def longsklearn(code='999999', ptype='f', dtype='d', start=None, end=None): # code='999999' # dtype = 'w' # start = '2014-09-01' # start = None # end='2015-12-23' # end = None df = tdd.get_tdx_append_now_df(code, ptype, start, end).sort_index(ascending=True) # if not dtype == 'd': # df = tdd.get_tdx_stock_period_to_type(df, dtype).sort_index(ascending=True) dw = tdd.get_tdx_stock_period_to_type(df, dtype).sort_index(ascending=True) # print df[:1] h = df.loc[:, ['open', 'close', 'high', 'low']] highp = h['high'].values lowp = h['low'].values openp = h['open'].values closep = h['close'].values lr = LinearRegression() x = np.atleast_2d(np.linspace(0, len(closep), len(closep))).T lr.fit(x, closep) LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False) xt = np.atleast_2d(np.linspace(0, len(closep) + 200, len(closep) + 200)).T yt = lr.predict(xt) # plt.plot(xt,yt,'-g',linewidth=5) # plt.plot(closep) bV = [] bP = [] uV = [] uP = [] for i in range(1, len(highp) - 1): # if highp[i] <= highp[i - 1] and highp[i] < highp[i + 1] and lowp[i] <= lowp[i - 1] and lowp[i] < lowp[i + 1]: if lowp[i] <= lowp[i - 1] and lowp[i] < lowp[i + 1]: bV.append(lowp[i]) bP.append(i) for i in range(1, len(highp) - 1): # if highp[i] >= highp[i - 1] and highp[i] > highp[i + 1] and lowp[i] >= lowp[i - 1] and lowp[i] > lowp[i + 1]: if highp[i] >= highp[i - 1] and highp[i] > highp[i + 1]: uV.append(highp[i]) uP.append(i) print(highp) print("uV:%s" % uV[:1]) print("uP:%s" % uP[:1]) print("bV:%s" % bV[:1]) print("bP:%s" % bP[:1]) sV, sP = LIS(uV) dV, dP = LIS(bV) print("sV:%s" % sV[:1]) print("sP:%s" % sP[:1]) print("dV:%s" % dV[:1]) print("dP:%s" % dP[:1]) sidx = [] didx = [] for i in range(len(sP)): # idx.append(bP[p[i]]) sidx.append(uP[sP[i]]) for i in range(len(dP)): # idx.append(bP[p[i]]) didx.append(bP[dP[i]]) print("sidx:%s" % sidx[:1]) print("didx:%s" % didx[:1]) # plt.plot(closep) # plt.plot(idx,d,'ko') lr = LinearRegression() X = np.atleast_2d(np.array(sidx)).T Y = np.array(sV) lr.fit(X, Y) estV = lr.predict(xt) fig = plt.figure(figsize=(16, 10), dpi=72) # plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9) plt.subplots_adjust(left=0.05, bottom=0.08, right=0.95, top=0.95, wspace=0.15, hspace=0.25) # set (gca,'Position',[0,0,512,512]) # fig.set_size_inches(18.5, 10.5) # fig=plt.fig(figsize=(14,8)) ax = fig.add_subplot(111) plt.grid(True) # print h.index[:5], h['close'] ax = h['close'].plot() # ax.plot(pd.datetime(h.index),h['close'], linewidth=1) # ax.plot(uP, uV, linewidth=1) # ax.plot(uP, uV, 'ko') # ax.plot(bP, bV, linewidth=1) # ax.plot(bP, bV, 'bo') # # ax.plot(sP, sV, linewidth=1) # # ax.plot(sP, sV, 'yo') # ax.plot(sidx, sV, linewidth=1) # ax.plot(sidx, sV, 'ro') # ax.plot(didx, dV, linewidth=1) # ax.plot(didx, dV, 'co') df['mean'] = list( map(lambda h, l: (h + l) / 2, df.high.values, df.low.values)) print(df['mean'][:1]) # d=df.mean dw = dw.set_index('date') # print dw[:2] # ax.plot(df.index,df['mean'],'g',linewidth=1) ax.plot(df.index, pd.rolling_mean(df['mean'], 60), 'g', linewidth=1) ax.plot(dw.index, pd.rolling_mean(dw.close, 5), 'r', linewidth=1) ax.plot(dw.index, pd.rolling_min(dw.close, 5), 'bo') ax.plot(dw.index, pd.rolling_max(dw.close, 5), 'yo') ax.plot(dw.index, pd.expanding_max(dw.close, 5), 'ro') ax.plot(dw.index, pd.expanding_min(dw.close, 5), 'go') # print pd.rolling_min(df.close,20)[:1],pd.rolling_min(df.close,20)[-1:] # print pd.rolling_min(df.close,20) # print pd.rolling_max(df.close,20)[:1],pd.rolling_max(df.close,20)[-1:] # print pd.rolling_max(df.close,20) # ax.plot(idx, d, 'ko') # ax.plot(xt, estV, '-r', linewidth=5) # ax.plot(xt, yt, '-g', linewidth=5) # ax2 = fig.add_subplot(122) # print len(closep),len(idx),len(d),len(xt),len(estV),len(yt) # f=lambda x:x[-int(len(x)/10):] # ax2.plot(f(closep)) # ax2.plot(f(idx),f(d),'ko') # ax2.plot(f(xt),f(estV),'-r',linewidth=5) # ax2.plot(f(xt),f(yt),'-g',linewidth=5) # # plt.show() scale = 1.1 zp = zoompan.ZoomPan() figZoom = zp.zoom_factory(ax, base_scale=scale) figPan = zp.pan_factory(ax) show()
def show_chan_mpl(code, start_date, end_date, stock_days, resample, show_mpl=True, least_init=3, chanK_flag=False, windows=20): def get_least_khl_num(resample, idx=0, init_num=3): # init = 3 if init_num - idx > 0: initw = init_num - idx else: initw = 0 return init_num if resample == 'd' else initw if resample == 'w' else init_num-idx-1 if init_num-idx-1 >0 else 0\ if resample == 'm' else 5 stock_code = code # 股票代码 # stock_code = '002176' # 股票代码 # start_date = '2017-09-05' # start_date = None # end_date = '2017-10-12 15:00:00' # 最后生成k线日期 # end_date = None # stock_days = 60 # 看几天/分钟前的k线 # resample = 'd' # resample = 'w' x_jizhun = 3 # window 周期 x轴展示的时间距离 5:日,40:30分钟, 48: 5分钟 least_khl_num = get_least_khl_num(resample, init_num=least_init) # stock_frequency = '5m' # 1d日线, 30m 30分钟, 5m 5分钟,1m 1分钟 stock_frequency = resample # 1d日线, 30m 30分钟, 5m 5分钟,1m 1分钟 w:week # chanK_flag = chanK # True 看缠论K线, False 看k线 # chanK_flag = True # True 看缠论K线, False 看k线 show_mpl = show_mpl def con2Cxianduan(stock, k_data, chanK, frsBiType, biIdx, end_date, cur_ji=1, recursion=False, dl=None, chanK_flag=False, least_init=3): max_k_num = 4 if cur_ji >= 6 or len(biIdx) == 0 or recursion: return biIdx idx = biIdx[len(biIdx) - 1] k_data_dts = list(k_data.index) st_data = chanK['enddate'][idx] if st_data not in k_data_dts: return biIdx # 重构次级别线段的点到本级别的chanK中 def refactorXd(biIdx, xdIdxc, chanK, chanKc, cur_ji): new_biIdx = [] biIdxB = biIdx[len(biIdx) - 1] if len(biIdx) > 0 else 0 for xdIdxcn in xdIdxc: for chanKidx in range(len(chanK.index))[biIdxB:]: if judge_day_bao(chanK, chanKidx, chanKc, xdIdxcn, cur_ji): new_biIdx.append(chanKidx) break return new_biIdx # 判断次级别日期是否被包含 def