Claude/busy ptolemy virivd

.claude/settings.json

@@ -1,7 +1,14 @@
 {
+  "permissions": {
+    "allow": [
+      "Bash(agy *)",
+      "PowerShell(agy *)"
+    ]
+  },
   "enabledPlugins": {
     "frontend-design@claude-plugins-official": true,
     "context7@claude-plugins-official": true,
-    "playwright@claude-plugins-official": true
+    "playwright@claude-plugins-official": true,
+    "independent-reviwer@Dobi-tools": true
   }
 }

.github/workflows/claude-code-review.yml

@@ -38,7 +38,8 @@ jobs:
           claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
           plugin_marketplaces: 'https://github.com/anthropics/claude-code.git'
           plugins: 'code-review@claude-code-plugins'
-          prompt: '/code-review:code-review ${{ github.repository }}/pull/${{ github.event.pull_request.number }}'
+          prompt: '/code-review:code-review --comment ${{ github.repository }}/pull/${{ github.event.pull_request.number }}'
+          claude_args: '--allowedTools "mcp__github_inline_comment__create_inline_comment"'
           # See https://github.com/anthropics/claude-code-action/blob/main/docs/usage.md
           # or https://code.claude.com/docs/en/cli-reference for available options
 

.github/workflows/claude.yml

@@ -46,5 +46,5 @@ jobs:
           # Optional: Add claude_args to customize behavior and configuration
           # See https://github.com/anthropics/claude-code-action/blob/main/docs/usage.md
           # or https://code.claude.com/docs/en/cli-reference for available options
-          # claude_args: '--allowed-tools Bash(gh pr:*)'
+          # claude_args: '--allowed-tools Bash(gh pr *)'
 

HI.md

@@ -0,0 +1,51 @@
+# HI — How this remote Claude Code session worked
+
+A short note written by Claude Code itself, from inside the session that created this file.
+
+## The short version
+
+You asked for a file; an agent running on a throwaway machine in the cloud wrote it,
+committed it, and pushed it back to GitHub. Nothing ran on your laptop.
+
+## The pieces
+
+**1. A container, not your machine.**
+The session runs in an isolated, ephemeral Linux container. The repo
+(`dobidobi77-bot/finally`) was cloned fresh when the container started. The container
+is reclaimed after inactivity, so anything worth keeping has to be committed and
+pushed — the working directory is not durable storage.
+
+**2. Context loaded up front.**
+Before the first instruction, the harness injected the project's `CLAUDE.md` and the
+`planning/PLAN.md` it imports. That is why the agent already knows FinAlly is a
+FastAPI + Next.js trading workstation with an SSE price stream, without reading a
+single file.
+
+**3. Tools instead of a terminal for you.**
+The agent acts through a fixed tool surface: `Bash`, file read/write/edit, `Grep`,
+`Glob`, sub-agents, and MCP servers (GitHub, Playwright, Context7). Each call runs
+under a permission mode you chose. Some tools are *deferred* — only their names are
+loaded until the agent searches for and pulls in the full schema, which keeps the
+prompt small.
+
+**4. Git is the delivery mechanism.**
+Work happens on a designated branch (`claude/busy-ptolemy-virivd`), never directly on
+`main`. Commits carry a co-author trailer and a link back to the session. A pull
+request is only opened if you ask for one.
+
+**5. It can outlive the turn.**
+The session can subscribe to PR webhooks, so CI failures and review comments wake it
+back up later and it pushes fixes without you re-prompting. It can also schedule its
+own check-ins.
+
+## What this session actually did
+
+Read the repo state, wrote this file, committed it to the feature branch, and pushed.
+
+## Why it's useful
+
+You can start work from a phone or a browser tab, close it, and come back to a branch
+with commits on it. The tradeoffs: no access to your local environment or secrets
+beyond what the container was given, and outbound network access is limited by the
+environment's policy — which is exactly why the Context7 documentation server failed
+to connect during this session.

README.md

@@ -1,60 +1,79 @@
 # FinAlly — AI Trading Workstation
 
-A visually stunning AI-powered trading workstation that streams live market data, simulates portfolio trading, and integrates an LLM chat assistant that can analyze positions and execute trades via natural language.
+An AI-powered trading workstation that streams live market data, simulates portfolio trading, and integrates an LLM assistant that can analyse positions and execute trades through natural language.
 
-Built entirely by coding agents as a capstone project for an agentic AI coding course.
+Built by coding agents as the capstone project for an agentic AI coding course. The full specification is in [planning/PLAN.md](planning/PLAN.md), which agents use as their shared contract.
 
