🎨 Palette: Add tooltips to financial parameters

.Jules/palette.md

@@ -0,0 +1,3 @@
+## 2024-10-24 - Tooltips with Concrete Financial Examples
+**Learning:** In quantitative dashboards, users often struggle with abstract financial or statistical inputs (like basis points, quantiles, or volume percentages). Providing just the parameter name is insufficient.
+**Action:** Always include a `help` parameter on statistical or financial input widgets (e.g., `st.slider`, `st.number_input`) that provides a concrete, easy-to-understand example of what the value means in practice (e.g., "10 bps = 0.10%").

src/dashboard.py

@@ -168,9 +168,30 @@ def get_cache_key(*args) -> str:
             help="Lookback months for Momentum signal."
         )
     else:
-        factor_window = st.slider("Factor Beta Window (days)", 20, 252, 63, 7)
-        vol_window = st.slider("Regime Vol Window (days)", 10, 60, 21, 5)
-        adv_pct = st.slider("ADV Participation %", 0.01, 0.30, float(DEFAULT_ADV_PCT), 0.01)
+        factor_window = st.slider(
+            "Factor Beta Window (days)",
+            20,
+            252,
+            63,
+            7,
+            help="Lookback window for factor beta calculation (e.g., 63 days = 3 months).",
+        )
+        vol_window = st.slider(
+            "Regime Vol Window (days)",
+            10,
+            60,
+            21,
+            5,
+            help="Lookback window for realized volatility calculation (e.g., 21 days = 1 month).",
+        )
+        adv_pct = st.slider(
+            "ADV Participation %",
+            0.01,
+            0.30,
+            float(DEFAULT_ADV_PCT),
+            0.01,
+            help="Max percentage of Average Daily Volume to trade (e.g., 0.10 = 10% of ADV).",
+        )
 
     st.markdown("---")
     st.subheader("4. Research Rigor")
@@ -185,12 +206,25 @@ def get_cache_key(*args) -> str:
         st.info("Using full-sample quantiles (exploratory mode)")
 
     vol_q_high = st.slider(
-        "High Volatility Quantile", 0.5, 0.95, DEFAULT_VOL_QUANTILE_HIGH, 0.05
+        "High Volatility Quantile",
+        0.5,
+        0.95,
+        DEFAULT_VOL_QUANTILE_HIGH,
+        0.05,
+        help="Threshold for 'High Volatility' regime (e.g., 0.75 = top 25% most volatile days).",
     )
 
     if mode == "Single-Asset":
         st.subheader("5. Backtest Settings")
-        bt_cost = st.number_input("Transaction Cost (bps)", value=DEFAULT_COST_BPS, step=1) / 10000
+        bt_cost = (
+            st.number_input(
+                "Transaction Cost (bps)",
+                value=DEFAULT_COST_BPS,
+                step=1,
+                help="Cost per trade in basis points (e.g., 10 bps = 0.10%).",
+            )
+            / 10000
+        )
         allow_short = st.checkbox("Allow Short Selling?", value=False)
     else:
         st.subheader("5. Alert Thresholds")