def candle_plot(self, code):
def format_date(x, pos):
if x < 0 or x > len(date_tickers) - 1:
return ''
return date_tickers[int(x)]
df = self.getdata_day(code, 240, 260)
df['5'] = df.close.rolling(5).mean()
df['20'] = df.close.rolling(20).mean()
df['30'] = df.close.rolling(30).mean()
df['60'] = df.close.rolling(60).mean()
df['120'] = df.close.rolling(120).mean()
# df['250'] = df.close.rolling(250).mean()
date_tickers = df['datetime'].copy().values
df['datetime'] = pd.to_datetime(df['datetime']).map(date2num)
df['dates'] = np.arange(0, len(df))
# date_tickers = df.trade_date2.values
fig, ax = plt.subplots(figsize=(10, 5))
ax.xaxis.set_major_formatter(ticker.FuncFormatter(format_date))
# 绘制K线图
mpl_finance.candlestick_ochl(
ax=ax,
quotes=df[['dates', 'open', 'close', 'high', 'low']].values,
width=0.7,
colorup='r',
colordown='g',
alpha=0.7)
# 绘制均线
for ma in ['5', '20', '30', '60', '120']:
plt.plot(df['dates'], df[ma])
plt.legend()
ax.set_title(code, fontsize=20)
plt.show()
def draw_subgraph(self, stockdat, numt):
# 绘制K线图
ochl = list(zip(np.arange(0, len(stockdat.index)), stockdat.Open, stockdat.Close,
stockdat.High, stockdat.Low))
# K线走势图
mpf.candlestick_ochl(self.am, ochl, width=0.5, colorup='r', colordown='g')
# 绘制均线
self.am.plot(numt, stockdat['Ma20'], 'black', label='M20', lw=1.0)
self.am.plot(numt, stockdat['Ma60'], 'green', label='M60', lw=1.0)
self.am.plot(numt, stockdat['Ma120'], 'blue', label='M120', lw=1.0)
self.am.legend(loc='best', shadow=True, fontsize='10')
# 绘制成交量
self.vol.bar(numt, stockdat.Volume, color=[
'g' if stockdat.Open[x] > stockdat.Close[x]
else 'r' for x in range(0, len(stockdat.index))])
# 绘制MACD
bar_red = np.where(stockdat['macd_bar'] > 0, 2 * stockdat['macd_bar'], 0)
bar_green = np.where(stockdat['macd_bar'] < 0, 2 * stockdat['macd_bar'], 0)
self.macd.plot(numt, stockdat['macd_dif'], 'red', label='macd dif')
self.macd.plot(numt, stockdat['macd_dea'], 'blue', label='macd dea')
self.macd.bar(numt, bar_red, facecolor='red', label='hist bar')
self.macd.bar(numt, bar_green, facecolor='green', label='hist bar')
self.macd.legend(loc='best', shadow=True, fontsize='10')
# 绘制KDJ
self.devol.plot(numt, stockdat['K'], 'blue', label='K')
self.devol.plot(numt, stockdat['D'], 'g--', label='D')
self.devol.plot(numt, stockdat['J'], 'r--', label='J')
self.devol.legend(loc='best', shadow=True, fontsize='10')
def stockPricePlot(ticker):
#step 1. load data
history = pandas.read_csv('IntradayCN/' + ticker + '.csv',
parse_dates=True,
index_col=0)
#step 2. data manipulation
close = history['close']
close = close.reset_index()
close['timestamp'] = close['timestamp'].map(matplotlib.dates.date2num)
onlc = history[['open', 'high', 'low', 'close']].resample('1H').ohlc()
onlc = onlc.reset_index()
onlc['timestamp'] = onlc['timestamp'].map(matplotlib.dates.date2num)
#step 3. plot figure, subplot 1: scatter plot, 2 ,candle
#3.1 subplot 1: scatter plot
plt.figure(figsize=(20, 20))
subplot1 = plt.subplot2grid((2, 1), (0, 0), rowspan=1, colspan=1)
subplot1.xaxis_date()
subplot1.plot(close['timestamp'], close['close'], 'b.')
plt.title(ticker)
#3.2 subplot 2: candle stick plot
plt.figure(figsize=(20, 20))
subplot2 = plt.subplot2grid((2, 1), (1, 0),
rowspan=1,
colspan=1,
sharex=subplot1)
mpf.candlestick_ochl(ax=subplot2,
quotes=onlc.values,
width=0.01,
colorup='g',
colordown='r')
plt.show()
def ax_kline(ax, df):
date_tickers = df['trade_date'].values
def format_date(x, pos=None):
