群益海期 ticks 報價範例，本範例直接在 OnNotifyTicksNineDigitLONG, OnNotifyHistoryTicksNineDigitLONG 裡做一些資料處理，當海期報價資料量很大的時候，處理速度變很慢，kline 轉換也有點慢，日後都還需要改進，朋友呀，如果有什麼更快的方法請教教我。
Notebook 範例版 jupyter notebook
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| # # 強烈建議用 jupyterlab or ipython 來測試 | |
| # # 海期 tick 報價範例 | |
| import pythoncom | |
| import asyncio | |
| import datetime | |
| import pandas as pd | |
| import comtypes.client as cc | |
| import plotly.graph_objects | |
| # 只有第一次使用 api ，或是更新 api 版本時，才需要呼叫 GetModule | |
| # 會將 SKCOM api 包裝成 python 可用的 package ，並存放在 comtypes.gen 資料夾下 | |
| # 更新 api 版本時，記得將 comtypes.gen 資料夾 SKCOMLib 相關檔案刪除，再重新呼叫 GetModule | |
| cc.GetModule('C:\\skcom\\CapitalAPI_2.13.39\\x64\\SKCOM.dll') | |
| import comtypes.gen.SKCOMLib as sk | |
| # login ID and PW | |
| # 身份證 | |
| ID = '' | |
| # 密碼 | |
| PW = '' | |
| print(datetime.datetime.now().strftime("%Y/%m/%d %H:%M:%S,"), 'Set ID and PW') | |
| # # 建立 event pump and event loop | |
| # 新版的jupyterlab event pump 機制好像有改變，因此自行打造一個 event pump機制， | |
| # 目前在 jupyterlab 環境下使用，也有在 spyder IDE 下測試過，都可以正常運行 | |
| # working functions, async coruntime to pump events | |
| async def pump_task(): | |
| '''在背景裡定時 pump windows messages''' | |
| while True: | |
| pythoncom.PumpWaitingMessages() | |
| # 想要反應更快 可以將 0.1 取更小值 | |
| await asyncio.sleep(0.1) | |
| # 將ticks 轉為Kline | |
| def convert_to_kline(query_stock, freq): | |
| '''將ticks 轉為Kline | |
| query_stock: 欲查詢的商品代號 ex. "YM2212" | |
| freq: 請參考 pandas resample 用法, "T" 為分, "S"為秒 | |
| "5T" 為 5分Kline, "30S" 為30秒Kline | |
| return a kline dataframe | |
| ''' | |
| # 只保留成交時間,成交價與量資料 | |
| df = EventOSQ.ticks.query(f'bstrStockNo == "{query_stock}"').copy() | |
| df = df.filter(['Datetime', 'price', 'volume'], axis=1) | |
| # 將成交時間欄位資料按格式轉換為 datetime 資料 | |
| df['Datetime'] = pd.to_datetime(df['Datetime'], format='%Y%m%d%H%M%S') | |
| # 設定資料以成交時間欄位為序列索引 | |
| df = df.set_index('Datetime') | |
| # return OHLCV Kline | |
| kline = df.resample(rule=freq).agg({'price': 'ohlc', 'volume': 'sum'}).dropna() | |
| kline.columns = kline.columns.get_level_values(1) | |
| return kline | |
| # plot kline | |
| def plot_candlestick(df): | |
| figure = plotly.graph_objects.Figure( | |
| data=[ | |
| plotly.graph_objects.Candlestick( | |
| x=df.index, | |
| open=df['open'], | |
| high=df['high'], | |
| low=df['low'], | |
| close=df['close'], | |
| name='K line', | |
| ), | |
| ], | |
| # 設定 XY 顯示格式 | |
| layout=plotly.graph_objects.Layout( | |
| xaxis=plotly.graph_objects.layout.XAxis( | |
| tickformat='%Y-%m-%d %H:%M' | |
| ), | |
| yaxis=plotly.graph_objects.layout.YAxis( | |
| tickformat='.2f' | |
| ) | |
| ) | |
| ) | |
| figure.show() | |
| # get an event loop | |
| loop = asyncio.get_event_loop() | |
| pumping_loop = loop.create_task(pump_task()) | |
| print(datetime.datetime.now().strftime("%Y/%m/%d %H:%M:%S,"), "Event pumping is ready!") | |
| # 建立物件，避免重複 createObject | |
| # 登錄物件 | |
| if 'skC' not in globals(): | |
| skC = cc.CreateObject(sk.SKCenterLib, interface=sk.ISKCenterLib) | |
| # 海期報價物件 | |
| if 'skOSQ' not in globals(): | |
| skOSQ = cc.CreateObject(sk.SKOSQuoteLib , interface=sk.ISKOSQuoteLib) | |
| # 回報物件 | |
| if 'skR' not in globals(): | |
| skR = cc.CreateObject(sk.SKReplyLib, interface=sk.ISKReplyLib) | |
| # # # 建立 event handler | |
| # SKOSQ event handler | |
| class skOSQ_events: | |
| def __init__(self): | |
| self.OverseaProductsDetail = [] | |
| # 以dataframe方式存放ticks | |
| self.ticks = pd.DataFrame( | |
| {'Datetime': pd.Series(dtype='str'), | |
| 'price': pd.Series(dtype='float'), | |
| 'volume': pd.Series(dtype='int')}, | |
| index = pd.MultiIndex(levels=[[],[],[],[]], | |
| codes=[[],[],[],[]], | |
| names=['bstrStockNo', 'nPtr', 'nDate', 'nTime']), | |
