433 lines
13 KiB
Python
433 lines
13 KiB
Python
"""
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策略配置加载器 — 从 rules.yaml 加载所有参数，支持热更新
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review_engine 调整权重后直接写回 yaml，下次运行自动生效
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"""
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import copy
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import datetime
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import os
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from pathlib import Path
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import yaml
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REPO_ROOT = Path(__file__).resolve().parents[2]
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RULES_PATH = str(REPO_ROOT / "rules.yaml")
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_cache = None
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_cache_mtime = None
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# 兼容旧代码中的信号名写法
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_SIGNAL_NAME_ALIASES = {
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"N倍放量(≥10x)": "N倍放量",
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"连续3x放量(≥3根)": "连续3x放量",
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"静K→动K转折": "静K动K转折",
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"Q≥7供给区突破": "Q7供给区突破",
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"1H放量(量价背离)": "1H放量",
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}
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def load_rules(force_reload=False):
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"""加载 rules.yaml，带文件变更检测自动刷新缓存"""
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global _cache, _cache_mtime
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mtime = os.path.getmtime(RULES_PATH) if os.path.exists(RULES_PATH) else 0
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if not force_reload and _cache and _cache_mtime == mtime:
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return _cache
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with open(RULES_PATH, "r", encoding="utf-8") as f:
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_cache = yaml.safe_load(f) or {}
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_cache_mtime = mtime
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return _cache
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def save_rules(rules_dict):
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"""保存修改后的 rules.yaml（review_engine 调整权重后用）"""
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global _cache, _cache_mtime
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with open(RULES_PATH, "w", encoding="utf-8") as f:
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yaml.dump(rules_dict, f, default_flow_style=False, allow_unicode=True, sort_keys=False)
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_cache = rules_dict
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_cache_mtime = os.path.getmtime(RULES_PATH)
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def _get_section(section_name, default=None):
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rules = load_rules()
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section = rules.get(section_name, default if default is not None else {})
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return copy.deepcopy(section)
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def _get_nested(section_name, path, default=None):
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node = load_rules().get(section_name, {})
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for key in path:
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if not isinstance(node, dict):
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return copy.deepcopy(default)
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node = node.get(key)
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if node is None:
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return copy.deepcopy(default)
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return copy.deepcopy(node)
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def normalize_signal_name(signal_name):
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"""统一信号名，兼容 rules.yaml 与旧 Python 代码中的不同写法"""
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return _SIGNAL_NAME_ALIASES.get(signal_name, signal_name)
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def get_strategy_params():
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"""返回全局策略约束"""
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return _get_section("strategy")
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def get_strategy_direction(default="多头启动"):
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return get_strategy_params().get("direction", default)
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def is_long_only():
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return get_strategy_params().get("mode", "") == "long_only"
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def allow_short(default=False):
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return bool(get_strategy_params().get("allow_short", default))
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def get_pa_params():
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"""返回 PA 引擎所有参数"""
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return _get_section("pa_engine")
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def get_pa_section(name=None):
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"""返回 PA 引擎某个子区块；name=None 时返回整个 pa_engine"""
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if name is None:
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return get_pa_params()
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return _get_nested("pa_engine", [name], {})
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def get_screener_params():
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"""返回粗筛所有参数"""
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return _get_section("screener")
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def get_event_driven_params():
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"""返回事件驱动舆情触发选币参数"""
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return _get_section("event_driven")
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def get_event_driven_section(name=None):
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"""返回 event_driven 某个子区块；name=None 时返回整个 event_driven"""
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if name is None:
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return get_event_driven_params()
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return _get_nested("event_driven", [name], {})
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def get_screener_section(name=None):
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"""返回 screener 某个子区块；name=None 时返回整个 screener"""
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if name is None:
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return get_screener_params()
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return _get_nested("screener", [name], {})
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def get_confirm_params():
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"""返回确认层所有参数"""
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return _get_section("confirm")
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def get_confirm_section(name=None):
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"""返回 confirm 某个子区块；name=None 时返回整个 confirm"""
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if name is None:
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return get_confirm_params()
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return _get_nested("confirm", [name], {})
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def get_tracker_params():
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"""返回跟踪层所有参数"""
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return _get_section("tracker")
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def get_signal_weights():
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"""返回信号权重 dict（优先用 DB signal_performance 的动态权重，fallback 到 yaml）
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兼容层要求：
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- 规则侧统一存 canonical key（如 "1H放量"）
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- 旧脚本仍可能用历史 key（如 "1H放量(量价背离)"）直接查 weights[...]
