Update app.py

app.py

@@ -256,6 +256,9 @@ def build_synthetic_dataset(universe_user: List[str],
                             erp_ann: float,
                             sigma_mkt: float,
                             n_rows: int = SYNTH_ROWS) -> pd.DataFrame:
+    """
+    Generate long-only mixes **from exactly the user's tickers** (VOO included only if the user holds it).
+    """
     rng = np.random.default_rng(12345)
     assets = list(universe_user)
     if len(assets) == 0:
@@ -370,7 +373,7 @@ def suggest_one_per_band(synth: pd.DataFrame, sigma_mkt: float, universe_user: L
         out[band.lower()] = chosen
     return out
 
-# -------------- UI helpers (restored) --------------
+# -------------- UI helpers --------------
 def empty_positions_df():
     return pd.DataFrame(columns=["ticker", "amount_usd", "weight_exposure", "beta"])
 
@@ -458,18 +461,18 @@ def compute(
 
     symbols = [t for t in df["ticker"].tolist() if t]
     if len(symbols) == 0:
-        hide = gr.update(visible=False)
         return (
             None, "Add at least one ticker.", empty_positions_df(), empty_suggestion_df(), None,
-            hide, hide, hide, hide, hide, hide, hide
+            "", "", "",
+            None, None, None, None, None, None, None, None, None
         )
 
     symbols = validate_tickers(symbols, years_lookback)
     if len(symbols) == 0:
-        hide = gr.update(visible=False)
         return (
             None, "Could not validate any tickers.", empty_positions_df(), empty_suggestion_df(), None,
-            hide, hide, hide, hide, hide, hide, hide
+            "", "", "",
+            None, None, None, None, None, None, None, None, None
         )
 
     global UNIVERSE
@@ -486,10 +489,10 @@ def compute(
     # Weights
     gross = sum(abs(v) for v in amounts.values())
     if gross <= 1e-12:
-        hide = gr.update(visible=False)
         return (
             None, "All amounts are zero.", empty_positions_df(), empty_suggestion_df(), None,
-            hide, hide, hide, hide, hide, hide, hide
+            "", "", "",
+            rf_ann, erp_ann, sigma_mkt, None, None, None, None, None, None
         )
     weights = {k: v / gross for k, v in amounts.items()}
 
@@ -500,7 +503,7 @@ def compute(
     a_sigma, b_sigma, mu_eff_same_sigma = efficient_same_sigma(sigma_hist, rf_ann, erp_ann, sigma_mkt)
     a_mu, b_mu, sigma_eff_same_mu = efficient_same_return(mu_capm, rf_ann, erp_ann, sigma_mkt)
 
-    # Synthetic dataset & suggestions
+    # Synthetic dataset & suggestions — exactly the user's tickers
     user_universe = list(symbols)
     synth = build_synthetic_dataset(user_universe, covA, betas, rf_ann, erp_ann, sigma_mkt, n_rows=SYNTH_ROWS)
     csv_path = os.path.join(DATA_DIR, f"investor_profiles_{int(time.time())}.csv")
@@ -529,6 +532,7 @@ def compute(
     else:
         chosen_sigma = float(chosen["sigma_hist"])
         chosen_mu = float(chosen["mu_capm"])
+        # holdings table from chosen suggestion
         sugg_table = _holdings_table_from_row(chosen, budget=gross)
 
     pos_table = pd.DataFrame(
@@ -548,6 +552,7 @@ def compute(
         sugg_sigma_hist=chosen_sigma, sugg_mu_capm=chosen_mu
     )
 
+    # ---------- summary text ----------
     info = "\n".join([
         "### Inputs",
         f"- Lookback years {years_lookback}",
@@ -564,18 +569,17 @@ def compute(
         f"- **Same σ as your portfolio** → Market weight **{a_sigma:.2f}**, Bills weight **{b_sigma:.2f}** → E[r] **{mu_eff_same_sigma:.2%}**",
         f"- **Same E[r] as your portfolio** → Market weight **{a_mu:.2f}**, Bills weight **{b_mu:.2f}** → σ **{sigma_eff_same_mu:.2%}**",
         "",
-        "_How to replicate:_ use a broad market ETF (e.g., VOO) for **Market** and a T-bill/MMF for **Bills**. "
-        "Weights may exceed 1 (leverage) or be negative (borrowing). If leverage isn’t allowed, scale toward 1.0."
+        "_How to replicate:_ use a broad market ETF (e.g., VOO) for the **Market** leg and a T-bill/money-market fund for **Bills**. ",
+        "Weights can be >1 or negative (e.g., Market > 1 and Bills < 0 implies leverage/borrowing). ",
+        "If leverage isn’t allowed, scale both weights proportionally toward 1.0 to fit your constraints.",
     ])
+    # -----------------------------------
 
