π Live Demo: mitrui.com/PokieTicker | Buy Me A Coffee
Since I wanted to understand the stories behind candlestick charts, I vibe coded this app.
As a stock market beginner, the news was too fragmented and I was wasting a lot of time, never understanding why prices went up or down. I built PokieTicker to develop event-driven thinking β to understand the "why" behind every price move.
Click to watch the full demo video
- News on the chart β Dots on the candlestick chart represent news for each date. Click any dot to see the news impacting that company at the time.
- Filter by impact type β Market, earnings, product, policy, competition, or management. Click a category to see related bullish or bearish news.
- Find similar events β Discover historical days with similar news patterns and see what happened to the stock afterward.
- AI explains price moves β Select a date range and ask AI why the stock dropped or rallied. It tells you what events caused it.
- Predict trends β Based on the past 30 days of news events, the model predicts how these events might affect future price direction.
PokieTicker includes an XGBoost-based prediction system that combines news sentiment with technical indicators:
Features (31 total):
- News features β article count, sentiment score, positive/negative ratio, 3/5/10-day rolling averages, sentiment momentum
- Technical features β price returns (1/3/5/10-day), volatility, volume ratio, RSI-14, moving average crossover
How it works:
- News articles are scored for sentiment by Claude Haiku (batch API) β each article gets a sentiment label, key discussion summary, and bullish/bearish reasons
- These are combined with OHLC price data into daily feature vectors
- XGBoost classifiers predict up/down direction at T+1, T+3, and T+5 horizons
- The system also finds historically similar periods (cosine similarity on feature vectors) and shows what happened next
Why news-driven prediction can work:
- Stock prices are driven by information. Major news events (earnings, policy changes, product launches) create predictable short-term momentum
- Sentiment clustering matters β when multiple negative articles cluster on the same day, the downward pressure tends to persist for 1-3 days
- Historical pattern matching works because markets react similarly to similar events (e.g., tariff announcements, FDA approvals, earnings beats)
This is an experimental tool for learning, not financial advice. Markets are complex and no model captures everything.
Frontend (React + Vite + D3.js) Backend (FastAPI + SQLite)
+---------------------------------+ +----------------------------+
| CandlestickChart (D3.js) | | /api/stocks/{sym}/ohlc |
| +- news dots on each date |----->| /api/news/{sym}?date= |
| +- crosshair + click to lock | | /api/news/{sym}/categories|
| | | |
| NewsPanel (right sidebar) |<-----| SQLite: pokieticker.db |
| +- sentiment sorted | | +- ohlc (51K+ rows) |
| +- up/down reasons | | +- news_raw (61K+) |
| +- T+1/T+5 returns | | +- layer1_results (97K+) |
| | | |
| PredictionPanel |<-----| /api/predict/{sym}/forecast|
| +- 7-day & 30-day forecasts | | +- XGBoost models |
| +- similar historical periods | | +- cosine similarity |
+---------------------------------+ +----------------------------+
Polygon API --> Layer 0 (rule filter) --> Layer 1 (Haiku Batch API) --> Layer 2 (Sonnet, on-demand)
OHLC + News reject spam/listicles sentiment + up/down reasons deep analysis on click
~17% rejected 50 articles per batch call cached in DB
The repo includes a pre-built database (pokieticker.db) with historical data, so you can run it immediately β no API keys needed.
git clone https://github.com/owengetinfo-design/PokieTicker.git cd PokieTicker # Unpack the pre-built database and models gunzip -k pokieticker.db.gz tar xzf models.tar.gz -C backend/ml/ # Backend (Python 3.10+) python -m venv venv source venv/bin/activate # Windows: venv\Scripts\activate pip install -r requirements.txt # Frontend (Node.js 18+) cd frontend && npm install && cd ..
Then start both services (in two terminal windows):
# Terminal 1: Backend source venv/bin/activate uvicorn backend.api.main:app --reload # Terminal 2: Frontend cd frontend && npm run dev
Open http://localhost:7777/PokieTicker/ and you're done.
Just want to see the demo? Visit mitrui.com/PokieTicker β no setup needed.
If you want to fetch the latest stock data and run AI analysis, you'll need API keys:
cp .env.example .env
# Edit .env and fill in your keys:| Key | Where to get it | Cost |
|---|---|---|
POLYGON_API_KEY |
polygon.io | Free tier |
ANTHROPIC_API_KEY |
console.anthropic.com | Pay-as-you-go |
# Fetch new OHLC + news python -m backend.bulk_fetch # Run AI analysis python -m backend.batch_submit --top 50 python -m backend.batch_collect <batch_id>
.env # API keys (gitignored)
pokieticker.db # SQLite database (gitignored)
requirements.txt # Python dependencies
backend/
config.py # pydantic-settings, loads .env
database.py # 9-table SQLite schema
bulk_fetch.py # Bulk download OHLC + news for many tickers
batch_submit.py # Submit Layer 1 to Anthropic Batch API
batch_collect.py # Collect Batch API results
weekly_update.py # Incremental weekly data update
polygon/
client.py # Polygon API with retry/backoff
pipeline/
layer0.py # Rule-based filter (free)
layer1.py # Claude Haiku batch analysis
layer2.py # Sonnet on-demand deep analysis
alignment.py # News -> trading day + forward returns
similarity.py # Similar news pattern matching
ml/
features.py # 31-feature engineering (news + technical)
model.py # XGBoost training (per-ticker + unified)
inference.py # Forecast generation + similar period analysis
backtest.py # Backtesting framework
lstm_model.py # Experimental LSTM model
api/
main.py # FastAPI app + CORS
routers/
stocks.py # GET /api/stocks, /search, /{sym}/ohlc
news.py # GET /api/news/{sym}, /{sym}/timeline
analysis.py # POST /api/analysis/deep, /story
pipeline.py # POST /api/pipeline/fetch, /process
predict.py # GET /api/predict/{sym}/forecast
frontend/
src/
App.tsx # Main layout (chart + panels)
components/
CandlestickChart.tsx # D3.js chart with news dots
NewsPanel.tsx # Sentiment-sorted news cards
NewsCategoryPanel.tsx # Filter by impact category
PredictionPanel.tsx # AI forecast + similar periods
RangeQueryPopup.tsx # "Why did it drop?" range analysis
SimilarDaysPanel.tsx # Historical pattern matches
StockSelector.tsx # Ticker search + tabs
# Fetch new OHLC + news since last update python -m backend.weekly_update # Run AI analysis on new articles python -m backend.batch_submit --top 50 python -m backend.batch_collect <batch_id>
| Item | Cost |
|---|---|
| Polygon data (free tier) | 0γγ« |
| Layer 1 Batch API (per 1000 articles) | ~0γγ«.35 |
| Layer 2 on-demand (per article) | ~0γγ«.003 |
| Weekly incremental update | ~1γγ«-2 |
- Frontend: React, TypeScript, Vite, D3.js
- Backend: FastAPI, SQLite (WAL mode), Pydantic
- AI: Claude Haiku 4.5 (batch sentiment), Claude Sonnet (deep analysis)
- ML: XGBoost (prediction), cosine similarity (pattern matching)
- Data: Polygon.io REST API
MIT β see LICENSE for details.