{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "start: 2020-10-1 00:00:00\n",
    "end: 2020-10-15 16:00:00\n",
    "period: 1h\n",
    "basePeriod: 1h\n",
    "exchanges: [{\"eid\":\"OKEX\",\"currency\":\"BTC_USDT\",\"stocks\":1}]\n",
    "'''\n",
    "import pandas as pd\n",
    "from fmz import * # 导入所有FMZ函数\n",
    "task = VCtx(__doc__) # 初始化\n",
    "#!pip install --user mplfinance\n",
    "#import sys\n",
    "#sys.path.append('/home/quant/.local/lib/python3.6/site-packages')\n",
    "#import mplfinance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "处理K线...\n",
      "绘制K线\n",
      "[0, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 0] ['2020-09-22 16:00:00', '2020-09-22 17:00:00', '2020-09-23 01:00:00', '2020-09-23 07:00:00', '2020-09-23 13:00:00', '2020-09-23 15:00:00', '2020-09-23 17:00:00', '2020-09-23 21:00:00', '2020-09-24 03:00:00', '2020-09-24 05:00:00', '2020-09-24 17:00:00', '2020-09-24 19:00:00', '2020-09-24 23:00:00', '2020-09-25 10:00:00', '2020-09-25 20:00:00', '2020-09-26 01:00:00', '2020-09-26 06:00:00', '2020-09-26 10:00:00', '2020-09-26 18:00:00', '2020-09-26 22:00:00', '2020-09-27 01:00:00', '2020-09-27 12:00:00', '2020-09-27 16:00:00', '2020-09-27 18:00:00', '2020-09-28 02:00:00', '2020-09-28 09:00:00', '2020-09-28 13:00:00', '2020-09-28 15:00:00', '2020-09-28 17:00:00', '2020-09-28 19:00:00', '2020-09-28 21:00:00', '2020-09-29 02:00:00', '2020-09-29 04:00:00', '2020-09-29 06:00:00', '2020-09-29 11:00:00', '2020-09-29 17:00:00', '2020-09-30 00:00:00', '2020-09-30 09:00:00', '2020-09-30 17:00:00', '2020-09-30 20:00:00', '2020-10-01 00:00:00']\n",
      "中枢： (151.5, 10681.6) 43.0 30.299999999999272\n",
      "中枢： (126.0, 10855.3) 22.0 31.80000000000109\n",
      "中枢： (43.0, 10702.8) 78.0 31.80000000000109\n",
      "中枢： (27.0, 10270.8) 9.0 70.60000000000036\n",
      "中枢： (0, 10450.0) 24.0 19.549999999999272\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3600x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 第三方函数库\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "#import mplfinance as mpf\n",
    "import matplotlib.patches as patches\n",
    "import talib\n",
    "import datetime\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "def get_k_series():\n",
    "    #\n",
    "    # 获取k线序列，默认为30分钟级别\n",
    "    # 输入：n是级别，单位是分钟\n",
    "    # 输出：pandas, k线序列\n",
    "    n = 1\n",
    "\n",
    "    one_min_data = pd.DataFrame(exchange.GetRecords())\n",
    "    one_min_data = one_min_data.rename(columns={'Time':'date','Open':'open','Close':'close','High':'high','Low':'low'})\n",
    "    one_min_data['date'] = one_min_data['date'].apply(lambda x:_D(x/1000))\n",
    "    one_min_data['Date'] = one_min_data['date'].apply(lambda x:pd.to_datetime(x))\n",
    "    one_min_data.set_index('Date',inplace=True)\n",
    "    \n",
    "    #print(one_min_data)\n",
    "    n_min_data = pd.DataFrame()\n",
    "    for i in range(n, len(one_min_data) + 1, n):\n",
    "        \n",
    "        interval = one_min_data.iloc[i - n:i]\n",
    "        interval_open = interval.open.iloc[0]\n",
    "        interval_high = max(interval.high)\n",
    "        interval_low = min(interval.low)\n",
    "        interval_date = interval.date\n",
    "        interval_k = pd.DataFrame(interval[-1:])  # 新建DataFrame，否则会报SettingWithCopyWarning\n",
    "        interval_k.open = interval_open\n",
    "        interval_k.high = interval_high\n",
    "        interval_k.low = interval_low\n",
    "        interval_k.date = interval_date\n",
    "        #print(interval_k)\n",
