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).

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.

Compilation of technical analysis tools (EMA, Bollinger bands), fundamental analysis, machine learning models (LSTM, Random forest, ARIMA, GARCH, Markov Regime Switching), traditional stock prediction tools (Monte Carlo), sentiment analysis (NLP) as well as portfolio optimization, with purpose to provide a better understanding regarding possible future price movements of the analyzed stock and propose an optimal asset allocation.

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.

A python based tool to build agents and models for stock price prediction and trade automation

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.

https://www.kaggle.com/c/jane-street-market-prediction/overview “Buy low, sell high.” It sounds so easy…. In reality, trading for profit has always been a difficult problem to solve, even more so in today’s fast-moving and complex financial markets. Electronic trading allows for thousands of transactions to occur within a fraction of a second, resulting in nearly unlimited opportunities to potentially find and take advantage of price differences in real time. In a perfectly efficient market, buyers and sellers would have all the agency and information needed to make rational trading decisions. As a result, products would always remain at their “fair values” and never be undervalued or overpriced. However, financial markets are not perfectly efficient in the real world. Developing trading strategies to identify and take advantage of inefficiencies is challenging. Even if a strategy is profitable now, it may not be in the future, and market volatility makes it impossible to predict the profitability of any given trade with certainty. As a result, it can be hard to distinguish good luck from having made a good trading decision. In the first three months of this challenge, you will build your own quantitative trading model to maximize returns using market data from a major global stock exchange. Next, you’ll test the predictiveness of your models against future market returns and receive feedback on the leaderboard. Your challenge will be to use the historical data, mathematical tools, and technological tools at your disposal to create a model that gets as close to certainty as possible. You will be presented with a number of potential trading opportunities, which your model must choose whether to accept or reject. In general, if one is able to generate a highly predictive model which selects the right trades to execute, they would also be playing an important role in sending the market signals that push prices closer to “fair” values. That is, a better model will mean the market will be more efficient going forward. However, developing good models will be challenging for many reasons, including a very low signal-to-noise ratio, potential redundancy, strong feature correlation, and difficulty of coming up with a proper mathematical formulation.

A Stock Market price prediction tool polyglot project using Neural Networks, Clojure, Java with Seesaw Swing library

StockAI a simple, modern platform for stock prediction and analysis. It combines deep learning (LSTM) for price forecasting, technical analysis tools, and an AI-powered chat advisor all in a clean, responsive web app.

In this project we have implemented python code for 4 different Technical analysis tools for Stock Price Prediction namely MACD, RSI, Bollinger Band and Fibonacci Retracement.

An advanced, full-stack web application designed to provide AI-driven stock price predictions and comprehensive portfolio management. This tool features a modern, dark-themed UI built with React.js, a robust backend powered by Node.js/Express, and a dedicated Python/Flask service for sophisticated machine learning predictions.

Stock Price Prediction with the help of statistical tools, python and python Libraries

chartVantage is a Python-based project designed to identify and analyze chart patterns in financial time series data. This tool enables traders and analysts to recognize key patterns in stock price movements, aiding in market predictions and decision-making.

StockVision is an advanced stock price prediction and analysis tool that leverages machine learning to provide insightful forecasts. It offers detailed data visualizations and comprehensive stock analysis for informed decision-making.

The main feature that this project provides is predicting the future trend of stock prices. About 3 months of data have been scraped through pakistan data portal website and on the basis of that data stock prices will be predicted. Only the companies that listed in pakistan data portal are enlisted for stock prediction. A web platform using advanced technologies has been built. Stock management is basically between buyer, seller and comapany. For gold and currency rates, an api has been created. Main tools and technologies involved are node.js, react, web scrapping, python, machine learning , Material design bootstrap.

An AI-powered stock price prediction tool built using Streamlit, LSTM neural networks, and sentiment analysis from news, Twitter, and Google Trends data.

This project is an AI-powered stock price prediction tool.

"Advanced stock price prediction and analysis tool built with Streamlit and machine learning"

Tools for stock price prediction based on k-line history powered by Kronos models

A deep learning-based stock market prediction tool that utilizes fundamental financial data to forecast stock prices for the next quarter.

A comprehensive stock analysis tool built with Streamlit that provides real-time stock data, technical analysis, and machine learning-powered price predictions.

Advanced Stock Price Prediction Tool using ARIMA and LSTM models with a Streamlit-based web interface for visualization and analysis.

This python3 program contains the code used to develop an Artificial intelligence based prediction tool to predict Apple stock price market.

Stock Predictions is a Python-based project that explores models for forecasting stock prices using the Black–Scholes formula and other time-series techniques. It includes tools for data ingestion, model training, evaluation, and price projection.

An AI-based stock prediction tool developed by me, using Python and publicly available historical data. The project aims to explore machine learning and data science approaches to predict stock price movements.

Stock Market prediction tool leveraging Hidden Markov Models (HMM) for time series analysis and pattern recognition in stock prices. Focused on Apple stock, the model uses fractional price changes for training, automated data fetching from Yahoo Finance, and MSE for performance evaluation. Visualizes predicted vs actual trends with matplotlib.

This innovative project, "Stock Market Prediction with LSTM," harnesses Python's powerful tools to analyze historical stock market data. Employing advanced machine learning techniques, including Long Short-Term Memory (LSTM) neural networks, the project aims to provide actionable insights for investors by predicting stock prices accurately.

A comprehensive project leveraging big data techniques for stock market prediction and analysis. This repository includes data collection, processing, and visualization tools, alongside machine learning models for predicting stock prices and analyzing market trends. Ideal for financial analytics and investment strategies.

🔮 Stock Price Predictor with Interactive Toggle A Python-based financial analysis tool with an enhanced interactive UI. Enter any stock ticker (e.g., AAPL, TSLA) to fetch historical data, calculate technical indicators (MA50, MA200, RSI), and visualize predictions using a linear regression model.

Alpha Trend AI is a deep learning-powered tool designed to predict stock price trends based on historical data using LSTM (Long Short-Term Memory) networks. It provides an intuitive, interactive dashboard for visualizing predictions and exploring financial indicators like Moving Averages and RSI.