def chart():
df = dataframe()
df.index = pd.DatetimeIndex(df['timestamp'])
df = df.iloc[::-1]
s = mpf.make_mpf_style(base_mpf_style='charles', gridcolor='#555555', gridstyle="--", rc={'axes.edgecolor': 'white', 'font.size': 5})
fig, axlist = mpf.plot(df, type='candle', style=s, title= coin, ylabel = 'Price (€)', volume=True, warn_too_much_data=9999999, returnfig=True)
mpf.show(block=True)
def show_adv(self, ftitle="Stock", **kwargs):
"""
顯示進階K線圖，K線搭配一主圖指標與兩個副圖指標
使用mplfinance實現之(panel method)
已定義的作圖參數：
ti_main = "SMA" / "VWMA"
ti_sub1 = "KD" / "MACD" / "STD" / "VIX" / "CVOL"
ti_sub2 = "KD" / "MACD" / "STD" / "VIX" / "CVOL"
"""
self._setrun(**kwargs) # 進行參數驗證與指標計算
dest = Dpath + '\\' + ftitle + ".png"
mc = mpf.make_marketcolors(up='r', down='g', inherit=True)
ms = mpf.make_mpf_style(base_mpf_style = "yahoo", \
marketcolors = mc, y_on_right = True)
# 不支援中文字型，圖表文字需為英文
mfargs = dict(type = "candle", volume = True, \
columns = ("O", "H", "L", "C", "V"), \
show_nontrading = False, returnfig = True, \
figsize = (self.fx, self.fy), \
title = ftitle, style = ms, ylabel = "Price", \
ylabel_lower = "Volume in shares", \
panel_ratios = (1, 1))
aps = []
if "ti_main" in kwargs:
tidf = self.val_main.copy()
isma = False
if kwargs["ti_main"] == "SMA" or kwargs["ti_main"] == "VWMA":
isma = True # 主圖技術指標為均線類型
for i in range(0, tidf.shape[1]):
aps.append(mpf.make_addplot(tidf.iloc[:, i], \
color = MA_Colors[i]))
if isma: # 補均線扣抵記號，配色同均線
aps.append(mpf.make_addplot(self.sig_main.iloc[:, i], \
type = "scatter", \
markersize = 200, \
marker = '^', \
color = MA_Colors[i]))
pid = 1 # Panel ID = 0 for candlestick, 1 for volume
if "ti_sub1" in kwargs: # pid = 2
pid += 1
ap2 = self._setsubti(self.val_sub1, kwargs["ti_sub1"], pid)
for item in ap2:
aps.append(item)
if "ti_sub2" in kwargs: # pid = 3 (or 2 if ti_sub1 is not assigned)
pid += 1
ap3 = self._setsubti(self.val_sub2, kwargs["ti_sub2"], pid)
for item in ap3:
aps.append(item)
if len(aps) > 0:
if pid == 2:
mfargs["panel_ratios"] = (2, 1, 1)
elif pid == 3:
mfargs["panel_ratios"] = (3, 1, 1, 1)
fig, axes = mpf.plot(self.df, addplot=aps, **mfargs)
else:
fig, axes = mpf.plot(self.df, **mfargs)
mpf.show()
fig.savefig(dest)
return None
def updateChart(q, x):
global timeP
global client
global ax
timeP = x
fig, ax, client = main()
ani = animation.FuncAnimation(fig, animateFunc, interval=200)
mpf.show()
def check(cls,
data_grabber: DataGrabber,
major_timeframe_in_minutes: int = 60,
minor_timeframe_in_minutes: int = 5,
show_chart: bool = False,
save_chart: bool = False) -> None:
df = data_grabber.fetch()
if df is None:
return
df_resampled_into_major_timeframe = cls.resample(
df).iloc[-cls.BAR_NUMS_SLIDING_WINDOW:]
df = df.iloc[-cls.BAR_NUMS_SLIDING_WINDOW:]
zones, levels = ZoneFinder.find_key_zones_and_levels(
df_resampled_into_major_timeframe,
find_levels_threshold=1,
merge_levels_threshold=0.0002,
zone_weight_threshold=5)
market_name = data_grabber.get_symbols()
df_latest = df.tail(3)
setup = SetupFinder.current_setup(zones, df_latest)
if not setup:
return
message = f'[{get_now_timestamp_string()}] {market_name} setup: {setup}'
image_path = None
if show_chart or save_chart:
plot_config = dict(
df_in_major_timeframe=df_resampled_into_major_timeframe,
major_timeframe_in_minutes=major_timeframe_in_minutes,
df_in_minor_timeframe=df,
minor_timeframe_in_minutes=minor_timeframe_in_minutes,
zones=zones,
levels=levels,
title=market_name,
)
ChartPlotter.plot(**plot_config)
if save_chart:
file_name = f"fig_{market_name}_{setup.replace(' ', '-')}_{get_now_timestamp_string()}.jpg"
image_path = LOGS_DIR / file_name
plt.savefig(fname=image_path, bbox_inches='tight')
if show_chart:
mpf.show()
if save_chart:
if cls.bot_send_image(image_path, message):
logging.info(f'Bot sent image: `{image_path}` `{message}`')
else:
logging.error(f'Failed to send image via bot!')
else:
if cls.bot_send_message(message):
logging.info(f'Bot sent message: `{message}`')
else:
logging.error(f'Failed to send message via bot!')
