def prepareData(code):
df_all = base.getOneStockData(code)
if df_all.shape[0] < 300:
raise Exception('data is short.' + str(df_all.shape[0]))
in_columns = ['open','close','high','low','volume']
indexes = []
for i in range(OLD_DAYS):
for in_column in in_columns:
index = in_column + '__' + str(i)
indexes.append(index)
df_all[index] = df_all[in_column].shift(0 - i)
futureIndexes = []
for i in range(1, FUTURE_DAYS):
index = 'high_' + str(i)
futureIndexes.append(index)
df_all[index] = df_all.high.shift(i)
index = 'high_future'
df_all[index] = df_all[futureIndexes].apply(max, 1)
# indexes.append(index)
index = 'operate'
df_all[index] = df_all.apply(calcOperate, 1)
indexes.append(index)
# print df_all.head(10)
df = df_all[indexes].dropna()
return df
def checkOne(code_str):
df = base.getOneStockData(code_str)
df['MINUS_DM_' + str(adx_timeperiod)] = ta.MINUS_DM(
np.array(df['high']), np.array(df['low']), timeperiod=adx_timeperiod)
df['PLUS_DM_' + str(adx_timeperiod)] = ta.PLUS_DM(
np.array(df['high']), np.array(df['low']), timeperiod=adx_timeperiod)
df['TR_' + str(adx_timeperiod)] = ta.TRANGE(np.array(df['high']),
np.array(df['low']),
np.array(df['close']))
df['MINUS_DI_' + str(adx_timeperiod)] = ta.MINUS_DI(
np.array(df['high']),
np.array(df['low']),
np.array(df['close']),
timeperiod=adx_timeperiod)
df['PLUS_DI_' + str(adx_timeperiod)] = ta.PLUS_DI(
np.array(df['high']),
np.array(df['low']),
np.array(df['close']),
timeperiod=adx_timeperiod)
df['ADX_' + str(adx_timeperiod)] = ta.ADX(np.array(df['high']),
np.array(df['low']),
np.array(df['close']),
timeperiod=adx_timeperiod)
df['ADXR_' + str(adx_timeperiod)] = ta.ADXR(np.array(df['high']),
np.array(df['low']),
np.array(df['close']),
timeperiod=adx_timeperiod)
df.to_csv(ADX_DIR + code_str + '.csv')
def markVolumePrice(code):
df = None
output_path = getVolumePricePath(code)
if os.path.exists(output_path):
df = pd.read_csv(output_path, index_col=0)
else:
df = base.getOneStockData(code)
if df.empty:
return
if df.shape[0] < 100:
return
df['close_diff'] = df.close.pct_change()
for index,row in df[df.close_diff < 0].iterrows():
df.loc[index,'close_diff'] = row['close_diff'] / (1+row['close_diff'])
df['close_diff_abs'] = abs(df.close_diff)
df['volume_change'] = df.volume.diff()
df['volume_diff'] = df.volume.pct_change()
for index,row in df[df.volume_diff < 0].iterrows():
df.loc[index,'volume_diff'] = row['volume_diff'] / (1+row['volume_diff'])
df['volume_diff_abs'] = abs(df.volume_diff)
df.to_csv(output_path)
def markVolumePrice(code):
df = None
output_path = getVolumePricePath(code)
if os.path.exists(output_path):
df = pd.read_csv(output_path, index_col=0)
else:
df = base.getOneStockData(code)
if df.empty:
return
if df.shape[0] < 100:
return
df['close_diff'] = df.close.pct_change()
for index, row in df[df.close_diff < 0].iterrows():
df.loc[index,
'close_diff'] = row['close_diff'] / (1 + row['close_diff'])
