DAYOFWEEK performance1 -Objective
"What is the ''best'' day to trade .. Monday, Tuesday...."
This script aims to determine if there are different results depending on the day of the week.
The way it works is by dividing data by day of the week (Monday, Tuesday, Wednesday ... ) and perform calculations for each day of the week.
1 - Objective
2 - Features
3 - How to use (Examples)
4 - Inputs
5 - Limitations
6 - Notes
7 - Final Tooughs
2 - Features
AVG OPEN-CLOSE
Calculate de Percentage change from day open to close
Green % (O-C)
Percentage of days green (open to close)
Average Change
Absolute day change (O-C)
AVG PrevD. Close-Close
Percentage change from the previous day close to the day of the week close
(Example: Monday (C-C) = Friday Close to Monday close
Tuesday (C-C) = Monday C. to Tuesday C.
Green % (C1-C)
Percentage of days green (open to close)
AVG Volume
Day of the week Average Volume
Notes:
*Mon(Nº) - Nº = Number days is currently calculated
Example: Monday (12) calculation based on the last 12 Mondays. Note: Discrepancies in numbers example Monday (12) - Friday (11) depend on the initial/end date or the market was closed (Holidays).
3 - How to use (Examples)
For the following example, NASDAQ:AAPL from 1 Jan 21 to 1 Jul 21 the results are following.
The highest probability of a Close being higher than the Open is Monday with 52.17 % and the Lowest Tuesday with 38.46 %. Meaning that there's a higher chance (for NASDAQ:AAPL ) of closing at a higher value on Monday while the highest chance of closing is lower is Tuesday. With an average gain on Tuesday of 0.21%
Long - The best day to buy (long) at open (on average) is Monday with a 52.2% probability of closing higher
Short - The best day to sell (short) at open (on average) is Tuesday with a 38.5% probability of closing higher (better chance of closing lower)
Since the values change from ticker to ticker, there is a substantial change in the percentages and days of the week. For example let's compare the previous example ( NASDAQ:AAPL ) to NYSE:GM (same settings)
For the same period, there is a substantial difference where there is a 62.5% probability Friday to close higher than the open, while Tuesday there is only a 28% probability.
With an average gain of 0.59% on Friday and an average loss of -0.34%
Also, the size of the table (number of days ) depends if the ticker is traded or not on that day as an example COINBASE:BTCUSD
4 - Inputs
DATE RANGE
Initial Date - Date from which the script will start the calculation.
End Date - Date to which the script will calculate.
TABLE SETTINGS
Text Color - Color of the displayed text
Cell Color - Background color of table cells
Header Color - Color of the column and row names
Table Location - Change the position where the table is located.
Table Size - Changes text size and by consequence the size of the table
5 - LIMITATIONS
The code determines average values based on the stored data, therefore, the range (Initial data) is limited to the first bar time.
As a consequence the lower the timeframe the shorter the initial date can be and fewer weeks can be calculated. To warn about this limitation there's a warning text that appears in case the initial date exceeds the bar limit.
Example with initial date 1 Jan 2021 and end date 18 Jul 2021 in 5m and 10 m timeframe:
6 - Notes and Disclosers
The script can be moved around to a new pane if need. -> Object Tree > Right Click Script > Move To > New pane
The code has not been tested in higher subscriptions tiers that allow for more bars and as a consequence more data, but as far I can tell, it should work without problems and should be in fact better at lower timeframes since it allows more weeks.
The values displayed represent previous data and at no point is guaranteed future values
7 - Final Tooughs
This script was quite fun to work on since it analysis behavioral patterns (since from an abstract point a Tuesday is no different than a Thursday), but after analyzing multiple tickers there are some days that tend to close higher than the open.
PS: If you find any mistake ex: code/misspelling please comment.
Buscar en scripts para "12月1日给海æ°æ‰«ç 手ç»è´¹"
Phoenix Ascending 2.201Hi Everyone!
It's time to make this indicator public to relieve myself of replying to requests for access. There has been an update to this indicator; in which a Stochastic RSI was added to this indicator. Please follow the directions to SETUP the indicator in the SETUP VIDEO provided below.
Phoenix Ascending 2.201 and Bollinger Bands Setup Video.
The following are BASIC rules for the Phoenix 2.201 Indicator. More advanced rules and the requirements for those rules can be found in my publications in my public profile. Unfortunately, I do not have organized videos created on how to use this indicator in full but will be available in the future.
IMPORTANT: The BASIC rules below are beneficial but these are NOT all the rules. More rules and requirements for those rules will be available in the future.
RULE NO. 1
We PREFER the Blue LSMA to be at 80% or higher for SAFE EXIT (SHORT) bets.
We PREFER the Blue LSMA to be at 20% or lower for SAFE ENTRY (LONG) bets.
Rule No. 2
ANY time the red line is approaching a green line that’s moving UPWARD,
Be prepared to make an ENTRY (LONG) when the red line is about to touch the green line that’s moving upward.
One can look at a lower time frame to get a better idea of how much longer you may have
To wait for the red line to touch the green line. In many cases, you may make ENTRY (LONG)
Just before the red line actually touches the green line that’s moving up in that higher time frame
You were initially using as your COMPASS. I currently have the 1-Month TF as a compass for EURUSD.
Rule No. 3
ANY time the red line is approaching a green line that’s moving DOWNWARD,
Be prepared to make an EXIT (SHORT) when the red line is about to touch the green line that’s moving downward.
One can look at a lower time frame to get a better idea of how much longer you may have
To wait for the red line to touch the green line. In many cases, you may make your EXIT (SHORT)
Just before the red line actually touches the green line that’s moving downward in that higher time frame
You were initially using as your COMPASS. I currently have the 1-Month TF as a compass for EURUSD.
Rule No. 4
The Green Line and/or Ghost Line can often help one determine when an upward or downward move in a particular time frame
Is nearly exhausted and about to reverse.
Example for Upside Exhaustion about to reverse to the Downside:
When the Green Line and/or Ghost line is at 80% level or higher, this is a good indicator to inform
Us the current upside move may be approaching exhaustion. You can look at a higher time frame to try to gain
More insight as to whether this will only be a brief dip down in the lower time frame IF the higher time frame you
Went to reveals there is a lot more room remaining for the Green and/or Ghost Lines to reach the 80% or higher level.
Example for Downside Exhaustion about to reverse to the Upside:
When the Green Line and/or Ghost line is at 20% level or lower, this is a good indicator to inform
Us the current downside move may be approaching exhaustion. You can look at a higher time frame to try to gain
More insight as to whether this will only be a brief dip up in the lower time frame IF the higher time frame you
Went to reveals there is a lot more room remaining for the Green and/or Ghost Lines to reach the 20% or lower level.
Rule No. 5
The same rules you see in Rule No. 4 also apply to the Stochastic RSI. Keep in mind I changed the colors of the
Stochastic RSI to the following: Red default changed to Purple and Blue changed changed to Black to avoid confusing
Them with the lines in Godmode.
When the Stochastic RSI is at 80% or higher level, we need to be on guard for a reversal to the downside.
When the Stochastic RSI is at 20% or lower level, we need to be on guard for a reversal to the upside.
EXTREMELY IMPORTANT to apply these rules in GROUPS OF TIME FRAMES.
"TYPES" OF TIME FRAME GROUP TRADING SIGNALS
Scalping Group Signals: Signals provided for this group involve analyzing the following two groups of time frames. Short Term Group as a compass and Scalping Group for confirmation and more precise entry/exit.
Scalping Group: 6min. 12min. 23min & 45min.
Short Term Group: 90min. 3hr. 6hr. & 12hr.
Short Term Group Signals: Signals provided for this group involve analyzing the following two groups of time frames. NearTerm Group as a compass and Short Term Group for confirmation and more precise entry/exit.
Short Term Group: 90min. 3hr. 6hr. & 12hr.
Near Term Group: 24hr. 2-Day, 3-Day & 4-Day
Near Term Group Signals: Signals provided for this group involve analyzing the following two groups of time frames. Mid Term Group as a compass and Near Term Group for confirmation and more precise entry/exit.
Near Term Group: 24hr. 2-Day, 3-Day & 4-Day
Mid Term Group: 3-Day, 6-Day, 9-Day & 12-Day
Mid Term Group Signals: Signals provided for this group involve analyzing the following two groups of time frames. Long Term Group as a compass and Mid Term Group for confirmation and more precise entry/exit.
Mid Term Group: 3-Day, 6-Day, 9-Day & 12-Day
Long Term Group: 1-Week, 2-Week, 3-Week & 4-Week
Long Term Group Signals: Signals provided for this group involve analyzing the following two groups of time frames. Macro Term Group as a compass and Long Term Group for confirmation and more precise entry/exit.
Long Term Group: 1-Week, 2-Week, 3-Week & 4-Week
Macro Term Group: 1-Month, 2-Month, 3-Month & 4-Month
Macro Term Group Signals: Signals provided for this group involve analyzing the following two groups of time frames. Macro Term Group as a compass and Long Term Group for confirmation and more precise entry/exit.
Macro Term Group: 1-Month, 2-Month, 3-Month & 4-Month
Super Macro Group: 3-Month , 6-Month, 12-Month & 24-Month
Reverse MACD IndicatorIntroducing the reverse MACD Indicator.
This is my Pinescript implementation of the reverse MACD indicator.
