SQA CLI Usage Examples¶

This guide demonstrates how to use the SQA CLI with the sample data files in the data/ directory.

Prerequisites¶

# Install dependencies in this project
bundle install

# Install dependencies in the SQA gem
cd ../sqa/main && bundle install && cd -

# Make sure the executable is available
chmod +x bin/sqa-cli

Note: The SQA CLI requires the SQA gem to be properly installed with all its dependencies. The commands in this guide should be run with bundle exec to ensure all dependencies are available.

Current Status: The integration between sqa-cli and the SQA gem is still being finalized. Some commands may require additional configuration of the SQA gem's data directory and API keys.

Basic Commands¶

Show Help¶

# General help
bundle exec ./bin/sqa-cli help

# Command-specific help
bundle exec ./bin/sqa-cli analyze --help
bundle exec ./bin/sqa-cli backtest --help
bundle exec ./bin/sqa-cli pattern --help

Check Version¶

bundle exec ./bin/sqa-cli version

Stock Analysis Commands¶

1. Analyze Stock Data¶

Run various analysis methods on historical stock data:

# Run all analysis methods on AAPL
./bin/sqa-cli analyze --ticker AAPL --methods all

# Run specific analysis methods
./bin/sqa-cli analyze --ticker AAPL --methods fpop,regime
./bin/sqa-cli analyze --ticker MSFT --methods seasonal

# Analyze with custom parameters
./bin/sqa-cli analyze --ticker GOOGL --methods fpop --fpop-periods 15
./bin/sqa-cli analyze --ticker TSLA --methods regime --regime-window 90

# Verbose output
./bin/sqa-cli analyze --ticker AAPL --methods all --verbose

What it does: - FPOP: Future Period of Performance analysis - identifies potential buy/sell opportunities - Regime: Detects market regime (bull/bear/sideways) - Seasonal: Analyzes seasonal patterns and best/worst months

2. Backtest Trading Strategies¶

Test trading strategies against historical data:

# Backtest RSI strategy on AAPL
./bin/sqa-cli backtest --ticker AAPL --strategy RSI

# Test different strategies
./bin/sqa-cli backtest --ticker MSFT --strategy SMA
./bin/sqa-cli backtest --ticker GOOGL --strategy MACD
./bin/sqa-cli backtest --ticker TSLA --strategy BollingerBands

# Compare all strategies
./bin/sqa-cli backtest --ticker AAPL --compare

# Custom capital and commission
./bin/sqa-cli backtest --ticker AAPL --strategy RSI --capital 50000 --commission 5.0

# Verbose output
./bin/sqa-cli backtest --ticker AAPL --strategy RSI --verbose

Available Strategies: - RSI, SMA, EMA, MACD - BollingerBands, Stochastic - VolumeBreakout, KBS, Consensus, Random

Output includes: - Total return percentage - Sharpe ratio - Maximum drawdown - Win rate - Number of trades

3. Discover Trading Patterns¶

Find profitable trading patterns using reverse engineering:

# Find patterns with minimum 10% gain
./bin/sqa-cli pattern --ticker AAPL --min-gain 10

# More aggressive pattern search
./bin/sqa-cli pattern --ticker TSLA --min-gain 15 --min-frequency 2

# Generate strategies from patterns
./bin/sqa-cli pattern --ticker AAPL --min-gain 10 --generate

# Export patterns to CSV
./bin/sqa-cli pattern --ticker AAPL --min-gain 10 --export patterns_aapl.csv

# Custom parameters
./bin/sqa-cli pattern --ticker MSFT \
  --min-gain 12 \
  --fpop 15 \
  --min-frequency 3 \
  --window 5 \
  --max-patterns 15

# Verbose output
./bin/sqa-cli pattern --ticker AAPL --min-gain 10 --verbose

What it does: - Identifies historical inflection points - Analyzes indicator combinations at those points - Finds patterns that preceded profitable trades - Can generate and backtest strategies from patterns

4. Genetic Programming Optimization¶

Evolve optimal strategy parameters using genetic algorithms:

