Quick and Simple - WPR+RSI+CCITake a look.
Couple of confluencial reversal signals from popular indicators (W%R, RSI & CCI). I can only say this shows how random the "stanard tools" are and how the market makers "play" these kind of tools to their advantage.
That said. It's better tha average, but not top-class, so expect to have to take signals with other confluence. DON'T take the plots or signals as buy / sell signals, they are just confluencial movements from these indicators based on how they should be "traditionally" used. Instead, use it as a guide as to what other traders may be thinking, or as a pull-back identifier.
Included 100 period ema as basic trend filter.
Not my normal type of script + been away for some time so be kind, lol :)
You might find it useful however so sharing.
More stuff to follow :)
Centered Oscillators
[blackcat] L3 God Hunter ScalpingLevel 3
Background
An ultra-short scaler that I integrate with multiple custom function implementations. Because of its responsiveness it is suitable for small cycle applications.
Function
The first technical indicator to integrate is the stoch. By combining the stoch indicators of long and short periods, I can not only ensure its high-speed reaction speed, but also be compatible with stability.
The second is the improved KDJ indicator to further strengthen buying and selling conditions. Because the final trend output is relatively fast, I used a variety of long-short conditions to improve adaptability. and minimize noise. It is well known that price fluctuations in small cycles are more random.
The third feature is the classification of buying and selling points, not only through the reversal of the trend curve, but also several other buying and selling point conditions, oversold and overbought signals, signal divergence techniques, etc.
Finally, through the nested RSI, the momentum trend strength of the trend signal is represented by a gradient color to assist in judging whether the reversal point is approaching.
Remarks
For differnent instruments and time frames, overbought and oversold threshold should be adjusted accordingly, or it may not work well.
Feedbacks are appreciated.
Wavetrend DivergencesCreated for the MarketCipher Community and friends :)
This indicator is partly based on Wavetrend Oscillator by LazyBear / blue momentum waves on MarketCipher B.
The Wavetrend indicator is a combination of 2 oscillator lines that signals the short term direction of the price once the lines cross. The Wavetrend indicator is useful but only once a divergence has been identified based on the crosses and the price which is what this strategy partly uses to open trades. This indicator signals divergences in the wavetrend, both regular and hidden divergences.
This indicator utilizes support and resistances to make sure that the indicator only signals high probability winning divergences. Supports represents a low level a stock price reaches over time, while resistance represents a high level a stock price reaches over time. Support materializes when a stock price drops to a level that prompts traders to buy. This reactionary buying causes a stock price to stop dropping and start rising and this is where the indicator will be looking for a divergence at a price point of your choosing.
To make it easier i have added a support and resistance drawing indicator that will help you find price points on the chart that the price is likely to get a reaction from. There are right now only 4 support or resistances that can be drawn at one time so make sure to update the levels as the market changes.
I have helped update and modify from the original script. Here it is:
On top of these indicators i have added my own indicator that will signal a short term trend reversal that is based on pivot points and moving averages. This will usually signal reversals earlier than divergences and is very effective when following the trend and using support and resistances and can be used as an extra confirmation that there will be a reaction from the support or resistance and that the divergence will play out like you want it to. These trend reversal dots can also be used to take profit.
Trade setup example:
As seen in the picture below price comes down to a previously drawn support line, then there is a trend reversal dot that signal a potential reversal and finally a divergence is signalled once there is a clear reaction to the support. When all these signals come together there is a high probability that the trade will end up in profit. To take profit in this trade setup you can use the trend reversal dots, the drawn resistances or your own intuition and technical analysis with Marketcipher B and DBSI. A stop loss in this trade setup could be at the swing low, below the blue or teal line.
There are alerts for everything so that you wont miss a trade setup. Hope you like it :)
I have some ideas on how to improve the indicator so there will be updates in the future.
Cutlers RSICutlers' RSI is a variation of the original RSI Developed by Welles Wilder.
This variation uses a simple moving average instead of an exponetial.
Since a simple moving average is used by this variation, a longer length tends to give better results compared to a shorter length.
CALCULATION
Step1: Calculating the Gains and Losses within the chosen period.
Step2: Calculating the simple moving averages of gains and losses.
