| """
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| Gold-set generator for Wyckoff structure labelling (2026-10-05).
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|
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| Each sample = the last W H1 bars ending at a decision bar t. CAUSAL: nothing after t is drawn, so a
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| labeller (a vision model or a human) sees exactly what a trader sees at t. ANONYMOUS: no dates, no
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| symbol, price rescaled to ATR units from the window's first close, volume to the window's median, so
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| the labeller cannot recall the history. The key (symbol, timestamp) goes to a separate CSV.
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| Sealed: nothing dated >= 2025-01-01 is ever sampled.
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| python wyckoff_gold.py SP500,NAS100 40 pilot # symbols, n per symbol, set name
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| """
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| import os, sys, numpy as np, pandas as pd
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| import matplotlib; matplotlib.use("Agg")
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| import matplotlib.pyplot as plt
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| sys.path.insert(0, os.path.dirname(__file__))
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| import discover as D
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|
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| W = 120
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| OUT = os.path.join(os.path.dirname(__file__), "wyckoff_gold")
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|
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| def render(b: pd.DataFrame, path: str):
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| c0 = b.c.iloc[0]
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| atr = (b.h - b.l).rolling(14, min_periods=1).mean().iloc[-1]
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| sc = lambda x: (x - c0) / atr
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| v = b.tv / b.tv.median()
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| fig, (a, v_ax) = plt.subplots(2, 1, figsize=(11, 6.5), sharex=True, gridspec_kw={"height_ratios": [3, 1]})
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| x = np.arange(len(b))
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| up = b.c >= b.o
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| a.vlines(x, sc(b.l), sc(b.h), color="k", lw=0.8)
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| a.bar(x, (sc(b.c) - sc(b.o)).abs().clip(lower=0.02), bottom=np.minimum(sc(b.o), sc(b.c)), width=0.7,
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| color=np.where(up, "#2a9d4a", "#d62f2f"))
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| a.set_ylabel("price (ATR units)"); a.grid(alpha=.25)
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| v_ax.bar(x, v, color=np.where(up, "#2a9d4a", "#d62f2f"), width=0.8); v_ax.set_ylabel("rel. volume")
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| v_ax.set_xlabel("bar (last bar = now; nothing to the right exists)"); v_ax.grid(alpha=.25)
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| fig.tight_layout(); fig.savefig(path, dpi=80); plt.close(fig)
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| def main():
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| syms = sys.argv[1].split(","); n = int(sys.argv[2]); name = sys.argv[3]
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| d = os.path.join(OUT, name); os.makedirs(d, exist_ok=True)
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| rng = np.random.default_rng(int(os.environ.get("SEED","7"))); key = []
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| for s in syms:
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| h = D.load_h1(s); h = h[h.index < D.SEAL]
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| # event-driven sampling: decision bars where price moved >=2 ATR from the 20-bar mean or at random
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| idx = rng.choice(np.arange(W, len(h)), n, replace=False)
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| for i in sorted(idx):
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| sid = f"{len(key):04d}"
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| render(h.iloc[i - W + 1:i + 1], os.path.join(d, sid + ".png"))
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| key.append((sid, s, h.index[i], h.c.iloc[i]))
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| pd.DataFrame(key, columns=["id", "sym", "t", "close"]).to_csv(os.path.join(d, "_key.csv"), index=False)
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| print(len(key), "charts ->", d)
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| if __name__ == "__main__":
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| main()
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