' <Close>': 'close',
' <High>': 'high',
' <Low>': 'low',
' <Volume>': 'volume'
})
for e in range(len(df['time'])):
df['time'][e] = datetime.strptime(df['time'][e], "%m/%d/%Y %H:%M:%S")
df = df.astype({'time': 'datetime64[ns]'})
print(df)
# create three plots
ax, ax2, ax3 = fplt.create_plot(symbol, rows=3)
# plot candle sticks
candles = df[['time', 'open', 'close', 'high', 'low']]
fplt.candlestick_ochl(candles, ax=ax)
# put an MA in there
#fplt.plot(df['time'], df['close'].rolling(25).mean(), ax=ax, color='#00f', legend='ma-25')
# place some dumb markers
hi_wicks = df['high'] - df[['open', 'close']].T.max()
df.loc[(hi_wicks > hi_wicks.quantile(0.99)), 'marker'] = df['high']
#fplt.plot(df['time'], df['marker'], ax=ax, color='#f44', style='^', legend='dumb mark')
# draw some random crap on our second plot
fplt.plot(df['time'],
np.random.normal(size=len(df)),
ax=ax2,
color='#927',
legend='stuff')
'T': 'time',
'O': 'open',
'C': 'close',
'H': 'high',
'L': 'low',
'V': 'volume'
})
df = df.astype({'time': 'datetime64[ns]'})
# create three plots
ax, ax2, ax3 = fplt.create_plot(symbol, rows=3)
# plot candle sticks
candle_src = fplt.PandasDataSource(df[['time', 'open', 'close', 'high',
'low']])
fplt.candlestick_ochl(candle_src, ax=ax)
# put an MA in there
fplt.plot(df['time'],
df['close'].rolling(25).mean(),
ax=ax,
color='#0000ff',
legend='ma-25')
# place some dumb markers
hi_wicks = df['high'] - df[['open', 'close']].T.max().T
df.loc[(hi_wicks > hi_wicks.quantile(0.99)), 'marker'] = df['close']
fplt.plot(df['time'],
df['marker'],
ax=ax,
color='#000000',
def plotCandles(cls, df, ax):
#return df[["Open", "Close", "High", "Low"]].plot(ax=ax, kind="candle")
return fplt.candlestick_ochl(df[cls.cs_keys], ax=ax)
import finplot as fplt
import pandas as pd
bars_df = pd.read_feather('bars.feather')
bars_df = bars_df.set_index('close_at')
fplt.candlestick_ochl(bars_df[['price_open','price_close','price_high','price_low']])
fplt.show()
def plot_bars(bars:pd.DataFrame):
ax1, ax2 = fplt.create_plot('Prices+Vol', rows=2)
fplt.candlestick_ochl(bars[['price_open','price_close','price_high','price_low']], ax=ax1)
fplt.volume_ocv(bars[['price_open','price_close','volume_sum']], ax=ax2)
fplt.show()
# candle stick example
from math import pi
import pandas as pd
from bokeh.plotting import figure, output_file, show
from bokeh.sampledata.stocks import MSFT
df = pd.DataFrame(MSFT)[:50]
df["date"] = pd.to_datetime(df["date"])
inc = df.close > df.open
dec = df.open > df.close
w = 12*60*60*1000 # half day in ms
def plot_bars(bars:pd.DataFrame):
ax1, ax2 = fplt.create_plot('Prices+Vol', rows=2)
fplt.candlestick_ochl(bars[['price_open','price_close','price_high','price_low']], ax=ax1)
fplt.volume_ocv(bars[['price_open','price_close','volume_sum']], ax=ax2)
fplt.show()
import datetime as dt
mt5.initialize()
#________Setting up Data_________#
symbol = input('Enter symbol: ').upper()
rate = pd.DataFrame(mt5.copy_rates_from_pos(symbol, mt5.TIMEFRAME_H1, 0, 200))
rate['time'] = pd.to_datetime(rate['time'], unit='s')
#________Settingup plot________#
# creating multiple windows
ax, ax2 = fplt.create_plot(title=symbol, rows=2)
fplt.candlestick_ochl(rate[['time', 'open', 'close', 'high', 'low']], ax=ax)
# creating volume overlay on 1st window
axo = ax.overlay()
fplt.volume_ocv(rate[['time', 'open', 'close', 'tick_volume']], ax=axo)
#_________plotting MACD on 2nd window__________#
# MACD line
macd = rate['close'].ewm(span=12).mean() - rate['close'].ewm(span=26).mean()
#signal line
signal = macd.ewm(span=9).mean()
rate['macd_diff'] = macd - signal
# plotting MACD histogram
def plt_chart(fa_info, save_chart=False, interactive=False):
global df
global symbol
global file_png
# load data and convert date
file_png = ""
symbol,sector,industry,website,num_emp,profile,pct_shorts,book_value = fa_info
return_result = (None, None)
end_t = int(time())
start_t = end_t - NUM_OF_MONTHS*30*24*60*60
interval = '1d'
url = 'https://query1.finance.yahoo.com/v7/finance/download/%s?period1=%s&period2=%s&interval=%s&events=history' % (symbol, start_t, end_t, interval)
try:
r = requests.get(url)
df = pd.read_csv(StringIO(r.text))
if df.empty:
print(f"[Warn] symbol {symbol} has no data")
return return_result
if df.shape[0] < MA_SLOW:
print(f"[Warn] symbol {symbol} has fewer than {MA_SLOW} data points")
return return_result
except Exception as ex:
print(f"[Error] failed to download quote from Yahoo finance for {symbol}")
return return_result
last_date = str(df.tail(1)['Date'].values[0]).split("T")[0]
df['Date'] = pd.to_datetime(df['Date']).astype('int64') # use finplot's internal representation, which is ns
log_msg = ""
if sector:
title = f"[{symbol}] {website} - {sector} - {industry} - emp: {num_emp} - shorts: {pct_shorts}% - book: {book_value} {last_date}"
log_msg += title + "\n" + sanitize(profile)
else:
title = f"[{symbol}] {last_date}"
log_msg += title
log_msg += "\n======================================================="
# print(log_msg)
# with open("_finplot-watchlist.log", "a") as f:
# f.write(log_msg)
ax,ax1,ax2 = finplot.create_plot(title, rows=3)
# plot price
finplot.candlestick_ochl(df[['Date','Open','Close','High','Low']], ax=ax)
hover_label = finplot.add_legend(symbol, ax=ax)
ma15=df.Close.ewm(span=MA_FAST).mean()
ma50=df.Close.ewm(span=MA_SLOW).mean()
ma200=df.Close.ewm(span=MA_LONG).mean()
finplot.plot(ma15, ax=ax, color=COLOR_IDX["blue"], width=1)
finplot.plot(ma50, ax=ax, color=COLOR_IDX["red"], width=2)
finplot.plot(ma200, ax=ax, color=COLOR_IDX["green"], width=3)
# plot macd with standard colors first
macd = ma15-ma50
signal = macd.ewm(span=MACD_AVG).mean()
trend = TREND_FACTOR*(ma50-ma200)
df['macd_diff'] = MACD_FACTOR*(macd - signal)
finplot.volume_ocv(df[['Date','Open','Close','macd_diff']], ax=ax1, colorfunc=finplot.strength_colorfilter)
finplot.plot(macd, ax=ax1)
# finplot.plot(signal, ax=ax1, legend='Signal')
finplot.plot(macd, ax=ax1, width=COLOR_IDX["red"])
finplot.plot(signal, ax=ax1, color=COLOR_IDX["black"])
finplot.plot(trend, ax=ax1, width=2, color=COLOR_IDX["blue"])
# # change to b/w coloring templates for next plots
# finplot.candle_bull_color = finplot.candle_bear_color = '#000'
# finplot.volume_bull_color = finplot.volume_bear_color = '#333'
# finplot.candle_bull_body_color = finplot.volume_bull_body_color = '#fff'
# plot volume
# axo = ax.overlay()
vol_factor = 100000
df['Volume'] = df['Volume']/vol_factor
finplot.volume_ocv(df[['Date','Open','Close','Volume']], ax=ax2)
finplot.plot(df.Volume.ewm(span=20).mean(), ax=ax2, color=COLOR_IDX["black"], width=2)
# if interactive:
# finplot.set_time_inspector(update_legend_text, ax=ax, when='hover')
# finplot.add_crosshair_info(update_crosshair_text, ax=ax)
if save_chart:
# print(__file__)
pardir = os.path.dirname(__file__)
chart_dir = os.path.join(pardir, "charts", today_str)
if not os.path.exists(chart_dir):
os.mkdir(chart_dir)
file_png = os.path.join(chart_dir, f"{symbol}_{today_str}.png")
# print(file_png)
finplot.timer_callback(save, 0.5, single_shot=True) # wait some until we're rendered
# print(chart_info)
return file_png, log_msg
def change_asset(*args, **kwargs):
'''Resets and recalculates everything, and plots for the first time.'''
