def plot_ts(df,
dv=[],
fig=None,
ax=None,
figsize=(11, 6),
backend='Agg',
title='',
chartformat='svg',
debugTF=False,
**optx):
if len(df) < 1:
return '', None, None
sys.stderr.write("===start:{}()\n".format("plot_ts"))
src = getKeyVal(optx, 'src', 'yh')
#if isinstance(df,str):
if not isinstance(df, pd.DataFrame):
ticker = df
from _alan_calc import pullStockHistory as psd
ranged = getKeyVal(optx, 'ranged', '1d')
gap = getKeyVal(optx, 'gap', '1m')
datax = psd(ticker, gap=gap, ranged=ranged, days=1500, src=src)
datax.rename(columns={'close': ticker}, inplace=True)
df = datax[[ticker]]
if src == 'fred':
tk2 = ticker + '_PCTCHG'
dx2 = psd(tk2, src=src, days=1500)
if len(dx2) > 0:
dv = datax[[ticker]]
df = dx2[['close']]
elif 'volume' in datax:
dv = datax[['volume']]
if 'volume' in df:
dv = df[['volume']].copy()
cs = list(df.columns)
cs.pop(cs.index('volume'))
df = df[cs]
if backend is not None and len(backend) > 1:
plt.switch_backend(backend)
trendTF = getKeyVal(optx, 'trendTF', True)
bottom = optx['bottom'] if 'bottom' in optx else 0.35
chartpath, chartname = gen_chartpath(**optx)
try:
from _alan_pppscf import vertex_locator
if not isinstance(df, pd.DataFrame):
df = pd.DataFrame(data=df)
if fig is None:
sns.set_style("whitegrid", {'grid.linestyle': '--'})
fig, ax = plt.subplots(figsize=figsize)
ax.grid(False, axis='x')
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.spines['bottom'].set_color('none')
ax.spines['left'].set_color('none')
plt.plot(df.index, df.iloc[:, 0])
plt.title(title)
x_fmt = getKeyVal(optx, 'x_fmt', '')
yunit = ['', '1,000', 'Million', 'Billion', 'Trillion']
ymax = abs(df.iloc[:, 0]).max()
ndg = max(int(math.log(ymax, 10) / 3), 1)
nadj = (ndg - 1) * 3
ylabel = yunit[ndg - 1] or ''
if debugTF:
sys.stderr.write("+++++ {} {} {} {}\n".format(
ymax, ndg, nadj, yunit[ndg - 1]))
if ndg > 1:
ax.yaxis.set_major_formatter(
FuncFormatter(lambda x, pos: '{:,g}'.format(x / 10**nadj)))
else:
ax.yaxis.set_major_formatter(
FuncFormatter(lambda x, pos: '{:,g}'.format(x)))
ax.set_ylabel(ylabel)
if trendTF is True:
npar = getKeyVal(optx, 'npar', 6)
trendName = df._get_numeric_data().columns[0]
dy = df[trendName]
dg, dh = vertex_locator(dy, npar=npar, debugTF=debugTF)
dg.plot(ax=ax, legend=False)
if debugTF:
sys.stderr.write("===plot data:\n {}\n".format(dg.tail()))
if len(dv) > 0 and isinstance(dv, pd.DataFrame):
axv = ax.twinx()
kind2 = 'area' if src != 'fred' else 'line'
color2 = 'lightgray' if src != 'fred' else 'gray'
axv = dv.plot(ax=axv,
kind=kind2,
color=color2,
alpha=.4,
legend=False)
y2max = dv.iloc[:, 0].max()
ndg = max(int(math.log(y2max, 10) / 3), 1)
nadj = (ndg - 1) * 3
if ndg > 1:
axv.yaxis.set_major_formatter(
FuncFormatter(lambda x, pos: '{:,g}'.format(x / 10**nadj)))
else:
axv.yaxis.set_major_formatter(
FuncFormatter(lambda x, pos: '{:,g}'.format(x)))
ylabel2 = yunit[ndg - 1] or ''
axv.set_ylabel(ylabel2)
if debugTF:
sys.stderr.write("===plot data:\n {}\n".format(dv.tail()))
if debugTF:
sys.stderr.write("===plot data:\n {}\n".format(df.tail(20)))
plt.subplots_adjust(left=0.1, bottom=bottom)
#if len(x_fmt)>0: # TBD, specify xticks format
# ax.xaxis.set_major_formatter(mdates.DateFormatter(x_fmt))
ax.grid(linestyle='dotted', linewidth=0.5)
if chartname is not None and len(chartname) > 5:
plt.savefig(chartpath,
format=chartformat) #, bbox_inches='tight',dpi=1000)
elif backend is not None and backend.lower() == 'tkagg':
plt.show(ax)
except Exception as e:
sys.stderr.write("**ERROR: {} @{}\n".format(str(e), "plot_ts"))
