Articles. StockSharp

Assign, install and work with StockSharp Installer.

2020-09-01T08:41:16Z

The Installer allows you to:

download and install Designer, Terminal, Hydra
buy or install connectors MT 4, MT 5, MatLab
buy and install additional software
install the program together with bought connectors
track the process of purchasing new programs
download the API library and automatically tracks its updates.
download the purchased training lessons, the Shell.

How to install the program:

Download 142263 (.NET 6 will be installed automatically).
Start the installation file stocksharp_setup.exe and follow the instructions. Sometimes Windows prevent the installation and pop up the warning like below: In this case click <u>More info</u> in the warning window. You'll get the next one: After clicking "Run anyway" button the installation begins.
After installation, open the program window.

How to work with Installer:

Installer allows you to:

select programs available for download
manage updates of downloaded programs
purchase available paid programs

For the convenience of finding the necessary product in the program, you can choose the type of application.

To installthe desired application, you need to:

Select the app, click install, agree to the license agreement, and click Continue.

After this you need to choose the installation path. **IMPORTANT!**It is necessary that the folder where the program will be installed is empty.

After selecting the directory, click Continue.
Select Execute and the installation process begins.

After installation, you can use the program.

To deletethe program, select delete and click Continue.

To repair, select repair and click Continue.

Installer independently tracks all software updates and updates itself automatically. Therefore, there is no need to delete it after installation. Installer is not closed by clicking on the "X" in the program window, but is closed via the toolbar, select the menu (using the right mouse button) Close.

You can also check for updates by clicking the check for updates button in the right corner of the program window.

If updates are available, a notification will appear.

To purchase paid apps, select the software you need and click the Buy button.

Using Candlestick Patterns ️ in StockSharp

2024-09-26T10:31:23Z

Candlestick patterns are one of the most common tools in technical analysis. They help traders recognize potential market movements based on combinations of candles on the price chart. In this article, we will explore what candlestick patterns are, how to use them in trading, and what features the StockSharp platform offers for working with them.

What Are Candlestick Patterns?

Candlestick patterns are specific combinations of Japanese candlesticks that indicate possible changes in market dynamics. Each candle displays information about the opening price, closing price, high, and low over a specific time period. By analyzing sequences of these candles, traders can draw conclusions about market sentiment and potential points of reversal or trend continuation.

How to Use Candlestick Patterns in Trading?

Using candlestick patterns allows traders to:

Identify trends: Determine the direction in which the market is moving—upward, downward, or in a flat state. Recognize reversal signals: Find moments when the current trend may change. Confirm trading decisions: Use patterns in combination with other indicators for more justified trades. It's important to understand that candlestick patterns are not guaranteed signals but serve as an additional tool for market analysis.

StockSharp's Capabilities for Working with Candlestick Patterns

The StockSharp platform provides extensive functionality for working with candlestick patterns, making this process intuitive and flexible.

Easy Addition of Indicators

Adding a candlestick pattern indicator in the StockSharp terminal is very simple:

Click on the button in the form of tiles:

The Pattern Editor will open, where you can select from a list of existing patterns or create your own.

Candlestick Pattern Editor

The Pattern Editor in StockSharp allows you to:

Create your own patterns: Set unique combinations of candles that match your trading strategies. Adjust parameters: Change the conditions for pattern formation, such as candle length, their ratio, and other characteristics. Save and load patterns: Manage your templates for quick access in the future.

Visualization on the Chart

After adding a pattern, it will be displayed directly on the price chart, which allows you to:

Quickly identify necessary formations: Instantly see signals without the need for complex analysis. Improve decision-making: Visual representation of patterns contributes to more prompt and well-founded actions.

Integration with Strategy Designer

Candlestick patterns in StockSharp are available not only for manual trading but also for automation:

Usage in Designer: Include patterns in automated strategies without the need to write code. Examples of free strategies: Check out our Pattern 3 white soldiers, demonstrating the application of candlestick patterns in automated trading.

Conclusion

Candlestick patterns are a powerful tool in a trader's arsenal. Understanding and correctly applying these patterns can significantly enhance trading efficiency. The StockSharp platform provides all the necessary tools for working with candlestick patterns, making the analysis and trading process more convenient and productive.

StockSharp Designer Training Program

2024-08-10T16:31:46Z

1. Introduction to StockSharp Designer

Description: This lesson covers the basics of using StockSharp Designer, including an overview of the interface and key features.
Video link: Introduction to StockSharp Designer

2. Trading Strategy Visual Builder

Description: The lesson focuses on creating and modifying trading strategies using the visual builder tool.
Video link: Trading Strategy Visual Builder

3. Cubes Variable, Logic, Math

Description: Learn about variables, logical operations, and mathematical cubes used in strategy creation.
Video link: Cubes Variable, Logic, Math

4. Debugging trading strategies

Description: This lesson focuses on debugging trading strategies, helping you identify and fix errors in your code.
Video link: Debugging trading strategies

5. Protect position

Description: Overview of strategies to protect your position in the market.
Video link: Protect position

6. Math and indicators. Formulas and moving averages

Description: Study of mathematical cubes and indicators such as moving averages, along with their formulas.
Video link: Math and indicators. Formulas and moving averages

7. More strategies. Bollinger and MACD

Description: Explore additional strategies using Bollinger Bands and MACD indicators.
Video link: More strategies. Bollinger and MACD

8. Multi-timeframe strategies

Description: Learn to create strategies that analyze multiple timeframes simultaneously.
Video link: Multi-timeframe strategies

9. Time is...

Description: Focuses on the importance of time-based conditions within trading strategies.
Video link: Time is...

