Quantitative technologies.jpg Greetings from the StockSharp team! π₯π₯ Our latest article is about the S#.Data program (better known as Hydra). In this article, we will explain (and demonstrate) the completely redesigned functionality of the program - Analytics what made our Hydra like fully quantitative analytics tool. π€ If you\u0027re already a pro at dissecting market data, feel free to watch the video below. https://youtu.be/wp_l0VBfY2o π€ However, if this is still a relatively unfamiliar area, please read this article below. π₯π₯ We\u0027ve long known that Hydra is a program for downloading historical market data from various sources (open or provided for a fee by brokers or exchanges). But now we want to explain how you can work with this data directly, without jumping into developing trading strategies just yet. So, why is this necessary? Primarily, it\u0027s to conduct quick data analysis on large volumes of data and to present the results visually. During trading, it\u0027s not always obvious whether the required conditions existed in the trading data, as historical data might suggest. In short, it\u0027s quantitative analysis tool through the Hydra program. Let\u0027s say a few words about quantitative analysis. π₯π₯ Quantitative analysis (or quantitative financial analytics) in trading is an approach that uses concepts and methods from quantitative mechanics to attempt to predict the future movements of securities and other financial instruments. This approach is mainly applied to high-frequency and short-term trading, where data analysis and decision-making occur on very short timeframes. Here are a few key elements of quantitative analysis in trading: π Securities Modeling: Quantitative traders use mathematical models and algorithms, sometimes based on quantitative mechanics, to describe and predict the behavior of securities. These models can take into account fundamental and technical factors, as well as statistical market patterns. π Big Data Analysis: Quantitative analysis requires extensive data collection and analysis of price, trading volume, and other financial parameters. With the use of powerful computing resources, traders can search for hidden patterns and signals in large volumes of information. π Machine Learning and Artificial Intelligence: Quantitative traders often employ machine learning and artificial intelligence methods to automate the decision-making process and search for optimal trading strategies. π Risk and Portfolio Management: Quantitative traders are also actively involved in risk management, using mathematical methods to assess and manage risks in their investment portfolios. π₯π₯ It\u0027s important to note that quantitative analysis in trading doesn\u0027t always guarantee profitability, and there is a risk of losing funds, just like in any other form of investment. This approach requires a high level of expertise in mathematics, programming, finance, and access to high-speed computing resources for successful strategy implementation. π₯π₯ The Hydra program allows you to work directly with downloaded data through embedded C# code. But don\u0027t be fooled. This is not a primitive script but a full-fledged language - C# - that allows you to work with a variety of mathematical and financial packages (Analytics already uses MathNet.Numerics, but you can connect other packages as well). π₯π₯ All the magic happens thanks to our advanced data access system - Storage API - which is used in all our programs, including Hydra. This provides both speed in obtaining large volumes of data and access to any type of market data (ticks, order books, candles). β‘οΈβ‘οΈ Yes, you can work with data directly through Storage API from Visual Studio. But is it convenient to install a separate program just to write a few queries to test your ideas? That\u0027s why we\u0027ve incorporated all of this into the Hydra program. β‘οΈAll the functions related to data downloading, as well as analytics, are free and available in our free plan. You can use it without any time or capability limitations. π₯π₯ Quantitative analysis in trading is a fascinating field that combines several sciences and areas, including finance, mathematics, computer science, and physics. It\u0027s a modern and innovative way of analyzing and making decisions in financial markets, allowing traders and investors to discover hidden opportunities and better understand complex market behaviors. β‘οΈβ‘οΈ However, like in any field, successfully applying quantitative analysis requires extensive knowledge, skills, and resources. Research and practice in this area can be lengthy and sometimes challenging, but it can lead to potentially high returns and better risk management. π₯π₯ We wish you an enjoyable exploration of this exciting realm of finance, and we hope that the knowledge you gain will help you develop successful trading and investment strategies. Remember that there is always a certain level of risk in the world of finance, so it\u0027s important to apply quantitative methods carefully and thoughtfully. Good luck on your journey into the world of quantitative analysis in trading! 01.png