Spaces:
Running
Running
File size: 5,165 Bytes
e30925f 6509beb 71c00fd e30925f 6509beb e30925f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 |
import pandas as pd
from . import nsepythonmodified as ns
from . 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 = ns.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>" |