ICT IPDAGuided by ICT tutoring, I create this versatile indicator "IPDA".
This indicator shows a different way of viewing the “IPDA” by calculating from START
(-20 / -40 / -60) to (+20 /+40 /+60) Days, showing the Highs and Lows of the IPDA of the Previous days and both of the subsequent ones, the levels of (-20 / -40 / -60) Days can be taken into consideration as objectives to be achieved in the range of days (+20 /+40 /+60)
The user has the possibility to:
- Choose whether to display IPDAs before and after START
- Choose to show High and Low levels
- Choose to show Prices
The indicator should be used as ICT shows in its concepts.
Example on how to evaluate a possible Start IPDA:
Example for Entry targeting IPDAs :
If something is not clear, comment below and I will reply as soon as possible.
Cerca negli script per "股价站上60月线"
Swing Action PriceEnglish:
**Description of "Swing Action Price" TradingView Script**
"Swing Action Price" is a custom technical indicator designed to identify swing highs and swing lows in a financial market. The script calculates and plots various lines on the chart to visualize these swing points. Swing highs are points where the price has made a local peak, while swing lows are points where the price has made a local trough.
The indicator displays the following lines on the chart:
1. Dotted lines representing each individual swing high and swing low identified on different timeframes (10, 30, 60, 100, 150, 200, 700, and 1000 bars).
2. Dotted lines representing the most recent swing high and swing low for the current bar.
How the indicator works:
1. The script uses historical price data to calculate swing highs and swing lows based on specific conditions.
2. For each of the mentioned timeframes, the indicator identifies the highest high and lowest low within a defined number of bars (10, 30, 60, etc.).
3. Once a new swing high or swing low is identified, the corresponding dotted lines are drawn on the chart, extending from the previous swing point to the current one.
The "Swing Action Price" indicator can be used by traders to visually identify key support and resistance levels in the market. It helps them recognize potential trend reversals or continuation points, which may be valuable for making trading decisions.
Please note that trading indicators should always be used in conjunction with other technical and fundamental analysis tools to make informed trading choices. The "Swing Action Price" indicator is offered under the Mozilla Public License 2.0, and the developer's username is "damianjorgeportillo."
Remember that past performance is not indicative of future results, and it's essential to exercise caution and apply risk management strategies when trading financial markets.
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Spanish:
**Descripción del Script "Swing Action Price" en TradingView**
"Swing Action Price" es un indicador técnico personalizado diseñado para identificar máximos y mínimos en un mercado financiero. El script calcula y muestra diversas líneas en el gráfico para visualizar estos puntos de inflexión. Los máximos se producen cuando el precio alcanza un pico local, mientras que los mínimos ocurren cuando el precio alcanza un valle local.
El indicador muestra las siguientes líneas en el gráfico:
1. Líneas punteadas que representan cada máximo y mínimo individual identificado en diferentes marcos de tiempo (10, 30, 60, 100, 150, 200, 700 y 1000 barras).
2. Líneas punteadas que representan el máximo y mínimo más reciente para la barra actual.
Cómo funciona el indicador:
1. El script utiliza datos históricos de precios para calcular los máximos y mínimos en función de ciertas condiciones.
2. Para cada uno de los marcos de tiempo mencionados, el indicador identifica el máximo más alto y el mínimo más bajo dentro de un número específico de barras (10, 30, 60, etc.).
3. Una vez que se identifica un nuevo máximo o mínimo, se dibujan las líneas punteadas correspondientes en el gráfico, extendiéndose desde el punto de inflexión anterior hasta el actual.
El indicador "Swing Action Price" puede ser utilizado por traders para identificar visualmente niveles clave de soporte y resistencia en el mercado. Ayuda a reconocer posibles puntos de inversión o continuación de tendencia, lo que puede ser valioso para tomar decisiones comerciales.
