Create bhabcopy_html.py

bhabcopy_html.py

@@ -0,0 +1,146 @@
+import pandas as pd
+import datetime
+from nsepython import *
+
+
+def build_bhavcopy_html(date_str):
+    key = f"bhavcopy_{date_str}"
+
+    # -------------------------------------------------------
+    # 0) Use cached HTML if present
+    # -------------------------------------------------------
+    if exists(key, "html"):
+        cached = load(key, "html")
+        if cached is not False:
+            print(
+                f"[{datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] "
+                f"Using cached bhavcopy for {date_str}"
+            )
+            return cached
+
+    try:
+        # -------------------------------------------------------
+        # 1) Validate Date
+        # -------------------------------------------------------
+        try:
+            datetime.datetime.strptime(date_str, "%d-%m-%Y")
+        except:
+            html = "<h3>Invalid date format. Use DD-MM-YYYY.</h3>"
+            save(key, html, "html")
+            return html
+
+        # -------------------------------------------------------
+        # 2) Fetch Bhavcopy
+        # -------------------------------------------------------
+        try:
+            df = nse_bhavcopy(date_str)
+            df.columns = df.columns.str.strip()
+        except:
+            html = f"<h3>No Bhavcopy found for {date_str}.</h3>"
+            save(key, html, "html")
+            return html
+
+        # -------------------------------------------------------
+        # 3) Drop unwanted columns
+        # -------------------------------------------------------
+        remove = ["DATE1", "LAST_PRICE", "AVG_PRICE"]
+        df.drop(columns=[c for c in remove if c in df.columns], inplace=True)
+
+        # -------------------------------------------------------
+        # 4) Convert numeric columns
+        # -------------------------------------------------------
+        numeric_cols = [
+            "PREV_CLOSE", "OPEN_PRICE", "HIGH_PRICE", "LOW_PRICE",
+            "CLOSE_PRICE", "TTL_TRD_QNTY", "TURNOVER_LACS",
+            "NO_OF_TRADES", "DELIV_QTY", "DELIV_PER"
+        ]
+
+        for col in numeric_cols:
+            if col in df.columns:
+                df[col] = (
+                    df[col]
+                    .astype(str)
+                    .str.replace(",", "", regex=False)
+                    .str.strip()
+                )
+                df[col] = pd.to_numeric(df[col], errors="coerce").fillna(0)
+
+        # -------------------------------------------------------
+        # 5) Filter & sort
+        # -------------------------------------------------------
+        df = df[df["TURNOVER_LACS"] > 1000]
+        df = df.sort_values("TURNOVER_LACS", ascending=False)
+
+        # -------------------------------------------------------
+        # 6) Computed columns
+        # -------------------------------------------------------
+        df["change"] = df["CLOSE_PRICE"] - df["PREV_CLOSE"]
+        df["perchange"] = (df["change"] / df["PREV_CLOSE"].replace(0, 1)) * 100
+        df["pergap"] = (
+            (df["OPEN_PRICE"] - df["PREV_CLOSE"]) /
+            df["PREV_CLOSE"].replace(0, 1)
+        ) * 100
+
+        # -------------------------------------------------------
+        # 7) HTML Output
+        # -------------------------------------------------------
+        main_html = f"""
+        <div class="main-table-container">
+            {df.to_html(index=False, escape=False)}
+        </div>
+        """
+
+        metrics = ["perchange", "pergap", "TURNOVER_LACS", "NO_OF_TRADES", "DELIV_PER"]
+        col_html = []
+
+        for m in metrics:
+            if m in df.columns:
+                temp = df[["SYMBOL", m]].sort_values(m, ascending=False)
+                col_html.append(
+                    f"""
+                    <div class="col">
+                        <h4>{m}</h4>
+                        {temp.to_html(index=False, escape=False)}
+                    </div>
+                    """
+                )
+
+        grid_html = f"""
+        <div class="grid">
+            {''.join(col_html)}
+        </div>
+        """
+
+        css = """
+        <style>
+            .grid { display: grid; grid-template-columns: repeat(5, 1fr); gap: 10px; }
+            .col, .main-table-container {
+                max-height: 480px; overflow-y: auto;
+                border: 1px solid #ccc; padding: 4px;
+            }
+            table { font-size: 12px; width: 100%; border-collapse: collapse; }
+            th, td { border: 1px solid #ddd; padding: 4px; }
+            th {
+                background: #2E7D32; color: white;
+                position: sticky; top: 0;
+            }
+        </style>
+        """
+
+        html = (
+            css +
+            "<h2>Main Bhavcopy Table</h2>" +
+            main_html +
+            "<h2>Matrix/Grid Table</h2>" +
+            grid_html
+        )
+
+        save(key, html, "html")
+        return html
+
+    except Exception as e:
+        print(
+            f"[{datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] "
+            f"Error build_bhavcopy_html: {e}"
+        )
+        return f"<h3>Error: {e}</h3>"