If you are looking for the original repo please checkout og branch.

Montreal Exchange (TMX)

The Montreal Exchange is the oldest exchange in Canada and has a very important history to Canada's economy. Taken from Wikipedia,

The Montreal Exchange, formerly the Montreal Stock Exchange (MSE), is a derivatives exchange, located in Montreal, Quebec, Canada that trades futures contracts and options on equities, indices, currencies, ETFs, energy and interest rates. Since 1965, it has been located in the Tour de la Bourse (Stock Exchange Tower), Montreal's third-tallest building. It is owned by the Toronto-based TMX Group.

This README file is to showcase the functionality of this small web scraping module. It can be used to get options prices, both calls and puts for index and ETF options, equity options, currency options, and weekly options.

These prices are then displayed in a Pandas Dataframe for further analysis. This could include simple plots for visualizing the data or creating machine learning model or neural network to predict future prices. One could also use Black-Scholes model to gain further insight.

---

Requirements

1. Python 3.11.5
2. Pandas 2.1.0 (older version will probably work)
3. Requests 2.31.0 (older version will probably work)
4. bs4 4.12.2 (for BeautifulSoup)
   • lxml 5.3.0 used with bs4 to process xml and html

---

We start by showing a list of options available from TMX. There is one parameter for the function get_list() and it can take one of four stings:

'Index'/'ETF', 'Equity', 'Currency', or 'Weekly'.

The webpage looks like:

get_list('equity')

	Name of underlying instrument	Option symbol	Underlying symbol
0	Aecon Group Inc.	ARE	ARE
1	AGF Management Ltd., Cl. B	AGF	AGF.B
2	Agnico Eagle Mines Limited	AEM	AEM
3	Air Canada	AC	AC
4	Alamos Gold Inc.	AGI	AGI
...	...	...	...
264	Wheaton Precious Metals Corp.	WPM	WPM
265	Whitecap Resources Inc.	WCP	WCP
266	Winpak Ltd.	WPK	WPK
267	WSP Global Inc.	WSP	WSP
268	Yamana Gold Inc.	YRI	YRI

269 rows × 3 columns

The above DataFrame that we get with get_list('equity') is from:

Now we can grab stock prices for Air Canada (ticker symbol AC) so we can compare them to the stock options:

get_stock('AC')

	TICKER	Last price	Net change	Bid price	Ask price
0	AC	15.520	0.000	15.520	15.550

We can also input a list of stock symbols to get a Pandas DataFrame of said stocks:

get_stock(['AC','ARE'])

	TICKER	Last price	Net change	Bid price	Ask price
0	AC	15.520	0.000	15.520	15.550
1	ARE	14.220	0.100	14.200	14.250

Finally, we obtain the a Pandas Dataframe of TMX stock options for Air Canada which comes from:

get('AC')

	Call	Bid price	Ask price	Last price	Impl. vol.	Open int.	Vol.	Strike	Put	Bid price_	Ask price_	Last price.1	Impl. vol_	Open int_	Vol_
0	Oct 23, 2020-W	2.05	2.20	2.20	86.5%	10	0	13.5	Oct 23, 2020-W	0.01	0.12	0.12	75.8%	17	0
1	Oct 23, 2020-W	1.57	1.77	1.77	79.0%	0	0	14.0	Oct 23, 2020-W	0.08	0.14	0.14	71.0%	30	0
2	Oct 23, 2020-W	1.12	1.24	1.24	62.5%	10	0	14.5	Oct 23, 2020-W	0.10	0.18	0.18	59.2%	23	10
3	Oct 23, 2020-W	0.73	0.84	0.84	57.0%	7	0	15.0	Oct 23, 2020-W	0.19	0.29	0.29	53.8%	57	7
4	Oct 23, 2020-W	0.44	0.54	0.54	56.0%	47	141	15.5	Oct 23, 2020-W	0.37	0.53	0.53	53.7%	46	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
153	Jan 20, 2023	3.55	7.35	7.35	76.4%	2	0	21.0	Jan 20, 2023	8.85	12.50	12.50	74.4%	0	0
154	Jan 20, 2023	3.30	7.20	7.20	76.3%	0	0	22.0	Jan 20, 2023	9.60	13.30	13.30	74.2%	0	0
155	Jan 20, 2023	3.10	7.00	7.00	76.2%	0	0	23.0	Jan 20, 2023	10.35	14.10	14.10	73.8%	0	0
156	Jan 20, 2023	2.84	6.80	6.80	75.6%	0	0	24.0	Jan 20, 2023	11.10	14.85	14.85	73.0%	0	0
157	Jan 20, 2023	3.15	5.00	5.00	69.3%	32	7	25.0	Jan 20, 2023	11.90	15.75	15.75	73.3%	0	0

