According to @camillionaire_m, optimists make money; traders can operationalize this by adding risk when sentiment turns up using measurable triggers such as the Crypto Fear and Greed Index rebounding from extreme fear, AAII bearish sentiment at extreme levels reverting, and BTC perpetual funding flipping from negative to positive with rising open interest; source: @camillionaire_m; Alternative.me Crypto Fear and Greed Index; American Association of Individual Investors Sentiment Survey; CoinGlass derivatives data. Evidence shows sentiment and trend filters improve entry quality, so use a simple trend confirmation such as price above the 200-day moving average and validate with on-chain realized profit and loss ratios before scaling into BTC and ETH; source: Baker and Wurgler 2006 Journal of Finance; Meb Faber 2006 A Quantitative Approach to Tactical Asset Allocation; Glassnode on-chain metrics.

According to @sama, there are now many LLM-run accounts on X, pointing to increased AI-generated activity on the platform (Source: Sam Altman on X, Sep 3, 2025). For crypto traders, a higher bot load can distort social-sentiment signals used in trading models and dashboards (Source: Ferrara et al., The Rise of Social Bots, Communications of the ACM). Studies have linked Twitter sentiment and activity to short-horizon moves and volume in Bitcoin and other cryptocurrencies, making data integrity critical for BTC and ETH strategies (Source: Stenqvist and Lönnqvist, 2017; Phillips and Gorse, 2017). Regulators warn investors not to trade solely on social-media hype due to manipulation risks (Source: U.S. SEC Office of Investor Education and Advocacy, Investor Alert: Social Media and Investment Fraud). In practice, traders should prioritize signals from verified accounts, apply bot-detection screening, and cross-check with on-chain flows and order book depth to reduce noise from AI-driven accounts (Source: X Help Center, Verification; Indiana University OSoMe, Botometer; Chainalysis Market Intel, on-chain analytics notes).