{"id":326,"date":"2020-05-11T10:33:56","date_gmt":"2020-05-11T10:33:56","guid":{"rendered":"http:\/\/lucylabs.gatech.edu\/ml4t\/?page_id=326"},"modified":"2020-08-14T04:02:51","modified_gmt":"2020-08-14T04:02:51","slug":"manipulating-financial-data-in-python","status":"publish","type":"page","link":"https:\/\/lucylabs.gatech.edu\/ml4t\/manipulating-financial-data-in-python\/","title":{"rendered":"Manipulating Financial Data in Python"},"content":{"rendered":"\n\n[et_pb_section fb_built=”1″ _builder_version=”4.4.6″][et_pb_row _builder_version=”4.4.6″][et_pb_column _builder_version=”4.4.6″ type=”4_4″][et_pb_text _builder_version=”4.4.6″ hover_enabled=”0″ header_font=”|||on|||||”]

Manipulating Financial Data in Python<\/span><\/h1>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.4.6″][et_pb_column _builder_version=”4.4.6″ type=”4_4″][et_pb_text _builder_version=”4.4.6″ hover_enabled=”0″]

Lesson 1: Reading, slicing and plotting stock data<\/span><\/h2>\n
    \n
  • Overview of the data we’ll be working with (from Yahoo!)<\/li>\n
  • Introduction to our primary library: Pandas<\/li>\n
  • Reading CSV data into Pandas<\/li>\n
  • Filtering to specific dates<\/li>\n
  • Plotting<\/li>\n<\/ul>\n

    Reading: “Python for Finance”, Chapter 6: Financial time series<\/p>\n

    Lesson 2: Working with many stocks at once<\/span><\/h2>\n
      \n
    • Our target data frame structure<\/li>\n
    • Address reverse order issue<\/li>\n
    • Reading data for multiple stocks into the structure<\/li>\n
    • Date slicing<\/li>\n
    • Symbol slicing<\/li>\n
    • Plotting<\/li>\n
    • Normalizing<\/li>\n<\/ul>\n

      Lesson 3: The power of Numpy<\/span><\/h2>\n
        \n
      • What is Numpy and how it relates to Pandas<\/li>\n
      • Why is Numpy powerful\/important?<\/li>\n
      • Creating Numpy arrays<\/li>\n
      • Indexing and slicing Numpy arrays<\/li>\n
      • Important data processing on Numpy arrays<\/li>\n
      • Example use with pandas too<\/li>\n<\/ul>\n

        Reading: “Python for Finance”, Chapter 4: Data types and structures<\/p>\n

        Lesson 4: Statistical analysis of time series<\/span><\/h2>\n
          \n
        • Gross statistics on dataframes<\/li>\n
        • Rolling statistics on dataframes<\/li>\n
        • Plotting a technical indicator (Bollinger Bands)<\/li>\n<\/ul>\n

          Reading: “Python for Finance”, Chapter 6: Financial time series<\/p>\n

          Lesson 5: Incomplete data<\/span><\/h2>\n
            \n
          • How incomplete data arises in financial data<\/li>\n
          • Different approaches to dealing with it<\/li>\n<\/ul>\n

            Lesson 6: Histograms and scatter plots<\/span><\/h2>\n
              \n
            • Histogram of daily returns<\/li>\n
            • Compare SPY with XOM<\/li>\n
            • Scatter plots<\/li>\n
            • Correlation is not slope!<\/li>\n
            • Compare SPY vs XOM, with SPY vs GLD scatter plots<\/li>\n<\/ul>\n

              Reading: “Python for Finance”, Chapter 5: Data Visualization<\/p>\n

              Lesson 7: Sharpe ratio & other portfolio statistics<\/span><\/h2>\n
                \n
              • Speed up reading data by memoizing<\/li>\n
              • Average daily return<\/li>\n
              • Volatility: stddev of daily return (don’t count first day)<\/li>\n
              • Cumulative return<\/li>\n
              • Relationship between cumulative and daily<\/li>\n
              • Sharpe Ratio<\/li>\n
              • How to model a buy and hold portfolio<\/li>\n<\/ul>\n

                Lesson 8: Optimizers: Building a parameterized model<\/span><\/h2>\n
                  \n
                • What does an optimizer do?<\/li>\n
                • Syntax of optimizer use<\/li>\n
                • Problem statement for an optimizer (inputs, outputs, assumptions)<\/li>\n
                • How to find X that minimizes f(X) with a minimizer<\/li>\n
                • How to build a parameterized polynomial model from real data using an optimizer<\/li>\n<\/ul>\n

                  Lesson 9: Optimizers: How to optimize a portfolio<\/span><\/h2>\n
                    \n
                  • What does it mean to “optimize” a portfolio<\/li>\n
                  • Framing the problem for an optimizer<\/li>\n
                  • Constraints on X for an optimizer<\/li>\n
                  • Ranges on X for an optimizer<\/li>\n<\/ul>\n

                    Reading: “Python for Finance”, Chapter 11: Statistics-Portfolio Optimization<\/p>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]\n\n","protected":false},"excerpt":{"rendered":"

                    Manipulating Financial Data in Python Lesson 1: Reading, slicing and plotting stock data Overview of the data we’ll be working with (from Yahoo!) Introduction to our primary library: Pandas Reading CSV data into Pandas Filtering to specific dates Plotting Reading: “Python for Finance”, Chapter 6: Financial time series Lesson 2: Working with many stocks at […]<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":7,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"class_list":["post-326","page","type-page","status-publish","hentry"],"jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/lucylabs.gatech.edu\/ml4t\/wp-json\/wp\/v2\/pages\/326","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lucylabs.gatech.edu\/ml4t\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/lucylabs.gatech.edu\/ml4t\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/lucylabs.gatech.edu\/ml4t\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/lucylabs.gatech.edu\/ml4t\/wp-json\/wp\/v2\/comments?post=326"}],"version-history":[{"count":3,"href":"https:\/\/lucylabs.gatech.edu\/ml4t\/wp-json\/wp\/v2\/pages\/326\/revisions"}],"predecessor-version":[{"id":648,"href":"https:\/\/lucylabs.gatech.edu\/ml4t\/wp-json\/wp\/v2\/pages\/326\/revisions\/648"}],"wp:attachment":[{"href":"https:\/\/lucylabs.gatech.edu\/ml4t\/wp-json\/wp\/v2\/media?parent=326"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}