[@btc_charlie] Trader XO Macro Trend ScannerWhat is this script?
This script has two main functions focusing on EMAs (Exponential Moving Average) and Stochastic RSI.
EMAs
EMAs are typically used to give a view of bullish / bearish momentum. When the shorter EMA (calculated off more recent price action) crosses, or is above, the slower moving EMA (calculated off a longer period of price action), it suggests that the market is in an uptrend. This can be an indication to either go long on said asset, or that it is more preferable to take long setups over short setups. Invalidation on long setups is usually found via price action (e.g. previous lows) or simply waiting for an EMA cross in the opposite direction (i.e. shorter EMA crosses under longer term EMA).
This is not a perfect system for trade entry or exit, but it does give a good indication of market trends. The settings for the EMAs can be changed based on user inputs, and by default the candles are coloured based on the crosses to make it more visual. The default settings are based on “Trader XO’s” settings who is an exceptional swing trader.
RSI
Stochastic RSI is a separate indicator that has been added to this script. RSI measures Relative Strength (RSI = Relative Strength Index). When RSI is <20 it is considered oversold, and when >80 it is overbought. These conditions suggests that momentum is very strong in the direction of the trend.
If there is a divergence between the price (e.g. price is creating higher highs, and stoch RSI is creating lower highs) it suggests the strength of the trend is weakening. Whilst this script does not highlight divergences, what it does highlight is when the shorter term RSI (K) crosses over D (the average of last 3 periods). This can give an indication that the trend is losing strength.
Combination
The EMAs indicate when trend shifts (bullish or bearish).
The RSI indicates when the trend is losing momentum.
The combination of the two can be used to suggest when to prefer a directional bias, and subsequently shift in anticipation of a trend reversal.
Note that no signal is 100% accurate and an interpretation of market conditions and price action will need to be overlayed to
Why is it different to others?
I have not found other scripts that are available in this way visually including alerts when Stoch RSI crosses over/under the extremes; or the mid points.
Whilst these indicators are default, the combination of them and how they are presented is not and makes use of the TradingView colouring functionalities.
What are the features?
Customise the variables (averages) used in the script.
Display as one EMA or two EMAs (the crossing ones).
Alerts on EMA crosses.
Alerts on Stoch RSI crosses - slow/fast, upper, lower areas.
- Currently set on the chart to show alerts when Stoch RSI is above 80, then falls below 80 (and colours it red).
Customisable colours.
What are the best conditions for this?
It is designed for high timeframe charts and analysis in crypto, since crypto tends to trend.
It can however be used for lower timeframes.
Disclaimer/Notes:
I have noticed several videos appearing suggesting that this is a "100% win rate indicator" .
NO indicator has 100% win rate.
An indicator is an *indicator* that is all.
Please use responsibly and let me know if there are any mods or updates you would like to see.
Oscillators
DEVIATION OF THE STOCHASTIC INDICATORThis new technical indicator uses the stochastic oscillator as its base and calculates the deviation of its moving average, generating an alternative view of market volatility.
Any Oscillator Underlay [TTF]We are proud to release a new indicator that has been a while in the making - the Any Oscillator Underlay (AOU) !
Note: There is a lot to discuss regarding this indicator, including its intent and some of how it operates, so please be sure to read this entire description before using this indicator to help ensure you understand both the intent and some limitations with this tool.
Our intent for building this indicator was to accomplish the following:
Combine all of the oscillators that we like to use into a single indicator
Take up a bit less screen space for the underlay indicators for strategies that utilize multiple oscillators
Provide a tool for newer traders to be able to leverage multiple oscillators in a single indicator
Features:
Includes 8 separate, fully-functional indicators combined into one
Ability to easily enable/disable and configure each included indicator independently
Clearly named plots to support user customization of color and styling, as well as manual creation of alerts
Ability to customize sub-indicator title position and color
Ability to customize sub-indicator divider lines style and color
Indicators that are included in this initial release:
TSI
2x RSIs (dubbed the Twin RSI )
Stochastic RSI
Stochastic
Ultimate Oscillator
Awesome Oscillator
MACD
Outback RSI (Color-coding only)
Quick note on OB/OS:
Before we get into covering each included indicator, we first need to cover a core concept for how we're defining OB and OS levels. To help illustrate this, we will use the TSI as an example.
