def __init__(self, dates, open, high, low, close, vol, start, end):
if start > end:
(start, end) = (end, start)
self.report_log = []
max = None
max_date = None
min = None
min_date = None
seq_start = dates[0]
seq_end = dates[0]
series = []
n = 0
for i in range(len(dates)):
d = dates[i]
if (d > start) and (d < end):
series.append(close[i])
if (d < seq_start):
seq_start = d
if (d > seq_end):
seq_end = d
n = n + 1
h = high[i]
if max == None:
max = h
max_date = d
else:
if h > max:
max = h
max_date = d
l = low[i]
if min == None:
min = l
min_date = d
else:
if l < min:
min = l
min_date = d
self.report_log.append('%s - %s' % (seq_start, seq_end))
self.report_log.append('%d trading days' % n)
self.report_log.append('Max = %s - %s' % (str(max), max_date))
self.report_log.append('Min = %s - %s' % (str(min), min_date))
h = Histogram(series)
for l in h.report():
self.report_log.append(l)
def gen_intraday_volatility_plots(symbol, dates, high, low, close, start_date, end_date, gen_title) :
(start_idx, end_idx) = get_start_end_idxs(dates, start_date, end_date)
# cut slice
date_slice = dates[start_idx : end_idx]
slice_start_date = dates[start_idx]
slice_end_date = dates[end_idx]
low_slice = low[start_idx : end_idx]
high_slice = high[start_idx : end_idx]
close_slice = close[start_idx : end_idx]
delta = []
for i in range(len(high_slice)):
delta.append(float(100 * (high_slice[i] - low_slice[i]) / close_slice[i]) )
# generate matplotlib plot
x = np.array(date_slice)
y = np.array(delta)
fever_fig = plt.figure()
ax = fever_fig.add_subplot(111)
ax.plot(x,y)
# leg = ax.legend(('Model length'), 'upper center', shadow=True)
ax.grid(False)
ax.set_ylabel('100 * (High - Low) / Close')
title = gen_title(symbol, 'Intra-Day Range as a Percentage of Closing Price', slice_start_date, slice_end_date)
ax.set_title(title)
# date intervals & markers
(formatter, locator) = tick_info(slice_start_date, slice_end_date)
ax.xaxis.set_major_formatter(formatter)
ax.xaxis.set_major_locator(locator)
fever_fig.autofmt_xdate(rotation=90)
ax.set_xlim([slice_start_date, slice_end_date])
ax.set_xlabel('Date')
# ------------
h = Histogram(delta)
left_edge = []
height = []
for bin in h.bins:
left_edge.append(float(bin.floor))
height.append(h.bin_contrib_perc(bin))
x = np.array(left_edge)
y = np.array(height)
dist_fig = plt.figure()
ax = dist_fig.add_subplot(111)
ax.bar(x, y, width=h.bins[0].range)
ax.set_xlim(h.min, h.max)
ax.set_ylabel('% of Population')
ax.set_xlabel('Intra-Day Range i.t.o Close : 100 * (High - Low) / Close')
title = gen_title(symbol, 'Distribution of Intra-Day Range', slice_start_date, slice_end_date)
ax.set_title(title)
reports = []
reports.append(AnalysisReport(symbol, slice_start_date, slice_end_date, 'Intra-Day Range Fever', '', fever_fig))
reports.append(AnalysisReport(symbol, slice_start_date, slice_end_date, 'Distribution of Intra-day Range', h.report(date_slice, delta), dist_fig))
return reports
