General Filter Estimator-An Experiment on Estimating EverythingIntroduction
The last indicators i posted where about estimating the least squares moving average, the task of estimating a filter is a funny one because its always a challenge and it require to be really creative. After the last publication of the 1LC-LSMA , who estimate the lsma with 1 line of code and only 3 functions i felt like i could maybe make something more flexible and less complex with the ability to approximate any filter output. Its possible, but the methods to do so are not something that pinescript can do, we have to use another base for our estimation using coefficients, so i inspired myself from the alpha-beta filter and i started writing the code.
Calculation and The Estimation Coefficients
Simplicity is the key word, its also my signature style, if i want something good it should be simple enough, so my code look like that :
p = length/beta
a = close - nz(b ,close)
b = nz(b ,close) + a/p*gamma
3 line, 2 function, its a good start, we could put everything in one line of code but its easier to see it this way. length control the smoothing amount of the filter, for any filter f(Period) Period should be equal to length and f(Period) = p , it would be inconvenient to have to use a different length period than the one used in the filter we want to estimate (imagine our estimation with length = 50 estimating an ema with period = 100) , this is where the first coefficients beta will be useful, it will allow us to leave length as it is. In general beta will be greater than 1, the greater it will be the less lag the filter will have, this coefficient will be useful to estimate low lagging filters, gamma however is the coefficient who will estimate lagging filters, in general it will range around .
We can get loose easily with those coefficients estimation but i will leave a coefficients table in the code for estimating popular filters, and some comparison below.
Estimating a Simple Moving Average
Of course, the boxcar filter, the running mean, the simple moving average, its an easy filter to use and calculate.
For an SMA use the following coefficients :
beta = 2
gamma = 0.5
Our filter is in red and the moving average in white with both length at 50 (This goes for every comparison we will do)
Its a bit imprecise but its a simple moving average, not the most interesting thing to estimate.
Estimating an Exponential Moving Average
The ema is a great filter because its length times more computing efficient than a simple moving average. For the EMA use the following coefficients :
beta = 3
gamma = 0.4
N.B : The EMA is rougher than the SMA, so it filter less, this is why its faster and closer to the price
Estimating The Hull Moving Average
Its a good filter for technical analysis with tons of use, lets try to estimate it ! For the HMA use the following coefficients :
beta = 4
gamma = 0.85
Looks ok, of course if you find better coefficients i will test them and actualize the coefficient table, i will also put a thank message.
Estimating a LSMA
Of course i was gonna estimate it, but this time this estimation does not have anything a lsma have, no moving average, no standard deviation, no correlation coefficient, lets do it.
For the LSMA use the following coefficients :
beta = 3.5
gamma = 0.9
Its far from being the best estimation, but its more efficient than any other i previously made.
Estimating the Quadratic Least Square Moving Average
I doubted about this one but it can be approximated as well. For the QLSMA use the following coefficients :
beta = 5.25
gamma = 1
Another ok estimate, the estimate filter a bit more than needed but its ok.
Jurik Moving Average
Its far from being a filter that i like and its a bit old. For the comparison i will use the JMA provided by @everget described in this article : c.mql5.com
For the JMA use the following coefficients :
for phase = 0
beta = pow*2 (pow is a parameter in the Jma)
gamma = 0.5
Here length = 50, phase = 0, pow = 5 so beta = 10
Looks pretty good considering the fact that the Jma use an adaptive architecture.
Discussion
I let you the task to judge if the estimation is good or not, my motivation was to estimate such filters using the less amount of calculations as possible, in itself i think that the code is quite elegant like all the codes of IIR filters (IIR Filters = Infinite Impulse Response : Filters using recursion) .
It could be possible to have a better estimate of the coefficients using optimization methods like the gradient descent. This is not feasible in pinescript but i could think about it using python or R.
Coefficients should be dependant of length but this would lead to a massive work, the variation of the estimation using fixed coefficients when using different length periods is just ok if we can allow some errors of precision.
I dont think it should be possible to estimate adaptive filter relying a lot on their adaptive parameter/smoothing constant except by making our coefficients adaptive (gamma could be)
So at the end ? What make a filter truly unique ? From my point of sight the architecture of a filter and the problem he is trying to solve is what make him unique rather than its output result. If you become a signal, hide yourself into noise, then look at the filters trying to find you, what a challenging game, this is why we need filters.
Conclusion
I wanted to give a simple filter estimator relying on two coefficients in order to estimate both lagging and low-lagging filters. I will try to give more precise estimate and update the indicator with new coefficients.
Thanks for reading !
Search in scripts for "one一季度财报"
BTC Volume Lines [v2018-11-17] @ LekkerCryptisch.nlCombine the volume of 8 BTCUSD exchanges in one graph.
Three use cases:
1) See the absolute volumes in one graph
2) See the relative volumes in one graph
3) See the deviation of the EMA the volumes in one graph
Tillson T3 Moving Average MTFMULTIPLE TIME FRAME version of Tillson T3 Moving Average Indicator
Developed by Tim Tillson, the T3 Moving Average is considered superior -1.60% to traditional moving averages as it is smoother, more responsive and thus performs better in ranging market conditions as well. However, it bears the disadvantage of overshooting the price as it attempts to realign itself to current market conditions.
It incorporates a smoothing technique which allows it to plot curves more gradual than ordinary moving averages and with a smaller lag. Its smoothness is derived from the fact that it is a weighted sum of a single EMA , double EMA , triple EMA and so on. When a trend is formed, the price action will stay above or below the trend during most of its progression and will hardly be touched by any swings. Thus, a confirmed penetration of the T3 MA and the lack of a following reversal often indicates the end of a trend.
The T3 Moving Average generally produces entry signals similar to other moving averages and thus is traded largely in the same manner. Here are several assumptions:
If the price action is above the T3 Moving Average and the indicator is headed upward, then we have a bullish trend and should only enter long trades (advisable for novice/intermediate traders). If the price is below the T3 Moving Average and it is edging lower, then we have a bearish trend and should limit entries to short. Below you can see it visualized in a trading platform.
Although the T3 MA is considered as one of the best swing following indicators that can be used on all time frames and in any market, it is still not advisable for novice/intermediate traders to increase their risk level and enter the market during trading ranges (especially tight ones). Thus, for the purposes of this article we will limit our entry signals only to such in trending conditions.
Once the market is displaying trending behavior, we can place with-trend entry orders as soon as the price pulls back to the moving average (undershooting or overshooting it will also work). As we know, moving averages are strong resistance/support levels, thus the price is more likely to rebound from them and resume its with-trend direction instead of penetrating it and reversing the trend.
And so, in a bull trend, if the market pulls back to the moving average, we can fairly safely assume that it will bounce off the T3 MA and resume upward momentum, thus we can go long. The same logic is in force during a bearish trend .
And last but not least, the T3 Moving Average can be used to generate entry signals upon crossing with another T3 MA with a longer trackback period (just like any other moving average crossover). When the fast T3 crosses the slower one from below and edges higher, this is called a Golden Cross and produces a bullish entry signal. When the faster T3 crosses the slower one from above and declines further, the scenario is called a Death Cross and signifies bearish conditions.
I Personally added a second T3 line with a volume factor of 0.618 (Fibonacci Ratio) and length of 3 (fibonacci number) which can be added by selecting the box in the input section. traders can combine the two lines to have Buy/Sell signals from the crosses.
Developed by Tim Tillson
Inverse Fisher Transform on STOCHASTIC (modified graphics)Modified the graphic representation of the script from John Ehlers - From California, USA, he is a veteran trader. With 35 years trading experience he has seen it all. John has an engineering background that led to his technical approach to trading ignoring fundamental analysis (with one important exception). John strongly believes in cycles. He’d rather exit a trade when the cycle ends or a new one starts. He uses the MESA principle to make predictions about cycles in the market and trades one hundred percent automatically.
In the show John reveals:
• What is more appropriate than trading individual stocks
• The one thing he relies upon in his approach to the market
• The detail surrounding his unique trading style
• What important thing underpins the market and gives every trader an edge
About INVERSE FISHER TRANSFORM:
The purpose of technical indicators is to help with your timing decisions to buy or sell. Hopefully, the signals are clear and unequivocal. However, more often than not your decision to pull the trigger is accompanied by crossing your fingers. Even if you have placed only a few trades you know the drill. In this article I will show you a way to make your oscillator-type indicators make clear black-or-white indication of the time to buy or sell. I will do this by using the Inverse Fisher Transform to alter the Probability Distribution Function (PDF) of your indicators. In the past12 I have noted that the PDF of price and indicators do not have a Gaussian, or Normal, probability distribution. A Gaussian PDF is the familiar bell-shaped curve where the long “tails” mean that wide deviations from the mean occur with relatively low probability. The Fisher Transform can be applied to almost any normalized data set to make the resulting PDF nearly Gaussian, with the result that the turning points are sharply peaked and easy to identify. The Fisher Transform is defined by the equation
1)
Whereas the Fisher Transform is expansive, the Inverse Fisher Transform is compressive. The Inverse Fisher Transform is found by solving equation 1 for x in terms of y. The Inverse Fisher Transform is:
2)
The transfer response of the Inverse Fisher Transform is shown in Figure 1. If the input falls between –0.5 and +0.5, the output is nearly the same as the input. For larger absolute values (say, larger than 2), the output is compressed to be no larger than unity. The result of using the Inverse Fisher Transform is that the output has a very high probability of being either +1 or –1. This bipolar probability distribution makes the Inverse Fisher Transform ideal for generating an indicator that provides clear buy and sell signals.
Tillson T3 Moving Average by KIVANÇ fr3762Developed by Tim Tillson, the T3 Moving Average is considered superior to traditional moving averages as it is smoother, more responsive and thus performs better in ranging market conditions as well. However, it bears the disadvantage of overshooting the price as it attempts to realign itself to current market conditions.
