OpenAlgo Python Library

A Python library for algorithmic trading using OpenAlgo's REST APIs and WebSocket feeds, with 100+ high-performance technical indicators powered by a Rust core.

• Python Library Docs: https://docs.openalgo.in/trading-platform/python
• Technical Indicators (100+): https://docs.openalgo.in/trading-platform/python/indicators
• WebSocket & Verbose Control: https://docs.openalgo.in/trading-platform/python/websockets-verbose-control
• API Reference: https://docs.openalgo.in/api-documentation/v1
• General Documentation: https://docs.openalgo.in
• Source: https://github.com/marketcalls/openalgo-python-library

What's New in 2.0.0

Version 2.0.0 replaces the old Numba/JIT indicator engine with a Rust core (via PyO3):

• No optional extra, no Numba. Indicators are compiled into the wheel. The legacy pip install openalgo[indicators] extra and the numba/llvmlite dependencies are removed; pip install openalgo is all you need.
• Python 3.12, 3.13 and 3.14 are all supported (abi3 wheels). Numba previously blocked newer Python/NumPy versions.
• New TA-Lib-compatible indicators: mom, rocp, rocr, rocr100, apo, midpoint, midprice, avgprice, medprice, typprice, wclprice, plus_dm, minus_dm, dx, adxr, stochf, linregangle, linregintercept.
• Performance: every indicator is O(n). Benchmarked head-to-head with TA-Lib on 924k bars, the regression/statistics family (linreg, tsf, stddev, cci, macd, ...) runs faster than TA-Lib; the rest are on par. See the performance comparison and TA-Lib compatibility notes.
• Backward compatible: the from openalgo import ta API is unchanged - existing code keeps working without modification.

Installation

To install the OpenAlgo Python library, use pip:

pip install openalgo

The 100+ technical indicators are built in (powered by a Rust core); no extra install step or optional dependency is required.

Get the OpenAlgo apikey

Make sure that your OpenAlgo Application is running. Login to OpenAlgo Application with valid credentials and get the OpenAlgo apikey.

For detailed function parameters refer to the API Documentation.

Getting Started with OpenAlgo

First, import the api class from the OpenAlgo library and initialize it with your API key:

from openalgo import api

# Replace 'your_api_key_here' with your actual API key
# Specify the host URL with your hosted domain or ngrok domain.
# If running locally in windows then use the default host value.
client = api(api_key='your_api_key_here', host='http://127.0.0.1:5000')

Check OpenAlgo Version

import openalgo
openalgo.__version__

Technical Indicators (100+)

OpenAlgo ships 100+ technical indicators powered by a Rust core (via PyO3) — including trend, momentum, volatility, volume, oscillators, statistics, and hybrid indicators. They are built in; no optional dependency or extra install step:

pip install openalgo

Quick example:

import numpy as np
from openalgo import ta

close = np.array([100, 101, 102, 103, 104, 105, 106, 107, 108, 109], dtype=float)
high  = close + 0.5
low   = close - 0.5

# Trend
sma   = ta.sma(close, period=5)
ema   = ta.ema(close, period=5)
supertrend, direction = ta.supertrend(high, low, close, period=7, multiplier=3.0)

# Momentum
rsi   = ta.rsi(close, period=14)
macd_line, signal_line, hist = ta.macd(close, fast=12, slow=26, signal=9)

# Volatility
atr   = ta.atr(high, low, close, period=14)
upper, middle, lower = ta.bbands(close, period=20, std=2.0)

Many indicators are value-compatible with TA-Lib; where OpenAlgo intentionally follows TradingView/Pine conventions instead (EMA/ATR/ADX seeding, etc.), the differences are documented in the TA-Lib compatibility notes.

Full indicator catalog and parameter reference: https://docs.openalgo.in/trading-platform/python/indicators

WebSocket Verbose Control

The streaming feed supports verbosity levels (0 silent, 1 connection/auth/subscription info, 2 full debug with every tick):

client = api(
    api_key="your_api_key",
    host="http://127.0.0.1:5000",
    ws_url="ws://127.0.0.1:8765",
    verbose=1,                 # 0 / 1 / True / 2
)

Details: https://docs.openalgo.in/trading-platform/python/websockets-verbose-control

Examples

Please refer to the documentation on order constants, and consult the API reference for details on optional parameters.

