Update kennis_streamlit.py
This commit is contained in:
KennisFx
+176
-118
@@ -5,61 +5,57 @@ 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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# ---------- Config ----------
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st.set_page_config(
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page_title="Kennis - Institucional FX Markets",
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layout="wide",
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initial_sidebar_state="expanded",
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)
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# Small styling for institutional look
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# ----- Page config and minimal styling (institutional) -----
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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: #0f1724; color: #e6eef8; }
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.header { color: #e6eef8; font-weight:700; }
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.subtle { color: #9fb0c8; }
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.big-decision { font-size:20px; font-weight:700; }
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.card { background:#0b1220; padding:12px; border-radius:8px; box-shadow: 0 1px 6px rgba(0,0,0,0.6); }
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.stApp { background-color: #0b0f14; color: #e6eef8; }
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.header { color: #e6eef8; font-weight:700; font-size:22px; }
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.subtitle { color: #b7c9d9; margin-top:-8px; }
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.card { background:#0f1720; padding:14px; border-radius:10px; }
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.muted { color:#9fb0c8; font-size:13px; }
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.decision { font-weight:700; font-size:18px; }
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.explain { color:#dfeffb; font-size:14px; line-height:1.5; }
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.small { font-size:12px; color:#9fb0c8; }
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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 ----------
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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 - Institucional FX Markets</div>", unsafe_allow_html=True)
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st.markdown("<div class='subtle'>Bayesian probabilistic FX signal — USD/JPY</div>", unsafe_allow_html=True)
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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.</div>", unsafe_allow_html=True)
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with col2:
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st.markdown("") # space for logo placeholder
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st.markdown("<div class='card'><strong style='font-size:12px'>© Kennis - Institucional FX Markets</strong></div>", unsafe_allow_html=True)
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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 ----------
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st.sidebar.header("Inputs (última fila / live)")
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uploaded = st.sidebar.file_uploader("Sube CSV histórico (opcional) — columnas oblig.: cot_long,cot_short,fed_prob,retail_pct,target_up", type=["csv"])
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st.sidebar.markdown("O usa los controles para ingresar la fila *live*:")
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# ----- Sidebar: inputs & upload -----
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st.sidebar.header("Live inputs (tactical / swing)")
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uploaded = st.sidebar.file_uploader("Upload historical CSV (optional). Required columns: cot_long,cot_short,fed_prob,retail_pct,target_up", type=["csv"])
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st.sidebar.markdown("Or use the live controls below:")
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cot_long = st.sidebar.number_input("COT Long (No-Commercial)", value=13662, step=1)
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cot_short = st.sidebar.number_input("COT Short (No-Commercial)", 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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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 (%) for current bin", 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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# Model hyperparams
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st.sidebar.markdown("---")
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st.sidebar.header("Model")
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prior_var = st.sidebar.number_input("Prior variance (gauss.)", value=0.5, step=0.1)
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s_cot = st.sidebar.number_input("S_cot (escala normalización)", value=50000, step=1000)
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retrain = st.sidebar.button("Retrain / Fit model (si subes CSV)")
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st.sidebar.header("Model & normalization")
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s_cot = st.sidebar.number_input("S_cot (scale for COT)", value=50000, step=1000)
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prior_var = st.sidebar.number_input("Prior variance (gauss)", value=0.5, step=0.1)
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retrain = st.sidebar.button("Retrain model (if CSV uploaded)")
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# ---------- Utilities ----------
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# ----- Utilities -----
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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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# MAP via Newton-Raphson with Gaussian prior (0, prior_var)
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# MAP (Newton-Raphson) with Gaussian prior N(0, prior_var)
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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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@@ -68,8 +64,7 @@ def fit_bayesian_logit(X, y, prior_var=0.5, maxiter=200, tol=1e-8):
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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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# avoid singular by flooring W
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W = np.maximum(W, 1e-8)
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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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@@ -84,23 +79,23 @@ def fit_bayesian_logit(X, y, prior_var=0.5, maxiter=200, tol=1e-8):
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return beta, cov
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def to_excel(df):
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output = BytesIO()
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with pd.ExcelWriter(output, engine="openpyxl") as writer:
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out = BytesIO()
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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 output.getvalue()
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return out.getvalue()
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# ---------- Data preparation ----------
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# ----- Data ingestion and feature building -----
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if uploaded is not None:
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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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st.info(f"CSV loaded: {uploaded.name} — {len(df)} rows")
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else:
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# demo synthetic historical data for training (keeps app usable without CSV)
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# synthetic demo history so app is usable without CSV
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np.random.seed(42)
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N = 400
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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, 10, size=N), 0, 100)
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retail_demo = np.clip(np.random.normal(50, 10, size=N), 0, 100)
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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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@@ -111,14 +106,13 @@ else:
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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 demo histórico (sube tu CSV para resultados reales).")
