Omotola Olasope

Portrait of Omotola Olasope

Transaction Cost Analysis (TCA) Trade Desk Specialist at Bloomberg, serving EMEA and drawing on a unique background in Data Engineering, Security Intelligence, and Cross-Domain Analytics. I turn market complexity into clarity for investors and business leaders.

  • TCA
  • Commodities
  • The Terminal
  • Python
  • AWS
  • Bloomberg BQuant
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Projects

LNG netback app showing a world map with shipping routes from Bonny Island to ranked destinations, and a table of net all in prices per MMBtu

Commodities, LNG and Freight Analytics

Interactive LNG Arbitrage and Netback Pricing App

An interactive LNG arbitrage app that prices cargoes from a selected origin to 40+ destinations, calculating netback per MMBtu across gas, freight, fuel, port, carbon, financing and FX costs.

What it does: ranks every destination on a single compute, then lets you interrogate the result through route comparison, spread matrices, forward curves, netback term structure, carbon detail and freight breakeven views.

Value add: one click destination ranking, fully transparent cost attribution so every dollar of the netback is traceable, replaces the manual spreadsheets desks maintain by hand, and enables live what if scenario testing without disturbing the base case.

Built for: LNG traders and analysts, commercial and origination teams, shipping and chartering desks, and market analysts.

  • Python
  • Freight Economics
  • Scenario Modelling
  • Geospatial
Contact me for a walkthrough
Campaign coverage dashboard showing target counts, a coverage donut split into complete, in progress and not started, and activity tiles for reps and touches

Sales Analytics and Coverage Intelligence

Campaign Coverage Dashboard: Turning Raw Call Notes Into Client Coverage

A dashboard for tracking sales campaigns. Each campaign carries a list of clients the team is meant to reach. The app takes the raw call and visit notes our reps log every day, matches them against those client lists, and shows how much of each campaign has actually been covered.

What it answers: who has been contacted and who has not, how they were reached (call, in person or virtual), and by which rep. Coverage is broken down into complete, in progress and never touched, with activity measured across the campaign window.

Why it matters: it turns tens of thousands of scattered, unstructured notes into a clear picture of progress, so team leaders can see the gaps while there is still time to act on them rather than after the campaign closes.

  • Python
  • Entity Matching
  • Text Processing
  • Dashboarding
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Commitments of Traders analytics dashboard on Bloomberg BQuant showing positioning extremes and trader category breakdowns

Commodities & Derivatives Analytics

Commodities and Commitment of Traders (COT) Analytics Platform on Bloomberg BQuant

I built a comprehensive multi tab analytical dashboard on Bloomberg's BQuant platform that transforms CFTC Commitments of Traders data into actionable market intelligence for commodity traders and portfolio managers.

Market Positioning & Sentiment: Multi-commodity COT snapshot with Z-score extremes, long/short ratios, and trader category breakdowns (Commercial, Managed Money, Producers) with color-coded statistical significance.

Futures Curve Analysis: Interactive term structure dashboard featuring normalized forward curves, calendar spreads, roll yield calculations, and automated regime classification (Contango/Backwardation with intensity scoring).

Advanced Analytics: Market regime detection via Hurst exponents, volatility analysis (IV vs RV), position dynamics tracking, 5-year seasonality patterns, cross-commodity correlations, and notional value exposure analysis.

  • Bloomberg BQuant
  • Python
  • CFTC Data
  • Statistical Analysis
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Analyst Recommendation Visualizer showing aggregated buy, hold and sell consensus ratings across a portfolio

Equities & Portfolio Insights

Building BQuant Apps on Bloomberg for Equities and Portfolio Analytics

I developed and customised a suite of BQuant apps on Bloomberg to streamline stock and fund portfolio workflows, making market analysis faster and more intuitive. One of them is the Analyst Recommendation Visualizer where I extended Bloomberg’s draft app to support both Portfolio ID and Security List inputs, enabling aggregated buy/hold/sell consensus ratings and target prices.

Features include sector grouping, time-frame filters, and drill-downs into historical recommendation trends.

  • Bloomberg BQuant
  • Python
  • Equities
  • Portfolio Analytics
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RivHealth geospatial platform mapping river monitoring sites with time series water quality readings

River Health

Building A River Health Monitoring and Analysis Platform: RivHealth

This project, a proof-of-concept (POC), involved creating a geospatial platform to visualize telemetry data and identify pollution sources. Users can click on any monitoring site to view time series data for water readings and quality metrics. As the data engineer, I built a scalable data platform, connecting to open-source APIs for EDM data, rainfall, groundwater, and river levels. I integrated multiple datasets for comprehensive river health analysis, including data from citizen scientists and water companies.

  • Python
  • Streamlit
  • Geospatial
  • API Integration
View the POC Here
Abstract stock market imagery representing algorithmic trading data quality

Stock Data Processing

Building Trustworthy Data for Algorithmic Trading

This project delves into the critical role of data quality for algorithmic trading robots. I designed and implemented a robust data pipeline to ensure the models were trained on reliable and accurate data. Read the full story on Medium.