| ) | |
| def OnConnect(self, nKind, nCode): | |
| '''連線海期主機狀況回報''' | |
| print(f'skOSQ_OnConnect nCode={nCode}, nKind={nKind}') | |
| def OnOverseaProductsDetail(self, bstrValue): | |
| '''查詢海期/報價下單商品代號''' | |
| if "##" not in self.OverseaProductsDetail: | |
| self.OverseaProductsDetail.append(bstrValue.split(',')) | |
| else: | |
| print("skOSQ_OverseaProductsDetail downloading is completed.") | |
| def OnNotifyQuoteLONG(self, sIndex): | |
| '''requestStock 報價回報''' | |
| # 儘量避免在這裡使用繁複的運算，這裡僅在 console 端印出報價 | |
| ts = sk.SKFOREIGNLONG() | |
| skOSQ.SKOSQuoteLib_GetStockByIndexLONG(sIndex, ts) | |
| print(ts.bstrExchangeNo, ts.bstrStockNo, ts.nClose, ts.nTickQty) | |
| def OnNotifyTicksNineDigitLONG (self, nIndex, nPtr, nDate, nTime, | |
| nClose, nQty): | |
| '''requestTick 回報''' | |
| # 儘量避免在這裡使用繁複的運算 | |
| ts = sk.SKFOREIGN_9LONG() | |
| skOSQ.SKOSQuoteLib_GetStockByIndexNineDigitLONG(nIndex, ts) | |
| self.ticks.loc[(ts.bstrStockNo, nPtr, nDate, nTime), | |
| ["Datetime", "price", "volume"]] = [f"{nDate}{nTime:06}", | |
| nClose/10**ts.sDecimal, | |
| nQty] | |
| def OnNotifyHistoryTicksNineDigitLONG (self, nIndex, nPtr, | |
| nDate, nTime, nClose, nQty): | |
| ''' History tick 回報''' | |
| # 儘量避免在這裡使用繁複的運算 | |
| ts = sk.SKFOREIGN_9LONG() | |
| ncode = skOSQ.SKOSQuoteLib_GetStockByIndexNineDigitLONG(nIndex, ts) | |
| self.ticks.loc[(ts.bstrStockNo, nPtr, nDate, nTime), | |
| ["Datetime", "price", "volume"]] = [f"{nDate}{nTime:06}", | |
| nClose/10**ts.sDecimal, | |
| nQty] | |
| # SKReplyLib event handler | |
| class skR_events: | |
| def OnReplyMessage(self, bstrUserID, bstrMessage): | |
| '''API 2.13.17 以上一定要返回 sConfirmCode=-1''' | |
| sConfirmCode = -1 | |
| print('skR_OnReplyMessage ok') | |
| return sConfirmCode | |
| # # 建立 event 跟 event handler 的連結 | |
| # Event sink, 事件實體化 | |
| EventOSQ = skOSQ_events() | |
| EventR = skR_events() | |
| # 建立 event 跟 event handler 的連結 | |
| ConnOSQ = cc.GetEvents(skOSQ, EventOSQ) | |
| ConnR = cc.GetEvents(skR, EventR) | |
| # # 登入及各項初始化作業 | |
| # login | |
| print('Login', skC.SKCenterLib_GetReturnCodeMessage(skC.SKCenterLib_Login(ID,PW))) | |
| # 海期商品初始化 | |
| nCode = skOSQ.SKOSQuoteLib_Initialize() | |
| print("SKOSQuoteLib_Initialize", skC.SKCenterLib_GetReturnCodeMessage(nCode)) | |
| ################################################################################### | |
| # 以下皆以手動輸入 | |
| # 登入海期報價主機 | |
| nCode = skOSQ.SKOSQuoteLib_LeaveMonitor() | |
| nCode = skOSQ.SKOSQuoteLib_EnterMonitorLONG() | |
| print('SKOSQuoteLib_EnterMonitorLONG()', skC.SKCenterLib_GetReturnCodeMessage(nCode)) | |
| # 登入海期報價主機，確認 OnConnect 出現 3001 回報後 | |
| # 才可 requesttick | |
| StockNo ="CBOT,YM2212" | |
| nCode = skOSQ.SKOSQuoteLib_RequestTicks(0, StockNo) | |
| print(f"Requesting ticks, {StockNo}", skC.SKCenterLib_GetReturnCodeMessage(nCode[1])) | |
| # 檢視 ticks 狀態，熱門商品資料數量很多，可能要等一下資料回傳完畢 | |
| EventOSQ.ticks | |
| # 轉換為 1分K,並畫出來，pandas 轉 kline 數據一多，好像有點慢 | |
| df_1k = convert_to_kline(query_stock="YM2212" ,freq="1T") | |
| plot_candlestick(df_1k) | |
| # 轉換為 5分K,並畫出來 | |
| df_5k = convert_to_kline(query_stock="YM2212",freq="5T") | |
| plot_candlestick(df_5k) | |
| # 多商品 tick 報價 | |
| # 一個 page 放一檔 tick 報價商品. page 放 0，好像群益系統會自動分配 page | |
| strCode = ['CBOT,YM0000', 'CBOT,YM2306'] | |
| for page, code in enumerate(strCode): | |
| print(skOSQ.SKOSQuoteLib_RequestTicks(page+1, code)) | |
| # 轉換多商品 tick to kline | |
| stockList = ['YM0000', 'YM2306'] | |
| kline_data = {} | |
| for s in stockList: | |
| kline_data[s] = convert_to_kline(s, '1T')['close'] | |
| # 順便計算 20 ma | |
| kline_data[f'{s}_ma20'] = convert_to_kline(s, '1T')['close'].rolling(20).mean() | |
| df_kline = pd.DataFrame(kline_data) | |
| df_kline.plot() |