python實現股票歷史數據可視化分析案例

投資有風險,選擇需謹慎。 股票交易數據分析可直觀股市走向,對於如何把握股票行情,快速解讀股票交易數據有不可替代的作用!

1 數據預處理

1.1 股票歷史數據csv文件讀取

import pandas as pd
import csv
df = pd.read_csv("/home/kesci/input/maotai4154/maotai.csv")

1.2 關鍵數據——在csv文件中選擇性提取“列”

df_high_low = df[['date','high','low']]

1.3 數據類型轉換

df_high_low_array = np.array(df_high_low)
df_high_low_list =df_high_low_array.tolist()

1.4 數據按列提取並累加性存入列表

price_dates, heigh_prices, low_prices = [], [], []
for content in zip(df_high_low_list):
    price_date = content[0][0]
    heigh_price = content[0][1]
    low_price = content[0][2]
    price_dates.append(price_date)
    heigh_prices.append(heigh_price)
    low_prices.append(low_price)

 

2 pyecharts實現數據可視化

2.1 導入庫

import pyecharts.options as opts
from pyecharts.charts import Line

2.2 初始化畫佈

Line(init_opts=opts.InitOpts(width="1200px", height="600px"))

2.3 根據需要傳入關鍵性數據並畫圖

    .add_yaxis(
        series_name="最低價",
        y_axis=low_prices,
        markpoint_opts=opts.MarkPointOpts(
            data=[opts.MarkPointItem(value=-2, name="周最低", x=1, y=-1.5)]
        ),
        markline_opts=opts.MarkLineOpts(
            data=[
                opts.MarkLineItem(type_="average", name="平均值"),
                opts.MarkLineItem(symbol="none", x="90%", y="max"),
                opts.MarkLineItem(symbol="circle", type_="max", name="最高點"),
            ]
        ),
    )
tooltip_opts=opts.TooltipOpts(trigger="axis"),
toolbox_opts=opts.ToolboxOpts(is_show=True),
xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=True)

2.4 將生成的文件形成HTML代碼並下載

.render("HTML名字填這裡.html")

2.5 完整代碼展示

import pyecharts.options as opts
from pyecharts.charts import Line
 
(
    Line(init_opts=opts.InitOpts(width="1200px", height="600px"))
    .add_xaxis(xaxis_data=price_dates)
    .add_yaxis(
        series_name="最高價",
        y_axis=heigh_prices,
        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(
            data=[opts.MarkLineItem(type_="average", name="平均值")]
        ),
    )
    .add_yaxis(
        series_name="最低價",
        y_axis=low_prices,
        markpoint_opts=opts.MarkPointOpts(
            data=[opts.MarkPointItem(value=-2, name="周最低", x=1, y=-1.5)]
        ),
        markline_opts=opts.MarkLineOpts(
            data=[
                opts.MarkLineItem(type_="average", name="平均值"),
                opts.MarkLineItem(symbol="none", x="90%", y="max"),
                opts.MarkLineItem(symbol="circle", type_="max", name="最高點"),
            ]
        ),
    )
    .set_global_opts(
        title_opts=opts.TitleOpts(title="茅臺股票歷史數據可視化", subtitle="日期、最高價、最低價可視化"),
        tooltip_opts=opts.TooltipOpts(trigger="axis"),
        toolbox_opts=opts.ToolboxOpts(is_show=True),
        xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=True),
    )
    .render("everyDayPrice_change_line_chart2.html")
)

3 結果展示

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