增加交易策略、交易指标、量化库代码等文件夹
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999.账户相关/simnow_trader/traderdata/temp/app_temp.py
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999.账户相关/simnow_trader/traderdata/temp/app_temp.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()
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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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# df = ak.futures_zh_minute_sina(symbol=df["symbol"].iloc[-1], period="5")
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# df['datetime'] = pd.to_datetime(df['datetime'],format='mixed')
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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['终极平滑值'],df['趋势方向'] = ultimate_smoother(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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# df = df.set_index('datetime')
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# # 使用 join 进行对齐
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# df = 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 = df.reset_index()
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df['datetime'] = df['datetime'].dt.strftime("%Y-%m-%d %H:%M:%S")
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# 增加'POC'列
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df['POC'] = add_poc_column(df)
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# df['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 = df[cols]
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# del df,df
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if len(df) >=5*time_period and (df['趋势方向'].iloc[-1] != df['趋势方向'].iloc[-2]):
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table_text = df.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.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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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 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:
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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 None # 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] = (1 - c1) * price[i] + (2 * c1 - c2) * price[i-1] \
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- (c1 + c3) * price[i-2] + c2 * us[i-1] + c3 * us[i-2]
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us_new = np.around(us, decimals=2)
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ma_close = price.rolling(window=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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