FusionFinance

Quantitative discipline meets agentic investigation—with evidence and risk gates between language and capital.

FusionFinance is an auditable autonomous-trading research system. Quantitative ML proposes opportunities. Agent roles investigate context and falsifiers. A deterministic evidence gate challenges citations and numbers. An independent market verifier and portfolio-risk layer are the required downstream capital gates. The checked-in agent runtime stops at a sealed evidence precheck; it does not claim that the full capital path is wired end to end.

ML proposes. Agents investigate. Evidence challenges. Risk decides.

Open the no-auth demo · View the public repository

Research and paper-trading software only. Not financial advice, a profit guarantee, or an authorization for real-money execution.

The experiment

The system defines three distinct arms under one intended experiment contract:

Arm	Inputs	What it tests
Pure ML	Structured point-in-time market and filing features	Numerical consistency without narrative interpretation
Pure LLM	Agent research without the proprietary ML proposal	Context synthesis without quantitative calibration
FusionFinance	ML proposal, agent investigation, evidence and market verification, risk sizing	Whether defined checks improve decision quality

FusionFinance is not a vote or an average. A fluent thesis can be rejected, and a strong model score can remain untraded. The current Micron example does exactly that: the proposal schema passes, but the retrospective thesis lacks a prospective seal and citation-complete evidence, so the final action is rejected with a zero position.

The shared execution foundation freezes the universe, market clock, capital, costs, slippage, rebalance cadence, leverage, and position limits in configs/fusionfinance-demo.json. It is tested infrastructure for the next controlled run; it is not the provenance of the legacy replay shown below.

Current replay—useful, but provisional

The public February–July 2026 replay contains 109 stored observations and 12 visible rejected decisions. Metrics are recomputed from the checked-in curves:

Stored arm	Return	Sharpe	Sortino	Max drawdown	Volatility
Pure ML	+1.7%	+0.76	+1.03	−2.9%	5.4%
Pure LLM	−10.3%	−0.90	−1.28	−23.8%	24.5%
Legacy Fusion line	+10.1%	+2.05	+3.48	−3.9%	11.1%
SPY benchmark	+8.7%	+1.39	+2.10	−8.9%	14.6%

These numbers are provisional, not causal proof of fusion outperformance. The legacy arms were not produced by the new shared execution kernel, their historical LLM theses were not prospectively sealed, and those theses fail the current evidence contract. The replay demonstrates the product, metric pipeline, verifier behavior, and claim boundary—not universal alpha.

The legacy Fusion curve is also sampled differently: it contains only 21 nonzero changes across 109 stored account values, in an apparent five-session marking cadence, while the comparison arms change more frequently. That sparse cadence can hide intra-interval troughs and weakens cross-arm drawdown comparability.

Turnover, cost, exposure, cash, win rate, and trade count are shown as an em dash because the legacy trade ledger was not retained. The dash means unavailable, not zero trades.

Judge quickstart

The default demo is static, fast, and offline. It needs no login, API key, model endpoint, or market-data connection. Python 3.11+ runs the reproducibility suite; FFmpeg's ffprobe validates the checked-in release video.

# Instant judge path
python3 -m http.server 8000 --bind 127.0.0.1 --directory demo
# Open http://127.0.0.1:8000/index.html

# Reproducibility and tests
python3 -m venv .venv
. .venv/bin/activate
python -m pip install -e '.[dev]'
make artifacts
make verify
make test

The artifact build reads only checked-in files under evidence/. It performs no network request and uses no private cache. Running it twice produces byte-identical public artifacts.

The judge path also includes a real provider-agnostic analyst runtime under alpha/agents/. Market, news, fundamentals, and risk roles run concurrently against one hash-sealed source snapshot. The default provider is deterministic and offline; an optional OpenAI-compatible adapter uses only the Python standard library and environment variables. Both paths must emit the same strict JSON contract before an immutable thesis can reach the existing evidence audit and veto-first candidate decision. Sources newer than the proposal cutoff are rejected before any role sees them, and quantitative claims must use reconciled citation fields:

pytest -q tests/test_agent_runtime.py -p no:capture

The focused test runs unseen structured fixtures through approval, malformed output, unknown-citation, contradiction, and risk-veto paths. It does not retroactively turn the legacy replay into a prospective agent run.