def plot(df: pd.DataFrame,
title: Optional[str] = None,
mav: Optional[Tuple[int]] = None,
alines: Optional[dict] = None):
color = mpf.make_marketcolors(up='red', down='cyan', inherit=True)
style = mpf.make_mpf_style(marketcolors=color)
# # 设置外观效果
# plt.rc('font', family='Microsoft YaHei') # 用中文字体，防止中文显示不出来
# plt.rc('figure', fc='k') # 绘图对象背景图
# plt.rc('text', c='#800000') # 文本颜色
# plt.rc('axes', axisbelow=True, xmargin=0, fc='k', ec='#800000', lw=1.5, labelcolor='#800000',
# unicode_minus=False) # 坐标轴属性(置底，左边无空隙，背景色，边框色，线宽，文本颜色，中文负号修正)
# plt.rc('xtick', c='#d43221') # x轴刻度文字颜色
# plt.rc('ytick', c='#d43221') # y轴刻度文字颜色
# plt.rc('grid', c='#800000', alpha=0.9, ls=':', lw=0.8) # 网格属性(颜色，透明值，线条样式，线宽)
# plt.rc('lines', lw=0.8) # 全局线宽
#
# # 创建绘图对象和4个坐标轴
# figure = plt.figure(figsize=(16, 8))
# left, width = 0.01, 0.98
# ax1 = figure.add_axes([left, 0.6, width, 0.35]) # left, bottom, width, height
# ax2 = figure.add_axes([left, 0.45, width, 0.15], sharex=ax1) # 共享ax1轴
# ax3 = figure.add_axes([left, 0.25, width, 0.2], sharex=ax1) # 共享ax1轴
# ax4 = figure.add_axes([left, 0.05, width, 0.2], sharex=ax1) # 共享ax1轴
# plt.setp(ax1.get_xticklabels(), visible=False) # 使x轴刻度文本不可见，因为共享，不需要显示
# plt.setp(ax2.get_xticklabels(), visible=False) # 使x轴刻度文本不可见，因为共享，不需要显示
# plt.setp(ax3.get_xticklabels(), visible=False) # 使x轴刻度文本不可见，因为共享，不需要显示
#
# # 绘制蜡烛图
# mpf.c(ax, data['open'], data['close'], data['high'], data['low'],
# width=0.5, colorup='r', colordown='green',
# alpha=0.6)
kwargs: dict = {
'type':
'candle',
'style':
style,
'volume':
True,
'figratio': (3, 1),
'figscale':
20,
'savefig':
dict(fname=
f'D:\\Test_{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}.png',
bbox_inches='tight')
}
if title:
kwargs['title'] = title
if mav:
kwargs['mav'] = mav
if alines:
kwargs['alines'] = alines
mpf.plot(df, **kwargs)
mpf.show()
def candle_stick(ticker, start_date=(datetime.now() - timedelta(days=1)).strftime("%Y%m%d"), end_date=datetime.now().strftime("%Y%m%d")):
start_date = datetime.strptime(str(start_date), '%Y%m%d').strftime('%Y-%m-%d')
end_date = datetime.strptime(str(end_date), '%Y%m%d').strftime('%Y-%m-%d')
tkr = yf.Ticker(ticker)
stock = tkr.history(interval="1m", start=start_date, end=end_date)
stock.drop(columns=['Dividends', 'Stock Splits'], inplace=True)
print(f"plotting chart for {ticker} ...")
# print(stock.head(5))
fig_d = mpf.figure(style="yahoo")
ax1_d = fig_d.add_subplot(1, 1, 1)
mpf.plot(stock, ax=ax1_d, type='candle')
mpf.show()
pass
def animate(ival):
if ival+rn < calc_window:
ival = calc_window - rn
if (ival+rn) > len(rates):
print('no more data to plot')
ani.event_source.interval *= 3
if ani.event_source.interval > 12000:
exit()
return
data = rates.iloc[ival:ival+rn]
ax1.clear()
ax2.clear()
mpf.plot(data,ax=ax1,volume=ax2,type='candle')
mpf.show()
def plotSpread(ticker1, ticker2, years):
fig = mpf.figure(figsize=(15, 10))
axs = fig.add_subplot(1, 1, 1, style='yahoo')
endDate = date.today()
startDate = endDate - datetime.timedelta(days=years * 365)
data1 = pdr.get_data_yahoo(ticker1, start=startDate, end=endDate)
data2 = pdr.get_data_yahoo(ticker2, start=startDate, end=endDate)
firstPrice = data1['Adj Close']
secondPrice = data2['Adj Close']
df1 = pd.DataFrame({'secondPrice': secondPrice, 'firstPrice': firstPrice})
df1.index = pd.to_datetime(df1.index)
state_means = regression(firstPrice, secondPrice)
df1['hr'] = -state_means[:, 0]
df1['spread'] = df1.secondPrice + (df1.firstPrice * df1.hr)
df2 = pd.DataFrame({
'Open': df1['spread'].shift(periods=1),
'High': df1['spread'],
'Low': df1['spread'],
'Close': df1['spread'],
'Volume': df1['spread']
})
df2 = df2[1:]
dt = 1
mu = 0
theta = 1
sigma = np.std(df1['spread'])
ts = np.arange(0, len(df1['spread'].values), dt)
var = np.array(
[sigma**2 / (2 * theta) * (1 - np.exp(-2 * theta * t)) for t in ts])
std = 2 * np.sqrt(var)
std = std[-1]
upper = mu + std
lower = mu - std
mpf.plot(df2,
type='candle',
ax=axs,
axtitle=f"Spread for {ticker2} / {ticker1}",
hlines=dict(hlines=[mu, upper, lower],
linestyle='-.',
colors=['b', 'b', 'b']),
xrotation=0)
mpf.show()
def show_basic(self, ftitle="Stock"):
"""
顯示基本K線圖，僅K線與成交量
使用mplfinance實現之
"""
dest = Dpath + '\\' + ftitle + ".png"
mc = mpf.make_marketcolors(up='r', down='g', inherit=True)
ms = mpf.make_mpf_style(base_mpf_style = "yahoo", \
marketcolors = mc)
# 不支援中文字型，圖表文字需為英文
mfargs = dict(type = "candle", volume = True, \
columns = ("O", "H", "L", "C", "V"), \
show_nontrading = False, returnfig = True, \
figsize = (self.fx, self.fy), \
title = ftitle, style = ms, ylabel = "Price", \
ylabel_lower = "Volume in shares")
fig, axes = mpf.plot(self.df, **mfargs)
mpf.show()
fig.savefig(dest)
return None
def show_graphic_from_finance(archivo, numero_velas, moneda):
df = pd.read_csv(archivo, index_col='Datetime')
df.index = pd.to_datetime(df.index, utc=True)
fig = mpf.figure(style='charles', figsize=(7, 7))
ax1 = fig.add_subplot(2, 1, 1)
ax2 = fig.add_subplot(3, 1, 3)
def animate(ival):
data = update_data_from_finance(archivo, moneda, "5m")
print("UPDATED")
print(data.index.max())
data_to_print = data.iloc[(len(data) - numero_velas):len(data)]
ax1.clear()
ax2.clear()
mpf.plot(data_to_print, ax=ax1, volume=ax2, type='candle')
ani = animation.FuncAnimation(fig, animate, interval=300000)
mpf.show()
def show(df):
buy, sell = label(df)
df['buy'] = buy
df['sell'] = sell
print(df.head(3))
apd = [
mpf.make_addplot(df['buy'],
type="scatter",
scatter=True,
markersize=20,
marker='^'),
mpf.make_addplot(df['sell'],
type="scatter",
scatter=True,
markersize=20),
]
mpf.plot(df, type="candle", volume=True, addplot=apd, style="sas")
mpf.show()
def show_graphic_from_finance_and_predict(cuda):
df = pd.read_csv("data/ETH-USD-5m.csv", index_col='Datetime')
df.index = pd.to_datetime(df.index, utc=True)
fig = mpf.figure(style='charles', figsize=(7, 7))
ax1 = fig.add_subplot(2, 1, 1)
ax2 = fig.add_subplot(3, 1, 3)
list_models_load = []
list_models = get_list_models()
# Cargamos los modelos
print('Cargando modelos...')