    "        n_min_data = pd.concat([n_min_data, interval_k], axis=0)\n",
    "    n_min_data = n_min_data.reset_index()\n",
    "    #del n_min_data['instrument']\n",
    "    #del n_min_data['index']\n",
    "    #print(n_min_data)\n",
    "    return n_min_data\n",
    "\n",
    "\n",
    "def get_binary_positions(k_data):\n",
    "    #\n",
    "    # 计算k线序列的二分位值\n",
    "    # 输入：k线序列\n",
    "    # 输出：list, k线序列对应的二分位值\n",
    "    binary_positions = []\n",
    "    for i in range(len(k_data)):\n",
    "        temp_y = (k_data.high[i] + k_data.low[i]) / 2.0\n",
    "        binary_positions.append(temp_y)\n",
    "    return binary_positions\n",
    "\n",
    "\n",
    "def adjust_by_cintainment(k_data):\n",
    "    #\n",
    "    # 判断k线的包含关系，便于寻找顶分型和底分型\n",
    "    # 输入：k线序列\n",
    "    # 输出：adjusted_k_data, 处理后的k线序列\n",
    "    trend = [0]\n",
    "    adjusted_k_data = pd.DataFrame()\n",
    "    temp_data = k_data[:1]\n",
    "    #print(temp_data)\n",
    "    #return\n",
    "    for i in range(len(k_data)):\n",
    "        #print(\"处理：\",i)\n",
    "        is_equal = temp_data.high.iloc[-1] == k_data.high.iloc[i] and temp_data.low.iloc[-1] == k_data.low.iloc[i]  # 第1根等于第2根\n",
    "\n",
    "        # 向右包含\n",
    "        if temp_data.high.iloc[-1] >= k_data.high.iloc[i] and temp_data.low.iloc[-1] <= k_data.low.iloc[i] and not is_equal:\n",
    "            if trend[-1] == -1:\n",
    "                temp_data.high.iloc[-1] = k_data.high.iloc[i]\n",
    "            else:\n",
    "                temp_data.low.iloc[-1] = k_data.low.iloc[i]\n",
    "\n",
    "        # 向左包含\n",
    "        elif temp_data.high.iloc[-1] <= k_data.high.iloc[i] and temp_data.low.iloc[-1] >= k_data.low.iloc[i] and not is_equal:\n",
    "            if trend[-1] == -1:\n",
    "                temp_data.low.iloc[-1] = k_data.low.iloc[i]\n",
    "            else:\n",
    "                temp_data.high.iloc[-1] = k_data.high.iloc[i]\n",
    "\n",
    "        elif is_equal:\n",
    "            trend.append(0)\n",
    "\n",
    "        elif temp_data.high.iloc[-1] > k_data.high.iloc[i] and temp_data.low.iloc[-1] > k_data.low.iloc[i]:\n",
    "            trend.append(-1)\n",
    "            temp_data = k_data[i:i + 1]\n",
    "\n",
    "        elif temp_data.high.iloc[-1] < k_data.high.iloc[i] and temp_data.low.iloc[-1] < k_data.low.iloc[i]:\n",
    "            trend.append(1)\n",
    "            temp_data = k_data[i:i + 1]\n",
    "        \n",
    "        #print(\"处理判断完毕：\",i)\n",
    "        \n",
    "        #print(\"调整收盘价和开盘价：\",i)\n",
    "        # 调整收盘价和开盘价\n",
    "        if temp_data.open.iloc[-1] > temp_data.close.iloc[-1]:\n",
    "            if temp_data.open.iloc[-1] > temp_data.high.iloc[-1]:\n",
    "                temp_data.open.iloc[-1] = temp_data.high.iloc[-1]\n",
    "            if temp_data.close.iloc[-1] < temp_data.low.iloc[-1]:\n",
    "                temp_data.close.iloc[-1] = temp_data.low.iloc[-1]\n",
    "        else:\n",
    "            if temp_data.open.iloc[-1] < temp_data.low.iloc[-1]:\n",
    "                temp_data.open.iloc[-1] = temp_data.low.iloc[-1]\n",
    "            if temp_data.close.iloc[-1] > temp_data.high.iloc[-1]:\n",
    "                temp_data.close.iloc[-1] = temp_data.high.iloc[-1]\n",
    "\n",
    "        adjusted_data = k_data[i:i + 1]\n",
    "        adjusted_data.open.iloc[-1] = temp_data.open.iloc[-1]\n",
    "        adjusted_data.close.iloc[-1] = temp_data.close.iloc[-1]\n",
    "        adjusted_data.high.iloc[-1] = temp_data.high.iloc[-1]\n",
    "        adjusted_data.low.iloc[-1] = temp_data.low.iloc[-1]\n",
    "        #print(\"调整收盘价和开盘价完毕：\",i)\n",
    "        adjusted_k_data = pd.concat([adjusted_k_data, adjusted_data], axis=0)\n",
    "\n",
    "    return adjusted_k_data\n",
    "\n",
    "\n",
    "def get_fx(adjusted_k_data):\n",
    "    #\n",
    "    # 寻找顶分型和底分型\n",
    "    # 1）连续分型选择最极端值\n",
    "    # 2）分型之间保证3根k线\n",
    "    # 输入：调整后的k线序列\n",
    "    # 输出：顶分型和底分型的位置\n",
    "\n",
    "    temp_num = 0  # 上一个顶或底的位置\n",
    "    temp_high = 0  # 上一个顶的high值\n",
    "    temp_low = 0  # 上一个底的low值\n",
    "    temp_type = 0  # 上一个记录位置的类型\n",
    "\n",
    "    fx_type = []  # 记录分型点的类型，1为顶分型，-1为底分型\n",
    "    fx_time = []  # 记录分型点的时间\n",
    "    fx_plot = []  # 记录点的数值，为顶分型取high值，为底分型取low值\n",
    "    fx_data = pd.DataFrame()  # 记录分型\n",
    "    fx_offset = []\n",
    "\n",
    "    # 加上线段起点\n",
    "    fx_type.append(0)\n",
    "    fx_offset.append(0)\n",
    "    #fx_time.append(adjusted_k_data.index[0].strftime(\"%Y-%m-%d %H:%M:%S\"))\n",
    "    fx_time.append(adjusted_k_data.date[0])\n",
    "    fx_data = pd.concat([fx_data, adjusted_k_data[:1]], axis=0)\n",
    "    fx_plot.append((adjusted_k_data.low[0] + adjusted_k_data.high[0]) / 2)\n",
    "\n",
    "    i = 1\n",
    "    while (i < len(adjusted_k_data) - 1):\n",
    "\n",
    "        top = adjusted_k_data.high[i - 1] <= adjusted_k_data.high[i] \\\n",
    "              and adjusted_k_data.high[i] > adjusted_k_data.high[i + 1]  # 顶分型\n",
    "        bottom = adjusted_k_data.low[i - 1] >= adjusted_k_data.low[i] \\\n",
    "                 and adjusted_k_data.low[i] < adjusted_k_data.low[i + 1]  # 底分型\n",
    "\n",
    "        if top:\n",
    "            if temp_type == 1:\n",
    "                # 如果上一个分型为顶分型，则进行比较，选取高点更高的分型\n",
    "                if adjusted_k_data.high[i] <= temp_high:\n",
    "                    i += 1\n",
    "                else:\n",
    "                    temp_high = adjusted_k_data.high[i]\n",
    "                    temp_low = adjusted_k_data.low[i]\n",
    "                    temp_num = i\n",
    "                    temp_type = 1\n",
    "                    i += 2  # 两个分型之间至少有3根k线\n",
    "            elif temp_type == -1:\n",
    "                # 如果上一个分型为底分型，则记录上一个分型，用当前分型与后面的分型比较，选取同向更极端的分型\n",
    "                if temp_low >= adjusted_k_data.high[i]:\n",
    "                    # 如果上一个底分型的底比当前顶分型的顶高，则跳过当前顶分型。\n",
    "                    i += 1\n",
    "                else:\n",
    "                    fx_type.append(-1)\n",
    "                    #fx_time.append(adjusted_k_data.index[temp_num].strftime(\"%Y-%m-%d %H:%M:%S\"))\n",
    "                    fx_time.append(adjusted_k_data.date.iloc[temp_num])\n",
    "                    fx_data = pd.concat([fx_data, adjusted_k_data[temp_num:temp_num + 1]], axis=0)\n",
    "                    fx_plot.append(temp_low)\n",
    "                    fx_offset.append(temp_num)\n",
    "                    temp_high = adjusted_k_data.high[i]\n",
    "                    temp_low = adjusted_k_data.low[i]\n",
    "                    temp_num = i\n",
    "                    temp_type = 1\n",
    "                    i += 2  # 两个分型之间至少有3根k线\n",
    "            else:\n",
    "                temp_high = adjusted_k_data.high[i]\n",
    "                temp_low = adjusted_k_data.low[i]\n",
    "                temp_num = i\n",
    "                temp_type = 1\n",
    "                i += 2\n",
    "\n",
    "        elif bottom:\n",
    "            if temp_type == -1:\n",
