313 lines
15 KiB
Python
313 lines
15 KiB
Python
# kennis_streamlit.py
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import streamlit as st
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import numpy as np
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import pandas as pd
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from io import BytesIO
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import plotly.graph_objects as go
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# ------------------ Configuración de la página ------------------
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st.set_page_config(page_title="Kennis FX Markets", layout="wide", initial_sidebar_state="expanded")
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st.markdown(
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"""
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<style>
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.stApp { background-color: #f4f6f9; color: #1a1f2b; font-family: 'Segoe UI', Roboto, Arial; }
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.header { color: #0f2742; font-weight:700; font-size:22px; }
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.subtitle { color: #4a6578; margin-top:-6px; }
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.card { background:#ffffff; padding:14px; border-radius:10px; box-shadow: 0 2px 8px rgba(20,30,40,0.06); }
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.muted { color:#5b6b75; font-size:13px; }
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.decision { font-weight:700; font-size:18px; color:#0f2742; }
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.explain { color:#22303a; font-size:14px; line-height:1.45; }
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.small { font-size:12px; color:#6f8190; }
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hr { border:none; border-top: 1px solid #e6eef8; }
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</style>
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""",
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unsafe_allow_html=True,
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)
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# ------------------ Header / Hero ------------------
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col1, col2 = st.columns([4,1])
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with col1:
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st.markdown("<div class='header'>Kennis FX Markets</div>", unsafe_allow_html=True)
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st.markdown("<div class='subtitle'>Institutional Bayesian Intelligence Engine</div>", unsafe_allow_html=True)
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st.markdown("<div class='muted'>Detection and strategic allocation guidance. (Edición táctica — swing / corto plazo)</div>", unsafe_allow_html=True)
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with col2:
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st.markdown("<div class='card'><div class='small'>Kennis FX — Tactical edition</div></div>", unsafe_allow_html=True)
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st.markdown("---")
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# ------------------ Sidebar: Inputs & opciones ------------------
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st.sidebar.header("Entradas (táctica / swing)")
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uploaded = st.sidebar.file_uploader("Sube CSV histórico (opcional). Columnas requeridas: date,cot_long,cot_short,fed_prob,retail_pct,target_up", type=["csv"])
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st.sidebar.markdown("O ingresa la fila live abajo:")
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par = st.sidebar.selectbox("Par de divisas", ["USD/JPY", "EUR/USD", "GBP/USD", "AUD/USD", "USD/CHF"], index=0)
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cot_long = st.sidebar.number_input("COT No-Commercial — Long", value=13662, step=1)
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cot_short = st.sidebar.number_input("COT No-Commercial — Short", value=13546, step=1)
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fed_prob = st.sidebar.number_input("FedWatch Prob (%) para bin actual", min_value=0.0, max_value=100.0, value=96.0, step=0.1)
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retail_pct = st.sidebar.number_input("Retail % Buy (0-100)", min_value=0.0, max_value=100.0, value=47.0, step=0.1)
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st.sidebar.markdown("---")
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st.sidebar.header("Modelo y normalización")
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s_cot = st.sidebar.number_input("S_cot (escala para COT)", value=50000, step=1000)
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prior_var = st.sidebar.number_input("Varianza prior gaussiano", value=0.5, step=0.1)
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retrain = st.sidebar.button("Reentrenar (si subes CSV)")
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# ------------------ Utilidades matemáticas ------------------
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def sigmoid(x):
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return 1.0 / (1.0 + np.exp(-x))
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def fit_bayesian_logit(X, y, prior_var=0.5, maxiter=200, tol=1e-8):
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n, p = X.shape
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beta = np.zeros(p)
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prior_prec = np.eye(p) / prior_var
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H = None
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for i in range(maxiter):
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eta = X.dot(beta)
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p_vec = sigmoid(eta)
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W = p_vec * (1.0 - p_vec)
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W = np.clip(W, 1e-8, None)
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H = X.T.dot(W[:, None] * X) + prior_prec
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grad = X.T.dot(y - p_vec) - prior_prec.dot(beta)
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try:
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delta = np.linalg.solve(H, grad)
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except np.linalg.LinAlgError:
