Update app/build_nse_fno.py

app/build_nse_fno.py

@@ -1,67 +1,30 @@
-# fno.py
+# build_nse_fno.py
 import os
 import subprocess
 import zipfile
-import pandas as pd
-import datetime as dt
 import tempfile
-import pickle
-
-# ============================================================
-# CONFIG
-# ============================================================
-CACHE_DIR = "./cache/fno"
-os.makedirs(CACHE_DIR, exist_ok=True)
-
-
-# ============================================================
-# CACHE HELPERS (DATE-BASED)
-# ============================================================
-def _cache_path(key):
-    return os.path.join(CACHE_DIR, f"{key}.pkl")
-
-
-def exists(key):
-    return os.path.exists(_cache_path(key))
-
+import pandas as pd
+from datetime import datetime as dt
 
-def load(key):
-    try:
-        with open(_cache_path(key), "rb") as f:
-            return pickle.load(f)
-    except Exception:
-        return None
+from .persist import exists, load, save
 
 
-def save(key, obj):
-    with open(_cache_path(key), "wb") as f:
-        pickle.dump(obj, f)
+NSE_FO_BASE = "https://nsearchives.nseindia.com/content/fo"
 
 
 # ============================================================
-# FETCH FO BHAVCOPY (RAW)
+# FETCH FO BHAVCOPY (RAW DF)
 # ============================================================
-def fo_bhavcopy(date_input) -> pd.DataFrame:
-    """
-    Download NSE F&O bhavcopy for a given date
-    date_input: dd-mm-yyyy | datetime.date | datetime.datetime
-    """
-    if isinstance(date_input, str):
-        date = dt.datetime.strptime(date_input, "%d-%m-%Y").date()
-    elif isinstance(date_input, dt.datetime):
-        date = date_input.date()
-    elif isinstance(date_input, dt.date):
-        date = date_input
-    else:
-        raise ValueError("Invalid date format. Use dd-mm-yyyy")
+def _fetch_fo_bhavcopy(fo_date: str) -> pd.DataFrame:
+    date = dt.strptime(fo_date, "%d-%m-%Y").date()
+    ymd = date.strftime("%Y%m%d")
 
-    dmy = date.strftime("%d%m%Y")
     file_name = f"BhavCopy_NSE_FO_0_0_0_{ymd}_F_0000.csv"
     zip_name = f"{file_name}.zip"
-    url = f"https://nsearchives.nseindia.com/content/fo/{zip_name}"
+    url = f"{NSE_FO_BASE}/{zip_name}"
 
-    with tempfile.TemporaryDirectory() as tmpdir:
-        zip_path = os.path.join(tmpdir, zip_name)
+    with tempfile.TemporaryDirectory() as tmp:
+        zip_path = os.path.join(tmp, zip_name)
 
         cmd = [
             "curl", "-L",
@@ -75,26 +38,17 @@ def fo_bhavcopy(date_input) -> pd.DataFrame:
         res = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
 
         if res.returncode != 0 or not os.path.exists(zip_path) or os.path.getsize(zip_path) < 1024:
-            raise RuntimeError("FO Bhavcopy download failed or blocked")
+            raise RuntimeError("FO bhavcopy download failed")
 
         with zipfile.ZipFile(zip_path) as z:
             with z.open(file_name) as f:
-                df = pd.read_csv(f)
-
-    return df
+                return pd.read_csv(f)
 
 
 # ============================================================
 # OPTION CHAIN BUILDER
 # ============================================================
-def build_option_chain(opt_df: pd.DataFrame) -> pd.DataFrame:
-    drop = [
-        "FininstrmActlXpryDt", "FinInstrmTp", "TckrSymb",
-        "TtlNbOfTxsExctd", "NewBrdLotQty",
-        "EXP_DMY", "SttlmPric",
-        "OpnPric", "HghPric", "LwPric", "TtlTrfVal"
-    ]
-
+def _build_option_chain(df: pd.DataFrame) -> pd.DataFrame:
     rename = {
         "ClsPric": "close",
         "PrvsClsgPric": "pre",
@@ -103,14 +57,10 @@ def build_option_chain(opt_df: pd.DataFrame) -> pd.DataFrame:
         "TtlTradgVol": "vol"
     }
 
