def _set_axes(self, xlim, ylim, pot_energy_lim, kin_energy_lim, tot_energy_lim, pressure_lim):
axes = []
axes.append(plt.subplot2grid((2, 4), (0, 0), colspan=2, rowspan=2, xlim=xlim, ylim=ylim, aspect="equal"))
axes.append(plt.subplot2grid((2, 4), (0, 2), title="Potential Energy", ylim=pot_energy_lim))
axes.append(plt.subplot2grid((2, 4), (1, 2), title="Kinetic Energy", ylim=kin_energy_lim))
axes.append(plt.subplot2grid((2, 4), (0, 3), title="Total Energy", ylim=tot_energy_lim))
axes.append(plt.subplot2grid((2, 4), (1, 3), title="Pressure", ylim=pressure_lim))
for a in axes[1:]:
a.grid()
a.set_xticks([])
return axes
def old_candles_plot(self, data, slide=-1):
data.index.name = 'Date'
data = data.rename(
columns={
"askopen": "Open",
"askclose": "Close",
"askhigh": "High",
"asklow": "Low",
"tickqty": "Volume"
})
data['3T'] = data['Close'].rolling(window=3, min_periods=0).mean()
data.dropna(inplace=True)
df_ohlc = data['Close'].resample('3T').ohlc() # not a moving avg
df_volume = data['Volume'].resample('3T').sum()
dataPeak = data.copy()
dataTrough = data.copy()
dataPeak["Open"].where(dataPeak['TP'] == 1, np.nan, inplace=True)
dataTrough["Open"].where(dataTrough['TP'] == -1, np.nan, inplace=True)
dataPeak.reset_index(inplace=True)
dataTrough.reset_index(inplace=True)
df_ohlc.reset_index(inplace=True)
dataPeak['Date'] = dataPeak['Date'].map(mdates.date2num)
dataTrough['Date'] = dataTrough['Date'].map(mdates.date2num)
df_ohlc['Date'] = df_ohlc['Date'].map(mdates.date2num)
ax1 = plt.subplot2grid((5, 1), (0, 0), rowspan=3, colspan=1)
ax2 = plt.subplot2grid((5, 1), (4, 0),
rowspan=1,
colspan=1,
sharex=ax1)
ax1.xaxis_date() #display dates as dates
candlestick_ohlc(ax1, df_ohlc.values, width=0.001, colorup='g')
ax2.fill_between(df_volume.index.map(mdates.date2num),
df_volume.values, 0)
ax1.plot(data.index, data['3T'])
ax1.plot(data.index, data['Open'], color="k", linestyle='dashed')
ax1.scatter(dataPeak["Date"].shift(slide),
dataPeak["Open"],
marker="^")
ax1.scatter(dataTrough["Date"].shift(slide),
dataTrough["Open"],
marker="v")
ax1.set_ylabel('Price')
ax2.set_ylabel('Volume')
ax2.set_xlabel('Date')
ax1.set_title('EUR/USD')
plt.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)
from statistics import mean
import seaborn as sns
#import pymc3 as pm
from scipy import stats
plt.style.use('seaborn')
mpl.rcParams['font.family'] = 'serif'
np.random.seed(1000)
raw = pd.read_csv('2510_input.csv', nrows=300, index_col=0,
parse_dates=True).dropna()
df = DataFrame(raw)
gridsize = (3, 2)
fig = plt.figure(figsize=(12, 8))
ax1 = plt.subplot2grid(gridsize, (0, 0), colspan=2, rowspan=2)
ax2 = plt.subplot2grid(gridsize, (2, 0))
ax3 = plt.subplot2grid(gridsize, (2, 1))
fig.tight_layout()
#AVG_2Y5Y10Y = df['CZK 2Y5Y10Y'+'PLN 2Y5Y10Y'+'ILS 2Y5Y10Y'+'HUF 2Y5Y10Y'+'CHF 2Y5Y10Y'+'EUR 2Y5Y10Y'+'GBP 2Y5Y10Y'+'SEK 2Y5Y10Y'][-250:]
df_5y5y = df[[
'CZK 5Y X 5Y Fwd Swap Rate', 'PLN 5Y X 5Y Fwd Swap Rate',
'ILS 5Y X 5Y Fwd Swap Rate', 'HUF 5Y X 5Y Fwd Swap Rate',
'5Y X 5Y CHF Fwd Swap Rate', '5Y X 5Y EUR Fwd Swap Rate',
'5Y X 5Y GBP Fwd Swap Rate', '5Y X 5Y SEK Fwd Swap Rate'
]][:-150]
avg_5y5y = df_5y5y.sum(axis=1) / 8
legend = ['CZK', 'PLN', 'ILS', 'HUF', 'CHF', 'EUR', 'GBP', 'SEK']
def weatherstat(df, destguid=None):
dfduplicates = df.groupby('date').apply(
lambda d: tuple(d.index) if len(d.index) > 1 else None).dropna()
