def longsklearn(code='999999', ptype='f', dtype='d', start=None, end=None):
# code='999999'
# dtype = 'w'
# start = '2014-09-01'
# start = None
# end='2015-12-23'
# end = None
df = tdd.get_tdx_append_now_df(code, ptype, start,
end).sort_index(ascending=True)
# if not dtype == 'd':
# df = tdd.get_tdx_stock_period_to_type(df, dtype).sort_index(ascending=True)
dw = tdd.get_tdx_stock_period_to_type(df, dtype).sort_index(ascending=True)
# print df[:1]
h = df.loc[:, ['open', 'close', 'high', 'low']]
highp = h['high'].values
lowp = h['low'].values
openp = h['open'].values
closep = h['close'].values
lr = LinearRegression()
x = np.atleast_2d(np.linspace(0, len(closep), len(closep))).T
lr.fit(x, closep)
LinearRegression(copy_X=True,
fit_intercept=True,
n_jobs=1,
normalize=False)
xt = np.atleast_2d(np.linspace(0, len(closep) + 200, len(closep) + 200)).T
yt = lr.predict(xt)
# plt.plot(xt,yt,'-g',linewidth=5)
# plt.plot(closep)
bV = []
bP = []
uV = []
uP = []
for i in range(1, len(highp) - 1):
# if highp[i] <= highp[i - 1] and highp[i] < highp[i + 1] and lowp[i] <= lowp[i - 1] and lowp[i] < lowp[i + 1]:
if lowp[i] <= lowp[i - 1] and lowp[i] < lowp[i + 1]:
bV.append(lowp[i])
bP.append(i)
for i in range(1, len(highp) - 1):
# if highp[i] >= highp[i - 1] and highp[i] > highp[i + 1] and lowp[i] >= lowp[i - 1] and lowp[i] > lowp[i + 1]:
if highp[i] >= highp[i - 1] and highp[i] > highp[i + 1]:
uV.append(highp[i])
uP.append(i)
print(highp)
print("uV:%s" % uV[:1])
print("uP:%s" % uP[:1])
print("bV:%s" % bV[:1])
print("bP:%s" % bP[:1])
sV, sP = LIS(uV)
dV, dP = LIS(bV)
print("sV:%s" % sV[:1])
print("sP:%s" % sP[:1])
print("dV:%s" % dV[:1])
print("dP:%s" % dP[:1])
sidx = []
didx = []
for i in range(len(sP)):
# idx.append(bP[p[i]])
sidx.append(uP[sP[i]])
for i in range(len(dP)):
# idx.append(bP[p[i]])
didx.append(bP[dP[i]])
print("sidx:%s" % sidx[:1])
print("didx:%s" % didx[:1])
# plt.plot(closep)
# plt.plot(idx,d,'ko')
lr = LinearRegression()
X = np.atleast_2d(np.array(sidx)).T
Y = np.array(sV)
lr.fit(X, Y)
estV = lr.predict(xt)
fig = plt.figure(figsize=(16, 10), dpi=72)
# plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
plt.subplots_adjust(left=0.05,
bottom=0.08,
right=0.95,
top=0.95,
wspace=0.15,
hspace=0.25)
# set (gca,'Position',[0,0,512,512])
# fig.set_size_inches(18.5, 10.5)
# fig=plt.fig(figsize=(14,8))
ax = fig.add_subplot(111)
plt.grid(True)
# print h.index[:5], h['close']
ax = h['close'].plot()
# ax.plot(pd.datetime(h.index),h['close'], linewidth=1)
# ax.plot(uP, uV, linewidth=1)
# ax.plot(uP, uV, 'ko')
# ax.plot(bP, bV, linewidth=1)
# ax.plot(bP, bV, 'bo')
# # ax.plot(sP, sV, linewidth=1)
# # ax.plot(sP, sV, 'yo')
# ax.plot(sidx, sV, linewidth=1)
# ax.plot(sidx, sV, 'ro')
# ax.plot(didx, dV, linewidth=1)
# ax.plot(didx, dV, 'co')
df['mean'] = list(
map(lambda h, l: (h + l) / 2, df.high.values, df.low.values))
print(df['mean'][:1])
# d=df.mean
dw = dw.set_index('date')
# print dw[:2]
# ax.plot(df.index,df['mean'],'g',linewidth=1)
ax.plot(df.index, pd.rolling_mean(df['mean'], 60), 'g', linewidth=1)
ax.plot(dw.index, pd.rolling_mean(dw.close, 5), 'r', linewidth=1)
ax.plot(dw.index, pd.rolling_min(dw.close, 5), 'bo')
ax.plot(dw.index, pd.rolling_max(dw.close, 5), 'yo')
ax.plot(dw.index, pd.expanding_max(dw.close, 5), 'ro')
ax.plot(dw.index, pd.expanding_min(dw.close, 5), 'go')
# print pd.rolling_min(df.close,20)[:1],pd.rolling_min(df.close,20)[-1:]
# print pd.rolling_min(df.close,20)
# print pd.rolling_max(df.close,20)[:1],pd.rolling_max(df.close,20)[-1:]
# print pd.rolling_max(df.close,20)
# ax.plot(idx, d, 'ko')
# ax.plot(xt, estV, '-r', linewidth=5)
# ax.plot(xt, yt, '-g', linewidth=5)
# ax2 = fig.add_subplot(122)
# print len(closep),len(idx),len(d),len(xt),len(estV),len(yt)
# f=lambda x:x[-int(len(x)/10):]
# ax2.plot(f(closep))
# ax2.plot(f(idx),f(d),'ko')
# ax2.plot(f(xt),f(estV),'-r',linewidth=5)
# ax2.plot(f(xt),f(yt),'-g',linewidth=5)
# # plt.show()
scale = 1.1
zp = zoompan.ZoomPan()
figZoom = zp.zoom_factory(ax, base_scale=scale)
figPan = zp.pan_factory(ax)
show()
def show_chan_mpl(code,
start_date,
end_date,
stock_days,
resample,
show_mpl=True,
least_init=3,
chanK_flag=False,
windows=20):
def get_least_khl_num(resample, idx=0, init_num=3):
# init = 3
if init_num - idx > 0:
initw = init_num - idx
else:
initw = 0
return init_num if resample == 'd' else initw if resample == 'w' else init_num-idx-1 if init_num-idx-1 >0 else 0\
if resample == 'm' else 5
stock_code = code # 股票代码
# stock_code = '002176' # 股票代码
# start_date = '2017-09-05'
# start_date = None
# end_date = '2017-10-12 15:00:00' # 最后生成k线日期
# end_date = None
# stock_days = 60 # 看几天/分钟前的k线
# resample = 'd'
# resample = 'w'
x_jizhun = 3 # window 周期 x轴展示的时间距离 5：日，40:30分钟， 48： 5分钟
least_khl_num = get_least_khl_num(resample, init_num=least_init)
# stock_frequency = '5m' # 1d日线， 30m 30分钟， 5m 5分钟，1m 1分钟
stock_frequency = resample # 1d日线， 30m 30分钟， 5m 5分钟，1m 1分钟 w:week
# chanK_flag = chanK # True 看缠论K线， False 看k线
# chanK_flag = True # True 看缠论K线， False 看k线
show_mpl = show_mpl
def con2Cxianduan(stock,
k_data,
chanK,
frsBiType,
biIdx,
end_date,
cur_ji=1,
recursion=False,
dl=None,
chanK_flag=False,
least_init=3):
max_k_num = 4
if cur_ji >= 6 or len(biIdx) == 0 or recursion:
return biIdx
idx = biIdx[len(biIdx) - 1]
k_data_dts = list(k_data.index)
st_data = chanK['enddate'][idx]
if st_data not in k_data_dts:
return biIdx
# 重构次级别线段的点到本级别的chanK中
def refactorXd(biIdx, xdIdxc, chanK, chanKc, cur_ji):
new_biIdx = []
biIdxB = biIdx[len(biIdx) - 1] if len(biIdx) > 0 else 0
for xdIdxcn in xdIdxc:
for chanKidx in range(len(chanK.index))[biIdxB:]:
if judge_day_bao(chanK, chanKidx, chanKc, xdIdxcn, cur_ji):
new_biIdx.append(chanKidx)
break
return new_biIdx
# 判断次级别日期是否被包含
def judge_day_bao(chanK, chanKidx, chanKc, xdIdxcn, cur_ji):
_end_date = chanK['enddate'][chanKidx] + datetime.timedelta(
hours=15) if cur_ji == 1 else chanK['enddate'][chanKidx]
_start_date = chanK.index[chanKidx] if chanKidx == 0\
else chanK['enddate'][chanKidx - 1] + datetime.timedelta(minutes=1)
return _start_date <= chanKc.index[xdIdxcn] <= _end_date
# cur_ji = 1 #当前级别
# 符合k线根数大于4根 1日级别， 2 30分钟， 3 5分钟， 4 一分钟
if not recursion:
resample = 'd' if cur_ji + 1 == 2 else '5m' if cur_ji + 1 == 3 else \
'd' if cur_ji + 1 == 5 else 'w' if cur_ji + 1 == 6 else 'd'
least_khl_num = get_least_khl_num(resample, 1, init_num=least_init)
print "次级:%s st_data:%s k_data_dts:%s least_khl_num:%s" % (
len(k_data_dts) - k_data_dts.index(st_data), str(st_data)[:10],
len(k_data_dts), least_khl_num)
if cur_ji + 1 != 2 and len(k_data_dts) - k_data_dts.index(
st_data) >= least_khl_num + 1:
frequency = '30m' if cur_ji + 1 == 2 else '5m' if cur_ji + 1 == 3 else '1m'
# else:
# frequency = 'd' if cur_ji+1==2 else '5m' if cur_ji+1==3 else \
# 'd' if cur_ji+1==5 else 'w' if cur_ji+1==6 else 'd'
start_lastday = str(chanK.index[biIdx[-1]])[0:10]
print "次级别为:%s cur_ji:%s %s" % (resample, cur_ji, start_lastday)
# print [chanK.index[x] for x in biIdx]
k_data_c, cname = get_quotes_tdx(stock,
start=start_lastday,
end=end_date,
dl=dl,
resample=resample)
print k_data_c.index[0], k_data_c.index[-1]
chanKc = chan.parse2ChanK(
k_data_c, k_data_c.values) if chanK_flag else k_data_c
fenTypesc, fenIdxc = chan.parse2ChanFen(chanKc, recursion=True)
if len(fenTypesc) == 0:
return biIdx
biIdxc, frsBiTypec = chan.parse2ChanBi(
fenTypesc, fenIdxc, chanKc, least_khl_num=least_khl_num - 1)
if len(biIdxc) == 0:
return biIdx
print "biIdxc:", [round(k_data_c.high[x], 2) for x in biIdxc
], [str(k_data_c.index[x])[:10] for x in biIdxc]
xdIdxc, xdTypec = chan.parse2Xianduan(
biIdxc, chanKc, least_windows=1 if least_khl_num > 0 else 0)
biIdxc = con2Cxianduan(stock,
k_data_c,
chanKc,
frsBiTypec,
biIdxc,
end_date,
cur_ji + 1,
recursion=True)
print "xdIdxc:%s xdTypec:%s biIdxc:%s" % (xdIdxc, xdTypec, biIdxc)
if len(xdIdxc) == 0:
return biIdx
# 连接线段位为上级别的bi
lastBiType = frsBiType if len(biIdx) % 2 == 0 else -frsBiType
if len(biIdx) == 0:
return refactorXd(biIdx, xdIdxc, chanK, chanKc, cur_ji)
lastbi = biIdx.pop()
firstbic = xdIdxc.pop(0)
# 同向连接
if lastBiType == xdTypec:
biIdx = biIdx + refactorXd(biIdx, xdIdxc, chanK, chanKc,
cur_ji)
# 逆向连接
else:
# print '开始逆向连接'
_mid = [lastbi] if (lastBiType == -1 and chanK['low'][lastbi] <= chanKc['low'][firstbic])\
or (lastBiType == 1 and chanK['high'][lastbi] >= chanKc['high'][firstbic]) else\
[chanKidx for chanKidx in range(len(chanK.index))[biIdx[len(biIdx) - 1]:]
if judge_day_bao(chanK, chanKidx, chanKc, firstbic, cur_ji)]
biIdx = biIdx + [_mid[0]] + refactorXd(biIdx, xdIdxc, chanK,
chanKc, cur_ji)
# print "次级:",len(biIdx),biIdx,[str(k_data_c.index[x])[:10] for x in biIdx]
return biIdx
def get_quotes_tdx(code,
start=None,
end=None,
dl=120,
resample='d',
show_name=True):
quotes = tdd.get_tdx_append_now_df_api(
code=stock_code, start=start, end=end,
dl=dl).sort_index(ascending=True)
if not resample == 'd' and resample in tdd.resample_dtype:
quotes = tdd.get_tdx_stock_period_to_type(quotes,
period_day=resample)
quotes.index = quotes.index.astype('datetime64')
if show_name:
if 'name' in quotes.columns:
cname = quotes.name[0]
# cname_g =cname
else:
dm = tdd.get_sina_data_df(code)
if 'name' in dm.columns:
cname = dm.name[0]
else:
cname = '-'
else:
cname = '-'
if quotes is not None and len(quotes) > 0:
quotes = quotes.loc[:, [
'open', 'close', 'high', 'low', 'vol', 'amount'
]]
else:
# log.error("quotes is None check:%s"%(code))
raise Exception("Code:%s error, df is None%s" % (code))
return quotes, cname
quotes, cname = get_quotes_tdx(stock_code,
start_date,
end_date,
dl=stock_days,
resample=resample,
show_name=show_mpl)
# quotes.rename(columns={'amount': 'money'}, inplace=True)
# quotes.rename(columns={'vol': 'vol'}, inplace=True)
# print quotes[-2:]
# print quotes[:1]
# 缠论k线
# open close high low volume money
# 2017-05-03 15.69 15.66 15.73 15.53 10557743 165075887
# 2017-05-04 15.66 15.63 15.70 15.52 8343270 130330396
# 2017-05-05 15.56 15.65 15.68 15.41 18384031 285966842
# 2017-05-08 15.62 15.75 15.76 15.54 12598891 197310688
quotes = chan.parse2ChanK(quotes, quotes.values) if chanK_flag else quotes
# print quotes[:1].index
# print quotes[-1:].index
quotes[quotes['vol'] == 0] = np.nan
quotes = quotes.dropna()
Close = quotes['close']
Open = quotes['open']
High = quotes['high']
Low = quotes['low']
T0 = quotes.index.values
# T0 = mdates.date2num(T0)
length = len(Close)
initial_trend = "down"
cur_ji = 1 if stock_frequency == 'd' else \
2 if stock_frequency == '30m' else \
3 if stock_frequency == '5m' else \
4 if stock_frequency == 'w' else \
5 if stock_frequency == 'm' else 6
log.debug('======笔形成最后一段未完成段判断是否是次级别的走势形成笔=======:%s %s' %
(stock_frequency, cur_ji))
x_date_list = quotes.index.values.tolist()
# for x_date in x_date_list:
# d = datetime.datetime.fromtimestamp(x_date/1000000000)
# print d.strftime("%Y-%m-%d %H:%M:%S.%f")
# print x_date_list
k_data = quotes
k_values = k_data.values
# 缠论k线
chanK = quotes if chanK_flag else chan.parse2ChanK(
k_data, k_values, chan_kdf=chanK_flag)
fenTypes, fenIdx = chan.parse2ChanFen(chanK)
# log.debug("code:%s fenTypes:%s fenIdx:%s k_data:%s" % (stock_code,fenTypes, fenIdx, len(k_data)))
biIdx, frsBiType = chan.parse2ChanBi(fenTypes,
fenIdx,
chanK,
least_khl_num=least_khl_num)
# log.debug("biIdx1:%s chanK:%s" % (biIdx, len(chanK)))
print("biIdx1:%s %s chanK:%s" %
(biIdx, str(chanK.index.values[biIdx[-1]])[:10], len(chanK)))
biIdx = con2Cxianduan(stock_code,
k_data,
chanK,
frsBiType,
biIdx,
end_date,
cur_ji,
least_init=least_init)
# log.debug("biIdx2:%s chanK:%s" % (biIdx, len(biIdx)))
chanKIdx = [(chanK.index[x]) for x in biIdx]
if len(biIdx) == 0 and len(chanKIdx) == 0:
print "BiIdx is None and chanKidx is None:%s" % (code)