-    opt_df = opt_df.drop(drop, axis=1, errors="ignore").rename(columns=rename)
+    df = df.rename(columns=rename)
 
-    ce = opt_df[opt_df["OptnTp"] == "CE"].rename(
-        columns={c: f"ce_{c}" for c in opt_df.columns}
-    )
-    pe = opt_df[opt_df["OptnTp"] == "PE"].rename(
-        columns={c: f"pe_{c}" for c in opt_df.columns}
-    )
+    ce = df[df["OptnTp"] == "CE"].rename(columns={c: f"ce_{c}" for c in df.columns})
+    pe = df[df["OptnTp"] == "PE"].rename(columns={c: f"pe_{c}" for c in df.columns})
 
     chain = pd.merge(
         ce, pe,
@@ -121,141 +71,96 @@ def build_option_chain(opt_df: pd.DataFrame) -> pd.DataFrame:
 
     chain["StrkPric"] = chain["ce_StrkPric"].combine_first(chain["pe_StrkPric"])
 
-    chain.drop(
-        columns=[
-            "ce_StrkPric", "pe_StrkPric",
-            "ce_OptnTp", "pe_OptnTp",
-            "ce_UndrlygPric", "pe_UndrlygPric"
-        ],
-        inplace=True,
-        errors="ignore"
-    )
-
-    chain = chain.fillna(0).sort_values("StrkPric").reset_index(drop=True)
-
-    cols = [
+    keep = [
         "ce_oi", "ce_oi_chg", "ce_vol", "ce_close", "ce_pre",
         "StrkPric",
         "pe_pre", "pe_close", "pe_vol", "pe_oi_chg", "pe_oi"
     ]
 
-    df = chain[cols].copy()
+    out = chain[keep].fillna(0).sort_values("StrkPric")
 
-    for c in ["ce_close", "ce_pre", "pe_close", "pe_pre"]:
-        df[c] = df[c].astype(float).round(2)
+    for c in out.columns:
+        out[c] = pd.to_numeric(out[c], errors="coerce").fillna(0)
 
-    for c in [
-        "ce_oi", "ce_oi_chg", "ce_vol",
-        "pe_vol", "pe_oi_chg", "pe_oi", "StrkPric"
-    ]:
-        df[c] = df[c].astype(int)
-
-    return df
+    return out.reset_index(drop=True)
 
 
 # ============================================================
-# HTML TABLE RENDER
+# MAIN HTML BUILDER (STYLE = build_index_live_html)
 # ============================================================
-def df_to_html(df: pd.DataFrame, title=None) -> str:
-    style = """
-    <style>
-        table {border-collapse: collapse; width:100%; font-family:Arial;}
-        th, td {border:1px solid #ddd; padding:6px; text-align:center;}
-        th {background:#2e7d32; color:white;}
-        tr:nth-child(even){background:#f2f2f2;}
-    </style>
+def build_nse_fno_html(fo_date: str, symbol: str) -> str:
     """
-
-    html = df.to_html(index=False, escape=False)
-    if title:
-        html = f"<h3>{title}</h3>" + html
-
-    return style + html
-
-
-# ============================================================
-# MAIN ENTRY (DAILY VALIDITY)
-# ============================================================
-def nse_fno_html(fo_date: str, symbol: str) -> str:
-    """
-    Daily-valid F&O HTML builder
-    Cache rules:
-    - HTML cached per (date + symbol)
-    - FO bhavcopy cached per date
+    Daily NSE F&O HTML
+    - Bhavcopy cached per DATE
+    - HTML cached per DATE + SYMBOL
     """
 
-    date_key = dt.datetime.strptime(fo_date, "%d-%m-%Y").strftime("%Y%m%d")
-
-    html_key = f"fno_html_{date_key}_{symbol}"
-    fo_key = f"fno_bhavcopy_{date_key}"
+    date_key = dt.strptime(fo_date, "%d-%m-%Y").strftime("%Y%m%d")
+    cache_html = f"NSE_FNO_HTML_{date_key}_{symbol}"
+    cache_df   = f"NSE_FNO_BHAVCOPY_{date_key}"
 
