Ai更新架构

TurtleOnTime copy.py

@@ -11,6 +11,14 @@ import mysql_database
 from EmailTest import send_email, parse_return_email
 from dataclasses import dataclass
 import time
+
+'''
+todo
+
+1 运行过程框架调整，支持多个turtle同时监测
+2 增加运行状态写入yaml文件，读取文件恢复状态
+'''
+
 @dataclass
 class BuyState:
     trigger_time: float # 触发次数
@@ -620,31 +628,9 @@ class TurtleTrading_OnTime(object):
                                     Net_return=abs(self.turtle.Capital - available_cash))
         self.turtle.tradeslog.append(sale_this_time)
 
-    def run_short_trading_loop(self):
+    def run_short_trading_loop(self, stock_data, etf_data):
-        while True:
+
-            # 获取当前时间
         now = datetime.now().time()
-
-            # 判断当前时间是否在交易时段内（9:30-11:30 或 13:00-15:00）
-            is_trading_time = (
-                (now.hour == 9 and now.minute >= 30) or
-                (now.hour == 10 and 0 <= now.minute <= 59) or
-                (now.hour == 11 and now.minute <= 30) or
-                (now.hour == 13 and 0 <= now.minute <= 59) or
-                (now.hour == 14 and 0 <= now.minute <= 59) or
-                (now.hour == 15 and now.minute <= 0)
-            )
-
-
-                
-            # if not is_trading_time:
-            #     # 非交易时间，等待 1 分钟后继续循环
-            #     time.sleep(60)
-            #     continue
-
-            # 获取股票和ETF数据
-            stock_data, etf_data = self.get_stocks_data()
-
         # 根据类型获取当前价格
         if self.turtle.type == "stock":
             self.turtle.PriceNow = float(stock_data.loc[etf_data['代码'] == self.turtle.TradeCode, '最新价'].values[0])
@@ -745,7 +731,34 @@ class TurtleTrading_OnTime(object):
             self.turtle.CalPositionSize()
             
         # 每分钟获取一次数据，判断是否触发条件 9:30-11:30 13:00-15:00
-        self.run_short_trading_loop()
+        while True:
+            # 获取当前时间
+            now = datetime.now().time()
+
+            # 判断当前时间是否在交易时段内（9:30-11:30 或 13:00-15:00）
+            is_trading_time = (
+                (now.hour == 9 and now.minute >= 30) or
+                (now.hour == 10 and 0 <= now.minute <= 59) or
+                (now.hour == 11 and now.minute <= 30) or
+                (now.hour == 13 and 0 <= now.minute <= 59) or
+                (now.hour == 14 and 0 <= now.minute <= 59) or
+                (now.hour == 15 and now.minute <= 0)
+            )
+
+
+                
+            if not is_trading_time:
+                # 非交易时间，等待 1 分钟后继续循环
+                time.sleep(60)
+                continue
+            
+            is_stop_time = (now.hour > 15 and now.minute > 0) #收盘时间
+            if is_stop_time:
+                break
+
+            # 获取股票和ETF数据
+            stock_data, etf_data = self.get_stocks_data()
+            self.run_short_trading_loop(stock_data, etf_data)
         # ------------------结束阶段--------------------
         # 数据库更新当天数据，增加ATR、donchian数据
         # 直接做个新表
@@ -754,6 +767,7 @@ class TurtleTrading_OnTime(object):
         time.sleep(16.5*600)
 
 if __name__ == '__main__':
+    
     user_email = "guoyize2209@163.com"
     t = TurtleTrading('513870', "etf", 0.0025, 100000, 200000)
     # t.get_ready(100)