return None
log.debug("con2Cxianduan:%s chanK:%s %s" %
(biIdx, len(chanK), chanKIdx[-1] if len(chanKIdx) > 0 else None))
# print quotes['close'].apply(lambda x:round(x,2))
# print '股票代码', get_security_info(stock_code).display_name
# print '股票代码', (stock_code), resample, least_khl_num
# 3.得到分笔结果，计算坐标显示
def plot_fenbi_seq(biIdx, frsBiType, plt=None, color=None):
x_fenbi_seq = []
y_fenbi_seq = []
for i in range(len(biIdx)):
if biIdx[i] is not None:
fenType = -frsBiType if i % 2 == 0 else frsBiType
# dt = chanK['enddate'][biIdx[i]]
# 缠论k线
dt = chanK.index[biIdx[i]] if chanK_flag else chanK['enddate'][
biIdx[i]]
# print i,k_data['high'][dt], k_data['low'][dt]
time_long = long(
time.mktime(
(dt + datetime.timedelta(hours=8)).timetuple()) *
1000000000)
# print x_date_list.index(time_long) if time_long in x_date_list else 0
if fenType == 1:
if plt is not None:
if color is None:
plt.text(x_date_list.index(time_long),
k_data['high'][dt],
str(k_data['high'][dt]),
ha='left',
fontsize=12)
else:
col_v = color[0] if fenType > 0 else color[1]
plt.text(x_date_list.index(time_long),
k_data['high'][dt],
str(k_data['high'][dt]),
ha='left',
fontsize=12,
bbox=dict(facecolor=col_v, alpha=0.5))
x_fenbi_seq.append(x_date_list.index(time_long))
y_fenbi_seq.append(k_data['high'][dt])
if fenType == -1:
if plt is not None:
if color is None:
plt.text(x_date_list.index(time_long),
k_data['low'][dt],
str(k_data['low'][dt]),
va='bottom',
fontsize=12)
else:
col_v = color[0] if fenType > 0 else color[1]
plt.text(x_date_list.index(time_long),
k_data['low'][dt],
str(k_data['low'][dt]),
va='bottom',
fontsize=12,
bbox=dict(facecolor=col_v, alpha=0.5))
x_fenbi_seq.append(x_date_list.index(time_long))
y_fenbi_seq.append(k_data['low'][dt])
# bottom_time = None
# for k_line_dto in m_line_dto.member_list[::-1]:
# if k_line_dto.low == m_line_dto.low:
# # get_price返回的日期，默认时间是08:00:00
# bottom_time = k_line_dto.begin_time.strftime('%Y-%m-%d') +' 08:00:00'
# break
# x_fenbi_seq.append(x_date_list.index(long(time.mktime(datetime.strptime(bottom_time, "%Y-%m-%d %H:%M:%S").timetuple())*1000000000)))
# y_fenbi_seq.append(m_line_dto.low)
return x_fenbi_seq, y_fenbi_seq
# print T0[-len(T0):].astype(dt.date)
T1 = T0[-len(T0):].astype(datetime.date) / 1000000000
Ti = []
if len(T0) / x_jizhun > 12:
x_jizhun = len(T0) / 12
for i in range(len(T0) / x_jizhun):
# print "len(T0)/x_jizhun:",len(T0)/x_jizhun
a = i * x_jizhun
d = datetime.date.fromtimestamp(T1[a])
# print d
T2 = d.strftime('$%Y-%m-%d$')
Ti.append(T2)
# print tab
d1 = datetime.date.fromtimestamp(T1[len(T0) - 1])
d2 = (d1 + datetime.timedelta(days=1)).strftime('$%Y-%m-%d$')
Ti.append(d2)
ll = Low.min() * 0.97
hh = High.max() * 1.03
# ht = HoverTool(tooltips=[
# ("date", "@date"),
# ("open", "@open"),
# ("close", "@close"),
# ("high", "@high"),
# ("low", "@low"),
# ("volume", "@volume"),
# ("money", "@money"),])
# TOOLS = [ht, WheelZoomTool(dimensions=['width']),\
# ResizeTool(), ResetTool(),\
# PanTool(dimensions=['width']), PreviewSaveTool()]
if show_mpl:
fig = plt.figure(figsize=(10, 6))
ax1 = plt.subplot2grid((10, 1), (0, 0), rowspan=8, colspan=1)
# ax1 = fig.add_subplot(2,1,1)
#fig = plt.figure()
#ax1 = plt.axes([0,0,3,2])
X = np.array(range(0, length))
pad_nan = X + nan
# 计算上 下影线
max_clop = Close.copy()
max_clop[Close < Open] = Open[Close < Open]
min_clop = Close.copy()
min_clop[Close > Open] = Open[Close > Open]
# 上影线
line_up = np.array([High, max_clop, pad_nan])
line_up = np.ravel(line_up, 'F')
# 下影线
line_down = np.array([Low, min_clop, pad_nan])
line_down = np.ravel(line_down, 'F')
# 计算上下影线对应的X坐标
pad_nan = nan + X
pad_X = np.array([X, X, X])
pad_X = np.ravel(pad_X, 'F')
# 画出实体部分,先画收盘价在上的部分
up_cl = Close.copy()
up_cl[Close <= Open] = nan
up_op = Open.copy()
up_op[Close <= Open] = nan
down_cl = Close.copy()
down_cl[Open <= Close] = nan
down_op = Open.copy()
down_op[Open <= Close] = nan
even = Close.copy()
even[Close != Open] = nan
# 画出收红的实体部分
pad_box_up = np.array([up_op, up_op, up_cl, up_cl, pad_nan])
pad_box_up = np.ravel(pad_box_up, 'F')
pad_box_down = np.array([down_cl, down_cl, down_op, down_op, pad_nan])
pad_box_down = np.ravel(pad_box_down, 'F')
pad_box_even = np.array([even, even, even, even, pad_nan])
pad_box_even = np.ravel(pad_box_even, 'F')
# X的nan可以不用与y一一对应
X_left = X - 0.25
X_right = X + 0.25
box_X = np.array([X_left, X_right, X_right, X_left, pad_nan])
# print box_X
box_X = np.ravel(box_X, 'F')
# print box_X
# Close_handle=plt.plot(pad_X,line_up,color='k')
vertices_up = np.array([box_X, pad_box_up]).T
vertices_down = np.array([box_X, pad_box_down]).T
vertices_even = np.array([box_X, pad_box_even]).T
handle_box_up = mat.patches.Polygon(vertices_up, color='r', zorder=1)
handle_box_down = mat.patches.Polygon(vertices_down,
color='g',
zorder=1)
handle_box_even = mat.patches.Polygon(vertices_even,
color='k',
zorder=1)
ax1.add_patch(handle_box_up)
ax1.add_patch(handle_box_down)
ax1.add_patch(handle_box_even)
handle_line_up = mat.lines.Line2D(pad_X,
line_up,
color='k',
linestyle='solid',
zorder=0)
handle_line_down = mat.lines.Line2D(pad_X,
line_down,
color='k',
linestyle='solid',
zorder=0)
ax1.add_line(handle_line_up)
ax1.add_line(handle_line_down)
v = [0, length, Open.min() - 0.5, Open.max() + 0.5]
plt.axis(v)
ax1.set_xticks(np.linspace(-2, len(Close) + 2, len(Ti)))
ax1.set_ylim(ll, hh)
ax1.set_xticklabels(Ti)
plt.grid(True)
plt.setp(plt.gca().get_xticklabels(),
rotation=30,
horizontalalignment='right')
'''
以上代码拷贝自https://www.joinquant.com/post/1756
感谢alpha-smart-dog
K线图绘制完毕
'''
# print "biIdx:%s chankIdx:%s"%(biIdx,str(chanKIdx[-1])[:10])
if show_mpl:
x_fenbi_seq, y_fenbi_seq = plot_fenbi_seq(biIdx, frsBiType, plt)
# plot_fenbi_seq(fenIdx,fenTypes[0], plt,color=['red','green'])
plot_fenbi_seq(fenIdx, frsBiType, plt, color=['red', 'green'])
else:
x_fenbi_seq, y_fenbi_seq = plot_fenbi_seq(biIdx, frsBiType, plt=None)
