def index_dr_json(_code=None):
if _code is None or len(_code) < 8:
return "error"
_code = _code.upper()
is_all = request.args.get('all')
data = []
_market = 1 if _code[:2] == "SH" else 0
_idx_code = _code[2:]
stock_model = StockModel()
_filter = {"code": _idx_code, "market": _market}
if is_all is None or len(is_all) <= 0:
from_date = date.datetime_to_str(date.years_ago(10))
_filter["date"] = {"$gte": from_date}
_idx_list = stock_model.stock_list(_filter=_filter, _sort=[("date", pymongo.ASCENDING)],
_fields={"date": 1, "dr": 1, "close": 1})
item_df = pd.DataFrame(list(_idx_list), columns=["date", "dr", "close"])
item_df = item_df.dropna(0)
for idx, row in item_df.iterrows():
data.append([row["date"], float("%.2f" % (row["dr"] * 100)), float("%.2f" % row["close"])])
return json.dumps(data)
def index(_code=None):
if _code is None or len(_code) < 8:
return "error"
"""获取某个指数的详情"""
_market = 1 if _code[:2] == "SH" else 0
_idx_code = _code[2:]
stock_model = StockModel()
_filter = {"code": _idx_code, "market": _market, "date": {"$gte": date.datetime_to_str(date.years_ago(10))}}
_idx_list = stock_model.stock_list(_filter=_filter, _sort=[("date", pymongo.ASCENDING)],
_fields={"date": 1, "name": 1, "pe_ttm": 1, "pb": 1, "dr": 1})
item_df = pd.DataFrame(list(_idx_list), columns=["date", "name", "pe_ttm", "pb", "dr"])
item_df = item_df.dropna(0)
_pe_series = 1 / item_df["pe_ttm"]
pe_ranks = _pe_series.rank(pct=True)
_pb_series = 1 / item_df["pb"]
pb_ranks = _pb_series.rank(pct=True)
return render_template('indexes/detail.mako', ctx={
"index_name": item_df["name"].values[0],
"index_code": _code,
"start_date": date.datetime_to_str(date.years_ago(years=3)),
"pe_max": "%.2f" % item_df["pe_ttm"].max(),
"pe_min": "%.2f" % item_df["pe_ttm"].min(),
"pe_mean": "%.2f" % item_df["pe_ttm"].mean(),
"pe_high": "%.2f" % item_df["pe_ttm"].quantile(0.75),
"pe_middle": "%.2f" % item_df["pe_ttm"].quantile(0.5),
"pe_low": "%.2f" % item_df["pe_ttm"].quantile(0.2),
"pe_value": "%.2f" % item_df["pe_ttm"].values[-1],
"pe_rate": "%.2f%%" % ((1 - pe_ranks.values[-1]) * 100),
"pb_max": "%.2f" % item_df["pb"].max(),
"pb_min": "%.2f" % item_df["pb"].min(),
"pb_mean": "%.2f" % item_df["pb"].mean(),
"pb_high": "%.2f" % item_df["pb"].quantile(0.75),
"pb_middle": "%.2f" % item_df["pb"].quantile(0.5),
"pb_low": "%.2f" % item_df["pb"].quantile(0.2),
"pb_value": "%.2f" % item_df["pb"].values[-1],
"pb_rate": "%.2f%%" % ((1 - pb_ranks.values[-1]) * 100),
"dr_max": "%.2f%%" % (item_df["dr"].max() * 100),
"dr_min": "%.2f%%" % (item_df["dr"].min() * 100),
"dr_mean": "%.2f%%" % (item_df["dr"].mean() * 100),
"dr_value": "%.2f%%" % (item_df["dr"].values[-1] * 100)
})
def stock_pe_json(_code: str = None):
if _code is None or len(_code) < 8:
return "error"
_code = _code.upper()
is_all = request.args.get('all')
data = []
_market = 1 if _code[:2] == "SH" else 0
_idx_code = _code[2:]
stockModel = StockModel()
_filter = {"code": _idx_code, "market": _market}
if is_all is None or len(is_all) <= 0:
from_date = date.datetime_to_str(date.years_ago(10))
_filter["date"] = {"$gte": from_date}
_idx_list = stockModel.stock_list(_filter=_filter,
_sort=[("date", pymongo.ASCENDING)],
_fields={
"date": 1,
"pe_ttm": 1,
"close": 1
})
item_df = pd.DataFrame(list(_idx_list),
columns=["date", "pe_ttm", "close"])
item_df = item_df.dropna(0)
item_df = item_df.reset_index()
_pe_series = 1 / item_df["pe_ttm"]
pe_ranks = _pe_series.rank(pct=True)
_pe_list = []
for idx, row in item_df.iterrows():
_pe_list.append(row["pe_ttm"])
