767 lines
25 KiB
Markdown
767 lines
25 KiB
Markdown
# OpenClaw Trading - 生存压力驱动的量化交易系统设计文档
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## 概述
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结合 ClawWork 的生存压力机制 + TradingAgents 的多智能体架构 + abu 的因子系统，创建一个**必须为自己的决策付费**的交易 Agent 系统。
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---
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## 1. 系统架构
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```
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┌─────────────────────────────────────────────────────────────────┐
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│ OpenClaw Trading │
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│ 生存压力驱动的量化系统 │
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├─────────────────────────────────────────────────────────────────┤
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│ │
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│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
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│ │ 资金层 │◄──►│ Agent 层 │◄──►│ 市场层 │ │
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│ │ Capital │ │ Multi-Agent │ │ Market │ │
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│ └──────────────┘ └──────────────┘ └──────────────┘ │
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│ │ │ │ │
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│ ▼ ▼ ▼ │
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│ ┌──────────────────────────────────────────────────────┐ │
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│ │ 生存压力引擎 │ │
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│ │ ┌──────────┐ ┌──────────┐ ┌──────────────────┐ │ │
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│ │ │ 成本计算 │ │ 收益评估 │ │ 生存状态管理 │ │ │
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│ │ │ Cost │ │ Reward │ │ Life State │ │ │
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│ │ └──────────┘ └──────────┘ └──────────────────┘ │ │
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│ └──────────────────────────────────────────────────────┘ │
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│ │
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└─────────────────────────────────────────────────────────────────┘
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```
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---
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## 2. 核心机制设计
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### 2.1 经济压力模型（借鉴 ClawWork）
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```python
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class TradingEconomicTracker:
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"""
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交易Agent经济追踪器
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每个Agent必须为自己的决策付费
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"""
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def __init__(self,
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agent_id: str,
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initial_capital: float = 10000.0, # 启动资金 $10,000
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token_cost_per_1m_input: float = 2.5,
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token_cost_per_1m_output: float = 10.0,
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trade_fee_rate: float = 0.001): # 交易手续费 0.1%
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self.balance = initial_capital
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self.token_costs = 0.0
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self.trade_costs = 0.0
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self.realized_pnl = 0.0
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# 生存状态阈值
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self.thresholds = {
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'thriving': initial_capital * 1.5, # 盈利 50%+
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'stable': initial_capital * 1.1, # 盈利 10%+
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'struggling': initial_capital * 0.8, # 亏损 20%+
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'bankrupt': initial_capital * 0.3 # 亏损 70%+
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}
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def calculate_decision_cost(self,
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tokens_input: int,
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tokens_output: int,
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market_data_calls: int) -> float:
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"""
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计算每次决策的成本
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"""
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llm_cost = (tokens_input / 1e6 * self.token_cost_per_1m_input +
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tokens_output / 1e6 * self.token_cost_per_1m_output)
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data_cost = market_data_calls * 0.01 # 每次数据调用 $0.01
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total_cost = llm_cost + data_cost
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self.token_costs += total_cost
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self.balance -= total_cost
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return total_cost
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def calculate_trade_cost(self,
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trade_value: float,
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is_win: bool,
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win_amount: float = 0.0,
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loss_amount: float = 0.0) -> dict:
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"""
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计算交易成本和收益
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"""
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fee = trade_value * self.trade_fee_rate
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self.trade_costs += fee
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self.balance -= fee
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pnl = win_amount - loss_amount - fee
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self.realized_pnl += pnl
