def plot(outright_index, fly_index, hedged_index):
date = m["DateTime"].tolist()
outright_prices = m[outright_index].values.tolist()
fly_prices = m[fly_index].values.tolist()
hedged_prices = m[hedged_index].values.tolist()
upper_band = (m[hedged_index+"_mean"]+EntryStd*m[hedged_index+"_std"]).values.tolist()
lower_band = (m[hedged_index+"_mean"]-EntryStd*m[hedged_index+"_std"]).values.tolist()
buy_dates = [x[0] for x in trade_recorder[hedged_index].trades if x[2]>0]
buy_prices = [x[1] for x in trade_recorder[hedged_index].trades if x[2]>0]
sell_dates = [x[0] for x in trade_recorder[hedged_index].trades if x[2]<0]
sell_prices = [x[1] for x in trade_recorder[hedged_index].trades if x[2]<0]
fig, (ax1, ax2, ax3) = plt.subplots(3, sharex = True)
mondays = WeekdayLocator(MONDAY)
mondays.MAXTICKS = 2000
alldays = DayLocator() # minor ticks on the days
alldays.MAXTICKS = 2000
weekFormatter = DateFormatter('%b %d') # e.g., Jan 12
dayFormatter = DateFormatter('%d') # e.g., 12
ax1.xaxis.set_major_locator(mondays)
ax1.xaxis.set_minor_locator(alldays)
ax1.xaxis.set_major_formatter(weekFormatter)
ax1.plot(date, outright_prices)
ax2.plot(date, fly_prices)
ax3.plot(date, hedged_prices)
ax3.plot(date, upper_band, color='k')
ax3.plot(date, lower_band, color='k')
ax3.plot(buy_dates, buy_prices, "^", markersize = 5, color='m')
ax3.plot(sell_dates, sell_prices, "v", markersize = 5, color='k')
ax1.xaxis_date()
ax1.autoscale_view()
def plot_contrast(self,moneyrecord1,moneyrecord2,date_ls,startdate,enddate):
startdate=self.datetransfer(startdate)
date_ls_change=[]
for i in date_ls:
if int(i)>=int(startdate) and int(i)<=int(enddate):
date_ls_change.append(i)
mondays = WeekdayLocator(MONDAY)
months = MonthLocator(range(1, 13), bymonthday=1, interval=3)
monthsFmt = DateFormatter("%b '%y")
print(date_ls_change)
#print(date_ls)
date = [str(e) for e in date_ls_change]
date_ls = [datetime.datetime.strptime(d,'%Y%m%d').date() for d in date]
algo_pv=moneyrecord1['money'].tolist()
print(algo_pv)
bench_pv=moneyrecord2['moneybenchmark'].tolist()
fig, ax = plt.subplots()
plt.gca().set_color_cycle(['blue', 'red'])
ax.plot_date(date_ls, algo_pv,'-')
ax.plot_date(date_ls, bench_pv,'-')
months.MAXTICKS=5000
mondays.MAXTICKS=5000
ax.xaxis.set_major_locator(months)
ax.xaxis.set_major_formatter(monthsFmt)
ax.xaxis.set_minor_locator(mondays)
ax.autoscale_view()
ax.grid(True)
fig.autofmt_xdate()
plt.title('P&L')
plt.legend(['strategy', 'benchmark'], loc='upper left')
plt.show()
def plot_trade(self, symbol, init_index = None, end_index=None, PnL=False, ATR=False):
if init_index == None:
init_index = 0
init_date = b.bars[symbol][init_index]["D"]
if end_index == None:
end_index = len(b.bars[symbol])
end_date = b.bars[symbol][end_index-1]["D"]
mondays = WeekdayLocator(MONDAY)
mondays.MAXTICKS = 2000
alldays = DayLocator() # minor ticks on the days
alldays.MAXTICKS = 2000
weekFormatter = DateFormatter('%b %d') # e.g., Jan 12
dayFormatter = DateFormatter('%d') # e.g., 12
history = [(date2num(x["D"]), x["O"], x["H"], x["L"], x["C"]) for x in b.bars[symbol][init_index:end_index]]
if PnL or ATR:
fig, (ax, ax2) = plt.subplots(2, sharex = True)
else:
fig, ax = plt.subplots()
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_minor_locator(alldays)
ax.xaxis.set_major_formatter(weekFormatter)
candlestick_ohlc(ax, history)
buys = [(x["Date"], x["Price"]) for x in self.trades[symbol] if (x["Lots"] > 0 and x["Date"] > init_date and x["Date"]<end_date) ]
buy_dates, buy_values = zip(*buys)
ax.plot(buy_dates, buy_values, "^", markersize = 5, color='m')
sells = [(x["Date"], x["Price"]) for x in self.trades[symbol] if (x["Lots"] < 0 and x["Date"] > init_date and x["Date"]<end_date)]
sell_dates, sell_values = zip(*sells)
ax.plot(sell_dates, sell_values, "v", markersize = 5, color='k')
ax.xaxis_date()
ax.autoscale_view()
if PnL:
equity_history = [(date, record["equity"], record["drawdown"]) for date, record in self.history.items()]
dates, values, drawdowns = zip(*equity_history)
ax2 = fig.add_subplot(212)
ax2.plot(dates, values, 'b-')
elif ATR:
equity_history = [(x["D"], x["ATR"]) for x in self.bars[symbol][init_index:end_index]]
dates, values = zip(*equity_history)
ax2 = fig.add_subplot(212)
ax2.plot(dates, values, 'b-')
## ax3 = ax2.twinx()
## ax3.plot(dates, drawdowns, 'r-')
plt.show()
def plot_alpha(self,alpha,date_ls,startdate,enddate):
startdate=self.datetransfer(startdate)
date_ls_change=[]
for i in date_ls:
if int(i)>=int(startdate) and int(i)<=int(enddate):
date_ls_change.append(i)
mondays = WeekdayLocator(MONDAY)
months = MonthLocator(range(1, 13), bymonthday=1, interval=3)
monthsFmt = DateFormatter("%b '%y")
#print(date_ls)
date = [str(e) for e in date_ls_change]
