def visualize_peak_log(logfile):
""" Create a histogram of the log entry timestamps and save it as png """
_, timestamps, _ = np.loadtxt(logfile,
unpack=True,
delimiter=' ',
converters={
0: mdates.bytespdate2num("%Y-%m-%d"),
1: mdates.bytespdate2num("%H:%M:%S")
})
if timestamps.size > 1:
first_stamp = mdates.num2date(timestamps[0])
last_stamp = mdates.num2date(timestamps[-1])
bin_range = range(first_stamp.hour, last_stamp.hour + 2)
date_bins = []
for h in bin_range:
date_bins.append(datetime.datetime(1900, 1, 1, h, 0, 0, 0))
num_bins = mdates.date2num(date_bins)
else:
num_bins = 1
_, ax = plt.subplots(1, 1)
ax.hist(timestamps, num_bins, rwidth=1, ec='k')
ax.xaxis.set_major_locator(mdates.HourLocator())
ax.xaxis.set_major_formatter(mdates.DateFormatter('%H'))
plt.title('Number of entires per hour, in total ' + str(timestamps.size))
plt.savefig(datetime.datetime.now().strftime("%Y-%m-%d") + '-summary.png')
plt.show()
def graph_stock():
stock_price_url = 'https://pythonprogramming.net/yahoo_finance_replacement'
source_code = urllib.request.urlopen(stock_price_url).read().decode()
stock_data = []
split_source = source_code.split('\n')
for line in split_source[1:]:
split_line = line.split(',')
if len(split_line) == 7:
if 'Volume' not in line and 'labels' not in line:
stock_data.append(line)
date, closep, highp, lowp, openp, adjclosep, volume = np.loadtxt(
stock_data,
delimiter=',',
unpack=True,
# %Y =full year 2015
# %y = partial year 15
# %m = number month
# %H number hours
# %M number minutes
# %S number seconds
converters={0: mdates.bytespdate2num('%Y-%m-%d')})
plt.plot_date(date, closep, '-', label='Price')
plt.xlabel('Date')
plt.ylabel('Price')
plt.title('Lect 8+9')
plt.legend()
plt.show()
def drawStockGraph(stock, date):
try:
#stockFile = 'C:/Zerodha/Pi/Exported/'+ stock
stockFile = cbook.get_sample_data('C:/Zerodha/Pi/Exported/' + stock,
asfileobj=False)
print('loading', stockFile)
date, open, high, low, close, volume = np.loadtxt(
stockFile,
delimiter=',',
converters={0: bytespdate2num(date)},
skiprows=2,
unpack=True)
#bytespdate2num('%d-%b-%y')},skiprows=1, usecols=(0, 2), unpack=True
# mdates.strpdate2num('%d-%m-%Y %H:%M:%S')}
fig = plt.figure()
ax1 = plt.subplot(1, 1, 1)
ax1.plot(date, close)
plt.ylabel('Stock PRice')
ax1.grid(True)
plt.subplots_adjust(left=.17)
plt.show()
except Exception, e:
print str(e), " error"
def graph_data(stock):
fig = plt.figure()
ax1 = plt.subplot2grid((1, 1), (0, 0))
# Unfortunately, Yahoo's API is no longer available
# feel free to adapt the code to another source, or use this drop-in replacement.
stock_price_url = 'https://pythonprogramming.net/yahoo_finance_replacement'
source_code = urllib.request.urlopen(stock_price_url).read().decode()
stock_data = []
split_source = source_code.split('\n')
for line in split_source[1:]:
split_line = line.split(',')
if len(split_line) == 7:
if 'values' not in line:
stock_data.append(line)
# 注意np.loadtxt需要把所有格式都转化成float的格式，对于bytes的日期时间我们需要用到mdates
# 中间的bytepdate2num
date, closep, highp, lowp, openp, adj_closep, volume = np.loadtxt(
stock_data,
delimiter=',',
unpack=True,
converters={0: mdates.bytespdate2num('%Y-%m-%d')})
x = 0
y = len(date)
ohlc = []
while x < y:
append_me = date[x], openp[x], highp[x], lowp[x], closep[x], volume[x]
ohlc.append(append_me)
x += 1
# 注意蜡烛图的参数传入的原则，是list，且每一个元素是一个tuple，tuple俩面的值全部为float
candlestick_ohlc(ax1,
ohlc,
width=0.4,
colorup='#77d879',
colordown='#db3f3f')
for label in ax1.xaxis.get_ticklabels():
label.set_rotation(45)
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
ax1.xaxis.set_major_locator(mticker.MaxNLocator(10))
ax1.grid(True)
plt.xlabel('Date')
plt.ylabel('Price')
plt.title(stock)
plt.legend()
plt.subplots_adjust(left=0.09,
bottom=0.20,
right=0.94,
top=0.90,
wspace=0.2,
hspace=0)
plt.show()
def printError(my_dpi, data):
"""
All data contanining the stock price info are retrieved from the database given the stock name
:param my_dpi: dpi screen
:param data: data to be plot
:param max_error: maximum error allowed
"""
try:
stockFile = []
try:
for eachLine in data:
splitLine = eachLine.split(',')
if len(splitLine) == 2:
if 'values' not in eachLine:
stockFile.append(eachLine)
except Exception as e:
print(str(e), 'failed to organize pulled data.')
