def now_to_mins_1_number():
snow=datetime.datetime.now().strftime('%Y-%m-%d %H')
snow1=datetime.datetime.now().strftime('%Y-%m-%d %H:%M')
v=date2num(datetime.datetime.strptime(snow,'%Y-%m-%d %H'))
tnow=date2num(datetime.datetime.strptime(snow1,'%Y-%m-%d %H:%M'))-v
x=math.floor(tnow*1440)/1440
return x+v
def now_to_hours_4_number():
snow=datetime.datetime.now().strftime('%Y-%m-%d')
snow1=datetime.datetime.now().strftime('%Y-%m-%d %H')
v=date2num(datetime.datetime.strptime(snow,'%Y-%m-%d'))
tnow=date2num(datetime.datetime.strptime(snow1,'%Y-%m-%d %H'))-v
x=math.floor(tnow*6)/6
easylog.info("flag_before: %s,%s,",snow1,x)
return x+v
def generate_xticks(self, all_days, monthsamp=1):
"""
generate xticks
all_days: array of times (days since 01-0-0)
"""
dd = pl.num2date(all_days)
xticks = []
xticklabels = []
last_month = dd[0].month
last_year = dd[0].year
xticks.append(all_days[0])
strmon = get_month_string(dd[0].month)
if self.yearonly:
xticklabels.append(str(dd[0].year))
else:
xticklabels.append(strmon + '\n' + str(dd[0].year))
for d in dd:
if (d.month != last_month) | (d.year != last_year):
xticks.append(pl.date2num(d)) # save tick location
mstr = get_month_string(d.month)
if self.yearonly:
xticklabels.append(str(d.year))
else:
if (d.year != last_year):
xticklabels.append(mstr + '\n' + str(d.year))
else:
xticklabels.append(mstr + '\n' + '')
last_month = d.month
last_year = d.year
if self.transpose:
scal = self.rescaley
else:
scal = self.rescalex
xticks = np.asarray(xticks)
# /// remap to image coordinates
ny, nx = np.shape(self.hov)
w = (xticks - pl.date2num(self.t_min)) / (
pl.date2num(self.t_max) - pl.date2num(self.t_min)
) # weights for positions on x-axis
xticks = w * nx * scal
xticks = list(xticks)
# here we have monthly ticks which we can now subsample
xticks = xticks[::monthsamp]
xticklabels = xticklabels[::monthsamp]
self.x_major_locator = pl.FixedLocator(xticks)
self.x_major_formatter = pl.FixedFormatter(xticklabels)
def generate_xticks(self, all_days, monthsamp=1):
"""
generate xticks
all_days: array of times (days since 01-0-0)
"""
dd = pl.num2date(all_days)
xticks = []
xticklabels = []
last_month = dd[0].month
last_year = dd[0].year
xticks.append(all_days[0])
strmon = get_month_string(dd[0].month)
if self.yearonly:
xticklabels.append(str(dd[0].year))
else:
xticklabels.append(strmon + '\n' + str(dd[0].year))
for d in dd:
if (d.month != last_month) | (d.year != last_year):
xticks.append(pl.date2num(d)) # save tick location
mstr = get_month_string(d.month)
if self.yearonly:
xticklabels.append(str(d.year))
else:
if (d.year != last_year):
xticklabels.append(mstr + '\n' + str(d.year))
else:
xticklabels.append(mstr + '\n' + '')
last_month = d.month
last_year = d.year
if self.transpose:
scal = self.rescaley
else:
scal = self.rescalex
xticks = np.asarray(xticks)
# /// remap to image coordinates
ny, nx = np.shape(self.hov)
w = (xticks - pl.date2num(self.t_min)) / (pl.date2num(self.t_max)
- pl.date2num(self.t_min)) # weights for positions on x-axis
xticks = w * nx * scal
xticks = list(xticks)
# here we have monthly ticks which we can now subsample
xticks = xticks[::monthsamp]
xticklabels = xticklabels[::monthsamp]
self.x_major_locator = pl.FixedLocator(xticks)
self.x_major_formatter = pl.FixedFormatter(xticklabels)
def date_to_num(dates, timecard):
num_time = []
if timecard == 'D':
for date in dates:
date_time = datetime.datetime.strptime(date, '%Y-%m-%d')
num_date = date2num(date_time)
num_time.append(num_date)
elif timecard == '30':
for date in dates:
date_time = datetime.datetime.strptime(date, '%Y-%m-%d %H:%M')
num_date = date2num(date_time)
num_time.append(num_date)
return num_time
def InputArgs():
parser = argparse.ArgumentParser(description="show example")
# parser.add_argument('hs300_stocks', default=['000300.sh'], nargs='*')
parser.add_argument("-a", "--append", default=1, help="append")
parser.add_argument("-c", "--count", default=20, help="stock count")
parser.add_argument("-e", "--end_date", default='', help="end date")
parser.add_argument("-f",
"--record_file",
default='hs300',
help="record file")
parser.add_argument("-l", "--stop_loss", default=3, help="stop loss")
parser.add_argument("-p",
"--data_path",
default='./data-qfq-13',
help="stock data path")
parser.add_argument("-s",
"--start_date",
default='20140101',
help="start date")
parser.add_argument("-S",
"--strong_cycle",
default=20,
help="strong cycle")
parser.add_argument("-u", "--loss_unit", default=0.01, help="loss unit")
ARGS = parser.parse_args()
global cmdline
if ARGS.start_date:
_date = dt.datetime.strptime(str(ARGS.start_date), '%Y%m%d')
cmdline['start_date'] = date2num(_date)
if ARGS.end_date:
_date = dt.datetime.strptime(str(ARGS.end_date), '%Y%m%d')
cmdline['end_date'] = date2num(_date)
if ARGS.data_path:
cmdline['data_path'] = ARGS.data_path
if ARGS.record_file:
cmdline['record_file'] = ARGS.record_file
if ARGS.count:
cmdline['stock_count'] = int(ARGS.count)
if ARGS.loss_unit:
cmdline['loss_unit'] = float(ARGS.loss_unit)
if ARGS.append:
cmdline['append'] = int(ARGS.append)
if ARGS.stop_loss:
cmdline['stop_loss'] = int(ARGS.stop_loss)
if ARGS.strong_cycle:
cmdline['strong_cycle'] = int(ARGS.strong_cycle)
def loadData(draw=False, count=100):
if (draw):
data = []
for i in range(1, 5):
with open(data_root_path + "/stock_data_" + str(i) + ".data") as f:
while (True):
line_str = f.readline().replace("\n", "").split(" ")
if (len(line_str) != 6):
break
results = list(map(float, line_str))
results[0] = (date2num(parse(str(results[0]))))
data.append(tuple(results))
pass
else:
data = []
for i in range(1, 5):
with open(data_root_path + "/stock_data_" + str(i) + ".data") as f:
while (True):
line_str = f.readline().replace("\n", "").split(" ")
if (len(line_str) != 6):
break
results = list(map(float, line_str))
#去除date
results.pop(0)
data.append(results)
pass
return data
def date_to_num(dates):
''' Function to convert tushare 'date' string to matplotlib datenum
Input
=====
dates: ndarray of tushare 'date' strings. Eg. ['2013-01-31', ...]
