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 all_sel_current_code(codes,names,writefileName='sel_codes.csv',writefile=True):
path='./选择/'
dir=Path(path)
if not dir.exists():
os.mkdir(dir)
begin_count=-200
end_count=-1
days=20
name_array=np.array([])
code_array=np.array([])
date_array=np.array([])
for i in range(len(codes)):
code=codes<i>
name=names<i>
#判断条件选择
ret,date=sel_current_code(code,name)
if ret:
name_array=np.append(name_array,name)
code_array=np.append(code_array,code)
date_array=np.append(date_array,date)
earnings,suc,fail,index_array,pct_array,hold_days=test(code,name=name,days=days,begin_count=begin_count,end_count=end_count)
if suc==-1:
continue
name=str.replace(name,'*','')
if earnings>0:
file_dir=path+'CurentAAA%s_%s%d%d++%.1f++%.1f.png'%(code,name,suc,fail,suc*100/(fail+suc+1),earnings)
else:
file_dir=path+'CurentBBB%s_%s%d%d——%.1f——%.1f.png'%(code,name,suc,fail,suc*100/(fail+suc+1),earnings)
plot_pct(code,index_array,pct_array,begin_count=begin_count,end_count=end_count,writefilename=file_dir)
print("codes%s%s earnings%.2f"%(code,name,earnings))
data=[code_array,name_array,date_array]
data=np.transpose(data)#矩阵转置
ser2=pd.DataFrame(data,columns=['ts_code','name','date'])
if writefile:
ser2.to_csv(writefileName,encoding="utf_8_sig")
#绘制指定代码价格图
def plot_pct(ts_code,index_array,pct_array,begin_count,end_count=-1,writefilename='temp.png'):
df=load_data(ts_code)
closes=df['close'].values
ma5=df['ma5'].values
ma10=df['ma10'].values
ma20=df['ma20'].values
if len(closes)<abs(begin_count):
print("TTTTTTT",len(closes),begin_count,ts_code)
begin_count=int(len(closes)*-1)
if len(closes)<end_count*-1:
end_count=-1
if begin_count+30>end_count:
return
x_array=np.linspace(begin_count,end_count,end_count-begin_count,dtype=np.int)
l=len(ma5)
x=len(x_array)
print('len',l,x)
ma5Mean_array=np.linspace(ma5.mean(),ma5.mean(),end_count-begin_count)
#plt.plot(x_array,closes,c='black')
start_date=df['trade_date'].values[begin_count]
end_date=df['trade_date'].values[end_count]
print(start_date,end_date)
name=get_code_name(ts_code)
money_df=get_dates_money(code=ts_code,name=name)
#print(money_df)
name=name.replace('*',"")
#money_df.to_csv(ts_code+name+"moneydf.csv",encoding='utf_8_sig')
money_dates=money_df['trade_date'].values
