stockcharts 0.2.0

NASDAQ stock screener using Heiken Ashi candles for trend reversal detection with volume and price filtering

StockCharts

A Python library for screening NASDAQ stocks using Heiken Ashi candles to detect trend reversals with volume filtering.

Features

� NASDAQ Screener

• Full NASDAQ Coverage: Automatically fetches all 5,120+ NASDAQ tickers from official FTP source
• Heiken Ashi Analysis: Detects red-to-green and green-to-red candle color changes
• Volume Filtering: Filter by average daily volume to focus on liquid, tradeable stocks
• Flexible Timeframes: Support for intraday (1m-1h), daily, weekly, and monthly charts
• Custom Date Ranges: Screen historical data with specific start/end dates
• CSV Export: Save screening results for further analysis

📊 Chart Generation

• Generate Heiken Ashi candlestick charts from screening results
• Support for multiple timeframes: 1m, 5m, 15m, 1h, 1d, 1wk, 1mo
• High-quality PNG output for technical analysis

🎯 Trading Styles Supported

• Day Trading: 1m-1h periods, high volume (2M+ shares/day)
• Swing Trading: Daily charts, moderate volume (500K-1M shares/day)
• Position Trading: Weekly/monthly charts, lower volume acceptable

Installation

From PyPI (coming soon)

pip install stockcharts

From Source

# Clone the repository
git clone https://github.com/paulboys/HeikinAshi.git
cd HeikinAshi

# Create conda environment
conda create -n stockcharts python=3.12 -y
conda activate stockcharts

# Install in editable mode
pip install -e .

Quick Start

After installation, you'll have two command-line tools available:

Usage

1. Screen for Trend Reversals

Find green reversals (red→green) for swing trading:

stockcharts-screen --color green --changed-only --min-volume 500000

Day trading setup (1-hour charts with high volume):

stockcharts-screen --color green --period 1h --lookback 1mo --min-volume 2000000 --changed-only

Weekly analysis over 6 months:

stockcharts-screen --color green --period 1wk --lookback 6mo --changed-only

Screen specific date range:

stockcharts-screen --color red --start 2024-01-01 --end 2024-12-31

2. Generate Charts from Results

Plot all screened stocks:

stockcharts-plot

Plot from specific CSV:

stockcharts-plot --input results/green_reversals.csv --output-dir my_charts/

Command-Line Options

stockcharts-screen

• --color: Filter by red or green candles (default: green)
• --period: Aggregation period: 1m, 5m, 15m, 1h, 1d, 1wk, 1mo (default: 1d)
• --lookback: Historical window: 1d, 5d, 1mo, 3mo, 6mo, 1y, 2y, max (default: 3mo)
• --start, --end: Custom date range in YYYY-MM-DD format
• --changed-only: Only show stocks where color changed in latest candle
• --min-volume: Minimum average daily volume (e.g., 500000)
• --output: CSV output path (default: results/nasdaq_screen.csv)
• --debug: Show detailed error messages

stockcharts-plot

• --input: Input CSV file from screener
• --output-dir: Directory for chart images (default: charts/)
• --period: Chart timeframe (default: 1d)
• --lookback: Historical data window (default: 3mo)

See QUICK_REFERENCE.md for parameter details.

Library API

You can also use StockCharts programmatically in your Python code:

from stockcharts.screener.screener import screen_nasdaq
from stockcharts.screener.nasdaq import get_nasdaq_tickers
from stockcharts.data.fetch import fetch_ohlc
from stockcharts.charts.heiken_ashi import heiken_ashi

# Screen for green reversals with volume filter
results = screen_nasdaq(
    color='green',
    period='1d',
    lookback='3mo',
    changed_only=True,
    min_volume=500000
)

# Get all NASDAQ tickers
tickers = get_nasdaq_tickers()
print(f"Found {len(tickers)} NASDAQ tickers")

# Fetch data and compute Heiken Ashi
data = fetch_ohlc('AAPL', period='1d', lookback='3mo')
ha_data = heiken_ashi(data)

Project Structure

StockCharts/
├── src/stockcharts/          # Main package
│   ├── cli.py                # Command-line entry points
│   ├── charts/               # Heiken Ashi computation
│   ├── data/                 # Data fetching (yfinance)
│   └── screener/             # NASDAQ screening logic
├── scripts/                  # Legacy CLI scripts
├── tests/                    # Unit tests
├── requirements.txt          # Dependencies
└── pyproject.toml            # Package configuration

Requirements

• Python 3.9+
• yfinance >= 0.2.38
• pandas >= 2.0.0
• matplotlib >= 3.7.0

Output Examples

Screener CSV Output

ticker,color,ha_open,ha_close,last_date,period,color_changed,avg_volume
AAPL,green,225.34,227.89,2024-01-15,1d,True,58234567
MSFT,green,402.15,405.67,2024-01-15,1d,True,25678901
NVDA,green,520.88,528.45,2024-01-15,1d,True,45123890

Chart Output

Charts include:

• Green candles for bullish moves (HA_Close >= HA_Open)
• Red candles for bearish moves (HA_Close < HA_Open)
• Full wicks showing HA_High and HA_Low
• Date labels on x-axis
• Automatic scaling based on price range

Documentation

• LIBRARY_GUIDE.md: Comprehensive usage guide with examples
• QUICK_REFERENCE.md: Parameter quick reference
• VOLUME_FILTERING_GUIDE.md: Volume filtering strategies
• TRADING_STYLE_GUIDE.md: Recommendations by trading style
• DISTRIBUTION.md: Build and distribution guide (for maintainers)

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Roadmap

• Publish to PyPI
• Add unit tests and CI/CD
• Additional technical indicators (RSI, MACD, Bollinger Bands)
• Multi-ticker comparison charts
• Backtesting framework
• Real-time streaming data support
• Alert/notification system

License

MIT License - see LICENSE file for details.

Acknowledgments

• yfinance: Yahoo Finance data API
• pandas: Data manipulation and analysis
• matplotlib: Chart generation
• NASDAQ: Official ticker data via FTP

Support

If you encounter any issues or have questions:

• Open an issue: https://github.com/paulboys/HeikinAshi/issues
• Check the documentation in this repository

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Happy Trading! 📈