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import pandas as pd
import nsepython as nse
import persist
from datetime import datetime as dt
def build_bhavcopy_html(date_str):
key = f"bhavcopy_{date_str}"
# -------------------------------------------------------
# 0) Use cached HTML if present
# -------------------------------------------------------
if persist.exists(key, "html"):
cached = persist.load(key, "html")
if cached is not False:
print(
f"[{dt.now().strftime('%Y-%m-%d %H:%M:%S')}] "
f"Using cached bhavcopy for {date_str}"
)
return cached
try:
# -------------------------------------------------------
# 1) Validate Date (DD-MM-YYYY)
# -------------------------------------------------------
try:
dt.strptime(date_str, "%d-%m-%Y")
except ValueError:
html = "<h3>Invalid date format. Use DD-MM-YYYY.</h3>"
persist.save(key, html, "html")
return html
# -------------------------------------------------------
# 2) Fetch Bhavcopy (nsepython expects DD-MM-YYYY)
# -------------------------------------------------------
try:
df = nse.nse_bhavcopy(date_str)
df.columns = df.columns.str.strip()
except Exception:
html = f"<h3>No Bhavcopy found for {date_str}.</h3>"
persist.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
)
persist.save(key, html, "html")
return html
except Exception as e:
print(
f"[{dt.now().strftime('%Y-%m-%d %H:%M:%S')}] "
f"Error build_bhavcopy_html: {e}"
)
return f"<h3>Error: {e}</h3>"
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