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tej87681088/TQuant Lab SuperTrend Strategy_SinoPac Shioaji.ipynb

Created November 19, 2024 09:01
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TQuant Lab SuperTrend Strategy_SinoPac Shioaji.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "93cc65cb-d9ad-4ae2-944d-d9ce3e3b1a3d",
"metadata": {},
"source": [
"# TQuant Lab SuperTrend Strategy - Sinopac Shioaji"
]
},
{
"cell_type": "markdown",
"id": "aea787ac-148e-4a3b-b65e-b2a0db85d898",
"metadata": {},
"source": [
"## Overview of the SuperTrend Strategy\n",
"The SuperTrend Strategy combines the SuperTrend Indicator and the ADX Indicator. The SuperTrend Indicator consists of upper and lower bands. When the stock price breaks above the upper band, it signals a breakout, indicating a potential upward trend. Conversely, when the price drops below the lower band, it indicates a breach of support and a potential downward trend.\r\n",
"\r\n",
"The ADX (Average Directional Index), ranging from 0 to 100, measures trend strength. Higher values signify stronger trends, helping confirm bullish or bearish conditions出。\n",
"\n",
"詳細的策略說明與 TQuant Lab 的回測流程請參考：[TQuant Lab 超級趨勢策略，低買高賣賺取波段價差](https://www.tejwin.com/insight/tquant-lab-%E8%B6%85%E7%B4%9A%E8%B6%A8%E5%8B%A2%E7%AD%96%E7%95%A5/)"
]
},
{
"cell_type": "markdown",
"id": "c9b23d34-4ab4-4faa-9cd1-cc4f063fc336",
"metadata": {},
"source": [
"## Identifying Daily Trading Targets"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "b621e62e-7e39-4a66-a49a-255cbc8229aa",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"# tej_key\n",
"tej_key = 'your key'\n",
"api_base = 'https://api.tej.com.tw'\n",
"\n",
"os.environ['TEJAPI_KEY'] = tej_key \n",
"os.environ['TEJAPI_BASE'] = api_base"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "2cb7b194-2eaf-4fb0-9e3a-45ef8fb32981",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Currently used TEJ API key call quota 230/100000 (0.23%)\n",
"Currently used TEJ API key data quota 787917/10000000 (7.88%)\n"
]
}
],
"source": [
"from zipline.utils.calendar_utils import get_calendar\n",
"from zipline.sources.TEJ_Api_Data import get_universe\n",
"\n",
"cal = get_calendar('TEJ').all_sessions\n",
"last_date = cal[-2] # last trading date\n",
"\n",
"pool = get_universe(start = last_date, \n",
" end = last_date,\n",
" mkt_bd_e = 'TSE', # Listed stock in Taiwan\n",
" stktp_e = 'Common Stock', \n",
" main_ind_e = 'M2300 Electronics' # Electronics Industry\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "d514b758-e8d5-413e-b81e-35c9e3678d98",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"407"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(pool)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "1a05c4bb-bb1a-424a-a953-293ad803830b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Currently used TEJ API key call quota 260/100000 (0.26%)\n",
"Currently used TEJ API key data quota 900607/10000000 (9.01%)\n"
]
},
{
"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>coid</th>\n",
" <th>mdate</th>\n",
" <th>Market_Cap_Dollars</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1471</td>\n",
" <td>2024-10-25</td>\n",
" <td>1.962910e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1582</td>\n",
" <td>2024-10-25</td>\n",
" <td>1.514820e+10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2059</td>\n",
" <td>2024-10-25</td>\n",
" <td>1.195978e+11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2301</td>\n",
" <td>2024-10-25</td>\n",
" <td>2.441140e+11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2302</td>\n",
