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This project forked from schustda/penny-stock-predictor

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galvanize data science immersive capstone project

Python 98.96% Shell 1.04%

penny-stock-predictor's Introduction

Penny-Stock-Predictor

Tasks:

  • Refine the target and make sure there are no outliers - COMPLETE
  • Begin running top_board / breakout_boards updater
  • Add top boards within the compiler - not till a later date...
  • Create log for AWS instance to update

Questions: Q: How do I treat weekends and market holidays? A: Remove them. The message boards are much less active on those days.

GOAL:

Predict a massive change in stock price for a given small cap stock

DATA SETS :

MY APPROACH:

  • Web scrape data from iHub
  • Compile with historical stock data
  • Create algorithm to determine success for target

MODEL

  • Feature Space:
    • 52-week prior stock volume
    • 52-week prior iHub message frequency
    • Others (promotional emails, iHub’s “top boards”)
  • Target:
    • ‘buy’ (1) : a significant stock price change is upcoming
    • ‘no buy’ (0): no significant stock price change is upcoming
Data Folder Structure
-- data

    -- raw_data
        -- ihub
            -- message_boards
            -- top_boards
            -- breakout_boards
        -- stock
            -- {raw stock data}

    -- data
        -- compiled data
SRC Folder Structure
-- src

    -- data_management
        -- ihub_data.py
            INPUT: Ticker Symbol
            OUTPUT: create/update the data/raw_data/ihub folder
        -- stock_data.py
            INPUT: Ticker Symbol
            OUTPUT: create/update the data/raw_data/stock folder
        -- compile_data.py
            INPUT: None
            OUTPUT: combined/manipulated data

    -- model
        -- model.py
            INPUT: data from the model_data folder
            OUTPUT: model

    -- data_visualization
        -- tbd

    -- web_app
        -- tbd

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