Output
======
Return: list of float datetime value compatible to matplotlib: floating point
numbers which represent time in days since 0001-01-01 UTC, plus 1.
For example, 0001-01-01, 06:00 is 1.25, not 0.25.
Example
=======
stock_data['mpl.date'] = date_to_num(stock_data['date'].values)
'''
num_time = []
for date in dates:
date_time = datetime.datetime.strptime(date, '%Y-%m-%d')
num_date = date2num(date_time)
'''
matplotlib.dates.date2num(d)
Converts datetime objects to Matplotlib dates
'''
num_time.append(num_date)
return num_time
def plot_chart(self, data, n, ticker):
''' plot the information for one stock (inclding: Moving Average, MACD, RSI, Volume)
param data: datafram for the stock
param n: n index before the end date
param ticker: single stock ticker
'''
data["macd"], data["macd_signal"], data["macd_hist"] = talib.MACD(data['Adj Close'])
# Get MA10 and MA30
data["ma10"] = talib.MA(data["Adj Close"], timeperiod=10)
data["ma30"] = talib.MA(data["Adj Close"], timeperiod=30)
# Get RSI
data["rsi"] = talib.RSI(data["Adj Close"])
# Filter number of observations to plot
data = data.iloc[-n:]
# Create figure and set axes for subplots
fig = plt.figure()
fig.set_size_inches((20, 16))
ax_candle = fig.add_axes((0, 0.72, 1, 0.32))
ax_macd = fig.add_axes((0, 0.48, 1, 0.2), sharex=ax_candle)
ax_rsi = fig.add_axes((0, 0.24, 1, 0.2), sharex=ax_candle)
ax_vol = fig.add_axes((0, 0, 1, 0.2), sharex=ax_candle)
# Format x-axis ticks as dates
ax_candle.xaxis_date()
# Get nested list of date, open, high, low and close prices
ohlc = []
for date, row in data.iterrows():
openp, highp, lowp, closep = row[:4]
ohlc.append([date2num(date), openp, highp, lowp, closep])
# Plot candlestick chart
ax_candle.plot(data.index, data["ma10"], label="MA10")
ax_candle.plot(data.index, data["ma30"], label="MA30")
# candlestick_ohlc(ax_candle, ohlc, colorup="g", colordown="r", width=0.8)
# ax_candle.legend()
ax_candle.plot(data.index, data['Adj Close'], label = 'Price')
# Plot MACD
ax_macd.plot(data.index, data["macd"], label="macd")
ax_macd.bar(data.index, data["macd_hist"] * 3, label="hist")
ax_macd.plot(data.index, data["macd_signal"], label="signal")
ax_macd.legend()
# Plot RSI
# Above 70% = overbought, below 30% = oversold
ax_rsi.set_ylabel("(%)")
ax_rsi.plot(data.index, [70] * len(data.index), label="overbought")
ax_rsi.plot(data.index, [30] * len(data.index), label="oversold")
ax_rsi.plot(data.index, data["rsi"], label="rsi")
ax_rsi.legend()
# Show volume in millions
ax_vol.bar(data.index, data["Volume"] / 1000000)
ax_vol.set_ylabel("(Million)")
# Save the chart as PNG
fig.savefig("charts/" + ticker + ".png", bbox_inches="tight")
plt.show()
def query_index_daily(self, code):
df = IndustryData().get_index_daily(code)
print( df.head(10) )
df['date']=df['date'].apply( lambda x:date2num(x))
ohlc=df[['date','open','high','low','close']]
ohlc.rename(columns={'date':'t'}, inplace=True)
ohlc['open']=ohlc['open'].apply( lambda x:float(x))
ohlc['high']=ohlc['high'].apply( lambda x:float(x))
ohlc['low']=ohlc['low'].apply( lambda x:float(x))
ohlc['close']=ohlc['close'].apply( lambda x:float(x))
print( ohlc.head(10))
self.f_plot.clear()
self.f_plot.xaxis_date()
self.f_plot.autoscale_view()
plt.xticks(rotation=45)
plt.yticks()
plt.title(" {0}".format(code))
plt.xlabel("date")
plt.ylabel("price")
print( ohlc.head(10))
print( ohlc.values)
mpf.candlestick_ohlc(self.f_plot, ohlc.values, colorup='red', colordown='green')
plt.legend(loc='best', shadow=True)
plt.grid()
self.canvs.draw()
def drawKFig(quote_code, start_date, end_date):
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
# 瀵箃ushare鑾峰彇鍒扮殑鏁版嵁杞崲鎴恈andlestick_ohlc()鏂规硶鍙鍙栫殑鏍煎紡
data_list = []
hist_data = ts.get_hist_data(quote_code, start=start_date, end=end_date) # 涓�娆℃�ц幏鍙栧叏閮ㄦ棩k绾挎暟鎹�
for date, row in hist_data.iterrows():
# 灏嗘椂闂磋浆鎹负鏁板瓧
date_time = datetime.datetime.strptime(date, '%Y-%m-%d')
t = date2num(date_time)
open, high, close, low = row[:4]
datas = (t, open, high, low, close)
print(datas)
data_list.append(datas)
# 鍒涘缓瀛愬浘
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.2)
