Pinned Repositories
anomaliesinoptions
In this notebook we will explore a machine learning approach to find anomalies in stock options pricing.
Deep-Trading
Algorithmic trading with deep learning experiments
lczero-training
For code etc relating to the network training process.
LearningX
Deep & Classical Reinforcement Learning + Machine Learning Examples in Python
piecewise
Functions for piecewise regression on time series data
portfolio_allocation_js
A JavaScript library to allocate and optimize financial portfolios.
predictions
PyPortfolioOpt
Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
qstrader
QSTrader
maslam's Repositories
maslam/anomaliesinoptions
In this notebook we will explore a machine learning approach to find anomalies in stock options pricing.
maslam/Deep-Trading
Algorithmic trading with deep learning experiments
maslam/lczero-training
For code etc relating to the network training process.
maslam/LearningX
Deep & Classical Reinforcement Learning + Machine Learning Examples in Python
maslam/piecewise
Functions for piecewise regression on time series data
maslam/portfolio_allocation_js
A JavaScript library to allocate and optimize financial portfolios.
maslam/predictions
maslam/PyPortfolioOpt
Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
maslam/qstrader
QSTrader
maslam/some-investment-books
maslam/Stock-Prediction-Models
Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations
maslam/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.
maslam/xgboost
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow