def get_ksh_tickbar(order_book_id, start_date, end_date, fields, market):
order_book_id = ensure_order_book_id(order_book_id)
start_date, end_date = ensure_date_range(start_date, end_date, datetime.timedelta(days=3))
data = get_client().execute(
"get_ksh_tickbar", order_book_id, start_date, end_date, fields, market
)
today = today_int()
if data:
data = [(obid, {k: np.frombuffer(*v) for k, v in d.items()}) for obid, d in data]
df_list = []
for obid, d in data:
df = pd.DataFrame(d)
df_list.append(df)
df = pd.concat(df_list) # type: pd.DataFrame
df["datetime"] = int17_to_datetime_v(df["datetime"].values)
history_latest_date = date_to_int8(df.iloc[-1]["datetime"])
df.set_index("datetime", inplace=True)
else:
df = None
history_latest_date = date_to_int8(get_previous_trading_date(today, market=market))
if history_latest_date >= end_date or start_date > today or history_latest_date >= today:
return df
live_df = get_today_ksh_tick(order_book_id, today, fields, market=market)
if live_df is None:
return df
if df is None:
return live_df
return pd.concat([df, live_df])
def get_ksh_minbar(order_book_ids, start_date, end_date, fields, duration, market):
data = get_client().execute(
"get_ksh_minbar", order_book_ids, start_date, end_date, fields, duration, market
)
if data:
data = [(obid, {k: np.frombuffer(*v) for k, v in d.items()}) for obid, d in data]
df = convert_bar_to_multi_df(data, 'datetime', fields, int14_to_datetime_v)
else:
df = None
today = today_int()
if df is None:
history_latest_date = date_to_int8(get_previous_trading_date(today, market=market))
else:
history_latest_date = date_to_int8(df.index.get_level_values(1).max())
if history_latest_date >= end_date or start_date > today or history_latest_date >= today:
return df
live_df = get_today_ksh_minbar(order_book_ids, today, fields, duration, market)
if live_df is None:
return df
if df is None:
return live_df
df = pd.concat([df, live_df])
df.sort_index(inplace=True)
return df
def get_capital_flow_tickbar(order_book_id, start_date, end_date, fields,
market):
ensure_string(order_book_id, "order_book_id")
start_date, end_date = ensure_date_range(start_date, end_date,
datetime.timedelta(days=3))
history_permission_denied = realtime_permission_denied = False
try:
data = get_client().execute("get_capital_flow_tickbar",
order_book_id,
start_date,
end_date,
fields,
market=market)
except PermissionDenied:
data = []
history_permission_denied = True
today = today_int()
if data:
data = [(obid, {k: np.frombuffer(*v)
for k, v in d.items()}) for obid, d in data]
df_list = []
for obid, d in data:
df = pd.DataFrame(d)
df_list.append(df)
df = pd.concat(df_list) # type: pd.DataFrame
df["datetime"] = int17_to_datetime_v(df["datetime"].values)
history_latest_date = date_to_int8(df.iloc[-1]["datetime"])
df.set_index("datetime", inplace=True)
else:
df = None
history_latest_date = date_to_int8(
get_previous_trading_date(today, market=market))
if history_latest_date >= end_date or start_date > today or history_latest_date >= today:
return df
try:
live_df = get_today_capital_flow_tick(order_book_id,
today,
market=market)
except PermissionDenied:
live_df = None
realtime_permission_denied = True
except MarketNotSupportError:
live_df = None
if history_permission_denied and realtime_permission_denied:
raise PermissionDenied("get_capital_flow_tick")
if live_df is None:
return df
if df is None:
return live_df
return pd.concat([df, live_df])
def get_capital_flow_minbar(order_book_ids, start_date, end_date, fields,
duration, market):
history_permission_denied = realtime_permission_denied = False
try:
data = get_client().execute("get_capital_flow_minbar",
order_book_ids,
start_date,
end_date,
fields,
duration,
market=market)
except PermissionDenied:
history_permission_denied = True
data = []
if data:
data = [(obid, {k: np.frombuffer(*v)
for k, v in d.items()}) for obid, d in data]
df = convert_bar_to_multi_df(data, 'datetime', fields,
int14_to_datetime_v)
else:
df = None
today = today_int()
if df is None:
history_latest_date = date_to_int8(
get_previous_trading_date(today, market=market))
else:
history_latest_date = date_to_int8(df.index.get_level_values(1).max())
if history_latest_date >= end_date or start_date > today or history_latest_date >= today:
return df
try:
live_df = get_today_capital_flow_minbar(order_book_ids, today, fields,
duration, market)
except PermissionDenied:
live_df = None
realtime_permission_denied = True
if history_permission_denied and realtime_permission_denied:
raise PermissionDenied("get_capital_flow_minbar")
if live_df is None:
return df
if df is None:
return live_df
df = pd.concat([df, live_df])
df.sort_index(inplace=True)
return df
def get_open_auction_info(order_book_ids, start_date=None, end_date=None, market="cn"):
"""获取盘前集合竞价数据
:param order_book_ids: 股票代码
:param start_date: 起始日期，默认为今天
:param end_date: 截止日期，默认为今天
:param market: (Default value = "cn")
:returns: pd.DataFrame or None
"""
order_book_ids = ensure_order_book_ids(order_book_ids)
start_date, end_date = ensure_date_range(start_date, end_date, datetime.timedelta(days=0))
history_permission_denied = realtime_permission_denied = False
try:
# obid add prefix 'OA_'
data = get_client().execute("get_open_auction_info_daybar", ["OA_" + obid for obid in order_book_ids],
start_date, end_date, OA_FIELDS + ["datetime", "date"], market=market)
except PermissionDenied:
data = []
history_permission_denied = True
today = today_int()
prev_trading_date = date_to_int8(get_previous_trading_date(today, market=market))
if data:
data = [(obid[3:], {k: np.frombuffer(*v) for k, v in d.items()}) for obid, d in data]
df = convert_bar_to_multi_df(data, 'datetime', OA_FIELDS + ["date"], int17_to_datetime_v, default=0.0)
if df is None:
history_latest_date = prev_trading_date
else:
history_latest_date = df["date"].max()
del df["date"]
else:
df = None
history_latest_date = prev_trading_date
if history_latest_date >= end_date or start_date > today or history_latest_date >= today or end_date < today:
return df
try:
live_df = get_today_open_auction(order_book_ids, today, market=market)
except PermissionDenied:
live_df = None
realtime_permission_denied = True
if history_permission_denied and realtime_permission_denied:
raise PermissionDenied("get_open_auction_info")
if live_df is None:
return df
if df is None:
return live_df
df = pd.concat([df, live_df])
df.sort_index(inplace=True)
return df
def get_tick_price_multi_df(order_book_ids, start_date, end_date, fields, market):
ins_list = ensure_instruments(order_book_ids)
order_book_ids = [ins.order_book_id for ins in ins_list]
types = {ins.type for ins in ins_list}
start_date, end_date = ensure_date_range(start_date, end_date, datetime.timedelta(days=3))
if "Future" in types or "Option" in types:
base_fields = FUTURE_TICK_FIELDS
base_columns = FUTURE_TICK_COLUMNS
else:
base_fields = EQUITIES_TICK_FIELDS
base_columns = EQUITIES_TICK_COLUMNS
if fields:
fields = ensure_list_of_string(fields, "fields")
check_items_in_container(fields, base_fields, "fields")
columns = [f for f in base_columns if f in fields]
else:
fields = base_fields
columns = base_columns
gtw_fields = set(fields) | {"date", "time"}
if set(fields) & RELATED_DABAR_FIELDS:
gtw_fields.update({"volume", "last"})
history_df = get_history_tick(order_book_ids, start_date, end_date, list(gtw_fields), columns, market)
history_latest_date = 0 if history_df is None else date_to_int8(get_current_trading_date(
history_df.index.get_level_values(1).max()))
today = today_int()
next_trading_date = date_to_int8(get_next_trading_date(today, market=market))
if history_latest_date >= end_date or start_date > next_trading_date or end_date < today:
return history_df
if end_date >= next_trading_date and (start_date > today or history_latest_date >= today):
live_date = next_trading_date
else:
live_date = today
if history_latest_date >= live_date:
return history_df
live_dfs = []
for ins in ins_list:
try:
live_df = get_ticks(ins.order_book_id, start_date=live_date, end_date=live_date, expect_df=True,
market=market)
except (PermissionDenied, MarketNotSupportError):
pass
else:
if live_df is None:
continue
if "trading_date" not in live_df.columns:
live_df["trading_date"] = int8_to_datetime(live_date)
else:
live_df["trading_date"] = live_df["trading_date"].apply(to_datetime)
if ins.type in ("Future", "Option"):
live_df["change_rate"] = live_df["last"] / live_df["prev_settlement"] - 1
else:
live_df["change_rate"] = live_df["last"] / live_df["prev_close"] - 1
live_df = live_df.reindex(columns=columns)
live_dfs.append(live_df)
if not live_dfs:
return history_df
if history_df is None:
return pd.concat(live_dfs)
return pd.concat([history_df] + live_dfs)
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]
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', 返回前复权数据。设置为'none'将返回原始数据,
'post'返回后复权数据,
