def update(txt):
df = download(txt)
if len(df) < 20: # symbol does not exist
return
info.setText('Loading symbol name...')
price = df['Open Close High Low'.split()]
ma20 = df.Close.rolling(20).mean()
ma50 = df.Close.rolling(50).mean()
volume = df['Open Close Volume'.split()]
if not plots:
plots.append(fplt.candlestick_ochl(price))
plots.append(fplt.plot(ma20, legend='MA-20'))
plots.append(fplt.plot(ma50, legend='MA-50'))
plots.append(fplt.volume_ocv(volume, ax=ax.overlay()))
else:
plots[0].update_data(price)
plots[1].update_data(ma20)
plots[2].update_data(ma50)
plots[3].update_data(volume)
Thread(target=lambda: info.setText(get_name(txt))).start(
) # slow, so use thread
def plot_rsi(df, ax):
diff = df.close.diff().values
gains = diff
losses = -diff
with np.errstate(invalid='ignore'):
gains[(gains < 0) | np.isnan(gains)] = 0.0
losses[(losses <= 0)
| np.isnan(losses)] = 1e-10 # we don't want divide by zero/NaN
n = 14
m = (n - 1) / n
ni = 1 / n
g = gains[n] = np.nanmean(gains[:n])
l = losses[n] = np.nanmean(losses[:n])
gains[:n] = losses[:n] = np.nan
for i, v in enumerate(gains[n:], n):
g = gains[i] = ni * v + m * g
for i, v in enumerate(losses[n:], n):
l = losses[i] = ni * v + m * l
rs = gains / losses
df['rsi'] = pd.Series(100 - (100 / (1 + rs)))
fplt.plot(df.time, df.rsi, ax=ax, legend='RSI')
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 search(self):
"""
This function displays given stock's history for the period of 4 years.
Args:
None
Returns:
None
"""
try:
df = yf.download(
self.search_entry.get(),
start="2017-01-01",
end=datetime.today().strftime("%Y-%m-%d"),
)
fplt.candlestick_ochl(df[["Open", "Close", "High", "Low"]])
fplt.plot(df.Close.rolling(50).mean())
fplt.plot(df.Close.rolling(200).mean())
fplt.show()
except IndexError:
messagebox.showerror("Stock Name Error", "Invalid Stock name")
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 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 plot(df, x, y, kind, **kwargs):
_x = df.index if y is None else df[x]
try:
_y = df[x].reset_index(drop=True) if y is None else df[y]
except:
_y = df.reset_index(drop=True)
kwargs = dict(kwargs)
if 'by' in kwargs:
del kwargs['by']
if kind in ('candle', 'candle_ochl', 'candlestick', 'candlestick_ochl',
'volume', 'volume_ocv', 'renko'):
if 'candle' in kind:
return finplot.candlestick_ochl(df, **kwargs)
elif 'volume' in kind:
return finplot.volume_ocv(df, **kwargs)
elif 'renko' in kind:
return finplot.renko(df, **kwargs)
elif kind == 'scatter':
if 'style' not in kwargs:
kwargs['style'] = 'o'
return finplot.plot(_x, _y, **kwargs)
elif kind == 'bar':
return finplot.bar(_x, _y, **kwargs)
elif kind in ('barh', 'horiz_time_volume'):
return finplot.horiz_time_volume(df, **kwargs)
elif kind in ('heatmap'):
return finplot.heatmap(df, **kwargs)
elif kind in ('labels'):
return finplot.labels(df, **kwargs)
elif kind in ('hist', 'histogram'):
return finplot.hist(df, **kwargs)
else:
if x is None:
_x = df
_y = None
if 'style' not in kwargs:
kwargs['style'] = None
return finplot.plot(_x, _y, **kwargs)
def update_plot():
global orderbook
calc_bollinger_bands(df)
candlesticks = df['t o c h l'.split()]
bollband_hi = df['t bbh'.split()]
bollband_lo = df['t bbl'.split()]
if not plots: # 1st time
candlestick_plot = fplt.candlestick_ochl(candlesticks)
plots.append(candlestick_plot)
plots.append(fplt.plot(bollband_hi, color='#4e4ef1'))
plots.append(fplt.plot(bollband_lo, color='#4e4ef1'))
fplt.fill_between(plots[1], plots[2], color='#9999fa')
# generate dummy orderbook plot, which we update next time
x = len(candlesticks)+0.5
y = candlesticks.c.iloc[-1]
orderbook = [[x,[(y,1)]]]
orderbook_colorfunc = fplt.horizvol_colorfilter([(0,'bull'),(10,'bear')])
