def data(order_book_id,
start_date=None,
end_date=None,
frequency="1d",
fields=None):
"""获取天猫电商销售额数据
:param order_book_id: 股票名
:param start_date: 开始日期，默认为结束日期前一个月, 必须在2016年6月30日之后 (Default value = None)
:param end_date: 结束日期 (Default value = None)
:param frequency: 如'1d', '1M' (Default value = "1d")
:param fields: 如'sales' (Default value = None)
:returns: 返回DataFrame
"""
order_book_id = ensure_order_book_id(order_book_id)
if not end_date:
end_date = datetime.date.today()
if not start_date:
start_date = end_date - datetime.timedelta(days=30)
end_date = to_datetime(end_date)
start_date = to_datetime(start_date)
if start_date < datetime.datetime(2016, 6, 30):
raise ValueError("start_date cannot be earlier than 2016-06-30")
if start_date > end_date:
raise ValueError()
ensure_string(frequency, "frequency")
check_items_in_container([frequency], {"1d", "1M"}, "frequency")
if fields is None:
fields = "sales"
else:
fields = ensure_string(fields, "fields")
check_items_in_container(fields, ["sales"], "fields")
data = get_client().execute("tmall.data", order_book_id, start_date,
end_date, frequency)
if not data:
return
df = pd.DataFrame(data)
df = df.set_index("date")
df.sort_index(inplace=True)
df.columns = ["sales"]
return df
def datetime(self):
if self._dt is not None:
return self._dt
if not self._isnan:
dt = self._data["datetime"]
return to_datetime(dt)
return datetime.datetime.min
def is_suspended(order_book_ids, start_date=None, end_date=None):
"""获取停牌信息
:param order_book_ids: 可转债ID
:param start_date: 开始日期, 如'2013-01-04' (Default value = None)
:param end_date: 结束日期，如'2014-01-04' (Default value = None)
:returns: DataFrame
"""
order_book_ids = ensure_order_book_ids(order_book_ids)
if len(order_book_ids) == 1:
instrument = instruments(order_book_ids[0], market="cn")
start_date, end_date = ensure_dates_base_on_listed_date(
instrument, start_date, end_date, "cn")
if start_date is None:
return
start_date, end_date = ensure_date_range(start_date, end_date)
trading_dates = pd.to_datetime(
get_trading_dates(start_date, end_date, market="cn"))
df = pd.DataFrame(data=False, columns=order_book_ids, index=trading_dates)
data = get_client().execute("convertible.is_suspended",
order_book_ids,
start_date,
end_date,
market="cn")
for (order_book_id, date) in data:
date = to_datetime(date)
df.at[date, order_book_id] = True
return df
def get_ratings(order_book_id, date=None, market="cn"):
"""获取距离指定日期最近发布的基金评级信息
:param order_book_id: 基金代码 str or list
:param date: 日期，为空则返回所有时间段的数据 (Default value = None)
:param market: (Default value = "cn")
:returns: DataFrame
"""
order_book_id = ensure_list_of_string(order_book_id)
if date is not None:
date = to_datetime(date)
if date > datetime.datetime.today():
return
date = ensure_date_int(date)
df = get_client().execute("fund.get_ratings_v2", order_book_id, date, market=market)
if not df:
return
df = pd.DataFrame(df, columns=["order_book_id", "datetime", "zs", "sh3", "sh5", "jajx"])
df.sort_values(["order_book_id", "datetime"], inplace=True)
if date is not None:
df.drop_duplicates(subset=['order_book_id'], keep='last', inplace=True)
df.set_index(["order_book_id", "datetime"], inplace=True)
df.fillna(np.nan, inplace=True)
return df
def get_bond_stru(order_book_id, date=None, market="cn"):
"""获取指定日期公募基金债券持仓券种明细信息
:param order_book_id: 基金代码 str, 仅支持传入单个基金代码
:param date: 日期，为空则返回所有时间段的数据 (Default value = None)
:param market: (Default value = "cn")
:returns: DataFrame
"""
order_book_id = ensure_string(order_book_id)
order_book_id = ensure_fund(order_book_id, market)
if date is not None:
date = to_datetime(date)
if date > datetime.datetime.today():
return
date = ensure_date_int(date)
data = get_client().execute("fund.get_bond_stru", order_book_id, date, market=market)
if not data:
return
df = pd.DataFrame(data)
df.set_index(['date'], inplace=True)
df.sort_index(inplace=True)
df = df[['order_book_id', 'bond_type', 'weight_nv', 'weight_bond_mv', 'market_value']]
return df
def is_st_stock(order_book_ids, start_date=None, end_date=None, market="cn"):
"""判断股票在给定的时间段是否是ST股, 返回值为一个DataFrame
:param order_book_ids: 股票 id
:param start_date: (Default value = None)
:param end_date: (Default value = None)
:param market: (Default value = "cn")
