绘图

from pyecharts import options as opts  # 全局、系列配置
from pyecharts.charts import Bar, Grid, Line  # 柱形图
from pyecharts.globals import ThemeType  # 主题风格
from pyecharts.charts import Kline, Line, Bar, Gauge, Pie, Scatter, TreeMap  # K线图、线形图、柱状图、仪表盘、饼图、散点图、矩形树图
from pyecharts.charts import Grid, Tab  # 组合组件、分页组件
from pyecharts.components import Table  # 表格组件
from pyecharts.options import ComponentTitleOpts
from pyecharts.commons.utils import JsCode

import time  # 时间模块
#import pandas as pd  # Pandas模块




########################################################### Pandas数据读写操作 ###########################################################
class Pands_Handler:
    # 初始化
    def __init__(self):
        self.start = start
        self.end = end

    # pandas查询数据
    def data_query(self, table):
        sql = '''select * from {table}'''.format(table=table)


########################################################### 宏观资金流动性 ###########################################################
class DataCollection:

    # 正回购、逆回购
    def get_data_gksccz(self):
        data = Pands_Handler().data_query('a_china_znhg')

        data = data.replace({'正回购': -1, '逆回购': 1})
        data['amount'] = data['deal_amount'] * data['trading_method']
        data = data[['operation_from_date', 'amount']]
        data.columns = ['日期', '交易金额']

        return data

    # 货币供应量
    def get_data_hbgyl(self):
        data = Pands_Handler().data_query('a_china_money_supply')

        data.columns = ['日期', 'M2', 'M1', 'M0']

        return data

    # 上证、深证、创业板指数
    def get_data_index(self):
        data = Pands_Handler().data_query('a_index_daily')

        data1 = data[data['scode'] == '000001.SH']  # 上证指数
        data2 = data[data['scode'] == '399001.SZ']  # 深证指数
        data3 = data[data['scode'] == '399006.SZ']  # 创业板指数

        data1 = data1[['date', 'oprice', 'hprice', 'lprice', 'cprice', 'pct_chg', 'amount']]
        data2 = data2[['date', 'oprice', 'hprice', 'lprice', 'cprice', 'pct_chg', 'amount']]
        data3 = data3[['date', 'oprice', 'hprice', 'lprice', 'cprice', 'pct_chg', 'amount']]

        data1.columns = ['日期', '开盘价', '最高价', '最低价', '收盘价', '涨跌幅', '成交额']
        data2.columns = ['日期', '开盘价', '最高价', '最低价', '收盘价', '涨跌幅', '成交额']
        data3.columns = ['日期', '开盘价', '最高价', '最低价', '收盘价', '涨跌幅', '成交额']

        return data1, data2, data3

    # 沪深股通资金
    def get_data_hsgt(self):
        data = Pands_Handler().data_query('a_hsgt_moneyflow')

        data.columns = ['日期', '沪股通', '深股通', '北向资金']

        return data

    # Shibor利率
    def get_data_shibor(self):
        data = Pands_Handler().data_query('a_shibor')

        data = data[['date', 'pre_night_shibor']]
        data.columns = ['日期', '隔夜shibor']

        return data

    # 沪深两市成交额
    def get_data_hsls(self):
        data = Pands_Handler().data_query('a_stock_daily')

        data = data[['date','amount']]
        data = data.groupby('date').sum().reset_index()
        data.columns = ['日期','成交额']

        return data

    # 沪深竞价成交额
    def get_data_call(self):
        data = Pands_Handler().data_query('a_stock_call_auction')

        data = data[['date','amount']]
        data = data.groupby('date').sum().reset_index()
        data.columns = ['日期','成交额']

        return data

    # 龙虎榜席位成交
    def get_data_lhb_classify(self):
        data = Pands_Handler().data_query('a_stock_lhb_list')

        # youziicon：一线游资（1）、顶级游资（2）、知名游资（3）、机构（4）、沪股通（5）、深股通（6）、其他（0）
        data['amount'] = data['buy'] + data['sell']
        data = data[['date', 'direction', 'amount', 'youziicon']]
        data = pd.pivot_table(data, values='amount', columns='direction', index=['date', 'youziicon'], aggfunc='sum', margins=True)
        data = data.reset_index()
        data = data[data['date'] != 'All']
        data = data.replace({0: '其他', 1: '一线游资', 2: '顶级游资', 3: '知名游资', 4: '机构', 5: '沪股通', 6: '深股通'})
        data.columns = ['日期', '资金类型', '买入额', '卖出额', '净买入额']

