Initial commit: MT5 EA Optimizer v1.0
Full optimization system for LEGSTECH_EA_V2: - Flask + SocketIO live dashboard (dark premium UI) - MT5 process control (auto-kill, clean launch, retry) - HTML report parser (UTF-16 LE, 597 trades, metrics) - Pre-run validation and actionable error messages - Analysis engines: Reversal, TimePerfomance, EntryExit, EquityCurve - Composite scoring (Calmar-primary) - Mutation engine with knowledge_base.yaml - Validation gate: IS + Walk-Forward - Reports folder with HTML/CSV per run - Double-click launcher batch file
This commit is contained in:
LEGSTECH Optimizer
@@ -0,0 +1,247 @@
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"""
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mutation/engine.py
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Translates analysis findings into concrete parameter hypotheses.
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Uses knowledge_base.yaml rules as a structured ruleset.
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"""
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from __future__ import annotations
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import json
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from pathlib import Path
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from typing import Any, Optional
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import numpy as np
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import yaml
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from loguru import logger
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from data.models import Finding, Hypothesis
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class MutationEngine:
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"""
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Finding → Hypothesis translator.
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Workflow:
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1. Load knowledge_base.yaml rules
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2. For each finding, find matching rules
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3. Filter rules already tested recently (dedup)
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4. Resolve dynamic mutation values (percentile-based, derived)
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5. Build Hypothesis objects
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6. Return sorted by estimated PnL impact
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"""
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def __init__(
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self,
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kb_path: str | Path = "mutation/knowledge_base.yaml",
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manifest_path: str | Path = "mutation/param_manifest.yaml",
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dedup_lookback: int = 10,
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):
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with open(kb_path) as f:
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self.kb = yaml.safe_load(f)["rules"]
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with open(manifest_path) as f:
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self.manifest = yaml.safe_load(f)["parameters"]
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self.dedup_lookback = dedup_lookback
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# ── Public ────────────────────────────────────────────────────────────────
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def propose(
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self,
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findings: list[Finding],
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current_params: dict[str, Any],
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recent_deltas: list[dict], # from store.get_recent_param_deltas()
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max_proposals: int = 3,
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) -> list[Hypothesis]:
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"""
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Generate hypotheses from findings, de-duplicate, and return top-N.
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"""
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hypotheses: list[Hypothesis] = []
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for finding in findings:
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for rule in self.kb:
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if not self._rule_matches(rule, finding, current_params):
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continue
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param_delta = self._build_delta(rule, finding, current_params)
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if not param_delta:
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continue
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# Skip if identical delta was recently tested
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if self._already_tested(param_delta, recent_deltas):
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logger.debug(f"Skipping rule {rule['id']} — already tested.")
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continue
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h = Hypothesis(
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parent_run_id=finding.run_id,
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finding_ids=[finding.finding_id],
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description=f"[{rule['id']}] {rule['action_label']}",
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param_delta=param_delta,
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strategy=rule.get("strategy", "targeted"),
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kb_rule_id=rule["id"],
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)
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hypotheses.append((h, finding.impact_estimate_pnl))
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# Sort by impact descending, deduplicate by KB rule
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seen_rules = set()
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ranked: list[Hypothesis] = []
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for h, impact in sorted(hypotheses, key=lambda x: x[1], reverse=True):
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if h.kb_rule_id not in seen_rules:
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seen_rules.add(h.kb_rule_id)
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ranked.append(h)
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if len(ranked) >= max_proposals:
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break
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logger.info(f"Proposed {len(ranked)} hypotheses from {len(findings)} findings.")
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return ranked
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# ── Rule matching ─────────────────────────────────────────────────────────
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def _rule_matches(
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self, rule: dict, finding: Finding, current_params: dict
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) -> bool:
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"""Check if a KB rule's trigger matches this finding and current params."""
