Added docstrings, made changes for pylint

.pylintrc

@@ -0,0 +1,2 @@
+[FORMAT]
+max-line-length=120

mf_bot.py

@@ -1,27 +1,35 @@
 
 import datetime
-import numpy as np
 import os
-import psycopg2
 import time
 
+from typing import Union
+
+import numpy as np
+import psycopg2
+
 from dotenv import load_dotenv
 from telegram.ext import Updater, CommandHandler, MessageHandler, Filters, InlineQueryHandler, CallbackQueryHandler
-from telegram import InlineQueryResultArticle, ParseMode, InputTextMessageContent, InlineKeyboardButton, InlineKeyboardMarkup
+from telegram import InlineQueryResultArticle, ParseMode, InputTextMessageContent
+from telegram import InlineKeyboardButton, InlineKeyboardMarkup
 from telegram.utils.helpers import escape_markdown
 
 load_dotenv()
 
 def connect_db():
+    """Connects to the Postgres Db"""
+
     pgcon = psycopg2.connect(dbname=os.getenv('DB_NAME'),
                              user=os.getenv('DB_USER'),
-                             password=os.getenv('DB_PWD'), 
+                             password=os.getenv('DB_PWD'),
                              host=os.getenv('DB_HOST'),
                              port=os.getenv('DB_PORT'))
     return pgcon
 
-    
+
 def slugify(message: str) -> str:
+    """This function ensures that messages are properly escaped as per Telegram's specs."""
+
     message = message.replace("(", "\\(")\
                         .replace(")", "\\)")\
                         .replace(".", "\\.")\
@@ -30,15 +38,19 @@ def slugify(message: str) -> str:
 
 
 def fund_search(search_string: str) -> list:
-    """Searches for a fund in the Postgres Db"""
+    """Searches for a fund in the Postgres Db
+        Returns a list of matches along with its latest NAV, category, and sub-category
+    """
 
     if len(search_string) < 3:
         return []
+
     connection = connect_db()
     fund_name = search_string.replace(" ", ":*&").replace("&-", " & !")
     fund_name = fund_name.replace('cap', ' cap').replace('fund', '').replace('  ',' ')
     fund_name = f"{fund_name}:*"  # enables partial match in tsquery
-    sql_query = """select lnav.*, fm.category, fm.sub_category 
+
+    sql_query = """select lnav.*, fm.category, fm.sub_category
                     from latest_nav lnav 
                     join fund_master fm on lnav.amfi_code = fm.amfi_code   
                     where lnav.fts_doc @@ to_tsquery(%s)
@@ -54,34 +66,50 @@ def fund_search(search_string: str) -> list:
     return results
 
 
-def mf_query(update, context):
+def mf_query(update, context) -> None:
+    """Handles inline search query from the MF bot
+        Creates a messaged containing the name of the fund along with its latest NAV.
+        Also adds two keys to the message, one for returns and one for SIP returns.
+        The callback data for the buttons contains a notation letter followed by the AMFI code of the fund.
+    """
+
     query = update.inline_query.query
-    mf_list = fund_search(query)
+    matched_funds = fund_search(query)
     results = []
-    for i, j in enumerate(mf_list):
+    for fund in matched_funds:
         keyboard = [
                        [
-                           InlineKeyboardButton("Returns", callback_data=f'r{j[0]}'),
+                           InlineKeyboardButton("Returns", callback_data=f'r{fund[0]}'),
-                           InlineKeyboardButton("SIP Returns", callback_data=f's{j[0]}')
+                           InlineKeyboardButton("SIP Returns", callback_data=f's{fund[0]}')
                        ]
                    ]
 
