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Quant

Simple Stock Price Forecasting using FinanceData

Package Structure

Below illustrates the file structure after every auto-generated file and folder is created.

.Quant
+-- Crawler
|   +-- crawled_result
|       +-- [stock_code]
|           +-- News
|               +-- fullPage
|                   +-- pages[from-to].csv
|               +-- page[page].csv
|           +-- Research
|               +-- fullPage
|                   +-- pages[from-to].csv
|               +-- page[page].csv
|   +-- __init__.py
|   +-- Google_Crawler.py
|   +-- Naver_Crawler.py
|
+-- FinanceData
|   +-- __init__.py
|   +-- FinanceDataCollection.py
|
+-- Model
|   +-- [stock_code]_final_data
|       +-- image.png
|   +-- [stock_code]_plots
|       +-- image.png
|   +-- Feature_Engineering
|       +-- image.png
|   +-- model_checkpoints
|       +-- [model_name].h5
|   +-- __init__.py
|   +-- data_config.py
|   +-- Data_preparation.py
|   +-- FeatureEngineering.py
|   +-- Gradient_Boosting.py
|   +-- LSTM_Model_.py
|   +-- Multivariate_LSTM.py
|   +-- Multivariage_supervised_LSTM.py
|   +-- Prophet_Model.py
|
+-- NLP
|   +-- __init__.py
|   +-- config.py  (credentials, thus gitignored)
|   +-- GCP_Language.py
|   +-- Saltlux_Language.py
|   +-- entities[id].json
|   +-- sentiment[id].json
|   +-- keyword[id].json
|
+-- __init__.py
+-- [your_GCP_credential].json
+-- Predict.py

How to Run

will be updated

Visualisation

Samsung Open, High, Low, Close Stock Price Prediction (LSTM)
Samsung_4_stock_prices

Samsung Golden and Dead Cross based on Moving Average
Samsung_Golden_Dead_Cross

Fourier Transforms on Samsung stock price for Denoising
Samsung_4_stock_prices

Feature Importance Test
Samsung_4_stock_prices

Facebook Prophet's prediction on Samsung Close price
Samsung_4_stock_prices

Facebook Prophet's prediction on ARKQ ETF
Samsung_4_stock_prices

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Simple Stock Price Forecasting

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