CCOMET_Scanner_LibraryLibrary "CCOMET_Scanner_Library"
- A Trader's Edge (ATE)_Library was created to assist in constructing CCOMET Scanners
Loc_tIDs_Col(_string, _firstLocation)
TickerIDs: You must form this single tickerID input string exactly as described in the scripts info panel (little gray 'i' that
is circled at the end of the settings in the settings/input panel that you can hover your cursor over this 'i' to read the
details of that particular input). IF the string is formed correctly then it will break up this single string parameter into
a total of 40 separate strings which will be all of the tickerIDs that the script is using in your CCOMET Scanner.
Locations: This function is used when there's a desire to print an assets ALERT LABELS. A set Location on the scale is assigned to each asset.
This is created so that if a lot of alerts are triggered, they will stay relatively visible and not overlap each other.
If you set your '_firstLocation' parameter as 1, since there are a max of 40 assets that can be scanned, the 1st asset's location
is assigned the value in the '_firstLocation' parameter, the 2nd asset's location is the (1st asset's location+1)...and so on.
Parameters:
_string (simple string) : (string)
A maximum of 40 Tickers (ALL joined as 1 string for the input parameter) that is formulated EXACTLY as described
within the tooltips of the TickerID inputs in my CCOMET Scanner scripts:
assets = input.text_area(tIDset1, title="TickerID (MUST READ TOOLTIP)", tooltip="Accepts 40 TICKERID's for each
copy of the script on the chart. TEXT FORMATTING RULES FOR TICKERID'S:
(1) To exclude the EXCHANGE NAME in the Labels, de-select the next input option.
(2) MUST have a space (' ') AFTER each TickerID.
(3) Capitalization in the Labels will match cap of these TickerID's.
(4) If your asset has a BaseCurrency & QuoteCurrency (ie. ADAUSDT ) BUT you ONLY want Labels
to show BaseCurrency(ie.'ADA'), include a FORWARD SLASH ('/') between the Base & Quote (ie.'ADA/USDT')", display=display.none)
_firstLocation (simple int) : (simple int)
Optional (starts at 1 if no parameter added).
Location that you want the first asset to print its label if is triggered to do so.
ie. loc2=loc1+1, loc3=loc2+1, etc.
Returns: Returns 40 output variables in the tuple (ie. between the ' ') with the TickerIDs, 40 variables for the locations for alert labels, and 40 Colors for labels/plots
TickeridForLabelsAndSecurity(_ticker, _includeExchange)
This function accepts the TickerID Name as its parameter and produces a single string that will be used in all of your labels.
Parameters:
_ticker (simple string) : (string)
For this parameter, input the varible named '_coin' from your 'f_main()' function for this parameter. It is the raw
Ticker ID name that will be processed.
_includeExchange (simple bool) : (bool)
Optional (if parameter not included in function it defaults to false ).
Used to determine if the Exchange name will be included in all labels/triggers/alerts.
Returns: ( )
Returns 2 output variables:
1st ('_securityTickerid') is to be used in the 'request.security()' function as this string will contain everything
TV needs to pull the correct assets data.
2nd ('lblTicker') is to be used in all of the labels in your CCOMET Scanner as it will only contain what you want your labels
to show as determined by how the tickerID is formulated in the CCOMET Scanner's input.
InvalID_LblSz(_barCnt, _close, _securityTickerid, _invalidArray, _tablePosition, _stackVertical, _lblSzRfrnce)
INVALID TICKERIDs: This is to add a table in the middle right of your chart that prints all the TickerID's that were either not formulated
correctly in the '_source' input or that is not a valid symbol and should be changed.
LABEL SIZES: This function sizes your Alert Trigger Labels according to the amount of Printed Bars the chart has printed within
a set time period, while also keeping in mind the smallest relative reference size you input in the 'lblSzRfrnceInput'
parameter of this function. A HIGHER % of Printed Bars(aka...more trades occurring for that asset on the exchange),
the LARGER the Name Label will print, potentially showing you the better opportunities on the exchange to avoid
exchange manipulation liquidations.
*** SHOULD NOT be used as size of labels that are your asset Name Labels next to each asset's Line Plot...
if your CCOMET Scanner includes these as you want these to be the same size for every asset so the larger ones dont cover the
smaller ones if the plots are all close to each other ***
Parameters:
_barCnt (float) : (float)
Get the 1st variable('barCnt') from the Security function's tuple and input it as this functions 1st input
parameter which will directly affect the size of the 2nd output variable ('alertTrigLabel') that is also outputted by this function.
_close (float) : (float)
Put your 'close' variable named '_close' from the security function here.
_securityTickerid (string) : (string)
Throughout the entire charts updates, if a '_close' value is never registered then the logic counts the asset as INVALID.
This will be the 1st TickerID variable (named _securityTickerid) outputted from the tuple of the TickeridForLabels()
function above this one.
_invalidArray (array) : (array string)
Input the array from the original script that houses all of the invalidArray strings.
_tablePosition (simple string) : (string)
Optional (if parameter not included, it defaults to position.middle_right). Location on the chart you want the table printed.
Possible strings include: position.top_center, position.top_left, position.top_right, position.middle_center,
position.middle_left, position.middle_right, position.bottom_center, position.bottom_left, position.bottom_right.
_stackVertical (simple bool) : (bool)
Optional (if parameter not included, it defaults to true). All of the assets that are counted as INVALID will be
created in a list. If you want this list to be prited as a column then input 'true' here, otherwise they will all be in a row.
_lblSzRfrnce (string) : (string)
Optional (if parameter not included, it defaults to size.small). This will be the size of the variable outputted
by this function named 'assetNameLabel' BUT also affects the size of the output variable 'alertTrigLabel' as it uses this parameter's size
as the smallest size for 'alertTrigLabel' then uses the '_barCnt' parameter to determine the next sizes up depending on the "_barCnt" value.
Returns: ( )
Returns 2 variables:
1st output variable ('AssetNameLabel') is assigned to the size of the 'lblSzRfrnceInput' parameter.
2nd output variable('alertTrigLabel') can be of variying sizes depending on the 'barCnt' parameter...BUT the smallest
size possible for the 2nd output variable ('alertTrigLabel') will be the size set in the 'lblSzRfrnceInput' parameter.
PrintedBarCount(_time, _barCntLength, _barCntPercentMin)
The Printed BarCount Filter looks back a User Defined amount of minutes and calculates the % of bars that have printed
out of the TOTAL amount of bars that COULD HAVE been printed within the same amount of time.
Parameters:
_time (int) : (int)
The time associated with the chart of the particular asset that is being screened at that point.
_barCntLength (int) : (int)
The amount of time (IN MINUTES) that you want the logic to look back at to calculate the % of bars that have actually
printed in the span of time you input into this parameter.
_barCntPercentMin (int) : (int)
The minimum % of Printed Bars of the asset being screened has to be GREATER than the value set in this parameter
for the output variable 'bc_gtg' to be true.
Returns: ( )
Returns 2 outputs:
1st is the % of Printed Bars that have printed within the within the span of time you input in the '_barCntLength' parameter.
2nd is true/false according to if the Printed BarCount % is above the threshold that you input into the '_barCntPercentMin' parameter.
ابحث في النصوص البرمجية عن "trigger"
A_Traders_Edge__LibraryLibrary "A_Traders_Edge__Library"
- A Trader's Edge (ATE)_Library was created to assist in constructing Market Overview Scanners (MOS)
LabelLocation(_firstLocation)
This function is used when there's a desire to print an assets ALERT LABELS at a set location on the scale that will
NOT change throughout the progression of the script. This is created so that if a lot of alerts are triggered, they
will stay relatively visible and not overlap each other. Ex. If you set your '_firstLocation' parameter as 1, since
there are a max of 40 assets that can be scanned, the 1st asset's location is assigned the value in the '_firstLocation' parameter,
the 2nd asset's location is the (1st asset's location+1)...and so on. If your first location is set to 81 then
the 1st asset is 81 and 2nd is 82 and so on until the 40th location = 120(in this particular example).
Parameters:
_firstLocation (simple int) : (simple int)
Optional(starts at 1 if no parameter added).
Location that you want the first asset to print its label if is triggered to do so.
ie. loc2=loc1+1, loc3=loc2+1, etc.
Returns: Returns 40 output variables each being a different location to print the labels so that an asset is asssigned to
a particular location on the scale. Regardless of if you have the maximum amount of assets being screened (40 max), this
function will output 40 locations… So there needs to be 40 variables assigned in the tuple in this function. What I
mean by that is you need to have 40 output location variables within your tuple (ie. between the ' ') regarless of
if your scanning 40 assets or not. If you only have 20 assets in your scripts input settings, then only the first 20
variables within the ' ' Will be assigned to a value location and the other 20 will be assigned 'NA', but their
variables still need to be present in the tuple.
SeparateTickerids(_string)
You must form this single tickerID input string exactly as described in the scripts info panel (little gray 'i' that
is circled at the end of the settings in the settings/input panel that you can hover your cursor over this 'i' to read the
details of that particular input). IF the string is formed correctly then it will break up this single string parameter into
a total of 40 separate strings which will be all of the tickerIDs that the script is using in your MO scanner.
Parameters:
_string (simple string) : (string)
A maximum of 40 Tickers (ALL joined as 1 string for the input parameter) that is formulated EXACTLY as described
within the tooltips of the TickerID inputs in my MOS Scanner scripts:
assets = input.text_area(tIDset1, title="TickerID (MUST READ TOOLTIP)", tooltip="Accepts 40 TICKERID's for each
copy of the script on the chart. TEXT FORMATTING RULES FOR TICKERID'S:
(1) To exclude the EXCHANGE NAME in the Labels, de-select the next input option.
(2) MUST have a space (' ') AFTER each TickerID.
(3) Capitalization in the Labels will match cap of these TickerID's.
(4) If your asset has a BaseCurrency & QuoteCurrency (ie. ADAUSDT ) BUT you ONLY want Labels
to show BaseCurrency(ie.'ADA'), include a FORWARD SLASH ('/') between the Base & Quote (ie.'ADA/USDT')", display=display.none)
Returns: Returns 40 output variables of the different strings of TickerID's (ie. you need to output 40 variables within the
tuple ' ' regardless of if you were scanning using all possible (40) assets or not.
If your scanning for less than 40 assets, then once the variables are assigned to all of the tickerIDs, the rest
of the 40 variables in the tuple will be assigned "NA".
TickeridForLabelsAndSecurity(_includeExchange, _ticker)
This function accepts the TickerID Name as its parameter and produces a single string that will be used in all of your labels.
Parameters:
_includeExchange (simple bool) : (bool)
Optional(if parameter not included in function it defaults to false ).
Used to determine if the Exchange name will be included in all labels/triggers/alerts.
_ticker (simple string) : (string)
For this parameter, input the varible named '_coin' from your 'f_main()' function for this parameter. It is the raw
Ticker ID name that will be processed.
Returns: ( )
Returns 2 output variables:
1st ('_securityTickerid') is to be used in the 'request.security()' function as this string will contain everything
TV needs to pull the correct assets data.
2nd ('lblTicker') is to be used in all of the labels in your MOS as it will only contain what you want your labels
to show as determined by how the tickerID is formulated in the MOS's input.
InvalidTID(_tablePosition, _stackVertical, _close, _securityTickerid, _invalidArray)
This is to add a table in the middle right of your chart that prints all the TickerID's that were either not formulated
correctly in the '_source' input or that is not a valid symbol and should be changed.
Parameters:
_tablePosition (simple string) : (string)
Optional(if parameter not included, it defaults to position.middle_right). Location on the chart you want the table printed.
Possible strings include: position.top_center, position.top_left, position.top_right, position.middle_center,
position.middle_left, position.middle_right, position.bottom_center, position.bottom_left, position.bottom_right.
_stackVertical (simple bool) : (bool)
Optional(if parameter not included, it defaults to true). All of the assets that are counted as INVALID will be
created in a list. If you want this list to be prited as a column then input 'true' here.
_close (float) : (float)
If you want them printed as a single row then input 'false' here.
This should be the closing value of each of the assets being tested to determine in the TickerID is valid or not.
_securityTickerid (string) : (string)
Throughout the entire charts updates, if a '_close' value is never regestered then the logic counts the asset as INVALID.
This will be the 1st TickerID varible (named _securityTickerid) outputted from the tuple of the TickeridForLabels()
function above this one.
_invalidArray (string ) : (array string)
Input the array from the original script that houses all of the invalidArray strings.
Returns: (na)
Returns a table with the screened assets Invalid TickerID's. Table draws automatically if any are Invalid, thus,
no output variable to deal with.
LabelSizes(_barCnt, _lblSzRfrnce)
This function sizes your Alert Trigger Labels according to the amount of Printed Bars the chart has printed within
a set time period, while also keeping in mind the smallest relative reference size you input in the 'lblSzRfrnceInput'
parameter of this function. A HIGHER % of Printed Bars(aka...more trades occurring for that asset on the exchange),
the LARGER the Name Label will print, potentially showing you the better opportunities on the exchange to avoid
exchange manipulation liquidations.
*** SHOULD NOT be used as size of labels that are your asset Name Labels next to each asset's Line Plot...
if your MOS includes these as you want these to be the same size for every asset so the larger ones dont cover the
smaller ones if the plots are all close to each other ***
Parameters:
_barCnt (float) : (float)
Get the 1st variable('barCnt') from the 'PrintedBarCount' function's tuple and input it as this functions 1st input
parameter which will directly affect the size of the 2nd output variable ('alertTrigLabel') outputted by this function.
_lblSzRfrnce (string) : (string)
Optional(if parameter not included, it defaults to size.small). This will be the size of the 1st variable outputted
by this function ('assetNameLabel') BUT also affects the 2nd variable outputted by this function.
Returns: ( )
Returns 2 variables:
1st output variable ('AssetNameLabel') is assigned to the size of the 'lblSzRfrnceInput' parameter.
2nd output variable('alertTrigLabel') can be of variying sizes depending on the 'barCnt' parameter...BUT the smallest
size possible for the 2nd output variable ('alertTrigLabel') will be the size set in the 'lblSzRfrnceInput' parameter.
AssetColor()
This function is used to assign 40 different colors to 40 variables to be used for the different labels/plots.
Returns: Returns 40 output variables each with a different color assigned to them to be used in your plots & labels.
Regardless of if you have the maximum amount of assets your scanning(40 max) or less,
this function will assign 40 colors to 40 variables that you have between the ' '.
PrintedBarCount(_time, _barCntLength, _barCntPercentMin)
The Printed BarCount Filter looks back a User Defined amount of minutes and calculates the % of bars that have printed
out of the TOTAL amount of bars that COULD HAVE been printed within the same amount of time.
Parameters:
_time (int) : (int)
The time associated with the chart of the particular asset that is being screened at that point.
_barCntLength (int) : (int)
The amount of time (IN MINUTES) that you want the logic to look back at to calculate the % of bars that have actually
printed in the span of time you input into this parameter.
_barCntPercentMin (int) : (int)
The minimum % of Printed Bars of the asset being screened has to be GREATER than the value set in this parameter
for the output variable 'bc_gtg' to be true.
Returns: ( )
Returns 2 outputs:
1st is the % of Printed Bars that have printed within the within the span of time you input in the '_barCntLength' parameter.
2nd is true/false according to if the Printed BarCount % is above the threshold that you input into the '_barCntPercentMin' parameter.
RCI(_rciLength, _source, _interval)
You will see me using this a lot. DEFINITELY my favorite oscillator to utilize for SO many different things from
timing entries/exits to determining trends.Calculation of this indicator based on Spearmans Correlation.
Parameters:
_rciLength (int) : (int)
Amount of bars back to use in RCI calculations.
_source (float) : (float)
Source to use in RCI calculations (can use ANY source series. Ie, open,close,high,low,etc).
_interval (int) : (int)
Optional(if parameter not included, it defaults to 3). RCI calculation groups bars by this amount and then will.
rank these groups of bars.
Returns: (float)
Returns a single RCI value that will oscillates between -100 and +100.
RCIAVG(firstLength, _amtBtLengths, _rciSMAlen, _source, _interval)
20 RCI's are averaged together to get this RCI Avg (Rank Correlation Index Average). Each RCI (of the 20 total RCI)
has a progressively LARGER Lookback Length. Though the RCI Lengths are not individually adjustable,
there are 2 factors that ARE:
(1) the Lookback Length of the 1st RCI and
(2) the amount of values between one RCI's Lookback Length and the next.
*** If you set 'firstLength' to it's default of 200 and '_amtBtLengths' to it's default of 120 (aka AMOUNT BETWEEN LENGTHS=120)...
then RCI_2 Length=320, RCI_3 Length=440, RCI_4 Length=560, and so on.
Parameters:
firstLength (int) : (int)
Optional(if parameter is not included when the function is called, then it defaults to 200).
This parameter is the Lookback Length for the 1st RCI used in the RCI Avg.
_amtBtLengths (int) : (int)
Optional(if parameter not included when the function is called, then it defaults to 120).
This parameter is the value amount between each of the progressively larger lengths used for the 20 RCI's that
are averaged in the RCI Avg.
***** BEWARE ***** Too large of a value here will cause the calc to look back too far, causing an error(thus the value must be lowered)
_rciSMAlen (int) : (int)
Unlike the Single RCI Function, this function smooths out the end result using an SMA with a length value that is this parameter.
