Add web application for real-time CSV data visualization and analysis
- Created Flask web application (app.py) to display and analyze trading data - Implemented data loading, processing, and email notification features - Added HTML templates for interactive data viewing - Included ultimate smoother and trend detection algorithms - Configured dynamic file selection and data rendering
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web/app.py
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web/app.py
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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 = ["240884432@qq.com"] # 设置邮件接收人地址
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subject = "TD_Simnow_Signal" # 设置邮件主题 订单流策略交易信号
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# 配置邮件服务器信息
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smtp_server = "smtp.qq.com" # 设置发送邮件的 SMTP 服务器地址
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smtp_port = 465 # 设置发送邮件的 SMTP 服务器端口号，一般为 25 端口 465
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sender = "240884432@qq.com" # 设置发送邮件的邮箱地址
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username = "240884432@qq.com" # 设置发送邮件的邮箱用户名
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password = "osjyjmbqrzxtbjbf" # zrmpcgttataabhjh，设置发送邮件的邮箱密码或授权码
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last_sent_time = 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(subset="datetime", keep="first").reset_index(drop=True)
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df = df[df["high"] != df["low"]]
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df["delta"] = df["delta"].astype(float)
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df["datetime"] = pd.to_datetime(
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df["datetime"], format="mixed"
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) # , dayfirst=True, format='mixed'
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df["delta累计"] = df.groupby(df["datetime"].dt.date)["delta"].cumsum()
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df = df.fillna("缺值")
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df["终极平滑值"], df["趋势方向"] = ultimate_smoother(df["close"], time_period)
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df["datetime"] = df["datetime"].dt.strftime("%Y-%m-%d %H:%M:%S")
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df["POC"] = add_poc_column(df)
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if len(df) >= 5 * time_period and (
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df["趋势方向"].iloc[-1] != df["趋势方向"].iloc[-2]
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):
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table_text = (
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df.iloc[:, 3:].tail(1).to_html(index=False)
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) # 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.iloc[-20:].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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def add_poc_column(df):
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# 安全转换列数据
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df["price"] = df["price"].apply(safe_literal_eval)
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df["Ask"] = df["Ask"].apply(lambda x: list(map(int, safe_literal_eval(x))))
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df["Bid"] = df["Bid"].apply(lambda x: list(map(int, safe_literal_eval(x))))
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# 定义处理函数（带数据验证）
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def find_poc(row):
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# 验证三个列表长度一致且非空
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if not (len(row["price"]) == len(row["Ask"]) == len(row["Bid"]) > 0):
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return "缺值" # 返回空值标记异常数据
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sums = [a + b for a, b in zip(row["Ask"], row["Bid"])]
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try:
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max_index = sums.index(max(sums))
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return row["price"][max_index]
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except ValueError:
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return "缺值" # 处理空求和列表情况
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# 应用处理函数
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df["POC"] = df.apply(find_poc, axis=1)
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# 可选：统计异常数据
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error_count = df["POC"].isnull().sum()
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if error_count > 0:
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print(f"警告：发现 {error_count} 行异常数据（已标记为NaN）")
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return df["POC"]
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def ultimate_smoother(price, period):
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# 初始化变量（修正角度单位为弧度）
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a1 = np.exp(-1.414 * np.pi / period)
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b1 = 2 * a1 * np.cos(1.414 * np.pi / period) # 将180改为np.pi
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c2 = b1
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c3 = -(a1**2)
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c1 = (1 + c2 - c3) / 4
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# 准备输出序列
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us = np.zeros(len(price))
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us_new = np.zeros(len(price))
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trend = [None] * (len(price))
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ma_close = np.zeros(len(price))
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# 前4个点用原始价格初始化
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for i in range(len(price)):
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if i < 4:
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us[i] = price[i]
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else:
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# 应用递归公式
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us[i] = (
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(1 - c1) * price[i]
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+ (2 * c1 - c2) * price[i - 1]
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- (c1 + c3) * price[i - 2]
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+ c2 * us[i - 1]
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+ c3 * us[i - 2]
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)
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us_new = np.around(us, decimals=2)
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ma_close = price.rolling(window=5 * period).mean()
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if us_new[i] > price[i] and ma_close[i] > price[i]:
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trend[i] = "空头趋势"
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elif us_new[i] < price[i] and ma_close[i] < price[i]:
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trend[i] = "多头趋势"
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else:
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trend[i] = "无趋势"
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return us_new, trend
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@app.route("/")
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def index():
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files = get_csv_files() # 获取所有符合条件的文件
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# 默认显示第一个文件的数据
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first_file = list(files.keys())[0] if files else None
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data = load_data(files[first_file]) if first_file else []
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return render_template("index.html", data=data, files=files, file_name=first_file)
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@app.route("/data/<file_name>")
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def switch_data(file_name):
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files = get_csv_files() # 获取所有符合条件的文件
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if file_name in files:
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data = load_data(files[file_name])
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return jsonify(data)
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return jsonify({"error": "File not found"}), 404
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if __name__ == "__main__":
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app.run(host="0.0.0.0", port=5000, debug=True) # 监听所有网络接口
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