# openInvest **A self-hosted investment decision engine built for modern AI agents. Multi-agent information isolation and cross-challenge protocol, providing an auditable decision trail (Audit Trail).** [![Python](https://img.shields.io/badge/Python-3.13+-3776AB?logo=python&logoColor=white)](https://www.python.org/) [![Agents](https://img.shields.io/badge/Agents-Claude%20Code%20%7C%20Codex%20%7C%20Hermes%20%7C%20OpenClaw-informational)](docs/wiki/20-agent-usage-tutorial.md) [![License](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE) [![Stars](https://img.shields.io/github/stars/longsizhuo/openInvest?style=social)](https://github.com/longsizhuo/openInvest) [![Glama MCP server](https://glama.ai/mcp/servers/longsizhuo/openInvest/badges/score.svg)](https://glama.ai/mcp/servers/longsizhuo/openInvest) [📚 Full Architecture Wiki](docs/wiki/README.md) · [🇨🇳 中文版](README_zh.md)

--- ## What is OpenInvest? OpenInvest is a self-hosted investment decision engine built for modern AI agents. It provides a verifiable investment committee, evidence-based reasoning, long-horizon backtesting, and auditable decision records. Instead of replacing Claude Code, Codex, Hermes, or OpenClaw, OpenInvest is designed to power them. --- ## Live Performance & PnL

_{Data feed is automatically updated every 2 hours using `jobs/pnl_snapshot` and pushed to the pnl-data branch}
_{Upper half: 30-day net asset value trend · Lower half: Net asset value comparison against 8 benchmark assets (transparent disclosure, not an alpha claim—the committee's proven value is discipline and transparency, not excess return, see ADR-023)}
_{📌 Note: The current chart shows the author's live production portfolio. After self-hosting, the system will automatically render your own equity curve based on the holdings defined in your `memory/` directory.}