It incorporates a smoothing technique which allows it to plot curves more gradual than ordinary moving averages and with a smaller lag. Its smoothness is derived from the fact that it is a weighted sum of a single EMA , double EMA , triple EMA and so on. When a trend is formed, the price action will stay above or below the trend during most of its progression and will hardly be touched by any swings. Thus, a confirmed penetration of the T3 MA and the lack of a following reversal often indicates the end of a trend.
The T3 Moving Average generally produces entry signals similar to other moving averages and thus is traded largely in the same manner. Here are several assumptions:
If the price action is above the T3 Moving Average and the indicator is headed upward, then we have a bullish trend and should only enter long trades (advisable for novice/intermediate traders). If the price is below the T3 Moving Average and it is edging lower, then we have a bearish trend and should limit entries to short. Below you can see it visualized in a trading platform.
Although the T3 MA is considered as one of the best swing following indicators that can be used on all time frames and in any market, it is still not advisable for novice/intermediate traders to increase their risk level and enter the market during trading ranges (especially tight ones). Thus, for the purposes of this article we will limit our entry signals only to such in trending conditions.
Once the market is displaying trending behavior, we can place with-trend entry orders as soon as the price pulls back to the moving average (undershooting or overshooting it will also work). As we know, moving averages are strong resistance/support levels, thus the price is more likely to rebound from them and resume its with-trend direction instead of penetrating it and reversing the trend.
And so, in a bull trend, if the market pulls back to the moving average, we can fairly safely assume that it will bounce off the T3 MA and resume upward momentum, thus we can go long. The same logic is in force during a bearish trend .
And last but not least, the T3 Moving Average can be used to generate entry signals upon crossing with another T3 MA with a longer trackback period (just like any other moving average crossover). When the fast T3 crosses the slower one from below and edges higher, this is called a Golden Cross and produces a bullish entry signal. When the faster T3 crosses the slower one from above and declines further, the scenario is called a Death Cross and signifies bearish conditions.
I Personally added a second T3 line with a volume factor of 0.618 (Fibonacci Ratio) and length of 3 (fibonacci number) which can be added by selecting the box in the input section. traders can combine the two lines to have Buy/Sell signals from the crosses.
Developed by Tim Tillson
Inverse Fisher Transform COMBO STO+RSI+CCIv2 by KIVANÇ fr3762A combined 3in1 version of pre shared INVERSE FISHER TRANSFORM indicators on RSI , on STOCHASTIC and on CCIv2 to provide space for 2 more indicators for users...
About John EHLERS:
From California, USA, John is a veteran trader. With 35 years trading experience he has seen it all. John has an engineering background that led to his technical approach to trading ignoring fundamental analysis (with one important exception).
John strongly believes in cycles. He’d rather exit a trade when the cycle ends or a new one starts. He uses the MESA principle to make predictions about cycles in the market and trades one hundred percent automatically.
In the show John reveals:
• What is more appropriate than trading individual stocks
• The one thing he relies upon in his approach to the market
• The detail surrounding his unique trading style
• What important thing underpins the market and gives every trader an edge
About INVERSE FISHER TRANSFORM:
The purpose of technical indicators is to help with your timing decisions to buy or
sell. Hopefully, the signals are clear and unequivocal. However, more often than
not your decision to pull the trigger is accompanied by crossing your fingers.
Even if you have placed only a few trades you know the drill.
In this article I will show you a way to make your oscillator-type indicators make
clear black-or-white indication of the time to buy or sell. I will do this by using the
Inverse Fisher Transform to alter the Probability Distribution Function ( PDF ) of
your indicators. In the past12 I have noted that the PDF of price and indicators do
not have a Gaussian, or Normal, probability distribution. A Gaussian PDF is the
familiar bell-shaped curve where the long “tails” mean that wide deviations from
the mean occur with relatively low probability. The Fisher Transform can be
applied to almost any normalized data set to make the resulting PDF nearly
Gaussian, with the result that the turning points are sharply peaked and easy to
identify. The Fisher Transform is defined by the equation
1)
Whereas the Fisher Transform is expansive, the Inverse Fisher Transform is
compressive. The Inverse Fisher Transform is found by solving equation 1 for x
in terms of y. The Inverse Fisher Transform is:
2)
The transfer response of the Inverse Fisher Transform is shown in Figure 1. If
the input falls between –0.5 and +0.5, the output is nearly the same as the input.
For larger absolute values (say, larger than 2), the output is compressed to be no
larger than unity . The result of using the Inverse Fisher Transform is that the
output has a very high probability of being either +1 or –1. This bipolar
probability distribution makes the Inverse Fisher Transform ideal for generating
an indicator that provides clear buy and sell signals.
Creator: John EHLERS
Inverse Fisher Transform on SMI (Stochastic Momentum Index)Inverse Fisher Transform on SMI (Stochastic Momentum Index)
About John EHLERS:
From California, USA, John is a veteran trader. With 35 years trading experience he has seen it all. John has an engineering background that led to his technical approach to trading ignoring fundamental analysis (with one important exception).
John strongly believes in cycles. He’d rather exit a trade when the cycle ends or a new one starts. He uses the MESA principle to make predictions about cycles in the market and trades one hundred percent automatically.
In the show John reveals:
• What is more appropriate than trading individual stocks
• The one thing he relies upon in his approach to the market
• The detail surrounding his unique trading style
• What important thing underpins the market and gives every trader an edge
About INVERSE FISHER TRANSFORM:
The purpose of technical indicators is to help with your timing decisions to buy or
sell. Hopefully, the signals are clear and unequivocal. However, more often than
not your decision to pull the trigger is accompanied by crossing your fingers.
Even if you have placed only a few trades you know the drill.
In this article I will show you a way to make your oscillator-type indicators make
clear black-or-white indication of the time to buy or sell. I will do this by using the
Inverse Fisher Transform to alter the Probability Distribution Function (PDF) of
your indicators. In the past12 I have noted that the PDF of price and indicators do
not have a Gaussian, or Normal, probability distribution. A Gaussian PDF is the
familiar bell-shaped curve where the long “tails” mean that wide deviations from
the mean occur with relatively low probability. The Fisher Transform can be
applied to almost any normalized data set to make the resulting PDF nearly
Gaussian, with the result that the turning points are sharply peaked and easy to
identify. The Fisher Transform is defined by the equation
1)
Whereas the Fisher Transform is expansive, the Inverse Fisher Transform is
compressive. The Inverse Fisher Transform is found by solving equation 1 for x
in terms of y. The Inverse Fisher Transform is:
2)
The transfer response of the Inverse Fisher Transform is shown in Figure 1. If
the input falls between –0.5 and +0.5, the output is nearly the same as the input.
For larger absolute values (say, larger than 2), the output is compressed to be no
larger than unity. The result of using the Inverse Fisher Transform is that the
output has a very high probability of being either +1 or –1. This bipolar
probability distribution makes the Inverse Fisher Transform ideal for generating
an indicator that provides clear buy and sell signals.
Big Snapper Alerts R2.0 by JustUncleLThis is a diversified Binary Option or Scalping Alert indicator originally designed for lower Time Frame Trend or Swing trading. Although you will find it a useful tool for higher time frames as well.
The Alerts are generated by the changing direction of the ColouredMA (HullMA by default), you then have the choice of selecting the Directional filtering on these signals or a Bollinger swing reversal filter.
The filters include:
Type 1 - The three MAs (EMAs 21,55,89 by default) in various combinations or by themselves. When only one directional MA selected then direction filter is given by ColouredMA above(up)/below(down) selected MA. If more than one MA selected the direction is given by MAs being in correct order for trend direction.
Type 2 - The SuperTrend direction is used to filter ColouredMA signals.
Type 3 - Bollinger Band Outside In is used to filter ColouredMA for swing reversals.
Type 4 - No directional filtering, all signals from the ColouredMA are shown.
Notes:
Each Type can be combined with another type to form more complex filtration.
Alerts can also be disabled completely if you just want one indicator with one colouredMA and/or 3xMAs and/or Bollinger Bands and/or SuperTrend painted on the chart.
Warning:
Be aware that combining Bollinger OutsideIn swing filter and a directional filter can be counter productive as they are opposites. So careful consideration is needed when combining Bollinger OutsideIn with any of the directional filters.
Hints:
For Binary Options try ColouredMA = HullMA(13) or HullMA(8) with Type 2 or 3 Filter.
When using Trend filters SuperTrend and/or 3xMA Trend, you will find if price reverses and breaks back through the Big Fat Signal line, then this can be a good reversal trade.
Some explanation about the what Hull Moving average and ideas of how the generated in Big Snapper can be used:
tradingsim.com
forextradingstrategies4u.com
Inspiration from @vdubus
Big Snapper's Bollinger OutsideIn Swing filter in Action:
2-step Moving Average by HAH Financial- longer SMAs tend to sit too far from daily action
- shorter SMAs are too jittery
- the idea here is to create a smooth line, that is sits much closer to the daily price ranges
- this is achieved by mixing 2 MAs, a longer one and a shorter one
- the long one gives smoothness
- while averaging it with a shorter one, brings it (much in some cases) closer to the daily range
Price Action Doji Harami v0.2 by JustUncleLThis is an updated and final version of this indicator. This version distinguishes between the true Harami and the other Doji candlestick patterns as used with the Heikin Ashi candle charts. These candle patterns indicate a potential trend reversal or pullback.
The patterns identified are:
- Bearish Harami (Red Highlight above Bar):
One to three (default 3) large body Bull (green) candles followed by a small (red)
or no body candle (less than 0.5pip) with wicks top and bottom that are at least 60% of candle.
- Bullish Harami (Green Highlight below Bar):
One to three (default 3) large body Bear (red) candles followed by a small (green)
or no body candle (less than 0.5pip) with wicks top and bottom that are at least 60% of candle.
- Bearish Doji (Fuchsia Highlight above Bar):
One to three (default 3) large body Bull (green) candles followed by a small (green)
with wicks top and bottom that are at least 60% of candle.
- Bullish Doji (Aqua Highlight below Bar):
One to three (default 3) large body Bear (red) candles followed by a small (red)
with wicks top and bottom that are at least 60% of candle.
