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Hyperoru is built around a few core abstractions that work together to automate trading.

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AI trader

An AI trader is the execution unit that connects everything together. Each trader has:
  • A name and display identity
  • An LLM model configuration (provider, model, API key)
  • A connected exchange wallet (Hyperliquid or Binance)
  • A bound strategy (prompt or program)
  • Signal pool triggers that decide when to analyze
You can run multiple AI traders simultaneously, each with different strategies, models, and risk profiles.

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Trading prompt

A trading prompt is a natural language template that tells an LLM how to make trading decisions. Prompts include:
  • Market context variables (prices, positions, indicators)
  • Trading rules (leverage limits, position sizing, risk management)
  • Output format instructions (buy/sell/hold decision)
The platform automatically injects real-time market data into the prompt before sending it to the LLM.

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Trading program

A trading program is a Python class with a should_trade(data) method that returns a trading decision. Programs provide:
  • Deterministic, reproducible behavior
  • Full access to market data, indicators, and positions
  • Backtesting against historical data
  • Execution in a secure sandbox (no filesystem or network access)

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Signal pool

A signal pool groups signal definitions with a set of symbols. It defines when a strategy should run. Signals can detect:
  • Price breakouts or breakdowns
  • Open interest surges
  • Funding rate spikes
  • Volume anomalies
  • Custom metric thresholds
Multiple signals can be combined with AND/OR logic.

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Binding

A binding connects a trading program to an AI trader. Each binding includes:
  • The program to execute
  • Signal pool triggers
  • Scheduled trigger interval
  • Exchange-specific configuration
One AI trader can have multiple program bindings, each triggered independently.

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Decision

Every time a strategy runs, it produces a decision with:
FieldDescription
operationbuy, sell, close, or hold
symbolThe trading pair (e.g., BTC, ETH)
target_portion_of_balancePosition size as a fraction (0.0 to 1.0)
leverageLeverage multiplier (1x to max)
max_price / min_priceSlippage protection limits
take_profit_priceAutomatic take-profit level
stop_loss_priceAutomatic stop-loss level
reasonExplanation for the decision

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Market regime

The platform classifies current market conditions into regimes:
  • Breakout — high volatility with strong directional movement
  • Trending — sustained directional movement
  • Ranging — sideways price action within a range
  • Volatile — high volatility without clear direction
  • Quiet — low volatility, low volume
Strategies can use regime information to adapt their behavior.