    "                # 如果上一个分型为底分型，则进行比较，选取低点更低的分型\n",
    "                if adjusted_k_data.low[i] >= temp_low:\n",
    "                    i += 1\n",
    "                else:\n",
    "                    temp_low = adjusted_k_data.low[i]\n",
    "                    temp_high = adjusted_k_data.high[i]\n",
    "                    temp_num = i\n",
    "                    temp_type = -1\n",
    "                    i += 2\n",
    "            elif temp_type == 1:\n",
    "                # 如果上一个分型为顶分型，则记录上一个分型，用当前分型与后面的分型比较，选取同向更极端的分型\n",
    "                if temp_high <= adjusted_k_data.low[i]:\n",
    "                    # 如果上一个顶分型的底比当前底分型的底低，则跳过当前底分型。\n",
    "                    i += 1\n",
    "                else:\n",
    "                    fx_type.append(1)\n",
    "                    #fx_time.append(adjusted_k_data.index[temp_num].strftime(\"%Y-%m-%d %H:%M:%S\"))\n",
    "                    fx_time.append(adjusted_k_data.date.iloc[temp_num])\n",
    "                    fx_data = pd.concat([fx_data, adjusted_k_data[temp_num:temp_num + 1]], axis=0)\n",
    "                    fx_plot.append(temp_high)\n",
    "                    fx_offset.append(temp_num)\n",
    "                    temp_low = adjusted_k_data.low[i]\n",
    "                    temp_high = adjusted_k_data.high[i]\n",
    "                    temp_num = i\n",
    "                    temp_type = -1\n",
    "                    i += 2\n",
    "            else:\n",
    "                temp_low = adjusted_k_data.low[i]\n",
    "                temp_high = adjusted_k_data.high[i]\n",
    "                temp_num = i\n",
    "                temp_type = -1\n",
    "                i += 2\n",
    "        else:\n",
    "            i += 1\n",
    "\n",
    "    # 加上最后一个分型（上面的循环中最后的一个分型并未处理）\n",
    "    if temp_type == -1:\n",
    "        fx_type.append(-1)\n",
    "        #fx_time.append(adjusted_k_data.index[temp_num].strftime(\"%Y-%m-%d %H:%M:%S\"))\n",
    "        fx_time.append(adjusted_k_data.date.iloc[temp_num])\n",
    "        fx_data = pd.concat([fx_data, adjusted_k_data[temp_num:temp_num + 1]], axis=0)\n",
    "        fx_plot.append(temp_low)\n",
    "        fx_offset.append(temp_num)\n",
    "    elif temp_type == 1:\n",
    "        fx_type.append(1)\n",
    "        #fx_time.append(adjusted_k_data.index[temp_num].strftime(\"%Y-%m-%d %H:%M:%S\"))\n",
    "        fx_time.append(adjusted_k_data.date.iloc[temp_num])\n",
    "        fx_data = pd.concat([fx_data, adjusted_k_data[temp_num:temp_num + 1]], axis=0)\n",
    "        fx_plot.append(temp_high)\n",
    "        fx_offset.append(temp_num)\n",
    "\n",
    "    # 加上线段终点\n",
    "    fx_type.append(0)\n",
    "    fx_offset.append(len(adjusted_k_data) - 1)\n",
    "    #fx_time.append(adjusted_k_data.index[-1].strftime(\"%Y-%m-%d %H:%M:%S\"))\n",
    "    fx_time.append(adjusted_k_data.date.iloc[-1])\n",
    "    fx_data = pd.concat([fx_data, adjusted_k_data[-1:]], axis=0)\n",
    "    fx_plot.append((adjusted_k_data.low.iloc[-1] + adjusted_k_data.high.iloc[-1]) / 2)\n",
    "\n",
    "    return fx_type, fx_time, fx_data, fx_plot, fx_offset\n",
    "\n",
    "\n",
    "def get_pivot(fx_plot, fx_offset, fx_observe):\n",
    "    #\n",
    "    # 计算最近的中枢\n",
    "    # 注意：一个中枢至少有三笔\n",
    "    # fx_plot 笔的节点股价\n",
    "    # fx_offset 笔的节点时间点（偏移）\n",
    "    # fx_observe 所观测的分型点\n",
    "\n",
    "    if fx_observe < 1:\n",
    "        # 处理边界\n",
    "        right_bound = 0\n",
    "        left_bount = 0\n",
    "        min_high = 0\n",
    "        max_low = 0\n",
