MoFin/strategy_tree.py

#!/usr/bin/env python3
"""
strategy_tree.py — 情景化多分支策略决策引擎

核心理念：
  每只股票不再只有一个买入区+止损，而是有一棵决策树。
  每个分支 = {条件, 动作, 优先级, 触发统计}
  当前宏观情景决定走哪个分支。

自成长：
  → 每次分支被触发，记录 trigger_count + 后续5日盈亏
  → success_rate < 30% 且触发≥5次 → 自动标记 pruning_candidate
  → 每周 pruning 时剪掉低效分支

数据存在 decisions.json 的 strategy_tree 字段。
"""

import json, os, sys, re
from datetime import datetime, date, timedelta
from mo_data import read_portfolio, read_decisions, read_watchlist

DECISIONS_PATH = "/home/hmo/web-dashboard/data/decisions.json"
PORTFOLIO_PATH = "/home/hmo/web-dashboard/data/portfolio.json"
MACRO_PATH = "/home/hmo/web-dashboard/data/macro_context.json"
MARKET_PATH = "/home/hmo/web-dashboard/data/market.json"
TREND_PATH = "/home/hmo/web-dashboard/data/trend_signals.json"

# ── 情景定义 ──────────────────────────────────────────────────────────────

SCENARIOS = [
    {
        "id": "sharp_decline",
        "label": "急跌防御",
        "desc": "大盘放量下跌，多板块共振杀跌",
        "rules": {"mood": "bearish", "sector_crash": True},
        "portfolio_action": "减仓至80%以下，优先出弱势深套",
    },
    {
        "id": "weak_consolidation",
        "label": "弱势震荡",
        "desc": "大盘缩量阴跌，结构分化",
        "rules": {"mood": "neutral", "breadth": "weak"},
        "portfolio_action": "保持仓位90%以内，调结构",
    },
    {
        "id": "sector_rotation",
        "label": "板块轮动",
        "desc": "大盘窄幅，强势板块切换",
        "rules": {"mood": "neutral", "rotation": True},
        "portfolio_action": "跟随板块切换，减旧加新",
    },
    {
        "id": "bullish_recovery",
        "label": "反弹上行",
        "desc": "大盘放量上涨，情绪回暖",
        "rules": {"mood": "bullish"},
        "portfolio_action": "加仓至95%，追随趋势",
    },
]


# ── 情景判定 ──────────────────────────────────────────────────────────────

def detect_scenario():
    """从宏观+市场数据判断当前情景
    
    返回:
        {"id": str, "label": str, "confidence": float, "portfolio_action": str}
    """
    scenario_id = "weak_consolidation"  # 默认
    confidence = 0.5
    
    try:
        # 优先 DB
        import sqlite3
        from pathlib import Path
        db = sqlite3.connect(str(Path(__file__).parent / "data" / "mofin.db"))
        mrow = db.execute(
            "SELECT indices, structure, sector_mood FROM macro_context_log "
            "WHERE has_valid_data=1 ORDER BY created_at DESC LIMIT 1"
        ).fetchone()
        db.close()
        if mrow:
            structure = json.loads(mrow[1]) if mrow[1] else {}
            overall = structure.get("overall", "").lower()
            mood = (mrow[2] or "").lower() if len(mrow) > 2 else ""
        else:
            raise ValueError("no db data")
    except Exception:
        try:
            macro = json.load(open(MACRO_PATH))
            market = json.load(open(MARKET_PATH))
            mood = market.get("mood", "").lower()
            structure = macro.get("structure", {})
            overall = structure.get("overall", "").lower()
        except Exception:
            return {"id": "weak_consolidation", "label": "默认-弱势震荡", "confidence": 0.3, "portfolio_action": "观望"}
    trend_desc = structure.get("description", "").lower()
    
    # Check for sharp decline
    if "bearish" in mood or "bearish" in overall:
        if "crash" in trend_desc or "跌幅" in trend_desc or "恐慌" in trend_desc:
            scenario_id = "sharp_decline"
            confidence = 0.7
        elif "弱势" in trend_desc or "疲弱" in trend_desc:
            scenario_id = "weak_consolidation"
            confidence = 0.6
        else:
            scenario_id = "weak_consolidation"
            confidence = 0.5
    elif "bullish" in mood or "bullish" in overall:
        scenario_id = "bullish_recovery"
        confidence = 0.6
    elif "neutral" in mood:
        # Check for rotation signals
        try:
            trend = json.load(open(TREND_PATH))
            if trend.get("rotation_detected"):
                scenario_id = "sector_rotation"
                confidence = 0.5
        except Exception:
            pass
        scenario_id = "weak_consolidation"
        confidence = 0.4
    
    sc = next((s for s in SCENARIOS if s["id"] == scenario_id), SCENARIOS[0])
    return {
        "id": scenario_id,
        "label": sc["label"],
        "desc": sc["desc"],
        "confidence": round(confidence, 2),
        "portfolio_action": sc["portfolio_action"],
    }


