Algorithmic Trading A-Z with Python, Machine Learning & AWS from Udemy

Welcome to the most comprehensive Algorithmic Trading Course. It´s the first 100% Data-driven Trading Course.

Course fully revised and updated in November 2025 with 100+ new or updated Lectures

Did you know that 75% of retail Traders lose money with Day Trading? (some sources say >95%)

For me as a Data Scientist and experienced Finance Professional this is not a surprise. Day Traders typically do not know/follow the five fundamental rules of (Day) Trading. This Course covers them all in detail.

1. Know and understand the Day Trading Business

Don´t start Trading if you are not familiar with terms like Bid-Ask Spread, Pips, Leverage, Margin Requirement, Half-Spread Costs, etc.

Part 1 of this course is all about Day Trading A-Z with the Brokers Oanda, Interactive Brokers, and FXCM. It deeply explains the mechanics, terms, and rules of Day Trading (covering Forex, Stocks, Indices, Commodities, Baskets, and more).

2. Use powerful and unique Trading Strategies

You need to have a Trading Strategy. Intuition or gut feeling is not a successful strategy in the long run (at least in 99.9% of all cases). Relying on simple Technical Rules doesn´t work either because everyone uses them.

You will learn how to develop more complex and unique Trading Strategies with Python. We will combine simple and also more complex Technical Indicators and we will also create Machine Learning- and Deep Learning- powered Strategies. The course covers all required coding skills (Python, Numpy, Pandas, Matplotlib, scikit-learn, Keras, Tensorflow) from scratch in a very practical manner.

3. Test your Strategies before you invest real money (Backtesting / Forward Testing)

Is your Trading Strategy profitable? You should rigorously test your strategy before 'going live'.

This course is the most comprehensive and rigorous Backtesting / Forward Testing course that you can find.

You will learn how to apply Vectorized Backtesting techniques, Iterative Backtesting techniques (event-driven), live Testing with play money, and more. And I will explain the difference between Backtesting and Forward Testing and show you what to use when. The backtesting techniques and frameworks covered in the course can be applied to long-term investment strategies as well.

4. Take into account Trading Costs - it´s all about Trading Costs.

"Trading with zero commissions? Great. " ... Well, there is still the Bid-Ask-Spread and even if 2 Pips seem to be very low, it isn´t.

The course demonstrates that finding profitable Trading Strategies before Trading Costs is simple. It´s way more challenging to find profitable Strategies after Trading Costs. Learn how to include Trading Costs into your Strategy and into Strategy Backtesting / Forward Testing. And most important: Learn how you can control and reduce Trading Costs.

5. Automate your Trades

Manual Trading is error-prone, time-consuming, and leaves room for emotional decision-making.

This course teaches how to implement and automate your Trading Strategies with Python, powerful Broker APIs, and Amazon Web Services (AWS). Create your own Trading Bot and fully automate/schedule your trading sessions in the AWS Cloud.

Finally... this is more than just a course on automated Day Trading:

the techniques and frameworks covered can be applied to long-term investing as well.

it´s an in-depth Python Course that goes beyond what you can typically see in other courses. Create Software with Python and run it in real-time on a virtual Server (AWS).

we will feed Machine Learning & Deep Learning Algorithms with real-time data and take ML/DL-based actions in real-time.

What are you waiting for? Join now. 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

Build automated trading bots with python and amazon web services (aws)
Create powerful and unique trading strategies based on technical indicators and machine learning / deep learning.
Rigorous testing of strategies: backtesting, forward testing and live testing with paper money.
Fully automate and schedule your trades on a virtual server in the aws cloud.
Truly data-driven trading and investing.
Python coding and object oriented programming (oop) in a way that everybody understands it.
Coding with numpy, pandas, matplotlib, scikit-learn, keras and tensorflow.

Understand day trading a-z: spread, pips, margin, leverage, bid and ask price, order types, charts & more.
Day trading with brokers oanda, interactive brokers (ibkr) and fxcm.
Stream high-frequency real-time data.
Understand, analyze, control and limit trading costs.
Use powerful broker apis and connect with python.
Show more
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Build automated trading bots with python and amazon web services (aws)
Create powerful and unique trading strategies based on technical indicators and machine learning / deep learning.
Rigorous testing of strategies: backtesting, forward testing and live testing with paper money.
Fully automate and schedule your trades on a virtual server in the aws cloud.
Truly data-driven trading and investing.
Python coding and object oriented programming (oop) in a way that everybody understands it.
Coding with numpy, pandas, matplotlib, scikit-learn, keras and tensorflow.
Understand day trading a-z: spread, pips, margin, leverage, bid and ask price, order types, charts & more.
Day trading with brokers oanda, interactive brokers (ibkr) and fxcm.
Stream high-frequency real-time data.
Understand, analyze, control and limit trading costs.
Use powerful broker apis and connect with python.
Show more
Show less

