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.

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%.

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.

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.

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 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.

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

### 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)

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)

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