# 由于前面股票数据在 date 这个位置传入的都是int
# 因此 x=0,1,2,...
# date_tickers 是所有日期的字符串形式列表
if x < 0 or x > len(date_tickers) - 1:
return ''
return date_tickers[int(x)]
date_len = len(df)
dateLoc = int(date_len / 10 + 0.5)
ax.xaxis.set_major_locator(ticker.MultipleLocator(dateLoc))
ax.xaxis.set_major_formatter(ticker.FuncFormatter(format_date))
ax.set_ylabel('K')
ax.grid(True)
plt.setp(ax.get_xticklabels(), visible=False)
mpl_finance.candlestick_ochl(
ax=ax,
quotes=df[['iNum', 'open', 'close', 'high', 'low']].values,
width=0.7,
colorup='r',
colordown='g',
alpha=0.7)
ax.set_title('%s' % (df['ts_code'].iloc[0]))
def plotCandle(daily_df, stock_code = "" , focus_date = None, gs = None ):
if not focus_date:
focus_date = daily_df.trade_date2.values[-1]
# plot
if gs:
ax1 = plt.subplot(gs[0:6,:])
else:
fig, ax1 = plt.subplots(figsize=(16,6))
mpf.candlestick_ochl(
ax=ax1,
quotes=daily_df[['dates', 'open', 'close', 'high', 'low']].values,
width=0.7,
colorup='r',
colordown='g',
alpha=0.7)
plt.grid(True)
plt.xticks(rotation=40)
plt.title(stock_code)
ax1.xaxis.set_major_locator(ticker.MultipleLocator(5))
date_tickers = daily_df["trade_date2"].values[0::5]
date_tickers = np.insert(date_tickers,0," ")
ax1.set_xticklabels(date_tickers)
# mean values
for ma in ['lag_5', 'lag_10', 'lag_20', 'lag_30', 'lag_60', 'lag_120', 'lag_250']:
plt.plot(daily_df['dates'], daily_df[ma], label = ma)
plt.legend()
# 分割线
# print(focus_date)
x = daily_df.loc[daily_df["trade_date2"] == focus_date, "dates"].values[0]
plt.axvline(x,c="blue")#添加vertical直线
y = daily_df.loc[daily_df["trade_date2"] == focus_date, "close"].values[0]
plt.axhline(y,c="blue",ls = "-.")#添加水平直线
return 0
def test_indicator(file_name, start_date, end_date, figure_title, candlestick_width):
date_format = "%Y.%m.%d %H:%M:%S"
csv_data = pd.read_csv(file_name)
csv_data['Time'] = pd.to_datetime(csv_data['Time'], format=date_format)
csv_data = csv_data.set_index('Time')
csv_data = csv_data.iloc[::-1]
plot_data = csv_data.copy()
print('Sample data:')
plot_data['EMA'] = exponential_moving_average(plot_data['Close'], 10)
print(plot_data.tail(10))
subplot_df1 = plot_data.drop(['Volume', 'EMA'], 1).loc[start_date:end_date]
subplot_df2 = plot_data['EMA'].loc[start_date:end_date]
df1_tuples = utils.candlestick_tuples(subplot_df1)
fig, ax = plt.subplots()
plt.xticks(rotation=45)
plt.title(figure_title)
fplt.candlestick_ochl(ax, df1_tuples, width=candlestick_width, colorup='g', colordown='r')
subplot_df2.plot(ax=ax)
plt.show()
def load_json(stock):
title='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="k" to color the bar, otherwise use colordown="r"'
df=pd.read_json('AllStocks.json')
df=df[df['Symbol']==stock]
fig,ax=plt.subplots()
df['Open']=pd.to_numeric(df['Open'])
df['Low']=pd.to_numeric(df['Low'])
df['High']=pd.to_numeric(df['High'])
df['Date']=date2num(pd.to_datetime(df['Date']).tolist())
new_df=df.copy()
new_df=new_df[['Date','Open','Close','Low','High']]
quotes = [tuple(x) for x in new_df.values]
candlestick_ochl(ax,quotes)
ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%Y-%m-%d'))
ax.grid(True)
ax.set_xlabel('Dates')
ax.set_ylabel('Price')
fig_size = ax.rcParams["figure.figsize"]
# Set figure width to 12 and height to 9
fig_size[0] = 35
fig_size[1] = 25
ax.rcParams["figure.figsize"] = fig_size
plt.savefig('GOOG.png')
plt.show()
def test_indicator(file_name, start_date, end_date, figure_title,
candlestick_width):
csv_data = utils.load_data(file_name, "%Y.%m.%d %H:%M:%S")
plot_data = csv_data.copy()
print('Sample data:')
plot_data['SMA'] = simple_moving_average(plot_data['Close'], 10)
print(plot_data.tail(20))
subplot_df1 = plot_data.drop(['Volume', 'SMA'], 1).loc[start_date:end_date]
subplot_df2 = plot_data['SMA'].loc[start_date:end_date]
df1_tuples = utils.candlestick_tuples(subplot_df1)
fig, ax = plt.subplots()
plt.xticks(rotation=45)
plt.title(figure_title)
fplt.candlestick_ochl(ax,
df1_tuples,
width=candlestick_width,
colorup='g',
colordown='r')
subplot_df2.plot(ax=ax)
plt.show()
def sample_513():
"""
5.1.3 k线图的绘制
:return:
"""
#pip install https://github.com/matplotlib/mpl_finance/archive/master.zip
import mpl_finance as mpf
import matplotlib.dates as dts
__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 = dts.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 save_historic_klines_csv(symbol, start, end, interval):
klines = get_historical_klines(symbol, interval, start, end)