_pe_high = np.percentile(_pe_list, 75)
_pe_middle = np.percentile(_pe_list, 50)
_pe_low = np.percentile(_pe_list, 20)
_item_rank = float("%.2f" % ((1 - pe_ranks.values[idx]) * 100))
data.append([
row["date"],
float("%.2f" % row["pe_ttm"]),
float("%.2f" % _pe_high),
float("%.2f" % _pe_middle),
float("%.2f" % _pe_low), _item_rank,
float("%.2f" % row["close"])
])
return json.dumps(data)
def pb_json():
"""首页图标需要的数据 """
data = []
stockModel = StockModel()
# '上证50', '沪深300', '中证500', '中小板指', '创业扳指'
_idx_list = stockModel.stock_list(_filter={"code": "000016", "market": 1}, _sort=[("date", pymongo.ASCENDING)],
_fields={"date": 1, "pb": 1})
_idx_df_50 = pd.DataFrame(list(_idx_list), columns=["date", "pb"])
_tmp_list_pb = _idx_df_50.pop("pb")
_idx_df_50.insert(1, 'pb50', _tmp_list_pb)
_idx_list = stockModel.stock_list(_filter={"code": "000300", "market": 1}, _sort=[("date", pymongo.ASCENDING)],
_fields={"date": 1, "pb": 1})
_idx_df_300 = pd.DataFrame(list(_idx_list), columns=["date", "pb"])
_tmp_list_pb = _idx_df_300.pop("pb")
_idx_df_300.insert(1, 'pb300', _tmp_list_pb)
_idx_list = stockModel.stock_list(_filter={"code": "000905", "market": 1}, _sort=[("date", pymongo.ASCENDING)],
_fields={"date": 1, "pb": 1})
_idx_df_500 = pd.DataFrame(list(_idx_list), columns=["date", "pb"])
_tmp_list_pb = _idx_df_500.pop("pb")
_idx_df_500.insert(1, 'pb500', _tmp_list_pb)
_idx_list = stockModel.stock_list(_filter={"code": "399005", "market": 0}, _sort=[("date", pymongo.ASCENDING)],
_fields={"date": 1, "pb": 1})
_idx_df_zx = pd.DataFrame(list(_idx_list), columns=["date", "pb"])
_tmp_list_pb = _idx_df_zx.pop("pb")
_idx_df_zx.insert(1, 'pbzx', _tmp_list_pb)
_idx_list = stockModel.stock_list(_filter={"code": "399006", "market": 0}, _sort=[("date", pymongo.ASCENDING)],
_fields={"date": 1, "pb": 1})
_idx_df_cy = pd.DataFrame(list(_idx_list), columns=["date", "pb"])
_tmp_list_pb = _idx_df_cy.pop("pb")
_idx_df_cy.insert(1, 'pbcy', _tmp_list_pb)
df = pd.merge(_idx_df_50, _idx_df_300, how='left', on=['date', 'date'])
df = pd.merge(df, _idx_df_500, how='left', on=['date', 'date'])
df = pd.merge(df, _idx_df_zx, how='left', on=['date', 'date'])
df = pd.merge(df, _idx_df_cy, how='left', on=['date', 'date'])
df = df.fillna('')
for _, row in df.iterrows():
data.append([row["date"],
"" if row["pb50"] == "" else float("%.3f" % row["pb50"]),
"" if row["pb300"] == "" else float("%.3f" % row["pb300"]),
"" if row["pb500"] == "" else float("%.3f" % row["pb500"]),
"" if row["pbzx"] == "" else float("%.3f" % row["pbzx"]),
"" if row["pbcy"] == "" else float("%.3f" % row["pbcy"])])
return json.dumps(data)
def indexes_json():
"""显示指数估值信息"""
# '上证50', '沪深300', '中证500', '中证1000', '上证红利', '中证红利'，'中小板指', '创业扳指'
# '食品饮料'
idx_list = config.get("indexes")
stock_model = StockModel()
result = []
for idx, item in enumerate(idx_list):
index_obj = {"idx": idx + 1}
_market = 'SZ' if item[0] == 0 else 'SH'
item_detail = stock_model.stock_item(_filter={"code": item[1], "market": item[0]},
_sort=[("date", pymongo.DESCENDING)])
index_obj["name"] = "<a href='/index/%s%s' title='%s的估值信息'> %s </a>" % \
(_market, item_detail["code"], item_detail["name"], item_detail["name"])
index_obj["interest"] = "%.2f%%" % (1 / item_detail["pe_ttm"] * 100)
index_obj["dr"] = "%.2f%%" % (item_detail["dr"] * 100)
index_obj["roe"] = "%.2f%%" % (item_detail["roe"] * 100)
index_obj["PE"] = "%.2f" % item_detail["pe_ttm"]
index_obj["PB"] = "%.2f" % item_detail["pb"]
_filter = {"code": item[1], "market": item[0], "date": {"$gte": date.datetime_to_str(date.years_ago(10))}}
