mirror of
https://github.com/wassname/catalyst.git
synced 2026-08-04 12:45:06 +08:00
Compare commits
| Author | SHA1 | Date | |
|---|---|---|---|
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8b141a0c28 | ||
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7f602d7fcc | ||
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1f56325895 |
@@ -215,13 +215,11 @@ cpdef _read_bcolz_data(ctable_t table,
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else:
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else:
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continue
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continue
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if column_name in ['open', 'high', 'low', 'close']:
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if column_name in ['open', 'high', 'low', 'close', 'volume']:
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where_nan = (outbuf == 0)
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where_nan = (outbuf == 0)
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outbuf_as_float = outbuf.astype(float64) * .000000001
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outbuf_as_float = outbuf.astype(float64) * .000000001
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outbuf_as_float[where_nan] = NAN
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outbuf_as_float[where_nan] = NAN
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results.append(outbuf_as_float)
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results.append(outbuf_as_float)
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elif column_name != 'volume':
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results.append(outbuf.astype(uint32))
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else:
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else:
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results.append(outbuf)
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results.append(outbuf)
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return results
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return results
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@@ -491,7 +491,7 @@ class BaseBundle(object):
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data_frequency,
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data_frequency,
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)
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)
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raw_data.index = pd.to_datetime(raw_data.index, utc=True)
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raw_data.index = pd.to_datetime(raw_data.index, utc=True)
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raw_data.index = raw_data.index.tz_localize('UTC')
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#raw_data.index = raw_data.index.tz_localize('UTC')
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# Filter incoming data to fit start and end sessions.
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# Filter incoming data to fit start and end sessions.
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raw_data = raw_data[
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raw_data = raw_data[
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@@ -18,6 +18,7 @@ from numpy import (
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full,
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full,
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nan,
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nan,
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int64,
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int64,
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float64,
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zeros
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zeros
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)
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)
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from six import iteritems, with_metaclass
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from six import iteritems, with_metaclass
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@@ -70,7 +71,9 @@ class AssetDispatchBarReader(with_metaclass(ABCMeta)):
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return self._dt_window_size(start_dt, end_dt), num_sids
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return self._dt_window_size(start_dt, end_dt), num_sids
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def _make_raw_array_out(self, field, shape):
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def _make_raw_array_out(self, field, shape):
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if field != 'volume' and field != 'sid':
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if field == 'volume':
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out = zeros(shape, dtype=float64)
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elif field != 'sid':
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out = full(shape, nan)
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out = full(shape, nan)
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else:
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else:
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out = zeros(shape, dtype=int64)
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out = zeros(shape, dtype=int64)
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@@ -39,7 +39,7 @@ from catalyst.data._minute_bar_internal import (
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from catalyst.gens.sim_engine import NANOS_IN_MINUTE
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from catalyst.gens.sim_engine import NANOS_IN_MINUTE
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from catalyst.data.bar_reader import BarReader, NoDataOnDate
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from catalyst.data.bar_reader import BarReader, NoDataOnDate
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from catalyst.data.us_equity_pricing import check_uint32_safe
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from catalyst.data.us_equity_pricing import check_uint64_safe
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from catalyst.utils.calendars import get_calendar
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from catalyst.utils.calendars import get_calendar
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from catalyst.utils.cli import maybe_show_progress
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from catalyst.utils.cli import maybe_show_progress
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from catalyst.utils.memoize import lazyval
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from catalyst.utils.memoize import lazyval
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@@ -52,7 +52,7 @@ FUTURES_MINUTES_PER_DAY = 1440
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DEFAULT_EXPECTEDLEN = US_EQUITIES_MINUTES_PER_DAY * 252 * 15
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DEFAULT_EXPECTEDLEN = US_EQUITIES_MINUTES_PER_DAY * 252 * 15
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OHLC_RATIO = 1000
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OHLC_RATIO = 100000000
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class BcolzMinuteOverlappingData(Exception):
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class BcolzMinuteOverlappingData(Exception):
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@@ -114,15 +114,15 @@ def _sid_subdir_path(sid):
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def convert_cols(cols, scale_factor, sid, invalid_data_behavior):
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def convert_cols(cols, scale_factor, sid, invalid_data_behavior):
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"""Adapt OHLCV columns into uint32 columns.
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"""Adapt OHLCV columns into uint64 columns.
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Parameters
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Parameters
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----------
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----------
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cols : dict
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cols : dict
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A dict mapping each column name (open, high, low, close, volume)
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A dict mapping each column name (open, high, low, close, volume)
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to a float column to convert to uint32.
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to a float column to convert to uint64.
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scale_factor : int
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scale_factor : int
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Factor to use to scale float values before converting to uint32.
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Factor to use to scale float values before converting to uint64.
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sid : int
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sid : int
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Sid of the relevant asset, for logging.
