增加交易策略、交易指标、量化库代码等文件夹
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@@ -0,0 +1,5 @@
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price,Ask,Bid,symbol,datetime,delta,close,open,high,low,volume,dj
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"['6438.6', '6439.4', '6439.6', '6440.0', '6440.4', '6441.0', '6441.4', '6441.6', '6442.0', '6442.2', '6442.4', '6442.6', '6442.8', '6443.0', '6443.2', '6443.4', '6443.6', '6443.8', '6444.0', '6444.2', '6444.4', '6444.6', '6445.0', '6445.6', '6446.0', '6446.2', '6446.4', '6447.4', '6447.6', '6447.8', '6448.0', '6448.2', '6448.4', '6448.6', '6448.8', '6449.0', '6449.2', '6449.4', '6449.8', '6450.0', '6450.2', '6450.4', '6450.6', '6450.8', '6451.0', '6451.2', '6451.6', '6451.8', '6452.0', '6452.2', '6452.4', '6452.6', '6452.8', '6453.4', '6453.6', '6453.8', '6454.0', '6454.4', '6454.6', '6455.0', '6455.2', '6455.8', '6456.4', '6456.6', '6457.4', '6457.6', '6457.8', '6458.0', '6458.2', '6459.0', '6459.2', '6459.4', '6459.6', '6459.8', '6460.0', '6461.0', '6461.2', '6461.4', '6461.8', '6462.0', '6462.2', '6462.4', '6462.6', '6462.8', '6463.0', '6463.2', '6463.4', '6463.6', '6463.8', '6464.0', '6464.2', '6464.4', '6464.6', '6464.8', '6465.0', '6465.2', '6465.4', '6465.6', '6465.8', '6466.0', '6466.2', '6466.4', '6466.6', '6466.8', '6467.0', '6467.2', '6467.4', '6467.6', '6467.8', '6468.0', '6468.2', '6468.4', '6468.6', '6468.8', '6469.0', '6469.2', '6469.4', '6469.6', '6469.8', '6470.0', '6470.2', '6470.4', '6470.6', '6470.8', '6471.0', '6471.2', '6471.4', '6471.6', '6471.8', '6472.0', '6472.4', '6472.6', '6472.8', '6473.0', '6473.2', '6473.6', '6474.0', '6474.4', '6474.6', '6475.0', '6476.0']","[0, 20, 31, 0, 0, 0, 63, 0, 0, 26, 37, 22, 24, 0, 0, 44, 131, 14, 19, 0, 6, 26, 30, 32, 0, 0, 0, 56, 0, 21, 42, 67, 38, 35, 12, 11, 33, 50, 11, 40, 157, 7, 149, 40, 0, 47, 73, 13, 58, 33, 0, 22, 0, 24, 54, 17, 27, 26, 32, 0, 0, 72, 100, 0, 25, 0, 33, 25, 14, 9, 0, 15, 13, 27, 16, 19, 13, 15, 0, 95, 24, 79, 0, 12, 16, 20, 26, 14, 15, 13, 18, 0, 26, 43, 63, 42, 45, 75, 13, 33, 23, 73, 64, 10, 47, 78, 0, 32, 61, 96, 61, 0, 0, 49, 32, 74, 49, 57, 23, 14, 11, 23, 35, 0, 61, 12, 13, 4, 2, 85, 70, 51, 36, 26, 4, 0, 137, 0, 27, 0, 30]","[19, 0, 0, 26, 31, 20, 0, 31, 17, 48, 52, 46, 0, 28, 18, 13, 27, 0, 17, 29, 0, 0, 0, 16, 48, 14, 51, 23, 13, 132, 137, 47, 11, 39, 16, 27, 0, 0, 38, 68, 52, 27, 0, 25, 43, 107, 0, 0, 45, 0, 42, 0, 30, 0, 0, 6, 34, 0, 41, 32, 20, 82, 0, 7, 6, 19, 0, 59, 46, 0, 28, 0, 0, 30, 8, 25, 41, 0, 12, 76, 0, 9, 40, 14, 4, 21, 0, 49, 46, 4, 41, 19, 38, 65, 125, 64, 24, 14, 0, 25, 24, 55, 20, 11, 12, 75, 45, 18, 38, 56, 23, 27, 53, 36, 64, 32, 46, 18, 66, 35, 31, 7, 12, 15, 42, 0, 61, 0, 0, 35, 124, 0, 34, 13, 0, 50, 44, 99, 0, 35, 0]",IM2503,2025-03-10 09:35:00,255,6446.0,6455.8,6476.0,6438.6,10242,1
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"['6422.6', '6423.0', '6423.2', '6423.6', '6423.8', '6424.0', '6424.4', '6424.8', '6425.0', '6425.4', '6425.8', '6426.0', '6426.2', '6426.4', '6426.6', '6426.8', '6427.0', '6427.2', '6427.4', '6427.6', '6427.8', '6428.0', '6428.2', '6428.4', '6428.6', '6428.8', '6429.0', '6429.2', '6429.4', '6429.6', '6429.8', '6430.0', '6430.2', '6430.4', '6430.8', '6431.0', '6431.2', '6431.4', '6431.6', '6431.8', '6432.0', '6432.2', '6432.4', '6432.6', '6432.8', '6433.0', '6433.2', '6433.4', '6433.6', '6433.8', '6434.0', '6434.2', '6434.4', '6434.6', '6434.8', '6435.0', '6435.8', '6436.0', '6436.2', '6436.4', '6437.0', '6437.2', '6437.4', '6437.8', '6438.0', '6438.2', '6438.4', '6438.6', '6438.8', '6439.0', '6439.2', '6439.4', '6439.6', '6439.8', '6440.0', '6440.2', '6440.4', '6440.6', '6440.8', '6441.0', '6441.2', '6441.4', '6441.6', '6441.8', '6442.2', '6442.4', '6442.6', '6443.0', '6443.2', '6443.6', '6443.8', '6444.0', '6444.2', '6444.6', '6444.8', '6445.0', '6445.2', '6445.8', '6446.2', '6446.6', '6446.8', '6447.2', '6447.4', '6447.8', '6448.0', '6448.2', '6448.4', '6448.6', '6449.2', '6449.4', '6449.6', '6449.8', '6450.0', '6450.2', '6450.4', '6450.8', '6451.0', '6451.6', '6451.8', '6452.0', '6452.2', '6452.4', '6453.0', '6453.2', '6453.4', '6454.0']","[0, 0, 20, 0, 0, 0, 19, 32, 107, 21, 0, 40, 86, 89, 61, 56, 93, 101, 61, 33, 41, 20, 21, 43, 27, 38, 0, 22, 42, 8, 0, 40, 0, 0, 29, 36, 18, 11, 0, 0, 52, 78, 48, 59, 19, 21, 30, 5, 51, 15, 18, 19, 25, 0, 39, 8, 0, 15, 0, 0, 28, 20, 0, 19, 29, 0, 0, 98, 80, 5, 29, 9, 32, 28, 10, 6, 0, 11, 38, 20, 0, 0, 5, 9, 12, 0, 96, 0, 7, 47, 18, 60, 35, 40, 28, 25, 22, 11, 0, 13, 41, 37, 25, 18, 11, 0, 65, 7, 0, 13, 0, 24, 62, 23, 0, 29, 0, 0, 30, 47, 69, 47, 25, 50, 8, 7]","[29, 9, 18, 30, 58, 63, 0, 40, 13, 57, 25, 59, 22, 73, 44, 67, 91, 8, 64, 28, 68, 7, 0, 89, 67, 0, 16, 0, 73, 0, 12, 119, 42, 33, 32, 59, 23, 6, 56, 3, 74, 5, 145, 41, 22, 21, 53, 8, 60, 15, 59, 26, 0, 7, 0, 57, 26, 90, 37, 14, 134, 14, 73, 13, 0, 44, 9, 29, 22, 12, 7, 38, 41, 3, 0, 0, 44, 0, 0, 0, 80, 88, 0, 0, 13, 11, 39, 34, 0, 19, 65, 17, 0, 16, 49, 65, 45, 0, 40, 0, 0, 0, 0, 0, 50, 22, 24, 15, 13, 11, 31, 0, 19, 14, 8, 41, 14, 12, 16, 8, 16, 122, 27, 34, 27, 0]",IM2503,2025-03-10 09:40:00,-606,6450.2,6446.6,6454.0,6422.6,8879,0,
|
||||