df['close_diff_abs'] = abs(df.close_diff)
df['volume_change'] = df.volume.diff()
df['volume_diff'] = df.volume.pct_change()
for index, row in df[df.volume_diff < 0].iterrows():
df.loc[index,
'volume_diff'] = row['volume_diff'] / (1 +
row['volume_diff'])
df['volume_diff_abs'] = abs(df.volume_diff)
df.to_csv(output_path)
def prepareData(code):
df_all = base.getOneStockData(code)
if df_all.shape[0] < 300:
raise Exception('data is short.' + str(df_all.shape[0]))
in_columns = ['open', 'close', 'high', 'low', 'volume']
indexes = []
for i in range(OLD_DAYS):
for in_column in in_columns:
index = in_column + '__' + str(i)
indexes.append(index)
df_all[index] = df_all[in_column].shift(0 - i)
futureIndexes = []
for i in range(1, FUTURE_DAYS):
index = 'high_' + str(i)
futureIndexes.append(index)
df_all[index] = df_all.high.shift(i)
index = 'high_future'
df_all[index] = df_all[futureIndexes].apply(max, 1)
# indexes.append(index)
index = 'operate'
df_all[index] = df_all.apply(calcOperate, 1)
indexes.append(index)
# print df_all.head(10)
df = df_all[indexes].dropna()
return df
def checkOne(code_str):
df = base.getOneStockData(code_str)
df['MINUS_DM_' + str(adx_timeperiod)] = ta.MINUS_DM(np.array(df['high']), np.array(df['low']), timeperiod=adx_timeperiod)
df['PLUS_DM_' + str(adx_timeperiod)] = ta.PLUS_DM(np.array(df['high']), np.array(df['low']), timeperiod=adx_timeperiod)
df['TR_' + str(adx_timeperiod)] = ta.TRANGE(np.array(df['high']), np.array(df['low']), np.array(df['close']))
df['MINUS_DI_' + str(adx_timeperiod)] = ta.MINUS_DI(np.array(df['high']), np.array(df['low']), np.array(df['close']), timeperiod=adx_timeperiod)
df['PLUS_DI_' + str(adx_timeperiod)] = ta.PLUS_DI(np.array(df['high']), np.array(df['low']), np.array(df['close']), timeperiod=adx_timeperiod)
df['ADX_' + str(adx_timeperiod)] = ta.ADX(np.array(df['high']), np.array(df['low']), np.array(df['close']), timeperiod=adx_timeperiod)
df['ADXR_' + str(adx_timeperiod)] = ta.ADXR(np.array(df['high']), np.array(df['low']), np.array(df['close']), timeperiod=adx_timeperiod)
df.to_csv(ADX_DIR + code_str + '.csv')
def checkByYearMonth(code):
results = pd.DataFrame()
df = base.getOneStockData(code)
print df
if df.empty:
return None
for index, item in df.iterrows():
# print item
date_str = item['date']
date = datetime.datetime.strptime(date_str, '%Y-%m-%d')
year_month = date.strftime('%Y-%m')
month = date.strftime('%m')
df.loc[index, 'year_month'] = year_month
df.loc[index, 'month'] = month
df['close_avg'] = df['close']
df['close_min'] = df['close']
df['close_max'] = df['close']
df['close_first'] = df['close']
df['close_last'] = df['close']
df['close_last'] = df['close']
df['asc_days'] = df['close']
df['desc_days'] = df['close']
group_month = df.groupby('year_month')
results = group_month.agg({
'close_avg': lambda g: np.average(g),
'close_min': lambda g: np.min(g),
'close_max': lambda g: np.max(g),
