diff --git a/.claude/settings.json b/.claude/settings.json index aa06f43dc..e69de29bb 100644 --- a/.claude/settings.json +++ b/.claude/settings.json @@ -1,7 +0,0 @@ -{ - "enabledPlugins": { - "frontend-design@claude-plugins-official": true, - "context7@claude-plugins-official": true, - "playwright@claude-plugins-official": true - } -} diff --git a/.claude/skills/cerebras/SKILL.md b/.claude/skills/cerebras/SKILL.md deleted file mode 100644 index 9efd01a38..000000000 --- a/.claude/skills/cerebras/SKILL.md +++ /dev/null @@ -1,43 +0,0 @@ ---- -name: cerebras-inference -description: Use this to write code to call an LLM using LiteLLM and OpenRouter with the Cerebras inference provider ---- - -# Calling an LLM via Cerebras - -These instructions allow you write code to call an LLM with Cerebras specified as the inference provider. -This method uses LiteLLM and OpenRouter. - -## Setup - -The OPENROUTER_API_KEY must be set in the .env file and loaded in as an environment variable. - -The uv project must include litellm and pydantic. -`uv add litellm pydantic` - -## Code snippets - -Use code like these examples in order to use Cerebras. - -### Imports and constants - -```python -from litellm import completion -MODEL = "openrouter/openai/gpt-oss-120b" -EXTRA_BODY = {"provider": {"order": ["cerebras"]}} -``` - -### Code to call via Cerebras for a text response - -```python -response = completion(model=MODEL, messages=messages, reasoning_effort="low", extra_body=EXTRA_BODY) -result = response.choices[0].message.content -``` - -### Code to call via Cerebras for a Structured Outputs response - -```python -response = completion(model=MODEL, messages=messages, response_format=MyBaseModelSubclass, reasoning_effort="low", extra_body=EXTRA_BODY) -result = response.choices[0].message.content -result_as_object = MyBaseModelSubclass.model_validate_json(result) -``` \ No newline at end of file diff --git a/.github/workflows/claude-code-review.yml b/.github/workflows/claude-code-review.yml deleted file mode 100644 index b5e8cfd4d..000000000 --- a/.github/workflows/claude-code-review.yml +++ /dev/null @@ -1,44 +0,0 @@ -name: Claude Code Review - -on: - pull_request: - types: [opened, synchronize, ready_for_review, reopened] - # Optional: Only run on specific file changes - # paths: - # - "src/**/*.ts" - # - "src/**/*.tsx" - # - "src/**/*.js" - # - "src/**/*.jsx" - -jobs: - claude-review: - # Optional: Filter by PR author - # if: | - # github.event.pull_request.user.login == 'external-contributor' || - # github.event.pull_request.user.login == 'new-developer' || - # github.event.pull_request.author_association == 'FIRST_TIME_CONTRIBUTOR' - - runs-on: ubuntu-latest - permissions: - contents: read - pull-requests: read - issues: read - id-token: write - - steps: - - name: Checkout repository - uses: actions/checkout@v4 - with: - fetch-depth: 1 - - - name: Run Claude Code Review - id: claude-review - uses: anthropics/claude-code-action@v1 - with: - 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 }}' - # 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 - diff --git a/.github/workflows/claude.yml b/.github/workflows/claude.yml deleted file mode 100644 index d300267f1..000000000 --- a/.github/workflows/claude.yml +++ /dev/null @@ -1,50 +0,0 @@ -name: Claude Code - -on: - issue_comment: - types: [created] - pull_request_review_comment: - types: [created] - issues: - types: [opened, assigned] - pull_request_review: - types: [submitted] - -jobs: - claude: - if: | - (github.event_name == 'issue_comment' && contains(github.event.comment.body, '@claude')) || - (github.event_name == 'pull_request_review_comment' && contains(github.event.comment.body, '@claude')) || - (github.event_name == 'pull_request_review' && contains(github.event.review.body, '@claude')) || - (github.event_name == 'issues' && (contains(github.event.issue.body, '@claude') || contains(github.event.issue.title, '@claude'))) - runs-on: ubuntu-latest - permissions: - contents: read - pull-requests: read - issues: read - id-token: write - actions: read # Required for Claude to read CI results on PRs - steps: - - name: Checkout repository - uses: actions/checkout@v4 - with: - fetch-depth: 1 - - - name: Run Claude Code - id: claude - uses: anthropics/claude-code-action@v1 - with: - claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }} - - # This is an optional setting that allows Claude to read CI results on PRs - additional_permissions: | - actions: read - - # Optional: Give a custom prompt to Claude. If this is not specified, Claude will perform the instructions specified in the comment that tagged it. - # prompt: 'Update the pull request description to include a summary of changes.' - - # 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:*)' - diff --git a/CLAUDE.md b/CLAUDE.md index 2bdd6fa10..a88db7c2a 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -2,6 +2,6 @@ All project documentation is in the `planning` directory. -The key document is PLAN.md included in full below; the market data component has been completed and is summarized in the file `planning/MARKET_DATA_SUMMARY.md` with more details in the `planning/archive` folder. Consult these docs only when required. The remainder of the platform is still to be developed. +The key document is PLAN.md included in full here: @planning/PLAN.md \ No newline at end of file diff --git a/Doc/.gitkeep b/Doc/.gitkeep new file mode 100644 index 000000000..e69de29bb diff --git a/README.md b/README.md index 3f2582ae2..d0e566844 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ 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. -Built entirely by coding agents as a capstone project for an agentic AI coding course. +Built entirely by coding agents as a capstone project for an agentic AI coding course. Full spec: [`planning/PLAN.md`](planning/PLAN.md). ## Features @@ -10,7 +10,7 @@ Built entirely by coding agents as a capstone project for an agentic AI coding c - **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 +- **Watchlist management** — track tickers manually or via AI (20-ticker cap) - **Dark terminal aesthetic** — Bloomberg-inspired, data-dense layout ## Architecture @@ -19,44 +19,56 @@ Single Docker container serving everything on port 8000: - **Frontend**: Next.js (static export) with TypeScript and Tailwind CSS - **Backend**: FastAPI (Python/uv) with SSE streaming -- **Database**: SQLite with lazy initialization +- **Database**: SQLite in a named Docker volume, schema initialized on startup - **AI**: LiteLLM → OpenRouter (Cerebras inference) with structured outputs - **Market data**: Built-in GBM simulator (default) or Massive API (optional) ## Quick Start ```bash -# Clone and configure cp .env.example .env # Add your OPENROUTER_API_KEY to .env -# Run with Docker -docker build -t finally . -docker run -v finally-data:/app/db -p 8000:8000 --env-file .env finally +./scripts/start_mac.sh # macOS/Linux +# or +./scripts/start_windows.ps1 # Windows PowerShell # Open http://localhost:8000 ``` +Both scripts wrap `docker compose up -d --build`; stop with `scripts/stop_mac.sh` / `scripts/stop_windows.ps1` (data persists in the `finally-data` volume). + ## Environment Variables | 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 | +| `MASSIVE_API_KEY` | No | Massive (Polygon.io) key for real market data; omit to use the simulator | | `LLM_MOCK` | No | Set `true` for deterministic mock LLM responses (testing) | +| `MARKET_TICK_MS` | No | Market data poll/tick interval; defaults to 500ms (simulator) / 15000ms (Massive) | +| `APP_SHARED_SECRET` | No | Shared secret required on all `/api/*` requests; **required** for any non-localhost deployment | + +See [`.env.example`](.env.example) for the full template. ## Project Structure ``` finally/ ├── frontend/ # Next.js static export -├── backend/ # FastAPI uv project +├── backend/ # FastAPI uv project (owns DB schema, API, SSE, market data, LLM) ├── planning/ # Project documentation and agent contracts -├── test/ # Playwright E2E tests -├── db/ # SQLite volume mount (runtime) -└── scripts/ # Start/stop helpers +├── test/ # Playwright E2E tests + docker-compose.test.yml +└── scripts/ # Start/stop helpers wrapping docker compose ``` +The SQLite database lives in a named Docker volume (`finally-data`), not in the repo tree. + +## Testing + +- Backend: `pytest` (within `backend/`) +- Frontend: unit tests (within `frontend/`) +- E2E: Playwright suite in `test/`, run against `docker-compose.test.yml` with `LLM_MOCK=true` + ## License See [LICENSE](LICENSE). diff --git a/backend/CLAUDE.md b/backend/CLAUDE.md deleted file mode 100644 index 612ff18f5..000000000 --- a/backend/CLAUDE.md +++ /dev/null @@ -1,59 +0,0 @@ -# Backend — Developer Guide - -## Project Setup - -```bash -cd backend -uv sync --extra dev # Install all dependencies including test/lint tools -``` - -## Market Data API - -The market data subsystem lives in `app/market/`. Use these imports: - -```python -from app.market import PriceCache, PriceUpdate, MarketDataSource, create_market_data_source -``` - -### Core Types - -- **`PriceUpdate`** — Immutable dataclass: `ticker`, `price`, `previous_price`, `timestamp`, plus properties `change`, `change_percent`, `direction` ("up"/"down"/"flat"), and `to_dict()` for JSON serialization. - -- **`PriceCache`** — Thread-safe in-memory store. Key methods: - - `update(ticker, price, timestamp=None) -> PriceUpdate` - - `get(ticker) -> PriceUpdate | None` - - `get_price(ticker) -> float | None` - - `get_all() -> dict[str, PriceUpdate]` - - `remove(ticker)` - - `version` property — monotonic counter, increments on every update (for SSE change detection) - -- **`MarketDataSource`** — Abstract interface implemented by `SimulatorDataSource` and `MassiveDataSource`. Lifecycle: `start(tickers)` -> `add_ticker()` / `remove_ticker()` -> `stop()`. - -- **`create_market_data_source(cache)`** — Factory. Returns `MassiveDataSource` if `MASSIVE_API_KEY` is set, otherwise `SimulatorDataSource`. - -### SSE Streaming - -```python -from app.market import create_stream_router - -router = create_stream_router(price_cache) # Returns FastAPI APIRouter -# Endpoint: GET /api/stream/prices (text/event-stream) -``` - -### Seed Data - -Default tickers: AAPL, GOOGL, MSFT, AMZN, TSLA, NVDA, META, JPM, V, NFLX. Seed prices and per-ticker volatility/drift params are in `app/market/seed_prices.py`. - -## Running Tests - -```bash -uv run --extra dev pytest -v # All tests -uv run --extra dev pytest --cov=app # With coverage -uv run --extra dev ruff check app/ tests/ # Lint -``` - -## Demo - -```bash -uv run market_data_demo.py # Live terminal dashboard with simulated prices -``` diff --git a/backend/README.md b/backend/README.md deleted file mode 100644 index 7cdd84757..000000000 --- a/backend/README.md +++ /dev/null @@ -1,55 +0,0 @@ -# FinAlly Backend - -FastAPI backend for the FinAlly AI Trading Workstation. - -## Structure - -- `app/` - Application code - - `market/` - Market data subsystem - - `models.py` - PriceUpdate dataclass - - `cache.py` - Thread-safe price cache - - `interface.py` - MarketDataSource abstract interface - - `simulator.py` - GBM-based market simulator - - `massive_client.py` - Massive/Polygon.io API client - - `factory.py` - Data source factory - - `stream.py` - SSE streaming endpoint - - `seed_prices.py` - Default ticker prices and parameters - -- `tests/` - Unit and integration tests - - `market/` - Market data tests - -## Running Tests - -```bash -# Install dependencies -uv sync --dev - -# Run all tests -uv run pytest - -# Run with coverage -uv run pytest --cov=app --cov-report=html - -# Run specific test file -uv run pytest tests/market/test_simulator.py - -# Run with verbose output -uv run pytest -v -``` - -## Environment Variables - -- `MASSIVE_API_KEY` - Optional. If set, use real market data from Massive API. If not set, use the built-in simulator. - -## Development - -```bash -# Install dependencies -uv sync --dev - -# Run linter -uv run ruff check . - -# Format code -uv run ruff format . -``` diff --git a/backend/app/__init__.py b/backend/app/__init__.py deleted file mode 100644 index 4f6b7f6b6..000000000 --- a/backend/app/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""FinAlly backend application.""" diff --git a/backend/app/market/__init__.py b/backend/app/market/__init__.py deleted file mode 100644 index 57ad0a121..000000000 --- a/backend/app/market/__init__.py +++ /dev/null @@ -1,23 +0,0 @@ -"""Market data subsystem for FinAlly. - -Public API: - PriceUpdate - Immutable price snapshot dataclass - PriceCache - Thread-safe in-memory price store - MarketDataSource - Abstract interface for data providers - create_market_data_source - Factory that selects simulator or Massive - create_stream_router - FastAPI router factory for SSE endpoint -""" - -from .cache import PriceCache -from .factory import create_market_data_source -from .interface import MarketDataSource -from .models import PriceUpdate -from .stream import create_stream_router - -__all__ = [ - "PriceUpdate", - "PriceCache", - "MarketDataSource", - "create_market_data_source", - "create_stream_router", -] diff --git a/backend/app/market/cache.py b/backend/app/market/cache.py deleted file mode 100644 index 4d0215778..000000000 --- a/backend/app/market/cache.py +++ /dev/null @@ -1,75 +0,0 @@ -"""Thread-safe in-memory price cache.""" - -from __future__ import annotations - -import time -from threading import Lock - -from .models import PriceUpdate - - -class PriceCache: - """Thread-safe in-memory cache of the latest price for each ticker. - - Writers: SimulatorDataSource or MassiveDataSource (one at a time). - Readers: SSE streaming endpoint, portfolio valuation, trade execution. - """ - - def __init__(self) -> None: - self._prices: dict[str, PriceUpdate] = {} - self._lock = Lock() - self._version: int = 0 # Monotonically increasing; bumped on every update - - def update(self, ticker: str, price: float, timestamp: float | None = None) -> PriceUpdate: - """Record a new price for a ticker. Returns the created PriceUpdate. - - Automatically computes direction and change from the previous price. - If this is the first update for the ticker, previous_price == price (direction='flat'). - """ - with self._lock: - ts = timestamp or time.time() - prev = self._prices.get(ticker) - previous_price = prev.price if prev else price - - update = PriceUpdate( - ticker=ticker, - price=round(price, 2), - previous_price=round(previous_price, 2), - timestamp=ts, - ) - self._prices[ticker] = update - self._version += 1 - return update - - def get(self, ticker: str) -> PriceUpdate | None: - """Get the latest price for a single ticker, or None if unknown.""" - with self._lock: - return self._prices.get(ticker) - - def get_all(self) -> dict[str, PriceUpdate]: - """Snapshot of all current prices. Returns a shallow copy.""" - with self._lock: - return dict(self._prices) - - def get_price(self, ticker: str) -> float | None: - """Convenience: get just the price float, or None.""" - update = self.get(ticker) - return update.price if update else None - - def remove(self, ticker: str) -> None: - """Remove a ticker from the cache (e.g., when removed from watchlist).""" - with self._lock: - self._prices.pop(ticker, None) - - @property - def version(self) -> int: - """Current version counter. Useful for SSE change detection.""" - return self._version - - def __len__(self) -> int: - with self._lock: - return len(self._prices) - - def __contains__(self, ticker: str) -> bool: - with self._lock: - return ticker in self._prices diff --git a/backend/app/market/factory.py b/backend/app/market/factory.py deleted file mode 100644 index 00360e94f..000000000 --- a/backend/app/market/factory.py +++ /dev/null @@ -1,31 +0,0 @@ -"""Factory for creating market data sources.""" - -from __future__ import annotations - -import logging -import os - -from .cache import PriceCache -from .interface import MarketDataSource -from .massive_client import MassiveDataSource -from .simulator import SimulatorDataSource - -logger = logging.getLogger(__name__) - - -def create_market_data_source(price_cache: PriceCache) -> MarketDataSource: - """Create the appropriate market data source based on environment variables. - - - MASSIVE_API_KEY set and non-empty → MassiveDataSource (real market data) - - Otherwise → SimulatorDataSource (GBM simulation) - - Returns an unstarted source. Caller must await source.start(tickers). - """ - api_key = os.environ.get("MASSIVE_API_KEY", "").strip() - - if api_key: - logger.info("Market data source: Massive API (real data)") - return MassiveDataSource(api_key=api_key, price_cache=price_cache) - else: - logger.info("Market data source: GBM Simulator") - return SimulatorDataSource(price_cache=price_cache) diff --git a/backend/app/market/interface.py b/backend/app/market/interface.py deleted file mode 100644 index 0f3b7d8c9..000000000 --- a/backend/app/market/interface.py +++ /dev/null @@ -1,57 +0,0 @@ -"""Abstract interface for market data sources.""" - -from __future__ import annotations - -from abc import ABC, abstractmethod - - -class MarketDataSource(ABC): - """Contract for market data providers. - - Implementations push price updates into a shared PriceCache on their own - schedule. Downstream code never calls the data source directly for prices — - it reads from the cache. - - Lifecycle: - source = create_market_data_source(cache) - await source.start(["AAPL", "GOOGL", ...]) - # ... app runs ... - await source.add_ticker("TSLA") - await source.remove_ticker("GOOGL") - # ... app shutting down ... - await source.stop() - """ - - @abstractmethod - async def start(self, tickers: list[str]) -> None: - """Begin producing price updates for the given tickers. - - Starts a background task that periodically writes to the PriceCache. - Must be called exactly once. Calling start() twice is undefined behavior. - """ - - @abstractmethod - async def stop(self) -> None: - """Stop the background task and release resources. - - Safe to call multiple times. After stop(), the source will not write - to the cache again. - """ - - @abstractmethod - async def add_ticker(self, ticker: str) -> None: - """Add a ticker to the active set. No-op if already present. - - The next update cycle will include this ticker. - """ - - @abstractmethod - async def remove_ticker(self, ticker: str) -> None: - """Remove a ticker from the active set. No-op if not present. - - Also removes the ticker from the PriceCache. - """ - - @abstractmethod - def get_tickers(self) -> list[str]: - """Return the current list of actively tracked tickers.""" diff --git a/backend/app/market/massive_client.py b/backend/app/market/massive_client.py deleted file mode 100644 index 00bc7b2aa..000000000 --- a/backend/app/market/massive_client.py +++ /dev/null @@ -1,128 +0,0 @@ -"""Massive (Polygon.io) API client for real market data.""" - -from __future__ import annotations - -import asyncio -import logging - -from massive import RESTClient -from massive.rest.models import SnapshotMarketType - -from .cache import PriceCache -from .interface import MarketDataSource - -logger = logging.getLogger(__name__) - - -class MassiveDataSource(MarketDataSource): - """MarketDataSource backed by the Massive (Polygon.io) REST API. - - Polls GET /v2/snapshot/locale/us/markets/stocks/tickers for all watched - tickers in a single API call, then writes results to the PriceCache. - - Rate limits: - - Free tier: 5 req/min → poll every 15s (default) - - Paid tiers: higher limits → poll every 2-5s - """ - - def __init__( - self, - api_key: str, - price_cache: PriceCache, - poll_interval: float = 15.0, - ) -> None: - self._api_key = api_key - self._cache = price_cache - self._interval = poll_interval - self._tickers: list[str] = [] - self._task: asyncio.Task | None = None - self._client: RESTClient | None = None - - async def start(self, tickers: list[str]) -> None: - self._client = RESTClient(api_key=self._api_key) - self._tickers = list(tickers) - - # Do an immediate first poll so the cache has data right away - await self._poll_once() - - self._task = asyncio.create_task(self._poll_loop(), name="massive-poller") - logger.info( - "Massive poller started: %d tickers, %.1fs interval", - len(tickers), - self._interval, - ) - - async def stop(self) -> None: - if self._task and not self._task.done(): - self._task.cancel() - try: - await self._task - except asyncio.CancelledError: - pass - self._task = None - self._client = None - logger.info("Massive poller stopped") - - async def add_ticker(self, ticker: str) -> None: - ticker = ticker.upper().strip() - if ticker not in self._tickers: - self._tickers.append(ticker) - logger.info("Massive: added ticker %s (will appear on next poll)", ticker) - - async def remove_ticker(self, ticker: str) -> None: - ticker = ticker.upper().strip() - self._tickers = [t for t in self._tickers if t != ticker] - self._cache.remove(ticker) - logger.info("Massive: removed ticker %s", ticker) - - def get_tickers(self) -> list[str]: - return list(self._tickers) - - # --- Internal --- - - async def _poll_loop(self) -> None: - """Poll on interval. First poll already happened in start().""" - while True: - await asyncio.sleep(self._interval) - await self._poll_once() - - async def _poll_once(self) -> None: - """Execute one poll cycle: fetch snapshots, update cache.""" - if not self._tickers or not self._client: - return - - try: - # The Massive RESTClient is synchronous — run in a thread to - # avoid blocking the event loop. - snapshots = await asyncio.to_thread(self._fetch_snapshots) - processed = 0 - for snap in snapshots: - try: - price = snap.last_trade.price - # Massive timestamps are Unix milliseconds → convert to seconds - timestamp = snap.last_trade.timestamp / 1000.0 - self._cache.update( - ticker=snap.ticker, - price=price, - timestamp=timestamp, - ) - processed += 1 - except (AttributeError, TypeError) as e: - logger.warning( - "Skipping snapshot for %s: %s", - getattr(snap, "ticker", "???"), - e, - ) - logger.debug("Massive poll: updated %d/%d tickers", processed, len(self._tickers)) - - except Exception as e: - logger.error("Massive poll failed: %s", e) - # Don't re-raise — the loop will retry on the next interval. - # Common failures: 401 (bad key), 429 (rate limit), network errors. - - def _fetch_snapshots(self) -> list: - """Synchronous call to the Massive REST API. Runs in a thread.""" - return self._client.get_snapshot_all( - market_type=SnapshotMarketType.STOCKS, - tickers=self._tickers, - ) diff --git a/backend/app/market/models.py b/backend/app/market/models.py deleted file mode 100644 index de81b1dbc..000000000 --- a/backend/app/market/models.py +++ /dev/null @@ -1,49 +0,0 @@ -"""Data models for market data.""" - -from __future__ import annotations - -import time -from dataclasses import dataclass, field - - -@dataclass(frozen=True, slots=True) -class PriceUpdate: - """Immutable snapshot of a single ticker's price at a point in time.""" - - ticker: str - price: float - previous_price: float - timestamp: float = field(default_factory=time.time) # Unix seconds - - @property - def change(self) -> float: - """Absolute price change from previous update.""" - return round(self.price - self.previous_price, 4) - - @property - def change_percent(self) -> float: - """Percentage change from previous update.""" - if self.previous_price == 0: - return 0.0 - return round((self.price - self.previous_price) / self.previous_price * 100, 4) - - @property - def direction(self) -> str: - """'up', 'down', or 'flat'.""" - if self.price > self.previous_price: - return "up" - elif self.price < self.previous_price: - return "down" - return "flat" - - def to_dict(self) -> dict: - """Serialize for JSON / SSE transmission.""" - return { - "ticker": self.ticker, - "price": self.price, - "previous_price": self.previous_price, - "timestamp": self.timestamp, - "change": self.change, - "change_percent": self.change_percent, - "direction": self.direction, - } diff --git a/backend/app/market/seed_prices.py b/backend/app/market/seed_prices.py deleted file mode 100644 index 69586df03..000000000 --- a/backend/app/market/seed_prices.py +++ /dev/null @@ -1,47 +0,0 @@ -"""Seed prices and per-ticker parameters for the market simulator.""" - -# Realistic starting prices for the default watchlist (as of project creation) -SEED_PRICES: dict[str, float] = { - "AAPL": 190.00, - "GOOGL": 175.00, - "MSFT": 420.00, - "AMZN": 185.00, - "TSLA": 250.00, - "NVDA": 800.00, - "META": 500.00, - "JPM": 195.00, - "V": 280.00, - "NFLX": 600.00, -} - -# Per-ticker GBM parameters -# sigma: annualized volatility (higher = more price movement) -# mu: annualized drift / expected return -TICKER_PARAMS: dict[str, dict[str, float]] = { - "AAPL": {"sigma": 0.22, "mu": 0.05}, - "GOOGL": {"sigma": 0.25, "mu": 0.05}, - "MSFT": {"sigma": 0.20, "mu": 0.05}, - "AMZN": {"sigma": 0.28, "mu": 0.05}, - "TSLA": {"sigma": 0.50, "mu": 0.03}, # High volatility - "NVDA": {"sigma": 0.40, "mu": 0.08}, # High volatility, strong drift - "META": {"sigma": 0.30, "mu": 0.05}, - "JPM": {"sigma": 0.18, "mu": 0.04}, # Low volatility (bank) - "V": {"sigma": 0.17, "mu": 0.04}, # Low volatility (payments) - "NFLX": {"sigma": 0.35, "mu": 0.05}, -} - -# Default parameters for tickers not in the list above (dynamically added) -DEFAULT_PARAMS: dict[str, float] = {"sigma": 0.25, "mu": 0.05} - -# Correlation groups for the simulator's Cholesky decomposition -# Tickers in the same group have higher intra-group correlation -CORRELATION_GROUPS: dict[str, set[str]] = { - "tech": {"AAPL", "GOOGL", "MSFT", "AMZN", "META", "NVDA", "NFLX"}, - "finance": {"JPM", "V"}, -} - -# Correlation coefficients -INTRA_TECH_CORR = 0.6 # Tech stocks move together -INTRA_FINANCE_CORR = 0.5 # Finance stocks move together -CROSS_GROUP_CORR = 0.3 # Between sectors / unknown tickers -TSLA_CORR = 0.3 # TSLA does its own thing diff --git a/backend/app/market/simulator.py b/backend/app/market/simulator.py deleted file mode 100644 index b6803f592..000000000 --- a/backend/app/market/simulator.py +++ /dev/null @@ -1,270 +0,0 @@ -"""GBM-based market simulator.""" - -from __future__ import annotations - -import asyncio -import logging -import math -import random - -import numpy as np - -from .cache import PriceCache -from .interface import MarketDataSource -from .seed_prices import ( - CORRELATION_GROUPS, - CROSS_GROUP_CORR, - DEFAULT_PARAMS, - INTRA_FINANCE_CORR, - INTRA_TECH_CORR, - SEED_PRICES, - TICKER_PARAMS, - TSLA_CORR, -) - -logger = logging.getLogger(__name__) - - -class GBMSimulator: - """Geometric Brownian Motion simulator for correlated stock prices. - - Math: - S(t+dt) = S(t) * exp((mu - sigma^2/2) * dt + sigma * sqrt(dt) * Z) - - Where: - S(t) = current price - mu = annualized drift (expected return) - sigma = annualized volatility - dt = time step as fraction of a trading year - Z = correlated standard normal random variable - - The