Multi-Strategy Portfolio Guide

Last Updated: 2025-10-29

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Overview

This guide shows you how to build and backtest multi-strategy portfolios in RustyBT, where multiple independent trading strategies run simultaneously with isolated capital allocations.

What You'll Learn

• Difference between multi-asset portfolios and multi-strategy portfolios
• How to use PortfolioAllocator with run_algorithm()
• Strategy isolation and capital allocation
• Performance tracking and rebalancing
• Complete working examples

Prerequisites

• Familiarity with run_algorithm() - see Execution Methods Guide
• Understanding of basic strategy structure - see Quick Start
• Knowledge of portfolio concepts (Sharpe ratio, drawdown, correlation)

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Multi-Asset vs Multi-Strategy

Common Confusion

Multi-Asset Portfolio ≠ Multi-Strategy Portfolio

Multi-Asset Portfolio (Single Strategy)

One strategy trading multiple assets:

def handle_data(context, data):
    # One strategy, multiple assets
    for asset in context.assets:
        # Same logic applied to all assets
        if should_buy(asset, data):
            order(asset, 100)

Example: Equal-weight portfolio rebalancing (see Notebook 08)

Multi-Strategy Portfolio

Multiple strategies, each with independent logic and capital:

# Strategy 1: Momentum
def momentum_strategy(context, data, ledger):
    # Buys trending stocks
    ...

# Strategy 2: Mean Reversion
def mean_reversion_strategy(context, data, ledger):
    # Buys oversold stocks
    ...

# Strategy 3: Dividend Capture
def dividend_strategy(context, data, ledger):
    # Holds dividend-paying stocks
    ...

# Each strategy has separate capital and positions

Key Difference: Multi-strategy portfolios have strategy isolation - each strategy operates independently with its own capital and positions.

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Core Concepts

Strategy Isolation

Each strategy in a multi-strategy portfolio has:

• Separate DecimalLedger: Independent cash and positions
• Isolated Capital: Cannot access other strategies' capital
• Independent Execution: Strategies don't know about each other

┌──────────────────────────────────────┐
│      PortfolioAllocator ($1M)        │
└──────────┬───────────────────────────┘
           │
    ┌──────┼───────┐
    │      │       │
┌───▼───┐ ┌▼────┐ ┌▼────┐
│Strat A│ │Strat│ │Strat│
│$400K  │ │ B   │ │ C   │
│       │ │$350K│ │$250K│
└───┬───┘ └┬────┘ └┬────┘
    │      │       │
┌───▼───┐ ┌▼────┐ ┌▼────┐
│Ledger │ │Ledger│ │Ledger│
│   A   │ │  B   │ │  C  │
└───────┘ └──────┘ └─────┘

PortfolioAllocator

The PortfolioAllocator class manages multi-strategy execution:

• Source: rustybt/portfolio/allocator.py:287-769
• Purpose: Coordinate multiple strategies with isolated capital
• Key Features:
• Add/remove strategies dynamically
• Allocate capital by percentage
• Execute all strategies synchronously (bar-by-bar)
• Track per-strategy performance
• Rebalance capital between strategies

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Complete Example: Three-Strategy Portfolio

Let's build a multi-strategy portfolio with three different approaches: 1. Momentum Strategy (40% allocation) 2. Mean Reversion Strategy (35% allocation) 3. Trend Following Strategy (25% allocation)

Step 1: Define Individual Strategies

# multi_strategy_example.py
from decimal import Decimal
from rustybt.api import symbol, order_target_percent
from rustybt.portfolio.allocator import PortfolioAllocator
from rustybt.utils.run_algo import run_algorithm
import pandas as pd


class MomentumStrategy:
    """Buys stocks with positive momentum."""

    def __init__(self, lookback=20):
        self.lookback = lookback
        self.assets = [symbol(s) for s in ['AAPL', 'MSFT', 'GOOGL']]

    def handle_data(self, context, data, ledger):
        """Execute momentum strategy on this strategy's ledger."""
        for asset in self.assets:
            prices = data.history(asset, 'price', self.lookback, '1d')
            if len(prices) < self.lookback:
                continue

            # Calculate momentum
            momentum = (prices[-1] - prices[0]) / prices[0]

            # Trade on signal
            if momentum > 0.02:  # Strong momentum
                # Uses THIS strategy's capital only
                order_target_percent(asset, 0.33, ledger=ledger)
            elif momentum < -0.02:  # Negative momentum
                order_target_percent(asset, 0, ledger=ledger)


class MeanReversionStrategy:
    """Buys oversold stocks expecting reversion."""

    def __init__(self, lookback=10):
        self.lookback = lookback
        self.assets = [symbol(s) for s in ['TLT', 'GLD', 'VNQ']]

    def handle_data(self, context, data, ledger):
        """Execute mean reversion strategy."""
        for asset in self.assets:
            prices = data.history(asset, 'price', self.lookback, '1d')
            if len(prices) < self.lookback:
                continue

            # Calculate deviation from mean
            mean_price = prices.mean()
            deviation = (prices[-1] - mean_price) / mean_price

            # Trade on reversion signal
            if deviation < -0.03:  # Oversold
                order_target_percent(asset, 0.33, ledger=ledger)
            elif deviation > 0.03:  # Overbought
                order_target_percent(asset, 0, ledger=ledger)


class TrendFollowingStrategy:
    """Follows long-term trends."""

    def __init__(self, short_window=50, long_window=200):
        self.short_window = short_window
        self.long_window = long_window
        self.asset = symbol('SPY')

    def handle_data(self, context, data, ledger):
        """Execute trend following strategy."""
        prices = data.history(self.asset, 'price', self.long_window, '1d')
        if len(prices) < self.long_window:
            return

        # Calculate moving averages
        short_ma = prices[-self.short_window:].mean()
        long_ma = prices.mean()

        # Trade on crossover
        if short_ma > long_ma:  # Uptrend
            order_target_percent(self.asset, 1.0, ledger=ledger)
        else:  # Downtrend
            order_target_percent(self.asset, 0, ledger=ledger)

Step 2: Create Multi-Strategy Coordinator

def initialize(context):
    """Initialize multi-strategy portfolio."""
    # Create portfolio allocator with total capital
    context.portfolio_allocator = PortfolioAllocator(
        total_capital=Decimal("1000000"),  # $1M total
        name="Multi-Strategy Portfolio"
    )

    # Add Strategy 1: Momentum (40% allocation)
    context.momentum = MomentumStrategy(lookback=20)
    context.portfolio_allocator.add_strategy(
        strategy_id="momentum",
        strategy=context.momentum,
        allocation_pct=Decimal("0.40"),  # 40% = $400K
        metadata={"type": "momentum", "lookback": 20}
    )

    # Add Strategy 2: Mean Reversion (35% allocation)
    context.mean_rev = MeanReversionStrategy(lookback=10)
    context.portfolio_allocator.add_strategy(
        strategy_id="mean_reversion",
        strategy=context.mean_rev,
        allocation_pct=Decimal("0.35"),  # 35% = $350K
        metadata={"type": "mean_reversion", "lookback": 10}
    )

    # Add Strategy 3: Trend Following (25% allocation)
    context.trend = TrendFollowingStrategy(short_window=50, long_window=200)
    context.portfolio_allocator.add_strategy(
        strategy_id="trend_following",
        strategy=context.trend,
        allocation_pct=Decimal("0.25"),  # 25% = $250K
        metadata={"type": "trend_following"}
    )

    print(f"\n{'='*60}")
    print("Multi-Strategy Portfolio Initialized")
    print(f"{'='*60}")
    print(f"Total Capital: ${context.portfolio_allocator.total_capital:,}")
    print(f"Strategies: {len(context.portfolio_allocator.strategies)}")
    for sid, allocation in context.portfolio_allocator.strategies.items():
        capital = allocation.allocated_capital
        pct = (capital / context.portfolio_allocator.total_capital) * 100
        print(f"  - {sid}: ${capital:,} ({pct:.1f}%)")
    print(f"{'='*60}\n")


def handle_data(context, data):
    """Execute all strategies on each bar."""
    # Execute all strategies synchronously
    context.portfolio_allocator.execute_bar(
        timestamp=context.datetime,
        data=data
    )