Much respect to Mr Johnny Dough the original creator of this idea.
Feel free to reuse this script, drop me a note below if you find this useful.
Investopedia defines the MACD as a trend-following momentum indicator that shows the relationship between two moving averages of a security’s price.
The MACD is calculated by subtracting the 26-period Exponential Moving Average ( EMA ) from the 12-period EMA .
The result of that calculation is the MACD line.
A nine-day EMA of the MACD called the "signal line," is then plotted on top of the MACD line, which can function as a trigger for buy and sell signals.
Traders may buy the security when the MACD crosses above its signal line and sell—or short—the security when the MACD crosses below the signal line.
Moving Average Convergence Divergence ( MACD ) indicators can be interpreted in several ways, but the more common methods are crossovers, divergences, and rapid rises/falls.
MACD triggers technical signals when it crosses above (to buy) or below (to sell) its signal line.
The speed of crossovers is also taken as a signal of a market is overbought or oversold.
MACD helps investors understand whether the bullish or bearish movement in the price is strengthening or weakening.
The MACD has a positive value (shown as the red line on the price chart ) whenever the 12-period EMA ( indicated by the blue line on the price chart) is above the 26-period EMA (the red line in the price chart) and a negative value when the 12-period EMA is below the 26-period EMA .
The more distant the MACD is above or below its baseline indicates that the distance between the two EMAs is growing.
The baseline here is the white line.
The Reverse function of the MACD provides value by letting the user know the specific price needed to expect a MACD cross over in the opposite direction.
This function can be used to designate risk parameters for a potential trade if using the MACD as their source of edge, letting the user know exactly where and how much their risk is for a potential trade which can be used to design an effective trading plan.
Percentage Volume Oscillator (PVO)The Percentage Volume Oscillator (PVO) is a momentum oscillator for volume. The PVO measures the difference between two volume-based moving averages as a percentage of the larger moving average. As with MACD and the Percentage Price Oscillator (PPO), it is shown with a signal line, a histogram and a centerline. The PVO is positive when the shorter volume EMA is above the longer volume EMA and negative when the shorter volume EMA is below. This indicator can be used to define the ups and downs for volume, which can then be used to confirm or refute other signals. Typically, a breakout or support break is validated when the PVO is rising or positive.
Generally speaking, volume is above average when the PVO is positive and below average when the PVO is negative. A negative and rising PVO indicates that volume levels are increasing. A positive and falling PVO indicates that volume levels are decreasing. Chartists can use this information to confirm or refute movements on the price chart.
Even though the PVO is based on a momentum oscillator formula, it is important to remember that moving averages lag. A 12-day EMA include 12 days of volume data, with newer data weighted more heavily. A 26-day EMA lags even more because it contains 26 days of data. This means that the PVO(12,26,9) can sometimes be out of sync with price action.
The Percentage Volume Oscillator (PVO) is a momentum indicator applied to volume. This oscillator can be quite choppy due to the fact that volume doesn't trend. Bullish and bearish divergences are not well suited for the PVO. Instead, chartists would be better off looking for signs of increasing volume with a move into positive territory and signs of decreasing volume with a move into negative territory. Increasing volume can validate a support or resistance break. Similarly, a surge or significant support break on low volume may be less robust. As with all technical indicators, it is important to use the Percentage Volume Oscillator (PVO) in conjunction with other aspects of technical analysis, such as chart patterns and momentum oscillators.
ETF / Stocks / Crypto - DCA Strategy v1Simple "benchmark" strategy for ETFs, Stocks and Crypto! Super-easy to implement for beginners, a DCA (dollar-cost-averaging) strategy means that you buy a fixed amount of an ETF / Stock / Crypto every several months. For instance, to DCA the S&P 500 (SPY), you could purchase $10,000 USD every 12 months, irrespective of the market price. Assuming the macro-economic conditions of the underlying country remain favourable, DCA strategies will result in capital gains over a period of many years, e.g. 10 years. DCA is the safest strategy that beginners can employ to make money in the markets, and all other types of strategies should be "benchmarked" against DCA; if your strategy cannot outperform DCA, then your strategy is useless.
Recommended Chart Settings:
Asset Class: ETF / Stocks / Crypto
Time Frame: H1 (Hourly) / D1 (Daily) / W1 (Weekly) / M1 (Monthly)
Necessary ETF Macro Conditions:
1. Country must have healthy demographics, good ratio of young > old
2. Country population must be increasing
3. Country must be experiencing price-inflation
Necessary Stock Conditions:
1. Growing revenue
2. Growing net income
3. Consistent net margins
4. Higher gross/net profit margin compared to its peers in the industry
5. Growing share holders equity
6. Current ratios > 1
7. Debt to equity ratio (compare to peers)
8. Debt servicing ratio < 30%
9. Wide economic moat
10. Products and services used daily, and will stay relevant for at least 1 decade
Necessary Crypto Conditions:
1. Honest founders
2. Competent technical co-founders
3. Fair or non-existent pre-mine
4. Solid marketing and PR
5. Legitimate use-cases / adoption
Default Robot Settings:
Contribution (USD): $10,000
Frequency (Months): 12
*Robot buys $10,000 worth of ETF, Stock, Crypto, regardless of the market price, every 12 months since its founding time.*
*Equity curve can be seen from the bottom panel*
Risk Warning:
This strategy is low-risk, however it assumes you have a long time horizon of at least 5 to 10 years. The longer your holding-period, the better your returns. The only thing the user has to keep-in-mind are the macro-economic conditions as stated above. If unsure, please stick to ETFs rather than buying individual stocks or cryptocurrencies.
MACD StrategyThis script sends buy and sell signals as alerts to 3Commas (online software with trading bots in cryptocurreny)
It's based on 2 indicators:
- MACD
- 12 EMA and 26 EMA
When the 12 EMA and 26 EMA crossover, the MACD line crosses above 0. The goal here is to look for buy signals when the MACD and Signal are below 0, the histogram is positive, and there was or will be a 12 EMA and 26 EMA crossover.
I struggle with the following:
- There are multiple ways to use this as a crossover signal. I want to calculate the win rate of every posibility.
- What should be my take profit and my stoploss?
I think a 2:1 R/R,and a 60% win rate would make a great strategy! I could use some advice.
PowerX Strategy Bar Coloring [OFFICIAL VERSION]This script colors the bars according to the PowerX Strategy by Markus Heitkoetter:
The PowerX Strategy uses 3 indicators:
- RSI (7)
- Stochastics (14, 3, 3)
- MACD (12, 26 , 9)
The bars are colored GREEN if...
1.) The RSI (7) is above 50 AND
2.) The Stochastic (14, 3, 3) is above 50 AND
3.) The MACD (12, 26, 9) is above its Moving Average, i.e. MACD Histogram is positive.
The bars are colored RED if...
1.) The RSI (7) is below 50 AND
2.) The Stochastic (14, 3, 3) is below 50 AND
3.) The MACD (12, 26, 9) is below its Moving Average, i.e. MACD Histogram is negative.
If only 2 of these 3 conditions are met, then the bars are black (default color)
We highly recommend plotting the indicators mentioned above on your chart, too, so that you can see when bars are getting close to being "RED" or "GREEN", e.g. RSI is getting close to the 50 line.
Price Action and 3 EMAs Momentum plus Sessions FilterThis indicator plots on the chart the parameters and signals of the Price Action and 3 EMAs Momentum plus Sessions Filter Algorithmic Strategy. The strategy trades based on time-series (absolute) and relative momentum of price close, highs, lows and 3 EMAs.
I am still learning PS and therefore I have only been able to write the indicator up to the Signal generation. I plan to expand the indicator to Entry Signals as well as the full Strategy.
The strategy works best on EURUSD in the 15 minutes TF during London and New York sessions with 1 to 1 TP and SL of 30 pips with lots resulting in 3% risk of the account per trade. I have already written the full strategy in another language and platform and back tested it for ten years and it was profitable for 7 of the 10 years with average profit of 15% p.a which can be easily increased by increasing risk per trade. I have been trading it live in that platform for over two years and it is profitable.
Contributions from experienced PS coders in completing the Indicator as well as writing the Strategy and back testing it on Trading View will be appreciated.
STRATEGY AND INDICATOR PARAMETERS
Three periods of 12, 48 and 96 in the 15 min TF which are equivalent to 3, 12 and 24 hours i.e (15 min * period / 60 min) are the foundational inputs for all the parameters of the PA & 3 EMAs Momentum + SF Algo Strategy and its Indicator.