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self.balance += pnl
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return {
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'fee': fee,
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'pnl': pnl,
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'balance': self.balance,
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'status': self.get_survival_status()
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}
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def get_survival_status(self) -> str:
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"""获取生存状态"""
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if self.balance >= self.thresholds['thriving']:
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return '🚀 thriving' # 繁荣 - 可扩张交易规模
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elif self.balance >= self.thresholds['stable']:
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return '💪 stable' # 稳定 - 正常交易
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elif self.balance >= self.thresholds['struggling']:
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return '⚠️ struggling' # 挣扎 - 只能做小单
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elif self.balance >= self.thresholds['bankrupt']:
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return '🔴 critical' # 危急 - 只能模拟交易
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else:
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return '💀 bankrupt' # 破产 - 被淘汰
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```
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### 2.2 多 Agent 角色设计（借鉴 TradingAgents）
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```python
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class TradingAgentTeam:
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"""
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交易Agent团队
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每个Agent都有自己的资金账户和生存压力
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"""
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def __init__(self):
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self.agents = {
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# 分析师团队 - 成本低，收费少
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'market_analyst': AnalystAgent(
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role='market',
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decision_cost=0.05, # 每次分析 $0.05
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min_balance=50
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),
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'sentiment_analyst': AnalystAgent(
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role='sentiment',
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decision_cost=0.08,
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min_balance=80
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),
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'fundamental_analyst': AnalystAgent(
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role='fundamental',
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decision_cost=0.10,
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min_balance=100
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),
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# 研究员团队 - 中等成本
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'bull_researcher': ResearcherAgent(
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stance='bull',
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decision_cost=0.15,
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min_balance=150
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),
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'bear_researcher': ResearcherAgent(
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stance='bear',
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decision_cost=0.15,
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min_balance=150
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),
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# 风险管理 - 高成本但必要
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'risk_manager': RiskManagerAgent(
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decision_cost=0.20,
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min_balance=200
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),
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# 交易员 - 执行决策，成本最高
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'trader': TraderAgent(
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decision_cost=0.30,
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min_balance=500,
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trade_fee_rate=0.001
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)
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}
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```
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### 2.3 工作-交易权衡机制（ClawWork 核心机制）
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```python
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class WorkTradeBalance:
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"""
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工作-交易权衡系统
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Agent需要决定：立即交易赚钱 vs 学习提升能力
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"""
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def decide_activity(self, agent: Agent) -> str:
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"""
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根据当前经济状态决定是交易还是学习
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"""
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status = agent.economic_tracker.get_survival_status()
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skill_level = agent.skill_level
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win_rate = agent.historical_win_rate
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# 决策逻辑
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if status == '💀 bankrupt':
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return 'liquidate' # 清仓，停止交易
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elif status == '🔴 critical':
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# 危急状态：只能做最有把握的交易
