chore(config): 添加环境变量示例及.gitignore更新
- 新增 .env.example，包含 Tushare API、钉钉机器人和PostgreSQL数据库配置模板 - 更新.gitignore，忽略本地配置文件如 .env.local 和 config_local.py - 添加对报表文件命名规则的支持，保留示例文件不忽略 - 删除废弃的 chart.py 及相关图表模块代码 - 新增 config/settings.py，实现从环境变量读取配置的统一接口 - 设置数据目录及缓存目录，确保目录存在，提高配置管理规范性
This commit is contained in:
0
core/__init__.py
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0
core/__init__.py
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0
core/common/__init__.py
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0
core/common/__init__.py
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96
core/common/db.py
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96
core/common/db.py
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@@ -0,0 +1,96 @@
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"""
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数据库配置和连接工具
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"""
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import psycopg2
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from psycopg2.extras import RealDictCursor
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from sqlalchemy import create_engine
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import pandas as pd
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from loguru import logger
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from typing import Optional
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from config.settings import get_db_config
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class DatabaseManager:
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"""数据库管理类"""
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def __init__(self, config: dict = None):
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self.config = config or get_db_config()
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self.engine = None
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def get_engine(self):
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"""获取SQLAlchemy引擎"""
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if self.engine is None:
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conn_str = (
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f"postgresql://{self.config['username']}:{self.config['password']}"
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f"@{self.config['host']}:{self.config['port']}/{self.config['database']}"
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)
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self.engine = create_engine(
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conn_str,
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pool_pre_ping=True,
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pool_recycle=300,
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echo=False,
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)
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return self.engine
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def get_connection(self):
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"""获取psycopg2连接"""
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return psycopg2.connect(
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host=self.config["host"],
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port=self.config["port"],
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database=self.config["database"],
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user=self.config["username"],
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password=self.config["password"],
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)
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def test_connection(self) -> bool:
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"""测试数据库连接"""
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try:
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with self.get_connection() as conn:
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with conn.cursor() as cursor:
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cursor.execute("SELECT 1")
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result = cursor.fetchone()
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logger.info("数据库连接测试成功")
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return True
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except Exception as e:
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logger.error(f"数据库连接测试失败: {e}")
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return False
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def execute_query(self, query: str, params: tuple = None) -> Optional[list]:
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"""执行查询并返回结果"""
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try:
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with self.get_connection() as conn:
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with conn.cursor(cursor_factory=RealDictCursor) as cursor:
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cursor.execute(query, params)
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result = cursor.fetchall()
