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Trading Lab 101 · Lesson 5
Algorithmic Trading – Rules, Backtesting & Automation
Explore how traders turn clear rules into executable code, test them on historical data, and then automate decisions so strategies can run without emotional interference.
Lesson Overview +
Lesson 5 moves learners from “clicking the button themselves” to letting a strategy think and act for them. Instead of watching every tick, traders define a repeatable rule set—signals, filters, exits—and translate it into code or platform logic.

Students see how algorithmic trading uses backtesting to test ideas against years of historical data before a single live dollar is put at risk. The emphasis is on understanding how rules, testing, and automation fit together: a clear signal, a structured process, and safeguards that keep emotion from breaking the system.
Lesson Walkthrough
Lesson 5 – Algorithmic Trading
Watch this walkthrough to see how the rules, backtesting, and automation concepts connect inside a real strategy example.
Full Lesson Text +

Informational Text – What Is Algorithmic Trading?

Algorithmic trading uses computer programs to execute trades automatically based on predefined rules. These rules might involve indicators (such as moving averages), price thresholds, or statistical patterns.

Algo traders use backtesting, where they test a strategy on past market data to determine whether it would have been profitable. Once a strategy performs well in backtesting, it can be automated to run live— without human emotion interfering.

Algorithmic trading ranges from simple moving-average systems to highly complex models using machine learning or high-speed execution. But at every level, the core idea is the same: translate a clear rule into code, then let the algorithm follow it consistently.

Scenario Example – A Moving Average Crossover Bot

You design an algorithm that buys a stock whenever the price crosses above the 20-day moving average and sells when it crosses back below. You test this rule on three years of historical data and discover it performs extremely well during trending markets but poorly when the price moves sideways.

You revise the rules to include a volume filter—only taking trades when trading volume is above average. After re-running the backtest, you see that the strategy now avoids many choppy periods and performs more consistently. Only after this process do you deploy the algorithm live.

Process Summary – How Algorithmic Trading Works

  • Define a clear, repeatable trading rule.
  • Translate the rule into code or trading platform logic.
  • Backtest the rule on historical market data.
  • Optimize the strategy by refining signals and filters.
  • Deploy the algorithm in a live or simulated environment.

Key Vocabulary

  • Backtesting – Testing a strategy using past market data to see how it would have performed.
  • Signal – A condition that tells the algorithm to buy or sell (for example, a moving-average crossover).
  • Slippage – The difference between the expected price of a trade and the price actually received.
  • Latency – The delay between sending an order and having it executed in the market.
  • Execution Speed – How quickly an algorithm can process data and send orders.

Cross-Strategy Vocabulary Use:

  • Signal → momentum systems, trend-following systems
  • Slippage → day trading, futures trading
  • Latency → high-frequency trading (HFT), arbitrage strategies

Lesson Flow – How the Session Unfolds

Learning Target: I can describe how automated trading strategies work and explain the role of backtesting.

Essential Question: How do trading algorithms make decisions without human involvement?

Bell Ringer: Students examine a simple IF–THEN rule (for example, “If price > moving average, then buy”) and predict whether it produces a buy or sell signal in a short example.

Mini-Lesson: The instructor explains how rules-based trading removes emotional bias, shows examples of simple algorithms, and introduces why backtesting is necessary to avoid costly mistakes.

Guided Practice: Students work with the teacher to convert a written trading idea into a set of coded rules, identifying the signal, filters, and exit logic.

Independent Practice: Students sketch a personal algo strategy flowchart showing the signal, filters, and exit rules their algorithm would follow.

Closure: Learners respond to a quick prompt: “Why is backtesting necessary before running a strategy live?”

Exit Ticket: Define “signal” in your own words.

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Trading Lab 101 · Lesson 5 Activity
Design an Algorithmic Strategy Blueprint
Define a clear rule set, sketch the signals and filters, plan your backtest, and identify how automation will keep emotion out of the loop.

Imagine that you are responsible for designing an algorithmic trading strategy that runs on its own. Use the panels below to describe your rule, identify the signal and filters, outline how you would backtest it, and explain how you would keep risk and emotion under control. When you are finished, generate your summary, optionally print/save a copy, and submit your work to your instructor.

1. Strategy Idea & Market +
2. Signal & Filters (Rule Definition) +
3. Backtesting Plan +
4. Parameters, Risk & Execution +
5. Emotion, Bias & Automation Benefits +
6. Next Iteration After Backtesting +
Generated Summary (copy, print, or save):
Mastery Check
Fill in all six sections with thoughtful responses. When everything is complete, this badge will glow to indicate that you’ve designed a structured algorithmic trading blueprint that connects rules, backtesting, and automation.