def __init__(self, pipe, init_data, init_sym, tech_indicators, fps=10):
super().__init__()
self.plots = []
self.tech_indicators = tech_indicators
self.data, self.watched_sym = init_data, init_sym
self.ax, self.ax2 = fplt.create_plot('Live Stock Trader',
init_zoom_periods=500,
maximize=False,
rows=2)
self.live_data = None
self.update_data()
self.fps = fps
self.plots = [
fplt.candlestick_ochl(self.live_data[0], ax=self.ax),
fplt.volume_ocv(self.live_data[1], ax=self.ax.overlay()),
fplt.plot(self.live_data[2], ax=self.ax, legend='VWAP')
]
self.plots.extend([
fplt.plot(ti_d, ax=self.ax2, legend=t)
for t, ti_d in zip(tech_indicators, self.live_data[3:])
])
fplt.timer_callback(
self.step,
1 / fps) # update (using synchronous rest call) every N seconds
self.pipe = pipe
def show_day_plot():
#self.fecha = instance.text
#print (self.fecha)
temp = lol
#temp = pd.date_range(start=temp['time'][self.fechas[0]], end=temp['time'][self.fechas[1]], freq='D')
#print (type(self.fechas[0]))
#print (self.fechas[0],self.fechas[1])
datex = fechas[3]
desde = fechas[1] + timedelta(hours=5)
hasta = desde + timedelta(hours=24)
temp = lol[(lol.time > desde) & (lol.time <= hasta)]
print(temp)
print('mierdaaa')
#print (emp['time'][self.fecha])
#temp = self.get_frac_df(temp,)
ax, ax2, ax3 = fplt.create_plot('NASDAQ', rows=3)
candle_src = fplt.PandasDataSource(
temp[['time', 'open', 'close', 'high', 'low']])
fplt.candlestick_ochl(candle_src, ax=ax)
fplt.plot(lol['time'],
lol['close'].rolling(25).mean(),
ax=ax,
color='#0000ff',
legend='ma-25')
#subprocess.Popen(['python','ploter.py'])
#df['rnd'] = np.random.normal(size=len(df))
#fplt.plot(df['time'], df['rnd'], ax=ax2, color='#992277', legend='stuff')
#fplt.set_y_range(ax2, -4.4, +4.4) # fix y-axis range
# finally a volume bar chart in our third plot
volume_src = fplt.PandasDataSource(
temp[['time', 'open', 'close', 'volume']])
fplt.volume_ocv(volume_src, ax=ax3)
fplt.show()
def setup_plot(self, plot_title, kbars):
import finplot as fplt
kbars = kbars.reset_index().rename(columns={'ts': 'time'})
# adopt TWSE style
fplt.display_timezone = gettz(self._exchange.brokerage.TIMEZONE)
fplt.candle_bull_color = '#ef5350'
fplt.candle_bull_body_color = fplt.candle_bull_color
fplt.candle_bear_color = '#26a69a'
fplt.volume_bull_color = '#f7a9a7'
fplt.volume_bull_body_color = fplt.volume_bull_color
fplt.volume_bear_color = '#92d2cc'
main_ax, rsi_ax, macd_ax = fplt.create_plot(plot_title, rows=3)
fplt.candlestick_ochl(kbars[['time', 'open', 'close', 'high', 'low']],
ax=main_ax)
fplt.volume_ocv(kbars[['time', 'open', 'close', 'volume']],
ax=main_ax.overlay())
fplt.plot(kbars['time'], kbars['rsi'], ax=rsi_ax, legend='RSI')
fplt.set_y_range(0, 100, ax=rsi_ax)
fplt.add_band(self.MIN_RSI, self.MAX_RSI, ax=rsi_ax)
fplt.volume_ocv(kbars[['time', 'open', 'close', 'macd_hist']],
ax=macd_ax,
colorfunc=fplt.strength_colorfilter)
fplt.plot(kbars['time'], kbars['macd'], ax=macd_ax, legend='MACD')
fplt.plot(kbars['time'],
kbars['macd_signal'],
ax=macd_ax,
legend='Signal')
return fplt
def worker(sym, inter, ops, csvlock, plots):
def drawplot():
url = 'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol=%s&interval=%s&apikey=%s&datatype=csv&outputsize=%s'%(sym, inter, APIKEY, ops)
res = rq.get(url)
with open(sym+'temp.csv', 'wb') as f:
for chunk in res.iter_content():
f.write(chunk)
try:
data = pd.read_csv(sym+'temp.csv', parse_dates=['timestamp'], index_col='timestamp').sort_index()
except:
return # if API does not respond with valid data.
