r3d0x3's Stars
freqtrade/freqtrade
Free, open source crypto trading bot
ctubio/Krypto-trading-bot
Self-hosted crypto trading bot (automated high frequency market making) written in C++
Alro10/deep-learning-time-series
List of papers, code and experiments using deep learning for time series forecasting
hfawaz/dl-4-tsc
Deep Learning for Time Series Classification
TreyThomas93/python-trading-bot-with-thinkorswim
This program is an automated trading bot that uses TDAmeritrades Thinkorswim trading platform's scanners and alerts systems to place trades dynamically using the TDAmeritrade API.
jinglescode/time-series-forecasting-pytorch
Acquiring data from Alpha Vantage and predicting stock prices with PyTorch's LSTM
duemig/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).
LiamConnell/deep-algotrading
A resource for learning about deep learning techniques from regression to LSTM and Reinforcement Learning using financial data and the fitness functions of algorithmic trading
thushv89/datacamp_tutorials
flo7up/relataly-public-python-tutorials
Beginner-friendly collection of Python notebooks for various use cases of machine learning, deep learning, and analytics. For each notebook there is a separate tutorial on the relataly.com blog.
pedrolarben/TimeSeriesForecasting-DeepLearning
An experiemtal review on deep learning architectures for time series forecasting
kennedyCzar/STOCK-RETURN-PREDICTION-USING-KNN-SVM-GUASSIAN-PROCESS-ADABOOST-TREE-REGRESSION-AND-QDA
Forecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning to Forecast Stock Return. Approach Used by Hedge Funds to Select Tradeable Stocks
sudharsan13296/Bitcoin-price-Prediction-using-LSTM
Bitcoin price Prediction ( Time Series ) using LSTM Recurrent neural network
SiaFahim/lstm-crypto-predictor
Predicting cryptocurrency price using RNN-LSTM networks
Cheng-Lin-Li/Market-Trend-Prediction
This is a project of build knowledge graph course. The project leverages historical stock price, and integrates social media listening from customers to predict market Trend On Dow Jones Industrial Average (DJIA).
Vanclief/algo-trading-crypto
Algorithmic Trading of Cryptocurrencies using Sentiment Analysis and Machine Learning
neha01/NIFTY_50_STOCK_PREDICTION
Predicting NIFTY_50 index price movement with LSTM Keras
ramtiin/Predicting-Stock-Prices-Using-FB-Prophet
Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well. In this notebook I'm going to try forecasting Google stock price using facebook's prophet model.
blurred-machine/RNN-based-Stock-Price-Prediction-using-LSTM
This repository will consist of a Long Short-Term Memory implementation of a Recurrent Neural Network used to predict the stock prices of Google Stocks for the next working day based on their past few days opening price trends.
JeetShah10/Time-Series-Forecasting-using-NN-LSTM-and-CNN
Predicted a stock price close of a day based on the last 7 day’s time series data using Neural Network, LSTM and CNN. Found the best number of the days should be considered in the past that yield the best model. Also, used LSTM to predict the stock prices for a company like Google and Apple for a continuous 5 days period.
luke4u/Time_Series_Forecasting
This is to create a repos for Time-series-predictions.
pathompong-y/stock_predictor
This is a project to try using machine learning to predict stock price. The scope of the stock is stock traded in Thailand.
akmuthun/Time-Series-Neural-Network-Grid-Search
Grid search for multilayer perceptron neural network for modelling time series data
castorgit/wind_code
Code and some notebooks from wind deep learning thesis
renatolfc/pystockml
A stock price predictor
dahhinc/Cryptocurrency-price-prediction-using-ANN
Deep learning RNN/LSTM model with sentiment analysis and grid search
edeng23/binance-trade-bot
Automated cryptocurrency trading bot
JordiCorbilla/G-Research-Crypto-Forecasting
G-Research Crypto-Forecasting Competition
abhiCoder123/Google-Stock-Price-Trend-Prediction
A Recurrent Neural Network(LSTM model) which predicts the upward and downward trends in the Google Stock Price.
paola-md/LSTM-GridSearch
Simple code to perform gridsearch for a LSTM RNN