Upload app.py with huggingface_hub

app.py

@@ -149,6 +149,22 @@ def refresh_market_data(tickers_str: str, period: str = "1y"):
     return tickers
 
 
+# -----------------------------------------------------------------------------
+# Clean Data Normalizer for Gradio / Plotly Web Serialization
+# (Prevents numpy arrays from serializing as bdata binary buffers in Gradio)
+# -----------------------------------------------------------------------------
+def to_clean_list(series, default_val=None):
+    if series is None:
+        return []
+    out = []
+    for val in series:
+        if val is None or pd.isna(val) or (isinstance(val, (int, float)) and np.isnan(val)):
+            out.append(default_val)
+        else:
+            out.append(round(float(val), 4))
+    return out
+
+
 # -----------------------------------------------------------------------------
 # Visual Component Builders (Continuous Marquee & Cyber HUDs)
 # -----------------------------------------------------------------------------
@@ -410,7 +426,22 @@ def plot_stock_candle(
         STATE["market_data"][cache_key] = df
     df = STATE["market_data"][cache_key]
 
-    dates = list(df.index)
+    dates = [str(d)[:10] for d in df.index]
+
+    open_vals = to_clean_list(df['open'])
+    high_vals = to_clean_list(df['high'])
+    low_vals = to_clean_list(df['low'])
+    close_vals = to_clean_list(df['close'])
+    vol_vals = to_clean_list(df['volume'], 0.0)
+    sma20_vals = to_clean_list(df['sma20']) if 'sma20' in df else []
+    sma50_vals = to_clean_list(df['sma50']) if 'sma50' in df else []
+    bb_u = to_clean_list(df['bb_upper']) if 'bb_upper' in df else []
+    bb_l = to_clean_list(df['bb_lower']) if 'bb_lower' in df else []
+    vwap_vals = to_clean_list(df['vwap']) if 'vwap' in df else []
+    rsi_vals = to_clean_list(df['rsi'], 50.0) if 'rsi' in df else []
+    macd_vals = to_clean_list(df['macd_line'], 0.0) if 'macd_line' in df else []
+    sig_vals = to_clean_list(df['macd_signal'], 0.0) if 'macd_signal' in df else []
+    hist_vals = to_clean_list(df['macd_hist'], 0.0) if 'macd_hist' in df else []
 
     # 4 rows: Price (52%), Volume (16%), RSI (16%), MACD (16%)
     fig = make_subplots(
@@ -421,67 +452,67 @@ def plot_stock_candle(
 
     # 1. Candlestick
     fig.add_trace(go.Candlestick(
-        x=dates, open=df['open'], high=df['high'], low=df['low'], close=df['close'],
+        x=dates, open=open_vals, high=high_vals, low=low_vals, close=close_vals,
         name="OHLC", increasing_line_color='#00ff87', decreasing_line_color='#ff3366', showlegend=False
     ), row=1, col=1)
 
     # Overlays: SMA 20 & SMA 50
     if "SMA 20 & SMA 50" in overlays:
-        if 'sma20' in df:
+        if sma20_vals:
             fig.add_trace(go.Scatter(
-                x=dates, y=df['sma20'], line=dict(color='#ffb703', width=1.5), name="SMA 20"
+                x=dates, y=sma20_vals, line=dict(color='#ffb703', width=1.5), name="SMA 20"
             ), row=1, col=1)
-        if 'sma50' in df:
+        if sma50_vals:
             fig.add_trace(go.Scatter(
-                x=dates, y=df['sma50'], line=dict(color='#00f2fe', width=1.5), name="SMA 50"
+                x=dates, y=sma50_vals, line=dict(color='#00f2fe', width=1.5), name="SMA 50"
             ), row=1, col=1)
 
     # Overlays: Bollinger Bands
-    if "Bollinger Bands (20, 2σ)" in overlays and 'bb_upper' in df and 'bb_lower' in df:
+    if "Bollinger Bands (20, 2σ)" in overlays and bb_u and bb_l:
         fig.add_trace(go.Scatter(
-            x=dates, y=df['bb_upper'], line=dict(color='rgba(0, 242, 254, 0.35)', width=1), showlegend=False
+            x=dates, y=bb_u, line=dict(color='rgba(0, 242, 254, 0.35)', width=1), showlegend=False
         ), row=1, col=1)
         fig.add_trace(go.Scatter(
-            x=dates, y=df['bb_lower'], line=dict(color='rgba(0, 242, 254, 0.35)', width=1),
+            x=dates, y=bb_l, line=dict(color='rgba(0, 242, 254, 0.35)', width=1),
             fill='tonexty', fillcolor='rgba(0, 242, 254, 0.05)', showlegend=False
         ), row=1, col=1)
 
     # Overlays: VWAP
-    if "VWAP" in overlays and 'vwap' in df:
+    if "VWAP" in overlays and vwap_vals:
         fig.add_trace(go.Scatter(
-            x=dates, y=df['vwap'], line=dict(color='#d500f9', width=1.2, dash='dash'), name="VWAP"
+            x=dates, y=vwap_vals, line=dict(color='#d500f9', width=1.2, dash='dash'), name="VWAP"
         ), row=1, col=1)
 
     # 2. Volume
-    vol_colors = ['#00ff87' if c >= o else '#ff3366' for c, o in zip(df['close'], df['open'])]
+    vol_colors = ['#00ff87' if (c or 0) >= (o or 0) else '#ff3366' for c, o in zip(close_vals, open_vals)]
     fig.add_trace(go.Bar(
-        x=dates, y=df['volume'], marker_color=vol_colors, name="Volume", showlegend=False
+        x=dates, y=vol_vals, marker_color=vol_colors, name="Volume", showlegend=False
     ), row=2, col=1)
     if "Volume + 20D MA" in overlays:
-        vol_ma = df['volume'].rolling(20, min_periods=1).mean()
+        vol_ma = to_clean_list(df['volume'].rolling(20, min_periods=1).mean(), 0.0)
         fig.add_trace(go.Scatter(
             x=dates, y=vol_ma, line=dict(color='#00f2fe', width=1.2), name="Vol MA20", showlegend=False
         ), row=2, col=1)
 
