Create preopen_html.py

app/preopen_html.py

@@ -0,0 +1,175 @@
+from nsepython import *
+import pandas as pd
+import re
+from datetime import datetime as dt
+
+# persist helpers (HF only)
+from persist import exists, load, save
+
+
+def build_preopen_html(key="NIFTY"):
+    """
+    Build full Pre-Open HTML
+    - Daily TTL (via persist.py)
+    - HTML only cache
+    """
+
+    # ================= CACHE (TTL via persist) =================
+    cache_name = f"DAILY_PREOPEN_{key.upper()}"
+
+    if exists(cache_name, "html"):
+        cached_html = load(cache_name, "html")
+        if isinstance(cached_html, str):
+            return cached_html
+
+    # ================= FETCH DATA =================
+    p = nsefetch(f"https://www.nseindia.com/api/market-data-pre-open?key={key}")
+
+    data_df = df_from_data(p.pop("data"))
+    rem_df  = df_from_data([p])
+
+    main_df  = data_df.iloc[[0]] if not data_df.empty else pd.DataFrame()
+    const_df = data_df.iloc[1:] if len(data_df) > 1 else pd.DataFrame()
+
+    # ================= REMOVE PATTERN COLUMNS =================
+    pattern_remove = re.compile(r"^(price_|buyQty_|sellQty_|iep_)\d+$")
+
+    def remove_pattern_cols(df):
+        if df is None or df.empty:
+            return df
+        return df[[c for c in df.columns if not pattern_remove.match(c)]]
+
+    main_df  = remove_pattern_cols(main_df)
+    const_df = remove_pattern_cols(const_df)
+    rem_df   = remove_pattern_cols(rem_df)
+
+    # ================= TABLE COLOR HELPER =================
+    def df_to_html_color(df, metric_col=None):
+        if df is None or df.empty:
+            return "<i>No data</i>"
+
+        df_html = df.copy()
+        top_up, top_down = [], []
+
+        if metric_col and metric_col in df_html.columns:
+            col_num = pd.to_numeric(df_html[metric_col], errors="coerce").dropna()
+            top_up = col_num.nlargest(3).index.tolist()
+            top_down = col_num.nsmallest(3).index.tolist()
+
+        for idx, row in df_html.iterrows():
+            for col in df_html.columns:
+                val = row[col]
+                cls = ""
+                if isinstance(val, (int, float)):
+                    val_fmt = f"{val:.2f}"
+                    if val > 0:
+                        cls = "numeric-positive"
+                    elif val < 0:
+                        cls = "numeric-negative"
+                    if metric_col and col == metric_col:
+                        if idx in top_up:
+                            cls += " top-up"
+                        elif idx in top_down:
+                            cls += " top-down"
+                    df_html.at[idx, col] = f'<span class="{cls.strip()}">{val_fmt}</span>'
+                else:
+                    df_html.at[idx, col] = str(val)
+
+        return df_html.to_html(index=False, escape=False, classes="compact-table")
+
+    # ================= MINI INFO CARDS =================
+    def build_info_cards(rem_df, main_df):
+        combined = pd.concat([rem_df, main_df], axis=1)
+        combined = combined.loc[:, ~combined.columns.duplicated()]
+        combined = remove_pattern_cols(combined)
+
+        html = '<div class="mini-card-container">'
+        for col in combined.columns:
+            val = combined.at[0, col] if not combined.empty else ""
+            html += f"""
+            <div class="mini-card">
+                <div class="card-key">{col}</div>
+                <div class="card-val">{val}</div>
+            </div>
+            """
+        html += '</div>'
+        return html
+
+    info_cards_html = build_info_cards(rem_df, main_df)
+
+    # ================= CONSTITUENTS TABLE =================
+    cons_html = df_to_html_color(const_df)
+
+    # ================= METRIC TABLES =================
+    metric_cols_allowed = [
+        "pChange",
+        "totalTurnover",
+        "marketCap",
+        "totalTradedVolume"
+    ]
+
+    metric_tables = ""
+    for col in metric_cols_allowed:
+        if col in const_df.columns:
+            df_m = const_df.copy()
+            df_m[col] = pd.to_numeric(df_m[col], errors="coerce")
+            df_m = df_m.sort_values(col, ascending=False)
+
+            show_cols = ["symbol", col] if "symbol" in df_m.columns else [col]
+            metric_tables += f"""
+            <div class="small-table">
+                <div class="st-title">{col}</div>
+                <div class="st-body">
+                    {df_to_html_color(df_m[show_cols], metric_col=col)}
+                </div>
+            </div>
+            """
+
+    # ================= FINAL HTML =================
+    html_out = f"""
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="UTF-8">
+<style>
+body {{ font-family: Arial; margin: 12px; background: #f5f5f5; font-size: 14px; }}
+h2, h3 {{ margin: 10px 0; }}
+table {{ border-collapse: collapse; width: 100%; }}
+th, td {{ border: 1px solid #bbb; padding: 6px; font-size: 13px; }}
+th {{ background: #333; color: #fff; }}
+.numeric-positive {{ color: green; font-weight: bold; }}
+.numeric-negative {{ color: red; font-weight: bold; }}
+.top-up {{ background: #b6f2b6; }}
+.top-down {{ background: #f2b6b6; }}
+.grid {{ display: grid; grid-template-columns: repeat(5, 1fr); gap: 12px; }}
+.small-table {{ background: #fff; padding: 8px; border-radius: 6px; border: 1px solid #ddd; }}
+.st-title {{ text-align: center; font-weight: bold; background: #222; color: #fff; padding: 6px; }}
+.st-body {{ max-height: 300px; overflow-y: auto; }}
+.mini-card-container {{ display: flex; flex-wrap: wrap; gap: 10px; }}
+.mini-card {{ background: #fff; padding: 8px 10px; border-radius: 6px; border: 1px solid #ddd; min-width: 120px; }}
+.card-key {{ font-weight: bold; }}
+</style>
+</head>
+<body>
+
+<h2>Pre-Open Market — {key}</h2>
+
+<h3>Info</h3>
+{info_cards_html}
+
+<h3>Constituents</h3>
+{cons_html}
+
+<h3>Key Metrics</h3>
+<div class="grid">
+{metric_tables}
+</div>
+
+</body>
+</html>
+"""
+
+    # ================= SAVE (HTML ONLY) =================
+    save(cache_name, html_out, "html")
+
+    return html_out