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因此返回值同时暴露 canonical key + alias key，避免旧调用方 KeyError。
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"""
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rules = load_rules()
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yaml_weights = copy.deepcopy(rules.get("signal_weights", {}))
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canonical = {}
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for sig, weight in yaml_weights.items():
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canonical[normalize_signal_name(sig)] = weight
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try:
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from app.db.altcoin_db import get_signal_weights as db_get_weights
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db_weights = db_get_weights()
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for sig, data in db_weights.items():
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norm_sig = normalize_signal_name(sig)
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if data.get("total_count", 0) >= 3:
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canonical[norm_sig] = data["weight"]
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except Exception:
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pass
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merged = dict(canonical)
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for alias, target in _SIGNAL_NAME_ALIASES.items():
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if target in canonical:
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merged[alias] = canonical[target]
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return merged
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def get_review_params():
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"""返回复盘参数"""
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return _get_section("review")
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def get_reverse_params():
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"""返回逆向分析参数"""
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return _get_section("reverse_analysis")
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def get_learned_rules(active_only=True):
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"""返回已学习的规律列表"""
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rules = load_rules()
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learned = copy.deepcopy(rules.get("learned_rules", []))
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if active_only:
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return [r for r in learned if r.get("active", True)]
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return learned
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def add_learned_rule(rule_dict):
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"""添加一条新学习规律"""
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rules = load_rules(force_reload=True)
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learned = rules.get("learned_rules", [])
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ts = datetime.datetime.now().strftime("%Y%m%d_%H%M")
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rule_dict["id"] = f"rule_{ts}_{len(learned)+1:03d}"
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rule_dict["created"] = datetime.datetime.now().strftime("%Y-%m-%d")
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rule_dict["hit_count"] = 0
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rule_dict["miss_count"] = 0
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rule_dict["active"] = True
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learned.append(rule_dict)
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rules["learned_rules"] = learned
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rules.setdefault("meta", {})["total_rules_learned"] = len(learned)
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save_rules(rules)
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return rule_dict["id"]
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def update_learned_rule(rule_id, updates):
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"""更新一条规律（如 hit_count/miss_count/active 状态）"""
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rules = load_rules(force_reload=True)
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learned = rules.get("learned_rules", [])
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for r in learned:
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if r.get("id") == rule_id:
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for k, v in updates.items():
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r[k] = v
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break
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rules["learned_rules"] = learned
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save_rules(rules)
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def update_signal_weight(signal_name, new_weight):
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"""更新单个信号权重（写回 yaml + DB）"""
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canonical_name = normalize_signal_name(signal_name)
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rules = load_rules(force_reload=True)
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rules.setdefault("signal_weights", {})[canonical_name] = new_weight
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save_rules(rules)
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try:
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from app.db.altcoin_db import update_signal_performance
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update_signal_performance(canonical_name, category=None, is_hit=None, pnl=None, weight_override=new_weight)
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except Exception:
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pass
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def get_meta():
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"""返回迭代元数据"""
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meta = _get_section("meta")
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if not meta.get("strategy_version"):
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version_num = meta.get("version", 1)
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iteration = meta.get("iteration_count", 0)
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meta["strategy_version"] = f"v{version_num}.{iteration}"
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return meta
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def get_rules_snapshot():
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"""返回完整 rules.yaml 快照（深拷贝）"""
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return copy.deepcopy(load_rules())
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def diff_rule_snapshots(before, after, prefix=""):
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"""递归比较两个配置快照，输出 changed/added/removed"""
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result = {"changed": [], "added": [], "removed": []}
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def walk(path, a, b):
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if isinstance(a, dict) and isinstance(b, dict):
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keys = set(a.keys()) | set(b.keys())
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for key in sorted(keys):
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next_path = f"{path}.{key}" if path else str(key)
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if key not in a:
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result["added"].append({"path": next_path, "new": copy.deepcopy(b[key])})
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elif key not in b:
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result["removed"].append({"path": next_path, "old": copy.deepcopy(a[key])})
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else:
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walk(next_path, a[key], b[key])
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return
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if isinstance(a, list) and isinstance(b, list):
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if a != b:
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result["changed"].append({"path": path, "old": copy.deepcopy(a), "new": copy.deepcopy(b)})
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return
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if a != b:
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result["changed"].append({"path": path, "old": copy.deepcopy(a), "new": copy.deepcopy(b)})
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walk(prefix, before or {}, after or {})
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return result
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def promote_candidate_rule_to_learned_rule(candidate, release_version=""):
