def extractArea():
# Load Area Data
area = loadArea()
if gs.is_debug:
print(area.head(10))
# Extract Area List
area_list = area.drop(['code', 'name'], axis=1)
area_list.drop_duplicates(['area'], inplace=True)
area_list.set_index('area', inplace=True)
if gs.is_debug:
print(area_list.index)
# Save to CSV File
if not u.isNoneOrEmpty(area_list):
u.to_csv(area_list, c.path_dict['area'], c.file_dict['area_list'])
# Extract Stocks for Each Area
area_number = len(area_list)
print('#Area =', area_number)
for i in range(area_number):
area_name = area_list.index[i]
area_stock = area[area.area.isin([area_name])]
area_stock['code'] = area_stock['code'].map(lambda x:str(x).zfill(6))
area_stock.set_index('code', inplace=True)
if gs.is_debug:
print(area_stock.head(10))
# Save to CSV File
if not u.isNoneOrEmpty(area_stock):
u.to_csv(area_stock, c.path_dict['area'], c.file_dict['area_stock'] % area_name)
def extractConceptSina():
# Load Sina Concept Data
concept_sina = loadConceptSina()
if gs.is_debug:
print(concept_sina.head(10))
# Extract Concept List
concept_list = concept_sina.drop(['code', 'name'], axis=1)
concept_list.drop_duplicates(['concept'], inplace=True)
concept_list.set_index('concept', inplace=True)
if gs.is_debug:
print(concept_list.index)
# Save to CSV File
if not u.isNoneOrEmpty(concept_list):
u.to_csv(concept_list, c.path_dict['conc_sina'], c.file_dict['conc_list'])
# Extract Stocks for Each Concept
concept_number = len(concept_list)
print('#Concept =', concept_number)
for i in range(concept_number):
concept_name = concept_list.index[i]
concept_stock = concept_sina[concept_sina.concept.isin([concept_name])]
concept_stock['code'] = concept_stock['code'].map(lambda x:str(x).zfill(6))
concept_stock.set_index('code', inplace=True)
if gs.is_debug:
print(concept_stock.head(10))
# Save to CSV File
if not u.isNoneOrEmpty(concept_stock):
u.to_csv(concept_stock, c.path_dict['conc_sina'], c.file_dict['conc_stock'] % concept_name)
def getRZRQDetailsSH():
# Download RZRQ Stock Data of SH Market
rzrq_sh_details = pd.DataFrame()
rzrq_sh = u.read_csv(c.fullpath_dict['rzrq'] % 'Market_SH')
date_number = len(rzrq_sh)
for i in range(date_number):
date = rzrq_sh.ix[i,'date']
rzrq = get_rzrq_sh_details(date, date)
print(rzrq.head(10))
if i == 0:
rzrq_sh_details = pd.DataFrame.copy(rzrq)
else:
rzrq_sh_details = pd.concat([rzrq_sh_details, rzrq])
print(rzrq_sh_details.head(10))
rzrq.set_index('date', inplace=True)
if not u.isNoneOrEmpty(rzrq):
u.to_csv(rzrq, c.path_dict['rzrq'], c.file_dict['rzrq'] % ('Details_SH_%s'%date))
# Process RZRQ Stock Data of SH Market
rzrq_sh_details.set_index('date', inplace=True)
rzrq_sh_details.sort_index(ascending=True,inplace=True)
if gs.is_debug:
print(rzrq_sh_details.head(10))
# Save to CSV File
if not u.isNoneOrEmpty(rzrq_sh_details):
u.to_csv(rzrq_sh_details, c.path_dict['rzrq'], c.file_dict['rzrq'] % 'Details_SH')
