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Trading Ops
Framework-driven trading workspace in Claude Code
Slash commands that scan stocks, crypto, FX, indices & commodities — structured verdict, ASCII price ladder, trade table every time. Version-controlled framework. Dated Markdown archive. Self-improving protocol. MIT, no paid APIs.
I've been building trading-ops for a few months and I'm excited to finally share it. Here's the full picture.
The problem
Most trading tools give you data. Fewer give you structure. Almost none give you an audit trail that lets you see how your analysis evolved — and whether your setups actually worked.
I wanted a workflow that was:
Consistent — the same checklist applies every time, every asset class, no improvisation
Auditable — every scan dated and archived; every rescan delta-compares against the prior
Improvable — the framework is version-controlled Markdown; it gets better over time, and you control every change
What it does
trading-ops is a workspace that runs inside Claude Code — Anthropic's CLI for agentic coding workflows. You run a slash command; Claude applies a framework to live data and saves a structured Markdown analysis.
The symbol auto-detects its asset class — same command works for everything.
What a verdict looks like
Every scan ends with a verb (LONG / SHORT / WAIT / SKIP), an ASCII price ladder with every level sourced, and a trade table with Entry / Stop / T1–T3 / R:R / Sizing per horizon:
If there's no setup, the verdict is WAIT — and the system says so explicitly. No prose triggers, no ambiguity.
Three horizons per name
Every scan produces three independent verdicts — Positional (weeks–months), Swing (3–15 days), Day (intraday). A positional Skip is compatible with a tradeable Swing around a catalyst.
Tiers: Top Pick (deploy on trigger) / Watchlist (live spec, act on conditions) / Bench (watching, no trigger) / Skip (auto-reject or hostile)
The framework (what the LLM applies)
All intelligence lives in four read-only documents in docs/. The LLM applies them — it doesn't improvise.
macro.md — 4-regime model (growth × inflation quadrants), indicator list, FOMC/CPI/NFP playbook. Every downstream scan is gated by the current regime — a great chart in a Risk-Off regime gets Bench, not a trade.
long-term-investing.md — 6-pillar scorecard: Quality (ROIC vs WACC, margin trend), Growth (3-quarter trajectory), Valuation (P/E vs history, PEG), Balance Sheet (leverage, runway), Capital Allocation (buyback quality, M&A), Smart Money (13F changes, Form 4 insider clusters, short interest). Auto-reject rules cut obvious misses before the full analysis runs.
volume-profile.md — POC / VAH / VAL / HVN / LVN, profile shapes (D / P / b / B), naked POCs as forward targets.
Add your own framework knowledge via /ingest input/your-paper.pdf — the command shows a write plan, waits for your consent, and extends docs/ without touching existing protocol.
The self-improvement loop
After every scan, Claude audits the process itself — broken data source, wrong filter code, ambiguous step, missing edge case. It surfaces fixes with a concrete file path and proposed change. It waits for consent before editing guide/ or .claude/commands/. It never touches docs/.
Over time, the protocol gets better without losing control of what changed.
Data pipeline — no paid subscriptions required
Nine Python scripts (yfinance, SEC EDGAR, FRED, CoinGecko, Google News RSS) pre-compute data — no paid APIs required. Everything works keyless; optional free keys unlock more.
Extensible via MCPs
The base layer is research only — intentionally. But Claude Code is MCP-native, so the stack is open. Examples:
Broker MCP (Alpaca, CCXT for Weex/Binance/Bybit) — execute directly from a verdict
Chrome DevTools MCP — auto-pull TradingView charts into every scan
Playwright MCP — scrape any page without an API (broker portals, paywalled flow data)
Slack / Telegram MCP — post verdict deltas to a channel when a rescan flips
Hermes (Nous Research) — run scans 24/7 on a $5 VPS, push alerts to Telegram or Discord
Every integration is additive. The research framework stays the anchor.
Would love to hear how others use it and what protocols you'd add. The framework is designed to grow — drop a comment if you want to talk architecture.
About Trading Ops on Product Hunt
“Framework-driven trading workspace in Claude Code”
Trading Ops was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #85 on the daily leaderboard. Slash commands that scan stocks, crypto, FX, indices & commodities — structured verdict, ASCII price ladder, trade table every time. Version-controlled framework. Dated Markdown archive. Self-improving protocol. MIT, no paid APIs.
