Bogleheads Forum Learner

Bogleheads Forum Learner Skill

Continuously monitors and learns from Bogleheads.org to extract investing wisdom and integrate insights into the RL trading engine.

Overview

Bogleheads is a community focused on passive investing, index funds, and long-term wealth building (inspired by Jack Bogle, founder of Vanguard). This skill:

• Monitors forum discussions for investing insights
• Extracts wisdom about market regimes, risk management, and strategy
• Stores insights in RAG for retrieval
• Integrates insights as a factor in RL engine decision-making

Why Bogleheads?

1. Wisdom of the Crowd: 147,000+ members, 8M+ posts
2. Long-Term Perspective: Focus on decades, not days
3. Risk Management: Strong emphasis on diversification and risk control
4. Market Regime Awareness: Discussions about market conditions
5. Contrarian Signals: Often identifies when markets are overheated/​oversold

Tools

monitor_bogleheads_forum

Monitor Bogleheads forum for new discussions and insights.

Parameters:

• topics: List of topics to monitor (default: ["Personal Investments", "Investing - Theory, News & General"])
• keywords: Keywords to filter for (default: ["market timing", "rebalancing", "risk", "volatility", "bear market", "bull market"])
• max_posts: Maximum posts to analyze per run (default: 50)
• min_replies: Minimum replies for post to be considered (default: 5)

Returns:

• posts_analyzed: Number of posts analyzed
• insights_extracted: Number of insights extracted
• topics_found: List of relevant topics found

extract_investing_insights

Extract investing insights from forum posts using Claude.

Parameters:

• post_content: Forum post content
• post_metadata: Post metadata (author, replies, date)

Returns:

• insight_type: Type of insight (market_regime, risk_management, strategy, sentiment)
• insight_text: Extracted insight
• confidence: Confidence score (0-1)
• relevance_score: Relevance to trading (0-1)
• actionable: Whether insight is actionable

store_insights_to_rag

Store extracted insights in RAG storage for retrieval.

Parameters:

• insights: List of insight dictionaries
• embedding_model: Model to use for embeddings (default: "text-embedding-3-small")

Returns:

• stored_count: Number of insights stored
• rag_path: Path to RAG storage

get_bogleheads_signal

Get trading signal based on Bogleheads forum wisdom.

Parameters:

• symbol: Symbol to analyze
• market_context: Current market context
• query: Specific query (e.g., "What do Bogleheads say about SPY in current market?")

Returns:

• signal: BUY/​SELL/​HOLD recommendation
• confidence: Confidence score (0-1)
• reasoning: Reasoning based on forum wisdom
• insights_used: List of insights that informed the signal

analyze_market_regime_bogleheads

Analyze current market regime based on Bogleheads discussions.

Parameters:

• timeframe: Timeframe to analyze (default: "30d")

Returns:

• regime: Market regime classification (bull, bear, choppy, uncertain)
• sentiment: Overall sentiment (bullish, bearish, neutral)
• key_themes: List of key themes discussed
• risk_level: Perceived risk level (low, medium, high)

Integration with RL Engine

Bogleheads insights are integrated as a factor in the RL engine:

1. State Space Enhancement: Adds "bogleheads_sentiment" feature
2. Signal Weighting: Bogleheads signal contributes 5-10% to ensemble voting
3. Risk Adjustment: Uses Bogleheads risk perception to adjust position sizing
4. Regime Detection: Uses Bogleheads regime analysis for context

Usage Example

from claude.skills.bogleheads_learner.scripts.bogleheads_learner import BogleheadsLearner

learner = BogleheadsLearner()

# Monitor forum
results = learner.monitor_bogleheads_forum(
    topics=["Personal Investments", "Investing - Theory"],
    keywords=["market timing", "risk"],
    max_posts=50
)

# Get signal for symbol
signal = learner.get_bogleheads_signal(
    symbol="SPY",
    market_context={"volatility": "high", "trend": "bullish"},
    query="What do Bogleheads recommend for SPY in high volatility?"
)

# Use in RL engine
rl_state["bogleheads_sentiment"] = signal["confidence"]
rl_state["bogleheads_regime"] = signal["regime"]

Continuous Learning Schedule

• Daily: Monitor new posts (runs at 2 AM UTC)
• Weekly: Deep analysis of trending topics
• Monthly: Regime analysis and strategy review

Data Privacy

• Respects forum terms of service
• Only analyzes publicly available posts
• No personal information stored
• Rate-limited to avoid overloading forum