in_python_

Hands on Machine Learning with PYTHON

inflationchart

The stock market inflation adjusted for the US-money supply

insight

Repository for Project Insight: NLP as a Service

integrated-credit-modeling-ccar-to-cecl

• Calculated the Capital Ratios,Risk Weighted Assets, Capital requirement over projected time horizon for both CCAR and CECL. • Created PD model using Time Series,Logistic regression,Random Forests,Neural Networks,Markov transition Matrix. • Software used various SAS 9.4, Python.

interest-rate-modelling-baruch

Interest Rate Models, Baruch group project

intermediate-macro

This repository contains material for a one-semester course in intermediate macroeconomics.

introduction-to-python-numerical-analysis-for-engineers-and-scientist

Introduction to Python: Numerical Analysis for Engineers and Scientist. In 2017, Python became the world's most popular programming language. This course covers the basic syntax, linear algebra, plotting, and more to prepare students for solving numerical problems with Python.

ipythonscripts

Tutorials about Quantitative Finance in Python and QuantLib: Pricing, xVAs, Hedging, Portfolio Optimisation, Machine Learning and Deep Learning

irb

The Internal Ratings-Based Approach

job_code_test

kalman-and-bayesian-filters-in-python

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

long-term-stock-price-growth-prediction-using-nlp-on-10-k-financial-reports

A 10-K FInancial Report is a comprehensive report which must be filed annually by all publicly traded companies about its financial performance. These reports are filed to the US Securities Exchange Commission (SEC). This is even more detailed than the annual report of a company. The 10K documents contain information about the Business' operations, risk factors, selected financial data, the Management's discussion and analysis (MD&A) and also Financial Statements and supplementary data. I have been expected to build an NLP pipeline that ingests 10-K reports of various publicly traded companies and build a machine learning model which can uncover the hidden signals to predict the long term stock performance of a company from the 10-K docs using the ‘Loughran McDonald Master Dictionary’. The Dictionary contain words that are specifically curated in the context of financial reports

lstm_option_pricing

Projeto de Pesquisa para a obtenção do título de Mestre em Engenharia Elétrica e Computação.

machine-learning-for-algorithmic-trading-second-edition

Machine Learning for Algorithmic Trading, Second Edition - published by Packt

machine-learning-for-asset-managers

Machine Learning for Asset Managers

machine-learning-for-trading

Code for Machine Learning for Algorithmic Trading, 2nd edition.

machine_learning_examples

A collection of machine learning examples and tutorials.

mall-customer-segmentation-data

Leveraging on Unsupervised Learning Techniques (K-Means and Hierarchical Clustering Implementation) to Perform Market Basket Analysis: Implementing Customer Segmentation Concepts to score a customer based on their behaviors and purchasing data

market-neutral-model

A Market Neutral Equity Model based on Barra's model

math-finance-cheat-sheet

Mathematical finance cheat sheet.

ml-hft

High frequency trading (HFT) strategies built for futures using machine learning and deep learning techniques.

ml-quant-interview-prep

Preparation material and resources for the ML (including DL) and Quant Research interviews

ml-trading

ml_optimal_trading

Collection of numerical methods for high frequency data, in Python notebooks

mlfinance

modeling-volatility

Modeling volatility project for ODSC East 2019

mth9815-asset-backed-security-modeling

A project of implementing, modeling, and simulating asset-backed securities.

mth9879-market-microstructure

mth9879-market-microstructure-models

A collection of homeworks of market microstructure models.

multi-factor-model

Build a statistical risk model using PCA. Optimize the portfolio using the risk model and factors using multiple optimization formulations.