Syllabus

Test your knowledge

Student FAQ

Getting Started

What is Algorithmic Trading / Course Overview

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Covers Day Trading A-Z, which provides a solid foundation for learners who are new to the world of finance and trading

Teaches how to automate trading strategies with Python, Broker APIs, and Amazon Web Services (AWS), which are essential skills for modern algorithmic trading

Explores backtesting and forward testing techniques, which are crucial for validating trading strategies before deploying them with real capital

Requires learners to install Anaconda, which may pose a barrier to entry for some learners without prior experience in software installation and package management

Features content updated in November 2025, which ensures that learners are exposed to the latest tools, techniques, and information in the field

Emphasizes the importance of understanding and controlling trading costs, which is a critical aspect of successful algorithmic trading often overlooked in other courses

Reviews summary

Comprehensive algorithmic trading with python, ml & aws

According to students, this is a highly comprehensive course teaching algorithmic trading using Python, Machine Learning, and AWS. Learners frequently praise the depth of coverage, including rigorous backtesting techniques, real-world broker API integration, and the critical focus on trading costs and risk. The course is described as hands-on and practical, with many appreciating the coding exercises and projects. While the instructor is seen as knowledgeable and engaging, some reviews suggest that the course pace can be fast for those without prior Python coding experience, recommending some preparation. Overall, reviews indicate this course provides a strong foundation for building automated trading systems.

Highlights often overlooked expenses.

"The focus on trading costs (spread, commissions) is a major strength of this course."

"Understanding how significant trading costs are and how to minimize them was an eye-opener."

"Most courses ignore this, but this one shows you how costs impact strategy profitability."

"I learned how to include trading costs into my strategy and backtesting."

Expertise is well-regarded.

"The instructor clearly knows his stuff and explains complex topics well."

"I found the lectures engaging and the instructor's passion for the subject is evident."

"His insights into the trading business and costs were particularly valuable."

"The explanations on backtesting and strategy development were very clear."

Strong emphasis on strategy testing.

"The section on backtesting is the most comprehensive I've seen; it's not just theoretical."

"Learning different backtesting techniques (vectorized vs iterative) was crucial for me."

"The course stresses the importance of rigorous testing before live trading, which is essential."

"I liked how the course showed how to incorporate trading costs into backtesting results."

Focuses on coding and real-world application.

"The hands-on coding and projects are the strongest part of the course for me, making the concepts concrete."

"I appreciated the practical examples of connecting to broker APIs and executing trades."

"Building the actual trading bot and deploying it to AWS was a fantastic practical exercise."

"This course provides practical tools and strategies I could immediately apply."

Covers Python, ML, AWS, backtesting.

"This course is incredibly comprehensive, covering everything from Python basics to advanced ML and AWS deployment."

"I found the coverage of backtesting methods and API integration particularly thorough and useful."

"It really does go A-Z, including not just coding but also broker mechanics and costs, which is rare."

"Learned so much about integrating ML models into a trading strategy and automating it on AWS."

Pace might be fast for absolute beginners.

"Although it says it covers Python from scratch, having a basic understanding beforehand is definitely recommended."

"The coding sections move quite quickly; beginners might need to supplement with other Python resources."

"I struggled a bit with the Python part initially as I had no prior experience, but caught up later."

"Be prepared to pause and re-watch the coding lectures if you're new to Python."

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 Algorithmic Trading A-Z with Python, Machine Learning & AWS with these activities:

Review Day Trading Terminology

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Reinforce your understanding of essential day trading terms to better grasp the course's foundational concepts.

Browse courses on Bid-Ask Spread

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Create a glossary of key day trading terms.
Find examples of each term in real-world scenarios.
Test your understanding with online quizzes.

Review 'Trading for Dummies'

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Solidify your understanding of basic trading principles before diving into algorithmic strategies.

View Trading For Dummies on Amazon

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Read the book, focusing on chapters about market mechanics.
Summarize key concepts from each chapter.
Identify areas where you need more clarification.

Practice Python Fundamentals

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Strengthen your Python skills to effectively implement and test algorithmic trading strategies.

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Complete Python tutorials on data structures and control flow.
Solve coding challenges on platforms like HackerRank or LeetCode.
Practice writing functions and classes in Python.

Five other activities

Expand to see all activities and additional details

Show all eight activities

Backtest a Simple Moving Average Strategy

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Apply your knowledge by backtesting a basic trading strategy using historical data.

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Gather historical price data for a specific asset.
Implement a Simple Moving Average (SMA) trading strategy in Python.
Backtest the strategy and analyze its performance.
Visualize the results using Matplotlib.