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H += np.eye(p) * 1e-8
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delta = np.linalg.solve(H, grad)
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beta = beta + delta
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if np.linalg.norm(delta) < tol:
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break
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cov = np.linalg.inv(H)
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return beta, cov
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def to_excel_bytes(df):
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out = BytesIO()
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# pandas con openpyxl como engine — openpyxl debe estar en requirements.txt
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with pd.ExcelWriter(out, engine="openpyxl") as writer:
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df.to_excel(writer, index=False, sheet_name="signal")
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return out.getvalue()
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# ------------------ Ingesta de datos ------------------
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if uploaded is not None:
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try:
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df = pd.read_csv(uploaded)
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st.info(f"CSV cargado: {uploaded.name} — {len(df)} filas")
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except Exception as e:
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st.error("Error leyendo CSV: " + str(e))
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df = pd.DataFrame()
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else:
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# demo sintético (multi-par no real) — solo para UI y pruebas
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np.random.seed(42)
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N = 420
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cot_long_demo = np.random.normal(12000, 3000, size=N).astype(int)
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cot_short_demo = np.random.normal(11000, 3000, size=N).astype(int)
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fed_demo = np.clip(np.random.normal(60, 12, size=N), 0, 100)
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retail_demo = np.clip(np.random.normal(50, 12, size=N), 0, 100)
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net_demo = cot_long_demo - cot_short_demo
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z_net_demo = (net_demo - net_demo.mean()) / (net_demo.std() if net_demo.std()>0 else 1)
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true_score = 0.8*z_net_demo + 1.5*((fed_demo - 50)/50) - 0.5*((retail_demo - 50)/50)
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prob_demo = sigmoid(true_score)
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target_demo = (np.random.rand(N) < prob_demo).astype(int)
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df = pd.DataFrame({
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"date": pd.date_range(end=pd.Timestamp.today(), periods=N),
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"cot_long": cot_long_demo, "cot_short": cot_short_demo,
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"fed_prob": fed_demo, "retail_pct": retail_demo,
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"target_up": target_demo
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})
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st.info("Usando datos demo. Suba CSV histórico para calibración con datos reales.")
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# ------------------ Feature engineering ------------------
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def build_features(df_in, s_cot_local=50000):
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df = df_in.copy()
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df["net"] = df["cot_long"] - df["cot_short"]
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df["net_scaled"] = df["net"] / float(s_cot_local)
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df["z_net"] = (df["net"] - df["net"].rolling(252, min_periods=1).mean()) / df["net"].rolling(252, min_periods=1).std().replace(0,1)
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df["delta_1w"] = df["net"].diff(5).fillna(0)
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df["delta_4w"] = df["net"].diff(20).fillna(0)
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df["z_delta_1w"] = (df["delta_1w"] - df["delta_1w"].rolling(252, min_periods=1).mean()) / df["delta_1w"].rolling(252, min_periods=1).std().replace(0,1)
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df["z_delta_4w"] = (df["delta_4w"] - df["delta_4w"].rolling(252, min_periods=1).mean()) / df["delta_4w"].rolling(252, min_periods=1).std().replace(0,1)
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df["signal_fed"] = (df["fed_prob"] - 50.0) / 50.0
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df["signal_retail"] = (df["retail_pct"] - 50.0) / 50.0
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return df
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df_feat = build_features(df, s_cot_local=s_cot)
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# Features used in el modelo táctico
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feature_cols = ["z_net", "z_delta_1w", "z_delta_4w", "signal_fed", "signal_retail"]
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if "target_up" in df_feat.columns:
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train_df = df_feat.copy()
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X_train = train_df[feature_cols].fillna(0).values
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y_train = train_df["target_up"].astype(int).values
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else:
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train_df = df_feat.copy()
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X_train = train_df[feature_cols].fillna(0).values
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y_train = train_df["target_up"].astype(int).values
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if retrain and uploaded is None:
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st.warning("Para reentrenar debe subir un CSV histórico con etiqueta 'target_up'.")
