Technical Analysis with Python for Algorithmic Trading from Udemy

Fully revised and updated in November 2024

"(How) Can I use Technical Analysis and Technical Indicators for Trading and Investing?" - This is one of the most frequently asked questions in trading and investing.

This course clearly goes beyond rules, theories, vague forecasts, and nice-looking charts. (These are useful but traders need more than that.) This is the first 100% data-driven course on Technical Analysis. We´ll use rigorous Backtesting / Forward Testing to identify and optimize proper Trading Strategies that are based on Technical Analysis / Indicators.

This course will allow you to test and challenge your trading ideas and hypothesis. It provides Python Coding Frameworks and Templates that will enable you to code and test thousands of trading strategies within minutes. Identify the profitable strategies and scrap the unprofitable ones.

The course covers the following Technical Analysis Tools and Indicators:

Interactive Line Charts and Candlestick Charts
Interactive Volume Charts
Trend, Support and Resistance Lines
Simple Moving Average (SMA)
Exponential Moving Average (EMA)
Moving Average Convergence Divergence (MACD)
Relative Strength Index (RSI)
Stochastic Oscillator
Bollinger Bands
Pivot Point (Price Action)
Fibonacci Retracement (Price Action)
combined/mixed Strategies and more.

This is not only a course on Technical Analysis and Trading. It´s an in-depth coding course on Python and its Data Science Libraries Numpy, Pandas, Matplotlib, Plotly, and more. You will learn how to use and master these Libraries for (Financial) Data Analysis, Technical Analysis, and Trading.

Please note: This is not a course for complete Python Beginners (check out my other courses. )

What are you waiting for? Join now and start making proper use of Technical Analysis.

As always, there is no risk for you as I provide a 30-Days-Money-Back Guarantee.

Thanks and looking forward to seeing you in the Course.

What's inside

Learning objectives

Make proper use of technical analysis and technical indicators.
Use technical analysis for (day) trading and algorithmic trading.
Convert technical indictors into sound trading strategies with python.
Backtest and forward test trading strategies that are based on technical analysis/indicators.
Create and backtest combined strategies with two or many technical indicators.
Create interactive charts (line, volume, ohlc, etc.) with python and plotly.
Visualize technical indicators and trend/support/resistance lines with python and plotly.
Use pandas, numpy and object oriented programming (oop) for technical analysis and trading.
Load financial data from local files and the web.
Simple moving average (sma) strategies

Exponential moving average (ema) strategies
Moving average convergence divergence (macd) strategies
Relative strength index (rsi) strategies
Stochastic oscillator strategies
Bollinger bands strategies
Pivot point strategies
Fibonacci retracement strategies
Mixed strategies (combining two or many indicators)
Show more
Show less

Make proper use of technical analysis and technical indicators.
Use technical analysis for (day) trading and algorithmic trading.
Convert technical indictors into sound trading strategies with python.
Backtest and forward test trading strategies that are based on technical analysis/indicators.
Create and backtest combined strategies with two or many technical indicators.
Create interactive charts (line, volume, ohlc, etc.) with python and plotly.
Visualize technical indicators and trend/support/resistance lines with python and plotly.
Use pandas, numpy and object oriented programming (oop) for technical analysis and trading.
Load financial data from local files and the web.
Simple moving average (sma) strategies
Exponential moving average (ema) strategies
Moving average convergence divergence (macd) strategies
Relative strength index (rsi) strategies
Stochastic oscillator strategies
Bollinger bands strategies
Pivot point strategies
Fibonacci retracement strategies
Mixed strategies (combining two or many indicators)
Show more
Show less

Syllabus

Getting Started

What is Technical Analysis? / Course Overview

Tips: How to get the most out of this course

Did you know...? (what Data can tell us about Technical Analysis)

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Uses Python and its data science libraries, such as Numpy and Pandas, which are essential for algorithmic trading and financial data analysis

Emphasizes backtesting and forward testing of trading strategies, which is crucial for validating and optimizing algorithmic trading models

Requires familiarity with Python, suggesting that learners should already possess a foundational understanding of programming concepts and syntax

Covers a wide range of technical indicators, including SMA, EMA, MACD, RSI, and Bollinger Bands, providing a comprehensive toolkit for technical analysis

