feat(step1): Universal EA Schema Layer — replaces param_manifest.yaml

NEW FILES:
  ea/__init__.py          - EA module package
  ea/schema.py            - ParameterDef + ParameterSchema (replaces YAML manifest)
  ea/set_parser.py        - Parse ANY MT5 .set file → ParameterSchema
  ea/registry.py          - EAProfile + EARegistry (ea_registry.yaml)
  ea_registry.yaml        - LEGSTECH_EA_V2 pre-registered with automation overrides

MODIFIED:
  mt5/ini_builder.py      - Accepts ParameterSchema OR legacy manifest_path
  mt5/runner.py           - Accepts optional EAProfile for EA-agnostic validation
  config.yaml             - Removed hardcoded ea: block; added ea_registry path

KEY DESIGN:
  - .set file IS the manifest: value|min|max|step auto-detects float/int/bool/enum/fixed
  - automation_overrides in EAProfile forces headless-safe values (InpShowPanel=0)
  - Phase A optimization INI: Optimization=2 (genetic), ranges from schema
  - Phase B single backtest: Optimization=0 (unchanged behavior)
  - LEGSTECH advanced mode: fully backwards compatible via legacy manifest fallback
  - runner.py still works for legacy callers (no EAProfile needed)

TESTED:
  - 49/49 LEGSTECH params parsed correctly
  - InpShowPanel=0 automation override applied
  - INI output correct for both Phase A (Optimization=2) and Phase B (Optimization=0)
  - All imports pass

ea/__init__.py

@@ -0,0 +1,3 @@
+"""
+ea/ — EA Profile management, .set file parsing, and parameter schema.
+"""

ea/registry.py

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+"""
+ea/registry.py
+EA Profile storage and retrieval.
+
+Profiles are stored in ea_registry.yaml (path from config.yaml paths.ea_registry).
+The registry is the single source of truth for which EAs are registered
+and how to find their .set files.
+"""
+from __future__ import annotations
+
+from dataclasses import dataclass, field, asdict
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Optional
+
+import yaml
+from loguru import logger
+
+from ea.schema import ParameterSchema
+from ea.set_parser import SetParser
+
+
+# ── EAProfile ─────────────────────────────────────────────────────────────────
+
+@dataclass
+class EAProfile:
+    """Configuration for one EA registered in the optimizer."""
+    name:          str           # Display name, e.g. "LEGSTECH_EA_V2"
+    ex5_file:      str           # MT5 Experts file name (without .ex5 extension)
+    set_template:  str           # Absolute path string to template .set file
+    symbol:        str           # e.g. "XAUUSD"
+    timeframe:     str           # e.g. "H1"
+    mode:          str = "generic"   # "generic" | "advanced"
+    registered_at: str = field(
+        default_factory=lambda: datetime.now(timezone.utc).isoformat()
+    )
+
+    # Params the user has chosen to optimize (param names → True/False).
+    # Empty dict means: use default_optimize logic in SetParser.
+    optimize_params: dict[str, bool] = field(default_factory=dict)
+
+    # Automation overrides: param values that must be used during backtesting,
+    # regardless of what the .set template says.
+    # Example: {"InpShowPanel": 0, "InpTesterMode": 1}
+    automation_overrides: dict[str, object] = field(default_factory=dict)
+
+    def __post_init__(self):
+        assert self.mode in ("generic", "advanced"), \
+            f"EAProfile.mode must be 'generic' or 'advanced', got {self.mode!r}"
+        assert self.timeframe in (
+            "M1","M5","M15","M30","H1","H4","D1","W1","MN"
+        ), f"Invalid timeframe: {self.timeframe}"
+
+    @property
+    def set_template_path(self) -> Path:
+        return Path(self.set_template)
+
+    def to_dict(self) -> dict:
+        return asdict(self)
+
+    @classmethod
+    def from_dict(cls, d: dict) -> "EAProfile":
+        return cls(**{k: v for k, v in d.items() if k in cls.__dataclass_fields__})
+
+
+# ── EARegistry ────────────────────────────────────────────────────────────────
+
+class EARegistry:
+    """
+    Manages registered EA profiles, persisted in ea_registry.yaml.
