Tushare接口文档：每日筹码分布（cyq_chips）

import tushare as ts
pro = ts.pro_api()
df = pro.cyq_chips(ts_code='600000.SH', trade_date='20260723')
df

import tushare as ts
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import gridspec
# 设置中文字体
plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'Arial Unicode MS']
plt.rcParams['axes.unicode_minus'] = False
def get_chips_data(pro, ts_code, trade_date):
    """获取筹码分布数据和当日收盘价"""
    df_chips = pro.cyq_chips(ts_code=ts_code, trade_date=trade_date)
    if df_chips.empty:
        raise ValueError(f"未获取到 {ts_code} 在 {trade_date} 的筹码数据")
    df_daily = pro.daily(ts_code=ts_code, trade_date=trade_date)
    if df_daily.empty:
        raise ValueError(f"未获取到 {ts_code} 在 {trade_date} 的日线行情")
    close = df_daily.iloc[0]['close']
    return df_chips, close
def calc_cost_percentile(df_sorted, p):
    """计算累计筹码占比达到 p% 对应的价格（线性插值）"""
    cumsum = np.cumsum(df_sorted['percent'].values)
    if p <= 0:
        return df_sorted.iloc[0]['price']
    if p >= 100:
        return df_sorted.iloc[-1]['price']
    idx = np.searchsorted(cumsum, p)
    if idx == 0:
        return df_sorted.iloc[0]['price']
    if idx >= len(df_sorted):
        return df_sorted.iloc[-1]['price']
    price_low = df_sorted.iloc[idx-1]['price']
    price_high = df_sorted.iloc[idx]['price']
    cum_low = cumsum[idx-1]
    cum_high = cumsum[idx]
    if cum_high == cum_low:
        return price_low
    return price_low + (price_high - price_low) * (p - cum_low) / (cum_high - cum_low)
def plot_chips(ts_code, trade_date,
               p_low=5, p_high=95,
               p_low_70=15, p_high_70=85,
               display_p_low=0, display_p_high=100,
               min_percent_filter=0.0):
    """
    绘制筹码分布图（水平柱状图）
    参数：
        ts_code: 股票代码，如 '000001.SZ'
        trade_date: 交易日期，格式 'YYYYMMDD'
        token: Tushare token（可选）
        p_low: 90%区间下轨百分位（默认5）
        p_high: 90%区间上轨百分位（默认95）
        p_low_70: 70%区间下轨百分位（默认15）
        p_high_70: 70%区间上轨百分位（默认85）
        display_p_low: 显示下限百分位（默认0）
        display_p_high: 显示上限百分位（默认100）
        min_percent_filter: 过滤筹码占比低于此值的价格点（默认0.0）
    """
    pro = ts.pro_api()
    # 获取全量数据
    df_full, close = get_chips_data(pro, ts_code, trade_date)
    if min_percent_filter > 0:
        df_full = df_full[df_full['percent'] >= min_percent_filter]
    df_full = df_full.sort_values('price').reset_index(drop=True)
    prices_full = df_full['price'].values
    percents_full = df_full['percent'].values
    # ---- 1. 计算各项指标（基于全量数据） ----
    # 平均成本
    avg_cost = np.sum(prices_full * percents_full) / 100.0
    # 获利比例
    profit_ratio = np.sum(percents_full[prices_full <= close])
    # 90% 和 70% 筹码区间
    low_90 = calc_cost_percentile(df_full, p_low)
    high_90 = calc_cost_percentile(df_full, p_high)
    low_70 = calc_cost_percentile(df_full, p_low_70)
    high_70 = calc_cost_percentile(df_full, p_high_70)
    range_90 = high_90 - low_90
    range_70 = high_70 - low_70
    overlap_ratio = range_70 / range_90 if range_90 > 0 else np.nan
    concentration_90 = (high_90 - low_90) / (high_90 + low_90) if (high_90 + low_90) > 0 else np.nan
    # 支撑位 = 获利盘的平均成本（价格 < 收盘价的筹码加权均价）
    profit_mask = prices_full < close
    if np.any(profit_mask):
        profit_prices = prices_full[profit_mask]
        profit_percents = percents_full[profit_mask]
        if np.sum(profit_percents) > 0:
            support = np.sum(profit_prices * profit_percents) / np.sum(profit_percents)
        else:
            support = prices_full[0]  # 无有效获利筹码时，取最低价
    else:
        support = prices_full[0]  # 全部套牢，最低价为支撑
    # 压力位 = 套牢盘的平均成本（价格 > 收盘价的筹码加权均价）
    loss_mask = prices_full > close
    if np.any(loss_mask):
        loss_prices = prices_full[loss_mask]
        loss_percents = percents_full[loss_mask]
        if np.sum(loss_percents) > 0:
