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Put_Options_Recommendor.py
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Put_Options_Recommendor.py
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"""
Put option recommendor
Stocks are checked for:
1. An underperform analyst rating at most
2. Either an increase of at least 40% or a decrease of at least 20%
3. A decrease in speculated Price to Earnings ratio by at least 10%
4. Suggested stocks are updated every 30 days
5. The parameters can be modified through the constructor
6. Final object is memoized in an external file to prevent needless recalculation
"""
#%%
# Importing packages
import pandas as pd
import yfinance as yf
from datetime import datetime
import os, sys
import pickle
#%%
# Function to hide the unnecessary output when downloading stocks
class HiddenPrints:
def __enter__(self):
self._original_stdout = sys.stdout
sys.stdout = open(os.devnull, 'w')
def __exit__(self, exc_type, exc_val, exc_tb):
sys.stdout.close()
sys.stdout = self._original_stdout
#%%
# Function to calculate percentage change from A to B
def perchange(A,B):
return (((B-A)/A)*100)
#%%
# Creating the class for put option investing
class PutInvest:
# Contructor
def __init__(self, maxrec = - 0.5, minychnge = 40, maxychnge = -20, maxPEchnge = -10, update_interval = 30):
self.maxrec = maxrec # maximum analyst recommendation
self.minychnge = minychnge # either the stock at least increases by min or decreases by max
self.maxychnge = maxychnge
self.maxPEchnge = maxPEchnge # Maximum change from trailing to forward PE
df= pd.read_csv("ftp://ftp.nasdaqtrader.com/SymbolDirectory/nasdaqlisted.txt", sep="|")
self.all_sym_lst = df["Symbol"] # All tickers on the Nasdaq exchange
self.opt = {"stocks":[], "last_updated": datetime.now()} # Tickers with options trading
self.rec = {"stocks":[], "last_updated": datetime.now()} # Tickers with desired ratings
self.trend = {"stocks":[], "last_updated": datetime.now()} # Tickers with desired trend
self.fin = {"stocks":[], "last_updated": datetime.now()} # Tickers with desired financials
self.update_interval = update_interval # Interval after which to update stocks
self.final_suggestions = [] # Final list of suggested stocks
# Function to update stocks with options
def update_opt_stocks(self):
temp_opt = []
for i in self.all_sym_lst: # Iterating through all the tickers
temp = yf.Ticker(i)
try: # temp.options will throw an error if it doesn't exist
temp.options
except:
print("Ticker ",i," doesn't support options trading")
continue
print("Options exist for ticker ",i)
temp_opt.append(i)
self.opt["stocks"] = temp_opt
self.opt["last_updated"] = datetime.now()
# Function to find stocks with desired trend
def update_trend(self):
temp_trend = []
if(self.opt["stocks"] == []):
self.update_opt_stocks()
for i in self.opt["stocks"]:
with HiddenPrints(): # To hide unnecessary output
data = yf.download(i,period = "1y")
temp = data.rolling(7).mean() # Calculating rolling mean
temp.drop(temp.index[0:6],inplace = True)
y_chng = perchange(temp["Adj Close"][0], temp["Adj Close"][-1]) # Change over the year
if((y_chng > self.minychnge) or (y_chng < self.maxychnge)):
print("Adding ",i," to trend stocks")
temp_trend.append(i)
self.trend["stocks"] = temp_trend
self.trend["last_updated"] = datetime.now()
# Function to find stocks with desired recommendations
def update_rec(self):
temp_recs = []
if(self.trend["stocks"] == []):
self.update_trend()
for i in self.trend["stocks"]:
data = yf.Ticker(i)
try: # if recommendations don't exist, the empty property will be undefined and throw an error
temp = data.recommendations
if(not temp.empty):
print("Recommendations exists for ",i)
except:
print("Recommendations do not exist for ",i)
continue
temp["To Grade"].replace("",None,inplace = True) # changing empty strings to null so that they're dropped
temp.dropna(inplace = True) # dropping empty values
temp["To Grade"] = temp["To Grade"].str.lower()
temp["To Grade"].replace(["strong buy","top pick"],"buy",inplace = True)
temp["To Grade"].replace(["outperform","overweight","positive","market outperform","sector outperform","long-term buy", "add","peer outperform","moderate buy","accumulate","conviction buy","overperformer"],"speculative buy", inplace = True)
temp["To Grade"].replace(["perform","equal-weight","neutral","market perform","sector perform","sector weight","in-line","peer perform","mixed","market performer","fair value",""],"hold", inplace = True)
temp["To Grade"].replace(["underperform","underweight","negative","market underperform","sector underperform","long-term sell","reduce","peer underperform","moderate sell","weak hold","conviction sell","underperformer"],"speculative sell", inplace = True)
temp["To Grade"].replace(["strong sell","bottom pick"],"sell",inplace = True)
temp.replace({'To Grade' : { 'buy' : 1.5, 'speculative buy' : 0.75, 'hold' : 0, 'speculative sell' : -0.75, 'sell' : -1.5}},inplace = True)
try:
rec = temp["To Grade"].mean()
if(rec<self.maxrec):
print("Adding ",i," to analyst recommendation stocks")
temp_recs.append(i)
except:
print("Error with symbol ",i)
self.rec["stocks"] = temp_recs
self.rec["last_updated"] = datetime.now()
# Function to find stocks with desired financials
def update_fin(self):
temp_fin = []
if(self.trend["stocks"] == []):
self.update_trend()
for i in self.trend["stocks"]:
data = yf.Ticker(i)
temp = data.info
temp_keys = list(temp.keys()) # Getting a list of what information about the stock is available
if(("forwardPE" in temp_keys) and (("trailingPE" in temp_keys) or ("priceToSalesTrailing12Months" in temp_keys))): # We can substitute Trailing PE with Trailing Price Sales ratio
if("trailingPE" in temp_keys):
chnge = perchange(temp["trailingPE"],temp["forwardPE"])
if(chnge < self.maxPEchnge):
print("Adding ticker ",i, "to financial stocks")
temp_fin.append(i)
else:
chnge = perchange(temp["priceToSalesTrailing12Months"],temp["forwardPE"])
if(chnge < self.maxPEchnge):
print("Adding ticker ",i, "to financial stocks")
temp_fin.append(i)
self.fin["stocks"] = temp_fin
self.fin["last_updated"] = datetime.now()
# Function to update outdated information
def check_for_updates(self):
if(((datetime.now() - self.fin["last_updated"]).days) >= self.update_interval):
self.update_fin()
if(((datetime.now() - self.rec["last_updated"]).days) >= self.update_interval):
self.update_rec()
self.final_suggestions = set(self.fin["stocks"])&set(self.rec["stocks"])
# Function to return the final list of suggested stocks
def get_suggestions(self):
return self.final_suggestions
# Function to store the object in an external file for future efficiency
def memo(self):
with open('memoPut.dictionary', 'wb') as memo_dictionary_file:
pickle.dump(self, memo_dictionary_file)
print("object memoized!")
#%%
# Example to use the above class
with open('memoPut.dictionary', 'rb') as memo_dictionary_file: # Checking if an object has already been memoized
obj = pickle.load(memo_dictionary_file)
if(obj.get_suggestions()==[]): # If the extracted object is empty, then a new object is created and memoized
obj = PutInvest()
obj.check_for_updates()
obj.get_suggestions()
obj.memo()
else:
obj.get_suggestions()