内容
# not-financial-advice
An agentic trading pipeline: Claude Code + a brokerage MCP connector,
running as two scheduled cloud sessions that screen, reason about, and
(under a narrow, explicit gate) execute real trades — with the actual
safety mechanism being mechanical, auditable risk rules, not the LLM's
judgment.
This is a template/framework extracted from a real, live deployment.
Adapt it, don't just run it blind — read "What this does and doesn't
solve" below before pointing it at real money.
## How it works
Trading runs as **two separate phases, on two separate schedules** — a
full trading day's closing data feeds the thesis, and a fresh opening
price is used for the actual order, rather than trading on a stale
overnight price.
- **Phase A** (Steps 1–3, ~4:30pm Central weekdays) — screens candidates,
gathers signals, writes a logged thesis per candidate to
`pending_proposals.jsonl`. Places no orders, not even dry-run ones.
Full spec: `PHASE_A_TASK.md`.
- **Phase B** (Steps 4–7, ~8:35am Central weekdays) — re-verifies Phase A's
proposals against fresh opening data, enforces `risk_rules.json`
mechanically, and dry-runs or (gated) places orders. Full spec:
`PHASE_B_TASK.md`.
Both are designed to run as cloud-hosted scheduled agent sessions,
independent of any local machine — each run clones this repo fresh and
commits/pushes its results back to `main`, so the repo itself is the
persistent state, not local disk.
## Files
- `risk_rules.json` — the hard, mechanical limits (position sizing, stop-
loss, loss limits, universe filters, execution mode). Nothing in this
system should be able to override these. **Fill in your own
`account_number` before using this** — the placeholder here is not a
real account. **Also set `universe.watchlist_name`** to the name of a
watchlist you actually have in your brokerage account — Phase A pulls
candidates from that list by name, not a hardcoded one. **Also fill in
`wash_sale_avoidance.linked_accounts`** with every brokerage account
number you personally control that might hold the same symbols (a
separate personal account, an IRA, etc.) — the IRS wash-sale rule
applies per taxpayer across all your accounts, not just this one, and
this list is how the guard checks the others. Set
`wash_sale_avoidance.enabled` to `false` if you'd rather not have buys
blocked by this check at all.
- `PHASE_A_TASK.md` / `PHASE_B_TASK.md` — the full, self-contained spec
each phase follows.
- `trade_log_template.jsonl` — the log line shapes; real logs should
accumulate in a file like `trade_log.jsonl` in this same style.
## Thesis record shape (Phase A, Step 3)
```json
{
"symbol": "XXXX",
"date": "YYYY-MM-DD",
"thesis": "1-3 sentences on what changed and why it might matter",
"conviction": "low | medium | high",
"invalidation": "what would prove this thesis wrong",
"direction": "long | avoid | exit_existing",
"sources": ["Outlet Name: https://...", "..."]
}
```
- **No price targets** — no reliable basis for a specific number, and it
invites false precision.
- **No forecasting language treated as fact** — "this suggests...", not
"this will...".
## First-time setup
1. Fill in `account_number` in `risk_rules.json` with your own brokerage
account number, set `universe.watchlist_name` to a watchlist you've
already created and populated in your brokerage account, and review
every other threshold — the defaults here are illustrative, not a
recommendation.
2. Fill in `wash_sale_avoidance.linked_accounts` with every brokerage
account number you personally control, not just this one — if this is
genuinely the only account you trade in, a single-entry list (just
this account's number) is enough. Leave `enabled: true` unless you
specifically want buys never blocked on wash-sale grounds.
3. Keep `execution.mode` set to `"dry_run"`. Leave it there for at least
the number of cycles set in `dry_run_min_cycles_before_live` — don't
shortcut this.
4. After each cycle, read `trade_log.jsonl` yourself. Look specifically
at rejected candidates and stop-loss triggers, not just the trades
that "worked" — that's where you'll see if the reasoning step is
actually sound or just getting lucky with an uptrend.
5. Only flip `execution.mode` to `"live"` yourself, by hand, after you've
reviewed enough dry-run cycles to trust the output. Do not let the
agent flip it for you as a shortcut.
## What this does and doesn't solve
- It gives you a structured, auditable version of "let an LLM screen and
reason about trades" instead of an opaque one.
- It does **not** make LLM-driven stock picking more likely to beat a
simple index fund — there's no established track record for that, and
this can't backtest the reasoning step honestly (news-based reasoning
can't be validated against historical data the model may already know
the outcome of).
- The risk rules are the actual safety mechanism here, not the reasoning
quality. Treat loosening them as the highest-risk change you can make
to this system.
- This is a template extracted from a real deployment trading a small
personal account, shared for others to learn from or adapt. It is
genuinely not financial advice, and running it against real money is
entirely your own decision and risk.
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