def extractIndustrySina():
# Load Sina Industry Data
industry_sina = loadIndustrySina()
if gs.is_debug:
print(industry_sina.head(10))
# Extract Industry List
industry_list = industry_sina.drop(['code', 'name'], axis=1)
industry_list.drop_duplicates(['industry'], inplace=True)
industry_list.set_index('industry', inplace=True)
if gs.is_debug:
print(industry_list.index)
# Save to CSV File
if not u.isNoneOrEmpty(industry_list):
u.to_csv(industry_list, c.path_dict['indu_sina'], c.file_dict['indu_list'])
# Extract Stocks for Each Industry
industry_number = len(industry_list)
print('#Industry =', industry_number)
for i in range(industry_number):
industry_name = industry_list.index[i]
industry_stock = industry_sina[industry_sina.industry.isin([industry_name])]
industry_stock['code'] = industry_stock['code'].map(lambda x:str(x).zfill(6))
industry_stock.set_index('code', inplace=True)
if gs.is_debug:
print(industry_stock.head(10))
# Save to CSV File
if not u.isNoneOrEmpty(industry_stock):
u.to_csv(industry_stock, c.path_dict['indu_sina'], c.file_dict['indu_stock'] % industry_name)
def generateIndexStatistics(index_names, series_name):
# Create Statistics DataFrame
columns = [
'name', 'date', 'c_number', 's_number', 'index', 'ratio', 'b_index',
'b_ratio', 'delta_ratio'
]
index_number = len(index_names)
stat = u.createDataFrame(index_number, columns)
# Calculate Index Statistics
for i in range(index_number):
index_name = index_names[i]
result = load_index_result(index_name)
if u.isNoneOrEmpty(result):
continue
result_number = len(result)
latest_date = result.ix[result_number - 1, 'date']
component_number, suspended_number = generateComponentStatistics(
index_name, latest_date)
stat.ix[i, 'name'] = index_name
stat.ix[i, 'date'] = latest_date
stat.ix[i, 'c_number'] = component_number
stat.ix[i, 's_number'] = suspended_number
stat.ix[i, 'index'] = result.ix[result_number - 1, 'index']
ratio = result.ix[result_number - 1, 'ratio']
stat.ix[i, 'ratio'] = ratio
stat.ix[i, 'b_index'] = result.ix[result_number - 1, 'b_index']
b_ratio = result.ix[result_number - 1, 'b_ratio']
stat.ix[i, 'b_ratio'] = b_ratio
stat.ix[i, 'delta_ratio'] = ratio - b_ratio
# Format Statistics DataFrame
for column in ['name', 'date']:
stat[column] = stat[column].astype(str)
for column in ['c_number', 's_number']:
stat[column] = stat[column].astype(int)
for column in ['index', 'b_index']:
stat[column] = stat[column].map(lambda x: '%.2f' % x)
stat[column] = stat[column].astype(float)
for column in ['ratio', 'b_ratio', 'delta_ratio']:
stat[column] = stat[column].map(lambda x: '%.2f%%' % (x * 100.0))
stat.set_index('name', inplace=True)
# Save to CSV File
if not u.isNoneOrEmpty(stat):
print(stat)
u.to_csv(stat, c.path_dict['index'],
c.file_dict['index_s'] % series_name)
def extractRollingBeta(postfix):
# Load Rolling Coefficient
fullpath = c.path_dict['strategy'] + c.file_dict['strategy'] % postfix