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if win_rate > 0.7 and skill_level > 0.8:
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return 'conservative_trade'
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else:
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return 'paper_trade' # 模拟交易，学习为主
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elif status == '⚠️ struggling':
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# 挣扎状态：谨慎交易，适当学习
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if win_rate < 0.5:
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return 'learn' # 胜率低，先学习
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else:
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return 'selective_trade' # 选择性交易
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elif status == '💪 stable':
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# 稳定状态：正常交易 + 适度学习
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if skill_level < 0.6:
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return 'learn' # 投资自己
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else:
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return 'normal_trade'
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elif status == '🚀 thriving':
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# 繁荣状态：可以承担更多风险
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if skill_level < 0.9:
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return 'aggressive_learn' # 大量投资学习
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else:
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return 'aggressive_trade' # 扩大交易规模
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```
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### 2.4 学习投资系统
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```python
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class LearningInvestment:
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"""
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学习投资系统
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Agent可以投资学习来提升交易能力
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"""
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LEARNING_COURSES = {
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'technical_analysis': {
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'cost': 100.0, # 学费 $100
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'duration_days': 7, # 学习周期 7天
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'skill_improvement': 0.1, # 技能提升 10%
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'win_rate_boost': 0.05 # 胜率提升 5%
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},
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'risk_management': {
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'cost': 150.0,
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'duration_days': 10,
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'skill_improvement': 0.15,
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'max_drawdown_reduction': 0.1
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},
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'market_psychology': {
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'cost': 200.0,
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'duration_days': 14,
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'skill_improvement': 0.2,
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'sentiment_accuracy_boost': 0.1
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},
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'advanced_strategies': {
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'cost': 500.0,
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'duration_days': 30,
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'skill_improvement': 0.3,
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'new_strategy_unlock': True
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}
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}
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def enroll_course(self, agent: Agent, course_name: str) -> bool:
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"""
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报名学习课程
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"""
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course = self.LEARNING_COURSES[course_name]
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# 检查是否有足够资金
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if agent.balance < course['cost'] * 1.5: # 保留50%安全边际
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return False
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# 扣除学费
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agent.balance -= course['cost']
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# 开始学习
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agent.learning_status = {
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'course': course_name,
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'start_date': datetime.now(),
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'end_date': datetime.now() + timedelta(days=course['duration_days']),
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'expected_improvement': course['skill_improvement']
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}
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return True
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```
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### 2.5 交易因子插件系统（借鉴 abu）
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```python
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class FactorPluginSystem:
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"""
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因子插件系统
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Agent可以购买/解锁因子来提升交易能力
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"""
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AVAILABLE_FACTORS = {
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# 基础因子 - 免费
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'moving_average_cross': {
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'cost': 0,
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'type': 'buy',
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'description': '均线金叉策略'