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return [dict(row) for row in result]
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except Exception as e:
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logger.error(f"执行查询失败: {e}")
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return None
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def insert_dataframe(
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self, df: pd.DataFrame, table_name: str, if_exists: str = "append"
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) -> bool:
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"""将DataFrame插入到数据库表"""
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try:
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engine = self.get_engine()
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df.to_sql(
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table_name,
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engine,
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if_exists=if_exists,
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index=False,
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method="multi",
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chunksize=1000,
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)
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logger.info(f"成功插入 {len(df)} 条记录到表 {table_name}")
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return True
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except Exception as e:
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logger.error(f"插入数据到表 {table_name} 失败: {e}")
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return False
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def close(self):
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"""关闭连接"""
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if self.engine:
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self.engine.dispose()
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self.engine = None
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210
core/common/notify.py
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210
core/common/notify.py
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@@ -0,0 +1,210 @@
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"""
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通知模块 - 支持钉钉、日志等多种通知方式
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"""
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import requests
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import time
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import hmac
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import hashlib
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import base64
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import urllib.parse
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from loguru import logger
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from typing import Optional
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from config.settings import get_dingtalk_config
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class DingTalkBot:
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"""钉钉机器人类"""
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def __init__(self, webhook: str = None, secret: str = None):
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"""
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初始化钉钉机器人
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Args:
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webhook: 钉钉自定义机器人webhook地址
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secret: 加签密钥（可选）
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"""
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config = get_dingtalk_config()
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self.webhook = webhook or config.get("webhook", "")
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self.secret = secret or config.get("secret", "")
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if not self.webhook:
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logger.warning("钉钉webhook未配置，消息将不会被发送")
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def _gen_signed_url(self) -> str:
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"""生成带签名的URL"""
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if not self.secret:
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return self.webhook
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timestamp = str(round(time.time() * 1000))
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secret_enc = self.secret.encode("utf-8")
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string_to_sign = f"{timestamp}\n{self.secret}"
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string_to_sign_enc = string_to_sign.encode("utf-8")
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hmac_code = hmac.new(
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secret_enc, string_to_sign_enc, digestmod=hashlib.sha256
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).digest()
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sign = urllib.parse.quote_plus(base64.b64encode(hmac_code))
