def SMA_dataPre(qx, xnam0, ksgn0):
''' 简单均线策略数据预处理函数
Args:
qx (zwQuantX): zwQuantX数据包
xnam0 (str)：函数标签
ksgn0 (str): 价格列名称，一般是'adj close'
:ivar xcod (int): 股票代码
'''
zwx.sta_dataPre0xtim(qx, xnam0)
#----对各只股票数据，进行预处理，提高后期运算速度
ksgn, qx.priceCalc = ksgn0, ksgn0
#'adj close';
for xcod in zw.stkLibCode:
d20 = zw.stkLib[xcod]
# 计算交易价格kprice和策略分析采用的价格dprice,kprice一般采用次日的开盘价
d20['dprice'] = d20['open'] * d20[ksgn] / d20['close']
d20['kprice'] = d20['dprice'].shift(-1)
#d20['kprice']=d20['dprice']
#
d = qx.staVars[0]
d20 = zwta.MA(d20, d, ksgn)
d = qx.staVars[1]
d20 = zwta.MA(d20, d, ksgn)
#
zw.stkLib[xcod] = d20
if qx.debugMod > 0:
print(d20.tail())
#---
fss = 'tmp\\' + qx.prjName + '_' + xcod + '.csv'
d20.to_csv(fss)
def tur10_dataPre(qx,xnam0,ksgn0):
'''
海龟策略:tur10, 数据预处理函数 说明
当今天的收盘价，大于过去n个交易日中的最高价时，以收盘价买入；
买入后，当收盘价小于过去n个交易日中的最低价时，以收盘价卖出。
Args:
qx (zwQuantX): zwQuantX数据包
xnam0 (str)：函数标签
ksgn0 (str): 价格列名称，一般是'adj close'
'''
zwx.sta_dataPre0xtim(qx,xnam0);
#----对各只股票数据，进行预处理，提高后期运算速度
ksgn,qx.priceCalc=ksgn0,ksgn0; #'adj close';
for xcod in zw.stkLibCode:
d20=zw.stkLib[xcod];
# 计算交易价格kprice和策略分析采用的价格dprice,kprice一般采用次日的开盘价
#d20['dprice']=d20['open']*d20[ksgn]/d20['close']
#d20['kprice']=d20['dprice'].shift(-1)
d20['dprice']=d20['close']
d20['kprice']=d20['dprice']
#
d=qx.staVars[0];ksgn='xhigh0';d20[ksgn]=pd.rolling_max(d20['high'],d)
d=qx.staVars[1];ksgn='xlow0';d20[ksgn]=pd.rolling_min(d20['low'],d)
d20['xhigh']=d20['xhigh0'].shift(1)
d20['xlow']=d20['xlow0'].shift(1)
#
zw.stkLib[xcod]=d20;
if qx.debugMod>0:
print(d20.tail())
#---
fss='tmp\\'+qx.prjName+'_'+xcod+'.csv'
d20.to_csv(fss)
def kdj10_dataPre(qx,xnam0,ksgn0):
'''
KDJ策略
Args:
qx (zwQuantX): zwQuantX数据包
xnam0 (str)：函数标签
ksgn0 (str): 价格列名称，一般是'adj close'
'''
zwx.sta_dataPre0xtim(qx,xnam0);
#----对各只股票数据，进行预处理，提高后期运算速度
ksgn,qx.priceCalc,qx.priceBuy=ksgn0,ksgn0,ksgn0 #'adj close';
for xcod in zw.stkLibCode:
d20=zw.stkLib[xcod];
# 计算交易价格kprice和策略分析采用的价格dprice,kprice一般采用次日的开盘价
#d20['dprice']=d20['open']*d20[ksgn]/d20['close']
#d20['kprice']=d20['dprice'].shift(-1)
d20['dprice']=d20['close']
d20['kprice']=d20['dprice']
#
d=qx.staVars[0];#d2=qx.staVars[1];
d20=zwta.STOD(d20,d,'close');
d20['stod1n']=d20['stod'].shift(1)
d20['stok1n']=d20['stok'].shift(1)
#
zw.stkLib[xcod]=d20;
if qx.debugMod>0:
print(d20.tail())
#---
