forked from animatedread/Warrior_EA
326 lines
14 KiB
Python
326 lines
14 KiB
Python
 | """Decoder for StrategyQuant X tick .dat files (format 4.2).
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Derived from SQX's own writer, disassembled out of internal/libs/SQDataLib.jar:
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com.strategyquant.datalib.data.io.newDataFormat.TickDataWriter
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com.strategyquant.datalib.data.io.newDataFormat.NewDataFormat{,Writter}
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Why bother, when Scripts/ExportTicks.mq5 pulls the same four fields from MT5: DEPTH. The
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broker's MT5 tick history goes back a few years; these files start in 2011. Sample size has
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been the binding constraint on this entire investigation, so 15 years of ticks is worth a
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decoder.
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FORMAT
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------
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Header: four Java writeUTF strings (version "4.2", a type char, a column string, and an
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empty one), ten zero bytes, then one more writeUTF. Data follows.
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Records are (time, ASK, BID, volume) - ask before bid, and the writer swaps them if bid>ask,
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so ask is always the larger. Every BLOCK_LENGTH=1000 records a block header is emitted:
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MAGIC (15 bytes: 0x00..0x0e) + int32 block index + config + four raw int64 values
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and in between, delta records:
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config + four variable-width deltas against the previous record
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CONFIG is two bytes = four nibbles, one per field, laid out high-nibble-first:
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nibble = (logicType << 2) | dataType
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dataType : 0=BYTE(1) 1=SHORT(2) 2=INT(4) 3=LONG(8) <- chosen by |value| magnitude
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logicType: 0=MINUS 1=PLUS 2=ASIS <- sign carried here, not in the bytes
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MINUS/PLUS payloads are UNSIGNED magnitudes with the sign supplied by logicType; ASIS is a
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plain signed read (used for the full records at block starts).
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SCALING
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-------
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Prices are integers scaled by 10**decimals. SQX carries `decimals` in its own metadata, not
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in this file, so it is inferred here by checking which power of ten yields a sane price -
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and validated against the first record, which is uncompressed and therefore unambiguous.
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Volume divides by VOLUME_CONSTANT = 100000.0 (OLD_VOLUME_CONSTANT = 100.0 for old files).
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A volume of Long.MAX_VALUE is the writer's "no volume" sentinel and decodes to NaN.
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"""
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import struct, sys, datetime as dt
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import numpy as np
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MAGIC = bytes(range(15))
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BLOCK_LENGTH = 1000
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VOLUME_CONSTANT = 100000.0
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_SIZE = (1, 2, 4, 8)
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MINUS, PLUS, ASIS = 0, 1, 2
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NO_VOLUME = (1 << 63) - 1
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def read_header(b):
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"""Returns the offset at which the first block MAGIC starts, plus the header strings."""
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p, strs = 0, []
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for _ in range(4):
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n = struct.unpack_from('>H', b, p)[0]
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strs.append(b[p + 2:p + 2 + n].decode('latin1'))
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p += 2 + n
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p += 10 # ten zero bytes
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n = struct.unpack_from('>H', b, p)[0]
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strs.append(b[p + 2:p + 2 + n].decode('latin1'))
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p += 2 + n
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if b[p:p + 15] != MAGIC: # be forgiving: just find it
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p = b.find(MAGIC)
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if p < 0:
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raise ValueError("no block magic found in first chunk")
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return p, strs
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def _fields(cfg):
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"""Unpack the two config bytes into four (logicType, dataType) pairs."""
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out = []
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for i in range(4):
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nib = (cfg[i >> 1] >> (4 if i % 2 == 0 else 0)) & 0xF
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out.append((nib >> 2, nib & 3))
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return out
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def decode(path, max_records=None, progress=None):
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"""Whole-file decode into four int64 arrays. Convenient, but a full symbol is ~458M
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ticks = ~15 GB of arrays - use decode_iter() for anything beyond a sample."""
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parts = [[], [], [], []]
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for ts, a, b, v in decode_iter(path, max_records=max_records, progress=progress):
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parts[0].append(ts); parts[1].append(a); parts[2].append(b); parts[3].append(v)
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if not parts[0]:
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return tuple(np.empty(0, dtype=np.int64) for _ in range(4))
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return tuple(np.concatenate(p) for p in parts)
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def find_block(f, approx, size, window=1 << 20):
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"""Byte offset of the first VALID block header at or after `approx`, or None.
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This is what makes the format parallel-decodable: a block header restates all four
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fields as absolute int64s, so a decoder can start there cold with no history. The catch
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is that MAGIC is 0x00..0x0e, a byte run that delta payloads produce by coincidence
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fairly often - a small negative delta on a LONG field is a lot of zero bytes. So a
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candidate is only accepted when the NEXT candidate downstream carries the next
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sequential block index, which junk essentially never does.
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"""
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def candidate(off):
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"""(offset, block_index, timestamp) of the first plausible header at/after off."""
