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@misango

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Frankline Oyolo, Misango

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Summary

I am a Quantitative developer with 3+ years of entrepreneurial and contractual experience building high-performance trading systems and financial models. My mission is to democratize access to low-latency markets free to developing world traders through optimized engineering practices.

Skills & Technologies
PythonRustC++C#AWSDockerKubernetesPostgreSQLTensorFlowPyTorchGitLinux
Education
The University of Hong Kong
Bachelor of Engineering, Data Science and Engineering
2022 - 2027
School of Computing and Data Science · Hong Kong SAR
Relevant courses:
  • MATH1851 — Calculus & ODEs
  • MATH1853 — Linear Algebra & Probability
  • MATH2014 — Multivariable Calculus & Linear Alg
  • STAT2601 — Probability & Statistics I
  • STAT2602 — Probability & Statistics II
  • COMP2119 — Data Structures & Algorithms
  • COMP2501 — Data Science & Engineering
  • ENGG1330 — Computer Programming I
  • ENGG1340 — Computer Programming II
  • COMP3278 — Database Management Systems
  • COMP3314 — Machine Learning
  • COMP3312 — Law & Ethics in Data Science
  • COMP3340 — Applied Deep Learning
  • COMP3353 — Bioinformatics
  • COMP3355 — Cyber Security
  • COMP3516 — Data Analytics for IoT
  • COMP3520 — Special Topics in Data Science
  • COMP3522 — Real-life Data Science
  • COMP4501 — Final Year Project
Independent Courses
  • Analysis of Algorithms (Math) — Stanford
  • Analysis of Algorithms (Pract.) — Princeton
  • Algorithms, Part I — Princeton
  • Algorithms, Part II — Princeton
  • Financial Markets — Yale (Completed)
  • Analysis of Algorithms — Princeton
  • Mathematics for Machine Learning — Imperial
  • Machine Learning Operations — Duke
  • Machine Learning — CU Boulder
  • Portfolio Construction with Python — EDHEC
  • IoT & Embedded Systems — UC Irvine
  • Advanced Data Structures & Quantum — CU Boulder
    Quantitative Research

    AI Economy Score Predictor

    Uses LLMs to score macroeconomic transcripts and news, aggregate economic sentiment, and predict future asset and economic outcomes. This is an alternative-data signal hypothesis with model-drift and limited out-of-sample validation risks.

    AI-Enhanced 60/40 Portfolio

    Uses machine learning and regime detection to dynamically adjust a traditional stock and bond portfolio, with optional Bitcoin exposure. The intended alpha comes from avoiding adverse regimes while controlling overfitting and turnover.

    Canadian Bond Day-Count Arbitrage

    Exploits pricing differences caused by Canadian bond day-count conventions and accrued-interest calculations. The opportunity requires precise institutional bond pricing, financing, and execution data.