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"""把通过发布门槛的候选规则正式写入 learned_rules。
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候选规则来自 DB strategy_rule_candidate；只有发布闸门通过时才调用，
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避免日常研究直接污染主策略。
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"""
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desc = (candidate.get("rule_description") or "").strip()
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if not desc:
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return None
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rules = load_rules(force_reload=True)
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learned_rules = rules.setdefault("learned_rules", [])
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for existing in learned_rules:
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if existing.get("description") == desc:
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return existing.get("id") or existing.get("rule_id")
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rule_id = f"candidate_{candidate.get('id')}_{datetime.datetime.now().strftime('%Y%m%d%H%M%S')}"
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rule = {
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"id": rule_id,
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"type": candidate.get("rule_type") or "bonus",
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"description": desc,
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"conditions": {
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"candidate_id": candidate.get("id"),
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"signal_name": candidate.get("signal_name") or "",
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"source": candidate.get("source") or "strategy_rule_candidate",
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"sample_size": candidate.get("sample_size") or 0,
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"confidence_score": candidate.get("confidence_score") or 0,
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},
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"score_adjust": 2 if (candidate.get("rule_type") or "") == "bonus" else -2,
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"source": "candidate_release_gate",
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"release_version": release_version or get_meta().get("strategy_version", ""),
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"created_at": datetime.datetime.now().isoformat(),
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}
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learned_rules.append(rule)
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save_rules(rules)
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return rule_id
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def bump_strategy_patch_version(note=""):
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"""正式发布时才提升 patch 版本号。"""
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import re
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meta = get_meta()
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current_ver = str(meta.get("strategy_version") or "v1.0.0").strip()
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m = re.match(r"^v(\d+)\.(\d+)\.(\d+)$", current_ver)
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if m:
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major, minor, patch = map(int, m.groups())
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new_ver = f"v{major}.{minor}.{patch + 1}"
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else:
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new_ver = current_ver + ".1"
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update_meta("strategy_version", new_ver)
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update_meta("strategy_revision_note", f"{new_ver}: {note}" if note else new_ver)
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update_meta("strategy_revision_started_at", datetime.datetime.now().isoformat())
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return current_ver, new_ver
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def update_meta(key, value):
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"""更新迭代元数据"""
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rules = load_rules(force_reload=True)
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rules.setdefault("meta", {})[key] = value
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save_rules(rules)
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# === 快捷取值函数（给各模块直接 import 用）===
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def dynamic_k_thresholds():
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p = get_pa_section("dynamic_k")
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return p["body_ratio_min"], p["atr_ratio_min"]
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def static_k_thresholds():
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p = get_pa_section("static_k")
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return p["body_ratio_max"], p["atr_ratio_max"]
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def zone_params():
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p = get_pa_section("supply_demand")
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return p["lookback"], p["min_static_count"], p["q_score_breakpoints"]
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def ignition_params():
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p = get_pa_section("ignition")
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return p["lookback"], p["min_static_count"], p["static_search_range"], p["confirm_search_range"]
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def continuous_k_params():
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return get_pa_section("continuous_k")
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def exhaustion_params():
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return get_pa_section("exhaustion")
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def entry_point_params():
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return get_pa_section("entry_point")
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def burst_thresholds():
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p = get_screener_section("burst_threshold")
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return p["main"], p["meme"], p["overbought_multiplier"]
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def volume_thresholds():
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p = get_screener_section("volume")
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return p["min_usd"], p["meme_min_usd"]
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def vp_fly_params():
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return get_screener_section("vp_fly")
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def bollinger_squeeze_params():
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return get_screener_section("bollinger_squeeze")
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def funding_rate_params():
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return get_screener_section("funding_rate")
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def top_trader_params():
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return get_screener_section("top_trader")
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def state_score_thresholds():
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p = get_screener_section("state_threshold")
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return p["accelerate_main"], p["accelerate_meme"], p["accumulate"]
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def confirm_min_score():
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return get_confirm_params().get("min_score", 6)
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def confirm_volume_breakout_ratio():
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return get_confirm_params().get("volume_breakout_ratio", 2.0)
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def confirm_state_cooldown_hours():
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return get_confirm_params().get("state_cooldown_hours", 6)
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def confirm_atr_multipliers():
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return get_confirm_section("atr_multiplier")
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def confirm_stop_loss_params():
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return get_confirm_section("stop_loss")
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def get_sentiment_params():
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"""返回舆情监控所有参数"""
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return _get_section("sentiment")
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def sentiment_max_bonus():
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return get_sentiment_params().get("max_bonus", 2)
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