for name, pretrained in list_models:
model = AdaptativeNet(pretrained)
model.load_state_dict(torch.load('trained_nets/' + name + '.pth'))
list_models_load.append((name, model))
print('Modelos cargados')
def animate(ival):
data = update_data_from_finance("data/ETH-USD-5m.csv", "ETH-USD", "5m")
print("UPDATED")
print(data.index.max())
data_to_print = data.iloc[(len(data) - 150):len(data)]
net_dataset = Net_dataset_show(data_to_print)
loader = torch.utils.data.DataLoader(net_dataset,
shuffle=True,
num_workers=2)
with torch.no_grad():
prediction = predict(list_models_load, loader, cuda)
print('La prediccion de la proxima vela sera: ' + prediction)
ax1.clear()
ax2.clear()
mpf.plot(data_to_print, ax=ax1, volume=ax2, type='candle')
ani = animation.FuncAnimation(fig, animate, interval=300000)
mpf.show()
def draw(self):
intraday: pd.DataFrame
intraday = self.candlestick.loc[:, ['open', 'high', 'low', 'close', 'volume']]
intraday.index = pd.to_datetime(self.candlestick['datetime'])
color = mpf.make_marketcolors(up='red', down='cyan', inherit=True)
style = mpf.make_mpf_style(marketcolors=color)
# # 设置外观效果
# plt.rc('font', family='Microsoft YaHei') # 用中文字体，防止中文显示不出来
# plt.rc('figure', fc='k') # 绘图对象背景图
# plt.rc('text', c='#800000') # 文本颜色
# plt.rc('axes', axisbelow=True, xmargin=0, fc='k', ec='#800000', lw=1.5, labelcolor='#800000',
# unicode_minus=False) # 坐标轴属性(置底，左边无空隙，背景色，边框色，线宽，文本颜色，中文负号修正)
# plt.rc('xtick', c='#d43221') # x轴刻度文字颜色
# plt.rc('ytick', c='#d43221') # y轴刻度文字颜色
# plt.rc('grid', c='#800000', alpha=0.9, ls=':', lw=0.8) # 网格属性(颜色，透明值，线条样式，线宽)
# plt.rc('lines', lw=0.8) # 全局线宽
#
# # 创建绘图对象和4个坐标轴
# figure = plt.figure(figsize=(16, 8))
# left, width = 0.01, 0.98
# ax1 = figure.add_axes([left, 0.6, width, 0.35]) # left, bottom, width, height
# ax2 = figure.add_axes([left, 0.45, width, 0.15], sharex=ax1) # 共享ax1轴
# ax3 = figure.add_axes([left, 0.25, width, 0.2], sharex=ax1) # 共享ax1轴
# ax4 = figure.add_axes([left, 0.05, width, 0.2], sharex=ax1) # 共享ax1轴
# plt.setp(ax1.get_xticklabels(), visible=False) # 使x轴刻度文本不可见，因为共享，不需要显示
# plt.setp(ax2.get_xticklabels(), visible=False) # 使x轴刻度文本不可见，因为共享，不需要显示
# plt.setp(ax3.get_xticklabels(), visible=False) # 使x轴刻度文本不可见，因为共享，不需要显示
#
# # 绘制蜡烛图
# mpf.c(ax, data['open'], data['close'], data['high'], data['low'],
# width=0.5, colorup='r', colordown='green',
# alpha=0.6)
mpf.plot(intraday, type='candle', style=style, mav=(5, 10), volume=True)
mpf.show()
def candle_stick_daily(ticker, start_date=(datetime.now() - timedelta(days=60)).strftime("%Y%m%d"), end_date=datetime.now().strftime("%Y%m%d")):
start_date = datetime.strptime(str(start_date), '%Y%m%d').strftime('%Y-%m-%d')
end_date = datetime.strptime(str(end_date), '%Y%m%d').strftime('%Y-%m-%d')
tkr = yf.Ticker(ticker)
stock = tkr.history(interval="1d", start=start_date, end=end_date)
stock.drop(columns=['Dividends', 'Stock Splits'], inplace=True)
"""
Open High Low Close Volume
Date
2021-01-20 858.739990 859.500000 837.280029 850.450012 25665900
2021-01-21 855.000000 855.719971 841.419983 844.989990 20521100
2021-01-22 834.309998 848.000000 828.619995 846.640015 20066500
2021-01-25 855.000000 900.400024 838.820007 880.799988 41173400
2021-01-26 891.380005 895.900024 871.599976 883.090027 23131600
"""
print(f"plotting chart for {ticker} ...")