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return f"{self.webhook}×tamp={timestamp}&sign={sign}"
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def send_text(
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self, content: str, at_mobiles: list = None, is_at_all: bool = False
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) -> bool:
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"""
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发送文本消息
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Args:
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content: 消息内容
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at_mobiles: 需要@的手机号列表
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is_at_all: 是否@所有人
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Returns:
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bool: 是否发送成功
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"""
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if not self.webhook:
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logger.warning(f"[钉钉消息未发送] {content[:100]}...")
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return False
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at_mobiles = at_mobiles or []
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data = {
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"msgtype": "text",
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"text": {"content": content},
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"at": {"atMobiles": at_mobiles, "isAtAll": is_at_all},
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}
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url = self._gen_signed_url()
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try:
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response = requests.post(url, json=data, timeout=5)
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response.raise_for_status()
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result = response.json()
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if result.get("errcode", -1) != 0:
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logger.error(f"钉钉消息发送失败: {result}")
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return False
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logger.info("钉钉消息发送成功")
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return True
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except Exception as e:
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logger.error(f"钉钉消息发送异常: {e}")
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return False
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def send_markdown(
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self,
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title: str,
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text: str,
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at_mobiles: list = None,
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is_at_all: bool = False,
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) -> bool:
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"""
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发送markdown消息
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Args:
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title: 消息标题
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text: markdown格式的消息内容
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at_mobiles: 需要@的手机号列表
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is_at_all: 是否@所有人
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Returns:
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bool: 是否发送成功
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"""
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if not self.webhook:
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logger.warning(f"[钉钉Markdown未发送] {title}")
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return False
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at_mobiles = at_mobiles or []
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data = {
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"msgtype": "markdown",
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"markdown": {"title": title, "text": text},
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"at": {"atMobiles": at_mobiles, "isAtAll": is_at_all},
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}
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url = self._gen_signed_url()
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try:
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response = requests.post(url, json=data, timeout=5)
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response.raise_for_status()
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result = response.json()
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if result.get("errcode", -1) != 0:
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logger.error(f"钉钉markdown消息发送失败: {result}")
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return False
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logger.info("钉钉markdown消息发送成功")
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return True