" <td>2024-10-25</td>\n",
" <td>3.201330e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>402</th>\n",
" <td>8215</td>\n",
" <td>2024-10-25</td>\n",
" <td>1.032572e+10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>403</th>\n",
" <td>8249</td>\n",
" <td>2024-10-25</td>\n",
" <td>9.068506e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>404</th>\n",
" <td>8261</td>\n",
" <td>2024-10-25</td>\n",
" <td>9.226725e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>405</th>\n",
" <td>8271</td>\n",
" <td>2024-10-25</td>\n",
" <td>6.603811e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>406</th>\n",
" <td>9912</td>\n",
" <td>2024-10-25</td>\n",
" <td>7.050700e+08</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>407 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" coid mdate Market_Cap_Dollars\n",
"0 1471 2024-10-25 1.962910e+09\n",
"1 1582 2024-10-25 1.514820e+10\n",
"2 2059 2024-10-25 1.195978e+11\n",
"3 2301 2024-10-25 2.441140e+11\n",
"4 2302 2024-10-25 3.201330e+09\n",
".. ... ... ...\n",
"402 8215 2024-10-25 1.032572e+10\n",
"403 8249 2024-10-25 9.068506e+09\n",
"404 8261 2024-10-25 9.226725e+09\n",
"405 8271 2024-10-25 6.603811e+09\n",
"406 9912 2024-10-25 7.050700e+08\n",
"\n",
"[407 rows x 3 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import TejToolAPI\n",
"\n",
"mktcap_data = TejToolAPI.get_history_data(start = last_date,\n",
" end = last_date,\n",
" ticker = pool,\n",
" columns = ['Market_Cap_Dollars']\n",
" )\n",
"\n",
"mktcap_data"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "943deaef-f891-4cb4-aa62-6f0f3281e2e6",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"['2330',\n",
" '2317',\n",
" '2454',\n",
" '2382',\n",
" '2308',\n",
" '2412',\n",
" '3711',\n",
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" '3045',\n",
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" '3035',\n",
" '2388',\n",
" '2404',\n",
" '3023',\n",
" '6285',\n",
" '5434',\n",
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" '3706',\n",
" '6139',\n",
" '3189',\n",
" '3406',\n",
" '2492',\n",
" '6412',\n",
" '6531',\n",
" '2337',\n",
" '3532',\n",
" '2458',\n",
" '6515',\n",
" '4915',\n",
" '8070',\n",
" '6414',\n",
" '2451',\n",
" '4919',\n",
" '3042',\n",
" '2312',\n",
" '6257',\n",
" '2498',\n",
" '2363',\n",
" '2362',\n",
" '3413',\n",
" '3583',\n",
" '2393',\n",
" '6442',\n",
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" '8112',\n",
" '8210',\n",
" '3376',\n",
" '3714',\n",
" '3596',\n",
" '5388',\n",
" '3030',\n",
" '6691']"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tickers = mktcap_data.nlargest(100, 'Market_Cap_Dollars')['coid'].tolist()\n",
"tickers"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e49f10b8-ffe4-42f5-aa69-fb6e04798a29",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Merging daily equity files:\n",
"Currently used TEJ API key call quota 266/100000 (0.27%)\n",
"Currently used TEJ API key data quota 914122/10000000 (9.14%)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[2024-10-28 03:45:19.943089] INFO: zipline.data.bundles.core: Ingesting tquant.\n",
"[2024-10-28 03:45:22.401729] INFO: zipline.data.bundles.core: Ingest tquant successfully.\n"
]
}
],
"source": [
"from datetime import timedelta\n",
"\n",
"start = (last_date - timedelta(days=90)).strftime('%Y-%m-%d') # We want 3 months (90 days) data\n",
"end = last_date.strftime('%Y-%m-%d')\n",
"\n",
"os.environ['mdate'] = start + ' ' + end\n",
"os.environ['ticker'] = ' '.join(tickers)\n",
"\n",
"!zipline ingest -b tquant"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "9a7f3390-046f-423d-a197-690964aec630",
