What you’ll need
- Basic Python knowledge.
- Python environment configured with
requests
andpandas
. - An API key from the Dashboard (only for production use, you can use the API without an API key in development mode)
Walkthrough
1
Setting Up the Environment and Connecting to Mobula APIs
Install Dependencies:Mobula API Configuration: Obtain your free API key from Dashboard.Connecting to Mobula APIs: Use Mobula’s endpoints for market and wallet data:Market DataRetrieve prices, volumes, and market caps using the Relevant guides:
/market/data
endpoint. Refer to the Market Data Documentation for more information.Wallet DataAccess balances and transactions via wallet endpoints:2
Adding an AI Layer
Add an AI layer to analyze and interpret collected data and turn it into actionable insights.Choosing a Model: Use GPT (e.g., OpenAI’s API) or a local ML model to analyze data retrieved from Mobula APIs.Example Analysis:
3
Enabling the LLM to Use the API
To enhance automation and flexibility, provide the LLM with Mobula’s API context. This will allow the AI to understand the API’s functions and generate requests or interpret responses dynamically.Defining the API Context: Provide detailed documentation of Mobula’s API endpoints, parameters, and expected responses to the LLM. For example:Prompting the LLM: Use the API context and an instruction to generate a query or analyze data dynamically.
4
Automating and Alerting
Automate trend detection and set up real-time alerts for significant changes, ensuring quick market response.Trend Detection: Write scripts to detect significant price or volume changes.Sending Alerts: Configure email or Slack notifications based on specific conditions:
5
(Optional) Simple User Interface
Use frameworks like Flask or Streamlit to create a basic interface for displaying real-time insights.