You can optionally specify how large the candles prior to Harami/Doji are in pips, default is 0 pip.
If you set this to zero then it will have no candle size consideration. You can also specify how many look back candles (1-3) are used in Harami/Doji calculations (default 3).
Included option to perform Calculations purely on Heikin Ashi candles, this helps when you want to see the HA Doji/Harami bars with the normal candle stick chart.
Also can optionally set an alert condition for when Harami/Doji found, this also displays a circle on the bottom of the screen when alert is triggered.
extended session - Regular Opening-Range- JayyOpening Range and some other scripts updated to plot correctly (see comments below.) There are three variations of the fibonacci expansion beyond the opening range and retracements within the opening range of the US Market session - I have not put in the script for the other markets yet.
The three scripts have different uses and strengths:
The extended session script (with the script here below) will plot the opening range whether you are using the extended session or the regular session. (that is to say whether "ext" in the lower right hand corner is highlighted or not.). While in the extended session the opening range has some plotting issues with periods like 13 minutes or any period that is not divisible into 330 mins with a round number outcome (eg 330/60 =5.5. Therefore an hour long opening range has problems in the extended session.
The pre session script is only for the premarket. You can select any opening range period you like. I have set the opening range to be the full premarket session. If you select a different session you will have to unselect "pre open to 9:30 EST for Opening Range?" in the format section. The script defaults to 15 minutes in the "period Of Pre Opening Range?". To go back to the 4 am to 9:30 pre opening range select "pre open to 9:30 EST for Opening Range?" there is no automatic 330 minute selection.
The past days offset script only works in 5 min or 15 minute period. It will show the opening range from up to 20 days past over the current days price action. Use this for the regular session only. 0 shows the current day's opening range. Use the positive integers for number of days back ie 1, 2, 3 etc not -1, -2, -3 etc. The script is preprogrammed to use the current day (0).
Scripts updated to plot correctly: One thing they all have in common is a way of they deal with a somewhat random problem that shifts the plots 4 hours in one direction or the other ie the plot started at 9:30 EST or 1:30PM EST. This issue started to occur approximately June 22, 2015 and impacts any script that tried to use "session" times to manage a plot in my scripts. The issue now seems to have been resolved during this past week.
Just in case the problem reoccurs I have added a "Switch session plot?" to each script. If the plot looks funny check or uncheck the "Switch session plot?" and see the difference. Of course if a new issue crops up it will likely require a different fix.
I have updated all of the scripts shown on this chart. If you are using a script of mine that suffers from the compiler issue then you will find an update on this chart. You can get any and all of the scripts by clicking on the small sideways wishbone on the left middle of the chart. You will see a dialogue box. Then click "make it mine". This will import all of the scripts to your computer and you can play around with them all to decide what you want and what you don't want. This is the easiest way to get all of the scripts in one fell swoop. It is also the easiest way for me to make all of the scripts available. I do not have all of the plots visible since it is too messy and one of the scripts (pre OR) is only for the regular session. To view the scripts click on the blue eye to the right of the script title to show it on this script. If you can only use the regular session. The scripts will all (with the exception of the pre OR) work fine.
If for any reason this script seems flakey refresh the page r try a slightly different period. I have noticed that sometimes randomly the script loves to return to the 5 min OR. This is a very new issue transient issue. As always if you see an issue please let me know.
Cheers Jayy
Camarilla - formula updated for 5 and 6 levelsSince levels 5 and 6 formulas are kind of surrounded in mystery it's difficult to find a widely agreed one.
While for the level 5 there is some consensus the 6th one is hard to find. I updated level 5 with the most common use of lvl 5 formula , some links like this one or from books (Secrets of a Pivot Boss) . Level 6 is a tough one, so please use this one experimentally . If you have other formulas for level 6, let me know. The 5 and 6 lvls are useful in volatile days.
forums.babypips.com
Grok/Claude Turtle Soup Strategy # 🥣 Turtle Soup Strategy (Enhanced)
## A Mean-Reversion Strategy Based on Failed Breakouts
---
## Historical Origins
### The Original Turtle Traders (1983-1988)
The Turtle Trading system is one of the most famous experiments in trading history. In 1983, legendary commodities trader **Richard Dennis** made a bet with his partner **William Eckhardt** about whether great traders were born or made. Dennis believed trading could be taught; Eckhardt believed it was innate.
To settle the debate, Dennis recruited 23 ordinary people through newspaper ads—including a professional blackjack player, a fantasy game designer, and an accountant—and taught them his trading system in just two weeks. He called them "Turtles" after turtle farms he had visited in Singapore, saying *"We are going to grow traders just like they grow turtles in Singapore."*
The results were extraordinary. Over the next five years, the Turtles reportedly earned over **$175 million in profits**. The experiment proved Dennis right: trading could indeed be taught.
#### The Original Turtle Rules:
- **Entry:** Buy when price breaks above the 20-day high (System 1) or 55-day high (System 2)
- **Exit:** Sell when price breaks below the 10-day low (System 1) or 20-day low (System 2)
- **Stop Loss:** 2x ATR (Average True Range) from entry
- **Position Sizing:** Based on volatility (ATR)
- **Philosophy:** Pure trend-following—catch big moves by riding breakouts
The Turtle system was a **trend-following** strategy that assumed breakouts would lead to sustained trends. It worked brilliantly in trending markets but suffered during choppy, range-bound conditions.
---
### The Turtle Soup Strategy (1990s)
In the 1990s, renowned trader **Linda Bradford Raschke** (along with Larry Connors) observed something interesting: many of the breakouts that the Turtle system traded actually *failed*. Price would spike above the 20-day high, trigger Turtle buy orders, then immediately reverse—trapping the breakout traders.
Raschke realized these failed breakouts were predictable and tradeable. She developed the **Turtle Soup** strategy, which does the *exact opposite* of the original Turtle system:
> *"Instead of buying the breakout, we wait for it to fail—then fade it."*
The name "Turtle Soup" is a clever play on words: the strategy essentially "eats" the Turtles by trading against them when their breakouts fail.
#### Original Turtle Soup Rules:
- **Setup:** Price makes a new 20-day high (or low)
- **Qualifier:** The previous 20-day high must be at least 3-4 days old (not a fresh breakout)
- **Entry Trigger:** Price reverses back inside the channel (failed breakout)
- **Entry:** Go SHORT (against the failed breakout above), or LONG (against the failed breakdown below)
- **Philosophy:** Mean-reversion—fade false breakouts and profit from trapped traders
#### Turtle Soup Plus One Variant:
Raschke also developed a more conservative variant called "Turtle Soup Plus One" which waits for the *next bar* after the breakout to confirm the failure before entering. This reduces false signals but may miss some opportunities.
---
## Our Enhanced Turtle Soup Strategy
We have taken the classic Turtle Soup concept and enhanced it with modern technical indicators and filters to improve signal quality and adapt to today's markets.
### Core Logic Preserved
The fundamental strategy remains true to Raschke's original concept:
| Turtle (Original) | Turtle Soup (Our Strategy) |
|-------------------|---------------------------|
| BUY breakout above 20-day high | SHORT when that breakout FAILS |
| SELL breakout below 20-day low | LONG when that breakdown FAILS |
| Trend-following | Mean-reversion |
| "The trend is your friend" | "Failed breakouts trap traders" |
---
### Enhancements & Improvements
#### 1. RSI Exhaustion Filter
**Addition:** RSI must confirm exhaustion before entry
- **For SHORT entries:** RSI > 60 (buyers exhausted)
- **For LONG entries:** RSI < 40 (sellers exhausted)
**Why:** The original Turtle Soup had no momentum filter. Adding RSI ensures we only fade breakouts when the market is showing signs of exhaustion, significantly reducing false signals. This enhancement was inspired by later traders who found RSI extremes (originally 90/10, softened to 60/40) dramatically improved win rates.
#### 2. ADX Trending Filter
**Addition:** ADX must be > 20 for trades to execute
**Why:** While the original Turtle Soup was designed for ranging markets, we found that requiring *some* trend strength (ADX > 20) actually improves results. This ensures we're trading in markets with enough directional movement to create meaningful failed breakouts, rather than random noise in dead markets.
#### 3. Heikin Ashi Smoothing
**Addition:** Optional Heikin Ashi calculations for breakout detection
**Why:** Heikin Ashi candles smooth out price noise and make trend reversals more visible. When enabled, the strategy uses HA values to detect breakouts and failures, reducing whipsaws from erratic price spikes.
#### 4. Dynamic Donchian Channels with Regime Detection
**Addition:** Color-coded channels based on market regime
- 🟢 **Green:** Bullish regime (uptrend + DI+ > DI- + OBV bullish)
- 🔴 **Red:** Bearish regime (downtrend + DI- > DI+ + OBV bearish)
- 🟡 **Yellow:** Neutral regime
**Why:** Visual regime detection helps traders understand the broader market context. The original Turtle Soup had no regime awareness—our enhancement lets traders see at a glance whether conditions favor the strategy.
#### 5. Volume Spike Detection (Optional)
**Addition:** Optional filter requiring volume surge on the breakout bar
**Why:** Failed breakouts are more significant when they occur on high volume. A volume spike on the breakout bar (default 1.2x average) indicates more traders got trapped, creating stronger reversal potential.
#### 6. ATR-Based Stops and Targets
**Addition:** Configurable ATR-based stop losses and profit targets
- **Stop Loss:** 1.5x ATR (default)
- **Profit Target:** 2.0x ATR (default)
**Why:** The original Turtle Soup used fixed stop placement. ATR-based stops adapt to current volatility, providing tighter stops in calm markets and wider stops in volatile conditions.
