From 6ff02825af9ed01ad2e5bec704e1337ce6e07b64 Mon Sep 17 00:00:00 2001
From: 4rumprom <84041172+4rumprom@users.noreply.github.com>
Date: Mon, 4 Mar 2024 14:44:18 -0500
Subject: [PATCH] Update example_options.py

---
 example/example_options.py | 42 +++++++++++++++++++-------------------
 1 file changed, 21 insertions(+), 21 deletions(-)

diff --git a/example/example_options.py b/example/example_options.py
index e9b8d5c..959c44d 100644
--- a/example/example_options.py
+++ b/example/example_options.py
@@ -21,50 +21,50 @@
     totp_secret=totp_secret # Get this using itsjafer.com/#/schwab.
 )
 
-#get quotes for options.
-OptionChain = api.get_options_chains_v2('$RUT') #try also with parameter greeks = True 
+# Get quotes for options.
+option_chain = api.get_options_chains_v2('$RUT') #try also with parameter greeks = True 
 
-#the json output is deeply nested so here is how you can work with it
-#normalizing the data into a pandas DataFrame
-df1 = pd.json_normalize(OptionChain,['Expirations','Chains','Legs'],[['Expirations','ExpirationGroup']])
-#normalizing Expirations.ExpirationGroup
+# The json output is deeply nested so here is how you can work with it:
+# Normalizing the data into a pandas DataFrame
+df1 = pd.json_normalize(option_chain,['Expirations','Chains','Legs'],[['Expirations','ExpirationGroup']])
+# Normalizing Expirations.ExpirationGroup
 df2 = pd.json_normalize(df1['Expirations.ExpirationGroup'])
-#dropping the column Expirations.ExpirationGroup in df1 and concatenating the two dataframes (side by side)
+# Dropping the column Expirations.ExpirationGroup in df1 and concatenating the two dataframes (side by side)
 df1.drop('Expirations.ExpirationGroup',axis=1, inplace=True)
 df = pd.concat([df1,df2],axis=1)
-#converting strings to numbers when relevant. Keeping strings is conversion is not possible.
+# Converting strings to numbers when relevant. Keeping strings is conversion is not possible.
 df = df.apply(lambda col: pd.to_numeric(col, errors='coerce')).fillna(df)
 
-#let's isolate options with closest expiration date:
+# Let's isolate options with closest expiration date:
 closest_expiration_options = df[(df.DaysUntil==df.DaysUntil.min())]
 
-#let's find the call and put options with closest strike price to current price:
-#first let's grab the current price. No need to use api.quote_v2(), it's already in chains
-current_price = float(OptionChain['UnderlyingData']['Last'])
-#finding the index of the closest strike prices
+# Let's find the call and put options with closest strike price to current price:
+# First let's grab the current price. No need to use api.quote_v2(), it's already in chains
+current_price = float(option_chain['UnderlyingData']['Last'])
+# Finding the index of the closest strike prices
 ATM_call_index = abs(closest_expiration_options[closest_expiration_options.OptionType=="C"].Strk - current_price).idxmin()
 ATM_put_index = abs(closest_expiration_options[closest_expiration_options.OptionType=="P"].Strk - current_price).idxmin()
-#grabbing the rows at those indexes 
+# Grabbing the rows at those indexes 
 ATM_call_option = closest_expiration_options.iloc[ATM_call_index]
 ATM_put_option = closest_expiration_options.iloc[ATM_put_index]
 print(f"Call and Put ATM options (At The Money) with the closest expiration:")
 print(f"Call: {ATM_call_option.Sym}         Ask: {ATM_call_option.Ask}      Bid: {ATM_call_option.Bid}")
 print(f"Put:  {ATM_put_option.Sym}         Ask: {ATM_put_option.Ask}      Bid: {ATM_put_option.Bid}")
 
-#now let's place an at the money straddle for the closest expiration date
-#preparing the parameters
-#setting the straddle strategy code:
+# Now let's place an at the money straddle for the closest expiration date
+# Preparing the parameters
+# Setting the straddle strategy code:
 strategy = 226 # for more codes, look at the comment section of option_trade_v2().
 symbols = [ATM_call_option.Sym,ATM_put_option.Sym]
 instructions = ["BTO","BTO"] #Buy To Open. To close the position, it would be STC (Sell To Close)
 quantities = [1,1]
-#note that the elements are paired. So the first symbol of the list will be associated with the first element of instructions and quantities.
+# Note that the elements are paired. So the first symbol of the list will be associated with the first element of instructions and quantities.
 account_info = api.get_account_info_v2()
 account_id = next(iter(account_info))
 order_type = 202 #net debit. 201 for net credit. You probably should avoid 49 market with options...
-# let's set the limit price at the median between bid and ask.
+# Let's set the limit price at the median between bid and ask.
 limit_price = (ATM_call_option.Ask + ATM_call_option.Bid + ATM_put_option.Ask + ATM_put_option.Bid) / 2
-#let's place the trade:
+# Let's place the trade:
 messages, success = api.option_trade_v2(
     strategy=strategy, 
     symbols = symbols, 
@@ -80,4 +80,4 @@
 print("The order verification produced the following messages: ")
 pprint.pprint(messages)
 
-#Happy coding!!
\ No newline at end of file
+# Happy coding!!