sys.stderr.write("{}\n".format(locals()))
return '', None, None
return (chartpath, ax, fig)
def plot_stock_performance(df,
fig=None,
ax=None,
figsize=(11, 6),
backend='Agg',
title='',
chartformat='svg',
debugTF=True,
**optx):
sys.stderr.write("===start:{}()\n".format("plot_stock_performance"))
if not isinstance(df, pd.DataFrame):
df = pd.DataFrame(data=df)
if len(df) < 1:
return '', None, None
if backend is not None and len(backend) > 1:
plt.switch_backend(backend)
bottom = optx['bottom'] if 'bottom' in optx else 0.2
chartpath, chartname = gen_chartpath(**optx)
trendTF = getKeyVal(optx, 'trendTF', True)
max_num = df['close'].max()
max_num_date = df[df['close'] == max_num].iloc[-1].name
min_num = np.min(df['close'])
min_num_date = df[df['close'] == min_num].iloc[-1].name
curr_num = df['close'][-1]
curr_num_date = df[df['close'] == curr_num].iloc[-1].name
open_num = df['close'].iloc[0]
max_index = np.max(df.index)
min_index = np.min(df.index)
x_lim = min_index.replace(hour=16, minute=31)
xprice = getKeyVal(optx, 'xprice', open_num)
cpchg = getKeyVal(optx, 'cpchg', round((curr_num / open_num - 1) * 100, 2))
max_ylim = np.max([max_num, xprice])
min_ylim = np.min([min_num, xprice])
try:
from _alan_pppscf import vertex_locator
if trendTF is True:
try:
npar = getKeyVal(optx, 'npar', 6)
trendName = df._get_numeric_data().columns[0]
dy = df[trendName]
dg, dh = vertex_locator(dy, npar=npar, debugTF=debugTF)
dh = dg
except Exception as e:
sys.stderr.write("**ERROR:{} @ {}\n".format(
str(e), "running vertex_locator()"))
dh = {}
#print(dh)
else:
dh = {}
if not isinstance(df, pd.DataFrame):
df = pd.DataFrame(data=df)
if fig is None:
plt.rcParams.update(plt.rcParamsDefault)
sns.set_style("whitegrid", {'grid.linestyle': '--'})
fig, ax = plt.subplots(figsize=figsize)
fig.autofmt_xdate()
ax.yaxis.set_major_formatter(StrMethodFormatter('{x:,.0f}'))
plt.xlim(min_index - timedelta(minutes=5),
x_lim + timedelta(minutes=5))
plt.ylim(min_ylim - (max_ylim - min_ylim) / 2,
max_ylim + (max_ylim - min_ylim) / 5)
plt.subplots_adjust(bottom=0.2)
#plt.xlabel('Time',fontsize=15)
#plt.ylabel('price',fontsize=15)
#hours = mdates.HourLocator(interval = 1)
h_fmt = mdates.DateFormatter('%H:%M')
#ax.xaxis.set_major_locator(hours)
ax.xaxis.set_major_formatter(h_fmt)
ax.grid(False, axis='x')
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.spines['bottom'].set_color('none')
ax.spines['left'].set_color('none')
plt.title(title, fontproperties=fontProp(size=15))
plt.legend(frameon=False)
plt.plot(df.index, df['close'])
plt.plot([min_index, x_lim], [xprice, xprice], 'k--',
lw=1) # plot open dash line
plt.plot([curr_num_date, curr_num_date], [xprice, curr_num],
'k--',
lw=1) # plot day pchg vertical line
#plt.annotate('{}%'.format(round((curr_num/open_num-1)*100,2)), xy=(curr_num_date,open_num+(curr_num-open_num)/2),xytext=(0,6),fontsize=10,annotation_clip=True, textcoords="offset points",ha=('left'), va='center') # plot pchg number
plt.scatter(max_num_date, max_num, s=20, color='g',
zorder=19) #plot max point
plt.scatter(min_num_date, min_num, s=20, color='r',
zorder=18) #plot min point
plt.scatter(curr_num_date, curr_num, s=40, zorder=20) #plot curr point
plt.annotate('{}'.format(round(max_num, 2)),
xy=(max_num_date, max_num),
xytext=(0, 20),
fontsize=10,
arrowprops=dict(arrowstyle='simple'),
annotation_clip=True,
textcoords="offset points",
ha=('right'),
va='bottom') # plot max point number
plt.annotate('{}'.format(round(min_num, 2)),
xy=(min_num_date, min_num),
xytext=(0, -20),
fontsize=10,
arrowprops=dict(arrowstyle='simple'),
annotation_clip=True,
textcoords="offset points",
ha=('left'),
va='top') # plot min point number
plt.annotate('{} | {}%'.format(round(curr_num, 2), round(cpchg, 2)),
xy=(curr_num_date, curr_num),
xytext=(0, 20),
fontsize=10,
arrowprops=dict(arrowstyle='simple'),
annotation_clip=True,
textcoords="offset points",
ha=('left'),
va='bottom') # plot curr point number
if len(dh) > 1:
if dh.iloc[-1].values < dh.iloc[-2].values:
ar_color = 'red'
else:
ar_color = 'green'
plt.annotate('',
xy=(dh.iloc[-1].name, dh.iloc[-1] * 1.00),
xytext=(dh.iloc[-2].name, dh.iloc[-2] * 1.00),
arrowprops=dict(facecolor=ar_color, shrink=0.05),
annotation_clip=True) # plot trend arrow
plt.plot(dh.index, dh.values)
if 'volume' in df:
axv = ax.twinx()
axv.grid(False, axis='both')
axv.spines['right'].set_color('none')
axv.spines['top'].set_color('none')
axv.spines['bottom'].set_color('none')
axv.spines['left'].set_color('none')
axv.xaxis.set_major_formatter(h_fmt)
axv.set_yticklabels([])
axv.tick_params(right="off")
plt.ylim(0, df['volume'].max() * 3.5)
#axv.plot(df['volume'].index, df['volume'], color='gray',alpha=0.8)
axv.fill_between(df['volume'].index, df['volume'], alpha=0.8)
if debugTF:
sys.stderr.write("===plot data:\n {}\n".format(df.tail()))
plt.subplots_adjust(left=0.1, bottom=bottom)
if chartname is not None and len(chartname) > 5:
plt.savefig(chartpath,
format=chartformat) #, bbox_inches='tight',dpi=1000)
elif backend is not None and backend.lower() == 'tkagg':
plt.show(ax)
except Exception as e:
sys.stderr.write("**ERROR: {} @{}\n".format(str(e),
"plot_stock_performance"))
return '', None, None
return (chartpath, ax, fig)
def plot_candlestick(datax,
tsidx=None,
chartType='chart',
title='',
block=True,
debugTF=False,
ax=None,
colorUD=['green', 'red'],
trendTF=False,
npar=12):
'''
plot candlestick ohlc/volume graph
'''
if chartType == 'minute':
wadj = 24 * 60
dtfmt = '%H:%M'
else:
wadj = 1
dtfmt = '%m/%d/%y'
if 'mpldatetime' not in datax and isinstance(
tsidx[0], (datetime.date, datetime.datetime)):
datax['mpldatetime'] = [mdates.date2num(x) for x in tsidx]
newAr = datax[['mpldatetime', 'open', 'high', 'low', 'close']].values
if debugTF is True:
pqint(newAr[:10], file=sys.stderr)
SP = datax.shape[0]
if ax is None:
fig = plt.figure(facecolor='#07000d', figsize=(11, 6))
ax = plt.subplot2grid((1, 2), (0, 0), rowspan=1, colspan=2)
colorup, colordown = colorUD
fillcolor = '#00ffe8'
candlestick_ohlc(ax,
newAr[-SP:],
width=0.6 / wadj,
colorup=colorup,
colordown=colordown)
ax.xaxis_date()
ax.autoscale_view()
ax.xaxis.set_major_formatter(mdates.DateFormatter(dtfmt))
ax.grid(True, color='w', linestyle='dotted', linewidth=0.5)
plt.xticks(rotation=30)
plt.ylabel('OHLC Price/Volume')
plt.xlabel('Date Time')
if trendTF is True:
from _alan_pppscf import vertex_locator
mpldate, closep = datax[['mpldatetime', 'close']].T.values
dg, dh = vertex_locator(closep, npar=npar, debugTF=debugTF)
trend = dg['trend'].values
#axt = ax.twinx()
#ax.plot(mpldate[-SP:],trend[-SP:], color='cyan',alpha=.5)
ax.plot(mpldate[dg.index.values], trend, color='cyan', alpha=.5)
if debugTF:
pqint("time/trend: ",
mpldate[dg.index.values],
trend,
file=sys.stderr)
if 'volume' in datax or 'marketVolume' in datax:
if 'volume' not in datax and 'marketVolume' in datax:
datax.rename(columns={'marketVolume': 'volume'}, inplace=True)
mpldate, volume = datax[['mpldatetime', 'volume']].T.values
axv = ax.twinx()
axv.fill_between(mpldate[-SP:],
0,
volume[-SP:],
facecolor='lightblue',
alpha=.4)
axv.axes.yaxis.set_ticklabels([])
axv.grid(False)
###Edit this to 3, so it's a bit larger
axv.set_ylim(0, 3 * volume.max())
if len(title) > 0:
ax.set_title("")
if block is True:
plt.show(ax)
return ax
def plot_candlestickCombo(data,
stock,
MA1=5,
MA2=30,
savePng=False,
block=False,
pngDIR='.',
chartType='chart',
trendTF=False,
npar=15,
debugTF=False,
colorUD=['green', 'red'],
title=None):
'''
plot candlestick ohlc/macd/rsi/ma combo graph
'''
colorup, colordown = colorUD
# prepare data
if 'mpldatetime' not in data:
data['mpldatetime'] = [mdates.date2num(x) for x in data.index]
newAr = data[['mpldatetime', 'open', 'high', 'low', 'close',
'volume']].values
mpldate, closep, volume = data[['mpldatetime', 'close', 'volume']].T.values
rsi, macd, ema9, macdsigv = data[[
'rsi', 'macd_ema', 'signal_macd', 'signal_value_macd'
]].T.values
ma1c, ma2c = 'ma{}'.format(MA1), 'ma{}'.format(MA2)
Av1, Av2 = data[[ma1c, ma2c]].T.values
#SP = len(mpldate[MA2-1:])
SP = newAr.shape[0]
# start plotting
fig = plt.figure(facecolor='#07000d', figsize=(11, 6))
ax1 = plt.subplot2grid((6, 4), (1, 0),
rowspan=4,
colspan=4,
facecolor='#07000d')
if chartType == 'minute':
wadj = 24 * 60
dtfmt = '%H:%M'
bym = [0, 15, 30, 45] if SP <= 120 else [0, 30] if SP <= 360 else [0]
else:
wadj = 1
dtfmt = '%m/%d/%y'
candlestick_ohlc(ax1,
newAr[-SP:],
width=.6 / wadj,
colorup=colorup,
colordown=colordown)
ax1.xaxis_date()
ax1.autoscale_view()
Label1 = str(MA1) + ' SMA'
Label2 = str(MA2) + ' SMA'
ax1.plot(mpldate[-SP:], Av1[-SP:], '#e1edf9', label=Label1, linewidth=1.5)
ax1.plot(mpldate[-SP:], Av2[-SP:], '#4ee6fd', label=Label2, linewidth=1.5)
if trendTF is True:
from _alan_pppscf import vertex_locator
dg, dh = vertex_locator(closep, npar=npar, debugTF=debugTF)
trend = dg['trend'].values
ax1.plot(mpldate[dg.index.values],
trend,
label='Trend',
color='cyan',
alpha=.5)
#ax1.plot(mpldate[-SP:],trend[-SP:], label='Trend',color='cyan',alpha=.5)
if debugTF is True:
pqint("trendline:\n", dh, file=sys.stderr)
ax1.grid(True, color='w', linestyle='dotted', linewidth=0.5)
if chartType == 'minute':
xlocator = mdates.MinuteLocator(byminute=bym, interval=1)
ax1.xaxis.set_major_locator(xlocator)
else:
nbins = 8
nsp = (SP / nbins) if SP > nbins * 2 else SP
bymd = [1, 5, 10, 15, 20, 25
] if SP < 50 else [1, 15] if SP < 120 else [1]
itv = 1 if SP < 160 else int(nsp / 30. + 0.97)
xlocator = mdates.MonthLocator(bymonthday=bymd, interval=itv)
ax1.xaxis.set_major_locator(xlocator)
# check if min/max of xaxis should be included major ticks
xtcks = list(ax1.get_xticks())
x1, x2 = xtcks[:2]
xmin, xmax = ax1.get_xlim()
if (x1 - xmin) > (x2 - x1) * 0.6:
xtcks = [xmin] + xtcks
if (xmax - xtcks[-1]) > (x2 - x1) * 0.6:
xtcks = xtcks + [xmax]
ax1.set_xticks(xtcks)
ax1.xaxis.set_minor_locator(mdates.MonthLocator(interval=1))
#original
#ax1.xaxis.set_major_locator(mticker.MaxNLocator(10))
ax1.xaxis.set_major_formatter(mdates.DateFormatter(dtfmt))
ax1.yaxis.label.set_color("w")
ax1.spines['bottom'].set_color("#5998ff")
ax1.spines['top'].set_color("#5998ff")
ax1.spines['left'].set_color("#5998ff")
ax1.spines['right'].set_color("#5998ff")
ax1.tick_params(axis='y', color='w')
plt.gca().yaxis.set_major_locator(mticker.MaxNLocator(prune='upper'))
ax1.tick_params(axis='x', color='w')
plt.ylabel('Stock price and Volume')
maLeg = plt.legend(loc=9,
ncol=2,
prop={'size': 7},
fancybox=True,
borderaxespad=0.)
