Python for Finance and Algorithmic Trading with QuantConnect
About Course
Learn Python for Finance and Algorithmic Trading with QuantConnect, completely free! This comprehensive course, offered by Udemy, will guide you from beginner to expert in using Python for financial analysis and algorithmic trading.
Master essential libraries like NumPy, Pandas, and Matplotlib for data analysis and visualization. Explore crucial financial concepts, including stock returns analysis, portfolio allocation optimization, efficient frontier, and Markowitz optimization. Dive into algorithmic trading with QuantConnect’s powerful LEAN engine, covering futures trading, options trading, and more.
This course includes high-quality videos, Jupyter notebooks with explanatory code, and a supportive online community with dedicated teaching assistants. Learn how to implement your ideas as algorithms and gain hands-on experience in the world of financial technology. Get started today and unlock your potential in the exciting field of finance and algorithmic trading!
What Will You Learn?
- Learn to use powerful Python libraries such as NumPy, Pandas, and Matplotlib
- Understand Modern Portfolio Theory
- Use Monte Carlo simulation techniques to optimize portfolio allocation
- Understand SciPy minimization algorithms to create optimized portfolio holdings
- Use and understand stock fundamentals data, such as CFC, Revenue, and EPS
- Calculate the Sharpe Ratio for any stock
- Understand cumulative returns and daily average returns in stocks
- Learn to use QuantConnect's LEAN engine for automated trading
- Learn about Bollinger Bands and other classic technical analysis
- Use algorithmic trading to trade derivative futures contracts
- Dive into understanding CAPM - Capital Asset Pricing Model
- Use fundamental stock company data to create rules based trading algorithms
- Learn about alternatives to the Sharpe Ratio, such as the Sortino Ratio
- Learn to read and understand a Backtest, including Probabilistic Sharpe Ratios
- Conduct Research on QuantConnect, including full universe stock selection screening
Course Content
Course Welcome and Overview
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A Message from the Professor
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Course Curriculum Overview
07:08 -
Course Overview Lecture (PLEASE DO NOT SKIP)
04:17 -
Installation and Jupyter Setup
13:49 -
Course Material Download Link
00:00
Python Crash Course
-
Introduction to Python Crash Course Section
01:16 -
Python Crash Course – Part One
19:00 -
Python Crash Course – Part Two
13:37 -
Python Crash Course – Part Three
15:02 -
Python Crash Course Exercise – Overview
04:13 -
Python Crash Course Exercise – Solutions
09:06
NumPy
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Introduction to NumPy Section
02:14 -
NumPy Arrays
22:41 -
NumPy – Indexing and Selection
11:06 -
NumPy Operations
08:14 -
NumPy Exercise Overview
01:18 -
NumPy Exercise Solutions
07:05
Core Pandas
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Introduction to Core Pandas Topics
02:00 -
Pandas Series – Part One
09:28 -
Pandas Series – Part Two
10:41 -
Pandas DataFrames – Part One – Creating a DataFrame
19:27 -
Pandas DataFrames – Part Two – Basic Properties
08:18 -
Pandas DataFrames – Part Three – Working with Columns
13:57 -
Pandas DataFrames – Part Four – Working with Rows
14:30 -
Pandas – Conditional Filtering
17:41 -
Pandas – Useful Methods – Apply on Single Column
13:47 -
Pandas – Useful Methods – Apply on Multiple Columns
17:23 -
Pandas – Useful Methods – Statistical Information
15:48 -
Pandas – Combining DataFrames – Concatenation
10:24 -
Pandas – Combining DataFrames – Inner Merge
12:04 -
Pandas – Combining DataFrames – Left and Right Merge
06:07 -
Pandas – Combining DataFrames – Outer Merge
10:38 -
Pandas IO -CSV Files
10:20 -
Pandas IO – HTML
14:41 -
Pandas IO – Excel Files
07:20 -
Pandas IO – SQL
18:19 -
Pandas Exercise Project
04:24 -
Pandas Exercise Project Solutions
18:39
Matplotlib
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Introduction to Matplotlib
04:06 -
Matplotlib Basics
12:35 -
Matplotlib – Understanding the Figure Object
07:32 -
Matplotlib – Implementing Figures and Axes
14:31 -
Matplotlib – Figure Parameters
04:56 -
Matplotlib – Subplots Functionality
19:17 -
Matplotlib Styling – Legends
