def result_list_to_array(data):
sdata = []
def fixBadData(x):
return '-' if math.isnan(x) else x
for index, row in data.iterrows():
sdata.append(
{'name': row['name'],
'code': row.code,
'industry': row.industry,
'tag': row.tag if hasattr(row, 'tag') else '',
'ncode': fn.code_to_ncode(row.code),
'pcode': row['code'] + ('01' if row['code'][:2] == '60'else '02'),
'price': fixBadData(row.close),
'mvalue': fixBadData(round(row.t_cap / (10000 * 10000), 2)),
'pe': fixBadData(round(row.t_cap / (row.jlr_ttm * 10000), 2)),
'ps': fixBadData(round(row.t_cap / (row.zyysr_ttm * 10000), 2)),
'pcf': fixBadData(round(row.t_cap / (row.jyjxjl_ttm * 10000), 2)),
'pb': fixBadData(round(row.t_cap / (row.gdqy * 10000), 2)),
'roe': fixBadData(round(row.jlr_ttm * 100.0 / row.gdqy, 2)),
'dar': fixBadData(round(row.zfz * 100.0 / (row.zzc), 2)),
'jlr_rate': fixBadData(round(row['jlr_rate'] * 100.0, 2)),
'cash_rate': fixBadData(round(row.jyjxjl_ttm * 1.0 / row.jlr_ttm, 2)), # 现金净利润比
'report_type': '-' if pd.isnull(row.report_type) else row.report_type
}
)
return sdata
def query():
query = request.args.get('query')
df = ds.get_global_basic_data()
df = df[(df['name'].str.contains(query)) | (df.index.str.contains(query))]
result = []
for index, row in df.iterrows():
result.append(
{'id': index, 'name': row['name'], 'ncode': fn.code_to_ncode(index)}
)
return jsonify(result=result)
def findStockJson():
skey = request.args.get('skey')
result = []
st_result = dts.findStocksByHolder(skey)
def fixBadData(x):
import math
return '-' if math.isnan(x) else round(x, 2)
for r in st_result:
report_date = r['report_date'].strftime('%Y-%m-%d')
df = r['data']
tableData = []
for index, row in df.iterrows():
tableData.append({
'name':
row['name'],
'code':
row.code,
'holder_name':
row.holder_name,
'holder_code':
row.holder_code,
'hold_length':
row.hold_length,
'stock_industry':
row.industry,
'ncode':
fn.code_to_ncode(row.code),
'pcode':
row['code'] + ('01' if row['code'][:2] == '60' else '02'),
'price':
fixBadData(row.close),
'rate':
fixBadData(row.rate),
'mvalue':
fixBadData(round(row.hold_amt / (10000 * 10000), 2)),
'pe':
fixBadData(row.pe),
'ps':
fixBadData(row.ps),
'pcf':
fixBadData(row.pcf),
'pb':
fixBadData(row.pb),
'report_type':
row.report_date.strftime('%Y-%m-%d')
})
result.append({"r_date": report_date, "data": tableData})
return jsonify(data=result)
def person_stockholder_rank():
data = hs.getStockHolderRank()
sdata = []
for index, row in data.iterrows():
sdata.append({
'name': row['name'],
'code': row.code,
'ncode': fn.code_to_ncode(row.code),
'sum': row['sum'],
'size': int(row['count']),
'avg': round(row['avg'],3),
'launch_date': row.launch_date
})
return render_template('stock/stockholder_rank.html', title='自然人持股排行', stocks=sdata)
def query():
query = request.args.get('query')
df = ds.get_global_basic_data()
df = df[(df['name'].str.contains(query)) | (df.index.str.contains(query))]
result = []
for index, row in df.iterrows():
result.append({
'id': index,
'name': row['name'],
'ncode': fn.code_to_ncode(index)
})
return jsonify(result=result)
def person_stockholder_rank():
data = hs.getStockHolderRank()
sdata = []
for index, row in data.iterrows():
sdata.append({
'name': row['name'],
'code': row.code,
'ncode': fn.code_to_ncode(row.code),
'sum': row['sum'],
'size': int(row['count']),
'avg': round(row['avg'], 3),
'launch_date': row.launch_date
})
return render_template('stock/stockholder_rank.html',
title='自然人持股排行',
stocks=sdata)
def result_list_to_array(data):
sdata = []
def fixBadData(x):
return '-' if math.isnan(x) else x
for index, row in data.iterrows():
sdata.append({
'name':
row['name'],
'code':
row.code,
'industry':
row.industry,
'tag':
row.tag if hasattr(row, 'tag') else '',
