The Price Movement Calculator estimates how long it takes for a stock, cryptocurrency, or futures contract to reach a given price target. Rather than relying on a single model, it combines multiple quantitative approaches — from classical volatility estimation to advanced stochastic processes — to produce probability-weighted time estimates and confidence intervals.

• —Supports stocks, crypto, and futures with real-time pricing
• —Calculates probability of reaching a target within 1 day, 1 week, and 1 month
• —Provides historical occurrence analysis showing past instances of similar moves
• —Offers side-by-side comparison of up to 4 assets simultaneously

Volatility is the foundation of every time estimate. The calculator offers multiple volatility estimation methods that users can switch between depending on their needs. Each method captures different aspects of price dynamics.

• —Close-to-Close: Standard deviation of log returns over a configurable lookback window
• —Parkinson: Uses daily high-low range for more efficient estimation with fewer data points
• —Garman-Klass: Combines open, high, low, close for the most efficient single-day estimator
• —Yang-Zhang: Accounts for overnight jumps by combining overnight and intraday volatility
• —EWMA: Exponentially weighted moving average that gives more weight to recent observations

The calculator uses geometric Brownian motion as its baseline model to estimate the probability of reaching a price target within specific time horizons. It accounts for both the drift (expected return) and diffusion (volatility) components of price dynamics.

• —First-passage time distribution for barrier-crossing probabilities
• —Drift-adjusted estimates using historical mean returns
• —Separate up-move and down-move probability calculations
• —Quantile projection cones showing 10th, 25th, 50th, 75th, and 90th percentile paths

The GARCH(1,1) model captures volatility clustering — the empirical observation that large price moves tend to be followed by large moves, and small moves by small moves. This produces more realistic probability cones than constant-volatility models, especially during turbulent markets.

• —Maximum likelihood estimation of GARCH(1,1) parameters (omega, alpha, beta)
• —Forward volatility term structure projection
• —Volatility regime classification (low, normal, high, extreme)
• —Monte Carlo simulation with time-varying volatility for probability cones

Beyond GARCH, the calculator implements several stochastic volatility and jump-diffusion models that capture different market dynamics. Each model adds a layer of realism to the probability estimates.

• —Heston Model: Stochastic volatility with mean-reversion and vol-of-vol, capturing the leverage effect
• —Merton Jump-Diffusion: Adds Poisson-distributed jumps to geometric Brownian motion for tail risk
• —Hidden Markov Model: Identifies latent market regimes (bull, bear, sideways) with transition probabilities
• —Hurst Exponent: Measures long-range dependence to detect trending vs mean-reverting behavior

The Log-Periodic Power Law (LPPL) model detects unsustainable super-exponential growth patterns that precede market crashes or corrections. It fits a parametric model to price data and evaluates whether the current trajectory matches historical bubble signatures.

• —Multi-timeframe analysis across hourly, daily, weekly, and monthly data
• —Quality gates checking omega frequency, damping ratio, and oscillation count
• —Cross-timeframe consensus scoring for robust bubble confidence
• —Predicted critical time (tc) and price level at the bubble peak

The regime dashboard scans multiple assets simultaneously, classifying each into a market regime based on GARCH volatility levels and trend direction. A composite signal combines direction forecasts, bubble detection, and volatility regime into a single actionable score.

• —GARCH-based regime classification: low-vol uptrend, high-vol downtrend, crisis, etc.
• —Direction forecast using an ensemble of online-learning predictors across multiple timeframes
• —Composite signal score from -1 (strongly bearish) to +1 (strongly bullish)
• —Granger causality network showing lead-lag relationships between assets

Every probability estimate is logged and evaluated against actual market outcomes. The accuracy tracking system measures calibration — whether events predicted at 70% probability actually occur 70% of the time — across all models and time horizons.

• —Per-model calibration scores (GARCH, Heston, Merton)
• —Breakdown by time horizon (1-day, 7-day, 30-day)
• —Historical hit rate tracking with statistical significance
• —Continuous recalibration as new outcomes are observed