Token Metrics Launches API for Python Developers with 35% Discount for $TMAI Staking

Token Metrics has introduced a comprehensive API designed to empower Python developers with reliable data and AI-powered insights for building advanced crypto applications, bots, and dashboards. This API provides a seamless integration of live market data, Trader Grades, and historical OHLCV data, making it an essential tool for developers aiming to enhance their crypto projects.

The quick setup process for developers begins with installing the official Token Metrics Python SDK, which can be done using the command "pip install tokenmetrics." Alternatively, developers can work directly with requests, as both methods are supported. The first step involves generating an API key by logging into the Token Metrics account and navigating to the API Keys Dashboard. Once the key is generated, it can be used to monitor usage, rate limits, and quotas directly from the dashboard.

Retrieving crypto prices in Python is straightforward with the Token Metrics API. For instance, fetching the latest price data for Ethereum (ETH) involves importing the requests module, setting the API key, and making a GET request to the specified URL. The response is then parsed to extract the date and close price for each data point, providing a working Python crypto price API pipeline. Developers can customize the start and end dates to retrieve specific ranges of historical data.

One of the standout features of the Token Metrics API is its AI-driven token ratings, known as Trader Grades. These grades can be accessed by making a GET request to the appropriate endpoint and parsing the JSON response. The data can be used to automate trading logic, such as entering trades when the Trader Grade exceeds a certain threshold, or overlaying the grades on charts for visual analysis.

For developers looking to backtest their strategies, the API allows for the merging of OHLCV data and Trader Grades. This can be achieved using the pandas library to create dataframes and merge them based on the date. The combined data can then be used to run simulations, build analytics dashboards, or train machine learning models.

The Token Metrics API offers a range of endpoints, including historical price data, AI signal grades, trading signals, sentiment scores, and a natural language interface for asking questions in plain English. All endpoints return structured JSON and can be queried using modern clients like requests or axios. The API also provides real-time tracking of credits, rate limits, and usage, with a free plan offering 5,000 calls per month, making it ideal for testing and building minimum viable products (MVPs).

Developers can further optimize their API usage by staking and paying with Token Metrics’ native token, $TMAI, which can reduce the API bill by up to 35%. This feature is available via the settings and payments page on the Token Metrics platform.

In summary, the Token Metrics API is a powerful tool for Python developers seeking to build advanced crypto applications. It combines market data with proprietary AI intelligence, trader/investor grades, sentiment scores, and backtest-ready endpoints, all accessible through a user-friendly platform. With real-time and historical data, RESTful endpoints, Python-ready SDKs, and comprehensive documentation, developers can start building their projects today. For more information, developers can visit the Token Metrics API documentation and explore the full Python Quick Start Guide.