"['6432.0', '6432.2', '6432.4', '6433.0', '6433.2', '6433.4', '6433.6', '6433.8', '6434.0', '6434.2', '6434.4', '6434.6', '6434.8', '6435.0', '6435.2', '6435.4', '6435.6', '6435.8', '6436.2', '6436.4', '6437.2', '6438.0', '6438.6', '6438.8', '6439.0', '6439.8', '6440.6', '6441.0', '6441.2', '6441.6', '6441.8', '6442.0', '6442.2', '6442.4', '6442.6', '6442.8', '6443.0', '6443.2', '6443.4', '6443.6', '6443.8', '6444.0', '6444.2', '6444.4', '6444.6', '6444.8', '6445.0', '6445.2', '6445.4', '6445.6', '6445.8', '6446.0', '6446.2', '6446.4', '6446.6', '6446.8', '6447.0', '6447.2', '6447.4', '6447.6', '6447.8', '6448.0', '6448.2', '6448.4', '6448.6', '6448.8', '6449.0', '6449.2', '6449.4', '6449.6', '6449.8', '6450.0', '6450.4', '6450.6', '6450.8', '6451.0', '6451.2', '6451.4', '6451.6', '6451.8', '6452.0', '6452.2', '6452.4', '6452.6', '6453.0', '6453.2', '6453.6', '6453.8', '6454.0', '6454.4', '6454.6', '6455.0']","[0, 0, 8, 19, 0, 13, 10, 43, 19, 22, 29, 0, 23, 14, 13, 19, 22, 1, 0, 6, 0, 0, 9, 13, 0, 0, 0, 0, 0, 16, 18, 12, 19, 14, 0, 8, 25, 0, 28, 0, 8, 16, 16, 3, 0, 14, 31, 4, 63, 24, 46, 67, 0, 50, 10, 44, 25, 47, 58, 43, 82, 38, 51, 38, 56, 101, 46, 4, 113, 61, 0, 20, 0, 17, 49, 25, 25, 49, 5, 0, 37, 50, 33, 7, 5, 0, 55, 18, 29, 15, 0, 7]","[51, 10, 0, 17, 26, 0, 48, 2, 6, 0, 3, 4, 24, 2, 23, 20, 7, 0, 2, 21, 38, 27, 8, 0, 8, 13, 11, 23, 25, 33, 46, 29, 0, 7, 4, 33, 14, 40, 13, 10, 13, 23, 27, 45, 13, 4, 8, 24, 31, 38, 20, 38, 21, 56, 59, 63, 35, 25, 19, 18, 37, 45, 26, 11, 64, 58, 58, 8, 0, 35, 23, 33, 17, 0, 51, 52, 21, 0, 34, 21, 0, 14, 8, 0, 0, 14, 42, 0, 12, 0, 37, 0]",IM2503,2025-03-10 09:45:00,79,6432.0,6449.6,6455.0,6432.0,4881,-1,
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"['6419.8', '6420.0', '6420.2', '6420.4', '6420.6', '6420.8', '6421.0', '6421.2', '6421.4', '6421.6', '6421.8', '6422.0', '6422.2', '6422.4', '6422.6', '6423.0', '6423.2', '6423.4', '6423.6', '6424.2', '6424.4', '6424.6', '6424.8', '6425.0', '6425.2', '6425.4', '6426.0', '6426.2', '6426.4', '6426.6', '6426.8', '6427.0', '6427.2', '6427.4', '6427.6', '6427.8', '6428.0', '6428.2', '6428.6', '6428.8', '6429.0', '6429.2', '6429.4', '6429.6', '6429.8', '6430.0', '6430.2', '6430.4', '6430.6', '6430.8', '6431.0', '6431.2', '6431.4', '6431.6', '6431.8', '6432.0', '6432.4', '6432.6', '6432.8', '6433.0', '6433.4', '6433.6', '6433.8', '6434.0', '6434.2', '6434.4', '6434.6', '6434.8', '6435.0', '6435.2', '6435.4', '6435.6', '6435.8', '6436.0', '6436.2', '6436.4', '6436.6', '6436.8', '6437.0', '6437.2', '6437.4', '6437.6', '6437.8', '6438.0', '6438.2', '6438.4', '6438.6', '6438.8', '6439.0', '6439.2', '6439.4', '6439.6', '6439.8', '6440.0', '6440.2', '6440.4', '6440.6', '6441.0', '6441.2', '6441.6', '6442.0']","[39, 45, 0, 19, 28, 44, 35, 9, 7, 29, 25, 16, 60, 25, 0, 23, 9, 0, 3, 41, 0, 38, 0, 26, 9, 16, 0, 23, 4, 0, 0, 32, 28, 0, 9, 54, 0, 18, 17, 0, 37, 33, 25, 13, 6, 28, 25, 2, 2, 0, 31, 8, 15, 14, 19, 0, 34, 36, 17, 4, 7, 8, 21, 10, 0, 3, 22, 10, 4, 3, 0, 0, 9, 15, 13, 6, 20, 23, 36, 21, 55, 18, 68, 45, 12, 33, 25, 10, 24, 15, 34, 47, 39, 12, 0, 4, 24, 0, 6, 8, 10]","[49, 98, 41, 31, 24, 24, 155, 2, 5, 19, 0, 29, 31, 3, 29, 87, 3, 58, 0, 0, 19, 0, 10, 83, 3, 21, 31, 10, 3, 8, 24, 19, 51, 10, 23, 24, 42, 31, 1, 23, 20, 16, 20, 8, 0, 72, 20, 5, 23, 7, 13, 19, 4, 24, 38, 35, 10, 39, 13, 0, 10, 23, 17, 1, 11, 7, 14, 33, 16, 6, 19, 5, 2, 29, 0, 10, 35, 53, 33, 4, 7, 36, 20, 15, 15, 10, 11, 46, 17, 19, 34, 15, 13, 10, 8, 0, 5, 5, 0, 0, 16]",IM2503,2025-03-10 09:50:00,-340,6423.2,6431.8,6442.0,6419.8,4510,-3,
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@@ -0,0 +1,220 @@
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from flask import Flask, render_template, jsonify
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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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app = Flask(__name__)
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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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# from email.mime.application import MIMEApplication
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# 配置邮件信息
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receivers = ["[email protected]"] # 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 = "[email protected]" # 设置发送邮件的邮箱地址
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username = "[email protected]" # 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 = 0
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time_period = 48
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# current_dir = os.path.dirname(os.path.abspath(__file__))
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# os.chdir(current_dir)
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# print("已更改为新的工作目录：", current_dir)
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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 = "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(text):
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global last_sent_time
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# 检查时间间隔
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current_time = time.time()
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if current_time - last_sent_time < 60:
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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 # 直接退出，不执行发送
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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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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)) # 只读取前12列
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df = df.drop_duplicates()
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df["delta"] = df["delta"].astype(float)
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df['datetime'] = pd.to_datetime(df['datetime'],format='mixed')#, dayfirst=True, format='mixed'
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# 增加'delta累计'列
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df['delta累计'] = df.groupby(df['datetime'].dt.date)['delta'].cumsum()
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# 使用akshare获取5分钟k线的全部数据
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futures_zh_minute_sina_df = ak.futures_zh_minute_sina(symbol=df["symbol"].iloc[-1], period="5")
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futures_zh_minute_sina_df['datetime'] = pd.to_datetime(futures_zh_minute_sina_df['datetime'],format='mixed')
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futures_zh_minute_sina_df['终极平滑值'],futures_zh_minute_sina_df['趋势方向'] = ultimate_smoother(futures_zh_minute_sina_df["close"],time_period)
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# 设置索引
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df = df.set_index('datetime')
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futures_zh_minute_sina_df = futures_zh_minute_sina_df.set_index('datetime')
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# 使用 join 进行对齐
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df_new = futures_zh_minute_sina_df.join(df[['price', 'Ask', 'Bid', 'delta','dj','delta累计']], how='left')
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# 重置索引（可选）
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df_new = df_new.reset_index()
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df_new['datetime'] = df_new['datetime'].dt.strftime("%Y-%m-%d %H:%M:%S")
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# 增加'POC'列
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df_new['POC'] = add_poc_column(df_new)
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df_new['symbol'] = df["symbol"].iloc[-1]
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cols = ['price', 'Ask', 'Bid', 'symbol','datetime','delta','close', 'open', 'high', 'low', 'volume','hold','dj','delta累计','POC', '终极平滑值', '趋势方向']
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df_new = df_new[cols]
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del futures_zh_minute_sina_df,df
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if len(df_new) >=5*time_period and (df_new['趋势方向'].iloc[-1] != df_new['趋势方向'].iloc[-2]):
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table_text = df_new.iloc[:,3:].tail(1).to_html(index=False) #price,Ask,Bid,symbol,datetime,delta,close,open,high,low,volume,dj
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send_mail(table_text)
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else:
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pass
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return df_new.iloc[-11:-1].to_dict(orient="records")
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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:
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return [] # 返回空列表作为占位符
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||||
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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):
|
||||
return None # 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:
|
||||
max_index = sums.index(max(sums))
|
||||
return row['price'][max_index]
|
||||
except ValueError:
|
||||
return None # 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]<price[i] and ma_close[i]<price[i]:
|
||||
trend[i] = '多头趋势'
|
||||
else:
|
||||
trend[i] = '无趋势'
|
||||
|
||||
|
||||
return us_new,trend
|
||||
|
||||
|
||||
@app.route("/")
|
||||
def index():
|
||||
files = get_csv_files() # 获取所有符合条件的文件
|
||||
# 默认显示第一个文件的数据
|
||||
first_file = list(files.keys())[0] if files else None
|
||||
data = load_data(files[first_file]) if first_file else []
|
||||
return render_template("index.html", data=data, files=files, file_name=first_file)
|
||||
|
||||
|
||||
@app.route("/data/<file_name>")
|
||||
def switch_data(file_name):
|
||||
files = get_csv_files() # 获取所有符合条件的文件
|
||||
if file_name in files:
|
||||
data = load_data(files[file_name])
|
||||
return jsonify(data)
|
||||
return jsonify({"error": "File not found"}), 404
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
app.run(host='0.0.0.0', port=5000, debug=True) # 监听所有网络接口
|
||||
@@ -0,0 +1,251 @@
|
||||
from flask import Flask, render_template, jsonify
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import os
|
||||
import ast
|
||||
import time
|
||||
|
||||
app = Flask(__name__)
|
||||
|
||||
# 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 = ["[email protected]"] # 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 = "[email protected]" # 设置发送邮件的邮箱地址
|
||||
username = "[email protected]" # 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 = "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"""
|
||||
<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())
|
||||
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()
|
||||
# 筛选 Ask 和 Bid 均不等于 [0] 的行
|
||||
# mask_ask = df['Ask'] != [0] # Ask 列不等于 [0]
|
||||
# mask_bid = df['Bid'] != [0] # Bid 列不等于 [0]
|
||||
# df = df[mask_ask & mask_bid]
|
||||
# 创建掩码，标记需要删除的行（Ask或Bid等于[0]）
|
||||
# mask = df['Ask'].apply(lambda x: x == [0]) | df['Bid'].apply(lambda x: x == [0])
|
||||
|
||||
# # 使用取反操作符~保留无需删除的行
|
||||
# df = df[~mask]
|
||||
# df = df[df['high'] != df['low']]
|
||||
df["delta"] = df["delta"].astype(float)
|
||||
df['datetime'] = pd.to_datetime(df['datetime'],format='mixed')#, dayfirst=True, format='mixed'
|
||||
df['delta累计'] = df.groupby(df['datetime'].dt.date)['delta'].cumsum()
|
||||
# 使用filter方法，itemwise=True确保按列进行操作
|
||||
# filtered_df = df[(df['delta'] >= 300) | (df['delta'] <= -300) | (df['dj'] >= 5) | (df['dj'] <= -5)]
|
||||
futures_zh_minute_sina_df = ak.futures_zh_minute_sina(symbol=df["symbol"].iloc[-1], period="5")
|
||||
futures_zh_minute_sina_df['datetime'] = pd.to_datetime(futures_zh_minute_sina_df['datetime'],format='mixed')
|
||||
futures_zh_minute_sina_df['终极平滑值'],futures_zh_minute_sina_df['趋势方向'] = ultimate_smoother(futures_zh_minute_sina_df["close"],time_period)
|
||||
# 设置索引
|
||||
df = df.set_index('datetime')
|
||||
futures_zh_minute_sina_df = futures_zh_minute_sina_df.set_index('datetime')
|
||||
|
||||
# 使用 join 进行对齐
|
||||
# df = df.join(futures_zh_minute_sina_df[['终极平滑值', '趋势方向']], how='left')
|
||||
df_new = futures_zh_minute_sina_df.join(df[['price', 'Ask', 'Bid', 'delta','dj','delta累计']], how='left')
|
||||
# df = df.join(futures_zh_minute_sina_df[['趋势方向']], how='left')
|
||||
|
||||
# 重置索引（可选）
|
||||
df_new = df_new.reset_index()
|
||||
# df = pd.merge(df,futures_zh_minute_sina_df[['datetime', '终极平滑值']], on='datetime', how='left' )
|
||||
# df = pd.merge(df,futures_zh_minute_sina_df[['datetime', ]], on='datetime', how='left' )
|
||||
# df['终极平滑值'],df['趋势方向'] = ultimate_smoother(df['close'],time_period)
|
||||
df_new['datetime'] = df_new['datetime'].dt.strftime("%Y-%m-%d %H:%M:%S")
|
||||
df_new['POC'] = add_poc_column(df_new)
|
||||
df_new['symbol'] = df["symbol"].iloc[-1]
|
||||
# print(df.tail(1))
|
||||
# cols = df_new.columns.tolist()
|
||||
# cols.insert(3, cols.pop(0))
|
||||
cols = ['price', 'Ask', 'Bid', 'symbol','datetime','delta','close', 'open', 'high', 'low', 'volume','hold','dj','delta累计','POC', '终极平滑值', '趋势方向']
|
||||
df_new = df_new[cols]
|
||||
# print(cols)
|
||||
del futures_zh_minute_sina_df,df
|
||||
|
||||
if len(df_new) >=5*time_period and (df_new['趋势方向'].iloc[-1] != df_new['趋势方向'].iloc[-2]):
|
||||
table_text = df_new.iloc[:,3:].tail(1).to_html(index=False) #price,Ask,Bid,symbol,datetime,delta,close,open,high,low,volume,dj
|
||||
send_mail(table_text)
|
||||
# time.sleep(60)
|
||||
else:
|
||||
pass
|
||||
return df_new.iloc[-11:-1].to_dict(orient="records")
|
||||
|
||||
# def add_poc_column(df):
|
||||
# # 转换字符串列到实际列表
|
||||
# df['price'] = df['price'].apply(ast.literal_eval)
|
||||
# df['Ask'] = df['Ask'].apply(lambda x: list(map(int, ast.literal_eval(x))))
|
||||
# df['Bid'] = df['Bid'].apply(lambda x: list(map(int, ast.literal_eval(x))))
|
||||
|
||||
# # 定义处理函数（向量化操作更高效）
|
||||
# def find_poc(row):
|
||||
# sums = [a + b for a, b in zip(row['Ask'], row['Bid'])]
|
||||
# max_index = sums.index(max(sums))
|
||||
# return row['price'][max_index]
|
||||
|
||||
# # 应用处理函数
|
||||
# df['POC'] = df.apply(find_poc, axis=1)
|
||||
# return df['POC']
|
||||
|
||||
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 None # 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 None # 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]<price[i] and ma_close[i]<price[i]:
|
||||
trend[i] = '多头趋势'
|
||||
else:
|
||||
trend[i] = '无趋势'
|
||||
|
||||
|
||||
return us_new,trend
|
||||
|
||||
|
||||
@app.route("/")
|
||||
def index():
|
||||
files = get_csv_files() # 获取所有符合条件的文件
|
||||
# 默认显示第一个文件的数据
|
||||
first_file = list(files.keys())[0] if files else None
|
||||
data = load_data(files[first_file]) if first_file else []
|
||||
return render_template("index.html", data=data, files=files, file_name=first_file)
|
||||
|
||||
|
||||
@app.route("/data/<file_name>")
|
||||
def switch_data(file_name):
|
||||
files = get_csv_files() # 获取所有符合条件的文件
|
||||
if file_name in files:
|
||||
data = load_data(files[file_name])
|
||||
return jsonify(data)
|
||||
return jsonify({"error": "File not found"}), 404
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
app.run(host='0.0.0.0', port=5000, debug=True) # 监听所有网络接口
|
||||
@@ -0,0 +1,200 @@
|
||||
from flask import Flask, render_template, jsonify
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import os
|
||||
import ast
|
||||
import time
|
||||
|
||||
app = Flask(__name__)
|
||||
|
||||
# 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 = ["[email protected]"] # 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 = "[email protected]" # 设置发送邮件的邮箱地址
|
||||
username = "[email protected]" # 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 = 60
|
||||
|
||||
# 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 = "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"""
|
||||
<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())
|
||||
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='mixed')#, 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(5))
|
||||
|
||||
# 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]<price[i] and ma_close[i]<price[i]:
|
||||
trend[i] = '多头趋势'
|
||||
else:
|
||||
trend[i] = '无趋势'
|
||||
|
||||
|
||||
return us_new,trend
|
||||
|
||||
|
||||
@app.route("/")
|
||||
def index():
|
||||
files = get_csv_files() # 获取所有符合条件的文件
|
||||
# 默认显示第一个文件的数据
|
||||
first_file = list(files.keys())[0] if files else None
|
||||
data = load_data(files[first_file]) if first_file else []
|
||||
return render_template("index.html", data=data, files=files, file_name=first_file)
|
||||
|
||||
|
||||
@app.route("/data/<file_name>")
|
||||
def switch_data(file_name):
|
||||
files = get_csv_files() # 获取所有符合条件的文件
|
||||
if file_name in files:
|
||||
data = load_data(files[file_name])
|
||||
return jsonify(data)
|
||||
return jsonify({"error": "File not found"}), 404
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
app.run(host='0.0.0.0', port=5000, debug=True) # 监听所有网络接口
|
||||
@@ -0,0 +1,235 @@
|
||||
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 = ["[email protected]"] # 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 = "[email protected]" # 设置发送邮件的邮箱地址
|
||||
username = "[email protected]" # 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"""
|
||||
<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())
|
||||
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]<price[i] and ma_close[i]<price[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("/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 % 5 == 0:
|
||||
# 检查是否在5秒内
|
||||
if now.second < 5:
|
||||
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
|
||||
@@ -0,0 +1,220 @@
|
||||
from flask import Flask, render_template, jsonify
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import os
|
||||
import ast
|
||||
import time
|
||||
|
||||
app = Flask(__name__)
|
||||
|
||||
# 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 = ["[email protected]"] # 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 = "[email protected]" # 设置发送邮件的邮箱地址
|
||||
username = "[email protected]" # 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 = "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"""
|
||||
<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())
|
||||
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()
|
||||
df["delta"] = df["delta"].astype(float)
|
||||
df['datetime'] = pd.to_datetime(df['datetime'],format='mixed')#, dayfirst=True, format='mixed'
|
||||
|
||||
# 增加'delta累计'列
|
||||
df['delta累计'] = df.groupby(df['datetime'].dt.date)['delta'].cumsum()
|
||||
|
||||
# # 使用akshare获取5分钟k线的全部数据
|
||||
# df = ak.futures_zh_minute_sina(symbol=df["symbol"].iloc[-1], period="5")
|
||||
# df['datetime'] = pd.to_datetime(df['datetime'],format='mixed')
|
||||
# df['终极平滑值'],df['趋势方向'] = ultimate_smoother(df["close"],time_period)
|
||||
df['终极平滑值'],df['趋势方向'] = ultimate_smoother(df['close'],time_period)
|
||||
# # 设置索引
|
||||
# df = df.set_index('datetime')
|
||||
# df = df.set_index('datetime')
|
||||
|
||||
# # 使用 join 进行对齐
|
||||
# df = df.join(df[['price', 'Ask', 'Bid', 'delta','dj','delta累计']], how='left')
|
||||
|
||||
# # 重置索引（可选）
|
||||
# df = df.reset_index()
|
||||
df['datetime'] = df['datetime'].dt.strftime("%Y-%m-%d %H:%M:%S")
|
||||
|
||||
# 增加'POC'列
|
||||
df['POC'] = add_poc_column(df)
|
||||
# df['symbol'] = df["symbol"].iloc[-1]
|
||||
# cols = ['price', 'Ask', 'Bid', 'symbol','datetime','delta','close', 'open', 'high', 'low', 'volume','hold','dj','delta累计','POC', '终极平滑值', '趋势方向']
|
||||
# df = df[cols]
|
||||
# del df,df
|
||||
|
||||
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[-11:-1].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 None # 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 None # 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]<price[i] and ma_close[i]<price[i]:
|
||||
trend[i] = '多头趋势'
|
||||
else:
|
||||
trend[i] = '无趋势'
|
||||
|
||||
|
||||
return us_new,trend
|
||||
|
||||
|
||||
@app.route("/")
|
||||
def index():
|
||||
files = get_csv_files() # 获取所有符合条件的文件
|
||||
# 默认显示第一个文件的数据
|
||||
first_file = list(files.keys())[0] if files else None
|
||||
data = load_data(files[first_file]) if first_file else []
|
||||
return render_template("index.html", data=data, files=files, file_name=first_file)
|
||||
|
||||
|
||||
@app.route("/data/<file_name>")
|
||||
def switch_data(file_name):
|
||||
files = get_csv_files() # 获取所有符合条件的文件
|
||||
if file_name in files:
|
||||
data = load_data(files[file_name])
|
||||
return jsonify(data)
|
||||
return jsonify({"error": "File not found"}), 404
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
app.run(host='0.0.0.0', port=5000, debug=True) # 监听所有网络接口
|
||||
@@ -0,0 +1,136 @@
|
||||
from flask import Flask, render_template, jsonify
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import os
|
||||
import ast
|
