Python for Finance and Algorithmic Trading with QuantConnect

Learn to use Python, Pandas, Matplotlib, and the QuantConnect Lean Engine to perform financial analysis and trading
Python for Finance and Algorithmic Trading with QuantConnect
File Size :
8.76 GB
Total length :
22h 51m



Jose Portilla


Last update




Python for Finance and Algorithmic Trading with QuantConnect

What you’ll 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

Python for Finance and Algorithmic Trading with QuantConnect


Basic Python Experience


Welcome to the ultimate online course to go from zero to hero in Python for Finance, including Algorithmic Trading with LEAN Engine!This course will guide you through everything you need to know to use Python for Finance and conducting Algorithmic Trading on the QuantConnect platform with the powerful LEAN engine!This course is specifically design to connect core financial concepts to clear Python code. You will learn about in-demand real world skills that are highly sought after in the fintech ecosystem.We’ll cover the following topics used by financial professionals:Python Crash Course FundamentalsNumPy for High Speed Numerical ProcessingPandas for Efficient Data AnalysisMatplotlib for Data VisualizationStock Returns AnalysisCumulative Daily ReturnsVolatility and Securities RiskEWMA (Exponentially Weighted Moving Average)Sharpe RatioPortfolio Allocation OptimizationEfficient Frontier and Markowitz OptimizationTypes of FundsOrder BooksShort SellingCapital Asset Pricing ModelStock Splits and DividendsEfficient Market HypothesisAlgorithmic Trading with QuantConnectFutures TradingOptions Tradingand much more!Why choose this specific course to learn Python, Finance, and Algorithmic Trading?This course starts by teaching you some of the most important and popular libraries in Python for Data Analysis and Visualization, includign NumPy, Pandas, and Matplotlib.Each lecture includes a high quality HD video with clear instructions and relevant theory slides as well as a full Jupyter Notebook with explanatory code and text.This course has complete coverage allowing you to actually implement your ideas as algorithms, other courses online never actually show you how to trade with your new knowledge!Powerful online community with our QA Forums with thousands of students and dedicated Teaching Assistants, as well as student interaction on our Discord Server.All of this comes with a 30-day money back guarantee, so you can try out the course absolutely risk free!


Section 1: Course Welcome and Overview

Lecture 1 Course Welcome Message

Lecture 2 Course Curriculum Overview

Lecture 3 Course Overview Lecture (PLEASE DO NOT SKIP)

Lecture 4 Installation and Jupyter Setup

Section 2: Python Crash Course

Lecture 5 Introduction to Python Crash Course Section

Lecture 6 Python Crash Course – Part One

Lecture 7 Python Crash Course – Part Two

Lecture 8 Python Crash Course – Part Three

Lecture 9 Python Crash Course Exercise – Overview

Lecture 10 Python Crash Course Exercise – Solutions

Section 3: NumPy

Lecture 11 Introduction to NumPy Section

Lecture 12 NumPy Arrays

Lecture 13 NumPy – Indexing and Selection

Lecture 14 NumPy Operations

Lecture 15 NumPy Exercise Overview

Lecture 16 NumPy Exercise Solutions

Section 4: Core Pandas

Lecture 17 Introduction to Core Pandas Topics

Lecture 18 Pandas Series – Part One

Lecture 19 Pandas Series – Part Two

Lecture 20 Pandas DataFrames – Part One – Creating a DataFrame

Lecture 21 Pandas DataFrames – Part Two – Basic Properties

Lecture 22 Pandas DataFrames – Part Three – Working with Columns

Lecture 23 Pandas DataFrames – Part Four – Working with Rows

Lecture 24 Pandas – Conditional Filtering

Lecture 25 Pandas – Useful Methods – Apply on Single Column

Lecture 26 Pandas – Useful Methods – Apply on Multiple Columns

Lecture 27 Pandas – Useful Methods – Statistical Information

Lecture 28 Pandas – Combining DataFrames – Concatenation

Lecture 29 Pandas – Combining DataFrames – Inner Merge

Lecture 30 Pandas – Combining DataFrames – Left and Right Merge

Lecture 31 Pandas – Combining DataFrames – Outer Merge

Lecture 32 Pandas IO -CSV Files

Lecture 33 Pandas IO – HTML

Lecture 34 Pandas IO – Excel Files

Lecture 35 Pandas IO – SQL

Lecture 36 Pandas Exercise Project

Lecture 37 Pandas Exercise Project Solutions

Section 5: Matplotlib

Lecture 38 Introduction to Matplotlib

Lecture 39 Matplotlib Basics

Lecture 40 Matplotlib – Understanding the Figure Object

Lecture 41 Matplotlib – Implementing Figures and Axes

Lecture 42 Matplotlib – Figure Parameters

Lecture 43 Matplotlib – Subplots Functionality

Lecture 44 Matplotlib Styling – Legends

Lecture 45 Matplotlib Styling – Colors and Styles

Lecture 46 Advanced Matplotlib Commands (Optional)

Lecture 47 Matplotlib Exercise Questions – Overview

Lecture 48 Matplotlib Exercise Questions – Solutions

Section 6: Pandas and Finance

Lecture 49 Introduction to Pandas and Finance

Lecture 50 Core Pandas Time Methods

Lecture 51 Pandas Visualizations

Lecture 52 Visualizing Time Series Data with Pandas – Part One

Lecture 53 Visualizing Time Series Data with Pandas – Part Two (Optional)

