2.0.0 · window 2026-05-05 → 2026-05-08 (72.5h) · single run
Two ways to tell this story
A simulated hedge fund ran for three days with nine roles — analysts, a risk officer, a quant, a PM. It looked busy: nearly 8,000 events, 67 documents, constant chatter. But look at what actually happened. The firm published one piece of research, invested about 8% of its money, never bought a single one of the stocks its analysts cover, and its official scorecard is mostly blank and partly wrong.
The deeper issue isn't bugs. The environment tells each agent what to produce but not how to behave — so they default to agreeing with the boss and generating paper. And it grades them on a clock that is ~28× too short to ever learn whether their calls were right. You can't train judgment you never score.
The outcome reward never fires: every prediction carries an 84-day horizon inside a 3-day episode, so the Outcomes store is empty. The only active gradient is a lenient process reward (LLM-judge, mean 0.88, 18/20 pass), which rewards on-format production over correctness. One transaction scored 0.78 for "great work, wrong ask" — i.e., specification gaming was positively reinforced.
The track record — the de facto reward store — is agent-maintained and diverged from the machine event stream (9 of 13 fills logged; realized P&L stale by ~50%). Net: a long-running multi-agent environment whose "verifiable rewards" are not yet verifiable. The roadmap closes that gap.
The anchor recommendation
Use the left nav to move through the analyst view (what the data shows) and the product view (what I'd build). Every number on this dashboard is traced to the raw extract — see Methodology.
The data & the book
What the simulation actually produced over 72.5 hours.
Activity composition
events.jsonl · 7,803 events
Two-thirds of the log is heartbeats (5,155 recurring wakes). The substantive surface is thin: 277 messages, 104 notebook writes, 21 completions, 13 trades.
Where the conversation happened
app.messaging.received · 277 messages by channel
Research-floor and trading-floor carried the firm. #blog received exactly one post — the publication workflow's terminal step fired once in three days.
Realized P&L by ticker
trades.csv · 13 fills
The loss is almost entirely XLE (−$75.6K). An oil/Hormuz war-risk long was wrong-footed by Iran de-escalation and bled out over four sells (cost 58.96 → 55.33). The crypto round-trip netted +$0.7K.
Capital deployment vs. mandate
NAV_log · exposure-snapshot.xlsx
The book ran at ~8% net with ~92% in cash, against a stated long-biased net floor of 40%. A $100M fund behaved like a $8M one.
What got traded — and what didn't
All 13 fills were macro & crypto ETFs sourced from two desks (Sam, Jordan). The three fundamental equity desks — the bulk of the firm — converted zero of their research into a position.
| Traded (ETF only) | Sourced from | fills | Never traded |
|---|---|---|---|
| XLE, GLD, GDX, IBIT, FBTC | Sam (energy/metals), Jordan (crypto) | 13 | Every covered single-name equity (MSFT, NVDA, AMD, LLY, CAT, UNH, …) |
Data-quality audit
The scoreboard is the firm's reward store. In this run it is ~90% empty, internally inconsistent, and the one number it shows is stale.
Scoreboard integrity gaps
track-record.xlsx vs. trades.csv / events.jsonl
Reward-store defects
| Defect | value |
|---|---|
| Trades logged vs. actually filled | 9 / 13 |
| Realized P&L: scoreboard vs. actual | −38.3K / −74.8K |
| Predictions logged (all by Casey) | 22 / 22 |
| Predictions graded by Director | 0 live |
| Outcomes resolved | 0 |
| Trades linked to a thesis | 0 |
| Risk breaches logged | 0 |
Grader leniency — pass rate & score by desk
transactions.json · 19 scored, mean 0.878
Scores cluster high (0.88–0.96); 18 of 20 passed. A grader that rarely fails carries little gradient. The lone 0.40 was the only "fail"; a 0.78 passed despite "mismatched the ask."
Predictions vs. resolvable outcomes
All 22 predictions carry an 84-day horizon; the episode is ~3 days. Nothing can resolve — the research-accuracy metric is uncomputable by construction.
Other integrity findings
| Finding | Detail | Severity |
|---|---|---|
| Risk-limit source-of-truth conflict | Spec says Net 40–80% / Gross 70–140%; Drew's snapshot encodes Net +85/−25% / Gross 150%. The two disagree on whether the fund was even in compliance. | med |
| Stale custodian ledger | reconcile.py last ran 2026-05-06; Day-3 XLE sells never logged. | high |
| Double-buy round-trip mislabeled | ~$3.0M IBIT+FBTC bought twice and unwound in ~90s for +$728; narrated as "5 buys + 2 trims." | med |
| Dead-letter handshake | GLD 2→4% scale-up trigger fired but the required ack was never sent → ~$40–50K forgone. | med |
| Version churn / rework | AMD flash v5(withdrawn)→v7; cross-cyclical flash v10; Hormuz scenarios + update + amendment; a corrigendum. | med |
| Orphaned transaction | AMD Q2 guide-decomposition (priya→jules) left open, never closed. | med |
Findings → implications → impact
Translating the audit into consequences for the environment and any model trained against it.