tiny dt (~8.5e-8 for 500ms ticks over 252 trading days * 6.5h/day) - produces sub-cent moves per tick that accumulate naturally over time. - """ - - # 500ms expressed as a fraction of a trading year - # 252 trading days * 6.5 hours/day * 3600 seconds/hour = 5,896,800 seconds - TRADING_SECONDS_PER_YEAR = 252 * 6.5 * 3600 # 5,896,800 - DEFAULT_DT = 0.5 / TRADING_SECONDS_PER_YEAR # ~8.48e-8 - - def __init__( - self, - tickers: list[str], - dt: float = DEFAULT_DT, - event_probability: float = 0.001, - ) -> None: - self._dt = dt - self._event_prob = event_probability - - # Per-ticker state - self._tickers: list[str] = [] - self._prices: dict[str, float] = {} - self._params: dict[str, dict[str, float]] = {} - - # Cholesky decomposition of the correlation matrix (for correlated moves) - self._cholesky: np.ndarray | None = None - - # Initialize all starting tickers - for ticker in tickers: - self._add_ticker_internal(ticker) - self._rebuild_cholesky() - - # --- Public API --- - - def step(self) -> dict[str, float]: - """Advance all tickers by one time step. Returns {ticker: new_price}. - - This is the hot path — called every 500ms. Keep it fast. - """ - n = len(self._tickers) - if n == 0: - return {} - - # Generate n independent standard normal draws - z_independent = np.random.standard_normal(n) - - # Apply Cholesky to get correlated draws - if self._cholesky is not None: - z_correlated = self._cholesky @ z_independent - else: - z_correlated = z_independent - - result: dict[str, float] = {} - for i, ticker in enumerate(self._tickers): - params = self._params[ticker] - mu = params["mu"] - sigma = params["sigma"] - - # GBM: S(t+dt) = S(t) * exp((mu - 0.5*sigma^2)*dt + sigma*sqrt(dt)*Z) - drift = (mu - 0.5 * sigma**2) * self._dt - diffusion = sigma * math.sqrt(self._dt) * z_correlated[i] - self._prices[ticker] *= math.exp(drift + diffusion) - - # Random event: ~0.1% chance per tick per ticker - # With 10 tickers at 2 ticks/sec, expect an event ~every 50 seconds - if random.random() < self._event_prob: - shock_magnitude = random.uniform(0.02, 0.05) - shock_sign = random.choice([-1, 1]) - self._prices[ticker] *= 1 + shock_magnitude * shock_sign - logger.debug( - "Random event on %s: %.1f%% %s", - ticker, - shock_magnitude * 100, - "up" if shock_sign > 0 else "down", - ) - - result[ticker] = round(self._prices[ticker], 2) - - return result - - def add_ticker(self, ticker: str) -> None: - """Add a ticker to the simulation. Rebuilds the correlation matrix.""" - if ticker in self._prices: - return - self._add_ticker_internal(ticker) - self._rebuild_cholesky() - - def remove_ticker(self, ticker: str) -> None: - """Remove a ticker from the simulation. Rebuilds the correlation matrix.""" - if ticker not in self._prices: - return - self._tickers.remove(ticker) - del self._prices[ticker] - del self._params[ticker] - self._rebuild_cholesky() - - def get_price(self, ticker: str) -> float | None: - """Current price for a ticker, or None if not tracked.""" - return self._prices.get(ticker) - - def get_tickers(self) -> list[str]: - """Return the list of currently tracked tickers.""" - return list(self._tickers) - - # --- Internals --- - - def _add_ticker_internal(self, ticker: str) -> None: - """Add a ticker without rebuilding Cholesky (for batch initialization).""" - if ticker in self._prices: - return - self._tickers.append(ticker) - self._prices[ticker] = SEED_PRICES.get(ticker, random.uniform(50.0, 300.0)) - self._params[ticker] = TICKER_PARAMS.get(ticker, dict(DEFAULT_PARAMS)) - - def _rebuild_cholesky(self) -> None: - """Rebuild the Cholesky decomposition of the ticker correlation matrix. - - Called whenever tickers are added or removed. O(n^2) but n < 50. - """ - n = len(self._tickers) - if n <= 1: - self._cholesky = None - return - - # Build the correlation matrix - corr = np.eye(n) - for i in range(n): - for j in range(i + 1, n): - rho = self._pairwise_correlation(self._tickers[i], self._tickers[j]) - corr[i, j] = rho - corr[j, i] = rho - - self._cholesky = np.linalg.cholesky(corr) - - @staticmethod - def _pairwise_correlation(t1: str, t2: str) -> float: - """Determine correlation between two tickers based on sector grouping. - - Correlation structure: - - Same tech sector: 0.6 - - Same finance sector: 0.5 - - TSLA with anything: 0.3 (it does its own thing) - - Cross-sector: 0.3 - - Unknown tickers: 0.3 - """ - tech = CORRELATION_GROUPS["tech"] - finance = CORRELATION_GROUPS["finance"] - - # TSLA is in tech set but behaves independently - if t1 == "TSLA" or t2 == "TSLA": - return TSLA_CORR - - if t1 in tech and t2 in tech: - return INTRA_TECH_CORR - if t1 in finance and t2 in finance: - return INTRA_FINANCE_CORR - - return CROSS_GROUP_CORR - - -class SimulatorDataSource(MarketDataSource): - """MarketDataSource backed by the GBM simulator. - - Runs a background asyncio task that calls GBMSimulator.step() every - `update_interval` seconds and writes results to the PriceCache. - """ - - def __init__( - self, - price_cache: PriceCache, - update_interval: float = 0.5, - event_probability: float = 0.001, - ) -> None: - self._cache = price_cache - self._interval = update_interval - self._event_prob = event_probability - self._sim: GBMSimulator | None = None - self._task: asyncio.Task | None = None - - async def start(self, tickers: list[str]) -> None: - self._sim = GBMSimulator( - tickers=tickers, - event_probability=self._event_prob, - ) - # Seed the cache with initial prices so SSE has data immediately - for ticker in tickers: - price = self._sim.get_price(ticker) - if price is not None: - self._cache.update(ticker=ticker, price=price) - self._task = asyncio.create_task(self._run_loop(), name="simulator-loop") - logger.info("Simulator started with %d tickers", len(tickers)) - - async def stop(self) -> None: - if self._task and not self._task.done(): - self._task.cancel() - try: - await self._task - except asyncio.CancelledError: - pass - self._task = None - logger.info("Simulator stopped") - - async def add_ticker(self, ticker: str) -> None: - if self._sim: - self._sim.add_ticker(ticker) - # Seed cache immediately so the ticker has a price right away - price = self._sim.get_price(ticker) - if price is not None: - self._cache.update(ticker=ticker, price=price) - logger.info("Simulator: added ticker %s", ticker) - - async def remove_ticker(self, ticker: str) -> None: - if self._sim: - self._sim.remove_ticker(ticker) - self._cache.remove(ticker) - logger.info("Simulator: removed ticker %s", ticker) - - def get_tickers(self) -> list[str]: - return self._sim.get_tickers() if self._sim else [] - - async def _run_loop(self) -> None: - """Core loop: step the simulation, write to cache, sleep.""" - while True: - try: - if self._sim: - prices = self._sim.step() - for ticker, price in prices.items(): - self._cache.update(ticker=ticker, price=price) - except Exception: - logger.exception("Simulator step failed") - await asyncio.sleep(self._interval) diff --git a/backend/app/market/stream.py b/backend/app/market/stream.py deleted file mode 100644 index 7fd974b7c..000000000 --- a/backend/app/market/stream.py +++ /dev/null @@ -1,87 +0,0 @@ -"""SSE streaming endpoint for live price updates.""" - -from __future__ import annotations - -import asyncio -import json -import logging -from collections.abc import AsyncGenerator - -from fastapi import APIRouter, Request -from fastapi.responses import StreamingResponse - -from .cache import PriceCache - -logger = logging.getLogger(__name__) - -router = APIRouter(prefix="/api/stream", tags=["streaming"]) - - -def create_stream_router(price_cache: PriceCache) -> APIRouter: - """Create the SSE streaming router with a reference to the price cache. - - This factory pattern lets us inject the PriceCache without globals. - """ - - @router.get("/prices") - async def stream_prices(request: Request) -> StreamingResponse: - """SSE endpoint for live price updates. - - Streams all tracked ticker prices every ~500ms. The client connects - with EventSource and receives events in the format: - - data: {"AAPL": {"ticker": "AAPL", "price": 190.50, ...}, ...} - - Includes a retry directive so the browser auto-reconnects on - disconnection (EventSource built-in behavior). - """ - return StreamingResponse( - _generate_events(price_cache, request), - media_type="text/event-stream", - headers={ - "Cache-Control": "no-cache", - "Connection": "keep-alive", - "X-Accel-Buffering": "no", # Disable nginx buffering if proxied - }, - ) - - return router - - -async def _generate_events( - price_cache: PriceCache, - request: Request, - interval: float = 0.5, -) -> AsyncGenerator[str, None]: - """Async generator that yields SSE-formatted price events. - - Sends all prices every `interval` seconds. Stops when the client - disconnects (detected via request.is_disconnected()). - """ - # Tell the client to retry after 1 second if the connection drops - yield "retry: 1000\n\n" - - last_version = -1 - client_ip = request.client.host if request.client else "unknown" - logger.info("SSE client connected: %s", client_ip) - - try: - while True: - # Check for client disconnect - if await request.is_disconnected(): - logger.info("SSE client disconnected: %s", client_ip) - break - - current_version = price_cache.version - if current_version != last_version: - last_version = current_version - prices = price_cache.get_all() - - if prices: - data = {ticker: update.to_dict() for ticker, update in prices.items()} - payload = json.dumps(data) - yield f"data: {payload}\n\n" - - await asyncio.sleep(interval) - except asyncio.CancelledError: - logger.info("SSE stream cancelled for: %s", client_ip) diff --git a/backend/market_data_demo.py b/backend/market_data_demo.py deleted file mode 100644 index 7414416c4..000000000 --- a/backend/market_data_demo.py +++ /dev/null @@ -1,272 +0,0 @@ -"""FinAlly Market Data Simulator Demo. - -Run with: uv run market_data_demo.py - -Displays a live-updating terminal dashboard of simulated stock prices -using the GBM simulator and Rich library. -""" - -from __future__ import annotations - -import asyncio -import time -from collections import deque - -from rich.console import Console -from rich.layout import Layout -from rich.live import Live -from rich.panel import Panel -from rich.table import Table -from rich.text import Text - -from app.market.cache import PriceCache -from app.market.seed_prices import SEED_PRICES -from app.market.simulator import SimulatorDataSource - -# Sparkline characters, low to high -SPARK_CHARS = "▁▂▃▄▅▆▇█" - -# Ordered ticker list matching the default watchlist -TICKERS = ["AAPL", "GOOGL", "MSFT", "AMZN", "TSLA", "NVDA", "META", "JPM", "V", "NFLX"] - -DURATION = 60 # seconds - - -def sparkline(values: list[float]) -> str: - """Render a sequence of values as a unicode sparkline.""" - if len(values) < 2: - return "" - lo, hi = min(values), max(values) - spread = hi - lo - if spread == 0: - return SPARK_CHARS[3] * len(values) - n = len(SPARK_CHARS) - 1 - return "".join(SPARK_CHARS[int((v - lo) / spread * n)] for v in values) - - -def format_price(price: float) -> str: - """Format a price with comma separator.""" - if price >= 1000: - return f"{price:,.2f}" - return f"{price:.2f}" - - -def build_table( - cache: PriceCache, - history: dict[str, deque], - elapsed: float, -) -> Table: - """Build the price table.""" - table = Table( - title=None, - expand=True, - border_style="bright_black", - header_style="bold bright_white", - pad_edge=True, - padding=(0, 1), - ) - table.add_column("Ticker", style="bold bright_white", width=8) - table.add_column("Price", justify="right", width=10) - table.add_column("Change", justify="right", width=9) - table.add_column("Chg %", justify="right", width=8) - table.add_column("", width=3) # arrow - table.add_column("Sparkline", width=42, no_wrap=True) - - for ticker in TICKERS: - update = cache.get(ticker) - if update is None: - table.add_row(ticker, "---", "---", "---", "", "") - continue - - # Direction styling - if update.direction == "up": - color = "green" - arrow = "[bold green]\u25b2[/]" - elif update.direction == "down": - color = "red" - arrow = "[bold red]\u25bc[/]" - else: - color = "bright_black" - arrow = "[bright_black]\u2500[/]" - - price_str = f"[{color}]${format_price(update.price)}[/]" - change_str = f"[{color}]{update.change:+.2f}[/]" - pct_str = f"[{color}]{update.change_percent:+.2f}%[/]" - - # Sparkline from history - vals = list(history.get(ticker, [])) - spark_str = f"[bright_cyan]{sparkline(vals)}[/]" if len(vals) > 1 else "" - - table.add_row(ticker, price_str, change_str, pct_str, arrow, spark_str) - - return table - - -def build_event_log(events: deque) -> Panel: - """Build the event log panel.""" - text = Text() - for evt in events: - text.append(evt) - text.append("\n") - if not events: - text.append("Watching for notable moves (>1% change)...", style="bright_black italic") - return Panel( - text, - title="[bold bright_yellow]Recent Events[/]", - border_style="bright_black", - height=8, - ) - - -def build_dashboard( - cache: PriceCache, - history: dict[str, deque], - events: deque, - start_time: float, -) -> Layout: - """Build the full dashboard layout.""" - elapsed = time.time() - start_time - remaining = max(0, DURATION - elapsed) - - layout = Layout() - layout.split_column( - Layout(name="header", size=3), - Layout(name="body"), - Layout(name="footer", size=10), - ) - - # Header - header_text = Text.assemble( - (" FinAlly ", "bold bright_yellow"), - ("Market Data Simulator", "bold bright_white"), - (" | ", "bright_black"), - (f"{elapsed:5.1f}s elapsed", "bright_cyan"), - (" | ", "bright_black"), - (f"{remaining:4.1f}s remaining", "bright_cyan"), - (" | ", "bright_black"), - (f"{len(cache)} tickers", "bright_white"), - (" | ", "bright_black"), - ("Ctrl+C to exit", "bright_black italic"), - ) - layout["header"].update(Panel(header_text, border_style="bright_yellow")) - - # Body: price table - layout["body"].update( - Panel( - build_table(cache, history, elapsed), - title="[bold bright_white]Live Prices[/]", - border_style="bright_black", - ) - ) - - # Footer: event log - layout["footer"].update(build_event_log(events)) - - return layout - - -def print_summary(cache: PriceCache) -> None: - """Print final summary comparing to seed prices.""" - console = Console() - console.print() - console.print("[bold bright_yellow] FinAlly[/] [bold]Session Summary[/]") - console.print() - - table = Table(border_style="bright_black", header_style="bold bright_white", expand=False) - table.add_column("Ticker", style="bold bright_white", width=8) - table.add_column("Seed Price", justify="right", width=12) - table.add_column("Final Price", justify="right", width=12) - table.add_column("Session Change", justify="right", width=14) - - for ticker in TICKERS: - seed = SEED_PRICES.get(ticker, 0) - update = cache.get(ticker) - if update is None: - continue - final = update.price - session_change = ((final - seed) / seed) * 100 if seed else 0 - - if session_change > 0: - color = "green" - elif session_change < 0: - color = "red" - else: - color = "bright_black" - - table.add_row( - ticker, - f"${format_price(seed)}", - f"[{color}]${format_price(final)}[/]", - f"[{color}]{session_change:+.2f}%[/]", - ) - - console.print(table) - console.print() - - -async def run() -> None: - """Main demo loop.""" - cache = PriceCache() - source = SimulatorDataSource(price_cache=cache, update_interval=0.5) - - # Per-ticker price history for sparklines - history: dict[str, deque] = {t: deque(maxlen=40) for t in TICKERS} - - # Recent event log - events: deque = deque(maxlen=12) - - await source.start(TICKERS) - start_time = time.time() - - # Seed initial history points - for ticker in TICKERS: - update = cache.get(ticker) - if update: - history[ticker].append(update.price) - - try: - with Live( - build_dashboard(cache, history, events, start_time), - refresh_per_second=4, - screen=True, - ) as live: - last_version = cache.version - while time.time() - start_time < DURATION: - await asyncio.sleep(0.25) - - # Check for updates - if cache.version == last_version: - continue - last_version = cache.version - - # Record history & detect events - for ticker in TICKERS: - update = cache.get(ticker) - if update is None: - continue - history[ticker].append(update.price) - - # Log notable moves - if abs(update.change_percent) > 1.0: - direction = "\u25b2" if update.direction == "up" else "\u25bc" - color = "green" if update.direction == "up" else "red" - timestamp = time.strftime("%H:%M:%S") - events.appendleft( - f"[bright_black]{timestamp}[/] " - f"[bold {color}]{direction} {ticker}[/] " - f"[{color}]{update.change_percent:+.2f}%[/] " - f"${format_price(update.price)}" - ) - - live.update(build_dashboard(cache, history, events, start_time)) - - except KeyboardInterrupt: - pass - finally: - await source.stop() - - print_summary(cache) - - -if __name__ == "__main__": - asyncio.run(run()) diff --git a/backend/pyproject.toml b/backend/pyproject.toml deleted file mode 100644 index e172cca22..000000000 --- a/backend/pyproject.toml +++ /dev/null @@ -1,58 +0,0 @@ -[project] -name = "finally-backend" -version = "0.1.0" -description = "FinAlly backend - AI Trading Workstation" -readme = "README.md" -requires-python = ">=3.12" -dependencies = [ - "fastapi>=0.115.0", - "uvicorn[standard]>=0.32.0", - "numpy>=2.0.0", - "massive>=1.0.0", - "rich>=13.0.0", -] - -[project.optional-dependencies] -dev = [ - "pytest>=8.3.0", - "pytest-asyncio>=0.24.0", - "pytest-cov>=5.0.0", - "ruff>=0.7.0", -] - -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" - -[tool.hatch.build.targets.wheel] -packages = ["app"] - -[tool.pytest.ini_options] -testpaths = ["tests"] -python_files = ["test_*.py"] -python_classes = ["Test*"] -python_functions = ["test_*"] -asyncio_mode = "auto" -asyncio_default_fixture_loop_scope = "function" - -[tool.ruff] -line-length = 100 -target-version = "py312" - -[tool.ruff.lint] -select = ["E", "F", "I", "N", "W"] -ignore = ["E501"] # Line too long (handled by formatter) - -[tool.coverage.run] -source = ["app"] -omit = ["tests/*"] - -[tool.coverage.report] -exclude_lines = [ - "pragma: no cover", - "def __repr__", - "raise AssertionError", - "raise NotImplementedError", - "if __name__ == .__main__.:", - "if TYPE_CHECKING:", -] diff --git a/backend/tests/__init__.py b/backend/tests/__init__.py deleted file mode 100644 index 6c957488c..000000000 --- a/backend/tests/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Tests for FinAlly backend.""" diff --git a/backend/tests/conftest.py b/backend/tests/conftest.py deleted file mode 100644 index 14545f124..000000000 --- a/backend/tests/conftest.py +++ /dev/null @@ -1,11 +0,0 @@ -"""Pytest configuration and fixtures.""" - -import pytest - - -@pytest.fixture -def event_loop_policy(): - """Use the default event loop policy for all async tests.""" - import asyncio - - return asyncio.DefaultEventLoopPolicy() diff --git a/backend/tests/market/__init__.py b/backend/tests/market/__init__.py deleted file mode 100644 index c614bf5c9..000000000 --- a/backend/tests/market/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Tests for market data subsystem.""" diff --git a/backend/tests/market/test_cache.py b/backend/tests/market/test_cache.py deleted file mode 100644 index b5ab3d55d..000000000 --- a/backend/tests/market/test_cache.py +++ /dev/null @@ -1,103 +0,0 @@ -"""Tests for PriceCache.""" - -from app.market.cache import PriceCache - - -class TestPriceCache: - """Unit tests for the PriceCache.""" - - def test_update_and_get(self): - """Test updating and getting a price.""" - cache = PriceCache() - update = cache.update("AAPL", 190.50) - assert update.ticker == "AAPL" - assert update.price == 190.50 - assert cache.get("AAPL") == update - - def test_first_update_is_flat(self): - """Test that the first update has flat direction.""" - cache = PriceCache() - update = cache.update("AAPL", 190.50) - assert update.direction == "flat" - assert update.previous_price == 190.50 - - def test_direction_up(self): - """Test price update with upward direction.""" - cache = PriceCache() - cache.update("AAPL", 190.00) - update = cache.update("AAPL", 191.00) - assert update.direction == "up" - assert update.change == 1.00 - - def test_direction_down(self): - """Test price update with downward direction.""" - cache = PriceCache() - cache.update("AAPL", 190.00) - update = cache.update("AAPL", 189.00) - assert update.direction == "down" - assert update.change == -1.00 - - def test_remove(self): - """Test removing a ticker from cache.""" - cache = PriceCache() - cache.update("AAPL", 190.00) - cache.remove("AAPL") - assert cache.get("AAPL") is None - - def test_remove_nonexistent(self): - """Test removing a ticker that doesn't exist.""" - cache = PriceCache() - cache.remove("AAPL") # Should not raise - - def test_get_all(self): - """Test getting all prices.""" - cache = PriceCache() - cache.update("AAPL", 190.00) - cache.update("GOOGL", 175.00) - all_prices = cache.get_all() - assert set(all_prices.keys()) == {"AAPL", "GOOGL"} - - def test_version_increments(self): - """Test that version counter increments.""" - cache = PriceCache() - v0 = cache.version - cache.update("AAPL", 190.00) - assert cache.version == v0 + 1 - cache.update("AAPL", 191.00) - assert cache.version == v0 + 2 - - def test_get_price_convenience(self): - """Test the convenience get_price method.""" - cache = PriceCache() - cache.update("AAPL", 190.50) - assert cache.get_price("AAPL") == 190.50 - assert cache.get_price("NOPE") is None - - def test_len(self): - """Test __len__ method.""" - cache = PriceCache() - assert len(cache) == 0 - cache.update("AAPL", 190.00) - assert len(cache) == 1 - cache.update("GOOGL", 175.00) - assert len(cache) == 2 - - def test_contains(self): - """Test __contains__ method.""" - cache = PriceCache() - cache.update("AAPL", 190.00) - assert "AAPL" in cache - assert "GOOGL" not in cache - - def test_custom_timestamp(self): - """Test updating with a custom timestamp.""" - cache = PriceCache() - custom_ts = 1234567890.0 - update = cache.update("AAPL", 190.50, timestamp=custom_ts) - assert update.timestamp == custom_ts - - def test_price_rounding(self): - """Test that prices are rounded to 2 decimal places.""" - cache = PriceCache() - update = cache.update("AAPL", 190.12345) - assert update.price == 190.12 diff --git a/backend/tests/market/test_factory.py b/backend/tests/market/test_factory.py deleted file mode 100644 index 5ff5dd49e..000000000 --- a/backend/tests/market/test_factory.py +++ /dev/null @@ -1,79 +0,0 @@ -"""Tests for market data source factory.""" - -import os -from unittest.mock import patch - -from app.market.cache import PriceCache -from app.market.factory import create_market_data_source -from app.market.massive_client import MassiveDataSource -from app.market.simulator import SimulatorDataSource - - -class TestFactory: - """Tests for create_market_data_source factory.""" - - def test_creates_simulator_when_no_api_key(self): - """Test that simulator is created when MASSIVE_API_KEY is not set.""" - cache = PriceCache() - - with patch.dict(os.environ, {}, clear=True): - source = create_market_data_source(cache) - - assert isinstance(source, SimulatorDataSource) - - def test_creates_simulator_when_api_key_empty(self): - """Test that simulator is created when MASSIVE_API_KEY is empty.""" - cache = PriceCache() - - with patch.dict(os.environ, {"MASSIVE_API_KEY": ""}, clear=True): - source = create_market_data_source(cache) - - assert isinstance(source, SimulatorDataSource) - - def test_creates_simulator_when_api_key_whitespace(self): - """Test that simulator is created when MASSIVE_API_KEY is whitespace.""" - cache = PriceCache() - - with patch.dict(os.environ, {"MASSIVE_API_KEY": " "}, clear=True): - source = create_market_data_source(cache) - - assert isinstance(source, SimulatorDataSource) - - def test_creates_massive_when_api_key_set(self): - """Test that Massive client is created when MASSIVE_API_KEY is set.""" - cache = PriceCache() - - with patch.dict(os.environ, {"MASSIVE_API_KEY": "test-key"}, clear=True): - source = create_market_data_source(cache) - - assert isinstance(source, MassiveDataSource) - - def test_massive_receives_api_key(self): - """Test that Massive client receives the API key.""" - cache = PriceCache() - - with patch.dict(os.environ, {"MASSIVE_API_KEY": "test-key-123"}, clear=True): - source = create_market_data_source(cache) - - assert isinstance(source, MassiveDataSource) - assert source._api_key == "test-key-123" - - def test_simulator_receives_cache(self): - """Test that simulator receives the cache reference.""" - cache = PriceCache() - - with patch.dict(os.environ, {}, clear=True): - source = create_market_data_source(cache) - - assert isinstance(source, SimulatorDataSource) - assert source._cache is cache - - def test_massive_receives_cache(self): - """Test that Massive client receives the cache reference.""" - cache = PriceCache() - - with patch.dict(os.environ, {"MASSIVE_API_KEY": "test-key"}, clear=True): - source = create_market_data_source(cache) - - assert isinstance(source, MassiveDataSource) - assert source._cache is cache diff --git a/backend/tests/market/test_massive.py b/backend/tests/market/test_massive.py deleted file mode 100644 index cdd7dbd24..000000000 --- a/backend/tests/market/test_massive.py +++ /dev/null @@ -1,201 +0,0 @@ -"""Tests for MassiveDataSource (mocked).""" - -from unittest.mock import MagicMock, patch - -import pytest - -from app.market.cache import PriceCache -from app.market.massive_client import MassiveDataSource - - -def _make_snapshot(ticker: str, price: float, timestamp_ms: int) -> MagicMock: - """Create a mock Massive snapshot object.""" - snap = MagicMock() - snap.ticker = ticker - snap.last_trade = MagicMock() - snap.last_trade.price = price - snap.last_trade.timestamp = timestamp_ms - return snap - - -@pytest.mark.asyncio -class TestMassiveDataSource: - """Unit tests for MassiveDataSource with mocked API.""" - - async def test_poll_updates_cache(self): - """Test that polling updates the cache.""" - cache = PriceCache() - source = MassiveDataSource( - api_key="test-key", - price_cache=cache, - poll_interval=60.0, # Long interval so the loop doesn't auto-poll - ) - source._tickers = ["AAPL", "GOOGL"] - source._client = MagicMock() # Satisfy the _poll_once guard - - mock_snapshots = [ - _make_snapshot("AAPL", 190.50, 1707580800000), - _make_snapshot("GOOGL", 175.25, 1707580800000), - ] - - with patch.object(source, "_fetch_snapshots", return_value=mock_snapshots): - await source._poll_once() - - assert cache.get_price("AAPL") == 190.50 - assert cache.get_price("GOOGL") == 175.25 - - async def test_malformed_snapshot_skipped(self): - """Test that malformed snapshots are skipped gracefully.""" - cache = PriceCache() - source = MassiveDataSource( - api_key="test-key", - price_cache=cache, - poll_interval=60.0, - ) - source._tickers = ["AAPL", "BAD"] - source._client = MagicMock() # Satisfy the _poll_once guard - - good_snap = _make_snapshot("AAPL", 190.50, 1707580800000) - bad_snap = MagicMock() - bad_snap.ticker = "BAD" - bad_snap.last_trade = None # Will cause AttributeError - - with patch.object(source, "_fetch_snapshots", return_value=[good_snap, bad_snap]): - await source._poll_once() - - # Good ticker processed, bad one skipped - assert cache.get_price("AAPL") == 190.50 - assert cache.get_price("BAD") is None - - async def test_api_error_does_not_crash(self): - """Test that API errors don't crash the poller.""" - cache = PriceCache() - source = MassiveDataSource( - api_key="test-key", - price_cache=cache, - poll_interval=60.0, - ) - source._tickers = ["AAPL"] - source._client = MagicMock() # Satisfy the _poll_once guard - - with patch.object(source, "_fetch_snapshots", side_effect=Exception("network error")): - await source._poll_once() # Should not raise - - assert cache.get_price("AAPL") is None # No update happened - - async def test_timestamp_conversion(self): - """Test that timestamps are converted from milliseconds to seconds.""" - cache = PriceCache() - source = MassiveDataSource( - api_key="test-key", - price_cache=cache, - poll_interval=60.0, - ) - source._tickers = ["AAPL"] - source._client = MagicMock() # Satisfy the _poll_once guard - - mock_snapshots = [_make_snapshot("AAPL", 190.50, 1707580800000)] - - with patch.object(source, "_fetch_snapshots", return_value=mock_snapshots): - await source._poll_once() - - update = cache.get("AAPL") - assert update is not None - assert update.timestamp == 1707580800.0 # Converted to seconds - - async def test_add_ticker(self): - """Test adding a ticker.""" - cache = PriceCache() - source = MassiveDataSource(api_key="test-key", price_cache=cache) - - await source.add_ticker("AAPL") - assert "AAPL" in source.get_tickers() - - async def test_add_ticker_uppercase_normalization(self): - """Test that tickers are normalized to uppercase.""" - cache = PriceCache() - source = MassiveDataSource(api_key="test-key", price_cache=cache) - - await source.add_ticker("aapl") - assert "AAPL" in source.get_tickers() - - async def test_add_ticker_strips_whitespace(self): - """Test that ticker whitespace is stripped.""" - cache = PriceCache() - source = MassiveDataSource(api_key="test-key", price_cache=cache) - - await source.add_ticker(" AAPL ") - assert "AAPL" in source.get_tickers() - - async def test_remove_ticker(self): - """Test removing a ticker.""" - cache = PriceCache() - source = MassiveDataSource(api_key="test-key", price_cache=cache) - source._tickers = ["AAPL", "GOOGL"] - cache.update("AAPL", 190.00) - - await source.remove_ticker("AAPL") - assert "AAPL" not in source.get_tickers() - assert cache.get("AAPL") is None - - async def test_get_tickers(self): - """Test getting the list of active tickers.""" - cache = PriceCache() - source = MassiveDataSource(api_key="test-key", price_cache=cache) - source._tickers = ["AAPL", "GOOGL"] - - tickers = source.get_tickers() - assert tickers == ["AAPL", "GOOGL"] - - async def test_empty_tickers_skips_poll(self): - """Test that polling is skipped when there are no tickers.""" - cache = PriceCache() - source = MassiveDataSource(api_key="test-key", price_cache=cache) - source._tickers = [] - - # Should not call _fetch_snapshots - with patch.object(source, "_fetch_snapshots") as mock_fetch: - await source._poll_once() - mock_fetch.assert_not_called() - - async def test_stop_is_idempotent(self): - """Test that stop() can be called multiple times.""" - cache = PriceCache() - source = MassiveDataSource(api_key="test-key", price_cache=cache) - - await source.stop() - await source.stop() # Should not raise - - async def test_stop_cancels_task(self): - """Test that stop() cancels the polling task.""" - cache = PriceCache() - source = MassiveDataSource(api_key="test-key", price_cache=cache, poll_interval=10.0) - - # Mock the client and start - with patch("app.market.massive_client.RESTClient"): - with patch.object(source, "_fetch_snapshots", return_value=[]): - await source.start(["AAPL"]) - - # Verify task is running - assert source._task is not None - assert not source._task.done() - - # Stop and verify task is cancelled - await source.stop() - assert source._task is None - - async def test_start_immediate_poll(self): - """Test that start() does an immediate poll before starting the loop.""" - cache = PriceCache() - source = MassiveDataSource(api_key="test-key", price_cache=cache, poll_interval=60.0) - - mock_snapshots = [_make_snapshot("AAPL", 190.50, 1707580800000)] - - with patch("app.market.massive_client.RESTClient"): - with patch.object(source, "_fetch_snapshots", return_value=mock_snapshots): - await source.start(["AAPL"]) - - # Cache should have data immediately from the first poll - assert cache.get_price("AAPL") == 190.50 - - await source.stop() diff --git a/backend/tests/market/test_models.py b/backend/tests/market/test_models.py deleted file mode 100644 index 21600dfd6..000000000 --- a/backend/tests/market/test_models.py +++ /dev/null @@ -1,77 +0,0 @@ -"""Tests for PriceUpdate dataclass.""" - -import pytest - -from app.market.models import PriceUpdate - - -class TestPriceUpdate: - """Unit tests for the PriceUpdate model.""" - - def test_price_update_creation(self): - """Test basic PriceUpdate creation.""" - update = PriceUpdate(ticker="AAPL", price=190.50, previous_price=190.00, timestamp=1234567890.0) - assert update.ticker == "AAPL" - assert update.price == 190.50 - assert update.previous_price == 190.00 - assert update.timestamp == 1234567890.0 - - def test_change_calculation(self): - """Test price change calculation.""" - update = PriceUpdate(ticker="AAPL", price=190.50, previous_price=190.00, timestamp=1234567890.0) - assert update.change == 0.50 - - def test_change_negative(self): - """Test negative price change.""" - update = PriceUpdate(ticker="AAPL", price=189.50, previous_price=190.00, timestamp=1234567890.0) - assert update.change == -0.50 - - def test_change_percent_up(self): - """Test percentage change calculation (up).""" - update = PriceUpdate(ticker="AAPL", price=190.00, previous_price=100.00, timestamp=1234567890.0) - assert update.change_percent == 90.0 - - def test_change_percent_down(self): - """Test percentage change calculation (down).""" - update = PriceUpdate(ticker="AAPL", price=100.00, previous_price=200.00, timestamp=1234567890.0) - assert update.change_percent == -50.0 - - def test_change_percent_zero_previous(self): - """Test percentage change with zero previous price.""" - update = PriceUpdate(ticker="AAPL", price=100.00, previous_price=0.00, timestamp=1234567890.0) - assert update.change_percent == 0.0 - - def test_direction_up(self): - """Test direction calculation (up).""" - update = PriceUpdate(ticker="AAPL", price=191.00, previous_price=190.00, timestamp=1234567890.0) - assert update.direction == "up" - - def test_direction_down(self): - """Test direction calculation (down).""" - update = PriceUpdate(ticker="AAPL", price=189.00, previous_price=190.00, timestamp=1234567890.0) - assert update.direction == "down" - - def test_direction_flat(self): - """Test direction calculation (flat).""" - update = PriceUpdate(ticker="AAPL", price=190.00, previous_price=190.00, timestamp=1234567890.0) - assert update.direction == "flat" - - def test_to_dict(self): - """Test serialization to dictionary.""" - update = PriceUpdate(ticker="AAPL", price=190.50, previous_price=190.00, timestamp=1234567890.0) - result = update.to_dict() - - assert result["ticker"] == "AAPL" - assert result["price"] == 190.50 - assert result["previous_price"] == 190.00 - assert result["timestamp"] == 1234567890.0 - assert result["change"] == 0.50 - assert result["change_percent"] == 0.2632 # (0.50 / 190.00) * 100 - assert result["direction"] == "up" - - def test_immutability(self): - """Test that PriceUpdate is immutable.""" - update = PriceUpdate(ticker="AAPL", price=190.50, previous_price=190.00, timestamp=1234567890.0) - - with pytest.raises(AttributeError): - update.price = 200.00 # Should raise error diff --git a/backend/tests/market/test_simulator.py b/backend/tests/market/test_simulator.py deleted file mode 100644 index 1845ec16b..000000000 --- a/backend/tests/market/test_simulator.py +++ /dev/null @@ -1,131 +0,0 @@ -"""Tests for GBMSimulator.""" - -from app.market.seed_prices import SEED_PRICES -from app.market.simulator import GBMSimulator - - -class TestGBMSimulator: - """Unit tests for the GBM price simulator.""" - - def test_step_returns_all_tickers(self): - """Test that step() returns prices for all tickers.""" - sim = GBMSimulator(tickers=["AAPL", "GOOGL"]) - result = sim.step() - assert set(result.keys()) == {"AAPL", "GOOGL"} - - def test_prices_are_positive(self): - """GBM prices can never go negative (exp() is always positive).""" - sim = GBMSimulator(tickers=["AAPL"]) - for _ in range(10_000): - prices = sim.step() - assert prices["AAPL"] > 0 - - def test_initial_prices_match_seeds(self): - """Test that initial prices match seed prices.""" - sim = GBMSimulator(tickers=["AAPL"]) - # Before any step, price should be the seed price - assert sim.get_price("AAPL") == SEED_PRICES["AAPL"] - - def test_add_ticker(self): - """Test adding a ticker dynamically.""" - sim = GBMSimulator(tickers=["AAPL"]) - sim.add_ticker("TSLA") - result = sim.step() - assert "TSLA" in result - - def test_remove_ticker(self): - """Test removing a ticker.""" - sim = GBMSimulator(tickers=["AAPL", "GOOGL"]) - sim.remove_ticker("GOOGL") - result = sim.step() - assert "GOOGL" not in result - assert "AAPL" in result - - def test_add_duplicate_is_noop(self): - """Test that adding a duplicate ticker is a no-op.""" - sim = GBMSimulator(tickers=["AAPL"]) - sim.add_ticker("AAPL") - assert len(sim._tickers) == 1 - - def test_remove_nonexistent_is_noop(self): - """Test that removing a non-existent ticker is a no-op.""" - sim = GBMSimulator(tickers=["AAPL"]) - sim.remove_ticker("NOPE") # Should not raise - - def test_unknown_ticker_gets_random_seed_price(self): - """Test that unknown tickers get random seed prices.""" - sim = GBMSimulator(tickers=["ZZZZ"]) - price = sim.get_price("ZZZZ") - assert price is not None - assert 50.0 <= price <= 300.0 - - def test_empty_step(self): - """Test stepping with no tickers.""" - sim = GBMSimulator(tickers=[]) - result = sim.step() - assert result == {} - - def test_prices_change_over_time(self): - """After many steps, prices should have drifted from their seeds.""" - sim = GBMSimulator(tickers=["AAPL"]) - initial_price = sim.get_price("AAPL") - - for _ in range(1000): - sim.step() - - final_price = sim.get_price("AAPL") - # Price should have changed (extremely unlikely to be exactly the seed) - assert final_price != initial_price - - def test_cholesky_rebuilds_on_add(self): - """Test that Cholesky matrix is rebuilt when tickers are added.""" - sim = GBMSimulator(tickers=["AAPL"]) - assert sim._cholesky is None # Only 1 ticker, no correlation matrix - sim.add_ticker("GOOGL") - assert sim._cholesky is not None # Now 2 tickers, matrix exists - - def test_cholesky_none_with_one_ticker(self): - """Test that Cholesky is None with only one ticker.""" - sim = GBMSimulator(tickers=["AAPL"]) - assert sim._cholesky is None - - def test_get_price_returns_none_for_unknown(self): - """Test that get_price returns None for unknown ticker.""" - sim = GBMSimulator(tickers=["AAPL"]) - assert sim.get_price("UNKNOWN") is None - - def test_pairwise_correlation_tech_stocks(self): - """Test that tech stocks have high correlation.""" - corr = GBMSimulator._pairwise_correlation("AAPL", "GOOGL") - assert corr == 0.6 - - def test_pairwise_correlation_finance_stocks(self): - """Test that finance stocks have moderate correlation.""" - corr = GBMSimulator._pairwise_correlation("JPM", "V") - assert corr == 0.5 - - def test_pairwise_correlation_tsla(self): - """Test that TSLA has lower correlation with everything.""" - corr = GBMSimulator._pairwise_correlation("TSLA", "AAPL") - assert corr == 0.3 - corr = GBMSimulator._pairwise_correlation("TSLA", "JPM") - assert corr == 0.3 - - def test_pairwise_correlation_cross_sector(self): - """Test cross-sector correlation.""" - corr = GBMSimulator._pairwise_correlation("AAPL", "JPM") - assert corr == 0.3 - - def test_default_dt_is_reasonable(self): - """Test that default dt is a reasonable small value.""" - assert 0 < GBMSimulator.DEFAULT_DT < 0.0001 - - def test_prices_rounded_to_two_decimals(self): - """Test that prices are rounded to 2 decimal places.""" - sim = GBMSimulator(tickers=["AAPL"]) - result = sim.step() - price_str = str(result["AAPL"]) - # Check that we have at most 2 decimal places - if '.' in price_str: - decimal_part = price_str.split('.')[1] - assert len(decimal_part) <= 2 diff --git a/backend/tests/market/test_simulator_source.py b/backend/tests/market/test_simulator_source.py deleted file mode 100644 index 515ce7290..000000000 --- a/backend/tests/market/test_simulator_source.py +++ /dev/null @@ -1,138 +0,0 @@ -"""Integration tests for SimulatorDataSource.""" - -import asyncio - -import pytest - -from app.market.cache import PriceCache -from app.market.simulator import SimulatorDataSource - - -@pytest.mark.asyncio -class TestSimulatorDataSource: - """Integration tests for the SimulatorDataSource.""" - - async def test_start_populates_cache(self): - """Test that start() immediately populates the cache.""" - cache = PriceCache() - source = SimulatorDataSource(price_cache=cache, update_interval=0.1) - await source.start(["AAPL", "GOOGL"]) - - # Cache should have seed prices immediately (before first loop tick) - assert cache.get("AAPL") is not None - assert cache.get("GOOGL") is not None - - await source.stop() - - async def test_prices_update_over_time(self): - """Test that prices are updated periodically.""" - cache = PriceCache() - source = SimulatorDataSource(price_cache=cache, update_interval=0.05) - await source.start(["AAPL"]) - - initial_version = cache.version - await asyncio.sleep(0.3) # Several update cycles - - # Version should have incremented (prices updated) - assert cache.version > initial_version - - await source.stop() - - async def test_stop_is_clean(self): - """Test that stop() is clean and idempotent.""" - cache = PriceCache() - source = SimulatorDataSource(price_cache=cache, update_interval=0.1) - await source.start(["AAPL"]) - await source.stop() - # Double stop should not raise - await source.stop() - - async def test_add_ticker(self): - """Test adding a ticker dynamically.""" - cache = PriceCache() - source = SimulatorDataSource(price_cache=cache, update_interval=0.1) - await source.start(["AAPL"]) - - await source.add_ticker("TSLA") - assert "TSLA" in source.get_tickers() - assert cache.get("TSLA") is not None - - await source.stop() - - async def test_remove_ticker(self): - """Test removing a ticker.""" - cache = PriceCache() - source = SimulatorDataSource(price_cache=cache, update_interval=0.1) - await source.start(["AAPL", "TSLA"]) - - await source.remove_ticker("TSLA") - assert "TSLA" not in source.get_tickers() - assert cache.get("TSLA") is None - - await source.stop() - - async def test_get_tickers(self): - """Test getting the list of active tickers.""" - cache = PriceCache() - source = SimulatorDataSource(price_cache=cache, update_interval=0.1) - await source.start(["AAPL", "GOOGL"]) - - tickers = source.get_tickers() - assert set(tickers) == {"AAPL", "GOOGL"} - - await source.stop() - - async def test_empty_start(self): - """Test starting with no tickers.""" - cache = PriceCache() - source = SimulatorDataSource(price_cache=cache, update_interval=0.1) - await source.start([]) - - assert len(cache) == 0 - assert source.get_tickers() == [] - - await source.stop() - - async def test_exception_resilience(self): - """Test that simulator continues running after errors.""" - cache = PriceCache() - source = SimulatorDataSource(price_cache=cache, update_interval=0.05) - - # Start with a valid ticker - await source.start(["AAPL"]) - - # Wait for some updates - await asyncio.sleep(0.15) - - # Task should still be running - assert source._task is not None - assert not source._task.done() - - await source.stop() - - async def test_custom_update_interval(self): - """Test using a custom update interval.""" - cache = PriceCache() - source = SimulatorDataSource(price_cache=cache, update_interval=0.01) - await source.start(["AAPL"]) - - initial_version = cache.version - await asyncio.sleep(0.05) # Should get ~5 updates - - # Should have multiple updates with fast interval - assert cache.version > initial_version + 2 - - await source.stop() - - async def test_custom_event_probability(self): - """Test creating source with custom event probability.""" - cache = PriceCache() - # Very high event probability for testing - source = SimulatorDataSource( - price_cache=cache, update_interval=0.1, event_probability=1.0 - ) - await source.start(["AAPL"]) - - # Just verify it starts and stops cleanly - await asyncio.sleep(0.2) - await source.stop() diff --git a/backend/uv.lock b/backend/uv.lock deleted file mode 100644 index 67d471b2d..000000000 --- a/backend/uv.lock +++ /dev/null @@ -1,813 +0,0 @@ -version = 1 -revision = 3 -requires-python = ">=3.12" - 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-### Architecture - -``` -MarketDataSource (ABC) -├── SimulatorDataSource → GBM simulator (default, no API key needed) -└── MassiveDataSource → Polygon.io REST poller (when MASSIVE_API_KEY set) - │ - ▼ - PriceCache (thread-safe, in-memory) - │ - ├──→ SSE stream endpoint (/api/stream/prices) - ├──→ Portfolio valuation - └──→ Trade execution -``` - -### Modules - -| File | Purpose | -|------|---------| -| `models.py` | `PriceUpdate` — immutable frozen dataclass (ticker, price, previous_price, timestamp, change, direction) | -| `interface.py` | `MarketDataSource` — abstract base class defining `start/stop/add_ticker/remove_ticker/get_tickers` | -| `cache.py` | `PriceCache` — thread-safe price store with version counter for SSE change detection | -| `seed_prices.py` | Realistic seed prices, per-ticker GBM params (drift/volatility), correlation groups | -| `simulator.py` | `GBMSimulator` (Geometric Brownian Motion with Cholesky-correlated moves) + `SimulatorDataSource` | -| `massive_client.py` | `MassiveDataSource` — REST polling client for Polygon.io via the `massive` package | -| `factory.py` | `create_market_data_source()` — selects simulator or Massive based on `MASSIVE_API_KEY` env var | -| `stream.py` | `create_stream_router()` — FastAPI SSE endpoint factory using version-based change detection | - -### Key Design Decisions - -- **Strategy pattern** — both data sources implement the same ABC; downstream code is source-agnostic -- **PriceCache as single point of truth** — producers write, consumers read; no direct coupling -- **GBM with correlated moves** — Cholesky decomposition of sector-based correlation matrix; tech stocks correlate at 0.6, finance at 0.5, cross-sector at 0.3 -- **Random shock events** — ~0.1% chance per tick per ticker of a 2-5% move for visual drama -- **SSE over WebSockets** — simpler, one-way push, universal browser support - -## Test Suite - -**73 tests, all passing.** 6 test modules in `backend/tests/market/`. - -| Module | Tests | Coverage | -|--------|-------|----------| -| test_models.py | 11 | models.py: 100% | -| test_cache.py | 13 | cache.py: 100% | -| test_simulator.py | 17 | simulator.py: 98% | -| test_simulator_source.py | 10 | (integration tests) | -| test_factory.py | 7 | factory.py: 100% | -| test_massive.py | 13 | massive_client.py: 56% (expected — API methods mocked) | - -Overall coverage: 84%. - -## Code Review & Fixes Applied - -A comprehensive code review identified 7 issues. All were resolved: - -1. **pyproject.toml build config** — added `[tool.hatch.build.targets.wheel] packages = ["app"]` -2. **Lazy imports removed** — `massive` is a core dependency; imports moved to top level -3. **SSE return type fixed** — `_generate_events` annotated as `AsyncGenerator[str, None]` -4. **Public `get_tickers()`** — added to `GBMSimulator` to avoid private attribute access -5. **Correlation constants cleaned up** — removed unused `DEFAULT_CORR`, consolidated into `CROSS_GROUP_CORR` -6. **Unused test imports removed** — `pytest`, `math`, `asyncio` cleaned from 4 test files -7. **Massive test mocks fixed** — `source._client` set in tests, patches target correct names - -## Demo - -A Rich terminal demo is available at `backend/market_data_demo.py`: - -```bash -cd backend -uv run market_data_demo.py -``` - -Displays a live-updating dashboard with all 10 tickers, sparklines, color-coded direction arrows, and an event log for notable price moves. Runs 60 seconds or until Ctrl+C. - -## Usage for Downstream Code - -```python -from app.market import PriceCache, create_market_data_source - -# Startup -cache = PriceCache() -source = create_market_data_source(cache) # Reads MASSIVE_API_KEY -await source.start(["AAPL", "GOOGL", "MSFT", ...]) - -# Read prices -update = cache.get("AAPL") # PriceUpdate or None -price = cache.get_price("AAPL") # float or None -all_prices = cache.get_all() # dict[str, PriceUpdate] - -# Dynamic watchlist -await source.add_ticker("TSLA") -await source.remove_ticker("GOOGL") - -# Shutdown -await source.stop() -``` diff --git a/planning/PLAN.md b/planning/PLAN.md index bc1811b33..9e2e769d2 100644 --- a/planning/PLAN.md +++ b/planning/PLAN.md @@ -1,13 +1,13 @@ # FinAlly — AI Trading Workstation -## Project Specification - ## 1. Vision FinAlly (Finance Ally) is a visually stunning AI-powered trading workstation that streams live market data, lets users trade a simulated portfolio, and integrates an LLM chat assistant that can analyze positions and execute trades on the user's behalf. It looks and feels like a modern Bloomberg terminal with an AI copilot. This is the capstone project for an agentic AI coding course. It is built entirely by Coding Agents demonstrating how orchestrated AI agents can produce a production-quality full-stack application. Agents interact through files in `planning/`. +> **How to read this document.** Sections marked **NORMATIVE** define contracts shared between agents built in isolation (frontend ↔ backend ↔ tests). Where a normative section gives a literal JSON shape, field name, or numeric rule, implementations MUST match it exactly — do not "improve" a field name. Everything else is guidance, and internal structure within an agent's own directory is that agent's call. + ## 2. User Experience ### First Launch @@ -22,11 +22,12 @@ The user runs a single Docker command (or a provided start script). A browser op ### What the User Can Do - **Watch prices stream** — prices flash green (uptick) or red (downtick) with subtle CSS animations that fade -- **View sparkline mini-charts** — price action beside each ticker in the watchlist, accumulated on the frontend from the SSE stream since page load (sparklines fill in progressively) +- **View sparkline mini-charts** — price action beside each ticker in the watchlist, accumulated on the frontend from the SSE stream since page load (sparklines fill in progressively). No price-history table backs this — a page refresh intentionally resets sparklines to empty; this is an accepted simplification, not a gap. **The same applies to the main chart** (§11): it starts as a single point at page load and fills in from the stream. - **Click a ticker** to see a larger detailed chart in the main chart area - **Buy and sell shares** — market orders only, instant fill at current price, no fees, no confirmation dialog - **Monitor their portfolio** — a heatmap (treemap) showing positions sized by weight and colored by P&L, plus a P&L chart tracking total portfolio value over time - **View a positions table** — ticker, quantity, average cost, current price, unrealized P&L, % change +- **Review trade history** — an append-only blotter of every fill - **Chat with the AI assistant** — ask about their portfolio, get analysis, and have the AI execute trades and manage the watchlist through natural language - **Manage the watchlist** — add/remove tickers manually or via the AI chat @@ -34,14 +35,18 @@ The user runs a single Docker command (or a provided start script). A browser op - **Dark theme**: backgrounds around `#0d1117` or `#1a1a2e`, muted gray borders, no pure black - **Price flash animations**: brief green/red background highlight on price change, fading over ~500ms via CSS transitions -- **Connection status indicator**: a small colored dot (green = connected, yellow = reconnecting, red = disconnected) visible in the header +- **Connection status indicator**: a small colored dot visible in the header. Because `EventSource` retries forever and never reports permanent failure, the tri-state is derived explicitly, not read off `readyState` alone: + - **green** — connected, and a `price` event or `: ping` comment arrived within the last 30s + - **yellow** — `onerror` fired and the browser is retrying, or nothing has arrived for 30s + - **red** — 3 consecutive reconnect attempts have failed, or nothing has arrived for 90s +- **Market-closed badge**: in Massive mode outside market hours, real prices genuinely stop changing and no events fire (§7). A distinct "MARKET CLOSED / STALE" badge, driven by the last-tick timestamp exceeding the expected poll interval, MUST be shown so the app does not appear broken. This is separate from the connection dot, which stays green. - **Professional, data-dense layout**: inspired by Bloomberg/trading terminals — every pixel earns its place - **Responsive but desktop-first**: optimized for wide screens, functional on tablet ### Color Scheme -- Accent Yellow: `#ecad0a` -- Blue Primary: `#209dd7` -- Purple Secondary: `#753991` (submit buttons) +- Accent Yellow: `#ecad0a` — alerts, highlights, price-flash-adjacent emphasis (not the flash itself, which is green/red) +- Blue Primary: `#209dd7` — links, secondary actions, selected/active states (e.g. selected ticker) +- Purple Secondary: `#753991` — submit buttons (buy/sell confirm, send chat message) ## 3. Architecture Overview @@ -64,7 +69,7 @@ The user runs a single Docker command (or a provided start script). A browser op - **Frontend**: Next.js with TypeScript, built as a static export (`output: 'export'`), served by FastAPI as static files - **Backend**: FastAPI (Python), managed as a `uv` project -- **Database**: SQLite, single file at `db/finally.db`, volume-mounted for persistence +- **Database**: SQLite, single file at `/app/db/finally.db`, volume-mounted for persistence - **Real-time data**: Server-Sent Events (SSE) — simpler than WebSockets, one-way server→client push, works everywhere - **AI integration**: LiteLLM → OpenRouter (Cerebras for fast inference), with structured outputs for trade execution - **Market data**: Environment-variable driven — simulator by default, real