openalgo 1.0.13

A Python library for interacting with OpenAlgo's trading APIs

OpenAlgo Python Library

A Python library for algorithmic trading using OpenAlgo's REST APIs. This library provides a comprehensive interface for order management, market data, account operations, and strategy automation.

Installation

pip install openalgo

Quick Start

from openalgo import api

# Initialize the client
client = api(
    api_key="your_api_key",
    host="http://127.0.0.1:5000"  # or your OpenAlgo server URL
)

API Categories

1. Strategy API

Strategy Management Module

OpenAlgo's Strategy Management Module allows you to automate your trading strategies using webhooks. This enables seamless integration with any platform or custom system that can send HTTP requests. The Strategy class provides a simple interface to send signals that trigger orders based on your strategy configuration in OpenAlgo.

from openalgo import Strategy
import requests

# Initialize strategy client
client = Strategy(
    host_url="http://127.0.0.1:5000",  # Your OpenAlgo server URL
    webhook_id="your-webhook-id"        # Get this from OpenAlgo strategy section
)

try:
    # Long entry (BOTH mode with position size)
    response = client.strategyorder("RELIANCE", "BUY", 1)
    print(f"Long entry successful: {response}")

    # Short entry
    response = client.strategyorder("ZOMATO", "SELL", 1)
    print(f"Short entry successful: {response}")

    # Close positions
    response = client.strategyorder("RELIANCE", "SELL", 0)  # Close long
    response = client.strategyorder("ZOMATO", "BUY", 0)     # Close short

except requests.exceptions.RequestException as e:
    print(f"Error sending order: {e}")

Strategy Modes:

• LONG_ONLY: Only processes BUY signals for long-only strategies
• SHORT_ONLY: Only processes SELL signals for short-only strategies
• BOTH: Processes both BUY and SELL signals with position sizing

The Strategy Management Module can be integrated with:

• Custom trading systems
• Technical analysis platforms
• Alert systems
• Automated trading bots
• Any system capable of making HTTP requests

2. Accounts API

Funds

Get funds and margin details of the trading account.

result = client.funds()
# Returns:
{
    "data": {
        "availablecash": "18083.01",
        "collateral": "0.00",
        "m2mrealized": "0.00",
        "m2munrealized": "0.00",
        "utiliseddebits": "0.00"
    },
    "status": "success"
}

Orderbook

Get orderbook details with statistics.

result = client.orderbook()
# Returns order details and statistics including:
# - Total buy/sell orders
# - Total completed/open/rejected orders
# - Individual order details with status

Tradebook

Get execution details of trades.

result = client.tradebook()
# Returns list of executed trades with:
# - Symbol, action, quantity
# - Average price, trade value
# - Timestamp, order ID

Positionbook

Get current positions across all segments.

result = client.positionbook()
# Returns list of positions with:
# - Symbol, exchange, product
# - Quantity, average price

Holdings

Get stock holdings with P&L details.

result = client.holdings()
# Returns:
# - List of holdings with quantity and P&L
# - Statistics including total holding value
# - Total investment value and P&L

3. Orders API

Place Order

Place a regular order.

result = client.placeorder(
    symbol="RELIANCE",
    exchange="NSE",
    action="BUY",
    quantity=1,
    price_type="MARKET",
    product="MIS"
)

Place Smart Order

Place an order with position sizing.

result = client.placesmartorder(
    symbol="RELIANCE",
    exchange="NSE",
    action="BUY",
    quantity=1,
    position_size=100,
    price_type="MARKET",
    product="MIS"
)

Basket Order

Place multiple orders simultaneously.

orders = [
    {
        "symbol": "RELIANCE",
        "exchange": "NSE",
        "action": "BUY",
        "quantity": 1,
        "pricetype": "MARKET",
        "product": "MIS"
    },
    {
        "symbol": "INFY",
        "exchange": "NSE",
        "action": "SELL",
        "quantity": 1,
        "pricetype": "MARKET",
        "product": "MIS"
    }
]
result = client.basketorder(orders=orders)

Split Order

Split a large order into smaller ones.

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

Order Status

Check status of a specific order.

result = client.orderstatus(
    order_id="24120900146469",
    strategy="Test Strategy"
)

Open Position

Get current open position for a symbol.

result = client.openposition(
    symbol="YESBANK",
    exchange="NSE",
    product="CNC"
)

Modify Order

Modify an existing order.

result = client.modifyorder(
    order_id="24120900146469",
    symbol="RELIANCE",
    action="BUY",
    exchange="NSE",
    quantity=2,
    price="2100",
    product="MIS",
    price_type="LIMIT"
)

Cancel Order

Cancel a specific order.

result = client.cancelorder(
    order_id="24120900146469"
)

Cancel All Orders

Cancel all open orders.

result = client.cancelallorder()

Close Position

Close all open positions.

result = client.closeposition()

4. WebSocket Feed API

The WebSocket Feed API provides real-time market data through WebSocket connections. The API supports three types of market data:

LTP (Last Traded Price) Feed

Get real-time LTP updates for multiple instruments:

from openalgo import api
import time

# Initialize the client with explicit WebSocket URL
client = api(
    api_key="your_api_key",
    host="http://127.0.0.1:5000",  # REST API host
    ws_url="ws://127.0.0.1:8765"   # WebSocket server URL (can be different from REST API)
)

# Define instruments to subscribe to
instruments = [
    {"exchange": "MCX", "symbol": "GOLDPETAL30MAY25FUT"},
    {"exchange": "MCX", "symbol": "GOLD05JUN25FUT"}
]

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

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

# Poll LTP data
print(client.get_ltp())
# Returns nested format:
# {"ltp": {"MCX": {"GOLDPETAL30MAY25FUT": {"timestamp": 1747761583959, "ltp": 9529.0}}}}

# Cleanup
client.unsubscribe_ltp(instruments)
client.disconnect()

Quote Feed

Get real-time quote updates with OHLC data:

from openalgo import api

# Initialize the client
client = api(
    api_key="your_api_key",
    host="http://127.0.0.1:5000",
    ws_url="ws://127.0.0.1:8765"
)

# Define instruments
instruments = [
    {"exchange": "MCX", "symbol": "GOLDPETAL30MAY25FUT"}
]

# Connect and subscribe
client.connect()
client.subscribe_quote(instruments)

# Poll quote data
print(client.get_quotes())
# Returns nested format:
# {"quote": {"MCX": {"GOLDPETAL30MAY25FUT": {
#   "timestamp": 1747767126517,
#   "open": 9430.0,
#   "high": 9544.0,
#   "low": 9390.0,
#   "close": 9437.0,
#   "ltp": 9535.0
# }}}}

# Cleanup
client.unsubscribe_quote(instruments)
client.disconnect()

Market Depth Feed

Get real-time market depth (order book) data:

from openalgo import api

# Initialize the client
client = api(
    api_key="your_api_key",
    host="http://127.0.0.1:5000",
    ws_url="ws://127.0.0.1:8765"
)

# Define instruments
instruments = [
    {"exchange": "MCX", "symbol": "GOLDPETAL30MAY25FUT"}
]

# Connect and subscribe
client.connect()
client.subscribe_depth(instruments)

# Poll depth data
print(client.get_depth())
# Returns nested format with order book:
# {"depth": {"MCX": {"GOLDPETAL30MAY25FUT": {
#   "timestamp": 1747767126517,
#   "ltp": 9535.0,
#   "buyBook": {"1": {"price": "9533.0", "qty": "53332", "orders": "0"}, ...},
#   "sellBook": {"1": {"price": "9535.0", "qty": "53332", "orders": "0"}, ...}
# }}}}

# Cleanup
client.unsubscribe_depth(instruments)
client.disconnect()

5. REST Data API

Quotes

Get real-time quotes for a symbol using REST API.

result = client.quotes(
    symbol="RELIANCE",
    exchange="NSE"
)
# Returns bid/ask, LTP, volume and other quote data

Market Depth

Get market depth (order book) data.

result = client.depth(
    symbol="RELIANCE",
    exchange="NSE"
)
# Returns market depth with top 5 bids/asks

Historical Data

Get historical price data.

result = client.history(
    symbol="RELIANCE",
    exchange="NSE",
    interval="5m",  # Use intervals() to get supported intervals
    start_date="2024-01-01",
    end_date="2024-01-31"
)
# Returns pandas DataFrame with OHLC data

Intervals

Get supported time intervals for historical data.

result = client.intervals()
# Returns:
{
    "status": "success",
    "data": {
        "seconds": ["1s"],
        "minutes": ["1m", "2m", "3m", "5m", "10m", "15m", "30m", "60m"],
        "hours": [],
        "days": ["D"],
        "weeks": [],
        "months": []
    }
}

Note: The legacy interval() method is still available but will be deprecated in future versions.

Symbol

Get details for a specific trading symbol.

result = client.symbol(
    symbol="NIFTY24APR25FUT",
    exchange="NFO"
)
# Returns:
{
    "status": "success",
    "data": {
        "brexchange": "NFO",
        "brsymbol": "NIFTY24APR25FUT",
        "exchange": "NFO",
        "expiry": "24-APR-25",
        "id": 39521,
        "instrumenttype": "FUTIDX",
        "lotsize": 75,
        "name": "NIFTY",
        "strike": -0.01,
        "symbol": "NIFTY24APR25FUT",
        "tick_size": 0.05,
        "token": "54452"
    }
}

Examples

Check the examples directory for detailed usage:

• account_test.py: Test account-related functions
• order_test.py: Test order management functions
• data_examples.py: Test market data functions
• feed_examples.py: Test WebSocket LTP feeds
• quote_example.py: Test WebSocket quote feeds
• depth_example.py: Test WebSocket market depth feeds

Publishing to PyPI

1. Update version in openalgo/__init__.py

2. Build the distribution:

python -m pip install --upgrade build
python -m build

3. Upload to PyPI:

python -m pip install --upgrade twine
python -m twine upload dist/*

License

This project is licensed under the MIT License - see the LICENSE file for details.