567 lines
345 KiB
Plaintext
567 lines
345 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"import tushare as ts\n",
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"import matplotlib.pyplot as plt\n",
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"import pandas as pd\n",
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"import MySQLdb\n",
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"import time\n",
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"import datetime\n",
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"import warnings\n",
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"warnings.filterwarnings(\"ignore\")\n",
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"from imp import reload\n",
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"import chan\n",
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"ts.set_token('3aceab1cc8a61e74fc0a1d481d64bf0b6d4e177a90f4c3936014c0ac')\n",
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"pro = ts.pro_api()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.read_csv('E:\\jupyter\\qushi-20190525-20190702.csv',index_col='index')\n",
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"df_down = df[df.direction=='downdown']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"collapsed": true
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"600618.SH\n",
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]
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}
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],
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"source": [
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"result_df=pd.DataFrame()\n",
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"min_day_n=30\n",
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"for n, k in df_down.iterrows():\n",
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" ts_code = k.ts_code\n",
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" trade_price = k.trade_price\n",
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" trade_time = datetime.datetime.strptime(k.trade_time,'%Y-%m-%d %H:%M')\n",
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" delta10 = datetime.timedelta(days=30)\n",
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" end_time = trade_time+delta10\n",
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" print(ts_code)\n",
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" day_df=None\n",
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" has_gotdata=False\n",
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" while not has_gotdata:\n",
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" try:\n",
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" day_df = pro.daily(ts_code=ts_code, start_date=trade_time.strftime('%Y%m%d'), end_date=end_time.strftime('%Y%m%d'))\n",
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" has_gotdata=True\n",
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" except OSError as e:\n",
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" print('pause 5s...',e)\n",
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" time.sleep(5) \n",
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" day_df = day_df.iloc[::-1] #倒序\n",
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" day=0\n",
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" for day_n, day_k in day_df.iterrows():\n",
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" if day_k.vol>0:\n",
