import os, sys, requests, pandas as pd, json, random, datetime, time, logging, re, urllib.parse from collections import Counter mode = 'local' # ------------------------- NSE FETCH ------------------------- if mode == "vpn": def nsefetch(payload): def encode(url): return url if "%26" in url or "%20" in url else urllib.parse.quote(url, safe=":/?&=") def refresh_cookies(): os.popen(f'curl -c cookies.txt "https://www.nseindia.com" {curl_headers}').read() os.popen(f'curl -b cookies.txt -c cookies.txt "https://www.nseindia.com/option-chain" {curl_headers}').read() if not os.path.exists("cookies.txt"): refresh_cookies() encoded = encode(payload) cmd = f'curl -b cookies.txt "{encoded}" {curl_headers}' raw = os.popen(cmd).read() try: return json.loads(raw) except: refresh_cookies() raw = os.popen(cmd).read() try: return json.loads(raw) except: return {} if mode == 'local': def nsefetch(payload): try: s = requests.Session() s.get("https://www.nseindia.com", headers=headers, timeout=10) s.get("https://www.nseindia.com/option-chain", headers=headers, timeout=10) return s.get(payload, headers=headers, timeout=10).json() except: return {} # ------------------------- HEADERS ------------------------- headers = { "accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7", "accept-language": "en-US,en;q=0.9,en-IN;q=0.8,en-GB;q=0.7", "cache-control": "max-age=0", "priority": "u=0, i", "sec-ch-ua": '"Microsoft Edge";v="129","Not=A?Brand";v="8","Chromium";v="129"', "sec-ch-ua-mobile": "?0", "sec-ch-ua-platform": '"Windows"', "sec-fetch-dest": "document", "sec-fetch-mode": "navigate", "sec-fetch-site": "none", "sec-fetch-user": "?1", "upgrade-insecure-requests": "1", "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/129.0.0.0 Safari/537.36 Edg/129.0.0.0" } niftyindices_headers = { 'Connection': 'keep-alive', 'sec-ch-ua': '"Not;A Brand";v="99","Google Chrome";v="91","Chromium";v="91"', 'Accept': 'application/json,text/javascript,*/*;q=0.01', 'DNT': '1', 'X-Requested-With': 'XMLHttpRequest', 'sec-ch-ua-mobile': '?0', 'User-Agent': 'Mozilla/5.0', 'Content-Type': 'application/json; charset=UTF-8', 'Origin': 'https://niftyindices.com', 'Sec-Fetch-Site': 'same-origin', 'Sec-Fetch-Mode': 'cors', 'Sec-Fetch-Dest': 'empty', 'Referer': 'https://niftyindices.com/reports/historical-data', 'Accept-Language': 'en-US,en;q=0.9,hi;q=0.8' } curl_headers = ''' -H "authority: beta.nseindia.com" -H "cache-control: max-age=0" -H "dnt: 1" -H "upgrade-insecure-requests: 1" -H "user-agent: Mozilla/5.0" -H "sec-fetch-user: ?1" -H "accept: */*" -H "sec-fetch-site: none" -H "accept-language: en-US,en;q=0.9" --compressed''' run_time = datetime.datetime.now() indices = ['NIFTY','FINNIFTY','BANKNIFTY'] # ------------------------- HELPERS ------------------------- def nsesymbolpurify(s): return s.replace('&','%26') def flatten_dict(d, parent="", sep="."): items={} for k,v in d.items(): nk = f"{parent}{sep}{k}" if parent else k if isinstance(v, dict): items.update(flatten_dict(v, nk, sep)) else: items[nk] = v return items def flatten_nested(d, prefix=""): flat={} for k,v in d.items(): nk = f"{prefix}{k}" if prefix=="" else f"{prefix}.{k}" if isinstance(v, dict): flat.update(flatten_nested(v, nk)) elif isinstance(v, list): if v and isinstance(v[0], dict): for i,x in enumerate(v): flat.update(flatten_nested(x, f"{nk}.