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| from . import nsepythonmodified as ns | |
| import pandas as pd | |
| from datetime import datetime as dt | |
| # persist helpers (HF only) | |
| from .persist import exists, load, save | |
| def build_index_live_html(index_name ="NIFTY 50"): | |
| """ | |
| Live HTML for NIFTY 50 | |
| - Intraday TTL (15 minutes) | |
| - HTML only cache | |
| - persist.py controls validity | |
| """ | |
| # ================= CACHE (TTL via persist) ================= | |
| cache_name = "INTRADAY_INDEX_LIVE_NIFTY50" | |
| if exists(cache_name, "html"): | |
| cached_html = load(cache_name, "html") | |
| if isinstance(cached_html, str): | |
| return cached_html | |
| # ================= LIVE FETCH ================= | |
| p = ns.nse_index_live(index_name) | |
| full_df = p.get("data", pd.DataFrame()) | |
| rem_df = p.get("rem", pd.DataFrame()) | |
| if full_df.empty: | |
| main_df = pd.DataFrame() | |
| const_df = pd.DataFrame() | |
| else: | |
| main_df = full_df.iloc[[0]] | |
| const_df = full_df.iloc[1:] | |
| if not const_df.empty: | |
| const_df = const_df.iloc[:, 1:] | |
| move_to_info = [c for c in ["segment", "equityTime", "preOpenTime"] if c in const_df.columns] | |
| if move_to_info: | |
| rem_df = pd.concat([rem_df, const_df[move_to_info].iloc[[0]]], axis=1) | |
| const_df = const_df.drop(columns=move_to_info) | |
| drop_cols_const = [ | |
| "identifier","ffmc","stockIndClosePrice","lastUpdateTime", | |
| "chartTodayPath","chart30dPath","chart365dPath","series", | |
| "symbol_meta","activeSeries","debtSeries","isFNOSec", | |
| "isCASec","isSLBSec","isDebtSec","isSuspended", | |
| "tempSuspendedSeries","isETFSec","isDelisted", | |
| "slb_isin","isMunicipalBond","isHybridSymbol","QuotePreOpenFlag" | |
| ] | |
| const_df = const_df.drop(columns=[c for c in drop_cols_const if c in const_df.columns]) | |
| drop_cols_main = [ | |
| "series","symbol_meta","companyName","industry", | |
| "activeSeries","debtSeries","isFNOSec","isCASec", | |
| "isSLBSec","isDebtSec","isSuspended","tempSuspendedSeries", | |
| "isETFSec","isDelisted","isin","slb_isin","listingDate", | |
| "isMunicipalBond","isHybridSymbol", | |
| "segment","equityTime","preOpenTime","QuotePreOpenFlag" | |
| ] | |
| main_df = main_df.drop(columns=[c for c in drop_cols_main if c in main_df.columns]) | |
| if "pChange" in const_df.columns: | |
| const_df["pChange"] = pd.to_numeric(const_df["pChange"], errors="coerce") | |
| const_df = const_df.sort_values("pChange", ascending=False) | |
| # ================= HTML HELPERS ================= | |
| def df_to_html_color(df, metric_col=None): | |
| 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") | |
| def build_info_cards(rem_df, main_df): | |
| combined = pd.concat([rem_df, main_df], axis=1) | |
| combined = combined.loc[:, ~combined.columns.duplicated()] | |
| 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) | |
| cons_html = df_to_html_color(const_df) | |
| metric_cols = [ | |
| "pChange","totalTradedValue","nearWKH", | |
| "nearWKL","perChange365d","perChange30d" | |
| ] | |
| metric_tables = "" | |
| for col in metric_cols: | |
| if col not in const_df.columns: | |
| continue | |
| df_m = const_df[["symbol", col]].copy() | |
| df_m[col] = pd.to_numeric(df_m[col], errors="coerce") | |
| df_m = df_m.sort_values(col, ascending=False) | |
| metric_tables += f""" | |
| <div class="small-table"> | |
| <div class="st-title">{col}</div> | |
| <div class="st-body">{df_to_html_color(df_m, 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; }} | |
| table {{ border-collapse: collapse; width: 100%; }} | |
| th, td {{ border: 1px solid #bbb; padding: 5px 8px; }} | |
| .numeric-positive {{ color: green; font-weight: bold; }} | |
| .numeric-negative {{ color: red; font-weight: bold; }} | |
| .top-up {{ background: #a8f0a5; }} | |
| .top-down {{ background: #f0a8a8; }} | |
| .mini-card-container {{ display: flex; flex-wrap: wrap; gap: 10px; }} | |
| .mini-card {{ background: #fff; padding: 8px; border-radius: 6px; }} | |
| .grid {{ display: grid; grid-template-columns: repeat(5, 1fr); gap: 12px; }} | |
| .small-table {{ background: white; padding: 8px; border-radius: 6px; }} | |
| .st-title {{ background: #222; color: white; text-align: center; padding: 5px; }} | |
| .st-body {{ max-height: 300px; overflow-y: auto; }} | |
| </style> | |
| </head> | |
| <body> | |
| <h3>Index Info</h3> | |
| {info_cards_html} | |
| <h3>Constituents</h3> | |
| {cons_html} | |
| <h3>Metric Tables</h3> | |
| <div class="grid"> | |
| {metric_tables} | |
| </div> | |
| </body> | |
| </html> | |
| """ | |
| # ================= SAVE (HTML ONLY) ================= | |
| save(cache_name, html_out, "html") | |
| return html_out |