Update daily.py

daily.py

@@ -1,395 +1,35 @@
 # daily.py
 import yfinance as yf
 import pandas as pd
-import numpy as np
-import plotly.graph_objs as go
-from plotly.subplots import make_subplots
+from chart_builder import build_chart
 
-# Try to import TA-Lib; if unavailable, will use fallback implementations
-try:
-    import talib
-    TALIB_AVAILABLE = True
-except Exception:
-    TALIB_AVAILABLE = False
+def fetch_daily(symbol):
+    yfs = f"{symbol}.NS"
+    df = yf.download(yfs, period="1y", interval="1d").round(2)
 
-from common import wrap_html, STYLE_BLOCK
+    if df.empty:
+        return {
+            "html": f"<h1>No daily data for {symbol}</h1>",
+            "data": {}
+        }
 
-# --- Helper fallback implementations (used if TA-Lib isn't available) ---
-def sma_fallback(series, period):
-    return series.rolling(period).mean()
+    chart_html = build_chart(df)
 
-def ema_fallback(series, period):
-    return series.ewm(span=period, adjust=False).mean()
-
-def macd_fallback(close, fast=12, slow=26, signal=9):
-    fast_ema = ema_fallback(close, fast)
-    slow_ema = ema_fallback(close, slow)
-    macd = fast_ema - slow_ema
-    macd_signal = ema_fallback(macd, signal)
-    macd_hist = macd - macd_signal
-    return macd, macd_signal, macd_hist
-
-def atr_fallback(high, low, close, period=14):
-    tr1 = high - low
-    tr2 = (high - close.shift()).abs()
-    tr3 = (low - close.shift()).abs()
-    tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
-    atr = tr.rolling(period).mean()
-    return atr
-
-def stoch_fallback(high, low, close, k_period=14, d_period=3):
-    low_min = low.rolling(k_period).min()
-    high_max = high.rolling(k_period).max()
-    k = 100 * (close - low_min) / (high_max - low_min)
-    d = k.rolling(d_period).mean()
-    return k, d
-
-# SuperTrend (custom)
-def supertrend_fallback(df, period=10, multiplier=3):
-    hl2 = (df['High'] + df['Low']) / 2
-    atr = atr_fallback(df['High'], df['Low'], df['Close'], period=period)
-    upperband = hl2 + multiplier * atr
-    lowerband = hl2 - multiplier * atr
-
-    st = pd.Series(index=df.index, dtype=float)
-    trend = pd.Series(index=df.index, dtype=bool)  # True = uptrend, False = downtrend
-
-    # initialize
-    st.iloc[0] = np.nan
-    trend.iloc[0] = True
-
-    for i in range(1, len(df)):
-        if df['Close'].iat[i] > upperband.iat[i-1]:
-            trend.iat[i] = True
-        elif df['Close'].iat[i] < lowerband.iat[i-1]:
-            trend.iat[i] = False
-        else:
-            trend.iat[i] = trend.iat[i-1]
-            if trend.iat[i] and lowerband.iat[i] < st.iat[i-1]:
-                lowerband.iat[i] = st.iat[i-1]
-            if not trend.iat[i] and upperband.iat[i] > st.iat[i-1]:
-                upperband.iat[i] = st.iat[i-1]
-
-        st.iat[i] = lowerband.iat[i] if trend.iat[i] else upperband.iat[i]
-
-    return st, trend
-
-# ZigZag (simple percent threshold)
-def zigzag_fallback(close_series, pct=5.0):
-    zz = pd.Series(index=close_series.index, dtype=float)
-    last_extreme_idx = 0
-    last_extreme_price = close_series.iat[0]
-    last_trend = 0  # 1 = up, -1 = down, 0 = unknown
-    for i in range(1, len(close_series)):
-        change = (close_series.iat[i] - last_extreme_price) / last_extreme_price * 100.0
-        if last_trend >= 0 and change <= -pct:
-            zz.iat[i] = close_series.iat[i]
-            last_extreme_idx = i
-            last_extreme_price = close_series.iat[i]
-            last_trend = -1
-        elif last_trend <= 0 and change >= pct:
-            zz.iat[i] = close_series.iat[i]
-            last_extreme_idx = i
-            last_extreme_price = close_series.iat[i]
-            last_trend = 1
-    return zz
-
-# Swing High/Low via rolling window
-def swing_high_low(df, window=5):
-    df = df.copy()
-    df['SwingHigh'] = df['High'].rolling(window, center=True).max()
-    df['SwingLow'] = df['Low'].rolling(window, center=True).min()
-    # Only keep values where the center equals the extreme (to mark pivot)
-    center = window // 2
