34 lines
1.3 KiB
Python
 | # SPDX-License-Identifier: LicenseRef-PolyForm-Noncommercial-1.0.0 (see LICENSE)
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"""Count pattern marks per 1,000 closed bars from the OHLC columns of dump CSVs (aggregate numbers only).
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python3 tests/frequency.py <dump dir> H1 > docs/data/pattern-frequency.json
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Reads <dump dir>/dump/cd_default_<SYMBOL>_<TF>.csv; every closed bar is scanned with tests/reference.py."""
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import json
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import sys
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from pathlib import Path
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import pandas as pd
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sys.path.insert(0, str(Path(__file__).parent))
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from reference import NAMES, NP, run # noqa: E402
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root, tf = Path(sys.argv[1]), sys.argv[2]
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tot = [0] * NP
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bars = 0
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per_symbol = {}
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for f in sorted(root.glob(f"dump/cd_default_*_{tf}.csv")):
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d = pd.read_csv(f)
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o, h, l, c = (d[k].to_numpy(float)[:-1] for k in ("open", "high", "low", "close"))
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rows = run(o, h, l, c)
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cnt = [0] * NP
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for r in rows:
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for p, _ in r["ev"]:
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cnt[p] += 1
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sym = f.stem.split("_")[2]
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per_symbol[sym] = dict(bars=len(c), marks=sum(cnt))
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bars += len(c)
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tot = [a + b for a, b in zip(tot, cnt)]
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out = dict(timeframe=tf, closed_bars=bars, per_symbol=per_symbol,
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marks_per_1000_bars={NAMES[i]: round(1000 * tot[i] / bars, 1) for i in range(NP)},
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marks_per_1000_bars_total=round(1000 * sum(tot) / bars, 1))
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print(json.dumps(out, indent=1))
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