How to run a backtest using the Indicator Docker image?

Use the provided Docker command with your Tiingo API key and asset tickers, e.g., `docker run -v $(pwd)/output:/app/output ghcr.io/cinar/indicator:latest --api-key YOUR_KEY --assets aapl`. This fetches historical data, runs strategies, and outputs HTML reports to the mounted directory.

Indicator Go vs TA-Lib for algorithmic trading in Go?

Indicator is a pure Go library with no C dependencies, offering over 70 indicators, strategies, and backtesting, while TA-Lib is a C library with Go bindings that might have performance edges but less integrated tooling. Choose Indicator for a self-contained Go ecosystem with modern features like generics and channels.

Can I use Indicator for live trading with Alpaca Markets?

Yes, but it requires the separate indicatoralpaca repository for Alpaca integration. The core library focuses on indicators and backtesting, so you'll need to combine it with that repo for live data and order execution, adding complexity.

How to convert slice data to channels in Indicator v2?

Use helper functions like `helper.SliceToChan` and `helper.ChanToSlice` provided in the library. This allows bridging between traditional slice-based data and the channel-based streaming model introduced in v2 for flexibility.

What is the MCP server in Indicator and how do I use it?

The MCP (Multi-Client Protocol) server enables real-time strategy execution and integration with AI tools. It's built into the library; you can start it programmatically to serve trading strategies over a network, facilitating scalable client interactions.

Is Indicator suitable for high-frequency trading systems?

Not ideally; it uses Go channels for streaming which are efficient, but lacks optimizations for ultra-low latency. It's better suited for backtesting, medium-frequency strategies, or research rather than nanosecond-level HFT.

How to contribute a new indicator to the Indicator library?

Follow the contribution guidelines in CONTRIBUTING.md, which includes signing a CLA. Ensure high test coverage with CSV test data, as the project emphasizes modularity and configurability, similar to existing indicators.

indicator — Go Technical Analysis Library

What is indicator?

Indicator is a Go module for financial market analysis that provides a comprehensive collection of over 70 technical indicators, configurable trading strategies, and a robust backtesting framework. It enables developers and traders to build, test, and evaluate algorithmic trading systems with precision, supporting real-time data stream processing and integration with multiple data sources.

Target Audience

Go developers and quantitative traders building algorithmic trading systems, backtesting engines, or financial analysis tools. It is also suitable for researchers and analysts who need to implement and test technical trading strategies programmatically.

Value Proposition

Developers choose Indicator for its extensive, dependency-free pure Go implementation with high test coverage, leveraging generics for type flexibility. Its unique selling points include a built-in MCP server for AI tool integration, a repository system for multiple data sources, and a focus on modularity with configurable indicators and strategies.

Indicator Go delivers a rich set of technical analysis indicators, customizable strategies, and a powerful backtesting framework. No dependencies, just pure simplicity. ✨ See how! 👀

Use Cases

Best For

Building algorithmic trading systems in Go that require real-time technical indicator calculations.
Backtesting trading strategies on historical asset data with detailed performance reports.
Integrating financial market analysis into AI tools via its built-in Multi-Client Protocol (MCP) server.
Synchronizing and storing asset data from multiple sources like Tiingo and Alpaca Markets using its repository system.
Developing custom trading strategies by combining pre-built indicators with compound and decorator logic.
Running containerized backtesting workflows using the provided Docker image for easy deployment.

Not Ideal For

Teams building high-frequency trading systems requiring nanosecond-level latency optimizations.
Projects deeply integrated into Python ecosystems or relying on popular libraries like TA-Lib.
Developers needing drag-and-drop strategy builders or graphical user interfaces for non-technical users.
Organizations requiring permissive licensing for proprietary software without AGPL compliance.

Pros & Cons

Pros

Extensive Indicator Library

Includes over 70+ indicators across trend, momentum, volatility, and volume categories, such as MACD and RSI, providing comprehensive coverage for technical analysis.

