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Simple RSI Strategy

Classic momentum strategy using RSI oversold/overbought levels.

Strategy Overview​

  • Type: Momentum
  • Indicators: RSI (14 periods)
  • Risk Level: Medium
  • Assets: Single asset (BTC)

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/strategy"
	"github.com/shopspring/decimal"
)

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

func NewRSI(k kronos.Kronos) strategy.Strategy {
	return &RSIStrategy{k: k}
}

func (s *RSIStrategy) GetSignals() ([]*strategy.Signal, error) {
	btc := s.k.Asset("BTC")
	rsi, _ := s.k.Indicators().RSI(btc, 14)

	// Buy oversold
	if rsi.LessThan(decimal.NewFromInt(30)) {
		s.k.Log().Opportunity("RSI", "BTC", "RSI oversold: %s", rsi.String())
		signal := s.k.Signal(s.GetName()).
			Buy(btc, connector.Binance, decimal.NewFromFloat(0.1)).
			Build()
		return []*strategy.Signal{signal}, nil
	}

	// Sell overbought
	if rsi.GreaterThan(decimal.NewFromInt(70)) {
		s.k.Log().Opportunity("RSI", "BTC", "RSI overbought: %s", rsi.String())
		signal := s.k.Signal(s.GetName()).
			Sell(btc, connector.Binance, decimal.NewFromFloat(0.1)).
			Build()
		return []*strategy.Signal{signal}, nil
	}

	return nil, nil
}

func (s *RSIStrategy) GetName() strategy.StrategyName { return "RSI" }
func (s *RSIStrategy) GetDescription() string { return "Simple RSI momentum" }
func (s *RSIStrategy) GetRiskLevel() strategy.RiskLevel { return strategy.RiskLevelMedium }
func (s *RSIStrategy) GetStrategyType() strategy.StrategyType { return strategy.StrategyTypeTechnical }

How It Works​

  1. Check RSI: Get the 14-period RSI for BTC
  2. Oversold: When RSI < 30, buy
  3. Overbought: When RSI > 70, sell
  4. Wait: Otherwise, do nothing

Key Concepts​

  • RSI < 30: Asset is oversold, potential reversal up
  • RSI > 70: Asset is overbought, potential reversal down
  • Fixed quantity: Always trades 0.1 BTC

Backtesting​

Run with:

kronos backtest

Expected characteristics:

  • Moderate trade frequency
  • Works best in ranging markets
  • May whipsaw in strong trends

Improvements​

Consider adding:

  • Trend filter (only buy in uptrend)
  • Stop loss protection
  • Multiple timeframe confirmation
  • Dynamic position sizing