Update app/daily.py

app/daily.py

@@ -3,15 +3,17 @@ import yfinance as yf
 import pandas as pd
 from datetime import datetime as dt
 import traceback
-
 from . import persist
-from .common import wrap_html
+from .common import wrap_html, format_large_number
+
+# Plotly
+import plotly.graph_objs as go
+from plotly.subplots import make_subplots
 
 # ===========================================================
 # RAW DAILY FETCHER
 # ===========================================================
 def daily(symbol, date_end, date_start):
-    """Fetch daily OHLCV from Yahoo Finance."""
     print(f"[{dt.now().strftime('%Y-%m-%d %H:%M:%S')}] yf called for {symbol}")
     
     start = dt.strptime(date_start, "%d-%m-%Y").strftime("%Y-%m-%d")
@@ -19,60 +21,168 @@ def daily(symbol, date_end, date_start):
     
     df = yf.download(symbol + ".NS", start=start, end=end)
     
-    # Flatten MultiIndex columns if present
     if isinstance(df.columns, pd.MultiIndex):
         df.columns = df.columns.get_level_values(0)
 
-    # Remove column names / DataFrame name to avoid "Price" display
     df.columns.name = None
     df.index.name = None
     
     return df
 
 # ===========================================================
-# FETCH DAILY HTML TABLE
+# TECHNICAL INDICATORS
+# ===========================================================
+def add_indicators(df):
+    df["SMA10"] = df["Close"].rolling(10).mean()
+    df["SMA50"] = df["Close"].rolling(50).mean()
+    df["SMA200"] = df["Close"].rolling(200).mean()
+    df["EMA20"] = df["Close"].ewm(span=20, adjust=False).mean()
+    delta = df["Close"].diff()
+    gain = (delta.where(delta>0,0)).rolling(14).mean()
+    loss = (-delta.where(delta<0,0)).rolling(14).mean()
+    rs = gain/loss
+    df["RSI14"] = 100 - (100/(1+rs))
+    df["Change %"] = ((df["Close"] - df["Open"])/df["Open"]*100).round(2)
+    return df
+
+# ===========================================================
+# SPARKLINE & MINI CANDLE
 # ===========================================================
-def fetch_daily(symbol, date_end, date_start):
+def sparkline(values, height=20, color="#1a4f8a"):
+    if len(values) == 0: return ""
+    max_val = max(values) if max(values)!=0 else 1
+    bars = "".join([f'<div style="display:inline-block;width:3px;height:{int(v/max_val*height)}px;margin-right:1px;background:{color};"></div>' for v in values])
+    return f'<div style="display:flex; align-items:flex-end;">{bars}</div>'
+
+def volume_bar(values, height=20, color="#5584d6"):
+    if len(values) == 0: return ""
+    max_val = max(values) if max(values)!=0 else 1
+    bars = "".join([f'<div style="display:inline-block;width:3px;height:{int(v/max_val*height)}px;margin-right:1px;background:{color};"></div>' for v in values])
+    return f'<div style="display:flex; align-items:flex-end;">{bars}</div>'
+
+# ===========================================================
+# PLOTLY CHART GENERATOR
+# ===========================================================
+def plotly_dashboard(df, symbol):
+    fig = make_subplots(rows=2, cols=1, shared_xaxes=True,
+                        vertical_spacing=0.05, row_heights=[0.7,0.3],
+                        subplot_titles=[f"{symbol} OHLC + SMA/EMA", "Volume"])
+    
+    # Candlestick
+    fig.add_trace(go.Candlestick(
+        x=df.index, open=df["Open"], high=df["High"], low=df["Low"], close=df["Close"], 
+        name="OHLC"), row=1, col=1)
+    
+    # Moving averages
+    for col, name in [("SMA10","SMA10"),("SMA50","SMA50"),("SMA200","SMA200"),("EMA20","EMA20")]:
+        fig.add_trace(go.Scatter(
+            x=df.index, y=df[col], mode="lines", line=dict(width=1.5), name=name), row=1, col=1)
+    
+    # Volume bars
+    fig.add_trace(go.Bar(
+        x=df.index, y=df["Volume"], marker_color="#5584d6", name="Volume"), row=2, col=1)
+    
+    fig.update_layout(height=700, showlegend=True, margin=dict(t=50,b=50), template="plotly_white")
+    
+    # Return div as HTML
+    return fig.to_html(full_html=False, include_plotlyjs='cdn')
+
+# ===========================================================
+# DASHBOARD GENERATOR
+# ===========================================================
+def fetch_daily(symbol, date_end, date_start, spark_days=10):
     key = f"daily_{symbol}"
     if persist.exists(key, "html"):
         cached = persist.load(key, "html")
         if cached:
-            print(f"[{date_end}] Using cached daily for {symbol}")
             return cached
 
     try:
         df = daily(symbol, date_end, date_start)
-        if df is None or df.empty:
-            return wrap_html(f'<div id="daily_wrapper"><h1>No daily data for {symbol}</h1></div>')
+        if df.empty:
+            return wrap_html(f"<h1>No daily data for {symbol}</h1>")
 