# print(stock.head(5))
fig_d = mpf.figure(style="yahoo")
ax1_d = fig_d.add_subplot(1,1,1)
mpf.plot(stock, ax=ax1_d, type='candle')
mpf.show()
pass
class Monitor:
BAR_NUMS_SLIDING_WINDOW = 50 # Determine and draw S/R by how many bars?
@classmethod
def bot_send_message(cls,
message: str,
disable_notification: bool = False) -> bool:
SEND_MESSAGE_API_ENDPOINT = f'https://api.telegram.org/bot{os.getenv("TELEGRAM_BOT_TOKEN")}/sendMessage'
RETRY_LIMIT = 3
data = {
'chat_id': os.getenv('TELEGRAM_BOT_SEND_MESSAGE_GROUP_ID'),
'text': message,
'disable_notification': disable_notification,
}
for _ in range(RETRY_LIMIT):
response = requests.post(SEND_MESSAGE_API_ENDPOINT, data=data)
if response.status_code == 200:
return True
time.sleep(3)
logging.error(f'Failed to send bot message [{message}]!')
return False
@classmethod
def bot_send_image(cls,
image_path: str,
caption: str = '',
disable_notification: bool = False) -> bool:
SEND_PHOTO_API_ENDPOINT = f'https://api.telegram.org/bot{os.getenv("TELEGRAM_BOT_TOKEN")}/sendPhoto'
RETRY_LIMIT = 3
files = {'photo': open(image_path, 'rb')}
data = {
'chat_id': os.getenv('TELEGRAM_BOT_SEND_MESSAGE_GROUP_ID'),
'caption': caption,
'disable_notification': disable_notification,
}
for _ in range(RETRY_LIMIT):
response = requests.post(SEND_PHOTO_API_ENDPOINT,
files=files,
data=data)
if response.status_code == 200:
return True
time.sleep(3)
logging.error(f'Failed to send photo [{caption}]!')
return False
@classmethod
def resample(cls,
df: pd.DataFrame,
major_timeframe_in_minutes: int = 60) -> pd.DataFrame:
RESAMPLE_MAP = {
'Open': 'first',
'High': 'max',
'Low': 'min',
'Close': 'last',
}
return df.resample(f'{major_timeframe_in_minutes}T').agg(
RESAMPLE_MAP).dropna()
def check(cls,
data_grabber: DataGrabber,
major_timeframe_in_minutes: int = 60,
minor_timeframe_in_minutes: int = 5,
show_chart: bool = False,
save_chart: bool = False) -> None:
df = data_grabber.fetch()
if df is None:
return
df_resampled_into_major_timeframe = cls.resample(
df).iloc[-cls.BAR_NUMS_SLIDING_WINDOW:]
df = df.iloc[-cls.BAR_NUMS_SLIDING_WINDOW:]
zones, levels = ZoneFinder.find_key_zones_and_levels(
df_resampled_into_major_timeframe,
find_levels_threshold=1,
merge_levels_threshold=0.0002,
zone_weight_threshold=5)
market_name = data_grabber.get_symbols()
df_latest = df.tail(3)
setup = SetupFinder.current_setup(zones, df_latest)
if not setup:
return
message = f'[{get_now_timestamp_string()}] {market_name} setup: {setup}'
image_path = None
if show_chart or save_chart:
plot_config = dict(
df_in_major_timeframe=df_resampled_into_major_timeframe,
major_timeframe_in_minutes=major_timeframe_in_minutes,
df_in_minor_timeframe=df,
minor_timeframe_in_minutes=minor_timeframe_in_minutes,
zones=zones,
levels=levels,
title=market_name,
)
ChartPlotter.plot(**plot_config)
if save_chart:
file_name = f"fig_{market_name}_{setup.replace(' ', '-')}_{get_now_timestamp_string()}.jpg"
image_path = LOGS_DIR / file_name
plt.savefig(fname=image_path, bbox_inches='tight')
if show_chart:
mpf.show()
if save_chart:
if cls.bot_send_image(image_path, message):
logging.info(f'Bot sent image: `{image_path}` `{message}`')
else:
logging.error(f'Failed to send image via bot!')
else:
if cls.bot_send_message(message):
logging.info(f'Bot sent message: `{message}`')
else:
logging.error(f'Failed to send message via bot!')
@classmethod
def run_simulation(cls,
df_minor_timeframe_full: pd.DataFrame,
start_from_frame_num: int = 0,
major_timeframe_in_minutes: int = 60,
minor_timeframe_in_minutes: int = 5) -> None:
rtapi = RealTimeAPIMock(df_minor_timeframe_full,
cls.BAR_NUMS_SLIDING_WINDOW)
df = rtapi.initial_fetch()
def step_forward_one_frame():
nxt = rtapi.fetch_next()
if nxt is None:
print('no more data to plot')
anim.event_source.interval *= 3
if anim.event_source.interval > 12000:
exit()
return
nonlocal df
df = pd.concat([df, nxt])
for _ in range(start_from_frame_num):
step_forward_one_frame()
df_resampled_into_major_timeframe = cls.resample(
df).iloc[-cls.BAR_NUMS_SLIDING_WINDOW:]
plot_config = dict(
df_in_major_timeframe=df_resampled_into_major_timeframe,
major_timeframe_in_minutes=major_timeframe_in_minutes,
df_in_minor_timeframe=df.iloc[-cls.BAR_NUMS_SLIDING_WINDOW:],
minor_timeframe_in_minutes=minor_timeframe_in_minutes,
title='EURUSD',
)
fig, ax_major_timeframe, ax_minor_timeframe = ChartPlotter.plot(
**plot_config)
pause = False
def on_click(event):
nonlocal pause
pause ^= True
if pause:
anim.event_source.stop()
else:
anim.event_source.start()
fig.canvas.mpl_connect('button_press_event', on_click)
def animate(frame_num):
print('Frame:', start_from_frame_num + frame_num)
ax_major_timeframe.clear()
ax_minor_timeframe.clear()
step_forward_one_frame()
df_resampled_into_major_timeframe = cls.resample(
df).iloc[-cls.BAR_NUMS_SLIDING_WINDOW:]
zones, levels = ZoneFinder.find_key_zones_and_levels(
df_resampled_into_major_timeframe,
find_levels_threshold=1,
merge_levels_threshold=0.0002,
zone_weight_threshold=5)
plot_config = dict(
df_in_major_timeframe=df_resampled_into_major_timeframe,
major_timeframe_in_minutes=major_timeframe_in_minutes,
df_in_minor_timeframe=df.iloc[-cls.BAR_NUMS_SLIDING_WINDOW:],
minor_timeframe_in_minutes=minor_timeframe_in_minutes,
zones=zones,
levels=levels,
fig=fig,
ax_major_timeframe=ax_major_timeframe,
ax_minor_timeframe=ax_minor_timeframe,
)
ChartPlotter.plot(**plot_config)
# plt.pause(0.001) # reduce GUI freeze
df_latest = df.tail(3)
if setup := SetupFinder.current_setup(zones, df_latest):
print(setup)
on_click(None)
anim = animation.FuncAnimation(fig, animate, interval=100)
mpf.show()
def plot_ohlc_all(self, data, block=True, save=False):
"""
:param data: [{code:code0, df:dataframe0(ohlc)}, {code:code1, df:dataframe1(ohlc)}, ...]