# 璁剧疆X杞村埢搴︿负鏃ユ湡鏃堕棿
ax.xaxis_date()
plt.xticks(rotation=45)
plt.yticks()
plt.title(u"鑲＄エ浠ｇ爜锛�" + quote_code)
plt.xlabel(u"鏃堕棿")
plt.ylabel(u"鑲′环锛堝厓锛�")
mpf.candlestick_ohlc(ax, data_list, width=0.9, colorup='red', colordown='green')
ax.autoscale_view()
plt.grid()
plt.show()
pass
def date_to_num(dates):
num_time = []
for date in dates:
date_time = datetime.datetime.strptime(date, '%Y-%m-%d')
num_date = date2num(date_time)
num_time.append(num_date)
return num_time
def draw_l1_fcbs_(fEPs, sSatLst, plt):
xtm = []
for ss in sSatLst:
xp=[]
yp=[]
for fE0 in fEPs:
for fb in fE0.inf_fbs:
if fb.id == ss:
xp.append(fE0.tm)
yp.append(fb.val)
if len(yp)<3:
continue
plt.plot(xp, yp, '.', label=ss)
xtm.append(xp[0])
xtm.append(xp[-1])
if len(xtm)>3:
tmflt = date2num(xtm)
plt.xlim(tmflt.min() - 0.000001, tmflt.max() + 0.000001)
plt.grid(True)
def sample_513():
"""
5.1.3 k线图的绘制
:return:
"""
# import matplotlib.finance as mpf
import mpl_finance as mpf
from matplotlib.pylab import date2num
__colorup__ = "red"
__colordown__ = "green"
# 为了示例清晰，只拿出前30天的交易数据绘制蜡烛图，
tsla_part_df = tsla_df[:30]
fig, ax = plt.subplots(figsize=(14, 7))
qutotes = []
for index, (d, o, c, h, l) in enumerate(
zip(tsla_part_df.index, tsla_part_df.open, tsla_part_df.close,
tsla_part_df.high, tsla_part_df.low)):
# 蜡烛图的日期要使用matplotlib.finance.date2num进行转换为特有的数字值
d = date2num(d)
# 日期，开盘，收盘，最高，最低组成tuple对象val
val = (d, o, c, h, l)
# 加val加入qutotes
qutotes.append(val)
# 使用mpf.candlestick_ochl进行蜡烛绘制，ochl代表：open，close，high，low
mpf.candlestick_ochl(ax,
qutotes,
width=0.6,
colorup=__colorup__,
colordown=__colordown__)
ax.autoscale_view()
ax.xaxis_date()
plt.show()
def printKline(stock):
re = getFullQuoteByStock(stock)
re = re.set_index('date')
#re.index = pd.to_datetime(re.index)
data_list = []
re = re.drop('index', 1)
for dates, row in re.iterrows():
date_time = datetime.datetime.strptime(dates, '%Y-%m-%d')
t = date2num(date_time)
open, high, low, close = row[:4]
datas = (t, open, high, low, close)
data_list.append(datas)
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.2)
ax.xaxis_date()
plt.xticks(rotation=45)
plt.yticks()
plt.title("STOCK:" + stock)
plt.xlabel("time")
plt.ylabel("price")
mpf.candlestick(ax, data_list, width=1.5, colorup='r', colordown='green')
plt.grid()
plt.show()
plt.savefig('/Users/momo/Programs/python/result/' + stock + '.png')
def printKlines(stocks, savepath, fileName):
stockNum = len(stocks)
fig = plt.figure()
fig.set_size_inches(18.5, 18.5)
i = 1
for stock in stocks:
re = getFullQuoteByStock(stock)
re = re.set_index('date')
#re.index = pd.to_datetime(re.index)
data_list = []
re = re.drop('index', 1)
for dates, row in re.iterrows():
date_time = datetime.datetime.strptime(dates, '%Y-%m-%d')
t = date2num(date_time)
open, high, low, close = row[:4]
datas = (t, open, high, low, close)
data_list.append(datas)
ax = fig.add_subplot(stockNum, 1, i)
#fig.subplots_adjust(bottom=0.2)
ax.xaxis_date()
#plt.xticks(rotation=45)
plt.xticks()
plt.yticks()
plt.title("STOCK:" + stock)
plt.xlabel("time")
plt.ylabel("price")
mpf.candlestick(ax, data_list, width=1, colorup='r', colordown='green')
plt.grid()
i = i + 1
#plt.show()
plt.savefig(savepath + fileName, dpi=100)
def eg9_10():
"""
9.10 动手实践：绘制股价分布直方图
我们来绘制从雅虎财经频道下载的股价数据的分布直方图。
"""
# (1) 下载一年以来的数据：
today = date.today()
start = date(today.year - 1, today.month, today.day)
# (2) 上一步得到的股价数据存储在Python列表中。将其转化为NumPy数组并提取出收盘价数据：
df_stock = pd.read_csv('output/AAPL.csv', header=0, index_col=None)
quotes = []
for i in range(df_stock.shape[0]): # 遍历每行，shape为dataframe大小(x,y)，表示x行y列
if i == 0:
# 将交易日期日期转为数字
date_num = date2num(datetime.strptime(df_stock.ix[[i]].values[0][0], '%Y-%m-%d')) # 1表示第一列，为交易日
date_plt = date_num
else:
date_plt = date_num + i # 由于csv文件中日期为升序排序，这里为加号
open = df_stock.ix[[i]].values[0][1] # 开盘价： i行第2列
close = df_stock.ix[[i]].values[0][2] # 收盘价：i行第3列
high = df_stock.ix[[i]].values[0][3] # 最高价：i行第4列
low = df_stock.ix[[i]].values[0][4] # 最低价：i行第5列
datas = (date_plt, open, close, high, low)
quotes.append(datas)