Teaches object-oriented programming (OOP) principles, which are valuable for creating modular and reusable code in trading strategy development

Focuses on using the Plotly library to create interactive charts, which can enhance the visualization and analysis of financial data and trading performance

Reviews summary

Technical analysis & backtesting with python

According to learners, this course offers a practical and data-driven approach to technical analysis and algorithmic trading using Python. Many praise the reusable Python code framework provided, which is described as well-structured and useful for backtesting strategies. Students particularly appreciate the hands-on coding sections and the instructor's ability to explain complex concepts clearly. The course is considered highly valuable for those looking to convert technical indicators into executable trading strategies, making it ideal for quantitative trading interests. However, it's frequently noted that a prior understanding of Python is essential, as the course is not designed for complete beginners.

Recently revised with updated libraries.

"The course content feels very current, incorporating recent library versions and best practices."

"Glad to see the course is updated regularly. Using the latest versions of Pandas, Plotly, etc., is important."

"The revision clearly shows in the updated coding sections and use of modern tools like Plotly."

"It's valuable that the instructor keeps the material fresh and relevant to the current state of Python libraries for finance."

"The updated materials were helpful and covered contemporary methods for data analysis and charting."

Strong emphasis on applying theory with code.

"This course is great because it focuses on actually *using* technical analysis with code, not just discussing theory."

"The hands-on application of technical indicators using Python is exactly what I was looking for."

"It’s very practical, showing how to translate theoretical TA concepts into testable trading strategies."

"I learned how to take indicators like RSI and MACD and build concrete trading rules that I could then backtest effectively."

"The blend of technical analysis theory and Python coding for real-world application is very effective."

Instructor explains complex topics well.

"The instructor does a fantastic job of explaining complex technical analysis concepts and how to apply them with Python."

"I found the explanations to be very clear and easy to follow, even for topics I wasn't previously familiar with."

"The way the instructor breaks down the strategies and the associated code is excellent. It makes learning much smoother."

"His explanations are concise yet thorough, which is perfect for understanding both the theory and the practical application."

"I appreciated the clarity of the lectures. It helped me grasp the 'why' behind the code and the analysis."

Provides a reusable Python code framework.

"The Python code framework provided in this course is excellent. It's well-structured and highly reusable for backtesting various strategies."

"I really appreciate the reusable code framework. It's a solid foundation for implementing and testing different technical analysis strategies efficiently."

"The practical backtesting code using Python is the strongest part of this course. It gives you tangible tools to test your trading ideas."

"The code provided is clean, well-commented, and easy to adapt for personal projects. This is a massive plus for me."

"I found the Python code to be a great starting point for building my own algorithmic trading systems."

Not suitable for complete Python beginners.

"This course is definitely NOT for complete Python beginners. You need a solid intermediate grasp of Python, especially Pandas and NumPy."

"As the description warns, come prepared with Python knowledge. The coding sections move quickly assuming you know the basics."

"If you're new to Python, take a beginner course first. This one dives straight into applying libraries for finance."

"The prerequisite knowledge of Python is real; don't underestimate it if you want to keep up with the coding."

"While the explanations are good, the coding part assumes you are already comfortable writing and understanding Python code."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Technical Analysis with Python for Algorithmic Trading with these activities:

Review Python Fundamentals

Show steps

Solidify your understanding of Python syntax, data structures, and control flow. This will make it easier to follow the course's coding examples and implement your own trading strategies.

Browse courses on Python Basics

Show steps

Review Python tutorials and documentation.
Practice writing basic Python scripts.
Complete online Python exercises.

Brush up on Pandas and NumPy

Show steps

Strengthen your skills in data manipulation and analysis using Pandas and NumPy. This will enable you to efficiently process financial data and implement technical indicators.

Browse courses on Pandas

Show steps

Work through Pandas and NumPy tutorials.
Practice data cleaning and transformation.
Implement basic statistical calculations.

Read 'Python for Data Analysis' by Wes McKinney

Show steps

Deepen your understanding of Pandas and data analysis techniques. This book provides a solid foundation for working with financial data in Python.

View Python for Data Analysis on Amazon

Show steps

Read the chapters on data cleaning and transformation.
Work through the examples in the book.
Apply the techniques to financial datasets.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Implement Technical Indicators

Show steps

Practice implementing the technical indicators covered in the course. This will solidify your understanding of the formulas and how they are calculated.

