feat: Streamlit dashboard with sidebar, detail, portfolio, and main app

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

dashboard/sidebar.py

@@ -0,0 +1,46 @@
+import streamlit as st
+import json
+from config import DEFAULT_WEIGHTS
+
+def render_sidebar(latest_results: dict, db):
+    st.sidebar.title("Crypto Signals")
+    page = st.sidebar.radio("View", ["Signals", "Portfolio"], label_visibility="collapsed")
+    st.sidebar.divider()
+
+    if latest_results:
+        coins = sorted(latest_results.values(), key=lambda x: x["composite"], reverse=True)
+        for coin in coins:
+            signal = coin["signal"]
+            color = {"BUY": "green", "HOLD": "orange", "SELL": "red"}.get(signal, "gray")
+            symbol_short = coin["symbol"].replace("USDT", "")
+            label = f":{color}[{signal}] **{symbol_short}** — {coin['composite']:.0f}"
+            if st.sidebar.button(label, key=coin["symbol"], use_container_width=True):
+                st.session_state["selected_coin"] = coin["symbol"]
+
+    st.sidebar.divider()
+    st.sidebar.subheader("Signal Weights")
+    weights = _load_weights(db)
+    new_weights = {}
+    new_weights["technical"] = st.sidebar.slider("Technical", 0.0, 1.0, weights["technical"], 0.05)
+    new_weights["news"] = st.sidebar.slider("News", 0.0, 1.0, weights["news"], 0.05)
+    new_weights["social"] = st.sidebar.slider("Social", 0.0, 1.0, weights["social"], 0.05)
+    new_weights["ai"] = st.sidebar.slider("AI", 0.0, 1.0, weights["ai"], 0.05)
+
+    total = sum(new_weights.values())
+    if abs(total - 1.0) > 0.01:
+        st.sidebar.warning(f"Weights sum: {total:.2f} (must be 1.0)")
+    else:
+        if new_weights != weights:
+            db.save_setting("weights", json.dumps(new_weights))
+            st.session_state["weights_changed"] = True
+
+    return page
+
+def _load_weights(db) -> dict:
+    raw = db.load_setting("weights")
+    if raw:
+        try:
+            return json.loads(raw)
+        except json.JSONDecodeError:
+            pass
+    return dict(DEFAULT_WEIGHTS)