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trigger = rule.get("trigger", {})
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# Analyzer match
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if trigger.get("analyzer") and trigger["analyzer"] != finding.analyzer:
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return False
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# Evaluate condition expression against finding evidence + current params
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condition = trigger.get("condition", "")
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if condition:
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env = {**finding.evidence, **current_params}
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# Simple boolean parsing for conditions like "reversal_rate > 0.15"
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try:
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if not self._eval_condition(condition, env):
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return False
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except Exception as e:
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logger.debug(f"Rule {rule['id']} condition eval error: {e}")
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return False
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return True
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def _eval_condition(self, condition: str, env: dict) -> bool:
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"""
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Evaluate a simple condition string.
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Supports: >, <, >=, <=, ==, AND, OR
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Variables are looked up in env dict.
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"""
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# Replace variable names with their values
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tokens = condition.split()
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resolved_tokens = []
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for token in tokens:
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if token in ("AND", "OR", "and", "or", ">", "<", ">=", "<=", "==", "!="):
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resolved_tokens.append(token.lower())
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elif token in env:
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val = env[token]
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resolved_tokens.append(str(val) if not isinstance(val, str) else f'"{val}"')
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else:
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resolved_tokens.append(token)
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expr = " ".join(resolved_tokens)
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return bool(eval(expr, {"__builtins__": {}})) # restricted eval
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# ── Delta building ────────────────────────────────────────────────────────
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def _build_delta(
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self,
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rule: dict,
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finding: Finding,
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current_params: dict,
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) -> dict[str, Any]:
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"""
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Translate a KB mutation spec into a concrete {param_name: new_value} dict.
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Handles: set, multiply, derive_from, set_to_percentile.
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"""
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delta: dict[str, Any] = {}
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mutations = rule.get("mutations", {})
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for param_name, mutation_spec in mutations.items():
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current = current_params.get(param_name)
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spec = self.manifest.get(param_name, {})
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ptype = spec.get("type", "float")
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p_min = spec.get("min")
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p_max = spec.get("max")
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new_val = self._resolve_mutation(
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mutation_spec, current, ptype, p_min, p_max, finding
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)
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if new_val is not None:
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delta[param_name] = new_val
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# Auto-cascade: if enabling a bool, set defaults for depends_on params
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if ptype == "bool" and new_val is True:
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delta.update(self._cascade_dependencies(param_name, current_params))
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return delta
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def _resolve_mutation(
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self,
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spec: dict | Any,
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current: Any,
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ptype: str,
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p_min: Optional[float],
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p_max: Optional[float],
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finding: Finding,
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) -> Optional[Any]:
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"""Resolve a single mutation spec into a concrete value."""
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if not isinstance(spec, dict):
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return spec # bare value
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# set: directly set to a value
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if "set" in spec:
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return spec["set"]
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# multiply: multiply current value by factor
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if "multiply" in spec and current is not None:
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result = float(current) * spec["multiply"]
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if "clamp_min" in spec:
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result = max(spec["clamp_min"], result)
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if p_min is not None:
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result = max(p_min, result)
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if p_max is not None:
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result = min(p_max, result)
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return round(result, 2) if ptype == "float" else int(result)
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# set_to_percentile: use Nth percentile of a finding evidence list
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if "set_to_percentile" in spec:
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pct = spec["set_to_percentile"] / 100.0
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data = finding.evidence.get("mfe_pips_distribution", [])
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if data:
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val = float(np.percentile(data, pct * 100))
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if "scale" in spec:
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val *= spec["scale"]
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if p_min is not None:
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val = max(p_min, val)
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if p_max is not None:
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val = min(p_max, val)
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return round(val, 1)
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# derive_from: use evidence field
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if "derive_from" in spec:
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key = spec["derive_from"]
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val = finding.evidence.get(key)
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if val is not None:
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return val
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return None
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def _cascade_dependencies(
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self, bool_param: str, current_params: dict
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) -> dict[str, Any]:
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"""When a bool param is enabled, fill in sensible defaults for its dependents."""
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cascade = {}
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for name, spec in self.manifest.items():
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if spec.get("depends_on") != bool_param:
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continue
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# Only set if not already in current params or at a sub-optimal default
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if name not in current_params:
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cascade[name] = spec.get("default", 0)
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return cascade
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# ── Deduplication ─────────────────────────────────────────────────────────
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def _already_tested(self, delta: dict, recent_deltas: list[dict]) -> bool:
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"""Check if an identical param delta was tested recently."""
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delta_str = json.dumps(delta, sort_keys=True)
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for past in recent_deltas:
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if json.dumps(past, sort_keys=True) == delta_str:
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return True
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return False
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@@ -0,0 +1,187 @@
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rules:
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# ── Trailing / Exit Rules ────────────────────────────────────────────────────
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- id: KB001
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trigger:
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analyzer: reversal
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condition: "reversal_rate > 0.15"
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action_label: "Enable trailing stop to protect in-profit trades (reversal rate high)"
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mutations:
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InpUseTrailing:
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set: true
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InpTrailStartPips:
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derive_from: "mfe_p25" # 25th percentile of reversal MFE
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fallback: 20.0
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InpTrailStepPips:
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set: 10.0
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strategy: targeted
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- id: KB002
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trigger:
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analyzer: reversal
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condition: "reversal_rate > 0.20"
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action_label: "Tighten TP ratio — too many trades reversing before target hit"
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mutations:
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InpRRRatio:
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multiply: 0.80
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clamp_min: 1.0
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strategy: targeted
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- id: KB003
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trigger:
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analyzer: reversal
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condition: "mean_capture_ratio < 0.55"
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action_label: "Low MFE capture on winners — enable or tighten trailing"
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mutations:
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InpUseTrailing:
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set: true
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InpTrailStartPips:
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set: 15.0
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InpTrailStepPips:
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set: 8.0
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strategy: targeted
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# ── Session / Time Filter Rules ───────────────────────────────────────────────
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- id: KB004
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trigger:
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analyzer: time_performance
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condition: "type == 'hour_window'"
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action_label: "Exclude identified negative-edge UTC time window via session filter"
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mutations:
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InpUseSession:
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set: true
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InpSessionEnd:
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derive_from: "broker_start" # end session before bad window starts
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strategy: targeted
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- id: KB005
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trigger:
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analyzer: time_performance
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condition: "type == 'session'"
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action_label: "Negative-edge session detected — tighten or disable session window"
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mutations:
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InpUseSession:
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set: true
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strategy: targeted
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# ── Entry Quality Rules ────────────────────────────────────────────────────────
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- id: KB006
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trigger:
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analyzer: entry_exit_quality
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condition: "diagnosis == 'poor_entry'"
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action_label: "Poor entry quality — tighten ATR filter and score gate"
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mutations:
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InpUseSpreadGuard:
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set: true
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InpMinScore:
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multiply: 1.125
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clamp_min: 6
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InpATRMultiplier:
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multiply: 1.20
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clamp_min: 0.3
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strategy: targeted
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- id: KB007
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trigger:
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analyzer: entry_exit_quality
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condition: "diagnosis == 'good_entry_poor_exit'"
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action_label: "Good entries, poor exits — enable trailing with conservative start"
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mutations:
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InpUseTrailing:
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set: true
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InpTrailStartPips:
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set: 18.0
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InpTrailStepPips:
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set: 10.0
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strategy: targeted
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- id: KB008
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trigger:
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analyzer: entry_exit_quality
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condition: "diagnosis == 'both_broken'"
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action_label: "Both entry and exit quality poor — test conservative bot mode"
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mutations:
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InpBotMode:
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set: 2
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InpMinScore:
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multiply: 1.25
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clamp_min: 6
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strategy: compound
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# ── Risk / Drawdown Rules ─────────────────────────────────────────────────────
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- id: KB009
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trigger:
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analyzer: equity_curve
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condition: "flatness_score > 0.50"
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action_label: "Equity spending too much time in drawdown — reduce risk per trade"
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mutations:
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InpRiskPercent:
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multiply: 0.75
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clamp_min: 0.5
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InpMaxDailyLossPct:
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multiply: 0.80
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clamp_min: 1.0
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strategy: targeted
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- id: KB010
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trigger:
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analyzer: equity_curve
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condition: "cluster_count > 3"
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action_label: "Repeated loss clusters — limit consecutive trades and daily risk"
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mutations:
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InpMaxTradesPerDay:
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multiply: 0.75
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clamp_min: 2
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InpMaxDailyLossPct:
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set: 2.0
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strategy: targeted
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# ── Breakeven Rules ────────────────────────────────────────────────────────────
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- id: KB011
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trigger:
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analyzer: reversal
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condition: "reversal_rate > 0.12"
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action_label: "Enable breakeven stop to lock in partial profit before reversal"
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mutations:
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InpUseBreakeven:
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set: true
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InpBEPips:
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derive_from: "mfe_p25"
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fallback: 15.0
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InpBEBufferPips:
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set: 2.0
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strategy: targeted
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# ── Filter Tightening Rules ────────────────────────────────────────────────────
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- id: KB012
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trigger:
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analyzer: entry_exit_quality
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condition: "high_mae_loser_count > 10"
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action_label: "High MAE losers — require EMA slope confirmation"
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mutations:
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InpUseEMA:
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set: true
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InpRequireEMASlope:
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set: true
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InpEMASlopeBars:
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set: 2
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strategy: targeted
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- id: KB013
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trigger:
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analyzer: entry_exit_quality
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condition: "mean_entry_quality < 0.35"
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action_label: "Very poor entry quality — tighten minimum RR gate"
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mutations:
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InpUseMinRR:
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set: true
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InpMinRRRatio:
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multiply: 1.25
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clamp_min: 1.0
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strategy: targeted
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@@ -0,0 +1,394 @@
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# Parameter Manifest — LEGSTECH_EA_V2
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# Generated from: LEGSTECH_EA_V2.set
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# Format: value = default, min/max/step = optimization bounds
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# Types: float | int | bool | enum
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# ─────────────────────────────────────────────────────────────
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parameters:
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# ── Bot / Mode ─────────────────────────────────────────────
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InpBotMode:
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type: enum
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values: [0, 1, 2] # 0, 1, 2 per .set range 0→2 step 1
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default: 1
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category: mode
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description: "Bot operating mode"
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# ── Timeframe Selection (PERIOD_ codes) ────────────────────
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# These are MT5 ENUM_TIMEFRAMES integer codes. Not optimized — fixed.
