Building an Agentic Data Pipeline for Predictive Trading
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the gist
An agentic trading workflow that aggregates sentiment from social media, news, and whale activity into a master file to inform betting decisions on platforms like Polymarket.
The Data Aggregation Strategy
The author utilizes a multi-source data pipeline to feed an AI agent with real-time context, arguing that the quality of the data pipeline is more critical to trading success than the underlying model. The system compiles unstructured data from five distinct sources into a single master text file, which the agent then parses to calculate expected value for specific market bets.
Pipeline Architecture
- Market Data: Uses the Kali API and websockets to retrieve competitor market pricing and threshold data.
- Social Sentiment: Employs a browser-based "surf agent" to scrape news, X (formerly Twitter), and Reddit for keyword-specific sentiment analysis.
- Whale Tracking: Monitors large-scale betting activity on the blockchain via the Polymarket API to identify high-conviction market movements.
- Compilation: All gathered information is appended to a
master_unstructured.txtfile, providing a unified context window for the agent. - Decision Logic: The agent executes a goal-oriented prompt against the compiled master file and the Polymarket API to identify trades with positive expected value.
Context
The author demonstrates this workflow by placing a bet on a Formula 1 outcome, citing a 28% gain within 15 minutes of execution. The process relies on browser automation to navigate live sites, allowing the agent to synthesize disparate signals—such as negative Bitcoin sentiment and liquidation headlines—into a high-level trade recommendation.