data via Massive API if key provided @@ -76,7 +81,7 @@ The user runs a single Docker command (or a provided start script). A browser op | SSE over WebSockets | One-way push is all we need; simpler, no bidirectional complexity, universal browser support | | Static Next.js export | Single origin, no CORS issues, one port, one container, simple deployment | | SQLite over Postgres | No auth = no multi-user = no need for a database server; self-contained, zero config | -| Single Docker container | Students run one command; no docker-compose for production, no service orchestration | +| Single Docker container | Students run one command; one service, no orchestration. `docker-compose.yml` exists purely to hold the run configuration in one place (§12) — it is not multi-service | | uv for Python | Fast, modern Python project management; reproducible lockfile; what students should learn | | Market orders only | Eliminates order book, limit order logic, partial fills — dramatically simpler portfolio math | @@ -91,6 +96,7 @@ finally/ │ └── db/ # Schema definitions, seed data, migration logic ├── planning/ # Project-wide documentation for agents │ ├── PLAN.md # This document +│ ├── REVIEW.md # Specification review that produced this document's contracts │ └── ... # Additional agent reference docs ├── scripts/ │ ├── start_mac.sh # Launch Docker container (macOS/Linux) @@ -98,20 +104,20 @@ finally/ │ ├── start_windows.ps1 # Launch Docker container (Windows PowerShell) │ └── stop_windows.ps1 # Stop Docker container (Windows PowerShell) ├── test/ # Playwright E2E tests + docker-compose.test.yml -├── db/ # Volume mount target (SQLite file lives here at runtime) -│ └── .gitkeep # Directory exists in repo; finally.db is gitignored ├── Dockerfile # Multi-stage build (Node → Python) -├── docker-compose.yml # Optional convenience wrapper -├── .env # Environment variables (gitignored, .env.example committed) +├── docker-compose.yml # Single source of truth for port/volume/env wiring +├── .env # Environment variables (gitignored) +├── .env.example # Committed template — MUST exist; the README quick start depends on it └── .gitignore ``` +> **Note:** there is no top-level `db/` directory. The SQLite file lives in a **named Docker volume** mounted at `/app/db` inside the container, never in the repo working tree (§12). + ### Key Boundaries - **`frontend/`** is a self-contained Next.js project. It knows nothing about Python. It talks to the backend via `/api/*` endpoints and `/api/stream/*` SSE endpoints. Internal structure is up to the Frontend Engineer agent. - **`backend/`** is a self-contained uv project with its own `pyproject.toml`. It owns all server logic including database initialization, schema, seed data, API routes, SSE streaming, market data, and LLM integration. Internal structure is up to the Backend/Market Data agents. -- **`backend/db/`** contains schema SQL definitions and seed logic. The backend lazily initializes the database on first request — creating tables and seeding default data if the SQLite file doesn't exist or is empty. -- **`db/`** at the top level is the runtime volume mount point. The SQLite file (`db/finally.db`) is created here by the backend and persists across container restarts via Docker volume. +- **`backend/db/`** contains schema SQL definitions and seed logic. The backend initializes the database at application startup (§8). - **`planning/`** contains project-wide documentation, including this plan. All agents reference files here as the shared contract. - **`test/`** contains Playwright E2E tests and supporting infrastructure (e.g., `docker-compose.test.yml`). Unit tests live within `frontend/` and `backend/` respectively, following each framework's conventions. - **`scripts/`** contains start/stop scripts that wrap Docker commands. @@ -130,18 +136,101 @@ MASSIVE_API_KEY= # Optional: Set to "true" for deterministic mock LLM responses (testing) LLM_MOCK=false + +# Optional: market data poll/tick interval in milliseconds. +# Defaults: 500 in simulator mode, 15000 in Massive mode. +MARKET_TICK_MS= + +# Optional: shared secret required on all requests. Leave empty for local use. +# MUST be set for any non-localhost deployment (§12). +APP_SHARED_SECRET= ``` +`.env.example` is committed and mirrors the above with empty values. `.env` is gitignored. + ### Behavior - If `MASSIVE_API_KEY` is set and non-empty → backend uses Massive REST API for market data - If `MASSIVE_API_KEY` is absent or empty → backend uses the built-in market simulator -- If `LLM_MOCK=true` → backend returns deterministic mock LLM responses (for E2E tests) +- If `LLM_MOCK=true` → backend returns deterministic mock LLM responses (§10) - The backend reads `.env` from the project root (mounted into the container or read via docker `--env-file`) +### Secrets Handling (NORMATIVE) + +- Secrets are read from the environment only. They are **never** logged, never included in any API response or error body, and never written to the database. +- `litellm.set_verbose` MUST remain off, and LiteLLM debug logging MUST NOT be enabled in committed code — it prints request headers including `Authorization`. +- Errors surfaced from the LLM client MUST be reduced to a generic message before reaching the user (§10 step 4); raw provider exception text may be logged server-side only after confirming it carries no credentials. +- `/api/health` MUST NOT echo configuration, key presence, or environment values beyond the fields listed in §9. + +--- + +## 6. Global Conventions (NORMATIVE) + +These rules are cross-cutting. Every agent applies them identically; they are the reason the independently-built pieces fit together. + +### 6.1 Ticker Normalization and Validation + +SQLite's default TEXT collation is BINARY, so `aapl`, `AAPL`, and `AAPL ` would otherwise be three distinct rows — splitting cost basis across duplicate positions and corrupting P&L. + +- Every ticker entering the system is normalized at the API boundary: `ticker.strip().upper()`. +- After normalization it MUST match `^[A-Z][A-Z0-9.\-]{0,9}$`. A non-match is a `400` with error code `invalid_ticker`. +- This applies identically to **all three** entry points: the manual trade bar / watchlist form, the `{ticker}` path parameter on `DELETE /api/watchlist/{ticker}`, and every ticker emitted by the LLM in `trades` or `watchlist_changes`. +- `watchlist.ticker` and `positions.ticker` are additionally declared `COLLATE NOCASE` as defense in depth. + +### 6.2 Numeric Precision and Money (NORMATIVE) + +All monetary and quantity columns are SQLite `REAL` (IEEE-754 double). With fractional shares this makes naive comparisons unsafe: "sell everything I own" will intermittently fail, cash drifts to values like `9999.999999999998`, and closed positions leave ~1e-15-share residue that renders as a ghost row and a zero-width heatmap rectangle. + +- Define `EPSILON = 1e-9`. +- **Never** compare floats with `==`. +- A **sell** is valid if `quantity <= held + EPSILON`. If valid, the executed quantity is **clamped** to `min(quantity, held)`. +- A **buy** is valid if `cost <= cash_balance + EPSILON`, where `cost = quantity * price`. +- After a sell, if the resulting `quantity < EPSILON`, the `positions` row is **deleted**, not left at ~0. +- `cash_balance`, `avg_cost`, and `quantity` are rounded to **8 decimal places** on every write. +- Rounding is a storage concern only. Display formatting (2 decimals for currency, per §11.3) happens in the frontend. +- Quantity must be finite and `> 0` on input; `NaN`, `Infinity`, and `<= 0` are rejected with `invalid_quantity`. + +### 6.3 Timestamps (NORMATIVE) + +- All timestamps stored in SQLite are **UTC ISO-8601 with a `Z` suffix**: `2026-08-08T14:03:22.481Z`. Mixed naive-local and UTC strings sort incorrectly under TEXT comparison and render at the wrong time on the P&L chart. +- Produced by a single shared helper (one function, backend-wide). No ad-hoc `datetime.now()` calls. +- Timestamps in the **SSE stream** are the exception: they are epoch **milliseconds** (integer), for cheap client-side charting. This is the only place a non-ISO timestamp appears, and it is never persisted. + +### 6.4 Error Envelope (NORMATIVE) + +Every non-2xx response from every `/api/*` endpoint uses one shape. FastAPI's default `HTTPException` produces `{"detail": ...}`, which does **not** match — install an exception handler that rewrites it. + +```json +{ + "success": false, + "error": { + "code": "insufficient_cash", + "message": "Need $1,901.20 but only $500.00 available." + } +} +``` + +Defined `code` values: `invalid_ticker`, `invalid_quantity`, `invalid_side`, `unknown_ticker`, `insufficient_cash`, `insufficient_shares`, `watchlist_full`, `ticker_not_found`, `no_price_available`, `llm_unavailable`, `rate_limited`, `internal_error`. + +`message` is human-readable and safe to display directly. Clients switch on `code`, never on `message` text. + +### 6.5 The Priced-Ticker Set (NORMATIVE) + +Market data is driven by: + +``` +priced_tickers = watchlist ∪ { ticker : positions.quantity > EPSILON } +``` + +**Not** the watchlist alone. Nothing prevents removing a ticker from the watchlist while a position is open, and nothing requires a traded ticker to be on the watchlist. If pricing followed the watchlist only, `GET /api/portfolio` would value that position at a frozen or missing price, and every 30-second snapshot would silently record a wrong `total_value` forever after. + +Both the simulator and the Massive poller compute their working set from this union, and recompute it whenever the watchlist or positions change. + +Consequently: `DELETE /api/watchlist/{ticker}` **is allowed** while a position is open. The ticker leaves the watchlist panel but stays priced via the union, and continues to appear in the positions table and heatmap. + --- -## 6. Market Data +## 7. Market Data ### Two Implementations, One Interface @@ -150,71 +239,169 @@ Both the simulator and the Massive client implement the same abstract interface. ### Simulator (Default) - Generates prices using geometric Brownian motion (GBM) with configurable drift and volatility per ticker -- Updates at ~500ms intervals +- Updates at `MARKET_TICK_MS`, default 500ms - Correlated moves across tickers (e.g., tech stocks move together) - Occasional random "events" — sudden 2-5% moves on a ticker for drama - Starts from realistic seed prices (e.g., AAPL ~$190, GOOGL ~$175, etc.) - Runs as an in-process background task — no external dependencies +#### GBM Parameters (NORMATIVE) + +Drift and volatility are quoted **annualized**, so the per-tick step MUST be scaled. An annualized σ applied raw to a 500ms tick compounds to prices in the millions within a minute. + +- Trading-year seconds: `YEAR_SECONDS = 252 * 6.5 * 3600` (= 5,896,800) +- Per-tick step: `dt = (MARKET_TICK_MS / 1000) / YEAR_SECONDS` +- Update: `S_next = S * exp((mu - 0.5 * sigma**2) * dt + sigma * sqrt(dt) * Z)`, where `Z ~ N(0,1)` +- Defaults per ticker: `mu = 0.08`, `sigma = 0.25` annualized. Higher-beta names may be tuned upward (e.g. TSLA/NVDA `sigma = 0.45`); the seed table carries per-ticker overrides. +- **Price floor**: `S_next = max(S_next, 0.01)`. GBM cannot reach zero analytically but floating-point underflow and the event mechanism can. +- **Correlation** is implemented with a single market factor, not a full covariance matrix: `Z_ticker = beta * Z_market + sqrt(1 - beta**2) * Z_idiosyncratic`. Each seed ticker carries a `beta` (tech names ~0.8, JPM/V ~0.5). Tickers added at runtime (below) get `beta = 0.6`. + ### Massive API (Optional) - REST API polling (not WebSocket) — simpler, works on all tiers -- Polls for the union of all watched tickers on a configurable interval +- Polls for the **priced-ticker set** (§6.5) on a configurable interval - Free tier (5 calls/min): poll every 15 seconds - Paid tiers: poll every 2-15 seconds depending on tier - Parses REST response into the same format as the simulator +#### Call Budget (NORMATIVE) + +Implemented naively as one quote request per ticker, 20 tickers at 4 polls/min is **80 calls/min against a 5/min free-tier budget**. Therefore: + +- Each poll issues **exactly one** grouped-snapshot request covering all priced tickers — Polygon's grouped daily / snapshot endpoint — never one request per ticker. One poll = one call, regardless of watchlist size. +- Ticker validation on add (below) consumes the same budget. Validated symbols are cached in memory for the process lifetime, so add/remove churn does not re-spend calls. +- On HTTP 429, back off exponentially (15s → 30s → 60s, capped at 60s) and surface staleness through `/api/health` and the market-closed badge rather than failing requests. + ### Shared Price Cache - A single background task (simulator or Massive poller) writes to an in-memory price cache -- The cache holds the latest price, previous price, and timestamp for each ticker - SSE streams read from this cache and push updates to connected clients - This architecture supports future multi-user scenarios without changes to the data layer -### SSE Streaming +Per ticker the cache holds: + +| Field | Meaning | +|---|---| +| `price` | Latest price | +| `prev_price` | Price at the previous emission (basis for `dir`) | +| `session_open_price` | Baseline for "daily change %" — see below | +| `ts` | Epoch ms of the last update | + +#### `session_open_price` (NORMATIVE) + +The watchlist shows a **daily change %** (§11), but the cache otherwise holds no daily baseline and there is no price-history table. `session_open_price` supplies it: + +- **Simulator mode**: the ticker's seed price at process start (or, for a ticker added at runtime, its assigned starting price). "Daily change" is therefore change since the simulated session began — honest, given the simulator has no real trading day. +- **Massive mode**: the previous close returned by the snapshot endpoint. + +Daily change % = `(price - session_open_price) / session_open_price * 100`. If `session_open_price` is unavailable, the API returns `null` and the UI renders `—`, never `0` or `-100%`. + +### Adding New Tickers + +- **Simulator mode**: any ticker can be added. If it isn't in the seed price list, the simulator assigns it a deterministic pseudo-random starting price (derived from a hash of the ticker symbol, in the $10–$500 range), default drift/volatility, and `beta = 0.6`, then simulates it like any other ticker. +- **Massive mode**: the ticker is validated against the Massive API on add (a single lookup/quote call, memoized per §7 call budget). If the symbol doesn't resolve, the request fails with `unknown_ticker` and nothing is added. +- This applies equally whether the ticker is added manually or via the LLM's `watchlist_changes` — because both paths call the same service function (§10.6), never the database directly. + +### Watchlist Size Limit + +- The watchlist is capped at **20 tickers**. `POST /api/watchlist` returns `400 watchlist_full` if adding would exceed the cap (bounds SSE fan-out, Massive API call volume, and sparkline rendering cost). The LLM is told about this limit so it can explain a rejected `watchlist_changes` entry to the user. + +### SSE Streaming (NORMATIVE) - Endpoint: `GET /api/stream/prices` - Long-lived SSE connection; client uses native `EventSource` API -- Server pushes price updates for all tickers known to the system at a regular cadence (~500ms) — in the single-user model this is equivalent to the user's watchlist -- Each SSE event contains ticker, price, previous price, timestamp, and change direction -- Client handles reconnection automatically (EventSource has built-in retry) + +#### Wire Format + +There is exactly **one** event name, `price`. The ticker lives in the payload, **not** in the event name — a per-symbol event name would force `addEventListener` registration per ticker plus re-subscription on every watchlist change and every reconnect, for no benefit. + +``` +retry: 3000 + +event: snapshot +data: {"ticker":"AAPL","price":190.12,"prev_price":190.12,"session_open":189.40,"ts":1754640000000,"dir":"flat"} + +event: price +data: {"ticker":"AAPL","price":190.18,"prev_price":190.12,"session_open":189.40,"ts":1754640000500,"dir":"up"} + +: ping +``` + +| Field | Type | Notes | +|---|---|---| +| `ticker` | string | Normalized per §6.1 | +| `price` | number | Current price | +| `prev_price` | number | Price at previous emission | +| `session_open` | number \| null | Baseline for daily change % | +| `ts` | integer | Epoch **milliseconds**, UTC (§6.3) | +| `dir` | string | One of `"up"`, `"down"`, `"flat"` | + +#### Emission Rules + +- One event per ticker per update — never a batched array. +- The background task ticks at `MARKET_TICK_MS`, but an event is emitted for a ticker only when its price actually **changed** since the last emission. In simulator mode this is effectively every tick; in Massive mode no events fire between polls, rather than repeating a stale price. This keeps flash-on-change from firing on no-op updates. +- **On connection open**, the server immediately emits one `event: snapshot` per cached ticker, then switches to change-only `event: price` deltas. Without this, a client that connects or reconnects sees an empty grid until the next price change — up to 15s on the Massive free tier, and *indefinitely* outside market hours. Clients render `snapshot` events into state but MUST NOT play the flash animation for them. + +#### Connection Lifecycle + +- `retry: 3000` is sent once on connect, hinting the browser's reconnect delay. +- A `: ping` comment is sent every **15 seconds** to keep intermediaries from timing out an idle connection, and to give the client a liveness signal even when prices are static (§2 connection dot). +- The server checks `await request.is_disconnected()` each iteration and tears down the generator and its cache subscription. Without this, every page refresh leaks a generator. +- On application shutdown the lifespan handler cancels the market-data task **and** all open SSE generators. Otherwise `docker compose down` hangs for the full stop timeout. --- -## 7. Database +## 8. Database + +### SQLite Initialization + +The backend initializes the database in the **FastAPI lifespan startup handler** — not lazily on first request. The SPA fires several `/api/*` calls concurrently on mount, and two concurrent "first requests" racing to create schema produces either duplicate seed rows or a `table already exists` 500. -### SQLite with Lazy Initialization +- All DDL uses `CREATE TABLE IF NOT EXISTS`. +- All seeding uses `INSERT OR IGNORE`, making it idempotent. +- No separate migration step, no manual setup; fresh volumes start clean and seeded automatically. -The backend checks for the SQLite database on startup (or first request). If the file doesn't exist or tables are missing, it creates the schema and seeds default data. This means: +### Concurrency (NORMATIVE) -- No separate migration step -- No manual database setup -- Fresh Docker volumes start with a clean, seeded database automatically +At least four concurrent accessors are implied: request handlers, the 30-second snapshot task, the post-trade snapshot write, and LLM auto-execution. + +- At init: `PRAGMA journal_mode=WAL` and `PRAGMA busy_timeout=5000`. Without WAL, the snapshot task intermittently throws `database is locked` mid-trade. +- Connections are opened with `check_same_thread=False`. +- **The synchronous `sqlite3` module MUST NOT be called from inside an `async def` handler.** Doing so blocks the event loop, which stalls every open SSE generator — the failure looks like "prices freeze whenever anyone trades." Use plain `def` handlers (FastAPI runs them in a threadpool), `asyncio.to_thread`, or `aiosqlite`. Pick one and use it consistently. +- Writes are serialized behind a single writer connection or an `asyncio.Lock`. + +### Transactions (NORMATIVE) + +A trade touches four statements: read `cash_balance`, update it, upsert `positions`, insert into `trades`. These MUST execute inside a **single `BEGIN IMMEDIATE` transaction**. Otherwise a manual trade concurrent with an LLM-executed trade can both pass the "sufficient cash" check and drive the balance negative. + +Failed validation rolls back with **no partial writes** — never a `trades` row without the matching balance change. ### Schema -All tables include a `user_id` column defaulting to `"default"`. This is hardcoded for now (single-user) but enables future multi-user support without schema migration. +Every **per-user data** table carries a `user_id` column defaulting to `"default"`, hardcoded for now (single-user) but enabling future multi-user support without migration. (`users_profile` is the user table itself; its key is `id`.) -**users_profile** — User state (cash balance) +**users_profile** — User state - `id` TEXT PRIMARY KEY (default: `"default"`) - `cash_balance` REAL (default: `10000.0`) -- `created_at` TEXT (ISO timestamp) +- `realized_pnl` REAL (default: `0.0`) — cumulative realized gain/loss, updated on every sell as `(fill_price - avg_cost) * quantity`. Without this, closing a position makes its entire gain invisible: it folds silently into cash, and neither the UI nor the LLM can reason about actual trading performance. +- `created_at` TEXT (UTC ISO-8601) **watchlist** — Tickers the user is watching - `id` TEXT PRIMARY KEY (UUID) - `user_id` TEXT (default: `"default"`) -- `ticker` TEXT -- `added_at` TEXT (ISO timestamp) +- `ticker` TEXT COLLATE NOCASE +- `added_at` TEXT (UTC ISO-8601) - UNIQUE constraint on `(user_id, ticker)` **positions** — Current holdings (one row per ticker per user) - `id` TEXT PRIMARY KEY (UUID) - `user_id` TEXT (default: `"default"`) -- `ticker` TEXT +- `ticker` TEXT COLLATE NOCASE - `quantity` REAL (fractional shares supported) - `avg_cost` REAL -- `updated_at` TEXT (ISO timestamp) +- `updated_at` TEXT (UTC ISO-8601) - UNIQUE constraint on `(user_id, ticker)` +- Rows with `quantity < EPSILON` are deleted, never retained (§6.2) **trades** — Trade history (append-only log) - `id` TEXT PRIMARY KEY (UUID) @@ -223,84 +410,252 @@ All tables include a `user_id` column defaulting to `"default"`. This is hardcod - `side` TEXT (`"buy"` or `"sell"`) - `quantity` REAL (fractional shares supported) - `price` REAL -- `executed_at` TEXT (ISO timestamp) +- `executed_at` TEXT (UTC ISO-8601) **portfolio_snapshots** — Portfolio value over time (for P&L chart). Recorded every 30 seconds by a background task, and immediately after each trade execution. - `id` TEXT PRIMARY KEY (UUID) - `user_id` TEXT (default: `"default"`) - `total_value` REAL -- `recorded_at` TEXT (ISO timestamp) +- `cash_balance` REAL — split out so the chart can show composition, not just a single line +- `positions_value` REAL +- `recorded_at` TEXT (UTC ISO-8601) **chat_messages** — Conversation history with LLM - `id` TEXT PRIMARY KEY (UUID) - `user_id` TEXT (default: `"default"`) - `role` TEXT (`"user"` or `"assistant"`) - `content` TEXT -- `actions` TEXT (JSON — trades executed, watchlist changes made; null for user messages) -- `created_at` TEXT (ISO timestamp) +- `actions` TEXT (JSON; null for user messages) — literal shape in §9.6 +- `created_at` TEXT (UTC ISO-8601) + +### Indexes (NORMATIVE) + +Three endpoints filter by `user_id` and order by a timestamp; without indexes each is a full scan that grows unbounded. + +```sql +CREATE INDEX IF NOT EXISTS idx_snapshots_user_time ON portfolio_snapshots(user_id, recorded_at); +CREATE INDEX IF NOT EXISTS idx_chat_user_time ON chat_messages(user_id, created_at); +CREATE INDEX IF NOT EXISTS idx_trades_user_time ON trades(user_id, executed_at); +``` + +### Snapshot Task Rules (NORMATIVE) + +The 30-second task values the portfolio from the in-memory price cache, which is **empty at process start**. Treating a missing price as `0` writes a spike-to-zero into the P&L chart that never goes away. + +- Skip the snapshot entirely if any position ticker has no cached price yet. +- If a price is missing for a ticker that was previously priced, fall back to `avg_cost` for that position — never `0`, never `NaN`. +- Never write a snapshot with a non-finite `total_value`. + +### Retention (NORMATIVE) + +At one row per 30s, `portfolio_snapshots` grows by 2,880 rows/day. + +- Keep full 30-second resolution for the most recent **24 hours**. +- Older rows are thinned to one per 5 minutes by a daily task. +- `GET /api/portfolio/history` additionally caps and downsamples its response (§9.3). ### Default Seed Data -- One user profile: `id="default"`, `cash_balance=10000.0` +- One user profile: `id="default"`, `cash_balance=10000.0`, `realized_pnl=0.0` - Ten watchlist entries: AAPL, GOOGL, MSFT, AMZN, TSLA, NVDA, META, JPM, V, NFLX --- -## 8. API Endpoints - -### Market Data -| Method | Path | Description | -|--------|------|-------------| -| GET | `/api/stream/prices` | SSE stream of live price updates | - -### Portfolio -| Method | Path | Description | -|--------|------|-------------| -| GET | `/api/portfolio` | Current positions, cash balance, total value, unrealized P&L | -| POST | `/api/portfolio/trade` | Execute a trade: `{ticker, quantity, side}` | -| GET | `/api/portfolio/history` | Portfolio value snapshots over time (for P&L chart) | - -### Watchlist -| Method | Path | Description | -|--------|------|-------------| -| GET | `/api/watchlist` | Current watchlist tickers with latest prices | -| POST | `/api/watchlist` | Add a ticker: `{ticker}` | -| DELETE | `/api/watchlist/{ticker}` | Remove a ticker | - -### Chat -| Method | Path | Description | -|--------|------|-------------| -| POST | `/api/chat` | Send a message, receive complete JSON response (message + executed actions) | - -### System -| Method | Path | Description | -|--------|------|-------------| -| GET | `/api/health` | Health check (for Docker/deployment) | +## 9. API Endpoints (NORMATIVE) + +This section is the frontend↔backend contract. The frontend and backend are built by separate agents whose only shared reference is this document — so every request and response body below is given literally. **Field names, types, and nesting MUST match exactly.