def a(x1, x2, L):
d = []
x1 = int(x1)
x2 = int(x2)
for i in range(x1, x2):
if (i not in date2):
continue
d.append(i)
start1 = datetime.strptime(start, "%Y-%m-%d")
t1 = str(start1 + timedelta(x1 - datestr2num(start)))[0:10]
t2 = str(start1 + timedelta(x2 - datestr2num(start)))[0:10]
ti = str(start1 + timedelta(i - datestr2num(start)))[0:10]
d.append(
culDistence(x1, closePrice[t1], x2, closePrice[t2], i,
closePrice[ti]))
# index=d.index(max(d))
if len(d) == 0:
return
d1 = [d[i] for i in range(1, len(d), 2)] #不包含索引的list
maxD = max(d1)
if maxD < L:
return
else:
index = d[d.index(maxD) - 1]
result.append(index)
a(x1, index, L)
a(index, x2, L)
def setUp(self):
n=1000 # slows down significantly! constraint is percentile test
x = sc.randn(n)*100. # generate dummy data
self.D = Data(None, None)
d=np.ones((n, 1, 1))
self.D.data = d
self.D.data[:,0,0]=x
self.D.data = np.ma.array(self.D.data, mask=self.D.data != self.D.data)
self.D.verbose = True
self.D.unit = 'myunit'
self.D.label = 'testlabel'
self.D.filename = 'testinputfilename.nc'
self.D.varname = 'testvarname'
self.D.long_name = 'This is the longname'
self.D.time = np.arange(n) + pl.datestr2num('2001-01-01')
self.D.time_str = "days since 0001-01-01 00:00:00"
self.D.calendar = 'gregorian'
self.D.oldtime=False
# generate dummy Model object
data_dir = './test/'
varmethods = {'albedo':'get_albedo()', 'sis': 'get_sis()'}
self.model = models.Model(data_dir, varmethods, name='testmodel', intervals='monthly')
sis = self.D.copy()
sis.mulc(5., copy=False)
sis.label='sisdummy'
alb = self.D.copy()
alb.label='albedodummy'
# add some dummy data variable
self.model.variables = {'albedo':alb, 'sis':sis}
def setUp(self):
self.map_plot = mapping.MapPlotGeneric()
# data for testing
n=1000 # slows down significantly! constraint is percentile test
x = sc.randn(n)*100. # generate dummy data
self.D = Data(None, None)
ny=10
nx=20
d=np.ones((n, ny, nx))
self.D.data = d
for i in xrange(ny):
for j in xrange(nx):
self.D.data[:, i, j] = x[:]
self.D.data = np.ma.array(self.D.data, mask=self.D.data != self.D.data)
self.D.verbose = True
self.D.unit = 'myunit'
self.D.label = 'testlabel'
self.D.filename = 'testinputfilename.nc'
self.D.varname = 'testvarname'
self.D.long_name = 'This is the longname'
self.D.time = np.arange(n) + pl.datestr2num('2001-01-01')
self.D.time_str = "days since 0001-01-01 00:00:00"
self.D.calendar = 'gregorian'
self.D.cell_area = np.ones((ny, nx))
self.D.lon = np.random.random((ny,nx))*10.
self.D.lat = np.random.random((ny,nx))*20.