judge_day_bao(chanK, chanKidx, chanKc, xdIdxcn, cur_ji): _end_date = chanK['enddate'][chanKidx] + datetime.timedelta( hours=15) if cur_ji == 1 else chanK['enddate'][chanKidx] _start_date = chanK.index[chanKidx] if chanKidx == 0\ else chanK['enddate'][chanKidx - 1] + datetime.timedelta(minutes=1) return _start_date <= chanKc.index[xdIdxcn] <= _end_date # cur_ji = 1 #当前级别 # 符合k线根数大于4根 1日级别, 2 30分钟, 3 5分钟, 4 一分钟 if not recursion: resample = 'd' if cur_ji + 1 == 2 else '5m' if cur_ji + 1 == 3 else \ 'd' if cur_ji + 1 == 5 else 'w' if cur_ji + 1 == 6 else 'd' least_khl_num = get_least_khl_num(resample, 1, init_num=least_init) print "次级:%s st_data:%s k_data_dts:%s least_khl_num:%s" % ( len(k_data_dts) - k_data_dts.index(st_data), str(st_data)[:10], len(k_data_dts), least_khl_num) if cur_ji + 1 != 2 and len(k_data_dts) - k_data_dts.index( st_data) >= least_khl_num + 1: frequency = '30m' if cur_ji + 1 == 2 else '5m' if cur_ji + 1 == 3 else '1m' # else: # frequency = 'd' if cur_ji+1==2 else '5m' if cur_ji+1==3 else \ # 'd' if cur_ji+1==5 else 'w' if cur_ji+1==6 else 'd' start_lastday = str(chanK.index[biIdx[-1]])[0:10] print "次级别为:%s cur_ji:%s %s" % (resample, cur_ji, start_lastday) # print [chanK.index[x] for x in biIdx] k_data_c, cname = get_quotes_tdx(stock, start=start_lastday, end=end_date, dl=dl, resample=resample) print k_data_c.index[0], k_data_c.index[-1] chanKc = chan.parse2ChanK( k_data_c, k_data_c.values) if chanK_flag else k_data_c fenTypesc, fenIdxc = chan.parse2ChanFen(chanKc, recursion=True) if len(fenTypesc) == 0: return biIdx biIdxc, frsBiTypec = chan.parse2ChanBi( fenTypesc, fenIdxc, chanKc, least_khl_num=least_khl_num - 1) if len(biIdxc) == 0: return biIdx print "biIdxc:", [round(k_data_c.high[x], 2) for x in biIdxc ], [str(k_data_c.index[x])[:10] for x in biIdxc] xdIdxc, xdTypec = chan.parse2Xianduan( biIdxc, chanKc, least_windows=1 if least_khl_num > 0 else 0) biIdxc = con2Cxianduan(stock, k_data_c, chanKc, frsBiTypec, biIdxc, end_date, cur_ji + 1, recursion=True) print "xdIdxc:%s xdTypec:%s biIdxc:%s" % (xdIdxc, xdTypec, biIdxc) if len(xdIdxc) == 0: return biIdx # 连接线段位为上级别的bi lastBiType = frsBiType if len(biIdx) % 2 == 0 else -frsBiType if len(biIdx) == 0: return refactorXd(biIdx, xdIdxc, chanK, chanKc, cur_ji) lastbi = biIdx.pop() firstbic = xdIdxc.pop(0) # 同向连接 if lastBiType == xdTypec: biIdx = biIdx + refactorXd(biIdx, xdIdxc, chanK, chanKc, cur_ji) # 逆向连接 else: # print '开始逆向连接' _mid = [lastbi] if (lastBiType == -1 and chanK['low'][lastbi] <= chanKc['low'][firstbic])\ or (lastBiType == 1 and chanK['high'][lastbi] >= chanKc['high'][firstbic]) else\ [chanKidx for chanKidx in range(len(chanK.index))[biIdx[len(biIdx) - 1]:] if judge_day_bao(chanK, chanKidx, chanKc, firstbic, cur_ji)] biIdx = biIdx + [_mid[0]] + refactorXd(biIdx, xdIdxc, chanK, chanKc, cur_ji) # print "次级:",len(biIdx),biIdx,[str(k_data_c.index[x])[:10] for x in biIdx] return biIdx def get_quotes_tdx(code, start=None, end=None, dl=120, resample='d', show_name=True): quotes = tdd.get_tdx_append_now_df_api( code=stock_code, start=start, end=end, dl=dl).sort_index(ascending=True) if not resample == 'd' and resample in tdd.resample_dtype: quotes = tdd.get_tdx_stock_period_to_type(quotes, period_day=resample) quotes.index = quotes.index.astype('datetime64') if show_name: if 'name' in quotes.columns: cname = quotes.name[0] # cname_g =cname else: dm = tdd.get_sina_data_df(code) if 'name' in dm.columns: cname = dm.name[0] else: cname = '-' else: cname = '-' if quotes is not None and len(quotes) > 0: quotes = quotes.loc[:, [ 'open', 'close', 'high', 'low', 'vol', 'amount' ]] else: # log.error("quotes is None check:%s"%(code)) raise Exception("Code:%s error, df is None%s" % (code)) return quotes, cname quotes, cname = get_quotes_tdx(stock_code, start_date, end_date, dl=stock_days, resample=resample, show_name=show_mpl) # quotes.rename(columns={'amount': 'money'}, inplace=True) # quotes.rename(columns={'vol': 'vol'}, inplace=True) # print quotes[-2:] # print quotes[:1] # 缠论k线 # open close high low volume money # 2017-05-03 15.69 15.66 15.73 15.53 10557743 165075887 # 2017-05-04 15.66 15.63 15.70 15.52 8343270 130330396 # 2017-05-05 15.56 15.65 15.68 15.41 18384031 285966842 # 2017-05-08 15.62 15.75 15.76 15.54 12598891 197310688 quotes = chan.parse2ChanK(quotes, quotes.values) if chanK_flag else quotes # print quotes[:1].index # print quotes[-1:].index quotes[quotes['vol'] == 0] = np.nan quotes = quotes.dropna() Close = quotes['close'] Open = quotes['open'] High = quotes['high'] Low = quotes['low'] T0 = quotes.index.values # T0 = mdates.date2num(T0) length = len(Close) initial_trend = "down" cur_ji = 1 if stock_frequency == 'd' else \ 2 if stock_frequency == '30m' else \ 3 if stock_frequency == '5m' else \ 4 if stock_frequency == 'w' else \ 5 if stock_frequency == 'm' else 6 log.debug('======笔形成最后一段未完成段判断是否是次级别的走势形成笔=======:%s %s' % (stock_frequency, cur_ji)) x_date_list = quotes.index.values.tolist() # for x_date in x_date_list: # d = datetime.datetime.fromtimestamp(x_date/1000000000) # print d.strftime("%Y-%m-%d %H:%M:%S.%f") # print x_date_list k_data = quotes k_values = k_data.values # 缠论k线 chanK = quotes if chanK_flag else chan.parse2ChanK( k_data, k_values, chan_kdf=chanK_flag) fenTypes, fenIdx = chan.parse2ChanFen(chanK) # log.debug("code:%s fenTypes:%s fenIdx:%s k_data:%s" % (stock_code,fenTypes, fenIdx, len(k_data))) biIdx, frsBiType = chan.parse2ChanBi(fenTypes, fenIdx, chanK, least_khl_num=least_khl_num) # log.debug("biIdx1:%s chanK:%s" % (biIdx, len(chanK))) print("biIdx1:%s %s chanK:%s" % (biIdx, str(chanK.index.values[biIdx[-1]])[:10], len(chanK))) biIdx = con2Cxianduan(stock_code, k_data, chanK, frsBiType, biIdx, end_date, cur_ji, least_init=least_init) # log.debug("biIdx2:%s chanK:%s" % (biIdx, len(biIdx))) chanKIdx = [(chanK.index[x]) for x in biIdx] if len(biIdx) == 0 and len(chanKIdx) == 0: print "BiIdx is None and chanKidx is None:%s" % (code) return None log.debug("con2Cxianduan:%s chanK:%s %s" % (biIdx, len(chanK), chanKIdx[-1] if len(chanKIdx) > 0 else None)) # print quotes['close'].apply(lambda x:round(x,2)) # print '股票代码', get_security_info(stock_code).display_name # print '股票代码', (stock_code), resample, least_khl_num # 3.