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