"metadata": {},
"outputs": [],
"source": [
"from zipline.pipeline import CustomFactor\n",
"from zipline.pipeline.data import TWEquityPricing\n",
"\n",
"class Supertrend(CustomFactor):\n",
" inputs = [\n",
" TWEquityPricing.high,\n",
" TWEquityPricing.low,\n",
" TWEquityPricing.close,\n",
" ]\n",
" window_length = 50 # Use a 50-day window length\n",
" outputs = ['atr', 'basic_upperband', 'basic_lowerband', 'final_upperband', 'final_lowerband']\n",
"\n",
" def compute(self, today, assets, out, highs, lows, closes):\n",
" # Calculate TR (True Range)\n",
" high_low = highs[1:] - lows[1:]\n",
" high_close = abs(highs[1:] - closes[:-1])\n",
" low_close = abs(lows[1:] - closes[:-1])\n",
" tr = np.maximum.reduce([high_low, high_close, low_close])\n",
"\n",
" # Calculate ATR (Average True Range)\n",
" atr = np.mean(tr, axis=0)\n",
" out.atr = atr\n",
"\n",
" # Calculate basic upperband & lowerband\n",
" hl2 = (highs + lows) / 2\n",
" basic_upperband = hl2 + 4 * atr\n",
" basic_lowerband = hl2 - 4 * atr\n",
"\n",
" # Initialize final upperband & lowerband\n",
" final_upperband = np.zeros_like(basic_upperband)\n",
" final_lowerband = np.zeros_like(basic_lowerband)\n",
"\n",
" # Calculate fianl upperband & lowerband\n",
" for i in range(1, len(closes)):\n",
" final_upperband[i] = np.where(\n",
" (basic_upperband[i] < final_upperband[i-1]) | (closes[i-1] > final_upperband[i-1]),\n",
" basic_upperband[i],\n",
" final_upperband[i-1]\n",
" )\n",
" \n",
" final_lowerband[i] = np.where(\n",
" (basic_lowerband[i] > final_lowerband[i-1]) | (closes[i-1] < final_lowerband[i-1]),\n",
" basic_lowerband[i],\n",
" final_lowerband[i-1]\n",
" )\n",
" \n",
" out.basic_upperband = basic_upperband[-1]\n",
" out.basic_lowerband = basic_lowerband[-1]\n",
" out.final_upperband = final_upperband[-1]\n",
" out.final_lowerband = final_lowerband[-1]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "ae6c4acd-4f5d-432d-b5c7-62c2e4ab1a22",
"metadata": {},
"outputs": [],
"source": [
"class ADX(CustomFactor):\n",
" inputs = [TWEquityPricing.high, TWEquityPricing.low, TWEquityPricing.close]\n",
" window_length = 14 # The ADX is typically used with a default period of 14 days\n",
" outputs = ['adx']\n",
" \n",
" def compute(self, today, assets, out, highs, lows, closes):\n",
" # Calculate TR (True Range)\n",
" high_low = highs[1:] - lows[1:]\n",
" high_close = abs(highs[1:] - closes[:-1])\n",
" low_close = abs(lows[1:] - closes[:-1])\n",
" tr = np.maximum.reduce([high_low, high_close, low_close])\n",
"\n",
" # Calculate ATR (Average True Range)\n",
" atr = np.mean(tr, axis=0)\n",
" \n",
" # Calculate +DM, -DM (Directional Movement)\n",
" up_move = highs[1:] - lows[:-1]\n",
" down_move = lows[:-1] - lows[1:]\n",
" plus_dm = np.where((up_move > down_move) & (up_move > 0), up_move, 0)\n",
" minus_dm = np.where((down_move > up_move) & (down_move > 0), down_move, 0)\n",
" \n",
" # Calaulate rolling mean of +DM & -DM (smoothing)\n",
" plus_dm_smooth = np.mean(plus_dm, axis=0)\n",
" minus_dm_smooth = np.mean(minus_dm, axis=0)\n",
" \n",
" # Calculate +DI, -DI (Directional Indicator)\n",
" di_plus = 100 * np.divide(plus_dm_smooth, atr)\n",
" di_minus = 100 * np.divide(minus_dm_smooth, atr)\n",
" \n",
" # Calculate DX\n",
" dx = 100 * np.divide(np.abs(di_plus - di_minus), np.abs(di_plus + di_minus))\n",
" \n",
" # Calculate ADX (rolling mean of DX)\n",
" dx_series = pd.DataFrame(dx)\n",
" adx = dx_series.rolling(window = 50, min_periods = 1).mean().values.flatten()\n",
"\n",