Show steps

Choose a technical indicator (e.g., SMA, RSI).
Write a Python function to calculate the indicator.
Test the function with sample data.
Compare your results with a known implementation.

Backtest a Simple Trading Strategy

Show steps

Apply your knowledge by backtesting a simple trading strategy using historical data. This will give you hands-on experience with the entire process, from data loading to performance evaluation.

Show steps

Choose a simple trading strategy (e.g., SMA crossover).
Collect historical price data for a stock or currency.
Implement the strategy in Python.
Backtest the strategy and evaluate its performance.

Write a Blog Post on a Technical Indicator

Show steps

Reinforce your understanding by explaining a technical indicator in a blog post. This will force you to think critically about the indicator and its applications.

Show steps

Choose a technical indicator.
Research the indicator and its applications.
Write a clear and concise explanation of the indicator.
Include examples and visualizations.

Read 'Algorithmic Trading: Winning Strategies and Their Rationale' by Ernie Chan

Show steps

Expand your knowledge of algorithmic trading strategies. This book provides a broader perspective on the field and helps you understand the rationale behind different approaches.

View Algorithmic Trading: Winning Strategies and... on Amazon

Show steps

Read the chapters on different trading strategies.
Analyze the rationale behind each strategy.
Consider how to implement the strategies in Python.

Career center

Learners who complete Technical Analysis with Python for Algorithmic Trading will develop knowledge and skills that may be useful to these careers:

Algorithmic Trader

An Algorithmic Trader designs, develops, and implements automated trading systems. This course helps build a strong foundation for automating trading strategies by leveraging Python. The course focuses on backtesting and forward testing trading strategies based on technical analysis and indicators. You can use the Python coding frameworks and templates to test thousands of trading strategies. The course's coverage of technical indicators such as Simple Moving Averages, Exponential Moving Averages, and MACD strategies is particularly relevant. This course may be useful to test and challenge your trading ideas and hypotheses.

See salaries and explore the career path for Algorithmic Trader

Trading System Developer

A Trading System Developer designs, codes, and tests automated trading systems for financial markets. A trading system developer directly benefits from the course's emphasis on Python coding for technical analysis. The course covers the creation of interactive charts and the implementation of various technical indicators such as Simple Moving Average, Exponential Moving Average, and MACD. The course teaches students how to use Python coding frameworks to test trading strategies. The course is especially valuable if you want to challenge your trading ideas and hypotheses.

See salaries and explore the career path for Trading System Developer

Financial Data Analyst

A Financial Data Analyst collects, cleans, and analyzes financial data to provide insights and support decision making. The course provides hands on experience using Python to perform financial data analysis and technical analysis, which is very valuable. The course teaches how to load financial data, create interactive charts, and visualize trend lines using Plotly. The course also provides coverage of backtesting and forward testing trading strategies.

See salaries and explore the career path for Financial Data Analyst

Quantitative Analyst

A Quantitative Analyst develops and implements mathematical and statistical models for financial analysis and trading. Should you wish to pursue this career, this course may be useful as it offers a practical introduction to using Python for financial data analysis and technical analysis. The course covers essential Python libraries such as NumPy and Pandas, which are widely used in quantitative finance. By learning how to backtest and optimize trading strategies using various technical indicators like RSI, MACD, and Bollinger Bands, you can gain valuable skills for quantitative analysis. The course's coverage of object oriented programming is helpful for building reusable and scalable models.

See salaries and explore the career path for Quantitative Analyst

Risk Manager

A Risk Manager identifies, assesses, and mitigates risks that could negatively impact an organization. Typically, this role requires an advanced degree. You can use skills taught in this course. The course provides a foundation for using Python to analyze financial data and develop trading strategies, which can be used to assess and manage risk. This course may be useful to examine potential high risk trades.

See salaries and explore the career path for Risk Manager

Financial Analyst

A Financial Analyst analyzes financial data, provides investment recommendations, and helps companies make informed decisions. While technical analysis is a subset of the broader skill set, it may be useful for you to take this course. The course teaches you how to load financial data, create interactive charts, and visualize technical indicators using Python and Plotly. The course's focus on backtesting and forward testing trading strategies may also be useful. While a financial analyst uses a variety of tools, this course hones a particular set of skills around technical analysis.