  • Python
  • Data Quality
  • Pipelines
Read More Here
End to end data pipeline architecture diagram from extraction through to a PostgreSQL database on AWS

Stock Data Processing

Building a Streamlined Stock Data Pipeline to PostgreSQL on AWS

In this data engineering project, I built an automated data pipeline on AWS to streamline stock data for select companies. You can read my Medium post where I explained more on: extracting and transforming data with Python and pandas, securely storing it in a PostgreSQL database on RDS, and automating everything with Airflow, Docker and Lambda.

  • AWS
  • Airflow
  • Docker
  • PostgreSQL
Read More Here
Server racks representing on premise infrastructure migrated to the cloud

Data Migration

Large Scale On Premise to Cloud Data Migration with AWS CloudFormation, File Gateway and CloudWatch Alarms

This project involved the successful migration of a large organization's on-premise data to the cloud. Leveraging the power of AWS CloudFormation, I created a secure and scalable infrastructure, including: VPC Base: Defined the network infrastructure for deploying resources. Windows Instance: Established an on-premise machine for file server access. File Gateway: Facilitated seamless data transfer from the on-premise network. NFS File Share: Received migrated data in the cloud. CloudWatch Alarm: Triggered alerts for exceeding data transfer thresholds.

  • AWS CloudFormation
  • File Gateway
  • CloudWatch
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Entity relationship diagram showing a normalised database schema with connected tables

Database and Schema Design

Strategic Database Design And Schema Development For A Sensitive Government Project

I led the comprehensive development of a database, meticulously crafting an intricately connected schema, and weaving together tables for optimal data organization. My focus extended beyond mere structural design, I prioritized data integrity, strategically normalizing the schema to enhance both accessibility and security. This initiative resulted in a resilient and efficient system that seamlessly integrates Online Transaction Processing (OLTP) for real-time data management and Online Analytical Processing (OLAP) for in-depth analysis and reporting.

  • Schema Design
  • OLTP and OLAP
  • Data Integrity
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Physical architecture model of a real time streaming pipeline built on AWS Lambda, DynamoDB and SNS

Streaming Pipeline

Dynamic Real Time Streaming Pipeline Architecture

In this project, I restructured a Python script into a dynamic Lambda function, leveraging AWS for the development of a real-time financial data streaming pipeline. The orchestration is handled through an IAM role endowed with Admin privileges, ensuring seamless execution. Key components include the establishment of a DynamoDB table for data storage, the implementation of automated Lambda triggers using CloudWatch, and the incorporation of proactive monitoring through AWS SNS notifications. This optimized architecture on AWS enhances the efficiency and responsiveness of the financial data streaming process.

  • AWS Lambda
  • DynamoDB
  • CloudWatch
  • SNS
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Diagram illustrating database normalisation into Third Normal Form

Normalisation

Database Normalisation

I transformed a denormalized database into Third Normal Form (3NF), resulting in a reduction in data storage and an improvement in query response time.

  • SQL
  • 3NF
  • Query Optimisation
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Natural language processing concept art for an AI powered serverless website

Website

AI Powered Website

Leveraging Python and AWS, I developed a serverless portfolio website with integrated Natural Language Processing (NLP). This technology enables the website to automate personalized email responses by intelligently analyzing user messages. Reach out through the website, and experience intelligent interactions.

  • Python
  • AWS Amplify
  • NLP
  • Serverless
View Here
DNA double helix representing RNA code validation in Python

Python

Python Genetic Code Validation

Using Python, I designed and implemented RNA code validation and complementary strand generation functions, reducing manual validation time and ensuring accuracy in identifying valid RNA codes.

  • Python
  • Validation Logic
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About Me.

Omotola Olasope

My experience spans diverse domains including security intelligence, data engineering, finance, equities, and commodities research. This multidisciplinary background allows me to approach complex challenges with unique insights and innovative solutions.

Currently, I serve as a Transaction Cost Analysis (TCA) Trade Desk Specialist at Bloomberg, helping desks across EMEA understand what their execution actually costs them and where that cost can be recovered.

Previously, I was an Advanced Commodities Specialist at Bloomberg, serving EMEA and delivering strategic market insights that empower high stakes financial decisions. Alongside the coverage work I build the analytics I wished existed, from LNG arbitrage and netback pricing to positioning and campaign coverage tooling.

Before Bloomberg, I was a Data Engineering Graduate Intern at Sand Technologies, where I supported critical insurance domain projects, optimized river health assessment systems, and developed enterprise data solutions.

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Services.

Commodities Research & Analytics

The full commodities complex, across energy, metals, agriculture and freight. Positioning and flow analysis, curve and spread work, arbitrage and netback modelling, and the whole Bloomberg commodities workflow end to end.

Trading & Execution Analytics

Transaction cost analysis for trading desks. Execution quality benchmarking, cost and slippage attribution, venue and broker performance, and the measurement framework that tells a desk where its money is actually going.

Data Engineering & Pipelines

The engineering behind the analytics. Ingestion from messy real world sources, orchestration with Airflow, Docker and AWS, schema and warehouse design, and the data quality controls that make a model worth trusting.

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If you have any questions or are interested in discussing potential opportunities, feel free to send me an email using the form.

I would also love to hear your thoughts! Leave a comment to share your feedback on my portfolio.

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