补全代码def predict_price(model, data, window_size): # 预测股票价格 obs = np.array([ data.loc[len(data) - window_size:len(data), 'Open'].values / 200, data.loc[len(data) - window_size:len(data), 'High'].values / 200, data.loc[len(data) - window_size:len

好的，补全代码如下：

def predict_price(model, data, window_size):
     # 预测股票价格
     obs = np.array([
         data.loc[len(data) - window_size:len(data), 'Open'].values / 200, 
         data.loc[len(data) - window_size:len(data), 'High'].values / 200, 
         data.loc[len(data) - window_size:len(data), 'Low'].values / 200, 
         data.loc[len(data) - window_size:len(data), 'Close'].values / 200, 
         data.loc[len(data) - window_size:len(data), 'Volume'].values / 100000000
     ])
     obs = np.reshape(obs, (obs.shape[1], obs.shape[0], 1))
     prediction = model.predict(obs)
     prediction = prediction.reshape(-1)
     return prediction * 200

这段代码将股票数据中最后 `window_size` 天的开盘价、最高价、最低价、收盘价和成交量作为输入数据 `obs`，然后将其传入已训练好的 `model` 中进行预测。最后将预测值乘以200，得到预测的股票价格。