self._tmpdir = tempfile.mkdtemp()
def get_stock_plot(code, start, end, session_id=''):
# get history
res_table = ts.get_hist_data(code, start, end)
sh_res_table = ts.get_hist_data('sh', start, end)
print(res_table)
# string to date
print(res_table.index.values)
x_date = [datestr2num(i) for i in res_table.index.values]
# draw
fig = plt.figure(figsize=(10, 5))
# matplotlib doesn't support chinese
plt.title(u"percentage - date")
plt.xlabel(u"date")
plt.ylabel(u"percentage")
plt.plot_date(x_date,
res_table['p_change'],
'-',
label="change percentage")
plt.plot_date(x_date,
sh_res_table['p_change'],
'-',
label="change percentage")
# plt.legend()
plt.grid(True)
# fig.savefig('test.png', format='png')
# plt.show()
sio = BytesIO()
fig.savefig(sio, format='png')
data = base64.encodebytes(sio.getvalue()).decode()
return data
def fun(x1, x2, L):
global result
result = []
a(x1, x2, L)
result = [date2[0]] + sorted(result) + [date2[-1]]
# 画图部分
start1 = datetime.strptime(start, "%Y-%m-%d")
profitRate = 1
for i in range(0, len(result) - 1):
ii = str(start1 +
timedelta(result[i] - datestr2num(start)))[0:10] #数字转化为日期
ii1 = str(start1 + timedelta(result[i + 1] - datestr2num(start)))[0:10]
p0 = closePrice[ii]
p1 = closePrice[ii1]
plt.plot_date([result[i], result[i + 1]], [p0, p1], 'r-')
if p1 > p0:
profitRate *= 1 + ((1 - Fee) * p1 -
(1 + Fee) * p0) / ((1 + Fee) * p0)
print(profitRate)
def setUp(self):
#init Data object for testing
n=4 #slows down significantly! constraint is percentile test
x = sc.randn(n)*100. #generate dummy data
self.D = Data(None,None)
d=np.ones((n,1,2))
self.D.data = d
self.D.data[:,0,0]=x
self.D.data = np.ma.array(self.D.data,mask=self.D.data != self.D.data)
self.D.verbose = True
self.D.unit = 'myunit'
self.D.time = np.arange(n) + pl.datestr2num('2001-01-01') - 1
def fun(x1, x2, L):
global result
result = []
a(x1, x2, L)
# fileHeader = ["start", "end","duration","slope"]
datacsv = open("data.csv", "w", newline="")
csvwriter = csv.writer(datacsv, dialect=("excel"))
result = [date2[0]] + sorted(result) + [date2[-1]]
# 画图部分
start1 = datetime.strptime(start, "%Y-%m-%d")
for i in range(0, len(result) - 1):
ii = str(start1 +
timedelta(result[i] - datestr2num(start)))[0:10] #数字转化为日期
ii1 = str(start1 + timedelta(result[i + 1] - datestr2num(start)))[0:10]
p0 = closePrice[ii]
p1 = closePrice[ii1]
plt.plot_date([result[i], result[i + 1]], [p0, p1], 'r-')
#保存为csv格式
csvwriter.writerow([
ii, ii1,
date2.index(result[i + 1]) - date2.index(result[i]),
(p1 - p0) / (result[i + 1] - result[i])
])
def sort_MMSI(traj):
##############筛选特定海域的，以及满足各种特定限制范围的正常数据####################
traj = traj[(traj['YCoord'] <= 43.2) &
(traj['YCoord'] >= 29.7)] #海域范围北纬29.7-43.2之间
traj = traj[(traj['STATUS'] <= 8)] #正常航行或者停泊状态
traj = traj[(traj['HEADING'] < 360)] #船头朝向范围[0,360）
traj = traj[(traj['SOG'] <= 50)] #速度小于50节
traj = traj[~((traj['SOG'] == 0) &
(traj['STATUS'] == 0))] #船速为0，但是状态却为航行的错误数据
###################按照MMSI和时间顺序，排列出每条轨迹############################
a = traj['BASEDATETIME']
traj['BASEDATETIME'] = datestr2num(a)
traj = traj.sort_values(by=['MMSI', 'BASEDATETIME'])
traj = traj.reset_index(drop=True)
return traj
def generate_monthly_timeseries(t, sday='01'):
"""
generate a vector monthly timeseries
t: time (numeric)
"""
tn = []
d = pl.num2date(t)
for i in range(len(t)):
y = str(d[i].year).zfill(4)
m = str(d[i].month).zfill(2)
s = y + '-' + m + '-' + sday
tn.append(pl.datestr2num(s))
return tn
def generate_monthly_timeseries(t, sday='01'):
"""
generate a vector monthly timeseries
t: time (numeric)
"""
tn = []
d = pl.num2date(t)
