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

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

QuantTraderDL combines quantitative finance and AI, using TFT models to forecast major indices (S&P 500, Nasdaq, IBEX 35, Dow Jones, EURO STOXX 50) and optimize portfolios. A DRL trading bot adapts to market changes to maximize returns and manage risk with prediction confidence and trend detection.

ML Crypto-Price Prediction Model using NASDAQ API.

Tesla/Nasdaq USD Prediction with Artificial Intelligence RNN Neural Network

To create a data-web application deployed using the azure app service, which was made on Streamlit, the leading Pythonic data application service. On this website, we display candlestick plots of various stocks listed on the Nasdac, according to the option of the user; and utilize the Garch based time forecasting algorithm done using Seasonal arima model and conduct a virtual future prediction for the given stock, so as to be able to conduct non-pairs algorithmic trading using time forecasting and Garch-based deep learning.

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.

This project was completed with the intention of helping Tesla stock investors better understand how to make decisions where the stock market is very volatile by training different models through historical and social media data analytics. Behavioral economics shows that public emotions can profoundly affect individual behavior and decision making. In order for investors to utilize it, business analysts must understand the behaviors and attitudes of the public within the finance context. Nowadays, social media perfectly tracked by data reflects the public emotions and sentiment about stock movement. Also, tremendous stock marketing news can be used to capture a trend of stock movement. The fundamental trading and decision making for main techniques rely on expert training and prediction. This article concentrated on tweets and stock news, and I applied sentiment analysis and machine learning models, especially, XGBoost to tweets and news extracted from Elon Musk tweets, Nasdaq and New York Times News about Tesla. Only by understanding the values and priorities of the public sentiment of Tesla stock will investors be able to make significant decisions. In addition, I conducted two models- ARIMA and RNN(LSTM) in forecasting the Tesla stock price. I compare their results with the prediction performances of the classical ARIMA and RNN.

predict the future volatility of the NASDAQ index using various econometric and machine learning models, including ARCH, GARCH, EGARCH, SVM, and ANN

This project is a working Stock Price predictor . The user can give it any company's stock from any Index like NASDAQ, NSE,etc. It can be used on any type of market like Stock(Equity), FX (Currency Exchange), FNO (Futures n Options), etc. and for any time frame like 1 min, 5 min, End Of the day data. Be careful to remove the irrelevant code from comment for 1 min and 5 min code. Be careful to change the READ_CSV line according to your Dataset and add columns or if not added then do not remove "USE_COLS" or replace it with a list of names of your columns like LIST=["Date","Time","Open", "High"...and so on] as"NAMES=LIST". I am open to changes and suggestions. If there is a query kindly add it in the issues section, I will try my best to help you.Stock traders mainly use three indicators for prediction: OHLC average (average of Open, High, Low and Closing Prices), HLC average (average of High, Low and Closing Prices) and Closing price, In this project, OHLC average has been used.

This is the the final project of the course: L330 Data Science: principles and practice at the University Of Cambridge. The task for this project is stock market prediction using a diverse set of variables. In particular, given a dataset representing days of trading in the NASDAQ Composite stock market, our aim is to predict the daily movement of the market up or down conditioned on the values of the features in the dataset over the previous N (trading) days.

This project is a working Stock Price predictor . The user can give it any company's stock from any Index like NASDAQ, NSE,etc. It can be used on any type of market like Stock(Equity), FX (Currency Exchange), FNO (Futures n Options), etc. Be careful to change the READ_CSV line according to your Dataset and add columns if not added but removing "USE_COLS"and replacing it with a list of names of your columns like LIST=["Date","Time","Open", "High"...and so on] as"NAMES=LIST". I am open to changes and suggestions. If there is a query kindly add it in the issues section, I will try my best to help you.Stock traders mainly use three indicators for prediction: OHLC average (average of Open, High, Low and Closing Prices), HLC average (average of High, Low and Closing Prices) and Closing price, In this project, OHLC average has been used.

I Developed a robust CNN model for both classification and regression tasks, leveraging a 2K-day dataset of S&P500 features and 80 other indicators. Executed a trading strategy based on the predictions of the model, achieving a 1.25 Sharpe ratio on S&P500 and averaging 1.05 across major indices including NASDAQ, DJI, NYSE, and RUSSELL.

Automatically fine-tunes the ML model with previously learned data. A sophisticated, cloud-native machine learning pipeline designed to fully automate the process of stock price prediction for equities from both the NASDAQ and NEPSE stock exchanges.

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Predicting NASDAQ with PyTorch Deep Learning model

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Applied LSTM on NASDAQ data for analysis and prediction

stock price prediction using Auto-Arima and linear Regression on Nasdaq Finance dataset!!

Prediction for nasdaq values and other tech companies by using dl4j in java .

Stock fluctuation prediction project - deep learning LSTM model for NASDAQ S&P500 & DOW JONES. Project horizon : Add sentiment analysis from twitter as input + Indicator combination Variation

Collected financial news via NYT API and NASDAQ stock data via yfinance, extracted sentiment scores using FinBERT, merged them with stock data, and trained an LSTM model for stock trend prediction.

A python based terminal interface to analyze the real-time performance of 3000+ company securities listed on NASDAQ. An extensive set of modules based on Pandas, NumPy, Matplotlib and Seaborn libraries. Also deployed the prediction of stock prices using Linear Regression.

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.

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.

A python program , for prededict some nasdaq close values ,with using neural network ,and a deep recurent neural network , and a lstm

StockWiz is an AI-powered stock prediction website for companies listed on NASDAQ using Tensorflow.js (Disclaimer: just for fun!)

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A project focused on data extraction from various sources, data preprocessing, and machine learning. The goal is to build a robust pipeline to collect, process, and analyze data, ultimately enhancing my skills in data science and machine learning. I also aim to share insights and discoveries with the community through this project.

Predicting Nasdaq Movements using Daily News using Recurrent Neural Networks. Multiple models tested such as multi layer perceptron, char/word level RNNs. Used Pytorch to train and run the models.