[置顶] Python绘制股票移动均线

1. 前沿

移动均线是股票最进本的指标，本文采用numpy.convolve计算股票的移动均线

2. numpy.convolve

numpy.convolve(a, v, mode=’full’)

Returns the discrete, linear convolution of two one-dimensional sequences.

The convolution operator is often seen in signal processing, where it models the effect of a linear time-invariant system on a signal [R17]. In probability theory, the sum of two independent random variables is distributed according to the convolution of their individual distributions.

If v is longer than a, the arrays are swapped before computation.

Parameters:

 a : (N,) array_like

    First one-dimensional input array.

 v : (M,) array_like

    Second one-dimensional input array.

 mode : {‘full’, ‘valid’, ‘same’}, optional

    ‘full’:

        By default, mode is ‘full’. This returns the convolution at each point of overlap, with an output shape of (N+M-1,). At the end-points of the convolution, the signals do not overlap completely, and boundary effects may be seen.
    ‘same’:

        Mode same returns output of length max(M, N). Boundary effects are still visible.
    ‘valid’:

        Mode valid returns output of length max(M, N) - min(M, N) + 1. The convolution product is only given for points where the signals overlap completely. Values outside the signal boundary have no effect.

Returns:

out : ndarray

    Discrete, linear convolution of a and v.

计算公式：

eg:

>>> import numpy as np
>>> 
>>> np_list = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> 
>>> np_list
array([1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> x = np.convolve(np_list, 2)
>>> x
array([ 2,  4,  6,  8, 10, 12, 14, 16, 18])
>>> x = np.convolve(np_list, [0.5, 0.5])
>>> x
array([ 0.5,  1.5,  2.5,  3.5,  4.5,  5.5,  6.5,  7.5,  8.5,  4.5])

3. 移动均线计算

def moving_average(x, n, type='simple'):
    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

    ma10 = moving_average(close_data, 10, 'simple')
    ma20 = moving_average(close_data, 20, 'simple')

    ax1.plot(data['date'], ma10, color='c', lw=2, label='MA (10)')
    ax1.plot(data['date'], ma20, color='red', lw=2, label='MA (20)')

4. 效果图

5. 参考文献

[1] http://docs.scipy.org/doc/numpy/reference/generated/numpy.convolve.html