for i in range(len(t)):
y = str(d[i].year).zfill(4)
m = str(d[i].month).zfill(2)
s = y + '-' + m + '-' + sday
tn.append(pl.datestr2num(s))
return tn
def setUp(self):
# init Data object for testing
n=100 # slows down significantly! constraint is percentile test
x = sc.randn(n)*100. # generate dummy data
self.D = Data(None, None)
d=np.ones((n, 1, 1))
self.D.data = d
self.D.data[:,0,0]=x
self.D.data = np.ma.array(self.D.data, mask=self.D.data != self.D.data)
self.D.verbose = True
self.D.unit = 'myunit'
self.D.label = 'testlabel'
self.D.filename = 'testinputfilename.nc'
self.D.varname = 'testvarname'
self.D.long_name = 'This is the longname'
self.D.time = np.arange(n) + pl.datestr2num('2001-01-01') - 1
self.D.time_str = "days since 0001-01-01 00:00:00"
self.D.calendar = 'gregorian'
self.D.cell_area = np.ones_like(self.D.data[0,:,:])
def setUp(self):
# init Data object for testing
n = 100 # slows down significantly! constraint is percentile test
x = sc.randn(n) * 100. # generate dummy data
self.D = Data(None, None)
d = np.ones((n, 1, 1))
self.D.data = d
self.D.data[:, 0, 0] = x
self.D.data = np.ma.array(self.D.data, mask=self.D.data != self.D.data)
self.D.verbose = True
self.D.unit = 'myunit'
self.D.label = 'testlabel'
self.D.filename = 'testinputfilename.nc'
self.D.varname = 'testvarname'
self.D.long_name = 'This is the longname'
self.D.time = np.arange(n) + pl.datestr2num('2001-01-01') - 1
self.D.time_str = "days since 0001-01-01 00:00:00"
self.D.calendar = 'gregorian'
self.D.cell_area = np.ones_like(self.D.data[0, :, :])
def mainPieceWise(L, path): #分段主函数
files = os.listdir(path) #得到文件夹下的所有文件名称
for code in files: #遍历文件夹
closePrice = [] #收盘价
date = [] #字符串日期
filePath = path + "/" + code + "/" + code + ".csv" #csv文件名
with open(filePath) as f:
f_csv = csv.reader(f)
headers = next(f_csv) # 略过第一行列名
for row in f_csv:
closePrice.append(row[3])
date.append(row[0])
os.chdir(path + "/" + code) #更改当前文件夹，进行文件的保存
closePrice = [float(i) for i in closePrice][::-1]
date = [str(i)[:10] for i in date][::-1] #字符串日期
dateToNum = [datestr2num(i) for i in date] #将日期转为数字进行坐标表示
#画原始数据图
plt.gcf().set_size_inches(12, 4)
plt.plot_date(dateToNum, closePrice, 'b-')
#调用线性分段函数
pieceWise(dateToNum, L, closePrice, code)
def get_bond_stocks_pe_infos():
addr = host_address + '/stock/get_stocks_bonds_pe'
r = requests.get(addr)
if r.status_code == 200:
result = json.loads(r.text)
pe_history = json.loads(result['pe_history'])
data = pd.DataFrame(pe_history)
data['bond_yield'] = 100 / data['bond_pe']
data['stock_yield'] = 100 / data['stocks_pe']
x = range(len(data))
x_date = [datestr2num(i) for i in data['trade_date']]
plt.style.use('ggplot')
plt.figure(figsize=(14, 4))
plt.title("10年期国债收益率与全市场股票收益率(%)")
plt.xticks(rotation=45)
plt.ylabel("收益率")
plt.plot_date(x_date, data['bond_yield'], '-', label="10年期国债收益率")
plt.plot_date(x_date, data['stock_yield'], '-', label="全市场股票收益率")
plt.legend()
plt.grid(True)
pic_path = sys.path[0] + '\\bond_stocks_yield.png'
if os.path.exists(pic_path):
os.remove(pic_path)
print('删除历史文件bond_stocks_yield.png')
print('生成图片：' + pic_path)
plt.savefig(pic_path) # 保存图片
with open(pic_path, "rb") as f:
base64_data = base64.b64encode(f.read())
global pic_base64
pic_base64 = pic_base64 + str(base64_data)[2:-1]
print('将图片转化为base64')
print(pic_base64)
else:
raise Exception('更新每日数据接口【get_bond_stocks_pe_infos】调用失败')
def test_gleckler_index(self):
"""
test Reichler index/Gleckler plot
"""
# generate sample data
# sample data
tmp = np.zeros((5, 3, 1))
tmp[:, 0, 0] = np.ones(5) * 1.
tmp[:, 1, 0] = np.ones(5) * 2.
tmp[:, 2, 0] = np.ones(5) * 5.