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p = off
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while p < size:
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f.seek(p)
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buf = f.read(window + 64)
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if len(buf) < 21 + 32:
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return None
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k = 0
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while True:
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k = buf.find(MAGIC, k)
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if k < 0 or k + 15 + 4 + 2 + 32 > len(buf):
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break
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idx = struct.unpack_from('>i', buf, k + 15)[0]
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t, a, b, _v = struct.unpack_from('>4q', buf, k + 15 + 4 + 2)
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#--- a real header: sane block index, epoch-ms in [2000, 2035], ask >= bid > 0
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if (0 <= idx < (1 << 28) and 946684800000 <= t <= 2051222400000
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and a >= b > 0):
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return (p + k, idx, t)
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k += 1
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p += window
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return None
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first = candidate(approx)
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while first is not None:
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nxt = candidate(first[0] + 15 + 4 + 2 + 32)
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if nxt is None:
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return first[0] # last block in the file
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if nxt[1] == first[1] + 1 and nxt[2] >= first[2]:
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return first[0]
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first = nxt # the first was noise; try the next
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return None
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def split_offsets(path, nchunks):
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"""Byte ranges [(start, stop), ...] on block boundaries covering the whole file.
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Half-open and contiguous: chunk k's stop IS chunk k+1's start, so every record is
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decoded exactly once with no duplicates and no gaps.
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"""
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import os
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size = os.path.getsize(path)
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with open(path, 'rb') as f:
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head = f.read(1 << 16)
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first, _ = read_header(head)
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if nchunks <= 1:
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return [(first, size)]
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cuts = [first]
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span = (size - first) // nchunks
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for k in range(1, nchunks):
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off = find_block(f, first + k * span, size)
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#--- keep it only if it is strictly after the previous cut
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if off is not None and off > cuts[-1]:
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cuts.append(off)
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cuts.append(size)
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return [(cuts[i], cuts[i + 1]) for i in range(len(cuts) - 1)]
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def decode_iter(path, batch=2000000, max_records=None, progress=None, start=None, stop=None):
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"""Streaming decode. Yields (time_ms, ask_i, bid_i, vol_i) int64 arrays in batches.
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A full symbol here is hundreds of millions of ticks, so nothing that wants the whole
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file can afford to materialise it. Everything downstream aggregates to bars in one pass.
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start/stop: decode only the byte range [start, stop), which MUST begin on a block
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boundary - see split_offsets(). Used to decode one file across many processes, since
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this per-record loop is pure Python and saturates exactly one core.
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"""
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f = open(path, 'rb')
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if start is None:
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head = f.read(1 << 16)
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start, strs = read_header(head)
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f.seek(start)
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ts, asks, bids, vols = [], [], [], []
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t = a = bd = v = 0
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buf = b''
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pos = 0
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base = start # absolute file offset of buf[0]
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CH = 1 << 22
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eof = False
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n = 0
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while True:
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if len(buf) - pos < 64 and not eof:
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buf = buf[pos:]
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base += pos
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pos = 0
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more = f.read(CH)
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if not more:
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eof = True
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buf += more
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if len(buf) - pos < 2:
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break
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if stop is not None and base + pos >= stop:
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break
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if buf[pos:pos + 15] == MAGIC:
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pos += 15 + 4 # magic + int32 block index
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if len(buf) - pos < 2 + 32:
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break
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cfg = buf[pos:pos + 2]; pos += 2
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t, a, bd, v = struct.unpack_from('>4q', buf, pos)
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pos += 32
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else:
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cfg = buf[pos:pos + 2]
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fs = _fields(cfg)
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need = 2 + sum(_SIZE[d] for _, d in fs)
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if len(buf) - pos < need:
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if eof:
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break
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buf = buf[pos:]; base += pos; pos = 0
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more = f.read(CH)
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if not more:
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eof = True
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buf += more
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if len(buf) < need:
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break
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continue
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q = pos + 2
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vals = []
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for logic, dtp in fs:
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sz = _SIZE[dtp]
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raw = int.from_bytes(buf[q:q + sz], 'big', signed=(logic == ASIS))
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q += sz
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vals.append(-raw if logic == MINUS else raw)
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pos = q
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t += vals[0]; a += vals[1]; bd += vals[2]; v += vals[3]
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ts.append(t); asks.append(a); bids.append(bd); vols.append(v)
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n += 1
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if progress and n % progress == 0:
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print(f" {n:,} ticks {dt.datetime.fromtimestamp(t/1000, dt.UTC):%Y-%m-%d}", flush=True)
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if len(ts) >= batch:
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yield (np.array(ts, dtype=np.int64), np.array(asks, dtype=np.int64),
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np.array(bids, dtype=np.int64), np.array(vols, dtype=np.int64))
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ts, asks, bids, vols = [], [], [], []
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if max_records and n >= max_records:
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break
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f.close()
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if ts:
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yield (np.array(ts, dtype=np.int64), np.array(asks, dtype=np.int64),
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np.array(bids, dtype=np.int64), np.array(vols, dtype=np.int64))
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def calibrate_decimals(ts, ask_i, ref_time_s, ref_close, verbose=True):
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"""Determine the price scale by matching against a KNOWN reference series.