Robust Backtesting Framework

Offers tools to test strategies on historical data and generate detailed HTML reports, with command-line and Docker support for easy execution, as shown in the backtesting examples.

Modern Go Architecture

Leverages Go generics for type flexibility and uses channels for efficient data stream processing, improving performance and testability with high code coverage.

AI Integration Ready

Includes a built-in Multi-Client Protocol server for seamless integration with AI tools, enabling real-time strategy execution and data processing.

Easy Deployment with Docker

Provides a Docker image that handles data syncing from sources like Tiingo and backtesting in a single command, simplifying setup and reproducibility.

Cons

Restrictive AGPL License

The AGPLv3 license requires open-sourcing derived works, which can be prohibitive for commercial projects without purchasing a separate commercial license.

Channel-Based Learning Curve

Version 2's shift to Go channels for data streams, while efficient, may confuse developers used to slice-based operations, despite helper functions for conversion.

Language and Ecosystem Lock-in

As a Go-only library, it doesn't support other popular languages in quantitative finance like Python, and key features like Alpaca integration are in separate repositories.

Limited Live Trading Focus

Primarily geared towards backtesting; live trading capabilities require additional setup with external repositories like indicatoralpaca, lacking built-in execution.

Frequently Asked Questions

What is indicator?

Target Audience

Value Proposition

Use Cases

Best For

Building algorithmic trading systems in Go that require real-time technical indicator calculations.
Backtesting trading strategies on historical asset data with detailed performance reports.
Integrating financial market analysis into AI tools via its built-in Multi-Client Protocol (MCP) server.
Synchronizing and storing asset data from multiple sources like Tiingo and Alpaca Markets using its repository system.
Developing custom trading strategies by combining pre-built indicators with compound and decorator logic.
Running containerized backtesting workflows using the provided Docker image for easy deployment.

Not Ideal For

Teams building high-frequency trading systems requiring nanosecond-level latency optimizations.
Projects deeply integrated into Python ecosystems or relying on popular libraries like TA-Lib.
Developers needing drag-and-drop strategy builders or graphical user interfaces for non-technical users.
Organizations requiring permissive licensing for proprietary software without AGPL compliance.

Pros & Cons

Pros

Extensive Indicator Library

Includes over 70+ indicators across trend, momentum, volatility, and volume categories, such as MACD and RSI, providing comprehensive coverage for technical analysis.

Robust Backtesting Framework

Offers tools to test strategies on historical data and generate detailed HTML reports, with command-line and Docker support for easy execution, as shown in the backtesting examples.

Modern Go Architecture

Leverages Go generics for type flexibility and uses channels for efficient data stream processing, improving performance and testability with high code coverage.

AI Integration Ready

Includes a built-in Multi-Client Protocol server for seamless integration with AI tools, enabling real-time strategy execution and data processing.

Easy Deployment with Docker

Provides a Docker image that handles data syncing from sources like Tiingo and backtesting in a single command, simplifying setup and reproducibility.

Cons

Restrictive AGPL License

The AGPLv3 license requires open-sourcing derived works, which can be prohibitive for commercial projects without purchasing a separate commercial license.

Channel-Based Learning Curve

Version 2's shift to Go channels for data streams, while efficient, may confuse developers used to slice-based operations, despite helper functions for conversion.

Language and Ecosystem Lock-in

As a Go-only library, it doesn't support other popular languages in quantitative finance like Python, and key features like Alpaca integration are in separate repositories.

Limited Live Trading Focus

Primarily geared towards backtesting; live trading capabilities require additional setup with external repositories like indicatoralpaca, lacking built-in execution.

Frequently Asked Questions

indicator

What is indicator?

Overview

Use Cases

Best For

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions

Related Projects

Found a gem we're missing?

indicator

What is indicator?

Overview

Use Cases

Best For

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions

Related Projects

Found a gem we're missing?