architecture_plan.md

@@ -0,0 +1,59 @@
+# Architecture Plan for TurtleOnTime.py
+
+## Overview
+
+This document outlines a plan to refactor the `TurtleOnTime.py` file to improve its architecture, maintainability, and testability.
+
+## Current Issues
+
+*   Single Responsibility Principle violation
+*   Tight coupling
+*   Lack of modularity
+*   Limited testability
+
+## Proposed Solution
+
+1.  Refactor the `TurtleTrading` class into smaller, more focused classes.
+2.  Introduce dependency injection to decouple components.
+3.  Create a configuration class to store configuration parameters.
+4.  Implement robust error handling.
+5.  Add logging.
+6.  Create unit tests.
+
+## Class Diagram
+```mermaid
+graph TD
+    A[Understand Current Architecture] --> B{Read TurtleOnTime.py};
+    B --> C[Identify Key Components];
+    C --> D[Analyze Data Flow];
+    D --> E[Assess Code Structure & Modularity];
+    E --> F[Identify Potential Bottlenecks & Areas for Improvement];
+    F --> G[Propose Architectural Changes & Refactoring];
+    G --> H[Create Detailed Improvement Plan];
+    H --> I[Present Improvement Plan (Mermaid Diagram)];
+    I --> J[Seek User Approval];
+    J -- Approved --> K[Write to File (if approved)];
+    J -- Rejected --> L[Revise Plan];
+    L --> I;
+```
+
+```mermaid
+graph TD
+    A[TurtleTrading Class] --> B(DataFetcher Class)
+    A --> C(DonchianChannelCalculator Class)
+    A --> D(PositionSizer Class)
+    A --> E(TradingStrategy Class)
+    F[Config Class] --> A
+    G[Unit Tests] --> A
+```
+
+## Detailed Steps
+
+*   **DataFetcher Class:** Responsible for fetching data from various sources (e.g., Akshare).  Should be configurable to support different data sources.
+*   **DonchianChannelCalculator Class:**  Calculates Donchian channels.  Should be reusable for different timeframes.
+*   **PositionSizer Class:**  Determines the size of positions based on risk tolerance and other factors.
+*   **TradingStrategy Class:**  Encapsulates the trading logic.  Should be easily swappable to test different strategies.
+*   **Config Class:**  Stores configuration parameters, such as data source URLs and trading strategy parameters.
+*   **Error Handling:** Implement try-except blocks to catch and handle exceptions gracefully. Log errors to a file for debugging.
+*   **Logging:** Use a logging library to track important events and debug issues.
+*   **Unit Tests:** Create unit tests to verify the correctness of individual components.

config.py

@@ -0,0 +1,6 @@
+class Config:
+    def __init__(self, symbol):
+        self.symbol = symbol
+
+    def get_symbol(self):
+        return self.symbol

data_fetcher.py

@@ -0,0 +1,23 @@
+import ak
+
+class DataFetcher:
+    def __init__(self, config):
+        self.config = config
+
+    def fetch_stock_data(self):
+        """Fetches stock data from Akshare."""
+        try:
+            stock_data = ak.stock_zh_a_spot(symbol=self.config.symbol)
+            return stock_data
+        except Exception as e:
+            print(f"Error fetching stock data: {e}")
+            return None
+
+    def fetch_etf_data(self):
+        """Fetches ETF data from Akshare."""
+        try:
+            etf_data = ak.fund_etf_spot(symbol=self.config.symbol)
+            return etf_data
+        except Exception as e:
+            print(f"Error fetching ETF data: {e}")
+            return None

run.py

@@ -0,0 +1,6 @@
+from turtle_trading import TurtleTrading
+
+if __name__ == "__main__":
+    symbol = "000001"  # Example symbol
+    turtle = TurtleTrading(symbol)
+    turtle.run()

turtle_trading.py

@@ -0,0 +1,16 @@
+from data_fetcher import DataFetcher
+from config import Config
+
+class TurtleTrading:
+    def __init__(self, symbol):
+        self.symbol = symbol
+        self.config = Config(self.symbol)
+        self.data_fetcher = DataFetcher(self.config)
+
+    def run(self):
+        stock_data = self.data_fetcher.fetch_stock_data()
+        if stock_data is not None:
+            print(f"Fetched stock data for {self.symbol}:")
+            print(stock_data)
+        else:
+            print(f"Failed to fetch stock data for {self.symbol}.")