#fss='tmp\\'+qx.prjName+'_'+xcod+'.csv'
fss=os.path.join('tmp',qx.prjName+'_'+xcod+'.csv')
d20.to_csv(fss)
def BBANDS_dataPre(qx,xnam0,ksgn0):
''' 布林带数据预处理函数
Args:
qx (zwQuantX): zwQuantX数据包
xnam0 (str)：函数标签
ksgn0 (str): 价格列名称，一般是'adj close'
'''
zwx.sta_dataPre0xtim(qx,xnam0);
ksgn,qx.priceCalc=ksgn0,ksgn0; #'adj close';'close';
for xcod in zw.stkLibCode:
d20=zw.stkLib[xcod];
#
#d20['dprice']=d20['open']*d20['adj close']/d20['close']
d20['dprice']=d20[ksgn]
d20['kprice']=d20['dprice']
#d20['kprice']=d20['dprice'].shift(-1)
#d20['kprice']=d20['open'].shift(-1)
#
dnum=qx.staVars[0];
d20=zwta.BBANDS_UpLow(d20,dnum,ksgn)
#---
zw.stkLib[xcod]=d20;
if qx.debugMod>0:
print(d20.tail())
#fss='tmp\\'+qx.prjName+'_'+xcod+'.csv'
fss=os.path.join('tmp',qx.prjName+'_'+xcod+'.csv')
d20.to_csv(fss)
def rsi10_dataPre(qx, xnam0, ksgn0):
'''
RSI策略, 数据预处理函数
Args:
qx (zwQuantX): zwQuantX数据包
xnam0 (str)：函数标签
ksgn0 (str): 价格列名称，一般是'adj close'
'''
zwx.sta_dataPre0xtim(qx, xnam0)
#----对各只股票数据，进行预处理，提高后期运算速度
ksgn, qx.priceCalc, qx.priceBuy = ksgn0, ksgn0, ksgn0 #'adj close';
for xcod in zw.stkLibCode:
d20 = zw.stkLib[xcod]
# 计算交易价格kprice和策略分析采用的价格dprice,kprice一般采用次日的开盘价
#d20['dprice']=d20['open']*d20[ksgn]/d20['close']
#d20['kprice']=d20['dprice'].shift(-1)
d20['dprice'] = d20['close']
d20['kprice'] = d20['dprice']
#
d = qx.staVars[0]
#d2=qx.staVars[1];
d20 = zwta.RSI(d20, d)
#d20['macd1n']=d20['macd'].shift(1)
#d20['msign1n']=d20['msign'].shift(1)
#
zw.stkLib[xcod] = d20
if qx.debugMod > 0:
print(d20.tail())
#---
fss = 'tmp\\' + qx.prjName + '_' + xcod + '.csv'
d20.to_csv(fss)
def BBANDS_dataPre(qx,xnam0,ksgn0):
''' 布林带数据预处理函数
Args:
qx (zwQuantX): zwQuantX数据包
xnam0 (str)：函数标签
ksgn0 (str): 价格列名称，一般是'adj close'
'''
zwx.sta_dataPre0xtim(qx,xnam0);
ksgn,qx.priceCalc=ksgn0,ksgn0; #'adj close';'close';
for xcod in zw.stkLibCode:
d20=zw.stkLib[xcod];
#
#d20['dprice']=d20['open']*d20['adj close']/d20['close']
d20['dprice']=d20[ksgn]
d20['kprice']=d20['dprice']
#d20['kprice']=d20['dprice'].shift(-1)
#d20['kprice']=d20['open'].shift(-1)
#
dnum=qx.staVars[0];
d20=zwta.BBANDS_UpLow(d20,dnum,ksgn)
#---
zw.stkLib[xcod]=d20;
if qx.debugMod>0:
print(d20.tail())
fss='tmp\\'+qx.prjName+'_'+xcod+'.csv'
d20.to_csv(fss)
def rsi10_dataPre(qx, xnam0, ksgn0):
'''
RSI策略, 資料預處理函數
Args:
qx (zwQuantX): zwQuantX數據包
xnam0 (str)：函數標籤
ksgn0 (str): 價格列名稱，一般是'adj close'
