Aryan SinghandClaude Opus 4.8
52450c0835
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_016DvdRZkyCy9NJpHYDdPqmS
75 lines
3.8 KiB
Markdown
75 lines
3.8 KiB
Markdown
# Overnight Local-LLM Paper-Trading Agent
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Every night the Mac wakes itself, runs **3 trader personas** one at a time against **one local LLM**
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(gpt-oss-20b via [oMLX](https://omlx.ai), an Apple-MLX inference server). Each persona researches the
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web + live market data, forms a stock thesis, and writes JSON. In the morning at market open a
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separate job reads that JSON and places
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**Alpaca paper trades** with programmatic risk guardrails. Everything is visible in a local
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Streamlit dashboard.
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> **Paper trading only.** Fake money, real prices. "The build works" ≠ "it's profitable" — the
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> quality of a local model's theses is the open question you tune over time.
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## Requirements
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- Apple Silicon Mac (built/tested on M4 Pro, 24 GB), **plugged in, lid open, logged in** overnight.
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- [oMLX](https://omlx.ai) (Apple-MLX server) auto-running on `:8090` with a model downloaded
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(default `gpt-oss-20b`; toggle `qwen35_27b`). Enforces an API key, auto-read from `~/.omlx/settings.json`.
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- [uv](https://docs.astral.sh/uv/) for Python env management.
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- Alpaca **paper** account + Finnhub free key.
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## Setup
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```bash
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# 1. Python env + deps
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uv sync
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# 2. Secrets
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cp .env.example .env # then paste your Alpaca paper + Finnhub keys
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# 3. Model host — oMLX auto-starts on login; if not, bring it up + verify tool-calling
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omlx start # OpenAI-compatible API on http://127.0.0.1:8090
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uv run python scripts/smoke_toolcalling.py # GATE: multi-turn tool calls must PASS
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# 4. Phase-0 gate
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uv run pytest
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```
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Switch models any time with one line in `.env`: `MODEL_PROFILE=gpt_oss` or `qwen35_27b`.
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## Entry points
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| Command | What it does |
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|---|---|
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| `uv run python -m orchestration.run_nightly` | Nightly research run (3 personas → JSON). `--compressed` for a fast test |
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| `uv run python -m execution.run_market_open` | Morning: JSON → guardrails → Alpaca paper orders. `--force` to test while closed |
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| `uv run streamlit run web/dashboard.py` | Dashboard: positions, PnL, theses, orders, logs |
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| `uv run python -m reporting.report` | CLI summary |
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## How it works
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`run_nightly` starts oMLX, then runs Nova → Warren-lite → Atlas **sequentially** (one model,
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time-sliced with rollover budgets). Each agent researches with tools (`run_python` for all math,
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web search/fetch, Alpaca/Finnhub/yfinance market data, `get_portfolio` for continuity) and its
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output is checkpointed incrementally to `state/output/<date>/`. A min-steps nudge keeps agents
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researching broadly instead of stopping early.
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**Each persona trades its own Alpaca paper account** (Nova→agent1, Warren-lite→agent2, Atlas→agent3),
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so the three books — and their PnL — stay separate and comparable. `run_market_open` gates on
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Alpaca's clock, then per account applies `execution/guardrails.py` (position/day caps, sizing,
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buying-power) and places **GTC bracket** orders (marketable-limit entry so stop/take legs survive
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multi-day holds).
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## Automate it (America/Chicago)
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- **00:00–07:00** research (~2.3 hr/agent), machine woken at **23:55**.
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- **08:30** execution (= 09:30 ET open; gated on Alpaca's clock, never local time).
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```bash
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scripts/install_launchd.sh # loads the 3 launchd jobs
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sudo pmset repeat wakeorpoweron MTWRFSU 23:55:00 # one-time: wake the Mac nightly (needs sudo)
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```
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Overnight requirements: **plugged in, lid open, logged in** (lock screen OK), oMLX running, `.env` filled.
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Remove with `scripts/uninstall_launchd.sh` + `sudo pmset repeat cancel`.
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## Instruments & safety
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- Trades **stocks (long or short)** and **single-leg long options** (buy calls/puts). Per-trade
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+ per-day caps; stops/takes enforced in code, not trusted from the LLM. Shorts use inverted
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brackets; options are sized by premium×100 (max loss = premium; see `docs/options-phase.md`).
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- `run_python` executes model-generated code locally — kept math-only, sandboxed, no network.
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