"""
order_book_ids = ensure_order_book_ids(order_book_ids,
type="CS",
market=market)
if len(order_book_ids) == 1:
instrument = instruments(order_book_ids[0], market=market)
start_date, end_date = ensure_dates_base_on_listed_date(
instrument, start_date, end_date, market)
if start_date is None:
return
start_date, end_date = ensure_date_range(start_date, end_date)
trading_dates = pd.to_datetime(
get_trading_dates(start_date, end_date, market=market))
data = get_client().execute("get_st_days",
order_book_ids,
start_date=start_date,
end_date=end_date)
df = pd.DataFrame(data=False, columns=order_book_ids, index=trading_dates)
for idx, dates in data.items():
for date in dates:
date = to_datetime(date)
df.at[date, idx] = True
return df
def is_suspended(order_book_ids, start_date=None, end_date=None, market="cn"):
"""获取股票停牌信息
:param order_book_ids: 股票名称
:param start_date: 开始日期, 如'2013-01-04' (Default value = None)
:param end_date: 结束日期，如'2014-01-04' (Default value = None)
:param market: 地区代码, 如 'cn' (Default value = "cn")
:returns: DataFrame
"""
order_book_ids = ensure_order_book_ids(order_book_ids, type="CS", market=market)
if len(order_book_ids) == 1:
instrument = instruments(order_book_ids[0], market=market)
start_date, end_date = ensure_dates_base_on_listed_date(instrument, start_date, end_date, market)
if start_date is None:
return
if end_date is None:
end_date = datetime.date.today()
start_date, end_date = ensure_date_range(start_date, end_date)
trading_dates = pd.to_datetime(get_trading_dates(start_date, end_date, market=market))
df = pd.DataFrame(data=False, columns=order_book_ids, index=trading_dates)
data = get_client().execute("get_suspended_days", order_book_ids, start_date, end_date, market=market)
for idx, dates in data.items():
for date in dates:
date = to_datetime(int(date))
df.at[date, idx] = True
df.sort_index(inplace=True)
return df
def all_instruments(date=None, market="cn"):
"""获取所有可转债详细信息
:param market: (Default value = "cn")
:returns: DataFrame
"""
profile = lambda v: (
v.order_book_id,
v.symbol,
v.full_name,
v.exchange,
v.bond_type,
v.trade_type,
v.value_date,
v.maturity_date,
v.par_value,
v.coupon_rate,
v.coupon_frequency,
v.coupon_method,
v.compensation_rate,
v.total_issue_size,
v.de_listed_date,
v.stock_code,
v.conversion_start_date,
v.conversion_end_date,
v.redemption_price,
v.issue_price,
v.call_protection,
v.listed_date,
)
def judge(listed_date, de_listed_date):
if listed_date and de_listed_date:
return listed_date <= date and de_listed_date > date
if listed_date:
return listed_date <= date
else:
return False
if date:
date = to_datetime(date)
data = [
profile(v) for v in _all_instruments_dict(market).values()
if judge(v.listed_date, v.de_listed_date)
]
else:
data = [profile(v) for v in _all_instruments_dict(market).values()]
df = pd.DataFrame(
data,
columns=INS_COLUMNS,
)
df.sort_values('order_book_id', inplace=True)
return df.reset_index(drop=True)
def get_units_change(order_book_id, date=None, market="cn"):
"""获取距离指定日期最近发布的基金认购赎回信息
:param order_book_id: 基金代码 str
:param date: 日期，为空则返回所有时间段的数据 (Default value = None)
:param market: (Default value = "cn")
:returns: DataFrame
"""
order_book_id = ensure_fund(order_book_id, market)
if date is not None:
date = to_datetime(date)
if date > datetime.datetime.today():
return None
date = ensure_date_int(date)
df = get_client().execute("fund.get_units_change", order_book_id, date, market=market)
df = pd.DataFrame(df, columns=["subscribe_units", "redeem_units", "units", "datetime"])
df["datetime"] = pd.to_datetime(df["datetime"])
return df.set_index("datetime").sort_index()
def get_industry_allocation(order_book_id, date=None, market="cn"):
"""获取指定日期最近发布的基金行业配置信息
:param order_book_id: 基金代码 str
:param date: 日期，为空则返回所有时间段的数据 (Default value = None)
:param market: (Default value = "cn")
:returns: DataFrame
"""
order_book_id = ensure_fund(order_book_id, market)
if date is not None:
date = to_datetime(date)
if date > datetime.datetime.today():
return
date = ensure_date_int(date)
df = get_client().execute("fund.get_industry_allocation", order_book_id, date, market=market)
df = pd.DataFrame(df, columns=["industry", "weights", "market_value", "datetime"])