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InpHTF:
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type: fixed
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default: 16408 # PERIOD_H4
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category: timeframe
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InpMTF:
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type: fixed
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default: 16388 # PERIOD_H1
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category: timeframe
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InpLTF:
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type: fixed
|
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default: 16385 # PERIOD_M30
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category: timeframe
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||||
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# ── Risk Management ────────────────────────────────────────
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InpRiskType:
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type: enum
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values: [0, 1] # 0=fixed lot, 1=percent risk
|
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default: 1
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category: risk
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||||
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InpFixedLot:
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type: float
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min: 0.01
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max: 1.0
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step: 0.01
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default: 0.01
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category: risk
|
||||
depends_on_value: {InpRiskType: 0} # only active when using fixed lot mode
|
||||
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InpRiskPercent:
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type: float
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min: 0.5
|
||||
max: 3.0
|
||||
step: 0.5
|
||||
default: 1.0
|
||||
category: risk
|
||||
depends_on_value: {InpRiskType: 1} # only active when using percent risk mode
|
||||
|
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InpMaxDailyLossPct:
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type: float
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min: 1.0
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max: 5.0
|
||||
step: 0.5
|
||||
default: 3.0
|
||||
category: risk
|
||||
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||||
InpMaxTradesPerDay:
|
||||
type: int
|
||||
min: 1
|
||||
max: 10
|
||||
step: 1
|
||||
default: 5
|
||||
category: risk
|
||||
|
||||
# ── Stop Loss ──────────────────────────────────────────────
|
||||
InpSLType:
|
||||
type: enum
|
||||
values: [0, 1] # 0=fixed pips, 1=ATR-based
|
||||
default: 0
|
||||
category: sl
|
||||
|
||||
InpSLBuffer:
|
||||
type: float
|
||||
min: 5.0
|
||||
max: 30.0
|
||||
step: 5.0
|
||||
default: 10.0
|
||||
category: sl
|
||||
description: "Buffer pips added to SL"
|
||||
|
||||
InpFixedSLPips:
|
||||
type: float
|
||||
min: 50.0
|
||||
max: 200.0
|
||||
step: 10.0
|
||||
default: 100.0
|
||||
category: sl
|
||||
depends_on_value: {InpSLType: 0}
|
||||
|
||||
InpMaxSLPips:
|
||||
type: float
|
||||
min: 100.0
|
||||
max: 400.0
|
||||
step: 50.0
|
||||
default: 200.0
|
||||
category: sl
|
||||
description: "Hard cap on calculated SL size"
|
||||
|
||||
InpUseFractalSL:
|
||||
type: bool
|
||||
default: false # 0 in .set
