Development
QLS
Business
Healthcare
Health & Fitness
Technology
Teaching
Lifestyle
Design
About This Course
- Making Automated Trading Bot Using Python Overview
Making Automated Trading Bot Using Python provides comprehensive knowledge of algorithmic trading systems, programming logic, and automated financial execution techniques used in modern trading environments. The course introduces essential concepts of Python programming, trading automation, market data processing, and broker API integration for building intelligent trading systems. Learners gain practical understanding of how automated bots execute trades based on predefined strategies and market conditions.
Key development areas include Python fundamentals for trading, bot architecture design, market data handling, strategy implementation, and risk control mechanisms. Learners study how trading signals are generated, processed, and executed automatically through coded systems connected to financial markets. Version control using GitHub, development tools, and integration methods for brokerage platforms are also covered.
Strengthens programming logic, analytical thinking, and financial automation skills required within modern fintech and trading industries. Learners build confidence in designing, developing, and managing automated trading systems for real-world market applications. Training supports career opportunities within algorithmic trading, financial technology development, quantitative analysis, and software-based investment solutions.
- What You Will Learn from Making Automated Trading Bot Using Python
- Understand Python programming for trading automation systems
- Build automated trading bot architecture from scratch
- Integrate broker APIs for real-time trade execution
- Develop trading strategies using coded algorithms effectively
- Process and analyse financial market data efficiently
- Use GitHub for version control and collaboration
- Implement risk management within automated trading systems
- Run and manage live automated trading bots successfully
- Why Should You Choose this Making Automated Trading Bot Using Python?
Develops strong programming skills, algorithmic trading knowledge, and financial automation expertise required within modern fintech industries. Learners gain practical understanding of Python-based trading systems, strategy development, and automated market execution processes.
Flexible online learning and structured coding lessons support hands-on skill development. Professional training improves technical confidence, problem-solving abilities, and employability opportunities within algorithmic trading, financial technology, and quantitative development sectors.
- Key Topics Covered
- Introduction to automated trading systems
- Python programming fundamentals for trading
- Trading strategy development and implementation
- Bot architecture and system design principles
- Broker API integration and connectivity methods
- GitHub version control and project management
- Market data analysis and processing techniques
- Running and monitoring automated trading bots
- Who Should Take This Course?
Suitable for beginners, programmers, finance learners, traders, and technology enthusiasts interested in algorithmic trading and financial automation. Training provides practical understanding of Python programming and automated trading system development used in modern fintech industries.
Individuals aiming to build coding skills, develop trading bots, and understand financial technology systems will benefit greatly. Skills gained support career progression within algorithmic trading, fintech development, quantitative analysis, and software engineering sectors.
- Entry Requirements
No formal academic qualifications are required for joining this course. Learners should possess basic computer knowledge, interest in Python programming and financial trading concepts, and access to a computer or laptop with stable internet connectivity for successful online learning and practical coding exercises.
- Why Should You Learn with Imperial Academy
- Flexible online learning suitable for coding learners
- Industry-focused curriculum designed by experts
- Structured lessons supporting practical bot development
- 24/7 learner support and technical assistance available
- Recognised certification supporting career growth
- Hands-on study materials improving coding skills
- Career Prospects
- Algorithmic Trading Developer (£40,000 – £90,000 per year)
- Python Developer (£35,000 – £80,000 per year)
- Quantitative Analyst (£50,000 – £120,000 per year)
- Fintech Software Engineer (£45,000 – £95,000 per year)
- Trading Systems Developer (£42,000 – £88,000 per year)
- Data Scientist – Finance (£50,000 – £110,000 per year)
Course Curriculum
-
About the course structure
00:05:00 -
Why working is important?
