I used to work in investment banking back in the early 2000s. Our "tech stack" was an Excel spreadsheet and a Bloomberg terminal. The problem? It relied on human input. And humans are terrible at processing fear.
Today, Dec 16, Bitcoin dropped ~4% to $86,241 and Ethereum broke $3,000 support to hit $2,928.
If I were trading manually, my amygdala (the fear center of the brain) would be screaming "SELL!" But today, I let my code handle the decision-making. Here is the logic behind the "AI Toolbox" I use at Blue Ocean Wealth.
- The "Whale Watcher" Function Price is a lagging indicator. Volume is leading. My script monitors on-chain wallet movements for addresses holding >1000 BTC. Today, while price dropped, large wallet accumulation remained neutral.
Human: "Price down = Bad."
Algo: "Price down + Whale holdings stable = Discount."
Sentiment Scrubbing (NLP) Using Natural Language Processing on social data, we track keywords like "Rekt," "Crash," and "Sell." Today, the "Fear Index" spiked. Historically, when retail fear hits >80 on my custom index, a bounce is statistically probable within 48 hours.
Cross-Asset Correlation The algorithm checks the correlation coefficient between BTC and Gold. Today, Gold also dropped to $4,290. This high correlation (moving together) implies a macro liquidity event (Pre-CPI de-risking), not a crypto-specific failure.
Conclusion Building trading bots isn't just about execution; it's about removing cognitive bias. Today's "crash" is just a data point in a larger array.
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