Basics of Python for Quant Finance

Name: Basics of Python for Quant Finance
Rating: 4.0 (3 reviews)

Learn data analysis, and build real crypto & stock trading systems using Python

Created byEbi Mindset

Last updated 4/2026

English

English [Auto],

What you'll learn

Python for Finance:
Python Algo Trading Masterclass (Beginner → Advanced)
Build Trading Bots & Strategies
Financial Data Analysis & Automation with Python

Course content

3 sections • 14 lectures • 1h 57m total length

Introduction4:00
Learn python for finance from beginner to intermediate, covering concepts, data analysis, and models for financial markets. Build skills with hands-on examples using price data and balance sheets from scratch.

Why Python for Finance16:22
Explains why Python is the preferred tool for finance, highlighting its simplicity, vast libraries (NumPy, Pandas, and Matplotlib), and capabilities for backtesting, automation, and algorithmic trading.
Data Types & Structures16:18
Explore Python data types and structures essential for quant finance, including integers, floats, booleans, and strings, with practical examples for pricing, trading decisions, and printing results.
Basic Data Structures16:18
Explore the four Python data types for quant finance—integers, floats, booleans, and strings—and how they support data structures, pricing, and symbols.
Learn NumPy and Pandas for Beginners7:59
Explore numpy and pandas basics for beginners, managing arrays and data frames, and computing mean and max prices to analyze daily price fluctuations.
Data Visualization with Python Explained Simply9:23
Learn data visualization with Python for finance by mastering 2d and 3d plotting, interactive charts, and candlestick charts with Matplotlib to interpret market moves.
Financial Time Series Explained Simply6:07
Explore financial time series as the base for market moves, linking time, price, and volatility to reveal trends; learn candles, data import, and pandas plots for returns and correlations.
Static vs Stochastic: What’s the Difference?6:31
Differentiate statistics from stochastics using simple train and climate examples, then explore how random market data informs probability and stochastic modeling for backtesting strategies.

Simple Moving Average Crossover11:49
Learn to calculate simple moving averages (SMA 10, SMA 20) and use crossovers to generate buy, hold, or sell signals with yfinance data and pandas in Python.
RSI4:53
Learn to implement RSI from scratch using close prices, 14 periods, average gain/loss, and hourly data, with RSI above 70 as short and below 30 as long.
MACD Crossover2:17
Implement a momentum-based MACD crossover strategy using EMA 12 and 26 on close prices, generating buy, hold, or sell signals and simulating the code to observe signals.
Bollinger Band4:38
Explore Bollinger bands with a simple pullback strategy around the middle line, using SMA 20 with upper and lower bands, demonstrated through a small Python code using yfinance and pandas.
OBV Indicator Strategy5:31
Apply the on balance volume (obv) indicator to identify buying and selling pressure, divergences, and potential breakouts. Use volume-based strategies with confirmation and risk management on bitcoin data examples.
ADX Strategy for Strong Trends5:35
Discover how ADX detects trends from sideways to strong and how EMA confirmations improve entries, with Python code using yfinance, pandas, and numpy.

Requirements

Basic computer knowledge
No coding needed

Description

This course focuses on helping you understand how financial markets operate and how to analyze them using Python tools.

Financial Market Basics
Understand how stock and cryptocurrency markets work, including price movement, trends, and volume.
Data Analysis with Python
Learn how to use Python (Pandas, NumPy) to read, clean, and analyze market data.
Technical Indicators
Build and understand indicators like RSI, Moving Averages, and MACD to identify market opportunities.
Beginner to Intermediate Strategy Building
Create simple and effective Buy/Sell strategies based on real market logic.
Basic Backtesting Concepts
Learn how to test your strategies on past data to evaluate performance.

In addition to these core concepts, the course also focuses on developing a strong analytical mindset required for trading and investing. You will learn how to interpret charts, identify trends, and understand market behavior without relying on guesswork or emotions. The course gradually introduces you to real-world scenarios, helping you connect theory with practical application. Each concept is explained in a simple and structured manner so that beginners can easily follow along and build confidence step by step.

You will also explore how different market conditions affect trading strategies, such as trending markets, sideways movements, and volatile price action. This helps you understand when to apply specific strategies and when to avoid risky situations. By practicing with real data, you will gain hands-on experience that prepares you for actual market environments.

By the end of this course, you will not only understand how financial markets function but also gain the ability to analyze data, build basic strategies, and improve them over time. This strong foundation will allow you to confidently move toward more advanced trading and algorithmic concepts in the future.

Who this course is for:

Beginner traders
Crypto traders
Engineering students
People interested in passive income

Basics of Python for Quant Finance

What you'll learn

Explore related topics

Course content

Introduction1 lecture • 4min

Python Data Types & Data Structures Explained Simply7 lectures • 1hr 19min

Real Time Projects6 lectures • 35min

Requirements

Description

Who this course is for: