Technical Analysis of Financial Data.
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Updated
Jun 26, 2019 - Jupyter Notebook
Technical Analysis of Financial Data.
Notebook for Data Science - Machine Learning
In this Jupyter Notebook, I've used LSTM RNN with Technical Indicators namely Simple Moving Average (SMA), Exponential Moving Average (EMA), Moving Average Convergence Divergence (MACD), and Bollinger Bands to predict the price of Bank Nifty.
A collection of notebooks I used in my Medium articles.
A Jupyter notebook and report for ECM3420 focused on predicting the stock price using technical analysis
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Python functions and an associated Jupyter notebook for technical analysis of stock price data. Numpy is used for calculating technical indicators. Matplotlib and mpl_finance are used for plotting data.
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