:param block: if block is False when plot shows, plot run backend
:param save: if save is True, plot is saved, don't show
"""
if save:
if os.path.exists(self._save_path):
save_path_names = []
for i in range(len(data)):
save_path_name = self._save_path + '\\chart' + f"_{i}" + self._suffix
save_path_names.append(save_path_name)
mpf.plot(data[i]['df'],
style='yahoo',
type='candle',
title=data[i]['code'],
savefig=save_path_name)
return save_path_names
else:
raise Exception('Save path {} does not exist.'.format(
self._save_path))
else:
page, limit = 0, min(8, len(data)) # page starts 0
max_page = math.ceil(len(data) / limit)
fig = mpf.figure(figsize=(12, 9), style='yahoo')
fig.subplots_adjust(left=0.03,
bottom=0.2,
right=0.95,
top=0.95,
hspace=0.2)
axes = []
for i in range(limit):
ax = fig.add_subplot(2, 4, i + 1)
axes.append(ax)
mpf.plot(data[i]['df'],
ax=ax,
type='candle',
axtitle=data[i]['code'])
def button_prev(event):
nonlocal page, limit, max_page
nonlocal data
nonlocal axes, axtext
page = (page - 1) if page > 0 else max_page - 1
replot_ohlc(data, axes, page, limit, axtext)
def button_next(event):
nonlocal page, limit, max_page
nonlocal data
nonlocal axes, axtext
page = (page + 1) % max_page
replot_ohlc(data, axes, page, limit, axtext)
def replot_ohlc(data, axes, page, limit, axtext):
axtext.clear()
axtext.text(0.4, 0.2,
'{:2d} / {:2d}'.format(page + 1, max_page))
for ax in axes:
ax.clear()
for i, ax in enumerate(axes):
idx = i + (page * limit)
if idx >= len(data):
break
mpf.plot(data[idx]['df'],
ax=ax,
type='candle',
axtitle=data[idx]['code'])
# make prev or next button
axprev = fig.add_axes([0.7, 0.05, 0.1, 0.075])
axnext = fig.add_axes([0.81, 0.05, 0.1, 0.075])
bprev = Button(axprev, '◀')
bnext = Button(axnext, '▶')
bnext.on_clicked(button_next)
bprev.on_clicked(button_prev)
# make current page representation
axtext = fig.add_axes([0.5, 0.05, 0.1, 0.075])
axtext.text(0.4, 0.2, '{:2d} / {:2d}'.format(page + 1, max_page))
mpf.show(block=block)
def Grafico(indicadorTec,
numSubplots1,
numSubplots2,
vr=None,
pi=None,
pf=None):
"""
Permite hacer la gráfica del activo en un periodo de tiempo
"""
fig = mpf.figure(figsize=(12, 9))
s = mpf.make_mpf_style(base_mpf_style='yahoo', y_on_right=False)
if numSubplots1 == 1 and numSubplots2 == 0:
ax1 = fig.add_subplot(2, 2, 2, style=s)
ax11 = ax1.twinx()
ap = mpf.make_addplot(indicadorTec.DataStock['RSI'],
ax=ax11,
ylabel='RSI')
vol = fig.add_subplot(2, 2, 4, sharex=ax1, style=s)
mpf.plot(indicadorTec.DataStock,
volume=vol,
ax=ax1,
addplot=ap,
xrotation=10,
ylabel='Precio',
type='candle',
axtitle='Gráfica de valor con indicador RSI')
elif numSubplots1 == 0 and numSubplots2 == 1:
ax2 = fig.add_subplot(2, 2, 2, style=s)
ax22 = ax2.twinx()
ap = mpf.make_addplot(indicadorTec.DataStock['SMA'],
ax=ax22,
ylabel='SMA')
vol = fig.add_subplot(2, 2, 4, sharex=ax2, style=s)
mpf.plot(indicadorTec.DataStock,
volume=vol,
ax=ax2,
addplot=ap,
xrotation=10,
ylabel='Precio',
type='candle',
axtitle='Gráfica de valor con indicador SMA')
elif numSubplots1 == 1 and numSubplots2 == 1:
ax1 = fig.add_subplot(2, 2, 2, style=s)
ax11 = ax1.twinx()
ap = mpf.make_addplot(indicadorTec.DataStock['RSI'],
ax=ax11,
ylabel='RSI')
mpf.plot(indicadorTec.DataStock,
ax=ax1,
addplot=ap,
xrotation=10,
ylabel='Precio',
type='candle',
axtitle='Gráfica de valor con indicador RSI')
ax2 = fig.add_subplot(2, 2, 4, style=s)
ax22 = ax2.twinx()
ap = mpf.make_addplot(indicadorTec.DataStock['SMA'],
ax=ax22,
ylabel='SMA')
mpf.plot(indicadorTec.DataStock,
ax=ax2,
addplot=ap,
xrotation=10,
ylabel='Precio',
type='candle',
axtitle='Gráfica de valor con indicador SMA')
if pi == None and pf == None:
axp = fig.add_subplot(2, 2, 1, style=s)
volp = fig.add_subplot(2, 2, 3, sharex=axp, style=s)
mpf.plot(indicadorTec.DataStock,
ax=axp,
volume=volp,
xrotation=10,
ylabel='Precio',
type='candle',
axtitle='Gráfica del valor (últimos 30 días) ')
mpf.show()
else:
axp = fig.add_subplot(2, 2, 1, style=s)
volp = fig.add_subplot(2, 2, 3, sharex=axp, style=s)
mpf.plot(indicadorTec.DataStock,
ax=axp,
volume=volp,
xrotation=10,
ylabel='Precio',
type='candle',
axtitle=f'Gráfica del valor (De {pi} a {pf}) ')
mpf.show()
def main():
"""
Despliega un menú de opciones en la consola del terminal
"""
ot1 = 0
ValorGrafico = Valor()
while ot1 == 0:
# Primer while principal para la identificación del código y el país del valor
global num_exis
