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Algorithmic Stock Trading Equity Investing Python

Discover the world of stock trading and investing with Python. Master equity analysis, API trading, ETF investing, and backtesting. Build stock portfolios and implement algorithmic strategies. Learn Python libraries and financial data analysis. Excel in equity investing decisions. UK English course.




Algorithmic Stock Trading Equity Investing Python Algorithmic Stock Trading Equity Investing Python
Course Price:
Original price was: £194.00.Current price is: £19.99.
Course Duration:
1 day, 6 hours
Total Lectures:
323
Total Students:
76
Average Rating:
4.5

Overview

The "Algorithmic Stock Trading Equity Investing Python" course, presented in UK English, provides a comprehensive journey into the world of stock trading and investing using Python. Starting with the basics and prerequisites, participants will explore equity markets, install Python and Jupyter Notebooks, and perform equity analysis using Python. Crucial insights on avoiding and debugging coding errors will be emphasized.

Students will then delve into interactive brokers and API trading, financial data analysis, and performance evaluation. The course progresses to ETF trading and equity portfolio investing with Python and IKBR, covering stock index building, ETF investing, and equity portfolio optimization.

The final segment focuses on algorithmic stock trading with Python and IKBR, where learners will understand various trading strategies, technical analysis, and backtesting using Python. The course includes appendices for a Python crash course, user-defined functions, essential libraries (NumPy, Pandas, Matplotlib, Seaborn), advanced Pandas time series topics, and object-oriented programming (OOP). Upon completion, participants will be well-equipped to build and analyze stock portfolios, implement algorithmic trading strategies, and leverage Python's power for effective equity investing and stock trading decisions.

What Will You Learn?

  • Basics and prerequisites for equity markets and stock trading.
  • How to install Python and Jupyter Notebooks for data analysis.
  • Perform equity analysis using Python for stock evaluation.
  • Avoid and debug coding errors for smooth programming.
  • Explore Interactive Brokers (IKBR) and API trading for real-time data.
  • Analyze financial data and evaluate portfolio performance.
  • Build and analyze a stock index and implement ETF investing.
  • Optimize equity portfolios and understand portfolio optimization theory.
  • Implement algorithmic stock trading strategies using Python.
  • Conduct technical analysis and backtesting with Python.
  • Gain essential Python skills and libraries for finance.
  • Learn advanced Pandas time series topics and object-oriented programming (OOP).

Who Should Take The Course?

  • Aspiring and existing traders interested in algorithmic stock trading.
  • Investors seeking to enhance their equity portfolio management skills.
  • Python enthusiasts looking to apply Python in financial analysis and trading.
  • Financial professionals aiming to build technical analysis expertise.
  • Individuals interested in understanding stock markets and ETF investing.
  • Students and professionals seeking to develop data analysis skills in finance.
  • Anyone keen on exploring interactive brokers and API trading in Python.
  • Those looking to gain practical insights into stock index analysis and optimization.
  • Python programmers wishing to specialize in finance and stock trading algorithms.

Requirements

  • Basic understanding of finance and stock markets.
  • Familiarity with Python programming language.
  • Access to a computer with internet connectivity.
  • Prior knowledge of Jupyter Notebooks is beneficial but not mandatory.
  • Interest in algorithmic stock trading and equity portfolio investing.
  • Willingness to engage in hands-on data analysis and backtesting using Python.
  • Openness to learning technical analysis and financial performance evaluation.
  • No specific educational background required, but financial literacy is advantageous.