158 rows × 15 columns

import pandas as pd
import requests
from bs4 import BeautifulSoup

"""
Get a list of options from https://m-x.ca/nego_liste_en.php
TMX website

Index and ETF options
Equity options
Currency options
Weekly options
"""
def get_list(market=str) -> pd.DataFrame:
    tmx = "https://m-x.ca/nego_liste_en.php" # TMX website, where data is taken from
    
    #check that parameter is of type string
    is_str1 = isinstance(market, str)
    if not is_str1:
        raise TypeError("market parameter must be of type string")
        
    try:
        market = market.lower()
    except Exception as e:
        print(e)
    else:
        if market == 'index' or market == 'etf':
            market = 0
        elif market == 'equity':
            market = 1
        elif market == 'currency':
            market = 2
        elif market == 'weekly':
            market = 3
        else:
            raise Exception("Did not enter market type, choose from Index or ETF, Equity, Currency, Weekly.")
        df = pd.read_html(tmx)
        return df[market]

"""
Get options prices at predetermined dates from TMX website
Call/Puts
strike price
Bid/Ask spreads
open interest
implied volatility
volume
"""
def get(ticker_symbol=str) -> pd.DataFrame:
    tmx = "https://m-x.ca/nego_cotes_en.php" # TMX website, where data is taken from

    is_str1 = isinstance(ticker_symbol, str)
    if not is_str1:
        raise TypeError("ticker_symbol parameter must be of type string")
        
    try:
        ticker_symbol = ticker_symbol.upper()
    except Exception as e:
        print(e)
    else:
        url = tmx + '?symbol=' + ticker_symbol + '*'
        df = pd.read_html(url)
        df[0].rename(columns={'Bid price.1':'Bid price_', 'Ask price.1':'Ask price_', 'Last Price.1':'Last Price_',
                             'Impl. vol..1':'Impl. vol_', 'Open int..1':'Open int_', 'Vol..1':'Vol_'}, inplace=True)
        return df[0].iloc[:-1] #do not include last row, rubbish information

"""
Get stock price from TMX to compare to strike price
can accept string or list of strings
"""
def get_stock(ticker_symbol=str) -> pd.DataFrame:
    tmx = "https://m-x.ca/nego_cotes_en.php" # TMX website, where data is taken from
    
    #check that parameter is of type string
    is_str1 = checktype(ticker_symbol)
    if not is_str1:
        raise TypeError("market parameter must be of type string")

    #download stock price, remember it is 15 minutes delayed
    try:
        symbols = []
        for n in ticker_symbol: 
            symbols.append(n.upper())

    except Exception as e:
        print(e)
    else:
        price_dict = {}
        is_list = isinstance(ticker_symbol, list)
        if is_list:
            df_list = []
            for m in symbols:
                URL = tmx + '?symbol=' + m + '*'
                response = requests.get(URL)
                soup = BeautifulSoup(response.text, 'html.parser')
                x = soup.find('div', class_ = 'quote-info', attrs = 'ul')
                y = x.ul.text.split('\n')[1:-2]
                
                price_dict['TICKER'] = m
                for z in y:
                    key, value = z.split(':')
                    price_dict[key] = value
                tmp_df = pd.DataFrame.from_dict(price_dict, orient='index').T
                df_list.append(tmp_df)
            return pd.concat(df_list, ignore_index=True)
        else:
            ticker_symbol = ticker_symbol.upper()
            URL = tmx + '?symbol=' + ticker_symbol + '*'
            response = requests.get(URL)
            soup = BeautifulSoup(response.text, 'html.parser')
            x = soup.find('div', class_ = 'quote-info', attrs = 'ul')
            y = x.ul.text.split('\n')[1:-2]

            price_dict['TICKER'] = ticker_symbol
            for z in y:
                key, value = z.split(':')
                price_dict[key] = value
            tmp_df = pd.DataFrame.from_dict(price_dict, orient='index').T
            return tmp_df
            

def checktype(obj):
        return bool(obj) and all(isinstance(elem, str) for elem in obj)