|
||||
category: sl
|
||||
|
||||
# ── Take Profit ────────────────────────────────────────────
|
||||
InpTPType:
|
||||
type: enum
|
||||
values: [0, 1] # 0=RR ratio, 1=fixed pips
|
||||
default: 0
|
||||
category: tp
|
||||
|
||||
InpRRRatio:
|
||||
type: float
|
||||
min: 1.0
|
||||
max: 3.0
|
||||
step: 0.5
|
||||
default: 1.5
|
||||
category: tp
|
||||
depends_on_value: {InpTPType: 0}
|
||||
|
||||
InpFixedTPPips:
|
||||
type: float
|
||||
min: 50.0
|
||||
max: 200.0
|
||||
step: 10.0
|
||||
default: 100.0
|
||||
category: tp
|
||||
depends_on_value: {InpTPType: 1}
|
||||
|
||||
InpUseFractalFilter:
|
||||
type: bool
|
||||
default: false # 0 in .set
|
||||
category: tp
|
||||
|
||||
# ── Session Filter ─────────────────────────────────────────
|
||||
InpUseSession:
|
||||
type: bool
|
||||
default: true # 1 in .set
|
||||
category: filter_session
|
||||
|
||||
InpSessionStart:
|
||||
type: int
|
||||
min: 0
|
||||
max: 23
|
||||
step: 1
|
||||
default: 7
|
||||
category: filter_session
|
||||
depends_on: InpUseSession
|
||||
description: "Session start hour (broker local time)"
|
||||
|
||||
InpSessionEnd:
|
||||
type: int
|
||||
min: 0
|
||||
max: 23
|
||||
step: 1
|
||||
default: 20
|
||||
category: filter_session
|
||||
depends_on: InpUseSession
|
||||
description: "Session end hour (broker local time)"
|
||||
|
||||
# ── Trade Limits ───────────────────────────────────────────
|
||||
InpMaxOpenTrades:
|
||||
type: int
|
||||
min: 1
|
||||
max: 3
|
||||
step: 1
|
||||
default: 1
|
||||
category: risk
|
||||
|
||||
InpAllowMultiple:
|
||||
type: bool
|
||||
default: false # 0 in .set
|
||||
category: risk
|
||||
|
||||
# ── Execution ─────────────────────────────────────────────
|
||||
InpMagicNumber:
|
||||
type: fixed
|
||||
default: 202402
|
||||
category: execution
|
||||
description: "Fixed — do not optimize"
|
||||
|
||||
InpSlippage:
|
||||
type: int
|
||||
min: 5
|
||||
max: 30
|
||||
step: 5
|
||||
default: 10
|
||||
category: execution
|
||||
|
||||
# ── Trailing Stop ─────────────────────────────────────────
|
||||
InpUseTrailing:
|
||||
type: bool
|
||||
default: true # 1 in .set
|
||||
category: exit_trail
|
||||
|
||||
InpTrailStartPips:
|
||||
type: float
|
||||
min: 10.0
|
||||
max: 50.0
|
||||
step: 5.0
|
||||
default: 20.0
|
||||
category: exit_trail
|
||||
depends_on: InpUseTrailing
|
||||
|
||||
InpTrailStepPips:
|
||||
type: float
|
||||
min: 5.0
|
||||
max: 30.0
|
||||
step: 5.0
|
||||
default: 10.0
|
||||
category: exit_trail
|
||||
depends_on: InpUseTrailing
|
||||
|
||||
# ── Break Even ────────────────────────────────────────────
|
||||
InpUseBreakeven:
|
||||
type: bool
|
||||
default: true # 1 in .set
|
||||
category: exit_be
|
||||
|
||||
InpBEPips:
|
||||
type: float
|
||||
min: 10.0
|
||||
max: 40.0
|
||||
step: 5.0
|
||||
default: 15.0
|
||||
category: exit_be
|
||||
depends_on: InpUseBreakeven
|
||||
description: "Pips in profit to activate breakeven"
|
||||
|
||||
InpBEBufferPips:
|
||||
type: float
|
||||
min: 1.0
|
||||
max: 5.0
|
||||
step: 1.0
|
||||
default: 2.0
|
||||
category: exit_be
|
||||
depends_on: InpUseBreakeven
|
||||
description: "Buffer pips above entry for breakeven SL"
|
||||
|
||||
# ── EMA Filter ────────────────────────────────────────────
|
||||
InpUseEMA:
|
||||
type: bool
|
||||
default: true # 1 in .set
|
||||
category: filter_ema
|
||||
|
||||
InpEMAPeriod:
|
||||
type: int
|
||||
min: 20
|
||||
max: 100
|
||||
step: 10
|
||||
default: 50
|
||||
category: filter_ema
|
||||
depends_on: InpUseEMA
|
||||
|
||||
InpRequireEMASlope:
|
||||
type: bool
|
||||
default: true # 1 in .set
|
||||
category: filter_ema
|
||||
depends_on: InpUseEMA
|
||||
|
||||
