MACDR2 model and random walk hypothesis

MACDR2 model and random walk hypothesis

In this study, which compared with a success rate of the method MACDR2 hold riskless security, 10-year Treasury bonds over 10 years
research period. The date of this study, a 10-year Treasury yield is 8.268%. Without reinvesting coupons, after 10 years, $ 1,000 will increase to 1,000 x [1 + (0.08268 x 10)] = $ 1,826.80

Benchmark for the maintenance of assets, the NASDAQ-100 index, results in an average annual increase of 45.62% over the 10-year study period, whereas the Nasdaq was at 444.21 points at the beginning and in 2470, 52 points at the end of a 10-year study period. In dollar terms, $ 1,000 invested at the beginning of the study will increase to 1,000 x [1 + (0.4562 x 10)] = $ 5,562.00 excluding the reinvestment of profits either. Holding the NASDAQ-100 can be conveniently achieved by keeping the NASDAQ-100 Index Tracking Stock, the symbol QQQ. The QQQ has a zero dividend yield, therefore, dividends can be neglected in calculating the increase in QQQ.

Using daily closing prices, the average profits and losses of the method MACDR2 were largely symmetrical. True 3% gain or loss on each trade can be achieved with intra-day trading.

This requires high liquidity, which is given, the QQQ trades of $ 1 / 64, and $ 0.01656 minimum bid-offer spread. The minimum spread quickly, and even dropped to $ 0.01 by the use of decimals.

Table 3 summarizes performance MACDR2
$ 10 fee for trading natural MACDR2 deteriorating performance (column 5) further. Along with the maximum trading limit (column 6), none of the methods outperform the


FRA-100, and only 3 of 7 outperform the Treasury bonds.

It can be expected to reduce costs further in future trading. Some brokerage houses provide an annual subscription with unlimited trading trading. However, there is usually a maximum dollar trade balance. At the time of the survey (fall 2000), Merrill Lynch charges $ 1,500 annual fee for unlimited trading trading. However, if the trade balance increases by more than $ 150,000, the annual trading fee increases to 1% of the trade balance. Of course, reducing the cost of trading will increase the efficiency of the method MACDR2.

The basic dollar amount invested at the beginning of the simulation, shown in Table 3, is $ 1,000. Higher amounts of dollars are raised mainly in relation to the performance of method 2, the trading price as the rate decreases. In fact, though, the play including $ 5 trading fee and maximum trading constraint increases only slightly. Initial amount of $ 10,000 leads to better performance by maintaining the NASDAQ-100 for 1.5% and 2% version (with $ 1000 only 2% version was superior). The same result is achieved for a higher initial amount of $ 10,000, as a trading fee of $ 5 and $ 10.

One disadvantage of the method MACDR2 is that the developer may be unfortunate in the sense that his first trades lead to losses. He could not then reinvest the initial investment amount, as it is assumed that in this study


Previous article :

Model MACDR2 and scope

Another interesting question is whether the success of the model MACDR2 is positively correlated with trade volume of stocks. The rationale is that larger scale, more technical analysis used in marketing decision. However, all regression coefficients indicated no significant correlation.

Model MACDR2 and scope

Model MACDR2 and scope

Another interesting question is whether the success of the model MACDR2 is positively correlated with trade volume of stocks. The rationale is that larger scale, more technical analysis used in marketing decision. However, all regression coefficients indicated no significant correlation.


Traditional method MACDR2 MACD and Moving Average for different time periods

In trading practice, traders often use the difference between 26 and 12-day exponential moving average to use MACD1 indicator. Then the 9-day moving average of MACD1 indicator is calculated and called the indicator signals.

Interesting question is whether the moving averages of different time lengths give better results compared to traditional trading MACD




indicator, our method MACDR2. In our study moving averages of 5 days to 100 days, leaving the participation of 26, 12 and 9 does not change, rounding to the nearest whole number.