Course Curriculum

    • Did you know… (a Sneak Preview on Stock Investing) 00:03:00
    • How to get the best out of this course 00:05:00
    • Course Overview 00:04:00
    • Introduction and Overview PART 1 00:04:00
    • Asset Classes – Overview 00:09:00
    • Equities vs. Fixed Income 00:07:00
    • Equities – Categories and Sub Classes 00:07:00
    • Top-Down vs. Bottom-Up 00:04:00
    • Investing vs. Trading 00:15:00
    • Introduction 00:02:00
    • Download and Install Anaconda 00:08:00
    • How to open Jupyter Notebooks 00:09:00
    • How to work with Jupyter Notebooks 00:14:00
    • Tips for python beginners 00:02:00
    • Yahoo Finance – Overview 00:05:00
    • How to open and work with the Course Notebooks 00:03:00
    • How to Install yfinance 00:02:00
    • yfinance API – first steps 00:11:00
    • Excursus Versions and Package Updates 00:02:00
    • Analysis Period 00:05:00
    • Data Frequency 00:06:00
    • Dividends 00:08:00
    • What´s the Adjusted Close Price 00:08:00
    • Stock Splits 00:08:00
    • Stocks from other Countries Exchanges 00:04:00
    • Multiple Tickers 00:05:00
    • Saving and Loading Data (Local Files) 00:07:00
    • Coding Challenge 00:04:00
    • Introduction 00:03:00
    • Test your debugging skills! 00:11:00
    • Major reasons for Coding Errors 00:01:00
    • The most commonly made Errors at a glance 00:06:00
    • Omitting cells, changing the sequence and more 00:07:00
    • IndexErrors 00:05:00
    • Indentation Errors 00:03:00
    • Misuse of function names and keywords 00:03:00
    • TypeErrors and ValueErrors 00:04:00
    • Getting help on StackOverflow 00:06:00
    • How to traceback more complex Errors 00:10:00
    • Problems with the Python Installation 00:06:00
    • External Factors and Issues 00:04:00
    • Errors related to the course content (Transcription Errors) 00:04:00
    • Summary and Debugging Flow-Chart 00:07:00
    • Getting more Information on Stocks – the Ticker Object 00:04:00
    • Price, Shares Outstanding _ Market Capitalization 00:05:00
    • Price vs. Value and Market Efficiency 00:10:00
    • Equity Value, Firm Value and Financial Distress 00:04:00
    • Market Value vs. Book Value (Part 1) 00:13:00
    • Market Value vs. Book Value (Part 2) 00:08:00
    • Liquidation Value 00:05:00
    • Market Value vs. Book Value (Part 3) 00:07:00
    • How to load Financial Statements 00:04:00
    • Project – Introduction 00:03:00
    • How to load the Dow Jones Constituents from the Web 00:05:00
    • Historical Prices (Time-Series Data) 00:04:00
    • Cross-Sectional Data 00:04:00
    • Stock Analysis and Comparison 00:06:00
    • Hot Topic How to get complete Lists with Stock Tickers 00:03:00
    • Hot Topic How to load all exchange tickers (Indian Stock Market) 00:05:00
    • Welcome to IKBR 00:04:00
    • How to create a Paper Trading Account 00:04:00
    • How to Install the IB Trader Workstation (TWS) 00:03:00
    • TWS – First Steps 00:04:00
    • The first Trades on TWS 00:06:00
    • Trading Hours 00:05:00
    • Cash Account vs. Margin Account 00:04:00
    • Fractional Trading 00:02:00
    • Trading Costs – Commissions 00:09:00
    • Trading Costs – other (hidden) Costs 00:07:00
    • How to download and install the API Wrapper _ other Preparations 00:03:00
    • Connecting to the API 00:03:00
    • Contracts (Introduction) 00:05:00
    • How to get Market Data 00:07:00
    • Data Streaming for Mulitple Tickers 00:02:00
    • Introduction and Overview 00:04:00
    • Initial Data Inspection and Visualization 00:05:00
    • Normalizing Time Series to a Base Value (100) 00:07:00
    • Coding Challenge #1 00:05:00
    • Price changes and Financial Returns 00:09:00
    • Reward and Risk of Financial Instruments 00:06:00
    • Investment Multiple and CAGR 00:07:00
    • Compound Returns _ Geometric Mean Return 00:04:00
    • Discrete Compounding 00:08:00
    • Continuous Compounding 00:06:00
    • Log Returns 00:02:00
    • Simple Returns vs Log Returns ( Part 1) 00:06:00
    • Simple Returns vs Log Returns ( Part 2) 00:05:00