10. Connect Designer with Hydra

Description: How to integrate StockSharp Designer with the Hydra data management tool.
Video link: Connect Designer with Hydra

11. Optimization of trading strategies

Description: Techniques for optimizing trading strategies to improve performance.
Video link: Optimization of trading strategies

12. Live trading

Description: Practical guide to using StockSharp Designer for live trading.
Video link: Live trading

13. Building complex cubes

Description: Advanced lesson on creating complex cubes for sophisticated trading strategies.
Video link: Building complex cubes

14. Candles compression

Description: Techniques for compressing candle data to create clearer market signals.
Video link: Candles compression

15. Using Flag

Description: How to use the "Flag" feature to manage conditional strategy behavior.
Video link: Using Flag

16. Maximum Volume strategy

Description: Strategy focused on identifying and trading based on maximum volume levels.
Video link: Maximum Volume strategy

17. Financial indexes

Description: Analyzing and incorporating financial indexes into your trading strategies.
Video link: Financial indexes

18. Multi instruments trading strategies

Description: Develop strategies that involve trading across multiple instruments.
Video link: Multi instruments trading strategies

19. Multi assets and financial indexes

Description: How to combine multiple assets and financial indexes in a single strategy.
Video link: Multi assets and financial indexes

20. C# code editor

Description: A detailed look at the C# code editor in StockSharp Designer for custom strategy development.
Video link: C# code editor

21. Writing trading strategies in Visual Studio

Description: Learn how to write and debug trading strategies using Visual Studio.
Video link: Writing trading strategies in Visual Studio

22. Import and export trading strategies

Description: Guide on importing and exporting trading strategies in StockSharp Designer.
Video link: Import and export trading strategies

Additional Resources:
Chat: StockSharp Telegram Chat Documentation: StockSharp Documentation Source codes: Lessons sources

How AI Helps Us Write Connectors for Crypto Exchanges

2024-09-04T21:43:46Z

Or how AI can save you hours of work, but not from the necessity of verifying every step.

September 2024. We, the StockSharp team, are actively using AI to write connectors for crypto exchanges. But let me warn you — if you are reading this in 2025 or later, all of this might already be outdated. If you’re from the future, welcome to the past! And don’t forget to check if our methods are still relevant.

Our journey with AI started with ChatGPT 3.5, which, frankly, could not write even a simple trading strategy, let alone a crypto exchange connector. However, with the arrival of ChatGPT 4.0 and Claude Sonnet 3.5, things drastically changed. Now, AI can write complex code modules, though with caveats: you still have to intervene, clarify, and fix bugs, which has become a normal part of our process.

Step 1. Starting a project in Claude.ai

Before we start writing a new connector, the first thing we do is create a project in Claude.ai. This is not just a chat that will forget everything once you close it. The project allows you to save everything you upload: code, documents, comments. It’s like Custom GPT, where AI "learns" from your examples and instructions, not just answering questions.

Project creation interface in Claude.ai. Here, all data and examples are saved, allowing you to maintain the context of the work.

Claude.ai is like your personal developer, who understands some things, but without your supervision, can write something that might make your hair stand on end. So keep your documentation and vigilance close.

Step 2. Copying existing code

To avoid reinventing the wheel every time, we base our work on an already existing connector, for example, for Coinbase. We copy the project structure and adapt all the key classes and methods for the new exchange. This is just the beginning — the real fun starts now.

Step 3. Adapting the WebSocket client

Now it’s time to set up the WebSocket client for the new exchange. For example, let's take GateIO Spot WebSocket v4. Claude.ai helps generate code, but this is where our ongoing battle with inaccuracies begins. The AI often generates code that theoretically seems correct, but in practice, needs to be supplemented and fixed.

Original WebSocket client code for one of the exchanges, generated by Claude.ai. The AI suggests expanding the data types and events.

When it comes to authentication, AI often makes mistakes, and we have to manually add the missing elements. Claude can forget even basic things like authorization before subscribing to WebSocket channels.

Claude.ai made a mistake in implementing WebSocket authentication. We corrected the code and added proper authentication before connecting.

We also rewrite the code to make it more flexible by passing the WebSocket address as a parameter. This allows us to easily adapt to various exchanges and their APIs.

Example code where the WebSocket address is passed as a parameter, making the architecture more flexible.

Step 4. Setting up the REST client and adapter

After setting up the WebSocket client, we move on to the REST client. Claude.ai generates basic requests, but we have to manually clarify whether they are correct. Each exchange's API differs, and you need to be careful not to miss important details in the documentation.

Once the client is ready, we move on to the adapter for StockSharp, where the main magic happens — data processing and bringing them to a common format. Here, AI helps generate classes, but every step must be double-checked. This is especially important when working with JSON data transmitted via the exchange’s API.

Generated domain model classes. We manually supplemented them with attributes and corrected the data types.