_source (float) : (float)
Source to use in RCI calculations (can use ANY source series. Ie, open,close,high,low,etc).
_interval (int) : (int)
Optional(if parameter not included, it defaults to 3). Within the RCI calculation, bars next to each other are grouped together
and then these groups are Ranked against each other. This parameter is the number of adjacent bars that are grouped together.
Returns: (float)
Returns a single RCI value that is the Avg of many RCI values that will oscillate between -100 and +100.
PercentChange(_startingValue, _endingValue)
This is a quick function to calculate how much % change has occurred between the '_startingValue' and the '_endingValue'
that you input into the function.
Parameters:
_startingValue (float) : (float)
The source value to START the % change calculation from.
_endingValue (float) : (float)
The source value to END the % change caluclation from.
Returns: Returns a single output being the % value between 0-100 (with trailing numbers behind a decimal). If you want only
a certain amount of numbers behind the decimal, this function needs to be put within a formatting function to do so.
Rescale(_source, _oldMin, _oldMax, _newMin, _newMax)
Rescales series with a known '_oldMin' & '_oldMax'. Use this when the scale of the '_source' to
rescale is known (bounded).
Parameters:
_source (float) : (float)
Source to be normalized.
_oldMin (int) : (float)
The known minimum of the '_source'.
_oldMax (int) : (float)
The known maximum of the '_source'.
_newMin (int) : (float)
What you want the NEW minimum of the '_source' to be.
_newMax (int) : (float)
What you want the NEW maximum of the '_source' to be.
Returns: Outputs your previously bounded '_source', but now the value will only move between the '_newMin' and '_newMax'
values you set in the variables.
Normalize_Historical(_source, _minimumLvl, _maximumLvl)
Normalizes '_source' that has a previously unknown min/max(unbounded) determining the max & min of the '_source'
FROM THE ENTIRE CHARTS HISTORY. ]
Parameters:
_source (float) : (float)
Source to be normalized.
_minimumLvl (int) : (float)
The Lower Boundary Level.
_maximumLvl (int) : (float)
The Upper Boundary Level.
Returns: Returns your same '_source', but now the value will MOSTLY stay between the minimum and maximum values you set in the
'_minimumLvl' and '_maximumLvl' variables (ie. if the source you input is an RSI...the output is the same RSI value but
instead of moving between 0-100 it will move between the maxand min you set).
Normailize_Local(_source, _length, _minimumLvl, _maximumLvl)
Normalizes series with previously unknown min/max(unbounded). Much like the Normalize_Historical function above this one,
but rather than using the Highest/Lowest Values within the ENTIRE charts history, this on looks for the Highest/Lowest
values of '_source' within the last ___ bars (set by user as/in the '_length' parameter. ]
Parameters:
_source (float) : (float)
Source to be normalized.
_length (int) : (float)
The amount of bars to look back to determine the highest/lowest '_source' value.
_minimumLvl (int) : (float)
The Lower Boundary Level.
_maximumLvl (int) : (float)
The Upper Boundary Level.
Returns: Returns a single output variable being the previously unbounded '_source' that is now normalized and bound between
the values used for '_minimumLvl'/'_maximumLvl' of the '_source' within the user defined lookback period.
RSI Trendlines with BreakoutsA pivot-based breakout indicator that attempts to provide traders with a visual aid for finding breakouts on the RSI. Similar to how we use trendlines on our charts, using them on the Relative Strength Index can also give us a sense of direction in the markets.
This script uses its own pivot-based system that checks for real-time swing levels and triggers a new pivot event after every dip and nth bars. The breakout alerts that are given were not designed to be taken as signals since their purpose is to provide an extra bit of confluence. Because of this, I added no other conditions that try to make the alerts "perfect", but instead, print every breakout that is detected. Despite stating this, I did happen to add a condition that checks the difference in RSI and the breakout value, but that's as far as it'll go.
There are alerts built-in to the script, along with adjustable repainting options.
🔳 Settings
Lookback Range: Lookback period to trigger a new pivot point when conditions are met.
RSI Difference: The difference between the current RSI value and the breakout value. How much higher in value should the current RSI be compared to the breakout value in order to detect a breakout?
RSI Settings
Styling Options
🔳 Repaint Options
On: Allows repainting
Off - Bar Confirmation: Prevents repainting and generates alerts when the bar closes. (1 candle later)
🔳 How it Works
Before a trendline is drawn, the script retrieves the slope between the previous pivot point and the current. Then it adds or subtracts the slope x amount of times (based on the lookback range) from the current pivot value until the current x-axis is reached. By doing this we can get a trendline that will detect a breakout accurately.
The result
When using the RSI Difference condition, the script will print breakouts whenever the condition is true, because of this dotted lines were added to track where the alert was triggered.
🔳 Alerts
Crossover Alerts for Yesterday O/H/L/C , Today Vwap [Zero54]This is a very simple script/indicator that trigger alerts every time the script triggers the following conditions.
1) Script crosses yesterday's (previous day's) high
2) Script crosses yesterday's (previous day's) low
3) Script crosses yesterday's (previous day's) open
4) Script crosses yesterday's (previous day's) close
5) Script crosses today's vwap.
I developed this to keep track of the scripts I follow and I find it useful. Hope you will find it useful too.
Steps to use:
1) Open the ticker for which you want to set the alerts.
2) Add this indicator to the chart.
3) Right Click on the text and set choose "Add Alert"
4) After you have done with setting up the alert, feel free to remove the indicator from the chart. It is not necessary for the indicator to be added in the chart in order for it to work.
5) Repeat 1-4 for all the scripts for which you want to set the alerts.
Be advised: During market open, if you have set alerts for multiple scripts, a tsunami of alerts may be triggered.
If you like this alert indicator, please like/boost it. Feel free to re-use this code however you may wish to. Cheers!
TradingView Alerts (Expo)█ Overview
The TradingView inbuilt alert feature inspires this alert tool.
TradingView Alerts (Expo) enables traders to set alerts on any indicator on TradingView, both public, protected, and invite-only scripts (if you are granted access). In this way, traders can set the alerts they want for any indicator they have access to. This feature is highly needed since many indicators on TradingView do not have the particular alert the trader looks for, this alert tool solves that problem and lets everyone create the alert they need. Many predefined conditions are included, such as "crossings," "turning up/down," "entering a channel," and much more.
█ TradingView alerts
TradingView alerts are a popular and convenient way of getting an immediate notification when the asset meets your set alert criteria. It helps traders to stay updated on the assets and timeframe they follow.
█ Alert table
Keep track of the average amount of alerts that have been triggered per day, per month, and per week. It helps traders to understand how frequently they can expect an alert to trigger.
█ Predefined alerts types
Crossing
The Crossing alert is triggered when the source input crosses (up or down) from the selected price or value.
Crossing Down / Crossing Up
The Crossing Down alert is triggered when the source input crosses down from the selected price or value.
The Crossing Up alert is triggered when the source input crosses up from the selected price or value.
Greater Than / Less Than
The Greater Than alert is triggered when the source input reaches the selected value or price.
The Less Than alert is triggered when the source input reaches the selected value or price.
Entering Channel / Exiting Channel
The Entering Channel alert is triggered when the source input enters the selected channel value.
The Exiting Channel alert is triggered when the source input exits the selected channel value.
Notice that this alert only works if you have selected "Channels."
Inside Channel / Outside Channel
The Inside Channel alert is triggered when the source input is within the selected Upper and Lower Channel boundaries.
The Outside Channel alert is triggered when the source input is outside the selected Upper and Lower Channel boundaries.
Notice that this alert only works if you have selected "Channels."
Moving Up / Moving Down
This alert is the same as "crossing up/down" within x-bars.
The Moving Up alert is triggered when the source input increases by a certain value within x-bars.
The Moving Down alert is triggered when the source input decreases by a certain value within x-bars.
Notice that you have to set the Number of Bars parameter!
The calculation starts from the last formed candlestick.
Moving Up % / Moving Down %
The Moving Up % alert is triggered when the source input increases by a certain percentage value within x-bars.
The Moving Down % alert is triggered when the source input decreases by a certain percentage value within x-bars.
Notice that you have to set the Number of Bars parameter!
The calculation starts from the last formed candlestick.
Turning Up / Turning Down
The Turning Up alert is triggered when the source input turns up.
The Turning Down alert is triggered when the source input turns down.
-----------------
Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Jake Bernstein - Moving Average ChannelWe all know that moving averages, in particular, moving averages of closing prices tend to be highly inaccurate indicators and frequently miss major tops and bottoms. In backtesting, they tend to be accurate some 30 to 40% of the time which is to my way of thinking unacceptable. On the contrary moving averages of opens versus closes for highs versus lows, when used properly avoid the drawbacks of closing moving averages, particularly when combined with a trigger. Shown above is my moving average channel method which uses the 57 SMA of Williams accumulation distribution as a setup or trigger. As shown by the arrows two consecutive price bars completely below the MA channel low and triggered by Williams below SMA constitutes a sell signal. Conversely, two consecutive price bars or more above the moving average channel high accompanied by Williams above its moving average constitutes a sell trigger. The moving average channel high, the red line is a 10 period Moving average of highs. The Moving average channel low, the green line is an 8 period Moving average of the low. There are at least a dozen applications of this methodology including its ability to spot trend changes, support, resistance, swing trades, market strength, market weakness, and more. I will post some of these additional uses of the moving average channel as they present themselves. Do note that in this chart there were two instances above the moving average channel high but these were not triggered by Williams AD and therefore the trend remains down for the duration of this chart. The methodology associated with my MAC is completely rules-based and works in any timeframe. Thank you my friend Larry Williams for developing your excellent version of accumulation-distribution
3Commas BotBjorgum 3Commas Bot
A strategy in a box to get you started today
With 3rd party API providers growing in popularity, many are turning to automating their strategies on their favorite assets. With so many options and layers of customization possible, TradingView offers a place no better for young or even experienced coders to build a platform from to meet these needs. 3Commas has offered easy access with straight forward TradingView compatibility. Before long many have their brokers hooked up and are ready to send their alerts (or perhaps they have been trying with mixed success for some time now) only they realize there might just be a little bit more to building a strategy that they are comfortable letting out of their sight to trade their money while they eat, sleep, etc. Many may have ideas for entry criteria they are excited to try, but further questions arise... "What about risk mitigation?" "How can I set stop or limit orders?" "Is there not some basic shell of a strategy that has laid some of this out for me to get me going?"
Well now there is just that. This strategy is meant for those that have begun to delve into the world of algorithmic trading providing a template that offers risk defined positions complete with stops, limit orders, and even trailing stops should one so choose to employ any of these criteria. It provides a framework that is easily manipulated (with some basic working knowledge of pine coding) to encompass ones own ideas and entry criteria, while also providing an already functioning strategy.
The default settings have a basic 1:1 risk to reward ratio, which sets a limit and a stop equal distance from the entry. The entry is a simple MA cross (up for long, down for short). There a variety of MA's to choose from and the user can define the lengths of the averages. The ratio can be adjusted from the menu along with a volatility based adder (ATR) that helps to distance a stop from support or resistance. These values are calculated off the swing low/high of the user defined lookback period. Risk is calculated from position entry to stop, and projected upwards to the limit as a function of the desired risk to reward ratio. Of note: the default settings include 0.05% commissions. Competitive commissions of the leading cryptocurrency exchanges are .1% round trip (one buy and one sell) for market orders. There is also some slippage to allow time for alerts to be sent and orders to fill giving the back test results a more accurate representation of real time conditions. Its recommended to research the going rates for your exchange and set them to default for the strategy you use or build.
To get started a user would:
1) Make a copy of the code and paste in their bot keys in the area provided under the "3Comma Keys" section
- eg. Long bot "start deal" copied from 3commas in to define "Long" etc. (code is commented)
2) Place alert on desired asset with desired settings ensuring to select "Order fills and alert() function calls"
3) Paste webhook into the webhook box and select webhook URL alerts (3rd party provided webhook)
3) Delete contents of alert message box and replace with {{strategy.order.alert_message}} and nothing else
- the codes will be sent to the webhook appropriately as the strategy enters and exits positions. Only 1 alert is needed
settings used for the display image:
1hr chart on BTCUSD
-ATR stop
-Risk adjustment 1.2
-ATR multiplier 1.3
-RnR 0.6
-MAs HEMA/SMA
-MA Length 50/100
-Order size percent of equity
-Trail trigger 60% of target
Experiment with your own settings on your crypto of choice or implement your own code!
Implementing your trailing stop (optional)
Among the options for possible settings is a trailing stop. This stop will ratchet higher once triggered as a function of the Average True Range (ATR). There is a variable level to choose where the user would like to begin trailing the stop during the trade. The level can be assigned with a decimal between 0 and 1 (eg. 0.5 = 50% of the distance between entry and the target which must be exceeded before the trail triggers to begin). This can allow for some dips to occur during the trade possibly keeping you in the trade for longer, while potentially reducing risk of drawdown over time. The default for this setting is 0 meaning unless adjusted, the trail will trigger on entry if the trailing stop exit method is selected. An example can be seen below:
Again, optional as well is the choice to implement a limit order. If one were to select a trailing stop they could choose not to set a limit, which could allow a trail to run further until hit. Drawdowns of this strategy would be foregoing locking gains at highs on target on other trades. This is a trade-off the user can decide on and test. An example of this working in favor can be observed below:
Conclusion
Although a simple strategy is implemented here, the benefits of this script allow a user a starting platform to build their strategies from with built in risk mitigation. This allows the user to sidestep some of the potential difficulties' that can arise while learning Pine and taking on the endeavor of automating their trading strategies. It is meant as an aid, a structure, and an educational piece that can be seen as a "pick-up-and-go" strategy with easy 3Commas compatibility. Additionally, this can help users become more comfortable with strategy alert messages and sending strings in the form of alerts from Pine. As well, FAQs are often littered with questions regarding "strategy.exit" calls, how to implement stops. how to properly set a trailing stop based on ATR, and more. The time this can save an individual to get started is likely of the best "take-aways" here.
Happy trading
Ruckard TradingLatinoThis strategy tries to mimic TradingLatino strategy.
The current implementation is beta.
Si hablas castellano o espanyol por favor consulta MENSAJE EN CASTELLANO más abajo.
It's aimed at BTCUSDT pair and 4h timeframe.
STRATEGY DEFAULT SETTINGS EXPLANATION
max_bars_back=5000 : This is a random number of bars so that the strategy test lasts for one or two years
calc_on_order_fills=false : To wait for the 4h closing is too much. Try to check if it's worth entering a position after closing one. I finally decided not to recheck if it's worth entering after an order is closed. So it is false.
calc_on_every_tick=false
pyramiding=0 : We only want one entry allowed in the same direction. And we don't want the order to scale by error.
initial_capital=1000 : These are 1000 USDT. By using 1% maximum loss per trade and 7% as a default stop loss by using 1000 USDT at 12000 USDT per BTC price you would entry with around 142 USDT which are converted into: 0.010 BTC . The maximum number of decimal for contracts on this BTCUSDT market is 3 decimals. E.g. the minimum might be: 0.001 BTC . So, this minimal 1000 amount ensures us not to entry with less than 0.001 entries which might have happened when using 100 USDT as an initial capital.
slippage=1 : Binance BTCUSDT mintick is: 0.01. Binance slippage: 0.1 % (Let's assume). TV has an integer slippage. It does not have a percentage based slippage. If we assume a 1000 initial capital, the recommended equity is 142 which at 11996 USDT per BTC price means: 0.011 BTC. The 0.1% slippage of: 0.011 BTC would be: 0.000011 . This is way smaller than the mintick. So our slippage is going to be 1. E.g. 1 (slippage) * 0.01 (mintick)
commission_type=strategy.commission.percent and commission_value=0.1 : According to: binance . com / en / fee / schedule in VIP 0 level both maker and taker fees are: 0.1 %.
BACKGROUND
Jaime Merino is a well known Youtuber focused on crypto trading
His channel TradingLatino
features monday to friday videos where he explains his strategy.
JAIME MERINO STANCE ON BOTS
Jaime Merino stance on bots (taken from memory out of a 2020 June video from him):
'~
You know. They can program you a bot and it might work.
But, there are some special situations that the bot would not be able to handle.
And, I, as a human, I would handle it. And the bot wouldn't do it.
~'
My long term target with this strategy script is add as many
special situations as I can to the script
so that it can match Jaime Merino behaviour even in non normal circumstances.
My alternate target is learn Pine script
and enjoy programming with it.
WARNING
This script might be bigger than other TradingView scripts.
However, please, do not be confused because the current status is beta.
This script has not been tested with real money.
This is NOT an official strategy from Jaime Merino.
This is NOT an official strategy from TradingLatino . net .
HOW IT WORKS
It basically uses ADX slope and LazyBear's Squeeze Momentum Indicator
to make its buy and sell decisions.
Fast paced EMA being bigger than slow paced EMA
(on higher timeframe) advices going long.
Fast paced EMA being smaller than slow paced EMA
(on higher timeframe) advices going short.
It finally add many substrats that TradingLatino uses.