#### 7. Signal Cooldown
**Addition:** Minimum bars between trades (default 5)
**Why:** Prevents overtrading during choppy conditions where multiple failed breakouts might occur in quick succession.
#### 8. Real-Time Info Panel
**Addition:** Comprehensive dashboard showing:
- Current regime (Bullish/Bearish/Neutral)
- RSI value and zone
- ADX value and trending status
- Breakout status
- Bars since last high/low
- Current setup status
- Position status
**Why:** Gives traders instant visibility into all strategy conditions without needing to check multiple indicators.
---
## Entry Rules Summary
### SHORT Entry (Fading Failed Breakout Above)
1. ✅ Price breaks ABOVE the 20-period Donchian high
2. ✅ Previous 20-period high was at least 1 bar ago
3. ✅ Price closes back BELOW the Donchian high (failed breakout)
4. ✅ RSI > 60 (exhausted buyers)
5. ✅ ADX > 20 (trending market)
6. ✅ Cooldown period met
→ **Enter SHORT**, betting the breakout will fail
### LONG Entry (Fading Failed Breakdown Below)
1. ✅ Price breaks BELOW the 20-period Donchian low
2. ✅ Previous 20-period low was at least 1 bar ago
3. ✅ Price closes back ABOVE the Donchian low (failed breakdown)
4. ✅ RSI < 40 (exhausted sellers)
5. ✅ ADX > 20 (trending market)
6. ✅ Cooldown period met
→ **Enter LONG**, betting the breakdown will fail
---
## Exit Rules
1. **ATR Stop Loss:** Position closed if price moves 1.5x ATR against entry
2. **ATR Profit Target:** Position closed if price moves 2.0x ATR in favor
3. **Channel Exit:** Position closed if price breaks the exit channel in the opposite direction
4. **Mid-Channel Exit:** Position closed if price returns to channel midpoint
---
## Best Market Conditions
The Turtle Soup strategy performs best when:
- ✅ Markets are prone to false breakouts
- ✅ Volatility is moderate (not too low, not extreme)
- ✅ Price is oscillating within a broader range
- ✅ There are clear support/resistance levels
The strategy may struggle when:
- ❌ Strong trends persist (breakouts follow through)
- ❌ Volatility is extremely low (no meaningful breakouts)
- ❌ Markets are in news-driven directional moves
---
## Default Settings
| Parameter | Default | Description |
|-----------|---------|-------------|
| Lookback Period | 20 | Donchian channel period |
| Min Bars Since Extreme | 1 | Bars since last high/low |
| RSI Length | 14 | RSI calculation period |
| RSI Short Level | 60 | RSI must be above this for shorts |
| RSI Long Level | 40 | RSI must be below this for longs |
| ADX Length | 14 | ADX calculation period |
| ADX Threshold | 20 | Minimum ADX for trades |
| ATR Period | 20 | ATR calculation period |
| ATR Stop Multiplier | 1.5 | Stop loss distance in ATR |
| ATR Target Multiplier | 2.0 | Profit target distance in ATR |
| Cooldown Period | 5 | Minimum bars between trades |
| Volume Multiplier | 1.2 | Volume spike threshold |
---
## Philosophy
> *"The Turtle system made millions by following breakouts. The Turtle Soup strategy makes money when those breakouts fail. In trading, there's always someone on the other side of the trade—this strategy profits by being the smart money that fades the trapped breakout traders."*
The beauty of the Turtle Soup strategy is its elegant simplicity: it exploits a known, repeatable pattern (failed breakouts) while using modern filters (RSI, ADX) to improve timing and reduce false signals.
---
## Credits
- **Original Turtle System:** Richard Dennis & William Eckhardt (1983)
- **Turtle Soup Strategy:** Linda Bradford Raschke & Larry Connors (1990s)
- **RSI Enhancement:** Various traders who discovered RSI extremes improve reversal detection
- **This Implementation:** Enhanced with Heikin Ashi smoothing, regime detection, ADX filtering, and comprehensive visualization
---
*"We're not following the turtles—we're making soup out of them."* 🥣
Expected Move BandsExpected move is the amount that an asset is predicted to increase or decrease from its current price, based on the current levels of volatility.
In this model, we assume asset price follows a log-normal distribution and the log return follows a normal distribution.
Note: Normal distribution is just an assumption, it's not the real distribution of return
Settings:
"Estimation Period Selection" is for selecting the period we want to construct the prediction interval.
For "Current Bar", the interval is calculated based on the data of the previous bar close. Therefore changes in the current price will have little effect on the range. What current bar means is that the estimated range is for when this bar close. E.g., If the Timeframe on 4 hours and 1 hour has passed, the interval is for how much time this bar has left, in this case, 3 hours.
For "Future Bars", the interval is calculated based on the current close. Therefore the range will be very much affected by the change in the current price. If the current price moves up, the range will also move up, vice versa. Future Bars is estimating the range for the period at least one bar ahead.
There are also other source selections based on high low.
Time setting is used when "Future Bars" is chosen for the period. The value in time means how many bars ahead of the current bar the range is estimating. When time = 1, it means the interval is constructing for 1 bar head. E.g., If the timeframe is on 4 hours, then it's estimating the next 4 hours range no matter how much time has passed in the current bar.
Note: It's probably better to use "probability cone" for visual presentation when time > 1
Volatility Models :
Sample SD: traditional sample standard deviation, most commonly used, use (n-1) period to adjust the bias
Parkinson: Uses High/ Low to estimate volatility, assumes continuous no gap, zero mean no drift, 5 times more efficient than Close to Close
Garman Klass: Uses OHLC volatility, zero drift, no jumps, about 7 times more efficient
Yangzhang Garman Klass Extension: Added jump calculation in Garman Klass, has the same value as Garman Klass on markets with no gaps.
about 8 x efficient
Rogers: Uses OHLC, Assume non-zero mean volatility, handles drift, does not handle jump 8 x efficient
EWMA: Exponentially Weighted Volatility. Weight recently volatility more, more reactive volatility better in taking account of volatility autocorrelation and cluster.
YangZhang: Uses OHLC, combines Rogers and Garmand Klass, handles both drift and jump, 14 times efficient, alpha is the constant to weight rogers volatility to minimize variance.
Median absolute deviation: It's a more direct way of measuring volatility. It measures volatility without using Standard deviation. The MAD used here is adjusted to be an unbiased estimator.
Volatility Period is the sample size for variance estimation. A longer period makes the estimation range more stable less reactive to recent price. Distribution is more significant on a larger sample size. A short period makes the range more responsive to recent price. Might be better for high volatility clusters.
Standard deviations:
Standard Deviation One shows the estimated range where the closing price will be about 68% of the time.
Standard Deviation two shows the estimated range where the closing price will be about 95% of the time.
Standard Deviation three shows the estimated range where the closing price will be about 99.7% of the time.
Note: All these probabilities are based on the normal distribution assumption for returns. It's the estimated probability, not the actual probability.
Manually Entered Standard Deviation shows the range of any entered standard deviation. The probability of that range will be presented on the panel.
People usually assume the mean of returns to be zero. To be more accurate, we can consider the drift in price from calculating the geometric mean of returns. Drift happens in the long run, so short lookback periods are not recommended. Assuming zero mean is recommended when time is not greater than 1.
When we are estimating the future range for time > 1, we typically assume constant volatility and the returns to be independent and identically distributed. We scale the volatility in term of time to get future range. However, when there's autocorrelation in returns( when returns are not independent), the assumption fails to take account of this effect. Volatility scaled with autocorrelation is required when returns are not iid. We use an AR(1) model to scale the first-order autocorrelation to adjust the effect. Returns typically don't have significant autocorrelation. Adjustment for autocorrelation is not usually needed. A long length is recommended in Autocorrelation calculation.
Note: The significance of autocorrelation can be checked on an ACF indicator.
ACF
The multimeframe option enables people to use higher period expected move on the lower time frame. People should only use time frame higher than the current time frame for the input. An error warning will appear when input Tf is lower. The input format is multiplier * time unit. E.g. : 1D
Unit: M for months, W for Weeks, D for Days, integers with no unit for minutes (E.g. 240 = 240 minutes). S for Seconds.
Smoothing option is using a filter to smooth out the range. The filter used here is John Ehler's supersmoother. It's an advance smoothing technique that gets rid of aliasing noise. It affects is similar to a simple moving average with half the lookback length but smoother and has less lag.
Note: The range here after smooth no long represent the probability
Panel positions can be adjusted in the settings.
X position adjusts the horizontal position of the panel. Higher X moves panel to the right and lower X moves panel to the left.
Y position adjusts the vertical position of the panel. Higher Y moves panel up and lower Y moves panel down.
Step line display changes the style of the bands from line to step line. Step line is recommended because it gets rid of the directional bias of slope of expected move when displaying the bands.
Warnings:
People should not blindly trust the probability. They should be aware of the risk evolves by using the normal distribution assumption. The real return has skewness and high kurtosis. While skewness is not very significant, the high kurtosis should be noticed. The Real returns have much fatter tails than the normal distribution, which also makes the peak higher. This property makes the tail ranges such as range more than 2SD highly underestimate the actual range and the body such as 1 SD slightly overestimate the actual range. For ranges more than 2SD, people shouldn't trust them. They should beware of extreme events in the tails.
Different volatility models provide different properties if people are interested in the accuracy and the fit of expected move, they can try expected move occurrence indicator. (The result also demonstrate the previous point about the drawback of using normal distribution assumption).
Expected move Occurrence Test
The prediction interval is only for the closing price, not wicks. It only estimates the probability of the price closing at this level, not in between. E.g., If 1 SD range is 100 - 200, the price can go to 80 or 230 intrabar, but if the bar close within 100 - 200 in the end. It's still considered a 68% one standard deviation move.
️Omega RatioThe Omega Ratio is a risk-return performance measure of an investment asset, portfolio, or strategy. It is defined as the probability-weighted ratio, of gains versus losses for some threshold return target. The ratio is an alternative for the widely used Sharpe ratio and is based on information the Sharpe ratio discards.
█ OVERVIEW
As we have mentioned many times, stock market returns are usually not normally distributed. Therefore the models that assume a normal distribution of returns may provide us with misleading information. The Omega Ratio improves upon the common normality assumption among other risk-return ratios by taking into account the distribution as a whole.