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st.info("Running on demo historical data. Upload CSV for real-data calibration.")
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# Build feature engineering (robust, reproducible)
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def build_features(df, s_cot_local=50000):
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df = df.copy()
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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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# normalized net (z-style): scale by s_cot (user set) and also produce standardized z
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df["net_scaled"] = df["net"] / float(s_cot_local)
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# rolling z using up to 252 rows (business-year style)
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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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@@ -130,122 +124,186 @@ def build_features(df, s_cot_local=50000):
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df_feat = build_features(df, s_cot_local=s_cot)
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# training set (if user uploaded data and target_up exists, use it)
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# features chosen for the tactical model
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feature_cols = ["z_net", "z_delta_1w", "z_delta_4w", "signal_fed", "signal_retail"]
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# prepare training data (if uploaded and has target_up will be used)
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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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# synthetic fallback
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# fallback (demo includes target_up)
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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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# Fit model
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with st.spinner("Modelo: calibrando (MAP + Laplace)..."):
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# If user pressed retrain and uploaded provided CSV, we would re-fit (button here for UI clarity)
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if retrain and uploaded is None:
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st.warning("No CSV uploaded — retrain requires historical CSV with 'target_up' labels.")
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# Fit model (MAP + Laplace)
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with st.spinner("Calibrating Bayesian tactical model (MAP + Laplace)..."):
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beta_map, cov_post = fit_bayesian_logit(X_train, y_train, prior_var=prior_var)
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# ---------- Live row features (from sidebar inputs) ----------
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live = {
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"cot_long": cot_long,
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"cot_short": cot_short,
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"fed_prob": fed_prob,
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"retail_pct": retail_pct
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}
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live_df = pd.DataFrame([live])
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live_feat = build_features(pd.concat([df.head(1), live_df], ignore_index=True), s_cot_local=s_cot).iloc[-1]
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# ----- Live row features (from sidebar) -----
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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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# to compute z etc we build by appending the live row to the history head
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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 and predictive
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# ----- Posterior sampling and predictive distribution -----
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# defensive: ensure covariance is PSD-ish
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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 as e:
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# fallback to point estimate if covariance numerically invalid
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eta = x_live.dot(beta_map)
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except Exception:
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# fallback to point probability
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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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# ---------- Decision logic ----------
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# Conviction index: transforms p_mean and dispersion into 0-100
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dispersion = max(1e-6, float(np.std(sample_probs))) if 'sample_probs' in locals() else 0.0
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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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# Decision thresholds (tactical)
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if p_mean >= 0.60:
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decision = "COMPRAR (LONG)"
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banner_color = "✅"
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banner_style = "success"
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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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banner_color = "🚫"
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banner_style = "error"
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decision_flag = "sell"
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else:
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decision = "ESPERAR (NO OPERAR)"
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banner_color = "🟡"
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banner_style = "warning"
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decision_flag = "wait"
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# ---------- UI: top result ----------
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colA, colB = st.columns([2,3])
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with colA:
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# Compute contribution breakdown (feature * beta) and normalize for narrative
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contrib_raw = np.array(beta_map) * np.array(x_live)
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# normalize to percent contribution with sign
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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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# ----- Expert narrative generator (deterministic, audit-friendly) -----
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def narrative_tactical(p_mean, p_low, p_high, conviction, decision_flag, x_live, contrib_pct, feature_cols):
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# Build human expert style narrative using magnitudes and signed contributions
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lines = []
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# regime sentence
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fed_s = x_live[3] # signal_fed
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cot_s = x_live[0] # z_net
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retail_s = x_live[4]
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lines.append("Executive summary (tactical):")
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lines.append(f"- Bayesian posterior P(USD/JPY ↑) = {p_mean*100:.1f}% (95% CI: {p_low*100:.1f}%–{p_high*100:.1f}%). Conviction: {conviction}/100.")
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# interpretation
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if fed_s > 0.4:
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lines.append("- Macro: FedWatch is strongly hawkish — this structurally supports USD strength versus JPY over short horizon.")
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elif fed_s < -0.4:
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lines.append("- Macro: FedWatch is dovish — downside pressure on USD is likely.")
