fsonmez's repositories
atlite
Atlite: Light-weight version of Aarhus RE Atlas for converting weather data to power systems data
atspy
AtsPy: Automated Time Series Models in Python (by @firmai)
awesome-energy-forecasting
list of papers, code, and other resources
awesome-quant
A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)
Awesome-Quant-Machine-Learning-Trading
Quant/Algorithm trading resources with an emphasis on Machine Learning
Corr_Prediction_ARIMA_LSTM_Hybrid
Applied an ARIMA-LSTM hybrid model to predict future price correlation coefficients of two assets
Deep-Learning-in-Asset-Pricing
https://arxiv.org/abs/1805.01104
deep-quant
Deep learning for forecasting company fundamental data
Deep_Learning_in_Asset_Pricing
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3350138
fecon235
Notebooks for financial economics. Keywords: Jupyter notebook pandas Federal Reserve FRED Ferbus GDP CPI PCE inflation unemployment wage income debt Case-Shiller housing asset portfolio equities SPX bonds TIPS rates currency FX euro EUR USD JPY yen XAU gold Brent WTI oil Holt-Winters time-series forecasting statistics econometrics
Hands-On-Deep-Learning-for-Finance
Hands-on Deep Learning for Finance published by Packt.
IBM_FINANCE_AP19
Deep learning in Finance with Keras. - IBM and NVIDIA workshop (Frankfurt, 2019)
kaggle-web-traffic
1st place solution
linearmodels
Linear models including instrumental variable estimators and panel data models
machine-learning-asset-management
Machine Learning in Asset Management (by @firmai)
Machine-Learning-for-Solar-Energy-Prediction
Predict the Power Production of a solar panel farm from Weather Measurements using Machine Learning
MachineLearningStocks
Using python and scikit-learn to make stock predictions
ml-for-finance
Machine learning techniques for financial datasets
notes
Contains Example Programs and Notebooks for some courses at Bogazici University, Department of Computer Engineering
playground
Play with neural networks!
probability
Probabilistic reasoning and statistical analysis in TensorFlow
PyPortfolioOpt
Financial portfolio optimisation in python, including classical efficient frontier and advanced methods.
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).
Stock-Prediction-Models
Gathers machine learning and deep learning models for Stock forecasting, included 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.
Test-stock-prediction-algorithms
Use deep learning, genetic programming and other methods to predict stock and market movements
value-investing-studies
Data Analysis Studies on Value Investing
WindTurbineClassification
Some Python codes from my master's thesis on wind turbine fault prediction using machine learning
workshop-ml-finance
git repo for the Machine Learning in Finance of Applied Machine Learning Days 2019