url = 'https://finance.naver.com/item/sise_day.nhn?code=028300&page=1'
with urlopen(url) as doc:
html = BeautifulSoup(doc, 'lxml')
pgrr = html.find('td', class_='pgRR')
s = str(pgrr.a['href']).split('=')
last_page = s[-1]
df = pd.DataFrame()
sise_url = 'https://finance.naver.com/item/sise_day.nhn?code=028300'
for page in range(1, int(last_page) + 1):
page_url = '{}&page={}'.format(sise_url, page)
df = df.append(pd.read_html(page_url, header=0)[0])
df = df.dropna()
df = df.iloc[0:30]
df = df.sort_values(by='날짜')
for idx in range(0, len(df)):
dt = datetime.strptime(df['날짜'].values[idx], '%Y.%m.%d').date()
df['날짜'].values[idx] = mdates.date2num(dt)
ohlc = df[['날짜', '시가', '고가', '저가', '종가']]
plt.figure(figsize=(9, 6))
ax = plt.subplot(1, 1, 1)
plt.title('HLB candle stick')
candlestick_ohlc(ax, ohlc.values, width=0.7, colorup='red', colordown='blue')
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
plt.xticks(rotation=45)
plt.grid(color='gray', linestyle='--')
plt.show()
def plot_chart(outputpath, data, title):
data.to_excel(title + '.xlsx')
print(data)
fig = plt.figure() #创建绘图区，包含四个子图
fig.set_size_inches((20, 16))
ax_candle = fig.add_axes((0, 0.75, 1, 0.25)) #蜡烛图子图
# left, bottom, width, height = 0.1, 0.1, 0.8, 0.8
ax_macd = fig.add_axes((0, 0.60, 1, 0.15), sharex=ax_candle) #macd子图
ax_rsi = fig.add_axes((0, 0.45, 1, 0.15), sharex=ax_candle) #rsi子图
ax_vol = fig.add_axes((0, 0.30, 1, 0.15), sharex=ax_candle) #成交量子图
ax_kjd = fig.add_axes((0, 0.15, 1, 0.15), sharex=ax_candle)
ax_candle.grid(True)
# ax_macd.grid(True)
# ax_rsi.grid(True)
# ax_vol.grid(True)
# ax_kjd.grid(True)
# print(data)
ohlc = [] #存放行情数据，candlestick_ohlc需要传入固定格式的数据
row_number = 0
for date, row in data.iterrows():
date, highp, lowp, openp, closep = row[:5]
ohlc.append([row_number, openp, highp, lowp, closep])
row_number = row_number + 1
# print(ohlc)
date_tickers = data.Date.values #获取Date数据
# print(date_tickers)
#绘制蜡烛图
candlestick_ohlc(ax_candle, ohlc, colorup="r", colordown="g")
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)]
# ax_candle = fig.add_axes((0, 0.75, 1, 0.25)) # 蜡烛图子图
# stockData=data.copy()
# stockData['Date'] = pd.to_datetime(stockData['Date'])
# stockData.set_index('Date',inplace=True)
# mpf.plot(stockData, type='candle', figscale=0.9,ax=ax_candle)
ax_candle.plot(data.index, data["ma10"], label="MA10")
ax_candle.plot(data.index, data["ma30"], label="MA30")
ax_candle.xaxis.set_major_formatter(ticker.FuncFormatter(format_date))
ax_candle.xaxis.set_major_locator(ticker.MultipleLocator(2)) # 设置间隔为6个交易日
ax_candle.grid(True)
ax_candle.set_title(title, fontsize=20)
ax_candle.legend()
for label in ax_kjd.xaxis.get_ticklabels():
label.set_rotation(60)
# 绘制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.set_title('MACD')
ax_macd.legend()
#绘制RSI
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.set_title('RSI')
ax_rsi.legend()
#绘制成交量
ax_vol.bar(data.index, data["Volume"] / 1000000)
ax_vol.set_ylabel("(Million)")
#KDJ
ax_kjd.plot(data.index, data["K"], label="K", color='blue')
ax_kjd.plot(data.index, data["D"], label="D", color='g')
ax_kjd.plot(data.index, data["J"], label="J", color='r')
ax_kjd.set_title('KDJ')
# graph_KDJ.plot(np.arange(0, len(figData.index)), figData['K'], 'blue', label='K') # K
# graph_KDJ.plot(np.arange(0, len(figData.index)), figData['D'], 'g--', label='D') # D
# graph_KDJ.plot(np.arange(0, len(figData.index)), figData['J'], 'r-', label='J') # J
ax_kjd.legend()
#这里个人选择不要plt.show()，因为保存图片到本地的
#plt.show()
# 保存图片到本地
fig.savefig(outputpath, bbox_inches="tight")
return outputpath
# 黄昏之星 函数名：CDLEVENINGSTAR
data['EveningStar'] = talib.CDLEVENINGSTAR(Open, High, Low, Close)
data[data['EveningStar'] == -100] # 负100
# 乌云压顶 函数名：CDLDARKCLOUDCOVER
data['DarkCloud'] = talib.CDLDARKCLOUDCOVER(Open, High, Low, Close)
data[data['DarkCloud'] == -100] # 负100
from mplfinance.original_flavor import candlestick_ohlc
import matplotlib.dates as mdates
date_wewannaanalyse = data['2018-05-10':'2018-06-05']
ax1 = plt.subplot()
ax1.xaxis_date()
df_ohlc = date_wewannaanalyse[['open', 'high', 'low', 'close']]
df_ohlc = df_ohlc.reset_index()
df_ohlc['trade_date'] = df_ohlc['trade_date'].map(mdates.date2num)
candlestick_ohlc(ax1,
df_ohlc.values,
width=1,
colordown='green',
colorup='red',
alpha=0.75)
plt.title('浦发银行历史股价K线图')
plt.show()
# 预测信号并不一定正确，不能只靠信号判断，要综合各种信息
# 尝试使用别的形态识别函数
import pandas_datareader.data as web
from mplfinance.original_flavor import candlestick_ohlc
import matplotlib.dates as mdates
style.use("ggplot")
#Start date for series
start = dt.datetime(2000, 1, 1)
#End date for series, can be changed to a specific date
end = dt.datetime.today()
#The tikcer name, can be found on yahoo
ticker = "AZN"
df = web.DataReader(ticker, "yahoo", start, end)
#"Adj Close" can be edited, and so can resampling size
df_ohlc = df["Adj Close"].resample("5D").ohlc()
#"Volume" can be edited, but is a useful insight, resampling resolution can be changed
df_volume = df["Volume"].resample("5D").sum()
df_ohlc.reset_index(inplace=True)
df_ohlc["Date"] = df_ohlc["Date"].map(mdates.date2num)
ax1 = plt.subplot2grid((6, 1), (0, 0), rowspan=5, colspan=1)
plt.title(f"{ticker} Candlestick and Volume")
ax2 = plt.subplot2grid((6, 1), (5, 0), rowspan=1, colspan=1, sharex=ax1)
ax1.xaxis_date()
candlestick_ohlc(ax1, df_ohlc.values, width=2, colorup="g")
ax2.fill_between(df_volume.index.map(mdates.date2num), df_volume.values, 0)
plt.show()
# Map traded to matplotlib date
df['traded'] = df['traded'].map(mdates.date2num)
# Create 10-day and 3-day moving averages over settlement price
# (for demonstration purposes, not for analysis)
df['10ma'] = df['settlement'].rolling(window=10).mean()
df['3ma'] = df['settlement'].rolling(window=3).mean()
# Apply ggplot style to matplotlib charts
style.use('ggplot')
# Create two subplots (5-to-1 ratio)
ax1 = plt.subplot2grid((6, 1), (0, 0), rowspan=5, colspan=1)
ax2 = plt.subplot2grid((6, 1), (5, 0), rowspan=1, colspan=1, sharex=ax1)
ax1.xaxis_date()
# Add OHLC and moving averages to upper plot
candlestick_ohlc(ax1,
df[['traded', 'open', 'high', 'low', 'close']].values,
width=0.5,
colorup='g',
colordown='r')
ax1.plot(df['traded'], df['10ma'])
ax1.plot(df['traded'], df['3ma'])
# Add volume to lower plot
ax2.bar(df['traded'], df['volume'])
# Display plot
plt.show()
df_volume = df['Volume'].resample('5D').sum()
df_ohlc.reset_index(inplace=True)
df_ohlc['Date'] = df_ohlc['Date'].map(mdates.date2num)
print(df_ohlc.head())
df.dropna(inplace=True)
print(df.head())
ax1 = plt.subplot2grid((10, 1), (0, 0), rowspan=5, colspan=1)
ax2 = plt.subplot2grid((10, 1), (5, 0), rowspan=1, colspan=1, sharex=ax1)
# Will take MDates and make them 'nice' looking showing actual text
ax1.xaxis_date()
# Define size of candlesticks and color, I have blue set to up and red set to down
candlestick_ohlc(ax1, df_ohlc.values, width=3, colorup='b')
# Have to map for the x and then the volume will be the y (the zero will fill x and y)
ax2.fill_between(df_volume.index.map(mdates.date2num), df_volume.values, 0)
ax1.plot(df.index, df['Adj Close'])
#ax1.plot(df.index, df['100ma'])
ax2.bar(df.index, df['Volume'])
print(df[['Open', 'High']].head())
df.plot()
# Generates second grid where you can see legend and more data
plt.legend(loc='best')
plt.show()
df['number'] = df.index.map(mdates.date2num)
ohlc = df[['number', 'open', 'high', 'low', 'close']]
ndays_high = df.high.rolling(window=14, min_periods=1).max()
ndays_low = df.low.rolling(window=14, min_periods=1).min()
fast_k = (df.close - ndays_low) / (ndays_high - ndays_low) * 100
slow_d = fast_k.rolling(window=3).mean()
df = df.assign(fast_k=fast_k, slow_d=slow_d).dropna()
plt.figure(figsize=(9, 9))
p1 = plt.subplot(3, 1, 1)
plt.title('Triple Screen Trading (NCSOFT)')
plt.grid(True)
candlestick_ohlc(p1, ohlc.values, width=.6, colorup='red', colordown='blue')
