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When you send a message, your agent fetches fresh data from all of the following sources before responding. Every answer is grounded in real numbers.

Live Account & Positions

Your agent has a real-time view of your Hyperliquid account:
  • Account value — total portfolio value including unrealized P&L
  • Margin used — how much of your capital is actively committed
  • Withdrawable balance — free margin available to deploy
  • Open positions — every current position with:
    • Coin, direction (long/short), and size
    • Entry price and current mark price
    • Unrealized P&L in USD and as a percentage
    • Leverage
    • Estimated liquidation price
Example questions:
  • “What’s my total account value right now?”
  • “What positions are you holding?”
  • “How far am I from liquidation on my SOL long?”
  • “How much margin do I have left to deploy?”

Performance & P&L

Your agent has access to aggregated performance statistics across your trading history:
  • Total trades executed
  • Win rate — percentage of profitable trades
  • Total P&L — cumulative dollar return
  • 7-day P&L — performance over the past week
  • 30-day P&L — performance over the past month
  • Per-token breakdown — win rate, total P&L, and trade count for each coin in your whitelist
Example questions:
  • “How am I performing this month?”
  • “What’s my win rate over the last 30 days?”
  • “Which token has made me the most money?”
  • “Am I profitable overall?”

Trade & Decision History

Your agent has the last 30 trading decisions stored in its context, including:
  • The token analyzed
  • The action taken (LONG, SHORT, CLOSE, or WAIT)
  • The full reasoning — what data the agent saw and why it acted
  • Whether the decision was executed or skipped, and why it was skipped if not
  • The timestamp of the decision
This lets you audit exactly what the agent was thinking at any point. Example questions:
  • “Walk me through your last 5 trades.”
  • “Why did you go long on SOL yesterday?”
  • “What was your reasoning on the BTC short last week?”
  • “How many times have you waited vs. traded this month?”
  • “Why did you skip the ETH trade on Monday?”

Strategy & Configuration

Your agent knows its own configuration completely:
  • The full strategy prompt you wrote
  • Your important notes (hard constraints)
  • Current risk parameters (max positions, position size range, drawdown limits)
  • Token whitelist
  • AI model and run schedule
  • Data sources enabled
This lets you ask the agent to reflect on whether its settings make sense for current market conditions. Example questions:
  • “What’s your current strategy?”
  • “Do you think my max drawdown limit is appropriate?”
  • “Is my whitelist still the right set of tokens?”
  • “Should I tighten my position size limits?”

Strategy Memory

Between runs, your agent maintains an ongoing strategy memory — a running self-analysis note it updates over time. This includes observations about what’s working, market regime notes, and self-corrections. The agent references this memory in chat to give you context on how its thinking has evolved. Example questions:
  • “What has your experience been with this strategy so far?”
  • “Have you noticed any patterns in your wins and losses?”
  • “What would you change about how you’ve been trading?”

Backtest History

Your agent has access to your last 5 completed backtests, including:
  • Backtest name and date range
  • Tokens tested and model used
  • Total return %
  • Sharpe ratio
  • Win rate
  • Max drawdown
  • Profit factor
  • Run duration and interval
This enables powerful comparisons between simulated and live performance. Example questions:
  • “Compare my live results to my last backtest.”
  • “Based on my last 3 backtests, what’s working?”
  • “Why is my live win rate lower than the backtest showed?”
  • “Which model performed best in my backtests?”
  • “What did the backtest predict my Sharpe ratio would be?”

What Happens When You Ask

Every chat message triggers a fresh data fetch in parallel:
  1. Agent config pulled from the database
  2. Live portfolio and positions fetched from Hyperliquid
  3. Performance metrics aggregated from trade history
  4. Last 30 decisions loaded from logs
  5. Strategy memory loaded
  6. Last 5 backtest reports loaded
All of this happens before the AI model sees your message. The response you get is always grounded in current data, not a cached snapshot.