plot_fenbi_seq(fenIdx, frsBiType, plt=None, color=['red', 'green'])
# 在原图基础上添加分笔蓝线
inx_value = chanK.high.values
inx_va = [round(inx_value[x], 2) for x in biIdx]
log.debug("inx_va:%s count:%s" % (inx_va, len(quotes.high)))
log.debug("yfenbi:%s count:%s" % ([round(y, 2)
for y in y_fenbi_seq], len(chanK)))
j_BiType = [
-frsBiType if i % 2 == 0 else frsBiType for i in range(len(biIdx))
]
BiType_s = j_BiType[-1] if len(j_BiType) > 0 else -2
# bi_price = [str(chanK.low[idx]) if i % 2 == 0 else str(chanK.high[idx]) for i,idx in enumerate(biIdx)]
# print ("笔 :%s %s"%(biIdx,bi_price))
# fen_dt = [str(chanK.index[fenIdx[i]])[:10] if chanK_flag else str(chanK['enddate'][fenIdx[i]])[:10]for i in range(len(fenIdx))]
fen_dt = [(chanK.index[fenIdx[i]]) if chanK_flag else
(chanK['enddate'][fenIdx[i]]) for i in range(len(fenIdx))]
if len(fenTypes) > 0:
if fenTypes[0] == -1:
# fen_price = [str(k_data.low[idx]) if i % 2 == 0 else str(k_data.high[idx]) for i,idx in enumerate(fen_dt)]
low_fen = [idx for i, idx in enumerate(fen_dt) if i % 2 == 0]
high_fen = [idx for i, idx in enumerate(fen_dt) if i % 2 <> 0]
else:
# fen_price = [str(k_data.high[idx]) if i % 2 == 0 else str(k_data.low[idx]) for i,idx in enumerate(fen_dt)]
high_fen = [idx for i, idx in enumerate(fen_dt) if i % 2 == 0]
low_fen = [idx for i, idx in enumerate(fen_dt) if i % 2 <> 0]
# fen_duration =[fenIdx[i] - fenIdx[i -1 ] if i >0 else 0 for i,idx in enumerate(fenIdx)]
else:
# fen_price = fenTypes
# fen_duration = fenTypes
low_fen = []
high_fen = []
# fen_dt = [str(k_data.index[idx])[:10] for i,idx in enumerate(fenIdx)]
# print low_fen,high_fen
def dataframe_mode_round(df):
roundlist = [1, 0]
df_mode = []
# df.high.cummin().value_counts()
for i in roundlist:
df_mode = df.apply(lambda x: round(x, i)).mode()
if len(df_mode) > 0:
break
return df_mode
kdl = k_data.loc[low_fen].low
kdl_mode = dataframe_mode_round(kdl)
kdh = k_data.loc[high_fen].high
kdh_mode = dataframe_mode_round(kdh)
print("kdl:%s" % (kdl.values))
print("kdh:%s" % (kdh.values))
print("kdl_mode:%s kdh_mode%s chanKidx:%s" %
(kdl_mode.values, kdh_mode.values, str(chanKIdx[-1])[:10]))
lastdf = k_data[k_data.index >= chanKIdx[-1]]
if BiType_s == -1:
keydf = lastdf[((lastdf.close >= kdl_mode.max()) &
(lastdf.low >= kdl_mode.max()))]
elif BiType_s == 1:
keydf = lastdf[((lastdf.close >= kdh_mode.max()) &
(lastdf.high >= kdh_mode.min()))]
else:
keydf = lastdf[((lastdf.close >= kdh_mode.max()) &
(lastdf.high >= kdh_mode.min())) |
((lastdf.close <= kdl_mode.min()) &
(lastdf.low <= kdl_mode.min()))]
print("BiType_s:%s keydf:%s key:%s" %
(BiType_s, None if len(keydf) == 0 else str(
keydf.index.values[0])[:10], len(keydf)))
# return BiType_s,None if len(keydf) == 0 else str(keydf.index.values[0])[:10],len(keydf)
# import ipdb;ipdb.set_trace()
log.debug("Fentype:%s " % (fenTypes))
log.debug("fenIdx:%s " % (fenIdx))
# print ("fen_duration:%s "%(fen_duration))
# print ("fen_price:%s "%(fen_price))
# print ("fendt:%s "%(fen_dt))
print("BiType :%s frsBiType:%s" % (j_BiType, frsBiType))
if len(j_BiType) > 0:
if j_BiType[0] == -1:
tb_price = [
str(quotes.low[idx]) if i % 2 == 0 else str(quotes.high[idx])
for i, idx in enumerate(x_fenbi_seq)
]
else:
tb_price = [
str(quotes.high[idx]) if i % 2 == 0 else str(quotes.low[idx])
for i, idx in enumerate(x_fenbi_seq)
]
tb_duration = [
x_fenbi_seq[i] - x_fenbi_seq[i - 1] if i > 0 else 0
for i, idx in enumerate(x_fenbi_seq)
]
else:
tb_price = j_BiType
tb_duration = j_BiType
print "图笔 :", x_fenbi_seq, tb_price
print "图笔dura :", tb_duration
# 线段画到笔上
xdIdxs, xfenTypes = chan.parse2ChanXD(frsBiType, biIdx, chanK)
print '线段', xdIdxs, xfenTypes
x_xd_seq = []
y_xd_seq = []
for i in range(len(xdIdxs)):
if xdIdxs[i] is not None:
fenType = xfenTypes[i]
# dt = chanK['enddate'][biIdx[i]]
# 缠论k线
dt = chanK.index[xdIdxs[i]] if chanK_flag else chanK['enddate'][
xdIdxs[i]]
# print k_data['high'][dt], k_data['low'][dt]
time_long = long(
time.mktime((dt + datetime.timedelta(hours=8)).timetuple()) *
1000000000)
# print x_date_list.index(time_long) if time_long in x_date_list else 0
if fenType == 1:
x_xd_seq.append(x_date_list.index(time_long))
y_xd_seq.append(k_data['high'][dt])
if fenType == -1:
x_xd_seq.append(x_date_list.index(time_long))
y_xd_seq.append(k_data['low'][dt])
# bottom_time = None
# for k_line_dto in m_line_dto.member_list[::-1]:
# if k_line_dto.low == m_line_dto.low:
# # get_price返回的日期，默认时间是08:00:00
# bottom_time = k_line_dto.begin_time.strftime('%Y-%m-%d') +' 08:00:00'
# break
# x_fenbi_seq.append(x_date_list.index(long(time.mktime(datetime.strptime(bottom_time, "%Y-%m-%d %H:%M:%S").timetuple())*1000000000)))
# y_fenbi_seq.append(m_line_dto.low)
# 在原图基础上添加分笔蓝线
print("线段 :%s" % (x_xd_seq))
print("笔值 :%s" % ([str(x) for x in (y_xd_seq)]))
# Y_hat = X * b + a
if show_mpl:
plt.plot(x_fenbi_seq, y_fenbi_seq)
plt.legend([stock_code, cname], loc=0)
plt.title(stock_code + " | " + cname + " | " +
str(quotes.index[-1])[:10],
fontsize=14)
plt.plot(x_xd_seq, y_xd_seq)
if len(quotes) > windows:
roll_mean = pd.rolling_mean(quotes.close, window=windows)
plt.plot(roll_mean, 'r')
zp = zoompan.ZoomPan()
figZoom = zp.zoom_factory(ax1, base_scale=1.1)
figPan = zp.pan_factory(ax1)
'''#subplot2 bar
ax2 = plt.subplot2grid((10, 1), (8, 0), rowspan=2, colspan=1)
# ax2.plot(quotes.vol)
# ax2.set_xticks(np.linspace(-2, len(quotes) + 2, len(Ti)))
ll = min(quotes.vol.values.tolist()) * 0.97
hh = max(quotes.vol.values.tolist()) * 1.03
ax2.set_ylim(ll, hh)
# ax2.set_xticklabels(Ti)
# plt.hist(quotes.vol, histtype='bar', rwidth=0.8)
plt.bar(x_date_list,quotes.vol, label="Volume", color='b')
'''
#画Volume no tight_layout()
'''
pad = 0.25
yl = ax1.get_ylim()
ax1.set_ylim(yl[0]-(yl[1]-yl[0])*pad,yl[1])
ax2 = ax1.twinx()
ax2.set_position(mat.transforms.Bbox([[0.125,0.1],[0.9,0.32]]))
volume = np.asarray(quotes.amount)