p1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
plt.plot(df.number, df['ema130'], color='c', label='EMA130')
for i in range(1, len(df.close)):
if df.ema130.values[i - 1] < df.ema130.values[i] and df.slow_d.values[
i - 1] >= 20 and df.slow_d.values[i] < 20:
plt.plot(df.number.values[i], 40000, 'r^')
elif df.ema130.values[i - 1] > df.ema130.values[i] and df.slow_d.values[
i - 1] <= 80 and df.slow_d.values[i] > 80:
plt.plot(df.number.values[i], 40000, 'bv')
plt.legend(loc='best')
p2 = plt.subplot(3, 1, 2)
plt.grid(True)
p2.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
plt.bar(df.number, df['macdhist'], color='m', label='MACD-Hist')
mondays = WeekdayLocator(MONDAY) # major ticks on the mondays
alldays = DayLocator() # minor ticks on the days
weekFormatter = DateFormatter('%b %d') # e.g., Jan 12
dayFormatter = DateFormatter('%d') # e.g., 12
ax.xaxis.set_major_locator(mondays)
#ax.xaxis.set_minor_locator(alldays)
ax.xaxis.set_major_formatter(weekFormatter)
#ax.xaxis.set_minor_formatter(dayFormatter)
plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')
# grid
plt.rc('axes', grid=True)
# the plots
candlestick_ohlc(ax, zip(mdates.date2num(amzn_piece_d.index.to_pydatetime()),
amzn_piece_d['Open'], amzn_piece_d['High'],amzn_piece_d['Low'], amzn_piece_d['Close']),
colorup='g', colordown='r', width=0.6)
ax.plot(amzn_bm_piece_bm['Close'].index, amzn_bm_piece_bm['Close'], label='AMZN monthly')
ax.plot(amzn_bm_piece_bm['Close'].index, amzn_bm_piece_bm['Close'], label='AMZN marker', marker='o', markersize=16, color='C0')
ax.plot(amzn_piece_d['SMA25'].index, amzn_piece_d['SMA25'], label='AMZN sma25')
# other configurations
ax.set_title('AMZN')
#ax.set_xlabel('Date')
ax.set_ylabel('Price')
ax.legend()
plt.savefig('plot.with.candle.daily.monthly.points.monthly.line.sma.25.matplotlib.style.little.range.png')
# the previous plot with matplotlib style without gaps
import numpy as np
df_train = pd.read_csv('./data/training.csv', header=None)
df_train.columns = ['Open', 'High', 'Low', 'Close']
# open-high-low-close
############################################################################
fig, ax = plt.subplots(2, 1, sharex=True)
# CANDLESTICK
ohlc = df_train
ohlc['Date'] = range(len(ohlc.index))
ohlc = ohlc.loc[:, ['Date', 'Open', 'High', 'Low', 'Close']]
ohlc = ohlc[-180:]
candlestick_ohlc(ax[0], ohlc.values, width=0.8,
colorup='green', colordown='red', alpha=0.8)
ax[0].set_xticklabels([])
# KD
def KD(data):
data_df = data.copy()
data_df['min'] = data_df['Low'].rolling(9).min()
data_df['max'] = data_df['High'].rolling(9).max()
data_df['RSV'] = (data_df['Close'] - data_df['min']) / \
(data_df['max'] - data_df['min'])
data_df = data_df.dropna()
# 計算K
# K的初始值定為50
K_list = [50]
def candlestick(candles):
candlestick_ohlc(plt.axes(), candles)
df_ohlc = df['Adj Close'].resample('240H').ohlc()
df_volume = df['Volume'].resample('240H').sum()
#print(df_ohlc.head())
ax1 = plt.subplot(111) # plot 1
ax1.xaxis_date()
ax1.plot(df.index, df['Adj Close'], label = "close")
ax1.plot(df.index, df['10RollMean'], label = "10d rolling mean")
ax1.plot(df.index, df['100RollMean'], label = "100d rolling mean")
df_ohlc.reset_index(inplace=True) # reset index so all columns consist of data
df_ohlc['Date'] = df_ohlc['Date'].map(mdates.date2num)
candlestick_ohlc(ax1, df_ohlc.values, width = 5, colorup = 'g')
# ---- #
# CURRENTLY DEVELOPING. Need to fix date history
# call FTSE function to plot over company trends.
# see 8.1.7 for timestamp definition - https://docs.python.org/2/library/datetime.html#strftime-and-strptime-behavior
print(FTSE100_index_price()[0])
#df_FTSE_date = df_FTSE_date.map(mdates.date2num)
#ax1.plot(FTSE[0], FTSE[1], label = "FTSE index")
ax1.set_facecolor('w')
ax1.legend(fancybox = True, framealpha = 1, facecolor = 'white')
ax1.grid(color = 'xkcd:grey')
def render(self, df, net_worth, trades):
Date = df["Date"]
Open = df["Open"]
High = df["High"]
Low = df["Low"]
Close = df["Close"]
Volume = df["Volume"]
# append volume and net_worth to deque list
self.Volume.append(Volume)
self.net_worth.append(net_worth)
# before appending to deque list, need to convert Date to special format
Date = mpl_dates.date2num([pd.to_datetime(Date)])[0]
self.render_data.append([Date, Open, High, Low, Close])
# Clear the frame rendered last step
self.ax1.clear()
candlestick_ohlc(self.ax1,
self.render_data,
width=0.8 / 24,
colorup='green',
colordown='red',
alpha=0.8)
# Put all dates to one list and fill ax2 sublot with volume
Date_Render_range = [i[0] for i in self.render_data]
self.ax2.clear()
self.ax2.fill_between(Date_Render_range, self.Volume, 0)
if self.Show_indicators:
self.Plot_indicators(df, Date_Render_range)
# draw our net_worth graph on ax3 (shared with ax1) subplot
self.ax3.clear()
self.ax3.plot(Date_Render_range, self.net_worth, color="blue")
# beautify the x-labels (Our Date format)
self.ax1.xaxis.set_major_formatter(self.date_format)
self.fig.autofmt_xdate()
minimum = np.min(np.array(self.render_data)[:, 1:])
maximum = np.max(np.array(self.render_data)[:, 1:])
RANGE = maximum - minimum
# sort sell and buy orders, put arrows in appropiate order positions
for trade in trades:
trade_date = mpl_dates.date2num([pd.to_datetime(trade['Date'])])[0]
if trade_date in Date_Render_range:
if trade['type'] == 'buy':
high_low = trade['Low'] - RANGE * 0.02
ycoords = trade['Low'] - RANGE * 0.08
self.ax1.scatter(trade_date,
high_low,
c='green',
label='green',
s=120,
edgecolors='none',
marker="^")
else:
high_low = trade['High'] + RANGE * 0.02
ycoords = trade['High'] + RANGE * 0.06
self.ax1.scatter(trade_date,
high_low,
c='red',
label='red',
s=120,
edgecolors='none',
marker="v")
if self.Show_reward:
try:
self.ax1.annotate('{0:.2f}'.format(trade['Reward']),
(trade_date - 0.02, high_low),
xytext=(trade_date - 0.02, ycoords),
bbox=dict(boxstyle='round',
fc='w',
ec='k',
lw=1),
fontsize="small")
except:
pass
# we need to set layers every step, because we are clearing subplots every step
self.ax2.set_xlabel('Date')
self.ax1.set_ylabel('Price')
self.ax3.set_ylabel('Balance')
# I use tight_layout to replace plt.subplots_adjust
self.fig.tight_layout()
"""Display image with matplotlib - interrupting other tasks"""
# Show the graph without blocking the rest of the program
#plt.show(block=False)
# Necessary to view frames before they are unrendered
#plt.pause(0.001)
"""Display image with OpenCV - no interruption"""
# redraw the canvas
self.fig.canvas.draw()
# convert canvas to image
img = np.fromstring(self.fig.canvas.tostring_rgb(),
dtype=np.uint8,
sep='')
img = img.reshape(self.fig.canvas.get_width_height()[::-1] + (3, ))
# img is rgb, convert to opencv's default bgr
image = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
# display image with OpenCV or any operation you like
cv2.imshow("Bitcoin trading bot", image)
if cv2.waitKey(25) & 0xFF == ord("q"):
cv2.destroyAllWindows()
return
else:
return img
def draw_stock_data(stock,
draw_candle_stick=True,
draw_roi=True,
draw_share_holder=True,
period=Constants.MONTH,
save_fig=False):
# read and reformat data
stock_data_dict = pandas.read_csv(
Financial.get_stock_data_file_name(stock),
parse_dates=True,
index_col=0)
stock_data_dict.reset_index(inplace=True)
stock_data_dict['date'] = mdates.date2num(stock_data_dict['date'])
# setup moving average models
x = stock_data_dict['date']
roi = stock_data_dict['roi']
#
# EMA_1_span = 7
# EMA_1 = stock_data['Close'].ewm(span=EMA_1_span, min_periods=EMA_1_span).mean()
#
# EMA_2_span = 30
# EMA_2 = stock_data['Close'].ewm(span=EMA_2_span, min_periods=EMA_2_span).mean()
#
# SMA_2_span = EMA_2_span
# SMA_2 = stock_data['Close'].rolling(window=SMA_2_span, center=False).mean()
#
# MACD = EMA_1 - EMA_2
financial_data_file_name = Financial.get_financial_data_file_name(stock)