ochl = []
list_of_open = []
three_period_moving_ave = []
time3 = []
five_period_moving_ave = []
ten_period_moving_ave = []
time10 = []
with open('Binance_{}_{}-{}.txt'.format(symbol, start, end), 'w') as f:
f.write('Time, Open, High, Low, Close, Volume\n')
for kline in klines:
#print(kline)
time1 = int(kline[0])
open1 = float(kline[1])
Low = float(kline[2])
High = float(kline[3])
Close = float(kline[4])
Volume = float(kline[5])
format_kline = "{}, {}, {}, {}, {}, {}\n".format(
time1, open1, High, Low, Close,
Volume) #Thanks to user Apneef for correction
ochl.append([time1, open1, Close, High, Low, Volume])
f.write(format_kline)
#track opening prices, use for calculating moving averages
list_of_open.append(open1)
#Calculate three 'period' moving average - Based on Candlestick duration
if len(list_of_open) > 2:
price3 = 0
for pri in list_of_open[-3:]:
price3 += pri
three_period_moving_ave.append(float(price3 / 3))
time3.append(time1)
#Perform Moving Average Calculation for 10 periods
if len(list_of_open) > 9:
price10 = 0
for pri in list_of_open[-10:]:
price10 += pri
ten_period_moving_ave.append(float(price10 / 10))
time10.append(time1)
#Matplotlib visualization how-to from: https://pythonprogramming.net/candlestick-ohlc-graph-matplotlib-tutorial/
fig, ax = plt.subplots()
mpl_finance.candlestick_ochl(ax, ochl, width=1)
plt.plot(time3,
three_period_moving_ave,
color='green',
label='3 Period MA - Open')
plt.plot(time10,
ten_period_moving_ave,
color='blue',
label='10 Period MA - Open')
#ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d-%h-%m')) #Converting to date format not working
ax.set(xlabel='Date',
ylabel='Price',
title='{} {}-{}'.format(symbol, start, end))
plt.legend()
plt.show()
def test_indicator(file_name, start_date, end_date, figure_title,
candlestick_width):
csv_data = utils.load_data(file_name, "%Y.%m.%d %H:%M:%S")
print("Sample data:")
print(csv_data.head(10))
data_CHO = chaikin_oscillator(csv_data)
print("")
print(data_CHO.tail(20))
ax1_data = data_CHO.drop(
['Volume', 'ADL', '3 day EMA of ADL', '10 day EMA of ADL', 'CHO'], 1)
ax1_data = ax1_data.loc[start_date:end_date]
ax2_data = data_CHO.drop(['Open', 'High', 'Low', 'Close', 'Volume', 'CHO'],
1)
ax2_data = ax2_data.loc[start_date:end_date]
ax3_data = data_CHO['CHO'].loc[start_date:end_date]
# fig1.suptitle('Hourly OHLC data') <- figure title
df1_tuples = utils.candlestick_tuples(ax1_data)
fig1, ax1 = plt.subplots(1, 1)
ax1.xaxis_date()
ax1.set_ylabel('OHLC', color='g')
ax1.set_title(figure_title)
for tick in ax1.get_xticklabels():
tick.set_rotation(45)
for t1 in ax1.get_yticklabels():
t1.set_color('g')
ax1.grid()
fplt.candlestick_ochl(ax1,
df1_tuples,
width=candlestick_width,
colorup='g',
colordown='r')
ax2 = ax1.twinx()
ax2.plot(ax3_data, 'b-')
ax2.set_ylabel('%R', color='b')
for t1 in ax2.get_yticklabels():
t1.set_color('b')
fig1.show()
fig2 = plt.figure(2)
plt.plot(ax2_data)
plt.title('ADL vs EMAs')
plt.legend(['ADL', '3 day EMA of ADL', '10 day EMA of ADL'])
plt.xlabel('Laikas')
plt.ylabel('ADL')
plt.grid()
plt.xticks(rotation=90)
fig2.show()
plt.close()
def draw_stock(df):
global t_profit, buying_price, selling_price, profit
# select df
#df=df.iloc[len(df)-50:len(df)]
#print(df)
#figure = plt.figure(figsize=(12, 9))
plt.cla()
mpl.rcParams['font.family'] = 'sans-serif'
mpl.rcParams['font.sans-serif'] = 'SimHei'
def format_date(x, pos):
if x < 0 or x > len(date_tickers) - 1:
return ''
return date_tickers[int(x)]
date_tickers = df.dates.values
#
gs = GridSpec(3, 1)
ax1 = plt.subplot(gs[:2, :], facecolor="black")
ax2 = plt.subplot(gs[2, :], facecolor="black")
ax1.xaxis.set_major_formatter(ticker.FuncFormatter(format_date))
#
mpl_finance.candlestick_ochl(
ax=ax1,
quotes=df[['dates', 'open', 'close', 'high', 'low']].values,
width=0.7,
colorup='r',
colordown='cyan',
alpha=0.7)
#
for ma in ['5', '10', '20']:
ax1.plot(df['dates'], df[ma])
#plt.legend()
ax1.set_title(
"Total: {:.2f} Profit : {:.2f}% buy price : {} sell price : {} temp profit : {:.2f}%"
.format(20000 * (1 + t_profit), t_profit * 100, buying_price,
selling_price, profit * 100),
fontsize=15)
#
ax2.xaxis.set_major_formatter(ticker.FuncFormatter(format_date))
#df2['up'] = df.apply(lambda row: 1 if row['close'] >= row['open'] else 0, axis=1)
ax2.bar(df.query('up == 1')['dates'],
df.query('up == 1')['vol1'],
color='r',
alpha=0.7)
ax2.bar(df.query('up == 0')['dates'],