-    swing_high = [np.nan]*len(df)
-    swing_low = [np.nan]*len(df)
-    highs = df['High'].values
-    lows = df['Low'].values
-    for i in range(center, len(df)-center):
-        window_high = highs[i-center:i+center+1]
-        window_low = lows[i-center:i+center+1]
-        if highs[i] == np.max(window_high):
-            swing_high[i] = highs[i]
-        if lows[i] == np.min(window_low):
-            swing_low[i] = lows[i]
-    df['SwingHigh'] = swing_high
-    df['SwingLow'] = swing_low
-    return df['SwingHigh'], df['SwingLow']
-
-# Keltner Channel (EMA ± ATR * mult)
-def keltner_channel(df, ema_period=20, atr_period=10, atr_mult=2):
-    if TALIB_AVAILABLE:
-        ema = talib.EMA(df['Close'], timeperiod=ema_period)
-        atr = talib.ATR(df['High'], df['Low'], df['Close'], timeperiod=atr_period)
-    else:
-        ema = ema_fallback(df['Close'], ema_period)
-        atr = atr_fallback(df['High'], df['Low'], df['Close'], period=atr_period)
-    upper = ema + atr_mult * atr
-    lower = ema - atr_mult * atr
-    return upper, lower
-
-# --- Main fetch function ---
-def fetch_daily(symbol,
-                period="1y",
-                interval="1d",
-                supertrend_period=10,
-                supertrend_mult=3,
-                keltner_ema=20,
-                keltner_atr=10,
-                keltner_mult=2,
-                zigzag_pct=5.0):
-    """
-    Returns full HTML with multi-panel Plotly chart (price, volume, MACD, Stochastic)
-    and controls (buttons) to toggle indicators.
-    """
-    yfsymbol = f"{symbol}.NS"
-    try:
-        df = yf.download(yfsymbol, period=period, interval=interval).round(6)
-    except Exception as e:
-        return wrap_html("Error", f"<h1>Error fetching data for {symbol}</h1><p>{str(e)}</p>")
-
-    if df is None or df.empty:
-        return wrap_html("No Data", f"<h1>No {interval} data for {symbol} (period={period})</h1>")
-
-    # Ensure datetime index is proper
-    df = df.copy()
-    if isinstance(df.columns, pd.MultiIndex):
-        df.columns = df.columns.get_level_values(0)
-
-    # Compute indicators (TA-Lib if available, else fallback)
-    if TALIB_AVAILABLE:
-        # moving averages
-        df['SMA20'] = talib.SMA(df['Close'], timeperiod=20)
-        df['SMA50'] = talib.SMA(df['Close'], timeperiod=50)
-        df['EMA20'] = talib.EMA(df['Close'], timeperiod=20)
-        # MACD
-        macd, macd_signal, macd_hist = talib.MACD(df['Close'], fastperiod=12, slowperiod=26, signalperiod=9)
-        df['MACD'], df['MACD_Signal'], df['MACD_Hist'] = macd, macd_signal, macd_hist
-        # Stochastic
-        slowk, slowd = talib.STOCH(df['High'], df['Low'], df['Close'], fastk_period=14, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0)
-        df['StochK'], df['StochD'] = slowk, slowd
-        # ATR for SuperTrend/Keltner
-        df['ATR14'] = talib.ATR(df['High'], df['Low'], df['Close'], timeperiod=14)
-    else:
-        # SMA/EMA fallback
-        df['SMA20'] = sma_fallback(df['Close'], 20)
-        df['SMA50'] = sma_fallback(df['Close'], 50)
-        df['EMA20'] = ema_fallback(df['Close'], 20)
-        # MACD fallback
-        macd, macd_signal, macd_hist = macd_fallback(df['Close'], 12, 26, 9)
-        df['MACD'], df['MACD_Signal'], df['MACD_Hist'] = macd, macd_signal, macd_hist
-        # Stoch fallback
-        stoch_k, stoch_d = stoch_fallback(df['High'], df['Low'], df['Close'], 14, 3)
-        df['StochK'], df['StochD'] = stoch_k, stoch_d
-        df['ATR14'] = atr_fallback(df['High'], df['Low'], df['Close'], period=14)
-
-    # SuperTrend (custom using ATR)
-    try:
-        st_values, st_trend = supertrend_fallback(df, period=supertrend_period, multiplier=supertrend_mult)
-        df['SuperTrend'] = st_values
-        df['SuperTrendDir'] = st_trend
-    except Exception:
-        # if fails, fill NaN
-        df['SuperTrend'] = np.nan
-        df['SuperTrendDir'] = np.nan
-
-    # Keltner Channel
-    try:
-        kc_upper, kc_lower = keltner_channel(df, ema_period=keltner_ema, atr_period=keltner_atr, atr_mult=keltner_mult)