update_csv(sym, data)
candles = data[['open','close','high','low']]
volumes = data[['open','close','volume']]
if len(plots) == 0:
plots.append(fplt.volume_ocv(volumes, ax=ax2))
plots.append(fplt.candlestick_ochl(candles, ax=ax))
else:
plots[0].update_data(volumes)
plots[1].update_data(candles)
#==============================================#
ax, ax2 = fplt.create_plot(sym+' Stock Time Series - '+inter+' - '+ops, rows=2)
drawplot()
fplt.timer_callback(drawplot, 3600.0) # draw the graphs every 60 mins (3600 secs)
fplt.show()
def init_chart(self):
canvas = QtWidgets.QGraphicsView()
ax = fplt.create_plot(init_zoom_periods=10)
print(type(ax))
canvas.axs = [ax]
self._ui.centralLayout.addWidget(ax.vb.win, 1, 0, 1, 2)
canvas.show()
def __init__(self):
super().__init__()
self.setWindowTitle("QGraphicsView")
layout = QGridLayout()
self.setLayout(layout)
self.resize(800, 300)
self.ws = BinanceFutureWebsocket()
plots = {}
fplt.y_pad = 0.07 # pad some extra (for control panel)
fplt.max_zoom_points = 7
fplt.autoviewrestore()
self.ax, self.ax_rsi = fplt.create_plot('Complicated Binance Futures Example', rows=2, init_zoom_periods=3000)
self.axo = self.ax.overlay()
layout.addWidget(self.ax.vb.win, 0, 0)
self.ax_rsi.hide()
self.ax_rsi.vb.setBackgroundColor(None) # don't use odd background color
self.ax.set_visible(xaxis=True)
self.symbol = "BTCUSDT"
self.interval = "1m"
self.change_asset()
fplt.timer_callback(self.realtime_update_plot, 1) # update every second
fplt.show(qt_exec=False)
def __init__(self, platform, symbol, time_frame):
self.__platform = platform
self.__symbol = symbol
self.__time_frame = time_frame
self.__market = MARKET(self.__platform, self.__symbol,
self.__time_frame)
# pull some data
self.__indicators = INDICATORS(self.__platform, self.__symbol,
self.__time_frame)
self.__kline = platform.get_kline(self.__time_frame)
self.__kline.reverse()
# format it in pandas
try: # dataframe有7列的情况
self.__df = pd.DataFrame(self.__kline,
columns=[
'time', 'open', 'high', 'low',
'close', 'volume', 'currency_volume'
])
self.__df = self.__df.astype({
'time': 'datetime64[ns]',
'open': 'float64',
'close': 'float64',
'high': 'float64',
'low': 'float64',
'volume': 'float64',
'currency_volume': 'float64'
})
except: # dataframe只有6列的情况，如okex的现货k线数据
self.__df = pd.DataFrame(
self.__kline,
columns=['time', 'open', 'high', 'low', 'close', 'volume'])
self.__df = self.__df.astype({
'time': 'datetime64[ns]',
'open': 'float64',
'close': 'float64',
'high': 'float64',
'low': 'float64',
'volume': 'float64'
})
# create three plot 创建三层图纸，第一层画k线，第二层画成交量，第三层画一些适宜于副图显示的指标
fplt.foreground = '#FFFFFF' # 前景色
fplt.background = '#333333' # 背景色
fplt.odd_plot_background = '#333333' # 第二层图纸的背景色
fplt.cross_hair_color = "#FFFFFF" # 准星的颜色
self.__ax, self.__ax2, self.__ax3 = fplt.create_plot(symbol, rows=3)
# plot candle sticks
candles = self.__df[['time', 'open', 'close', 'high', 'low']]
fplt.candlestick_ochl(candles, ax=self.__ax)
# overlay volume on the plot
volumes = self.__df[['time', 'open', 'close', 'volume']]
fplt.volume_ocv(volumes, ax=self.__ax2)
fplt.add_legend("VOLUME", self.__ax2) # 增加"VOLUME"图例
def __init__(self, data_host):
self.plots = []
self.data_host = data_host
symbol = data_host.request['symbol']
fplt.create_plot(f"{symbol}", init_zoom_periods=75, maximize=True)
self.update_plot()
def upd():
try:
if self.data_host.changed:
self.update_plot()
self.data_host.changed = False
except Exception as ex:
print(ex)
fplt.timer_callback(upd, 0.1) # update in 10 Hz
#fplt.autoviewrestore()
fplt.show()
def plotter():
# getting the selections
selection = assetListBox.curselection()[0]
asset = asset_dict[assetListBox.get(selection)]
currency = denomination_dict[opMenuVar.get()]
periods = periodEntryVar.get()
if currency is None: # No currency is selected.