'close_first': lambda g: g[g.first_valid_index()],
'close_last': lambda g: g[g.last_valid_index()],
'month': lambda g: g[g.first_valid_index()],
'asc_days': _calcAscDays,
'desc_days': _calcDescDays,
})
return results
def checkByYearMonth(code):
results = pd.DataFrame()
df = base.getOneStockData(code)
print df
if df.empty:
return None
for index,item in df.iterrows():
# print item
date_str = item['date']
date = datetime.datetime.strptime(date_str, '%Y-%m-%d')
year_month = date.strftime('%Y-%m')
month = date.strftime('%m')
df.loc[index, 'year_month'] = year_month
df.loc[index, 'month'] = month
df['close_avg'] = df['close']
df['close_min'] = df['close']
df['close_max'] = df['close']
df['close_first'] = df['close']
df['close_last'] = df['close']
df['close_last'] = df['close']
df['asc_days'] = df['close']
df['desc_days'] = df['close']
group_month = df.groupby('year_month')
results = group_month.agg({'close_avg':lambda g: np.average(g),
'close_min':lambda g: np.min(g),
'close_max':lambda g: np.max(g),
'close_first':lambda g: g[g.first_valid_index()],
'close_last':lambda g: g[g.last_valid_index()],
'month':lambda g: g[g.first_valid_index()],
'asc_days': _calcAscDays,
'desc_days': _calcDescDays,
})
return results
def test2():
df_all = base.getOneStockData('000002')
n_lag = 3
n_seq = 1
n_test = 100
series = df_all[['close']].dropna()
# out = series_to_supervised(df_all[['close','volume']], 3, 1, True)
# print out
# df['max_close'] = df[['close1','close2','close3']].apply(max, 1)
train, test = prepare_data(series, n_test, n_lag, n_seq)
# print(test)
print('Train: %s, Test: %s' % (train.shape, test.shape))
# make forecasts
forecasts = make_forecasts(train, test, n_lag, n_seq)
# evaluate forecasts
# evaluate_forecasts(test, forecasts, n_lag, n_seq)
# plot forecasts
y = series.tail(n_test)
# print '------------------'
# print y.head(10)
# print '-----------------'
# print forecasts
plot_forecasts(y, forecasts, n_test + 2)
def test():
df_all = base.getOneStockData('000002')
df_all['volume_diff'] = df_all.volume.pct_change()
for index, row in df_all[df_all.volume_diff < 0].iterrows():
df_all.loc[index, 'volume_diff'] = row['volume_diff'] / (1 + row['volume_diff'])
df_all['close_diff'] = df_all.close.pct_change() * 100
df_all = df_all.dropna(subset=['volume_diff', 'close_diff'])
df_all = df_all[df_all.close_diff < 11]
df_all = df_all[df_all.close_diff > -11]
df_all = df_all[abs(df_all.volume_diff) > 1]
dfx = df_all[['volume_diff']]
dfy = df_all[['close_diff']]
# X_train = [[6], [8], [10], [14], [18]]
# y_train = [[7], [9], [13], [17.5], [18]]
# X_test = [[6], [8], [11], [16]]
# y_test = [[8], [12], [15], [18]]
print df_all.shape
count = dfx.shape[0] / 2 - 3
X_train = dfx[:count]
y_train = dfy[1:count + 1]
X_test = dfx[count:count + count]
y_test = dfy[count + 1:count + count + 1]
runplt(X_train, y_train, X_test, y_test)
plt.plot(X_train, y_train, 'k.')