SETTINGS
__ SETTINGS - Basics
____ SETTINGS - Basics - ADX
(ADX) Smoothing {14}
(ADX) DI Length {14}
(ADX) key level {23}
____ SETTINGS - Basics - LazyBear Squeeze Momentum
(SQZMOM) BB Length {20}
(SQZMOM) BB MultFactor {2.0}
(SQZMOM) KC Length {20}
(SQZMOM) KC MultFactor {1.5}
(SQZMOM) Use TrueRange (KC) {True}
____ SETTINGS - Basics - EMAs
(EMAS) EMA10 - Length {10}
(EMAS) EMA10 - Source {close}
(EMAS) EMA55 - Length {55}
(EMAS) EMA55 - Source {close}
____ SETTINGS - Volume Profile
Lowest and highest VPoC from last three days
is used to know if an entry has a support
VPVR of last 100 4h bars
is also taken into account
(VP) Use number of bars (not VP timeframe): Uses 'Number of bars {100}' setting instead of 'Volume Profile timeframe' setting for calculating session VPoC
(VP) Show tick difference from current price {False}: BETA . Might be useful for actions some day.
(VP) Number of bars {100}: If 'Use number of bars (not VP timeframe)' is turned on this setting is used to calculate session VPoC.
(VP) Volume Profile timeframe {1 day}: If 'Use number of bars (not VP timeframe)' is turned off this setting is used to calculate session VPoC.
(VP) Row width multiplier {0.6}: Adjust how the extra Volume Profile bars are shown in the chart.
(VP) Resistances prices number of decimal digits : Round Volume Profile bars label numbers so that they don't have so many decimals.
(VP) Number of bars for bottom VPOC {18}: 18 bars equals 3 days in suggested timeframe of 4 hours. It's used to calculate lowest session VPoC from previous three days. It's also used as a top VPOC for sells.
(VP) Ignore VPOC bottom advice on long {False}: If turned on it ignores bottom VPOC (or top VPOC on sells) when evaluating if a buy entry is worth it.
(VP) Number of bars for VPVR VPOC {100}: Number of bars to calculate the VPVR VPoC. We use 100 as Jaime once used. When the price bounces back to the EMA55 it might just bounce to this VPVR VPoC if its price it's lower than the EMA55 (Sells have inverse algorithm).
____ SETTINGS - ADX Slope
ADX Slope
help us to understand if ADX
has a positive slope, negative slope
or it is rather still.
(ADXSLOPE) ADX cut {23}: If ADX value is greater than this cut (23) then ADX has strength
(ADXSLOPE) ADX minimum steepness entry {45}: ADX slope needs to be 45 degrees to be considered as a positive one.
(ADXSLOPE) ADX minimum steepness exit {45}: ADX slope needs to be -45 degrees to be considered as a negative one.
(ADXSLOPE) ADX steepness periods {3}: In order to avoid false detection the slope is calculated along 3 periods.
____ SETTINGS - Next to EMA55
(NEXTEMA55) EMA10 to EMA55 bounce back percentage {80}: EMA10 might bounce back to EMA55 or maybe to 80% of its complete way to EMA55
(NEXTEMA55) Next to EMA55 percentage {15}: How much next to the EMA55 you need to be to consider it's going to bounce back upwards again.
____ SETTINGS - Stop Loss and Take Profit
You can set a default stop loss or a default take profit.
(STOPTAKE) Stop Loss % {7.0}
(STOPTAKE) Take Profit % {2.0}
____ SETTINGS - Trailing Take Profit
You can customize the default trailing take profit values
(TRAILING) Trailing Take Profit (%) {1.0}: Trailing take profit offset in percentage
(TRAILING) Trailing Take Profit Trigger (%) {2.0}: When 2.0% of benefit is reached then activate the trailing take profit.
____ SETTINGS - MAIN TURN ON/OFF OPTIONS
(EMAS) Ignore advice based on emas {false}.
(EMAS) Ignore advice based on emas (On closing long signal) {False}: Ignore advice based on emas but only when deciding to close a buy entry.
(SQZMOM) Ignore advice based on SQZMOM {false}: Ignores advice based on SQZMOM indicator.
(ADXSLOPE) Ignore advice based on ADX positive slope {false}
(ADXSLOPE) Ignore advice based on ADX cut (23) {true}
(STOPTAKE) Take Profit? {false}: Enables simple Take Profit.
(STOPTAKE) Stop Loss? {True}: Enables simple Stop Loss.
(TRAILING) Enable Trailing Take Profit (%) {True}: Enables Trailing Take Profit.
____ SETTINGS - Strategy mode
(STRAT) Type Strategy: 'Long and Short', 'Long Only' or 'Short Only'. Default: 'Long and Short'.
____ SETTINGS - Risk Management
(RISKM) Risk Management Type: 'Safe', 'Somewhat safe compound' or 'Unsafe compound'. ' Safe ': Calculations are always done with the initial capital (1000) in mind. The maximum losses per trade/day/week/month are taken into account. ' Somewhat safe compound ': Calculations are done with initial capital (1000) or a higher capital if it increases. The maximum losses per trade/day/week/month are taken into account. ' Unsafe compound ': In each order all the current capital is gambled and only the default stop loss per order is taken into account. That means that the maximum losses per trade/day/week/month are not taken into account. Default : 'Somewhat safe compound'.
(RISKM) Maximum loss per trade % {1.0}.
(RISKM) Maximum loss per day % {6.0}.
(RISKM) Maximum loss per week % {8.0}.
(RISKM) Maximum loss per month % {10.0}.
____ SETTINGS - Decimals
(DECIMAL) Maximum number of decimal for contracts {3}: How small (3 decimals means 0.001) an entry position might be in your exchange.
EXTRA 1 - PRICE IS IN RANGE indicator
(PRANGE) Print price is in range {False}: Enable a bottom label that indicates if the price is in range or not.
(PRANGE) Price range periods {5}: How many previous periods are used to calculate the medians
(PRANGE) Price range maximum desviation (%) {0.6} ( > 0 ): Maximum positive desviation for range detection
(PRANGE) Price range minimum desviation (%) {0.6} ( > 0 ): Mininum negative desviation for range detection
EXTRA 2 - SQUEEZE MOMENTUM Desviation indicator
(SQZDIVER) Show degrees {False}: Show degrees of each Squeeze Momentum Divergence lines to the x-axis.
(SQZDIVER) Show desviation labels {False}: Whether to show or not desviation labels for the Squeeze Momentum Divergences.
(SQZDIVER) Show desviation lines {False}: Whether to show or not desviation lines for the Squeeze Momentum Divergences.
EXTRA 3 - VOLUME PROFILE indicator
WARNING: This indicator works not on current bar but on previous bar. So in the worst case it might be VP from 4 hours ago. Don't worry, inside the strategy calculus the correct values are used. It's just that I cannot show the most recent one in the chart.
(VP) Print recent profile {False}: Show Volume Profile indicator
(VP) Avoid label price overlaps {False}: Avoid label prices to overlap on the chart.
EXTRA 4 - ZIGNALY SUPPORT
(ZIG) Zignaly Alert Type {Email}: 'Email', 'Webhook'. ' Email ': Prepare alert_message variable content to be compatible with zignaly expected email content format. ' Webhook ': Prepare alert_message variable content to be compatible with zignaly expected json content format.
EXTRA 5 - DEBUG
(DEBUG) Enable debug on order comments {False}: If set to true it prepares the order message to match the alert_message variable. It makes easier to debug what would have been sent by email or webhook on each of the times an order is triggered.
HOW TO USE THIS STRATEGY
BOT MODE: This is the default setting.
PROPER VOLUME PROFILE VIEWING: Click on this strategy settings. Properties tab. Make sure Recalculate 'each time the order was run' is turned off.
NEWBIE USER: (Check PROPER VOLUME PROFILE VIEWING above!) You might want to turn on the 'Print recent profile {False}' setting. Alternatively you can use my alternate realtime study: 'Resistances and supports based on simplified Volume Profile' but, be aware, it might consume one indicator.
ADVANCED USER 1: Turn on the 'Print price is in range {False}' setting and help us to debug this subindicator. Also help us to figure out how to include this value in the strategy.
ADVANCED USER 2: Turn on the all the (SQZDIVER) settings and help us to figure out how to include this value in the strategy.
ADVANCED USER 3: (Check PROPER VOLUME PROFILE VIEWING above!) Turn on the 'Print recent profile {False}' setting and report any problem with it.
JAIME MERINO: Just use the indicator as it comes by default. It should only show BUY signals, SELL signals and their associated closing signals. From time to time you might want to check 'ADVANCED USER 2' instructions to check that there's actually a divergence. Check also 'ADVANCED USER 1' instructions for your amusement.
EXTRA ADVICE
It's advised that you use this strategy in addition to these two other indicators:
* Squeeze Momentum Indicator
* ADX
so that your chart matches as close as possible to TradingLatino chart.
ZIGNALY INTEGRATION
This strategy supports Zignaly email integration by default. It also supports Zignaly Webhook integration.
ZIGNALY INTEGRATION - Email integration example
What you would write in your alert message:
||{{strategy.order.alert_message}}||key=MYSECRETKEY||
ZIGNALY INTEGRATION - Webhook integration example
What you would write in your alert message:
{ {{strategy.order.alert_message}} , "key" : "MYSECRETKEY" }
CREDITS
I have reused and adapted some code from
'Directional Movement Index + ADX & Keylevel Support' study
which it's from TradingView console user.
I have reused and adapted some code from
'3ema' study
which it's from TradingView hunganhnguyen1193 user.
I have reused and adapted some code from
'Squeeze Momentum Indicator ' study
which it's from TradingView LazyBear user.
I have reused and adapted some code from
'Strategy Tester EMA-SMA-RSI-MACD' study
which it's from TradingView fikira user.
I have reused and adapted some code from
'Support Resistance MTF' study
which it's from TradingView LonesomeTheBlue user.
I have reused and adapted some code from
'TF Segmented Linear Regression' study
which it's from TradingView alexgrover user.
I have reused and adapted some code from
"Poor man's volume profile" study
which it's from TradingView IldarAkhmetgaleev user.
FEEDBACK
Please check the strategy source code for more detailed information
where, among others, I explain all of the substrats
and if they are implemented or not.
Q1. Did I understand wrong any of the Jaime substrats (which I have implemented)?
Q2. The strategy yields quite profit when we should long (EMA10 from 1d timeframe is higher than EMA55 from 1d timeframe.
Why the strategy yields much less profit when we should short (EMA10 from 1d timeframe is lower than EMA55 from 1d timeframe)?
Any idea if you need to do something else rather than just reverse what Jaime does when longing?
FREQUENTLY ASKED QUESTIONS
FAQ1. Why are you giving this strategy for free?
TradingLatino and his fellow enthusiasts taught me this strategy. Now I'm giving back to them.
FAQ2. Seriously! Why are you giving this strategy for free?
I'm confident his strategy might be improved a lot. By keeping it to myself I would avoid other people contributions to improve it.
Now that everyone can contribute this is a win-win.
FAQ3. How can I connect this strategy to my Exchange account?
It seems that you can attach alerts to strategies.
You might want to combine it with a paying account which enable Webhook URLs to work.
I don't know how all of this works right now so I cannot give you advice on it.
You will have to do your own research on this subject. But, be careful. Automating trades, if not done properly,
might end on you automating losses.
FAQ4. I have just found that this strategy by default gives more than 3.97% of 'maximum series of losses'. That's unacceptable according to my risk management policy.
You might want to reduce default stop loss setting from 7% to something like 5% till you are ok with the 'maximum series of losses'.
FAQ5. Where can I learn more about your work on this strategy?
Check the source code. You might find unused strategies. Either because there's not a substantial increases on earnings. Or maybe because they have not been implemented yet.
FAQ6. How much leverage is applied in this strategy?
No leverage.
FAQ7. Any difference with original Jaime Merino strategy?
Most of the times Jaime defines an stop loss at the price entry. That's not the case here. The default stop loss is 7% (but, don't be confused it only means losing 1% of your investment thanks to risk management). There's also a trailing take profit that triggers at 2% profit with a 1% trailing.
FAQ8. Why this strategy return is so small?
The strategy should be improved a lot. And, well, backtesting in this platform is not guaranteed to return theoric results comparable to real-life returns. That's why I'm personally forward testing this strategy to verify it.
MENSAJE EN CASTELLANO
En primer lugar se agradece feedback para mejorar la estrategia.
Si eres un usuario avanzado y quieres colaborar en mejorar el script no dudes en comentar abajo.
Ten en cuenta que aunque toda esta descripción tenga que estar en inglés no es obligatorio que el comentario esté en inglés.
CHISTE - CASTELLANO
¡Pero Jaime!
¡400.000!
¡Tu da mun!
3 EMAs with Crossing Alerts & 2 SMAsHi all,
This is a simple indicator that plots 3 EMA lines and 2MAs of your choice to the screen and has built in alert triggers.
Features:
1) Alerts are triggered when EMAs 1 (Signal line) and 2 (Baseline) cross - a Long signal is called if the cross is above EMA 3 ( Trendline ), a short if the cross is below EMA3
2) Signals are represented visually as a triangle on the chart, below the candles is a long, above is a short
3) TradingView Alerts can be easily set as I have labelled the signals clearly as many other Indicators like this aren’t easy to work out if trying to create alerts to trigger a 3commas bot, for example!
Each EMA is fully customisable and if you wish to take advantage of the alerts, only a few simple rules need to be followed:
EMA1 needs to be less than EMA2.
EMA2 needs to be the same or greater than EMA3
That’s it, happy trading!
Advanced Larry Williams 9.2- By EduHit rate greater than Setup 9.1
However, the stop of this setup becomes more expensive in certain situations.
PURCHASE SIGN
1 - Paper comes in a bullish trend in the operational term to be operated.
2 - Exponential moving average of 9 upward periods.
3 - Wait for a candle to make the largest closing (candle reference).
4 - If the next candle CLOSES below the minimum of the candle reference the setup is armed.
5 - Mark the candle maxim that closed below the reference. It's the trigger!
6 - If the next candle exceeds this maximum by 1 cent the trade is triggered. Put the stop loss at the low of the candle that closed below (0.01 to 0.10 below)
7 - If the next candle does not fire, let's lower the trigger to the lower maximums, SINCE the mm9exp does not turn down.
8 - It exceeded the maximum we will have the entrance.
9 - Original stop-loss in the minimum of the candle we set the maximum activated.
SIGN OF SALE
1 - Paper comes in a downtrend in the operating period to be operated.
2 - Exponential moving average of 9 periods descending.
3 - Wait for a candle that makes the lowest closing (candle reference).
4 - If the next candle CLOSE above the maximum of the reference candle the setup is armed.
5 - Bookmark the candle that closed above the reference. It's the trigger!
6 - If the next candle breaks this minimum, the trade is triggered.
7 - Place the stop-loss at the maximum of the candle that closed up.
8 - If the next candle does not trigger, we will raise the trigger to the highest minimums SINCE the exponential moving average of 9 periods does not turn upwards.
9 - It broke the minimum we will have the entrance.
10 - Stop-loss original in the maximum of the candle that we set the minimum activated.
*********************************************************************************************************************************************************
Índice de acerto Superior ao Setup 9.1
Porém o stop deste setup acaba se tornando mais caro em determinadas situações.
SINAL DE COMPRA
1 - Papel vem em tendência de alta no prazo operacional a ser operado.
2 - Média móvel exponencial de 9 períodos ascendente.
3 - Aguardar um candle que faça o maior fechamento (candle referência).
4 - Se o próximo candle FECHAR abaixo da mínima do candle referência o setup está armado.
5 - Marcar a máxima do candle que fechou abaixo do referência. É o gatilho!
6 - Se o próximo candle superar essa máxima em 1 centavo o trade é acionado. Colocar o stop-loss na mínima do candle que fechou abaixo (0,01 a 0,10 abaixo)
7 - Se o próximo candle não acionar, vamos abaixando o gatilho para as máximas menores DESDE QUE a mm9exp não vire para baixo.
8 - Superou a máxima teremos a entrada.
9 - Stop-loss original na mínima do candle que marcamos a máxima ativada.
SINAL DE VENDA
1 - Papel vem em tendência de baixa no prazo operacional a ser operado.
2 - Média móvel exponencial de 9 períodos descendente.
3 - Aguardar um candle que faça o menor fechamento (candle referência).
4 - Se o próximo candle FECHAR acima da máxima do candle referência o setup está armado.
5 - Marcar a mínima do candle que fechou acima do referência. É o gatilho!
6 - Se o próximo candle romper essa mínima o trade é acionado.
7 - Colocar o stop-loss na máxima do candle que fechou acima.
8 - Se o próximo candle não acionar, vamos levantando o gatilho para as mínimas maiores DESDE QUE a média móvel exponencial de 9 períodos não vire para cima.
9 - Rompeu a mínima teremos a entrada.
10 - Stop-loss original na máxima do candle que marcamos a mínima ativada.
Trend Stress Quant [MarkitTick]💡This indicator combines a liquidity-based stress model with a dynamic linear regression channel to identify potential market exhaustion points and assess trend quality. By merging volume impact analysis with statistical deviation, this tool aims to highlight moments where price action may be overextended relative to the underlying liquidity conditions.
● Originality and Utility
Standard volatility indicators often rely solely on price range (like Bollinger Bands). This script introduces a Stress Engine that normalizes the relationship between Price Range (True Range) and Volume. This helps distinguish between healthy price movements and liquidity-stress events (illiquidity). Furthermore, instead of using a fixed-length channel, this tool offers a Dynamic Mode that anchors the regression channel to recent pivot points, ensuring the statistical analysis aligns with the current market structure rather than an arbitrary timeframe.