Figure 6 shows the results of different moving averages. The horizontal axis represents the time lengths of moving average. Figures on the horizontal axis reflect the average longest. For example, the number 55 represents the combined average of 55, 25 and 19th Figure 6 from, we can see that the traditional MACD did not improve for a variety of moving averages. The success rate is fairly constant around a disappointing 30% level.

Figure 6 also shows a slightly positive correlation method MACDR2 (with 1.5% crossing level) with multiple moving averages. The best result is achieved using a combination of moving average 73, 34 and 25th Here's the success rate is 90.74%.




Previous article :

Model MACDR2 and volatility


Interesting question is whether the success of the model MACDR2 is correlated with the volatility of stocks. We calculated the 30-day volatility of any kind, and annually by a factor of 260 trading days. The average annual volatility was then used to chart below.

Model MACDR2 and volatility

Model MACDR2 and volatility

Interesting question is whether the success of the model MACDR2 is correlated with the volatility of stocks. We calculated the 30-day volatility of any kind, and annually by a factor of 260 trading days. The average annual volatility was then used to chart below. Figure 4 shows the success rate of Model MACDR2 0.5% crossing level and volatility of stock.

Figure 4 shows a positive correlation with the level of p-8.18E-09, t-statistics of 6.32 and the F-value of 40.02. The correlation coefficient R is unsatisfactory low with 0.54. The overall positive correlation has little sense from an intuitive point of view: High volatility stocks produce stronger and longer lasting trends, which can be better utilized by moving averages.

The correlation analysis of the level crossing from 1% to 3.5% produce slightly worse results than 0.5 crossing level.


Previous article :

MACDR2 Model and Option Trading

The features of the model MACDR2 a very convenient option trading for the maintenance period is on average only 5.06 days,
Thus, since the breakup of a long option position is small. The model MACDR2, 1.5% crossing level, 70.76% of all trades generated an average profit of 5.94%.

Model MACDR2 indicator

Model MACDR2 indicator

The model results MACDR2, which gives a buy or sell signal if the difference between MACD1 signal is greater than a certain percentage of stock price, is seen in table 2.

MACDR2 model improves the results of the model MACDR1 significantly. As to be expected, with higher levels of crossing the line MACD1 and signals, the higher the success rate was. This comes at the expense of less trading signals.



Previous article :

Model MACDR1

As mentioned in section second, Model MACDR1 addresses these two weaknesses. MACDR1 model results are shown in Table 1:
Two scenarios are explored in terms of model MACDR1. The position is closed when the profit from greater equal 3% be achieved when a more equal 5% is achieved.

Model MACDR1 indicator

Model MACDR1

As mentioned in section second, Model MACDR1 addresses these two weaknesses. MACDR1 model results are shown in Table 1:
Two scenarios are explored in terms of model MACDR1. The position is closed when the profit from greater equal 3% be achieved when a more equal 5% is achieved. The 3% goal, actually achieved average profit is 4.92%. About 5% goal, actually achieved average profit is 6.88%.

As seen in Table 1, MACDR1 model outperforms the traditional MACD model, which resulted in a 32.73% success rate. Success rate of the model MACDR1 is an average 61,62% 3% target. The success rates are bullish as expected slightly higher than bearish, given that this research was done during the bull market. It is also encouraging to see that the bearish signals are successful more than 57% in a bull market. This study shows that the model MACDR1 been able to find profitable short sales opportunities even in a strong bull market.

MACDR1 model generated significantly better trading results than traditional MACD model. But when analyzing the simulated trades became clear that the model MACDR1 sometimes generated trade signals even if the trend was weak. Model MACDR2 is to improve the model MACDR1 in terms of trend-identification



Previous article :

The traditional MACD

The results of empirical testing of the traditional MACD indicator were surprisingly poor. For each individual sample tested out of FRA-100 stocks over a 10-year period, only 32.73% of total trades generated profits. A similar result is obtained with a sample tested 30 stocks of the Dow Jones Industrial Average over the last 10 years. Here are just 32.14% of all trades resulted in profits.

Model MACDR2

Model MACDR2

Model MACDR2 is another method of purification MACDR1. Method MACDR2 produce trading signals when the trend is stronger than MACDR1 method. It naturally creates less buy and sell signals from the model MACDR1, but it has a higher success rate for each transaction.

