Trading Strategy
HALO provides multiple ways to profit from prediction markets — from manual analysis using AI signals to fully automated trading bots.
Overview
The platform supports multiple trading approaches:
- AI Signal Trading: Use fair value signals and mispricing detection to find underpriced markets
- Whale Following: Track smart money and follow high-conviction bets from top traders
- Cross-Platform Arbitrage: Exploit price differences between Polymarket and Limitless
- Automated Market Making: Deploy bots that provide liquidity and capture spreads automatically
Market Making Strategy
Core Principle
The AMM places simultaneous buy and sell orders around the current market price, capturing the bid-ask spread.
spread = ask_price - bid_price
Spread Optimization
Our AI dynamically adjusts spreads using:
- Kelly Criterion: Optimal position sizing based on edge and bankroll
- Avellaneda-Stoikov Model: Real-time spread adjustment based on volatility, liquidity, and market conditions
- Market volatility analysis
- Liquidity depth assessment
- Detected mispricing levels
Spreads are optimized in real-time to maximize fill rates while maintaining profitability, adapting to changing market conditions.
Order Placement
Orders are placed using a sophisticated algorithm that:
- Analyzes current market depth
- Calculates optimal price levels
- Sizes positions based on available capital
- Monitors and adjusts in real-time
Mispricing Arbitrage
Detection Algorithm
Our ensemble ML models analyze 45+ features to predict true probability:
- Historical price data
- Market sentiment indicators
- Volume patterns
- Time to resolution
mispricing = |predicted_probability - market_price|
Execution Criteria
Trades are executed when:
mispricing 5% AND model_confidence 60%
This dual-threshold approach ensures we only trade on high-confidence opportunities.
Profit Mechanism
When both legs of a binary market are purchased at prices where P_UP + P_DOWN 1:
guaranteed_profit = 1 - (P_UP + P_DOWN)
This profit is realized at market settlement, regardless of the outcome.
AI Fair Value Model
Our ensemble model combines:
- XGBoost: Gradient boosting for feature importance and pattern recognition
- LSTM: Time series analysis for temporal patterns and sequential dependencies
- PPO: Reinforcement learning for optimal trading policy
- LLM: Large language model for sentiment and context analysis
- Ensemble: Weighted combination for final prediction
Model Performance
The ensemble model analyzes 45 features (25 technical indicators, 4 sentiment signals, 8 funding rates, 8 order book depth metrics) to discover fair value, significantly outperforming naive market prices.
Capital Allocation
Capital is allocated across Polymarket and Limitless using a priority queue system:
- High Confidence Arbitrage: Markets with 60% confidence where mispricing is detected
- Market Making: Liquid markets with stable spreads and high trading volume
- Dynamic Rebalancing: Real-time adjustment based on Kelly Criterion and market conditions
The AI bot maintains balanced inventory and minimizes risks while maximizing spread capture opportunities.
Combining Strategies
The most effective approach combines multiple signal types:
- AI + Whales: When AI detects mispricing and whales are trading the same direction
- Arbitrage + AI: Cross-platform spreads confirmed by AI fair value analysis
- Manual + Bots: Use the trading terminal for analysis, deploy bots for execution
Risk Controls
Each strategy includes built-in risk management:
- Position Limits: Max 5% of vault per market
- Stop Losses: 10% loss threshold triggers exit
- Time Decay: Reduce positions as resolution approaches
- Correlation Limits: Avoid overexposure to related markets
Performance Metrics
Track strategy performance through:
- Sharpe Ratio: Risk-adjusted returns
- Win Rate: Percentage of profitable trades
- Average Spread Captured: Mean profit per trade
- Capital Efficiency: Returns per dollar deployed
Next Steps
- AI Fair Value - Deep dive into our ML models
- Risk Management - Comprehensive risk analysis