quotes = np.array(quotes)
close = quotes.T[4]
print close
# (3) 指定合理数量的柱形，绘制分布直方图：
print len(close)
print np.sqrt(len(close))
plt.hist(close, int(np.sqrt(len(close)))) # 返回len(close)的平方根，保留为整数
plt.show()
def get_monthly_abstract_price(company):
print("Company: ", company)
# get data from iexfinance
goal = Stock(company, token=Token)
mon_pri_dics = goal.get_historical_prices()
# create a new list
mon_pri_data = []
# change the data into the form that mpf.candlestick_ochl function can read
for dic in mon_pri_dics:
# get date open high low close volume
list(dic.values())[0:6]
# change date into number
t = date2num(datetime.datetime.strptime(list(dic.values())[0], '%Y-%m-%d'))
# change sequence into the form that mpf.candlestick_ochl function can read
ope, close, high, low, vol = list(dic.values())[1:6]
# print(ope, close, high, low, vol)
# change the data into the form that mpf.candlestick_ochl function can read
data = [t, ope, close, high, low, vol]
mon_pri_data.append(data)
# draw the figure
# set ax as the style of figure
fig, ax = plt.subplots(figsize = (15,3.5))
plt.rcParams['savefig.dpi'] = 500
# set parameters of plot_day_summary_oclh: ax = ax sample; mon_pri_data = data color up = close > open; color down = open > close
plot_day_summary_oclh(ax, mon_pri_data, colorup = 'r', colordown = 'g')
ax.set_title('Candlestick Abstract Chart of' + ' ' + company)
plt.grid(True)
# set price label
plt.ylabel('Price')
# set x axis as date
ax.xaxis_date ()
plt.show()
return plt.savefig('monthabstractprice.png')
def initial_figure(self):
DB = MongoDB.MongoDBData()
df = DB.datafromMongoDB(self.dbname, self.tick)
df = df.loc[self.start_date:self.end_date, ['open', 'high', 'low', 'close']]
Data_list = []
for date, row in df.iterrows():
Date = date2num(dt.datetime.strptime(date, "%Y-%m-%d"))
Open, High, Low, Close = row[:4]
Data = (Date, Open, High, Low, Close)
Data_list.append(Data)
self.axes.xaxis_date()
# self.axes.plot(df['open'], df['close'])
# fig, ax = plt.subplots()
# fig.subplots_adjust(bottom=0.2)
# 设置X轴刻度为日期时间
# ax.xaxis_date()
# plt.xticks(rotation=45)
# plt.yticks()
# plt.title("股票代码：601558两年K线图")
# plt.xlabel("时间")
# plt.ylabel("股价（元）")
plt.tight_layout()
mpf.candlestick_ohlc(self.axes, Data_list, width=1.5, colorup='r', colordown='green')
def get_candles_data(url,contractSize):
print(url)
response = requests.get(url)
data_arr = response.text.replace("[[",'').replace("]]",'').replace("\"","").split("],[")
close = []
high = []
low = []
tradeTime = []
for item_str in reversed(data_arr):
item = item_str.split(",")
sdatetime_num = date2num(datetime.strptime(item[0].replace("T",' ').replace('.000Z',''),'%Y-%m-%d'))
# datas = (sdatetime_num,float(item[1]),float(item[2]),float(item[3]),float(item[4])) # 按照 candlestick_ohlc 要求的数据结构准备数据
# quotes.append(datas)
tradeTime.append(sdatetime_num)
high.append(float(item[2])*contractSize)
low.append(float(item[3])*contractSize)
close.append(float(item[4])*contractSize)
dt_dict = {'tradeTime':tradeTime,
'high':high,
'low':low,
'close':close}
data_df = DataFrame(dt_dict)
atr = ta.ATR(data_df['high'],data_df['low'],data_df['close'],timeperiod=14)
data_df["atr"]=atr
data_df["atrRatio"]=data_df["atr"]/data_df['close']
atrRatio = data_df["atrRatio"]
if len(data_df["atrRatio"])>300:
atrRatio = data_df["atrRatio"][len(data_df["atrRatio"])-300:len(data_df["atrRatio"])-1]
# print(atrRatio)
return list(atrRatio)
def drawK(k_dict):
k_dict = dict(sorted(k_dict.items()))
quotes = list()
for k, v in k_dict.items():
sdate_num = date2num(datetime.datetime.strptime(
k, '%Y-%m-%d')) # 日期需要特定形式，这里进行转换
sopen = v[0]
sclose = v[-1]
shigh = max(v)
slow = min(v)
datas = (sdate_num, sopen, shigh, slow, sclose)
quotes.append(datas)
fig, ax = plt.subplots(facecolor=(1, 0.6, 0.6), figsize=(12, 8))
fig.subplots_adjust(bottom=0.1)
ax.xaxis_date()
plt.xticks(rotation=45) # 日期显示的旋转角度
plt.title('K')
plt.xlabel('Time')
plt.ylabel('Money')
mpf.candlestick_ohlc(ax, quotes, width=0.7, colorup='r',
colordown='green') # 上涨为红色K线，下跌为绿色，K线宽度为0.7
plt.grid(True)
plt.show()
print("Done.")
