predsci/sunRunner3D-manuscript

Repository containing codes and scripts to reproduce the sunRunner3D manuscript

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NDelventhal/cot_reports

cot_reports is a Python library for fetching the Commitments of Trader reports of the Commodity Futures Trading Commission (CFTC). The following COT reports are supported: Legacy Futures-only, Legacy Futures-and-Options Combined, Supplemental Futures-and-Options Combined, Disaggregated Futures-only, Disaggregated Futures-and-Options Combined, Traders in Financial Futures (TFF) Futures-only and Traders in Financial Futures (TFF) Futures-and-Options Combined.

Language: Python - Size: 72.3 KB - Last synced at: 24 days ago - Pushed at: about 1 year ago - Stars: 151 - Forks: 41

bbcho/risktools-dev

Risk tools for commodities trading and finance

Language: Jupyter Notebook - Size: 46.3 MB - Last synced at: 12 days ago - Pushed at: 5 months ago - Stars: 28 - Forks: 4

databricks-industry-solutions/causal-incentive

Accelerator for customer incentive investment using causal inference techniques

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dod38fr/config-model

Perl module to create configuration editor with semantic validation

Language: Perl - Size: 20.4 MB - Last synced at: 3 days ago - Pushed at: 5 months ago - Stars: 59 - Forks: 12

alexpinel/CatView

CME SHARP Active region visualisation tool

Language: JavaScript - Size: 646 KB - Last synced at: 2 days ago - Pushed at: 5 months ago - Stars: 3 - Forks: 0

epam/java-cme-mdp3-handler

Java Market Data Handler for CME Market Data (MDP 3.0)

Language: Java - Size: 100 MB - Last synced at: 4 months ago - Pushed at: over 2 years ago - Stars: 78 - Forks: 31

johan12345/gcs_python

Graduated cylindrical shell CME model in Python

Language: Python - Size: 3.18 MB - Last synced at: 8 days ago - Pushed at: 8 months ago - Stars: 18 - Forks: 10

mariamkauchersusefuwa/CME

Size: 1000 Bytes - Last synced at: 9 months ago - Pushed at: 9 months ago - Stars: 0 - Forks: 0

vincent212/FIX-Order-Routing-Client

FIX order manager client for fix order routing in C++ using QuickFIX engine can be used for Trading Technologies (TT) or CQG and others

Language: C++ - Size: 128 KB - Last synced at: 9 months ago - Pushed at: 9 months ago - Stars: 11 - Forks: 5

pachterlab/biophysics

Repository for Pachter Lab Biophysics

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databricks-industry-solutions/media-mix-modeling

Language: Python - Size: 34.6 MB - Last synced at: 9 months ago - Pushed at: 9 months ago - Stars: 6 - Forks: 2

yuzokune/CME

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burlingamegordanaxu/CME

Size: 1000 Bytes - Last synced at: 10 months ago - Pushed at: 10 months ago - Stars: 0 - Forks: 0

hileshammur/CME

Size: 1000 Bytes - Last synced at: 10 months ago - Pushed at: 10 months ago - Stars: 0 - Forks: 0

vincent212/CME-Market-Data-Handler

A minimalist, low-latency, HFT CME MDP 3.0 C++ market data feed handler implementing all required features

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astronish16/Cone_Model_for_CME

This repository provides a python code to infer morphological parameters of Coronal Mass Ejection using Cone model given by Xie et al.,2004.

Language: Python - Size: 8.96 MB - Last synced at: 2 months ago - Pushed at: over 3 years ago - Stars: 3 - Forks: 1

cyschneck/CME_Sunbody-I-Used-to-Know

CME CNN

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npredey/CMEParser

Language: Python - Size: 20.4 MB - Last synced at: about 1 month ago - Pushed at: almost 6 years ago - Stars: 3 - Forks: 0

CJuanvip/CMEOptions

Analyze the CME grain options markets in python

Language: Python - Size: 298 KB - Last synced at: about 1 year ago - Pushed at: over 4 years ago - Stars: 6 - Forks: 1

imandra-ai/cme-mdp

Imandra Modelling Language CME MDP Model

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pupak250194/Physical-Methods-of-Biology---Enzyme-Reactions

Project for Physical Methods of Biology based on Kim JK, Tyson JJ (2020) Misuse of the Michaelis–Menten rate law for protein interaction networks and its remedy. PLoS Comput Biol 16(10): e1008258. https://doi.org/10.1371/journal. pcbi.1008258.