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SQX keeps `decimals` in metadata outside the .dat, and it cannot be inferred from the
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file alone: 1216010000 is a plausible price at 10^3 (1216010), 10^5 (12160.1) or 10^6
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(1216.01), and nothing in the bytes distinguishes them. Guessing here would be exactly
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the kind of silent, plausible-looking error this project keeps getting burned by - a
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100x price scale error would not crash anything, it would just quietly rescale every
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ATR-normalised feature downstream.
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So instead: take a reference series for the same instrument (the MT5 rate exports under
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Common\\Files\\Warrior_EA\\Research), find the tick nearest each reference bar time, and
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pick the power of ten minimising median relative error. A correct scale lands at ~0;
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every wrong one is off by a factor of ten or more, so the decision is never marginal.
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"""
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ref_ms = (ref_time_s * 1000).astype(np.int64)
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lo, hi = ts[0], ts[-1]
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m = (ref_ms >= lo) & (ref_ms <= hi)
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if m.sum() < 50:
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raise ValueError(f"only {int(m.sum())} overlapping reference bars - cannot calibrate")
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rt, rc = ref_ms[m], ref_close[m]
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idx = np.searchsorted(ts, rt).clip(1, len(ts) - 1)
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got = ask_i[idx].astype(float)
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best, best_err = None, np.inf
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for d in range(0, 11):
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err = float(np.median(np.abs(got / (10.0 ** d) - rc) / np.maximum(np.abs(rc), 1e-9)))
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if verbose:
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print(f" decimals={d:<3} median rel.err {err:12.6f}")
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if err < best_err:
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best, best_err = d, err
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if best_err > 0.05:
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raise ValueError(f"best scale (decimals={best}) still off by {best_err:.1%} - "
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"reference series may be a different instrument")
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return best, best_err
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def to_frame(path, max_records=None, decimals=None, volume_constant=VOLUME_CONSTANT,
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progress=None, ref=None):
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"""decimals: pass explicitly, or pass ref=(time_s, close) to calibrate against a known
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series. There is no safe default - see calibrate_decimals()."""
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ts, ai, bi, vi = decode(path, max_records, progress)
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if decimals is None:
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if ref is None:
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raise ValueError("pass decimals=... or ref=(time_s, close) - see calibrate_decimals()")
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decimals, _ = calibrate_decimals(ts, ai, ref[0], ref[1])
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scale = 10.0 ** decimals
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ask = ai / scale
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bid = bi / scale
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vol = np.where(vi == NO_VOLUME, np.nan, vi / volume_constant)
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return ts, bid, ask, vol, decimals
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if __name__ == '__main__':
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# usage: python sqx.py <file.dat> [max_records] [--ref SYMBOL | --decimals N]
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sys.stdout.reconfigure(encoding='utf-8', errors='replace')
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path = sys.argv[1]
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lim = int(sys.argv[2]) if len(sys.argv) > 2 and not sys.argv[2].startswith('-') else None
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dec_arg, ref_sym = None, None
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if '--decimals' in sys.argv:
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dec_arg = int(sys.argv[sys.argv.index('--decimals') + 1])
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if '--ref' in sys.argv:
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ref_sym = sys.argv[sys.argv.index('--ref') + 1]
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ref = None
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if ref_sym:
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from kit import load_rates
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rt, ro, rh, rl, rc, rv, rs = load_rates(ref_sym, 16385)
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ref = (rt, rc)
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print(f"calibrating scale against {ref_sym} H1 closes:")
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ts, bid, ask, vol, dec = to_frame(path, max_records=lim, decimals=dec_arg,
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progress=5000000, ref=ref)
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print(f"\ndecoded {len(ts):,} ticks decimals={dec}")
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print(f"span {dt.datetime.fromtimestamp(ts[0]/1000, dt.UTC)}"
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f" -> {dt.datetime.fromtimestamp(ts[-1]/1000, dt.UTC)}")
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print(f"bid min {np.nanmin(bid):.5f} max {np.nanmax(bid):.5f}")
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spread = ask - bid
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print(f"spread median {np.nanmedian(spread):.5f} negative: {(spread<0).sum()}")
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print(f"time monotonic: {bool((np.diff(ts)>=0).all())} "
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f"max gap {np.max(np.diff(ts))/1000/3600:.1f} h")
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print(f"volume: median {np.nanmedian(vol)} nan {int(np.isnan(vol).sum()):,}")
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print("\nfirst 5:")
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for i in range(5):
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print(f" {dt.datetime.fromtimestamp(ts[i]/1000, dt.UTC)} "
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f"bid {bid[i]:.5f} ask {ask[i]:.5f} vol {vol[i]}")
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