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
def get_tdx_barfeed(code, start=None, dl=30):
df = tdd.get_tdx_append_now_df_api(code, start=start)
# df2 = get_tdx_append_now_df_api(code=stock_code, start=start, end=end, dl=dl).sort_index(ascending=True)
df.rename(columns={'vol': 'volume'}, inplace=True)
df = df.sort_index(ascending=True)
barfeed = dataFramefeed.Feed()
barfeed.addBarsFromDataFrame(code, df)
# print barfeed.get_dataFrame()
return barfeed
def get_duration_filter(code, df=None, dtype='d', start=None, end=None, dl=None, filter=True, ptype='low', power=True):
if start is not None and end is None and filter:
index_d = cct.day8_to_day10(start)
start = tdd.get_duration_price_date(code, ptype=ptype, dt=start, df=df, dl=dl, power=power)
log.debug("start is not None start: %s index_d:%s" % (start, index_d))
elif end is not None and filter:
df = tdd.get_tdx_append_now_df_api(code, start=start, end=end, df=df, dl=dl, power=power).sort_index(ascending=True)
index_d = cct.day8_to_day10(start)
start = tdd.get_duration_price_date(code, ptype=ptype, dt=start, df=df, dl=dl, power=power)
df = df[df.index >= start]
if len(df) > 2 and dl is None:
if df.index.values[0] < index_d:
df = df[df.index >= index_d]
if dl is not None:
if power:
start, index_d, df = tdd.get_duration_price_date(
code, ptype=ptype, dl=dl, filter=False, df=df, power=True)
else:
start, index_d = tdd.get_duration_price_date(
code, ptype=ptype, dl=dl, filter=False, df=df, power=False)
log.debug("dl not None code:%s start: %s index_d:%s" % (code, start, index_d))
if len(df) > 0 and df is not None:
df = df.sort_index(ascending=True)
df = df[df.index >= start]
if len(df) == 0 or df is None:
if start is not None and len(start) > 8 and int(start[:4]) > 2500:
log.warn("code:%s ERROR:%s" % (code, start))
start = '2016-01-01'
df = tdd.get_tdx_append_now_df_api(
code, start, end).sort_index(ascending=True)
if start is None:
start = df.index.values[0]
if len(df) > 2 and dl is None and start is not None and filter:
if df.index.values[0] < index_d:
df = df[df.index >= index_d]
if not dtype == 'd':
df = tdd.get_tdx_stock_period_to_type(
df, dtype).sort_index(ascending=True)
return df
def fibonacciCount(code, dl=60, start=None, days=0):
fibl = []
if not isinstance(code, list):
codes = [code]
else:
codes = code
for code in codes:
df = tdd.get_tdx_append_now_df_api(code, dl=dl)
for ptype in ['low', 'high']:
if ptype == 'low':
op, ra, st, daysData = pct.get_linear_model_status(code,
df=df,
filter='y',
dl=dl,
ptype=ptype,
days=days)
dd, boll = getab.Get_BBANDS(df, days=days)
else:
# df = tdd.get_tdx_append_now_df_api(code,dl=dl)
op, ra, st, daysData = pct.get_linear_model_status(code,
df=df,
filter='y',
dl=dl,
ptype=ptype,
days=days)
dd, boll = getab.Get_BBANDS(df, dtype='d')
fib = cct.getFibonacci(300, daysData[0])
# log.debug('st:%s days:%s fib:%s'%(st,days,fib))
# print "%s op:%s ra:%s days:%s fib:%s %s" % (code, op,
# ra,days,fib, st)
if not daysData[1].ma5d[0]:
daysData[1].ma5d[0] = 0
fibl.append([
code, op, ra, [daysData[0],
int(daysData[1].ma5d[0])], fib, st
])
return fibl