synthetic_asset = pd.DataFrame(
mt5.copy_rates_from_pos(
asset, mt5.TIMEFRAME_H1, 1,
periods)).drop(columns=['spread', 'real_volume'])
synthetic_asset['time'] = pd.to_datetime(synthetic_asset['time'],
unit='s')
synthetic_asset.set_index(keys=['time'], inplace=True)
else: # Cross-currency is selected
synthetic_asset = synthesize(asset, currency, periods)
synthetic_asset.dropna(inplace=True)
# Calculaating Simple Moving averages
synthetic_asset['SMA 1'] = synthetic_asset['close'].rolling(
sma1boxVar.get()).mean()
synthetic_asset['SMA 2'] = synthetic_asset['close'].rolling(
sma2boxVar.get()).mean()
# Calculating Standard-deviation.
synthetic_asset['pct_change'] = synthetic_asset['close'].pct_change() * 100
synthetic_asset['std-dev'] = synthetic_asset['pct_change'].ewm(
stddevVar.get()).std()
candle_stick_data = fplt.PandasDataSource(
synthetic_asset[['open', 'close', 'high', 'low']])
ax, ax2 = fplt.create_plot(title=f'{asset}/{currency}', rows=2)
candle_plot = fplt.candlestick_ochl(candle_stick_data, ax=ax)
# Plotting SMAs
sma1 = fplt.plot(synthetic_asset['SMA 1'],
legend=f'{sma1boxVar.get()} SMA',
ax=ax)
sma2 = fplt.plot(synthetic_asset['SMA 2'],
legend=f'{sma2boxVar.get()} SMA',
ax=ax)
# Plotting Std-dev
stdDevPlot = fplt.plot(synthetic_asset['std-dev'],
legend=f'Std Dev {stddevVar.get()}',
ax=ax2)
fplt.add_text(pos=(synthetic_asset.index[-1],
synthetic_asset['std-dev'].iloc[-1]),
s=f"{synthetic_asset['std-dev'].iloc[-1].round(3)}",
ax=ax2)
fplt.show()
def plot_kline(kline):
"""
回测结束时绘制k线图
:param kline: 回测时传入指定的k线数据
:return:
"""
kline = kline
# kline.reverse()
# format it in pandas
try: # dataframe有7列的情况
df = pd.DataFrame(kline,
columns=[
'time', 'open', 'high', 'low', 'close', 'volume',
'currency_volume'
])
df = df.astype({
'time': 'datetime64[ns]',
'open': 'float64',
'close': 'float64',
'high': 'float64',
'low': 'float64',
'volume': 'float64',
'currency_volume': 'float64'
})
except: # dataframe只有6列的情况，如okex的现货k线数据
df = pd.DataFrame(
kline, columns=['time', 'open', 'high', 'low', 'close', 'volume'])
df = df.astype({
'time': 'datetime64[ns]',
'open': 'float64',
'close': 'float64',
'high': 'float64',
'low': 'float64',
'volume': 'float64'
})
# create three plot 创建三层图纸，第一层画k线，第二层画成交量，第三层画一些适宜于副图显示的指标
fplt.foreground = '#FFFFFF' # 前景色
fplt.background = '#333333' # 背景色
fplt.odd_plot_background = '#333333' # 第二层图纸的背景色
fplt.cross_hair_color = "#FFFFFF" # 准星的颜色
ax, ax2 = fplt.create_plot("KLINE", rows=2)
# plot candle sticks
candles = df[['time', 'open', 'close', 'high', 'low']]
fplt.candlestick_ochl(candles, ax=ax)
# overlay volume on the plot
volumes = df[['time', 'open', 'close', 'volume']]
fplt.volume_ocv(volumes, ax=ax2)
fplt.add_legend("VOLUME", ax2) # 增加"VOLUME"图例
fplt.show()
def plotter():
# getting the selections
selection = assetListBox.curselection()[0]
asset = asset_dict[assetListBox.get(selection)]
currency = denomination_dict[opMenuVar.get()]
periods = periodEntryVar.get()
synthetic_asset = synthesize(asset, currency, periods)
synthetic_asset.dropna(inplace=True)
candle_stick_data = fplt.PandasDataSource(
synthetic_asset[['open', 'close', 'high', 'low']])
ax = fplt.create_plot(title=f'{asset}/{currency}', rows=1)
candle_plot = fplt.candlestick_ochl(candle_stick_data, ax=ax)
def plot_price_in_range(df, name, start, end):
new_df = get_time_period(df, start, end)
ax = fplt.create_plot(name, rows=1)
# plot candle stick
candles = new_df[['Open', 'Close', 'High', 'Low']]
fplt.candlestick_ochl(candles, ax=ax)
