EC_2024_Q4_SPLibrary "EC_2024_Q4_SP"
output2024()
Returns the list of events during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;;; ...
Where: is expressed as date + characteristics: YYYY,MM,DD,hh,mm,ss,x,y,z
x impact in numbers
y event name in numbers
z currency in numbers
name2024()
Returns the list of event names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, name
index: related to event name y
name event: event name related to y text
impact2024()
Returns the list of impact names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, impact
index: related to impact name x
impact: impact name related to x text
currency2024()
Returns the list of currencies during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, currency
index: related to currency name z
currency: currency name related to z text
Search in scripts for "text"
EC_2024_Q3_SPLibrary "EC_2024_Q3_SP"
output2024()
Returns the list of events during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;;; ...
Where: is expressed as date + characteristics: YYYY,MM,DD,hh,mm,ss,x,y,z
x impact in numbers
y event name in numbers
z currency in numbers
name2024()
Returns the list of event names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, name
index: related to event name y
name event: event name related to y text
impact2024()
Returns the list of impact names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, impact
index: related to impact name x
impact: impact name related to x text
currency2024()
Returns the list of currencies during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, currency
index: related to currency name z
currency: currency name related to z text
EC_2024_Q4_ENLibrary "EC_2024_Q4_EN"
output2024()
Returns the list of events during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;;; ...
Where: is expressed as date + characteristics: YYYY,MM,DD,hh,mm,ss,x,y,z
x impact in numbers
y event name in numbers
z currency in numbers
name2024()
Returns the list of event names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, name
index: related to event name y
name event: event name related to y text
impact2024()
Returns the list of impact names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, impact
index: related to impact name x
impact: impact name related to x text
currency2024()
Returns the list of currencies during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, currency
index: related to currency name z
currency: currency name related to z text
EC_2024_Q3_ENLibrary "EC_2024_Q3_EN"
output2024()
Returns the list of events during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;;; ...
Where: is expressed as date + characteristics: YYYY,MM,DD,hh,mm,ss,x,y,z
x impact in numbers
y event name in numbers
z currency in numbers
name2024()
Returns the list of event names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, name
index: related to event name y
name event: event name related to y text
impact2024()
Returns the list of impact names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, impact
index: related to impact name x
impact: impact name related to x text
currency2024()
Returns the list of currencies during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, currency
index: related to currency name z
currency: currency name related to z text
EC_2024_Q2_ENLibrary "EC_2024_Q2_EN"
output2024()
Returns the list of events during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;;; ...
Where: is expressed as date + characteristics: YYYY,MM,DD,hh,mm,ss,x,y,z
x impact in numbers
y event name in numbers
z currency in numbers
name2024()
Returns the list of event names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, name
index: related to event name y
name event: event name related to y text
impact2024()
Returns the list of impact names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, impact
index: related to impact name x
impact: impact name related to x text
currency2024()
Returns the list of currencies during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, currency
index: related to currency name z
currency: currency name related to z text
EC_2024_Q2_SPLibrary "EC_2024_Q2_SP"
output2024()
Returns the list of events during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;;; ...
Where: is expressed as date + characteristics: YYYY,MM,DD,hh,mm,ss,x,y,z
x impact in numbers
y event name in numbers
z currency in numbers
name2024()
Returns the list of event names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, name
index: related to event name y
name event: event name related to y text
impact2024()
Returns the list of impact names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, impact
index: related to impact name x
impact: impact name related to x text
currency2024()
Returns the list of currencies during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, currency
index: related to currency name z
currency: currency name related to z text
EC_2024_Q1_SPLibrary "EC_2024_Q1_SP"
output2024()
Returns the list of events during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;;; ...
Where: is expressed as date + characteristics: YYYY,MM,DD,hh,mm,ss,x,y,z
x impact in numbers
y event name in numbers
z currency in numbers
name2024()
Returns the list of event names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, name
index: related to event name y
name event: event name related to y text
impact2024()
Returns the list of impact names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, impact
index: related to impact name x
impact: impact name related to x text
currency2024()
Returns the list of currencies during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, currency
index: related to currency name z
currency: currency name related to z text
EC_2024_Q1_ENLibrary "EC_2024_Q1_EN"
output2024()
Returns the list of events during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;;; ...
Where: is expressed as date + characteristics: YYYY,MM,DD,hh,mm,ss,x,y,z
x impact in numbers
y event name in numbers
z currency in numbers
name2024()
Returns the list of event names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, name
index: related to event name y
name event: event name related to y text
impact2024()
Returns the list of impact names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, impact
index: related to impact name x
impact: impact name related to x text
currency2024()
Returns the list of currencies during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, currency
index: related to currency name z
currency: currency name related to z text
EC_2023_Q4_SPLibrary "EC_2023_Q4_SP"
output2023()
Returns the list of events during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;;; ...
Where: is expressed as date + characteristics: YYYY,MM,DD,hh,mm,ss,x,y,z
x impact in numbers
y event name in numbers
z currency in numbers
name2023()
Returns the list of event names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, name
index: related to event name y
name event: event name related to y text
impact2023()
Returns the list of impact names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, impact
index: related to impact name x
impact: impact name related to x text
currency2023()
Returns the list of currencies during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, currency
index: related to currency name z
currency: currency name related to z text
EC_2023_Q3_SPLibrary "EC_2023_Q3_SP"
output2023()
Returns the list of events during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;;; ...