# save window zoom position before resetting
fplt._savewindata(fplt.windows[0])
symbol = ctrl_panel.symbol.currentText()
interval = ctrl_panel.interval.currentText()
ws.df = None
df = load_price_history(symbol, interval=interval)
ws.reconnect(symbol, interval, df)
# remove any previous plots
ax.reset()
axo.reset()
ax_rsi.reset()
# calculate plot data
indicators = ctrl_panel.indicators.currentText().lower()
data, price_data = calc_plot_data(df, indicators)
# some space for legend
ctrl_panel.move(100 if 'clean' in indicators else 200, 0)
# plot data
global plots
plots = {}
plots['price'] = fplt.candlestick_ochl(data['price'], ax=ax)
plots['volume'] = fplt.volume_ocv(data['volume'], ax=axo)
if data['ma50'] is not None:
plots['ma50'] = fplt.plot(data['ma50'], legend='MA-50', ax=ax)
plots['ma200'] = fplt.plot(data['ma200'], legend='MA-200', ax=ax)
plots['vema24'] = fplt.plot(data['vema24'],
color=4,
legend='V-EMA-24',
ax=axo)
if data['rsi'] is not None:
ax.set_visible(xaxis=False)
ax_rsi.show()
fplt.set_y_range(0, 100, ax=ax_rsi)
fplt.add_band(30, 70, color='#6335', ax=ax_rsi)
plots['sar'] = fplt.plot(data['sar'],
color='#55a',
style='+',
width=0.6,
legend='SAR',
ax=ax)
plots['rsi'] = fplt.plot(data['rsi'], legend='RSI', ax=ax_rsi)
plots['stoch'] = fplt.plot(data['stoch'],
color='#880',
legend='Stoch',
ax=ax_rsi)
plots['stoch_s'] = fplt.plot(data['stoch_s'], color='#650', ax=ax_rsi)
else:
ax.set_visible(xaxis=True)
ax_rsi.hide()
# price line
ax.price_line = pg.InfiniteLine(
angle=0,
movable=False,
pen=fplt._makepen(fplt.candle_bull_body_color, style='.'))
