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Multi-Asset Portfolio Strategy

Trade multiple assets with individual analysis and allocation.

Strategy Overview​

  • Type: Portfolio
  • Indicators: RSI (14), SMA(200)
  • Risk Level: Medium
  • Assets: Multiple (BTC, ETH, SOL)

Complete Code​

package main

import (
	"github.com/backtesting-org/kronos-sdk/pkg/types/connector"
	"github.com/backtesting-org/kronos-sdk/pkg/types/kronos"
	"github.com/backtesting-org/kronos-sdk/pkg/types/portfolio"
	"github.com/backtesting-org/kronos-sdk/pkg/types/strategy"
	"github.com/shopspring/decimal"
)

type Portfolio struct {
	strategy.BaseStrategy
	k kronos.Kronos
}

func NewPortfolio(k kronos.Kronos) strategy.Strategy {
	return &Portfolio{k: k}
}

func (s *Portfolio) GetSignals() ([]*strategy.Signal, error) {
	btc := s.k.Asset("BTC")
	eth := s.k.Asset("ETH")
	sol := s.k.Asset("SOL")

	var signals []*strategy.Signal

	// Check each asset
	assets := []struct {
		asset portfolio.Asset
		size  float64
	}{
		{btc, 0.1},
		{eth, 1.0},
		{sol, 10.0},
	}

	for _, a := range assets {
		rsi, _ := s.k.Indicators().RSI(a.asset, 14)
		sma200, _ := s.k.Indicators().SMA(a.asset, 200)
		price, _ := s.k.Market().Price(a.asset)

		// Buy if oversold and in uptrend
		if rsi.LessThan(decimal.NewFromInt(30)) && price.GreaterThan(sma200) {
			s.k.Log().Opportunity("Portfolio", a.asset.Symbol(), "Oversold in uptrend")
			signal := s.k.Signal(s.GetName()).
				Buy(a.asset, connector.Binance, decimal.NewFromFloat(a.size)).
				Build()
			signals = append(signals, signal)
		}
	}

	return signals, nil
}

func (s *Portfolio) GetName() strategy.StrategyName         { return "Portfolio" }
func (s *Portfolio) GetDescription() string                 { return "Multi-asset portfolio strategy" }
func (s *Portfolio) GetRiskLevel() strategy.RiskLevel       { return strategy.RiskLevelMedium }
func (s *Portfolio) GetStrategyType() strategy.StrategyType { return strategy.StrategyTypeTechnical }

How It Works​

  1. Define Universe: Set up multiple assets to trade
  2. Individual Analysis: Check each asset independently
  3. Entry Criteria: Buy when oversold AND in uptrend
  4. Return Multiple Signals: Can trade multiple assets simultaneously

Key Concepts​

  • Parallel Analysis: Kronos handles data for all assets automatically
  • Different Sizes: Position sizes vary by asset (0.1 BTC, 1.0 ETH, 10.0 SOL)
  • Diversification: Spreads risk across multiple assets
  • Same Logic: Each asset uses identical entry criteria

Backtesting​

Run with:

kronos backtest

Expected characteristics:

  • More trading opportunities (multiple assets)
  • Better diversification
  • Reduced overall portfolio risk
  • May require more capital

Improvements​

Consider adding:

  • Dynamic allocation (adjust sizes based on volatility)
  • Correlation filtering (avoid highly correlated positions)
  • Total exposure limits
  • Individual stops per asset
  • Rebalancing logic