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