date_ls = [datetime.datetime.strptime(d,'%Y%m%d').date() for d in date]
algo_pv=alpha['alpha'].tolist()
#print(algo_pv)
fig, ax = plt.subplots()
plt.gca().set_color_cycle(['blue', 'red'])
ax.plot_date(date_ls, algo_pv,'-')
months.MAXTICKS=5000
mondays.MAXTICKS=5000
ax.xaxis.set_major_locator(months)
ax.xaxis.set_major_formatter(monthsFmt)
ax.xaxis.set_minor_locator(mondays)
ax.autoscale_view()
ax.grid(True)
fig.autofmt_xdate()
plt.title('Alpha')
plt.legend(['alpha'], loc='upper left')
plt.show()
#KJ_profit_df=pd.DataFrame(columns=[i/10 for i in range(1,121)])
#for K in list_K:
#for J in list_J:
#KJ=B(cash,account,startdate,enddate,K,J)
#KJ_total_portfolio=KJ.test(startdate,enddate,K,J,cash)
#KJ_profit_df.loc[10*J-1,K]=KJ_total_portfolio
#KJ_profit_df.to_csv('KJ_finalprofit.csv')
data = sbi.copy()
data.columns
data.dtypes
data.head()
data.index
data[['Open', 'High', 'Low', 'Close']]
data[['Open', 'High', 'Low', 'Close', 'WAP']].plot()
data[['Open', 'Close']].plot()
#%%%
#Libraries
#pip install mpl_finance
from mpl_finance import candlestick_ohlc
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
from matplotlib.dates import MONDAY, DateFormatter, DayLocator, WeekdayLocator
mondays = WeekdayLocator(MONDAY) # major ticks on the mondays
alldays = DayLocator() # minor ticks on the days
weekFormatter = DateFormatter('%b %d') # e.g., Jan 12
dayFormatter = DateFormatter('%d') # e.g., 12
#prepare OHLC
sbi.head()
data3 = sbi[['Open', 'High', 'Low', 'Close']].copy()
data3.head()
date1 = "2019-8-1"
date2 = "2019-8-30"
data3b = data3[(data3.index >= date1) & (data3.index <= date2)]
data3b
#%%% Plots
#run together till plt.show
fig, ax = plt.subplots(figsize=(10, 8))
def pandas_candlestick_ohlc(dat, stick = "day", otherseries = None):
"""
:param dat: pandas DataFrame object with datetime64 index, and float columns "Open", "High", "Low", and "Close", likely created via DataReader from "yahoo"
:param stick: A string or number indicating the period of time covered by a single candlestick. Valid string inputs include "day", "week", "month", and "year", ("day" default), and any numeric input indicates the number of trading days included in a period
:param otherseries: An iterable that will be coerced into a list, containing the columns of dat that hold other series to be plotted as lines
This will show a Japanese candlestick plot for stock data stored in dat, also plotting other series if passed.
"""
mondays = WeekdayLocator(MONDAY) # major ticks on the mondays
alldays = DayLocator() # minor ticks on the days
#dayFormatter = DateFormatter('%d') # e.g., 12
# Create a new DataFrame which includes OHLC data for each period specified by stick input
transdat = dat.loc[:,["Open", "High", "Low", "Close"]]
if (type(stick) == str):
if stick == "day":
plotdat = transdat
stick = 1 # Used for plotting
elif stick in ["week", "month", "year"]:
if stick == "week":
transdat["week"] = pd.to_datetime(transdat.index).map(lambda x: x.isocalendar()[1]) # Identify weeks
elif stick == "month":
transdat["month"] = pd.to_datetime(transdat.index).map(lambda x: x.month) # Identify months
transdat["year"] = pd.to_datetime(transdat.index).map(lambda x: x.isocalendar()[0]) # Identify years
grouped = transdat.groupby(list(set(["year",stick]))) # Group by year and other appropriate variable
plotdat = pd.DataFrame({"Open": [], "High": [], "Low": [], "Close": []}) # Create empty data frame containing what will be plotted
for name, group in grouped:
plotdat = plotdat.append(pd.DataFrame({"Open": group.iloc[0,0],
"High": max(group.High),
"Low": min(group.Low),
"Close": group.iloc[-1,3]},
index = [group.index[0]]))
if stick == "week": stick = 5
elif stick == "month": stick = 30
elif stick == "year": stick = 365
elif (type(stick) == int and stick >= 1):
transdat["stick"] = [np.floor(i / stick) for i in range(len(transdat.index))]
grouped = transdat.groupby("stick")
plotdat = pd.DataFrame({"Open": [], "High": [], "Low": [], "Close": []}) # Create empty data frame containing what will be plotted
for name, group in grouped:
plotdat = plotdat.append(pd.DataFrame({"Open": group.iloc[0,0],
"High": max(group.High),
"Low": min(group.Low),
"Close": group.iloc[-1,3]},
index = [group.index[0]]))
else:
raise ValueError('Valid inputs to argument "stick" include the strings "day", "week", "month", "year", or a positive integer')
# Set plot parameters, including the axis object ax used for plotting
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.2)
if plotdat.index[-1] - plotdat.index[0] < pd.Timedelta('730 days'):
weekFormatter = DateFormatter('%b %d') # e.g., Jan 12
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_minor_locator(alldays)