-        # Reset index if not simple RangeIndex
         if not isinstance(df.index, pd.RangeIndex):
             df.reset_index(inplace=True)
-
-        # Convert numeric columns safely
-        numeric_cols = ["Open","High","Low","Close","Adj Close","Volume"]
-        for col in numeric_cols:
+        
+        # Numeric conversion
+        for col in ["Open","High","Low","Close","Adj Close","Volume"]:
             if col in df.columns:
                 df[col] = pd.to_numeric(df[col], errors='coerce')
-
-        # Drop rows with missing essential data
         df = df.dropna(subset=["Open","High","Low","Close","Volume"]).reset_index(drop=True)
 
         # Format date
-        if "Date" in df.columns:
-            df["Date"] = pd.to_datetime(df["Date"], errors='coerce')
-            df = df.dropna(subset=["Date"]).reset_index(drop=True)
-            df["Date"] = df["Date"].dt.strftime("%d-%b-%Y")
+        df["Date"] = pd.to_datetime(df.index if "Date" not in df.columns else df["Date"], errors='coerce')
+        df = df.dropna(subset=["Date"]).reset_index(drop=True)
+        df["Date"] = df["Date"].dt.strftime("%d-%b-%Y")
+        
+        # Add indicators
+        df = add_indicators(df)
+
+        # Summary
+        summary_html = f"""
+        <div style="margin-bottom:10px; font-family:Arial,sans-serif;">
+            <h3>{symbol} Summary</h3>
+            <table border="1" style="border-collapse:collapse; width:400px;">
+                <tr><th>Metric</th><th>Value</th></tr>
+                <tr><td>Start Date</td><td>{df['Date'].iloc[0]}</td></tr>
+                <tr><td>End Date</td><td>{df['Date'].iloc[-1]}</td></tr>
+                <tr><td>Min Close</td><td>{format_large_number(df['Close'].min())}</td></tr>
+                <tr><td>Max Close</td><td>{format_large_number(df['Close'].max())}</td></tr>
+                <tr><td>Mean Close</td><td>{format_large_number(df['Close'].mean())}</td></tr>
+                <tr><td>Total Volume</td><td>{format_large_number(df['Volume'].sum())}</td></tr>
+                <tr><td>Avg Daily Change %</td><td>{df['Change %'].mean():.2f}%</td></tr>
+                <tr><td>Latest Close</td><td>{df['Close'].iloc[-1]}</td></tr>
+                <tr><td>Prev Close</td><td>{df['Close'].iloc[-2] if len(df)>1 else df['Close'].iloc[-1]}</td></tr>
+            </table>
+        </div>
+        """
+
+        # Table with sparkline, volume, mini candle
+        html_table = f"""
+        <div style="max-height:400px; overflow:auto; font-family:Arial,sans-serif;">
+        <table border="1" style="border-collapse:collapse; width:100%;">
+            <thead style="position:sticky; top:0; background:#1a4f8a; color:white;">
+                <tr>
+                    <th>Date</th><th>Open</th><th>High</th><th>Low</th><th>Close</th><th>Adj Close</th>
+                    <th>Volume</th><th>Change %</th><th>Close Trend</th><th>Vol Trend</th><th>Mini Candle</th>
+                </tr>
+            </thead>
+            <tbody>
+        """
+
+        for idx, r in df.iterrows():
+            row_color = "#e8f5e9" if idx%2==0 else "#f5f5f5"
+            change_color = "green" if r["Change %"]>0 else "red" if r["Change %"]<0 else "black"
+            start_idx = max(0, idx-spark_days+1)
+            close_trend = sparkline(df["Close"].iloc[start_idx:idx+1].tolist())
+            vol_trend = volume_bar(df["Volume"].iloc[start_idx:idx+1].tolist())
+            mini_c = sparkline([r["Open"], r["High"], r["Low"], r["Close"]], height=20, color="#1a4f8a")
 
-        # Remove column name again just in case
-        df.columns.name = None
+            html_table += f"""
+                <tr style='background:{row_color};'>
+                    <td>{r['Date']}</td>
+                    <td>{r['Open']}</td>
+                    <td>{r['High']}</td>
+                    <td>{r['Low']}</td>
+                    <td>{r['Close']}</td>
+                    <td>{r.get('Adj Close','')}</td>
+                    <td>{r['Volume']}</td>
+                    <td style='color:{change_color}; font-weight:600;'>{r['Change %']}%</td>
+                    <td>{close_trend}</td>
+                    <td>{vol_trend}</td>
+                    <td>{mini_c}</td>
+                </tr>
+            """
+        html_table += "</tbody></table></div>"
 
-        # Build HTML table WITHOUT any DataFrame name
-        html_table = f'<div id="daily_table"><h2>{symbol} Daily Data</h2>{df.to_html(index=False, header=True, border=1, classes="daily-data", escape=False)}</div>'
+        # Plotly chart
+        chart_html = plotly_dashboard(df, symbol)
 
-        # Save to cache
-        persist.save(key, html_table, "html")
-        return html_table
+        # Combine full HTML
+        full_html = summary_html + html_table + chart_html
+        persist.save(key, full_html, "html")
+        return full_html
 
     except Exception as e:
-        return wrap_html(f'<div id="daily_wrapper"><h1>Error fetch_daily: {e}</h1><pre>{traceback.format_exc()}</pre></div>')
+        return wrap_html(f"<h1>Error fetch_daily: {e}</h1><pre>{traceback.format_exc()}</pre>")