def plot_index(index_name, benchmark_name):
# Load Index Data File
index_path = c.path_dict['index']
index_file = c.file_dict['index_r'] % index_name
df = u.read_csv(index_path + index_file)
# Plot Figure
fig, (ax1, ax2, ax3) = plt.subplots(3, sharex=True)
fig.set_size_inches(32, 18)
# Define Font
font = {
'family': 'serif',
'color': 'black',
'weight': 'normal',
'size': 18,
}
# Plot Sub-figure 1
title = '%s vs. %s' % (index_name, benchmark_name)
ax1.set_title(title, fontdict=font)
ax1.set_xlabel('', fontdict=font)
ax1.set_ylabel('Ratio', fontdict=font)
for column in ['ratio', 'b_ratio']:
df.plot(x='date', y=column, ax=ax1)
# Plot Sub-figure 2
title = 'Index %s' % index_name
ax2.set_title(title, fontdict=font)
ax2.set_xlabel('', fontdict=font)
ax2.set_ylabel('Close Price', fontdict=font)
df.plot(x='date', y='index', ax=ax2)
# Plot Sub-figure 3
title = 'Index %s' % benchmark_name
ax3.set_title(title, fontdict=font)
ax3.set_xlabel('', fontdict=font)
ax3.set_ylabel('Close Price', fontdict=font)
df.plot(x='date', y='b_index', ax=ax3)
# Common Format for Both Sub-figures
for ax in [ax1, ax2, ax3]:
ax.grid(True)
fig.autofmt_xdate()
fig.tight_layout()
plt.setp(plt.gca().get_xticklabels(), rotation=30)
plt.show()
# Save Figure
fig_key = 'fig_index'
fig_path = c.path_dict[fig_key]
fig_file = c.file_dict[fig_key] % (index_name + '_' +
u.dateToStr(u.today()))
u.saveFigure(fig, fig_path, fig_file)
def plot_index_series(index_names, series_name, benchmark_name):
# Load Index Data Files
series_path = c.path_dict['index']
series_file = c.file_dict['index_r'] % series_name
df = u.read_csv(series_path + series_file)
# Plot Figure
fig = plt.figure(figsize=(32, 18), dpi=72, facecolor="white")
axes = plt.subplot(111)
axes.cla() # Clear Axes
# Define Font
font = {
'family': 'serif',
'color': 'black',
'weight': 'normal',
'size': 18,
}
# Plot Sub-figure 1
title = '%s vs. %s' % (series_name, benchmark_name)
plt.title(title, fontdict=font)
axes.set_xlabel('', fontdict=font)
axes.set_ylabel('Ratio', fontdict=font)
df.plot(x='date',
y='ratio_benchmark',
ax=axes,
color='grey',
lw=2.0,
ls='--')
index_number = len(index_names)
for i in range(index_number):
index_name = index_names[i]
column = 'ratio_' + index_name
df.plot(x='date', y=column, ax=axes)
# Common Format for Both Sub-figures
axes.grid(True)
fig.autofmt_xdate()
fig.tight_layout()
plt.setp(plt.gca().get_xticklabels(), rotation=30)
plt.show()
# Save Figure
fig_key = 'fig_index'
fig_path = c.path_dict[fig_key]
fig_file = c.file_dict[fig_key] % (series_name + '_' +
u.dateToStr(u.today()))
u.saveFigure(fig, fig_path, fig_file)
def generateIndex(index_name, base_date, base_point, weight_method,
benchmark_id):
# Load Index Component Stocks
component = load_component(index_name)
if u.isNoneOrEmpty(component):
print('Index Component Not Available:', index_name)
raise SystemExit
if gs.is_debug:
print(component.head(10))
# Update Benchmark Index LSHQ to Latest
date_start = u.dateFromStr(base_date)
date_end = u.today()
getDailyHFQ(stock_id=benchmark_id,
is_index=True,
date_start=date_start,
date_end=date_end,
time_to_market=None,
incremental=True)
print('Update Price:', benchmark_id)
# Update Component Stock LSHQ to Latest
component_number = len(component)
for i in range(component_number):
stock_id = u.stockID(component.ix[i, 'code'])
getDailyHFQ(stock_id=stock_id,
is_index=False,
date_start=date_start,
date_end=date_end,
time_to_market=None,
incremental=True)
print('Update Price:', stock_id)
# Generate Index
generate_index(index_name, base_date, base_point, weight_method,
benchmark_id)
