Portfolio Management

💼 Professional Portfolio Management

Quantbot's portfolio management system provides institutional-grade tools for tracking, analyzing, and optimizing your investments with real-time insights and AI-powered recommendations.

Portfolio Overview

Total Value

Example: $XXX,XXX

+18.7% Total Return

Today's P&L

Example: +$X,XXX

+1.85% Today

Active Positions

12

8 Profitable

Cash Available

$8,432

5.4% Allocation

Key Features

📊 Real-Time Portfolio Tracking

Monitor your investments with live updates and comprehensive analytics:

Live Position Tracking - Real-time P&L updates
Performance Attribution - Understand what's driving returns
Risk Metrics - Beta, Sharpe ratio, maximum drawdown
Sector Allocation - Diversification analysis
Geographic Exposure - Regional investment breakdown

🎯 Asset Allocation

Asset Class	Current	Target	Difference
US Stocks	65%	60%	+5%
International	15%	20%	-5%
Bonds	10%	15%	-5%
Cash	5%	3%	+2%
Alternatives	5%	2%	+3%

🔄 Rebalancing Recommendations

Get Rebalancing Suggestions

from quantbot import QuantbotClient

client = QuantbotClient()

# Get rebalancing recommendations
rebalance = client.get_rebalancing_recommendations()

print("🎯 Rebalancing Suggestions:")
for suggestion in rebalance.suggestions:
    print(f"- {suggestion.action} {suggestion.quantity} shares of {suggestion.symbol}")
    print(f"  Reason: {suggestion.reason}")
    print(f"  Expected impact: {suggestion.expected_impact}")

Rebalancing Alert: Technology Overweight

AI Recommendation

Your tech allocation is 5% above target. Consider taking profits.

Optimize

Position Management

Individual Position Tracking

GET /api/v1/portfolio/positions

Retrieve detailed information about all portfolio positions.

Position Details Response

{
  "positions": [
    {
      "symbol": "AAPL",
      "quantity": 100,
      "current_price": 175.43,
      "cost_basis": 150.00,
      "market_value": 17543.00,
      "unrealized_pnl": 2543.00,
      "unrealized_pnl_percent": 16.95,
      "day_change": 234.00,
      "day_change_percent": 1.35,
      "weight": 11.2,
      "sector": "Technology",
      "last_updated": "2024-07-04T14:30:00Z"
    }
  ]
}

Position Analytics

For each position, Quantbot provides:

Cost Basis Tracking - FIFO, LIFO, or specific lot identification
Tax Optimization - Tax-loss harvesting opportunities
Performance Metrics - Alpha, beta, correlation analysis
Risk Assessment - Position-specific risk metrics
News Impact - How recent news affects the position

Risk Management

Portfolio Risk Metrics

Portfolio Beta

1.15

vs S&P 500

Sharpe Ratio

1.32

Excellent

Max Drawdown

-8.4%

Low Risk

VaR (95%)

-$4,200

1-Day Risk

Risk Alerts

Set Up Risk Alerts

# Portfolio-level alerts
client.set_portfolio_alert({
    "type": "max_drawdown",
    "threshold": 0.10,  # 10% maximum drawdown
    "action": "email_and_sms"
})

# Position-level alerts
client.set_position_alert("AAPL", {
    "type": "stop_loss",
    "threshold": 0.05,  # 5% stop loss
    "action": "auto_sell"  # Automatic execution
})

# Sector concentration alert
client.set_sector_alert("Technology", {
    "type": "overweight",
    "threshold": 0.30,  # 30% maximum sector allocation
    "action": "rebalance_suggestion"
})

Performance Analysis

Historical Performance

Period	Portfolio	S&P 500	NASDAQ	Outperformance
1 Month	+3.2%	+2.1%	+2.8%	+1.1%
3 Months	+8.7%	+6.2%	+7.1%	+2.5%
6 Months	+12.4%	+9.8%	+11.2%	+2.6%
1 Year	+18.7%	+12.3%	+15.6%	+6.4%

Attribution Analysis

Performance Attribution

# Analyze what's driving your returns
attribution = client.get_performance_attribution(period="1Y")

print("📈 Performance Attribution (1 Year):")
print(f"Total Return: {attribution.total_return:.2%}")
print("\nContributions by:")
print(f"- Asset Allocation: {attribution.asset_allocation:.2%}")
print(f"- Security Selection: {attribution.security_selection:.2%}")
print(f"- Timing: {attribution.timing:.2%}")
print(f"- Interaction: {attribution.interaction:.2%}")

print("\nTop Contributors:")
for contributor in attribution.top_contributors:
    print(f"- {contributor.symbol}: +{contributor.contribution:.2%}")

