# tuner_plan.py
# Search plan for CrossSectionalRSHeat v2 on 5m, with a special branch for long windows (e.g., 2880 bars).
# The auto_tuner will import default_plan(limit_bars) if present.
#
# Keys map to YAML as follows:
# - "tp"/"sl" -> strategy_params.tp_atr_mult / strategy_params.sl_atr_mult
# - "min-mom" -> strategy_params.min_momentum_sum
# - "min-atr" -> strategy_params.min_atr_ratio
# - "top-n"   -> top-n (top-level)
# - "side"    -> side (top-level)
# - Other keys are addressed via full paths (e.g., strategy_params.min_vol_surge_mult).
#
# Rationale: based on current 2880/5m backtest showing steady equity decay and
# broad drawdown, we bias the search toward **stricter entry filters** (ATR,
# momentum, liquidity, volume surge), **heat gating**, and **smaller top‑N**.
# This should cut trades during adverse regimes and prioritize higher‑quality
# breakouts.
#
# Works with auto_tuner_rays2grid (rays -> grid).

def _frange(start, stop, step):
    vals, x = [], float(start)
    for _ in range(10000):
        if x > float(stop) + 1e-12:
            break
        vals.append(round(x, 10))
        x += float(step)
    return vals

def default_plan(limit_bars: int = None):
    """
    Returns a list[tuple[str, dict]] of (mode, params) steps.
    The tuner will iterate over the steps in order.
    """
    long_window = (limit_bars or 1440) >= 2000

    if long_window:
        # ---- Plan tuned for 5m / 2880 bars ----
        return [
            # Direction & universe size
            ("rays", {"side": ["LONG", "BOTH", "SHORT"]}),
            ("rays", {"top-n": [6, 8, 10, 12]}),

            # Core entry quality thresholds (tighten first)
            ("rays", {"min-mom": [0.03, 0.05, 0.07, 0.09]}),     # momentum_sum = dp6h + dp12h
            ("rays", {"min-atr": [0.008, 0.012, 0.016, 0.020]}), # ATR/close

            # Liquidity / surge (tight → fewer trades in chop/illiquid)
            ("rays", {"strategy_params.min_vol_surge_mult": [1.10, 1.30, 1.50]}),
            ("rays", {"strategy_params.min_qv_24h": [300_000, 500_000, 800_000, 1_200_000]}),
            ("rays", {"strategy_params.min_qv_1h":  [20_000,  40_000,  60_000,   80_000]}),
            ("rays", {"strategy_params.min_breadth": [0.00, 0.05, 0.10, 0.20]}),

            # Brackets from ATR (prefer asymmetric exploration: wider SL may help trend capture)
            ("rays", {"tp": [3.2, 3.6, 4.0, 4.4]}),
            ("rays", {"sl": [1.0, 1.2, 1.4, 1.6]}),

            # Heat-first gating
            ("rays", {"open_on_heat": [False, True]}),
            ("rays", {"open_heat_min": [0.75, 0.85, 0.90, 0.95]}),

            # Local refinement near current bests
            ("grid", {
                "min-mom": "around:0.01",
                "min-atr": "around:0.004",
                "strategy_params.min_vol_surge_mult": "around:0.10",
                "strategy_params.min_qv_24h": "around:200000",
                "strategy_params.min_qv_1h":  "around:10000",
                "top-n": "around:2",
                "tp": "around:0.2",
                "sl": "around:0.1",
                "open_heat_min": "around:0.05",
            }),
        ]

    # ---- Default plan (e.g., 1440 bars) ----
    return [
        ("rays", {"tp": _frange(3.25, 3.75, 0.05)}),
        ("rays", {"sl": _frange(1.02, 1.12, 0.02)}),
        ("rays", {"min-mom": _frange(0.019, 0.024, 0.001)}),
        ("rays", {"min-atr": [0, 0.0005, 0.0008, 0.0010, 0.0012]}),
        ("rays", {"side": ["LONG", "BOTH"]}),
        ("rays", {"top-n": [10, 12, 14, 16]}),
        ("rays", {"strategy_params.min_vol_surge_mult": [1.05, 1.20, 1.35]}),
        ("rays", {"strategy_params.min_qv_24h": [200_000, 350_000, 500_000]}),
        ("rays", {"strategy_params.min_qv_1h":  [10_000,  20_000,  30_000]}),
        ("rays", {"open_on_heat": [False, True]}),
        ("rays", {"open_heat_min": [0.80, 0.90, 0.95]}),
        ("grid", {
            "tp": "around:0.02",
            "sl": "around:0.02",
            "min-mom": "around:0.001",
            "min-atr": "around:0.0002",
            "top-n": "around:2",
            "strategy_params.min_vol_surge_mult": "around:0.10",
            "strategy_params.min_qv_24h": "around:150000",
            "strategy_params.min_qv_1h":  "around:5000",
            "open_heat_min": "around:0.05",
        }),
    ]