def sample_513():
"""
5.1.3 k线图的绘制
:return:
"""
import matplotlib.finance as mpf
__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 = mpf.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 sample_513():
"""
5.1.3 k线图的绘制
:return:
"""
import matplotlib.finance as mpf
__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 = mpf.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 dataframe2quote(data):
quote = []
for i in range(data.shape[0]):
d = data.iloc[i]
time = date2num(data.index[i])
quote.append((i, d.open, d.highest, d.lowest, d.close, d.turnoverValue))
return quote
def loadStockQuotes(symbol, dates):
col_names = ['Open', 'Close', 'High', 'Low']
df = load_OHLCV(symbol, dates, column_names=col_names, base_dir=DIR_SEC_CSV)
# quotes = [ (date, open, close, high, low), .....]
quotes = df.to_records(convert_datetime64=True).tolist()
quotes = [(date2num(d), o, c, h, l) for d, o, c, h, l in quotes]
return quotes
def loadStockQuotes(symbol, startdate, enddate):
#[ (Date, Open, High, Low, Close, Adj Close, Volume), .....]
df = load_OHLCV(symbol, startdate, enddate)
#quotes = [ (date, open, close, high, low), .....]
quotes = df.to_records(convert_datetime64=True).tolist()
quotes = [ (date2num(d), o, h, l, c) for d, o, h, l, c, adj, v in quotes ]
return quotes
def loadStockQuotes(symbol, dates): col_names=['<OPEN>', '<CLOSE>', '<HIGH>', '<LOW>'] df = load_OHLCV(symbol, dates, column_names=col_names, base_dir=default_path) #quotes = [ (date, open, close, high, low), .....] quotes = df.to_records(convert_datetime64=True).tolist() quotes = [ (date2num(d), o, c, h, l) for d,o,c,h,l in quotes ] return quotes
def import_stock_data_from_zszq(self, path, identifier):
# This method loads trading data of a specified stock from Zhaoshang Securities data
# path of the data source
if path[-1] == "/":
path = path + identifier + ".txt"
else:
path = path + "/" + identifier + ".txt"
# read data
if os.path.isfile(path):
file_input = codecs.open(path, "r", "gbk").readlines()
else:
raise IOError("File Not Found.")
# header - id, stock name
##header1 = input[0].split(" ")
# header - date, open, highest, lowest, close, volume, total
header2 = file_input[1].split("\t")
# price data
prices = file_input[2:-1]
header = {}
# header indices
for x in header2:
if codecs.encode(x.strip(), "gbk") == codecs.encode("日期", "gbk"):
header["date"] = header2.index(x)
elif codecs.encode(x.strip(), "gbk") == codecs.encode("开盘", "gbk"):
header["open"] = header2.index(x)
elif codecs.encode(x.strip(), "gbk") == codecs.encode("最高", "gbk"):
header["highest"] = header2.index(x)
elif codecs.encode(x.strip(), "gbk") == codecs.encode("最低", "gbk"):
header["lowest"] = header2.index(x)
elif codecs.encode(x.strip(), "gbk") == codecs.encode("收盘", "gbk"):
header["close"] = header2.index(x)
elif codecs.encode(x.strip(), "gbk") == codecs.encode("成交量", "gbk"):
header["volume"] = header2.index(x)
elif codecs.encode(x.strip(), "gbk") == codecs.encode("成交额", "gbk"):
header["total"] = header2.index(x)
# generate price vectors
for day in prices:
data = day.split("\t")
pdate = finance_plot.date2num(datetime.datetime.strptime(data[header["date"]].strip(), "%Y/%m/%d"))
