Stock Market Prediction Web App based on Machine Learning and Sentiment Analysis of Tweets (API keys included in code). The front end of the Web App is based on Flask and Wordpress. The App forecasts stock prices of the next seven days for any given stock under NASDAQ or NSE as input by the user. Predictions are made using three algorithms: ARIMA, LSTM, Linear Regression. The Web App combines the predicted prices of the next seven days with the sentiment analysis of tweets to give recommendation whether the price is going to rise or fall

A stock trading bot that uses machine learning to make price predictions.

Stanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions [1] or [2]. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts [3], [4] or [5]. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train a LSTM neural network with time series price-volume data and compare its out-of-sample return predictability with the performance of a simple logistic regression (our baseline model).

Pull stock prices from online API and perform predictions using Long Short Term Memory (LSTM) with TensorFlow.js framework

Try to predict stock price with LSTM、GAN and DRL, exploring the features of news and technical indicators，which help improving perfomance of predictions.

🚀 Professional AI-powered stock market dashboard with real-time technical analysis, machine learning price predictions, and intelligent market insights. Built with Python, Streamlit, and scikit-learn.

The Stock Price Prediction App is a Streamlit-based web application that provides users with tools to analyze historical stock price data, visualize technical indicators, and make short-term price predictions using different machine learning models.

Calculate technical indicators from historical stock data Create features and targets out of the historical stock data. Prepare features for linear models, xgboost models, and neural network models. Use linear models, decision trees, random forests, and neural networks to predict the future price of stocks in the US markets. Evaluate performance of the models in order to optimize them Get predictions with enough accuracy to make a stock trading strategy profitable.

A web-based stock analysis application built with React and Flask, offering interactive visualizations and machine learning-based stock price predictions.

Implementation of stock technical indicators and deep LSTM for closing price 30-day lookahead predictions (for learning purposes only).

This project implements a time series multivariate analysis using RNN/LSTM for stock price predictions. A deep RNN model was created and trained on five years of historical Google stock price data to forecast the stock performance over a two-month period.

Jupyter notebook for performing price predictions of stock data using Facebook's Prophet package.

Applying Reinforcement learning models for stock price predictions

Stock Market Prediction Web App based on Machine Learning and Sentiment Analysis of Tweets **(API keys included in code)**. The front end of the Web App is based on Flask and Wordpress. The App forecasts stock prices of the next seven days for any given stock under NASDAQ or NSE as input by the user. Predictions are given for three algorithms: ARIMA, LSTM, Linear Regression. The Web App combines the predicted prices for the next seven days with the sentiment analysis of tweets to give recommendation whether the price is going to rise or fall

Stock Market Forecasting: Using Technical Indicators for Price Movement Predictions.

Visualising stock price predictions for several Machine Learning models using DASH, Python

Stock Market predictions are one of the most difficult problems to solve, and during the looming days of recession it’s extremely difficult and next to impossible to do. This is because there are numerous patterns in the stock prices trend throughout the day and every variation from the normal trend could mean something new, since stocks is ever expanding and hence new problems and pattern in the trends are visible which needs to be studied but these new trends are usually generated each and every day possible of the trade and to keep up with the change is a very lofty task to do especially for an individual who has a large or even semi large portfolio to maintain over a period of time. Stocks and bonds are immensely important for a country’s economy to boom and it’s collapse means the collapse of country’s economy and since these markets are linked with every possible sector that contribute to the economy, mostly organised sectors, it’s collapse would be seen on every sector linked in those markets by what the economists call as “Ripple effect” and this goes other way around as well that if a particular sector’s firm performs poorly, then that would be reflected in the other firms of that sector.

Sentiment analysis of the collected tweets is used for prediction model for finding and analysing correlation between contents of news articles and stock prices and then making predictions for future prices will be developed by using machine learning.

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.

Interactive data science and machine learning dashboards on the stock market analysis and making predictions for companies' stock prices.

A machine learning-driven platform that integrates sentiment analysis and advanced predictive modeling to forecast stock market trends. It leverages TensorFlow, financial news analysis, and technical indicators within a Django web interface to offer insightful stock price predictions.

We compiled the analyst reports from Morningstar for 15 largest companies in retail and technology sector and extracted the specific text. Then extracteed sentiments using VADER general sentiment lexicon and through Loughran and MCdonald financial sentiment lexicon. S&P Capital IQ and Yahoo Finance was also our data source. We applied statistical modeling, both linear and logisitc regressions to predict the percentage change in the stock price from day of publication of report to 3 time periods and our model showed some sigificant results with over 95% accuracy and validated our hypothesis.

Solana-based Stock Prediction DApp: Bet on stock prices, win rewards for accurate predictions. Explore financial markets like never before

Stock Price Predictions from Financial Statements using Machine Learning and Deep Learning algorithms augmented with Knowledge Graph Embeddings

Market Pulse is a web app built with Streamlit that provides real-time stock data, technical analysis, and price predictions. It features key tools like RSI, SMA, and Random Forest for stock price forecasting. Key Features: Real-time Stock Data: Get the latest stock prices and metrics. Technical Analysis: Calculate indicators like RSI and SMA.

This is a model that has been trained on historical data obtained from Yahoo Finance. The data set comprises of all data records starting from the launch date of this stock in India (1996). This model aims to pick up key trends in the stock price fluctuations based on Time Series mapping. It is able to render predictions for the upcoming time period. The accuracy as obtained on the training data-set is about 90 percent and it successfully demonstrates key trends. It can be simulated on any stock in the market provided their historical data is made available. (One could use the yfinance API or download manually). Keras is used extensively along with Tensorflow for training. The model features 100 epochs of Base size 64. The training time depends on the hardware being used by the user. It is advisable to be performed on Google Colaboratory. For any issues/suggestions write to somshankar97@gmail.com

A Stock Prices Analytics Dashboard, comprising of python codes for price predictions, technical indicators, and dashboard hosting

LSTM with attention for time series predictions of stock prices using own Ticker Embedding model

This is a data pipeline for predicting stock prices using Apache Spark, Apache Cassandra, and machine learning techniques. It collects and preprocesses stock data from Alpha Vantage API, engineers features, trains models, and performs data analysis and predictions.

This project is a Stock Market Prediction App built using Python, Keras, Streamlit, and yFinance. It predicts stock prices based on historical data and visualizes trends with moving averages and ML-based predictions.