-        df['KC_Upper'] = kc_upper
-        df['KC_Lower'] = kc_lower
-    except Exception:
-        df['KC_Upper'] = np.nan
-        df['KC_Lower'] = np.nan
-
-    # ZigZag
-    try:
-        df['ZigZag'] = zigzag_fallback(df['Close'], pct=zigzag_pct)
-    except Exception:
-        df['ZigZag'] = np.nan
-
-    # Swing High/Low
-    try:
-        sh, sl = swing_high_low(df, window=5)
-        df['SwingHigh'] = sh
-        df['SwingLow'] = sl
-    except Exception:
-        df['SwingHigh'] = np.nan
-        df['SwingLow'] = np.nan
-
-    # OBV or Volume smoothing if needed (not required but left as data)
-    df['VolumeSmoothed'] = df['Volume'].rolling(3).mean()
-
-    # Build Plotly subplots: 4 rows (price, volume, MACD, Stochastic)
-    fig = make_subplots(rows=4, cols=1, shared_xaxes=True,
-                        row_heights=[0.5, 0.12, 0.18, 0.18],
-                        specs=[[{"secondary_y": False}],
-                               [{"secondary_y": False}],
-                               [{"secondary_y": False}],
-                               [{"secondary_y": False}]])
-
-    x = df.index
-
-    # Row 1: Candlestick
-    fig.add_trace(go.Candlestick(x=x, open=df['Open'], high=df['High'], low=df['Low'], close=df['Close'],
-                                 name='Price', increasing_line_color='green', decreasing_line_color='red'), row=1, col=1)
-
-    # SuperTrend (line, colored by trend)
-    fig.add_trace(go.Scatter(x=x, y=df['SuperTrend'], mode='lines', name='SuperTrend', visible=True,
-                             line=dict(width=2, dash='dash')), row=1, col=1)
-
-    # SMA & EMA
-    fig.add_trace(go.Scatter(x=x, y=df['SMA20'], mode='lines', name='SMA20', visible=True, line=dict(width=1)), row=1, col=1)
-    fig.add_trace(go.Scatter(x=x, y=df['SMA50'], mode='lines', name='SMA50', visible=False, line=dict(width=1)), row=1, col=1)
-    fig.add_trace(go.Scatter(x=x, y=df['EMA20'], mode='lines', name='EMA20', visible=False, line=dict(width=1, dash='dot')), row=1, col=1)
-
-    # Keltner Channels
-    fig.add_trace(go.Scatter(x=x, y=df['KC_Upper'], mode='lines', name='KC Upper', visible=False, line=dict(width=1)), row=1, col=1)
-    fig.add_trace(go.Scatter(x=x, y=df['KC_Lower'], mode='lines', name='KC Lower', visible=False, line=dict(width=1)), row=1, col=1)
-
-    # ZigZag and Swing markers as markers on price panel
-    fig.add_trace(go.Scatter(x=x, y=df['ZigZag'], mode='markers', name='ZigZag', visible=False,
-                             marker=dict(symbol='diamond', size=8)), row=1, col=1)
-    fig.add_trace(go.Scatter(x=x, y=df['SwingHigh'], mode='markers', name='Swing High', visible=False,
-                             marker=dict(symbol='triangle-up', size=8, color='purple')), row=1, col=1)
-    fig.add_trace(go.Scatter(x=x, y=df['SwingLow'], mode='markers', name='Swing Low', visible=False,
-                             marker=dict(symbol='triangle-down', size=8, color='brown')), row=1, col=1)
-
-    # Row 2: Volume
-    fig.add_trace(go.Bar(x=x, y=df['Volume'], name='Volume', marker_color='lightblue', visible=True), row=2, col=1)
-
-    # Row 3: MACD (line + signal + histogram as bar)
-    fig.add_trace(go.Scatter(x=x, y=df['MACD'], mode='lines', name='MACD', visible=False), row=3, col=1)
-    fig.add_trace(go.Scatter(x=x, y=df['MACD_Signal'], mode='lines', name='MACD Signal', visible=False), row=3, col=1)
-    fig.add_trace(go.Bar(x=x, y=df['MACD_Hist'], name='MACD Hist', visible=False), row=3, col=1)
-
-    # Row 4: Stochastic (%K & %D)
-    fig.add_trace(go.Scatter(x=x, y=df['StochK'], mode='lines', name='Stoch %K', visible=False), row=4, col=1)
-    fig.add_trace(go.Scatter(x=x, y=df['StochD'], mode='lines', name='Stoch %D', visible=False), row=4, col=1)
-
-    # Layout defaults
-    fig.update_layout(
-        template="plotly_white",
-        xaxis_rangeslider_visible=False,
-        height=1000,
-        legend=dict(orientation='h', yanchor='bottom', y=1.02, xanchor='right', x=1)
+    table_html = df.tail(30).to_html(
+        classes="styled-table",
+        border=0
     )
 
