(iwencai量化策略)kdj(k)增长值_、竞价涨幅>-2<5、今日增仓占比_5%

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2023-08-31 发布

问财量化选股策略逻辑

今日增仓占比>5%,竞价涨幅>-2<5,kdj(k)增长值

选股逻辑分析

这个策略基于三个指标来筛选股票。首先,要求股票今日的增仓比例超过5%,这意味着机构投资者对这只股票的买入行为较为积极。其次,要求股票在竞价阶段的涨幅超过-2%,这意味着股票的价格表现良好。最后,要求股票的KDJ(K)增长值大于0,这意味着股票的短期趋势较为向上。

有何风险?

这个策略的风险主要在于选取的股票可能不符合投资者的预期。如果机构投资者买入的股票表现不佳,或者股票的价格涨幅不符合预期,那么投资者可能会遭受损失。此外,KDJ指标也存在一定的局限性,它可能会产生虚假信号,因此投资者需要谨慎使用。

如何优化?

为了优化这个策略,投资者可以考虑以下几点:

  1. 增加更多的筛选指标,例如市盈率、市净率等,以更全面地评估股票的价值和风险。
  2. 使用更复杂的算法来计算KDJ指标,以提高其准确性和可靠性。
  3. 考虑使用技术指标的组合,例如移动平均线、布林线等,以提高策略的准确性和稳定性。

最终的选股逻辑

最终的选股逻辑如下:

def select_stock():
    # 获取今日所有股票的增仓比例和涨幅数据
    today_data = get_todays_data()
    
    # 筛选出今日增仓比例超过5%的股票
    stocks = today_data[today_data['net_percent'] > 0.05]['symbol']
    
    # 筛选出在竞价阶段涨幅超过-2%的股票
    stocks = stocks[today_data[today_data['symbol']]['pre_close'] > today_data[today_data['symbol']]['pre_close'] - 0.02]['symbol']
    
    # 筛选出KDJ指标增长值大于0的股票
    stocks = stocks[today_data[today_data['symbol']]['k'] > 0]['symbol']
    
    return stocks

python代码参考

def get_todays_data():
    # 获取所有股票的开盘价、收盘价、最高价、最低价和成交量数据
    data = get_data()
    
    # 计算今日增仓比例
    data['net_percent'] = data['net_amount'] / data['close'] * 100
    
    # 计算今日竞价涨幅
    data['pre_close'] = data['close']
    data['pre_open'] = data['open']
    data['pre_high'] = data['high']
    data['pre_low'] = data['low']
    data['pre_volume'] = data['volume']
    data['pre_bid'] = data['bid']
    data['pre_ask'] = data['ask']
    data['pre_bid_amount'] = data['bid_amount']
    data['pre_ask_amount'] = data['ask_amount']
    data['pre_bid_percent'] = data['bid_percent']
    data['pre_ask_percent'] = data['ask_percent']
    data['pre_bid_ask_diff'] = data['bid_ask_diff']
    data['pre_bid_ask_ratio'] = data['bid_ask_ratio']
    data['pre_bid_ask_volume'] = data['bid_ask_volume']
    data['pre_open_price'] = data['pre_open'] + data['pre_bid_ask_diff']
    data['pre_close_price'] = data['pre_close'] + data['pre_ask_bid_diff']
    data['pre_high_price'] = max(data['pre_open_price'], data['pre_high'])
    data['pre_low_price'] = min(data['pre_open_price'], data['pre_low'])
    data['pre_open_percent'] = (data['pre_close'] - data['pre_open']) / data['pre_open'] * 100
    data['pre_close_percent'] = (data['pre_close'] - data['pre_close']) / data['pre_close'] * 100
    data['pre_high_percent'] = (data['pre_high'] - data['pre_open']) / data['pre_open'] * 100
    data['pre_low_percent'] = (data['pre_low'] - data['pre_open']) / data['pre_open'] * 100
    data['pre_volume'] = data['pre_bid_amount'] + data['pre_ask_amount']
    data['pre_bid_percent'] = data['pre_bid_amount'] / data['pre_volume'] * 100
    data['pre_ask_percent'] = data['pre_ask_amount'] / data['pre_volume'] * 100
    data['pre_bid_ask_diff'] = data['pre_bid_amount'] - data['pre_ask_amount']
    data['pre

## 如何进行量化策略实盘?
请把您优化好的选股语句放入文章最下面模板的选股语句中即可。

select_sentence = '市值小于100亿' #选股语句。

模板如何使用?

点击图标右上方的复制按钮,复制到自己的账户即可使用模板进行回测。


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