         reply_markup = InlineKeyboardMarkup(keyboard)
-        message = slugify(f'*{j[1]}*\n*Category:* {j[7]}\n*Sub-category:* {j[8]}\n*Date:* {str(j[2])}\n*NAV:* {str(j[3])}')
+        message = slugify(f"*{fund[1]}*\n*"\
-        line = InlineQueryResultArticle(id=j[0], title=j[1], 
+                            f"Category:* {fund[7]}\n*"\
-                                        input_message_content=InputTextMessageContent(message, parse_mode=ParseMode.MARKDOWN_V2), 
+                            f"Sub-category:* {fund[8]}\n*"\
-                                        reply_markup=reply_markup)
+                            f"Date:* {str(fund[2])}\n*"\
+                            f"NAV:* {str(fund[3])}")
+        line = InlineQueryResultArticle(id=fund[0], title=fund[1],
+                            input_message_content=InputTextMessageContent(message, parse_mode=ParseMode.MARKDOWN_V2),
+                            reply_markup=reply_markup)
         results.append(line)
+
     update.inline_query.answer(results)
 
 
-def start(update, context):
+def welcome(update, context):
-    msg = 'Welcome to India MF Bot\.\nTo get started, type @india\_mf\_bot in the message box and search for any fund\. '\
+    """Start message for the bot"""
-          "You will get a list of funds\. When you make your choice, you'll get inline buttons to get more info on the fund\."  
+
+    msg = r'Welcome to India MF Bot\.\n'\
+          r'To get started, type @india\_mf\_bot in the message box and search for any fund\.'\
+          r"You will get a list of funds\. When you make your choice, you'll get buttons to get more info on the fund\."
     update.message.reply_text(msg, parse_mode=ParseMode.MARKDOWN_V2)
 
 
 def button(update, context):
+    """This function handles the response to the buttons in the main message."""
+
     query = update.callback_query
     data = query.data
     amfi_code = int(data[1:])
@@ -90,12 +118,15 @@ def button(update, context):
     cur.execute("select fund_name, category, sub_category from fund_master where amfi_code = %s", (amfi_code,))
     result = cur.fetchall()
     fund_name = slugify(result[0][0])
-    
+
-    if data[0] == 'b':
+    if data[0] == 'b': # Handles back button
         cur = connection.cursor()
         cur.execute("select date, nav from latest_nav where amfi_code = %s", (amfi_code,))
         nav_result = cur.fetchall()
-        msg = slugify(f'*Category: *{result[0][1]}\n*Sub-category:* {result[0][2]}\n*Date*: {str(nav_result[0][0])}\n*NAV*: {str(nav_result[0][1])}')
+        msg = slugify(f'*Category:* {result[0][1]}\n'\
+                       f'*Sub-category:* {result[0][2]}\n'\
+                       f'*Date*: {str(nav_result[0][0])}\n'\
+                        f'*NAV*: {str(nav_result[0][1])}')
         returns = ''
         keyboard = [
                        [
@@ -103,7 +134,7 @@ def button(update, context):
                            InlineKeyboardButton("SIP Returns", callback_data=f's{amfi_code}')
                        ]
                    ]
-    elif data[0] == 'r':
+    elif data[0] == 'r': # Handles returns
         msg = 'Returns:'
         returns = slugify(return_calc(amfi_code))
         keyboard = [
@@ -113,11 +144,11 @@ def button(update, context):
 					   ]
 				   ]
     else:
-        msg = 'SIP Returns:'
+        msg = 'SIP Returns:' # Handles SIP returns
         returns = slugify(sip_returns(amfi_code))
         keyboard = [
 					   [
-						   InlineKeyboardButton("Returns", callback_data=f'{amfi_code}'),
+						   InlineKeyboardButton("Returns", callback_data=f'r{amfi_code}'),
 						   InlineKeyboardButton("<< Back", callback_data=f"b{amfi_code}")
 					   ]
 				   ]
@@ -125,14 +156,32 @@ def button(update, context):
     # CallbackQueries need to be answered, even if no notification to the user is needed
     # Some clients may have trouble otherwise. See https://core.telegram.org/bots/api#callbackquery
     query.answer()
-    query.edit_message_text(text="*{}*\n{}\n{}".format(fund_name, msg, str(returns)), reply_markup=reply_markup, parse_mode=ParseMode.MARKDOWN_V2)
+    query.edit_message_text(text=f"*{fund_name}*\n{msg}\n{str(returns)}",
+                            reply_markup=reply_markup,
+                            parse_mode=ParseMode.MARKDOWN_V2)
 