{i}")) else: flat[nk]=v else: flat[nk]=v return flat def rename_col(cols): child=[c.split('.')[-1] for c in cols] cnt=Counter(child) new=[] for c,ch in zip(cols,child): if cnt[ch]==1: new.append(ch) else: p=c.split('.') new.append(f"{p[-1]}_{p[-2]}" if len(p)>=2 else p[-1]) return new def df_from_data(data): rows=[ flatten_nested(x) if isinstance(x,dict) else {"value":x} for x in data ] df=pd.DataFrame(rows) df.columns=rename_col(df.columns) return df # ------------------------- API FUNCTIONS ------------------------- def indices(): p=nsefetch("https://www.nseindia.com/api/allIndices") return {"data":pd.DataFrame(p.pop("data")), "dates":pd.DataFrame([p.pop("dates")]), "indices":pd.DataFrame([p])} def eq(symbol): symbol=nsesymbolpurify(symbol) df=nsefetch(f'https://www.nseindia.com/api/quote-equity?symbol={symbol}') pre=df.pop('preOpenMarket') out={ "securityInfo": pd.DataFrame([df["securityInfo"]]), "priceInfo": pd.DataFrame([flatten_dict(df["priceInfo"])]), "industryInfo": pd.DataFrame([df["industryInfo"]]), "pdSectorIndAll": pd.DataFrame([df["metadata"].pop("pdSectorIndAll")]), "metadata": pd.DataFrame([df["metadata"]]), "info": pd.DataFrame([df["info"]]), "preOpen": pd.DataFrame(pre.pop('preopen')), "preOpenMarket": pd.DataFrame([pre]) } return out def eq_fno(): return nsefetch('https://www.nseindia.com/api/equity-stockIndices?index=SECURITIES%20IN%20F%26O') def eq_der(symbol): return nsefetch('https://www.nseindia.com/api/quote-derivative?symbol='+nsesymbolpurify(symbol)) def index_chain(symbol): return nsefetch('https://www.nseindia.com/api/option-chain-indices?symbol='+nsesymbolpurify(symbol)) def eq_chain(symbol): return nsefetch('https://www.nseindia.com/api/option-chain-equities?symbol='+nsesymbolpurify(symbol)) def nse_holidays(t="trading"): return nsefetch('https://www.nseindia.com/api/holiday-master?type='+t) def nse_results(index="equities",period="Quarterly"): if index in ["equities","debt","sme"] and period in ["Quarterly","Annual","Half-Yearly","Others"]: return pd.json_normalize(nsefetch(f'https://www.nseindia.com/api/corporates-financial-results?index={index}&period={period}')) print("Invalid Input") def nse_events(): return pd.json_normalize(nsefetch('https://www.nseindia.com/api/event-calendar')) def nse_past_results(symbol): return nsefetch('https://www.nseindia.com/api/results-comparision?symbol='+nsesymbolpurify(symbol)) def nse_blockdeal(): return nsefetch('https://nseindia.com/api/block-deal') def nse_marketStatus(): return nsefetch('https://nseindia.com/api/marketStatus') def nse_circular(mode="latest"): return nsefetch('https://www.nseindia.com/api/latest-circular' if mode=="latest" else 'https://www.nseindia.com/api/circulars') def nse_fiidii(mode="pandas"): p=nsefetch('https://www.nseindia.com/api/fiidiiTradeReact') return pd.DataFrame(p) def nsetools_get_quote(symbol): p=nsefetch('https://www.nseindia.com/api/equity-stockIndices?index=SECURITIES%20IN%20F%26O') for x in p['data']: if x['symbol']==symbol.upper(): return x def nse_index(): p=nsefetch('https://iislliveblob.niftyindices.com/jsonfiles/LiveIndicesWatch.json') return pd.DataFrame(p['data']) def