" out.adx = adx"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "1049f7b3-7f5f-41b8-bdb0-0efac8a83631",
"metadata": {},
"outputs": [],
"source": [
"from zipline.pipeline import Pipeline\n",
"from zipline.TQresearch.tej_pipeline import run_pipeline\n",
"from zipline.pipeline.filters import StaticAssets\n",
"\n",
"def make_pipeline():\n",
"\n",
" close = TWEquityPricing.close.latest\n",
" supertrend = Supertrend()\n",
" adx = ADX()\n",
" \n",
" return Pipeline(\n",
" columns={\n",
" 'close': close,\n",
" 'final_upperband': supertrend.final_upperband,\n",
" 'final_lowerband': supertrend.final_lowerband,\n",
" 'ADX': adx.adx\n",
" }\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "5e8695ca-81f1-4741-a020-defb9d8715f3",
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>close</th>\n",
" <th>final_upperband</th>\n",
" <th>final_lowerband</th>\n",
" <th>ADX</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">2024-10-25 00:00:00+00:00</th>\n",
" <th>Equity(16 [2354])</th>\n",
" <td>69.8</td>\n",
" <td>69.014286</td>\n",
" <td>63.985714</td>\n",
" <td>79.029377</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(73 [4919])</th>\n",
" <td>94.1</td>\n",
" <td>89.441837</td>\n",
" <td>78.708163</td>\n",
" <td>79.987217</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" close final_upperband \\\n",
"2024-10-25 00:00:00+00:00 Equity(16 [2354]) 69.8 69.014286 \n",
" Equity(73 [4919]) 94.1 89.441837 \n",
"\n",
" final_lowerband ADX \n",
"2024-10-25 00:00:00+00:00 Equity(16 [2354]) 63.985714 79.029377 \n",
" Equity(73 [4919]) 78.708163 79.987217 "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"last_data = run_pipeline(make_pipeline(), last_date, last_date)\n",
"buy_data = last_data[last_data['close'] > last_data['final_upperband']]\n",
"buy_data"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "854a55a6-7925-4ed6-8e9a-2f87c03df2da",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['2354', '4919']"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"buy_list = []\n",
"for i in range(len(buy_data)):\n",
" stock = buy_data.index.get_level_values(1)[i].symbol\n",
" buy_list.append(stock)\n",
"\n",
"buy_list"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "19d00454-737e-4bfb-88fd-d73e5018275c",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"\n",
" .dataframe thead th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>close</th>\n",
" <th>final_upperband</th>\n",
" <th>final_lowerband</th>\n",
" <th>ADX</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">2024-10-25 00:00:00+00:00</th>\n",
" <th>Equity(42 [2498])</th>\n",
" <td>45.85</td>\n",
" <td>52.910204</td>\n",
" <td>47.089796</td>\n",
" <td>80.747982</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(93 [6770])</th>\n",
" <td>19.70</td>\n",
" <td>21.993878</td>\n",
" <td>19.831122</td>\n",
" <td>82.509597</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" close final_upperband \\\n",
"2024-10-25 00:00:00+00:00 Equity(42 [2498]) 45.85 52.910204 \n",
" Equity(93 [6770]) 19.70 21.993878 \n",
"\n",
" final_lowerband ADX \n",
"2024-10-25 00:00:00+00:00 Equity(42 [2498]) 47.089796 80.747982 \n",
" Equity(93 [6770]) 19.831122 82.509597 "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sell_data = last_data[(last_data['close'] < last_data['final_lowerband']) & (last_data['ADX'] > 50)]\n",
"sell_data"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "8a9e0d06-8c05-4249-87f9-4810f9eaee66",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['2498', '6770']"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sell_list = []\n",
"for i in range(len(sell_data)):\n",