coef = u.read_csv(fullpath)
if u.isNoneOrEmpty(coef):
print('Require Coefficient File: %s!' % fullpath)
return False
# Extract Rolling Beta
row_number = len(coef)
beta = u.createDataFrame(row_number, ['date', 'beta'])
beta['date'] = coef['date']
for column in coef.columns:
if len(column) >= 4 and column[0:4] == 'beta':
beta[column] = coef[column]
# Calculate Rolling Beta Average
beta_number = len(beta.columns) - 2
for i in range(row_number):
beta_avg = 0.0
beta_count = 0
for j in range(beta_number):
b = beta.ix[i, beta.columns[j + 2]]
if not np.isnan(b):
beta_avg = beta_avg + b
beta_count = beta_count + 1
if beta_count > 0:
beta.ix[i, 'beta'] = beta_avg / float(beta_count)
beta.set_index('date', inplace=True)
postfix = '_'.join([postfix, 'Beta'])
u.to_csv(beta, c.path_dict['strategy'], c.file_dict['strategy'] % postfix)
def get_commodity_price(code, retry_count=3, pause=0.01):
"""
获取生意社（100PPI）商品价格
Parameters
--------
code:string 商品代码 e.g. 67(钴的代码)
Return
--------
DataFrame
发布时间
报价机构
报价类型
报价
规格
产地
"""
# Download Commodity Data
pageNo = 1
df_all = _get_commodity_price(code, pageNo, pd.DataFrame(), retry_count,
pause)
if not u.isNoneOrEmpty(df_all):
df_all.drop_duplicates(inplace=True)
df_all.set_index(u'发布时间', inplace=True)
return df_all
def get_stock_list(cutoff_date):
'''
函数功能：
--------
获取所有指定日期之前（含）已经上市的股票列表。
输入参数：
--------
date,截止时间 e.g. '2016-12-31'
输出参数：
--------
DataFrame
code,代码
name,名称
industry,所属行业
area,地区
timeToMarket,上市日期
'''
# Check pre-requisite
basics = loadStockBasics()
if u.isNoneOrEmpty(basics):
print('Need to have stock basics!')
raise SystemExit
# Extract Stock List
stocks = basics[basics.timeToMarket <= cutoff_date]
stocks = stocks[['code', 'name', 'industry', 'area', 'timeToMarket']]
stocks = stocks.sort_values('timeToMarket', axis=0, ascending=True)
# Return Dataframe
return stocks
def loadAllIndex():
'''
函数功能：
--------
加载所有指数列表。
输入参数：
--------
无
输出参数：
--------
加载成功时，index_ids : pandas.Series, 所有指数列表。
加载失败时，None
'''
# Load Local Cache
file_postfix = '_'.join(['Common', 'AllIndex'])
fullpath = c.path_dict['strategy'] + c.file_dict['strategy'] % file_postfix
allindex = u.read_csv(fullpath)
if not u.isNoneOrEmpty(allindex):
allindex['code'] = allindex['code'].map(lambda x: str(x).zfill(6))
return allindex['code']
print('Failed to Load File: %s!' % fullpath)
return None
def updateSamplePriceAllIndex(benchmark_id, period):
'''
函数功能：
--------
根据基准指数的时间范围和采样周期，对所有指数的收盘价格进行全采样。
输入参数：
--------
benchmark_id : string, 指数代码 e.g. '000300'，隐含起止时间。
period : string, 采样周期 e.g. 'M'，支持'D', 'W', and 'M'。
输出参数：
--------
True/False : boolean，是否采样成功。
数据文件
Strategy_Common_AllPrice_Benchmark_Period_AllStock.csv : 所有指数收盘价格的采样结果数据文件。
'''
# Sample Price for All Index
allindex = loadAllIndex()
allprice = updateSamplePrice(benchmark_id, allindex, True, period)
# Save to CSV File
if not u.isNoneOrEmpty(allindex):
