karagul Goto Github PK

happiness-level-prediction-model-

Used model selection and stepwise regression techniques to find the best fitting model in R to predict happiness levels

harvardx-capstone-project-nyc-house-price-forecast

harvest-fund-management

heavydutyfixedincomepricing_juliacon2017

Slides for the lightning talk at JuliaCon 2017

hedge-fund-stock-market-analysis

Developed a deep learning model that allows trading firms to analyze large patterns of stock market data and look for possible permutations to increase returns and reduce risk. Trained the model using a Multilayer Perceptron Neural Network on a vast set of features that influence the stock market indices. Performed technical analysis using historical stock prices and fundamental analysis using social media dat

hedge_fund_strategy

This is for the paper on hedge fund strategies

hello-world

MyFiles

heptet

Pair Trading - Reinforcement Learning - with Oanda Trading API

heston-model

Research work on Implied volatility of Stock Prices

hft

compute section volatility of high frequency trading data using pyspark

hft-pairs-trading

High Frequency Pairs Trading Based on Statistical Arbitrage (Python) :moneybag:

hierarchical-portfolio-construction

The implementation of Hierarchical Risk Parity algorithm

hierarchical-risk-parity

This is the implementation for Hierarchical Risk Parity approach to portfolio optimization

hierarchical-risk-parity-approach

Hierarchical Risk Parity Approach in portfolio optimization

high-claimers

A banking insurance dataset where premium holders are categorized by value of claims. A value greater than median can be study of investigation to ascertain risk. Logistic regression with L2 regularization is deployed, further an unsupervised classification model is trained to identify premium holders with high risk. The model is cross validated with custom labels.

high-frequency-trading-model-with-ib

A high-frequency trading model using Interactive Brokers API with pairs and mean-reversion in Python

high-frequency-trading-simulation-system

HFT, A high-frequency trading simulation package in R

high-volatility-momentum-trading

An algorithm replicating a manual trading strategy I invested in for 2018

highfrequencytrading

A repository for sharing trading simulation codes

hilpisch

time series data modeling

home-credit

Home-Credit Default Risk using Deep Learning

home-credit-default-risk-

Credit risk is the probability that companies or individuals will be unable to make the required payments on their debt obligations. Performed prediction on more than 3,00,000 individuals. Model achieved AUC metric of 0.786 and predicted correctly almost 81% of people who will have difficulty paying loan.

home-credit-default-risk-baseline-model

home-loan-default-

In this project, we’ll be focusing on a specific type of risk called Credit or Default Risk, which has both systemic and unsystemic drivers. The main point is that the drivers of default risk can be measured and analyzed for patterns related to default. As a result, the probability of default for a person or an institution is not random. This is where machine learning can help.

home_credit_default_risk

First_deep_learning_project_

home_credit_default_risk-1

https://www.kaggle.com/c/home-credit-default-risk#description

home_credit_risk_rmarkdown

homecreditdefaultrisk

Home Credit Default Risk Competition on Kaggle

hotel-management-dashboard

basic python console dashboard

hotel-price-recommender

School Project - build tool to recommend profit-maximizing future pricing strategies for hotel owners.