🎨 Palette: Add UX tooltips to financial parameters

.Jules/palette.md

@@ -0,0 +1,5 @@
+## 2024-03-13 - [Initial]
+
+## 2024-03-13 - [Tooltip UX for Statistical Inputs]
+**Learning:** Statistical inputs (like quantiles, bps, adv) without concrete examples cause confusion.
+**Action:** Use native Streamlit `help` parameter to provide contextual examples (e.g. 10 bps = 0.10%) for all financial widgets.

src/dashboard.py

@@ -141,7 +141,8 @@ def get_cache_key(*args) -> str:
         portfolio_value = st.number_input(
             "Portfolio Value (USD)",
             value=float(DEFAULT_PORTFOLIO_VALUE),
-            step=100000.0
+            step=100000.0,
+            help="Total capital allocated to the portfolio (e.g., 1,000,000 USD).",
         )
         benchmark_ticker = st.text_input("Benchmark Ticker", value=DEFAULT_BENCHMARK).upper()
 
@@ -160,37 +161,79 @@ def get_cache_key(*args) -> str:
     st.subheader("3. Signal Parameters")
     if mode == "Single-Asset":
         sma_window = st.slider(
-            "Trend SMA Window", 10, 200, DEFAULT_SMA_WINDOW, 10,
-            help="Lookback days for Simple Moving Average trend signal."
+            "Trend SMA Window",
+            10,
+            200,
+            DEFAULT_SMA_WINDOW,
+            10,
+            help="Lookback days for Simple Moving Average trend signal.",
         )
         mom_window = st.slider(
-            "Momentum Lookback (Months)", 1, 24, DEFAULT_MOMENTUM_WINDOW, 1,
-            help="Lookback months for Momentum signal."
+            "Momentum Lookback (Months)",
+            1,
+            24,
+            DEFAULT_MOMENTUM_WINDOW,
+            1,
+            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="Rolling window for calculating factor betas (e.g., 63 days ≈ 3 months).",
+        )
+        vol_window = st.slider(
+            "Regime Vol Window (days)",
+            10,
+            60,
+            21,
+            5,
+            help="Rolling window for calculating annualized volatility (e.g., 21 days ≈ 1 month).",
+        )
+        adv_pct = st.slider(
+            "ADV Participation %",
+            0.01,
+            0.30,
+            float(DEFAULT_ADV_PCT),
+            0.01,
+            help="Maximum percentage of Average Daily Volume to trade (e.g., 0.10 = 10% of ADV).",
+        )
 
     st.markdown("---")
     st.subheader("4. Research Rigor")
     use_oos = st.toggle(
         "Out-of-Sample Mode",
         value=False,
-        help="Uses expanding-window quantiles for regime classification to avoid look-ahead bias. Enable for rigorous backtesting."
+        help="Uses expanding-window quantiles for regime classification to avoid look-ahead bias. Enable for rigorous backtesting.",
     )
     if use_oos:
         st.success("✓ Look-ahead bias removed")
     else:
         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 means top 25% of historically 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="Transaction friction per trade (e.g., 10 bps = 0.10%).",
+            )
+            / 10000
+        )
         allow_short = st.checkbox("Allow Short Selling?", value=False)
     else:
         st.subheader("5. Alert Thresholds")