-    # Prepare updatemenu buttons for toggling indicators
-    # Determine trace indices in the order they were added:
-    # 0 price candlestick
-    # 1 supertrend
-    # 2 SMA20
-    # 3 SMA50
-    # 4 EMA20
-    # 5 KC Upper
-    # 6 KC Lower
-    # 7 ZigZag
-    # 8 SwingHigh
-    # 9 SwingLow
-    # 10 Volume
-    # 11 MACD
-    # 12 MACD Signal
-    # 13 MACD Hist
-    # 14 StochK
-    # 15 StochD
-
-    total_traces = len(fig.data)  # should be 16
-
-    # Helper to create visible array with selective traces visible
-    def visible_array(show_indices):
-        arr = [False] * total_traces
-        # keep price and volume visible by default in any selection for context
-        arr[0] = True   # candle
-        arr[10] = True  # volume (index may vary—check length; we've added bar at position 10)
-        for i in show_indices:
-            if 0 <= i < total_traces:
-                arr[i] = True
-        return arr
-
-    # Map indicator buttons to the trace indices they affect
-    buttons = []
-
-    # SuperTrend
-    buttons.append(dict(label="SuperTrend",
-                        method="restyle",
-                        args=[{"visible": visible_array([1])}]))
-
-    # SMA20
-    buttons.append(dict(label="SMA20",
-                        method="restyle",
-                        args=[{"visible": visible_array([2])}]))
-
-    # SMA50
-    buttons.append(dict(label="SMA50",
-                        method="restyle",
-                        args=[{"visible": visible_array([3])}]))
-
-    # EMA20
-    buttons.append(dict(label="EMA20",
-                        method="restyle",
-                        args=[{"visible": visible_array([4])}]))
-
-    # Keltner Channel
-    buttons.append(dict(label="Keltner",
-                        method="restyle",
-                        args=[{"visible": visible_array([5,6])}]))
-
-    # ZigZag
-    buttons.append(dict(label="ZigZag",
-                        method="restyle",
-                        args=[{"visible": visible_array([7])}]))
-
-    # Swing HI/LO
-    buttons.append(dict(label="Swing H/L",
-                        method="restyle",
-                        args=[{"visible": visible_array([8,9])}]))
-
-    # MACD
-    buttons.append(dict(label="MACD",
-                        method="restyle",
-                        args=[{"visible": visible_array([11,12,13])}]))
-
-    # Stochastic
-    buttons.append(dict(label="Stochastic",
-                        method="restyle",
-                        args=[{"visible": visible_array([14,15])}]))
-
-    # Show All (turn on all traces)
-    buttons.append(dict(label="All On",
-                        method="restyle",
-                        args=[{"visible": [True]*total_traces}]))
-
-    # All Off (only show price and volume)
-    buttons.append(dict(label="All Off",
-                        method="restyle",
-                        args=[{"visible": visible_array([])}]))
-
-    # Reset (default view: price, volume, supertrend, SMA20)
-    buttons.append(dict(label="Reset",
-                        method="restyle",
-                        args=[{"visible": visible_array([1,2])}]))
-
-    fig.update_layout(
-        updatemenus=[dict(type="buttons", direction="down", buttons=buttons, x=1.02, xanchor="left", y=0.95, yanchor="top")]
-    )
-
-    # Finalize: add a table (recent 30 rows) below chart area as HTML
-    table_html = df.tail(30)[['Open', 'High', 'Low', 'Close', 'Volume']].round(2).to_html(classes="styled-table", border=0)
-
-    chart_div = fig.to_html(full_html=False, include_plotlyjs='cdn')
-
-    content_html = f"""
-    <div class='card'>
-        <h2>Daily Chart - {symbol.upper()}</h2>
-        <div class='card-content-grid'>
-            <div style="width:100%">{chart_div}</div>
-        </div>
-    </div>
-    <div class='big-box'>
-        <h2>Recent Daily Data (last 30 rows)</h2>
+    final = f"""
+    <div class="group">
+        <h2>Daily Chart — {symbol}</h2>
+        {chart_html}
+        <h3>Last 30 Days Data</h3>
         {table_html}
     </div>
     """
 
-    return wrap_html(f"Daily Chart for {symbol}", content_html, style_block=STYLE_BLOCK)
+    return {
+        "html": final,
+        "data": df.tail(30).to_dict()
+    }