log.info(dfduplicates)
df.drop_duplicates(inplace=True) # 去重，去除可能重复的天气数据记录，原因可能是邮件重复发送等
# print(df.head(30))
df['date'] = df['date'].apply(lambda x: pd.to_datetime(x))
# 日期做索引，去重并重新排序
df.index = df['date']
df = df[~df.index.duplicated()]
df.sort_index(inplace=True)
# print(df)
df.dropna(how='all', inplace=True) # 去掉空行，索引日期，后面索引值相同的行会被置空，需要去除
# print(len(df))
# df['gaowen'] = df['gaowen'].apply(lambda x: np.nan if str(x).isspace() else int(x)) #处理空字符串为空值的另外一骚
df['gaowen'] = df['gaowen'].apply(lambda x: int(x)
if x else None) # 数字串转换成整数，如果空字符串则为空值
df['diwen'] = df['diwen'].apply(lambda x: int(x) if x else None)
df['fengsu'] = df['fengsu'].apply(lambda x: int(x) if x else None)
df['shidu'] = df['shidu'].apply(lambda x: int(x) if x else None)
# df['gaowen'] = df['gaowen'].astype(int)
# df['diwen'] = df['diwen'].astype(int)
# df['fengsu'] = df['fengsu'].astype(int)
# df['shidu'] = df['shidu'].astype(int)
df.fillna(method='ffill', inplace=True) # 向下填充处理可能出现的空值，bfill是向上填充
df['wendu'] = (df['gaowen'] + df['diwen']) / 2
df['richang'] = df['sunoff'] - df['sunon']
df['richang'] = df['richang'].astype(int)
df['wendu'] = df['wendu'].astype(int)
# print(df.tail(30))
df_recent_year = df.iloc[-365:]
# print(df_recent_year)
# print(df[df.gaowen == df.iloc[-364:]['gaowen'].max()])
# df_before_year = df.iloc[:-364]
plt.figure(figsize=(16, 20))
ax1 = plt.subplot2grid((4, 2), (0, 0), colspan=2, rowspan=2)
ax1.plot(df['gaowen'], lw=0.3, label=u'日高温')
ax1.plot(df['diwen'], lw=0.3, label=u'日低温')
ax1.plot(df['wendu'], 'g', lw=0.7, label=u'日温度（高温低温平均）')
quyangtianshu = 10
ax1.plot(df['wendu'].resample('%dD' % quyangtianshu).mean(),
'b',
lw=1.2,
label='日温度（每%d天平均）' % quyangtianshu)
ax1.plot(df[df.fengsu > 5]['fengsu'], '*', label='风速（大于五级）')
plt.legend(loc=2)
# 起始统计日
kedu = df.iloc[0]
ax1.plot([kedu['date'], kedu['date']], [0, kedu['wendu']], 'c--', lw=0.4)
ax1.scatter([
kedu['date'],
], [kedu['wendu']], 50, color='Wheat')
fsize = 8
txt = str(kedu['wendu'])
ax1.annotate(txt,
xy=(kedu['date'], kedu['wendu']),
xycoords='data',
xytext=(-(len(txt) * fsize), +20),
textcoords='offset points',
fontsize=fsize,
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3,rad=.2",
color='Purple'))
dates = "%02d-%02d" % (kedu['date'].month, kedu['date'].day)
ax1.annotate(dates,
xy=(kedu['date'], 0),
xycoords='data',
xytext=(-10, -20),
textcoords='offset points',
fontsize=8,
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3,rad=0"))
# 去年今日，如果数据不足一年，取今日
if len(df) >= 366:
locqnjr = -365
else:
locqnjr = -1
kedu = df.iloc[locqnjr]
# kedu = df.iloc[-364]
print(kedu)
ax1.plot([kedu['date'], kedu['date']], [0, kedu['wendu']], 'c--')
ax1.scatter([
kedu['date'],
], [kedu['wendu']], 50, color='Wheat')
ax1.annotate(str(kedu['wendu']),
xy=(kedu['date'], kedu['wendu']),
xycoords='data',
xytext=(-5, +20),
textcoords='offset points',
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3,rad=.2",
color='Purple'))
dates = "%02d-%02d" % (kedu['date'].month, kedu['date'].day)
ax1.annotate(dates,
xy=(kedu['date'], 0),
xycoords='data',
xytext=(-10, -35),
textcoords='offset points',
fontsize=8,
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3,rad=0"))
# 今日
kedu = df.iloc[-1]
# print(kedu)
ax1.plot([kedu['date'], kedu['date']], [0, kedu['wendu']], 'c--')
ax1.scatter([
kedu['date'],
], [kedu['gaowen']], 50, color='BlueViolet')
ax1.annotate(str(kedu['gaowen']),