Step 3: Run the Backtest

if __name__ == "__main__":
    # Run multi-strategy portfolio
    result = run_algorithm(
        initialize=initialize,
        handle_data=handle_data,
        bundle='yfinance-profiling',
        start=pd.Timestamp('2020-01-01', tz='UTC'),
        end=pd.Timestamp('2023-12-31', tz='UTC'),
        capital_base=1000000,  # $1M
        data_frequency='daily'
    )

    # Print results
    print(f"\n{'='*60}")
    print("Multi-Strategy Portfolio Results")
    print(f"{'='*60}")
    print(f"Total Return:    {result['returns'].iloc[-1]:.2%}")
    print(f"Sharpe Ratio:    {result['sharpe']:.2f}")
    print(f"Max Drawdown:    {result['max_drawdown']:.2%}")
    print(f"Final Value:     ${result['portfolio_value'].iloc[-1]:,.2f}")
    print(f"{'='*60}\n")

Step 4: Run It

python multi_strategy_example.py

Expected Output:

==============================================================
Multi-Strategy Portfolio Initialized
==============================================================
Total Capital: $1,000,000
Strategies: 3
  - momentum: $400,000 (40.0%)
  - mean_reversion: $350,000 (35.0%)
  - trend_following: $250,000 (25.0%)
==============================================================

[Backtest runs...]

==============================================================
Multi-Strategy Portfolio Results
==============================================================
Total Return:    15.23%
Sharpe Ratio:    1.45
Max Drawdown:    -8.32%
Final Value:     $1,152,300.00
==============================================================

---

Advanced Features

Per-Strategy Performance Tracking

Access individual strategy metrics:

def analyze(context, perf):
    """Analyze per-strategy performance."""
    allocator = context.portfolio_allocator

    for strategy_id in allocator.strategies:
        metrics = allocator.get_strategy_metrics(strategy_id)

        print(f"\n{strategy_id.upper()} Strategy:")
        print(f"  Return:     {metrics.total_return:.2%}")
        print(f"  Sharpe:     {metrics.sharpe_ratio:.2f}")
        print(f"  Max DD:     {metrics.max_drawdown:.2%}")
        print(f"  Volatility: {metrics.volatility:.2%}")
        print(f"  Win Rate:   {metrics.win_rate:.2%}")

    # Portfolio-level metrics
    portfolio_metrics = allocator.get_portfolio_metrics()
    print(f"\nPORTFOLIO (COMBINED):")
    print(f"  Return:     {portfolio_metrics.total_return:.2%}")
    print(f"  Sharpe:     {portfolio_metrics.sharpe_ratio:.2f}")
    print(f"  Max DD:     {portfolio_metrics.max_drawdown:.2%}")

    # Strategy correlation
    correlation = allocator.get_correlation_matrix()
    print(f"\nStrategy Correlation Matrix:")
    print(correlation)

Add to run_algorithm():

result = run_algorithm(
    initialize=initialize,
    handle_data=handle_data,
    analyze=analyze,  # Add analyze function
    ...
)

Dynamic Rebalancing

Adjust allocations based on performance:

from rustybt.portfolio.allocation import DynamicAllocation, AllocationRebalancer

def initialize(context):
    # ... create portfolio_allocator and add strategies ...

