CLI Reference

Command-line interface reference for QuantumVest administration, data operations, and development tasks.

Overview
Installation
Global Options
Commands

Overview

QuantumVest provides CLI tools through shell scripts and Python scripts for administration, development, and operational tasks.

CLI Components

Component	Location	Purpose
Setup Scripts	`scripts/`	Environment setup and installation
Backend CLI	`code/backend/`	Database, API, and data operations
AI Model CLI	`code/ai_models/`	Model training and evaluation
DevOps Scripts	`scripts/`	Deployment and maintenance

Installation

No separate CLI installation required. Scripts are available after repository clone.

git clone https://github.com/quantsingularity/QuantumVest.git
cd QuantumVest
chmod +x scripts/*.sh  # Make scripts executable

Global Options

Most scripts support common options:

Option	Description	Example
`--help`	Show help message	`./script.sh --help`
`--verbose`	Enable verbose output	`./script.sh --verbose`
`--config FILE`	Use custom config file	`./script.sh --config custom.conf`

Commands

Setup Commands

setup_quantumvest_env.sh

Complete environment setup for QuantumVest.

Usage:

./scripts/setup_quantumvest_env.sh [OPTIONS]

Description: Installs all dependencies, creates virtual environments, and configures the application.

What it does:

Creates Python virtual environment
Installs backend dependencies
Installs frontend dependencies
Sets up database connections
Initializes configuration files

Example:

# Standard setup
./scripts/setup_quantumvest_env.sh

# With verbose output
VERBOSE=1 ./scripts/setup_quantumvest_env.sh

env_setup.sh

Environment-specific configuration setup.

Usage:

./scripts/env_setup.sh [environment]

Arguments:

Argument	Description	Example
environment	Target environment (dev/staging/prod)	`staging`

Example:

# Setup development environment
./scripts/env_setup.sh dev

# Setup production environment
./scripts/env_setup.sh prod

Server Commands

run_quantumvest.sh

Start all QuantumVest services.

Usage:

./scripts/run_quantumvest.sh [OPTIONS]

Options:

Option	Description	Example
`--backend-only`	Start only backend	`--backend-only`
`--frontend-only`	Start only frontend	`--frontend-only`
`--port PORT`	Custom port	`--port 8080`

Example:

# Start all services
./scripts/run_quantumvest.sh

# Start backend only on port 8000
./scripts/run_quantumvest.sh --backend-only --port 8000

Backend Server (app.py)

Start Flask backend server directly.

Usage:

cd code/backend
source venv/bin/activate
python app.py [OPTIONS]

Environment Variables:

Variable	Default	Description
FLASK_APP	app.py	Application entry point
FLASK_ENV	development	Environment mode
FLASK_HOST	0.0.0.0	Server host
FLASK_PORT	5000	Server port

Example:

# Development server
FLASK_ENV=development python app.py

# Production with Gunicorn
gunicorn -w 4 -b 0.0.0.0:5000 app:app

Database Commands

migrate_db.py

Database migration and initialization.

Usage:

cd code/backend
source venv/bin/activate
python migrate_db.py [COMMAND]

Commands:

Command	Description	Example
`init`	Initialize database	`python migrate_db.py init`
`migrate`	Create migration	`python migrate_db.py migrate`
`upgrade`	Apply migrations	`python migrate_db.py upgrade`
`downgrade`	Revert migration	`python migrate_db.py downgrade`
`seed`	Seed default data	`python migrate_db.py seed`

Examples:

# Initialize database
python migrate_db.py init

# Create migration for model changes
python migrate_db.py migrate -m "Add portfolio metrics table"

# Apply all pending migrations
python migrate_db.py upgrade

# Revert last migration
python migrate_db.py downgrade

# Seed database with default assets
python migrate_db.py seed

Data Pipeline Commands

Data Fetcher

Fetch market data for assets.

Usage:

cd code/backend
python -m data_pipeline.data_fetcher [OPTIONS]

Options:

Option	Description	Example
`--symbol SYMBOL`	Asset symbol	`--symbol AAPL`
`--type TYPE`	Asset type (stock/crypto)	`--type stock`
`--days DAYS`	Historical days	`--days 365`
`--output FILE`	Output file	`--output data.csv`

Examples:

# Fetch Apple stock data for 1 year
python -m data_pipeline.data_fetcher --symbol AAPL --type stock --days 365

# Fetch Bitcoin data
python -m data_pipeline.data_fetcher --symbol BTC --type crypto --days 180

# Fetch and save to file
python -m data_pipeline.data_fetcher --symbol TSLA --days 730 --output tsla_data.csv

Stock API

Direct stock data operations.