except Exception as e:
print(str(e), 'failed to pull pricing data')
try:
date, closep_raw = np.loadtxt(
stockFile,
delimiter=',',
unpack=True,
converters={0: mdates.bytespdate2num('%Y%m%d')})
closep = closep_raw[::-1]
max_error = 0.1
while max_error < float(2 * max(closep)):
segments1 = segment.slidingwindowsegment(closep, fit.interpolate,
fit.sumsquared_error,
max_error)
segments2 = segment.slidingwindowsegment(closep, fit.regression,
fit.sumsquared_error,
max_error)
err1 = str(evaluate_MSE(closep, segments1))
err4 = str(evaluate_MSE(closep, segments2))
segments1 = segment.topdownsegment(closep, fit.interpolate,
fit.sumsquared_error, max_error)
segments2 = segment.topdownsegment(closep, fit.regression,
fit.sumsquared_error, max_error)
err2 = str(evaluate_MSE(closep, segments1))
err5 = str(evaluate_MSE(closep, segments2))
segments1 = segment.bottomupsegment(closep, fit.interpolate,
fit.sumsquared_error,
max_error)
segments2 = segment.bottomupsegment(closep, fit.regression,
fit.sumsquared_error,
max_error)
err3 = str(evaluate_MSE(closep, segments1))
err6 = str(evaluate_MSE(closep, segments2))
print(
str(max_error) + " " + err1 + " " + err2 + " " + err3 + " " +
err4 + " " + err5 + " " + err6)
max_error += 0.1
except e:
print("Error")
def graph_raw_fx():
date, bid, ask = np.loadtxt(
"GBPUSD1d.txt",
unpack=True,
delimiter=",",
converters={0: mdates.bytespdate2num("%Y%m%d%H%M%S")})
# define figure
fig = plt.figure(figsize=(10, 7))
ax1 = plt.subplot2grid(shape=(40, 40), loc=(0, 0), rowspan=40, colspan=40)
# plot data
#ax1.plot(date, bid)
#ax1.plot(date, ask)
# format x-axis so that it is in date format.
ax1.xaxis.set_major_formatter(mdates.DateFormatter("%Y-%m-%d %H:%M:%S"))
for label in ax1.xaxis.get_ticklabels():
label.set_rotation(45)
ax1_2 = ax1.twinx()
ax1_2.fill_between(date, 0, (ask - bid), facecolor="g", alpha=.3)
plt.gca().get_yaxis().get_major_formatter().set_useOffset(False)
plt.subplots_adjust(bottom=.23) # add additional space below date ticks
plt.grid(True)
def graph_data(stock):
fig = plt.figure()
ax1 = plt.subplot2grid((1, 1), (0, 0))
# Unfortunately, Yahoo's API is no longer available
# feel free to adapt the code to another source, or use this drop-in replacement.
stock_price_url = 'https://pythonprogramming.net/yahoo_finance_replacement'
source_code = urllib.request.urlopen(stock_price_url).read().decode()
stock_data = []
split_source = source_code.split('\n')
for line in split_source[1:]:
split_line = line.split(',')
if len(split_line) == 7:
if 'values' not in line and 'labels' not in line:
stock_data.append(line)
date, closep, highp, lowp, openp, adj_closep, volume = np.loadtxt(
stock_data,
delimiter=',',
unpack=True,
converters={0: bytespdate2num('%Y-%m-%d')})
x, y, ohlc = 0, len(date), []
while x < y:
append_me = date[x], openp[x], highp[x], lowp[x], closep[x], volume[x]
ohlc.append(append_me)
x += 1
candlestick_ohlc(ax1, ohlc, width=0.4, colorup='g', colordown='r')
for label in ax1.xaxis.get_ticklabels():
label.set_rotation(45)
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
ax1.xaxis.set_major_locator(mticker.MaxNLocator(10))
ax1.grid(True)
# annotation example with arror
ax1.annotate('You are here!', (date[9], highp[9]),
xytext=(0.8, 0.9),
textcoords='axes fraction',
arrowprops=dict(facecolor='grey', color='grey'))
# font dic example
font_dict = {'family': 'sherif', 'color': 'darkred', 'size': 15}
# hard coded text
ax1.text(date[4410], closep[1], 'Text Example', fontdict=font_dict)
plt.xlabel('Date')
plt.ylabel('Price')
plt.title(stock)
# plt.legend()
plt.subplots_adjust(left=0.09,
bottom=0.2,
right=0.94,
top=0.9,
wspace=0.2,
hspace=0)
plt.show()
def graph_data(stock):
fig = plt.figure()
ax1 = plt.subplot2grid((6, 1), (0, 0), rowspan=1, colspan=1)
plt.title(stock)
ax2 = plt.subplot2grid((6, 1), (1, 0), rowspan=4, colspan=1)
plt.xlabel('Date')
plt.ylabel('Price')
ax3 = plt.subplot2grid((6, 1), (5, 0), rowspan=1, colspan=1)
# Unfortunately, Yahoo's API is no longer available
# feel free to adapt the code to another source, or use this drop-in replacement.
stock_price_url = 'https://pythonprogramming.net/yahoo_finance_replacement'
source_code = urllib.request.urlopen(stock_price_url).read().decode()
stock_data = []
split_source = source_code.split('\n')
for line in split_source[1:]:
split_line = line.split(',')
if len(split_line) == 7:
if 'values' not in line and 'labels' not in line:
stock_data.append(line)
date, closep, highp, lowp, openp, adj_closep, volume = np.loadtxt(
stock_data,
delimiter=',',
unpack=True,
converters={0: bytespdate2num('%Y-%m-%d')})
x, y, ohlc = 0, len(date), []
while x < y:
append_me = date[x], openp[x], highp[x], lowp[x], closep[x], volume[x]
ohlc.append(append_me)
x += 1
candlestick_ohlc(ax2, ohlc, width=0.4, colorup='g', colordown='r')
for label in ax2.xaxis.get_ticklabels():
label.set_rotation(45)
ax2.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
ax2.xaxis.set_major_locator(mticker.MaxNLocator(10))
ax2.grid(True)
bbox_props = dict(boxstyle='larrow', fc='w', ec='k', lw=1)
ax2.annotate(str(closep[-1]), (date[0], closep[-1]),
xytext=(date[0] + 400, closep[-1]),
bbox=bbox_props)
# plt.legend()
plt.subplots_adjust(left=0.11,
bottom=0.24,
right=0.86,
top=0.9,
wspace=0.2,
hspace=0)
plt.show()
def graph_data(stock):
fig = plt.figure()
ax1 = plt.subplot2grid((1, 1), (0, 0))
