上海开心男人的美篇

上海开心男人

温和放量趋势选股公式}

涨幅 := (CLOSE - REF(CLOSE,1)) / REF(CLOSE,1) * 100;

量比 := VOL / MA(VOL, 5);

换手率 := VOL / FINANCE(7) * 100; { 假设FINANCE(7)为流通股本，请根据软件实际函数调整 }

市值 := FINANCE(总市值或流通市值函数); { 请根据软件实际函数调整，例如 FINANCE(41) }

{核心选股条件}

条件1 := 涨幅 >= 3 AND 涨幅 <= 5;

条件2 := 量比 >= 1;

条件3 := 换手率 >= 5 AND 换手率 <= 10;

条件4 := 市值 >= 5000000000 AND 市值 <= 200000000000; { 单位：元，50亿=5e9, 200亿=2e11 }

条件5 := COUNT(VOL > REF(VOL,1), 3) >= 2; { 最近3日内，至少有2天成交量放大 }

条件6 := MA(CLOSE, 60) > REF(MA(CLOSE, 60), 1); { 60日均线今日值大于昨日值，表示向上 }

选股: 条件1 AND 条件2 AND 条件3 AND 条件4 AND 条件5 AND 条件6;

import pandas as pd

import akshare as ak # 示例数据库，需提前安装

# 定义选股参数

params = {

'涨幅下限': 3,

'涨幅上限': 5,

'量比下限': 1,

'换手率下限': 5,

'换手率上限': 10,

'市值下限_亿元': 50,

'市值上限_亿元': 200,

'成交量持续天数': 3 # 判断成交量放大的时间窗口

}

def stock_selection_logic(stock_data, params):

"""

应用选股逻辑到单只股票的数据上

"""

# 计算基本指标

stock_data['涨幅'] = (stock_data['收盘价'] - stock_data['收盘价'].shift(1)) / stock_data['收盘价'].shift(1) * 100

stock_data['量比'] = stock_data['成交量'] / stock_data['成交量'].rolling(5).mean()

stock_data['换手率'] = stock_data['成交量'] / stock_data['流通股本'] * 100 # 需要流通股本数据

stock_data['MA60'] = stock_data['收盘价'].rolling(60).mean()

# 获取最新一天的数据

latest = stock_data.iloc[-1]

# 条件判断

cond_price = params['涨幅下限'] <= latest['涨幅'] <= params['涨幅上限']

cond_vol_ratio = latest['量比'] >= params['量比下限']

cond_turnover = params['换手率下限'] <= latest['换手率'] <= params['换手率上限']

cond_market_cap = params['市值下限_亿元'] <= latest['总市值'] <= params['市值上限_亿元']

cond_ma60_trend = latest['MA60'] > stock_data['MA60'].iloc[-2] # 60日均线向上

# 判断成交量持续放大：例如，最近N天有N-1天是放大的

recent_vol = stock_data['成交量'].tail(params['成交量持续天数']+1) # 多取一天用于比较

vol_increase_days = sum(recent_vol.iloc[i] > recent_vol.iloc[i-1] for i in range(1, len(recent_vol)))

cond_vol_increase = vol_increase_days >= (params['成交量持续天数'] - 1)

return cond_price and cond_vol_ratio and cond_turnover and cond_market_cap and cond_vol_increase and cond_ma60_trend

# 主程序流程示意

def main():

all_stocks_codes = ['000001', '600036', ...] # 需要获取股票列表

selected_stocks = []

for code in all_stocks_codes:

try:

# 获取单个股票的历史日线数据，需要包含收盘价、成交量、总市值、流通股本等字段

stock_data = ak.stock_zh_a_hist(symbol=code, period="daily")

# 这里需要将数据整理成规范的DataFrame格式

if stock_selection_logic(stock_data, params):

selected_stocks.(code)

except Exception as e:

print(f"处理股票 {code} 时出错: {e}")

print("筛选出的股票代码:", selected_stocks)

if __name__ == "__main__":

main()

先编二个试试……真的要感谢上苍赐给我的这个礼物，这不是什么人都可以做的事情！前段时间自己自由放任了自己，现在该醒醒了……不然我是自毁前程。