183 lines
29 KiB
Plaintext
183 lines
29 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": 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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"text/plain": [
|
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"<Figure size 432x288 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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"source": [
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"result_df.plot()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python [conda root]",
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"language": "python",
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"name": "conda-root-py"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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|
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"name": "python",
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|
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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