'ncode':
fn.code_to_ncode(row.code),
'pcode':
row['code'] + ('01' if row['code'][:2] == '60' else '02'),
'price':
fixBadData(row.close),
'mvalue':
fixBadData(round(row.t_cap / (10000 * 10000), 2)),
'pe':
fixBadData(round(row.t_cap / (row.jlr_ttm * 10000), 2)),
'ps':
fixBadData(round(row.t_cap / (row.zyysr_ttm * 10000), 2)),
'pcf':
fixBadData(round(row.t_cap / (row.jyjxjl_ttm * 10000), 2)),
'pb':
fixBadData(round(row.t_cap / (row.gdqy * 10000), 2)),
'roe':
fixBadData(round(row.jlr_ttm * 100.0 / row.gdqy, 2)),
'dar':
fixBadData(round(row.zfz * 100.0 / (row.zzc), 2)),
'jlr_rate':
fixBadData(round(row['jlr_rate'] * 100.0, 2)),
'sh_rate':
fixBadData(row['count']),
'cash_rate':
fixBadData(round(row.jyjxjl_ttm * 1.0 / row.jlr_ttm, 2)), # 现金净利润比
'report_type':
'-' if pd.isnull(row.report_type) else row.report_type
})
return sdata
def queryComment(uid,code,page=1):
pageSize = 10
if code.strip() == '':
totalPage = db.session.query(Comment).filter_by(user_id=uid).count()
comments = db.session.query(Comment). \
filter_by(user_id=uid).order_by(Comment.created_time.desc()).offset(
(page - 1) * pageSize).limit(pageSize)
else:
code = fn.code_to_ncode(code) if len(code) <= 6 else code
totalPage = db.session.query(Comment).filter_by(stock=code, user_id=uid).count()
comments = db.session.query(Comment). \
filter_by(stock=code, user_id=uid).order_by(Comment.created_time.desc()).offset(
(page - 1) * pageSize).limit(pageSize)
# group_by(Comment.parent_id,Comment.created_time,Comment.id).all()
return totalPage,comments
def queryComment(uid, code, page=1):
pageSize = 10
if code.strip() == '':
totalPage = db.session.query(Comment).filter_by(user_id=uid).count()
comments = db.session.query(Comment). \
filter_by(user_id=uid).order_by(Comment.created_time.desc()).offset(
(page - 1) * pageSize).limit(pageSize)
else:
code = fn.code_to_ncode(code) if len(code) <= 6 else code
totalPage = db.session.query(Comment).filter_by(stock=code,
user_id=uid).count()
comments = db.session.query(Comment). \
filter_by(stock=code, user_id=uid).order_by(Comment.created_time.desc()).offset(
(page - 1) * pageSize).limit(pageSize)
# group_by(Comment.parent_id,Comment.created_time,Comment.id).all()
return totalPage, comments
def stockList(pageNum=None):
pageSize = 200
page = 0 if pageNum is None else int(pageNum)
df = ds.get_global_basic_data()
totalPages = int(df.index.size / pageSize);
df = df[(page*pageSize+1):(page+1)*pageSize]
data = []
for index, row in df.iterrows():
data.append({
'code': index,
'name': row['name'],
'ncode': fn.code_to_ncode(index)
})
cuPage = 1 if page==0 else page
return render_template('stock_list.html',title="股票清单",stocks=data,currentPage=cuPage,totalPages=totalPages)
def findStockJson():
skey = request.args.get('skey')
result = []
st_result = dts.findStocksByHolder(skey)
def fixBadData(x):
import math
return '-' if math.isnan(x) else round(x,2)
for r in st_result:
report_date = r['report_date'].strftime('%Y-%m-%d')
df = r['data']
tableData = []
for index, row in df.iterrows():
tableData.append(
{'name': row['name'],
'code': row.code,
'holder_name': row.holder_name,
'holder_code': row.holder_code,
'hold_length': row.hold_length,
'stock_industry': row.industry,
'ncode': fn.code_to_ncode(row.code),
'pcode': row['code'] + ('01' if row['code'][:2] == '60'else '02'),
'price': fixBadData(row.close),
'rate': fixBadData(row.rate),
'mvalue': fixBadData(round(row.hold_amt / (10000 * 10000), 2)),
'pe': fixBadData(row.pe),
'ps': fixBadData(row.ps),
'pcf': fixBadData(row.pcf),
'pb': fixBadData(row.pb),
'report_type': row.report_date.strftime('%Y-%m-%d')
}
)
result.append({"r_date": report_date, "data": tableData})
return jsonify(data=result)
