ai-agent

Artificial Intelligence For Trading

Name: Artificial Intelligence For Trading
Author: udacity

Content for Udacity's AI in Trading NanoDegree.

775 stars575 forksJupyter NotebookUpdated 4/9/2023100% free · open source

What it does

This tool provides a set of Jupyter Notebooks and exercises to help users learn and apply artificial intelligence and machine learning concepts to trading and finance

Install / run

git clone https://github.com/udacity/artificial-intelligence-for-trading.git

When to use it

•When you want to learn how to use AI and ML to make informed trading decisions
•When you need to analyze and visualize large financial datasets
•When you want to backtest and evaluate trading strategies using historical data

Quick start

1Navigate to the cloned repository and open the '001_intro_to_ai_in_trading' notebook in Jupyter Notebook
2Run the cells in the '001_intro_to_ai_in_trading' notebook to complete the introductory exercises
3Move on to the '002_data_preprocessing' notebook and follow the instructions to complete the data preprocessing tasks
4Use the 'backtrader' library to backtest a simple moving average crossover strategy in the '003_backtrader' notebook
5Experiment with different trading strategies and parameters in the '004_strategy_experiments' notebook

Ready-to-paste prompt

Run the following cell in the '002_data_preprocessing' notebook: `data = pd.read_csv('stock_data.csv', index_col='Date', parse_dates=['Date'])` to load and preprocess historical stock data

Heads up: Make sure you have the required libraries installed, including 'pandas', 'numpy', and 'matplotlib', and that you have the necessary dependencies installed, such as 'backtrader', before running the notebooks

Saves to your device

What's inside — free to inspect

No purchase needed

Read the entire source before you build — unlike paid marketplaces that hide it behind a buy button.

top-level files

folders

8.9M

repo size

—

license

Key files

README.md