Step3: Calculating Cutler’s Relative Strength (RS). Calculated using the following:
-> Cutler’s RS = SMA(gains,length) / SMA(losses,length)
Step 4: Calculating the Cutler’s Relative Strength Index (RSI). Calculated used the following:
-> RSI = 100 —
I have added some signals and filtering options with moving averages:
Trend OB/OS: Uptrend after above Overbought Level. Downtrend after below Oversold Level.
OB/OS: When above Overbought, or below oversold
50-Cross: Above 50 line is uptrend, below is downtrend
Direction: Moving up or down
RSI vs MA: RSI above MA is an uptrend, RSI below MA is a downtrend
The signals I added are just some potential ideas, always backtest your own strategies.
Harris RSIThis is a variation of Wilder's RSI that was altered by Michael Harris.
CALCULATION
The average change of each of the length's source value is compared to the more recent source value.
The average difference of both positive or negative changes is found.
The range of 100 is divided by the divided result of the average incremented and decremented ratio plus one.
This result of the above is subracted from the range value of 100
I have added some signals and filtering options with moving averages:
Trend OB/OS: Uptrend after above Overbought Level. Downtrend after below Oversold Level (For the traditional RSI OB=60 and OS=40 is used)
OB/OS: When above Overbought, or below oversold
50-Cross: Above 50 line is uptrend, below is downtrend
Direction: Moving up or down
RSI vs MA: RSI above MA is an uptrend, RSI below MA is a downtrend
The signals I added are just some potential ideas, always backtest your own strategies.
TomSeb StrategyRSI & MACD based. The parameters can be fine tuned to suit the symbol. 0 and 2 are default parameters which work for most symbols.
TMO ArrowsTMO - (T)rue (M)omentum (O)scillator) MTF Arrows
Do you want to use TMO but you lack space on the chart? This study is just for you. This is the more user-friendly version of the TMO Oscillator. In terms of the indicator there are no changes except the indicator is converted in to the simple arrows.
There are Four Types of Arrows:
1. TMO Arrow Up - Visualizes the TMO bullish crosses.
2. TMO Arrow Down - Visualizes the TMO bearish crosses.
3. TMO Arrow Up (Oversolds Only) - Visualizes only the bullish crosses that are at or below the oversold zone.
4. TMO Arrow Down (Overboughts Only) - Visualizes only the bearish crosses that are at or above the overbought zone.
In case you only want the arrows for extremes, turn off the Arrow Up / Arrow Down first. Arrows for extremes only are turned off by default.
Hope it helps.
MTF TMOTMO - (T)rue (M)omentum (O)scillator) MTF (Higher Aggregation) Version
TMO calculates momentum using the DELTA of price. Giving a much better picture of the trend, reversals & divergences than most momentum oscillators using price. Aside from the regular TMO, this study combines four different TMO aggregations into one indicator for an even better picture of the trend. Once you look deeper into this study you will realize how complex this tool is. This version also produce much more information like crosses, divergences, overbought / oversold signals, higher aggregation fades etc. It is probably not even possible to explain them all, there could easily be an entire e-book about this study.
I have been using this tool for a couple of years now, and this is what i have learned so far:
Favorite Time Frame Variations:
1. 1m / 5m / 30m - Great for intraday futures or options scalps. 30m TMO serves as the overall trend gauge for the day. 5min dictates the longer term intraday moves as well as direction of the 1min. 1min is for the scalps. When the 5min TMO is sloping higher focus should be on 1min buy signals (red to green cross) and vice versa for the 5min agg. sloping down.
2. 5m / 30m / 60m - Also an interesting variation for day trading the 3-5 min charts. Producing more cleaner & beginner-friendly signals that lasts couple of minutes instead of seconds.
3. 120m / Day / 2 Day - For the 30m to 1H or 2H timeframes. Daily & 2 Day dictates the overall trend. 120 min for the signals. Great for a multi-day swings.
4. Day / 2 Day / Week - Good for the daily charts, swing trading analysis as the weekly dictates the overall trend, daily dictates the signals and the 2 day cleans out the daily signals. If the daily & 2 day are not aligned togather, daily signal means nothing. Weekly dictates 2 day - 2 day dictates daily.
5. Week / Month / 3 Month - Same thing as the previous variation but for the weekly charts.
TMO Length:
The default vanilla settings are 14,5,3. Some traders prefer 21,5,3 as the TMO length is litle higher = TMO will potenially last little longer which could teoretically produce less false signals but slower crosses which means signals will lag more behind price. The lower the length, the faster the oscillator oscillates. It is the noice vs. the lag debate. The Length can be changed, but i would not personally touch the other two. Few points up or down on length will not drastically change much. But changes on Calc Length and Smooth Length can produce totally different signals from the original.