    "        pivot_x_interval = [left_bount, right_bound]\n",
    "        pivot_price_interval = [max_low, min_high]\n",
    "        return pivot_x_interval, pivot_price_interval\n",
    "\n",
    "    right_bound = (fx_offset[fx_observe] + fx_offset[fx_observe - 1]) / 2\n",
    "    # 右边界是所观察分型的上一笔中位\n",
    "    left_bount = 0\n",
    "    min_high = 0\n",
    "    max_low = 0\n",
    "\n",
    "    if fx_plot[fx_observe] >= fx_plot[fx_observe - 1]:\n",
    "        # 所观察分型的上一笔是往上的一笔\n",
    "        min_high = fx_plot[fx_observe]\n",
    "        max_low = fx_plot[fx_observe - 1]\n",
    "    else:  # 所观察分型的上一笔是往下的一笔\n",
    "        max_low = fx_plot[fx_observe]\n",
    "        min_high = fx_plot[fx_observe - 1]\n",
    "\n",
    "    i = fx_observe - 1\n",
    "    cover = 0  # 记录走势的重叠区，至少为3才能画中枢\n",
    "    while (i >= 1):\n",
    "        if fx_plot[i] >= fx_plot[i - 1]:\n",
    "            # 往上的一笔\n",
    "            if fx_plot[i] < max_low or fx_plot[i - 1] > min_high:\n",
    "                # 已经没有重叠区域了\n",
    "                left_bount = (fx_offset[i] + fx_offset[i + 1]) / 2\n",
    "                break\n",
    "            else:\n",
    "                # 有重叠区域\n",
    "                # 计算更窄的中枢价格区间\n",
    "                cover += 1\n",
    "                min_high = min(fx_plot[i], min_high)\n",
    "                max_low = max(fx_plot[i - 1], max_low)\n",
    "\n",
    "\n",
    "        elif fx_plot[i] < fx_plot[i - 1]:\n",
    "            # 往下的一笔\n",
    "            if fx_plot[i] > min_high or fx_plot[i - 1] < max_low:\n",
    "                # 已经没有重叠区域了\n",
    "                left_bount = (fx_offset[i] + fx_offset[i + 1]) / 2\n",
    "                break\n",
    "            else:\n",
    "                # 有重叠区域\n",
    "                # 计算更窄的中枢价格区间\n",
    "                cover += 3\n",
    "                min_high = min(fx_plot[i - 1], min_high)\n",
    "                max_low = max(fx_plot[i], max_low)\n",
    "\n",
    "        i -= 1\n",
    "\n",
    "    if cover < 3:\n",
    "        # 不满足中枢定义\n",
    "        right_bound = -1\n",
    "        left_bount = -1\n",
    "        min_high = -1\n",
    "        max_low = -1\n",
    "\n",
    "    pivot_x_interval = [left_bount, right_bound]\n",
    "    pivot_price_interval = [max_low, min_high]\n",
    "    return pivot_x_interval, pivot_price_interval,i\n",
    "\n",
    "\n",
    "def plot_k_series(ax,k_data):\n",
    "    # 画k线\n",
    "    num_of_ticks = len(k_data)\n",
    "\n",
    "   # fig, ax = plt.subplots(figsize=(num_of_ticks, 20))\n",
    "   # fig.subplots_adjust(bottom=0.2)\n",
    "    dates = k_data.date\n",
    "    # print dates\n",
    "    ax.set_xticks(np.linspace(1, num_of_ticks, num_of_ticks))\n",
    "    ax.set_xticklabels(list(dates))\n",
    "    \"\"\"\n",
    "    xticks = list(range(0, len(dates), 10))  # 这里设置的是x轴点的位置（40设置的就是间隔了）\n",
    "    xlabels = [dates[x] for x in xticks ]  # 这里设置X轴上的点对应在数据集中的值（这里用的数据为totalSeed）\n",
    "    xticks.append(len(dates))\n",
    "    xlabels.append(dates[-1])\n",
    "    ax.set_xticks(xticks)\n",
    "    ax.set_xticklabels(xlabels, rotation=40)\n",
    "    \"\"\"\n",
    "    #T.plot(k_data,candlestick=True)\n",
    "    print(\"绘制K线\")\n",
    "    plt.plot(k_data.close)\n",
    "    #print(1)\n",
    "#     mpf.candlestick2_ochl(\n",
    "#         ax,\n",
    "#         list(k_data.open), list(k_data.close), list(k_data.high), list(k_data.low),\n",
    "#         width=0.6, colorup='r', colordown='b', alpha=0.75\n",
    "#     )\n",