Document Your Trading Strategy

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Improve your understanding by clearly documenting your trading strategy, assumptions, and results.

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Describe your trading strategy in detail.
Explain the rationale behind your strategy's rules.
Document the backtesting process and results.
Reflect on the strengths and weaknesses of your strategy.

Review 'Python for Data Analysis'

Show steps

Enhance your data analysis skills with Python to improve your trading strategy development.

View Python for Data Analysis on Amazon

Show steps

Read the book, focusing on Pandas and data manipulation.
Practice the examples in the book using your own data.
Apply the techniques to analyze trading data.

Explore AWS CloudWatch Tutorials

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Learn how to monitor your automated trading bots in the AWS cloud using CloudWatch.

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Find tutorials on setting up CloudWatch for monitoring applications.
Configure CloudWatch to track key metrics of your trading bot.
Create dashboards to visualize the performance of your bot.

Contribute to a Trading Library

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Deepen your understanding by contributing to an open-source algorithmic trading library.

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Find an open-source trading library on GitHub.
Identify a bug or feature to work on.
Contribute code, documentation, or tests to the library.

Career center

Learners who complete Algorithmic Trading A-Z with Python, Machine Learning & AWS will develop knowledge and skills that may be useful to these careers:

Quantitative Analyst

A quantitative analyst, often called a quant, develops and implements mathematical and statistical models for financial markets. This course is directly relevant to this role, as it teaches the core skills of developing algorithmic trading strategies using Python, machine learning, and deep learning. A quant applies these models to make trading or investment decisions, and this course provides a foundation in testing those strategies through backtesting and forward testing. The specific focus on automating trades with broker APIs and cloud services like AWS makes this course particularly helpful for anyone aiming for a quantitative analyst position to understand the full scope of creating a data-driven trading system from strategy to automation. This may be a typical role for someone with a masters or PhD.

See salaries and explore the career path for Quantitative Analyst

Algorithmic Trader

An algorithmic trader designs, develops, and implements automated trading strategies. This course is an ideal fit for someone pursuing this role. The course covers the development of trading strategies using technical indicators, machine learning, and deep learning, and it includes rigorous testing methodologies such as backtesting and forward testing. Learning how to automate trading strategies with Python and broker APIs is paramount for an algorithmic trader. Additionally, the course’s discussion of trading costs and how to reduce them is vital to building profitable strategies. All of these directly prepare a learner to succeed as an algorithmic trader.

See salaries and explore the career path for Algorithmic Trader

Financial Data Scientist

A financial data scientist uses data analysis, machine learning, and statistical modeling to solve financial problems. This position often includes the development of trading strategies and risk management tools. This course is helpful for this role, as it teaches data analysis in Python through libraries like Numpy, Pandas, and Matplotlib. The course also provides experience with machine learning and deep learning algorithms for financial applications, which a financial data scientist would often use. The focus on building and testing trading strategies and automating them with cloud services is an excellent application of data science principles to finance, and the course provides a practical, hands-on approach to learning these skills.

See salaries and explore the career path for Financial Data Scientist

Trading Systems Developer

A trading systems developer builds and maintains the software infrastructure for trading desks. This course can help someone interested in this role, as it teaches the practical coding skills using Python necessary to build and automate trading systems. The course also covers essential topics such as connecting to broker APIs and streaming real-time market data, which are crucial components in building trading platforms and applications. For a trading systems developer, this course’s emphasis on deploying automated trading strategies on AWS cloud services is especially beneficial, as it prepares them to write and maintain software that is ready to scale for real-world use.

See salaries and explore the career path for Trading Systems Developer

Financial Software Engineer

Financial software engineers create and maintain the software applications used in the financial industry. The role often involves working with trading platforms, risk management systems, and data analytics tools. This course is directly relevant as it teaches Python programming, a popular language in finance, and provides practical experience in connecting to broker APIs, retrieving real-time data, and building automated trading systems. The course also covers how to deploy trading systems in the cloud with Amazon Web Services (AWS), which is an important skill for financial software engineers who need to create scalable and robust applications for real-time trading.

See salaries and explore the career path for Financial Software Engineer

Hedge Fund Analyst

A hedge fund analyst researches investment opportunities and supports portfolio managers in making decisions. This position could benefit from the course, as it covers many aspects of algorithmic trading, including strategy design, backtesting, and automation. The course's approach to using Python and machine learning in trading can offer valuable insights into how hedge funds operate, especially those using quantitative strategies. The specific focus on high-frequency data, broker APIs, and cloud deployment in this course can make a hedge fund analyst more effective.