█ CONCEPTS
Two distributions with the same mean and variance, would according to the most commonly used Sharpe Ratio suggest that the underlying assets of the distribution offer the same risk-return ratio. But as we have mentioned in our Moments indicator, variance and standard deviation are not a sufficient measure of risk in the stock market since other shape features of a distribution like skewness and excess kurtosis come into play. Omega Ratio tackles this problem by employing all four Moments of the distribution and therefore taking into account the differences in the shape features of the distributions. Another important feature of the Omega Ratio is that it does not require any estimation but is rather calculated directly from the observed data. This gives it an advantage over standard statistical estimators that require estimation of parameters and are therefore sampling uncertainty in its calculations.
█ WAYS TO USE THIS INDICATOR
Omega calculates a probability-adjusted ratio of gains to losses, relative to the Minimum Acceptable Return (MAR). This means that at a given MAR using the simple rule of preferring more to less, an asset with a higher value of Omega is preferable to one with a lower value. The indicator displays the values of Omega at increasing levels of MARs and creating the so-called Omega Curve. Knowing this one can compare Omega Curves of different assets and decide which is preferable given the MAR of your strategy. The indicator plots two Omega Curves. One for the on chart symbol and another for the off chart symbol that u can use for comparison.
When comparing curves of different assets make sure their trading days are the same in order to ensure the same period for the Omega calculations. Value interpretation: Omega<1 will indicate that the risk outweighs the reward and therefore there are more excess negative returns than positive. Omega>1 will indicate that the reward outweighs the risk and that there are more excess positive returns than negative. Omega=1 will indicate that the minimum acceptable return equals the mean return of an asset. And that the probability of gain is equal to the probability of loss.
█ FEATURES
• "Low-Risk security" lets you select the security that you want to use as a benchmark for Omega calculations.
• "Omega Period" is the size of the sample that is used for the calculations.
• “Increments” is the number of Minimal Acceptable Return levels the calculation is carried on. • “Other Symbol” lets you select the source of the second curve.
• “Color Settings” you can set the color for each curve.
Historical Volatility EstimatorsHistorical volatility is a statistical measure of the dispersion of returns for a given security or market index over a given period. This indicator provides different historical volatility model estimators with percentile gradient coloring and volatility stats panel.
█ OVERVIEW There are multiple ways to estimate historical volatility. Other than the traditional close-to-close estimator. This indicator provides different range-based volatility estimators that take high low open into account for volatility calculation and volatility estimators that use other statistics measurements instead of standard deviation. The gradient coloring and stats panel provides an overview of how high or low the current volatility is compared to its historical values.
█ CONCEPTS We have mentioned the concepts of historical volatility in our previous indicators, Historical Volatility, Historical Volatility Rank, and Historical Volatility Percentile. You can check the definition of these scripts. The basic calculation is just the sample standard deviation of log return scaled with the square root of time. The main focus of this script is the difference between volatility models.
Close-to-Close HV Estimator: Close-to-Close is the traditional historical volatility calculation. It uses sample standard deviation. Note: the TradingView build in historical volatility value is a bit off because it uses population standard deviation instead of sample deviation. N – 1 should be used here to get rid of the sampling bias.
Pros:
• Close-to-Close HV estimators are the most commonly used estimators in finance. The calculation is straightforward and easy to understand. When people reference historical volatility, most of the time they are talking about the close to close estimator.
Cons:
• The Close-to-close estimator only calculates volatility based on the closing price. It does not take account into intraday volatility drift such as high, low. It also does not take account into the jump when open and close prices are not the same.
• Close-to-Close weights past volatility equally during the lookback period, while there are other ways to weight the historical data.
• Close-to-Close is calculated based on standard deviation so it is vulnerable to returns that are not normally distributed and have fat tails. Mean and Median absolute deviation makes the historical volatility more stable with extreme values.
Parkinson Hv Estimator:
• Parkinson was one of the first to come up with improvements to historical volatility calculation. • Parkinson suggests using the High and Low of each bar can represent volatility better as it takes into account intraday volatility. So Parkinson HV is also known as Parkinson High Low HV. • It is about 5.2 times more efficient than Close-to-Close estimator. But it does not take account into jumps and drift. Therefore, it underestimates volatility. Note: By Dividing the Parkinson Volatility by Close-to-Close volatility you can get a similar result to Variance Ratio Test. It is called the Parkinson number. It can be used to test if the market follows a random walk. (It is mentioned in Nassim Taleb's Dynamic Hedging book but it seems like he made a mistake and wrote the ratio wrongly.)
Garman-Klass Estimator:
• Garman Klass expanded on Parkinson’s Estimator. Instead of Parkinson’s estimator using high and low, Garman Klass’s method uses open, close, high, and low to find the minimum variance method.
• The estimator is about 7.4 more efficient than the traditional estimator. But like Parkinson HV, it ignores jumps and drifts. Therefore, it underestimates volatility.
Rogers-Satchell Estimator:
• Rogers and Satchell found some drawbacks in Garman-Klass’s estimator. The Garman-Klass assumes price as Brownian motion with zero drift.
• The Rogers Satchell Estimator calculates based on open, close, high, and low. And it can also handle drift in the financial series.
• Rogers-Satchell HV is more efficient than Garman-Klass HV when there’s drift in the data. However, it is a little bit less efficient when drift is zero. The estimator doesn’t handle jumps, therefore it still underestimates volatility.
Garman-Klass Yang-Zhang extension:
• Yang Zhang expanded Garman Klass HV so that it can handle jumps. However, unlike the Rogers-Satchell estimator, this estimator cannot handle drift. It is about 8 times more efficient than the traditional estimator.
• The Garman-Klass Yang-Zhang extension HV has the same value as Garman-Klass when there’s no gap in the data such as in cryptocurrencies.
Yang-Zhang Estimator:
• The Yang Zhang Estimator combines Garman-Klass and Rogers-Satchell Estimator so that it is based on Open, close, high, and low and it can also handle non-zero drift. It also expands the calculation so that the estimator can also handle overnight jumps in the data.
• This estimator is the most powerful estimator among the range-based estimators. It has the minimum variance error among them, and it is 14 times more efficient than the close-to-close estimator. When the overnight and daily volatility are correlated, it might underestimate volatility a little.
• 1.34 is the optimal value for alpha according to their paper. The alpha constant in the calculation can be adjusted in the settings. Note: There are already some volatility estimators coded on TradingView. Some of them are right, some of them are wrong. But for Yang Zhang Estimator I have not seen a correct version on TV.
EWMA Estimator:
• EWMA stands for Exponentially Weighted Moving Average. The Close-to-Close and all other estimators here are all equally weighted.
• EWMA weighs more recent volatility more and older volatility less. The benefit of this is that volatility is usually autocorrelated. The autocorrelation has close to exponential decay as you can see using an Autocorrelation Function indicator on absolute or squared returns. The autocorrelation causes volatility clustering which values the recent volatility more. Therefore, exponentially weighted volatility can suit the property of volatility well.
• RiskMetrics uses 0.94 for lambda which equals 30 lookback period. In this indicator Lambda is coded to adjust with the lookback. It's also easy for EWMA to forecast one period volatility ahead.
• However, EWMA volatility is not often used because there are better options to weight volatility such as ARCH and GARCH.
Adjusted Mean Absolute Deviation Estimator:
• This estimator does not use standard deviation to calculate volatility. It uses the distance log return is from its moving average as volatility.
• It’s a simple way to calculate volatility and it’s effective. The difference is the estimator does not have to square the log returns to get the volatility. The paper suggests this estimator has more predictive power.
• The mean absolute deviation here is adjusted to get rid of the bias. It scales the value so that it can be comparable to the other historical volatility estimators.
• In Nassim Taleb’s paper, he mentions people sometimes confuse MAD with standard deviation for volatility measurements. And he suggests people use mean absolute deviation instead of standard deviation when we talk about volatility.
Adjusted Median Absolute Deviation Estimator:
• This is another estimator that does not use standard deviation to measure volatility.
• Using the median gives a more robust estimator when there are extreme values in the returns. It works better in fat-tailed distribution.
• The median absolute deviation is adjusted by maximum likelihood estimation so that its value is scaled to be comparable to other volatility estimators.
█ FEATURES
• You can select the volatility estimator models in the Volatility Model input
• Historical Volatility is annualized. You can type in the numbers of trading days in a year in the Annual input based on the asset you are trading.
• Alpha is used to adjust the Yang Zhang volatility estimator value.
• Percentile Length is used to Adjust Percentile coloring lookbacks.
• The gradient coloring will be based on the percentile value (0- 100). The higher the percentile value, the warmer the color will be, which indicates high volatility. The lower the percentile value, the colder the color will be, which indicates low volatility.
• When percentile coloring is off, it won’t show the gradient color.
• You can also use invert color to make the high volatility a cold color and a low volatility high color. Volatility has some mean reversion properties. Therefore when volatility is very low, and color is close to aqua, you would expect it to expand soon. When volatility is very high, and close to red, you would it expect it to contract and cool down.
• When the background signal is on, it gives a signal when HVP is very low. Warning there might be a volatility expansion soon.
• You can choose the plot style, such as lines, columns, areas in the plotstyle input.
• When the show information panel is on, a small panel will display on the right.
• The information panel displays the historical volatility model name, the 50th percentile of HV, and HV percentile. 50 the percentile of HV also means the median of HV. You can compare the value with the current HV value to see how much it is above or below so that you can get an idea of how high or low HV is. HV Percentile value is from 0 to 100. It tells us the percentage of periods over the entire lookback that historical volatility traded below the current level. Higher HVP, higher HV compared to its historical data. The gradient color is also based on this value.
█ HOW TO USE If you haven’t used the hvp indicator, we suggest you use the HVP indicator first. This indicator is more like historical volatility with HVP coloring. So it displays HVP values in the color and panel, but it’s not range bound like the HVP and it displays HV values. The user can have a quick understanding of how high or low the current volatility is compared to its historical value based on the gradient color. They can also time the market better based on volatility mean reversion. High volatility means volatility contracts soon (Move about to End, Market will cooldown), low volatility means volatility expansion soon (Market About to Move).