Tips & Tricks:
1. Observe
- This is the best tip & trick I can give you. The #1 best way to learn how any study operates is to just observe how it works in certain situations from the past. MTF TMO is not
an exception.
2. The Power of the Higher Aggregation
- The higher aggregation ALWAYS dictates the lower one. Best way to see this? Just 2x the current timeframe aggregation = so on daily chart, plot the daily & two day TMOs and you will notice how the higher agg. smooths out the current agg. The higher the aggregation is, the smoother (but slower) will the TMO turn. The real power kicks in when the 3 or 4 aggregations are aligned togather in one direction.
3. Position of the Higher Aggregation in Relation to the Extremes
- Overbought / oversold signals might not really work on the current aggregation. But pay attention to the higher aggregations in relation to the extremes. Ex: on the daily chart - daily TMO inside the OB / OS extremes might not mean much. But once the higher aggregations such as 3 day or Weekly TMO enters OB/OS zone togather with the daily, this can be a very powerful signal for a TMO reversion to the zeroline.
4. Crosses
- Yes, crosses do work. Personally, I never really focused on them. The thing about the crosses is that it is crucial to pick the right higher aggregation to the combination of the current one that would be reliable but also print enough signals. The closer the cross is to the OB / OS extremes, the more bigger move can occur. Crosses around the zero line can be considered as less quality crosses.
5. Divergences
- TMO can print awesome divergences. The best divergences are on the current aggregation (TMO agg. same as the chart) since the current agg. oscillates fast, it can usually produce lower lows & higher highs faster then any higher aggregations. Easy setup: wait for the higher aggregation to reach the OB / OS extremes and watch the current (chart) aggregation to print a divergence.
6. Three is Enough
- I personally find more than three aggregations messy and hard to read. But there is always the option to turn on the 4th one. Just switch the TMO 4 Main, TMO 4 Signal and TMO 4 Fill in the style settings.
Hope it helps.
Strategy Myth-Busting #6 - PSAR+MA+SQZMOM+HVI - [MYN]This is part of a new series we are calling "Strategy Myth-Busting" where we take open public manual trading strategies and automate them. The goal is to not only validate the authenticity of the claims but to provide an automated version for traders who wish to trade autonomously.
Our sixth one we are automating is " I Tested ''7% Profit Per Day" Scalping Strategy 100 Times ( Unexpected Results ) " from " TradeIQ " which claims to have made 175% profit on the 5 min chart of BTCUSD with a having a 61% win rate in just 32 days.
Originally, we mimicked verbatim the indicators and settings TradeIQ was using however weren't getting promising results anything close to the claim so we decided to try and improve on it. We changed the static Parabolic SAR to be adaptive based upon the timeframe. We did this by using an adjustable multiplier for the PSAR Max. Also, In TradeIQ's revised version he substituted Hawkeye's Volume Indicator in lieu of Squeeze Momentum. We found that including both indicators we were getting better results so included them both. Feel free to experiment more. Would love to see how this could be improved on.
This strategy uses a combination of 4 open-source public indicators:
Parabolic Sar (built in)
10 in 1 MA's by hiimannshu
Squeeze Momentum by lazybear
HawkEYE Volume Indicator by lazybear
Trading Rules
5m timeframe and above. We saw equally good results in the higher (3h - 4h) timeframes as well.
Long Entry:
Parabolic Sar shifts below price at last dot above and then previous bar needs to breach above that.
Price action has to be below both MA's and 50MA needs to be above 200MA
Squeeze Momentum needsd to be in green or close to going green
HawkEYE Volume Indicator needs to be show a green bar on the histagram
Short Entry:
Parabolic Sar shifts above price at last dot below and then previous bar needs to breach below that.
Price action needs to be above both MA's and 50MA needs to be below 200MA
Squeeze Momentum needsd to be in red or close to going red
HawkEYE Volume Indicator needs to be show a red bar on the histagram
If you know of or have a strategy you want to see myth-busted or just have an idea for one, please feel free to message me.
Chop and explode (ps5)Description : This is a renovated version of my previous mod that was based on the original script from fhenry0331.
Added are:
a data cleaning function
a seasonal random index function
an updated scaler and
a signalling procedure.
-
The following description is moved here from the old script.
