Quickstart Tutorial

Get up and running with VectorAlpha in just a few minutes. This tutorial will guide you through installation and your first indicator calculation.

Build in 10 Minutes

This quickstart guide will have you calculating indicators and running your first backtest in under 10 minutes. We'll build a simple moving average crossover strategy using real market data.

Step 1: Installation

Install Rust

First, ensure you have Rust installed. VectorAlpha requires Rust 1.70 or later. 

# Install Rust via rustup
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

# Verify installation
rustc --version
cargo --versio

Create a New Project

# Create a new Rust project
cargo new my_trading_bot
cd my_trading_bot

# Add VectorAlpha dependencies
cat >> Cargo.toml << 'EOF'

[dependencies]
vectoralpha-ta = "0.5"
vectoralpha-backtest = "0.3"
tokio = { version = "1", features = ["full"] }
serde = { version = "1.0", features = ["derive"] }
csv = "1.3"
anyhow = "1.0"
EO

Step 2: Load Market Data

Let's start by loading some historical price data. Create a file called src/main.rs:

Code Example Coming Soon

Full code examples with syntax highlighting will be available in the next update.

Sample Data Format

Your CSV file should have this format: 

date,open,high,low,close,volume
2024-01-02,187.15,188.44,186.86,187.21,45123000
2024-01-03,187.84,188.63,187.05,188.22,4823400

Step 3: Calculate Technical Indicators

Now let's calculate some common technical indicators:

Code Example Coming Soon

Full code examples with syntax highlighting will be available in the next update.

Step 4: Build a Simple Strategy

Let's implement a basic moving average crossover strategy:

Moving Average Crossover Strategy

This example shows how to implement a classic moving average crossover strategy using VectorAlpha's backtest framework.

Code Example Coming Soon

Full code examples with syntax highlighting will be available in the next update.

Step 5: Run a Backtest

Now let's test our strategy on historical data:

Code Example Coming Soon

Full code examples with syntax highlighting will be available in the next update.

Complete Working Example

Here's the complete code you can run:

Code Example Coming Soon

Full code examples with syntax highlighting will be available in the next update.

Step 6: Optimize and Improve

Now that you have a working strategy, here are ways to improve it:

1. Add Risk Management

Code Example Coming Soon

Full code examples with syntax highlighting will be available in the next update.

2. Use Multiple Timeframes

Code Example Coming Soon

Full code examples with syntax highlighting will be available in the next update.

3. Add More Indicators

Code Example Coming Soon

Full code examples with syntax highlighting will be available in the next update.

Performance Tips

  • • Use cargo build --release for 10-50x performance improvement
  • • Enable SIMD with RUSTFLAGS="-C target-cpu=native"
  • • Process data in batches for better cache utilization
  • • Use rayon for parallel indicator calculations

Next Steps

Congratulations! You've built your first quantitative trading system with VectorAlpha. Here's where to go next:

More Examples →

Explore advanced strategy examples

Indicator Theory →

Deep dive into technical indicators

Strategy Development →

Build more sophisticated strategies

Deploy to Production →

Scale your strategies to the cloud

Join the Community

Have questions? Join our Discord server or check out the GitHub discussions. The VectorAlpha community is here to help you succeed in quantitative finance with Rust.