PlaceOrder example

To place a new market order:

response = client.placeorder(
    strategy="Python",
    symbol="NHPC",
    action="BUY",
    exchange="NSE",
    price_type="MARKET",
    product="MIS",
    quantity=1
)
print(response)

Place Market Order Response:

{"orderid": "250408000989443", "status": "success"}

To place a new limit order:

response = client.placeorder(
    strategy="Python",
    symbol="YESBANK",
    action="BUY",
    exchange="NSE",
    price_type="LIMIT",
    product="MIS",
    quantity="1",
    price="16",
    trigger_price="0",
    disclosed_quantity="0",
)
print(response)

Place Limit Order Response:

{"orderid": "250408001003813", "status": "success"}

PlaceSmartOrder Example

To place a smart order considering the current position size:

response = client.placesmartorder(
    strategy="Python",
    symbol="TATAMOTORS",
    action="SELL",
    exchange="NSE",
    price_type="MARKET",
    product="MIS",
    quantity=1,
    position_size=5
)
print(response)

Place Smart Market Order Response:

{"orderid": "250408000997543", "status": "success"}

OptionsOrder Example

To place an ATM options order:

response = client.optionsorder(
    strategy="python",
    underlying="NIFTY",
    exchange="NSE_INDEX",
    expiry_date="28OCT25",
    offset="ATM",
    option_type="CE",
    action="BUY",
    quantity=75,
    pricetype="MARKET",
    product="NRML",
    splitsize=0
)
print(response)

Place Options Order Response:

{
  "exchange": "NFO",
  "offset": "ATM",
  "option_type": "CE",
  "orderid": "25102800000006",
  "status": "success",
  "symbol": "NIFTY28OCT2525950CE",
  "underlying": "NIFTY28OCT25FUT",
  "underlying_ltp": 25966.05
}

To place an ITM options order:

response = client.optionsorder(
    strategy="python",
    underlying="NIFTY",
    exchange="NSE_INDEX",
    expiry_date="28OCT25",
    offset="ITM4",
    option_type="PE",
    action="BUY",
    quantity=75,
    pricetype="MARKET",
    product="NRML",
    splitsize=0
)
print(response)

Place Options Order Response:

{
  "exchange": "NFO",
  "offset": "ITM4",
  "option_type": "PE",
  "orderid": "25102800000007",
  "status": "success",
  "symbol": "NIFTY28OCT2526150PE",
  "underlying": "NIFTY28OCT25FUT",
  "underlying_ltp": 25966.05
}

To place an OTM options order:

response = client.optionsorder(
    strategy="python",
    underlying="NIFTY",
    exchange="NSE_INDEX",
    expiry_date="28OCT25",
    offset="OTM5",
    option_type="CE",
    action="BUY",
    quantity=75,
    pricetype="MARKET",
    product="NRML",
    splitsize=0
)
print(response)

Place Options Order Response:

{
  "exchange": "NFO",
  "mode": "analyze",
  "offset": "OTM5",
  "option_type": "CE",
  "orderid": "25102800000008",
  "status": "success",
  "symbol": "NIFTY28OCT2526200CE",
  "underlying": "NIFTY28OCT25FUT",
  "underlying_ltp": 25966.05
}

OptionsMultiOrder Example

To place an Iron Condor (same expiry):

response = client.optionsmultiorder(
    strategy="Iron Condor Test",
    underlying="NIFTY",
    exchange="NSE_INDEX",
    expiry_date="25NOV25",
    legs=[
        {"offset": "OTM6", "option_type": "CE", "action": "BUY", "quantity": 75},
        {"offset": "OTM6", "option_type": "PE", "action": "BUY", "quantity": 75},
        {"offset": "OTM4", "option_type": "CE", "action": "SELL", "quantity": 75},
        {"offset": "OTM4", "option_type": "PE", "action": "SELL", "quantity": 75}
    ]
)
print(response)

Place OptionsMultiOrder Response:

{
  "status": "success",
  "underlying": "NIFTY",
  "underlying_ltp": 26050.45,
  "results": [
    {
      "action": "BUY",
      "leg": 1,
      "mode": "analyze",
      "offset": "OTM6",
      "option_type": "CE",
      "orderid": "25111996859688",
      "status": "success",
      "symbol": "NIFTY25NOV2526350CE"
    },
    {
      "action": "BUY",
      "leg": 2,
      "mode": "analyze",
      "offset": "OTM6",
      "option_type": "PE",
      "orderid": "25111996042210",
      "status": "success",
      "symbol": "NIFTY25NOV2525750PE"
    },
    {
      "action": "SELL",
      "leg": 3,
      "mode": "analyze",
      "offset": "OTM4",
      "option_type": "CE",
      "orderid": "25111922189638",
      "status": "success",
      "symbol": "NIFTY25NOV2526250CE"
    },
    {
      "action": "SELL",
      "leg": 4,
      "mode": "analyze",
      "offset": "OTM4",
      "option_type": "PE",
      "orderid": "25111919252668",
      "status": "success",
      "symbol": "NIFTY25NOV2525850PE"
    }
  ]
}

To place a Diagonal Spread (different expiry):

response = client.optionsmultiorder(
    strategy="Diagonal Spread Test",
    underlying="NIFTY",
    exchange="NSE_INDEX",
    legs=[
        {"offset": "ITM2", "option_type": "CE", "action": "BUY", "quantity": 75, "expiry_date": "30DEC25"},
        {"offset": "OTM2", "option_type": "CE", "action": "SELL", "quantity": 75, "expiry_date": "25NOV25"}
    ]
)
print(response)

Place OptionsMultiOrder Response:

{
  "results": [
    {
      "action": "BUY",
      "leg": 1,
      "mode": "analyze",
      "offset": "ITM2",
      "option_type": "CE",
      "orderid": "25111933337854",
      "status": "success",
      "symbol": "NIFTY30DEC2525950CE"
    },
    {
      "action": "SELL",
      "leg": 2,
      "mode": "analyze",
      "offset": "OTM2",
      "option_type": "CE",
      "orderid": "25111957475473",
      "status": "success",
      "symbol": "NIFTY25NOV2526150CE"
    }
  ],
  "status": "success",
  "underlying": "NIFTY",
  "underlying_ltp": 26052.65
}

BasketOrder example

To place a new basket order:

basket_orders = [
    {
        "symbol": "BHEL",
        "exchange": "NSE",
        "action": "BUY",
        "quantity": 1,
        "pricetype": "MARKET",
        "product": "MIS"
    },
    {
        "symbol": "ZOMATO",
        "exchange": "NSE",
        "action": "SELL",
        "quantity": 1,
        "pricetype": "MARKET",
        "product": "MIS"
    }
]
response = client.basketorder(orders=basket_orders)
print(response)

Basket Order Response:

{
  "status": "success",
  "results": [
    {"symbol": "BHEL", "status": "success", "orderid": "250408000999544"},
    {"symbol": "ZOMATO", "status": "success", "orderid": "250408000997545"}
  ]
}

SplitOrder example

To place a new split order:

response = client.splitorder(
    symbol="YESBANK",
    exchange="NSE",
    action="SELL",
    quantity=105,
    splitsize=20,
    price_type="MARKET",
    product="MIS"
)
print(response)

SplitOrder Response:

{
  "status": "success",
  "split_size": 20,
  "total_quantity": 105,
  "results": [
    {"order_num": 1, "orderid": "250408001021467", "quantity": 20, "status": "success"},
    {"order_num": 2, "orderid": "250408001021459", "quantity": 20, "status": "success"},
    {"order_num": 3, "orderid": "250408001021466", "quantity": 20, "status": "success"},
    {"order_num": 4, "orderid": "250408001021470", "quantity": 20, "status": "success"},
    {"order_num": 5, "orderid": "250408001021471", "quantity": 20, "status": "success"},
    {"order_num": 6, "orderid": "250408001021472", "quantity": 5, "status": "success"}
  ]
}

ModifyOrder Example

To modify an existing order:

response = client.modifyorder(
    order_id="250408001002736",
    strategy="Python",
    symbol="YESBANK",
    action="BUY",
    exchange="NSE",
    price_type="LIMIT",
    product="CNC",
    quantity=1,
    price=16.5
)
print(response)

Modify Order Response:

{"orderid": "250408001002736", "status": "success"}

CancelOrder Example

To cancel an existing order:

response = client.cancelorder(
    order_id="250408001002736",
    strategy="Python"
)
print(response)

Cancelorder Response:

{"orderid": "250408001002736", "status": "success"}

CancelAllOrder Example

To cancel all open orders and trigger pending orders:

response = client.cancelallorder(strategy="Python")
print(response)

Cancelallorder Response:

{
  "status": "success",
  "message": "Canceled 5 orders. Failed to cancel 0 orders.",
  "canceled_orders": [
    "250408001042620",
    "250408001042667",
    "250408001042642",
    "250408001043015",
    "250408001043386"
  ],
  "failed_cancellations": []
}

ClosePosition Example

To close all open positions across various exchanges:

response = client.closeposition(strategy="Python")
print(response)

ClosePosition Response:

{"message": "All Open Positions Squared Off", "status": "success"}

OrderStatus Example

To get the current order status:

response = client.orderstatus(
    order_id="250828000185002",
    strategy="Test Strategy"
)
print(response)

Orderstatus Response:

{
  "data": {
    "action": "BUY",
    "average_price": 18.95,
    "exchange": "NSE",
    "order_status": "complete",
    "orderid": "250828000185002",
    "price": 0,
    "pricetype": "MARKET",
    "product": "MIS",
    "quantity": "1",
    "symbol": "YESBANK",
    "timestamp": "28-Aug-2025 09:59:10",
    "trigger_price": 0
  },
  "status": "success"
}

OpenPosition Example

To get the current open position:

response = client.openposition(
    strategy="Test Strategy",
    symbol="YESBANK",
    exchange="NSE",
    product="MIS"
)
print(response)

OpenPosition Response:

{"quantity": "-10", "status": "success"}

Quotes Example

response = client.quotes(symbol="RELIANCE", exchange="NSE")
print(response)

Quotes Response:

{
  "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
  }
}

MultiQuotes Example

response = client.multiquotes(symbols=[
    {"symbol": "RELIANCE", "exchange": "NSE"},
    {"symbol": "TCS", "exchange": "NSE"},
    {"symbol": "INFY", "exchange": "NSE"}
])
print(response)

MultiQuotes Response:

{
  "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": 0, "oi": 0, "volume": 14054299
      }
    },
    {
      "symbol": "TCS",
      "exchange": "NSE",
      "data": {
        "open": 3118.8, "high": 3178, "low": 3117, "ltp": 3162.9,
        "prev_close": 3119.2, "ask": 0, "bid": 3162.9, "oi": 0, "volume": 2508527
      }
    },
    {
      "symbol": "INFY",
      "exchange": "NSE",
      "data": {
        "open": 1532.1, "high": 1560.3, "low": 1532.1, "ltp": 1557.9,
        "prev_close": 1530.6, "ask": 0, "bid": 1557.9, "oi": 0, "volume": 7575038
      }
    }
  ]
}

Depth Example

response = client.depth(symbol="SBIN", exchange="NSE")
print(response)

Depth Response:

{
  "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}
    ]
  }
}

History Example

Download data directly from broker API:

response = client.history(
    symbol="SBIN",
    exchange="NSE",
    interval="5m",
    start_date="2025-04-01",
    end_date="2025-04-08",
    source="api"
)
print(response)

Download data from Historify DuckDB (stored data):

response = client.history(
    symbol="SBIN",
    exchange="NSE",
    interval="5m",
    start_date="2025-04-01",
    end_date="2025-04-08",
    source="db"
)
print(response)

History Response:

                            close    high     low    open  volume
timestamp
2025-04-01 09:15:00+05:30  772.50  774.00  763.20  766.50  318625
2025-04-01 09:20:00+05:30  773.20  774.95  772.10  772.45  197189
2025-04-01 09:25:00+05:30  775.15  775.60  772.60  773.20  227544
2025-04-01 09:30:00+05:30  777.35  777.50  774.85  775.15  134596
2025-04-01 09:35:00+05:30  778.00  778.00  776.25  777.50  145385
...                           ...     ...     ...     ...     ...
2025-04-08 14:00:00+05:30  768.25  770.70  767.85  768.50  142478
2025-04-08 14:05:00+05:30  769.10  769.80  766.60  768.15  128283
2025-04-08 14:10:00+05:30  769.05  769.85  768.40  769.10  119084
2025-04-08 14:15:00+05:30  770.05  770.50  769.05  769.05  158299
2025-04-08 14:20:00+05:30  769.95  770.50  769.40  770.05  125485

[437 rows x 5 columns]

Intervals Example

response = client.intervals()
print(response)

Intervals Response:

{
  "status": "success",
  "data": {
    "months": [],
    "weeks": [],
    "days": ["D"],
    "hours": ["1h"],
    "minutes": ["10m", "15m", "1m", "30m", "3m", "5m"],
    "seconds": []
  }
}

