Programming for Finance

https://www.youtube.com/watch?v=2BrpKpWwT2A&list=PLQVvvaa0QuDcOdF96TBtRtuQksErCEBYZ

pythonprogramming.net for explanations

https://quantnet.com/threads/so-you-want-to-be-a-financial-engineer.11338/

coding will primarily focus on building, optimizing, and maintaining financial models, trading

1. High-Performance Computing and optimization

Writing low-latency, high-throughput code for trading systems

Languages: C++, Java, Rust, Python

Concepts: Multithreading, parallel computing, vectorization, efficient memory management

2. Financial and Quantitative Modeling

Implement pricing models, risk assessment tools, market simulators

Langs: Python (NumPy, Pandas, SciPy), MATLAB, R

Concepts: Stochastic calculus, Monte Carlo simulations, Black-Scholes model, time-series analysis

3. Data Engineering for Market Data processing

Cleaning, storing, streamlining financial data (tick data, order books, historical prices)

Langs: Python, SQL, Scala, C++ (for low-latency data processing)

Concepts: Database indexing, real-time data processing (Kafka, Redis, KDB+/q)

4. Algorithmic Trading & Market Microstructure

Developing market-making, arbitrage, and trend-following strategies

Langs: C++, Python, Java

Concepts: Order book dynamics, execution algos (VWAP, TWAP), latency optimization

5. Risk & Portfolio Management

Coding risk models to measure volatility, VaR (Value at risk) and exposure

Langs: Python, R

Concepts: Factor models, stress testing, portfolio optimization

6. Distributed & Cloud Computing

Scaling computations for backtesting analytics

Tools: AWS, Kubernetes, Spark

Concepts: Distributed computing, parallel backtesting, cloud-based execution

7. Machine Learning in Quant Finance

Applying ML for predictive analytics

Langs: Python (TensorFlow, PyTorch, scikit-learn)

Concepts: Time-series forecasting, reinforcement learning, NLP for sentiment analysis

Potential LOBs

1. Corporate & Investment Bank (CIB)

Markets (Sales and Trading)

Stochastic calculus

Linear algebra

Spatial Geometry

Partial differential equations, ordinary diff equations

Pandas -datareader

matplotlib

beautifulsoup4

scikit-learn / sklearn

Types of Coding in Hedge Funds

1. Quant Dev

- Implement mathematical models for trading strategies

- Python, C++, R, Matlab, Java

- Statistical arbitrage, time-series analysis, Matlab

- Monte Carlo simulations, stochastic calculus, optimization algos

2. Algorithmic Trading & High Freq Trading

- Low-latency algos; execute in microsecs

- C++, some Java or Rust

- Direct Market Access (DMA, order book analysis, real time data processing)

3. Data Engineering and Infra Development

- Support quant teams by handling large-scale financial data processing

- Python, SQL, Spark, cloud comp (AWs, Google Cloud)

- Building data pipelines, cleaning data, efficient storage and retreival

4. Backtesting and simulations

- Code that tests trading strategies on historical data before deploy

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