The TSI by default has a mid-point of 0 and a range of -100 to 100. As a result, a common practice is to place lines on the -30 and +30 levels to represent OS and OB zones, respectively. Most people tend to view these levels as distance from the edges/outer bounds or as absolute levels, but we feel a more way to frame the OB/OS concept is to instead define it as distance ("offset") from the mid-line. In keeping with the -30 and +30 levels in our example, the offset in this case would be "30".
Taking this a step further, let's say we decided we wanted an offset of 25. Since the mid-point is 0, we'd then calculate the OB level as 0 + 25 (+25), and the OS level as 0 - 25 (-25).
Now that we've covered the concept of how we approach defining OB and OS levels (based on offset/distance from the mid-line), and since we did apply some transformations, rescaling, and/or repositioning to all of the indicators noted above, we are going to discuss each component indicator to detail both how it was modified from the original to fit the stacked-indicator model, as well as the various major components that the indicator contains.
TSI:
This indicator contains the following major elements:
TSI and TSI Signal Line
Color-coded fill for the TSI/TSI Signal lines
Moving Average for the TSI
TSI Histogram
Mid-line and OB/OS lines
Default TSI fill color coding:
Green : TSI is above the signal line
Red : TSI is below the signal line
Note: The TSI traditionally has a range of -100 to +100 with a mid-point of 0 (range of 200). To fit into our stacking model, we first shrunk the range to 100 (-50 to +50 - cut it in half), then repositioned it to have a mid-point of 50. Since this is the "bottom" of our indicator-stack, no additional repositioning is necessary.
Twin RSI:
This indicator contains the following major elements:
Fast RSI (useful if you want to leverage 2x RSIs as it makes it easier to see the overlaps and crosses - can be disabled if desired)
Slow RSI (primary RSI)
Color-coded fill for the Fast/Slow RSI lines (if Fast RSI is enabled and configured)
Moving Average for the Slow RSI
Mid-line and OB/OS lines
Default Twin RSI fill color coding:
Dark Red : Fast RSI below Slow RSI and Slow RSI below Slow RSI MA
Light Red : Fast RSI below Slow RSI and Slow RSI above Slow RSI MA
Dark Green : Fast RSI above Slow RSI and Slow RSI below Slow RSI MA
Light Green : Fast RSI above Slow RSI and Slow RSI above Slow RSI MA
Note: The RSI naturally has a range of 0 to 100 with a mid-point of 50, so no rescaling or transformation is done on this indicator. The only manipulation done is to properly position it in the indicator-stack based on which other indicators are also enabled.
Stochastic and Stochastic RSI:
These indicators contain the following major elements:
Configurable lengths for the RSI (for the Stochastic RSI only), K, and D values
Configurable base price source
Mid-line and OB/OS lines
Note: The Stochastic and Stochastic RSI both have a normal range of 0 to 100 with a mid-point of 50, so no rescaling or transformations are done on either of these indicators. The only manipulation done is to properly position it in the indicator-stack based on which other indicators are also enabled.
Ultimate Oscillator (UO):
This indicator contains the following major elements:
Configurable lengths for the Fast, Middle, and Slow BP/TR components
Mid-line and OB/OS lines
Moving Average for the UO
Color-coded fill for the UO/UO MA lines (if UO MA is enabled and configured)
Default UO fill color coding:
Green : UO is above the moving average line
Red : UO is below the moving average line
Note: The UO naturally has a range of 0 to 100 with a mid-point of 50, so no rescaling or transformation is done on this indicator. The only manipulation done is to properly position it in the indicator-stack based on which other indicators are also enabled.
Awesome Oscillator (AO):
This indicator contains the following major elements:
Configurable lengths for the Fast and Slow moving averages used in the AO calculation
Configurable price source for the moving averages used in the AO calculation
Mid-line
Option to display the AO as a line or pseudo-histogram
Moving Average for the AO
Color-coded fill for the AO/AO MA lines (if AO MA is enabled and configured)
Default AO fill color coding (Note: Fill was disabled in the image above to improve clarity):
Green : AO is above the moving average line
Red : AO is below the moving average line
Note: The AO is technically has an infinite (unbound) range - -∞ to ∞ - and the effective range is bound to the underlying security price (e.g. BTC will have a wider range than SP500, and SP500 will have a wider range than EUR/USD). We employed some special techniques to rescale this indicator into our desired range of 100 (-50 to 50), and then repositioned it to have a midpoint of 50 (range of 0 to 100) to meet the constraints of our stacking model. We then do one final repositioning to place it in the correct position the indicator-stack based on which other indicators are also enabled. For more details on how we accomplished this, read our section "Binding Infinity" below.
