import lab, rules as Q, rotmap as R, math, json, datetime as dt, statistics as st
R.INDEX_NEED = 1
R.RISK_MA = 50   # baseline; the script sets its own risk series where it matters
M = lab.load(); M._risk = Q.trend_state(M.idx["BTC"], 100, 0.01)
S = {dt.datetime.utcfromtimestamp(int(r["date"])).strftime("%Y-%m-%d"): r["totalCirculatingUSD"].get("peggedUSD") for r in json.load(open("stables.json"))}
sc = [S.get(d) for d in M.days]
for i in range(1, M.N):
    if sc[i] is None: sc[i] = sc[i - 1]
def g(i, n): return (sc[i] / sc[i - n] - 1) if i >= n and sc[i] and sc[i - n] else None
P = [("2020-06-01", "2024-06-30"), ("2024-07-01", None)]
def both(rule):
    out = []
    for a, z in P:
        s = R.stats(M.backtest(rule(), M.pos[a], M.pos[z] if z else M.N - 1)[0]); out.append("%+6.0f%% dd %4.0f%%" % (s["total"], s["maxdd"]))
    return "   |   ".join(out)
print("LIQUIDITY LAYER (stablecoin supply growth) as an extra condition")
print("  G on 100d (base)                 ", both(lambda: M.book_rule()[0]))
for n in (30, 60, 90):
    base = list(M._risk)
    M._risk = [base[i] and (g(i, n) or 0) > 0 for i in range(M.N)]
    print("  G, risk also needs stables %2dd > 0" % n, both(lambda: M.book_rule()[0]))
    M._risk = base
# how often is stables growth negative while BTC>100d? informative
neg = sum(1 for i in range(M.pos["2020-06-01"], M.N) if M._risk[i] and (g(i, 60) or 0) <= 0)
print("  days risk-on with stables 60d shrinking:", neg)
# forward alt vs BTC return by stablecoin growth tercile
print("\nSTABLES 60d growth vs next 60d ALTS minus BTC (alts basket)")
A = lab.alts_idx(M)
rows = []
for i in range(M.pos["2020-06-01"], M.N - 60, 5):
    x = g(i, 60)
    if x is None or not A[i] or not A[i + 60]: continue
    rows.append((x, math.log(A[i + 60] / A[i]) - math.log(M.idx["BTC"][i + 60] / M.idx["BTC"][i]), M.days[i]))
rows.sort()
k = len(rows) // 3
for lab_, part in (("low", rows[:k]), ("mid", rows[k:2*k]), ("high", rows[2*k:])):
    for per, f in (("hold", lambda d: d < "2024-07-01"), ("recent", lambda d: d >= "2024-07-01")):
        v = [r[1] for r in part if f(r[2])]
        if v: print("  %-4s tercile %-6s n=%3d  median alts-vs-BTC %+5.1f%%  win %2.0f%%" % (lab_, per, len(v), 100 * (math.exp(st.median(v)) - 1), 100 * sum(x > 0 for x in v) / len(v)))
# TRENCH window back-test on the MEMES basket (tradeable proxy, 2020+)
print("\nTRENCH WINDOW PROXY: BTC 7d crosses +10%, then MEMES vs BTC over 14d")
b = M.idx["BTC"]; mm = M.idx["MEMES"]
r7 = [(b[i] / b[i - 7] - 1) if i >= 7 and b[i] and b[i - 7] else None for i in range(M.N)]
ev = [i for i in range(M.pos["2020-06-01"], M.N - 14) if r7[i] and r7[i - 1] is not None and r7[i] >= 0.10 > r7[i - 1]]
ev2 = []
for i in ev:
    if not ev2 or i - ev2[-1] > 14: ev2.append(i)
res = []
for i in ev2:
    if mm[i] and mm[i + 14]:
        rel = (mm[i + 14] / mm[i]) / (b[i + 14] / b[i]) - 1
        res.append((M.days[i], rel, mm[i + 14] / mm[i] - 1))
for per, f in (("hold", lambda d: d < "2024-07-01"), ("recent", lambda d: d >= "2024-07-01")):
    v = [r for r in res if f(r[0])]
    print("  %-6s n=%2d  memes beat BTC %d/%d  median rel %+.1f%%  memes up %d/%d" % (per, len(v), sum(r[1] > 0 for r in v), len(v), 100 * st.median([r[1] for r in v]), sum(r[2] > 0 for r in v), len(v)))
# base rate: any random 14d window
base = [(mm[i + 14] / mm[i]) / (b[i + 14] / b[i]) - 1 for i in range(M.pos["2020-06-01"], M.N - 14) if mm[i] and mm[i + 14]]
print("  base rate: memes beat BTC in any 14d window %.0f%%, median %+.1f%%" % (100 * sum(x > 0 for x in base) / len(base), 100 * st.median(base)))