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.


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

BBANDS               Bollinger Bands
DEMA                 Double Exponential Moving Average
EMA                  Exponential Moving Average
HT_TRENDLINE         Hilbert Transform - Instantaneous Trendline
KAMA                 Kaufman Adaptive Moving Average
MA                   Moving average
MAMA                 MESA Adaptive 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


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


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


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


Kalman Filter:

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

and the corresponding implementation:


Fourier decomposition (FFT, STFT), Hilber filter and others (standard python library, very useful!)


Time Series Prediction with LSTM (Keras)

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


Some more advanced topics:

Dynamic Time Warping

Global alignment and triangular global alignment kernels (implementation in C provided in the web site):


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