ax.price_line.setPos(price_data['last_close'])
ax.price_line.pen.setColor(pg.mkColor(price_data['last_col']))
ax.addItem(ax.price_line, ignoreBounds=True)
# restores saved zoom position, if in range
fplt.refresh()
def updateCandlePane(self, quotes):
self.ax.reset()
finplot.candlestick_ochl(quotes)
finplot.refresh()
self.hoverLabel = finplot.add_legend('', ax=self.ax)
finplot.set_time_inspector(self.updateLegend, ax=self.ax, when='hover')
import finplot as fplt
import pandas as pd
import requests
# download, extract times and candles
url = 'https://www.tensorcharts.com/tensor/bitmex/XBTUSD/heatmapCandles/15min'
data = requests.get(url).json()
times = pd.to_datetime([e['T'] for e in data])
candles = [(e['open'], e['close'], e['high'], e['low']) for e in data]
df_candles = pd.DataFrame(index=times, data=candles)
# extract volume heatmap as a PRICE x VOLUME matrix
orderbooks = [(e['heatmapOrderBook'] or []) for e in data]
prices = sorted(set(prc for ob in orderbooks for prc in ob[::2]))
vol_matrix = [[0] * len(prices) for _ in range(len(times))]
for i, orderbook in enumerate(orderbooks):
for price, volume in zip(orderbook[::2], orderbook[1::2]):
j = prices.index(price)
vol_matrix[i][j] = volume
df_volume_heatmap = pd.DataFrame(index=times, columns=prices, data=vol_matrix)
# plot
fplt.create_plot('BitMEX BTC 15m orderbook heatmap')
fplt.candlestick_ochl(df_candles)
fplt.heatmap(df_volume_heatmap, filter_limit=0.2, whiteout=0.3)
fplt.show()
def plot_signal(kline, buy_signal=None, sell_signal=None, *args):
"""回测完成后调用此函数绘制k线图与指标"""
fplt.foreground = '#FFFFFF'
fplt.background = '#333333'
fplt.odd_plot_background = '#333333'
fplt.cross_hair_color = "#FFFFFF"
ax, ax2, ax3 = fplt.create_plot('历史K线图',
init_zoom_periods=100,
maximize=True,
rows=3)
fplt.add_legend("K线主图", ax)
fplt.add_legend("成交量", ax2)
fplt.add_legend("指标副图", ax3)
df = pd.DataFrame(kline)
df = df[[0, 1, 2, 3, 4, 5]]
columns = ['time', 'open', 'high', 'low', 'close', 'volume']
df.columns = columns
df = df.astype({
'time': 'datetime64[ns]',
'open': 'float64',
'high': 'float64',
'low': 'float64',
'close': 'float64',
'volume': 'float64'
})
candlesticks = df['time open close high low'.split()]
volumes = df['time open close volume'.split()]
fplt.candlestick_ochl(candlesticks)
fplt.volume_ocv(volumes, ax=ax2)
if args:
count = 1
for i in args:
indicators = pd.Series(i)
fplt.plot(df['time'],
indicators,
legend="指标{}".format(count),
ax=ax3)
count += 1
if buy_signal:
for i in buy_signal:
df.loc[df['high'] == buy_signal[buy_signal.index(i)],
"buy_signal"] = df['high']
fplt.plot(df['time'],
df['buy_signal'],
ax=ax,
color="#FF0000",
style='^',
width=2,
legend='买入信号')
if sell_signal:
for i in sell_signal:
df.loc[df['low'] == sell_signal[sell_signal.index(i)],
"sell_signal"] = df['low']
fplt.plot(df['time'],
df['sell_signal'],
ax=ax,
color="#00FF00",
style='v',
width=2,
legend='卖出信号')
fplt.show()
import requests
import pandas as pd
now = date.today().strftime('%Y-%m-%d')
r = requests.get(