Claude can offer interesting solutions, but you need to add attributes like JsonProperty to correctly process exchange data. If you don't, be prepared for surprises like incorrect data in reports.

We added JsonProperty attributes for proper work with JSON data.

Processing trade data is another important step where AI doesn’t always manage on its own. For example, trade data and their processing can lead to errors.

Claude fixes code for processing trade data transmitted via WebSocket and REST API.

Step 5. Optimization and new methods

Claude.ai does a decent job of writing basic methods like working with candlesticks and trade data. However, its suggestions require optimization. We often refactor logic into separate classes for better structure.

Optimized methods for working with candlesticks and trade data via WebSocket, refactored into separate classes.

We also have to refine methods for working with trade operations via WebSocket. Even though AI helps automate such processes, the code still requires final polishing.

Claude.ai added methods for trade operations in SocketClient, but they had to be optimized and corrected for errors.

Step 6. Testing — the code doesn’t always work on the first try

When the code is ready, it's time to test it. This is where AI comes to the rescue again, but all of its suggestions need to be thoroughly checked because things often don’t work correctly the first time. For example, when testing the exchange’s API, we encounter errors in instrument and position requests.

Claude.ai suggested code for working with REST API Gate.io, but it needed to be tested and refined.

Step 7. Data conversion for spot and derivatives

Claude.ai helps generate classes for working with spot and derivatives, but again — be prepared to manually adjust methods and data types. For example, working with derivatives on Gate.io requires additional checks and adjustments.

Claude.ai suggested the Extensions class to support data conversion between spot and derivatives on Gate.io.

We also adapt SpotAdapter to correctly process exchange data using the conversion methods suggested by Claude.

SpotAdapter was rewritten with changes in HttpClient and SocketClient, using methods from Extensions.

Step 8. Futures adapter and error corrections

Claude.ai helps write methods for working with futures, but as before, careful checking of logic and fixing errors is required. For example, when building the order book data, we encountered an issue with exception handling.

Generated HttpClient for working with futures via REST API Gate.io.

Claude also sometimes makes mistakes with data handling in the futures adapter. We have to manually adjust the methods for correct order book processing.

Fixed futures adapter with correct order book processing logic.

As a result, after corrections, AI proposed an optimized method for working with order books and data recovery.

Optimized methods for data recovery and order book processing based on Claude.ai’s suggestions.

GitHub Copilot — overhyped toy or real helper?

GitHub Copilot, which Microsoft and GitHub actively promote as a tool of the future, is actually more of an interesting toy than a full-fledged developer assistant at its current stage. Copilot helps write needed sections of code, but expecting groundbreaking results from it is premature — its strength lies in refining and suggesting code improvements. However, its key advantage is its tight integration with the development environment, making it convenient for quick fixes and completing template code, which it handles much better than writing complex logic modules.

AI: an enhancer for professionals and a barrier for beginners

If you’re a beginner developer just diving into the world of programming, you’re, to put it mildly, out of luck. Currently, AI won’t boost your efficiency in any significant way. Moreover, trusting AI with complex tasks can get you even more tangled in code and problems it generates. It’s a different story for experienced programmers.

For professionals, AI becomes a powerful tool that amplifies their capabilities: it speeds up development, offers solutions, and enriches knowledge of various technologies and libraries. In such a tandem, AI helps you focus on key project aspects, leaving the routine to it.

However, as we have already mentioned, AI hits the weakest spot - beginner developers, making the gap between them and professionals even wider. Experienced programmers can quickly identify AI errors, correct them, and continue working, while a novice will simply drown in these problems. Ironically, at a time when AI was conceived as a help for everyone, it currently only increases this gap.

But we should hope that in the future, AI will become more independent in programming, and then this gap may not only stop growing but disappear altogether, leveling the playing field for everyone, regardless of their level of expertise.

Designer Knowledge Base

2019-04-25T12:28:29Z

Frequently asked questions about Designer:

How to download market data to the program?
How and where to specify an instrument for strategy backtesting?
How to create a condition in the strategy?
Compound indicators, methods of their decomposition.

What’s new and how AI helped us expand the tech indicator database

2024-09-08T20:56:03Z

We have released new versions of our programs, which include several improvements, but the main update is the significant expansion of our technical indicators database. The number of indicators on our platform has more than doubled and now stands at around 160.

With the help of AI, we have quickly entered the niche of leading global platforms, securing a strong competitive advantage in innovation and development. We have already matched the codebase for indicators with top platforms and are ready to significantly expand it in the near future, thanks to AI. This will allow us to offer even more tools for analysis and market operations.

Previously, developing the first half of these indicators required considerable efforts from our users, who created them as part of open source projects. Their work laid the foundation for training the AI, which now generates new indicators fully adapted to our platform.

During the process, it became clear that the AI initially had difficulties with our indicators API. This led us to revisit and simplify the internal code architecture, which not only helped the AI but also improved the overall flexibility of the system. Interestingly, the AI itself suggested optimal solutions, based on the best programming practices.

The process of developing indicators is similar to what we described in our article about creating connectors using AI.

We are happy to note that all indicators remain in open access, and we would greatly appreciate your feedback. You can leave your feedback in our chat or on GitHub to help us improve the product even further.