The purpose of this script is to decipher chop zones from runs/movement/explosion spans. The chop is RSI movement between 40 and 60. Tight chop is RSI movement between 45 and 55. There should be an explosion after RSI breaks through 60 (long) or 40 (short). Tight chop bars are colored gray, a series of gray bars indicates a tight consolidation and should explode imminently. The longer the chop the longer the explosion will go for. The tighter the better. Loose chop (jig saw/gray bars on the silver background) will range between 40 and 60. The move begins with green and red bars.
Couple it with your trading system to help stay out of chop and enter when there is a movement.
Open Interest StochasticStochastic Money Flow Index(MFI) using open interest instead of volume.
Open Interest data for Binance, Bitmex, and Kraken
EMA and MACD with Trailing Stop Loss (by Coinrule)An exponential moving average ( EMA ) is a type of moving average (MA) that places a greater weight and significance on the most recent data points. The exponential moving average is also referred to as the exponentially weighted moving average. An exponentially weighted moving average reacts more significantly to recent price changes than a simple moving average simple moving average ( SMA ), which applies an equal weight to all observations in the period.
Moving average convergence divergence ( MACD ) is a trend-following momentum indicator that shows the relationship between two moving averages of a security’s price. The MACD is calculated by subtracting the 26-period exponential moving average ( EMA ) from the 12-period EMA.
The result of that calculation is the MACD line. A nine-day EMA of the MACD called the "signal line," is then plotted on top of the MACD line, which can function as a trigger for buy and sell signals. Traders may buy the security when the MACD crosses above its signal line and sell—or short—the security when the MACD crosses below the signal line. Moving average convergence divergence ( MACD ) indicators can be interpreted in several ways, but the more common methods are crossovers, divergences, and rapid rises/falls.
The Strategy enters and closes the trade when the following conditions are met:
LONG
The MACD histogram turns bearish
EMA7 is greater than EMA14
EXIT
Price increases 3% trailing
Price decreases 1% trailing
This strategy is back-tested from 1 January 2022 to simulate how the strategy would work in a bear market and provides good returns.
Pairs that produce very strong results include XRPUSDT on the 1-minute timeframe. This short timeframe means that this strategy opens and closes trades regularly
In order to further improve the strategy, the EMA can be changed from 7 and 14 to, say, EMA20 and EMA50. Furthermore, the trailing stop loss can also be changed to ideally suit the user to match their needs.
The strategy assumes each order is using 30% of the available coins to make the results more realistic and to simulate you only ran this strategy on 30% of your holdings. A trading fee of 0.1% is also taken into account and is aligned to the base fee applied on Binance.
Percentile Rank of Moving Average Convergence DivergenceThis simple indicator provides you three useful information of the Moving Average Convergence Divergence (MACD) indicator:
The percentile rank of the current value of the MACD line, displayed by the bright blue line.
The percentile rank of the current value of the Signal line, displayed by the dark blue line.
The percentile rank of the current value of the Histogram line, displayed by the olive histogram.
This indicator can be useful to identify the strength of trend. This indicator makes the assumption that market tends to revert into the opposite direction. If the market has been trending a lot, it should consolidate for a while later. If the market has been consolidating for a long time, it would begin trending real soon.
When we see a low percentile rank, no matter which line it is, this tells that the market hasn't been moving much, or there is little momentum. If the percentile rank stays below the median or even below the first quartile for a long time, this could suggest that the market is ready for the next trend since it has stored quite some energy.
When we see a high percentile rank, no matter which line it is, this tells that the market has been trending a lot, or there is much momentum. If the percentile rank stays above the median or even above the third quartile for a long time, it is probable that the market has used up much of its energy and is going to take a rest (consolidate).
Divergence Cheat Sheet'Divergence Cheat Sheet' helps in understanding what to look for when identifying divergences between price and an indicator. The strength of a divergence can be strong, medium, or weak. Divergences are always most effective when references prior peaks and on higher time frames. The most common indicators to identify divergences with are the Relative Strength Index (RSI) and the Moving average convergence divergence (MACD).
Regular Bull Divergence: Indicates underlying strength. Bears are exhausted. Warning of a possible trend direction change from a downtrend to an uptrend.
Hidden Bull Divergence: Indicates underlying strength. Good entry or re-entry. This occurs during retracements in an uptrend. Nice to see during the price retest of previous lows. “Buy the dips."
