HQM Momentum Scanner

A web application implementing the High Quality Momentum (HQM) investment strategy, inspired by Qullamaggie's quantitative trading approach. It identifies stocks showing consistent "slow and steady" momentum across multiple timeframes, filtering out low-quality momentum caused by short-term news events.

Strategy Overview

The HQM strategy differentiates between:

• High-quality momentum: Stocks showing strong performance across 1-month, 3-month, 6-month, and 1-year timeframes
• Low-quality momentum: Short-term spikes caused by news (e.g., FDA approvals) unlikely to sustain

By requiring consistency across all timeframes, the strategy filters out stocks with unreliable momentum patterns.

Features

• Multi-timeframe Momentum Analysis: Prevents false signals from single-period spikes
• Quality Filter: Requires minimum 25th percentile in ALL timeframes
• SMA10 Entry Filter: Identifies optimal entry points based on 10-day moving average distance
• SQLite Caching: Fast scans without excessive API calls
• Historical Tracking: Enables backtesting and performance analysis
• TradingView Integration: Direct links to stock charts
• CSV Export: Download scan results for external analysis
• Dark Theme UI: Professional, low-eye-strain interface

Tech Stack

Layer	Technology
Backend	Python 3, Flask
Database	SQLite
Frontend	HTML5, Bootstrap 5, Chart.js
Data APIs	FinViz, yfinance
Data Science	Pandas, NumPy, SciPy

Installation

Prerequisites

• Python 3.8+
• pip or conda

Setup

# Clone the repository
git clone <repository-url>
cd momentum_trader

# Install dependencies
pip install -r requirements.txt

Usage

Web Application

python app.py

Open your browser at http://localhost:5000

Workflow:

1. Click "Refresh" to fetch stock data from FinViz
2. Configure portfolio size and number of positions
3. Optionally enable SMA10 filter for better entry timing
4. Click "Run Scan" to calculate HQM scores
5. View results, charts, and export to CSV

Command Line

python momentum.py

Follow the prompts to enter portfolio size and number of positions. Results are exported to hqm_portfolio.xlsx.

Configuration

Default settings in momentum.py:

PORTFOLIO_SIZE = 10000          # Portfolio size in USD
NUM_POSITIONS = 8               # Number of stocks to hold
MIN_MARKET_CAP = '+Mid (over $2bln)'  # Market cap filter
EXCHANGES = ['NYSE', 'NASDAQ']  # Exchanges to scan

API Endpoints

Endpoint	Method	Description
/api/data-status	GET	Current data age & stock count
/api/refresh	POST	Fetch fresh data from FinViz
/api/scan	POST	Run HQM scan with parameters
/api/status	GET	Get operation progress
/api/results	GET	Fetch latest scan results
/api/history	GET	Get scan history
/api/ticker-history/<ticker>	GET	Get HQM history for specific ticker

Project Structure

momentum_trader/
├── app.py                 # Flask server & API endpoints
├── momentum.py            # Core HQM strategy algorithm
├── database.py            # SQLite database management
├── requirements.txt       # Python dependencies
├── templates/
│   └── index.html        # Web UI template
├── static/
│   ├── js/app.js         # Frontend logic
│   └── css/style.css     # Dark theme styling
└── hqm_data.db           # SQLite database (auto-generated)

How HQM Scoring Works

1. Percentile Calculation: Each stock is ranked by return performance in each timeframe (0-100)
2. Composite Score: Average of percentiles across 4 timeframes
3. Quality Filter: Stocks below 25th percentile in ANY timeframe are removed
4. Ranking: Results sorted by HQM Score (highest first)
5. Equal-weight Allocation: Portfolio divided equally among selected positions

SMA10 Distance Indicator

The optional SMA10 filter helps time entries:

• 🟢 Green (≤5%): Good entry point
• 🟡 Yellow (5-15%): Moderately extended
• 🔴 Red (>15%): Overextended, consider waiting

Dependencies

numpy
pandas
scipy
xlsxwriter
finvizfinance
flask
yfinance

License

MIT License

Acknowledgments

• Strategy inspired by Qullamaggie momentum trading approach
• Data provided by FinViz and Yahoo Finance