'''
zwx.sta_dataPre0xtim(qx, xnam0)
#----對各檔股票資料，進行預處理，提高後期運算速度
ksgn, qx.priceCalc, qx.priceBuy = ksgn0, ksgn0, ksgn0 #'adj close';
for xcod in zw.stkLibCode:
d20 = zw.stkLib[xcod]
# 計算交易價格kprice和策略分析採用的價格dprice,kprice一般採用次日的開盤價
#d20['dprice']=d20['open']*d20[ksgn]/d20['close']
#d20['kprice']=d20['dprice'].shift(-1)
d20['dprice'] = d20['close']
d20['kprice'] = d20['dprice']
#
d = qx.staVars[0]
#d2=qx.staVars[1];
d20 = zwta.RSI(d20, d)
#d20['macd1n']=d20['macd'].shift(1)
#d20['msign1n']=d20['msign'].shift(1)
#
zw.stkLib[xcod] = d20
if qx.debugMod > 0:
print(d20.tail())
#---
fss = 'tmp\\' + qx.prjName + '_' + xcod + '.csv'
d20.to_csv(fss)
def BBANDS_dataPre(qx, xnam0, ksgn0):
''' 布林帶資料預處理函數
Args:
qx (zwQuantX): zwQuantX數據包
xnam0 (str)：函數標籤
ksgn0 (str): 價格列名稱，一般是'adj close'
'''
zwx.sta_dataPre0xtim(qx, xnam0)
ksgn, qx.priceCalc = ksgn0, ksgn0
#'adj close';'close';
for xcod in zw.stkLibCode:
d20 = zw.stkLib[xcod]
#
#d20['dprice']=d20['open']*d20['adj close']/d20['close']
d20['dprice'] = d20[ksgn]
d20['kprice'] = d20['dprice']
#d20['kprice']=d20['dprice'].shift(-1)
#d20['kprice']=d20['open'].shift(-1)
#
dnum = qx.staVars[0]
d20 = zwta.BBANDS_UpLow(d20, dnum, ksgn)
#---
zw.stkLib[xcod] = d20
if qx.debugMod > 0:
print(d20.tail())
fss = 'tmp\\' + qx.prjName + '_' + xcod + '.csv'
d20.to_csv(fss)
def CMA_dataPre(qx,xnam0,ksgn0):
''' 均线交叉策略数据预处理函数
Args:
qx (zwQuantX): zwQuantX数据包
xnam0 (str)：函数标签
ksgn0 (str): 价格列名称，一般是'adj close'
'''
zwx.sta_dataPre0xtim(qx,xnam0);
#----对各只股票数据，进行预处理，提高后期运算速度
ksgn,qx.priceCalc=ksgn0,ksgn0; #'adj close';
for xcod in zw.stkLibCode:
d20=zw.stkLib[xcod];
#---------------dprice,kprice
#d20['dprice']=d20['open']*d20['adj close']/d20['close']
d20['dprice']=d20[ksgn]
d20['kprice']=d20['dprice']
#d20['kprice']=d20['dprice'].shift(-1)
#
d=qx.staVars[0];d20=zwta.MA(d20,d,ksgn);k0ma='ma_%d' %qx.staVars[0]
#d=qx.staVars[1];d20=zwta.MA(d20,d,ksgn);k1ma='ma_%d' %qx.staVars[1]
#
#d20['ma1n']=d20[k0ma].shift(1)
d20['ma2n']=d20[k0ma].shift(2)
#d20['dp1n']=d20['dprice'].shift(1)
d20['dp2n']=d20['dprice'].shift(2)
#---
d20=np.round(d20,3);
zw.stkLib[xcod]=d20;
if qx.debugMod>0:
print(d20.tail())
fss='tmp\\'+qx.prjName+'_'+xcod+'.csv'
d20.to_csv(fss)
def SMA_dataPre(qx, xnam0, ksgn0):
''' 簡單均線策略資料預處理函數
Args:
qx (zwQuantX): zwQuantX數據包
xnam0 (str)：函數標籤
