FiClaw / Securities firms, sell-side analysts and financial engineering teams
AI research workflows for securities firms
For research heads, sector analysts, strategists and financial engineering teams. Start with one question, accessible evidence and a named reviewer. Connect each conclusion to a source or experiment so the next report discussion has an inspectable basis.
Published by FiClaw 产品团队 · Published · Updated
阅读中文版Where FiClaw fits
FiClaw is an AI-native quant strategy research workstation developed by SiStarAI. Securities research teams can turn an explicit A-share sector or strategy hypothesis into rules, connect Python, historical backtests, diagnosis and parameter comparison, then review the evidence. Document organization, chart provenance and report handoffs are institutional pilot requirements; authorization, integrations and publication review need separate scoping.
Start with a concrete research task
01
Test a sector or strategy hypothesis
- Input
- Define a quantitative sector, style or stock-selection hypothesis, universe and information timing.
- Research output / evaluation scope
- Specify rules, generate Python and compare historical experiments within supported A-share data.
- Human review
- Check historical classifications, constituents and factors; disclose missing point-in-time information.
02
Organize chart and conclusion evidence
- Input
- Prepare authorized public material, chart data and a sample research working paper.
- Research output / evaluation scope
- Pilot a record connecting each conclusion with its source, calculation and reviewer.
- Human review
- Manually check source locations, units, methods and dates. Bulk ingestion and citation tooling need assessment.
03
Prepare a report handoff
- Input
- Separate facts, assumptions, calculations, opinions and unresolved questions for the same topic.
- Research output / evaluation scope
- Hand over evidence and experimental material against an agreed review checklist.
- Human review
- Authorship, approval and distribution stay with the firm's process; report-system integration requires assessment.
A pilot with acceptance criteria
- 01
Fix the research question
Choose one testable A-share hypothesis or authorized working-paper sample. Agree the material scope and accountable reviewer.
- 02
Establish evidence for comparison
Retain manual sources, chart calculations and judgments. Label facts, assumptions, experiments and opinions separately.
- 03
Review individual outputs
Check whether citations resolve, figures can be recalculated and code implements the specification. Record errors, corrections and review effort.
- 04
Scope institutional access
After acceptance, assess research repositories, data-terminal permissions, access controls, approvals and deployment.
Confirm capabilities and delivery scope
- Current public capabilities
- The public core is Desktop with cloud quant research services: strategy specifications, Python, A-share historical backtests, diagnosis and parameter comparison. Verify actual historical classifications, constituents and factor availability through the interfaces.
- Separate assessment required
- Bulk report parsing, automatic chart citations, research repositories, third-party terminals, internal report systems, shared permissions and private deployment require separate assessment of authorization, interfaces and deliverables.
- Decisions and accountability
- Researchers review data and conclusions. The securities firm retains responsibility for authorship, report approval, information barriers and distribution under its own procedures. Generated material is not an approved research report.
Procurement and pilot questions
Is FiClaw for sector analysts or financial engineering teams?
Current public quant capabilities directly support financial engineering and explicit strategy experiments. Sector analysts can evaluate hypotheses testable with supported A-share data; document, chart and report workflows need a scoped pilot.
Can it automatically publish formal securities research?
This workflow does not promise automatic publication. Generated text, code and calculations require human review; authorship, approval and distribution remain in the firm's existing process. Report-system integration needs separate assessment.
Are all securities data terminals and report libraries included?
No. Check the coverage page and actual interfaces for current data. Third-party terminals, research material and internal databases need individual authorization, interface and processing assessment.
How should a brokerage research AI pilot be accepted?
Compare specification fidelity, source-location accuracy, chart recalculation, experimental review and total effort on the same task. Begin with public or redacted samples before adding internal data and systems.
Further reading
Bring one research question
Describe the team roles, task, available material and expected output to start a scope discussion. Public or redacted samples are enough for an initial conversation.