userFunding | Hyperliquid info

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Parameters

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Request body

• type (string, required) — The request type. Must be "userFunding" to retrieve user funding history.
• user (string, required) — Address in 42-character hexadecimal format; e.g. 0x0000000000000000000000000000000000000000.
• startTime (integer, required) — Start time in milliseconds, inclusive.
• endTime (integer, optional) — End time in milliseconds, inclusive. Defaults to current time.

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Response

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Response structure

• time — Unix timestamp in milliseconds when the funding occurred
• hash — Transaction hash (typically zero hash for funding events)

• delta.type — Always “funding” for funding events
• delta.coin — Asset symbol (e.g., “BTC”, “ETH”, “SOL”)
• delta.usdc — Funding amount in USDC (negative = paid, positive = received)
• delta.szi — Position size at the time of funding
• delta.fundingRate — Funding rate applied for this period

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Data interpretation

• Negative usdc values indicate funding paid (user is long in a positive funding environment or short in negative funding)
• Positive usdc values indicate funding received (user is short in positive funding environment or long in negative funding)

• szi shows the position size when funding was applied
• Positive szi indicates long positions, negative indicates short positions
• Larger absolute position sizes result in proportionally larger funding payments/receipts

• fundingRate represents the rate charged per unit of time
• Positive rates favor shorts (longs pay shorts)
• Negative rates favor longs (shorts pay longs)
• Rates are typically charged every 8 hours (3 times daily)

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Trading insights

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Funding cost analysis

• Total funding cost = sum of all usdc values for a specific asset
• Daily funding impact = funding payments/receipts over 24-hour periods
• Funding rate trends indicate market sentiment and position bias

• High funding costs may indicate overleverage or poor market timing
• Consistent funding receipts suggest alignment with market structure
• Funding patterns help optimize position entry and exit timing

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Market sentiment indicators

• Consistently positive funding rates indicate bullish bias (demand for long positions)
• Negative funding rates suggest bearish sentiment (demand for short positions)
• Extreme funding rates often coincide with market turning points

• Monitor funding costs relative to trading profits
• Consider position size adjustments during high funding periods
• Use funding rate changes as market sentiment indicators

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Historical analysis

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Performance tracking

• Track funding costs as percentage of trading profits
• Compare funding expenses across different assets
• Identify optimal position sizing for funding cost management

• Analyze funding rate cycles for entry/exit optimization
• Monitor funding rate extremes for reversal signals
• Use historical funding patterns for strategy backtesting

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Example request

curl -X POST \
  -H "Content-Type: application/json" \
  -d '{
    "type": "userFunding",
    "user": "0x2ba553d9f990a3b66b03b2dc0d030dfc1c061036",
    "startTime": 1681923833000,
    "endTime": 1681924833000
  }' \
  https://api.hyperliquid.xyz/info

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Example response

[
  {
    "delta": {
      "coin": "ETH",
      "fundingRate": "0.0000417",
      "szi": "49.1477",
      "type": "funding",
      "usdc": "-3.625312"
    },
    "hash": "0xa166e3fa63c25663024b03f2e0da011a00307e4017465df020210d3d432e7cb8",
    "time": 1681222254710
  }
]

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Use cases

• Trading analytics: Analyze funding costs and their impact on trading performance
• Portfolio management: Track funding expenses across multiple positions and assets
• Strategy optimization: Optimize position sizing and timing based on funding patterns
• Performance attribution: Separate trading gains from funding costs for accurate measurement
• Tax reporting: Calculate funding payments and receipts for tax documentation
• Risk management: Monitor funding costs as part of overall trading risk assessment
• Market analysis: Study funding rate patterns to understand market sentiment
• Algorithm development: Incorporate funding costs into automated trading strategies
• Position management: Make informed decisions about position holds based on funding trends
• Cost analysis: Compare funding efficiency across different trading strategies
• Backtesting: Include historical funding costs in strategy backtesting and simulation
• Reporting tools: Generate comprehensive trading reports including funding breakdowns