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else:
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lines.append("- Macro: FedWatch neutral/moderately balanced — macro is not the dominant driver.")
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# COT
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if abs(cot_s) < 0.15:
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lines.append("- Positioning: COT non-commercials are currently near neutral (low extremeness); tail risk from crowded positioning is limited.")
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elif cot_s >= 0.15:
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lines.append("- Positioning: COT shows noticeable long bias among non-commercials — structural support exists but be wary of profit-taking risk.")
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else:
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lines.append("- Positioning: COT shows notable short bias — risk of short-squeeze exists if macro tilts hawkish.")
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# Retail
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if retail_s > 0.15:
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lines.append("- Retail: retail crowd leaning long — often a contrarian warning for short-term reversals; exercise caution.")
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elif retail_s < -0.15:
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lines.append("- Retail: retail lean short — can be supportive for medium momentum if institutional flow aligns.")
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else:
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lines.append("- Retail: retail positioning near balanced — not a dominant contrarian signal now.")
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# Contributions
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lines.append("- Signal decomposition (signed % contribution):")
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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}% (value {val:.3f})")
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# Decision rationale
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if decision_flag == "buy":
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lines.append("- Tactical recommendation: Gradual LONG accumulation. Rationale: hawkish Fed probabilities dominate neutral institutional positioning; market structure allows tactical upside.")
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lines.append("- Suggested execution (tactical): scale-in (3 tranches), initial exposure 0.5–1.5% notional, use volatility stop (1.0–1.5 × recent ATR) and target R:R ≥ 1:1.5.")
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elif decision_flag == "sell":
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lines.append("- Tactical recommendation: Consider SHORT exposure or reduce existing long exposure. Rationale: posterior favors downside with sufficient conviction.")
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lines.append("- Suggested execution: tight initial sizing, stop at 1.0–1.5 × ATR, target adapt to event risk.")
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else:
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lines.append("- Tactical recommendation: No trade. Rationale: posterior is near-neutral and uncertainty is significant; wait for clearer regime signal or confirmatory flows.")
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# Caveats
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lines.append("- Caveats: model uses weekly-positioning (COT) which is lagged; complement with intraday order flow and option skew for execution decisions.")
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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)
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# ----- UI: top result and narrative -----
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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"**Señal (decisión):** <div class='big-decision'>{banner_color} {decision}</div>", unsafe_allow_html=True)
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st.markdown(f"**Prob. (media):** {p_mean*100:.2f}% **95% CI:** [{p_low*100:.1f}%, {p_high*100:.1f}%]")
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st.markdown("**Resumen breve de por qué:**")
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# compute component contributions (normalized by assumed weights)
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w = np.array([0.30, 0.20, 0.10, 0.30, 0.10]) # chosen weights (documentado)
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contribs = w * np.array([x_live[0], x_live[1], x_live[2], x_live[3], x_live[4]])
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comp_names = ["COT (z_net)", "Δ1w (z)", "Δ4w (z)", "Fed (signal)", "Retail (signal)"]
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explanation_lines = []
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for nm, val, c in zip(comp_names, x_live, contribs):
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explanation_lines.append(f"- {nm}: {val:.3f} → contrib {c:.3f}")