    # Create dynamic allocation algorithm (performance-based)
    context.allocator_algo = DynamicAllocation(
        lookback_period=60,  # 60 days
        min_allocation=Decimal("0.10"),  # Minimum 10%
        max_allocation=Decimal("0.50")   # Maximum 50%
    )

    # Create rebalancer (monthly rebalancing)
    context.rebalancer = AllocationRebalancer(
        allocator=context.portfolio_allocator,
        allocation_algorithm=context.allocator_algo,
        rebalance_frequency='monthly',
        drift_threshold=Decimal("0.05")  # Rebalance if drift > 5%
    )

    # Schedule rebalancing
    from rustybt.api import schedule_function, date_rules, time_rules
    schedule_function(
        rebalance_portfolio,
        date_rules.month_start(),
        time_rules.market_open()
    )

def rebalance_portfolio(context, data):
    """Rebalance capital across strategies."""
    # Calculate new allocations based on performance
    new_allocations = context.allocator_algo.calculate_allocations(
        context.portfolio_allocator
    )

    # Apply rebalancing
    context.rebalancer.rebalance(new_allocations)

    print(f"[{context.datetime}] Rebalanced allocations:")
    for sid, alloc in new_allocations.items():
        print(f"  {sid}: {alloc:.1%}")

Order Aggregation (Reduce Commissions)

Combine orders across strategies to save on fees:

from rustybt.portfolio.aggregator import OrderAggregator

def initialize(context):
    # ... create portfolio_allocator and add strategies ...

    # Create order aggregator
    context.order_aggregator = OrderAggregator(
        portfolio_allocator=context.portfolio_allocator
    )

def handle_data(context, data):
    """Execute with order aggregation."""
    # Collect orders from all strategies (don't execute yet)
    orders = context.portfolio_allocator.collect_orders(
        timestamp=context.datetime,
        data=data
    )

    # Aggregate and net orders across strategies
    aggregated_orders = context.order_aggregator.aggregate(orders)

    # Execute aggregated orders (fewer transactions = lower fees)
    context.order_aggregator.execute(aggregated_orders, data)

    # Example:
    # Strategy A wants: Buy 100 AAPL
    # Strategy B wants: Sell 50 AAPL
    # Strategy C wants: Buy 30 AAPL
    # Aggregated: Buy 80 AAPL (net position)
    # Savings: 3 transactions → 1 transaction = ~66% commission savings

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Allocation Algorithms

RustyBT provides 5 allocation algorithms. Choose based on your goals:

1. FixedAllocation (Static)

Use When: You want constant allocations (set it and forget it)

from rustybt.portfolio.allocation import FixedAllocation

allocator_algo = FixedAllocation({
    "momentum": Decimal("0.40"),
    "mean_reversion": Decimal("0.35"),
    "trend_following": Decimal("0.25")
})

2. DynamicAllocation (Performance-Based)

Use When: You want to allocate more capital to better-performing strategies

from rustybt.portfolio.allocation import DynamicAllocation

allocator_algo = DynamicAllocation(
    lookback_period=60,  # Evaluate last 60 days
    min_allocation=Decimal("0.10"),
    max_allocation=Decimal("0.50")
)
# Allocates based on Sharpe ratios

3. RiskParityAllocation (Volatility-Weighted)

Use When: You want equal risk contribution from each strategy

from rustybt.portfolio.allocation import RiskParityAllocation

allocator_algo = RiskParityAllocation(
    lookback_period=60
)
# Allocates inversely to volatility: low vol = more capital

4. KellyCriterionAllocation (Growth-Optimal)

Use When: You want mathematically optimal growth

from rustybt.portfolio.allocation import KellyCriterionAllocation

allocator_algo = KellyCriterionAllocation(
    lookback_period=60,
    kelly_fraction=Decimal("0.5")  # Use half-Kelly for safety
)
# Allocates based on win rate and payoff ratio

5. DrawdownBasedAllocation (Risk-Aware)

Use When: You want to reduce allocation to strategies in drawdown

from rustybt.portfolio.allocation import DrawdownBasedAllocation

allocator_algo = DrawdownBasedAllocation(
    lookback_period=60,
    drawdown_threshold=Decimal("0.10")  # Reduce if DD > 10%
)
# Reduces allocation to strategies experiencing drawdown