See salaries and explore the career path for Financial Analyst

Data Scientist

A Data Scientist uses statistical techniques, machine learning, and data visualization tools to analyze large datasets and extract meaningful insights. You may find this course helpful for a niche application of your skills. The course provides hands on experience using Python and data science libraries to perform technical analysis and develop trading strategies. The course teaches you how to create interactive charts and visualize technical indicators using Plotly. The course's coverage of backtesting and forward testing trading strategies may be useful.

See salaries and explore the career path for Data Scientist

Investment Strategist

An Investment Strategist develops investment strategies for institutional investors, such as hedge funds and pension funds. You may find this course helpful, as it provides a hands on introduction to using Python for technical analysis and algorithmic trading. The course teaches you how to backtest and forward test trading strategies using various technical indicators. The course's coverage of combined strategies with two or more technical indicators may be useful for developing more sophisticated investment strategies.

See salaries and explore the career path for Investment Strategist

Hedge Fund Manager

A Hedge Fund Manager oversees investment decisions and manages a portfolio of assets for a hedge fund. You may find that this course gives you a narrow but potentially useful set of skills. The course provides a hands on introduction to using Python for technical analysis and algorithmic trading. The course teaches you how to backtest and forward test trading strategies using various technical indicators. The course's coverage of combined strategies and object oriented programming may also be valuable.

See salaries and explore the career path for Hedge Fund Manager

Portfolio Manager

A Portfolio Manager manages a portfolio of investments for individuals or institutions, with the goal of maximizing returns while minimizing risk. This course may be useful, as it provides a practical introduction to using Python for technical analysis and algorithmic trading, which are components of the wider skills of a portfolio manager. The course focuses on backtesting and forward testing trading strategies, which is an essential skill for portfolio management. The course's coverage of technical indicators such as moving averages, MACD, and RSI may also be useful.

See salaries and explore the career path for Portfolio Manager

Financial Consultant

A Financial Consultant provides financial advice to individuals and businesses, helping them to achieve their financial goals. You may find this course helpful, as it provides a hands on introduction to using Python for technical analysis and algorithmic trading. The course teaches you how to backtest and forward test trading strategies using various technical indicators. The course's coverage of interactive charts and data visualization may also be useful for communicating financial information to clients.

See salaries and explore the career path for Financial Consultant

Investment Banker

An Investment Banker assists companies with raising capital through the issuance of stocks and bonds, as well as providing advice on mergers and acquisitions. You can apply skills taught in this course. The course provides a skill set for using Python for financial data analysis and algorithmic trading. The course teaches students how to backtest and forward test strategies. Technical analysis tools may be helpful to assist in making recommendations.

See salaries and explore the career path for Investment Banker

Business Intelligence Analyst

A Business Intelligence Analyst analyzes business data to identify trends, patterns, and insights that can help companies make better decisions. You may find this course helpful, as it provides a practical introduction to using Python for data analysis and visualization. The course teaches you how to load data, create interactive charts, and perform technical analysis using various indicators. The course's coverage of Pandas and Matplotlib may also be useful.

See salaries and explore the career path for Business Intelligence Analyst

Actuary

An Actuary analyzes statistical data to assess risk and calculate insurance rates and pension plans. This role typically requires an advanced degree. You can apply skills taught in this course. The course offers a practical introduction to using Python for data analysis and technical analysis. The course covers essential Python libraries such as NumPy and Pandas, which are widely used in statistical modeling.

See salaries and explore the career path for Actuary

Management Consultant

A Management Consultant advises organizations on how to improve their performance and efficiency. You may find this course helpful, as it provides a hands on introduction to using Python for data analysis and visualization. The course teaches you how to load data, create interactive charts, and perform technical analysis using various indicators. The course's coverage of Pandas and Matplotlib may also be useful. Data analysis may form a small part of your wider roles and responsibilities.

See salaries and explore the career path for Management Consultant

Technical Analysis with Python for Algorithmic Trading

Here's a deal for you

What's inside

Learning objectives

Syllabus

Traffic lights

Save this course

Reviews summary

Technical analysis & backtesting with python

Activities

Career center

Reading list

Share

Similar courses