Explore 11,183 indicators. Filter beyond the obvious.
Discover the tools that fit the way you trade.
11,183 tools to explore
A probabilistic trading indicator using Normal-Inverse Gaussian distribution to estimate future price movements by analyzing log returns and calculating real-time return probability distributions.
A sophisticated moving average technique using Gaussian kernel smoothing to reduce price noise and capture underlying market trends with dynamic bandwidth adaptation.
A statistical trading signal that analyzes historical returns, projects future confidence levels, and provides insights into potential price movements through probability and distribution analysis.
A statistical indicator that calculates cumulative distribution function, assessing probability distributions, kurtosis, and skewness for a given dataset to understand price movement characteristics.
A frequency histogram indicator that creates a bell curve distribution, analyzing market data's statistical properties and revealing price accumulation zones through comprehensive statistical analysis.
A trading indicator that analyzes price distribution skewness and volatility to identify potential buy and sell zones, offering unique market movement insights.
Bandwidth Bands use Silverman's Rule of Thumb to create dynamic volatility bands around a moving average, offering insights into price distribution and market volatility.
A trading signal that calculates logarithmic returns and generates projections using both statistical and empirical methods to forecast potential asset performance.
A volatility and skew-based oscillator that helps traders identify market trends by measuring price movement direction and market momentum using standard deviation and skewness.
A third-order moment indicator that analyzes price distribution skewness to help traders identify potential market turning points and trend asymmetries.
N-Degree Moment-Based Adaptive Detection (NDMBAD) is an advanced polynomial regression method for analyzing market trends and detecting deviations from data's core process.
Likes, views, and comments are historical TradingView snapshots. They describe attention, not trading performance.
![[SGM Return Distribution] chart preview](/_next/image?url=%2Fmedia%2F21219.png&w=3840&q=90&dpl=dpl_Jt27hmzh6tNsGuJ13vyBBWgv8XSW)
![Cumulative Distribution of a Dataset [SS] chart preview](/_next/image?url=%2Fmedia%2F29861.png&w=3840&q=90&dpl=dpl_Jt27hmzh6tNsGuJ13vyBBWgv8XSW)
![Distribution Histogram [SS] chart preview](https://s3.tradingview.com/m/MV0YbQui.png?v=1703150716)
![[SGM VaR Stats VS Empirical] chart preview](https://s3.tradingview.com/w/wOuCfQOu.png?v=1732082135)