'https://www.bitstamp.net/api-internal/tradeview/price-history/BTC/USD/?step=86400&start_datetime=2011-08-18T00:00:00.000Z&end_datetime=%sT00:00:00.000Z'
% now)
df = pd.DataFrame(r.json()['data']).astype({
'timestamp': int,
'open': float,
'close': float,
'high': float,
'low': float
}).set_index('timestamp')
# plot price
fplt.create_plot('Bitcoin 2011-%s' % now.split('-')[0], yscale='log')
fplt.candlestick_ochl(df['open close high low'.split()])
# monthly separator lines
months = pd.to_datetime(df.index, unit='s').strftime('%m')
last_month = ''
for x, (month, price) in enumerate(zip(months, df.close)):
if month != last_month:
fplt.add_line((x - 0.5, price * 0.5), (x - 0.5, price * 2),
color='#bbb',
style='--')
last_month = month
fplt.show()
macd = df.Close.ewm(span=12).mean() - df.Close.ewm(span=26).mean()
signal = macd.ewm(span=9).mean()
df['macd_diff'] = macd - signal
fplt.volume_ocv(df[['Date', 'Open', 'Close', 'macd_diff']],
ax=ax2,
colorfunc=fplt.strength_colorfilter)
fplt.plot(macd, ax=ax2, legend='MACD')
fplt.plot(signal, ax=ax2, legend='Signal')
# change to b/w coloring templates for next plots
fplt.candle_bull_color = fplt.candle_bear_color = '#000'
fplt.volume_bull_color = fplt.volume_bear_color = '#333'
fplt.candle_bull_body_color = fplt.volume_bull_body_color = '#fff'
# plot price and volume
fplt.candlestick_ochl(df[['Date', 'Open', 'Close', 'High', 'Low']], ax=ax)
hover_label = fplt.add_legend('', ax=ax)
axo = ax.overlay()
fplt.volume_ocv(df[['Date', 'Open', 'Close', 'Volume']], ax=axo)
fplt.plot(df.Volume.ewm(span=24).mean(), ax=axo, color=1)
#######################################################
## update crosshair and legend when moving the mouse ##
def update_legend_text(x, y):
row = df.loc[df.Date == x]
# format html with the candle and set legend
fmt = '<span style="color:#%s">%%.2f</span>' % ('0b0' if (
row.Open < row.Close).all() else 'a00')
rawtxt = '<span style="font-size:13px">%%s %%s</span> O%s C%s H%s L%s' % (
def plotter():
"""Takes the input value from widgets and creates a candlestick plot."""
base = (symbolVar.get()).upper()
quote = quoteCurrency.get()
start_date = startDateCal.get_date()
end_date = endDateCal.get_date()
interval = intervalVar.get()
#______If no symbol entered_______#
if len(base) == 0:
errorBox()
return None
else:
pass
base_ticker = yf.Ticker(base)
#_____If symbol is invalid________#
try:
base_currency = base_ticker.info['currency']
except ImportError:
errorBox2()
return None
else:
pass
if quote == 'Select' or quote == base_currency: # No currency is selected or base denomination = selected currency.
base_ticker = yf.Ticker(base)
base_quote = base_ticker.history(
start=start_date, end=end_date, interval=interval).drop(
columns=['Dividends', 'Stock Splits', 'Volume'])
else: # A currency is selected.
base_quote = synthesize(base, quote, start_date, end_date, interval)
base_quote.dropna(inplace=True)
# print(base_quote)
# Calculating Simple Moving averages
base_quote['SMA 1'] = base_quote['Close'].rolling(sma1Var.get()).mean()
base_quote['SMA 2'] = base_quote['Close'].rolling(sma2Var.get()).mean()