" stock = sell_data.index.get_level_values(1)[i].symbol\n",
" sell_list.append(stock)\n",
"\n",
"sell_list"
]
},
{
"cell_type": "markdown",
"id": "11865644-970a-4d5f-9c26-79c5ede4627a",
"metadata": {},
"source": [
"## Algorithmic Trading with SinoPac API\r",
"For a detailed introduction to the Yongfeng API, please refer to: [Shioaji Documentation](https://sinotrade.github.io/)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "debd0485-df3f-4217-a68f-4b4f15822062",
"metadata": {},
"outputs": [],
"source": [
"import shioaji as sj\n",
"\n",
"api = sj.Shioaji(simulation=True) # Simulation mode\n",
"api.login(\n",
" api_key = 'your api_key', \n",
" secret_key = 'your secret_key',\n",
" contracts_cb=lambda security_type: print(f\"{repr(security_type)} fetch done.\") # Fetch Contracts Callback\n",
")"
]
},
{
"cell_type": "markdown",
"id": "266d425c-3c74-4614-b4a7-a1cecc064ddc",
"metadata": {},
"source": [
"### Retrieving Current Holdings\n",
"The `list_positions` function in Sinopac API provides current holdings, including stock symbols, quantities, average prices, current prices, and profit/loss, etc. \n",
"\n",
"**Note**: If no positions are held, the function returns an empty DataFrame."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "54e25daf-1626-44f9-ad79-9aee443ec461",
"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",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: []\n",
"Index: []"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"positions = api.list_positions(api.stock_account)\n",
"position_data = pd.DataFrame(s.__dict__ for s in positions)\n",
"position_data"
]
},
{
"cell_type": "markdown",
"id": "d9ee5695-d1cc-45a6-8c5b-9d590fa81d47",
"metadata": {},
"source": [
"### Fetching the list of holding stocks\n",
"Extracting the *code* column from the position data will provide the current list of held stocks, referred to as *hold_list*."
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "bde4829b-b7a5-482a-a87b-a50ff8ab4f30",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"if position_data.empty:\n",
" hold_list = []\n",
"else:\n",
" hold_list = position_data['code'].to_list()\n",
" \n",
"hold_list"
]
},
{
"cell_type": "markdown",
"id": "f080e563-6a73-4e56-9dfe-488441f9ae40",
"metadata": {},
"source": [
"### Setting Up Order Callback\n",
"We define the order callback function `order_cb` and pass it to `set_order_callback`. Once an order is executed, it will return order details and trade execution messages."
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "94e95db1-303b-4d61-beb1-197fae70abd0",
"metadata": {},
"outputs": [],
"source": [
"def order_cb(stat, msg):\n",
" print('my_order_callback')\n",
" print(stat, msg)\n",
"\n",
"api.set_order_callback(order_cb)"
]
},
{
"cell_type": "markdown",
"id": "a2fa141d-8f85-40ac-adb3-d47718b233d1",
"metadata": {},
"source": [
"### Placing Orders\n",
"The place_order function is used to execute trades. It requires contract (stock information) and order (trade details). Key parameters include action (Buy/Sell), quantity, price type (MKT, MKP), order type (ROD, IOC, FOK), and order lot (Common, Odd), etc. \n",
"\n",
"The trading rules in this article are as follows:\n",
" - If a stock in the buy_list is not in the hold_list, a MKT order will be placed to buy one share.\n",
" - If a stock in the sell_list is in the hold_list, a MKT order will be placed to sell one share.\n",
"\n",
"**Note**: Using price_type as MKP (market price with range) may result in the following error and prevent successful execution. \n",
"```python\n",
