256 lines
8.9 KiB
Python
256 lines
8.9 KiB
Python
"""
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LLM Gate - 极简门控系统，以频率控制为主
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核心原则:
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1. 频率限制 - 每天最多12次，间隔≥15分钟（核心控制！）
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2. 数据基本可用 - 至少100根K线，基础指标完整
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3. 信号基本质量 - 综合得分≥15（只过滤完全中性的信号）
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"""
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import logging
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import os
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import json
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from typing import Dict, Any, Tuple, Optional
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from datetime import datetime, timedelta
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from pathlib import Path
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logger = logging.getLogger(__name__)
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class LLMGate:
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"""
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极简 LLM 门控系统 - 以频率控制为主，量化初筛为辅
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设计原则:
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- 频率限制是核心（防止过度调用）
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- 量化分析做初步筛选（过滤完全中性信号）
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- 尽可能让LLM有机会深度分析
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"""
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def __init__(
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self,
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# 数据要求
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min_candles: int = 100, # 最少K线数量
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# 信号质量（极简 - 只检查综合得分）
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min_composite_score: float = 15.0, # 最小综合得分（过滤完全中性信号）
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# 频率限制（核心控制！）
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max_calls_per_day: int = 12, # 每天最多调用次数
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min_call_interval_minutes: int = 15, # 最小调用间隔（分钟）
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# 状态存储
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state_file: str = '/app/data/llm_gate_state.json',
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):
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"""
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初始化极简 LLM Gate
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Args:
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min_candles: 最少K线数量
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min_composite_score: 最小综合得分（唯一的质量检查）
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max_calls_per_day: 每天最多调用次数
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min_call_interval_minutes: 最小调用间隔
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state_file: 状态文件路径
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"""
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# 数据要求
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self.min_candles = min_candles
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# 信号质量（极简）
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self.min_composite_score = min_composite_score
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# 频率限制
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self.max_calls_per_day = max_calls_per_day
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self.min_call_interval_minutes = min_call_interval_minutes
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# 状态管理
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self.state_file = state_file
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self.state = self._load_state()
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logger.info(
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f"🚦 LLM Gate 初始化 (极简模式): "
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f"每天最多{max_calls_per_day}次, "
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f"间隔≥{min_call_interval_minutes}分钟, "
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f"综合得分≥{min_composite_score} (唯一质量检查)"
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)
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def should_call_llm(
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self,
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quant_signal: Dict[str, Any],
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analysis: Dict[str, Any]
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) -> Tuple[bool, str]:
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"""
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判断是否应该调用 LLM（优化版：简化检查，主要靠频率限制）
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检查顺序 (快速失败原则):
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1. 频率限制 (核心!)
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2. 数据基本可用性
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3. 信号基本质量 (量化初筛)
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Returns:
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(should_call, reason)
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"""
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# Check 1: 频率限制 (核心控制！)
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freq_check, freq_reason = self._check_frequency_limit()
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if not freq_check:
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logger.info(f"🚫 LLM Gate: 频率限制 - {freq_reason}")
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return False, freq_reason
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# Check 2: 数据基本可用性（简化版）
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data_check, data_reason = self._check_data_sufficiency(analysis)
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if not data_check:
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logger.info(f"🚫 LLM Gate: 数据不足 - {data_reason}")
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return False, data_reason
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# Check 3: 信号基本质量（量化初筛，门槛很低）
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quality_check, quality_reason = self._check_signal_quality(quant_signal, analysis)
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if not quality_check:
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logger.info(f"🚫 LLM Gate: 信号质量不足 - {quality_reason}")
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return False, quality_reason
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# ✅ 所有检查通过 - 让 LLM 进行深度分析
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logger.info(
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f"✅ LLM Gate: PASSED! "
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f"{quality_reason}, "
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f"今日已调用{self.state['today_calls']}/{self.max_calls_per_day}次"
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)
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# 记录本次调用
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self._record_call()
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return True, f"量化初筛通过: {quality_reason}"
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def _check_frequency_limit(self) -> Tuple[bool, str]:
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"""检查频率限制"""
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now = datetime.now()
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today_str = now.strftime('%Y-%m-%d')
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# 重置每日计数
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if self.state.get('last_date') != today_str:
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self.state['last_date'] = today_str
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self.state['today_calls'] = 0
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self._save_state()
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# Check 1: 每日调用次数
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if self.state['today_calls'] >= self.max_calls_per_day:
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return False, f"今日已调用{self.state['today_calls']}次，达到上限{self.max_calls_per_day}次"
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# Check 2: 调用间隔
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last_call_time = self.state.get('last_call_time')
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if last_call_time:
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last_call = datetime.fromisoformat(last_call_time)
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elapsed = (now - last_call).total_seconds() / 60 # 转为分钟
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if elapsed < self.min_call_interval_minutes:
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return False, f"距离上次调用仅{elapsed:.1f}分钟，需≥{self.min_call_interval_minutes}分钟"
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return True, "频率检查通过"
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def _check_data_sufficiency(self, analysis: Dict[str, Any]) -> Tuple[bool, str]:
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"""检查数据充足性 (提高到200根K线)"""
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metadata = analysis.get('metadata', {})
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candle_count = metadata.get('candle_count', 0)
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if candle_count < self.min_candles:
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return False, f"K线数量不足: {candle_count}/{self.min_candles}根"
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# 确保所有必要的指标都已计算
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required_keys = ['trend_analysis', 'momentum', 'support_resistance', 'orderflow']
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for key in required_keys:
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if key not in analysis:
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return False, f"缺少关键指标: {key}"
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return True, f"数据充足: {candle_count}根K线"
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def _check_signal_quality(
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self,
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quant_signal: Dict[str, Any],
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analysis: Dict[str, Any]
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) -> Tuple[bool, str]:
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"""
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检查信号质量（极简版 - 只检查综合得分）
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只要量化分析给出了明确信号（不是完全中性），就让LLM来深度分析
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"""
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# 唯一检查: 综合得分强度 (门槛非常低，只过滤完全中性的信号)
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composite_score = abs(quant_signal.get('composite_score', 0))
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if composite_score < self.min_composite_score:
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return False, f"综合得分不足: {composite_score:.1f} < {self.min_composite_score}"
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# ✅ 通过 - 其他所有检查都删除了
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signal_type = quant_signal.get('signal_type', 'HOLD')
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return True, f"信号类型: {signal_type}, 综合得分: {composite_score:.1f}"
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def _load_state(self) -> Dict[str, Any]:
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"""加载状态文件"""
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# 确保目录存在
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state_path = Path(self.state_file)
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state_path.parent.mkdir(parents=True, exist_ok=True)
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if state_path.exists():
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try:
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with open(self.state_file, 'r') as f:
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return json.load(f)
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except Exception as e:
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logger.warning(f"加载状态文件失败: {e}")
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# 默认状态
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return {
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'last_date': '',
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'today_calls': 0,
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'last_call_time': None,
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'total_calls': 0,
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}
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def _save_state(self):
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"""保存状态文件"""
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try:
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with open(self.state_file, 'w') as f:
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json.dump(self.state, f, indent=2)
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except Exception as e:
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logger.error(f"保存状态文件失败: {e}")
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def _record_call(self):
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"""记录本次调用"""
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now = datetime.now()
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self.state['today_calls'] += 1
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self.state['total_calls'] = self.state.get('total_calls', 0) + 1
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self.state['last_call_time'] = now.isoformat()
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self._save_state()
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logger.info(
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f"📝 记录LLM调用: 今日第{self.state['today_calls']}次, "
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f"累计第{self.state['total_calls']}次"
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)
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def get_stats(self) -> Dict[str, Any]:
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"""获取统计信息"""
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now = datetime.now()
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today_str = now.strftime('%Y-%m-%d')
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# 重置每日计数
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if self.state.get('last_date') != today_str:
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today_calls = 0
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else:
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today_calls = self.state['today_calls']
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# 计算距离上次调用的时间
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last_call_time = self.state.get('last_call_time')
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if last_call_time:
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last_call = datetime.fromisoformat(last_call_time)
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minutes_since_last = (now - last_call).total_seconds() / 60
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else:
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minutes_since_last = None
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return {
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'today_calls': today_calls,
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'max_calls_per_day': self.max_calls_per_day,
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'remaining_calls_today': max(0, self.max_calls_per_day - today_calls),
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'total_calls': self.state.get('total_calls', 0),
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'last_call_time': last_call_time,
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'minutes_since_last_call': minutes_since_last,
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'can_call_now': minutes_since_last is None or minutes_since_last >= self.min_call_interval_minutes,
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}
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