Where: is expressed as date + characteristics: YYYY,MM,DD,hh,mm,ss,x,y,z
x impact in numbers
y event name in numbers
z currency in numbers
name2023()
Returns the list of event names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, name
index: related to event name y
name event: event name related to y text
impact2023()
Returns the list of impact names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, impact
index: related to impact name x
impact: impact name related to x text
currency2023()
Returns the list of currencies during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, currency
index: related to currency name z
currency: currency name related to z text
EC_2023_Q2_SPLibrary "EC_2023_Q2_SP"
output2023()
Returns the list of events during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;;; ...
Where: is expressed as date + characteristics: YYYY,MM,DD,hh,mm,ss,x,y,z
x impact in numbers
y event name in numbers
z currency in numbers
name2023()
Returns the list of event names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, name
index: related to event name y
name event: event name related to y text
impact2023()
Returns the list of impact names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, impact
index: related to impact name x
impact: impact name related to x text
currency2023()
Returns the list of currencies during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, currency
index: related to currency name z
currency: currency name related to z text
EC_2023_Q1_SPLibrary "EC_2023_Q1_SP"
output2023()
Returns the list of events during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;;; ...
Where: is expressed as date + characteristics: YYYY,MM,DD,hh,mm,ss,x,y,z
x impact in numbers
y event name in numbers
z currency in numbers
name2023()
Returns the list of event names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, name
index: related to event name y
name event: event name related to y text
impact2023()
Returns the list of impact names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, impact
index: related to impact name x
impact: impact name related to x text
currency2023()
Returns the list of currencies during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, currency
index: related to currency name z
currency: currency name related to z text
EC_2023_Q4_ENLibrary "EC_2023_Q4_EN"
output2023()
Returns the list of events during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;;; ...
Where: is expressed as date + characteristics: YYYY,MM,DD,hh,mm,ss,x,y,z
x impact in numbers
y event name in numbers
z currency in numbers
name2023()
Returns the list of event names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, name
index: related to event name y
name event: event name related to y text
impact2023()
Returns the list of impact names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, impact
index: related to impact name x
impact: impact name related to x text
currency2023()
Returns the list of currencies during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, currency
index: related to currency name z
currency: currency name related to z text
EC_2023_Q3_ENLibrary "EC_2023_Q3_EN"
output2023()
Returns the list of events during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;;; ...
Where: is expressed as date + characteristics: YYYY,MM,DD,hh,mm,ss,x,y,z
x impact in numbers
y event name in numbers
z currency in numbers
name2023()
Returns the list of event names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, name
index: related to event name y
name event: event name related to y text
impact2023()
Returns the list of impact names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, impact
index: related to impact name x
impact: impact name related to x text
currency2023()
Returns the list of currencies during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, currency
index: related to currency name z
currency: currency name related to z text
EC_2023_Q2_ENLibrary "EC_2023_Q2_EN"
output2023()
Returns the list of events during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;;; ...
Where: is expressed as date + characteristics: YYYY,MM,DD,hh,mm,ss,x,y,z
x impact in numbers
y event name in numbers
z currency in numbers
name2023()
Returns the list of event names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, name
index: related to event name y
name event: event name related to y text
impact2023()
Returns the list of impact names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, impact
index: related to impact name x
impact: impact name related to x text
currency2023()
Returns the list of currencies during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, currency
index: related to currency name z
currency: currency name related to z text
EC_2023_Q1_ENLibrary "EC_2023_Q1_EN"
output2023()
Returns the list of events during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;;; ...
Where: is expressed as date + characteristics: YYYY,MM,DD,hh,mm,ss,x,y,z
x impact in numbers
y event name in numbers
z currency in numbers
name2023()
Returns the list of event names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, name
index: related to event name y
name event: event name related to y text
impact2023()
Returns the list of impact names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, impact
index: related to impact name x
impact: impact name related to x text
currency2023()
Returns the list of currencies during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, currency
index: related to currency name z
currency: currency name related to z text
Auto-magnifier / quantifytools- Overview
Auto-magnifier shows a lower timeframe view of candles and volume bars inside any main timeframe candle by zooming into it. Candles and volume bars as they develop are shown chronologically from left to right. By default, magnifier is triggered when less than 3 candles are visible on the chart.
By default, 20 lower timeframe candles are displayed by splitting main timeframe into 20 parts. The amount of candles displayed is a target rate, meaning the script will use a lower timeframe that has the closest match to 20 candles and therefore will vary a bit. Users can override automatic timeframe calculation and opt in to display any specific lower timeframe or adjust amount of candles shown (e.g. 20 -> 30 candles) per each main timeframe candle.
Example
Main timeframe set to 30 minute, candles displayed set to 20 -> Magnifying using 2 minute candles (30 minute/20 candles = 1.5 min, rounded to 2 min)
Main timeframe set to 30 minute, override set to 5 minutes -> Displaying 5 minute candles
Size of volume bars is calculated using relative volume (volume relative to volume SMA20), lowest bar representing relative volume values of under or equal to 1x the moving average and from there onwards progressively growing.