orderbook_plot = fplt.horiz_time_volume(orderbook, candle_width=1, draw_body=10, colorfunc=orderbook_colorfunc)
plots.append(orderbook_plot)
# use bitmex colors
candlestick_plot.colors.update(dict(
bull_shadow = '#388d53',
bull_frame = '#205536',
bull_body = '#52b370',
bear_shadow = '#d56161',
bear_frame = '#5c1a10',
bear_body = '#e8704f'))
orderbook_plot.colors.update(dict(
bull_frame = '#52b370',
bull_body = '#bae1c6',
bear_frame = '#e8704f',
bear_body = '#f6c6b9'))
else: # update
plots[0].update_data(candlesticks)
plots[1].update_data(bollband_hi)
plots[2].update_data(bollband_lo)
plots[3].update_data(orderbook)
def update_plot():
calc_bollinger_bands(df)
candlesticks = df['t o c h l'.split()]
bollband_hi = df['t bbh'.split()]
bollband_lo = df['t bbl'.split()]
if not plots:
candlestick_plot = fplt.candlestick_ochl(candlesticks)
plots.append(candlestick_plot)
plots.append(fplt.plot(bollband_hi, color='#4e4ef1'))
plots.append(fplt.plot(bollband_lo, color='#4e4ef1'))
fplt.fill_between(plots[1], plots[2], color='#9999fa')
# redraw using bitmex colors
candlestick_plot.colors.update(
dict(bull_shadow='#388d53',
bull_frame='#205536',
bull_body='#52b370',
bear_shadow='#d56161',
bear_frame='#5c1a10',
bear_body='#e8704f'))
candlestick_plot.repaint()
else:
plots[0].update_data(candlesticks)
plots[1].update_data(bollband_hi)
plots[2].update_data(bollband_lo)
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 update():
df = delta_bars.df
# Меняем индекс и делаем его типом datetime
df = df.set_index(
pd.to_datetime(df['date_time'], format='%Y-%m-%d %H:%M:%S'))
df = df.drop(
'date_time', 1
) # Удаляем колонку с датой и временем, т.к. дата у нас теперь в индексе
play_sound(df.tail(2)) # Вызов функции звукового сигнала
print(df)
# pick columns for our three data sources: candlesticks and TD
candlesticks = df['open close high low'.split()]
deltabarsec = df['open close sec'.split()]
delta = df['open close delta'.split()]
maxvolcluster = df['max_vol']
if not plots:
# first time we create the plots
global ax
plots.append(fplt.candlestick_ochl(candlesticks, candle_width=0.8))
plots.append(fplt.volume_ocv(deltabarsec, ax=ax2))
fplt.add_legend('Время формирования бара', ax=ax2)
plots.append(
fplt.volume_ocv(delta, colorfunc=fplt.strength_colorfilter,
ax=ax3))
fplt.add_legend('Дельта', ax=ax3)
plots.append(
fplt.plot(maxvolcluster,
legend='Max volume',
style='o',
color='#00f'))
else:
# every time after we just update the data sources on each plot
plots[0].update_data(candlesticks)
plots[1].update_data(deltabarsec)
plots[2].update_data(delta)
plots[3].update_data(maxvolcluster)
def plot_macd(self,
fastperiod,
slowperiod,
signalperiod,
color1=None,
color2=None,
color3=None):
"""
在图上画出MACD指标
:param fastperiod:
:param slowperiod:
:param signalperiod:
:param color1:
:param color2:
:param color3:
:return:
"""
color1 = color1 if color1 is not None else "#FF0000" # 默认设置为红色
color2 = color2 if color2 is not None else "#00FF00" # 默认设置为绿色
color3 = color3 if color3 is not None else "#0000FF" # 默认设置为蓝色
dif = self.__indicators.MACD(fastperiod, slowperiod,
signalperiod)['DIF']
dea = self.__indicators.MACD(fastperiod, slowperiod,
signalperiod)["DEA"]
macd = self.__indicators.MACD(fastperiod, slowperiod,
signalperiod)["MACD"]
fplt.plot(self.__df['time'],
dif,
color=color1,
ax=self.__ax3,
legend='MACD({}, {}, {})-DIF'.format(fastperiod, slowperiod,
signalperiod))
fplt.plot(self.__df['time'],
dea,
color=color2,
ax=self.__ax3,
legend='MACD({}, {}, {})-DEA'.format(fastperiod, slowperiod,
signalperiod))
fplt.plot(self.__df['time'],
macd,
color=color3,
ax=self.__ax3,
legend='MACD({}, {}, {})-MACD'.format(
fastperiod, slowperiod, signalperiod))
def plot(txt):
# Get data
df = download(txt)
if len(df) < 20: # symbol does not exist
return
info.setText('Loading symbol name...')