+
+    Usage:
+        reg = EARegistry("config.yaml")
+        profile = reg.get("LEGSTECH_EA_V2")
+        schema  = reg.get_schema(profile)
+    """
+
+    def __init__(self, config_path: str | Path = "config.yaml"):
+        config_path = Path(config_path)
+        with open(config_path) as f:
+            cfg = yaml.safe_load(f)
+
+        registry_rel = cfg.get("paths", {}).get("ea_registry", "ea_registry.yaml")
+        self._registry_path = config_path.parent / registry_rel
+        self._parser = SetParser()
+        self._profiles: dict[str, EAProfile] = {}
+        self._load()
+
+    # ── Public API ────────────────────────────────────────────────────────────
+
+    def register(self, profile: EAProfile) -> None:
+        """Add or update an EA profile."""
+        self._profiles[profile.name] = profile
+        self._save()
+        logger.info(f"EARegistry: registered {profile.name!r} (mode={profile.mode})")
+
+    def get(self, name: str) -> EAProfile:
+        """Get a registered EA profile by name. Raises KeyError if not found."""
+        if name not in self._profiles:
+            available = list(self._profiles.keys())
+            raise KeyError(
+                f"EA {name!r} not registered. Available: {available}"
+            )
+        return self._profiles[name]
+
+    def list_all(self) -> list[EAProfile]:
+        """Return all registered profiles."""
+        return list(self._profiles.values())
+
+    def remove(self, name: str) -> None:
+        """Unregister an EA."""
+        self._profiles.pop(name, None)
+        self._save()
+        logger.info(f"EARegistry: removed {name!r}")
+
+    def exists(self, name: str) -> bool:
+        return name in self._profiles
+
+    def get_schema(
+        self,
+        profile: EAProfile,
+        apply_optimize_selection: bool = True,
+    ) -> ParameterSchema:
+        """
+        Parse the EA's .set file and return a ParameterSchema.
+        Applies automation_overrides to ensure headless-safe defaults.
+        Applies optimize_params selection if present.
+        """
+        if not profile.set_template_path.exists():
+            raise FileNotFoundError(
+                f"Set template not found: {profile.set_template}\n"
+                f"Please update the path in EA Registry for {profile.name!r}."
+            )
+
+        schema = self._parser.parse(
+            path=profile.set_template_path,
+            ea_name=profile.name,
+            default_optimize=False,
+        )
+
+        # Apply automation overrides: force specific param values
+        # (e.g. InpShowPanel=0 so no GUI renders during headless backtests)
+        for pname, value in profile.automation_overrides.items():
+            if pname in schema.parameters:
+                schema.parameters[pname].default = value
+                schema.parameters[pname].type    = "fixed"
+                schema.parameters[pname].optimize = False
+
+        if apply_optimize_selection and profile.optimize_params:
+            for pname, should_opt in profile.optimize_params.items():
+                if pname in schema.parameters:
+                    p = schema.parameters[pname]
+                    if p.type != "fixed":
+                        p.optimize = should_opt
+        elif not profile.optimize_params:
+            # No selection yet — mark all non-fixed as optimizable by default
+            for p in schema.parameters.values():
+                if p.type != "fixed":
+                    p.optimize = True
+
+        return schema
+
+    def update_optimize_params(self, name: str, optimize_params: dict[str, bool]) -> None:
+        """Update which parameters to optimize for a registered EA."""
+        profile = self.get(name)
+        profile.optimize_params = optimize_params
+        self._save()
+
+    # ── Internal ─────────────────────────────────────────────────────────────
+
+    def _load(self) -> None:
+        """Load profiles from YAML. Creates the file if it doesn't exist."""