"2024-09-24 09:52:51.492 | ERROR | shioaji.shioaji:place_order:419 - {'status': {'status_code': 500}, 'response': {'detail': 'Internal Server Error'}}\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "e3a60547-e7c3-40bf-940e-06a1c0a2e1f1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"processing 2354\n",
"2354 not in hold_list\n",
"Ready to buy!\n",
"====================================================================================================\n",
"processing 4919\n",
"4919 not in hold_list\n",
"Ready to buy!\n",
"my_order_callback\n",
"OrderState.StockOrder {'operation': {'op_type': 'New', 'op_code': '00', 'op_msg': ''}, 'order': {'id': '000401', 'seqno': '000401', 'ordno': '000106', 'account': {'account_type': 'S', 'person_id': '', 'broker_id': '9A95', 'account_id': '3369822', 'signed': True}, 'action': 'Buy', 'price': 0, 'quantity': 1, 'order_cond': 'Cash', 'order_lot': 'Common', 'custom_field': '', 'order_type': 'IOC', 'price_type': 'MKT'}, 'status': {'id': '000401', 'exchange_ts': 1730087221.832041, 'order_quantity': 1, 'modified_price': 0, 'cancel_quantity': 0, 'web_id': '137'}, 'contract': {'security_type': 'STK', 'exchange': 'TSE', 'code': '2354'}}\n",
"my_order_callback\n",
"OrderState.StockOrder {'operation': {'op_type': 'New', 'op_code': '00', 'op_msg': ''}, 'order': {'id': '000402', 'seqno': '000402', 'ordno': '0000EE', 'account': {'account_type': 'S', 'person_id': '', 'broker_id': '9A95', 'account_id': '3369822', 'signed': True}, 'action': 'Buy', 'price': 0, 'quantity': 1, 'order_cond': 'Cash', 'order_lot': 'Common', 'custom_field': '', 'order_type': 'IOC', 'price_type': 'MKT'}, 'status': {'id': '000402', 'exchange_ts': 1730087221.86957, 'order_quantity': 1, 'modified_price': 0, 'cancel_quantity': 0, 'web_id': '137'}, 'contract': {'security_type': 'STK', 'exchange': 'TSE', 'code': '4919'}}\n",
"====================================================================================================\n",
"my_order_callback\n",
"OrderState.StockDeal {'trade_id': '000402', 'seqno': '000402', 'ordno': '0000EE', 'exchange_seq': '000001', 'broker_id': '9A95', 'account_id': '3369822', 'action': 'Buy', 'code': '4919', 'order_cond': 'Cash', 'order_lot': 'Common', 'price': 95.4, 'quantity': 1, 'web_id': '137', 'custom_field': '', 'ts': 1730087222.687482}\n",
"my_order_callback\n",
"OrderState.StockDeal {'trade_id': '000401', 'seqno': '000401', 'ordno': '000106', 'exchange_seq': '000001', 'broker_id': '9A95', 'account_id': '3369822', 'action': 'Buy', 'code': '2354', 'order_cond': 'Cash', 'order_lot': 'Common', 'price': 72.9, 'quantity': 1, 'web_id': '137', 'custom_field': '', 'ts': 1730087229.40197}\n"
]
}
],
"source": [
"for i in buy_list:\n",
" print('processing %s' % i)\n",
" \n",
" if i not in hold_list:\n",
" print('%s not in hold_list' % i)\n",
" print('Ready to buy!')\n",
" \n",
" contract = api.Contracts.Stocks.TSE[i]\n",
"\n",
" order = api.Order(\n",
" action = 'Buy',\n",
" price = 0, # MKT, MKP will not use price parameter\n",
" quantity = 1,\n",
" price_type = 'MKT', # MKT or MKP\n",
" order_type = 'IOC', # ROD, IOC, FOK\n",
" order_lot = 'Common', # Common:整股, Fixing:定盤, Odd:盤後零股, IntradayOdd:盤中零股\n",
" account = api.stock_account\n",
" )\n",
"\n",
" trade = api.place_order(contract, order)\n",
" trade\n",
"\n",
" else:\n",
" print('%s in hold_list' % i)\n",
"\n",
" print('=' * 100)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "37fa5021-2a45-46c6-9957-14a0bb70d960",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"processing 2498\n",
"2498 not in hold_list\n",