● Methodology
The script operates on two distinct mathematical models:
• Illiquidity Stress Engine
The core formula calculates a raw illiquidity metric based on the log-normal distribution of the ratio between True Range and Volume. A Z-Score (standard score) is then derived from this data over a specific lookback period. High Z-Scores indicate that price is moving disproportionately fast relative to the available volume, often a signature of panic selling or euphoric buying (exhaustion).
• Linear Regression Channel
The script calculates an Ordinary Least Squares (OLS) regression line (the line of best fit) to determine the mean price trend.
Standard Deviation Bands are plotted parallel to this mean.
Pearson Correlation Coefficient (R) is calculated to quantify the strength of the linear trend. Values closer to 1 or -1 indicate a strong trend, while values near 0 indicate a chaotic or ranging market.
📑 How to Use
Traders can utilize the visual outputs for mean reversion or trend continuation context:
• Exhaustion Signals (SE / BE Labels)
SE (Seller Exhaustion): Appears when the market is in a downtrend, but the Stress Engine detects a statistical anomaly (High Z-Score) on a down candle. This suggests panic selling may be peaking.
BE (Buyer Exhaustion): Appears when the market is in an uptrend, but the Stress Engine detects high stress on an up candle, suggesting a potential blow-off top.
• Regression Channel
The dashed middle line represents the fair value (mean) of the current trend.
The outer bands represent statistical extremes. Price interacting with the outer bands (default 2 Standard Deviations) while coincident with an Exhaustion Signal provides a high-confluence area of interest.
• Metrics Dashboard
A dashboard displays the current Trend Regime, Exhaustion Status, and Channel Width (volatility percentage).
● Settings
• Exhaustion Model
Trend Filter Length: Sets the baseline EMA to determine if the market is bullish or bearish.
Stress Threshold (Sigma): The Z-Score required to trigger an exhaustion signal (default is 2.0).
• Channel Configuration
Dynamic Pivot Mode: If enabled, automatically calculates the channel length based on recent pivots. If disabled, uses the Fixed Length.
Standard Deviations: Controls the width of the inner and outer channel bands.
📖This guide explains how to interpret and utilize signals for trading:
The script is designed primarily for Mean Reversion and Exhaustion trading strategies.
● The Core Strategy: Volatility Exhaustion
The script uses a "Stress Engine" to identify when price movement is statistically overextended relative to the available liquidity (Volume).
• Setup A: The "Seller Exhaustion" (Bullish Bounce)
Look for this setup during a downtrend to catch a temporary bottom or a reversal.
Trend Condition: The dashboard shows Bearish (Price is below the trend filter).
Trigger: The label SE (Seller Exhaustion) appears below a candle.
Why? This indicates that selling pressure was intense but likely panic-driven (High Z-Score/Stress) and may be drying up.
Confluence: Ideally, this signal appears when the price is touching or piercing the Lower Channel Band (dotted or solid lines).
Action: Traders often use this as a signal to close Short positions or enter a speculative Long (counter-trend) targeting the middle line.
• Setup B: The "Buyer Exhaustion" (Bearish Pullback)
Look for this setup during an uptrend to catch a local top.
Trend Condition: The dashboard shows Bullish .
Trigger: The label BE (Buyer Exhaustion) appears above a candle.
Why? This indicates euphoric buying on low liquidity or extreme volatility that is statistically unsustainable.
Confluence: Look for price rejection at the Upper Channel Band.
Action: Traders often use this to close Long positions or enter a Short targeting the mean.
● The Filter: Trend & Correlation
The script includes a Linear Regression Channel that quantifies the quality of the trend.
• Channel Slope
If the channel is angling steeply up or down, the trend is strong.
• Pearson R (Correlation)
The script calculates the Pearson R coefficient.
Weak Correlation: If the channel turns Gray/Neutral (or the fill becomes weak), it means the correlation is below the threshold (default 0.5).
Trading Rule: Avoid trading exhaustion signals when the channel is Gray/Neutral, as the market is likely chopping sideways with no clear direction.
● Risk Management & Targets
• Stop Loss
Since this is a volatility tool, a common technique is to place stops just outside the Outer Deviation Band (the widest line). If price expands beyond the outer band with no exhaustion signal, the trend may be entering a "runaway" phase.
• Take Profit
Target 1: The Middle Regression Line (The dashed center line). Prices tend to revert to this mean after an exhaustion event.
Target 2: The opposite channel band (e.g., if you bought at the bottom, hold until the top).
● Summary of Dashboard Metrics
The table on your chart provides a quick snapshot:
Trend Regime: Tells you if you should fundamentally look for Shorts (Bearish) or Longs (Bullish).
Seller/Buyer Status: Alerts you if the current bar is EXHAUSTED or Normal .
Channel Width %: Indicates volatility. If the width is very low (percentage is small), a breakout might be imminent (squeezing). If high, be careful of chop.
⚙️ Indicator settings
• Signal Parameters
Exhaustion & Stress Model: Controls signal sensitivity.
Trend Filter: Decides if the market is Bullish or Bearish.
Stress Threshold (Sigma): Higher values (e.g., 2.5) make the script stricter, showing fewer but potentially stronger signals.
• Channel Configuration
Dynamic Pivot Mode: If ON, the channel length auto-adjusts to recent market pivots. If OFF, it uses the Fixed Length you set.
Channel Bands: Adjusts the channel width.
Outer Deviation: The boundary for "extreme" moves. Price hitting this often signals a reversal.
• Quality Filter
Filter Weak Correlations: If enabled, the channel turns gray during choppy/sideways markets to warn you not to trust trend signals.
• Visuals
Display Options: Toggles the "Stats" dashboard and adjusts volatility coloring.
● Disclaimer
All provided scripts and indicators are strictly for educational exploration and must not be interpreted as financial advice or a recommendation to execute trades. I expressly disclaim all liability for any financial losses or damages that may result, directly or indirectly, from the reliance on or application of these tools. Market participation carries inherent risk where past performance never guarantees future returns, leaving all investment decisions and due diligence solely at your own discretion.
11-MA Institutional System (ATR+HTF Filters)11-MA Institutional Trading System Analysis.
This is a comprehensive Trading View Pine Script indicator that implements a sophisticated multi-timeframe moving average system with institutional-grade filters. Let me break down its key components and functionality:
🎯 Core Features
1. 11 Moving Average System. The indicator plots 11 customizable moving averages with different roles:
MA1-MA4 (5, 8, 10, 12): Fast-moving averages for short-term trends
MA5 (21 EMA): Short-term anchor - critical pivot point
MA6 (34 EMA): Intermediate support/resistance
MA7 (50 EMA): Medium-term bridge between short and long trends
MA8-MA9 (89, 100): Transition zone indicators
MA10-MA11 (150, 200): Long-term anchors for major trend identification
Each MA is fully customizable:
Type: SMA, EMA, WMA, TMA, RMA
Color, width, and enable/disable toggle
📊 Signal Generation System
Three Signal Tiers: Short-Term Signals (ST)
Trigger: MA8 (EMA 8) crossing MA21 (EMA 21)
Filters Applied:
✅ ATR-based post-cross confirmation (optional)
✅ Momentum confirmation (RSI > 50, MACD positive)
✅ Volume spike requirement
✅ HTF (Higher Timeframe) alignment
✅ Strong candle body ratio (>50%)
✅ Multi-MA confirmation (3+ MAs supporting direction)
✅ Price beyond MA21 with conviction
✅ Minimum bar spacing (prevents signal clustering)
✅ Consolidation filter
✅ Whipsaw protection (ATR-based price threshold)
Medium-Term Signals (MT)
Trigger: MA21 crossing MA50
Less strict filtering for swing trades
Major Signals
Golden Cross: MA50 crossing above MA200 (major bullish)
Death Cross: MA50 crossing below MA200 (major bearish)
🔍 Advanced Filtering System1. ATR-Based ConfirmationPrice must move > (ATR × 0.25) beyond the MA after crossover
This prevents false signals during low-volatility consolidation.2. Momentum Filters
RSI (14)
MACD Histogram
Rate of Change (ROC)
Composite momentum score (-3 to +3)
3. Volume Analysis
Volume spike detection (2x MA)
Volume classification: LOW, MED, HIGH, EXPL
Directional volume confirmation
4. Higher Timeframe Alignment
HTF1: 60-minute (default)
HTF2: 4-hour (optional)
HTF3: Daily (optional)
Signals only trigger when current TF aligns with HTF trend
5. Market Structure Detection
Break of Structure (BOS): Price breaking recent swing highs/lows
Order Blocks (OB): Institutional demand/supply zones
Fair Value Gaps (FVG): Imbalance areas for potential fills
📈 Comprehensive DashboardReal-Time Metrics Display: {scrollbar-width:none;-ms-overflow-style:none;-webkit-overflow-scrolling:touch;} ::-webkit-scrollbar{display:none}MetricDescriptionPriceCurrent close priceTimeframeCurrent chart timeframeSHORT/MEDIUM/MAJORTrend classification (🟢BULL/🔴BEAR/⚪NEUT)HTF TrendsHigher timeframe alignment indicatorsMomentumSTR↑/MOD↑/WK↑/WK↓/MOD↓/STR↓VolatilityLOW/MOD/HIGH/EXTR (based on ATR%)RSI(14)Color-coded: >70 red, <30 greenATR%Volatility as % of priceAdvanced Dashboard Features (Optional):
Price Distance from Key MAs
vs MA21, MA50, MA200 (percentage)
Color-coded: green (above), red (below)
MA Alignment Score
Calculates % of MAs in proper order
🟢 for bullish alignment, 🔴 for bearish
Trend Strength
Based on separation between MA21 and MA200
NONE/WEAK/MODERATE/STRONG/EXTREME
Consolidation Detection
Identifies low-volatility ranges
Prevents signals during sideways markets
⚙️ Customization OptionsFilter Toggles:
☑️ Require Momentum
☑️ Require Volume
☑️ Require HTF Alignment
☑️ Use ATR post-cross confirmation
☑️ Whipsaw filter
Min bars between signals (default: 5)
Dashboard Styling:
9 position options
6 text sizes
Custom colors for header, rows, and text
Toggle individual metrics on/off
🎨 Visual Elements
Signal Labels:
ST▲/ST▼ (green/red) - Short-term
MT▲/MT▼ (blue/orange) - Medium-term
GOLDEN CROSS / DEATH CROSS - Major signals
Volume Spikes:
Small labels showing volume class + direction
Example: "HIGH🟢" or "EXPL🔴"
Market Structure:
Dashed lines for Break of Structure levels
Automatic detection of swing highs/lows
🔔 Alert Conditions
Pre-configured alerts for:
Short-term bullish/bearish crosses
Medium-term bullish/bearish crosses
Golden Cross / Death Cross
Volume spikes
💡 Key Strengths
Institutional-Grade Filtering: Multiple confirmation layers reduce false signals
Multi-Timeframe Analysis: Ensures alignment across timeframes
Adaptive to Market Conditions: ATR-based thresholds adjust to volatility
Comprehensive Dashboard: All critical metrics in one view
Highly Customizable: 100+ input parameters
Signal Quality Over Quantity: Strict filters prioritize high-probability setups
⚠️ Usage Recommendations
Best for: Swing trading and position trading
Timeframes: Works on all TFs, optimized for 15m-Daily
Markets: Stocks, Forex, Crypto, Indices
Signal Frequency: Conservative (quality over quantity)
Combine with: Support/resistance, price action, risk management
🔧 Technical Implementation Notes
Uses Pine Script v6 syntax
Efficient calculation with minimal repainting
Maximum 500 labels for performance
Security function for HTF data (no lookahead bias)
Array-based MA alignment calculation
State variables to track signal spacing
This is a professional-grade trading system that combines classical technical analysis (moving averages) with modern institutional concepts (market structure, order blocks, multi-timeframe alignment).
The extensive filtering system is designed to eliminate noise and focus on high-probability trade setups.
Bayesian Liquidity Pain & Gain [Instit. Vol Weighted]Bayesian Liquidity Pain & Gain Indicator
Stop guessing where support and resistance are.
The Bayesian Liquidity Pain & Gain indicator moves beyond arbitrary lines and raw price action. It quantifies Institutional Intent by calculating the exact price levels where large volume has been accumulated and visualizes the "Pain" (stress) those participants feel when the market moves against them.
The Logic: Quantified Institutional Stress
Institutions don't trade single candles; they accumulate positions over time. This indicator tracks their Volume-Weighted Average Cost Basis to answer two critical questions:
Where did they enter? (The Cost Basis Lines)
Are they underwater? (The Pain Clouds)
By normalizing price distance using volatility (ATR) and statistical deviation (Z-Score), we filter out noise and only highlight zones where "Smart Money" is statistically forced to defend their positions or capitulate.
How to Read the Chart
1. The Cost Basis Lines (Anchors)
• 🟢 Green Line (Buyer Cost Basis): The average price where institutions accumulated long positions. This acts as dynamic Support.
• 🔴 Red Line (Seller Cost Basis): The average price where institutions accumulated short positions. This acts as dynamic Resistance.
2. The Pain Clouds (Signals)
When price moves significantly away from the cost basis (Z-Score > 2.0), "Clouds" appear to visualize the PnL status of the participants:
• 🔴 Red Cloud (Buyer Pain): Price is below the buyer's entry. Buyers are losing money (in the red). This creates a "Discount" zone where they may defend support.
• 🟢 Green Cloud (Seller Pain): Price is above the seller's entry. Sellers are losing money (shorts are squeezed). This indicates strong bullish momentum.
3. The Multi-Timeframe Dashboard
A real-time HUD showing the Z-Score status across 4 timeframes (1m, 5m, 15m, 1h):
• 🟢 Green: Profitable/Neutral (Trend Continuation)
• 🟠 Orange: Warning (Pressure Building)
• 🔴 Red: Critical Pain (High Probability Reversal)
Trading Strategies
Setup 1: The Defensive Bounce (Long)
• Context: Price drops into a 🔴 Red Cloud (Buyer Pain).
• Trigger: Price touches the 🟢 Green Line (Buyer Cost Basis) and shows a rejection wick.
• Logic: Institutional buyers defend their cost basis to avoid realizing losses.
Setup 2: The Short Squeeze (Momentum)
• Context: Price rallies into a 🟢 Green Cloud (Seller Pain).
• Trigger: Price holds above the 🔴 Red Line (Seller Cost Basis).
• Logic: Short sellers are trapped and forced to buy back (cover), fueling the rally.
Fractal Alignment:
For high-conviction trades, wait for the Dashboard to show "Pain" signals on both the 1h (Anchor) and 5m (Trigger) timeframes simultaneously.
Settings
• Memory Length (Default 144): The lookback period for the institutional cost basis. Increase for swing trading, decrease for scalping.
• Sigma Threshold (Default 2.0): The statistical confidence level for "Pain". Higher values = fewer, stronger signals.
• Volume Amp: When enabled, high volume amplifies the pain signal, giving more weight to institutional footprints.
Smart RSI MTF [DotGain]Summary
Are you tired of constantly switching between timeframes to check the RSI, only to miss the bigger picture?
The Smart RSI MTF (Multi-Timeframe) is designed to solve this exact problem. It is a streamlined chart overlay that monitors RSI conditions across up to 10 different timeframes simultaneously —from the 1-minute chart all the way up to the Monthly view.
This indicator removes the need for multiple open tabs and declutters your analysis by plotting signals directly on your main chart using a smart "visual hierarchy" system based on transparency.
⚙️ Core Components and Logic
The Smart RSI MTF relies on a sophisticated 3-layer logic to deliver clear, actionable context:
Multi-Timeframe Engine: The script runs 10 independent RSI calculations in the background. It checks standard intervals (5m, 15m, 1h, 4h, Daily, Weekly, Monthly) to ensure you never miss a momentum extreme on any scale.
Classic RSI Thresholds:
Overbought (> 70): Indicates price may be extended to the upside.
Oversold (< 30): Indicates price may be extended to the downside.
Smart Visibility System (The "Secret Sauce"): Not all signals are equal. A 5-minute Overbought signal is "noise" compared to a Weekly Overbought signal. This indicator automatically applies Transparency to differentiate importance:
Minutes = High Transparency (Faint).
Hours = Medium Transparency.
Days/Weeks/Months = No Transparency (Solid/Bold).
🚦 How to Read the Indicator
The indicator plots shapes (Labels by default) directly above or below the candles. The appearance tells you the direction and the timeframe significance:
🟥 RED SIGNALS (Overbought Condition)
Trigger: RSI is above 70 on a specific timeframe.
Location: Placed above the candle bar.
Meaning: Potential bearish reversal or pullback.
🟩 GREEN SIGNALS (Oversold Condition)
Trigger: RSI is below 30 on a specific timeframe.
Location: Placed below the candle bar.
Meaning: Potential bullish reversal or bounce.
👻 TRANSPARENCY (Signal Strength)
Faint/Ghostly: The signal comes from a lower timeframe (e.g., 5m, 15m). Use for scalping or entry timing.
Solid/Bright: The signal comes from a major timeframe (e.g., Daily, Weekly). Use for swing trading and identifying major market turns.
Visual Elements
Symbol Shapes: Fully customizable (Label, Diamond, Circle, Triangle, etc.) via settings.
Stacking: If multiple timeframes trigger at once, symbols will overlay, creating a visually denser and darker color, indicating Confluence .
