Java Market Data Handler for CME Market Data (MDP 3.0)
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Updated
Oct 25, 2022 - Java
Java Market Data Handler for CME Market Data (MDP 3.0)
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, Tr…
Perl module to create configuration editor with semantic validation
A minimalist, low-latency, HFT CME MDP 3.0 C++ market data feed handler implementing all required features
Graduated cylindrical shell CME model in Python
Perform fine-grained forecasting at the store-item level in an efficient manner, leveraging the distributed computational power of the Databricks Lakehouse Platform.
Bootstrap your large scale forecasting solution on Databricks with Many Models Forecasting (MMF) Project.
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.
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
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.
CME Arrival Time Prediction Using Convolutional Neural Network
FIX order manager client for fix order routing in C++ using QuickFIX engine can be used for Trading Technologies (TT) or CQG and others
Imandra Modelling Language CME MDP Model
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.
Risk tools for commodities trading and finance
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.
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
This repository provides the python-based code for Coronal Mass Ejection(CME) arrival forecast using Drag Based Model(DBM).
This repository provides a python code to infer morphological parameters of Coronal Mass Ejection using Cone model given by Xie et al.,2004.
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