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Sid of the relevant asset, for logging.
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invalid_data_behavior : str
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invalid_data_behavior : str
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@@ -135,6 +135,7 @@ def convert_cols(cols, scale_factor, sid, invalid_data_behavior):
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scaled_highs = np.nan_to_num(cols['high']) * scale_factor
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scaled_highs = np.nan_to_num(cols['high']) * scale_factor
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scaled_lows = np.nan_to_num(cols['low']) * scale_factor
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scaled_lows = np.nan_to_num(cols['low']) * scale_factor
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scaled_closes = np.nan_to_num(cols['close']) * scale_factor
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scaled_closes = np.nan_to_num(cols['close']) * scale_factor
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scaled_volumes = np.nan_to_num(cols['volume']) * scale_factor
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exclude_mask = np.zeros_like(scaled_opens, dtype=bool)
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exclude_mask = np.zeros_like(scaled_opens, dtype=bool)
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@@ -143,11 +144,12 @@ def convert_cols(cols, scale_factor, sid, invalid_data_behavior):
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('high', scaled_highs),
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('high', scaled_highs),
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('low', scaled_lows),
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('low', scaled_lows),
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('close', scaled_closes),
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('close', scaled_closes),
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('volume', scaled_volumes),
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]:
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]:
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max_val = scaled_col.max()
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max_val = scaled_col.max()
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try:
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try:
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check_uint32_safe(max_val, col_name)
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check_uint64_safe(max_val, col_name)
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except ValueError:
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except ValueError:
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if invalid_data_behavior == 'raise':
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if invalid_data_behavior == 'raise':
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raise
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raise
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@@ -155,20 +157,20 @@ def convert_cols(cols, scale_factor, sid, invalid_data_behavior):
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if invalid_data_behavior == 'warn':
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if invalid_data_behavior == 'warn':
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logger.warn(
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logger.warn(
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'Values for sid={}, col={} contain some too large for '
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'Values for sid={}, col={} contain some too large for '
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'uint32 (max={}), filtering them out',
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'uint64 (max={}), filtering them out',
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sid, col_name, max_val,
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sid, col_name, max_val,
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)
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)
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# We want to exclude all rows that have an unsafe value in
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# We want to exclude all rows that have an unsafe value in
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# this column.
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# this column.
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exclude_mask &= (scaled_col >= np.iinfo(np.uint32).max)
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exclude_mask &= (scaled_col >= np.iinfo(np.uint64).max)
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# Convert all cols to uint32.
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# Convert all cols to uint32.
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opens = scaled_opens.astype(np.uint32)
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opens = scaled_opens.astype(np.uint64)
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highs = scaled_highs.astype(np.uint32)
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highs = scaled_highs.astype(np.uint64)
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lows = scaled_lows.astype(np.uint32)
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lows = scaled_lows.astype(np.uint64)
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closes = scaled_closes.astype(np.uint32)
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closes = scaled_closes.astype(np.uint64)
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volumes = cols['volume'].astype(np.uint32)
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volumes = scaled_volumes.astype(np.uint64)
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# Exclude rows with unsafe values by setting to zero.
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# Exclude rows with unsafe values by setting to zero.
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opens[exclude_mask] = 0
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opens[exclude_mask] = 0
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@@ -288,7 +290,7 @@ class BcolzMinuteBarMetadata(object):
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ohlc_ratio : int
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ohlc_ratio : int
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The default ratio by which to multiply the pricing data to
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The default ratio by which to multiply the pricing data to
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convert the floats from floats to an integer to fit within
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convert the floats from floats to an integer to fit within
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the np.uint32. If ohlc_ratios_per_sid is None or does not
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the np.uint64. If ohlc_ratios_per_sid is None or does not
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contain a mapping for a given sid, this ratio is used.
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contain a mapping for a given sid, this ratio is used.
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ohlc_ratios_per_sid : dict
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ohlc_ratios_per_sid : dict
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A dict mapping each sid in the output to the factor by
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A dict mapping each sid in the output to the factor by
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@@ -372,13 +374,13 @@ class BcolzMinuteBarWriter(object):
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The last trading session in the data set.
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The last trading session in the data set.
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default_ohlc_ratio : int, optional
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default_ohlc_ratio : int, optional
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The default ratio by which to multiply the pricing data to
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The default ratio by which to multiply the pricing data to
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convert from floats to integers that fit within np.uint32. If
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convert from floats to integers that fit within np.uint64. If
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ohlc_ratios_per_sid is None or does not contain a mapping for a
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ohlc_ratios_per_sid is None or does not contain a mapping for a
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given sid, this ratio is used. Default is OHLC_RATIO (1000).
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given sid, this ratio is used. Default is OHLC_RATIO (10^8).
