EL PSY CONGROO

发起AJAX请求

AJAX使用Script 内置，下面是DEMO GET与POST

注意：

// @grant GM_xmlhttpRequest
// @connect httpbin.org

上面两个 必须拥有 ，可以将GM_xmlhttpRequest封装成函数调用。

// ==UserScript==
// @name         AJAX DEMO GET-POST
// @namespace    https://docs.scriptcat.org/
// @version      0.1.0
// @description  try to take over the world!
// @author       You
// @match        https://el.psy.congroo.com/*
// @icon         https://www.google.com/s2/favicons?sz=64&domain=el.psy.congroo.com
// @grant        GM_xmlhttpRequest
// @connect      httpbin.org

// ==/UserScript==

(function() {
    'use strict';

// GET DEMO
    GM_xmlhttpRequest({
        method: "GET",
        url: "https://httpbin.org/get?name=Saya&to=lora",
        onload: function(response) {
            console.log('请求成功，状态码：', response.status);
            console.log('响应内容：', response.responseText);
        },
        onerror: function(error) {
            console.error('请求失败：', error);
        }
    });

// POST表单 传入 数据 JSON返回 DEMO
    const searchData = new URLSearchParams({
        keyword: "JavaScript教程",
        page: "1",
        sort: "latest",
        category: "编程"
    });

    GM_xmlhttpRequest({
        method: "POST",
        url: "https://httpbin.org/post",
        headers: {
            "Content-Type": "application/x-www-form-urlencoded"
        },
        data: searchData.toString(),
        responseType: 'json',  // ✅ 加上这一行
        onload: function(res) {
            const results = res.response;
            // results 为队像直接处理(res.response)
            console.log('搜索结果：json字符串', res.responseText);
            console.log('搜索结果：json对象', res.response);
        }
    });



    // Your code here...
})();

动态网站。# 访问 不刷新网页

hash事件监听【需要在监听函数创建后执行】
window.addEventListener(‘hashchange’, pageChanged);
自动调用函数pageChanged

关键变量参数，获取当前网页#后面的数据

    function pageChanged() {
        const hash = window.location.hash;
        console.log('当前页面：', hash || '首页');
        
        // 根据不同 hash 做不同事
        if (hash === '#/list') {
            console.log('📋 列表页，抓取数据...');
            // 你的抓取代码
        } else if (hash === '#/detail') {
            console.log('📄 详情页，抓取数据...');
            // 你的抓取代码
        }
    }



    // 首次加载执行
    pageChanged();

加上延时 动态网站必备

# 还是之前的change函数
// ============ 延迟包装 ============
    let timer = null;

    function pageChangedWithDelay(delay = 400) {
        clearTimeout(timer);
        timer = setTimeout(() => {
            pageChanged();
        }, delay);
    }

    // ============ 监听 ============
    // ✅ hash 变化时自动调用
    window.addEventListener('hashchange', pageChangedWithDelay);

可以使用1分钟 或者5分钟线

买入点MA5 上穿MA10 必须上升趋势下，前提MA5 MA10 都是上升趋势下，在MA5 MA10下降去情况下慢慢MA5 穿MA10 不要买入，趋势反转处 附近K线 没有上影线。

卖出点MA5下穿MA10 可以在MA5 下穿MA10 前卖出 只要有足够利润，MA5下穿MA10时候已经是利润非最大话，可以在K线见顶 即多根 上影子线时候下手。

import pandas as pd

def first(df):
    #流通市值 30 45 60 90 120 日均价 以及在4日内高点发生回撤时候的比率
    df['lt']=df['amount']/(1000000*df['turn'])
    df['hc30']=(df['high']-df['p30'])/df['p30']
    df['hc45']=(df['high']-df['p45'])/df['p45']
    df['hc60']=(df['high']-df['p60'])/df['p60']
    df['hc90']=(df['high']-df['p90'])/df['p90']
    df['hc120']=(df['high']-df['p120'])/df['p120']
    is_pivot_high = df['high'] == df['high'].rolling(window=5, center=True).max()
    high_df = df[is_pivot_high][['mcode','date','open','close','low','high','p30','p45','p60','p90','p120','amount','turn','lt','hc30','hc45','hc60','hc90','hc120']]
    return high_df

        
results=[]
# 执行每个
results.append(first(df))

final_df=pd.concat(results,ignore_index=True)
final_df.to_csv("xxx.csv",index=False,encoding="utf-8-sig")

ADD

import pymysql
from pymysql.err import OperationalError, ProgrammingError
import pandas as pd
import time



def first(df):
    #流通市值 30 45 60 90 120 日均价 以及在4日内高点发生回撤时候的比率
    df['lt']=df['amount']/(1000000*df['turn'])
    df['hc30']=(df['high']-df['p30'])/df['p30']
    df['hc45']=(df['high']-df['p45'])/df['p45']
    df['hc60']=(df['high']-df['p60'])/df['p60']
    df['hc90']=(df['high']-df['p90'])/df['p90']
    df['hc120']=(df['high']-df['p120'])/df['p120']
    is_pivot_high = df['high'] == df['high'].rolling(window=5, center=True).max()
    high_df = df[is_pivot_high][['mcode','date','open','close','low','high','p30','p45','p60','p90','p120','amount','turn','lt','hc30','hc45','hc60','hc90','hc120']]
    return high_df

def get_all_data(mcode):
    start = time.time()
    sql = "SELECT * FROM cmf_quant5 WHERE mcode = %s AND date>2025-10-01 ORDER BY date ASC"
    df= pd.read_sql(sql, conn, params=(mcode,))
     # 清理异常值（必须保留）
    df = df.replace([float('inf'), -float('inf')], 0)
    df = df.fillna(0)
    return df


