from flask import Flask, render_template, jsonify from flask_socketio import SocketIO import pandas as pd import numpy as np import os import ast import time from datetime import datetime app = Flask(__name__) app.config['SECRET_KEY'] = 'secret!' socketio = SocketIO(app) # 加入邮件通知 import smtplib from email.mime.text import MIMEText # 导入 MIMEText 类发送纯文本邮件 from email.mime.multipart import ( MIMEMultipart, ) import akshare as ak # from email.mime.application import MIMEApplication # 配置邮件信息 receivers = ["240884432@qq.com"] # 设置邮件接收人地址 subject = "TD_Simnow_Signal" # 设置邮件主题订单流策略交易信号 # 配置邮件服务器信息 smtp_server = "smtp.qq.com" # 设置发送邮件的 SMTP 服务器地址 smtp_port = 465 # 设置发送邮件的 SMTP 服务器端口号，一般为 25 端口 465 sender = "240884432@qq.com" # 设置发送邮件的邮箱地址 username = "240884432@qq.com" # 设置发送邮件的邮箱用户名 password = "osjyjmbqrzxtbjbf" # zrmpcgttataabhjh，设置发送邮件的邮箱密码或授权码 last_sent_time = 0 time_period = 48 # current_dir = os.path.dirname(os.path.abspath(__file__)) # os.chdir(current_dir) # print("已更改为新的工作目录：", current_dir) # 获取当前工作目录 current_directory = os.getcwd() print("当前工作目录：", current_directory) # 设置新的工作目录 new_directory = r"C:\simnow_trader\traderdata" os.chdir(new_directory) # 验证新的工作目录 updated_directory = os.getcwd() print("已更改为新的工作目录：", updated_directory) # 获取当前文件夹中所有包含"ofdata"字符的CSV文件 def get_csv_files(): files = {} for filename in os.listdir(): if "ofdata" in filename and filename.endswith(".csv"): files[filename] = os.path.join(os.getcwd(), filename) return files def send_mail(text): global last_sent_time # 检查时间间隔 current_time = time.time() if current_time - last_sent_time < 60: print("current_time:",current_time) print("last_sent_time:",last_sent_time) print("一分钟内已发送过邮件，本次跳过") return # 直接退出，不执行发送 msg = MIMEMultipart() msg["From"] = sender msg["To"] = ";".join(receivers) msg["Subject"] = subject html_content = f"""

以下是数据的最后一列：

{text} """ msg.attach(MIMEText(html_content, 'html')) smtp = smtplib.SMTP_SSL(smtp_server, smtp_port) smtp.login(username, password) smtp.sendmail(sender, receivers, msg.as_string()) smtp.quit() # 根据文件路径加载数据，只读取前12列 def load_data(file_path): df = pd.read_csv(file_path, usecols=range(12)) # 只读取前12列 df = df.drop_duplicates(subset='datetime', keep='first').reset_index(drop=True) df = df[df['high'] != df['low']] df["delta"] = df["delta"].astype(float) df['datetime'] = pd.to_datetime(df['datetime'],format='ISO8601')#, dayfirst=True, format='mixed' df['delta累计'] = df.groupby(df['datetime'].dt.date)['delta'].cumsum() df = df.fillna('缺值') df['终极平滑值'],df['趋势方向'] = ultimate_smoother(df['close'],time_period) df['datetime'] = df['datetime'].dt.strftime("%Y-%m-%d %H:%M:%S") df['POC'] = add_poc_column(df) print(df.tail(2)) if len(df) >=5*time_period and (df['趋势方向'].iloc[-1] != df['趋势方向'].iloc[-2]): table_text = df.iloc[:,3:].tail(1).to_html(index=False) #price,Ask,Bid,symbol,datetime,delta,close,open,high,low,volume,dj send_mail(table_text) else: pass return df.iloc[-60:].to_dict(orient="records") def safe_literal_eval(x): """带异常处理的安全转换""" try: return ast.literal_eval(x) except: return [] # 返回空列表作为占位符 def add_poc_column(df): # 安全转换列数据 df['price'] = df['price'].apply(safe_literal_eval) df['Ask'] = df['Ask'].apply(lambda x: list(map(int, safe_literal_eval(x)))) df['Bid'] = df['Bid'].apply(lambda x: list(map(int, safe_literal_eval(x)))) # 定义处理函数（带数据验证） def find_poc(row): # 验证三个列表长度一致且非空 if not (len(row['price']) == len(row['Ask']) == len(row['Bid']) > 0): return '缺值' # 返回空值标记异常数据 sums = [a + b for a, b in zip(row['Ask'], row['Bid'])] try: max_index = sums.index(max(sums)) return row['price'][max_index] except ValueError: return '缺值' # 处理空求和列表情况 # 应用处理函数 df['POC'] = df.apply(find_poc, axis=1) # 可选：统计异常数据 error_count = df['POC'].isnull().sum() if error_count > 0: print(f"警告：发现 {error_count} 行异常数据（已标记为NaN）") return df['POC'] def ultimate_smoother(price, period): # 初始化变量（修正角度单位为弧度） a1 = np.exp(-1.414 * np.pi / period) b1 = 2 * a1 * np.cos(1.414 * np.pi / period) # 将180改为np.pi c2 = b1 c3 = -a1 ** 2 c1 = (1 + c2 - c3) / 4 # 准备输出序列 us = np.zeros(len(price)) us_new = np.zeros(len(price)) trend = [None]*(len(price)) ma_close = np.zeros(len(price)) # 前4个点用原始价格初始化 for i in range(len(price)): if i < 4: us[i] = price[i] else: # 应用递归公式 us[i] = (1 - c1) * price[i] + (2 * c1 - c2) * price[i-1] \ - (c1 + c3) * price[i-2] + c2 * us[i-1] + c3 * us[i-2] us_new = np.around(us, decimals=2) ma_close = price.rolling(window=5*period).mean() if us_new[i]>price[i] and ma_close[i]>price[i]: trend[i] = '空头趋势' elif us_new[i]