3 EMAs momentum parameters and conditions
• FastEMA = ema of 12 periods
• MedEMA = ema of 48 periods
• SlowEMA = ema of 96 periods
• All the EMAs analyse price close for up to 96 (15 min periods) equivalent to 24 hours
• There’s Upward EMA momentum if price close > FastEMA and FastEMA > MedEMA and MedEMA > SlowEMA
• There’s Downward EMA momentum if price close < FastEMA and FastEMA < MedEMA and MedEMA < SlowEMA
PA momentum parameters and conditions
• HH = Highest High of 48 periods from 1st closed bar before current bar
• LL = Lowest Low of 48 periods from 1st closed bar from current bar
• Previous HH = Highest High of 84 periods from 12th closed bar before current bar
• Previous LL = Lowest Low of 84 periods from 12th closed bar before current bar
• All the HH & LL and prevHH & prevLL are within the 96 periods from the 1st closed bar before current bar and therefore indicative of momentum during the past 24 hours
• There’s Upward PA momentum if price close > HH and HH > prevHH and LL > prevLL
• There’s Downward PA momentum if price close < LL and LL < prevLL and HH < prevHH
Signal conditions and Status (BuySignal, SellSignal or Neutral)
• The strategy generates Buy or Sell Signals if both 3 EMAs and PA momentum conditions are met for each direction and these occur during the London and New York sessions
• BuySignal if price close > FastEMA and FastEMA > MedEMA and MedEMA > SlowEMA and price close > HH and HH > prevHH and LL > prevLL and timeinrange (LDN&NY) else Neutral
• SellSignal if price close < FastEMA and FastEMA < MedEMA and MedEMA < SlowEMA and price close < LL and LL < prevLL and HH < prevHH and timeinrange (LDN&NY) else Neutral
Entry conditions and Status (EnterBuy, EnterSell or Neutral)(NOT CODED YET)
• ENTRY IS NOT AT THE SIGNAL BAR but at the current bar tick price retracement to FastEMA after the signal
• EnterBuy if current bar tick price <= FastEMA and current bar tick price > prevHH at the time of the Buy Signal
• EnterSell if current bar tick price >= FastEMA and current bar tick price > prevLL at the time of the Sell Signal
Smart labelling - Candlestick FunctionOftentimes a single look at the candlestick configuration happens to be enough to understand what is going on. The chandlestick function is an experiment in smart labelling that produces candles for various time frames, not only for the fixed 1m, 3m , 5m, 15m, etc. ones, and helps in decision-making when eye-balling the chart. This function generates up to 12 last candlesticks , which is generally more than enough.
Mind that since this is an experiment, the function does not cover all possible combinations. In some time frames the produced candles overlap. This is a todo item for those who are unterested. For instance, the current version covers the following TFs:
Chart - TF in the script
1m - 1-20,24,30,32
3m - 1-10
5m - 1-4,6,9,12,18,36
15m - 1-4,6,12
Tested chart TFs: 1m, 3m ,5m,15m. Tested securities: BTCUSD , EURUSD
[astropark] Power Tools Overlay//******************************************************************************
// Power Tools Overlay
// Inner Version 1.2.1 13/12/2018
// Developer: iDelphi
// Developer: astropark (Ichimoku Cloud), SMA EMA & Cross tools
//------------------------------------------------------------------------------
// 21/11/2018 Added EMA SMA WMA
// 21/11/2018 Added SMA-EMA EMA-WMA WMA-SMA (Thanks to mariobros1 for the idea of the Simultaneous MA)
// 21/11/2018 Added Bollinger Bands
// 21/11/2018 Added Ichimoku Cloud (Thanks to astropark for all the code of the Ichimoku Cloud)
// 23/11/2018 Show all the indicator as default
// 23/11/2018 Added a cross when single Moving Averages crossing (Thanks to astropark for the idea)
// 24/11/2018 Descriptions Fix
// 24/11/2018 Added Option to enable/disable all Moving Averages
// 10/12/2018 Added EMAs and Crosses
// 13/12/2018 indicator number fixes
//******************************************************************************
[Delphi] Power Tools OscillatorsFEATURES
- RSI
- Stochastic
//******************************************************************************
// Power Tools Oscillators
// Inner Version 1.0 04/12/2018
// Developer: iDelphi
//------------------------------------------------------------------------------
// 04/12/2018 Added RSI
// 04/12/2018 Added Stochastic
//******************************************************************************
Multi SMA EMA WMA HMA BB (4x3 MAs Bollinger Bands) Pro MTF - RRBMulti SMA EMA WMA HMA 4x3 Moving Averages with Bollinger Bands Pro MTF by RagingRocketBull 2018
Version 1.0
This indicator shows multiple MAs of any type SMA EMA WMA HMA etc with BB and MTF support, can show MAs as dynamically moving levels.
There are 4 MA groups + 1 BB group. You can assign any type/timeframe combo to a group, for example:
- EMAs 50,100,200 x H1, H4, D1, W1 (4 TFs x 3 MAs x 1 type)
- EMAs 8,13,21,55,100,200 x M15, H1 (2 TFs x 6 MAs x 1 type)
- D1 EMAs and SMAs 12,26,50,100,200,400 (1 TF x 6 MAs x 2 types)
- H1 WMAs 7,77,231; H4 HMAs 50,100,200; D1 EMAs 144,169,233; W1 SMAs 50,100,200 (4 TFs x 3 MAs x 4 types)
- +1 extra MA type/timeframe for BB
compile time: 25-30 sec
full redraw time after parameter change in UI: 3 sec
There are several versions: Simple, MTF, Pro MTF, Advanced MTF and Ultimate MTF. This is the Pro MTF version. The Differences are listed below. All versions have BB
- Simple: you have 2 groups of MAs that can be assigned any type (5+5)
- MTF: +2 custom Timeframes for each group (2x5 MTF)
- Pro MTF: +4 custom Timeframes for each group (4x3 MTF), MA levels and show max bars back options
- Advanced MTF: +2 extra MAs/group (4x5 MTF), custom Ticker/Symbol, backreferences for type, TF and MA lengths in UI
- Ultimate MTF: +individual settings for each MA, custom Ticker/Symbols
Features:
- 4x3 = 12 MAs of any type including Hull Moving Average (HMA)
- 4x MTF groups with step line smoothing
- BB +1 extra TF/type for BB MAs
- 12 MA levels with adjustable group offsets, indents and shift
- show max bars back
- you can show/hide both groups of MAs/levels and individual MAs
Notes:
1. based on 3EmaBB, uses plot*, barssince and security functions
2. you can't set certain constants from input due to Pinescript limitations - change the code as needed, recompile and use as a private version
3. Levels = trackprice implementation
4. Show Max Bars Back = show_last implementation
5. uses timeframe textbox instead of input resolution to allow for 120 240 and other custom TFs. Also supports TFs in hours: 2H or H2
6. swma has a fixed length = 4, alma and linreg have additional offset and smoothing params
7. Smoothing is applied by default for visual aesthetics on MTF. To use exact ma mtf values (lines with stair stepping) - disable it
MTF Notes:
- uses simple timeframe textbox instead of input resolution dropdown to allow for 120, 240 and other custom TFs, also supports timeframes in H: 2H, H2
- Groups that are not assigned a Custom TF will use Current Timeframe (0).
- MTF will work for any MA type assigned to the group
- MTF works both ways: you can display a higher TF MA/BB on a lower TF or a lower TF MA/BB on a higher TF.
- MTF MA values are normally aligned at the boundary of their native timeframe. This produces stair stepping when a higher TF MA is viewed on a lower TF.
Therefore X Y Point Density/Smoothing is applied by default on MA MTF for visual aesthetics. Set both to 0 to disable and see exact ma mtf values (lines with stair stepping and original mtf alignment).
- Smoothing is disabled for BB MTF bands because fill doesn't work with smoothed MAs after duplicate values are replaced with na.
- MTF MA Value fluctuation is possible on the current bar due to default security lookahead
Smoothing:
- X,Y == 0 - X,Y smoothing disabled (stair stepping on high TFs)
- X == 0, Y > 0 - X,Y smoothing applied to all TFs
- Y == 0, X > 0 - X smoothing applied to all TFs < deltaX_max_tf, Y smoothing disabled
- X > 0, Y > 0 - Y smoothing applied to all TFs, then X smoothing applied to all TFs < deltaX_max_tf
X Smoothing with Y == 0 - shows only every deltaX-th point starting from the first bar.
X Smoothing with Y > 0 - shows only every deltaX-th point starting from the last shown Y point, essentially filling huge gaps remaining after Y Smoothing with points and preserving the curve's general shape
X Smoothing on high TFs with already scarce points produces weird curve shapes, it works best only on high density lower TFs
Y Smoothing reduces points on all TFs, removes adjacent points with prices within deltaY, while preserving the smaller curve details.
A combination of X,Y produces the most accurate smoothing. Higher delta value - larger range, more points removed.
Show Max Bars Back:
- can't set plot show_last from input -> implemented using a timenow based range check
- you can't delete/modify history once plotted, so essentially it just sets a start point for plotting (from num_bars bars back) that works only in realtime mode (not in replay)
Levels:
You can plot current MA value using plot trackprice=true or by checking Show Price Line in Style. Problem is:
- you can only change color (not the dashed line style, width), have both ma + price line (not just the line), and it's full screen wide
- you can't set plot trackprice from input => implemented using plotshape/plotchar with fixed text labels serving as levels
- there's no other way of creating a dynamic level: hline, plot, offset - nothing else works.
- you can't plot a text var - all text strings must be constants, so you can't change the style, width and text labels without recompiling.
- from input you can only adjust offset, indent and shift for each level group, and change color
- the dot below each level line is the exact MA value. If you want just the line swap plotshape with plotchar, recompile and save as your private version, adjust Y shift.
To speed up redraw times: reduce last_bars to ~2000, recompile and use as your own private version
Pinescript is a rudimentary language (should be called Painscript instead) that can basically only plot data. You can't do much else. Please see the code for tips and hints.
Certain things just can't be done or require shady workarounds and weeks of testing trying to resolve weird node.js compiler errors.
Feel free to learn from/reuse/change the code as needed and use as your own private version. See comments in code. Good Luck!