-    # ---------------- HTML CACHE FIRST ----------------
-    if exists(html_key):
-        html = load(html_key)
-        if html:
-            return html
+    # ================= HTML CACHE =================
+    if exists(cache_html, "html"):
+        cached = load(cache_html, "html")
+        if isinstance(cached, str):
+            return cached
 
-    # ---------------- FO CACHE ----------------
-    if exists(fo_key):
-        fo_df = load(fo_key)
+    # ================= BHAVCOPY CACHE =================
+    if exists(cache_df):
+        fo_df = load(cache_df)
     else:
-        fo_df = fo_bhavcopy(fo_date)
-        save(fo_key, fo_df)
-
-    # ---------------- BUILD DATA ----------------
-    fo = fo_df.copy().drop(
-        ["ISIN", "Rmks", "SctySrs", "Rsvd1", "Rsvd2", "Rsvd3", "Rsvd4"],
-        axis=1,
-        errors="ignore"
-    )
+        fo_df = _fetch_fo_bhavcopy(fo_date)
+        save(cache_df, fo_df)
 
+    # ================= PROCESS =================
+    fo = fo_df.copy()
     exp = pd.to_datetime(fo["FininstrmActlXpryDt"], errors="coerce")
     today = pd.Timestamp.today().normalize()
 
-    monthly = (
-        exp[exp >= today]
-        .groupby([exp.dt.year, exp.dt.month])
-        .max()
-        .sort_values()
-    )
-
+    monthly = exp[exp >= today].groupby([exp.dt.year, exp.dt.month]).max()
     if monthly.empty:
-        return "<h3>No valid expiry found</h3>"
+        return "<h3>No valid expiry</h3>"
 
     expiry = monthly.iloc[0].strftime("%d-%m-%Y")
+    fo["EXP"] = exp.dt.strftime("%d-%m-%Y")
 
-    fo["EXP_DMY"] = exp.dt.strftime("%d-%m-%Y")
-
-    df = fo[
-        (fo["TckrSymb"] == symbol) &
-        (fo["EXP_DMY"] == expiry)
-    ].copy()
-
+    df = fo[(fo["TckrSymb"] == symbol) & (fo["EXP"] == expiry)]
     if df.empty:
         return f"<h3>No F&O data for {symbol}</h3>"
 
-    # ---------------- COMMON ----------------
-    common_cols = [
-        "TradDt", "BizDt", "Sgmt", "Src", "SsnId",
-        "FinInstrmId", "XpryDt", "FinInstrmNm", "LastPric"
-    ]
-
-    common_df = pd.DataFrame([df.iloc[0][common_cols]])
-    common_df.insert(0, "Expiry", expiry)
-
-    # ---------------- FUTURE + OPTION ----------------
-    future_df = df[df["FinInstrmTp"].isin(["STF", "IDF"])]
-    option_df = df[df["FinInstrmTp"].isin(["STO", "IDO"])]
-
-    option_chain_df = build_option_chain(option_df)
-
-    html = (
-        df_to_html(common_df, "Common Info") + "<br>"
-        + df_to_html(future_df, "Future Contracts") + "<br>"
-        + df_to_html(option_chain_df, "Option Chain")
-    )
-
-    # ---------------- SAVE HTML ----------------
-    save(html_key, html)
-
-    return html
+    fut_df = df[df["FinInstrmTp"].isin(["STF", "IDF"])]
+    opt_df = df[df["FinInstrmTp"].isin(["STO", "IDO"])]
+
+    opt_chain = _build_option_chain(opt_df)
+
+    # ================= HTML =================
+    html = f"""
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="UTF-8">
+<style>
+body {{ font-family: Arial; margin: 12px; background:#f5f5f5; }}
+table {{ border-collapse: collapse; width: 100%; background:white; }}
+th, td {{ border:1px solid #bbb; padding:6px; text-align:center; }}
+th {{ background:#2e7d32; color:white; }}
+</style>
+</head>
+<body>
+
+<h2>NSE F&O : {symbol}</h2>
+<h4>Expiry: {expiry}</h4>
+
+<h3>Futures</h3>
+{fut_df.to_html(index=False)}
+
+<h3>Option Chain</h3>
+{opt_chain.to_html(index=False)}
+
+</body>
+</html>
+"""
+
+    # ================= SAVE =================
+    save(cache_html, html, "html")
+    return html