def get_linear_model_histogramDouble(code, ptype='f', dtype='d', start=None, end=None, vtype='close', filter='n',
df=None):
# 399001','cyb':'zs399006','zxb':'zs399005
# code = '999999'
# code = '601608'
# code = '000002'
# asset = ts.get_hist_data(code)['close'].sort_index(ascending=True)
# df = tdd.get_tdx_Exp_day_to_df(code, 'f').sort_index(ascending=True)
# vtype='close'
# if vtype == 'close' or vtype==''
# 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, 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)
asset = df[vtype]
log.info("df:%s" % asset[:1])
asset = asset.dropna()
dates = asset.index
if not code.startswith('999') or not code.startswith('399'):
if code[:1] in ['5', '6', '9']:
code2 = '999999'
elif code[:1] in ['3']:
code2 = '399006'
else:
code2 = '399001'
df1 = tdd.get_tdx_append_now_df_api(code2, ptype, 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)
asset1 = df1.loc[asset.index, vtype]
startv = asset1[:1]
# asset_v=asset[:1]
# print startv,asset_v
asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2))
# print asset1[:4]
# 画出价格随时间变化的图像
# _, ax = plt.subplots()
# fig = plt.figure()
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[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)
ax2 = fig.add_subplot(323)
# ax2.plot(asset)
# ticks = ax2.get_xticks()
ax2.set_xticklabels([dates[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])
# 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'):
ax3.plot(asset)
plt.plot(distance)
ticks = ax3.get_xticks()
ax3.set_xticklabels([dates[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)
plt.grid(True)
zp3 = zoompan.ZoomPan()
figZoom = zp3.zoom_factory(ax3, base_scale=scale)
figPan = zp3.pan_factory(ax3)
# plt.title(code, fontsize=14)
# plt.legend([code])
# 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)
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
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)
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)
# plt.ion()
plt.show(block=False)
import matplotlib import matplotlib.pyplot as plt from matplotlib.finance import candlestick from matplotlib.finance import volume_overlay3 from matplotlib.dates import num2date from matplotlib.dates import date2num import matplotlib.mlab as mlab import datetime stock_code = '000002' start = None end= None dl=60 df = tdd.get_tdx_append_now_df_api(code=stock_code, start=start, end=end, dl=dl).sort_index(ascending=True) # print df.close.T fig = plt.figure() # ax = fig.add_subplot(211, sharex=None, sharey=None) ax = fig.add_subplot(211) ax.plot(df.close) ax.set_xticklabels(df.index) plt.xticks(rotation=30, horizontalalignment='center') # plt.subplots_adjust(left=0.05, bottom=0.08, right=0.95, top=0.95, wspace=0.15, hspace=0.25) pad = 0.25 yl = ax.get_ylim() ax.set_ylim(yl[0]-(yl[1]-yl[0])*pad,yl[1]) # ax2 = ax.twinx() ax2 = fig.add_subplot(211,sharex=ax)
(date[i]),
float(openp[i]),
float(highp[i]),
float(lowp[i]),
float(closep[i]),
]],
axis=0)
# data, [[float(date[i]), float(openp[i]), float(highp[i]), float(lowp[i]), float(closep[i]), ]], axis=0)
data = np.delete(data, 0, 0)
return data
#simple data
data = [ ## fields are (time, open, close, min, max).
