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Timeframe Quality Analyzer - MetaTrader 5 için gösterge

Rajesh Kumar Nait
Görüntülemeler:
1143
Derecelendirme:
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Güncellendi:
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This indicator tests if a timeframe is tradeable by checking

1. Signal-to-Noise Ratio (SNR)

Fit a linear regression over a rolling window of N bars.

Compute:

Explained variance (trend component)

Residual variance (noise component)

SNR = Explained Variance / Residual Variance

2. Autocorrelation (Memory Test)

Compute autocorrelation of returns for lag=1 over rolling window.

Display value.

Color code:

Green if > 0.1 (persistence)

Red if near 0 (noise)

3. Hurst Exponent (Fractal Memory)

Interpret:

H ≈ 0.5 → random walk

H > 0.55 → trending

H < 0.45 → mean reverting

4. Volatility Clustering (Variance Stability)

Display as oscillator between 0 and 1.

5. Shannon Entropy (Randomness Test)

Discretize returns into bins.

Compute Shannon entropy:

H = - Σ p(x) log(p(x))

Normalize between 0 and 1.

Higher entropy = more randomness.


BEST INPUT SETTINGS

A. Default Balanced (Recommended Start)

InpWindow        = 120
InpEntropyBins   = 25

InpWeightSNR     = 0.30
InpWeightAC      = 0.10
InpWeightHurst   = 0.25
InpWeightDER     = 0.20
InpWeightEntropy = 0.15

Why:

  • Larger window → reduces noise

  • Higher SNR weight → trend detection

  • Lower AC → autocorrelation is unstable

B. Trend Following Mode (Best for Breakouts)

InpWindow        = 150
InpEntropyBins   = 30

InpWeightSNR     = 0.35
InpWeightAC      = 0.05
InpWeightHurst   = 0.30
InpWeightDER     = 0.20
InpWeightEntropy = 0.10

Use when:

  • Market trending strongly

  • Trading breakout / momentum

 Focus: trend + persistence

C. Scalping / Intraday Mode

InpWindow        = 80
InpEntropyBins   = 20

InpWeightSNR     = 0.20
InpWeightAC      = 0.20
InpWeightHurst   = 0.20
InpWeightDER     = 0.25
InpWeightEntropy = 0.15

Use when:

  • M1–M15 trading

  • Need fast adaptation

Focus: structure + efficiency

D. Anti-Chop Filter Mode (VERY POWERFUL)

InpWindow        = 100
InpEntropyBins   = 25

InpWeightSNR     = 0.25
InpWeightAC      = 0.10
InpWeightHurst   = 0.20
InpWeightDER     = 0.15
InpWeightEntropy = 0.30

Use when:

  • You want to avoid bad market conditions

High entropy weight = avoid noise


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