From 641d04928c1d6080c5024baf3dd48f99203f3f79 Mon Sep 17 00:00:00 2001 From: Toh4iem9 Date: Sat, 20 Jun 2026 16:34:01 +0200 Subject: [PATCH] docs: --- .../MyIndicators/Quant/Autocorrelation_Pro.md | 110 +++++++++++++----- 1 file changed, 79 insertions(+), 31 deletions(-) diff --git a/Indicators/MyIndicators/Quant/Autocorrelation_Pro.md b/Indicators/MyIndicators/Quant/Autocorrelation_Pro.md index 99aa82b..0177769 100644 --- a/Indicators/MyIndicators/Quant/Autocorrelation_Pro.md +++ b/Indicators/MyIndicators/Quant/Autocorrelation_Pro.md @@ -1,49 +1,97 @@ -# Autocorrelation Pro (Indicator) +# Autocorrelation Pro Suite (Standard & MTF) ## 1. Summary -**Autocorrelation Pro** measures the "Serial Dependence" of market returns. It answers the question: *"Does yesterday's price move predict today's price move?"* +The **Autocorrelation Pro Suite** is an institutional-grade, high-performance statistical suite comprising two advanced indicators: -Unlike trend indicators that follow price levels, this proprietary statistical tool analyzes the relationship between the *Current Return* ($R_t$) and the *Previous Return* ($R_{t-1}$). It is a powerful filter for distinguishing between Momentum Regimes and Mean Reversion Regimes. +* `Autocorrelation_Pro` (Single timeframe separate window oscillator) +* `Autocorrelation_MTF_Pro` (Multi-Timeframe separate window oscillator) -## 2. Methodology +This proprietary suite measures the "Serial Dependence" of market returns in real-time. It directly answers the core quantitative question: *"Does the direction and magnitude of the current price move statistically predict the direction of the next price move?"* -The indicator calculates the **Lag-1 Pearson Correlation Coefficient** over a rolling window. +Unlike trend indicators that lag behind price levels (e.g., Moving Averages), the Autocorrelation Suite analyzes the mathematical relationship between the *Current Log-Return* ($R_t$) and the *Previous Log-Return* ($R_{t-1}$). It serves as an ultimate regime filter, allowing quantitative systems to instantly distinguish between persistent Trend Regimes, mean-reverting Oscillation Regimes, and non-directional Random Walks. -### The Formula +--- -$$\rho_1 = \frac{\text{Cov}(R_t, R_{t-1})}{\sigma_{R_t} \sigma_{R_{t-1}}}$$ +## 2. Mathematical Methodology -* **Positive Correlation ($\rho > 0$):** **Momentum Effect.** A positive return is likely followed by another positive return. "Winners keep winning." -* **Negative Correlation ($\rho < 0$):** **Reversion Effect.** A positive return is likely followed by a negative return (pullback). The market is "choppy" or "elastic". -* **Zero Correlation ($\rho \approx 0$):** **Random Walk.** The market has no memory; price changes are independent. +To perform mathematically rigorous correlation sweeps, price series $P_t$ are first transformed into stationary logarithmic returns to prevent spurious statistics: -*Note: The calculation uses Logarithmic Returns ($\ln(P_t/P_{t-1})$) for statistical accuracy.* +$$R_t = \ln\left(\frac{P_t}{P_{t-1}}\right)$$ + +The calculator computes the **Lag-1 Pearson Correlation Coefficient** ($\rho_1$) over a rolling window of size $N$ (`InpPeriod`): + +$$\rho_1 = \frac{\text{Covariance}(R_t, R_{t-1})}{\sigma_{R_t} \times \sigma_{R_{t-1}}}$$ + +### Statistical Interpretations + +* **Positive Correlation ($\rho_1 > \text{Threshold}$):** **Serial Persistence (Momentum).** A positive return is statistically likely to be followed by another positive return. "Winners keep winning." The market is in a sustained Trend Regime. +* **Negative Correlation ($\rho_1 < -\text{Threshold}$):** **Serial Mean-Reversion (Mean Reverting).** A positive return is statistically likely to be followed by a negative return (immediate pullback). The market is in an oscillating, range-bound, or "choppy" regime. +* **Zero Correlation ($\rho_1 \approx 0.0$):** **Random Walk (Noise).** Price returns are independent and possess no memory. There is no statistical edge for continuation or reversion. + +--- ## 3. Visualization & Interpretation -The indicator displays a colored Histogram oscillating between -1.0 and +1.0. +The indicators display a colored Histogram oscillating between the absolute boundaries of $-1.0$ and $+1.0$. -* **Green Bars (> 0.1):** **Trend Mode.** The market is exhibiting serial persistence. This is the optimal environment for: - * Breakout strategies. - * Moving Average crossovers. - * Adding to winning positions. -* **Red Bars (< -0.1):** **Mean Reversion Mode.** The market is oscillating. Prices are rejecting new highs/lows. This is the optimal environment for: - * Buying Support / Selling Resistance (Range Trading). - * Fading breakouts (False Breakouts are common). - * Bollinger Band / RSI strategies. -* **Gray Bars (-0.1 to 0.1):** **Random Noise.