else:
weekFormatter = DateFormatter('%b %d, %Y')
ax.xaxis.set_major_formatter(weekFormatter)
ax.grid(True)
# Create the candelstick chart
candlestick_ohlc(ax, list(zip(list(date2num(plotdat.index.tolist())), plotdat["Open"].tolist(), plotdat["High"].tolist(),
plotdat["Low"].tolist(), plotdat["Close"].tolist())),
colorup = "black", colordown = "red", width = stick * .4)
# Plot other series (such as moving averages) as lines
if otherseries != None:
if type(otherseries) != list:
otherseries = [otherseries]
dat.loc[:,otherseries].plot(ax = ax, lw = 1.3, grid = True)
ax.xaxis_date()
ax.autoscale_view()
plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')
plt.show()
value + 0.5 * rect.get_height(),
"{} - {:.2%}".format(
int(width),
float(width) / float(session_with_command)),
ha='center',
va='bottom')
print("plotting")
fig, ax = plt.subplots()
ax.plot(dates_x, y, linestyle='None', marker='o')
# every monday
mondays = WeekdayLocator(MONDAY)
months = MonthLocator(range(1, 13), bymonthday=1)
days = DayLocator(interval=1)
monthsFmt = DateFormatter("%d %b '%y")
daysFmt = DateFormatter("%d/%m/%y")
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_major_formatter(monthsFmt)
ax.xaxis.set_minor_locator(days)
ax.autoscale_view()
fig.autofmt_xdate()
ax.set_title(
"Repartition of the credentials used during a telnet sessions over time (filtered: <"
+ str(thres_om) + ")")
ax.yaxis.grid(True, which='both')
import datetime
from pylab import *
from matplotlib.dates import MONDAY, SATURDAY
from matplotlib.finance import quotes_historical_yahoo
from matplotlib.dates import MonthLocator, WeekdayLocator, DateFormatter
from matplotlib.ticker import FormatStrFormatter
# the start and end date range for the financial plots
date1 = datetime.date(2003, 1, 1)
date2 = datetime.date(2004, 4, 12)
# the tick locators and formatters
mondays = WeekdayLocator(MONDAY) # every monday
months = MonthLocator() # every month
monthsFmt = DateFormatter('%b %d') # looks like May 01
dollarFmt = FormatStrFormatter('$%0.2f') # dollars!
# get some financial data from the finance module
quotes = quotes_historical_yahoo('INTC', date1, date2)
if not quotes: raise SystemExit # failsafe
# extract the date and opening prices from the quote tuples
dates = [q[0] for q in quotes]
opens = [q[1] for q in quotes]
# plot_date will choose a default date ticker and formatter
ax = subplot(111)
plot_date(dates, opens, markeredgecolor='k')
# but we'll override the default with our custom locators and
# formatters
def drawPic(df, code, name):
mondays = WeekdayLocator(MONDAY) # 主要刻度
alldays = DayLocator() # 次要刻度
# weekFormatter = DateFormatter('%b %d') # 如：Jan 12
mondayFormatter = DateFormatter('%m-%d-%Y') # 如：2-29-2015
dayFormatter = DateFormatter('%d') # 如：12
xx = []
t = 0
for DAT, DD in df.iterrows():
t = t + 10
xx.append(t)
# --------------------------------------------------------------------
plt.figure(4) # 创建图表2
ax1 = plt.subplot(411) # 在图表2中创建子图1
ax2 = plt.subplot(412) # 在图表2中创建子图2
ax3 = plt.subplot(413) # 在图表2中创建子图2
ax4 = plt.subplot(414) # 在图表2中创建子图2
# --------------------------------------------(子图表1)
plt.sca(ax1)
ax1.xaxis.set_major_locator(mondays)
ax1.xaxis.set_minor_locator(alldays)
ax1.xaxis.set_major_formatter(mondayFormatter)
_candlestick(ax1, df, width=0.6, colorup='r', colordown='g')
ax1.xaxis_date()
plt.setp(plt.gca().get_xticklabels(),
rotation=45,
horizontalalignment='right')
"""
plt.plot( xx, df['ma5'], linewidth = 0.5 )
plt.plot( xx, df['ma10'], linewidth = 0.5 )
plt.plot( xx, df['ma20'], linewidth = 0.5 )
plt.plot( xx, df['ma30'], linewidth = 0.5 )
plt.plot( xx, df['ma40'], linewidth = 0.5 )
plt.plot( xx, df['ma60'], linewidth = 0.5 )
plt.plot( xx, df['ma89'], linewidth = 0.5 )
plt.plot( xx, df['ma144'], linewidth = 0.5 )
plt.plot( xx, df['ma233'], linewidth = 0.5 )
plt.plot( xx, df['ma377'], linewidth = 0.5 )
plt.plot( xx, df['ma610'], linewidth = 0.5 )
"""
# ax.grid(True)
# --------------------------------------------(子图表2)
plt.sca(ax2)
# plt.plot( xx[len(xx)-500:len(xx)], df['j1'].tail(500), linewidth = 0.5 )
plt.plot(xx, df['j1'], linewidth=0.5)
plt.plot(xx, df['e1'], linewidth=0.5)
plt.plot(xx, df['j2'], linewidth=0.5)
plt.plot(xx, df['e2'], linewidth=0.5)
plt.plot(xx, df['j3'], linewidth=0.5)
plt.plot(xx, df['e3'], linewidth=0.5)
plt.plot(xx, df['j4'], linewidth=0.5)
plt.plot(xx, df['e4'], linewidth=0.5)
plt.sca(ax3)
plt.plot(xx, df['x1'], linewidth=0.5)
plt.plot(xx, df['x2'] * 7, linewidth=0.5)
plt.plot(xx, df['x3'] * 30, linewidth=0.5)
plt.plot(xx, df['x4'] * 80, linewidth=0.5)
plt.sca(ax4)
plt.plot(xx, df['gain'], linewidth=0.5)
plt.show()
def drawgraph(shcode, startdate=0, period=0, discover=None, pearson=None):
# 1st parameter, shcode is stock code. # e.g., shcode = "000020" #동화약품(000020)
# 2nd parameter, startdate is an index of DB data.
# 3rd parameter, period is a number of DB data from startdate. default 0 means all period.
# Fetch
rows = shquery.getstockprice(shcode)
# exception of function
if len(rows) - 1 < startdate + period:
return -1
# create List type array
marketdate, openprice, closeprice, highprice, lowprice = [], [], [], [], []
for row in rows:
marketdate.append(dates.date2num(row['marketdate']))
openprice.append(int(row['openprice']))
closeprice.append(int(row['closeprice']))
highprice.append(int(row['highprice']))
lowprice.append(int(row['lowprice']))
j = period if period != 0 else len(rows) - 1 - startdate # set period
# create DataFrame
k = startdate
graph = {
'marketdate': marketdate[k:k + j],
'openprice': openprice[k:k + j],
'closeprice': closeprice[k:k + j],
'highprice': highprice[k:k + j],
'lowprice': lowprice[k:k + j]
}
graph = DataFrame(graph)
quotes = zip(graph['marketdate'], graph['openprice'], graph['highprice'],
graph['lowprice'], graph['closeprice'])
# print(graph)
# Graph Drawing
mondays = WeekdayLocator(MONDAY) # major ticks on the mondays
alldays = DayLocator() # minor ticks on the days
weekFormatter = DateFormatter('%b %d, %Y') # e.g., Jan 12, 2014
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.2)
# ax.xaxis.set_major_locator(mondays)
# ax.xaxis.set_minor_locator(alldays)
# ax.xaxis.set_major_formatter(weekFormatter)
ax.xaxis_date()
ax.autoscale_view()
ax.grid(True)
# print(graph['marketdate'][396])
if discover is not None:
if len(discover) != len(pearson):
return -1
j = -1
for i in discover:
j += 1
ymin, ymax = graph['highprice'][i], graph['lowprice'][
i] # initialize
for n in range(i, i + 30):
ymin = min(ymin, graph['lowprice'][n]) # min value
ymax = max(ymax, graph['highprice'][n]) # max value
ax.set_title("Stock Code: " + shcode) # set graph title
ax.text(
graph['marketdate'][i],
ymax,
str(float("{0:.2f}".format(pearson[j] * 100))) + "%",
fontsize=8
) # print similarity percent (limiting floats to four decimal points)
if i + (30 * 2) < len(rows) - 1:
earning = ((int(graph['closeprice'][i + 50]) -
int(graph['closeprice'][i + 30])) /
int(graph['closeprice'][i + 30])
) * 100 # calculate earning ratio
earning = round(earning, 2)
ax.text(graph['marketdate'][i + 50],
graph['highprice'][i + 50] + 250,
str(earning) + "%",
color='green',
fontsize=8) # print earning ratio
# Draw Rectangle
# ax.text(graph['marketdate'][i], ymax - ymin, str(pearson[j])+"%") # print earning ratio
ax.add_patch(
patches.Rectangle(
(graph['marketdate'][i], ymin), # (x,y)
graph['marketdate'][i + 30] -
graph['marketdate'][i], # width
ymax - ymin, # height
fill=False, # remove background
linewidth=1))
candlestick_ohlc(ax, quotes, colorup="red", colordown="blue", width=0.6)
plt.setp(plt.gca().get_xticklabels(),
rotation=45,
horizontalalignment='right',
size=10)
# Save graph
filename = datetime.today().strftime("%Y%m%d_") + str(shcode) + "_" + str(
startdate) + "_" + str(
period) + "_" + "temp.png" # e.g., 19931004_000020_0_0_temp.png
filepath = "temp/" + filename
if not os.path.isfile(filepath):
plt.savefig(filepath, format='png')
print("검색결과를 다음의 경로로 저장합니다. ", filename)
else:
print("기존의 검색결과가 존재하므로, 저장하지 않습니다. ", filename)
plt.show()
# End of function
return filename
def visualize(self, elements="C,CL,LMK,PV,PVL,ODR", ylimits=None):
"""
elements: elements that should be plotted, see below.
ylimits: range of y axis. e.g. (0, 100)
{ # element and its dependent columns
# Basic
"C" : ["Close",], # Mark the tick['Close'] value with point.
"CL" : ["Close",], # Mark the tick['Close'] value as a line.
"HLC" : ["High", "Low", "Close"], # Plot the HLC values in the tick.
# Derived
"ODR" : ["Open", "High", "Low", "Close", "Volume"], # One Day Reversal. Mark the ODR ticks.
"PV" : ["Close", "Top", "Btm"], # Label the pivot point value.
"PVL" : ["Close", "Top", "Btm"], # Plot a Line that connects the pivot points.
"EE" : ["Buy", "Sell"], # Mark the idea Buy/Sell point.
"BAND" : ["Band", "WM"], # Mark the point with its current LMK Band Level.
"BANDL" : ["Band", "Buy", "Sell"], # Mark line segment according to its current band level.
