# Overnight Local-LLM Paper-Trading Agent

Every night the Mac wakes itself, runs **4 trader personas** one at a time against **one local LLM**
(gpt-oss-20b via [oMLX](https://omlx.ai), an Apple-MLX inference server). Each persona researches the
web + live market data, forms up to **4 trade theses — at least one of them an options trade** —
and writes JSON. In the morning at market open a separate job reads that JSON and places
**Alpaca paper trades** with programmatic risk guardrails. Everything is visible in a local
Streamlit dashboard, including an **Agents tab** that manages the roster (add/edit/disable agents,
per-agent research budgets) via `state/agents.json` — no code edits needed.

> **Paper trading only.** Fake money, real prices. "The build works" ≠ "it's profitable" — the
> quality of a local model's theses is the open question you tune over time.

## Requirements
- Apple Silicon Mac (built/tested on M4 Pro, 24 GB), **plugged in, lid open, logged in** overnight.
- [oMLX](https://omlx.ai) (Apple-MLX server) auto-running on `:8090` with a model downloaded
  (default `gpt-oss-20b`; toggle `qwen35_27b`). Enforces an API key, auto-read from `~/.omlx/settings.json`.
- [uv](https://docs.astral.sh/uv/) for Python env management.
- Alpaca **paper** account + Finnhub free key.

## Setup
```bash
# 1. Python env + deps
uv sync

# 2. Secrets
cp .env.example .env      # then paste your Alpaca paper + Finnhub keys

# 3. Model host — oMLX auto-starts on login; if not, bring it up + verify tool-calling
omlx start                                  # OpenAI-compatible API on http://127.0.0.1:8090
uv run python scripts/smoke_toolcalling.py  # GATE: multi-turn tool calls must PASS

# 4. Phase-0 gate
uv run pytest
```

Switch models any time with one line in `.env`: `MODEL_PROFILE=gpt_oss` or `qwen35_27b`.

## Entry points
| Command | What it does |
|---|---|
| `uv run python -m orchestration.run_nightly` | Nightly research run (all enabled agents → JSON). `--compressed` for a fast test |
| `uv run python -m execution.run_market_open` | Morning: JSON → guardrails → Alpaca paper orders. `--force` to test while closed |
| `uv run streamlit run web/dashboard.py` | Dashboard: positions, PnL, theses, orders, logs |
| `uv run python -m reporting.report` | CLI summary |

## How it works
`run_nightly` starts oMLX, then runs Nova → Warren-lite → Atlas → Maverick **sequentially** (one
model, time-sliced with rollover budgets; per-agent budgets are dashboard-editable). The roster
comes from `config/agent_registry.py` (built-ins merged with `state/agents.json`). Each agent
researches with tools (`run_python` for math, `run_analysis` for data/charts, web search/fetch,
Alpaca/Finnhub/yfinance market data, options chains, `get_portfolio` for continuity) and its
output is checkpointed incrementally to `state/output/<date>/`. Nudges keep agents researching
broadly, steer them to the **options mandate** (each night's picks must include a researched
single-leg option), and converge them near the end of their budget.

**Each persona trades its own Alpaca paper account** (Nova→agent1, Warren-lite→agent2,
Atlas→agent3, Maverick→agent4 — Maverick is the deliberately aggressive options/shorts book),
so the books — and their PnL — stay separate and comparable. `run_market_open` gates on
Alpaca's clock, then per account applies `execution/guardrails.py` (position/day caps, sizing,
buying-power, an options conviction floor + premium cap, and a reserved slot so the mandated
option trade isn't trimmed) and places **GTC bracket** orders (marketable-limit entry so
stop/take legs survive multi-day holds).

> **Adding an agent:** use the dashboard's 🤖 Agents tab (clone a template, tweak knobs), create
> a fresh Alpaca paper account for it, enable **options trading (level 2)** on that account, and
> add `ALPACA_<ID>_KEY` / `ALPACA_<ID>_SECRET` to `.env`. Maverick ships built-in but still needs
> its own account keys the same way.

### The differentiating layer
- **Market regime snapshot** — SPY trend / VIX / sector momentum computed in code each night and
  injected into every agent's kickoff (shared ground truth).
- **Self-learning journal** — each agent sees its own past picks marked-to-market before
  researching; losses and wins become explicit lessons.
- **AI Investment Committee** — a Chief-Risk-Officer LLM pass reviews the whole book after
  research and issues per-pick verdicts (APPROVE / REDUCE / VETO with reasons). The memo lands in
  the Reports tab and the morning executor **enforces** the verdicts.

## Automate it (America/Chicago)
- **00:00–07:00** research (fair-share time-slicing: ~105 min/agent with 4 agents; tune
  per-agent budgets from the dashboard), machine woken at **23:55**.
- **08:30** execution (= 09:30 ET open; gated on Alpaca's clock, never local time).

```bash
scripts/install_launchd.sh                      # loads the 3 launchd jobs
sudo pmset repeat wakeorpoweron MTWRFSU 23:55:00 # one-time: wake the Mac nightly (needs sudo)
```
Overnight requirements: **plugged in, lid open, logged in** (lock screen OK), oMLX running, `.env` filled.
Remove with `scripts/uninstall_launchd.sh` + `sudo pmset repeat cancel`.

## Instruments & safety
- Trades **stocks (long or short)** and **single-leg long options** (buy calls/puts). Per-trade
  + per-day caps; stops/takes enforced in code, not trusted from the LLM. Shorts use inverted
  brackets; options are sized by premium×100 (max loss = premium; see `docs/options-phase.md`).
- `run_python` executes model-generated code locally — kept math-only, sandboxed, no network.