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Mackenna Dsouza

Category Archives: Python

Artificial Intelligence, Data Cleansing, Deep Learning, ICR, Land Records, LSTM, Machine Learning, NLP, OCR, Python, Search, Surveyors, Technology, Title Research

DevStory #16: Image Enhancement, OCR / ICR , NLP, Full text search on information goldmine extracted from 900 million pages / 150 million documents

An information goldmine exists in about 150 million TIFF images containing information about natural resources in rural Texas. An easy way to extract and search this information using AI was developed as part of this prototype. The corpus generated results in options to build a significant number of value-added applications that benefit the real estate,…

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Commodity, Crude Oil, CTRM, Dev Story, ETRM, FAAS, Front Office, Natural Gas, Options, Python, Trading

DEVSTORY #13: Pricing CME Natural Gas American Options (ON) using options pricing functions as a service library

CME Natural Gas American Options (ON) priced using an American Call Option (Bjerksund Stensland 2002 approximation) model for live pricing and greeks. Low code, HA, hybrid cloud and kubernetes deployed containerized model accessed from any where through a https endpoint in a performant manner

Cassandra, Columnar Database, Commodity, CTRM, Curve Management, Deal Capture, Dev Story, ETRM, Front Office, MarkToMarket, Mid office, MTM, Natural Gas, Position Management, Projects, Python, Risk management, Technology, Trading

DEVSTORY #8: Greenfield natural gas portfolio mid-office profit & loss platform for startup natural gas trading desk

mid office required a system to capture trades, capture trader marks and report daily p&l to match trader estimates. An operational, reliable, cheap system was required for this USD 30 million dollar startup portfolio. The long term plan was to migrate to a traditional ETRM system as the portfolio grew to USD 120 million. Since the startup decided to move quickly, the time and cost of evaluating and implementing a traditional ETRM would set then back several months if not a year at best.

A quick comprehensive and complete solution implemented in 2 months was delivered that met needs of desk.

Accounting, Accounts Payable, Accounts Receivable, Back office, Dev Story, ETRM, FAAS, MarkToMarket, Mid office, MTM, Projects, Python, Risk management, Trading

DEVSTORY #14: Quantitative analysts rollout derivative pricing models globally to desktop pricers, ETRM, Accounting, and Risk systems in minutes not months

A low code, highly available, planet-scale, containerized, Kubernetes deployed, functions as a service approach, that allowed the quant and IT teams to roll out models using a Jenkins-based CI/CD process several times a day. Models were deployed as stateless functions called using simple HTTP get requests accessible from software clients such as spreadsheets, javascript, python, Java, C++, C#, and Golang programs. Several research quant groups could leverage these models from tools like R, SAS, and Matlab.