07:02 -
Matplotlib Styling – Colors and Styles
14:29 -
Advanced Matplotlib Commands (Optional)
03:52 -
Matplotlib Exercise Questions – Overview
06:10 -
Matplotlib Exercise Questions – Solutions
16:39
Pandas and Finance
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Introduction to Pandas and Finance
01:47 -
Core Pandas Time Methods
21:00 -
Pandas Visualizations
20:14 -
Visualizing Time Series Data with Pandas – Part One
17:11 -
Visualizing Time Series Data with Pandas – Part Two (Optional)
08:55 -
Pandas Rolling Statistics
08:12 -
Pandas Time Shifting and Row Calculations
12:19 -
Python API Based Data Sources
12:17 -
Alternative Data Sources and Platforms
08:08 -
Pandas and Finance – Exercise Overview
05:33 -
Pandas and Finance – Exercise Solutions
19:56
Financial Concepts with Python
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Introduction to Financial Concepts with Python
01:53 -
Efficient Market Hypothesis
14:12 -
Measurements of Return
07:36 -
Measurements of Risk
05:37 -
Sharpe Ratio – Theory and Intuition
08:55 -
Sharpe Ratio with Python
08:04 -
Sortino Ratio – Theory and Intuition
03:26 -
Sortino Ratio with Python
07:29 -
Probabilistic Sharpe Ratio – Theory and Intuition
06:49 -
Probabilistic Sharpe Ratio with Python
08:09 -
Modern Portfolio Theory
10:16 -
Equal Weighted Portfolio in Python
23:02 -
Log Returns – Theory and Intuition
06:12 -
Monte Carlo Simulation with Python
15:26 -
Minimization Search with SciPy
11:22 -
Efficient Frontier in Python
12:51 -
Capital Asset Pricing Model
13:07 -
CAPM with Python – Part One – Exploring Data and Market
21:51 -
CAPM with Python – Part Two – Beta and Alpha
15:38
Stock Market Analysis Capstone Project
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Introduction to Capstone Project
06:05 -
Capstone Project Solutions – Part One – Returns Analysis
17:14 -
Capstone Project Solutions – Part Two – Volume Analysis
06:01 -
Capstone Project Solutions – Part Three – Technical Analysis
07:04
Algorithmic Trading Basics with QuantConnect
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Algorithmic Trading Basics Overview
01:38 -
Algorithmic Trading Basics – Learning Pathway
04:16 -
Algorithmic Trading – Core Concepts
09:54 -
QuantConnect Platform Tour
12:11 -
Buying Shares of Stock – Core Concepts – Part One
24:06 -
Buying Shares of Stock – Core Concepts – Part Two
17:17 -
Buying Securities on QuantConnect – Part One – Initialize Method
09:39 -
Buying Securities on QuantConnect – Part Two – OnData Method
05:42 -
Backtesting – Core Concepts
11:18 -
Buying Securities on QuantConnect – Part 3 – Backtesting and Multiple Securities
11:39 -
Selling Securities – Part One – Portfolio Liquidation
18:46 -
Selling Securities – Part Two – Time Based Exit
11:18 -
Selling Securities – Part Three – Profit Threshold Exit
15:33 -
Order System
02:37 -
MarketOrder on QuantConnect
13:40 -
LimitOrder on QuantConnect
10:09 -
StopMarketOrder on QuantConnect
16:13 -
StopLimitOrder on QuantConnect
09:17 -
MarketOnOpen and MarketOnClose Orders on QuantConnect
02:48 -
Getting Price and Share Information
17:17 -
OrderTicket System Overview
07:09 -
Interacting with and Updating OrderTickets – Part One
15:43 -
Interacting with and Updating OrderTickets – Part Two
10:55 -
Conditional Purchasing – Scheduling Functions
15:42 -
Conditional Purchasing – Price Comparison
12:02 -
Leverage – Theory and Intuition
05:12 -
Leverage Example – QuantConnect
12:11 -
Shorting – Theory and Intuition
06:14 -
Shorting Example – QuantConnect
07:48 -
Margin Calls
17:23
QuantConnect Research, Plotting, Universe Selection
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Introduction to Research and Plotting Section
01:19 -
QuantConnect Charts
06:30 -
Custom Charts
09:51 -
CandleStick Plots
09:33 -
Combining Plots
08:27 -
Modifying Plot Properties
05:36 -
QuantBook and Research Notebooks Overview
03:17 -
Research Notebooks – Part One – Securities Historical Data
12:23 -
Research Notebooks – Part Two – Fundamental Data
17:09 -
Research Notebooks – Part Three – Technical Indicators
12:51 -
Universe Selection – Key Ideas
10:48 -
Universe Selection – Part One- Coarse Filter
24:32 -
Universe Selection – Part Two – OnSecuritiesChanged Mthod
10:21 -
Universe Selection – Part Three – Fine Filter
16:24
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