popen = float(data[header["open"]].strip())
phighest = float(data[header["highest"]].strip())
plowest = float(data[header["lowest"]].strip())
pclose = float(data[header["close"]].strip())
pvolume = float(data[header["volume"]].strip())
ptotal = float(data[header["total"]].strip())
self.quotes.append([pdate, popen, phighest, plowest, pclose, pvolume, ptotal])
print("Quotes of stock {0} from date {1} to {2} have been loaded".format(identifier, datetime.date.fromordinal(int(self.quotes[0][0])), datetime.date.fromordinal(int(self.quotes[-1][0]))))
return 0
def plot_data():
df = pd.read_csv("sp_500_joined.csv")
tickers = df.dtypes.index
df.fillna(0, inplace=True)
val = []
for i in df['Date'][1800:]:
val.append(date2num(datetime.datetime.strptime(i, '%Y-%m-%d')))
y = numpy.array(df[tickers[-1]][1800:])
x = numpy.array(val)
plt.plot(x, y)
plt.show()
def prepare_plot_trade_args(self, trade_idx, preoffset_bars,
postoffset_bars):
entry_time = self.detailed_results["EntryTime"][trade_idx]
entry_idx = self.data[self.data["time"] ==
entry_time].index.tolist()[0]
start_idx = max([0, entry_idx - preoffset_bars])
exit_time = self.detailed_results["ExitTime"][trade_idx]
exit_idx = self.data[self.data["time"] == exit_time].index.tolist()[0]
end_idx = min([len(self.data), exit_idx + postoffset_bars + 1])
ts = self.ts
opens = self.opens
highs = self.highs
lows = self.lows
closes = self.closes
d = [(date2num(ts[i]), opens[i], highs[i], lows[i], closes[i])
for i in range(start_idx, end_idx)]
position = self.detailed_results["Position"][trade_idx]
arrow_color = 'green' if position > 0 else 'red'
entry_price = self.detailed_results["EntryPrice"][trade_idx]
exit_price = self.detailed_results["ExitPrice"][trade_idx]
return (d, self.interval, (date2num(entry_time), entry_price),
(date2num(exit_time),
exit_price), arrow_color, "Trade #" + str(trade_idx))
def drawData(ohlcv, macd, trade_loop_back, pdays):
dif = macd['dif']
dea = macd['dea']
bar = macd['bar']
fig, axs = plt.subplots(nrows=3,
ncols=1,
facecolor=(0.5, 0.5, 0.5),
figsize=(14, 7))
draw1, draw2, draw3 = axs.ravel()
# 可视化profit_array
draw1.plot(np.array(trade_loop_back.profit_array).cumsum(),
label='profit_array')
# #print(trade_loop_back.days_array)
#
#可视化K线和交易日
qutotes = []
for index, (d, o, c, h, l) in enumerate(
zip(ohlcv.index, ohlcv.open, ohlcv.close, ohlcv.highest,
ohlcv.lowest)):
d = mpf.date2num(d)
val = (d, o, c, h, l)
qutotes.append(val)
mpf.candlestick_ochl(draw2,
qutotes,
width=0.4,
colorup="green",
colordown="red")
#draw2.autoscale_view()
#draw2.xaxis_date()
act = pdays[(pdays.stock - pdays.shift(1).stock) != 0]
# draw2.axvline(np.array([736875, 736876]), label = 'buy', color="green")
# for index, line in act[act.stock == 1].date:
# draw2.axvline(qutotes[index][0], label = 'buy', color="green")
# for index, line in act[act.stock == 0].date:
# draw2.axvline(qutotes[index][0], label = 'sell', color="red")
# draw2.axvline(np.float32(act[act.stock == 1].date), label = 'buy', color="green")
# draw2.axvline(np.float32(act[act.stock == 0].date), label = 'sell', color="red")
draw3.plot(ohlcv.index, dif, label='macd dif')
draw3.plot(ohlcv.index, dea, label='signal dea')
bar_red = np.where(bar > 0, bar, 0)