Por favor, ten en cuenta que los indicadores de trading siempre deben utilizarse junto con otras herramientas de análisis técnico y fundamental para tomar decisiones comerciales informadas. El indicador "Swing Action Price" se ofrece bajo la Licencia Pública de Mozilla 2.0, y el nombre de usuario del desarrollador es "damianjorgeportillo".
Recuerda que el rendimiento pasado no garantiza resultados futuros, y es esencial ser cauteloso y aplicar estrategias de gestión de riesgos al operar en los mercados financieros.
Crypto Trend IndicatorThe Crypto Trend Indicator is a trend-following indicator specifically designed to identify bullish and bearish trends in the price of Bitcoin, and other cryptocurrencies. This indicator doesn't provide explicit instructions on when to buy or sell, but rather offers an understanding of whether the trend is bullish or bearish. It's important to note that this indicator is only useful for trend trading.
The band is a visual representation of the 30-day and 60-day Exponential Moving Average (EMA). When the 30-day EMA is above the 60-day EMA, the trend is bullish and the band is green. When the 30-day EMA is below the 60-day EMA, the trend is bearish and the band is red. When the 30-day EMA starts to converge with the 60-day EMA, the trend is neutral and the band is grey.
The line is a visual representation of the 20-week Simple Moving Average (SMA) in the daily timeframe. "Bull" and "Bear" signals are generated when the 20-day EMA is either above or below the 20-week SMA, in conjunction with a bullish or bearish trend. When the band is green and the 20-day EMA is above the 20-week SMA, a “Bull” signal emerges. When the band is red and the 20-day EMA is below the 20-week SMA, a “Bear” signal emerges. The 20-week SMA can potentially also function as a leading indicator, as substantial price deviations from the SMA typically indicate an overextended market.
While this indicator has traditionally identified bullish and bearish trends in various cryptocurrency assets, past performance does not guarantee future results. Therefore, it is advisable to supplement this indicator with other technical tools. For instance, range-bound indicators can greatly improve the decision-making process when planning for entries and exits points.
Biddles OI Weighted Average PriceAhoy!
This script calculates Open Interested Weighted Average Price for the following lookback periods:
- 7, 30, 60
e.g. On the 1D chart, you will see OIWAP for the past 7, 30, and 60 days. It works on any timeframe though.
It works with any ticker that TV's OI indicator supports, and has ticker override if you are looking at an exchange that's unsupported, but for an asset that is.
e.g. If you're looking at Bybit's BTCUSDT.P which is unsupported- you can override to get OI data from Binance's BTCUSDT.P which is supported.
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Open-Sourced + Crowd Sourcing Goals
=====
I am open sourcing this in hopes we can work together to find interesting signal/observation, and make the script better.
The only way I could think of to calculate the OIWAP for the lookback periods was to manually factor in each period in the formula.
e.g. For the 60-period lookback, it's manually taking price and OI for each individual period.
I am also hoping other folks will make interesting observations.
With the few hours I've spent thus far, they seem to operate much like MA bands, with crossovers having similar implications.
But I feel like there are many other observations left unnoticed!
If you find any, hmu on twitter: @thalamu_
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Interesting Calculations in the Script, but not Plotted on the Chart
=====
There are calculations for up to 60 days of OIWAP taking change in OI rather than just OI.
There's one set for absolute value of change in OI, and one set for raw change in OI.
I didn't notice anything spectacularly interesting - but perhaps you will if you tinker with it!
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Find something cool? Have an improvement?
=====
Hmu on twitter: @thalamu_
RSI Candle Advanced V2RSI Advanced
As the period value is longer than 14, the RSI value sticks to the value of 50 and becomes useless.
Also, when the period value is less than 14, it moves excessively, so it is difficult for us to see the movement of the RSI .
So, using the period value and the RSI value as variables, I tried to make it easier to identify the RSI value through a new function expression.
This is how RSI Advanced was developed.