index_history(sym,sd,ed): d={'cinfo':f"{{'name':'{sym}','startDate':'{sd}','endDate':'{ed}','indexName':'{sym}'}}"} p=json.loads(requests.post('https://niftyindices.com/Backpage.aspx/getHistoricaldatatabletoString', headers=niftyindices_headers, json=d).json()["d"]) return pd.DataFrame.from_records(p) def index_pe_pb_div(sym,sd,ed): d={'cinfo':f"{{'name':'{sym}','startDate':'{sd}','endDate':'{ed}','indexName':'{sym}'}}"} p=json.loads(requests.post('https://niftyindices.com/Backpage.aspx/getpepbHistoricaldataDBtoString', headers=niftyindices_headers, json=d).json()["d"]) return pd.DataFrame.from_records(p) def index_total_returns(sym,sd,ed): d={'cinfo':f"{{'name':'{sym}','startDate':'{sd}','endDate':'{ed}','indexName':'{sym}'}}"} p=json.loads(requests.post('https://niftyindices.com/Backpage.aspx/getTotalReturnIndexString', headers=niftyindices_headers, json=d).json()["d"]) return pd.DataFrame.from_records(p) def nse_bulkdeals(): return pd.read_csv("https://archives.nseindia.com/content/equities/bulk.csv") def nse_blockdeals(): return pd.read_csv("https://archives.nseindia.com/content/equities/block.csv") #nse daily report def nse_bhavcopy(d): return pd.read_csv("https://archives.nseindia.com/products/content/sec_bhavdata_full_"+d.replace("-","")+".csv") def nse_highlow(d: str) -> pd.DataFrame: """ NSE 52-week High/Low CSV Real header starts from row 3 """ date_str = d.replace("-", "") url = f"https://archives.nseindia.com/content/CM_52_wk_High_low_{date_str}.csv" df = pd.read_csv( url, skiprows=2, # 🔥 key fix engine="python" ) df.columns = df.columns.str.strip() return df def nse_preopen(key="NIFTY"): p=nsefetch("https://www.nseindia.com/api/market-data-pre-open?key="+key) return {"data":df_from_data(p.pop("data")), "rem":df_from_data([p])} def nse_most_active(t="securities",s="value"): return pd.DataFrame(nsefetch(f"https://www.nseindia.com/api/live-analysis-most-active-{t}?index={s}")["data"]) def nse_eq_symbols(): return pd.read_csv('https://archives.nseindia.com/content/equities/EQUITY_L.csv')['SYMBOL'].tolist() def nse_price_band_hitters(b="both",v="AllSec"): p=nsefetch("https://www.nseindia.com/api/live-analysis-price-band-hitter") return {"data":pd.DataFrame(p[b][v]["data"]), "count":pd.DataFrame([p['count']])} def nse_largedeals(mode="bulk_deals"): p=nsefetch('https://www.nseindia.com/api/snapshot-capital-market-largedeal') return pd.DataFrame(p["BULK_DEALS_DATA" if mode=="bulk_deals" else "SHORT_DEALS_DATA" if mode=="short_deals" else "BLOCK_DEALS_DATA"]) def nse_largedeals_historical(f,t,mode="bulk_deals"): m = "bulk-deals" if mode=="bulk_deals" else "short-selling" if mode=="short_deals" else "block-deals" p=nsefetch(f'https://www.nseindia.com/api/historical/{m}?from={f}&to={t}') return pd.DataFrame(p["data"]) def nse_stock_hist(f,t,symbol,series="ALL"): url=f"https://www.nseindia.com/api/historical/securityArchives?from={f}&to={t}&symbol={symbol.upper()}&dataType=priceVolumeDeliverable&series={series}" return pd.DataFrame(nsefetch(url)['data']) def nse_index_live(name="NIFTY 50"): p=nsefetch(f"https://www.nseindia.com/api/equity-stockIndices?index={name.replace(' ','%20')}") return {"data":df_from_data(p.pop("data")) if "data" in p else pd.DataFrame(), "rem":df_from_data([p])}