||||
|
||||
app = Flask(__name__)
|
||||
|
||||
# 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 = "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
|
||||
|
||||
|
||||
# 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["delta"] = df["delta"].astype(float)
|
||||
df['datetime'] = pd.to_datetime(df['datetime'], format='mixed')
|
||||
df['delta累计'] = df.groupby(df['datetime'].dt.date)['delta'].cumsum()
|
||||
df['终极平滑值'],df['趋势方向'] = ultimate_smoother(df["close"],48)
|
||||
df['datetime'] = df['datetime'].dt.strftime("%Y-%m-%d %H:%M:%S")
|
||||
df['POC'] = add_poc_column(df)
|
||||
return df.tail(20).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 0 # 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 0 # 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=period).mean()
|
||||
|
||||
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'
|
||||
|
||||
|
||||
return us_new,trend
|
||||
|
||||
|
||||
@app.route("/")
|
||||
def index():
|
||||
files = get_csv_files() # 获取所有符合条件的文件
|
||||
# 默认显示第一个文件的数据
|
||||
first_file = list(files.keys())[0] if files else None
|
||||
data = load_data(files[first_file]) if first_file else []
|
||||
return render_template("index.html", data=data, files=files, file_name=first_file)
|
||||
|
||||
|
||||
@app.route("/data/<file_name>")
|
||||
def switch_data(file_name):
|
||||
files = get_csv_files() # 获取所有符合条件的文件
|
||||
if file_name in files:
|
||||
data = load_data(files[file_name])
|
||||
return jsonify(data)
|
||||
return jsonify({"error": "File not found"}), 404
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
app.run(host='0.0.0.0', port=5000, debug=True) # 监听所有网络接口
|
||||
@@ -0,0 +1,222 @@
|
||||
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
|
||||
|
||||
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 = ["[email protected]"] # 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 = "[email protected]" # 设置发送邮件的邮箱地址
|
||||
username = "[email protected]" # 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"""
|
||||
<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())
|
||||
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,
|
||||
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]<price[i] and ma_close[i]<price[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("/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 background_thread():
|
||||
"""后台线程，定期发送数据更新"""
|
||||
while True:
|
||||
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)
|
||||
time.sleep(1) # 每秒更新一次
|
||||
|
||||
@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
|
||||
@@ -0,0 +1,234 @@
|
||||
from flask import Flask, render_template, jsonify
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import os
|
||||
import ast
|
||||
import time
|
||||
|
||||
app = Flask(__name__)
|
||||
|
||||
|
||||
# # from flask import Flask
|
||||
# from flasgger import Swagger
|
||||
# from flasgger import Talisman
|
||||
|
||||
# app = Flask(__name__)
|
||||
# Talisman(app) # This will add X-Content-Type-Options among other headers.
|
||||
# Swagger(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 = ["[email protected]"] # 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 = "[email protected]" # 设置发送邮件的邮箱地址
|
||||
username = "[email protected]" # 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
|
||||
|
||||
# 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 = "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"""
|
||||
<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())
|
||||
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()
|
||||
df["delta"] = df["delta"].astype(float)
|
||||
df['datetime'] = pd.to_datetime(df['datetime'],format='mixed')#, dayfirst=True, format='mixed'
|
||||
|
||||
# 增加'delta累计'列和'POC'列
|
||||
df['delta累计'] = df.groupby(df['datetime'].dt.date)['delta'].cumsum()
|
||||
|
||||
|
||||
# 使用akshare获取5分钟k线的全部数据
|
||||
symbol_name =df["symbol"].iloc[-1]
|
||||
futures_zh_minute_sina_df = ak.futures_zh_minute_sina(symbol=symbol_name, period="5")
|
||||
futures_zh_minute_sina_df['datetime'] = pd.to_datetime(futures_zh_minute_sina_df['datetime'],format='mixed')
|
||||
futures_zh_minute_sina_df['终极平滑值'],futures_zh_minute_sina_df['趋势方向'] = ultimate_smoother(futures_zh_minute_sina_df["close"],time_period)
|
||||
|
||||
# 设置索引
|
||||
df = df.set_index('datetime')
|
||||
futures_zh_minute_sina_df = futures_zh_minute_sina_df.set_index('datetime')
|
||||
|
||||
# 使用 join 进行对齐
|
||||
df = futures_zh_minute_sina_df.join(df[['price', 'Ask', 'Bid', 'delta','dj','delta累计']], how='left')
|
||||
|
||||
# 重置索引（可选）
|
||||
df = df.reset_index()
|
||||
|
||||
# 修改symbol列全部值为统一值
|
||||
df['symbol'] = symbol_name
|
||||
|
||||
# df['POC'] = add_poc_column(df)
|
||||
|
||||
# 重新排列数据并删除其他dataframe
|
||||
cols = ['price', 'Ask', 'Bid', 'symbol','datetime','delta','close', 'open', 'high', 'low', 'volume','hold','dj','delta累计', '终极平滑值', '趋势方向']
|
||||
df = df[cols]
|
||||
# print(cols)
|
||||
|
||||
# datetime修改为字符串
|
||||
df['datetime'] = df['datetime'].dt.strftime("%Y-%m-%d %H:%M:%S")
|
||||
|
||||
|
||||
|
||||
# del futures_zh_minute_sina_df,df
|
||||
|
||||
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)
|
||||
send_mail(table_text)
|
||||
else:
|
||||