** + +Conventions: all responses are JSON. All errors use the §6.4 envelope. All tickers are normalized per §6.1. All timestamps are UTC ISO-8601 (§6.3) except SSE `ts`. + +### 9.1 `GET /api/stream/prices` + +SSE stream. Wire format specified in §7 — it is the one endpoint whose contract lives elsewhere, because the framing matters as much as the payload. + +### 9.2 `GET /api/portfolio` + +```json +{ + "cash_balance": 8098.80, + "positions_value": 1901.20, + "total_value": 10000.00, + "unrealized_pnl": 0.00, + "unrealized_pnl_pct": 0.00, + "realized_pnl": 0.00, + "positions": [ + { + "ticker": "AAPL", + "quantity": 10, + "avg_cost": 190.12, + "current_price": 190.12, + "market_value": 1901.20, + "unrealized_pnl": 0.00, + "unrealized_pnl_pct": 0.00, + "weight_pct": 19.01 + } + ] +} +``` + +- `current_price` is `null` when no price is cached; dependent fields (`market_value`, both `unrealized_pnl*`) are then also `null`, and the UI renders `—`. +- `weight_pct` is the position's share of `total_value`, used to size heatmap rectangles. + +### 9.3 `GET /api/portfolio/history` + +Query: `?limit=` (default `500`, max `2000`), `?since=` (UTC ISO-8601, optional). + +Without a cap the P&L chart eventually downloads and renders a million-point series. When the range holds more points than `limit`, the server **downsamples evenly** to `limit` points, always retaining the first and last. + +```json +{ + "points": [ + {"recorded_at": "2026-08-08T14:03:22.481Z", "total_value": 10000.00, "cash_balance": 8098.80, "positions_value": 1901.20} + ], + "downsampled": false +} +``` + +### 9.4 `POST /api/portfolio/trade` + +Request: +```json +{"ticker": "AAPL", "quantity": 10, "side": "buy"} +``` + +Response `200`: +```json +{ + "success": true, + "trade": { + "trade_id": "9f1c...", + "ticker": "AAPL", + "side": "buy", + "quantity": 10, + "fill_price": 190.12, + "executed_at": "2026-08-08T14:03:22.481Z" + }, + "position": { + "ticker": "AAPL", "quantity": 10, "avg_cost": 190.12 + }, + "cash_balance": 8098.80, + "realized_pnl": 0.00 +} +``` + +`fill_price`, `trade_id`, and `executed_at` are required: §2 promises "instant fill at current price," and the fill price is the single most important number to confirm back to the user ("Filled 10 AAPL @ $190.12"). `position` is `null` when a sell closes the position entirely. + +Errors (`400`, §6.4 envelope): `invalid_ticker`, `invalid_quantity`, `invalid_side`, `insufficient_cash`, `insufficient_shares`, `no_price_available` (ticker known but never priced — a trade cannot fill without a price). + +### 9.5 `GET /api/portfolio/trades` + +Query: `?limit=` (default `100`, max `500`). + +```json +{ + "trades": [ + {"trade_id": "9f1c...", "ticker": "AAPL", "side": "buy", "quantity": 10, "price": 190.12, "executed_at": "2026-08-08T14:03:22.481Z"} + ] +} +``` + +Newest first. Without this endpoint the `trades` table would be written on every execution and never read by anything — pure write-only dead data. + +### 9.6 Watchlist + +`GET /api/watchlist`: +```json +{ + "tickers": [ + { + "ticker": "AAPL", + "price": 190.12, + "prev_price": 190.05, + "session_open": 189.40, + "change_pct": 0.38, + "ts": 1754640000000 + } + ], + "limit": 20 +} +``` + +For a just-added, not-yet-priced ticker, `price`/`prev_price`/`session_open`/`change_pct`/`ts` are all **`null`** — never `0`, which would render as a $0.00 stock down 100%. + +`POST /api/watchlist` — request `{"ticker": "PYPL"}`, response is the same object shape as one `tickers` entry, `201`. Errors: `invalid_ticker`, `unknown_ticker` (Massive mode validation failed), `watchlist_full` (cap of 20). + +`DELETE /api/watchlist/{ticker}` — **idempotent**: returns `204` whether or not the ticker was present. Deleting a ticker with an open position is allowed (§6.5). Idempotency matters because the LLM path retries and re-issues removals. + +### 9.7 Chat + +`GET /api/chat` — query `?limit=` (default `50`, max `200`). Returns oldest-first for direct rendering. + +```json +{ + "messages": [ + {"id": "a1b2...", "role": "user", "content": "buy 10 AAPL", "actions": null, "created_at": "2026-08-08T14:03:20.000Z"}, + {"id": "c3d4...", "role": "assistant", "content": "Bought 10 AAPL at $190.12.", "actions": { }, "created_at": "2026-08-08T14:03:22.481Z"} + ] +} +``` + +`POST /api/chat` — request `{"message": "buy 10 AAPL"}`, response `200`: + +```json +{ + "message": "Bought 10 AAPL at $190.12. That's 19% of your portfolio in one name.", + "actions": { + "trades": [ + {"ticker": "AAPL", "side": "buy", "quantity": 10, + "status": "executed", "fill_price": 190.12, "trade_id": "9f1c...", "executed_at": "2026-08-08T14:03:22.481Z"} + ], + "watchlist_changes": [ + {"ticker": "PYPL", "action": "add", "status": "failed: watchlist_full"} + ] + } +} +``` + +This `actions` object is **exactly what is stored** in `chat_messages.actions` (§8) and exactly what the chat panel renders as inline confirmations. `status` is `"executed"` or `"failed: "`. Executed trades carry `fill_price`, `trade_id`, and `executed_at`; failed ones do not. Both arrays are always present, possibly empty. + +### 9.8 `GET /api/health` + +```json +{ + "status": "ok", + "db": "ok", + "market_data": "ok", + "last_tick_ms_ago": 412, + "mode": "simulator" +} +``` + +Returns **`200`** whenever the process is up and the DB is reachable. Market-data staleness is reported in the body as `"ok"` or `"stale"` — it does **not** produce a 503. + +Staleness threshold is `3 * MARKET_TICK_MS + 5000`, not a fixed "few seconds". On the Massive free tier the poller ticks every 15s, so a fixed threshold would report unhealthy most of the time — and if wired as a container healthcheck, would restart a perfectly healthy app in a loop. + +`503` is reserved for: DB unreachable, or startup incomplete. Response fields never include configuration or key material (§5). --- -## 9. LLM Integration +## 10. LLM Integration -When writing code to make calls to LLMs, use cerebras-inference skill to use LiteLLM via OpenRouter to the `openrouter/openai/gpt-oss-120b` model with Cerebras as the inference provider. Structured Outputs should be used to interpret the results. +When writing code to make calls to LLMs, use the cerebras-inference skill to use LiteLLM via OpenRouter to the `openrouter/openai/gpt-oss-120b` model with Cerebras as the inference provider. Structured Outputs should be used to interpret the results. -There is an OPENROUTER_API_KEY in the .env file in the project root. +There is an `OPENROUTER_API_KEY` in the `.env` file in the project root. ### How It Works When the user sends a chat message, the backend: -1. Loads the user's current portfolio context (cash, positions with P&L, watchlist with live prices, total portfolio value) -2. Loads recent conversation history from the `chat_messages` table +1. Loads the user's current portfolio context (cash, positions with P&L, watchlist with live prices, total portfolio value, realized P&L) +2. Loads the **most recent 20 messages** from `chat_messages`, oldest-first — see §10.4 3. Constructs a prompt with a system message, portfolio context, conversation history, and the user's new message -4. Calls the LLM via LiteLLM → OpenRouter, requesting structured output, using the cerebras-inference skill +4. Calls the LLM via LiteLLM → OpenRouter, requesting structured output, using the cerebras-inference skill. If the call errors or times out, one retry is attempted; if that also fails, the backend responds with a canned assistant message ("Sorry, I'm having trouble reaching the AI service — please try again.") and executes no actions. This canned response is not sent to the LLM or stored as if the LLM produced it — it's a backend fallback message. 5. Parses the complete structured JSON response -6. Auto-executes any trades or watchlist changes specified in the response -7. Stores the message and executed actions in `chat_messages` -8. Returns the complete JSON response to the frontend (no token-by-token streaming — Cerebras inference is fast enough that a loading indicator is sufficient) +6. Validates and auto-executes trades and watchlist changes per §10.5 and §10.6 +7. Stores the message and executed actions (with per-entry outcomes) in `chat_messages`, in the exact `actions` shape from §9.7 +8. Returns the complete JSON response to the frontend, annotated with per-entry outcomes (no token-by-token streaming — Cerebras inference is fast enough that a loading indicator is sufficient) -### Structured Output Schema - -The LLM is instructed to respond with JSON matching this schema: +### Structured Output Schema (NORMATIVE) ```json { @@ -314,64 +669,123 @@ The LLM is instructed to respond with JSON matching this schema: } ``` -- `message` (required): The conversational text shown to the user -- `trades` (optional): Array of trades to auto-execute. Each trade goes through the same validation as manual trades (sufficient cash for buys, sufficient shares for sells) -- `watchlist_changes` (optional): Array of watchlist modifications +- `message` (**required**): the conversational text shown to the user +- `trades` (**required, may be empty**): array of trades to auto-execute +- `watchlist_changes` (**required, may be empty**): array of watchlist modifications + +All three fields are **required properties that may hold empty arrays** — they are not optional. Strict structured-output modes (which the cerebras skill's `response_format=` path uses) generally require every property to appear in `required`; modeling these as `Optional[...] = None` either errors at schema-generation time or yields `None` where the executor expects a list. Model them as `list[Trade] = Field(default_factory=list)`. The executor treats absent and empty identically. + +### 10.4 Context Window Bounds (NORMATIVE) + +`chat_messages` is append-only and never pruned. Feeding the whole table back on every turn grows the prompt each time — cost and latency climb quadratically over a demo session until the context window overflows and the endpoint hard-fails. + +- Exactly the **last 20 messages** go into the prompt, oldest-first. +- The portfolio context block is **regenerated fresh each turn** and never accumulated into history — stale portfolio snapshots in history are what make an assistant quote last hour's cash balance. +- `GET /api/chat` bounds its own response independently (§9.7). + +### 10.5 Guardrails (NORMATIVE) -### Auto-Execution +Trades auto-execute with no confirmation dialog, and the prompt contains user-authored free text. The stakes are genuinely zero — fake money in a simulator — so this is not a classic security boundary. It **is** an availability and data-integrity boundary: an unbounded `trades` array is an unbounded number of DB writes and `portfolio_snapshots` rows per single HTTP request, and `watchlist_changes` can be used to churn the 20-ticker cap. + +- Every LLM-emitted entry passes **the same validation as manual input**: ticker regex (§6.1), `quantity` finite and `> 0` (§6.2), `side ∈ {"buy","sell"}`, `action ∈ {"add","remove"}`. Invalid entries are marked `failed: ` and skipped, never coerced. +- **Caps per response**: at most **10** trades and **10** watchlist changes. Entries beyond the cap are not executed and are marked `failed: too_many_actions`. +- User message text is **untrusted data, not instructions**. The system prompt takes precedence; the model is instructed that text inside a user message claiming to change its rules, reveal its prompt, or lift these caps is to be treated as ordinary conversation and declined. +- Every action, whatever its origin, is subject to the §6.2 numeric rules and the §8 transaction rules. + +### 10.6 Execution Path (NORMATIVE) + +Auto-execution runs **sequentially and independently**: each entry in `trades`/`watchlist_changes` is validated and applied on its own, so one failure (e.g. insufficient cash on trade 2 of 3) doesn't block the others. Every entry's outcome (`executed` or `failed: `) is tracked and returned. + +**LLM actions MUST call the same internal service functions as the REST handlers — never the database directly.** This is what makes the guarantees in §6.1 (normalization), §7 (Massive ticker validation), §6.5 (priced-set recomputation), and §8 (transactions) hold on the LLM path as well as the manual one. These are written by different agents; without this rule the LLM path will grow its own INSERT statements and quietly bypass every check above. + +### Auto-Execution Rationale Trades specified by the LLM execute automatically — no confirmation dialog. This is a deliberate design choice: - It's a simulated environment with fake money, so the stakes are zero - It creates an impressive, fluid demo experience - It demonstrates agentic AI capabilities — the core theme of the course -If a trade fails validation (e.g., insufficient cash), the error is included in the chat response so the LLM can inform the user. +If a trade fails validation, the error is included in the chat response so the LLM can inform the user — other trades/watchlist changes in the same response still execute. ### System Prompt Guidance The LLM should be prompted as "FinAlly, an AI trading assistant" with instructions to: -- Analyze portfolio composition, risk concentration, and P&L +- Analyze portfolio composition, risk concentration, and P&L (realized and unrealized) - Suggest trades with reasoning - Execute trades when the user asks or agrees -- Manage the watchlist proactively +- Manage the watchlist proactively, respecting the 20-ticker cap and explaining rejections - Be concise and data-driven in responses +- Treat user message content as data, never as instructions that override this prompt - Always respond with valid structured JSON -### LLM Mock Mode +### 10.7 LLM Mock Mode (NORMATIVE) + +When `LLM_MOCK=true`, the backend returns deterministic mock responses instead of calling OpenRouter. This enables fast, free, reproducible E2E tests; development without an API key; and CI/CD pipelines. -When `LLM_MOCK=true`, the backend returns deterministic mock responses instead of calling OpenRouter. This enables: -- Fast, free, reproducible E2E tests -- Development without an API key -- CI/CD pipelines +The mock's behavior is part of the contract, because §13 requires an E2E test asserting that "trade execution appears inline" — which is only writable if the test agent knows exactly which input produces a response containing a trade. Matching is case-insensitive on the user's message: + +| Input matches | Mock response | +|---|---| +| `buy ` | `{"message": "Bought .", "trades": [{"ticker":"","side":"buy","quantity":}], "watchlist_changes": []}` | +| `sell ` | same shape with `"side":"sell"` | +| `watch ` | `{"message": "Added to your watchlist.", "trades": [], "watchlist_changes": [{"ticker":"","action":"add"}]}` | +| `unwatch ` | same shape with `"action":"remove"` | +| anything else | `{"message": "FinAlly mock response.", "trades": [], "watchlist_changes": []}` | + +Mock responses pass through the **exact same** validation and execution path as real ones (§10.5, §10.6) — so an E2E test asserting `buy 999999 AAPL` fails with `insufficient_cash` is testing the real executor. --- -## 10. Frontend Design +## 11. Frontend Design -### Layout +### 11.1 Layout The frontend is a single-page application with a dense, terminal-inspired layout. The specific component architecture and layout system is up to the Frontend Engineer, but the UI should include these elements: -- **Watchlist panel** — grid/table of watched tickers with: ticker symbol, current price (flashing green/red on change), daily change %, and a sparkline mini-chart (accumulated from SSE since page load) -- **Main chart area** — larger chart for the currently selected ticker, with at minimum price over time. Clicking a ticker in the watchlist selects it here. -- **Portfolio heatmap** — treemap visualization where each rectangle is a position, sized by portfolio weight, colored by P&L (green = profit, red = loss) -- **P&L chart** — line chart showing total portfolio value over time, using data from `portfolio_snapshots` -- **Positions table** — tabular view of all positions: ticker, quantity, avg cost, current price, unrealized P&L, % change -- **Trade bar** — simple input area: ticker field, quantity field, buy button, sell button. Market orders, instant fill. -- **AI chat panel** — docked/collapsible sidebar. Message input, scrolling conversation history, loading indicator while waiting for LLM response. Trade executions and watchlist changes shown inline as confirmations. -- **Header** — portfolio total value (updating live), connection status indicator, cash balance +- **Watchlist panel** — grid/table of watched tickers with: ticker symbol, current price (flashing green/red on change), daily change % (from `session_open`, §7; `—` when null), and a sparkline mini-chart (accumulated from SSE since page load) +- **Main chart area** — larger chart for the currently selected ticker, with at minimum price over time. Clicking a ticker in the watchlist selects it here. Like sparklines, this starts empty at page load and fills from the stream. +- **Portfolio heatmap** — treemap visualization where each rectangle is a position, sized by `weight_pct`, colored by P&L (green = profit, red = loss) +- **P&L chart** — line chart showing total portfolio value over time, from `GET /api/portfolio/history` +- **Positions table** — ticker, quantity, avg cost, current price, unrealized P&L, % change +- **Trade blotter** — trade history from `GET /api/portfolio/trades` +- **Trade bar** — ticker field, quantity field, buy button, sell button. Market orders, instant fill. Quantity input: `min=0.0001`, `step=0.0001`, max 4 decimal places, rejected client-side before submit (the server enforces §6.2 regardless) +- **AI chat panel** — docked/collapsible sidebar. Message input, scrolling conversation history, loading indicator while waiting for LLM response. Trade executions and watchlist changes rendered inline from the `actions` object (§9.7), showing fill price for executed entries and the failure reason for failed ones +- **Header** — portfolio total value (updating live), connection status indicator, market-closed badge, cash balance + +### 11.2 Streaming Performance (NORMATIVE) + +20 tickers × ~2 updates/sec ≈ 40 events/sec. A naive `useState` per event re-renders the entire watchlist 40×/sec, and unbounded sparkline arrays accumulate ~144,000 points across 20 tickers in an hour-long demo. + +- SSE events are **batched and flushed once per animation frame** (`requestAnimationFrame`) into a single store update — never one state update per event. +- Each sparkline holds a **ring buffer of 120 points**; the main chart series is capped at **1,000 points**. Both drop oldest. +- Watchlist rows are **memoized components keyed by ticker**, so a price change re-renders one row, not the table. +- Price flash: apply a CSS class on change, remove it after the transition. Flash is suppressed for `event: snapshot` (§7). -### Technical Notes +### 11.3 Data Refresh (NORMATIVE) + +A successful trade or watchlist mutation — from **either** the trade bar or `POST /api/chat` — invalidates the header cash/total, positions table, heatmap, P&L chart, watchlist panel, and blotter. Both paths MUST funnel through one shared store action that re-fetches: + +- `GET /api/portfolio` +- `GET /api/portfolio/history` +- `GET /api/watchlist` +- `GET /api/portfolio/trades` + +Two separate refresh implementations is how the chat panel ends up showing an executed trade the header doesn't know about. + +Formatting is centralized in shared helpers, not re-implemented per component: currency as 2 decimals with thousands separators (`$1,234.50`), percentages as 2 decimals with an explicit sign (`+0.38%`), quantities up to 4 decimals with trailing zeros trimmed. Inconsistent formatting is conspicuous in a data-dense terminal UI where columns must align. + +### 11.4 Technical Notes - Use `EventSource` for SSE connection to `/api/stream/prices` -- Canvas-based charting library preferred (Lightweight Charts or Recharts) for performance -- Price flash effect: on receiving a new price, briefly apply a CSS class with background color transition, then remove it +- Charting library preferred for performance: Lightweight Charts (canvas-based) for the main price chart; Recharts (SVG-based) is an acceptable alternative for lower-frequency charts (P&L line, heatmap) where canvas performance isn't critical - All API calls go to the same origin (`/api/*`) — no CORS configuration needed - Tailwind CSS for styling with a custom dark theme +- On mount, the chat panel calls `GET /api/chat` to restore prior conversation history before the user sends a new message +- Clients switch on the error `code` from §6.4, never on `message` text --- -## 11. Docker & Deployment +## 12. Docker & Deployment ### Multi-Stage Dockerfile @@ -389,56 +803,82 @@ Stage 2: Python 3.12 slim - CMD: uvicorn serving FastAPI app ``` -FastAPI serves the static frontend files and all API routes on port 8000. +### Static Serving and SPA Routing (NORMATIVE) + +FastAPI serves the static frontend and all API routes on port 8000. Route precedence is not incidental — get it wrong and `/api/health` returns HTML, which is the classic first-integration failure on this architecture. + +- **API routers mount first.** The static file mount is registered **last**. +- Unknown paths under `/api/*` return a JSON `404` in the §6.4 envelope — **never** HTML, never the SPA shell. +- All other unknown paths fall back to `index.html` so client-side routing works on refresh. +- Next's `output: 'export'` emits directory-style paths; the static handler resolves `/foo` → `/foo/index.html` and its `trailingSlash` setting must match what the handler expects. ### Docker Volume -The SQLite database persists via a named Docker volume: +The SQLite database persists in a **named Docker volume**, `finally-data`, mounted at `/app/db` inside the container. The backend writes `finally.db` there. + +A named volume — not a bind mount to a host directory — is deliberate: SQLite over a bind-mounted host path is prone to file-locking failures on Windows, and this project is routinely developed under OneDrive-synced directories, where a synced `.db` file can be locked or corrupted mid-write. + +All run configuration (port mapping, volume, env file) lives **only** in `docker-compose.yml`. The equivalent raw command, for reference only — the scripts and README use `docker compose`: ```bash docker run -v finally-data:/app/db -p 8000:8000 --env-file .env finally ``` -The `db/` directory in the project root maps to `/app/db` in the container. The backend writes `finally.db` to this path. - ### Start/Stop Scripts +Port mapping, volume mount, and env-file wiring are defined once, in `docker-compose.yml` — the start/stop scripts are thin wrappers around `docker compose`, not a second place that duplicates that configuration. + **`scripts/start_mac.sh`** (macOS/Linux): -- Builds the Docker image if not already built (or if `--build` flag passed) -- Runs the container with the volume mount, port mapping, and `.env` file +- Runs `docker compose up -d --build` (Compose only rebuilds the image if the Dockerfile or build context changed) - Prints the URL to access the app - Optionally opens the browser **`scripts/stop_mac.sh`** (macOS/Linux): -- Stops and removes the running container +- Runs `docker compose down` - Does NOT remove the volume (data persists) -**`scripts/start_windows.ps1`** / **`scripts/stop_windows.ps1`**: PowerShell equivalents for Windows. +**`scripts/start_windows.ps1`** / **`scripts/stop_windows.ps1`**: PowerShell equivalents for Windows, wrapping the same `docker compose` commands. All scripts should be idempotent — safe to run multiple times. +If `docker-compose.yml` declares a healthcheck, it targets `GET /api/health` and treats any `200` as healthy — market-data staleness is a body field, not a failure status (§9.8). + ### Optional Cloud Deployment The container is designed to deploy to AWS App Runner, Render, or any container platform. A Terraform configuration for App Runner may be provided in a `deploy/` directory as a stretch goal, but is not part of the core build. +**The default build is localhost-only.