Module 1: Introduction to Python for Financial Data Analysis

• Introduction to Python and its application in finance
• Setting up the Python environment for financial analysis
• Python basics and its libraries used for financial data analysis

Module 2: Scraping Historical Crypto Price Data

• Understanding APIs and Web Scraping
• Data scraping from leading cryptocurrency exchanges
• Cleaning, organizing, and storing data in Python

Module 3: Backtesting Trading Strategies and Visualising Historical Performance

• Introduction to backtesting and its importance in trading
• Creating and backtesting simple to complex trading strategies
• Visualizing historical performance using Python libraries

Module 4: Automating Trades on Live Exchanges

• Understanding trading APIs
• Coding your first trading bot in Python
• Connecting your bot to live exchanges and automating trades

Module 5: Deploying Trading Bots for 100% Uptime

• Introduction to cloud services for trading bots
• Deploying Python trading bots on the cloud
• Ensuring 100% uptime and handling potential errors

Module 6: Analysing Trade Performance

• Understanding and calculating key trading performance metrics
• Analysing trade data using Python
• Visualizing and interpreting performance results

Module 7: Final Project and Course Wrap-Up

• Applying all course concepts in a comprehensive project
• Review and discussion of various Python resources for further learning
• Final thoughts and next steps in your algorithmic trading journey

This course is designed for:

1. Aspiring data analysts and data scientists looking to apply Python skills in financial markets.
2. Finance professionals wanting to improve their data analysis skills and understanding of algorithmic trading.
3. Software engineers and developers interested in finance and wanting to diversify their skill set.
4. Investors and traders wanting to leverage Python to create, backtest, and deploy automated trading strategies.

To make the most of this course, you should have:

1. Prior experience with Python: You should have a good understanding of Python programming basics such as variables, data types, loops, functions, and classes. Familiarity with Python libraries like NumPy, pandas, and matplotlib would also be advantageous.
2. Basic understanding of financial markets: Having a basic knowledge of financial markets and trading will help you grasp the concepts and strategies discussed in the course more effectively.
3. Familiarity with mathematical concepts: Concepts such as statistics and probability are often used in financial data analysis and algorithmic trading. Therefore, having a basic understanding of these will be beneficial.
4. Problem-solving mindset: Algorithmic trading involves devising and testing trading strategies, which requires a creative and analytical mindset.
5. Access to a Python development environment: You should have Python installed on your computer, along with necessary libraries for data analysis such as pandas, NumPy, and matplotlib. An Integrated Development Environment (IDE) like Jupyter Notebook or PyCharm is also recommended.

Note: If you’re completely new to Python or financial markets, don’t worry! We have resources available online to help you get up to speed with these concepts before you start the course.

Upon completing this course, you will be able to:

1. Scrape and preprocess financial data from various sources such as cryptocurrency exchanges.
2. Develop and backtest trading strategies using Python and relevant libraries.
3. Automate trades using APIs and manage them on live exchanges.
4. Deploy trading bots to the cloud for 100% uptime and maintain them effectively.
5. Analyze and visualize trade performance to make informed decisions and optimize your strategies.