得到分笔结果,计算坐标显示 def plot_fenbi_seq(biIdx, frsBiType, plt=None, color=None): x_fenbi_seq = [] y_fenbi_seq = [] for i in range(len(biIdx)): if biIdx[i] is not None: fenType = -frsBiType if i % 2 == 0 else frsBiType # dt = chanK['enddate'][biIdx[i]] # 缠论k线 dt = chanK.index[biIdx[i]] if chanK_flag else chanK['enddate'][ biIdx[i]] # print i,k_data['high'][dt], k_data['low'][dt] time_long = long( time.mktime( (dt + datetime.timedelta(hours=8)).timetuple()) * 1000000000) # print x_date_list.index(time_long) if time_long in x_date_list else 0 if fenType == 1: if plt is not None: if color is None: plt.text(x_date_list.index(time_long), k_data['high'][dt], str(k_data['high'][dt]), ha='left', fontsize=12) else: col_v = color[0] if fenType > 0 else color[1] plt.text(x_date_list.index(time_long), k_data['high'][dt], str(k_data['high'][dt]), ha='left', fontsize=12, bbox=dict(facecolor=col_v, alpha=0.5)) x_fenbi_seq.append(x_date_list.index(time_long)) y_fenbi_seq.append(k_data['high'][dt]) if fenType == -1: if plt is not None: if color is None: plt.text(x_date_list.index(time_long), k_data['low'][dt], str(k_data['low'][dt]), va='bottom', fontsize=12) else: col_v = color[0] if fenType > 0 else color[1] plt.text(x_date_list.index(time_long), k_data['low'][dt], str(k_data['low'][dt]), va='bottom', fontsize=12, bbox=dict(facecolor=col_v, alpha=0.5)) x_fenbi_seq.append(x_date_list.index(time_long)) y_fenbi_seq.append(k_data['low'][dt]) # bottom_time = None # for k_line_dto in m_line_dto.member_list[::-1]: # if k_line_dto.low == m_line_dto.low: # # get_price返回的日期,默认时间是08:00:00 # bottom_time = k_line_dto.begin_time.strftime('%Y-%m-%d') +' 08:00:00' # break # x_fenbi_seq.append(x_date_list.index(long(time.mktime(datetime.strptime(bottom_time, "%Y-%m-%d %H:%M:%S").timetuple())*1000000000))) # y_fenbi_seq.append(m_line_dto.low) return x_fenbi_seq, y_fenbi_seq # print T0[-len(T0):].astype(dt.date) T1 = T0[-len(T0):].astype(datetime.date) / 1000000000 Ti = [] if len(T0) / x_jizhun > 12: x_jizhun = len(T0) / 12 for i in range(len(T0) / x_jizhun): # print "len(T0)/x_jizhun:",len(T0)/x_jizhun a = i * x_jizhun d = datetime.date.fromtimestamp(T1[a]) # print d T2 = d.strftime('$%Y-%m-%d$') Ti.append(T2) # print tab d1 = datetime.date.fromtimestamp(T1[len(T0) - 1]) d2 = (d1 + datetime.timedelta(days=1)).strftime('$%Y-%m-%d$') Ti.append(d2) ll = Low.min() * 0.97 hh = High.max() * 1.03 # ht = HoverTool(tooltips=[ # ("date", "@date"), # ("open", "@open"), # ("close", "@close"), # ("high", "@high"), # ("low", "@low"), # ("volume", "@volume"), # ("money", "@money"),]) # TOOLS = [ht, WheelZoomTool(dimensions=['width']),\ # ResizeTool(), ResetTool(),\ # PanTool(dimensions=['width']), PreviewSaveTool()] if show_mpl: fig = plt.figure(figsize=(10, 6)) ax1 = plt.subplot2grid((10, 1), (0, 0), rowspan=8, colspan=1) # ax1 = fig.add_subplot(2,1,1) #fig = plt.figure() #ax1 = plt.axes([0,0,3,2]) X = np.array(range(0, length)) pad_nan = X + nan # 计算上 下影线 max_clop = Close.copy() max_clop[Close < Open] = Open[Close < Open] min_clop = Close.copy() min_clop[Close > Open] = Open[Close > Open] # 上影线 line_up = np.array([High, max_clop, pad_nan]) line_up = np.ravel(line_up, 'F') # 下影线 line_down = np.array([Low, min_clop, pad_nan]) line_down = np.ravel(line_down, 'F') # 计算上下影线对应的X坐标 pad_nan = nan + X pad_X = np.array([X, X, X]) pad_X = np.ravel(pad_X, 'F') # 画出实体部分,先画收盘价在上的部分 up_cl = Close.copy() up_cl[Close <= Open] = nan up_op = Open.copy() up_op[Close <= Open] = nan down_cl = Close.copy() down_cl[Open <= Close] = nan down_op = Open.copy() down_op[Open <= Close] = nan even = Close.copy() even[Close != Open] = nan # 画出收红的实体部分 pad_box_up = np.array([up_op, up_op, up_cl, up_cl, pad_nan]) pad_box_up = np.ravel(pad_box_up, 'F') pad_box_down = np.array([down_cl, down_cl, down_op, down_op, pad_nan]) pad_box_down = np.ravel(pad_box_down, 'F') pad_box_even = np.array([even, even, even, even, pad_nan]) pad_box_even = np.ravel(pad_box_even, 'F') # X的nan可以不用与y一一对应 X_left = X - 0.25 X_right = X + 0.25 box_X = np.array([X_left, X_right, X_right, X_left, pad_nan]) # print box_X box_X = np.ravel(box_X, 'F') # print box_X # Close_handle=plt.plot(pad_X,line_up,color='k') vertices_up = np.array([box_X, pad_box_up]).T vertices_down = np.array([box_X, pad_box_down]).T vertices_even = np.array([box_X, pad_box_even]).T handle_box_up = mat.patches.Polygon(vertices_up, color='r', zorder=1) handle_box_down = mat.patches.Polygon(vertices_down, color='g', zorder=1) handle_box_even = mat.patches.Polygon(vertices_even, color='k', zorder=1) ax1.add_patch(handle_box_up) ax1.add_patch(handle_box_down) ax1.add_patch(handle_box_even) handle_line_up = mat.lines.Line2D(pad_X, line_up, color='k', linestyle='solid', zorder=0) handle_line_down = mat.lines.Line2D(pad_X, line_down, color='k', linestyle='solid', zorder=0) ax1.add_line(handle_line_up) ax1.add_line(handle_line_down) v = [0, length, Open.min() - 0.5, Open.max() + 0.5] plt.axis(v) ax1.set_xticks(np.linspace(-2, len(Close) + 2, len(Ti))) ax1.set_ylim(ll, hh) ax1.set_xticklabels(Ti) plt.grid(True) plt.setp(plt.gca().get_xticklabels(), rotation=30, horizontalalignment='right') ''' 以上代码拷贝自https://www.joinquant.com/post/1756 感谢alpha-smart-dog