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arfh

Accounts Receivable Forward Hedge

arima

2.6.3 Arima Time Series Modeling

arima-energy-prediction-v2

Predicting energy usage with the ARIMA algorithm in R

arima-lstm

a model for traffic flow forecast

arima-trading-strategy

Trading strategy for the S&P 500 index based on ARIMA models.

arima_models

Shiny examples from Hyndman & Athanasopoulos

arima_shiny

arima_shiny-1

arimatimeseries

arimaui

Shiny gadget/add in arimaUI to pick arima parameters

armmh

Accounts Receivable Money Market Hedge

arpc-conf-yves-hilpisch

article.eikonapi.python.equitypairtrading

Jupyter Notebook introducing Equity Pair Trading use case

article.eikonapi.python.evolutionofesgdisclosures

This Jupyter notebook examines the evolution of ESG disclosures by companies over time. It uses the universe of companies, for which Refinitiv has been collacting ESG data since 2002, and examines how the number of ESG metrics that these companies report on evolved between 2002 and now.

articles

Collateral for blog posts and articles

artifitial-neural-network-churn-modeling

Business Problem: Dataset of a bank with 10,000 customers measured lots of attributes of the customer and is seeing unusual churn rates at a high rate. Want to understand what the problem is, address the problem, and give them insights. 10,000 is a sample, millions of customer across Europe. Took a sample of 10,000 measured six months ago lots of factors (name, credit score, grography, age, tenure, balance, numOfProducts, credit card, active member, estimated salary, exited, etc.). For these 10,000 randomly selected customers and track which stayed or left. Goal: create a geographic segmentation model to tell which of the customers are at highest risk of leaving. Valuable to any customer-oriented organisations. Geographic Segmentation Modeling can be applied to millions of scenarios, very valuable. (doesn't have to be for banks, churn rate, etc.). Same scenario works for (e.g. should this person get a loan or not? Should this be approved for credit => binary outcome, model, more likely to be reliable). Fradulant transactions (which is more likely to be fradulant) Binary outcome with lots of independent variables you can build a proper robust model to tell you which factors influence the outcome. alt text Problem: Classification problem with lots of independent variables (credit score, balance, number of products) and based on these variables we're predicting which of these customers will leave the bank. Artificial Neural Networks can do a terrific job with Classification problems and making those kind of predictions. Libraries used: Theano numerical computation library, very efficient for fast numerical computations based on Numpy syntax GPU is much more powerful than CPU, as there are many more cores and run more floating points calculations per second GPU is much more specialized for highly intensive computing tasks and parallel computations, exactly for the case for neural networks When we're forward propogating the activations of the different neurons in the neural network thanks to the activation function well that involves parallel computations When errors are backpropagated to the neural networks that again involves parallel computation GPU is a much better choice for deep neural network than CPU - simple neural networks, CPU is sufficient Created by Machine Learning group at the Univeristy of Montreal Tensorflow Another numerical computation library that runs very fast computations that can run on your CPU or GPU Google Brain, Apache 2.0 license Theano & Tensorflow are used primarily for research and development in the deep learning field Deep Learning neural network from scratch, use the above Great for inventing new deep learning neural networks, deep learning models, lots of line of code Keras Wrapper for Theano + Tensorflow Amazing library to build deep neural networks in a few lines of code Very powerful deep neural networks in few lines of code based on Theano and Tensorflow Sci-kit Learn (Machine Learning models), Keras (Deep Learning models) Installing Theano, Tensorflow in three steps with Anaconda installed: $ pip install theano $ pip install tensorflow $ pip install keras $ conda update --all

asset-price-prediction_machine-learning

Works with any open, high, low, close prices. It works with QuantConnect CSV data downloaded by default.

asset-pricing

Asset Pricing using Python

asset-pricing-1

asset-pricing-models

Building the CAPM model

asset-pricing-predictor

assetcorr

:exclamation: This is a read-only mirror of the CRAN R package repository. AssetCorr — Estimating Asset Correlations from Default Data

assetliabilitysimulations

SCRS Asset Liability Simulations

assetpricing

assetpricing-1

assetpricing1

assetpricing2

assetpricing2-1

create assetPricing2

auquan-toolbox-python

Backtesting toolbox for trading strategies - DEPRECATED

auto-randomforest-kfold-function

Automatically executes Randomforest Function with k-fold.