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5 fichiers name market-data description Market data retrieval with OpenAlgo - real-time quotes, historical OHLCV, market depth, option chains, WebSocket streaming, and symbol search
OpenAlgo Market Data
Access real-time and historical market data using OpenAlgo's unified Python SDK. Supports REST API for on-demand data and WebSocket for real-time streaming.
Environment Setup
from openalgo import api
client = api(
api_key='your_api_key_here' ,
host='http://127.0.0.1:5000'
)
client = api(
api_key='your_api_key_here' ,
host='http://127.0.0.1:5000' ,
ws_url='ws://127.0.0.1:8765' ,
verbose=True
)
Quick Start Scripts
Get Quotes
python scripts/quotes.py --symbol RELIANCE --exchange NSE
python scripts/quotes.py --symbols RELIANCE,TCS,INFY --exchange NSE
Get Historical Data
python scripts/history.py --symbol SBIN --exchange NSE --interval 5m --start 2025-01-01 --end 2025-01-15
Get Market Depth
python scripts/depth.py --symbol SBIN --exchange NSE
Stream Live Data
python scripts/stream.py --symbols NIFTY,BANKNIFTY --exchange NSE_INDEX --mode ltp
REST API Methods
1. Single Quote
Get current market quote for a symbol:
response = client.quotes(symbol="RELIANCE" , exchange="NSE" )
{
"status" : "success" ,
"data" : {
"open" : 1172.0 ,
"high" : 1196.6 ,
"low" : 1163.3 ,
"ltp" : 1187.75 ,
"ask" : 1188.0 ,
"bid" : 1187.85 ,
"prev_close" : 1165.7 ,
"volume" : 14414545
}
}
2. Multiple Quotes Get quotes for multiple symbols in one call:
response = client.multiquotes(symbols=[
{"symbol" : "RELIANCE" , "exchange" : "NSE" },
{"symbol" : "TCS" , "exchange" : "NSE" },
{"symbol" : "INFY" , "exchange" : "NSE" },
{"symbol" : "NIFTY" , "exchange" : "NSE_INDEX" }
])
{
"status" : "success" ,
"results" : [
{
"symbol" : "RELIANCE" ,
"exchange" : "NSE" ,
"data" : {
"open" : 1542.3 ,
"high" : 1571.6 ,
"low" : 1540.5 ,
"ltp" : 1569.9 ,
"prev_close" : 1539.7 ,
"ask" : 1569.9 ,
"bid" : 1569.8 ,
"oi" : 0 ,
"volume" : 14054299
}
} ,
...
]
}
3. Market Depth (Level 2) Get order book with 5 best bid/ask levels:
response = client.depth(symbol="SBIN" , exchange="NSE" )
{
"status" : "success" ,
"data" : {
"open" : 760.0 ,
"high" : 774.0 ,
"low" : 758.15 ,
"ltp" : 769.6 ,
"ltq" : 205 ,
"prev_close" : 746.9 ,
"volume" : 9362799 ,
"oi" : 161265750 ,
"totalbuyqty" : 591351 ,
"totalsellqty" : 835701 ,
"asks" : [
{ "price" : 769.6 , "quantity" : 767 } ,
{ "price" : 769.65 , "quantity" : 115 } ,
{ "price" : 769.7 , "quantity" : 162 } ,
{ "price" : 769.75 , "quantity" : 1121 } ,
{ "price" : 769.8 , "quantity" : 430 }
] ,
"bids" : [
{ "price" : 769.4 , "quantity" : 886 } ,
{ "price" : 769.35 , "quantity" : 212 } ,
{ "price" : 769.3 , "quantity" : 351 } ,
{ "price" : 769.25 , "quantity" : 343 } ,
{ "price" : 769.2 , "quantity" : 399 }
]
}
}
4. Historical Data (OHLCV) Get historical candlestick data:
response = client.history(
symbol="SBIN" ,
exchange="NSE" ,
interval="5m" ,
start_date="2025-01-01" ,
end_date="2025-01-15"
)
Response (Pandas DataFrame):
close high low open volume
timestamp
2025-01-01 09:15:00+05:30 772.50 774.00 763.20 766.50 318625
2025-01-01 09:20:00+05:30 773.20 774.95 772.10 772.45 197189
2025-01-01 09:25:00+05:30 775.15 775.60 772.60 773.20 227544
...
response = client.intervals()
5. Option Chain Get complete option chain for an underlying:
chain = client.optionchain(
underlying="NIFTY" ,
exchange="NSE_INDEX" ,
expiry_date="30JAN25" ,
strike_count=10
)
{
"status" : "success" ,
"underlying" : "NIFTY" ,
"underlying_ltp" : 26215.55 ,
"expiry_date" : "30JAN25" ,
"atm_strike" : 26200.0 ,
"chain" : [
{
"strike" : 26100.0 ,
"ce" : {
"symbol" : "NIFTY30JAN2526100CE" ,
"label" : "ITM2" ,
"ltp" : 490 ,
"bid" : 490 ,
"ask" : 491 ,
"volume" : 1195800 ,
"oi" : 5000000 ,
"lotsize" : 75
} ,
"pe" : {
"symbol" : "NIFTY30JAN2526100PE" ,
"label" : "OTM2" ,
"ltp" : 193 ,
"bid" : 191.2 ,
"ask" : 193 ,
"volume" : 1832700 ,
"oi" : 4500000 ,
"lotsize" : 75
}
} ,
...