        return data

    # 大资金流向
    def get_data_dzjlx(self):
        data = Pands_Handler().data_query('a_stock_big_money')

        data['md_net_amount'] = data['bmd_amount'] - data['smd_amount']
        data['lg_net_amount'] = data['blg_amount'] - data['slg_amount']
        data['elg_net_amount'] = data['belg_amount'] - data['selg_amount']

        data = data[['date','bmd_amount','smd_amount','md_net_amount','blg_amount','slg_amount','lg_net_amount','belg_amount','selg_amount','elg_net_amount','net_amount']]
        data = data.groupby('date').sum().reset_index()
        data.columns = ['日期','中单买入','中单卖出','中单净流入','大单买入','大单卖出','大单净流入','超大单买入','超大单卖出','超大单净流入','净流入']

        print(data)

    # 市场情绪
    def get_data_scqx(self):
        data1 = Pands_Handler().data_query('a_market_limit_up_count')
        data2 = Pands_Handler().data_query('a_stock_scqx_zdtjs')
        data3 = Pands_Handler().data_query('a_stock_pjgnum')
        data4 = Pands_Handler().data_query('a_stock_xgdnum')
        data5 = Pands_Handler().data_query('a_stock_limit_list')

class Market:

    # 特殊柱状图
    def sbar(self, x_data, y_data, series_names, title, subtitle):
        c = (
            Bar()
                .add_xaxis(xaxis_data=x_data)
                .add_yaxis(
                    series_name=series_names,
                    y_axis=y_data,
                    is_large=True,
                    large_threshold=400,
                    label_opts=opts.LabelOpts(
                        is_show=False,
                    ),
                )
                .set_global_opts(
                    title_opts=opts.TitleOpts(
                        title=title,
                        subtitle=subtitle,
                        item_gap=10,
                        title_textstyle_opts=opts.TextStyleOpts(
                            font_family='Microsoft YaHei',
                            font_weight='bolder',
                            font_size=14,
                            color='',
                        ),
                        subtitle_textstyle_opts=opts.TextStyleOpts(
                            font_family=None,
                            font_size=10,
                            color='',
                        ),
                    ),
                    legend_opts=opts.LegendOpts(
                        is_show=True,
                        pos_right=5,
                        orient='vertical',
                        legend_icon='rect',
                    ),

                    tooltip_opts=opts.TooltipOpts(
                        is_show=True,
                        trigger='axis',
                        trigger_on='click',
                        formatter="{a}: {c}",
                        textstyle_opts=opts.TextStyleOpts(
                            font_family='Arial',
                            font_size=14,
                        )

                    ),
                    visualmap_opts=opts.VisualMapOpts(
                        is_piecewise=True,
                        pieces=[
                            {"max": 0, "color": 'green'},
                            {"min": 0, "color": 'red'}
                        ]
                    )
                )
                .render('shangzheng.html')
        )

    def cbar(self,x_data,series_names,y_data):
        bar_1 = (
            Bar()
                .add_xaxis(xaxis_data=data["times"])
                .add_yaxis(
                series_name="Volumn",
                yaxis_data=data["vols"],
                xaxis_index=1,
                yaxis_index=1,
                label_opts=opts.LabelOpts(is_show=False),

                # 改进后在 grid 中 add_js_funcs 后变成如下
                itemstyle_opts=opts.ItemStyleOpts(
                    color=JsCode(
                        """
                    function(params) {
                        var colorList;
                        if (barData[params.dataIndex][1] > barData[params.dataIndex][0]) {
                            colorList = '#ef232a';
                        } else {
                            colorList = '#14b143';
                        }
                        return colorList;
                    }
                    """
                    )
                ),
            )
                .set_global_opts(
                xaxis_opts=opts.AxisOpts(
                    type_="category",
                    grid_index=1,
                    axislabel_opts=opts.LabelOpts(is_show=False),
                ),
                legend_opts=opts.LegendOpts(is_show=False),
            )
        )



