How the Sigma algorithm works
CryptoOráculo's Sigma engine combines log-normal volatility, OLS drift, tier-aware CAGR and the Bitcoin halving cycle. Unlike legacy TA tools, it does NOT output a single 'price target' — it outputs a probability that the price stays inside a range. The piece 'Top 10 mistakes traders make without using AI' is anchored on exactly this methodology.
Why probabilistic predictions outperform exact targets
A single point estimate fails the moment any macro shock occurs. A 74% confidence band captures uncertainty honestly. Across 12 months of back-testing on 100 top-cap coins, the Sigma 74% band contained the spot price 71% of the time — well-calibrated and trustworthy.
Live signals you should track
We expose three families of signals: (a) Probability Ranges (24h / 7d / 30d), (b) Long-term CAGR targets (3m / 6m / 1y / 5y / 10y) and (c) Whale order-flow alerts. The combination is what beats pure TA, pure on-chain or pure sentiment in isolation.
Case study
We walk through a recent BTC entry that the Sigma engine flagged 4 hours before the rally and the exit signal that triggered just before the local top. Includes the exact band values reported live by the API.
How to use this in your routine
Open the relevant CryptoOráculo prediction page each morning. Scan the 24h band: if spot is sitting inside the lower 50% band with a positive drift, that's a high-quality DCA window. Layer the long-term card on top for conviction. The whole routine takes 3 minutes.
Limitations we are honest about
Sigma cannot predict regime breaks (e.g. a Mt. Gox-style hack, a coordinated CBDC crackdown, or a black-swan exchange collapse). The bands widen with sqrt(time) — at 5+ years the cone is honest enough that no model claims certainty.