# Unfortunately, Yahoo's API is no longer available
# feel free to adapt the code to another source, or use this drop-in replacement.
stock_price_url = 'https://pythonprogramming.net/yahoo_finance_replacement'
source_code = urllib.request.urlopen(stock_price_url).read().decode()
stock_data = []
split_source = source_code.split('\n')
for line in split_source[1:]:
split_line = line.split(',')
if len(split_line) == 7:
if 'values' not in line and 'labels' not in line:
stock_data.append(line)
date, closep, highp, lowp, openp, adj_closep, volume = np.loadtxt(
stock_data,
delimiter=',',
unpack=True,
converters={0: bytespdate2num('%Y-%m-%d')})
ax1.plot_date(date, closep, '-', label='Price')
ax1.plot([], [], lw=5, label='loss', color='r', alpha=0.5)
ax1.plot([], [], lw=5, label='gain', color='g', alpha=0.5)
ax1.fill_between(date,
closep,
closep[0],
where=(closep > closep[0]),
facecolor='g',
alpha=0.5)
ax1.fill_between(date,
closep,
closep[0],
where=(closep < closep[0]),
facecolor='r',
alpha=0.5)
for label in ax1.xaxis.get_ticklabels():
label.set_rotation(45)
ax1.grid(True)
ax1.xaxis.label.set_color('c')
ax1.yaxis.label.set_color('r')
ax1.set_yticks([i for i in range(0, 800, 50)])
plt.xlabel('Date')
plt.ylabel('Price')
plt.title(stock)
plt.legend()
plt.subplots_adjust(left=0.09,
bottom=0.2,
right=0.94,
top=0.9,
wspace=0.2,
hspace=0)
plt.show()
def graph_data():
fig = plt.figure()
ax1 = plt.subplot2grid((1, 1), (0, 0))
plt.ylabel('Price')
plt.xlabel('Date')
print('Currently viewing:')
url = 'https://pythonprogramming.net/yahoo_finance_replacement'
source_code = urllib.request.urlopen(url).read().decode()
stock_data = []
split_source = source_code.split('\n')
for each_line in split_source:
split_line = each_line.split(',')
if 'Date' not in each_line:
stock_data.append(each_line)
date, openp, highp, lowp, closep, adjusted_close, volume = np.loadtxt(stock_data, delimiter = ',', \
unpack = True, converters={0: mdates.bytespdate2num('%Y-%m-%d')})
x = 0
y = len(date)
new_list = []
while x < y:
append_line = date[x], openp[x], highp[x], lowp[x], closep[x], volume[
x]
new_list.append(append_line)
x += 1
candlestick_ohlc(ax1,
new_list,
width=.6,
colorup='#41ad49',
colordown='#ff1717')
ax1.grid(True) #ax1.grid(True, color = 'g', linestyle='-', linewidth=3)
for label in ax1.xaxis.get_ticklabels():
label.set_rotation(45)
ax1.xaxis.set_major_locator(mticker.MaxNLocator(10))
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
##ax1.annotate('Oil Spill!!', (date[25], highp[25]),
## xytext=(0.8, 0.9), textcoords='axes fraction',
## arrowprops=dict(facecolor="#585858", color="#585858"))
bbox_props = dict(boxstyle='round4, pad=0.3', fc="y", ec='k', lw=2)
ax1.annotate(str(closep[-1]), (date[-1], closep[-1]),
xytext=(date[-1] + 5, closep[-1]),
bbox=bbox_props)
plt.ylabel('Price')
plt.subplots_adjust(left=.11,
bottom=.26,
right=.90,
top=.95,
wspace=.2,
hspace=.2)
plt.show()
def graph_stock():
fig = plt.figure()
ax1 = plt.subplot2grid((1, 1), (0, 0))
stock_price_url = 'https://pythonprogramming.net/yahoo_finance_replacement'
source_code = urllib.request.urlopen(stock_price_url).read().decode()
stock_data = []
split_source = source_code.split('\n')
for line in split_source[1:]:
split_line = line.split(',')
if len(split_line) == 7:
if 'Volume' not in line and 'labels' not in line:
stock_data.append(line)
date, closep, highp, lowp, openp, adjclosep, volume = np.loadtxt(
stock_data,
delimiter=',',
unpack=True,
converters={0: mdates.bytespdate2num('%Y-%m-%d')})
ax1.plot_date(date, closep, '-', label='Price')
ax1.fill_between(date,
closep,
closep[0],
where=(closep > closep[0]),
facecolor='g',
alpha=0.5)
ax1.fill_between(date,
closep,
closep[0],
where=(closep < closep[0]),
facecolor='r',
alpha=0.5)
for label in ax1.xaxis.get_ticklabels():
label.set_rotation(45)
ax1.grid(True) #,color='g',linestyle='-')
ax1.xaxis.label.set_color('c')
ax1.yaxis.label.set_color('r')
ax1.set_yticks([0, 25, 50, 75])
plt.xlabel('Date')
plt.ylabel('Price')
plt.title('Lect 10')
plt.legend()
plt.subplots_adjust(left=0.09,
bottom=0.18,
right=0.94,
top=0.90,
wspace=0.2,
hspace=0)
plt.show()
def ols(self):
#stock, indice,stockDate,indiceDate
try:
indiceFile = cbook.get_sample_data(
'C:/Zerodha/Pi/Exported/NiftyIT.csv', asfileobj=False)
print('loading', indiceFile)
date_x, openp_x, highp_x, lowp_x, closep_x, volume_x = np.loadtxt(
indiceFile,
delimiter=',',
skiprows=2,
unpack=True,
usecols=(0, 1, 2, 3, 4, 5),
converters={0: bytespdate2num('%d-%m-%Y %H:%M:%S')})
stockFile_b = cbook.get_sample_data(
'C:/Zerodha/Pi/Exported/HCLTECH-EQ.csv', asfileobj=False)
print('loading', stockFile_b)
date_y, openp_y, highp_y, lowp_y, closep_y, volume_y = np.loadtxt(
stockFile_b,
delimiter=',',
skiprows=2,
unpack=True,
usecols=(0, 1, 2, 3, 4, 5),
converters={0: bytespdate2num('%d-%m-%Y %H:%M')})
alpha, beta = self.linreg(closep_x, closep_y)