00:04:00
-
The free and perfect tools
00:07:00
-
Our editor: Atom
00:04:00
-
Version control: Github
00:07:00
-
Python download (Mac)
00:05:00
-
Python download (Windows)
00:02:00
-
Introduction
00:03:00
-
Python Libraries
00:05:00
-
Iterators: for
00:08:00
-
Iterators: while
00:08:00
-
Conditionals: if & else
00:10:00
-
Logic gates: and & or
00:09:00
-
Error handling: try & except
00:09:00
-
Functions and calls to libraries
00:13:00
-
Objects and classes (1)
00:10:00
-
Objects and classes (2)
00:07:00
-
Debugging the code
00:12:00
-
Closing and wrap up
00:01:00
-
Introduction
00:03:00
-
Fundamental vs Technical Analysis
00:04:00
-
Stocks vs CFDs
00:05:00
-
Long and Short positions
00:04:00
-
Takeprofit and Stoploss
00:03:00
-
Setting a real Stoploss
00:08:00
-
Limit and Market orders
00:10:00
-
Don’t forget the spread
00:04:00
-
Stock data visualisation: candles
00:08:00
-
Technical Indicators: about
00:05:00
-
Exponential Moving Average
00:08:00
-
EMA use and interpretation
00:06:00
-
Relative Strength Index
00:07:00
-
Stochastic Oscillator
00:09:00
-
Introduction
00:02:00
-
Overview
00:08:00
-
The Entry Strategy
00:10:00
-
About Tradingview
00:12:00
-
When to enter (1)
00:08:00
-
When to enter (2)
00:08:00
-
Open and hold a position
00:12:00
-
Closing a position
00:08:00
-
Review (1)
00:06:00
-
Review (2)
00:13:00
-
Closing
00:02:00
-
Introduction
00:04:00
-
Download and install Github
00:01:00
-
Create a repo
00:10:00
-
Working with branches
00:13:00
-
Introduction
00:05:00
-
Create the first bot file
00:07:00
-
Building the bot scheme
00:08:00
-
Complete your code scheme (1)
00:11:00
-
Complete your code scheme (2)
00:11:00
-
Complete your code scheme (3)
00:18:00
-
A logger to remember (1)
00:14:00
-
A logger to remember (2)
00:14:00
-
Organising your code
00:07:00
-
Main function: run bot
00:24:00
-
Link the bot and the library
00:08:00
-
Traderlib control functions (1)
00:12:00
-
Traderlib control functions (2)
00:13:00
-
Check if tradable function
00:06:00
-
Set stoploss function
00:10:00
-
Set takeprofit function
00:04:00
-
Load historical data function
00:01:00
-
Get open positions function
00:04:00
-
Submit and cancel order functions
00:04:00
-
Check positions function
00:09:00
-
The Tulipy libraries
00:07:00
-
Importing all the libraries
00:03:00
-
First filter: get general trend
00:19:00
-
Second filter: get instant trend
00:14:00
-
Third filter: RSI
00:08:00
-
Fourth filter: Stochastic Oscillator
00:14:00
-
Enter position (1)
00:13:00
-
Enter position (2)
00:11:00
-
Enter position (3)
00:15:00
-
Enter position (4)
00:08:00
-
Last check before opening
00:13:00
-
Exit position and get out
00:10:00
-
Linking everything (1)
00:12:00
-
Linking everything (2)
00:08:00
-
Linking everything (3)
00:16:00
-
Fixing a mistake: going Short
00:05:00
-
Handling all your variables
00:18:00
-
Run function scheme clarification and rebuild
00:13:00
-
Closing and wrap up
00:01:00
-
Introduction
00:03:00
-
The Alpaca Python API Wrapper
00:07:00
-
Initialising the REST API
00:09:00
-
Running the program (crash!)
00:06:00
-
Integration with check account (1)
00:08:00
-
Integration with check account (2)
00:05:00
-
Clean open orders function
00:10:00
-
Importing the trading library
00:04:00
-
Running the main
00:05:00
-
Check position function
00:09:00
-
Check if asset exists function
00:08:00
-
Fetching barset data (theory)
00:07:00
-
Fetching barset data (practice)
00:13:00
-
Updating the code for the Alpaca API V2 (explanation)
00:03:00
-
Updating the code for the Alpaca API V2 (implementation)
00:08:00
-
Organizing data with Pandas
00:06:00
-
Get general trend function (1)
00:08:00
-
Reframing the timeframe with Pandas
00:23:00
-
Get general trend function (2)
00:13:00
-
Get instant trend function
00:08:00
-
Get RSI function
00:06:00
-
Get Stochastic function
00:10:00
-
Get current price function
00:05:00
-
Finishing get shares amount
00:09:00
-
Opening a position (1)
00:12:00
-
Opening a position (2)
00:09:00
-
Check the open position
00:07:00
-
Cancelling the order (1)
00:20:00
-
Cancelling the order (2)
00:08:00
-
Making sure we cancelled
00:03:00
-
Get average entry price function
00:10:00
-
Fixing bugs when getting price
00:18:00
-
Check Stochastic crossing
00:02:00
-
Holding an open position
00:11:00
-
Submitting the exit order
00:08:00
-
Closing position and out (1)
00:08:00
-
Closing position and out (2)
00:10:00
-
Closing and wrap up
00:01:00
-
Introduction
00:03:00
-
Filtering asset by price and volume
00:07:00
-
Get the bot ready to trade
00:04:00
-
Running the Trading Bot with AAPL
00:09:00
-
A real open position
00:08:00
-
Debugging and bug fixing
00:12:00
-
Fixing one (last) bug
00:02:00
-
Running the bot with TSLA
00:09:00
-
Discussing EMA implementations
00:12:00
-
Closing and wrap up
00:02:00
Subscribe to this course and 2,000+ top‑rated Training Express courses for your organization.
Try imperial academy business
Add to Cart