MACD:
This indicator contains the following major elements:
Configurable lengths for the Fast and Slow moving averages used in the MACD calculation
Configurable price source for the moving averages used in the MACD calculation
Configurable length and calculation method for the MACD Signal Line calculation
Mid-line
Note: Like the AO, the MACD also technically has an infinite (unbound) range. We employed the same principles here as we did with the AO to rescale and reposition this indicator as well. For more details on how we accomplished this, read our section "Binding Infinity" below.
Outback RSI (ORSI):
This is a stripped-down version of the Outback RSI indicator (linked above) that only includes the color-coding background (suffice it to say that it was not technically feasible to attempt to rescale the other components in a way that could consistently be clearly seen on-chart). As this component is a bit of a niche/special-purpose sub-indicator, it is disabled by default, and we suggest it remain disabled unless you have some pre-defined strategy that leverages the color-coding element of the Outback RSI that you wish to use.
Binding Infinity - How We Incorporated the AO and MACD (Warning - Math Talk Ahead!)
Note: This applies only to the AO and MACD at time of original publication. If any other indicators are added in the future that also fall into the category of "binding an infinite-range oscillator", we will make that clear in the release notes when that new addition is published.
To help set the stage for this discussion, it's important to note that the broader challenge of "equalizing inputs" is nothing new. In fact, it's a key element in many of the most popular fields of data science, such as AI and Machine Learning. They need to take a diverse set of inputs with a wide variety of ranges and seemingly-random inputs (referred to as "features"), and build a mathematical or computational model in order to work. But, when the raw inputs can vary significantly from one another, there is an inherent need to do some pre-processing to those inputs so that one doesn't overwhelm another simply due to the difference in raw values between them. This is where feature scaling comes into play.
With this in mind, we implemented 2 of the most common methods of Feature Scaling - Min-Max Normalization (which we call "Normalization" in our settings), and Z-Score Normalization (which we call "Standardization" in our settings). Let's take a look at each of those methods as they have been implemented in this script.
Min-Max Normalization (Normalization)
This is one of the most common - and most basic - methods of feature scaling. The basic formula is: y = (x - min)/(max - min) - where x is the current data sample, min is the lowest value in the dataset, and max is the highest value in the dataset. In this transformation, the max would evaluate to 1, and the min would evaluate to 0, and any value in between the min and the max would evaluate somewhere between 0 and 1.
The key benefits of this method are:
It can be used to transform datasets of any range into a new dataset with a consistent and known range (0 to 1).
It has no dependency on the "shape" of the raw input dataset (i.e. does not assume the input dataset can be approximated to a normal distribution).
But there are a couple of "gotchas" with this technique...
First, it assumes the input dataset is complete, or an accurate representation of the population via random sampling. While in most situations this is a valid assumption, in trading indicators we don't really have that luxury as we're often limited in what sample data we can access (i.e. number of historical bars available).
Second, this method is highly sensitive to outliers. Since the crux of this transformation is based on the max-min to define the initial range, a single significant outlier can result in skewing the post-transformation dataset (i.e. major price movement as a reaction to a significant news event).
You can potentially mitigate those 2 "gotchas" by using a mechanism or technique to find and discard outliers (e.g. calculate the mean and standard deviation of the input dataset and discard any raw values more than 5 standard deviations from the mean), but if your most recent datapoint is an "outlier" as defined by that algorithm, processing it using the "scrubbed" dataset would result in that new datapoint being outside the intended range of 0 to 1 (e.g. if the new datapoint is greater than the "scrubbed" max, it's post-transformation value would be greater than 1). Even though this is a bit of an edge-case scenario, it is still sure to happen in live markets processing live data, so it's not an ideal solution in our opinion (which is why we chose not to attempt to discard outliers in this manner).
