Simplified Portfolio - Saumik Dana

Technical Adaptation of a Fusion Framework for Stock Market Analysis

Right after I got my green card, I joined a fusion startup, and I was tasked with a challenging yet exhilarating project: adapting a high-tech feature engineering framework, initially designed for analyzing intricate fusion experiment data, to the complex and unpredictable realm of stock market analysis. This endeavor required a meticulous blend of physics, finance, and advanced machine learning techniques. The technical intricacies involved in this adaptation were manifold. It entailed reengineering the framework's capability to process and interpret the volatile nature of stock data, applying concepts like temporal and phase derivatives, and translating the stochastic and nonlinear behaviors of market dynamics into predictive models.

Refining the Greedy Algorithm for Stock Data Analysis

Central to this adaptation was the development of an advanced greedy algorithm. This algorithm, originally successful in parsing fusion data, was now reconceived to navigate the complexities of financial markets. It involved an elaborate exploration of temporal and phase derivatives, crafting a multidimensional search space that could capture the multifaceted nature of stock price movements and market indicators. Notable technical achievements of this project included:

Engineering a robust algorithm that could handle the intricacies of high-frequency financial data.
Implementing sophisticated methods for noise reduction and signal extraction in derivative calculations.
Adapting the model to dynamically respond to market volatility and emerging trends.

Temporal and Phase Derivatives in PDE Construction

The advanced aspect of my work involved the integration of temporal and phase derivatives to construct complex PDEs from stock market data. This process was critical in capturing the intricate dynamics of the financial markets.

Temporal derivatives, such as first and second order, were crucial in analyzing time-dependent dynamics in the market. Proper data sampling and noise reduction techniques were implemented to accurately estimate these derivatives from time-series data.
Phase derivatives provided insights into spatial or phase-space relationships, essential for understanding interactions between different market variables.
The greedy algorithm process was iterative, starting with a single term and progressively adding more terms, re-evaluating coefficients at each step. This method ensured that the final sparse PDE model was both accurate and efficient, effectively capturing the underlying phenomena of the market.

Iterative addition and re-evaluation of terms for optimal model complexity.
Use of loss minimization to guide the selection of terms in the model.
Adaptation of the model to uncover hidden interactions and trends.