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The Composite Predictive Index-(CPI-IG v5)**

The Composite Predictive Index (CPI-IG v5) is a comprehensive Market Institutional Indicator created by Alcides Davila and is an overlay indicator designed for institutional-grade market analysis and trading signals. Nevertheless, Daily-Short-Term Traders may also take advantage of this robust and efficient indicator. Still, they must make the necessary adjustments for scalping and for short-, medium-, and long-term trading. It synthesizes multiple technical factors (e.g., RSI, MACD, Bollinger Bands, VWAP, EMAs/SMAs, volume pressure, delta volume, manual sentiment/news inputs) into a weighted Z-score-based probability model (probUp) for forecasting price direction—generating buy/sell gates, strong/ultra signals, and short-term projections. It supports multi-timeframe alignment (HTF/LTF), breakout/breakdown detection with retests, internal backtesting, and alerts, while displaying dashboards for probabilities, stats, oscillators (bull/bear/neutral), major indexes (S&P, DJIA, Nasdaq), and ETFs (SPY, QQQ, etc.).
In terms of structure, it's highly reliable and productive: modular code with error-handling (safe divisions, approximations for tanh/erf), customizable modes (scalp to long-term), efficient resource use (max_bars_back=500), and cooldowns to prevent alert spam. Quality is strong, with transparent math, visual flexibility, and no apparent logic bugs—though real-world performance depends on market conditions and user tuning.
Investors can benefit significantly by using it for data-driven decisions, reducing bias through probability scores (e.g., >68% for buys), timing entries/exits with cross-confirmations, and monitoring broader market context via indexes/ETFs. It's especially useful for trend-following or reversal strategies, potentially improving win rates in volatile markets, but, like all indicators, it's not foolproof—use it in combination with risk management.
Strongest feature: The probability engine, which normalizes diverse signals into a robust, Z-scaled probUp metric (via the normal CDF or a logistic), enabling a quantifiable edge over traditional oscillators. Cheers...!!!
In terms of structure, it's highly reliable and productive: modular code with error-handling (safe divisions, approximations for tanh/erf), customizable modes (scalp to long-term), efficient resource use (max_bars_back=500), and cooldowns to prevent alert spam. Quality is strong, with transparent math, visual flexibility, and no apparent logic bugs—though real-world performance depends on market conditions and user tuning.
Investors can benefit significantly by using it for data-driven decisions, reducing bias through probability scores (e.g., >68% for buys), timing entries/exits with cross-confirmations, and monitoring broader market context via indexes/ETFs. It's especially useful for trend-following or reversal strategies, potentially improving win rates in volatile markets, but, like all indicators, it's not foolproof—use it in combination with risk management.
Strongest feature: The probability engine, which normalizes diverse signals into a robust, Z-scaled probUp metric (via the normal CDF or a logistic), enabling a quantifiable edge over traditional oscillators. Cheers...!!!
Korumalı komut dosyası
Bu komut dosyası kapalı kaynak olarak yayınlanmaktadır. Ancak, ücretsiz ve herhangi bir sınırlama olmaksızın kullanabilirsiniz – daha fazla bilgiyi buradan edinebilirsiniz.
Feragatname
Bilgiler ve yayınlar, TradingView tarafından sağlanan veya onaylanan finansal, yatırım, alım satım veya diğer türden tavsiye veya öneriler anlamına gelmez ve teşkil etmez. Kullanım Koşulları bölümünde daha fazlasını okuyun.
Korumalı komut dosyası
Bu komut dosyası kapalı kaynak olarak yayınlanmaktadır. Ancak, ücretsiz ve herhangi bir sınırlama olmaksızın kullanabilirsiniz – daha fazla bilgiyi buradan edinebilirsiniz.
Feragatname
Bilgiler ve yayınlar, TradingView tarafından sağlanan veya onaylanan finansal, yatırım, alım satım veya diğer türden tavsiye veya öneriler anlamına gelmez ve teşkil etmez. Kullanım Koşulları bölümünde daha fazlasını okuyun.