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Merged
walkerhughes merged 2 commits into from
Aug 4, 2026
Merged

feat(tastytrade): a skill for ranking earnings calendar spreads #23

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a29c886
feat(tastytrade): a skill for ranking earnings calendar spreads
walkerhughes Aug 4, 2026
669bec9
fix(tastytrade): bump to 0.4.0 and run the skill selftest in CI
walkerhughes Aug 4, 2026
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2 changes: 1 addition & 1 deletion plugins/tastytrade/.claude-plugin/plugin.json
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
{
"name": "tastytrade",
"version": "0.3.0",
"version": "0.4.0",
"description": "MCP server for the TastyTrade Open API: brokerage accounts, positions, market data, option chains, transactions, and order preview.",
"author": {
"name": "Walker Hughes"
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9 changes: 7 additions & 2 deletions plugins/tastytrade/Makefile
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@@ -1,4 +1,4 @@
.PHONY: lint lint-fix format typecheck check test test-unit test-integration coverage install-hooks validate-tasks mock-api benchmark-build benchmark benchmark-view
.PHONY: lint lint-fix format typecheck check selftest test test-unit test-integration coverage install-hooks validate-tasks mock-api benchmark-build benchmark benchmark-view

lint:
uv run ruff check .
Expand All @@ -12,7 +12,12 @@ format:
typecheck:
uv run mypy src/

check: lint typecheck test-unit
check: lint typecheck selftest test-unit

# Skill scripts are shipped payload but live outside src/, so pytest never sees
# them. Each carries its own assertions; this is what runs them.
selftest:
python3 scripts/calendars.py --selftest

test:
uv run pytest
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23 changes: 23 additions & 0 deletions plugins/tastytrade/README.md
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Expand Up @@ -33,6 +33,29 @@ Authentication uses the OAuth2 refresh-token flow with automatic token refresh.
`TT_ENABLE_TRADING=true`, **and** each call must pass `confirm=true`. Otherwise the order is not
sent and the previewed effect is returned. Always call `preview_order` first.

## Skills

| Skill | What it does |
|---|---|
| `earnings-calendars` | Analyse an option chain for calendar spreads around an earnings event and rank them by risk-adjusted return. |

`earnings-calendars` decomposes the vol term structure into base vol plus a one-time event
jump, then prices every candidate calendar against three move regimes using real bid/ask.
The screening rule it enforces is that a calendar only has edge if its profit band is wider
than the implied expected move, which is usually not the case and is the point.

Its analysis runs in [`scripts/calendars.py`](scripts/calendars.py), which is standard
library only (no numpy) and self-testing:

```bash
python3 scripts/calendars.py --selftest
python3 scripts/calendars.py fit skills/earnings-calendars/reference/pltr-2026-08-03.json
python3 scripts/calendars.py rank skills/earnings-calendars/reference/pltr-2026-08-03.json
```

That reference file is a real PLTR chain captured the afternoon of its 2026-08-03 print, and
doubles as a regression fixture. The skill never places orders; it is analysis only.

## Architecture

The design follows the Honeycomb MCP: a few curated tools, responses shaped for a model rather
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