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