# moving averages
fplt.plot(new_df.Close.rolling(50).mean(), legend='ma50')
fplt.plot(new_df.Close.rolling(100).mean(), legend='ma100')
fplt.plot(new_df.Close.rolling(150).mean(), legend='ma150')
fplt.plot(new_df.Close.rolling(200).mean(), legend='ma200')
# overlay volume on the top plot
volumes = new_df[['Open', 'Close', 'Volume']]
fplt.volume_ocv(volumes, ax=ax.overlay())
fplt.show()
def drawCandleStickChart(timestamp, openVal, high, low, closeVal, volume,
exchange, symbol):
# create two plots
ax = fplt.create_plot(exchange + '-' + symbol, rows=1)
# plot candle sticks
candles = [timestamp, openVal, closeVal, high, low]
fplt.candlestick_ochl(candles, ax=ax)
# # put an MA on the close price
# fplt.plot(timestamp, closeVal.rolling(25).mean(), ax=ax, legend='ma-25')
# overlay volume on the top plot
volumes = [timestamp, openVal, closeVal, volume]
fplt.volume_ocv(volumes, ax=ax.overlay())
# restore view (X-position and zoom) if we ever run this example again
fplt.autoviewrestore()
# we're done
fplt.show()
def show_day():
#lol = df
temp = lol
#temp = self.get_frac_df(temp,)
ax, ax2, ax3 = fplt.create_plot('NASDAQ', rows=3)
candle_src = fplt.PandasDataSource(
lol[['time', 'open', 'close', 'high', 'low']])
fplt.candlestick_ochl(candle_src, ax=ax)
fplt.plot(lol['time'],
lol['close'].rolling(25).mean(),
ax=ax,
color='#0000ff',
legend='ma-25')
#df['rnd'] = np.random.normal(size=len(df))
#fplt.plot(df['time'], df['rnd'], ax=ax2, color='#992277', legend='stuff')
#fplt.set_y_range(ax2, -4.4, +4.4) # fix y-axis range
# finally a volume bar chart in our third plot
volume_src = fplt.PandasDataSource(lol[['time', 'open', 'close',
'volume']])
fplt.volume_ocv(volume_src, ax=ax3)
# we're done
fplt.show()
def plt_finplot_show(df, symbol):
import finplot as fplt
df.reset_index(drop=True, inplace=True)
df = df.astype({'date': 'datetime64[ns]'})
ax, ax2 = fplt.create_plot(symbol, rows=2)
# plot candle sticks
candle_src = fplt.PandasDataSource(df[['date', 'open', 'close', 'high', 'low']])
fplt.candlestick_ochl(candle_src, ax=ax)
# put an MA in there
fplt.plot(df['date'], 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['date'], df['marker'], ax=ax, color='#000000', style='^', legend='dumb mark')
# draw some random crap on our second plot
df['rnd'] = np.random.normal(size=len(df))
fplt.plot(df['date'], df['rnd'], ax=ax2, color='#992277', legend='stuff')
fplt.set_y_range(ax2, -1.4, +1.7) # fix y-axis range
fplt.show()
for window in windows:
ma = df['high'].rolling(window).max() # volatility box top
mi = df['low'].rolling(window).min() # volatility box bottom}
c = ma - mi # volatility box center
vol = c / ma # volatility
diff = vol.diff()
volatility.append(vol)
volatility_diff.append(diff)
#volatility = volatility.rolling(60*6).mean()
#open_price_position_relative_to_volatility_box = (df['open']-df['low'])/center
#%% Plot
# create two 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)
# overlay volume on the top plot
volumes = df[['time', 'open', 'close', 'volume']]
fplt.volume_ocv(volumes, ax=ax.overlay())
# put an MA on the close price
#fplt.plot(df['time'], df['close'].rolling(window).mean(), ax=ax, legend='ma-25')
#fplt.plot(df['time'], maxim, ax=ax, legend='maxim')
#fplt.plot(df['time'], minim, ax=ax, legend='minim')
# draw second plot
win.setWindowTitle('TradingView wannabe')
layout = QGridLayout()
win.setLayout(layout)
win.showMaximized()
combo = QComboBox()
combo.setEditable(True)
[
combo.addItem(i)