-## Features
+## Status
 
-- **Live price streaming** via SSE with green/red flash animations
-- **Simulated portfolio** — $10k virtual cash, market orders, instant fills
-- **Portfolio visualizations** — heatmap (treemap), P&L chart, positions table
-- **AI chat assistant** — analyzes holdings, suggests and auto-executes trades
-- **Watchlist management** — track tickers manually or via AI
-- **Dark terminal aesthetic** — Bloomberg-inspired, data-dense layout
+Early development. Only the market data subsystem is built.
 
-## Architecture
+| Component | State |
+| --- | --- |
+| Market data — simulator, Massive API client, price cache, SSE endpoint | Built, 73 tests passing |
+| Database, portfolio, trading | Not started |
+| LLM chat assistant | Not started |
+| Frontend | Not started |
+| Docker packaging | Not started |
 
-Single Docker container serving everything on port 8000:
+There is no runnable application yet — no Dockerfile, no frontend, no API server. The sections below describe what exists today.
 
-- **Frontend**: Next.js (static export) with TypeScript and Tailwind CSS
-- **Backend**: FastAPI (Python/uv) with SSE streaming
-- **Database**: SQLite with lazy initialization
-- **AI**: LiteLLM → OpenRouter (Cerebras inference) with structured outputs
-- **Market data**: Built-in GBM simulator (default) or Massive API (optional)
+## Running what exists
 
-## Quick Start
+Requires Python 3.12+ and [uv](https://docs.astral.sh/uv/).
 
 ```bash
-# Clone and configure
-cp .env.example .env
-# Add your OPENROUTER_API_KEY to .env
+cd backend
+uv sync
 
-# Run with Docker
-docker build -t finally .
-docker run -v finally-data:/app/db -p 8000:8000 --env-file .env finally
+# Live terminal dashboard: 10 tickers with sparklines and colour-coded moves.
+# Runs 60 seconds, or until Ctrl+C. No API key needed.
+uv run market_data_demo.py
 
-# Open http://localhost:8000
+# Test suite
+uv run pytest
 ```
 
-## Environment Variables
+## Market data
+
+Two interchangeable sources sit behind one abstract interface (`MarketDataSource`):
+
+- **Simulator** (default) — geometric Brownian motion with per-ticker drift and volatility, sector-correlated moves, and occasional random shocks. Runs in-process with no external dependencies.
+- **Massive API** (optional) — REST polling against Polygon.io. Selected automatically when `MASSIVE_API_KEY` is set.
+
+Both write to a thread-safe `PriceCache`. Everything downstream — the SSE endpoint, and later portfolio valuation and trade execution — reads from that cache and never touches the source directly, so the rest of the system does not care which one is running.
+
+Module-level detail is in [planning/MARKET_DATA_SUMMARY.md](planning/MARKET_DATA_SUMMARY.md).
+
+## Environment variables
+
+Create a `.env` file in the project root:
 
 | Variable | Required | Description |
-|---|---|---|
-| `OPENROUTER_API_KEY` | Yes | OpenRouter API key for AI chat |
-| `MASSIVE_API_KEY` | No | Massive (Polygon.io) key for real market data; omit to use simulator |
-| `LLM_MOCK` | No | Set `true` for deterministic mock LLM responses (testing) |
+| --- | --- | --- |
+| `OPENROUTER_API_KEY` | Later | OpenRouter key for the AI chat assistant. Not used yet. |
+| `MASSIVE_API_KEY` | No | Polygon.io key for real market data. Omit to use the simulator. |
+| `LLM_MOCK` | No | Set `true` for deterministic mock LLM responses in tests. |
+
+## Planned architecture
+
+A single Docker container serving everything on port 8000:
+
+- **Frontend** — a static build served by FastAPI, so there is one origin and no CORS setup
+- **Backend** — FastAPI managed with uv, pushing live prices over SSE
+- **Database** — SQLite, a single volume-mounted file
+- **AI** — LiteLLM to OpenRouter, using structured outputs to drive trade execution
 
-## Project Structure
+## Project structure
 
 ```
 finally/
-├── frontend/    # Next.js static export
-├── backend/     # FastAPI uv project
-├── planning/    # Project documentation and agent contracts
-├── test/        # Playwright E2E tests
-├── db/          # SQLite volume mount (runtime)
-└── scripts/     # Start/stop helpers
+├── backend/              FastAPI uv project
+│   ├── app/market/       Market data subsystem (built)
+│   └── tests/            Unit and integration tests
+└── planning/             Specification and agent contracts
+    ├── PLAN.md
+    └── MARKET_DATA_SUMMARY.md
 ```
 
 ## License