def init_finance_table(self, start_date=0):
self.authenticate()
self.stock = jdk.normalize_code(self.stock)
self.stock_info = jdk.get_security_info(self.stock)
if self.stock_info is None:
print('Error: Cannot get data or stock code not correct!')
'''Internal usage. To get finance table'''
q = jdk.query(jdk.valuation).filter(jdk.valuation.code == self.stock)
end_date = datetime.now().date()
if start_date == 0:
df = jdk.get_fundamentals_continuously(q, end_date=end_date, count=5000)
else:
# get the days between start date and end date
date_span = jdk.get_trade_days(start_date=start_date, end_date=end_date)
if len(date_span) == 0:
return
df = jdk.get_fundamentals_continuously(q, end_date=end_date, count=len(date_span))
if df is None:
return
self.finance_data = df.swapaxes('items', 'minor_axis')
self.finance_data = self.finance_data[self.stock]
del self.finance_data['day.1']
del self.finance_data['code.1']
del self.finance_data['id']
return
def get_single_price(code: str, freq: str, start=None, end=None) -> pd.DataFrame:
if start is None:
start = jq.get_security_info(code).start_date
if end is None:
end = datetime.today()
df = jq.get_price(code, start_date=start, end_date=end, frequency=freq, panel=False)
print(df)
return df
def delect_stop(stocks,begin_date,n=30*3):
stock_list = list()
begin_date = datetime.datetime.strptime(begin_date,"%Y-%m-%d")
for stock in stocks:
start_date = jq.get_security_info(stock).start_date
if start_date < (begin_date - datetime.timedelta(days=n)).date():
stock_list.append(stock)
return stock_list
def init_light(self):
self.authenticate()
self.stock = jdk.normalize_code(self.stock)
self.stock_info = jdk.get_security_info(self.stock)
if self.stock_info is None:
print('Error: Cannot get data or stock code not correct!')
self.finance_table = self.__generate_table_name() + '_finance'
self.price_table = self.__generate_table_name() + '_price'
def getDateData(self, jqID, intDate):
dataTable = self.cfg.dictDataTable[jq.get_security_info(jqID).type]
sqlword = "SELECT * FROM %s where id='%s' and CONVERT(varchar(10), tm, 112)='%d'" % (
dataTable, jqID, intDate)
data = pd.read_sql(sqlword, con=self.conn)
if len(data) > 0:
return data
data = getJQTickData(jqID, intDate)
updateTickData(data, dataTable)
return data
def get_price(self, code, start_date='2005-01-01', end_date='2018-11-30'):
'''Download stock data (adjusted close)
Parameters
----------------------
code: str or int
code of stock
start_date: str
start_date
end_date: str
end_date
Returns
----------------------
normalized_code, pd.DataFrame
'''
try:
normalized_code = self.normalize_code(str(code))
except Exception as e:
print(e)
return [], None
if normalized_code == "000001.XSHE":
price_df = pd.read_csv("finance/data/000001.SXHE.csv", index_col=0)
price_df = price_df.loc[start_date:end_date,
['open', 'close', 'high', 'low', 'volume']]
else:
security_info = get_security_info(normalized_code)
if security_info is None:
return [], None
security_start_date = security_info.start_date
security_end_date = security_info.end_date
# compare date information of security and requery date
if datetime.datetime.strptime(
start_date, "%Y-%m-%d").date() < security_start_date:
start_date = security_start_date
if datetime.datetime.strptime(
end_date, "%Y-%m-%d").date() > security_end_date:
end_date = security_end_date
price_df = get_price(
normalized_code,
start_date=start_date,
end_date=end_date,
frequency='daily',
fields=['open', 'close', 'high', 'low', 'volume'],
skip_paused=False,
fq='pre')
self.fill_missing_values(price_df)
return normalized_code, price_df
def get_index_data(self, index_code, trade_days):
open_date = jqdatasdk.get_security_info(index_code).start_date
if (open_date > trade_days[0]):
trade_days = jqdatasdk.get_trade_days(start_date=open_date)
df = pd.DataFrame()
for day in trade_days:
dic = self._calc_pe_pb(index_code, day)
if dic:
df = df.append(dic, ignore_index=True)
if not df.empty:
df.set_index('day', inplace=True)
return df
def remove_new(stocks, tradeDate=None, day=1):
"""
list: 去掉新股股票之后的股票列表
"""
tradeDate = tradeDate if tradeDate is not None else datetime.datetime.now()
nday = helper.get_ndays_before(str(tradeDate.date()), day)
rm_news = []
for s in stocks:
info = jq.get_security_info(s)
# 上市日期小于指定日期
if str(info.start_date) < nday:
rm_news.append(s)
return rm_news
def draw_line(stock_name, type, days=600):
info = jqdatasdk.get_security_info(stock_name)
if info is None:
return 'error'
q = jqdatasdk.query(jqdatasdk.valuation).filter(jqdatasdk.valuation.code == stock_name)
now = datetime.now()
date = '%d-%d-%d' % (now.year, now.month, now.day)
df = jqdatasdk.get_fundamentals_continuously(q, end_date=date, count=days)
print(df['pe_ratio'].index)
if type == 'PE':
return draw_diagram(df['pe_ratio'], 'PE-TTM', stock_name, info.display_name)
elif type == 'PB':
return draw_diagram(df['pb_ratio'], 'PB', stock_name, info.display_name)
def get_all_financial_data():
df_set = []
jdk.auth('18951518869', 'ceshi123')
for stock in stock_set:
stock_info = jdk.get_security_info(stock)
stock = stock.split('.')[0]
df = read_finance_data(stock, 'pe_ratio', '2016-1-1', '2018-10-4')
if df is None:
print(stock)
continue
df_set.append(df)
try:
waveline.draw_diagram(df, 'PE-TTM', stock, stock_info.display_name)
except:
continue
def init(self):
'''
Get price, open, close, high, low, volume. And finantial data, TTM, PB, etc.