K线图绘制完毕 ''' # print "biIdx:%s chankIdx:%s"%(biIdx,str(chanKIdx[-1])[:10]) if show_mpl: x_fenbi_seq, y_fenbi_seq = plot_fenbi_seq(biIdx, frsBiType, plt) # plot_fenbi_seq(fenIdx,fenTypes[0], plt,color=['red','green']) plot_fenbi_seq(fenIdx, frsBiType, plt, color=['red', 'green']) else: x_fenbi_seq, y_fenbi_seq = plot_fenbi_seq(biIdx, frsBiType, plt=None) plot_fenbi_seq(fenIdx, frsBiType, plt=None, color=['red', 'green']) # 在原图基础上添加分笔蓝线 inx_value = chanK.high.values inx_va = [round(inx_value[x], 2) for x in biIdx] log.debug("inx_va:%s count:%s" % (inx_va, len(quotes.high))) log.debug("yfenbi:%s count:%s" % ([round(y, 2) for y in y_fenbi_seq], len(chanK))) j_BiType = [ -frsBiType if i % 2 == 0 else frsBiType for i in range(len(biIdx)) ] BiType_s = j_BiType[-1] if len(j_BiType) > 0 else -2 # bi_price = [str(chanK.low[idx]) if i % 2 == 0 else str(chanK.high[idx]) for i,idx in enumerate(biIdx)] # print ("笔 :%s %s"%(biIdx,bi_price)) # fen_dt = [str(chanK.index[fenIdx[i]])[:10] if chanK_flag else str(chanK['enddate'][fenIdx[i]])[:10]for i in range(len(fenIdx))] fen_dt = [(chanK.index[fenIdx[i]]) if chanK_flag else (chanK['enddate'][fenIdx[i]]) for i in range(len(fenIdx))] if len(fenTypes) > 0: if fenTypes[0] == -1: # fen_price = [str(k_data.low[idx]) if i % 2 == 0 else str(k_data.high[idx]) for i,idx in enumerate(fen_dt)] low_fen = [idx for i, idx in enumerate(fen_dt) if i % 2 == 0] high_fen = [idx for i, idx in enumerate(fen_dt) if i % 2 <> 0] else: # fen_price = [str(k_data.high[idx]) if i % 2 == 0 else str(k_data.low[idx]) for i,idx in enumerate(fen_dt)] high_fen = [idx for i, idx in enumerate(fen_dt) if i % 2 == 0] low_fen = [idx for i, idx in enumerate(fen_dt) if i % 2 <> 0] # fen_duration =[fenIdx[i] - fenIdx[i -1 ] if i >0 else 0 for i,idx in enumerate(fenIdx)] else: # fen_price = fenTypes # fen_duration = fenTypes low_fen = [] high_fen = [] # fen_dt = [str(k_data.index[idx])[:10] for i,idx in enumerate(fenIdx)] # print low_fen,high_fen def dataframe_mode_round(df): roundlist = [1, 0] df_mode = [] # df.high.cummin().value_counts() for i in roundlist: df_mode = df.apply(lambda x: round(x, i)).mode() if len(df_mode) > 0: break return df_mode kdl = k_data.loc[low_fen].low kdl_mode = dataframe_mode_round(kdl) kdh = k_data.loc[high_fen].high kdh_mode = dataframe_mode_round(kdh) print("kdl:%s" % (kdl.values)) print("kdh:%s" % (kdh.values)) print("kdl_mode:%s kdh_mode%s chanKidx:%s" % (kdl_mode.values, kdh_mode.values, str(chanKIdx[-1])[:10])) lastdf = k_data[k_data.index >= chanKIdx[-1]] if BiType_s == -1: keydf = lastdf[((lastdf.close >= kdl_mode.max()) & (lastdf.low >= kdl_mode.max()))] elif BiType_s == 1: keydf = lastdf[((lastdf.close >= kdh_mode.max()) & (lastdf.high >= kdh_mode.min()))] else: keydf = lastdf[((lastdf.close >= kdh_mode.max()) & (lastdf.high >= kdh_mode.min())) | ((lastdf.close <= kdl_mode.min()) & (lastdf.low <= kdl_mode.min()))] print("BiType_s:%s keydf:%s key:%s" % (BiType_s, None if len(keydf) == 0 else str( keydf.index.values[0])[:10], len(keydf))) # return BiType_s,None if len(keydf) == 0 else str(keydf.index.values[0])[:10],len(keydf) # import ipdb;ipdb.set_trace() log.debug("Fentype:%s " % (fenTypes)) log.debug("fenIdx:%s " % (fenIdx)) # print ("fen_duration:%s "%(fen_duration)) # print ("fen_price:%s "%(fen_price)) # print ("fendt:%s "%(fen_dt)) print("BiType :%s frsBiType:%s" % (j_BiType, frsBiType)) if len(j_BiType) > 0: if j_BiType[0] == -1: tb_price = [ str(quotes.low[idx]) if i % 2 == 0 else str(quotes.high[idx]) for i, idx in enumerate(x_fenbi_seq) ] else: tb_price = [ str(quotes.high[idx]) if i % 2 == 0 else str(quotes.low[idx]) for i, idx in enumerate(x_fenbi_seq) ] tb_duration = [ x_fenbi_seq[i] - x_fenbi_seq[i - 1] if i > 0 else 0 for i, idx in enumerate(x_fenbi_seq) ] else: tb_price = j_BiType tb_duration = j_BiType print "图笔 :", x_fenbi_seq, tb_price print "图笔dura :", tb_duration # 线段画到笔上 xdIdxs, xfenTypes = chan.parse2ChanXD(frsBiType, biIdx, chanK) print '线段', xdIdxs, xfenTypes x_xd_seq = [] y_xd_seq = [] for i in range(len(xdIdxs)): if xdIdxs[i] is not None: fenType = xfenTypes[i] # dt = chanK['enddate'][biIdx[i]] # 缠论k线 dt = chanK.index[xdIdxs[i]] if chanK_flag else chanK['enddate'][ xdIdxs[i]] # print k_data['high'][dt], k_data['low'][dt] time_long = long( time.mktime((dt + datetime.timedelta(hours=8)).timetuple()) * 1000000000) # print x_date_list.index(time_long) if time_long in x_date_list else 0 if fenType == 1: x_xd_seq.append(x_date_list.index(time_long)) y_xd_seq.append(k_data['high'][dt]) if fenType == -1: x_xd_seq.append(x_date_list.index(time_long)) y_xd_seq.append(k_data['low'][dt]) # bottom_time = None # for k_line_dto in m_line_dto.member_list[::-1]: # if k_line_dto.low == m_line_dto.low: # # get_price返回的日期,默认时间是08:00:00 # bottom_time = k_line_dto.begin_time.strftime('%Y-%m-%d') +' 08:00:00' # break # x_fenbi_seq.append(x_date_list.index(long(time.mktime(datetime.strptime(bottom_time, "%Y-%m-%d %H:%M:%S").timetuple())*1000000000))) # y_fenbi_seq.append(m_line_dto.low) # 在原图基础上添加分笔蓝线 print("线段 :%s" % (x_xd_seq)) print("笔值 :%s" % ([str(x) for x in (y_xd_seq)])) # Y_hat = X * b + a if show_mpl: plt.plot(x_fenbi_seq, y_fenbi_seq) plt.legend([stock_code, cname], loc=0) plt.title(stock_code + " | " + cname + " | " + str(quotes.index[-1])[:10], fontsize=14) plt.plot(x_xd_seq, y_xd_seq) if len(quotes) > windows: roll_mean = pd.rolling_mean(quotes.close, window=windows) plt.plot(roll_mean, 'r') zp = zoompan.ZoomPan() figZoom = zp.zoom_factory(ax1, base_scale=1.1) figPan = zp.pan_factory(ax1) '''#subplot2 bar ax2 = plt.subplot2grid((10, 1), (8, 0), rowspan=2, colspan=1) # ax2.plot(quotes.vol) # ax2.set_xticks(np.linspace(-2, len(quotes) + 2, len(Ti))) ll = min(quotes.vol.values.tolist()) * 0.97 hh = max(quotes.vol.values.tolist()) * 1.03 ax2.set_ylim(ll, hh) # ax2.set_xticklabels(Ti) # plt.hist(quotes.vol, histtype='bar', rwidth=0.8) plt.bar(x_date_list,quotes.vol, label="Volume", color='b') ''' #画Volume no tight_layout() ''' pad = 0.25 yl = ax1.get_ylim() ax1.set_ylim(yl[0]-(yl[1]-yl[0])*pad,yl[1]) ax2 = ax1.twinx() ax2.set_position(mat.transforms.Bbox([[0.125,0.1],[0.9,0.32]])) volume = np.asarray(quotes.amount) pos = quotes['open']-quotes['close']<0 neg = quotes['open']-quotes['close']>=0 idx = quotes.reset_index().index ax2.bar(idx[pos],volume[pos],color='red',width=1,align='center') ax2.bar(idx[neg],volume[neg],color='green',width=1,align='center') yticks = ax2.get_yticks() ax2.set_yticks(yticks[::3]) ''' # same sharex plt.subplots_adjust(left=0.05, bottom=0.08, right=0.95, top=0.95, wspace=0.15, hspace=0.00) plt.setp(ax1.get_xticklabels(), visible=False) yl = ax1.get_ylim() # ax2 = plt.subplot(212, sharex=ax1) ax2 = plt.subplot2grid((10, 1), (8, 0), rowspan=2, colspan=1, sharex=ax1) # ax2.set_position(mat.transforms.Bbox([[0.125,0.1],[0.9,0.32]])) volume = np.asarray(quotes.amount) pos = quotes['open'] - quotes['close'] < 0 neg = quotes['open'] - quotes['close'] >= 0 idx = quotes.reset_index().index ax2.bar(idx[pos], volume[pos], color='red', width=1, align='center') ax2.bar(idx[neg], volume[neg], color='green', width=1, align='center') yticks = ax2.get_yticks() ax2.set_yticks(yticks[::3]) # plt.tight_layout() # plt.subplots_adjust(hspace=0.00, bottom=0.08) plt.xticks(rotation=15, horizontalalignment='center') # plt.bar(x_date_list,quotes.vol, label="Volume", color='b') # quotes['vol'].plot(kind='bar', ax=ax2, color='g', alpha=0.1) # ax2.set_ylim([0, ax2.get_ylim()[1] * 2]) # plt.gcf().subplots_adjust(bottom=0.15) # fig.subplots_adjust(left=0.05, bottom=0.08, right=0.95, top=0.95, wspace=0.15, hspace=0.25) #scale the x-axis tight # ax2.set_xlim(min(x_date_list),max(x_date_list)) # the y-ticks for the bar were too dense, keep only every third one # plt.grid(True) # plt.xticks(rotation=30, horizontalalignment='center') # plt.setp( axs[1].xaxis.get_majorticklabels(), rotation=70 ) # plt.legend() # plt.tight_layout() # plt.draw() # plt.show() plt.show(block=False)
def longsklearn(code='999999'): # code='999999' df = tdd.get_tdx_append_now_df(code, 'f').sort_index(ascending=True) # print df[:1] h = df.loc[:, ['open', 'close', 'high', 'low']] highp = h['high'].values lowp = h['low'].values openp = h['open'].values closep = h['close'].values lr = LinearRegression() x = np.atleast_2d(np.linspace(0, len(closep), len(closep))).T lr.fit(x, closep) LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False) xt = np.atleast_2d(np.linspace(0, len(closep) + 200, len(closep) + 200)).T yt = lr.predict(xt) # plt.plot(xt,yt,'-g',linewidth=5) # plt.plot(closep) bV = [] bP = [] for i in range(1, len(highp) - 1): if highp[i] <= highp[i - 1] and highp[i] < highp[ i + 1] and lowp[i] <= lowp[i - 1] and lowp[i] < lowp[i + 1]: bV.append(lowp[i]) bP.append(i) d, p = LIS(bV) idx = [] for i in range(len(p)): idx.append(bP[p[i]]) # plt.plot(closep) # plt.plot(idx,d,'ko') lr = LinearRegression() X = np.atleast_2d(np.array(idx)).T Y = np.array(d) lr.fit(X, Y) estV = lr.predict(xt) fig = plt.figure(figsize=(16, 10), dpi=72) # plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9) plt.subplots_adjust(left=0.05, bottom=0.08, right=0.95, top=0.95, wspace=0.15, hspace=0.25) # set (gca,'Position',[0,0,512,512]) # fig.set_size_inches(18.5, 10.5) # fig=plt.fig(figsize=(14,8)) ax = fig.add_subplot(111) plt.grid(True) ax.plot(closep, linewidth=2) ax.plot(idx, d, 'ko') ax.plot(xt, estV, '-r', linewidth=5) ax.plot(xt, yt, '-g', linewidth=5) # ax2 = fig.add_subplot(122) # print len(closep),len(idx),len(d),len(xt),len(estV),len(yt) # f=lambda x:x[-int(len(x)/10):] # ax2.plot(f(closep)) # ax2.plot(f(idx),f(d),'ko') # ax2.plot(f(xt),f(estV),'-r',linewidth=5) # ax2.plot(f(xt),f(yt),'-g',linewidth=5) # # plt.show() scale = 1.1 zp = zoompan.ZoomPan() figZoom = zp.zoom_factory(ax, base_scale=scale) figPan = zp.pan_factory(ax) show()
sys.exit(0) import matplotlib.pyplot as plt import numpy as np t = np.arange(0.01, 5.0, 0.01) s1 = np.sin(2 * np.pi * t) s2 = np.exp(-t) s3 = np.sin(4 * np.pi * t) ax1 = plt.subplot(311) plt.plot(t, s1) plt.setp(ax1.get_xticklabels(), fontsize=6) # share x only ax2 = plt.subplot(312, sharex=ax1) plt.plot(t, s2) # make these tick labels invisible plt.setp(ax2.get_xticklabels(), visible=False) # share x and y ax3 = plt.subplot(313, sharex=ax1, sharey=ax1) plt.plot(t, s3) plt.xlim(0.01, 5.0) import sys sys.path.append('../../') from JohnsonUtil import zoompan zp = zoompan.ZoomPan() figZoom = zp.zoom_factory(ax1, base_scale=1.1) figPan = zp.pan_factory(ax1) plt.show()
def get_linear_model_histogram(code, ptype='low', dtype='d', start=None, end=None, vtype='f', filter='n', df=None): # 399001','cyb':'zs399006','zxb':'zs399005 # code = '999999' # code = '601608' # code = '000002' # asset = get_kdate_data(code)['close'].sort_index(ascending=True) # df = tdd.get_tdx_Exp_day_to_df(code, 'f').sort_index(ascending=True) # ptype='close' # if ptype == 'close' or ptype=='' # ptype= if start is not None and filter == 'y': if code not in ['999999', '399006', '399001']: index_d, dl = tdd.get_duration_Index_date(dt=start) log.debug("index_d:%s dl:%s" % (str(index_d), dl)) else: index_d = cct.day8_to_day10(start) log.debug("index_d:%s" % (index_d)) start = tdd.get_duration_price_date(code, ptype='low', dt=index_d) log.debug("start:%s" % (start)) if df is None: # df = tdd.get_tdx_append_now_df(code, ptype, start, end).sort_index(ascending=True) df = tdd.get_tdx_append_now_df_api(code, start, end).sort_index(ascending=True) if