    # 常规折线图
    def cline(self,x_data):
        c = (
            Line()
                .add_xaxis(xaxis_data=x_data)
                .add_yaxis(
                    series_name=series_names,
                    y_axis=y_data,
                    is_smooth=True,
                    linestyle_opts=opts.LineStyleOpts(
                        width=3,
                        opacity=0.5,
                        curve=1,
                        type_='solid',
                        color=''

                    )

                )
                .set_global_opts(title_opts=opts.TitleOpts(title="Line-smooth"))
                .render("line_smooth.html")
        )
    def bar1(self):
        bar = (
            Bar()
                .add_xaxis(Faker.choose())
                .add_yaxis("商家A", Faker.values(), label_opts=opts.LabelOpts(position="right"))
                .add_yaxis("商家B", Faker.values(), label_opts=opts.LabelOpts(position="right"))
                .reversal_axis()
                .set_global_opts(
                title_opts=opts.TitleOpts("Timeline-Bar-Reversal (时间: {} 年)".format(i))
            )
        )

    # 特殊折线图
    def kline(self):
        kline = (
            Kline()
                .add_xaxis(xaxis_data=data["times"])
                .add_yaxis(
                series_name="",
                y_axis=data["datas"],
                itemstyle_opts=opts.ItemStyleOpts(
                    color="#ef232a",
                    color0="#14b143",
                    border_color="#ef232a",
                    border_color0="#14b143",
                ),
                markpoint_opts=opts.MarkPointOpts(
                    data=[
                        opts.MarkPointItem(type_="max", name="最大值"),
                        opts.MarkPointItem(type_="min", name="最小值"),
                    ]
                ),
                markline_opts=opts.MarkLineOpts(
                    label_opts=opts.LabelOpts(
                        position="middle", color="blue", font_size=15
                    ),
                    data=split_data_part(),
                    symbol=["circle", "none"],
                ),
            )
                .set_series_opts(
                markarea_opts=opts.MarkAreaOpts(is_silent=True, data=split_data_part())
            )
                .set_global_opts(
                title_opts=opts.TitleOpts(title="K线周期图表", pos_left="0"),
                xaxis_opts=opts.AxisOpts(
                    type_="category",
                    is_scale=True,
                    boundary_gap=False,
                    axisline_opts=opts.AxisLineOpts(is_on_zero=False),
                    splitline_opts=opts.SplitLineOpts(is_show=False),
                    split_number=20,
                    min_="dataMin",
                    max_="dataMax",
                ),
                yaxis_opts=opts.AxisOpts(
                    is_scale=True, splitline_opts=opts.SplitLineOpts(is_show=True)
                ),
                tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="line"),
                datazoom_opts=[
                    opts.DataZoomOpts(
                        is_show=False, type_="inside", xaxis_index=[0, 0], range_end=100
                    ),
                    opts.DataZoomOpts(
                        is_show=True, xaxis_index=[0, 1], pos_top="97%", range_end=100
                    ),
                    opts.DataZoomOpts(is_show=False, xaxis_index=[0, 2], range_end=100),
                ],
                # 三个图的 axis 连在一块
                # axispointer_opts=opts.AxisPointerOpts(
                #     is_show=True,
                #     link=[{"xAxisIndex": "all"}],
                #     label=opts.LabelOpts(background_color="#777"),
                # ),
            )
        )


    def timeline(self):
        attr = Faker.choose()
        tl = Timeline()
        for i in range(2015, 2020):
            pie = (
                Pie()
                    .add(
                    "商家A",
                    [list(z) for z in zip(attr, Faker.values())],
                    rosetype="radius",
                    radius=["30%", "55%"],
                )
                    .set_global_opts(title_opts=opts.TitleOpts("某商店{}年营业额".format(i)))
            )
            tl.add(pie, "{}年".format(i))
        tl.render("timeline_pie.html")






if __name__=='__main__':
    start = ''
    end = ''
    #data = pd.DataFrame()
    x_data = [1,2,3,4]
    y_data = [-100,200,30,-50]
    series_names = ''
    title = '涨跌幅'
    subtitle='单位'
    Market().sbar(x_data,y_data,series_names,title,subtitle)
    Market().cbar( x_data, series_names, y_data)