file_postfix = '_'.join(
['Common', 'AllPrice', benchmark_id, period, 'AllIndex'])
u.to_csv(allprice, c.path_dict['strategy'],
c.file_dict['strategy'] % file_postfix)
return True
return False
def loadSamplePriceAllIndex(benchmark_id, period):
'''
函数功能：
--------
根据基准指数的时间范围和采样周期，加载所有指数的收盘价格采样文件。
输入参数：
--------
benchmark_id : string, 指数代码 e.g. '000300'，隐含起止时间。
period : string, 采样周期 e.g. 'M'，支持'D', 'W', and 'M'。
输出参数：
--------
加载成功时，allprice : pandas.DataFrame, 所有指数的收盘价格采样结果。
加载失败时，None
'''
# Check if AllPrice File Already Exists
file_postfix = '_'.join(
['Common', 'AllPrice', benchmark_id, period, 'AllIndex'])
fullpath = c.path_dict['strategy'] + c.file_dict['strategy'] % file_postfix
allprice = u.read_csv(fullpath)
if not u.isNoneOrEmpty(allprice):
return allprice
print('Failed to Load File: %s!' % fullpath)
return None
def updateAllStocks():
'''
函数功能：
--------
更新所有股票列表。
输入参数：
--------
无
输出参数：
--------
True/False : boolean，是否更新成功。
数据文件
Strategy_Common_AllStock.csv : 参与策略计算的所有股票列表
'''
# Load from Fundamental Stock Basics
allstock = loadStockBasics()
if not u.isNoneOrEmpty(allstock):
# Save to CSV File
allstock.set_index('code', inplace=True)
file_postfix = '_'.join(['Common', 'AllStock'])
u.to_csv(allstock, c.path_dict['strategy'],
c.file_dict['strategy'] % file_postfix)
return True
return False
def updateAllIndex():
'''
函数功能：
--------
更新所有指数列表。
输入参数：
--------
无
输出参数：
--------
True/False : boolean，是否更新成功。
数据文件
Strategy_Common_AllIndex.csv : 参与策略计算的所有指数列表
'''
# Load from Constants.index_list
allindex = pd.DataFrame({'code': c.index_list})
if not u.isNoneOrEmpty(allindex):
# Save to CSV File
allindex.set_index('code', inplace=True)
file_postfix = '_'.join(['Common', 'AllIndex'])
u.to_csv(allindex, c.path_dict['strategy'],
c.file_dict['strategy'] % file_postfix)
return True
return False
def extractCommodityPrice(code, column):
# Check Pre-requisite
fullpath = c.fullpath_dict['commodity'] % code
if not u.hasFile(fullpath):
print('Require File Exists:', fullpath)
return
# Load Commodity Data
data = loadCommodityPrice(code)
data.set_index(u'发布时间', inplace=True)
print(data.head(10))
# Extract Price Data based on Given Column
market = data[column].drop_duplicates()
market_number = len(market)
print('Market Number:', market_number)
print('Markets:', market)
i = 0
for m in market:
print('Market %s: %s' % (i + 1, m))
m_name = 'Market_%s' % (i + 1)
m_data = data[data[column].isin([m])]
if not u.isNoneOrEmpty(m_data):
u.to_csv(m_data, c.path_dict['commodity'],
c.file_dict['commodity_m'] % (code, m_name))
i = i + 1
'''
def get_cxg(date):
'''
函数功能：
--------
获取所有次新股的股票列表，根据给定的上市时间。
输入参数：
--------
date,上市时间 e.g. '2016-01-01'
输出参数：
--------
DataFrame
code,代码
name,名称
industry,所属行业
area,地区
timeToMarket,上市日期
'''
# Check pre-requisite
basics = loadStockBasics()
if u.isNoneOrEmpty(basics):
print('Need to have stock basics!')
