Mastering Python for Trading: From Beginner to Advanced Strategies

🚀 The complete course that takes you from first lines of Python code to building trading systems, backtesting strategies, and even applying machine learning.

✅ Why This Course?

Most “Python for finance” courses are either too basic or too academic.

This course is different: it’s built by a professional trader for traders who want practical skills they can use immediately.

No fluff.
No jargon for the sake of it.
Just the exact coding and trading skills you need to compete at a professional level.

📘 What You’ll Learn

By the end of this course, you will:

Write clean, efficient Python code with confidence.
Build your own trading utilities (PnL calculators, trade journals, portfolio trackers).
Pull and visualise real market data (stocks, futures, forex).
Code technical indicators (MA, Bollinger, ATR, Stochastics, HV, Z-score).
Generate and test trading signals on candlestick charts.
Run backtests with commissions, slippage, and real risk metrics.
Apply Monte Carlo simulations and Kelly criterion to stress-test strategies.
Take your first steps into machine learning for trading — train and test models, and compare them to rule-based systems.

🧩 Course Structure

Stage 1 – Python Foundations for Traders

From first script to building real trading utilities (profit calculators, trade journals, portfolio trackers).

Stage 2 – Python for Trading Systems

Data → Indicators → Signals → Backtesting → Risk. Build your first complete trading system using candlestick data.

Stage 3 – Intro to Machine Learning for Trading

Step into the world of ML with simple, practical models — logistic regression, testing vs backtests, and side-by-side comparisons.

📈 Who This Is For

Traders who want to add coding to their skillset.
Investors who want to backtest and measure risk like professionals.
Beginners who want a structured path into Python.
Experienced traders looking to future-proof with ML and automation.

🖥️ How It Works

Course access: The full course is now live! All content updates will automatically unlock in your Payhip account.

Lifetime access: You keep the course forever and receive every future update free.

Course curriculum:

1

SECTION 0 - INTRODUCTION (HOUSEKEEPING & SETUP)
- Welcome & How to Use the Course Free Preview
- Installing Python & Your IDE
- How Code Access Works (GitHub + ZIP)
- How to Request GitHub Access
- Support Rules & Expectations
- BONUS SECTION Free Preview
- Summary
2

SECTION 1 — PYTHON FOUNDATIONS (STAGE 1)
- Module 1: Getting Started
- Getting Started: L1: Understanding Python better
- Getting Started: L2: VS Code – practical walk through
- Getting Started: L3: • Libraries – the ones you’ll need and how to instal them
- Getting Started: L4: Writing and running your first program – code structure
- Getting Started: L5: Understanding indentation, comments, keywords
- Getting Started: L6: Practicals
- Module 2: Python Basics
- Python Basics: L1: Variables & data types: int, float, string, bool
- Python Basics: L2: Operators: arithmetic, comparison, logical
- Python Basics: L3: Type casting & type checking
- Python Basics: L4: Input/output - printing results cleanly
- Python Basics: L5: f-Strings: the professional way to print variables
- Python Basics : L6: Practicals
- Module 3: Control Flow
- Control Flow: L1: • If/elif/else — decision making
- Control Flow L2: • For and while loops — repeating tasks
- Control Flow: L3: • Loop control: break, continue, pass
- Control Flow: L4: Practicals
- Module 4: Data Structures
- Data Structures L1: Strings: slicing, formatting tickers, useful methods
- Data Structures: L2: Lists: storing trade results
- Data Structures: L3: Tuples: OHLC data
- Data Structures: L4: Dictionaries: storing performance metrics
- Data Structures: L5: Sets: handling unique tickers
- Data Structures: L6: Practicals
- Module 5: Functions & Modules
- Functions & Modules: L1: Functions
- Functions & Modules: L2: Modules
- Functions & Modules: L3: Practicals
- Module 6: Writing Robust Code
- Writing Robust Code: L1: File handling: open, read, write (CSV basics)
- Writing Robust Code: L2: Exception handling with try/except
- Writing Robust Code: L3: Logging and debugging simple scripts
- Writing Robust Code: L4: Practicals
- Module 7: Object Oriented Programming Basics
- OOP: L2: What classes and objects are
- OOP: L3: Attributes and methods
- OOP: L4: Simple inheritance
- OOP: L5: Practicals
- Module 8: Mini Projects
- Mini Project: L1: Practical
3

