PythagorasGoal/scripts/research/xsec/runs/XM08.py

"""XM08 — Momentum Consistency (Frog-in-Pan)
Score = past_return(close, L) * fraction_of_up_days(ret, L)

Smooth momentum beats jumpy. "Frog-in-pan" from Ang, Goetzmann, Schaefer (2012):
consistent trends accumulating through many small daily gains dominate short sharp jumps.
Score is higher (more long) when returns over L days are both large AND consistent.

Grid: L=60 fixed (canonical), vary universe / H / k / long_short (<=5 calls total).
"""
import sys
sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/xsec")
import xslib as xs
import numpy as np

# ---------------------------------------------------------------------------
# SCORE: causal frog-in-pan
# ---------------------------------------------------------------------------
def fip_score(P, L=60):
    """
    score[i, a] = past_return(close[i], L) * frac_up_days(ret[i-L+1..i], L)
    Causal: only uses ret and close up to row i.
    """
    close = P.close   # (n, A)
    ret   = P.ret     # (n, A) simple daily returns

    n, A = close.shape

    # past return over L days (causal)
    pr = xs.past_return(close, L)  # (n, A), nan for i < L

    # fraction of positive days over rolling window L
    pos = (ret > 0).astype(float)                          # 1 if up day
    frac_up = xs.roll_mean(pos, L)                         # causal rolling mean -> (n, A)

    score = pr * frac_up
    return score


# ---------------------------------------------------------------------------
# GRID  (<=5 calls)
# ---------------------------------------------------------------------------
results = []

# 1. Base: majors, L=60, H=10, k=5, long_short
rep1 = xs.study_xs(
    "XM08_majors_H10_k5_ls",
    lambda P: fip_score(P, 60),
    universe="majors", H=10, k=5, long_short=True, target_vol=0.20
)
print(xs.fmt(rep1))
print("JSON:", xs.as_json(rep1))
results.append(rep1)

# 2. All assets, L=60, H=10, k=5, long_short
rep2 = xs.study_xs(
    "XM08_all_H10_k5_ls",
    lambda P: fip_score(P, 60),
    universe="all", H=10, k=5, long_short=True, target_vol=0.20
)
print(xs.fmt(rep2))
print("JSON:", xs.as_json(rep2))
results.append(rep2)

# 3. All assets, H=20, k=5, long_short (slower rebal)
rep3 = xs.study_xs(
    "XM08_all_H20_k5_ls",
    lambda P: fip_score(P, 60),
    universe="all", H=20, k=5, long_short=True, target_vol=0.20
)
print(xs.fmt(rep3))
print("JSON:", xs.as_json(rep3))
results.append(rep3)

# 4. Majors, H=10, k=5, long-only
rep4 = xs.study_xs(
    "XM08_majors_H10_k5_lo",
    lambda P: fip_score(P, 60),
    universe="majors", H=10, k=5, long_short=False, target_vol=0.20
)
print(xs.fmt(rep4))
print("JSON:", xs.as_json(rep4))
results.append(rep4)

# 5. All assets, H=10, k=7, long_short (wider top/bottom bucket)
rep5 = xs.study_xs(
    "XM08_all_H10_k7_ls",
    lambda P: fip_score(P, 60),
    universe="all", H=10, k=7, long_short=True, target_vol=0.20
)
print(xs.fmt(rep5))
print("JSON:", xs.as_json(rep5))
results.append(rep5)

# ---------------------------------------------------------------------------
# PICK BEST
# ---------------------------------------------------------------------------
def score_result(r):
    """Prefer earns_slot, then hold-out sharpe, then distinctness."""
    earns = r.get("earns_slot", False)
    ho = r.get("holdout", {}).get("sharpe", -999)
    corr = abs(r.get("corr_xs01", 1.0))
    return (int(earns), ho, -corr)

best = max(results, key=score_result)
print("\n=== BEST CONFIG ===")
print(xs.fmt(best))
print("JSON:", xs.as_json(best))