# Cleanup
ax.reset() # remove previous plots
axo.reset() # remove previous plots
# Process result
price = df['Open Close High Low'.split()]
volume = df['Open Close Volume'.split()]
# Indicators
emaShort = df.Close.ewm(span=10,
min_periods=0,
adjust=False,
ignore_na=False).mean()
emaMedium = df.Close.ewm(span=20,
min_periods=0,
adjust=False,
ignore_na=False).mean()
emaLong = df.Close.ewm(span=50,
min_periods=0,
adjust=False,
ignore_na=False).mean()
# Plot data
fplt.candlestick_ochl(price)
fplt.plot(emaShort, legend='EMA-10', color='#00FFFF', width=2)
fplt.plot(emaMedium, legend='EMA-20', width=2)
fplt.plot(emaLong, legend='EMA-50', color='#FF0000', width=2)
fplt.volume_ocv(volume, ax=axo)
fplt.refresh() # refresh autoscaling when all plots complete
Thread(target=lambda: info.setText(get_name(txt))).start(
) # slow, so use thread
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()
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'
# 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)
#######################################################
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()
axo = ax.overlay()
ind.df.replace([np.inf, -np.inf], np.nan, inplace=True)
ind.df.fillna(0)
ind.df['ts'] = ind.df.index.values.astype(np.int64) // 10 ** 9
ind.df.loc[ind.df['fractals_high'] == 0,'fractals_high'] = np.nan
ind.df.loc[ind.df['fractals_high'] > 0,'fractals_high'] = ind.df['High']+1
ind.df.loc[ind.df['fractals_low'] == 0,'fractals_low'] = np.nan
ind.df.loc[ind.df['fractals_low'] > 0,'fractals_low'] = ind.df['Low']-1
angularation = myutils.angularation(ind.df)
ind.df['angularation'] = angularation
ind.df.loc[ind.df['angularation'] == True,'angularation'] = ind.df['High']+2
fplt.set_y_range(-10, 10, ax=ax2)
fplt.plot(ind.df['fractals_high'], ax=ax, color='#4a5', style='^', legend='fractals_high')
fplt.plot(ind.df['angularation'], ax=ax, color='#0f0', style='^', legend='angularation')
fplt.plot(ind.df['fractals_low'], ax=ax, color='#f00', style="v", legend='fractals_low')
fplt.plot(ind.df[['alligator_jaws']], ax=ax, legend=['alligator_jaws'])
fplt.plot(ind.df[['alligator_teeth']], ax=ax, legend=['alligator_teeth'])
fplt.plot(ind.df[['alligator_lips']], ax=ax, legend=['alligator_lips'])
fplt.volume_ocv(ind.df[['Open','Close','ao']], ax=ax2)
fplt.volume_ocv(df[['Open','Close','Volume']], ax=axo)
fplt.candlestick_ochl(df[['Open', 'Close', 'High', 'Low']],ax=ax)
#######################################################
## update crosshair and legend when moving the mouse ##
hover_label = fplt.add_legend('', ax=ax)
def update_legend_text(x, y):
#!/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)
# 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']
fplt.plot(df['time'], df['marker'], ax=ax, color='#4a5', style='^')
# draw something random on our second plot
fplt.plot(df['time'], np.random.normal(size=len(df)), ax=ax2, color='#927', legend='random')
fplt.set_y_range(-1.4, +3.7, ax=ax2) # hard-code y-axis range limitation
# we're done
fplt.show()
#!/usr/bin/env python3
import finplot as fplt
import numpy as np
import pandas as pd
dates = pd.date_range('01:00', '01:00:01.200', freq='1ms')
prices = pd.Series(np.random.random(len(dates))).rolling(30).mean() + 4
fplt.plot(dates, prices, width=3)
line = fplt.add_line((dates[100], 4.4), (dates[1100], 4.6), color='#9900ff', interactive=True)
## fplt.remove_primitive(line)
text = fplt.add_text((dates[500], 4.6), "I'm here alright!", color='#bb7700')
## fplt.remove_primitive(text)
rect = fplt.add_rect((dates[700], 4.5), (dates[850], 4.4), color='#8c8', interactive=True)
## fplt.remove_primitive(rect)
def save():
fplt.screenshot(open('screenshot.png', 'wb'))
fplt.timer_callback(save, 0.5, single_shot=True) # wait some until we're rendered
fplt.show()
def plot_vma(df, ax):
fplt.plot(df.time, df.volume.rolling(20).mean(), ax=ax, color='#c0c030')
import finplot as fplt
import numpy as np
import pandas as pd
dates = pd.date_range('01:00', '21:30', freq='1min')
prices = pd.Series(np.random.random(len(dates))).rolling(30).mean() + 4
plot = fplt.plot(dates, prices)
line = fplt.add_line(plot.ax, (dates[100], 4.4), (dates[1100], 4.6), color='#9900ff', interactive=True)
#fplt.remove_line(plot.ax, line)
text = fplt.add_text(plot.ax, (dates[500], 4.6), "I'm here alright!", color='#bb7700')
#fplt.remove_text(plot.ax, text)
fplt.show()
