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…

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

Perl module to create configuration editor with semantic validation

A minimalist, low-latency, HFT CME MDP3.0 C market data feed handler and pcap file reader (MDP 3.0)

Risk tools for commodities trading and finance

Accelerator for customer incentive investment using causal inference techniques

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

Graduated cylindrical shell CME model in Python

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.

Imandra Modelling Language CME MDP Model

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

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

CME Arrival Time Prediction Using Convolutional Neural Network

CME iLINK3 Connectivity

Use futures symbols to search and get their contract specs From CME website

Analyze the CME grain options markets in python

Repository for Pachter Lab Biophysics

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.

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