Usage:

python -c "from data_pipeline.stock_api import StockDataFetcher; \
fetcher = StockDataFetcher(); \
data = fetcher.get_stock_data('AAPL'); \
print(data)"

Example:

# Get current stock price
python -c "from data_pipeline.stock_api import StockDataFetcher; \
print(StockDataFetcher().get_stock_data('AAPL')['price'])"

# Get historical data
python -c "from data_pipeline.stock_api import StockDataFetcher; \
history = StockDataFetcher().get_historical_data('MSFT', period='1y'); \
print(history.head())"

Model Commands

Train Prediction Model

Train AI prediction models.

Usage:

cd code/ai_models
python train_prediction_model.py [OPTIONS]

Options:

Option	Type	Required	Default	Description	Example
--asset_type	string	No	stock	Asset type	--asset_type crypto
--symbol	string	Yes	-	Asset symbol	--symbol AAPL
--epochs	int	No	100	Training epochs	--epochs 200
--batch_size	int	No	32	Batch size	--batch_size 64
--lookback	int	No	60	Lookback window	--lookback 90
--output	string	No	models/	Model output dir	--output trained/

Examples:

# Train AAPL stock model
python train_prediction_model.py --symbol AAPL --epochs 150

# Train BTC crypto model with custom params
python train_prediction_model.py \
  --asset_type crypto \
  --symbol BTC \
  --epochs 200 \
  --batch_size 64 \
  --lookback 90

# Train and save to custom location
python train_prediction_model.py --symbol GOOGL --output ./trained_models/

Train Optimization Model

Train portfolio optimization models.

Usage:

cd code/ai_models
python train_optimization_model.py [OPTIONS]

Options:

Option	Description	Example
--data_path PATH	Training data path	--data_path ../resources/datasets/
--model_type TYPE	Model type (rf/xgb/nn)	--model_type xgb
--evaluate	Run evaluation	--evaluate

Example:

# Train optimization model
python train_optimization_model.py --data_path ../resources/datasets/

# Train with XGBoost
python train_optimization_model.py --model_type xgb --evaluate

# Evaluate existing model
python train_optimization_model.py --evaluate

Data Preprocessing

Preprocess data for model training.

Usage:

cd code/ai_models/training_scripts
python data_preprocessing.py [OPTIONS]

Options:

Option	Description	Example
--input FILE	Input data file	--input raw_data.csv
--output FILE	Output file	--output processed.csv
--features LIST	Feature columns	--features price,volume

Example:

# Preprocess raw data
python data_preprocessing.py \
  --input ../../resources/datasets/raw_stock_data.csv \
  --output ../../resources/datasets/processed_data.csv

# Select specific features
python data_preprocessing.py \
  --input raw.csv \
  --output processed.csv \
  --features price,volume,ma_50,ma_200

Testing Commands

run_all_tests.sh

Run complete test suite.

Usage:

./scripts/run_all_tests.sh [OPTIONS]

Options:

Option	Description	Example
--coverage	Generate coverage report	--coverage
--verbose	Verbose output	--verbose
--fast	Skip slow tests	--fast

Example:

# Run all tests
./scripts/run_all_tests.sh

# Run with coverage
./scripts/run_all_tests.sh --coverage

# Fast test run
./scripts/run_all_tests.sh --fast

run_backend_tests.sh

Run backend tests only.

Usage:

./scripts/run_backend_tests.sh

Example:

# Run backend tests
./scripts/run_backend_tests.sh

# Run specific test file
cd code/backend
pytest tests/test_endpoints.py -v

# Run with coverage
pytest --cov=. --cov-report=html

Pytest Commands

Direct pytest usage for backend.

Usage:

cd code/backend
pytest [OPTIONS] [PATH]

Common Options:

Option	Description	Example
-v	Verbose output	pytest -v
-k EXPR	Run tests matching expression	pytest -k "test_login"
--cov	Coverage report	pytest --cov=.
-x	Stop on first failure	pytest -x
--pdb	Debug on failure	pytest --pdb

Examples:

# Run all tests
pytest

# Run specific test file
pytest tests/test_endpoints.py

# Run tests matching pattern
pytest -k "test_portfolio"

# Run with coverage
pytest --cov=. --cov-report=html

# Stop on first failure
pytest -x

# Run in parallel (requires pytest-xdist)
pytest -n auto

Deployment Commands

deploy.sh

Deploy to environment.

