Xiaoxuan Zhang.
An engineer by training, a quant by aspiration — I build high-performance trading systems powered by parallel computing, with a long-term vision of becoming an independent quantitative trader in the crypto space.
This is,
Experience
- Researching Time Series Foundation Models (TSFMs) with a focus on the Chronos model family — pre-trained probabilistic forecasters built on language model architectures — as the core subject of the master’s thesis at UvA.
- Applying Chronos2 to cryptocurrency price series for multi-horizon trend forecasting, evaluating zero-shot and fine-tuned performance on volatile, non-stationary financial data.
- In-depth understanding of probabilistic forecast objectives, especially pinball (quantile) loss, with hands-on analysis of their behavior on fat-tailed distributions.
- Investigating the transferability and fine-tuning generalization of large-scale TSFMs for forecasting cryptocurrencies such as BTC, ETH, and LTC under rapidly shifting market regimes.
- Built a real-time market data pipeline syncing order book snapshots, trades, and OHLCV streams from Binance into MongoDB (cold storage) and Redis (hot cache), forming the data infrastructure layer for all downstream strategy research.
- Ported and adapted statistical trading strategies from TradingView Pine Script — covering momentum, volatility breakout, and candlestick pattern signals — into a live execution framework on Binance, with real-money validation.
- Fine-tuned quantile-regression-based time series foundation models on cryptocurrency price series to capture asymmetric return distributions; constructed and backtested AI-driven alpha signals derived from predicted quantile spreads and directional probabilities.
- Completed Optiver’s market-making training program, gaining structured exposure to delta-neutral positioning, and bid-ask spread management.
- Led the full lifecycle development of Lava, a proof-of-capacity (PoC) based cryptocurrency forked from Bitcoin Core.
- Architected and implemented consensus modifications to support PoC mining, enhancing energy efficiency compared to PoW.
- Oversaw system integration, wallet and miner tools, and network bootstrapping.
- Led the High-Availability (HA) system design and development of Mavenir’s distributed element control platform, ensuring robust uptime and fault tolerance.
- Focused on telecom-grade reliability, implementing failover, state recovery, and multi-node synchronization.
Projects
Crypto Trading Infrastructure
C++
Redis/MongoDB
Binance API
ChomoSyncer
A high-performance Binance market data synchronizer that streams multi-symbol cryptocurrency Kline data in real time, distributes updates through Redis Streams, and persists historical data into MongoDB for research, backtesting, and downstream trading systems.
Python
Binance
WebSocket
Binance Orderbook Visualizer
A real-time 3D visualization app for Binance order books, using streaming depth data to reveal market microstructure, liquidity shifts, best bid/ask trajectories, and transient order-book imbalances.
OHLCV
TA
Kline Preprocess
A cryptocurrency K-line preprocessing pipeline that standardizes Binance OHLCV data and generates no-lookahead technical indicator features for downstream consumers such as traditional price-volume models and time-series AI foundation models.
Financial Applications
Computational finance, option pricing, and trading-oriented financial models.
Option Pricing
Volatility
Smile
Option Pricing
Numerical methods for option pricing — volatility smile modeling and computational finance assignments.
Blockchain
Cryptocurrency protocol engineering, consensus modifications, and network tooling.
Articles
[1]
Twintinuum: Advancing Self-Calibrating Physical-Digital Continuum
IEEE INFOCOM 2026
· 2026
— Top-tier networking & communications conference (CORE A*)
[2]
The Simple Math Behind Foundation Models(e.g. LLM): Cross-Entropy, Not Magic
LinkedIn Pulse
· Sep 9, 2025
Education
2024 - 2026
Master of Computational Science
University of Amsterdam
GPA:
7.7/10.0
- Built a solid foundation in stochastic differential equations (SDEs), Monte Carlo simulations, and numerical methods for option pricing.
- Gained in-depth understanding of numerical PDE solvers, discretization techniques, and iterative schemes in the context of scientific computation.
2013 - 2015
Master of Control Engineering
Wuhan University
GPA:
3.5/4.0
- Core courses included Matrix Theory, Control Theory, Numerical Analysis, and Optimization.
- Proficient in modeling and simulation of industry processing, focusing on industry efficiency solutions by regression and classification tools like SVM, LS-SVM, and PSO.