TradingView Charts
This guide explains how to implement TradingView charts in OpenBB using TradingView's Universal Data Feed (UDF) protocol. The UDF protocol allows you to create custom data feeds for TradingView charts.
Prerequisites
- FastAPI or similar web framework for implementing the UDF endpoints
- Data source for market data (OHLCV)
Required UDF Endpoints
To implement TradingView charts, you need to create the following UDF endpoints:
1. Configuration Endpoint (/udf/config)
@app.get("/udf/config")
async def get_config():
"""UDF configuration endpoint"""
return {
"supported_resolutions": ["1", "5", "15", "30", "60", "D", "W", "M"],
"supports_group_request": False,
"supports_marks": False,
"supports_search": True,
"supports_timescale_marks": False,
"supports_time": True,
"exchanges": [
{"value": "", "name": "All Exchanges", "desc": ""},
{"value": "NASDAQ", "name": "NASDAQ", "desc": "NASDAQ Stock Exchange"}
],
"symbols_types": [
{"name": "All types", "value": ""},
{"name": "Stocks", "value": "stock"}
]
}
2. Symbol Search Endpoint (/udf/search)
@app.get("/udf/search")
async def search_symbols(
query: str = Query("", description="Search query"),
limit: int = Query(30, description="Limit of results")
):
"""UDF symbol search endpoint"""
results = []
# Implement your symbol search logic here
return results
3. Symbol Info Endpoint (/udf/symbols)
@app.get("/udf/symbols")
async def get_symbol_info(symbol: str = Query(..., description="Symbol to get info for")):
"""UDF symbol info endpoint"""
return {
"name": symbol,
"description": "Symbol Description",
"type": "stock",
"exchange": "NASDAQ",
"pricescale": 100,
"minmov": 1,
"volume_precision": 0,
"has_volume": True,
"has_intraday": True,
"has_daily": True,
"has_weekly_and_monthly": True,
"supported_resolutions": ["1", "5", "15", "30", "60", "D", "W", "M"],
"session-regular": "0930-1600",
"timezone": "America/New_York"
}
4. Historical Data Endpoint (/udf/history)
@app.get("/udf/history")
async def get_history(
symbol: str = Query(..., description="Symbol"),
resolution: str = Query(..., description="Resolution"),
from_time: int = Query(..., alias="from", description="From timestamp"),
to_time: int = Query(..., alias="to", description="To timestamp")
):
"""UDF historical data endpoint"""
# Implement your data fetching logic here
return {
"s": "ok",
"t": [timestamp1, timestamp2, ...], # Time array
"o": [open1, open2, ...], # Open prices array
"h": [high1, high2, ...], # High prices array
"l": [low1, low2, ...], # Low prices array
"c": [close1, close2, ...], # Close prices array
"v": [volume1, volume2, ...] # Volume array
}
5. Server Time Endpoint (/udf/time)
@app.get("/udf/time")
async def get_server_time():
"""UDF server time endpoint"""
return int(datetime.now().timestamp())
Widget Registration
To register the TradingView chart widget in OpenBB:
@register_widget({
"name": "TradingView Chart",
"description": "Advanced charting with TradingView",
"category": "Finance",
"type": "advanced_charting",
"endpoint": "/udf",
"gridData": {
"w": 20,
"h": 20
},
"data": {
"defaultSymbol": "AAPL",
"updateFrequency": 60000,
"chartConfig": {
"upColor": "#26a69a",
"downColor": "#ef5350",
"borderUpColor": "#26a69a",
"borderDownColor": "#ef5350",
"wickUpColor": "#26a69a",
"wickDownColor": "#ef5350",
"volumeUpColor": "#26a69a",
"volumeDownColor": "#ef5350",
"showVolume": True
}
}
})
def tradingview_chart():
"""Dummy function for TradingView chart widget registration"""
pass
Data Format Requirements
The historical data endpoint must return data in the following format:
{
"s": "ok", # Status: ok
"t": [timestamp1, timestamp2, ...], # Time array (Unix timestamps)
"o": [open1, open2, ...], # Open prices array
"h": [high1, high2, ...], # High prices array
"l": [low1, low2, ...], # Low prices array
"c": [close1, close2, ...], # Close prices array
"v": [volume1, volume2, ...] # Volume array
}
Best Practices
- Error Handling: Implement proper error handling for all endpoints
- Data Validation: Validate all input parameters
- Caching: Implement caching for frequently accessed data
- Rate Limiting: Add rate limiting to prevent abuse
- Security: Implement proper authentication and authorization
- Performance: Optimize data fetching and processing
- Documentation: Document all endpoints and their parameters
Example Implementation
Here's a complete example of implementing the historical data endpoint with mock data:
def generate_mock_price_data(symbol: str, from_time: int, to_time: int, resolution: str) -> dict:
"""Generate mock OHLCV data for a symbol"""
resolution_minutes = {
"1": 1, "5": 5, "15": 15, "30": 30, "60": 60,
"D": 1440, "W": 10080, "M": 43200
}.get(resolution, 60)
current_time = from_time
timestamps = []
while current_time <= to_time:
timestamps.append(current_time)
current_time += resolution_minutes * 60
base_price = 100.0
prices = []
current_price = base_price
for _ in timestamps:
change = random.uniform(-2, 2)
current_price += change
current_price = max(current_price, 1.0)
prices.append(current_price)
opens = []
highs = []
lows = []
closes = []
volumes = []
for price in prices:
is_bullish = random.random() > 0.5
if is_bullish:
open_price = price * 0.99
close_price = price * 1.01
else:
open_price = price * 1.01
close_price = price * 0.99
high_price = max(open_price, close_price) * 1.02
low_price = min(open_price, close_price) * 0.98
opens.append(open_price)
highs.append(high_price)
lows.append(low_price)
closes.append(close_price)
volume = int(1000000 * (1 + random.uniform(-0.2, 0.2)))
volumes.append(volume)
return {
"s": "ok",
"t": timestamps,
"o": opens,
"h": highs,
"l": lows,
"c": closes,
"v": volumes
}