- Limitations and considerations
Amount of candles shown might flow over from the background on smaller screen sizes, in which case you would want to decrease the amount shown. Opposite is true for bigger screens, this value can be increased as more candles fit.
This indicator involves a lot of tricks with text elements to make it work automatically by zooming in. Size of wicks, bodies and volume bars are calculated by adding more text elements on big candles and less text elements on smaller candles. This means the displayed candles won't be a 100% match, but a rather a fair representation of the view, e.g. candle is green = lower timeframe candle is green, candle has a big wick = lower timeframe candle has a big wick (but not a 100% match).
Example
Magnified lower timeframe chart vs. Actual lower timeframe chart
Most mismatch will be found on the price levels where lower timeframe candles are shown, which is sacrificed for the sake of getting a better readability on the overall shape of lower timeframe price action. Users can alternatively optimize calculations for more accuracy, giving a better representation of the price levels where candles truly originated. This typically comes with the cost of worse readability however.
Example
Optimized for readability vs. Optimized for accuracy
- Visuals
All visual elements are fully customizable.
Monte Carlo Future Moves [ChartPrime]ORIGINS AND HISTORICAL BACKGROUND:
Prior to the the advent of the Monte Carlo method, examining well-understood deterministic problems via simulation generally utilized statistical sampling to gauge uncertainty estimations. The Monte Carlo (MC) approach inverts this paradigm by modeling with probabilistic metaheuristics to address deterministic problems. Addressing Buffon's needle problem, an early form of the Monte Carlo method estimated π (3.14159) by dropping needles on a floor. Later, the modern MC inception primarily began when Stanislaw Ulam was playing solitaire games while experiencing illness and recovery.
Ulam further developed, applied, and ascribed "Monte Carlo" as a classified code name to maintain a level of secrecy for the modern method applications during collaborative investigations on neutron diffusion and collision intricacies with John von Neumann. Despite having relevant data, physicist's conventional deterministic mathematical methods were unable to solve mysterious "neutronion problems". Monte Carlo filled in the gaps necessary to resolve this perplexing neutron problem with innovative statistics, and the resilient MC continues onward to have diverse application in many fields of science. MC also extends into the realm of relevance within finance.
APPLICATION IN FINANCE:
Building on its historical roots, the Monte Carlo method's transition into finance opened new avenues for risk assessment and predictive analysis. In financial markets, characterized by uncertainty and complex variables, this method offers a powerful tool for simulating a wide range of scenarios and assessing probabilities of different outcomes. By employing probabilistic models to predict price movements, the Monte Carlo method helps in creating more resilient and informed trading strategies. This approach is particularly valuable in options pricing, portfolio management, and risk assessment, where understanding the range of potential outcomes is crucial for making sound investment decisions. Our indicator utilizes this methodology, blending traditional financial analysis with advanced statistical techniques.
THE INDICATOR:
The Monte Carlo Future Moves (ChartPrime) indicator is designed to predict future price movements. It simulates various possible price paths, showing the likelihood of different outcomes. We have designed it to be simple to use and understand by displaying lines indicating the most likely bullish and bearish outcomes. The arrows point to these areas making it intuitive to understand. Also included is extreme price levels shown in blue and yellow. This is the most likely extreme range that the price will move to. The outcome distribution is there to show you the range of outcomes along with a visual representation of the possible future outcomes. To make things more user friendly we have also included a representation of this distribution as a background heatmap. The brighter the price level, the more likely the price will end at that level. Finally, we have also included a market bias indication on the side that shows you the general bullish/bearish probabilities.
HOW TO USE:
To use this indicator you want to first assess the market bias. From there you want to target the most likely polar outcome. You can use the range of outcomes to assess your risk and set a stop within a reasonable range of the desired target. By default the indicator projects 10 steps into the future, however this can be easily adjusted in the settings. Generally this indicator excels at mid-term estimations and may yield inconclusive results if the prediction period is too short or too long. You can change the granularity of the outcomes to give you a more or less detailed view of the future. That being said, a lower resolution can make the predictions less useful while a higher resolution can give you a less useful picture. If you decide to use a higher resolution we have included an option to smooth the final result. This is intended to reduce the uncertainty and noise in the predicted outcomes. It is advised to use the minimum level of smoothing possible as a high level of smoothing will greatly reduce the accuracy.
INPUT SECTION:
Derivative Source changes how the indicator sees the price movements. When you set this to Candle it will use the difference between the open and close of each candle. If set to Move, it will use the difference between closing prices. If you are in a market with gaps, you might want to use Candle as this will prevent the indicator from seeing gaps.
Number of Simulations is a crucial setting as it is the core of this indicator. This determines the number of simulations the indicator will use to get its final result. By default it is set to 1000 as we feel like that is around the minimum number of simulations required to get a reasonable output while maintaining stability. In tests the maximum number of simulations we have been able to consistently achieve is 2000.
Lookback is the number of historical candles to account for. A lookback that is too short will not have enough data to accurately assess the likelihood of a price movement, while a period that is too large can make the data less relevant. By default this is set to 1000 as we feel like this is a reasonable tradeoff between volume of data and relevance.
Steps Into Future is the prediction period. By default we have picked a period of 10 steps as this has a good balance between accuracy and usability. The more steps into the future you go, the more uncertain the future outcome will be.