pos = quotes['open']-quotes['close']<0
neg = quotes['open']-quotes['close']>=0
idx = quotes.reset_index().index
ax2.bar(idx[pos],volume[pos],color='red',width=1,align='center')
ax2.bar(idx[neg],volume[neg],color='green',width=1,align='center')
yticks = ax2.get_yticks()
ax2.set_yticks(yticks[::3])
'''
# same sharex
plt.subplots_adjust(left=0.05,
bottom=0.08,
right=0.95,
top=0.95,
wspace=0.15,
hspace=0.00)
plt.setp(ax1.get_xticklabels(), visible=False)
yl = ax1.get_ylim()
# ax2 = plt.subplot(212, sharex=ax1)
ax2 = plt.subplot2grid((10, 1), (8, 0),
rowspan=2,
colspan=1,
sharex=ax1)
# ax2.set_position(mat.transforms.Bbox([[0.125,0.1],[0.9,0.32]]))
volume = np.asarray(quotes.amount)
pos = quotes['open'] - quotes['close'] < 0
neg = quotes['open'] - quotes['close'] >= 0
idx = quotes.reset_index().index
ax2.bar(idx[pos], volume[pos], color='red', width=1, align='center')
ax2.bar(idx[neg], volume[neg], color='green', width=1, align='center')
yticks = ax2.get_yticks()
ax2.set_yticks(yticks[::3])
# plt.tight_layout()
# plt.subplots_adjust(hspace=0.00, bottom=0.08)
plt.xticks(rotation=15, horizontalalignment='center')
# plt.bar(x_date_list,quotes.vol, label="Volume", color='b')
# quotes['vol'].plot(kind='bar', ax=ax2, color='g', alpha=0.1)
# ax2.set_ylim([0, ax2.get_ylim()[1] * 2])
# plt.gcf().subplots_adjust(bottom=0.15)
# fig.subplots_adjust(left=0.05, bottom=0.08, right=0.95, top=0.95, wspace=0.15, hspace=0.25)
#scale the x-axis tight
# ax2.set_xlim(min(x_date_list),max(x_date_list))
# the y-ticks for the bar were too dense, keep only every third one
# plt.grid(True)
# plt.xticks(rotation=30, horizontalalignment='center')
# plt.setp( axs[1].xaxis.get_majorticklabels(), rotation=70 )
# plt.legend()
# plt.tight_layout()
# plt.draw()
# plt.show()
plt.show(block=False)
def longsklearn(code='999999'):
# code='999999'
df = tdd.get_tdx_append_now_df(code, 'f').sort_index(ascending=True)
# print df[:1]
h = df.loc[:, ['open', 'close', 'high', 'low']]
highp = h['high'].values
lowp = h['low'].values
openp = h['open'].values
closep = h['close'].values
lr = LinearRegression()
x = np.atleast_2d(np.linspace(0, len(closep), len(closep))).T
lr.fit(x, closep)
LinearRegression(copy_X=True,
fit_intercept=True,
n_jobs=1,
normalize=False)
xt = np.atleast_2d(np.linspace(0, len(closep) + 200, len(closep) + 200)).T
yt = lr.predict(xt)
# plt.plot(xt,yt,'-g',linewidth=5)
# plt.plot(closep)
bV = []
bP = []
for i in range(1, len(highp) - 1):
if highp[i] <= highp[i - 1] and highp[i] < highp[
i + 1] and lowp[i] <= lowp[i - 1] and lowp[i] < lowp[i + 1]:
bV.append(lowp[i])
bP.append(i)
d, p = LIS(bV)
idx = []
for i in range(len(p)):
idx.append(bP[p[i]])
# plt.plot(closep)
# plt.plot(idx,d,'ko')
lr = LinearRegression()
X = np.atleast_2d(np.array(idx)).T
Y = np.array(d)
lr.fit(X, Y)
estV = lr.predict(xt)
fig = plt.figure(figsize=(16, 10), dpi=72)
# plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
plt.subplots_adjust(left=0.05,
bottom=0.08,
right=0.95,
top=0.95,
wspace=0.15,
hspace=0.25)
# set (gca,'Position',[0,0,512,512])
# fig.set_size_inches(18.5, 10.5)
# fig=plt.fig(figsize=(14,8))
ax = fig.add_subplot(111)
plt.grid(True)
ax.plot(closep, linewidth=2)
ax.plot(idx, d, 'ko')
ax.plot(xt, estV, '-r', linewidth=5)
ax.plot(xt, yt, '-g', linewidth=5)
# ax2 = fig.add_subplot(122)
# print len(closep),len(idx),len(d),len(xt),len(estV),len(yt)
# f=lambda x:x[-int(len(x)/10):]
# ax2.plot(f(closep))
# ax2.plot(f(idx),f(d),'ko')
# ax2.plot(f(xt),f(estV),'-r',linewidth=5)
# ax2.plot(f(xt),f(yt),'-g',linewidth=5)
# # plt.show()
scale = 1.1
zp = zoompan.ZoomPan()
figZoom = zp.zoom_factory(ax, base_scale=scale)
figPan = zp.pan_factory(ax)
show()
sys.exit(0)
import matplotlib.pyplot as plt
import numpy as np
t = np.arange(0.01, 5.0, 0.01)
s1 = np.sin(2 * np.pi * t)
s2 = np.exp(-t)
s3 = np.sin(4 * np.pi * t)
ax1 = plt.subplot(311)
plt.plot(t, s1)
plt.setp(ax1.get_xticklabels(), fontsize=6)
# share x only
ax2 = plt.subplot(312, sharex=ax1)
plt.plot(t, s2)
# make these tick labels invisible
plt.setp(ax2.get_xticklabels(), visible=False)
# share x and y
ax3 = plt.subplot(313, sharex=ax1, sharey=ax1)
plt.plot(t, s3)
plt.xlim(0.01, 5.0)
import sys
sys.path.append('../../')
from JohnsonUtil import zoompan
zp = zoompan.ZoomPan()
figZoom = zp.zoom_factory(ax1, base_scale=1.1)
figPan = zp.pan_factory(ax1)
plt.show()
def get_linear_model_histogram(code,
ptype='low',
dtype='d',
start=None,
end=None,
vtype='f',
filter='n',
df=None):
# 399001','cyb':'zs399006','zxb':'zs399005
# code = '999999'
# code = '601608'
# code = '000002'
# asset = get_kdate_data(code)['close'].sort_index(ascending=True)
# df = tdd.get_tdx_Exp_day_to_df(code, 'f').sort_index(ascending=True)
# ptype='close'
# if ptype == 'close' or ptype==''
# ptype=
if start is not None and filter == 'y':
if code not in ['999999', '399006', '399001']:
index_d, dl = tdd.get_duration_Index_date(dt=start)
log.debug("index_d:%s dl:%s" % (str(index_d), dl))
else:
index_d = cct.day8_to_day10(start)
log.debug("index_d:%s" % (index_d))
start = tdd.get_duration_price_date(code, ptype='low', dt=index_d)
log.debug("start:%s" % (start))
if df is None:
# df = tdd.get_tdx_append_now_df(code, ptype, start, end).sort_index(ascending=True)
df = tdd.get_tdx_append_now_df_api(code, start,
end).sort_index(ascending=True)
if not dtype == 'd':
df = tdd.get_tdx_stock_period_to_type(df,
dtype).sort_index(ascending=True)
asset = df[ptype]
log.info("df:%s" % asset[:1])
asset = asset.dropna()
dates = asset.index
if not code.startswith('999') and not code.startswith('399'):
# print "code:",code
if code[:1] in ['5', '6', '9']:
code2 = '999999'
elif code[:2] in ['30']:
# print "cyb"
code2 = '399006'
else:
code2 = '399001'
df1 = tdd.get_tdx_append_now_df_api(code2, start,
end).sort_index(ascending=True)
