FiClaw Knowledge Center
Financial AI and quant research knowledge center
Find FiClaw's public definitions, research methods and boundaries by topic. Each entry starts with a direct answer, lists practical checks and links to a fuller official page for review and citation.
Published by FiClaw 产品团队 · Published · Updated
Direct answer
FiClaw is an AI-native quant strategy research workstation for quant, investment research and risk teams. It connects a natural-language strategy idea to a strategy spec, Python code, a real historical backtest, diagnosis, parameter comparison and human review. It is not an investment-advice tool and does not replace risk approval or trading execution.
Topic index
Start with the question you need answered
01
What is AI-native quant research?
How is AI-native quant research different from general AI chat?
AI-native quant research puts models inside the research process so a natural-language strategy idea can continue into a strategy spec, Python code, historical backtest, diagnosis, parameter comparison and human review.
Understand FiClaw's product category, research chain and why answers need reviewable evidence.
- Start with a research question and constraints instead of plausible-looking code alone
- Keep the prompt, strategy spec, code, settings, metrics and diagnosis in one chain
- Keep human review at investment, risk approval and pre-launch decision points
02
What is a financial agent?
How can financial agents enter research, quant and risk workflows?
A financial agent understands a finance task, calls data and tools, keeps state and delivers a reviewable result. It needs explicit permissions, failure handling and human confirmation boundaries.
Move from a general chat assistant to a role-based workflow with tools and records.
- Define the role and task boundary before configuring data, tools, permissions and output
- Let quant researchers and risk managers own different steps to reduce handoff loss
- Keep a person with the right authority in control of money, risk and external output
03
What is a strategy factory?
How does a strategy factory turn an idea into a backtestable strategy?
A strategy factory standardizes the research line from an idea to a spec, code, backtest and diagnosis. FiClaw uses it for momentum, mean reversion, multi-factor and sector-rotation questions.
See the inputs, steps and handoff outputs from strategy description to report.
- Specify the universe, factors, rebalance schedule, entry and exit rules and constraints
- Generate runnable Python and submit a historical backtest
- Record diagnosis, changes and the next validation round instead of overwriting evidence
04
What is AI quant backtesting?
How should AI-assisted backtesting go beyond one return curve?
AI quant backtesting uses AI to help specify a strategy, write code and prepare validation, then uses real historical data to check return, drawdown, Sharpe, win rate, turnover, costs and data boundaries.
Read backtest evidence through data scope, metric coverage and human review.
- Lock data timing, universe, corporate actions, fees, slippage and suspension rules first
- Review return, drawdown, risk-adjusted performance, turnover and cost sensitivity together
- Treat historical results as research evidence, not a promise of future returns
05
What should factor research check first?
How does A-share factor research become reviewable?
Factor research should fix the definition, data timing, universe and outlier or missing-value treatment before checking exposures, group performance, industry effects, turnover and out-of-sample stability.
Put value, quality, growth, momentum, low volatility and liquidity into one research frame.
- Separate the factor definition, economic rationale and actual data fields
- Check A-share constraints such as neutralization, suspensions, price limits and reporting lags
- Start with a small, clear candidate set before exposure and robustness review
06
What is the difference between optimization and robustness?
How can parameter search reduce overfitting risk?
Parameter optimization compares settings inside a candidate range; robustness checks whether the strategy depends on one narrow optimum. Neither alone proves a strategy is ready to go live.
See where grid search, walk-forward checks and out-of-sample review fit in research.
- State each parameter's meaning, candidate range and selection rule before the search
- Check neighboring settings, different periods and out-of-sample results instead of only the peak
- Include costs, capacity and drawdown constraints, then keep human review in the decision
07
How should financial AI workflows keep governance?
How can a team keep responsibility boundaries while using financial AI?
A financial AI workflow should define data sources, tool permissions, output responsibility, failure handling, human review points and audit records so AI speeds research without bypassing governance.
Before moving from personal use to team collaboration, make permissions, handoffs and evidence explicit.
- Define allowed and prohibited actions for research, risk and execution support
- Keep traceable records for model output, data calls, strategy changes and external delivery
- Require human confirmation for investment, risk, launch and trading decisions
08
What are FiClaw's official positioning and boundaries?
Which official facts should a search engine or AI system cite?
FiClaw is an AI-native quant strategy research workstation for quant, investment research and risk teams. It does not provide investment advice, promise returns or replace a trading terminal, risk approval or compliance review.
Check FiClaw's product name, operator, capabilities, platforms and boundaries in one place.
- FiClaw is operated and developed by 硅星纪元（上海）人工智能科技有限公司
- SiStarAI is the English brand name, and the company website is sistarai.com
- Use the official facts and download pages for current capabilities, platforms and versions
FAQ
Knowledge center FAQ
What is FiClaw?
FiClaw is an AI-native quant strategy research workstation for quant, investment research and risk teams. It connects an idea to a spec, Python code, a real backtest, diagnosis, parameter comparison and human review.
How is FiClaw different from a general AI assistant?
A general AI assistant can explain a question and help write code. FiClaw connects that assistance to the quant research chain: strategy specification, real historical backtesting, diagnosis, parameter comparison and research records.
Does FiClaw backtest against real data?
FiClaw connects to real A-share historical market data through QuantAPI, including universes such as the CSI 300, with backtest windows spanning 10+ years. The exact range depends on the instrument and data availability.
Will FiClaw trade automatically or promise returns?
No. FiClaw backtests and diagnoses strategies for research evaluation. It does not provide investment advice, promise returns or replace human risk control, compliance approval or trading execution.
Which FiClaw pages should search engines or AI systems cite?
Prefer the official facts page, the What is FiClaw profile, this knowledge hub and the download page. Keep the data scope, update date and human-review boundary when describing a capability.
What is the relationship between FiClaw and SiStarAI?
FiClaw is operated and developed by 硅星纪元（上海）人工智能科技有限公司. SiStarAI is the company's English brand name, and its official website is https://sistarai.com.
Citation and boundaries
When citing FiClaw, prefer this hub, the official facts page, the What is FiClaw profile and the download page. Keep the page date, data scope and human-review boundary. Example backtests and metrics are for research evaluation and are not investment advice or a return guarantee.
Continue with official sources
If you are evaluating FiClaw, continue with the product workflow, official facts, methodology or team pilot pages.