🎨 Palette: Add contextual tooltips to financial parameters

.Jules/palette.md

@@ -0,0 +1,4 @@
+
+## 2024-05-24 - Concrete Tooltips for Financial Parameters
+**Learning:** Users struggle to interpret abstract statistical inputs (like quantiles, days, or basis points). Providing concrete, easily understandable examples within tooltips significantly improves confidence and usability.
+**Action:** Always include concrete examples in tooltips for financial or statistical parameters (e.g., '10 bps = 0.10%', '63 days = ~3 months').

src/dashboard.py

@@ -160,44 +160,114 @@ 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. 63 days = ~3 months.",
+        )
+        vol_window = st.slider(
+            "Regime Vol Window (days)",
+            10,
+            60,
+            21,
+            5,
+            help="Rolling window for realized volatility. 21 days = ~1 trading 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. 0.10 = 10% of daily volume.",
+        )
 
     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. 0.80 = Top 20% of historical volatility.",
     )
 
     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. 10 bps = 0.10%.",
+            )
+            / 10000
+        )
         allow_short = st.checkbox("Allow Short Selling?", value=False)
     else:
         st.subheader("5. Alert Thresholds")
-        dd_alert = st.slider("Max Drawdown Alert", -0.6, -0.05, -0.2, 0.05)
-        vol_alert = st.slider("Volatility Alert (ann.)", 0.1, 1.0, 0.35, 0.05)
-        beta_alert = st.slider("Beta Alert", 0.5, 2.0, 1.3, 0.1)
-        dttl_alert = st.slider("Days-to-Liquidate Alert", 1.0, 20.0, 5.0, 1.0)
+        dd_alert = st.slider(
+            "Max Drawdown Alert",
+            -0.6,
+            -0.05,
+            -0.2,
+            0.05,
+            help="Alert when drawdown exceeds this negative threshold. -0.20 = 20% loss.",
+        )
+        vol_alert = st.slider(
+            "Volatility Alert (ann.)",
+            0.1,
+            1.0,
+            0.35,
+            0.05,
+            help="Alert when annualized volatility exceeds this level. 0.35 = 35% volatility.",
+        )
+        beta_alert = st.slider(
+            "Beta Alert",
+            0.5,
+            2.0,
+            1.3,
+            0.1,
+            help="Alert when portfolio beta to benchmark exceeds this value.",
+        )
+        dttl_alert = st.slider(
+            "Days-to-Liquidate Alert",
+            1.0,
+            20.0,
+            5.0,
+            1.0,
+            help="Alert when estimated days to liquidate the portfolio exceeds this value.",
+        )
 
 
 # --- Portfolio Mode ---
@@ -916,8 +986,12 @@ def get_cache_key(*args) -> str:
             """)
 
             wf_col1, wf_col2 = st.columns(2)
-            wf_train = wf_col1.number_input("Training Window (months)", value=24, min_value=6, max_value=60)
-            wf_test = wf_col2.number_input("Test Window (months)", value=6, min_value=1, max_value=12)
+            wf_train = wf_col1.number_input(
+                "Training Window (months)", value=24, min_value=6, max_value=60
+            )
+            wf_test = wf_col2.number_input(
+                "Test Window (months)", value=6, min_value=1, max_value=12
+            )
 
             if st.button("Run Walk-Forward Analysis"):
                 with st.spinner("Running walk-forward validation..."):