Key Benefit
The goal of the Smart RSI MTF is to help traders instantly spot Confluence . When you see a faint short-term signal align with a solid long-term signal, you have identified a high-probability reversal zone without leaving your chart.
Have fun :)
Disclaimer
This "Smart RSI MTF" indicator is provided for informational and educational purposes only. It does not, and should not be construed as, financial, investment, or trading advice.
The signals generated by this tool (both "Buy" and "Sell" indications) are the result of a specific set of algorithmic conditions. They are not a direct recommendation to buy or sell any asset. All trading and investing in financial markets involves substantial risk of loss. You can lose all of your invested capital.
Past performance is not indicative of future results. The signals generated may produce false or losing trades. The creator (© DotGain) assumes no liability for any financial losses or damages you may incur as a result of using this indicator.
You are solely responsible for your own trading and investment decisions. Always conduct your own research (DYOR) and consider your personal risk tolerance before making any trades.
BTC -50% Crash to Recovery ZoneGeneral Overview This is a macro-analysis tool designed to visualize the true duration of Bitcoin’s "Suffering & Recovery Cycles." Unlike standard oscillators that only signal oversold conditions, this script highlights the entire timeline required for the market to flush out leverage and return to All-Time Highs (ATH).
Operational Logic The algorithm tracks Bitcoin’s historical All-Time High (ATH).
The Trigger: It activates automatically when the price drops 50% below the last recorded ATH.
The "Recovery Zone": Once triggered, the chart background turns red (indicating a "Drawdown" state). This zone remains active persistently, even during intermediate relief rallies.
The Reset: The zone deactivates only when the price breaks above the previous ATH, marking the official start of a new Price Discovery phase.
How to Read It
Red Background: We are officially in a Bear Market or Recovery Phase. The asset is technically "underwater." For the long-term investor with a low time preference, this visually defines the accumulation window.
Red Horizontal Line: Indicates the "Target." This is the exact price level of the old ATH that Bitcoin must reclaim to close the bearish cycle.
No Background Color: We are in Price Discovery. The market is healthy and pushing for new highs.
The Financial Lesson This indicator visually demonstrates a fundamental market truth: "Price takes the elevator down, but takes the stairs up." It shows that after a halving of value (-50%), Bitcoin may take months or years to recover previous levels, helping investors filter out the noise of short-term pumps that fail to break the macro-bearish structure.
DCA Bot v7 - Cryptosa Nostra 1.0Technical Overview: Adaptive RSI DCA Bot
This is a sophisticated DCA (Dollar Cost Averaging) indicator designed for accumulating assets and managing portfolio distribution. It does not trade on simple RSI crosses. Instead, it combines multi-zone RSI analysis with ATR-based volatility triggers to execute staggered, dynamically-sized trades.
Its core feature is a "learning" engine that adapts its own settings over time. This "brain" can be trained on historical data and then applied to your real-time portfolio holdings via a "Live Override" feature.
Core Logic: How It Works
A trade is only executed when two conditions are met simultaneously:
The RSI Condition: The RSI must be inside one of the four pre-defined zones.
The Price Condition: The price must cross a "trigger line" (the green or red line) that is dynamically calculated based on volatility.
1. The Four RSI Zones
This script uses four distinct zones to determine the intent to trade:
Deep Buy Zone (Default: RSI <= 35 & Downtrend): This is the primary "value" buy signal. It only activates if the RSI is deeply oversold and the price is below the 200-period Trend MA.
Reload Buy Zone (Default: RSI 40-50 & Uptrend): This is a "buy the dip" signal. It looks for minor pullbacks during an established uptrend (price above the 200-period Trend MA).
Profit-Taking Zone (Default: RSI 70-80): Triggers a standard, small sell when the market is overbought.
Euphoria Zone (Default: RSI >= 80): Triggers a larger, more aggressive sell during extreme "blow-off" tops.
2. Dynamic Trade Sizing
The amount to buy or sell is not fixed. It scales dynamically based on how high or low the RSI is:
Buy Sizing: Spends a higher percentage of available cash when RSI is at its lowest (e.g., 35) and a smaller percentage when it's at the top of the reload zone (e.g., 50).
Sell Sizing: Sells a smaller percentage of holdings when RSI just enters the overbought zone (e.g., 70) and a much larger percentage when it's in the euphoria zone (e.g., 80+).
3. The "Adaptive Brain" (ATR Multipliers)
This is the script's learning mechanism. The green/red trigger lines are calculated as: Last Trade Price +/- (ATR * Multiplier).
This "Multiplier" is the brain. It adapts based on trade performance.
After a successful trade (as defined by profit_target_multiplier), the bot gets more confident and reduces the multiplier. This places the next trigger line closer to the price, making it more aggressive.
After a losing trade (as defined by loss_limit_multiplier), the bot gets more cautious and increases the multiplier. This places the next trigger line further away, making it more patient.
How to Use This Indicator
This script is designed to be "trained" on historical data to provide relevant signals for today.
To Train the Brain: In the settings, go to "1. Backtest Settings". Set the "Start Date (For Learning)" to a date in the past (e.g., 6 months or 1 year ago). The script will run a simulation from that date, allowing its Adaptive Multipliers (the "brain") to adjust to the market's volatility.
To See Live Signals: In "2. Live Portfolio Override", check the box "Override Backtest Balance?" and enter your real current coin and USD holdings.
Result: The "Live Status" table (top-right) will now display signals from the trained brain but will calculate the "Potential Buy %" and "Potential Sell %" based on your real portfolio. The "Buy Multi" and "Sell Multi" fields show you the brain's current learned values.
DCA Percent SignalOverview
The DCA Percent Signal Indicator generates buy and sell signals based on percentage drops from all-time highs and percentage gains from lowest lows since ATH. This indicator is designed for pyramiding strategies where each signal represents a configurable percentage of equity allocation.
Definitions
DCA (Dollar-Cost Averaging): An investment strategy where you invest a fixed amount at regular intervals, regardless of price fluctuations. This indicator generates signals for a DCA-style pyramiding approach.
Gann Bar Types: Classification system for price bars based on their relationship to the previous bar:
Up Bar: High > previous high AND low ≥ previous low
Down Bar: High ≤ previous high AND low < previous low
Inside Bar: High ≤ previous high AND low ≥ previous low
Outside Bar: High > previous high AND low < previous low
ATH (All-Time High): The highest price level reached during the entire chart period
ATL (All-Time Low): The lowest price level reached since the most recent ATH
Pyramiding: A trading strategy that adds to positions on favorable price movements
Look-Ahead Bias: Using future information that wouldn't be available in real-time trading
Default Properties
Signal Thresholds:
Buy Threshold: 10% (triggers every 10% drop from ATH)
Sell Threshold: 30% (triggers every 30% gain from lowest low since ATH)
Price Sources:
ATH Tracking: High (ATH detection)
ATL Tracking: Low (low detection)
Buy Signal Source: Low (buy signals)
Sell Signal Source: High (sell signals)
Filter Options:
Apply Gann Filter: False (disabled by default)
Buy Sets ATL: False (disabled by default)
Display Options:
Show Buy/Sell Signals: True
Show Reference Lines: True
Show Info Table: False
Show Bar Type: False
How It Works
Buy Signals: Trigger every 10% drop from the all-time highest price reached
Sell Signals: Trigger every 30% increase from the lowest low since the most recent all-time high
Smart Tracking: Uses configurable price sources for signal generation
Key Features
Configurable Thresholds: Adjustable buy/sell percentage thresholds (default: 10%/30%)
Separate Price Sources: Independent sources for ATH tracking, ATL tracking, and signal triggers
Configurable Signals: Uses low for buy signals and high for sell signals by default
Optional Gann Filter: Apply Gann bar analysis for additional signal filtering
Optional Buy Sets ATL: Option to set ATL reference point when buy signals occur
Visual Debug: Detailed labels showing signal parameters and values
Usage Instructions
Apply to Chart: Use on any timeframe (recommended: 1D or higher for better signal quality)
Risk Management: Adjust thresholds based on your risk tolerance and market volatility
Signal Analysis: Monitor debug labels for detailed signal information and validation
Signal Logic
Buy signals are blocked when ATH increases to prevent buying at peaks
Sell signals are blocked when ATL decreases to prevent selling at lows
This ensures signals only trigger on subsequent bars, not the same bar that establishes new reference points
Buy Signals:
Calculate drop percentage from ATH to buy signal source
Trigger when drop reaches threshold increments (10%, 20%, 30%, etc.)
Always blocked on ATH bars to prevent buying at peaks
Optional: Also blocked on up/outside bars when Gann filter enabled
Sell Signals:
Calculate gain percentage from lowest low to sell signal source
Trigger when gain reaches threshold increments (30%, 60%, 90%, etc.)
Always blocked when ATL decreases to prevent selling at lows
Optional: Also blocked on down bars when Gann filter enabled
Limitations
Designed for trending markets; may generate many signals in sideways/ranging markets
Requires sufficient price movement to be effective
Not suitable for scalping or very short timeframes
Implementation Notes
Signals use optimistic price sources (low for buys, high for sells), these can be configured to be more conservative
Gann filter provides additional signal filtering based on bar types
Debug information available in data window for real-time analysis
Detailed labels on each signal show ATH, lowest low, buy level, sell level, and drop/gain percentages
Dynamic Equity Allocation Model"Cash is Trash"? Not Always. Here's Why Science Beats Guesswork.
Every retail trader knows the frustration: you draw support and resistance lines, you spot patterns, you follow market gurus on social media—and still, when the next bear market hits, your portfolio bleeds red. Meanwhile, institutional investors seem to navigate market turbulence with ease, preserving capital when markets crash and participating when they rally. What's their secret?
The answer isn't insider information or access to exotic derivatives. It's systematic, scientifically validated decision-making. While most retail traders rely on subjective chart analysis and emotional reactions, professional portfolio managers use quantitative models that remove emotion from the equation and process multiple streams of market information simultaneously.
This document presents exactly such a system—not a proprietary black box available only to hedge funds, but a fully transparent, academically grounded framework that any serious investor can understand and apply. The Dynamic Equity Allocation Model (DEAM) synthesizes decades of financial research from Nobel laureates and leading academics into a practical tool for tactical asset allocation.
Stop drawing colorful lines on your chart and start thinking like a quant. This isn't about predicting where the market goes next week—it's about systematically adjusting your risk exposure based on what the data actually tells you. When valuations scream danger, when volatility spikes, when credit markets freeze, when multiple warning signals align—that's when cash isn't trash. That's when cash saves your portfolio.
The irony of "cash is trash" rhetoric is that it ignores timing. Yes, being 100% cash for decades would be disastrous. But being 100% equities through every crisis is equally foolish. The sophisticated approach is dynamic: aggressive when conditions favor risk-taking, defensive when they don't. This model shows you how to make that decision systematically, not emotionally.
Whether you're managing your own retirement portfolio or seeking to understand how institutional allocation strategies work, this comprehensive analysis provides the theoretical foundation, mathematical implementation, and practical guidance to elevate your investment approach from amateur to professional.
The choice is yours: keep hoping your chart patterns work out, or start using the same quantitative methods that professionals rely on. The tools are here. The research is cited. The methodology is explained. All you need to do is read, understand, and apply.
The Dynamic Equity Allocation Model (DEAM) is a quantitative framework for systematic allocation between equities and cash, grounded in modern portfolio theory and empirical market research. The model integrates five scientifically validated dimensions of market analysis—market regime, risk metrics, valuation, sentiment, and macroeconomic conditions—to generate dynamic allocation recommendations ranging from 0% to 100% equity exposure. This work documents the theoretical foundations, mathematical implementation, and practical application of this multi-factor approach.
1. Introduction and Theoretical Background
1.1 The Limitations of Static Portfolio Allocation
Traditional portfolio theory, as formulated by Markowitz (1952) in his seminal work "Portfolio Selection," assumes an optimal static allocation where investors distribute their wealth across asset classes according to their risk aversion. This approach rests on the assumption that returns and risks remain constant over time. However, empirical research demonstrates that this assumption does not hold in reality. Fama and French (1989) showed that expected returns vary over time and correlate with macroeconomic variables such as the spread between long-term and short-term interest rates. Campbell and Shiller (1988) demonstrated that the price-earnings ratio possesses predictive power for future stock returns, providing a foundation for dynamic allocation strategies.
The academic literature on tactical asset allocation has evolved considerably over recent decades. Ilmanen (2011) argues in "Expected Returns" that investors can improve their risk-adjusted returns by considering valuation levels, business cycles, and market sentiment. The Dynamic Equity Allocation Model presented here builds on this research tradition and operationalizes these insights into a practically applicable allocation framework.
1.2 Multi-Factor Approaches in Asset Allocation
Modern financial research has shown that different factors capture distinct aspects of market dynamics and together provide a more robust picture of market conditions than individual indicators. Ross (1976) developed the Arbitrage Pricing Theory, a model that employs multiple factors to explain security returns. Following this multi-factor philosophy, DEAM integrates five complementary analytical dimensions, each tapping different information sources and collectively enabling comprehensive market understanding.
2. Data Foundation and Data Quality
2.1 Data Sources Used
The model draws its data exclusively from publicly available market data via the TradingView platform. This transparency and accessibility is a significant advantage over proprietary models that rely on non-public data. The data foundation encompasses several categories of market information, each capturing specific aspects of market dynamics.
First, price data for the S&P 500 Index is obtained through the SPDR S&P 500 ETF (ticker: SPY). The use of a highly liquid ETF instead of the index itself has practical reasons, as ETF data is available in real-time and reflects actual tradability. In addition to closing prices, high, low, and volume data are captured, which are required for calculating advanced volatility measures.
Fundamental corporate metrics are retrieved via TradingView's Financial Data API. These include earnings per share, price-to-earnings ratio, return on equity, debt-to-equity ratio, dividend yield, and share buyback yield. Cochrane (2011) emphasizes in "Presidential Address: Discount Rates" the central importance of valuation metrics for forecasting future returns, making these fundamental data a cornerstone of the model.
Volatility indicators are represented by the CBOE Volatility Index (VIX) and related metrics. The VIX, often referred to as the market's "fear gauge," measures the implied volatility of S&P 500 index options and serves as a proxy for market participants' risk perception. Whaley (2000) describes in "The Investor Fear Gauge" the construction and interpretation of the VIX and its use as a sentiment indicator.
Macroeconomic data includes yield curve information through US Treasury bonds of various maturities and credit risk premiums through the spread between high-yield bonds and risk-free government bonds. These variables capture the macroeconomic conditions and financing conditions relevant for equity valuation. Estrella and Hardouvelis (1991) showed that the shape of the yield curve has predictive power for future economic activity, justifying the inclusion of these data.
2.2 Handling Missing Data
A practical problem when working with financial data is dealing with missing or unavailable values. The model implements a fallback system where a plausible historical average value is stored for each fundamental metric. When current data is unavailable for a specific point in time, this fallback value is used. This approach ensures that the model remains functional even during temporary data outages and avoids systematic biases from missing data. The use of average values as fallback is conservative, as it generates neither overly optimistic nor pessimistic signals.
3. Component 1: Market Regime Detection
3.1 The Concept of Market Regimes
The idea that financial markets exist in different "regimes" or states that differ in their statistical properties has a long tradition in financial science. Hamilton (1989) developed regime-switching models that allow distinguishing between different market states with different return and volatility characteristics. The practical application of this theory consists of identifying the current market state and adjusting portfolio allocation accordingly.
DEAM classifies market regimes using a scoring system that considers three main dimensions: trend strength, volatility level, and drawdown depth. This multidimensional view is more robust than focusing on individual indicators, as it captures various facets of market dynamics. Classification occurs into six distinct regimes: Strong Bull, Bull Market, Neutral, Correction, Bear Market, and Crisis.
3.2 Trend Analysis Through Moving Averages
Moving averages are among the oldest and most widely used technical indicators and have also received attention in academic literature. Brock, Lakonishok, and LeBaron (1992) examined in "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns" the profitability of trading rules based on moving averages and found evidence for their predictive power, although later studies questioned the robustness of these results when considering transaction costs.
The model calculates three moving averages with different time windows: a 20-day average (approximately one trading month), a 50-day average (approximately one quarter), and a 200-day average (approximately one trading year). The relationship of the current price to these averages and the relationship of the averages to each other provide information about trend strength and direction. When the price trades above all three averages and the short-term average is above the long-term, this indicates an established uptrend. The model assigns points based on these constellations, with longer-term trends weighted more heavily as they are considered more persistent.
3.3 Volatility Regimes
Volatility, understood as the standard deviation of returns, is a central concept of financial theory and serves as the primary risk measure. However, research has shown that volatility is not constant but changes over time and occurs in clusters—a phenomenon first documented by Mandelbrot (1963) and later formalized through ARCH and GARCH models (Engle, 1982; Bollerslev, 1986).
DEAM calculates volatility not only through the classic method of return standard deviation but also uses more advanced estimators such as the Parkinson estimator and the Garman-Klass estimator. These methods utilize intraday information (high and low prices) and are more efficient than simple close-to-close volatility estimators. The Parkinson estimator (Parkinson, 1980) uses the range between high and low of a trading day and is based on the recognition that this information reveals more about true volatility than just the closing price difference. The Garman-Klass estimator (Garman and Klass, 1980) extends this approach by additionally considering opening and closing prices.