ksgn0 (str): 價格列名稱，一般是'adj close'
:ivar xcod (int): 股票代碼
'''
zwx.sta_dataPre0xtim(qx, xnam0)
#----對各檔股票資料，進行預處理，提高後期運算速度
ksgn, qx.priceCalc = ksgn0, ksgn0
#'adj close';
for xcod in zw.stkLibCode:
d20 = zw.stkLib[xcod]
# 計算交易價格kprice和策略分析採用的價格dprice,kprice一般採用次日的開盤價
d20['dprice'] = d20['open'] * d20[ksgn] / d20['close']
d20['kprice'] = d20['dprice'].shift(-1)
#d20['kprice']=d20['dprice']
#
d = qx.staVars[0]
d20 = zwta.MA(d20, d, ksgn)
d = qx.staVars[1]
d20 = zwta.MA(d20, d, ksgn)
#
zw.stkLib[xcod] = d20
if qx.debugMod > 0:
print(d20.tail())
#---
fss = 'tmp\\' + qx.prjName + '_' + xcod + '.csv'
d20.to_csv(fss)
def rsi10_dataPre(qx,xnam0,ksgn0):
'''
RSI策略, 数据预处理函数
Args:
qx (zwQuantX): zwQuantX数据包
xnam0 (str)：函数标签
ksgn0 (str): 价格列名称，一般是'adj close'
'''
zwx.sta_dataPre0xtim(qx,xnam0);
#----对各只股票数据，进行预处理，提高后期运算速度
ksgn,qx.priceCalc,qx.priceBuy=ksgn0,ksgn0,ksgn0 #'adj close';
for xcod in zw.stkLibCode:
d20=zw.stkLib[xcod];
# 计算交易价格kprice和策略分析采用的价格dprice,kprice一般采用次日的开盘价
#d20['dprice']=d20['open']*d20[ksgn]/d20['close']
#d20['kprice']=d20['dprice'].shift(-1)
d20['dprice']=d20['close']
d20['kprice']=d20['dprice']
#
d=qx.staVars[0];#d2=qx.staVars[1];
d20=zwta.RSI(d20,d);
#d20['macd1n']=d20['macd'].shift(1)
#d20['msign1n']=d20['msign'].shift(1)
#
zw.stkLib[xcod]=d20;
if qx.debugMod>0:
print(d20.tail())
#---
fss='tmp\\'+qx.prjName+'_'+xcod+'.csv'
d20.to_csv(fss)
def tur10_dataPre(qx,xnam0,ksgn0):
''' 策略.数据预处理函数 说明
Args:
qx (zwQuantX): zwQuantX数据包
xnam0 (str)：函数标签
ksgn0 (str): 价格列名称，一般是'adj close'
'''
zwx.sta_dataPre0xtim(qx,xnam0);
#----对各只股票数据，进行预处理，提高后期运算速度
ksgn,qx.priceCalc=ksgn0,ksgn0; #'adj close';
for xcod in zw.stkLibCode:
d20=zw.stkLib[xcod];
# 计算交易价格kprice和策略分析采用的价格dprice,kprice一般采用次日的开盘价
#d20['dprice']=d20['open']*d20[ksgn]/d20['close']
d20['dprice']=d20['close']
#d20['kprice']=d20['dprice'].shift(-1)
d20['kprice']=d20['dprice']
#
d=qx.staVars[0];d20=zwta.MA(d20,d,ksgn);
d=qx.staVars[1];d20=zwta.MA(d20,d,ksgn);
#
zw.stkLib[xcod]=d20;
if qx.debugMod>0:
print(d20.tail())
#---
fss='tmp\\'+qx.prjName+'_'+xcod+'.csv'
d20.to_csv(fss)
def SMA_dataPre(qx,xnam0,ksgn0):
''' 简单均线策略数据预处理函数
Args:
qx (zwQuantX): zwQuantX数据包
xnam0 (str)：函数标签
ksgn0 (str): 价格列名称，一般是'adj close'
:ivar xcod (int): 股票代码
'''
zwx.sta_dataPre0xtim(qx,xnam0);
#----对各只股票数据，进行预处理，提高后期运算速度
ksgn,qx.priceCalc=ksgn0,ksgn0; #'adj close';