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ohlc_ratios_per_sid : dict, optional
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ohlc_ratios_per_sid : dict, optional
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A dict mapping each sid in the output to the ratio by which to
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A dict mapping each sid in the output to the ratio by which to
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multiply the pricing data to convert the floats from floats to
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multiply the pricing data to convert the floats from floats to
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an integer to fit within the np.uint32.
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an integer to fit within the np.uint64.
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expectedlen : int, optional
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expectedlen : int, optional
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The expected length of the dataset, used when creating the initial
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The expected length of the dataset, used when creating the initial
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bcolz ctable.
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bcolz ctable.
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@@ -401,11 +403,9 @@ class BcolzMinuteBarWriter(object):
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Each individual asset's data is stored as a bcolz table with a column for
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Each individual asset's data is stored as a bcolz table with a column for
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each pricing field: (open, high, low, close, volume)
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each pricing field: (open, high, low, close, volume)
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|
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The open, high, low, and close columns are integers which are 1000 times
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The open, high, low, close and volume columns are integers which are 10^8 times
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the quoted price, so that the data can represented and stored as an
|
the quoted price, so that the data can represented and stored as an
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np.uint32, supporting market prices quoted up to the thousands place.
|
np.uint64, supporting market prices quoted up to the 1/10^8-th place.
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|
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volume is a np.uint32 with no mutation of the tens place.
|
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|
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The 'index' for each individual asset are a repeating period of minutes of
|
The 'index' for each individual asset are a repeating period of minutes of
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length `minutes_per_day` starting from each market open.
|
length `minutes_per_day` starting from each market open.
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@@ -573,7 +573,7 @@ class BcolzMinuteBarWriter(object):
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if not os.path.exists(sid_containing_dirname):
|
if not os.path.exists(sid_containing_dirname):
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# Other sids may have already created the containing directory.
|
# Other sids may have already created the containing directory.
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os.makedirs(sid_containing_dirname)
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os.makedirs(sid_containing_dirname)
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initial_array = np.empty(0, np.uint32)
|
initial_array = np.empty(0, np.uint64)
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table = ctable(
|
table = ctable(
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rootdir=path,
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rootdir=path,
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columns=[
|
columns=[
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@@ -610,7 +610,7 @@ class BcolzMinuteBarWriter(object):
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minute_offset = len(table) % self._minutes_per_day
|
minute_offset = len(table) % self._minutes_per_day
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num_to_prepend = numdays * self._minutes_per_day - minute_offset
|
num_to_prepend = numdays * self._minutes_per_day - minute_offset
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||||||
|
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prepend_array = np.zeros(num_to_prepend, np.uint32)
|
prepend_array = np.zeros(num_to_prepend, np.uint64)
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# Fill all OHLCV with zeros.
|
# Fill all OHLCV with zeros.
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table.append([prepend_array] * 5)
|
table.append([prepend_array] * 5)
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table.flush()
|
table.flush()
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@@ -815,11 +815,11 @@ class BcolzMinuteBarWriter(object):