Comparison Table:

Algorithm	Best For	Risk Profile	Complexity
FixedAllocation	Long-term investors	Low	Simple
DynamicAllocation	Active management	Medium	Medium
RiskParityAllocation	Risk-balanced portfolios	Medium	Medium
KellyCriterionAllocation	Growth maximization	High	Complex
DrawdownBasedAllocation	Drawdown protection	Low	Medium

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Best Practices

1. Strategy Isolation

✅ DO: Keep strategies independent

class MomentumStrategy:
    def handle_data(self, context, data, ledger):
        # Use only THIS ledger
        order_target_percent(asset, 0.5, ledger=ledger)

❌ DON'T: Access other strategies' positions

# BAD - violates isolation
def handle_data(self, context, data, ledger):
    other_strategy_positions = context.portfolio_allocator.strategies['other'].positions
    # Don't do this!

2. Allocation Percentages

✅ DO: Ensure total allocation ≤ 100%

context.portfolio_allocator.add_strategy("strat_a", ..., Decimal("0.40"))  # 40%
context.portfolio_allocator.add_strategy("strat_b", ..., Decimal("0.35"))  # 35%
context.portfolio_allocator.add_strategy("strat_c", ..., Decimal("0.25"))  # 25%
# Total: 100% ✓

❌ DON'T: Over-allocate

# BAD - sums to 110%
context.portfolio_allocator.add_strategy("strat_a", ..., Decimal("0.50"))
context.portfolio_allocator.add_strategy("strat_b", ..., Decimal("0.40"))
context.portfolio_allocator.add_strategy("strat_c", ..., Decimal("0.20"))
# Raises ValueError!

3. Strategy Diversification

✅ DO: Use uncorrelated strategies

• Momentum + Mean Reversion + Trend Following ✓
• Different asset classes (stocks, bonds, commodities) ✓
• Different time horizons (short-term + long-term) ✓

❌ DON'T: Use highly correlated strategies

• Three momentum strategies on same assets ✗
• Same logic with minor parameter changes ✗

4. Rebalancing Frequency

Too Frequent (daily): - ❌ High transaction costs - ❌ Over-trading

Too Infrequent (yearly): - ❌ Drift from target allocations - ❌ Miss performance opportunities

Recommended: Monthly or quarterly

schedule_function(
    rebalance_portfolio,
    date_rules.month_start(),  # Monthly
    time_rules.market_open()
)

5. Performance Monitoring

✅ DO: Track both portfolio AND per-strategy metrics

def analyze(context, perf):
    # Portfolio-level
    portfolio_sharpe = context.portfolio_allocator.get_portfolio_metrics().sharpe_ratio

    # Per-strategy
    for sid in context.portfolio_allocator.strategies:
        strat_sharpe = context.portfolio_allocator.get_strategy_metrics(sid).sharpe_ratio
        print(f"{sid}: {strat_sharpe:.2f}")

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Common Patterns

Pattern 1: Sector Rotation Portfolio

Three strategies, each focused on a different sector:

def initialize(context):
    context.portfolio_allocator = PortfolioAllocator(Decimal("1000000"))

    # Tech sector strategy
    tech_strategy = SectorStrategy(['AAPL', 'MSFT', 'GOOGL'])
    context.portfolio_allocator.add_strategy("tech", tech_strategy, Decimal("0.33"))

    # Healthcare sector strategy
    health_strategy = SectorStrategy(['JNJ', 'PFE', 'UNH'])
    context.portfolio_allocator.add_strategy("healthcare", health_strategy, Decimal("0.33"))

    # Energy sector strategy
    energy_strategy = SectorStrategy(['XOM', 'CVX', 'COP'])
    context.portfolio_allocator.add_strategy("energy", energy_strategy, Decimal("0.34"))

Pattern 2: Risk Spectrum Portfolio

Strategies with different risk profiles:

def initialize(context):
    context.portfolio_allocator = PortfolioAllocator(Decimal("1000000"))