Regular Bear Divergence: Indicates underlying weakness. The bulls are exhausted. Warning of a possible trend direction change from an uptrend to a downtrend.
Hidden Bear Divergence: Indicates underlying weakness. Found during retracements in a downtrend. Nice to see during price retests of previous highs. “Sell the rallies.”
Divergences can have different strengths.
Strong Bull Divergence
Price: Lower Low
Indicator: Higher Low
Medium Bull Divergence
Price: Equal Low
Indicator: Higher Low
Weak Bull Divergence
Price: Lower Low
Indicator: Equal Low
Hidden Bull Divergence
Price: Higher Low
Indicator: Higher Low
Strong Bear Divergence
Price: Higher High
Indicator: Lower High
Medium Bear Divergence
Price: Equal High
Indicator: Lower High
Weak Bear Divergence
Price: Higher High
Indicator: Equal High
Hidden Bull Divergence
Price: Lower High
Indicator: Higher High
Barndorff-Nielsen and Shephard Jump Statistic [Loxx]The following comments and descriptions are from from "Problems in the Application of Jump Detection Tests to Stock Price Data" by Michael William Schwert; Professor George Tauchen, Faculty Advisor.
This indicator applies several jump detection tests to intraday stock price data sampled at various frequencies. It finds that the choice of sampling frequency has an effect on both the amount of jumps detected by these tests, as well as the timing of those jumps. Furthermore, although these tests are designed to identify the same phenomenon, they find different amounts and timing of jumps when performed on the same data. These results suggest that these jump detection tests are probably identifying different types of jump behavior in stock price data, so they are not really substitutes for one another.
In recent years there has been a great deal of interest in studying jumps in asset price movements. Reasons why it is important to know when and how frequently jumps occur include risk management and the pricing and hedging of derivative contracts. Investors would benefit greatly from knowing the properties of jumps, since large instantaneous drops in asset prices result in large instantaneous losses. The effect of jumps on derivative pricing is equally significant, especially considering the important role derivatives play in modern financial markets. When asset price movements are continuous, investors can perfectly hedge derivative contracts such as options, but when jumps occur, they cause a change in the derivative price that is non-linear to the change in the price of the underlying asset. Thus, jumps introduce an unhedgeable risk to the holders of derivative contracts.
The ability to identify realized jumps in the financial markets could provide helpful information such as how frequently jumps occur, how large the jumps are, and whether they tend to occur in clusters. With this goal in mind, several authors have developed tests to determine whether or not an asset price movement is a statistically significant jump. These tests take advantage of the high-frequency intraday price data available today through electronic sources. Barndorff-Nielsen and Shephard (2004, 2006) use the difference between an estimate of variance and a jump-robust measure of variance to detect jumps over the course of a day. Approaching the problem differently, Jiang and Oomen (2007) exploit high order sample moments of returns to identify days that include jumps. Aїt-Sahalia and Jacod (2008) also exploit high order sample moments of returns to detect jumps by comparing price data sampled at two different frequencies. Lee and Mykland (2007) test for jumps at individual price observations by scaling returns by a local volatility measure. While these tests employ different strategies for detecting jumps, they are all designed to identify the same phenomenon.
For this indicator we are focused on the Barndorff-Nielsen and Shephard jump statistic.
Barndorff-Nielsen and Shephard (2004, 2006) developed a test that uses high-frequency price data to determine whether there is a jump over the course of a day. Their test compares two measures of variance: Realized Variance, which converges to the integrated variance plus a jump component as the time between observations approaches zero; and Bipower Variation, which converges to the integrated variance as the time between observations approaches zero, and is robust to jumps in the price path, an important fact for this application. The integrated variance of a price process is the integral of the square of the σ(t) term in (2.2.2), taken over the course of a day. Since prices cannot be observed continuously, one cannot calculate integrated variance exactly, and must estimate it instead.
For our purposes here, this is calculated as:
r = log(p /p )
This the geometric return from time ti-1 to time ti.
Then, Realized Variance and Bipower Variation are described by the following functions (see code for details)
realizedVariance(float src, int per)
and
bipowerVariance(float src, int per)
Huang and Tauchen (2005) also consider Relative Jump, a measure that approximates the percentage of total variance attributable to jumps:
RJ = (RV - BV) / RV
This statistic approximates the ratio of the sum of squared jumps to the total variance and is useful because it scales out long-term trends in volatility so one can compare the relative contribution of jumps to the variance of two price series with different volatilities.