import finplot as fplt
import numpy as np
import pandas as pd
# Creating a randon Datetime index for x-axes.
dates = pd.date_range('01:00', '01:00:01.200', freq='1ms')
# Creating a random price Series for y-axes
prices = pd.Series(np.random.random(len(dates))).rolling(30).mean() + 4
# plotting a line
fplt.plot(dates, prices, legend='Nonscence')
# Drawing a interactive line
line = fplt.add_line((dates[100], 4.4), (dates[1100], 4.6), color='#9900ff', interactive=True)
# removing the interactive line
# fplt.remove_line(line)
# adding an immovable text.
text = fplt.add_text(pos=(dates[50], 4.4), s="I'm here alright!", color='#bb7700')
# removing the immovable text.
# fplt.remove_text(text)
# displaying the plot
fplt.show()
def plot_bubble_pass(price, price_col, size_col, min_val, max_val, scale, color, ax):
price = price.copy()
price.loc[(price[size_col]<min_val)|(price[size_col]>max_val), price_col] = np.nan
fplt.plot(price[price_col], style='o', width=scale, color=color, ax=ax)
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
df['ret_alphabet'] = df.Close.pct_change()
df['ret_microsoft'] = dfms.Close.pct_change()
dfc = df.dropna().reset_index(drop=True)[['ret_alphabet', 'ret_microsoft']]
fplt.plot(dfc, style='o', color=1, ax=ax2)
# draw least-square line
errfun = lambda arr: [
y - arr[0] * x + arr[1]
for x, y in zip(dfc.ret_alphabet, dfc.ret_microsoft)
]
line = scipy.optimize.least_squares(errfun, [0.01, 0.01]).x
linex = [dfc.ret_alphabet.min(), dfc.ret_alphabet.max()]
liney = [linex[0] * line[0] + line[1], linex[1] * line[0] + line[1]]
fplt.add_line((linex[0], liney[0]), (linex[1], liney[1]), color='#993', ax=ax2)
fplt.add_text((linex[1], liney[1]), 'k=%.2f' % line[0], color='#993', ax=ax2)
fplt.add_legend('Alphabet vs. Microsft 90m correlation', ax=ax2)
fplt.show()
def plot_ema(df, ax):
fplt.plot(df.time, df.close.ewm(span=9).mean(), ax=ax, legend='EMA')
df = pd.read_csv(symbol + '.csv')
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',
style='^',
legend='dumb mark')
# draw some random crap on our second plot
df['rnd'] = np.random.normal(size=len(df))
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
for i, window in enumerate(windows):
if (i < 5):
fplt.plot(df['time'],
volatility[i],
ax=ax2,
legend='volatility ' + str(window))
if (i >= 5):
fplt.plot(df['time'],
volatility[i],
ax=ax3,
legend='volatility ' + str(window))
#fplt.set_y_range(0, 0.005, ax=ax2) # hard-code y-axis range limitation
# draw third plot
#fplt.plot(df['time'], volatility_diff, ax=ax3, color='#927', legend='volatility_diff')
# draw fourth plot
#fplt.plot(df['time'], open_price_position_relative_to_volatility_box, ax=ax4, color='#927', legend='open_price_position_relative_to_volatility_box')
# restore view (X-position and zoom) if we ever run this example again
def plot_on_balance_volume(df, ax):
obv = df.volume.copy()
obv[df.close < df.close.shift()] = -obv
obv[df.close == df.close.shift()] = 0
df['obv'] = obv.cumsum()
fplt.plot(df.time, df.obv, ax=ax, legend='OBV', color='#008800')
