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automated_report

automatically_generate_pdf_and_send_email

This script generate the PDF report of the data and sends the email to the given address.

automation-post

📝 A blog post about report generation and automation in python

autoreporting

autoreportr

Auto Generate PDF Reports With R, Markdown, and Bash

autotrader_forex

A Python-based development platform for automated trading systems - from backtesting to optimisation to livetrading.

av_mckinsey_time_series_prediction

Code for time series prediction - Mckinsey competion held on Analytics Vidya

avq

Excel-Add-In (UDF) to get stock data from the Alpha Vantage API

awesome-algorithmic-trading

A curated list of awesome algorithmic trading frameworks, libraries, software and resources

awesome-quant

A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)

awesome-r

A curated list of awesome R packages, frameworks and software.

awesome-rshiny

An awesome R-shiny list!

awesome-streamlit

The purpose of this project is to share knowledge on how awesome Streamlit is and can be

awesomermarkdownwordreport

b76

R package to compute the Implied Volatility and plot the Volatility Skew using the Black-76 option pricing model

backpropagation-with-kfold

backtest

Backtesting code for portfolio management

backtest.rsi3

Backtest RSI(3) for SPY with Zipline & Quantopian

backtesting.py

:mag_right: :chart_with_upwards_trend: :snake: :moneybag: Backtest trading strategies in Python.

backtestvar

Shiny app for Value at Risk (VaR) Backtesting

backtrader

Python Backtesting library for trading strategies

backtrader-mql5-api

Python Backtrader - Metaquotes MQL5 - API

baktst_org

BakTst_Org is a backtesting system for quantitative transactions.

balm-studium

Bank Asset Liability Management Application

bank

A painfully simple bank simulation

bank-1

Simple simulation of bank loan

bank-case-study

Big Data from the world bank is extracted, organized and analyzed. https://datacatalog.worldbank.org/dataset/world-development-indicators

bank-loan-default-prediction

Bharat Bank is a banking institution. Every year, a large number of people take home loans from Bharat Bank, and many of them default on their loans which results in huge losses for the bank. In the data file attached are the details of some of the loan applications given earlier along with the details of whether the borrower was defaulted or not. Based on the given dataset, predicted whether the applicant will default or not.

bank-loss-warning-model

Bank churn refers to the bank's customers terminating all business in the bank and selling them. However, in actual operations, for a specific business unit, bank customer churn can be positioned as a specific business termination behavior. The loss of customers in commercial banks is more serious, and the turnover rate can reach 20%. The cost of getting a new customer is five times that of maintaining an old customer. Therefore, it is especially important to dig out the information that has an impact on the loss from the massive customer transaction data and establish an efficient customer turnover warning system. The main reasons for customer loss are: price loss, product loss, service loss, market loss, loss of promotion, technology loss, and political loss. There are times when the price is caused by customer churn, but in fact multiple factors work together to lead to the loss of customers. For example, unrealistic profit targets, unreasonable price structure, too complicated business processes, and unreasonable organizational structure. The basic methods of maintaining customer relationships: tracking systems, product follow-up, sales expansion, maintenance access, and mechanism maintenance. Therefore, it is necessary to establish a quantitative model to reasonably predict the risk of loss of the customer group. For example: commonly used risk factors, the number and type of products held by customers, the age and gender of customers, the impact of geographic regions, the impact of product categories, the time interval of transactions, the means of promotion, and so on. Based on these factors and historical data of customer churn, the existing customers are predicted to be lost, and different maintenance methods are provided for different customer groups, thereby reducing the customer churn rate.

bank-simulation

A simulation of a bank during a day taking into account many variables