量化交易系統(tǒng)是基于算法和模型的自動(dòng)化交易系統(tǒng)，可以通過(guò)計(jì)算機(jī)程序快速進(jìn)行市場(chǎng)分析、預(yù)測(cè)和交易決策。下面介紹量化交易系統(tǒng)開(kāi)發(fā)的步驟和注意事項(xiàng)：

　　策略設(shè)計(jì)和建模：在量化交易系統(tǒng)開(kāi)發(fā)前，需要先確定交易策略并進(jìn)行建模。Strategies can be designed based on historical data and market conditions,including various types such as trend trading and market arbitrage.During the modeling process,it is necessary to select appropriate mathematical models and algorithms,and conduct testing and verification.

　　系統(tǒng)框架搭建：根據(jù)策略設(shè)計(jì)和數(shù)據(jù)采集，需要構(gòu)建系統(tǒng)框架，Including data storage and management,transaction execution,risk control,and other aspects.同時(shí)，還需要選擇適當(dāng)?shù)募夹g(shù)框架和編程語(yǔ)言，如Python、Java等。

　　算法實(shí)現(xiàn)和優(yōu)化：在系統(tǒng)框架搭建好后，需要進(jìn)行算法實(shí)現(xiàn)和優(yōu)化。This process requires the implementation of existing models and algorithms,and Code refactoring and performance optimization to improve the efficiency and stability of the system.

　　def merge_codes():

　　markets=['SSE','SZSE']

　　all_codes=np.array([])

　　all_names=np.array([])

　　all_industrys=np.array([])

　　all_markets=np.array([])

　　for market in markets:

　　code_list=load_code_list(market=market)

　　codes=code_list['ts_code'].values

　　names=code_list['name'].values

　　industrys=code_list['industry'].values

　　marketList=code_list['market'].values

　　all_codes=np.append(all_codes,codes)

　　all_names=np.append(all_names,names)

　　all_industrys=np.append(all_industrys,industrys)

　　all_markets=np.append(all_markets,marketList)

　　data=[all_codes,all_names,all_industrys,all_markets]

　　data=np.transpose(data)

　　ser2=pd.DataFrame(data,columns=['ts_code','name','industry','market'])

　　writefile='all_codes.csv'

　　ser2.to_csv(writefile,encoding="utf_8_sig")

　　print("merge_codes EEEEEEEEE")

　　def load_data(ts_code,start_date='20160101',end_date=''):

　　data_dir='./data/'

　　name=get_code_name(ts_code)

　　name=name.replace('*','')

　　file=data_dir+ts_code+name+'.csv'

　　if not os.path.exists(file):

　　#初始化pro接口

　　#pro=ts.pro_api

　　#df=ts.pro_bar(ts_code=ts_code,start_date=start_date,end_date=end_date,ma=[5,10,20,30,50,120,200])

　　df=ts.pro_bar(ts_code=ts_code,ma=[5,10,20,30,50,120,200])

　　if df is None:

　　print('can not get data')

　　return

　　df.to_csv(file,index=False,encoding="utf_8_sig")

　　print('new file',file)

　　df=pd.read_csv(file)

　　#ts_code,trade_date,open,high,low,close,pre_close,change,pct_chg,vol,amount,adj_factor

　　df.dropna(inplace=True)

　　df=df.sort_index(ascending=False)

　　df.reset_index(drop=True,inplace=True)

　　return df

　　def load_code_list(market='SZSE',sel=False):#交易所SSE上交所SZSE深交所HKEX港交所(未上線)

　　path='./data/'

　　faceDir=Path(path)

　　if faceDir.exists():

　　file_dir=path+'code_list_'+market+'.csv'

　　else:

　　os.mkdir(faceDir)

　　file_dir=path+'code_list_'+market+'.csv'

　　if os.path.exists(file_dir):

　　code_list=pd.read_csv(file_dir)

　　else:

　　pro=ts.pro_api('ee5c0e991e17949cdafbcf8ec42321ef4bac94e9ca3474e4d62313a3')

　　#code_list=pro.stock_basic(exchange=market,list_status='L',fields='ts_code')#,symbol,name,market,list_date

　　#code_list=pro.stock_basic(exchange=market,list_status='L')#,symbol,name,market,list_date

　　code_list=pro.stock_basic(exchange=market,list_status='L')#,symbol,name,market,list_date

　　code_list.to_csv(file_dir,index=False,encoding="utf_8_sig")

　　#code_list=code_list[['ts_code']].values.flatten()