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3 posts tagged with "Trading Algorithms"

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Understanding the Sniper Algorithm Implementation in Algorithmic Trading

· 8 min read
Vadim Nicolai
Vadim Nicolai
Senior Software Engineer at Vitrifi

Introduction​

In the realm of algorithmic trading, execution algorithms play a pivotal role in optimizing trade orders to minimize market impact and slippage. One such algorithm is the Sniper Algorithm, which is designed to execute trades discreetly and efficiently by capitalizing on favorable market conditions.

This article aims to review and understand the implementation of the Sniper Algorithm as provided in the VeighNa trading platform's open-source repository. By dissecting the code and explaining its components, we hope to provide clarity on how the algorithm functions and how it can be utilized in practical trading scenarios.

Understanding Gradient Descent and Its Applications in Trading Algorithms

· 6 min read
Vadim Nicolai
Vadim Nicolai
Senior Software Engineer at Vitrifi

Introduction​

Gradient Descent is a fundamental optimization algorithm used in machine learning and quantitative finance. In the context of algorithmic trading, it helps in optimizing predictive models, from price forecasting to portfolio optimization. Understanding how Gradient Descent works and how it can be applied in the financial markets is crucial for developing effective trading strategies.

In this article, we will explore the concept of Gradient Descent, its variations, and its applications in trading.

Understanding Euclidean Distance and Its Applications in Trading Algorithms

· 5 min read
Vadim Nicolai
Vadim Nicolai
Senior Software Engineer at Vitrifi

Introduction​

Euclidean distance is not just a mathematical concept but a crucial tool for data analysis in various fields, including trading and quantitative finance. In algorithmic trading, Euclidean distance can be applied to evaluate the similarity between financial assets, identify trading signals, and optimize portfolio allocation. As a distance metric, it helps in quantifying the relationship between different financial data points, allowing for more effective trading strategies.

In this article, we will discuss what Euclidean distance is, how it's calculated, and where it fits in the world of financial markets and algorithmic trading.