def candle_draw(self, id='', df=None):
wx = lg.get_handle()
if df is None:
wx.info("[mp_painter][candle_draw] dataframe is Empty, return")
return
fake_date_arr = ['20000101']
fake_date = datetime.strptime('20000101', '%Y%m%d')
i = 1
while i < len(df):
fake_date += timedelta(days=1)
fake_date_arr.append(fake_date.strftime('%Y%m%d'))
i += 1
# 日期处理为时间戳
real_trade_date = df['trade_date'].copy().tolist()
df['trade_date'] = fake_date_arr
df.trade_date = df.trade_date.apply(
lambda x: date2num(datetime.strptime(x, "%Y%m%d")))
# Tushare 返回的数据 按日期倒序，按日期升序后，需要把倒序的index先删掉，再重新生成升序的 index
# 用 index 填充 日期列，当做日期使用
# df.reset_index(drop=False, inplace=True)
# df.drop('index', axis=1, inplace=True)
# df.reset_index(drop=False, inplace=True)
# df.drop('trade_date', axis=1, inplace=True)
# df.index = df.index.apply(lambda x: date2num(x))
candle_array = df.values
# 创建一个子图
fig, ax = plt.subplots(facecolor=(0.5, 0.5, 0.5))
fig.subplots_adjust(bottom=0.1)
# 设置X轴刻度为日期时间
ax.xaxis_date()
ax.autoscale_view()
candlestick_ohlc(ax,
candle_array,
colordown='#53c156',
colorup='#ff1717',
width=0.3,
alpha=1)
# ax.xaxis.set_major_locator(ticker.MultipleLocator(6))
# ax.set_xticks(range(len(real_trade_date)))
# ax.set_xticklabels(real_trade_date)
formatter = MyFormatter(real_trade_date)
ax.xaxis.set_major_formatter(formatter)
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
# X轴刻度文字倾斜45度
# plt.xticks( rotation=45)
plt.grid(True)
plt.title("回测结果:[" + id + "] K线图")
plt.xlabel("日期")
plt.ylabel("价格(元)")
# candlestick_ochl(ax, candle_array, colordown='#53c156', colorup='#ff1717', width=0.3, alpha=1)
plt.show()
def drawAbsRBacktest(self,portReturn,portReturn1,dailyDate,path,name):
pylab.clf()
pylab.plot_date(pylab.date2num(dailyDate),portReturn,'r',linewidth=0.8,linestyle='-')
pylab.plot_date(pylab.date2num(dailyDate),portReturn1,'b',linewidth=0.8,linestyle='-')
xtext = pylab.xlabel('Out-Of-Sample Date')
ytext = pylab.ylabel('Cumulative Return')
ttext = pylab.title('Portfolio Return Vs Benchmark Return')
pylab.grid(True)
pylab.setp(ttext, size='large', color='r')
pylab.setp(xtext, size='large', weight='bold', color='g')
pylab.setp(ytext, size='large', weight='light', color='b')
yearFormat=matDate.DateFormatter('%Y%m%d')
ax=pylab.gca()
ax.xaxis.set_major_formatter(yearFormat)
pylab.savefig('%s%sAbsRbacktest.png'%(path,name))
def date_to_num(dates):
"""将日期转换为matplotlib可以识别的日期格式"""
num_time = []
for date in dates:
date_time = datetime.datetime.strptime(date, "%Y-%m-%d")
num_date = date2num(date_time)
num_time.append(num_date)
return num_time
def float_date(_date):
if isinstance(_date, np.int64):
_date = str(_date)
if isinstance(_date, str):
_date = dt.datetime.strptime(_date, '%Y%m%d')
if isinstance(_date, dt.datetime):
_date = date2num(_date)
return _date
def plot_figure(self):
item = self.item
mpl_dates = date2num(item.index)
plt.figure(figsize=(8, 4))
plt.scatter(item['GLD'], item['SLV'], c=mpl_dates, marker='o', s=8)
plt.grid(True)
plt.xlabel('GLD')
plt.ylabel('SLV')
plt.colorbar(ticks=DayLocator(interval=250), format=DateFormatter('%d %b %y'))
def filllist(res):
data_list = []
for block in res['data']:
dt = datetime.datetime.utcfromtimestamp(
block['id']) + datetime.timedelta(hours=8)
# mpf库中要求的顺序是：时间、开盘价、最高价、最低价、收盘价
data_list.append((date2num(dt), block['open'], block['high'],
block['low'], block['close']))
return data_list
def float_date(_date):
if isinstance(_date, numpy.int64) or isinstance(_date, int):
_date = str(_date)
if isinstance(_date, str):
_date = datetime.datetime.strptime(_date, '%Y%m%d')
if isinstance(_date, datetime.datetime) or isinstance(
_date, datetime.date):
_date = date2num(_date)
return _date
def historico():
symbol = input("Input a symbol: ")
historico = DatabaseActions.getHistoric(symbol)
historicDF = {}
for key in historico:
historicDF[datetime.datetime.fromisoformat(key)] = historico[key]
df = pd.DataFrame.from_dict(historicDF,
orient='index').tail(50).rename(columns={
'o': 'Open',
'h': 'High',
'l': 'Low',
'c': 'Close'
})
op = input("¿Agregar RSI? S/N: ")
if op.lower() == 's':
titulo = 'RSI'
periodo = int(input("Ingrese el periodo: "))
RSI = RelativeStrengthIndex.RSI(historico, periodo)
df[titulo] = df.index.to_series().map(RSI)
op = input("¿Agregar EMA? S/N: ")
while op.lower() == 's':
titulo = input("Ingrese un titulo para la EMA: ")
periodo = int(input("Ingrese el periodo: "))
EMAS = ExponentialMovingAverage.EMA(historico, periodo)
df[titulo] = df.index.to_series().map(EMAS)
op = input("¿Agregar otra EMA? S/N: ")
print(df)
data = df.tail(30)
columns = list(data.columns)[4:]
fig = plt.figure()
fig.set_size_inches((20, 16))
ax_candle = fig.add_axes((0, 0.05, 1, 0.9))
ax_candle.xaxis_date()
if 'RSI' in columns:
ax_candle = fig.add_axes((0, 0.30, 1, 0.7))
ax_rsi = fig.add_axes((0, 0.05, 1, 0.2), sharex=ax_candle)
ax_rsi.set_ylabel("(%)")
ax_rsi.plot(data.index, [70] * len(data.index), label="overbought")
ax_rsi.plot(data.index, [30] * len(data.index), label="oversold")
ax_rsi.plot(data.index, data["RSI"], label="RSI")
ax_rsi.legend(loc="center left")
ohlc = []
for date, row in data.iterrows():
Open, High, Low, Close = row[:4]
ohlc.append([date2num(date), Open, High, Low, Close])
for column in columns:
if column != 'RSI':
ax_candle.plot(data.index, data[column], label=column)
candlestick_ohlc(ax_candle, ohlc, colorup="g", colordown="r")
ax_candle.legend()
plt.show()
def date_to_num(dates):
num_time = []
for date in dates:
#为什么int转换后会有.0这样的问题
date = (str)(date)
date = date[:-2]
date_time = datetime.datetime.strptime(date, '%Y%m%d')
num_date = date2num(date_time)
num_time.append(num_date)
return num_time
def get_current_selection(self):
"""
Returns a mask array where 1 == selected data point and 0 == not
selected data point.