Lecture 54 Pandas Rolling Statistics

Lecture 55 Pandas Time Shifting and Row Calculations

Lecture 56 Python API Based Data Sources

Lecture 57 Alternative Data Sources and Platforms

Lecture 58 Pandas and Finance – Exercise Overview

Lecture 59 Pandas and Finance – Exercise Solutions

Section 7: Financial Concepts with Python

Lecture 60 Introduction to Financial Concepts with Python

Lecture 61 Efficient Market Hypothesis

Lecture 62 Measurements of Return

Lecture 63 Measurements of Risk

Lecture 64 Sharpe Ratio – Theory and Intuition

Lecture 65 Sharpe Ratio with Python

Lecture 66 Sortino Ratio – Theory and Intuition

Lecture 67 Sortino Ratio with Python

Lecture 68 Probabilistic Sharpe Ratio – Theory and Intuition

Lecture 69 Probabilistic Sharpe Ratio with Python

Lecture 70 Modern Portfolio Theory

Lecture 71 Equal Weighted Portfolio in Python

Lecture 72 Log Returns – Theory and Intuition

Lecture 73 Monte Carlo Simulation with Python

Lecture 74 Minimization Search with SciPy

Lecture 75 Efficient Frontier in Python

Lecture 76 Capital Asset Pricing Model

Lecture 77 CAPM with Python – Part One – Exploring Data and Market

Lecture 78 CAPM with Python – Part Two – Beta and Alpha

Section 8: Stock Market Analysis Capstone Project

Lecture 79 Introduction to Capstone Project

Lecture 80 Capstone Project Solutions – Part One – Returns Analysis

Lecture 81 Capstone Project Solutions – Part Two – Volume Analysis

Lecture 82 Capstone Project Solutions – Part Three – Technical Analysis

Section 9: Algorithmic Trading Basics with QuantConnect

Lecture 83 QuantConnect Access Link

Lecture 84 Algorithmic Trading Basics Overview

Lecture 85 Algorithmic Trading Basics – Learning Pathway

Lecture 86 Algorithmic Trading – Core Concepts

Lecture 87 QuantConnect Platform Tour

Lecture 88 Buying Shares of Stock – Core Concepts – Part One

Lecture 89 Buying Shares of Stock – Core Concepts – Part Two

Lecture 90 Buying Securities on QuantConnect – Part One – Initialize Method

Lecture 91 Buying Securities on QuantConnect – Part Two – OnData Method

Lecture 92 Backtesting – Core Concepts

Lecture 93 Buying Securities on QuantConnect – Part 3 – Backtesting and Multiple Securities

Lecture 94 Quick Check-in — Buy and Hold

Lecture 95 Selling Securities – Part One – Portfolio Liquidation

Lecture 96 Selling Securities – Part Two – Time Based Exit

Lecture 97 Quick Check-In – Liquidate Remaining Portfolio

Lecture 98 Selling Securities – Part Three – Profit Threshold Exit

Lecture 99 Quick Check-in – Locking in Profits

Lecture 100 Order System

Lecture 101 MarketOrder on QuantConnect

Lecture 102 LimitOrder on QuantConnect

Lecture 103 StopMarketOrder on QuantConnect

Lecture 104 StopLimitOrder on QuantConnect

Lecture 105 MarketOnOpen and MarketOnClose Orders on QuantConnect

Lecture 106 Getting Price and Share Information

Lecture 107 Quick Check-in – Stopping Losses

Lecture 108 OrderTicket System Overview

Lecture 109 Interacting with and Updating OrderTickets – Part One

Lecture 110 Interacting with and Updating OrderTickets – Part Two

Lecture 111 Conditional Purchasing – Scheduling Functions

Lecture 112 Quick Check-in – Trailing Stop Loss

Lecture 113 Quick Note on Next Lecture

Lecture 114 Conditional Purchasing – Price Comparison

Lecture 115 Leverage – Theory and Intuition

Lecture 116 Leverage Example – QuantConnect

Lecture 117 Shorting – Theory and Intuition

Lecture 118 Shorting Example – QuantConnect

Lecture 119 Margin Calls

Section 10: QuantConnect Research, Plotting, Universe Selection

Lecture 120 Research, Plotting, and Universe Selection Notebooks

Lecture 121 Introduction to Research and Plotting Section

Lecture 122 QuantConnect Charts

Lecture 123 Custom Charts

Lecture 124 CandleStick Plots

Lecture 125 Combining Plots

Lecture 126 Modifying Plot Properties

Lecture 127 QuantBook and Research Notebooks Overview

Lecture 128 Research Notebooks – Part One – Securities Historical Data

Lecture 129 Research Notebooks – Part Two – Fundamental Data

Lecture 130 Research Notebooks – Part Three – Technical Indicators

Lecture 131 Universe Selection – Key Ideas

Lecture 132 Universe Selection – Part One- Coarse Filter

Lecture 133 Universe Selection – Part Two – OnSecuritiesChanged Mthod

Lecture 134 Universe Selection – Part Three – Fine Filter

Section 11: Derivative Contracts

Lecture 135 Options Notebooks Download

Python developers interested in learning more about finance, markets, and algorithmic trading.

Course Information:

Udemy | English | 22h 51m | 8.76 GB
Created by: Jose Portilla

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