# The data is like ...
#| 1 | 2 | 5 |
#| 1 | 2 | 5 |
#| 1 | 2 | 5 |
#| 1 | 2 | 5 |
#| 1 | 2 | 5 |
x = self.D.copy()
x._temporal_subsetting(0, 4)
x.data = np.ma.array(tmp, mask=tmp != tmp)
x.std = np.ones(x.data.shape)
x.time[0] = pl.datestr2num('2000-02-15')
x.time[1] = pl.datestr2num('2000-03-15')
x.time[2] = pl.datestr2num('2000-04-15')
x.time[3] = pl.datestr2num('2000-05-15')
x.time[4] = pl.datestr2num('2000-06-15')
y = self.D.copy()
y._temporal_subsetting(0, 4)
tmp = np.ones(x.data.shape) # sample data 2
y.data = np.ma.array(tmp, mask=tmp != tmp)
y.time[0] = pl.datestr2num('2000-02-15')
y.time[1] = pl.datestr2num('2000-03-15')
y.time[2] = pl.datestr2num('2000-04-15')
y.time[3] = pl.datestr2num('2000-05-15')
y.time[4] = pl.datestr2num('2000-06-15')
# Case 1: same area weights
# cell area
tmp = np.ones((3, 1))
x.cell_area = tmp * 1.
#| 1-1 | 2-1 | 5-1 |
#| 1-1 | 2-1 | 5-1 |
#| 1-1 | 2-1 | 5-1 |
#| 1-1 | 2-1 | 5-1 |
#| 1-1 | 2-1 | 5-1 |
#===================
#| 0 | 5 | 5*4**2=5*16. = 80 |
#==> E2 = sqrt(85./(15.))
D = GlecklerPlot()
r = D.calc_index(x, y, 'a', 'b', time_weighting=False)
wt = np.ones(5) / 5.
ref = np.sqrt(((85. / 15.) * wt).sum())
t = np.abs(1. - r / ref)
self.assertLess(t, 0.000001) # relative error
D = GlecklerPlot()
r = D.calc_index(x, y, 'a', 'b')
wt = np.asarray([29., 31., 30., 31., 30.])
wt = wt / wt.sum()
ref = np.sqrt(((85. / 15.) * wt).sum())
t = np.abs(1. - r / ref)
self.assertLess(t, 0.000001) # relative error
# Case 2: Different area weights
# cell area
tmp = np.ones((3, 1))
tmp[1, 0] = 2.
x.cell_area = tmp * 1.
#| 1-1=0 | 2-1=1 | 5-1=16 |
#| 1-1=0 | 2-1=1 | 5-1=16 |
#| 1-1=0 | 2-1=1 | 5-1=16 |
#| 1-1=0 | 2-1=1 | 5-1=16 |
#| 1-1=0 | 2-1=1 | 5-1=16 |
#--------------------------
# w = 0.25 w = 0.5 w=0.25|
#--------------------------
# 0.25*0 + 0.5 * 1 + 0.25 * 16 = 0 + 0.5 + 4 = 4.5
# the mean of that is 4.5 for each timestep
# mean because the overall weights are calculated as such that
# they give a total weight if 1
# diagnostic
D = GlecklerPlot()
r = D.calc_index(x, y, 'a', 'b', time_weighting=False)
wt = np.ones(5) / 5.
ref = np.sqrt((4.5 * wt).sum())
t = np.abs(1. - r / ref)
self.assertLess(t, 0.000001) # relative error
wt = np.asarray([29., 31., 30., 31., 30.])
wt = wt / wt.sum()
ref = np.sqrt((4.5 * wt).sum())
t = np.abs(1. - r / ref)
self.assertLess(t, 0.000001) # relative error
# Case 3: use different std
x.std = np.ones(x.data.shape)
x.std[:, 2, 0] = 0.5
#| 1-1=0 | 2-1=1 | 5-1=16 / 0.5 |
#| 1-1=0 | 2-1=1 | 5-1=16 / 0.5 |
#| 1-1=0 | 2-1=1 | 5-1=16 / 0.5 |
#| 1-1=0 | 2-1=1 | 5-1=16 / 0.5 |
#| 1-1=0 | 2-1=1 | 5-1=16 / 0.5 |
#--------------------------------
# w = 0.25 w = 0.5 w=0.25|
# 0 + 0.5 + 0.25*32 = 0.5 + 8 = 8.5
D = GlecklerPlot()
r = D.calc_index(x, y, 'a', 'b', time_weighting=False)
wt = np.ones(5) / 5.
ref = np.sqrt((8.5 * wt).sum())
t = np.abs(1. - r / ref)
self.assertLess(t, 0.000001) # relative error
wt = np.asarray([29., 31., 30., 31., 30.])
