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?
- Wisdom of the Crowd: 147,000+ members, 8M+ posts
- Long-Term Perspective: Focus on decades, not days
- Risk Management: Strong emphasis on diversification and risk control
- Market Regime Awareness: Discussions about market conditions
- 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 analyzedinsights_extracted: Number of insights extractedtopics_found: List of relevant topics found
extract_investing_insights
Extract investing insights from forum posts using Claude.
Parameters:
post_content: Forum post contentpost_metadata: Post metadata (author, replies, date)
Returns:
insight_type: Type of insight (market_regime, risk_management, strategy, sentiment)insight_text: Extracted insightconfidence: 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 dictionariesembedding_model: Model to use for embeddings (default: "text-embedding-3-small")
Returns:
stored_count: Number of insights storedrag_path: Path to RAG storage
get_bogleheads_signal
Get trading signal based on Bogleheads forum wisdom.
Parameters:
symbol: Symbol to analyzemarket_context: Current market contextquery: Specific query (e.g., "What do Bogleheads say about SPY in current market?")
Returns:
signal: BUY/SELL/HOLD recommendationconfidence: Confidence score (0-1)reasoning: Reasoning based on forum wisdominsights_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 discussedrisk_level: Perceived risk level (low, medium, high)
Integration with RL Engine
Bogleheads insights are integrated as a factor in the RL engine:
- State Space Enhancement: Adds "bogleheads_sentiment" feature
- Signal Weighting: Bogleheads signal contributes 5-10% to ensemble voting
- Risk Adjustment: Uses Bogleheads risk perception to adjust position sizing
- 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
1---2name: bogleheads-forum-learner3description: Continuously learns from Bogleheads.org forum to extract investing wisdom and integrate into RL trading engine4---5
6# Bogleheads Forum Learner Skill
7
8Continuously monitors and learns from [Bogleheads.org](https://www.bogleheads.org/forum/index.php) to extract investing wisdom and integrate insights into the RL trading engine.
9
10## Overview
11
12Bogleheads is a community focused on passive investing, index funds, and long-term wealth building (inspired by Jack Bogle, founder of Vanguard). This skill:
13
14- **Monitors** forum discussions for investing insights
15- **Extracts** wisdom about market regimes, risk management, and strategy
16- **Stores** insights in RAG for retrieval
17- **Integrates** insights as a factor in RL engine decision-making
18
19## Why Bogleheads?
20
211. **Wisdom of the Crowd**: 147,000+ members, 8M+ posts
222. **Long-Term Perspective**: Focus on decades, not days
233. **Risk Management**: Strong emphasis on diversification and risk control
244. **Market Regime Awareness**: Discussions about market conditions
255. **Contrarian Signals**: Often identifies when markets are overheated/oversold
26
27## Tools
28
29### `monitor_bogleheads_forum`
30
31Monitor Bogleheads forum for new discussions and insights.
32
33**Parameters**:
34- `topics`: List of topics to monitor (default: ["Personal Investments", "Investing - Theory, News & General"])
35- `keywords`: Keywords to filter for (default: ["market timing", "rebalancing", "risk", "volatility", "bear market", "bull market"])
36- `max_posts`: Maximum posts to analyze per run (default: 50)
37- `min_replies`: Minimum replies for post to be considered (default: 5)
38
39**Returns**:
40- `posts_analyzed`: Number of posts analyzed
41- `insights_extracted`: Number of insights extracted
42- `topics_found`: List of relevant topics found
43
44### `extract_investing_insights`
45
46Extract investing insights from forum posts using Claude.
47
48**Parameters**:
49- `post_content`: Forum post content
50- `post_metadata`: Post metadata (author, replies, date)
51
52**Returns**:
53- `insight_type`: Type of insight (market_regime, risk_management, strategy, sentiment)
54- `insight_text`: Extracted insight
55- `confidence`: Confidence score (0-1)
56- `relevance_score`: Relevance to trading (0-1)
57- `actionable`: Whether insight is actionable
58
59### `store_insights_to_rag`
60
61Store extracted insights in RAG storage for retrieval.
62
63**Parameters**:
64- `insights`: List of insight dictionaries
65- `embedding_model`: Model to use for embeddings (default: "text-embedding-3-small")
66
67**Returns**:
68- `stored_count`: Number of insights stored
69- `rag_path`: Path to RAG storage
70
71### `get_bogleheads_signal`
72
73Get trading signal based on Bogleheads forum wisdom.
74
75**Parameters**:
76- `symbol`: Symbol to analyze
77- `market_context`: Current market context
78- `query`: Specific query (e.g., "What do Bogleheads say about SPY in current market?")
79
80**Returns**:
81- `signal`: BUY/SELL/HOLD recommendation
82- `confidence`: Confidence score (0-1)
83- `reasoning`: Reasoning based on forum wisdom
84- `insights_used`: List of insights that informed the signal
85
86### `analyze_market_regime_bogleheads`
87
88Analyze current market regime based on Bogleheads discussions.
89
90**Parameters**:
91- `timeframe`: Timeframe to analyze (default: "30d")
92
93**Returns**:
94- `regime`: Market regime classification (bull, bear, choppy, uncertain)
95- `sentiment`: Overall sentiment (bullish, bearish, neutral)
96- `key_themes`: List of key themes discussed
97- `risk_level`: Perceived risk level (low, medium, high)
98
99## Integration with RL Engine
100
101Bogleheads insights are integrated as a **factor** in the RL engine:
102
1031. **State Space Enhancement**: Adds "bogleheads_sentiment" feature
1042. **Signal Weighting**: Bogleheads signal contributes 5-10% to ensemble voting
1053. **Risk Adjustment**: Uses Bogleheads risk perception to adjust position sizing
1064. **Regime Detection**: Uses Bogleheads regime analysis for context
107
108## Usage Example
109
110```python
111from claude.skills.bogleheads_learner.scripts.bogleheads_learner import BogleheadsLearner
112
113learner = BogleheadsLearner()
114
115# Monitor forum
116results = learner.monitor_bogleheads_forum(
117 topics=["Personal Investments", "Investing - Theory"],
118 keywords=["market timing", "risk"],
119 max_posts=50
120)
121
122# Get signal for symbol
123signal = learner.get_bogleheads_signal(
124 symbol="SPY",
125 market_context={"volatility": "high", "trend": "bullish"},
126 query="What do Bogleheads recommend for SPY in high volatility?"
127)
128
129# Use in RL engine
130rl_state["bogleheads_sentiment"] = signal["confidence"]
131rl_state["bogleheads_regime"] = signal["regime"]
132```
133
134## Continuous Learning Schedule
135
136- **Daily**: Monitor new posts (runs at 2 AM UTC)
137- **Weekly**: Deep analysis of trending topics
138- **Monthly**: Regime analysis and strategy review
139
140## Data Privacy
141
142- Respects forum terms of service
143- Only analyzes publicly available posts
144- No personal information stored
145- Rate-limited to avoid overloading forum
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