#!/usr/bin/env python
# coding: utf-8

# # Part 0: Import 套件

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#下載資料套件
import urllib3
from bs4 import BeautifulSoup

#資料處理套件
import pandas as pd
from datetime import datetime, date

#畫圖套件
import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')

from plotly.subplots import make_subplots
import plotly.graph_objects as go


# # Part 1: 下載期交所 臺指選擇權 每日交易行情

# ### 股海小英雄youtube影片：[【Python 爬蟲】台指選擇權Put/Call比免費下載 (上)｜股市多空頭趨勢預測](https://youtu.be/RqodmAWC8EE)
# ### 資料來源：[期交所 臺指選擇權 每日交易行情](https://www.taifex.com.tw/cht/3/dlOptDailyMarketView)

# **注意:查詢區間不可超過30日**

# 用urllib3下載選擇權每日交易行情資料

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http = urllib3.PoolManager()
url = "https://www.taifex.com.tw/cht/3/dlOptDataDown"
res = http.request(
     'POST',
      url,
      fields={
         'down_type': 1,
         'commodity_id': 'TXO',
         'queryStartDate': '2021/08/04',
         'queryEndDate': '2021/08/11'
      }
 )

html_doc = res.data
html_doc


# 用BeautifulSoup解析資料

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soup = BeautifulSoup(html_doc, 'html.parser')
soup


# 把資料依行數切割

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soup_str = str(soup)
lines = soup_str.split('\r\n')

for i in range(5):
    print(lines[i])
    print()


# 把下載的選擇權每日交易行情資料存入dataframe內

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# 新增空的dataframe,定義欄位名稱
df = pd.DataFrame(columns = lines[0].split(','))

# 把選擇權資料一行一行寫入dataframe內
for i in range(1, len(lines) - 1):
    list_ = lines[i].split(',')[:-1]
    df_length = len(df)
    df.loc[df_length] = list_

#顯示dataframe前20行的資料    
df.head(20)


# # Part 2: 將下載的臺指選擇權 每日交易行情 另存成csv檔

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df.to_csv('options.csv')


# # Part 3: 畫出臺指選擇權每日交易行情

# ### 資料轉型：把日期從字串(string)換成時間(datetime)/浮點數(float)

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# 資料轉型
for col in [0, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]:
    for row in range(df.shape[0]):
        # 把"日期"從字串(string)換成時間(datetime)
        if col == 0:
            day = df.iloc[row,0].split('/')
            df.iloc[row, 0] = datetime(int(day[0]), int(day[1]), int(day[2]))  
        # 把字串(string)換成浮點數(float): "履約價", "開盤價", "最高價", "最低價", "收盤價", "成交量", "結算價", "未沖銷契約數", "最後最佳買價", "最後最佳賣價", "歷史最高價", "歷史最低價" 
        elif col != 0 and df.iloc[row, col] != '-':
            df.iloc[row, col] = float(df.iloc[row,col])


# ### 資料篩選：取出臺指選擇權202108W2的資料

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df.head(20)


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df.describe()


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df['到期月份(週別)'].unique()


# 選到期月份(週別)是'202108W2'的資料

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df.loc[df['到期月份(週別)'] == '202108W2']


# 選到期月份(週別)是'202108W2'&買權的資料

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df.loc[(df['到期月份(週別)']  == '202108W2') & (df['買賣權'] == '買權')]


# 選到期月份(週別)是'202108W2'&買權&履約價17800的資料

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df.loc[(df['到期月份(週別)']  == '202108W2') & (df['買賣權'] == '買權') & (df['履約價'] == 17800)]


# 選到期月份(週別)是'202108W2'&買權&履約價17800&一般交易時段的資料

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df.loc[(df['到期月份(週別)']  == '202108W2') & (df['買賣權'] == '買權') & (df['履約價'] == 17800) & (df['交易時段'] == '一般')]


# 用get_options函式篩選資料

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def get_options(option_df, contract_period, put_or_call, strike_price, trade_period):
    
    option_df = df.loc[(df['到期月份(週別)'] == contract_period) & \
                       (df['買賣權'] == put_or_call) & \
                       (df['履約價'] == strike_price) & \
                       (df['交易時段'] == trade_period)]
    return option_df


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get_options(df, '202108W2', '買權', 17800, '一般')


# 拿選擇權履約價17400-17700的資料

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txo_17400_c = get_options(df, '202108W2', '買權', 17400, '一般')
txo_17500_c = get_options(df, '202108W2', '買權', 17500, '一般')
txo_17600_c = get_options(df, '202108W2', '買權', 17600, '一般')
txo_17700_c = get_options(df, '202108W2', '買權', 17700, '一般')


txo_17400_p = get_options(df, '202108W2', '賣權', 17400, '一般')
txo_17500_p = get_options(df, '202108W2', '賣權', 17500, '一般')
txo_17600_p = get_options(df, '202108W2', '賣權', 17600, '一般')
txo_17700_p = get_options(df, '202108W2', '賣權', 17700, '一般')


# ### 用matplotlib套件：畫出選擇權202108W2走勢圖

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fig = plt.figure(figsize = (20, 5))
plt.title('TXO Option 202108W2 Call 17400 Historical Data')

plt.plot(txo_17400_c['交易日期'], txo_17400_c['收盤價'])

plt.legend(['Close'])


# ### 用plotly套件：畫出選擇權202108W2各履約價走勢圖

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# Initialize figure with subplots
fig = make_subplots(
    rows=7, cols=2, subplot_titles=("買權", "賣權")
)

# Add traces
fig.add_trace(go.Scatter(x = txo_17400_c["交易日期"], y = txo_17400_c["收盤價"], name = "買權17400"), row=1, col=1)
fig.add_trace(go.Scatter(x = txo_17500_c["交易日期"], y = txo_17500_c["收盤價"], name = "買權17500"), row=2, col=1)
fig.add_trace(go.Scatter(x = txo_17600_c["交易日期"], y = txo_17600_c["收盤價"], name = "買權17600"), row=3, col=1)
fig.add_trace(go.Scatter(x = txo_17700_c["交易日期"], y = txo_17700_c["收盤價"], name = "買權17700"), row=4, col=1)

fig.add_trace(go.Scatter(x = txo_17400_p["交易日期"], y = txo_17400_p["收盤價"], name = "賣權17400"), row=1, col=2)
fig.add_trace(go.Scatter(x = txo_17500_p["交易日期"], y = txo_17500_p["收盤價"], name = "賣權17500"), row=2, col=2)
fig.add_trace(go.Scatter(x = txo_17600_p["交易日期"], y = txo_17600_p["收盤價"], name = "賣權17600"), row=3, col=2)
fig.add_trace(go.Scatter(x = txo_17700_p["交易日期"], y = txo_17700_p["收盤價"], name = "賣權17700"), row=4, col=2)

# Update yaxis properties
fig.update_yaxes(title_text="17400", row=1, col=2)
fig.update_yaxes(title_text="17500", row=2, col=2)
fig.update_yaxes(title_text="17600", row=3, col=2)
fig.update_yaxes(title_text="17700", row=4, col=2)

# Update title and height
fig.update_layout(title_text="台指選 每日交易行情 - 202108W2", width = 1000, height=2000)

fig.show()