Building an Open-Source Financial Data Platform with OpenBB: A Complete Guide to Replacing Commercial APIs

# Install venv module (requires sudo)
sudo apt install python3.13-venv

# Create virtual environment
python3 -m venv ~/.openclaw/openbb-env

# Activate virtual environment
source ~/.openclaw/openbb-env/bin/activate

# Upgrade pip
pip install --upgrade pip

# Install OpenBB with all extensions
pip install "openbb[all]"

python3 -c "from openbb import obb; print('✅ OpenBB installed successfully')"

vim ~/.openclaw/openbb-env/bin/activate

# OpenBB API Keys
export FRED_API_KEY="your_fred_key_here"
export AV_API_KEY="your_alpha_vantage_key_here"

#!/usr/bin/env python3
import sys
sys.path.insert(0, '/path/to/your/openbb-env/lib/python3.13/site-packages')

from openbb import obb

# Get Apple stock historical data
output = obb.equity.price.historical('AAPL', provider='yfinance', limit=30)
df = output.to_dataframe()

# View latest data
latest = df.iloc[-1]
print(f"Current Price: ${latest['close']:.2f}")
print(f"Volume: {int(latest['volume']):,}")

from openbb import obb

# Get Bitcoin data
output = obb.crypto.price.historical('BTC-USD', provider='yfinance', limit=30)
df = output.to_dataframe()

latest = df.iloc[-1]
print(f"BTC Current Price: ${latest['close']:,.2f}")

from openbb import obb

# Get UK GDP
try:
    output = obb.economy.gdp(country='united_kingdom', provider='oecd')
    df = output.to_dataframe()
    print(df.tail(5))
except Exception as e:
    print(f"GDP data fetch failed: {e}")

# Get unemployment rate
try:
    output = obb.economy.unemployment(country='united_kingdom')
    df = output.to_dataframe()
    print(df.tail(5))
except Exception as e:
    print(f"Unemployment data fetch failed: {e}")

#!/usr/bin/env python3
"""OpenBB Stock Analysis Script - Replacement for TwelveData"""

import sys
sys.path.insert(0, '/path/to/your/openbb-env/lib/python3.13/site-packages')

import os
from datetime import datetime, timedelta
from openbb import obb

# Stock list
STOCKS = ['MSFT', 'AAPL', 'GOOGL']

def calculate_rsi(prices, period=14):
    """Calculate RSI indicator"""
    if len(prices) < period + 1:
        return 50
    
    deltas = [prices[i] - prices[i-1] for i in range(1, len(prices))]
    gains = [d if d > 0 else 0 for d in deltas[-period:]]
    losses = [-d if d < 0 else 0 for d in deltas[-period:]]
    
    avg_gain = sum(gains) / period
    avg_loss = sum(losses) / period
    
    if avg_loss == 0:
        return 100
    
    rs = avg_gain / avg_loss
    rsi = 100 - (100 / (1 + rs))
    return rsi

def calculate_ma(prices, period):
    """Calculate Moving Average"""
    if len(prices) < period:
        return prices[-1]
    return sum(prices[-period:]) / period

def get_stock_analysis(symbol):
    """Get complete stock analysis"""
    try:
        # Get 30 days of historical data
        output = obb.equity.price.historical(symbol, provider='yfinance', limit=35)
        df = output.to_dataframe()
        
        if df.empty:
            return None
        
        # Latest data
        latest = df.iloc[-1]
        prev = df.iloc[-2]
        
        # Price data
        current = latest['close']
        change = current - prev['close']
        change_pct = (change / prev['close']) * 100
        volume = int(latest['volume'])
        
        # 30-day statistics
        high_30 = df['high'].max()
        low_30 = df['low'].min()
        prices = df['close'].tolist()
        
        # Technical indicators
        rsi = calculate_rsi(prices)
        ma20 = calculate_ma(prices, 20)
        
        # Trend judgment
        if current > ma20:
            trend = "📈 Uptrend"
        elif current < ma20:
            trend = "📉 Downtrend"
        else:
            trend = "➡️ Sideways"
        
        # RSI signal
        if rsi > 70:
            rsi_signal = "⚠️ Overbought"
        elif rsi < 30:
            rsi_signal = "💡 Oversold"
        else:
            rsi_signal = "📊 Normal"
        
        # 52-week position (estimated from 30-day data)
        week52_position = ((current - low_30) / (high_30 - low_30)) * 100 if high_30 != low_30 else 50
        
        return {
            'symbol': symbol,
            'current': current,
            'change': change,
            'change_pct': change_pct,
            'volume': volume,
            'high_30': high_30,
            'low_30': low_30,
            'rsi': rsi,
            'rsi_signal': rsi_signal,
            'ma20': ma20,
            'trend': trend,
            'week52_position': week52_position
        }
        
    except Exception as e:
        print(f"❌ {symbol} error: {str(e)[:50]}", file=sys.stderr)
        return None

def main():
    """Main function"""
    print("📊 **Stock Technical Analysis Report** - {} ({})")
    
    for symbol in STOCKS:
        data = get_stock_analysis(symbol)
        if data:
            # Format output
            emoji = "🟢" if data['change'] >= 0 else "🔴"
            print(f"{emoji} **{data['symbol']}**")
            print(f"   Current: ${data['current']:.2f} ({data['change']:+.2f}, {data['change_pct']:+.2f}%)")
            print(f"   Volume: {data['volume']:,}")
            print(f"   Trend: {data['trend']}")
            print(f"   RSI(14): {data['rsi']:.1f} {data['rsi_signal']}")
            print(f"   MA20: ${data['ma20']:.2f}")
            print(f"   30-Day Range: ${data['low_30']:.2f} - ${data['high_30']:.2f}")
            print(f"   Range Position: {data['week52_position']:.1f}%")
            print()
    
    print("💡 Data Source: OpenBB (yfinance)")
    print("⚠️ For reference only, not investment advice")

if __name__ == '__main__':
    main()

{
  "id": "your-job-id-here",
  "name": "Daily Stock Analysis - 8:30 AM",
  "enabled": true,
  "schedule": {
    "kind": "cron",
    "expr": "0 30 8 * * 1-5",
    "tz": "Asia/Shanghai"
  },
  "payload": {
    "kind": "agentTurn",
    "message": "Execute script: python3 ~/.openclaw/workspace/openbb_stock_analysis.py. Send the script output as your reply without any additional explanation."
  },
  "delivery": {
    "mode": "announce",
    "to": "discord:YOUR_CHANNEL_ID"
  }
}

pip install openbb-fred        # FRED data source
pip install openbb-polygon     # Polygon.io data source