# 建立线性回归，并用训练的模型绘图
regressor = LinearRegression()
regressor.fit(X_train, y_train)
yy = regressor.predict(X_test)
# df_all['LR1'] = pd.Series()
# df_all['LR1'][count+1:count+count+1] = yy
plt.plot(y_train, yy, 'y-')
quadratic_featurizer = PolynomialFeatures(degree=2)
X_train_quadratic = quadratic_featurizer.fit_transform(X_train)
X_test_quadratic = quadratic_featurizer.transform(X_test)
regressor_quadratic = LinearRegression()
regressor_quadratic.fit(X_train_quadratic, y_train)
xx_quadratic = quadratic_featurizer.transform(X_test)
plt.plot(X_test, regressor_quadratic.predict(xx_quadratic), 'r-')
cubic_featurizer = PolynomialFeatures(degree=3)
X_train_cubic = cubic_featurizer.fit_transform(X_train)
X_test_cubic = cubic_featurizer.transform(X_test)
regressor_cubic = LinearRegression()
regressor_cubic.fit(X_train_cubic, y_train)
xx_cubic = cubic_featurizer.transform(X_test)
plt.plot(X_test, regressor_cubic.predict(xx_cubic), 'g')
seventh_featurizer = PolynomialFeatures(degree=7)
X_train_seventh = seventh_featurizer.fit_transform(X_train)
X_test_seventh = seventh_featurizer.transform(X_test)
regressor_seventh = LinearRegression()
regressor_seventh.fit(X_train_seventh, y_train)
xx_seventh = seventh_featurizer.transform(X_test)
plt.plot(X_test, regressor_seventh.predict(xx_seventh), 'b')
plt.plot(X_test, y_test, 'm+')
plt.show()
# print(X_train_cubic)
# print(X_test_cubic)
# print(X_train_seventh)
# print(X_test_seventh)
print('1 r-liner', regressor.score(X_test, y_test))
print('2 r-squared', regressor_quadratic.score(X_test_quadratic, y_test))
print('3 r-squared', regressor_cubic.score(X_test_cubic, y_test))
print('7 r-squared', regressor_seventh.score(X_test_seventh, y_test))
#predictions = model.predict(X_test)
#for i, prediction in enumerate(predictions):
# print('Predicted: %s, Target: %s' % (prediction, y_test[i]))
#print('R-squared: %.2f' % model.score(X_test, y_test))
#
import base
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
df_all = base.getOneStockData('000002')
columns = []
for i in range(1, 5):
column = 'close(t-' + str(i) + ')'
df_all[column] = df_all.close.shift(i)
columns.append(column)
df_all = df_all.dropna(subset=columns)
#df_all = df_all[df_all.close_diff < 11]
#df_all = df_all[df_all.close_diff > -11]
#df_all = df_all[abs(df_all.volume_diff) > 1]
dfx = df_all[columns]
dfy = df_all[['close']]
#X_train = [[6], [8], [10], [14], [18]]
#y_train = [[7], [9], [13], [17.5], [18]]
#X_test = [[6], [8], [11], [16]]
def calcMACD(code):
success_count = 0
failed_count = 0
output_path = getMacdPath(code)
last_operator_price_close = 0
last_operator = 0
if os.path.exists(output_path):
df = pd.read_csv(output_path, index_col=0)
success_count = df.macd_result[df.macd_result>0].count()
failed_count = df.macd_result[df.macd_result<0].count()
return (success_count,failed_count)
df = base.getOneStockData(code)
if df.empty:
return (success_count,failed_count)
dflen = df.shape[0]
if dflen<35:
return (success_count,failed_count)
df['macd_DIFF_DEA'] = pd.Series()
