How to Code a AI Trading bot (so you can make $$$)

Nicholas Renotte
18 Jan 202435:08

TLDRThis script outlines the process of creating an AI-powered trading bot, capable of executing automated trades. The creator shares a step-by-step guide, starting with setting up a baseline bot, then moving on to position sizing and risk management. The bot is further enhanced by integrating news sentiment analysis using machine learning models, allowing it to make informed trading decisions. The script concludes with a backtesting of the bot over a four-year period, demonstrating its potential to generate significant returns, albeit in a paper trading environment.

Takeaways

  • 🤖 The video outlines a step-by-step process to create an AI-powered, fully automated trading bot.
  • 🔍 The trading bot is developed using machine learning models and backtesting strategies.
  • 📈 The bot is designed to mimic the sophisticated algorithms used by hedge funds.
  • 🛠️ The process involves setting up a baseline trading bot and then enhancing it with AI capabilities.
  • 🔧 The script details the use of the Lumo library for creating the trading framework and backtesting.
  • 📊 The bot uses Yahoo data for backtesting and Alpaca API for live trading capabilities.
  • 📈 Position sizing and risk management are crucial components of the bot's strategy.
  • 📰 The bot incorporates news sentiment analysis to make trading decisions.
  • 🔧 The script provides a detailed guide on how to set up API keys and manage cash in the trading bot.
  • 🔄 The bot includes logic for dynamic order placement and management based on market conditions.
  • 📝 The video emphasizes the importance of paper trading before deploying the bot in a live environment.
  • 🚀 The final bot is capable of short selling and long buying based on sentiment analysis and market trends.

Q & A

  • What is the main focus of the video script?

    -The main focus of the video script is to guide the audience through the process of creating an AI-powered, fully automated trading bot.

  • What are some of the biggest hedge funds in the world using for their trading strategies?

    -Some of the biggest hedge funds are using algorithmic trading, with examples like Renaissance and Two Sigma.

  • What is the significance of the Lumino library in the script?

    -The Lumino library provides a trading framework, which includes functionalities for brokers, data backtesting, and strategy implementation.

  • What is the purpose of creating a 'Baseline' in the trading bot development process?

    -Creating a Baseline serves as the starting point for the trading bot, allowing the developer to establish a basic trading mechanism before adding more complex features.

  • How does the trading bot determine when to execute a trade?

    -The trading bot executes a trade when it receives a new data tick, which is a piece of data from the data source, and it has a trading logic encapsulated within the strategy class.

  • What is the role of the 'ml Trader' class in the script?

    -The 'ml Trader' class forms the backbone of the actual trading bot, containing all the trading logic and setup procedures.

  • How does the script handle API keys and secrets for accessing the Alpaca broker?

    -The script creates variables to hold the API keys and secrets, which are then used to authenticate and interact with the Alpaca broker for live trading or backtesting.

  • What is the purpose of the 'position sizing' and 'limits' in the trading bot?

    -Position sizing and limits are crucial for cash management and risk control, allowing the bot to dynamically determine the appropriate amount to trade based on the available cash and risk tolerance.

  • How does the trading bot integrate machine learning into its decision-making process?

    -The trading bot uses a machine learning model to analyze news sentiment, which is then used as a signal to decide whether to buy, sell, or hold positions in the market.

  • What are the performance metrics mentioned in the script for the backtesting period?

    -The performance metrics mentioned include a cumulative return of 234.40% over 4 years and a compound annual growth rate of 23.8%.

Outlines

00:00

🤖 Building an AI Trading Bot

The script outlines the process of creating an AI-powered trading bot that is fully automated. It discusses the secretive world of hedge funds using algorithmic trading and the author's ambition to create their own bot. The author shares their months of research, strategies, and over 600 lines of code experiments, breaking down the creation process into five steps. The first step involves setting up a baseline trading bot using the Lumo library for a trading framework, importing necessary dependencies, and setting up API keys for data access.

05:01

📈 Establishing the Trading Framework

The paragraph details the initial setup for the trading bot, including creating a new file, importing libraries, and setting up variables for API keys and base URLs. The author explains the importance of the Lumo library components for brokering, backtesting, and strategy implementation. The script also covers the creation of a trading bot class, the initialization method, and the on_trading_iteration function, which will be crucial for the bot's operation.

10:02

🚀 Position Sizing and Risk Management

This part of the script focuses on dynamic position sizing and risk management. The author introduces the concept of cash at risk, which determines how much of the cash balance is put at risk per trade. The script includes code to calculate position size based on the last price and cash at risk, and introduces the idea of setting take profit and stop loss parameters to manage risk effectively.

15:04

📰 Incorporating News Data

The script moves on to integrating news data into the trading bot. The author explains the need for an API to fetch news and the use of the Alpaca trade API for this purpose. The script includes methods to get news and process it, with the aim of using this data to inform trading decisions. The author also discusses the creation of methods to get current and past dates for backtesting purposes.