|
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|
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minutes_count = all_minutes_in_window.size
|
minutes_count = all_minutes_in_window.size
|
||||||
|
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open_col = np.zeros(minutes_count, dtype=np.uint32)
|
open_col = np.zeros(minutes_count, dtype=np.uint64)
|
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high_col = np.zeros(minutes_count, dtype=np.uint32)
|
high_col = np.zeros(minutes_count, dtype=np.uint64)
|
||||||
low_col = np.zeros(minutes_count, dtype=np.uint32)
|
low_col = np.zeros(minutes_count, dtype=np.uint64)
|
||||||
close_col = np.zeros(minutes_count, dtype=np.uint32)
|
close_col = np.zeros(minutes_count, dtype=np.uint64)
|
||||||
vol_col = np.zeros(minutes_count, dtype=np.uint32)
|
vol_col = np.zeros(minutes_count, dtype=np.uint64)
|
||||||
|
|
||||||
dt_ixs = np.searchsorted(all_minutes_in_window.values,
|
dt_ixs = np.searchsorted(all_minutes_in_window.values,
|
||||||
dts.astype('datetime64[ns]'))
|
dts.astype('datetime64[ns]'))
|
||||||
@@ -1125,8 +1125,8 @@ class BcolzMinuteBarReader(MinuteBarReader):
|
|||||||
else:
|
else:
|
||||||
return np.nan
|
return np.nan
|
||||||
|
|
||||||
if field != 'volume':
|
#if field != 'volume':
|
||||||
value *= self._ohlc_ratio_inverse_for_sid(sid)
|
value *= self._ohlc_ratio_inverse_for_sid(sid)
|
||||||
return value
|
return value
|
||||||
|
|
||||||
def get_last_traded_dt(self, asset, dt):
|
def get_last_traded_dt(self, asset, dt):
|
||||||
@@ -1248,7 +1248,7 @@ class BcolzMinuteBarReader(MinuteBarReader):
|
|||||||
if field != 'volume':
|
if field != 'volume':
|
||||||
out = np.full(shape, np.nan)
|
out = np.full(shape, np.nan)
|
||||||
else:
|
else:
|
||||||
out = np.zeros(shape, dtype=np.uint32)
|
out = np.zeros(shape, dtype=np.float64)
|
||||||
|
|
||||||
for i, sid in enumerate(sids):
|
for i, sid in enumerate(sids):
|
||||||
carray = self._open_minute_file(field, sid)
|
carray = self._open_minute_file(field, sid)
|
||||||
@@ -1262,11 +1262,11 @@ class BcolzMinuteBarReader(MinuteBarReader):
|
|||||||
where = values != 0
|
where = values != 0
|
||||||
# first slice down to len(where) because we might not have
|
# first slice down to len(where) because we might not have
|
||||||
# written data for all the minutes requested
|
# written data for all the minutes requested
|
||||||
if field != 'volume':
|
#if field != 'volume':
|
||||||
out[:len(where), i][where] = (
|
out[:len(where), i][where] = (
|
||||||
values[where] * self._ohlc_ratio_inverse_for_sid(sid))
|
values[where] * self._ohlc_ratio_inverse_for_sid(sid))
|
||||||
else:
|
#else:
|
||||||
out[:len(where), i][where] = values[where]
|
# out[:len(where), i][where] = values[where]
|
||||||
|
|
||||||
results.append(out)
|
results.append(out)
|
||||||
return results
|
return results
|
||||||
|
|||||||
@@ -441,7 +441,7 @@ class BcolzDailyBarWriter(object):
|
|||||||
dates = raw_data.index.values.astype('datetime64[s]')
|
dates = raw_data.index.values.astype('datetime64[s]')
|
||||||
check_uint32_safe(dates.max().view(np.int64), 'day')
|
check_uint32_safe(dates.max().view(np.int64), 'day')
|
||||||
processed['day'] = dates.astype('uint32')
|
processed['day'] = dates.astype('uint32')
|
||||||
processed['volume'] = raw_data.volume.astype('uint64')
|
processed['volume'] = (raw_data.volume * PRICE_ADJUSTMENT_FACTOR).astype('uint64')
|
||||||
return ctable.fromdataframe(processed)
|
return ctable.fromdataframe(processed)
|
||||||
|
|
||||||
|
|
||||||
@@ -494,9 +494,8 @@ class BcolzDailyBarReader(SessionBarReader):
|
|||||||
|
|
||||||
The data in these columns is interpreted as follows:
|
The data in these columns is interpreted as follows:
|
||||||
|
|
||||||
- Price columns ('open', 'high', 'low', 'close') are interpreted as 1000 *
|
- Price columns ('open', 'high', 'low', 'close') and Volume are interpreted
|
||||||
as-traded dollar value.
|
as 10^9 * as-traded dollar value.
|
||||||
- Volume is interpreted as as-traded volume.
|
|
||||||
- Day is interpreted as seconds since midnight UTC, Jan 1, 1970.
|
- Day is interpreted as seconds since midnight UTC, Jan 1, 1970.
|
||||||
- Id is the asset id of the row.
|
- Id is the asset id of the row.
|
||||||
|
|
||||||
@@ -762,13 +761,10 @@ class BcolzDailyBarReader(SessionBarReader):
|
|||||||
"""
|
"""
|
||||||
ix = self.sid_day_index(sid, dt)
|
ix = self.sid_day_index(sid, dt)
|
||||||
price = self._spot_col(field)[ix]
|
price = self._spot_col(field)[ix]
|
||||||
if field != 'volume':
|
if field != 'volume' and price == 0:
|
||||||
if price == 0:
|
return nan
|
||||||
return nan
|
|
||||||
else:
|
|
||||||
return price / PRICE_ADJUSTMENT_FACTOR
|
|
||||||
else:
|
else:
|
||||||
return price
|
return price / PRICE_ADJUSTMENT_FACTOR
|
||||||
|
|
||||||
|
|
||||||
class PanelBarReader(SessionBarReader):
|
class PanelBarReader(SessionBarReader):
|
||||||
|
|||||||
@@ -1,7 +1,7 @@
|
|||||||
# Incompatible with earlier PIP versions
|
# Incompatible with earlier PIP versions
|
||||||
pip>=7.1.0
|
pip>=7.1.0
|
||||||
# bcolz fails to install if this is not in the build_requires.
|
# bcolz fails to install if this is not in the build_requires.
|
||||||
setuptools>18.0
|
setuptools>36.0
|
||||||
|
|
||||||
# Logging
|
# Logging
|
||||||
Logbook==0.12.5
|
Logbook==0.12.5
|
||||||
|
|||||||
Reference in New Issue
Block a user