NEURAL_QUANT

Algorithmic Trader // ML Engineer // Financial Hacker

function predictMarket(data) {
  const signal = runNeuralNet(data);
  if (signal > threshold) {
    return 'BUY';
  }
  return 'SELL';
}

class QuantStrategy:
  def __init__(self):
    self.model = MLPClassifier()
    self.features = extract_features()
    
  def trade(self):
    # Execute algorithm
    signal = self.model.predict()

quant@neural-net:~

quant@neural-net:~$ python analyze_market_data.py

Loading market data... Analyzing patterns... Generating alpha signals... Market inefficiencies detected in 3 sectors.

quant@neural-net:~$ cat investment_philosophy.md

Systematic approach leveraging statistical arbitrage and machine learning. Focus on risk-adjusted returns through market-neutral strategies. Combining quantitative rigor with innovative financial engineering.

quant@neural-net:~$ ./run_monte_carlo.sh --iterations=10000

Running Monte Carlo simulation... Analyzing 10,000 market scenarios... Simulation complete: 95% confidence interval established. Strategy Sharpe ratio: 2.34

quant@neural-net:~$ _

PROFILE::DEBUG

Self-taught algorithmic trader with expertise in machine learning and statistical arbitrage. I design and deploy quantitative trading systems that extract alpha from complex market patterns.

8+ years experience implementing cutting-edge ML models for financial prediction and portfolio optimization. My algorithms combine neural networks with traditional econometric models to identify market inefficiencies.

Former Goldman Sachs quant turned independent researcher. Currently focused on developing open-source tools for decentralized finance and automated trading systems with robust risk management.

Years Experience

24/7

Algorithm Uptime

Trading Systems

NEURAL·NETWORK·VISUALIZATION

CORE::FUNCTIONS

Statistical Arbitrage

Mean-reversion and pairs trading strategies

{x→y}

Machine Learning

Neural networks and gradient boosting models

∑f(x)

Risk Management

Value-at-Risk and portfolio optimization

σ²

Financial Modeling

Stochastic processes and option pricing

∂S/∂t

Algorithmic Trading

High-frequency and execution algorithms

if(){}

Market Microstructure

Order book dynamics and liquidity analysis

L₂

CAREER::TRAJECTORY

2023

Launched QuantAlpha Fintech

Founder & Lead Quant

Established independent quantitative research firm focusing on ML-driven trading strategies and risk management solutions for institutional clients.

2021

Citadel Securities

Senior Quantitative Researcher

Led development of high-frequency trading algorithms and market-making strategies. Implemented deep reinforcement learning systems for optimal execution.

2019

Two Sigma Investments

Machine Learning Engineer

Developed natural language processing systems to extract alpha signals from financial news, earnings calls, and social media for systematic trading strategies.

2017

Goldman Sachs

Quantitative Analyst

Built statistical arbitrage models and derivatives pricing systems. Designed portfolio optimization algorithms using stochastic calculus and machine learning.

2015

Stanford University

MSc in Financial Mathematics

Specialized in computational finance and machine learning applications in financial markets. Research focused on deep learning for time series forecasting.

PROJECT::REPOSITORY

QuantML Framework

Open-Source Library

High-performance Python library for quantitative finance with integrated deep learning capabilities. Optimized for backtesting and live trading.

Python PyTorch C++ CUDA

GitHub Documentation

MarketSentiment API

Financial NLP Service

Real-time sentiment analysis API for financial news, social media, and earnings calls. Uses transformer models to extract tradable signals.

NLP BERT FastAPI AWS

API Docs Demo

DeepOptions

Volatility Modeling

Neural network-based options pricing and volatility surface prediction framework. Outperforms traditional models in abnormal market conditions.

TensorFlow LSTM R C++

Research Paper Code

CryptoAlpha

Digital Asset Trading

Advanced cryptocurrency trading system with market-making, arbitrage, and liquidity provision strategies across decentralized exchanges.

Rust Solidity WebSockets Redis

Architecture Performance

RESEARCH::PUBLICATIONS

Reinforcement Learning for Optimal Execution with Non-Linear Market Impact

Journal of Financial Economics 2023

Novel approach to optimal trade execution using deep reinforcement learning, achieving 18% reduction in implementation shortfall compared to traditional strategies.

PDF Code

Attention Mechanisms for Time Series Forecasting in Financial Markets

Advances in Neural Information Processing Systems 2022

Self-attention architecture for multi-asset time series prediction, capturing cross-asset dependencies and outperforming traditional recurrent models.

PDF Code

Neural Network Approaches to Implied Volatility Surface Modeling

Quantitative Finance 2021

Deep learning framework for volatility surface prediction that maintains no-arbitrage constraints and outperforms parametric models during market stress events.

PDF Code

INITIATE::CONSULTATION

Full Name

Email Address

Company/Fund

Project Details