# Tech stack suggestions: Cryptocurrency Challenge

As asked by the organizers of the Datathon here are some suggestions about possible tech stack I found useful for time series analysis, which can be applied for the Cryptocurrency Challenge like libraries/articles/blogs about Moving Average and its variations, Kalman Filter, Fourier and Hilbert Decomposition, LSTM Recurrent Neural Network and others.

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First of all I would suggest the library TA-Lib (Python interface to the C++ implementation)

https://github.com/mrjbq7/ta-lib

``````BBANDS               Bollinger Bands
DEMA                 Double Exponential Moving Average
EMA                  Exponential Moving Average
HT_TRENDLINE         Hilbert Transform - Instantaneous Trendline
MA                   Moving average
MAVP                 Moving average with variable period
MIDPOINT             MidPoint over period
MIDPRICE             Midpoint Price over period
SAR                  Parabolic SAR
SAREXT               Parabolic SAR - Extended
SMA                  Simple Moving Average
T3                   Triple Exponential Moving Average (T3)
TEMA                 Triple Exponential Moving Average
TRIMA                Triangular Moving Average
WMA                  Weighted Moving Average``````

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Introductory to Middle level:

Moving average smoothing (introduction with examples in Python):

Moving Average Smoothing for Data Preparation, Feature Engineering, and Time Series Forecasting with Python

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How to Create an ARIMA (Autoregressive Integrated Moving Average ) Model for Time Series Forecasting with Python:

How to Create an ARIMA Model for Time Series Forecasting with Python

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Very useful and complete explanation about ARIMA and its variations /theory and applications/:

https://people.duke.edu/~rnau/411sdif.htm

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Kalman Filter:

An introduction to smoothing time series in python. Part III: Kalman Filter

and the corresponding implementation:
https://github.com/tmramalho/smallParticleFilter/blob/master/kalmanFilter.py

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Fourier decomposition (FFT, STFT), Hilber filter and others (standard python library, very useful!)
https://docs.scipy.org/doc/scipy/reference/tutorial/signal.html

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Time Series Prediction with LSTM (Keras)

Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras

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