def get_stock_connect(order_book_ids, start_date=None, end_date=None, fields=None, expect_df=False):
"""获取"陆股通"的持股、持股比例
:param order_book_ids: 股票列表
:param start_date: 开始日期: 如'2017-03-17' (Default value = None)
:param end_date: 结束日期: 如'2018-03-16' (Default value = None)
:param fields: 默认为所有字段，可输入shares_holding或者holding_ratio (Default value = None)
:param expect_df: 返回 MultiIndex DataFrame (Default value = False)
:returns: 返回pandas.DataFrame or pandas.Panel
"""
if order_book_ids not in ("shanghai_connect", "shenzhen_connect", "all_connect"):
order_book_ids = ensure_order_book_ids(order_book_ids, type="CS")
start_date, end_date = ensure_date_range(start_date, end_date)
if fields is not None:
fields = ensure_list_of_string(fields)
for f in fields:
if f not in ("shares_holding", "holding_ratio"):
raise ValueError("invalid field: {}".format(f))
else:
fields = ["shares_holding", "holding_ratio"]
data = get_client().execute("get_stock_connect", order_book_ids, start_date, end_date, fields)
if not data:
return None
df = pd.DataFrame(data, columns=["trading_date", "order_book_id"] + fields)
if not expect_df and not is_panel_removed:
df = df.set_index(["trading_date", "order_book_id"])
df = df.to_panel()
df.major_axis.name = None
df.minor_axis.name = None
if len(order_book_ids) == 1:
df = df.minor_xs(order_book_ids[0])
if len(fields) == 1:
df = df[fields[0]]
if len(order_book_ids) != 1 and len(fields) != 1:
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 df
else:
df.sort_values(["order_book_id", "trading_date"], inplace=True)
df.set_index(["order_book_id", "trading_date"], inplace=True)
if expect_df:
return df
if len(order_book_ids) != 1 and len(fields) != 1:
raise_for_no_panel()
if len(order_book_ids) == 1:
df.reset_index(level=0, drop=True, inplace=True)
if len(fields) == 1:
df = df[fields[0]]
return df
else:
df = df.unstack(0)[fields[0]]
df.index.name = None
df.columns.name = None
return df
def parse_results(records, quarters, expect_df):
if not records:
return None
removed_items_size = 3
base_fields = ["stockcode", "rpt_year", "rpt_quarter"]
field_names = base_fields + list(set(records[0].keys()) - set(base_fields))
items = field_names[removed_items_size:]
if not expect_df and not is_panel_removed:
# 只有一个查询日期时, 保持顺序
stocks = list(set(r[field_names[0]] for r in records))
stock_index = {s: i for i, s in enumerate(stocks)}
quarter_index = {q: i for i, q in enumerate(quarters)}
array = np.ndarray(
(len(records[0]) - removed_items_size, len(quarters), len(stocks)), dtype=object
)
array.fill(np.nan)
for r in records:
istock = stock_index[r[field_names[0]]]
iquarter = quarter_index[(r[field_names[1]], r[field_names[2]])]
for i in range(removed_items_size, len(r)):
if field_names[i] == "announce_date":
announce_date = r[field_names[i]]
array[i - removed_items_size, iquarter, istock] = (
np.nan if announce_date is None else announce_date
)
else:
array[i - removed_items_size, iquarter, istock] = np.float64(r[field_names[i]])
s_quarters = ["%dq%d" % (year, quarter) for year, quarter in quarters]
results = pd.Panel(data=array, items=items, major_axis=s_quarters, minor_axis=stocks)
item_size, major_size, minor_size = results.shape
if minor_size == 1:
ret = results.minor_xs(*stocks)
return ret[field_names[removed_items_size]] if item_size == 1 else ret
elif item_size == 1:
return results[field_names[removed_items_size]]
elif major_size == 1:
return results.major_xs(*s_quarters)
else:
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 results
else:
df = pd.DataFrame(records)
df.rename(columns={"stockcode": "order_book_id"}, inplace=True)
df["quarter"] = df["rpt_year"].map(str) + "q" + df["rpt_quarter"].map(str)
df.sort_values(["order_book_id", "quarter"], ascending=[True, False], inplace=True)
df.set_index(["order_book_id", "quarter"], inplace=True)
for item in items:
if item != "announce_date":
df[item] = df[item].astype(np.float64)
df = df[items]
if expect_df:
return df
else:
if len(df.index.get_level_values(0).unique()) == 1:
df.reset_index(level=0, inplace=True, drop=True)
df.index.name = None
df.sort_index(ascending=False, inplace=True)
if len(df.columns) == 1:
df = df[df.columns[0]]
return df
elif len(df.columns) == 1:
field = df.columns[0]
df = df.unstack(0)[field]
df.index.name = None
df.columns.name = None
df.sort_index(ascending=False, inplace=True)
return df
elif len(df.index.get_level_values(1).unique()) == 1:
df.reset_index(level=1, inplace=True, drop=True)
df.index.name = None
return df
raise_for_no_panel()