"====================================================================================================\n",
"processing 6770\n",
"6770 not in hold_list\n",
"====================================================================================================\n"
]
}
],
"source": [
"for i in sell_list:\n",
" print('processing %s' % i)\n",
" \n",
" if i in hold_list:\n",
" print('%s in hold_list' % i)\n",
" print('Ready to sell!')\n",
" \n",
" contract = api.Contracts.Stocks.TSE[i]\n",
"\n",
" order = api.Order(\n",
" action = 'Sell',\n",
" price = 0, # MKT, MKP will not use price parameter\n",
" quantity = 1,\n",
" price_type = 'MKT', # MKT or MKP\n",
" order_type = 'IOC', # ROD, IOC, FOK\n",
" order_lot = 'Common', # Common:整股, Fixing:定盤, Odd:盤後零股, IntradayOdd:盤中零股\n",
" account = api.stock_account\n",
" )\n",
"\n",
" trade = api.place_order(contract, order)\n",
" trade\n",
"\n",
" else:\n",
" print('%s not in hold_list' % i)\n",
"\n",
" print('=' * 100)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "71d58839-3b74-4f4a-8b12-666bde3d234c",
"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>id</th>\n",
" <th>code</th>\n",
" <th>direction</th>\n",
" <th>quantity</th>\n",
" <th>price</th>\n",
" <th>last_price</th>\n",
" <th>pnl</th>\n",
" <th>yd_quantity</th>\n",
" <th>cond</th>\n",
" <th>margin_purchase_amount</th>\n",
" <th>collateral</th>\n",
" <th>short_sale_margin</th>\n",
" <th>interest</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>2354</td>\n",
" <td>Action.Buy</td>\n",
" <td>1</td>\n",
" <td>72.9</td>\n",
" <td>72.9</td>\n",
" <td>-292.0</td>\n",
" <td>0</td>\n",
" <td>StockOrderCond.Cash</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>4919</td>\n",
" <td>Action.Buy</td>\n",
" <td>1</td>\n",
" <td>95.4</td>\n",
" <td>95.4</td>\n",
" <td>-381.0</td>\n",
" <td>0</td>\n",
" <td>StockOrderCond.Cash</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id code direction quantity price last_price pnl yd_quantity \\\n",
"0 0 2354 Action.Buy 1 72.9 72.9 -292.0 0 \n",
"1 1 4919 Action.Buy 1 95.4 95.4 -381.0 0 \n",
"\n",
" cond margin_purchase_amount collateral short_sale_margin \\\n",
"0 StockOrderCond.Cash 0 0 0 \n",
"1 StockOrderCond.Cash 0 0 0 \n",
"\n",
" interest \n",
"0 0 \n",
"1 0 "
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"positions = api.list_positions(api.stock_account)\n",
"position_data = pd.DataFrame(s.__dict__ for s in positions)\n",
"position_data"
]
},
{
"cell_type": "markdown",
"id": "c2016606-1e59-403e-b865-e2989447edcf",
"metadata": {},
"source": [
"### Viewing Realized P&L\n",
"The `list_profit_loss` function provides realized profit/loss data for a specified date range, including stock symbols, prices, quantities, and profit/loss details."
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "6e258cb1-8f0f-4031-8ea5-61011c6f71ec",
"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>id</th>\n",
" <th>code</th>\n",
" <th>quantity</th>\n",
" <th>pnl</th>\n",
" <th>date</th>\n",
" <th>dseq</th>\n",
" <th>price</th>\n",
" <th>pr_ratio</th>\n",
" <th>cond</th>\n",
" <th>seqno</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>2330</td>\n",
" <td>1</td>\n",
" <td>112818.0</td>\n",
" <td>20241028</td>\n",
" <td>000158</td>\n",
" <td>1060.00</td>\n",
" <td>11.96379</td>\n",
" <td>StockOrderCond.Cash</td>\n",
" <td>00055D</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>2454</td>\n",
" <td>1</td>\n",
" <td>109777.0</td>\n",
" <td>20241028</td>\n",
" <td>000027</td>\n",
" <td>1320.00</td>\n",
" <td>9.11017</td>\n",
" <td>StockOrderCond.Cash</td>\n",