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" result_df=result_df.append({'ts_code':ts_code, 'trade_price':trade_price, 'trade_time':trade_time, 'date':day_k.trade_date ,'day': day, 'day_close_price':day_k.close, 'percent': (day_k.close-trade_price)*100/trade_price},ignore_index=True)\n",
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" day+=1\n",
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" min_day_n=min(min_day_n,day)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": true
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"ts_code\n",
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"000923.SZ -9.190992\n",
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" ... \n",
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"Name: percent, Length: 97, dtype: float64"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"result_df.groupby('ts_code')['percent'].mean().sort_values()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"-0.5834096631447335"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"result_df[result_df.day<=7]['percent'].mean()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"2.270976915772038"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"result_df['percent'].mean()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {
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"collapsed": true
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<matplotlib.axes._subplots.AxesSubplot at 0x23ee39524a8>"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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|
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 432x288 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"result_df.groupby('day')['percent'].mean().plot()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 51,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAABIQAAAEyCAYAAACLeQv5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzs3Xd8zff3B/DXOwmJWdSoUTSCWrFiVGsTO6qiZu29itrUVtpa34pdqmqWtlQRhNo1EqPor4qi9qqaQSLv3x8HGW6Se5N77+fem9fz8bgPce9nnJtccT/nnvc5SmsNIiIiIiIiIiJKOdyMDoCIiIiIiIiIiOyLCSEiIiIiIiIiohSGCSEiIiIiIiIiohSGCSEiIiIiIiIiohSGCSEiIiIiIiIiohSGCSEiIiIiIiIiohSGCSEiIiIiIiIiohTGrISQUuq8Uuq4UuqoUirUxOONlVK/v3hcKfWe9UMlIiIiIiIiIiJrUFrrxDdS6jwAP631rXgeTw/godZaK6V8AXyvtX7bqpESEREREREREZFVeFjjIFrrBzH+mg5AolmmrFmz6vz581vj9EREREREREREBCAsLOyW1jpbYtuZmxDSALYopTSAeVrr+XE3UEo1ATAJQHYADUwdRCnVFUBXAMibNy9CQ19ZfUZEREREREREREmklLpgznbmNpV+V2tdBkA9AL2UUlXibqC1/un5MrH3AYw3dRCt9XyttZ/W2i9btkSTVUREREREREREZANmJYS01lee/3kDwE8Ayiew7S4ABZRSWa0SIRERERERERERWVWiCSGlVDqlVIYXXwPwB3AizjY+Sin1/OsyAFIDuG39cImIiIiIiIiIKLnM6SGUA8BPz/M9HgCWa62DlVLdAUBrPRdAUwBtlVIRAMIBNNfmjC8jIiIiIiIiIiK7M2vsvC34+flpNpUmIiIiIiIiIrIepVSY1tovse3MbSpNREREREREREQuggkhIiIiIiIiIqIUhgkhIiIiIiIiIqIUhgkhIiIiIiIiIqIUhgkhIiIiSrHu3AGOHTM6CiIiIiL7Y0KIiIiIUqzmzYEqVYBz54yOhIiIiMi+mBAiIiKiFGnFCuD8eWD0aKBqVeCvv4yOiIiIiMh+PIwOgIiIiMjeTp8G+vYFtmwBSpcGMmUCqlcHtm4FihY1OjoiIiIi22NCiIiIiFKUJ09kqdiYMZIMAoCOHQFPT6BmTSA4GChZ0tAQiYiIiGyOCSEiIiJKUQYNAt56C+jZM/b9rVtLUsjfH/jlF6BcOWPiIyIiIrIHJoSIiIgoxfjpJ0n2HD4MKPXq44GBkhRq0ABYuxaoVMn+MRIRERHZA5tKExERUYpw/jzQrRuwcqX0DIpPo0bAkiVA48bAzp12C4+IiIjIrpgQIiIiIpf39Kn0DRo6FChfPvHt69YFVq0CmjWTxtNEREREroYJISIiInJ5I0YA2bMD/fubv0+NGsCPPwJt2sgyMyIiIiJXwoQQERERubQNG6TaZ/Fi032DEvLee5IM6tRJkkNEREREroJNpYlcyM2bQFQUkCOH0ZEQETmGS5ckmbNmDfD660k7RvnyMoq+Xj0ZWd+ypXVjJCIiIjICK4SIXMhHHwFvvw0cOGB0JERExouMBFq1Avr2lUqf5ChdGggJAQYOlEojIiIiImfHhBCRizh6FDhyBJg6FQgIAMaNk4shIqKUauxYwMtLGklbQ/HiwPbtwKefAvPmWeeYREREREZhQojIBURFAT16ABMnAh07AocPA7t3A1WqAH//bXR0RET2FxICLFoEfPcd4GbFdzuFCwM7dgCTJgH/+5/1jktERERkb0wIEbmAr7+WRqkdO8rfc+cGNm8GPvwQqFBBljdobWiIRER2c+0a0LYtsGSJbXqqFSgA7NwJzJwJfP659Y9PREREZA9MCBE5uRs3gJEjgblzY38K7uYG9OsnyxumTQOaNQNu3zYuTiIie3j2TMbEd+kC1Kxpu/PkyydJoW++kaVpTLoTERGRs2FCiMjJDR4szaR9fU0/XqIEcPCgXLyULAls3Wrf+IiI7GnSJOmfNmqU7c+VO7ckhdasAYYPZ1KIiIiInAvHzhM5sZ07gW3bgD/+SHg7Ly9pNl2/PtC+PRAYKBdNXl52CZOIyC527QKCgoCwMMDd3T7nzJED+PVXwN8fePxYKjKVss+5iYiIiJKDFUJETurpU2kkPWMGkCGDefvUrAkcOwZcugSUKwf8/rttYyQispebN4HWrWUJV+7c9j131qySnP/tN6BnT2n0T0REROTomBAiclLTpgH58wMffGDZflmyAN9/DwwaJAmiadN48UJEzi0qCmjXDmjVCqhXz5gYMmcGtmwBTpwAOneWXkZEREREjowJISIndP48MGWKLI1IytIEpWQCz8GDwA8/yFKHS5esHiYRkV1MmwbcuQNMmGBsHBkzAsHBwIUL8js2MtLYeIiIiIgSwoQQkRPq2xfo3x/w9k7ecd56S/oQVasGlC0LrF5tlfAM9/SpXBw6i6go4NYto6Mgck779wNffgmsXAmkSmV0NEC6dMAvvwD//gu0aCG/j4iIiIgcERNCRE5m3Trgr7+AgQOtczwPDxlbv349MGKELLu4d886xzbK5MlA3rzA9etGR2KehQslXi4xIbLMnTuSdJk3TyYpOoo0aYC1a4GICKBpU2k2TURERORozEoIKaXOK6WOK6WOKqVCTTzeWin1+/PbPqVUSeuHSkQPH0p10OzZgKendY9dvjxw5IhMHitVCti717rHt6e7d6Vpds2a0mjW0R06BGTLJqOricg8WgOdOgGNGwPvv290NK/y9JR/02nSSIyPHhkdEREREVFsllQIVddal9Ja+5l47ByAqlprXwDjAcy3SnREFMu4cUDlykCNGrY5frp08kn7jBkymv7TT+UTbmezd6/E3rgxUKsWcPu20RHF7+lT4McfgbFjpf8JG3wTmScoCPjnH+CLL4yOJH6pUgHLlwPZswMNGgAPHhgdEREREVE0qywZ01rv01q/6NixH0AeaxyXiKKdOAEsWgRMnWr7cwUESLVQWBhQqZIsUXMW4eHA8eNAhQqSYKlTB6hd23F7CoWEAG+/LUv1PD2Bn382OiLriYoCxo8H/vvP6EhsY9Ys4Nw5o6NImcLC5LW1apX1qyWtzcMDWLwY8PGR30d37xodEREREZEwNyGkAWxRSoUppbomsm0nAJtMPaCU6qqUClVKhd50hnUcRA5Ca6BnT6kiyZHDPud84w1gwwagQwfg3XeB+fMlDkd36BBQrBiQNq1MU/v8c2ma7e/vmImJFSukB4pS0stp/Hjn+D4n5M4dmfpUsKAkMIcPNzoi6woPBwYMAIYOlaWJy5c7/8/Mmdy7BzRvLhVCBQoYHY153N2l+rJ0aala/PdfoyMiIiIiMj8h9K7WugyAegB6KaWqmNpIKVUdkhAaYupxrfV8rbWf1tovW7ZsSQqYKCX69lu5CO3Wzb7nVUoSUbt2AXPnyhKsGzfsG4Ol9u6VBNYLSklS4p13gLp1Hathdni4TCNq1kz+HhAgS8g2mUypO77ffwe6dpXpd4cPA8uWAf/3f8APPwChr3Sfc06HDgFlygBXrgDnz0vS9PPP5bV19qzR0bk+reU1VqsW8OGHRkdjGTc3YOZMoEoV5+lvRkRERK7NrISQ1vrK8z9vAPgJQPm42yilfAF8DaCx1tqBO3YQOZfbt6USYe5c+ZTZCEWKyGjnokWl4fTGjcbEYY49e2InhABJCv3vf3IhX68ecP++MbHFtWED4OcXXfXl5uZ8VUIREcDq1XKRW7++TEv7809g6VKgYkUgZ05g+nSgfXvgyROjo026p0+lL1XDhsCYMTLi/PXXZWliaKhc4FeoIBPunLHvlrP4+mvgjz/kNeWMlAKmTJF+QtWqAdeuGR0RERERpWRKJ3LVoZRKB8BNa33/+ddbAYzTWgfH2CYvgO0A2mqt95lzYj8/Px3qKh8Zk0NZWPJ/WHG5CkLeGfXqg716yUf5ABAcLE1A4rN+ffTX/frF//F/nTpA797y9ZkzQP/+8R9
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1440x360 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
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"text/plain": [
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|||
|
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"<Figure size 1440x360 with 1 Axes>"
|
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]
|
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},
|
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"metadata": {},
|
|||
|
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAABIQAAAEyCAYAAACLeQv5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzs3XmcTfUfx/HXmRlkya5sLfZkl0KyJNnpV9miUNaiLCVUWhRppUKWFKXFUkILJZRKZCtbw1jKkkj2fcz5/fExZjHLvTP3zp07834+HvPInHvu93zvjMydz/ksjuu6iIiIiIiIiIhI5hES6A2IiIiIiIiIiEjaUkBIRERERERERCSTUUBIRERERERERCSTUUBIRERERERERCSTUUBIRERERERERCSTUUBIRERERERERCSTUUBIRERERERERCSTUUBIRERERERERCSTUUBIRERERERERCSTCQvUhQsWLOhee+21gbq8iIiIiIiIiEiGs3r16n9d1y2U3HkBCwhde+21rFq1KlCXFxERERERERHJcBzH+dOT81QyJiIiIiIiIiKSySQbEHIcp5zjOOtifRx1HKd/Iufe6DjOecdx2vh+qyIiIiIiIiIi4gvJloy5rhsOVAVwHCcU2APMiX/ehcdeAhb6eI8iIiIiIiIiIuJD3paM3QZsc103oXq0h4FPgf2p3pWIiIiIiIiIiPiNtwGhDsDH8Q86jlMMuBOYkNSTHcfp6TjOKsdxVh04cMDLS4uIiIiIiIiIiC94HBByHCcr0BqYlcDDY4DBruueT2oN13Unua5bw3XdGoUKJTsBTURERERERERE/MCbsfPNgDWu6/6TwGM1gE8cxwEoCDR3HCfSdd3PfbBHERERERERERHxIW8CQveQQLkYgOu6JaL/7DjOVOALBYNERERERERERNInj0rGHMfJAdwOfBbrWG/HcXr7a2MiIiIiIiIiIuIfHmUIua57EigQ71iCDaRd1+2a+m2JiIhIUqKi4IcfoEGDQO9ERERERIKRt1PGREREJB1YuxYaNoSjRwO9ExEREREJRgoIiYiIBKG9e6FQIfg4we5+IiIiIiJJU0BIREQkCK1fD3XrwuTJgd6JiIiIiAQjBYRERESC0IYN0LIl/PsvrF4d6N2IiIiISLBRQEhERCQIrV8PVapA9+7KEhIRERER7ykgJCIiEmTOnoWICLjuOrj/fpg5E44fD/SuRERERCSYKCAkIiISZLZsgWuugezZoVgx6yU0Y0agdyUiIiIiwUQBIRERkSCzfj1UqhTzeY8eKhsTEREREe8oICQiIhJk1q+HihVjPm/aFPbsgd9/D9yeRERERCS4KCAkIiISZOJnCIWFQbduyhISEREREc8pICQiIhJkNmyIGxACeOAB+OgjOHkyMHsSERERkeCigJCIiEgQOXYM9u+HkiXjHr/6aqhVC2bPDsy+RERERCS4KCAkIiISRDZsgPLlITT00sd69IBJk9J+TyIiIiISfBQQEhERCSIJlYtFa9ECtm+HjRvTdk8iIiIiEnwUEBIREQki8RtKx5YlC9x/P7zzTtruSURERESCjwJCIiIiQST+yPn4uneH6dPh9Om025OIiIiIBB8FhERERIKE6yadIQRQogRUrw6ffZZ2+xIRERGR4KOAkIiISJD45x/7b+HCSZ/ni+bS06bBM8+kbg0RERERSb8UEBIREQkS0dlBjpP0ea1bwx9/wJYtKb/W22/D3Lkpf76IiIiIpG8KCImIiASJ5PoHRcuaFbp0gcmTU3advXth0yY4cSJlzxcRERGR9E8BIRERkSCR1Mj5+Lp3t7KvM2e8v84770D79rBrl5pTi4iIiGRUCgiJiIgEieQaSsdWpoxlE3lb9hUZaf2H+vaFUqUgPNz7fYqIiIhI+qeAkIiISBA4f97KuCpU8Pw5PXt631x6/ny45hqoUsWutWGDd88XERERkeCggJCIiEgQ2L4dChWC3Lk9f86dd8Jvv8G2bZ4/Z/x4eOgh+3OFCrBxo3f7FBEREZHgoICQiIhIEPCmf1C0bNmgc2frCeSJLVvg99+hTRv7vGJFBYREREREMioFhERERIKAN/2DYuvRA6ZOhXPnkj93wgR44AELJIFKxkREREQyMgWERERE/ODECbj2Wjh61DfreTpyPr7rrrMG0/PnJ33eyZPw/vvQq1fMsdKlbQT9yZPeX1dERERE0jcFhERERPzg8GHYtw9GjPDNeinNEAJrLj15ctLnfPIJ1KplQaxoYWFQtixs3pyy64qIiIhI+qWAkIiIiB+Eh0PVqlautWZN6tY6fRr+/BPKlUvZ8+++G379FXbuTPyct9+OaSYdm8rGRERERDImBYRERET8IDzcMnpefhm6dfOsh09iNm+GUqUga9aUPT97dujUCaZMSfjxX3+FgwehSZNLH9OkMREREZGMSQEhERERPwgPt4yezp3hiivgtddSvlZqysWi9egB774LkZGXPjZ+vPUOCg299DFNGhMRERHJmBQQEhER8YPogJDjwMSJ8OqrNtY9JVIycj6+ihXhmmvgq6/iHv/vP5gzx6aLJUQlYyIiIiIZkwJCIiIifhAdEAJr1DxsmGXpREV5v5YvMoQg4ebSU6dCq1ZQqFDCzylRAg4cgGPHUn99EREREUk/FBASERHxsdOnbVx7iRIxx/r2hTNnkp/2lZCUjpyPr21b+Okn2L3bPo+KSryZdLTQUChfHjZtSv31RURERCT9UEBIRETExyIiLCsoS5aYY6Gh1tT5qadgzx7P1zp0CI4etXKv1MqZE+65x3oJASxaZMdq1Ur6eSobExEREcl4FBASERHxsS1bEh4RX6EC9OljGTmu69laGzbY80J89BO7Rw945x04f96aST/0kPU5SoomjYmIiIhkPAoIiYiI+Fjs/kHxDR1qGUSzZnm2lq/KxaJVrQqFC8P06bBsGXTsmPxzNGlMREREJONRQEhERMTHwsOhbNmEH8uWzTJ0+vWDgweTX8tXDaVj69EDRo6ETp0gV67kz1fJmIiIiEjGo4CQiIiIjyWVIQRQuza0awePPpr8Wr4YOR9f27awdSu0b+/Z+VdfbX2MDh/27T5EREREJHAUEBIREfEh100+IAQwYgQsXQrffJP0Wr4uGQPImxd+/x3q1PHs/JAQuP56lY2JiIiIZCQKCImIiPjQv/9aIKdQoaTPy5ULJk6EXr3g+PGEz9m9Gy67LPm1UsLbIJPKxkREREQyFgWEREREfCg6Oyi5yV0ATZpA3bowbFjCj/ujf1BKqbG0iIiISMaigJCIiIgPeVIuFtvo0fDJJ7BixaWP+aN/UEpp9LyIiIhIxhIW6A2IiIgEmtuyFf+ezU2hbEfjPlCqFIwZE/N5q1aJL9KnDzRtagEhZwu0SqJj9Pz5F/9Y4Pn+jC5SlO63t2N13QFkDYm8+Nj6tQO5dfit3r4cv1DJmIiIiEjGogwhERHJ9L45UJWii6Zx3k39j8XwcChX/IRXz2lfdBnXZt/PqIg2cY6vp2K6yRAqVgzOnLEeSSIiIiIS/BzXdQNy4Ro1arirVq0KyLVFRERiG9lqOW8srcIHn+agcePUrVW+PMya5X3T5t27oVo1+P57m+gVGQm5c1sAJkeO1O3JV+rUgZEjoX79QO9ERERERBLjOM5q13VrJHeeMoRERCTTm3egNm275GDatNStExkJO3ZYpZm3iheH4cOhe3c4fx62brWsnPQSDAKVjYmIiGQ0CxYsoFy5cpQuXZpRo0YBsGPHDmrWrEmZMmVo3749Z8+eBeDMmTO0b9+e0qVLU7NmTXbu3AnAzp07yZ49O1WrVqVq1ar07t374vpNmzalSpUqVKhQgd69e3P+/HkAZs2aRYUKFQgJCSGpRJERI0ZQoUIFKleuTNWqVVmxYgW7du26eK3oj9y5czN48GA/fZUyLgWEREQkU9u3z8q8nngCvvwSjh5N/jmJ2bEDihSB7NlT9vxevSAsDMaPh/Xf7qNiiUTm0QeIJo2JiIhkHOfPn6dPnz58/fXXbNq0iY8//phNmzYxePBgBgwYwNa
|
|||
|
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"text/plain": [
|
|||
|
|
"<Figure size 1440x360 with 1 Axes>"
|
|||
|
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]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
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},
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{
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"data": {
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|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1440x360 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