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st.write("\n".join(explanation_lines))
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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'>Prob. (mean): <strong>{p_mean*100:.1f}%</strong> 95% CI: <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'>Conviction: <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("### Tactical trade plan (concise)", unsafe_allow_html=True)
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if decision_flag == "buy":
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st.markdown("- Scale-in (3 tranches). Initial tranche: 0.5–1% notional.", unsafe_allow_html=True)
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st.markdown("- Stop: technical stop (1.0–1.5×ATR) or below nearest structure.", unsafe_allow_html=True)
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st.markdown("- Target: prefer R:R ≥ 1:1.5; manage increments.", unsafe_allow_html=True)
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elif decision_flag == "sell":
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st.markdown("- Consider reducing longs or initiating small short (0.5–1%).", unsafe_allow_html=True)
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st.markdown("- Tight stops; monitor macro headlines closely.", unsafe_allow_html=True)
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else:
|
||||
st.markdown("- No trade. Await clearer signal or follow confirmatory flow.", unsafe_allow_html=True)
|
||||
st.markdown("</div>", unsafe_allow_html=True)
|
||||
|
||||
with colB:
|
||||
with right:
|
||||
# gauge
|
||||
fig = go.Figure(go.Indicator(
|
||||
mode="gauge+number",
|
||||
mode="gauge+number+delta",
|
||||
value=p_mean*100,
|
||||
number={'suffix': '%'},
|
||||
domain={'x': [0,1], 'y': [0,1]},
|
||||
title={'text': "Probabilidad USD/JPY al alza"},
|
||||
gauge={'axis': {'range': [0,100]},
|
||||
'bar': {'color': "darkcyan"},
|
||||
'steps': [
|
||||
{'range': [0,40], 'color': "red"},
|
||||
{'range': [40,60], 'color': "gold"},
|
||||
{'range': [60,100], 'color': "green"},
|
||||
]} ))
|
||||
title={'text': "P(USD/JPY ↑)"},
|
||||
gauge={
|
||||
'axis': {'range': [0,100]},
|
||||
'bar': {'color': "#1f7a8c"},
|
||||
'steps': [
|
||||
{'range':[0,40], 'color':'#a62b2b'},
|
||||
{'range':[40,60], 'color':'#bfae59'},
|
||||
{'range':[60,100], 'color':'#2a8f6b'}
|
||||
],
|
||||
}
|
||||
))
|
||||
st.plotly_chart(fig, use_container_width=True)
|
||||
|
||||
# ---------- Component table ----------
|
||||
st.markdown("### Componentes y valores normalizados")
|
||||
st.markdown("### Contextual narrative — expert style", unsafe_allow_html=True)
|
||||
st.markdown(f"<div class='card'><div class='explain'>{narrative.replace(chr(10), '<br/>')}</div></div>", unsafe_allow_html=True)
|
||||
|
||||
# ----- Component table and coefficients -----
|
||||
st.markdown("### Components (normalized features) and MAP coefficients")
|
||||
comp_df = pd.DataFrame({
|
||||
"Componente": comp_names,
|
||||
"Valor (normalizado)": [f"{v:.4f}" for v in x_live],
|
||||
"Peso aplicado": list(w)
|
||||
"Componente": feature_cols,
|
||||
"Valor (normalized)": [f"{v:.4f}" for v in x_live],
|
||||
"beta_MAP": [float(b) for b in beta_map],
|
||||
"Signed % contrib": [f"{float(c):.1f}%" for c in contrib_pct]
|
||||
})
|
||||
st.table(comp_df)
|
||||
|
||||
# ---------- Optional: show coefficients and metrics ----------
|
||||
st.markdown("### Coeficientes MAP (modelo bayesiano)")
|
||||
coef_df = pd.DataFrame({
|
||||
"feature": feature_cols,
|
||||
"beta_map": [float(b) for b in beta_map],
|
||||
"post_var_diag": [float(v) for v in np.diag(cov_post)]
|
||||
})
|
||||
st.table(coef_df)
|
||||
|
||||
# ---------- Export / download ----------
|
||||
# ----- Export & footer -----
|
||||
out_df = pd.DataFrame([{
|
||||
"cot_long": cot_long, "cot_short": cot_short, "net": net,
|
||||
"fed_prob": fed_prob, "retail_pct": retail_pct,
|
||||
"p_mean": p_mean, "p_2.5": p_low, "p_97.5": p_high, "decision": decision
|
||||
"p_mean": p_mean, "p_2.5": p_low, "p_97.5": p_high,
|
||||
"conviction": conviction, "decision": decision
|
||||
}])
|
||||
st.download_button("Exportar resultado (Excel)", data=to_excel(out_df),
|
||||
file_name="kennis_usdjpy_signal.xlsx",
|
||||
mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet")
|
||||
st.download_button("Export result (Excel)", data=to_excel(out_df), file_name="kennis_usdjpy_signal.xlsx")
|
||||
|
||||
# ---------- Footer ----------
|
||||
st.markdown("---")
|
||||
st.markdown("<div class='subtle'>Implementación: MAP logistic (Laplace) — Posterior sampling para incertidumbre. Reemplaza demo subiendo CSV histórico con 'target_up' (0/1) para entrenamiento real.</div>", unsafe_allow_html=True)
|
||||
st.markdown("© Kennis - Institucional FX Markets")
|
||||
st.markdown("<div class='small'>Implementation: Tactical Bayesian logistic (MAP + Laplace). Replace demo with real historical CSV (with 'target_up' labels) for production-grade calibration. Use with execution policy and risk controls.</div>", unsafe_allow_html=True)
|
||||
st.markdown("© Kennis FX Markets — Institutional Bayesian Intelligence Engine", unsafe_allow_html=True)
|
||||
|
||||
Reference in New Issue
Block a user