#!/usr/bin/env python3
"""step37_final_v3.py — 最终模拟 v3（阶段过滤 + 支撑压力 + 最优参数）
规则（全部数据驱动）：
- 信号: 预测信号 + 大盘阶段过滤（排除高位/牛市）
  保留: mkt_dd60 <= -5（大盘已回撤，非高位）
- 入场: 信号日收盘买
- 止损: 支撑下方5%（step34验证）
- 止盈: 压力位分批卖
- 兜底: 40日
- 资金: 10槽 + 15%仓位（step35最优）
"""
import numpy as np
import pandas as pd
import sqlite3
import sys
sys.path.insert(0, "/tmp")
from sr_calculator import SRCalculator

print("=== 加载 ===", flush=True)
panel = pd.read_pickle("/tmp/panel_12d.pkl")
panel = panel.sort_values(["code", "date"]).reset_index(drop=True)
panel["_key"] = panel["code"] + "_" + panel["date"]
pos_map = {k: i for i, k in enumerate(panel["_key"])}
dates = sorted(panel["date"].unique())

# 大盘回撤
conn = sqlite3.connect("file:/home/hmo/MoFin/data/mofin.db?mode=ro", uri=True)
idx_df = pd.read_sql("SELECT date, close, high FROM stock_daily WHERE code='sh000001' ORDER BY date", conn)
idx_df["date"] = idx_df["date"].astype(str)
idx_df["hi60"] = idx_df["high"].rolling(60).max()
idx_df["mkt_dd60"] = (idx_df["close"] / idx_df["hi60"] - 1) * 100
dd_map = dict(zip(idx_df["date"], idx_df["mkt_dd60"]))
panel["mkt_dd60"] = panel["date"].map(dd_map)

# 信号：预测信号 + 阶段过滤（大盘已回撤，非高位）
sig_cond = (
    (panel["mkt_rsi"] < 50) & (panel["mcap_q"] < 0.2) & (panel["pe_q"] < 0.2) &
    (panel["news3"] >= 1) & (panel["sec_ret20"] < 0) & (panel["bias60"] < -20) &
    (panel["mkt_dd60"] <= -5)  # 阶段过滤：大盘已回撤(排除高位/牛市)
)
cand = panel[sig_cond][["code", "date"]].copy()
cand = cand.sort_values(["code", "date"])
cand["prev"] = cand.groupby("code")["date"].shift(1)
cand["gap"] = (pd.to_datetime(cand["date"]) - pd.to_datetime(cand["prev"])).dt.days
cand = cand[(cand["prev"].isna()) | (cand["gap"] > 30)]
cand["year"] = cand["date"].str[:4]
print("信号(含阶段过滤):", len(cand), flush=True)
print("月均: {:.1f}".format(len(cand)/max(len(cand["year"].unique()),1)/12), flush=True)
print("分年:", {str(y): int(c) for y, c in cand["year"].value_counts().sort_index().items()}, flush=True)

codes = cand["code"].unique().tolist()
ph = ",".join("?" * len(codes))
df = pd.read_sql("SELECT code, date, close, high, low FROM stock_daily WHERE code IN ({}) ORDER BY code, date".format(ph), conn, params=codes)
df["date"] = df["date"].astype(str)
df["code"] = df["code"].astype(str).str.zfill(6)
df = df.sort_values(["code", "date"]).reset_index(drop=True)
df["_key"] = df["code"] + "_" + df["date"]
dpos = {k: i for i, k in enumerate(df["_key"])}
closes = df["close"].values
highs = df["high"].values
lows = df["low"].values

cand["kidx"] = (cand["code"] + "_" + cand["date"]).map(dpos)
cand = cand.dropna(subset=["kidx"]).copy()
cand["kidx"] = cand["kidx"].astype(int)
print("可定位:", len(cand), flush=True)

# 支撑压力
sr = SRCalculator()
print("=== 计算支撑压力 ===", flush=True)
sig_list = []
for r in cand.itertuples():
    bars_code = sr.get_bars(r.code)
    d_idx = bars_code.index[bars_code["date"] == r.date]
    if len(d_idx) == 0:
        continue
    sr_full = sr.sr_full(r.code, d_idx[0])
    pv = sr_full["pivot"]
    chip = sr_full["chip"]
    if not pv:
        continue
    sig_close = closes[r.kidx]
    support = pv["s2"]
    if chip and chip["chip_ss"] < sig_close:
        support = max(pv["s2"], chip["chip_ss"])
    resist = pv["r2"]
    if chip and chip["chip_sr"] > sig_close:
        resist = min(pv["r2"], chip["chip_sr"])
    if support >= sig_close or resist <= sig_close:
        continue
    sig_list.append({"code": r.code, "date": r.date, "kidx": r.kidx,
                     "sig_close": sig_close, "support": support, "resist": resist})

sigdf = pd.DataFrame(sig_list)
print("有支撑压力:", len(sigdf), flush=True)

