288 lines
8.6 KiB
Python
288 lines
8.6 KiB
Python
#!/usr/bin/env python3
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"""
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新架构集成示例
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展示如何使用重构后的模块化架构进行策略执行和任务调度
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"""
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import sys
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import time
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from pathlib import Path
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from datetime import datetime
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# 添加src目录到路径
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current_dir = Path(__file__).parent
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src_dir = current_dir.parent / "src"
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sys.path.insert(0, str(src_dir))
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from loguru import logger
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from src.data.tushare_fetcher import TushareFetcher
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from src.data.stock_pool_manager import StockPoolManager
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from src.strategy.kline_pattern_strategy import KLinePatternStrategy
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from src.execution.strategy_executor import StrategyExecutor
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from src.execution.task_scheduler import TaskScheduler
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from src.utils.notification import NotificationManager
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from src.utils.config_loader import config_loader
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def demo_new_architecture():
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"""演示新架构的完整工作流程"""
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print("=" * 80)
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print("🚀 新架构演示 - 模块化策略执行系统")
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print("=" * 80)
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print("📋 系统架构:")
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print(" 数据层: TushareFetcher + StockPoolManager")
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print(" 策略层: BaseStrategy + KLinePatternStrategy")
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print(" 执行层: TaskScheduler + StrategyExecutor")
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print(" 通知层: NotificationManager")
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print()
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# 1. 初始化所有组件
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print("📦 第1步: 初始化所有组件")
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print("-" * 60)
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# 数据层
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fetcher = TushareFetcher()
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pool_manager = StockPoolManager(fetcher)
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# 通知层
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notification_config = config_loader.get('notification', {})
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notification_manager = NotificationManager(notification_config)
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# 策略层
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strategy_config = {
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'min_entity_ratio': 0.55,
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'final_yang_min_ratio': 0.40,
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'max_turnover_ratio': 40.0,
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'timeframes': ['daily'],
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'pullback_tolerance': 0.02,
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'monitor_days': 30,
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'pullback_confirmation_days': 7
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}
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kline_strategy = KLinePatternStrategy(
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data_fetcher=fetcher,
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notification_manager=notification_manager,
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config=strategy_config
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)
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# 执行层
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executor = StrategyExecutor(pool_manager, notification_manager)
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scheduler = TaskScheduler()
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print("✅ 所有组件初始化完成")
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print()
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# 2. 注册策略到执行器
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print("📋 第2步: 注册策略")
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print("-" * 60)
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executor.register_strategy("kline_pattern", kline_strategy)
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print("已注册策略:")
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for strategy_id, strategy_name in executor.get_registered_strategies().items():
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print(f" {strategy_id}: {strategy_name}")
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print()
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# 3. 展示股票池规则
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print("🎯 第3步: 可用股票池规则")
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print("-" * 60)
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available_rules = pool_manager.get_available_rules()
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print("可用规则:")
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for rule_id, rule_name in available_rules.items():
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print(f" {rule_id}: {rule_name}")
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print()
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# 4. 手动执行单个任务
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print("⚡ 第4步: 手动执行策略任务")
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print("-" * 60)
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task_id = f"manual_task_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
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print(f"执行任务: {task_id}")
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print("参数:")
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print(f" 策略: kline_pattern")
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print(f" 股票池规则: tushare_hot")
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print(f" 最大股票数: 5")
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print()
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result = executor.execute_task(
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task_id=task_id,
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strategy_id="kline_pattern",
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stock_pool_rule="tushare_hot",
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stock_pool_params={"limit": 10},
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max_stocks=5,
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send_notification=False # 演示时不发送通知
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)
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# 显示执行结果
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print("📊 执行结果摘要:")
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summary = result.get_summary()
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for key, value in summary.items():
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print(f" {key}: {value}")
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print()
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# 5. 设置定时任务
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print("⏰ 第5步: 设置定时任务")
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print("-" * 60)
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# 创建任务函数
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task_function_1 = executor.create_task_function(
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strategy_id="kline_pattern",
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stock_pool_rule="tushare_hot",
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stock_pool_params={"limit": 20},
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max_stocks=10,
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send_notification=False
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)
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task_function_2 = executor.create_task_function(
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strategy_id="kline_pattern",
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stock_pool_rule="combined_hot",
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stock_pool_params={"limit_per_source": 15, "final_limit": 25},
