from flask import Flask, render_template, jsonify, make_response
from flask_socketio import SocketIO
import pandas as pd
import numpy as np
import os
import ast
import time
from datetime import datetime
import requests
# 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
app = Flask(__name__)
app.config['SECRET_KEY'] = 'secret!'
socketio = SocketIO(app)
# 添加安全响应头
@app.after_request
def add_security_headers(response):
response.headers['X-Content-Type-Options'] = 'nosniff'
response.headers['Cache-Control'] = 'no-store, no-cache, must-revalidate, max-age=0'
response.headers['Pragma'] = 'no-cache'
response.headers['Expires'] = '0'
return response
# 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
count = 0
time_period = 30
delta_sum_trend=0
delta_trend=0
dj_trend = 0
delta_rate = 0.8
dj_rate = 0.8
# 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(subject, text):
global last_sent_time, count
# 检查时间间隔
current_time = time.time()
print('count:',count)
if count == 1 and current_time - last_sent_time <1:
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
elif count ==1 and current_time - last_sent_time >1:
count = 0
if count == 0 and current_time - last_sent_time < 1:
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())
count = 1
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)).iloc[-1200:] # 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()
# 自定义分组逻辑：前一日21:00至当日15:00为一天
def get_trading_day(dt):
# 如果时间在21:00之后，属于下一个交易日
if dt.hour >= 21:
return (dt + pd.Timedelta(days=1)).date()
# 如果时间在15:00之前，属于当前交易日
elif dt.hour < 15:
return dt.date()
# 15:00-21:00之间的数据属于当前交易日
else:
return dt.date()
# 添加交易日列并转换为字符串
df['trading_day'] = df['datetime'].apply(get_trading_day)
df['trading_day'] = df['trading_day'].astype(str) # 将日期转换为字符串
# 按交易日计算delta累计
df['delta累计'] = df.groupby('trading_day')['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)
df['最终趋势'] = finall_trend(df['delta累计'],df['趋势方向'])
# print(df.tail(1))
# print(type(df['delta累计'].iloc[-1]))
def send_feishu_message(text):
headers = {
"Content-Type": "application/json"
}
table_html = f'\n \n \n | symbol | \n datetime | \n delta | \n close | \n open | \n high | \n low | \n volume | \n dj | \n trading_day | \n delta累计 | \n 终极平滑值 | \n 趋势方向 | \n POC | \n 最终趋势 | \n
\n \n \n{text}\n \n
'
data = {
"msg_type": "text",
"content": {
"text": table_html
}
}
response = requests.post("https://open.feishu.cn/open-apis/bot/v2/hook/8608dfa4-e599-462a-8dba-6ac72873dd27", headers=headers, json=data)
if response.status_code != 200:
print(f"飞书消息发送失败，状态码: {response.status_code}, 响应内容: {response.text}")
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':
table_text = df.iloc[:,3:].tail(1).to_html(index=False)
send_feishu_message("日内delta累计多头信号\n" + table_text)
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':
table_text = df.iloc[:,3:].tail(1).to_html(index=False)
send_feishu_message("日内delta累计空头信号\n" + table_text)
else:
pass
# djValues[i] >= maxDJ * 0.8 && ultimateValues[i] > ma120[i]
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' :
table_text = df.iloc[:,3:].tail(1).to_html(index=False)
# send_mail("dj多头信号",table_text)
send_feishu_message("dj多头信号\n" + table_text)
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' :
table_text = df.iloc[:,3:].tail(1).to_html(index=False)
# send_mail("dj空头信号",table_text)
send_feishu_message("dj空头信号\n" + table_text)
else:
pass
# deltaValues[i] >= maxDelta * 0.8 && ultimateValues[i] > ma120[i])
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' :
table_text = df.iloc[:,3:].tail(1).to_html(index=False)
# send_mail("delta多头信号",table_text)
send_feishu_message("delta多头信号\n" + table_text)
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' :
table_text = df.iloc[:,3:].tail(1).to_html(index=False)
# send_mail("delta空头信号",table_text)
send_feishu_message("delta空头信号\n" + table_text)
else:
pass
return df.to_dict(orient="records")#.iloc[-48:]
# return df.iloc[-60:].iloc[::-1].to_dict(orient="records")
def finall_trend(delta_sum,trend):
f_trend = [None]*(len(delta_sum))
# delta_sum = delta_sum.astype(float)
for i in range(len(delta_sum)):
if (delta_sum[i] == '缺值') or (trend[i] == '缺值'):
f_trend[i] = '方向不明'
# return f_trend
else:
if delta_sum[i] > 0 and (trend[i] == '多头趋势'):
f_trend[i] = '强多头'
elif delta_sum[i] < 0 and (trend[i] == '空头趋势'):
f_trend[i] = '强空头'
else:
f_trend[i] = '方向不明'
return f_trend
def safe_literal_eval(x):
"""带异常处理的安全转换"""
try:
return ast.literal_eval(x)
except ValueError:
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.iloc[i]
else:
# 应用递归公式
us[i] = (1 - c1) * price.iloc[i] + (2 * c1 - c2) * price.iloc[i-1] \
- (c1 + c3) * price.iloc[i-2] + c2 * us[i-1] + c3 * us[i-2]
us_new = np.around(us, decimals=2)
ma_close = price.rolling(window=4*period).mean()#5*
# if us_new[i]>price[i] and ma_close[i]>price[i]:
# trend[i] = '空头趋势'
# elif us_new[i] ma_close.iloc[i]:
trend[i] = '多头趋势'
else:
trend[i] = '无趋势'
return us_new,trend
@app.route("/")
def index():
return render_template("index.html")
@app.route("/kline")
def kline():
return render_template("kline.html")
@app.route("/api/data")
def get_data():
try:
files = get_csv_files()
data = {}
for symbol, filename in files.items():
loaded_data = load_data(filename)
if loaded_data:
data[symbol] = loaded_data
return jsonify(data)
except Exception as e:
return jsonify({"error": str(e)})
def should_update():
"""检查是否应该在当前时间更新数据"""
now = datetime.now()
# 检查是否是整点5分钟
if now.minute % 2 == 0:
# 检查是否在5秒内
if now.second < 2:
return True
return False
def background_thread():
"""后台线程，在每整点5分钟的5秒内发送数据更新"""
while True:
if should_update():
files = get_csv_files()
data = {}
for file_name, file_path in files.items():
data[file_name] = load_data(file_path)
socketio.emit('data_update', data)
print(f"数据更新完成 - {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
time.sleep(1) # 每秒检查一次
@socketio.on('connect')
def handle_connect():
print('Client connected')
# 启动后台线程
socketio.start_background_task(background_thread)
@socketio.on('disconnect')
def handle_disconnect():
print('Client disconnected')
if __name__ == "__main__":
socketio.run(app, host='0.0.0.0', port=5000, debug=True) # 监听所有网络接口