Unfortunately, we’ve encountered some limitations in using AI tools. Claude is not available for public access or through an API, so we cannot provide these capabilities directly to our clients. However, we are hopeful that the upcoming GPT-5 will address the competition and give a much-needed "kick" to others in terms of speed of development and accessibility of technologies.

Hey, big news – we've teamed up with Gate.io!

2024-08-20T22:02:46Z

Greetings to all our amazing users! We couldn’t just sit still, so we’ve made yet another awesome connection – StockSharp now has a connector for Gate.io. That’s right – we’re talking direct access to one of the top crypto exchanges! Break out the champagne because you’ve now got a golden ticket to the world of crypto trading.

So, what’s the scoop?

Connector to Gate.io: Yup, now you can trade on Gate.io without leaving the comfort of our cozy programs. Cryptocurrencies, charts, trades – all at your fingertips. Ready to dive into the market and make waves? We’ve got your back!
Available in all programs: No matter what you’re using – whether it’s Designer, Terminal, or who knows, maybe even S#.Buddy – the new connector is here to impress. No extra steps needed, everything’s set up and running like clockwork!
No license check: And here’s the cherry on top! We’ve decided to give you a gift and turned off the license check for this connector. Now you can use it in your own projects without any “Can I really do that?” moments. Total freedom, folks!

So go ahead – trade, create, profit!

If something isn’t working right, or if you just need someone to chat with, our support team is always on hand (sipping coffee and ready to help). And don’t forget to tell us how you’re conquering the market with our new connector!

Thanks for sticking with us, and get ready – there’s a lot more exciting stuff on the way. Let’s fly!

Trading Strategy Creation: Blocks vs. Code

2024-05-07T11:29:57Z

"Designer" from StockSharp offers two powerful approaches to creating trading strategies: visual construction using blocks and programming using code. These methods provide unique opportunities for traders, offering both the simplicity of the visual approach and the depth and flexibility of coding.

But how to understand which method is better suited for you personally? Let's take a closer look at the strengths and weaknesses of each approach so you can choose the most suitable one for your needs.

Advantages of Creating Strategies with Blocks in "Designer":

1. Accessibility and ease of use: Visual blocks allow you to easily build strategies by dragging elements onto the workspace. This makes the process intuitively understandable even for those who do not have programming skills.

2. Quick debugging and modification: Changes to the strategy can be made on the fly, speeding up the testing and adaptation process under changing market conditions.

3. Visual representation of logic: Visual blocks allow you to "see" the logic of the strategy, simplifying the process of understanding and adjusting it.

Limitations of the visual approach: 1. Limited flexibility: Despite the convenience, visual constructors may limit user capabilities due to a predefined set of functions and parameters.

2. Difficulties with scalability: As the complexity of the strategy increases, managing a large number of blocks can become labor-intensive and confusing.

Advantages of Creating Strategies with (https://doc.stocksharp.com/topics/designer/strategies/using_csharp.html) in "Designer":

1. High flexibility and precision: Coding allows you to precisely adjust every aspect of the strategy, providing limitless possibilities for implementing complex algorithms.

2. Scalability: Code can easily be adapted to any requirements, whether it's handling large amounts of data or integrating with various systems and APIs.

3. Control and transparency: Full control over every line of code provides a deep understanding and detailed view of each element of the strategy.

Limitations of the programming approach: 1. High entry threshold: The need for programming skills can be a barrier for beginners.

2. More time for development and testing: Creating and testing code requires significant time and attention to detail.

Conclusion:

"Designer" from StockSharp combines the best of both worlds, offering users the flexibility to choose between visual and programming approaches depending on their skill level and specific needs. This unique choice makes "Designer" the perfect tool for any trader looking to maximize the effectiveness and efficiency of their trading strategies.

Introducing the New Three White Soldiers Strategy on StockSharp

2024-06-14T08:06:39Z

We are excited to share great news – a new strategy called "Three White Soldiers" has been added to our trading strategy gallery for Designer. This powerful bullish reversal model is now available for all users absolutely free!

Why Try "Three White Soldiers"?

Easy installation through the gallery – quick and simple.
Perfect for both beginners and professionals.
Impressive effectiveness in a bullish market.

Remember Our Features:

Free live trading Designer.
One free connector of your choice.

For any questions, join our chat here.

Learn more about the strategy and how to install it Pattern 3 white soldiers. Happy trading!

AI in Algorithmic Trading: The Revolution Has Begun

2024-06-29T07:49:50Z

Artificial intelligence is gradually permeating all spheres, including algorithmic trading. We plan to release various AI-based features, as this trend cannot be ignored. This article discusses how AI is developing in the field of algo trading and the changes it brings.

Impact of AI on Algorithmic Trading

1. Reduced Need for Connector Developers

With the development of AI, the need for connector developers is decreasing. Basic connectors, such as those for cryptocurrency exchanges, are easier and faster to create through AI with proper model fine-tuning. If AI learns to adapt in real-time, it can automatically track errors and protocol updates, promptly adjusting the code.

Screenshot of the process, showing how a connector adapts to a changed crypto exchange protocol:

One of the key advantages of AI is the significant acceleration of development. In our company, we use AI to create connectors, which reduces their development time by several orders of magnitude. Previously, writing and testing a new connector could take several weeks, but now it takes only a few days. This demonstrates how AI can transform processes, increasing efficiency and reducing costs. We will write a separate article on our approach to developing connectors through AI.