pass
|
||||
return df.iloc[-21:-1].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 None # 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 None # 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]<price[i] and ma_close[i]<price[i]:
|
||||
trend[i] = '多头趋势'
|
||||
else:
|
||||
trend[i] = '无趋势'
|
||||
|
||||
|
||||
return us_new,trend
|
||||
|
||||
|
||||
@app.route("/")
|
||||
def index():
|
||||
files = get_csv_files() # 获取所有符合条件的文件
|
||||
# 默认显示第一个文件的数据
|
||||
first_file = list(files.keys())[0] if files else None
|
||||
data = load_data(files[first_file]) if first_file else []
|
||||
return render_template("index.html", data=data, files=files, file_name=first_file)
|
||||
|
||||
|
||||
@app.route("/data/<file_name>")
|
||||
def switch_data(file_name):
|
||||
files = get_csv_files() # 获取所有符合条件的文件
|
||||
if file_name in files:
|
||||
data = load_data(files[file_name])
|
||||
return jsonify(data)
|
||||
return jsonify({"error": "File not found"}), 404
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
app.run(host='0.0.0.0', port=5000, debug=True) # 监听所有网络接口
|
||||
@@ -0,0 +1,67 @@
|
||||
price,Ask,Bid,symbol,datetime,delta,close,open,high,low,volume,dj
|
||||
"['3273.0', '3274.0', '3275.0', '3276.0']","[0, 2239, 2381, 3]","[864, 1357, 206, 0]",rb2505,2025-03-06 21:35:00,2196,3275.0,3275.0,3276.0,3273.0,7050,0
|
||||
"['3271.0', '3272.0', '3273.0', '3274.0', '3275.0', '3276.0']","[0, 604, 1068, 2615, 2480, 433]","[1145, 5423, 3975, 2348, 312, 0]",rb2505,2025-03-06 21:40:00,-6003,3272.0,3275.0,3276.0,3271.0,20442,0,
|
||||
"['3270.0', '3271.0', '3272.0', '3273.0']","[0, 946, 5050, 905]","[1459, 4097, 1983, 0]",rb2505,2025-03-06 21:45:00,-638,3273.0,3271.0,3273.0,3270.0,14440,0,
|
||||
"['3271.0', '3272.0', '3273.0', '3274.0']","[0, 3303, 1202, 116]","[2189, 1249, 804, 0]",rb2505,2025-03-06 21:50:00,379,3271.0,3272.0,3274.0,3271.0,8863,0,
|
||||
"['3271.0', '3272.0', '3273.0']","[0, 1259, 960]","[118, 1545, 0]",rb2505,2025-03-06 21:55:00,556,3271.0,3272.0,3273.0,3271.0,3882,0,
|
||||
"['3270.0', '3271.0', '3272.0', '3273.0']","[0, 2564, 2309, 360]","[1248, 2593, 366, 0]",rb2505,2025-03-06 22:00:00,1026,3271.0,3272.0,3273.0,3270.0,9440,0,
|
||||
"['3270.0', '3271.0', '3272.0', '3273.0', '3274.0', '3275.0', '3276.0']","[0, 1290, 703, 1821, 1779, 2845, 3380]","[248, 659, 459, 297, 539, 1570, 0]",rb2505,2025-03-06 22:05:00,8046,3276.0,3272.0,3276.0,3270.0,15590,1,
|
||||
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||||
|
@@ -0,0 +1,106 @@
|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
cursor: pointer;
|
||||
}
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<h1>Real-Time CSV Data Viewer</h1>
|
||||
|
||||
<!-- 动态生成按钮 -->
|
||||
{% for file_name, file_path in files.items() %}
|
||||
<button onclick="loadData('{{ file_name }}')">{{ file_name }}</button>
|
||||
{% endfor %}
|
||||
|
||||
<h3>Data for {{ file_name }}</h3>
|
||||
<table id="data-table">
|
||||
<thead>
|
||||
<tr>
|
||||
<th>Symbol</th>
|
||||
<th>Datetime</th>
|
||||
<th>Delta</th>
|
||||
<th>Close</th>
|
||||
<th>Open</th>
|
||||
<th>High</th>
|
||||
<th>Low</th>
|
||||
<th>Volume</th>
|
||||
<th>DJ</th>
|
||||
<th>Delta累计</th>
|
||||
<th>POC</th>
|
||||
<th>终极平滑值</th>
|
||||
<th>趋势方向</th>
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody>
|
||||
{% for row in data %}
|
||||
<tr>
|
||||
<td>{{ row.symbol }}</td>
|
||||
<td>{{ row.datetime }}</td>
|
||||
<td>{{ row.delta }}</td>
|
||||
<td>{{ row.close }}</td>
|
||||
<td>{{ row.open }}</td>
|
||||
<td>{{ row.high }}</td>
|
||||
<td>{{ row.low }}</td>
|
||||
<td>{{ row.volume }}</td>
|
||||
<td>{{ row.dj }}</td>
|
||||
<td>{{ row.delta累计 }}</td>
|
||||
<td>{{ row.POC }}</td>
|
||||
<td>{{ row.终极平滑值 }}</td>
|
||||
<td>{{ row.趋势方向 }}</td>
|
||||
</tr>
|
||||
{% endfor %}
|
||||
</tbody>
|
||||
</table>
|
||||
|
||||
<script>
|
||||
function loadData(fileName) {
|
||||
fetch('/data/' + fileName)
|
||||
.then(response => response.json())
|
||||
.then(data => {
|
||||
let tableBody = document.querySelector("#data-table tbody");
|
||||
tableBody.innerHTML = ''; // 清空现有数据行
|
||||
data.forEach(row => {
|
||||
let rowElement = document.createElement('tr');
|
||||
rowElement.innerHTML = `
|
||||
<td>${row.symbol}</td>
|
||||
<td>${row.datetime}</td>
|
||||
<td>${row.delta}</td>
|
||||
<td>${row.close}</td>
|
||||
<td>${row.open}</td>
|
||||
<td>${row.high}</td>
|
||||
<td>${row.low}</td>
|
||||
<td>${row.volume}</td>
|
||||
<td>${row.dj}</td>
|
||||
<td>${row.delta累计}</td>
|
||||
<td>${row.POC}</td>
|
||||
<td>${row.终极平滑值}</td>
|
||||
<td>${row.趋势方向}</td>
|
||||
`;
|
||||
tableBody.appendChild(rowElement);
|
||||
});
|
||||
});
|
||||
}
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
@@ -0,0 +1,199 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<title>CSV Data Viewer</title>
|
||||
<style>
|
||||
table {
|
||||
width: 100%;
|
||||
border-collapse: collapse;
|
||||
}
|
||||
table, th, td {
|
||||
border: 1px solid black;
|
||||
}
|
||||
th, td {
|
||||
padding: 8px;
|
||||
text-align: center;
|
||||
}
|
||||
button {
|
||||
margin: 10px;
|
||||
padding: 10px;
|
||||
cursor: pointer;
|
||||
}
|
||||
select {
|
||||
width: 100%;
|
||||
padding: 5px;
|
||||
text-align: center;
|
||||
}
