qlib/qlib/data/storage/file_storage.py

# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.

import struct
from pathlib import Path
from typing import Iterator, Iterable, Union, Dict, Mapping, Tuple

import numpy as np
import pandas as pd

from qlib.data.storage import CalendarStorage, InstrumentStorage, FeatureStorage, CalVT, InstKT, InstVT


class FileCalendarStorage(CalendarStorage):
    def __init__(self, freq: str, future: bool, uri: str):
        super(FileCalendarStorage, self).__init__(freq, future, uri)
        _file_name = f"{freq}_future.txt" if future else f"{freq}.txt"
        self.uri = Path(self.uri).expanduser().joinpath(_file_name.lower())

    def _read_calendar(self, skip_rows: int = 0, n_rows: int = None) -> np.ndarray:
        if not self.uri.exists():
            self._write_calendar(values=[])
        with self.uri.open("rb") as fp:
            return np.loadtxt(fp, str, skiprows=skip_rows, max_rows=n_rows, encoding="utf-8")

    def _write_calendar(self, values: Iterable[CalVT], mode: str = "wb"):
        with self.uri.open(mode=mode) as fp:
            np.savetxt(fp, values, fmt="%s", encoding="utf-8")

    @property
    def data(self) -> Iterable[CalVT]:
        return self._read_calendar()

    def extend(self, values: Iterable[CalVT]) -> None:
        self._write_calendar(values, mode="ab")

    def clear(self) -> None:
        self._write_calendar(values=[])

    def index(self, value: CalVT) -> int:
        calendar = self._read_calendar()
        return int(np.argwhere(calendar == value)[0])

    def insert(self, index: int, value: CalVT):
        calendar = self._read_calendar()
        calendar = np.insert(calendar, index, value)
        self._write_calendar(values=calendar)

    def remove(self, value: CalVT) -> None:
        index = self.index(value)
        calendar = self._read_calendar()
        calendar = np.delete(calendar, index)
        self._write_calendar(values=calendar)

    def __setitem__(self, i: Union[int, slice], values: Union[CalVT, Iterable[CalVT]]) -> None:
        calendar = self._read_calendar()
        calendar[i] = values
        self._write_calendar(values=calendar)

    def __delitem__(self, i: Union[int, slice]) -> None:
        calendar = self._read_calendar()
        calendar = np.delete(calendar, i)
        self._write_calendar(values=calendar)

    def __getitem__(self, i: Union[int, slice]) -> Union[CalVT, Iterable[CalVT]]:
        return self._read_calendar()[i]

    def __len__(self) -> int:
        return len(self._read_calendar())

    def __iter__(self):
        return iter(self._read_calendar())


class FileInstrumentStorage(InstrumentStorage):

    INSTRUMENT_SEP = "\t"
    INSTRUMENT_START_FIELD = "start_datetime"
    INSTRUMENT_END_FIELD = "end_datetime"
    SYMBOL_FIELD_NAME = "instrument"

    def __init__(self, market: str, uri: str):
        super(FileInstrumentStorage, self).__init__(market, uri)
        self.uri = Path(self.uri).expanduser().joinpath(f"{market.lower()}.txt")

    def _read_instrument(self) -> Dict[InstKT, InstVT]:
        if not self.uri.exists():
            self._write_instrument()

        _instruments = dict()
        df = pd.read_csv(
            self.uri,
            sep="\t",
            usecols=[0, 1, 2],
            names=[self.SYMBOL_FIELD_NAME, self.INSTRUMENT_START_FIELD, self.INSTRUMENT_END_FIELD],
            dtype={self.SYMBOL_FIELD_NAME: str},
            parse_dates=[self.INSTRUMENT_START_FIELD, self.INSTRUMENT_END_FIELD],
        )
        for row in df.itertuples(index=False):
            _instruments.setdefault(row[0], []).append((row[1], row[2]))
        return _instruments

    def _write_instrument(self, data: Dict[InstKT, InstVT] = None) -> None:
        if not data:
            with self.uri.open("w") as _:
                pass
            return

        res = []
        for inst, v_list in data.items():
            _df = pd.DataFrame(v_list, columns=[self.INSTRUMENT_START_FIELD, self.INSTRUMENT_END_FIELD])
            _df[self.SYMBOL_FIELD_NAME] = inst
            res.append(_df)

        df = pd.concat(res, sort=False)
        df.loc[:, [self.SYMBOL_FIELD_NAME, self.INSTRUMENT_START_FIELD, self.INSTRUMENT_END_FIELD]].to_csv(
            self.uri, header=False, sep=self.INSTRUMENT_SEP, index=False
        )
        df.to_csv(self.uri, sep="\t", encoding="utf-8", header=False, index=False)

    def clear(self) -> None:
        self._write_instrument(data={})

