Financial Modeling for Algorithmic Trading using Python – Matthew Macarty & Others

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Description

Video Learning Path Overview

A 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 Features

• Get hands-on with financial forecasting using machine learning with Python, Keras, scikit-learn, and pandas
• Use libraries like Numpy, Pandas, Scipy and Matplotlib for data analysis, manipulation and visualization
• Be comfortable with Monte Carlo Simulation, Value at Risk, and Options Valuation
• Grasp Machine Learning forecasting on a specific real-world financial data

Who this course is for:

• 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.

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Trading foreign exchange, cryptocurrencies, and algorithmic assets on margin carries a high level of risk and may not be suitable for all investors. Past performance of any trading system or quantitative blueprint does not guarantee future results.<br><br><b>Asset Classification:</b>