if not os.path.exists(financial_data_file_name):
print(financial_data_file_name + " not exist, return")
return
financial_data_dict = pandas.read_csv(
Financial.get_financial_data_file_name(stock),
parse_dates=True,
index_col=0)
financial_data_dict.reset_index(inplace=True)
financial_data_dict['date'] = mdates.date2num(financial_data_dict['date'])
x1 = financial_data_dict['date']
roe = financial_data_dict['roe']
book_value_per_share = financial_data_dict['book_value_per_share']
net_profit_per_share = financial_data_dict['net_profit_per_share'] * 10
cash_flow_per_share = financial_data_dict['cash_flow_per_share']
total_current_assets = financial_data_dict['total_current_assets']
total_assets = financial_data_dict['total_assets']
total_long_term_liabilities = financial_data_dict[
'total_long_term_liabilities']
main_business_income = financial_data_dict['main_business_income']
financial_expenses = financial_data_dict['financial_expenses']
net_profit = financial_data_dict['net_profit']
# roe = financial_data_dict['roe']
share_bonus_file_name = Financial.get_share_bonus_file_name(stock)
if not os.path.exists(share_bonus_file_name):
print(share_bonus_file_name + " not exist, return")
return
share_bonus_dict = pandas.read_csv(share_bonus_file_name,
parse_dates=True,
index_col=0)
share_bonus_dict.reset_index(inplace=True)
share_bonus_dict['date'] = mdates.date2num(share_bonus_dict['date'])
x2 = share_bonus_dict['date']
dividend = share_bonus_dict['dividend']
if draw_share_holder:
share_holder_file_name = Financial.get_share_holder_file_name(stock)
if not os.path.exists(share_holder_file_name):
print(share_holder_file_name + " not exist, return")
return
share_holder_dict = pandas.read_csv(share_holder_file_name,
parse_dates=True,
index_col=0)
share_holder_dict.reset_index(inplace=True)
share_holder_dict['date'] = mdates.date2num(share_holder_dict['date'])
x3 = share_holder_dict['date']
holder_number = share_holder_dict['number']
share_ratio = share_holder_dict['ratio']
# plot
fig, (ax1, ax3) = plt.subplots(2, sharex=True, figsize=(10, 12))
# plot candlestick, SAM, EMA in subplot_1
if draw_candle_stick:
candlestick_ohlc(ax1,
stock_data_dict.values,
width=0.5,
colorup='r',
colordown='g')
# p1 = ax.plot(x, EMA_1, label='EMA(' + str(EMA_1_span) + ')')
# p2 = ax.plot(x, EMA_2, label='EMA(' + str(EMA_2_span) + ')')
# p3 = ax.plot(x, SMA_2, label='SMA(' + str(SMA_2_span) + ')')
ax1.step(x1, roe, label='Roe')
ax1.step(x1, cash_flow_per_share, label='CashFlowPerShare')
ax1.step(x1, net_profit_per_share, label='NetProfitPerShare')
ax1.step(x1, book_value_per_share, label='BookValuePerShare')
ax1.step(x2, dividend, label='Dividend')
if draw_roi:
ax1.step(x, roi, label='ROI')
if draw_share_holder:
ax1.step(x3, holder_number, label='HolderNumber')
ax1.step(x3, share_ratio, label='ShareRatio')
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
ax1.xaxis.set_major_locator(mdates.MonthLocator([1, 4, 7, 10]))
ax1.xaxis.set_major_formatter(mdates.DateFormatter("%b '%y"))
ax1.set_ylabel('Price', fontsize=16)
ax1.legend()
# plot volume in subplot_2
# ax2.bar(x, stock_data_dict['volume'])
# ax2.set_ylabel('volume', fontsize=16)
# plot MACD in subplot_3
# ax3.plot(x, MACD, label='MACD (' + 'EMA(' + str(EMA_1_span) + '), ' + 'EMA(' + str(EMA_2_span) + '))')
# ax3.plot(x1, total_current_assets, label='TotalCurrentAssets')
# ax3.plot(x1, total_assets, label='TotalAssets')
ax3.plot(x1, total_long_term_liabilities, label='TotalLongTermLiabilities')
ax3.plot(x1, main_business_income, label='MainBusinessIncome')
ax3.plot(x1, financial_expenses, label='FinancialExpenses')
ax3.plot(x1, net_profit, label='NetProfit')
# ax3.plot(x1, roe, label='ROE')
ax3.axhline(0, color='gray', linestyle='--')
ax3.set_xlabel('date')
ax3.set_ylabel('MACD', fontsize=16)
ax3.legend()
# # Pandas 无法显示中文问题 解决方案##
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
plt.title(stock.to_string())
if save_fig:
fig.savefig(Constants.DATA_FIGURE_PATH + stock.get_name())
plt.close(fig)
else:
plt.show()
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--driver', default=None, choices=['inky', 'inkyphat', 'waveshare_epd'])
parser.add_argument("--pair", default='XETHZUSD', help="currency pair")
parser.add_argument('--flip', dest='flip', action='store_true', help="rotate the display")
parser.set_defaults(flip=False)
parser.add_argument("--output", help="save plot as png")
args = parser.parse_args()
yesterday = datetime.now() - timedelta(hours=12)
quotes = quotes_historical_kraken_ohlc(args.pair, yesterday, interval=15)
if not quotes:
raise ValueError('Empty OHLC data')
candlestick_ohlc(ax, quotes, width=1)
ax.xaxis_date()
ax.autoscale_view()
ymin, ymax = ax.get_ylim()
last_low = quotes[-1][3]
last_high = quotes[-1][2]
last_close = quotes[-1][4]
ttf = pkg_resources.open_binary(fonts, '04B_03__.TTF')
font = ImageFont.truetype(ttf, 8)
RED = (255,0,0)
BLACK = (0,0,0)
display = None
driver = args.driver
if driver == 'inky':
from inky import InkyPHAT
RED = InkyPHAT.RED
BLACK = InkyPHAT.BLACK
display = InkyPHAT('black')
elif driver == 'inkyphat':
from inkyphat import RED as inky_RED, BLACK as inky_BLACK, set_image as _set_image, show as _show
RED = inky_RED
BLACK = inky_BLACK
class InkyPHAT():
def __init__(*args):
pass
set_image = staticmethod(_set_image)
show = staticmethod(_show)
display = InkyPHAT()
elif driver == 'waveshare_epd':
from waveshare_epd import epd2in13_V2
class EPD():
def __init__(self, *args):
self.epd = epd2in13_V2.EPD()
self.epd.init(self.epd.FULL_UPDATE)
self.buf = []
def set_image(self, image):
self.buf = self.epd.getbuffer(image)
def show(self):
self.epd.display(self.buf)
display = EPD()
with io.BytesIO() as f:
fig.savefig(f, dpi=dpi, cmap="bwr", interpolation="none", origin="lower", pad_inches=0)
f.seek(0)
i = Image.open(f)
d = ImageDraw.Draw(i)
ypos = 0 if ymax - last_high > last_low - ymin else h - 6
d.text((148, ypos), '{:.2f}'.format(last_close), BLACK, font)
d.text((176, ypos), args.pair, RED, font)
if args.flip:
i = i.transpose(Image.ROTATE_180)
if display:
display.set_image(i.convert("P"))
display.show()
if args.output:
i.save(args.output)
def plot_ticker_analysis(market, ticker):
ticker_file = 'stock_data/'+ticker+'.csv'
df = pd.read_csv(ticker_file, parse_dates=True, index_col=0)
df_rsi = df.copy()
#df['100ma'] = df['Adj Close'].rolling(window=100, min_periods=0).mean()
df_ohlc = df['Adj Close'].resample('2D').ohlc()
df_volume = df['Volume'] #.resample('2D').sum()
#df_vol_mean = df['Volume'].mean()
#print(df_vol_mean)
df_ohlc.reset_index(inplace=True)
df_ohlc['Date'] = df_ohlc['Date'].map(dates.date2num)
df.drop(['Open','High','Low','Close','Volume'],1,inplace=True)
## Bollinger Computation *********************
mma_30 = df.rolling(window=20).mean()
std_30 = df.rolling(window=20).std()
upper = mma_30 + (std_30 * 2)
lower = mma_30 - (std_30 * 2)
## *******************************************
## RSI Computation ***************************
close = df['Adj Close']
delta = close.diff()
delta = delta[1:]
up, down = delta.copy(), delta.copy()
up [up < 0] = 0
down [down > 0] = 0
#roll_up1 = pd.DataFrame.ewm( up, 14)
#roll_dn1 = pd.DataFrame.ewm(down, 14)
#RS1 = roll_up1 / roll_dn1
#RSI1 = 100.0 - (100.0 / (1.0 + RS1))
roll_up2 = up.rolling(window=14).mean().abs()
roll_dn2 = down.rolling(window=14).mean().abs()
RS2 = roll_up2 / roll_dn2
RSI2 = 100.0 - (100.0 / (1.0 + RS2))
## *******************************************
## MACD Computation **************************
exp1 = df.ewm(span=12, adjust=False).mean()
exp2 = df.ewm(span=26, adjust=False).mean()
macd = exp1-exp2
exp3 = macd.ewm(span=9, adjust=False).mean()
## *******************************************
## Graph organization ***********************
#ax1 = plt.subplot2grid((10,1),(0,0), rowspan=4, colspan=1 )