Usage:

./scripts/deploy.sh [environment] [OPTIONS]

Arguments:

Argument	Description	Example
environment	Target environment	staging, production

Options:

Option	Description	Example
--skip-tests	Skip test execution	--skip-tests
--no-backup	Skip database backup	--no-backup
--force	Force deployment	--force

Examples:

# Deploy to staging
./scripts/deploy.sh staging

# Deploy to production with tests
./scripts/deploy.sh production

# Force deploy without backup
./scripts/deploy.sh production --force --no-backup

build_frontend.sh

Build frontend for production.

Usage:

./scripts/build_frontend.sh

Example:

# Build frontend
./scripts/build_frontend.sh

# The built files will be in web-frontend/build/

lint-all.sh

Run code quality checks.

Usage:

./scripts/lint-all.sh [OPTIONS]

Options:

Option	Description	Example
--fix	Auto-fix issues	--fix
--check-only	Check without fixing	--check-only

Example:

# Check code quality
./scripts/lint-all.sh

# Auto-fix issues
./scripts/lint-all.sh --fix

# Check only (CI mode)
./scripts/lint-all.sh --check-only

maintenance.sh

System maintenance operations.

Usage:

./scripts/maintenance.sh [COMMAND]

Commands:

Command	Description	Example
backup	Backup database	backup
restore	Restore database	restore
clean	Clean old logs	clean
optimize	Optimize database	optimize

Examples:

# Backup database
./scripts/maintenance.sh backup

# Restore from backup
./scripts/maintenance.sh restore

# Clean old logs
./scripts/maintenance.sh clean

# Optimize database
./scripts/maintenance.sh optimize

Command Cheat Sheet

Quick Reference

# Setup
./scripts/setup_quantumvest_env.sh

# Start services
./scripts/run_quantumvest.sh

# Database operations
cd code/backend && python migrate_db.py upgrade

# Fetch data
python -m data_pipeline.data_fetcher --symbol AAPL --days 365

# Train model
cd code/ai_models && python train_prediction_model.py --symbol AAPL

# Run tests
./scripts/run_all_tests.sh

# Deploy
./scripts/deploy.sh staging

# Lint code
./scripts/lint-all.sh --fix

# Maintenance
./scripts/maintenance.sh backup

Troubleshooting

Common Issues

1. Permission Denied

chmod +x scripts/*.sh

2. Virtual Environment Not Found

cd code/backend
python3 -m venv venv
source venv/bin/activate

3. Module Not Found

cd code/backend
source venv/bin/activate
pip install -r requirements.txt

4. Database Connection Failed

# Check PostgreSQL is running
sudo systemctl status postgresql

# Check connection string in .env
cat .env | grep DATABASE_URL

Next Steps

Usage Guide: See USAGE.md for practical examples
API Reference: Check API.md for REST API usage
Developer Guide: Read CONTRIBUTING.md for development workflow
Troubleshooting: See TROUBLESHOOTING.md for more issues

For script internals and customization, refer to inline script documentation

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CLI Reference

Table of Contents

Overview

CLI Components

Installation

Global Options

Commands

Setup Commands

setup_quantumvest_env.sh

env_setup.sh

Server Commands

run_quantumvest.sh

Backend Server (app.py)

Database Commands

migrate_db.py

Data Pipeline Commands

Data Fetcher

Stock API

Model Commands

Train Prediction Model

Train Optimization Model

Data Preprocessing

Testing Commands

run_all_tests.sh

run_backend_tests.sh

Pytest Commands

Deployment Commands

deploy.sh

build_frontend.sh

lint-all.sh

maintenance.sh

Command Cheat Sheet

Quick Reference

Troubleshooting

Common Issues

Next Steps

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CLI Reference

Table of Contents

Overview

CLI Components

Installation

Global Options

Commands

Setup Commands

setup_quantumvest_env.sh

env_setup.sh

Server Commands

run_quantumvest.sh

Backend Server (app.py)

Database Commands

migrate_db.py

Data Pipeline Commands

Data Fetcher

Stock API

Model Commands

Train Prediction Model

Train Optimization Model

Data Preprocessing

Testing Commands

run_all_tests.sh

run_backend_tests.sh

Pytest Commands

Deployment Commands

deploy.sh

build_frontend.sh

lint-all.sh

maintenance.sh

Command Cheat Sheet

Quick Reference

Troubleshooting

Common Issues

Next Steps