OptionChain Example

Note: To fetch the entire option chain for an expiry, omit the strike_count parameter.

chain = client.optionchain(
    underlying="NIFTY",
    exchange="NSE_INDEX",
    expiry_date="30DEC25",
    strike_count=10
)

OptionChain Response:

{
  "status": "success",
  "underlying": "NIFTY",
  "underlying_ltp": 26215.55,
  "expiry_date": "30DEC25",
  "atm_strike": 26200.0,
  "chain": [
    {
      "strike": 26100.0,
      "ce": {
        "symbol": "NIFTY30DEC2526100CE", "label": "ITM2",
        "ltp": 490, "bid": 490, "ask": 491,
        "open": 540, "high": 571, "low": 444.75,
        "prev_close": 496.8, "volume": 1195800, "oi": 0,
        "lotsize": 75, "tick_size": 0.05
      },
      "pe": {
        "symbol": "NIFTY30DEC2526100PE", "label": "OTM2",
        "ltp": 193, "bid": 191.2, "ask": 193,
        "open": 204.1, "high": 229.95, "low": 175.6,
        "prev_close": 215.95, "volume": 1832700, "oi": 0,
        "lotsize": 75, "tick_size": 0.05
      }
    },
    {
      "strike": 26200.0,
      "ce": {
        "symbol": "NIFTY30DEC2526200CE", "label": "ATM",
        "ltp": 427, "bid": 425.05, "ask": 427,
        "open": 449.95, "high": 503.5, "low": 384,
        "prev_close": 433.2, "volume": 2994000, "oi": 0,
        "lotsize": 75, "tick_size": 0.05
      },
      "pe": {
        "symbol": "NIFTY30DEC2526200PE", "label": "ATM",
        "ltp": 227.4, "bid": 227.35, "ask": 228.5,
        "open": 251.9, "high": 269.15, "low": 205.95,
        "prev_close": 251.9, "volume": 3745350, "oi": 0,
        "lotsize": 75, "tick_size": 0.05
      }
    }
  ]
}

Symbol Example

response = client.symbol(
    symbol="NIFTY30DEC25FUT",
    exchange="NFO"
)
print(response)

Symbol Response:

{
  "data": {
    "brexchange": "NSE_FO",
    "brsymbol": "NIFTY FUT 30 DEC 25",
    "exchange": "NFO",
    "expiry": "30-DEC-25",
    "freeze_qty": 1800,
    "id": 57900,
    "instrumenttype": "FUT",
    "lotsize": 75,
    "name": "NIFTY",
    "strike": 0,
    "symbol": "NIFTY30DEC25FUT",
    "tick_size": 10,
    "token": "NSE_FO|49543"
  },
  "status": "success"
}

Search Example

response = client.search(query="NIFTY 26000 DEC CE", exchange="NFO")
print(response)

Search Response:

{
  "data": [
    {
      "brexchange": "NSE_FO",
      "brsymbol": "NIFTY 26000 CE 30 DEC 25",
      "exchange": "NFO",
      "expiry": "30-DEC-25",
      "freeze_qty": 1800,
      "instrumenttype": "CE",
      "lotsize": 75,
      "name": "NIFTY",
      "strike": 26000,
      "symbol": "NIFTY30DEC2526000CE",
      "tick_size": 5,
      "token": "NSE_FO|71399"
    }
  ],
  "message": "Found 7 matching symbols",
  "status": "success"
}

OptionSymbol Example

ATM Option:

response = client.optionsymbol(
    underlying="NIFTY",
    exchange="NSE_INDEX",
    expiry_date="30DEC25",
    offset="ATM",
    option_type="CE"
)
print(response)

OptionSymbol Response:

{
  "status": "success",
  "symbol": "NIFTY30DEC2525950CE",
  "exchange": "NFO",
  "lotsize": 75,
  "tick_size": 5,
  "freeze_qty": 1800,
  "underlying_ltp": 25966.4
}

ITM Option:

response = client.optionsymbol(
    underlying="NIFTY",
    exchange="NSE_INDEX",
    expiry_date="30DEC25",
    offset="ITM3",
    option_type="PE"
)
print(response)

OptionSymbol Response:

{
  "status": "success",
  "symbol": "NIFTY30DEC2526100PE",
  "exchange": "NFO",
  "lotsize": 75,
  "tick_size": 5,
  "freeze_qty": 1800,
  "underlying_ltp": 25966.4
}