#ax2 = plt.subplot2grid((10,1),(4,0), rowspan=3, colspan=1 )
#ax3 = plt.subplot2grid((10,1),(7,0), rowspan=1, colspan=1 )
#ax4 = plt.subplot2grid((10,1),(8,0), rowspan=2, colspan=1 )
fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, sharex=True)
#ax3.xaxis_date()
ax3.xaxis.set_major_formatter(day_fmt)
ax3.xaxis.set_minor_formatter(day_fmt)
## Plot ADJ CLOSE
ax1.plot(df['Adj Close'], label = 'Adj Close', color = 'blue')
## Plot Bollinger upper and lower
ax1.plot(upper, label = 'upper', color = 'black', lw=0.3)
ax1.plot(lower, label = 'lower', color = 'black', lw=0.3)
ax1.plot(mma_30, label = 'mma', color = 'black' , lw=0.3)
## Plot candlestick
candlestick_ohlc(ax1, df_ohlc.values, width=2, colorup='g')
#ax3.plot(df_volume)
ax3.fill_between(df_volume.index.map(dates.date2num), df_volume.values, 0)
#ax3.plot(df_vol_mean, label='Volume mean',color = 'black')
#ax4.plot(RSI1, label = 'RSI EWMA', color = 'green')
ax4.plot(RSI2, label = 'RSI SMA ', color = 'red')
ax2.plot(macd, label='MACD', color = 'black' )
ax2.plot(exp3, label='Signal', color = 'red' )
#plt.setp(ax1.get_xticklabels(), visible=False)
#plt.setp(ax2.get_xticklabels(), visible=False)
multi = MultiCursor(fig.canvas, (ax1, ax2, ax3, ax4), color = 'r', lw=1)
cursor1 = Cursor(ax1, useblit=True, color='red', linewidth=2 )
cursor2 = Cursor(ax2, useblit=True, color='red', linewidth=2 )
cursor3 = Cursor(ax3, useblit=True, color='red', linewidth=2 )
cursor4 = Cursor(ax4, useblit=True, color='red', linewidth=2 )
plt.show()
dayFormatter = DateFormatter('%d') # e.g., 12
infile = os.path.join('data', 'yahoofinance-INTC-19950101-20040412.csv')
quotes = pd.read_csv(infile,
index_col=0,
parse_dates=True,
infer_datetime_format=True)
# select desired range of dates
quotes = quotes[(quotes.index >= date1) & (quotes.index <= date2)]
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.2)
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_minor_locator(alldays)
ax.xaxis.set_major_formatter(weekFormatter)
# ax.xaxis.set_minor_formatter(dayFormatter)
# plot_day_summary(ax, quotes, ticksize=3)
candlestick_ohlc(ax,
zip(mdates.date2num(quotes.index.to_pydatetime()),
quotes['Open'], quotes['High'], quotes['Low'],
quotes['Close']),
width=0.6)
ax.xaxis_date()
ax.autoscale_view()
plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')
plt.show()
def chart1():
# １データからチャートをplotする
def get_OHLC(before, after):
# ここからデータ取得
url = 'https://api.cryptowat.ch/markets/bitflyer/btcjpy/ohlc'
query = {
'periods': 60,
'before': before,
'after': after,
}
res = requests.get(url, params=query).json()['result']['60']
return res
# UNIX時間に変換するためのもの
# ４取得した現在時刻をUNIX時間に変換
def unixTime(y, m, d, h):
return int(time.mktime(datetime(y, m, d, h).timetuple()))
# ３現在時刻を取得
now = datetime.now()
y, m, d, h = now.year, now.month, now.day, now.hour
# ５直近１時間の間のデータを取得する
data = get_OHLC(unixTime(y, m, d, h), unixTime(y, m, d, h - 1))
# 空リスト
Time, Open, High, Low, Close = [], [], [], [], []
# 6取得したデータをfor文で仕分ける
for i in data:
Time.append(datetime.fromtimestamp(i[0]))
Open.append(i[1])
High.append(i[2])
Low.append(i[3])
Close.append(i[4])
# 7取得したデータを表示
pd.DataFrame({
'date': Time,
'open': Open,
'high': High,
'low': Low,
'close': Close
})
# チャートに描画
Date = [
datetime(y, m, d, h - 1) + dt.timedelta(minutes=mi) for mi in range(60)
]
ohlc = zip(mdates.date2num(Date), Open, High, Low, Close)
ax = plt.subplot()
# 時間を15分単位で区切る
ax.xaxis.set_major_locator(mdates.MinuteLocator([0, 15, 30, 45]))
ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))
# mpl_financeのメソッドを仕様。（描画幅やチャートの色などを指定。デフォルトは赤黒）
# 上昇の時は緑、下落は赤
candlestick_ohlc(ax,
ohlc,
width=(1 / 24 / 60) * 0.7,
colorup='g',
colordown='r')
# チャートタイトルのテキスト
plt.title('BTC / JPY by Cryptowatch API')
plt.savefig('app/static/price.png')
def pandas_candlestick_ohlc(dat, days='day', adj=False, otherseries=None):
mondays = WeekdayLocator(MONDAY)
all_days = DayLocator()
fields = ['Open', 'High', 'Low', 'Close']
transdat = dat.loc[:,
fields] # Selects single column or subset of columns by label
transdat.columns = pd.Index(['Open', 'High', 'Low', 'Close'])
if (type(days) == str):
if days == "day":
plotdat = transdat
days = 1
elif days in ['week', 'month', 'year']:
if days == 'week':
transdat['week'] = pd.to_datetime(transdat.index).map(
lambda x: x.isocalender()[1]
) #Return a 3-tuple, (ISO year, ISO week number, ISO weekday).
elif days == 'month':
transdat['month'] = pd.to_datetime(
transdat.index).map(lambda x: x.month)
transdat['year'] = pd.to_datetime(
transdat.index).map(lambda x: x.isocalendar()[0])
grouped = transdat.groupby(list({'year', days}))
plotdat = pd.DataFrame({
"Open": [],
"High": [],
"Low": [],
"Close": []
})
for name, group in grouped:
plotdat = plotdat.append(
pd.DataFrame(
{
'Open': group.iloc[0, 0],
'High': max(group.High),
'Low': min(group.Low),
'Close': group.iloc[-1, 3]
},
index=[group.index[0]]))
if days == 'week':
days = 5
elif days == 'month':
days = 30
elif days == 'year':
days = 365
elif (type(days) == int and days >= 1):
transdat['stick'] = [
pd.np.floor(i / days) for i in range(len(transdat.index))
]
grouped = transdat.groupby('stick')
plotdat = pd.DataFrame({
"Open": [],
"High": [],
"Low": [],
"Close": []
})
for name, group in grouped:
plotdat = plotdat.append(
pd.DataFrame(
{
'Open': group.iloc[0, 0],
'High': max(group.High),
'Low': min(group.Low),
'Close': group.iloc[-1, 3]
},
index=[group.index[0]]))
else:
raise ValueError(
'Valid inputs - "days" include the strings or a positive integer')
# Set plot parameters, including the axis object ax used for plotting
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.2)
if plotdat.index[-1] - plotdat.index[0] < pd.Timedelta('730 days'):
weekFormatter = DateFormatter('%b %d') # for example Jan 12
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_minor_locator(all_days)
else:
weekFormatter = DateFormatter('%b %d, %Y')
ax.xaxis.set_major_formatter(weekFormatter)
ax.grid(True)
# Create the candelstick chart
candlestick_ohlc(ax,
list(
zip(list(date2num(plotdat.index.tolist())),
plotdat["Open"].tolist(),
plotdat["High"].tolist(), plotdat["Low"].tolist(),
plotdat["Close"].tolist())),
colorup="black",
colordown="red",
width=days * .4)
# Plot other series (such as moving averages) as lines
if otherseries != None:
if type(otherseries) != list:
otherseries = [otherseries]
dat.loc[:, otherseries].plot(ax=ax, lw=1.3, grid=True)
ax.xaxis_date()
ax.autoscale_view()
plt.setp(plt.gca().get_xticklabels(),
rotation=45,
horizontalalignment='right')
plt.show()
def draw(listStockPrices,
begin=None,
end=None,
field="Close",
date_format=None,
existing=None,
axis=1,
ax=None,
label_prefix=None,
color=None,
**args):
"""
Draws a graph showing one or several time series.
The example was taken
`date_demo.py <https://matplotlib.org/examples/api/date_demo.html>`_.
@param listStockPrices list of @see cl StockPrices (or one @see cl StockPrices if it is the only one)
@param begin first date (datetime) or None to take the first one
@param end last included date (datetime) or None to take the last one
@param field Open, High, Low, Close, Adj Close, Volume
@param date_format ``%Y`` or ``%Y-%m`` or ``%Y-%m-%d`` or None if you prefer the function to choose
@param args other arguments to send to ``plt.subplots``
@param axis 1 or 2, it only works if existing is not None.
If axis is 2, the function draws the curves on the second axis.
@param label_prefix to prefix curve label
@param color curve color
@param args other parameters to give method ``plt.subplots``
@param ax use existing `axes <http://matplotlib.org/api/axes_api.html>`_
@return `axes <http://matplotlib.org/api/axes_api.html>`_
The parameter ``figsize`` of the method
`subplots <https://matplotlib.org/api/pyplot_api.html?highlight=subplots#matplotlib.pyplot.subplots>`_
can change the graph size (see the example below).
