Short volatility exposure requires managing gamma risk under discrete hedging conditions, where replication is imperfect.

A short option position is dynamically delta-hedged using Black-Scholes Greeks to approximate continuous replication.
The simulation focuses on how hedging frequency and volatility estimation impact PnL outcomes.

The position reflects standard short vol exposures:
- Short gamma → losses from large directional moves
- Short vega → sensitivity to volatility changes
- Long theta → time decay benefit

PnL is driven by the interaction between:
- Spot movement
- Gamma exposure
- Discrete hedging adjustments

At each time step:
- Compute option delta (Black-Scholes-Merton)
- Rebalance underlying position to maintain delta neutrality
- Track cash + financing cost
- Update portfolio value under self-financing constraint

This replicates:
- Dynamic hedging of short option exposure
- Real-world gamma risk under imperfect replication
- Volatility mispricing impact on PnL
- Execution friction from discrete rebalancing

- Discrete hedging creates unavoidable replication error
- Gamma dominates PnL near expiry and during large moves
- Volatility mis-specification is the main driver of PnL deviation
- Option PnL is path-dependent rather than mark-to-model static

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

Trading models are only as reliable as the market data they consume.Inconsistent or incomplete market snapshots introduce direct distortion into pricing, risk, and execution signals.

This system converts raw exchange-style market data into structured, time-consistent inputs suitable for trading models.
It focuses on ensuring data integrity, continuity, and reproducibility across time-series market snapshots.

Market Data → Pricing Models → Risk Systems → Execution Decisions

This pipeline sits at the foundation of the trading stack, ensuring all downstream components operate on consistent inputs.

- 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.