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max_stocks=15,
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send_notification=False
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)
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# 添加定时任务
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scheduler.add_task(
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task_id="hot_stocks_scan",
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name="同花顺热榜K线形态扫描",
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func=task_function_1,
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schedule_rule="every 30 minutes",
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enabled=False # 演示时不启用
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)
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scheduler.add_task(
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task_id="combined_hot_scan",
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name="合并热门股票K线形态扫描",
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func=task_function_2,
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schedule_rule="daily at 09:30",
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enabled=False # 演示时不启用
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)
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scheduler.add_task(
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task_id="leading_stocks_scan",
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name="龙头股K线形态扫描",
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func=executor.create_task_function(
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strategy_id="kline_pattern",
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stock_pool_rule="leading_stocks",
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stock_pool_params={"top_boards": 5, "stocks_per_board": 2},
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max_stocks=10,
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send_notification=False
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),
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schedule_rule="weekdays at 14:30",
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enabled=False # 演示时不启用
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)
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print("已添加定时任务:")
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task_status = scheduler.get_task_status()
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for task_id, status in task_status.items():
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print(f" {task_id}:")
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print(f" 名称: {status['name']}")
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print(f" 规则: {status['schedule_rule']}")
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print(f" 状态: {status['status']}")
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print(f" 启用: {status['enabled']}")
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print()
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# 6. 演示立即执行定时任务
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print("🎬 第6步: 演示立即执行定时任务")
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print("-" * 60)
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print("立即执行任务: hot_stocks_scan")
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success = scheduler.execute_task_now("hot_stocks_scan")
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print(f"执行结果: {'成功' if success else '失败'}")
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print()
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# 7. 显示任务执行历史
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print("📈 第7步: 任务执行历史")
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print("-" * 60)
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updated_status = scheduler.get_task_status()
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for task_id, status in updated_status.items():
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if status['total_executions'] > 0:
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print(f"任务: {status['name']}")
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print(f" 总执行次数: {status['total_executions']}")
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print(f" 成功率: {status['success_rate']:.1f}%")
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print(f" 最后执行: {status['last_execution_time']}")
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print()
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# 8. 系统功能总结
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print("🎯 第8步: 新架构优势总结")
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print("-" * 60)
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advantages = """
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✅ 模块化设计:
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• 数据获取、股票池管理、策略执行、任务调度完全分离
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• 每个模块职责单一，易于维护和扩展
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• 支持插件化添加新的股票池规则和策略
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✅ 灵活的股票池管理:
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• 支持多种数据源：同花顺热榜、合并热门、龙头股
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• 可配置参数：股票数量、筛选条件等
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• 易于添加新的股票池规则
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✅ 标准化的策略接口:
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• BaseStrategy抽象基类统一策略接口
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• StrategyResult标准化输出格式
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• 支持多时间周期分析
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✅ 强大的任务调度:
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• 支持多种调度规则：间隔时间、每日定时、工作日定时
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• 任务执行历史和成功率统计
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• 支持立即执行和定时执行
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✅ 统一的执行协调:
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• StrategyExecutor协调股票池获取→策略分析→结果通知
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• 完整的执行结果记录和统计
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• 支持并发执行多个策略
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✅ 完善的通知系统:
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• 支持多种通知方式
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• 策略结果汇总推送
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• 回踩提醒等特殊通知
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"""
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print(advantages)
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print("🚀 架构使用示例:")
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example_usage = """
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# 基础用法 - 立即执行
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result = executor.execute_task(
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task_id="my_task",
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strategy_id="kline_pattern",
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stock_pool_rule="tushare_hot",
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max_stocks=20
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)
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# 定时任务 - 每天9:30执行
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scheduler.add_task(
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task_id="morning_scan",
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name="晨间形态扫描",
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func=executor.create_task_function(...),
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schedule_rule="daily at 09:30"
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)
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# 启动调度器
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scheduler.start()
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"""
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print(example_usage)
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print("=" * 80)
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print("🎉 新架构演示完成!")
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print("💡 现在可以通过简单配置实现复杂的策略执行和调度")
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print("🔧 支持灵活的股票池规则和多策略并行执行")
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print("📱 统一的结果通知和监控体系")
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print("=" * 80)
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if __name__ == "__main__":
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# 设置日志
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logger.remove()
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logger.add(sys.stdout, level="INFO", format="{time:HH:mm:ss} | {level} | {message}")
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try:
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demo_new_architecture()
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except Exception as e:
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logger.error(f"演示过程中发生错误: {e}")
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import traceback
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traceback.print_exc() |