openenv.yaml

name: "Quant-Gym"
description: "Financial analysis environment for testing AI agents"
version: "1.0.0"

environment:
  name: "QuantGym"
  class: "TradingEnvironment"
  module: "server.environment"

tasks:
  - id: "task1"
    name: "Fetch Market Data"
    description: "Agent must retrieve current price for AAPL"
    difficulty: "easy"
    grader:
      type: deterministic
      endpoint: /grader
    max_score: 1.0
    
  - id: "task2"
    name: "News Sentiment Analysis"
    description: "Agent must analyze news and recommend action"
    difficulty: "medium"
    grader:
      type: llm_judge
      endpoint: /grader
    max_score: 1.0
    
  - id: "task3"
    name: "Backtest Strategy"
    description: "Agent must backtest a trading strategy"
    difficulty: "hard"
    grader:
      type: deterministic
      endpoint: /grader
    max_score: 1.0

action_schema:
  type: object
  properties:
    type:
      type: string
      enum: [GET_PRICE, GET_NEWS, BUY, SELL, BACKTEST]
    symbol:
      type: string
    amount:
      type: integer
    explanation:
      type: string
    strategy:
      type: string

observation_schema:
  type: object
  properties:
    timestamp:
      type: string
    price:
      type: number
    balance:
      type: number
    holdings:
      type: integer
    portfolio_value:
      type: number
    last_news:
      type: object
    backtest_results:
      type: object