2. Strategy Writing

Almost all developers engaged in custom strategy writing may disappear as a class. AI can generate individual strategies faster and more accurately than a human. In algorithmic trading, strategy creators are often not professional programmers (which we understand, offering a solution based on Designer). Therefore, replacing this direction with AI is already possible. Users will be able to receive personalized solutions tailored to their trading style and preferences.

3. Disappearance of Popular Open-Source Projects

As AI develops, the need for popular open-source projects based on ready-made robots or strategies will decrease. It will be easier for people to ask AI to create a suitable solution for their needs and the software they are familiar with, rather than searching for a ready-made one and trying to adapt it.

4. Strategy Rental Services and Purchase of Ready-Made Robots

Creating personal strategies through AI will become easier than purchasing ready-made solutions or renting strategies. Users will be able to generate their strategies considering their personal preferences and market specifics.

5. Level of Programming Knowledge and Its Necessity

The question of the necessity of learning programming is becoming increasingly relevant. AI can already generate code better than many average programmers. This raises the question of why spend years learning when a machine can perform the same tasks faster and more efficiently.

6. Disappearance of Juniors in Algorithmic Trading

With the development of AI, juniors (entry-level developers) in algorithmic trading may disappear. Simple tasks that were previously given to newcomers for training are now performed by AI. This makes entering the profession more challenging, and tasks for seniors are becoming increasingly complex.

The Future of AI in Algorithmic Trading

In the future, AI is expected to become increasingly integrated into algorithmic trading, offering more advanced and tailored solutions for each user. Tracking protocol changes, writing unique strategies, and creating new connectors will all become commonplace. The role of humans in this process will be to control and adapt AI for specific needs and to solve complex tasks that remain beyond the capabilities of machines.

Conclusion

In conclusion, it should be noted that the use of AI in programming opens up many opportunities but also requires adaptation and new approaches to human resource management. We are on the brink of a new era, and our future depends on how we can integrate these technologies into our daily work.

Accelerate your trading with the new Runner!

2024-07-13T08:28:10Z

Friends!

We are excited to introduce our latest development — the Runner program! This powerful tool is designed specifically to make it easy for you to run and manage your trading strategies on the StockSharp platform.

Why did we create it?

We strive to make your trading even more efficient and convenient. Runner allows you to run strategies written in C#, schemas, and ready-made DLL assemblies, as well as integrate with Telegram for remote control.

What is Runner for?

Integration with Designer

Runner is integrated with Designer, which means you can run strategies written with blocks. Learn more about it here.

Live Trading

Use your strategies in real time on VPS to get current results. We have specially designed Runner to minimize resource consumption, especially when many trading robots are running. This allows for optimal performance for complex operations.

Command Line Execution

Runner is executed via the command line, which is especially convenient for developers working in Visual Studio. Since Runner consumes virtually no resources, it is highly efficient for heavy operations such as optimization. Moreover, backtesting is available for free within the Runner program. Details can be found here.

Remote Management via Telegram

Runner supports remote management via Telegram. No coding is required for this — you just need to be registered on our website and pass verification in our chat bot. Read more here.

Key Features:

Cross-Platform: Works on various operating systems.
Ease of Use: Intuitive interface and ease of setup.

We are confident that Runner will become an indispensable assistant for all traders and developers striving for new heights in algorithmic trading.

Learn more about Runner and download the program on our website StockSharp.

Sincerely,
The StockSharp Team

Join Us in Developing Connectors for Our Platform!

2024-06-21T12:16:54Z

We are excited to announce an open invitation for anyone interested in developing connectors for our platform.

Why Participate?

Challenge Yourself: Developing connectors is an excellent opportunity to test your skills and expand your professional horizons.
Promote Yourself: Showcase your developments on GitHub and to our community. Our repository on GitHub is one of the most popular in the field of trading.
Earn Money: If your connector is well-made, we are ready to offer it to clients through our marketplace, where you can sell your solutions. Learn more about the marketplace here.
Risk-Free Strategy: Developing and sharing your connectors is a risk-free way to gain recognition and potential income.

We not only allow the creation of custom connectors for our platform, but we also encourage it. We are ready to assist by answering your questions in our chat. Join us on Telegram: https://t.me/stocksharpchat/361

We have significantly expanded our documentation to help you with this. Detailed instructions on creating your own connectors can be found here.

Additionally, we have made several connectors available as examples. You can check them out at the following link: GitHub: StockSharp Connectors.

We look forward to your contributions and ideas!

New Connectors: Alpaca Markets, ByBit, PolygonIO

2024-06-17T10:42:59Z

New Connectors on StockSharp!

We are pleased to announce that new connectors have appeared on the StockSharp platform in recent months, significantly expanding our users' capabilities for trading and market analysis. We present to you the new connectors:

1. Alpaca Markets

Alpaca Markets - is a platform for algorithmic trading, offering an API for automating trading strategies in the stock market. With the Alpaca Markets connector, StockSharp users can easily integrate their trading strategies and automate the trading process.