]
}
6. Expiry Dates Get available expiry dates:
response = client.expiry(
symbol="NIFTY" ,
exchange="NFO" ,
instrumenttype="options"
)
Symbol Search & Discovery
Search Symbols response = client.search(query="NIFTY 26000 JAN CE" , exchange="NFO" )
{
"status" : "success" ,
"message" : "Found 7 matching symbols" ,
"data" : [
{
"symbol" : "NIFTY30JAN2526000CE" ,
"exchange" : "NFO" ,
"expiry" : "30-JAN-25" ,
"strike" : 26000 ,
"instrumenttype" : "CE" ,
"lotsize" : 75
} ,
...
]
}
Get Symbol Details response = client.symbol(symbol="NIFTY30JAN25FUT" , exchange="NFO" )
{
"status" : "success" ,
"data" : {
"symbol" : "NIFTY30JAN25FUT" ,
"exchange" : "NFO" ,
"name" : "NIFTY" ,
"expiry" : "30-JAN-25" ,
"instrumenttype" : "FUT" ,
"lotsize" : 75 ,
"freeze_qty" : 1800 ,
"tick_size" : 10
}
}
Get All Instruments Download complete instrument list for an exchange:
instruments = client.instruments(exchange="NSE" )
WebSocket Streaming
Connection Setup from openalgo import api
import time
client = api(
api_key='your_api_key' ,
host='http://127.0.0.1:5000' ,
ws_url='ws://127.0.0.1:8765' ,
verbose=True
)
client.connect()
Verbose Levels Level Value Description Silent False or 0Errors only (default) Basic True or 1Connection, auth, subscription logs Debug 2All market data updates
Stream LTP (Last Traded Price) instruments = [
{"exchange" : "NSE" , "symbol" : "RELIANCE" },
{"exchange" : "NSE" , "symbol" : "INFY" },
{"exchange" : "NSE_INDEX" , "symbol" : "NIFTY" }
]
def on_ltp (data ):
print (f"{data['symbol' ]} : {data['data' ]['ltp' ]} " )
client.subscribe_ltp(instruments, on_data_received=on_ltp)
time.sleep(60 )
client.unsubscribe_ltp(instruments)
client.disconnect()
Stream Quotes (OHLC + Bid/Ask) def on_quote (data ):
d = data['data' ]
print (f"{data['symbol' ]} : O={d['open' ]} H={d['high' ]} L={d['low' ]} LTP={d['ltp' ]} " )
client.subscribe_quote(instruments, on_data_received=on_quote)
Stream Market Depth def on_depth (data ):
d = data['data' ]
print (f"{data['symbol' ]} : Best Bid={d['bids' ][0 ]['price' ]} Best Ask={d['asks' ][0 ]['price' ]} " )
client.subscribe_depth(instruments, on_data_received=on_depth)
Get Cached Data Access latest cached data without callback:
ltp_data = client.get_ltp()
quote_data = client.get_quotes()
depth_data = client.get_depth()
nifty_ltp = ltp_data['ltp' ]['NSE_INDEX' ]['NIFTY' ]['ltp' ]
Market Information
Trading Holidays response = client.holidays(year=2025 )
{
"data" : [
{
"date" : "2025-01-26" ,
"description" : "Republic Day" ,
"holiday_type" : "TRADING_HOLIDAY" ,
"closed_exchanges" : [ "NSE" , "BSE" , "NFO" , "MCX" ]
} ,
...
]
}
Exchange Timings response = client.timings(date="2025-01-15" )
{
"data" : [
{ "exchange" : "NSE" , "start_time" : 1705293300000 , "end_time" : 1705315800000 } ,
{ "exchange" : "BSE" , "start_time" : 1705293300000 , "end_time" : 1705315800000 } ,
{ "exchange" : "MCX" , "start_time" : 1705293000000 , "end_time" : 1705346700000 }
]
}
Common Patterns
Build a Watchlist watchlist = [
{"symbol" : "NIFTY" , "exchange" : "NSE_INDEX" },
{"symbol" : "BANKNIFTY" , "exchange" : "NSE_INDEX" },
{"symbol" : "RELIANCE" , "exchange" : "NSE" },
{"symbol" : "HDFCBANK" , "exchange" : "NSE" },
{"symbol" : "INFY" , "exchange" : "NSE" }
]
quotes = client.multiquotes(symbols=watchlist)
for item in quotes.get('results' , []):
data = item.get('data' , {})
change = ((data['ltp' ] - data['prev_close' ]) / data['prev_close' ]) * 100
print (f"{item['symbol' ]} : {data['ltp' ]} ({change:+.2 f} %)" )
Fetch Intraday Data from datetime import date
today = date.today().strftime("%Y-%m-%d" )
intraday = client.history(
symbol="NIFTY" ,
exchange="NSE_INDEX" ,
interval="1m" ,
start_date=today,
end_date=today
)
print (f"Today's range: High={intraday['high' ].max ()} , Low={intraday['low' ].min ()} " )
Monitor Option Chain Changes import time
while True :
chain = client.optionchain(
underlying="NIFTY" ,
exchange="NSE_INDEX" ,
expiry_date="30JAN25" ,
strike_count=5
)
atm = chain.get('atm_strike' )
print (f"\nNIFTY ATM: {atm} , LTP: {chain.get('underlying_ltp' )} " )
for strike in chain.get('chain' , []):
if strike['strike' ] == atm:
ce = strike['ce' ]
pe = strike['pe' ]
print (f" CE: {ce['ltp' ]} (Vol: {ce['volume' ]} )" )
print (f" PE: {pe['ltp' ]} (Vol: {pe['volume' ]} )" )
time.sleep(5 )
Notes
Use WebSocket for real-time data (lower latency, no rate limits)
REST API is better for on-demand queries
Historical data returns Pandas DataFrame for easy analysis
Option chain includes OI, volume, bid/ask for all strikes
Use verbose=2 for debugging WebSocket issues