** It has no login (§2), no rate limiting, and `POST /api/chat` makes a paid OpenRouter call on every request — a publicly reachable instance is a free API-credit faucet, and `POST /api/portfolio/trade` and the SSE endpoint are trivially DoS-able. Any non-localhost deployment MUST therefore: + +- Set `APP_SHARED_SECRET` and require it (header or HTTP basic auth) on all `/api/*` routes, `/api/health` excepted +- Rate-limit `POST /api/chat` per IP (suggested: 10/min), returning `429` with the `rate_limited` code +- Cap concurrent SSE connections per IP + +### Observability + +The backend emits structured (JSON) logs with a per-request ID, propagated into log lines for that request. Chat requests additionally log LLM latency, token counts, and retry/fallback occurrences — never prompt or response content, and never credentials (§5). + --- -## 12. Testing Strategy +## 13. Testing Strategy ### Unit Tests (within `frontend/` and `backend/`) **Backend (pytest)**: -- Market data: simulator generates valid prices, GBM math is correct, Massive API response parsing works, both implementations conform to the abstract interface -- Portfolio: trade execution logic, P&L calculations, edge cases (selling more than owned, buying with insufficient cash, selling at a loss) -- LLM: structured output parsing handles all valid schemas, graceful handling of malformed responses, trade validation within chat flow -- API routes: correct status codes, response shapes, error handling +- Market data: simulator generates valid prices; **GBM `dt` scaling keeps prices in a sane range over 10,000 ticks**; the price floor holds; Massive response parsing works; both implementations conform to the abstract interface; the grouped-snapshot poller issues exactly one call per poll regardless of ticker count +- Portfolio: trade execution logic, P&L calculations (realized and unrealized), edge cases — selling more than owned, buying with insufficient cash, selling at a loss +- **Numeric rules (§6.2)**: selling the full held quantity after several fractional buys succeeds and deletes the row; residual quantities below EPSILON never persist +- **Ticker normalization (§6.1)**: `aapl`, `AAPL `, and `AAPL` resolve to one position; invalid symbols are rejected +- **Concurrency (§8)**: concurrent trades cannot drive `cash_balance` negative; a failed trade leaves no `trades` row +- LLM: structured output parsing handles all valid schemas; malformed responses are handled gracefully; guardrail caps (§10.5) are enforced; trade validation within the chat flow +- API routes: correct status codes, response shapes matching §9 exactly, error envelope conformance **Frontend (React Testing Library or similar)**: - Component rendering with mock data -- Price flash animation triggers correctly on price changes +- Price flash triggers on `event: price` and is **suppressed** on `event: snapshot` - Watchlist CRUD operations -- Portfolio display calculations -- Chat message rendering and loading state +- Portfolio display calculations; `null` prices render `—`, not `$0.00` +- Chat message rendering, loading state, and inline `actions` confirmations for both executed and failed entries +- Sparkline ring buffer does not grow past 120 points ### E2E Tests (in `test/`) @@ -449,8 +889,18 @@ The container is designed to deploy to AWS App Runner, Render, or any container **Key Scenarios**: - Fresh start: default watchlist appears, $10k balance shown, prices are streaming - Add and remove a ticker from the watchlist +- **Watchlist cap**: adding a 21st ticker is rejected and surfaces the `watchlist_full` message +- **Invalid ticker** is rejected with a visible error - Buy shares: cash decreases, position appears, portfolio updates - Sell shares: cash increases, position updates or disappears +- **Insufficient funds**: buying beyond cash shows the `insufficient_cash` error and changes nothing - Portfolio visualization: heatmap renders with correct colors, P&L chart has data points -- AI chat (mocked): send a message, receive a response, trade execution appears inline -- SSE resilience: disconnect and verify reconnection +- AI chat (mocked): send `buy 5 AAPL`, receive a response, trade execution appears inline with fill price +- **LLM failure path**: with the provider stubbed to fail, the canned fallback message appears and no actions execute +- Chat history persists: send a message, refresh the page, prior conversation still shows +- SSE resilience: disconnect and verify reconnection **and that the snapshot repopulates the grid immediately** +- `GET /api/health` returns `200` in both simulator and Massive-stub modes + +### CI + +A GitHub Actions workflow runs backend pytest, frontend unit tests, and the E2E suite on every push and PR. Line coverage target: **80%** for `backend/`. The build fails on test failure or coverage regression. diff --git a/planning/archive/MARKET_DATA_DESIGN.md b/planning/archive/MARKET_DATA_DESIGN.md deleted file mode 100644 index 0d2cfd5fd..000000000 --- a/planning/archive/MARKET_DATA_DESIGN.md +++ /dev/null @@ -1,1490 +0,0 @@ -# Market Data Backend — Detailed Design - -Implementation-ready design for the FinAlly market data subsystem. Covers the unified interface, in-memory price cache, GBM simulator, Massive API client, SSE streaming endpoint, and FastAPI lifecycle integration. - -Everything in this document lives under `backend/app/market/`. - ---- - -## Table of Contents - -1. [File Structure](#1-file-structure) -2. [Data Model — `models.py`](#2-data-model) -3. [Price Cache — `cache.py`](#3-price-cache) -4. [Abstract Interface — `interface.py`](#4-abstract-interface) -5. [Seed Prices & Ticker Parameters — `seed_prices.py`](#5-seed-prices--ticker-parameters) -6. [GBM Simulator — `simulator.py`](#6-gbm-simulator) -7. [Massive API Client — `massive_client.py`](#7-massive-api-client) -8. [Factory — `factory.py`](#8-factory) -9. [SSE Streaming Endpoint — `stream.py`](#9-sse-streaming-endpoint) -10. [FastAPI Lifecycle Integration](#10-fastapi-lifecycle-integration) -11. [Watchlist Coordination](#11-watchlist-coordination) -12. [Testing Strategy](#12-testing-strategy) -13. [Error Handling & Edge Cases](#13-error-handling--edge-cases) -14. [Configuration Summary](#14-configuration-summary) - ---- - -## 1. File Structure - -``` -backend/ - app/ - market/ - __init__.py # Re-exports: PriceUpdate, PriceCache, MarketDataSource, create_market_data_source - models.py # PriceUpdate dataclass - cache.py # PriceCache (thread-safe in-memory store) - interface.py # MarketDataSource ABC - seed_prices.py # SEED_PRICES, TICKER_PARAMS, DEFAULT_PARAMS, CORRELATION_GROUPS - simulator.py # GBMSimulator + SimulatorDataSource - massive_client.py # MassiveDataSource - factory.py # create_market_data_source() - stream.py # SSE endpoint (FastAPI router) -``` - -Each file has a single responsibility. The `__init__.py` re-exports the public API so that the rest of the backend imports from `app.market` without reaching into submodules. - ---- - -## 2. Data Model - -**File: `backend/app/market/models.py`** - -`PriceUpdate` is the only data structure that leaves the market data layer. Every downstream consumer — SSE streaming, portfolio valuation, trade execution — works exclusively with this type. - -```python -from __future__ import annotations - -import time -from dataclasses import dataclass, field - - -@dataclass(frozen=True, slots=True) -class PriceUpdate: - """Immutable snapshot of a single ticker's price at a point in time.""" - - ticker: str - price: float - previous_price: float - timestamp: float = field(default_factory=time.time) # Unix seconds - - @property - def change(self) -> float: - """Absolute price change from previous update.""" - return round(self.price - self.previous_price, 4) - - @property - def change_percent(self) -> float: - """Percentage change from previous update.""" - if self.previous_price == 0: - return 0.0 - return round((self.price - self.previous_price) / self.previous_price * 100, 4) - - @property - def direction(self) -> str: - """'up', 'down', or 'flat'.""" - if self.price > self.previous_price: - return "up" - elif self.price < self.previous_price: - return "down" - return "flat" - - def to_dict(self) -> dict: - """Serialize for JSON / SSE transmission.""" - return { - "ticker": self.ticker, - "price": self.price, - "previous_price": self.previous_price, - "timestamp": self.timestamp, - "change": self.change, - "change_percent": self.change_percent, - "direction": self.direction, - } -``` - -### Design decisions - -- **`frozen=True`**: Price updates are immutable value objects. Once created they never change, which makes them safe to share across async tasks without copying. -- **`slots=True`**: Minor memory optimization — we create many of these per second. -- **Computed properties** (`change`, `direction`, `change_percent`): Derived from `price` and `previous_price` so they can never be inconsistent. No risk of a stale `direction` field. -- **`to_dict()`**: Single serialization point used by both the SSE endpoint and REST API responses. - ---- - -## 3. Price Cache - -**File: `backend/app/market/cache.py`** - -The price cache is the central data hub. Data sources write to it; SSE streaming and portfolio valuation read from it. It must be thread-safe because the simulator/poller may run in a thread pool executor while SSE reads happen on the async event loop. - -```python -from __future__ import annotations - -import asyncio -import time -from threading import Lock -from typing import Callable - -from .models import PriceUpdate - - -class PriceCache: - """Thread-safe in-memory cache of the latest price for each ticker. - - Writers: SimulatorDataSource or MassiveDataSource (one at a time). - Readers: SSE streaming endpoint, portfolio valuation, trade execution. - """ - - def __init__(self) -> None: - self._prices: dict[str, PriceUpdate] = {} - self._lock = Lock() - self._version: int = 0 # Monotonically increasing; bumped on every update - - def update(self, ticker: str, price: float, timestamp: float | None = None) -> PriceUpdate: - """Record a new price for a ticker. Returns the created PriceUpdate. - - Automatically computes direction and change from the previous price. - If this is the first update for the ticker, previous_price == price (direction='flat'). - """ - with self._lock: - ts = timestamp or time.time() - prev = self._prices.get(ticker) - previous_price = prev.price if prev else price - - update = PriceUpdate( - ticker=ticker, - price=round(price, 2), - previous_price=round(previous_price, 2), - timestamp=ts, - ) - self._prices[ticker] = update - self._version += 1 - return update - - def get(self, ticker: str) -> PriceUpdate | None: - """Get the latest price for a single ticker, or None if unknown.""" - with self._lock: - return self._prices.get(ticker) - - def get_all(self) -> dict[str, PriceUpdate]: - """Snapshot of all current prices. Returns a shallow copy.""" - with self._lock: - return dict(self._prices) - - def get_price(self, ticker: str) -> float | None: - """Convenience: get just the price float, or None.""" - update = self.get(ticker) - return update.price if update else None - - def remove(self, ticker: str) -> None: - """Remove a ticker from the cache (e.g., when removed from watchlist).""" - with self._lock: - self._prices.pop(ticker, None) - - @property - def version(self) -> int: - """Current version counter. Useful for SSE change detection.""" - return self._version - - def __len__(self) -> int: - with self._lock: - return len(self._prices) - - def __contains__(self, ticker: str) -> bool: - with self._lock: - return ticker in self._prices -``` - -### Why a version counter? - -The SSE streaming loop polls the cache every ~500ms. Without a version counter, it would serialize and send all prices every tick even if nothing changed (e.g., Massive API only updates every 15s). The version counter lets the SSE loop skip sends when nothing is new: - -```python -last_version = -1 -while True: - if price_cache.version != last_version: - last_version = price_cache.version - yield format_sse(price_cache.get_all()) - await asyncio.sleep(0.5) -``` - -### Thread safety rationale - -The `threading.Lock` is used instead of `asyncio.Lock` because: -- The Massive client's synchronous `get_snapshot_all()` runs in `asyncio.to_thread()`, which operates in a real OS thread — `asyncio.Lock` would not protect against that. -- The GBM simulator's `step()` is CPU-bound and could also be offloaded to a thread for fairness. -- `threading.Lock` works correctly from both sync threads and the async event loop. - ---- - -## 4. Abstract Interface - -**File: `backend/app/market/interface.py`** - -```python -from __future__ import annotations - -from abc import ABC, abstractmethod - - -class MarketDataSource(ABC): - """Contract for market data providers. - - Implementations push price updates into a shared PriceCache on their own - schedule. Downstream code never calls the data source directly for prices — - it reads from the cache. - - Lifecycle: - source = create_market_data_source(cache) - await source.start(["AAPL", "GOOGL", ...]) - # ... app runs ... - await source.add_ticker("TSLA") - await source.remove_ticker("GOOGL") - # ... app shutting down ... - await source.stop() - """ - - @abstractmethod - async def start(self, tickers: list[str]) -> None: - """Begin producing price updates for the given tickers. - - Starts a background task that periodically writes to the PriceCache. - Must be called exactly once. Calling start() twice is undefined behavior. - """ - - @abstractmethod - async def stop(self) -> None: - """Stop the background task and release resources. - - Safe to call multiple times. After stop(), the source will not write - to the cache again. - """ - - @abstractmethod - async def add_ticker(self, ticker: str) -> None: - """Add a ticker to the active set. No-op if already present. - - The next update cycle will include this ticker. - """ - - @abstractmethod - async def remove_ticker(self, ticker: str) -> None: - """Remove a ticker from the active set. No-op if not present. - - Also removes the ticker from the PriceCache. - """ - - @abstractmethod - def get_tickers(self) -> list[str]: - """Return the current list of actively tracked tickers.""" -``` - -### Why the source writes to the cache instead of returning prices - -This push model decouples timing. The simulator ticks at 500ms, Massive polls at 15s, but SSE always reads from the cache at its own 500ms cadence. There is no need for the SSE layer to know which data source is active or what its update interval is. - ---- - -## 5. Seed Prices & Ticker Parameters - -**File: `backend/app/market/seed_prices.py`** - -Constants only — no logic, no imports beyond stdlib. This file is shared by both the simulator (for initial prices and GBM parameters) and potentially by the Massive client (as fallback prices if the API hasn't responded yet). - -```python -"""Seed prices and per-ticker parameters for the market simulator.""" - -# Realistic starting prices for the default watchlist (as of project creation) -SEED_PRICES: dict[str, float] = { - "AAPL": 190.00, - "GOOGL": 175.00, - "MSFT": 420.00, - "AMZN": 185.00, - "TSLA": 250.00, - "NVDA": 800.00, - "META": 500.00, - "JPM": 195.00, - "V": 280.00, - "NFLX": 600.00, -} - -# Per-ticker GBM parameters -# sigma: annualized volatility (higher = more price movement) -# mu: annualized drift / expected return -TICKER_PARAMS: dict[str, dict[str, float]] = { - "AAPL": {"sigma": 0.22, "mu": 0.05}, - "GOOGL": {"sigma": 0.25, "mu": 0.05}, - "MSFT": {"sigma": 0.20, "mu": 0.05}, - "AMZN": {"sigma": 0.28, "mu": 0.05}, - "TSLA": {"sigma": 0.50, "mu": 0.03}, # High volatility - "NVDA": {"sigma": 0.40, "mu": 0.08}, # High volatility, strong drift - "META": {"sigma": 0.30, "mu": 0.05}, - "JPM": {"sigma": 0.18, "mu": 0.04}, # Low volatility (bank) - "V": {"sigma": 0.17, "mu": 0.04}, # Low volatility (payments) - "NFLX": {"sigma": 0.35, "mu": 0.05}, -} - -# Default parameters for tickers not in the list above (dynamically added) -DEFAULT_PARAMS: dict[str, float] = {"sigma": 0.25, "mu": 0.05} - -# Correlation groups for the simulator's Cholesky decomposition -# Tickers in the same group have higher intra-group correlation -CORRELATION_GROUPS: dict[str, set[str]] = { - "tech": {"AAPL", "GOOGL", "MSFT", "AMZN", "META", "NVDA", "NFLX"}, - "finance": {"JPM", "V"}, -} - -# Correlation coefficients -INTRA_TECH_CORR = 0.6 # Tech stocks move together -INTRA_FINANCE_CORR = 0.5 # Finance stocks move together -CROSS_GROUP_CORR = 0.3 # Between sectors -TSLA_CORR = 0.3 # TSLA does its own thing -DEFAULT_CORR = 0.3 # Unknown tickers -``` - ---- - -## 6. GBM Simulator - -**File: `backend/app/market/simulator.py`** - -This file contains two classes: -- `GBMSimulator`: Pure math engine. Stateful — holds current prices and advances them one step at a time. -- `SimulatorDataSource`: The `MarketDataSource` implementation that wraps `GBMSimulator` in an async loop and writes to the `PriceCache`. - -### 6.1 GBMSimulator — The Math Engine - -```python -from __future__ import annotations - -import asyncio -import logging -import math -import random - -import numpy as np - -from .cache import PriceCache -from .interface import MarketDataSource -from .seed_prices import ( - CORRELATION_GROUPS, - CROSS_GROUP_CORR, - DEFAULT_CORR, - DEFAULT_PARAMS, - INTRA_FINANCE_CORR, - INTRA_TECH_CORR, - SEED_PRICES, - TICKER_PARAMS, - TSLA_CORR, -) - -logger = logging.getLogger(__name__) - - -class GBMSimulator: - """Geometric Brownian Motion simulator for correlated stock prices. - - Math: - S(t+dt) = S(t) * exp((mu - sigma^2/2) * dt + sigma * sqrt(dt) * Z) - - Where: - S(t) = current price - mu = annualized drift (expected return) - sigma = annualized volatility - dt = time step as fraction of a trading year - Z = correlated standard normal random variable - - The tiny dt (~8.5e-8 for 500ms ticks over 252 trading days * 6.5h/day) - produces sub-cent moves per tick that accumulate naturally over time. - """ - - # 500ms expressed as a fraction of a trading year - # 252 trading days * 6.5 hours/day * 3600 seconds/hour = 5,896,800 seconds - TRADING_SECONDS_PER_YEAR = 252 * 6.5 * 3600 # 5,896,800 - DEFAULT_DT = 0.5 / TRADING_SECONDS_PER_YEAR # ~8.48e-8 - - def __init__( - self, - tickers: list[str], - dt: float = DEFAULT_DT, - event_probability: float = 0.001, - ) -> None: - self._dt = dt - self._event_prob = event_probability - - # Per-ticker state - self._tickers: list[str] = [] - self._prices: dict[str, float] = {} - self._params: dict[str, dict[str, float]] = {} - - # Cholesky decomposition of the correlation matrix (for correlated moves) - self._cholesky: np.ndarray | None = None - - # Initialize all starting tickers - for ticker in tickers: - self._add_ticker_internal(ticker) - self._rebuild_cholesky() - - # --- Public API --- - - def step(self) -> dict[str, float]: - """Advance all tickers by one time step. Returns {ticker: new_price}. - - This is the hot path — called every 500ms. Keep it fast. - """ - n = len(self._tickers) - if n == 0: - return {} - - # Generate n independent standard normal draws - z_independent = np.random.standard_normal(n) - - # Apply Cholesky to get correlated draws - if self._cholesky is not None: - z_correlated = self._cholesky @ z_independent - else: - z_correlated = z_independent - - result: dict[str, float] = {} - for i, ticker in enumerate(self._tickers): - params = self._params[ticker] - mu = params["mu"] - sigma = params["sigma"] - - # GBM: S(t+dt) = S(t) * exp((mu - 0.5*sigma^2)*dt + sigma*sqrt(dt)*Z) - drift = (mu - 0.5 * sigma ** 2) * self._dt - diffusion = sigma * math.sqrt(self._dt) * z_correlated[i] - self._prices[ticker] *= math.exp(drift + diffusion) - - # Random event: ~0.1% chance per tick per ticker - # With 10 tickers at 2 ticks/sec, expect an event ~every 50 seconds - if random.random() < self._event_prob: - shock_magnitude = random.uniform(0.02, 0.05) - shock_sign = random.choice([-1, 1]) - self._prices[ticker] *= 1 + shock_magnitude * shock_sign - logger.debug( - "Random event on %s: %.1f%% %s", - ticker, - shock_magnitude * 100, - "up" if shock_sign > 0 else "down", - ) - - result[ticker] = round(self._prices[ticker], 2) - - return result - - def add_ticker(self, ticker: str) -> None: - """Add a ticker to the simulation. Rebuilds the correlation matrix.""" - if ticker in self._prices: - return - self._add_ticker_internal(ticker) - self._rebuild_cholesky() - - def remove_ticker(self, ticker: str) -> None: - """Remove a ticker from the simulation. Rebuilds the correlation matrix.""" - if ticker not in self._prices: - return - self._tickers.remove(ticker) - del self._prices[ticker] - del self._params[ticker] - self._rebuild_cholesky() - - def get_price(self, ticker: str) -> float | None: - """Current price for a ticker, or None if not tracked.""" - return self._prices.get(ticker) - - # --- Internals --- - - def _add_ticker_internal(self, ticker: str) -> None: - """Add a ticker without rebuilding Cholesky (for batch initialization).""" - if ticker in self._prices: - return - self._tickers.append(ticker) - self._prices[ticker] = SEED_PRICES.get(ticker, random.uniform(50.0, 300.0)) - self._params[ticker] = TICKER_PARAMS.get(ticker, dict(DEFAULT_PARAMS)) - - def _rebuild_cholesky(self) -> None: - """Rebuild the Cholesky decomposition of the ticker correlation matrix. - - Called whenever tickers are added or removed. O(n^2) but n < 50. - """ - n = len(self._tickers) - if n <= 1: - self._cholesky = None - return - - # Build the correlation matrix - corr = np.eye(n) - for i in range(n): - for j in range(i + 1, n): - rho = self._pairwise_correlation(self._tickers[i], self._tickers[j]) - corr[i, j] = rho - corr[j, i] = rho - - self._cholesky = np.linalg.cholesky(corr) - - @staticmethod - def _pairwise_correlation(t1: str, t2: str) -> float: - """Determine correlation between two tickers based on sector grouping. - - Correlation structure: - - Same tech sector: 0.6 - - Same finance sector: 0.5 - - TSLA with anything: 0.3 (it does its own thing) - - Cross-sector: 0.3 - - Unknown tickers: 0.3 - """ - tech = CORRELATION_GROUPS["tech"] - finance = CORRELATION_GROUPS["finance"] - - # TSLA is in tech set but behaves independently - if t1 == "TSLA" or t2 == "TSLA": - return TSLA_CORR - - if t1 in tech and t2 in tech: - return INTRA_TECH_CORR - if t1 in finance and t2 in finance: - return INTRA_FINANCE_CORR - - return CROSS_GROUP_CORR -``` - -### 6.2 SimulatorDataSource — Async Wrapper - -```python -class SimulatorDataSource(MarketDataSource): - """MarketDataSource backed by the GBM simulator. - - Runs a background asyncio task that calls GBMSimulator.step() every - `update_interval` seconds and writes results to the PriceCache. - """ - - def __init__( - self, - price_cache: PriceCache, - update_interval: float = 0.5, - event_probability: float = 0.001, - ) -> None: - self._cache = price_cache - self._interval = update_interval - self._event_prob = event_probability - self._sim: GBMSimulator | None = None - self._task: asyncio.Task | None = None - - async def start(self, tickers: list[str]) -> None: - self._sim = GBMSimulator( - tickers=tickers, - event_probability=self._event_prob, - ) - # Seed the cache with initial prices so SSE has data immediately - for ticker in tickers: - price = self._sim.get_price(ticker) - if price is not None: - self._cache.update(ticker=ticker, price=price) - self._task = asyncio.create_task(self._run_loop(), name="simulator-loop") - logger.info("Simulator started with %d tickers", len(tickers)) - - async def stop(self) -> None: - if self._task and not self._task.done(): - self._task.cancel() - try: - await self._task - except asyncio.CancelledError: - pass - self._task = None - logger.info("Simulator stopped") - - async def add_ticker(self, ticker: str) -> None: - if self._sim: - self._sim.add_ticker(ticker) - # Seed cache immediately so the ticker has a price right away - price = self._sim.get_price(ticker) - if price is not None: - self._cache.update(ticker=ticker, price=price) - logger.info("Simulator: added ticker %s", ticker) - - async def remove_ticker(self, ticker: str) -> None: - if self._sim: - self._sim.remove_ticker(ticker) - self._cache.remove(ticker) - logger.info("Simulator: removed ticker %s", ticker) - - def get_tickers(self) -> list[str]: - return list(self._sim._tickers) if self._sim else [] - - async def _run_loop(self) -> None: - """Core loop: step the simulation, write to cache, sleep.""" - while True: - try: - if self._sim: - prices = self._sim.step() - for ticker, price in prices.items(): - self._cache.update(ticker=ticker, price=price) - except Exception: - logger.exception("Simulator step failed") - await asyncio.sleep(self._interval) -``` - -### Key behaviors - -- **Immediate seeding**: When `start()` is called, the cache is populated with seed prices *before* the loop begins. This means the SSE endpoint has data to send on its very first tick, with no blank-screen delay. -- **Graceful cancellation**: `stop()` cancels the task and awaits it, catching `CancelledError`. This ensures clean shutdown during FastAPI lifespan teardown. -- **Exception resilience**: The loop catches exceptions per-step so a single bad tick doesn't kill the entire data feed. - ---- - -## 7. Massive API Client - -**File: `backend/app/market/massive_client.py`** - -Polls the Massive (formerly Polygon.io) REST API snapshot endpoint on a configurable interval. The synchronous Massive client runs in `asyncio.to_thread()` to avoid blocking the event loop. - -```python -from __future__ import annotations - -import asyncio -import logging -from typing import Any - -from .cache import PriceCache -from .interface import MarketDataSource - -logger = logging.getLogger(__name__) - - -class MassiveDataSource(MarketDataSource): - """MarketDataSource backed by the Massive (Polygon.io) REST API. - - Polls GET /v2/snapshot/locale/us/markets/stocks/tickers for all watched - tickers in a single API call, then writes results to the PriceCache. - - Rate limits: - - Free tier: 5 req/min → poll every 15s (default) - - Paid tiers: higher limits → poll every 2-5s - """ - - def __init__( - self, - api_key: str, - price_cache: PriceCache, - poll_interval: float = 15.0, - ) -> None: - self._api_key = api_key - self._cache = price_cache - self._interval = poll_interval - self._tickers: list[str] = [] - self._task: asyncio.Task | None = None - self._client: Any = None # Lazy import to avoid hard dependency - - async def start(self, tickers: list[str]) -> None: - # Lazy import: only import massive when actually using real market data. - # This means the massive package is not required when using the simulator. - from massive import RESTClient - - self._client = RESTClient(api_key=self._api_key) - self._tickers = list(tickers) - - # Do an immediate first poll so the cache has data right away - await self._poll_once() - - self._task = asyncio.create_task(self._poll_loop(), name="massive-poller") - logger.info( - "Massive poller started: %d tickers, %.1fs interval", - len(tickers), - self._interval, - ) - - async def stop(self) -> None: - if self._task and not self._task.done(): - self._task.cancel() - try: - await self._task - except asyncio.CancelledError: - pass - self._task = None - self._client = None - logger.info("Massive poller stopped") - - async def add_ticker(self, ticker: str) -> None: - ticker = ticker.upper().strip() - if ticker not in self._tickers: - self._tickers.append(ticker) - logger.info("Massive: added ticker %s (will appear on next poll)", ticker) - - async def remove_ticker(self, ticker: str) -> None: - ticker = ticker.upper().strip() - self._tickers = [t for t in self._tickers if t != ticker] - self._cache.remove(ticker) - logger.info("Massive: removed ticker %s", ticker) - - def get_tickers(self) -> list[str]: - return list(self._tickers) - - # --- Internal --- - - async def _poll_loop(self) -> None: - """Poll on interval. First poll already happened in start().""" - while True: - await asyncio.sleep(self._interval) - await self._poll_once() - - async def _poll_once(self) -> None: - """Execute one poll cycle: fetch snapshots, update cache.""" - if not self._tickers or not self._client: - return - - try: - # The Massive RESTClient is synchronous — run in a thread to - # avoid blocking the event loop. - snapshots = await asyncio.to_thread(self._fetch_snapshots) - processed = 0 - for snap in snapshots: - try: - price = snap.last_trade.price - # Massive timestamps are Unix milliseconds → convert to seconds - timestamp = snap.last_trade.timestamp / 1000.0 - self._cache.update( - ticker=snap.ticker, - price=price, - timestamp=timestamp, - ) - processed += 1 - except (AttributeError, TypeError) as e: - logger.warning( - "Skipping snapshot for %s: %s", - getattr(snap, "ticker", "???"), - e, - ) - logger.debug("Massive poll: updated %d/%d tickers", processed, len(self._tickers)) - - except Exception as e: - logger.error("Massive poll failed: %s", e) - # Don't re-raise — the loop will retry on the next interval. - # Common failures: 401 (bad key), 429 (rate limit), network errors. - - def _fetch_snapshots(self) -> list: - """Synchronous call to the Massive REST API. Runs in a thread.""" - from massive.rest.models import SnapshotMarketType - - return self._client.get_snapshot_all( - market_type=SnapshotMarketType.STOCKS, - tickers=self._tickers, - ) -``` - -### Error handling philosophy - -The Massive poller is intentionally resilient: - -| Error | Behavior | -|-------|----------| -| **401 Unauthorized** | Logged as error. Poller keeps running (user might fix `.env` and restart). | -| **429 Rate Limited** | Logged as error. Next poll retries after `poll_interval` seconds. | -| **Network timeout** | Logged as error. Retries automatically on next cycle. | -| **Malformed snapshot** | Individual ticker skipped with warning. Other tickers still processed. | -| **All tickers fail** | Cache retains last-known prices. SSE keeps streaming stale data (better than no data). | - -### Lazy import strategy - -`from massive import RESTClient` happens inside `start()`, not at module import time. This means: -- The `massive` package is only required when `MASSIVE_API_KEY` is set. -- Students who don't have a Massive API key don't need the package installed at all. -- The simulator path has zero external dependencies beyond `numpy`. - ---- - -## 8. Factory - -**File: `backend/app/market/factory.py`** - -```python -from __future__ import annotations - -import logging -import os - -from .cache import PriceCache -from .interface import MarketDataSource - -logger = logging.getLogger(__name__) - - -def create_market_data_source(price_cache: PriceCache) -> MarketDataSource: - """Create the appropriate market data source based on environment variables. - - - MASSIVE_API_KEY set and non-empty → MassiveDataSource (real market data) - - Otherwise → SimulatorDataSource (GBM simulation) - - Returns an unstarted source. Caller must await source.start(tickers). - """ - api_key = os.environ.get("MASSIVE_API_KEY", "").strip() - - if api_key: - from .massive_client import MassiveDataSource - - logger.info("Market data source: Massive API (real data)") - return MassiveDataSource(api_key=api_key, price_cache=price_cache) - else: - from .simulator import SimulatorDataSource - - logger.info("Market data source: GBM Simulator") - return SimulatorDataSource(price_cache=price_cache) -``` - -### Usage at app startup - -```python -price_cache = PriceCache() -source = create_market_data_source(price_cache) -await source.start(initial_tickers) # e.g., ["AAPL", "GOOGL", ...] -``` - ---- - -## 9. SSE Streaming Endpoint - -**File: `backend/app/market/stream.py`** - -The SSE endpoint is a FastAPI route that holds open a long-lived HTTP connection and pushes price updates to the client as `text/event-stream`. - -```python -from __future__ import annotations - -import asyncio -import json -import logging -import time - -from fastapi import APIRouter, Request -from fastapi.responses import StreamingResponse - -from .cache import PriceCache - -logger = logging.getLogger(__name__) - -router = APIRouter(prefix="/api/stream", tags=["streaming"]) - - -def create_stream_router(price_cache: PriceCache) -> APIRouter: - """Create the SSE streaming router with a reference to the price cache. - - This factory pattern lets us inject the PriceCache without globals. - """ - - @router.get("/prices") - async def stream_prices(request: Request) -> StreamingResponse: - """SSE endpoint for live price updates. - - Streams all tracked ticker prices every ~500ms. The client connects - with EventSource and receives events in the format: - - data: {"AAPL": {"ticker": "AAPL", "price": 190.50, ...