To develop a statistical test to determine whether there is a significant difference between RV and BV, one needs an estimate of integrated quarticity. Andersen, Bollerslev, and Diebold (2004) recommend using a jump-robust realized Tri-Power Quarticity, I've included commentary in code to better explain how this indicator is collocated. See code for details.
How to use this indicator
When the bars turn gray, it's an indication that a jump has occurred in the market. It serves a warning that price jumped. I've included a percent point function (or inverse cumulative distribution function) to cutoff Z-score values depicted by histogram values. The top line at 3 is the empirical maximum Z-score value a serves merely as a point of reference. The Red line is the cutoff line calculated using PPF. When the histogram is green, no jumps have been detected. This indicator also includes alerts, signals, and bar coloring. I've also expanded the possible source types using my own Expanded Source Types library so you can test different log return methods as inputs. It is recommended to use window sizes of 7, 16, 78, 110, 156, and 270 returns for sampling intervals of 1 week, 1 day, 1 hour, 30 minutes, 15 minutes, and 5 minutes, respectively.
If you'ed like to better understand PPF, see here: Distributions in python
Included:
Bar coloring
Signals
Alerts
Loxx's Expanded Source Types
MACD strategy + Trailstop indicatorWelcome traveler !
Here is my first indicator I made after 3 days of hardlearning pine code (beginner in coding).
I hope it will please you, if you have any suggestion to enhance this indicator, do not hesitate to give me your thoughts in the comments section or by Private message on trading View !
How does it works ?
It's a simple MACD strategy as describe here :
Uses of EMA 200 as a trend confirmer,
For sells :
When above Zero line (MACD) and under EMA200, we go on sell (background color is red)
For buys:
When under Zero line (MACD) and above EMA 200, we go on Buy (back ground color is green)
FILTERS !
I haded one filter to reduce noise on the indicator :
Signals aren't taken if one of the 14 last candles closed on the other side of the EMA 14.
What are the green and red lines ?
The green line is equivalent of a potential stop loss as a buyer side, same for the red one on seller side !
To make the space with the price bigger, please use "ATR multiplier" in the input options of the indicator while on your chart !
Is it timeframe specific ?
Hell no it is not timeframe specific ! You can try to use it on every timeframe !
As usual, I like to remind you that the best way to test an indicator is to go backtest it or to paper trade before using it on real market conditions !
If you find an idea of filter for a specific timeframe, do not hesitate to contact me ! I'll try to do my best to enhance this indicator as the time goes !
Is there repainting ?
There is no repainting on confirmation !
There's only a movement that I don't know how to ignore on the current open candle for the trail stop indicator I built, it should not be a problem if you place alerts to automatise your trading on the close of the candle, and not the high or low !
If you know how to resolve this problem with my code, I would be glad to get your tips to enhance the script ! :)
Example of the indicator in market (backtest, as said, no repaint on confirmation) :
BUY/SELL arvwis STORMASBuy/sell indikatorius, geriausia naudoti ant didesnių timefreimų, bet tinka ir ant mažesnių
End-Pointed SSA of Normalized Price Corridor [Loxx]End-Pointed SSA of Normalized Price Corridor is an end-pointed SSA of normalized input price to output a smoothed normalized oscillator of price. Corridors are added in attempt to decipher larger trend direction of price. These corridor trend lines are based on highs and lows of price. Due to the SSA algorithm, this indicator takes some time load on the chat, so be patient. You can adjust the lag parameter downward to speed up the indicator load time but this will also degrade the signal. There are many different ways to use this indicator. It is also Renko chart friendly.
An example of emerging trends (these do not repaint)
What is Singular Spectrum Analysis ( SSA )?
Singular spectrum analysis ( SSA ) is a technique of time series analysis and forecasting. It combines elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing. SSA aims at decomposing the original series into a sum of a small number of interpretable components such as a slowly varying trend, oscillatory components and a ‘structureless’ noise. It is based on the singular value decomposition ( SVD ) of a specific matrix constructed upon the time series. Neither a parametric model nor stationarity-type conditions have to be assumed for the time series. This makes SSA a model-free method and hence enables SSA to have a very wide range of applicability.
