Algorithmic Trading Basics for Engineers

Explore the fundamentals of algorithmic trading through a math-heavy, code-first lens tailored for engineers diving into quantitative finance.

Introduction to Algorithmic Trading

It was 3 AM, and I was staring at a trading dashboard that looked like a rollercoaster. My bot had just executed 50 trades in the last hour, and I was trying to figure out why my portfolio was bleeding money. Turns out, a single bug in my code had flipped a buy signal into a sell signal, and the market wasn’t forgiving.

Algorithmic trading, at its core, is about using math and code to make trading decisions faster and more precise than any human could. For engineers, it’s an exciting blend of data science, software engineering, and financial theory. Done right, it can scale your trading strategies to levels impossible with manual trading.

Here’s why it matters:

• Speed: Algorithms can react to market changes in milliseconds.
• Precision: Decisions are based on data, not emotions.
• Scalability: You can monitor and trade hundreds of instruments simultaneously.

In this article, we’ll explore the fundamentals of algorithmic trading, from core concepts to building your first bot.

Core Concepts in Quantitative Finance

Before diving into code, let’s cover some foundational concepts in quantitative finance. These are the building blocks for any trading strategy.

Time Series Data and Financial Instruments

Financial data is typically represented as time series: sequences of data points indexed by time. Examples include stock prices, exchange rates, and commodity prices. Understanding time series is critical for analyzing trends and patterns.

Statistical Foundations

Statistics are your best friend in algorithmic trading. Key metrics include:

• Mean: The average value, useful for identifying trends.
• Variance: Measures how spread out the data is—important for assessing risk.
• Correlation: Helps identify relationships between assets.

Risk and Return

Every trading strategy balances risk and return. The Sharpe ratio is a popular metric for evaluating this balance. It’s calculated as: