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()]
ax.reset() # remove previous plots
axo.reset() # remove previous plots
qplt.candlestick_ochl(price)
qplt.plot(ma20, legend='MA-20')
qplt.plot(ma50, legend='MA-50')
qplt.volume_ocv(volume, ax=axo)
qplt.refresh() # refresh autoscaling when all plots complete
Thread(target=lambda: info.setText(get_name(txt))).start(
) # slow, so use thread
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 quantlplot.candlestick_ochl(df, **kwargs)
elif 'volume' in kind:
return quantlplot.volume_ocv(df, **kwargs)
elif 'renko' in kind:
return quantlplot.renko(df, **kwargs)
elif kind == 'scatter':
if 'style' not in kwargs:
kwargs['style'] = 'o'
return quantlplot.plot(_x, _y, **kwargs)
elif kind == 'bar':
return quantlplot.bar(_x, _y, **kwargs)
elif kind in ('barh', 'horiz_time_volume'):
return quantlplot.horiz_time_volume(df, **kwargs)
elif kind in ('heatmap'):
return quantlplot.heatmap(df, **kwargs)
elif kind in ('labels'):
return quantlplot.labels(df, **kwargs)
elif kind in ('hist', 'histogram'):
return quantlplot.hist(df, **kwargs)
else:
if x is None:
_x = df
_y = None
if 'style' not in kwargs:
kwargs['style'] = None
return quantlplot.plot(_x, _y, **kwargs)
dfd['High'] = df.High.resample('D').max()
dfd['Low'] = df.Low.resample('D').min()
ax,ax2 = qplt.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 = qplt.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
qplt.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']]
qplt.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]]
qplt.add_line((linex[0],liney[0]), (linex[1],liney[1]), color='#993', ax=ax2)
qplt.add_text((linex[1],liney[1]), 'k=%.2f'%line[0], color='#993', ax=ax2)
qplt.add_legend('Alphabet vs. Microsft 90m correlation', ax=ax2)
qplt.show()
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
qplt.create_plot('Binance BTC futures weekly volume profile')
qplt.plot(df.time, df.close, legend='Price')
qplt.plot(df.time, vwap, style='--', legend='VWAP')
qplt.horiz_time_volume(time_volume_profile, draw_va=0.7, draw_poc=1.0)
qplt.show()