Allow me to introduce myself—I am Teddy Ambona, a driven and self-taught Python/DevOps Engineer. With over 5 years of focused expertise in SaaS, cloud development, automation, and APIs, I bring a wealth of knowledge to the table.
My proficiency as a Certified Kubernetes Administrator and AWS Solutions Architect enables me to build cutting-edge toolchains from the ground up, ensuring seamless code delivery to production. From orchestrating cloud-based solutions to creating robust automation processes, I am up for any challenge!
Currently, I am actively seeking fully-remote DevOps/Python developer roles within EMEA or APAC time zones. Beyond my technical acumen, I have a keen interest in natural resources investing and stay active with Thai boxing.
If you are seeking a versatile professional to elevate your team’s SaaS initiatives, I would be delighted to connect and explore new opportunities with you. Looking forward to collaborating on exciting projects ahead!
Master thesis: “Can we generate Alpha with Alternative Data ?”
– Analysis of the predictive power of Google trends and “Sentdex sentiment analysis”(NLP) for the S&P 500 underlyings over the period 2015-2018
– Webscraping (BeautifulSoup) / Analysis of the factor’s excess return with the python library Alphalens
– Spearman’s rank correlation, L/S spread analysis, data analytics with python (pandas, numpy)
– Ensemble learning (Random forest, KNN, SVM) to classify the observations
– Algorithmic trading with the web-based backtesting platform Quantopian
– Exotic options, Stochastic Differential Equations (Ito’s lemma), Black Scholes, Greeks
Fixed-Income & ccy derivatives:
– Cap/Floor pricing, FX/currency swaps arbitrage, Bond Futures (CTD arbitrage)
Financial Risk Management:
– Portfolio VAR backtests
– Normality test QQ plot/ jarque-bera
– GARCH(1,1), Maximum Likelihood Estimators
– Monte Carlo simulations, CreditMetrics modelling, CDS valuation
Power Trading Desk Analytics: Optimizing revenue through reducing power imbalances.
Quant dev missions:
– Forecasting solar PV and wind turbines energy production based on sensors and third-party weather data.
– Statistical analysis, Seasonal ARIMAX, Recurrent Neural Networks (LSTM), Principal Component Regression, Savitzky–Golay filter, STL decomposition.
– Signal processing algorithms: Augmented Kalman filter for sensors and data fusion to estimate temperature and irradiance.
Pricing (Mentored a team of 4 engineers to deliver the end product) :
– UK/European markets power price forward curves modelling. Taking low granularity marks and shaping half-hourly forward curves. Refactored SDLC from clunky Excel files to battle-tested python code deployed with cloud-based GitOps architecture
– Built a Github Actions CI/CD pipeline to improve the development workflow an productionize the code.
– Developed a branching pattern strategy for release management and improved SDLC by evangelizing DevOps principles across the team
– Improved deployment strategy with Gitops: better rollback strategy, testing framework
Technology used: Microsoft Azure, Databricks, Spark, Github, ML Ops, Terraform
Python libraries: numpy, pandas, pyastral, pyspark, statsmodels, keras, scipy
– Options strategies, Interest rates derivatives
– Statistical analysis, PCA, Rates modelling, Volatility Models (SABR, Polynomial), in-house pricing library
Quant dev missions:
– Continuous deployment (Jenkins, Gitlab CI) of high quality Dockerized python code, linted (flake8, piprot) and tested (unit/integration) on a cloud-native development platform
– Implemented extensive option pricing library in python, stress tests scenarios and other key features for the trading analytics platform
– Microservices architecture using flask, implemented a distributed system instrumentation (with Opentracing and Jaeger) to monitor the speed of execution of our application workflow
– Caching LRU with memoization python library
– Deployed financial data pipelines with Airflow
– Deployed new containerized applications with Kubernetes / Docker / Helm
– Integrated Airflow on Kubernetes to enhance data pipelines scalability
– Implemented SQS queues with Lambda workers for infrequent workload processing
Technology used: Airflow, PostgreSQL, AWS, Kubernetes, Docker, Linux, Vagrant, VirtualBox, Loggly, OAuth2, Helm, Terraform, Opentracing, Grafana
Python libraries: numpy, pandas, flake8, PyPI server, unittests, mock, SQL Alchemy ORM(database agnositc), flask(microservices architecture), QuantLib, asyncio
Lyxor ETF was the 2nd European Exchange Traded Funds provider and second in terms of market liquidity.
– Data pipelines owner (end to end)
– Pre-trade analytics / Spread analysis / Market microstructure (tick data) / Market impact / Stock-exchange mecanisms
– Database administration, SQL Performance Tuning / Ability to handle intensive computation with Python, vectorization of loops with numpy (slicing, masking)
– Developed a Bid/Ask premium arbitrage tool to identify over/under-priced ETFs. Defining methodologies and computing metrics from tick data
– Developed a web-app from scratch with flask (blueprint, MVC, logging design patterns, front+back end)
– Maintaining and improving the ETF Spreads monitoring tool (real-time data)
– ETL processes in python (OO programming), Advanced SQL queries, VBA, Tableau, BQUANT, Batch
– UML diagrams / Version control (Git) / Agile-Rad SDLC
– Exposure to ETF Launches / New listings, replication strategies, performance analysis
– Assisting Fund Managers and Sales with Data Analysis in Python (pandas) and VBA developments
– Big Data analysis and visualization with Tableau
– SQL DB management
– Developed python algorithms for data quality monitoring / Web scraping / Web services (flask)
– Managing FX and cash positions
– Trading FX and money market instruments, equities, bonds, options, futures, funds, ETFs, Gold
– Dealing orders from direct access clients
– Troubleshooting issues with Back Office and Middle Officers
– Back testing performance of structured products with VBA