Period below 14 reduce the volatility of RSI , and period above 14 increase the volatility of RSI, allowing overbought and oversold zones to work properly and give you a better view of the trend.
By applying the custom algorithm so that the 'RSI Advanced' with period on a 5-minute timeframe has the same value as the 'original RSI' with period on a 60-minute timeframe.
As another example, an 'RSI Advanced' with a period in a 60-minute time frame has the same value as an 'original RSI' with a period in a 240-minute time frame.
Compare the difference in the RSI with a period value of 200 in the snapshot.
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RSI Candlestick
RSI derives its value using only the closing price as a variable.
I solved the RSI equation in reverse and tried to include the high and low prices of candlesticks in the equation.
As a result, 'if the high or low was the closing price, the value of RSI would be like this' was implemented.
Just like when a candle comes down after setting a high price, an upper tail is formed when RSI Candle goes down after setting a high price!!
In divergence, we had to look only at the relationship between closing prices, but if we use RSI candles, we can find divergences in highs and highs, and lows and lows.
Existing indicators could not express "gap", but Version 2 made it possible to express "gap"!!!!!!
RSI can be displayed as candlesticks, bars and lines
Then enjoy my RSI!
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RSI Advanced
기간값이 14보다 길어질수록 RSI값은 50값에 달라붙게 되어서 쓸모가 없어집니다.
또 기간값이 14보다 줄어들수록 과도하게 움직여서 우리는 RSI의 움직임을 보기가 힘듭니다.
그래서 기간 값과 RSI 값을 변수로 사용하여 새로운 함수 식을 통해 RSI 값을 식별하기 편하도록 해보았습니다.
이렇게 RSI Advanced가 개발되었습니다.
기간값이 14보다 낮으면 rsi의 변동폭이 줄어들고, 기간값이 14보다 크면 변동폭이 넓어져 과매수 및 과매도 영역이 제대로 작동하여 추세를 더 잘 볼 수 있습니다.
또한 저는 5분 타임프레임의 기간값이 168(=14*12)인 RSI가 주기 값이 14인 60분 타임프레임의 RSI와 동일한 값을 갖도록 적절한 함수 표현식을 적용하여 RSI를 변경했습니다.
다른 예로, 15분 시간 프레임에서 기간값이 56(=14*4)인 RSI는 60분 시간 프레임의 기간값이 14인 RSI와 동일한 값을 갖습니다.
기간값이 200인 RSI의 차이를 스냅샷에서 비교해보십시오.
-----------------------------
RSI Candlestick
RSI는 종가만을 변수로 사용하여 값을 도출해냅니다.
저는 RSI 식을 역으로 풀어내어서 캔들스틱의 고가와 저가, 시가를 식에 포함시켜보았습니다.
결과적으로, '만약 고가나 저가가 종가였다면 RSI의 값이 이럴것이다'를 구현해내었습니다.
캔들이 고가를 찍고 내려오면 윗꼬리가 생기듯 RSI Candle에서도 고가를 찍고 내려오면 윗꼬리가 생기는겁니다!!
다이버전스 또한 원래는 종가끼리의 관계만 봐야했지만 RSI 캔들을 이용한다면 고가와 고가, 저가와 저가에서도 다이버전스를 발견할 수 있습니다.
기존의 지표는 "갭"을 표현하지 못했지만 Version 2 에서는 "갭"을 표현할 수 있게 만들었습니다!!!!!!
그럼 잘 사용해주십시오!!!
PIVOT STRATEGY [INDIAN MARKET TIMING]
A Back-tested Profitable Strategy for Free!!
A PIVOT INTRADAY STRATEGY for 5 minute Time-Frame , that also explains the time condition for Indian Markets
The Timing can be changed to fit other markets, scroll down to "TIME CONDITION" to know more.
The commission is also included in the strategy .
The basic idea is when ,
1) Price crosses above ema1 ,indicated by pivot highest line in green color .