maLeg.get_frame().set_alpha(0.4)
textEd = pylab.gca().get_legend().get_texts()
pylab.setp(textEd[0:5], color='w')
volumeMin = 0
ax0 = plt.subplot2grid((6, 4), (5, 0),
sharex=ax1,
rowspan=1,
colspan=4,
facecolor='#07000d')
rsiCol = '#c1f9f7'
posCol = '#386d13'
negCol = '#8f2020'
ax0.plot(mpldate[-SP:], rsi[-SP:], rsiCol, linewidth=1.5)
ax0.axhline(70, color=negCol)
ax0.axhline(30, color=posCol)
ax0.fill_between(mpldate[-SP:],
rsi[-SP:],
70,
where=(rsi[-SP:] >= 70),
facecolor=negCol,
edgecolor=negCol,
alpha=0.5)
ax0.fill_between(mpldate[-SP:],
rsi[-SP:],
30,
where=(rsi[-SP:] <= 30),
facecolor=posCol,
edgecolor=posCol,
alpha=0.5)
plt.gca().yaxis.set_major_locator(mticker.MaxNLocator(prune='upper'))
ax0.set_yticks([30, 70])
ax0.yaxis.label.set_color("w")
ax0.spines['bottom'].set_color("#5998ff")
ax0.spines['top'].set_color("#5998ff")
ax0.spines['left'].set_color("#5998ff")
ax0.spines['right'].set_color("#5998ff")
ax0.tick_params(axis='y', color='w')
ax0.tick_params(axis='x', color='w')
plt.ylabel('RSI')
ax1v = ax1.twinx()
ax1v.fill_between(mpldate[-SP:],
volumeMin,
volume[-SP:],
facecolor='lightgray',
alpha=.4,
step='pre')
ax1v.axes.yaxis.set_ticklabels([])
ax1v.grid(False)
###Edit this to 3, so it's a bit larger
ax1v.set_ylim(0, 3 * volume.max())
ax1v.spines['bottom'].set_color("#5998ff")
ax1v.spines['top'].set_color("#5998ff")
ax1v.spines['left'].set_color("#5998ff")
ax1v.spines['right'].set_color("#5998ff")
ax1v.tick_params(axis='x', color='w')
ax1v.tick_params(axis='y', color='w')
ax2 = plt.subplot2grid((6, 4), (0, 0),
sharex=ax1,
rowspan=1,
colspan=4,
facecolor='#07000d')
fillcolor = '#00ffe8'
ax2.plot(mpldate[-SP:], macd[-SP:], color='#4ee6fd', lw=2)
ax2.plot(mpldate[-SP:], ema9[-SP:], color='#e1edf9', lw=1)
# calc (macd-ema9) as signal_macd
ax2.fill_between(mpldate[-SP:],
macdsigv[-SP:],
0,
alpha=0.5,
facecolor=fillcolor,
edgecolor=fillcolor)
plt.gca().yaxis.set_major_locator(mticker.MaxNLocator(prune='upper'))
ax2.spines['bottom'].set_color("#5998ff")
ax2.spines['top'].set_color("#5998ff")
ax2.spines['left'].set_color("#5998ff")
ax2.spines['right'].set_color("#5998ff")
ax2.tick_params(axis='x', colors='w')
ax2.tick_params(axis='y', colors='w')
plt.ylabel('MACD', color='w')
ax2.yaxis.set_major_locator(mticker.MaxNLocator(nbins=5, prune='upper'))
if title is None or len(title) < 1:
title = stock.upper()
plt.suptitle(title, color='w', fontproperties=prop)
plt.setp(ax0.get_xticklabels(), rotation=30, visible=True)
plt.setp(ax2.get_xticklabels(), rotation=30, visible=False)
plt.setp(ax1.get_xticklabels(), rotation=30, visible=False)
"""
locNews=int(len(mpldate)*.66)
ax1.annotate('News!',(mpldate[locNews],Av1[locNews]),
xytext=(0.8, 0.9), textcoords='axes fraction',
arrowprops=dict(facecolor='white', shrink=0.05),
fontsize=14, color = 'w',
horizontalalignment='right', verticalalignment='bottom')
"""
plt.subplots_adjust(left=.09,
bottom=.14,
right=.94,
top=.95,
wspace=.20,
hspace=0.10)
wintitle = "Candlestick OHLC View"
fig.canvas.set_window_title(wintitle)
if savePng is True:
chartName = '{0}/OHLC_MACD_RSI_{1}.png'.format(pngDIR, stock.upper())
fig.savefig(chartName, facecolor=fig.get_facecolor())
else:
plt.show(block=block)
return fig, [ax0, ax1, ax2]
def plot_csvdata(df, nbins=6,rsiYN=False,title=None,pivot_value=None,pivot_group=None,pngname=None,x_fmt="%b-%d-%y",interpolateYN=True,backend="tkAgg",npar=15,tsTF=True,xaxis=None,trendTF=False,debugTF=False,ohlcTF=False,ohlcComboTF=False,lang='en',**kwargs):
if debugTF:
sys.stderr.write("===plot_csvdata VARS:\t{}\n".format(locals()))
import matplotlib.dates as mdates
import matplotlib.image as mimage
import matplotlib.ticker as mticker
#pltStyle=getKeyVal(kwargs,'pltStyle','dark_background')
pltStyle=getKeyVal(kwargs,'pltStyle','classic')
figsize=getKeyVal(kwargs,'figsize',(11,6))
ylabel=getKeyVal(kwargs,'ylabel',None)
fontpath=getKeyVal(kwargs,'fontpath',None)
if fontpath is not None:
prop.set_file(fontpath)
if pltStyle in plt.style.available:
plt.style.use(pltStyle)
#- Use backend to 'tkAgg' for cronjob
if pngname is None or len(pngname)<=4:
plt.switch_backend(backend)
#- Rename columns
renColumns=getKeyVal(kwargs,'renColumns',{})
if len(renColumns)>0:
df = renameDict(df,mapper=renColumns)
#- Create datetime index
idxname='date'
pbname=xaxis
if isinstance(df.index,pd.DatetimeIndex):
pass
elif pbname in df.columns:
sdate = str(df[pbname].iloc[0])
if sdate.isdigit() == True:
if int(sdate)>123456789:
idxpt=[epoch_parser(x) for x in df[pbname]]
else:
idxpt=[ymd_parser(x,fmt="%Y%m%d") for x in df[pbname]]
else:
idxpt=[ymd_parser(x,fmt=x_fmt) for x in df[pbname]]
df.set_index(pd.DatetimeIndex(idxpt),inplace=True)
df.index.rename(idxname,inplace=True)
df = df.drop(pbname,1)
elif idxname in df.columns:
df[idxname] = pd.to_datetime(df[idxname])
df.set_index(idxname,inplace=True)
else:
df = df.reset_index(drop=True)
#- Create 2nd dataframe 'df2' for 2-Yaxis plot
from _alan_calc import subDF
dux=getKeyVal(kwargs,'columns2',None)
if dux is not None:
colLst2=dux.split(',')
df2=subDF(df,colLst2)
df=subDF(df,colLst2,reverseTF=True)
else:
df2={}
#- Create a pivot table
trendName = None
if pivot_group in df.columns and pivot_value in df.columns:
trendName = df[pivot_group][0]
df=df.pivot_table(index='date',columns=pivot_group,values=pivot_value)
#- Rename columns
renColumns=getKeyVal(kwargs,'renColumns',{})
if len(renColumns)>0:
df = renameDict(df,mapper=renColumns)
#- Create linear-interpolation for missing data
if interpolateYN is True:
df=df.apply(extrapolate_series,axis=0)
#- Create return since inception
if rsiYN is True:
de=[]
for j in range(df.shape[1]):
inix = df.iloc[0,j] if df.iloc[0,j]>1 else 1
de.append(df.iloc[:,j]/inix*100.-100)
#de = [df.iloc[:,j]/df.iloc[0,j]*100.-100 for j in range(df.shape[1])]
df = pd.concat(de,axis=1)
#- NO PLOTTING, just return data
if 'plotTF' in kwargs and kwargs['plotTF'] is False:
return df,{},{}
#- Create trend curve
if trendTF is True:
try:
from _alan_pppscf import vertex_locator
if trendName is None:
trendName = df._get_numeric_data().columns[0]
dg, dh = vertex_locator(df[trendName],npar=npar,debugTF=debugTF)
#df['trend'] = dg['trend'].values
if debugTF is True:
sys.stderr.write("{}\n{}\n".format("Trendline dh:",dh))
except Exception as e:
sys.stderr.write("**ERROR: {} @ {}\n".format(str(e),'trendline'))
if title is None:
title="/".join(df.columns).upper()
if rsiYN is True:
title += " Return Since Inception"
#- plot simple line plot
if tsTF is False:
df = df.reset_index(drop=True)
if debugTF is True:
sys.stderr.write("{}\n".format(df.head()))
sys.stderr.write("{}\n".format(df.tail()))
nobs=len(df.index)
nsp = (nobs/nbins) if nobs>nbins*2 else nobs
#ds=[y for j,y in enumerate(df.index) if j%nsp==0]
#ax=df.plot(xticks=ds,title=title)
colorUD = ['red','green'] if lang=='cn' else ['green','red']
if ohlcComboTF is True:
from alan_plot import plot_candlestickCombo
