OpenInvestOp

This project systematically attempts to falsify its own edge and publishes negative results as-is. The deterministic features the committee reads, and the timing signals around them, were tested against pre-registered statistical gates — none survived as tradable alpha.

Methodology: Newey-West HAC t-statistics, Deflated Sharpe Ratio (Bailey & López de Prado 2014, re-derived equation by equation), Holm correction, zero lookahead, and LLM training-cutoff probes.

Most AI investment assistants try to become better chatbots. OpenInvest instead builds a transparent, verifiable, and auditable decision engine that plugs into personal agents such as Claude Code, Codex, Hermes, and OpenClaw — every improvement in those agents automatically makes OpenInvest more capable.

The division of labor is deliberate: your agent handles long-term memory, natural conversation, and user understanding; OpenInvest handles the verifiable investment committee, evidence-based reasoning, long-horizon backtesting, and auditable decision records.

User │ ┌───────────┴───────────┐ ▼ ▼ Your Agent OpenInvest (User Understanding) (Market Understanding) │ │ └───────────┬───────────┘ ▼ Better Investment Decisions

Your agent knows you. OpenInvest knows investing.

OpenInvest intentionally avoids "owning" the user. Most AI products try to own everything—memory, persona, chat history, and workspaces. OpenInvest takes a back seat. It exposes clean APIs, CLI commands, and agent skills (Claude Code / Codex / Hermes / OpenClaw), letting your primary agent manage the conversation and context while OpenInvest powers the underlying investment intelligence.

- Multi-Agent Investment Committee: Isolated analysis and round-rebuttal debate.
- Coordinator-Worker Architecture: Prevents context contamination and role hallucination.
- Information Isolation: Rigidly blocks quant and risk analysts from out-of-boundary contexts.
- Auditable Decision Trail: Clean logs showing exactly "why" each decision was made.
- Markdown-as-a-Database: Frontmatter (YAML) + Markdown (Body) as the single source of truth.
- Long-Horizon Backtesting: Built-in test harness with lookahead bias protections.
- Dreaming-Based Memory Consolidation: Nightly memory distillation to prevent context drift.
- Self-Hosted / Zero-Cost: Powered directly by your local agent's reasoning resources.
- Agent Skill: Lightweight plugin for Claude Code / Codex / Hermes / OpenClaw, with interactive bootstrap wizard.
- Automated Deployment: GitHub Actions workflow to run the committee and email reports daily.

1. Integrate with your agent (Recommended)

Add the lightweight skill from your agent's plugin registry. The host agent will automatically pull the core code and align dependencies on first run:

# Claude Code /plugin marketplace add longsizhuo/openInvest /plugin install invest@openinvest # Codex codex plugin marketplace add longsizhuo/openInvest # Hermes Agent hermes plugins install longsizhuo/openInvest --enable # OpenClaw openclaw plugins install clawhub:openinvest

2. Standalone — MCP server or CLI (no clone needed)

# MCP (18 tools, any MCP client; add --http for a remote streamable-HTTP server — BETA) claude mcp add openinvest -e INVEST_HOME=~/openInvest -- uvx openinvest-mcp # or plain CLI INVEST_HOME=~/openInvest uvx openinvest status

Sendset up invest(or帮我初始化 invest) to any skill-enabled AI terminal. The system will trigger an interactive bootstrap wizard to guide you through:
- Detecting thememory/state storage path and.envconfiguration.
- 5-dimensional profiling (Legal name, Risk capacity, Debt structure, Initial holdings, and optional keys).
- Running static data migration to immediately generate your first asset exposure memo.

💡Zero-Cost Execution: In skill interactive mode, the committee's underlying reasoning relies entirely on the host agent's (e.g. Claude Code) reasoning pipeline.No third-party API Key is consumed. You only need to configure an API key when setting up automated crons or calling independent Web APIs.

3. Serverless Self-Hosting (GitHub Actions)

Run the committee automatically via GitHub Actions and receive daily digest emails.

⚠️Fork must be set to Private: State files (holdings, verdicts) will be committed back to your fork. Public forks will leak your private financial information.
- Fork this repositoryand change its visibility toPrivate(Settings -> Visibility).
- Runset up investlocally to generate the initialmemory/folder, then commit and push it to your private fork:

git add -f memory/ && git commit -m "chore: init memory state" && git push

Architecture & Multi-Agent Orchestration

openInvest does not run a mock debate in a single LLM session. The system enforces anInformation Isolation Contractat thecore/committee/layer, orchestrating 4 independent LLM processes in a directed acyclic graph (DAG):

- Coordinator-Worker Pattern: Workers operate in isolated namespaces. Boundary constraints are hardcoded at the framework layer in Python to prevent attention contamination in large multi-role prompts.
- Markdown-as-a-Database: The system uses Frontmatter (YAML) + Markdown (Body) as the single source of truth. Leveragingfcntl.flockprocess file locks and temporary atomic file replacement, it provides a tamper-proof investment audit trail natively tracked by Git.
- Three-Phase Dreaming Consolidation: Distills daily decisions against actual market outcomes nightly (Light Sleep $\rightarrow$ REM $\rightarrow$ Deep Sleep) to consolidate long-term insights, preventing Large Language Model (LLM) context drift over long execution spans.

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