# 数据库配置
config = {
    "host": "localhost",
    "port": 3306,
    "user": "root",
    "password": "123456",
    "database": "gupiao",
    "charset": "utf8mb4"
}

conn = pymysql.connect(**config)
cursor = conn.cursor()

results=[]
df = pd.read_csv('/home/may/gupiao/all.csv', dtype={'code': str})
i=0
for index, row in df.iterrows():
    df=get_all_data(row['code'])
    results.append(first(df))
final_df=pd.concat(results,ignore_index=True)
final_df.to_csv("xxx.csv",index=False,encoding="utf-8-sig")

首先 获取pandas对象下的单个股票df 可以塞选，但是必须对于该股票之前到数据进行初步塞选

使用 pandas 判断 执行 买入 然后在相隔固定时间 一天 三天 五天 7天 10天 后的收盘价 记录 与之前买入价比较。若大于其中为胜。 且记录差值，因为是盘中数值 所以 使用当日收盘价 第二日开盘价比较

满足条件。

a当日买入 收盘价

b 下一日开盘价

比较后 1天 2天 3天 5天 7天 10天

更具多个股票信息分别统计 单因子规律

---

简化步骤

触发买入

触发卖出

止损卖出

倒入个股的 pandas数据

执行 买入信号 买入

盈利止盈 卖出atr 止损atr清仓

执行到最后一天看整体数据

也可以使用backtrader进行回测

import pandas as pd
import numpy as np


#价
def price(df):
    return abs(df['high'].max()-df['low'].min())/df['low'].min()

def vper(df):
    return (abs((df['close']-df['open'])/df['open'])<=0.02).mean()

#量 前后对比
def volume(df_pre,df):
    return df_pre['volume'].mean()>=2*df['volume'].mean()
#红肥绿
def redgreen(df):
    red=(df['close']>=df['open'])
    green=(df['close']<df['open'])
    return df[red]['volume'].sum()>=2*df[green]['volume'].sum()
#涨跌段量比 
def vol_zd(df, min_days=1):
    df = df.copy()
    df['flag'] = np.where(df['close'] > df['open'], 1,
                 np.where(df['close'] < df['open'], -1, 0))
    df['group'] = (df['flag'] != df['flag'].shift()).cumsum()
    
    seg_info = []
    for _, g in df.groupby('group'):
        f = g['flag'].iloc[0]
        if f in (1, -1) and len(g) >= min_days:
            seg_info.append((f, g['volume'].mean()))
    
    pair_list = []
    for i in range(len(seg_info)-1):
        curr_f, curr_v = seg_info[i]
        next_f, next_v = seg_info[i+1]
        if curr_f == 1 and next_f == -1:
            pair_list.append((curr_v, next_v))
    
    if not pair_list:
        return False, [], 0.0
    
    each_ok = [up > down for up, down in pair_list]
    pass_rate = sum(each_ok) / len(each_ok)
    all_pass = all(each_ok)
    return all_pass, each_ok, pass_rate

def merge_intervals(intervals):
    """合并重叠或相邻的区间，intervals: [(start, end), ...]，左闭右开"""
    if not intervals:
        return []
    intervals.sort(key=lambda x: x[0])          # 按起点排序
    merged = [list(intervals[0])]               # 用列表方便修改
    for start, end in intervals[1:]:
        if start <= merged[-1][1]:              # 重叠或刚好相邻
            merged[-1][1] = max(merged[-1][1], end)
        else:
            merged.append([start, end])
    return [tuple(m) for m in merged]           # 转回元组

def cpdf(df,k=60):
    valid_intervals = []
    for i in range(k,len(df)-k-1):
        pre_df=df.iloc[i-k:i].copy()
        next_df=df.iloc[i:i+k].copy()
        #价
        if price(next_df)<0.15:
            continue
        if vper(next_df)<0.7:
            continue
        #量
        if not volume(pre_df,next_df):
            continue
        if not redgreen(next_df):
            continue
        a,b,c=vol_zd(next_df)
        if c<0.7:
            continue
        valid_intervals.append((i, i + k))
    
    if not valid_intervals:
        return []
    
    # 合并区间（使用之前的通用函数）
    merged_rows = merge_intervals(valid_intervals)  # 例如 [(100, 220), (350, 500)]
    
    # 将行号区间转换为日期区间（方便阅读）
    result = []
    for start_row, end_row in merged_rows:
        start_date = df.iloc[start_row]['date']   # 假设date列是日期
        end_date = df.iloc[end_row - 1]['date']   # 因为右开，取前一行日期
        result.append((start_row, end_row, start_date, end_date))
    
    return result