Outcome Granularity controls the precision of the simulated outcomes. By default this is set to 40 as its a good balance between resolution and accuracy.
Outcome Smoothing allows you to smooth the outcome distribution. By default this is set to 0 as it is generally not needed for lower resolutions. Smoothing levels beyond 2 are not recommended as it will negatively impact the output.
Returns Granularity controls the level of definition in the collected price movements. This directly impacts indicator performance and is set to 50 by default because its a good balance between fidelity and usability. When this number is too small, the simulations will be less accurate while numbers too large will negatively impact the probabilities of the movements.
Drift is the trend component in the simulation. This adds the directionality of the simulations by biasing the movements in the current direction of the market. We have included both the standard formula for drift and linear regression. Both methods are well suited for simulating future price movements and have their own advantages. The drift period is set to 100 by default as its a good balance between current and historical directionality. You may want to increase or decrease this number depending on the current market conditions but it is advised to use a period that isn't too small. If your period is too small it can skew the outcomes too much resulting in poor performance. When this is set to 0 it will use the same period as your lookback.
Volatility Adjust , adjusts the simulation to include current volatility. This makes sure that the price movements in the simulation reflects the current market conditions better by making sure that each price move is at least a minimum size.
Returns Style allows you to pick between using percent moves and log returns. We have opted to make percent move the default as it is more intuitive for beginners however both settings yield similar results. Log returns can be less cpu intensive so it might be desirable for longer term predictions.
Precision adjusts the rounding of used when collecting the frequency of price movement sizes. By default this is set to 4 as its is fairly accurate without impacting performance too much. A larger number will make the indicator more precise but at the cost of cpu time. Precision levels that are too small can greatly reduce the accuracy of the simulation and even break the indicator all together.
Update Every Bar allows you to recalculate the prediction every bar and is there for you if you want to strictly use the market bias. It is not recommended to enable this feature but it is there for flexibility.
Side of Chart allows you to pick what side of the price action you want the visuals to be on. When its set to the right everything will be to the right of the starting point and when its set to Left it will position everything to the left of the starting point.
Move Visualization is there to give you an arrow to the most likely bullish and bearish moves. It is meant as a visual aid and visualization tool. The color of these arrows use the same colors as the distribution.
Most Likely Move is a horizontal line that indicates the most likely move. It is positioned in the same location as the Move Visualization.
Standard Deviation is horizontal lines at the extremities of the simulated price action. These represent the most likely range of the future outcomes. You can adjust the multiplier of the standard deviation but by default it is set to 2.
Most Likely Direction is a vertical bar that shows you the sum of the up and down probabilities. It is there to show you the bias of the outcomes and guide you in decision making.
Max Probability Zone is a horizontal line that highlights the location of the highest probability move. You can think of it almost like the POC in a volume distribution but in this case it is the "most likely" single outcome.
Outcome Distribution allows you to toggle the distribution on or off. This is the distribution of all of the simulated outcomes. You can toggle the scale width of the distribution to fit your visual style.
Distribution Text toggles the probability text inside of the distribution bars. When you have a large number for the outcome granularity this text may not be visible and you may want to disable this feature.
Background is a heatmap of the outcome distribution. This allows you to visualize the underlying distribution without the need for the distribution histogram. The brighter the color, the more likely the outcome is for that level. It can be useful for visualizing the range of possible outcomes.
Starting Line is simply a horizontal line indicating the starting point of the simulation. It just the opening price for the starting position.
Extend Lines allows you to extend the lines and background past the prediction period.
CONCLUSION:
With its intuitive visuals and flexible settings, the Monte Carlo Future Moves (ChartPrime) indicator is practice and easy to use. It brings clarity to price movement predictions, helping you to build confidence in your strategies. This indicator not only reflects the evolution of technical analysis but also touches on data-driven insights.
Enjoy
forex_factory_utilityLibrary "forex_factory_utility"
Supporting Utility Library for the Live Economic Calendar by toodegrees Indicator; responsible for data handling, and plotting news event data.
isLeapYear()
Finds if it's currently a leap year or not.
Returns: Returns True if the current year is a leap year.
daysMonth(M)
Provides the days in a given month of the year, adjusted during leap years.
Parameters:
M (int) : Month in numerical integer format (i.e. Jan=1).
Returns: Days in the provided month.
size(S, N)
Converts a size string into the corresponding Pine Script v5 format, or N times smaller/bigger.
Parameters:
S (string) : Size string: "Tiny", "Small", "Normal", "Large", or "Huge".
N (int) : Size variation, can be positive (larger than S), or negative (smaller than S).
Returns: Size string in Pine Script v5 format.
lineStyle(S)
Converts a line style string into the corresponding Pine Script v5 format.
Parameters:
S (string) : Line style string: "Dashed", "Dotted" or "Solid".
Returns: Line style string in Pine Script v5 format.
lineTrnsp(S)
Converts a transparency style string into the corresponding integer value.
Parameters:
S (string) : Line style string: "Light", "Medium" or "Heavy".
Returns: Transparency integer.
boxLoc(X, Y)
Converts position strings of X and Y into a table position in Pine Script v5 format.
Parameters:
X (string) : X-axis string: "Left", "Center", or "Right".
Y (string) : Y-axis string: "Top", "Middle", or "Bottom".