2) Price crosses below ema1 ,indicated by pivot lowest line in red color .
3) Candle high crosses above pivot highest , is the Long condition .
4) Candle low crosses below pivot lowest , is the Short condition .
5) Maximum Risk per trade for the intraday trade can be changed .
6) Default_qty_size is set to 60 contracts , which can be changed under settings → properties → order size .
7) ATR is used for trailing after entry, as mentioned in the inputs below.
// ═════════════════════════//
// ————————> INPUTS <————————— //
// ═════════════════════════//
Leftbars —————> Length of pivot highs and lows
Rightbars —————> Length of pivot highs and lows
Price Cross Ema —————> Added condition
ATR LONG —————> ATR stoploss trail for Long positions
ATR SHORT —————> ATR stoploss trail for Short positions
RISK —————> Maximum Risk per trade for the day
The strategy was back-tested on RELIANCE ,the input values and the results are mentioned under "BACKTEST RESULTS" below .
// ═════════════════════════ //
// ————————> PROPERTIES<——————— //
// ═════════════════════════ //
Default_qty_size ————> 60 contracts , which can be changed under settings
↓
properties
↓
order size
// ═══════════════════════════════//
// ————————> TIME CONDITION <————————— //
// ═══════════════════════════════//
The time can be changed in the script , Add it → click on ' { } ' → Pine editor→ making it a copy [right top corner} → Edit the line 25 .
The Indian Markets open at 9:15am and closes at 3:30pm .
The 'time_cond' specifies the time at which Entries should happen .
"Close All" function closes all the trades at 3pm, at the open of the next candle.
To change the time to close all trades , Go to Pine Editor → Edit the line 103 .
All open trades get closed at 3pm , because some brokers don't allow you to place fresh intraday orders after 3pm .
NSE:RELIANCE
// ═══════════════════════════════════════════════ //
// ————————> BACKTEST RESULTS ( 128 CLOSED TRADES )<————————— //
// ═══════════════════════════════════════════════ //
INPUTS can be changed for better back-test results.
The strategy applied to NIFTY ( 5 min Time-Frame and contract size 60 ) gives us 60% profitability y , as shown below
It was tested for a period a 6 months with a Profit Factor of 1.45 ,net Profit of 21,500Rs profit .
Sharpe Ratio : 0.311
Sortino Ratio : 0.727
The graph has a Linear Curve with consistent profits .
The INPUTS are as follows,
1) Leftbars ————————> 3
2) Rightbars ————————> 5
3) Price Cross Ema ——————> 150
4) ATR LONG ————————> 2.7
5) ATR SHORT ———————> 2.9
6) RISK —————————> 2500
7) Default qty size ——————> 60
NSE:RELIANCE
Save it to favorites.
Apply it to your charts Now !!
↓
FOLLOW US FOR MORE !
Thank me later ;)
RSI Trend Heatmap in Multi TimeframesRSI Trend Heatmap in Multi Timeframes
Description
Sometimes you want to look at the RSI Trend across multiple time frames.
You have to waste time browsing through them.
So we've put together every time frame you want to see in one indicator.
We have 10 layers of RSI Trend heatmap available for you.
You can set the timeframe as you want on the Settings page.
Description of Parameter RSI Setting ** You can change it by setting.
RSI Trend Length : (Default 50)
Source : (Default close)
RSI Sideways Length : (Default 2 = RSI between 48 .. 52)
Description of Parameter RSI Timeframe ** You can change it by setting.
