import csv
from datetime import datetime, timezone
import matplotlib.pyplot as plt

# ---------------- 配置 ----------------
CSV_FILE = "bitmart/kline_3.csv"  # 你的 CSV 文件路径
LEVERAGE = 100
CAPITAL = 10000
POSITION_RATIO = 0.01
FEE_RATE = 0.00015

# ---------------- 读取 CSV ----------------
data = []
with open(CSV_FILE, 'r') as f:
    reader = csv.DictReader(f)
    for row in reader:
        ts = int(row['id'])
        dt = datetime.fromtimestamp(ts, tz=timezone.utc)
        if dt.year == 2025 and dt.month == 1:
            data.append({
                'time': dt,
                'open': float(row['open']),
                'high': float(row['high']),
                'low': float(row['low']),
                'close': float(row['close']),
            })

# ---------------- 识别交易信号 ----------------
trades = []
position_usdt = CAPITAL * POSITION_RATIO
leveraged_position = position_usdt * LEVERAGE

for i in range(len(data)-1):
    k1 = data[i]
    k2 = data[i+1]

    # 刺透形态（Piercing Line，多头）
    if k1['close'] < k1['open'] and k2['close'] > k2['open']:
        midpoint = (k1['open'] + k1['close']) / 2
        if k2['open'] < k1['close'] and k2['close'] > midpoint:
            trades.append({
                '方向': '多',
                '开仓时间': k2['time'],
                '开仓价格': k2['open'],
                '平仓时间': k2['time'],
                '平仓价格': k2['close']
            })

    # 乌云盖顶（Dark Cloud Cover，空头）
    elif k1['close'] > k1['open'] and k2['close'] < k2['open']:
        midpoint = (k1['open'] + k1['close']) / 2
        if k2['open'] > k1['close'] and k2['close'] < midpoint:
            trades.append({
                '方向': '空',
                '开仓时间': k2['time'],
                '开仓价格': k2['open'],
                '平仓时间': k2['time'],
                '平仓价格': k2['close']
            })

# ---------------- 绘制 K 线图 ----------------
plt.figure(figsize=(16,6))

times = [k['time'] for k in data]
opens = [k['open'] for k in data]
closes = [k['close'] for k in data]
highs = [k['high'] for k in data]
lows = [k['low'] for k in data]

# 绘制 K 线（用竖线表示最高最低价，用矩形表示开收盘价）
for i in range(len(data)):
    color = 'green' if closes[i] >= opens[i] else 'red'
    plt.plot([times[i], times[i]], [lows[i], highs[i]], color='black')  # 高低价
    plt.plot([times[i]-0.0005, times[i]+0.0005], [opens[i], opens[i]], color=color, linewidth=5)  # 开盘价
    plt.plot([times[i]-0.0005, times[i]+0.0005], [closes[i], closes[i]], color=color, linewidth=5)  # 收盘价

# ---------------- 标注交易信号 ----------------
for t in trades:
    if t['方向'] == '多':
        plt.scatter(t['开仓时间'], t['开仓价格'], color='green', marker='^', s=100, label='多开' if '多开' not in plt.gca().get_legend_handles_labels()[1] else "")
        plt.scatter(t['平仓时间'], t['平仓价格'], color='red', marker='v', s=100, label='多平' if '多平' not in plt.gca().get_legend_handles_labels()[1] else "")
    else:
        plt.scatter(t['开仓时间'], t['开仓价格'], color='red', marker='v', s=100, label='空开' if '空开' not in plt.gca().get_legend_handles_labels()[1] else "")
        plt.scatter(t['平仓时间'], t['平仓价格'], color='green', marker='^', s=100, label='空平' if '空平' not in plt.gca().get_legend_handles_labels()[1] else "")

plt.xlabel('时间')
plt.ylabel('价格(USDT)')
plt.title('ETH 永续合约 1 月交易回测（刺透 & 乌云形态）')
plt.legend()
plt.grid(True)
plt.gcf().autofmt_xdate()
plt.show()