# ── 分支评估 ──────────────────────────────────────────────────────────────

def evaluate_branches(code, scenario_id, price, shares, cost):
    """评估某只股票在当前情景下的所有分支
    
    从 decisions.json 读取 strategy_tree.branches[]
    返回: [{branch_id, action_type, action_detail, priority, applicable}]
    """
    try:
        dec = mo_data.read_decisions()
    except Exception:
        return []
    
    entry = None
    for e in dec.get("decisions", []):
        if e.get("code") == code:
            entry = e
            break
    if not entry:
        return []
    
    branches = entry.get("strategy_tree", {}).get("branches", [])
    if not branches:
        return []
    
    results = []
    for br in sorted(branches, key=lambda b: b.get("priority", 999)):
        applicable = _check_branch_condition(br, scenario_id, price, shares, cost)
        results.append({
            "branch_id": br.get("id"),
            "action_type": br.get("action", {}).get("type", "hold"),
            "action_detail": br.get("action", {}),
            "priority": br.get("priority", 999),
            "rationale": br.get("rationale", ""),
            "applicable": applicable,
        })
    
    return results


def _check_branch_condition(branch, scenario_id, price, shares, cost):
    """检查分支条件是否满足"""
    cond = branch.get("condition", {})
    required_scenario = cond.get("scenario", "")
    if required_scenario and required_scenario != scenario_id:
        return False
    
    # Price conditions
    price_cond = cond.get("price", "")
    if price_cond:
        ops = re.findall(r'([<>=!]+)\s*([\d.]+)', price_cond)
        for op, val_str in ops:
            val = float(val_str)
            op = op.strip()
            if op == "<" and not (price < val):
                return False
            if op == ">" and not (price > val):
                return False
            if op == "<=" and not (price <= val):
                return False
            if op == ">=" and not (price >= val):
                return False
            if op == "==" and not (abs(price - val) < 0.01):
                return False

    # Price lower bound (separate field)
    price_lower = cond.get("price_lower", "")
    if price_lower:
        ops = re.findall(r'([<>=!]+)\s*([\d.]+)', price_lower)
        for op, val_str in ops:
            val = float(val_str)
            op = op.strip()
            if op == "<" and not (price < val):
                return False
            if op == ">" and not (price > val):
                return False
            if op == "<=" and not (price <= val):
                return False
            if op == ">=" and not (price >= val):
                return False
            if op == "==" and not (abs(price - val) < 0.01):
                return False
    
    # Trend condition
    trend = cond.get("trend", "")
    if trend and trend == "uptrend":
        pass  # TODO: check multi_timeframe
    
    # Loss condition
    loss_pct = cond.get("loss_pct", "")
    if loss_pct and cost > 0:
        actual_loss = (price - cost) / cost * 100
        if "<" in str(loss_pct):
            limit = float(str(loss_pct).replace("<", "").replace("%", ""))
            if not (actual_loss < limit):
                return False
    
    return True


# ── 分支触发记录 ──────────────────────────────────────────────────────────

def record_branch_trigger(code, branch_id):
    """记录分支被触发了一次，用于自成长统计"""
    try:
        dec = mo_data.read_decisions()
        for e in dec.get("decisions", []):
            if e.get("code") == code:
                st = e.setdefault("strategy_tree", {})
                for br in st.get("branches", []):
                    if br.get("id") == branch_id:
                        br["trigger_count"] = br.get("trigger_count", 0) + 1
                        br["last_triggered"] = datetime.now().isoformat()
                        break
                break
        json.dump(dec, open(DECISIONS_PATH, "w"), ensure_ascii=False, indent=2)
    except Exception:
        pass


# ── 分支剪枝（自成长核心）─────────────────────────────────────────────────

def prune_low_performance_branches(min_triggers=5, min_success_rate=0.3):
    """剪掉低成功率分支——自成长机制
    
    条件：触发≥min_triggers 次 且 success_rate < min_success_rate
    被剪的分支移入 history 字段，不打删除（可追溯）
    """
    try:
        dec = mo_data.read_decisions()
    except Exception:
        return []
    
    pruned = []
    for e in dec.get("decisions", []):
        st = e.setdefault("strategy_tree", {})
        branches = st.get("branches", [])
        kept = []
        for br in branches:
            tc = br.get("trigger_count", 0)
            sr = br.get("success_rate")
            if sr is not None and tc >= min_triggers and sr < min_success_rate:
                # 移入 history
                history = st.setdefault("pruned_branches", [])
                br["pruned_at"] = datetime.now().isoformat()
                br["prune_reason"] = f"低成功率: {sr:.0%} (触发{tc}次)"
                history.append(br)
                pruned.append(f'{e.get("code")}:{br.get("id")} ({sr:.0%} < {min_success_rate:.0%})')
            else:
                kept.append(br)
        st["branches"] = kept
    
    if pruned:
        json.dump(dec, open(DECISIONS_PATH, "w"), ensure_ascii=False, indent=2)
    
    return pruned


# ── 初始化策略树（为一只票创建默认分支）─────────────────────────────────────

def init_default_branches(code, name, entry_low, entry_high, stop_loss, take_profit):
    """为 stock 创建默认多分支策略——由 per_stock_reassess 调用"""
    base_price = (entry_low + entry_high) / 2 if entry_low and entry_high else 0
    
    branches = []
    