(20190101., 10, 13, 5, 15),
(20190102., 13, 17, 9, 20),
(20190103., 17, 14, 11, 23),
(20190104., 14, 15, 5, 19),
(20190105., 15, 9, 8, 22),
(20190106., 9, 15, 8, 16),
]
start = '2020-01-01'
code = '000002'
df = tdd.get_tdx_append_now_df_api(code,
start=start).sort_index(ascending=True)
data = getCandlesData(df)
print(data)
item = CandlestickItem(data)
plt = pg.plot()
plt.addItem(item)
QtGui.QApplication.exec_()
def get_diff_index(code, df=None, start=None, end=None, dl=None, dtype='d', ptype='close'):
df = get_duration_filter(code, df=df, dl=dl, ptype=ptype)
if not dtype == 'd':
df = tdd.get_tdx_stock_period_to_type(df, dtype).sort_index(ascending=True)
if dl is not None and len(df) > dl:
df = df[-dl:]
# print df.index[0]
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'
start = asset.index[0]
# print start,end
df1 = tdd.get_tdx_append_now_df_api(code2, start=start, end=end).sort_index(ascending=True)
if not dtype == 'd':
df1 = tdd.get_tdx_stock_period_to_type(df1, dtype).sort_index(ascending=True)
# print df1,asset
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], 3))
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], 3))
else:
if code.startswith('399001'):
code2 = '399006'
elif code.startswith('399006'):
code2 = '399005'
else:
code2 = '399006'
if code2.startswith('3990'):
start = asset.index[0]
df1 = tdd.get_tdx_append_now_df_api(code2, start=start, end=end).sort_index(ascending=True)
if len(df1) < int(len(df) / 4):
code2 = '399001'
df1 = tdd.get_tdx_append_now_df_api(code2, start=start, end=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], 3))
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], 3))
# log.info("code2:%s"%(code2))
asset2 = asset.apply(lambda x: round(x / asset[:1], 3))
# print asset.index[-1],asset1.index[-1]
log.info("code:%s codeR:%s index:%s coden:%s codeRn:%s indexn:%s" %
(round(asset[0], 2), asset2[0], asset1[0], round(asset[-1], 2), round(asset2[-1], 2), asset1[-1]))
return round(asset2[-1], 3), round(asset1[-1], 3)
def get_linear_model_status(code, df=None, dtype='d', type='m', start=None, end=None, days=1, filter='n',
dl=None, countall=True, ptype='low', power=True):
"""[pct test get_linear_model_status]
[description]
Arguments:
code {[type]} -- [description]
Keyword Arguments:
df {[type]} -- [description] (default: {None})
dtype {str} -- [description] (default: {'d'})
type {str} -- [description] (default: {'m'})
start {[type]} -- [description] (default: {None})
end {[type]} -- [description] (default: {None})
days {number} -- [description] (default: {1})
filter {str} -- [description] (default: {'n'})
dl {[type]} -- [description] (default: {None})
countall {bool} -- [description] (default: {True})
ptype {str} -- [description] (default: {'low'})
power {bool} -- [description] (default: {True})
Returns:
[type] -- [description]
"""
if start is not None and end is None and filter == 'y':
index_d = cct.day8_to_day10(start)
start = tdd.get_duration_price_date(code, ptype=ptype, dt=start, df=df, dl=dl, power=power)
log.debug("start is not None start: %s index_d:%s" % (start, index_d))
elif end is not None and filter == 'y':
df = tdd.get_tdx_append_now_df_api(code, start=start, end=end, df=df, dl=dl, power=power).sort_index(ascending=True)
index_d = cct.day8_to_day10(start)
start = tdd.get_duration_price_date(code, ptype=ptype, dt=start, df=df, dl=dl, power=power)
df = df[df.index >= start]
