Crypto price prediction — the Consensus Score from 6 derivatives signals.

The 6 sub-signals are: funding rate trend (measures leveraged sentiment direction), open interest momentum (measures whether new positions are entering or exiting), long/short ratio (measures top-trader crowding), liquidation bias (tracks forced buying/selling pressure), RSI (relative strength index momentum oscillator), and EMA cross (exponential moving average trend direction). Each signal is scored independently on a -100 to +100 scale before combination into the 0-100 consensus score.

The score is a directional bias indicator, not a guaranteed prediction or published hit-rate model. It is most useful when multiple derivatives and momentum signals agree. When signals are mixed or data coverage is thin, the score stays closer to neutral instead of creating false precision. Use it as one market-structure input, not as a standalone trading system.

Conviction weighting combines the 6 independent sub-signals by magnitude. A barely positive funding rate should not move the score much, while an extreme funding or liquidation reading should carry more weight. Conflicting signals pull the final score back toward neutral, which prevents small or noisy readings from dominating the output.

The complete methodology is documented at /price-prediction/methodology on Sharpe Terminal. The documentation covers the formula for each of the 6 sub-signals, the conviction weighting algorithm, low-coverage behavior, and the data sources used for each signal. This level of transparency lets you evaluate the score's logic before using it in your own analysis.

The live score is a current directional read, refreshed hourly, and is best interpreted over hours-to-weeks positioning horizons. Sharpe also publishes year-specific SEO pages for common forecast queries, but those pages provide scenario framing rather than separate point-price targets or separate model weights.

No. Price Prediction is a quantitative analysis tool that combines 6 market signals into a directional score. It is provided for informational and educational purposes only and should not be considered financial advice. The score should be one input among many in your decision-making process. No model can account for black swan events, regulatory changes, or other unpredictable market shocks.

Scores are recalculated hourly in two staggered cron batches. Funding rates, open interest, long/short ratios, liquidation data, RSI, and EMA inputs are refreshed from the latest available exchange and market data before the score is cached for the terminal and API.

Yes. Price Prediction is available free on Sharpe Terminal with no account required. The coin screener, detail pages, 6 sub-signals, conviction-weighted scores, and the full methodology documentation are accessible immediately. The same prediction data is available through Sharpe's REST API.

Crypto price predictions vary enormously in reliability. Quantitative signals built on real market structure (funding, open interest, liquidations, RSI, and EMA) can be useful over short horizons when several inputs agree, but they are still uncertain and regime-dependent. Long-horizon predictions are inherently unreliable because crypto is subject to regulatory, macro, and black-swan shocks that no model can anticipate. Treat every score as a probabilistic input, not a point forecast. Any model advertising ">90% accuracy" is likely overfit or misrepresented.

No credible model can give a point-estimate answer to year-end price targets. Bitcoin's price is driven by institutional flows, macro conditions, supply dynamics, regulatory developments, and liquidity shocks. Rather than anchoring to a specific target, use the live consensus score to understand current derivatives positioning and update your view as new data arrives.

Technical analysis (TA) is the study of historical price and volume patterns to frame future price movement. Common TA tools include moving averages, momentum oscillators, trend indicators, support and resistance, and chart patterns. Sharpe's prediction score uses RSI and EMA cross alongside derivatives data, blending momentum with positioning inputs instead of relying on chart patterns alone.