+        if not self._registry_path.exists():
+            logger.info(f"EARegistry: creating new registry at {self._registry_path}")
+            self._registry_path.write_text("profiles: []\n", encoding="utf-8")
+            return
+
+        with open(self._registry_path, encoding="utf-8") as f:
+            data = yaml.safe_load(f) or {}
+
+        profiles_raw = data.get("profiles", [])
+        for item in profiles_raw:
+            try:
+                p = EAProfile.from_dict(item)
+                self._profiles[p.name] = p
+            except Exception as e:
+                logger.warning(f"EARegistry: skipped malformed profile {item}: {e}")
+
+        logger.info(f"EARegistry: loaded {len(self._profiles)} profile(s) from {self._registry_path.name}")
+
+    def _save(self) -> None:
+        """Persist all profiles to YAML."""
+        data = {"profiles": [p.to_dict() for p in self._profiles.values()]}
+        with open(self._registry_path, "w", encoding="utf-8") as f:
+            yaml.dump(data, f, default_flow_style=False, allow_unicode=True, sort_keys=False)
+        logger.debug(f"EARegistry: saved {len(self._profiles)} profile(s)")
+
+    def verify_integrity(self) -> list[str]:
+        """
+        Check that all registered EAs have accessible .set files.
+        Returns a list of error messages (empty = all OK).
+        """
+        errors = []
+        for name, profile in self._profiles.items():
+            if not profile.set_template_path.exists():
+                errors.append(f"{name}: .set file missing at {profile.set_template}")
+        return errors

ea/schema.py

@@ -0,0 +1,231 @@
+"""
+ea/schema.py
+ParameterDef and ParameterSchema — the universal parameter representation.
+Replaces mutation/param_manifest.yaml entirely.
+"""
+from __future__ import annotations
+
+import math
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Optional
+
+
+# ── Parameter types ───────────────────────────────────────────────────────────
+
+PARAM_TYPES = {"float", "int", "bool", "enum", "fixed"}
+
+
+@dataclass
+class ParameterDef:
+    """
+    One EA input parameter, as parsed from a .set file.
+
+    type:
+        float  — continuous, has decimal step
+        int    — discrete integer range
+        bool   — true/false toggle (min=0, max=1, step=1)
+        enum   — small discrete set of integer values
+        fixed  — never changed during optimization (min==max, or zeroed range)
+    """
+    name:        str
+    default:     Any                        # Value at default (typed: float/int/bool)
+    type:        str                        # float | int | bool | enum | fixed
+    min:         Optional[float] = None
+    max:         Optional[float] = None
+    step:        Optional[float] = None
+    optimize:    bool = False               # User has selected this for optimization
+    enum_values: list = field(default_factory=list)  # Populated for type==enum
+
+    def __post_init__(self):
+        assert self.type in PARAM_TYPES, f"Unknown param type '{self.type}' for {self.name}"
+        if self.type == "enum" and not self.enum_values and self.min is not None:
+            # Auto-populate enum values from range
+            v = self.min
+            while v <= self.max + 1e-9:
+                self.enum_values.append(int(round(v)))
+                v += self.step
+
+    @property
+    def range_label(self) -> str:
+        """Human-readable range string, e.g. '0.5 – 3.0 (step 0.5)'."""
+        if self.type == "fixed":
+            return f"{self.default} (fixed)"
+        if self.type == "bool":
+            return "true / false"
+        if self.type == "enum":
+            return " | ".join(str(v) for v in self.enum_values)
+        return f"{self.min} – {self.max} (step {self.step})"
+
+    def clamp(self, value: float) -> Any:
+        """Clamp a proposed value to valid range, return correctly typed value."""
