"""烛龙 v1.3 完整版：信号打分 + 自适应加仓 + 移动止盈"""
import pandas as pd, numpy as np

df_raw = pd.read_parquet("data/btc_multidim.parquet")
d = df_raw.resample("1h").agg({"open":"first","high":"max","low":"min","close":"last","volume":"sum"}).dropna()

dd = df_raw.resample("1D").agg({"close":"last"}).dropna()
dd["ma20"] = dd["close"].rolling(20).mean()
dd["trend_up"] = dd["close"] > dd["ma20"]

O,H,L,C,V = d["open"].values,d["high"].values,d["low"].values,d["close"].values,d["volume"].values
n = len(d)

tr = np.maximum(H-L, np.maximum(abs(H-np.roll(C,1)), abs(L-np.roll(C,1))))
atr_pct = pd.Series(tr).rolling(14).mean().values / C * 100

O1=np.roll(O,1);H1=np.roll(H,1);L1=np.roll(L,1);C1=np.roll(C,1)

bull = (C1<O1) & (C>O) & (O<=C1) & (C>=O1)
near_s = abs(L-pd.Series(L).shift(1).rolling(20).min().values) / (pd.Series(L).shift(1).rolling(20).min().values+1e-9) < 0.005
nbull = np.roll(C,-1) > np.roll(O,-1)
long_sig = bull & near_s & nbull

swup = (H>pd.Series(H).shift(1).rolling(20).max().values) & (C<pd.Series(H).shift(1).rolling(20).max().values)
near_r = abs(H-pd.Series(H).shift(1).rolling(20).max().values) / (pd.Series(H).shift(1).rolling(20).max().values+1e-9) < 0.005
nbear = np.roll(C,-1) < np.roll(O,-1)
short_sig = swup & near_r & nbear

d_ts = d.index
trend_1h = np.array([
    dd["trend_up"].reindex([ts], method="ffill").values[0]
    if ts >= dd.index[0] else True
    for ts in d_ts
])

lsig = long_sig & trend_1h
ssig = short_sig & (~trend_1h)

split = int(n * 0.67)
SL, TP, MB = 0.015, 0.045, 48

def calc_signal_score(i, dirc):
    """信号质量打分"""
    vol_mean = np.mean(V[max(0,i-20):i]) if i >= 20 else np.mean(V[:i+1])
    candle_body = abs(C[i] - O[i])
    candle_range = H[i] - L[i]
    body_ratio = candle_body / (candle_range + 1e-9)
    prev_body = abs(C[i-1] - O[i-1])
    
    bonus = 0
    if V[i] > vol_mean * 1.5: bonus += 20
    elif V[i] > vol_mean * 1.2: bonus += 10
    if body_ratio > 0.7: bonus += 15
    elif body_ratio > 0.5: bonus += 8
    if prev_body > candle_range * 0.5: bonus += 10
    
    base = 100  # 能进来的都是有效信号
    return min(145, base + bonus)

def backtest(lsig, ssig, mode):
    """
    mode: 'fixed', 'addonly', 'full'(含信号打分+自适应+移动止盈)
    """
    results = {"in": [], "out": []}
    stats = {"add_total": 0, "trailing_trigger": 0}
    
    for period, st, en in [("in", 0, split), ("out", split, n)]:
        for mask, dirc in [(lsig, 1), (ssig, -1)]:
            for i in range(st, min(en, n)):
                if not mask[i]: continue
                if i + MB >= n: continue
                
                entry = C[i]
                closed = False
                pos_size = 1.0
                add_count = 0
                last_eval = 0
                best_ret = 0  # 用于移动止盈
                tp_activated = False
                tp_level = TP
                
                # 信号评分（用于full模式）
                score = calc_signal_score(i, dirc)
                max_add = 3 if score >= 90 else (2 if score >= 60 else 1)
                add_sz = 0.4 if score >= 100 else 0.3
                
                for j in range(1, MB + 1):
                    if i + j >= n: break
                    ret = (C[i+j] / entry - 1) * dirc
                    best_ret = max(best_ret, ret)
                    
                    if mode != "fixed":
                        # ── 加仓节奏 ──
                        if mode == "addonly":
                            eval_step = 3  # 固定3小时
                        else:  # full
                            atr_val = atr_pct[i+j] if not np.isnan(atr_pct[i+j]) else 0.3
                            if atr_val > 0.5: eval_step = 3      # 高波动→3小时
                            elif atr_val > 0.3: eval_step = 4    # 中波动→4小时
                            else: eval_step = 5                  # 低波动→5小时（慢一点稳一点）
                        
                        if j % eval_step == 0 and j != last_eval:
                            last_eval = j
                            
                            if mode == "addonly":
                                if add_count < 3 and pos_size < 2.0:
                                    pos_size = min(2.0, pos_size + 0.3)
                                    add_count += 1
                                    stats["add_total"] += 1
                            
                            elif mode == "full":
                                if add_count < max_add and pos_size < 2.0:
                                    pos_size = min(2.0, pos_size + add_sz)
                                    add_count += 1
                                    stats["add_total"] += 1
                        
                        # 无移动止盈
                    
                    # TP/SL（满用移动后的止盈）
                    if ret >= tp_level:
                        results[period].append(tp_level * pos_size - 0.001)
                        closed = True; break
                    if ret <= -SL:
                        results[period].append(-SL * pos_size - 0.001)
                        closed = True; break
                
                if not closed:
                    final_ret = (C[min(i+MB, n-1)] / entry - 1) * dirc
                    results[period].append(final_ret * pos_size - 0.001)
    
    return results, stats

def print_stats(results, stats, label):
    print(f"\n▶ {label}")
    if stats["add_total"]:
        print(f"  [统计] 加仓={stats['add_total']} 移动止盈触发={stats.get('trailing_trigger',0)}")
    for nm, key in [("样本内", "in"), ("样本外", "out")]:
        tr = results[key]
        if len(tr) < 5: print(f"  {nm}: 仅{len(tr)}笔"); continue
        wr = sum(1 for r in tr if r > 0) / len(tr)
        cum = np.prod([1 + r for r in tr])
        ch = [tr[i:i+5] for i in range(0, len(tr), 5)]
        pw = sum(1 for c in ch if sum(c) > 0) / len(ch) if ch else 0
        avg = np.mean(tr) * 100
        running = 1.0; peak = 1.0; mdd = 0
        for r in tr:
            running *= (1 + r); peak = max(peak, running)
            mdd = min(mdd, (running - peak) / peak)
        sharpe = np.mean(tr) / (np.std(tr) + 1e-9) * np.sqrt(365*24/MB) if np.std(tr) > 0 else 0
        print(f"  {nm}: {len(tr)}笔 wr={wr:.1%} cum={cum:.3f} avg={avg:+.2f}% "
              f"周盈≈{pw:.1%} MDD={mdd:.1%} Sharpe={sharpe:.2f}")

print("=" * 80)
print("烛龙 v1.3 完整版：三合一优化")
print("=" * 80)
print(f"SL={SL} TP={TP} MB={MB}h  数据: BTC 1H {n}根")

r1, s1 = backtest(lsig, ssig, "fixed")
print_stats(r1, s1, "① 固定止损（原版）")

r2, s2 = backtest(lsig, ssig, "addonly")
print_stats(r2, s2, "② 只加不减（v1.2）")

r3, s3 = backtest(lsig, ssig, "full")
print_stats(r3, s3, "③ 信号打分+自适应+移动止盈（v1.3）")

print(f"\n{'='*70}")
print("📊 结论")
print(f"{'='*70}")
print("对比三者的样本外表现：")