'''
self.authenticate()
self.stock = jdk.normalize_code(self.stock)
self.stock_info = jdk.get_security_info(self.stock)
if self.stock_info is None:
print('Error: Cannot get data or stock code not correct!')
self.finance_table = self.__generate_table_name() + '_finance'
self.price_table = self.__generate_table_name() + '_price'
price_data = jdk.get_price(self.stock, start_date=self.stock_info.start_date, end_date=datetime.now())
# remove NaN data
self.price_data = price_data.dropna()
self.price_data.index.name = 'date'
print('%s[%s] from %s - %s ' % (self.stock_info.display_name, self.stock,
self.stock_info.start_date, self.stock_info.end_date))
def plot_index(index_code, df_, anno_text='', show=True):
data = df_
PEs = data['pe']
low = round(PEs.quantile(0.3), 2)
high = round(PEs.quantile(0.7), 2)
plt.title(
'%s-%s' %
(index_code, jqdatasdk.get_security_info(index_code).display_name))
#l1, = plt.plot(data.index, data.pe, lw=LINE_W)
l1, = plt.plot(data.index, data.pe)
#latest day annotation
mid_idx = int(data.shape[0] / 2)
txt = 'Data: %s, PE: %.2f, %s' % (str(
data.index[-1])[:10], PEs.iloc[-1], str(anno_text))
plt.text(data.index[mid_idx],
PEs.max() - 1,
txt,
horizontalalignment='center')
plt.axhline(high, ls='--', c='r', label=high)
plt.axhline(low, ls='--', c='g', label=low)
plt.legend()
plt.show()
def getedate(self, c):
return jq.get_security_info(c).end_date
def fill_stock_name(l):
for stock in l:
si = jq.get_security_info(stock.code)
stock.name = si.display_name
def get_code_name(code):
si = jq.get_security_info(code)
return si.display_name
def convert_stk_code_to_ism_id(stk):
return jq.get_security_info(stk).name
def run_chan_analysis(stock_code, end_date, stock_days, x_jizhun,
stock_frequency, chanK_flag):
jqdatasdk.auth('13381719059', 'GoodLuck2018')
quotes = jqdatasdk.get_price(stock_code, datetime.datetime.strptime(end_date, "%Y-%m-%d %H:%M:%S")-datetime.timedelta(days=stock_days) , end_date,\
frequency=stock_frequency,skip_paused=False,fq='pre')
# print quotes
# 缠论k线
quotes = parse2ChanK(quotes, quotes.values) if chanK_flag else quotes
# print quotes
quotes[quotes['volume'] == 0] = np.nan
quotes = quotes.dropna()
Close = quotes['close']
Open = quotes['open']
High = quotes['high']
Low = quotes['low']
T0 = quotes.index.values
length = len(Close)
# 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()]
fig = plt.figure(figsize=(20, 10))
ax1 = plt.subplot2grid((10, 4), (0, 0), rowspan=10, colspan=4)
#fig = plt.figure()
#ax1 = plt.axes([0,0,3,2])
X = np.array(range(0, length))
pad_nan = X + np.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 = np.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] = np.nan
up_op = Open.copy()
up_op[Close <= Open] = np.nan
down_cl = Close.copy()
down_cl[Open <= Close] = np.nan
down_op = Open.copy()
down_op[Open <= Close] = np.nan
even = Close.copy()
even[Close != Open] = np.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 = matplt.patches.Polygon(vertices_up, color='r', zorder=1)
handle_box_down = matplt.patches.Polygon(vertices_down,
color='g',
zorder=1)
handle_box_even = matplt.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 = matplt.lines.Line2D(pad_X,
line_up,
color='k',
linestyle='solid',
