Create nse.py

nse.py

@@ -0,0 +1,220 @@
+# ================================
+# NSE Fetch Module (DF Only)
+# ================================
+
+import datetime
+import pandas as pd
+import time
+import requests
+import nsepython # Moved import here
+
+HEADERS = {
+    "User-Agent": "Mozilla/5.0",
+    "Accept-Language": "en-US,en;q=0.9",
+}
+
+session = requests.Session()
+session.get("https://www.nseindia.com", headers=HEADERS, timeout=5)
+
+# ---------------------------------------------------
+# Helper: JSON Fetch
+# ---------------------------------------------------
+def fetch_data(url):
+    try:
+        response = session.get(url, headers=HEADERS, timeout=5)
+        response.raise_for_status()
+        return response.json()
+    except:
+        return None
+
+# ---------------------------------------------------
+# Clean DF
+# ---------------------------------------------------
+def clean_dataframe(df):
+    df.columns = df.columns.str.strip()
+    str_cols = df.select_dtypes(include=["object"]).columns
+    df[str_cols] = df[str_cols].apply(lambda x: x.str.strip())
+    df.fillna(0.01, inplace=True)
+    return df
+
+# ---------------------------------------------------
+# Bhavcopy Fetch → DataFrame
+# ---------------------------------------------------
+def fetch_bhavcopy_df(date):
+    """Returns Cleaned Bhavcopy DF for EQ / BE / SM"""
+    date_str = date.strftime("%d-%m-%Y")
+    print(f"Attempting to fetch bhavcopy for date: {date_str}")
+
+    try:
+        df = nsepython.get_bhavcopy(date_str) # Direct call
+        if df is None or df.empty:
+            print(f"No bhavcopy data or empty DataFrame returned for {date_str}")
+            return None, None
+
+        actual_bhavcopy_date = datetime.datetime.strptime(
+            df.iloc[2, 2].strip(), "%d-%b-%Y"
+        ).date()
+
+        df = clean_dataframe(df)
+        df_filtered = df[df.iloc[:, 1].isin(["EQ", "BE", "SM"])]
+
+        return df_filtered, actual_bhavcopy_date
+
+    except Exception as e:
+        print(f"An error occurred while fetching bhavcopy for {date_str}: {e}")
+        return None, None
+
+# ---------------------------------------------------
+# Stock Deliverable DF (security-wise archive)
+# ---------------------------------------------------
+def fetch_stock_df(nse_module, stock, start, end, series="ALL"):
+    """Return DF for security-wise archive (deliverable + all columns)"""
+
+    df = nse_module.security_wise_archive(start, end, stock, series)
+    if df is not None and not df.empty:
+        return df
+    return None
+
+# ---------------------------------------------------
+# All NSE Indices → DataFrames
+# ---------------------------------------------------
+def nse_indices_df():
+    url = "https://www.nseindia.com/api/allIndices"
+    data = fetch_data(url)
+    if data is None:
+        return None, None, None
+
+    df_dates = pd.DataFrame([data["dates"]])
+    df_meta = pd.DataFrame([{k: v for k, v in data.items() if k not in ["data", "dates"]}])
+    df_data = pd.DataFrame(data["data"])
+
+    return df_dates, df_meta, df_data
+
+# ---------------------------------------------------
+# Specific Index → DataFrames
+# ---------------------------------------------------
+def nse_index_df(index_name="NIFTY 50"):
+    url = f"https://www.nseindia.com/api/equity-stockIndices?index={index_name.replace(' ', '%20')}"
+    data = fetch_data(url)
+    if data is None:
+        return None, None, None, None
+
+    df_market = pd.DataFrame([data["marketStatus"]])
+    df_adv = pd.DataFrame([data["advance"]])
+    df_meta = pd.DataFrame([data["metadata"]])
+    df_data = pd.DataFrame(data["data"])
+
+    return df_market, df_adv, df_meta, df_data
+
+# ---------------------------------------------------
+# Option Chain DF (Raw CE/PE)
+# ---------------------------------------------------
+def fetch_option_chain_df(symbol="NIFTY"):