if len(df) > 2 and dl is None:
if df.index.values[0] < index_d:
df = df[df.index >= index_d]
if dl is not None:
if power:
start, index_d, df = tdd.get_duration_price_date(
code, ptype=ptype, dl=dl, filter=False, df=df, power=power)
else:
start, index_d = tdd.get_duration_price_date(
code, ptype=ptype, dl=dl, filter=False, df=df, power=power)
log.debug("dl not None code:%s start: %s index_d:%s" % (code, start, index_d))
if len(df) > 0 and df is not None:
df = df.sort_index(ascending=True)
df = df[df.index >= start]
if len(df) == 0 or df is None:
if start is not None and len(start) > 8 and int(start[:4]) > 2500:
log.warn("code:%s ERROR:%s" % (code, start))
start = '2016-01-01'
df = tdd.get_tdx_append_now_df_api(
code, start, end).sort_index(ascending=True)
if start is None:
start = df.index.values[0]
if len(df) > 2 and dl is None and start is not None and filter == 'y':
if df.index.values[0] < index_d:
df = df[df.index >= index_d]
if not dtype == 'd':
df = tdd.get_tdx_stock_period_to_type(
df, dtype).sort_index(ascending=True)
df = df.fillna(0)
if len(df) > 1 + days:
if days != 0:
asset = df[:-days]
else:
asset = df
else:
asset = df
if len(asset) > 1:
operationcount = 0
# ratio_l = []
if countall:
assetratio = asset
nowpratio = df['close'][-days] if len(df) > 1 + days else None
# print assetratio
op, ratio_l, status, sdl = get_linear_model_rule(code, df=assetratio, nowP=nowpratio, ptype=ptype)
# print op,ratio,status,sdl
# ratio_l.append(round(ratio, 2))
operationcount += op
else:
assetratio = asset
nowpratio = df['close'][-days] if len(df) > 1 + days else None
op, ratio_l, status, sdl = get_linear_model_rule(code, df=assetratio, nowP=nowpratio, ptype=ptype)
# ratio_l.append(round(ratio, 2))
operationcount += op
return operationcount, (ratio_l), df[:1].index.values[0], [len(df), df[:1]]
elif len(asset) == 1:
## log.error("powerCompute code:%s"%(code))
if ptype == 'high':
if df.close[-1] >= df.high[-1] * 0.99 and df.close[-1] >= df.open[-1]:
return 12, 0, df.index.values[0], [len(df), df[:1]]
elif df.close[-1] > df.open[-1]:
if df.close[-1] > df.high[-1] * 0.97:
if len(df) > 2 and df.close[-1] > df.close[-2]:
return 10, 0, df.index.values[0], [len(df), df[:1]]
else:
return 11, 0, df.index.values[0], [len(df), df[:1]]
else:
return 9, 0, df.index.values[0], [len(df), df[:1]]
else:
if len(df) >= 2:
if df.close[-1] > df.close[-2] * 1.01:
return 9, 0, df.index.values[0], [len(df), df[:1]]
elif df.close[-1] > df.close[-2]:
return 8, 0, df.index.values[0], [len(df), df[:1]]
elif df.low[-1] > df.low[-2]:
return 6, 0, df.index.values[0], [len(df), df[:1]]
else:
return 3, 0, df.index.values[0], [len(df), df[:1]]
else:
return 1, 0, df.index.values[0], [len(df), df[:1]]
else:
return -10, 0, df.index.values[0], [len(df), df[:1]]
else:
if ptype == 'high':
return 13, 1, cct.get_today(), [len(df), df[:1]]
else:
return -10, -10, cct.get_today(), [len(df), df[:1]]
plt.plot(x, 'b-')
plt.plot(ind, x[ind], 'r*', markersize=9)
# plt.axes()
plt.show(block=True)
# plt.show(block=False)
return ind, x[ind]
if __name__ == "__main__":
# df = tdd.get_tdx_append_now_df_api('399006',start=20160912)
# df = tdd.get_tdx_append_now_df_api('399006',dl=30)
# print df[-1:]
code = '999999'
# code = '999999'
df = tdd.get_tdx_append_now_df_api(code, dl=60).sort_index(ascending=True)
# ind = detect_peaks(df.close, mph=0,mpd=10,edge='falling',show=True)
print(detect_peaks(df.close, mph=0, mpd=2, valley=False, show=True))