df["datetime"] = pd.to_datetime(df["datetime"])
return df.set_index("datetime").sort_index()
def get_asset_allocation(order_book_id, date=None, market="cn"):
"""获取指定日期最近发布的基金资产配置信息
:param order_book_id: 基金代码 str
:param date: 日期，为空则返回所有时间段的数据 (Default value = None)
:param market: (Default value = "cn")
:returns: DataFrame
"""
order_book_id = ensure_fund(order_book_id, market)
if date is not None:
date = to_datetime(date)
if date > datetime.datetime.today():
return None
date = ensure_date_int(date)
df = get_client().execute("fund.get_asset_allocation", order_book_id, date, market=market)
if not df:
return
df = pd.DataFrame(df,
columns=["stock", "bond", "fund", "cash", 'other', 'nav', "net_asset", 'total_asset', "datetime"])
df["datetime"] = pd.to_datetime(df["datetime"])
warnings.warn("'nav' is deprecated. Please use 'net_asset' instead")
return df.set_index("datetime").sort_index()
def get_fundamentals(query, entry_date, interval=None, report_quarter=False, expect_df=False, market="cn"):
"""获取财务数据
:param query: query 对象
:param entry_date: 日期
:param interval: (Default value = None)
:param report_quarter: (Default value = False)
:param expect_df: 返回 MultiIndex DataFrame (Default value = False)
:param market: (Default value = "cn")
"""
if market != "cn":
raise MarketNotSupportError("don't support market {} yet.", market)
if not isinstance(query, Query):
raise ValueError("a sqlalchemy's Query object expected: {}".format(type(query)))
raise_for_no_panel(expect_df)
entry_date = to_datetime(entry_date)
delta = 0
duration = 0
if interval is not None:
if not isinstance(interval, str):
raise ValueError(
"invalid interval: {} should be a string like 1d, 5y, 3m, 2q".format(interval)
)
if interval[-1] not in __TIME_DELTA_MAP__:
raise ValueError(
"invalid interval: {}, interval unit should be d(day), "
"m(month), q(quarter) or y(year)".format(interval)
)
delta = __TIME_DELTA_MAP__[interval[-1]]
try:
duration = int(interval[:-1])
except ValueError:
raise ValueError(
"invalid interval: {}, should be a string like 1d, 5y, 3m, 2q".format(interval)
)
trading_dates = [get_previous_trading_date(entry_date + __TIME_DELTA_MAP__["d"], market=market)]
if duration > 0:
current_date = trading_dates[0]
one_day = __TIME_DELTA_MAP__["d"]
for i in range(duration - 1):
current_date = get_previous_trading_date(current_date - delta + one_day, market=market)
trading_dates.append(current_date)
query = _unsafe_apply_query_filter(query, trading_dates)
sql = _compile_query(query)
records = get_client().execute("get_fundamentals", sql, market=market)
if not records:
warnings.warn("No record found")
return None
base_fields = ["STOCKCODE", "TRADEDATE", "RPT_YEAR", "RPT_QUARTER"]
field_names = base_fields + list(set(records[0].keys()) - set(base_fields))
items = ["report_quarter"] + field_names[4:] if report_quarter else field_names[4:]
if expect_df:
df = pd.DataFrame(records)
df.rename(columns={"STOCKCODE": "order_book_id", "TRADEDATE": "date"}, inplace=True)
df["report_quarter"] = df["RPT_YEAR"].map(str) + "q" + df["RPT_QUARTER"].map(str)
df.sort_values(["order_book_id", "date"], ascending=[True, False], inplace=True)
df.set_index(["order_book_id", "date"], inplace=True)
for item in items:
if item != "report_quarter":
df[item] = df[item].astype(np.float64)
return df[items]
# 只有一个查询日期时, 保持顺序
if len(trading_dates) > 1:
stocks = list(set([r[field_names[0]] for r in records]))
else:
stocks = [r[field_names[0]] for r in records]
stock_index = {s: i for i, s in enumerate(stocks)}
day_index = {d: i for i, d in enumerate(trading_dates)}
removed_items_size = 3 if report_quarter else 4
array = np.ndarray(
((len(records[0]) - removed_items_size), len(trading_dates), len(stocks)), dtype=object
)
array.fill(np.nan)
for r in records:
istock = stock_index[r[field_names[0]]]
iday = day_index[int8_to_date(r[field_names[1]])]
for i in range(4, len(r)):
array[(i - removed_items_size, iday, istock)] = np.float64(r[field_names[i]])
if report_quarter:
array[(0, iday, istock)] = (
np.nan
if None in (r[field_names[2]], r[field_names[3]])
else str(r[field_names[2]]) + "q" + str(r[field_names[3]])
)
trading_dates = pd.to_datetime(trading_dates)
warnings.warn("Panel is removed after pandas version 0.25.0."
