Quantitative Trading Guide¶

A comprehensive introduction to quantitative trading with sigc.

Overview¶

This guide takes you from fundamentals to production trading:

Chapter	Topic	Level
1. Introduction	What is quant trading?	Beginner
2. Fundamentals	Market data and returns	Beginner
3. Signals	Building alpha signals	Beginner
4. Language	sigc DSL mastery	Intermediate
5. Backtesting	Proper validation	Intermediate
6. Risk	Risk management	Intermediate
7. Production	Going live	Advanced
8. Advanced Analytics	Factor models	Advanced
9. Deployment	Safety and ops	Advanced

Who This Is For¶

Quant Researchers¶

Learn to implement trading signals
Understand backtesting best practices
Build robust research workflows

Software Developers¶

Understand quant finance concepts
Learn the sigc DSL
Deploy production systems

Finance Professionals¶

Move from Excel to programmatic trading
Automate research workflows
Build systematic strategies

Learning Path¶

Week 1-2: Foundations¶

Introduction - Understand quant trading landscape
Fundamentals - Learn about returns, volatility, correlation
Installation - Set up your environment

Week 3-4: Building Signals¶

Signals - Construct trading signals
Language - Master sigc syntax
Operators - Learn available tools

Week 5-6: Testing¶

Backtesting - Validate strategies properly
Risk - Manage portfolio risk
Tutorials - Hands-on practice

Week 7-8: Production¶

Production - Deploy trading systems
Advanced Analytics - Factor models
Deployment - Safety and monitoring

Key Concepts Preview¶

What is Quantitative Trading?¶

Using mathematical models to identify trading opportunities:

Text Only

Data → Analysis → Signal → Portfolio → Execution

The sigc Approach¶

Text Only

// 1. Load data
data:
  source = "prices.parquet"
  format = parquet

// 2. Compute signal
signal momentum:
  emit zscore(ret(prices, 60))

// 3. Construct portfolio
portfolio main:
  weights = rank(momentum).long_short(top=0.2, bottom=0.2)
  backtest from 2020-01-01 to 2024-12-31

Why Systematic Trading?¶

Aspect	Discretionary	Systematic
Emotions	Affected	Removed
Consistency	Variable	Consistent
Scalability	Limited	High
Backtesting	Difficult	Easy
Speed	Slow	Fast

Prerequisites¶

Technical¶

Basic programming knowledge
Command line familiarity
Text editor proficiency

Finance¶

Understanding of stocks/bonds
Basic statistics (mean, std, correlation)
Interest in markets

No Prerequisites¶

No Rust knowledge needed
No advanced math required
No prior quant experience needed

Tools You'll Need¶

Tool	Purpose
sigc	Strategy development
Text editor/VSCode	Code editing
Terminal	Running sigc
Sample data	Testing strategies

Quick Start¶

Bash

# Install sigc
cargo install sigc

# Create first strategy
cat > momentum.sig << 'EOF'
data:
  source = "prices.csv"
  format = csv
  columns:
    date: Date
    ticker: Symbol
    close: Numeric as prices

signal momentum:
  emit zscore(ret(prices, 60))

portfolio main:
  weights = rank(momentum).long_short(top=0.2, bottom=0.2)
  backtest from 2020-01-01 to 2024-12-31
EOF

# Run backtest
sigc run momentum.sig

What You'll Build¶

By the end of this guide, you'll be able to:

Research - Build and test trading signals
Backtest - Validate strategies properly
Risk Manage - Control portfolio risk
Deploy - Run strategies in production
Monitor - Track and manage live trading

Next Steps¶

Start with Chapter 1: Introduction to begin your quant trading journey.