The basic concept of the model MACDR2 is the same as in the model MACDR1. However, to buy or sell signal is given, if the difference between moving average is greater or equal than a certain percentage of share price at the end of the third day after passage. We test-point level of 0.5% to 3.5%. For levels above 3.5%, hardly any trading signals occur.

To illustrate how MACDR2, suppose the stock price is $ 100, MACD1 = 2 and = 1 signal on the third day after passage. The difference between the averages 1, which is 1% of the shares. This will generate a trading signal for crossing-levels greater than or equal to 1%. This method assures that the action moving at the beginning of this trend is significant and not accidental movement in a narrow trading range.

In this study, the correlation between model MACDR2 and volatility of each type, and market capitalization of each type are tested.

Furthermore, results showed that trading can be improved significantly when combined with the option of trading.

We also test if the results from the traditional MACD indicator and MACDR2 our method can be improved when the moving averages of different time lengths are used.

Finally, compare the results of the method MACDR2 with two benchmarks, less risk government bonds and the underlying instrument, NASDAQ-100 to challenge the random walk hypothesis.



Previous article :

Model MACDR1


A key issue when using moving averages to determine the exact timing of opening buy or sell. The first model, called MACDR1, attempts to eliminate buy and sell signals when averaged MACD1 and signal often crossing each other in a short period of time, thus in a case where no clear trend.

Model MACDR1

Model MACDR1

A key issue when using moving averages to determine the exact timing of opening buy or sell. The first model, called MACDR1, attempts to eliminate buy and sell signals when averaged MACD1 and signal often crossing each other in a short period of time, thus in a case where no clear trend. Instead, the trading signal is given three days after the actual crossing if the trend is still intact. Thus, the position is opened at the closing price on the third day of crossing, if no transition appeared on day 2 and 3.

Another important issue is to close the trade at the right moment in time. Because the MACD indicator is lagging indicator, the change of successful trading is often done too late, especially since it often happens very fast turnaround trend. This quick turnaround is difficult to predict, but it is disastrous, when he failed to predict, because the first few days after the reversal often have the most price ranges.

Model MACDR1 (and model MACDR2) solved this problem by stating upfront closing signal when the profit is achieved. In this study we test profit levels of 3% and 5%. Thus, the model gives a signal to close an open position when 3% or 5% gain target is reached or if another crossing occurs before the goal is achieved.

A lower target will naturally get more, but there is an opportunity cost involved to close the position too early and missing the bigger profits.


Previous article :

study tests the traditional MACD indicator

This study tests the traditional MACD indicator and obtained two methods, and MACDR1 MACDR2 (R: Refinement), which significantly improve the MACD trading results and outperform the benchmark of holding non-security risk, the bond asset and maintenance of the basic instrument, FRA -100.

MACD divergence trading system


Currency: EUR/USD
Time frame: 30 min.
Indicators: MACD (5, 26, 1), EMA 3, SMA 13

Entry rule MACD divergence trading system
1. Look for MACD divergence between the price and the chart
2. Enter when EMA 3 and SMA 13 cross

