hforay's Stars
kieranjwood/slow-momentum-fast-reversion
This code accompanies the the paper Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection (https://arxiv.org/pdf/2105.13727.pdf).
llSourcell/Stock_Market_Prediction
This is the code for "Stock Market Prediction" by Siraj Raval on Youtube
maxlamberti/time-series-momentum
🚂💨 Deep Momentum Networks for Time Series Strategies
Daniblit/Ensemble-Predictive-Model-Forecasting-AMGEN-stock-price-at-year-end-31s
The basis of this project involves analyzing Amgen future profitability based on its current business environment and financial performance. Technical Analysis, on the other hand, includes reading the charts and using statistical figures to identify the trends in the stock market. The dataset used for this analysis was downloaded from Yahoo finance for year 2009 to 2019. There are multiple variables in the dataset – date, open, high, low, volume. Adjusted close. The columns Open and Close represent the starting and final price at which the stock is traded on a day. High and Low represent the maximum, minimum price of the share for the day. The profit or loss calculation is usually determined by the closing price of a stock for the day, I used the adjusted closing price as the target variable. I downloaded data on the inflation rate, unemployment rate, Industrial Production Index, Consumer Price Index for All Urban Consumers: All Items and Real Gross Domestic Product as independent variables, Quarterly Financial Report: U.S. Corporations: Cash Dividends Charged to Retained Earnings All Manufacturing: All Nondurable Manufacturing: Chemicals: Pharmaceuticals and Medicines Industry, Producer Price Index by Industry: Pharmaceutical Preparation Manufacturing, 30-Year Treasury Constant Maturity Rate, and Producer Price Index by Industry: Pharmaceutical and Medicine Manufacturing Index. The independent variables are economic parameters which was obtained from Federal Reserve Economic Data (FRED) website. Methodology 1. Linear Regression: The linear regression model returns an equation that determines the relationship between the independent variables and the dependent variable. I used linear regression tool in Alteryx with ARIMA tool to forecast the stock prices for the year. The algorithm was trained with the historical data to see how the variables impact on the dependent variable. The test data was used to predict the adjusted closing price for the year and predicted a stock price of $193.38. 2. Support Vector Machines (SVM): Support Vector Networks (SVN), are a popular set of supervised learning algorithms originally developed for classification (categorical target) problems and can be used for regression (numerical target) problems. SVMs are memory efficient and can address many predictor variables. This model finds the best equation of one predictor, a plane (two predictors) or a hyperplane (three or more predictors) that maximally separates the groups of records, based on a measure of distance into different groups based on the target variable. A kernel function provides the measure of distance that causes to records to be placed in the same or different groups and involves taking a function of the predictor variables to define the distance metric. I used the SVM tool in Alteryx with ARIMA tool to forecast the stock prices for the year and predicted a stock price of $189.44. 3. Spline Model: The Spline Model tool was used because it provides the multivariate adaptive regression splines (or MARS) algorithm of Friedman. This statistical learning model self-determines which subset of fields best predict a target field of interest and can capture highly nonlinear relationships and interactions between fields. I used the Spline tool in Alteryx with ARIMA tool to forecast the stock prices for the year and predicted a stock price of $201.84. The results from the models was weighted by comparing the RMSE of each model. A lower RMSE indicates that the model’s predictions were closer to the actual values. However, a simpler model with the same RMSE as a more complex model is generally better, as simpler models are less likely to be overfit. Though the Spline model had a lower RMSE, the Linear Regression model had fewer variables. Thus, we combined the 3 models with the ARIMA forecast in a model ensemble, which allows us to use the results of multiple models. The forecasted stock price is $197.99 with 1.5% increase for 31st December 2019. Apart from economic parameters, stock price is affected by the news about the company and other factors like demonetization or merger/demerger of the companies. There are certain intangible factors which can often be impossible to predict beforehand hence the model predicts that the stock price of Amgen will continue to rise except there is a drastic downturn of the company.
dorianbaranes/LSTM_prediction_stock_market
This project implements an LSTM (Long Short-Term Memory) model for predicting the future trends of the NASDAQ 100 index. The model is trained using different combinations of hyperparameters to find the best configuration for accurate predictions.
lfcabhi/Forecasting-of-Indian-IT-Sector-Index-using-Machine-Learning-techniques-and-LSTM
Stock market indexes predictions have always been under the radar of stalwarts belonging from the domains of econometrics, statistics, and mathematics. This has been a fascinating challenge to deal with since a major portion of the research community who promotes the idea of the efficient market hypothesis (EMH) believes that no predictive model can accurately predict the fluctuations of the ever-changing market, while recent works in this field using more advanced techniques like statistical modelling and machine learning can be used to demonstrate the gesticulations of a time series data with exceptional levels of accuracy. The stock market of a given country can be divided into its constituent sectors which represent the entire behaviour of a particular domain instead of the performance of individual companies. In this dissertation, it has been proposed how machine learning and deep neural network technique like Long Short Term Memory (LSTM) can be used to obtain fantastic results for prediction of stock value. Here, the IT sector of India has been taken into account to analyse its characteristic features about its ascend and descend according to the trend of the market. Regression techniques have been used to predict the probable indexes of the closing values and classification methods for identifying their pattern of movement. At first, a detailed machine learning approach has been adopted by using all adept methods like ensemble techniques like bagging and boosting, random forest, multivariate regression, decision tree, support vector machines, MARS, logistic regression and artificial neural networks. The application of univariate time series (with 5 input) deep learning model for regression was also implemented which has outperformed all the machine techniques as expected.
