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网页网站设计价格,界面网站建设,seo推广软件排名,wordpress+HTML5游戏周末#xff0c;有读者加我#xff0c; 说 之前的涨停分析 是否可以增加连板分析。 这个可以加上。 先看效果 这里附上完整代码#xff1a; import streamlit as st import pywencai import pandas as pd from datetime import datetime, timedelta import plotly.graph_o…周末有读者加我 说 之前的涨停分析 是否可以增加连板分析。 这个可以加上。 先看效果 这里附上完整代码 import streamlit as st import pywencai import pandas as pd from datetime import datetime, timedelta import plotly.graph_objects as go from chinese_calendar import is_workday, is_holiday# Setting up pandas display options pd.set_option(display.unicode.ambiguous_as_wide, True) pd.set_option(display.unicode.east_asian_width, True) pd.set_option(display.max_rows, None) pd.set_option(display.max_columns, None) pd.set_option(display.expand_frame_repr, False) pd.set_option(display.max_colwidth, 100)def get_previous_trading_day(date):previous_date date - timedelta(days1)while not is_workday(previous_date) or is_holiday(previous_date):previous_date - timedelta(days1)return previous_datedef get_limit_up_data(date):param f{date.strftime(%Y%m%d)}涨停成交金额排序df pywencai.get(queryparam, sort_key成交金额, sort_orderdesc, loopTrue)return dfdef analyze_continuous_limit_up(df, date):# 提取连续涨停天数列和涨停原因类别列continuous_days_col f连续涨停天数[{date.strftime(%Y%m%d)}]reason_col f涨停原因类别[{date.strftime(%Y%m%d)}]# 确保涨停原因类别列存在if reason_col not in df.columns:df[reason_col] 未知# 按连续涨停天数降序排序然后按涨停原因类别排序df_sorted df.sort_values([continuous_days_col, reason_col], ascending[False, True])# 创建结果DataFrameresult pd.DataFrame(columns[连续涨停天数, 股票代码, 股票简称, 涨停原因类别])# 遍历排序后的DataFrame为每只股票创建一行for _, row in df_sorted.iterrows():new_row pd.DataFrame({连续涨停天数: [row[continuous_days_col]],股票代码: [row[股票代码]],股票简称: [row[股票简称]],涨停原因类别: [row[reason_col]]})result pd.concat([result, new_row], ignore_indexTrue)return resultdef get_concept_counts(df, date):concepts df[f涨停原因类别[{date.strftime(%Y%m%d)}]].str.split().explode().reset_index(dropTrue)concept_counts concepts.value_counts().reset_index()concept_counts.columns [概念, 出现次数]return concept_countsdef calculate_promotion_rates(current_df, previous_df, current_date, previous_date):Calculate promotion rates between consecutive dayscurrent_days_col f连续涨停天数[{current_date.strftime(%Y%m%d)}]previous_days_col f连续涨停天数[{previous_date.strftime(%Y%m%d)}]promotion_data []# Calculate for each level (from 1 to max consecutive days)max_days max(current_df[current_days_col].max(), previous_df[previous_days_col].max())for days in range(1, int(max_days)):# Previous day count for current levelprev_count len(previous_df[previous_df[previous_days_col] days])# Current day count for next levelcurr_count len(current_df[current_df[current_days_col] days 1])if prev_count 0:promotion_rate f{curr_count}/{prev_count}{round(curr_count / prev_count * 100 if prev_count 0 else 0)}%else:promotion_rate N/A# Get stocks that promotedpromoted_stocks current_df[current_df[current_days_col] days 1][[股票简称, f涨停原因类别[{current_date.strftime(%Y%m%d)}]]]promotion_data.append({连板数: f{days}板{days 1},晋级率: promotion_rate,股票列表: promoted_stocks})return pd.DataFrame(promotion_data)def app():st.title(A股涨停概念分析)# Date selectionmax_date datetime.now().date()selected_date st.date_input(选择分析日期, max_valuemax_date, valuemax_date)if not is_workday(selected_date) or is_holiday(selected_date):st.write(所选日期不是A股交易日请选择其他日期。)