for i in 'SPY ^AXJO GC=F GLD ^TNX BTC-USD ETH-USD XRP-USD'.split()
]
layout.addWidget(combo, 0, 0, 1, 1)
info = QLabel()
layout.addWidget(info, 0, 1, 1, 1)
ax = fplt.create_plot(init_zoom_periods=100)
cross_hair_color = '#000000'
ax.crosshair = fplt.FinCrossHair(ax, color=cross_hair_color)
win.axs = [ax] # finplot requres this property
axo = ax.overlay()
layout.addWidget(ax.vb.win, 1, 0, 1, 2)
# Data downloading
@lru_cache(maxsize=15)
def download(symbol):
return yf.download(symbol, '2019-01-01')
@lru_cache(maxsize=100)
def get_name(symbol):
plots.append(fplt.plot(middleband, legend="MIDDLEBAND"))
plots.append(fplt.plot(lowerband, legend="LOWERBAND"))
else:
# every time after we just update the data sources on each plot
plots[0].update_data(candlesticks)
plots[1].update_data(volumes)
plots[2].update_data(upperband)
plots[3].update_data(middleband)
plots[4].update_data(lowerband)
if __name__ == "__main__":
try:
kline = Kline()
plots = []
fplt.foreground = '#FFFFFF' # 前景色
fplt.background = '#333333' # 背景色
fplt.odd_plot_background = '#333333' # 第二层图纸的背景色
fplt.cross_hair_color = "#FFFFFF" # 准星的颜色
ax, ax2 = fplt.create_plot('Realtime kline',
init_zoom_periods=100,
maximize=False,
rows=2)
fplt.add_legend("VOLUME", ax2) # 增加"VOLUME"图例
kline.update()
fplt.timer_callback(
kline.update,
5.0) # update (using synchronous rest call) every N seconds
fplt.show()
except:
logger.error()
l = losses[i] = ni * v + m * l
rs = gains / losses
df['rsi'] = 100 - (100 / (1 + rs))
df.rsi.plot(ax=ax, legend='RSI')
fplt.set_y_range(0, 100, ax=ax)
fplt.add_band(30, 70, ax=ax)
def plot_vma(df, ax):
df.volume.rolling(20).mean().plot(ax=ax, color='#c0c030')
symbol = 'XBTUSD'
df = download_price_history(symbol=symbol)
ax, axv, ax2, ax3, ax4 = fplt.create_plot(
'BitMEX %s heikin-ashi price history' % symbol, rows=5)
ax.set_visible(xgrid=True, ygrid=True)
# price chart
plot_heikin_ashi(df, ax)
plot_bollinger_bands(df, ax)
plot_ema(df, ax)
# volume chart
plot_heikin_ashi_volume(df, axv)
plot_vma(df, ax=axv)
# some more charts
plot_accumulation_distribution(df, ax2)
plot_on_balance_volume(df, ax3)
plot_rsi(df, ax4)
from io import StringIO
from time import time
# load data and convert date
end_t = int(time())
start_t = end_t - 12 * 30 * 24 * 60 * 60 # twelve months
symbol = 'SPY'
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)
r = requests.get(url)
df = pd.read_csv(StringIO(r.text))
df['Date'] = pd.to_datetime(df['Date']).astype(
'int64') // 1_000_000 # use finplot's internal representation, which is ms
ax, ax2 = fplt.create_plot('S&P 500 MACD', rows=2)
# plot macd with standard colors first
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'
def plt_chart(fa_info, save_chart=False, interactive=False):
global df
global symbol
global file_png
# load data and convert date
symbol, sector, industry, website, num_emp, profile = fa_info
return_result = (None, None)
end_t = int(time())
start_t = end_t - NUM_OF_MONTHS * 30 * 24 * 60 * 60 # twelve months
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} {last_date}"
log_msg += title + "\n" + 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)
# 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)
ax, ax1, ax2 = fplt.create_plot(title, rows=3)
# plot price
fplt.candlestick_ochl(df[['Date', 'Open', 'Close', 'High', 'Low']], ax=ax)
hover_label = fplt.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()
fplt.plot(ma15, ax=ax, color=COLOR_IDX["blue"], width=1)