""=None,
"M"=1Month, "2W"=2Weeks, "W"=1Week,
"3D"=3Days, "2D"=2Days, "D"=1Day,
"720"=12Hours, "480"=4Hours, "240"=4Hours, "180"=3Hours, "120"=2Hours,
"60"=60Minutes, "30"=30Minutes, "15"=15Minutes, "5"=5Minutes, "1"=1Minute
Default Configurate of RSI Timeframe (for a time frame of 1 hour to 1 day)
"W"= Timeframe 1 month shown in line 90-100 --> Represent Long Trend of RSI
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"D2"= Timeframe 2 days shown in line 70-80 --> Represent Trend of RSI
"D"= Timeframe 1 day shown in line 60-70 --> Represent Trend of RSI
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"240"= Timeframe 3 hours shown in line 40-50 --> Represent Signal Up/Signal Down/Divergence of RSI
"120"= Timeframe 2 hours shown in line 30-40 --> Represent Signal Up/Signal Down/Divergence of RSI
"60"= Timeframe 1 hour shown in line 20-30 --> Represent Signal Up/Signal Down/Divergence of RSI
"30"= Timeframe 30 minutes shown in line 10-20 --> Represent Signal Up/Signal Down/Divergence of RSI
"15"= Timeframe 15 minutes shown in line 00-10 --> Represent Signal Up/Signal Down/Divergence of RSI
Description of Colors
Dark Bule = Extreme Uptrend / Overbought / Bull Market (RSI > 67)
Light Bule = Uptrend (RSI between 50-52 .. 67)
Yellow = Sideways Trend / Trend Reversal (RSI between 48 .. 52) ** You can change it by setting.
Light Red = Downtrend (RSI between 33 .. 48-50)
Dark Red = Extreme Downtrend / Oversold / Bear Market (RSI < 33)
How to use
1. You must first know what the main trend of the RSI is (look at the 60-80 line). If it is red, it is a downtrend. and if it's blue shows that it is an uptrend
2. Throughout the period of the main trend There will always be a reversal of the sub-trend. (Can see from the 0-50 line), but eventually will return to follow the main trend.
3. Unless the sub trend persists for a long time until the main trend changes.
Runners & Laggers (scanner)Firstly, seems to me this may only work with crypto but I know nothing about the other sectors so i could be wrong. I was trying to think up a good way to find moving coins(other than by volume bc theres holes in the results when using it this way). Thought this was an interesting concept so decided to publish it as I've seen no others like it (though i did not extensively search for it. We need to start with a little Tradingview(TV) common knowledge. When there is no update of trades/volume in a candle TV does not print the candle. So when looking at (let's say) a 1 second chart, if the coin being observed by the user has no update from a trade in the time of that 1 sec candle it is skipped over. This means that a coin with a ton of volume might fill an entire 60 seconds with 60 candles and conversely with a low volume coin there could be as little as 0 1-second candles. BUT even for normally low volume coins, when a pump is beginning with the coin it could literally go from 0 1-second candles within a minute to 60 1-second candles within the next minute. ***NOTE: This DOES NOT show ANY information if the coin is going up or down but rather that a LOT more trading volume is occurring than normal.*** What this script does is scans (via request.security feature) up to 40 coins at a time and counts how many candles are printed within a user set timespan calculated in minute. 1 candle print per incremented timeframe that the chart is on. ie. if the chart is a 1 min chart it counts how many 1 min candles are printed. So, (as is in the captured image for the script) if you wanted to count how many 5 second candles are printed for each coin in 1 min then you would have to put the charts timeframe on 5sec and the setting titled 'Window of TIME(in minutes) to count bars' as 1.0 (which bc it's in minutes 1.0m = 60sec and bc 60s / 5s = 12 there would be 12 possible values that each coin can be at depending on how many bars are counted within that 1min/60sec. *** I will update to show an image of what I'm talking about here. Now, the exchange I'm scanning here is Kucoin's Margin Coins. There are 170 something coins total but I removed a few i didn't care for to make it a round 40 coins per set (there being 4 sets of 40 coins total=160 coins being scanned). To scan all 4 sets the indicator must be added 4 times to the chart and a different 'set' selected for each iteration of the script on the chart. Free users can only scan 3 at the most. All others can scan all 4 sets. In the script you can change the exchange and coins as necessary. If there done so and there are not 40 coins total just put '' '' in the extra coins spots that are not filled and the script will skip over these blankly filled spots. The suffix (traded pair) for the tickerID on all Kucoin's