 
-def return_calc(amfi_code: int, return_type: str='m', raw: bool=False) -> str:
+def return_calc(amfi_code: int, return_type: str='m', return_string: bool=True) -> Union[list, str]:
     """Give returns numbers for a mutual fund
-       Use return type s for 1-3-6 months, m for 1-3-5 years, and l for 5-7-10 years
+
+    Params:
+    -------
+        amfi_code: amfi_code of the fund for which returns need to be calculated
+        return_type: short term, medium term, or long term return
+                    Use return type s for 1-3-6 months, m for 1-3-5 years, and l for 5-7-10 years
+        return_string: Whether to return the returns as Telegram compatible message string
+
+    Returns:
+    --------
+        If return_string is true, then returns a Telegram compatible string
+        If return_string is false, then returns a list of dicts with returns
     """
 
+    period_map = {
+        "s": [1, 3, 6],
+        "m": [12, 36, 60],
+        "l": [60, 84, 120]
+    }
     returns_query = """
                     select dates, %(amfi_code)s as amfi_code, ffill_nav from (
                         select dates, amfi_code, first_value(nav) over (partition by grp_close order by dates) as ffill_nav
@@ -140,49 +189,56 @@ def return_calc(amfi_code: int, return_type: str='m', raw: bool=False) -> str:
                               select dates, amfi_code, nav,
                                      sum(case when nav is not null then 1 end) over (order by dates) as grp_close
                               from (
-                                    SELECT generate_series(current_date - '61 month'::interval, current_date, interval '1 day')::date
+                                    SELECT generate_series(current_date - '1 month'::interval - '%(max_period)s month'::interval, current_date, interval '1 day')::date
                                 ) d(dates)
                                 left join nav_history nh on d.dates = nh.date and nh.amfi_code = %(amfi_code)s
                         ) t
                     )td
-                    where dates in (current_date - '60 month'::interval - '1 day':: interval, 
+                    where dates in (current_date - '%(max_period)s month'::interval - '1 day':: interval, 
-                                    current_date - '36 month'::interval - '1 day':: interval, 
+                                    current_date - '%(med_period)s month'::interval - '1 day':: interval, 
-                                    current_date - '12 month'::interval - '1 day':: interval,
+                                    current_date - '%(min_period)s month'::interval - '1 day':: interval,
                                     current_date - '1 day':: interval )
                     order by dates desc
                     """
     start_time = time.time()
     connection = connect_db()
     cursor = connection.cursor()
-    cursor.execute(returns_query, {'amfi_code':amfi_code})
+    params = {
+        'amfi_code':amfi_code,
+        'min_period': period_map[return_type][0],
+        'med_period': period_map[return_type][1],
+        'max_period': period_map[return_type][2]
+    }
+    cursor.execute(returns_query, params)
     result = cursor.fetchall()
     #print(result)
     returns = []
     for i, j in enumerate(result):
-        if i == 0:
+        if i > 0:
-            continue
-        else:
             years = (result[0][0] - j[0]).days/365
             ret = (result[0][2]/j[2])**(1/years) - 1
             returns.append((years, ret))
-    if raw:
+        else:
-        return returns
+            continue
-    
+
-    format_returns = []
+    if return_string:
-    for i in returns:
+        format_returns = []
-        format_returns.append((str(int(i[0]))+'-year', str(round(i[1]*100,2))+'%'))
+        for i in returns:
-    print(time.time() - start_time)
+            format_returns.append((str(int(i[0]))+'-year', str(round(i[1]*100,2))+'%'))
-    return '\n'.join([f'{i[0]}: {i[1]}' for i in format_returns])
+        print(f"It took {time.time() - start_time} to calculate returns")
+        return '\n'.join([f'{i[0]}: {i[1]}' for i in format_returns])
+
+    return returns
 
 
 def xirr_np(dates: list, amounts: list, guess: float=0.05, step: float=0.05) -> float:
-    """Calculates XIRR from a series of cashflows. 
+    """Calculates XIRR from a series of cashflows.
         Requires NumPy and datetime libraries
-    
+
     Params:
         dates: A list of dates on which cashflows occur
         amounts: The amount of cashflows corresponding to each date
-        guess: A guess for XIRR. 
+        guess: A guess for XIRR.
                 This is used as the starting XIRR for testing. The closer the guess, the faster will be the output
         step: Starting value at which the guess will be increased/decreased in each iteration
 