SECTION 2 — PYTHON FOR TRADING SYSTEMS (STAGE 2)
- MODULE 1 : Getting Market Data
- Getting Market Data L1: How to get market data – best sources, API basics
- Getting Market Data L2: Data sources (what to use, why, and when)
- Getting Market Data L3: Practicals
- MODULE 2: Visualising The Market
- Visualising The Market L1: Charting intro – how to and best libraries to use
- Visualising The Market L2: Line charts (fast scanning, sanity checks, regime detection)
- Visualising The Market L3: Plotting Bar Charts (OHLC bar charts)
- Visualising The Market L4: Plotting Candlestick Charts
- Visualising The Market L5: Plotting alternative chart types: Renko, Line Break, Kagi, Point & Figure
- Visualising The Market L6: Adding overlays and volume (the professional workflow)
- Visualising The Market L7: Making your charts interactive (zoom / pan / drag like TradingView)
- Visualising The Market L8: Practicals
- MODULE 3: Technical Indicators (Core Toolkit)
- Technical Indicators L1: Moving Averages, Bollinger Bands, Stochastics.
- Technical Indicators L2: ATR, Historical Volatility, Z-score.
- Technical Indicators L3: Chart pattern scanning – how to recognise using python
- Technical Indicators L4: Practicals
- MODULE 4: Generating Trading Signals
- Generating Trading Signals L1: Turning indicators into rules
- Generating Trading Signals L2: Boolean signals and confirmations
- Generating Trading Signals L3: Getting automated trade signals – by email / text
- Generating Trading Signals L4: Practicals
- MODULE 5: TradingView Webhooks & Python Trade Execution
- Tradingview Webhooks & Python Trade Execution L1: Webhook Signals & Python Trade Execution (TradingView-Ready
- Tradingview Webhooks & Python Trade Execution L2: Practicals
- MODULE 6: Backtesting Basics
- Backtesting Basics L2: Vectorised backtesting
- Backtesting Basics L3: Adding commissions and slippage
- Backtesting Basics L4: Backtest Librarie (why / who / what / how)
- Backtesting Basics L4: Practicals
- MODULE 7: Risk & Performance Metrics
- Risk & Performance Metrics L2: Win rate, risk/reward, Sharpe ratio
- Risk & Performance Metrics L3: Equity Curve, Drawdowns and VaR basics
- Risk & Performance Metrics L4: Practicals
- MODULE 8: Monte Carlo & Kelly Basics
- Monte Carlo & Kelly Basics L1: Why single backtests lie
- Monte Carlo & Kelly Basics L2: Monte Carlo resampling for robustness
- Monte Carlo & Kelly Basics L3: Kelly Criterion for optimal sizing
- Monte Carlo & Kelly Basics L4: Practicals
- MODULE 9: Building Your First Trading System
- Building Your First Trading System L1
4

SECTION 3 — INTRO TO MACHINE LEARNING FOR TRADING (STAGE 3)
- MODULE 1: What ML Really Means
- What ML Really Means L2: Demystifying ML vs hype
- What ML Really Means L3: How hedge funds use ML in trading
- What ML Really Means L4: The Tools / Libraries
- What ML Really Means L5: Practicals
- MODULE 2: Preparing Your Data
- Preparing Your Data L1: Features and labels for trading
- Preparing Your Data L2: Training Set (Dataset)
- Preparing Your Data L3: Building training sets from candlestick data
- Preparing Your Data L4: Practicals
- MODULE 3: Simple ML Models
- Simple ML Models L2: Linear Regression: predicting price changes
- Simple ML Models L3: Logistic Regression: predicting up vs down days
- Simple ML Models L4: Practicals
- MODULE 4: Testing ML Models
- Testing ML Models L1: Train/test split
- Testing ML Models L2: Overfitting (Confusion Matrix + Accuracy Score)
- Testing ML Models L3: Practicals
- MODULE 5: Looking Ahead
- Looking Ahead L1: Why most ML trading fails
- Looking Ahead L2: Next steps in Machine Learning and Python
- Looking Ahead L3: Practicals
- Looking Ahead L4: Stage 3 Takeaway Project (Home Assignment): Build an ML Trade Filter for a Rule-Based Strategy
5

SECTION 4 - END GAME
- Conclusions, Whats Next, Pathways
6

SECTION 5 - BONUS PYTHON
- BONUS PYTHON
- GITHUB CODE
- FAQ's
- OTHER - Documentation, Tips, Extras