"""烛龙 v1.3 完整回测：信号质量打分 + 加仓节奏自适应"""
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_strength(i, dirc):
    """信号质量打分：放量+实体+前阴大小"""
    vol_mean = np.mean(V[max(0,i-20):i])
    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 dirc == 1 and bull[i] and prev_body > candle_range * 0.5: bonus += 10
    if dirc == -1 and (C[i-1] > O[i-1]) and (C[i] < O[i]) and prev_body > candle_range * 0.5: bonus += 10
    
    base = 100 if (bull[i] and near_s[i]) or (swup[i] and near_r[i]) else 80
    return min(145, base + bonus)

def backtest(lsig, ssig, mode):
    """
    mode: 'fixed'原版, 'addonly'只加不减, 'full'信号打分+自适应加仓
    """
    results = {"in": [], "out": []}
    stats = {"add":0, "reduce":0, "hold":0, "early_close":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
                
                for j in range(1, MB + 1):
                    if i + j >= n: break
                    ret = (C[i+j] / entry - 1) * dirc
                    
                    if mode != "fixed" and j % 3 == 0 and j != last_eval:
                        last_eval = j
                        
                        if mode == "addonly":
                            # 只加不减（原版）
                            if add_count < 3 and pos_size < 2.0:
                                pos_size = min(2.0, pos_size + 0.3)
                                add_count += 1
                                stats["add"] += 1
                        
                        elif mode == "full":
                            # 信号质量打分
                            signal_score = calc_signal_strength(i, dirc)
                            max_add = 2 if signal_score >= 120 else (1 if signal_score >= 80 else 0)
                            add_size = 0.5 if signal_score >= 120 else 0.3
                            
                            # 加仓节奏自适应（基于ATR）
                            atr_val = atr_pct[i+j] if not np.isnan(atr_pct[i+j]) else 0.3
                            if atr_val > 0.5:
                                eval_step = 2  # 高波动→加仓快
                            elif atr_val > 0.3:
                                eval_step = 3
                            else:
                                eval_step = 4  # 低波动→加仓慢
                            
                            if add_count < max_add and pos_size < 2.0:
                                pos_size = min(2.0, pos_size + add_size)
                                add_count += 1
                                stats["add"] += 1
                    
                    # TP/SL
                    if ret >= TP:
                        results[period].append(TP * 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"]:
        print(f"  [推理] 加仓={stats['add']} 保持={stats['hold']} 减仓={stats['reduce']} 早退={stats['early_close']}")
    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 完整策略对比：信号质量打分 + 加仓节奏自适应")
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, "固定止损（原版）")

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

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