Welcome to download



Do not remember me!

 
We have Tested and found Below Host Trustable, Please Buy Premium account From Below Host.
UploadGIG.com nitroflare.com
Note: Do not Buy Premium account from Reseller

Latest Comments

    No comments
» » » Python and Machine Learning in Financial Analysis

Python and Machine Learning in Financial Analysis


Python and Machine Learning in Financial Analysis
Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 5.94 GB | Duration: 20h 17m

Using Python and machine learning in financial analysis with step-by-step coding (with all codes)
What you'll learn
You will be able to use the functions provided to download financial data from a number of sources and preprocess it for further analysis
You will be able to draw some insights into patterns emerging from a selection of the most commonly used metrics (such as MACD and RSI)
Introduces the basics of time series modeling. Then, we look at exponential smoothing methods and ARIMA class models.
shows you how to estimate various factor models in Python. one ,three-, four-, and five-factor models.
introduces you to the concept of volatility forecasting using (G)ARCH class models, how to choose the best-fitting model, and how to interpret your results.
Description
In this course, you will become familiar with a variety of up-to-date financial analysis content, as well as algorithms techniques of machine learning in the Python environment, where you can perform highly specialized financial analysis. You will get acquainted with technical and fundamental analysis and you will use different tools for your analysis. You will get acquainted with technical and fundamental analysis and you will use different tools for your analysis. You will learn the Python environment completely. You will also learn deep learning algorithms and artificial neural networks that can greatly enhance your financial analysis skills and expertise.
This tutorial begins by exploring various ways of downloading financial data and preparing it for modeling. We check the basic statistical properties of asset prices and returns, and investigate the existence of so-called stylized facts. We then calculate popular indicators used in technical analysis (such as Bollinger Bands, Moving Average Convergence Divergence (MACD), and Relative Strength Index (RSI)) and backtest automatic trading strategies built on their basis.
The next section introduces time series analysis and explores popular models such as exponential smoothing, AutoRegressive Integrated Moving Average (ARIMA), and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) (including multivariate specifications). We also introduce you to factor models, including the famous Capital Asset Pricing Model (CAPM) and the Fama-French three-factor model. We end this section by demonstrating different ways to optimize asset allocation, and we use Monte Carlo simulations for tasks such as calculating the price of American options or estimating the Value at Risk (VaR).
In the last part of the course, we carry out an entire data science project in the financial domain. We approach credit card fraud/default problems using advanced classifiers such as random forest, XGBoost, LightGBM, stacked models, and many more. We also tune the hyperparameters of the models (including Bayesian optimization) and handle class imbalance. We conclude the book by demonstrating how deep learning (using PyTorch) can solve numerous financial problems.
Who this course is for:
Developers
Financial Analysts
Data Analysts
Data Scientists
Stock and cryptocurrency traders
Students
Teachers
Researchers

Download link:

Links are Interchangeable - Single Extraction - Premium is support resumable
UploadGIG.com

Please login or register

Dear visitor, you are browsing our website as Guest. We strongly recommend you to register and login to view hidden contents.

Comments (0)

Leave Comment

Name:*
E-Mail:
Security Code: *
Click on the image to refresh the code if it cannot be viewed