xy=(kedu['date'], kedu['gaowen']),
xycoords='data',
xytext=(-10, +20),
textcoords='offset points',
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3,rad=.2",
color='Purple'))
ax1.scatter([
kedu['date'],
], [kedu['wendu']], 50, color='BlueViolet')
ax1.annotate(str(kedu['wendu']),
xy=(kedu['date'], kedu['wendu']),
xycoords='data',
xytext=(10, +5),
textcoords='offset points',
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3,rad=.2",
color='Purple'))
ax1.scatter([
kedu['date'],
], [kedu['diwen']], 50, color='BlueViolet')
ax1.annotate(str(kedu['diwen']),
xy=(kedu['date'], kedu['diwen']),
xycoords='data',
xytext=(-10, -20),
textcoords='offset points',
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3,rad=.2",
color='Purple'))
dates = "%02d-%02d" % (kedu['date'].month, kedu['date'].day)
ax1.annotate(dates,
xy=(kedu['date'], 0),
xycoords='data',
xytext=(-10, -35),
textcoords='offset points',
fontsize=8,
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3,rad=0"))
# 最近一年最高温
kedu = df_recent_year[df_recent_year.gaowen == df_recent_year.iloc[-364:]
['gaowen'].max()].iloc[0]
# print(kedu)
ax1.plot([kedu['date'], kedu['date']], [0, kedu['gaowen']], 'c--')
ax1.scatter([
kedu['date'],
], [kedu['gaowen']], 50, color='Wheat')
ax1.annotate(str(kedu['gaowen']),
xy=(kedu['date'], kedu['gaowen']),
xycoords='data',
xytext=(-20, +5),
textcoords='offset points',
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3,rad=.2",
color='Purple'))
dates = "%02d-%02d" % (kedu['date'].month, kedu['date'].day)
ax1.annotate(dates,
xy=(kedu['date'], 0),
xycoords='data',
xytext=(-10, -20),
textcoords='offset points',
fontsize=8,
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3,rad=0"))
# 最近一年最低温
kedu = df_recent_year[df_recent_year.diwen == df_recent_year.iloc[-364:]
['diwen'].min()].iloc[0]
ax1.plot([kedu['date'], kedu['date']], [0, kedu['diwen']], 'c--')
ax1.scatter([
kedu['date'],
], [kedu['diwen']], 50, color='Wheat')
ax1.annotate(str(kedu['diwen']),
xy=(kedu['date'], kedu['diwen']),
xycoords='data',
xytext=(-20, +5),
textcoords='offset points',
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3,rad=.2",
color='Purple'))
dates = "%02d-%02d" % (kedu['date'].month, kedu['date'].day)
ax1.annotate(dates,
xy=(kedu['date'], 0),
xycoords='data',
xytext=(-10, -20),
textcoords='offset points',
fontsize=8,
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3,rad=0"))
# 最高温
kedu = df[df.gaowen == df['gaowen'].max()].iloc[0]
ax1.plot([kedu['date'], kedu['date']], [0, kedu['gaowen']], 'c--')
ax1.scatter([
kedu['date'],
], [kedu['gaowen']], 50, color='Wheat')
ax1.annotate(str(kedu['gaowen']),
xy=(kedu['date'], kedu['gaowen']),
xycoords='data',
xytext=(-20, +5),
textcoords='offset points',
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3,rad=.2",
color='Purple'))
dates = "%02d-%02d" % (kedu['date'].month, kedu['date'].day)
ax1.annotate(dates,
xy=(kedu['date'], 0),
xycoords='data',
xytext=(-10, -20),
textcoords='offset points',
fontsize=8,
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3,rad=0"))
# 最低温
kedu = df[df.diwen == df['diwen'].min()].iloc[0]
ax1.plot([kedu['date'], kedu['date']], [0, kedu['diwen']], 'c--')
ax1.scatter([
kedu['date'],
], [kedu['diwen']], 50, color='Wheat')
ax1.annotate(str(kedu['diwen']),
xy=(kedu['date'], kedu['diwen']),
xycoords='data',
xytext=(-20, +5),
textcoords='offset points',
