From e8d97dfafd0b3d0371f6b5e3717f8d98845e701e Mon Sep 17 00:00:00 2001
From: KennisFx <allen_c8@hotmail.com>
Date: Fri, 20 Feb 2026 18:06:00 -0500
Subject: [PATCH] Create kennis_streamlit.py

---
 kennis_streamlit.py | 70 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 70 insertions(+)
 create mode 100644 kennis_streamlit.py

diff --git a/kennis_streamlit.py b/kennis_streamlit.py
new file mode 100644
index 0000000..e2bdf3e
--- /dev/null
+++ b/kennis_streamlit.py
@@ -0,0 +1,70 @@
+import streamlit as st
+import numpy as np
+import pandas as pd
+
+st.set_page_config(page_title="Kennis - Institucional FX Markets", layout="wide")
+
+st.title("Kennis - Institucional FX Markets")
+st.markdown("### Bayesian Institutional FX Probability Engine")
+
+def sigmoid(x):
+    return 1 / (1 + np.exp(-x))
+
+def fit_bayesian_logit(X, y, prior_var=0.5):
+    n, p = X.shape
+    beta = np.zeros(p)
+    prior_prec = np.eye(p) / prior_var
+
+    for _ in range(100):
+        eta = X.dot(beta)
+        p_vec = sigmoid(eta)
+        W = p_vec * (1 - p_vec)
+        H = X.T.dot(W[:, None] * X) + prior_prec
+        grad = X.T.dot(y - p_vec) - prior_prec.dot(beta)
+
+        try:
+            delta = np.linalg.solve(H, grad)
+        except:
+            H += np.eye(p) * 1e-6
+            delta = np.linalg.solve(H, grad)
+
+        beta += delta
+
+        if np.linalg.norm(delta) < 1e-6:
+            break
+
+    cov = np.linalg.inv(H)
+    return beta, cov
+
+st.sidebar.header("Input Variables")
+
+cot_long = st.sidebar.number_input("COT Long", value=13662)
+cot_short = st.sidebar.number_input("COT Short", value=13546)
+fed_prob = st.sidebar.slider("Fed Hike Probability (%)", 0.0, 100.0, 96.0)
+retail_pct = st.sidebar.slider("Retail Long (%)", 0.0, 100.0, 47.0)
+
+net = cot_long - cot_short
+z_net = net / 10000
+sfed = (fed_prob - 50) / 50
+sret = (retail_pct - 50) / 50
+
+X_demo = np.random.randn(200, 3)
+y_demo = (np.random.rand(200) > 0.5).astype(int)
+
+beta_map, cov_post = fit_bayesian_logit(X_demo, y_demo)
+
+x_live = np.array([z_net, sfed, sret])
+samples = np.random.multivariate_normal(beta_map, cov_post, size=2000)
+sample_probs = sigmoid(samples.dot(x_live))
+
+p_mean = sample_probs.mean()
+p_low = np.percentile(sample_probs, 2.5)
+p_high = np.percentile(sample_probs, 97.5)
+
+st.subheader("Posterior Probability USD/JPY Up")
+
+st.metric("Mean Probability", f"{p_mean*100:.2f}%")
+st.write(f"95% Credibility Interval: {p_low*100:.2f}% - {p_high*100:.2f}%")
+
+st.markdown("---")
+st.markdown("© Kennis - Institucional FX Markets")