Algo Trading with Python: Build Indicators and Manage Risks
Algo Trading with Python: Build Indicators and Manage Risks, available at $34.99, has an average rating of 3.55, with 61 lectures, based on 11 reviews, and has 91 subscribers.
You will learn about Build a complete algorithm, from scanning market, placing trades, to managing trades Learn how to build indicators that work for you Learn how to interact with your brokers programmatically and directly Use code to be systematic in trading and get your time back Learn how to enter a trade, exit a trade, and get other account data programmatically Learn the Python skills to protect your accounts and profits Learn all the skills so that you are able to build a backtesitng system for your algorithm from this course This course is ideal for individuals who are Forex and crypto traders who are interested in algorithimic trading or Forex and crypto traders who are interested in using machine learning in your algorithms in the future It is particularly useful for Forex and crypto traders who are interested in algorithimic trading or Forex and crypto traders who are interested in using machine learning in your algorithms in the future.
Enroll now: Algo Trading with Python: Build Indicators and Manage Risks
Summary
Title: Algo Trading with Python: Build Indicators and Manage Risks
Price: $34.99
Average Rating: 3.55
Number of Lectures: 61
Number of Published Lectures: 61
Number of Curriculum Items: 61
Number of Published Curriculum Objects: 61
Original Price: $79.99
Quality Status: approved
Status: Live
What You Will Learn
- Build a complete algorithm, from scanning market, placing trades, to managing trades
- Learn how to build indicators that work for you
- Learn how to interact with your brokers programmatically and directly
- Use code to be systematic in trading and get your time back
- Learn how to enter a trade, exit a trade, and get other account data programmatically
- Learn the Python skills to protect your accounts and profits
- Learn all the skills so that you are able to build a backtesitng system for your algorithm from this course
Who Should Attend
- Forex and crypto traders who are interested in algorithimic trading
- Forex and crypto traders who are interested in using machine learning in your algorithms in the future
Target Audiences
- Forex and crypto traders who are interested in algorithimic trading
- Forex and crypto traders who are interested in using machine learning in your algorithms in the future
Are you a trader who is interested in learning how to build their own trading algorithm?
If so, this course will teach you the basics of Python – you will learn about all the native data types in Python, and know how to work with control flow structure so that you can have decision logic built into your code.
Trading is hard, but it is also highly rewarding. With Python, you can put in a methodical system to build your own rule-based algorithm in order to get to your goal efficiently.
Python is also the perfect language for AI-based algorithms using a variety of machine learning techniques. The AI-based courses are coming soon.
This complete course of algo trading with Python will teach all the Python syntax you need to know, and get you ready for working with Pandas, taking care of the DateTime objects, and handling errors. You will then immediately learn about how to use Python to connect to the MetaTrader5 terminal, and get market data as well as account information programmatically from your broker directly. You will also learn about constructing indicators that work for your style of trading, by using Pandas, Ta-Lib, or writing your own user-defined functions. You will learn how to programmatically compute position size but first decide where you would like to place your stop loss level, and then query some account information to complete the computation. You will learn about the specific ways that you will communicate, using Python, with your brokers to enter and/or exit trades with the computed size for your risk tolerance. Further, you will also learn about how to set the stop loss and take profit, how to move stop loss to breakeven, and how to update (trail) your stop-loss level after that point. Once you have learned all about these topics, you are able to build as many elaborate risk management strategies as you want in a way that you like to trade.
At the end of this course, you will have a firm understanding of what goes into building an algorithmic trading strategy from scratch. You will have not only all the tools necessary to create your own algorithmic strategies, but you will also know how to manage your positions, as well as take what you already know and set up a backtesting environment for yourself, so that you are able to systematically build and test strategies on an on-going basis.
The goal of the series is to give you an understanding of what goes into building an algorithmic trading strategy from scratch. By the end of the three-part series, you should have not only all the tools necessary to create your own algorithmic strategies, but you will also know how to manage your positions.
Course Curriculum
Chapter 1: Do This If You Are On a Mac
Lecture 1: Optional (For Mac Users)
Chapter 2: Installing Anaconda
Lecture 1: Installing Anaconda
Chapter 3: Install Ta-Lib
Lecture 1: Installing Ta-Lib
Chapter 4: Python Primer
Lecture 1: Python Expressions
Lecture 2: Overview of Data Types
Lecture 3: Numerical Data Type
Lecture 4: String Data Type
Lecture 5: List Data Type
Lecture 6: Tuple Data Type
Lecture 7: Set Data Type
Lecture 8: Dictionary Data Type
Lecture 9: Control Flow Structure
Chapter 5: Learning Pandas
Lecture 1: Course Introductions and Overview
Lecture 2: Working with Pandas DataFrame
Lecture 3: DataFrame at a Glance
Lecture 4: Working with DataFrame – Part 2
Lecture 5: DataFrame at a Glance – Part 2
Lecture 6: Accessing Columns
Lecture 7: Accessing Columns – Part 2
Lecture 8: Subsetting, Indexing and Slicing
Lecture 9: Unique Values
Lecture 10: Selecting Rows
Lecture 11: Selecting Rows – Part 2
Lecture 12: Subsetting
Lecture 13: Adding New Columns
Lecture 14: Sorting
Lecture 15: Using Groupby
Lecture 16: Pandas Summary
Chapter 6: Writing User-Defined Functions
Lecture 1: Python Function Overview
Lecture 2: Functions
Lecture 3: Error Handling – Try / Except
Lecture 4: Why Using Error-Handling
Lecture 5: Functions – Part 2
Lecture 6: Best Practices
Chapter 7: Handling Datetime
Lecture 1: Datetime Objects in Python
Lecture 2: Timezones
Lecture 3: Timedelta
Lecture 4: Converting Datetime to String
Lecture 5: Conveting String to Datetime
Chapter 8: Getting Broker Data
Lecture 1: Installing MetaTrader5 Module
Lecture 2: Connecting to MT5 Terminal
Lecture 3: Setting Timezones to Match Market Watch Time
Chapter 9: Building Indicators
Lecture 1: Overview
Lecture 2: Building Indicators via Pandas
Lecture 3: Building Indicators via Ta-Lib
Chapter 10: Getting Account Info
Lecture 1: Getting Account Info
Chapter 11: Position Sizing
Lecture 1: Recap
Lecture 2: Stoploss
Lecture 3: Calculate Size
Chapter 12: Putting It Together
Lecture 1: Putting It Together
Lecture 2: Putting It Together for Long Positions
Lecture 3: Putting It Together for Short
Lecture 4: One More Thing
Lecture 5: Summary
Lecture 6: Documentation and Summary
Chapter 13: Trailing
Lecture 1: Recap of What We Have So Far
Lecture 2: Updating Stoploss
Lecture 3: Updating Stoploss – Part 2
Lecture 4: Move Stops to Breakeven and Trail Afterwards for Long Positions
Lecture 5: Move Stops to Breakeven and Trail Afterwards for Short Positions
Lecture 6: Overall Summary
Instructors
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Jenny Hung
Data Scientist, Mentor, and Trader
Rating Distribution
- 1 stars: 1 votes
- 2 stars: 2 votes
- 3 stars: 1 votes
- 4 stars: 5 votes
- 5 stars: 2 votes
Frequently Asked Questions
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You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
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