Returns: Table location string in Pine Script v5 format.
method bubbleSort_NewsTOD(N)
Performs bubble sort on a Forex Factory News array of all news from the same date, ordering them in ascending order based on the time of the day.
Namespace types: News
Parameters:
N (News ) : Forex Factory News array.
Returns: void
bubbleSort_News(N)
Performs bubble sort on a Forex Factory News array, ordering them in ascending order based on the time of the day, and date.
Parameters:
N (News ) : Forex Factory News array.
Returns: Sorted Forex Factory News array.
weekNews(N, C, I)
Creates a Forex Factory News array containing the current week's Forex Factory News.
Parameters:
N (News ) : Forex Factory News array containing this week's unfiltered Forex Factory News.
C (string ) : Currency filter array (string array).
I (color ) : Impact filter array (color array).
Returns: Forex Factory News array containing the current week's Forex Factory News.
todayNews(W, D, M)
Creates a Forex Factory News array containing the current day's Forex Factory News.
Parameters:
W (News ) : Forex Factory News array containing this week's Forex Factory News.
D (News ) : Forex Factory News array for the current day's Forex Factory News.
M (bool) : Boolean that marks whether the current chart has a Day candle-switch at Midnight New York Time.
Returns: Forex Factory News array containing the current day's Forex Factory News.
impFilter(X, L, M, H)
Creates a filter array from the User's desired Forex Facory News to be shown based on Impact.
Parameters:
X (bool) : Boolean - if True Holidays listed on Forex Factory will be shown.
L (bool) : Boolean - if True Low Impact listed on Forex Factory News will be shown.
M (bool) : Boolean - if True Medium Impact listed on Forex Factory News will be shown.
H (bool) : Boolean - if True High Impact listed on Forex Factory News will be shown.
Returns: Color array with the colors corresponding to the Forex Factory News to be shown.
curFilter(A, C1, C2, C3, C4, C5, C6, C7, C8, C9)
Creates a filter array from the User's desired Forex Facory News to be shown based on Currency.
Parameters:
A (bool) : Boolean - if True News related to the current Chart's symbol listed on Forex Factory will be shown.
C1 (bool) : Boolean - if True News related to the Australian Dollar listed on Forex Factory will be shown.
C2 (bool) : Boolean - if True News related to the Canadian Dollar listed on Forex Factory will be shown.
C3 (bool) : Boolean - if True News related to the Swiss Franc listed on Forex Factory will be shown.
C4 (bool) : Boolean - if True News related to the Chinese Yuan listed on Forex Factory will be shown.
C5 (bool) : Boolean - if True News related to the Euro listed on Forex Factory will be shown.
C6 (bool) : Boolean - if True News related to the British Pound listed on Forex Factory will be shown.
C7 (bool) : Boolean - if True News related to the Japanese Yen listed on Forex Factory will be shown.
C8 (bool) : Boolean - if True News related to the New Zealand Dollar listed on Forex Factory will be shown.
C9 (bool) : Boolean - if True News related to the US Dollar listed on Forex Factory will be shown.
Returns: String array with the currencies corresponding to the Forex Factory News to be shown.
FF_OnChartLine(N, T, S)
Plots vertical lines where a Forex Factory News event will occur, or has already occurred.
Parameters:
N (News ) : News-type array containing all the Forex Factory News.
T (int) : Transparency integer value (0-100) for the lines.
S (string) : Line style in Pine Script v5 format.
Returns: void
method updateStringMatrix(M, P, V)
Namespace types: matrix
Parameters:
M (matrix)
P (int)
V (string)
FF_OnChartLabel(N, Y, S)
Plots labels where a Forex Factory News has already occurred based on its/their impact.
Parameters:
N (News ) : News-type array containing all the Forex Factory News.
Y (string) : String that gives direction on where to plot the label (options= "Above", "Below", "Auto").
S (string) : Label size in Pine Script v5 format.
Returns: void
historical(T, D, W, X)
Deletes Forex Factory News drawings which are ourside a specific Time window.
Parameters:
T (int) : Number of days input used for Forex Factory News drawings' history.
D (bool) : Boolean that when true will only display Forex Factory News drawings of the current day.
W (bool) : Boolean that when true will only display Forex Factory News drawings of the current week.
X (string) : String that gives direction on what lines to plot based on Time (options= "Past", "Future", "Both").
Returns: void
newTable(P)
Creates a new Table object with parameters tailored to the Forex Factory News Table.
Parameters:
P (string) : Position string for the Table, in Pine Script v5 format.
Returns: Empty Forex Factory News Table.
resetTable(P, S, headTextC, headBgC)
Resets a Table object with parameters and headers tailored to the Forex Factory News Table.
Parameters:
P (string) : Position string for the Table, in Pine Script v5 format.
S (string) : Size string for the Table's text, in Pine Script v5 format.
headTextC (color)
headBgC (color)
Returns: Empty Forex Factory News Table.
logNews(N, TBL, R, S, rowTextC, rowBgC)
Adds an event to the Forex Factory News Table.
Parameters:
N (News) : News-type object.
TBL (table) : Forex Factory News Table object to add the News to.
R (int) : Row to add the event to in the Forex Factory News Table.
