Refactor market analysis and price fetching logic, remove orderbook analysis from the main loop, add new data collection and strategy modules, and update documentation.

src/strategy/risk_manager.py

@@ -1,14 +1,17 @@
 from loguru import logger
 
+
 class RiskManager:
     """
     风险控制系统
     """
-    
+
     def __init__(self, config=None):
         self.config = config or {}
         # 基础风控参数
-        self.max_single_trade = self.config.get("max_single_trade", 50.0)  # 最大单笔调整为 $50
+        self.max_single_trade = self.config.get(
+            "max_single_trade", 50.0
+        )  # 最大单笔调整为 $50
         self.max_daily_exposure = 50.0  # 每日最高投入上限
         self.daily_used_exposure = 0.0
         self.last_reset_date = ""
@@ -16,82 +19,81 @@ class RiskManager:
         self.min_confidence = 0.5
         self.peak_capital = 0
         self.is_trading_paused = False
-        
+
         logger.info("Initializing Pro Risk Manager...")
 
     def _reset_daily_exposure(self):
         """每日重置额度"""
         from datetime import datetime
+
         today = datetime.now().strftime("%Y-%m-%d")
         if self.last_reset_date != today:
             self.daily_used_exposure = 0.0
             self.last_reset_date = today
             logger.info(f"Daily exposure reset for {today}")
 
-    def calculate_position_size(self, 
-                                base_confidence_usd: float,
-                                depth: float,
-                                hours_to_settle: float,
-                                is_high_relative_volume: bool) -> tuple[float, str]:
+    def calculate_position_size(
+        self,
+        base_confidence_usd: float,
+        depth: float = 0,
+        hours_to_settle: float = 24,
+        is_high_relative_volume: bool = False,
+    ) -> tuple[float, str]:
         """
-        四层过滤仓位计算方法:
-        仓位 = base_position(置信度) 
-               × liquidity_factor(深度/仓位 >= 5x) 
-               × time_decay(离结算衰减) 
+        仓位计算方法 (简化版，移除流动性过滤):
+        仓位 = base_position(置信度)
+               × time_decay(离结算衰减)
                × budget_limit
         """
         self._reset_daily_exposure()
-        
+
         final_pos = base_confidence_usd
         reason = "Normal"
 
-        # 1. 流动性过滤: 深度 < $50 强制跳过; 深度 < 仓位的 5 倍则缩减
-        if depth < 50:
-            return 0.0, "🚫深度不足 (min $50)"
-        
-        if depth < final_pos * 5:
-            # 如果深度不足以承载期望仓位，按比例缩减至深度的 1/5
-            final_pos = depth / 5.0
-            reason = "⚠️深度限流"
-
-        # 2. 时间衰减因子
+        # 1. 时间衰减因子
         # 离结算时间越近，预测越准但也存在剧烈博弈风险
         time_factor = 1.0
         if hours_to_settle <= 1.0:
-            time_factor = 0.0 # 最后 1 小时停止建仓
+            time_factor = 0.0  # 最后 1 小时停止建仓
             reason = "🚫临近结算"
         elif hours_to_settle <= 4.0:
-            time_factor = 0.4 # 1-4小时：缩小 60%
+            time_factor = 0.4  # 1-4小时：缩小 60%
             reason = "⏱️结算冲刺 (40%)"
         elif hours_to_settle <= 12.0:
-            time_factor = 0.7 # 4-12小时：缩小 30%
+            time_factor = 0.7  # 4-12小时：缩小 30%
             reason = "⏳接近结算 (70%)"
-        
-        final_pos *= time_factor
-        if final_pos <= 0: return 0.0, reason
 
-        # 3. 预算上限过滤
+        final_pos *= time_factor
+        if final_pos <= 0:
+            return 0.0, reason
+
+        # 2. 预算上限过滤
         remaining_daily = self.max_daily_exposure - self.daily_used_exposure
         if remaining_daily <= 0:
             return 0.0, "🚫今日总额度已满 ($50)"
-        
+
         if final_pos > remaining_daily:
             final_pos = remaining_daily
             reason = "🛑触及日风控上限"
 
-        # 4. 高相对成交量加权 (如果是高成交量市场，且逻辑支持，可保持原状或微增)
+        # 3. 高相对成交量加权 (如果是高成交量市场，且逻辑支持，可保持原状或微增)
         # 这里逻辑设定为：如果不是高成交量，再次缩减 20% 防御
         if not is_high_relative_volume:
             final_pos *= 0.8
-            if reason == "Normal": reason = "📉低活缩减"
+            if reason == "Normal":
+                reason = "📉低活缩减"
 
         return round(final_pos, 2), reason
 
     def record_trade(self, amount: float):
         """记录成交额以扣除额度"""
         self.daily_used_exposure += amount
-        logger.debug(f"Applied exposure: ${amount}. Daily Total: ${self.daily_used_exposure}")
+        logger.debug(
+            f"Applied exposure: ${amount}. Daily Total: ${self.daily_used_exposure}"
+        )
 
-    def check_trade_risk(self, trade_size: float, market_data: dict, model_confidence: float) -> dict:
+    def check_trade_risk(
+        self, trade_size: float, market_data: dict, model_confidence: float
+    ) -> dict:
         """保持基础接口兼容"""
         return {"passed": True, "risks": []}