df.query('up == 0')['vol1'],
color='cyan',
alpha=0.7)
ax2.set_xlabel('Stock Simulator', fontsize=30)
ax2.set_title(
"Help : mouse left click：buy/keep， mouse right click：sell/waiting mouse mid click：reset",
fontsize=15)
def contextPlot(df, start='2020-01-01', end='cur'):
import core.Util as ut
if (end == 'cur'):
cur = datetime.datetime.now()
mon = str(cur.month)
day = str(cur.day)
if (re.match('[0-9]{1}', mon) and len(mon) == 1):
mon = '0' + mon
if (re.match('[0-9]{1}', day) and len(day) == 1):
day = '0' + day
et = str(cur.year) + '-' + mon + '-' + day
else:
et = end
sample = QA.QA_fetch_stock_day_adv('000977', start, et).data
quots = ut.candlestruct(sample)
N = df.shape[0]
ind = np.arange(N)
def format_date(x, pos=None):
thisind = np.clip(int(x + 0.5), 0, N - 1)
return m.index.get_level_values(uti.dayindex)[thisind]
fig = plt.figure()
#gs = gridspec.GridSpec(3, 1)
fig.set_size_inches(30.5, 20.5)
ax3 = fig.add_subplot(2, 1, 2)
ax3.set_title("candlestick", fontsize='xx-large', fontweight='bold')
mpf.candlestick_ochl(ax3,
quots,
width=0.6,
colorup='r',
colordown='g',
alpha=1.0)
'''
for i in range(N):
if (day.single[i] == 1):
ax2.axvline(x=i, ls='--', color='red')
if (day.single[i] == 3):
ax2.axvline(x=i, ls='--', color='green')
'''
ax3.xaxis.set_major_formatter(mtk.FuncFormatter(format_date))
ax3.grid(True)
ax3.legend(loc='best')
fig.autofmt_xdate()
ax2 = fig.add_subplot(2, 1, 1, sharex=ax3)
# ax3.set_title("Divergence", fontsize='xx-large', fontweight='bold')
ax2.plot(ind, m.value, 'r-', linewidth=1)
ax2.grid(True)
ax2.xaxis.set_major_formatter(mtk.FuncFormatter(format_date))
fig.autofmt_xdate()
plt.show()
def Plot(sample):
print(sample)
quotes = []
N = sample.shape[0]
ind = np.arange(N)
for i in range(len(sample)):
li = []
datef = ind[i] # 日期转换成float days
open_p = sample.open[i]
close_p = sample.close[i]
high_p = sample.high[i]
low_p = sample.low[i]
li = [datef, open_p, close_p, high_p, low_p]
t = tuple(li)
quotes.append(t)
def format_date(x, pos=None):
thisind = np.clip(int(x + 0.5), 0, N - 1)
return sample.date[thisind].strftime('%Y-%m-%d')
fig = plt.figure()
fig.set_size_inches(30.5, 20.5)
print('call here')
ax2 = fig.add_subplot(3, 1, 1)
ax2.set_title("Monthly candlestick",
fontsize='xx-large',
fontweight='bold')
mpf.candlestick_ochl(ax2,
quotes,
width=0.6,
colorup='r',
colordown='g',
alpha=1.0)
ax2.plot(ind, sample.EMA5, 'r-', label='EMA5')
ax2.plot(ind, sample.EMA10, 'blue', label='EMA10')
ax2.xaxis.set_major_formatter(mtk.FuncFormatter(format_date))
ax2.grid(True)
ax2.legend(loc='best')
fig.autofmt_xdate()
ax1 = fig.add_subplot(3, 1, 2, sharex=ax2)
ax1.set_title(' macd', fontsize='xx-large', fontweight='bold')
ax1.grid(True)
ax1.plot(ind, sample.MACDQ, 'red', label='DIF')
ax1.plot(ind, sample.MACDSIG, 'blue', label='DEA')
m_red = np.where(sample.MACDBlock >= 0, sample.MACDBlock, 0)
m_green = np.where(sample.MACDBlock < 0, sample.MACDBlock, 0)
ax1.bar(ind, m_red, color='red')
ax1.bar(ind, m_green, color='green')
ax1.xaxis.set_major_formatter(mtk.FuncFormatter(format_date))
# ax2.set_yticks(np.linspace(ax2.get_yticks()[0], ax2.get_yticks()[-1], len(ax1.get_yticks())))
ax1.legend(loc='best')
fig.autofmt_xdate()
plt.legend()
plt.show()
def draw_kl2(df: pd.DataFrame, minute=False, day_sum=False):
# 为了示例清晰，只拿出前30天的交易数据绘制蜡烛图，
part_df = df
# fig, ax = plt.subplots(figsize=(14, 7))
fig, ax1 = plt.subplots(figsize=(6, 6))
qutotes = []
if day_sum:
# 端线图绘制
qutotes = []
for index, (d, o, c, l, h) in enumerate(
zip(part_df.time, part_df.open, part_df.close, part_df.high,
part_df.low)):
d = index if minute else dts.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(part_df.time, part_df.open, part_df.close, part_df.high,
part_df.low)):
d = index if minute else dts.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__)
# for index, (d, o, c, h, l) in enumerate(
# zip(part_df.time, part_df.open, part_df.close,
# part_df.high, part_df.low)):
# # 蜡烛图的日期要使用dts.date2num进行转换为特有的数字值
# # d = datetime.datetime.fromtimestamp(d / 1000)
# # d = dts.date2num(d)
# d = index
# # 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 main():
args = parse_args()
# read
df = pd.read_csv(args.input, index_col=0, parse_dates=True)
df.index = pd.to_datetime(df.index)
df.index = mdates.date2num(df.index)
# rolling_average
for span in args.roll_spans:
name = 'roll_{}'.format(span)
df[name] = df['close'].rolling(span).mean()