# ── 模拟（10槽+40日+15%+支撑保护）──
INIT_CAP = 1_000_000
SLOTS = 10
HOLD = 40
POS_FRAC = 0.15
STOP_BUF = 0.05

sig_by_date = {}
for r in sigdf.itertuples():
    sig_by_date.setdefault(r.date, []).append(r)

positions = {}
cash = INIT_CAP
nav_history = []
trades = []

for di, d in enumerate(dates):
    for code in list(positions.keys()):
        pos = positions[code]
        k = dpos.get(code + "_" + d)
        if k is None:
            continue
        hi, lo, cl = highs[k], lows[k], closes[k]
        if hi >= pos["tp"]:
            sell_qty = pos["qty"] // 2
            if sell_qty > 0:
                cash += sell_qty * pos["tp"]
                pos["qty"] -= sell_qty
                trades.append({"code": code, "date": d, "ret": (pos["tp"]/pos["avg_cost"]-1)*100, "reason": "tp"})
            if pos["qty"] <= 0:
                del positions[code]
                continue
        if lo <= pos["stop"]:
            cash += pos["qty"] * lo
            trades.append({"code": code, "date": d, "ret": (lo/pos["avg_cost"]-1)*100, "reason": "stop"})
            del positions[code]
            continue
        if di - pos["entry_di"] >= HOLD:
            cash += pos["qty"] * cl
            trades.append({"code": code, "date": d, "ret": (cl/pos["avg_cost"]-1)*100, "reason": "hold40"})
            del positions[code]
            continue

    if d in sig_by_date:
        for r in sig_by_date[d]:
            if len(positions) >= SLOTS:
                break
            if r.code in positions:
                continue
            cur_nav = cash
            for c, p in positions.items():
                k = dpos.get(c + "_" + d)
                px = closes[k] if k is not None else p["avg_cost"]
                cur_nav += p["qty"] * px
            pos_val = cur_nav * POS_FRAC
            price = r.sig_close
            if price <= 0:
                continue
            qty = int(pos_val / price)
            if qty <= 0 or qty * price > cash:
                continue
            cash -= qty * price
            positions[r.code] = {
                "entry_di": di, "qty": qty, "avg_cost": price,
                "stop": r.support * (1 - STOP_BUF), "tp": r.resist,
            }

    nav = cash
    for c, p in positions.items():
        k = dpos.get(c + "_" + d)
        px = closes[k] if k is not None else p["avg_cost"]
        nav += p["qty"] * px
    nav_history.append({"date": d, "nav": nav, "n_pos": len(positions), "cash": cash})

nav_df = pd.DataFrame(nav_history)
nav_df.to_csv("/tmp/step37_nav.csv", index=False)
tr_df = pd.DataFrame(trades)
tr_df.to_csv("/tmp/step37_trades.csv", index=False)

final_nav = nav_df["nav"].iloc[-1]
total_ret = (final_nav / INIT_CAP - 1) * 100
years = (pd.to_datetime(nav_df["date"].iloc[-1]) - pd.to_datetime(nav_df["date"].iloc[0])).days / 365
cagr = ((final_nav / INIT_CAP) ** (1 / years) - 1) * 100 if years > 0 else 0
cummax = nav_df["nav"].cummax()
dd = (nav_df["nav"] / cummax - 1) * 100
max_dd = dd.min()
print("\n=== 最终v3（阶段过滤）结果 ===", flush=True)
print("总收益: {:.1f}%".format(total_ret))
print("年化(CAGR): {:.2f}%".format(cagr))
print("最大回撤: {:.1f}%".format(max_dd))
print("持仓峰值: {}".format(nav_df["n_pos"].max()))
print("交易笔数: {}".format(len(tr_df)))
if len(tr_df) > 0:
    print("平均收益: {:.2f}% 胜率: {:.1f}%".format(tr_df["ret"].mean(), (tr_df["ret"]>0).mean()*100))
    print("卖出原因:", tr_df["reason"].value_counts().to_dict())
nav_df["year"] = nav_df["date"].str[:4]
yr_end = nav_df.groupby("year")["nav"].last()
print("\n分年净值:")
for y, v in yr_end.items():
    print("  {}: {:.0f} ({}%)".format(y, v, (v/INIT_CAP-1)*100))
print("现金占比均值: {:.1f}%".format((nav_df["cash"]/nav_df["nav"]).mean()*100))
print("\n=== 完成 ===", flush=True)