def train(high, low, dopen, close, vol, ratio, set_bool, methodss):
datasets = get_factors(high, low, close, vol, set_bool)
#train_data,test_data=train_test_split(datasets)
label = generate_label(close, sign=1)
methods = [
"DecisionTreeClassifier", "MLPClassifier", "KNeighborsClassifier",
"AdaBoostClassifier", "RandomForestClassifier",
"GradientBoostingClassifier"
]
for i in range(len(methods)):
if methodss == methods[i]:
num = i
#train_label,test_label=train_test_split(label)
pred = []
true_value = []
start = int(len(datasets) * ratio)
for i in range(start, len(datasets) - 1):
#print(datasets)
#print(label)
method[num].fit(datasets[:i, :], label[:i])
pred.append(method[num].predict(np.array(datasets[i]).reshape(1, -1)))
true_value.append(label[i])
#print(classification_report(np.array(true_value),pred))
return classification_report(np.array(true_value), pred)
def train(high, low, dopen, close, vol, ratio, set_bool):
BBANDS_1 = False
DEMA_1 = False
EMA_1 = False
HT_TRENDLINE_1 = False
KAMA_1 = False
MA_1 = False
MAMA_1 = False
MIDPOINT_1 = False
MIDPRICE_1 = False
SAR_1 = False
SAREXT_1 = False
SMA_1 = False
T3_1 = False
TEMA_1 = False
TRIMA_1 = False
WMA_1 = False
AD_1 = False
ADOSC_1 = False
OBV_1 = False
HT_DCPERIOD_1 = False
HT_DCPHASE_1 = False
HT_PHASOR_1 = False
HT_SINE_1 = False
HT_TRENDMODE_1 = False
AVGPRICE_1 = False
MEDPRICE_1 = False
TYPPRICE_1 = False
WCLPRICE_1 = False
ATR_1 = False
NATR_1 = False
TRANGE_1 = False
ADX_1 = False
ADXR_1 = False
APO_1 = False
AROON_1 = False
AROONOSC_1 = False
BOP_1 = False
CCI_1 = False
CMO_1 = False
DX_1 = False
MACD_1 = False
ivergence_1 = False
MACDEXT_1 = False
MACDFIX_1 = False
MFI, MINUS_DI_1 = False
MINUS_DM_1 = False
MOM_1 = False
PLUS_DI_1 = False
PLUS_DM_1 = False
PPO_1 = False
ROC_1 = False
ROCP_1 = False
ROCR_1 = False
ROCR100_1 = False
RSI_1 = False
STOCH_1 = False
STOCHF_1 = False
STOCHRSI_1 = False
TRIX_1 = False
ULTOSC_1 = False
WILLR_1 = False
if 'BBANDS' in set_bool:
BBANDS_1 = True
if 'DEMA' in set_bool:
DEMA_1 = True
if 'EMA' in set_bool:
EMA_1 = True
if 'HT_TRENDLINE' in set_bool:
HT_TRENDLINE_1 = True
if 'KAMA' in set_bool:
KAMA_1 = True
if 'MA' in set_bool:
MA_1 = True
if 'MAMA' in set_bool:
MAMA_1 = True
if 'MIDPOINT' in set_bool:
MIDPOINT_1 = True
if 'MIDPRICE' in set_bool:
MIDPRICE_1 = True
if 'SAR' in set_bool:
SAR_1 = True
if 'SAREXT' in set_bool:
SAREXT_1 = True
if 'SMA' in set_bool:
SMA_1 = True
if 'T3' in set_bool:
T3_1 = True
if 'TEMA' in set_bool:
TEMA_1 = True
if 'TRIMA' in set_bool:
TRIMA_1 = True
if 'WMA' in set_bool:
WMA_1 = True
if 'AD' in set_bool:
AD_1 = True
if 'ADOSC' in set_bool:
ADOSC_1 = True
if 'OBV' in set_bool:
OBV_1 = True
if 'HT_DCPERIOD' in set_bool:
HT_DCPERIOD_1 = True
if 'HT_DCPHASE' in set_bool:
HT_DCPHASE_1 = True
if 'HT_PHASOR' in set_bool:
HT_PHASOR_1 = True
if 'HT_SINE' in set_bool:
HT_SINE_1 = True
if 'HT_TRENDMODE' in set_bool:
HT_TRENDMODE_1 = True
if 'AVGPRICE' in set_bool:
AVGPRICE_1 = True
if 'MEDPRICE' in set_bool:
MEDPRICE_1 = True
if 'TYPPRICE' in set_bool:
TYPPRICE_1 = True
if 'WCLPRICE' in set_bool:
WCLPRICE_1 = True
if 'ATR' in set_bool:
ATR_1 = True
if 'NATR' in set_bool:
NATR_1 = True
if 'TRANGE' in set_bool:
TRANGE_1 = True
if 'ADX' in set_bool:
ADX_1 = True
if 'ADXR' in set_bool:
ADXR_1 = True
if 'APO' in set_bool:
APO_1 = True
if 'AROON' in set_bool:
AROON_1 = True
if 'AROONOSC' in set_bool:
AROONOSC_1 = True
if 'BOP' in set_bool:
BOP_1 = True
if 'CCI' in set_bool:
CCI_1 = True
if 'CMO' in set_bool:
CMO_1 = True
if 'DX' in set_bool:
DX_1 = True
if 'MACD' in set_bool:
MACD_1 = True
if 'ivergence' in set_bool:
ivergence_1 = True
if 'MACDEXT' in set_bool:
MACDEXT_1 = True
if 'MACDFIX' in set_bool:
MACDFIX_1 = True
if 'MFI,MINUS_DI' in set_bool:
MFI, MINUS_DI_1 = True
if 'MINUS_DM' in set_bool:
MINUS_DM_1 = True
if 'MOM' in set_bool:
MOM_1 = True
if 'PLUS_DI' in set_bool:
PLUS_DI_1 = True
if 'PLUS_DM' in set_bool:
PLUS_DM_1 = True
if 'PPO' in set_bool:
PPO_1 = True
if 'ROC' in set_bool:
ROC_1 = True
if 'ROCP' in set_bool:
ROCP_1 = True
if 'ROCR' in set_bool:
ROCR_1 = True
if 'ROCR100' in set_bool:
ROCR100_1 = True
if 'RSI' in set_bool:
RSI_1 = True
if 'STOCH' in set_bool:
STOCH_1 = True
if 'STOCHF' in set_bool:
STOCHF_1 = True
if 'STOCHRSI' in set_bool:
STOCHRSI_1 = True
if 'TRIX' in set_bool:
TRIX_1 = True
if 'ULTOSC' in set_bool:
ULTOSC_1 = True
if 'WILLR' in set_bool:
WILLR_1 = True
datasets = get_factors(high=high,
low=low,
close=close,
volume=vol,
BBANDS=BBANDS_1,
DEMA=DEMA_1,
EMA=EMA_1,
HT_TRENDLINE=HT_TRENDLINE_1,
KAMA=KAMA_1,
MA=MA_1,
MAMA=MAMA_1,
MIDPOINT=MIDPOINT_1,
MIDPRICE=MIDPRICE_1,
SAR=SAR_1,
SAREXT=SAREXT_1,
SMA=SMA_1,
T3=T3_1,
TEMA=TEMA_1,
TRIMA=TRIMA_1,
WMA=WMA_1,
AD=AD_1,
ADOSC=ADOSC_1,
OBV=OBV_1,
HT_DCPERIOD=HT_DCPERIOD_1,
HT_DCPHASE=HT_DCPHASE_1,
HT_PHASOR=HT_PHASOR_1,
HT_SINE=HT_SINE_1,
HT_TRENDMODE=HT_TRENDMODE_1,
AVGPRICE=AVGPRICE_1,
MEDPRICE=MEDPRICE_1,
TYPPRICE=TYPPRICE_1,
WCLPRICE=WCLPRICE_1,
ATR=ATR_1,
NATR=NATR_1,
TRANGE=TRANGE_1,
ADX=ADX_1,
ADXR=ADXR_1,
APO=APO_1,
AROON=AROON_1,
AROONOSC=AROONOSC_1,
BOP=BOP_1,
CCI=CCI_1,
CMO=CMO_1,
DX=DX_1,
MACD=MACD_1,
ivergence=ivergence_1,
MACDEXT=MACDEXT_1,
MACDFIX=MACDFIX_1,
MFI,
MINUS_DI=MFI,
MINUS_DI_1,
MINUS_DM=MINUS_DM_1,
MOM=MOM_1,
PLUS_DI=PLUS_DI_1,
PLUS_DM=PLUS_DM_1,
PPO=PPO_1,
ROC=ROC_1,
ROCP=ROCP_1,
ROCR=ROCR_1,
ROCR100=ROCR100_1,
RSI=RSI_1)
#train_data,test_data=train_test_split(datasets)
label = generate_label(close, sign=1)
#train_label,test_label=train_test_split(label)
pred = []
true_value = []
start = int(len(datasets) * ratio)
for i in range(start, len(datasets) - 1):
#print(datasets)
#print(label)
method[5].fit(datasets[:i, :], label[:i])
pred.append(method[5].predict(np.array(datasets[i]).reshape(1, -1)))
true_value.append(label[i])
#print(classification_report(np.array(true_value),pred))
return classification_report(np.array(true_value), pred)
'603989', '603990', '603991', '603993', '603996', '603997', '603998',
'603999', '900901', '900902', '900903', '900904', '900905', '900906',
'900907', '900908', '900909', '900910', '900911', '900912', '900913',
'900914', '900915', '900916', '900917', '900918', '900919', '900920',
'900921', '900922', '900923', '900924', '900925', '900926', '900927',
'900928', '900929', '900930', '900932', '900933', '900934', '900936',
'900937', '900938', '900939', '900940', '900941', '900942', '900943',
'900945', '900946', '900947', '900948', '900951', '900952', '900953',
'900955', '900956', '900957'
]
filename = 'E:/300354.csv' #使用文件
tablename = 'stock_' + stockinfo[2019] #使用数据库
#high,low,dopen,close,vol=get_data(filename)
high, low, dopen, close, vol = get_data_fromDB(tablename) #high类型: ndarry 维数1
datasets = get_factors(high,
low,
close,
vol,
BBANDS=True,
DEMA=True,
EMA=True,
AD=True,
ADOSC=True,
OBV=True)
train_data, test_data = train_test_split(datasets)
label = generate_label(close, sign=1)
train_label, test_label = train_test_split(label)
method[5].fit(train_data, train_label)
pred = method[5].predict(test_data)
print(classification_report(test_label, pred))