returnprevious_date get_previous_trading_day(selected_date)st.write(f分析日期: {selected_date} 和 {previous_date} (前一交易日))# Fetch data for both daysselected_df get_limit_up_data(selected_date)previous_df get_limit_up_data(previous_date)# Analyze continuous limit-up for both daysselected_continuous analyze_continuous_limit_up(selected_df, selected_date)previous_continuous analyze_continuous_limit_up(previous_df, previous_date)# Get concept counts for both daysselected_concepts get_concept_counts(selected_df, selected_date)previous_concepts get_concept_counts(previous_df, previous_date)# Merge concept countsmerged_concepts pd.merge(selected_concepts, previous_concepts, on概念, howouter,suffixes(_selected, _previous))merged_concepts merged_concepts.fillna(0)# Calculate changemerged_concepts[变化] merged_concepts[出现次数_selected] - merged_concepts[出现次数_previous]# Sort by 出现次数_selected in descending ordersorted_concepts merged_concepts.sort_values(出现次数_selected, ascendingFalse)# Display total limit-up stocks for both daysst.subheader(涨停股票数量变化)selected_total len(selected_continuous)previous_total len(previous_continuous)change selected_total - previous_totalcol1, col2, col3 st.columns(3)col1.metric(前一交易日涨停数, previous_total)col2.metric(选定日期涨停数, selected_total)col3.metric(变化, change, f{change:d})# Display concept changesst.subheader(涨停概念变化)st.dataframe(sorted_concepts)# Create a bar chart for top 10 conceptstop_10_concepts sorted_concepts.head(10)fig go.Figure(data[go.Bar(name选定日期, xtop_10_concepts[概念], ytop_10_concepts[出现次数_selected]),go.Bar(name前一交易日, xtop_10_concepts[概念], ytop_10_concepts[出现次数_previous])])fig.update_layout(barmodegroup, titleTop 10 涨停概念对比)st.plotly_chart(fig)# Display continuous limit-up analysisst.subheader(连续涨停天数分析)st.dataframe(selected_continuous)# Create a bar chart for continuous limit-up days distributioncontinuous_days_count selected_continuous[连续涨停天数].value_counts().sort_index()fig_continuous go.Figure(data[go.Bar(xcontinuous_days_count.index, ycontinuous_days_count.values)])fig_continuous.update_layout(title连续涨停天数分布,xaxis_title连续涨停天数,yaxis_title股票数量,xaxisdict(tickmodelinear))st.plotly_chart(fig_continuous)# Display raw datast.subheader(选定日期涨停股票详情)st.dataframe(selected_df)st.subheader(连板晋级率分析)promotion_rates calculate_promotion_rates(selected_df, previous_df, selected_date, previous_date)# Display promotion rates in a custom formatfor _, row in promotion_rates.iterrows():col1, col2 st.columns([1, 3])with col1:st.write(f{row[连板数]})st.write(f晋级率: {row[晋级率]})with col2:if not row[股票列表].empty:for _, stock in row[股票列表].iterrows():concept stock[f涨停原因类别[{selected_date.strftime(%Y%m%d)}]]st.write(f{stock[股票简称]} ({concept}))st.markdown(—)# Create visualization for promotion ratespromotion_rates_fig go.Figure()# Extract numeric values from promotion ratesrates []labels []for _, row in promotion_rates.iterrows():if row[晋级率] ! N/A:rate int(row[晋级率].split()[1].replace(%, ))rates.append(rate)labels.append(row[连板数])promotion_rates_fig.add_trace(go.Bar(xlabels,yrates,text[f{rate}% for rate in rates],textpositionauto,))promotion_rates_fig.update_layout(title连板晋级率分布,xaxis_title连板数,yaxis_title晋级率 (%),yaxis_range[0, 100])st.plotly_chart(promotion_rates_fig)if name main:app()在原来代码基础上增加了 根据连板天数排序 每支个股的涨停原因分析。 这个我之前没加 是因为我很少关注连续涨停股 毕竟我不是龙头选手。 另外增加了连板晋级率 比如1进2,2进3 可以分析涨停板晋级概率。对于龙头选手有一定的辅助效果。 另外交易日判断我之前肤浅了主要是脑子短路了。根据读者提醒改为引入日历控件chinese_calendar的is_workday, is_holiday 判断工作日、节假日。 原文链接 【Python技术】同花顺wencai涨停分析基础上增加连板分析