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except Exception as e:
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logger.error(f"钉钉markdown消息发送异常: {e}")
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return False
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class NotificationManager:
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"""通知管理器 - 统一管理多种通知渠道"""
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def __init__(self):
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self.dingtalk = DingTalkBot()
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def notify(self, message: str, title: str = "系统通知", use_markdown: bool = False):
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"""
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发送通知（优先使用钉钉，失败则记录日志）
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Args:
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message: 消息内容
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title: 消息标题（markdown模式使用）
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use_markdown: 是否使用markdown格式
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"""
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if use_markdown:
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success = self.dingtalk.send_markdown(title, message)
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else:
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success = self.dingtalk.send_text(message)
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if not success:
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# 钉钉发送失败，记录到日志
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logger.info(f"[通知] {title}: {message}")
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def notify_error(self, error_msg: str):
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"""发送错误通知"""
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markdown = f"""## 错误告警
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**时间**: {time.strftime('%Y-%m-%d %H:%M:%S')}
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**错误信息**:
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```
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{error_msg}
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```
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"""
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self.notify(markdown, title="系统错误", use_markdown=True)
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def notify_signal(self, signals: list, signal_type: str = "CCI超卖"):
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"""
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发送交易信号通知
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Args:
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signals: 信号列表，每项为dict包含code, name等指标
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signal_type: 信号类型名称
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"""
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if not signals:
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logger.info(f"[{signal_type}] 无信号")
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return
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# 构建markdown表格
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if signals:
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headers = signals[0].keys()
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header_line = " | ".join(headers)
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separator = " | ".join(["---"] * len(headers))
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rows = []
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for s in signals:
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row = " | ".join(str(v) for v in s.values())
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rows.append(row)
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table = f"{header_line}\n{separator}\n" + "\n".join(rows)
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else:
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table = "无"
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markdown = f"""## {signal_type}信号
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**时间**: {time.strftime('%Y-%m-%d %H:%M:%S')}
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**筛选结果**:
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{table}
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共 {len(signals)} 个标的符合筛选条件。
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"""
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self.notify(markdown, title=f"{signal_type}信号", use_markdown=True)
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190
core/common/utils.py
Normal file
190
core/common/utils.py
Normal file
@@ -0,0 +1,190 @@
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"""
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通用工具函数
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"""
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import pandas as pd
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import numpy as np
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from datetime import datetime, timedelta