Forex Custom Indicators part 2

501. dinapolitargets
502. gmacd_signals
503. float#data_2
504. macd_4in1_v2
505. macd_cja m1d1
506. advanced_adx
507. mtf_stoch_fibbreak_1.3
508. delta
509. mtf_machnl810_env
510. #mtf_bbands
511. adx_wildersdmi_v1m
512. velocity_v2
513. t3 alpha
514. doublecciwoody
515. cci_woodies_lnx_v3_1
516. mtf_ma-4h
517. #mtf_stochastic
518. rsi-tc_new
519. solar_wind_clean_x
520. chin breakout alert
521. bobokusfibo_v2
522. nonlagzigzag_v2
523. qqe
524. mtf_wpr_1_fixed
525. fisher_m11
526. advancedgetoscillator
527. bb_prc-1
528. expected_volumes_
529. mba
530. labtrend1_v2
531. !linregrbuf
532. inout
533. trendlinesday
534. qqe with alerts
535. tdi-with alerts
536. fibopiv_v2
537. [] fractal ama
538. accelerator_4cm_mtf
539. nonlagdot
540. fn signal
541. dinapolitarget_malay
542. hma4
543. pfe
544. mtf_demarker
545. mtf_customcandle[hl]
546. bollstarc-tc
549. #heiken_ashi_ma 20
550. gann hi-lo activator ssl
547. mtf_atrstops
548. atrstops_v1[1].1
551. #mtf stochastic v2.0
552. #mtf sr
553. #mtf macd x
554. 3macross_alertwarnsigonlynoshift
555. mtf_adx_wildersdmi_v1m
556. mtf_osma_lc
557. mtf_stochastic_sml
558. dolly_graphics_v11-gmtshift
559. dolly_trading times _3
560. ama_slope
561. iinwmarrows
562. fansimple8_4men
563. mtf_waddah_attar_explosionsa
564. all adx
565. all cci
566. all rsi
567. mtf_ad
568. mtf_mamy3
569. mtf_hi_low_v1
570. mtf_4 tf xo_m30d1
571. damiani_volatmeter
572. ang_azad_css[cw]
573. aroon_horn_oscillator_v1
574. mtf_heiken_ashi
575. mtf_heiken_ashi_[sw]
576. mtf_macd_bars
578. trix
579. buy_sell
580. rzi
581. levels
582. filter overwpr
583. +rsi-tc
584. bat atrv1
585. ema_prediction
586. ffs_crosstiming
587. mtf_bb_squeeze
588. fx5_selfadjustingrsi_v1.0[1]
589. onchart rsi
590. range_v2.2
591. avgrangem
592. heiken ashi v
600. onchart stochastic
599. x_o_serg153_test
598. sar_color
597. ifish
596. kaufwmacross
593. trend_alexcud 2
594. trend_alexcud
595. sdx-tzpivots-alerts
577. mtf_alligator+t3
601. hull_o_h_l_c
602. auto_stop_revers
603. mtf_chandelierstops_60m
604. #mtf stochastic standard
605. tor_1.20
606. ppo
607. mtf_4tf_supertrend_barm
608. traders dynamic index visual alerts
609. stoch crossing
610. stochcandles
611. ga-ind
612. roundlevels
613. fxmfish[1]-2
614. vusual_start
615. nonlagzigzag_v2[1]-2
616. zigzag-2
617. trend_alexcud[1]--2
618. afirma_[1]-2
619. visual_start_all
620. specification
621. impulse cdc
622. #(sf_trend_lines)
623. _signal_bars_mfi
624. bbflat_sw
625. Squize_MA
626. osc-mtf_cf_sysv1.1
627. ga-ind_2color
628. currencypositions
629. _grachi_mika53
630. zigzag2_r_
631. levels_a_vlad
632. _fast2
633. _fast3
634. fx-trend
635. zigzag-fractals
636. zzf
637. reluptrlen_forcodebase_v01
638. reldowntrlen_forcodebase_v01
639. fx-ao
640. clock_v1_3
641. calc
642. grfleadingedge
643. szzreader
644. szz_without_zz
645. sr-rate indicator
646. support-resistance indicator
647. onesidegaussian
648. macd_color
649. volatility2
650. volatility4
651. volatility 3 pairs
652. painter_v1
653. adx_ma
654. triggerlines shift modified
655. tickwatcher 2.0
656. _tro_mid
657. _linreg
658. _digistoch-1
659. -- heiken_ashi_ma_t3
660. linearregslope_v1
661. breakout_panca_eagle__indicator