df['macd_DEA_K'] = pd.Series()
df['macd_MACD_SELF'] = pd.Series()
df['macd_SUM'] = pd.Series()
df['macd_result'] = pd.Series()
macd, macdsignal, macdhist = ta.MACD(np.array(df['close']), fastperiod=MACD_FASTPERIOD, slowperiod=MACD_SLOWPERIOD, signalperiod=MACD_SIGNALPERIOD)
MA5_index = df.shape[1]
df['MA' + str(MA_FAST)]=pd.Series(ta.MA(np.array(df['close']), timeperiod=MA_FAST, matype=0),index=df.index)
MA10_index = df.shape[1]
df['MA' + str(MA_MIDDLE)]=pd.Series(ta.MA(np.array(df['close']), timeperiod=MA_MIDDLE, matype=0),index=df.index)
MA20_index = df.shape[1]
df['MA' + str(MA_SLOW)]=pd.Series(ta.MA(np.array(df['close']), timeperiod=MA_SLOW, matype=0),index=df.index)
macd_index = df.shape[1]
df['macd']=pd.Series(macd,index=df.index) #DIFF
macdsignal_index = df.shape[1]
df['macdsignal']=pd.Series(macdsignal,index=df.index)#DEA
macdhist_index = df.shape[1]
df['macdhist']=pd.Series(macdhist,index=df.index)#DIFF-DEA
SignalMA5 = ta.MA(macdsignal, timeperiod=MA_FAST, matype=0)
SignalMA10 = ta.MA(macdsignal, timeperiod=MA_MIDDLE, matype=0)
SignalMA20 = ta.MA(macdsignal, timeperiod=MA_SLOW, matype=0)
for dflen in range(36, df.shape[0] + 1):
operate = 0
#在后面增加3列，对应的是 DIFF DEA DIFF-DEA
MAlen = dflen
#2个数组 1.DIFF、DEA均为正，DIFF向上突破DEA，买入信号。 2.DIFF、DEA均为负，DIFF向下跌破DEA，卖出信号。
#待修改
if df.iat[(dflen-1),macd_index]>0:
if df.iat[(dflen-1),macdsignal_index]>0:
if df.iat[(dflen-1),macd_index]>df.iat[(dflen-1),macdsignal_index] and df.iat[(dflen-2),macd_index]<=df.iat[(dflen-2),macdsignal_index]:
# operate = operate + 10#买入
df.loc[dflen - 1, 'macd_DIFF_DEA'] = 1
else:
if df.iat[(dflen-1),macdsignal_index]<0:
if df.iat[(dflen-1),macd_index] == df.iat[(dflen-2),macdsignal_index]:
# operate = operate - 10#卖出
df.loc[dflen - 1, 'macd_DIFF_DEA'] = -1
#3.DEA线与K线发生背离，行情反转信号。
if df.iat[(dflen-1),MA5_index]>=df.iat[(dflen-1),MA10_index] and df.iat[(dflen-1),MA10_index]>=df.iat[(dflen-1),MA20_index]:#K线上涨
if SignalMA5[MAlen-1]<=SignalMA10[MAlen-1] and SignalMA10[MAlen-1]<=SignalMA20[MAlen-1]: #DEA下降
operate = operate - 1
df.loc[dflen - 1, 'macd_DEA_K'] = 1
elif df.iat[(dflen-1),MA5_index]<=df.iat[(dflen-1),MA10_index] and df.iat[(dflen-1),MA10_index]<=df.iat[(dflen-1),MA20_index]:#K线下降
if SignalMA5[MAlen-1]>=SignalMA10[MAlen-1] and SignalMA10[MAlen-1]>=SignalMA20[MAlen-1]: #DEA上涨
operate = operate + 1
df.loc[dflen - 1, 'macd_DEA_K'] = -1
#4.分析MACD柱状线，由负变正，买入信号。
if df.iat[(dflen-1),macdhist_index]>0 and dflen >30 :
for i in range(1,26):
if df.iat[(dflen-1-i),macdhist_index]<=0:#
# operate = operate + 5
df.loc[dflen - 1, 'macd_MACD_SELF'] = 1
break
#由正变负，卖出信号
if df.iat[(dflen-1),macdhist_index]<0 and dflen >30 :
for i in range(1,26):
if df.iat[(dflen-1-i),macdhist_index]>=0:#
# operate = operate - 5
df.loc[dflen - 1, 'macd_MACD_SELF'] = -1
break
if operate != 0:
df.loc[dflen - 1, 'macd_SUM'] = operate
if operate == 0:
continue
cur_operator_price_close = df['close'][dflen - 1]
if last_operator * operate > 0:
continue