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databricks-industry-solutions/toxicity-detection-in-gaming

Build a lakehouse for all your gamer data and use natural language processing techniques to flag questionable comments for moderation.

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astronish16/DBM--Drag-Based-Model

This repository provides the python-based code for Coronal Mass Ejection(CME) arrival forecast using Drag Based Model(DBM).

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databricks-industry-solutions/customer-er

Translating text attributes (like name, address, phone number) into quantifiable numerical representations Training ML models to determine if these numerical labels form a match Scoring the confidence of each match

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manves/CrackMapexec-6

Crackmapexec 6.0.0 Bane Edition Re-upload

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yiminking/CME-CNN

CME Arrival Time Prediction Using Convolutional Neural Network

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databricks-industry-solutions/wide-and-deep

Build a wide-and-deep recommender with collaborative filters that takes advantage of patterns of repeat purchases to suggest both previously purchased and related products.

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databricks-industry-solutions/real-time-bidding

From display to video, the value of an impression can only be realized if an ad is viewed by a user. Therefore, when using programmatic advertising to buy inventory, it’s important to take viewability into account. In this Solution Accelerator, learn how to predict ad viewability to optimize your real-time bidding strategy.

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databricks-industry-solutions/multi-touch-attribution

Connect the impact of marketing and your ad spend to sales. Efficiently pinpoint the impact of various revenue-generating marketing activities to understand what works best. Focus on the best-performing channels to optimize media mix and drive revenue.

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sambacha/CME-iLink3

CME iLINK3 Connectivity

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databricks-industry-solutions/survival-analysis

Survival analysis is a collection of statistical methods used to examine and predict the time until an event of interest occurs. In this Solution Accelerator, learn how to use different survival analysis techniques for predicting churn and calculating lifetime value.

Language: Python - Size: 73.2 KB - Last synced at: over 1 year ago - Pushed at: almost 2 years ago - Stars: 4 - Forks: 3

databricks-industry-solutions/fine-grained-demand-forecasting

Perform fine-grained forecasting at the store-item level in an efficient manner, leveraging the distributed computational power of the Databricks Lakehouse Platform.

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databricks-industry-solutions/survival

Preempt churn with the Databricks Solution Accelerator for predicting subscriber attrition. Learn how to analyze behavioral data to identify subscribers with an increased risk of cancellation. Then use machine learning to quantify the likelihood to churn as well as indicate which factors explain that risk.

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databricks-industry-solutions/propensity

Get started with our Solution Accelerator for Propensity Scoring to build effective propensity scoring pipelines that: Enable the persistence, discovery and sharing of features across various model training exercises Quickly generate models by leveraging industry best practices Track and analyze the various model iterations generated

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a-sfrank/macro-tpt

Macro-tpt: A program to quantify macrophage phenotype transitions in a stochastic model by applying a clustering technique and transition path theory

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databricks-industry-solutions/video-streaming-qoe

Increase viewer retention through data-driven engagement strategies: analyze both streaming and batch data sets to ensure a performant streaming content experience that drives engagement and loyalty.

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Clutchisback1/GIT_THEM

Just a quick and dirty little script import all the github goodies I like to play with.

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databricks-industry-solutions/campaign-effectiveness

Identifying Campaign Effectiveness For Forecasting Foot Traffic

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RosesHaveThorns/LASCOdownloader

Download images from the LASCO coronagraphs for CME analysis

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Alek-S/Derivative-Exchange-Lookup

Financial derivative exchange lookup by market code

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CME-OS/cme

Campaigns Made Easy - Open Source Email Campaign Sysetm

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DexterHaslem/cme-tools

various scripts to parse CME group end of day settlements and such

Language: Python - Size: 14.6 KB - Last synced at: about 2 years ago - Pushed at: almost 7 years ago - Stars: 0 - Forks: 0

asaxena2019/Coronal-Mass-Ejections-Year-1

A computational model predicting the physical behavior of coronal mass ejections

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asaxena2019/Modelling-the-Forecast-of-Solar-Energetic-Particle-Events-Year-2

A computational model predicting the physical behavior of solar energetic particle events

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danielkrizian/q4q Fork of psaris/q4q

Source Code for "Q for Quants"

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