fplt.plot(ma50, ax=ax, color=COLOR_IDX["red"], width=2)
fplt.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)
fplt.volume_ocv(df[['Date', 'Open', 'Close', 'macd_diff']],
ax=ax1,
colorfunc=fplt.strength_colorfilter)
fplt.plot(macd, ax=ax1)
# fplt.plot(signal, ax=ax1, legend='Signal')
fplt.plot(macd, ax=ax1, width=COLOR_IDX["red"])
fplt.plot(signal, ax=ax1, color=COLOR_IDX["black"])
fplt.plot(trend, ax=ax1, width=2, color=COLOR_IDX["blue"])
# # 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 volume
# axo = ax.overlay()
vol_factor = 100000
df['Volume'] = df['Volume'] / vol_factor
fplt.volume_ocv(df[['Date', 'Open', 'Close', 'Volume']], ax=ax2)
fplt.plot(df.Volume.ewm(span=20).mean(),
ax=ax2,
color=COLOR_IDX["black"],
width=2)
# if interactive:
# fplt.set_time_inspector(update_legend_text, ax=ax, when='hover')
# fplt.add_crosshair_info(update_crosshair_text, ax=ax)
if save_chart:
fplt.timer_callback(save, 0.5,
single_shot=True) # wait some until we're rendered
# print(chart_info)
return_result = (fplt, file_png)
return return_result
for iv, vol in zip(volbins, g.volume):
price2vol[iv.left] += vol
data.append([t, sorted(price2vol.items())])
return data
def calc_vwap(period):
vwap = pd.Series([], dtype='float64')
df['hlc3v'] = df['hlc3'] * df.volume
for _, g in df.groupby(pd.Grouper(key='time', freq=period)):
i0, i1 = g.index[0], g.index[-1]
vwap = vwap.append(g.hlc3v.loc[i0:i1].cumsum() /
df.volume.loc[i0:i1].cumsum())
return vwap
# download and calculate indicators
df = download_price_history(
interval_mins=30) # reduce to [15, 5, 1] minutes to increase accuracy
time_volume_profile = calc_volume_profile(
df, period='W',
bins=100) # try fewer/more horizontal bars (graphical resolution only)
vwap = calc_vwap(period='W') # try period='D'
# plot
fplt.create_plot('Binance BTC futures weekly volume profile')
fplt.plot(df.time, df.close, legend='Price')
fplt.plot(df.time, vwap, style='--', legend='VWAP')
fplt.horiz_time_volume(time_volume_profile, draw_va=0.7, draw_poc=1.0)
fplt.show()
df = yf.download('GOOG',
start_day.isoformat(),
now.isoformat(),
interval='90m')
dfms = yf.download('MSFT',
start_day.isoformat(),
now.isoformat(),
interval='90m')
# resample to daily candles, i.e. five 90-minute candles per business day
dfd = df.Open.resample('D').first().to_frame()
dfd['Close'] = df.Close.resample('D').last()
dfd['High'] = df.High.resample('D').max()
dfd['Low'] = df.Low.resample('D').min()
ax, ax2 = fplt.create_plot('Alphabet Inc.', rows=2, maximize=False)
ax2.disable_x_index() # second plot is not timebased
# plot down-sampled daily candles first
daily_plot = fplt.candlestick_ochl(dfd.dropna(), candle_width=5)
daily_plot.colors.update(
dict(bull_body='#bfb',
bull_shadow='#ada',
bear_body='#fbc',
bear_shadow='#dab'))
daily_plot.x_offset = 3.1 # resample() gets us start of day, offset +1.1 (gap+off center wick)
# plot high resolution on top
fplt.candlestick_ochl(df[['Open', 'Close', 'High', 'Low']])
# scatter plot correlation between Google and Microsoft stock
tdup,tddn = td_sequential(df['close'])
df['tdup'] = [('%i'%i if 0<i<10 else '') for i in tdup]
df['tddn'] = [('%i'%i if 0<i<10 else '') for i in tddn]
# pick columns for our three data sources: candlesticks and TD sequencial labels for up/down
candlesticks = df['time open close high low'.split()]
volumes = df['time open close volume'.split()]
td_up_labels = df['time high tdup'.split()]
td_dn_labels = df['time low tddn'.split()]
if not plots:
# first time we create the plots
global ax
plots.append(fplt.candlestick_ochl(candlesticks))
plots.append(fplt.volume_ocv(volumes, ax=ax.overlay()))
plots.append(fplt.labels(td_up_labels, color='#009900'))
plots.append(fplt.labels(td_dn_labels, color='#990000', anchor=(0.5,0)))
else:
# every time after we just update the data sources on each plot
plots[0].update_data(candlesticks)
plots[1].update_data(volumes)
plots[2].update_data(td_up_labels)
plots[3].update_data(td_dn_labels)
plots = []
ax = fplt.create_plot('Realtime Bitcoin/Dollar 1m TD Sequential (BitFinex REST)', init_zoom_periods=100, maximize=False)
update()
fplt.timer_callback(update, 5.0) # update (using synchronous rest call) every N seconds
fplt.show()
'''Plot quote table (in millions). We're using lables on top of a heatmap to create sort of a table.'''
ax.set_visible(yaxis=False) # Y axis is useless on our table
def skip_y_crosshair_info(x, y, xt, yt): # we don't want any Y crosshair info on the table
return xt, ''
fplt.add_crosshair_info(skip_y_crosshair_info, ax=ax)
fplt.set_y_range(0, 2, ax) # 0-1 for bid row, 1-2 for ask row
# add two columns for table cell colors
quotes[1] = -quotes['askSize'] * 0.5 / quotes['askSize'].max() + 0.5
quotes[0] = +quotes['bidSize'] * 0.5 / quotes['bidSize'].max() + 0.5
ts = [int(t.timestamp()) for t in quotes.index]
colmap = fplt.ColorMap([0.0, 0.5, 1.0], [[200, 80, 60], [200, 190, 100], [40, 170, 30]]) # traffic light colors
fplt.heatmap(quotes[[1, 0]], colmap=colmap, colcurve=lambda x: x, ax=ax) # linear color mapping
fplt.labels(ts, [1.5]*count, ['%.1f'%(v/1e6) for v in quotes['askSize']], ax=ax2, anchor=(0.5, 0.5))
fplt.labels(ts, [0.5]*count, ['%.1f'%(v/1e6) for v in quotes['bidSize']], ax=ax2, anchor=(0.5, 0.5))
prices, quotes = download_resample()
fplt.max_zoom_points = 5
fplt.right_margin_candles = 0
ax,ax2 = fplt.create_plot(f'BitMEX {downsample}m quote bubble plot + quote table', rows=2, maximize=False)
fplt.windows[0].ci.layout.setRowStretchFactor(0, 10) # make primary plot large, and implicitly table small
candles = fplt.candlestick_ochl(prices[['Open','Close','High','Low']], ax=ax)
candles.colors.update(dict(bear_body='#fa8')) # bright red, to make bubbles visible
fplt.volume_ocv(prices[['Open','Close','Volume']], ax=ax.overlay())
plot_quote_bubbles(quotes, ax=ax)
plot_quote_table(quotes, ax=ax2)
fplt.show()
#!/usr/bin/env python3
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=0.1, whiteout=0.1)
fplt.show()
#!/usr/bin/env python3
import finplot as fplt
import numpy as np
import pandas as pd
import yfinance as yf
btc = yf.download('BTC-USD', '2014-09-01')
ax1, ax2, ax3, ax4, ax5 = fplt.create_plot('Bitcoin/Dollar long term analysis',
rows=5,
maximize=False)
fplt.set_y_scale(ax=ax1, yscale='log')
fplt.plot(btc.Close, color='#000', legend='Log price', ax=ax1)
btc['ma200'] = btc.Close.rolling(200).mean()
btc['ma50'] = btc.Close.rolling(50).mean()
fplt.plot(btc.ma200, legend='MA200', ax=ax1)
fplt.plot(btc.ma50, legend='MA50', ax=ax1)
btc['one'] = 1
fplt.volume_ocv(btc[['ma200', 'ma50', 'one']],
candle_width=1,
ax=ax1.overlay(scale=0.02))
daily_ret = btc.Close.pct_change() * 100
fplt.plot(daily_ret, width=3, color='#000', legend='Daily returns %', ax=ax2)
fplt.add_legend('Daily % returns histogram', ax=ax3)
fplt.hist(daily_ret, bins=60, ax=ax3)
fplt.add_legend('Yearly returns in %', ax=ax4)
basequote = base_asset[[
'open', 'high', 'low', 'close'
]] * quote_asset[['open', 'high', 'low', 'close']]