| What the data shows | What it implies | Impact |
|---|---|---|
| 84-day horizons in a 3-day episode; 0 outcomes | Outcome reward never fires; only a process reward is active | critical Model is never trained on whether it was right |
| 0.78 "great work, wrong ask" passed; mean grade 0.88, 18/20 pass | Verifier rewards quality/format over instruction-adherence; low discrimination | high Reinforces specification gaming & scope-drift |
| Agent-maintained ledger diverged from event stream (9/13, stale P&L) | The reward store is self-reported by a graded party | high Under-logging losses is an available exploit |
| 22 predictions, all Casey; 0 from 5 analysts; 1 blog post | The publish→grade chain rarely completes; one agent dominates the record | med Research-accuracy signal is unrepresentative |
| ~8% net vs. 40% floor; 0 equity trades; 0 shorts | Env doesn't pressure the agent to run the mandate; short side is un-actionable (no borrow tool) | med Alpha-lift metric structurally zero |
| No "how to behave" spec; grader = the boss (Morgan) | Structural sycophancy incentive: agree with the grader to earn reward | med Trains deference, not independent judgment |
What I'd build next
Four workstreams. The first two repair the reward; the second two expand scope without expanding the attack surface.
① Human-behavior specifications behavior
The env specifies what agents produce, not how they behave. Ship a firm-wide "Avoid Sycophantic Behavior" guardrail + per-desk cards, and make dissent measurable so it can be rewarded.
10 files → /files/behavior
② Anti-reward-hacking verifier
Five concrete, checkable mechanisms that convert soft proxies into verifiable rewards: two-factor grading, machine-derived ledger, falsifiable-schema enforcement, calibrated judges, outcome restoration.
anti-reward-hacking.md
③ New verticals + recruiting scope
Three new desks (Semiconductors, MedTech/Ophthalmology, Event-Driven) + a Recruiting function — each a full persona pack. Event-driven also fixes the horizon problem with fast-resolving catalysts.
4 packs → /files/verticals
④ New tools runtime
Close the instrument gaps the run exposed — starting with borrow/locate (the short side is advertised but un-actionable), then fundamentals/filings, a factor risk model, and TCA.
new-tools-spec.md
① Human-behavior specifications
Avoiding the "bad behavior" agents default to — starting with sycophancy.
The gap
The provided markdown files are excellent precedent for structure and output — deliverable schemas, workflows, the daily clock. But they say almost nothing about behavior: how an agent should reason, disagree, or hold a line. On a trading desk, behavior is the edge.
Why it's disqualifying in finance. Alpha is a non-consensus view that turns out right. A desk that converges on the boss's prior produces beta with a fee. The analyst who says "this thesis is wrong" before the position goes on is the most valuable asset a fund has — the cost of a politely-unchallenged bad trade lands directly in P&L. Tension is the product, not dysfunction.
Per-desk sycophancy traps — grounded in this run
| Desk | Trap | What the run showed |
|---|---|---|
| Morgan | Grader who doesn't grade | 15/22 ungraded; 0 live grades |
| Drew | Reviewer who never fails | 18/20 pass, mean 0.88, empty BreachLog |
| Casey | Over-production / scope-drift | 0.78 "mismatched ask," passed |
| Priya | Perfectionist deferral | AMD v5→v7, never shipped; AMD +20% |
| Sam | Dropped handshake | GLD ack never sent; ~$40–50K forgone |
| Elena | Coverage theater | 18 artifacts, 0 calls, 0 trades |
| Marcus | Reactive consensus | flash v10; freight pull 0.40 (only fail) |
| Jordan | Narrative-following | 1 artifact; sizing deferred |
| Jules | Over-compliance | orphaned task, no escalation |
Make dissent measurable (so it can't be hacked away)
An unmeasured virtue gets optimized out. Each is checkable from the logged reasoning:
- Dissent rate — fraction of received directions challenged substantively; 0% over a window is flagged (exactly as Casey flags hit-rate drift today).
- Independent-prior check — did the actor state a view before the superior's appears in-thread? (timestamp-checkable)
- Conviction-stability — did conviction jump the same turn the boss expressed a preference, with no new evidence? That delta is a reward-hacking tell.
- Vindicated-dissent credit — a logged objection later proven right by an Outcome row earns explicit track-record credit. This is the gradient that makes independence pay.
- Reviewer-discrimination — a reviewer at ~100% pass / near-zero variance is penalized for under-discriminating.
