20250314修改:增加dingdanliu_nb_mflow

999.账户相关/test/akshare_view.py

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+import akshare as ak
+import pandas as pd
+import numpy as np
+import os
+import ast
+import time
+import mplfinance as mpf
+import matplotlib.pyplot as plt
+
+# from flask import Flask, render_template, jsonify
+
+# # 读取数据（注意替换实际文件路径）
+file_path = r"C:\Users\zhouj\Desktop\IM888_5MIN_2024_ofdata.csv"
+df = pd.read_csv(file_path, parse_dates=["datetime"], index_col="datetime").sort_index()
+
+# 使用akshare获取数据 
+symbol_name = 'IM2503'
+time_period = 30
+# df = ak.futures_zh_minute_sina(symbol=symbol_name, period="15")
+
+# ultimate_smoother函数
+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
+
+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']
+df.reset_index()
+# df['datetime'] = pd.to_datetime(df['datetime'])
+df['终极平滑值'], df['趋势方向'] = ultimate_smoother(df["close"], time_period)
+df['POC'] = add_poc_column(df)
+df.index = pd.to_datetime(df.index)
+print(df.head(5))
+
+# 规范列名（兼容MA列）
+df = df.rename(columns={"close": "Close", "open": "Open", "high": "High", "low": "Low", "volume":"Volume"})
+
+# 创建一个新的DataFrame来存储有效的POC值
+valid_poc = pd.DataFrame(index=df.index)  # 创建与主数据相同索引的DataFrame
+valid_poc['POC'] = df['POC'].apply(lambda x: float(x) if x != '缺值' else np.nan)  # 将有效POC值转换为float，无效值设为NaN
+
+# 创建附加绘图对象（关键步骤）
+apd = [
+    mpf.make_addplot(
+        df["终极平滑值"],  # MA数据列
+        color="dodgerblue",  # 线条颜色
+        width=1,  # 线宽
+        panel=0,  # 显示在主图区域
+        ylabel="终极平滑值",  # 右侧Y轴标签
+        mav=(60),#绘制均线
+        secondary_y=True,  # 与主图共享左侧Y轴
+    ),
+    mpf.make_addplot(
+        valid_poc['POC'],  # 使用处理后的POC值
+        type='scatter',  # 使用散点图
+        marker='o',  # 圆形标记
+        markersize=100,  # 标记大小
+        color='yellow',  # 标记颜色
+        alpha=0.5,  # 透明度
+        panel=0,  # 显示在主图区域
+        ylabel="POC",  # 右侧Y轴标签
+        secondary_y=False,  # 与主图共享左侧Y轴
+    )
+]
+
+# 定义 mplfinance 的自定义风格
+mc = mpf.make_marketcolors(up='r', down='g', volume='inherit')
+s = mpf.make_mpf_style(base_mpf_style='charles', marketcolors=mc, rc={'font.sans-serif': ['Microsoft YaHei']})
+
+# 创建图形
+fig, axes = mpf.plot(
+    df,
+    type="candle",
+    style=s,
+    addplot=apd,  # 添加ultimate_smoother线和POC散点
+    title=f"{symbol_name} K-Line with ultimate_smoother and POC",
+    ylabel="Price",
+    show_nontrading=False,
+    returnfig=True  # 返回图形对象
+)
+
+# 获取主图轴对象
+ax = axes[0]
+
+# 添加POC值标注
+for idx, row in valid_poc.iterrows():
+    if pd.notnull(row['POC']):
+        ax.annotate(
+            f'{row["POC"]:.2f}',
+            xy=(idx, row['POC']),
+            xytext=(10, 10),
+            textcoords='offset points',
+            color='black',
+            fontsize=8,
+            bbox=dict(boxstyle='round,pad=0.5', fc='yellow', alpha=0.5),
+            arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0')
+        )
+
+# 显示图形
+plt.show()
+