知微
102a64d856
补齐「顺势而为 环境预判 策略多分枝」体系中缺失的组件： branch_evaluator.py（新增）— 每30min评估所有策略树分支 1. detect_scenario() 获取当前宏观情景 2. 对42只股票评估哪个分支当前适用 3. 适用分支 trigger_count+1, last_triggered=now 4. 触发>=3次且成功率<30%→标记pruning_candidate 5. 无决策树的股票自动初始化（init_default_branches） prune_branches.py（新增）— 每日16:30收盘后剪枝 阈值：触发>=3次且成功率<30%→裁掉并归档到pruned_branches Dad说「每周太低频」→改为每日 stale_push_wlin.py（修改）— 报告每只股增加分支行： 【弱势震荡→buy_dip】价格回调到支撑区，弱势市场低吸 cron更新： 分支扫描(b809fcabfa5b) → 指向branch_evaluator.py, 每30min 剪枝(a3697c108c7b) → 指向prune_branches.py, 每日16:30 自成长核心：branch_evaluator 运行时自动发现并初始化无策略树股票, 252个分支已生成, trigger_count已开始累积, 反馈循环上线
149 lines
5.1 KiB
Python
149 lines
5.1 KiB
Python
#!/usr/bin/env python3
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"""
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branch_evaluator.py — 分支自成长引擎
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每30分钟评估所有策略树的当前适用性：
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1. 读取 decisions.json 中所有 strategy_tree.branches
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2. 获取当前宏观情景（detect_scenario）
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3. 对每只股票获取实时价，评估哪些分支条件命中
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4. 命中的分支 → trigger_count+1, last_triggered=now
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5. 后续跟进：成功/失败取决于该分支被选中后5日盈亏（由price_monitor回填success_rate）
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6. 触发≥3次且成功率<30% → 标记 pruning_candidate
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7. 写回 decisions.json
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设计为 no_agent cron 脚本：非空输出→推送到XMPP，空输出→静默
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"""
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import json, sys, os, re
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from datetime import datetime, date
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# 路径
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DECISIONS_PATH = "/home/hmo/web-dashboard/data/decisions.json"
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PORTFOLIO_PATH = "/home/hmo/web-dashboard/data/portfolio.json"
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# 引入 strategy_tree 模块
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sys.path.insert(0, "/home/hmo/MoFin")
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try:
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import strategy_tree as st
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except ImportError:
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# 如果 MoFin 路径下找不到，尝试直接 exec
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import importlib.util
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spec = importlib.util.spec_from_file_location("st", "/home/hmo/MoFin/strategy_tree.py")
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st = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(st)
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def get_live_prices():
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"""从 portfolio.json 读取实时价格"""
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prices = {}
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try:
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with open(PORTFOLIO_PATH) as f:
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pf = json.load(f)
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for h in pf.get("holdings", []):
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code = str(h.get("code", ""))
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prices[code] = h.get("price", 0)
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except Exception:
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pass
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return prices
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def evaluate_all():
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"""评估所有已触发策略树的分支"""
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try:
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with open(DECISIONS_PATH) as f:
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data = json.load(f)
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except Exception as e:
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print(f"[错误] 读 decisions.json 失败: {e}", file=sys.stderr)
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return
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# 当前情景
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scenario = st.detect_scenario()
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scenario_id = scenario.get("id", "")
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scenario_label = scenario.get("label", "未知")
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prices = get_live_prices()
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decisions = data.get("decisions", [])
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total_triggered = 0
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auto_init_count = 0
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pruning_flags = []
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for entry in decisions:
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code = entry.get("code", "")
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tree = entry.get("strategy_tree")
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if not tree:
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# 自初始化：无决策树的股票自动生成默认分支
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try:
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branches = st.init_default_branches(
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code=code,
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name=entry.get("name", ""),
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entry_low=entry.get("entry_low", 0),
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entry_high=entry.get("entry_high", 0),
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stop_loss=entry.get("stop_loss", 0),
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take_profit=entry.get("take_profit", 0),
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)
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tree = {"branches": branches, "initialized_at": datetime.now().isoformat()}
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entry["strategy_tree"] = tree
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auto_init_count += 1
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except Exception:
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continue
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branches = tree.get("branches", [])
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if not branches:
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continue
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price = prices.get(code, 0) or entry.get("price", 0)
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shares = entry.get("shares", 0)
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cost = entry.get("cost", 0)
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# 评估所有分支
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results = st.evaluate_branches(code, scenario_id, price, shares, cost)
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now_ts = datetime.now().isoformat()
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updated = False
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for result in results:
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br_id = result.get("branch_id", "")
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# 找到对应分支更新trigger_count
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for br in branches:
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if br.get("id") == br_id:
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if result.get("applicable"):
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# 分支命中 → 增加触发计数
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br["trigger_count"] = br.get("trigger_count", 0) + 1
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br["last_triggered"] = now_ts
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total_triggered += 1
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updated = True
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# 检查是否需要标记剪枝候补
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tc = br["trigger_count"]
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sr = br.get("success_rate")
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if tc >= 3 and sr is not None and sr < 30:
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br["pruning_candidate"] = True
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pruning_flags.append(f"{code}/{br_id}(触发{tc}次/成功率{sr}%)")
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break
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if updated:
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# 回写 strategy_tree
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entry["strategy_tree"] = tree
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# 标记评估时间
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tree["last_evaluated"] = now_ts
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# 写回文件
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with open(DECISIONS_PATH, "w") as f:
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json.dump(data, f, indent=2, ensure_ascii=False)
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# 输出摘要（空 = 静默）
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lines = []
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init_note = f" | 自动初始化{auto_init_count}只" if auto_init_count else ""
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lines.append(f"【分支评估】情景{scenario_label}({scenario_id}) | 命中{total_triggered}次{init_note}")
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if pruning_flags:
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lines.append(f"需剪枝{len(pruning_flags)}个分支:")
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for f in pruning_flags:
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lines.append(f" ⚠ {f}")
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else:
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lines.append("无需剪枝的分支")
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out = "\n".join(lines)
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print(out)
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return out
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if __name__ == "__main__":
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evaluate_all()
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