prernamishra08 Goto Github PK

pymail

POP3/SMTP clients based on sockets

python-algorithms

Collection of algorithm implementations from various sources plus own creations.

pytorch-nlp-notebooks

Learn how to use PyTorch to solve some common NLP problems with deep learning.

r-stats-machine-learning

Misc Statistics and Machine Learning codes in R

recommender-system-dsga1004

A recommender system built in PySpark and to run on HPC environment. Submitted as term project for DS-GA 1004 Big Data at NYU.

reducingmlcostswithaws

reinforcement-learning

Research and implementations of Deep Learning / Reinforcement Learning agents and their implementations / applications

rl_basics

Basic Reinforcement Learning algorithms

rnn-stocks-prediction

Another attempt to use Deep-Learning in the financial markets

robo-advisor-and-systematic-trading

Basis, execution, and evaluation of execution strategies used in financial markets, and exploring the use of AI in driving decision making.

rss-to-email

Quick and dirty dockerized rss feed reader that supports HTTP basic auth and sends new articles as emails via Amazon SES

sample_http_app

Samp HTTP server (Flask/Python)

sharkstock

Automate swing trading using deep reinforcement learning. The deep deterministic policy gradient-based neural network model trains to choose an action to sell, buy, or hold the stocks to maximize the gain in asset value. The paper also acknowledges the need for a system that predicts the trend in stock value to work along with the reinforcement learning algorithm. We implement a sentiment analysis model using a recurrent convolutional neural network to predict the stock trend from the financial news. The objective of this paper is not to build a better trading bot, but to prove that reinforcement learning is capable of learning the tricks of stock trading.

springboard

stanford-project-predicting-stock-prices-using-a-lstm-network

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).

stanford_data_strucutre_and_algo

In this repository, I try to learn and implement some of the algorithms taught in Coursera's Stanford Data Structure and Algorithm Course

statub18--forecasting-time-series-data

A statistics course covers practical time series forecasting techniques with particular emphasis on the Box-Jenkins (ARIMA) method and conditional volatility (ARCH) models. Offered by NYU Stern.

stock-market-prediction-using-gan-1

#LSTM #1D CNN #GAN #Stock Prediction #Indicators #AMD #PlaidML #Crawling

stock-market-prediction-using-natural-language-processing

stock-market-prediction-using-sentiment-analysis

stock market predictions using sentiment analysis, a deep learning project(data and news based on pakistani stock exchange and news(Dawn news))

stock-market-prediction-web-app-using-machine-learning-and-sentiment-analysis

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-predictions

Predicting the stock market opening values using Deep learning's Model Recurrent Neural Networks which is a very powerful model.

stock-market-price-prediction

Analysis of various deep learning based models for financial time series data using convolutions, recurrent neural networks (lstm), dilated convolutions and residual learning

stock-prediction

LSTM RNN for sentiment-based stock prediction

stock-prediction-1

Technical and sentiment analysis to predict the stock market with machine learning models based on historical time series data and news article sentiment collected using APIs and web scraping.

stock-prediction-models

Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations

stock-price-prediction

This project seeks to utilize Deep Learning models, Long-Short Term Memory (LSTM) Neural Networks to predict stock prices.

stocker

Financial Web Scraper & Sentiment Classifier

stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

stockpredictor

Predict stock movement with Machine Learning and Deep Learning algorithms