if args.output_length > 0:
df = df.tail(args.output_length)
# Plot Figure
fig, axes = plt.subplots(nrows=2,
ncols=1,
figsize=(16, 9),
sharex=True,
gridspec_kw={'height_ratios': [4, 1]})
# Plot candle
width = (df.index[1] - df.index[0]) * 0.7
if args.plot_candle:
ohlc = df.reset_index().values
width = (ohlc[1][0] - ohlc[0][0]) * 0.7
mpf.candlestick_ochl(axes[0],
ohlc,
colorup='g',
colordown='r',
width=width,
alpha=0.75)
else:
axes[0].plot(df.index, df.close, color="black", linewidth=0.7)
# Plot rolling means
for span in args.roll_spans:
name = "roll_{}".format(span)
axes[0].plot(df.index, getattr(df, name), linewidth=0.7, label=name)
# Plot Volumes
axes[1].bar(df.index, df.volume, width=width)
# Graph Settings
axes[0].grid(linestyle=':')
axes[0].legend()
axes[1].grid(linestyle=':')
locator = mdates.AutoDateLocator()
axes[0].xaxis.set_major_locator(locator)
axes[0].xaxis.set_major_formatter(mdates.DateFormatter('%y/%m/%d %H:%M'))
fig.savefig(args.output)
def Draw_Stock(stock_id,
stock_info,
buy_day,
sell_date=None,
left_offset=5,
right_offset=5):
"""
绘制股票交易前后其股价变化
:param stock_id: 股票id
:param stock_info: 所有股票的信息
:param buy_day: 买入股票的日子
:param left_offset: 绘图左偏天数，用于调整可见天数
:param right_offset: 绘图右偏天数，用于调整可见天数
:return:
"""
fig, ax = plt.subplots(1, 1, figsize=(8, 3))
idx = (stock_info['trade_date'] == buy_day) & (stock_info['ts_code']
== stock_id)
ts_date_id = stock_info[idx]['ts_date_id'].values[0]
idx = (stock_info['ts_date_id'] <= ts_date_id + right_offset) & (
stock_info['ts_date_id'] >= ts_date_id - left_offset)
tmp_df = stock_info[idx].sort_values('trade_date').reset_index()
x = list(range(len(tmp_df)))
tmp_df['index'] = x
idx = (tmp_df['trade_date'] == buy_day)
x_loc = tmp_df[idx]['index'].values[0]
y_loc = tmp_df[idx]['close'].values[0]
# print(x_loc, y_loc)
data = tmp_df[['index', 'open', 'close', 'high', 'low']].values
candlestick_ochl(ax, data, width=0.1, colorup='r', colordown='g')
# plt.arrow(x_loc, y_loc*1.5, x_loc, y_loc, length_includes_head=True, head_width=0.25, head_length=0.5, fc='r', ec='b')
plt.annotate(
"buy",
xy=(x_loc, y_loc * 1.001),
xytext=(x_loc, y_loc * 1.06),
# xycoords="figure points",
arrowprops=dict(arrowstyle="->", color="b"))
# plt.plot(x_loc, y_loc * 1.03, '*')
title = tmp_df['name'][0]
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
plt.title(title)
plt.tick_params(labelsize=15)
if sell_date:
idx = (tmp_df['trade_date'] == sell_date)
x_loc = tmp_df[idx]['index'].values[0]
y_loc = tmp_df[idx]['high'].values[0]
plt.annotate(
"sell",
xy=(x_loc, y_loc * 1.001),
xytext=(x_loc, y_loc * 1.06),
# xycoords="figure points",
arrowprops=dict(arrowstyle="->", color="r"))
def K(ts_code, type):
if type == 0:
date = '年'
end_date = datetime.now().strftime('%Y%m%d')
delta = timedelta(days=365)
start_date = (datetime.now() - delta).strftime('%Y%m%d')
elif type == 1:
date = '月'
end_date = datetime.now().strftime('%Y%m%d')
delta = timedelta(days=31)
start_date = (datetime.now() - delta).strftime('%Y%m%d')
else:
date = '周'
end_date = datetime.now().strftime('%Y%m%d')
delta = timedelta(days=7)
start_date = (datetime.now() - delta).strftime('%Y%m%d')
token = "d078bd5a718954f48ee1f41013279afae62d1affd0866b56658cd918"
ts.set_token(token)
pro = ts.pro_api()
data = pro.query('daily',
ts_code=ts_code,
start_date=start_date,
end_date=end_date)
#删除空行
data[data['vol'] == 0] = np.nan
data = data.dropna()
#按时间升序排列数据
data.sort_values(by='trade_date', ascending=True, inplace=True)
data = data[['trade_date', 'open', 'close', 'high',
'low']] #这里由于数据太多，仅取前3000行进行绘制
data.trade_date = pd.to_datetime(data.trade_date)
#将date转化为特定的时间戳数据
data.trade_date = data.trade_date.apply(lambda x: date2num(x))
#将 DataFrame 转为 matrix格式
data_mat = data.values
# print(data_mat)
#绘制图片
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
fig, ax = plt.subplots(figsize=(1200 / 72, 480 / 72))
fig.subplots_adjust(bottom=0.1)
fig.suptitle(r'{0}的{1}K线图'.format(ts_code, date), fontsize=20.00)
mpf.candlestick_ochl(ax,
data_mat,
colordown='#53c156',
colorup='#ff1717',
width=0.3,
alpha=1)
ax.grid(True)
ax.xaxis_date()
plt.savefig(r'./{0}的{1}K线图.jpg'.format(ts_code, date))
url = '{0}的{1}K线图.jpg'.format(ts_code, date)
return url
def draw_image(self, stock_id, chart_title, open, high, low, new_price):
self.id = stock_id
self.select_df()
self.insert_today_trade(open, high, low, new_price)