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from typing import Optional
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def format_date(date_str: str, output_format: str = "%Y-%m-%d") -> str:
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"""
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统一日期格式
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Args:
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date_str: 输入日期字符串（支持 YYYY-MM-DD 或 YYYYMMDD）
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output_format: 输出格式
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Returns:
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str: 格式化后的日期字符串
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"""
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# 尝试解析多种格式
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for fmt in ["%Y-%m-%d", "%Y%m%d", "%Y/%m/%d"]:
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try:
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dt = datetime.strptime(date_str, fmt)
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return dt.strftime(output_format)
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except ValueError:
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continue
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raise ValueError(f"无法解析日期格式: {date_str}")
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def get_date_range(
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start_date: Optional[str] = None,
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end_date: Optional[str] = None,
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lookback_days: int = 365,
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) -> tuple[str, str]:
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"""
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获取日期范围
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Args:
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start_date: 开始日期，None则根据lookback_days计算
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end_date: 结束日期，None则使用今天
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lookback_days: 回溯天数
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Returns:
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tuple: (start_date, end_date) 格式为 YYYY-MM-DD
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"""
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if end_date is None:
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end = datetime.now()
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else:
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end = datetime.strptime(format_date(end_date), "%Y-%m-%d")
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if start_date is None:
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start = end - timedelta(days=lookback_days)
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else:
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start = datetime.strptime(format_date(start_date), "%Y-%m-%d")
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return start.strftime("%Y-%m-%d"), end.strftime("%Y-%m-%d")
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def calculate_cagr(
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nav_series: pd.Series,
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method: str = "natural_days",
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) -> float:
|
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"""
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计算年化收益率（CAGR）
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Args:
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nav_series: 净值序列（index=日期）
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method: 'natural_days' 或 'trading_days'
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||||
Returns:
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float: CAGR值
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"""
|
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total_return = nav_series.iloc[-1] / nav_series.iloc[0]
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|
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if method == "natural_days":
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days = (nav_series.index[-1] - nav_series.index[0]).days
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years = days / 365.0
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elif method == "trading_days":
|
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years = len(nav_series) / 252.0
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else:
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raise ValueError(f"不支持的CAGR计算方式: {method}")
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||||
|
||||
if years <= 0:
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return 0.0
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||||
return total_return ** (1 / years) - 1
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|
||||
|
||||
def calculate_max_drawdown(nav_series: pd.Series) -> tuple[float, datetime, datetime]:
|
||||
"""
|
||||
计算最大回撤
|
||||
|
||||
Returns:
|
||||
tuple: (最大回撤比例, 回撤起始日, 回撤结束日)
|
||||
"""
|
||||
cummax = nav_series.cummax()
|