" <td>0000AB</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>2890</td>\n",
" <td>1</td>\n",
" <td>-1244.0</td>\n",
" <td>20241028</td>\n",
" <td>000030</td>\n",
" <td>23.35</td>\n",
" <td>-5.07745</td>\n",
" <td>StockOrderCond.Cash</td>\n",
" <td>0000D0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>6152</td>\n",
" <td>1</td>\n",
" <td>-2915.0</td>\n",
" <td>20241028</td>\n",
" <td>000028</td>\n",
" <td>15.85</td>\n",
" <td>-15.58730</td>\n",
" <td>StockOrderCond.Cash</td>\n",
" <td>0000B2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>2424</td>\n",
" <td>1</td>\n",
" <td>18687.0</td>\n",
" <td>20241028</td>\n",
" <td>00002C</td>\n",
" <td>80.60</td>\n",
" <td>30.33620</td>\n",
" <td>StockOrderCond.Cash</td>\n",
" <td>0000B0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>5</td>\n",
" <td>6916</td>\n",
" <td>1</td>\n",
" <td>397.0</td>\n",
" <td>20241028</td>\n",
" <td>00002D</td>\n",
" <td>25.85</td>\n",
" <td>1.56548</td>\n",
" <td>StockOrderCond.Cash</td>\n",
" <td>0000B3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>6</td>\n",
" <td>2488</td>\n",
" <td>1</td>\n",
" <td>-5294.0</td>\n",
" <td>20241028</td>\n",
" <td>000026</td>\n",
" <td>47.90</td>\n",
" <td>-9.98896</td>\n",
" <td>StockOrderCond.Cash</td>\n",
" <td>0000B1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>7</td>\n",
" <td>6792</td>\n",
" <td>1</td>\n",
" <td>-2067.0</td>\n",
" <td>20241028</td>\n",
" <td>00019D</td>\n",
" <td>66.60</td>\n",
" <td>-3.02237</td>\n",
" <td>StockOrderCond.Cash</td>\n",
" <td>00066C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>8</td>\n",
" <td>2466</td>\n",
" <td>1</td>\n",
" <td>-1540.0</td>\n",
" <td>20241028</td>\n",
" <td>00018D</td>\n",
" <td>34.90</td>\n",
" <td>-4.24325</td>\n",
" <td>StockOrderCond.Cash</td>\n",
" <td>00066B</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id code quantity pnl date dseq price pr_ratio \\\n",
"0 0 2330 1 112818.0 20241028 000158 1060.00 11.96379 \n",
"1 1 2454 1 109777.0 20241028 000027 1320.00 9.11017 \n",
"2 2 2890 1 -1244.0 20241028 000030 23.35 -5.07745 \n",
"3 3 6152 1 -2915.0 20241028 000028 15.85 -15.58730 \n",
"4 4 2424 1 18687.0 20241028 00002C 80.60 30.33620 \n",
"5 5 6916 1 397.0 20241028 00002D 25.85 1.56548 \n",
"6 6 2488 1 -5294.0 20241028 000026 47.90 -9.98896 \n",
"7 7 6792 1 -2067.0 20241028 00019D 66.60 -3.02237 \n",
"8 8 2466 1 -1540.0 20241028 00018D 34.90 -4.24325 \n",
"\n",
" cond seqno \n",
"0 StockOrderCond.Cash 00055D \n",
"1 StockOrderCond.Cash 0000AB \n",
"2 StockOrderCond.Cash 0000D0 \n",
"3 StockOrderCond.Cash 0000B2 \n",
"4 StockOrderCond.Cash 0000B0 \n",
"5 StockOrderCond.Cash 0000B3 \n",
"6 StockOrderCond.Cash 0000B1 \n",
"7 StockOrderCond.Cash 00066C \n",
"8 StockOrderCond.Cash 00066B "
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"profitloss = api.list_profit_loss(api.stock_account,'2024-10-28','2024-10-28')\n",
"profitloss_data = pd.DataFrame(pnl.__dict__ for pnl in profitloss)\n",
"profitloss_data"
]
},
{
"cell_type": "markdown",
"id": "2d749d21-d7ab-4451-9c6d-7ebdea305772",
"metadata": {},
"source": [
"### Logging Out\n",
"Due to API usage limits, it’s recommended to log out after completing your operations."
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "ec5874dd-8d59-4e7d-8667-328a5b33fed8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"api.logout()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "94cae78a-d68f-49df-a2bd-9a6d8386d2fd",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.11.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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