not dtype == 'd': df = tdd.get_tdx_stock_period_to_type(df, dtype).sort_index(ascending=True) asset = df[ptype] log.info("df:%s" % asset[:1]) asset = asset.dropna() dates = asset.index if not code.startswith('999') and not code.startswith('399'): # print "code:",code if code[:1] in ['5', '6', '9']: code2 = '999999' elif code[:2] in ['30']: # print "cyb" code2 = '399006' else: code2 = '399001' df1 = tdd.get_tdx_append_now_df_api(code2, start, end).sort_index(ascending=True) # df1 = tdd.get_tdx_append_now_df(code2, ptype, start, end).sort_index(ascending=True) if not dtype == 'd': df1 = tdd.get_tdx_stock_period_to_type( df1, dtype).sort_index(ascending=True) # if len(asset) < len(df1): # asset1 = df1.loc[asset.index, ptype] # else: # asset1 = df1.loc[asset.index, ptype] # startv = asset1[:1] # asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2)) # print asset[:1].index[0] , df1[:1].index[0] if asset[:1].index[0] > df1[:1].index[0]: asset1 = df1.loc[asset.index, ptype] startv = asset1[:1] asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2)) else: df = df[df.index >= df1.index[0]] asset = df[ptype] asset = asset.dropna() dates = asset.index asset1 = df1.loc[df.index, ptype] asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2)) else: if code.startswith('399001'): code2 = '999999' elif code.startswith('399006'): code2 = '399005' else: code2 = '399001' df1 = tdd.get_tdx_append_now_df_api(code2, start, end).sort_index(ascending=True) # print df1[:1] # df1 = tdd.get_tdx_append_now_df(code2, ptype, start, end).sort_index(ascending=True) if not dtype == 'd': df1 = tdd.get_tdx_stock_period_to_type( df1, dtype).sort_index(ascending=True) if len(asset) < len(df1): asset1 = df1.loc[asset.index, ptype] asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2)) else: df = df[df.index >= df1.index[0]] asset = df[ptype] asset = asset.dropna() dates = asset.index asset1 = df1.loc[df.index, ptype] asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2)) # print len(df),len(asset),len(df1),len(asset1) if end is not None: # print asset[-1:] asset = asset[:-1] dates = asset.index asset1 = asset1[:-1] asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2)) # 画出价格随时间变化的图像 # _, ax = plt.subplots() # fig = plt.figure() fig = plt.figure(figsize=(16, 5)) # fig = plt.figure(figsize=(16, 10), dpi=72) # fig.autofmt_xdate() #(no fact) # plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9) plt.subplots_adjust(left=0.05, bottom=0.08, right=0.95, top=0.95, wspace=0.15, hspace=0.25) # set (gca,'Position',[0,0,512,512]) # fig.set_size_inches(18.5, 10.5) # fig=plt.fig(figsize=(14,8)) ax1 = fig.add_subplot(121) # asset=asset.apply(lambda x:round( x/asset[:1],2)) ax1.plot(asset) # ax1.plot(asset1,'-r', linewidth=2) ticks = ax1.get_xticks() # start, end = ax1.get_xlim() # print start, end, len(asset) # print ticks, ticks[:-1] # (ticks[:-1] if len(asset) > end else np.append(ticks[:-1], len(asset) - 1)) ax1.set_xticklabels( [dates[int(i)] for i in (np.append(ticks[:-1], len(asset) - 1))], rotation=15) # Label x-axis with dates # 拟合 X = np.arange(len(asset)) x = sm.add_constant(X) model = regression.linear_model.OLS(asset, x).fit() a = model.params[0] b = model.params[1] # log.info("a:%s b:%s" % (a, b)) log.info("X:%s a:%s b:%s" % (len(asset), a, b)) Y_hat = X * b + a # 真实值-拟合值,差值最大最小作为价值波动区间 # 向下平移 i = (asset.values.T - Y_hat).argmin() c_low = X[i] * b + a - asset.values[i] Y_hatlow = X * b + a - c_low # 向上平移 i = (asset.values.T - Y_hat).argmax() c_high = X[i] * b + a - asset.values[i] Y_hathigh = X * b + a - c_high plt.plot(X, Y_hat, 'k', alpha=0.9) plt.plot(X, Y_hatlow, 'r', alpha=0.9) plt.plot(X, Y_hathigh, 'r', alpha=0.9) # plt.xlabel('Date', fontsize=12) plt.ylabel('Price', fontsize=12) plt.title(code + " | " + str(dates[-1])[:11], fontsize=14) plt.legend([asset.iat[-1]], fontsize=12, loc=4) plt.grid(True) # plt.legend([code]); # plt.legend([code, 'Value center line', 'Value interval line']); # fig=plt.fig() # fig.figsize = [14,8] scale = 1.1 zp = zoompan.ZoomPan() figZoom = zp.zoom_factory(ax1, base_scale=scale) figPan = zp.pan_factory(ax1) # 将Y-Y_hat股价偏离中枢线的距离单画出一张图显示,对其边界线之间的区域进行均分,大于0的区间为高估,小于0的区间为低估,0为价值中枢线。 ax3 = fig.add_subplot(122) # distance = (asset.values.T - Y_hat) distance = (asset.values.T - Y_hat)[0] # if code.startswith('999') or code.startswith('399'): if len(asset) > len(df1): ax3.plot(asset) plt.plot(distance) ticks = ax3.get_xticks() ax3.set_xticklabels( [dates[int(i)] for i in (np.append(ticks[:-1], len(asset) - 1))], rotation=15) n = 5 d = (-c_high + c_low) / n c = c_high while c <= c_low: Y = X * b + a - c plt.plot(X, Y - Y_hat, 'r', alpha=0.9) c = c + d ax3.plot(asset) ## plt.xlabel('Date', fontsize=12) plt.ylabel('Price-center price', fontsize=14) plt.grid(True) else: as3 = asset.apply(lambda x: round(x / asset[:1], 2)) ax3.plot(as3) ax3.plot(asset1, '-r', linewidth=2) # assvol = df.loc[asset.index]['vol'] # assvol = assvol.apply(lambda x: round(x / assvol[:1], 2)) # ax3.plot(assvol, '-g', linewidth=2) plt.grid(True) zp3 = zoompan.ZoomPan() figZoom = zp3.zoom_factory(ax3, base_scale=scale) figPan = zp3.pan_factory(ax3) # plt.title(code, fontsize=14) if 'name' in df.columns: plt.legend([df.name[-1], df1.name[-1]], loc=0) else: dm = tdd.get_sina_data_df(code) if 'name' in dm.columns: cname = dm.name[0] else: cname = '-' # plt.legend([code, code2], loc=0) plt.legend([cname, code2], loc=0) plt.show(block=False)
