def getMAMid(MA_Short, MA_Mid, symbol, K_MIN, indexoffset=0):
#计算ma以及ma_cross
bardata = pd.read_csv(DC.BAR_DATA_PATH + symbol + '\\' + symbol + ' ' +
str(K_MIN) + '.csv')
madf = pd.DataFrame({'close': bardata.close})
madf['utc_time'] = bardata['utc_time']
madf['strtime'] = bardata['strtime']
madf['index'] = bardata['Unnamed: 0'] - indexoffset
#madf['MA_Short'] = MA.calMA(madf['close'], MA_Short)
madf['MA_Mid'] = MA.calMA(madf['close'], MA_Mid)
madf['MA_True'], madf['MA_Cross'] = MA.dfCross(madf, 'close', 'MA_Mid')
#madf.to_csv("mamid.csv")
return madf
def BOLL(df, N=26, M=26, P=2):
print 'start computing DMI'
mid = MA.calMA(df['close'], N)
tmp2 = pd.rolling_std(df['close'], M)
TOP = mid + P * tmp2
BOTTOM = mid - P * tmp2
df1 = pd.DataFrame({
'close': df['close'],
'MID': mid,
'TOP': TOP,
'BOTTOM': BOTTOM
})
print 'DMI compute finished'
return df1
def calDMI(df, N=14, M=6):
high = df.high
low = df.low
close = df.close
closeshift1 = close.shift(1).fillna(0)
open = df.open
c = high - low
d = high - closeshift1
df1 = pd.DataFrame({'c': c, 'd': d})
df1['A'] = df1.max(axis=1)
df1.drop('c', axis=1, inplace=True)
df1.drop('d', axis=1, inplace=True)
df1['B'] = np.abs(low - closeshift1)
df1['C'] = df1.max(axis=1)
# df1.drop('A',axis=1,inplace=True)
# df1.drop('B',axis=1,inplace=True)
df1['TR'] = df1['C'].rolling(N).sum()
# 2、HD=最高价-昨日最高价
# 3、LD=昨日最低价-最低价
HD = high - high.shift(1).fillna(0)
LD = low.shift(1).fillna(0) - low
df1['HD'] = HD
df1['LD'] = LD
# DMP:= SUM(IFELSE(HD>0 && HD>LD,HD,0),N);
# DMM:= SUM(IFELSE(LD>0 && LD>HD,LD,0),N);
df2 = pd.DataFrame({'HD': HD, 'LD': LD})
df2['DMP_1'] = df2[(df2['HD'] > df2['LD']) & (df2['HD'] > 0)]['HD']
df2['DMM_1'] = df2[(df2['LD'] > df2['HD']) & (df2['LD'] > 0)]['LD']
df2 = df2.fillna(0)
df1['DMP'] = df2['DMP_1'].rolling(N).sum()
df1['DMM'] = df2['DMM_1'].rolling(N).sum()
del df2
# 6、PDI=DMP*100/TR
# 7、MDI=DMM*100/TR
df1['PDI'] = df1['DMP'] * 100 / df1['TR']
df1['MDI'] = df1['DMM'] * 100 / df1['TR']
# ADX: MA(ABS(MDI-PDI)/(MDI+PDI)*100,M);
# ADXR:(ADX+REF(ADX,M))/2;
df1['ADX'] = MA.calMA(
np.abs(df1['MDI'] - df1['PDI']) / (df1['MDI'] + df1['PDI']) * 100, M)
df1['ADXR'] = (df1['ADX'] + df1['ADX'].shift(M).fillna(0)) / 2
return df1['PDI'], df1['MDI'], df1['ADX'], df1['ADXR']
def Lvyi3MAWin(symbolinfo, rawdata, paraset):
setname = paraset['Setname']
MA_Short = paraset['MA_Short']
MA_Mid = paraset['MA_Mid']
MA_Long = paraset['MA_Long']
rawdata['Unnamed: 0'] = range(rawdata.shape[0])
beginindex = rawdata.ix[0, 'Unnamed: 0']
#df_MA = MA.MA(rawdata['close'], MA_Short, MA_Mid)
#df_MA.drop('close', axis=1, inplace=True)
#df = pd.concat([rawdata, df_MA], axis=1)
df = rawdata
df['MA_Short'] = MA.calMA(df['close'],MA_Short)
df['MA_Mid'] = MA.calMA(df['close'],MA_Mid)
df['MA_Long'] = MA.calMA(df['close'],MA_Long)
df['MA_True'],df['MA_Cross'] = MA.dfCross(df,'MA_Short','MA_Mid')
# 找出买卖点：
# 1.先找出MA金叉的买卖点
# 2.找到结合判决条件的买点
# 3.从MA买点中滤出真实买卖点
# 取出金叉点
goldcrosslist = pd.DataFrame({'goldcrosstime': df.loc[df['MA_Cross'] == 1, 'strtime']})
goldcrosslist['goldcrossutc'] = df.loc[df['MA_Cross'] == 1, 'utc_time']
goldcrosslist['goldcrossindex'] = df.loc[df['MA_Cross'] == 1, 'Unnamed: 0'] - beginindex
goldcrosslist['goldcrossprice'] = df.loc[df['MA_Cross'] == 1, 'close']
# 取出死叉点
deathcrosslist = pd.DataFrame({'deathcrosstime': df.loc[df['MA_Cross'] == -1, 'strtime']})
deathcrosslist['deathcrossutc'] = df.loc[df['MA_Cross'] == -1, 'utc_time']
deathcrosslist['deathcrossindex'] = df.loc[df['MA_Cross'] == -1, 'Unnamed: 0'] - beginindex
deathcrosslist['deathcrossprice'] = df.loc[df['MA_Cross'] == -1, 'close']
goldcrosslist = goldcrosslist.reset_index(drop=True)
deathcrosslist = deathcrosslist.reset_index(drop=True)
# 生成多仓序列（金叉在前，死叉在后）
if goldcrosslist.ix[0, 'goldcrossindex'] < deathcrosslist.ix[0, 'deathcrossindex']:
longcrosslist = pd.concat([goldcrosslist, deathcrosslist], axis=1)
else: # 如果第一个死叉的序号在金叉前，则要将死叉往上移1格
longcrosslist = pd.concat([goldcrosslist, deathcrosslist.shift(-1)], axis=1)
longcrosslist = longcrosslist.set_index(pd.Index(longcrosslist['goldcrossindex']), drop=True)
# 生成空仓序列（死叉在前，金叉在后）
if deathcrosslist.ix[0, 'deathcrossindex'] < goldcrosslist.ix[0, 'goldcrossindex']:
shortcrosslist = pd.concat([deathcrosslist, goldcrosslist], axis=1)
else: # 如果第一个金叉的序号在死叉前，则要将金叉往上移1格
shortcrosslist = pd.concat([deathcrosslist, goldcrosslist.shift(-1)], axis=1)
shortcrosslist = shortcrosslist.set_index(pd.Index(shortcrosslist['deathcrossindex']), drop=True)
# 取出开多序号和开空序号
openlongindex = df.loc[(df['MA_Cross'] == 1) & (df['MA_Short'] >= df['MA_Long']) & (df['MA_Mid'] >= df['MA_Long'])].index
openshortindex = df.loc[(df['MA_Cross'] == -1) & (df['MA_Short'] <= df['MA_Long']) & (df['MA_Mid'] <= df['MA_Long'])].index
# 从多仓序列中取出开多序号的内容，即为开多操作
longopr = longcrosslist.loc[openlongindex]
longopr['tradetype'] = 1
longopr.rename(columns={'goldcrosstime': 'opentime',
'goldcrossutc': 'openutc',
'goldcrossindex': 'openindex',
'goldcrossprice': 'openprice',
'deathcrosstime': 'closetime',
'deathcrossutc': 'closeutc',
'deathcrossindex': 'closeindex',
'deathcrossprice': 'closeprice'}, inplace=True)
# 从空仓序列中取出开空序号的内容，即为开空操作
shortopr = shortcrosslist.loc[openshortindex]
shortopr['tradetype'] = -1
shortopr.rename(columns={'deathcrosstime': 'opentime',
'deathcrossutc': 'openutc',
'deathcrossindex': 'openindex',
'deathcrossprice': 'openprice',
'goldcrosstime': 'closetime',
'goldcrossutc': 'closeutc',
'goldcrossindex': 'closeindex',
'goldcrossprice': 'closeprice'}, inplace=True)
# 结果分析
result = pd.concat([longopr, shortopr])
result = result.sort_index()
result = result.reset_index(drop=True)
#result.drop(result.shape[0] - 1, inplace=True)
result = result.dropna()
# 去掉跨合约的操作
# 使用单合约，不用再去掉跨合约
#result = removeContractSwap(result, contractswaplist)
slip = symbolinfo.getSlip()
result['ret'] = ((result['closeprice'] - result['openprice']) * result['tradetype']) - slip
result['ret_r'] = result['ret'] / result['openprice']
results = {}
'''
# 使用单合约，策略核心内不再计算结果
if calcResult:
result['commission_fee'], result['per earn'], result['own cash'], result['hands'] = RS.calcResult(result,
symbolinfo,
initialCash,
positionRatio)
endcash = result['own cash'].iloc[-1]
Annual = RS.annual_return(result)
Sharpe = RS.sharpe_ratio(result)
DrawBack = RS.max_drawback(result)[0]
SR = RS.success_rate(result)
max_single_loss_rate = abs(result['ret_r'].min())
results = {
'Setname':setname,
'opentimes': result.shape[0],
'end_cash': endcash,
'SR': SR,
'Annual': Annual,
'Sharpe': Sharpe,
'DrawBack': DrawBack,
'max_single_loss_rate': max_single_loss_rate
}
closeopr = result.loc[:, 'closetime':'tradetype']
return result, df, closeopr, results
'''
return result
#准备画布
_xlength = 8
_ylength = 4
_Fig = plt.figure(figsize=(_xlength, _ylength),
dpi=200,
facecolor=PlotLib.__color_pink__,
edgecolor=PlotLib.__color_navy__,
linewidth=1.0) # Figure 对象
axes = _Fig.add_axes([0.1, 0.1, 0.8, 0.8], facecolor='black')
bardata = DC.getBarData(symbol,
K_MIN,
starttime=starttime,
endtime=endtime)
bardata['ms'] = MA.calMA(bardata['close'], ms)
bardata['mm'] = MA.calMA(bardata['close'], mm)
bardata['ml'] = MA.calMA(bardata['close'], ml)
for w in range(1, len(weeklist)):
weekstart = weeklist[w - 1]
weekend = weeklist[w]
weekstartutc = float(
time.mktime(
time.strptime(weekstart + ' 00:00:00', "%Y-%m-%d %H:%M:%S")))
weekendutc = float(
time.mktime(
time.strptime(weekend + ' 00:00:00', "%Y-%m-%d %H:%M:%S")))
weekbar = bardata.loc[(bardata['utc_time'] >= weekstartutc)
& (bardata['utc_time'] < weekendutc)]
#这里没有reset_index！