S (string) : Size string for the event's text, in Pine Script v5 format.
rowTextC (color)
rowBgC (color)
Returns: void
FF_Table(N, P, S, headTextC, headBgC, rowTextC, rowBgC)
Creates the Forex Factory News Table.
Parameters:
N (News ) : News-type array containing all the Forex Factory News.
P (string) : Position string for the Table, in Pine Script v5 format.
S (string) : Size string for the Table's text, in Pine Script v5 format.
headTextC (color)
headBgC (color)
rowTextC (color)
rowBgC (color)
Returns: Forex Factory News Table.
timeline(N, T, F, D)
Shades Forex Factory News events in the Forex Factory News Table after they occur.
Parameters:
N (News ) : News-type array containing all the Forex Factory News.
T (table) : Forex Facory News table object.
F (color) : Color used as shading once the Forex Factory News has occurred.
D (bool) : Daily Forex Factory News flag.
Returns: Forex Factory News Table.
News
Custom News type which contains informatino about a Forex Factory News Event.
Fields:
dow (series string) : Day of the week, in DDD format (i.e. 'Mon').
dat (series string) : Date, in MMM D format (i.e. 'Jan 1').
_t (series int)
tod (series string) : Time of the day, in hh:mm 24-Hour format (i.e 17:10).
cur (series string) : Currency, in CCC format (i.e. "USD").
imp (series color) : Impact, the respective impact color for Forex Factory News Events.
ttl (series string) : Title, encoded in a custom number mapping (see the toodegrees/toodegrees_forex_factory library to learn more).
tmst (series int)
ln (series line)
Minervini Stage 2 AnalysisHandbook for Minervini Stage 2 Analysis Indicator
Introduction
This handbook provides detailed instructions and guidelines for using the Minervini Stage 2 Analysis Indicator based on Mark Minervini's swing trading methodology. This indicator is designed for traders focusing on US stocks, aiming to capture gains in medium to short-term uptrends (swing trading).
Understanding Stage 2
Stage 2 represents a bullish uptrend in a stock's price. Mark Minervini emphasizes entering long positions during this phase. The stage is identified using four key criteria related to moving averages (MAs).
Indicator Criteria
Stock Price Above MA 150 and 200: Indicates an overall uptrend.
MA 150 Above MA 200: Signals a stronger medium-term trend compared to the long-term trend.
MA 200 Trending Up for At Least 1 Month (22 Days): Confirms a stable uptrend.
MA 50 Above Both MA 150 and 200: Shows short-term strength and momentum.
Using the Indicator
Entering Trades: Consider long positions when all four criteria are met. This signifies that the stock is in a Stage 2 uptrend.
Monitoring Trades: Regularly check if the stock continues to meet these criteria. The indicator provides a clear visual and textual representation for ease of monitoring.
Alarm Signals and Exit Strategy
One Criterion Not Met: This serves as an alarm signal. Increased vigilance is required, and traders should prepare for a potential exit.
Two Criteria Not Met: Strong indication to close the trade. This suggests the stock may be transitioning out of Stage 2, increasing the risk of holding the position.
Risk Management
Stop-Loss Orders: Consider setting a trailing stop-loss to protect profits and minimize losses.
Position Sizing: Adjust position sizes according to your risk tolerance and portfolio strategy.
Volume and Relative Strength Analysis
Volume Analysis: Look for increased trading volume as confirmation when the stock price moves above key MAs.
Relative Strength (RS) Rating: Compare the stock's performance to the broader market to gauge its strength.
Limitations and Considerations
Market Conditions: The indicator's effectiveness may vary with market conditions. It is more reliable in a bullish market environment.
Supplementary Analysis: Combine this indicator with other analysis methods (fundamental, technical) for a holistic approach.
Continuous Learning: Stay updated with market trends and adjust your strategy accordingly.
Conclusion
The Minervini Stage 2 Analysis Indicator is a powerful tool for identifying potential long positions in uptrending stocks. Its reliance on specific criteria aligns with Mark Minervini's proven swing trading strategy. However, always exercise due diligence and risk management in your trading decisions.
Adaptive MFT Extremum Pivots [Elysian_Mind]Adaptive MFT Extremum Pivots
Overview:
The Adaptive MFT Extremum Pivots indicator, developed by Elysian_Mind, is a powerful Pine Script tool that dynamically displays key market levels, including Monthly Highs/Lows, Weekly Extremums, Pivot Points, and dynamic Resistances/Supports. The term "dynamic" emphasizes the adaptive nature of the calculated levels, ensuring they reflect real-time market conditions. I thank Zandalin for the excellent table design.
---
Chart Explanation:
The table, a visual output of the script, is conveniently positioned in the bottom right corner of the screen, showcasing the indicator's dynamic results. The configuration block, elucidated in the documentation, empowers users to customize the display position. The default placement is at the bottom right, exemplified in the accompanying chart.
The deliberate design ensures that the table does not obscure the candlesticks, with traders commonly situating it outside the candle area. However, the flexibility exists to overlay the table onto the candles. Thanks to transparent cells, the underlying chart remains visible even with the table displayed atop.
In the initial column of the table, users will find labels for the monthly high and low, accompanied by their respective numerical values. The default precision for these values is set at #.###, yet this can be adjusted within the configuration block to suit markets with varying degrees of volatility.