See salaries and explore the career path for Hedge Fund Analyst

Investment Analyst

An investment analyst researches investment opportunities and makes recommendations to portfolio managers or individual clients. This course may be useful to an investment analyst, as it teaches the fundamentals of trading, including market mechanics, order types, and trading costs which are important for evaluating investment opportunities. Though the primary aim of the course is to design and build algorithmic trading systems, the understanding gained from backtesting and forward testing strategies and the use of technical indicators can provide an analyst with a unique perspective on market dynamics and enhance their analytical toolkit.

See salaries and explore the career path for Investment Analyst

Risk Manager

A risk manager identifies, assesses, and mitigates financial risk within a company or organization. This course may be helpful to a risk manager in gaining an understanding of the mechanics of trading and the tools used in algorithmic trading. Such an understanding helps a risk manager evaluate the risks associated with automated trading strategies or systems. While the course is primarily focused on strategy development and automation, the topics of trading cost control and backtesting do have relevance to a risk manager's role. Learning Python in this course may also be useful when creating dashboards and risk reporting.

See salaries and explore the career path for Risk Manager

Portfolio Manager

A portfolio manager is in charge of making investment decisions for a fund or a client's portfolio. While this course mainly focuses on algorithmic trading, it may be of value for a portfolio manager to understand the mechanics of automated trading, the importance of risk management, and the role of trading costs in investment performance. A portfolio manager may find useful the techniques taught for backtesting and forward testing trading strategies, which can provide an empirical perspective on potential investment decisions. The course can help a portfolio manager who wishes to deepen their understanding of automated strategies.

See salaries and explore the career path for Portfolio Manager

Financial Consultant

A financial consultant provides advice on financial planning, investing, and risk management. While this position does not directly involve algorithmic trading, this course may be useful for a financial consultant to help understand the mechanics, challenges, and opportunities within the world of algorithmic trading. The course’s emphasis on data-driven strategies and rigorous testing may help a consultant better understand and articulate the potential rewards and risks associated with automated trading, which can be valuable for clients who are considering these strategies. A financial consultant will also gain a deeper understanding of risk management.

See salaries and explore the career path for Financial Consultant

Data Analyst

A data analyst interprets and presents data to help organizations make strategic decisions. While not specific to finance, the techniques and tools taught in this course may be helpful to a data analyst who wishes to enter the financial or algorithmic trading space. The course teaches Python programming and data manipulation libraries such as Numpy and Pandas, which are essential for handling and analyzing financial data. A data analyst will also gain experience with machine learning and deep learning techniques that this course covers, which can be applied to solve problems and improve workflows specific to finance.

See salaries and explore the career path for Data Analyst

Operations Analyst

An operations analyst in finance ensures that all trading activities happen smoothly, efficiently, and according to regulations. This course may be helpful for this role because it provides insights into the world of automated trading, including how trades are executed, how data is managed, and how systems are integrated. An operations analyst may find especially useful the course topics covering the various exchanges and trading platforms, the importance of APIs, and the need for robust and scalable systems for trading. Understanding these core elements will help an operations analyst in their role.

See salaries and explore the career path for Operations Analyst

Business Analyst

A business analyst focuses on improving organizational processes and systems, often with a focus on efficiency and efficacy. This course may be useful to a business analyst who seeks to bridge the gap between business needs and technical solutions within a financial institution. The course provides an overview of algorithmic trading, and a business analyst may benefit from learning the basics of Python, machine learning, and cloud computing to better understand the automation involved in trading. This will give a business analyst a better perspective on how to improve the systems used by financial professionals.

See salaries and explore the career path for Business Analyst

Software Developer

A software developer designs and builds software applications, and while not specific to finance, this course may be useful to a software developer who seeks a role in fintech or financial applications. The course teaches Python programming, which is widely used in the financial industry, and provides hands-on experience with libraries, APIs, and cloud services like Amazon Web Services (AWS). Understanding the concepts of algorithmic trading and the use of machine learning may be of interest for a software developer looking for a career in a quantitatively driven financial institution.

See salaries and explore the career path for Software Developer

Market Research Analyst

A market research analyst studies market conditions to help their firm make strategic decisions. This course may be helpful to a market research analyst in finance because it provides a general understanding of how markets work from an algorithmic perspective. The course will teach an analyst the factors that go into constructing trading strategies, how trading systems are built, and gives them knowledge of some current technologies in finance, including Python, API usage, and cloud computing. These areas, while not part of a traditional market analysis role, can enhance an analyst's understanding of financial markets.

See salaries and explore the career path for Market Research Analyst

Algorithmic Trading A-Z with Python, Machine Learning & AWS

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What's inside

Learning objectives

Syllabus

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Reviews summary

Comprehensive algorithmic trading with python, ml & aws

Activities

Career center

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