for xcod in zw.stkLibCode:
d20=zw.stkLib[xcod];
# 计算交易价格kprice和策略分析采用的价格dprice,kprice一般采用次日的开盘价
d20['dprice']=d20['open']*d20[ksgn]/d20['close']
d20['kprice']=d20['dprice'].shift(-1)
#d20['kprice']=d20['dprice']
#
d=qx.staVars[0];d20=zwta.MA(d20,d,ksgn);
d=qx.staVars[1];d20=zwta.MA(d20,d,ksgn);
#
zw.stkLib[xcod]=d20;
if qx.debugMod>0:
print(d20.tail())
#---
fss='tmp\\'+qx.prjName+'_'+xcod+'.csv'
d20.to_csv(fss)
def CMA_dataPre(qx,xnam0,ksgn0):
''' 均线交叉策略数据预处理函数
Args:
qx (zwQuantX): zwQuantX数据包
xnam0 (str)：函数标签
ksgn0 (str): 价格列名称，一般是'adj close'
'''
zwx.sta_dataPre0xtim(qx,xnam0);
#----对各只股票数据，进行预处理，提高后期运算速度
ksgn,qx.priceCalc=ksgn0,ksgn0; #'adj close';
for xcod in zw.stkLibCode:
d20=zw.stkLib[xcod];
#---------------dprice,kprice
#d20['dprice']=d20['open']*d20['adj close']/d20['close']
d20['dprice']=d20[ksgn]
d20['kprice']=d20['dprice']
#d20['kprice']=d20['dprice'].shift(-1)
#
d=qx.staVars[0];d20=zwta.MA(d20,d,ksgn);k0ma='ma_%d' %qx.staVars[0]
#d=qx.staVars[1];d20=zwta.MA(d20,d,ksgn);k1ma='ma_%d' %qx.staVars[1]
#
#d20['ma1n']=d20[k0ma].shift(1)
d20['ma2n']=d20[k0ma].shift(2)
#d20['dp1n']=d20['dprice'].shift(1)
d20['dp2n']=d20['dprice'].shift(2)
#---
d20=np.round(d20,3);
zw.stkLib[xcod]=d20;
if qx.debugMod>0:
print(d20.tail())
fss='tmp\\'+qx.prjName+'_'+xcod+'.csv'
d20.to_csv(fss)
def VWAP_dataPre(qx, xnam0, ksgn0):
'''
vwap 資料預處理函數,vwap策略，成交量加權平均價
Args:
qx (zwQuantX): zwQuantX數據包
xnam0 (str)：函數標籤
ksgn0 (str): 價格列名稱，一般是'adj close'
'''
zwx.sta_dataPre0xtim(qx, xnam0)
#
ksgn, qx.priceCalc = ksgn0, ksgn0
#'adj close';'close';
for xcod in zw.stkLibCode:
d20 = zw.stkLib[xcod]
#---------------dprice,kprice
#d20['dprice']=d20['open']*d20['adj close']/d20['close']
d20['dprice'] = d20[ksgn]
#d20['kprice']=d20['dprice'].shift(-1)
#d20['kprice']=d20['dprice'].shift(-1)
d20['kprice'] = d20['open'].shift(-1)
#
#d=qx.staVars[0];d20=zwta.MA(d20,d,ksgn);
#d=qx.staVars[1];d20=zwta.MA(d20,d,ksgn);
#
#d20=zwta.MA(d20,qx.staMA_short,'adj close');
#d20=zwta.MA(d20,qx.staMA_long,'adj close');
#ksma='ma_'+str(qx.staMA_long);
#d20['ma1n']=d20[ksma].shift(1)
#d20['ma1n']=d20[ksma]
#
#---------------dprice,kprice
#d20['dprice']=d20['open']*d20['adj close']/d20['close']
#d20['dprice']=d20['adj close']
#d20['kprice']=d20['dprice']
#vwap,基於成交量的加權平均價
#vwap = (prices * volume).sum(n) / volume.sum(n) #sum函數自動忽略NaN值
#vwapWindowSize,threshold