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},
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'rsi_oversold': {
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'cost': 0,
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'type': 'buy',
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'description': 'RSI超卖反弹'
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},
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# 进阶因子 - 付费解锁
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'bollinger_squeeze': {
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'cost': 50.0,
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'type': 'buy',
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'description': '布林带挤压突破'
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},
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'macd_divergence': {
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'cost': 80.0,
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'type': 'buy',
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'description': 'MACD背离信号'
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},
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# 高级因子 - 昂贵但强大
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'machine_learning_pred': {
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'cost': 500.0,
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'type': 'buy',
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'description': '机器学习预测模型'
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},
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'sentiment_momentum': {
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'cost': 300.0,
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'type': 'buy',
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'description': '情绪动量策略'
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},
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# 卖出因子
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'atr_trailing_stop': {
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'cost': 100.0,
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'type': 'sell',
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'description': 'ATR追踪止损'
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},
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'time_decay_exit': {
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'cost': 150.0,
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'type': 'sell',
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'description': '时间衰减退出'
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}
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}
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def purchase_factor(self, agent: Agent, factor_name: str) -> bool:
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"""
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购买因子
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"""
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factor = self.AVAILABLE_FACTORS[factor_name]
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if agent.balance < factor['cost'] * 1.2: # 保留20%缓冲
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return False
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agent.balance -= factor['cost']
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agent.unlocked_factors.append(factor_name)
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return True
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```
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### 2.6 风险评估与拦截系统（借鉴 abu UMP）
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```python
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class SurvivalRiskManager:
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"""
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生存风险管理系统
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防止Agent因高风险交易而破产
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"""
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def evaluate_trade_risk(self, agent: Agent, trade_signal: dict) -> dict:
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"""
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评估交易对Agent生存的风险
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"""
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balance = agent.balance
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status = agent.economic_tracker.get_survival_status()
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# 风险指标
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position_size = trade_signal['position_size']
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stop_loss = trade_signal['stop_loss']
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max_loss = position_size * stop_loss
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risk_assessment = {
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'approved': False,
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'risk_level': 'unknown',
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'max_position_size': 0,
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'reason': ''
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}
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# 根据生存状态限制风险
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if status == '💀 bankrupt':
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risk_assessment['reason'] = 'Agent已破产，禁止交易'
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return risk_assessment
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elif status == '🔴 critical':
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# 危急状态：最大损失不能超过余额的1%
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max_risk = balance * 0.01
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if max_loss > max_risk:
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risk_assessment['reason'] = f'风险过高，最大允许损失: ${max_risk:.2f}'
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risk_assessment['max_position_size'] = max_risk / stop_loss
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else:
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risk_assessment['approved'] = True
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risk_assessment['risk_level'] = 'extreme_low'
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elif status == '⚠️ struggling':