Tax Optimization

Tax-Loss Harvesting

Tax-Loss Harvesting Opportunity

Tax Optimization

Sell TSLA (-$1,200) to offset AAPL gains (+$2,500)

Tax Savings

Tax Optimization

# Get tax-loss harvesting opportunities
tax_opportunities = client.get_tax_opportunities()

for opportunity in tax_opportunities:
    print(f"💰 Tax Opportunity:")
    print(f"- Sell: {opportunity.sell_symbol} (Loss: ${opportunity.loss:,.2f})")
    print(f"- Offset: {opportunity.offset_symbol} (Gain: ${opportunity.gain:,.2f})")
    print(f"- Tax Savings: ${opportunity.tax_savings:,.2f}")
    print(f"- Wash Sale Risk: {opportunity.wash_sale_risk}")

Tax Reporting

Realized Gains/Losses - Detailed transaction history
Dividend Tracking - Qualified vs non-qualified dividends
Cost Basis Methods - FIFO, LIFO, specific identification
Tax Forms - 1099-B reconciliation and export
Estimated Taxes - Quarterly tax payment calculations

Advanced Features

Portfolio Optimization

Modern Portfolio Theory Optimization

# Optimize portfolio using Modern Portfolio Theory
optimization = client.optimize_portfolio({
    "objective": "maximize_sharpe",  # or "minimize_risk", "maximize_return"
    "constraints": {
        "max_position_weight": 0.10,  # 10% max per position
        "min_positions": 15,          # Minimum diversification
        "sector_limits": {
            "Technology": 0.30,       # Max 30% in tech
            "Healthcare": 0.25        # Max 25% in healthcare
        }
    },
    "risk_tolerance": "moderate"
})

print("🎯 Optimization Results:")
print(f"Expected Return: {optimization.expected_return:.2%}")
print(f"Expected Risk: {optimization.expected_risk:.2%}")
print(f"Sharpe Ratio: {optimization.sharpe_ratio:.2f}")

print("\nRecommended Changes:")
for change in optimization.recommended_changes:
    print(f"- {change.action} {change.symbol}: {change.weight_change:+.1%}")

Scenario Analysis

Stress Testing

# Test portfolio under different market scenarios
scenarios = client.run_scenario_analysis([
    {"name": "Market Crash", "sp500_change": -0.20, "bond_change": 0.05},
    {"name": "Interest Rate Spike", "sp500_change": -0.10, "bond_change": -0.15},
    {"name": "Tech Bubble", "nasdaq_change": -0.30, "sp500_change": -0.15},
    {"name": "Recession", "sp500_change": -0.25, "bond_change": 0.10}
])

for scenario in scenarios:
    print(f"📊 {scenario.name}:")
    print(f"  Portfolio Impact: {scenario.portfolio_impact:+.2%}")
    print(f"  Worst Position: {scenario.worst_position} ({scenario.worst_impact:+.2%})")
    print(f"  Best Position: {scenario.best_position} ({scenario.best_impact:+.2%})")

API Integration

Portfolio API Endpoints

GET /api/v1/portfolio/summary

Get complete portfolio summary with performance metrics.

POST /api/v1/portfolio/rebalance

Execute portfolio rebalancing based on target allocation.

GET /api/v1/portfolio/risk-metrics

Retrieve comprehensive risk analysis and metrics.

Python SDK Examples

Portfolio Management SDK

from quantbot import QuantbotClient

client = QuantbotClient(api_key="your_api_key")

# Get portfolio summary
portfolio = client.portfolio.get_summary()
print(f"Total Value: ${portfolio.total_value:,.2f}")

# Add position
client.portfolio.add_position(
    symbol="AAPL",
    quantity=100,
    price=175.43
)

# Get performance metrics
metrics = client.portfolio.get_performance_metrics(period="1Y")
print(f"Annual Return: {metrics.annual_return:.2%}")

# Optimize allocation
optimization = client.portfolio.optimize(
    objective="maximize_sharpe",
    constraints={"max_weight": 0.10}
)

Ready to Optimize Your Portfolio? 📈

Start using Quantbot's advanced portfolio management tools today.

Get Started API Documentation