Author: Arunangshu Das

Software architecture plays a crucial role in how applications are built, scaled, and maintained. For years, the monolithic architecture was the dominant approach, where an entire application was developed as a single, unified codebase. However, with the rise of microservices architecture, companies have started shifting toward a more modular and scalable way of building applications. But why exactly are microservices gaining so much traction? What makes them better than monolithic architecture in modern software development? 1. Scalability: Scale What You Need, When You Need One of the biggest advantages of microservices is scalability. In a monolithic architecture, scaling an application…

When it comes to modern web development, the smooth integration between the backend and frontend is what makes an application function seamlessly. A well-integrated system ensures data consistency, performance efficiency, and a better user experience. But here’s the challenge—how do you pick the right technology to bridge the gap between the frontend and backend? Should you go for GraphQL or REST APIs? What about WebSockets for real-time communication? 1. REST APIs (Representational State Transfer) Best for: Standardized web services, mobile apps, and most modern web applications. REST APIs have been the go-to method for backend-frontend integration for years. Based on…

When people talk about trading success, they usually focus on strategy, risk management, and market trends. But behind every successful trading system is a solid database architecture. If your database is slow, unreliable, or not optimized for real-time decision-making, your trading performance will suffer—regardless of how good your strategy is. Let’s break down why database architecture is a critical factor in trading success and how you can optimize it for speed, reliability, and scalability. Why Database Architecture Matters in Trading 1. Speed: Every Millisecond Counts Trading—especially algorithmic and high-frequency trading (HFT)—is all about speed. If your system is too slow…

In the world of trading, speed and reliability are everything. Whether you’re building an automated trading system or managing large-scale financial data, the database you choose plays a crucial role. A poorly optimized database can lead to slow query times, data inconsistencies, and even financial losses. But not all databases are created equal. To ensure your trading operations run smoothly, here are six key features to look for in a trading database. 1. Low-Latency Performance In trading, every millisecond counts. A good trading database must be optimized for ultra-low-latency transactions. When handling real-time market data, execution speeds can determine profitability,…

Choosing the right database for a trading application is a critical decision that can significantly impact performance, reliability, and scalability. Traders and developers often focus on execution strategies, backtesting models, or real-time analytics but overlook the database that powers these systems. A poor database choice can lead to slow queries, data inconsistencies, or even system failures during peak trading hours. 1. Ignoring Latency Requirements Speed is everything in trading. Whether you’re handling order execution, market data storage, or real-time analytics, your database must be optimized for low-latency reads and writes. One of the biggest mistakes is choosing a traditional relational…

When it comes to algorithmic trading, every millisecond counts. Financial markets generate massive amounts of data in real-time, and efficiently storing, retrieving, and analyzing this data is crucial. This is where time-series databases (TSDBs) come in. Unlike traditional relational databases, TSDBs are optimized for handling timestamped data, making them perfect for market feeds, historical price analysis, and backtesting trading strategies. If you’re developing an algo trading system, choosing the right database can significantly impact performance. 1. TimescaleDB Why It’s Great for Trading TimescaleDB is built on top of PostgreSQL, combining the familiarity of SQL with the performance benefits of a…

SEO is constantly evolving, and staying ahead of the game is crucial for ranking high on search engines. While off-page strategies like backlinks are important, on-page SEO is where you have the most control. In 2025, search engines are smarter than ever, prioritizing user experience, content relevance, and technical optimizations. If you want your website to dominate the search results, here are seven essential on-page SEO techniques you need to implement right now. 1. Optimize for Search Intent, Not Just Keywords Gone are the days when stuffing keywords into content would get you ranked. In 2025, Google focuses on search…

The stock market has always been a game of numbers, patterns, and predictions. But now, with AI stepping into the financial world, things are moving faster and becoming more accurate than ever before. From hedge funds to retail investors, AI is changing the way we analyze and predict stock movements. 1. AI-Powered Algorithmic Trading Algorithmic trading (or algo trading) isn’t new, but AI is taking it to another level. Traditional trading algorithms followed predefined rules, but AI-driven models are self-learning. They can analyze vast amounts of market data in real time, detect patterns, and execute trades at lightning speed. Hedge…

Financial markets are complex, noisy, and influenced by countless factors—from global economic shifts to investor sentiment. Traditional statistical models often fall short of capturing these intricacies, which is why machine learning (ML) has become a game-changer in financial predictions. From stock price forecasting to fraud detection, ML techniques offer powerful ways to analyze and predict market trends with greater accuracy. 1. Regression Models (Linear & Logistic Regression) Best for: Predicting stock prices, interest rates, and economic indicators Regression models are one of the simplest yet most effective machine learning techniques in finance. Linear regression helps predict continuous values, like future…

Artificial Intelligence (AI) has transformed financial markets, helping firms analyze vast amounts of data, predict trends, and automate trading. However, integrating AI into finance isn’t as straightforward as plugging in an algorithm and watching the money roll in. The complexities of financial systems, regulations, and human behavior pose unique challenges that can make AI adoption tricky. Let’s explore eight major challenges financial institutions face when implementing AI in the markets. 1. Data Quality and Availability AI thrives on data. But in financial markets, high-quality, reliable data is hard to come by. Market data is often fragmented across different sources, and…