Skills

Pairs trading, cointegration, and mean-reversion strategies for market-neutral statistical arbitrage.

Developing and backtesting momentum, trend-following, and time-series momentum trading strategies.

Mean reversion trading strategies including Bollinger Bands, RSI-based, and z-score entry/exit systems.

Using ML models for signal generation, including random forests, neural networks, and ensemble methods.

Building automated cryptocurrency trading bots with exchange APIs, strategy management, and risk controls.

Developing quant strategies for Chinese A-share markets using local data sources and market-specific conventions.

Using computer vision on satellite imagery to estimate retail traffic, crop yields, and supply chain activity.

Analyzing web traffic, app downloads, and e-commerce data as leading indicators for company performance.

Building event-driven backtesting engines that simulate order execution, slippage, and market impact.

Using Monte Carlo methods for option pricing, risk assessment, portfolio simulation, and scenario analysis.

Robust out-of-sample testing methodology using walk-forward optimization to prevent overfitting in strategy development.

Using HMMs, clustering, and changepoint detection to identify market regimes for adaptive strategy switching.

Analyzing blockchain data including transaction flows, wallet activity, and smart contract interactions for crypto research.

Analyzing DeFi protocols using on-chain data to assess usage, TVL, yield sources, and protocol risk.

Using ML and graph analytics to detect money laundering, rug pulls, wash trading, and other blockchain fraud.

Black-Scholes, binomial trees, stochastic volatility models, and numerical methods for pricing financial derivatives.

Historical, parametric, and Monte Carlo VaR methodologies for measuring and managing portfolio risk.

Designing and running stress test scenarios for portfolio resilience analysis under extreme market conditions.

Self-directed learning path covering mathematics, statistics, programming, and finance fundamentals for quantitative finance.

Preparing for quantitative finance interviews covering probability, statistics, brainteasers, and financial reasoning.

Mastering the Python ecosystem for quant finance: NumPy, Pandas, SciPy, statsmodels, and specialized quant libraries.

Building real-time data pipelines for market data ingestion, normalization, and storage using streaming technologies.

Normalizing market data from multiple providers into consistent schemas for analysis and backtesting.

Designing feature stores for financial ML with point-in-time correctness, lookahead bias prevention, and feature versioning.

Domain-adapting large language models with financial corpora using supervised fine-tuning and instruction tuning.

Continuing pretraining of language models on domain-specific financial corpora to improve downstream task performance.

Benchmarking and evaluating LLM performance on financial tasks including reporting, analysis, and compliance.

NLP techniques for extracting sentiment from financial news, earnings calls, and social media data.

Named entity recognition for extracting financial entities, tickers, metrics, and events from unstructured text.

Using LLMs to automate generation of financial summaries, research reports, and investment memos.

NLP pipelines for analyzing earnings call transcripts to extract sentiment, key metrics, and forward guidance.

Abstractive and extractive summarization of financial documents including 10-K filings, prospectuses, and research notes.

Building QA systems that answer complex financial questions from structured and unstructured data sources.

Latency-sensitive trading strategies, order book dynamics, colocation, and market making fundamentals.

Analyzing limit order book data for liquidity, order flow imbalance, and short-term price prediction.

Algorithms for minimizing market impact and execution costs including VWAP, TWAP, implementation shortfall.

Building autonomous AI agents that research, analyze, and execute trades using LLMs and tool-use frameworks.

Designing collaborative multi-agent systems where specialized agents handle research, analysis, risk, and execution.

Mean-variance optimization, efficient frontier analysis, and modern extensions of Markowitz portfolio theory.

Risk parity and risk budgeting approaches to portfolio construction that balance risk contributions across assets.

Implementing the Black-Litterman model for combining market equilibrium with investor views for portfolio optimization.

Discovering and validating factor models for asset pricing, including statistical factors and fundamental factors.

Systematic approaches to discovering predictive signals (alpha) using ML, alternative data, and quantitative research.

ML approaches for market risk, credit risk, and operational risk modeling in financial institutions.

Applying XGBoost, LightGBM, and CatBoost to classification and regression problems in financial prediction.

Applying transformers, LSTMs, CNNs, and attention mechanisms to financial prediction and classification tasks.

Implementing retrieval-augmented generation for financial document QA, regulatory compliance, and research synthesis.

Applying DRL algorithms like PPO, SAC, and DQN to automated trading and portfolio management tasks.

Multi-agent reinforcement learning frameworks where multiple agents collaborate or compete in market environments.

Using reinforcement learning for dynamic portfolio allocation, rebalancing, and risk-adjusted return optimization.

Real-time portfolio risk monitoring, attribution analysis, factor exposure tracking, and risk budgeting.

Managing portfolio drawdowns and tail risk through position sizing, stop-losses, and hedging strategies.

Optimal position sizing methods including Kelly criterion, fixed fraction, volatility-adjusted, and risk-based sizing.

Statistical and ML methods for forecasting financial time series including price, volatility, and volume.

GARCH, stochastic volatility, and ML-based approaches for modeling and predicting financial market volatility.

Detecting anomalous trading patterns, market manipulation, and regime changes using statistical and ML techniques.

Creating informative financial charts, dashboards, and interactive visualizations for market analysis and reporting.

Operating cloud-based quant platforms including QuantConnect, OpenBB, and backtesting infrastructure.

Deploying production trading systems with cloud infrastructure, Docker, CI/CD, monitoring, and failover strategies.

Designing and comparing backtesting engines including vectorized, event-driven, and distributed simulation approaches.