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"data": {
|
|||
|
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1440x360 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
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|
|||
|
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"text/plain": [
|
|||
|
|
"<Figure size 1440x360 with 1 Axes>"
|
|||
|
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]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
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},
|
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAABIsAAAEyCAYAAAB6clB0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzs3XmczfUXx/HX15Y1EcoWIVEITatf1hCifqHNr5QWZQkp6vdToRStiBbtRQuSLNmLNttYIvtWWYvIvs/398eZO5t75947c7eZeT8fj/sw93u/y7lzp3n0PXPO+Tiu6yIiIiIiIiIiIgKQK9oBiIiIiIiIiIhI7FCySEREREREREREkihZJCIiIiIiIiIiSZQsEhERERERERGRJEoWiYiIiIiIiIhIEiWLREREREREREQkiZJFIiIiIiIiIiKSRMkiERERERERERFJomSRiIiIiIiIiIgkyRPtALwpUaKEW7FixWiHISIiIiIiIiKSbSxZsmSP67ol/e0Xk8miihUrEh8fH+0wRERERERERESyDcdxfg9kP7WhiYiIiIiIiIhIEiWLREREREREREQkiZJFIiIiIiIiIiKSRMkiERERERERERFJomSRiIiIiIiIiIgkUbJIRERERERERESSKFkkIiIiIiIiIiJJlCwSEREREREREZEkShaJiIiIiIiIiEgSJYsk2/ntN9i8OdpRiIiIiIiIiGRNeaIdgEiodegAR4/C0qXRjkREREREREQk61GySLKVhATYtMmSRX/9BaVKRTsiERERERERkaxFbWiSrfz0E5x3Htx2G7z1VrSjEREREREREcl6lCySbGXsWLj1VujRA958E44fj3ZEIiIiIiIiIlmLkkWSbZw+DePHQ/v2cOmlULMmfPFFtKMSERERERERyVqULJJs48cf4fzzoWpVe96jBwwdCq4b3bhEREREREREshIliyTb8LSgebRoAYcOWRJJRERERERERAKjZJFkC6dPw5dfWguaR65cydVFIiIiIiIiIhIYJYskW/j+eyhbFqpUSb29Y0eYOxe2bIlKWCIiIiIiIiJZjpJFki2kbUHzKFwYOnWCESMiH5OIiIiIiIhIVqRkkWR5p06d2YKWUrdu8OGHcPBgRMMSERERERERyZL8JoscxynvOM53juOscRxnleM4PdK8/pjjOK7jOCV8HN/RcZwNiY+OoQpcxGPePKhQASpV8v56hQrQqBF89FFk4xIRERERERHJigKpLDoF9HZdtzpwNdDVcZxLwBJJQFPgD28HOo5THHgGuAq4EnjGcZxioQhcxMNXC1pKPXvCsGGQkBCZmERERERERESyKr/JItd1d7quuzTx64PAGqBs4suvAX0A18fhzYFZruvudV13HzALuCHTUYskOnUKJkzw3YLmUa8eFC0K33wTmbhEREREREREsqqgZhY5jlMRqAMsdBynDbDddd1f0jmkLLA1xfNtJCeaRDLtu++s/axixfT3cxyrLho6NCJhiYiIiIiIiGRZASeLHMcpDHwJ9MRa0/4HPO3vMC/bvFYhOY7zoOM48Y7jxO/evTvQsCSHC6QFzePWW2HVKli5MrwxiYiIiIiIiGRlASWLHMfJiyWKxriuOwGoDFwI/OI4zm9AOWCp4zjnpzl0G1A+xfNywA5v13Bdd5TrunGu68aVLFkyuHchOdLJk/DVV9CuXWD758sHXbrA8OHhjUtEREREREQkKwtkNTQHeA9Y47ruqwCu6650XbeU67oVXdetiCWF6rquuyvN4TOAZo7jFEscbN0scZtIpn37LVx0ka12FqjOnWH8eFDxmoiIiIiIiIh3gVQW1QPuAho7jrM88dHS186O48Q5jvMugOu6e4FngcWJj4GJ20QyLZgWNI9SpeCWW2DUqPDEJCIiIiIiIpLVOa7rayGz6ImLi3Pj4+OjHYbEsBMnoHRpWL4cypf3v39KK1ZAixawZYu1pomIiIiIiIjkBI7jLHFdN87ffkGthiYSK+bMgWrVgk8UAdSqBRdfDOPGhT4uERERERERkaxOySLJkjLSgpZSz54wdCjEYGGdiIiIiIiISFQpWSRZzokT8PXXga+C5k2rVrBvH8yfH7q4RERERERERLIDJYsky5k1Cy69FMqWzfg5cueGRx6x6iIJv8OHYevWaEchIiIiIiIigVCySLKczLagedx7r80++v33zJ9L0jdgAFx+ebSjEBERERERkUAoWSRZyvHjMGkStG2b+XMVKQJ33w0jR2b+XJK+7dvhwAFrIRQREREREZHYpmSRZCkzZ9pqZmXKhOZ83bvD++9bm5SEh+vCvHlQuTIsXhztaERERERERMQfJYskSwlVC5pHpUpw3XXw8cehO6ektmYN5M0LzZrB3LnRjkZERERERET8UbJIsoxjx2DKlNC0oKXUsycMGwYJCaE9r5iZMy1R1KiRkkUiIiIiIiJZgZJFkmXMmAG1a8P554f2vPXrQ4ECdn4JPU+y6LrrYMECzS0SERERERGJdUoWSZYR6hY0D8eBHj1g6NDQnzunO3YMfvwRGjeGYsXgoosgPj7aUYmIiIiIiEh6lCySqPvjD//7HD0KU6fCLbeEJ4bbb4dffoHVq8Nz/pzqp5/g0kstUQTQsKFa0URERERERGKdkkUSVZs2QYUKVoGSnunT4fLL4bzzwhNH/vzw0EMwfHh4zp9TeVrQPBo0ULJIREREREQk1ilZJFE1bRqcfTZs3Jj+fmPHQvv24Y3loYfgiy/g77/De52cJG2y6LrrYP58zS0SERERERGJZUoWSVSNHWtDq5cu9b3PkSPwzTfha0HzOP98uOkmeOed8F4np/jzT/jtN7jqquRtxYtDlSqaWyQiIiIiIhLLlCySqNmxA379FW64AZYt873ftGlwxRVQqlT4Y+rRA0aMgJMnw3+t7G72bGjUCPLkSb1dc4tERERERERim5JFEjVffgmtW1siKL3KonCtguZNnTpQuTJMmBCZ62VnaVvQPBo2hHnzIh6OiIiIiIiIBEjJIokaTxKoTh1YvhwSEs7c5/BhG279739HLq6ePWHo0MhdLztyXd/JIs/cIlVviYiIiIiIxCYliyQqtm+HVaugaVM491w45