bar_green = np.where(bar < 0, bar, 0)
draw3.bar(ohlcv.index, bar_red, facecolor='red', label='hist bar')
draw3.bar(ohlcv.index, bar_green, facecolor='green', label='hist bar')
draw3.legend(loc='best')
plt.show()
def main():
hb = ccxt.huobipro()
hb.proxies = {
'http': 'http://127.0.0.1:8123',
'https': 'http://127.0.0.1:8123',
}
data = hb.fetch_ohlcv('BTC/USDT', '15m')
arr = np.array(data)
ohlcv = pd.DataFrame(arr,
columns=('time', 'open', 'highest', 'lowest', 'close',
'volume'))
ohlcv = ohlcv.sort_index(by='time')
timeIndex = pd.date_range(time.strftime(
'%Y-%m-%d %H:%M:%S', time.localtime(int(ohlcv.head(1).time) / 1000)),
periods=ohlcv.shape[0],
freq='15m')
ohlcv.index = timeIndex
dif, dea, bar = talib.MACD(ohlcv.close,
fastperiod=12,
slowperiod=26,
signalperiod=9)
fig, axs = plt.subplots(figsize=(14, 7))
#可视化K线和交易日
qutotes = []
for index, (d, o, c, h, l) in enumerate(
zip(ohlcv.index, ohlcv.open, ohlcv.close, ohlcv.highest,
ohlcv.lowest)):
print(d)
d = mpf.date2num(d)
print(d)
val = (d, o, c, h, l)
qutotes.append(val)
mpf.candlestick_ochl(axs,
qutotes,
width=1,
colorup="green",
colordown="red")
print(qutotes)
axs.autoscale_view()
#axs.xaxis_date()#仅日线时使用
plt.show()
def candle_draw():
__colorup__ = "red"
__colordown__ = "green"
pingan_part_df = pingan_df[:100]
fig, ax = plt.subplots(figsize=(14, 7))
qutotes = []
for index, (d, o, c, h, l) in enumerate(
zip(pingan_part_df.index, pingan_part_df.OPEN,
pingan_part_df.CLOSE, pingan_part_df.HIGH,
pingan_part_df.LOW)):
d = mpf.date2num(d)
# 日期、开盘、收盘、最高、最低组成一个tuple对象val
val = (d, o, c, h, l)
qutotes.append(val)
mpf.candlestick_ochl(ax,
qutotes,
width=0.6,
colorup=__colorup__,
colordown=__colordown__)
ax.autoscale_view()
ax.xaxis_date()
plt.grid(True)
plt.show()
def _do_plot_candle(date, p_open, high, low, close, volume, view_index, symbol,
day_sum, save, minute):
"""
绘制不可交互的k线图
param date: 融时间序列交易日时间，pd.DataFrame.index对象
:param p_open: 金融时间序列开盘价格序列，np.array对象
:param high: 金融时间序列最高价格序列，np.array对象
:param low: 金融时间序列最低价格序列，np.array对象
:param close: 金融时间序列收盘价格序列，np.array对象
:param volume: 金融时间序列成交量序列，np.array对象
:param symbol: symbol str对象
:param day_sum: 端线图 matplotlib的版本有些有bug显示不对
:param save: 是否保存可视化结果在本地
:param minute: 是否是绘制分钟k线图
"""
# 需要内部import不然每次import abupy都有warning，特别是子进程很烦人
try:
# noinspection PyUnresolvedReferences, PyDeprecation
import matplotlib.finance as mpf
except ImportError:
# 2.2 才会有
# noinspection PyUnresolvedReferences, PyDeprecation
import matplotlib.mpl_finance as mpf
if not g_only_draw_price:
# 成交量，价格都绘制
# noinspection PyTypeChecker
fig, (ax1, ax2) = plt.subplots(2, sharex=True, figsize=(20, 12))
else:
# 只绘制价格
fig, ax1 = plt.subplots(figsize=(6, 6))
if day_sum:
# 端线图绘制
qutotes = []
for index, (d, o, c, l,
h) in enumerate(zip(date, p_open, close, low, high)):
d = index if minute else mpf.date2num(d)
val = (d, o, c, l, h)
qutotes.append(val)
# plot_day_summary_oclh接口，与mpf.candlestick_ochl不同，即数据顺序为开收低高
mpf.plot_day_summary_oclh(ax1,
qutotes,
ticksize=5,
colorup=__colorup__,
colordown=__colordown__)
else:
# k线图绘制
qutotes = []
for index, (d, o, c, h,
l) in enumerate(zip(date, p_open, close, high, low)):
d = index if minute else mpf.date2num(d)