** No clear statistical edge. +* **Green Bars ($> \text{InpThreshold}$):** **Persistent Trend Regime.** The market exhibits serial continuation. + * *Optimal Trading Style:* Breakout strategies, Moving Average crossovers, and adding to winning positions (pyramiding). +* **Red Bars ($< -\text{InpThreshold}$):** **Mean-Reversion Regime.** The market exhibits immediate rejection of new highs and lows. + * *Optimal Trading Style:* Buying support / selling resistance (Range Trading), fading breakouts, and employing Bollinger Bands or RSI oscillator overbought/oversold systems. +* **Gray Bars (Neutral Zone):** **Random Noise.** No statistical edge is present. Quantitative systems should stand aside or tighten execution filters. -## 4. Parameters +--- -* `InpPeriod`: The lookback window for the correlation calculation (Default: `20`). - * *Shorter Period (e.g., 10):* Highly reactive, good for scalping. - * *Longer Period (e.g., 50):* Institutional regime filter. -* `InpThreshold`: The significance level for coloring (Default: `0.1`). -* `InpPrice`: The price source to analyze (Default: `PRICE_CLOSE`). +## 4. Multi-Timeframe Step Alignment (Solving the Live-Bar Warping Bug) -## 5. Strategic Usage +Traditional MTF separate-window indicators often suffer from severe visual warping on their right edge. Since the MTF calculation updates tick-by-tick, updating only the latest lower timeframe (LTF) index (`rates_total - 1`) causes the older LTF bars belonging to the current forming HTF block to retain outdated tick values, creating a jagged, diagonal, or fűrészfog-like distortion. -1. **Trend Filter:** Before entering a trend trade, check the Autocorrelation. If it is **Red (Negative)**, the probability of a sustained trend run is statistically low, even if other indicators say "Buy". Wait for it to turn Green. -2. **Volatile Breakouts:** If volatility is high but Autocorrelation is **Red**, expect a "V-Top" or "V-Bottom" reversal rather than a clean trend. -3. **Regime Change:** The transition from Red to Green often marks the moment a consolidation turns into a breakout. +### The Forming LTF Block Flat-Force Algorithm + +`Autocorrelation_MTF_Pro` resolves this issue by implementing a robust step-blocking design pattern. On every live tick, the indicator dynamically traces back to the very first LTF bar matching the active forming HTF bar: + +```mql5 +int first_bar_of_forming_htf = rates_total - 1; +while(first_bar_of_forming_htf > 0 && + iBarShift(_Symbol, InpTimeframe, time[first_bar_of_forming_htf], false) == 0) + { + first_bar_of_forming_htf--; + } +first_bar_of_forming_htf++; // Start index of the forming HTF step block +``` + +By forcing the calculation's `start` index back to the beginning of the active block on every tick, the indicator completely overwrites the entire active block uniformly. This guarantees a mathematically correct, perfectly flat horizontal block (Flat Step) on the right edge of the chart in real-time. + +--- + +## 5. Input Parameters + +### A. Common Parameters + +* **Period (`InpPeriod`):** The lookback window size for the Pearson correlation calculation (Default: `20`). + * *Short Window (5 - 15):* Highly reactive, ideal for scalp-level momentum transitions. + * *Long Window (20 - 50):* Stabilized institutional regime filter. + +* **Significance Threshold (`InpThreshold`):** The correlation level required to trigger color transitions (Default: `0.1`). +* **Applied Price (`InpPrice`):** The price series source used to compute log-returns (Default: `PRICE_CLOSE`). + +### B. MTF Specific Parameters + +* **Target Timeframe (`InpTimeframe`):** The higher timeframe to calculate autocorrelation on, which is then mapped down to the current chart (Default: `PERIOD_M5`). + +--- + +## 6. Strategic Quantitative Usage + +### A. Top-Down Macro Regime Filter (MTF Strategy) + +Before deploying capital on a lower timeframe trend strategy, execute a macro persistence check: + +1. Apply `Autocorrelation_MTF_Pro` on an `M1` execution chart, set to monitor the **`PERIOD_H1`** target timeframe. +2. If the H1 MTF histogram is strongly **Green**, macro serial persistence is active. You are cleared to trade lower timeframe breakout and momentum trend-following strategies. +3. If the H1 MTF histogram turns **Red**, macro serial mean-reversion is dominant. Immediately halt trend-following bots and activate range-bound, support/resistance fading systems. + +### B. Regime-Change Breakout Confirmation + +Consolidation phases are characterized by low volatility and near-zero or negative autocorrelation (Red/Gray). The transition of the autocorrelation histogram from **Red/Gray directly into Green** is a leading statistical indicator that a consolidation is turning into a high-velocity, persistent breakout.