662. bid_view2.0
663. marketprofile
664. customcandle
665. normalizedvolume
666. pldot
667. _tro_range
668. prusax_v61
669. price_barsm2_mtf
670. xpoints
671. #stochastic_cross_alert_sigoverlaym_cw
672. i4_goldenliontrend_v3
673. pvt
674. mvv_linearregression
675. fractals[1]
676. signaltable
677. zigandzag
678. priliv_s
679. # otcfx_b-clock modified v3.2-sw
680. news
681. pro
682. lsma in color
683. normalized volume oscillator
684. zigzaghistory
685. macdonrsi
686. vinini_nema
687. vinini lrma color
688. trendlinerange
689. weekly_hilo_shj
690. ^l_correlation
691. i-levels_rs
692. dailypivotpoints
693. waddah attar hidden level
694. waddah attar pivot
695. servertime
696. timeout
697. amplitude_short
698. amplitude_all
699. multitrandoscilator2
700. _bz_tl_skylinem
701. stepma_stoch_nk
702. catfx50
703. i-sessions
704. 3d oscilator
705. bw mfi+volumes
706. t3.lnx
707. macd_histogram
708. kijun-sen+
709. silver-sen
710. ozfx_d1_indaes_v1.0
711. entropymath
712. ma-wpr
713. priliv
714. innbar_mtf
715. innoutbar_mtf
716. pricetrender2kbarshift_sw
717. voltychannel_stop_v2.1m
718. mor
719. ind-wso+wro+trend line
720. mt4-psychological
721. macd_colored_v103
722. multi pair macd mtf
723. coloured days on chart
724. doublecci_woodies
725. supertrend
726. autodayfibs
727. rads macd
728. cci customcandles
729. rsl1
730. allaverages_v1-lab_tab1
731. trand
732. lrma_bb
733. sell zone fibs
734. ozfx signals v1.7
735. ma chanels
736. kanal_ant
737. corelation
738. zz_ensign_fibo
739. fibopiv_daily_dk
740. macd with crossing
741. vq bars
742. onesidegaussianlibrary
743. os gaussian ma
744. os gaussian sr rate
745. os gaussian support resistance
746. os gaussian trend
747. fibo_s
748. ssl_fast_sbar_mtf
749. ma_dash_cobra
750. ma_parabolic_alert_2
751. ssl_channel_chart_alert
752. cycleidentifier
753. ssl
754. akf
755. channel zz
756. cam_h2_h5_historical
757. spread swap
758. dayofweek
759. #-pirson
760. bs_#marketprice
761. cci_onma
762. xaosexplore_perkymod
763. rads mtf has bar a
764. magnified market price
765. natusekoprotrader4hstrategy
766. cci_onma_mas
767. xaosexplorer
768. multima
769. waddah attar super support resistance
770. rsi_dots
771. ma_gideon2
772. dss bressert
773. tema_rlh
774. mindhero
775. hemnina
776. kijuntenkan+
777. silver-channels
778. 3linebreak
779. fractal_level_xrust_v2
780. fractal_level_xrust
781. fractallevels
782. ball
783. ama_bands
784. matwo
785. mmlevls_vg
786. parma_bb
787. cci_woodies_paterns_v1
788. wccipatterns
789. nrtr
790. schaff trend
791. t3 bands2
792. t3 taotra
793. taf
794. ultitimate oscillator
795. laguerre
796. camarilladt7v1
797. iavgvol
798. elliott wave oscillator
799. instrend
800. aroon oscillator_v1
801. dpo
802. cci_woodies
803. keltner channel
804. high_low (zigzag)
805. asctrend1sig
806. altrtrend_signal_v2_2
807. hi-lo
808. tii_rlh
809. itrend
810. spyker
811. coppock
812. sgmar
813. dema
814. beginneralert
815. wolfwave_nen
816. [i]gordagoelder
817. #mama
818. --aroon horn----
819. ema_trend_indicator
820. istochtxt
821. ergodic oscillator
822. fisher_org_v12
823. deltaforce
824. demarker pivots
825. chf_corr_eur
826. tsi-osc
827. dfc next
828. bandslsma
