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ppnn

post-processing experiments with neural networks

pydata2016_tutorial-arima-jeffrey-yau

pydata_2018-tidy

PyData 2018 tutorial for tidying data

pyfolio

Portfolio and risk analytics in Python

pyquantregforest

This package aims to compute conditional quantiles using random regression forests from the scikit-learn library.

pyquantrf

Here is a [quantile random forest](http://jmlr.org/papers/v7/meinshausen06a.html) implementation that utilizes the [SciKitLearn](https://scikit-learn.org/stable/) RandomForestRegressor. This implementation uses [numba](https://numba.pydata.org) to improve efficiency.

qp

Quantile Parametrization for probability distribution functions module

qr-hfdtd-rnn

Quantile Regression of High-Frequency Data Tail Dynamics via a Recurrent Neural Network

qrnn_bayesian_remote_sensing_retrievals

A validation study for the application of quantile regression neural networks to Bayesian remote sensing retrievals

qrnn_cloud_top_pressure

Predicting cloud top pressure from MODIS observations using QRNNs.

quantile-regression-tensorflow

Tensorflow implementation of deep quantile regression

quantile_detector

Qauntile autoregressive neural network for time series anamoly detection.

quantile_regression

Quantile regression using keras multi output

quantnn

Quantile regression neural networks

quantregforest

R package - Quantile Regression Forests, a tree-based ensemble method for estimation of conditional quantiles (Meinshausen, 2006).

regime-switching-copula-rsc-toolbox

A selection of MATLAB frunctions to estimate regime switching copula models

robgarchboot

sedumi

SeDuMi: A linear/quadratic/semidefinite solver for Matlab and Octave

sfe-ready

Quantnet: validated SFE quantlets

stock-index-prediciton

stock-prediction-models

Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations

stock_prediction_ai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

stock_price_direction_prediction_neural_networks

Using neural networks to predict stock price direction

tenet

TENET: Tail-Event driven NETwork Risk

w_zhang_qrnn

QRNN (Quantile Regression Neural Network) Keras version

xfg3

Q for Applied Quantitative Finance (3rd edition)

xu_probabilistic_load_forecasting

Probabilistic Load Forecasting