# Evolve RSI strategy for AAPL
./bin/sqa-cli genetic --ticker AAPL

# Custom population and generations
./bin/sqa-cli genetic --ticker TSLA --population 30 --generations 20

# Adjust mutation and crossover rates
./bin/sqa-cli genetic --ticker MSFT \
  --population 25 \
  --generations 15 \
  --mutation-rate 0.2 \
  --crossover-rate 0.8

# Verbose output to see evolution progress
./bin/sqa-cli genetic --ticker AAPL --verbose

What it does: - Evolves RSI strategy parameters (period, buy/sell thresholds) - Shows evolution history across generations - Tests best parameters with full backtest - Reports optimal configuration found

5. Knowledge-Based Strategy (KBS)¶

Use rule-based inference engine for trading decisions:

# Run with default rules
./bin/sqa-cli kbs --ticker AAPL --rules default

# Show loaded rules
./bin/sqa-cli kbs --ticker AAPL --rules default --show-rules

# Show asserted facts
./bin/sqa-cli kbs --ticker AAPL --rules default --show-facts

# Backtest the KBS strategy
./bin/sqa-cli kbs --ticker AAPL --rules default --backtest

# Use custom rule sets
./bin/sqa-cli kbs --ticker MSFT --rules custom
./bin/sqa-cli kbs --ticker GOOGL --rules minimal

# Full verbose output
./bin/sqa-cli kbs --ticker AAPL --rules default --show-rules --show-facts --backtest --verbose

What it does: - Uses RETE forward-chaining inference - Combines multiple technical indicators - Applies trading rules based on market conditions - Generates buy/sell/hold signals

6. Real-time Streaming Simulation¶

Simulate real-time price streaming with strategy execution:

# Stream AAPL with RSI strategy
./bin/sqa-cli stream --ticker AAPL --strategies RSI

# Multiple strategies
./bin/sqa-cli stream --ticker TSLA --strategies RSI,MACD

# Custom window and update count
./bin/sqa-cli stream --ticker MSFT \
  --strategies RSI,MACD \
  --window 150 \
  --updates 100

# Verbose output to see updates
./bin/sqa-cli stream --ticker AAPL --strategies RSI --verbose

What it does: - Simulates real-time price updates from historical data - Executes strategies on each update - Tracks generated signals - Shows signal summary

7. Portfolio Optimization¶

Optimize portfolio allocation across multiple stocks:

# Optimize using Sharpe ratio (default)
./bin/sqa-cli optimize --tickers AAPL,MSFT,GOOGL

# All four stocks from our data
./bin/sqa-cli optimize --tickers AAPL,MSFT,GOOGL,TSLA

# Use different optimization methods
./bin/sqa-cli optimize --tickers AAPL,MSFT,GOOGL --method variance
./bin/sqa-cli optimize --tickers AAPL,MSFT,GOOGL --method risk_parity
./bin/sqa-cli optimize --tickers AAPL,MSFT,GOOGL --method efficient_frontier

# Show risk metrics for each stock
./bin/sqa-cli optimize --tickers AAPL,MSFT,GOOGL,TSLA --risk-metrics

# Custom risk-free rate
./bin/sqa-cli optimize --tickers AAPL,MSFT,GOOGL --method sharpe --risk-free-rate 0.03

# Verbose output
./bin/sqa-cli optimize --tickers AAPL,MSFT,GOOGL,TSLA --risk-metrics --verbose

Optimization Methods: - sharpe: Maximum Sharpe ratio - variance: Minimum variance - risk_parity: Equal risk contribution - efficient_frontier: Calculate entire efficient frontier

Working with Portfolio Data¶

Load Portfolio from CSV¶

# Ruby example
require 'csv'

portfolio = CSV.read('data/portfolios/my_portfolio.csv', headers: true)
portfolio.each do |position|
  puts "#{position['ticker']}: #{position['shares']} shares @ $#{position['purchase_price']}"
end

Load Portfolio from JSON¶

# Ruby example
require 'json'

portfolio = JSON.parse(File.read('data/portfolios/my_portfolio.json'))
puts "Portfolio: #{portfolio['portfolio_name']}"
puts "Total Invested: $#{portfolio['total_invested']}"

portfolio['positions'].each do |position|
  puts "#{position['ticker']}: #{position['shares']} shares (avg: $#{position['average_purchase_price']})"
end

