ta.py

### Technical Analisis functions
import numpy as np
import pandas as pd


def EMA(values, window):
    """ Numpy implementation of EMA
    """
    weights = np.exp(np.linspace(-1., 0., window))
    weights /= weights.sum()
    a =  np.convolve(values, weights, mode='full')[:len(values)]
    a[:window] = a[window]
    return a
    
def MACD(x, nslow=26, nfast=12):
    emaslow = EMA(x, nslow)
    emafast = EMA(x, nfast)
    macd = emafast - emaslow
    signal = EMA(macd, 9)
    hist = macd - signal
    return macd, signal,hist
    
def RSI(prices, n=14):
        
    deltas = np.diff(prices)
    seed = deltas[:n+1]
    up = seed[seed >= 0].sum()/n
    down = -seed[seed < 0].sum()/n
    rs = up/down
    rsi = np.zeros_like(prices)
    rsi[:n] = 100. - 100./(1. + rs)

    for i in range(n, len(prices)):
        delta = deltas[i - 1]  # cause the diff is 1 shorter

        if delta > 0:
            upval = delta
            downval = 0.
        else:
            upval = 0.
            downval = -delta

        up = (up*(n - 1) + upval)/n
        down = (down*(n - 1) + downval)/n

        rs = up/down
        rsi[i] = 100. - 100./(1. + rs)

    return rsi
    
def BB():
    pass
    
def moving_average(x, n, type='simple'):
    """
    compute an n period moving average.

    type is 'simple' | 'exponential


    """
    x = np.asarray(x)
    if type == 'simple':
        weights = np.ones(n)
    else:
        weights = np.exp(np.linspace(-1., 0., n))

    weights /= weights.sum()

    a = np.convolve(x, weights, mode='full')[:len(x)]
    a[:n] = a[n]
    return a