    • Comparing the Performance of Financial Instruments 00:10:00
    • Price Return vs. Total Return (Stocks) 00:03:00
    • (Non-) Normality of Financial Returns 00:13:00
    • Annualizing Return and Risk 00:05:00
    • Resampling Smoothing of Financial Data 00:08:00
    • Rolling Statistics 00:09:00
    • Short Selling and Short Position Returns (Part 1) 00:03:00
    • Introduction to Currencies (Forex) and Trading 00:07:00
    • Short Selling and Short Position Returns (Part 2) 00:05:00
    • Short Selling and Short Position Returns (Part 3) 00:04:00
    • Covariance and Correlation 00:07:00
    • Portfolios and Portfolio Returns 00:04:00
    • Margin Trading and Levered Returns (Part 1) 00:05:00
    • Margin Trading and Levered Returns (Part 2) 00:09:00
    • Introduction and Overview PART 2 00:02:00
    • Investment Strategies, Indices, Portfolios _ Benchmarks 00:07:00
    • Financial Indices – an Overview 00:09:00
    • Getting started 00:02:00
    • Price-Weighted Index – Theory 00:08:00
    • Building the Dow Jones Industrial Average Index from scratch 00:05:00
    • Equal-Weighted Index – Theory 00:06:00
    • Creating an Equal-Weighted Stock Index with Python 00:02:00
    • Market Value-Weighted Index – Theory 00:09:00
    • Creating a Market Value-Weighted Stock Index with Python (Part 1) 00:05:00
    • Creating a Market Value-Weighted Stock Index with Python (Part 2) 00:04:00
    • Comparison of weighting methods (Part 1) 00:02:00
    • Comparison of weighting methods (Part 2) 00:04:00
    • Price Index vs. PerformanceTotal Return Index 00:04:00
    • Why ETF Investing 00:06:00
    • Index Replication Tracking – Intro 00:07:00
    • The S_P500 Index and its ETFs – Full Replication 00:08:00
    • Active Return and Active Risk (Tracking Error) 00:05:00
    • The Russell 3000 Index and its ETFs – Representative Sampling 00:06:00
    • ETF Investing with IBKR 00:05:00
    • Index Tracking with Optimization (Part 1) 00:08:00
    • Index Tracking with Optimization (Part 2) 00:05:00
    • Index Tracking with Optimization (Part 3) 00:03:00
    • Index Tracking with Optimization (Part 4) 00:05:00
    • Index Tracking with Optimization (Part 5) 00:05:00
    • Index Tracking with Optimization (Part 6) 00:05:00
    • Optimization and out-sample Testing (Part 1) 00:05:00
    • Optimization and out-sample Testing (Part 2) 00:04:00
    • Getting Started 00:02:00
    • Creating Random Portfolios (Part 1) 00:07:00
    • Creating Random Portfolios (Part 2) 00:02:00
    • Performance Measurement The Risk-adjusted Return 00:04:00
    • Portfolio Optimization 00:08:00
    • Minimum Variance Portfolio 00:03:00
    • Maximum Return Portfolio 00:02:00
    • The Efficient Frontier 00:06:00
    • Portfolio Optimization with frequent Rebalancing 00:03:00
    • Comparison daily Rebalancing vs. no Rebalancing 00:03:00
    • Introduction 00:02:00
    • Getting Started 00:03:00
    • 2-Asset-Case (Intro) 00:02:00
    • Portfolio Return (2-Asset-Case) 00:05:00
    • Portfolio Risk (2-Asset-Case) – a (too) simple solution 00:05:00
    • Crash Course Statistics Variance and Standard Deviation 00:01:00
    • Crash Course Statistics Covariance and Correlation (Part 1) 00:07:00
    • Crash Course Statistics Covariance and Correlation (Part 2) 00:02:00
    • Portfolio Risk (2-Asset-Case) 00:04:00
    • Correlation and the Portfolio Diversification Effect 00:05:00
    • Multiple Asset Case 00:04:00
    • Forward-looking Optimization 00:05:00
    • Forward-looking Mean-Variance Optimization (MVO) Pitfalls (1) 00:06:00
    • Forward-looking Mean-Variance Optimization (MVO) Pitfalls (2) 00:05:00
    • Introduction of a Risk-Free Asset 00:07:00
    • The Sharpe Ratio Graphical Interpretation 00:02:00
    • Portfolio Optimization with Risk-free Asset (Part 1) 00:03:00
    • Portfolio Optimization with Risk-free Asset (Part 2) 00:03:00
    • Implications and the Two-Fund-Theorem 00:03:00
    • Introduction and Motivation 00:05:00
    • Getting started (Inputs for reverse Optimization) 00:01:00