||||
drawdown = (nav_series - cummax) / cummax
|
||||
|
||||
max_dd = drawdown.min()
|
||||
end_idx = drawdown.idxmin()
|
||||
start_idx = nav_series[:end_idx].idxmax()
|
||||
|
||||
return max_dd, start_idx, end_idx
|
||||
|
||||
|
||||
def calculate_sharpe(
|
||||
returns: pd.Series,
|
||||
rf: float = 0.0,
|
||||
periods: int = 252,
|
||||
) -> float:
|
||||
"""
|
||||
计算年化夏普比率
|
||||
|
||||
Args:
|
||||
returns: 日收益率序列
|
||||
rf: 无风险利率（年化）
|
||||
periods: 年化系数
|
||||
|
||||
Returns:
|
||||
float: 夏普比率
|
||||
"""
|
||||
excess_returns = returns - rf / periods
|
||||
if excess_returns.std() == 0:
|
||||
return 0.0
|
||||
return excess_returns.mean() / excess_returns.std() * np.sqrt(periods)
|
||||
|
||||
|
||||
def resample_data(
|
||||
df: pd.DataFrame,
|
||||
timeframe: str,
|
||||
time_col: str = "time",
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
对数据进行重采样
|
||||
|
||||
Args:
|
||||
df: 原始数据
|
||||
timeframe: 目标周期 ('1D', '1W', '1M', '1Y')
|
||||
time_col: 时间列名
|
||||
|
||||
Returns:
|
||||
DataFrame: 重采样后的数据
|
||||
"""
|
||||
timeframe_map = {
|
||||
"1D": "D",
|
||||
"1W": "W",
|
||||
"1M": "M",
|
||||
"3M": "3M",
|
||||
"1Y": "Y",
|
||||
}
|
||||
|
||||
if timeframe not in timeframe_map:
|
||||
return df
|
||||
|
||||
df = df.copy()
|
||||
if time_col in df.columns:
|
||||
df[time_col] = pd.to_datetime(df[time_col])
|
||||
df.set_index(time_col, inplace=True)
|
||||
|
||||
rule = timeframe_map[timeframe]
|
||||
|
||||
resampled = (
|
||||
df.resample(rule)
|
||||
.agg(
|
||||
{
|
||||
"open": "first",
|
||||
"high": "max",
|
||||
"low": "min",
|
||||
"close": "last",
|
||||
"volume": "sum",
|
||||
}
|
||||
)
|
||||
.dropna()
|
||||
)
|
||||
|
||||
return resampled.reset_index()
|
||||
|
||||
|
||||
def safe_divide(a: float, b: float, default: float = 0.0) -> float:
|
||||
"""安全除法，避免除以0"""
|
||||
return a / b if b != 0 else default
|
||||
|
||||
|
||||
def truncate_string(s: str, max_length: int = 50, suffix: str = "...") -> str:
|
||||
"""截断字符串"""
|
||||
if len(s) <= max_length:
|
||||
return s
|
||||
return s[: max_length - len(suffix)] + suffix
|
||||
0
core/factors/__init__.py
Normal file
0
core/factors/__init__.py
Normal file
137
core/factors/momentum.py
Normal file
137
core/factors/momentum.py
Normal file
@@ -0,0 +1,137 @@
|
||||
"""
|
||||
动量因子计算模块
|
||||
|
||||
支持两种动量因子：
|
||||
1. N日涨幅（简单动量）
|
||||
2. 斜率×R²趋势得分（改进版）
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from sklearn.linear_model import LinearRegression
|
||||
|
||||
|
||||
def calculate_momentum(price_series: pd.Series, n: int) -> pd.Series:
|
||||
"""
|
||||
计算 N 日涨幅（简单动量）
|
||||
|
||||
Args:
|
||||
price_series: 价格序列
|
||||
n: 动量窗口天数
|
||||
|
||||
Returns:
|
||||
Series: N日涨幅
|
||||
"""
|
||||
return price_series / price_series.shift(n + 1) - 1.0
|
||||
|
||||
|
||||
def _slope_r2_score(srs: pd.Series, n: int = 25) -> float:
|
||||
"""
|
||||
单次计算斜率×R²趋势得分
|
||||
|
||||
Args:
|
||||
srs: 价格窗口序列（长度为 n）
|
||||
n: 窗口长度
|
||||
|
||||
Returns:
|
||||
float: 斜率 × R² × 10000
|
||||
"""
|
||||
if srs.shape[0] < n:
|
||||
return np.nan
|
||||
|
||||
x = np.arange(1, n + 1).reshape(-1, 1)
|
||||
y = srs.values / srs.values[0] # 归一化
|
||||
|
||||
lr = LinearRegression().fit(x, y)
|
||||
slope = lr.coef_[0]
|
||||
r_squared = lr.score(x, y)
|
||||
score = 10000 * slope * r_squared
|
||||
|
||||
return score
|
||||
|
||||
|
||||
def calculate_slope_r2(price_series: pd.Series, n: int = 25) -> pd.Series:
|
||||
"""
|
||||
计算斜率×R²趋势得分序列
|
||||
|
||||
Args:
|
||||
price_series: 价格序列
|
||||
n: 滚动窗口天数
|
||||
|
||||
Returns:
|
||||
Series: 趋势得分序列
|
||||
"""
|
||||
return price_series.rolling(n).apply(
|
||||
lambda x: _slope_r2_score(x, n), raw=False
|
||||
)
|
||||
|
||||
|
||||
def calculate_daily_return(price_series: pd.Series) -> pd.Series:
|
||||
"""
|
||||
计算日收益率
|
||||
|
||||
Args:
|
||||
price_series: 价格序列
|
||||
|
||||
Returns:
|
||||
Series: 日收益率
|
||||
"""
|
||||
return price_series / price_series.shift(1) - 1
|
||||
|
||||
|
||||
def compute_factors(
|
||||
etf_data: pd.DataFrame,
|
||||
code_list: list,
|
||||
n: int = 25,
|
||||
factor_type: str = "slope_r2",
|
||||
) -> tuple[pd.DataFrame, list]:
|
||||
"""
|
||||
计算所有指数的因子和日收益率
|
||||
|
||||
Args:
|
||||
etf_data: DataFrame, 宽表格式的收盘价
|
||||
code_list: 指数代码列表
|
||||
n: 动量/趋势窗口
|
||||
factor_type: 'momentum' 或 'slope_r2'
|
||||
|
||||
Returns:
|
||||
tuple: (result_df, valid_codes)
|
||||
"""
|
||||
result = etf_data.copy()
|
||||
|
||||
# 过滤掉缺失值过多的指数
|
||||
total_rows = len(result)
|
||||
valid_codes = []
|
||||
for code in code_list:
|
||||
if code not in result.columns:
|
||||
print(f" ⚠ 跳过 {code}: 不在数据中")
|
||||
continue
|
||||
null_pct = result[code].isnull().sum() / total_rows
|
||||
if null_pct > 0.2:
|
||||
print(f" ⚠ 剔除 {code}: 缺失率 {null_pct:.1%} 过高")
|
||||
result = result.drop(columns=[code])
|
||||
else:
|
||||
valid_codes.append(code)
|
||||
|
||||
# 对有效指数计算因子
|
||||
for code in valid_codes:
|
||||
result[f"日收益率_{code}"] = calculate_daily_return(result[code])
|
||||
|
||||
if factor_type == "momentum":
|
||||
result[f"得分_{code}"] = calculate_momentum(result[code], n)
|
||||
elif factor_type == "slope_r2":
|
||||
result[f"得分_{code}"] = calculate_slope_r2(result[code], n)
|
||||
else:
|
||||
raise ValueError(f"不支持的因子类型: {factor_type}")
|
||||
|