            resistance = np.sum(loss_prices * loss_percents) / np.sum(loss_percents)
        else:
            resistance = prices_full[-1]  # 无有效套牢筹码时，取最高价
    else:
        resistance = prices_full[-1]  # 全部获利，最高价为压力
    # ---- 2. 准备绘图数据（仅用于显示） ----
    price_min_display = calc_cost_percentile(df_full, display_p_low)
    price_max_display = calc_cost_percentile(df_full, display_p_high)
    mask = (df_full['price'] >= price_min_display) & (df_full['price'] <= price_max_display)
    if min_percent_filter > 0:
        mask = mask & (df_full['percent'] >= min_percent_filter)
    df_display = df_full[mask].copy()
    if df_display.empty:
        print("警告：应用显示过滤后无数据可绘，将显示全部数据")
        df_display = df_full.copy()
    prices_display = df_display['price'].values
    percents_display = df_display['percent'].values
    # ---- 3. 绘制 ----
    fig = plt.figure(figsize=(14, 8))
    gs = gridspec.GridSpec(1, 2, width_ratios=[0.7, 0.3])
    ax = plt.subplot(gs[0])
    # 柱子高度
    if len(prices_display) > 1:
        gaps = np.diff(prices_display)
        min_gap = np.min(gaps)
        if min_gap == 0:
            min_gap = (prices_display[-1] - prices_display[0]) / max(1, len(prices_display)-1)
        bar_height = min_gap
    else:
        bar_height = 0.01
    # 颜色
    colors = []
    for p in prices_display:
        if p > close:
            colors.append('green')
        elif p < close:
            colors.append('red')
        else:
            colors.append('gray')
    ax.barh(
        y=prices_display,
        width=percents_display,
        height=bar_height,
        color=colors,
        edgecolor='none',
        alpha=0.8
    )
    # ---- 4. 标注线（使用新的支撑位和压力位） ----
    ax.axhline(y=avg_cost, color='blue', linestyle='--', linewidth=1.5, label=f'平均成本 {avg_cost:.2f}')
    ax.axhline(y=support, color='orange', linestyle='-.', linewidth=1.5, label=f'支撑位 {support:.2f} (获利盘均价)')
    ax.axhline(y=resistance, color='purple', linestyle='-.', linewidth=1.5, label=f'压力位 {resistance:.2f} (套牢盘均价)')
    ax.axhline(y=close, color='black', linestyle='-', linewidth=0.8, alpha=0.5, label=f'收盘价 {close:.2f}')
    ax.set_xlabel('筹码占比 (%)', fontsize=16)
    ax.set_ylabel('价格 (元)', fontsize=16)
    ax.set_title(f'{ts_code} 筹码分布图 ({trade_date})', fontsize=20)
    ax.grid(True, axis='x', alpha=0.3, linestyle=':')
    ax.tick_params(axis='both', labelsize=14)
    ax.legend(loc='upper right', fontsize=14)
    ax.yaxis.set_major_formatter(plt.FormatStrFormatter('%.2f'))
    # ---- 5. 右侧指标文本 ----
    ax_text = plt.subplot(gs[1])
    ax_text.axis('off')
    text_lines = [
        "【成本指标】",
        f"获利比例: {profit_ratio:.2f}%",
        f"压力位: {resistance:.2f}",
        f"平均成本: {avg_cost:.2f}",
        f"支撑位: {support:.2f}",
        "",
        "【集中度指标】",
        f"90%筹码区间: [{low_90:.2f}, {high_90:.2f}]",
        f"70%筹码区间: [{low_70:.2f}, {high_70:.2f}]",
        f"区间重合度: {overlap_ratio:.2%}" if not np.isnan(overlap_ratio) else "区间重合度: --",
        f"90%筹码集中度: {concentration_90:.2%}" if not np.isnan(concentration_90) else "90%筹码集中度: --"
    ]
    y_pos = 0.95
    for line in text_lines:
        ax_text.text(0.1, y_pos, line, fontsize=16, verticalalignment='top')
        y_pos -= 0.06
    plt.tight_layout()
    plt.show()
    # 返回所有指标
    return {
        'avg_cost': avg_cost,
        'profit_ratio': profit_ratio,
        'support': support,
        'resistance': resistance,
        'low_90': low_90,
        'high_90': high_90,
        'low_70': low_70,
        'high_70': high_70,
        'overlap_ratio': overlap_ratio,
        'concentration_90': concentration_90
    }
# ========== 使用示例 ==========
if __name__ == "__main__":
    plot_chips('000001.SZ', '20260723',
           display_p_low=2, display_p_high=98,
           min_percent_filter=0.01)