"""
xdata, ydata = self.series.get_data()
selection_mask = numpy.zeros(len(xdata), dtype='int')
#if xdata are datetimes, then need to convert them to numbers
#first
if len(xdata) > 0 and type(xdata[0]) is datetime.datetime:
xdata = numpy.array([date2num(d) for d in xdata])
#if ydata are datetimes, then need to convert them to numbers
#first
if len(ydata) > 0 and type(ydata[0]) is datetime.datetime:
ydata = numpy.array([date2num(d) for d in ydata])
if self.direction == 'rectangular':
for xmin_sel, ymin_sel, xmax_sel, ymax_sel in self.current_selection:
tmp_mask = numpy.where(
numpy.logical_and(
numpy.logical_and(xdata >= xmin_sel,
xdata <= xmax_sel),
numpy.logical_and(ydata >= ymin_sel,
ydata <= ymax_sel)))
selection_mask[tmp_mask] = 1
elif self.direction == 'horizontal':
for xmin_sel, ymin_sel, xmax_sel, ymax_sel in self.current_selection:
tmp_mask = numpy.where(
numpy.logical_and(xdata >= xmin_sel, xdata <= xmax_sel))
selection_mask[tmp_mask] = 1
else:
for xmin_sel, ymin_sel, xmax_sel, ymax_sel in self.current_selection:
tmp_mask = numpy.where(
numpy.logical_and(ydata >= ymin_sel, ydata <= ymax_sel))
selection_mask[tmp_mask] = 1
return selection_mask
def get_one(gu_piao):
sql = 'SELECT mai_chu_er,mai_ru_er,date,shou_pan_jia FROM shen_zheng_gushi.shen_gutong where dai_ma=%s;'
cur.execute(sql, (gu_piao))
result = cur.fetchall()
money_data_list = []
for i in result:
t = date2num(i[2])
jin_er_du = i[1] - i[0]
shou_pan_jia = i[3]
money_data_list.append((t, shou_pan_jia, jin_er_du))
return money_data_list
def get_current_selection(self):
"""
Returns a mask array where 1 == selected data point and 0 == not
selected data point.
"""
xdata, ydata = self.series.get_data()
selection_mask = numpy.zeros(len(xdata),dtype='int')
#if xdata are datetimes, then need to convert them to numbers
#first
if len(xdata)>0 and type(xdata[0]) is datetime.datetime:
xdata = numpy.array([date2num(d) for d in xdata])
#if ydata are datetimes, then need to convert them to numbers
#first
if len(ydata)>0 and type(ydata[0]) is datetime.datetime:
ydata = numpy.array([date2num(d) for d in ydata])
if self.direction == 'rectangular':
for xmin_sel, ymin_sel, xmax_sel, ymax_sel in self.current_selection:
tmp_mask = numpy.where(numpy.logical_and(numpy.logical_and(xdata >= xmin_sel,
xdata <= xmax_sel),
numpy.logical_and(ydata >= ymin_sel,
ydata <= ymax_sel)))
selection_mask[tmp_mask] = 1
elif self.direction == 'horizontal':
for xmin_sel, ymin_sel, xmax_sel, ymax_sel in self.current_selection:
tmp_mask = numpy.where(numpy.logical_and(xdata >= xmin_sel,
xdata <= xmax_sel))
selection_mask[tmp_mask] = 1
else:
for xmin_sel, ymin_sel, xmax_sel, ymax_sel in self.current_selection:
tmp_mask = numpy.where(numpy.logical_and(ydata >= ymin_sel,
ydata <= ymax_sel))
selection_mask[tmp_mask] = 1
return selection_mask
def str_epoch2num(s):
"""
input is either a string or and time in seconds since epoch
output is the date format of matplotlib
Without a timezone in the input string, the local timezone is used.
To enter a timezone you can use UTC, GMT,Z or something like
-0500. It also knows about EST and DST.