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3,rad=.2",
color='Purple'))
dates = "%02d-%02d" % (kedu['date'].month, kedu['date'].day)
ax1.annotate(dates,
xy=(kedu['date'], 0),
xycoords='data',
xytext=(-10, -20),
textcoords='offset points',
fontsize=8,
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3,rad=0"))
ax1.set_ylabel(u'（摄氏度℃）')
ax1.grid(True)
ax1.set_title(u'最高气温、最低气温和均值温度图')
ax3 = plt.subplot2grid((4, 2), (2, 0), colspan=2, rowspan=2)
# print(type(ax3))
ax3.plot(df_recent_year['shidu'], 'c.', lw=0.3, label=u'湿度')
ax3.plot(df_recent_year['shidu'].resample('15D').mean(), 'g', lw=1.5)
ax3.set_ylabel(u'（百分比%）')
ax3.set_title(u'半月平均湿度图')
img_wenshifeng_path = dirmainpath / "img" / 'weather' / 'wenshifeng.png'
img_wenshifeng_path_str = str(img_wenshifeng_path)
touchfilepath2depth(img_wenshifeng_path)
plt.legend(loc='lower left')
plt.savefig(img_wenshifeng_path_str)
imglist = list()
imglist.append(img_wenshifeng_path_str)
plt.close()
plt.figure(figsize=(16, 10))
fig, ax1 = plt.subplots()
plt.plot(df['date'], df['sunon'], lw=0.8, label=u'日出')
plt.plot(df['date'], df['sunoff'], lw=0.8, label=u'日落')
ax = plt.gca()
# ax.yaxis.set_major_formatter(FuncFormatter(min_formatter)) # 主刻度文本用pi_formatter函数计算
ax.yaxis.set_major_formatter(
FuncFormatter(lambda x, pos: "%02d:%02d" %
(int(x / 60), int(x % 60)))) # 主刻度文本用pi_formatter函数计算
plt.ylim((0, 24 * 60))
plt.yticks(np.linspace(0, 24 * 60, 25))
plt.xlabel(u'日期')
plt.ylabel(u'时刻')
plt.legend(loc=6)
plt.title(u'日出日落时刻和白天时长图')
plt.grid(True)
ax2 = ax1.twinx()
print(ax2)
plt.plot(df_recent_year['date'],
df_recent_year['richang'],
'r',
lw=1.5,
label=u'日长')
ax = plt.gca()
# ax.yaxis.set_major_formatter(FuncFormatter(min_formatter)) # 主刻度文本用pi_formatter函数计算
ax.yaxis.set_major_formatter(
FuncFormatter(lambda x, pos: "%02d:%02d" %
(int(x / 60), int(x % 60)))) # 主刻度文本用pi_formatter函数计算
# ax.set_xticklabels(rotation=45, horizontalalignment='right')
plt.ylim((3 * 60, 12 * 60))
plt.yticks(np.linspace(3 * 60, 15 * 60, 13))
plt.ylabel(u'时分')
plt.legend(loc=5)
plt.grid(True)
# plt.show()
img_sunonoff_path = dirmainpath / 'img' / 'weather' / 'sunonoff.png'
img_sunonoff_path_str = str(img_sunonoff_path)
touchfilepath2depth(img_sunonoff_path)
plt.savefig(img_sunonoff_path_str)
imglist.append(img_sunonoff_path_str)
plt.close()
imglist2note(get_notestore(), imglist, destguid, '武汉天气图')
print_frame = False file_name = "square_lattice" L = 10 dims = 2 initializer = ParticleInitialize() c = Container(dims,10) c = initializer(file_name,c) ################################# distance_matrix = PeroidicDistanceMatrix(L) force = LeonardJonesForce(distance_matrix,c.masses) integrate = VerletIntegrator(.01) ########### FORCED ANIMATION ########## circles = [] fig = plt.figure(figsize=(10,5)) ax = plt.subplot2grid((2,2),(0,0),rowspan=2,xlim=(0,c.L[0]),ylim=(0,c.L[1]),aspect='equal') ax2 = plt.subplot2grid((2,2),(0,1),title='Potential Energy',ylim=(-1,potential_ylim)) ax2.grid() ax2.set_xticklabels([]) ax3 = plt.subplot2grid((2,2),(1,1),title='Kinetic Energy') ax3.grid() ax3.set_xticklabels([]) ax3.text(.4,.5,"????",fontsize=25, transform=ax3.transAxes) fig.subplots_adjust(wspace=.4) # this makes space between subplots fig.subplots_adjust(hspace=.4) # this makes space between subplots def prettify_circle(e): color="lightsteelblue" facecolor="green" alpha=.6 e.set_clip_box(ax.bbox)