The receipt is explicitly an agent_evidence_precheck. It proves schema, point-in-time, citation, numeric-reconciliation, and decision self-consistency; it does not prove semantic entailment, authenticity, market-verifier approval, portfolio construction, or execution. Its provisional_weight_cap is not an executable target; those remain separate downstream gates. The current receipt embeds the immutable proposal and source snapshot, then rederives their hashes, evidence audit, decision, reasons, and provisional cap during validation.

Architecture

Point-in-time data
       │
       ├── Pure ML ───────────────────────────────────────────┐
       ├── Pure LLM agent roles ──────────────────────────────┤
       └── ML proposal → agents → evidence audit             │
                                  → market verifier           ├→ shared execution → metrics
                                  → risk sizing ──────────────┘

• alpha/filing_alpha/ contains attributed XBRL, filing-text, point-in-time fusion, and evaluation transforms selected from the supplied filing_alpha_sharpe6 archive. Its rejected strategy and unsafe timestamp parser were not imported.
• alpha/agents/ contains the four-role concurrent desk, deterministic offline and optional HTTP providers, strict immutable input and output contracts, and the agent/evidence pre-gate orchestrator. The market verifier, portfolio policy, and execution remain downstream.
• alpha/verifier/ defines immutable theses, exact-quote and numeric reconciliation, source-availability checks, calibration, market verification, and veto-first adjudication.
• demo/ contains the immutable experiment contracts, common next-session execution/metrics kernel, and the deployed replay interface.
• evidence/ is the complete public rebuild input and AMD receipt chain.

See architecture, methodology, and limitations for the implemented boundary and the work still required before a comparative claim.

AMD Compute Usage

AMD compute powers a recorded expanding walk-forward quantitative model training workload—not a decorative logo. This workload is separate from and did not generate the displayed legacy replay curves. Self-reported receipts record:

• AMD vendor and gfx1100 architecture;
• PyTorch on ROCm/HIP 7.2.53211-e1a6bc5663;
• 51,522,830,336 reported VRAM bytes;
• 72 expanding walk-forward ranker retrains; and
• 231.29 seconds of recorded GPU training.

The builder derives those fields from the receipt contents and cross-checks the environment, hardware, and training records. SHA-256 protects their published byte integrity; it is not independent hardware attestation. The raw device-name field and utilization are unavailable, so FusionFinance does not invent an exact commercial SKU or utilization value. Verify the published chain locally:

python3 scripts/verify_amd.py

Full evidence and caveats: AMD Compute Usage.

Submission artifacts

• Live replay
• Demo video
• 10-page slide deck
• Timed video storyboard and narration
• Replay result, metrics, decisions, and AMD manifest
• Submission copy and checklist

Repository map

alpha/          focused filing, agent, verifier, and AMD-backed quantitative modules
configs/        frozen controlled-comparison contract
demo/           no-auth UI plus shared execution and metrics kernel
docs/           architecture, methodology, AMD evidence, and limitations
evidence/       sealed replay input and three byte-preserved AMD receipts
presentation/   submitted deck/video sources and artifacts
results/        deterministic public outputs
scripts/        artifact builder, AMD verifier, and curated archive builder
tests/          focused unit, integration, publication, and release tests

The public tree intentionally excludes vendored research archives, obsolete demo surfaces, private caches, fine-tuning corpora, raw portrait/voice media, and historical experiment operators. The project acknowledges external ideas through links and notices instead of shipping unused upstream repositories.

License and attribution

Original FusionFinance work is licensed under GPL-3.0-only. The filing-alpha subset retains its MIT notice in alpha/filing_alpha/NOTICE. See NOTICE for attribution and boundaries.