20:05

🧠 Introducing the Machine Learning Model

This section introduces the machine learning component of the trading bot. The author plans to use a sentiment analysis model to process news data and make trading decisions based on the sentiment of the market. The script includes the process of getting news, estimating sentiment, and integrating this sentiment analysis into the trading loop. The author also provides examples of how the sentiment analysis works with sample news headlines.

25:05

🔄 Integrating Trading Signals

The final part of the script focuses on integrating the sentiment analysis into the trading strategy. The author explains how to create buy and sell orders based on the sentiment and probability output from the sentiment analysis. The script includes decision logic to issue buy or sell orders, manage existing orders, and adjust take profit and stop loss parameters accordingly. The author also discusses the importance of testing the bot and provides a summary of the bot's performance over a 4-year backtesting period.

Mindmap

Keywords

💡AI trading bot

An AI trading bot is a software application that uses artificial intelligence to make trading decisions. In the video, the creator aims to build a fully automated trading bot that can execute trades based on sophisticated algorithms, similar to those used by major hedge funds. The bot is designed to analyze market data and make decisions without human intervention, potentially leading to profitable outcomes.

💡Algorithmic trading

Algorithmic trading refers to the use of computer algorithms to execute trades in financial markets. It is a method that allows for high-speed and efficient trading, often beyond human capabilities. In the context of the video, the creator is exploring how to develop an algorithm that can outperform the market, which is a common goal in algorithmic trading.

💡Backtesting

Backtesting is the process of evaluating the performance of a trading strategy using historical data. It is a crucial step in developing a trading bot, as it helps to validate the strategy's effectiveness before deploying it in live markets. In the video, the creator uses backtesting to simulate how the AI trading bot would have performed in the past, which is essential for assessing its potential success.

💡Machine Learning (ML) models

ML models are algorithms that allow computers to learn from and make predictions or decisions based on data. In the video, the creator is researching and experimenting with different ML models to improve the trading bot's strategy. These models can help the bot to identify patterns and trends in the market, which can lead to better trading decisions.

💡Position sizing

Position sizing is a risk management strategy that determines the appropriate amount of capital to allocate to a trade. It is critical in trading to avoid overexposure to any single trade, which can lead to significant losses. The video discusses dynamic position sizing, which adjusts the trade size based on the available capital and the risk tolerance set by the trader.

💡Stop loss and take profit

Stop loss and take profit are orders used in trading to automatically sell a security when it reaches a certain price, either to secure a profit or to limit a loss. In the video, the creator sets up these orders to protect the trading bot's capital and to ensure that profits are taken when the market moves favorably. This is a common practice in algorithmic trading to manage risk and maximize returns.

💡News sentiment analysis

News sentiment analysis involves using natural language processing (NLP) techniques to assess the emotional tone of news articles and its potential impact on financial markets. In the video, the creator plans to incorporate news sentiment into the trading bot's strategy, using it as a signal to inform buy or sell decisions. This approach leverages the idea that market sentiment can influence asset prices.

💡API keys

API keys are unique codes that allow software applications to access specific services or data from a third-party provider. In the context of the video, the creator needs API keys to connect the trading bot to the Alpaca API, which provides access to the stock market data and trading functionality. These keys are sensitive information and must be kept secure.

💡Paper trading

Paper trading is a simulation of trading where no real money is used. It allows traders to test their strategies and learn about trading without the risk of financial loss. The video mentions paper trading as a safe environment to develop and test the AI trading bot before moving to live trading, which involves real capital.

💡Deployment

Deployment in the context of trading bots refers to the process of putting the bot into a live trading environment. This means the bot will start executing trades with real money based on its programmed strategy. The video discusses the transition from paper trading to live deployment, highlighting the importance of careful planning and risk management.

Highlights

Creating an AI-powered, fully automated trading bot.

Inspiration from hedge funds using algorithmic trading.

Importing dependencies from the Lumo library for trading framework.

Setting up API keys for Alpaca Broker to access market data.

Building a Baseline trading bot with a 15-minute timer.

Creating a new file named 'trading bot' for the project.

Backtesting using Yahoo data and Lumo's backtesting capabilities.

Creating a class 'mlTrader' to form the backbone of the trading bot.

Setting up a strategy with a 'initialize' method and 'on_trading_iteration' method.

Creating a baseline trade by randomly buying 10 units of a stock.

Implementing position sizing and limits for dynamic trading.

Calculating position size based on cash at risk and last price.

Setting take profit and stop loss parameters for risk management.

Integrating news data into the trading bot for sentiment analysis.

Using a machine learning model to analyze news sentiment.

Adjusting trading decisions based on sentiment analysis results.

Backtesting the trading bot over a 4-year period.

Achieving a cumulative return of 234.40% over 4 years in paper trading.

Highlighting the importance of considering real trading conditions like fees and commissions.

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