Data Science Expert

12/2019 – offen

Advanced Backtesting framework

Description:
Generic platform agnostic framework to allow for advanced trading strategies definition and execution combining multiple data resolution and providing an enhance trading description language
Functionality/Methodology:
• Design of highly efficient and highly adaptable data models to represent trading strategies.
• Financial data sourcing and quality control for futures, commodities, forex, etc from different commercial and open sources
• Brain-2-code: a Natural language like interface to specify trading conditions at different execution times (entry point, stay-in-trade, dynamic trailing, dynamic take profit, stop loss, max trade duration conditions)
• Queuing mechanism to support the parallel execution of back-testing jobs.
• Highly scalable processing engine supporting high dimensional volumes of historical data in fine granular resolutions (minutes to days) [10 Mio data points with 1500+ columns]
• Optimized reporting of the results including machine learning generated optimization hints to aid traders, including trade-level insights.
Technologies
• Data manipulation packages (dplyr, lubridate, zoo in R and Pandas in Python)
• Trading specific packages (TTR, Quantmod, Candlestick, QuantPy, SciPy, Pynance)
• Serialization formats (json, yaml)
• Advanced time series in-memory technologies (XTS, TSeries, pandas and scikit-learn) as well as purpose-specific data bases (Influx DB, Timescale DB)
• Custom visualization and dashboarding (ggplot, rcharts, PowerBI, matplotlib, dash)
• Workload management (Slurm)

High-availability intelligent trading bot

Description: multi-level risk-aware machine learning based stochastic optimization for trading strategies
Functionality/Methodology:
• Bayesian hyperparameter tuning for dynamic execution
• Assemble learning and super-learning based facetted optimization
• Enhanced tailor-made Support-Resistance and channels modelling.
• In-trade prediction for dynamic optimal behavior based on Deep Neural Networks
• Optimization based on different risks profiles (from highly-conservative to highly-aggressive)
• Customization of objective function (cumulative net profit, Sortino rate, Sharpe rate, etc)
• Time Series multi-granular prediction techniques
Technologies
• Bayesian optimization frameworks (Scikit-Optimizer)
• Advanced CNN architecture (TensorFlow/Keras)
• Features optimization packages (Boruta, BorutaPy, etc)
• Ensemble learning packages (Superlearner, Scikit-Learb)
• Advanced time series (Prophet, LSTMs, etc)

High-availability intelligent trading bot

Description: robust micro-services-based strategy execution trading bot
Functionality/Methodology:
• Custom integration with industry leading brokers
• Integration with market standards (MT4/MT5)
• Advanced self-healing runtime to ensure minimum downtime and trading state preserving
• Highly scalability supporting the execution several instances in parallel
• Remote client steering based on instant-messaging custom commands
• Instant reporting and secured panic mode
• Customizable risk management and enhanced collision handling for overall portfolio optimization
Technologies:
• High performance asynchronous messaging (ZeroMQ)
• R2MT (for MetaTrader integration), IB-Insync, etc
• Telegram custom development (SM Manager)
• Watchdog steering for self-healing and keep-alive
• Instance balancing with shared data live-feed

Data Mining, Data Science, Datenanalyse, Maschinelles Lernen, Python, Technische Konzeption

12/2016 – offen

Accredited professor by the Spanish minister of Education
NoSQL Data Base technologies (I) & (II)
Description: Introduction to NoSQL DB - MongoDB
• Introduction to NoSQL, ACID vs BASE, SQL vs NoSQL
• Different NoSQL Data Bases and when to use them
• MongoDB
• Graph Data Bases
• Neo4j

Creation of Data Science B. Sc. curriculum: Validated by the Ministry of Education

NonStop SQL & SQL/MX (TANDEM)

12/2014 – 12/2019

Team Lead of cross-country team and product owner

Machine Learning Look-Alike Modeling for advanced targeting
Description: Machine Learning Look-alike modeling segmentation from anonymized data in programmatic media

Functionality/Methodology:
• Design of highly efficient and highly adaptable data models to represent users’ behavior and segments
• Features Selection based on K-NN algorithm
• K-NN/Random Forest/K-Means models training
• >80% Accuracy. >50Mio Advertising Ids estimated
Technologies:
• Data manipulation packages Scikit-learn
• Exploratory Data Analysis (numpy, matplotlib, etc.)
• Outlier detection (Boxplot, IQR Score, etc)

Data Anonymization Platform

Description: Design and launch anonymization platform enabling products monetization previously impacted by GDPR

Functionality/Methodology:
• Privacy by design. Design of highly efficient and highly scalable platform to anonymize opted-in customer data in a GDPR compliance way
• Annual and daily encryption seeds to protect customer data. In memory protection for encryption keys
• More than 100Mio records / hour processed
Technologies:
• In memory protection for encryption keys (MEM06-C – mlock)
• AWS – Tailor made Python API for customer data and Pixel API
• MongoDB & Json/bjson file format

Bulk-messaging fraud detection

Description: Real Time monitor for data analysis and automatic fraud detection in Bulk messaging
Functionality/Methodology:
• Design real time monitor for bulk messaging usage for insight extraction and fraud detection prevention
• Real Time detection of fraud usage of bulk message for SIM deactivation
• 75% fraud reduction
Technologies:
• Graphana dashboard and Kafka
• Machine Learning model training (Decision Trees, Random Forest, XGBoost, etc.)

Data Mining, Data Science, Product Owner, Projektmanagement (IT)