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 get_price(
order_book_ids,
start_date=None,
end_date=None,
frequency="1d",
fields=None,
adjust_type="pre",
skip_suspended=False,
expect_df=False,
market="cn",
**kwargs
):
"""获取证券的历史数据
:param order_book_ids: 股票列表
:param market: 地区代码, 如 'cn' (Default value = "cn")
: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 frequency: 可选参数, 默认为日线。日线使用 '1d', 分钟线 '1m' (Default value = "1d")
:param fields: 可选参数。默认为所有字段。 (Default value = None)
:param adjust_type: 可选参数,默认为‘pre', 返回开盘价，收盘价，最高价，最低价依据get_ex_factor 复权因子（包含分红，拆分），volume依据get_split 复权因子（仅涵盖拆分）计算的前复权数据
'none'将返回原始数据
'post'返回开盘价，收盘价，最高价，最低价依据get_ex_factor 复权因子（包含分红，拆分），volume依据get_split 复权因子（仅涵盖拆分）计算的后复权数据
'pre_volume'返回开盘价，收盘价，最高价，最低价,成交量依据get_ex_factor 复权因子（包含分红，拆分）计算的前复权数据
'post_volume'返回开盘价，收盘价，最高价，最低价,成交量依据get_ex_factor 复权因子（包含分红，拆分）计算的后复权数据
'internal'返回只包含拆分的前复权数据。 (Default value = "pre")
:param skip_suspended: 可选参数，默认为False；当设置为True时，返回的数据会过滤掉停牌期间，
此时order_book_ids只能设置为一只股票 (Default value = False)
:param expect_df: 返回 MultiIndex DataFrame (Default value = False)
:returns: 如果仅传入一只股票, 返回一个 pandas.DataFrame
如果传入多只股票, 则返回一个 pandas.Panel
"""
# tick数据
if frequency == "tick":
return get_tick_price(order_book_ids, start_date, end_date, fields, expect_df, market)
elif frequency.endswith(("d", "m", "w")):
duration = int(frequency[:-1])
frequency = frequency[-1]
assert 1 <= duration <= 240, "frequency should in range [1, 240]"
if market == "hk" and frequency == "m" and duration not in (1, 5, 15, 30, 60):
raise ValueError("frequency should be str like 1m, 5m, 15m 30m,or 60m")
elif frequency == 'w' and duration not in (1,):
raise ValueError("Weekly frequency should be str '1w'")
else:
raise ValueError("frequency should be str like 1d, 1m, 5m or tick")
# 验证adjust_type
if "adjusted" in kwargs:
adjusted = kwargs.pop("adjusted")
adjust_type = "pre" if adjusted else "none"
if kwargs:
raise ValueError('unknown kwargs: {}'.format(kwargs))
valid_adjust = ["pre", "post", "none", "pre_volume", "post_volume"]
ensure_string(adjust_type, "adjust_type")
check_items_in_container(adjust_type, valid_adjust, "adjust_type")
order_book_ids = ensure_list_of_string(order_book_ids, "order_book_ids")
if skip_suspended and len(order_book_ids) > 1:
raise ValueError("only accept one order_book_id or symbol if skip_suspended is True")
assert isinstance(skip_suspended, bool), "'skip_suspended' should be a bool"
assert isinstance(expect_df, bool), "'expect_df' should be a bool"
order_book_ids, stocks, funds, indexes, futures, futures888, spots, options, convertibles, repos = classify_order_book_ids(
order_book_ids, market
)
if not order_book_ids:
warnings.warn("no valid instrument")
return
start_date, end_date = _ensure_date(
start_date, end_date, stocks, funds, indexes, futures, spots, options, convertibles, repos
)
from rqdatac.services.detail.get_price_df import get_price_df, get_week_df
if frequency != 'w':
df = get_price_df(
order_book_ids, start_date, end_date, frequency, duration, fields, adjust_type, skip_suspended,
stocks, funds, indexes, futures, futures888, spots, options, convertibles, repos, market
)
if df is None or expect_df:
return df
# 单个合约
if len(df.index.get_level_values(0).unique()) == 1:
df.reset_index(level=0, inplace=True, drop=True)
# df.index.name = None
if len(df.columns) == 1:
df = df[df.columns[0]]
return df
# 单个字段
elif len(df.columns) == 1:
field = df.columns[0]
df = df.unstack(0)[field]
# df.index.name = None
df.columns.name = None
return df
raise_for_no_panel(False)
# 交换index的顺序，以制作panel
return df.swaplevel().to_panel()
else:
if not expect_df:
raise ValueError(
"Weekly frequency can only return a DataFrame object, set 'expect_df' to True to resolve this")
if skip_suspended:
raise ValueError(
"Weekly frequency does not support skipping suspended trading days, set 'skip_suspended' to False to resolve this")
start_date, end_date = _weekly_start_end_date_handler(start_date, end_date)
if start_date > end_date:
# 如果*当周没有结束*
# 或者start date 和 end date 不能涵盖当周所有的交易日，查询该周的数据时返回为空。
return None
return get_week_df(order_book_ids, start_date, end_date, fields, adjust_type, market,
*(stocks, funds, indexes, futures, futures888, spots, options, convertibles, repos))
def get_turnover_rate(order_book_ids,
start_date=None,
end_date=None,
fields=None,
expect_df=False,
market="cn"):
"""获取股票换手率数据
:param order_book_ids: 股票代码或股票代码列表
:param start_date: 开始时间
:param end_date: 结束时间；在 start_date 和 end_date 都不指定的情况下，默认为最近3个月
:param fields: str或list类型. 默认为None, 返回所有fields.
