目錄
- K線圖
- K線圖系列模板
- 最簡單的K線圖繪制
- K線圖鼠標縮放
- 有刻度標簽的K線圖
- K線圖鼠標無縮放
- 大量數據K線圖繪制（X軸鼠標可移動）
K線圖
概念
股市及期貨市bai場中的K線圖的du畫法包含四個zhi數據，即開盤dao價、最高價、最低價zhuan、收盤價，所有的shuk線都是圍繞這四個數據展開，反映大勢的狀況和價格信息。如果把每日的K線圖放在一張紙上，就能得到日K線圖，同樣也可畫出周K線圖、月K線圖。研究金融的小伙伴肯定比較熟悉這個，那么我們看起來比較復雜的K線圖，又是這樣畫出來的，本文我們將一起探索K線圖的魅力與神奇之處吧！
K線圖
用處
K線圖用處于股票分析，作為數據分析，以后的進入大數據肯定是一個趨勢和熱潮，K線圖的專業知識，說實話肯定比較的復雜，這里就不做過多的展示了，有興趣的小伙伴去問問百度小哥哥喲！
K線圖系列模板
最簡單的K線圖繪制
第一個K線圖繪制，來看看需要哪些參數吧，數據集都有四個必要的喲！
import pyecharts.options as opts
from pyecharts.charts import Candlestick
x_data = ["2017-10-24", "2017-10-25", "2017-10-26", "2017-10-27"]
y_data = [[20, 30, 10, 35], [40, 35, 30, 55], [33, 38, 33, 40], [40, 40, 32, 42]]
(
Candlestick(init_opts=opts.InitOpts(width="1200px", height="600px"))
.add_xaxis(xaxis_data=x_data)
.add_yaxis(series_name="", y_axis=y_data)
.set_series_opts()
.set_global_opts(
yaxis_opts=opts.AxisOpts(
splitline_opts=opts.SplitLineOpts(
is_show=True, linestyle_opts=opts.LineStyleOpts(width=1)
)
)
)
.render("簡單K線圖.html")
)
K線圖鼠標縮放
大量的數據集的時候，我們不可以全部同時展示，我們可以縮放來進行定向展示。
from pyecharts import options as opts
from pyecharts.charts import Kline
data = [
[2320.26, 2320.26, 2287.3, 2362.94],
[2300, 2291.3, 2288.26, 2308.38],
[2295.35, 2346.5, 2295.35, 2345.92],
[2347.22, 2358.98, 2337.35, 2363.8],
[2360.75, 2382.48, 2347.89, 2383.76],
[2383.43, 2385.42, 2371.23, 2391.82],
[2377.41, 2419.02, 2369.57, 2421.15],
[2425.92, 2428.15, 2417.58, 2440.38],
[2411, 2433.13, 2403.3, 2437.42],
[2432.68, 2334.48, 2427.7, 2441.73],
[2430.69, 2418.53, 2394.22, 2433.89],
[2416.62, 2432.4, 2414.4, 2443.03],
[2441.91, 2421.56, 2418.43, 2444.8],
[2420.26, 2382.91, 2373.53, 2427.07],
[2383.49, 2397.18, 2370.61, 2397.94],
[2378.82, 2325.95, 2309.17, 2378.82],
[2322.94, 2314.16, 2308.76, 2330.88],
[2320.62, 2325.82, 2315.01, 2338.78],
[2313.74, 2293.34, 2289.89, 2340.71],
[2297.77, 2313.22, 2292.03, 2324.63],
[2322.32, 2365.59, 2308.92, 2366.16],
[2364.54, 2359.51, 2330.86, 2369.65],
[2332.08, 2273.4, 2259.25, 2333.54],
[2274.81, 2326.31, 2270.1, 2328.14],
[2333.61, 2347.18, 2321.6, 2351.44],
[2340.44, 2324.29, 2304.27, 2352.02],
[2326.42, 2318.61, 2314.59, 2333.67],
[2314.68, 2310.59, 2296.58, 2320.96],
[2309.16, 2286.6, 2264.83, 2333.29],
[2282.17, 2263.97, 2253.25, 2286.33],
[2255.77, 2270.28, 2253.31, 2276.22],
]
c = (
Kline()
.add_xaxis(["2017/7/{}".format(i + 1) for i in range(31)])
.add_yaxis(
"kline",
data,
itemstyle_opts=opts.ItemStyleOpts(
color="#ec0000",
color0="#00da3c",
border_color="#8A0000",
border_color0="#008F28",
),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
),
