<commit_before># FRAMA (Fractal Adaptive Moving Average) in Python
## Introduction
The aim of this repository is to provide functions of FRAMA written
in python for both educational and industrial purposes. FRAMA is a
state of the art Moving Average estimator(or was by the time u read this...).
It is used in economics for stock prices and Wall Street stuff, but
it can also be used from anyone as a better moving average algorithm.
It works better on time series which present the same shape despite the
length of the data timestamps type (sec, minutes, hours, days,...).


## Libraries & Dependencies
+ `Numpy`

+ Compiled with `Python 2.7.6`
## More Details
To present more details we wil use a top-down approach.
The algorithm uses an adaptive low-pass filter with one term `alpha`.
So it should look something like this:
```python
    for i in range(1,N):
        Filt[i+1] = alpha * InputPrice[i] + (1 - alpha) * Filt[i]
```
Now the problem is how do we calculate alpha at each step?
<commit_msg>spelling<commit_after># FRAMA (Fractal Adaptive Moving Average) in Python
## Introduction
The aim of this repository is to provide functions of FRAMA written
in python for both educational and industrial purposes. FRAMA is a
state of the art Moving Average estimator(or was by the time u read this...).
It is used in economics for stock prices and Wall Street stuff, but
it can also be used from anyone as a better moving average algorithm.
It works better on time series which present the same shape despite the
length of the data timestamps type (sec, minutes, hours, days,...).


## Libraries & Dependencies
+ `Numpy`

+ Compiled with `Python 2.7.6`
## More Details
To present more details we will use a top-down approach.
The algorithm uses an adaptive low-pass filter with one term `alpha`.
So it should look something like this:
```python
    for i in range(1,N):
        Filt[i+1] = alpha * InputPrice[i] + (1 - alpha) * Filt[i]
```
Now the problem is how do we calculate alpha at each step?

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