Remove multiple Pine Script indicators: SSFDSP, STARCHANNEL, STBANDS, STC, UBANDS, UCHANNEL, VWAPBANDS, and VWAPSD. These indicators were deleted to streamline the library and remove unused or redundant code.

2026-08-21 12:08:05 +00:00 · 2026-02-20 18:44:56 -08:00
parent 3dd05f23e4
commit cbeefc9d64
283 changed files with 23963 additions and 3838 deletions
@@ -0,0 +1,100 @@
+// The MIT License (MIT)
+// © mihakralj
+//@version=6
+indicator("Binomial Distribution CDF (BINOMDIST)", "BINOMDIST", overlay=false, precision=6)
+
+//@function Log-gamma via Lanczos approximation (g=7, 9 coefficients)
+//@param z Input value (z > 0)
+//@returns ln(Gamma(z))
+lnGamma(simple float z) =>
+    float g = 7.0
+    array<float> c = array.from(
+         0.99999999999980993,
+       676.5203681218851,
+     -1259.1392167224028,
+       771.32342877765313,
+      -176.61502916214059,
+        12.507343278686905,
+        -0.13857109526572012,
+         9.9843695780195716e-6,
+         1.5056327351493116e-7)
+    float zz = z - 1.0
+    float x = array.get(c, 0)
+    for i = 1 to 8
+        x += array.get(c, i) / (zz + i)
+    float t = zz + g + 0.5
+    0.5 * math.log(2.0 * math.pi) + (zz + 0.5) * math.log(t) - t + math.log(x)
+
+//@function Log of binomial coefficient C(n, i) = ln(n!) - ln(i!) - ln((n-i)!)
+//@param n Number of trials
+//@param i Number of successes
+//@returns ln(C(n, i))
+lnBinom(simple int n, int i) =>
+    lnGamma(float(n + 1)) - lnGamma(float(i + 1)) - lnGamma(float(n - i + 1))
+
+//@function Binomial Distribution CDF: P(X <= k) = sum_{i=0}^{k} C(n,i) * p^i * (1-p)^(n-i)
+//@param p Probability of success per trial (0 <= p <= 1)
+//@param n Number of trials
+//@param k Threshold (compute P(X <= k))
+//@returns CDF value in [0,1]
+//@optimized Log-space summation avoids factorial overflow for large n
+binomCdf(series float p, simple int n, simple int k) =>
+    if p <= 0.0
+        k >= 0 ? 1.0 : 0.0
+    else if p >= 1.0
+        k >= n ? 1.0 : 0.0
+    else
+        float lnP = math.log(p)
+        float lnQ = math.log(1.0 - p)
+        float cdf = 0.0
+        int kk = math.min(k, n)
+        for i = 0 to kk
+            float lnTerm = lnBinom(n, i) + i * lnP + (n - i) * lnQ
+            cdf += math.exp(lnTerm)
+        math.min(cdf, 1.0)
+
+//@function Computes Binomial Distribution CDF for a normalized price series
+//@param source Series to transform
+//@param period Lookback period for min-max normalization to [0,1] as probability p
+//@param trials Number of Bernoulli trials (n)
+//@param threshold Success threshold (k) — compute P(X <= k)
+//@returns Binomial CDF value in [0,1]
+binomdist(series float source, simple int period, simple int trials, simple int threshold) =>
+    if period <= 0
+        runtime.error("Period must be greater than 0")
+    if trials <= 0
+        runtime.error("Trials must be greater than 0")
+    if threshold < 0
+        runtime.error("Threshold must be non-negative")
+
+    float minVal = source
+    float maxVal = source
+    for i = 1 to period - 1
+        float v = source[i]
+        if not na(v)
+            if v < minVal
+                minVal := v
+            if v > maxVal
+                maxVal := v
+
+    float range = maxVal - minVal
+    float p = range > 0.0 ? (source - minVal) / range : 0.5
+
+    binomCdf(p, trials, threshold)
+
+// ---------- Main loop ----------
+
+// Inputs
+i_source = input.source(close, "Source")
+i_period = input.int(50, "Lookback Period", minval=2, maxval=5000, tooltip="Min-max normalization window")
+i_trials = input.int(20, "Trials (n)", minval=1, maxval=1000, tooltip="Number of Bernoulli trials")
+i_threshold = input.int(10, "Threshold (k)", minval=0, maxval=1000, tooltip="Compute P(X <= k)")
+
+// Calculation
+result = binomdist(i_source, i_period, i_trials, i_threshold)
+
+// Plot
+plot(result, "BINOMDIST", color=color.yellow, linewidth=2)
+hline(0.5, "Midline", color=color.gray, linestyle=hline.style_dotted)
+hline(0.95, "Upper", color=color.red, linestyle=hline.style_dashed)
+hline(0.05, "Lower", color=color.green, linestyle=hline.style_dashed)