raise SystemExit
# Extract CXG Data
cxg = basics[basics.timeToMarket >= date]
cxg = cxg[['code', 'name', 'industry', 'area', 'timeToMarket']]
cxg = cxg.sort_values('timeToMarket', axis=0, ascending=True)
# Return Dataframe
return cxg
def getStockBasics():
# Download Stock Basics
basics = get_stock_basics()
if gs.is_debug:
print(basics.head(10))
# Save to CSV File
if not u.isNoneOrEmpty(basics):
u.to_csv(basics, c.path_dict['basics'], c.file_dict['basics'])
def getFinanceSummary(stock_id):
# Download Finance Summary for Given Stock
fs = get_finance_summary(stock_id)
if gs.is_debug:
print(fs.head(10))
# Save to CSV File
if not u.isNoneOrEmpty(fs):
u.to_csv(fs, c.path_dict['finsum'], c.file_dict['finsum'] % stock_id)
def topCorrelation(postfix, completeness_threshold, top_number):
# Load Coefficient File
allcoef = loadCoefficient(postfix, completeness_threshold)
if u.isNoneOrEmpty(allcoef):
return False
# Calculate Coefficient Statistics
allcoef.set_index('code', inplace=True)
allcoef['correlation_abs'] = allcoef['correlation'].map(lambda x: abs(x))
allcoef['correlation_abs'] = allcoef['correlation_abs'].astype(float)
negative_correlation = allcoef[allcoef.correlation < 0.0]
positive_correlation = allcoef[allcoef.correlation > 0.0]
zero_correlation = allcoef[allcoef.correlation_abs < 0.25]
# Filter Out Top Number of Positive Correlation
positive_correlation = positive_correlation.sort_values('correlation',
axis=0,
ascending=False)
positive_correlation = positive_correlation[0:(
top_number if len(positive_correlation
) >= top_number else len(positive_correlation))]
# Save to CSV File
file_postfix = '_'.join(
[postfix, completeness_threshold, 'PositiveCorrelation'])
u.to_csv(positive_correlation, c.path_dict['strategy'],
c.file_dict['strategy'] % file_postfix)
# Filter Out Top Number of Zero Correlation
zero_correlation = zero_correlation.sort_values('correlation_abs',
axis=0,
ascending=True)
zero_correlation = zero_correlation[0:(
top_number if len(zero_correlation
) >= top_number else len(zero_correlation))]
# Save to CSV File
file_postfix = '_'.join(
[postfix, completeness_threshold, 'ZeroCorrelation'])
u.to_csv(zero_correlation, c.path_dict['strategy'],
c.file_dict['strategy'] % file_postfix)
# Filter Out Top Number of Negative Correlation
negative_correlation = negative_correlation.sort_values('correlation',
axis=0,
ascending=True)
negative_correlation = negative_correlation[0:(
top_number if len(negative_correlation
) >= top_number else len(negative_correlation))]
# Save to CSV File
file_postfix = '_'.join(
[postfix, completeness_threshold, 'NegativeCorrelation'])
u.to_csv(negative_correlation, c.path_dict['strategy'],
c.file_dict['strategy'] % file_postfix)
return True
def getCommodityPrice(code):
# Download Commodity Data
data = get_commodity_price(code)
if gs.is_debug:
print(data.head(10))
# Save to CSV File
if not u.isNoneOrEmpty(data):
u.to_csv(data, c.path_dict['commodity'],
c.file_dict['commodity'] % code)
def loadCoefficient(postfix, completeness_threshold):
# Load Coefficient File
fullpath = c.path_dict['strategy'] + c.file_dict['strategy'] % '_'.join(
[postfix, completeness_threshold])
allcoef = u.read_csv(fullpath)
if u.isNoneOrEmpty(allcoef):
