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This indicator is deliberately constructed to avoid repainting. Once a candle has closed, the output it produces remains fixed and unchanged. This behavior ensures accuracy during backtesting and provides reliability in real-time strategy execution.
Unlike traditional methods such as linear or polynomial regression, kernel-based methods don't assume any specific form or distribution for the data. Instead, they rely on a kernel function —a mathematical tool that assigns influence to each nearby
High Timeframe Exponential Moving Average (HTF EMA) Spot the Trend with Clarity – No Matter Your Timeframe!
The HTF EMA Indicator brings the power of higher timeframe analysis right onto your current chart. Whether you're trading M5,M15, H1, or any intraday timeframe, this tool overlays the Exponential Moving Average (EMA) from your chosen higher timeframe—such as H1, H4, or D1—so you can make smarter, more confident decisions.
Key Features: Displays EMA from Higher Timeframes directly o
Common Channel Index (CCI) Multi-Period Overlay Overview:
The Common Channel Index Multi-Period Overlay is a powerful -based tool that brings clarity and multi-timeframe insight into your trading decisions. By combining three different Commodity Channel Index (CCI) periods — 5 , 15 , and 60 — into a single, unified display, this indicator offers a layered view of market momentum and overbought/oversold conditions. Key Features: Triple CCI Overlay:
See short-term (5), mid-term (15), a
Keltner Channel MultiBand – Period 20 | ATR 10 | Bands 1.5, 2.0, 2.5 Unlock Precise Market Timing with Triple-Band Trend Mapping The Keltner Channel MultiBand is a powerful technical tool designed to help traders identify trend opportunities , optimize entry points , and strategically place Take Profit (TP) and Stop Loss (SL) levels. Key Features: Triple Bands (1.5x, 2.0x, 2.5x ATR) : Offers a multi-layered view of price volatility, helping you gauge the strength of market momentum with gr
This indicator is deliberately constructed to avoid repainting. Once a candle has closed, the output it produces remains fixed and unchanged. This behavior ensures accuracy during backtesting and provides reliability in real-time strategy execution.
Unlike traditional methods such as linear or polynomial regression, kernel-based methods don't assume any specific form or distribution for the data. Instead, they rely on a kernel function —a mathematical tool that assigns influence to each nearb