xzYvPnM/b75xmbelCwZudjatIFdu2DBgshdM7v59VcoVAgqVTrzteLFrXpLc4tERERERERik5JF2cCOHdGOIHjjx1tSJl8+e163rvdWtEi2oHnkzg2PPALDhkX2utmJr6oiD80tEhERERERiV1KFmVxK1dC+fK28lRWMm5c6iRQ3bpnDrk+dMiSDpFsQfPo1AlmzIBt2yJ/7ezAX7KoQQMli0RERERERGKVkkVZ3Ny5ULUqDBoU7UgCt20brFkD11+fvK1OnTMri6ZOhWuusTa1SCtaFP7zH3jjjchfO6s7ehR+/tlWQvOlfn3NLRIREREREYlVShZlcV9+Cf/7H3z6KWzcGO1oAjN+PNx0U3ILGiS3oblu8rZotKCl1L07vPMOHDkSvRiyoh9+gMsus4SbL8WL2zyjJUsiF5eIiIiIiIgERsmiKDh5Eg4cyPx5/vzTVhFr1w5694Ynn8z8OSNh3Dho3z71ttKlIVcuG3wN1oI2ezbcfHPk4/O46CKrbBo9OnoxZEX+WtA8NLdIREREREQkNilZFAUvvuh9lahgTZwILVpA/vzQo4et3hXrK3ht3Qpr10KTJqm3O07qIddTpkC9elaBEk09e8LQoakrniR9M2dC8+b+91OySEREREREJDYpWRQFf/8Nhw9nPgHx5ZfQtq19XbAgDBwIjz8e24mN8eOtWihlC5pHyiHX0W5B82jUyFZHmzUr2pFkDTt32kyquDj/+9avb7ONNLdIREREREQktihZFAWrVkGhQvD77xk/x969sHChVRZ53H037N8PX3+d+RjDZezYM1vQPDxDrg8ehDlzbK5RtDmOVRcNGxbtSLKGWbOsaix3bv/7am6RpOehh+Cll6IdhYiIiIhIzqRkUYQlJMCiRalbrjJi0iRbTaxQoeRtuXNbi1vfvrFZrfHHH7Bhw5ktaB6e78n
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1440x360 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
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},
|
|||
|
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{
|
|||
|
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"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1440x360 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1440x360 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"ename": "KeyboardInterrupt",
|
|||
|
|
"evalue": "",
|
|||
|
|
"output_type": "error",
|
|||
|
|
"traceback": [
|
|||
|
|
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
|||
|
|
"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
|
|||
|
|
"\u001b[1;32m<ipython-input-51-ec7e098ed8e3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[0mcursor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[0mreload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchan\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#重新加载外部chan.py\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m \u001b[0mdf_dr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mchan\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_dr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m72\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
|||
|
|
"\u001b[1;32mE:\\jupyter\\quant\\chan.py\u001b[0m in \u001b[0;36mbuild\u001b[1;34m(df_dr)\u001b[0m\n\u001b[0;32m 77\u001b[0m \u001b[0mdf_dr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mat\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'group_no'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mgroup_no\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 78\u001b[0m \u001b[0mdf_dr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mat\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'group_high'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mgroup_high\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 79\u001b[1;33m \u001b[0mdf_dr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mat\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'group_low'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mgroup_low\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 80\u001b[0m \u001b[0mdf_dr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mat\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'group_master'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mis_group_m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 81\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mis_group\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
|||
|
|