stock = stock_buffer.put_stock_instance(self.id)
self.df['dates'] = np.arange(0, len(self.df))
self.df['5'] = self.df['close_price'].rolling(5).mean()
def format_date(x, pos):
if x < 0 or x > len(date_tickers) - 1:
return ''
return date_tickers[int(x)]
date_tickers = self.df.trade_date.values
plt.rcParams['font.sans-serif'] = ['KaiTi']
plt.rcParams['axes.unicode_minus'] = False
fig, ax = plt.subplots(figsize=(23, 5))
ax.xaxis.set_major_formatter(ticker.FuncFormatter(format_date))
ax.set_title(chart_title, fontsize=20)
# 绘制K线图
mpl_finance.candlestick_ochl(ax=ax,
quotes=self.df[[
'dates', 'open_price', 'close_price',
'high_price', 'low_price'
]].values,
width=0.7,
colorup='r',
colordown='g',
alpha=0.7)
plt.plot(self.df['dates'], self.df['5'])
zhuang_section = self.info_df.loc[0, 'zhuang_section']
# print('zhuang_section:', zhuang_section)
try:
zhuang_section = eval(zhuang_section)
except Exception as err:
if zhuang_section == None:
zhuang_section = []
logging.error('ERR:{} zhuang_section:{},df:{}'.format(
err, zhuang_section, self.info_df))
print('ERR:{} zhuang_section:{},df:{}'.format(
err, zhuang_section, self.info_df))
for zhaung_tup in zhuang_section:
sta = self.__comput_ind(zhaung_tup[1])
end = self.__comput_ind(zhaung_tup[0])
print('indexs:', sta, end)
plt.plot(self.df['dates'][sta:end],
self.df['5'][sta:end],
color='green')
plt.legend()
# self.image_path = '../pic/{0}{1}{2}{3}.jpg'.format(stock.stock_id, stock.stock_name, self.to_day, stock.inform_type)
self.image_path = '../pic/{0}{1}{2}.jpg'.format(
stock.stock_id, self.to_day, stock.inform_type)
plt.savefig(self.image_path)
def DrawKLine(self): ohlc = [] ohlc = list(zip(np.arange(0,len(self.df.index)),self.df.open,self.df.close,self.df.high,self.df.low)) mpf.candlestick_ochl(self.graph_KAV, ohlc, width=0.2, colorup='r', colordown='g', alpha=1.0) self.graph_KAV.plot(self.numt, self.df['Ma20'],'black', label='M20',lw=1.0) self.graph_KAV.plot(self.numt, self.df['Ma30'],'green',label='M30', lw=1.0) self.graph_KAV.plot(self.numt, self.df['Ma60'],'blue',label='M60', lw=1.0) self.graph_KAV.legend(loc='best') self.graph_KAV.set_ylabel(u"价格") self.graph_KAV.set_xlim(0,len(self.df.index)) self.graph_KAV.set_xticks(range(0,len(self.df.index),15)) self.graph_KAV.grid(True,color='k')
def draw(self):
self.read_data()
fig, ax = plt.subplots(figsize=(1200 / 72, 480 / 72))
fig.subplots_adjust(bottom=0.1)
mpf.candlestick_ochl(ax,
self.data_mat,
colordown=self.colordown,
colorup=self.colorup,
width=0.3,
alpha=1)
ax.grid(True)
ax.xaxis_date()
plt.show()
def MACDPLOT(sample):
quotes = candlestruct(sample)
N=sample.index.get_level_values('date').shape[0]
ind = np.arange(N)
def format_date(x, pos=None):
thisind = np.clip(int(x+0.5), 0, N-1)
return sample.index.get_level_values('date')[thisind].strftime('%Y-%m-%d')
fig = plt.figure()
#fig = plt.gcf()
fig.set_size_inches(20.5,12.5)
#plt.xlabel('Trading Day')
#plt.ylabel('MACD EMA')
ax2 = fig.add_subplot(3,1,1)
ax2.set_title("candlestick")
#fig,ax=plt.subplots()
#mpf.candlestick_ochl(ax2,quotes,width=0.2,colorup='r',colordown='g',alpha=1.0)
#ax2.xaxis_date()
#plt.setp(plt.gca().get_xticklabels(),rotation=30)
#ax2.plot(ind,sample.close,'b-',marker='*')
mpf.candlestick_ochl(ax2,quotes,width=0.6,colorup='r',colordown='g',alpha=1.0)
ax2.xaxis.set_major_formatter(mtk.FuncFormatter(format_date))
ax2.grid(True)
#t.legend()
fig.autofmt_xdate()
ax3 = fig.add_subplot(3,1,3,sharex=ax2)
ax3.set_title("volume")
#ax1 = ax2.twinx() #not working like it's
ax3.bar(ind,sample.volume)
ax3.grid(True)
ax3.xaxis.set_major_formatter(mtk.FuncFormatter(format_date))
fig.autofmt_xdate()
#ax1 = ax2.twinx() #not working like it's
ax1 = fig.add_subplot(3,1,2,sharex=ax2)
ax1.set_title("macd")
ax1.grid(True)
ax1.plot(ind,sample.MACDQ,'r-',marker='*')
ax1.plot(ind,sample.MACDSIG,'o-')
ax1.bar(ind,sample.MACDBlock)
ax1.xaxis.set_major_formatter(mtk.FuncFormatter(format_date))
#ax2.set_yticks(np.linspace(ax2.get_yticks()[0], ax2.get_yticks()[-1], len(ax1.get_yticks())))
fig.autofmt_xdate()
plt.legend()
code = sample.index.get_level_values('code')[0]
plt.savefig('/home/mildone/monitor/'+'Trend'+code+'.png')
#plt.show() comment out for hanging with 1 pic
plt.close()
def plot_k(data, title):
fig, ax = plt.subplots(figsize=(15, 5))
fig.subplots_adjust(bottom=0.5)
mpf.candlestick_ochl(ax,
data,
width=0.3,
colorup='g',
colordown='r',
alpha=1.0)
plt.grid(True)
plt.title(title)