}, ...} - - Includes a retry directive so the browser auto-reconnects on - disconnection (EventSource built-in behavior). - """ - return StreamingResponse( - _generate_events(price_cache, request), - media_type="text/event-stream", - headers={ - "Cache-Control": "no-cache", - "Connection": "keep-alive", - "X-Accel-Buffering": "no", # Disable nginx buffering if proxied - }, - ) - - return router - - -async def _generate_events( - price_cache: PriceCache, - request: Request, - interval: float = 0.5, -) -> None: - """Async generator that yields SSE-formatted price events. - - Sends all prices every `interval` seconds. Stops when the client - disconnects (detected via request.is_disconnected()). - """ - # Tell the client to retry after 1 second if the connection drops - yield "retry: 1000\n\n" - - last_version = -1 - client_ip = request.client.host if request.client else "unknown" - logger.info("SSE client connected: %s", client_ip) - - try: - while True: - # Check for client disconnect - if await request.is_disconnected(): - logger.info("SSE client disconnected: %s", client_ip) - break - - current_version = price_cache.version - if current_version != last_version: - last_version = current_version - prices = price_cache.get_all() - - if prices: - data = { - ticker: update.to_dict() - for ticker, update in prices.items() - } - payload = json.dumps(data) - yield f"data: {payload}\n\n" - - await asyncio.sleep(interval) - except asyncio.CancelledError: - logger.info("SSE stream cancelled for: %s", client_ip) -``` - -### SSE wire format - -Each event the client receives looks like this: - -``` -data: {"AAPL":{"ticker":"AAPL","price":190.50,"previous_price":190.42,"timestamp":1707580800.5,"change":0.08,"change_percent":0.042,"direction":"up"},"GOOGL":{"ticker":"GOOGL","price":175.12,...}} - -``` - -The client parses this with: - -```javascript -const eventSource = new EventSource('/api/stream/prices'); -eventSource.onmessage = (event) => { - const prices = JSON.parse(event.data); - // prices is { "AAPL": { ticker, price, previous_price, ... }, ... } -}; -``` - -### Why poll-and-push instead of event-driven? - -The SSE endpoint polls the cache on a fixed interval rather than being notified by the data source. This is simpler and produces predictable, evenly-spaced updates for the frontend. The frontend accumulates these into sparkline charts — regular spacing is important for clean visualization. - ---- - -## 10. FastAPI Lifecycle Integration - -The market data system starts and stops with the FastAPI application using the `lifespan` context manager pattern. - -**In `backend/app/main.py`:** - -```python -from contextlib import asynccontextmanager - -from fastapi import FastAPI - -from app.market.cache import PriceCache -from app.market.factory import create_market_data_source -from app.market.interface import MarketDataSource -from app.market.stream import create_stream_router - - -@asynccontextmanager -async def lifespan(app: FastAPI): - """Manage startup and shutdown of background services.""" - - # --- STARTUP --- - - # 1. Create the shared price cache - price_cache = PriceCache() - app.state.price_cache = price_cache - - # 2. Create and start the market data source - source = create_market_data_source(price_cache) - app.state.market_source = source - - # 3. Load initial tickers from the database watchlist - initial_tickers = await load_watchlist_tickers() # reads from SQLite - await source.start(initial_tickers) - - # 4. Register the SSE streaming router - stream_router = create_stream_router(price_cache) - app.include_router(stream_router) - - yield # App is running - - # --- SHUTDOWN --- - await source.stop() - - -app = FastAPI(title="FinAlly", lifespan=lifespan) - - -# Dependency for injecting the price cache into route handlers -def get_price_cache() -> PriceCache: - return app.state.price_cache - - -def get_market_source() -> MarketDataSource: - return app.state.market_source -``` - -### Accessing market data from other routes - -Other parts of the backend (trade execution, portfolio valuation, watchlist management) access the price cache and data source via FastAPI's dependency injection: - -```python -from fastapi import APIRouter, Depends - -router = APIRouter(prefix="/api") - -@router.post("/portfolio/trade") -async def execute_trade( - trade: TradeRequest, - price_cache: PriceCache = Depends(get_price_cache), -): - current_price = price_cache.get_price(trade.ticker) - if current_price is None: - raise HTTPException(404, f"No price available for {trade.ticker}") - # ... execute trade at current_price ... - - -@router.post("/watchlist") -async def add_to_watchlist( - payload: WatchlistAdd, - source: MarketDataSource = Depends(get_market_source), - price_cache: PriceCache = Depends(get_price_cache), -): - # Add to database ... - # Then tell the data source to start tracking it - await source.add_ticker(payload.ticker) - # ... - - -@router.delete("/watchlist/{ticker}") -async def remove_from_watchlist( - ticker: str, - source: MarketDataSource = Depends(get_market_source), -): - # Remove from database ... - # Then stop tracking - await source.remove_ticker(ticker) - # ... -``` - ---- - -## 11. Watchlist Coordination - -When the watchlist changes (via REST API or LLM chat), the market data source must be notified so it tracks the right set of tickers. - -### Flow: Adding a Ticker - -``` -User (or LLM) → POST /api/watchlist {ticker: "PYPL"} - → Insert into watchlist table (SQLite) - → await source.add_ticker("PYPL") - Simulator: adds to GBMSimulator, rebuilds Cholesky, seeds cache - Massive: appends to ticker list, appears on next poll - → Return success (ticker + current price if available) -``` - -### Flow: Removing a Ticker - -``` -User (or LLM) → DELETE /api/watchlist/PYPL - → Delete from watchlist table (SQLite) - → await source.remove_ticker("PYPL") - Simulator: removes from GBMSimulator, rebuilds Cholesky, removes from cache - Massive: removes from ticker list, removes from cache - → Return success -``` - -### Edge case: Ticker has an open position - -If the user removes a ticker from the watchlist but still holds shares, the ticker should remain in the data source so portfolio valuation stays accurate. The watchlist route should check for this: - -```python -@router.delete("/watchlist/{ticker}") -async def remove_from_watchlist( - ticker: str, - source: MarketDataSource = Depends(get_market_source), -): - # Remove from watchlist table - await db.delete_watchlist_entry(ticker) - - # Only stop tracking if no open position - position = await db.get_position(ticker) - if position is None or position.quantity == 0: - await source.remove_ticker(ticker) - - return {"status": "ok"} -``` - ---- - -## 12. Testing Strategy - -### 12.1 Unit Tests for GBMSimulator - -**File: `backend/tests/market/test_simulator.py`** - -```python -import math -import pytest -from app.market.simulator import GBMSimulator -from app.market.seed_prices import SEED_PRICES - - -class TestGBMSimulator: - """Unit tests for the GBM price simulator.""" - - def test_step_returns_all_tickers(self): - sim = GBMSimulator(tickers=["AAPL", "GOOGL"]) - result = sim.step() - assert set(result.keys()) == {"AAPL", "GOOGL"} - - def test_prices_are_positive(self): - """GBM prices can never go negative (exp() is always positive).""" - sim = GBMSimulator(tickers=["AAPL"]) - for _ in range(10_000): - prices = sim.step() - assert prices["AAPL"] > 0 - - def test_initial_prices_match_seeds(self): - sim = GBMSimulator(tickers=["AAPL"]) - # Before any step, price should be the seed price - assert sim.get_price("AAPL") == SEED_PRICES["AAPL"] - - def test_add_ticker(self): - sim = GBMSimulator(tickers=["AAPL"]) - sim.add_ticker("TSLA") - result = sim.step() - assert "TSLA" in result - - def test_remove_ticker(self): - sim = GBMSimulator(tickers=["AAPL", "GOOGL"]) - sim.remove_ticker("GOOGL") - result = sim.step() - assert "GOOGL" not in result - assert "AAPL" in result - - def test_add_duplicate_is_noop(self): - sim = GBMSimulator(tickers=["AAPL"]) - sim.add_ticker("AAPL") - assert len(sim._tickers) == 1 - - def test_remove_nonexistent_is_noop(self): - sim = GBMSimulator(tickers=["AAPL"]) - sim.remove_ticker("NOPE") # Should not raise - - def test_unknown_ticker_gets_random_seed_price(self): - sim = GBMSimulator(tickers=["ZZZZ"]) - price = sim.get_price("ZZZZ") - assert 50.0 <= price <= 300.0 - - def test_empty_step(self): - sim = GBMSimulator(tickers=[]) - result = sim.step() - assert result == {} - - def test_prices_change_over_time(self): - """After many steps, prices should have drifted from their seeds.""" - sim = GBMSimulator(tickers=["AAPL"]) - for _ in range(1000): - sim.step() - # Price should have changed (extremely unlikely to be exactly the seed) - assert sim.get_price("AAPL") != SEED_PRICES["AAPL"] - - def test_cholesky_rebuilds_on_add(self): - sim = GBMSimulator(tickers=["AAPL"]) - assert sim._cholesky is None # Only 1 ticker, no correlation matrix - sim.add_ticker("GOOGL") - assert sim._cholesky is not None # Now 2 tickers, matrix exists -``` - -### 12.2 Unit Tests for PriceCache - -**File: `backend/tests/market/test_cache.py`** - -```python -import pytest -from app.market.cache import PriceCache - - -class TestPriceCache: - - def test_update_and_get(self): - cache = PriceCache() - update = cache.update("AAPL", 190.50) - assert update.ticker == "AAPL" - assert update.price == 190.50 - assert cache.get("AAPL") == update - - def test_first_update_is_flat(self): - cache = PriceCache() - update = cache.update("AAPL", 190.50) - assert update.direction == "flat" - assert update.previous_price == 190.50 - - def test_direction_up(self): - cache = PriceCache() - cache.update("AAPL", 190.00) - update = cache.update("AAPL", 191.00) - assert update.direction == "up" - assert update.change == 1.00 - - def test_direction_down(self): - cache = PriceCache() - cache.update("AAPL", 190.00) - update = cache.update("AAPL", 189.00) - assert update.direction == "down" - assert update.change == -1.00 - - def test_remove(self): - cache = PriceCache() - cache.update("AAPL", 190.00) - cache.remove("AAPL") - assert cache.get("AAPL") is None - - def test_get_all(self): - cache = PriceCache() - cache.update("AAPL", 190.00) - cache.update("GOOGL", 175.00) - all_prices = cache.get_all() - assert set(all_prices.keys()) == {"AAPL", "GOOGL"} - - def test_version_increments(self): - cache = PriceCache() - v0 = cache.version - cache.update("AAPL", 190.00) - assert cache.version == v0 + 1 - cache.update("AAPL", 191.00) - assert cache.version == v0 + 2 - - def test_get_price_convenience(self): - cache = PriceCache() - cache.update("AAPL", 190.50) - assert cache.get_price("AAPL") == 190.50 - assert cache.get_price("NOPE") is None -``` - -### 12.3 Integration Test: SimulatorDataSource - -**File: `backend/tests/market/test_simulator_source.py`** - -```python -import asyncio -import pytest -from app.market.cache import PriceCache -from app.market.simulator import SimulatorDataSource - - -@pytest.mark.asyncio -class TestSimulatorDataSource: - - async def test_start_populates_cache(self): - cache = PriceCache() - source = SimulatorDataSource(price_cache=cache, update_interval=0.1) - await source.start(["AAPL", "GOOGL"]) - - # Cache should have seed prices immediately (before first loop tick) - assert cache.get("AAPL") is not None - assert cache.get("GOOGL") is not None - - await source.stop() - - async def test_prices_update_over_time(self): - cache = PriceCache() - source = SimulatorDataSource(price_cache=cache, update_interval=0.05) - await source.start(["AAPL"]) - - initial = cache.get("AAPL").price - await asyncio.sleep(0.3) # Several update cycles - current = cache.get("AAPL").price - - # Extremely unlikely to be identical after many steps - # (but not impossible, so this is a probabilistic test) - assert current != initial or True # Soft assertion - - await source.stop() - - async def test_stop_is_clean(self): - cache = PriceCache() - source = SimulatorDataSource(price_cache=cache, update_interval=0.1) - await source.start(["AAPL"]) - await source.stop() - # Double stop should not raise - await source.stop() - - async def test_add_and_remove_ticker(self): - cache = PriceCache() - source = SimulatorDataSource(price_cache=cache, update_interval=0.1) - await source.start(["AAPL"]) - - await source.add_ticker("TSLA") - assert "TSLA" in source.get_tickers() - assert cache.get("TSLA") is not None - - await source.remove_ticker("TSLA") - assert "TSLA" not in source.get_tickers() - assert cache.get("TSLA") is None - - await source.stop() -``` - -### 12.4 Unit Test: MassiveDataSource (Mocked) - -**File: `backend/tests/market/test_massive.py`** - -```python -import asyncio -from unittest.mock import MagicMock, patch -import pytest -from app.market.cache import PriceCache -from app.market.massive_client import MassiveDataSource - - -def _make_snapshot(ticker: str, price: float, timestamp_ms: int) -> MagicMock: - """Create a mock Massive snapshot object.""" - snap = MagicMock() - snap.ticker = ticker - snap.last_trade.price = price - snap.last_trade.timestamp = timestamp_ms - return snap - - -@pytest.mark.asyncio -class TestMassiveDataSource: - - async def test_poll_updates_cache(self): - cache = PriceCache() - source = MassiveDataSource( - api_key="test-key", - price_cache=cache, - poll_interval=60.0, # Long interval so the loop doesn't auto-poll - ) - - mock_snapshots = [ - _make_snapshot("AAPL", 190.50, 1707580800000), - _make_snapshot("GOOGL", 175.25, 1707580800000), - ] - - with patch.object(source, "_fetch_snapshots", return_value=mock_snapshots): - await source._poll_once() - - assert cache.get_price("AAPL") == 190.50 - assert cache.get_price("GOOGL") == 175.25 - - async def test_malformed_snapshot_skipped(self): - cache = PriceCache() - source = MassiveDataSource( - api_key="test-key", - price_cache=cache, - poll_interval=60.0, - ) - source._tickers = ["AAPL", "BAD"] - - good_snap = _make_snapshot("AAPL", 190.50, 1707580800000) - bad_snap = MagicMock() - bad_snap.ticker = "BAD" - bad_snap.last_trade = None # Will cause AttributeError - - with patch.object(source, "_fetch_snapshots", return_value=[good_snap, bad_snap]): - await source._poll_once() - - # Good ticker processed, bad one skipped - assert cache.get_price("AAPL") == 190.50 - assert cache.get_price("BAD") is None - - async def test_api_error_does_not_crash(self): - cache = PriceCache() - source = MassiveDataSource( - api_key="test-key", - price_cache=cache, - poll_interval=60.0, - ) - source._tickers = ["AAPL"] - - with patch.object(source, "_fetch_snapshots", side_effect=Exception("network error")): - await source._poll_once() # Should not raise - - assert cache.get_price("AAPL") is None # No update happened -``` - ---- - -## 13. Error Handling & Edge Cases - -### 13.1 Startup: Empty Watchlist - -If the database has no watchlist entries (user deleted everything), `start()` receives an empty list. Both data sources handle this gracefully — the simulator produces no prices, the Massive poller skips its API call. The SSE endpoint sends empty events. When the user adds a ticker, the source starts tracking it immediately. - -### 13.2 Price Cache Miss During Trade - -If a user tries to trade a ticker that has no cached price (e.g., just added to watchlist, Massive hasn't polled yet): - -```python -price = price_cache.get_price(ticker) -if price is None: - raise HTTPException( - status_code=400, - detail=f"Price not yet available for {ticker}. Please wait a moment and try again.", - ) -``` - -The simulator avoids this by seeding the cache in `add_ticker()`. The Massive client may have a brief gap — the HTTP 400 with a clear message is the correct response. - -### 13.3 Massive API Key Invalid - -If the API key is set but invalid, the first poll will fail with a 401. The poller logs the error and keeps retrying. The SSE endpoint streams empty data. The user sees no prices and a connection status indicator showing "connected" (SSE is working, just no data). The fix is to correct the API key and restart. - -### 13.4 Thread Safety Under Load - -The `PriceCache` uses `threading.Lock` which is a mutex — only one thread can hold it at a time. Under normal load (10 tickers, 2 updates/sec), lock contention is negligible. The critical section is tiny (dict lookup + assignment). - -If this ever became a bottleneck (hundreds of tickers, many concurrent SSE readers), the fix would be a `ReadWriteLock` — but that level of optimization is unnecessary for this project. - -### 13.5 Simulator Precision - -GBM with tiny `dt` produces very small per-tick moves. Floating-point precision is not a concern because: -- Prices are `round()`ed to 2 decimal places in `GBMSimulator.step()` -- The exponential formulation (`exp(drift + diffusion)`) is numerically stable -- Prices are always positive (exponential function) - ---- - -## 14. Configuration Summary - -All tunable parameters and their defaults: - -| Parameter | Location | Default | Description | -|-----------|----------|---------|-------------| -| `MASSIVE_API_KEY` | Environment variable | `""` (empty) | If set, use Massive API; otherwise use simulator | -| `update_interval` | `SimulatorDataSource.__init__` | `0.5` (seconds) | Time between simulator ticks | -| `poll_interval` | `MassiveDataSource.__init__` | `15.0` (seconds) | Time between Massive API polls | -| `event_probability` | `GBMSimulator.__init__` | `0.001` | Chance of a random shock event per ticker per tick | -| `dt` | `GBMSimulator.__init__` | `~8.5e-8` | GBM time step (fraction of a trading year) | -| SSE push interval | `_generate_events()` | `0.5` (seconds) | Time between SSE pushes to the client | -| SSE retry directive | `_generate_events()` | `1000` (ms) | Browser EventSource reconnection delay | - -### Package `__init__.py` - -**File: `backend/app/market/__init__.py`** - -```python -"""Market data subsystem for FinAlly. - -Public API: - PriceUpdate - Immutable price snapshot dataclass - PriceCache - Thread-safe in-memory price store - MarketDataSource - Abstract interface for data providers - create_market_data_source - Factory that selects simulator or Massive - create_stream_router - FastAPI router factory for SSE endpoint -""" - -from .cache import PriceCache -from .factory import create_market_data_source -from .interface import MarketDataSource -from .models import PriceUpdate -from .stream import create_stream_router - -__all__ = [ - "PriceUpdate", - "PriceCache", - "MarketDataSource", - "create_market_data_source", - "create_stream_router", -] -``` diff --git a/planning/archive/MARKET_DATA_REVIEW.md b/planning/archive/MARKET_DATA_REVIEW.md deleted file mode 100644 index 61b4d6bf4..000000000 --- a/planning/archive/MARKET_DATA_REVIEW.md +++ /dev/null @@ -1,173 +0,0 @@ -# Market Data Backend — Code Review - -**Date:** 2026-02-10 -**Scope:** `backend/app/market/` (8 source files) and `backend/tests/market/` (6 test files) - ---- - -## 1. Test Results Summary - -**73 tests collected, 68 passed, 5 failed.** - -All failures are in `test_massive.py` and stem from the same root cause: the `massive` package is not installed in the test environment, so `patch("app.market.massive_client.RESTClient")` fails with `AttributeError` because the module-level name `RESTClient` was never imported (it is lazy-imported inside methods). This is an environment issue, not a logic bug — the tests are correctly structured but require the `massive` package to be available (or `create=True` on the patch) so that the mock target exists. - -Failing tests: -- `test_poll_updates_cache` — `asyncio.to_thread` fails because `_fetch_snapshots` is not properly mocked when `massive` is absent -- `test_malformed_snapshot_skipped` — same cause -- `test_timestamp_conversion` — same cause -- `test_stop_cancels_task` — `patch("app.market.massive_client.RESTClient")` fails because the name doesn't exist at module level -- `test_start_immediate_poll` — same as above - -The underlying `_poll_once()` logic itself is correct. The 3 tests that mock `source._fetch_snapshots` directly fail because `asyncio.to_thread(self._fetch_snapshots)` calls the real method which tries to import `massive`. The 2 tests that use `patch("app.market.massive_client.RESTClient")` fail because the name doesn't exist in the module's namespace (lazy import). Both issues resolve when the `massive` package is installed. - -**Lint (ruff):** Source code passes clean. Tests have 5 unused-import warnings (`pytest`, `math`, `asyncio` imported but not used in some test files). - -**Coverage:** 84% overall. -| Module | Coverage | Notes | -|---|---|---| -| models.py | 100% | | -| cache.py | 100% | | -| interface.py | 100% | | -| seed_prices.py | 100% | | -| factory.py | 100% | | -| simulator.py | 98% | Uncovered: `_add_ticker_internal` duplicate guard (L145), exception log in `_run_loop` (L264-265) | -| massive_client.py | 56% | Expected — real API methods can't run without the massive package | -| stream.py | 31% | Expected — SSE generator requires a running ASGI server to test | - ---- - -## 2. Architecture Assessment - -The market data subsystem is well-designed. It follows a clean strategy pattern: - -``` -MarketDataSource (ABC) -├── SimulatorDataSource (GBM simulator) -└── MassiveDataSource (Polygon.io REST poller) - │ - ▼ - PriceCache (shared, thread-safe) - │ - ▼ - SSE stream → Frontend -``` - -**Strengths:** -- Clear separation of concerns across 8 focused modules -- Factory pattern with lazy imports — the `massive` package is only needed when `MASSIVE_API_KEY` is set -- PriceCache as the single point of truth decouples producers from consumers -- Immutable `PriceUpdate` dataclass with `frozen=True, slots=True` is correct and efficient -- The GBM math is proper: log-normal price paths via `exp((mu - 0.5*sigma^2)*dt + sigma*sqrt(dt)*Z)` -- Correlated moves via Cholesky decomposition are a nice touch for realism -- All background tasks are properly cancellable and idempotent on stop() - ---- - -## 3. Issues Found - -### 3.1 Build Configuration Bug (Severity: High) - -`pyproject.toml` is missing the hatchling package discovery configuration. Running `uv sync` fails: - -``` -ValueError: Unable to determine which files to ship inside the wheel -``` - -**Fix:** Add to `pyproject.toml`: -```toml -[tool.hatch.build.targets.wheel] -packages = ["app"] -``` - -This will block Docker builds and any fresh `uv sync` until fixed. - -### 3.2 Massive Test Fragility (Severity: Medium) - -Five tests in `test_massive.py` fail when the `massive` package is not installed. The root cause is twofold: - -1. **`_poll_once` uses `asyncio.to_thread(self._fetch_snapshots)`** — even when `_fetch_snapshots` is patched on the instance, `to_thread` runs it in a thread executor. Three tests mock `_fetch_snapshots` as a `MagicMock` (synchronous), but `asyncio.to_thread` wraps it in `loop.run_in_executor`, which works... except that when `_fetch_snapshots` is NOT patched, the real method tries `from massive.rest.models import SnapshotMarketType` and fails. - -2. **`patch("app.market.massive_client.RESTClient")`** targets a name that doesn't exist at module level because `massive_client.py` uses a lazy import inside `start()`. The patch needs `create=True` or the import needs to be at module level behind a `TYPE_CHECKING` guard. - -These tests pass when `massive>=1.0.0` is installed (as `pyproject.toml` declares it as a core dependency), so this is technically a test-environment issue, not a code bug. However, since the whole point of lazy imports is to make `massive` optional for simulator-only use, the tests should also work without it. - -### 3.3 `_generate_events` Return Type Annotation (Severity: Low) - -`stream.py:54` declares the return type as `-> None` but the function is an async generator (it uses `yield`). The correct annotation would be `-> AsyncGenerator[str, None]` or simply removing the annotation. This doesn't cause runtime issues but is misleading for type checkers and developers. - -### 3.4 `version` Property Not Under Lock (Severity: Low) - -`PriceCache.version` reads `self._version` without acquiring `self._lock`: - -```python -@property -def version(self) -> int: - return self._version -``` - -On CPython with the GIL, reading a single `int` is atomic, so this won't cause corruption. However, it's inconsistent with the rest of the class, and if the project ever runs on a no-GIL Python build (PEP 703, Python 3.13t+), this could become a race. A minor concern given the current context. - -### 3.5 `SimulatorDataSource.get_tickers` Accesses Private State (Severity: Low) - -`simulator.py:254`: -```python -def get_tickers(self) -> list[str]: - return list(self._sim._tickers) if self._sim else [] -``` - -This reaches into `GBMSimulator._tickers` (private attribute). `GBMSimulator` should expose a `get_tickers()` method or a `tickers` property to keep the boundary clean. - -### 3.6 Module-Level Router Instance (Severity: Low) - -`stream.py:16` creates a module-level `router` object, and `create_stream_router()` registers a route on it via closure. If `create_stream_router` were called twice (e.g., in tests), the `/prices` route would be registered twice on the same router. In practice this won't happen because the function is called once during app startup, but it's a latent footgun for testing. - -### 3.7 Unused Imports in Tests (Severity: Trivial) - -Five lint warnings from `ruff`: -- `test_cache.py`: unused `pytest` -- `test_factory.py`: unused `pytest` -- `test_massive.py`: unused `asyncio` -- `test_simulator.py`: unused `math`, unused `pytest` - ---- - -## 4. Design Observations - -### 4.1 Things Done Well - -- **GBM parameter tuning is thoughtful.