Welcome to my GitHub profile! I'm currently pursuing a double major in Quantitative Finance and Computer Science and Econometrics at the University of Warsaw, in my final year for both. My academic journey is a blend of finance, economics, and technology, fueling my passion for algorithmic trading, time series forecasting, and machine learning, with a special focus on reinforcement learning.
I've always been fascinated by the intersection of finance and technology, especially the dynamic field of algorithmic trading. My main area of interest lies in reinforcement learning, where I've implemented a reinforcement agent that actively trades in the FOREX market. This project is a testament to my dedication and skill in navigating complex financial markets through advanced machine learning techniques.
Professionally, I'm navigating the intricate world of financial data as a Market Data Scientist/Data Engineer in the banking sector. My role involves harnessing vast amounts of data to uncover insights that drive strategic decisions, leveraging my programming skills in Python, SQL, R, and C++.
I'm always open to connecting with fellow data enthusiasts, finance professionals, and anyone curious about the potential of technology to transform traditional industries.
- 📧 Email: jmaskiew@gmail.com
- LinkedIn: TODO
This project stands as the crown jewel of my portfolio, where I've poured my passion and expertise into developing a Reinforcement Learning agent that navigates the complexities of the FOREX market. This isn't just any implementation; it represents a significant leap beyond conventional models. I've customized and enhanced algorithms like Temporal-difference (TD) and Generalized Advantage Estimation (GAE), crafting my own versions tailored to significantly boost the predictive power and efficiency of the trading agent. link to Project Reinforcement learning for Stock market
- Languages: Python
- Key Libraries: Torch, Pandas, NumPy, Numba, Gym
- Custom TD and GAE Algorithms: Innovated beyond traditional models by modifying and enhancing Temporal-difference and Generalized Advantage Estimation algorithms to suit the unique challenges of FOREX trading.
- Written From Scratch: The Reinforcement Learning agents are meticulously crafted from the ground up, allowing for unparalleled optimization and integration with the custom TD and GAE algorithms.
Here's a quick overview of my other projects. Each of these has taught me valuable lessons and allowed me to apply my skills in diverse contexts.
| Repository Title | Languages & Packages Used | Description |
|---|---|---|
| Text mining of annual reports | Python, nltk, transformers, gensim | This project harnesses the power of text mining to analyze annual reports, extracting key insights through techniques such as sentiment analysis, topic modeling, and word clouds. Utilizing a sophisticated Python stack including nltk for natural language processing, transformers, and gensim for unsupervised topic modeling, it aims to unveil the underlying themes, sentiments, and focal points within various sections of corporate annual reports. The outcome is a comprehensive understanding of a firm’s yearly performance, strategic direction, and market position, distilled from extensive textual data. |
| Markov Models | Python, statsmodels, hmmlearn | This project delves into the analysis of different Markov models, with a significant emphasis on the Hidden Markov Model (HMM), in forecasting the daily price regimes of the EUR/USD currency pair. Utilizing Python's statsmodels and hmmlearn, it provides an in-depth exploration of how these probabilistic models can identify and predict the underlying financial regimes. |
| BubbleShooter | Python, PyTorch | Application of the Deep Q-Network (DQN) algorithm, this project teaches a machine learning model to master game of Bubble Shooter. By leveraging PyTorch, the project demonstrates the capability of DQN to learn game strategies and decision-making. |