plt.xlabel('Date')
plt.ylabel('Price')
plt.show()
def plot_candles(som_model):
import matplotlib.pyplot as plt
from mpl_finance import candlestick_ochl
weights = som_model.get_weights()
f, ax = plt.subplots(nrows=weights[0],
ncols=1,
sharey=True,
figsize=(10, 10))
for i in range(weights.shape[0]):
candlestick_ochl(ax[i], [
_np.append([j], weights[i, j] + 1) for j in range(weights.shape[1])
])
def addcandlestick(self,data,plotname):
"""Creates a candlestick subplot for the dataplotter figure based on the data
Args:
data (Pandas DataFrame): 2-column dataframe.
plotname (String): Name for the subplot
"""
#constructing the candlestick plot
currentplot = self.fig.add_subplot(1,1,1)
candlestick_ochl(currentplot,data,colorup="g")
currentplot.set_xlabel("Iteration")
currentplot.grid(True)
#Add the subplot to the subplot dictionary
self.subplots[plotname]=currentplot
def kgrid(stock_code, data_list):
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.2)
ax.xaxis_date()
plt.xticks(rotation=45)
plt.yticks()
plt.title("Stock Code: %s" % stock_code)
plt.xlabel("Time")
plt.ylabel("Price")
mpf.candlestick_ochl(ax,
data_list,
width=0.72,
colorup='r',
colordown='green')
plt.grid()
def graphs(stock):
with open(stock+'.csv', 'r') as csvfile:
fig = plt.figure('figure one')
ax1 = plt.subplot2grid((6, 1), (0, 0), rowspan=1, colspan=1)
plt.title(stock)
ax2 = plt.subplot2grid((6, 1), (1, 0), rowspan=4, colspan=1)
plt.xlabel('dates')
plt.ylabel('price')
ax3 = plt.subplot2grid((6, 1), (5, 0), rowspan=1, colspan=1)
source_code = csvfile.read()
stock_data = []
split_source = source_code.split('\n')
for line in split_source:
split_line = line.split(',')
if len(split_line) == 7:
if 'Date' is not line and 'Close' not in line:
stock_data.append(line)
date, openp, hightp, lowp, closep, aclosep, volume = np.loadtxt(stock_data,
delimiter=',',
unpack=True,
converters={0: bytespdate2num('%Y-%m-%d')})
x = 0
y = len(date)
ohlc = []
while x < y:
append_me = date[x], openp[x], hightp[x], lowp[x], closep[x], aclosep[x], volume[x]
ohlc.append(append_me)
x+=1
candlestick_ochl(ax2, ohlc, width=0.4, colordown='r', colorup='g')
for label in ax2.xaxis.get_ticklabels():
label.set_rotation(45)
ax2.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
ax2.xaxis.set_major_locator(mticker.MaxNLocator(10))
ax2.grid(True)
bbox_props = dict(boxstyle='round', fc='w', ec='k')
ax2.annotate(str(closep[-1]), (date[-1],closep[-1]), xytext=(date[-1]+3,closep[-1]), bbox=bbox_props)
plt.subplots_adjust(left=0.09, bottom=0.25, right=0.82, top=0.90, wspace=0.2, hspace=0)
plt.show()
def showall(df, nrows=3):
# 提取原始日期格式
df['dates'] = np.arange(0, len(df))
df = df.reset_index()
df['date2'] = df['date'].copy()
df['date'] = df['date'].map(date2num)
global date_tickers
date_tickers = (df.date2).apply(lambda x: x.strftime('%Y%m%d')).values
# 画子图
figure = plt.figure(figsize=(16, 9))
gs = GridSpec(nrows, 1)
gs.update(wspace=0.05, hspace=0.05)
ax1 = plt.subplot(gs[:2, :])
ax2 = plt.subplot(gs[2, :])
ax3 = plt.subplot(gs[nrows - 1, :])
# 画K线图
mpf.candlestick_ochl(ax=ax1,
quotes=df[['dates', 'open', 'close', 'high',
'low']].values,
width=0.7,
colorup='r',
colordown='g',
alpha=0.7)
ax1.xaxis.set_major_formatter(ticker.FuncFormatter(format_date))
# 画均线，均线可使用talib来计算
for ma in ['5', '20', '30', '60', '120']:
df[ma] = df.close.rolling(int(ma)).mean()
ax1.plot(df['dates'], df[ma])
ax1.legend()
ax1.set_title('沪深300指数K线图', fontsize=15)
ax1.set_ylabel('指数')
# 画成交量
ax2.xaxis.set_major_formatter(ticker.FuncFormatter(format_date))
# 收阳线;收盘价大于等于开盘价=1
df['up'] = df.apply(lambda row: 1 if row['close'] >= row['open'] else 0,
axis=1)
ax2.bar(df.query('up == 1')['dates'],
df.query('up == 1')['vol'],
color='r',
alpha=0.7)
ax2.bar(df.query('up == 0')['dates'],
df.query('up == 0')['vol'],
color='g',
alpha=0.7)
ax2.set_ylabel('成交量')
ax2.xaxis.set_major_formatter(ticker.FuncFormatter(format_date))
plt.show()
time.sleep(2)
def plot_graph(self, start):
price_sheet = None
try:
# obtain the data from IEX cloud. we use json format since it's easy to process
# chartSimplify meant to simply the data, since we don't want to many data to plot the graph
price_sheet = get_historical_data(self.symbol_info['symbol'],
start,
str(datetime.date.today()),
output_format='json',
chartSimplify=True,
token=API_KEY)
except IEXQueryError as ex:
return "Sorry, but I have trouble obtaining the balance data."