num_exis = 0
a = -1
while a != 1 and a != 2 and a != 3:
# Bucle que repite la opción de la elección de lista
print(
"\n\nEste programa realiza recomendaciones de compra o venta de un valor según un análisis técnico con los datos obtenidos de Investing.com\n"
)
print("(1) Ver lista de países ")
print("(2) Ver lista de valores de todos los países ")
print("(3) Ver lista de valores según el país ")
a = int(input("\n¿Qué desea hacer?\n"))
if a == 1:
# Se imprimen todos los países que están registrados en el mercado de valores
for i in investpy.get_stock_countries():
print("País {}: {}".format(
investpy.get_stock_countries().index(i) + 1, i))
elif a == 2:
# Se imprime un panda.DataFrame de todos los valores sin importar el país
print(investpy.get_stocks(country=None))
elif a == 3:
# Se imprime un panda.DataFrame de todos los valores según el país indicado
otra = 1
while otra != 0:
try:
b = input("Escribe el país de donde quieres buscar:\n")
print(investpy.get_stocks(country=b))
otra = 0
except ValueError:
# Evita que se ejecute el error ValueError cuando la palabra insertada no se encuentra entre los países de la lista
print("Escribe un pais existente\n")
ott = 1
while ott != 0:
# Permite la opción de salida del primer bucle while principal
ot = str(input("¿Desea ver otra lista? Sí(S) o No (N):\n"))
if ot == 'S' or ot == 's':
ot1 = 0
ott = 0
elif ot == 'N' or ot == 'n':
ot1 = 1
ott = 0
else:
ott = 1
ot2 = 0
while ot2 == 0:
# Segundo while principal para ingresar el país y el valor, o salir del programa
a1 = -1
while a1 != 1 and a1 != 2:
# Bucle que repite la opción de la elección de continuar con el análisis o salir del programa
print(
"\nElija si desea realizar un análisis técnico de un valor en el mercado de valores\n"
)
print("(1) Analizar un valor ")
print("(2) Salir ")
a1 = int(input("\n¿Qué desea hacer?\n"))
if a1 == 1:
# Se ingresa el país y el valor para su análisis técnico
otr = 1
while otr != 0:
try:
print("¿Qué valor deseas ver?\n")
pais = input(
"Ingresa el país (Segunda columna en la lista de valores)\n"
)
stock_valor = input(
"Ingresa el código del valor (Séptima columna en la lista de valores)\n"
).upper()
p = 0
while p == 0:
# Permite la opción de especificar un rango de tiempo
p1 = str(
input(
"¿Desea establecer un rango de tiempo? Sí(S) o No (N):\n"
))
if p1 == 'S' or p1 == 's':
print(
"Escriba el rango de tiempo con el formato (dd/mm/aaaa)"
)
pi = str(input("Escriba el día de inicio : "))
pf = str(input("Escriba el día de término: "))
df = extraer(1, stock_valor, pais, pi, pf)
print(df)
mpf.plot(
df,
axtitle=f'Gráfica del valor (De {pi} a {pf})',
style='yahoo',
xrotation=15,
type='candle')
mpf.show()
p = 1
elif p1 == 'N' or p1 == 'n':
df = extraer(2, stock_valor, pais)
print(df)
mpf.plot(
df,
axtitle='Gráfica del valor (últimos 30 días)',
style='yahoo',
xrotation=15,
type='candle')
mpf.show()
p = 1
else:
p = 0
otr = 0
except ValueError:
print("Escribe un país existente\n")
elif a1 == 2:
# Sale del programa
break
otq = 0
while otq == 0:
z = -1
while z != 1 and z != 2 and z != 3:
# Bucle que repite la opción de la elección de indicador
print("\n¿Cuál indicador deseas ver?\n")
print(
"(1) RSI (Relative Strength Index) "
)
print(
"(2) SMA (Simple Moving Average) "
)
print(
"(3) Ambos "
)
z = int(input("\n¿Qué desea hacer?\n"))
indic1 = Indicador_tecnico(df)
if z == 1:
indic1.RSI()
t = "RSI"
elif z == 2:
indic1.SMA()
t = "SMA"
elif z == 3:
indic1.All()
Grafico(indic1, indic1._RSI, indic1._SMA)
señ1 = señal(z, stock_valor, pais)
if z != 3:
print(
f"\n\nEl indicador {t} en el valor búrsatil '{señ1[0]}' es de {señ1[1]}, e indica que se recomienda {señ1[2]}.\n\n"
)
else:
print(
f"\n\nCon el valor búrsatil '{señ1[0]}', con el indicador RSI, el cual es de {señ1[1]}, recomienda {señ1[2]}; mientras que el indicador SMA, el cual es de {señ1[3]}, recomienda {señ1[4]}.\n\n"
)
ott2 = 1
while ott2 != 0:
# Permite la opción de salida del bucle while
otp = str(input(
"¿Desea ver otro indicador? Sí(S) o No (N):\n")).upper()
if otp == 'S':
otq = 0
ott2 = 0
elif otp == 'N':
otq = 1
ott2 = 0
else:
ott2 = 1
ValorGrafico.agregar_valor(stock_valor, indic1)
otw = 0
while otw == 0:
ddd = 1
while ddd != 0:
# Permite la opción de salida del bucle while
otp5 = str(
input(
"¿Desea ver algún valor registrado? Sí(S) o No (N):\n")
).upper()
