| # SPDX-License-Identifier: LicenseRef-PolyForm-Noncommercial-1.0.0 (see LICENSE)
|
| """Count completed setups (9), perfected 9s and countdown 13s per 1,000 closed bars from the OHLC columns of dump CSVs
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| (aggregate numbers only).
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|
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| python3 tests/frequency.py <dump dir> M15 H1 H4 > docs/data/count-frequency.json
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| Reads <dump dir>/dump/sq_default_<SYMBOL>_<TF>.csv; every closed bar is counted 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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|
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| sys.path.insert(0, str(Path(__file__).parent))
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| from reference import run # noqa: E402
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|
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| root = Path(sys.argv[1])
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| result = {}
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| for tf in sys.argv[2:]:
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| bars = nine = perf = c13 = 0
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| per_symbol = {}
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| for f in sorted(root.glob(f"dump/sq_default_*_{tf}.csv")):
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| d = pd.read_csv(f)
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| h, l, c = (d[k].to_numpy(float)[:-1] for k in ("high", "low", "close"))
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| rows = run(h, l, c)
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| n9 = sum(1 for r in rows if r["ev_setup"])
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| np_ = sum(1 for r in rows if r["ev_setup"] and r["perf"])
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| n13 = sum(1 for r in rows if r["ev_cd"])
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| per_symbol[f.stem.split("_")[2]] = dict(bars=len(c), setups_9=n9, perfected=np_, countdown_13=n13)
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| bars += len(c); nine += n9; perf += np_; c13 += n13
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| result[tf] = dict(closed_bars=bars, per_symbol=per_symbol, setups_9_per_1000_bars=round(1000 * nine / bars, 2),
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| perfected_share_of_9s=round(perf / max(1, nine), 3),
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| countdown_13_per_1000_bars=round(1000 * c13 / bars, 2),
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| thirteens_per_nine=round(c13 / max(1, nine), 3))
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| print(json.dumps(result, indent=1))
|
