unifai-trading-suite

⚠Review·Scanned 2/19/2026

This skill is an AI-powered trading suite for prediction markets and social signals, providing CLI scripts and a FastAPI web UI. It requires UNIFAI_AGENT_API_KEY/GOOGLE_API_KEY, makes network calls to https://api.elections.kalshi.com/trade-api/v2 and UnifAI/LLM services, and instructs running python3 scripts and copying skills to ~/.clawdbot/skills/.

from clawhub.ai·v1.0.0·163.8 KB·0 installs
Scanned from 1.0.0 at 701454c · Transparency log ↗
$ vett add clawhub.ai/zbruceli/unifai-trading-suiteReview findings below

AI Trader for Prediction Markets

An AI-powered trading agent for prediction markets that leverages LLMs to create and execute trading strategies based on social network signals and on-chain analysis.

Features

  • Multi-Platform Support: Trade on Polymarket and Kalshi prediction markets
  • Social Signal Analysis: Track KOL mentions, sentiment, and trending tokens
  • LLM-Powered Strategies: Uses Google Gemini 3.0 Flash for intelligent analysis
  • UnifAI Integration: Dynamic tool discovery and agent-to-agent communication
  • Web Interface: Simple chat-based frontend for trading queries
  • Moltbot Skills: Packaged as reusable skills for AI agents

Quick Start

Prerequisites

  • Python 3.10+
  • UnifAI API key (for social signals and Polymarket)
  • Google API key (for Gemini LLM)

Installation

# Clone the repository
git clone https://github.com/zbruceli/trading.git
cd trading

# Create virtual environment
python -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

Environment Variables

export UNIFAI_AGENT_API_KEY="your-unifai-key"
export GOOGLE_API_KEY="your-google-key"
export LLM_MODEL="gemini/gemini-3-flash-preview"

Running

# Run the trading agent demo
python -m src.agents.trading_agent --demo

# Interactive mode
python -m src.agents.trading_agent

# Start web interface
uvicorn src.api.server:app --port 8080

Usage

Trading Agent

from src.agents import TradingAgent

agent = TradingAgent()

# Analyze a token with price + social + news signals
analysis = await agent.analyze_token("SOL")

# Get trending tokens from KOL discussions
trending = await agent.get_trending_signals()

# Natural language queries
response = await agent.chat("Get ETH price and recent news")

Kalshi Markets

from src.markets import KalshiClient

client = KalshiClient()

# Get Fed interest rate markets
fed_markets = await client.get_fed_markets(limit=10)

# Search markets
results = await client.search_markets("bitcoin", limit=5)

Social Signals

from src.signals import SocialSignalProcessor

processor = SocialSignalProcessor()

# Get token sentiment
sentiment = await processor.get_token_sentiment("ETH")

# Get trending tokens from KOLs
trending = await processor.get_trending_tokens(time_window="24h")

Prediction Market Integrations

PlatformIntegrationMarket Types
PolymarketUnifAI SDKCrypto, politics, sports
KalshiDirect APIEconomics, politics, events

Project Structure

trading/
├── src/
│   ├── agents/        # Trading agents
│   ├── api/           # Web API & frontend
│   ├── markets/       # Market clients (Kalshi, Polymarket)
│   ├── signals/       # Social signal processors
│   └── strategies/    # Trading strategies
├── skills/            # Moltbot skill definitions
└── tests/

Moltbot Skills

Pre-packaged skills for AI agent platforms:

  • prediction-trader - Cross-platform trading assistant
  • kalshi-trader - Kalshi market queries
  • polymarket-trader - Polymarket integration
  • social-signals - Social signal analysis

See CLAUDE.md for detailed skill documentation.

Technology Stack

  • LLM: Google Gemini 3.0 Flash (via LiteLLM)
  • Agent Framework: UnifAI SDK
  • Skills Platform: Moltbot (AgentSkills-compatible)
  • Language: Python 3.10+

License

MIT

References