datazoom_opts=[opts.DataZoomOpts(type_="inside")],
title_opts=opts.TitleOpts(title="Kline-ItemStyle"),
)
.render("K線圖鼠標縮放.html")
)
有刻度標簽的K線圖
我們知道一個數據節點，但是我們不能在圖像里面一眼看出有哪些數據量超出了它的范圍，刻度標簽就可以派上用場了。
from pyecharts import options as opts
from pyecharts.charts import Kline
data = [
[2320.26, 2320.26, 2287.3, 2362.94],
[2300, 2291.3, 2288.26, 2308.38],
[2295.35, 2346.5, 2295.35, 2345.92],
[2347.22, 2358.98, 2337.35, 2363.8],
[2360.75, 2382.48, 2347.89, 2383.76],
[2383.43, 2385.42, 2371.23, 2391.82],
[2377.41, 2419.02, 2369.57, 2421.15],
[2425.92, 2428.15, 2417.58, 2440.38],
[2411, 2433.13, 2403.3, 2437.42],
[2432.68, 2334.48, 2427.7, 2441.73],
[2430.69, 2418.53, 2394.22, 2433.89],
[2416.62, 2432.4, 2414.4, 2443.03],
[2441.91, 2421.56, 2418.43, 2444.8],
[2420.26, 2382.91, 2373.53, 2427.07],
[2383.49, 2397.18, 2370.61, 2397.94],
[2378.82, 2325.95, 2309.17, 2378.82],
[2322.94, 2314.16, 2308.76, 2330.88],
[2320.62, 2325.82, 2315.01, 2338.78],
[2313.74, 2293.34, 2289.89, 2340.71],
[2297.77, 2313.22, 2292.03, 2324.63],
[2322.32, 2365.59, 2308.92, 2366.16],
[2364.54, 2359.51, 2330.86, 2369.65],
[2332.08, 2273.4, 2259.25, 2333.54],
[2274.81, 2326.31, 2270.1, 2328.14],
[2333.61, 2347.18, 2321.6, 2351.44],
[2340.44, 2324.29, 2304.27, 2352.02],
[2326.42, 2318.61, 2314.59, 2333.67],
[2314.68, 2310.59, 2296.58, 2320.96],
[2309.16, 2286.6, 2264.83, 2333.29],
[2282.17, 2263.97, 2253.25, 2286.33],
[2255.77, 2270.28, 2253.31, 2276.22],
]
c = (
Kline()
.add_xaxis(["2017/7/{}".format(i + 1) for i in range(31)])
.add_yaxis(
"kline",
data,
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="max", value_dim="close")]
),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
),
title_opts=opts.TitleOpts(title="標題"),
)
.render("刻度標簽.html")
)
K線圖鼠標無縮放
前面的是一個有縮放功能的圖例代碼，但是有時候我們不想要那么修改一下參數就可以了。
from pyecharts import options as opts
from pyecharts.charts import Kline
data = [
[2320.26, 2320.26, 2287.3, 2362.94],
[2300, 2291.3, 2288.26, 2308.38],
[2295.35, 2346.5, 2295.35, 2345.92],
[2347.22, 2358.98, 2337.35, 2363.8],
[2360.75, 2382.48, 2347.89, 2383.76],
[2383.43, 2385.42, 2371.23, 2391.82],
[2377.41, 2419.02, 2369.57, 2421.15],
[2425.92, 2428.15, 2417.58, 2440.38],
[2411, 2433.13, 2403.3, 2437.42],
[2432.68, 2334.48, 2427.7, 2441.73],
[2430.69, 2418.53, 2394.22, 2433.89],
[2416.62, 2432.4, 2414.4, 2443.03],
[2441.91, 2421.56, 2418.43, 2444.8],
[2420.26, 2382.91, 2373.53, 2427.07],
[2383.49, 2397.18, 2370.61, 2397.94],
[2378.82, 2325.95, 2309.17, 2378.82],
[2322.94, 2314.16, 2308.76, 2330.88],
[2320.62, 2325.82, 2315.01, 2338.78],