Analyze Portfolio Positions¶

# Analyze each stock in the portfolio
./bin/sqa-cli analyze --ticker AAPL --methods all
./bin/sqa-cli analyze --ticker MSFT --methods all
./bin/sqa-cli analyze --ticker GOOGL --methods all
./bin/sqa-cli analyze --ticker TSLA --methods all

# Optimize the portfolio allocation
./bin/sqa-cli optimize --tickers AAPL,MSFT,GOOGL,TSLA --risk-metrics

Advanced Usage¶

Combining Commands¶

# Analyze, backtest, and find patterns for AAPL
./bin/sqa-cli analyze --ticker AAPL --methods all
./bin/sqa-cli backtest --ticker AAPL --compare
./bin/sqa-cli pattern --ticker AAPL --min-gain 10 --generate

# Complete analysis workflow
./bin/sqa-cli analyze --ticker TSLA --methods all --verbose
./bin/sqa-cli genetic --ticker TSLA --population 30 --generations 20
./bin/sqa-cli backtest --ticker TSLA --strategy RSI
./bin/sqa-cli kbs --ticker TSLA --backtest

Save Output to Files¶

# Redirect output to files
./bin/sqa-cli analyze --ticker AAPL --methods all > analysis_aapl.txt
./bin/sqa-cli backtest --ticker AAPL --compare > backtest_comparison.txt

# Export patterns
./bin/sqa-cli pattern --ticker AAPL --min-gain 10 --export data/patterns_aapl.csv

Using Different Tickers¶

# The CLI works with any ticker that has data in data/stocks/
# Available tickers: AAPL, MSFT, GOOGL, TSLA

for ticker in AAPL MSFT GOOGL TSLA; do
  echo "Analyzing $ticker..."
  ./bin/sqa-cli analyze --ticker $ticker --methods regime
done

Common Workflows¶

1. Quick Stock Analysis¶

# Get a quick overview of a stock
./bin/sqa-cli analyze --ticker AAPL --methods all
./bin/sqa-cli backtest --ticker AAPL --strategy RSI

2. Strategy Development¶

# Find patterns, evolve parameters, test strategy
./bin/sqa-cli pattern --ticker AAPL --min-gain 10 --generate
./bin/sqa-cli genetic --ticker AAPL --population 30 --generations 20
./bin/sqa-cli backtest --ticker AAPL --compare

3. Portfolio Analysis¶

# Analyze individual positions
for ticker in AAPL MSFT GOOGL TSLA; do
  ./bin/sqa-cli analyze --ticker $ticker --methods regime,seasonal
done

# Optimize allocation
./bin/sqa-cli optimize --tickers AAPL,MSFT,GOOGL,TSLA --risk-metrics

4. Strategy Comparison¶

# Compare all strategies on multiple stocks
./bin/sqa-cli backtest --ticker AAPL --compare
./bin/sqa-cli backtest --ticker MSFT --compare
./bin/sqa-cli backtest --ticker GOOGL --compare
./bin/sqa-cli backtest --ticker TSLA --compare

Tips and Best Practices¶

Start with Analysis: Always run analyze first to understand the stock's characteristics
Use Verbose Mode: Add --verbose to see detailed processing information
Compare Strategies: Use --compare in backtest to find the best strategy
Iterate on Patterns: Adjust --min-gain and --min-frequency to find optimal patterns
Genetic Programming: Use larger populations and more generations for better results
Portfolio Optimization: Include --risk-metrics to understand individual stock risks

Troubleshooting¶

If a command doesn't work:¶

Check that the SQA gem is properly installed:
```
bundle install
```
Verify the ticker has data:
```
ls data/stocks/
```
Use --help to see all options:
```
./bin/sqa-cli <command> --help
```

Enable verbose mode for debugging:

./bin/sqa-cli <command> --ticker AAPL --verbose

Next Steps¶

Explore the data/ directory to understand the data format
Read data/README.md for detailed data documentation
Check the main README.md for architecture details
Run bundle exec rake test to verify everything works