Develop a Live Trend-Following Algorithmic Trading Bot for Gold (Python Tutorial)

Matt Macarty · Beginner ·🛠️ AI Tools & Apps ·1y ago

About this lesson

@MattMacarty #lumibot #algotrading #pythonfinance #goldtrading **Build a live Trend-Following Algorithmic Trading Bot for Gold using Python and Alpaca.** In Part 3 of our LumiBot series, we implement a classic Simple Moving Average (SMA) crossover strategy to automate trading for the Gold ETF (GLD). We move beyond basic execution to show how to "close and flip" positions—automatically transitioning from long to short as market trends shift. This tutorial covers the end-to-end workflow: from defining signal logic in Pandas to live deployment on the Alpaca Paper Trading API. ### ⏱️ Technical Milestones 0:00 - Introduction & Series Overview 0:41 - **Strategy Architecture:** 9-Day vs. 21-Day SMA Crossover 2:01 - Class Structure and Strategy Inheritance in Python 3:03 - Defining Strategy Parameters (Symbols, Moving Averages, Quantity) 4:32 - **The Lifecycle:** Implementing the `on_trading_iteration` method 5:03 - Using Pandas to Generate Technical Indicators 5:40 - **Signal Logic:** Preventing "Double-Firing" with Previous Day Checks 7:02 - Connecting to the Alpaca Paper Trading API 7:42 - **Dynamic Position Sizing:** Calculating Quantity based on Cash Balance 8:35 - Backtesting the Strategy Performance 10:42 - **Analysis:** Benchmarking against GLD Buy-and-Hold 11:27 - Preview: Moving to Options Trading Bots --- ### 🏛️ Implementation Highlights This project demonstrates how to bridge the gap between technical indicators and automated execution: **1. The "Close and Flip" Logic:** * Unlike basic strategies, this bot is programmed to recognize trend reversals. It automatically closes a long position and initiates a short position in a single iteration when the fast SMA crosses below the slow SMA. **2. Signal Integrity:** * Learn how to use the `np.where` function combined with a "previous day" check to ensure the bot only generates a trade signal at the exact moment of the crossover, preventing unnecessary order submissions. **3. Paper Trading Integration:** * We

Original Description

@MattMacarty #lumibot #algotrading #pythonfinance #goldtrading **Build a live Trend-Following Algorithmic Trading Bot for Gold using Python and Alpaca.** In Part 3 of our LumiBot series, we implement a classic Simple Moving Average (SMA) crossover strategy to automate trading for the Gold ETF (GLD). We move beyond basic execution to show how to "close and flip" positions—automatically transitioning from long to short as market trends shift. This tutorial covers the end-to-end workflow: from defining signal logic in Pandas to live deployment on the Alpaca Paper Trading API. ### ⏱️ Technical Milestones 0:00 - Introduction & Series Overview 0:41 - **Strategy Architecture:** 9-Day vs. 21-Day SMA Crossover 2:01 - Class Structure and Strategy Inheritance in Python 3:03 - Defining Strategy Parameters (Symbols, Moving Averages, Quantity) 4:32 - **The Lifecycle:** Implementing the `on_trading_iteration` method 5:03 - Using Pandas to Generate Technical Indicators 5:40 - **Signal Logic:** Preventing "Double-Firing" with Previous Day Checks 7:02 - Connecting to the Alpaca Paper Trading API 7:42 - **Dynamic Position Sizing:** Calculating Quantity based on Cash Balance 8:35 - Backtesting the Strategy Performance 10:42 - **Analysis:** Benchmarking against GLD Buy-and-Hold 11:27 - Preview: Moving to Options Trading Bots --- ### 🏛️ Implementation Highlights This project demonstrates how to bridge the gap between technical indicators and automated execution: **1. The "Close and Flip" Logic:** * Unlike basic strategies, this bot is programmed to recognize trend reversals. It automatically closes a long position and initiates a short position in a single iteration when the fast SMA crosses below the slow SMA. **2. Signal Integrity:** * Learn how to use the `np.where` function combined with a "previous day" check to ensure the bot only generates a trade signal at the exact moment of the crossover, preventing unnecessary order submissions. **3. Paper Trading Integration:** * We
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Chapters (12)

Introduction & Series Overview
0:41 **Strategy Architecture:** 9-Day vs. 21-Day SMA Crossover
2:01 Class Structure and Strategy Inheritance in Python
3:03 Defining Strategy Parameters (Symbols, Moving Averages, Quantity)
4:32 **The Lifecycle:** Implementing the `on_trading_iteration` method
5:03 Using Pandas to Generate Technical Indicators
5:40 **Signal Logic:** Preventing "Double-Firing" with Previous Day Checks
7:02 Connecting to the Alpaca Paper Trading API
7:42 **Dynamic Position Sizing:** Calculating Quantity based on Cash Balance
8:35 Backtesting the Strategy Performance
10:42 **Analysis:** Benchmarking against GLD Buy-and-Hold
11:27 Preview: Moving to Options Trading Bots
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