val = (d, o, c, h, l)
qutotes.append(val)
# mpf.candlestick_ochl即数据顺序为开收高低
mpf.candlestick_ochl(ax1,
qutotes,
width=0.6,
colorup=__colorup__,
colordown=__colordown__)
if not g_only_draw_price:
# 开始绘制成交量
ax1.set_title(symbol)
ax1.set_ylabel('ochl')
ax1.grid(True)
if not minute:
ax1.xaxis_date()
if view_index is not None:
# 开始绘制买入交易日，卖出交易日，重点突出的点位
e_list = date.tolist()
# itertools.cycle循环使用备选的颜色
for v, csColor in zip(view_index,
itertools.cycle(K_PLT_MAP_STYLE)):
try:
v_ind = e_list.index(v)
except Exception as e:
# logging.exception(e)
logging.debug(e)
# 向前倒一个
v_ind = len(close) - 1
label = symbol + ': ' + str(date[v_ind])
ax1.plot(v,
close[v_ind],
'ro',
markersize=12,
markeredgewidth=4.5,
markerfacecolor='None',
markeredgecolor=csColor,
label=label)
# 因为candlestick_ochl 不能label了，所以使用下面的显示文字
# noinspection PyUnboundLocalVariable
ax2.plot(v,
0,
'ro',
markersize=12,
markeredgewidth=0.5,
markerfacecolor='None',
markeredgecolor=csColor,
label=label)
plt.legend(loc='best')
# 成交量柱子颜色，收盘价格 > 开盘，即红色
# noinspection PyTypeChecker
bar_red = np.where(close >= p_open, volume, 0)
# 成交量柱子颜色，开盘价格 > 收盘。即绿色
# noinspection PyTypeChecker
bar_green = np.where(p_open > close, volume, 0)
date = date if not minute else np.arange(0, len(date))
ax2.bar(date, bar_red, facecolor=__colorup__)
ax2.bar(date, bar_green, facecolor=__colordown__)
ax2.set_ylabel('volume')
ax2.grid(True)
ax2.autoscale_view()
plt.setp(plt.gca().get_xticklabels(), rotation=30)
else:
ax1.grid(False)
if save:
# 保存可视化结果在本地
from pylab import savefig
save_dir = os.path.join(K_SAVE_CACHE_PNG_ROOT,
ABuDateUtil.current_str_date())
png_dir = os.path.join(save_dir, symbol)
ABuFileUtil.ensure_dir(png_dir)
r_cnt = 0
while True:
png_name = '{}{}.png'.format(
png_dir, '' if r_cnt == 0 else '-{}'.format(r_cnt))
if not ABuFileUtil.file_exist(png_name):
break
r_cnt += 1
# noinspection PyUnboundLocalVariable
savefig(png_name)
fig.clf()
plt.close(fig)
else:
"""
save 了就不show了
"""
plt.show()
bbox_to_anchor=(1.05, 1),
loc=2,
borderaxespad=0.)
# 绘制K线图，通过 matplotlib.finance.candlestick_ochl 以及 matplotlib.finance.candlestick2_ohlc 函数
import matplotlib.finance as mpf
__colorup__ = "red"
__colordown__ = "green"
tsla_part_df = tsla_df[:30]
fig, ax = plt.subplots(figsize=(14, 7))
quotes = []
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)):
d = mpf.date2num(d)
val = (d, o, c, h, l)
quotes.append(val)
mpf.candlestick_ochl(ax,
quotes,
width=0.6,
colorup=__colorup__,
colordown=__colordown__)
ax.autoscale_view()
ax.xaxis_date()
# 引用 Bokeh 支持交互可视化
from abupy import ABuMarketDrawing
ABuMarketDrawing.plot_candle_form_klpd(tsla_df, html_bk=True)
# 使用 Pandas 可视化数据
def calcDiscountFactor(self, discount_curve):
dates = [date2num(p.EndDate) for p in discount_curve]
dfs = [p.DiscountFactor for p in discount_curve]
cs = CubicSpline(dates, dfs)
self.StartDiscountFactor = cs(date2num(self.StartDate))
self.EndDiscountFactor = cs(date2num(self.EndDate))
def plot_history_day(trade_record,
data_financial,
pre=50,
post=20,
save=False,
record_index=-1):
if not trade_record:
raise ValueError('Empty record. %s' % trade_record)
symbol = trade_record[0]
stock_history = dp.read(config.DATA_PATH_STOCK, symbol)