.. exref::
:title: graph of a financial series
::
from pyensae.finance import StockPrices
stocks = [ StockPrices("NASDAQ:MSFT", folder = cache),
StockPrices("NASDAQ:GOOGL", folder = cache),
StockPrices("NASDAQ:AAPL", folder = cache)]
fig, ax, plt = StockPrices.draw(stocks)
fig.savefig("image.png")
fig, ax, plt = StockPrices.draw(stocks, begin="2010-01-01", figsize=(16,8))
plt.show()
You can also chain the graphs and add a series on a second graph:
::
from pyensae.finance import StockPrices
stock = StockPrices("NASDAQ:MSFT", folder = cache)
stock2 = StockPrices "NASDAQ:GOOGL", folder = cache)
fig, ax, plt = stock.plot(figsize=(16,8))
fig, ax, plt = stock2.plot(existing=(fig,ax), axis=2)
plt.show()
.. versionchanged:: 1.1
Parameter *existing* was removed and parameter *ax* was added.
If the date overlaps, the method
`autofmt_xdate <https://matplotlib.org/api/figure_api.html#matplotlib.figure.Figure.autofmt_xdate>`_
should be called.
"""
if isinstance(listStockPrices, StockPrices):
listStockPrices = [listStockPrices]
data = StockPrices.available_dates(listStockPrices,
missing=False,
field=field)
if begin is None:
if end is not None:
data = data[data.index <= end]
else:
if end is not None:
data = data[(data.index >= begin) & (data.index <= end)]
else:
data = data[data.index >= begin]
dates = [datetime.datetime.strptime(_, '%Y-%m-%d') for _ in data.index]
begin = dates[0]
end = dates[-1]
def price(x):
"local formatting"
return '%1.2f' % x
import matplotlib.pyplot as plt # pylint: disable=C0415
import matplotlib.dates as mdates # pylint: disable=C0415
if ax is not None:
ex_h, ex_l = ax.get_legend_handles_labels()
ex_l = tuple(ex_l)
ex_h = tuple(ex_h)
if axis == 2:
ax = ax.twinx()
fig = None
else:
if 'label' in args:
args_ = {k: v for k, v in args.items() if k not in ('label', )}
else:
args_ = args
fig, ax = plt.subplots(**args_)
ex_h, ex_l = tuple(), tuple()
curve = []
if field == "ohlc":
from mplfinance.original_flavor import candlestick_ohlc # pylint: disable=E0401
ohlc = list(
list(data.iloc[i, :4]) for i in range(0, data.shape[0]))
ohlc = [[mdates.date2num(t)] + v for t, v in zip(dates, ohlc)]
candlestick_ohlc(ax, ohlc, colorup="g")
else:
if label_prefix is None:
label_prefix = ""
add_args = {}
if color:
add_args['c'] = color
for stock in data.columns:
if axis == 2:
curve.append(
ax.plot(dates,
data[stock],
"r",
linestyle='solid',
label=label_prefix + str(stock),
**add_args))
else:
curve.append(
ax.plot(dates,
data[stock],
linestyle='solid',
c=color,
label=label_prefix + str(stock),
**add_args))
if existing is None:
ax.format_xdata = mdates.DateFormatter('%Y-%m-%d')
if len(dates) < 30:
days = mdates.DayLocator()
ax.xaxis.set_major_locator(days)
ax.xaxis.set_minor_locator(days)
if date_format is not None:
fmt = mdates.DateFormatter(date_format)
ax.xaxis.set_major_formatter(fmt)
else:
ax.xaxis.set_major_formatter(
mdates.DateFormatter("%Y-%m-%d"))
elif len(dates) < 500:
months = mdates.MonthLocator()
days = mdates.DayLocator()
ax.xaxis.set_major_locator(months)
ax.xaxis.set_minor_locator(days)
ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y-%m"))
if date_format is not None:
fmt = mdates.DateFormatter(date_format)
ax.xaxis.set_major_formatter(fmt)
else:
ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y-%m"))
else:
years = mdates.YearLocator()
months = mdates.MonthLocator()
ax.xaxis.set_major_locator(years)
ax.xaxis.set_minor_locator(months)
if date_format is not None:
fmt = mdates.DateFormatter(date_format)
ax.xaxis.set_major_formatter(fmt)
else:
ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y"))
ax.set_xlim(begin, end)
ax.format_ydata = price
if fig is not None:
fig.autofmt_xdate()
if axis == 2:
if isinstance(curve, list):
curve = [_[0] for _ in curve]
ax.legend(ex_h + tuple(curve), ex_l + tuple(data.columns))
else:
ax.grid(True)
ax.legend(ex_l + tuple(data.columns))
return ax
def plot_stock_curve(self):
self.fig = plt.figure()
ax2 = self.fig.add_subplot(111, ylabel='Stock value: %')
ohlc = self.stock_data[['open', 'high', 'low', 'close']]
ohlc = ohlc.reset_index().to_numpy()
date = date2num(ohlc[:, 0])
ohlc[:, 0] = date
le_y = self.summary_recording[self.summary_recording['direction'] ==
'LONG']['close_price']
le_x = self.summary_recording[self.summary_recording['direction'] ==
'LONG'].index.to_list()
le_x_value = date2num(le_x)
le_y_value = le_y.values
lexit_y = self.summary_recording[self.summary_recording['direction'] ==
'EXIT']['close_price']
lexit_x = self.summary_recording[self.summary_recording['direction'] ==
'EXIT'].index.to_list()
lexit_x_value = date2num(lexit_x)
lexit_y_value = lexit_y.to_numpy()
candlestick_ohlc(ax2,
ohlc,
width=0.4,
colorup='red',
colordown='green')
for label in ax2.xaxis.get_ticklabels():
label.set_rotation(45)
ax2.plot(le_x_value,
le_y_value,
'^',
color='lime',
markersize=8,
label='long enter')
for index in range(len(le_x_value)):
plt.text(le_x_value[index],
le_y_value[index] * 1.05,
"Buy",
ha='center',
va='bottom',
fontsize=8)
ax2.plot(lexit_x_value,
lexit_y_value,
'v',
color='red',
markersize=8,
label='Exit')
plt.text(lexit_x_value[0],
lexit_y_value[0] * 1.05,
"Sell",
ha='center',
va='bottom',
fontsize=8)
ax2.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
ax2.xaxis.set_major_locator(mticker.MaxNLocator(10))
plt.grid(True)
plt.show()
from matplotlib import pyplot as plt
from matplotlib import style
import pandas as pd
import matplotlib.dates as mdates
from mplfinance.original_flavor import candlestick_ohlc
import csv
style.use('ggplot')
Analysis = "2313.TW.csv"
data = pd.read_csv(Analysis, parse_dates=True, index_col='Date')
price = data['Close']
price.head()
moving_avg = price.rolling(20).mean()
fig = plt.figure()
ax1 = fig.add_subplot(111)
data = data.reset_index()
data['Date'] = data['Date'].apply(lambda d: mdates.date2num(d.to_pydatetime()))
candlestick = [tuple(x) for x in data[['Date','Open','High','Low','Close']].values]
candlestick_ohlc(ax1, candlestick[-120:], width=0.7,colorup='r',colordown='green',alpha=0.8)
plt.plot(moving_avg[-120:],linewidth=1,alpha=0.7,label="MA20")
plt.legend()
plt.show()
lastD = prices['D'][i]
lastLow = prices['Low'][i]
lastClose = prices['Adj Close'][i]
lastLowBB = prices['LowerBand'][i]
#Plot moving averages and BBands
for x in smasUsed:
sma = x
prices['SMA_' + str(sma)].plot(label='close')
prices['UpperBand'].plot(label='close', color='lightgray')
prices['LowerBand'].plot(label='close', color='lightgray')
#plot candlesticks
candlestick_ohlc(ax1,
ohlc,
width=.5,
colorup='k',
colordown='r',
alpha=0.75)
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
ax1.xaxis.set_major_locator(mticker.MaxNLocator(8))
plt.tick_params(axis='x', rotation=45)
#Pivot Points
pivots = []
dates = []
counter = 0
lastPivot = 0
Range = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
dateRange = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
from matplotlib import style
from mplfinance.original_flavor import candlestick_ohlc
from matplotlib.dates import date2num
import pandas as pd
import pandas_datareader.data as web
style.use('ggplot') #set a plot style
df = pd.read_csv('nflx.csv', parse_dates=True,
index_col=0) #read csv file as pandas dataframe
df_ohlc = df['Adj Close'].resample(
'10D').ohlc() #create a new dataframe with resampling of 10 days
df_volume = df['Volume'].resample(
'10D').sum() ##create a new dataframe with resampling of 10 days
df_ohlc.reset_index(inplace=True) #reset date as index
df_ohlc['Date'] = df_ohlc['Date'].map(
date2num) #convert datetime object to matplotlib dates
print(df_ohlc.head()) #print the first five rows of the dataframe