#qx.staVarLst=[15,0.01]#
nwin = qx.staVars[0]
d20['vw_sum'] = pd.rolling_sum(d20['dprice'] * d20['volume'], nwin)
d20['vw_vol'] = pd.rolling_sum(d20['volume'], nwin)
d20['vwap'] = d20['vw_sum'] / d20['vw_vol']
#---
zw.stkLib[xcod] = d20
if qx.debugMod > 0:
print(d20.tail())
fss = 'tmp\\' + qx.prjName + '_' + xcod + '.csv'
d20.to_csv(fss)
def VWAP_dataPre(qx,xnam0,ksgn0):
'''
vwap 数据预处理函数,vwap策略，成交量加权平均价
Args:
qx (zwQuantX): zwQuantX数据包
xnam0 (str)：函数标签
ksgn0 (str): 价格列名称，一般是'adj close'
'''
zwx.sta_dataPre0xtim(qx,xnam0);
#
ksgn,qx.priceCalc=ksgn0,ksgn0; #'adj close';'close';
for xcod in zw.stkLibCode:
d20=zw.stkLib[xcod];
#---------------dprice,kprice
#d20['dprice']=d20['open']*d20['adj close']/d20['close']
d20['dprice']=d20[ksgn]
#d20['kprice']=d20['dprice'].shift(-1)
#d20['kprice']=d20['dprice'].shift(-1)
d20['kprice']=d20['open'].shift(-1)
#
#d=qx.staVars[0];d20=zwta.MA(d20,d,ksgn);
#d=qx.staVars[1];d20=zwta.MA(d20,d,ksgn);
#
#d20=zwta.MA(d20,qx.staMA_short,'adj close');
#d20=zwta.MA(d20,qx.staMA_long,'adj close');
#ksma='ma_'+str(qx.staMA_long);
#d20['ma1n']=d20[ksma].shift(1)
#d20['ma1n']=d20[ksma]
#
#---------------dprice,kprice
#d20['dprice']=d20['open']*d20['adj close']/d20['close']
#d20['dprice']=d20['adj close']
#d20['kprice']=d20['dprice']
#vwap,基于成交量的加权平均价
#vwap = (prices * volume).sum(n) / volume.sum(n) #sum函数自动忽略NaN值
#vwapWindowSize,threshold
#qx.staVarLst=[15,0.01]#
nwin=qx.staVars[0];
d20['vw_sum']=pd.rolling_sum(d20['dprice']*d20['volume'],nwin);
d20['vw_vol']=pd.rolling_sum(d20['volume'],nwin);
d20['vwap']=d20['vw_sum']/d20['vw_vol']
#---
zw.stkLib[xcod]=d20;
if qx.debugMod>0:
print(d20.tail())
#fss='tmp\\'+qx.prjName+'_'+xcod+'.csv'
fss=os.path.join('tmp',qx.prjName+'_'+xcod+'.csv')
d20.to_csv(fss)
def VWAP_dataPre(qx,xnam0,ksgn0):
'''
vwap 数据预处理函数,vwap策略，成交量加权平均价
Args:
qx (zwQuantX): zwQuantX数据包
xnam0 (str)：函数标签
ksgn0 (str): 价格列名称，一般是'adj close'
'''
zwx.sta_dataPre0xtim(qx,xnam0);
#
ksgn,qx.priceCalc=ksgn0,ksgn0; #'adj close';'close';
for xcod in zw.stkLibCode:
d20=zw.stkLib[xcod];
#---------------dprice,kprice
#d20['dprice']=d20['open']*d20['adj close']/d20['close']
d20['dprice']=d20[ksgn]
#d20['kprice']=d20['dprice'].shift(-1)
#d20['kprice']=d20['dprice'].shift(-1)
d20['kprice']=d20['open'].shift(-1)
#
#d=qx.staVars[0];d20=zwta.MA(d20,d,ksgn);