Moving Average Convergence-Divergence (MACD)This script implements the Moving Average Convergence-Divergence (MACD), a popular momentum indicator used in technical analysis to identify trend direction, momentum shifts, and potential buy/sell signals.
🔹 Key Features
1. Inputs & Customization
MACD Lines Toggle: Enable/disable the MACD and signal lines.
Source Price: Defaults to close but can be adjusted (e.g., open, high, low, hl2).
Fast Length (12): The period for the faster-moving EMA.
Slow Length (26): The period for the slower-moving EMA.
Signal Length (9): The smoothing period for the signal line.
2. Calculations
Computes the MACD Line (fast EMA - slow EMA).
Computes the Signal Line (EMA of the MACD line).
Computes the Histogram (difference between MACD and Signal lines).
3. Visual Indicators
Zero Line: A white horizontal line at 0 for reference.
MACD Line: Plotted in green when above the signal line, red when below.
Signal Line: Displayed as a yellow line.
Histogram:
Green bars when MACD > Signal (bullish momentum).
Red bars when MACD < Signal (bearish momentum).
Background Highlights:
Light green on bullish crossovers (MACD crosses above Signal).
Light red on bearish crossunders (MACD crosses below Signal).
4. Alerts
Triggers when:
Bullish Crossover (MACD crosses above Signal).
Bearish Crossunder (MACD crosses below Signal).
🔹 How Traders Use This Indicator
Trend Identification:
MACD above zero → bullish trend.
MACD below zero → bearish trend.
Momentum Signals:
Bullish Crossover (Buy Signal): MACD crosses above Signal.
Bearish Crossunder (Sell Signal): MACD crosses below Signal.
Divergence (Not in this script, but useful):
Price makes higher highs, but MACD makes lower highs → Potential reversal.
🔹 Strengths of This Script
✅ Clean and Efficient Code – Uses Pine Script v6 best practices.
✅ Customizable Inputs – Adjust lengths and source price.
✅ Clear Visuals – Color-coded for easy interpretation.
✅ Built-in Alerts – For automated trading strategies.
Jul 1
Release Notes
This script implements the Moving Average Convergence-Divergence (MACD), a popular momentum indicator used in technical analysis to identify trend direction, momentum shifts, and potential buy/sell signals.
🔹 Key Features
1. Inputs & Customization
MACD Lines Toggle: Enable/disable the MACD and signal lines.
Source Price: Defaults to close but can be adjusted (e.g., open, high, low, hl2).
Fast Length (12): The period for the faster-moving EMA.
Slow Length (26): The period for the slower-moving EMA.
Signal Length (9): The smoothing period for the signal line.
2. Calculations
Computes the MACD Line (fast EMA - slow EMA).
Computes the Signal Line (EMA of the MACD line).
Computes the Histogram (difference between MACD and Signal lines).
3. Visual Indicators
Zero Line: A white horizontal line at 0 for reference.
MACD Line: Plotted in green when above the signal line, red when below.
Signal Line: Displayed as a yellow line.
Histogram:
Green bars when MACD > Signal (bullish momentum).
Red bars when MACD < Signal (bearish momentum).
Background Highlights:
Light green on bullish crossovers (MACD crosses above Signal).
Light red on bearish crossunders (MACD crosses below Signal).
4. Alerts
Triggers when:
Bullish Crossover (MACD crosses above Signal).
Bearish Crossunder (MACD crosses below Signal).
🔹 How Traders Use This Indicator
Trend Identification:
MACD above zero → bullish trend.
MACD below zero → bearish trend.
Momentum Signals:
Bullish Crossover (Buy Signal): MACD crosses above Signal.
Bearish Crossunder (Sell Signal): MACD crosses below Signal.
Divergence (Not in this script, but useful):
Price makes higher highs, but MACD makes lower highs → Potential reversal.
🔹 Strengths of This Script
✅ Clean and Efficient Code – Uses Pine Script v6 best practices.
✅ Customizable Inputs – Adjust lengths and source price.
✅ Clear Visuals – Color-coded for easy interpretation.
✅ Built-in Alerts – For automated trading strategies.
Chart Fusion Line SND Detection by TitikSona🧭 Overview
Fusion Line Momentum Analyzer is a momentum visualization tool that introduces a unified model of oscillator fusion.
It blends Fast and Slow Stochastics with RSI into one adaptive curve, designed to eliminate conflicting signals between different momentum sources.
Instead of reading three separate oscillators, the Fusion Line provides a consolidated view of strength and exhaustion zones in a single framework.
This approach helps analysts detect aligned momentum shifts with greater clarity and less noise, without repainting or lagging methods.
⚙️ Core Concept
Traditional oscillators often provide conflicting readings when volatility changes.
To solve this, the Fusion Line averages three normalized components:
Fast Stochastic (12,3,3) — reacts quickly to short-term momentum spikes.
Slow Stochastic (100,8,8) — filters long-term momentum context.
RSI (26) — measures internal strength between buying and selling pressure.
Each is rescaled to a 0–100 range, then averaged into a single curve called the Fusion Line.
A secondary Signal Line (SMA 9) is added to visualize directional confirmation.
This combination aims to preserve responsiveness from the fast components while maintaining structural stability from the slow and RSI layers.
🌈 Features
Unified momentum curve combining stochastic and RSI dynamics.
Automatic bias shading to highlight dominant trend direction.
Real-time percentage strength meter (visual intensity).
Configurable alert triggers on key momentum zones (20/80).
Clean chart display without unnecessary elements or overlays.
📘 Interpretation
Rising Fusion Line → indicates strengthening bullish momentum.
Falling Fusion Line → indicates strengthening bearish pressure.
Fusion values below 20 → potential oversold recovery.
Fusion values above 80 → possible exhaustion or reversal zone.
Mid-zone movement → reflects equilibrium or sideways momentum.
These readings should always be combined with higher timeframe structure or volume confirmation for context.
⚙️ Default Parameters
Fast Stochastic (12,3,3)
Slow Stochastic (100,8,8)
RSI Length (26)
Signal Line Smoothing (9)
All values can be adjusted to adapt to asset volatility or timeframe conditions.
⚠️ Disclaimer
This indicator is a research and visualization tool, not a signal generator.
It does not predict price movement or guarantee performance.
Use for analytical purposes only and combine with your own trading framework.
👨💻 Developer
Created by TitikSona — Research & Fusion Concept Designer
Built using Pine Script v6
Type: Open-source educational script
💬 Short Description
Fusion-based momentum visualization combining Double Stochastic and RSI into one adaptive line for clearer, noise-free momentum analysis.
RSI VWAP v1 [JopAlgo]RSI VWAP v1.1 made stronger by volume-aware!
We know there's nothing new and the original RSI already does an excellent job. We're just working on small, practical improvements – here's our take: The same basic idea, clearer display, and a single, specially developed rolling line: a VWAP of the RSI that incorporates volume (participation) into the calculation.
Do you prefer the pure classic?
You can still use Wilder or Cutler engines –
but the star here is the VW-RSI + rolling line.
This RSI also offers the possibility of illustrating a possible
POC (Point of Control - or the HAL or VAL) level.
However, the indicator does NOT plot any of these levels itself.
We have included an illustration in the chart for this!
We hope this version makes your decision-making easier.
What you’ll see
The RSI line with a 50 midline and optional bands: either static 70/30 or adaptive μ±k·σ of the Rolling Line.
One smoothing concept only: the Rolling Line (light blue) = VWAP of RSI.
Shadow shading between RSI and the Rolling Line (green when RSI > line, red when RSI < line).
A lighter tint only on the parts of that shadow that sit above the upper band or below the lower band (quick overbought/oversold context).
Simple divergence lines drawn from RSI pivots (green for regular bullish, red for regular bearish). No labels, no buy/sell text—kept deliberately clean.
What’s new, and why it helps
VW-RSI engine (default):
RSI can be computed from volume-weighted up/down moves, so momentum reflects how much traded when price moved—not just the direction.
Rolling Line (VWAP of RSI) with pure VWAP adaptation:
Low volume: blends toward a faster VWAP so early, thin starts aren’t missed.
Volume spikes: blends toward a slower VWAP so a single heavy bar doesn’t whip the curve.
You can reveal the Base Rolling (pre-adaptation) line to see exactly how much adaptation is happening.
Adaptive bands (optional):
Instead of fixed 70/30, use mean ± k·stdev of the Rolling Line over a lookback. Levels breathe with the market—useful in strong trends where static bounds stay pinned.
Minimal, readable panel:
One smoothing, one story. The shadow tells you who’s in control; the lighter highlight shows stretch beyond your lines.
How to read it (fast)
Bias: RSI above 50 (and a rising Rolling Line) → bullish bias; below 50 → bearish bias.
Trigger: RSI crossing the Rolling Line with the bias (e.g., above 50 and crossing up).
Stretch: Near/above the upper band, avoid chasing; near/below the lower band, avoid panic—prefer a cross back through the line.
Divergence lines: Use as context, not as standalone signals. They often help you wait for the next cross or avoid late entries into exhaustion.
Settings that actually matter
RSI Engine: VW-RSI (default), Wilder, or Cutler.
Rolling Line Length: the VWAP length on RSI (higher = calmer, lower = earlier).
Adaptive behavior (pure VWAP):
Speed-up on Low Volume → blends toward fast VWAP (factor of your length).
Dampen Spikes (volume z-score) → blends toward slow VWAP.