Margin Coin's is USDT so that's what i have inputted in the main function on line 46 (will need to be changed if that differs from the coins you want to scan. Next in the line of settings is 'Window of TIME(in minutes) to count bars' which has already been discussed. Following that is the setting "Table Shows" which the results are all in a table and the table will present the coins that have either "Passed" or "Failed" depending on which you choose. The next setting determines what passes or fails. If there are 12 possible rows for the coins to be in (as described above) then this setting is the "Pass/Fail Cutoff" level. So if you want to show all the coins that are in rows 11 and 12 (as in the image at top) then 11 should be selected here. At this point you will see all the coins that have a lot of volume in them. Finding coin names in the table that are usually not with a ton of volume will present your present movers. NOTE: coins like BTC and ETH will almost always be in these levels so it does not indicate anything different from the norm of these coins. Last setting is the ability to show the table on the main window or not. Hope you enjoy and find use in it. BTW this screener format is the same as the others I have published. If you like, check those out too. If you find difficulty using then refer to those as well as they have additional info in them on how to use the scanner and its format. Lastly, in the script is the ability to print the plots and labels but I commented them out bc its really just a jumbled mess. In the commented out sections there is a Random Color Function (provided by @hewhomustnotbenamed which was developed on the basis of Function-HSL-color by @RicardoSantos. All right, peace brothers....and sisters.
**** Also, I see how the "levels" could be confusing so I will put them into a % format soon (probably not today) so that the "Pass/Fail Cutoff" can be in % format so that if "passed" is chosen and 50% is chosen (in the new setting that will be changed) then it'll show you all the coins that have more than 50% of the bars printed within the time window chosen. Goodluck in all your trading adventures. ChasinAlts out.
P/L panelThis is not a indicator or strategy.
I thought of having a table showing running profit or loss on chart from a specific price.
I tried to put the same in code and ended up with this code.
This is a table showing the running profit or loss from a manually specified price and quantity.
when you add the code, This table asks us to input the entry price and quantity.
It will calculate the running profit or loss with respect to running price and puts that in the table.
We will have to input two things.
1.) entry price: the price at which a position(long/short) is taken.
2.) Quantity: A +value need to be entered for Long position and -value for short position.
code detects whether its a long position or short position based on the quantity info.
for example if a LONG position is taken at a price 60 of 100 quantity,
then in price we need to enter 60
and in quantity 100 (+ve value)
for SHORT position at a price of 60 of 100 quantity,
in price we need to enter 60
and in quantity -100 (-ve value)
once the table is added to the chart.
Just double click on the table, it will open the settings tab and we can provide new inputs price/quantity/position.
positioning of table is optional and all possible positioning options are provided.
Advise further improvements required if any in this code.
This piece of code can be used along with any indicator.
For which we may need to use valuewhen() additionally.
Try it yourself and ping me if required.
RSI Candle with Advanced RSI fomulaRSI Advanced
As the period value is longer than 14, the RSI value sticks to the value of 50 and becomes useless.
Also, when the period value is less than 14, it moves excessively, so it is difficult for us to see the movement of the RSI.
So, using the period value and the RSI value as variables, I tried to make it easier to identify the RSI value through a new function expression.
This is how RSI Advanced was developed.
Period values below 14 reduce the volatility of RSI, and period values greater than 14 allow wider fluctuations, allowing overbought and oversold zones to work properly and give you a better view of the trend.
I also changed the RSI by applying the appropriate function expression so that the RSI with a period value of 168 (=14*12) on a 5 minute timeframe has the same value as the RSI on a 60 minute timeframe with a period value of 14.
As another example, an RSI with a period value of 56 (=14*4) in a 15-minute time frame has the same value as an RSI with a period value of 14 in a 60-minute time frame.