from _alan_calc import run_tech
chartType = 'minute' if pbname == 'epochs' else 'chart'
#ma1=5;ma2=30
ma1,ma2=sma=getKeyVal(kwargs,'sma',[5,30])
datax = run_tech(df, pcol='close',winLst=sma,debugTF=debugTF,nanTF=True)
if 'open' not in datax:
return datax, None, None
fig, axes = plot_candlestickCombo(datax,title,ma1,ma2,block=False,chartType=chartType,trendTF=trendTF,npar=npar,debugTF=debugTF,colorUD=colorUD,title=title)
#plt.suptitle(title,fontsize=18,fontproperties=prop)
if pngname is not None and len(pngname)>4 and '.' in pngname:
ghLst = plt.gcf().canvas.get_supported_filetypes().keys()
ghx = pngname.split('.')[-1]
format = ghx.lower()
if ghx.lower() in ghLst:
format = ghx.lower()
else:
format = 'svg'
pngname = pngname.replace(ghx,'svg')
plt.savefig(pngname, format=format) #, bbox_inches='tight',dpi=1000)
# skip the plot if pngname='noshow'
elif pngname is None:
plt.show(axes)
return datax, fig, axes
fig, ax=plt.subplots(figsize=figsize)
if ohlcTF is True:
if 'marketVolume' in df:
df.rename(columns={'marketVolume': 'volume'},inplace=True)
if 'open' not in df and 'close' in df:
df['open']=df['high']=df['low']=df['close']
elif 'open' not in df:
return df, None, None
from alan_plot import plot_candlestick
chartType = 'minute' if pbname == 'epochs' else 'chart'
ax = plot_candlestick(df,tsidx=df.index,chartType=chartType,title=title,block=False,debugTF=debugTF,ax=ax,trendTF=trendTF,npar=npar,colorUD=colorUD)
x_fmt = "%H:%M" if chartType == 'minute' else x_fmt
else:
colorLst=['blue','red','green','salmon','lightgray','cyan']
df.plot(ax=ax,grid=True,color=colorLst)
#ax=df.plot(figsize=(11,6))
ax.set_ylabel(df.columns[0])
if trendTF is True:
dg.plot(ax=ax)
if len(df2)>0:
if debugTF:
sys.stderr.write("DF2:\n{}\n{}\n".format(df2.tail(),df2.shape))
axv = ax.twinx()
df2.plot(ax=axv,kind='area',alpha=0.4,legend=False)
axv.yaxis.set_major_formatter(FuncFormatter(lambda x,pos: '{:,.0f}'.format(x)))
if rsiYN is True: # calc Returns Since Incept
ax.set_ylabel("Returns Since Inception (%)")
if ylabel is not None and len(ylabel)>0:
ax.set_ylabel(ylabel,fontproperties=fontProp(size=12))
ax.grid(linestyle='dotted',linewidth=0.5)
if df.index._typ == "datetimeindex":
mddfmt=mdates.DateFormatter(x_fmt)
ax.xaxis.set_major_formatter(mddfmt)
xtinterval=(df.index[1]-df.index[0])
if xtinterval.days < 7 and xtinterval.days>=1 : # daily data
ax.set_xlim(df.index[0], df.index[-1])
#ax.xaxis.set_major_locator(mdates.MonthLocator(interval=int(nsp/30.+0.97)))
bymd = [1,5,10,15,20,25] if nobs<50 else [1,15] if nobs<120 else [1]
itv = 1 if nobs<160 else int(nsp/30.+0.97)
xlocator = mdates.MonthLocator(bymonthday=bymd,interval=itv)
ax.xaxis.set_major_locator(xlocator)
# check if min/max of xaxis should be included major ticks
if debugTF is True:
sys.stderr.write("{} {}\n".format( ax.get_xticks(),ax.get_xlim()))
xtcks = list(ax.get_xticks())
x1,x2 = xtcks[:2]
xmin,xmax = ax.get_xlim()
if (x1-xmin)>(x2-x1)*0.6:
xtcks = [xmin] + xtcks
if (xmax-xtcks[-1])>(x2-x1)*0.6:
xtcks = xtcks + [xmax]
ax.set_xticks(xtcks)
ax.xaxis.set_minor_locator(mdates.MonthLocator(interval=1))
if debugTF is True:
sys.stderr.write("{}\n".format(ax.get_xticks()))
sys.stderr.write("{}\n".format( "Daily data use MonthLocator"))
elif xtinterval.seconds < 30: # second data
locator = mdates.AutoDateLocator()
locator.intervald[5] = [0,5,10,15,20,25,30,35,40,45,55]
mddfmt = mdates.AutoDateFormatter(locator)
mddfmt.scaled[1/(24.*60.)] = '%M:%S'