def get_linear_model_histogramDouble(code, ptype='low', dtype='d', start=None, end=None, vtype='f', filter='n', df=None, dl=None): # 399001','cyb':'zs399006','zxb':'zs399005 # code = '999999' # code = '601608' # code = '000002' # asset = get_kdate_data(code)['close'].sort_index(ascending=True) # df = tdd.get_tdx_Exp_day_to_df(code, 'f').sort_index(ascending=True) # ptype='close' # if ptype == 'close' or ptype=='' # ptype= if start is not None and filter == 'y': if code not in ['999999', '399006', '399001']: index_d, dl = tdd.get_duration_Index_date(dt=start) log.debug("index_d:%s dl:%s" % (str(index_d), dl)) else: index_d = cct.day8_to_day10(start) log.debug("index_d:%s" % (index_d)) start = tdd.get_duration_price_date(code, ptype='low', dt=index_d) log.debug("start:%s" % (start)) if start is None and df is None and dl is not None: start = cct.last_tddate(dl) # print start df = tdd.get_tdx_append_now_df_api(code, start=start, end=end).sort_index(ascending=True) if df is None: # df = tdd.get_tdx_append_now_df(code, ptype, start, end).sort_index(ascending=True) df = tdd.get_tdx_append_now_df_api(code, start, end).sort_index(ascending=True) if not dtype == 'd': df = tdd.get_tdx_stock_period_to_type(df, dtype).sort_index(ascending=True) if len(df) == 0: raise Exception("Code:%s error, df is None" % (code)) asset = df[ptype].round(2) log.info("df:%s" % asset[:1]) asset = asset.dropna() dates = asset.index if not code.startswith('999') and not code.startswith('399'): # print "code:",code if code[:1] in ['5', '6', '9']: code2 = '999999' elif code[:2] in ['30']: # print "cyb" code2 = '399006' else: code2 = '399001' df1 = tdd.get_tdx_append_now_df_api(code2, start, end).sort_index(ascending=True) # df1 = tdd.get_tdx_append_now_df(code2, ptype, start, end).sort_index(ascending=True) if not dtype == 'd': df1 = tdd.get_tdx_stock_period_to_type( df1, dtype).sort_index(ascending=True) # if len(asset) < len(df1): # asset1 = df1.loc[asset.index, ptype] # else: # asset1 = df1.loc[asset.index, ptype] # startv = asset1[:1] # asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2)) # print asset[:1].index[0] , df1[:1].index[0] if asset[:1].index[0] > df1[:1].index[0]: asset1 = df1.loc[asset.index, ptype] startv = asset1[:1] asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2)) else: df = df[df.index >= df1.index[0]] asset = df[ptype] asset = asset.dropna() dates = asset.index asset1 = df1.loc[df.index, ptype] asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2)) else: if code.startswith('399001'): code2 = '399006' elif code.startswith('399006'): code2 = '399005' else: code2 = '399006' if code2.startswith('3990'): df1 = tdd.get_tdx_append_now_df_api(code2, start, end).sort_index(ascending=True) if len(df1) < int(len(df) / 4): code2 = '399001' df1 = tdd.get_tdx_append_now_df_api( code2, start, end).sort_index(ascending=True) # df1 = tdd.get_tdx_append_now_df(code2, ptype, start, end).sort_index(ascending=True) if not dtype == 'd': df1 = tdd.get_tdx_stock_period_to_type( df1, dtype).sort_index(ascending=True) if len(asset) < len(df1): asset1 = df1.loc[asset.index, ptype] asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2)) else: df = df[df.index >= df1.index[0]] asset = df[ptype] asset = asset.dropna() dates = asset.index asset1 = df1.loc[df.index, ptype] asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2)) # print len(df),len(asset),len(df1),len(asset1) if end is not None: # print asset[-1:] asset = asset[:-1] dates = asset.index asset1 = asset1[:-1] asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2)) # 画出价格随时间变化的图像 # _, ax = plt.subplots() # fig = plt.figure() # plt.ion() fig = plt.figure(figsize=(16, 10)) # fig = plt.figure(figsize=(16, 10), dpi=72) # fig.autofmt_xdate() #(no fact) # plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9) plt.subplots_adjust(left=0.05, bottom=0.08, right=0.95, top=0.95, wspace=0.15, hspace=0.25) # set (gca,'Position',[0,0,512,512]) # fig.set_size_inches(18.5, 10.5) # fig=plt.fig(figsize=(14,8)) ax1 = fig.add_subplot(321) # asset=asset.apply(lambda x:round( x/asset[:1],2)) ax1.plot(asset) # ax1.plot(asset1,'-r', linewidth=2) ticks = ax1.get_xticks() # start, end = ax1.get_xlim() # print start, end, len(asset) # print ticks, ticks[:-1] # (ticks[:-1] if len(asset) > end else np.append(ticks[:-1], len(asset) - 1)) ax1.set_xticklabels( [dates[int(i)] for i in (np.append(ticks[:-1], len(asset) - 1))], rotation=15) # Label x-axis with dates # 拟合 X = np.arange(len(asset)) x = sm.add_constant(X) model = regression.linear_model.OLS(asset, x).fit() a = model.params[0] b = model.params[1] # log.info("a:%s b:%s" % (a, b)) log.info("X:%s a:%s b:%s" % (len(asset), a, b)) Y_hat = X * b + a # 真实值-拟合值,差值最大最小作为价值波动区间 # 向下平移 i = (asset.values.T - Y_hat).argmin() c_low = X[i] * b + a - asset.values[i] Y_hatlow = X * b + a - c_low # 向上平移 i = (asset.values.T - Y_hat).argmax() c_high = X[i] * b + a - asset.values[i] Y_hathigh = X * b + a - c_high plt.plot(X, Y_hat, 'k', alpha=0.9) plt.plot(X, Y_hatlow, 'r', alpha=0.9) plt.plot(X, Y_hathigh, 'r', alpha=0.9) # plt.xlabel('Date', fontsize=12) plt.ylabel('Price', fontsize=12) plt.title(code + " | " + str(dates[-1])[:11], fontsize=14) plt.legend([asset.iat[-1]], fontsize=12, loc=4) plt.grid(True) # #plot volume # pad = 0.25 # yl = ax1.get_ylim() # ax1.set_ylim(yl[0]-(yl[1]-yl[0])*pad,yl[1]) # axx = ax1.twinx() # axx.set_position(transforms.Bbox([[0.125,0.1],[0.9,0.32]])) # volume = np.asarray(df.vol) # pos = df['open']-df['close']<0 # neg = df['open']-df['close']>=0 # idx = np.asarray([x for x in range(len(df))]) # axx.bar(idx[pos],volume[pos],color='red',width=1,align='center') # axx.bar(idx[neg],volume[neg],color='green',width=1,align='center') # plt.legend([code]); # plt.legend([code, 'Value center line', 'Value interval line']); # fig=plt.fig() # fig.figsize = [14,8] scale = 1.1 zp = zoompan.ZoomPan() figZoom = zp.zoom_factory(ax1, base_scale=scale) figPan = zp.pan_factory(ax1) # 将Y-Y_hat股价偏离中枢线的距离单画出一张图显示,对其边界线之间的区域进行均分,大于0的区间为高估,小于0的区间为低估,0为价值中枢线。 