About ExSan

A b o u t   E x S a n Roberto Santander °|°  Not Afraid Of Pointers °|° ExSan is a sophisticated software component designed for handling complex, multi-dimensional data with efficiency and scalability. It can be visualized as either a matrix or a worksheet, tailored for demanding computational tasks. Coding to solve a particular problem using the ExSan tool offers a significant advantage over traditional top-down or bottom-up programming methods. ExSan, by mimicking the functionality of a spreadsheet tool, allows for a more intuitive and simplified approach. Instead of writing complex code, the programming process in ExSan closely resembles the experience of working with a spreadsheet itself. Features Dynamic Worksheet Structure: A single layout declaration generates a versatile worksheet, supporting multiple layers and diverse data types (e.g., d...

ExSan's Novel Data Structure ExSan Abstract Data Structure Not Afraid Of Pointers The ExSan Abstract Data Structure is built upon a node design that incorporates eight directional pointers. Specifically, these pointers are divided into two groups: Cardinal Directions: North, South, East, and West Diagonal Directions: Northeast, Northwest, Southeast, and Southwest (45-degree intervals) This design enables the creation of a highly interconnected and spatially comprehensive graph structure, suitable for modeling multidimensional relationships or spatial data. Integration with Red-Black Binary Search Tree Each node in the ExSan structure is further integrated into a master Red-Black Binary Search Tree (RB-BST) , ensuring the overall structure remains balanced and efficiently searchable . The Red-Black Tree provides O(log n) time complexity for operations such as insertion, deletion, and lookup. ...

Setting Up a Scenario for Backtesting Setting Up a Scenario for Backtesting Backtesting is essential in quantitative finance, allowing traders to evaluate strategies against historical data to assess performance and risk. A robust backtesting framework simulates realistic market conditions and ensures unbiased results. This document outlines the process of setting up backtesting scenarios within the ExSan framework. Step 1: Configuring the Backtesting Scenario Begin by defining the parameters of the backtesting scenario, including asset selection, historical data time horizon, and specific features or metrics for evaluation. Once defined, stream the data to ExSan, the core analytical engine for backtesting. A critical task here is data clustering. Data Clustering ExSan uses advanced clustering algorithms to group data points based on statistical properties. These clusters help construct the covariance matrix, essential in portfolio optim...

Advanced Clustering Technique For High-Frequency Low-Latency Analysis After configuring the backtesting scenario , data is streamed to ExSan to identify clusters that will subsequently be utilized for constructing the covariance matrix. This process is fundamental to modern portfolio theory, particularly due to the asynchronous nature of market data. Only clusters with overlapping time periods (i.e., concurrent in time) are eligible for inclusion in the estimation of the covariance matrix. The aim of this work is to establish Covariance and Correlation of Asynchronous Market Tick Data Using Cluster Computing . An algorithm is implemented to correlate asynchronous tick data from the market using cluster computing. Each asset generates a unique number of ticks distributed over a given time frame, with some assets being more active and others less so, reflecting varying levels of volatility and activity. Challenges ...