Update app/daily.py

app/daily.py

@@ -4,16 +4,51 @@ import pandas as pd
 from datetime import datetime as dt
 from plotly import graph_objs as go
 from plotly.subplots import make_subplots
-import traceback
+import traceback, io, base64
 
 from . import persist
 from . import backblaze as b2
 from .common import wrap_html, format_large_number
 
+# ===========================================================
+# Candlestick Pattern Detection
+# ===========================================================
+def detect_patterns(df):
+    patterns = []
+
+    for i in range(1, len(df)):
+        open_today, close_today = df.loc[i, "Open"], df.loc[i, "Close"]
+        open_prev, close_prev = df.loc[i-1, "Open"], df.loc[i-1, "Close"]
+        high, low = df.loc[i, "High"], df.loc[i, "Low"]
+
+        # Bullish Engulfing
+        if close_prev < open_prev and close_today > open_today and close_today > open_prev and open_today < close_prev:
+            patterns.append({"Date": df.loc[i, "Date"], "Pattern": "Bullish Engulfing"})
+        # Bearish Engulfing
+        elif close_prev > open_prev and close_today < open_today and open_today > close_prev and close_today < open_prev:
+            patterns.append({"Date": df.loc[i, "Date"], "Pattern": "Bearish Engulfing"})
+        # Doji
+        elif abs(close_today - open_today)/ (high-low+1e-6) < 0.1:
+            patterns.append({"Date": df.loc[i, "Date"], "Pattern": "Doji"})
+        # Hammer / Hanging Man
+        elif (high - max(open_today, close_today)) > 2*(max(open_today, close_today)-min(open_today, close_today)) and \
+             (min(open_today, close_today) - low) < 0.1*(high-low):
+            if close_today > open_today:
+                patterns.append({"Date": df.loc[i, "Date"], "Pattern": "Hammer"})
+            else:
+                patterns.append({"Date": df.loc[i, "Date"], "Pattern": "Hanging Man"})
+        # Gap Up / Gap Down
+        if df.loc[i, "Open"] > df.loc[i-1, "Close"] * 1.01:
+            patterns.append({"Date": df.loc[i, "Date"], "Pattern": "Gap Up"})
+        elif df.loc[i, "Open"] < df.loc[i-1, "Close"] * 0.99:
+            patterns.append({"Date": df.loc[i, "Date"], "Pattern": "Gap Down"})
+
+    return pd.DataFrame(patterns)
+
+# ===========================================================
+# Ultimate Daily Analysis Dashboard
+# ===========================================================
 def fetch_daily(symbol, date_end, date_start, b2_save=False):
-    """
-    Fetch daily historical stock data and generate full analysis dashboard with Plotly charts.
-    """
     key = f"daily_{symbol}"
     if persist.exists(key, "html"):
         cached = persist.load(key, "html")
@@ -22,76 +57,98 @@ def fetch_daily(symbol, date_end, date_start, b2_save=False):
             return cached
 
     try:
+        # Download data
         start = dt.strptime(date_start, "%d-%m-%Y").strftime("%Y-%m-%d")
         end = dt.strptime(date_end, "%d-%m-%Y").strftime("%Y-%m-%d")
-
         print(f"[{dt.now().strftime('%Y-%m-%d %H:%M:%S')}] Fetching daily for {symbol}")
         df = yf.download(symbol + ".NS", start=start, end=end)
         if df.empty:
             return wrap_html(f"<h1>No daily data for {symbol}</h1>")
 
-        # Reset index and format Date
         df.reset_index(inplace=True)
         df["Date"] = df["Date"].dt.strftime("%d-%b-%Y")
 
-        # Optional save to Backblaze
         if b2_save:
             b2.upload_file("eshanhf", f"daily/{symbol}.csv", df)
 
-        # Calculate additional metrics
-        df["Daily Return %"] = df["Close"].pct_change() * 100
+        # Indicators
+        df["Daily Return %"] = df["Close"].pct_change()*100
         df["SMA20"] = df["Close"].rolling(20).mean()
         df["SMA50"] = df["Close"].rolling(50).mean()
         df["EMA20"] = df["Close"].ewm(span=20, adjust=False).mean()
+        df["EMA50"] = df["Close"].ewm(span=50, adjust=False).mean()
         df["UpperBB"] = df["Close"].rolling(20).mean() + 2*df["Close"].rolling(20).std()
         df["LowerBB"] = df["Close"].rolling(20).mean() - 2*df["Close"].rolling(20).std()
+        df["ATR"] = df["High"].combine(df["Low"], lambda h,l: h-l).rolling(14).mean()
 
         # Summary stats
         summary = pd.DataFrame({
-            "Metric": ["Start Date","End Date","Min Price","Max Price","Mean Price","Total Volume"],
-            "Value": [df["Date"].iloc[0], df["Date"].iloc[-1],
-                      format_large_number(df["Close"].min()),
-                      format_large_number(df["Close"].max()),
-                      format_large_number(df["Close"].mean()),
-                      format_large_number(df["Volume"].sum())]
+            "Metric": ["Start Date","End Date","Min Price","Max Price","Mean Price","Total Volume","Avg Daily Return %","Volatility ATR"],
+            "Value": [
+                df["Date"].iloc[0],
+                df["Date"].iloc[-1],
+                format_large_number(df["Close"].min()),
+                format_large_number(df["Close"].max()),
+                format_large_number(df["Close"].mean()),
+                format_large_number(df["Volume"].sum()),
+                f"{df['Daily Return %'].mean():.2f}%",
+                f"{df['ATR'].mean():.2f}"
+            ]
         })
 
