# 添加一个计算过去K线波动率的函数，并在主函数中增加相关逻辑。

# 计算过去K线波动率
def calculate_kline_volatility(close_prices, period):
    """
    计算K线波动率

    :param close_prices: 收盘价序列
    :param period: 计算波动率所用的时间窗口大小
    :return: 价格波动率
    """
    try:
        # 计算每个周期内价格变化率
        price_returns = np.abs(np.diff(close_prices)) / close_prices[:-1]

        # 计算波动率
        volatility = np.sqrt(np.sum(price_returns ** 2) / (period - 1)) * np.sqrt(period)

    except Exception as e:
        # 发生错误时返回0
        print(f'计算波动率时发生错误: {e}')
        volatility = 0.0

    # 返回波动率
    return volatility


# 实例化OKXTrader类
trader = OKXTrader(api_key, secret_key, passphrase)

# 获取历史K线数据
times, high_prices, low_prices, close_prices = trader.get_historical_klines(symbol, interval, limit=200)

# 计算过去K线波动率
kline_volatility = calculate_kline_volatility(close_prices, period=20)

# 如果波动率小于0.2%，则不开仓交易，直到波动率大于这一水平再进行接下来的开仓操作
if kline_volatility > 0.002:
    # 计算超级趋势指标
    upper_bound, lower_bound, buy_signal, sell_signal = trader.calculate_super_trend(close_prices, period=10, multiplier=3)

    # 执行交易策略
    if buy_signal or sell_signal:
        # 获取最新成交价作为下单价格
        price = float(trader.future_api.get_last_trade(symbol)['price'])

        # 计算下单数量
        account_data = trader.account_api.get_account_info()
        available_balance = float(account_data['data'][0]['details']['available_balance'])
        quantity = min(quantity, int(available_balance / price))

        # 执行交易策略并打印订单结果
        order_result = trader.execute_trade_strategy(symbol, quantity, price, buy_signal, sell_signal, stop_loss_pct, take_profit_pct)
        print(f'执行交易策略，订单结果: {order_result}')
    else:
        print('没有发出买入或卖出信号')
else:
    print('过去K线波动率小于0.2%，不开仓交易')