galdamour's repositories
Deep-Learning-Papers-Reading-Roadmap
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
deep_learning_object_detection
A paper list of object detection using deep learning.
Forex-and-Stock-Python-Pattern-Recognizer
A machine learning program that is able to recognize patterns inside Forex or stock data
FundamentalAnalysis
Fully-fledged Fundamental Analysis package capable of collecting 20 years of Company Profiles, Financial Statements, Ratios and Stock Data of 13.000+ companies.
FX_Prediction
If you search for "Technical analysis and AI" in Persian you will find this post among the first 3 results of Google.
IntroNeuralNetworks
Introducing neural networks to predict stock prices
learning_to_beat_the_random_walk
In this project, I explore various machine learning techniques including Principal Component Analysis (PCA), Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Sentiment Analysis in an effort to predict the directional changes in exchange rates for a list of developed and developing countries.
LSTM-NeuralNetwork-Forex
Analysis and implementation of an LSTM Neural Network to predict foreign exchange of currencies.
LSTM-Text-Generation
Tons of fun with text and recurrent neural networks! Let your computer read a book and tell you its own story. 🤣
Machine-Learning-and-AI-in-Trading
Applying Machine Learning and AI Algorithms applied to Trading for better performance and low Std.
MachineLearning
Examples on building machine learning models
MachineLearningStocks
Using python and scikit-learn to make stock predictions
Options-Trading-Strategies-in-Python
Developing Options Trading Strategies using Technical Indicators and Quantitative Methods
Predicting-Nordea-stock-price-using-an-LSTM-neural-network-
Using an 80/20 split in the historical data daily closing prices where predicted using a LSTM network based on data observed in the past 30 days for each prediction
QuantConnect-Trading-Strategies
Forex & Equities Trading Strategies using Machine Learning, Deep Learning and Statistical Techniques
Quantitative-Notebooks
Educational notebooks on quantitative finance, algorithmic trading, financial modelling and investment strategy
Statistical-Arbitrage-Algorithmic-Trading
A Project to identify statistical arbitrage opportunities between cointegrated pairs. This is referred to as 'Pairs Trading' which is a bet on the mean reversion property of the spread.
stock-analysis-engine
Backtest 1000s of minute-by-minute trading algorithms for training AI with automated pricing data from: IEX, Tradier and FinViz. Datasets and trading performance automatically published to S3 for building AI training datasets for teaching DNNs how to trade. Runs on Kubernetes and docker-compose. >150 million trading history rows generated from +5000 algorithms. Heads up: Yahoo's Finance API was disabled on 2019-01-03 https://developer.yahoo.com/yql/
Stock-Portfolio-Suggestion-Engine
A group project for CMPE285 at SJSU in Python
Stock-Prediction
Smart Algorithms to predict buying and selling of stocks on the basis of Mutual Funds Analysis, Stock Trends Analysis and Prediction, Portfolio Risk Factor, Stock and Finance Market News Sentiment Analysis and Selling profit ratio. Project developed as a part of NSE-FutureTech-Hackathon 2018, Mumbai. Team : Semicolon
Stock-Price-Prediction-LSTM
OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network
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
stocksight
Stock market analyzer and predictor using Elasticsearch, Twitter, News headlines and Python natural language processing and sentiment analysis
Twitter-moods-as-stock-price-predictors-on-Nasdaq
An attempt to predict next day's stock price movements using sentiments in tweets with cashtags. Six different ML algorithms were deployed (LogReg, KNN, SVM etc.). Main libraries used: Pandas & Numpy