wt = wt / wt.sum()
ref = np.sqrt((8.5 * wt).sum())
t = np.abs(1. - r / ref)
self.assertLess(t, 0.000001) # relative error
DM = '000002'
global csvInput
csvInput = []
sns.set_style("whitegrid")
end = datetime.today() #开始时间结束时间，选取最近一年的数据
start = datetime(end.year, end.month - 3, end.day)
end = str(end)[0:10]
start = str(start)[0:10]
getData = ts.get_hist_data(DM, start, end)
closePrice = getData.close #收盘价
closePrice = closePrice[::-1] #按日期从低到高
date1 = getData.index #日期
date1 = date1[::-1] #按日期从低到高
date2 = [datestr2num(i) for i in date1] #将日期转为数字进行坐标表示
plt.gcf().set_size_inches(12, 4)
plt.plot_date(date2, closePrice, 'b-')
#买卖的的判断画图
chiyou = [] #预计持有日期
uptrend = [] #预计上升日期
downtrend = [] #预计下跌日期
chiyouPrice = [] #持有价钱
uptrendPrice = [] #上升价钱
downtrendPrice = [] #下跌价钱
for i in date1:
cul, count1 = culKDJ.cul_KDJ(DM, i, 9)
count += count1
if cul == 0:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
import matplotlib.mlab as mlab
from scipy.interpolate import UnivariateSpline as spline
from glob import glob
files = sorted(glob("grand-island-test-wenzel-*.csv"))
# pumping start and end times
t0 = pylab.datestr2num('07/29/1931 06:05:00')
t1 = pylab.datestr2num('07/31/1931 06:04:00')
print 'test length in seconds:', (t1 - t0) * 1440 * 60
tlen = (t1 - t0) * 1440
# column 0: date
# column 1: time
# column 2: drawdown (ft)
individualplots = True
computesplinederiv = True
drawdowncheck = True
mapcheckplot = True
saveMetricDecimalData = True
# apply uniform min/max derivatives to all data
ydmin, ydmax = (-0.2, 1.0)
if drawdowncheck:
def test_gleckler_index(self):
"""
test Reichler index/Gleckler plot
"""
# generate sample data
# sample data
tmp = np.zeros((5, 3, 1))
tmp[:,0,0] = np.ones(5)*1.
tmp[:,1,0] = np.ones(5)*2.
tmp[:,2,0] = np.ones(5)*5.
# The data is like ...
#| 1 | 2 | 5 |
#| 1 | 2 | 5 |
#| 1 | 2 | 5 |
#| 1 | 2 | 5 |
#| 1 | 2 | 5 |
x = self.D.copy()
x._temporal_subsetting(0, 4)
x.data = np.ma.array(tmp, mask=tmp!=tmp)
x.std = np.ones(x.data.shape)
x.time[0] = pl.datestr2num('2000-02-15')
x.time[1] = pl.datestr2num('2000-03-15')
x.time[2] = pl.datestr2num('2000-04-15')
x.time[3] = pl.datestr2num('2000-05-15')
x.time[4] = pl.datestr2num('2000-06-15')
y = self.D.copy()
y._temporal_subsetting(0, 4)
tmp = np.ones(x.data.shape) # sample data 2
y.data = np.ma.array(tmp, mask=tmp!=tmp)
y.time[0] = pl.datestr2num('2000-02-15')
y.time[1] = pl.datestr2num('2000-03-15')
y.time[2] = pl.datestr2num('2000-04-15')
y.time[3] = pl.datestr2num('2000-05-15')
y.time[4] = pl.datestr2num('2000-06-15')
# Case 1: same area weights
# cell area
tmp = np.ones((3, 1))
x.cell_area = tmp*1.
#| 1-1 | 2-1 | 5-1 |
#| 1-1 | 2-1 | 5-1 |
#| 1-1 | 2-1 | 5-1 |
#| 1-1 | 2-1 | 5-1 |
#| 1-1 | 2-1 | 5-1 |
#===================
#| 0 | 5 | 5*4**2=5*16. = 80 |
#==> E2 = sqrt(85./(15.))
D = GlecklerPlot()
r = D.calc_index(x, y, 'a', 'b', time_weighting=False)
wt = np.ones(5) / 5.
ref = np.sqrt(((85./15.) * wt).sum())
t = np.abs(1. - r / ref)
self.assertLess(t, 0.000001) # relative error
D = GlecklerPlot()
r = D.calc_index(x, y, 'a', 'b')
wt = np.asarray([29., 31., 30., 31., 30.])
wt = wt / wt.sum()
ref = np.sqrt(((85./15.) * wt).sum())
t = np.abs(1. - r / ref)
self.assertLess(t, 0.000001) # relative error
# Case 2: Different area weights
# cell area
tmp = np.ones((3, 1))
tmp[1, 0] = 2.
x.cell_area = tmp*1.