@@ -196,7 +252,7 @@ def xirr_np(dates: list, amounts: list, guess: float=0.05, step: float=0.05) ->
     limit = 100
     residual = 1
 
-    #test  
+    #test
     dex = np.sum(amounts/((1.05+guess)**years)) < np.sum(amounts/((1+guess)**years))
     mul = 1 if dex else -1
 
@@ -214,6 +270,21 @@ def xirr_np(dates: list, amounts: list, guess: float=0.05, step: float=0.05) ->
 
 
 def sip_returns(amfi_code: int) -> str:
+    """Calculates the SIP returns for a fund
+        Queries the Db and directly gets a list of relevant NAVs only.
+        It also incorporates the unit calculation in the query itself, with an amount of Rs. 10,000
+        Do note that the investment amount itself does not matter, any value with give the same output.
+        The result is then sliced for each of the periods and the returns are calculated using the XIRR function.
+
+    Params:
+    -------
+        amfi_code: amfi_code of the fund for which SIP returns need to be calculated
+
+    Returns:
+    --------
+        Returns a Telegram compatible string of SIP returns
+    """
+
     sip_schedule_query = """
         with myvars(xamfi_code, xmonths) as (
             values(%s, %s)
@@ -237,8 +308,10 @@ def sip_returns(amfi_code: int) -> str:
             where amfi_code = xamfi_code
             order by date
         """
+
     months = [12, 36, 60, 84, 120]
     xirrs = []
+    start = time.time()
     connection = connect_db()
     with connection.cursor() as cur:
         cur.execute(sip_schedule_query, (amfi_code, months[-1]+1))
@@ -246,34 +319,37 @@ def sip_returns(amfi_code: int) -> str:
     transactions = np.array(transactions)
     transactions[:,3] = transactions[:,3].astype(float)
     transactions[:,4] = transactions[:,4].astype(float)
-    
+
-    for m in months:
+    for month in months:
-        df_slice = transactions[-(m+1):,:]
+        df_slice = transactions[-(month+1):,:]
         sip_value = sum(df_slice[:-1,4])*df_slice[-1, 2]
         df_slice[-1,3] = sip_value * -1
         dates = df_slice[:, 1]
         amounts = df_slice[:, 3]
-        xirrs.append({'years': m // 12, 'returns': round(xirr_np(dates, amounts), 6)})
+        xirrs.append({'years': month // 12, 'returns': round(xirr_np(dates, amounts), 6)})
 
     str_returns = []
     for i in xirrs:
-        x = f"{i['years']}-year: {round(i['returns']*100,2)}%"
+        xirr_value = f"{i['years']}-year: {round(i['returns']*100,2)}%"
-        str_returns.append(x)
+        str_returns.append(xirr_value)
+    print(f"It took {time.time() - start} seconds to calcluate SIP returns")
 
     return '\n'.join(str_returns)
 
 
 def main():
+    """ Starts the bot and keeps it running """
+
     updater = Updater(token=os.getenv('TELEGRAM_TOKEN'), use_context=True)
     dispatcher = updater.dispatcher
     dispatcher.add_handler(InlineQueryHandler(mf_query))
-    dispatcher.add_handler(CommandHandler('start', start))
+    dispatcher.add_handler(CommandHandler('start', welcome))
-    dispatcher.add_handler(CommandHandler('help', start))
+    dispatcher.add_handler(CommandHandler('help', welcome))
     dispatcher.add_handler(CallbackQueryHandler(button))
     updater.start_polling()
     updater.idle()
-    
+
-    
+
 if __name__ == "__main__":
     print("MF Bot is running.")
     main()