"the default value of 'expect_df' will change to True in the future.")
return pd.Panel(data=array, items=items, major_axis=trading_dates, minor_axis=stocks)
def convert_history_tick_to_multi_df(data, dt_name, fields, convert_dt):
line_no = 0
dt_set = set()
obid_level = []
obid_slice_map = {}
for i, (obid, d) in enumerate(data):
dates = d.pop("date")
if len(dates) == 0:
continue
times = d.pop("time")
dts = d[dt_name] = [_convert_int_to_datetime(dt, tm) for dt, tm in zip(dates, times)]
dts_len = len(dts)
if not obid_level or obid_level[-1] != obid:
obid_level.append(obid)
obid_slice_map[(i, obid)] = slice(line_no, line_no + dts_len, None)
dt_set.update(dts)
line_no += dts_len
if line_no == 0:
return
daybars = {}
if set(fields) & RELATED_DABAR_FIELDS:
for obid in obid_level:
daybar = daybar_for_tick_price(obid)
if daybar is not None:
daybar['prev_close'] = daybar['close'].shift(1)
daybars[obid] = daybar
fields_ = list(set(fields) | {"last", "volume"})
else:
fields_ = fields
obid_idx_map = {o: i for i, o in enumerate(obid_level)}
obid_label = np.empty(line_no, dtype=object)
dt_label = np.empty(line_no, dtype=object)
arr = np.full((line_no, len(fields_)), np.nan)
r_map_fields = {f: i for i, f in enumerate(fields_)}
dt_arr_sorted = np.array(sorted(dt_set))
dt_level = convert_dt(dt_arr_sorted)
for i, (obid, d) in enumerate(data):
if dt_name not in d:
continue
dts = d[dt_name]
slice_ = obid_slice_map[(i, obid)]
for f, value in d.items():
if f == dt_name:
dt_label[slice_] = dt_arr_sorted.searchsorted(dts, side='left')
else:
arr[slice_, r_map_fields[f]] = value
obid_label[slice_] = obid_idx_map[obid]
trading_date = to_datetime(get_current_trading_date(int17_to_datetime(dts[-1])))
if "trading_date" in r_map_fields:
trading_date_int = date_to_int8(trading_date)
arr[slice_, r_map_fields["trading_date"]] = trading_date_int
daybar = daybars.get(obid)
if daybar is not None:
try:
last = daybar.loc[trading_date]
except KeyError:
continue
day_open = last["open"]
if "open" in r_map_fields:
arr[slice_, r_map_fields["open"]] = [day_open if v > 0 else 0.0 for v in d["volume"]]
if "prev_close" in r_map_fields:
arr[slice_, r_map_fields["prev_close"]] = last["prev_close"]
if instruments(obid).type in ("CS", "Future"):
if "limit_up" in r_map_fields:
arr[slice_, r_map_fields["limit_up"]] = last["limit_up"]
if "limit_down" in r_map_fields:
arr[slice_, r_map_fields["limit_down"]] = last["limit_down"]
if instruments(obid).type in ("Future", "Option"):
if "prev_settlement" in r_map_fields:
arr[slice_, r_map_fields["prev_settlement"]] = last["prev_settlement"]
if "change_rate" in r_map_fields:
arr[slice_, r_map_fields["change_rate"]] = arr[slice_, r_map_fields["last"]] / last[
"prev_settlement"] - 1
elif "change_rate" in r_map_fields:
arr[slice_, r_map_fields["change_rate"]] = arr[slice_, r_map_fields["last"]] / last["prev_close"] - 1
try:
func_is_singletz = getattr(pd._libs.lib, 'is_datetime_with_singletz_array')
setattr(pd._libs.lib, 'is_datetime_with_singletz_array', lambda *args: True)
except AttributeError:
func_is_singletz = None
multi_idx = pd.MultiIndex(
levels=[obid_level, dt_level],
labels=[obid_label, dt_label],
names=('order_book_id', dt_name)
)
df = pd.DataFrame(data=arr, index=multi_idx, columns=fields_)
if "trading_date" in r_map_fields:
df["trading_date"] = df["trading_date"].astype(int).apply(int8_to_datetime)
if func_is_singletz is not None:
setattr(pd._libs.lib, 'is_datetime_with_singletz_array', func_is_singletz)
return df[fields]