Mirroring this layout, the last column of the table presents the weekly high and low data. This arrangement is part of the upper half of the table. Transitioning to the lower half, users encounter the resistance levels in the first column and the support levels in the last column.
At the center of the table, prominently displayed, is the monthly pivot point. For a comprehensive understanding of the calculations governing these values, users can refer to the documentation. Importantly, users retain the freedom to modify these mathematical calculations, with the table seamlessly updating to reflect any adjustments made.
Noteworthy is the table's persistence; it continues to display reliably even if users choose to customize the mathematical calculations, providing a consistent and adaptable tool for informed decision-making in trading.
This detailed breakdown offers traders a clear guide to interpreting the information presented by the table, ensuring optimal use and understanding of the Adaptive MFT Extremum Pivots indicator.
---
Usage:
Table Layout:
The table is a crucial component of this indicator, providing a structured representation of various market levels. Color-coded cells enhance readability, with blue indicating key levels and a semi-transparent background to maintain chart visibility.
1. Utilizing a Table for Enhanced Visibility:
In presenting this wealth of information, the indicator employs a table format beneath the chart. The use of a table is deliberate and offers several advantages:
2. Structured Organization:
The table organizes the diverse data into a structured format, enhancing clarity and making it easier for traders to locate specific information.
3. Concise Presentation:
A table allows for the concise presentation of multiple data points without cluttering the main chart. Traders can quickly reference key levels without distraction.
4. Dynamic Visibility:
As the market dynamically evolves, the table seamlessly updates in real-time, ensuring that the most relevant information is readily visible without obstructing the candlestick chart.
5. Color Coding for Readability:
Color-coded cells in the table not only add visual appeal but also serve a functional purpose by improving readability. Key levels are easily distinguishable, contributing to efficient analysis.
Data Values:
Numerical values for each level are displayed in their respective cells, with precision defined by the iPrecision configuration parameter.
Configuration:
// User configuration: You can modify this part without code understanding
// Table location configuration
// Position: Table
const string iPosition = position.bottom_right
// Width: Table borders
const int iBorderWidth = 1
// Color configuration
// Color: Borders
const color iBorderColor = color.new(color.white, 75)
// Color: Table background
const color iTableColor = color.new(#2B2A29, 25)
// Color: Title cell background
const color iTitleCellColor = color.new(#171F54, 0)
// Color: Characters
const color iCharColor = color.white
// Color: Data cell background
const color iDataCellColor = color.new(#25456E, 0)
// Precision: Numerical data
const int iPrecision = 3
// End of configuration
The code includes a configuration block where users can customize the following parameters:
Precision of Numerical Table Data (iPrecision):
// Precision: Numerical data
const int iPrecision = 3
This parameter (iPrecision) sets the precision of the numerical values displayed in the table. The default value is 3, displaying numbers in #.### format.
Position of the Table (iPosition):
// Position: Table
const string iPosition = position.bottom_right
This parameter (iPosition) sets the position of the table on the chart. The default is position.bottom_right.
Color preferences
Table borders (iBorderColor):
// Color: Borders
const color iBorderColor = color.new(color.white, 75)
This parameters (iBorderColor) sets the color of the borders everywhere within the window.
Table Background (iTableColor):
// Color: Table background
const color iTableColor = color.new(#2B2A29, 25)
This is the background color of the table. If you've got cells without custom background color, this color will be their background.
Title Cell Background (iTitleCellColor):
// Color: Title cell background
const color iTitleCellColor = color.new(#171F54, 0)
This is the background color the title cells. You can set the background of data cells and text color elsewhere.
Text (iCharColor):
// Color: Characters
const color iCharColor = color.white
This is the color of the text - titles and data - within the table window. If you change any of the background colors, you might want to change this parameter to ensure visibility.
Data Cell Background: (iDataCellColor):
// Color: Data cell background
const color iDataCellColor = color.new(#25456E, 0)
The data cells have a background color to differ from title cells. You can configure this is a different parameter (iDataColor). You might even set the same color for data as for the titles if you will.
---
Mathematical Background:
Monthly and Weekly Extremums:
The indicator calculates the High (H) and Low (L) of the previous month and week, ensuring accurate representation of these key levels.