index_history = dp.read(config.DATA_PATH_INDEX_DICT, 'SH#000001')
dates = [trade_record[1], trade_record[1], trade_record[1]]
stock_history_dates = [r['date'] for r in stock_history]
dates_index = [
bisect.bisect_left(stock_history_dates, date) for date in dates
]
stock_history_to_plot = stock_history[dates_index[0] -
pre:dates_index[-1] + post]
index_history_to_plot = [
index_history[r['date']] for r in stock_history_to_plot
]
stock_history_dates = [r['date'] for r in stock_history_to_plot]
dates_index = [
bisect.bisect_left(stock_history_dates, date) for date in dates
]
marker_position = {0: 'open', 1: 'open', 2: 'open'}
stock_history_markers = [
stock_history_to_plot[j][marker_position[i]]
for i, j in enumerate(dates_index)
]
profit = (stock_history_markers[2] / stock_history_markers[1] - 1.0) * 100
hold_time = (dates[2] - dates[1]).days
if profit > 0:
return
plt.rcParams['font.size'] = 15
fig, ax1 = plt.subplots(figsize=(25, 10))
ax1.grid(True)
ax1.plot([r['date'] for r in stock_history_to_plot],
[r['ma10'] for r in stock_history_to_plot],
'--',
color='#0094af',
alpha=0.7)
ax1.plot([r['date'] for r in stock_history_to_plot],
[r['ma20'] for r in stock_history_to_plot],
'--',
color='#434051',
alpha=0.7)
ax1.plot(dates, stock_history_markers, 'bo', alpha=0.7)
quotes = [[
date2num(stock_history_to_plot[i]['date']),
stock_history_to_plot[i]['open'],
stock_history_to_plot[i]['high'],
stock_history_to_plot[i]['low'],
stock_history_to_plot[i]['close'],
] for i in range(len(stock_history_to_plot))]
ax1.axhline(
y=stock_history_to_plot[dates_index[0]]['lo50'],
# xmin=stock_history_to_plot[0]['date'],
# xmax=stock_history_to_plot[-1]['date'],
# color='b',
alpha=0.3,
linewidth=2)
candlestick_ohlc(ax1,
quotes,
width=0.7,
colorup='#d81715',
colordown='#0c8731',
alpha=0.9)
# ax1.set_xlim(dates[0] - datetime.timedelta(days=20),
# dates[-1] + datetime.timedelta(days=20))
ax1.set_xlabel('date')
# Make the y-axis label, ticks and tick labels match the line color.
ax1.set_ylabel('stock price of %s' % stock_history[0]['symbol'], color='k')
ax1.tick_params('y', colors='k')
horizontal = {
0: dates[0] - datetime.timedelta(days=pre // 3),
1: dates[1] - datetime.timedelta(days=pre // 4),
2: dates[2] - datetime.timedelta(days=(dates[2] - dates[1]).days // 3)
}
max_price = max([r['close'] for r in stock_history_to_plot])
min_price = min([r['close'] for r in stock_history_to_plot])
vertical = {
0:
(max_price - stock_history_markers[0]) / 3 + stock_history_markers[0],
1:
(max_price - stock_history_markers[1]) / 2 + stock_history_markers[1],
2:
(min_price - stock_history_markers[2]) / 2 + stock_history_markers[2]
}
# 个股基本面数据全，每股收益大于2毛，ROE大于10%，营收增长大于30%。
stock_basic_info = ''
report_index = dp.find_financial_report_index(data_financial, symbol,
dates[1])
if report_index > 0: # 可以比较成长性
current_report, previous_report = data_financial[symbol][report_index], \
data_financial[symbol][report_index - 1]
if any([
r[item] == '' or float(r[item]) == 0
for r in [current_report, previous_report]
for item in ['每股收益TTM', '净资产收益率TTM', '营业总收入']
]):
pass
else:
stock_basic_info = 'EPSTTM: %.2f' % float(current_report['每股收益TTM']) \
+ '\nROETTM: %.2f %%' % (float(current_report['净资产收益率TTM']) * 100) \
