Udemy - Building an Investment Trading System in Python

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Free Download Udemy - Building an Investment Trading System in Python
Published: 4/2025
Created by: Cordell Tanny, CFA, FRM, FDP
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 26 Lectures ( 5h 38m ) | Size: 2.2 GB

Build your first Python-based trading strategy with simple, real techniques used by a 25-year investment veteran.
What you'll learn
Build a Python-based trading system using real historical market data.
Learn a simple trading strategy that has historically produced profitable results.
Analyze investment performance through cumulative returns and data visualization.
Apply Python and data science techniques to create and test trading strategies easily.
Learn techniques from a 25-year investment veteran who managed a $2B CAD mutual fund program.
Develop confidence in building and testing simple trading strategies independently.
Requirements
Basic familiarity with Python (understanding variables, loops, and functions).
General knowledge of investing concepts (stocks, ETFs, returns).
A computer with internet access to run Google Colab (free, no installations required)
Curiosity and willingness to learn practical trading strategies!
Description
Learn to build a real investment trading system in Python with the practical techniques used by a 25-year investment veteran who managed a $2B CAD portfolio.This course will guide you through every step - from analyzing financial data, calculating Z-scores, and adding a volatility overlay, to running a simple backtest - using real historical market data.You don't need advanced math or expensive software. With just basic Python skills and Google Colab (free), you'll learn how to design strategies that adapt to changing market conditions.Along the way, you'll develop the ability to evaluate strategy performance using key financial metrics like Sharpe ratios, cumulative returns, and out-of-sample validation - skills professional portfolio managers use daily.This course focuses on practical, real-world applications - not theory. You will work directly with live market data, building strategies you can understand, test, and improve.Whether you're a trader looking to automate your ideas, a Python learner seeking real-world projects, or an investor curious about systematic strategies, this course will provide a strong, practical foundation.By the end, you'll have the confidence to build, test, and refine your own trading systems independently - and you'll understand the key techniques that professionals use to manage market risks.If you're serious about applying Python to real investments, not just running academic exercises, this course is for you.Enroll today and start building smarter trading strategies!
Who this course is for
Aspiring traders and investors who want to learn how to build trading systems using Python.
Python learners interested in applying their skills to real-world financial markets.
Investors looking for simple, proven strategies to improve their portfolio results.
Data science and finance students seeking hands-on projects with live market data.
Anyone curious about how professional investment managers design and test trading strategies.
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Building an Investment Trading System in Python
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 5h 37m | 2.19 GB
Instructor: Cordell Tanny, CFA, FRM, FDP​

Build your first Python-based trading strategy with simple, real techniques used by a 25-year investment veteran

What you'll learn

  • Build a Python-based trading system using real historical market data.
  • Learn a simple trading strategy that has historically produced profitable results.
  • Analyze investment performance through cumulative returns and data visualization.
  • Apply Python and data science techniques to create and test trading strategies easily.
  • Learn techniques from a 25-year investment veteran who managed a $2B CAD mutual fund program.
  • Develop confidence in building and testing simple trading strategies independently.

Requirements

  • Basic familiarity with Python (understanding variables, loops, and functions).
  • General knowledge of investing concepts (stocks, ETFs, returns).
  • A computer with internet access to run Google Colab (free, no installations required)
  • Curiosity and willingness to learn practical trading strategies!

Description

Learn to build a real investment trading system in Python with the practical techniques used by a 25-year investment veteran who managed a $2B CAD portfolio.

This course will guide you through every step - from analyzing financial data, calculating Z-scores, and adding a volatility overlay, to running a simple backtest - using real historical market data.

You don't need advanced math or expensive software. With just basic Python skills and Google Colab (free), you'll learn how to design strategies that adapt to changing market conditions.
Along the way, you'll develop the ability to evaluate strategy performance using key financial metrics like Sharpe ratios, cumulative returns, and out-of-sample validation - skills professional portfolio managers use daily.

This course focuses on practical, real-world applications - not theory. You will work directly with live market data, building strategies you can understand, test, and improve.
Whether you're a trader looking to automate your ideas, a Python learner seeking real-world projects, or an investor curious about systematic strategies, this course will provide a strong, practical foundation.

By the end, you'll have the confidence to build, test, and refine your own trading systems independently - and you'll understand the key techniques that professionals use to manage market risks.

If you're serious about applying Python to real investments, not just running academic exercises, this course is for you.

Enroll today and start building smarter trading strategies!

Who this course is for:

  • Aspiring traders and investors who want to learn how to build trading systems using Python.
  • Python learners interested in applying their skills to real-world financial markets.
  • Investors looking for simple, proven strategies to improve their portfolio results.
  • Data science and finance students seeking hands-on projects with live market data.
  • Anyone curious about how professional investment managers design and test trading strategies.

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