增加交易策略、交易指标、量化库代码等文件夹

999.账户相关/simnow_trader/traderdata/temp/app——.py

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+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