20250408修改
This commit is contained in:
377
temp/app.py
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377
temp/app.py
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from flask import Flask, render_template, jsonify, make_response
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from flask_socketio import SocketIO
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import pandas as pd
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import numpy as np
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import os
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import ast
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import time
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from datetime import datetime
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import requests
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# 加入邮件通知
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import smtplib
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from email.mime.text import MIMEText # 导入 MIMEText 类发送纯文本邮件
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from email.mime.multipart import (
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MIMEMultipart,
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)
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# import akshare as ak
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app = Flask(__name__)
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app.config['SECRET_KEY'] = 'secret!'
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socketio = SocketIO(app)
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# 添加安全响应头
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@app.after_request
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def add_security_headers(response):
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response.headers['X-Content-Type-Options'] = 'nosniff'
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response.headers['Cache-Control'] = 'no-store, no-cache, must-revalidate, max-age=0'
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response.headers['Pragma'] = 'no-cache'
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response.headers['Expires'] = '0'
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return response
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# from email.mime.application import MIMEApplication
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# 配置邮件信息
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receivers = ["240884432@qq.com"] # 设置邮件接收人地址
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# subject = "TD_Simnow_Signal" # 设置邮件主题 订单流策略交易信号
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# 配置邮件服务器信息
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smtp_server = "smtp.qq.com" # 设置发送邮件的 SMTP 服务器地址
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smtp_port = 465 # 设置发送邮件的 SMTP 服务器端口号，一般为 25 端口 465
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sender = "240884432@qq.com" # 设置发送邮件的邮箱地址
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username = "240884432@qq.com" # 设置发送邮件的邮箱用户名
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password = "osjyjmbqrzxtbjbf" # zrmpcgttataabhjh，设置发送邮件的邮箱密码或授权码
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last_sent_time_1 = None
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last_sent_time_2 = None
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last_sent_time_3 = None
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count = 0
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time_period = 30
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delta_sum_trend=0
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delta_trend=0
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dj_trend = 0
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delta_rate = 0.8
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dj_rate = 0.8
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# 获取当前工作目录
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current_directory = os.getcwd()
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print("当前工作目录：", current_directory)
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# 设置新的工作目录
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new_directory = r"C:/simnow_trader/traderdata"
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os.chdir(new_directory)
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# 验证新的工作目录
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updated_directory = os.getcwd()
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print("已更改为新的工作目录：", updated_directory)
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# 获取当前文件夹中所有包含"ofdata"字符的CSV文件
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def get_csv_files():
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files = {}
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for filename in os.listdir():
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if "ofdata" in filename and filename.endswith(".csv"):
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files[filename] = os.path.join(os.getcwd(), filename)
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return files
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# def send_mail(subject, text):
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# global last_sent_time, count
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# # 检查时间间隔
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# current_time = time.time()
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# print('count:',count)
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# if count == 1 and current_time - last_sent_time <1:
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# print("current_time:",current_time)
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# print("last_sent_time:",last_sent_time)
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# print("一分钟内已发送过邮件，本次跳过")
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# return
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# elif count ==1 and current_time - last_sent_time >1:
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# count = 0
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# if count == 0 and current_time - last_sent_time < 1:
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# msg = MIMEMultipart()
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# msg["From"] = sender
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# msg["To"] = ";".join(receivers)
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# msg["Subject"] = subject
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# html_content = f"""
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# <html>
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# <body>
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# <p>以下是数据的最后一列：</p>
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# {text}
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# </body>
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# </html>
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# """
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# msg.attach(MIMEText(html_content, 'html'))
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# smtp = smtplib.SMTP_SSL(smtp_server, smtp_port)
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# smtp.login(username, password)
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# smtp.sendmail(sender, receivers, msg.as_string())
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# count = 1
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# smtp.quit()
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# 根据文件路径加载数据，只读取前12列
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def load_data(file_path):
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df = pd.read_csv(file_path, usecols=range(12)).iloc[-1200:] # 只读取前12列
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df = df.drop_duplicates(subset='datetime', keep='first').reset_index(drop=True)
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# df = df[df['high'] != df['low']]
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df["delta"] = df["delta"].astype(float)
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df['datetime'] = pd.to_datetime(df['datetime'],format='ISO8601')#, dayfirst=True, format='mixed'
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# df['delta累计'] = df.groupby(df['datetime'].dt.date)['delta'].cumsum()
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# 自定义分组逻辑：前一日21:00至当日15:00为一天
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def get_trading_day(dt):
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# 如果时间在21:00之后，属于下一个交易日
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if dt.hour >= 21:
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return (dt + pd.Timedelta(days=1)).date()
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# 如果时间在15:00之前，属于当前交易日
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elif dt.hour < 15:
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return dt.date()
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# 15:00-21:00之间的数据属于当前交易日
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else:
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return dt.date()
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# 添加交易日列并转换为字符串
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df['trading_day'] = df['datetime'].apply(get_trading_day)
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df['trading_day'] = df['trading_day'].astype(str) # 将日期转换为字符串
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# 按交易日计算delta累计
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df['delta累计'] = df.groupby('trading_day')['delta'].cumsum()
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df = df.fillna('缺值')
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df['终极平滑值'],df['趋势方向'] = ultimate_smoother(df['close'],time_period)
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df['datetime'] = df['datetime'].dt.strftime("%Y-%m-%d %H:%M:%S")
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df['POC'] = add_poc_column(df)
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df['最终趋势'] = finall_trend(df['delta累计'],df['趋势方向'])
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# print(df.tail(1))
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# print(type(df['delta累计'].iloc[-1]))
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def send_feishu_message(text):
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headers = {
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"Content-Type": "application/json"
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}
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data = {
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"msg_type": "text",
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"content": {
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"text": text
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}
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}
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response = requests.post("https://open.feishu.cn/open-apis/bot/v2/hook/8608dfa4-e599-462a-8dba-6ac72873dd27", headers=headers, json=data)
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if response.status_code != 200:
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print(f"飞书消息发送失败，状态码: {response.status_code}, 响应内容: {response.text}")
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# if df['close'].iloc[-1]>1000:
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# table_text = df.iloc[:,3:].tail(1).to_markdown(index=False)
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# print(table_text)
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# send_feishu_message("close多头信号\n" + table_text)
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# else:
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# pass
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global last_sent_time_1, last_sent_time_2, last_sent_time_3
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if df['delta累计'].iloc[-2] < 0 and df['delta累计'].iloc[-1] > 0 and df['趋势方向'].iloc[-1] == '多头趋势':
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table_text = df.iloc[:,3:].tail(1).to_markdown(index=False)
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current_time = df['datetime'].tail(1)#time.time()
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if current_time != last_sent_time_1:
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send_feishu_message("日内delta累计多头信号\n" + table_text)
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last_sent_time_1 = current_time
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elif df['delta累计'].iloc[-2] > 0 and df['delta累计'].iloc[-1] < 0 and df['趋势方向'].iloc[-1] == '空头趋势':
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table_text = df.iloc[:,3:].tail(1).to_markdown(index=False)
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current_time = df['datetime'].tail(1)
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if current_time != last_sent_time_1:
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send_feishu_message("日内delta累计空头信号\n" + table_text)
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last_sent_time_1 = current_time
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else:
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pass
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# djValues[i] >= maxDJ * 0.8 && ultimateValues[i] > ma120[i]
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if df['dj'].iloc[-1] >= 0.8 * max(df['dj'].iloc[-121:-1] ) and df['趋势方向'].iloc[-1] == '多头趋势' :
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table_text = df.iloc[:,3:].tail(1).to_markdown(index=False)
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# send_mail("dj多头信号",table_text)
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current_time = df['datetime'].tail(1)
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if current_time != last_sent_time_2:
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send_feishu_message("dj多头信号\n" + table_text)
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last_sent_time_2 = current_time
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elif df['dj'].iloc[-1] <= 0.8 * min(df['dj'].iloc[-121:-1] ) and df['趋势方向'].iloc[-1] == '空头趋势' :
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table_text = df.iloc[:,3:].tail(1).to_markdown(index=False)
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# send_mail("dj空头信号",table_text)
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current_time = df['datetime'].tail(1)
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if current_time != last_sent_time_2:
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send_feishu_message("dj空头信号\n" + table_text)
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last_sent_time_2 = current_time
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else:
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pass
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# deltaValues[i] >= maxDelta * 0.8 && ultimateValues[i] > ma120[i])
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if df['delta'].iloc[-1] >= 0.8 * max(df['delta'].iloc[-121:-1] ) and df['趋势方向'].iloc[-1] == '多头趋势' :
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table_text = df.iloc[:,3:].tail(1).to_markdown(index=False)
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# send_mail("delta多头信号",table_text)
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current_time = df['datetime'].tail(1)
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if current_time != last_sent_time_3:
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send_feishu_message("delta多头信号\n" + table_text)
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last_sent_time_3 = current_time
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elif df['delta'].iloc[-1] <= 0.8 * min(df['delta'].iloc[-121:-1] ) and df['趋势方向'].iloc[-1] == '空头趋势' :
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table_text = df.iloc[:,3:].tail(1).to_markdown(index=False)
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# send_mail("delta空头信号",table_text)
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current_time = df['datetime'].tail(1)
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if current_time != last_sent_time_3:
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send_feishu_message("delta空头信号\n" + table_text)
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last_sent_time_3 = current_time
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else:
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pass
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return df.to_dict(orient="records")#.iloc[-48:]
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# return df.iloc[-60:].iloc[::-1].to_dict(orient="records")
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def finall_trend(delta_sum,trend):
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f_trend = [None]*(len(delta_sum))
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# delta_sum = delta_sum.astype(float)
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for i in range(len(delta_sum)):
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if (delta_sum[i] == '缺值') or (trend[i] == '缺值'):
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f_trend[i] = '方向不明'
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# return f_trend
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else:
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if delta_sum[i] > 0 and (trend[i] == '多头趋势'):
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f_trend[i] = '强多头'
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elif delta_sum[i] < 0 and (trend[i] == '空头趋势'):
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f_trend[i] = '强空头'
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else:
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f_trend[i] = '方向不明'
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return f_trend
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def safe_literal_eval(x):
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"""带异常处理的安全转换"""
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try:
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return ast.literal_eval(x)
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except ValueError:
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return [] # 返回空列表作为占位符
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def add_poc_column(df):
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# 安全转换列数据
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df['price'] = df['price'].apply(safe_literal_eval)
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df['Ask'] = df['Ask'].apply(lambda x: list(map(int, safe_literal_eval(x))))
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df['Bid'] = df['Bid'].apply(lambda x: list(map(int, safe_literal_eval(x))))
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# 定义处理函数（带数据验证）
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def find_poc(row):
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# 验证三个列表长度一致且非空
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if not (len(row['price']) == len(row['Ask']) == len(row['Bid']) > 0):
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return '缺值' # 返回空值标记异常数据
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sums = [a + b for a, b in zip(row['Ask'], row['Bid'])]
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try:
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max_index = sums.index(max(sums))
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return row['price'][max_index]
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except ValueError:
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return '缺值' # 处理空求和列表情况
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# 应用处理函数
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df['POC'] = df.apply(find_poc, axis=1)
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# 可选：统计异常数据
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error_count = df['POC'].isnull().sum()
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if error_count > 0:
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print(f"警告：发现 {error_count} 行异常数据（已标记为NaN）")
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return df['POC']
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def ultimate_smoother(price,period):
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# 初始化变量（修正角度单位为弧度）
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a1 = np.exp(-1.414 * np.pi / period)
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b1 = 2 * a1 * np.cos(1.414 * np.pi / period) # 将180改为np.pi
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c2 = b1
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c3 = -a1 ** 2
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c1 = (1 + c2 - c3) / 4
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# 准备输出序列
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us = np.zeros(len(price))
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us_new = np.zeros(len(price))
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trend = [None]*(len(price))
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ma_close = np.zeros(len(price))
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# 前4个点用原始价格初始化
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for i in range(len(price)):
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if i < 4:
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us[i] = price.iloc[i]
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else:
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# 应用递归公式
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us[i] = (1 - c1) * price.iloc[i] + (2 * c1 - c2) * price.iloc[i-1] \
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- (c1 + c3) * price.iloc[i-2] + c2 * us[i-1] + c3 * us[i-2]
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us_new = np.around(us, decimals=2)
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ma_close = price.rolling(window=4*period).mean()#5*
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# if us_new[i]>price[i] and ma_close[i]>price[i]:
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# trend[i] = '空头趋势'
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# elif us_new[i]<price[i] and ma_close[i]<price[i]:
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# trend[i] = '多头趋势'
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# else:
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# trend[i] = '无趋势'
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if us_new[i] < ma_close.iloc[i]:
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trend[i] = '空头趋势'
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elif us_new[i] > ma_close.iloc[i]:
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trend[i] = '多头趋势'
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else:
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trend[i] = '无趋势'
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return us_new,trend
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@app.route("/")
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def index():
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return render_template("index.html")
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@app.route("/kline")
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def kline():
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return render_template("kline.html")
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@app.route("/api/data")
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def get_data():
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try:
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files = get_csv_files()
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data = {}
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for symbol, filename in files.items():
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loaded_data = load_data(filename)
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if loaded_data:
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data[symbol] = loaded_data
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return jsonify(data)
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except Exception as e:
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return jsonify({"error": str(e)})
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def should_update():
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"""检查是否应该在当前时间更新数据"""
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now = datetime.now()
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# 检查是否是整点5分钟
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if now.minute % 2 == 0:
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# 检查是否在5秒内
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if now.second < 2:
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return True
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return False
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def background_thread():
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"""后台线程，在每整点5分钟的5秒内发送数据更新"""
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while True:
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if should_update():
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files = get_csv_files()
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data = {}
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for file_name, file_path in files.items():
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data[file_name] = load_data(file_path)
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socketio.emit('data_update', data)
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print(f"数据更新完成 - {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
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time.sleep(1) # 每秒检查一次
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@socketio.on('connect')
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def handle_connect():
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print('Client connected')
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# 启动后台线程
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socketio.start_background_task(background_thread)
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@socketio.on('disconnect')
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def handle_disconnect():
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print('Client disconnected')
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if __name__ == "__main__":
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socketio.run(app, host='0.0.0.0', port=5000, debug=True) # 监听所有网络接口
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Reference in New Issue
Block a user