Z-Score Normalization (Standardization)
This method of rescaling is a bit more complex than the Min-Max Normalization method noted above, but it is also a widely used process. The basic formula is: y = (x – μ) / σ - where x is the current data sample, μ is the mean (average) of the input dataset, and σ is the standard deviation of the input dataset. While this transformation still results in a technically-infinite possible range, the output of this transformation has a 2 very significant properties - the mean (average) of the output dataset has a mean (μ) of 0 and a standard deviation (σ) of 1.
The key benefits of this method are:
As it's based on normalizing the mean and standard deviation of the input dataset instead of a linear range conversion, it is far less susceptible to outliers significantly affecting the result (and in fact has the effect of "squishing" outliers).
It can be used to accurately transform disparate sets of data into a similar range regardless of the original dataset's raw/actual range.
But there are a couple of "gotchas" with this technique as well...
First, it still technically does not do any form of range-binding, so it is still technically unbounded (range -∞ to ∞ with a mid-point of 0).
Second, it implicitly assumes that the raw input dataset to be transformed is normally distributed, which won't always be the case in financial markets.
The first "gotcha" is a bit of an annoyance, but isn't a huge issue as we can apply principles of normal distribution to conceptually limit the range by defining a fixed number of standard deviations from the mean. While this doesn't totally solve the "infinite range" problem (a strong enough sudden move can still break out of our "conceptual range" boundaries), the amount of movement needed to achieve that kind of impact will generally be pretty rare.
The bigger challenge is how to deal with the assumption of the input dataset being normally distributed. While most financial markets (and indicators) do tend towards a normal distribution, they are almost never going to match that distribution exactly. So let's dig a bit deeper into distributions are defined and how things like trending markets can affect them.
Skew (skewness): This is a measure of asymmetry of the bell curve, or put another way, how and in what way the bell curve is disfigured when comparing the 2 halves. The easiest way to visualize this is to draw an imaginary vertical line through the apex of the bell curve, then fold the curve in half along that line. If both halves are exactly the same, the skew is 0 (no skew/perfectly symmetrical) - which is what a normal distribution has (skew = 0). Most financial markets tend to have short, medium, and long-term trends, and these trends will cause the distribution curve to skew in one direction or another. Bullish markets tend to skew to the right (positive), and bearish markets to the left (negative).
Kurtosis: This is a measure of the "tail size" of the bell curve. Another way to state this could be how "flat" or "steep" the bell-shape is. If the bell is steep with a strong drop from the apex (like a steep cliff), it has low kurtosis. If the bell has a shallow, more sweeping drop from the apex (like a tall hill), is has high kurtosis. Translating this to financial markets, kurtosis is generally a metric of volatility as the bell shape is largely defined by the strength and frequency of outliers. This is effectively a measure of volatility - volatile markets tend to have a high level of kurtosis (>3), and stable/consolidating markets tend to have a low level of kurtosis (<3). A normal distribution (our reference), has a kurtosis value of 3.
So to try and bring all that back together, here's a quick recap of the Standardization rescaling method:
The Standardization method has an assumption of a normal distribution of input data by using the mean (average) and standard deviation to handle the transformation
Most financial markets do NOT have a normal distribution (as discussed above), and will have varying degrees of skew and kurtosis
Q: Why are we still favoring the Standardization method over the Normalization method, and how are we accounting for the innate skew and/or kurtosis inherent in most financial markets?
A: Well, since we're only trying to rescale oscillators that by-definition have a midpoint of 0, kurtosis isn't a major concern beyond the affect it has on the post-transformation scaling (specifically, the number of standard deviations from the mean we need to include in our "artificially-bound" range definition).
Q: So that answers the question about kurtosis, but what about skew?
A: So - for skew, the answer is in the formula - specifically the mean (average) element. The standard mean calculation assumes a complete dataset and therefore uses a standard (i.e. simple) average, but we're limited by the data history available to us. So we adapted the transformation formula to leverage a moving average that included a weighting element to it so that it favored recent datapoints more heavily than older ones. By making the average component more adaptive, we gained the effect of reducing the skew element by having the average itself be more responsive to recent movements, which significantly reduces the effect historical outliers have on the dataset as a whole. While this is certainly not a perfect solution, we've found that it serves the purpose of rescaling the MACD and AO to a far more well-defined range while still preserving the oscillator behavior and mid-line exceptionally well.