# print code, name, 'operate=', operate, ' last_operator=', last_operator,
# print ' cur_operator_price_close=', cur_operator_price_close,
# print ' last_operator_price_close=', last_operator_price_close
if operate > 0:
# print 'operate=买入 price=',cur_operator_price_close
pass
else:
# print 'operate=卖出 ',
# print ' success=', (cur_operator_price_close > last_operator_price_close)
# print ' curprice=',cur_operator_price_close,
# print ' last_operator_price_close=', last_operator_price_close
if last_operator_price_close != 0:
if cur_operator_price_close > last_operator_price_close:
success_count += 1
df.loc[dflen - 1, 'macd_result'] = 1
else:
failed_count += 1
df.loc[dflen - 1, 'macd_result'] = -1
last_operator = operate
last_operator_price_close = cur_operator_price_close
df.to_csv(output_path)
return (success_count,failed_count)
#predictions = model.predict(X_test)
#for i, prediction in enumerate(predictions):
# print('Predicted: %s, Target: %s' % (prediction, y_test[i]))
#print('R-squared: %.2f' % model.score(X_test, y_test))
#
import base
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
df_all = base.getOneStockData('000002')
columns = []
for i in range(1, 5):
column = 'close(t-' + str(i) + ')'
df_all[column] = df_all.close.shift(i)
columns.append(column)
df_all = df_all.dropna(subset=columns)
#df_all = df_all[df_all.close_diff < 11]
#df_all = df_all[df_all.close_diff > -11]
#df_all = df_all[abs(df_all.volume_diff) > 1]
dfx = df_all[columns]
dfy = df_all[['close']]
#X_train = [[6], [8], [10], [14], [18]]
#y_train = [[7], [9], [13], [17.5], [18]]
#X_test = [[6], [8], [11], [16]]
def calcMACD(code):
success_count = 0
failed_count = 0
output_path = getMacdPath(code)
last_operator_price_close = 0
last_operator = 0
if os.path.exists(output_path):
df = pd.read_csv(output_path, index_col=0)
success_count = df.macd_result[df.macd_result > 0].count()
failed_count = df.macd_result[df.macd_result < 0].count()
return (success_count, failed_count)
df = base.getOneStockData(code)
if df.empty:
return (success_count, failed_count)
dflen = df.shape[0]
if dflen < 35:
return (success_count, failed_count)
df['macd_DIFF_DEA'] = pd.Series()
df['macd_DEA_K'] = pd.Series()
df['macd_MACD_SELF'] = pd.Series()
df['macd_SUM'] = pd.Series()
df['macd_result'] = pd.Series()
macd, macdsignal, macdhist = ta.MACD(np.array(df['close']),
fastperiod=MACD_FASTPERIOD,
slowperiod=MACD_SLOWPERIOD,
signalperiod=MACD_SIGNALPERIOD)
MA5_index = df.shape[1]
df['MA' + str(MA_FAST)] = pd.Series(ta.MA(np.array(df['close']),
timeperiod=MA_FAST,
matype=0),
index=df.index)
MA10_index = df.shape[1]
df['MA' + str(MA_MIDDLE)] = pd.Series(ta.MA(np.array(df['close']),
timeperiod=MA_MIDDLE,
matype=0),
index=df.index)
MA20_index = df.shape[1]
df['MA' + str(MA_SLOW)] = pd.Series(ta.MA(np.array(df['close']),
timeperiod=MA_SLOW,
matype=0),
index=df.index)
macd_index = df.shape[1]
df['macd'] = pd.Series(macd, index=df.index) #DIFF
macdsignal_index = df.shape[1]
df['macdsignal'] = pd.Series(macdsignal, index=df.index) #DEA
macdhist_index = df.shape[1]