The calculated volatility is annualized by multiplying it by the square root of 252 (the average number of trading days per year), enabling standardized comparability. The model compares current volatility with the VIX, the implied volatility from option prices. A low VIX (below 15) signals market comfort and increases the regime score, while a high VIX (above 35) indicates market stress and reduces the score. This interpretation follows the empirical observation that elevated volatility is typically associated with falling markets (Schwert, 1989).
3.4 Drawdown Analysis
A drawdown refers to the percentage decline from the highest point (peak) to the lowest point (trough) during a specific period. This metric is psychologically significant for investors as it represents the maximum loss experienced. Calmar (1991) developed the Calmar Ratio, which relates return to maximum drawdown, underscoring the practical relevance of this metric.
The model calculates current drawdown as the percentage distance from the highest price of the last 252 trading days (one year). A drawdown below 3% is considered negligible and maximally increases the regime score. As drawdown increases, the score decreases progressively, with drawdowns above 20% classified as severe and indicating a crisis or bear market regime. These thresholds are empirically motivated by historical market cycles, in which corrections typically encompassed 5-10% drawdowns, bear markets 20-30%, and crises over 30%.
3.5 Regime Classification
Final regime classification occurs through aggregation of scores from trend (40% weight), volatility (30%), and drawdown (30%). The higher weighting of trend reflects the empirical observation that trend-following strategies have historically delivered robust results (Moskowitz, Ooi, and Pedersen, 2012). A total score above 80 signals a strong bull market with established uptrend, low volatility, and minimal losses. At a score below 10, a crisis situation exists requiring defensive positioning. The six regime categories enable a differentiated allocation strategy that not only distinguishes binarily between bullish and bearish but allows gradual gradations.
4. Component 2: Risk-Based Allocation
4.1 Volatility Targeting as Risk Management Approach
The concept of volatility targeting is based on the idea that investors should maximize not returns but risk-adjusted returns. Sharpe (1966, 1994) defined with the Sharpe Ratio the fundamental concept of return per unit of risk, measured as volatility. Volatility targeting goes a step further and adjusts portfolio allocation to achieve constant target volatility. This means that in times of low market volatility, equity allocation is increased, and in times of high volatility, it is reduced.
Moreira and Muir (2017) showed in "Volatility-Managed Portfolios" that strategies that adjust their exposure based on volatility forecasts achieve higher Sharpe Ratios than passive buy-and-hold strategies. DEAM implements this principle by defining a target portfolio volatility (default 12% annualized) and adjusting equity allocation to achieve it. The mathematical foundation is simple: if market volatility is 20% and target volatility is 12%, equity allocation should be 60% (12/20 = 0.6), with the remaining 40% held in cash with zero volatility.
4.2 Market Volatility Calculation
Estimating current market volatility is central to the risk-based allocation approach. The model uses several volatility estimators in parallel and selects the higher value between traditional close-to-close volatility and the Parkinson estimator. This conservative choice ensures the model does not underestimate true volatility, which could lead to excessive risk exposure.
Traditional volatility calculation uses logarithmic returns, as these have mathematically advantageous properties (additive linkage over multiple periods). The logarithmic return is calculated as ln(P_t / P_{t-1}), where P_t is the price at time t. The standard deviation of these returns over a rolling 20-trading-day window is then multiplied by √252 to obtain annualized volatility. This annualization is based on the assumption of independently identically distributed returns, which is an idealization but widely accepted in practice.
The Parkinson estimator uses additional information from the trading range (High minus Low) of each day. The formula is: σ_P = (1/√(4ln2)) × √(1/n × Σln²(H_i/L_i)) × √252, where H_i and L_i are high and low prices. Under ideal conditions, this estimator is approximately five times more efficient than the close-to-close estimator (Parkinson, 1980), as it uses more information per observation.
4.3 Drawdown-Based Position Size Adjustment
In addition to volatility targeting, the model implements drawdown-based risk control. The logic is that deep market declines often signal further losses and therefore justify exposure reduction. This behavior corresponds with the concept of path-dependent risk tolerance: investors who have already suffered losses are typically less willing to take additional risk (Kahneman and Tversky, 1979).
The model defines a maximum portfolio drawdown as a target parameter (default 15%). Since portfolio volatility and portfolio drawdown are proportional to equity allocation (assuming cash has neither volatility nor drawdown), allocation-based control is possible. For example, if the market exhibits a 25% drawdown and target portfolio drawdown is 15%, equity allocation should be at most 60% (15/25).
4.4 Dynamic Risk Adjustment
An advanced feature of DEAM is dynamic adjustment of risk-based allocation through a feedback mechanism. The model continuously estimates what actual portfolio volatility and portfolio drawdown would result at the current allocation. If risk utilization (ratio of actual to target risk) exceeds 1.0, allocation is reduced by an adjustment factor that grows exponentially with overutilization. This implements a form of dynamic feedback that avoids overexposure.
Mathematically, a risk adjustment factor r_adjust is calculated: if risk utilization u > 1, then r_adjust = exp(-0.5 × (u - 1)). This exponential function ensures that moderate overutilization is gently corrected, while strong overutilization triggers drastic reductions. The factor 0.5 in the exponent was empirically calibrated to achieve a balanced ratio between sensitivity and stability.
5. Component 3: Valuation Analysis
5.1 Theoretical Foundations of Fundamental Valuation
DEAM's valuation component is based on the fundamental premise that the intrinsic value of a security is determined by its future cash flows and that deviations between market price and intrinsic value are eventually corrected. Graham and Dodd (1934) established in "Security Analysis" the basic principles of fundamental analysis that remain relevant today. Translated into modern portfolio context, this means that markets with high valuation metrics (high price-earnings ratios) should have lower expected returns than cheaply valued markets.
Campbell and Shiller (1988) developed the Cyclically Adjusted P/E Ratio (CAPE), which smooths earnings over a full business cycle. Their empirical analysis showed that this ratio has significant predictive power for 10-year returns. Asness, Moskowitz, and Pedersen (2013) demonstrated in "Value and Momentum Everywhere" that value effects exist not only in individual stocks but also in asset classes and markets.
5.2 Equity Risk Premium as Central Valuation Metric
The Equity Risk Premium (ERP) is defined as the expected excess return of stocks over risk-free government bonds. It is the theoretical heart of valuation analysis, as it represents the compensation investors demand for bearing equity risk. Damodaran (2012) discusses in "Equity Risk Premiums: Determinants, Estimation and Implications" various methods for ERP estimation.
DEAM calculates ERP not through a single method but combines four complementary approaches with different weights. This multi-method strategy increases estimation robustness and avoids dependence on single, potentially erroneous inputs.
The first method (35% weight) uses earnings yield, calculated as 1/P/E or directly from operating earnings data, and subtracts the 10-year Treasury yield. This method follows Fed Model logic (Yardeni, 2003), although this model has theoretical weaknesses as it does not consistently treat inflation (Asness, 2003).
The second method (30% weight) extends earnings yield by share buyback yield. Share buybacks are a form of capital return to shareholders and increase value per share. Boudoukh et al. (2007) showed in "The Total Shareholder Yield" that the sum of dividend yield and buyback yield is a better predictor of future returns than dividend yield alone.
The third method (20% weight) implements the Gordon Growth Model (Gordon, 1962), which models stock value as the sum of discounted future dividends. Under constant growth g assumption: Expected Return = Dividend Yield + g. The model estimates sustainable growth as g = ROE × (1 - Payout Ratio), where ROE is return on equity and payout ratio is the ratio of dividends to earnings. This formula follows from equity theory: unretained earnings are reinvested at ROE and generate additional earnings growth.
The fourth method (15% weight) combines total shareholder yield (Dividend + Buybacks) with implied growth derived from revenue growth. This method considers that companies with strong revenue growth should generate higher future earnings, even if current valuations do not yet fully reflect this.
The final ERP is the weighted average of these four methods. A high ERP (above 4%) signals attractive valuations and increases the valuation score to 95 out of 100 possible points. A negative ERP, where stocks have lower expected returns than bonds, results in a minimal score of 10.
5.3 Quality Adjustments to Valuation
Valuation metrics alone can be misleading if not interpreted in the context of company quality. A company with a low P/E may be cheap or fundamentally problematic. The model therefore implements quality adjustments based on growth, profitability, and capital structure.
Revenue growth above 10% annually adds 10 points to the valuation score, moderate growth above 5% adds 5 points. This adjustment reflects that growth has independent value (Modigliani and Miller, 1961, extended by later growth theory). Net margin above 15% signals pricing power and operational efficiency and increases the score by 5 points, while low margins below 8% indicate competitive pressure and subtract 5 points.
Return on equity (ROE) above 20% characterizes outstanding capital efficiency and increases the score by 5 points. Piotroski (2000) showed in "Value Investing: The Use of Historical Financial Statement Information" that fundamental quality signals such as high ROE can improve the performance of value strategies.
Capital structure is evaluated through the debt-to-equity ratio. A conservative ratio below 1.0 multiplies the valuation score by 1.2, while high leverage above 2.0 applies a multiplier of 0.8. This adjustment reflects that high debt constrains financial flexibility and can become problematic in crisis times (Korteweg, 2010).
6. Component 4: Sentiment Analysis
6.1 The Role of Sentiment in Financial Markets
Investor sentiment, defined as the collective psychological attitude of market participants, influences asset prices independently of fundamental data. Baker and Wurgler (2006, 2007) developed a sentiment index and showed that periods of high sentiment are followed by overvaluations that later correct. This insight justifies integrating a sentiment component into allocation decisions.
Sentiment is difficult to measure directly but can be proxied through market indicators. The VIX is the most widely used sentiment indicator, as it aggregates implied volatility from option prices. High VIX values reflect elevated uncertainty and risk aversion, while low values signal market comfort. Whaley (2009) refers to the VIX as the "Investor Fear Gauge" and documents its role as a contrarian indicator: extremely high values typically occur at market bottoms, while low values occur at tops.
6.2 VIX-Based Sentiment Assessment
DEAM uses statistical normalization of the VIX by calculating the Z-score: z = (VIX_current - VIX_average) / VIX_standard_deviation. The Z-score indicates how many standard deviations the current VIX is from the historical average. This approach is more robust than absolute thresholds, as it adapts to the average volatility level, which can vary over longer periods.
A Z-score below -1.5 (VIX is 1.5 standard deviations below average) signals exceptionally low risk perception and adds 40 points to the sentiment score. This may seem counterintuitive—shouldn't low fear be bullish? However, the logic follows the contrarian principle: when no one is afraid, everyone is already invested, and there is limited further upside potential (Zweig, 1973). Conversely, a Z-score above 1.5 (extreme fear) adds -40 points, reflecting market panic but simultaneously suggesting potential buying opportunities.
6.3 VIX Term Structure as Sentiment Signal
The VIX term structure provides additional sentiment information. Normally, the VIX trades in contango, meaning longer-term VIX futures have higher prices than short-term. This reflects that short-term volatility is currently known, while long-term volatility is more uncertain and carries a risk premium. The model compares the VIX with VIX9D (9-day volatility) and identifies backwardation (VIX > 1.05 × VIX9D) and steep backwardation (VIX > 1.15 × VIX9D).
Backwardation occurs when short-term implied volatility is higher than longer-term, which typically happens during market stress. Investors anticipate immediate turbulence but expect calming. Psychologically, this reflects acute fear. The model subtracts 15 points for backwardation and 30 for steep backwardation, as these constellations signal elevated risk. Simon and Wiggins (2001) analyzed the VIX futures curve and showed that backwardation is associated with market declines.
6.4 Safe-Haven Flows
During crisis times, investors flee from risky assets into safe havens: gold, US dollar, and Japanese yen. This "flight to quality" is a sentiment signal. The model calculates the performance of these assets relative to stocks over the last 20 trading days. When gold or the dollar strongly rise while stocks fall, this indicates elevated risk aversion.
The safe-haven component is calculated as the difference between safe-haven performance and stock performance. Positive values (safe havens outperform) subtract up to 20 points from the sentiment score, negative values (stocks outperform) add up to 10 points. The asymmetric treatment (larger deduction for risk-off than bonus for risk-on) reflects that risk-off movements are typically sharper and more informative than risk-on phases.
Baur and Lucey (2010) examined safe-haven properties of gold and showed that gold indeed exhibits negative correlation with stocks during extreme market movements, confirming its role as crisis protection.
7. Component 5: Macroeconomic Analysis
7.1 The Yield Curve as Economic Indicator
The yield curve, represented as yields of government bonds of various maturities, contains aggregated expectations about future interest rates, inflation, and economic growth. The slope of the yield curve has remarkable predictive power for recessions. Estrella and Mishkin (1998) showed that an inverted yield curve (short-term rates higher than long-term) predicts recessions with high reliability. This is because inverted curves reflect restrictive monetary policy: the central bank raises short-term rates to combat inflation, dampening economic activity.
DEAM calculates two spread measures: the 2-year-minus-10-year spread and the 3-month-minus-10-year spread. A steep, positive curve (spreads above 1.5% and 2% respectively) signals healthy growth expectations and generates the maximum yield curve score of 40 points. A flat curve (spreads near zero) reduces the score to 20 points. An inverted curve (negative spreads) is particularly alarming and results in only 10 points.
The choice of two different spreads increases analysis robustness. The 2-10 spread is most established in academic literature, while the 3M-10Y spread is often considered more sensitive, as the 3-month rate directly reflects current monetary policy (Ang, Piazzesi, and Wei, 2006).
7.2 Credit Conditions and Spreads
Credit spreads—the yield difference between risky corporate bonds and safe government bonds—reflect risk perception in the credit market. Gilchrist and Zakrajšek (2012) constructed an "Excess Bond Premium" that measures the component of credit spreads not explained by fundamentals and showed this is a predictor of future economic activity and stock returns.
The model approximates credit spread by comparing the yield of high-yield bond ETFs (HYG) with investment-grade bond ETFs (LQD). A narrow spread below 200 basis points signals healthy credit conditions and risk appetite, contributing 30 points to the macro score. Very wide spreads above 1000 basis points (as during the 2008 financial crisis) signal credit crunch and generate zero points.
Additionally, the model evaluates whether "flight to quality" is occurring, identified through strong performance of Treasury bonds (TLT) with simultaneous weakness in high-yield bonds. This constellation indicates elevated risk aversion and reduces the credit conditions score.
7.3 Financial Stability at Corporate Level
While the yield curve and credit spreads reflect macroeconomic conditions, financial stability evaluates the health of companies themselves. The model uses the aggregated debt-to-equity ratio and return on equity of the S&P 500 as proxies for corporate health.
A low leverage level below 0.5 combined with high ROE above 15% signals robust corporate balance sheets and generates 20 points. This combination is particularly valuable as it represents both defensive strength (low debt means crisis resistance) and offensive strength (high ROE means earnings power). High leverage above 1.5 generates only 5 points, as it implies vulnerability to interest rate increases and recessions.
Korteweg (2010) showed in "The Net Benefits to Leverage" that optimal debt maximizes firm value, but excessive debt increases distress costs. At the aggregated market level, high debt indicates fragilities that can become problematic during stress phases.
8. Component 6: Crisis Detection
8.1 The Need for Systematic Crisis Detection
Financial crises are rare but extremely impactful events that suspend normal statistical relationships. During normal market volatility, diversified portfolios and traditional risk management approaches function, but during systemic crises, seemingly independent assets suddenly correlate strongly, and losses exceed historical expectations (Longin and Solnik, 2001). This justifies a separate crisis detection mechanism that operates independently of regular allocation components.
Reinhart and Rogoff (2009) documented in "This Time Is Different: Eight Centuries of Financial Folly" recurring patterns in financial crises: extreme volatility, massive drawdowns, credit market dysfunction, and asset price collapse. DEAM operationalizes these patterns into quantifiable crisis indicators.
8.2 Multi-Signal Crisis Identification
The model uses a counter-based approach where various stress signals are identified and aggregated. This methodology is more robust than relying on a single indicator, as true crises typically occur simultaneously across multiple dimensions. A single signal may be a false alarm, but the simultaneous presence of multiple signals increases confidence.
The first indicator is a VIX above the crisis threshold (default 40), adding one point. A VIX above 60 (as in 2008 and March 2020) adds two additional points, as such extreme values are historically very rare. This tiered approach captures the intensity of volatility.
The second indicator is market drawdown. A drawdown above 15% adds one point, as corrections of this magnitude can be potential harbingers of larger crises. A drawdown above 25% adds another point, as historical bear markets typically encompass 25-40% drawdowns.
The third indicator is credit market spreads above 500 basis points, adding one point. Such wide spreads occur only during significant credit market disruptions, as in 2008 during the Lehman crisis.
The fourth indicator identifies simultaneous losses in stocks and bonds. Normally, Treasury bonds act as a hedge against equity risk (negative correlation), but when both fall simultaneously, this indicates systemic liquidity problems or inflation/stagflation fears. The model checks whether both SPY and TLT have fallen more than 10% and 5% respectively over 5 trading days, adding two points.
The fifth indicator is a volume spike combined with negative returns. Extreme trading volumes (above twice the 20-day average) with falling prices signal panic selling. This adds one point.
A crisis situation is diagnosed when at least 3 indicators trigger, a severe crisis at 5 or more indicators. These thresholds were calibrated through historical backtesting to identify true crises (2008, 2020) without generating excessive false alarms.