The most difficult parts to compensate for are periods where markets have low volatility for an extended period of time - to the point where the oscillators are hovering around the 0/midline (in the case of the AO), or when the oscillator and signal lines converge and remain close to each other (in the case of the MACD). It's during these periods where even our best attempt at ensuring accurate mirrored-behavior when compared to the original can still occasionally lead or lag by a candle.
Note: If this is a make-or-break situation for you or your strategy, then we recommend you do not use any of the included indicators that leverage this kind of bounding technique (the AO and MACD at time of publication) and instead use the Trandingview built-in versions!
We know this is a lot to read and digest, so please take your time and feel free to ask questions - we will do our best to answer! And as always, constructive feedback is always welcome!
VolumeFlowVolume & price have a direct correlation with each other. If the fundamental value changes, the price changes and volume follows. If the technicals change, volume changes and price follows.
Because the relationship between volume and price is so connected, I created a script highlighting important volume flow measurements.
The VolumeFlow indicator combines several volume measurements into 1 indicator.
1) Volume net inflow / outflow
2) Volume total flow change
3) Volume cumulation flow
The VolumeFlow indicator uses a scale from 100 high to -100 low, with the zero level being neutral.
The VolumeFlow indicator has 4 inputs:
1) +Volume-
2) VolumeFast
3) VolumeSlow
4) Accum/Dist
Default inputs:
+Volume-
length = 1, color = + green or - red
VolumeFast
length = 2, color = blue
VolumeSlow
length = 3, color = white
Accum/Dist
length = 5, color = brown
Horizontal lines
length = 100, 50, 0, -50, -100, color = white
* The VolumeFlow indicator uses altered pieces of code from my Options360 FibVIP indicator, Tradingview "Up / down volume" indicator and Tradingview "Accumulation/Distribution" indicator. *
VIX OscillatorThis is my VIX Oscillator indicator.
About it:
This indicator takes the Z-Score of the VIX and of the current ticker you are on and presents them in the format of an oscillator.
Key parts of the indicator:
A diagram of the key elements of the indicator are displayed above.
Purple Line: Represents the Z-Score of the current Ticker.
Blue Line: Represents the Z-Score of the VIX
Green fill line: Represents bullish divergence
Red fill line: Represents bearish divergence
How to use it:
Characteristics for long entries:
- Look for recent bullish divergence (green fill line)
- Look for the ticker line (purple line) to be holding above 0 (neutrality)
- look for a bullish cross (purple line (ticker) crossing over blue line (VIX))
Characteristics for short entries:
- Look for recent Bearish divergence
- Look for the VIX line (blue line) to be holding above 0 and the Ticker
- Look for the ticker line to be holding below 0
- Look for a bearish cross (blue crossing above purple)
Some principles:
The bands represent oversold, overbought and neutral.
0 is absolute neutrality. No bias here.
Anything towards + 2.5 is considered normal, moving towards overbought (2.5 or higher).
Anything towards -2.5 is considered normal, moving towards oversold (-2.5 or lower).
+2.5 or higher is overbought.
-2.5 or lower is oversold.
As always, I have prepared a quick tutorial video for your reference of this indicator:
Please let me know your questions, comments or suggestions about this indicator below.
Thank you for checking it out!
Composite Cosmetic CandlesThis is effectively version 2 of my script "Candle Fill % Meter", with a few different/more options available in a more compact form. Choose between multiple oscillator sources, # of dividing lines, and solid or gradient candle fill. Once again this script is intended for use with hollow candles! This script enables you to see more information with less screen space taken up, not to mention it looks nice. Labels by last bar also toggleable in the settings.
Multi Timeframe Stochastic RSI ScreenerThis script is also a Stochastic RSI Screener, but it allows users to choose one specific symbol and three timeframes of that symbol to monitor at once.
[blackcat] L3 Jurik MACDLevel: 3
Background
Use Jurik MA to build MACD and many people need to judge the market trend against the main candlestick chart when using MACD .
Function
First of all, the MACD function is built with Jurik MA and ALMA for better performance.
