以下內容，都是 shioaji 的官網文件的內容，只是加了一些我自己的理解，感謝永豐提供這麼完整的 sample code

繪圖會用到 numpy, pandas, bqplot...等套件，如果之前沒有安裝，可以先行安裝

pip install numpy pandas bqplot

• 引用套件

import json
import datetime as dt
import numpy as np
import pandas as pd
import bqplot as bq
import shioaji as sj

• 登入 shioaji

api = sj.Shioaji()
api.login(person_id="帳號", passwd="密碼")

• 建立表格的資料集

today = dt.date.today()
df_min = pd.DataFrame(
    index=pd.date_range(
        start=dt.datetime(*(today.year, today.month, today.day, 8, 45)),
        end=dt.datetime(*(today.year, today.month, today.day, 13, 45)),
        freq='1T'), 
    columns=['open', 'high', 'low', 'close'], 
    dtype=float,
)

• 建立繪圖的方法

# ohlc: open high low close
def init_ohlc_chart():
    global ohlc_chart
    sc = bq.LinearScale()
    dt_scale = bq.DateScale()
    ax_x = bq.Axis(label="datetime", scale=dt_scale)
    ax_y = bq.Axis(lable="price", scale=sc, orientation="vertical", tick_format="0.0f")

    # 建立標記
    ohlc_chart = bq.OHLC(
        x=df_min.index,
        y=df_min.dropna().values,
        marker='candle',
        scales={ 'x': dt_scale, 'y': sc },
        format='ohlc',
        opcities=[0.85 for _ in df_min.index],
        display_legend=True,
        labels=['台積電']
    )
    
    fig = bq.Figure(axes=[ax_x, ax_y], marks=[ohlc_chart])

    return fig

• 建立資料處理方法

def proc_ohlc_data(quote_msg, new_deal_price):
    global df_min
    ts = pd.Timestamp("{Date} {Time}".format(**quote_msg)).replace(second=0, microsecond=0)
    df_min.loc[ts, 'open'] = new_deal_price if np.isnan(df_min.loc[ts, 'open']) else df_min.loc[ts, 'open']
    df_min.loc[ts, 'high'] = new_deal_price if np.isnan(df_min.loc[ts, 'high']) else df_min.loc[ts, 'high']
    df_min.loc[ts, 'low'] = new_deal_price if np.isnan(df_min.loc[ts, 'low']) else df_min.loc[ts, 'low']
    df_min.loc[ts, 'close'] = new_deal_price

• 建立圖表更新方法

def update_ohlc_chart():
    global ohlc_chart
    with ohlc_chart.hold_sync():
        ohlc_chart.x = df_min.dropna().index
        ohlc_chart.y = df_min.dropna().values

• 建立取得價格後的回呼方法

@sj.on_quote
def quote_callback(topic, quote_msg):
    global new_deal_price
    print(topic, quote_msg)
    proc_ohlc_data(quote_msg, new_deal_price)
    update_ohlc_chart()

• 建立 solace 事件回呼方法

@sj.on_event
def event_callback(resp_code, event_code, event):
    print("Response Code: {} | Event Code: {} | Event: {}".format(resp_code, event_code, event))

• 設定回呼方法

api.quote.set_callback(quote_callback)
api.quote.set_event_callback(event_callback)

• 繪圖

init_ohlc_chart()

因為我是在盤後才跑這個程式，所以沒有辦法取得即資料，所以暫時不確定以上的程式能不能正確運行，之後如果有機會在交易時間跑的話，會再驗證，如果有問題的話，會再修改。
另外，我自己用 api.ticks() 的方法，把當天的 ticks 資料抓下來再去進行繪制，我想應該和最後的結果是一樣的吧

ticks2330 = api.ticks(stock2330)
dt_index = list(map(lambda x:dt.datetime.utcfromtimestamp(x/1000000000), ticks2330.ts))

print(df_min)

prevTime = ""
open_price = 0
high_price = 0
low_price = 0
close_price = 0

for i in range(len(dt_index)):
    currTime = dt_index[i].strftime("%Y-%m-%d %H:%M:00")

    if currTime != prevTime:
        open_price = ticks2330.close[i]
        high_price = ticks2330.close[i]
        low_price = ticks2330.close[i]
        close_price = ticks2330.close[i]
        prevTime = currTime
    else:
        high_price = max(high, ticks2330.close[i])
        low_price = min(low, ticks2330.close[i])
        close_price = ticks2330.close[i]

    df_min.loc[currTime, "open"] = open_price
    df_min.loc[currTime, "high"] = high_price
    df_min.loc[currTime, "low"] = low_price
    df_min.loc[currTime, "close"] = close_price
    
init_ohlc_chart()