Trading Signals
@@ -240,10 +262,25 @@ function renderAll(data) {
renderTomorrow(data.tomorrow);
renderCharts(data.forecast);
renderComparison(data.forecast);
- renderSignals(data.signals);
- renderKelly(data.kelly);
- renderRaw(data.forecast);
- renderTyphoon(data.typhoon);
+ renderSignals(data.signals);
+ renderKelly(data.kelly);
+ renderRaw(data.forecast);
+ renderTyphoon(data.typhoon);
+ }
+ // ML data refreshes independently (slower)
+ fetchML();
+}
+
+async function fetchML() {
+ try {
+ const resp = await fetch('/api/ml');
+ const data = await resp.json();
+ renderML(data);
+ renderTyphoonML(data.typhoon);
+ } catch(e) {
+ document.getElementById('ml-loading').style.display = 'block';
+ document.getElementById('ml-content').style.display = 'none';
+ }
}
function renderCurrent(current, typhoon) {
@@ -467,6 +504,59 @@ function renderRaw(fc) {
div.innerHTML = '
' + JSON.stringify(fc, null, 2) + '
';
}
+function renderML(data) {
+ document.getElementById('ml-loading').style.display = 'none';
+ const div = document.getElementById('ml-content');
+ div.style.display = '';
+
+ if (data.error) {
+ div.innerHTML = '
ML models not available (train first: python ml/train.py)
';
+ return;
+ }
+
+ const models = data.models || {};
+ if (Object.keys(models).length === 0) {
+ div.innerHTML = '
No model predictions yet
';
+ return;
+ }
+
+ let html = '';
+ const sorted = Object.entries(models).sort((a, b) => {
+ const x = Math.abs(a[1].calibrated - 50);
+ const y = Math.abs(b[1].calibrated - 50);
+ return y - x;
+ });
+
+ for (const [name, m] of sorted) {
+ const cls = m.calibrated > 75 ? 'badge-green' : (m.calibrated < 25 ? 'badge-danger' : 'badge-info');
+ html += `
+
${m.description} ${m.calibrated.toFixed(1)}%
+
raw=${m.raw.toFixed(1)}% · ${m.method}
+
`;
+ }
+ div.innerHTML = html;
+}
+
+function renderTyphoonML(typhoon) {
+ const div = document.getElementById('typhoon-content');
+ if (!typhoon || Object.keys(typhoon).length === 0) {
+ div.innerHTML = '
No data';
+ return;
+ }
+ let html = '';
+ const levels = ['typhoon_T1', 'typhoon_T3', 'typhoon_T8'];
+ for (const k of levels) {
+ if (typhoon[k] !== undefined) {
+ const cls = typhoon[k] > 30 ? 'badge-warn' : (typhoon[k] > 10 ? 'badge-info' : '');
+ html += `
${k.replace('typhoon_','')}: ${typhoon[k].toFixed(1)}% `;
+ }
+ }
+ if (typhoon['typhoon_T8_72h'] !== undefined) {
+ html += `
T8/72h: ${typhoon['typhoon_T8_72h'].toFixed(1)}% · T8/120h: ${(typhoon['typhoon_T8_120h']||0).toFixed(1)}%
`;
+ }
+ div.innerHTML = html;
+}
+
refresh();
setInterval(refresh, 300000); // Every 5 min
@@ -629,6 +719,53 @@ def api_dashboard():
return jsonify({"error": str(e)}), 500
+@app.route("/api/ml")
+def api_ml():
+ """Return ML model predictions."""
+ if not _ml_available:
+ return jsonify({"error": "ML not available"}), 503
+
+ try:
+ predictor = MLPredictor()
+ predictor.fetch_and_predict()
+ predictions = predictor._last_predictions or {}
+
+ # Per-model raw vs calibrated
+ models = {}
+ for target, model in predictor.ensemble.models.items():
+ if predictor._last_features is not None:
+ raw = float(model.predict_raw(predictor._last_features[1:2])[0]) if len(predictor._last_features) > 1 else 0.5
+ cal = float(model.predict_proba(predictor._last_features[1:2])[0]) if len(predictor._last_features) > 1 else 50.0
+ else:
+ raw, cal = 0.5, 50.0
+ models[target] = {
+ "description": model.target_def["description"],
+ "raw": round(raw * 100, 1),
+ "calibrated": round(cal, 1),
+ "method": model.calibrator.method,
+ }
+
+ # Typhoon
+ typhoon = {}
+ if predictor._last_typhoon:
+ for k in ["typhoon_T1", "typhoon_T3", "typhoon_T8",
+ "typhoon_T8_72h", "typhoon_T8_120h"]:
+ if k in predictor._last_typhoon:
+ typhoon[k] = round(predictor._last_typhoon[k], 1)
+
+ # Spatial
+ spatial = predictor._last_spatial or {}
+
+ return jsonify({
+ "fetch_time": datetime.now().strftime("%H:%M:%S"),
+ "models": models,
+ "typhoon": typhoon,
+ "spatial": spatial,
+ })
+ except Exception as e:
+ return jsonify({"error": str(e)}), 500
+
+
@app.route("/")
def index():
return render_template_string(HTML_TEMPLATE)