"\u001b[1;32me:\\app\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__setitem__\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m 2157\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_key\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mis_setter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2158\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2159\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_set_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtakeable\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_takeable\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2160\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2161\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
|
|||
|
|
"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"db = MySQLdb.connect(host=\"127.0.0.1\",user=\"sa\",passwd=\"sasasa\",db=\"quant\",charset=\"utf8\")\n",
|
|||
|
|
"\n",
|
|||
|
|
"df = pd.read_csv('E:\\jupyter\\qushi-20190901-20190930.csv',index_col='index')\n",
|
|||
|
|
"df_down = df[df.direction=='downdown']\n",
|
|||
|
|
"for stock_n, stock_k in df_down.iterrows():\n",
|
|||
|
|
" ts_code=stock_k.ts_code\n",
|
|||
|
|
" trade_time=stock_k.trade_time\n",
|
|||
|
|
" trade_price=stock_k.trade_price\n",
|
|||
|
|
" cursor = db.cursor()\n",
|
|||
|
|
" sql = \"select * from stock_min where ts_code='\"+ts_code+\"' and trade_time>'2019-09-01' and trade_time<'2019-10-15' order by trade_time\"\n",
|
|||
|
|
" df_dr = pd.read_sql(sql,db)\n",
|
|||
|
|
" cursor.close()\n",
|
|||
|
|
" reload(chan) #重新加载外部chan.py\n",
|
|||
|
|
" df_dr=chan.build(df_dr)\n",
|
|||
|
|
" \n",
|
|||
|
|
" plt.figure(figsize=(20,5),dpi=72)\n",
|
|||
|
|
" #画线段\n",
|
|||
|
|
" draw_dot_n_pre=None\n",
|
|||
|
|
" draw_dot_y_pre=None\n",
|
|||
|
|
" for n, k in df_dr[df_dr.line!=''].iterrows():\n",
|
|||
|
|
" line_type = k.line\n",
|
|||
|
|
" low = k.low\n",
|
|||
|
|
" high = k.high \n",
|
|||
|
|
" if line_type==\"top\" or line_type=='bottom':\n",
|
|||
|
|
" y=low if line_type=='bottom' else high\n",
|
|||
|
|
" if draw_dot_n_pre!=None:\n",
|
|||
|
|
" plt.plot([draw_dot_n_pre,n],[draw_dot_y_pre,y],'b-',lw=1)\n",
|
|||
|
|
" draw_dot_n_pre=n\n",
|
|||
|
|
" draw_dot_y_pre=y\n",
|
|||
|
|
" #画中枢\n",
|
|||
|
|
" draw_zs_no_pre=0\n",
|
|||
|
|
" draw_zg_pre=0\n",
|
|||
|
|
" draw_zd_pre=0\n",
|
|||
|
|
" draw_zs_start_n_pre=0\n",
|
|||
|
|
" draw_zs_n_pre=0\n",
|
|||
|
|
" for n, k in df_dr[df_dr.zs_no>0].iterrows():\n",
|
|||
|
|
" zg = k.zg\n",
|
|||
|
|
" zd = k.zd\n",
|
|||
|
|
" zs_no = k.zs_no\n",
|
|||
|
|
" if draw_zs_no_pre!=zs_no: #开始了新的中枢，画上一个\n",
|
|||
|
|
" if draw_zs_no_pre!=0: \n",
|
|||
|
|
" #print(draw_zs_start_n_pre,draw_zg_pre,draw_zd_pre,draw_zs_n_pre-draw_zs_start_n_pre,draw_zd_pre)\n",
|
|||
|
|
" plt.bar((draw_zs_start_n_pre+draw_zs_n_pre)/2,draw_zg_pre-draw_zd_pre,draw_zs_n_pre-draw_zs_start_n_pre,draw_zd_pre, color='w',alpha=0.7, edgecolor='r', ls='--',lw=2)\n",
|
|||
|
|
" draw_zs_start_n_pre=n\n",
|
|||
|
|
" draw_zs_no_pre=zs_no\n",
|
|||
|
|
" draw_zg_pre=zg\n",
|
|||
|
|
" draw_zd_pre=zd\n",
|
|||
|
|
" draw_zs_n_pre=n\n",
|
|||
|
|
" if draw_zs_no_pre!=0: #画最后一个\n",
|
|||
|
|
" #print(draw_zs_start_n_pre,draw_zg_pre,draw_zd_pre,draw_zs_n_pre-draw_zs_start_n_pre,draw_zd_pre)\n",
|
|||
|
|
" plt.bar((draw_zs_start_n_pre+draw_zs_n_pre)/2,draw_zg_pre-draw_zd_pre,draw_zs_n_pre-draw_zs_start_n_pre,draw_zd_pre, color='w',alpha=0.7, edgecolor='r', ls='--',lw=2)\n",
|
|||
|
|
"\n",
|
|||
|
|
" #画交易点\n",
|
|||
|
|
" trade_n=df_dr[df_dr.trade_time==trade_time].index[0]\n",
|
|||
|
|
" #print(trade_n,trade_price)\n",
|
|||
|
|
