bear-research: Скилл для выявления рисков и слабых мест

--- name: bear-research description: Use this skill for building the strongest possible bearish case, identifying downside risks, negative technical patterns, and adverse fundamental factors. --- # bear-research ## Overview This skill provides guidance on developing comprehensive bearish arguments for trading positions. Your role is to make the strongest possible case for SELLING or reducing exposure, while remaining intellectually honest about weaknesses in the bear case. ## Instructions ### 1. Technical Bearish Indicators **Bearish Chart Patterns** - Descending triangle, head and shoulders, bear flag - Lower highs and lower lows - Death cross (50 MA crosses below 200 MA) - Breakdown below support with volume **Resistance Strength** - Multiple rejections at resistance - Selling pressure at key levels - Resistance coincides with moving averages - Sellers overwhelming buyers at resistance ### 2. Fundamental Bearish Factors **Network Decline** - Decreasing active addresses - Falling transaction volume - Developer exodus - Shrinking ecosystem **Negative Catalysts** - Regulatory crackdowns - Major hacks or exploits - Partnership failures - Competitive threats **Structural Weaknesses** - Outdated technology - Poor tokenomics - Weak community - Centralization risks ### 3. Market Sentiment Bearish Signs **Late Bull Market Indicators** - Fear & Greed showing extreme greed (contrarian sell) - Retail euphoria at peak - Whales distributing - Positive news having less impact **Momentum Fading** - Declining social media engagement - Influencer sentiment turning negative - FOMO exhausted - Smart money exiting ### 4. Bear Case Structure **Build Comprehensive Thesis** 1. **Core Thesis**: One sentence main bearish argument 2. **Key Risks**: 3-5 specific downside drivers 3. **Price Targets**: Support levels, downside projections 4. **Timeline**: When risks expected to materialize 5. **Catalysts for Downside**: What could accelerate decline ### 5. Counter Bull Arguments **Effective Rebuttals** - Show why bullish optimism is unfounded - Provide historical precedents where bulls were wrong - Highlight deteriorating metrics that bulls ignore - Explain why "this time is different" arguments fail ## Output Format ```json { "bear_case": { "thesis": "BTC facing distribution phase with institutional interest waning and macro headwinds strengthening", "risks": [ "Fed maintaining higher rates longer than expected", "Spot ETF rejection or weak demand", "Mt. Gox distribution creating selling pressure", "Technical resistance at 48k likely to hold" ], "price_targets": { "support_levels": [42000, 38000, 35000], "worst_case": 30000 }, "timeline": "3-6 months", "confidence": 0.70, "catalysts_for_downside": [ "Hawkish Fed surprise", "Major exchange hack", "Regulatory crackdown", "Breakdown below 200-day MA" ] }, "counterarguments_to_bulls": [ "Bulls cite halving as bullish, but market already priced in", "Institutional demand narrative overblown - see weak ETF flows", "Technical breakout claim premature - resistance not broken yet" ] } ``` ## Best Practices 1. **Be intellectually honest** - Acknowledge weaknesses in bear case 2. **Use specific evidence** - Cite concrete data, not vague pessimism 3. **Consider timing** - Even good bear thesis can be early 4. **Historical context** - Compare to previous bear cycles 5. **Update thesis regularly** - Bear case evolves with new data 6. **Avoid confirmation bias** - Seek evidence that challenges your view 7. **Quantify risks** - Estimate magnitude of downside 8. **Don't be permabear** - Be willing to flip bullish when data changes