print('Require Coefficient File: %s!' % fullpath)
return None
return allcoef
def calcQFQ(stock_id, period):
# Calculate QFQ DataFrame
df = calc_qfq(stock_id, period)
if gs.is_debug:
print(df.head(10))
# Save to CSV File
if not u.isNoneOrEmpty(df):
path = c.path_dict['qfq'] % period
file = c.file_dict['qfq'] % (period, stock_id)
u.to_csv(df, path, file)
def histogramAlpha(postfix, completeness_threshold):
# Load Coefficient File
allcoef = loadCoefficient(postfix, completeness_threshold)
if u.isNoneOrEmpty(allcoef):
return False
# Calculate Coefficient Histogram
columns = [
'Total', 'Very High', 'High', 'Medium', 'Low', 'Very Low',
'Negative Very Low', 'Negative Low', 'Negative Medium',
'Negative High', 'Negative Very High'
]
histogram = u.createDataFrame(1, columns, 0)
stock_number = len(allcoef)
histogram.ix[0, 'Total'] = stock_number
for i in range(stock_number):
alpha = allcoef.ix[i, 'alpha']
if alpha >= 0.4: # [0.4, +Infinity)
histogram.ix[0, 'Very High'] = histogram.ix[0, 'Very High'] + 1
elif alpha >= 0.3: # [0.3, 0.4)
histogram.ix[0, 'High'] = histogram.ix[0, 'High'] + 1
elif alpha >= 0.2: # [0.2, 0.3)
histogram.ix[0, 'Medium'] = histogram.ix[0, 'Medium'] + 1
elif alpha >= 0.1: # [0.1, 0.2)
histogram.ix[0, 'Low'] = histogram.ix[0, 'Low'] + 1
elif alpha >= 0.0: # [0.0, 0.1)
histogram.ix[0, 'Very Low'] = histogram.ix[0, 'Very Low'] + 1
elif alpha >= -0.1: # [-0.1, 0.0)
histogram.ix[
0,
'Negative Very Low'] = histogram.ix[0, 'Negative Very Low'] + 1
elif alpha >= -0.2: # [-0.2, -0.1)
histogram.ix[0,
'Negative Low'] = histogram.ix[0, 'Negative Low'] + 1
elif alpha >= -0.3: # [-0.3, -0.2)
histogram.ix[
0, 'Negative Medium'] = histogram.ix[0, 'Negative Medium'] + 1
elif alpha >= -0.4: # [-0.4, -0.3)
histogram.ix[0,
'Negative High'] = histogram.ix[0,
'Negative High'] + 1
else: # (-Infinity, -0.4)
histogram.ix[0, 'Negative Very High'] = histogram.ix[
0, 'Negative Very High'] + 1
# Save to CSV File
histogram.set_index('Total', inplace=True)
file_postfix = '_'.join(
[postfix, completeness_threshold, 'HistogramAlpha'])
u.to_csv(histogram, c.path_dict['strategy'],
c.file_dict['strategy'] % file_postfix)
return True
def calcHPE(stock_id, period, ratio):
# Calculate HPE DataFrame
df = calc_hpe(stock_id, period, ratio)
if gs.is_debug:
print(df.head(10))
# Save to CSV File
if not u.isNoneOrEmpty(df):
key = 'hpe' if ratio == 'PE' else 'hep'
path = c.path_dict[key] % period
file = c.file_dict[key] % (period, stock_id)
u.to_csv(df, path, file)
def histogramCorrelation(postfix, completeness_threshold):
# Load Coefficient File
allcoef = loadCoefficient(postfix, completeness_threshold)
if u.isNoneOrEmpty(allcoef):
return False
# Calculate Coefficient Histogram
columns = [
'Total', 'Very Strong', 'Strong', 'Medium', 'Weak', 'Very Weak',
'Negative Very Weak', 'Negative Weak', 'Negative Medium',
'Negative Strong', 'Negative Very Strong'
]
histogram = u.createDataFrame(1, columns, 0)
stock_number = len(allcoef)
histogram.ix[0, 'Total'] = stock_number
for i in range(stock_number):
correlation = allcoef.ix[i, 'correlation']
if correlation > 0.8: # (0.8, 1.0]
histogram.ix[0, 'Very Strong'] = histogram.ix[0, 'Very Strong'] + 1
elif correlation > 0.6: # (0.6, 0.8]
histogram.ix[0, 'Strong'] = histogram.ix[0, 'Strong'] + 1
