guardrailed-llm-agent — Step 8: The Walk-Forward Loop: Keeping It Honest (snippet 1)

step_8_the_walk_forward_loop_keeping_it__1.py

def run_walk_forward_oos(feat_all):
    oos_start = pd.to_datetime(OOS_START)
    months    = month_starts(feat_all.index, oos_start)
    cache     = load_cache(POLICY_CACHE_FILE)

    # Compute lags over FULL history so month boundaries never force flat.
    # state_lag[t] = state at close of t-1, available before t opens.
    state_lag_full = feat_all['state'].shift(1)
    vol_lag_full   = feat_all['vol'].shift(1)

    month_blocks = []   # (test_index, policy, guard_params)
    best_guard   = None

    for m in months:
        next_m = (m + pd.offsets.MonthBegin(1)).normalize()
        test   = feat_all.loc[(feat_all.index >= m) & (feat_all.index < next_m)]
        if len(test) < 5: continue

        train_start = (m - pd.DateOffset(years=TRAIN_YEARS)).normalize()
        train = feat_all.loc[(feat_all.index >= train_start) & (feat_all.index < m)]
        if len(train) < 252: continue      # need at least ~1 year

        policy = build_policy_for_month(m, train, cache)

        if best_guard is None or ((m.month - 1) % OPT_FREQ_MONTHS == 0):
            best_guard = optimize_guardrails(train, policy)

        month_blocks.append((test.index, policy, best_guard))