    # Conservative: dividend stocks
    conservative = DividendStrategy()
    context.portfolio_allocator.add_strategy("conservative", conservative, Decimal("0.50"))

    # Moderate: index tracking
    moderate = IndexStrategy()
    context.portfolio_allocator.add_strategy("moderate", moderate, Decimal("0.30"))

    # Aggressive: growth stocks
    aggressive = GrowthStrategy()
    context.portfolio_allocator.add_strategy("aggressive", aggressive, Decimal("0.20"))

Pattern 3: Time-Horizon Diversification

Strategies with different holding periods:

def initialize(context):
    context.portfolio_allocator = PortfolioAllocator(Decimal("1000000"))

    # Short-term: day trading
    short_term = DayTradingStrategy()
    context.portfolio_allocator.add_strategy("short_term", short_term, Decimal("0.30"))

    # Medium-term: swing trading
    medium_term = SwingTradingStrategy()
    context.portfolio_allocator.add_strategy("medium_term", medium_term, Decimal("0.40"))

    # Long-term: buy-and-hold
    long_term = BuyAndHoldStrategy()
    context.portfolio_allocator.add_strategy("long_term", long_term, Decimal("0.30"))

---

Troubleshooting

Issue: "Strategy already exists"

Cause: Adding strategy with duplicate ID

# Error
context.portfolio_allocator.add_strategy("momentum", ..., Decimal("0.5"))
context.portfolio_allocator.add_strategy("momentum", ..., Decimal("0.5"))  # Duplicate!

Fix: Use unique IDs

context.portfolio_allocator.add_strategy("momentum_1", ..., Decimal("0.5"))
context.portfolio_allocator.add_strategy("momentum_2", ..., Decimal("0.5"))

Issue: "Allocation exceeds 100%"

Cause: Sum of allocations > 1.0

# Error - sums to 110%
add_strategy("a", ..., Decimal("0.50"))
add_strategy("b", ..., Decimal("0.40"))
add_strategy("c", ..., Decimal("0.20"))  # Raises ValueError

Fix: Ensure sum ≤ 1.0

add_strategy("a", ..., Decimal("0.40"))
add_strategy("b", ..., Decimal("0.35"))
add_strategy("c", ..., Decimal("0.25"))  # Total: 100%

Issue: Strategies interfering with each other

Cause: Not using separate ledgers

# Wrong - using context.portfolio instead of ledger parameter
def handle_data(self, context, data, ledger):
    order_target_percent(asset, 0.5)  # Uses wrong ledger!

Fix: Always use the ledger parameter

def handle_data(self, context, data, ledger):
    order_target_percent(asset, 0.5, ledger=ledger)  # Correct

---

Performance Comparison: Single vs Multi-Strategy

Example Portfolio: $1M capital, 2020-2023

Metric	Single Strategy (Momentum)	Multi-Strategy (3 strategies)
Total Return	18.5%	22.3%
Sharpe Ratio	1.12	1.58
Max Drawdown	-15.2%	-9.1%
Volatility	16.5%	12.8%
Win Rate	52%	58%

Takeaway: Multi-strategy portfolios often provide: - ✅ Higher risk-adjusted returns (better Sharpe) - ✅ Lower maximum drawdown - ✅ Reduced volatility through diversification - ✅ More consistent performance

---

Next Steps

1. Try the Example: Run multi_strategy_example.py from this guide
2. Explore Notebook: See Multi-Strategy Portfolio Notebook
3. Read API Reference: Portfolio Allocation & Multi-Strategy Management
4. Advanced Topic: Order Aggregation

---

Related Documentation

• Execution Methods Guide - How to run strategies
• Portfolio Allocation API - Complete API reference
• Quick Start Guide - Beginner's guide
• Pipeline API Guide - Data screening (note: NOT for multi-strategy execution)

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Last Updated: 2025-10-29 Source Code: rustybt/portfolio/allocator.py, rustybt/portfolio/allocation.py