"WM" : ["Trend", "WM"], # Mark the current resistant or support line
}
"""
h = self.history
# --------------------------------------------------------------
# ---- Background and Limits ----
ax0 = plt.subplot2grid((5, 1), (0, 0), rowspan=4)
ax0.set_xmargin(0.02)
#http://stackoverflow.com/questions/3305865/what-is-the-difference-between-log-and-symlog
#ax0.set_yscale("symlog", linthreshy=30)
ax1 = plt.subplot2grid((5, 1), (4, 0), rowspan=1, sharex=ax0)
ax1.yaxis.set_visible(False)
#ax1.set_yscale("symlog", linthreshy=1000)
self.figure = plt.gcf()
#self.figure.clear()
self.figure.suptitle("%s%s" %
(self.symbol, "" if self.symbol == self.name else
("(%s)" % self.name)))
self.figure.subplots_adjust(hspace=0)
if ylimits is not None:
ax0.set_ylim(*ylimits)
else:
close_min = min(h["Close"])
close_max = max(h["Close"])
height = close_min * .5
ymin = close_min * 0.98
ymax = close_min + (height * 1.02)
if ymax < close_max:
height = close_max - close_min
ymax = close_min + height * 1.02
ax0.set_ylim(ymin, ymax)
ax0.set_axis_bgcolor('white')
ax1.set_axis_bgcolor('white')
# --------------------------------------------------------------
# http://stackoverflow.com/questions/1166118/how-to-strip-decorators-from-a-function-in-python
def plot_elements(*names):
def decorated(f):
@functools.wraps(f)
def wrapper(*argv, **kargs):
return f(*argv, **kargs)
wrapper.elements = names
return wrapper
return decorated
def columns(*names):
def decorated(f):
@functools.wraps(f)
def wrapper(*argv, **kargs):
h = argv[1]
ensure_columns_exist(h, names)
return f(*argv, **kargs)
return wrapper
return decorated
# -- Basic Volume/Price --
@plot_elements("V")
@columns("CC", "Volume")
def plot_V(ax, h):
up = h[h["CC"] >= 0]
ax.bar(up.index,
up["Volume"],
width=1,
color="black",
edgecolor="black",
linewidth=1,
alpha=.3,
align="center")
dn = h[h["CC"] < 0]
ax.bar(dn.index,
dn["Volume"],
width=1,
color="red",
edgecolor="red",
linewidth=1,
alpha=.3,
align="center")
@plot_elements("C")
@columns("Close", "CC")
def plot_C(ax, h):
up = h[h["CC"] >= 0]
ax.plot(up.index,
up["Close"],
"_",
color="black",
alpha=.5,
markeredgewidth=2)
dn = h[h["CC"] < 0]
ax0.plot(dn.index,
dn["Close"],
"_",
color="red",
alpha=.5,
markeredgewidth=2)
@plot_elements("CL")
@columns("Close")
def plot_CL(ax, h):
ax.plot(h.index, h["Close"], "-", color="black", alpha=0.5)
@plot_elements("HLC")
@columns("High", "Low", "Close", "CC")
def plot_HLC(ax, h):
up = h[h["CC"] >= 0]
ax.plot(up.index,
up["Close"],
"_",
color="black",
alpha=1,
markeredgewidth=1)
up = h[h["Close"] >= h["Open"]]
ax.vlines(up.index,
up["Low"],
up["High"],
color="black",
edgecolor="black",
alpha=1,
linewidth=1)
dn = h[h["CC"] < 0]
ax.plot(dn.index,
dn["Close"],
"_",
color="red",
alpha=1,
markeredgewidth=1)
dn = h[h["Close"] < h["Open"]]
ax.vlines(dn.index,
dn["Low"],
dn["High"],
color="red",
edgecolor="red",
alpha=1,
linewidth=1)
# -- ODR --
@plot_elements("ODR")
@columns("ODR", "Close")
def plot_ODR(ax, h):
r = h[h["ODR"]]
ax.plot(r.index,
r["Close"],
"rx",
markersize=8,
markeredgewidth=3,
alpha=1)
# -- Pivots --
@plot_elements("PV")
@columns("Top", "Btm", "Close", "Low", "High")
def plot_PV(ax, h):
pivots = h[h["Top"] | h["Btm"]]
for x, tick in pivots.iterrows():
label = "%.2f" % tick["Close"]
if tick["Top"]: # crest
y = tick["High"]
ax.text(x, y, label, color="g", alpha=.8)
else: # trough
y = tick["Low"]
ax.text(x, y, label, color="r", alpha=.8)
@plot_elements("PVL")
@columns("Top", "Btm", "Close")
def plot_PVL(ax, h):
pivots = h[h["Top"] | h["Btm"]]
ax.plot(pivots.index, pivots["Close"], "-", color="blue", alpha=.3)
r = h[h["Top"]]
ax.plot(r.index, r["Close"], "g^", alpha=1.0)
r = h[h["Btm"]]
ax.plot(r.index, r["Close"], "rv", alpha=1.0)
# -- LMK --
BAND_STYLE_MAP = {
BAND_DNWARD: "rv",
BAND_NAT_REACT: "m<",
BAND_SEC_REACT: "m*",
BAND_SEC_RALLY: "c*",
BAND_NAT_RALLY: "c>",
BAND_UPWARD: "g^",
}
@plot_elements("BAND", "LMK")
@columns("Close", "WM", "Band")
def plot_BAND(ax, h):
for band in range(BAND_DNWARD, BAND_UPWARD + 1):
#if band in (BAND_SEC_REACT, BAND_SEC_RALLY): continue
r = h[(h["WM"] == h["Close"]) & (h["Band"] == band)]
ax0.plot(r.index, r["Close"], BAND_STYLE_MAP[band], alpha=1.0)
@plot_elements("BANDL")
@columns("Band", "Buy", "Sell", "Close")
def plot_BANDL(ax, h):
chosen = ma.masked_where(~(h["Band"] >= BAND_NAT_RALLY),
h["Close"])
if chosen.any():
ax.plot(h.index, chosen, "g-", linewidth=1, alpha=1)
chosen = ma.masked_where(~(h["Band"] <= BAND_NAT_REACT),
h["Close"])
if chosen.any():
ax.plot(h.index, chosen, "r-", linewidth=1, alpha=1)
@plot_elements("WM")
@columns("Trend", "WM", "ATR")
def plot_WM(ax, h):
chosen = ma.masked_where(~(h['Trend'] == TREND_UP), h["WM"])
ax.plot(h.index,
chosen,
drawstyle="steps-post",
color="g",
linewidth=1.5)
chosen = ma.masked_where(~(h['Trend'] == TREND_UP),
h["WM"] - h["ATR"] * 2.0)
ax.plot(h.index,
chosen,
drawstyle="steps-post",
color="r",
alpha=.5)
chosen = ma.masked_where(~(h['Trend'] == TREND_UP),
h["WM"] - h["ATR"])
ax.plot(h.index,
chosen,
drawstyle="steps-post",
color="b",
alpha=.2)
chosen = ma.masked_where(~(h['Trend'] == TREND_DN), h["WM"])
ax.plot(h.index,
chosen,
drawstyle="steps-post",
color="r",
linewidth=1.5)
chosen = ma.masked_where(~(h['Trend'] == TREND_DN),
h["WM"] + h["ATR"] * 2.0)
ax.plot(h.index,
chosen,
drawstyle="steps-post",
color="g",
alpha=.5)
chosen = ma.masked_where(~(h['Trend'] == TREND_DN),
h["WM"] + h["ATR"])
ax.plot(h.index,
chosen,
drawstyle="steps-post",
color="b",
alpha=.2)
@plot_elements("EE", "BS")
@columns("Buy", "Sell", "Close")
def plot_EE(ax, h):
r = h[h["Buy"]]
ax0.plot(r.index,
r["Close"],
"g+",
markersize=8,
markeredgewidth=3,
alpha=1)
r = h[h["Sell"]]
ax0.plot(r.index,
r["Close"],
"r_",
markersize=8,
markeredgewidth=3,
alpha=1)