item_list = stock_model.stock_list(_filter=_filter, _sort=[("date", pymongo.ASCENDING)],
_fields={"pe_ttm": 1, "pb": 1})
item_df = pd.DataFrame(list(item_list), columns=["pe_ttm", "pb"])
item_df = item_df.dropna(0)
_pe_series = 1 / item_df["pe_ttm"]
pe_ranks = _pe_series.rank(pct=True)
_pb_series = 1 / item_df["pb"]
pb_ranks = _pb_series.rank(pct=True)
index_obj["PE_RATE"] = "%.2f%%" % ((1 - pe_ranks.values[-1]) * 100)
index_obj["PB_RATE"] = "%.2f%%" % ((1 - pb_ranks.values[-1]) * 100)
# 本周以来的涨跌幅
now = datetime.today()
_tmp_date = (now - timedelta(days=now.weekday())).strftime('%Y%m%d')
item_last_week = stock_model.stock_item(_filter={"code": item[1], "market": item[0],
"date": _tmp_date if date.is_work_day(int(_tmp_date))
else str(date.next_day(int(_tmp_date)))})
if item_last_week is None:
item_last_week = item_detail
# 本月以来
_tmp_date = datetime(now.year, now.month, 1).strftime('%Y%m%d')
item_last_month = stock_model.stock_item(_filter={"code": item[1], "market": item[0],
"date": _tmp_date if date.is_work_day(int(_tmp_date))
else str(date.next_day(int(_tmp_date)))})
if item_last_month is None:
item_last_month = item_detail
# 本季度以来
month = (now.month - 1) - (now.month - 1) % 3 + 1
_tmp_date = datetime(now.year, month, 1).strftime('%Y%m%d')
item_last_quarter = stock_model.stock_item(_filter={"code": item[1], "market": item[0],
"date": _tmp_date if date.is_work_day(int(_tmp_date))
else str(date.next_day(int(_tmp_date)))})
if item_last_quarter is None:
item_last_quarter = item_detail
# 本年度以来
_tmp_date = datetime(now.year, 1, 1).strftime('%Y%m%d')
item_last_year = stock_model.stock_item(_filter={"code": item[1], "market": item[0],
"date": _tmp_date if date.is_work_day(int(int(_tmp_date)))
else str(date.next_day(int(_tmp_date)))})
if item_last_year is None:
item_last_year = item_detail
index_obj["last_week"] = "%.2f%%" % (
(item_detail["close"] - item_last_week["close"]) / item_last_week["close"] * 100)
index_obj["last_month"] = "%.2f%%" % (
(item_detail["close"] - item_last_month["close"]) / item_last_month["close"] * 100)
index_obj["last_quarter"] = "%.2f%%" % (
(item_detail["close"] - item_last_quarter["close"]) / item_last_quarter["close"] * 100)
index_obj["last_year"] = "%.2f%%" % (
(item_detail["close"] - item_last_year["close"]) / item_last_year["close"] * 100)
result.append(index_obj)
# 追加A股市场的估值信息
stock_a_df = pd.read_csv(config.get("files").get("stock_a"), header=0, encoding="utf8")
stock_a_df = stock_a_df.dropna(0)
if len(stock_a_df) > 0:
item_last = stock_a_df.loc[stock_a_df.index[-1]]
pe_array = 1 / stock_a_df["pe"]
pb_array = 1 / stock_a_df["pb"]
pe_ranks = pe_array.rank(pct=True)
pb_ranks = pb_array.rank(pct=True)
stock_a_item = {"idx": len(idx_list) + 1, "name": "A股市场", "interest": "%.2f%%" % (1 / item_last["pe"] * 100),
"dr": "%.2f%%" % (item_last["dr"] * 100), "roe": "%.2f%%" % (item_last["roe"] * 100),
"PE": "%.2f" % item_last["pe"], "PB": "%.2f" % item_last["pb"],
"PE_RATE": "%.2f%%" % ((1 - pe_ranks.values[-1]) * 100),
"PB_RATE": "%.2f%%" % ((1 - pb_ranks.values[-1]) * 100)}
result.append(stock_a_item)
# 追加国债收益供参考
bond_df = pd.read_csv(config.get("files").get("bond"), header=0, encoding="utf8")
if len(bond_df) > 0:
item_last = bond_df.loc[bond_df.index[-1]]
bond_item = {"idx": len(idx_list) + 2,
"name": "<a href='http://www.pbc.gov.cn/rmyh/108976/index.html#Container' "
"title='%s的历史利率' target='_blank'> 十年国债利率 </a>",
"interest": "%.2f%%" % item_last["10year"]}
result.append(bond_item)
return json.dumps(result)