2. ByBit

ByBit - is a leading cryptocurrency exchange specializing in derivative trading. The new ByBit connector will allow traders to access the cryptocurrency market and take advantage of high-frequency trading and flexible risk management tools.

3. PolygonIO

PolygonIO - is a platform providing an API for obtaining real-time market data and historical data on stocks, options, and cryptocurrencies. With the PolygonIO connector, StockSharp users can obtain high-quality data for analysis and developing trading strategies.

These new connectors open new horizons for our users in trading and market analysis. We continue to work on improving our platform and hope that these innovations will help you achieve even greater success in your trading endeavors.

Sincerely, The StockSharp Team

Hydra Analytics - Volume Profile

2023-10-04T13:47:46Z

Hello again!

In today's episode, we will talk about another script in Hydra Analytics - the volume profile. But first, let's cover the basics.

The volume profile in financial charts is a graph that displays the trading volume for each price level in the market over a specific period of time. It allows traders to analyze which prices are traded most actively and which price levels may be key for the market. The volume profile can help in identifying support and resistance levels, as well as in making decisions to buy or sell assets.

The volume profile in the form of a histogram is a graphical representation of trading volumes at specific price levels. The histogram consists of vertical bars that represent trading volumes at each price level. The higher the bar, the more trading activity occurred at that price level. Thus, the volume profile visually assesses which price levels had more trading activity concentrated and which price levels may be important for identifying support and resistance levels in the market. Additionally, the volume profile can help traders identify potential entry and exit points for positions based on volume levels and changes in trading volumes at different price levels.
Traders use the volume profile in trading to analyze the market and make trading decisions. They can use the volume profile to determine support and resistance levels and to identify key price levels. Furthermore, the volume profile can help traders understand which prices are traded most actively and which price levels may be crucial for the market. This allows traders to make more informed decisions to buy or sell assets based on trading volume and other factors influencing the market.
In quantitative analytics, the volume profile can be used to create price forecasting models and assess risks. Analyzing the volume profile can help identify hidden patterns and trends in the market that can be used to create more accurate forecasting models. Additionally, the volume profile can be used to assess the probability of a specific price level being reached in the future, enabling traders to make more informed decisions about buying or selling assets. Overall, volume profile analysis is an important tool for quantitative analysts looking to create more accurate forecasting and risk management models in financial markets.
The volume profile can be used in trading robots to make automated decisions to buy or sell assets based on trading volumes. A trading robot can use the volume profile to determine support and resistance levels in the market, as well as to identify potential entry and exit points for positions. Additionally, the volume profile can help the trading robot assess the probability of a specific price level being reached in the future and make appropriate decisions to buy or sell assets. Overall, using the volume profile in trading robots can help improve the quality of decisions made and increase trading efficiency in financial markets.

We hope our latest script will be very useful for you. Download our Hydra and use it for free today.

Hydra Analytics - Charts Feature in S#.Data(Hydra).

2023-09-27T05:34:29Z

Trading in financial markets, whether it's stocks, currencies (forex), or cryptocurrencies, requires a deep understanding of market data. One of the most powerful tools available to traders is trading charts. They provide a visual representation of historical price and volume data, allowing traders to analyze trends, identify patterns, and make informed decisions. Hydra Analytics, a versatile trading analytics platform, offers a range of features to help traders harness the power of trading charts.

What are Trading Charts? Trading charts are graphical representations of price movements over time. They display historical data points as candlesticks, lines, or bars, depending on the chosen chart type. Traders use these charts to perform technical analysis, which involves studying past price data to predict future price movements.

Common Types of Trading Charts: Candlestick Charts: These are the most popular charts among traders. Each candlestick represents a specific time period (e.g., one day), showing the opening, closing, high, and low prices during that period. Candlestick patterns are widely used to make predictions.

Line Charts: Line charts connect closing prices over a specified period with a continuous line. They provide a clear view of trends but lack the details of candlestick charts.

Bar Charts: Bar charts display price data as vertical bars, with the top of the bar representing the high price and the bottom representing the low. A horizontal line on the left indicates the opening price, while a line on the right shows the closing price.

⚡️Using Trading Charts in Hydra Analytics⚡️ Hydra Analytics offers a suite of tools to analyze trading charts effectively. Here's how you can use it:

1. Chart Types: Hydra Analytics provides multiple chart types, allowing you to choose the one that suits your analysis style. Whether you prefer candlestick charts for detailed analysis or line charts for an overview, you'll find the right chart type.

2. Timeframes: You can customize the timeframe of your charts. Whether you're a day trader who focuses on short-term movements or a long-term investor interested in the bigger picture, Hydra Analytics allows you to select timeframes that match your strategy.

3. Technical Indicators: Hydra Analytics offers a vast library of technical indicators that you can overlay on your charts. These indicators, such as moving averages, relative strength index (RSI), and stochastic oscillators, help you make informed trading decisions.

4. Drawing Tools: To perform in-depth technical analysis, you can use drawing tools like trendlines, support and resistance lines, and Fibonacci retracements. These tools help you identify key price levels and patterns.

5. Backtesting: Hydra Analytics allows you to backtest your trading strategies using historical data. You can apply your strategy to past price movements to see how it would have performed, helping you refine your approach.