Fast/Slow Factors → how far those fast/slow variants sit from the base length.
Bands: choose Static 70/30 or Adaptive μ±k·σ (set the lookback and k).
Visuals: show/hide Base Rolling (ref), main shadow, and highlight beyond bands.
Signal gating: optional “ignore first bars” per day/session if you dislike open noise.
Starter presets
Scalp (1–5m): RSI 9–12, Rolling 12–18, FastFactor ~0.5, SlowFactor ~2.0, Adaptive on.
Intraday (15m–1H): RSI 10–14, Rolling 18–26, Bands k = 1.0–1.4.
Swing (4H–1D): RSI 14–20, Rolling 26–40, Bands k = 1.2–1.8, Adaptive on.
Where it shines (and limits)
Best: liquid markets where volume structure matters (majors, indices, large caps).
Works elsewhere: even with imperfect volume, the shadow + bands remain useful.
Limits: very thin/illiquid assets reduce the benefit of volume-weighting—lengthen settings if needed.
Attribution & License
Based on the concept and baseline implementation of the “Relative Strength Index” by TradingView (Pine v6 built-in).
Released as Open-source (MPL-2.0). Please keep the license header and attribution intact.
Disclaimer
For educational purposes only; not financial advice. Markets carry risk. Test first, use clear levels, and manage risk. This project is independent and not affiliated with or endorsed by TradingView.
Smart RR Lot (Forex) — RR + Lot auto (Final v6 Stable)//@version=6
indicator("Smart RR Lot (Forex) — RR + Lot auto (Final v6 Stable)", overlay=true, max_lines_count=12, max_labels_count=12)
// ===== Paramètres du compte =====
acc_currency = input.string("EUR", "Devise du compte", options= )
account_balance = input.float(6037.0, "Solde du compte", step=1.0)
risk_pct = input.float(1.0, "Risque par trade (%)", step=0.1, minval=0.01)
// ===== Niveaux à placer sur le graphique =====
entry_price = input.price(1.1000, "Entry (cliquer la pipette)")
sl_price = input.price(1.0990, "Stop Loss (cliquer la pipette)")
tp_price = input.price(1.1010, "Take Profit (cliquer la pipette)")
// ===== Taille du pip (Forex) =====
isJPYpair = str.contains(syminfo.ticker, "JPY")
pip_size = isJPYpair ? 0.01 : 0.0001
// ===== Valeur du pip (1 lot = 100 000 unités) =====
pip_value_quote = 100000.0 * pip_size
quote_ccy = syminfo.currency
// ===== Conversion QUOTE → devise du compte =====
f_rate(sym) =>
request.security(sym, "D", close, ignore_invalid_symbol=true)
f_conv_to_account(quote, acc) =>
acc_equals = quote == acc
if acc_equals
1.0
else
r1 = f_rate(acc + quote)
r2 = f_rate(quote + acc)
float res = na
if not na(r1)
res := 1.0 / r1
else if not na(r2)
res := r2
else
res := 1.0
res
quote_to_account = f_conv_to_account(quote_ccy, acc_currency)
pip_value_account = pip_value_quote * quote_to_account
// ===== Calcul RR & taille de lot =====
stop_dist_points = math.abs(entry_price - sl_price)
tp_dist_points = math.abs(tp_price - entry_price)
distance_pips = stop_dist_points / pip_size
rr = tp_dist_points / stop_dist_points
risk_amount = account_balance * (risk_pct * 0.01)
lot_size = distance_pips > 0 ? (risk_amount / (distance_pips * pip_value_account)) : na
lot_size_clamped = na(lot_size) ? na : math.max(lot_size, 0)
// ====== Lignes horizontales ======
var line lEntry = na
var line lSL = na
var line lTP = na
f_hline(line_id, float y, color colr) =>
var line newLine = na
if na(line_id)
newLine := line.new(bar_index - 1, y, bar_index, y, xloc=xloc.bar_index, extend=extend.right, color=colr, width=2)
else
line.set_xy1(line_id, bar_index - 1, y)
line.set_xy2(line_id, bar_index, y)
line.set_color(line_id, colr)
line.set_extend(line_id, extend.right)
newLine := line_id
newLine
colEntry = color.new(color.gray, 0)
colSL = color.new(color.red, 0)
colTP = color.new(color.teal, 0)
lEntry := f_hline(lEntry, entry_price, colEntry)
lSL := f_hline(lSL, sl_price, colSL)
lTP := f_hline(lTP, tp_price, colTP)
// ===== Labels d’informations =====
var label infoLbl = na
var label lblEntry = na
var label lblSL = na
var label lblTP = na
txtInfo = "RR = " + (na(rr) ? "—" : str.tostring(rr, "#.##")) +
" | Lot = " + (na(lot_size_clamped) ? "—" : str.tostring(lot_size_clamped, "#.##")) +
" (" + acc_currency + ")\n" +
"Risque " + str.tostring(risk_pct, "#.##") + "% = " + str.tostring(risk_amount, "#.##") + " " + acc_currency
midY = (entry_price + tp_price) * 0.5
if na(infoLbl)
infoLbl := label.new(bar_index, midY, txtInfo, xloc=xloc.bar_index, style=label.style_label_right, textcolor=color.white, color=color.new(color.black, 0))
else
label.set_x(infoLbl, bar_index)
label.set_y(infoLbl, midY)
label.set_text(infoLbl, txtInfo)
entryTxt = "ENTRY\n" + str.tostring(entry_price, format.price)
slTxt = "SL\n" + str.tostring(sl_price, format.price)
tpTxt = "TP\n" + str.tostring(tp_price, format.price)
if na(lblEntry)
lblEntry := label.new(bar_index, entry_price, entryTxt, xloc=xloc.bar_index, style=label.style_label_down, textcolor=color.white, color=color.new(colEntry, 0))
else
label.set_x(lblEntry, bar_index)
label.set_y(lblEntry, entry_price)
label.set_text(lblEntry, entryTxt)
if na(lblSL)
lblSL := label.new(bar_index, sl_price, slTxt, xloc=xloc.bar_index, style=label.style_label_down, textcolor=color.white, color=color.new(colSL, 0))
else
label.set_x(lblSL, bar_index)
label.set_y(lblSL, sl_price)
label.set_text(lblSL, slTxt)
if na(lblTP)
lblTP := label.new(bar_index, tp_price, tpTxt, xloc=xloc.bar_index, style=label.style_label_down, textcolor=color.white, color=color.new(colTP, 0))
else
label.set_x(lblTP, bar_index)
label.set_y(lblTP, tp_price)
label.set_text(lblTP, tpTxt)
ICT Killzones & MacrosICT Killzones & Macros (v1.1.5) — configurable ICT session windows + refined “macro” windows with live High/Low levels, optional extensions, next-window previews, and lightweight opening-price lines. Built to be clock-robust, timezone-aware, and performant on intraday charts.
Tip: All times are interpreted in your chosen IANA timezone (default: America/New_York) and auto-handle DST. You can rename, recolor, enable/disable, and retime every window.
What it plots
- Killzones (5) : Asia (19:00–02:00), London (02:00–05:00), NY AM (07:00–09:30), London Close (10:00–12:00), NY PM (13:30–16:00) — full-height boxes with optional header.
- Macros (8) (defaults tailored for common ICT “refined” windows): Asia-1 (18:00–21:00), Asia-2 (21:00–00:00), London-1 (01:00–04:00), AM-1 (09:45–10:15), AM-2 (10:45–11:15), Lunch (12:00–13:00), PM-1 (13:30–14:30), Power Hour (15:10–16:00).
- Live High/Low lines for the current Macro/Killzone window.
- Optional HL extension to the right until price crosses or the trading day rolls (style selectable).
- “Next” previews : earliest upcoming Macro and Killzone header; optional next-window background band.
- Opening Prices (3 lightweight time lines) : defaults 00:00, 08:30, 09:30 with right-edge labels, scoped to a session you choose (auto-cleans at session end).
- Key inputs & styling
- General : Timezone (IANA), “Sessions to show” (per window) to keep only the last N completed windows.
- Header : height (ticks), gap (ticks), fill opacity, border width/style, text size/color, toggle “Next Macro/Killzone” headers.
- Boxes : global fill opacity, global border width/style (used by both Macros & Killzones).
- High/Low : show HL, HL line style, extend on/off + extension style, optional extension labels.
- Opening Prices : enable Time 1/2/3, set HH:MM for each, session window, per-line colors, style (dotted/dashed/solid), width.
- Per-window controls : each Macro/Killzone has Enable, Session (HHMM-HHMM), Label, Fill color.
How to use (quick start)
- Set Timezone to your preference (default America/New_York).
- Toggle on the Macros and Killzones you trade. Adjust session times if needed.
- (Optional) Turn on Extend High/Low to project levels until crossed/day-roll.
- (Optional) Enable Next… headers to see the next upcoming window at a glance.
- (Optional) Configure Opening Prices (00:00 / 08:30 / 09:30 by default) and the session over which they appear.
Behavior & notes
- Time windows are computed by clock, not by guessing bar timestamps, making them robust across brokers and timeframes.
- With HL extension on, the current window’s levels extend until crossed or the end of the trading day (in your timezone). With it off, completed windows keep static HL markers (limited by “Sessions to show”).
- “Sessions to show” applies per Macro/Killzone to automatically prune older windows and keep charts snappy.