Blog
Awards
  • Dec 2025: Granted The All Builders Welcome Grant by Amazon to attend the AWS Re:Invent conference in Las Vegas
  • Apr 2025: Granted The All Builders Welcome Grant by Amazon to attend the AWS Reinforce conference in Philadelphia
  • Mar 2025: Granted the PyconUS Travel Grant by the Python Software Foundation to attend the PyCON in Pennsylvania
  • Jan 2025: Awarded the Summer @ EPFL Grant of CHF 5000 of a 1.6% competitive rate globally to complete the SURF
  • Dec 2024: Granted The All Builders Welcome Grant by Amazon to attend the AWS Re:Invent conference in Las Vegas
  • Apr 2023: Awarded $5000 for winning the AI Tech4good Hackathon organized by Accenture
  • Sep 2022: Awarded $10,000 as African Impact Grant Award for winning the Healthcare theme by Mastercard Foundation
  • Aug 2022: Granted $150,000 for 4 years, termed Future Leaders Scholarship by HKU, to study Engineering
  • May 2022: Granted CHF 4000 by Glencore to attend the Swiss-African Hydrogen case challenge in Switzerland
Certifications
  • • Citi — Markets Quantitative Analysis (MQA) Job Simulation — Forage (May 2026)
  • • Citi — Markets Sales & Trading Job Simulation — Forage (Mar 2026)
  • • AWS Certified Machine Learning – Specialty — Amazon Web Services (Jun 2025)
  • • AWS Solutions Architect Professional — Amazon Web Services
  • • Financial Modeling & Valuation Analyst (FMVA)® — Corporate Finance Institute® (CFI) (Dec 2022)
  • • Android Developer Associate — Google (Mar 2023)
  • • Student Member — Hong Kong Computer Society (Oct 2025)
  • • Learn Ethical Hacking From A-Z: Beginner To Expert Course — Udemy (Feb 2022)
  • • The Complete Android Ethical Hacking Practical Course C|AEHP — Udemy (Oct 2022)
  • • Scientific Computing with Python — freeCodeCamp (Jan 2023)
  • • Data Analysis with Python — freeCodeCamp (Mar 2023)
  • • Machine Learning with Python — freeCodeCamp (Jan 2023)
  • • Back End Development and APIs — freeCodeCamp (Mar 2023)
  • • Front End Development Libraries — freeCodeCamp (Feb 2023)
  • • Responsive Web Design — freeCodeCamp (Oct 2022)
Interests & Activities
Languages: English (Native), German (A2 Fluency), French (A2 Fluency), Mandarin (Beginner)
Interests: Chess (Blitz 1800, Rapid 1500), Leetcode (top 5%), Cycling, International travel
Self Readings
Teaching Experience
School of Computing and Data Science, HKU
Student Teaching Assistant
Sep 2026 - Present
Hong Kong SAR · On-site
  • Teach ENGG1330, an introductory Python programming course.
Entrepreneurship
Arithmax Research
Lead Quantitative Developer
Jan 2026 - May 2026 · 5 mos
Wilmington, Delaware, United States · Remote
  • Engineered lightweight hardware accelerator cores in SystemVerilog featuring ultra-fast ITCH market data parsing, real-time order book reconstruction, and sub-microsecond risk-check logic.
  • Developed bare-metal, deterministic operating systems optimized for high-frequency trading (HFT) written in C/ASM, utilizing custom thread scheduling to achieve nanosecond-level execution latency.
  • Designed customized digital logic for high-frequency trading FPGA boards using Verilog and VHDL to process high-throughput live market feeds.
  • Programmed LOB analytics engines in C++ to ingest and analyze order book imperfections, structural imbalances, and momentary pricing mishaps via low-latency broker websockets.
  • Formulated multi-language trading frameworks leveraging Rust for production execution speed alongside Python for rapid alpha signal generation and automated circuit breaker deployment.
  • Built a retrieval-augmented generation (RAG) system utilizing Semantic Kernel architecture in C# to automate quantitative research workflows by processing unstructured alternative data and market trends.
  • Designed interactive global maritime data pipelines and alternative macroeconomic ingestion engines to map global trade flows and isolate predictive macro factors.
  • Constructed a unified historical data-fetching pipeline and high-performance simulation engine in C# via Lean CLI with dynamic slippage and risk modeling.
Arithmax Research
Associate Founder
Sep 2025 - Jan 2026 · 5 mos
Remote
  • Fundraised proprietary trading capital alongside structured Limited Partnerships, managing small client advisory relationships while sustaining high annualized returns.
  • Scaled a cross-functional team of quantitative researchers and engineers by strategically sourcing top STEM friends from elite global institutions.
  • Ideated systematic multi-asset strategies across equities, FX, and crypto, strictly maintaining maximum drawdown under 8% via dynamic position sizing.
  • Spearheaded a proprietary cross-asset correlation trading framework to capture structural relationship deviations, generating approximately 30% in annual alpha with a 1.5 Sharpe ratio.
  • Enhanced traditional carry strategies using GARCH volatility forecasting and forward rate bias corrections to achieve atleast 20% annual returns.
  • Directed the ingestion of alternative economic indicators, satellite data, and social sentiment into core investment models, boosting strategy performance.
  • Authored QR research papers validating a statistical edge, predictability, and full reproducibility of the firm's potential proprietary strategies.
Skills: Startup Development, Portfolio Management, +3 skills
Industrial Experience
Citi
Markets - Sales and Trading Summer Analyst · Internship
Jun 2026 - Aug 2026 · 3 mos
Hong Kong SAR · On-site
  • Formulated a quantitative options framework in Python based on Break-Even Volatility, deriving the continuous-time PDE relationship to prove BEV functions as a Gamma-weighted average of realized variance across historical price paths.
  • Applied the BEV engine to unlisted and illiquid equities lacking active option markets, integrating rolling factor models and proxy asset variance to generate theoretical option price distributions and risk metrics.
  • Evaluated listed market options by comparing executable bid/ask implied volatilities against model BEV surfaces to isolate structural mispricings and identify delta-hedged volatility-arbitrage opportunities.
  • Built a self-financing discrete delta-hedging backtester accounting for pathwise cash-flow dynamics, continuous risk-free carry, dividend yields , borrow fees, and transaction costs across daily and intraday rebalancing schedules.
  • Modernized legacy dashboard pipelines for volatility measurement against proprietary benchmarks, speeding up the time required for a quantitative structurer to generate reports under complex client requirements.
BASF
Solutions Architect - Global Supply Chain and Digital Business Solutions · Internship
Jan 2025 - Jun 2025 · 6 mos
Hong Kong, Hong Kong SAR · Hybrid
  • Architected RAG pipeline using GPT-4 Turbo (128k context) with Azure Cognitive Search vector indexing, achieving 93% query accuracy on BASF's TPM KB (58k+ entries) through hybrid semantic/keyword search optimization
  • Developed multi-agent system with AutoGen Framework v1.2, implementing custom tooling for SAP TPM integration that reduced shipment cost analysis runtime from 14min to 38sec (22x improvement)
  • Containerized Python analytics tools using Docker/Kubernetes on Azure Kubernetes Service (AKS), achieving 99.95% uptime through pod auto-scaling (HPAv2) and distributed Redis caching
  • Optimized PySpark ETL jobs on Databricks (Delta Lake 3.1), processing 18TB weekly Inventory and Order data with 92% parallelization efficiency using optimized shuffle partitions (200+ core cluster)
  • Implemented a genetic algorithm for route optimization using DEAP framework, reducing transportation costs by 31.4% on BASF's EU logistics network (4,200+ nodes)
Manulife
Data Governance Summer Analyst - Asia Data Office · Internship
Jun 2024 - Aug 2024 · 3 mos
Hong Kong, Hong Kong SAR · On-site
  • Engineered hybrid contextual generation pipeline using LangChain + Azure OpenAI GPT-4 Turbo, achieving 92.4% description accuracy across 312k Purview assets (38% error reduction vs manual)
  • Implemented dynamic few-shot learning with Azure Cognitive Search index (1.2M reference docs) to cut manual metadata remediation by 650h/month
  • Developed automated GPT-Lint test suite with PyTest + Azure ML, increasing compliance with 23 Manulife global standards (GDPR/CCPA) by 41% YoY. Reduced hallucination rates from 12.7% → 3.8% through: Bi-weekly prompt toxicity scans (OWASP LLM Top 10 aligned) and Output validation against SWIFT financial taxonomy (87.3% precision/recall)