On the analytics side, Trading Ops competes within Fintech, Artificial Intelligence, GitHub and Finance — topics that collectively have 562.5k followers on Product Hunt. The dashboard above tracks how Trading Ops performed against the three products that launched closest to it on the same day.
Who hunted Trading Ops?
Trading Ops was hunted by l3lackcurtains. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Trading Ops including community comment highlights and product details, visit the product overview.
l3lackcurtains
Hey Product Hunt 👋
I've been building trading-ops for a few months and I'm excited to finally share it. Here's the full picture.
The problem
Most trading tools give you data. Fewer give you structure. Almost none give you an audit trail that lets you see how your analysis evolved — and whether your setups actually worked.
I wanted a workflow that was:
Consistent — the same checklist applies every time, every asset class, no improvisation
Auditable — every scan dated and archived; every rescan delta-compares against the prior
Improvable — the framework is version-controlled Markdown; it gets better over time, and you control every change
What it does
trading-ops is a workspace that runs inside Claude Code — Anthropic's CLI for agentic coding workflows. You run a slash command; Claude applies a framework to live data and saves a structured Markdown analysis.
Five asset classes, one command:
The symbol auto-detects its asset class — same command works for everything.
What a verdict looks like
Every scan ends with a verb (LONG / SHORT / WAIT / SKIP), an ASCII price ladder with every level sourced, and a trade table with Entry / Stop / T1–T3 / R:R / Sizing per horizon:
If there's no setup, the verdict is WAIT — and the system says so explicitly. No prose triggers, no ambiguity.
Three horizons per name
Every scan produces three independent verdicts — Positional (weeks–months), Swing (3–15 days), Day (intraday). A positional Skip is compatible with a tradeable Swing around a catalyst.
Tiers: Top Pick (deploy on trigger) / Watchlist (live spec, act on conditions) / Bench (watching, no trigger) / Skip (auto-reject or hostile)
The framework (what the LLM applies)
All intelligence lives in four read-only documents in docs/. The LLM applies them — it doesn't improvise.
macro.md — 4-regime model (growth × inflation quadrants), indicator list, FOMC/CPI/NFP playbook. Every downstream scan is gated by the current regime — a great chart in a Risk-Off regime gets Bench, not a trade.
long-term-investing.md — 6-pillar scorecard: Quality (ROIC vs WACC, margin trend), Growth (3-quarter trajectory), Valuation (P/E vs history, PEG), Balance Sheet (leverage, runway), Capital Allocation (buyback quality, M&A), Smart Money (13F changes, Form 4 insider clusters, short interest). Auto-reject rules cut obvious misses before the full analysis runs.
volume-profile.md — POC / VAH / VAL / HVN / LVN, profile shapes (D / P / b / B), naked POCs as forward targets.
vwap.md — Session / weekly / monthly / quarterly / anchored VWAP, ±1σ / ±2σ bands, reclaim/lose patterns.
Add your own framework knowledge via /ingest input/your-paper.pdf — the command shows a write plan, waits for your consent, and extends docs/ without touching existing protocol.
The self-improvement loop
After every scan, Claude audits the process itself — broken data source, wrong filter code, ambiguous step, missing edge case. It surfaces fixes with a concrete file path and proposed change. It waits for consent before editing guide/ or .claude/commands/. It never touches docs/.
Over time, the protocol gets better without losing control of what changed.
Data pipeline — no paid subscriptions required
Nine Python scripts (yfinance, SEC EDGAR, FRED, CoinGecko, Google News RSS) pre-compute data — no paid APIs required. Everything works keyless; optional free keys unlock more.
Extensible via MCPs
The base layer is research only — intentionally. But Claude Code is MCP-native, so the stack is open. Examples:
Broker MCP (Alpaca, CCXT for Weex/Binance/Bybit) — execute directly from a verdict
Chrome DevTools MCP — auto-pull TradingView charts into every scan
Playwright MCP — scrape any page without an API (broker portals, paywalled flow data)
Slack / Telegram MCP — post verdict deltas to a channel when a rescan flips
Hermes (Nous Research) — run scans 24/7 on a $5 VPS, push alerts to Telegram or Discord
Every integration is additive. The research framework stays the anchor.
Completely free
MIT license
No paid data subscriptions
Claude Code free tier works
Python 3.10+
GitHub: https://github.com/l3lackcurtains/trading-ops
Would love to hear how others use it and what protocols you'd add. The framework is designed to grow — drop a comment if you want to talk architecture.