For our purposes here, we are only concerned with the "Caterpillar" SSA . This methodology was developed in the former Soviet Union independently (the ‘iron curtain effect’) of the mainstream SSA . The main difference between the main-stream SSA and the "Caterpillar" SSA is not in the algorithmic details but rather in the assumptions and in the emphasis in the study of SSA properties. To apply the mainstream SSA , one often needs to assume some kind of stationarity of the time series and think in terms of the "signal plus noise" model (where the noise is often assumed to be ‘red’). In the "Caterpillar" SSA , the main methodological stress is on separability (of one component of the series from another one) and neither the assumption of stationarity nor the model in the form "signal plus noise" are required.
"Caterpillar" SSA
The basic "Caterpillar" SSA algorithm for analyzing one-dimensional time series consists of:
Transformation of the one-dimensional time series to the trajectory matrix by means of a delay procedure (this gives the name to the whole technique);
Singular Value Decomposition of the trajectory matrix;
Reconstruction of the original time series based on a number of selected eigenvectors.
This decomposition initializes forecasting procedures for both the original time series and its components. The method can be naturally extended to multidimensional time series and to image processing.
The method is a powerful and useful tool of time series analysis in meteorology, hydrology, geophysics, climatology and, according to our experience, in economics, biology, physics, medicine and other sciences; that is, where short and long, one-dimensional and multidimensional, stationary and non-stationary, almost deterministic and noisy time series are to be analyzed.
Included
Bar coloring
Signals
Alerts
Loxx's Expanded Source Types
Scalper RibbonThis Scalper Ribbon is a combination of 6 different oscillators with a sprinkle of secret sauce . It’s smoothed out so it’s easy to read, but is quick enough to catch reversals early and helps you spot divergences. It will turn green or red according to the bullish or bearish nature of the ticker you are viewing without all of the noise that most oscillators give you.
It combines price action, momentum, rsi and a few other oscillators together to give an overall trend strength line that is smoothed out and coupled with a moving average to make it less noisy. Use it as an identifier of the underlying trend so you can make better decisions on scalp trades as well as swing trades on longer timeframes. Wait for the ribbon to break out/down from the middle blue range to avoid chop and get in when price is actually moving.
***HOW TO USE***
Find tops and bottoms of the market by looking for reversals in the ribbon when it is either very high or very low. The white line is the midline and the ribbon is overall bullish when above the midline and overall bearish when below the midline. There are also two blue lines just above and below the midline that is a buffer area I like to call the neutral range. When the ribbon is in the neutral range, expect indecision in the market and look for the ribbon to break out or down from that range for continuation of a trend. The farther away from the neutral range the ribbon is, the stronger the trend is. Take a look at how it performs across multiple timeframes and tickers and get a feel for it before using it in your strategy. It will help you spot reversals early and show you hidden divergences in price action before the reversals happen.
***CUSTOMIZATION***
You can adjust the length of the oscillators and the moving average ribbon to be faster or slower to suit your preferences. The lower the number used, the faster it will detect changes, but the more noise it will have. The higher the number used, the slower it will detect changes, but there will be less noise and easier to follow.
***MARKETS***
This indicator can be used as a signal on all markets, including stocks, crypto, futures and forex.
***TIMEFRAMES***
This Scalper Ribbon indicator can be used on all timeframes.
Moving Average Convergence Divergence On Alter OBVOBV:
The OBV is perfect indicator to understand the strength of the particular stock. As the strength increase, the trend of the stock goes high along with price. But, the OBV is considered only with close of previous close which is to make sure the double confirmation on the price to accumulate the volume.
Altered OBV:
So, here is the altered OBV, which basically consider the close of previous close and also buying interested of the day when close is higher than open.
MACD:
I always admire the magic of MACD with pre-defined timeframe. Now, this MACD applied on top of altered OBV to signal us the moving of the ticker strength.
I hope the another MACDAltOBV would help on your swing trading strategy.
Happy Investing.
Adaptive Two-Pole Super Smoother Entropy MACD [Loxx]Adaptive Two-Pole Super Smoother Entropy (Math) MACD is an Ehlers Two-Pole Super Smoother that is transformed into an MACD oscillator using entropy mathematics. Signals are generated using Discontinued Signal Lines.
What is Ehlers; Two-Pole Super Smoother?
