Private fund research
How private funds can evaluate AI research tools: a FiClaw pilot checklist
A polished demonstration shows that a workflow ran once. A research team needs to know whether another person can explain and rerun it, errors are visible, and coding time saved exceeds added review effort. This is a pilot design template, not a customer case study or a measured productivity claim.
Published by FiClaw 产品团队 · Published · Updated
阅读中文版Quick answer
Evaluate an AI research tool on one reviewable task. Fix the data, strategy rules and evaluation assumptions, then compare deliverables, completion time, correction effort and rerun consistency against a manual baseline. FiClaw can connect specifications, code, A-share historical backtests, diagnosis and parameter comparison; organizational permissions, integrations and deployment need separate acceptance criteria.
Key takeaways
- Choose a task with existing manual evidence to detect unintended changes to the question.
- Evaluate fidelity, rerun feasibility and total cost together.
- Accept the current workflow before scoping shared access, integrations and deployment.
Example pilot: review an existing A-share strategy hypothesis
Suppose a team already has a momentum screen with risk constraints. Before the pilot, specify the historical universe, information availability, signal window, rebalance, weights, costs and execution assumptions. Another strategy is fine; changing the task after seeing a favorable demonstration defeats the comparison.
Ask FiClaw to organize the specification and open questions, then check the code and run settings. Keep unsupported rules or unavailable data visible. Model-inferred defaults are not approved requirements.
| Stage | Agreed evidence | Acceptance question |
|---|---|---|
| Task definition | Question, fixed rules and adjustable parameters | Did AI change the universe, signals or trading times? |
| Experiment | Data scope, code, settings and run records | Can another researcher establish what actually ran? |
| Interpretation | Report, diagnosis, exceptions and failed runs | Do conclusions point to specific metrics and experiments? |
| Handoff | Review comments, changes and next questions | Can the reviewer rerun it or explain the blocker? |
Record fidelity, workload and cost
Define acceptance criteria before seeing the curve. Tool evaluation asks whether the task was executed correctly; strategy research asks whether the hypothesis is supported. Record these judgments separately.
Record active work, waiting, retries, corrections and review. Total cost includes service or credit usage, researcher effort, integration and maintenance. Faster generation alone does not establish a cheaper completed task.
- 01Specification fidelity: inspect signals, rebalance and constraints against the approved rules; log deviations and owners.
- 02Review feasibility: have another researcher inspect code, configuration and results; record time and obstacles.
- 03Visible exceptions: identify missing data, no-trade results, failed runs and unsupported requirements.
- 04Total effort: compare time, corrections, service costs and handoff effort with the manual baseline.
- 05Expansion criteria: agree acceptable deviations, resolution times and cost ranges for this team rather than assuming a universal efficiency percentage.
A copyable pilot brief
Research question: [hypothesis] Owner / independent reviewer: [roles] Permitted material: [sources, authorization and redaction] Fixed rules: [universe, signals, timing, rebalance, holdings, execution and costs] Baseline: [manual evidence and effort] FiClaw task: [approve specification before code, runs and diagnosis] Deliverables: [specification, code, settings, results, exceptions and changes] Acceptance: [pre-agreed fidelity, review and cost criteria] Stop conditions: [unavailable data, unresolved permission, unexplained implementation deviation] Before expansion: [collaboration, shared material, interfaces, deployment and operations] Investment conclusions and launch approval: [existing institutional process]
Assess institutional access after the pilot
The public entry point is Desktop and cloud research services. Verify A-share data and backtest scope against actual interfaces. Internal data, shared institutional agents, centralized permissions and audit, and VPC, hybrid or private deployment need separate requirements and acceptance evidence. A desktop interface does not establish entirely local processing.
A critical data gap, unreviewable rules or unsuitable total cost can justify stopping or narrowing the pilot. That is a useful result. Profitability or live deployment need not be the success criterion for evaluating a research tool.
Frequently asked questions
Should a private fund choose its highest-return strategy for a pilot?
Prefer explicit rules, available data and existing manual evidence so the team can check execution fidelity. Historical returns alone cannot establish research tool reliability.
Can FiClaw replace independent research review?
No. It can help implement rules, organize experiments and explain results. Authorized people still review data assumptions, code behavior, risks and deployment decisions.
Must the pilot include sensitive positions?
Public or authorized redacted samples can support an initial evaluation. Confirm data flows, processing and permission before introducing internal positions, trades or research material.
Method and product references
Related reading
Evaluate FiClaw on one explicit task
Bring the research question, available material types and acceptance criteria to a private fund or asset manager pilot discussion.