345 lines
13 KiB
Python
345 lines
13 KiB
Python
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"""
|
||
<html>
|
||
<body>
|
||
<p>以下是数据的最后一列：</p>
|
||
{text}
|
||
</body>
|
||
</html>
|
||
"""
|
||
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'<table border="1" class="dataframe">\n <thead>\n <tr style="text-align: right;">\n <th>symbol</th>\n <th>datetime</th>\n <th>delta</th>\n <th>close</th>\n <th>open</th>\n <th>high</th>\n <th>low</th>\n <th>volume</th>\n <th>dj</th>\n <th>trading_day</th>\n <th>delta累计</th>\n <th>终极平滑值</th>\n <th>趋势方向</th>\n <th>POC</th>\n <th>最终趋势</th>\n </tr>\n </thead>\n <tbody>\n{text}\n </tbody>\n</table>'
|
||
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]<price[i] and ma_close[i]<price[i]:
|
||
# trend[i] = '多头趋势'
|
||
# else:
|
||
# trend[i] = '无趋势'
|
||
|
||
if us_new[i] < ma_close.iloc[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
|