█ FINAL THOUGHTS HV vs ATR The above volatility estimator concepts are a display of history in the quantitative finance realm of the research of historical volatility estimations. It's a timeline of range based from the Parkinson Volatility to Yang Zhang volatility. We hope these descriptions make more people know that even though ATR is the most popular volatility indicator in technical analysis, it's not the best estimator. Almost no one in quant finance uses ATR to measure volatility (otherwise these papers will be based on how to improve ATR measurements instead of HV). As you can see, there are much more advanced volatility estimators that also take account into open, close, high, and low. HV values are based on log returns with some calculation adjustment. It can also be scaled in terms of price just like ATR. And for profit-taking ranges, ATR is not based on probabilities. Historical volatility can be used in a probability distribution function to calculated the probability of the ranges such as the Expected Move indicator. Other Estimators There are also other more advanced historical volatility estimators. There are high frequency sampled HV that uses intraday data to calculate volatility. We will publish the high frequency volatility estimator in the future. There's also ARCH and GARCH models that takes volatility clustering into account. GARCH models require maximum likelihood estimation which needs a solver to find the best weights for each component. This is currently not possible on TV due to large computational power requirements. All the other indicators claims to be GARCH are all wrong.
Session Markers - JDK AnalysisSession Markers is a tool designed to study how markets behave during specific, recurring time windows. Many traders know that price behaves differently depending on the day of the week, the time of the day, or particular market sessions such as the weekly open, the London session, or the New York open. This indicator makes those recurring windows visible on the chart and then analyzes what price typically does inside them. The result is a clear statistical understanding of how a chosen session behaves, both in direction and in strength.
The script works by allowing the trader to define any time window using a start day and time and an end day and time. Every time this window occurs on the chart, the indicator highlights it with a full-height vertical band. These visual markers reveal patterns that are otherwise difficult to detect manually, such as whether certain sessions tend to trend, reverse, consolidate, or create large imbalances. They also help the trader quickly scan through historical price action to see how the market has behaved under similar conditions.
For every completed session window, the indicator measures how much price changed from the moment the window began to the moment it ended. Instead of using raw price differences, it converts these changes into percentage moves. This makes the measurement consistent across different price ranges and market regimes. A one-percent move always has the same meaning, whether the asset is trading at 100 or 50,000. These percentage moves are collected for a user-selected number of past sessions, creating a dataset of how the market has behaved in the chosen time window.
Based on this dataset, the indicator generates several statistics. It counts how many past sessions closed higher and how many closed lower, producing a directional tendency. It also computes the probability of an upward session by dividing the number of positive sessions by the total. More importantly, it calculates the average percentage movement for all sessions in the lookback period. This average move reflects not just the direction but also the magnitude of price changes. A session with frequent small upward moves but occasional large downward moves will show a negative average movement, even if more sessions ended positive. This creates a more realistic representation of true market behavior.
Using this average movement, the script determines a “Bias” for the session. If the average percentage move is positive, the bias is considered bullish. If it is negative, the bias is bearish. If the values are very close to zero, the bias is neutral. This way, the indicator takes both frequency and impact into account, producing a magnitude-aware assessment instead of one that only counts wins and losses. A sequence such as +5%, –1% results in a bullish bias because the overall impact is strongly positive. On the other hand, a series of small gains followed by a large drop produces a bearish bias even if more sessions ended positive, because the large move dominates the average. This provides a far more truthful picture of what the market tends to do during the chosen window.
All relevant statistics are displayed neatly in a small panel in the top-right corner of the chart. The panel updates in real time as new sessions complete and older ones fall out of the lookback range. It shows how many sessions were analyzed, how many ended up or down, the probability of an upward move, the average percentage change, and the final bias. The background color of the panel instantly reflects that bias, making it easy to interpret at a glance.
To use the tool effectively, the trader simply needs to define a time window of interest. This could be something like the weekly opening window from Sunday to Monday, the London open each day, or even a unique custom window. After selecting how many past sessions to analyze, the indicator takes care of the rest. The vertical session markers reveal the structure visually. The statistics summarize the historical behavior objectively. The magnitude-weighted bias provides a realistic indication of whether the window tends to produce upward or downward movement on average.
Session Markers is helpful because it translates repeated market timing behavior into measurable data. It exposes hidden tendencies that are easy to feel intuitively but hard to quantify manually. By analyzing both direction and magnitude, it prevents misleading interpretations that can arise from looking only at win rates. It helps traders understand whether a session typically produces meaningful moves or just small noise, whether it tends to trend or reverse, and whether its behavior has recently changed. Whether used for bias building, session filtering, or deeper market research, it offers a structured framework for understanding the market through time-based patterns.
MTC – Multi-Timeframe Trend Confirmator V2MTC – Multi-Timeframe Trend Confirmator V2
A comprehensive trend analysis indicator that systematically combines six technical indicators across three customizable timeframes, using a weighted scoring system to identify high-probability trend conditions.
ORIGINALITY AND CONCEPT
This indicator is original in its approach to multi-timeframe trend confirmation. Rather than relying on a single indicator or timeframe, it creates a composite score by evaluating six different technical conditions simultaneously across three timeframes. The scoring system weighs certain indicators more heavily based on their reliability in trend identification. The visual gauge provides an at-a-glance view of trend alignment across timeframes, making it easier to identify when multiple timeframes agree - a condition that typically produces stronger, more reliable trends.
HOW IT WORKS - DETAILED SCORING METHODOLOGY
The indicator evaluates six technical conditions on each timeframe. Each condition contributes to a composite score:
EMA 200 (Weight: 1 point)
Bullish: Price closes above EMA 200 (+1)
Bearish: Price closes below EMA 200 (-1)
Rationale: Long-term trend direction
SMA 50/200 Crossover (Weight: 1 point)
Bullish: SMA 50 above SMA 200 (+1)
Bearish: SMA 50 below SMA 200 (-1)
Rationale: Golden/Death cross confirmation
RSI 14 (Weight: 1 point)
Bullish: RSI above 55 (+1)
Bearish: RSI below 45 (-1)
Neutral: RSI between 45-55 (0)
Rationale: Momentum filter with buffer zone to avoid chop
MACD (12,26,9) (Weight: 1 point)
Bullish: MACD line above signal line (+1)
Bearish: MACD line below signal line (-1)
Rationale: Trend momentum confirmation
ADX 14 (Weight: 2 points - DOUBLE WEIGHTED)
Requires ADX above 25 to activate
Bullish: DI+ above DI- and ADX > 25 (+2)
Bearish: DI- above DI+ and ADX > 25 (-2)
Neutral: ADX below 25 (0)
Rationale: Trend strength filter - only counts when a strong trend exists. Double weighted because ADX is specifically designed to measure trend strength, making it more reliable than oscillators.
Supertrend (Factor: 3.0, ATR Period: 10) (Weight: 2 points - DOUBLE WEIGHTED)
Bullish: Direction indicator = -1 (+2)
Bearish: Direction indicator = +1 (-2)
Rationale: Dynamic support/resistance that adapts to volatility. Double weighted because Supertrend provides clear, objective trend signals with built-in stop-loss levels.
COMPOSITE SCORE CALCULATION:
Total possible score range: -10 to +10 points
Score interpretation:
Score > 2: UPTREND (majority of indicators bullish, especially weighted ones)
Score < -2: DOWNTREND (majority of indicators bearish, especially weighted ones)
Score between -2 and +2: NEUTRAL/RANGING (mixed signals or weak trend)
The threshold of +/- 2 was chosen because it requires more than just basic agreement - it typically means at least 3-4 indicators align, or that the heavily-weighted indicators (ADX, Supertrend) confirm the direction.
MULTI-TIMEFRAME LOGIC:
The indicator calculates the composite score independently for three timeframes:
Higher Timeframe (default: 4H) - Major trend direction
Mid Timeframe (default: 1H) - Intermediate trend
Lower Timeframe (default: 15min) - Entry timing
Main Trend Confirmation Rule:
The indicator only signals a confirmed trend when BOTH the higher timeframe AND mid timeframe scores agree (both > 2 for uptrend, or both < -2 for downtrend). This dual-timeframe confirmation significantly reduces false signals during choppy or ranging markets.
