A dynamic delta hedging system for a short NVDA European call option, replicating option exposure using the underlying asset and cash.

The system continuously recalibrates delta using Black-Scholes-Merton Greeks and evaluates hedging performance under discrete-time execution and market noise.

Short volatility strategies depend on managing gamma risk under imperfect replication and discrete execution.

A short call option position is dynamically hedged using delta replication to study:hedging error gamma exposure volatility sensitivity PnL path dependence

The portfolio is structured as a short call position:
- Short gamma → exposure to convex losses during large moves
- Short vega → benefits from volatility compression
- Long theta → captures time decay

This reflects a volatility risk premium strategy, where returns come from option selling while delta risk is dynamically hedged.

At each time step:
- Compute option delta using Black-Scholes-Merton
- Rebalance underlying position to maintain delta neutrality
- Adjust cash position under a self-financing constraint
- Track portfolio value and PnL evolution

This approximates a continuous-time hedging strategy using discrete execution intervals, introducing realistic replication error.

This replicates:
- Market making with dynamic hedging
- Short volatility exposure
- Gamma risk management
- Execution-constrained replication

- Discrete hedging introduces structural replication error
- Gamma risk dominates near expiry
- Volatility mis-specification is a primary PnL driver
- PnL is path-dependent, not state-dependent

Real-time simulation of portfolio rebalancing, gamma exposure, and hedging error under non-continuous execution constraints.

A market data ingestion system that transforms raw XML exchange feeds into structured inputs for quantitative trading models.

The system ensures that trading and pricing models operate on consistent, reliable market data, where input quality directly impacts signal stability, pricing accuracy, and execution decisions.

Market data quality is a first-order driver of trading performance.Small inconsistencies propagate into pricing, risk, and execution errors.

This system transforms raw exchange data into consistent, model-ready inputs for trading systems.

Market Data Feed → Structured Inputs → Trading Decisions

- Extraction of raw XML exchange feeds
- Structured transformation (ETL pipeline)
- Normalization of market variables
- Persistent storage for reproducibility
- Scheduled updates for time-series consistency

This layer supports:
- Volatility modeling
- Risk calculations
- Execution strategies
- Microstructure models

- Data consistency is critical for trading correctness
- Small schema inconsistencies amplify into PnL distortion
- Most model failures originate from data, not modeling
- Market data is a first-order driver of strategy quality

This project implements a market data transformation pipeline that converts raw XML-based financial feeds into structured, model-ready datasets used across pricing, risk, and execution systems.

Raw market data is not usable for trading. It must be structurally consistent before it can feed pricing, risk, or execution models.

- Converts hierarchical exchange data into tabular form
- Preserves schema relationships and traceability
- Normalizes inconsistent financial data structures
- Enables downstream model reliability

- XML feed → raw exchange data
- Parser → market data ingestion layer
- Structured output → model-ready inputs
- Transformation → pre-pricing / pre-risk conditioning

- Market data is a structural problem, not a data problem
- Schema consistency determines downstream model reliability
- Parsing design affects system correctness
- Data preprocessing is a prerequisite for any trading strategy

- High-level parsing for flexibility (xmltodict)
- Low-level streaming parsing for deterministic control (ElementTree)

XML-based market data transformation system converting nested exchange feeds into structured datasets for pricing, risk, and execution models.

Hierarchical financial data encoding demonstrating how raw exchange feeds represent instrument-level market structure used in downstream trading systems.