Second, the principle of MACD is the difference between EMA's long-term and short-term values. So, I wonder if it is possible to use EMA to construct a set of candle charts that are similar in proportion to MACD values for overlapping comparisons? Because this can greatly facilitate traders to make quick trend judgments. So I used the 3-8 lines of EMA to simulate the KD of KDJ, constructed a set of candle charts, and generated buying and selling points through conditional constraints. Do you like this MACD + Candlestick chart?
Key Signal
Traditional Jurik MACD output signal
Candlesticks
Near Top --> Top is reached and reversal may happen soon. (fuchsia labels)
Near Bottom --> Bottom is reached and reversal may happen soon. (yellow labels)
Remarks
Feedbacks are appreciated.
RSI TREND FILTERRSI TREND Filter on Chart
RSI scaled to fit on chart instead of oscillator, Trend Analysis is easy and Hidden Divergence is revealed using this indicator. This indicator is an aim to reduce confusing RSI Situations. The Oversold and Overbought lines help to determine the price conditions so its easy to avoid Traps.
Oversold and Overbought conditions are marked on Chart to make it useful to confirm a Buy or Sell Signals.
RSI 50 level is plotted with reference to EMA50 and Oversold and Overbought Conditions are calculated accordingly.
Uptrend: RSI Cloud / Candles above RSI 50 Level
Down Trend: RSI Cloud / Candles below RSI 50 Level
Sideways : Candles in the Gray Area above and below RSI 50 Level
Default RSI (14) : is the Candlestick pattern itself
Disclaimer: Use Solely at your own Risk.
Balance of Force (BOF)The script "Balance of Force" is an indicator that aims to provide insight into the bullish and bearish forces present in the market by analyzing the relationship between bullish and bearish true ranges. The indicator first calculates the bearish and bullish true ranges by taking the absolute difference between the open and close prices for each period and summing these values over a user-specified length. It then calculates the ratio of the bullish true range to the bearish true range and takes the natural logarithm of this value, resulting in the "bullish-bearish ratio".
The script then calculates the standard deviation of this ratio over a user-specified length to create a measure of volatility. Using this deviation and the dominant cycle, it then applies an exponential moving average to smooth the ratio. The indicator plots the smoothed ratio, the raw ratio, and the deviation of the ratio multiplied by 1, 2 and 3 in addition to filling the area between the deviation multiplied by 3 and the log(1) with red and green. The user can use the indicator to identify potential bullish or bearish market conditions by analyzing the relationship between the smoothed ratio and the log(1) and the deviation of the ratio.
Stochastic RSI ScreenerStochastic RSI Screener is built as an indicator and can be applied to any chart.
It gives users the ability to choose 5 specific symbols to watch and then specify the required options to change the RSI and Stochastic settings in a way that fits their needs.
This screener shows the values of (CURRENT PRICE, RSI, K-VALUE, D-VALUE) for each one of the specified symbols. It will do the calculations based on the currently opened timeframe for all symbols.
TradePro's 2 EMA + Stoch RSI + ATR StrategySaw TradePro's "NEW BEST HIGHEST PROFITING STRATEGY WITH CRAZY RESULTS - 2 EMA+ Stochastic RSI+ ATR", and was curious on the back testing results. This strategy is an attempt to recreate it.
This strategy uses 50 / 200 EMAs, Stochastic RSI and ATR.
Long Entry Criteria:
- 50 EMA > 200 EMA
- Price closes below 50 EMA
- Stochastic RSI has gone into oversold < 20
- Stochastic RSI crosses up while making higher low from previous cross up
Short Entry Criteria:
- 50 EMA < 200 EMA
- Price closes above 50 EMA
- Stochastic RSI has gone into overbought > 80
- Stochastic RSI crosses down while making lower high from previous cross down
Stop-loss is set to ATR stop-loss
Take Profit is 2x the risk
All parameters are configurable.