[2313.74, 2293.34, 2289.89, 2340.71],
[2297.77, 2313.22, 2292.03, 2324.63],
[2322.32, 2365.59, 2308.92, 2366.16],
[2364.54, 2359.51, 2330.86, 2369.65],
[2332.08, 2273.4, 2259.25, 2333.54],
[2274.81, 2326.31, 2270.1, 2328.14],
[2333.61, 2347.18, 2321.6, 2351.44],
[2340.44, 2324.29, 2304.27, 2352.02],
[2326.42, 2318.61, 2314.59, 2333.67],
[2314.68, 2310.59, 2296.58, 2320.96],
[2309.16, 2286.6, 2264.83, 2333.29],
[2282.17, 2263.97, 2253.25, 2286.33],
[2255.77, 2270.28, 2253.31, 2276.22],
]
c = (
Kline()
.add_xaxis(["2017/7/{}".format(i + 1) for i in range(31)])
.add_yaxis("kline", data)
.set_global_opts(
yaxis_opts=opts.AxisOpts(is_scale=True),
xaxis_opts=opts.AxisOpts(is_scale=True),
title_opts=opts.TitleOpts(title="Kline-基本示例"),
)
.render("鼠標無縮放.html")
)
大量數據K線圖繪制（X軸鼠標可移動）
雖然有時候縮放可以容納較多的數據量，但是還是不夠智能，可以利用這個
from pyecharts import options as opts
from pyecharts.charts import Kline
data = [
[2320.26, 2320.26, 2287.3, 2362.94],
[2300, 2291.3, 2288.26, 2308.38],
[2295.35, 2346.5, 2295.35, 2345.92],
[2347.22, 2358.98, 2337.35, 2363.8],
[2360.75, 2382.48, 2347.89, 2383.76],
[2383.43, 2385.42, 2371.23, 2391.82],
[2377.41, 2419.02, 2369.57, 2421.15],
[2425.92, 2428.15, 2417.58, 2440.38],
[2411, 2433.13, 2403.3, 2437.42],
[2432.68, 2334.48, 2427.7, 2441.73],
[2430.69, 2418.53, 2394.22, 2433.89],
[2416.62, 2432.4, 2414.4, 2443.03],
[2441.91, 2421.56, 2418.43, 2444.8],
[2420.26, 2382.91, 2373.53, 2427.07],
[2383.49, 2397.18, 2370.61, 2397.94],
[2378.82, 2325.95, 2309.17, 2378.82],
[2322.94, 2314.16, 2308.76, 2330.88],
[2320.62, 2325.82, 2315.01, 2338.78],
[2313.74, 2293.34, 2289.89, 2340.71],
[2297.77, 2313.22, 2292.03, 2324.63],
[2322.32, 2365.59, 2308.92, 2366.16],
[2364.54, 2359.51, 2330.86, 2369.65],
[2332.08, 2273.4, 2259.25, 2333.54],
[2274.81, 2326.31, 2270.1, 2328.14],
[2333.61, 2347.18, 2321.6, 2351.44],
[2340.44, 2324.29, 2304.27, 2352.02],
[2326.42, 2318.61, 2314.59, 2333.67],
[2314.68, 2310.59, 2296.58, 2320.96],
[2309.16, 2286.6, 2264.83, 2333.29],
[2282.17, 2263.97, 2253.25, 2286.33],
[2255.77, 2270.28, 2253.31, 2276.22],
]
c = (
Kline()
.add_xaxis(["2017/7/{}".format(i + 1) for i in range(31)])
.add_yaxis("kline", data)
.set_global_opts(
xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
),
datazoom_opts=[opts.DataZoomOpts(pos_bottom="-2%")],
title_opts=opts.TitleOpts(title="Kline-DataZoom-slider-Position"),
)
.render("大量數據展示.html")
)
K線圖的繪制需要有專業的基本知識喲，不然可能有點惱火了。
到此這篇關于Python繪制K線圖之可視化神器pyecharts的使用的文章就介紹到這了,更多相關Python繪制K線圖內容請搜索腳本之家以前的文章或繼續瀏覽下面的相關文章希望大家以后多多支持腳本之家！
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