OTM Option:

response = client.optionsymbol(
    underlying="NIFTY",
    exchange="NSE_INDEX",
    expiry_date="30DEC25",
    offset="OTM4",
    option_type="CE"
)
print(response)

OptionSymbol Response:

{
  "status": "success",
  "symbol": "NIFTY30DEC2526150CE",
  "exchange": "NFO",
  "lotsize": 75,
  "tick_size": 5,
  "freeze_qty": 1800,
  "underlying_ltp": 25966.4
}

SyntheticFuture Example

response = client.syntheticfuture(
    underlying="NIFTY",
    exchange="NSE_INDEX",
    expiry_date="25NOV25"
)
print(response)

SyntheticFuture Response:

{
  "atm_strike": 25900.0,
  "expiry": "25NOV25",
  "status": "success",
  "synthetic_future_price": 25980.05,
  "underlying": "NIFTY",
  "underlying_ltp": 25910.05
}

OptionGreeks Example

response = client.optiongreeks(
    symbol="NIFTY25NOV2526000CE",
    exchange="NFO",
    interest_rate=0.00,
    underlying_symbol="NIFTY",
    underlying_exchange="NSE_INDEX"
)
print(response)

OptionGreeks Response:

{
  "days_to_expiry": 28.5071,
  "exchange": "NFO",
  "expiry_date": "25-Nov-2025",
  "greeks": {
    "delta": 0.4967,
    "gamma": 0.000352,
    "rho": 9.733994,
    "theta": -7.919,
    "vega": 28.9489
  },
  "implied_volatility": 15.6,
  "interest_rate": 0.0,
  "option_price": 435,
  "option_type": "CE",
  "spot_price": 25966.05,
  "status": "success",
  "strike": 26000.0,
  "symbol": "NIFTY25NOV2526000CE",
  "underlying": "NIFTY"
}

Expiry Example

response = client.expiry(
    symbol="NIFTY",
    exchange="NFO",
    instrumenttype="options"
)
print(response)

Expiry Response:

{
  "data": [
    "10-JUL-25", "17-JUL-25", "24-JUL-25", "31-JUL-25",
    "07-AUG-25", "28-AUG-25", "25-SEP-25", "24-DEC-25",
    "26-MAR-26", "25-JUN-26", "31-DEC-26", "24-JUN-27",
    "30-DEC-27", "29-JUN-28", "28-DEC-28", "28-JUN-29",
    "27-DEC-29", "25-JUN-30"
  ],
  "message": "Found 18 expiry dates for NIFTY options in NFO",
  "status": "success"
}

Instruments Example

response = client.instruments(exchange="NSE")
print(response.tail())

Instruments Response:

     brexchange           brsymbol exchange expiry instrumenttype  lotsize  \
3041        NSE      NSE:NEOGEN-EQ      NSE   None             EQ        1
3042        NSE     NSE:ALANKIT-EQ      NSE   None             EQ        1
3043        NSE  NSE:EVERESTIND-EQ      NSE   None             EQ        1
3044        NSE   NSE:VIKASLIFE-EQ      NSE   None             EQ        1
3045        NSE    NSE:ONEPOINT-EQ      NSE   None             EQ        1

                          name  strike      symbol  tick_size           token
3041  NEOGEN CHEMICALS LIMITED    -1.0      NEOGEN       0.10  10100000009917
3042           ALANKIT LIMITED    -1.0     ALANKIT       0.01  10100000009921
3043    EVEREST INDUSTRIES LTD    -1.0  EVERESTIND       0.05   1010000000993
3044    VIKAS LIFECARE LIMITED    -1.0   VIKASLIFE       0.01  10100000009931
3045     ONE POINT ONE SOL LTD    -1.0    ONEPOINT       0.01  10100000009939

Telegram Alert Example

response = client.telegram(
    username="<openalgo_loginid>",
    message="NIFTY crossed 26000!"
)
print(response)

Telegram Alert Response:

{
  "message": "Notification sent successfully",
  "status": "success"
}

Funds Example

response = client.funds()
print(response)

Funds Response:

{
  "status": "success",
  "data": {
    "availablecash": "320.66",
    "collateral": "0.00",
    "m2mrealized": "3.27",
    "m2munrealized": "-7.88",
    "utiliseddebits": "679.34"
  }
}