#| 1-1=0 | 2-1=1 | 5-1=16 |
#| 1-1=0 | 2-1=1 | 5-1=16 |
#| 1-1=0 | 2-1=1 | 5-1=16 |
#| 1-1=0 | 2-1=1 | 5-1=16 |
#| 1-1=0 | 2-1=1 | 5-1=16 |
#--------------------------
# w = 0.25 w = 0.5 w=0.25|
#--------------------------
# 0.25*0 + 0.5 * 1 + 0.25 * 16 = 0 + 0.5 + 4 = 4.5
# the mean of that is 4.5 for each timestep
# mean because the overall weights are calculated as such that
# they give a total weight if 1
# diagnostic
D = GlecklerPlot()
r = D.calc_index(x, y, 'a', 'b', time_weighting=False)
wt = np.ones(5) / 5.
ref = np.sqrt((4.5 * wt).sum())
t = np.abs(1. - r / ref)
self.assertLess(t, 0.000001) # relative error
wt = np.asarray([29., 31., 30., 31., 30.])
wt = wt / wt.sum()
ref = np.sqrt((4.5 * wt).sum())
t = np.abs(1. - r / ref)
self.assertLess(t, 0.000001) # relative error
# Case 3: use different std
x.std = np.ones(x.data.shape)
x.std[:, 2, 0] = 0.5
#| 1-1=0 | 2-1=1 | 5-1=16 / 0.5 |
#| 1-1=0 | 2-1=1 | 5-1=16 / 0.5 |
#| 1-1=0 | 2-1=1 | 5-1=16 / 0.5 |
#| 1-1=0 | 2-1=1 | 5-1=16 / 0.5 |
#| 1-1=0 | 2-1=1 | 5-1=16 / 0.5 |
#--------------------------------
# w = 0.25 w = 0.5 w=0.25|
# 0 + 0.5 + 0.25*32 = 0.5 + 8 = 8.5
D = GlecklerPlot()
r = D.calc_index(x, y, 'a', 'b', time_weighting=False)
wt = np.ones(5) / 5.
ref = np.sqrt((8.5 * wt).sum())
t = np.abs(1. - r / ref)
self.assertLess(t, 0.000001) # relative error
wt = np.asarray([29., 31., 30., 31., 30.])
wt = wt / wt.sum()
ref = np.sqrt((8.5 * wt).sum())
t = np.abs(1. - r / ref)
self.assertLess(t, 0.000001) # relative error
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
import matplotlib.mlab as mlab
from scipy.interpolate import UnivariateSpline as spline
from glob import glob
files = sorted(glob("grand-island-test-wenzel-*.csv"))
# pumping start and end times
t0 = pylab.datestr2num("07/29/1931 06:05:00")
t1 = pylab.datestr2num("07/31/1931 06:04:00")
print "test length in seconds:", (t1 - t0) * 1440 * 60
tlen = (t1 - t0) * 1440
# column 0: date
# column 1: time
# column 2: drawdown (ft)
individualplots = True
computesplinederiv = True
drawdowncheck = True
mapcheckplot = True
saveMetricDecimalData = True
# apply uniform min/max derivatives to all data
ydmin, ydmax = (-0.2, 1.0)
if drawdowncheck:
def main():
funds = [
# # 股票基金
{"code": "004851", "name": "广发医疗保健股票"},
{"code": "000913", "name": "农银医疗保健股票"},
{"code": "000960", "name": "招商医药健康产业股票"},
# # 债券基金
{"code": "005461", "name": "南方希元转债"},
{"code": "000080", "name": "天治可转债增强债券A"},
{"code": "004993", "name": "中欧可转债债券A"},
#
# # 混合基金
{"code": "050026", "name": "博时医疗保健行业混合A"},
{"code": "003096", "name": "中欧医疗健康混合C"},
{"code": "005689", "name": "中银医疗保健混合"},
#
# # 大盘指数基金
{"code": "070039", "name": "嘉实中证500ETF联接C"},
{"code": "002987", "name": "广发沪深300ETF联接C"},
# # 行业指数基金
{"code": "161035", "name": "富国中证医药主题指数增强"},
{"code": "161122", "name": "易方达生物分级"},
{"code": "161726", "name": "招商国证生物医药指数分级"},
]
if USE_ALL_FUNDS:
print("拉取全部基金列表")
funds = []
allFunds = AllFundDownloader()
for fund in allFunds.funds:
funds.append({
"code": fund.code,
"name": fund.name,
})
# 下载每日净值图
if FETCH_FUNDS_GUZHI:
guzhi_images_dir = "guzhi_images"
print("下载每日净值图到{}".format(guzhi_images_dir))
# 创建结果目录
shutil.rmtree(guzhi_images_dir, True)