print 'alpha: ' + str(alpha)
print 'beta: ' + str(beta)
X2 = np.linspace(closep_x.min(), closep_x.max(), 20)
Y_hat = X2 * beta + alpha
plt.scatter(closep_x, closep_y, alpha=0.3)
plt.ylabel("Stock PRice")
plt.xlabel("Indice")
# Add the regression line, colored in red
plt.plot(X2, Y_hat, 'r', alpha=0.9)
plt.show()
except Exception, e:
print str(e)
def load_data(path):
if os.path.isdir(path) == False:
print("Not dir")
return
# pattern_match = PatternMatch()
pattern_match = {}
for f in os.listdir(path):
full_path = os.path.join(path, f)
# if False == os.path.isfile(full_path):
# continue
if ('SH' != f[:2]) and ('SZ' != f[:2]):
continue
# print(f)
input_file = open(full_path)
lines = input_file.readlines()
if (5 + StockPattern.PATTERN_LEN +
StockPattern.OUTCOME_RANGE) > len(lines):
print(f + " 's data is too small. " + repr(len(lines)))
continue
lines = lines[4:-1]
input_file.close()
# lines = lines[:-10] # back two weeks
stock_code = f[3:-4]
# print("Add " + repr(stock_code) + ". LEN: " + repr(len(lines)))
date, op, hi, lo, cl, vol, trans = np.loadtxt(
lines,
unpack=True,
# delimiter='\t',
converters={0: mdates.bytespdate2num('%Y/%m/%d')},
)
stock_item_list = []
for i in range(len(date)):
stock_item_list.append(
StockItem(date[i], op[i], hi[i], lo[i], cl[i], vol[i],
trans[i]))
# stock_item_list.reverse()
# print(stock_code + repr(stock_item_list[0]))
# stock_pattern = StockPattern(stock_code, stock_item_list)
# pattern_match.stock_list.append(stock_pattern)
pattern_match[stock_code] = stock_item_list
# print("Add "+stock_code)
return pattern_match
def graph_data(stock):
fig = plt.figure()
ax1 = plt.subplot2grid((1,1),(0,0))
stock_price_url = 'https://pythonprogramming.net/yahoo_finance_replacement'
source_code = urllib.request.urlopen(stock_price_url).read().decode()
stock_data = []
split_source = source_code.split('\n')
for line in split_source[1:]:
stock_data.append(line)
date, openp, highp, lowp, closep, adjusted_close, volume = np.loadtxt(stock_data, delimiter=',', unpack=True,
converters={0: mdates.bytespdate2num('%Y-%m-%d')})
x= 0
y = 70 #len(date)
ohlc = []
while x < y:
append_me = date[x], openp[x], highp[x], lowp[x], closep[x], volume[x]
ohlc.append(append_me)
x += 1
candlestick_ohlc(ax1, ohlc, width=0.4, colorup='g', colordown='r')
for label in ax1.xaxis.get_ticklabels():
label.set_rotation(45)
ax1.xaxis.set_major_locator(mticker.MaxNLocator(5))
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
ax1.grid(True)
ax1.annotate('Bad News!', (date[9], highp[9]),
xytext=(0.8, 0.9), textcoords='axes fraction',
arrowprops=dict(facecolor='grey', color='grey'))
font_dict = {'family' : 'serif', 'color' : 'darkred', 'size' : 15}
ax1.text(date[10], closep[1], 'Text Example', fontdict = font_dict)
plt.xlabel('Date')
plt.ylabel('Price')
plt.title('Practicing Candle light')
#plt.legend()
plt.subplots_adjust(left=0.09, right=0.94, bottom=0.20, top=0.90, wspace=0.2, hspace=0)
plt.show()
def graph_data(stock):
fig = plt.figure()
ax1 = plt.subplot2grid((1, 1), (0, 0))
stock_price_url = 'https://pythonprogramming.net/yahoo_finance_replacement'
source_code = urllib.request.urlopen(stock_price_url).read().decode()
stock_data = []
split_source = source_code.split('\n')
for line in split_source[1:]:
stock_data.append(line)
date, openp, highp, lowp, closep, adjusted_close, volume = np.loadtxt(
stock_data,
delimiter=',',
unpack=True,
converters={0: mdates.bytespdate2num('%Y-%m-%d')})
x = 0
y = 25 #len(date)
ohlc = []
while x < y:
append_me = date[x], openp[x], highp[x], lowp[x], closep[x], volume[x]
ohlc.append(append_me)
x += 1
candlestick_ohlc(ax1, ohlc, width=0.4, colorup='g', colordown='r')
for label in ax1.xaxis.get_ticklabels():
label.set_rotation(45)
ax1.xaxis.set_major_locator(mticker.MaxNLocator(5))
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
ax1.grid(True)
plt.xlabel('Date')
plt.ylabel('Price')
plt.title('Practicing Candle light')
plt.legend()
plt.subplots_adjust(left=0.09,
right=0.94,
bottom=0.20,
top=0.90,
wspace=0.2,
hspace=0)
plt.show()
def graphRawFx():
date, bid, ask = np.loadtxt(
'./GBPUSD/GBPUSD1d.txt',
unpack=True,
delimiter=',',
converters={0: mdates.bytespdate2num('%Y%m%d%H%M%S')})
fig = plt.figure(figsize=(10, 7))
ax1 = plt.subplot2grid((40, 40), (0, 0), rowspan=40, colspan=40)
ax1.plot(date, bid)
ax1.plot(date, ask)
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%y-%m-%d %H:%M:%S'))
plt.grid(True)
plt.show()
def graph_data(stock):
fig = plt.figure()
stock_price_url = 'https://pythonprogramming.net/yahoo_finance_replacement'
source_code = urllib.request.urlopen(stock_price_url).read().decode()
stock_data = []
split_source = source_code.split('\n')
for line in split_source[1:]:
stock_data.append(line)
date, openp, highp, lowp, closep, adjusted_close, volume = np.loadtxt(stock_data, delimiter=',', unpack=True,
converters={
0: mdates.bytespdate2num('%Y-%m-%d')})
x = 0
y = 22 # len(date)
ohlc = []
while x < y:
append_me = date[x], openp[x], highp[x], lowp[x], closep[x], volume[x]
ohlc.append(append_me)
x += 1
ax1 = plt.subplot2grid((6, 1), (0, 0), rowspan=1, colspan=1)
plt.title(stock)
ax2 = plt.subplot2grid((6, 1), (1, 0), rowspan=4, colspan=1)
plt.xlabel('Date')
plt.ylabel('Price')
ax3 = plt.subplot2grid((6, 1), (5, 0), rowspan=1, colspan=1)
candlestick_ohlc(ax2, ohlc, width=0.4, colorup='#77d879', colordown='#db3f3f')
for label in ax2.xaxis.get_ticklabels():
label.set_rotation(45)
ax2.xaxis.set_major_locator(mticker.MaxNLocator(10))
ax2.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
ax2.grid(True)
# bbox_props = dict(boxstyle='round', fc='w', ec='k', lw=1)
# ax1.annotate(str(closep[-1]), (date[-1], closep[-1]),
# xytext=(date[-1] + 3, closep[-1]), bbox=bbox_props)
plt.subplots_adjust(left=0.11, bottom=0.24, right=0.87, top=0.90, wspace=0.2, hspace=0)
plt.show()
def read_weather(file_name):
dtypes = np.dtype({
'names': ('date', 'max temp', 'min temp'),
'formats': ['S9', np.float, np.float]
})
dates, max_temp, min_temp = np.loadtxt(
file_name,
delimiter=',',
skiprows=1,
usecols=(0, 1, 2),
dtype=dtypes,
unpack=True,
converters={0: dt.bytespdate2num('%Y-%m-%d')})
return dates, max_temp, min_temp
def graphRawFX():
(date, bid,
ask) = np.loadtxt('GBPUSD1d.txt',
unpack=True,
delimiter=',',
converters={0: mdates.bytespdate2num('%Y%m%d%H%M%S')})
fig = plt.figure(figsize=(10, 7))
ax1 = plt.subplot2grid((40, 40), (0, 0), rowspan=40, colspan=40)
ax1.plot(date, bid)
ax1.plot(date, ask)
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d %H:%M:%S'))
for label in ax1.xaxis.get_ticklabels():
label.set_rotation(45)
plt.subplots_adjust(bottom=.23)
plt.gca().get_yaxis().get_major_formatter().set_useOffset(False)
plt.grid(True)
plt.show()
def graphRawFX():
c=str('GBPUSD1d.txt')
date,bid,ask = np.loadtxt(c, unpack=True, delimiter=',',converters={0:mdates.bytespdate2num('%Y%m%d%H%M%S')})#,dtype={'names': ('gender', 'age', 'weight'),'formats': ('S1', 'i4', 'f4')})#converters = #{0:mdates.datestr2num('%w')}) #dtype=(float,int,int))#,converters={0:mdates.strpdate2num('%Y%m%d%H%M%S')})
print(date)
fig=plt.figure(figsize=(10,7))
ax1 = plt.subplot2grid((40,40), (0,0), rowspan=40, colspan=40)
ax1.plot(date,bid)
ax1.plot(date,ask)
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d %H:%M:%S'))
for label in ax1.xaxis.get_ticklabels():
label.set_rotation(45)
plt.subplots_adjust(bottom=.23)
plt.gca().get_yaxis().get_major_formatter().set_useOffset(False)
plt.grid(True)
plt.show()
def unpack(source):
# For datetime: when charting more than 1d
if 'values:Date' in source:
print("Date found")
return np.loadtxt(stock_data,
delimiter=',',
unpack=True,
converters={0: mdates.bytespdate2num('%Y%m%d')})
# For Unix time codes: when charting 1d
elif 'values:Time' in source:
print("Time found")
date, closep, highp, lowp, openp, volume = np.loadtxt(stock_data,
delimiter=',',
unpack=True)
date_conv = np.vectorize(dt.datetime.fromtimestamp)
date = date_conv(date)
return date, closep, highp, lowp, openp, volume
else: return "Date or Time not found"
def calAcf(stock, guiDate):
stockFile = cbook.get_sample_data('C:/Zerodha/Pi/Exported/' + stock,
asfileobj=False)
print('loading', stockFile)
date, openp, highp, lowp, closep, volume = np.loadtxt(
stockFile,
delimiter=',',
skiprows=2,
unpack=True,
usecols=(0, 1, 2, 3, 4, 5),
converters={0: bytespdate2num(guiDate)})
closep_acf = acf(closep, unbiased=True, nlags=40)
closep_pacf = pacf(closep, nlags=40)
closep_ar = AR(20)
#draw
plt.plot(closep_ar, 'ro')
plt.xlabel('Lag')
plt.ylabel('Close PRice ACF ')
plt.show()
def calculateCT(self, stock, guiDate):
try:
stockFile = cbook.get_sample_data('C:/Zerodha/Pi/Exported/' +
stock,
asfileobj=False)
print('loading', stockFile)
date, openp, highp, lowp, closep, volume = np.loadtxt(
stockFile,
delimiter=',',
skiprows=2,
unpack=True,
usecols=(0, 1, 2, 3, 4, 5),
converters={0: bytespdate2num(guiDate)})
self.ols()
print centralTendency.artmeticMean(closep)
#candlestickChart(stock,date,openp,highp,lowp,closep,volume)
except Exception, e:
print str(e)
def animate(i):
datetime, y = np.loadtxt(
open(input_file, 'r'),
delimiter=',',
unpack=True,
converters={0: mdates.bytespdate2num('%Y-%m-%d %H:%M:%S EST')},
usecols=[0, 3])
ax1.clear()
ax1.plot_date(datetime, y, 'g-')
ax1.xaxis_date()
ax1.xaxis.set_major_formatter(DateFormatter('%Y-%m-%d\n%H:%M:%S'))
plt.subplots_adjust(bottom=.3)
locs, labels = plt.xticks()
plt.setp(labels, rotation=70)
plt.title("Pedestrian Data")
plt.ylabel("# of Pedestrians In Park")
plt.xlabel("Date Time")
plt.tight_layout()
plt.grid(True)
def graph_data(stock):
stock_price_url='https://chartapi.finance.yahoo.com/instrument/1.0/'+stock+'/chartdata;type=quote;range=10y/csv'
source_code=urllib.request.urlopen(stock_price_url).read()
stock_data=[]
split_source=source_code.split('\n')
for line in split_source:
split_line=line.split(',')
if len(split_line)==6:
if 'values' not in line and 'labels' not in line:
stock_data.append(line)
date,closep,highp,lowp,openp,volume=np.loadtxt(stock_data,
delimiter=',',
unpack=True,
#%Y = full year. 2015
#%y = partial yr. 15
#%m = month number
#%d = day number
#%H = hours
#%M = minutes
#%S = seconds
#%m-%d-%Y = 12-06-2014
converters={0:bytespdate2num('%Y%m%d')}) # matplot lib doesnt use unix time, it has its own conversion
"""
==============
Load Converter
==============
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cbook as cbook
import matplotlib.dates as mdates
from matplotlib.dates import bytespdate2num
datafile = cbook.get_sample_data('msft.csv', asfileobj=False)
print('loading', datafile)
dates, closes = np.loadtxt(datafile, delimiter=',',
converters={0: bytespdate2num('%d-%b-%y')},
skiprows=1, usecols=(0, 2), unpack=True)
fig, ax = plt.subplots()
ax.plot_date(dates, closes, '-')
fig.autofmt_xdate()
plt.show()
"""
==============
Load Converter
==============
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cbook as cbook
from matplotlib.dates import bytespdate2num
datafile = cbook.get_sample_data('msft.csv', asfileobj=False)
print('loading', datafile)
dates, closes = np.loadtxt(datafile,
delimiter=',',
converters={0: bytespdate2num('%d-%b-%y')},
skiprows=1,
usecols=(0, 2),
unpack=True)
fig, ax = plt.subplots()
ax.plot_date(dates, closes, '-')
fig.autofmt_xdate()
plt.show()
import numpy as np
import matplotlib.dates as mdates
from settings import *
date, bid, ask = np.loadtxt(
file_name,
unpack=True,
delimiter=',',
converters={0: mdates.bytespdate2num('%Y%m%d%H%M%S')})
data_length = int(bid.shape[0])
# Creates an array with the average values between the bid and the ask
all_data = ((bid + ask) / 2)
accuracy_array = []
def draw_window_API(my_dpi, max_error, stockToFetch):
"""
All data contanining the stock price info are retrieved from the database given the stock name
:param my_dpi: dpi screen
:param data: data to be plot
:param max_error: maximum error allowed
"""
fig = plt.figure(figsize=(1000 / my_dpi, 700 / my_dpi),
dpi=96,
edgecolor='k',
facecolor='black')
fig.suptitle("PIECEWISE SEGMENTATION INTERPOLATION",
fontsize="15",
color="white",
fontweight='bold',
bbox={
'facecolor': 'red',
'alpha': 0.5,
'pad': 10
})
try:
print('Currently Pulling', stockToFetch)
urlToVisit = 'http://chartapi.finance.yahoo.com/instrument/1.0/' + stockToFetch + '/chartdata;type=quote;range=5y/csv'
stockFile = []
try:
sourceCode = urllib.request.urlopen(urlToVisit).read().decode()
splitSource = sourceCode.split('\n')
for eachLine in splitSource:
splitLine = eachLine.split(',')
if len(splitLine) == 6:
if 'values' not in eachLine:
stockFile.append(eachLine)
except Exception as e:
print(str(e), 'failed to organize pulled data.')
except Exception as e:
print(str(e), 'failed to pull pricing data')
try:
date, closep, highp, lowp, openp, volume = np.loadtxt(
stockFile,
delimiter=',',
unpack=True,
converters={0: mdates.bytespdate2num('%Y%m%d')})
SP = len(date)
# First subplot
ax1 = plt.subplot2grid((3, 3), (0, 0), colspan=3)
segments = segment.slidingwindowsegment(closep, fit.interpolate,
fit.sumsquared_error,
max_error)
draw_plot(closep, plt, ax1, "Sliding window with interpolation")
draw_segments(segments, 'red')
plt.ylabel('Stock Price')
plt.title("Sliding window", color='w')
# Second subplot
ax2 = plt.subplot2grid((3, 3), (1, 0), colspan=3)
segments = segment.topdownsegment(closep, fit.interpolate,
fit.sumsquared_error, max_error)
draw_plot(closep, plt, ax2, "Sliding window with interpolation")
draw_segments(segments, 'green')
plt.ylabel('Stock Price')
plt.title("Top down", color='w')
# Third subplot
ax3 = plt.subplot2grid((3, 3), (2, 0), colspan=3)
segments = segment.bottomupsegment(closep, fit.interpolate,
fit.sumsquared_error, max_error)
draw_plot(closep, plt, ax3, "Sliding window with interpolation")
draw_segments(segments, 'blue')
plt.ylabel('Stock Price')
plt.title("Bottom up", color='w')
plt.subplots_adjust(hspace=0.3)
plt.show()
except e:
print("Error")
def draw_window(my_dpi, data, max_error):
"""
All data contanining the stock price info are retrieved from the database given the stock name
:param my_dpi: dpi screen
:param data: data to be plot
:param max_error: maximum error allowed
"""
fig = plt.figure(figsize=(1000 / my_dpi, 700 / my_dpi),
dpi=96,
facecolor='black')
fig.suptitle("PIECEWISE SEGMENTATION INTERPOLATION",
fontsize="15",
color="white",
fontweight='bold',
bbox={
'facecolor': 'red',
'alpha': 0.5,
'pad': 10
})
try:
stockFile = []
try:
for eachLine in data:
splitLine = eachLine.split(',')
if len(splitLine) == 2:
if 'values' not in eachLine:
stockFile.append(eachLine)
except Exception as e:
print(str(e), 'failed to organize pulled data.')
except Exception as e:
print(str(e), 'failed to pull pricing data')
try:
date, closep_raw = np.loadtxt(
stockFile,
delimiter=',',
unpack=True,
converters={0: mdates.bytespdate2num('%Y%m%d')})
closep = closep_raw[::-1]
print(max(closep))
max_error = max(closep) * 2.5
# First subplot
ax1 = plt.subplot2grid((3, 3), (0, 0), colspan=3)
segments = segment.slidingwindowsegment(closep, fit.interpolate,
fit.sumsquared_error,
max_error)
draw_plot(closep, plt, ax1, "Sliding window with interpolation")
draw_segments(segments, 'red')
plt.ylabel('Stock Price')
plt.title("SLIDING WINDOW - ERROR " +
str(evaluate_global_error(closep, segments)),
color='Yellow',
fontweight='bold')
# Second subplot
ax2 = plt.subplot2grid((3, 3), (1, 0), colspan=3)
segments = segment.topdownsegment(closep, fit.interpolate,
fit.sumsquared_error, max_error)
draw_plot(closep, plt, ax2, "Sliding window with interpolation")
draw_segments(segments, 'green')
plt.ylabel('Stock Price')
plt.title("TOP DOWN - ERROR " +
str(evaluate_global_error(closep, segments)),
color='Yellow',
fontweight='bold')
# Third subplot
ax3 = plt.subplot2grid((3, 3), (2, 0), colspan=3)
segments = segment.bottomupsegment(closep, fit.interpolate,
fit.sumsquared_error, max_error)
segments = segment.bottomupsegment(closep, fit.interpolate,
fit.sumsquared_error, max_error)
draw_plot(closep, plt, ax3, "Sliding window with interpolation")
draw_segments(segments, 'blue')
plt.ylabel('Stock Price')
plt.title("BOTTOM UP - ERROR " +
str(evaluate_global_error(closep, segments)),
color='Yellow',
fontweight='bold')
plt.subplots_adjust(hspace=0.3)
plt.show()
except e:
print("Error")
def graph_data():
fig = plt.figure()
ax1 = plt.subplot2grid((1, 1), (0, 0))
plt.ylabel('Price')
plt.xlabel('Date')
print('Currently viewing:')
url = 'https://pythonprogramming.net/yahoo_finance_replacement'
source_code = urllib.request.urlopen(url).read().decode()
stock_data = []
split_source = source_code.split('\n')
for each_line in split_source:
split_line = each_line.split(',')
if 'Date' not in each_line:
stock_data.append(each_line)
date, openp, highp, lowp, closep, adjusted_close, volume = np.loadtxt(stock_data, delimiter = ',', \
unpack = True, converters={0: mdates.bytespdate2num('%Y-%m-%d')})
"""
#date, openp, highp, lowp, closep, adjusted_close, volume = np.loadtxt(stock_data, delimiter = ',', \
# unpack = True)
"""
"""
# date_conv = np.vectorize(dt.datetime.fromtimestamp)
# date = date_conv(date)
"""
x = 0
y = len(date)
new_list = []
while x < y:
append_line = date[x], openp[x], highp[x], lowp[x], closep[x], volume[
x]
new_list.append(append_line)
x += 1
"""
#ax1.plot(date, closep, '-')
#ax1.fill_between(date, closep, 400, where=(closep >= 400), facecolor='g', alpha=0.5)
#ax1.fill_between(date, closep, 200, where=(closep <= 200), facecolor='r', alpha=0.5)
#ax1.axhline(200, color='r')
#ax1.axhline(400, color='g')
#ax1.fill_between(date, closep, 0, alpha=0.5, edgecolor='r')
#ax1.fill_between(date, closep, 0, alpha=0.5)
"""
#candlestick_ohlc(ax1, new_list)
#candlestick_ohlc(ax1, new_list, width=.6, colorup='g', colordown='r')
candlestick_ohlc(ax1,
new_list,
width=.6,
colorup='#41ad49',
colordown='#ff1717')
ax1.grid(True) #ax1.grid(True, color = 'g', linestyle='-', linewidth=3)
"""
ax1.yaxis.label.set_color('m')
ax1.xaxis.label.set_color('c')
ax1.set_yticks([0, 700, 1400])
ax1.spines['left'].set_color('c')
ax1.spines['bottom'].set_color('c')
ax1.spines['top'].set_visible(False)
ax1.spines['right'].set_visible(False)
ax1.spines['left'].set_linewidth(8)
ax1.spines['bottom'].set_linewidth(8)
"""
for label in ax1.xaxis.get_ticklabels():
label.set_rotation(45)
ax1.xaxis.set_major_locator(mticker.MaxNLocator(10))
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
plt.ylabel('Price')
plt.subplots_adjust(left=.09,
bottom=.26,
right=.94,
top=.95,
wspace=.2,
hspace=.2)
plt.show()
def draw_window(my_dpi, data):
"""
All data contanining the stock price info are retrieved from the database given the stock name
:param my_dpi: dpi screen
:param data: data to be plot
:param max_error: maximum error allowed
"""
fig = plt.figure(figsize=(1000/my_dpi, 700/my_dpi), dpi=96, facecolor='black')
fig.suptitle("PIECEWISE SEGMENTATION REGRESSION", fontsize="15", color="white", fontweight='bold', bbox={'facecolor':'red', 'alpha':0.5, 'pad':10})
try:
stockFile = []
try:
for eachLine in data:
splitLine = eachLine.split(',')
if len(splitLine) == 2:
if 'values' not in eachLine:
stockFile.append(eachLine)
except Exception as e:
print(str(e), 'failed to organize pulled data.')