'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
"""
if frequency == "tick":
return get_tick_price(order_book_ids, start_date, end_date, fields, expect_df, market)
elif frequency.endswith(("d", "m")):
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")
else:
raise ValueError("frequency should be str like 1d, 1m, 5m or tick")
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"]
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
)
if expect_df:
from rqdatac.services.detail.get_price_df import get_price_df
return 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 frequency == "d":
fields, has_dominant_id = _ensure_fields(fields, DAYBAR_FIELDS, stocks, funds, futures, spots, options, convertibles, indexes, repos)
pf = get_daybar(order_book_ids, start_date, end_date, fields, duration, market)
if pf is None:
return
else:
fields, has_dominant_id = _ensure_fields(fields, MINBAR_FIELDS, stocks, funds, futures, spots, options, convertibles, indexes, repos)
history_permission_denied, today_permission_denied = False, False
try:
pf = get_minbar(order_book_ids, start_date, end_date, fields, duration, market)
except (PermissionDenied, MarketNotSupportError):
pf = None
history_permission_denied = True
history_latest_day = 0 if pf is None else date_to_int8(pf.iloc[-1].index[-1])
if history_latest_day < end_date and end_date >= today_int():
try:
today_pf = get_today_minbar(order_book_ids, fields, duration, market)
except (PermissionDenied, MarketNotSupportError):
today_pf = None
today_permission_denied = True
if today_pf is None:
today_pf_latest_day = 0
else:
today_pf_latest_day = date_to_int8(get_current_trading_date(today_pf.iloc[-1].index[-1]))
if today_pf_latest_day > history_latest_day and today_pf_latest_day >= start_date:
if history_latest_day == 0:
pf = today_pf
else:
pf = pd.concat([pf, today_pf], axis=1)
if pf is None:
if history_permission_denied and today_permission_denied:
raise PermissionDenied("Not permit to get minbar price ")
elif history_permission_denied:
warnings.warn("Not permit to get history minbar price")
elif today_permission_denied:
warnings.warn("Not permit to get realtime minbar price")
return
result = _adjust_pf(
pf,
order_book_ids,
stocks,
funds,
futures888,
start_date,
end_date,
frequency,
fields,
has_dominant_id,
adjust_type,
skip_suspended,
market,
)
return result
def 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 frequency == "d":
fields, has_dominant_id = _ensure_fields(fields, DAYBAR_FIELDS, stocks, funds, futures, spots, options, convertibles, indexes, repos)
pf, obid_slice_map = get_daybar(order_book_ids, start_date, end_date, fields, duration, market)
if pf is None:
return
else:
fields, has_dominant_id = _ensure_fields(fields, MINBAR_FIELDS, stocks, funds, futures, spots, options, convertibles, indexes, repos)
history_permission_denied, today_permission_denied = False, False
try:
pf, obid_slice_map = get_minbar(order_book_ids, start_date, end_date, fields, duration, market)
except (PermissionDenied, MarketNotSupportError):
pf = obid_slice_map = None
history_permission_denied = True
history_latest_day = 0 if pf is None else date_to_int8(pf.index.get_level_values(1).max())
if history_latest_day < end_date and end_date >= today_int():
try:
today_pf, obid_slice_map = get_today_minbar(order_book_ids, fields, duration, market)
except (PermissionDenied, MarketNotSupportError):
today_pf = None
today_permission_denied = True
if today_pf is None:
today_pf_latest_day = 0
else:
today_pf_latest_day = date_to_int8(get_current_trading_date(today_pf.index.get_level_values(1).max()))
if today_pf_latest_day > history_latest_day and today_pf_latest_day >= start_date:
if history_latest_day == 0:
pf = today_pf
else:
pf = pd.concat([pf, today_pf])
line_no, obid_slice_map = 0, {}
obids, counts = np.unique(pf.index.get_level_values(0), return_counts=True)
for obid, ct in zip(obids, counts):
obid_slice_map[obid] = slice(line_no, line_no + ct, None)
line_no += ct
if pf is None:
if history_permission_denied and today_permission_denied:
raise PermissionDenied("Not permit to get minbar price ")
elif history_permission_denied:
warnings.warn("Not permit to get history minbar price")
elif today_permission_denied:
warnings.warn("Not permit to get realtime minbar price")
return
result = _adjust_pf(
pf,
order_book_ids,
stocks,
funds,
futures888,
start_date,
end_date,
has_dominant_id,
adjust_type,
skip_suspended,
obid_slice_map,
market,
)
return result