Standard Monthly Pivot Point:
The standard pivot point is determined based on the previous month's data using the formula:
PivotPoint = (PrevMonthHigh + PrevMonthLow + Close ) / 3
Monthly Pivot Points (R1, R2, R3, S1, S2, S3):
Additional pivot points are calculated for Resistances (R) and Supports (S) using the monthly data:
R1 = 2 * PivotPoint - PrevMonthLow
S1 = 2 * PivotPoint - PrevMonthHigh
R2 = PivotPoint + (PrevMonthHigh - PrevMonthLow)
S2 = PivotPoint - (PrevMonthHigh - PrevMonthLow)
R3 = PrevMonthHigh + 2 * (PivotPoint - PrevMonthLow)
S3 = PrevMonthLow - 2 * (PrevMonthHigh - PivotPoint)
---
Code Explanation and Interpretation:
The table displayed beneath the chart provides the following information:
Monthly Extremums:
(H) High of the previous month
(L) Low of the previous month
// Function to get the high and low of the previous month
getPrevMonthHighLow() =>
var float prevMonthHigh = na
var float prevMonthLow = na
monthChanged = month(time) != month(time )
if (monthChanged)
prevMonthHigh := high
prevMonthLow := low
Weekly Extremums:
(H) High of the previous week
(L) Low of the previous week
// Function to get the high and low of the previous week
getPrevWeekHighLow() =>
var float prevWeekHigh = na
var float prevWeekLow = na
weekChanged = weekofyear(time) != weekofyear(time )
if (weekChanged)
prevWeekHigh := high
prevWeekLow := low
Monthly Pivots:
Pivot: Standard pivot point based on the previous month's data
// Function to calculate the standard pivot point based on the previous month's data
getStandardPivotPoint() =>
= getPrevMonthHighLow()
pivotPoint = (prevMonthHigh + prevMonthLow + close ) / 3
Resistances:
R3, R2, R1: Monthly resistance levels
// Function to calculate additional pivot points based on the monthly data
getMonthlyPivotPoints() =>
= getPrevMonthHighLow()
pivotPoint = (prevMonthHigh + prevMonthLow + close ) / 3
r1 = (2 * pivotPoint) - prevMonthLow
s1 = (2 * pivotPoint) - prevMonthHigh
r2 = pivotPoint + (prevMonthHigh - prevMonthLow)
s2 = pivotPoint - (prevMonthHigh - prevMonthLow)
r3 = prevMonthHigh + 2 * (pivotPoint - prevMonthLow)
s3 = prevMonthLow - 2 * (prevMonthHigh - pivotPoint)
Initializing and Populating the Table:
The myTable variable initializes the table with a blue background, and subsequent table.cell functions populate the table with headers and data.
// Initialize the table with adjusted bgcolor
var myTable = table.new(position = iPosition, columns = 5, rows = 10, bgcolor = color.new(color.blue, 90), border_width = 1, border_color = color.new(color.blue, 70))
Dynamic Data Population:
Data is dynamically populated in the table using the calculated values for Monthly Extremums, Weekly Extremums, Monthly Pivot Points, Resistances, and Supports.
// Add rows dynamically with data
= getPrevMonthHighLow()
= getPrevWeekHighLow()
= getMonthlyPivotPoints()
---
Conclusion:
The Adaptive MFT Extremum Pivots indicator offers traders a detailed and clear representation of critical market levels, empowering them to make informed decisions. However, users should carefully analyze the market and consider their individual risk tolerance before making any trading decisions. The indicator's disclaimer emphasizes that it is not investment advice, and the author and script provider are not responsible for any financial losses incurred.
---
Disclaimer:
This indicator is not investment advice. Trading decisions should be made based on a careful analysis of the market and individual risk tolerance. The author and script provider are not responsible for any financial losses incurred.
Kind regards,
Ely
Time-itTime-it = Time based indicator
The Time-it indicator parses data by the day of week. Every tradeable instrument has its own personality. Some are more volatile on Mondays, and some are more bullish / bearish on Fridays or any day in between. The key metrics Time-it parses is range, open, high, low, close and +volume-.
The Time-it parsed data is printed in a table format. The table, position, size & color and text color & size can be changed to your preference. Each column parsed data is the last 10 which is numbered 0-9 which refers to the number of the selected day bars ago. For example: if Monday is chosen, 0 is the last closed Monday bar and 9 is the last closed Monday 9 Monday bars ago.
Range = measures the range between high and low for the day.
Open = is the opening price for the day.
High = is the high price for the day.
Low = is the low price for the day.
Close = is the closing price for the day.
+volume- = is the positive or negative volume for the day.
Default settings:
*Represents a how to use tooltip*
Source = ohlc4
* The source used for MA
MA length = 20
* The moving average used
Day bar color on / off
* checked on / unchecked off
Monday = blue
Tuesday = yellow
Wednesday = purple
Thursday = orange
Friday = white
Saturday = red
Sunday = green
Day M, T, W, TH, F, ST, SN.
* Parsed data for the day of week tables
Table, position, size & color:
Top, middle, bottom, left, center, right
* Table position on the chart.
Frame width & border width = 1
Text color and text size
Border color and frame color
Decimal place = 0
* example: use 0 for a round number, use 4 for Forex
*** The Time-it indicator uses parts and/or pieces of code from "Tradingview Up/Down Volume" and "Tradingview Financials on Chart".
Previous Day High and Low + Separators Daily/WeeklyPrevious Day High and Low + Separators Daily/Weekly is an indicator based on separators of days and weeks and at the same time points out the previous highs and lows, everything is marked by lines, it consists of creating a clean graph and separated by the different trading days, referring to the extreme points created the previous day.
USEAGE
Point to each day of the week at the top of the chart to get a time location in your trading week and day sparation determined by 00:00 of any timezone.
The reference of the previous day's higs and LOWS is vitally important to understand which direction is most likely for the next day, either continuation or reversal.
DETAILS
As you can see you will be able to adapt these lines according to your chart design and with the desired intensity of appearance.
SETTINGS
UTC OFFSET: Determine your TIMEZONE in this section.
DAILY SEPARATOR: You have the option to change the color, style, width and text color.
WEEKLY SEPARATOR: You have the option to change the color, style, width and text color.
PREVIOUS HIGS & LOWS: You have the option to change the color, style, width and text color.