field 包括： 'today', 'week', 'month', 'year', 'current_year'
(Default value = None)
:param expect_df: 返回 MultiIndex DataFrame (Default value = False)
:param market: 地区代码, 如: 'cn' (Default value = "cn")
:returns: 如果order_book_ids或fields为单个值 返回pandas.DataFrame, 否则返回pandas.Panel
"""
order_book_ids = ensure_order_book_ids(order_book_ids)
start_date, end_date = ensure_date_range(start_date, end_date)
if fields is not None:
fields, mapped_fields = _get_maped_fields(fields)
else:
fields, mapped_fields = list(TURNOVER_FIELDS_MAP.keys()), list(
TURNOVER_FIELDS_MAP.values())
df = get_client().execute("get_turnover_rate",
order_book_ids,
start_date,
end_date,
mapped_fields,
market=market)
df = pd.DataFrame(df,
columns=["tradedate", "order_book_id"] + mapped_fields)
df.rename(columns={v: k
for k, v in TURNOVER_FIELDS_MAP.items()},
inplace=True)
if not expect_df and not is_panel_removed:
df.set_index(["tradedate", "order_book_id"], inplace=True)
df.sort_index(inplace=True)
df = df.to_panel()
df = pf_fill_nan(df, order_book_ids)
if len(order_book_ids) == 1:
df = df.minor_xs(*order_book_ids)
if fields and len(fields) == 1:
return df[fields[0]]
return df
if fields and len(fields) == 1:
return df[fields[0]]
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 df
else:
df.sort_values(["order_book_id", "tradedate"], inplace=True)
df.set_index(["order_book_id", "tradedate"], inplace=True)
if expect_df:
return df
if len(order_book_ids) != 1 and len(fields) != 1:
raise_for_no_panel()
if len(order_book_ids) == 1:
df.reset_index(level=0, drop=True, inplace=True)
if len(fields) == 1:
df = df[fields[0]]
return df
else:
df = df.unstack(0)[fields[0]]
df.index.name = None
df.columns.name = None
return df
def get_shares(order_book_ids, start_date=None, end_date=None, fields=None, expect_df=False, market="cn"):
"""获取流通股本信息
: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 fields: 如'total', 'circulation_a' (Default value = None)
:param expect_df: 返回 MultiIndex DataFrame (Default value = False)
:param market: 地区代码，如'cn' (Default value = "cn")
:returns: 返回一个DataFrame
"""
order_book_ids = ensure_order_book_ids(order_book_ids, market=market)
start_date, end_date = ensure_date_range(start_date, end_date)
if fields:
fields = ensure_list_of_string(fields, "fields")
if 'management_circulation' in fields:
fields.remove('management_circulation')
if fields:
warnings.warn("management_circulation is removed")
else:
raise ValueError("management_circulation is removed")
check_items_in_container(fields, set(share_fields), "fields")
fields = [share_fields[i] for i in fields]
else:
fields = list(share_fields.values())
all_shares = get_client().execute("get_shares", order_book_ids, fields, market=market)
if not all_shares:
return
dates = get_trading_dates_in_type(start_date, end_date, expect_type="datetime", market=market)
df = pd.DataFrame(all_shares)
unique = set(df.order_book_id)
for order_book_id in order_book_ids:
if order_book_id not in unique:
df = df.append(
{"order_book_id": order_book_id, "date": df.date.iloc[-1]}, ignore_index=True
)
df.set_index(["date", "order_book_id"], inplace=True)
df.sort_index(inplace=True)
df = df.unstack(level=1)
index = df.index.union(dates)
df = df.reindex(index)
df = df.fillna(method="ffill")
df = df.loc[list(dates)]
df = df.dropna(how="all")
df = df[fields]
if not is_panel_removed and not expect_df:
pl = df.stack(1).to_panel()
pl.items = [anti_fields[i] for i in pl.items]
if len(order_book_ids) == 1:
pl = pl.minor_xs(order_book_ids[0])