-    show = gr.update(visible=True)
     return (
         img, info, pos_table, sugg_table, csv_path,
-        show,  # suggestions header visible
-        gr.update(value=txt_low,  visible=True),
-        gr.update(value=txt_med,  visible=True),
-        gr.update(value=txt_high, visible=True),
-        gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)  # buttons visible
+        txt_low, txt_med, txt_high,
+        rf_ann, erp_ann, sigma_mkt, sigma_hist, mu_capm, mu_eff_same_sigma, sigma_eff_same_mu,
+        chosen_sigma, chosen_mu
     )
 
 # -------------- UI --------------
@@ -586,93 +590,111 @@ with gr.Blocks(title="Efficient Portfolio Advisor") as demo:
         "Plot shows **your CAPM point on the CML** plus efficient market/bills points."
     )
 
-    # All left-stacked
-    with gr.Column():
-        # --- Search / Add flow (vertical) ---
-        q = gr.Textbox(label="Search symbol")
-        search_btn = gr.Button("Search")  # directly under the search box
-        search_note = gr.Markdown()
-        matches = gr.Dropdown(choices=[], label="Matches")
-        add_btn = gr.Button("Add selected to portfolio")  # after matches
-
-        # --- Portfolio table ---
-        gr.Markdown("### Portfolio positions")
-        table = gr.Dataframe(
-            headers=["ticker", "amount_usd"],
-            datatype=["str", "number"],
-            row_count=0,
-            col_count=(2, "fixed")
-        )
+    with gr.Row():
+        with gr.Column(scale=1):
+            # --- Vertical flow: Search -> Button -> Matches -> Add ---
+            q = gr.Textbox(label="Search symbol")
+            search_btn = gr.Button("Search")
+            search_note = gr.Markdown()
+            matches = gr.Dropdown(choices=[], label="Matches")
+            add_btn = gr.Button("Add selected to portfolio")
+            # ----------------------------------------------------------
+
+            gr.Markdown("### Portfolio positions")
+            table = gr.Dataframe(
+                headers=["ticker", "amount_usd"],
+                datatype=["str", "number"],
+                row_count=0,
+                col_count=(2, "fixed")
+            )
 
-        # --- Controls ---
-        horizon = gr.Number(label="Horizon in years (1–100)", value=HORIZON_YEARS, precision=0)
-        lookback = gr.Slider(1, 15, value=DEFAULT_LOOKBACK_YEARS, step=1, label="Lookback years for betas & covariances")
-        run_btn = gr.Button("Compute (build dataset & suggest)")  # right under the slider
-
-        # --- Results ---
-        plot = gr.Image(label="Capital Market Line (CAPM)", type="pil")
-        summary = gr.Markdown(label="Inputs & Results")
-        positions = gr.Dataframe(
-            label="Computed positions",
-            headers=["ticker", "amount_usd", "weight_exposure", "beta"],
-            datatype=["str", "number", "number", "number"],
-            col_count=(4, "fixed"),
-            value=empty_positions_df(),
-            interactive=False
-        )
-        sugg_table = gr.Dataframe(
-            label="Selected suggestion holdings (% / $)",
-            headers=["ticker", "weight_%", "amount_$"],
-            datatype=["str", "number", "number"],
-            col_count=(3, "fixed"),
-            value=empty_suggestion_df(),
-            interactive=False
-        )
-        dl = gr.File(label="Generated dataset CSV", value=None, visible=True)
-
-        # --- Suggestions (hidden until Compute) ---
-        sugg_hdr = gr.Markdown("### Suggestions", visible=False)
-        with gr.Row():
-            btn_low  = gr.Button("Show Low",    visible=False)
-            btn_med  = gr.Button("Show Medium", visible=False)
-            btn_high = gr.Button("Show High",   visible=False)
-        low_txt  = gr.Markdown(visible=False)
-        med_txt  = gr.Markdown(visible=False)
-        high_txt = gr.Markdown(visible=False)
-
-    # --- wiring ---
+            horizon = gr.Number(label="Horizon in years (1–100)", value=HORIZON_YEARS, precision=0)
+            lookback = gr.Slider(1, 15, value=DEFAULT_LOOKBACK_YEARS, step=1, label="Lookback years for betas & covariances")
+
+            # --- Compute button directly under lookback slider ---
+            run_btn = gr.Button("Compute (build dataset & suggest)")
+            # -----------------------------------------------------
+
+            gr.Markdown("### Suggestions")
+            with gr.Row():
+                btn_low = gr.Button("Show Low")
+                btn_med = gr.Button("Show Medium")
+                btn_high = gr.Button("Show High")
+            low_txt = gr.Markdown()
+            med_txt = gr.Markdown()
+            high_txt = gr.Markdown()
+
+        with gr.Column(scale=1):
+            plot = gr.Image(label="Capital Market Line (CAPM)", type="pil")
+            summary = gr.Markdown(label="Inputs & Results")
+            positions = gr.Dataframe(
+                label="Computed positions",
+                headers=["ticker", "amount_usd", "weight_exposure", "beta"],
+                datatype=["str", "number", "number", "number"],
+                col_count=(4, "fixed"),
+                value=empty_positions_df(),
+                interactive=False
+            )
+            sugg_table = gr.Dataframe(
+                label="Selected suggestion holdings (% / $)",
+                headers=["ticker", "weight_%", "amount_$"],
+                datatype=["str", "number", "number"],
+                col_count=(3, "fixed"),
+                value=empty_suggestion_df(),
+                interactive=False
+            )
+            dl = gr.File(label="Generated dataset CSV", value=None, visible=True)
+
+    # wire search / add / locking
     search_btn.click(fn=search_tickers_cb, inputs=q, outputs=[search_note, matches])
-    add_btn.click(fn=add_symbol,         inputs=[matches, table], outputs=[table, search_note])
-    table.change(fn=lock_ticker_column,  inputs=table,           outputs=table)
+    add_btn.click(fn=add_symbol, inputs=[matches, table], outputs=[table, search_note])
+    table.change(fn=lock_ticker_column, inputs=table, outputs=table)
 
