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@tej87681088
Created October 7, 2022 03:58
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "3160033a",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import tejapi\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.transforms as transforms\n",
"from matplotlib.font_manager import FontProperties\n",
"plt.rcParams['font.sans-serif'] = ['Arial Unicode MS'] # 解決MAC電腦 plot中文問題\n",
"plt.rcParams['axes.unicode_minus'] = False\n",
"tejapi.ApiConfig.api_key = \"Your Key\"\n",
"tejapi.ApiConfig.ignoretz = True"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "5b3a41fb",
"metadata": {},
"outputs": [],
"source": [
"data=tejapi.get('TWN/ANPRCSTD' ,chinese_column_name=True )\n",
"select=data[\"上市別\"].unique()\n",
"select=select[1:3]\n",
"condition =(data[\"上市別\"].isin(select)) & ( data[\"證券種類名稱\"]==\"普通股\" )\n",
"data=data[condition]\n",
"twid=data[\"證券碼\"].to_list() #取得上市櫃股票證券碼"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "b017876d",
"metadata": {},
"outputs": [],
"source": [
"def get_data(code:str, id_):\n",
" df = tejapi.get(code, #從TEJ api撈取所需要的資料\n",
" chinese_column_name = True,\n",
" paginate = True,\n",
" mdate = {'gt':'2013-04-01', 'lt':'2021-12-31'},\n",
" coid=id_,\n",
" opts={'columns':['coid','mdate','close_adj']})\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "be19d929",
"metadata": {},
"outputs": [],
"source": [
"def get_data1(code:str, id_):\n",
" df = tejapi.get(code, #從TEJ api撈取所需要的資料\n",
" chinese_column_name = True,\n",
" paginate = True,\n",
" mdate = {'gt':'2013-04-01', 'lt':'2021-12-31'},\n",
" coid=id_,\n",
" opts={'columns':['coid','annd_s', 'd0003', 'd0004']})\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "59962a59",
"metadata": {},
"outputs": [],
"source": [
"# df_1 = tejapi.get('TWN/APRCD1', #從TEJ api撈取所需要的資料\n",
"# chinese_column_name = True,\n",
"# paginate = True,\n",
"# mdate = {'gt':'2013-04-01', 'lt':'2021-12-31'},\n",
"# coid=1101,\n",
"# opts={'columns':['coid','mdate','close_adj']})"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "46da1da5",
"metadata": {},
"outputs": [],
"source": [
"# df = tejapi.get('TWN/ASALE', #從TEJ api撈取所需要的資料\n",
"# chinese_column_name = True,\n",
"# paginate = True,\n",
"# mdate = {'gt':'2013-04-01', 'lt':'2021-12-31'},\n",
"# coid = 1101,\n",
"# opts={'columns':['coid','annd_s', 'd0003', 'd0004']})"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "06bab173",
"metadata": {},
"outputs": [],
"source": [
"df_1 = get_data('TWN/APRCD1', i)\n",
"df = get_data1('TWN/ASALE', i)\n",
"df.rename(columns={'營收發布日':'年月日','單月營收成長率%':'yoy', '單月營收與上月比%':'mom', '公司':'證券代碼'}, inplace=True)\n",
"df['yoy3'] = df['yoy'].rolling(5).mean()\n",
"df['mom3'] = df['mom'].rolling(10).mean()\n",
"\n",
"df2 = df_1.merge(df, on=['證券代碼', '年月日'], how='outer')\n",
"df2 = df2.sort_values(by='年月日')\n",
"df2[['yoy', 'mom','yoy3','mom3']] = df2[['yoy', 'mom','yoy3',\"mom3\"]].shift(1)\n",
"df3 = df2.dropna()\n",
"df3.set_index(df3['年月日'], inplace=True)\n",
"df3.drop(columns={'年月日'}, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "a2798c8c",
"metadata": {},
"outputs": [],
"source": [
"def buysell(data, id_):\n",
" data =data.copy()\n",
" buy=[]\n",
" sell=[]\n",
" hold=0\n",
" for i in range(len(data)):\n",
" \n",
" if (data[\"yoy\"][i] > data[\"yoy3\"][i]) & (data[\"mom\"][i] > data[\"mom3\"][i]):\n",
" sell.append(np.nan)\n",
" if hold !=1:\n",
" buy.append(data[\"收盤價(元)\"][i])\n",
" \n",
" hold = 1\n",
" else: \n",
" buy.append(np.nan)\n",