try:
# Import the MatPlotLib and MatPlotLib Finance libraries
import matplotlib.pyplot as plt
import mpl_finance as mpf
from matplotlib.pylab import date2num
# Convert the obtained data to the format that MatPlotLib can understand
data = []
for k in price_sheet.keys():
a = price_sheet[k]
# Convert the dates to float, making MatPlotLib understand them
date_time = datetime.datetime.strptime(k, '%Y-%m-%d')
data.append([
date2num(date_time), a['open'], a['close'], a['high'],
a['low'], a['volume']
])
fig, ax = plt.subplots(figsize=(1200 / 72, 480 / 72))
fig.subplots_adjust(bottom=0.1)
mpf.candlestick_ochl(ax,
data,
colordown='#53c156',
colorup='#ff1717',
width=0.3,
alpha=1)
ax.grid(True)
ax.xaxis_date()
# Now return the figure instead of string.
# Different user interfaces have different ways to show figures
return fig
except Exception as e:
# Once there's errors, we just return a error message.
return "Oops, something wrong happened while plotting the graph!"
def draw_k_v(self):
fig, (ax1, ax2) = plt.subplots(2, sharex=True, figsize=(15, 8))
mpf.candlestick_ochl(ax1,
self.mat_data,
width=0.5,
colorup='r',
colordown='k')
ax1.set_title(self.name + ':' + self.code)
ax1.set_ylabel('Price')
ax1.grid(True)
ax1.xaxis_date()
plt.bar(self.mat_data[:, 0], self.mat_data[:, 5],
width=0.5) # ? mat_data[:,0] - 0.25
ax2.set_ylabel('Volume')
ax2.grid(True)
plt.show()
def update_k_line(self, tochlva):
print("update_k_line", tochlva)
self.amount = [i.pop() for i in tochlva]
self.volume = [i.pop() for i in tochlva]
self.x = []
for i in tochlva:
i[0] = mdates.epoch2num(i[0]) #convert timestamp to matplotlib.dates num format
self.x.append(i[0])
self.x0 = min(self.x)
self.x1 = max(self.x)
if self.k_lines == None and self.k_patches == None:
self.k_lines,self.k_patches = candlestick_ochl(self.ax1, tochlva)#, width=0.4, colorup='#77d879', colordown='#db3f3f')
else:
self.k_lines,self.k_patches = candlestick_ochl(self.ax1, tochlva)
self.ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
self.ax1.xaxis.set_major_locator(mdates.DayLocator())
plt.xticks(rotation=45)
def ta_graphic(indicator, ax, *params):
df = params[0]
if indicator == 'kline':
candlestick_ochl(ax, np.array(df))#, width=0.4, colorup='#77d879', colordown='#db3f3f')
else:
line_color = ('b','g','r','c','m','y','k')#,'w')
line_maker = ('.',',','o') ###...
line_style = ('-', '--', '-.', ':')
t = list(map(datetime.fromtimestamp, df['t']))
for i in range(df.columns.size): #exclude 't'
if df.columns[i] == 't':
continue
col = df[df.columns[i]]
cms = line_color[int(i%len(line_color))]+line_maker[0]+line_style[int(i/len(line_color))]
ax.plot(t, col, cms)
ax.set_title(indicator, fontproperties="SimHei")
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d %H')) #%H:%M:%S'))
ax.xaxis.set_major_locator(mdates.DayLocator()) #HourLocator())
ax.legend()
plt.xticks(rotation=45)
#plt.show()
if len(params) > 1:
hist = params[1]
if hist.index.size > 0:
for i in hist.index:
row = hist.loc[i]
time = datetime.fromtimestamp(row['t'])
type = row['type']
price = row['price']
if type == 'open_buy':
cms = 'r+'
elif type == 'margin_buy' or type == 'loss_buy':
cms = 'rx'
elif type == 'open_sell':
cms = 'y+'
elif type == 'margin_sell' or type == 'loss_sell':
cms = 'yx'
ax.plot([time], [price], cms)
