prompt
FreeQuantitative Trading Agent Architect prompt for autonomous research.
FreeFree tier
Inputs: textOutputs: text, code
About prompt
Open-source research workspace for turning finance questions into runnable analysis: natural-language strategy generation, cross-market backtesting, Shadow Account behavior extraction, multi-agent trading teams, and a 452-alpha factor zoo. Designed for research, simulation, and backtesting only; live trade execution is explicitly out of scope.
Key Features
Natural-language strategy generation from finance questions
Cross-market backtesting across equities (A-shares, HK, US), crypto, futures, and forex
Shadow Account behavior extraction from broker journals
Multi-agent trading teams
452-alpha factor zoo for research
AST purity gate for strategy code generation
Statistical validation with Monte Carlo, Bootstrap CI, and Walk-Forward analysis
Run card, HTML/PDF report, and export artifact emission
Research memory persistence for building on past sessions
Reproducible research harness with traceable claims
Pros & Cons
Pros
- Open source and free to use
- Comprehensive research workflow from question to report
- Statistical rigor with Monte Carlo, bootstrap, and walk-forward validation
- Multi-asset support for diverse financial instruments
- Reproducible and inspectable research artifacts
Cons
- Explicitly designed for research and simulation only; no live trading
- Requires integration with an LLM and data sources for execution
- Complex setup and technical knowledge needed
- Only a prompt; not a standalone tool
Best For
Backtesting quantitative strategies across multiple asset classesAnalyzing broker journals to extract shadow trading behaviorsResearching and validating alpha factors (e.g., GTJA 191 alphas)Simulating portfolio strategies with statistical rigorGenerating reproducible research reports for algorithmic trading
FAQ
Can this prompt be used for live trading?
No, it is explicitly designed for research, simulation, and backtesting only. Live trade execution is out of scope.
What asset classes are supported?
Equities (A-shares, HK, US), crypto, futures, and forex.
What statistical validations does the agent perform?
Monte Carlo simulation, Bootstrap confidence intervals, and Walk-Forward analysis.
How are results output?
Every backtest emits a run card, an HTML/PDF report, and an export artifact for reproducibility.