#d=qx.staVars[1];d20=zwta.MA(d20,d,ksgn);
#
#d20=zwta.MA(d20,qx.staMA_short,'adj close');
#d20=zwta.MA(d20,qx.staMA_long,'adj close');
#ksma='ma_'+str(qx.staMA_long);
#d20['ma1n']=d20[ksma].shift(1)
#d20['ma1n']=d20[ksma]
#
#---------------dprice,kprice
#d20['dprice']=d20['open']*d20['adj close']/d20['close']
#d20['dprice']=d20['adj close']
#d20['kprice']=d20['dprice']
#vwap,基于成交量的加权平均价
#vwap = (prices * volume).sum(n) / volume.sum(n) #sum函数自动忽略NaN值
#vwapWindowSize,threshold
#qx.staVarLst=[15,0.01]#
nwin=qx.staVars[0];
d20['vw_sum']=pd.rolling_sum(d20['dprice']*d20['volume'],nwin);
d20['vw_vol']=pd.rolling_sum(d20['volume'],nwin);
d20['vwap']=d20['vw_sum']/d20['vw_vol']
#---
zw.stkLib[xcod]=d20;
if qx.debugMod>0:
print(d20.tail())
fss='tmp\\'+qx.prjName+'_'+xcod+'.csv'
d20.to_csv(fss)
def tim0Trade_dataPre(qx,xnam0,ksgn0):
zwx.sta_dataPre0xtim(qx,xnam0);
#-------
ksgn,qx.priceCalc=ksgn0,ksgn0; #'adj close';
for xcod in zw.stkLibCode:
d20=zw.stkLib[xcod];
# 计算交易价格kprice和策略分析采用的价格dprice,kprice一般采用次日的开盘价
#d20['dprice']=d20['open']*d20[ksgn]/d20['close']
d20['dprice']=d20[ksgn]
#d20['kprice']=d20['dprice'].shift(-1)
d20['kprice']=d20['dprice']
#
d=qx.staVars[0];d20=zwta.MA(d20,d,ksgn);
d=qx.staVars[1];d20=zwta.MA(d20,d,ksgn);
#
zw.stkLib[xcod]=d20;
if qx.debugMod>0:
print(d20.tail())
fss='tmp\\'+qx.prjName+'_'+xcod+'.csv'
d20.to_csv(fss)
def tur10_dataPre(qx, xnam0, ksgn0):
'''
海龜策略:deal_stock_num, 資料預處理函數 說明
當今天的收盤價，大於過去n個交易日中的最高價時，以收盤價買入；
買入後，當收盤價小於過去n個交易日中的最低價時，以收盤價賣出。
Args:
qx (zwQuantX): zwQuantX數據包
xnam0 (str)：函數標籤
ksgn0 (str): 價格列名稱，一般是'adj close'
'''
#zwsta.zwx.sta_dataPre0xtim(qx,xnam0);
zwx.sta_dataPre0xtim(qx, xnam0)
#----對各檔股票資料，進行預處理，提高後期運算速度
ksgn, qx.priceCalc = ksgn0, ksgn0
#'adj close';
for xcod in zw.stkLibCode:
d20 = zw.stkLib[xcod]
# 計算交易價格kprice和策略分析採用的價格dprice,kprice一般採用次日的開盤價
#d20['dprice']=d20['open']*d20[ksgn]/d20['close']
#d20['kprice']=d20['dprice'].shift(-1)
d20['dprice'] = d20['close']
d20['kprice'] = d20['dprice']
#
d = qx.staVars[0]
ksgn = 'xhigh0'
d20[ksgn] = pd.rolling_max(d20['high'], d)
d = qx.staVars[1]
ksgn = 'xlow0'
d20[ksgn] = pd.rolling_min(d20['low'], d)
d20['xhigh'] = d20['xhigh0'].shift(1)
d20['xlow'] = d20['xlow0'].shift(1)
#
zw.stkLib[xcod] = d20
if qx.debugMod > 0:
print(d20.tail())
#---
fss = 'tmp\\' + qx.prjName + '_' + xcod + '.csv'
d20.to_csv(fss)
