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# Licensed under the MIT License.
"""
TODO:
- A more well-designed PIT database is required.
- separated insert, delete, update, query operations are required.
"""
import shutil
import struct
from pathlib import Path
from typing import Iterable
from functools import partial
from concurrent.futures import ProcessPoolExecutor
import fire
import pandas as pd
from tqdm import tqdm
from loguru import logger
from qlib.utils import fname_to_code, get_period_offset
from qlib.config import C
class DumpPitData:
PIT_DIR_NAME = "financial"
PIT_CSV_SEP = ","
DATA_FILE_SUFFIX = ".data"
INDEX_FILE_SUFFIX = ".index"
INTERVAL_quarterly = "quarterly"
INTERVAL_annual = "annual"
PERIOD_DTYPE = C.pit_record_type["period"]
INDEX_DTYPE = C.pit_record_type["index"]
DATA_DTYPE = "".join(
[
C.pit_record_type["date"],
C.pit_record_type["period"],
C.pit_record_type["value"],
C.pit_record_type["index"],
]
)
NA_INDEX = C.pit_record_nan["index"]
INDEX_DTYPE_SIZE = struct.calcsize(INDEX_DTYPE)
PERIOD_DTYPE_SIZE = struct.calcsize(PERIOD_DTYPE)
DATA_DTYPE_SIZE = struct.calcsize(DATA_DTYPE)
UPDATE_MODE = "update"
ALL_MODE = "all"
def __init__(
self,
csv_path: str,
qlib_dir: str,
backup_dir: str = None,
freq: str = "quarterly",
max_workers: int = 16,
date_column_name: str = "date",
period_column_name: str = "period",
value_column_name: str = "value",
field_column_name: str = "field",
file_suffix: str = ".csv",
exclude_fields: str = "",
include_fields: str = "",
limit_nums: int = None,
):
"""
Parameters
----------
csv_path: str
stock data path or directory
qlib_dir: str
qlib(dump) data director
backup_dir: str, default None
if backup_dir is not None, backup qlib_dir to backup_dir
freq: str, default "quarterly"
data frequency
max_workers: int, default None
number of threads
date_column_name: str, default "date"
the name of the date field in the csv
file_suffix: str, default ".csv"
file suffix
include_fields: tuple
dump fields
exclude_fields: tuple
fields not dumped
limit_nums: int
Use when debugging, default None
"""
csv_path = Path(csv_path).expanduser()
if isinstance(exclude_fields, str):
exclude_fields = exclude_fields.split(",")
if isinstance(include_fields, str):
include_fields = include_fields.split(",")
self._exclude_fields = tuple(filter(lambda x: len(x) > 0, map(str.strip, exclude_fields)))
self._include_fields = tuple(filter(lambda x: len(x) > 0, map(str.strip, include_fields)))
self.file_suffix = file_suffix
self.csv_files = sorted(csv_path.glob(f"*{self.file_suffix}") if csv_path.is_dir() else [csv_path])
if limit_nums is not None:
self.csv_files = self.csv_files[: int(limit_nums)]
self.qlib_dir = Path(qlib_dir).expanduser()
self.backup_dir = backup_dir if backup_dir is None else Path(backup_dir).expanduser()
if backup_dir is not None:
self._backup_qlib_dir(Path(backup_dir).expanduser())
self.works = max_workers
self.date_column_name = date_column_name
self.period_column_name = period_column_name
self.value_column_name = value_column_name
self.field_column_name = field_column_name
self._mode = self.ALL_MODE
def _backup_qlib_dir(self, target_dir: Path):
shutil.copytree(str(self.qlib_dir.resolve()), str(target_dir.resolve()))
def get_source_data(self, file_path: Path) -> pd.DataFrame:
df = pd.read_csv(str(file_path.resolve()), low_memory=False)
df[self.value_column_name] = df[self.value_column_name].astype("float32")
df[self.date_column_name] = df[self.date_column_name].str.replace("-", "").astype("int32")
# df.drop_duplicates([self.date_field_name], inplace=True)
return df
def get_symbol_from_file(self, file_path: Path) -> str:
return fname_to_code(file_path.name[: -len(self.file_suffix)].strip().lower())
def get_dump_fields(self, df: Iterable[str]) -> Iterable[str]:
return (
set(self._include_fields)
if self._include_fields
else (
set(df[self.field_column_name]) - set(self._exclude_fields)
if self._exclude_fields
else set(df[self.field_column_name])
)
)
def get_filenames(self, symbol, field, interval):
dir_name = self.qlib_dir.joinpath(self.PIT_DIR_NAME, symbol)
dir_name.mkdir(parents=True, exist_ok=True)
return (
dir_name.joinpath(f"{field}_{interval[0]}{self.DATA_FILE_SUFFIX}".lower()),
dir_name.joinpath(f"{field}_{interval[0]}{self.INDEX_FILE_SUFFIX}".lower()),
)
def _dump_pit(
self,
file_path: str,
interval: str = "quarterly",
overwrite: bool = False,
):
"""
dump data as the following format:
`/path/to/<field>.data`
[date, period, value, _next]
[date, period, value, _next]
[...]