pathlib.Path(guzhi_images_dir).mkdir(parents=True, exist_ok=True)
for idx, fund in enumerate(funds):
chartDownloader = FundGuzhiChartDownloader(fund["code"], fund["name"], guzhi_images_dir)
res = ""
if chartDownloader.save_to_local():
res = "成功"
else:
res = "失败"
print("[{}/{}] 下载{}-{}成功, url={}".format(idx + 1, len(funds), chartDownloader.code, chartDownloader.name, chartDownloader.url))
# 合并为一个图
print("合并为单个图")
images_to_merge = []
for filename in os.listdir(guzhi_images_dir):
filepath = os.path.join(guzhi_images_dir, filename)
if filename.endswith(".png"):
images_to_merge.append(filepath)
merge_images_vertically_and_display(images_to_merge)
peroids = [
7,
14,
30,
]
now = datetime.datetime.now().strftime("%Y-%m-%d")
lastMonth = (datetime.datetime.now() - datetime.timedelta(days=int(365 / 12 * 1))).strftime("%Y-%m-%d")
lastTwoMonth = (datetime.datetime.now() - datetime.timedelta(days=int(365 / 12 * 2))).strftime("%Y-%m-%d")
lastSeason = (datetime.datetime.now() - datetime.timedelta(days=int(365 / 12 * 3))).strftime("%Y-%m-%d")
lastFourMonth = (datetime.datetime.now() - datetime.timedelta(days=int(365 / 12 * 4))).strftime("%Y-%m-%d")
lastFiveMonth = (datetime.datetime.now() - datetime.timedelta(days=int(365 / 12 * 5))).strftime("%Y-%m-%d")
lastHalfYear = (datetime.datetime.now() - datetime.timedelta(days=int(365 / 12 * 6))).strftime("%Y-%m-%d")
lastYear = (datetime.datetime.now() - datetime.timedelta(days=int(365))).strftime("%Y-%m-%d")
times = [
{"start": "2016-01-01", "end": "2017-01-01"}, # 2016年
{"start": "2017-01-01", "end": "2018-01-01"}, # 2017年
{"start": "2018-01-01", "end": "2019-01-01"}, # 2018年
{"start": "2016-01-01", "end": now}, # 2016年至今
{"start": "2017-01-01", "end": now}, # 2017年至今
{"start": "2018-01-01", "end": now}, # 2018年至今
{"start": "2019-01-01", "end": now}, # 2019年至今
{"start": lastMonth, "end": now}, # 上个月
{"start": lastTwoMonth, "end": now}, # 前两个月
{"start": lastSeason, "end": now}, # 上个季度
{"start": lastFourMonth, "end": now}, # 前四个月
{"start": lastFiveMonth, "end": now}, # 前五个月
{"start": lastHalfYear, "end": now}, # 前半年
{"start": lastYear, "end": now}, # 前年
]
fund_deal_map = {}
for idx, fund in enumerate(funds):
# 获取基金数据
print("[%d/%d] Loading data for %s" % (idx + 1, len(funds), fund["name"]))
fund_deal_map[fund["name"]] = BackTrackingDeal(fund["code"], fund["name"], DingtouStrategy(days=1))
if DRAW_PLOTS:
# 绘走势图
# plt.style.use('ggplot')
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
# plt.title(u'基金走势图')
fig, axs = plt.subplots(len(fund_deal_map), figsize=(10, 5 * len(fund_deal_map)))
idx = 0
start_dingtou_time = "2019-10-15"
for name, fund in fund_deal_map.items():
data = fund.get_range_data(start_dingtou_time, now)
# data = fund.fund.data
x = range(len(data))
x_date = [datestr2num(i.time) for i in data]
y_data = [i.unit_net_value for i in data]
fund.strategy = DingtouStrategy(days=30)
profit = fund.run(start_dingtou_time, now)
total_change_rate = (data[-1].unit_net_value - data[0].unit_net_value) / data[0].unit_net_value * 100
axs[idx].plot_date(x_date, y_data, '-', label=u"%s-%s-[%f%%]-[%s]" % (fund.fund_code, fund.fund_name, total_change_rate, profit["profit_rate"]))
for day in get_dingtou_days():
