Financial Modeling For Algorithmic Trading Using Python

A practical guide to implementing financial analysis strategies using Python

What you'll learn

How to use Numpy, Pandas, and matplotlib to manipulate, analyze, and visualize financial data

Understand the Time Value of Money applications and project selection

Make use of Monte Carlo method to simulate portfolio ending values, value options, and calculate Value at Risk

Understand complex financial terminology and methodology in simple ways

Featuring a premiere on Ensemble Learning with Bagging & Boosting

How to apply your skills to real world cryptocurrency trading such as Bitcoin and Ethereum

Building high-frequency trading robots

Implementing backtesting econometrics for trading strategies evaluation

Get hands-on with financial forecasting using machine learning with Python, Keras, scikit-learn, and pandas

Requirements

Working knowledge of Python is required.

Description

Video Learning Path OverviewA Learning Path is a specially tailored course that brings together two or more different topics that lead you to achieve an end goal. Much thought goes into the selection of the assets for a Learning Path, and this is done through a complete understanding of the requirements to achieve a goal.Technology has become an asset in finance. Among the hottest programming languages, you’ll find Python becoming the technology of choice for Finance. The financial industry is increasingly adopting Python for general-purpose programming and quantitative analysis, ranging from understanding trading dynamics to building financial machine learning models.This well thought out Learning Path takes a step by step approach to teach you how to use Python for performing financial analysis and modeling on a day-to-day basis. Beginning with an introduction to Python and its third party libraries, you will learn how to apply basics of Finance such as Time Value of Money and time series in Python. You will also perform valuations, linear regressions, and Monte Carlo simulation for analyzing some basic models.Once you are comfortable in analyzing models with Python, you will learn to practically apply them to analyze machine learning models for your own financial data. You will then learn how to build machine learning models and trading algorithms as per your trade. You will also learn to build a trading bot for providing fully automated trading solutions to your trade. Next, you will learn to evaluate the models for value at risk using machine learning techniques.Now that you are being familiar with machine learning, you will step ahead with learning deep learning techniques for Financial forecasting, predicting Forex currency exchange rates, looking into financial loan approval, fraud detection, and forecasting stock prices.Towards the end of this course, you will be able to perform financial valuations, build algorithmic trading bots, and perform stock trading and financial analysis in different areas of finance.Key FeaturesGet hands-on with financial forecasting using machine learning with Python, Keras, scikit-learn, and pandasUse libraries like Numpy, Pandas, Scipy and Matplotlib for data analysis, manipulation and visualizationBe comfortable with Monte Carlo Simulation, Value at Risk, and Options ValuationGrasp Machine Learning forecasting on a specific real-world financial dataAuthor BiosMatthew Macarty has taught graduate and undergraduate business school students for over 15 years and currently teaches at Bentley University. He has taught courses in statistics, quantitative methods, information systems and database design.Mustafa Qamar-ud-Din is a machine learning engineer with over 10 years of experience in the software development industry. He is a specialist in image processing, machine learning and deep learning. He worked with many startups and understands the dynamics of agile methodologies and the challenges they face on a day to day basis. He is also quite aware of the professional skills which the recruiters are looking for when making hiring decisions.Jakub Konczyk has enjoyed and done programming professionally since 1995. He is a Python and Django expert and has been involved in building complex systems since 2006. He loves to simplify and teach programming subjects and share it with others. He first discovered Machine Learning when he was trying to predict the real estate prices in one of the early stage startups he was involved in. He failed miserably. Then he discovered a much more practical way to learn Machine Learning that he would like to share with you in this course. It boils down to “Keep it simple!” mantra.