" elif (data[\"yoy\"][i] < data[\"yoy3\"][i]) & (data[\"mom\"][i] < data[\"mom3\"][i]):\n",
" buy.append(np.nan)\n",
" if hold !=0:\n",
" sell.append(data[\"收盤價(元)\"][i])\n",
" hold = 0\n",
" else:\n",
" sell.append(np.nan)\n",
" else:\n",
" buy.append(np.nan)\n",
" sell.append(np.nan)\n",
" a=(buy,sell)\n",
" \n",
" data['Buy_Signal_Price']=a[0]\n",
" data['Sell_Signal_Price']=a[1]\n",
" data[\"買賣股數1\"]=data['Buy_Signal_Price'].apply(lambda x : 1 if x >0 else 0)\n",
" data[\"買賣股數2\"]=data['Sell_Signal_Price'].apply(lambda x : -1 if x >0 else 0 )\n",
" data[\"買賣股數\"]=data[\"買賣股數1\"]+ data[\"買賣股數2\"]\n",
" \n",
" b = data[['Buy_Signal_Price']].dropna()\n",
" c = data[['Sell_Signal_Price']].dropna()\n",
" d = pd.DataFrame(index=c.index, columns = {\"Buy_Signal_Price\", 'Sell_Signal_Price'})\n",
" for i in range(len(d.index)):\n",
" d[\"Buy_Signal_Price\"][i] = b[\"Buy_Signal_Price\"][i]\n",
" d['Sell_Signal_Price'][i] = c['Sell_Signal_Price'][i]\n",
" d['hold_return'] = (d['Sell_Signal_Price']-d['Buy_Signal_Price'])/d['Buy_Signal_Price']\n",
" win_rate = ((np.where(d['hold_return']>0, 1, 0).sum())/len(d.index))*100\n",
" hold_ret = (d['hold_return'].sum())*100\n",
" e = pd.DataFrame({'報酬':hold_ret, '勝率':win_rate}, index={str(id_)}) \n",
" return e"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "817728d6",
"metadata": {},
"outputs": [],
"source": [
"qq = pd.DataFrame()\n",
"for i in twid[:]:\n",
" df_1 = get_data(\"TWN/APRCD1\",i)\n",
" df = get_data1(\"TWN/ASALE\",i)\n",
" df.rename(columns={'營收發布日':'年月日','單月營收成長率%':'yoy', '單月營收與上月比%':'mom', '公司':'證券代碼'}, inplace=True)\n",
" df['yoy3'] = df['yoy'].rolling(5).mean()\n",
" df['mom3'] = df['mom'].rolling(10).mean()\n",
" df2 = df_1.merge(df, on=['證券代碼', '年月日'], how='outer')\n",
" df2 = df2.sort_values(by='年月日')\n",
" df2[['yoy', 'mom','yoy3','mom3']] = df2[['yoy', 'mom','yoy3',\"mom3\"]].shift(1)\n",
" df3 = df2.dropna()\n",
" df3.set_index(df3['年月日'], inplace=True)\n",
" df3.drop(columns={'年月日'}, inplace=True)\n",
" qq = qq.append(buysell(df3, i))\n",
" print(i)\n",
"qq.index.name = '證券碼'"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "44cea95d",
"metadata": {},
"outputs": [],
"source": [
"qq = pd.read_csv('月營收.csv')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "9e835549",
"metadata": {},
"outputs": [],
"source": [
"qq = qq.rename(columns={'Unnamed: 0':'證券碼'})"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "7834b747",
"metadata": {},
"outputs": [],
"source": [
"qq['證券碼'] = qq[['證券碼']].applymap(lambda x:str(x))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "b168c893",
"metadata": {},
"outputs": [],
"source": [
"qq.set_index(qq['證券碼'],inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "410f7b02",
"metadata": {},
"outputs": [],
"source": [
"qq.drop(columns={'證券碼'}, inplace = True)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "124b471a",
"metadata": {},
"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>報酬</th>\n",
" <th>勝率</th>\n",
" </tr>\n",
" <tr>\n",
" <th>證券碼</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1101</th>\n",
" <td>49.099029</td>\n",
" <td>54.545455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1102</th>\n",
" <td>55.589627</td>\n",
" <td>58.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1103</th>\n",
" <td>68.876273</td>\n",
" <td>60.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1104</th>\n",
" <td>28.355913</td>\n",
" <td>68.750000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1108</th>\n",