# Build the plotting function map ...
plot_functions = [
f for f in locals().values()
if callable(f) and hasattr(f, "elements")
]
plot_dict = {}
for f in plot_functions:
for element in f.elements:
plot_dict[element] = f
# do the real plotting ...
l = re.split("[-:,;.]", elements)
for c in l:
_plot = plot_dict[c]
if c != "V":
_plot(ax0, h)
else:
_plot(ax1, h)
# --------------------------------------------------------------
# ---- Axis and Grid ----
days = WeekdayLocator(MONDAY)
#days = WeekdayLocator(FRIDAY)
#dayFmt = DateFormatter("%d")
def _dayFmt(x, pos):
dt = num2date(x)
return dt.strftime("%d")[-1] if dt.day < 10 else dt.strftime("%d")
dayFmt = FuncFormatter(_dayFmt)
months = MonthLocator(range(1, 13), bymonthday=1, interval=1)
# http://stackoverflow.com/questions/11623498/date-formatting-with-matplotlib
def _monthFmt(x, pos):
dt = num2date(x)
return dt.strftime('\n%Y') if dt.month == 1 else dt.strftime(
"\n%b")
monthFmt = FuncFormatter(_monthFmt)
for ax in (ax0, ax1):
ax.xaxis.set_major_locator(months)
ax.xaxis.set_major_formatter(monthFmt)
plt.setp(ax.xaxis.get_majorticklabels(), rotation=45, color="blue")
if len(ax.get_xticks()) <= 12:
ax.xaxis.set_minor_locator(days)
ax.xaxis.set_minor_formatter(dayFmt)
ax0.xaxis.grid(True,
which='major',
color="blue",
linestyle="-",
alpha=1)
ax0.xaxis.grid(True,
which='minor',
color="gray",
linestyle="-",
alpha=.5)
ax0.tick_params(labelbottom="off", which="both")
ax0.yaxis.grid(True)
ax1.xaxis.grid(True,
which='major',
color="blue",
linestyle="-",
alpha=1)
ax1.xaxis.grid(True,
which='minor',
color="gray",
linestyle="-",
alpha=.3)
# http://stackoverflow.com/questions/12750355/python-matplotlib-figure-title-overlaps-axes-label-when-using-twiny
#ax0.set_title("%s%s" % (self.symbol, "" if self.symbol == self.name else ("(%s)" % self.name)), y=0.9)
plt.show()
t = []
for single_date in daterange(start_date, end_date):
times.append(PT.getTimes(single_date.timetuple(), cnf_city_coord, cnf_tz))
t.append(single_date.toordinal())
fajr = [time2minutes(x['fajr']) for x in times]
sunrise = [time2minutes(x['sunrise']) for x in times]
dhuhr = [time2minutes(x['dhuhr']) for x in times]
asr = [time2minutes(x['asr']) for x in times]
maghrib = [time2minutes(x['maghrib']) for x in times]
isha = [time2minutes(x['isha']) for x in times]
# Date Locators
everyday = DayLocator(interval=1)
fridays = WeekdayLocator(FRIDAY)
months = MonthLocator(range(1, 13), bymonthday=1, interval=1) # every month
monthsFmt = DateFormatter("%b '%y")
daysFmt = DateFormatter("%d %b")
fig, ax = plt.subplots()
plt.plot(t,fajr, 'k',t,sunrise, 'k',t,dhuhr, 'k',t,asr, 'k',t,maghrib, 'k',t,isha, 'k')
ax.xaxis.set_major_locator(fridays)
ax.xaxis.set_major_formatter(daysFmt)
ax.xaxis.set_minor_locator(everyday)
ax.xaxis_date()
ax.autoscale_view()
ax.xaxis.grid(True, 'major')
ax.xaxis.grid(False, 'minor')
ax.set_title('Annual Prayer Times for '+cnf_city_name+', '+str(cnf_year))
#fig.autofmt_xdate() #Autorotate labels
def _xAxis(self, ax):
# X-axis labels
# Don't create ticks when the x-axis range is too big. Likely this is because of
# a problem with the input data. Some versions of python crash when trying to
# create too many ticks
range = mpl.xlim()
if (range[1] - range[0] < 100):
if (self.shortRange):
mpl.gca().xaxis.set_major_locator(DayLocator(interval=1))
mpl.gca().xaxis.set_minor_locator(HourLocator(interval=6))
mpl.gca().xaxis.set_major_formatter(
DateFormatter('\n %a %d %b %Y'))
mpl.gca().xaxis.set_minor_formatter(DateFormatter('%H'))
else:
mpl.gca().xaxis.set_major_locator(
WeekdayLocator(byweekday=(MO, TU, WE, TH, FR)))
mpl.gca().xaxis.set_major_formatter(
DateFormatter('\n%Y-%m-%d'))
mpl.gca().xaxis.set_minor_locator(
WeekdayLocator(byweekday=(SA, SU)))
mpl.gca().xaxis.set_minor_formatter(
DateFormatter('\n%Y-%m-%d'))
mpl.xticks(rotation=90)
if (self.showX):
mpl.xlabel('Date', fontsize=self.labelFs)
majlabels = [tick.label1 for tick in mpl.gca().xaxis.get_major_ticks()]
for i in majlabels:
# Don't show the last label, since it will be outside the range
if (i == majlabels[len(majlabels) - 1]):
i.set_visible(0)
if (not self.showX):
i.set_visible(0)
else:
if (self.shortRange):
i.set_horizontalalignment('left')
i.set_position((0, -0.035))
else:
i.set_horizontalalignment('right')
i.set_rotation(30)
i.set_verticalalignment('top')