axs[idx].axvline(datestr2num(day), ymin=0.0, ymax=1.0, color="gray")
axs[idx].legend()
axs[idx].grid(True)
idx += 1
plt.xlabel(u'时间')
plt.ylabel(u'单位净值')
# plt.show()
plt.savefig("profit.png")
webbrowser.get("C:/Program Files (x86)/Google/Chrome/Application/chrome.exe %s").open("profit.png")
# 清空结果目录
shutil.rmtree("result")
print("output directory cleared")
for t in times:
start = t["start"]
end = t["end"]
duration = (datetime.datetime.strptime(end, "%Y-%m-%d") - datetime.datetime.strptime(start, "%Y-%m-%d")).days
file_name = "result/%s_%s.csv" % (start, end)
if not os.path.exists(os.path.dirname(file_name)):
os.makedirs(os.path.dirname(file_name))
summary = {}
for peroid in peroids:
summary[peroid] = {
"fund_name": "平均",
"duration": duration,
"strategy": "%d天" % peroid,
"invest_money": 0.001,
"profit": 0.0,
"profit_rate": "0%",
"annualized_profit_rate": "0%",
}
outputs = []
for fund in funds:
fund_deal = fund_deal_map[fund["name"]]
for peroid in peroids:
# 尝试不同策略
strategy = DingtouStrategy(days=peroid)
fund_deal.strategy = strategy
profit = fund_deal.run(start, end)
if len(profit) != 0:
outputs.append({
"fund_name": fund["name"],
"duration": duration,
"strategy": strategy.name(),
"invest_money": profit["invest_money"],
"profit": profit["profit"],
"profit_rate": profit["profit_rate"],
"annualized_profit_rate": profit["annualized_profit_rate"],
})
# show status
line = "%s,%s天,%s,%s,%s,%s,%s" % (
fund["name"],
duration,
strategy.name(),
profit["invest_money"],
profit["profit"],
profit["profit_rate"],
profit["annualized_profit_rate"])
print(line)
summary[peroid]["invest_money"] += profit["invest_money"]
summary[peroid]["profit"] += profit["profit"]
outputs.sort(key=lambda x: x["profit"], reverse=True)
for k, v in summary.items():
v["profit_rate"] = "%.4f%%" % (100 * v["profit"] / v["invest_money"])
v["annualized_profit_rate"] = "%.4f%%" % (100 * v["profit"] / v["invest_money"] * 365 / duration)
outputs.append(v)
with open(file_name, "w+") as ouput_file:
print("名称,时长,定投周期,总投入,总盈利,总盈利率,年化利率", file=ouput_file)
for output in outputs:
line = "%s,%s天,%s,%s,%s,%s,%s" % (
output["fund_name"],
output["duration"],
output["strategy"],
output["invest_money"],
output["profit"],
output["profit_rate"],
output["annualized_profit_rate"])
print(line, file=ouput_file)
return positions
#%%
positions = trading()
strategy_positions = pd.DataFrame(positions)
return_strategy = (strategy_positions / strategy_positions.shift()) - 1
return_strategy = return_strategy.cumsum()
hsa_closeIndex = pd.DataFrame(hsa.closeIndex)
return_hsa = (hsa_closeIndex / hsa_closeIndex.shift()) - 1
return_hsa = return_hsa.cumsum()
matplotlib.use('qt4agg')
myfont = FontProperties(fname='C:\Windows\Fonts\simhei.ttf')
matplotlib.rcParams['axes.unicode_minus'] = False
plt.figure(figsize=(10, 5))
plt.title(u"累计收益率", fontproperties=myfont)
x_date = [datestr2num(i) for i in hsa.tradeDate]
plt.plot_date(x_date, return_hsa, '-')
plt.plot_date(x_date, return_strategy, '-', color='r')
plt.legend(['恒生指数', '策略累计收益率'], prop=myfont)
#date = pd.to_datetime(hsa.tradeDate)
#hsa_hist = pd.DataFrame({'closeIndex':hsa.closeIndex},index=hsa.tradeDate)
#plt.plot(hsa_hist)
#plt.show()