garyxcheng/stock-prediction
Using google trends and Tensorflow to predict whether the s&p500 index goes up or down
gjwlsdnr0115/Project-Thematic-Investments
Predicting potential stock themes by using NLP
nhipqnguyen/Dailly_Stock_Index_Trend_Prediction
Using resources available on Reddit, a social news discussion website, and Sequenced Packet Exchange (SPX), a networking protocol, we performed analysis on the potential predictive variables of daily stock index trend. The process and results have been discussed and demonstrated in the below report.
hardika191/Stock-Price-Prediction
This project addresses the problem of predicting direction of movement of stock and stock price index for Indian stock markets. The study compares four prediction models, Gradient Boost Regression, Support Vector Machine (SVM), random forest regression and Reinforcement learning (Bagging Model) with two approaches for input to these models. The first approach for input data involves computation of ten technical parameters using stock trading data (open, high, low & close prices) while the second approach focuses on representing these technical parameters as trend deterministic data. Accuracy of each of the prediction models for each of the two input approaches is evaluated. Evaluation is carried out on specific years when stock prices rise or fall. Accuracy of each of the 4 machine learning algorithms is calculated and printed with the correlation between stock opening price and oil price.
kaushiksriram16/Mutual-funds-preformance-analysis-using-supervised-learning
mehra-deepak/Stock-Price-Prediction
Comparing Machine Learning algorithms for stock price prediction and stock index movement using trend deterministic data preparation.
NicoleLund/mutual_fund_diversification
UofA Data Analytics Bootcamp Project 2 - ETL (Extract, Transform, Load)
4869-book/Predicting-stock-market-trends-using-machine-learning-CSS341-Group-7
Stock index prediction
afahad0149/Stock-Market-Analysis-Using-Data-Mining-Techniques
Analysis and Prediction of Dhaka Stock Exchange Broad Index trends over the last 10 years.
DivyaJagarlapoodi/Amazon-Stock-Price-Prediction
The aim of this project is to perform data exploratory analysis and to use time series analysis in order to analyze data of Amazon Stocks to understand and forecast the trends. The time series analysis of Amazon index will be done over two parts. The first part will consist of the exploratory data analysis of available data of Amazon over the chosen time order to understand the reasons behind the recorded fluctuations, and the second part will consist of the time series analysis of the data. The data used for this analysis is from January 1, 2011 to present. The next step is to analyze the data and implement in Jupyter notebook using ARIMA
jmller/ml-prediction-project
A Project based on "Predicting Stock and Stock Price Index Movement using Trend Deterministic Data Preparation and Machine Learning Techniques" from "Jigar Patel, Sahil Shah, Priyank Thakkar, K Kotecha (2014)"
khaoulabia/Stock-market-prediction-on-the-Dow-Jones-index-dataset-Clustering-task
Predicting stock market trends on the Dow Jones index dataset through clustering, a machine learning task aiming to group similar stock behavior. This approach seeks to uncover hidden patterns and potentially guide investment strategies.
lightkun10/Mutual-Fund-Investment-Analysis
memetics19/Mutual_funds_predictor
A Predictor System which predicts the mutual funds returns
modanwan/Time-Serires_Forecasting-Stock-Closing-Price
The objective of this project is to analyze historical S&P 500 index from 2001-2018, explore the trend and pattern of the index over time and make prediction for the future using time series methods. The whole process includes: build four time series models -- regression, Seasonal Naive model, Smoothing and ARIMA model; divide the dataset into training and testing data based on two partition method and run four models respectively; choose a champion model with the smallest average validation MAPE in two scenarios; use this model to forecast the S&P 500 Index in the following year.
pablovicente/stock-index-prediction
An study of machine learning algorithms applied to stock index trend prediction
Realamitkumar/Top_50-Mutual-Funds
RichieGarafola/Mutual_Funds_2022
Analyze the top 10 performing mutual funds of 2022
rishikeshshede/StockMarket-Prediction
Stock price and Index trend prediction using Stacked-LSTM Model.
saikatdgp2001/Stock_Market_Trend_Analysis
The main objective of this project is to create and train a classification model that can detect patterns in past data from the Nifty 50 stock market index. By studying these patterns, our aim is to gain insights into market behavior and make reasonably accurate predictions about future trends.
samacker77/Major-Project-Final
Comparing Machine Learning algorithms for stock price prediction and stock index movement using trend deterministic data preparation.
Satyam-Jaiswal/Stock-Index-Trend-Prediction-Buy-Sell-recommendation-System.
Developed Python scripts for Forecasting & Recommending System for the Stock market that leverages information from a website using web Scraping.
sofman65/Stock-Trend-Prediction-Project-in-CSI-300-Index
CSI_300_Stock_Trend_Prediction
Yanjing-PENG/index_prediction
The project utilized deep learning methodology to predict index trends of Chinese A-share stock market.