" <td>12.914957</td>\n",
" <td>40.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9951</th>\n",
" <td>57.428088</td>\n",
" <td>57.142857</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9955</th>\n",
" <td>-29.987316</td>\n",
" <td>35.294118</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9958</th>\n",
" <td>109.106182</td>\n",
" <td>45.454545</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9960</th>\n",
" <td>118.700424</td>\n",
" <td>50.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9962</th>\n",
" <td>22.152303</td>\n",
" <td>50.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1663 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" 報酬 勝率\n",
"證券碼 \n",
"1101 49.099029 54.545455\n",
"1102 55.589627 58.333333\n",
"1103 68.876273 60.000000\n",
"1104 28.355913 68.750000\n",
"1108 12.914957 40.000000\n",
"... ... ...\n",
"9951 57.428088 57.142857\n",
"9955 -29.987316 35.294118\n",
"9958 109.106182 45.454545\n",
"9960 118.700424 50.000000\n",
"9962 22.152303 50.000000\n",
"\n",
"[1663 rows x 2 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"qq"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "a5daa494",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"報酬 0\n",
"勝率 54\n",
"dtype: int64\n"
]
}
],
"source": [
"print(qq.isna().sum())\n",
"qq.dropna(inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "c59834d4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"勝率: 52.03301257275607\n",
"報酬率: 66.44750522557626\n"
]
}
],
"source": [
"print('勝率:',qq['勝率'].mean())\n",
"print('報酬率:',qq['報酬'].mean())"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "fd44f463",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"57.73772529521442"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"qq['count'] = np.where( (qq['報酬']>0) &(qq['勝率']>= 50),1,0)\n",
"(qq['count'].sum()/qq['count'].count())*100"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "92c1c295",
"metadata": {},
"outputs": [],
"source": [
"qq['20%'] = np.where((qq['勝率']>= 80),1,0)\n",
"qq['40%'] = np.where((qq['勝率']>= 60)& (qq['勝率']< 80),1,0)\n",
"qq['60%'] = np.where((qq['勝率']>= 40)& (qq['勝率']< 60),1,0)\n",
"qq['80%'] = np.where((qq['勝率']>= 20)& (qq['勝率']< 40),1,0)\n",
"qq['100%'] = np.where((qq['勝率']<20),1,0)\n",
"z5 = [qq['20%'].sum(),qq['40%'].sum(), qq['60%'].sum(), qq['80%'].sum(),qq['100%'].sum()]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "76da1c56",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 576x720 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8,10))\n",
"plt.pie(z5,\n",
" radius=1,\n",
" labels=['勝率>80%','80%>勝率>60%','60%>勝率>40%','40%>勝率>20%','勝率<20%'],\n",
" autopct='%.1f%%', # %.1f%% 表示顯示小數點一位的浮點數,後方加上百分比符號 \n",
" pctdistance = 0.6, \n",
" textprops = {\"fontsize\" : 18}) # 文字大小) \n",
"plt.title('月營收成長率策略勝率佔比', {\"fontsize\":18})\n",
"plt.legend(loc = \"best\")\n",
"plt.axis('equal')\n",
" \n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "f5317472",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.6090739589807334"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len((qq[qq['勝率']>=50]).index)/len(qq.index)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "257b84df",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.9.12 ('base')",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.12"
},
"vscode": {
"interpreter": {
"hash": "3e06028060a7864164bfee2227bae8dfd39c60041c455d9e6b5a8dd3aec9b09f"
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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