if len(fields) == 1:
pl = pl[anti_fields[fields[0]]]
if len(order_book_ids) != 1 and len(fields) != 1:
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 pl
else:
df = df.stack(1)
df.index.set_names(["date", "order_book_id"], inplace=True)
df = df.reorder_levels(["order_book_id", "date"]).sort_index()
df = df.rename(columns=anti_fields)
if expect_df:
return df
if len(order_book_ids) != 1 and len(fields) != 1:
raise_for_no_panel()
if len(order_book_ids) == 1:
df.reset_index(level=0, drop=True, inplace=True)
if len(fields) == 1:
df = df[anti_fields[fields[0]]]
return df
else:
df = df.unstack(0)[anti_fields[fields[0]]]
df.index.name = None
df.columns.name = None
return df
def get_securities_margin(
order_book_ids, start_date=None, end_date=None, fields=None, expect_df=False, market="cn"
):
"""获取股票融资融券数据
:param order_book_ids: 股票代码或代码列表
:param start_date: 开始时间，支持 str, date, datetime, pandasTimestamp
默认为 end_date 之前一个月 (Default value = None)
:param end_date: 结束时间 默认为当前日期前一天 (Default value = None)
:param fields: str 或 list 类型. 默认为 None, 返回所有字段。可选字段包括：
today, week, month, three_month, six_month, year, current_year, total
(Default value = None)
:param expect_df: 返回 MultiIndex DataFrame (Default value = False)
:param market: 地区代码, 如: 'cn' (Default value = "cn")
:returns: 如果传入多个股票代码，且 fields 为多个或者 None，返回 pandas.Panel
如果传入一只股票或者 fields 为单个字段，则返回 pandas.DataFrame
如果传入的股票代码和字段数都是1，则返回 pandas.Series
"""
order_book_ids = ensure_list_of_string(order_book_ids, "order_book_ids")
all_list = []
for order_book_id in order_book_ids:
if order_book_id.upper() in MARGIN_SUMMARY_MAP:
all_list.append(MARGIN_SUMMARY_MAP[order_book_id.upper()])
else:
inst = instruments(order_book_id, market)
if inst.type in ["CS", "ETF", "LOF"]:
all_list.append(inst.order_book_id)
else:
warnings.warn("{} is not stock, ETF, or LOF.".format(order_book_id))
order_book_ids = all_list
if not order_book_ids:
raise ValueError("no valid securities in {}".format(order_book_ids))
if fields is None:
fields = list(MARGIN_FIELDS)
else:
fields = ensure_list_of_string(fields, "fields")
check_items_in_container(fields, MARGIN_FIELDS, "fields")
fields = ensure_order(fields, MARGIN_FIELDS)
start_date, end_date = ensure_date_range(start_date, end_date)
if end_date > ensure_date_or_today_int(None):
end_date = ensure_date_or_today_int(get_previous_trading_date(datetime.date.today()))
trading_dates = pd.to_datetime(get_trading_dates(start_date, end_date, market=market))
data = get_client().execute(
"get_securities_margin", order_book_ids, start_date, end_date, market=market
)
if not data:
return
if not expect_df and not is_panel_removed:
pl = pd.Panel(items=fields, major_axis=trading_dates, minor_axis=order_book_ids)
for r in data:
for field in fields:
value = r.get(field)
pl.at[field, r["date"], r["order_book_id"]] = value
if len(order_book_ids) == 1:
pl = pl.minor_xs(order_book_ids[0])
if len(fields) == 1:
pl = pl[fields[0]]
if len(order_book_ids) != 1 and len(fields) != 1:
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 pl
else:
df = pd.DataFrame(data)
df.sort_values(["order_book_id", "date"], inplace=True)
df.set_index(["order_book_id", "date"], inplace=True)
df = df.reindex(columns=fields)
if expect_df:
return df
if len(order_book_ids) != 1 and len(fields) != 1:
raise_for_no_panel()
if len(order_book_ids) == 1:
df.reset_index(level=0, drop=True, inplace=True)
if len(fields) == 1:
df = df[fields[0]]
return df
else:
df = df.unstack(0)[fields[0]]
df.index.name = None
df.columns.name = None
return df