I transform complex quantitative insights into scalable, revenue-generating decision systems. Specializing in algorithmic trading, statistical modeling, and machine learning.
class QuantStrategy:
def __init__(self):
self.models = ["LSTM", "XGBoost"]
self.markets = 15
self.revenue = "$1.1M+"
def execute(self, data):
signals = self.predict(data)
return self.optimize(signals)I'm a Quantitative Analyst and Researcher based in New York, NY, building systems that transform complex quantitative insights into scalable, revenue-generating decision engines.
Equally fluent in traditional financial analysis and modern ML methodologies, I leverage Python-based analytics, SQL-driven data pipelines, and deep learning techniques to design market-facing strategies under real-world constraints.
Hands-on experience across institutional trading, portfolio construction, pricing optimization, and research automation.
Quantitative Analyst
Generated through data-driven trade selection achieving 35% profit margin
Scaled crypto lending portfolio from $60M to $180M at TradeStation
Built implied volatility models across commodity, currency & index futures
Authored NFL ticket market analysis with 1K+ visualizations
Cloud-based real-time data pipeline capturing prediction market microstructure from Polymarket via WebSocket. Collects full order book depth and trade streams (650K+ rows, 13K+ trades) with automated cloud-to-local replication and daily archival. Early analysis reveals potential alpha signals in tail-size trades and asymmetric Yes/No position dynamics.
Advanced coursework in quantitative finance, financial engineering, and mathematical modeling for derivatives pricing.
Python, SQL, big data, Ethereum, blockchain, machine learning, neural networks, and algorithmic trading.
Financial analysis, portfolio theory, corporate finance, and investment strategies.
I'm actively exploring opportunities in quantitative research, algorithmic trading, and data science. Let's discuss how I can contribute to your team.