6. Real-time Data: For traders who need up-to-the-minute information, Hydra Analytics provides real-time data feeds, ensuring you have the latest price and volume information at your fingertips.

Conclusion Trading charts are an essential part of any trader's toolkit, providing valuable insights into market dynamics. Hydra Analytics enhances this by offering a range of chart types, technical indicators, and analysis tools. Whether you're a seasoned trader or just starting, understanding and effectively using trading charts within Hydra Analytics can significantly improve your trading decisions. Remember that while technical analysis is a powerful tool, it should be combined with a comprehensive trading strategy and risk management for the best results.

Analytics - a New Feature in S#.Data (Hydra). Quantitative analytics tool

2023-09-08T06:48:56Z

Greetings from the StockSharp team!

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.

If you're already a pro at dissecting market data, feel free to watch the video below.

However, if this is still a relatively unfamiliar area, please read this article below.

We've 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's to conduct quick data analysis on large volumes of data and to present the results visually. During trading, it's not always obvious whether the required conditions existed in the trading data, as historical data might suggest. In short, it's quantitative analysis tool through the Hydra program. Let's say a few words about quantitative analysis.

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:

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.

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.

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.

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.

It's important to note that quantitative analysis in trading doesn't 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.

The Hydra program allows you to work directly with downloaded data through embedded C# code. But don't 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).

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).

⚡️⚡️ 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's why we've incorporated all of this into the Hydra program.

⚡️All 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.

Quantitative analysis in trading is a fascinating field that combines several sciences and areas, including finance, mathematics, computer science, and physics. It's 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.

⚡️⚡️ 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.

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's important to apply quantitative methods carefully and thoughtfully. Good luck on your journey into the world of quantitative analysis in trading!

How to download free Forex market data from Dukascopy via S#.Data(Hydra)

2023-08-20T15:49:55Z

How can you download free Forex market data via S#.Data? This Video has answers for you.

In the fast-paced world of financial markets, knowledge truly is power. Historical market data plays a pivotal role in empowering traders and investors with insights into past price movements, trends, and patterns. This data serves as a valuable tool for making informed decisions, developing trading strategies, and understanding market dynamics. In this article, we delve into the importance of historical market data and explore where to find it for free, with a special focus on the offerings of Dukascopy.

The Value of Historical Market Data:

⚡️Historical market data provides a historical record of price movements, volume, and other relevant metrics for various financial instruments. Here's why it's crucial for traders and investors:

Pattern Recognition: Examining historical data allows traders to identify recurring patterns and trends that may indicate potential market movements in the future. Recognizing these patterns can inform trading strategies and decisions.

Backtesting Strategies: Historical data enables traders to test their trading strategies on past market conditions. This process, known as backtesting, helps traders understand how their strategies would have performed in different scenarios.

Risk Management: Analyzing historical data helps traders assess historical volatility, allowing them to manage risk more effectively. This is essential for determining appropriate position sizes and setting stop-loss levels.

Market Understanding: Historical data provides insight into how various economic events, news releases, and geopolitical developments have influenced market behavior in the past. This understanding can guide traders in predicting market reactions to similar events.

Strategy Development: Traders can use historical data to develop and refine trading algorithms and quantitative models. By testing these strategies on historical data, traders can fine-tune their approaches before applying them in live markets.

Accessing Historical Market Data for Free:

⚡️ Several online platforms offer historical market data for free, enabling traders to access and analyze the information they need. Some of these sources include:

Dukascopy: Dukascopy, a Swiss-based online bank and forex brokerage, provides historical market data through its "JForex" platform. Traders can access detailed historical tick data for a wide range of currency pairs and other instruments. This data can be invaluable for traders looking to conduct thorough analysis and backtesting.

Yahoo Finance: Yahoo Finance offers historical price data for stocks, indices, currencies, commodities, and more. Users can download historical data in various timeframes and formats.

Investing.com: Investing.com provides historical price data and charts for a wide range of financial instruments. The platform allows users to customize date ranges and chart types.

Free Charting Platforms: Some charting platforms, like TradingView, offer historical data for various markets. Users can access price data and apply technical analysis tools.

Central Banks and Government Agencies: Central banks and government agencies often provide economic and financial data, including historical statistics, through their official websites.

Dukascopy's Historical Market Data Offering:

Dukascopy's historical market data offering stands out due to its comprehensive and detailed nature. Traders can access tick-by-tick data for precise analysis and backtesting. The availability of such data can be invaluable for algorithmic traders and quantitative analysts aiming to develop and optimize trading strategies.

Additionally, Dukascopy's Swiss regulatory status adds an extra layer of reliability and credibility to its data offerings, enhancing the confidence of traders who rely on accurate historical data.

⚡️⚡️ Historical market data is a treasure trove of insights that traders and investors can leverage to enhance their decision-making and trading strategies. Accessing this data for free has become easier with various online platforms and resources. Dukascopy's contribution to this landscape, with its detailed historical tick data, serves as a testament to the importance of quality information in the world of trading. By harnessing the power of historical data, traders can gain a competitive edge in navigating the complexities of financial markets.

S#.Data(Hydra) - TOC

2023-04-08T16:20:24Z

How to use Support and Resistance technical analysis in trading?