# df1 = tdd.get_tdx_append_now_df(code2, ptype, start, end).sort_index(ascending=True)
if not dtype == 'd':
df1 = tdd.get_tdx_stock_period_to_type(
df1, dtype).sort_index(ascending=True)
# if len(asset) < len(df1):
# asset1 = df1.loc[asset.index, ptype]
# else:
# asset1 = df1.loc[asset.index, ptype]
# startv = asset1[:1]
# asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2))
# print asset[:1].index[0] , df1[:1].index[0]
if asset[:1].index[0] > df1[:1].index[0]:
asset1 = df1.loc[asset.index, ptype]
startv = asset1[:1]
asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2))
else:
df = df[df.index >= df1.index[0]]
asset = df[ptype]
asset = asset.dropna()
dates = asset.index
asset1 = df1.loc[df.index, ptype]
asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2))
else:
if code.startswith('399001'):
code2 = '999999'
elif code.startswith('399006'):
code2 = '399005'
else:
code2 = '399001'
df1 = tdd.get_tdx_append_now_df_api(code2, start,
end).sort_index(ascending=True)
# print df1[:1]
# df1 = tdd.get_tdx_append_now_df(code2, ptype, start, end).sort_index(ascending=True)
if not dtype == 'd':
df1 = tdd.get_tdx_stock_period_to_type(
df1, dtype).sort_index(ascending=True)
if len(asset) < len(df1):
asset1 = df1.loc[asset.index, ptype]
asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2))
else:
df = df[df.index >= df1.index[0]]
asset = df[ptype]
asset = asset.dropna()
dates = asset.index
asset1 = df1.loc[df.index, ptype]
asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2))
# print len(df),len(asset),len(df1),len(asset1)
if end is not None:
# print asset[-1:]
asset = asset[:-1]
dates = asset.index
asset1 = asset1[:-1]
asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2))
# 画出价格随时间变化的图像
# _, ax = plt.subplots()
# fig = plt.figure()
fig = plt.figure(figsize=(16, 5))
# fig = plt.figure(figsize=(16, 10), dpi=72)
# fig.autofmt_xdate() #(no fact)
# plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
plt.subplots_adjust(left=0.05,
bottom=0.08,
right=0.95,
top=0.95,
wspace=0.15,
hspace=0.25)
# set (gca,'Position',[0,0,512,512])
# fig.set_size_inches(18.5, 10.5)
# fig=plt.fig(figsize=(14,8))
ax1 = fig.add_subplot(121)
# asset=asset.apply(lambda x:round( x/asset[:1],2))
ax1.plot(asset)
# ax1.plot(asset1,'-r', linewidth=2)
ticks = ax1.get_xticks()
# start, end = ax1.get_xlim()
# print start, end, len(asset)
# print ticks, ticks[:-1]
# (ticks[:-1] if len(asset) > end else np.append(ticks[:-1], len(asset) - 1))
ax1.set_xticklabels(
[dates[int(i)] for i in (np.append(ticks[:-1],
len(asset) - 1))],
rotation=15) # Label x-axis with dates
# 拟合
X = np.arange(len(asset))
x = sm.add_constant(X)
model = regression.linear_model.OLS(asset, x).fit()
a = model.params[0]
b = model.params[1]
# log.info("a:%s b:%s" % (a, b))
log.info("X:%s a:%s b:%s" % (len(asset), a, b))
Y_hat = X * b + a
# 真实值-拟合值，差值最大最小作为价值波动区间
# 向下平移
i = (asset.values.T - Y_hat).argmin()
c_low = X[i] * b + a - asset.values[i]
Y_hatlow = X * b + a - c_low
# 向上平移
i = (asset.values.T - Y_hat).argmax()
c_high = X[i] * b + a - asset.values[i]
Y_hathigh = X * b + a - c_high
plt.plot(X, Y_hat, 'k', alpha=0.9)
plt.plot(X, Y_hatlow, 'r', alpha=0.9)
plt.plot(X, Y_hathigh, 'r', alpha=0.9)
# plt.xlabel('Date', fontsize=12)
plt.ylabel('Price', fontsize=12)
plt.title(code + " | " + str(dates[-1])[:11], fontsize=14)
plt.legend([asset.iat[-1]], fontsize=12, loc=4)
plt.grid(True)
# plt.legend([code]);
# plt.legend([code, 'Value center line', 'Value interval line']);
# fig=plt.fig()
# fig.figsize = [14,8]
scale = 1.1
zp = zoompan.ZoomPan()
figZoom = zp.zoom_factory(ax1, base_scale=scale)
figPan = zp.pan_factory(ax1)
# 将Y-Y_hat股价偏离中枢线的距离单画出一张图显示，对其边界线之间的区域进行均分，大于0的区间为高估，小于0的区间为低估，0为价值中枢线。
ax3 = fig.add_subplot(122)
# distance = (asset.values.T - Y_hat)
distance = (asset.values.T - Y_hat)[0]
# if code.startswith('999') or code.startswith('399'):
if len(asset) > len(df1):
ax3.plot(asset)
plt.plot(distance)
ticks = ax3.get_xticks()
ax3.set_xticklabels(
[dates[int(i)] for i in (np.append(ticks[:-1],
len(asset) - 1))],
rotation=15)
n = 5
d = (-c_high + c_low) / n
c = c_high
while c <= c_low:
Y = X * b + a - c
plt.plot(X, Y - Y_hat, 'r', alpha=0.9)
c = c + d
ax3.plot(asset)
## plt.xlabel('Date', fontsize=12)
plt.ylabel('Price-center price', fontsize=14)
plt.grid(True)
else:
as3 = asset.apply(lambda x: round(x / asset[:1], 2))
ax3.plot(as3)
ax3.plot(asset1, '-r', linewidth=2)
# assvol = df.loc[asset.index]['vol']
# assvol = assvol.apply(lambda x: round(x / assvol[:1], 2))
# ax3.plot(assvol, '-g', linewidth=2)
plt.grid(True)
zp3 = zoompan.ZoomPan()
figZoom = zp3.zoom_factory(ax3, base_scale=scale)
figPan = zp3.pan_factory(ax3)
# plt.title(code, fontsize=14)
if 'name' in df.columns:
plt.legend([df.name[-1], df1.name[-1]], loc=0)
else:
dm = tdd.get_sina_data_df(code)
if 'name' in dm.columns:
cname = dm.name[0]
else:
cname = '-'
# plt.legend([code, code2], loc=0)
plt.legend([cname, code2], loc=0)
plt.show(block=False)
def get_linear_model_histogramDouble(code,
ptype='low',
dtype='d',
start=None,
end=None,
vtype='f',
filter='n',
df=None,
dl=None):
# 399001','cyb':'zs399006','zxb':'zs399005
# code = '999999'
# code = '601608'
# code = '000002'
# asset = get_kdate_data(code)['close'].sort_index(ascending=True)
# df = tdd.get_tdx_Exp_day_to_df(code, 'f').sort_index(ascending=True)
# ptype='close'
# if ptype == 'close' or ptype==''
# ptype=
if start is not None and filter == 'y':
if code not in ['999999', '399006', '399001']:
index_d, dl = tdd.get_duration_Index_date(dt=start)
log.debug("index_d:%s dl:%s" % (str(index_d), dl))
else:
index_d = cct.day8_to_day10(start)
log.debug("index_d:%s" % (index_d))
start = tdd.get_duration_price_date(code, ptype='low', dt=index_d)
log.debug("start:%s" % (start))
if start is None and df is None and dl is not None:
start = cct.last_tddate(dl)
# print start
df = tdd.get_tdx_append_now_df_api(code, start=start,
