Jupyter notebooks and data files of the new EDHEC specialization on quantitative finance (completed Aug 2022)

Coursera Specialization Courses about Investment Management with Python and Machine Learning

A Data Science/Machine Learning Project. According to Bolster , Global Fraud Index (as at June 2022) is at 10,183 and growing. This is high risk to businesses and customers transacting online. This indicates that traditional rules-based methods of detecting and combating fraud are fast becoming less effective. It becomes imperative for stakeholders to develop innovative means to make transacting online as safe as possible. Artificial intelligence provides viable and efficient solutions via Machine Learning models/algorithms. In this project, I trained a fraud detection model to predict online payment fraud using Blossom Bank PLC as case study. Blosssom Bank ( BB PLC) is a multinational financial services group, that offers retail and investment banking, pension management, assets management and payment services, headquartered in London, UK. Blossom Bank wants to build a machine learning model to predict online payment fraud. Here is the dataset used for this task. With this model, BB PLC will: Keep up with fast evolving technological threats and better prevent the loss of funds (profit) to fraudsters. Accurately detect and identify anomalies in managing online transactions done on its platforms which may go undetected using traditional rules-based methods. 3.Improve quality assurance thus retaining old customers and acquire new ones. This will increase credit/profit base. Improve its policy and decision making. Steps: 1.Loading necessary python libraries. Loading Dataset. Exploratory Data Analysis. Higlighting Relationships and insights. Data Transformation; Using resampling techniques to address Class-imbalace.. Feature Engineering. Model Training. Model Evaluation. Challenges: I encountered a number of challenges during coding which made me run into error reports. these were due to improper documentations, syntax, especially during feature engineering (one-hot encoding: 'fit.transform'). This aspect consumed most of my time I was able to solve these challenges by making extensive research and paying close attention to syntax. I was able to selve the encoding by using 'pd.get_dummies() and making some specifications in the methods.

Python SDK for LUSID by FINBOURNE, a bi-temporal investment management data platform with portfolio accounting capabilities.

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Jupyter Notebooks created for solving Graded Quizzes part of Investment Management with Python and Machine Learning Specialization by EDHEC Business School (Sep 2020)

This is the Python examples repository for LUSID by FINBOURNE, a bi-temporal investment management data platform with portfolio accounting capabilities.

Investment Management with Python and Machine Learning Specialization by EDHEC Business School

Investment Management with Python and Machine Learning

Investment management with Python and Machine Learning

Investment Management with Python and Machine Learning Specialisation, EDHEC Business School | Coursera

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Financial Management Tool is a Python Django-based web application that helps users track and manage their income, expenses, debts, and investments. It provides an intuitive interface for recording and analyzing financial transactions, along with visualizations and search/filtering capabilities.

Sample code from the book <Quantitative Finance with Python: A Practical Guide to Investment Management, Trading and Financial Engineering> by Chris Kelliher

The Portfolio Management System Project is created based on Python, Django, and SQLITE3 Database. The Stocks, funds, insurances, and other investments can all be managed with this system.

Recent business research interests concentrated on areas of future predictions of stock prices movements which make it challenging and demanding. Researchers, business communities, and interested users who assume that future occurrence depends on present and past data, are keen to identify the stock price prediction of movements in stock markets. . Predicting market prices are seen as problematical, and as explained in the efficient market hypotheses (EMH) that was put forward by Fama (1990), the EMH is considered as bridging the gap between financial information and the financial market; it also affirms that the fluctuations in prices are only a result of newly available information; and that all available information reflected in market prices. We applied k-nearest neighbour algorithm in order to predict stock prices for a sample of five major companies listed on the NASDAQ stock market to assist investors, management, decision makers, and users in making correct and informed investments decisions. According to the results, the k-NN algorithm is mildly robust with a good accuracy; consequently, the results were rational and also reasonable. In addition, depending on the actual stock prices data; the prediction results were close and fairly parallel to actual stock prices. We implemented the k-NN algorithm from scratch on python 2.7 to conduct the experiments for the project. k-NN is an instance-based, competitive learning, and lazy learning algorithm. Instance based algorithms, sometimes called memory-based learning, are those algorithms that, instead of performing explicit generalization, use the instances seen in the training as a comparison standard. For k-NN, the entire training dataset is the model. When a prediction is required for an unseen data instance, the k-NN algorithm will search through the training dataset for the k-most similar instances. k-NN is a competitive learning model because a majority vote is performed among the selected k records to determine the class label and then assigned it to the query record. k-NN is considered a lazy learning that does not build a model or function previously, but yields the closest k records of the training data set that have the highest similarity to the test (i.e., query record). The prediction attribute of the most similar instances is summarized and returned as the prediction for the unseen instance. The similarity measure is dependent on the type of data. For real-valued data, the Euclidean distance can be used. Other types of data such as categorical or binary data, Hamming distance can be used. In the case of regression problems, the average of the predicted attribute may be returned. In the case of classification, the most prevalent class may be returned.

Coursera

Python-based automated software for the management of users investments/funds, their liquidation and rewards with Company acting as Custodian - through BitShares blockchain.

Course I did back in 2020

My first steps to combine DS and finance

EDHEC Business School

The Data Science and Machine Learning for Asset Management Specialization has been designed to deliver a broad and comprehensive introduction to modern methods in Investment Management, with a particular emphasis on the use of data science and machine learning techniques to improve investment decisions.By the end of this specialization, you will have acquired the tools required for making sound investment decisions, with an emphasis not only on the foundational theory and underlying concepts, but also on practical applications and implementation. Instead of merely explaining the science, we help you build on that foundation in a practical manner, with an emphasis on the hands-on implementation of those ideas in the Python programming language through a series of dedicated lab sessions.

This repository contains the notebooks and data used for the Investment Management with Python and Machine Learning Specialization. Also it contains my projects and quizes

Investment Management Specialization with Python and ML offered by EDHEC Business School in Coursera.

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Notes from the course "Investment Management with Python and Machine Learning Specialization"

You find here some of my projects based on the Investment Mangagement course by EDHEC Business School - In progress ...

InvestoMate is an AI-driven personal wealth management platform that provides portfolio management, smart spending insights, and investment recommendations. The platform combines a robust React-based front-end with a Python-powered back-end to deliver personalized financial insights and actionable investment recommendations.

Investment Management with Python and Machine Learning Specialization

A simple Investment Management System with MySQL and Python 🐍💰 Manage customers & investments easily | Python + MySQL project 📊 Investment tracker with customer reports, charts, and database integration 🚀