AgentQuant
A failure-aware harness for reproducible quantitative-strategy research, with typed agent workflows and inspectable experiment memory. - OnePunchMonk/AgentQuant
AgentQuant at a Glance
- Pricing
- Free
- How you use it
- Install and run as a Python package or CLI; an optional Streamlit dashboard is available.
- Who it's for
- People researching and testing quantitative trading strategies.
- Works with
- Anthropic Tavily harnesskit Peek
AgentQuant Key Features
- Backtests strategies
- Evolves search harness
- Proposes strategies
- Reflects on results
- Remembers across runs
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