import lab, rules as Q, rotmap as R, math
R.INDEX_NEED = 1
R.RISK_MA = 50   # baseline; the script sets its own risk series where it matters
M = lab.load(); label, runs = lab.oracle(M)
base_risk = list(M._risk)
btc = M.idx["BTC"]
def risk_series(n, band):
    return Q.trend_state(btc, n, band)
def ens(ns=(20, 50, 100, 200), band=0.01):
    ss = [risk_series(n, band) for n in ns]
    return [sum(s[i] for s in ss) / len(ss) for i in range(M.N)]
def wrap(rulef, frac):
    r = rulef()
    return lambda i: {k: v * frac[i] for k, v in r(i).items()}
P = [("HOLD", "2020-06-01", "2024-06-30"), ("RECENT", "2024-07-01", None)]
def show(name, mk):
    cells = []
    for _, a, z in P:
        s = lab.score(M, mk(), a, z, label=label, runs=runs)
        st = R.stats(M.backtest(mk(), M.pos[a], M.pos[z] if z else M.N - 1)[0])
        cells.append("cap %3d%% maj %d/%d ret %+6d%% dd %4.0f%% sw %3d" % (s["capture"], s["caught"], s["major"], st["total"], st["maxdd"], s["switches"]))
    print("%-34s" % name + "  ".join(cells))
for n in (20, 30, 50, 100, 200):
    for band in (0.0, 0.01, 0.03):
        M._risk = risk_series(n, band)
        show("BTC>%dd b%d%%  | trend" % (n, band*100), lambda: (lambda i: {"BTC": 1.0} if M._risk[i] else {}))
        show("BTC>%dd b%d%%  | book G" % (n, band*100), lambda: M.book_rule()[0])
for ns in ((20, 50, 100, 200), (50, 100, 200), (20, 50, 100)):
    f = ens(ns)
    M._risk = [x >= 0.5 for x in f]
    show("ENS %s | trend" % (ns,), lambda: (lambda i: {"BTC": f[i]} if f[i] else {}))
    show("ENS %s | book G" % (ns,), lambda: wrap(lambda: M.book_rule()[0], f))
M._risk = base_risk