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max_risk = balance * 0.03 # 3%风险
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if max_loss > max_risk:
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risk_assessment['max_position_size'] = max_risk / stop_loss
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else:
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risk_assessment['approved'] = True
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risk_assessment['risk_level'] = 'low'
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elif status == '💪 stable':
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max_risk = balance * 0.05 # 5%风险
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if max_loss > max_risk:
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risk_assessment['max_position_size'] = max_risk / stop_loss
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else:
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risk_assessment['approved'] = True
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risk_assessment['risk_level'] = 'medium'
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elif status == '🚀 thriving':
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max_risk = balance * 0.10 # 10%风险
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if max_loss > max_risk:
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risk_assessment['max_position_size'] = max_risk / stop_loss
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else:
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risk_assessment['approved'] = True
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risk_assessment['risk_level'] = 'high'
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return risk_assessment
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```
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---
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## 3. 核心工作流程
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```python
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class OpenClawTradingWorkflow:
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"""
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OpenClaw Trading 核心工作流
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"""
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async def execute_trading_cycle(self, agent_team: TradingAgentTeam, symbol: str):
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"""
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执行一个交易周期
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"""
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# Step 1: 检查每个Agent的生存状态
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for agent_name, agent in agent_team.agents.items():
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status = agent.economic_tracker.get_survival_status()
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if status == '💀 bankrupt':
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logger.warning(f'{agent_name} 已破产，跳过该Agent')
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continue
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# Step 2: 决定活动（交易还是学习）
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work_trade = WorkTradeBalance()
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activities = {}
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for agent_name, agent in agent_team.agents.items():
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activities[agent_name] = work_trade.decide_activity(agent)
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# Step 3: 如果决定学习，执行学习
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for agent_name, activity in activities.items():
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if 'learn' in activity:
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await self.execute_learning(agent_team.agents[agent_name])
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# Step 4: 市场分析（分析师团队）
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analysis_results = {}
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for analyst_name in ['market_analyst', 'sentiment_analyst', 'fundamental_analyst']:
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analyst = agent_team.agents[analyst_name]
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if activities[analyst_name] in ['normal_trade', 'selective_trade', 'conservative_trade']:
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# 扣除分析成本
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cost = analyst.economic_tracker.calculate_decision_cost(
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tokens_input=2000,
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tokens_output=500,
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market_data_calls=5
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)
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analysis_results[analyst_name] = await analyst.analyze(symbol)
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logger.info(f'{analyst_name} 分析完成，成本: ${cost:.4f}')
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# Step 5: 研究员辩论（看多 vs 看空）
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if activities['bull_researcher'] in ['normal_trade', 'selective_trade']:
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bull_view = await agent_team.agents['bull_researcher'].debate(
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analysis_results, 'bull'
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)
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if activities['bear_researcher'] in ['normal_trade', 'selective_trade']:
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bear_view = await agent_team.agents['bear_researcher'].debate(
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analysis_results, 'bear'
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)
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# Step 6: 风险评估
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risk_assessment = await agent_team.agents['risk_manager'].assess_risk(
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bull_view, bear_view, symbol
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)
|
||
|
||
# Step 7: 交易决策
|
||
if risk_assessment['approved']:
|
||
trader = agent_team.agents['trader']
|
||
|
||
# 生存风险管理
|
||
survival_risk = SurvivalRiskManager()