* **Benchmark Portfolio**: The system introduces 8 standard control benchmarks across 4 quadrants (AI advisors / Mutual funds / Wealth management / Broad market index). For details on the comparison methodology and data cleaning logic, see [docs/wiki/README.md](docs/wiki/README.md). --- ## Research & Falsification **System Self-Disclosure**: This system is an **auditing tool to eliminate human investment cognitive biases and enforce reasoning transparency**, not a return-amplifying black box. Latest automated audit (`docs/verdict_accuracy.md`): Directional verdicts (excluding HOLD) have a true hit rate of **42.2%** (n=56, **below random**); `HOLD` accounts for **56%** of all decisions. The system's value lies in transparency and discipline (mostly staying inactive, low turnover), **not directional prediction**. Detailed log stream can be found in [docs/verdict_accuracy.md](docs/verdict_accuracy.md). 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. | Test | Result | Verdict | |---|---|---| | Q1 cross-sectional stock picking | 6 features, mean-IC 0.025–0.067, Holm-corrected **p=0.397** | No significant stock-picking signal | | M1 multivariate GBM (out-of-sample) | mean OOS IC **+0.003**, p=0.925 | Feature combination doesn't help either — no signal | | Q2 gold MA200 trend | **p_holm=0.016**, significant — but `trend_dca` shows it is **beta, not tradable alpha**: timing terminal value **3.07 vs 15.10** buy-and-hold, Sharpe **+0.36 vs +0.68**, max drawdown deeper (**−57% vs −44%**) | Statistically significant, economically untradable | | Per-asset multi-signal families | 3 assets × 4 signal families × parameter grid = 24 variants per asset; after costs + DSR deflation, **none passes DSR > 0.95** | No tradable signal in any family | | Positive control | A cheating perfect-foresight timing signal scores **DSR = 1.00** | The harness can detect a real signal | 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. Details: [experiments/signal-eval/README.md](experiments/signal-eval/README.md) · [docs/verdict_accuracy.md](docs/verdict_accuracy.md) · [ADR-022](docs/wiki/adr/022-backtest-memory-contamination-and-holdout-discipline.md) · [ADR-023](docs/wiki/adr/023-honest-positioning-not-alpha.md) --- ## Product Philosophy 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.** ### Avoiding User Ownership 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. --- ## Features * **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. --- ## Quick Start ### 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: ```bash # 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 ``` Any other MCP client: register the MCP server from step 2 below (full walkthrough in the [agent tutorial](docs/wiki/20-agent-usage-tutorial.md)). ### 2. Standalone — MCP server or CLI (no clone needed) The backend ships on [PyPI](https://pypi.org/project/openinvest/); `~/openInvest` holds only your data: ```bash # 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 ``` Send `set 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: 1. Detecting the `memory/` state storage path and `.env` configuration. 2. 5-dimensional profiling (Legal name, Risk capacity, Debt structure, Initial holdings, and optional keys). 3. 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. For self-hosting details, see [docs/QUICK_START.md](docs/QUICK_START.md). (The bundled Web GUI was retired on 2026-07-05 — all capabilities are exposed via CLI/MCP; a standalone frontend may return later.) ### 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. 1. **Fork this repository** and change its visibility to **Private** (Settings -> Visibility). 2. Run `set up invest` locally to generate the initial `memory/` folder, then commit and push it to your private fork: ```bash git add -f memory/ && git commit -m "chore: init memory state" && git push ``` 3. In your fork's **Settings -> Secrets and variables -> Actions**, add the following Secrets: * `LLM_API_KEY` (or `DEEPSEEK_API_KEY`): API key to run the committee. * `EMAIL_SENDER` / `EMAIL_PASSWORD`: Gmail address + [App Password](https://support.google.com/accounts/answer/185833). * `DIGEST_EMAIL_TO`: Recipient email address. 4. **Enable Workflows** under the **Actions** tab. The workflow runs automatically at 10:00 AM (Beijing time) daily; you can also manually trigger `daily-report` via **Run workflow**. --- ## Architecture & Multi-Agent Orchestration openInvest does not run a mock debate in a single LLM session. The system enforces an **Information Isolation Contract** at the `core/committee/` layer, orchestrating 4 independent LLM processes in a directed acyclic graph (DAG): ``` [ Macro Data Injection ] │ ▼ 1. Macro Alignment Context ┌──────────────────────────┐ │ Macro Strategist │ (VIX / Interest rate spread / Currency momentum) └─────────────┬────────────┘ │ ▼ 2. Async Multi-Dimensional Scrutiny (Async DAG) ┌─────────────┴────────────┐ ▼ ▼ ┌──────────────────┐ ┌──────────────────┐ │ Quant Analyst │ │ Risk Officer │ │ (RSI / Momentum) │ │ (Concentration) │ │ │ │ │ │ 🛑 No Holdings │ │ 🛑 No Indicators │ └─────────┬────────┘ └─────────┬────────┘ │ │ └─────────────┬─────────────┘ │ ▼ 3. Round 2 Rebuttal & Cross-Challenge │ Mutual feedback loop for signal correction ▼ ┌──────────────────────────────────────────────┐ │ Chief Investment Officer (CIO) │ └───────────────────────┬──────────────────────┘ │ ▼ 4. Deterministic State Persistence [ BUY / ACCUMULATE / HOLD / TRIM / SELL ] ``` 1. **Macro Strategist**: Assesses the global macro landscape (VIX, yield curve spread, core currency matrix) to establish the portfolio's risk threshold. 2. **Quant Analyst**: A pure mathematical momentum and technical indicator filter. **Strictly blocked from knowing portfolio holdings** to eliminate human attachment and loss-aversion biases. 3. **Risk Officer**: Focuses entirely on tail risks (drawdown buffers, concentration limits, solvency multipliers). **Strictly blocked from technical indicators** to make objective asset exposure rulings. 4. **Round 2 Rebuttal**: Quant and Risk analysts are fed each other's Round 1 reports in Round 2, challenging boundaries until signals converge or safety valves trigger. 5. **CIO (Chief Investment Officer)**: Synthesizes the audited reports and outputs a structured `Verdict` (BUY / ACCUMULATE / HOLD / TRIM / SELL) with a confidence level. **No auto-order execution occurs**; final action remains strictly up to the human auditor. Key trade-offs behind this design are recorded as ADRs in [docs/wiki/adr/](docs/wiki/adr/) (24 to date), including rulings that overturned our own earlier designs — [ADR-007](docs/wiki/adr/007-few-shot-retirement.md) retired the few-shot CIO route, and [ADR-009](docs/wiki/adr/009-no-ta-style-analyst-agents.md) rejected TA-style analyst agents after a pre-registered experiment. --- ## Core Design * **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. Leveraging `fcntl.flock` process 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. --- ## Configuration The system defaults to DeepSeek endpoints and supports any standard OpenAI-compatible API. LLM provider setup and all tunable runtime overrides ([ADR-017](docs/wiki/adr/017-config-via-api.md)) are documented in [docs/wiki/22-configuration.md](docs/wiki/22-configuration.md). --- ## Disclaimers & Backtest Limitations 1. **No Financial Advice**: This system is a decision-support tool powered by LLMs. Output memos represent simulated reasoning based on deterministic data and do not constitute asset allocation advice. 2. **Backtest Time-Lock & Lookahead Guard**: The backtest engine (`scripts/backtest_runner.py`) has a hardcoded safety valve: **it rejects backtests for `decision_date > 2024-06-30` by default** (override with `--allow-lookahead`). Since mainstream foundation models have training cutoff dates around mid-2024, backtesting on later intervals introduces severe **Lookahead Bias** (model pre-training leakage). Parameter tuning, Optuna sweeps, and prompt optimization must run strictly on historical windows prior to June 30, 2024. --- ## Acknowledgments * [MiMo](https://mimo.mi.com/) — Special thanks to MiMo Quantitative Lab for sponsoring production-grade high-performance LLM inference (powering `mimo-v2.5-pro` long-horizon sweeps). * [OpenClaw Dreaming Guide](https://dev.to/czmilo/openclaw-dreaming-guide-2026-background-memory-consolidation-for-ai-agents-585e) — Theoretical foundation for the three-phase sleep-cycle memory distillation framework. --- ## License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.