"""
if isinstance(s, basestring):
# we replace pylab.datestr2num to better handle local timezone
dt = dateutil.parser.parse(s, tzinfos=_TZOFFSETS)
if dt.tzinfo is None:
dt = dt.replace(tzinfo=dateutil.tz.tzlocal())
return pylab.date2num(dt)
else:
return pylab.epoch2num(s)
def draw():
#获取时间轴
date_num = len(get_data_source('date'))
today = datetime.today() if LOG_DATE is 'today' else str2datetime(LOG_DATE)
x_time =[today+timedelta(-i) for i in range(date_num)]
dates = pl.date2num(x_time)
#创建图1
pl.figure(1)
#子图1
ax1 = pl.subplot(2,2,1)
update_list = get_data_source('number')
ax1.plot_date(dates, update_list, 'r-', marker='.', linewidth=1)
ax1.xaxis.set_major_formatter( DateFormatter('%m.%d'))
pl.xlabel('date')
pl.ylabel('album number')
pl.title('the number of album db')
#子图2
ax2 = pl.subplot(2,2,2)
insert_list = get_data_source('insert')
ax2.plot_date(dates, insert_list, 'b-', marker='*', linewidth=1)
ax2.xaxis.set_major_formatter( DateFormatter('%m.%d') )
pl.xlabel('date')
pl.ylabel('increasement')
pl.title('daily album increasement')
#子图3
ax3 = pl.subplot(2,2,3)
err_num_list = get_data_source('error_num')
ax3.plot_date(dates, err_num_list, 'y-', marker='+', linewidth=1)
ax3.xaxis.set_major_formatter( DateFormatter('%m.%d') )
pl.xlabel('date')
pl.ylabel('error')
pl.title('occurred errors')
#子图4
ax4 = pl.subplot(2,2,4)
err_num_list = get_data_source('update')
ax4.plot_date(dates, err_num_list, 'g-', marker='^', linewidth=1)
ax4.xaxis.set_major_formatter( DateFormatter('%m.%d') )
pl.xlabel('date')
pl.ylabel('update')
pl.title('updated albums')
pl.figure(1).autofmt_xdate()
pl.figure(1).tight_layout()
pl.savefig(IMAGE_PATH,dpi=DPI)
conn = sqlite3.connect('data_log.db')
c = conn.cursor()
c.execute("SELECT name FROM sqlite_master WHERE type='table' AND name LIKE '28.%';")
sensors = []
for row in c:
sensors.append(row[0])
print(sensors)
sdates = [];
svalues = [];
i = 1
for s in sensors:
c.execute("SELECT `datetime`, value FROM `%s` ORDER BY `datetime` DESC LIMIT 150" % (sensors[i-1]))
dates = []
values = []
for row in c:
dates.append(dateutil.parser.parse(row[0]))
values.append(row[1])
plo = pyplot.subplot(310+i)
if i < 3:
pylab.setp( plo.get_xticklabels(), visible=False)
plo.plot_date(pylab.date2num(dates),values)
i=i+1
pyplot.show()
def plot(self, xticks=None, xlabel=None, ylabel=None, title='',
grid=True, climits=None, figsize=None, origin=None,
xtickrotation=0, cmap='jet', showcolorbar=True, ax=None,
show_uncertainties=False, norm_uncertainties=False,
input=None, showxticks=True, nclasses=11, lat_tick=np.arange(-90., 90 + 30., 30.),
monthsamp=1, yearonly=True):
"""
Generates a Hovmoeller plot
Two different options are available to generate the plot. The plot can be either based on calculation from
the Hovmoeller class itself. This requires that self.hov was calculated already using e.g. time_to_lat().
The second option is, that a Data object is provided by the *input* variable. In this case, the zonal mean is calculated from
the Data object all required processing is done in this routine.
input : Data
C{Data} object that is used for the generation of the hovmoeller plot
grid : bool
plot tick grid in hovmoeller plot
ylabel : str
ylabel for plot
title : str
title for the plot
climits : list
limits for the colorbar; [vmin,vmax]
cmap : str
colormap to be used
xtickrotation : float
rotation of xticklabels 0 ... 90
showcolorbar : bool
show colorbar in plot
ax : axis
axis to plot to. If not specified (default), then a new figure will be created
showxticks : bool
show the xticks in the Hovmoeller plot
nclasses : int
number of classes for colorbar
lat_tick : ndarray
tick labels for latitude (default: every 30 degree)
monthsamp : int
sampling interval for months
yearonly : bool
if True, then only the years are shown as labels in the timeseries
input is expected to have dimensions [nlat,ntime] and can be precalculated using Data.get_zonal_mean().T
input is expected to be a Data object!
"""
if input is not None:
if self.hov is not None:
raise ValueError(
'If precalculated dat is provided as input, the data MUST not be calculated already by the class!')
else:
#////////////////////////////////////////////////
# HOVMOELLER PLOT FROM DATA OBJECT
#////////////////////////////////////////////////
input1 = input.get_zonal_mean(return_object=True)
input1.adjust_time(day=1) # , month=1)
nlat, nt = input1.data.shape
if nt != len(self.time):
print input1.shape
print nt, len(self.time)
raise ValueError('inconsistent time shape!')
self.hov = input1.data
self.yearonly = yearonly
#/// check if latitudes are in increasing order ?
lats = input1.lat * 1.
if np.all(np.diff(lats) >= 0):
f_invert = False
else:
f_invert = True
# change sequence of data!
lats = lats[::-1]
if not np.all(np.diff(lats) >= 0):
raise ValueError(
'Latitudes can not be put into ascending order!')