# averaging volume.
basequote['vol'] = (
(base_asset['tick_volume'] + quote_asset['tick_volume']) / 2)
basequote.set_index(keys=[base_asset['time']], inplace=True)
return basequote
symbol1 = input('Enter base symbol: ').upper()
symbol2 = input('Enter quote symbol: ').upper()
period = int(input('Enter period in Hours: '))
synthetic_asset = synthesize(symbol1, symbol2, period)
print(synthetic_asset)
candle_stick_data = fplt.PandasDataSource(
synthetic_asset[['open', 'close', 'high', 'low']])
volume = fplt.PandasDataSource(synthetic_asset[['open', 'close', 'vol']])
ax = fplt.create_plot(title=f'{symbol1}/{symbol2}', rows=1)
axo = ax.overlay()
candle_plot = fplt.candlestick_ochl(candle_stick_data, ax=ax)
volume_olay = fplt.volume_ocv(volume, ax=axo)
fplt.show()
mt5.shutdown()
t -= t % (60*interval_mins)
c = trade['price']
if t < df['t'].iloc[-1]:
# ignore already-recorded trades
continue
elif t > df['t'].iloc[-1]:
# add new candle
o = df['c'].iloc[-1]
h = c if c>o else o
l = o if o<c else c
df1 = pd.DataFrame(dict(t=[t], o=[o], c=[c], h=[l], l=[l]))
df = pd.concat([df, df1], ignore_index=True, sort=False)
else:
# update last candle
i = df.index.max()
df.loc[i,'c'] = c
if c > df.loc[i,'h']:
df.loc[i,'h'] = c
if c < df.loc[i,'l']:
df.loc[i,'l'] = c
update_plot(df)
if __name__ == '__main__':
df = pd.DataFrame(price_history())
ws = BitMEXWebsocket(endpoint=baseurl+'/v1', symbol='XBTUSD')
ax = fplt.create_plot('Realtime Bitcoin/Dollar 1m (BitMEX websocket)', init_zoom_periods=100, maximize=False)
update_plot(df)
fplt.timer_callback(update_data, 1.0) # update every second
fplt.show()
# define time range
start = dt.date.today() - dt.timedelta(days = 365*10)
end = dt.datetime.now()
stock = 'AAPL'
df = yf.download(stock,start, end, interval='1d')
# format it in pandas
df = df.reset_index()
df = df.drop(columns = ['Adj Close'])
df = df.rename(columns={'Date':'time', 'Open':'open', 'Close':'close', 'High':'high', 'Low':'low', 'Volume':'volume'})
df = df.astype({'time':'datetime64[ns]'})
# create two plots
ax,ax2 = fplt.create_plot(stock, rows=2)
# plot candle sticks
candles = df[['time','open','close','high','low']]
fplt.candlestick_ochl(candles, ax=ax)
# overlay volume on the top plot
volumes = df[['time','open','close','volume']]
fplt.volume_ocv(volumes, ax=ax.overlay())
# put an MA on the close price
fplt.plot(df['time'], df['close'].rolling(25).mean(), ax=ax, legend='ma-25')
# place some markers on low wicks
lo_wicks = df[['open','close']].T.min() - df['low']
df.loc[(lo_wicks>lo_wicks.quantile(0.99)), 'marker'] = df['low']