829. price channel
830. 003
831. fiboretracement3
832. raznost
833. vegas1hr
834. fibocalc_v31
835. relax_gotosleep_v01
836. stlm_hist
837. macdmversion2_mod
838. --pivotcustomtime----
839. ppo
840. markettime
841. mouteki-demark_trend_new
842. decema_v1
843. linefrakup
844. linefrakdown
845. elder_impulse_system
846. kwan
847. _i_ef_distance
848. v-tbv6
849. i-bandsprice
850. i-bandswidth
851. zigzag_ws_chanel
852. icci_m1+h1+d1
853. i-regr h&l
854. equity_v7
855. vinini_bb_ma_wpr(v1)
856. vinini_ma_wpr(v1)
857. vinini_mv_ma_wpr(v1)
858. vinini_mv_wpr(v1)
859. drp2
860. vinini_trend
861. vinini_trend_lrma
862. vinini_trend_ma_wpr
863. vinini_trend_wpr_ma
864. vinini_trend_wpr
865. japan
866. smi_correct-1
867. tiktakwav
868. tiktak
869. markettime
870. cloq
871. kuskus_starlight
872. takbir
873. discipline
874. dolly_v01
875. trend
876. time_h1
877. maangletony
878. turtlechanneli
879. bearnakedpattern1
880. bearnakedpattern1.1
881. bearnakedpattern1.2
882. pivot_mid_support_historical
883. newtrend
884. trendmanagernt
885. stoplevel
886. graphongrap
887. zerolagstochs
888. zigzag_ws_chanel_r
889. tradechannel
890. yefekt
891. i-cai
892. madeleine_v2.0
893. 172_grfleadingedge
894. batma
895. zonetrade_v2.3
896. candlestickscw
897. itrend_alexcud
898. math--system-trader
899. 54_search_patterns
900. tracktrend_macd_color
901. vkw bands modifay
902. metro
903. # tlb oc v02
904. moving averages
905. muv
906. ravi
907. daily range
908. cronex impulse cd
909. chandelierstops_v1
910. chandeqstik_v1
911. trendlinearreg
912. largetimeframe
913. median
914. yzchmc_v1
915. raznost2
916. will-spread
917. indicator volumes buys_sells
918. interpolation
919. traders dynamic index visual alerts
920. #---murreymath-timeframe
921. # murrey math black
922. normalizer
923. rsi##
924. osma
925. multizigzag
926. muv%
927. muv_diff
928. tickprice
929. dosr
930. supertrend
931. audioprice
932. bartimer
933. tracktrend_macd
934. ivar
935. nonlagama
936. multistrend
937. indexdollar
938. indexes_v4
939. autodayfibs
940. madlen_5_0
941. vkw bands - ibs
942. ibs
943. exoticwavein
944. candle patterns
945. detrended price oscillator
946. x-pair
947. price_alert
948. stopatr_auto
949. ind_rsicolored_v1
950. ind_divpeaktroughrsi_sw_v1
951. stik
952. ind_osmacolored_v1
953. osma_divergence_v1
954. waddah attar weekly fibo
955. tabelka_kontrolna_pl
956. timetoendbar
957. ryan_jones_sm
958. extrapolator
959. volatilityindicator
960. multyma for usd
961. adx+period
962. keltner_chanel
963. correlation
964. cosmod
965. period_converter_mn
966. equity_v8
967. m-candles
968. m-fibonacci
969. mf_breakdown_flat 0-1010
970. allframestrend
971. hp
972. hp extrapolator
973. ds_hdiv_osma_01
974. sidus v.2
975. mtrendline alert
976. chaos 2
977. 2_2_ma
978. i_sadukey_v1
979. bo
980. navelsma
981. ind_stocolored_v1
982. ind_divpeaktroughsto_sw_v1
983. parabolic_standart2
984. Pivot Points - Daily (Shifted)
985. riskreward ratio
986. riskreward ratio v0.1
987. mtfsr-kurl
988. mtfpi-sub1
989. mtfpi-sub2
990. mtfpi-sub3
991. mtfpi-sub4
992. clearsgnls
993. 1_min_microtrading
994. rsi+ma+label
995. frasma
996. fractal_dimension
997. ais1si
998. xo
999. ais1ai
1000. b-clock (h-m-s)