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devdataprod_courseproject

Course project for the Developing Data Products course through John Hopkins Bloomberg School of Public Health.

develop

generic project files

develop-excel-london-2018

Slides and content from the Python in Excel talk at DevelopExcel London 2018

digit_recognition_bestmodel

Predicting the Handwritten Digit with the best Classification model choosen by KFold cross validation technique

digitaltrader

An automated and adjustable high frequency cryptocurrency trading bot that connects to the Binance exchange and takes advantage of price trends, dips, volatility, etc.

dijkstra-algorithm-applied-in-profit-optimization-in-metro-system

use Dijkstra algorithm to find the best metro route scenario which is the most profitable for a metro company and satisfies the travel demand of consumers

direct-debit-churn

Customer churn model using Survival Analysis

dissertation_implied-volatility-surface-parameterization

Disseration for M.S. in Computer Science of class 2018 at HKU

distiller

Neural Network Distiller by Intel AI Lab: a Python package for neural network compression research. https://nervanasystems.github.io/distiller

django-beginners-guide

A Complete Beginner's Guide to Django - Code Samples

django-financial-monitor

This a demo web application for a thesis project

django-newsletter

An email newsletter application for the Django web application framework, including an extended admin interface, web (un)subscription, dynamic e-mail templates, an archive and HTML email support.

django-portfolio

A pluggable django application for really straight-forward portfolio management.

django-propaganda

Management of simple newsletters

django-report-builder

This is a github mirror for django-report-builder which is hosted on Gitlab. Django Report Builder is a GUI for Django ORM. Build custom queries and display results. Targets sys admins and capable end users who might not be able to program.

django-worldbank

Django model for storing World Bank indicator data.

django_invest

Investment performance application written in Python using the Django web framework

djangofullstackbootcamp_course

This repository has the files I produced from the exercises of the course Python and Django Full Stack Web Developer Bootcamp by Jose Portilla on Udemy (https://goo.gl/C8btdy)

dmac-btc-trading-example-with-tuning

I dug up some code that I played with a while ago as a toy to test out ideas. It takes a DMAC, Dual Moving Average Crossover and buys when the short average crosses the long up and sells on the reverse of that. It auto-optimizes on backtest data to pick the best combination of the length of the averages, using brute force search. This is a real simple strat, I would not advise trading with something like this without additional work and risk management, stops for instance. But, I did it as a check on the timing intervals of the BTC market at the time. This version of it runs through a years worth of data tunes and the spits out the results for various parameter tunings and prints out the HODL gain, as if you bought on Jan 1 and held for the year. The strat assumes $100000 in at Jan 1, so look at that as your 100% mark to see what the strat would gain for the year. It is revealing in that it shows that sometimes buy and hold is the winner other times the strat wins. Best Bank is the best amount it makes with the short and long averages (Averages are in terms of days in this example)adjusted as seen. Count is just that, a count of how many combinations it has tried. It prints out the best ones within the range. I am not blocking the averages from going upside down either. For example for 2018 the winner has a short avg of 35 days and a long of 30, it's "backwards" from the normal idea of DMAC but makes the best gains on backtest. I am posting this here for educational purposes for people that might be new-ish to trading just to show a comparison between a dirt simple strat and HODL. Notice how buy and hold beats the strat by a lot in 2011,2013,2017 and the strat beats HODL for 2014 and 2018 in this example. For 2011,2012 and 2017, the strat is a dud in that it doesn't find decent parameters for the average to make good gains. All this is just food for thought.

dnsc6211-worldbank

Individual project using World Bank data to examine GDP Growth versus Debt

doc2vecstockpredictionnlp

Predict the stock index by using financial data (prices/index) and news

dovizkurlari

TCMB'dan Tüm Döviz kurlarını alan Python 2 & 3 kodu

dowd

:exclamation: This is a read-only mirror of the CRAN R package repository. Dowd — Functions Ported from 'MMR2' Toolbox Offered in Kevin Dowd's Book Measuring Market Risk

downloadworldbank

Interactive application for programmatic data retrieval from Worldbank database

dpkg-wb-privatization

Worldbank Privatization Database

dream-economic-impact

The code used to generate an economic impact analysis of passing the dream act.

driving-factors-behind-loan-default

You work for a consumer finance company which specialises in lending various types of loans to urban customers. When the company receives a loan application, the company has to make a decision for loan approval based on the applicant’s profile. Two types of risks are associated with the bank’s decision: If the applicant is likely to repay the loan, then not approving the loan results in a loss of business to the company If the applicant is not likely to repay the loan, i.e. he/she is likely to default, then approving the loan may lead to a financial loss for the company The data given below contains the information about past loan applicants and whether they ‘defaulted’ or not. The aim is to identify patterns which indicate if a person is likely to default, which may be used for taking actions such as denying the loan, reducing the amount of loan, lending (to risky applicants) at a higher interest rate, etc. In this case study, you will use EDA to understand how consumer attributes and loan attributes influence the tendency of default

drl-portfolio-management

CSCI 599 deep learning and its applications final project

droughtecon

County level climate data for economic drought impact analysis

ds-cross-validation-kfolds