From "Cycle Analytics for Traders Advanced Technical Trading Concepts" by John F. Ehlers
A SuperSmoother filter is used anytime a moving average of any type would otherwise be used, with the result that the SuperSmoother filter output would have substantially less lag for an equivalent amount of smoothing produced by the moving average. For example, a five-bar SMA has a cutoff period of approximately 10 bars and has two bars of lag. A SuperSmoother filter with a cutoff period of 10 bars has a lag a half bar larger than the two-pole modified Butterworth filter.Therefore, such a SuperSmoother filter has a maximum lag of approximately 1.5 bars and even less lag into the attenuation band of the filter. The differential in lag between moving average and SuperSmoother filter outputs becomes even larger when the cutoff periods are larger.
Market data contain noise, and removal of noise is the reason for using smoothing filters. In fact, market data contain several kinds of noise. I’ll group one kind of noise as systemic, caused by the random events of trades being exercised. A second kind of noise is aliasing noise, caused by the use of sampled data. Aliasing noise is the dominant term in the data for shorter cycle periods.
It is easy to think of market data as being a continuous waveform, but it is not. Using the closing price as representative for that bar constitutes one sample point. It doesn’t matter if you are using an average of the high and low instead of the close, you are still getting one sample per bar. Since sampled data is being used, there are some dSP aspects that must be considered. For example, the shortest analysis period that is possible (without aliasing)2 is a two-bar cycle.This is called the Nyquist frequency, 0.5 cycles per sample.A perfect two-bar sine wave cycle sampled at the peaks becomes a square wave due to sampling. However, sampling at the cycle peaks can- not be guaranteed, and the interference between the sampling frequency and the data frequency creates the aliasing noise.The noise is reduced as the data period is longer. For example, a four-bar cycle means there are four samples per cycle. Because there are more samples, the sampled data are a better replica of the sine wave component. The replica is better yet for an eight-bar data component.The improved fidelity of the sampled data means the aliasing noise is reduced at longer and longer cycle periods.The rate of reduction is 6 dB per octave. My experience is that the systemic noise rarely is more than 10 dB below the level of cyclic information, so that we create two conditions for effective smoothing of aliasing noise:
1. It is difficult to use cycle periods shorter that two octaves below the Nyquist frequency.That is, an eight-bar cycle component has a quantization noise level 12 dB below the noise level at the Nyquist frequency. longer cycle components therefore have a systemic noise level that exceeds the aliasing noise level.
2. A smoothing filter should have sufficient selectivity to reduce aliasing noise below the systemic noise level. Since aliasing noise increases at the rate of 6 dB per octave above a selected filter cutoff frequency and since the SuperSmoother attenuation rate is 12 dB per octave, the Super- Smoother filter is an effective tool to virtually eliminate aliasing noise in the output signal.
What are DSL Discontinued Signal Line?
A lot of indicators are using signal lines in order to determine the trend (or some desired state of the indicator) easier. The idea of the signal line is easy : comparing the value to it's smoothed (slightly lagging) state, the idea of current momentum/state is made.
Discontinued signal line is inheriting that simple signal line idea and it is extending it : instead of having one signal line, more lines depending on the current value of the indicator.
"Signal" line is calculated the following way :
When a certain level is crossed into the desired direction, the EMA of that value is calculated for the desired signal line
When that level is crossed into the opposite direction, the previous "signal" line value is simply "inherited" and it becomes a kind of a level
This way it becomes a combination of signal lines and levels that are trying to combine both the good from both methods.
In simple terms, DSL uses the concept of a signal line and betters it by inheriting the previous signal line's value & makes it a level.
Included:
Bar coloring
Alerts
Signals
Loxx's Expanded Source Types
Leavitt Convolution Slope [CC]The Leavitt Convolution Slope indicator was created by Jay Leavitt (Stocks and Commodities Oct 2019, page 11), who is most well known for creating the Volume-Weighted Average Price indicator. This indicator is very similar to the Leavitt Convolution indicator but the big difference is that it is getting the slope instead of predicting the next Convolution value. I changed quite a few things from the original source code so let me know if you like these changes. I added a normalization function using code from a good friend @loxx that I recommend to leave on but feel free to experiment with it. Last but not least, the unsure levels are essentially acting as a buy or sell threshold. I personally recommend to buy or sell for zero crossovers but another option would be to buy or sell for crossovers using the unsure levels. I have color coded the lines to turn light green for a normal buy signal or dark green for a strong buy signal and light red for a normal sell signal, and dark red for a strong sell signal.
This is another indicator in a series that I'm publishing to fulfill a special request from @ashok1961 so let me know if you ever have any special requests for me.
