analyze_rebalance

Optimize portfolio allocations using four rebalancing methods with live market data. Get target weights, current weights, and specific actions (increase/reduce/hold) for each asset.

Instructions

Portfolio rebalancing recommendations with live market data. Four methods: profile (conservative/balanced/aggressive), risk_parity (inverse-vol weighting), min_variance (inverse-variance), equal_weight (1/N). Returns target weight, current weight, action (increase/reduce/hold), and delta per asset. Known crypto tickers auto-enriched with real volatility data. Payment: $0.02 USDC on Tempo.

Input Schema

TableJSON Schema

Name	Required	Description
`holdings`	Yes
`profile`	No	Risk profile — affects rebalance targets and scoring. Default: balanced.
`benchmarkReturn`	No	Annual benchmark return for Sharpe calculation, e.g. 0.08 = 8%. Default: 0.08.
`riskFreeRate`	No	Annual risk-free rate for Sortino and VaR excess return, e.g. 0.05 = 5%. Default: 0.05.
`rebalanceMethod`	No	Portfolio construction method for rebalance recommendations. Default: profile.
`marketIndicators`	No	Optional macro indicators — improves market regime detection confidence to HIGH when 3+ provided.

Implementation Reference

src/analyze/rebalance.ts:132-167 (handler)

The main handler function for rebalancing logic, which calculates target weights and generates rebalance suggestions based on the portfolio's method and profile.

export function analyzeRebalance(portfolio: Portfolio): RebalanceResult {
  const targets = computeTargets(portfolio.holdings, portfolio.profile, portfolio.rebalanceMethod)

  const roundedTargets = targets.map((t) => Number(t.toFixed(4)))
  // Absorb rounding error into the largest-weight suggestion so client sums always equal 1.0
  const roundedSum = roundedTargets.reduce((a, b) => a + b, 0)
  const roundingError = Number((1 - roundedSum).toFixed(4))
  if (roundingError !== 0 && roundedTargets.length > 0) {
    const maxIdx = roundedTargets.reduce((best, v, i) => (v > roundedTargets[best] ? i : best), 0)
    roundedTargets[maxIdx] = Number((roundedTargets[maxIdx] + roundingError).toFixed(4))
  }

  const suggestions: RebalanceSuggestion[] = portfolio.holdings.map((h, i) => {
    const delta = Number((roundedTargets[i] - h.weight).toFixed(4))
    const action: RebalanceSuggestion['action'] =
      Math.abs(delta) < 0.02 ? 'hold' : delta > 0 ? 'increase' : 'reduce'
    return {
      asset: h.asset,
      currentWeight: h.weight,
      targetWeight: roundedTargets[i],
      action,
      delta,
    }
  })

  const targetWeightsSum = Number(
    targets.reduce((a, b) => a + b, 0).toFixed(6),
  )

  return {
    method: portfolio.rebalanceMethod,
    profile: portfolio.profile,
    suggestions,
    targetWeightsSum,
  }
}

FinSight