`/path/to/<field>.index`
[first_year, index, index, ...]
`<field.data>` contains the data as the point-in-time (PIT) order: `value` of `period`
is published at `date`, and its successive revised value can be found at `_next` (linked list).
`<field>.index` contains the index of value for each period (quarter or year). To save
disk space, we only store the `first_year` as its followings periods can be easily infered.
Parameters
----------
symbol: str
stock symbol
interval: str
data interval
overwrite: bool
whether overwrite existing data or update only
"""
symbol = self.get_symbol_from_file(file_path)
df = self.get_source_data(file_path)
if df.empty:
logger.warning(f"{symbol} file is empty")
return
for field in self.get_dump_fields(df):
df_sub = df.query(f'{self.field_column_name}=="{field}"').sort_values(self.date_column_name)
if df_sub.empty:
logger.warning(f"field {field} of {symbol} is empty")
continue
data_file, index_file = self.get_filenames(symbol, field, interval)
## calculate first & last period
start_year = df_sub[self.period_column_name].min()
end_year = df_sub[self.period_column_name].max()
if interval == self.INTERVAL_quarterly:
start_year //= 100
end_year //= 100
# adjust `first_year` if existing data found
if not overwrite and index_file.exists():
with open(index_file, "rb") as fi:
(first_year,) = struct.unpack(self.PERIOD_DTYPE, fi.read(self.PERIOD_DTYPE_SIZE))
n_years = len(fi.read()) // self.INDEX_DTYPE_SIZE
if interval == self.INTERVAL_quarterly:
n_years //= 4
start_year = first_year + n_years
else:
with open(index_file, "wb") as f:
f.write(struct.pack(self.PERIOD_DTYPE, start_year))
first_year = start_year
# if data already exists, continue to the next field
if start_year > end_year:
logger.warning(f"{symbol}-{field} data already exists, continue to the next field")
continue
# dump index filled with NA
with open(index_file, "ab") as fi:
for year in range(start_year, end_year + 1):
if interval == self.INTERVAL_quarterly:
fi.write(struct.pack(self.INDEX_DTYPE * 4, *[self.NA_INDEX] * 4))
else:
fi.write(struct.pack(self.INDEX_DTYPE, self.NA_INDEX))
# if data already exists, remove overlapped data
if not overwrite and data_file.exists():
with open(data_file, "rb") as fd:
fd.seek(-self.DATA_DTYPE_SIZE, 2)
last_date, _, _, _ = struct.unpack(self.DATA_DTYPE, fd.read())
df_sub = df_sub.query(f"{self.date_column_name}>{last_date}")
# otherwise,
# 1) truncate existing file or create a new file with `wb+` if overwrite,
# 2) or append existing file or create a new file with `ab+` if not overwrite
else:
with open(data_file, "wb+" if overwrite else "ab+"):
pass
with open(data_file, "rb+") as fd, open(index_file, "rb+") as fi:
# update index if needed
for i, row in df_sub.iterrows():
# get index
offset = get_period_offset(first_year, row.period, interval == self.INTERVAL_quarterly)
fi.seek(self.PERIOD_DTYPE_SIZE + self.INDEX_DTYPE_SIZE * offset)
(cur_index,) = struct.unpack(self.INDEX_DTYPE, fi.read(self.INDEX_DTYPE_SIZE))
# Case I: new data => update `_next` with current index
if cur_index == self.NA_INDEX:
fi.seek(self.PERIOD_DTYPE_SIZE + self.INDEX_DTYPE_SIZE * offset)
fi.write(struct.pack(self.INDEX_DTYPE, fd.tell()))
# Case II: previous data exists => find and update the last `_next`
else:
_cur_fd = fd.tell()
prev_index = self.NA_INDEX
while cur_index != self.NA_INDEX: # NOTE: first iter always != NA_INDEX
fd.seek(cur_index + self.DATA_DTYPE_SIZE - self.INDEX_DTYPE_SIZE)
prev_index = cur_index
(cur_index,) = struct.unpack(self.INDEX_DTYPE, fd.read(self.INDEX_DTYPE_SIZE))
fd.seek(prev_index + self.DATA_DTYPE_SIZE - self.INDEX_DTYPE_SIZE)
fd.write(struct.pack(self.INDEX_DTYPE, _cur_fd)) # NOTE: add _next pointer
fd.seek(_cur_fd)
# dump data
fd.write(struct.pack(self.DATA_DTYPE, row.date, row.period, row.value, self.NA_INDEX))
def dump(self, interval="quarterly", overwrite=False):
logger.info("start dump pit data......")
_dump_func = partial(self._dump_pit, interval=interval, overwrite=overwrite)
with tqdm(total=len(self.csv_files)) as p_bar:
with ProcessPoolExecutor(max_workers=self.works) as executor:
for _ in executor.map(_dump_func, self.csv_files):
p_bar.update()
def __call__(self, *args, **kwargs):
self.dump()
if __name__ == "__main__":
fire.Fire(DumpPitData)
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