  • Architected 48 test scenarios for production GAI features using Azure: Stress-tested RAG pipelines at 12k QPS (SLA: 99.95% uptime) and Identified 17 critical path bugs pre-launch via synthetic data fuzzing
TalentLabs
Junior Data Engineer · Internship
Jun 2023 - Aug 2023 · 3 mos
Hong Kong, Hong Kong SAR · Hybrid
  • Built Looker-Python dashboard (Plotly/Dash) analyzing 12K+ user feedback events → 75%↑ task completion rate via A/B-tested UI changes and NLTK sentiment tagging (89.2% accuracy)
  • Automated GCP ETL with Prefect (20h→2h/week reporting)
  • Scraped 8K+/day HK job ads (Scrapy/Selenium + 2CAPTCHA) → 16 sector-specific DataFrames
  • Trained LightGBM model (AUC=0.91) for AI job matching using spaCy/BERT embeddings (93% user-interest recall)
Equity Bank Limited
Summer Intern: EGF College Counselling Division · Internship
Apr 2023 - Jul 2023 · 4 mos
Remote
  • Prepared and facilitated APAC College presentations to an audience of 300 students
  • Graded and prepared feedback on 50+ essay prompts from scholars and including practice resources to correct specific weaknesses
  • Introduced & Trained 10 scholars to the entrance exams: SATs, DTE and directed them on extra resources where they can practice further
  • Maintained records and progress reports for 10 scholars and flagged issues with the Study Group Leader for each respective country
  • Assisted in the Selection of 300 scholars from Rwanda, DRC and Uganda to join the College Counselling 2023/24 class
Scientific Research Experience
EPFL
Research Assistant · Internship
Jun 2025 - Aug 2025 · 3 mos
Lausanne, Vaud, Switzerland · On-site
  • Optimized distributed training workflows on the EPFL high-performance computing (HPC) cluster, utilizing multi-GPU parallelization and efficient data caching to maximize throughput.
  • Designed and implemented a multi-scale feature integration framework, extracting OpenStreetMap (OSM) and population features at 500m, 1000m, and 1500m radii to encode hierarchical urban context for over 1,000 stations.
  • Formulated the prediction task as a sequence-to-sequence learning problem, leveraging DCGRU cells to model spatial diffusion and temporal dependencies in mobility networks.
  • Constructed a comprehensive dataset comprising 10M+ trip records, multi-scale OSM features, and population grids, enabling robust model training and evaluation.
  • Demonstrated that the proposed DCRNN architecture achieves a 27% reduction in RMSE compared to ST-GCN and a 13.4% performance gain attributable to OSM feature integration, as shown by ablation studies.
  • Developed attention-based interpretability analyses, revealing that urban stations predominantly attend to local features (500m), while peripheral stations emphasize regional connectivity (1500m).
HKU Architecture
Research Assistant · Part-time
Nov 2023 - May 2024 · 7 mos
Hong Kong SAR · On-site
  • Built SAM (Segment Anything Model) pipeline processing 400+ daily images via DeepLab3+ (PyTorch), segmenting 1K+ architectural blueprints & HK public housing units to identify 12 key aeration features correlating with 23% airborne disease reduction (R²=0.81).
  • Automated 3 Roboflow COCO training pipelines with Apache Airflow/Kubernetes, orchestrating 150 weekly inference tasks (98.7% SLA) while cutting GPU idle time by 37% via spot instance scheduling.
  • Led academic-industry team (5 PhDs + HKU Architecture Dept) converting 8.7M Vector3D labels to ML-compatible formats (Blender/Python ETL), achieving 99.2% CAD-Mesh alignment accuracy for disease simulation models.
HKU TALIC
Research Assistant · Part-time
Mar 2023 - May 2023 · 3 mos
Hong Kong SAR · On-site
  • Implemented BERT-Large (340M parameters) with custom tokenization pipeline for semantic analysis of 30k+ student responses, extracting 128-dimensional embeddings with 94.2% topic classification accuracy
  • Developed Meta BART-based bidirectional transformer sentiment analysis system with attention visualization, achieving F1 score of 0.89 on 5-point sentiment scale across 17 pedagogical categories
  • Engineered distributed data lake architecture (S3-compatible object storage with 12TB capacity) processing 1000+ survey datasets with automated ETL pipelines, enabling sub-second query response for longitudinal analytics