def dataXY(ticker, alpha=0.02): data =fetch(ticker, start=datetime.datetime(2012, 1, 1)) data['pivots'] = peak_valley_pivots(data.Close, alpha, -alpha) rd = data.reset_index() X = rd[rd.pivots!=0][['Close', 'pivots']].reset_index().values X = np.dstack( [X[i:i+5, :] for i in range( X.shape[0] -4)] ) X[:,1,:] = X[:,1,:]/X[0,1,:] # covert to return axis F = map(lambda i: genFeature(X[..., i]), range(X.shape[-1])) F = np.array(F) return F, data
def main(ticker, idx=0, alpha = 0.02):
data =fetch(ticker)
data['pivots'] = peak_valley_pivots(data.Close, alpha, -alpha)
rd = data.reset_index()
X = rd[rd.pivots!=0][['Close', 'pivots']].reset_index().values
X = np.dstack( [X[i:i+5, :] for i in range( X.shape[0] -4)] )
features = []
for i in range(X.shape[-1]):
x = X[:,:,i]
x[:, 1] = x[:, 1]/x[0,1]
a1, b1 = trend(x[::4, 0], x[::4, 1])
a2, b2 = trend(x[1::2, 0], x[1::2, 1])
xm = a1*x[2,0] + b1
dy = xm - x[2,1]
features.append([idx, x[0,2], a1, a2, dy])
print idx, x[0,2], a1, a2, dy
#import ipdb; ipdb.set_trace()
raw = rd[int(x[0,0]): int(x[-1,0])]
ret = raw.Close/raw.Close.iloc[0]
ry1 = a1 * x[:,0] + b1
ry2 = a2 * x[:,0] + b2
fig = plt.figure(figsize=(6,3))
ax = fig.add_subplot(111)
ret.plot(style='g.', ax=ax)
ax.plot(x[:,0], x[:,1], '--')
ax.plot(x[:,0], ry1, 'r-', lw=2)
ax.plot(x[:,0], ry2, 'b-', lw=2)
ax.plot(x[::2, 0], x[::2, 1], 'ro')
ax.plot(x[1::2, 0], x[1::2, 1], 'bo')
ax.axhline(y=1.0, lw=1, c='k')
ax.annotate("%.3f%%"%(100*a1), (x[1,0], a1*x[1,0]+b1), color='r')
ax.annotate("%.3f%%"%(100*a2), (x[3,0], a2*x[3,0]+b2), color='b')
ax.annotate("%.3f%%"%(100*dy), (x[2,0], x[2,1]), color='k')
ax.set_title("#%s %s, %.3f%%, %.3f%%, %.3f%%"%(idx, x[0,2], 100*a1, 100*a2, 100*dy))
ax.set_xlim([x[0,0]-5, x[-1,0]+5])
ax.set_ylim([x[:,1].min()-0.03, x[:,1].max()+0.03 ])
plt.savefig("figs/%s.png"%(idx))
idx += 1
with open('data.csv', 'a') as fs:
writer = csv.writer(fs)
writer.writerows(features)
return idx