8.3 Crisis-Based Allocation Override
When a crisis is detected, the system overrides the normal allocation recommendation and caps equity allocation at maximum 25%. In a severe crisis, the cap is set at 10%. This drastic defensive posture follows the empirical observation that crises typically require time to develop and that early reduction can avoid substantial losses (Faber, 2007).
This override logic implements a "safety first" principle: in situations of existential danger to the portfolio, capital preservation becomes the top priority. Roy (1952) formalized this approach in "Safety First and the Holding of Assets," arguing that investors should primarily minimize ruin probability.
9. Integration and Final Allocation Calculation
9.1 Component Weighting
The final allocation recommendation emerges through weighted aggregation of the five components. The standard weighting is: Market Regime 35%, Risk Management 25%, Valuation 20%, Sentiment 15%, Macro 5%. These weights reflect both theoretical considerations and empirical backtesting results.
The highest weighting of market regime is based on evidence that trend-following and momentum strategies have delivered robust results across various asset classes and time periods (Moskowitz, Ooi, and Pedersen, 2012). Current market momentum is highly informative for the near future, although it provides no information about long-term expectations.
The substantial weighting of risk management (25%) follows from the central importance of risk control. Wealth preservation is the foundation of long-term wealth creation, and systematic risk management is demonstrably value-creating (Moreira and Muir, 2017).
The valuation component receives 20% weight, based on the long-term mean reversion of valuation metrics. While valuation has limited short-term predictive power (bull and bear markets can begin at any valuation), the long-term relationship between valuation and returns is robustly documented (Campbell and Shiller, 1988).
Sentiment (15%) and Macro (5%) receive lower weights, as these factors are subtler and harder to measure. Sentiment is valuable as a contrarian indicator at extremes but less informative in normal ranges. Macro variables such as the yield curve have strong predictive power for recessions, but the transmission from recessions to stock market performance is complex and temporally variable.
9.2 Model Type Adjustments
DEAM allows users to choose between four model types: Conservative, Balanced, Aggressive, and Adaptive. This choice modifies the final allocation through additive adjustments.
Conservative mode subtracts 10 percentage points from allocation, resulting in consistently more cautious positioning. This is suitable for risk-averse investors or those with limited investment horizons. Aggressive mode adds 10 percentage points, suitable for risk-tolerant investors with long horizons.
Adaptive mode implements procyclical adjustment based on short-term momentum: if the market has risen more than 5% in the last 20 days, 5 percentage points are added; if it has declined more than 5%, 5 points are subtracted. This logic follows the observation that short-term momentum persists (Jegadeesh and Titman, 1993), but the moderate size of adjustment avoids excessive timing bets.
Balanced mode makes no adjustment and uses raw model output. This neutral setting is suitable for investors who wish to trust model recommendations unchanged.
9.3 Smoothing and Stability
The allocation resulting from aggregation undergoes final smoothing through a simple moving average over 3 periods. This smoothing is crucial for model practicality, as it reduces frequent trading and thus transaction costs. Without smoothing, the model could fluctuate between adjacent allocations with every small input change.
The choice of 3 periods as smoothing window is a compromise between responsiveness and stability. Longer smoothing would excessively delay signals and impede response to true regime changes. Shorter or no smoothing would allow too much noise. Empirical tests showed that 3-period smoothing offers an optimal ratio between these goals.
10. Visualization and Interpretation
10.1 Main Output: Equity Allocation
DEAM's primary output is a time series from 0 to 100 representing the recommended percentage allocation to equities. This representation is intuitive: 100% means full investment in stocks (specifically: an S&P 500 ETF), 0% means complete cash position, and intermediate values correspond to mixed portfolios. A value of 60% means, for example: invest 60% of wealth in SPY, hold 40% in money market instruments or cash.
The time series is color-coded to enable quick visual interpretation. Green shades represent high allocations (above 80%, bullish), red shades low allocations (below 20%, bearish), and neutral colors middle allocations. The chart background is dynamically colored based on the signal, enhancing readability in different market phases.
10.2 Dashboard Metrics
A tabular dashboard presents key metrics compactly. This includes current allocation, cash allocation (complement), an aggregated signal (BULLISH/NEUTRAL/BEARISH), current market regime, VIX level, market drawdown, and crisis status.
Additionally, fundamental metrics are displayed: P/E Ratio, Equity Risk Premium, Return on Equity, Debt-to-Equity Ratio, and Total Shareholder Yield. This transparency allows users to understand model decisions and form their own assessments.
Component scores (Regime, Risk, Valuation, Sentiment, Macro) are also displayed, each normalized on a 0-100 scale. This shows which factors primarily drive the current recommendation. If, for example, the Risk score is very low (20) while other scores are moderate (50-60), this indicates that risk management considerations are pulling allocation down.
10.3 Component Breakdown (Optional)
Advanced users can display individual components as separate lines in the chart. This enables analysis of component dynamics: do all components move synchronously, or are there divergences? Divergences can be particularly informative. If, for example, the market regime is bullish (high score) but the valuation component is very negative, this signals an overbought market not fundamentally supported—a classic "bubble warning."
This feature is disabled by default to keep the chart clean but can be activated for deeper analysis.
10.4 Confidence Bands
The model optionally displays uncertainty bands around the main allocation line. These are calculated as ±1 standard deviation of allocation over a rolling 20-period window. Wide bands indicate high volatility of model recommendations, suggesting uncertain market conditions. Narrow bands indicate stable recommendations.
This visualization implements a concept of epistemic uncertainty—uncertainty about the model estimate itself, not just market volatility. In phases where various indicators send conflicting signals, the allocation recommendation becomes more volatile, manifesting in wider bands. Users can understand this as a warning to act more cautiously or consult alternative information sources.
11. Alert System
11.1 Allocation Alerts
DEAM implements an alert system that notifies users of significant events. Allocation alerts trigger when smoothed allocation crosses certain thresholds. An alert is generated when allocation reaches 80% (from below), signaling strong bullish conditions. Another alert triggers when allocation falls to 20%, indicating defensive positioning.
These thresholds are not arbitrary but correspond with boundaries between model regimes. An allocation of 80% roughly corresponds to a clear bull market regime, while 20% corresponds to a bear market regime. Alerts at these points are therefore informative about fundamental regime shifts.
11.2 Crisis Alerts
Separate alerts trigger upon detection of crisis and severe crisis. These alerts have highest priority as they signal large risks. A crisis alert should prompt investors to review their portfolio and potentially take defensive measures beyond the automatic model recommendation (e.g., hedging through put options, rebalancing to more defensive sectors).
11.3 Regime Change Alerts
An alert triggers upon change of market regime (e.g., from Neutral to Correction, or from Bull Market to Strong Bull). Regime changes are highly informative events that typically entail substantial allocation changes. These alerts enable investors to proactively respond to changes in market dynamics.
11.4 Risk Breach Alerts
A specialized alert triggers when actual portfolio risk utilization exceeds target parameters by 20%. This is a warning signal that the risk management system is reaching its limits, possibly because market volatility is rising faster than allocation can be reduced. In such situations, investors should consider manual interventions.
12. Practical Application and Limitations
12.1 Portfolio Implementation
DEAM generates a recommendation for allocation between equities (S&P 500) and cash. Implementation by an investor can take various forms. The most direct method is using an S&P 500 ETF (e.g., SPY, VOO) for equity allocation and a money market fund or savings account for cash allocation.
A rebalancing strategy is required to synchronize actual allocation with model recommendation. Two approaches are possible: (1) rule-based rebalancing at every 10% deviation between actual and target, or (2) time-based monthly rebalancing. Both have trade-offs between responsiveness and transaction costs. Empirical evidence (Jaconetti, Kinniry, and Zilbering, 2010) suggests rebalancing frequency has moderate impact on performance, and investors should optimize based on their transaction costs.
12.2 Adaptation to Individual Preferences
The model offers numerous adjustment parameters. Component weights can be modified if investors place more or less belief in certain factors. A fundamentally-oriented investor might increase valuation weight, while a technical trader might increase regime weight.
Risk target parameters (target volatility, max drawdown) should be adapted to individual risk tolerance. Younger investors with long investment horizons can choose higher target volatility (15-18%), while retirees may prefer lower volatility (8-10%). This adjustment systematically shifts average equity allocation.
Crisis thresholds can be adjusted based on preference for sensitivity versus specificity of crisis detection. Lower thresholds (e.g., VIX > 35 instead of 40) increase sensitivity (more crises are detected) but reduce specificity (more false alarms). Higher thresholds have the reverse effect.
12.3 Limitations and Disclaimers
DEAM is based on historical relationships between indicators and market performance. There is no guarantee these relationships will persist in the future. Structural changes in markets (e.g., through regulation, technology, or central bank policy) can break established patterns. This is the fundamental problem of induction in financial science (Taleb, 2007).
The model is optimized for US equities (S&P 500). Application to other markets (international stocks, bonds, commodities) would require recalibration. The indicators and thresholds are specific to the statistical properties of the US equity market.
The model cannot eliminate losses. Even with perfect crisis prediction, an investor following the model would lose money in bear markets—just less than a buy-and-hold investor. The goal is risk-adjusted performance improvement, not risk elimination.
Transaction costs are not modeled. In practice, spreads, commissions, and taxes reduce net returns. Frequent trading can cause substantial costs. Model smoothing helps minimize this, but users should consider their specific cost situation.
The model reacts to information; it does not anticipate it. During sudden shocks (e.g., 9/11, COVID-19 lockdowns), the model can only react after price movements, not before. This limitation is inherent to all reactive systems.
12.4 Relationship to Other Strategies
DEAM is a tactical asset allocation approach and should be viewed as a complement, not replacement, for strategic asset allocation. Brinson, Hood, and Beebower (1986) showed in their influential study "Determinants of Portfolio Performance" that strategic asset allocation (long-term policy allocation) explains the majority of portfolio performance, but this leaves room for tactical adjustments based on market timing.
The model can be combined with value and momentum strategies at the individual stock level. While DEAM controls overall market exposure, within-equity decisions can be optimized through stock-picking models. This separation between strategic (market exposure) and tactical (stock selection) levels follows classical portfolio theory.
The model does not replace diversification across asset classes. A complete portfolio should also include bonds, international stocks, real estate, and alternative investments. DEAM addresses only the US equity allocation decision within a broader portfolio.
13. Scientific Foundation and Evaluation
13.1 Theoretical Consistency
DEAM's components are based on established financial theory and empirical evidence. The market regime component follows from regime-switching models (Hamilton, 1989) and trend-following literature. The risk management component implements volatility targeting (Moreira and Muir, 2017) and modern portfolio theory (Markowitz, 1952). The valuation component is based on discounted cash flow theory and empirical value research (Campbell and Shiller, 1988; Fama and French, 1992). The sentiment component integrates behavioral finance (Baker and Wurgler, 2006). The macro component uses established business cycle indicators (Estrella and Mishkin, 1998).
This theoretical grounding distinguishes DEAM from purely data-mining-based approaches that identify patterns without causal theory. Theory-guided models have greater probability of functioning out-of-sample, as they are based on fundamental mechanisms, not random correlations (Lo and MacKinlay, 1990).
13.2 Empirical Validation
While this document does not present detailed backtest analysis, it should be noted that rigorous validation of a tactical asset allocation model should include several elements:
In-sample testing establishes whether the model functions at all in the data on which it was calibrated. Out-of-sample testing is crucial: the model should be tested in time periods not used for development. Walk-forward analysis, where the model is successively trained on rolling windows and tested in the next window, approximates real implementation.
Performance metrics should be risk-adjusted. Pure return consideration is misleading, as higher returns often only compensate for higher risk. Sharpe Ratio, Sortino Ratio, Calmar Ratio, and Maximum Drawdown are relevant metrics. Comparison with benchmarks (Buy-and-Hold S&P 500, 60/40 Stock/Bond portfolio) contextualizes performance.
Robustness checks test sensitivity to parameter variation. If the model only functions at specific parameter settings, this indicates overfitting. Robust models show consistent performance over a range of plausible parameters.
13.3 Comparison with Existing Literature
DEAM fits into the broader literature on tactical asset allocation. Faber (2007) presented a simple momentum-based timing system that goes long when the market is above its 10-month average, otherwise cash. This simple system avoided large drawdowns in bear markets. DEAM can be understood as a sophistication of this approach that integrates multiple information sources.
Ilmanen (2011) discusses various timing factors in "Expected Returns" and argues for multi-factor approaches. DEAM operationalizes this philosophy. Asness, Moskowitz, and Pedersen (2013) showed that value and momentum effects work across asset classes, justifying cross-asset application of regime and valuation signals.
Ang (2014) emphasizes in "Asset Management: A Systematic Approach to Factor Investing" the importance of systematic, rule-based approaches over discretionary decisions. DEAM is fully systematic and eliminates emotional biases that plague individual investors (overconfidence, hindsight bias, loss aversion).
References
Ang, A. (2014) *Asset Management: A Systematic Approach to Factor Investing*. Oxford: Oxford University Press.
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Trend Fib Zone Bounce (TFZB) [KedArc Quant]Description:
Trend Fib Zone Bounce (TFZB) trades with the latest confirmed Supply/Demand zone using a single, configurable Fib pullback (0.3/0.5/0.6). Trade only in the direction of the most recent zone and use a single, configurable fib level for pullback entries.
• Detects market structure via confirmed swing highs/lows using a rolling window.
• Draws Supply/Demand zones (bearish/bullish rectangles) from the latest MSS (CHOCH or BOS) event.
• Computes intra zone Fib guide rails and keeps them extended in real time.
• Triggers BUY only inside bullish zones and SELL only inside bearish zones when price touches the selected fib and closes back beyond it (bounce confirmation).
• Optional labels print BULL/BEAR + fib next to the triangle markers.
What it does
Finds structure using confirmed swing highs/lows (you choose the confirmation length).
Builds the latest zone (bullish = demand, bearish = supply) after a CHOCH/BOS event.
Draws intra-zone “guide rails” (Fib lines) and extends them live.
Signals only with the trend of that zone:
BUY inside a bullish zone when price tags the selected Fib and closes back above it.
SELL inside a bearish zone when price tags the selected Fib and closes back below it.
Optional labels print BULL/BEAR + Fib next to triangles for quick context
Why this is different
Most “zone + fib + signal” tools bolt together several indicators, or fire counter-trend signals because they don’t fully respect structure. TFZB is intentionally minimal:
Single bias source: the latest confirmed zone defines direction; nothing else overrides it.
Single entry rule: one Fib bounce (0.3/0.5/0.6 selectable) inside that zone—no counter-trend trades by design.
Clean visuals: you can show only the most recent zone, clamp overlap, and keep just the rails that matter.
Deterministic & transparent: every plot/label comes from the code you see—no external series or hidden smoothing
How it helps traders
Cuts decision noise: you always know the bias and the only entry that matters right now.
Forces discipline: if price isn’t inside the active zone, you don’t trade.
Adapts to volatility: pick 0.3 in strong trends, 0.5 as the default, 0.6 in chop.
Non-repainting zones: swings are confirmed after Structure Length bars, then used to build zones that extend forward (they don’t “teleport” later)
How it works (details)
*Structure confirmation
A swing high/low is only confirmed after Structure Length bars have elapsed; the dot is plotted back on the original bar using offset. Expect a confirmation delay of about Structure Length × timeframe.
*Zone creation
After a CHOCH/BOS (momentum shift / break of prior swing), TFZB draws the new Supply/Demand zone from the swing anchors and sets it active.
*Fib guide rails
Inside the active zone TFZB projects up to five Fib lines (defaults: 0.3 / 0.5 / 0.7) and extends them as time passes.
*Entry logic (with-trend only)
BUY: bar’s low ≤ fib and close > fib inside a bullish zone.
SELL: bar’s high ≥ fib and close < fib inside a bearish zone.
*Optionally restrict to one signal per zone to avoid over-trading.
(Optional) Aggressive confirm-bar entry
When do the swing dots print?
* The code confirms a swing only after `structureLen` bars have elapsed since that candidate high/low.
* On a 5-min chart with `structureLen = 10`, that’s about 50 minutes later.
* When the swing confirms, the script plots the dot back on the original bar (via `offset = -structureLen`). So you *see* the dot on the old bar, but it only appears on the chart once the confirming bar arrives.
> Practical takeaway: expect swing markers to appear roughly `structureLen × timeframe` later. Zones and signals are built from those confirmed swings.
Best timeframe for this Indicator
Use the timeframe that matches your holding period and the noise level of the instrument:
* Intraday :
* 5m or 15m are the sweet spots.
* Suggested `structureLen`:
* 5m: 10–14 (confirmation delay \~50–70 min)
* 15m: 8–10 (confirmation delay \~2–2.5 hours)
* Keep Entry Fib at 0.5 to start; try 0.3 in strong trends, 0.6 in chop.
* Tip: avoid the first 10–15 minutes after the open; let the initial volatility set the early structure.
* Swing/overnight:
* 1h or 4h.
* `structureLen`:
* 1h: 6–10 (6–10 hours confirmation)
* 4h: 5–8 (20–32 hours confirmation)
* 1m scalping: not recommended here—the confirmation lag relative to the noise makes zones less reliable.
Inputs (all groups)
Structure
• Show Swing Points (structureTog)
o Plots small dots on the bar where a swing point is confirmed (offset back by Structure Length).
• Structure Length (structureLen)
o Lookback used to confirm swing highs/lows and determine local structure. Higher = fewer, stronger swings; lower = more reactive.
