karagul Goto Github PK

deep-brain-simulation-for-alzheimer-s-patient

In this project I motivated by a hypothesis by a research team from University of Toronto that a particular area of the human brain named 'Fornix' is responsible for increasing memory in the brain and this may be a potential clinical cure for affected brain cell like Alzheimer's patient. Alzheimer's patient suffer from declination of memory. Alzheimer's disease can be clinically healed(not fully-cured though) by applying Deep Brain Stimulation for the patients are in early Alzheimer's disease. My quest to find various features and factors and give accurate prediction by building various Machine Learning model and Artificial Neural Network to detect early Alzheimer's patient, hence, help them to undergo Deep Brain Simulation(DBS) process for Alzheimer's treatment.

deep-forecast

Forecasting Macroeconomic Parameters with Deep Learning Neural Networks - Final Year Peoject

deep-learning-framework-for-financial-time-series-prediction-in-python-keras

Randomly partitions time series segments into train, development, and test sets; Trains multiple models optimizing parameters for development set, final cross-validation in test set; Calculates model’s annualized return, improvement from buy/hold, percent profitable trades, profit factor, max drawdown

deep-learning-in-asset-pricing

https://arxiv.org/abs/1805.01104

deep-learning-in-asset-pricing-1

deep-neural-networks-for-stock-price-prediction

Comparative Analysis of Conv1D-LSTM with CNN , LSTM for Stock Price Prediction

deep-portfolio-management-reinforcement-learning

This repository presents our work during a project realized in the context of the IEOR 8100 RL Class at Columbia University.

deep-reinforcement-learning-in-multidimensional-pairs-trading

A Deep Reinforcement Learning neural net for an original Multi-Dimensional Pairs Trading strategy is proposed

deep-reinforcement-learning-in-stock-trading

Using deep actor-critic model to learn best strategies in pair trading

deep_mortgage_risk

Deep Learning for Mortgage Risk

deep_news

A trading algorithm which uses news and ticker data to predict the volatility and volume of ETFs.

deep_portfolio_theory

Deep Portfolio Theory is a portfolio selection method published by J. B. Heaton, N. G. Polson, J. H. Witte from GreyMaths Inc. Authors' codes are proprietary, We tried to implement this method on BSE Healthcare stocks. We constructed the deep portfolio method over the modern portfolio theory, Markowitz’s classic risk-return trade-off. Four-step routine of encode, calibrate, validate and verify to formulate an automated and general portfolio selection process. At the heart of our algorithm are deep hierarchical compositions of portfolios constructed in the encoding step. The calibration step then provides multivariate payouts in the form of deep hierarchical portfolios that are designed to target a variety of objective functions. The validate step trades-off the amount of regularization used in the encode and calibrate steps. The verification step uses a cross validation approach to trace out an ex post deep portfolio efficient frontier. We demonstrate all four steps of our portfolio theory numerically.

deeptrade

A LSTM model using Risk Estimation loss function for stock trades in market

deeptrading

DQN for single stock, pair trading in KOR

default-correlation

I apply Merton's Single Factor Copula to Lending Club data to analyze the default correlation.

default-swaption-pricing-and-counterparty-credit-risk-valuation

defaulters-risk-prediction-banking

Buliding a predictive model to help the banks in predicting the probability of a customer defaulting in the future based on the historic data is the ultimate objective of the project . For which structured methodology is followed, in which the historic data of the customers are cleaned , splitted for testing and training , independency are checked ,classification algorithms were used like J48,Naïve Bayes, MLP,base line accuracy for the available data set is calculated, models were repeatedly tested and trained with Hold out and K-fold cross validation methods ,significant features are selected and the models were re evaluated for accuracy and precision comparison , model errors are address with the ROC .Individual model outputs are captured and compared with each other to get the best model for the Defaulter prediction.

demand-forecast

Demand forcast for home applinaces

demo_dashboard

Interactive Dashboard in R using flexdashboard, shiny and plotly

demo_powerbi_r_tradebalance

Power BI Desktop file and R Script used in Demo

departmentnewsletter-rmd-

An Rmarkdown that wrangles data from an open google sheet into a newsletter format and emails it to the department graduate student listserv.

dependence-modeling-with-copulas

deriv_models

Scripts to model various derivative instruments. Adaptations from QuantLib and Yves Hilpisch books

derivative-analytics-with-python-yves-hilpisch

Codes given in "Derivative Analytics with Python - Yves Hilpisch" Book

derivative_analytics

For Derivative valuation and analysis.

derivative_pricing

The aim of the project is to create application, which allows to determine the price of the financial derivatives, its sensitivities in different models (stochastic volatility, local volatility). Models will be calibrated the market on the basis of volatility surface.

derivatives-and-securities-research

Bid-ask spread analysis (cboe spxw), risk and volatility, trading strategies

descriptive_statistics

R Markdown file to run descriptive stats

detection-estimation-learning

Python notebooks for my graduate class on Detection, Estimation, and Learning. Intended for in-class demonstration. Notebooks illustrate a variety of concepts, from hypothesis testing to estimation to image denoising to Kalman filtering. Feel free to use or modify for your instruction or self-study.

deutsche-bundesbank-internship-2018-

R function including an extension of the EGHL test for cointegration for a monthly frequency.