def CMA_dataPre(qx, xnam0, ksgn0):
''' 均線交叉策略資料預處理函數
Args:
qx (zwQuantX): zwQuantX數據包
xnam0 (str)：函數標籤
ksgn0 (str): 價格列名稱，一般是'adj close'
'''
zwx.sta_dataPre0xtim(qx, xnam0)
#----對各檔股票資料，進行預處理，提高後期運算速度
ksgn, qx.priceCalc = ksgn0, ksgn0
#'adj close';
for xcod in zw.stkLibCode:
d20 = zw.stkLib[xcod]
#---------------dprice,kprice
#d20['dprice']=d20['open']*d20['adj close']/d20['close']
d20['dprice'] = d20[ksgn]
d20['kprice'] = d20['dprice']
#d20['kprice']=d20['dprice'].shift(-1)
#
d = qx.staVars[0]
d20 = zwta.MA(d20, d, ksgn)
k0ma = 'ma_%d' % qx.staVars[0]
#d=qx.staVars[1];d20=zwta.MA(d20,d,ksgn);k1ma='ma_%d' %qx.staVars[1]
#
#d20['ma1n']=d20[k0ma].shift(1)
d20['ma2n'] = d20[k0ma].shift(2)
#d20['dp1n']=d20['dprice'].shift(1)
d20['dp2n'] = d20['dprice'].shift(2)
#---
d20 = np.round(d20, 3)
zw.stkLib[xcod] = d20
if qx.debugMod > 0:
print(d20.tail())
fss = 'tmp\\' + qx.prjName + '_' + xcod + '.csv'
d20.to_csv(fss)
def tur10_dataPre(qx,xnam0,ksgn0):
'''
海龟策略:deal_stock_num, 数据预处理函数 说明
当今天的收盘价，大于过去n个交易日中的最高价时，以收盘价买入；
买入后，当收盘价小于过去n个交易日中的最低价时，以收盘价卖出。
Args:
qx (zwQuantX): zwQuantX数据包
xnam0 (str)：函数标签
ksgn0 (str): 价格列名称，一般是'adj close'
'''
#zwsta.zwx.sta_dataPre0xtim(qx,xnam0);
zwx.sta_dataPre0xtim(qx,xnam0);
#----对各只股票数据，进行预处理，提高后期运算速度
ksgn,qx.priceCalc=ksgn0,ksgn0; #'adj close';
for xcod in zw.stkLibCode:
d20=zw.stkLib[xcod];
# 计算交易价格kprice和策略分析采用的价格dprice,kprice一般采用次日的开盘价
#d20['dprice']=d20['open']*d20[ksgn]/d20['close']
#d20['kprice']=d20['dprice'].shift(-1)
d20['dprice']=d20['close']
d20['kprice']=d20['dprice']
#
d=qx.staVars[0];ksgn='xhigh0';d20[ksgn]=pd.rolling_max(d20['high'],d)
d=qx.staVars[1];ksgn='xlow0';d20[ksgn]=pd.rolling_min(d20['low'],d)
d20['xhigh']=d20['xhigh0'].shift(1)
d20['xlow']=d20['xlow0'].shift(1)
#
zw.stkLib[xcod]=d20;
if qx.debugMod>0:
print(d20.tail())
#---
fss='tmp\\'+qx.prjName+'_'+xcod+'.csv'
d20.to_csv(fss)
# -*- coding: utf-8 -*-
#zwQuant
import zwSys as zw
import zwQTBox as zwx
import zwBacktest as zwbt
#=======================
rss='dat\\' #rss='\\zwdat\\cn\\day\\'
xlst=['600401'] #600401,*ST海润
qx=zwbt.bt_init(xlst,rss,'inx',10000);
#读取大盘指数
qx.stkInxCode,qx.stkInxName,qx.stkInxCName='000001','sh001','上证指数'
zwx.stkInxLibRd(qx)
#切割大盘指数
#zwx.stkInxLibSet8XTim(qx,qx.xtim0,qx.xtim9)
qx.staVars=[0,'','']
zwx.sta_dataPre0xtim(qx,'inx')
#输出大盘指数
print(zw.stkInxLib)