||||
# 按得分列做 dropna
|
||||
score_cols = [f"得分_{code}" for code in valid_codes]
|
||||
result = result.dropna(subset=score_cols)
|
||||
|
||||
print("\n因子计算完成:")
|
||||
print(f" 因子类型: {factor_type}")
|
||||
print(f" 窗口天数: {n}")
|
||||
print(f" 有效指数: {len(valid_codes)}/{len(code_list)}")
|
||||
print(f" 有效数据: {len(result)} 行")
|
||||
|
||||
return result, valid_codes
|
||||
207
core/factors/technical.py
Normal file
207
core/factors/technical.py
Normal file
@@ -0,0 +1,207 @@
|
||||
"""
|
||||
技术指标计算模块
|
||||
|
||||
包含CCI、EMA、MACD等常用技术指标
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import talib as ta
|
||||
|
||||
|
||||
def calculate_cci(
|
||||
df: pd.DataFrame,
|
||||
period: int = 14,
|
||||
high_col: str = "high",
|
||||
low_col: str = "low",
|
||||
close_col: str = "close",
|
||||
) -> pd.Series:
|
||||
"""
|
||||
计算CCI指标（商品通道指数）
|
||||
|
||||
Args:
|
||||
df: DataFrame with OHLC data
|
||||
period: CCI周期
|
||||
high_col: 最高价列名
|
||||
low_col: 最低价列名
|
||||
close_col: 收盘价列名
|
||||
|
||||
Returns:
|
||||
Series: CCI值
|
||||
"""
|
||||
return ta.CCI(
|
||||
high=df[high_col],
|
||||
low=df[low_col],
|
||||
close=df[close_col],
|
||||
timeperiod=period,
|
||||
)
|
||||
|
||||
|
||||
def calculate_ema(
|
||||
price_series: pd.Series,
|
||||
period: int = 20,
|
||||
) -> pd.Series:
|
||||
"""
|
||||
计算指数移动平均线
|
||||
|
||||
Args:
|
||||
price_series: 价格序列
|
||||
period: EMA周期
|
||||
|
||||
Returns:
|
||||
Series: EMA值
|
||||
"""
|
||||
return ta.EMA(price_series, timeperiod=period)
|
||||
|
||||
|
||||
def calculate_macd(
|
||||
price_series: pd.Series,
|
||||
fastperiod: int = 12,
|
||||
slowperiod: int = 26,
|
||||
signalperiod: int = 9,
|
||||
) -> tuple[pd.Series, pd.Series, pd.Series]:
|
||||
"""
|
||||
计算MACD指标
|
||||
|
||||
Args:
|
||||
price_series: 价格序列
|
||||
fastperiod: 快线周期
|
||||
slowperiod: 慢线周期
|
||||
signalperiod: 信号线周期
|
||||
|
||||
Returns:
|
||||
tuple: (macd, signal, hist)
|
||||
"""
|
||||
macd, signal, hist = ta.MACD(
|
||||
price_series,
|
||||
fastperiod=fastperiod,
|
||||
slowperiod=slowperiod,
|
||||
signalperiod=signalperiod,
|
||||
)
|
||||
return macd, signal, hist
|
||||
|
||||
|
||||
def calculate_td_sequence(close_series: pd.Series) -> pd.Series:
|
||||
"""
|
||||
计算TD序列（Tom DeMark Sequential）
|
||||
|
||||
Args:
|
||||
close_series: 收盘价序列
|
||||
|
||||
Returns:
|
||||
Series: TD序列值（正数为上涨计数，负数为下跌计数）
|
||||
"""
|
||||
close = close_series.to_list()
|
||||
td = [0, 0, 0, 0]
|
||||
up = 0
|
||||
down = 0
|
||||
|
||||
for i in range(4, len(close)):
|
||||
if close[i] > close[i - 4]:
|
||||
up += 1
|
||||
down = 0
|
||||
td.append(up)
|
||||
else:
|
||||
down -= 1
|
||||
up = 0
|
||||
td.append(down)
|
||||
|
||||
return pd.Series(td, index=close_series.index)
|
||||
|
||||
|
||||
def resample_to_weekly(df: pd.DataFrame) -> pd.DataFrame:
|
||||
"""
|
||||
将日线数据重采样为周线数据
|
||||
|
||||
Args:
|
||||
df: DataFrame with columns: date, open, high, low, close, volume
|
||||
|
||||
Returns:
|
||||
DataFrame: 周线数据
|
||||
"""
|
||||
df = df.copy()
|
||||
if "date" in df.columns:
|
||||
df["date"] = pd.to_datetime(df["date"])
|
||||
df.set_index("date", inplace=True)
|
||||
|
||||
weekly = pd.DataFrame(
|
||||
{
|
||||
"code": df["code"].resample("W").first() if "code" in df.columns else None,
|
||||
"open": df["open"].resample("W").first(),
|
||||
"high": df["high"].resample("W").max(),
|
||||
"low": df["low"].resample("W").min(),
|
||||
"close": df["close"].resample("W").last(),
|
||||
"volume": df["volume"].resample("W").sum(),
|
||||
}
|
||||
)
|
||||
|
||||
return weekly.dropna()
|
||||
|
||||
|
||||
class TechnicalScreener:
|
||||
"""技术指标筛选器基类"""
|
||||
|
||||
def __init__(self, name: str):
|
||||
self.name = name
|
||||
|
||||
def screen(self, df: pd.DataFrame) -> bool:
|
||||
"""
|
||||
判断数据是否符合筛选条件
|
||||
|
||||
Args:
|
||||
df: DataFrame with OHLCV data
|
||||
|
||||
Returns:
|
||||
bool: 是否符合条件
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
class CCIScreener(TechnicalScreener):
|
||||
"""CCI超卖筛选器"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
day_period: int = 14,
|
||||
week_period: int = 14,
|
||||
threshold: float = -100,
|
||||
use_weekly: bool = True,
|
||||
):
|
||||
super().__init__("CCI超卖筛选")
|
||||
self.day_period = day_period
|
||||
self.week_period = week_period
|
||||
self.threshold = threshold
|
||||
self.use_weekly = use_weekly
|
||||
|
||||
def screen(self, df: pd.DataFrame) -> dict:
|
||||
"""
|
||||
筛选CCI超卖信号
|
||||
|
||||
Returns:
|
||||
dict: {
|
||||
'triggered': bool, # 是否触发信号
|
||||
'day_cci': float, # 日线CCI值
|
||||
'week_cci': float, # 周线CCI值（如启用）
|
||||
}
|
||||
"""
|
||||
# 计算日线CCI
|
||||
day_cci = calculate_cci(df, period=self.day_period).iloc[-1]
|
||||
|
||||
result = {
|
||||
"triggered": day_cci < self.threshold,
|
||||
"day_cci": day_cci,
|
||||
"week_cci": None,
|
||||
}
|
||||
|
||||
# 计算周线CCI（如果启用）
|
||||
if self.use_weekly:
|
||||
weekly_df = resample_to_weekly(df)
|
||||
if len(weekly_df) >= self.week_period:
|
||||
week_cci = calculate_cci(weekly_df, period=self.week_period).iloc[-1]
|
||||
result["week_cci"] = week_cci
|
||||
# 日线或周线任一超卖即触发
|
||||
result["triggered"] = (
|
||||
day_cci < self.threshold or week_cci < self.threshold
|
||||
)
|
||||
|
||||
return result
|
||||
Reference in New Issue
Block a user