    # 分支0：止损（始终有效）
    if stop_loss:
        branches.append({
            "id": f"{code}_stop_loss",
            "condition": {"price": f"<{stop_loss}"},
            "action": {"type": "sell", "amount": "all", "reason": "止损"},
            "priority": 0,
            "rationale": "止损保护本金",
            "trigger_count": 0,
            "success_rate": None,
            "last_triggered": None,
        })
    
    # 分支1：回调买入（弱势情景适用）
    if entry_low:
        branches.append({
            "id": f"{code}_buy_dip",
            "condition": {"scenario": "weak_consolidation", "price": f"<={entry_high}", "price_lower": f">={entry_low}"},
            "action": {"type": "buy", "amount": "normal", "limit": entry_low, "reason": "回调支撑买入"},
            "priority": 1,
            "rationale": "价格回调到支撑区，弱势市场低吸",
            "trigger_count": 0,
            "success_rate": None,
            "last_triggered": None,
        })
    
    # 分支2：突破追涨（强势情景适用）
    if take_profit:
        branches.append({
            "id": f"{code}_breakout_chase",
            "condition": {"scenario": "bullish_recovery", "price": f">={take_profit}"},
            "action": {"type": "buy", "amount": "normal", "limit": "market", "reason": "突破确认追涨"},
            "priority": 2,
            "rationale": "价格突破阻力，确认上升趋势后买入",
            "trigger_count": 0,
            "success_rate": None,
            "last_triggered": None,
        })
    
    # 分支3：减仓（急跌情景适用）
    branches.append({
        "id": f"{code}_trim",
        "condition": {"scenario": "sharp_decline", "loss_pct": "<-15%"},
        "action": {"type": "sell", "amount": "half", "reason": "急跌降风险"},
        "priority": 3,
        "rationale": "急跌市场，深套股减半仓减少敞口",
        "trigger_count": 0,
        "success_rate": None,
        "last_triggered": None,
    })
    
    # 分支4：止盈（浮盈较大）
    if take_profit and entry_low:
        branches.append({
            "id": f"{code}_take_profit",
            "condition": {"price": f">={take_profit}"},
            "action": {"type": "sell", "amount": "half", "reason": "止盈锁利"},
            "priority": 4,
            "rationale": "达到目标价，减半仓锁定利润",
            "trigger_count": 0,
            "success_rate": None,
            "last_triggered": None,
        })
    
    # 分支5：持有（默认）
    branches.append({
        "id": f"{code}_hold",
        "condition": {},
        "action": {"type": "hold", "reason": "无明确信号，继续持有"},
        "priority": 99,
        "rationale": "没有分支匹配时的默认动作",
        "trigger_count": 0,
        "success_rate": None,
        "last_triggered": None,
    })
    
    return branches


# ── 组合约束检查 ──────────────────────────────────────────────────────────

def check_portfolio_constraint(action_type, amount, cash_remain=None):
    """组合约束检查：现金够不够？仓位上限？"""
    try:
        pf = mo_data.read_portfolio()
    except Exception:
        return True, "无法读取组合"
    
    if action_type == "buy":
        # 估算买入金额
        cost_est = amount if amount else 100000  # default 10万
        if cash_remain is not None:
            cost_est = cash_remain
        if cost_est > pf.get("cash", 0):
            return False, f"现金不足: 需要~{cost_est:.0f}，可用{pf['cash']:.0f}"
    
    return True, "OK"


# ── CLI 入口 ──────────────────────────────────────────────────────────────

if __name__ == "__main__":
    import argparse
    parser = argparse.ArgumentParser(description="多分支策略决策引擎")
    parser.add_argument("--detect", action="store_true", help="检测当前情景")
    parser.add_argument("--evaluate", type=str, help="评估指定股票的分支")
    parser.add_argument("--prune", action="store_true", help="剪枝低效分支")
    args = parser.parse_args()
    
    if args.detect:
        sc = detect_scenario()
        print(f"情景: {sc['id']} ({sc['label']})")
        print(f"置信度: {sc['confidence']}")
        print(f"组合动作: {sc['portfolio_action']}")
    
    if args.evaluate:
        code = args.evaluate
        sc = detect_scenario()
        print(f"当前情景: {sc['id']} ({sc['label']})")
        print(f"评估 {code}:")
        results = evaluate_branches(code, sc["id"], 0, 0, 0)
        for r in results:
            status = "✅" if r["applicable"] else "  "
            print(f"  {status} [{r['priority']}] {r['branch_id']} → {r['action_type']}: {r['rationale']}")
    
    if args.prune:
        pruned = prune_low_performance_branches()
        if pruned:
            print(f"已剪枝: {len(pruned)} 条")
            for p in pruned:
                print(f"  - {p}")
        else:
            print("无需要剪枝的分支")