HOW TO USE IT
Setup:
Add indicator to chart
Customize timeframes based on your trading style:
Scalpers: 15min, 5min, 1min
Day traders: 4H, 1H, 15min (default)
Swing traders: Daily, 4H, 1H
Toggle individual indicators on/off based on your preference
Adjust Supertrend parameters if needed for your instrument's volatility
Reading the Gauge (Top Right Corner):
Each row shows one timeframe
Left column: Timeframe label
Middle column: Visual strength bars (10 bars = maximum score)
Green bars = Bullish score
Red bars = Bearish score
Yellow bars = Neutral/ranging
More filled bars = stronger trend
Right column: Numerical score
Trading Signals:
Entry Signals:
Long Entry: Wait for upward triangle arrow (appears when higher + mid TF both bullish)
Confirm gauge shows green bars on higher and mid timeframes
Lower timeframe should ideally turn green for entry timing
Chart background tints light green
Short Entry: Wait for downward triangle arrow (appears when higher + mid TF both bearish)
Confirm gauge shows red bars on higher and mid timeframes
Lower timeframe should ideally turn red for entry timing
Chart background tints light red
Position Management:
Stay in position while higher and mid timeframes remain aligned
Consider reducing position size when mid timeframe score weakens
Exit when higher timeframe trend reverses (daily label changes)
Avoiding False Signals:
Ignore signals when gauge shows mixed colors across timeframes
Avoid trading when scores are close to threshold (+/- 2 to +/- 4 range)
Best trades occur when all three timeframes align (all green or all red in gauge)
Use the numerical scores: higher absolute values (7-10) indicate stronger, more reliable trends
Practical Examples:
Example 1 - Strong Uptrend Entry:
Higher TF: +8 (strong green bars)
Mid TF: +6 (strong green bars)
Lower TF: +4 (moderate green bars)
Action: Look for long entries on lower timeframe pullbacks
Background is tinted green, upward arrow appears
Example 2 - Ranging Market (Avoid):
Higher TF: +3 (weak green)
Mid TF: -1 (weak red)
Lower TF: +2 (neutral yellow)
Action: Stay out, wait for alignment
Example 3 - Trend Reversal Warning:
Higher TF: +7 (still green)
Mid TF: -3 (turned red)
Lower TF: -5 (strong red)
Action: Consider exiting longs, prepare for potential higher TF reversal
Customization Options:
Timeframes: Adjust all three to match your trading horizon
Indicator Toggles: Disable indicators that don't suit your instrument:
Disable RSI for highly volatile crypto markets
Disable SMA crossover for range-bound instruments
Keep ADX and Supertrend enabled for trending markets
Visual Preferences:
Arrow size: 5 options from Tiny to Huge
Gauge size: Small/Medium/Large for different screen sizes
Toggle arrows on/off if you only want the gauge
Alert Setup:
Right-click chart, "Add Alert"
Condition: MTC v6 - UPTREND or DOWNTREND
Get notified when multi-timeframe confirmation occurs
Best Practices:
Use with Price Action: The indicator works best when combined with support/resistance levels, chart patterns, and volume analysis
Risk Management: Even with multi-timeframe confirmation, always use stop losses
Market Context: Works best in trending markets; less reliable in strong consolidation
Backtesting: Test the default settings on your specific instrument and timeframe before live trading
Patience: Wait for full multi-timeframe alignment rather than taking premature signals
Technical Notes:
All calculations use Pine Script's security function to fetch data from multiple timeframes
Prevents repainting by using confirmed bar data
Gauge updates in real-time on the last bar
Daily labels mark at the open of each new daily candle
Works on all instruments and timeframes
This indicator is ideal for traders who want objective, systematic trend identification without the complexity of analyzing multiple indicators manually across different timeframes.
-NATANTIA
Grok/Claude AI Regime Engine • Grok/Claude X SeriesGrok/Claude AI Regime Engine
This is a TradingView indicator designed to identify market regimes (bullish, bearish, or neutral) and generate buy/sell signals based on multiple technical factors working together.
Core Concept
At its heart, this indicator tries to answer a simple question: "What kind of market are we in right now, and when should I consider buying or selling?"
It does this by blending several well-known technical analysis tools into a unified system. Think of it as a dashboard that synthesizes multiple indicators into clear, actionable information.
How It Determines Market Regime
The indicator creates what it calls a "Money Line" by combining two exponential moving averages (EMAs) — a fast one (default 8 periods) and a slow one (default 24 periods). These are weighted together, with the fast EMA getting 60% influence by default. This blended line serves as the primary trend reference.
Bullish regime is declared when the short EMA crosses above the long EMA, provided the RSI isn't already in overbought territory. Bearish regime kicks in when the opposite happens — short EMA crosses below long, as long as RSI isn't oversold. Neutral regime occurs when the indicator detects sideways, choppy conditions.
The neutral detection is particularly interesting. It uses two optional methods: one looks at how flat the Money Line's slope is (compared to recent volatility via ATR), and the other checks how close together the two EMAs are as a percentage of price. When the market is grinding sideways, these methods help the indicator avoid falsely calling a trend.
Signal Generation Logic
Buy and sell signals are generated using Donchian Channel breakouts as the trigger mechanism. The Donchian Channel tracks the highest high and lowest low over a lookback period (default 20 bars), using the previous bar's values to avoid repainting issues.
A buy signal fires when price touches or breaks below the lower Donchian band, suggesting a potential reversal from oversold conditions. A sell signal fires when price reaches the upper band. However, these raw breakout signals pass through several filters before being displayed:
FilterPurposeADX thresholdOnly signals when the market has sufficient trend strength (default: ADX > 25)RSI filterBuy signals require RSI to be oversold; sell signals require overbought RSICooldown periodPrevents signal spam by requiring a minimum number of bars between signalsClose confirmationOptional setting to require a candle close beyond the band, not just a wick
Additional Metrics Displayed
The indicator calculates and displays several supplementary metrics in an information panel. ADX (Average Directional Index) measures trend strength — values below 15 suggest a weak, ranging market, while above 25 indicates a strong trend. The colored dots at the bottom of the chart reflect this: white for weak, orange for moderate, blue for strong.
BBWP (Bollinger Band Width Percentile) measures current volatility relative to historical volatility over roughly a year of data. High readings suggest volatility expansion; low readings suggest compression, which often precedes significant moves.
Alerts and Notifications
The indicator generates alerts in two scenarios: when the market regime changes (bullish to bearish, etc.) and when buy/sell signals trigger. Alert messages include the ticker symbol, timeframe, current price, RSI, ADX, and other relevant context so you can quickly assess the situation without opening the chart.
Visual Customization
Users can toggle various display elements on or off, including the EMA lines, Donchian bands, shaded regime zones between the bands, and price labels at signal points. The shading between the upper and lower bands changes color based on the current regime — green for bullish, magenta for bearish, and blue for neutral — providing an at-a-glance view of market conditions over time.
Summary
This is essentially a trend-following system with mean-reversion entry signals, filtered by momentum and trend strength indicators. It's designed to help traders identify favorable market conditions and time entries while avoiding signals during choppy, directionless periods. The multiple confirmation layers aim to reduce false signals, though like any technical system, it will still produce losing trades in certain market conditions.
Multi EMA and SMA with VWAP Indicator📊 Custom Multi-MA & VWAP Indicator
A comprehensive and fully customizable moving average indicator that combines 6 Exponential Moving Averages (EMAs), 3 Simple Moving Averages (SMAs), and VWAP in one clean, easy-to-use tool.
✨ Features:
6 Configurable EMAs:
• Default periods: 9, 21, 50, 100, 150, 200
• Fully adjustable lengths
• Individual color customization
• Show/hide toggles for each EMA
3 Configurable SMAs:
• Default periods: 20, 50, 100
• Fully adjustable lengths
• Individual color customization
• Show/hide toggles for each SMA
• Thicker lines for easy distinction from EMAs
VWAP (Volume Weighted Average Price):
• Toggle on/off
• Customizable color and line width
• Essential for intraday trading and institutional levels
🎯 Use Cases:
• Trend identification and confirmation
• Support and resistance levels
• Entry and exit signals
• Multi-timeframe analysis
• Day trading and swing trading strategies
• Institutional price levels (VWAP)
⚙️ Fully Customizable:
Every aspect of this indicator is configurable through the settings panel:
• Adjust any MA period to fit your trading strategy
• Choose your preferred colors for better chart visualization
• Enable/disable specific MAs to reduce chart clutter
• Customize VWAP line thickness
📈 Perfect For:
• Traders who use multiple moving averages in their strategy
• Those seeking an all-in-one MA solution
• Clean chart organization with one indicator instead of multiple
• Both beginners and experienced traders
💡 Tips:
• Use shorter EMAs (9, 21) for quick trend changes
• Longer EMAs (100, 150, 200) act as strong support/resistance
• VWAP is particularly useful for intraday trading
• Customize colors to match your chart theme
Version: Pine Script v6
Overlay: Yes (plots directly on price chart)
MTF EMA Hariss 369The strategy has been prepared in a simplistic manner and easy to understand the concept by any novice trader.
Indicators used:
Current Time frame 20 EMA- Gives clear look about current time frame dynamic support and resistance and trend as well.
Higher Time Frame 20 EMA: Gives macro level trend, support and resistance
Kama: Capture volatility and trend direction.
RVOL: Main factor of price movement.
Buy when price closes above current time frame 20 ema and current time frame 20 ema is above higher time frame 20 ema. Stop loss just below the low of last candle. One can use current time frame 20 ema, higher time frame 20 ema or kama as stop loss depending upon type of asset class and risk appetite. The ideal way is to keep 20 ema as trailing sl if one wants to trail with trend.
Sell when price closes below current time frame 20 ema and current time frame 20 ema is lower than higher time frame 20 ema. Stop loss just above high of last candle.
Ideal target is 1.5 or 2 times of stop loss.
Entry and exit time depends on trading style. Eg. if you want to enter and exit in 5 min time frame, then choose 15 min or 1h as higher time frame as trend filter. Buy and sell signals are also plotted based on this strategy. One should always go with the higher time frame trend. Opting higher time frame trend filter always filters out market noises.
CoreTFRSIMD CoreTFRSIMD library — Reusable TFRSI core for consistent momentum inputs across scripts
The library provides a reusable exported function such as calcTfrsi(src, len, signalLen) so you can compute TFRSI in your own indicators or strategies, e.g. tfrsi = CoreTFRSIMD.calcTfrsi(close, 6, 2)
Summary
CoreTFRSIMD is a Pine Script v6 library that implements a TFRSI-style oscillator core and exposes it as a reusable exported function. It is designed for authors who want the same TFRSI calculation across multiple indicators or strategies without duplicating logic. The library includes a simple demo plot and band styling so you can visually sanity-check the output. No higher-timeframe sampling is used, and there are no loops or arrays, so runtime cost is minimal for typical chart usage.
Motivation: Why this design?
When you reuse an oscillator across different tools, small implementation differences create inconsistent signals and hard-to-debug results. This library isolates the signal path into one exported function so that every dependent script consumes the exact same oscillator output. The design combines filtering, normalization, and a final smoothing pass to produce a stable, RSI-like readout intended for momentum and regime context.
What’s different vs. standard approaches?
Baseline: Traditional RSI computed directly from gains and losses with standard smoothing.
Architecture differences:
A high-pass stage to attenuate slower components before the main smoothing.
A multi-pole smoothing stage implemented with persistent state to reduce noise.
A running peak-tracker style normalization that adapts to changing signal amplitude.
A final signal smoothing layer using a simple moving average.