-        # Plotly figure
-        fig = make_subplots(rows=2, cols=1, shared_xaxes=True,
-                            vertical_spacing=0.1,
-                            row_heights=[0.7,0.3],
-                            specs=[[{"secondary_y": False}], [{"secondary_y": False}]])
+        # Detect patterns
+        patterns_df = detect_patterns(df)
 
-        # Candlestick
-        fig.add_trace(go.Candlestick(
-            x=df["Date"], open=df["Open"], high=df["High"], low=df["Low"], close=df["Close"],
-            name="OHLC"), row=1, col=1)
+        # Plotly dashboard
+        fig = make_subplots(rows=4, cols=1, shared_xaxes=True,
+                            vertical_spacing=0.05,
+                            row_heights=[0.4,0.2,0.2,0.2],
+                            specs=[[{}],[{}],[{}],[{}]])
 
-        # SMA/EMA overlays
+        # Candlestick + SMA/EMA/Bollinger
+        fig.add_trace(go.Candlestick(x=df["Date"], open=df["Open"], high=df["High"], low=df["Low"], close=df["Close"], name="OHLC"), row=1, col=1)
         fig.add_trace(go.Scatter(x=df["Date"], y=df["SMA20"], mode="lines", name="SMA20"), row=1, col=1)
         fig.add_trace(go.Scatter(x=df["Date"], y=df["SMA50"], mode="lines", name="SMA50"), row=1, col=1)
         fig.add_trace(go.Scatter(x=df["Date"], y=df["EMA20"], mode="lines", name="EMA20"), row=1, col=1)
-
-        # Bollinger Bands
+        fig.add_trace(go.Scatter(x=df["Date"], y=df["EMA50"], mode="lines", name="EMA50"), row=1, col=1)
         fig.add_trace(go.Scatter(x=df["Date"], y=df["UpperBB"], mode="lines", name="UpperBB", line=dict(dash="dot")), row=1, col=1)
         fig.add_trace(go.Scatter(x=df["Date"], y=df["LowerBB"], mode="lines", name="LowerBB", line=dict(dash="dot")), row=1, col=1)
 
-        # Volume bar chart
+        # Highlight patterns on chart
+        for _, row in patterns_df.iterrows():
+            fig.add_trace(go.Scatter(
+                x=[row["Date"]], y=[df.loc[df["Date"]==row["Date"], "High"].values[0]*1.01],
+                mode="markers+text",
+                marker=dict(color="red", size=10, symbol="triangle-up"),
+                text=[row["Pattern"]],
+                textposition="top center",
+                showlegend=False
+            ), row=1, col=1)
+
+        # Volume
         fig.add_trace(go.Bar(x=df["Date"], y=df["Volume"], name="Volume"), row=2, col=1)
+        # Daily Return %
+        fig.add_trace(go.Scatter(x=df["Date"], y=df["Daily Return %"], mode="lines+markers", name="Daily Return %"), row=3, col=1)
+        # ATR
+        fig.add_trace(go.Scatter(x=df["Date"], y=df["ATR"], mode="lines", name="ATR"), row=4, col=1)
 
-        fig.update_layout(height=700, width=1000, title=f"{symbol} Daily Analysis",
-                          xaxis_rangeslider_visible=False)
-
+        fig.update_layout(height=1000, width=1200, title=f"{symbol} Daily Analysis Dashboard", xaxis_rangeslider_visible=False)
         chart_html = fig.to_html(full_html=False, include_plotlyjs='cdn')
 
-        # Build table HTML
-        table_html = wrap_html(f"<h2>Summary</h2>{summary.to_html(index=False, escape=False)}")
+        # Tables
+        table_html = wrap_html(f"<h2>Summary Stats</h2>{summary.to_html(index=False, escape=False)}")
         data_table_html = wrap_html(f"<h2>OHLC Table</h2>{df.to_html(index=False, escape=False)}")
+        patterns_html = wrap_html(f"<h2>Detected Patterns</h2>{patterns_df.to_html(index=False, escape=False)}")
+
+        # CSV download
+        csv_buffer = io.StringIO()
+        df.to_csv(csv_buffer, index=False)
+        csv_base64 = base64.b64encode(csv_buffer.getvalue().encode()).decode()
+        download_html = f'<a href="data:text/csv;base64,{csv_base64}" download="{symbol}_daily.csv">Download CSV</a>'
 
-        # Combine full dashboard
-        full_html = chart_html + table_html + data_table_html
+        full_html = chart_html + table_html + patterns_html + data_table_html + download_html
 
-        # Cache HTML
+        # Cache
         persist.save(key, full_html, "html")
         return full_html