+ '\nIncome growth: %.2f %%\n' % \
((float(current_report['营业总收入']) / float(previous_report['营业总收入']) - 1.0) * 100)
if stock_basic_info:
plt.text(0.02,
0.90,
stock_basic_info,
horizontalalignment='left',
verticalalignment='center',
transform=ax1.transAxes,
color='#1d8450',
alpha=0.8)
annotate_text = {
0: 'Watch',
1: 'Buy',
2: 'Sell',
}
for i in range(len(dates)):
plt.annotate(
'%s: %s, %.2f' %
(annotate_text[i], dates[i], stock_history_markers[i]),
xy=(dates[i], stock_history_markers[i]),
xytext=(horizontal[i], vertical[i]),
arrowprops=dict(width=1,
headwidth=5,
facecolor='black',
shrink=0.15),
)
ax2 = ax1.twinx()
# ax1.rcParams['font.size'] = 13
ax2.plot([r['date'] for r in index_history_to_plot],
[r['close'] for r in index_history_to_plot],
'b',
alpha=0.5)
ax2.set_ylabel('SHCI', color='b')
ax2.tick_params('y', colors='b')
# ax2.set_ylim(min([r['close'] for r in index_history_to_plot]) * 0.7,
# max([r['close'] for r in index_history_to_plot]) * 1.3)
plt.title("record # %d: profit = %.2f %%, hold time = %d calendar days" %
(record_index, profit, hold_time))
fig.tight_layout()
plt.show(block=not save)
if save:
plt.savefig('visualizations/record # %d.pdf' % record_index,
format='pdf')
plt.close()
merged_dataframes = [input_dataset, input_dataset_label['total_cases']]
merged_data = pd.concat(merged_dataframes, axis=1)
input_dataset_sj = merged_data.loc[merged_data['city'] == 'sj']
input_dataset_iq = merged_data.loc[merged_data['city'] == 'iq']
import datetime
from matplotlib.finance import date2num
dateList = []
temp = np.array(input_dataset_sj['week_start_date'])
for i in range(len(temp)):
#print( str(input_dataset_sj['week_start_date'].iloc[[i]]) )
dateList.append(
date2num(datetime.datetime.strptime(temp[i], "%m/%d/%Y")) / 365)
plt.figure(figsize=(20, 10))
plt.plot(dateList, input_dataset_sj['total_cases'])
plt.title("Total Cases for San Juan")
plt.xlabel("Weeks")
plt.ylabel("Total Cases")
plt.show()
# In[677]:
dateList = []
temp = np.array(input_dataset_iq['week_start_date'])
for i in range(len(temp)):
#print( str(input_dataset_sj['week_start_date'].iloc[[i]]) )
column_names = [v for k,v in json['dataset'].items() if k=='column_names']
data = [v for k,v in json['dataset'].items() if k=='data']
dates = [x[0] for x in data[0]]
open = [x[1] for x in data[0]]
high = [x[2] for x in data[0]]
low = [x[3] for x in data[0]]
close = [x[4] for x in data[0]]
no_of_shares = [x[6] for x in data[0]]
print(dates[0])
year = [int(x[:4]) for x in dates]
month = [int(x[5:7]) for x in dates]
day = [int(x[8::]) for x in dates]
float_days=[]
for i in range(len(year)):
float_days.append(date2num(datetime.datetime(year[i], month[i], day[i])))
#print(float_days)
quotes=[[]]
for i in range(len(year)):
quotes.append([float_days[i],open[i],close[i],high[i],low[i]])
quotes = quotes[1::]
ax = plt.gca()
plt.title("RELIANCE COMMUNICATIONS STOCK PRICE TREND TILL TODAY")
plt.ylabel("Price")
plt.xlabel("Date(epoch)")
fin.candlestick_ochl(ax,quotes,width=0.2, colorup='k', colordown='r', alpha=1.0)
'''
Plot the time, open, close, high, low as a vertical line ranging from low to high. Use a rectangular bar to represent the open-close span.