Zones
• Show Last (zoneDispNum)
o Maximum number of zones kept on the chart when Display All Zones is off.
• Display All Zones (dispAll)
o If on, ignores Show Last and keeps all zones/levels.
• Zone Display (zoneFilter): Bullish Only / Bearish Only / Both
o Filters which zone types are drawn and eligible for signals.
• Clean Up Level Overlap (noOverlap)
o Prevents fib lines from overlapping when a new zone starts near the previous one (clamps line start/end times for readability).
Fib Levels
Each row controls whether a fib is drawn and how it looks:
• Toggle (f1Tog…f5Tog): Show/hide a given fib line.
• Level (f1Lvl…f5Lvl): Numeric ratio in . Defaults active: 0.3, 0.5, 0.7 (0 and 1 off by default).
• Line Style (f1Style…f5Style): Solid / Dashed / Dotted.
• Bull/Bear Colors (f#BullColor, f#BearColor): Per-fib color in bullish vs bearish zones.
Style
• Structure Color: Dot color for confirmed swing points.
• Bullish Zone Color / Bearish Zone Color: Rectangle fills (transparent by default).
Signals
• Entry Fib for Signals (entryFibSel): Choose 0.3, 0.5 (default), or 0.6 as the trigger line.
• Show Buy/Sell Signals (showSignals): Toggles triangle markers on/off.
• One Signal Per Zone (oneSignalPerZone): If on, suppresses additional entries within the same zone after the first trigger.
• Show Signal Text Labels (Bull/Bear + Fib) (showSignalLabels): Adds a small label next to each triangle showing zone bias and the fib used (e.g., BULL 0.5 or BEAR 0.3).
How TFZB decides signals
With trend only:
• BUY
1. Latest active zone is bullish.
2. Current bar’s close is inside the zone (between top and bottom).
3. The bar’s low ≤ selected fib and it closes > selected fib (bounce).
• SELL
1. Latest active zone is bearish.
2. Current bar’s close is inside the zone.
3. The bar’s high ≥ selected fib and it closes < selected fib.
Markers & labels
• BUY: triangle up below the bar; optional label “BULL 0.x” above it.
• SELL: triangle down above the bar; optional label “BEAR 0.x” below it.
Right-Panel Swing Log (Table)
What it is
A compact, auto-updating log of the most recent Swing High/Low events, printed in the top-right of the chart.
It helps you see when a pivot formed, when it was confirmed, and at what price—so you know the earliest bar a zone-based signal could have appeared.
Columns
Type – Swing High or Swing Low.
Date – Calendar date of the swing bar (follows the chart’s timezone).
Swing @ – Time of the original swing bar (where the dot is drawn).
Confirm @ – Time of the bar that confirmed that swing (≈ Structure Length × timeframe after the swing). This is also the earliest moment a new zone/entry can be considered.
Price – The swing price (high for SH, low for SL).
Why it’s useful
Clarity on repaint/confirmation: shows the natural delay between a swing forming and being usable—no guessing.
Planning & journaling: quick reference of today’s pivots and prices for notes/backtesting.
Scanning intraday: glance to see if you already have a confirmed zone (and therefore valid fib-bounce entries), or if you’re still waiting.
Context for signals: if a fib-bounce triangle appears before the time listed in Confirm @, it’s not a valid trade (you were too early).
Settings (Inputs → Logging)
Log swing times / Show table – turn the table on/off.
Rows to keep – how many recent entries to display.
Show labels on swing bar – optional tags on the chart (“Swing High 11:45”, “Confirm SH 14:15”) that match the table.
Recommended defaults
• Structure Length: 10–20 for intraday; 20–40 for swing.
• Entry Fib for Signals: 0.5 to start; try 0.3 in stronger trends and 0.6 in choppier markets.
• One Signal Per Zone: ON (prevents over trading).
• Zone Display: Both.
• Fib Lines: Keep 0.3/0.5/0.7 on; turn on 0 and 1 only if you need anchors.
Alerts
Two alert conditions are available:
• BUY signal – fires when a with trend bullish bounce at the selected fib occurs inside a bullish zone.
• SELL signal – fires when a with trend bearish bounce at the selected fib occurs inside a bearish zone.
Create alerts from the chart’s Alerts panel and select the desired condition. Use Once Per Bar Close to avoid intrabar flicker.
Notes & tips
• Swing dots are confirmed only after Structure Length bars, so they plot back in time; zones built from these confirmed swings do not repaint (though they extend as new bars form).
• If you don’t see a BUY where you expect one, check: (1) Is the active zone bullish? (2) Did the candle’s low actually pierce the selected fib and close above it? (3) Is One Signal Per Zone suppressing a second entry?
• You can hide visual clutter by reducing Show Last to 1–3 while keeping Display All Zones off.
Glossary
• CHOCH (Change of Character): A shift where price breaks beyond the last opposite swing while local momentum flips.
• BOS (Break of Structure): A cleaner break beyond the prior swing level in the current momentum direction.
• MSS: Either CHOCH or BOS – any event that spawns a new zone.
Extension ideas (optional)
• Add fib extensions (1.272 / 1.618) for target lines.
• Zone quality score using ATR normalization to filter weak impulses.
• HTF filter to only accept zones aligned with a higher timeframe trend.
⚠️ Disclaimer This script is provided for educational purposes only.
Past performance does not guarantee future results.
Trading involves risk, and users should exercise caution and use proper risk management when applying this strategy.
RSI DCA StrategyThis strategy combines RSI oversold signals with a Dollar-Cost Averaging (DCA) buying approach.
Trigger:
When the RSI (Relative Strength Index) crosses below 30, the strategy marks an oversold condition.
DCA Entry:
Once triggered, the strategy executes up to three consecutive daily entries (1 per day), splitting the predefined capital equally (configurable by user).
Position Management:
Take Profit at a configurable % above the average entry price.
Stop Loss at a configurable % below the average entry price.
Exit Conditions:
The strategy automatically exits either on reaching Take Profit or Stop Loss.
Visualization:
RSI plotted with oversold line (30).
Take Profit and Stop Loss lines displayed after entry.
Performance Reporting:
Includes an optional monthly performance table for evaluating results by month.
Note:
This strategy is for testing RSI-based mean reversion with staggered entries. It is not financial advice and should be optimized and validated for each market or timeframe before practical use.
ICT FVG Buy/Sell SignalsThis bot is built on ICT (Inner Circle Trader) concepts such as:
Fair Value Gaps (FVGs) – imbalance zones between candles.
Consequent Encroachment (CE) – the midpoint of a gap.
Premium / Discount Arrays – dealing ranges split into premium (sell-side) and discount (buy-side) zones.
Displacement candles – strong impulsive moves that confirm intent.
The bot scans for FVGs, marks CE levels, and waits for price to return to these levels.
When price revisits a valid FVG zone with displacement confirmation and in the correct PD array, the bot generates a BUY or SELL signal.
✅ Signal Rules
Buy Signal
Price trades back into a Bullish FVG.
Current bar shows bullish displacement (large bullish body relative to ATR).
Price is in discount territory of the current dealing range (if PD filter is enabled).
Close is above the CE line of the FVG.
Sell Signal
Price trades back into a Bearish FVG.
Current bar shows bearish displacement.
Price is in premium territory of the current dealing range.
Close is below the CE line of the FVG.
🎯 What You’ll See on the Chart
Green “BUY” labels below candles when long signals trigger.
Red “SELL” labels above candles when short signals trigger.
Shaded background:
Red = Premium zone (sell side).
Teal = Discount zone (buy side).
Yellow line = dealing range midpoint (equilibrium).
Dots on CE lines = midpoints of the latest bullish/bearish FVG.
🔔 Alerts
ICT Buy → Triggers when a bullish setup confirms.
ICT Sell → Triggers when a bearish setup confirms.
You can connect these alerts to:
TradingView notifications.
Webhooks (for brokers or bots like MetaTrader, NinjaTrader, or Discord).
⚙️ Settings
Swing length – how many bars to use when detecting swing highs/lows for the dealing range.
Use PD filter – toggle ON/OFF for requiring discount/premium alignment.
Displacement ATR multiple – how strong the candle body must be compared to ATR to count as a displacement.
ATR length – used for displacement filter.
📈 Supported Markets
Works on all symbols and timeframes.
Commonly applied to:
NASDAQ (NQ, QQQ)
S&P500 (ES, SPX, SPY)
Forex pairs
Crypto (BTC, ETH, etc.)
⚠️ Disclaimer
This bot is for educational purposes only. It does not guarantee profits and should be tested on demo accounts first.
Always apply proper risk management before trading live.
Opening Range BreakoutOpen Range Breakout (ORB) – Trading Strategy Documentation
Definition:
The Open Range Breakout (ORB) is a short-term trading strategy that identifies the price range established during the initial period of market opening (typically the first 15 to 60 minutes) and uses the high and low of that range as key reference levels for potential breakout entries.
Components:
Open Range High: The highest price traded during the defined opening period.
Open Range Low: The lowest price traded during the same period.
Breakout Trigger: A price move above the Open Range High or below the Open Range Low, signaling potential continuation momentum.
How It Works:
Define the Opening Period: Select a time window (e.g., 30 minutes) at market open to establish the initial range.
Identify Range Boundaries: Record the high and low prices during this period.
Monitor for Breakout: Watch for price to break and close above the Open Range High (bullish breakout) or below the Open Range Low (bearish breakout).
Enter Trade: Enter long on a confirmed break above the Open Range High, or short on a break below the Open Range Low. Entry may be triggered on a retest of the broken level or with volume confirmation.
Set Stop-Loss and Target:
Stop-loss: Placed just inside the open range (e.g., below the high for long, above the low for short).
Profit target: Based on volatility (e.g., ATR multiple) or support/resistance levels.
Key Assumptions:
Early price action reflects initial market sentiment.
A breakout from this range indicates strong directional momentum likely to continue.
Best Conditions:
High liquidity markets (e.g., major indices, large-cap stocks).
Volatile or news-driven trading sessions.
Used primarily in intraday trading.
Limitations:
Prone to false breakouts during low-volume or choppy markets.
Requires strict risk management due to reliance on timing and confirmation.
Conclusion:
The ORB strategy capitalizes on early market momentum by trading breakouts from the initial price range. Its effectiveness depends on precise range definition, timely execution, and disciplined risk control.
VWAP For Loop [BackQuant]VWAP For Loop
What this tool does—in one sentence
A volume-weighted trend gauge that anchors VWAP to a calendar period (day/week/month/quarter/year) and then scores the persistence of that VWAP trend with a simple for-loop “breadth” count; the result is a clean, threshold-driven oscillator plus an optional VWAP overlay and alerts.
Plain-English overview
Instead of judging raw price alone, this indicator focuses on anchored VWAP —the market’s average price paid during your chosen institutional period. It then asks a simple question across a configurable set of lookback steps: “Is the current anchored VWAP higher than it was i bars ago—or lower?” Each “yes” adds +1, each “no” adds −1. Summing those answers creates a score that reflects how consistently the volume-weighted trend has been rising or falling. Extreme positive scores imply persistent, broad strength; deeply negative scores imply persistent weakness. Crossing predefined thresholds produces objective long/short events and color-coded context.
Under the hood
• Anchoring — VWAP using hlc3 × volume resets exactly when the selected period rolls:
Day → session change, Week → new week, Month → new month, Quarter/Year → calendar quarter/year.
• For-loop scoring — For lag steps i = , compare today’s VWAP to VWAP .
– If VWAP > VWAP , add +1.
– Else, add −1.
The final score ∈ , where N = (end − start + 1). With defaults (1→45), N = 45.
• Signal logic (stateful)
– Long when score > upper (e.g., > 40 with N = 45 → VWAP higher than ~89% of checked lags).
– Short on crossunder of lower (e.g., dropping below −10).
– A compact state variable ( out ) holds the current regime: +1 (long), −1 (short), otherwise unchanged. This “stickiness” avoids constant flipping between bars without sufficient evidence.
Why VWAP + a breadth score?
• VWAP aggregates both price and volume—where participants actually traded.
• The breadth-style count rewards consistency of the anchored trend, not one-off spikes.
• Thresholds give you binary structure when you need it (alerts, automation), without complex math.
What you’ll see on the chart
• Sub-pane oscillator — The for-loop score line, colored by regime (long/short/neutral).
• Main-pane VWAP (optional) — Even though the indicator runs off-chart, the anchored VWAP can be overlaid on price (toggle visibility and whether it inherits trend colors).
• Threshold guides — Horizontal lines for the long/short bands (toggle).
• Cosmetics — Optional candle painting and background shading by regime; adjustable line width and colors.
Input map (quick reference)
• VWAP Anchor Period — Day, Week, Month, Quarter, Year.
• Calculation Start/End — The for-loop lag window . With 1→45, you evaluate 45 comparisons.
• Long/Short Thresholds — Default upper=40, lower=−10 (asymmetric by design; see below).
• UI/Style — Show thresholds, paint candles, background color, line width, VWAP visibility and coloring, custom long/short colors.
Interpreting the score
• Near +N — Current anchored VWAP is above most historical VWAP checkpoints in the window → entrenched strength.
• Near −N — Current anchored VWAP is below most checkpoints → entrenched weakness.
• Between — Mixed, choppy, or transitioning regimes; use thresholds to avoid reacting to noise.
Why the asymmetric default thresholds?
• Long = score > upper (40) — Demands unusually broad upside persistence before declaring “long regime.”
• Short = crossunder lower (−10) — Triggers only on downward momentum events (a fresh breach), not merely being below −10. This combination tends to:
– Capture sustained uptrends only when they’re very strong.
– Flag downside turns as they occur, rather than waiting for an extreme negative breadth.
Tuning guide
Choose an anchor that matches your horizon
– Intraday scalps : Day anchor on intraday charts.
– Swing/position : Month or Quarter anchor on 1h/4h/D charts to capture institutional cycles.
Pick the for-loop window
– Larger N (bigger end) = stronger evidence requirement, smoother oscillator.
– Smaller N = faster, more reactive score.
Set achievable thresholds
– Ensure upper ≤ N and lower ≥ −N ; if N=30, an upper of 40 can never trigger.
– Symmetric setups (e.g., +20/−20) are fine if you want balanced behavior.
Match visuals to intent
– Enabling VWAP coloring lets you see regime directly on price.
– Background shading is useful for discretionary reading; turn it off for cleaner automation displays.
Playbook examples
• Trend confirmation with disciplined entries — On Month anchor, N=45, upper=38–42: when the long regime engages, use pullbacks toward anchored VWAP on the main pane for entries, with stops just beyond VWAP or a recent swing.
• Downside transition detection — Keep lower around −8…−12 and watch for crossunders; combine with price losing anchored VWAP to validate risk-off.
• Intraday bias filter — Day anchor on a 5–15m chart, N=20–30, upper ~ 16–20, lower ~ −6…−10. Only take longs while score is positive and above a midline you define (e.g., 0), and shorts only after a genuine crossunder.
Behavior around resets (important)
Anchored VWAP is hard-reset each period. Immediately after a reset, the series can be young and comparisons to pre-reset values may span two periods. If you prefer within-period evaluation only, choose end small enough not to bridge typical period length on your timeframe, or accept that the breadth test intentionally spans regimes.
Alerts included
• VWAP FL Long — Fires when the long condition is true (score > upper and not in short).
• VWAP FL Short — Fires on crossunder of the lower threshold (event-driven).
Messages include {{ticker}} and {{interval}} placeholders for routing.
Strengths
• Simple, transparent math — Easy to reason about and validate.
• Volume-aware by construction — Decisions reference VWAP, not just price.
• Robust to single-bar noise — Needs many lags to agree before flipping state (by design, via thresholds and the stateful output).
Limitations & cautions
• Threshold feasibility — If N < upper or |lower| > N, signals will never trigger; always cross-check N.
• Path dependence — The state variable persists until a new event; if you want frequent re-evaluation, lower thresholds or reduce N.
• Regime changes — Calendar resets can produce early ambiguity; expect a few bars for the breadth to mature.
• VWAP sensitivity to volume spikes — Large prints can tilt VWAP abruptly; that behavior is intentional in VWAP-based logic.
Suggested starting profiles
• Intraday trend bias : Anchor=Day, N=25 (1→25), upper=18–20, lower=−8, paint candles ON.
• Swing bias : Anchor=Month, N=45 (1→45), upper=38–42, lower=−10, VWAP coloring ON, background OFF.
• Balanced reactivity : Anchor=Week, N=30 (1→30), upper=20–22, lower=−10…−12, symmetric if desired.
Implementation notes
• The indicator runs in a separate pane (oscillator), but VWAP itself is drawn on price using forced overlay so you can see interactions (touches, reclaim/loss).
• HLC3 is used for VWAP price; that’s a common choice to dampen wick noise while still reflecting intrabar range.
• For-loop cap is kept modest (≤50) for performance and clarity.
How to use this responsibly
Treat the oscillator as a bias and persistence meter . Combine it with your entry framework (structure breaks, liquidity zones, higher-timeframe context) and risk controls. The design emphasizes clarity over complexity—its edge is in how strictly it demands agreement before declaring a regime, not in predicting specific turns.
Summary
VWAP For Loop distills the question “How broadly is the anchored, volume-weighted trend advancing or retreating?” into a single, thresholded score you can read at a glance, alert on, and color through your chart. With careful anchoring and thresholds sized to your window length, it becomes a pragmatic bias filter for both systematic and discretionary workflows.