df['macdhist'] = pd.Series(macdhist, index=df.index) #DIFF-DEA
SignalMA5 = ta.MA(macdsignal, timeperiod=MA_FAST, matype=0)
SignalMA10 = ta.MA(macdsignal, timeperiod=MA_MIDDLE, matype=0)
SignalMA20 = ta.MA(macdsignal, timeperiod=MA_SLOW, matype=0)
for dflen in range(36, df.shape[0] + 1):
operate = 0
#在后面增加3列，对应的是 DIFF DEA DIFF-DEA
MAlen = dflen
#2个数组 1.DIFF、DEA均为正，DIFF向上突破DEA，买入信号。 2.DIFF、DEA均为负，DIFF向下跌破DEA，卖出信号。
#待修改
if df.iat[(dflen - 1), macd_index] > 0:
if df.iat[(dflen - 1), macdsignal_index] > 0:
if df.iat[(dflen - 1), macd_index] > df.iat[
(dflen - 1), macdsignal_index] and df.iat[
(dflen - 2), macd_index] <= df.iat[(dflen - 2),
macdsignal_index]:
# operate = operate + 10#买入
df.loc[dflen - 1, 'macd_DIFF_DEA'] = 1
else:
if df.iat[(dflen - 1), macdsignal_index] < 0:
if df.iat[(dflen - 1), macd_index] == df.iat[(dflen - 2),
macdsignal_index]:
# operate = operate - 10#卖出
df.loc[dflen - 1, 'macd_DIFF_DEA'] = -1
#3.DEA线与K线发生背离，行情反转信号。
if df.iat[(dflen - 1),
MA5_index] >= df.iat[(dflen - 1), MA10_index] and df.iat[
(dflen - 1), MA10_index] >= df.iat[(dflen - 1),
MA20_index]: #K线上涨
if SignalMA5[MAlen - 1] <= SignalMA10[MAlen - 1] and SignalMA10[
MAlen - 1] <= SignalMA20[MAlen - 1]: #DEA下降
operate = operate - 1
df.loc[dflen - 1, 'macd_DEA_K'] = 1
elif df.iat[(dflen - 1),
MA5_index] <= df.iat[(dflen - 1), MA10_index] and df.iat[
(dflen - 1), MA10_index] <= df.iat[(dflen - 1),
MA20_index]: #K线下降
if SignalMA5[MAlen - 1] >= SignalMA10[MAlen - 1] and SignalMA10[
MAlen - 1] >= SignalMA20[MAlen - 1]: #DEA上涨
operate = operate + 1
df.loc[dflen - 1, 'macd_DEA_K'] = -1
#4.分析MACD柱状线，由负变正，买入信号。
if df.iat[(dflen - 1), macdhist_index] > 0 and dflen > 30:
for i in range(1, 26):
if df.iat[(dflen - 1 - i), macdhist_index] <= 0: #
# operate = operate + 5
df.loc[dflen - 1, 'macd_MACD_SELF'] = 1
break
#由正变负，卖出信号
if df.iat[(dflen - 1), macdhist_index] < 0 and dflen > 30:
for i in range(1, 26):
if df.iat[(dflen - 1 - i), macdhist_index] >= 0: #
# operate = operate - 5
df.loc[dflen - 1, 'macd_MACD_SELF'] = -1
break
if operate != 0:
df.loc[dflen - 1, 'macd_SUM'] = operate
if operate == 0:
continue
cur_operator_price_close = df['close'][dflen - 1]
if last_operator * operate > 0:
continue
# print code, name, 'operate=', operate, ' last_operator=', last_operator,
# print ' cur_operator_price_close=', cur_operator_price_close,
# print ' last_operator_price_close=', last_operator_price_close
if operate > 0:
# print 'operate=买入 price=',cur_operator_price_close
pass
else:
# print 'operate=卖出 ',
# print ' success=', (cur_operator_price_close > last_operator_price_close)
# print ' curprice=',cur_operator_price_close,
# print ' last_operator_price_close=', last_operator_price_close
if last_operator_price_close != 0:
if cur_operator_price_close > last_operator_price_close:
success_count += 1
df.loc[dflen - 1, 'macd_result'] = 1
else:
failed_count += 1
df.loc[dflen - 1, 'macd_result'] = -1
last_operator = operate
last_operator_price_close = cur_operator_price_close
df.to_csv(output_path)
return (success_count, failed_count)