elif correlation > 0.4: # (0.4, 0.6]
histogram.ix[0, 'Medium'] = histogram.ix[0, 'Medium'] + 1
elif correlation > 0.2: # (0.2, 0.4]
histogram.ix[0, 'Weak'] = histogram.ix[0, 'Weak'] + 1
elif correlation >= 0.0: # [0.0, 0.2]
histogram.ix[0, 'Very Weak'] = histogram.ix[0, 'Very Weak'] + 1
elif correlation > -0.2: # (-0.2, 0.0)
histogram.ix[0, 'Negative Very Weak'] = histogram.ix[
0, 'Negative Very Weak'] + 1
elif correlation > -0.4: # (-0.4, -0.2]
histogram.ix[0,
'Negative Weak'] = histogram.ix[0,
'Negative Weak'] + 1
elif correlation > -0.6: # (-0.6, -0.4]
histogram.ix[
0, 'Negative Medium'] = histogram.ix[0, 'Negative Medium'] + 1
elif correlation > -0.8: # (-0.8, -0.6]
histogram.ix[
0, 'Negative Strong'] = histogram.ix[0, 'Negative Strong'] + 1
else: # [-1.0, -0.8]
histogram.ix[0, 'Negative Very Strong'] = histogram.ix[
0, 'Negative Very Strong'] + 1
# Save to CSV File
histogram.set_index('Total', inplace=True)
file_postfix = '_'.join(
[postfix, completeness_threshold, 'HistogramCorrelation'])
u.to_csv(histogram, c.path_dict['strategy'],
c.file_dict['strategy'] % file_postfix)
return True
def plotHPE(period='M', ratio='PE'):
# Check pre-requisite
basics = loadStockBasics()
if u.isNoneOrEmpty(basics):
print('Need to have stock basics!')
raise SystemExit
# Iterate over all stocks
basics_number = len(basics)
for i in range(basics_number):
stock_id = u.stockID(basics.loc[i, 'code'])
# Plot HPE Data
plot_HPE(stock_id=stock_id, period=period, ratio=ratio)
def getZZ500():
# Download ZZ500 Data
zz500 = get_zz500()
if not u.isNoneOrEmpty(zz500):
zz500.set_index('code', inplace=True)
if gs.is_debug:
print(zz500.head(10))
# Save to CSV File
u.to_csv(zz500, c.path_dict['classify'], c.file_dict['zz500'])
else:
print('getZZ500() Failed!')
raise SystemExit
def getTerminated():
# Download Terminated Stock Data
terminated = get_terminated()
if not u.isNoneOrEmpty(terminated):
terminated.set_index('code', inplace=True)
if gs.is_debug:
print(terminated.head(10))
# Save to CSV File
u.to_csv(terminated, c.path_dict['classify'], c.file_dict['terminated'])
else:
print('getTerminated() Failed!')
raise SystemExit
def getSuspended():
# Download Suspended Data
suspended = get_suspended()
if not u.isNoneOrEmpty(suspended):
suspended.set_index('code', inplace=True)
if gs.is_debug:
print(suspended.head(10))
# Save to CSV File
u.to_csv(suspended, c.path_dict['classify'], c.file_dict['suspended'])
else:
print('getSuspended() Failed!')
raise SystemExit
def getStockList(cutoff_date):
stocks = get_stock_list(cutoff_date)
if not u.isNoneOrEmpty(stocks):
stocks['code'] = stocks['code'].map(lambda x:str(x).zfill(6))
stocks.set_index('code', inplace=True)
if gs.is_debug:
print(stocks.head(10))
# Save to CSV File
u.to_csv(stocks, c.path_dict['classify'], c.file_dict['stock_list'] % cutoff_date)
else:
print('getStockList() Failed!')
raise SystemExit
def getConceptSina():
# Download Sina Concept Data
concept = get_concept_sina()
if not u.isNoneOrEmpty(concept):
concept.set_index('code', inplace=True)
if gs.is_debug:
print(concept.head(10))
# Save to CSV File
u.to_csv(concept, c.path_dict['classify'], c.file_dict['conc_sina'])
else:
print('getConceptSina() Failed!')
raise SystemExit
