Overnight Local-LLM Paper-Trading Agent

▶ Dashboard tour — portfolio & option positions, each persona's overnight theses, the AI Investment Committee verdicts, and the orders that got placed. Click for the full-quality MP4 (with sound).

Every night the Mac wakes itself, runs 4 trader personas one at a time against one local LLM (gpt-oss-20b via oMLX, 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 (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 for Python env management.
• Alpaca paper account + Finnhub free key.

Setup

# 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).

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.