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<h1>Real-Time CSV Data Viewer</h1>
|
||||
|
||||
<!-- 动态生成文件切换按钮 -->
|
||||
{% for file_name, file_path in files.items() %}
|
||||
<button onclick="loadData('{{ file_name }}')">{{ file_name }}</button>
|
||||
{% endfor %}
|
||||
|
||||
<h3>Data for {{ file_name }}</h3>
|
||||
<table id="data-table">
|
||||
<thead>
|
||||
<tr>
|
||||
<th>
|
||||
<select id="filter-price" onchange="filterTable()">
|
||||
<option value="">Price</option>
|
||||
</select>
|
||||
</th>
|
||||
<th>
|
||||
<select id="filter-Ask" onchange="filterTable()">
|
||||
<option value="">Ask</option>
|
||||
</select>
|
||||
</th>
|
||||
<th>
|
||||
<select id="filter-Bid" onchange="filterTable()">
|
||||
<option value="">Bid</option>
|
||||
</select>
|
||||
</th>
|
||||
<th>
|
||||
<select id="filter-symbol" onchange="filterTable()">
|
||||
<option value="">Symbol</option>
|
||||
</select>
|
||||
</th>
|
||||
<th>
|
||||
<select id="filter-datetime" onchange="filterTable()">
|
||||
<option value="">Datetime</option>
|
||||
</select>
|
||||
</th>
|
||||
<th>
|
||||
<select id="filter-delta" onchange="filterTable()">
|
||||
<option value="">Delta</option>
|
||||
</select>
|
||||
</th>
|
||||
<th>
|
||||
<select id="filter-close" onchange="filterTable()">
|
||||
<option value="">Close</option>
|
||||
</select>
|
||||
</th>
|
||||
<th>
|
||||
<select id="filter-open" onchange="filterTable()">
|
||||
<option value="">Open</option>
|
||||
</select>
|
||||
</th>
|
||||
<th>
|
||||
<select id="filter-high" onchange="filterTable()">
|
||||
<option value="">High</option>
|
||||
</select>
|
||||
</th>
|
||||
<th>
|
||||
<select id="filter-low" onchange="filterTable()">
|
||||
<option value="">Low</option>
|
||||
</select>
|
||||
</th>
|
||||
<th>
|
||||
<select id="filter-volume" onchange="filterTable()">
|
||||
<option value="">Volume</option>
|
||||
</select>
|
||||
</th>
|
||||
<th>
|
||||
<select id="filter-dj" onchange="filterTable()">
|
||||
<option value="">DJ</option>
|
||||
</select>
|
||||
</th>
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody>
|
||||
{% for row in data %}
|
||||
<tr>
|
||||
<td>{{ row.price }}</td>
|
||||
<td>{{ row.Ask }}</td>
|
||||
<td>{{ row.Bid }}</td>
|
||||
<td>{{ row.symbol }}</td>
|
||||
<td>{{ row.datetime }}</td>
|
||||
<td>{{ row.delta }}</td>
|
||||
<td>{{ row.close }}</td>
|
||||
<td>{{ row.open }}</td>
|
||||
<td>{{ row.high }}</td>
|
||||
<td>{{ row.low }}</td>
|
||||
<td>{{ row.volume }}</td>
|
||||
<td>{{ row.dj }}</td>
|
||||
</tr>
|
||||
{% endfor %}
|
||||
</tbody>
|
||||
</table>
|
||||
|
||||
<script>
|
||||
function loadData(fileName) {
|
||||
fetch('/data/' + fileName)
|
||||
.then(response => response.json())
|
||||
.then(data => {
|
||||
let tableBody = document.querySelector("#data-table tbody");
|
||||
tableBody.innerHTML = ''; // 清空现有数据行
|
||||
data.forEach(row => {
|
||||
let rowElement = document.createElement('tr');
|
||||
rowElement.innerHTML = `
|
||||
<td>${row.price}</td>
|
||||
<td>${row.Ask}</td>
|
||||
<td>${row.Bid}</td>
|
||||
<td>${row.symbol}</td>
|
||||
<td>${row.datetime}</td>
|
||||
<td>${row.delta}</td>
|
||||
<td>${row.close}</td>
|
||||
<td>${row.open}</td>
|
||||
<td>${row.high}</td>
|
||||
<td>${row.low}</td>
|
||||
<td>${row.volume}</td>
|
||||
<td>${row.dj}</td>
|
||||
`;
|
||||
tableBody.appendChild(rowElement);
|
||||
});
|
||||
|
||||
populateFilters(data);
|
||||
});
|
||||
}
|
||||
|
||||
function populateFilters(data) {
|
||||
let columns = ["price", "Ask", "Bid", "symbol", "datetime", "delta", "close", "open", "high", "low", "volume", "dj"];
|
||||
columns.forEach(col => {
|
||||
let uniqueValues = [...new Set(data.map(row => row[col]))];
|
||||
let select = document.getElementById(`filter-${col}`);
|
||||
select.innerHTML = '<option value="">All</option>';
|
||||
uniqueValues.forEach(value => {
|
||||
select.innerHTML += `<option value="${value}">${value}</option>`;
|
||||
});
|
||||
});
|
||||
}
|
||||
|
||||
function filterTable() {
|
||||
let filters = {
|
||||
price: document.getElementById("filter-price").value,
|
||||
Ask: document.getElementById("filter-Ask").value,
|
||||
Bid: document.getElementById("filter-Bid").value,
|
||||
symbol: document.getElementById("filter-symbol").value,
|
||||
datetime: document.getElementById("filter-datetime").value,
|
||||
delta: document.getElementById("filter-delta").value,
|
||||
close: document.getElementById("filter-close").value,
|
||||
open: document.getElementById("filter-open").value,
|
||||
high: document.getElementById("filter-high").value,
|
||||
low: document.getElementById("filter-low").value,
|
||||
volume: document.getElementById("filter-volume").value,
|
||||
dj: document.getElementById("filter-dj").value
|
||||
};
|
||||
|
||||
let rows = document.querySelectorAll("#data-table tbody tr");
|
||||
rows.forEach(row => {
|
||||
let cells = row.children;
|
||||
let show = true;
|
||||
|
||||
Object.keys(filters).forEach((col, index) => {
|
||||
if (filters[col] && cells[index].textContent !== filters[col]) {
|
||||
show = false;
|
||||
}
|
||||
});
|
||||
|
||||
row.style.display = show ? "" : "none";
|
||||
});
|
||||
}
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
Reference in new issue
Block a user