week现货合约跟单交易所系统开发实现技术方案及详细源码
量化交易系统是基于算法和模型的自动化交易系统,可以通过计算机程序快速进行市场分析、预测和交易决策。下面介绍量化交易系统开发的步骤和注意事项:
策略设计和建模:在量化交易系统开发前,需要先确定交易策略并进行建模。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.
系统框架搭建:根据策略设计和数据采集,需要构建系统框架,Including data storage and management,transaction execution,risk control,and other aspects.同时,还需要选择适当的技术框架和编程语言,如Python、Java等。
算法实现和优化:在系统框架搭建好后,需要进行算法实现和优化。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(未上线)
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()