def updateRZRQ(date_start='2010-03-31', date_end=str(u.today())):
getRZRQMarket(date_start, date_end)
getRZRQDetails(date_start, date_end)
from Data.GetClassifying import getIndustrySina, getConceptSina, getArea
from Data.GetClassifying import getSME, getGEM, getST
from Data.GetClassifying import getHS300, getSZ50, getZZ500
from Data.GetClassifying import getTerminated, getSuspended, getCXG, loadCXG
from Data.GetClassifying import extractIndustrySina, extractConceptSina, extractArea
from Data.GetReference import getRZRQMarket, getRZRQDetails
import Common.Utilities as u
import Common.Constants as c
import Common.GlobalSettings as gs
#
# Update Data Center Parameters
#
date_start = dt.date(2005, 1, 1)
date_end = u.today()
date_cxg = '2016-01-01'
###############################################################################
#
# Update Data Center Functions
#
def updateStockBasics():
getStockBasics()
cleanStockBasics()
def cleanStockBasics():
basics = loadStockBasics()
def plot_coefficient_price(stock_ids, allprice, postfix, series_name,
benchmark_name):
# If want to debug benchmark only (without stocks), set below flag to True.
debug_benchmark_only = False
# Extract Stock Prices and Normalize Them
row_number = len(allprice)
stock_number = len(stock_ids)
columns = ['date', benchmark_name]
if not debug_benchmark_only:
for i in range(stock_number):
stock_id = u.stockID(stock_ids[i])
columns.append(stock_id)
prices = u.createDataFrame(row_number, columns)
prices['date'] = allprice['date']
prices[benchmark_name] = allprice['close']
if not debug_benchmark_only:
for i in range(stock_number):
stock_id = u.stockID(stock_ids[i])
prices[stock_id] = allprice['close_' + stock_id]
if debug_benchmark_only:
print('Original Price')
print(prices)
# Normalize Price
for i in range(1, len(columns)):
column = columns[i]
prices[column] = normalize_price(prices[column])
if debug_benchmark_only:
print('Normalized Price')
print(prices)
# Calculate Relative Price w.r.t. First Valid Price
for i in range(1, len(columns)):
column = columns[i]
row = -1
for j in range(row_number):
if not np.isnan(prices.ix[j, column]): # Find first valid price
row = j
break
if row != -1:
if debug_benchmark_only:
print('Row =', row)
ref_price = prices.ix[
row,
column] # Need to be cached in the first place as it will be normalized to one later.
for j in range(row, row_number):
cur_price = prices.ix[j, column]
if not np.isnan(cur_price):
prices.ix[
j, column] = 1.0 + (cur_price - ref_price) / ref_price
if debug_benchmark_only:
print('Relative Price')
print(prices)
# Plot Figure
fig = plt.figure(figsize=(32, 18), dpi=72, facecolor="white")
axes = plt.subplot(111)
axes.cla() # Clear Axes
# Define Font
font = {
'family': 'serif',
'color': 'black',
'weight': 'normal',
'size': 18,
}
# Plot Sub-figure 1
title = '%s vs. %s' % (series_name, benchmark_name)
plt.title(title, fontdict=font)
axes.set_xlabel('', fontdict=font)
axes.set_ylabel('Ratio', fontdict=font)
prices.plot(x='date',
y=benchmark_name,
ax=axes,
color='grey',
lw=2.0,
ls='--')
if not debug_benchmark_only:
for i in range(stock_number):
column = u.stockID(stock_ids[i])
prices.plot(x='date', y=column, ax=axes)
# Common Format for Both Sub-figures
axes.grid(True)
fig.autofmt_xdate()
fig.tight_layout()
plt.setp(plt.gca().get_xticklabels(), rotation=30)
plt.show()
# Save Figure
fig_key = 'fig_coef'
fig_path = c.path_dict[fig_key]
fig_name = '_'.join(
[postfix, series_name, 'vs', benchmark_name,
u.dateToStr(u.today())])
fig_file = c.file_dict[fig_key] % fig_name
u.saveFigure(fig, fig_path, fig_file)