ax1 = plt.subplot2grid((6, 1), (0, 0), rowspan=5,
colspan=1) #add first subplot
ax2 = plt.subplot2grid((6, 1), (5, 0), rowspan=1, colspan=1,
sharex=ax1) #add second subplot
ax1.xaxis_date()
candlestick_ohlc(ax1, df_ohlc.values, width=2,
colorup='g') #plot a candlestick chart
ax2.fill_between(df_volume.index.map(date2num), df_volume.values, 0)
plt.show() #display the figure
def candlestickIt(data, inc, name, useCsv):
tickName = name
if useCsv == 1:
dataFrame = pd.read_csv("CsvFolder/" + tickName + ".csv",
parse_dates=['Date'])
df = dataFrame
else:
df = data
increment = inc
#matplot figure allows for multiple instances/windows to be made
plt.figure(increment)
plt.suptitle(tickName)
df = data
print(df.head())
#creates and resamples the dataframe to show the weekly change
#do not resample to less than 4D - bad things happen(down bars disappear)
df_ohlc = df['Adj Close'].resample('4D').ohlc()
df_volume = df['Adj Close'].resample('4D').sum()
df_ohlc.reset_index(inplace=True)
df_ohlc['Date'] = df_ohlc['Date'].map(mdates.date2num)
#create subplots to represent data
ax1 = plt.subplot2grid((6, 1), (0, 0), rowspan=5, colspan=1)
#need ax2 to match ax1 so that when zooming on one graph the other matches
ax2 = plt.subplot2grid((6, 1), (5, 0), rowspan=5, colspan=1, sharex=ax1)
ax2.set_title('Volume')
#date format for x axis
ax1.xaxis_date()
#formats the y axis ticker to represent dollars
formatter = ticker.FormatStrFormatter('$%1.2f')
ax1.yaxis.set_major_formatter(formatter)
for tick in ax1.yaxis.get_major_ticks():
tick.label1.set_visible(False)
tick.label2.set_visible(True)
#tick.label2.set_color('green')
#sets ticker to the left side
ax1.yaxis.tick_left()
ax1.yaxis.set_label_position("left")
#Build candlestick chart
try:
candlestick_ohlc(ax1,
df_ohlc.values,
width=1.8,
colorup='#00eeff',
colordown='#ff0044',
alpha=.4)
except:
print("ERROR build went wrong within candlestick method")
main()
ax2.fill_between(df_volume.index.map(mdates.date2num), df_volume.values, 0)
#add spacing between subplots
plt.subplots_adjust(hspace=0.7)
ax1.grid(b=True, which='major', color='grey', linestyle='-', alpha=0.4)
#maximizes window
#this backend is: TkAgg
manager = plt.get_current_fig_manager()
manager.resize(*manager.window.maxsize())
return
def rsiCheck(_shcode="", hname=''):
global g_rsiTgt
global g_simpleMA9Tgt
now = time.localtime()
tempToday = "%04d%02d%02d" % (now.tm_year, now.tm_mon, now.tm_mday)
if not os.path.exists(tempToday):
os.mkdir(tempToday)
df1475 = orderManager.t1475(_shcode)
if df1475.shape[0] == 0:
print("t1475 responded null")
elif df1475.shape[0] != 0 and float(df1475['당일VP'].values[0]) < 100:
strTemp = "체결강도 100 미만으로 후보 탈락:" + hname
print(strTemp)
# botManager.sendTeleMsg(strTemp)
return 0
df = orderManager.t8412(단축코드=_shcode, 단위="2", 요청건수="36")
if df.shape[0] > 0: # rsi 9 구하기
try:
taadx = ta.ADX(df['고가'], df['저가'], df['종가'], 9)
taadxSig = ta.MA(taadx, timeperiod=9)
taadx = np.asarray(taadx)
taadxSig = np.asarray(taadxSig)
print("ADX ", taadx)
print("ADX Sig", taadxSig)
if float(taadx[-1]) <= float(taadxSig[-1]):
print("ADX condition mismatched")
return 0
adxgrad, intercept, adi_r_square, p_value, std_err = stats.linregress(
list(range(len(taadx[-30:-1]))), taadx[-30:-1])
if adxgrad < 0:
print("ADX downturn")
return 0
except Exception as e:
print("Exception Occur : ", e)
# adi = calcADI(df)
# # mincond = adi['ADI'] < 0
# # adi = adi[mincond]
# tempY = list(map(float, np.asarray(adi)))
# sortTemp = sorted(tempY)
# sortTemp = sortTemp[0:5]
# print("저점 ADI: ",sortTemp)
# newTempY = []
# for i in tempY:
# if i in sortTemp:
# newTempY.append(i)
# print("저점 정렬 ADI: ", newTempY)
# tempX = list(range(len(newTempY)))
# adigrad, intercept, adi_r_square, p_value, std_err = stats.linregress(tempX, newTempY)
# print("adi 저점정보", adigrad, intercept, adi_r_square, p_value, std_err)
rsi9 = ta.RSI(np.asarray(df['종가']), 9)
rsi9 = rsi9[~np.isnan(rsi9)] # remove nan
if rsi9.size == 0:
print("rsi size exception")
return 0
rsiTgt = rsi9[-1]
simpleMA9 = ta.MA(rsi9, timeperiod=9)
simpleMA9Tgt = simpleMA9[-1]
print('----------[RSI SIGNAL JUDGE]----------')
if int(rsiTgt) < int(simpleMA9Tgt): # or int(rsiTgt) >= 75:
print('rsi', rsiTgt, 'signal', simpleMA9Tgt)
return 1
# elif int(rsiTgt) > int(simpleMA9Tgt) and int(rsiTgt) <= 30:
# print('rsi > ', rsiTgt, '>', 'signal', simpleMA9Tgt)
# g_rsiTgt = round(rsiTgt, 2)
# g_simpleMA9Tgt = round(simpleMA9Tgt,2)
# return 2
obv = ta.OBV(np.asarray(df['종가'], dtype=np.double),
np.asarray(df['거래량'], dtype=np.double))
if obv[-1] < 8000:
print("latest obv cut", obv[-1])
return 1
if obv[-1] - obv[-3] <= 0 or obv[-1] - obv[-2] <= 0:
print("obv is downturned")
return 1
obvSignal = ta.MA(obv, timeperiod=9)
#obvSignal = obvSignal[~np.isnan(obvSignal)]
# if obvSignal[-1] < 0:
# print("latest obv signal is under zero")
# return 1
tempX = list(range(len(obv)))
tempY = list(map(float, obv))
obvgrad, intercept, r_square, p_value, std_err = stats.linregress(
tempX, tempY)
print("OBV ", obvgrad, intercept, r_square, p_value, std_err)
if obvgrad <= 0:
print("OBV grad <= 0")
return 1
tempXSignal = list(range(len(obvSignal)))
tempYSignal = list(map(float, obvSignal))
tempXSignal = tempXSignal[-16:-1]
tempYSignal = tempYSignal[-16:-1]
obvSignalgrad, intercept, r_square, p_value, std_err = stats.linregress(
tempXSignal, tempYSignal)
print("OBV Signal ", obvSignalgrad, intercept, r_square, p_value,
std_err)
# if obvSignalgrad <=0:
# print("obvSignalgrad grad <= 0")
# return 1
# plt.show()
# print(obv)
# print(obvSignal)
#obv always above 0, but signal can be lower than 0
if obv[-1] < obvSignal[-1] or obv[-2] < obvSignal[-2]:
print("Signal Condition Unsuited")
return 1
# if abs(obvSignal[-1]) / (obv[-1] + (obvSignal[-1] < 0 and abs(obvSignal[-1]) or 0)) > 0.9 or abs(obvSignal[-2]) / (obv[-2]+(obvSignal[-1] < 0 and abs(obvSignal[-1]) or 0)) > 0.9:
# print("OBV Gap Flattened")
# return 1
# RSI Gap Condition Check : reg grad +, abs(gap[-1]) < 10
# rsiGap = rsi9 - simpleMA9
# rsiGap = rsiGap[~np.isnan(rsiGap)]
# rsiGap = abs(rsiGap)
# print("rsi gap : ", rsiGap)
# tempX = list(range(len(rsiGap)))
# tempY = list(map(float, rsiGap))
# tempX = tempX[-8:-1]
# tempY = tempY[-8:-1]
# g_rsiTgt = round(rsiTgt, 2)
# g_simpleMA9Tgt = round(simpleMA9Tgt, 2)
# grad, intercept, r_square, p_value, std_err = stats.linregress(tempX, tempY)
# print("regression result: ", grad, intercept, r_square, p_value, std_err)
# print("type rsi... ",type(rsi9[-1]))
if rsi9[-1] - rsi9[-3] <= 0 or rsi9[-1] - rsi9[-2] <= 0:
print("rsi is downturned")
return 0
print("type simpleMA9... ", type(simpleMA9[-1]))
# if grad <= 0 and rsi9[-1] > simpleMA9[-1] and rsi9[-2] > simpleMA9[-2]: # and r_square <= -0.6 and p_value < 0.05 and rsiGap[-2] >= rsiGap[-1]:
if rsi9[-1] > simpleMA9[-1] and rsi9[-2] > simpleMA9[
-2]: # and r_square <= -0.6 and p_value < 0.05 and rsiGap[-2] >= rsiGap[-1]:
# gapFlag = 0
# for i in tempY:
# if i > 11:
# gapFlag += 1
# if gapFlag < 2:
# print("no rsi condition(gap flag is flatten) matched")
# return 0
print("RSI Gap Condition Check OK")
plt.figure(figsize=(30, 30))
plt.subplot(221)
plt.title(str(_shcode))
plt.plot(obv)
plt.plot(obvSignal)
plt.text(0, 10, "og:" + str(format(obvgrad, "8.2%")))
plt.legend(["obv", "obv signal"])
# tempStr = tempToday + "/" + str(_shcode) + " obv" + ".png"
# plt.savefig(tempStr)
# botManager.sendImage(tempStr)
plt.subplot(222)
plt.plot(rsi9)
plt.plot(simpleMA9)
# plt.text(0, 10, "rg:" + str(format(grad, "3.2%")))
plt.legend(["rsi", "rsi signal"])