Compare the difference in the RSI with a period value of 200 in the snapshot.
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RSI Candlestick
RSI derives its value using only the closing price as a variable. I solved the RSI equation in reverse and tried to include the high and low prices of candlesticks in the equation.
As a result, 'if the high or low was the closing price, the value of RSI would be like this' was implemented. Just like when a candle comes down after setting a high price, an upper tail is formed when RSI Candle goes down after setting a high price!!
In divergence, we had to look only at the relationship between closing prices, but if we use RSI candles, we can find divergences in highs and highs, and lows and lows.
Then enjoy my RSI!
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RSI Advanced
기간값이 14보다 길어질수록 RSI값은 50값에 달라붙게되어서 쓸모가 없어집니다.
또 기간값이 14보다 줄어들수록 과도하게 움직여서 우리는 RSI의 움직임을 보기가 힘듭니다.
그래서 기간 값과 RSI 값을 변수로 사용하여 새로운 함수 식을 통해 RSI 값을 식별하기 편하도록 해보았습니다.
이렇게 RSI Advanced가 개발되었습니다.
기간값이 14보다 낮으면 rsi의 변동폭이 줄어들고, 기간값이 14보다 크면 변동폭이 넓어져 과매수 및 과매도 영역이 제대로 작동하여 추세를 더 잘 볼 수 있습니다.
또한 저는 5분 타임프레임의 기간값이 168(=14*12)인 RSI가 주기 값이 14인 60분 타임프레임의 RSI와 동일한 값을 갖도록 적절한 함수 표현식을 적용하여 RSI를 변경했습니다.
다른 예로, 15분 시간 프레임에서 기간값이 56(=14*4)인 RSI는 60분 시간 프레임의 기간값이 14인 RSI와 동일한 값을 갖습니다.
기간값이 200인 RSI의 차이를 스냅샷에서 비교해보십시오.
-----------------------------
RSI Candlestick
RSI는 종가만을 변수로 사용하여 값을 도출해냅니다. 저는 RSI 식을 역으로 풀어내어서 캔들스틱의 고가와 저가를 식에 포함시켜보았습니다.
결과적으로, '만약 고가나 저가가 종가였다면 RSI의 값이 이럴것이다'를 구현해내었습니다. 캔들이 고가를 찍고 내려오면 윗꼬리가 생기듯 RSI Candle에서도 고가를 찍고 내려오면 윗꼬리가 생기는겁니다!!
다이버전스 또한 원래는 종가끼리의 관계만 봐야했지만 RSI 캔들을 이용한다면 고가와 고가, 저가와 저가에서도 다이버전스를 발견할 수 있습니다.
그럼 잘 사용해주십시오!!!
Modified RSI Multi-Time Frame (HM)Effective RSI with Multi-Timeframe with Hilema - Milega(HM) concept (HM=WMA -EMA). RSI Script is included with WMA and EMA band for RSI1 and it works very simple
i) When the RSI band turns to Green its a Buy signal. Normally whenever Bearish strength weakens and move towards the Bullish area, the WMA and EMA cross each other and that tends to provide a possible trend change. A trade at crossover normally provides a very good trading oppertunity. One can combine with some other Price action if needed for double confirmation.
ii)When RSI band turns to RED its a Sell signal. As explained in the point 1 , its a vice-versa where a crossover of WMA and EMA is perfect entry to get a good swing trade. Once can combine this tool with Price action for double confirmation.
iii) Using the Multi timeframe user could able to find the trend at higher timeframe to take double confirm on the trend strength and take a perfect oppertunity to take the trade.
By default, script uses the RSI with length 14, WMA 21 and EMA 3 which perfectly working for Index in NSE. Please change as per your requirement.
