Just completed a fascinating test of my AI agent's dual-source EV analysis on a Royal Ascot race. The agent combined trading pattern analysis with semantic performance evaluation to select ROGUE LEGEND at 6.8 odds with +0.25 EV.
What makes this interesting? The AI didn't just pick a horse - it generated both human-readable tables AND machine-readable JSON output, then automatically executed a "Back trailing stop loss trading" strategy based on convergence between market signals and performance data.
Horse Racing Combined EV Analysis with Table and JSON Output!
But here's the REAL question: How do we know if the AI is actually good at this? 🤔
The Critical Missing Piece: Backtesting & Learning Loop
Right now, my AI agent can:
✅ Analyze current market data
✅ Evaluate performance semantics
✅ Calculate EV and execute strategies
❌ Learn from its mistakes
What I'm building next: A new MCP tool SetAIAgentDataContextForBetfairMarket that would:
Store AI predictions alongside actual race results
Track strategy performance over time
Identify covariance patterns between AI confidence levels and actual outcomes
Flag systematic biases (e.g., "AI overvalues recent winners by 15%")
Suggest model improvements based on historical prediction accuracy
Real Example Questions We Could Answer:
Does the AI's "18% win probability" actually translate to 18% win rate over 100 races?
Which semantic indicators are most/least predictive?
Are trading patterns better predictors than performance analysis?
How should we adjust EV calculations based on historical accuracy?
The Goal: Transform from "AI makes prediction" to "AI learns and improves predictions"
For the Community
For the community: Anyone else working on AI backtesting for betting strategies? What metrics do you track? How do you handle the feedback loop between predictions and results?
Would love to hear thoughts on building truly adaptive AI agents that get smarter with each race! 🏇
Currently testing with Bfexplorer + Claude via MCP integration. The JSON output makes backtesting analysis much easier than traditional betting logs.
TL;DR
AI picked a horse, executed a strategy, but without backtesting we're flying blind. Building tools to make AI agents learn from their wins AND losses.
Technical Implementation Notes
Current System Capabilities
Dual-source analysis: Trading patterns + performance semantics
Automated strategy execution: Based on EV and confidence thresholds
Dual output format: Human-readable tables + machine-readable JSON
Real-time market integration: Via Bfexplorer MCP tools
Proposed Enhancement: SetAIAgentDataContextForBetfairMarket
Purpose: Create a feedback loop for AI learning and improvement
Benefits of Backtesting Integration
Calibration Improvement: Adjust probability assessments based on historical accuracy
Strategy Optimization: Identify which strategies work best in different market conditions
Bias Detection: Uncover systematic errors in AI reasoning
Confidence Scoring: Build more accurate confidence intervals
Adaptive Learning: Continuously improve prediction models
Community Discussion Points
Metrics for Success: What KPIs matter most for AI betting agents?
Sample Size: How many races needed for statistical significance?
Market Evolution: How to handle changing market dynamics?
Overfitting Prevention: Avoiding optimization to historical data only
This post highlights the sophistication of current AI betting systems while emphasizing the crucial next step of creating learning feedback loops for continuous improvement.