Stock predictor app for NASDAQ stocks served on Streamlit. Data engineering side uses publicly available APIs to curate and form data, data science side offers a myriad of models.

A model to predict stock market behavior for NASDAQ 100 companies using Twitter Sentiment Analysis.

Predict short-term stock prices based on the first half of 2020 stock price history, covid 19 cases, and related stock news. Goals to implement machine learning models by tensorflow, data processing, and Restful API. My contribution is Apple and Tesla stock prediction from NASDAQ.

Long short-term memory (LSTM) model that predicts the closing stock prices for the NASDAQ 100 Technology Sector (NXDT) and NIFTY IT (CNXIT) stocks to compare side-by-side. Based on this a conclusion can be made about the economic development of the stock listing's respective country with the technology sector acting as the indicator.

This repository contains a machine learning project that predicts Apple (NASDAQ: AAPL) stock prices using sentiment analysis of news data and technical indicators, with implementations of Linear Regression, Support Vector Regression (SVR), and Long Short-Term Memory (LSTM) models, as well as an algorithmic trading bot for evaluation.

Recent business research interests concentrated on areas of future predictions of stock prices movements which make it challenging and demanding. Researchers, business communities, and interested users who assume that future occurrence depends on present and past data, are keen to identify the stock price prediction of movements in stock markets. . Predicting market prices are seen as problematical, and as explained in the efficient market hypotheses (EMH) that was put forward by Fama (1990), the EMH is considered as bridging the gap between financial information and the financial market; it also affirms that the fluctuations in prices are only a result of newly available information; and that all available information reflected in market prices. We applied k-nearest neighbour algorithm in order to predict stock prices for a sample of five major companies listed on the NASDAQ stock market to assist investors, management, decision makers, and users in making correct and informed investments decisions. According to the results, the k-NN algorithm is mildly robust with a good accuracy; consequently, the results were rational and also reasonable. In addition, depending on the actual stock prices data; the prediction results were close and fairly parallel to actual stock prices. We implemented the k-NN algorithm from scratch on python 2.7 to conduct the experiments for the project. k-NN is an instance-based, competitive learning, and lazy learning algorithm. Instance based algorithms, sometimes called memory-based learning, are those algorithms that, instead of performing explicit generalization, use the instances seen in the training as a comparison standard. For k-NN, the entire training dataset is the model. When a prediction is required for an unseen data instance, the k-NN algorithm will search through the training dataset for the k-most similar instances. k-NN is a competitive learning model because a majority vote is performed among the selected k records to determine the class label and then assigned it to the query record. k-NN is considered a lazy learning that does not build a model or function previously, but yields the closest k records of the training data set that have the highest similarity to the test (i.e., query record). The prediction attribute of the most similar instances is summarized and returned as the prediction for the unseen instance. The similarity measure is dependent on the type of data. For real-valued data, the Euclidean distance can be used. Other types of data such as categorical or binary data, Hamming distance can be used. In the case of regression problems, the average of the predicted attribute may be returned. In the case of classification, the most prevalent class may be returned.

An SVM-Regression model implementation to predict stock behavior(NASDAQ: AMZN, for instance)

Python-based Neural Network LSTM model that predicts the stock price of Apple Inc. using daily stock prices from Nasdaq.

It is technically a streamlit based web Stock price predictor having listed the stocks of NSE, BSE, Nasdaq using y-finance. For the model purpose, trained model Fbprophet was used an accuracy of 96% was achieved.

Developed and optimized a LightGBM model to accurately predict 60-second future movements in Nasdaq stock closing prices, utilizing meticulously engineered features from auction and order book data

A versatile stock portfolio management tool that accesses real-time Nasdaq data, utilizes technical analysis for predictive modeling, and generates accurate investment assessments for multiple unique user profiles.

This study compares the results of two completely different models: statistical one (ARIMA) and deep learning one (LSTM) based on a chosen set of NASDAQ data. Both models are used to predict daily or monthly average prices of chosen companies listed on the NASDAQ stock exchange.

Our neural network model will predict historical stock market trends for DJIA, S&P 500, and NASDAQ. Using this existing model we will adapt it to fit the needs of continuous training, anomaly detection, protection against adversarial attacks, as well as explainability of model predictions in the use case of orbital data.

ECE 5984 SP22 – Prof. Jones – Group Project II Due Tuesday, May 3, 2022 – 11:59 PM via Canvas In this project, your team will develop a model to predict the closing price of a specific stock as traded on the NASDAQ exchange.

In this project, we develop a model capable of predicting the closing price movements for hundreds of Nasdaq listed stocks using data from the order book and the closing auction of the stock. Information from the auction can be used to adjust prices, assess supply and demand dynamics, and identify trading opportunities.

A machine learning model predicting the daily movement of the NASDAQ Composite index. Using historical data and Random Forest Classifier, it classifies if the index will rise or fall the next day. Enhanced with trend and ratio features, backtested for precision, and includes interpretation of prediction accuracy for realistic insights.

Nasdaq stock price predictor with LSTM model

LSTM model for predicting stock prices using historical NASDAQ data

LSTM Model which predicts the Stock Price using NASDAQ data and Market sentiment

Model to predict Tesla stock high price based on Tesla and Nasdaq opening price

In this project, I used LSTM to predict the stock model on NASDAQ and HOSE

Developed a model to predict stock market behavior of NASDAQ 100 companies using Twitter sentiment analysis.

Training a machine learning model to predict movements in the NASDAQ stock index using alternative data.

A machine learning model that will predict the stock prices of any given company listed on Nasdaq.

This program is able to predict stock prices using NASDAQ's historical data. It makes use of the XGBClassifier model.

This program fetches a certain amount of stocks from the NASDAQ.com website and uses predictor module for each stock using a linear regression model.

Predict stock price using the LSTM model with historical data, gold, crude oil, DJIA index, NASDAQ 100 Index, Twitter score, and ratings.

A comparative study of SVM, Linear Regression, LSTM, and Random Forest models for predicting stock prices of five diverse companies using NASDAQ data.

A comparative study of SVM, Linear Regression, LSTM, and Random Forest models for predicting stock prices of five diverse companies using NASDAQ data.

The LSTM NASDAQ Stock Price Prediction Model uses Long Short-Term Memory (LSTM) networks to forecast stock prices for NASDAQ-listed companies. This project demonstrates how to apply LSTM, a type of recurrent neural network (RNN), to predict future stock prices based on historical data.