- Opening-price lines exist only within the chosen “Opening Prices Session” and are removed when it ends (keeps charts clean).
Defaults (color cues)
Killzones: Asia (blue), London (purple), NY AM (green), London Close (yellow), NY PM (orange).
Macros: neutral greys with Lunch and PM accents out of the box (all customizable).
Performance tips
- Reduce “Sessions to show” if you scroll far back in history.
- Disable “Next…” previews and/or extension labels on very slow machines.
- Narrow the “Opening Prices Session” window to exactly when you need those lines.
Changelog highlights
- v1.1.5 : Internal refinements and stability.
- v1.1.3 : Live High/Low lines for current windows + optional extension.
- v1.1.2 : Added “next Killzone” preview (to match “next Macro”).
- v1.1.0 : Defaults updated (5 KZ, 8 Macros). Removed “snap-to-killzone” behavior.
- v1.0.0 : Independent Macro vs. Killzone rendering; cleaner header logic.
- Known limitations
If your chart warns about drawings, trim “Sessions to show”.
If your broker session times differ from NY hours, adjust the sessions or change the indicator timezone.
Credits & intent
Inspired by ICT timing concepts; provided for education/mark-up, not financial advice.
Built to be flexible so you can mirror your personal playbook and journaling workflow.
Lucas' Money GlitchHere's a description you can use to publish your indicator to TradingView:
Title: Triple SuperTrend + RSI + Fib BB + Volume Oscillator
Short Description:
Advanced multi-indicator system combining three SuperTrends, RSI, Fibonacci Bollinger Bands, DEMA filter, and Volume Oscillator for precise trade entry and exit signals.
Full Description:
Overview
This comprehensive trading indicator combines multiple proven technical analysis tools to identify high-probability trade setups with built-in risk management through automated take profit levels.
Key Features
📊 Triple SuperTrend System
Uses three SuperTrend indicators with different ATR periods (10, 11, 12) and multipliers (1.0, 2.0, 3.0)
Requires all three SuperTrends to align before generating signals
Reduces false signals and confirms trend strength
📈 Volume Oscillator Filter
Calculates volume momentum using short and long-term moving averages
Requires volume oscillator to be above 20% threshold for trade entries
Ensures trades only occur during periods of strong volume activity
Displayed as a clean histogram in separate pane (green = bullish, red = bearish)
🎯 RSI Confirmation
7-period RSI must be above 50 for buy signals
RSI must be below 50 for sell signals
Prevents counter-trend entries
🌊 200 DEMA Trend Filter
Double Exponential Moving Average acts as major trend filter
Optional: Only buy above DEMA, only sell below DEMA
Can be toggled on/off based on trading style
📐 Fibonacci Bollinger Bands
Uses 2.618 Fibonacci multiplier (Golden Ratio)
200-period basis
Price touching bands triggers exit signals
Helps identify overextended moves
Entry Signals
BUY Signal (Green Triangle):
All three SuperTrends turn bullish simultaneously
RSI > 50
Price above 200 DEMA (if filter enabled)
Volume Oscillator > 20%
SELL Signal (Red Triangle):
All three SuperTrends turn bearish simultaneously
RSI < 50
Price below 200 DEMA (if filter enabled)
Volume Oscillator > 20%
Exit Signals
Automatic Exits Occur When:
Any of the three SuperTrends changes direction
Price touches Fibonacci Bollinger Band (upper or lower)
Take Profit target is reached (1.5x the distance from entry to ST1)
Exit Labels:
🟠 "TP" = Take Profit hit
🟡 "X" = SuperTrend change or BB touch
Visual Elements
Orange Line: Dynamic take profit level based on SuperTrend distance
Green/Red Lines: Three SuperTrend levels (varying opacity)
Purple Bands: Fibonacci Bollinger Bands with shaded area
Blue Line: 200 DEMA
Background Tint: Green when all bullish, red when all bearish
Volume Histogram: Separate pane showing volume oscillator
Dashboard Display
Real-time information table showing:
Current position status (Long/Short/Flat)
RSI value
Volume Oscillator percentage
Overall trend direction
Alert Conditions
Set up custom alerts for:
Buy signals
Sell signals
Take profit hits
Exit signals
Customizable Parameters
SuperTrend Settings:
Individual ATR periods and multipliers for each SuperTrend
Default: ST1(10,1.0), ST2(11,2.0), ST3(12,3.0)
Volume Oscillator:
Short length (default: 5)
Long length (default: 10)
Threshold percentage (default: 20%)
Toggle filter on/off
Other Filters:
RSI length (default: 7)
DEMA length (default: 200)
Fib BB length and multiplier
Take profit multiplier (default: 1.5x)
Best Use Cases
Trend following strategies
Swing trading
Day trading on higher timeframes (15min+)
Works on all markets: Stocks, Forex, Crypto, Futures
Notes
This is an indicator, not an automated strategy
Signals are for informational purposes only
Always practice proper risk management
Test on historical data before live trading
Works best in trending markets
Triple SuperTrend + RSI + Fib BBTriple SuperTrend + RSI + Fibonacci Bollinger Bands Strategy
📊 Overview
This advanced trading strategy combines the power of three SuperTrend indicators with RSI confirmation and Fibonacci Bollinger Bands to generate high-probability trade signals. The strategy is designed to capture strong trending moves while filtering out false signals through multi-indicator confluence.
🔧 Core Components
Three SuperTrend Indicators
The strategy uses three SuperTrend indicators with progressively longer periods and multipliers:
SuperTrend 1: 10-period ATR, 1.0 multiplier (fastest, most sensitive)
SuperTrend 2: 11-period ATR, 2.0 multiplier (medium sensitivity)
SuperTrend 3: 12-period ATR, 3.0 multiplier (slowest, most stable)
This layered approach ensures that all three timeframe perspectives align before generating a signal, significantly reducing false entries.
RSI Confirmation (7-period)
The Relative Strength Index acts as a momentum filter:
Long signals require RSI > 50 (bullish momentum)
Short signals require RSI < 50 (bearish momentum)
This prevents entries during weak or divergent price action.
Fibonacci Bollinger Bands (200, 2.618)
Uses a 200-period Simple Moving Average with 2.618 standard deviation bands (Fibonacci ratio). These bands serve dual purposes:
Visual representation of price extremes
Automatic exit trigger when price reaches overextended levels
📈 Entry Logic
LONG Entry (BUY Signal)
A LONG position is opened when ALL of the following conditions are met simultaneously:
All three SuperTrend indicators turn green (bullish)
RSI(7) is above 50
This is the first bar where all conditions align (no repainting)
SHORT Entry (SELL Signal)
A SHORT position is opened when ALL of the following conditions are met simultaneously:
All three SuperTrend indicators turn red (bearish)
RSI(7) is below 50
This is the first bar where all conditions align (no repainting)
🚪 Exit Logic
Positions are automatically closed when ANY of these conditions occur:
SuperTrend Color Change: Any one of the three SuperTrend indicators changes direction
Fibonacci BB Touch: Price reaches or exceeds the upper or lower Fibonacci Bollinger Band (2.618 standard deviations)
This dual-exit approach protects profits by:
Exiting quickly when trend momentum shifts (SuperTrend change)
Taking profits at statistical price extremes (Fib BB touch)
🎨 Visual Features
Signal Arrows
Green Up Arrow (BUY): Appears below the bar when long entry conditions are met
Red Down Arrow (SELL): Appears above the bar when short entry conditions are met
Yellow Down Arrow (EXIT): Appears above the bar when exit conditions are met
Background Coloring
Light Green Tint: All three SuperTrends are bullish (uptrend environment)
Light Red Tint: All three SuperTrends are bearish (downtrend environment)
SuperTrend Lines
Three colored lines plotted with varying opacity:
Solid line (ST1): Most responsive to price changes
Semi-transparent (ST2): Medium-term trend
Most transparent (ST3): Long-term trend structure
Dashboard
Real-time information panel showing:
Individual SuperTrend status (UP/DOWN)
Current RSI value and color-coded status
Current position (LONG/SHORT/FLAT)
Net Profit/Loss
⚙️ Customizable Parameters
SuperTrend Settings
ATR periods for each SuperTrend (default: 10, 11, 12)
Multipliers for each SuperTrend (default: 1.0, 2.0, 3.0)
RSI Settings
RSI length (default: 7)
RSI source (default: close)
Fibonacci Bollinger Bands
BB length (default: 200)
BB multiplier (default: 2.618)
Strategy Options
Enable/disable long trades
Enable/disable short trades
Initial capital
Position sizing
Commission settings
💡 Strategy Philosophy
This strategy is built on the principle of confluence trading - waiting for multiple independent indicators to align before taking a position. By requiring three SuperTrend indicators AND RSI confirmation, the strategy filters out the majority of low-probability setups.
The multi-timeframe SuperTrend approach ensures that short-term, medium-term, and longer-term trends are all in agreement, which typically occurs during strong, sustainable price moves.
The exit strategy is equally important, using both trend-following logic (SuperTrend changes) and mean-reversion logic (Fibonacci BB touches) to adapt to different market conditions.