end=end).sort_index(ascending=True)
if df is None:
# df = tdd.get_tdx_append_now_df(code, ptype, start, end).sort_index(ascending=True)
df = tdd.get_tdx_append_now_df_api(code, start,
end).sort_index(ascending=True)
if not dtype == 'd':
df = tdd.get_tdx_stock_period_to_type(df,
dtype).sort_index(ascending=True)
if len(df) == 0:
raise Exception("Code:%s error, df is None" % (code))
asset = df[ptype].round(2)
log.info("df:%s" % asset[:1])
asset = asset.dropna()
dates = asset.index
if not code.startswith('999') and not code.startswith('399'):
# print "code:",code
if code[:1] in ['5', '6', '9']:
code2 = '999999'
elif code[:2] in ['30']:
# print "cyb"
code2 = '399006'
else:
code2 = '399001'
df1 = tdd.get_tdx_append_now_df_api(code2, start,
end).sort_index(ascending=True)
# df1 = tdd.get_tdx_append_now_df(code2, ptype, start, end).sort_index(ascending=True)
if not dtype == 'd':
df1 = tdd.get_tdx_stock_period_to_type(
df1, dtype).sort_index(ascending=True)
# if len(asset) < len(df1):
# asset1 = df1.loc[asset.index, ptype]
# else:
# asset1 = df1.loc[asset.index, ptype]
# startv = asset1[:1]
# asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2))
# print asset[:1].index[0] , df1[:1].index[0]
if asset[:1].index[0] > df1[:1].index[0]:
asset1 = df1.loc[asset.index, ptype]
startv = asset1[:1]
asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2))
else:
df = df[df.index >= df1.index[0]]
asset = df[ptype]
asset = asset.dropna()
dates = asset.index
asset1 = df1.loc[df.index, ptype]
asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2))
else:
if code.startswith('399001'):
code2 = '399006'
elif code.startswith('399006'):
code2 = '399005'
else:
code2 = '399006'
if code2.startswith('3990'):
df1 = tdd.get_tdx_append_now_df_api(code2, start,
end).sort_index(ascending=True)
if len(df1) < int(len(df) / 4):
code2 = '399001'
df1 = tdd.get_tdx_append_now_df_api(
code2, start, end).sort_index(ascending=True)
# df1 = tdd.get_tdx_append_now_df(code2, ptype, start, end).sort_index(ascending=True)
if not dtype == 'd':
df1 = tdd.get_tdx_stock_period_to_type(
df1, dtype).sort_index(ascending=True)
if len(asset) < len(df1):
asset1 = df1.loc[asset.index, ptype]
asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2))
else:
df = df[df.index >= df1.index[0]]
asset = df[ptype]
asset = asset.dropna()
dates = asset.index
asset1 = df1.loc[df.index, ptype]
asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2))
# print len(df),len(asset),len(df1),len(asset1)
if end is not None:
# print asset[-1:]
asset = asset[:-1]
dates = asset.index
asset1 = asset1[:-1]
asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2))
# 画出价格随时间变化的图像
# _, ax = plt.subplots()
# fig = plt.figure()
# plt.ion()
fig = plt.figure(figsize=(16, 10))
# fig = plt.figure(figsize=(16, 10), dpi=72)
# fig.autofmt_xdate() #(no fact)
# plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
plt.subplots_adjust(left=0.05,
bottom=0.08,
right=0.95,
top=0.95,
wspace=0.15,
hspace=0.25)
# set (gca,'Position',[0,0,512,512])
# fig.set_size_inches(18.5, 10.5)
# fig=plt.fig(figsize=(14,8))
ax1 = fig.add_subplot(321)
# asset=asset.apply(lambda x:round( x/asset[:1],2))
ax1.plot(asset)
# ax1.plot(asset1,'-r', linewidth=2)
ticks = ax1.get_xticks()
# start, end = ax1.get_xlim()
# print start, end, len(asset)
# print ticks, ticks[:-1]
# (ticks[:-1] if len(asset) > end else np.append(ticks[:-1], len(asset) - 1))
ax1.set_xticklabels(
[dates[int(i)] for i in (np.append(ticks[:-1],
len(asset) - 1))],
rotation=15) # Label x-axis with dates
# 拟合
X = np.arange(len(asset))
x = sm.add_constant(X)
model = regression.linear_model.OLS(asset, x).fit()
a = model.params[0]
b = model.params[1]
# log.info("a:%s b:%s" % (a, b))
log.info("X:%s a:%s b:%s" % (len(asset), a, b))
Y_hat = X * b + a
# 真实值-拟合值，差值最大最小作为价值波动区间
# 向下平移
i = (asset.values.T - Y_hat).argmin()
c_low = X[i] * b + a - asset.values[i]
Y_hatlow = X * b + a - c_low
# 向上平移
i = (asset.values.T - Y_hat).argmax()
c_high = X[i] * b + a - asset.values[i]
Y_hathigh = X * b + a - c_high
plt.plot(X, Y_hat, 'k', alpha=0.9)
plt.plot(X, Y_hatlow, 'r', alpha=0.9)
plt.plot(X, Y_hathigh, 'r', alpha=0.9)
# plt.xlabel('Date', fontsize=12)
plt.ylabel('Price', fontsize=12)
plt.title(code + " | " + str(dates[-1])[:11], fontsize=14)
plt.legend([asset.iat[-1]], fontsize=12, loc=4)
plt.grid(True)
# #plot volume
# pad = 0.25
# yl = ax1.get_ylim()
# ax1.set_ylim(yl[0]-(yl[1]-yl[0])*pad,yl[1])
# axx = ax1.twinx()
# axx.set_position(transforms.Bbox([[0.125,0.1],[0.9,0.32]]))
# volume = np.asarray(df.vol)
# pos = df['open']-df['close']<0
# neg = df['open']-df['close']>=0
# idx = np.asarray([x for x in range(len(df))])
# axx.bar(idx[pos],volume[pos],color='red',width=1,align='center')
# axx.bar(idx[neg],volume[neg],color='green',width=1,align='center')
# plt.legend([code]);
# plt.legend([code, 'Value center line', 'Value interval line']);
# fig=plt.fig()
# fig.figsize = [14,8]
scale = 1.1
zp = zoompan.ZoomPan()
figZoom = zp.zoom_factory(ax1, base_scale=scale)
figPan = zp.pan_factory(ax1)
# 将Y-Y_hat股价偏离中枢线的距离单画出一张图显示，对其边界线之间的区域进行均分，大于0的区间为高估，小于0的区间为低估，0为价值中枢线。
ax3 = fig.add_subplot(322)
# distance = (asset.values.T - Y_hat)
distance = (asset.values.T - Y_hat)[0]
# if code.startswith('999') or code.startswith('399'):
if len(asset) > len(df1):
ax3.plot(asset)
plt.plot(distance)
ticks = ax3.get_xticks()
ax3.set_xticklabels(
[dates[int(i)] for i in (np.append(ticks[:-1],
len(asset) - 1))],
rotation=15)
n = 5
d = (-c_high + c_low) / n
c = c_high
while c <= c_low:
Y = X * b + a - c
plt.plot(X, Y - Y_hat, 'r', alpha=0.9)
c = c + d
ax3.plot(asset)
## plt.xlabel('Date', fontsize=12)
plt.ylabel('Price-center price', fontsize=14)
plt.grid(True)
else:
as3 = asset.apply(lambda x: round(x / asset[:1], 2))
ax3.plot(as3)
ticks = ax3.get_xticks()
ax3.plot(asset1, '-r', linewidth=2)
