def bio(name,surname,nick,location,job,education,company=None):
  name = Ognjen
  surname = Raketic
  nick= []
  for i in nick:
  if Family==True:
    return nick = Ogi
  elif CloseFriend== True:
    return nick = Serafim
  elif Colleague==True:
    return nick = Raketa
  else:
    return nick = name + surname
  location = Vrbas, Serbia
  job = Hired
  education = [Masters Student in Computational Science 2022-2023,
               Masters Degree in Engineering Management 2019-2020,
               Bachelors Degree in Engineering Management 2015-2019]
  company = Mutadich Financial Advisory & Vital A.D.

def family():
  fam = {"Wife" : Irena,
         "Since" : 11.01.2016,
         "Kid" : Srdja
         "Since": 27.11.2021
         "Kid": Another boy
         "Since": To be decided in May}

def projects(name,programming_language,date):
  project1 = {"name": Risk Contributions On Client Portfolio,
              "programming_language" : Python
              "date" : 01.10.2023}
  project2 = {"name": Making Real Portfolio & Prepare for MarketWatch,
              "programming_language" : Python
              "date" : 25.09.2023}
  project3 = {"name": CAPM & Factor Models,
              "programming_language" : Python
              "date" : 15.09.2023}
  project4 = {"name": Finding Efficient Frontier & Testing Portfolio Performances,
              "programming_language" : Python
              "date" : 30.08.2023}
  project5 = {"name": Portfolio Optimization in Investment Part 1,
              "programming_language" : Python
              "date" : 26.08.2023}

def past_experiences(position, company, job_description,location,years):
  company1 = { "position": Junior M&A Analyst - Sell Side,
              "company": Mutadich Financial Advisory,
              "job_description" : Financial Modelling, Consulting,
              "location": Remote,
              "year": 2024-Present}
  company2 = { "position": Production Conusltant,
              "company": Vital a.d.,
              "job_description" : Consulting & Coaching,
              "location": Vrbas,
              "year": 2023-Present}
  company3 = { "position": Consulting Engineer,
              "company": Furni,
              "job_description" : ML, Consulting & Coaching,
              "location": Vrbas,
              "year": 2022-2023}
  company4 = { "position":Process Engineer,
              "company": Vendomnia,
              "job_description" : Consulting, ISO Standards & Project Management,
              "location": Novi Sad,
              "year": 2022-2022}
  company5 = { "position":CTO & Engineer,
              "company": INKO National,
              "job_description" : Finance & Production,
              "location": Vrbas,
              "year": 2021-2022}
  company6 = { "position": Research Consultant,
              "company": PhiAcademy,
              "job_description" : Scientific Researching  & Product Management,
              "location": Fully Remote,
              "year": 2021-2021}
  company7 = { "position": Innovation Consultant,
              "company": INAT Centre,
              "job_description" : Agile & DT, Project Management,
              "location": Fully Remote,
              "year": 2017-2021}
  company8 = { "position": Shift Leader,
              "company": Vital A.D Vrbas,
              "job_description" : Production Planning & Agile Transformator,
              "location": Vrbas,
              "year": 2015-2019}

def technical_background(degree):
    if degree == Msc_Engineer**2:
      return ("Computer Science Engineer, Industiral Engineer")
    else:
      pass

def fields_of_interest(alpha,betha, gamma, research, readings, projects):
  FoE = { "alpha" : Investment Banking,
          "betha" : Mergers & Acquisition,
          "gamma" : Data Science,
          "research" : FinTech,
          "readings" : Financial Computings,
          "projects" : Optimizations In Investments}

def skills(languages,Frameworks, Libraries):
  languages = { "language1" : Python,
                "language2" : R,
                "language3" : SQL,
  DS libraries= {"lib1" = Pandas,
              "lib2"= Matplotlib,
              "lib3" = NumPy,
              "lib4"= SciPy,
              "lib5" = SymPy,
              "lib6"= Seaborn,
              "lib7" = SciKit-Learn,
              "lib8"= PyFolio,
              "lib9" = QuantFormulas(My Own Library)},

def currently_learning():
  reading = {"Investment" : Bodie et al.,
             "Storytelling with data": Cole et al,,
             "Options, Futures & Other Derivatives" : John C. Hull,
             "Effective Data Storytelling" : Brent Dykes,
             "Robust Portfolio Optimization & Management" : Frank J.Fabozzi et al.,
             "Asset Management" : Andrew Ang,
             "Black Swan" : Nassim Nicholas Taleb}
  testing = { "Portfolio" : Python,
              "Derivatives" : Python,
              "Financial Data Analysis" : R}

def hobbies():
  hobby = {"Recovery" : Bike,
           "Reading" : Books,
           "Training" : Judo/BJJ(On Hold Due To TCL),
           "Storytelling": Wines & Food,
           "Academics" : Write Sci Papers,
           "Travel" : Across The Globe}