# ==============================
# Imports
# ==============================
import requests
import zipfile
from io import BytesIO, StringIO
from datetime import datetime as dt
from typing import Dict, Union
import pandas as pd
# persist helpers
from .persist import exists, load, save
# ==============================
# Raw CSV Loader (NO parsing)
# ==============================
def load_csv(url: str) -> Union[str, Dict[str, str]]:
"""
Pure transport loader
- .csv -> raw CSV text (str)
- .zip -> {filename: raw CSV text}
NO parsing
NO cleaning
NO assumptions
"""
r = requests.get(url)
r.raise_for_status()
if url.lower().endswith(".zip"):
z = zipfile.ZipFile(BytesIO(r.content))
out: Dict[str, str] = {}
for name in z.namelist():
if name.lower().endswith(".csv"):
with z.open(name) as f:
out[name] = f.read().decode("utf-8", errors="ignore")
return out
return r.text
# ==============================
# NSE High-Low HTML Formatter
# ==============================
def _highlow_html_formatter(df: pd.DataFrame, date_str: str) -> str:
metric = "PERCENT_CHANGE"
df_html = df.copy()
top_up = df[metric].nlargest(3).index if metric in df else []
top_dn = df[metric].nsmallest(3).index if metric in df else []
for idx, row in df_html.iterrows():
for col in df_html.columns:
val = row[col]
style = ""
if isinstance(val, (int, float)):
txt = f"{val:.2f}"
if val > 0:
style = "pos"
elif val < 0:
style = "neg"
if col == metric:
if idx in top_up:
style += " top-up"
elif idx in top_dn:
style += " top-down"
df_html.at[idx, col] = f'{txt}'
else:
df_html.at[idx, col] = str(val)
return f"""
NSE High-Low {date_str}
NSE Index High / Low — {date_str}
{df_html.to_html(index=False, escape=False)}
"""
# ==============================
# NSE High-Low Master Function
# ==============================
def nse_highlow(date_str: str | None = None) -> str:
"""
Master NSE High-Low function
Responsibilities:
- Knows NSE CSV structure
- Header starts at row index 2 (skip 0 & 1)
- Uses raw CSV loader
- Builds HTML
- Persists ONLY HTML
"""
if not date_str:
date_str = dt.now().strftime("%d-%m-%Y")
cache_key = f"highlow_{date_str}"
if exists(cache_key, "html"):
return load(cache_key, "html")
d = dt.strptime(date_str, "%d-%m-%Y")
url = (
"https://archives.nseindia.com/content/indices/"
f"ind_close_all_{d.strftime('%d%m%Y')}.csv"
)
# 1️⃣ Load raw CSV text
csv_text = load_csv(url)
# 2️⃣ NSE-specific parsing (header row = 2)
df = pd.read_csv(
StringIO(csv_text),
header=0
)
# 3️⃣ Build HTML
html = _highlow_html_formatter(df, date_str)
# 4️⃣ Persist HTML only
save(cache_key, html, "html")
return html