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with st.spinner("Calibrando modelo bayesiano táctico (MAP + Laplace)..."):
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try:
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beta_map, cov_post = fit_bayesian_logit(X_train, y_train, prior_var=prior_var)
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except Exception as e:
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st.error("Fallo en calibración del modelo: " + str(e))
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# fallback: coeficientes neutros
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beta_map = np.zeros(len(feature_cols))
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cov_post = np.eye(len(feature_cols)) * 1.0
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# ------------------ Fila en vivo y predicción ------------------
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live = {"cot_long": float(cot_long), "cot_short": float(cot_short), "fed_prob": float(fed_prob), "retail_pct": float(retail_pct)}
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aug = pd.concat([df.head(1).copy(), pd.DataFrame([live])], ignore_index=True)
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live_feat = build_features(aug, s_cot_local=s_cot).iloc[-1]
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x_live = live_feat[feature_cols].fillna(0).values
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net = live_feat["net"]
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# Posterior sampling + predicción (defensiva)
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try:
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samples = np.random.multivariate_normal(beta_map, cov_post, size=4000)
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sample_probs = sigmoid(samples.dot(x_live))
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p_mean = float(sample_probs.mean())
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p_low = float(np.percentile(sample_probs, 2.5))
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p_high = float(np.percentile(sample_probs, 97.5))
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except Exception:
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eta = float(x_live.dot(beta_map))
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p_mean = float(sigmoid(eta))
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p_low, p_high = p_mean, p_mean
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sample_probs = np.array([p_mean])
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# Índice de convicción
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dispersion = max(1e-9, float(np.std(sample_probs)))
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conviction = min(100.0, max(0.0, 100.0 * (abs(p_mean - 0.5) * 2.0) * (1.0 / (1.0 + 5.0 * dispersion))))
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conviction = round(conviction, 1)
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# Decisión táctica
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if p_mean >= 0.60:
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decision = "COMPRAR (LONG)"
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decision_flag = "buy"
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elif p_mean <= 0.40:
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decision = "VENDER (SHORT)"
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decision_flag = "sell"
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else:
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decision = "ESPERAR (NO OPERAR)"
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decision_flag = "wait"
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# Descomposición de contribuciones
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contrib_raw = np.array(beta_map) * np.array(x_live)
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if np.sum(np.abs(contrib_raw)) > 0:
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contrib_pct = 100.0 * contrib_raw / np.sum(np.abs(contrib_raw))
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else:
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contrib_pct = np.zeros_like(contrib_raw)
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# ------------------ Narrativa experta (en español) ------------------
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def narrative_tactical(p_mean, p_low, p_high, conviction, decision_flag, x_live, contrib_pct, feature_cols, par):
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lines = []
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lines.append(f"Resumen ejecutivo (táctico) — activo: {par}")
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lines.append(f"- Probabilidad bayesiana P({par} ↑) = {p_mean*100:.1f}% (IC95%: {p_low*100:.1f}%–{p_high*100:.1f}%). Convicción: {conviction}/100.")
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fed_s = x_live[3]
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cot_s = x_live[0]
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retail_s = x_live[4]
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if fed_s > 0.4:
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lines.append("- Macro: FedWatch muestra sesgo hawkish — esto favorece fuerza del USD frente al JPY en el horizonte táctico.")
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elif fed_s < -0.4:
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lines.append("- Macro: FedWatch muestra sesgo dovish — presión a la baja sobre USD esperable.")
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else:
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lines.append("- Macro: FedWatch neutral/moderado — macro por sí sola no domina la señal táctica.")
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if abs(cot_s) < 0.15:
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lines.append("- Posicionamiento: COT de no-commercials cerca de neutral; riesgo de cola limitado por sobreposicionamiento.")
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elif cot_s >= 0.15:
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lines.append("- Posicionamiento: COT exhibe sesgo long entre no-commercials — soporte estructural, pero cuidado con toma de ganancias.")
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else:
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lines.append("- Posicionamiento: COT exhibe sesgo short — riesgo de short-squeeze si macro gira hawkish.")
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if retail_s > 0.15:
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lines.append("- Retail: minoristas inclinados a comprar — aviso contrarian en plazos cortos; prudencia en entradas agresivas.")
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elif retail_s < -0.15:
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lines.append("- Retail: minoristas inclinados a vender — puede reforzar momentum si flujo institucional coincide.")
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else:
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lines.append("- Retail: posicionamiento minorista balanceado — señal contraria no relevante ahora.")
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lines.append("- Descomposición de señales (contribución firmada en %):")
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for name, pct, val in zip(feature_cols, contrib_pct, x_live):
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sign = "+" if pct >= 0 else "-"
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lines.append(f" • {name}: {sign}{abs(pct):.1f}% (valor {val:.3f})")
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if decision_flag == "buy":
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lines.append("- Recomendación táctica: Acumulación gradual LONG. Razón: P posterior y convicción respaldan sesgo alcista táctico.")
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lines.append("- Ejecución sugerida: escala en 3 tramos; tamaño inicial 0.5–1% notional; stop técnico 1.0–1.5×ATR; objetivo R:R ≥ 1:1.5.")
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elif decision_flag == "sell":
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lines.append("- Recomendación táctica: Considerar SHORT táctico o reducción de posiciones largas. Razón: posterior favorece la baja con convicción.")
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lines.append("- Ejecución sugerida: tamaño reducido; stops ajustados; vigilar noticias macro.")