|
||
trade_approval = survival_risk.evaluate_trade_risk(
|
||
trader, risk_assessment['trade_signal']
|
||
)
|
||
|
||
if trade_approval['approved']:
|
||
# 执行交易
|
||
result = await trader.execute_trade(
|
||
symbol,
|
||
risk_assessment['trade_signal'],
|
||
max_position=trade_approval.get('max_position_size')
|
||
)
|
||
|
||
# 计算交易后的经济状态
|
||
trade_cost_result = trader.economic_tracker.calculate_trade_cost(
|
||
trade_value=result['value'],
|
||
is_win=result['pnl'] > 0,
|
||
win_amount=max(0, result['pnl']),
|
||
loss_amount=max(0, -result['pnl'])
|
||
)
|
||
|
||
logger.info(f'交易执行: {result}')
|
||
logger.info(f'经济状况: {trade_cost_result}')
|
||
|
||
# Step 8: 反思与学习（BM25 记忆系统）
|
||
await agent_team.reflect_and_learn()
|
||
|
||
return {
|
||
'activities': activities,
|
||
'analysis': analysis_results,
|
||
'trades': result if 'result' in locals() else None,
|
||
'economic_status': {
|
||
name: agent.economic_tracker.get_survival_status()
|
||
for name, agent in agent_team.agents.items()
|
||
}
|
||
}
|
||
```
|
||
|
||
---
|
||
|
||
## 4. 可视化仪表板
|
||
|
||
```python
|
||
class SurvivalDashboard:
|
||
"""
|
||
生存状态实时仪表板
|
||
"""
|
||
|
||
def render(self, agent_team: TradingAgentTeam):
|
||
"""
|
||
渲染实时仪表板
|
||
"""
|
||
console = Console()
|
||
|
||
# 创建Agent状态表格
|
||
table = Table(title="Agent 生存状态")
|
||
table.add_column("Agent", style="cyan")
|
||
table.add_column("余额", justify="right")
|
||
table.add_column("状态", justify="center")
|
||
table.add_column("胜率", justify="right")
|
||
table.add_column("技能等级", justify="right")
|
||
table.add_column("已解锁因子", justify="center")
|
||
|
||
for name, agent in agent_team.agents.items():
|
||
status = agent.economic_tracker.get_survival_status()
|
||
status_color = {
|
||
'🚀 thriving': 'green',
|
||
'💪 stable': 'blue',
|
||
'⚠️ struggling': 'yellow',
|
||
'🔴 critical': 'red',
|
||
'💀 bankrupt': 'dim'
|
||
}.get(status, 'white')
|
||
|
||
table.add_row(
|
||
name,
|
||
f"${agent.balance:,.2f}",
|
||
f"[{status_color}]{status}[/{status_color}]",
|
||
f"{agent.win_rate:.1%}",
|
||
f"{agent.skill_level:.1%}",
|
||
str(len(agent.unlocked_factors))
|
||
)
|
||
|
||
console.print(table)
|
||
|
||
# 经济压力曲线图
|
||
self.render_pressure_chart(agent_team)
|
||
|
||
# 交易盈亏分布
|
||
self.render_pnl_distribution(agent_team)
|
||
```
|
||
|
||
---
|
||
|
||
## 5. 配置示例
|
||
|
||
```json
|
||
{
|
||
"openclaw_trading": {
|
||
"initial_capital": {
|
||
"market_analyst": 1000,
|
||
"sentiment_analyst": 1000,
|
||
"fundamental_analyst": 1000,
|
||
"bull_researcher": 2000,
|
||
"bear_researcher": 2000,
|
||
"risk_manager": 3000,
|
||
"trader": 10000
|
||
},
|
||
|
||
"cost_structure": {
|
||
"llm_input_per_1m": 2.5,
|
||
"llm_output_per_1m": 10.0,
|
||
"market_data_per_call": 0.01,
|
||
"trade_fee_rate": 0.001
|
||
},
|
||
|
||
"survival_thresholds": {
|
||
"thriving_multiplier": 1.5,
|
||
"stable_multiplier": 1.1,
|
||
"struggling_multiplier": 0.8,
|
||
"bankrupt_multiplier": 0.3
|
||
},
|
||
|
||
"learning_courses": {
|
||
"enabled": true,
|
||
"auto_enroll": false,
|
||
"min_balance_ratio": 1.2
|
||
},
|
||
|
||
"factor_market": {
|
||
"enabled": true,
|
||
"free_factors": ["moving_average_cross", "rsi_oversold"],
|
||
"premium_factors": ["machine_learning_pred", "sentiment_momentum"]
|
||
}
|
||
}
|
||
}
|
||
```
|
||
|
||
---
|
||
|
||
## 6. 系统特点总结
|
||
|
||
| 特性 | 设计来源 | 说明 |
|
||
|------|---------|------|
|
||
| 💰 **经济压力** | ClawWork | 每个Agent必须为自己的token和交易付费 |
|
||
| 🎯 **生存状态** | ClawWork | 5级生存状态，影响交易权限和风险承受 |
|
||
| 📚 **学习投资** | ClawWork | Agent可投资学习提升能力，但需权衡成本 |
|
||
| 🤖 **多Agent协作** | TradingAgents | 分析师、研究员、风险管理、交易员分工 |
|
||
| 🧠 **记忆系统** | TradingAgents | BM25离线记忆，从过往交易中学习 |
|
||
| 📊 **因子插件** | abu | 可购买解锁的交易策略因子 |
|
||
| 🛡️ **风险拦截** | abu UMP | 基于生存状态的动态风险限制 |
|
||
|
||
---
|
||
|
||
## 7. 实现路线图
|
||
|
||
### 第一阶段：基础框架
|
||
- [ ] 实现基础经济追踪器
|
||
- [ ] 单Agent交易能力
|
||
- [ ] 生存状态管理
|
||
- [ ] 基础CLI界面
|
||
|
||
### 第二阶段：多Agent协作
|
||
- [ ] 添加多Agent协作架构
|
||
- [ ] 实现辩论机制
|
||
- [ ] 工作流编排
|
||
- [ ] 记忆系统集成
|
||
|
||
### 第三阶段：高级功能
|
||
- [ ] 因子市场系统
|
||
- [ ] 学习投资系统
|
||
- [ ] 风险管理拦截
|
||
- [ ] 可视化仪表板
|
||
|
||
### 第四阶段：生产就绪
|
||
- [ ] 完善监控系统
|
||
- [ ] 添加回测能力
|
||
- [ ] 性能优化
|
||
- [ ] 文档和示例
|
||
|
||
---
|
||
|
||
## 8. 参考项目
|
||
|
||
| 项目 | 核心借鉴 | 路径 |
|
||
|------|---------|------|
|
||
| ClawWork | 经济压力机制、生存状态 | `/Users/cillin/workspeace/stock/reference/ClawWork` |
|
||
| TradingAgents | 多智能体架构、BM25记忆 | `/Users/cillin/workspeace/stock/reference/TradingAgents` |
|
||
| abu | 因子插件系统、UMP风险拦截 | `/Users/cillin/workspeace/stock/reference/abu` |
|
||
| daily_stock_analysis | 数据源管理、通知推送 | `/Users/cillin/workspeace/stock/reference/daily_stock_analysis` |
|
||
| Lean | 回测引擎、性能优化 | `/Users/cillin/workspeace/stock/reference/Lean` |
|
||
|
||
---
|
||
|
||
## 9. 调研报告目录
|
||
|
||
所有参考项目的详细调研报告保存在：
|
||
|
||
```
|
||
/Users/cillin/workspeace/stock/report/
|
||
├── abu_report.md # 阿布量化系统
|
||
├── ClawWork_report.md # AI经济生存基准测试
|
||
├── daily_stock_analysis_report.md # 每日股票分析系统
|
||
├── Lean_report.md # 量化交易平台（待生成）
|
||
└── TradingAgents_report.md # 多智能体交易框架
|
||
```
|
||
|
||
---
|
||
|
||
## 10. Phase 4 实现完成
|
||
|
||
**完成时间**: 2026-02-25
|
||
|
||
### 已完成模块
|
||
|
||
| 任务 | 模块 | 文件数 | 状态 |
|
||
|------|------|--------|------|
|
||
| TASK-045 | 策略框架基类 | 7 | ✅ 完成 |
|
||
| TASK-046 | 策略组合管理器 | 5 | ✅ 完成 |
|
||
| TASK-047 | 策略回测对比 | 5 | ✅ 完成 |
|
||
| TASK-048 | Agent学习记忆 | 3 | ✅ 完成 |
|
||
| TASK-049 | 策略优化器 | 6 | ✅ 完成 |
|
||
| TASK-050 | 进化算法集成 | 6 | ✅ 完成 |
|
||
|
||
### 代码统计
|
||
- **Python 文件**: 64 个
|
||
- **测试文件**: 17 个
|
||
- **测试用例**: 300+
|
||
|
||
### 功能验证
|
||
运行 `python demo_phase4.py` 验证所有功能:
|
||
- ✅ 策略框架基类 (Strategy, Signal, StrategyContext)
|
||
- ✅ 策略组合管理 (StrategyPortfolio, 权重分配)
|
||
- ✅ 策略回测对比 (ComparisonMetrics, StrategyComparator)
|
||
- ✅ Agent学习记忆 (LearningMemory, BM25Index)
|
||
- ✅ 策略优化器 (GridSearch, RandomSearch, Bayesian)
|
||
- ✅ 进化算法 (GeneticAlgorithm, EvolutionEngine, NSGA2)
|
||
|
||
### 实现路线图更新
|
||
|
||
#### ✅ 第一阶段：基础框架
|
||
- [x] 实现基础经济追踪器
|
||
- [x] 单Agent交易能力
|
||
- [x] 生存状态管理
|
||
- [x] 基础CLI界面
|
||
|
||
#### ✅ 第二阶段：多Agent协作
|
||
- [x] 添加多Agent协作架构
|
||
- [x] 实现辩论机制
|
||
- [x] 工作流编排
|
||
- [x] 记忆系统集成
|
||
|
||
#### 🔄 第三阶段：高级功能 (进行中)
|
||
- [x] 因子市场系统
|
||
- [x] 学习投资系统
|
||
- [x] 风险管理拦截
|
||
- [ ] 可视化仪表板
|
||
|
||
#### ⏳ 第四阶段：生产就绪
|
||
- [ ] 完善监控系统
|
||
- [ ] 添加回测能力
|
||
- [ ] 性能优化
|
||
- [ ] 文档和示例
|
||
|
||
---
|
||
|
||
*设计文档版本: 1.1*
|
||
*Phase 4 完成时间: 2026-02-25*
|
||
*设计来源: Claude Code 基于多项目分析*
|