i.set_fontsize(self.fs)
i.set_position((0, -0.035))
minlabels = [tick.label1 for tick in mpl.gca().xaxis.get_minor_ticks()]
for i in minlabels:
if (not self.showX):
i.set_visible(0)
else:
if (self.shortRange):
i.set_horizontalalignment('center')
i.set_rotation(0)
i.set_color("k")
else:
i.set_horizontalalignment('right')
i.set_rotation(30)
i.set_color((1, 0, 1)) # Weekend days are magenta
i.set_verticalalignment('top')
i.set_fontsize(self.fs)
ylabels = [tick.label1 for tick in mpl.gca().yaxis.get_major_ticks()]
for i in ylabels:
i.set_fontsize(self.fs)
# Gridlines
mpl.gca().xaxis.grid(True,
which='major',
color='k',
zorder=-10,
linestyle='-')
if (self.shortRange):
mpl.gca().xaxis.grid(True,
which='minor',
color='k',
zorder=0,
linestyle=':')
else:
mpl.gca().xaxis.grid(True,
which='minor',
color=(1, 0, 1),
zorder=0,
linestyle='-')
minOffset = min(self.file.getOffsets())
maxOffset = max(self.file.getOffsets())
if (not np.isnan(self.maxOffset)):
maxOffset = minOffset + self.maxOffset / 24.0
mpl.xlim(minOffset, maxOffset)
def plot_covid(
loc,
cat,
cty_state,
wknd="skip weekends",
start="2020-03-02",
end=str(dt.date.today()),
in_fn=f"DL-us-mobility.ndjson",
out_fn=f"DL_mobility-index_{str(dt.date.today())}",
):
"""
Parameters
----------
loc : list
a list of strings with names of counties, states, or both. default is a
list containing: California, Texas, New York, Illinois, Washington, Florida.
cat : list
categorizes loc as states, counties, both, or rank; "rank" specifies
to rank top ten counties within a state or states. default is state.
cty_state : str or list
a string or list of strings with the state that corresponds to
counties if that is what is specified in loc
wknd : bool
if weekends, include weekend dates; if skip_weekends, only include
weekdays. default is False.
start : str
starting date for plot. optional.
end: str
ending date for plot. optional.
in_fn: str
input filename for plotting. optional.
out_fn: str
output filename for figure. default is "DL_mobility-index_{today}.png. optional.
Returns
-------
plot of mobility indices for specified locations and dates
"""
# organize data
data = {}
states = []
if (cat == "county" or cat == "both") and cty_state == None:
print(
"Please specify states for your county or counties to ensure you're getting the expected data."
)
for line in open(in_fn):
d = json.loads(line)
for aoi in loc:
aoi = aoi.title()
# state or default for county / both for if county states aren't specified
if (cat == "state" or (cat == "county" and cty_state == None)
or (cat == "both" and cty_state == None)):
if (aoi in d["admin1"] and d["admin_level"] == 1) or (
aoi in d["admin2"] and d["admin_level"] == 2):
states.append(d["admin1"])
data[aoi] = {}
data[aoi]["dates"] = d["date"]
data[aoi]["m50"] = d["m50_index"]
data[aoi]["samples"] = d["samples"]
# counties all from the same state
if (cat == "county" or cat == "both") and len(cty_state) == 1:
state = cty_state[0].title()
if (aoi in d["admin2"]
or aoi in d["admin1"]) and state in d["admin1"]:
states.append(d["admin1"])
data[aoi] = {}
data[aoi]["dates"] = d["date"]
data[aoi]["m50"] = d["m50_index"]
data[aoi]["samples"] = d["samples"]
# counties from different states
if (cat == "county" or cat == "both") and len(cty_state) > 1:
for cty_st in cty_state:
if loc.index(aoi.lower()) == cty_state.index(cty_st):
state = cty_st.title()
if aoi.title() in d["admin2"] and state in d["admin1"]:
states.append(d["admin1"])
data[aoi] = {}
data[aoi]["dates"] = d["date"]
data[aoi]["m50"] = d["m50_index"]
data[aoi]["samples"] = d["samples"]
data[aoi]["state"] = d["admin1"]
# counties ranked for a specific state
if cat == "rank":
if aoi in d["admin1"] and d["admin_level"] == 2:
data[d["admin2"]] = {}
data[d["admin2"]]["dates"] = d["date"]
data[d["admin2"]]["m50"] = d["m50_index"]
data[d["admin2"]]["samples"] = d["samples"]
# prepare for plotting
orddata = OrderedDict(
sorted(data.items(), key=lambda x: getitem(x[1], "samples")))
if cat == "rank":
items = list(orddata.items())[-10:]
else:
items = list(orddata.items())
mindex = {"x": [], "y": [], "label": []}
for item in items:
aoi = {"x": [], "y": []}
for n in range(len(item[1]["dates"])):
date = item[1]["dates"][n]
m50 = item[1]["m50"][n]
day = dt.date(int(date[:4]), int(date[5:7]),
int(date[8:10])).weekday()