# print(base_quote)
# Calculating Standard-deviation.
base_quote['pct_change'] = base_quote['Close'].pct_change() * 100
base_quote['std-dev'] = base_quote['pct_change'].rolling(
stdDevVar.get()).std()
# print(base_quote)
candle_stick_data = fplt.PandasDataSource(
base_quote[['Open', 'Close', 'High', 'Low']])
ax, ax2 = fplt.create_plot(title=f"{base}/{quote}", rows=2)
candle_plot = fplt.candlestick_ochl(candle_stick_data, ax=ax)
# Plotting SMAs
sma1 = fplt.plot(base_quote['SMA 1'], legend=f'{sma1Var.get()} SMA', ax=ax)
sma2 = fplt.plot(base_quote['SMA 2'], legend=f'{sma2Var.get()} SMA', ax=ax)
# Ploting StdDev
stdDevPlot = fplt.plot(base_quote['std-dev'],
legend=f'{stdDevVar.get()} period std-dev',
ax=ax2)
fplt.add_text(pos=(base_quote.index[-1], base_quote['std-dev'].iloc[-1]),
s=f"{base_quote['std-dev'].iloc[-1].round(2)}%",
ax=ax2)
fplt.show()
def plot_price(df):
df = df.set_index('time')
fplt.candlestick_ochl(df[['open', 'close', 'high', 'low']])
fplt.show()
def show_day_plot(self, instance):
aux = int(instance.text.split('-')[0])
#print ('fechaaa')
#print (aux)
'''TERMINA TUS WEADAS ACA'''
#print (self.marketdata)
#temp = pd.date_range(start=temp['time'][self.fechas[0]], end=temp['time'][self.fechas[1]], freq='D')
#print (self.fechas[0])
#print (self.fechas[0],self.fechas[1])
#datex = self.fechas[3]
#print (rt.Singleton.getInstance().result)
try:
self.marcadores = pd.read_pickle('estrategia1.pkl')
self.marketdata['marker'] = self.marcadores['marker']
except:
print('mierda')
desde = self.fechas[aux] + timedelta(hours=5)
hasta = desde + timedelta(hours=24)
temp = self.marketdata[(self.marketdata.time > desde)
& (self.marketdata.time <= hasta)]
#print (temp)
#print (temp['time'][self.fecha])
#temp = self.get_frac_df(temp,)
#print ('actualizado')
#print ('SELECCIONADO')
#print (temp)
#print (temp.dtypes)
'''TODO :MEJORA EL FFT que agrege en la ventana izquierda en matplotlib'''
#self.plot_fft(temp,'filtered')
ax, ax2 = fplt.create_plot('NASDAQ', rows=2) #,maximize=True)
try:
'''CANDLESTICK CHARTS'''
candle_src = fplt.PandasDataSource(
temp[['time', 'open', 'close', 'high', 'low']])
fplt.candlestick_ochl(candle_src,
ax=ax,
draw_body=True,
draw_shadow=True)
fplt.plot(temp['time'],
temp['sma200'],
ax=ax,
color='#0000ff',
legend='ma-200')
fplt.plot(temp['time'],
temp['filtered'],
ax=ax,
color='#ff00ff',
legend='filtrado',
width=2)
try:
'''OBTIENE LAS ORDENES DE COMPRA-VENTA'''
buys = pd.read_pickle("book_buy.pkl")
#sell = pd.read_pickle("book_sell.pkl")
'''PROCEDE A PLOTEAR'''
print('compras')
print(buys)
pass
except:
print("[ ERROR ] Fallo al obtener las ordenes de compra/venta")
pass
'''VOLUMEN CHARTS'''
volume_src = fplt.PandasDataSource(
temp[['time', 'open', 'close', 'volume']])
fplt.volume_ocv(volume_src, ax=ax2)
# place some dumb markers
#hi_wicks = df['high'] - df[['open','close']].T.max().T
#df.loc[(hi_wicks>hi_wicks.quantile(0.99)), 'marker'] = df['close']
fplt.plot(temp['time'],
temp['marker'],
ax=ax,
color='#000000',
style='^',
legend='dumb mark')
#with pd.option_context('display.max_rows', None, 'display.max_columns', None): # more options can be specified also
# print (self.marcadores)
'''INTERMEDIOS'''
#te = np.random.normal(size=len(temp))
#fplt.plot(temp['time'], te, ax=ax2, color='#001177', legend='vortex pos')
#fplt.plot(temp['time'], temp['vortex_n'], ax=ax2, color='#ff0000', legend='vortex neg')
#fplt.set_y_range(ax2, -4.4, +4.4) # fix y-axis range
# draw some random crap on our second plot
#temp['rnd'] = np.random.normal(size=len(temp))
#print (temp.vortexn)
#fplt.plot(temp['time'], temp['vortexn'], ax=ax3, color='#992277', legend='stuff')
#fplt.set_y_range(ax3, -10.4, +10.7) # fix y-axis range
# finally a volume bar chart in our third plot
fplt.show()
#pass
except Exception as e:
print(e)
pass