2022-11-04T12:35:19Z

Support and resistance levels are key concepts in technical analysis that can be used in various ways to inform trading decisions. Here are some ways to utilize support and resistance in trading:

1. Identifying Key Levels: Use support and resistance levels to identify significant price levels where buying or selling pressure has historically been concentrated. These levels can be identified by observing areas where the price has previously reversed or consolidated. By identifying these levels, traders can anticipate potential price reactions in the future.

2. Confirmation of Breakouts: Support and resistance levels can act as a confirmation tool for breakout trades. When the price breaks above a resistance level, it may indicate a bullish breakout and potential buying opportunities. Conversely, when the price breaks below a support level, it may indicate a bearish breakout and potential selling opportunities. Traders often wait for a breakout confirmation by observing the price closing above or below the level to reduce the risk of false breakouts.

3. Trade Entries and Exits: Support and resistance levels can be used to determine entry and exit points for trades. Traders may look for buying opportunities near support levels, expecting the price to bounce higher. Conversely, they may consider selling opportunities near resistance levels, anticipating a price decline. It's important to use additional technical indicators or price action patterns to confirm these potential trading opportunities.

4. Stop Loss Placement: Support and resistance levels can help determine appropriate stop loss levels. When entering a long trade, placing the stop loss below the support level can help limit potential losses if the support level fails. For short trades, placing the stop loss above the resistance level can provide protection if the price breaks through the resistance.

5. Profit Target Levels: Support and resistance levels can also serve as profit targets for trades. Traders may consider taking profits or scaling out of a position when the price reaches a known resistance level in a long trade. In short trades, profit targets can be set near known support levels.

6. Trend Analysis: Support and resistance levels can provide insights into the overall trend of an asset. When a series of higher highs and higher lows are established, it suggests an uptrend. In this case, support levels are viewed as potential buying opportunities, and traders may focus on long trades. Conversely, a series of lower highs and lower lows indicates a downtrend, where resistance levels can be seen as potential selling opportunities, and traders may consider short trades.

Remember, support and resistance levels are not foolproof and can be subject to breakouts or false signals. It's essential to combine them with other technical analysis tools, such as trendlines, chart patterns, and indicators, to strengthen the analysis and increase the probability of successful trades. Additionally, risk management techniques, such as setting appropriate stop losses and position sizing, should always be applied to manage potential losses.

How Adaptive Strategies working in market analysis trading robot.

2023-06-30T13:37:55Z

Adaptive strategies in a market analysis trading robot refer to the ability of the robot to adjust and modify its trading strategies based on changing market conditions. These strategies aim to adapt to the dynamic nature of the market and optimize trading performance. Here's how adaptive strategies work in a market analysis trading robot:

1. Market Monitoring: The trading robot continuously monitors market conditions, including price movements, volume, volatility, and other relevant indicators. It collects real-time data and analyzes it to identify changes in market trends, patterns, or volatility.

2. Strategy Evaluation: The robot evaluates the performance of its existing trading strategies based on historical data and current market conditions. It assesses the profitability, risk, and other performance metrics of each strategy.

3. Indicator Selection: The robot selects a set of indicators or parameters that are most relevant to the current market conditions. These indicators can be technical indicators, fundamental factors, sentiment analysis, or any other relevant data points.

4. Adaptive Rule Set: The trading robot uses predefined adaptive rules or algorithms to determine when and how to modify its trading strategies. These rules are based on the analysis of market data and indicators. For example, if the market becomes highly volatile, the robot may adjust its risk parameters or change its position sizing strategy.

5. Dynamic Parameter Adjustment: The robot adjusts its trading parameters, such as entry and exit thresholds, stop-loss levels, take-profit targets, or timeframes, based on the adaptive rules and the selected indicators. It recalibrates these parameters to align with the current market conditions and optimize trading performance.

6. Backtesting and Simulation: Before implementing adaptive strategies in live trading, the trading robot may conduct extensive backtesting and simulation. It tests the modified strategies on historical market data to evaluate their performance and assess their effectiveness under various market scenarios.

7. Real-Time Decision Making: In live trading, the robot continuously analyzes real-time market data and applies its adaptive strategies to make trading decisions. It assesses the suitability of each strategy based on the current market conditions and executes trades accordingly.

8. Performance Monitoring and Evaluation: The robot tracks and evaluates the performance of its adaptive strategies over time. It measures key performance metrics, such as profitability, risk-adjusted returns, drawdowns, and other relevant indicators. This monitoring allows the robot to assess the effectiveness of its adaptive strategies and make further adjustments if necessary.

9. Machine Learning and Artificial Intelligence: Some advanced trading robots employ machine learning and artificial intelligence techniques to enhance their adaptive strategies. They can learn from past market data, adapt their trading models, and improve their decision-making processes over time.

10. Continuous Improvement: The trading robot undergoes continuous improvement and refinement of its adaptive strategies. It incorporates feedback from market performance, user feedback, and ongoing research to enhance its trading algorithms and adaptability.

⚡️⚡️ Adaptive strategies in a market analysis trading robot enable it to respond to changing market conditions, optimize trading performance, and potentially capture more profitable trading opportunities. By dynamically adjusting trading parameters and strategies, the robot can adapt to different market phases, volatility levels, and trends, enhancing its ability to generate consistent returns in various market environments.