183 lines
29 KiB
Plaintext
183 lines
29 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 73,
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"metadata": {},
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"outputs": [],
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"source": [
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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\")"
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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": 168,
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"metadata": {},
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"outputs": [],
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"source": [
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"result_df=pd.DataFrame()\n",
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"for i in range(1,50):\n",
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" result_df=result_df.append({'up':0,'down':0},ignore_index=True)"
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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": 171,
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1 -2.57 -0.16 35\n",
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"2 -2.59 -0.2 35\n",
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"3 -2.72 -0.12 35\n",
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"4 -0.35 -0.04 35\n",
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"5 -0.29 0.03 35\n",
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"6 -3.31 0.21 35\n",
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"7 -2.65 0.25 35\n",
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"8 -5.11 0.04 35\n",
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"9 -10.34 -0.22 35\n",
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"10 -11.51 -0.4 35\n",
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"11 -12.29 -0.55 34\n",
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"12 -9.89 -0.42 34\n",
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"13 -8.52 -0.32 32\n",
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"14 -9.44 -0.35 32\n",
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"15 -7.0 -0.15 32\n",
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"16 -9.27 -0.32 31\n",
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"17 -4.03 0.17 31\n",
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"18 -7.95 -0.09 31\n",
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"19 -10.5 -0.28 30\n",
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"20 -13.46 -0.6 30\n",
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"21 -13.48 -0.52 29\n",
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"22 -11.2 -0.33 27\n",
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"23 -10.52 -0.46 25\n",
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"24 -13.36 -0.72 24\n",
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"25 -13.82 -0.75 24\n",
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"26 -12.83 -0.78 24\n",
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"27 -11.86 -0.68 23\n",
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"28 5.64 0.28 22\n",
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"29 3.85 0.15 20\n",
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"30 6.44 0.3 19\n",
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"31 7.28 0.47 18\n",
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"32 9.89 0.32 16\n",
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"33 11.96 0.5 15\n",
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"34 8.78 0.34 13\n",
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"35 8.75 0.35 13\n",
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"36 8.64 0.36 13\n",
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"37 8.49 0.37 13\n",
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"38 9.06 0.38 13\n",
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"39 16.09 0.58 12\n",
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"40 16.41 0.6 12\n",
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"41 17.67 0.61 12\n",
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"42 17.77 0.63 12\n",
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"43 18.64 0.86 11\n",
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"44 18.78 0.88 11\n",
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"45 23.13 1.35 9\n",
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"46 25.31 1.61 8\n",
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"47 26.31 1.65 8\n",
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"48 15.62 1.2 7\n",
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"49 12.83 0.73 6\n"
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]
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}
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],
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"source": [
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"db = MySQLdb.connect(host=\"127.0.0.1\",user=\"sa\",passwd=\"sasasa\",db=\"quant\",charset=\"utf8\")\n",
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"df = pd.read_csv('E:\\jupyter\\qushi-20190625-20190802.csv',index_col='index')\n",
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"df_down = df[df.direction=='downdown']\n",
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"\n",
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"for i in range(1,50):\n",
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" result=0\n",
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" result_rate=0\n",
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" raise_n=0\n",
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" decrease_n=0\n",
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" for stock_n, stock_k in df_down.iterrows():\n",
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" ts_code=stock_k.ts_code\n",
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" trade_time=stock_k.trade_time\n",
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" trade_price=stock_k.trade_price\n",
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" high=trade_price*(1+0.01*i)\n",
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" low=trade_price*(1-0.005*i)\n",
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" cursor = db.cursor()\n",
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" sql = \"select * from stock_min where ts_code='\"+ts_code+\"' and trade_time>'\"+trade_time+\"' and (close<\"+str(low)+\" or close>\"+str(high)+\") order by trade_time limit 1\"\n",
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" #print(sql)\n",
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" df_dr = pd.read_sql(sql,db)\n",
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" cursor.close()\n",
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" if df_dr.shape[0]>0:\n",
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" touch_time=df_dr.ix[0:1,'trade_time'][0]\n",
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" touch_price=df_dr.ix[0:1,'close'][0]\n",
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" spread=touch_price-trade_price\n",
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" if spread>0:\n",
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" raise_n+=1 \n",
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" result_rate+=0.01*i\n",
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" else:\n",
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" decrease_n+=1\n",
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" result_rate-=0.005*i\n",
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" result+=spread\n",
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" print(i,round(result,2),round(result_rate,2),raise_n+decrease_n)\n",
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" result_df.loc[i-1,['down']]=round(result_rate,2)\n",
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"\n",
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"db.close()"
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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": 172,
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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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"<matplotlib.axes._subplots.AxesSubplot at 0x245d186b6d8>"
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]
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},
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"execution_count": 172,
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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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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"result_df.plot()"
|
|
]
|
|
}
|
|
],
|
|
"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
|
|
}
|