fsonmez

100days

100 days of algorithms

ams-2013-2014-solar-energy

Asset_Pricing_Test

Asset-Pricing Testing with Many Assets

assetPricing

assetPricing2

Deep-Portfolio-Theory

Autoencoder framework for portfolio selection (paper published by J. B. Heaton, N. G. Polson, J. H. Witte.)

FF3Factor

Fama French 3 Factor Model

PGPortfolio

PGPortfolio: Policy Gradient Portfolio, the source code of "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem"(https://arxiv.org/pdf/1706.10059.pdf).

PyPortfolioOpt

Financial portfolio optimisation in python, including classical efficient frontier and advanced methods.

wpIonic

Ionic app with WordPress integration

fsonmez/PyPortfolioOpt

Financial portfolio optimisation in python, including classical efficient frontier and advanced methods.

fsonmez/atlite

Atlite: Light-weight version of Aarhus RE Atlas for converting weather data to power systems data

fsonmez/atspy

AtsPy: Automated Time Series Models in Python (by @firmai)

fsonmez/awesome-energy-forecasting

list of papers, code, and other resources

fsonmez/awesome-quant

A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)

fsonmez/Awesome-Quant-Machine-Learning-Trading

Quant/Algorithm trading resources with an emphasis on Machine Learning

fsonmez/Corr_Prediction_ARIMA_LSTM_Hybrid

Applied an ARIMA-LSTM hybrid model to predict future price correlation coefficients of two assets

fsonmez/Deep-Learning-in-Asset-Pricing

https://arxiv.org/abs/1805.01104

fsonmez/deep-quant

Deep learning for forecasting company fundamental data

fsonmez/Deep_Learning_in_Asset_Pricing

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3350138

fsonmez/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

fsonmez/Hands-On-Deep-Learning-for-Finance

Hands-on Deep Learning for Finance published by Packt.

fsonmez/IBM_FINANCE_AP19

Deep learning in Finance with Keras. - IBM and NVIDIA workshop (Frankfurt, 2019)

fsonmez/kaggle-web-traffic

1st place solution

fsonmez/linearmodels

Linear models including instrumental variable estimators and panel data models

fsonmez/machine-learning-asset-management

Machine Learning in Asset Management (by @firmai)

fsonmez/Machine-Learning-for-Solar-Energy-Prediction

Predict the Power Production of a solar panel farm from Weather Measurements using Machine Learning

fsonmez/MachineLearningStocks

Using python and scikit-learn to make stock predictions

fsonmez/ml-for-finance

Machine learning techniques for financial datasets

fsonmez/notes

Contains Example Programs and Notebooks for some courses at Bogazici University, Department of Computer Engineering

fsonmez/playground

Play with neural networks!

fsonmez/probability

Probabilistic reasoning and statistical analysis in TensorFlow

fsonmez/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).

fsonmez/Stock-Prediction-Models

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

fsonmez/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.

fsonmez/Test-stock-prediction-algorithms

Use deep learning, genetic programming and other methods to predict stock and market movements

fsonmez/value-investing-studies

Data Analysis Studies on Value Investing

fsonmez/Wind_Turbine_Predictions

fsonmez/WindTurbineClassification

Some Python codes from my master's thesis on wind turbine fault prediction using machine learning

fsonmez/workshop-ml-finance

git repo for the Machine Learning in Finance of Applied Machine Learning Days 2019