if not wknd and (day == 5 or day == 6):
continue
if not wknd and (day != 5 or day != 6):
aoi["x"].append([dt.datetime.strptime(date, "%Y-%m-%d")])
aoi["y"].append(m50)
if wknd:
aoi["x"].append([dt.datetime.strptime(date, "%Y-%m-%d")])
aoi["y"].append(m50)
mindex["x"].append(aoi["x"])
mindex["y"].append(aoi["y"])
mindex["label"].append(item[0])
# plot data
plt.rcParams["font.family"] = "sans-serif"
plt.rcParams["font.sans-serif"] = ["Source Sans Pro"]
fig, ax = plt.subplots(figsize=(8, 6))
filled_markers = ["v", "^", "<", ">", "8", "s", "p"]
for i in range(len(mindex["label"])):
if i <= 8:
plt.plot(mindex["x"][i],
mindex["y"][i],
marker="o",
label=mindex["label"][i])
if i > 8:
plt.plot(
mindex["x"][i],
mindex["y"][i],
marker=filled_markers[i % 7],
label=mindex["label"][i],
)
ax.set_ylabel("Mobility Index", fontsize=12)
ax.set_ylim(0, 200)
ax.set_xlim(dt.datetime.strptime(start, "%Y-%m-%d"),
dt.datetime.strptime(end, "%Y-%m-%d"))
plt.setp(ax.get_xticklabels(), rotation=50)
plt.grid(axis="y")
ax.xaxis.set_major_locator(WeekdayLocator(byweekday=MO))
ax.xaxis.set_major_formatter(DateFormatter("%b %d"))
logo = plt.imread(dl_logo64)
if len(loc) <= 5 and cat != "rank":
ax.legend(
loc="upper center",
fontsize=11,
bbox_to_anchor=(0.75, -0.16),
ncol=2,
frameon=False,
)
ax.figure.figimage(logo, 200, 100, alpha=1.0, zorder=1)
plt.figtext(
0.08,
-0.15,
"CC BY 4.0 descarteslabs.com/mobility",
ha="left",
va="bottom",
alpha=0.5,
fontsize=8,
)
if len(loc) > 5 and len(loc) <= 12 or cat == "rank":
ax.legend(
loc="upper center",
fontsize=11,
bbox_to_anchor=(0.75, -0.16),
ncol=2,
frameon=False,
)
ax.figure.figimage(logo, 200, 100, alpha=1.0, zorder=1)
plt.figtext(
0.08,
-0.05,
"CC BY 4.0 descarteslabs.com/mobility",
ha="left",
va="bottom",
alpha=0.5,
fontsize=8,
)
if len(loc) > 12:
ax.legend(
loc="upper center",
fontsize=11,
bbox_to_anchor=(0.75, -0.16),
ncol=3,
frameon=False,
)
ax.figure.figimage(logo, 130, 130, alpha=1.0, zorder=1)
plt.figtext(
0.08,
-0.05,
"CC BY 4.0 descarteslabs.com/mobility",
ha="left",
va="bottom",
alpha=0.5,
fontsize=8,
)
plt.subplots_adjust(bottom=0.7)
fig.tight_layout()
# format titles based on user input
if cat == "state":
plt.title("United States")
if cat == "county" or cat == "both":
if len(set(states)) == 1:
plt.title(states[0] + " Counties")
if len(set(states)) != 1:
plt.title("United States: Counties")
if cat == "rank" and len(loc) == 1:
plt.title(loc[0].title())
if cat == "rank" and len(loc) > 1:
plt.title("United States county comparisons")
if cat == "both" and len(set(states)) != 1:
plt.title("United States + Counties")
# plot and save figure
plt.draw()
fig.savefig("{}.png".format(out_fn),
bbox_inches="tight",
pad_inches=0.1,
dpi=200)
def candleLinePlots(candleData, candleTitle='a', **kwargs):
Date = [date2num(date) for date in candleData.index]
candleData.loc[:,'Date'] = Date
listData = []
for i in range(len(candleData)):
a = [candleData.Date[i],\
candleData.Open[i],candleData.High[i],\
candleData.Low[i],candleData.Close[i]]
listData.append(a)
# 如 果 不 定 长 参 数 无 取 值 ， 只 画 蜡 烛 图
ax = plt.subplot()
# 如 果 不 定 长 参 数 有 值 ， 则 分 成 两 个 子 图
flag=0
if kwargs:
if kwargs['splitFigures']:
ax = plt.subplot(211)
ax2= plt.subplot(212)
flag=1;
# 如 果 无 参 数 splitFigures ， 则 只 画 一 个 图 形 框
# 如 果 有 参 数 splitFigures ， 则 画 出 两 个 图 形 框
for key in kwargs:
if key=='title':
ax2.set_title(kwargs[key])
if key=='ylabel':
ax2.set_ylabel(kwargs[key])
if key=='grid':
ax2.grid(kwargs[key])
if key=='Data':
plt.sca(ax)
if flag:
plt.sca(ax2)
#一维数据
if kwargs[key].ndim==1:
plt.plot(kwargs[key],\
color='k',\
label=kwargs[key].name)
plt.legend(loc='best')
#二维数据有两个columns
elif all([kwargs[key].ndim==2,\
len(kwargs[key].columns)==2]):
plt.plot(kwargs[key].iloc[:,0], color='k',
label=kwargs[key].iloc[:,0].name)
plt.plot(kwargs[key].iloc[:,1],\
linestyle='dashed',\
label=kwargs[key].iloc[:,1].name)
plt.legend(loc='best')
mondays = WeekdayLocator(MONDAY)
weekFormatter = DateFormatter('%y %b %d')
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_minor_locator(DayLocator())
ax.xaxis.set_major_formatter(weekFormatter)
plt.sca(ax)
candlestick_ohlc(ax,listData, width=0.7,\
colorup='r',colordown='g')
ax.set_title(candleTitle)
plt.setp(ax.get_xticklabels(),\
rotation=20,\
horizontalalignment='center')
ax.autoscale_view()
return(plt.show())