+        if self.type == "fixed":
+            return self.default
+        if self.type == "bool":
+            return bool(round(value))
+        if self.min is not None:
+            value = max(float(self.min), min(float(self.max), float(value)))
+        if self.type == "int":
+            return int(round(value))
+        if self.type == "enum":
+            # Snap to nearest enum value
+            return min(self.enum_values, key=lambda v: abs(v - value))
+        # float — round to same decimal places as step
+        if self.step and self.step > 0:
+            decimals = len(str(self.step).rstrip("0").split(".")[-1]) if "." in str(self.step) else 0
+            return round(value, decimals)
+        return value
+
+    def step_up(self, current: Any) -> Optional[Any]:
+        """Return value one step above current, or None if already at max."""
+        if self.type in ("fixed", "bool"):
+            return None
+        if self.type == "enum":
+            idx = self.enum_values.index(int(current)) if int(current) in self.enum_values else -1
+            return self.enum_values[idx + 1] if idx < len(self.enum_values) - 1 else None
+        nxt = float(current) + float(self.step)
+        return self.clamp(nxt) if nxt <= self.max + 1e-9 else None
+
+    def step_down(self, current: Any) -> Optional[Any]:
+        """Return value one step below current, or None if already at min."""
+        if self.type in ("fixed", "bool"):
+            return None
+        if self.type == "enum":
+            idx = self.enum_values.index(int(current)) if int(current) in self.enum_values else -1
+            return self.enum_values[idx - 1] if idx > 0 else None
+        nxt = float(current) - float(self.step)
+        return self.clamp(nxt) if nxt >= self.min - 1e-9 else None
+
+
+# ── ParameterSchema ───────────────────────────────────────────────────────────
+
+class ParameterSchema:
+    """
+    Full parameter schema for one EA, derived from its .set file.
+    Replaces mutation/param_manifest.yaml.
+    """
+
+    def __init__(self, ea_name: str, source_set: Path, parameters: dict[str, ParameterDef]):
+        self.ea_name    = ea_name
+        self.source_set = source_set
+        self.parameters = parameters   # ordered dict: name → ParameterDef
+
+    # ── Accessors ─────────────────────────────────────────────────────────────
+
+    def optimizable(self) -> list[ParameterDef]:
+        """Parameters the user has selected to optimize."""
+        return [p for p in self.parameters.values() if p.optimize]
+
+    def fixed(self) -> list[ParameterDef]:
+        """Parameters that never change."""
+        return [p for p in self.parameters.values() if not p.optimize]
+
+    def all_params(self) -> list[ParameterDef]:
+        return list(self.parameters.values())
+
+    def get(self, name: str) -> Optional[ParameterDef]:
+        return self.parameters.get(name)
+
+    def __len__(self) -> int:
+        return len(self.parameters)
+
+    # ── Baseline / override helpers ───────────────────────────────────────────
+
+    def defaults(self) -> dict[str, Any]:
+        """All parameters at their default values."""
+        return {name: p.default for name, p in self.parameters.items()}
+
+    def with_overrides(self, overrides: dict[str, Any]) -> dict[str, Any]:
+        """
+        Merge override values with defaults.
+        Overrides only apply to known parameters; unknown keys are dropped.
+        Values are clamped to valid range.
+        """
+        result = self.defaults()
+        for name, value in overrides.items():
+            if name in self.parameters:
+                result[name] = self.parameters[name].clamp(value)
+        return result
+
+    # ── INI rendering ─────────────────────────────────────────────────────────
+
+    def to_ini_inputs(self, params: dict[str, Any], optimize_mode: bool = False) -> str:
+        """
+        Render [TesterInputs] block for an MT5 .ini file.
+
+        optimize_mode=False → plain values  (Phase B single backtest)
+        optimize_mode=True  → value|min|max|step ranges (Phase A genetic search)
+
+        Bool optimization ranges use 1/0 (not true/false) per MT5 spec.