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else:
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lines.append("- Recomendación táctica: No operar. Razón: incertidumbre relevante; esperar confirmación de flujo o ruptura macro.")
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lines.append("- Nota: COT es semanal (retardo); combinar con order flow intradía y skew de opciones para ejecución.")
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return "\n".join(lines)
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narrative = narrative_tactical(p_mean, p_low, p_high, conviction, decision_flag, x_live, contrib_pct, feature_cols, par)
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# ------------------ Interfaz: resultado y narrativa ------------------
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left, right = st.columns([2,3])
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with left:
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st.markdown("<div class='card'>", unsafe_allow_html=True)
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st.markdown(f"<div class='decision'>{decision}</div>", unsafe_allow_html=True)
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st.markdown(f"<div class='muted'>Probabilidad (media): <strong>{p_mean*100:.1f}%</strong> 95% IC: <strong>{p_low*100:.1f}%–{p_high*100:.1f}%</strong></div>", unsafe_allow_html=True)
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st.markdown(f"<div class='muted'>Convicción: <strong>{conviction}/100</strong></div>", unsafe_allow_html=True)
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st.markdown("<hr/>", unsafe_allow_html=True)
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st.markdown("### Plan táctico (resumen)", unsafe_allow_html=True)
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if decision_flag == "buy":
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st.markdown("- Escala en 3 tramos. Tramo inicial 0.5–1% notional.", unsafe_allow_html=True)
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st.markdown("- Stop: 1.0–1.5×ATR o por debajo de soporte técnico.", unsafe_allow_html=True)
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st.markdown("- Target: R:R ≥ 1:1.5.", unsafe_allow_html=True)
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elif decision_flag == "sell":
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st.markdown("- Reducir exposiciones largas o iniciar cortos pequeños (0.5–1%).", unsafe_allow_html=True)
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st.markdown("- Stops estrictos; vigila comunicados macro.", unsafe_allow_html=True)
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else:
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st.markdown("- No operar. Esperar señal clara o confirmación de flujos.", unsafe_allow_html=True)
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st.markdown("</div>", unsafe_allow_html=True)
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with right:
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fig = go.Figure(go.Indicator(
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mode="gauge+number",
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value=p_mean*100,
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number={'suffix': '%'},
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domain={'x': [0,1], 'y': [0,1]},
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title={'text': f"P({par} ↑)"},
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gauge={
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'axis': {'range': [0,100]},
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'bar': {'color': "#0f2742"},
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'steps': [
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{'range':[0,40], 'color':'#d9534f'},
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{'range':[40,60], 'color':'#f0ad4e'},
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{'range':[60,100], 'color':'#2a9d8f'}
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],
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}
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))
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st.plotly_chart(fig, use_container_width=True)
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st.markdown("### Narrativa contextual — experto", unsafe_allow_html=True)
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st.markdown(f"<div class='card'><div class='explain'>{narrative.replace(chr(10), '<br/>')}</div></div>", unsafe_allow_html=True)
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# ------------------ Tabla de componentes y coeficientes ------------------
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st.markdown("### Componentes (valores normalizados) y coeficientes MAP")
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comp_df = pd.DataFrame({
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"Componente": feature_cols,
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"Valor (normalizado)": [f"{v:.4f}" for v in x_live],
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"beta_MAP": [float(b) for b in beta_map],
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"Contribución firmada (%)": [f"{float(c):.1f}%" for c in contrib_pct]
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})
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st.table(comp_df)
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# ------------------ Exportar resultado ------------------
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out_df = pd.DataFrame([{
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"par": par, "cot_long": cot_long, "cot_short": cot_short, "net": net,
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"fed_prob": fed_prob, "retail_pct": retail_pct,
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"p_mean": p_mean, "p_2.5": p_low, "p_97.5": p_high,
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"conviction": conviction, "decision": decision
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}])
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try:
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excel_bytes = to_excel_bytes(out_df)
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st.download_button("Exportar resultado (Excel)", data=excel_bytes, file_name=f"kennis_{par.replace('/','')}_signal.xlsx")
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except Exception as e:
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st.error("Error exportando Excel: " + str(e))
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st.markdown("---")
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st.markdown("<div class='small'>Implementación: Logistic bayesiano táctico (MAP + Laplace). Suba CSV con 'target_up' para calibración real y backtests. Uso profesional: combine con price feed y motor de ejecución.</div>", unsafe_allow_html=True)
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st.markdown("© Kennis FX Markets — Institutional Bayesian Intelligence Engine", unsafe_allow_html=True)
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