kurucan

algorithmic_trading_book

2 books and related source codes for algorithmic trading.

Asset-Pricing-via-ML

Empirical asset pricing via Machine Learning in the Korean market

backtesting.py

:mag_right: :chart_with_upwards_trend: :snake: :moneybag: Backtest trading strategies in Python.

DeepLearningFinance

Deep Learning algorithms for forecasting in finance.

DEPBU

Hierarchical Probabilistic Forecasting with Intraday Smart Electricity Meter Demand

Empirical-Asset-Pricing-via-Machine-Learning-Evidence-from-the-German-Stock-Market

Machine learning methods for identifing investment factors

Factor-Models-Machine-Learning-and-Asset-Pricing-

Recent methodological contributions in asset pricing using factor models and machine learning. I presented Giglio et al. (2021) paper during Econometrics and ML reading group.

FeatureEngineering

The repository showcases feature engineering for finance data and use of automated libraries

qlib_predict_daily-stock_prices

This is test qlib from microsoft

quantstats

Portfolio analytics for quants, written in Python

kurucan/DEPBU

Hierarchical Probabilistic Forecasting with Intraday Smart Electricity Meter Demand

kurucan/Active-Portfolio-Management-Notes

Notes for Active Portfolio Management, by Grinold and Kahn

kurucan/ams-2013-2014-solar-energy

kurucan/bulbea

:boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling

kurucan/cpatest

Giacomini & White test of Predictive Ability

kurucan/CPSC540Project

Project on financial forecasting using ML. Made by Anson Wong, Juan Garcia & Gudbrand Tandberg

kurucan/Deep-Portfolio-Theory

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

kurucan/demos

Public facing demos

kurucan/Diebold-Mariano-Test

This Python function dm_test implements the Diebold-Mariano Test (1995) to statistically test forecast accuracy equivalence for 2 sets of predictions with modification suggested by Harvey et. al (1997).

kurucan/Duel-staged-Attention-for-NYC-Weather-prediction

Dual Staged Attention Model for Time Series prediction

kurucan/FinanceAndPython.com-CorporateFinance

kurucan/GEFCOM2014

Code used for GEFCOM2014. Predicting the distribution of electricity load.

kurucan/gym-trading

Environment for reinforcement-learning algorithmic trading models

kurucan/IRDM2016

UCL group project - Information Retrieval and Data Mining 2016 - Time Series Forecasting

kurucan/jupytercon2017

Material for Data analysis and machine learning in Jupyter

kurucan/keras

Deep Learning for Python. Runs on TensorFlow, Theano or CNTK.

kurucan/Machine_Learning_Problems

Testing repository for various machine learning algorithms and methods using public test data sets

kurucan/MicroGrids

Library of tools for the simulation and optimization of microgrids

kurucan/ML-Quant-Finance

Machine Learning for Quantitative Finance

kurucan/Mutual-Fund-Market-Clusters

Using kmeans clustering, hierarchical clustering, and dynamic time warp to find natural groups in mutual funds and broker dealer offices

kurucan/pairs-trading-with-ML

kurucan/PCEDL

Wind Turbine Power Curve Estimation using Deep Learning methods

kurucan/Power_Curve_Estimation

Wind energy is one of the fastest growing renewable energy sources. According to a report issued by the U.S. Department of Energy (DOE), wind power installation in the United States increased by nearly a factor of 10 in the past decade, from 6350 megawatts (MW) in 2003 to 61,108 MW by the end of 2013 (DOE 2014). The DOE advocates working toward the goal that wind power accounts for 20% of the total electricity generated in the United States by 2030 (DOE 2008).

kurucan/SGX-Full-OrderBook-Tick-Data-Trading-Strategy

Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.

kurucan/sonnet

Winning data science solution for Energy Hack NL 2018. Sonnet: forecasting station load caused by solar panels.

kurucan/stELMOD

stELMOD is a stochastic optimization model to analyze the impact of uncertain wind generation on the dayahead and intraday electricity markets as well as network congestion management. The consecutive clearing of the electricity markets is incorporated by a rolling planning procedure resembling the market process of most European markets.

kurucan/successful-algorithmic-trading

the book with script

kurucan/Thesis

Reinforcement Learning for Automated Trading

kurucan/WPPF

An early prototype of a time-series forecasting app predicting wind power production