Margin Example

response = client.margin(positions=[
    {
        "symbol": "NIFTY25NOV2525000CE",
        "exchange": "NFO",
        "action": "BUY",
        "product": "NRML",
        "pricetype": "MARKET",
        "quantity": "75"
    },
    {
        "symbol": "NIFTY25NOV2525500CE",
        "exchange": "NFO",
        "action": "SELL",
        "product": "NRML",
        "pricetype": "MARKET",
        "quantity": "75"
    }
])

Margin Response:

{
  "status": "success",
  "data": {
    "total_margin_required": 91555.7625,
    "span_margin": 0.0,
    "exposure_margin": 91555.7625
  }
}

OrderBook Example

response = client.orderbook()
print(response)

OrderBook Response:

{
  "status": "success",
  "data": {
    "orders": [
      {
        "action": "BUY",
        "symbol": "RELIANCE",
        "exchange": "NSE",
        "orderid": "250408000989443",
        "product": "MIS",
        "quantity": "1",
        "price": 1186.0,
        "pricetype": "MARKET",
        "order_status": "complete",
        "trigger_price": 0.0,
        "timestamp": "08-Apr-2025 13:58:03"
      },
      {
        "action": "BUY",
        "symbol": "YESBANK",
        "exchange": "NSE",
        "orderid": "250408001002736",
        "product": "MIS",
        "quantity": "1",
        "price": 16.5,
        "pricetype": "LIMIT",
        "order_status": "cancelled",
        "trigger_price": 0.0,
        "timestamp": "08-Apr-2025 14:13:45"
      }
    ],
    "statistics": {
      "total_buy_orders": 2.0,
      "total_sell_orders": 0.0,
      "total_completed_orders": 1.0,
      "total_open_orders": 0.0,
      "total_rejected_orders": 0.0
    }
  }
}

TradeBook Example

response = client.tradebook()
print(response)

TradeBook Response:

{
  "status": "success",
  "data": [
    {
      "action": "BUY",
      "symbol": "RELIANCE",
      "exchange": "NSE",
      "orderid": "250408000989443",
      "product": "MIS",
      "quantity": 0.0,
      "average_price": 1180.1,
      "timestamp": "13:58:03",
      "trade_value": 1180.1
    },
    {
      "action": "SELL",
      "symbol": "NHPC",
      "exchange": "NSE",
      "orderid": "250408001086129",
      "product": "MIS",
      "quantity": 0.0,
      "average_price": 83.74,
      "timestamp": "14:28:49",
      "trade_value": 83.74
    }
  ]
}

PositionBook Example

response = client.positionbook()
print(response)

PositionBook Response:

{
  "status": "success",
  "data": [
    {
      "symbol": "NHPC",
      "exchange": "NSE",
      "product": "MIS",
      "quantity": "-1",
      "average_price": "83.74",
      "ltp": "83.72",
      "pnl": "0.02"
    },
    {
      "symbol": "RELIANCE",
      "exchange": "NSE",
      "product": "MIS",
      "quantity": "0",
      "average_price": "0.0",
      "ltp": "1189.9",
      "pnl": "5.90"
    },
    {
      "symbol": "YESBANK",
      "exchange": "NSE",
      "product": "MIS",
      "quantity": "-104",
      "average_price": "17.2",
      "ltp": "17.31",
      "pnl": "-10.44"
    }
  ]
}

Holdings Example

response = client.holdings()
print(response)

Holdings Response:

{
  "status": "success",
  "data": {
    "holdings": [
      {"symbol": "RELIANCE",  "exchange": "NSE", "product": "CNC", "quantity": 1, "pnl": -149.0, "pnlpercent": -11.10},
      {"symbol": "TATASTEEL", "exchange": "NSE", "product": "CNC", "quantity": 1, "pnl": -15.0,  "pnlpercent": -10.41},
      {"symbol": "CANBK",     "exchange": "NSE", "product": "CNC", "quantity": 5, "pnl": -69.0,  "pnlpercent": -13.43}
    ],
    "statistics": {
      "totalholdingvalue": 1768.0,
      "totalinvvalue": 2001.0,
      "totalprofitandloss": -233.15,
      "totalpnlpercentage": -11.65
    }
  }
}

Holidays Example

response = client.holidays(year=2026)
print(response)

Holidays Response:

{
  "data": [
    {
      "closed_exchanges": ["NSE", "BSE", "NFO", "BFO", "CDS", "BCD", "MCX"],
      "date": "2026-01-26",
      "description": "Republic Day",
      "holiday_type": "TRADING_HOLIDAY",
      "open_exchanges": []
    },
    {
      "closed_exchanges": [],
      "date": "2026-02-19",
      "description": "Chhatrapati Shivaji Maharaj Jayanti",
      "holiday_type": "SETTLEMENT_HOLIDAY",
      "open_exchanges": []
    },
    {
      "closed_exchanges": ["NSE", "BSE", "NFO", "BFO", "CDS", "BCD"],
      "date": "2026-03-10",
      "description": "Holi",
      "holiday_type": "TRADING_HOLIDAY",
      "open_exchanges": [
        {"end_time": 1741677900000, "exchange": "MCX", "start_time": 1741624200000}
      ]
    }
  ]
}

Timings Example

response = client.timings(date="2025-12-19")
print(response)

Timings Response:

{
  "data": [
    {"end_time": 1766138400000, "exchange": "NSE", "start_time": 1766115900000},
    {"end_time": 1766138400000, "exchange": "BSE", "start_time": 1766115900000},
    {"end_time": 1766138400000, "exchange": "NFO", "start_time": 1766115900000},
    {"end_time": 1766138400000, "exchange": "BFO", "start_time": 1766115900000},
    {"end_time": 1766168700000, "exchange": "MCX", "start_time": 1766115000000},
    {"end_time": 1766143800000, "exchange": "BCD", "start_time": 1766115000000},
    {"end_time": 1766143800000, "exchange": "CDS", "start_time": 1766115000000}
  ],
  "status": "success"
}

Analyzer Status Example

response = client.analyzerstatus()
print(response)

Analyzer Status Response:

{
  "data": {"analyze_mode": true, "mode": "analyze", "total_logs": 2},
  "status": "success"
}

Analyzer Toggle Example

# Switch to analyze mode (simulated responses)
response = client.analyzertoggle(mode=True)
print(response)

Analyzer Toggle Response:

{
  "data": {
    "analyze_mode": true,
    "message": "Analyzer mode switched to analyze",
    "mode": "analyze",
    "total_logs": 2
  },
  "status": "success"
}

LTP Data (Streaming WebSocket)

from openalgo import api
import time

# Initialize OpenAlgo client
client = api(
    api_key="your_api_key",                  # Replace with your actual OpenAlgo API key
    host="http://127.0.0.1:5000",            # REST API host
    ws_url="ws://127.0.0.1:8765"             # WebSocket host
)

# Define instruments to subscribe for LTP
instruments = [
    {"exchange": "NSE", "symbol": "RELIANCE"},
    {"exchange": "NSE", "symbol": "INFY"}
]

# Callback function for LTP updates
def on_ltp(data):
    print("LTP Update Received:")
    print(data)

# Connect and subscribe
client.connect()
client.subscribe_ltp(instruments, on_data_received=on_ltp)

# Run for a few seconds to receive data
try:
    time.sleep(10)
finally:
    client.unsubscribe_ltp(instruments)
    client.disconnect()

Quotes (Streaming WebSocket)

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"
)

instruments = [
    {"exchange": "NSE", "symbol": "RELIANCE"},
    {"exchange": "NSE", "symbol": "INFY"}
]

def on_quote(data):
    print("Quote Update Received:")
    print(data)

client.connect()
client.subscribe_quote(instruments, on_data_received=on_quote)

try:
    time.sleep(10)
finally:
    client.unsubscribe_quote(instruments)
    client.disconnect()

Depth (Streaming WebSocket)

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"
)

instruments = [
    {"exchange": "NSE", "symbol": "RELIANCE"},
    {"exchange": "NSE", "symbol": "INFY"}
]

def on_depth(data):
    print("Market Depth Update Received:")
    print(data)

client.connect()
client.subscribe_depth(instruments, on_data_received=on_depth)

try:
    time.sleep(10)
finally:
    client.unsubscribe_depth(instruments)
    client.disconnect()

More Examples

The examples/ directory in the source repository contains runnable scripts:

• account_test.py — account-related functions
• margin_example.py — margin calculation for single and multiple positions
• order_test.py — order management
• data_examples.py — market data
• feed_examples.py — WebSocket LTP feeds
• quote_example.py — WebSocket quote feeds
• depth_example.py — WebSocket market depth feeds
• options_examples.py — Options API (Greeks, symbol resolution, orders)
• telegram_examples.py — Telegram notification API

License

MIT — see the LICENSE file for details.