+        """
+        lines = []
+        full = self.with_overrides(params)
+
+        for name, p in self.parameters.items():
+            value = full.get(name, p.default)
+
+            if optimize_mode and p.optimize and p.type != "fixed":
+                # Write optimization range — booleans use 1/0 in range format
+                if p.type == "bool":
+                    v = "1" if value else "0"
+                    lines.append(f"{name}={v}|0|1|1")
+                else:
+                    formatted = self._fmt(p, value)
+                    lines.append(
+                        f"{name}={formatted}|{self._fmt(p, p.min)}|{self._fmt(p, p.max)}|{self._fmt(p, p.step)}"
+                    )
+            else:
+                lines.append(f"{name}={self._fmt(p, value)}")
+
+        return "\n".join(lines)
+
+    def to_set_file(self, params: dict[str, Any], header_comment: str = "") -> str:
+        """
+        Render a clean output .set file (no optimization ranges).
+        This is what gets downloaded by the user.
+        """
+        lines = []
+        if header_comment:
+            for line in header_comment.strip().splitlines():
+                lines.append(f"; {line}")
+            lines.append("")
+
+        full = self.with_overrides(params)
+        for name, p in self.parameters.items():
+            value = full.get(name, p.default)
+            lines.append(f"{name}={self._fmt(p, value)}")
+
+        return "\n".join(lines)
+
+    # ── Internal formatting ───────────────────────────────────────────────────
+
+    @staticmethod
+    def _fmt(p: ParameterDef, value: Any) -> str:
+        """Format a value according to parameter type."""
+        if value is None:
+            return str(p.default)
+        if p.type == "bool":
+            # Accept int (0/1) or bool
+            if isinstance(value, str):
+                return value.lower()
+            return "true" if value else "false"
+        if p.type in ("int", "enum"):
+            return str(int(round(float(value))))
+        if p.type == "float":
+            step = p.step or 0.1
+            decimals = 0
+            if "." in str(step):
+                decimals = len(str(step).rstrip("0").split(".")[-1])
+            return f"{float(value):.{decimals}f}"
+        # fixed or unknown
+        return str(value)
+
+    # ── Summary ───────────────────────────────────────────────────────────────
+
+    def summary(self) -> str:
+        opt = self.optimizable()
+        return (
+            f"ParameterSchema({self.ea_name}): "
+            f"{len(self.parameters)} params total, "
+            f"{len(opt)} optimizable"
+        )

ea/set_parser.py

@@ -0,0 +1,278 @@
+"""
+ea/set_parser.py
+Parse any MT5 .set file into a ParameterSchema.
+
+Handles both .set formats:
+  value|min|max|step          (single pipe  — most common)
+  value||min||max||step||Y/N  (double pipe  — some MT5 builds)
+
+Fixed detection:
+  min == max                  → type="fixed" (e.g. InpMagicNumber=202402|202402|202402|1)
+  min == 0 AND max == 0       → type="fixed" (zeroed range = "don't optimize")
+  No range at all             → type="fixed"
+
+Type detection (non-fixed only):
+  min==0, max==1, step==1     → bool
+  "." in step string          → float
+  max - min <= 8, step==1     → enum  (small discrete integer set)
+  otherwise                   → int
+"""
+from __future__ import annotations
+
+import re
+from pathlib import Path
+from typing import Any, Optional
+
+from loguru import logger
+
+from ea.schema import ParameterDef, ParameterSchema
+
+
+# ── Tester section keys to skip (not EA inputs) ───────────────────────────────
+
+_TESTER_KEYS = {
+    "expert", "symbol", "period", "optimization", "model",
+    "fromdate", "todate", "forwardmode", "report", "replacereport",
+    "shutdownterminal", "deposit", "currency", "leverage",
+    "optimizationmode", "forwarddate", "optimizationiterations",
+}
+
+# EA params that should always be fixed even if they have a range
+_FORCE_FIXED_PATTERNS = [
+    "testermode", "testeri", "testerinit", "showpanel",
+    "magicnumber", "magic",
+]
+
+
+class SetParser:
+    """
+    Parses a MT5 .set file into a ParameterSchema.
