Create main.py

main.py

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+import yfinance as yf
+import os
+import pandas as pd
+import requests
+import json
+import datetime
+
+
+#calculating columns how many days to expiration dates
+def daysleft(inputdate):
+  import datetime
+  previous_date = datetime.datetime.strptime(inputdate, '%Y-%m-%d')
+  today = datetime.datetime.today()
+  ndays = (previous_date  - today).days +1
+  return ndays
+
+#get All Option Chain for a ticker, calculate additional columns and retun as a dataframe from Yahoo finance
+def get_DF_optionchain(ticker):
+    import requests_cache
+    session = requests_cache.CachedSession('yfinance.cache')
+    session.headers['User-agent'] = 'my-program/1.0'
+    # ticker = yf.Ticker('msft', session=session)
+
+    tkobj = yf.Ticker(ticker, session=session)
+    exps = tkobj.options
+
+    #   Current Price in Market Session
+    #   lastprice = tkobj.info['currentPrice']
+    lastprice = tkobj.history().iloc[-1]['Close']
+    
+
+  #base price for additional column 
+    bias_price = 'mark'
+#   bias_price = 'lastPrice'
+    options = pd.DataFrame()
+    for e in exps:
+        try:
+            print(e)
+            opt = tkobj.option_chain(e)
+            # opt_calls = pd.DataFrame().append(opt.calls)
+            opt_calls = opt.calls
+            opt_calls['Type'] = 'CALL'
+            # opt_puts = pd.DataFrame().append(opt.puts)
+            opt_puts = opt.puts
+            opt_puts['Type'] = 'PUT'
+            opt = opt_calls._append(opt_puts)
+            opt['expirationDate'] = e
+            opt['daysleft'] = daysleft(e)
+
+            opt[['bid', 'ask', 'strike']] = opt[['bid', 'ask', 'strike']].apply(pd.to_numeric)
+            opt['mark'] = (opt['bid'] + opt['ask']) / 2
+            opt['pricepercent'] = 100 * opt['mark'] / lastprice
+
+            opt[['bid', 'ask', 'strike']] = opt[['bid', 'ask', 'strike']].apply(pd.to_numeric)
+            opt['mark'] = (opt['bid'] + opt['ask']) / 2
+
+            opt['interinsicvalue'] = opt.apply(lambda row: abs(lastprice - row['strike']) if row['inTheMoney'] else 0, axis=1)
+            opt['timevalue'] = opt.apply(lambda row: row[bias_price] - row['interinsicvalue'], axis=1)
+            opt['breakevenprice'] = opt.apply(lambda row: row[bias_price]+ row['strike'] if row['Type']=='CALL' else row['strike'] - row['lastPrice'], axis=1)
+            options = options._append(opt, ignore_index=True)    
+        except:
+            print('ERROR')
+            pass
+    return options
+
+
+
+pathdatafolder = os.path.join('data' , datetime.datetime.today().strftime("%Y%m%d"))
+if not os.path.exists(fr"./{pathdatafolder}"):
+    os.mkdir(fr"./{pathdatafolder}")
+
+tickers = ['TSLA', 'META', 'GOOG', 'IBM', 'MSFT','NKE','DLTR','DG']
+
+for ticker in tickers:
+    print(ticker)
+    try:
+        options = get_DF_optionchain(ticker)
+        options = options.drop(['contractSize','currency'] , axis=1)
+        options.to_csv(fr"./{pathdatafolder}/{ticker}.csv",index=False)
+    except:
+        pass
+
+
+# df = pd.read_csv(r"\\PERSIA\New Volume\storage\premarket\america_2022-04-14.csv")
+
+# df2 = df.query("`Market Capitalization`>10e9")
+
+# import timeit
+# start = timeit.default_timer()
+
+
+# for index,row in df2.iterrows():
+#     ticker = row.Ticker
+#     print(ticker)
+#     try:
+#         options = get_DF_optionchain(ticker)
+#         options.to_csv(fr"./{pathdatafolder}/{ticker}.csv")
+#     except:
+#         pass
+
+
+
+# stop = timeit.default_timer()
+# print('Time: ', stop - start)  
+
+
+
+
+# import os
+# import glob
+
+# DFtotal = pd.DataFrame()
+# for csvfile in glob.glob(r'./data/*.csv'):
+#     DF = pd.read_csv(csvfile)
+#     DF['Ticker'] = os.path.basename(csvfile).split('.')[0]
+#     DFtotal = DFtotal.append(DF)
+# DFtotal.shape
+# DFtotal.columns
+
+
+# DFtotal.sort_values(by='impliedVolatility')[-10:].T
+# DFtotal.sort_values(by='impliedVolatility')[-10:].T
+
+
+# DFtotal.query('volume>100').sort_values(by='impliedVolatility')[-10:].T
+# DFtotal.query('daysleft>7 & daysleft<60 & not inTheMoney & Type=="CALL" & openInterest>1000').sort_values(by='pricepercent')[-5:].T
+
+# dftest = DFtotal.query('daysleft>7 & daysleft<60 & inTheMoney!=1 & Type=="CALL" &openInterest>100').sort_values(by='pricepercent')
+# dftest['lasttradedate']=dftest['lastTradeDate'].str.split(' ', 0, expand=False).str[0]
+# dftest.to_csv('test.csv')
+# # dftest['lasttradedate']=pd.to_datetime(dftest['lastTradeDate'])
+# dftest['lasttradedate2']=pd.to_datetime(dftest['lasttradedate'],format='%Y-%m-%d')
+# dftest['year'] = pd.DatetimeIndex(dftest['lasttradedate']).year
+# dftest['month'] = pd.DatetimeIndex(dftest['lasttradedate']).month
+# dftest['day'] = pd.DatetimeIndex(dftest['lasttradedate']).day
+# dftest.info()
+# dftest.query('year==2022 & month==4 & day==14').sort_values(by='pricepercent')
+# [-5:].T
+
+# # dftest.query('lasttradedate>2022-04-14')
+
+# df = pd.read_csv(r"./{pathdatafolder}/AAPL.csv")
+# df.columns
+# df.sort_values(by='impliedVolatility')[-5:].T
+
+# df.query('impliedVolatility>1').sort_values(by='openInterest')[-5:].T
+
+# df.query('daysleft>7 & daysleft<60 & not inTheMoney & Type=="CALL" & openInterest>10000').sort_values(by='bid')[-5:].T
+
+