ax3 = fig.add_subplot(322) # distance = (asset.values.T - Y_hat) distance = (asset.values.T - Y_hat)[0] # if code.startswith('999') or code.startswith('399'): if len(asset) > len(df1): ax3.plot(asset) plt.plot(distance) ticks = ax3.get_xticks() ax3.set_xticklabels( [dates[int(i)] for i in (np.append(ticks[:-1], len(asset) - 1))], rotation=15) n = 5 d = (-c_high + c_low) / n c = c_high while c <= c_low: Y = X * b + a - c plt.plot(X, Y - Y_hat, 'r', alpha=0.9) c = c + d ax3.plot(asset) ## plt.xlabel('Date', fontsize=12) plt.ylabel('Price-center price', fontsize=14) plt.grid(True) else: as3 = asset.apply(lambda x: round(x / asset[:1], 2)) ax3.plot(as3) ticks = ax3.get_xticks() ax3.plot(asset1, '-r', linewidth=2) # show volume bar !!! # assvol = df.loc[asset.index]['vol'] # assvol = assvol.apply(lambda x: round(x / assvol[:1], 2)) # ax3.plot(assvol, '-g', linewidth=0.5) ax3.set_xticklabels( [dates[int(i)] for i in (np.append(ticks[:-1], len(asset) - 1))], rotation=15) plt.grid(True) zp3 = zoompan.ZoomPan() figZoom = zp3.zoom_factory(ax3, base_scale=scale) figPan = zp3.pan_factory(ax3) # plt.title(code, fontsize=14) if 'name' in df.columns: plt.legend([df.name.values[-1:][0], df1.name.values[-1:][0]], loc=0) else: if code not in ['999999', '399006', '399001']: indexIdx = False else: indexIdx = True dm = tdd.get_sina_data_df(code, index=indexIdx) if 'name' in dm.columns: cname = dm.name[0] else: cname = '-' # plt.legend([code, code2], loc=0) plt.legend([cname, code2], loc=0) ax2 = fig.add_subplot(323) # ax2.plot(asset) # ticks = ax2.get_xticks() ax2.set_xticklabels( [dates[int(i)] for i in (np.append(ticks[:-1], len(asset) - 1))], rotation=15) # plt.plot(X, Y_hat, 'k', alpha=0.9) n = 5 d = (-c_high + c_low) / n c = c_high while c <= c_low: Y = X * b + a - c plt.plot(X, Y, 'r', alpha=0.9) c = c + d # asset=asset.apply(lambda x:round(x/asset[:1],2)) ax2.plot(asset) # ax2.plot(asset1,'-r', linewidth=2) # plt.xlabel('Date', fontsize=12) plt.ylabel('Price', fontsize=12) plt.grid(True) # plt.title(code, fontsize=14) # plt.legend([code]) if len(df) > 10: ax6 = fig.add_subplot(324) h = df.loc[:, ['open', 'close', 'high', 'low']] highp = h['high'].values lowp = h['low'].values openp = h['open'].values closep = h['close'].values # print len(closep) lr = LinearRegression() x = np.atleast_2d(np.linspace(0, len(closep), len(closep))).T lr.fit(x, closep) LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False) xt = np.atleast_2d(np.linspace(0, len(closep) + 200, len(closep) + 200)).T yt = lr.predict(xt) bV = [] bP = [] for i in range(1, len(highp) - 1): if highp[i] <= highp[i - 1] and highp[i] < highp[ i + 1] and lowp[i] <= lowp[i - 1] and lowp[i] < lowp[i + 1]: bV.append(lowp[i]) bP.append(i) else: bV.append(lowp[i - 1]) bP.append(i - 1) if len(bV) > 0: d, p = LIS(bV) idx = [] for i in range(len(p)): idx.append(bP[p[i]]) lr = LinearRegression() X = np.atleast_2d(np.array(idx)).T Y = np.array(d) lr.fit(X, Y) estV = lr.predict(xt) ax6.plot(closep, linewidth=2) ax6.plot(idx, d, 'ko') ax6.plot(xt, estV, '-r', linewidth=3) ax6.plot(xt, yt, '-g', linewidth=3) plt.grid(True) # plt.tight_layout() zp2 = zoompan.ZoomPan() figZoom = zp2.zoom_factory(ax6, base_scale=scale) figPan = zp2.pan_factory(ax6) # 统计出每个区域内各股价的频数,得到直方图,为了更精细的显示各个区域的频数,这里将整个边界区间分成100份。 ax4 = fig.add_subplot(325) log.info("assert:len:%s %s" % (len(asset.values.T - Y_hat), (asset.values.T - Y_hat)[0])) # distance = map(lambda x:int(x),(asset.values.T - Y_hat)/Y_hat*100) # now_distanse=int((asset.iat[-1]-Y_hat[-1])/Y_hat[-1]*100) # log.debug("dis:%s now:%s"%(distance[:2],now_distanse)) # log.debug("now_distanse:%s"%now_distanse) distance = (asset.values.T - Y_hat) now_distanse = asset.iat[-1] - Y_hat[-1] # distance = (asset.values.T-Y_hat)[0] pd.Series(distance).plot(kind='hist', stacked=True, bins=100) # plt.plot((asset.iat[-1].T-Y_hat),'b',alpha=0.9) plt.axvline(now_distanse, hold=None, label="1", color='red') # plt.axhline(now_distanse,hold=None,label="1",color='red') # plt.axvline(asset.iat[0],hold=None,label="1",color='red',linestyle="--") plt.xlabel( 'Undervalue ------------------------------------------> Overvalue', fontsize=12) plt.ylabel('Frequency', fontsize=14) # plt.title('Undervalue & Overvalue Statistical Chart', fontsize=14) plt.legend([code, asset.iat[-1], str(dates[-1])[5:11]], fontsize=12) plt.grid(True) # plt.show() # import os # print(os.path.abspath(os.path.curdir)) ax5 = fig.add_subplot(326) # fig.figsize=(5, 10) log.info("assert:len:%s %s" % (len(asset.values.T - Y_hat), (asset.values.T - Y_hat)[0])) # distance = map(lambda x:int(x),(asset.values.T - Y_hat)/Y_hat*100) distance = (asset.values.T - Y_hat) / Y_hat * 100 now_distanse = ((asset.iat[-1] - Y_hat[-1]) / Y_hat[-1] * 100) log.debug("dis:%s now:%s" % (distance[:2], now_distanse)) log.debug("now_distanse:%s" % now_distanse) # n, bins = np.histogram(distance, 50) # print n, bins[:2] pd.Series(distance).plot(kind='hist', stacked=True, bins=100) # plt.plot((asset.iat[-1].T-Y_hat),'b',alpha=0.9) plt.axvline(now_distanse, hold=None, label="1", color='red') # plt.axhline(now_distanse,hold=None,label="1",color='red') # plt.axvline(asset.iat[0],hold=None,label="1",color='red',linestyle="--") plt.xlabel( 'Undervalue ------------------------------------------> Overvalue', fontsize=14) plt.ylabel('Frequency', fontsize=12) # plt.title('Undervalue & Overvalue Statistical Chart', fontsize=14) plt.legend([code, asset.iat[-1]], fontsize=12) plt.grid(True) # plt.ion() plt.draw() plt.pause(0.001) # plt.show(block=False) # plt.draw() # plt.pause(0.001) # plt.close() # print plt.get_backend() # plt.show(block=True) return df