#/// monthly ticks ///
# convert times to monthly; apply date conversions to ensure
# that generate_monthly_timeseries() can work following the
# python convention
data_days = generate_monthly_timeseries(
pl.date2num(input1.date))
all_days = np.unique(data_days)
if showxticks:
self.generate_xticks(all_days, monthsamp=monthsamp)
# use only ticks that are within the current latitudes
mlat = (lat_tick >= lats.min()) & (lat_tick <= lats.max())
lat_tick1 = lat_tick[mlat]
# interpolate the tick grid to the data grid
# index positions
lat_pos = np.interp(lat_tick1, lats, np.arange(len(lats)))
if f_invert: # invert position back
#lat_tick = lat_tick[::-1]
lat_pos = lat_pos[::-1]
yticklabels = np.asarray(map(str, lat_tick1))
if self.transpose:
scal = self.rescalex
else:
scal = self.rescaley
yticks = lat_pos * scal
self.y_major_locator = pl.FixedLocator(yticks)
self.y_major_formatter = pl.FixedFormatter(yticklabels)
ylabel = 'latitude'
if climits is None:
raise ValueError('Hovmoeller, please specify climits')
if xlabel is None:
self.xlabel = 'x-label'
else:
self.xlabel = xlabel
if figsize is None:
if self.transpose:
figsize = (6, 11)
else:
figsize = (12, 4)
if ylabel is None:
self.ylabel = 'y-label'
else:
self.ylabel = ylabel
self.title = title
if ax is None:
self.fig = pl.figure(figsize=figsize)
self.ax = self.fig.add_subplot(111)
else:
self.ax = ax
self.fig = self.ax.figure
if self.transpose:
arr = self.hov.repeat(self.rescalex, axis=0).repeat(
self.rescaley, axis=1)
self.im = self.ax.imshow(arr.T, interpolation='Nearest', origin=origin, vmin=climits[
0], vmax=climits[1], cmap=pyplot.get_cmap(cmap, nclasses))
else:
# rescale array for visualization
arr = self.hov.repeat(self.rescaley, axis=0).repeat(
self.rescalex, axis=1)
self.im = self.ax.imshow(arr, interpolation='Nearest', origin=origin, cmap=pyplot.get_cmap(
cmap, nclasses), vmin=climits[0], vmax=climits[1])
if (show_uncertainties) & (self.hov_var is not None):
arr1 = self.hov_var.repeat(
self.rescaley, axis=0).repeat(self.rescalex, axis=1)
if norm_uncertainties:
# normalized by variance
arr1 = arr / arr1
self.ax.contour(arr1, linestyles='-', colors='black')
if xlabel is not None:
self.ax.set_xlabel(self.xlabel)
if ylabel is not None:
self.ax.set_ylabel(self.ylabel)
self.ax.set_title(self.title)
#/// ticks
if self.transpose:
self.ax.yaxis.set_major_locator(self.x_major_locator)
self.ax.yaxis.set_major_formatter(self.x_major_formatter)
self.ax.xaxis.set_major_locator(self.y_major_locator)
self.ax.xaxis.set_major_formatter(self.y_major_formatter)
else:
self.ax.yaxis.set_major_locator(self.y_major_locator)
self.ax.yaxis.set_major_formatter(self.y_major_formatter)
if showxticks:
self.ax.xaxis.set_major_locator(self.x_major_locator)
self.ax.xaxis.set_major_formatter(self.x_major_formatter)
if showxticks:
# pl.xticks(rotation=xtickrotation)
for t in self.ax.get_xticklabels():
t.set_rotation(xtickrotation)
else:
self.ax.set_xticks([])
if xticks is not None:
nx = 2
set_label(nx)
if grid: # show grid
self.ax.grid(color='white', linewidth=1, linestyle='-')
#/// colorbar
if showcolorbar:
if self.transpose:
self.fig.colorbar(self.im, orientation='vertical', shrink=0.5)
else:
# only do import here, as it does not work otherwise (todo)
from plots import add_nice_legend
add_nice_legend(self.ax, self.im,
pyplot.get_cmap(cmap, nclasses))
def __init__(self, time, value, var_unc=None, rescalex=1,
rescaley=1, lat=None, lon=None, transpose=False,
use_cdo=True):
"""
Hovmoeller class
This class implements the functionality to generate hovmoeller
plots. It allows to calculate all relevant data by itself or
using the CDO's for calculation of e.g. zonal means
time : datetime
vector with time information
value : ndarray
data to be analyzed. if the argument lat is provided it is
assumed that lat/lon are 2D matrices
In this case the value is expected to be a 3D variables as
value(time,ny,nx)
var_unc : ndarray
additional array with variance information (can be used to
show uncertainties) - not really validated so far
rescalex : int
rescaling factor for x-variable (used to blow up the plot)
rescaley : int
rescaling factor for y-variable (used to blow up the plot)
lat : ndarray
latitude coordinates
lon : ndarray
longitude coordinates
transpose : bool
transpose the data matrix
EXAMPLES
========
#a minimum example for a time-latitude plot
#get data from e.g. file; here random data
t1=datestr2num('2011-05-01'); t2=datestr2num('2011-08-31')
d = num2date(linspace(t1,t2,1000.))
lats=linspace(-90.,90.,181) + 0.5; lats = lats[:-1]
lons = linspace(-180.,180.,361) + 0.5; lons = lons[:-1]
LONS,LATS = meshgrid(lons,lats)
ny,nx = LATS.shape; nt = len(d)
dat = rand(nt,ny,nx)
#create a hovmoeller object
myhov = hovmoeller(d,dat,lat=LATS,rescaley=20,rescalex=10)
myhov.time_to_lat(dlat=1.,yticksampling=1)
myhov.plot(title='Test Hovmoeller plot',ylabel='lat',xlabel='days',origin='lower',xtickrotation=30,climits=[-1.,1.])
show()
Another example, that does NOT calculate the hovmoeller plot by itself, but uses functionality from pyCMBS Data object
file='wachmos_sm_1978-2010_int_monthly_t63.nc'
D = Data(file,'var100',read=True)
myhov = hovmoeller(num2date(D.time),None,rescaley=20,rescalex=20)
myhov.plot(climits=[0.,0.5],input=D,xtickrotation=90,cmap='jet_r')
show()
"""
#/// check consistency ///
if value is not None:
if len(time) == len(value):
pass
else:
sys.exit('Inconsistent sizes of time and value (hovmoeller)')
if lat is not None and shape(lat) != shape(value[0, :, :]):
print shape(lat), shape(value[0, :, :])
sys.exit('Inconsistent latitudes and data (hovmoeller)')
#/// set values of class ///
self.time = time
self.transpose = transpose
ntim = len(self.time)
t = pl.date2num(time)
self.t_min = pl.num2date(np.floor(t.min()))
self.t_max = pl.num2date(np.ceil(t.max()))
if value is not None:
self.value = value.copy()
self.value = ma.array(self.value, mask=isnan(self.value))
# now reshape everything to [time,ngridcells]
self.value.shape = (ntim, -1)
if var_unc is None:
self.var_unc = None
else:
self.var_unc = var_unc.copy()
self.var_unc.shape = (ntim, -1)
self.hov_var = None
self.rescalex = rescalex
self.rescaley = rescaley
if lat is not None:
self.lat = lat.copy()
self.lat.shape = (-1)
else:
self.lat = None
if lon is not None:
self.lon = lon.copy()
self.lon.shape = (-1)
else:
self.lon = None
self.hov = None
