My语言怎么实现SAR量化交易策略

# My语言怎么实现SAR量化交易策略

## 一、SAR指标原理与交易逻辑

### 1.1 SAR指标数学定义
抛物线转向指标（Stop and Reverse, SAR）由J. Welles Wilder Jr.在1978年提出，其核心计算公式为：

SAR(n) = SAR(n-1) + AF × (EP - SAR(n-1))

其中：
- AF（加速因子）通常从0.02开始，每创新高/新低增加0.02，最大不超过0.2
- EP（极值点）是多头期间的最高价或空头期间的最低价

### 1.2 交易信号生成规则
1. **多头信号**：当价格从下向上突破SAR值时
2. **空头信号**：当价格从上向下跌破SAR值时
3. **止损机制**：SAR值本身作为动态止损点

## 二、My语言环境配置

### 2.1 开发环境准备
```python
# My语言量化平台基础配置
import myquant as mq
mq.set_backtest(
    capital=100000,
    commission=0.0003,
    slippage=0.001
)

2.2 数据接口调用

# 获取沪深300指数数据
data = mq.get_history(
    symbol='000300.SH',
    frequency='1d',
    fields=['open','high','low','close'],
    start_date='20200101',
    end_date='20231231'
)

三、SAR策略完整实现

3.1 核心算法实现

def calculate_sar(data, af_start=0.02, af_step=0.02, af_max=0.2):
    """
    SAR指标计算函数
    :param data: 包含OHLC的DataFrame
    :param af_start: 初始加速因子
    :param af_step: 加速因子步长
    :param af_max: 最大加速因子
    :return: 添加SAR列的DataFrame
    """
    high = data['high'].values
    low = data['low'].values
    close = data['close'].values
    
    sar = np.zeros(len(close))
    trend = np.zeros(len(close), dtype=int)
    ep = np.zeros(len(close))
    af = np.zeros(len(close))
    
    # 初始化前两日值
    sar[0:2] = close[0:2]
    trend[0:2] = 1
    ep[0:2] = high[0:2]
    af[0:2] = af_start
    
    for i in range(2, len(close)):
        # 趋势延续处理
        if trend[i-1] == 1:
            sar[i] = sar[i-1] + af[i-1] * (ep[i-1] - sar[i-1])
            if low[i] < sar[i]:
                trend[i] = -1
                sar[i] = ep[i-1]
                ep[i] = low[i]
                af[i] = af_start
            else:
                trend[i] = 1
                if high[i] > ep[i-1]:
                    ep[i] = high[i]
                    af[i] = min(af[i-1] + af_step, af_max)
                else:
                    ep[i] = ep[i-1]
                    af[i] = af[i-1]
        else:
            sar[i] = sar[i-1] + af[i-1] * (ep[i-1] - sar[i-1])
            if high[i] > sar[i]:
                trend[i] = 1
                sar[i] = ep[i-1]
                ep[i] = high[i]
                af[i] = af_start
            else:
                trend[i] = -1
                if low[i] < ep[i-1]:
                    ep[i] = low[i]
                    af[i] = min(af[i-1] + af_step, af_max)
                else:
                    ep[i] = ep[i-1]
                    af[i] = af[i-1]
        
        # 价格跳空处理
        if trend[i] == 1:
            sar[i] = min(sar[i], low[i-1], low[i-2])
        else:
            sar[i] = max(sar[i], high[i-1], high[i-2])
    
    data['SAR'] = sar
    data['Trend'] = trend
    return data

3.2 交易信号生成

def generate_signals(data):
    signals = []
    position = 0  # 0:无持仓, 1:多头, -1:空头
    
    for i in range(1, len(data)):
        current_close = data['close'].iloc[i]
        prev_sar = data['SAR'].iloc[i-1]
        current_sar = data['SAR'].iloc[i]
        
        # 多头信号
        if position <= 0 and current_close > prev_sar:
            signals.append(1)
            position = 1
        # 空头信号
        elif position >= 0 and current_close < prev_sar:
            signals.append(-1)
            position = -1
        else:
            signals.append(0)
    
    data['Signal'] = signals + [0]  # 补齐最后一日
    return data

四、策略回测与优化

4.1 回测引擎实现

def backtest(data):
    returns = []
    position = 0
    entry_price = 0
    
    for i in range(len(data)):
        signal = data['Signal'].iloc[i]
        close = data['close'].iloc[i]
        
        if signal == 1 and position != 1:
            if position == -1:
                # 空头平仓
                returns.append((entry_price - close)/entry_price)
            # 开多头
            position = 1
            entry_price = close
        elif signal == -1 and position != -1:
            if position == 1:
                # 多头平仓
                returns.append((close - entry_price)/entry_price)
            # 开空头
            position = -1
            entry_price = close
    
    # 计算累计收益
    cumulative_returns = np.cumprod([1 + r for r in returns])
    return cumulative_returns

4.2 参数优化框架

def optimize_parameters(data):
    af_range = np.arange(0.01, 0.05, 0.01)
    max_af_range = np.arange(0.15, 0.25, 0.05)
    
    best_sharpe = -np.inf
    best_params = {}
    
    for af in af_range:
        for max_af in max_af_range:
            temp_data = calculate_sar(data.copy(), af_start=af, af_max=max_af)
            temp_data = generate_signals(temp_data)
            returns = backtest(temp_data)
            
            if len(returns) > 0:
                sharpe = np.mean(returns)/np.std(returns) * np.sqrt(252)
                if sharpe > best_sharpe:
                    best_sharpe = sharpe
                    best_params = {'af_start': af, 'af_max': max_af}
    
    return best_params

五、实盘部署要点

5.1 实时数据处理

class SAR_Trader:
    def __init__(self, af_start=0.02, af_max=0.2):
        self.af_start = af_start
        self.af_max = af_max
        self.prev_data = None
        
    def process_tick(self, new_tick):
        if self.prev_data is None:
            self.prev_data = new_tick
            return 0
            
        # 合成最新K线
        current_bar = {
            'high': max(self.prev_data['high'], new_tick['price']),
            'low': min(self.prev_data['low'], new_tick['price']),
            'close': new_tick['price']
        }
        
        # 计算SAR值
        sar_value = self.calculate_sar(current_bar)
        
        # 生成交易信号
        if new_tick['price'] > sar_value:
            return 1  # 买入信号
        elif new_tick['price'] < sar_value:
            return -1 # 卖出信号
        return 0

5.2 风险管理模块

def risk_management(position, portfolio, max_drawdown=0.2):
    current_value = portfolio['cash'] + portfolio['position_value']
    peak_value = max(portfolio['peak_value'], current_value)
    drawdown = (peak_value - current_value)/peak_value
    
    if drawdown > max_drawdown:
        # 触发风控平仓
        if position > 0:
            return -1  # 平多
        elif position < 0:
            return 1   # 平空
    return 0

六、策略评价与改进方向

6.1 绩效评估指标

6.2 常见改进方案

1. 趋势过滤：结合MA20判断大趋势方向
2. 动态参数：根据市场波动率调整AF参数
3. 混合策略：与RSI或MACD形成复合信号
4. 仓位管理：使用凯利公式动态调整仓位

注：实际应用中需注意My语言的版本差异，部分函数可能需要根据具体平台API调整实现方式。建议先在模拟盘运行至少3个月再投入实盘资金。 “`