    "    #mpf.plot(k_data,type='candle')\n",
    "    \n",
    "    plt.grid(True)\n",
    "    plt.setp(plt.gca().get_xticklabels(), rotation=30)\n",
    "    return dates\n",
    "\n",
    "\n",
    "def plot_lines(ax, fx_plot, fx_offset):\n",
    "    # 绘制笔和线段\n",
    "    # ax 绘图区域\n",
    "    # fx_plot\n",
    "\n",
    "    plt.plot(fx_offset, fx_plot, 'k', lw=1)\n",
    "    plt.plot(fx_offset, fx_plot, 'o')\n",
    "\n",
    "\n",
    "def plot_pivot(ax, pivot_date_interval, pivot_price_interval):\n",
    "    #\n",
    "    # 绘制中枢\n",
    "\n",
    "    start_point = (pivot_date_interval[0], pivot_price_interval[0])\n",
    "    width = pivot_date_interval[1] - pivot_date_interval[0]\n",
    "    height = pivot_price_interval[1] - pivot_price_interval[0]\n",
    "    print(\n",
    "        \"中枢：\",\n",
    "        start_point,  # (x,y)\n",
    "        width,  # width\n",
    "        height,  # height\n",
    "    )\n",
    "    plt.gca().add_patch(\n",
    "        patches.Rectangle(\n",
    "            start_point,  # (x,y)\n",
    "            width,  # width\n",
    "            height,  # height\n",
    "            linewidth=8,\n",
    "            edgecolor='g',\n",
    "            facecolor='none'\n",
    "        )\n",
    "    )\n",
    "    return\n",
    "\n",
    "\n",
    "def plot_all(select_deta=10,price_percent=0.01):#select_deta 中枢最小间隔  price_percent幅度小于某个值\n",
    "    k_series = get_k_series()\n",
    "    kk=k_series\n",
    "    if(len(kk)<10):\n",
    "        print('k线数量不足')\n",
    "        return\n",
    "    fig = plt.figure(figsize=(50, 20))\n",
    "    plt.rcParams.update({'figure.max_open_warning': 0})\n",
    "\n",
    "    ax2= fig.add_subplot(212)\n",
    "    \n",
    "    print(\"处理K线...\")\n",
    "    adjusted_k_data = adjust_by_cintainment(k_series)\n",
    "    plot_k_series(ax2,adjusted_k_data) # 调整后的k线图\n",
    "\n",
    "    fx_type, fx_time, fx_data, fx_plot, fx_offset = get_fx(adjusted_k_data)\n",
    "    print (fx_type, fx_time)\n",
    "    plot_lines(ax2, fx_plot, fx_offset)\n",
    "    pivot_x_interval, pivot_price_interval,now_index = None,None,len(fx_offset)-2\n",
    "    #last_pivot_x_interval, last_pivot_price_interval,last_index = get_pivot(fx_plot, fx_offset, now_index)\n",
    "    \n",
    "    while now_index >= 1:\n",
    "        pivot_x_interval, pivot_price_interval,now_index = get_pivot(fx_plot, fx_offset, now_index)\n",
    "        if pivot_x_interval[0] == -1:\n",
    "            break\n",
    "        else:\n",
    "            if pivot_x_interval[1] - pivot_x_interval[0] < select_deta:\n",
    "                print(\"pivot_x_interval[1] - pivot_x_interval[0] < select_deta\")\n",
    "                continue\n",
    "            hhv = max(k_series.high[int(pivot_x_interval[0]):int(pivot_x_interval[1])])\n",
    "            llv = min(k_series.low[int(pivot_x_interval[0]):int(pivot_x_interval[1])])\n",
    "            hhv_deta = abs((hhv - pivot_price_interval[1]) / pivot_price_interval[1])\n",
    "            llv_deta = abs((llv - pivot_price_interval[0]) / pivot_price_interval[0])\n",
    "            if (hhv_deta > price_percent or llv_deta > price_percent):\n",
    "                print(\" (hhv_deta > 1.0 / price_percent or llv_deta > 1.0 / price_percent)\")\n",
    "                continue\n",
    "            plot_pivot(ax2, pivot_x_interval, pivot_price_interval)\n",
    "    \n",
    "plot_all(\n",
    "    select_deta = 2,#中枢最小间隔\n",
    "    price_percent = 0.5#幅度小于某个值\n",
    ")"
   ]
  }
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