Practical effect:
The oscillator output tends to be less dominated by raw volatility spikes and more consistent across changing conditions.
The normalization step helps keep the output in an RSI-like reading space without relying on fixed scaling.
How it works (technical)
1. Input source: The exported function accepts a source series and two integer parameters controlling responsiveness and final smoothing.
2. High-pass stage: A recursive filter is applied to the source to emphasize shorter-term movement. This stage uses persistent storage so it can reference prior internal states across bars.
3. Smoothing stage: The filtered stream is passed through a SuperSmoother-like recursive smoother derived from the chosen length. This again uses persistent state and prior values for continuity.
4. Adaptive normalization: The absolute magnitude of the smoothed stream is compared to a slowly decaying reference level. If the current magnitude exceeds the reference, the reference is updated. This acts like a “peak hold with decay” so the oscillator scales relative to recent conditions.
5. Oscillator mapping: The normalized value is mapped into an RSI-like reading range.
6. Signal smoothing: A simple moving average is applied over the requested signal length to reduce bar-to-bar chatter.
7. Demo rendering: The library script plots the oscillator, draws horizontal guide levels, and applies background plus gradient fills for overbought and oversold regions.
Parameter Guide
Parameter — Effect — Default — Trade-offs/Tips
src — Input series used by the oscillator — close in demo — Use close for general momentum, or a derived series if you want to emphasize a specific behavior.
len — Controls the responsiveness of internal filtering and smoothing — six in demo — Smaller values react faster but can increase short-term noise; larger values smooth more but can lag turns.
signalLen — Controls the final smoothing of the mapped oscillator — two in demo — Smaller values preserve detail but can flicker; larger values reduce flicker but can delay transitions.
Reading & Interpretation
The plot is an oscillator intended to be read similarly to an RSI-style momentum gauge.
The demo includes three reference levels: upper at one hundred, mid at fifty, and lower at zero.
The fills visually emphasize zones above the midline and below the midline. Treat these as context, not as standalone entries.
If the oscillator appears unusually compressed or unusually jumpy, the normalization reference may be adapting to an abrupt change in amplitude. That is expected behavior for adaptive normalization.
Practical Workflows & Combinations
Trend following:
Use structure first, then confirm with oscillator behavior around the midline.
Prefer signals aligned with higher-high higher-low or lower-low lower-high context from price.
Exits/Stops:
Use oscillator loss of momentum as a caution flag rather than an automatic exit trigger.
In strong trends, consider keeping risk rules price-based and use the oscillator mainly to avoid adding into exhaustion.
Multi-asset/Multi-timeframe:
Start with the demo defaults when you want a responsive oscillator.
If an asset is noisier, increase the main length or the signal smoothing length to reduce false flips.
Behavior, Constraints & Performance
Repaint/confirmation: No higher-timeframe sampling is used. Output updates on the live bar like any normal series. There is no explicit closed-bar gating in the library.
security or HTF: Not used, so there is no HTF synchronization risk.
Resources: No loops, no arrays, no large history buffers. Persistent variables are used for filter state.
Known limits: Like any filtered oscillator, sharp gaps and extreme one-bar events can produce transient distortions. The adaptive normalization can also make early bars unstable until enough history has accumulated.
Sensible Defaults & Quick Tuning
Starting values: length six, signal smoothing two.
Too many flips: Increase signal smoothing length, or increase the main length.
Too sluggish: Reduce the main length, or reduce signal smoothing length.
Choppy around midline: Increase signal smoothing length slightly and rely more on price structure filters.
What this indicator is—and isn’t
This library is a reusable signal component and visualization aid. It is not a complete trading system, not predictive, and not a substitute for market structure, execution rules, and risk controls. Use it as a momentum and regime context layer, and validate behavior per asset and timeframe before relying on it.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Best regards and happy trading
Chervolino
The Strat Lite [rdjxyz]◆ OVERVIEW
The Strat Lite is a stripped down version of the Strat Assistant indicator by rickyzcarroll—focusing on visual simplicity and script performance. If you're new to The Strat, you may prefer the Strat Assistant as a learning aid.
◇ FEATURES REMOVED FROM THE ORIGINAL SCRIPT
Candle Numbering & Up/Down Arrows
Previous Week High & Low Lines
Previous Day High & Low Lines
Action Wick Percentage
Actionable Signals Plot
Strat Combo Plots
Extensive Alerts
◇ FEATURES KEPT FROM THE ORIGINAL SCRIPT
Full Timeframe Continuity
Candle Coloring
◇ FEATURES ADDED TO THE ORIGINAL SCRIPT
Failed 2 Down Classification
Failed 2 Up Classification
◆ DETAILS
The Strat is a trading methodology developed by Rob Smith that offers an objective approach to trading by focusing on the 3 universal scenarios regarding candle behavior:
SCENARIO ONE
The 1 Bar - Inside Bar: A candle that doesn't take out the highs or the lows of the previous candle; aka consolidation.
These are shown as gray candles by default.
SCENARIO TWO
The 2 Bar - Directional Bar: A candle that takes out one side of the previous candle; aka trending (or at least attempting to trend).
SCENARIO THREE
The 3 Bar - Outside Bar: A candle that takes out both sides of the previous candle; aka broadening formation.
In addition to Rob's 3 universal scenarios, this indicator identifies two variations of 2 bars:
Failed 2 up: A candle that takes out the high of the previous candle but closes bearish.
Failed 2 down: A candle that takes out the low of the previous candle but closes bullish.
◆ SETTINGS
◇ INPUTS
FTC (FULL TIMEFRAME CONTINUITY)
Show/hide FTC plots
Offset FTC plots from current bar
◇ STYLE
STRAT COLORS
Color 0 (Failed 2 Up) - Default is fuchsia
Color 1 (Failed 2 Down) - Default is teal
Color 2 (Inside 1) - Default is gray
Color 3 (Outside 3) - Default is dark purple
Color 4 (2 up) - Default is aqua
Color 5 (2 down) - Default is white
◆ USAGE
It's recommended to use The Strat Lite with a top down analysis so you can find lower timeframe positions with higher timeframe context.
◇ TOP DOWN ANALYSIS
MONTHLY LEVELS
Starting on a monthly chart, the previous month's high and low are manually plotted.
WEEKLY LEVELS
Dropping down to a weekly chart, the previous week's high and low are manually plotted.
DAILY LEVELS
Dropping down to a daily chart, the previous day's high and low are manually plotted.
12H LEVELS
Dropping down to a 12h chart, the previous 12h's high and low are manually plotted.
ANALYSIS
The monthly low was broken, creating a lower low (aka a broadening formation), signalling potential exhaustion risk, which can be a catalyst for reversals. The daily candle that tested the monthly low closed as a Failed 2 Down—potentially an early sign of a reversal. With these 2 confluences, it's reasonable to expect the next daily candle to be a 2 Up. Now it's time to look for a lower timeframe entry.
◇ LOWER TIMEFRAME POSITION
HOURLY PRICE ACTION
Dropping down to an hourly chart, we're anticipating a 2 Up on the daily timeframe, so we're looking for a bullish pattern to enter a position long. I personally like the 6:00 AM UTC-5 hourly candle, as it's the midpoint of the day (for futures).
In this specific example, we see the opening gap was filled and there's a potential 2-1-2 bullish reversal set up.
At this point, price can either do one of 5 things:
Form another 1 (inside) candle
Form a 2 up (directional) candle
Form a 2 down (directional) candle
Form a 2 up, fail, and potentially flip to form a bearish 3 (outside) candle
Form a 2 down, fail, and potentially flip to form a bullish 3 (outside) candle
Knowing the finite potential outcomes helps us set up our positions, manage them accordingly, and flip bias if needed.
POSITION SETUP
Here we can set up a position long AND short. To go long, we set a buy stop at the 1h high and stop loss just below the 50% level of the inside candle; to go short, we set a sell stop at 1h low and stop loss just above the 50% level of the inside candle.
If the short gets triggered first, we can wait for price to move in our favor before cancelling the buy order. If the short becomes a failed 2 down, potentially reversing to become a bullish 3, we can either wait for the stop loss to trigger and for the long position to trigger OR we can move the buy stop to our short stop loss and move the long stop loss to the low of the 1h candle.
POSITION REFINEMENT
For an even tighter risk-to-reward, we can drop to a lower timeframe and look for setups that would be an early trigger of the 1h entry. Just know, the lower you go the more noise there is—increasing risk of getting stopped out before the 1h trigger.
Above are 30m refined entries.
In this example, the long buy stop was triggered. It closed bullish, so the sell stop order can be cancelled.
◇ TARGETS & POSITION MANAGEMENT
TARGETS
These depend on whether you intend to scalp, day trade, or swing trade, but targets are typically the highs of previous candles (when bullish) and lows of previous candles (when bearish). It's advised to be cautious of swing pivots as there's a risk of exhaustion and reversal at these levels.
In this example, the nearest target was the previous 12h high and the next target was the previous day high; if you're a swing trader, you could target previous week's high and previous month's high.
POSITION MANAGEMENT
This largely depends on your risk tolerance, but it's common to either:
Move stop loss slightly into profit
Trail stop loss behind higher highs (bullish) or lower lows (bearish)
Scale out of positions at potential pivot points, leaving a runner
Scale into positions on pullbacks on the way to target
◆ WRAP UP
As demonstrated, The Strat Lite offers a stripped down version of the Strat Assistant—making it visually simple for more experienced Strat traders. By following a top-down approach with The Strat methodology, you can find high probability setups and manage risk with relative ease.
◆ DISCLAIMER
This indicator is a tool for visual analysis and is intended to assist traders who follow The Strat methodology. As with any trading methodology, there's no guarantee of profits; trading involves a high degree of risk and you could lose all of your invested capital. The example shown is of past performance and is not indicative of future results and does not constitute and should not be construed as investment advice. All trading decisions and investments made by you are at your own discretion and risk. Under no circumstances shall the author be liable for any direct, indirect, or incidental damages. You should only risk capital you can afford to lose.