# show volume bar !!!
# assvol = df.loc[asset.index]['vol']
# assvol = assvol.apply(lambda x: round(x / assvol[:1], 2))
# ax3.plot(assvol, '-g', linewidth=0.5)
ax3.set_xticklabels(
[dates[int(i)] for i in (np.append(ticks[:-1],
len(asset) - 1))],
rotation=15)
plt.grid(True)
zp3 = zoompan.ZoomPan()
figZoom = zp3.zoom_factory(ax3, base_scale=scale)
figPan = zp3.pan_factory(ax3)
# plt.title(code, fontsize=14)
if 'name' in df.columns:
plt.legend([df.name.values[-1:][0], df1.name.values[-1:][0]], loc=0)
else:
if code not in ['999999', '399006', '399001']:
indexIdx = False
else:
indexIdx = True
dm = tdd.get_sina_data_df(code, index=indexIdx)
if 'name' in dm.columns:
cname = dm.name[0]
else:
cname = '-'
# plt.legend([code, code2], loc=0)
plt.legend([cname, code2], loc=0)
ax2 = fig.add_subplot(323)
# ax2.plot(asset)
# ticks = ax2.get_xticks()
ax2.set_xticklabels(
[dates[int(i)] for i in (np.append(ticks[:-1],
len(asset) - 1))],
rotation=15)
# plt.plot(X, Y_hat, 'k', alpha=0.9)
n = 5
d = (-c_high + c_low) / n
c = c_high
while c <= c_low:
Y = X * b + a - c
plt.plot(X, Y, 'r', alpha=0.9)
c = c + d
# asset=asset.apply(lambda x:round(x/asset[:1],2))
ax2.plot(asset)
# ax2.plot(asset1,'-r', linewidth=2)
# plt.xlabel('Date', fontsize=12)
plt.ylabel('Price', fontsize=12)
plt.grid(True)
# plt.title(code, fontsize=14)
# plt.legend([code])
if len(df) > 10:
ax6 = fig.add_subplot(324)
h = df.loc[:, ['open', 'close', 'high', 'low']]
highp = h['high'].values
lowp = h['low'].values
openp = h['open'].values
closep = h['close'].values
# print len(closep)
lr = LinearRegression()
x = np.atleast_2d(np.linspace(0, len(closep), len(closep))).T
lr.fit(x, closep)
LinearRegression(copy_X=True,
fit_intercept=True,
n_jobs=1,
normalize=False)
xt = np.atleast_2d(np.linspace(0,
len(closep) + 200,
len(closep) + 200)).T
yt = lr.predict(xt)
bV = []
bP = []
for i in range(1, len(highp) - 1):
if highp[i] <= highp[i - 1] and highp[i] < highp[
i + 1] and lowp[i] <= lowp[i - 1] and lowp[i] < lowp[i +
1]:
bV.append(lowp[i])
bP.append(i)
else:
bV.append(lowp[i - 1])
bP.append(i - 1)
if len(bV) > 0:
d, p = LIS(bV)
idx = []
for i in range(len(p)):
idx.append(bP[p[i]])
lr = LinearRegression()
X = np.atleast_2d(np.array(idx)).T
Y = np.array(d)
lr.fit(X, Y)
estV = lr.predict(xt)
ax6.plot(closep, linewidth=2)
ax6.plot(idx, d, 'ko')
ax6.plot(xt, estV, '-r', linewidth=3)
ax6.plot(xt, yt, '-g', linewidth=3)
plt.grid(True)
# plt.tight_layout()
zp2 = zoompan.ZoomPan()
figZoom = zp2.zoom_factory(ax6, base_scale=scale)
figPan = zp2.pan_factory(ax6)
# 统计出每个区域内各股价的频数，得到直方图，为了更精细的显示各个区域的频数，这里将整个边界区间分成100份。
ax4 = fig.add_subplot(325)
log.info("assert:len:%s %s" % (len(asset.values.T - Y_hat),
(asset.values.T - Y_hat)[0]))
# distance = map(lambda x:int(x),(asset.values.T - Y_hat)/Y_hat*100)
# now_distanse=int((asset.iat[-1]-Y_hat[-1])/Y_hat[-1]*100)
# log.debug("dis:%s now:%s"%(distance[:2],now_distanse))
# log.debug("now_distanse:%s"%now_distanse)
distance = (asset.values.T - Y_hat)
now_distanse = asset.iat[-1] - Y_hat[-1]
# distance = (asset.values.T-Y_hat)[0]
pd.Series(distance).plot(kind='hist', stacked=True, bins=100)
# plt.plot((asset.iat[-1].T-Y_hat),'b',alpha=0.9)
plt.axvline(now_distanse, hold=None, label="1", color='red')
# plt.axhline(now_distanse,hold=None,label="1",color='red')
# plt.axvline(asset.iat[0],hold=None,label="1",color='red',linestyle="--")
plt.xlabel(
'Undervalue ------------------------------------------> Overvalue',
fontsize=12)
plt.ylabel('Frequency', fontsize=14)
# plt.title('Undervalue & Overvalue Statistical Chart', fontsize=14)
plt.legend([code, asset.iat[-1], str(dates[-1])[5:11]], fontsize=12)
plt.grid(True)
# plt.show()
# import os
# print(os.path.abspath(os.path.curdir))
ax5 = fig.add_subplot(326)
# fig.figsize=(5, 10)
log.info("assert:len:%s %s" % (len(asset.values.T - Y_hat),
(asset.values.T - Y_hat)[0]))
# distance = map(lambda x:int(x),(asset.values.T - Y_hat)/Y_hat*100)
distance = (asset.values.T - Y_hat) / Y_hat * 100
now_distanse = ((asset.iat[-1] - Y_hat[-1]) / Y_hat[-1] * 100)
log.debug("dis:%s now:%s" % (distance[:2], now_distanse))
log.debug("now_distanse:%s" % now_distanse)
# n, bins = np.histogram(distance, 50)
# print n, bins[:2]
pd.Series(distance).plot(kind='hist', stacked=True, bins=100)
# plt.plot((asset.iat[-1].T-Y_hat),'b',alpha=0.9)
plt.axvline(now_distanse, hold=None, label="1", color='red')
# plt.axhline(now_distanse,hold=None,label="1",color='red')
# plt.axvline(asset.iat[0],hold=None,label="1",color='red',linestyle="--")
plt.xlabel(
'Undervalue ------------------------------------------> Overvalue',
fontsize=14)
plt.ylabel('Frequency', fontsize=12)
# plt.title('Undervalue & Overvalue Statistical Chart', fontsize=14)
plt.legend([code, asset.iat[-1]], fontsize=12)
plt.grid(True)
# plt.ion()
plt.draw()
plt.pause(0.001)
# plt.show(block=False)
# plt.draw()
# plt.pause(0.001)
# plt.close()
# print plt.get_backend()
# plt.show(block=True)
return df