# plt.subplot(413)
# plt.plot(newTempY)
# plt.text(0, newTempY[0], "g"+str(format(adigrad,"3.2%"))+"r^2:" + str(format(adi_r_square*adi_r_square,"3.2%")))
# plt.legend(["new adi"])
plt.subplot(223)
plt.plot(taadx)
plt.plot(taadxSig)
plt.text(20, taadxSig[-1], "g:" + str(format(adxgrad, "3.2%")))
plt.legend(["adx", "adx sig"])
ax = plt.subplot(224)
dfnew = df[['시가', '고가', '저가', '종가']]
day_list = range(len(df))
dfnew.insert(0, '시각', day_list)
#dfnew = dfnew.set_index('날짜')
dfnew = dfnew.apply(pd.to_numeric)
candlestick_ohlc(ax,
dfnew.values,
width=0.5,
colorup='r',
colordown='b')
plt.grid()
tempStr = tempToday + "/" + str(_shcode) + " " + hname + ".png"
plt.savefig(tempStr)
plt.close()
# botManager.sendImage(tempStr)
# msrate check added
return 3
else:
print("no rsi condition matched")
return 0
print("RSI Calc Err")
return 0
#ax1.plot(df.index, df["vwap"], lw=1, alpha=0.8,
# label="Bitcoin Price (BitMex)")
dfn = df.resample('60min').agg({'open': 'first',
'high': 'max',
'low': 'min',
'close': 'last',
'vwap': 'mean'})
dfn["open_adj"] = np.where(dfn.open < dfn.low, dfn.low,
np.where(dfn.open > dfn.high, dfn.high,
dfn.open))
quotes = zip(mdates.date2num(dfn.index.to_pydatetime()),
dfn.open_adj, dfn.high, dfn.low, dfn.close)
candlestick_ohlc(ax1, quotes, width=0.01, colorup='g')
ema_fast_plot = ax1.plot(
df.index, df["ema-5hr"], lw=1, alpha=0.8,
label="EMA (Exponential Moving Average) - 5 hours")
ema_slow_plot = ax1.plot(
df.index, df["ema-1day"], lw=1, alpha=0.8,
label="EMA 1 day")
buy_signal_plot = ax1.plot(
df.loc[df["position"] == 1.0].index,
df["ema-5hr"][df["position"] == 1.0],
'^', markersize=10, color='#009900', label="Buy signal")
sell_signal_plot = ax1.plot(
df.loc[df["position"] == -1.0].index,
def chart_plot_upload(stock):
yf.pdr_override()
smaUsed = [5, 10, 20]
timed = dt.timedelta(120)
now = dt.datetime.now()
start = now - timed - dt.timedelta(max(smaUsed)) # Within one year
print(start)
# start = dt.datetime(2010,1,1)
# stock = input("Enter the stock symbol: ") #Asks for stock ticker
# stock = 'BRK-B'
prices = pdr.get_data_yahoo(
stock, start, now) #Fetches stock price data, saves as data frame
# Set Plot Grid
fig, (axc, axkd) = plt.subplots(2,
1,
gridspec_kw={'height_ratios': [3, 1]},
sharex=True,
figsize=(15, 10))
axv = axc.twinx()
# Caculated All SMA
for x in smaUsed:
prices['SMA_' + str(x)] = prices['Adj Close'].rolling(window=x).mean()
# Caculate Boolinger Bands
BBperiod = 15
stdev = 2
prices['Boolinger Band SMA' +
str(BBperiod)] = prices.iloc[:, 4].rolling(window=BBperiod).mean()
prices['STDEV'] = prices.iloc[:, 4].rolling(window=BBperiod).std()
prices['LowerBand'] = prices['Boolinger Band SMA' +
str(BBperiod)] - stdev * prices['STDEV']
prices['UpperBand'] = prices['Boolinger Band SMA' +
str(BBperiod)] + stdev * prices['STDEV']
prices['Date'] = mdates.date2num(
prices.index
) #creates a date column stored in number format (for OHCL bars)
# Caculated 10.4.4 KD Value
Period = 10
K = 4
D = 4
prices['RolHigh'] = prices['High'].rolling(window=Period).max()
prices['RolLow'] = prices['Low'].rolling(window=Period).min()
prices["stok"] = ((prices["Adj Close"] - prices["RolLow"]) /
(prices["RolHigh"] - prices["RolLow"])) * 100
prices["K"] = prices["stok"].rolling(window=K).mean() #Finds 10.4 stoch
prices["D"] = prices["K"].rolling(window=D).mean() #Finds 10.4.4 stoch
prices["GD"] = prices["High"] #Create GD column to store green dots
prices = prices[max(smaUsed):]
ohlc = []
for i in prices.index:
append_me = prices['Date'][i], prices['Open'][i], prices['High'][
i], prices["Low"][i], prices["Adj Close"][i], prices["Volume"][i]
ohlc.append(append_me)
print(ohlc)
print(prices)
# Plot
# Moving average
for x in smaUsed:
axc.plot(prices['SMA_' + str(x)], label='SMA_' + str(x))
# Volume
axv.plot(prices['Volume'], color='Navy')
# axv.axes.yaxis.set_ticklabels(range(0, 10000000))
axv.set_ylim(0, 5 * max(prices['Volume']))
axv.set_ylabel("Volume (K)")
axv.fill_between(prices['Date'],
prices['Volume'],
facecolor='Navy',
alpha=0.25)
# axv.yaxis.set_major_formatter(mticker.FormatStrFormatter('%0.0e'))
axv.yaxis.set_major_formatter(
mticker.FuncFormatter(lambda x, p: format(int(x / 1000), ',')))
candlestick_ohlc(
axc,
ohlc,
width=.5,
colorup='g',
colordown='r',
alpha=0.75,
)
axc.xaxis.set_major_formatter(
mdates.DateFormatter('%Y-%m-%d')) #change x axis back to datestamps
axc.xaxis.set_major_locator(
mticker.MaxNLocator(8)) #add more x axis labels
axc.legend()
axkd.plot(prices['K'], label='K')
axkd.plot(prices['D'], label='D')
axkd.axhline(y=80, xmin=0, xmax=1, color='gray', linestyle='--')
axkd.axhline(y=20, xmin=0, xmax=1, color='gray', linestyle='--')
axkd.legend(loc='center left', bbox_to_anchor=(1, 0.85))
axkd.tick_params(axis='x', rotation=45) #rotate dates for readability
axc.set_title(stock)
# build folder
# basedir = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
# newdir = os.path.join(basedir, 'chart')
# if os.path.exists(newdir) == False:
# os.mkdir(newdir)
print("Starting Saving....")
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
print('Base_dir:' + BASE_DIR)
fig.savefig(os.path.join(BASE_DIR, 'chart/fig1.png'))
CLIENT_ID = "f99b6417ee666d3"
PATH = os.path.join(BASE_DIR, 'chart/fig1.png')
im = pyimgur.Imgur(CLIENT_ID)
uploaded_image = im.upload_image(PATH, title="Uploaded with PyImgur")
# print(uploaded_image.link)
return (uploaded_image.link)
def draw(days: DataFrame, trades=None):
"""作图"""
days['date'] = pd.to_datetime(days['date'])
days['date'] = days['date'].apply(lambda x: dates.date2num(x))
# drop the date index from the dataframe & make a copy
days_reshape = days.reset_index()
days_reshape.drop('volume', axis=1, inplace=True)
days_reshape = days_reshape.reindex(
columns=['date', 'open', 'high', 'low', 'close'])
fig = plt.figure(facecolor='#07000d', figsize=(days.__len__() * 0.1, 10))
ax = plt.subplot2grid((6, 4), (1, 0), rowspan=4, colspan=4)
ax.set_facecolor('#07000d')
candlestick_ohlc(ax,
days_reshape.values,
width=.6,
colorup='#ff1717',
colordown='#53c156')
# 添加均线
short_rolling = days_reshape.close.rolling(window=slow_ma).mean()
long_rolling = days_reshape.close.rolling(window=fast_ma).mean()
SP = len(days_reshape.date.values[fast_ma - 1:])
ax.plot(days_reshape.date.values[-SP:],
short_rolling[-SP:],
'#e1edf9',
label=str(slow_ma) + 'SMA',
linewidth=1.5)
ax.plot(days_reshape.date.values[-SP:],
long_rolling[-SP:],
'#4ee6fd',
label=str(fast_ma) + 'SMA',
linewidth=1.5)
# 样式设置
ax.grid(True, color='w')
ax.xaxis.set_major_locator(
ticker.MaxNLocator(math.floor(days_reshape.__len__() / 22)))
ax.xaxis.set_major_formatter(dates.DateFormatter('%Y-%m-%d'))
ax.yaxis.label.set_color("w")
ax.spines['bottom'].set_color("#5998ff")
ax.spines['top'].set_color("#5998ff")
ax.spines['left'].set_color("#5998ff")
ax.spines['right'].set_color("#5998ff")
ax.tick_params(axis='y', colors='w')
plt.gca().yaxis.set_major_locator(ticker.MaxNLocator(prune='upper'))
ax.tick_params(axis='x', colors='w')
ax.set_ylabel('Stock price and Volume')
# 1、绘制成交量
volumeMin = 0
ax1v = ax.twinx()
ax1v.fill_between(days.date.values,
volumeMin,
days.volume.values,
facecolor='#00ffe8',
alpha=.4)
ax1v.axes.yaxis.set_ticklabels([])
ax1v.grid(False)
# Edit this to 3, so it's a bit larger
ax1v.set_ylim(0, 3 * days.volume.values.max())
ax1v.spines['bottom'].set_color("#5998ff")
ax1v.spines['top'].set_color("#5998ff")
ax1v.spines['left'].set_color("#5998ff")
ax1v.spines['right'].set_color("#5998ff")
ax1v.tick_params(axis='x', colors='w')
ax1v.tick_params(axis='y', colors='w')
# 2、绘制RSI
maLeg = plt.legend(loc=9,
ncol=2,
prop={'size': 7},
fancybox=True,
borderaxespad=0.)