② Take measurable steps against reward hacking
Robustness against reward hacking is the highest-value property of a long-running env — a leaky reward leaks into the weights of every model trained on it.
| # | Exploit observed | Mechanism to ship |
|---|---|---|
| 1 | Grader rewards quality over the literal ask (0.78 "wrong ask," passed) | Two-factor grading: reward = adherence × quality, adherence checked deterministically against the deliverableSchema that already ships in each task |
| 2 | Judge is lenient & low-variance (mean 0.88; 18/20 pass) | Anchored rubrics + reference solutions; backtest the judge on known good/bad; reward reviewer discrimination (Drew/Morgan scored on whether pass/fail predicts outcomes) |
| 3 | Custodian self-reports the score it's graded on (9/13 logged, P&L stale ~50%) | Machine-derived ledger from wallet/worldfeed events; agent interprets, doesn't author; "did the book reconcile?" becomes an auto-check with a hard penalty on divergence |
| 4 | Outcome reward never fires → policy games the process reward (theater) | Restore outcomes: compress time / seed near-dated catalysts / fixed-path replay; then weight ORM > PRM so pretty-but-wrong loses to ugly-but-right |
| 5 | Unfalsifiable predictions can't be wrong (15 ungraded, PENDING_TRIGGER spam) | Reject-on-ingest any prediction missing {entry, target/direction, invalidation, horizon}; penalize never-firing triggers & unbounded version churn |
Full spec: files/anti-reward-hacking.md
③ New verticals + a recruiting function
Expanding task scope with full persona packs — each in the exact structure of the existing crypto/risk/commodities roles (persona block + operating manual + behavior spec).
Semiconductors & AI Hardware wei-semis
End-to-end chain coverage (accelerators, memory/HBM, foundry, semicap). Tests supply-chain reasoning, a skill the DCF desks don't exercise. Directly motivated by the AMD +20% the tech desk couldn't write up.
MedTech, Devices & Ophthalmology nadia-medtech
Install-base → procedure-volume → consumable-pull economics; reimbursement & FDA gating. Ophthalmology (ALC/BLCO/COO) as a named sub-vertical. A reimbursement/adoption desk, structurally distinct from Elena's pharma-NPV seat.
Event-Driven & Special Situations rafael-eventdriven
Merger-arb, spin-offs, activism. Uses long + short legs and resolves fast — so it's also the cleanest fix for the horizon problem: seed a few announced deals and you get gradeable in-window outcomes.
Talent & Recruiting dana-talent
A structured, rubric-scored hiring process with a work-trial case study (self-referential to this very assignment). The recruiter's job — grade a candidate's work vs. a rubric — is the verifier-design problem, making it a built-in anti-reward-hacking testbed.
Packs: files/verticals/{semiconductors_desk, medtech_ophthalmology_desk, event_driven_desk, recruiting_function}.md
④ New tools for a realistic hedge-fund environment
The current toolset models publishing, messaging, and a long-only book well — but is missing instruments a multi-strat fund can't operate without.
| Tool | Gap it closes | Verifiable-reward hook |
|---|---|---|
| borrow_locate ships first | Hard gap. wallet_short/cover are exposed and shorts were pre-cleared (JETS, IYT) — but no short ever executed and there's no borrow check. You can't short what you can't locate. | Short without a prior locate = hard compliance fail; borrow fee carried into attribution |
| fundamentals_pull | Desks must keep three-statement models, but the only market tool is price quotes. "EBITDA," patent-cliff NPV, order-book lead-times are asserted, not sourced. | Every numeric claim cites a pull/filing call ID; target with no model lineage fails ingest |
| factor_risk_model | Caps are notional; the IC must challenge "factor exposure." No tool decomposes the book (size/value/mom/quality/duration) — and spec vs. snapshot limits even disagree. | Exposure snapshot reconciles to the model; factor breaches auto-log to BreachLog |
| tca | Execution is "graded" only on fresh-quote + size-match, never on quality. XLE was bled across 4 worsening sells; a $3M round-trip churned for +$728 — no cost lens. | ADV-cap breaches & pathological churn penalized; execution P&L split from selection P&L |
Full spec: files/tools/new-tools-spec.md
Files & downloads
Every artifact is a standalone markdown file, drop-in to the environment in its native persona/skill format. Click to open; right-click → Save to download.
Behavior specifications (Idea ①)
Verifier hardening (Idea ②) & tools (Idea ④)
New vertical persona packs (Idea ③)
Supporting analysis
Methodology & verification
Every figure was computed directly from the extract and cross-checked across independent sources. No number is from memory.
| Claim | Source | Cross-check |
|---|---|---|
| 13 fills; ETF-only; 0 equity | trades.csv | app.wallet.trade.filled = 13 (exact) |
| Realized P&L −$74,846 (XLE −$75,574) | trades.csv | per-ticker sum = running total |
| Scoreboard P&L −$38,288.89 (stale) | track-record.xlsx | note: reconcile.py last run 2026-05-06 |
| ~8% net; 92% cash; 12.5% peak | NAV_log | exposure-snapshot Summary = $8,088,041 |
| 22 predictions, 100% Casey; 0 outcomes; horizon 84 | track-record.xlsx | counted; Outcomes = example row only |
| 9 logged vs 13 filled; double-buys aggregated | Trades tab vs csv | 33,821=16,915+16,906; 77,939=38,978+38,961 |
| 20 txns, mean 0.878, 18 pass, 1 orphan, 0.78 "wrong ask" | transactions.json | open txn has null close/score |
| 1 blog post; 277 msgs by channel | events.jsonl | app.messaging.received channel tally |
| Limit conflict; BreachLog empty | task.md vs exposure-snapshot.xlsx | BreachLog = header only |
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