+    # horizon updates globals silently (no UI output)
     horizon.change(fn=set_horizon, inputs=horizon, outputs=[])
 
-    # compute (default Medium band); also reveal Suggestions UI on success
+    # compute + render (default to Medium band)
     run_btn.click(
         fn=compute,
         inputs=[lookback, table, gr.State("Medium")],
         outputs=[
             plot, summary, positions, sugg_table, dl,
-            sugg_hdr, low_txt, med_txt, high_txt,  # content + visibility
-            btn_low, btn_med, btn_high             # visibility only
+            low_txt, med_txt, high_txt,
+            gr.State(), gr.State(), gr.State(), gr.State(), gr.State(), gr.State(), gr.State(),
+            gr.State(), gr.State()
         ]
     )
 
-    # band buttons reuse compute; keep them visible after first compute
+    # band buttons recompute picks quickly
     btn_low.click(
         fn=compute,
         inputs=[lookback, table, gr.State("Low")],
-        outputs=[plot, summary, positions, sugg_table, dl, sugg_hdr, low_txt, med_txt, high_txt, btn_low, btn_med, btn_high]
+        outputs=[
+            plot, summary, positions, sugg_table, dl,
+            low_txt, med_txt, high_txt,
+            gr.State(), gr.State(), gr.State(), gr.State(), gr.State(), gr.State(), gr.State(),
+            gr.State(), gr.State()
+        ]
     )
     btn_med.click(
         fn=compute,
         inputs=[lookback, table, gr.State("Medium")],
-        outputs=[plot, summary, positions, sugg_table, dl, sugg_hdr, low_txt, med_txt, high_txt, btn_low, btn_med, btn_high]
+        outputs=[
+            plot, summary, positions, sugg_table, dl,
+            low_txt, med_txt, high_txt,
+            gr.State(), gr.State(), gr.State(), gr.State(), gr.State(), gr.State(), gr.State(),
+            gr.State(), gr.State()
+        ]
     )
     btn_high.click(
         fn=compute,
         inputs=[lookback, table, gr.State("High")],
-        outputs=[plot, summary, positions, sugg_table, dl, sugg_hdr, low_txt, med_txt, high_txt, btn_low, btn_med, btn_high]
+        outputs=[
+            plot, summary, positions, sugg_table, dl,
+            low_txt, med_txt, high_txt,
+            gr.State(), gr.State(), gr.State(), gr.State(), gr.State(), gr.State(), gr.State(),
+            gr.State(), gr.State()
+        ]
     )
 
 # initialize risk-free at launch