** TSLA at sigma=0.50 vs V at 0.17 reflects real-world volatility differences. The shock event system (~0.1% per tick, producing visible moves every ~50s) adds visual drama without destabilizing prices. -- **Cholesky decomposition for correlated moves** is the mathematically correct approach. The sector-based correlation structure (tech 0.6, finance 0.5, cross 0.3) is reasonable. -- **Defensive error handling in both data sources.** Both `_run_loop` (simulator) and `_poll_once`/`_poll_loop` (massive) catch exceptions and continue, which is essential for a long-running background service. -- **SSE implementation is clean.** The version-based change detection avoids sending redundant payloads. The `retry: 1000\n\n` directive ensures browser auto-reconnect. Nginx buffering is proactively disabled. -- **Seed prices in the cache at start** means the frontend gets data on the first SSE poll, with no visible delay. -- **Thread-safe cache with Lock** is the right choice since the Massive client runs API calls via `asyncio.to_thread`. - -### 4.2 Missing Tests - -- **SSE streaming (`stream.py`)** at 31% coverage has no dedicated tests. Testing SSE requires an ASGI test client (e.g., `httpx.AsyncClient` with `app`). Given that this is the primary consumer of PriceCache, even a basic integration test would add confidence. -- **No concurrent/thread-safety test for PriceCache.** The lock usage looks correct from inspection, but a test with multiple threads writing simultaneously would verify it empirically. -- **No test for `GBMSimulator` with all 10 default tickers.** Tests use 1-2 tickers. A test confirming the Cholesky decomposition succeeds for the full 10-ticker default set would catch correlation matrix issues. - -### 4.3 Potential Future Considerations - -- The `PriceCache` doesn't cap history; it only stores the latest price per ticker, so memory is bounded at O(tickers). Good. -- The `DEFAULT_CORR` constant (0.3, `seed_prices.py:48`) is defined but never referenced in `_pairwise_correlation`. The static method returns `CROSS_GROUP_CORR` (also 0.3) as the fallback. This is semantically confusing — `DEFAULT_CORR` seems intended for tickers not in any group, but the code returns `CROSS_GROUP_CORR` for all non-matched pairs. Both happen to be 0.3, so behavior is correct, but the naming is misleading. - ---- - -## 5. Verdict - -The market data backend is solid and well-structured. The GBM simulator, price cache, abstract interface, factory pattern, and SSE streaming all work correctly and follow good practices. The architecture will integrate cleanly with the rest of the application. - -**Must fix before proceeding:** -1. Add `[tool.hatch.build.targets.wheel] packages = ["app"]` to `pyproject.toml` — without this, `uv sync` and Docker builds fail. - -**Should fix:** -2. Make the Massive tests resilient to the `massive` package being absent (use `create=True` on patches, or restructure mocks). -3. Fix the `_generate_events` return type annotation. -4. Remove unused imports in test files. - -**Nice to have:** -5. Add a `get_tickers()` public method to `GBMSimulator`. -6. Add at least one SSE integration test. -7. Clarify `DEFAULT_CORR` vs `CROSS_GROUP_CORR` naming. diff --git a/planning/archive/MARKET_INTERFACE.md b/planning/archive/MARKET_INTERFACE.md deleted file mode 100644 index 156cad287..000000000 --- a/planning/archive/MARKET_INTERFACE.md +++ /dev/null @@ -1,273 +0,0 @@ -# Market Data Interface Design - -Unified Python interface for market data in FinAlly. Two implementations (simulator and Massive API) behind one abstract interface. All downstream code — SSE streaming, price cache, portfolio valuation — is source-agnostic. - -## Core Data Model - -```python -from dataclasses import dataclass - -@dataclass -class PriceUpdate: - """A single price update for one ticker.""" - ticker: str - price: float - previous_price: float - timestamp: float # Unix seconds - change: float # price - previous_price - direction: str # "up", "down", or "flat" -``` - -This is the only data structure that leaves the market data layer. Everything downstream works with `PriceUpdate` objects. - -## Abstract Interface - -```python -from abc import ABC, abstractmethod - -class MarketDataSource(ABC): - """Abstract interface for market data providers.""" - - @abstractmethod - async def start(self, tickers: list[str]) -> None: - """Begin producing price updates for the given tickers.""" - - @abstractmethod - async def stop(self) -> None: - """Stop producing price updates and clean up.""" - - @abstractmethod - async def add_ticker(self, ticker: str) -> None: - """Add a ticker to the active set.""" - - @abstractmethod - async def remove_ticker(self, ticker: str) -> None: - """Remove a ticker from the active set.""" - - @abstractmethod - def get_tickers(self) -> list[str]: - """Return the current list of active tickers.""" -``` - -Both implementations write to a shared `PriceCache` (see below). The interface does **not** return prices directly — it pushes updates into the cache on its own schedule. - -## Price Cache - -Shared in-memory store that both data sources write to and the SSE streamer reads from. - -```python -import time -from threading import Lock - -class PriceCache: - """Thread-safe cache of latest prices per ticker.""" - - def __init__(self): - self._prices: dict[str, PriceUpdate] = {} - self._lock = Lock() - - def update(self, ticker: str, price: float, timestamp: float | None = None) -> PriceUpdate: - """Update price for a ticker. Returns the PriceUpdate.""" - with self._lock: - ts = timestamp or time.time() - previous = self._prices.get(ticker) - previous_price = previous.price if previous else price - - if price > previous_price: - direction = "up" - elif price < previous_price: - direction = "down" - else: - direction = "flat" - - update = PriceUpdate( - ticker=ticker, - price=price, - previous_price=previous_price, - timestamp=ts, - change=price - previous_price, - direction=direction, - ) - self._prices[ticker] = update - return update - - def get(self, ticker: str) -> PriceUpdate | None: - """Get latest price for a ticker.""" - with self._lock: - return self._prices.get(ticker) - - def get_all(self) -> dict[str, PriceUpdate]: - """Get all current prices.""" - with self._lock: - return dict(self._prices) - - def remove(self, ticker: str) -> None: - """Remove a ticker from the cache.""" - with self._lock: - self._prices.pop(ticker, None) -``` - -## Factory Function - -Select the data source at startup based on environment: - -```python -import os - -def create_market_data_source(price_cache: PriceCache) -> MarketDataSource: - """Create the appropriate market data source based on environment.""" - api_key = os.environ.get("MASSIVE_API_KEY", "").strip() - - if api_key: - from .massive_client import MassiveDataSource - return MassiveDataSource(api_key=api_key, price_cache=price_cache) - else: - from .simulator import SimulatorDataSource - return SimulatorDataSource(price_cache=price_cache) -``` - -## Massive Implementation Sketch - -```python -import asyncio -from massive import RESTClient -from massive.rest.models import SnapshotMarketType - -class MassiveDataSource(MarketDataSource): - def __init__(self, api_key: str, price_cache: PriceCache, poll_interval: float = 15.0): - self._client = RESTClient(api_key=api_key) - self._cache = price_cache - self._interval = poll_interval - self._tickers: list[str] = [] - self._task: asyncio.Task | None = None - - async def start(self, tickers: list[str]) -> None: - self._tickers = list(tickers) - self._task = asyncio.create_task(self._poll_loop()) - - async def stop(self) -> None: - if self._task: - self._task.cancel() - - async def add_ticker(self, ticker: str) -> None: - if ticker not in self._tickers: - self._tickers.append(ticker) - - async def remove_ticker(self, ticker: str) -> None: - self._tickers = [t for t in self._tickers if t != ticker] - self._cache.remove(ticker) - - def get_tickers(self) -> list[str]: - return list(self._tickers) - - async def _poll_loop(self) -> None: - while True: - await self._poll_once() - await asyncio.sleep(self._interval) - - async def _poll_once(self) -> None: - if not self._tickers: - return - # Run synchronous Massive client in thread pool - snapshots = await asyncio.to_thread( - self._client.get_snapshot_all, - market_type=SnapshotMarketType.STOCKS, - tickers=self._tickers, - ) - for snap in snapshots: - self._cache.update( - ticker=snap.ticker, - price=snap.last_trade.price, - timestamp=snap.last_trade.timestamp / 1000, # ms -> seconds - ) -``` - -## Simulator Implementation Sketch - -```python -import asyncio - -class SimulatorDataSource(MarketDataSource): - def __init__(self, price_cache: PriceCache, update_interval: float = 0.5): - self._cache = price_cache - self._interval = update_interval - self._tickers: list[str] = [] - self._task: asyncio.Task | None = None - self._sim: GBMSimulator | None = None # See MARKET_SIMULATOR.md - - async def start(self, tickers: list[str]) -> None: - self._tickers = list(tickers) - self._sim = GBMSimulator(tickers=self._tickers) - self._task = asyncio.create_task(self._run_loop()) - - async def stop(self) -> None: - if self._task: - self._task.cancel() - - async def add_ticker(self, ticker: str) -> None: - if ticker not in self._tickers: - self._tickers.append(ticker) - self._sim.add_ticker(ticker) - - async def remove_ticker(self, ticker: str) -> None: - self._tickers = [t for t in self._tickers if t != ticker] - self._sim.remove_ticker(ticker) - self._cache.remove(ticker) - - def get_tickers(self) -> list[str]: - return list(self._tickers) - - async def _run_loop(self) -> None: - while True: - prices = self._sim.step() # Returns dict[str, float] - for ticker, price in prices.items(): - self._cache.update(ticker=ticker, price=price) - await asyncio.sleep(self._interval) -``` - -## Integration with SSE - -The SSE endpoint reads from the `PriceCache` and pushes to connected clients: - -```python -async def price_stream(price_cache: PriceCache): - """SSE generator that yields price updates.""" - while True: - prices = price_cache.get_all() - data = { - ticker: { - "ticker": p.ticker, - "price": p.price, - "previous_price": p.previous_price, - "change": p.change, - "direction": p.direction, - "timestamp": p.timestamp, - } - for ticker, p in prices.items() - } - yield f"data: {json.dumps(data)}\n\n" - await asyncio.sleep(0.5) -``` - -## File Structure - -``` -backend/ - app/ - market/ - __init__.py - models.py # PriceUpdate dataclass - interface.py # MarketDataSource ABC, PriceCache - factory.py # create_market_data_source() - massive_client.py # MassiveDataSource - simulator.py # SimulatorDataSource + GBMSimulator - seed_prices.py # Default ticker seed prices -``` - -## Lifecycle - -1. **App startup**: Create `PriceCache`, call `create_market_data_source(price_cache)`, then `await source.start(initial_tickers)` -2. **Watchlist changes**: Call `source.add_ticker()` or `source.remove_ticker()` -3. **SSE streaming**: Reads from `PriceCache.get_all()` every 500ms -4. **Trade execution**: Reads current price from `PriceCache.get(ticker)` -5. **App shutdown**: Call `await source.stop()` diff --git a/planning/archive/MARKET_SIMULATOR.md b/planning/archive/MARKET_SIMULATOR.md deleted file mode 100644 index e157b6efb..000000000 --- a/planning/archive/MARKET_SIMULATOR.md +++ /dev/null @@ -1,245 +0,0 @@ -# Market Simulator Design - -Approach and code structure for simulating realistic stock prices when no Massive API key is configured. - -## Overview - -The simulator uses **Geometric Brownian Motion (GBM)** to generate realistic stock price paths. GBM is the standard model underlying Black-Scholes option pricing — prices evolve continuously with random noise, can't go negative, and exhibit the lognormal distribution seen in real markets. - -Updates run at ~500ms intervals, producing a continuous stream of price changes that feel alive. - -## GBM Math - -At each time step, a stock price evolves as: - -``` -S(t+dt) = S(t) * exp((mu - sigma^2/2) * dt + sigma * sqrt(dt) * Z) -``` - -Where: -- `S(t)` = current price -- `mu` = annualized drift (expected return), e.g. 0.05 (5%) -- `sigma` = annualized volatility, e.g. 0.20 (20%) -- `dt` = time step as fraction of a trading year -- `Z` = standard normal random variable (drawn from N(0,1)) - -For our 500ms updates with ~252 trading days and ~6.5 hours per day: -``` -dt = 0.5 / (252 * 6.5 * 3600) = ~8.5e-8 -``` - -This tiny `dt` produces small, realistic per-tick moves. - -## Correlated Moves - -Real stocks don't move independently — tech stocks tend to move together, etc. We use a **Cholesky decomposition** of a correlation matrix to generate correlated random draws. - -Given a correlation matrix `C`, compute `L = cholesky(C)`. Then for independent standard normals `Z_independent`: -``` -Z_correlated = L @ Z_independent -``` - -Default correlation groups: -- **Tech**: AAPL, GOOGL, MSFT, AMZN, META, NVDA, NFLX — corr ~0.6 within group -- **Finance**: JPM, V — corr ~0.5 within group -- **Cross-group**: ~0.3 baseline correlation -- **TSLA**: lower correlation with everything (~0.3) — it does its own thing - -## Random Events - -Every step, each ticker has a small probability (~0.001) of a random event — a sudden 2-5% move. This adds drama and makes the dashboard visually interesting. - -```python -if random.random() < event_probability: - shock = random.uniform(0.02, 0.05) * random.choice([-1, 1]) - price *= (1 + shock) -``` - -## Seed Prices - -Realistic starting prices for the default watchlist: - -```python -SEED_PRICES: dict[str, float] = { - "AAPL": 190.0, - "GOOGL": 175.0, - "MSFT": 420.0, - "AMZN": 185.0, - "TSLA": 250.0, - "NVDA": 800.0, - "META": 500.0, - "JPM": 195.0, - "V": 280.0, - "NFLX": 600.0, -} -``` - -Tickers added dynamically (not in the seed list) start at a random price between $50-$300. - -## Per-Ticker Parameters - -Each ticker has its own volatility to reflect real-world behavior: - -```python -TICKER_PARAMS: dict[str, dict] = { - "AAPL": {"sigma": 0.22, "mu": 0.05}, - "GOOGL": {"sigma": 0.25, "mu": 0.05}, - "MSFT": {"sigma": 0.20, "mu": 0.05}, - "AMZN": {"sigma": 0.28, "mu": 0.05}, - "TSLA": {"sigma": 0.50, "mu": 0.03}, # High vol - "NVDA": {"sigma": 0.40, "mu": 0.08}, # High vol, strong drift - "META": {"sigma": 0.30, "mu": 0.05}, - "JPM": {"sigma": 0.18, "mu": 0.04}, # Low vol (bank) - "V": {"sigma": 0.17, "mu": 0.04}, # Low vol (payments) - "NFLX": {"sigma": 0.35, "mu": 0.05}, -} - -# Default for unknown tickers -DEFAULT_PARAMS = {"sigma": 0.25, "mu": 0.05} -``` - -## Implementation - -```python -import math -import random -import time -import numpy as np - -class GBMSimulator: - """Generates correlated GBM price paths for multiple tickers.""" - - def __init__( - self, - tickers: list[str], - dt: float = 8.5e-8, - event_probability: float = 0.001, - ): - self._dt = dt - self._event_prob = event_probability - self._prices: dict[str, float] = {} - self._params: dict[str, dict] = {} - self._tickers: list[str] = [] - self._cholesky: np.ndarray | None = None - - for ticker in tickers: - self.add_ticker(ticker) - - def add_ticker(self, ticker: str) -> None: - if ticker in self._prices: - return - self._tickers.append(ticker) - self._prices[ticker] = SEED_PRICES.get(ticker, random.uniform(50, 300)) - self._params[ticker] = TICKER_PARAMS.get(ticker, DEFAULT_PARAMS) - self._rebuild_cholesky() - - def remove_ticker(self, ticker: str) -> None: - if ticker not in self._prices: - return - self._tickers.remove(ticker) - del self._prices[ticker] - del self._params[ticker] - self._rebuild_cholesky() - - def step(self) -> dict[str, float]: - """Advance one time step. Returns {ticker: new_price}.""" - n = len(self._tickers) - if n == 0: - return {} - - # Generate correlated random normals - z_independent = np.random.standard_normal(n) - if self._cholesky is not None: - z = self._cholesky @ z_independent - else: - z = z_independent - - result = {} - for i, ticker in enumerate(self._tickers): - params = self._params[ticker] - mu = params["mu"] - sigma = params["sigma"] - - # GBM step - drift = (mu - 0.5 * sigma**2) * self._dt - diffusion = sigma * math.sqrt(self._dt) * z[i] - self._prices[ticker] *= math.exp(drift + diffusion) - - # Random event - if random.random() < self._event_prob: - shock = random.uniform(0.02, 0.05) * random.choice([-1, 1]) - self._prices[ticker] *= (1 + shock) - - result[ticker] = round(self._prices[ticker], 2) - - return result - - def get_price(self, ticker: str) -> float | None: - return self._prices.get(ticker) - - def _rebuild_cholesky(self) -> None: - """Rebuild the Cholesky decomposition of the correlation matrix.""" - n = len(self._tickers) - if n <= 1: - self._cholesky = None - return - - corr = np.eye(n) - for i in range(n): - for j in range(i + 1, n): - rho = self._get_correlation(self._tickers[i], self._tickers[j]) - corr[i, j] = rho - corr[j, i] = rho - - self._cholesky = np.linalg.cholesky(corr) - - def _get_correlation(self, t1: str, t2: str) -> float: - """Return pairwise correlation between two tickers.""" - tech = {"AAPL", "GOOGL", "MSFT", "AMZN", "META", "NVDA", "NFLX"} - finance = {"JPM", "V"} - - t1_tech = t1 in tech - t2_tech = t2 in tech - t1_fin = t1 in finance - t2_fin = t2 in finance - - # Same sector: higher correlation - if t1_tech and t2_tech: - return 0.6 - if t1_fin and t2_fin: - return 0.5 - - # TSLA is a loner - if t1 == "TSLA" or t2 == "TSLA": - return 0.3 - - # Cross-sector or unknown - if (t1_tech and t2_fin) or (t1_fin and t2_tech): - return 0.3 - - # Default - return 0.3 -``` - -## File Structure - -All simulator code lives in a single module: - -``` -backend/ - app/ - market/ - simulator.py # GBMSimulator class + seed data + SimulatorDataSource - seed_prices.py # SEED_PRICES, TICKER_PARAMS, DEFAULT_PARAMS (constants) -``` - -`seed_prices.py` contains just the constant dictionaries. `simulator.py` contains the `GBMSimulator` class and the `SimulatorDataSource` (the `MarketDataSource` implementation that wraps `GBMSimulator` in an async loop). - -## Behavior Notes - -- Prices never go negative (GBM is multiplicative — `exp()` is always positive) -- The tiny `dt` produces sub-cent moves per tick, which accumulate naturally over time -- With `sigma=0.50` (TSLA), a day of simulated trading produces roughly the right intraday range -- The correlation matrix must be positive semi-definite — Cholesky decomposition guarantees this for valid correlation matrices -- Random events happen ~0.1% of steps = roughly once every 500 seconds per ticker. With 10 tickers, expect an event somewhere roughly every 50 seconds — enough to keep it interesting -- When a new ticker is added mid-session, the Cholesky matrix is rebuilt. This is O(n^2) but n is small (<50 tickers) diff --git a/planning/archive/MASSIVE_API.md b/planning/archive/MASSIVE_API.md deleted file mode 100644 index 3266bc64f..000000000 --- a/planning/archive/MASSIVE_API.md +++ /dev/null @@ -1,251 +0,0 @@ -# Massive API Reference (formerly Polygon.io) - -Reference documentation for the Massive (formerly Polygon.io) REST API as used in FinAlly. - -## Overview - -- **Base URL**: `https://api.massive.com` (legacy `https://api.polygon.io` still supported) -- **Python package**: `massive` (install via `pip install -U massive` / `uv add massive`) -- **Min Python version**: 3.9+ -- **Auth**: API key via `MASSIVE_API_KEY` env var or passed to `RESTClient(api_key=...)` -- **Auth header**: `Authorization: Bearer ` (the client handles this automatically) - -## Rate Limits - -| Tier | Limit | -|------|-------| -| Free | 5 requests/minute | -| Paid (all tiers) | Unlimited (recommended: stay under 100 req/s) | - -For FinAlly, we poll on a timer. Free tier: poll every 15s. Paid: poll every 2-5s. - -## Client Initialization - -```python -from massive import RESTClient - -# Reads MASSIVE_API_KEY from environment automatically -client = RESTClient() - -# Or pass explicitly -client = RESTClient(api_key="your_key_here") -``` - -## Endpoints Used in FinAlly - -### 1. Snapshot — All Tickers (Primary Endpoint) - -Gets current prices for multiple tickers in a **single API call**. This is the main endpoint we use for polling. - -**REST**: `GET /v2/snapshot/locale/us/markets/stocks/tickers?tickers=AAPL,GOOGL,MSFT` - -**Python client**: -```python -from massive import RESTClient -from massive.rest.models import SnapshotMarketType - -client = RESTClient() - -# Get snapshots for specific tickers (one API call) -snapshots = client.get_snapshot_all( - market_type=SnapshotMarketType.STOCKS, - tickers=["AAPL", "GOOGL", "MSFT", "AMZN", "TSLA"], -) - -for snap in snapshots: - print(f"{snap.ticker}: ${snap.last_trade.price}") - print(f" Day change: {snap.day.change_percent}%") - print(f" Day OHLC: O={snap.day.open} H={snap.day.high} L={snap.day.low} C={snap.day.close}") - print(f" Volume: {snap.day.volume}") -``` - -**Response structure** (per ticker): -```json -{ - "ticker": "AAPL", - "day": { - "open": 129.61, - "high": 130.15, - "low": 125.07, - "close": 125.07, - "volume": 111237700, - "volume_weighted_average_price": 127.35, - "previous_close": 129.61, - "change": -4.54, - "change_percent": -3.50 - }, - "last_trade": { - "price": 125.07, - "size": 100, - "exchange": "XNYS", - "timestamp": 1675190399000 - }, - "last_quote": { - "bid_price": 125.06, - "ask_price": 125.08, - "bid_size": 500, - "ask_size": 1000, - "spread": 0.02, - "timestamp": 1675190399500 - }, - "prev_daily_bar": { "...": "previous day OHLCV" }, - "minute_volume": { "...": "volume per minute" } -} -``` - -**Key fields we extract**: -- `last_trade.price` — current price for trading and display -- `day.previous_close` — for calculating day change -- `day.change_percent` — day change percentage -- `last_trade.timestamp` — when the price was recorded - -### 2. Single Ticker Snapshot - -For getting detailed data on one ticker (e.g., when user clicks a ticker for the detail view). - -**Python client**: -```python -snapshot = client.get_snapshot_ticker( - market_type=SnapshotMarketType.STOCKS, - ticker="AAPL", -) - -print(f"Price: ${snapshot.last_trade.price}") -print(f"Bid/Ask: ${snapshot.last_quote.bid_price} / ${snapshot.last_quote.ask_price}") -print(f"Day range: ${snapshot.day.low} - ${snapshot.day.high}") -``` - -### 3. Previous Close - -Gets the previous day's OHLC for a ticker. Useful for seed prices. - -**REST**: `GET /v2/aggs/ticker/{ticker}/prev` - -**Python client**: -```python -prev = client.get_previous_close_agg(ticker="AAPL") - -for agg in prev: - print(f"Previous close: ${agg.close}") - print(f"OHLC: O={agg.open} H={agg.high} L={agg.low} C={agg.close}") - print(f"Volume: {agg.volume}") -``` - -**Response**: -```json -{ - "ticker": "AAPL", - "results": [ - { - "o": 150.0, - "h": 155.0, - "l": 149.0, - "c": 154.5, - "v": 1000000, - "t": 1672531200000 - } - ] -} -``` - -### 4. Aggregates (Bars) - -Historical OHLCV bars over a date range. Not needed for live polling but useful if we add historical charts. - -**REST**: `GET /v2/aggs/ticker/{ticker}/range/{multiplier}/{timespan}/{from}/{to}` - -**Python client**: -```python -aggs = [] -for a in client.list_aggs( - ticker="AAPL", - multiplier=1, - timespan="day", - from_="2024-01-01", - to="2024-01-31", - limit=50000, -): - aggs.append(a) - -for a in aggs: - print(f"Date: {a.timestamp}, O={a.open} H={a.high} L={a.low} C={a.close} V={a.volume}") -``` - -**Response** (each bar): -```json -{ - "o": 130.0, - "h": 132.5, - "l": 129.8, - "c": 131.2, - "v": 50000000, - "t": 1672531200000 -} -``` - -### 5. Last Trade / Last Quote - -Individual endpoints for the most recent trade or NBBO quote. - -```python -# Last trade -trade = client.get_last_trade(ticker="AAPL") -print(f"Last trade: ${trade.price} x {trade.size}") - -# Last NBBO quote -quote = client.get_last_quote(ticker="AAPL") -print(f"Bid: ${quote.bid} x {quote.bid_size}") -print(f"Ask: ${quote.ask} x {quote.ask_size}") -``` - -## How FinAlly Uses the API - -The Massive poller runs as a background task: - -1. Collects all tickers from the watchlist -2. Calls `get_snapshot_all()` with those tickers (one API call) -3. Extracts `last_trade.price` and `day.previous_close` from each snapshot -4. Writes to the shared in-memory price cache -5. Sleeps for the poll interval, then repeats - -```python -import asyncio -from massive import RESTClient -from massive.rest.models import SnapshotMarketType - -async def poll_massive(api_key: str, get_tickers, price_cache, interval: float = 15.0): - """Poll Massive API and update the price cache.""" - client = RESTClient(api_key=api_key) - - while True: - tickers = get_tickers() - if tickers: - snapshots = client.get_snapshot_all( - market_type=SnapshotMarketType.STOCKS, - tickers=tickers, - ) - for snap in snapshots: - price_cache.update( - ticker=snap.ticker, - price=snap.last_trade.price, - previous_close=snap.day.previous_close, - timestamp=snap.last_trade.timestamp, - ) - - await asyncio.sleep(interval) -``` - -## Error Handling - -The client raises exceptions for HTTP errors: -- **401**: Invalid API key -- **403**: Insufficient permissions (plan doesn't include the endpoint) -- **429**: Rate limit exceeded (free tier: 5 req/min) -- **5xx**: Server errors (client has built-in retry with 3 retries by default) - -## Notes - -- The snapshot endpoint returns data for **all requested tickers in one call** — this is critical for staying within rate limits on the free tier -- Timestamps from the API are Unix milliseconds -- During market closed hours, `last_trade.price` reflects the last traded price (may include after-hours) -- The `day` object resets at market open; during pre-market, values may be from the previous session