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sentiment-analysis-financial-health-nlp

Using NLTK library and annual report to determine financial health of a firm

simple-storage

soybean-image-classifier

sp1500stockpicker

A machine learning approach to investment portfolio composition. The program analyzes the fundamentals of the listed companies on the S&P1500 in order to emit monthly buy signals.

sp500_bullishbearishenglufing

standard-readme

A standard style for README files

stanford-project-predicting-stock-prices-with-lstm-networks

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).

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

stock-prediction-models

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

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.

streamlitforecast

successful-algorithmic-trading

the book with script

tda-api

A TDAmeritrade API client for Python

time-series-forecasting-of-amazon-stock-prices-using-neural-networks-lstm-and-gan-

Project analyzes Amazon Stock data using Python. Feature Extraction is performed and ARIMA and Fourier series models are made. LSTM is used with multiple features to predict stock prices and then sentimental analysis is performed using news and reddit sentiments. GANs are used to predict stock data too where Amazon data is taken from an API as Generator and CNNs are used as discriminator.

trading_strategies

Testing ideas for trading strategies

transfer-learning-pneumonia-image-classifier

tutorials

Ipython notebooks for math and finance tutorials

ucl-project-generating-normally-distributed-pseudo-random-numbers-

udacity

Nanodegree practical projects

watch-me-build-a-finance-startup

This is the code for "Watch Me Build a Finance Startup" by Siraj Raval on Youtube