C++ interfaces used to communicate with Roq's market gateways.

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)

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

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

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.

Accelerator for customer incentive investment using causal inference techniques

This is a Python 3.10+ trading bot that monitors CME E-mini S&P 500 futures (ES) vs SPDR S&P 500 ETF (SPY) and trades a simplified cash-and-carry / reverse cash-and-carry signal when the futures price deviates from a theoretical fair value:

Perl module to create configuration editor with semantic validation

Graduated cylindrical shell CME model in Python

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.

Create advanced customer segments to drive better purchasing predictions based on behaviors. Using sales data, campaigns and promotions systems, this solution helps derive a number of features that capture the behavior of various households. Build useful customer clusters to target with different promos and offers.

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

Risk tools for commodities trading and finance

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

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

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.