zorder=0)
handle_line_down = matplt.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)
T1 = T0[-len(T0):].astype(datetime.date) / 1000000000
# print T1
Ti = []
for i in range(int(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)
ax1.set_xticks(np.linspace(-2, len(Close) + 2, len(Ti)))
ll = Low.min() * 0.97
hh = High.max() * 1.03
ax1.set_ylim(ll, hh)
ax1.set_xticklabels(Ti)
plt.grid(True)
plt.setp(plt.gca().get_xticklabels(),
rotation=45,
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
'''
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 parse2ChanK(k_data, k_values)
fenTypes, fenIdx = parse2ChanFen(chanK)
#print "分型", fenTypes, fenIdx
biIdx, frsBiType = parse2ChanBi(fenTypes, fenIdx, chanK)
#biIdx = con2Cxianduan(stock_code, k_data, chanK, frsBiType, biIdx, end_date, cur_ji)
#print chanK
print("分笔", biIdx, frsBiType)
print('number#', jqdatasdk.get_security_info(stock_code).display_name)
# 3.得到分笔结果，计算坐标显示
x_fenbi_seq = []
y_fenbi_seq = []
for i in range(len(biIdx)):
if biIdx[i]:
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 k_data['high'][dt], k_data['low'][dt]
time_long = np.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:
plt.text(x_date_list.index(time_long), k_data['high'][_dt], \
str(k_data['high'][_dt]), ha='left', fontsize=12)
x_fenbi_seq.append(x_date_list.index(time_long))
y_fenbi_seq.append(k_data['high'][_dt])
if fenType == -1:
plt.text(x_date_list.index(time_long), k_data['low'][_dt], \
str(k_data['low'][_dt]), va='bottom', fontsize=12)
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)
# 在原图基础上添加分笔蓝线
plt.plot(x_fenbi_seq, y_fenbi_seq)
#线段画到笔上
xdIdxs, xfenTypes = parse2ChanXD(frsBiType, biIdx, chanK)
print('线段:', xdIdxs, xfenTypes)
x_xd_seq = []
y_xd_seq = []
for i in range(len(xdIdxs)):
if xdIdxs[i]:
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 = np.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
plt.plot(x_xd_seq, y_xd_seq)
high_point = k_data['high'].max()
low_point = k_data['low'].min()
end_x = max(x_date_list)
text_x = max(x_fenbi_seq)
high_x = [0, 0]
high_y = [0, 0]
high_x[1] = end_x
high_y[0] = high_point
high_y[1] = high_y[0]
plt.plot(high_x, high_y)
plt.text(text_x,
high_point,
'100% : ' + str(high_point),
ha='left',
fontsize=16)
low_x = [0, 0]
low_y = [0, 0]
low_x[1] = end_x
low_y[0] = low_point
low_y[1] = low_y[0]
plt.plot(low_x, low_y)
plt.text(text_x,
low_point,
'0% : ' + str(low_point),
ha='left',
fontsize=16)
increase = high_point - low_point
mid_x = [0, 0]
mid_y = [0, 0]
mid_x[1] = end_x
mid_y[0] = low_point + increase / 2
mid_y[1] = mid_y[0]
plt.plot(mid_x, mid_y)
plt.text(text_x,
mid_y[0],
'50% : ' + str(mid_y[0]),
ha='left',
fontsize=16)
mid_x_1 = [0, 0]
mid_y_1 = [0, 0]
mid_x_1[1] = end_x
mid_y_1[0] = low_point + increase * 0.618
mid_y_1[1] = mid_y_1[0]
plt.plot(mid_x_1, mid_y_1)
plt.text(text_x,
mid_y_1[0],
'61.8% : ' + str(mid_y_1[0]),
ha='left',
fontsize=16)
mid_x_2 = [0, 0]
mid_y_2 = [0, 0]
mid_x_2[1] = end_x
mid_y_2[0] = low_point + increase * 0.382
mid_y_2[1] = mid_y_2[0]
plt.plot(mid_x_2, mid_y_2)
plt.text(text_x,
mid_y_2[0],
'61.8% : ' + str(mid_y_2[0]),
ha='left',
fontsize=16)
plt.show()
def getstockname(self, code):
return jq.get_security_info(code).display_name