    • Black-Litterman Step 1 Reverse Optimization 00:04:00
    • Black-Litterman Step 2 Incorporating Investor Opinions 00:05:00
    • Introduction and Overview PART 3 00:01:00
    • Trading Strategies – Overview 00:05:00
    • How to create your own Trading Strategies 00:06:00
    • Technical Analysis vs Fundamental Analysis 00:06:00
    • Technical Analysis and the Efficient Market Hypothesis 00:05:00
    • Technical Analysis – Applications and Use Cases 00:09:00
    • Getting started and simple Price Charts 00:02:00
    • Charting – Interactive Line Charts with Cufflinks and Plotly 00:04:00
    • How to customize Plotly Charts 00:04:00
    • Candlestick and OHLC Bar Charts 00:06:00
    • Bar Size Granularity 00:08:00
    • Volume Charts 00:04:00
    • Technical Indicators – Overview and Examples 00:03:00
    • Trend Lines 00:04:00
    • Support and Resistance Lines 00:05:00
    • Getting started 00:03:00
    • A simple Buy and Hold Strategy 00:05:00
    • Defining an SMA Crossover Strategy 00:11:00
    • Vectorized Strategy Backtesting 00:07:00
    • Strategy Optimization 00:06:00
    • Transaction _ Trading Costs (Part 1) 00:05:00
    • The Backtester Class 00:07:00
    • Backtesting a Long-Only Strategy 00:02:00
    • Introduction and Overview 00:03:00
    • Intro to the Time Value of Money (TVM) Concept (Theory) 00:06:00
    • Calculate Future Values (FV) with Python Compounding 00:03:00
    • Calculate Present Values (PV) with Python Discounting 00:03:00
    • Interest Rates and Returns (Theory) 00:04:00
    • Calculate Interest Rates and Returns with Python 00:04:00
    • Introduction to Variables 00:05:00
    • Excursus How to add inline comments 00:03:00
    • Variables and Memory (Theory) 00:02:00
    • More on Variables and Memory 00:07:00
    • Variables – Dos, Don´ts and Conventions 00:04:00
    • The print() Function 00:04:00
    • Coding Exercise 1 00:09:00
    • TVM Problems with many Cashflows 00:03:00
    • Intro to Python Lists 00:02:00
    • Zero-based Indexing and negative Indexing in Python (Theory) 00:03:00
    • Indexing Lists 00:03:00
    • For Loops – Iterating over Lists 00:08:00
    • The range Object – another Iterable 00:05:00
    • Calculate FV and PV for many Cashflows 00:08:00
    • The Net Present Value – NPV (Theory) 00:08:00
    • Calculate an Investment Project´s NPV 00:03:00
    • Coding Exercise 2 00:09:00
    • Data Types in Action 00:06:00
    • The Data Type Hierarchy (Theory) 00:04:00
    • Excursus Dynamic Typing in Python 00:02:00
    • Build-in Functions 00:06:00
    • Integers 00:03:00
    • Floats 00:06:00
    • How to round Floats (and Integers) with round() 00:05:00
    • More on Lists 00:05:00
    • Lists and Element-wise Operations 00:04:00
    • Slicing Lists 00:05:00
    • Changing Elements in Lists 00:03:00
    • Sorting and Reversing Lists 00:04:00
    • Adding and removing Elements fromto Lists 00:10:00
    • Mutable vs. immutable Objects (Part 1) 00:09:00
    • Mutable vs. immutable Objects (Part 2) 00:05:00
    • Coding Exercise 3 00:12:00
    • Tuples 00:07:00
    • Dictionaries 00:06:00
    • Intro to Strings 00:09:00
    • String Replacement 00:04:00
    • Booleans 00:02:00
    • Operators (Theory) 00:05:00
    • Comparison, Logical and Membership Operators in Action 00:08:00
    • Coding Exercise 4 00:09:00
    • Conditional Statements 00:09:00
    • Keywords pass, continue and break 00:10:00
    • Calculate a Project´s Payback Period 00:05:00
    • Introduction to while loops 00:08:00
    • Defining your first user-defined Function 00:06:00
    • What´s the difference between Positional Arguments vs. Keyword Arguments 00:06:00
    • How to work with Default Arguments 00:05:00
    • The Default Argument None 00:06:00
    • How to unpack Iterables 00:05:00
    • Sequences as arguments and args 00:05:00
    • How to return many results 00:03:00
    • Scope – easily explained 00:08:00
    • Modules, Packages and Libraries – No need to reinvent the Wheel 00:08:00
    • Numpy Arrays 00:08:00
    • Indexing and Slicing Numpy Arrays 00:03:00
