Giter Site home page Giter Site logo

snowrr's Projects

a1 icon a1

Created with CodeSandbox

coursera-uiuc-applying-data-analytics-in-finance icon coursera-uiuc-applying-data-analytics-in-finance

Course Description In this course, we will introduce a number of financial analytic techniques. You will learn why, when, and how to apply financial analytics in real-world situations. We will explore techniques to analyze time series data and how to evaluate the risk-reward trade off expounded in modern portfolio theory. While most of the focus will be on the prices, returns, and risks of corporate stocks, the analytical techniques can be leveraged in other domains. Finally, a short introduction to algorithmic trading concludes the course. After completing this course, you should be able to understand time series data, create forecasts, and determine the efficacy of the estimates. Also, you will be able to create a portfolio of assets using actual stock price data while optimizing risk and reward. Understanding financial data is an important skill as an analyst, manager, or consultant. Course Goals and Objectives Upon completion of this course, you should be able to: Understand the forecasting process. Evaluate a forecast. Describe time series data. Perform moving average analysis. Perform exponential smoothing. Develop a Holt-Winters model. Develop an ARIMA model. Understand how to create a portfolio of assets. Understand a basic trading algorithm.

devtraining-needit-orlando icon devtraining-needit-orlando

This repository is used by the developer site training content, Orlando release. It is used for the Build the NeedIt App, Scripting in ServiceNow, Application Security, Importing Data, Automating Application Logic, Flow Designer, REST Integrations, Reporting and Analytics, Domain Separation, Mobile Applications, and Context-sensitive Help courses.

devtraining-needit-paris icon devtraining-needit-paris

This repository is used by the developer site training content, Paris release. It is used for the Build the NeedIt App, Scripting in ServiceNow, Application Security, Importing Data, Automating Application Logic, Flow Designer, REST Integrations, Reporting and Analytics, Domain Separation, Mobile Applications, and Context-sensitive Help courses.

empirical-method-in-finance icon empirical-method-in-finance

Winter 2020 Course description: Econometric and statistical techniques commonly used in quantitative finance. Use of estimation application software in exercises to estimate volatility, correlations, stability, regressions, and statistical inference using financial time series. Topic 1: Time series properties of stock market returns and prices  Class intro: Forecasting and Finance  The random walk hypothesis  Stationarity  Time-varying volatility and General Least Squares  Robust standard errors and OLS Topic 2: Time-dependence and predictability  ARMA models  The likelihood function, exact and conditional likelihood estimation  Predictive regressions, autocorrelation robust standard errors  The Campbell-Shiller decomposition  Present value restrictions  Multivariate analysis: Vector Autoregression (VAR) models, the Kalman Filter Topic 3: Heteroscedasticity  Time-varying volatility in the data  Realized Variance  ARCH and GARCH models, application to Value-at-Risk Topic 4: Time series properties of the cross-section of stock returns  Single- and multifactor models  Economic factors: Models and data exploration  Statistical factors: Principal Components Analysis  Fama-MacBeth regressions and characteristics-based factors

ml-mooc-nptel icon ml-mooc-nptel

This repository contains the Tutorials for the NPTEL MOOC on Machine Learning.

react-pdb-view icon react-pdb-view

React component for simple visualizations of .pdb (Protein Data Bank) molecules

stock_market_prediction_using_time_series_and_sentiment_analysis icon stock_market_prediction_using_time_series_and_sentiment_analysis

This project studies the possibilities of forecasting stock market prices of firms using the sentiments captured via web scrapping. We have experimented with the stock market price of Tesla and Moderna using sentiment analysis and the ARIMA model. An accuracy analysis was also carried out with an R- squared value of each of the models to evaluate how they faired in the forecasting. The aim is to help reduce participants in the loss while investing using Twitter data. The stock data was pulled from the Yahoo Finance API. The sentiments were obtained from the sentences of tweets from Twitter. Results have proved that the ARIMA model has an excellent R-Squared value for short-term prediction.

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.