maLeg.get_frame().set_alpha(0.4)
textEd = pylab.gca().get_legend().get_texts()
pylab.setp(textEd[0:5], color='w')
ax0 = plt.subplot2grid((6, 4), (0, 0), sharex=ax, rowspan=1, colspan=4)
ax0.set_facecolor('#07000d')
rsi = vutil.relative_strength_index(days_reshape, 5)['RSI_5']
rsiCol = '#c1f9f7'
posCol = '#386d13'
negCol = '#8f2020'
ax0.plot(days_reshape.date.values[-SP:], rsi[-SP:], rsiCol, linewidth=1.5)
ax0.axhline(70, color=negCol)
ax0.axhline(30, color=posCol)
ax0.fill_between(days_reshape.date.values[-SP:],
rsi[-SP:],
70,
where=(rsi[-SP:] >= 70),
facecolor=negCol,
edgecolor=negCol,
alpha=0.5)
ax0.fill_between(days_reshape.date.values[-SP:],
rsi[-SP:],
30,
where=(rsi[-SP:] <= 30),
facecolor=posCol,
edgecolor=posCol,
alpha=0.5)
ax0.set_yticks([30, 70])
ax0.yaxis.label.set_color("w")
ax0.spines['bottom'].set_color("#5998ff")
ax0.spines['top'].set_color("#5998ff")
ax0.spines['left'].set_color("#5998ff")
ax0.spines['right'].set_color("#5998ff")
ax0.tick_params(axis='y', colors='w')
ax0.tick_params(axis='x', colors='w')
ax0.set_ylabel('RSI')
# 2、绘制MACD
ax2 = plt.subplot2grid((6, 4), (5, 0), sharex=ax, rowspan=1, colspan=4)
ax2.set_facecolor('#07000d')
fillcolor = '#00ffe8'
nslow = 26
nfast = 12
nema = 9
df = vutil.macd(days_reshape, 12, 26)
ema9 = df['MACDsign_12_26']
macd = df['MACD_12_26']
ax2.plot(days_reshape.date.values[-SP:], macd[-SP:], color='#4ee6fd', lw=2)
ax2.plot(days_reshape.date.values[-SP:], ema9[-SP:], color='#e1edf9', lw=1)
# 红色填充大于0区域、绿色填充小于0区域
ax2.fill_between(days_reshape.date.values[-SP:],
macd[-SP:] - ema9[-SP:],
0,
where=(macd[-SP:] - ema9[-SP:] > 0),
alpha=0.5,
facecolor=negCol,
edgecolor=negCol)
ax2.fill_between(days_reshape.date.values[-SP:],
macd[-SP:] - ema9[-SP:],
0,
where=(macd[-SP:] - ema9[-SP:] < 0),
alpha=0.5,
facecolor=posCol,
edgecolor=posCol)
plt.gca().yaxis.set_major_locator(ticker.MaxNLocator(prune='upper'))
ax2.spines['bottom'].set_color("#5998ff")
ax2.spines['top'].set_color("#5998ff")
ax2.spines['left'].set_color("#5998ff")
ax2.spines['right'].set_color("#5998ff")
ax2.tick_params(axis='x', colors='w')
ax2.tick_params(axis='y', colors='w')
plt.ylabel('MACD', color='w')
ax2.yaxis.set_major_locator(ticker.MaxNLocator(nbins=5, prune='upper'))
for label in ax2.xaxis.get_ticklabels():
label.set_rotation(45)
ax2.set_ylabel('MACD')
# 增加提示点
plt.suptitle('000001.XSHG', color='w')
plt.setp(ax0.get_xticklabels(), visible=False)
plt.setp(ax.get_xticklabels(), visible=False)
# buy and sell annotate
# TODO: 箭头的长度可以根据ATR确定
if trades is not None:
for trade in trades.values():
info = '%s,%s,%s' % (trade.datetime.strftime("%Y/%m/%d"),
round_to(trade.price, 0.01), trade.volume)
if Direction.LONG == trade.direction:
ax.annotate(info,
xy=(dates.date2num(trade.datetime), trade.price),
xycoords='data',
color='yellow',
bbox=dict(boxstyle="round", fc="none",
ec="yellow"),
xytext=(0, -40),
textcoords='offset points',
ha='center',
arrowprops=dict(color='yellow', arrowstyle="->"))
if Direction.SHORT == trade.direction:
ax.annotate(info,
xy=(dates.date2num(trade.datetime), trade.price),
xycoords='data',
color='green',
bbox=dict(boxstyle="round", fc="none", ec="green"),
xytext=(0, 40),
textcoords='offset points',
ha='center',
arrowprops=dict(color='green', arrowstyle="->"))
plt.subplots_adjust(left=.09,
bottom=.14,
right=.94,
top=.95,
wspace=.20,
hspace=0)
plt.show()
dfc = df.copy()
dfc['VolumePositive'] = dfc['Open'] < dfc['Adj Close']
#dfc = dfc.dropna()
dfc = dfc.reset_index()
dfc['Date'] = mdates.date2num(dfc['Date'].astype(dt.date))
from mplfinance.original_flavor import candlestick_ohlc
fig = plt.figure(figsize=(14, 7))
ax1 = plt.subplot(2, 1, 1)
ax1.plot(df['MA5'], label='MA5')
ax1.plot(df['MA200'], label='MA200')
candlestick_ohlc(ax1,
dfc.values,
width=0.5,
colorup='g',
colordown='r',
alpha=1.0)
ax1.xaxis_date()
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%d-%m-%Y'))
ax1.grid(True, which='both')
ax1.minorticks_on()
ax1v = ax1.twinx()
colors = dfc.VolumePositive.map({True: 'g', False: 'r'})
ax1v.bar(dfc.Date, dfc['Volume'], color=colors, alpha=0.4)
ax1v.axes.yaxis.set_ticklabels([])
ax1v.set_ylim(0, 3 * df.Volume.max())
ax1.set_title('Stock ' + symbol + ' Closing Price')
ax1.set_ylabel('Price')
ax1.legend(loc='best')
def plot_cols(df,
y='fClose',
moving_averages=None,
vol=False,
candle=False,
figsize=(16, 8)):
"""Plot stock data. y can be a list."""
import matplotlib.pyplot as plt
from matplotlib import style
style.use('ggplot')
cols = df.columns
df = df.copy()
if candle:
import mplfinance as mpf
from mplfinance.original_flavor import candlestick_ohlc
import matplotlib.dates as mdates
# Determine if the date column is datetime (it should be)
# fClose is the default adjusted closing price
ma_list = ['10', '20', '50', '200']
try:
if df['date'].dtype != '<M8[ns]':
df['date'] = pd.to_datetime(df['date'], infer_datetime_format=True)
df['date_keys'] = df['date']
df = df.set_index(keys='date')
except KeyError:
print('Could not find date column')
pass
if vol and not candle:
fig = plt.figure(figsize=figsize)
ax1 = plt.subplot2grid((7, 1), (0, 0), rowspan=4, colspan=1)
ax2 = plt.subplot2grid((7, 1), (5, 0),
rowspan=2,
colspan=1,
sharex=ax1)
# ax1.plot(df.index, df[y], label='Close')
df_index = df.index.get_level_values('date')
df.plot(x='date_keys',
y='fOpen',
label='fOpen',
kind='scatter',
ax=ax1,
color='black')
df.plot(x='date_keys',
y='fClose',
label='fClose',
kind='scatter',
ax=ax1,
color='blue')
df.plot(x='date_keys',
y='fHigh',
label='fHigh',
kind='scatter',
ax=ax1,
color='green')
df.plot(x='date_keys',
y='fLow',
label='fLow',
kind='scatter',
ax=ax1,
color='red')
# print(dir(ax1))
if 'volume' in cols:
ax2.bar(df.index, df['volume'], label='Volume')
elif 'fVolume' in cols:
ax2.bar(df.index,
df['vol/mil'],
label='Volume (in millions)',
color='Blue')
if moving_averages:
ma = moving_averages
for m in ma_list:
ax1.plot(df.index, df[f"{ma}_{m}"], label=f"{ma.upper()} {m}")
elif not vol and not candle:
df[y].plot(label='Close', figsize=figsize, grid=True)
if moving_averages:
ma = moving_averages
df[f"{ma}_10"].plot(label=f"{ma.upper()} 10")
df[f"{ma}_20"].plot(label=f"{ma.upper()} 20")
df[f"{ma}_50"].plot(label=f"{ma.upper()} 50")
df[f"{ma}_200"].plot(label=f"{ma.upper()} 200")
elif vol and candle:
# df_mod = df.reset_index().copy(deep=True)
df_ohlc = df[['fOpen', 'fHigh', 'fLow', 'fClose']].copy(deep=True)
df_ohlc.reset_index(inplace=True)
df_ohlc['date'] = df_ohlc['date'].map(mdates.date2num)
fig = plt.figure(figsize=figsize)
ax1 = plt.subplot2grid((6, 1), (0, 0), rowspan=4, colspan=1)
ax2 = plt.subplot2grid((6, 1), (5, 0),
rowspan=1,
colspan=1,
sharex=ax1)
ax1.xaxis_date()
candlestick_ohlc(ax1, df_ohlc.values, colorup='g')
if moving_averages:
ma = moving_averages
for m in ma_list:
ax1.plot(df.index, df[f"{ma}_{m}"], label=f"{ma.upper()} {m}")
# ax2.fill_between(df.index, df['volume'], 0)
ax2.bar(df.index, df['volume'], label='Volume')
return df_ohlc
plt.legend()