print(detect_peaks(df.close, mph=0, mpd=2, valley=True, show=True))
sys.exit(0)
print(LDS(df.close))
# print detect_peaks(df.close, mph=0,mpd=3,threshold=0,valley=False,show=False)
print(detect_peaks_lis(df))
# ind = detect_peaks(df.close,show=True)
# print get_diff_index(code,dl=10)
# print get_linear_model_status(code,dl=30,days=1)
# print get_linear_model_status(code,dl=30,days=1,ptype='high')
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_status(code, ptype="f", df=None, dtype="d", type="m", start=None, end=None, days=1, filter="y"):
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, ptype, start, end).sort_index(ascending=True)
log.info("Code:%s start:%s end:%s" % (code, start, df[-1:].index.values[0]))
if not dtype == "d":
df = tdd.get_tdx_stock_period_to_type(df, dtype).sort_index(ascending=True)
# df = tdd.get_tdx_Exp_day_to_df(code, 'f').sort_index(ascending=True)
def get_linear_model_ratio(asset):
log.info("asset:%s" % asset[-1:])
duration = asset[-1:].index.values[0]
log.debug("duration:%s" % duration)
log.debug("duration:%s" % cct.get_today_duration(duration))
# log.debug("duration:%s"%cct.get_duration_date(duration))
asset = asset.dropna()
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("X:%s a:%0.1f b:%0.1f" % (len(asset), a, b))
Y = np.append(X, X[-1] + int(days))
log.debug("X:%s Y:%s" % (X[-1], Y[-1]))
# print ("X:%s" % (X[-1]))
Y_hat = X * b + a
# Y_hat_t = Y * b + a
# log.info("Y_hat:%s " % (Y_hat))
# log.info("asset:%s " % (asset.values))
ratio = b / a * 100
operation = 0
if Y_hat[-1] > Y_hat[1]:
log.debug("u-Y_hat[-1]:%0.1f" % (Y_hat[-1]))
log.debug("price:%0.1f" % asset.iat[-1])
log.debug("u:%0.1f" % Y_hat[1])
log.debug("price:%0.1f" % asset.iat[1])
if type.upper() == "M":
Y_Future = Y * b + a
# ratio = b/a*100
log.info("ratio: %0.1f %0.1f Y_Mid: %0.1f" % (b, ratio, Y_Future[-1]))
# diff = asset.iat[-1] - Y_hat[-1]
# if diff > 0:
# return True, len(asset), diff
# else:
# return False, len(asset), diff
elif type.upper() == "L":
i = (asset.values.T - Y_hat).argmin()
c_low = X[i] * b + a - asset.values[i]
# Y_hatlow = X * b + a - c_low
Y_Future = Y * b + a - c_low
log.info("b: %0.1f ratio:%0.1f Y_Mid: %0.1f" % (b, ratio, Y_Future[-1]))
# diff = asset.iat[-1] - Y_hatlow[-1]
# if asset.iat[-1] - Y_hatlow[-1] > 0:
# return True, len(asset), diff
# else:
# return False, len(asset), diff
elif type.upper() == "H":
i = (asset.values.T - Y_hat).argmax()
c_high = X[i] * b + a - asset.values[i]
# Y_hathigh = X * b + a - c_high
Y_Future = Y * b + a - c_high
log.info("ratio: %0.1f %0.1f Y_Mid: %0.1f" % (b, ratio, Y_Future[-1]))
diff = asset[-1] - Y_Future[-1]
# print ("as:%s Y:%s"%(asset[-1] ,Y_Future[-1]))
if diff > 0:
operation += 1
log.info("UP !!%s Y_Future: %0.1f b:%0.1f ratio:%0.1f " % (type.upper(), Y_Future[-1], b, ratio))
else:
log.info("Down %s Y_Future: %0.1f b:%0.1f ratio:%0.1f" % (type.upper(), Y_Future[-1], b, ratio))
return operation, ratio
else:
log.debug("down !!! d:%s" % Y_hat[1])
print ("down !!! d:%s" % Y_hat[1])
return 0, 0
# print "high:",
operationcount = 0
ratio_l = []
for co in ["high", "close", "low"]:
op, ratio = get_linear_model_ratio(df[co])
ratio_l.append(round(ratio, 2))
operationcount += op
log.info("op:%s min:%s ratio_l:%s" % (operationcount, min(ratio_l), ratio_l))
return operationcount, ratio.min()