+
+    Usage:
+        parser = SetParser()
+        schema = parser.parse(
+            path=Path("C:/MT5 Set files/LEGSTECH_EA_V2.set"),
+            ea_name="LEGSTECH_EA_V2",
+            default_optimize=False,  # user chooses via UI
+        )
+    """
+
+    def parse(
+        self,
+        path: Path,
+        ea_name: str,
+        default_optimize: bool = False,
+        force_optimize: Optional[set[str]] = None,
+        force_fixed:    Optional[set[str]] = None,
+    ) -> ParameterSchema:
+        """
+        Parse a .set file and return a ParameterSchema.
+
+        Args:
+            path:             Path to the .set file.
+            ea_name:          Display name for the EA.
+            default_optimize: Whether to mark all optimizable params as optimize=True by default.
+                              If False (default), the user selects via UI.
+            force_optimize:   Set of param names that are always optimize=True regardless.
+            force_fixed:      Set of param names that are always type="fixed".
+        """
+        force_optimize = force_optimize or set()
+        force_fixed    = force_fixed    or set()
+
+        path = Path(path)
+        if not path.exists():
+            raise FileNotFoundError(f".set file not found: {path}")
+
+        try:
+            text = path.read_text(encoding="utf-16")
+        except UnicodeError:
+            text = path.read_text(encoding="utf-8", errors="replace")
+
+        parameters: dict[str, ParameterDef] = {}
+        current_section = ""
+
+        for raw_line in text.splitlines():
+            line = raw_line.strip()
+            if not line or line.startswith(";"):
+                continue
+
+            # Section header
+            if line.startswith("[") and line.endswith("]"):
+                current_section = line[1:-1].lower()
+                continue
+
+            # Skip lines without "="
+            if "=" not in line:
+                continue
+
+            name, _, rest = line.partition("=")
+            name = name.strip()
+            rest = rest.strip()
+
+            # Skip tester-section metadata keys
+            if name.lower() in _TESTER_KEYS:
+                continue
+
+            # Parse the value + optional range
+            param = self._parse_param(name, rest)
+            if param is None:
+                logger.debug(f"SetParser: skipped unrecognised line: {line!r}")
+                continue
+
+            # Apply force-fixed overrides
+            if name in force_fixed or self._is_force_fixed(name):
+                param.type     = "fixed"
+                param.optimize = False
+            elif name in force_optimize:
+                param.optimize = True
+            elif default_optimize and param.type != "fixed":
+                param.optimize = True
+
+            parameters[name] = param
+
+        if not parameters:
+            raise ValueError(f"No EA input parameters found in .set file: {path}")
+
+        schema = ParameterSchema(ea_name=ea_name, source_set=path, parameters=parameters)
+        logger.info(f"SetParser: parsed {schema.summary()} from {path.name}")
+        return schema
+
+    # ── Internal ─────────────────────────────────────────────────────────────
+
+    def _parse_param(self, name: str, rest: str) -> Optional[ParameterDef]:
+        """
+        Parse a single parameter line.
+        rest is everything after the first "=" on the line.
