testh2 = testh[tic2].values testl1 = testl[tic1].values testl2 = testl[tic2].values x1 = crtf.ret(testc1) x2 = crtf.ret(testc2) xidx = ~np.isnan(x1) & ~np.isnan(x2) x1 = x1[xidx] x2 = x2[xidx] dt = testc['Date'].values dt = dt[xidx] # static x_act = np.corrcoef(x1, x2)[0, 1] x12_act = np.ones(x1.shape[0])*x_act crup.plot_ts(dt, x12_act) # base case n = x1.shape[0] mi = 240 mi2 = (2*mi+1)/3 # x12_corr2 = np.ones(n)*np.nan # x12_corr2 = (crtf.ema(x1*x2, mi)-crtf.ema(x1, mi)*crtf.ema(x2, mi))/\ # np.sqrt((crtf.ema(x1**2, mi)-crtf.ema(x1, mi)**2)*(crtf.ema(x2**2, mi)-crtf.ema(x2, mi)**2)) # crup.plot_ts(dt, x12_corr2) # x12_corr2 = np.ones(n)*np.nan # x12_corr2 = (crtf.sma(x1*x2, mi)-crtf.sma(x1, mi)*crtf.sma(x2, mi))/\ # np.sqrt((crtf.sma(x1**2, mi)-crtf.sma(x1, mi)**2)*(crtf.sma(x2**2, mi)-crtf.sma(x2, mi)**2)) # crup.plot_ts(dt, x12_corr2) x12_corr61 = crtf.cor_cc(testc1, testc2, mi, smth=7, zl=False)
xdt_ = xdt_[-3:-1] xview = [i for i, j in enumerate(xdt) if j.astype('datetime64[Y]') in xdt_] hl = np.zeros(xpr.shape[0]) # xprs1 = crtf2.lrma(xpr, 30, lg=True) # xprs2 = crtf2.lrma2(xpr, 30, lg=True) xprs3 = crtf2.lrma3(xpr, 30, lg=True) # xr1 = crtf2.ret(xprs1) # xr2 = crtf2.ret(xprs2) xr3 = crtf2.ret(xprs3) pl.figure(2) pl.subplot(311) crup.plot_ts(xdt[xview], xpr[xview]) # crup.plot_ts(xdt[xview], xprs1[xview]) # crup.plot_ts(xdt[xview], xprs2[xview]) crup.plot_ts(xdt[xview], xprs3[xview]) pl.subplot(312) crup.plot_ts(xdt[xview], hl[xview]) m = 30 # xr1_ = crtf2.return_estimates_normal(xr1, m) # crup.plot_ts(xdt[xview], xr1_[xview]) # xr2_ = crtf2.return_estimates_normal(xr2, m) # crup.plot_ts(xdt[xview], xr2_[xview]) xr3_ = crtf2.return_estimates_normal(xr3, m) crup.plot_ts(xdt[xview], xr3_[xview])
Y[i] = Ym[i] print(i) print(Value[0:20]) print(Yg[0:20]) print(Wg[0:20]) print(WgCs[0:20]) print(jIdx) print(WgCs[jIdx]) print(Ym[0:25]) print(Y[0:25]) crup.plot_ts(x['Date'].values, Input) crup.plot_ts(x['Date'].values, Y) Y2 = crtf.ema(Input, Periods) crup.plot_ts(x['Date'].values, Y2) # # def mma(x, m, lg=False): # n = x.shape[0] # ny = crtf.fst_nan(x) # mi = int(m) # x1 = crtf.fill(x) # y = np.empty(n)*np.nan # ym = np.empty(n)*np.nan
def _smoother(x, m): return crtf.fish_inv_trans(crtf.lrbeta(crtf.cumsum_special(crtf.fish_trans(x)), m, lg=False)) window = 241 window1 = int((3*window-1)/2) window2 = int((2*window+1)/3) for i in range(window1, n): # i = n test_c1[i] = stats.spearmanr(testr1[i-window2:i], testr2[i-window2:i])[0] test_c2[i] = stats.spearmanr(testr1[i-window:i], testr2[i-window:i])[0] test_c3[i] = stats.spearmanr(testr1[i-window1:i], testr2[i-window1:i])[0] dt = testc['Date'].values dt = dt[test_idx] crup.plot_ts(dt, crtf.fish_trans(test_c1)) crup.plot_ts(dt, crtf.fish_trans(test_c2)) crup.plot_ts(dt, crtf.fish_trans(test_c3)) test_c4= crtf.fish_inv_trans(crtf.lrbeta(crtf.cumsum_special(crtf.fish_trans(test_c2)), 7, lg=False)) crup.plot_ts(dt, test_c4) test_c5 = crtf.fish_inv_trans(crtf.lrbeta(crtf.cumsum_special(crtf.fish_trans(3*test_c2-2*test_c3)), 7, lg=False)) crup.plot_ts(dt, test_c5) #------------------------------------ test = d_comb[6].retrieve('Close') test1 = crtf.ret(test['SPY'].values) test2 = crtf.ret(test['TLT'].values) testidx = ~np.isnan(test1) & ~np.isnan(test2)