if otp5 == 'S':
otw = 0
ddd = 0
elif otp5 == 'N':
otw = 1
ddd = 0
break
else:
ddd = 1
if otp5 == 'S':
valReg = str(
input(
"Escriba el símbolo del valor registrado que quiere buscar: "
)).upper()
VR = ValorGrafico.mostrar_valor(valReg)
elif otp5 == 'N':
break
Grafico(VR[0], VR[1], VR[2])
if VR[1] == 1 and VR[2] == 0:
t1 = "RSI"
z1 = 1
elif VR[1] == 0 and VR[2] == 1:
t1 = "SMA"
z1 = 2
elif VR[1] == 1 and VR[2] == 1:
z1 = 3
señ2 = señal(z1, valReg, pais)
if z1 != 3:
print(
f"\n\nEl indicador {t1} en el valor búrsatil '{señ2[0]}' es de {señ2[1]}, e indica que se recomienda {señ2[2]}.\n\n"
)
else:
print(
f"\n\nCon el valor búrsatil '{señ2[0]}', con el indicador RSI, el cual es de {señ2[1]}, recomienda {señ2[2]}; mientras que el indicador SMA, el cual es de {señ2[3]}, recomienda {señ2[4]}.\n\n"
)
ott2 = 1
while ott2 != 0:
# Permite la opción de salida del bucle while
otp7 = str(
input(
"¿Desea ver otro valor registrado? Sí(S) o No (N):\n"))
if otp7 == 'S' or otp7 == 's':
otw = 0
ott2 = 0
elif otp7 == 'N' or otp7 == 'n':
otw = 1
ott2 = 0
break
else:
ott2 = 1
valReg = str(
input(
"Escriba el símbolo del valor registrado que quiere buscar: "
)).upper()
ValorGrafico.mostrar_valor(valReg)
VR1 = ValorGrafico.mostrar_valor(valReg)
Grafico(VR1[0], VR1[1], VR1[2])
if VR1[1] == 1 and VR1[2] == 0:
t2 = "RSI"
z2 = 1
elif VR1[1] == 0 and VR1[2] == 1:
t2 = "SMA"
z2 = 2
elif VR1[1] == 1 and VR1[2] == 1:
z2 = 3
señ3 = señal(z2, valReg, pais)
if z2 != 3:
print(
f"\n\nEl indicador {t2} en el valor búrsatil '{señ3[0]}' es de {señ3[1]}, e indica que se recomienda {señ3[2]}.\n\n"
)
else:
print(
f"\n\nCon el valor búrsatil '{señ3[0]}', con el indicador RSI, el cual es de {señ3[1]}, recomienda {señ3[2]}; mientras que el indicador SMA, el cual es de {señ3[3]}, recomienda {señ3[4]}.\n\n"
)
ott3 = 1
while ott3 != 0:
# Permite la opción de salida del segundo bucle while principal
otp3 = str(input("¿Desea ver otro valor? Sí(S) o No (N):\n"))
if otp3 == 'S' or otp3 == 's':
ot2 = 0
ott3 = 0
elif otp3 == 'N' or otp3 == 'n':
ot2 = 1
ott3 = 0
else:
ott3 = 1
def showStockData(stockData, stockName):
#marketcolor
mc = mpf.make_marketcolors(up="red",
down="green",
edge="i",
wick="i",
volume="in",
inherit=True)
#style
#mpf.available_styles()
#['binance',
# 'blueskies',
# 'brasil',
# 'charles',
# 'checkers',
# 'classic',
# 'default',
# 'mike',
# 'nightclouds',
# 'sas',
# 'starsandstripes',
# 'yahoo']
s = mpf.make_mpf_style(gridaxis='both',
base_mpf_style='blueskies',
gridstyle='-.',
y_on_right=False,
marketcolors=mc)
# 设置基本参数
# type:绘制图形的类型，有candle, renko, ohlc, line等
# 此处选择candle,即K线图
# mav(moving average):均线类型,此处设置7,30,60日线
# volume:布尔类型，设置是否显示成交量，默认False
# title:设置标题
# y_label_lower:设置成交量图一栏的标题
# figratio:设置图形纵横比
# figscale:设置图形尺寸(数值越大图像质量越高)
symbol = stockName.replace(".csv", "")
kwargs = dict(type='candle',
mav=(5),
volume=True,
title='\nStock %s ' % (symbol),
ylabel='OHLC Candles',
ylabel_lower='Trade volume',
figscale=1.25,
figratio=(16, 5))
#figratio=(10, 6),
#figscale=4)
#calculate above and below percentB
#low_signal = percentB_below(stockData)
#high_signal = percentB_above(stockData)
#Days = int(Days)
Days = 60
apds = [
mpf.make_addplot(stockData['Lower'][-Days:],
color='b',
width=0.75,
ylabel='Lower'),
mpf.make_addplot(stockData['Upper'][-Days:], color='b', width=0.75),
mpf.make_addplot(stockData['MA_20'][-Days:], color='b', width=0.75),
#mpf.make_addplot( low_signal[-Days:],color='g',markersize=200,marker='^',type='scatter' ),
#mpf.make_addplot( high_signal[-Days:],color='g',markersize=200,marker='x',type='scatter' ),
mpf.make_addplot(stockData['Bbandwidth'][-Days:],
color='r',
panel=3,
width=0.75,
ylabel="BD"),
mpf.make_addplot(stockData['percentB'][-Days:],
color='r',
panel=2,
width=0.75,
ylabel="%B"),
#mpf.make_addplot(stockData['RSI'][-Days:], color='g', panel=4, ylabel="RSI")
]
#add legend which doesn't available in current mplfinance library
fig, axes = mpf.plot(stockData[-Days:],
**kwargs,
style=s,
show_nontrading=False,
addplot=apds,
returnfig=True,
update_width_config=dict(candle_linewidth=2))
#fig, axes = mpf.plot(stockData[-Days:], **kwargs,style=s,show_nontrading= False,addplot=apds,returnfig=True)
#configure chart legend and title
#axes[0].legend(['MA_5'],['MA_20'],['Upper'],['Lower'])
#_, axs = plt.subplots(nrows=2,ncols=2,figsize=(7,5))
#todo_list:
#回測系統
#加入其他技術分析
#考慮網頁化?