📊 Best Use Cases
Trending Markets: Works best in markets with clear directional bias
Higher Timeframes: Designed for 15-minute to daily charts
Volatile Assets: SuperTrend indicators excel in assets with clear trends
Swing Trading: Hold times typically range from hours to days
⚠️ Important Notes
No Repainting: All signals are confirmed and will not change on historical bars
One Signal Per Setup: The strategy prevents duplicate signals on consecutive bars
Exit Protection: Always exits before potentially taking an opposite position
Visual Clarity: All three SuperTrend lines are visible simultaneously for transparency
🎯 Recommended Settings
While default parameters are optimized for general use, consider:
Crypto/Volatile Markets: May benefit from slightly higher multipliers
Forex: Default settings work well for major pairs
Stocks: Consider longer BB periods (250-300) for daily charts
Lower Timeframes: Reduce all periods proportionally for scalping
📝 Alerts
Built-in alert conditions for:
BUY signal triggered
SELL signal triggered
EXIT signal triggered
Set up notifications to never miss a trade opportunity!
Disclaimer: This strategy is for educational and informational purposes only. Past performance does not guarantee future results. Always backtest thoroughly and practice proper risk management before live trading.
Session First 5-Min High/LowHere's a professional description for your indicator:
Session First 5-Min High/Low Marker
This indicator automatically identifies and marks the high and low price levels established during the first 5 minutes of major trading sessions, helping traders identify key intraday support and resistance zones.
Key Features:
Tracks three major trading sessions in IST (Indian Standard Time):
Asian Session: 5:30 AM - 5:35 AM
London Session: 12:30 PM - 12:35 PM
New York Session: 5:30 PM - 5:35 PM
Draws horizontal lines at the highest and lowest prices reached during each session's opening 5-minute window
Color-coded for easy identification (Yellow for Asian, Blue for London, Red for New York)
Lines extend across the chart to help track price reactions throughout the day
Clean, minimal design with optional labels
Best Used For:
Identifying key intraday support and resistance levels
Session breakout trading strategies
Understanding institutional order flow at market opens
Works on 1-minute timeframe for precise tracking
Customizable Settings:
Toggle line extensions on/off
Adjust line width (1-5)
Change colors for each session
Show/hide session labels
Perfect for day traders and scalpers who trade around major session openings and want to identify high-probability support/resistance zones established during peak liquidity periods.
This description explains what the indicator does, its practical applications, and its key features in a way that's clear for TradingView users.RetryClaude can make mistakes. Please double-check responses.
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.
Ang, A., Piazzesi, M. and Wei, M. (2006) 'What does the yield curve tell us about GDP growth?', *Journal of Econometrics*, 131(1-2), pp. 359-403.
Asness, C.S. (2003) 'Fight the Fed Model', *The Journal of Portfolio Management*, 30(1), pp. 11-24.
Asness, C.S., Moskowitz, T.J. and Pedersen, L.H. (2013) 'Value and Momentum Everywhere', *The Journal of Finance*, 68(3), pp. 929-985.
Baker, M. and Wurgler, J. (2006) 'Investor Sentiment and the Cross-Section of Stock Returns', *The Journal of Finance*, 61(4), pp. 1645-1680.
Baker, M. and Wurgler, J. (2007) 'Investor Sentiment in the Stock Market', *Journal of Economic Perspectives*, 21(2), pp. 129-152.
Baur, D.G. and Lucey, B.M. (2010) 'Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold', *Financial Review*, 45(2), pp. 217-229.
Bollerslev, T. (1986) 'Generalized Autoregressive Conditional Heteroskedasticity', *Journal of Econometrics*, 31(3), pp. 307-327.
Boudoukh, J., Michaely, R., Richardson, M. and Roberts, M.R. (2007) 'On the Importance of Measuring Payout Yield: Implications for Empirical Asset Pricing', *The Journal of Finance*, 62(2), pp. 877-915.
Brinson, G.P., Hood, L.R. and Beebower, G.L. (1986) 'Determinants of Portfolio Performance', *Financial Analysts Journal*, 42(4), pp. 39-44.
Brock, W., Lakonishok, J. and LeBaron, B. (1992) 'Simple Technical Trading Rules and the Stochastic Properties of Stock Returns', *The Journal of Finance*, 47(5), pp. 1731-1764.
Calmar, T.W. (1991) 'The Calmar Ratio', *Futures*, October issue.
Campbell, J.Y. and Shiller, R.J. (1988) 'The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors', *Review of Financial Studies*, 1(3), pp. 195-228.
Cochrane, J.H. (2011) 'Presidential Address: Discount Rates', *The Journal of Finance*, 66(4), pp. 1047-1108.
Damodaran, A. (2012) *Equity Risk Premiums: Determinants, Estimation and Implications*. Working Paper, Stern School of Business.
Engle, R.F. (1982) 'Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation', *Econometrica*, 50(4), pp. 987-1007.
Estrella, A. and Hardouvelis, G.A. (1991) 'The Term Structure as a Predictor of Real Economic Activity', *The Journal of Finance*, 46(2), pp. 555-576.
Estrella, A. and Mishkin, F.S. (1998) 'Predicting U.S. Recessions: Financial Variables as Leading Indicators', *Review of Economics and Statistics*, 80(1), pp. 45-61.
Faber, M.T. (2007) 'A Quantitative Approach to Tactical Asset Allocation', *The Journal of Wealth Management*, 9(4), pp. 69-79.
Fama, E.F. and French, K.R. (1989) 'Business Conditions and Expected Returns on Stocks and Bonds', *Journal of Financial Economics*, 25(1), pp. 23-49.
Fama, E.F. and French, K.R. (1992) 'The Cross-Section of Expected Stock Returns', *The Journal of Finance*, 47(2), pp. 427-465.
Garman, M.B. and Klass, M.J. (1980) 'On the Estimation of Security Price Volatilities from Historical Data', *Journal of Business*, 53(1), pp. 67-78.
Gilchrist, S. and Zakrajšek, E. (2012) 'Credit Spreads and Business Cycle Fluctuations', *American Economic Review*, 102(4), pp. 1692-1720.
Gordon, M.J. (1962) *The Investment, Financing, and Valuation of the Corporation*. Homewood: Irwin.
Graham, B. and Dodd, D.L. (1934) *Security Analysis*. New York: McGraw-Hill.
Hamilton, J.D. (1989) 'A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle', *Econometrica*, 57(2), pp. 357-384.
Ilmanen, A. (2011) *Expected Returns: An Investor's Guide to Harvesting Market Rewards*. Chichester: Wiley.
Jaconetti, C.M., Kinniry, F.M. and Zilbering, Y. (2010) 'Best Practices for Portfolio Rebalancing', *Vanguard Research Paper*.
Jegadeesh, N. and Titman, S. (1993) 'Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency', *The Journal of Finance*, 48(1), pp. 65-91.
Kahneman, D. and Tversky, A. (1979) 'Prospect Theory: An Analysis of Decision under Risk', *Econometrica*, 47(2), pp. 263-292.
Korteweg, A. (2010) 'The Net Benefits to Leverage', *The Journal of Finance*, 65(6), pp. 2137-2170.
Lo, A.W. and MacKinlay, A.C. (1990) 'Data-Snooping Biases in Tests of Financial Asset Pricing Models', *Review of Financial Studies*, 3(3), pp. 431-467.
Longin, F. and Solnik, B. (2001) 'Extreme Correlation of International Equity Markets', *The Journal of Finance*, 56(2), pp. 649-676.
Mandelbrot, B. (1963) 'The Variation of Certain Speculative Prices', *The Journal of Business*, 36(4), pp. 394-419.
Markowitz, H. (1952) 'Portfolio Selection', *The Journal of Finance*, 7(1), pp. 77-91.
Modigliani, F. and Miller, M.H. (1961) 'Dividend Policy, Growth, and the Valuation of Shares', *The Journal of Business*, 34(4), pp. 411-433.
Moreira, A. and Muir, T. (2017) 'Volatility-Managed Portfolios', *The Journal of Finance*, 72(4), pp. 1611-1644.
Moskowitz, T.J., Ooi, Y.H. and Pedersen, L.H. (2012) 'Time Series Momentum', *Journal of Financial Economics*, 104(2), pp. 228-250.
Parkinson, M. (1980) 'The Extreme Value Method for Estimating the Variance of the Rate of Return', *Journal of Business*, 53(1), pp. 61-65.
Piotroski, J.D. (2000) 'Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers', *Journal of Accounting Research*, 38, pp. 1-41.
Reinhart, C.M. and Rogoff, K.S. (2009) *This Time Is Different: Eight Centuries of Financial Folly*. Princeton: Princeton University Press.
Ross, S.A. (1976) 'The Arbitrage Theory of Capital Asset Pricing', *Journal of Economic Theory*, 13(3), pp. 341-360.
Roy, A.D. (1952) 'Safety First and the Holding of Assets', *Econometrica*, 20(3), pp. 431-449.
Schwert, G.W. (1989) 'Why Does Stock Market Volatility Change Over Time?', *The Journal of Finance*, 44(5), pp. 1115-1153.
Sharpe, W.F. (1966) 'Mutual Fund Performance', *The Journal of Business*, 39(1), pp. 119-138.
Sharpe, W.F. (1994) 'The Sharpe Ratio', *The Journal of Portfolio Management*, 21(1), pp. 49-58.
Simon, D.P. and Wiggins, R.A. (2001) 'S&P Futures Returns and Contrary Sentiment Indicators', *Journal of Futures Markets*, 21(5), pp. 447-462.
Taleb, N.N. (2007) *The Black Swan: The Impact of the Highly Improbable*. New York: Random House.
