Narrative-Driven Crypto Swing Trading Playbook

You are a crypto trader who has consistently profited from narrative-driven price movements. You understand that crypto markets are heavily influenced by rotating narratives (AI tokens, RWA, DePIN, L2s, memecoins, etc.) and you have developed a systematic approach to trading these cycles.

CONTEXT: Crypto narratives drive massive capital flows between sectors. A single narrative like "AI tokens" or "Real World Assets" can cause 5-20x moves in associated tokens over weeks to months. I want a repeatable system to identify emerging narratives early, select the best tokens within each narrative, time my entries and exits for swing trades, and manage risk when narratives fade. I have $25,000 in trading capital.

TASK: Create a narrative-driven swing trading playbook:
1. Build a narrative detection system: list 10+ data sources and signals to monitor (Crypto Twitter sentiment, token launch trends, VC funding announcements, protocol TVL changes, Google Trends, Dune Analytics dashboards, etc.). Define what constitutes a confirmed narrative vs. noise.
2. Map the narrative lifecycle (emergence, early adoption, mainstream, euphoria, decline) with observable characteristics at each stage and recommended trading actions.
3. Create a token selection framework within a narrative: evaluate by market cap tier, team/backing quality, token utility within the narrative, liquidity depth, and relative strength vs. peers. Provide a scoring rubric.
4. Design entry strategy for each narrative lifecycle stage: early stage (higher risk, larger position building), mid stage (momentum entries with tighter stops), and late stage (quick scalps only or short setups).
5. Build exit rules that differentiate between narrative exhaustion signals (declining social volume, reduced new wallet creation, whale distribution, funding rate divergence) and temporary pullbacks.
6. Include a real-world case study analysis of 3 past narratives (e.g., AI tokens late 2024, Solana DeFi renaissance, memecoin season) showing how this framework would have been applied.