Overview

Section 1: Hands-on Python for Finance

Lecture 1 The Course Overview

Lecture 2 Installing the Anaconda Platform

Lecture 3 Launching the Python Environment

Lecture 4 String and Number Objects

Lecture 5 Python Lists

Lecture 6 Python Dictionaries (Dicts)

Lecture 7 Repetition in Python (For Loops)

Lecture 8 Branching Logic in Python (If Blocks)

Lecture 9 Introduction to Functions in Python

Lecture 10 Introduction to NumPy Arrays

Lecture 11 NumPy – A Deeper Dive

Lecture 12 Pandas – Part I

Lecture 13 Pandas – Part II

Lecture 14 Introduction to Scipy.stats

Lecture 15 Matplotlib – Part I

Lecture 16 Matplotlib – Part II

Lecture 17 Present Value of a Stream of Cash Flows

Lecture 18 Future Value of Single and Multiple Cash Flows

Lecture 19 Net Present Value of a Project

Lecture 20 Internal Rate of Return

Lecture 21 Introduction to Amortization

Lecture 22 Creating an Amortization Application

Lecture 23 Opening and Reading a .CSV File

Lecture 24 Getting and Evaluating Data

Lecture 25 Moving Average Forecasting

Lecture 26 Forecasting with Single Exponential Smoothing

Lecture 27 Creating and Testing a Simple Trading System

Lecture 28 Valuing Securities with Pricing Models

Lecture 29 Finding Correlations Between Securities

Lecture 30 Linear Regression

Lecture 31 Calculating Beta and Expected Return

Lecture 32 Constructing Portfolios Along the Efficient Frontier

Lecture 33 Introduction to Monte Carlo

Lecture 34 Monte Carlo Simulation

Lecture 35 Using Monte Carlo Technique to Calculate Value at Risk

Lecture 36 Putting It All Together – Monte Simulation Application

Section 2: Machine Learning for Algorithmic Trading Bots with Python

Lecture 37 The Course Overview

Lecture 38 Introduction to Financial Machine Learning and Algorithmic Trading

Lecture 39 Setting up the Environment

Lecture 40 Project Skeleton Overview

Lecture 41 Fetching and Understanding the Dataset

Lecture 42 Build the Conventional Buy and Hold Strategy

Lecture 43 Evaluate the Strategy’s Performance

Lecture 44 Intuition behind Random Forests Algorithm

Lecture 45 Build and Implement Random Forests Algorithm

Lecture 46 Plug-in Random Forests Implementation into Your Bot

Lecture 47 Evaluate Random Forest’s Performance

Lecture 48 Introducing Online Algorithms

Lecture 49 Getting Statistical Correlation

Lecture 50 Implement Exploit Correlation Strategy

Lecture 51 Evaluate the Strategy

Lecture 52 Ensemble Learning Theory

Lecture 53 Implementing GBoosting Using Python

Lecture 54 Evaluating the Model Performance

Lecture 55 Introduction to Scalpers Trading Strategy

Lecture 56 Implement Scalpers Trading Strategy

Lecture 57 Evaluate Scalpers Trading Strategy

Lecture 58 Introducing Value at Risk Backtest

Lecture 59 Implement Value at Risk Backtest

Lecture 60 Value at Risk with Machine Learning

Lecture 61 Implement VaR Using SVR

Lecture 62 Conclusion and Next steps

Section 3: AI for Finance

Lecture 63 The Course Overview

Lecture 64 What’s Financial Forecasting and Why It’s Important?

Lecture 65 Installing Pandas, Scikit-Learn, Keras, and TensorFlow

Lecture 66 Summary

Lecture 67 Getting and Preparing the Currency Exchange Data

Lecture 68 Building the MLP Model with Keras

Lecture 69 Training and Testing the Model

Lecture 70 Summary and What’s Next?

Lecture 71 Getting and Preparing the Loan Approval Data

Lecture 72 Creating, Training, Testing, and Using a GradientBoostingClassfier Model

Lecture 73 Summary and What’s Next?

Lecture 74 Getting and Preparing Financial Fraud Data

Lecture 75 Creating, Training, and Testing XGBoost Model

Lecture 76 Summary and What’s Next?

Lecture 77 Getting and Preparing the Stock Prices Data

Lecture 78 Building the LSTM Model with Keras

Lecture 79 Training and Testing the Model

Lecture 80 Summary and What’s Next?

This course is ideal for aspiring data scientists, Python developers and anyone who wants to start performing quantitative finance using Python. You can also make this beginner-level guide your first choice if you’re looking to pursue a career as a financial analyst or a data analyst.