    • Vectorized Operations with Numpy Arrays 00:04:00
    • Changing Elements in Numpy Arrays _ Mutability 00:06:00
    • View vs. copy – potential Pitfalls when slicing Numpy Arrays 00:05:00
    • Numpy Array Methods and Attributes 00:05:00
    • Numpy Universal Functions 00:04:00
    • Boolean Arrays and Conditional Filtering 00:05:00
    • Advanced Filtering _ Bitwise Operators 00:06:00
    • Determining a Project´s Payback Period with np.where() 00:05:00
    • Creating Numpy Arrays from Scratch 00:06:00
    • Coding Exercise 7 00:13:00
    • How to work with nested Lists 00:04:00
    • 2-dimensional Numpy Arrays 00:04:00
    • How to slice 2-dim Numpy Arrays (Part 1) 00:06:00
    • How to slice 2-dim Numpy Arrays (Part 2) 00:02:00
    • Recap Changing Elements in a Numpy Array slice 00:04:00
    • How to perform row-wise and column-wise Operations 00:05:00
    • Intro to Tabular Data Pandas 00:04:00
    • Create your very first Pandas DataFrame (from csv) 00:09:00
    • Pandas Display Options and the methods head() _ tail() 00:07:00
    • Selecting Rows with iloc (position-based indexing) 00:10:00
    • Slicing Rows and Columns with iloc (position-based indexing) 00:05:00
    • Selecting Rows with loc (label-based indexing) 00:03:00
    • Slicing Rows and Columns with loc (label-based indexing) 00:10:00
    • Summary, Best Practices and Outlook 00:07:00
    • First Steps with Pandas Series 00:04:00
    • Analyzing Numerical Series with unique(), nunique() and value_counts() 00:14:00
    • Analyzing non-numerical Series with unique(), nunique(), value_counts() 00:07:00
    • The copy() method 00:04:00
    • Sorting of Series and Introduction to the inplace – parameter 00:09:00
    • First Steps with Pandas Index Objects 00:06:00
    • Changing Row Index with set_index() and reset_index() 00:10:00
    • Changing Column Labels 00:03:00
    • Renaming Index _ Column Labels with rename() 00:04:00
    • Filtering DataFrames (one Condition) 00:10:00
    • Filtering DataFrames by many Conditions (AND) 00:05:00
    • Filtering DataFrames by many Conditions (OR) 00:05:00
    • Advanced Filtering with between(), isin() and ~ 00:09:00
    • Intro to NA Values missing Values 00:09:00
    • Handling NA Values missing Values 00:11:00
    • Exporting DataFrames to csv 00:02:00
    • Summary Statistics and Accumulations 00:10:00
    • Visualization with Matplotlib (Intro) 00:09:00
    • Customization of Plots 00:13:00
    • Histogramms (Part 1) 00:05:00
    • Histogramms (Part 2) 00:06:00
    • Scatterplots 00:07:00
    • First Steps with Seaborn 00:05:00
    • Categorical Seaborn Plots 00:14:00
    • Seaborn Regression Plots 00:12:00
    • Seaborn Heatmaps 00:08:00
    • Removing Columns 00:05:00
    • Introduction to GroupBy Operations 00:02:00
    • Understanding the GroupBy Object 00:08:00
    • Splitting with many Keys 00:07:00
    • split-apply-combine 00:10:00
    • Helpful DatetimeIndex Attributes and Methods 00:06:00
    • Filling NA Values with bfill, ffill and interpolation 00:10:00
    • Timezones and Converting (Part 1) 00:05:00
    • Timezones and Converting (Part 2) 00:05:00
    • Introduction to OOP and examples for Classes 00:11:00
    • The Financial Analysis Class live in action (Part 1) 00:05:00
    • The Financial Analysis Class live in action (Part 2) 00:04:00
    • The special method __init__() 00:08:00
    • The method get_data() 00:07:00
    • The method log_returns() 00:03:00
    • String representation and the special method __repr__() 00:04:00
    • The methods plot_prices() and plot_returns() 00:05:00
    • Encapsulation and protected Attributes 00:04:00
    • The method set_ticker() 00:03:00
    • Adding more methods and performance metrics 00:06:00
    • Inheritance 00:09:00
    • Inheritance and the super() Function 00:07:00
    • Adding meaningful Docstrings 00:06:00
    • Creating and Importing Python Modules (.py) 00:04:00
    • Coding Exercise Create your own Class 00:07:00
    • Order Certificate 00:05:00

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