If close >= open, use colorup to color the bar, otherwise use colordown
def _do_plot_candle(date, p_open, high, low, close, volume, view_index, symbol, day_sum, save, minute):
"""
绘制不可交互的k线图
param date: 融时间序列交易日时间，pd.DataFrame.index对象
:param p_open: 金融时间序列开盘价格序列，np.array对象
:param high: 金融时间序列最高价格序列，np.array对象
:param low: 金融时间序列最低价格序列，np.array对象
:param close: 金融时间序列收盘价格序列，np.array对象
:param volume: 金融时间序列成交量序列，np.array对象
:param symbol: symbol str对象
:param day_sum: 端线图 matplotlib的版本有些有bug显示不对
:param save: 是否保存可视化结果在本地
:param minute: 是否是绘制分钟k线图
"""
# 需要内部import不然每次import abupy都有warning，特别是子进程很烦人
try:
# noinspection PyUnresolvedReferences, PyDeprecation
import matplotlib.finance as mpf
except ImportError:
# 2.2 才会有
# noinspection PyUnresolvedReferences, PyDeprecation
import matplotlib.mpl_finance as mpf
if not g_only_draw_price:
# 成交量，价格都绘制
# noinspection PyTypeChecker
fig, (ax1, ax2) = plt.subplots(2, sharex=True, figsize=(20, 12))
else:
# 只绘制价格
fig, ax1 = plt.subplots(figsize=(6, 6))
if day_sum:
# 端线图绘制
qutotes = []
for index, (d, o, c, l, h) in enumerate(zip(date, p_open, close, low, high)):
d = index if minute else mpf.date2num(d)
val = (d, o, c, l, h)
qutotes.append(val)
# plot_day_summary_oclh接口，与mpf.candlestick_ochl不同，即数据顺序为开收低高
mpf.plot_day_summary_oclh(ax1, qutotes, ticksize=5, colorup=__colorup__, colordown=__colordown__)
else:
# k线图绘制
qutotes = []
for index, (d, o, c, h, l) in enumerate(zip(date, p_open, close, high, low)):
d = index if minute else mpf.date2num(d)
val = (d, o, c, h, l)
qutotes.append(val)
# mpf.candlestick_ochl即数据顺序为开收高低
mpf.candlestick_ochl(ax1, qutotes, width=0.6, colorup=__colorup__, colordown=__colordown__)
if not g_only_draw_price:
# 开始绘制成交量
ax1.set_title(symbol)
ax1.set_ylabel('ochl')
ax1.grid(True)
if not minute:
ax1.xaxis_date()
if view_index is not None:
# 开始绘制买入交易日，卖出交易日，重点突出的点位
e_list = date.tolist()
# itertools.cycle循环使用备选的颜色
for v, csColor in zip(view_index, itertools.cycle(K_PLT_MAP_STYLE)):
try:
v_ind = e_list.index(v)
except Exception as e:
# logging.exception(e)
logging.debug(e)
# 向前倒一个
v_ind = len(close) - 1
label = symbol + ': ' + str(date[v_ind])
ax1.plot(v, close[v_ind], 'ro', markersize=12, markeredgewidth=4.5,
markerfacecolor='None', markeredgecolor=csColor, label=label)
# 因为candlestick_ochl 不能label了，所以使用下面的显示文字
# noinspection PyUnboundLocalVariable
ax2.plot(v, 0, 'ro', markersize=12, markeredgewidth=0.5,
markerfacecolor='None', markeredgecolor=csColor, label=label)
plt.legend(loc='best')
# 成交量柱子颜色，收盘价格 > 开盘，即红色
# noinspection PyTypeChecker
bar_red = np.where(close >= p_open, volume, 0)
# 成交量柱子颜色，开盘价格 > 收盘。即绿色
# noinspection PyTypeChecker
bar_green = np.where(p_open > close, volume, 0)
date = date if not minute else np.arange(0, len(date))
ax2.bar(date, bar_red, facecolor=__colorup__)
ax2.bar(date, bar_green, facecolor=__colordown__)
ax2.set_ylabel('volume')
ax2.grid(True)
ax2.autoscale_view()
plt.setp(plt.gca().get_xticklabels(), rotation=30)
else:
ax1.grid(False)
if save:
# 保存可视化结果在本地
from pylab import savefig
save_dir = os.path.join(K_SAVE_CACHE_PNG_ROOT, ABuDateUtil.current_str_date())
png_dir = os.path.join(save_dir, symbol)
ABuFileUtil.ensure_dir(png_dir)
r_cnt = 0
while True:
png_name = '{}{}.png'.format(png_dir, '' if r_cnt == 0 else '-{}'.format(r_cnt))
if not ABuFileUtil.file_exist(png_name):
break
r_cnt += 1
# noinspection PyUnboundLocalVariable
savefig(png_name)
fig.clf()
plt.close(fig)
else:
"""
save 了就不show了
"""
plt.show()
def make_graphs(tickers,names,delt,matr,inp):
year_days = 260.0
if(inp[0] == 'Get quotes and show graphs' or 'Show graphs only'):
week = ['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday','Sunday']
nm = len(matr)
time_label = 'From ' + matr[0][0]+' to '+matr[nm-1][0] + ' (dd/mm/yyyy)'
date_now = datetime.datetime.strptime(matr[nm-1][0], "%d/%m/%Y").date()
wdn = date_now.weekday()
weekday = week[wdn]
if inp[1] == '1 month':
span = int(year_days / 12)
if inp[1] == '3 months':
span = int(0.25 * year_days)
if inp[1] == '6 months':
span = int(0.5 * year_days)
if inp[1] == '1 year':
span = int(year_days)
if inp[1] == '2 years':
span = int(2.0 * year_days)
if inp[1] == '3 years':
span = int(3.0 * year_days)
if inp[1] == '5 years':
span = int(5.0 * year_days)
if inp[1] == 'all':
span = nm
for j in range(len(tickers)): # for each ticker ...