     # 3. RSI (14)
-    if 'rsi' in df:
+    if rsi_vals:
         fig.add_trace(go.Scatter(
-            x=dates, y=df['rsi'], line=dict(color='#00f2fe', width=1.5), name="RSI(14)", showlegend=False
+            x=dates, y=rsi_vals, line=dict(color='#00f2fe', width=1.5), name="RSI(14)", showlegend=False
         ), row=3, col=1)
         fig.add_hline(y=70, line_dash='dot', line_color='#ff3366', line_width=1, row=3, col=1)
         fig.add_hline(y=30, line_dash='dot', line_color='#00ff87', line_width=1, row=3, col=1)
 
     # 4. MACD
-    if 'macd_line' in df and 'macd_signal' in df and 'macd_hist' in df:
+    if macd_vals and sig_vals and hist_vals:
         fig.add_trace(go.Scatter(
-            x=dates, y=df['macd_line'], line=dict(color='#00f2fe', width=1.2), name="MACD", showlegend=False
+            x=dates, y=macd_vals, line=dict(color='#00f2fe', width=1.2), name="MACD", showlegend=False
         ), row=4, col=1)
         fig.add_trace(go.Scatter(
-            x=dates, y=df['macd_signal'], line=dict(color='#ffb703', width=1.2), name="Signal", showlegend=False
+            x=dates, y=sig_vals, line=dict(color='#ffb703', width=1.2), name="Signal", showlegend=False
         ), row=4, col=1)
-        macd_colors = ['#00ff87' if h >= 0 else '#ff3366' for h in df['macd_hist']]
+        macd_colors = ['#00ff87' if (h or 0) >= 0 else '#ff3366' for h in hist_vals]
         fig.add_trace(go.Bar(
-            x=dates, y=df['macd_hist'], marker_color=macd_colors, name="Histogram", showlegend=False
+            x=dates, y=hist_vals, marker_color=macd_colors, name="Histogram", showlegend=False
         ), row=4, col=1)
 
     # Watermark Badges (Zero-collision top-left annotations)
@@ -524,7 +555,7 @@ def plot_stock_candle(
     )
 
     # X-Axes: Robust Category Axis with evenly spaced horizontal tick labels (No weekend gaps, zero collapse)
-    tick_indices = np.linspace(0, len(dates)-1, min(8, len(dates)), dtype=int)
+    tick_indices = [int(i) for i in np.linspace(0, len(dates)-1, min(8, len(dates)), dtype=int)]
     tickvals = [dates[i] for i in tick_indices]
 
     fig.update_xaxes(
@@ -550,6 +581,7 @@ def plot_stock_candle(
             tickfont=dict(family='JetBrains Mono', size=10, color='#94a3b8'),
             row=r, col=1
         )
+    fig.update_yaxes(range=[0, 100], row=3, col=1)
 
     kpi_card = generate_kpi_card_html(ticker)
     return fig, kpi_card
@@ -563,6 +595,7 @@ def plot_backtest_cumulative(record: dict) -> go.Figure:
     )
     days = 252
     dates = pd.date_range(end=pd.Timestamp.now(), periods=days, freq='B')
+    date_strs = [d.strftime('%Y-%m-%d') for d in dates]
     ann_ret = record.get("annualized_return_pct", 15.0) / 100.0
     sharpe = record.get("sharpe", 1.5)
     vol = abs(ann_ret / sharpe) if sharpe != 0 else 0.15
@@ -573,12 +606,15 @@ def plot_backtest_cumulative(record: dict) -> go.Figure:
     rolling_max = np.maximum.accumulate(cum_ret)
     dd = (cum_ret - rolling_max) / rolling_max
 
+    cum_ret_clean = [round(float(v), 4) for v in cum_ret]
+    dd_clean = [round(float(v * 100), 2) for v in dd]
+
     fig.add_trace(go.Scatter(
-        x=dates, y=cum_ret, line=dict(color='#00ff87', width=2), name="Strategy Equity"
+        x=date_strs, y=cum_ret_clean, line=dict(color='#00ff87', width=2), name="Strategy Equity"
     ), row=1, col=1)
 
     fig.add_trace(go.Scatter(
-        x=dates, y=dd * 100, fill='tozeroy', line=dict(color='#ff3366', width=1), name="Drawdown %"
+        x=date_strs, y=dd_clean, fill='tozeroy', line=dict(color='#ff3366', width=1), name="Drawdown %"
     ), row=2, col=1)
 
     fig.add_annotation(
@@ -646,7 +682,7 @@ def plot_factor_quantiles(record: dict) -> go.Figure:
 def plot_portfolio_allocation(weights: dict) -> go.Figure:
     """Plots interactive donut chart of optimized asset allocation."""
     labels = list(weights.keys())
-    values = [w * 100 for w in weights.values()]
+    values = [round(float(w * 100), 2) for w in weights.values()]
 
     fig = go.Figure(data=[go.Pie(
         labels=labels, values=values, hole=0.52,
@@ -734,8 +770,8 @@ def handle_train_xgboost():
 """
     importances = res.get("feature_importances", {})
     fig = go.Figure(data=[go.Bar(
-        x=list(importances.keys()),
-        y=list(importances.values()),
+        x=[str(k) for k in importances.keys()],
+        y=[round(float(v), 4) for v in importances.values()],
         marker_color='#00f2fe'
     )])
     fig.update_layout(