+    url = f"https://www.nseindia.com/api/option-chain-indices?symbol={symbol}"
+    data = fetch_data(url)
+
+    if data and "filtered" in data:
+        ce_df = pd.DataFrame([r["CE"] for r in data["filtered"]["data"] if "CE" in r])
+        pe_df = pd.DataFrame([r["PE"] for r in data["filtered"]["data"] if "PE" in r])
+        return ce_df, pe_df
+
+    return None, None
+
+# ---------------------------------------------------
+# Pre-open market → DataFrame
+# ---------------------------------------------------
+def nse_preopen_df(key="NIFTY"):
+    url = f"https://www.nseindia.com/api/market-data-pre-open?key={key}"
+    data = fetch_data(url)
+    if data:
+        return pd.DataFrame(data.get("data", []))
+    return None
+
+# ---------------------------------------------------
+# FNO Quote → DataFrames
+# ---------------------------------------------------
+def nse_fno_df(symbol):
+    payload = nsepython.nse_quote(symbol) # Direct call
+    if not payload:
+        return None
+
+    # info + timestamps + volatility info
+    info_keys = list(payload["info"].keys()) + [
+        "fut_timestamp",
+        "opt_timestamp",
+        "maxVolatility",
+        "minVolatility",
+        "avgVolatility",
+    ]
+
+    info_values = list(payload["info"].values()) + [
+        payload["fut_timestamp"],
+        payload["opt_timestamp"],
+        payload["underlyingInfo"]["volatility"][0]['maxVolatility'],
+        payload["underlyingInfo"]["volatility"][0]['minVolatility'],
+        payload["underlyingInfo"]["volatility"][0]['avgVolatility'],
+    ]
+
+    df_info = pd.DataFrame([info_values], columns=info_keys)
+
+    df_mcap = pd.DataFrame(payload["underlyingInfo"].get("marketCap", {}))
+    df_fno_list = pd.DataFrame(payload.get("allSymbol", []), columns=["FNO_Symbol"])
+
+    # Core stock depth
+    df_stock = process_stocks_df(payload["stocks"])
+
+    return {
+        "info": df_info,
+        "mcap": df_mcap,
+        "fno": df_fno_list,
+        "stock": df_stock
+    }
+
+# ---------------------------------------------------
+# Handle nested stock → DF
+# ---------------------------------------------------
+def process_stocks_df(data):
+    """Return final merged stock DF only"""
+    trade_info_list, other_info_list = [], []
+    bid_ask_list = []
+    stock_values = []
+    trade_keys = other_keys = bidask_keys = stock_keys = None
+
+    for i, stock in enumerate(data):
+        meta = stock.pop("metadata")
+        depth = stock.pop("marketDeptOrderBook")
+        parent = stock
+
+        trade_info = depth.pop("tradeInfo", {})
+        other_info = depth.pop("otherInfo", {})
+
+        trade_info_list.append(trade_info)
+        other_info_list.append(other_info)
+
+        # bid / ask
+        bid_ask_row = {}
+        for side in ["bid", "ask"]:
+            for j, entry in enumerate(depth.get(side, []), start=1):
+                bid_ask_row[f"{side}_price_{j}"] = entry.get("price")
+                bid_ask_row[f"{side}_qty_{j}"] = entry.get("quantity")
+
+        bid_ask_list.append(bid_ask_row)
+
+        if i == 0:
+            trade_keys = list(trade_info.keys())
+            other_keys = list(other_info.keys())
+            bidask_keys = list(bid_ask_row.keys())
+            stock_keys = list(meta.keys()) + list(depth.keys()) + list(parent.keys())
+
+        stock_values.append(
+            list(meta.values()) + list(depth.values()) + list(parent.values())
+        )
+
+    df_trade = pd.DataFrame(trade_info_list, columns=trade_keys)
+    df_other = pd.DataFrame(other_info_list, columns=other_keys)
+    df_bidask = pd.DataFrame(bid_ask_list, columns=bidask_keys)
+    df_stock = pd.DataFrame(stock_values, columns=stock_keys)
+
+    df_stock = df_stock.drop(columns=['bid', 'ask', 'carryOfCost'], errors="ignore")
+
+    return pd.concat([df_stock, df_trade, df_other, df_bidask], axis=1)