    @property
    def data(self) -> Dict[InstKT, InstVT]:
        return self._read_instrument()

    def __setitem__(self, k: InstKT, v: InstVT) -> None:
        inst = self._read_instrument()
        inst[k] = v
        self._write_instrument(inst)

    def __delitem__(self, k: InstKT) -> None:
        inst = self._read_instrument()
        del inst[k]
        self._write_instrument(inst)

    def __getitem__(self, k: InstKT) -> InstVT:
        return self._read_instrument()[k]

    def __len__(self) -> int:
        inst = self._read_instrument()
        return len(inst)

    def __iter__(self) -> Iterator[InstKT]:
        for _inst in self._read_instrument().keys():
            yield _inst

    def update(self, *args, **kwargs) -> None:

        if len(args) > 1:
            raise TypeError(f"update expected at most 1 arguments, got {len(args)}")
        inst = self._read_instrument()
        if args:
            other = args[0]  # type: dict
            if isinstance(other, Mapping):
                for key in other:
                    inst[key] = other[key]
            elif hasattr(other, "keys"):
                for key in other.keys():
                    inst[key] = other[key]
            else:
                for key, value in other:
                    inst[key] = value
        for key, value in kwargs.items():
            inst[key] = value

        self._write_instrument(inst)


class FileFeatureStorage(FeatureStorage):
    def __init__(self, instrument: str, field: str, freq: str, uri: str):
        super(FileFeatureStorage, self).__init__(instrument, field, freq, uri)
        self.uri = (
            Path(self.uri).expanduser().joinpath(instrument.lower()).joinpath(f"{field.lower()}.{freq.lower()}.bin")
        )

    def clear(self):
        with self.uri.open("wb") as _:
            pass

    @property
    def data(self) -> pd.Series:
        return self[:]

    def extend(self, series: pd.Series) -> None:
        extend_start_index = self[0][0] + len(self) if self.uri.exists() else series.index[0]
        series = series.reindex(pd.RangeIndex(extend_start_index, series.index[-1] + 1))
        with self.uri.open("ab") as fp:
            np.array(series.values).astype("<f").tofile(fp)

    def rebase(self, series: pd.Series) -> None:
        origin_series = self[:]
        series = series.append(origin_series.loc[origin_series.index > series.index[-1]])
        series = series.reindex(pd.RangeIndex(series.index[0], series.index[-1]))
        with self.uri.open("wb") as fp:
            np.array(series.values).astype("<f").tofile(fp)

    def __getitem__(self, i: Union[int, slice]) -> Union[Tuple[int, float], pd.Series]:
        if not self.uri.exists():
            if isinstance(i, int):
                return None, None
            elif isinstance(i, slice):
                return pd.Series()
            else:
                raise TypeError(f"type(i) = {type(i)}")

        with open(self.uri, "rb") as fp:
            ref_start_index = int(np.frombuffer(fp.read(4), dtype="<f")[0])

            if isinstance(i, int):
                if ref_start_index > i:
                    raise IndexError(f"{i}: start index is {ref_start_index}")
                fp.seek(4 * (i - ref_start_index) + 4)
                return i, struct.unpack("f", fp.read(4))[0]
            elif isinstance(i, slice):
                start_index = i.start
                end_index = i.stop - 1
                si = max(ref_start_index, start_index)
                if si > end_index:
                    return pd.Series()
                fp.seek(4 * (si - ref_start_index) + 4)
                # read n bytes
                count = end_index - si + 1
                data = np.frombuffer(fp.read(4 * count), dtype="<f")
                return pd.Series(data, index=pd.RangeIndex(si, si + len(data)))
            else:
                raise TypeError(f"type(i) = {type(i)}")

    def __len__(self) -> int:
        return self.uri.stat().st_size // 4 - 1 if self.uri.exists() else 0

    def __iter__(self):
        if not self.uri.exists():
            return
        with open(self.uri, "rb") as fp:
            ref_start_index = int(np.frombuffer(fp.read(4), dtype="<f")[0])
            fp.seek(4)
            while True:
                v = fp.read(4)
                if v:
                    yield ref_start_index, struct.unpack("f", v)[0]
                    ref_start_index += 1
                else:
                    break