FractalCycles Reviews & Product Details

FractalCycles is a market cycle detection platform that helps analysts identify statistically significant repeating patterns in financial time series. The software applies digital signal processing (DSP) techniques to OHLCV price data to surface hidden periodicities and quantify market structure. The platform is built for market analysts, investment researchers, and quantitative practitioners who need rigorous cycle analysis beyond moving averages and visual chart inspection. Users upload or fetch market data across equities, forex, commodities, crypto, and 800,000+ economic series via FRED, then run automated analyses that return dominant cycles, statistical significance scores, and regime classifications. Core methodology combines three complementary techniques: - Goertzel DFT for efficient spectral analysis and dominant period detection - Bartels test for statistical validation of each detected cycle - Hurst exponent for regime classification (trending, mean-reverting, random walk) Key capabilities include: - Composite cycle projections overlaid on price charts - Interactive cycle selection and weighting - Multi-timeframe analysis (daily, weekly, monthly, intraday) - Regime detection using rolling Hurst exponent calculations - Indicator suite including CCI, DEMA, dual EMA, ZigZag, and trendlines - Data sources including Yahoo Finance and FRED (Federal Reserve Economic Data) - Educational content library covering Hurst, Goertzel, Bartels, and cycle theory FractalCycles operates on a subscription model with three tiers: Free (limited monthly analyses, daily timeframe), Pro (expanded quotas and additional timeframes), and Ultra (maximum quotas and priority processing). The platform runs entirely in the browser with no software installation required. Typical use cases include identifying dominant market cycles across equities and commodities, validating the statistical significance of perceived periodicities, detecting regime transitions for asset allocation decisions, and supporting research with reproducible cycle analysis.