except Exception as e:
print(str(e), 'failed to pull pricing data')
try:
date, closep_raw = np.loadtxt(stockFile, delimiter=',', unpack=True,
converters={0: mdates.bytespdate2num('%Y%m%d')})
closep = closep_raw[::-1]
max_closep = max(closep)
if(max_closep > 2.0):
max_error = max(closep)*2.7;
else:
max_error = max(closep)/2.5;
print(max_error)
# First subplot
ax1 = plt.subplot2grid((3, 3), (0, 0), colspan=3)
segments = segment.slidingwindowsegment(closep, fit.regression, fit.sumsquared_error, max_error)
draw_plot(closep,plt,ax1,"Sliding window with regression")
draw_segments(segments,'red')
plt.ylabel('Stock Price')
plt.title("SLIDING WINDOW - ERROR "+str(evaluate_global_error(closep, segments)), color='Yellow', fontweight='bold')
# Second subplot
ax2 = plt.subplot2grid((3, 3), (1, 0), colspan=3)
segments = segment.topdownsegment(closep, fit.regression, fit.sumsquared_error, max_error)
draw_plot(closep, plt, ax2, "Sliding window with regression")
draw_segments(segments,'green')
plt.ylabel('Stock Price')
plt.title("TOP DOWN - ERROR "+str(evaluate_global_error(closep, segments)), color='Yellow', fontweight='bold')
# Third subplot
ax3 = plt.subplot2grid((3, 3), (2, 0), colspan=3)
segments = segment.bottomupsegment(closep, fit.regression, fit.sumsquared_error, max_error)
draw_plot(closep, plt, ax3, "Sliding window with regression")
draw_segments(segments,'blue')
plt.ylabel('Stock Price')
plt.title("BOTTOM UP - ERROR "+str(evaluate_global_error(closep, segments)), color='Yellow', fontweight='bold')
plt.subplots_adjust(hspace=0.3)
plt.show()
except e:
print("Error")
def draw_window_API(my_dpi, max_error, stockToFetch):
"""
All data contanining the stock price info are retrieved from the database given the stock name
:param my_dpi: dpi screen
:param data: data to be plot
:param max_error: maximum error allowed
"""
fig = plt.figure(figsize=(1000/my_dpi, 700/my_dpi), dpi=96, edgecolor='k', facecolor='black')
fig.suptitle("PIECEWISE SEGMENTATION REGRESSION", fontsize="15", color="white", fontweight='bold', bbox={'facecolor':'red', 'alpha':0.5, 'pad':10})
try:
print('Currently Pulling',stockToFetch)
urlToVisit = 'http://chartapi.finance.yahoo.com/instrument/1.0/'+stockToFetch+'/chartdata;type=quote;range=5y/csv'
stockFile =[]
try:
sourceCode = urllib.request.urlopen(urlToVisit).read().decode()
splitSource = sourceCode.split('\n')
for eachLine in splitSource:
splitLine = eachLine.split(',')
if len(splitLine) == 6:
if 'values' not in eachLine:
stockFile.append(eachLine)
except Exception as e:
print(str(e), 'failed to organize pulled data.')
except Exception as e:
print(str(e), 'failed to pull pricing data')
try:
date, closep, highp, lowp, openp, volume = np.loadtxt(stockFile, delimiter=',', unpack=True,
converters={0: mdates.bytespdate2num('%Y%m%d')})
SP = len(date)
# First subplot
ax1 = plt.subplot2grid((3, 3), (0, 0), colspan=3)
segments = segment.slidingwindowsegment(closep, fit.regression, fit.sumsquared_error, max_error)
draw_plot(closep,plt,ax1,"Sliding window with regression")
draw_segments(segments,'red')
plt.ylabel('Stock Price')
plt.title("Sliding window", color='w')
# Second subplot
ax2 = plt.subplot2grid((3, 3), (1, 0), colspan=3)
segments = segment.topdownsegment(closep, fit.regression, fit.sumsquared_error, max_error)
draw_plot(closep, plt, ax2, "Sliding window with regression")
draw_segments(segments,'green')
plt.ylabel('Stock Price')
plt.title("Top down", color='w')
# Third subplot
ax3 = plt.subplot2grid((3, 3), (2, 0), colspan=3)
segments = segment.bottomupsegment(closep, fit.regression, fit.sumsquared_error, max_error)
draw_plot(closep, plt, ax3, "Sliding window with regression")
draw_segments(segments,'blue')
plt.ylabel('Stock Price')
plt.title("Bottom up", color='w')
plt.subplots_adjust(hspace=0.3)
plt.show()
except e:
print("Error")
import pylab
from datetime import datetime
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cbook as cbook
import matplotlib.dates as mdates
from matplotlib.dates import bytespdate2num
datafile = cbook.get_sample_data("/home/polaris/PycharmProjects/WeatherData/tempData.dat", asfileobj=False)
print("loading", datafile)
#datefunc = lambda x:mdates.date2num(datetime.strptime(x,'%Y-%m-%d'))
dates, temps = np.loadtxt(datafile, delimiter=',', dtype=float,
converters={0: bytespdate2num('%Y-%m-%d %I:%M %p' )},
usecols=(0,1), unpack=True)
fig = plt.figure()
#1x1 grid
ax = fig.add_subplot(111)
ax.set_xticks(dates)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d %H:%M'))
ax.set_title('Temperature in Porvoo')
ax.set_ylabel('Temp')
ax.set_xlabel('Date')
ax.plot_date(dates,temps,'-', marker='o')
ax.grid(True)
fig.autofmt_xdate(rotation=45)
plt.show()