ax.xaxis.set_major_locator(locator)
ax.xaxis.set_major_formatter(mddfmt)
sys.stderr.write("{} {}\n".format( "Second data use AutoDateLocator",xtinterval.seconds))
elif xtinterval.seconds < 100 : # minute data
bym = [0,15,30,45] if nobs<=120 else [0,30] if nobs<=360 else [0]
xlocator = mdates.MinuteLocator(byminute=bym, interval = 1)
ax.xaxis.set_major_locator(xlocator)
sys.stderr.write("{} {}\n".format( "Minute data use MinuteLocator",xtinterval.days))
else: # periodic data
sys.stderr.write("{}\n".format( "Periodic data use DayLocator" ))
ax.xaxis.set_major_locator(mdates.DayLocator(interval=nsp))
ax.xaxis.label.set_visible(False)
plt.title(title,fontsize=30,fontproperties=prop)
plt.xticks(rotation='20',fontsize=12)#,fontproperties=prop)
if len(df.columns)>1 and ohlcTF is False:
ax.legend(loc="upper left",prop=prop)
#ax.legend().set_visible(False)
#logo = mimage.imread("aicaas_icon.png")
#plt.figimage(logo, xo=20,yo=420)
plt.subplots_adjust(left=0.1,bottom=0.30)
if pngname is not None and len(pngname)>4:
plt.savefig(pngname)#, bbox_inches='tight',dpi=1000)
sys.stderr.write("Save chart {} to {}\n".format(title,pngname))
else:
plt.show(ax)
return df, fig, [ax]
def run_csv2plot(args, opts=None, optx=None):
"""
plot time series data from csv file
"""
#- Set input parameters
if opts is None:
opts, _ = opt_csv2plot([])
if optx is not None:
opts.update(optx)
for ky, va in opts.items():
exec("{}=va".format(ky))
#- Get data in datafram with selected [columns]
df = get_csvdata(args,
sep=sep,
src=src,
days=days,
start=start,
end=end,
columns=columns,
hdrLst=hdrLst)
if df is None or len(df) < 1 or df.size < 1:
return None
if debugTF is True:
print >> sys.stderr, df.head()
#- Use backend to 'tkAgg' for cronjob
if pngname is None or len(pngname) <= 4:
plt.switch_backend(backend)
#- Create datetime index
idxname = 'date'
pbname = xaxis
if pbname in df.columns:
from _alan_date import ymd_parser, epoch_parser
sdate = str(df[pbname].iloc[0])
if sdate.isdigit() == True:
if int(sdate) > 123456789:
idxpt = [epoch_parser(x) for x in df[pbname]]
else:
idxpt = [ymd_parser(x, fmt="%Y%m%d") for x in df[pbname]]
else:
idxpt = [ymd_parser(x, fmt=x_fmt) for x in df[pbname]]
df.set_index(pd.DatetimeIndex(idxpt), inplace=True)
df.index.rename(idxname, inplace=True)
df = df.drop(pbname, 1)
elif idxname in df.columns:
df[idxname] = pd.to_datetime(df[idxname])
df.set_index(idxname, inplace=True)
else:
df = df.reset_index(drop=True)
#- Create a pivot table
trendName = None
if pivot_group in df.columns and pivot_value in df.columns:
trendName = df[pivot_group][0]
df = df.pivot_table(index='date',
columns=pivot_group,
values=pivot_value)
#- Create linear-interpolation for missing data
if interpolateYN is True:
df = df.apply(extrapolate_series, axis=0)
#- Create return since inception
if rsiYN is True:
de = []
for j in range(df.shape[1]):
inix = df.iloc[0, j] if df.iloc[0, j] > 1 else 1
de.append(df.iloc[:, j] / inix * 100. - 100)
#de = [df.iloc[:,j]/df.iloc[0,j]*100.-100 for j in range(df.shape[1])]
df = pd.concat(de, axis=1)
#- Create trend curve
if trendTF is True:
try:
from _alan_pppscf import vertex_locator
if trendName is None:
trendName = df._get_numeric_data().columns[0]
dg, dh = vertex_locator(df[trendName], npar=npar, debugTF=True)
#df['trend'] = dg['trend'].values
if debugTF is True:
print >> sys.stderr, "Trendline dg:\n", dg
except Exception, e:
print >> sys.stderr, "**ERROR: {} @ {}".format(
str(e), 'vertex_locator()')