InpEMASlopeBars:
|
||||
type: int
|
||||
min: 1
|
||||
max: 3
|
||||
step: 1
|
||||
default: 1
|
||||
category: filter_ema
|
||||
depends_on: InpRequireEMASlope
|
||||
|
||||
# ── Entry Mode ────────────────────────────────────────────
|
||||
InpEntryMode:
|
||||
type: enum
|
||||
values: [0, 1]
|
||||
default: 1
|
||||
category: entry
|
||||
|
||||
InpSLBufferMode:
|
||||
type: enum
|
||||
values: [0, 1]
|
||||
default: 1
|
||||
category: sl
|
||||
|
||||
# ── ATR ───────────────────────────────────────────────────
|
||||
InpATRPeriod:
|
||||
type: int
|
||||
min: 10
|
||||
max: 20
|
||||
step: 2
|
||||
default: 14
|
||||
category: atr
|
||||
|
||||
InpATRMultiplier:
|
||||
type: float
|
||||
min: 0.3
|
||||
max: 1.0
|
||||
step: 0.1
|
||||
default: 0.5
|
||||
category: atr
|
||||
|
||||
# ── Spread Guard ──────────────────────────────────────────
|
||||
InpUseSpreadGuard:
|
||||
type: bool
|
||||
default: true # 1 in .set
|
||||
category: filter_spread
|
||||
|
||||
InpMaxSpreadPips:
|
||||
type: float
|
||||
min: 10.0
|
||||
max: 50.0
|
||||
step: 5.0
|
||||
default: 30.0
|
||||
category: filter_spread
|
||||
depends_on: InpUseSpreadGuard
|
||||
|
||||
# ── Minimum R:R Gate ──────────────────────────────────────
|
||||
InpUseMinRR:
|
||||
type: bool
|
||||
default: true # 1 in .set
|
||||
category: filter_rr
|
||||
|
||||
InpMinRRRatio:
|
||||
type: float
|
||||
min: 1.0
|
||||
max: 3.0
|
||||
step: 0.5
|
||||
default: 1.5
|
||||
category: filter_rr
|
||||
depends_on: InpUseMinRR
|
||||
|
||||
# ── Score Gate ────────────────────────────────────────────
|
||||
InpUseScoreGate:
|
||||
type: bool
|
||||
default: true # 1 in .set
|
||||
category: filter_score
|
||||
|
||||
InpMinScore:
|
||||
type: int
|
||||
min: 6
|
||||
max: 11
|
||||
step: 1
|
||||
default: 8
|
||||
category: filter_score
|
||||
depends_on: InpUseScoreGate
|
||||
description: "Minimum signal quality score required to enter trade"
|
||||
|
||||
# ── Tester-Specific (fixed during automation) ─────────────
|
||||
InpTesterMode:
|
||||
type: fixed
|
||||
default: 1
|
||||
category: tester
|
||||
description: "Must be 1 during automated backtesting"
|
||||
|
||||
InpTesterInitDeposit:
|
||||
type: fixed
|
||||
default: 10000.0
|
||||
category: tester
|
||||
|
||||
InpTesterSpreadPts:
|
||||
type: int
|
||||
min: 10
|
||||
max: 50
|
||||
step: 5
|
||||
default: 20
|
||||
category: tester
|
||||
description: "Spread in points used in tester (20 pts = 2.0 pips for XAUUSD)"
|
||||
|
||||
InpShowPanel:
|
||||
type: fixed
|
||||
default: 0 # force off during automation (no GUI needed)
|
||||
category: tester
|
||||
|
||||
# ── Parameter categories (for mutation engine grouping) ──────
|
||||
categories:
|
||||
mode: [InpBotMode]
|
||||
risk: [InpRiskType, InpFixedLot, InpRiskPercent, InpMaxDailyLossPct, InpMaxTradesPerDay, InpMaxOpenTrades, InpAllowMultiple]
|
||||
sl: [InpSLType, InpSLBuffer, InpFixedSLPips, InpMaxSLPips, InpUseFractalSL, InpSLBufferMode]
|
||||
tp: [InpTPType, InpRRRatio, InpFixedTPPips, InpUseFractalFilter]
|
||||
exit_trail: [InpUseTrailing, InpTrailStartPips, InpTrailStepPips]
|
||||
exit_be: [InpUseBreakeven, InpBEPips, InpBEBufferPips]
|
||||
filter_session: [InpUseSession, InpSessionStart, InpSessionEnd]
|
||||
filter_ema: [InpUseEMA, InpEMAPeriod, InpRequireEMASlope, InpEMASlopeBars]
|
||||
filter_spread: [InpUseSpreadGuard, InpMaxSpreadPips]
|
||||
filter_rr: [InpUseMinRR, InpMinRRRatio]
|
||||
filter_score: [InpUseScoreGate, InpMinScore]
|
||||
entry: [InpEntryMode]
|
||||
atr: [InpATRPeriod, InpATRMultiplier]
|
||||
tester: [InpTesterMode, InpTesterInitDeposit, InpTesterSpreadPts, InpShowPanel]
|
||||
Reference in New Issue
Block a user