durgarao-guttula Goto Github PK

300-python-interview-questions-and-solutions

amibroker

automatic-trading-using-artificial-intelligence-machine-learning-on-indian-stock-market-_zerodha

Online trading using Artificial Intelligence Machine leaning with python on Indian Stock Market

beadtracker

Complete magnetic tweezers setup code in LabView as used in the Nynke Dekker Lab (TUDelft, Netherlands), using qtrk for image processing.

bioinformatics-with-python-cookbook-second-edition

Bioinformatics with Python Cookbook Second Edition, published by Packt

comp-phys

Computational Physics

complexin

Codes used in the article: "Focused clamping of a single neuronal SNARE complex by complexin under high mechanical tension"

computational-stat-mech

Algorithms used in Statistical Mechanics

computational-statistical-experiments

Courses in Computational Statistical Experiments through Matlab and SageMath

computationalphysics

For best reading of the website use

courses

Course materials for the Data Science Specialization: https://www.coursera.org/specialization/jhudatascience/1

datasciencecoursera

datasharing

The Leek group guide to data sharing

deep-learning-material

ebfret-gui

ebFRET (GUI version) – Empirical Bayes HMM analysis for multiple single-molecule FRET time series

gmx_tutorials_livecoms

LiveCoMS GROMACS Tutorials Paper

gotolong

GotoLong Investor Stock Advisor: Indian Stock Market BSE 500 & NSE 500

gramm

Gramm is a complete data visualization toolbox for Matlab. It provides an easy to use and high-level interface to produce publication-quality plots of complex data with varied statistical visualizations. Gramm is inspired by R's ggplot2 library.

gromacs_scripts

This repository creates GROMACS scripts for the tutorials offered by bevanlab (http://www.bevanlab.biochem.vt.edu/Pages/Personal/justin/gmx-tutorials/) .

kyotoux-009x

legacy-bhmm-force-spectroscopy-manuscript

Bayesian hidden Markov models for analysis of single-molecule trajectory data

machine-learning

Practical Full-Stack Machine Learning

magnetic-tweezers

market_profile

market_profile-1

How to make simple market profile charts in seaborn.

md_python

The repository with the lecture and tutorial for CPS teaching grant

models-for-intraday-trading-volume-prediction

Having effective intraday forecast for the level of trading volume is of vital importance to algorithmic trading and portfolio management since it attempts to minimize transaction costs by optimally scheduling and placing. The purpose of this project is to create dynamic statistical models of intraday trading volume prediction (in Python). By assuming the stable U shape distribution of intraday trading volume, we apply Deterministic blend, Lognormal Bayesian, Kalman filter and ARIMA model to estimate and generate out of sample forecast on 12 US equity sector ETFs. Results show that some of the proposed methods are able to obviously outperform common volume forecasting methods.

modules

mscl_survival

Store for all data analysis code for probing the relationship between MscL channel copy number and survival under shock.

nse-data

NSE Data [Both Live tick data and per minute data] is uploaded in this repository. NSE live tick data uploaded every day after market ends.