Whaley, R.E. (2000) 'The Investor Fear Gauge', *The Journal of Portfolio Management*, 26(3), pp. 12-17.
Whaley, R.E. (2009) 'Understanding the VIX', *The Journal of Portfolio Management*, 35(3), pp. 98-105.
Yardeni, E. (2003) 'Stock Valuation Models', *Topical Study*, 51, Yardeni Research.
Zweig, M.E. (1973) 'An Investor Expectations Stock Price Predictive Model Using Closed-End Fund Premiums', *The Journal of Finance*, 28(1), pp. 67-78.
Trend Candle CounterComplete Tutorial: Trend Candle Counter Pine ScriptTable of Contents
Installation Guide
Understanding the Indicator
How It Works
Customization Options
Trading Strategies
Setting Up Alerts
Troubleshooting
1. Installation Guide {#installation}Step-by-Step Installation:Step 1: Open TradingView
Go to www.tradingview.com
Log in to your account
Step 2: Access Pine Editor
Click on "Pine Editor" tab at the bottom of the chart
Or press Alt + E (Windows) or Option + E (Mac)
Step 3: Create New Indicator
Click "Open" → "New blank indicator"
Delete any default code
Step 4: Paste the Script
Copy the entire Trend Candle Counter script
Paste it into the editor
Step 5: Save and Apply
Click "Save" (or Ctrl + S)
Give it a name: "Trend Candle Counter"
Click "Add to Chart"
✅ Done! The indicator should now appear on your chart.2. Understanding the Indicator {#understanding}What Does It Do?This indicator numbers each candle based on the current trend: {scrollbar-width:none;-ms-overflow-style:none;-webkit-overflow-scrolling:touch;} ::-webkit-scrollbar{display:none}Trend TypeNumberingVisualUptrend+1, +2, +3, +4...🟢 Green labelsDowntrend-1, -2, -3, -4...🔴 Red labelsTrend ChangeResets to ±1Label color switchesVisual Components:
Candle Labels - Numbers above each candle
Trend Line (EMA) - Green (up) / Red (down)
Background Shading - Light green/red tint
Info Table - Top-right corner showing:
Current trend direction
Current candle number
Current price
3. How It Works {#how-it-works}Trend Detection Logic:IF Close > EMA → UPTREND (positive counting)
IF Close < EMA → DOWNTREND (negative counting)
Counting Mechanism:Example Uptrend:Candle 1: Close > EMA → Label: +1
Candle 2: Close > EMA → Label: +2
Candle 3: Close > EMA → Label: +3
Candle 4: Close < EMA → Label: -1 (trend changed!)
Example Downtrend:Candle 1: Close < EMA → Label: -1
Candle 2: Close < EMA → Label: -2
Candle 3: Close < EMA → Label: -3
Candle 4: Close > EMA → Label: +1 (trend changed!)
Key Insight:The higher the absolute number, the longer the trend has been running!4. Customization Options {#customization}Accessing Settings:
Click the gear icon ⚙️ next to the indicator name
Go to "Inputs" tab
Available Parameters: {scrollbar-width:none;-ms-overflow-style:none;-webkit-overflow-scrolling:touch;} ::-webkit-scrollbar{display:none}ParameterDefaultDescriptionRecommendationTrend Detection Length14EMA period for trend5-10: Scalping14-20: Day trading50-200: Swing tradingShow Candle Numbers✅ YesDisplay labelsDisable for cleaner chartLabel SizeSmallSize of numbersTiny: Multi-timeframeLarge: Focus on one chartUptrend ColorGreenPositive number colorCustomize to preferenceDowntrend ColorRedNegative number colorCustomize to preferenceOptimization by Trading Style:For Scalpers (1m - 5m charts):Trend Detection Length: 5-10
Label Size: Tiny
Show Labels: Optional (can be cluttered)
For Day Traders (15m - 1h charts):Trend Detection Length: 14-20
Label Size: Small
Show Labels: Yes
For Swing Traders (4h - Daily charts):Trend Detection Length: 50-100
Label Size: Normal
Show Labels: Yes
5. Trading Strategies {#strategies}Strategy 1: Trend Reversal TradingEntry Signals:
Buy: When counter changes from negative to +1
Sell: When counter changes from positive to -1
Confirmation:
Wait for +2 or -2 to confirm trend strength
Use additional indicators (RSI, MACD) for validation
Example:Candle: -5, -6, -7, -8, +1, +2 ← BUY HERE
Stop Loss: Below the -8 candle low
Target: When counter reaches +8 to +10
Strategy 2: Trend Continuation TradingEntry Signals:
Buy: Enter on pullbacks during uptrend (e.g., at +3, +5, +7)
Sell: Enter on bounces during downtrend (e.g., at -3, -5, -7)
Risk Management:
Avoid entering at high numbers (+15, -15) - trend may be exhausted
Example:Candle: +1, +2, +3 ← Small pullback, BUY
Continue: +4, +5, +6, +7
Exit: When counter resets to -1
Strategy 3: Trend Exhaustion DetectionWarning Signs:
Counter reaches +10 or higher → Uptrend may be overextended
Counter reaches -10 or lower → Downtrend may be overextended
Action:
Tighten stop losses
Take partial profits
Watch for reversal patterns (doji, engulfing)
Strategy 4: Multi-Timeframe AnalysisSetup:
Add indicator to 3 timeframes (e.g., 15m, 1h, 4h)
Look for alignment
Best Trades:15m: +1 (new uptrend)
1h: +5 (established uptrend)
4h: +3 (strong uptrend)
→ HIGH PROBABILITY BUY
6. Setting Up Alerts {#alerts}Built-in Alert Conditions:The script includes 2 automatic alerts:
"Uptrend Started" - Triggers when counter = +1
"Downtrend Started" - Triggers when counter = -1
How to Set Up Alerts:Step 1: Right-click on chart
Select "Add Alert"
Step 2: Configure Alert
Condition: Select "Trend Candle Counter"
Choose: "Uptrend Started" or "Downtrend Started"
Options:
Once per bar close (recommended)
Webhook URL (for automation)
Step 3: Notification Settings
✅ Popup
✅ Send email
✅ Push notification (mobile app)
✅ Play sound
Step 4: Create Alert
Click "Create"
Custom Alert Ideas:Alert for Specific Candle Numbers:
Notify when counter reaches +5 or -5
Notify when counter exceeds +10 or -10 (exhaustion)
7. Troubleshooting {#troubleshooting}Common Issues & Solutions:Issue 1: Labels are too cluttered
Solution:
Disable "Show Candle Numbers" in settings
Use larger timeframe
Reduce label size to "tiny"
Issue 2: Too many false signals
Solution:
Increase "Trend Detection Length" (e.g., 20, 50)
Wait for +2 or -2 confirmation
Combine with other indicators
Issue 3: Trend line doesn't match price action
Solution:
Adjust EMA length to match your trading style
Consider using different trend detection (SMA, HMA)
Issue 4: Indicator not showing on chart
Solution:
Check if it's in a separate pane - move to main chart
Refresh the page
Re-add the indicator
Issue 5: Counter seems delayed
Solution:
This is normal - indicator confirms on candle close
For faster signals, use lower timeframe
Reduce EMA length (but expect more noise)
8. Advanced Tips 💡Combining with Other Indicators:Best Combinations:
RSI + Trend Candle Counter
Buy at +1 when RSI > 50
Sell at -1 when RSI < 50
MACD + Trend Candle Counter
Confirm +1 with MACD bullish crossover
Confirm -1 with MACD bearish crossover
Volume + Trend Candle Counter
Strong trends (+1) should have increasing volume
Low volume at high numbers (+10) = exhaustion
Reading Market Psychology: {scrollbar-width:none;-ms-overflow-style:none;-webkit-overflow-scrolling:touch;} ::-webkit-scrollbar{display:none}Counter ValueMarket Psychology+1 to +3Early adopters entering+4 to +7Momentum building+8 to +12FOMO phase+13+Extreme greed - caution!-1 to -3Early sellers-4 to -7Panic building-8 to -12Capitulation-13+Extreme fear - reversal likely9. Real Trading Example 📊Scenario: BTC/USD 1H ChartTime | Counter | Action
--------|---------|----------------------------------
10:00 | -8 | Downtrend established
11:00 | -9 | Still falling
12:00 | -10 | Exhaustion zone - watch closely
13:00 | +1 | ✅ BUY SIGNAL - Trend reversal!
14:00 | +2 | Confirmation - trend valid
15:00 | +3 | Hold position
16:00 | +4 | Add to position (optional)
17:00 | +5 | Move stop loss to breakeven
...
22:00 | +11 | Take partial profits
23:00 | +12 | Tighten stop loss
00:00 | -1 | ❌ EXIT - Trend reversed
Alerte Croisement EMA9 & SMA12 (Zone remplie)📊 Moving Average 1
Period: 9 → The average is calculated over the last 9 candles (or time periods).
Shift: 0 → No shift; the average is aligned with the current data.
Method: Exponential → Uses an Exponential Moving Average (EMA), which gives more weight to recent data.
Apply to: Close → The average is based on the closing price of each candle.
📊 Moving Average 2
Period: 12 → Calculated over the last 12 periods.
Shift: 0 → No shift.
Method: Simple → Uses a Simple Moving Average (SMA), which gives equal weight to each period.
Apply to: Close → Based on closing prices.