Enjoy~~
AII - Average indicator of indicatorsThis Pine Script for TradingView is a technical analysis tool that visualizes the average of several popular indicators in the trading world. The indicators included are the RSI (Relative Strength Index), RVI (Relative Vigor Index), Stochastic RSI, Williams %R, relative MACD (ranging from 0 to 100), and Bollinger Bands price distance from 0 to 100. The script uses the "input" function to customize the length of the indicators and the "plot" function to display the results on the chart. In addition, options are included to turn off certain indicators and change the line colors if the user desires. All indicators can also be activated independently, allowing the user to see only the indicators they want. It is also mentioned that the script will be improved in the future to offer a better user experience. The calculated values are calculated with the default EMA of 14. Overall, this script is an excellent option for those looking for a combined view of several important indicators for making trading decisions.
Table rsi multiframes(by Lc_M)- Simultaneous display of RSI values on cells corresponding to each selected timeframe, organized in an intuitive table, adjustable in size and position.
- Color indicator on each cell that presents RSI values within the overbought and oversold levels. example: if the user wants to set the O.S/O.B levels to 20 - 80, the colored cells will only appear at "RSI" => 80 and "RSI" <= 20.
- Free configuration of graphic times, lengths and O.B/O.S, according to user standards
Fair Value Strategy UltimateThis is a strategy using an index's (SPX, NDX, RUT) Fair Value derived from Net Liquidity.
Net Liquidity function is simply: Fed Balance Sheet - Treasury General Account - Reverse Repo Balance
Formula for calculating the fair value of and Index using Net Liquidity looks like this: net_liquidity/1000000000/scalar - subtractor
The Index Fair Value is then subtracted from the Index value which creates an oscillating diff value.
When diff is greater than the overbought threshold, Index is considered overbought and we go short/sell.
When diff is less than the oversold signal, Index is considered oversold and we cover/buy.
The net liquidity values I calculate outside of TradingView. If you'd like the strategy to work for future dates, you'll need to update the reference to my NetLiquidityLibrary , which I update daily.
Parameters:
Index: SPX, NDX, RUT
Strategy: Short Only, Long Only, Long/Short
Inverse (bool): check if using an inverse ETF to go long instead of short.
Scalar (float)
Subtractor (int)
Overbought Threshold (int)
Oversold Threshold (int)
Start After Date: When the strategy should start trading
Close Date: Day to close open trades. I just like it to get complete results rather than the strategy ending with open trades.
Optimal Parameters:
I've optimized the parameters for each index using the python backtesting library and they are as follows =>
SPX
Scalar: 1.1
Subtractor: 1425
OB Threshold: 0
OS Threshold: -175
NDX
Scalar: 0.5
Subtractor: 250
OB Threshold: 0
OS Threshold: -25
RUT
Scalar: 3.2
Subtractor: 50
OB Threshold: 25
OS Threshold: -25
Strategy Myth-Busting #5 - POKI+GTREND+ADX - [MYN]This is part of a new series we are calling "Strategy Myth-Busting" where we take open public manual trading strategies and automate them. The goal is to not only validate the authenticity of the claims but to provide an automated version for traders who wish to trade autonomously.
Our fifth one we are automating is one of the strategies from "The Best 3 Buy And Sell Indicators on Tradingview + Confirmation Indicators ( The Golden Ones ))" from "Online Trading Signals (Scalping Channel)". No formal backtesting was done by them and resuructo messaged me asking if we could validate their claims.
Originally, we mimic verbatim the settings Online Trading Signals was using however weren't getting promising results. So before we stopped there we thought we might want to see if this could be improved on. So we adjusted the Renko Assignment modifier from ATR to Traditional and adjusted the value to be higher from 30 to 47. We also decided to try adding another signal confirmation to eliminate some of the ranged market conditions so we choose our favorite, ADX . Also, given we are using this on a higher time-frame we adjusted the G-Channel Trend detection source from close to OHLC4 to get better average price action indication and more accurate trend direction.
This strategy uses a combination of 2 open-source public indicators:
poki buy and sell Take profit and stop loss by RafaelZioni
G-Channel Trend Detection by jaggedsoft
Trading Rules
15m - 4h timeframe. We saw best results at the recommended 1 hour timeframe.
Long Entry:
When POKI triggers a buy signal
When G-Channel Trend Detection is in an upward trend (Green)
ADX Is above 25
Short Entry:
When POKI triggers a sell signal
When G-Channel Trend Detection is in an downward trend (red)
ADX Is above 25
If you know of or have a strategy you want to see myth-busted or just have an idea for one, please feel free to message me.