" plt.annotate(ts_code, xy=(trade_n, trade_price), xytext=(trade_n, trade_price+0.5),arrowprops=dict(facecolor='black', shrink=0.05),)\n",
|
|||
|
|
" #break\n",
|
|||
|
|
" \n",
|
|||
|
|
" plt.savefig('E:/jupyter/pic9/'+ts_code+'.png')\n",
|
|||
|
|
" plt.show()\n",
|
|||
|
|
"db.close() "
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 21,
|
|||
|
|
"metadata": {
|
|||
|
|
"scrolled": true
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/html": [
|
|||
|
|
"<div>\n",
|
|||
|
|
"<style scoped>\n",
|
|||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|||
|
|
" vertical-align: middle;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe tbody tr th {\n",
|
|||
|
|
" vertical-align: top;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe thead th {\n",
|
|||
|
|
" text-align: right;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"</style>\n",
|
|||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>ts_code</th>\n",
|
|||
|
|
" <th>trade_time</th>\n",
|
|||
|
|
" <th>open</th>\n",
|
|||
|
|
" <th>close</th>\n",
|
|||
|
|
" <th>high</th>\n",
|
|||
|
|
" <th>low</th>\n",
|
|||
|
|
" <th>vol</th>\n",
|
|||
|
|
" <th>amount</th>\n",
|
|||
|
|
" <th>trade_date</th>\n",
|
|||
|
|
" <th>pre_close</th>\n",
|
|||
|
|
" <th>...</th>\n",
|
|||
|
|
" <th>bi</th>\n",
|
|||
|
|
" <th>line</th>\n",
|
|||
|
|
" <th>zs_no</th>\n",
|
|||
|
|
" <th>zg</th>\n",
|
|||
|
|
" <th>zd</th>\n",
|
|||
|
|
" <th>gg</th>\n",
|
|||
|
|
" <th>dd</th>\n",
|
|||
|
|
" <th>zs_direction</th>\n",
|
|||
|
|
" <th>zs_confirm_time</th>\n",
|
|||
|
|
" <th>zs_confirm_price</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2091</th>\n",
|
|||
|
|
" <td>000650.SZ</td>\n",
|
|||
|
|
" <td>2019-06-06 13:44:00</td>\n",
|
|||
|
|
" <td>7.48</td>\n",
|
|||
|
|
" <td>7.45</td>\n",
|
|||
|
|
" <td>7.48</td>\n",
|
|||
|
|
" <td>7.45</td>\n",
|
|||
|
|
" <td>499400</td>\n",
|
|||
|
|
" <td>3731289</td>\n",
|
|||
|
|
" <td>2019-06-06</td>\n",
|
|||
|
|
" <td>7.46</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>top</td>\n",
|
|||
|
|
" <td>top</td>\n",
|
|||
|
|
" <td>4</td>\n",
|
|||
|
|
" <td>7.42</td>\n",
|
|||
|
|
" <td>7.33</td>\n",
|
|||
|
|
" <td>7.48</td>\n",
|
|||
|
|
" <td>7.2</td>\n",
|
|||
|
|
" <td>downup</td>\n",
|
|||
|
|
" <td>2019-06-11 13:29:00</td>\n",
|
|||
|
|
" <td>7.71</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"<p>1 rows × 27 columns</p>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" ts_code trade_time open close high low vol amount \\\n",
|
|||
|
|
"2091 000650.SZ 2019-06-06 13:44:00 7.48 7.45 7.48 7.45 499400 3731289 \n",
|
|||
|
|
"\n",
|
|||
|
|
" trade_date pre_close ... bi line zs_no zg zd \\\n",
|
|||
|
|
"2091 2019-06-06 7.46 ... top top 4 7.42 7.33 \n",
|
|||
|
|
"\n",
|
|||
|
|
" gg dd zs_direction zs_confirm_time zs_confirm_price \n",
|
|||
|
|
"2091 7.48 7.2 downup 2019-06-11 13:29:00 7.71 \n",
|
|||
|
|
"\n",
|
|||
|
|
"[1 rows x 27 columns]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 21,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"df_dr[df_dr.trade_time=='2019-06-06 13:44:00']"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "Python [conda root]",
|
|||
|
|
"language": "python",
|
|||
|
|
"name": "conda-root-py"
|
|||
|
|
},
|
|||
|
|
"language_info": {
|
|||
|
|
"codemirror_mode": {
|
|||
|
|
"name": "ipython",
|
|||
|
|
"version": 3
|
|||
|
|
},
|
|||
|
|
"file_extension": ".py",
|
|||
|
|
"mimetype": "text/x-python",
|
|||
|
|
"name": "python",
|
|||
|
|
"nbconvert_exporter": "python",
|
|||
|
|
"pygments_lexer": "ipython3",
|
|||
|
|
"version": "3.6.5"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 2
|
|||
|
|
}
|