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SQA - Simple Qualitative Analysis

SQA - Simple Qualitative Analysis

Key Features

  • High Performance - Rust-backed Polars DataFrames (30x faster)
  • 150+ Technical Indicators - TA-Lib integration via sqa-tai
  • 13+ Trading Strategies - RSI, MACD, Bollinger Bands, and more
  • Portfolio Management - Track positions, P&L, commissions
  • Backtesting Framework - Comprehensive performance metrics
  • Real-Time Streaming - Live price data with callbacks
  • Strategy Generation - Discover patterns from profitable trades
  • Genetic Programming - Evolve optimal parameters
  • Risk Management - VaR, CVaR, position sizing
  • Pattern Matching - Find similar historical patterns

Welcome to SQA

SQA (Simple Qualitative Analysis) is an educational Ruby library designed for stock market technical analysis and trading strategy development. Built with high-performance data structures and seamlessly integrated with TA-Lib, SQA provides a comprehensive toolkit for analyzing historical stock data, implementing trading strategies, and backtesting your ideas.

Educational Purpose Only

SQA is designed for educational purposes only. It should not be used for actual trading without extensive testing and professional financial advice. Trading stocks involves substantial risk of loss.

Why SQA?

⚡ High Performance

  • Polars DataFrames: Rust-backed data structures providing 30x faster operations than pure Ruby
  • TA-Lib Integration: Access to 150+ battle-tested technical indicators via the sqa-tai gem
  • Efficient Algorithms: Optimized for large historical datasets

🧰 Comprehensive Feature Set

  • 13+ Trading Strategies: From simple moving averages to advanced machine learning-based strategies
  • Portfolio Management: Track positions, calculate P&L, manage commissions
  • Backtesting Framework: Simulate strategies with comprehensive performance metrics
  • Real-Time Streaming: Process live price data with callback support
  • Strategy Generation: Reverse-engineer profitable trades to discover patterns
  • Genetic Programming: Evolve optimal strategy parameters

🎓 Educational Focus

  • Clear Documentation: Extensive guides and examples
  • Transparent Algorithms: Understand how each indicator and strategy works
  • Modular Design: Learn by building custom strategies
  • Risk Disclaimers: Honest about limitations and risks

Quick Example

require 'sqa'

# Initialize SQA
SQA.init

# Load stock data
stock = SQA::Stock.new(ticker: 'AAPL')

# Get price data
prices = stock.df["adj_close_price"].to_a

# Calculate RSI indicator
rsi = SQAI.rsi(prices, period: 14)

# Execute RSI trading strategy
require 'ostruct'
vector = OpenStruct.new(rsi: { trend: rsi.last < 30 ? :over_sold : :over_bought })
signal = SQA::Strategy::RSI.trade(vector)  # => :buy, :sell, or :hold

# Backtest the strategy
backtest = SQA::Backtest.new(
  stock: stock,
  strategy: SQA::Strategy::RSI,
  initial_cash: 10_000
)
results = backtest.run

puts "Total Return: #{results.total_return}%"
puts "Sharpe Ratio: #{results.sharpe_ratio}"
puts "Max Drawdown: #{results.max_drawdown}%"

Core Features

Data Management

  • Multiple Data Sources: Alpha Vantage, Yahoo Finance, CSV imports
  • Polars DataFrames: High-performance time series data manipulation
  • Automatic Updates: Keep historical data current

Technical Analysis

  • 150+ Indicators: SMA, EMA, RSI, MACD, Bollinger Bands, Stochastic, ADX, ATR, and more
  • Custom Calculations: Build your own indicators
  • Trend Detection: Identify market conditions automatically

Trading Strategies

  • Built-in Strategies: RSI, MACD, Bollinger Bands, Moving Average crossovers, Volume Breakout
  • Strategy Framework: Plugin architecture for custom strategies
  • Consensus Approach: Combine multiple strategies
  • Rule-Based (KBS): RETE-based forward-chaining inference engine

Advanced Analytics

  • Portfolio Tracking: Monitor positions, P&L, commissions
  • Backtesting: Historical simulation with performance metrics
  • Strategy Generator: Mine patterns from profitable trades
  • Genetic Programming: Evolutionary parameter optimization
  • FPOP Analysis: Future Period of Performance calculations
  • Real-Time Streaming: Process live data with event callbacks

Architecture

SQA Architecture

Getting Started

Ready to dive in? Check out our guides:

Key Resources

For Beginners

For Advanced Users

Reference

Demo Application

Want to see SQA in action? Check out the sqa_demo-sinatra gem - a web-based demonstration application that provides a visual interface for exploring stock analysis, technical indicators, and trading strategies.

Community & Support

License

SQA is released under the MIT License. See the LICENSE file for details.

Remember: The House Always Wins
Trade responsibly. Never risk more than you can afford to lose.