#2020-10-25
#優化視窗 鼠標可顯示該位置的價格 數值等等...
mpf.show()
histogram = macd - signal
apds = [
mpf.make_addplot(exp12, color='lime'),
mpf.make_addplot(exp26, color='c'),
mpf.make_addplot(histogram,
type='bar',
width=0.7,
panel=1,
color='dimgray',
alpha=1,
secondary_y=False),
mpf.make_addplot(macd, panel=1, color='fuchsia', secondary_y=True),
mpf.make_addplot(signal, panel=1, color='b', secondary_y=True),
]
fig, axlist = mpf.plot(df,
type='candle',
addplot=apds,
figscale=1.1,
figratio=(8, 5),
title='\nMACD',
style='blueskies',
volume=True,
volume_panel=2,
panel_ratios=(6, 3, 2),
returnfig=True)
multi = MultiCursor(fig.canvas, axlist, color='r', lw=1.2)
mpf.show()
def draw_k(code, date, a, title='', h=False):
if len(a) == 0:
return
d = stock.data.loc[code]
ds = stock.data.loc[2330].loc[name.DATE].tolist()
ds.index(date)
d = d.iloc[:, ds.index(date):90 + ds.index(date)].dropna(axis=1)
ds = d.loc[name.DATE].tolist()
if a[-1][0] > d.columns[-1]:
return
n = np.full(d.shape[1], np.nan)
dsd = d.loc[name.DATE].tolist()
for v in a:
n[dsd.index(v[1])] = d.loc[name.CLOSE][v[0]] * 0.99
apd = mpf.make_addplot(n[::-1], type='scatter', markersize=100, marker='^')
dd = pd.DataFrame({
name.DATE:
pd.to_datetime(d.loc[name.DATE].iloc[::-1]).tolist(),
name.OPEN:
d.loc[name.OPEN].iloc[::-1].tolist(),
name.CLOSE:
d.loc[name.CLOSE].iloc[::-1].tolist(),
name.HIGH:
d.loc[name.HIGH].iloc[::-1].tolist(),
name.LOW:
d.loc[name.LOW].iloc[::-1].tolist(),
name.VOLUME:
d.loc[name.VOLUME].iloc[::-1].tolist(),
})
fig, axlist = mpf.plot(dd.set_index(name.DATE),
type='candle',
addplot=apd,
ylabel=f"{code} {date} {title}",
returnfig=True)
if h:
ds = d.loc[name.DATE].tolist()
y = d.iloc[:, ds.index(a[0][1]):ds.index(a[1][1]) + 1]
c = y.loc[name.CLOSE]
o = y.loc[name.OPEN]
if c.max() > o.max():
axlist[0].axhline(y=c.max())
else:
axlist[0].axhline(y=o.max())
if c.min() > o.min():
axlist[0].axhline(y=c.min())
else:
axlist[0].axhline(y=o.min())
fig.set_tight_layout(False)
for ax in axlist:
ax.yaxis.set_major_locator(
k.PriceLocator(dd[name.HIGH].max(), dd[name.LOW].min()))
ax.yaxis.set_major_formatter(k.PriceFormatter())
ax.xaxis.set_major_locator(k.DateLocator(ds))
ax.xaxis.set_major_formatter(k.DateFormatter())
mpf.show()
def run(pickle_file=PICKLE_FILE):
ani = animation.FuncAnimation(fig, analysis, fargs=[pickle_file])
mpf.show()
def plot_data(self):
#generated_data = []
#for i in range(int(self.n_windows / self.batch_size)):
Z_ = next(self.random_series)
d = self.synthetic_data(Z_)
R_ = next(self.real_series_iter)
print('Este es el real')
print(R_)
real_data = np.array(R_)
print(real_data.shape)
generated_data = np.array(d)
print(generated_data.shape)
print(len(generated_data))
generated_data = (self.scaler.inverse_transform(
generated_data.reshape(-1, self.n_seq)).reshape(
-1, self.seq_len, self.n_seq))
real_data = (self.scaler.inverse_transform(
real_data.reshape(-1, self.n_seq)).reshape(-1, self.seq_len,
self.n_seq))
for i in range(3):
# print(' sequence:{}'.format(i))
generated = generated_data[i, :, :]
real = real_data[i, :, :]
# print(generated)
# numpy_data = np.array([[1, 2], [3, 4]])
base = datetime.datetime(2020, 11, 20)
arr = np.array(
[base + datetime.timedelta(minutes=j) for j in range(20)])
didx = pd.DatetimeIndex(data=arr, tz='America/New_York')
gen_df = pd.DataFrame(
data=generated,
index=didx,
columns=["Open", "High", "Low", "Close", "Volume"])
print(gen_df)
real_df = pd.DataFrame(
data=real,
index=didx,
columns=["Open", "High", "Low", "Close", "Volume"])
# mpf.plot(real_dat, type='candle',mav=(3,6,9),volume=True)
fig = mpf.figure(style='yahoo', figsize=(14, 16))
ax1 = fig.add_subplot(2, 2, 1)
ax2 = fig.add_subplot(2, 2, 2)
av1 = fig.add_subplot(3, 2, 5, sharex=ax1)
av2 = fig.add_subplot(3, 2, 6, sharex=ax2)
mpf.plot(gen_df,
type='candle',
ax=ax1,
volume=av1,
mav=(10, 20),
axtitle='generated data')
mpf.plot(real_df,
type='candle',
ax=ax2,
volume=av2,
mav=(10, 20),
axtitle='real data')
mpf.show()