y = [] # array of prices
t = []
t_float = []
v0 = 0
for i in range(nm): # along time line...
try:
v = float(matr[i][j+1])
if(i==0 or v != v0):
y.append(matr[i][j+1]) # from data list matr
t.append(datetime.datetime.strptime(matr[i][0], '%d/%m/%Y'))
v0 = v
t_float.append(date2num(datetime.datetime.strptime(matr[i][0], '%d/%m/%Y')))
except:
pass
t0 = t_float[0] #starting time point
for i in range(len(t)):
t_float[i] = t_float[i] - t0 #relative time in days starting at zero
trend_days = int(float(inp[2]) * year_days) #duration of trend approximation
n = len(y)
start_point = max(0, n - span)
i_start_trend = max(0, n - trend_days)
t_date_trend = list(t)
t_trend = list(t_float) #copy of t_float
t_trend[0:i_start_trend] = [] #time points in days of trend duration
y_trend = list(y)
y_trend[0:i_start_trend] = [] #price array for trend duration
try:
z = numpy.polyfit(t_trend, y_trend, 1)
p = numpy.poly1d(z)
y_lin = []
for i in range(len(t)):
y_lin.append(p(t_float[i]))
except:
print('no lin fit')
t_date_trend[0:i_start_trend] = []
y_lin[0:i_start_trend] = []
t[0:start_point] = []
y[0:start_point] = []
n = len(y)
delta = int((y[n-1]/y[n-2]-1)*100000)/1000
down = float(inp[3][:-1]) / 100.
up = float(inp[4][:-1]) / 100.
x = round(y[n-1]/y_lin[len(y_lin)-1] - 1. , 3)
action = 'Wait ('+ str(x*100)+'% relative to trend)'
if x >0 and x > up:
action = 'Sell ('+ str(x*100)+'% over trend)'
if x<0 and -x > down:
action = 'Buy ('+ str(-x*100)+'% below trend)'
si =''
if(delta > 0):
si = '+'
fig = pyplot.figure()
pyplot.title(tickers[j]+' ('+names[j]+') ' + weekday, color='white',fontweight='bold')
graphtext = 'Price today: ' +str(y[n-1]) + '\n(Change today '+ si + str(delta)+ '%) \n ' + action
pyplot.ylabel('Price',color='white',fontweight='bold',rotation = 45)
ax = pyplot.gca()
pyplot.text(0.95,0.95,graphtext,horizontalalignment='right',verticalalignment='top',transform = ax.transAxes,color='blue',fontweight='bold')
pyplot.grid()
graph = fig.add_subplot(111)
graph.patch.set_facecolor('cyan')
graph.tick_params(axis='x', colors='blue')
graph.tick_params(axis='y', colors='black')
graph.plot_date(t,y,'-',color='red',label= tickers[j])
graph.plot_date(t_date_trend, y_lin, '--', fillstyle='none',color='blue', label='xxoooooooooooooooooo')
pyplot.gcf().autofmt_xdate()
fig.show()
pyplot.savefig(tickers[j]+'.png')
messagebox.showinfo("showinfo",'\nClose all graph windows')
pyplot.close('all')