+        """
+        # Normalise: double-pipe "||" → single "|"
+        rest = re.sub(r"\|\|", "|", rest)
+
+        # Strip trailing Y/N optimize flag if present
+        yn_match = re.search(r"\|([YN])$", rest, re.IGNORECASE)
+        if yn_match:
+            rest = rest[:yn_match.start()]
+
+        parts = [p.strip() for p in rest.split("|")]
+
+        if len(parts) == 1:
+            # No range info → fixed
+            value = self._cast_value(parts[0])
+            return ParameterDef(
+                name=name, default=value, type="fixed",
+                min=None, max=None, step=None, optimize=False,
+            )
+
+        if len(parts) < 4:
+            # Incomplete range — treat as fixed  
+            value = self._cast_value(parts[0])
+            return ParameterDef(
+                name=name, default=value, type="fixed",
+                min=None, max=None, step=None, optimize=False,
+            )
+
+        raw_val, raw_min, raw_max, raw_step = parts[0], parts[1], parts[2], parts[3]
+
+        try:
+            default_f = float(raw_val)
+            min_f     = float(raw_min)
+            max_f     = float(raw_max)
+            step_f    = float(raw_step)
+        except ValueError:
+            value = self._cast_value(raw_val)
+            return ParameterDef(
+                name=name, default=value, type="fixed",
+                min=None, max=None, step=None, optimize=False,
+            )
+
+        # Fixed detection
+        is_fixed = (
+            abs(min_f - max_f) < 1e-9          # min == max
+            or (abs(min_f) < 1e-9 and abs(max_f) < 1e-9)  # both zero (zeroed range)
+        )
+        if is_fixed:
+            return ParameterDef(
+                name=name,
+                default=self._typed_default(raw_val, raw_step),
+                type="fixed",
+                min=min_f, max=max_f, step=step_f,
+                optimize=False,
+            )
+
+        # Type detection
+        ptype = self._detect_type(min_f, max_f, step_f, raw_step, raw_val)
+        default = self._typed_cast(ptype, default_f, raw_val)
+
+        return ParameterDef(
+            name=name,
+            default=default,
+            type=ptype,
+            min=min_f,
+            max=max_f,
+            step=step_f,
+            optimize=False,  # user sets this via UI; can be overridden by caller
+        )
+
+    @staticmethod
+    def _detect_type(min_f: float, max_f: float, step_f: float,
+                     raw_step: str, raw_val: str) -> str:
+        """Infer parameter type from its range."""
+        # Bool: exactly 0–1 with step 1
+        if abs(min_f) < 1e-9 and abs(max_f - 1.0) < 1e-9 and abs(step_f - 1.0) < 1e-9:
+            return "bool"
+
+        # Float: step has decimal component
+        if "." in raw_step and not raw_step.endswith(".0") and float(raw_step) % 1 != 0:
+            return "float"
+        # Also float if default value has meaningful decimal
+        if "." in raw_val and float(raw_val) % 1 != 0:
+            return "float"
+
+        # Enum: small integer set (≤ 8 distinct values, step 1)
+        n_values = int(round((max_f - min_f) / step_f)) + 1 if step_f > 0 else 1
+        if abs(step_f - 1.0) < 1e-9 and n_values <= 8:
+            return "enum"
+
+        return "int"
+
+    @staticmethod
+    def _typed_cast(ptype: str, value_f: float, raw: str) -> Any:
+        if ptype == "bool":
+            return value_f != 0 or raw.lower() in ("true", "1")
+        if ptype == "int":
+            return int(round(value_f))
+        if ptype == "enum":
+            return int(round(value_f))
+        return value_f  # float
+
+    @staticmethod
+    def _typed_default(raw: str, raw_step: str) -> Any:
+        """Cast a fixed-param value without range context."""
+        lower = raw.lower()
+        if lower in ("true", "false"):
+            return lower == "true"
+        try:
+            f = float(raw)
+            # Return int if it's a whole number and step is integer-like
+            if "." not in raw_step or raw_step.endswith(".0"):
+                if f == int(f):
+                    return int(f)
+            return f
+        except ValueError:
+            return raw
+
+    @staticmethod
+    def _cast_value(raw: str) -> Any:
+        lower = raw.lower()
+        if lower in ("true", "false"):
+            return lower == "true"
+        try:
+            f = float(raw)
+            return int(f) if f == int(f) and "." not in raw else f
+        except ValueError:
+            return raw
+
+    @staticmethod
+    def _is_force_fixed(name: str) -> bool:
+        """Return True for params that are always fixed regardless of their range."""
+        lower = name.lower()
+        return any(pat in lower for pat in _FORCE_FIXED_PATTERNS)