Apart from the above band, RSI is not have the different levels like 20/ 40 /50/60/80
Multi-timeframes currently set as
RSI1 - Same as Chart
RSI2 - 15 Min
RSI3 - 60 Min
RSI4 - Daily
Script has enabled the option to change the values for these timeframes as per the user requirement.
These ranges can be interpreted and acts as a probable swing points based on the trend and momentum.
40-60 - Neutral Range or Sideways
20 - 60 Bearish range
40 - 70 - Bullish range
Below 20 -- Over Sold Zone
Above 80 - over Bought zone
Also, the crossovers of the WMA and EMA on the RSI gives a very good momentum towards that trend.
[kai]Futility RatioAn indicator that measures movement inefficiency
Inefficient movement, that is, the range market becomes a high number, the limit is reached at about 60 and a trend occurs
When the range breaks and a trend occurs, the inefficiency drops to about 40 and many trends end.
The full-scale trend goes down further and goes down to about 25, which is evaluated as an efficient movement, the limit is reached and the trend ends.
As for how to use this Inge, the direction of the trend needs to be considered in other ways.
Create a position when you reach 60
Position closed or contrarian at 40 or 25
I assume the usage
動きの非効率性を測定するインジケーターです
非効率な動きをするつまりレンジ相場は高い数字になって、60程度で限界が訪れてトレンドが発生します
レンジがブレイクしトレンドが発生すると40程度まで非効率性は下がりって多くのトレンドは終了します
本格的なトレンドはさらに下がっていって効率的な動きと評価される25程度まで下がって限界が訪れてトレンドが終了します
このインジの使い方はトレンドの方向は他の方法で考える必要がありますが
60まで上がったときにポジション作成
40又は25でポジションクローズ又は逆張り
という使い方を想定しています
Waindrops [Makit0]█ OVERALL
Plot waindrops (custom volume profiles) on user defined periods, for each period you get high and low, it slices each period in half to get independent vwap, volume profile and the volume traded per price at each half.
It works on intraday charts only, up to 720m (12H). It can plot balanced or unbalanced waindrops, and volume profiles up to 24H sessions.
As example you can setup unbalanced periods to get independent volume profiles for the overnight and cash sessions on the futures market, or 24H periods to get the full session volume profile of EURUSD
The purpose of this indicator is twofold:
1 — from a Chartist point of view, to have an indicator which displays the volume in a more readable way
2 — from a Pine Coder point of view, to have an example of use for two very powerful tools on Pine Script:
• the recently updated drawing limit to 500 (from 50)
• the recently ability to use drawings arrays (lines and labels)
If you are new to Pine Script and you are learning how to code, I hope you read all the code and comments on this indicator, all is designed for you,
the variables and functions names, the sometimes too big explanations, the overall structure of the code, all is intended as an example on how to code
in Pine Script a specific indicator from a very good specification in form of white paper
If you wanna learn Pine Script form scratch just start HERE
In case you have any kind of problem with Pine Script please use some of the awesome resources at our disposal: USRMAN , REFMAN , AWESOMENESS , MAGIC
█ FEATURES
Waindrops are a different way of seeing the volume and price plotted in a chart, its a volume profile indicator where you can see the volume of each price level
plotted as a vertical histogram for each half of a custom period. By default the period is 60 so it plots an independent volume profile each 30m
You can think of each waindrop as an user defined candlestick or bar with four key values:
• high of the period
• low of the period
• left vwap (volume weighted average price of the first half period)
• right vwap (volume weighted average price of the second half period)
The waindrop can have 3 different colors (configurable by the user):
• GREEN: when the right vwap is higher than the left vwap (bullish sentiment )
• RED: when the right vwap is lower than the left vwap (bearish sentiment )
• BLUE: when the right vwap is equal than the left vwap ( neutral sentiment )
KEY FEATURES
• Help menu
• Custom periods
• Central bars
• Left/Right VWAPs
• Custom central bars and vwaps: color and pixels
• Highly configurable volume histogram: execution window, ticks, pixels, color, update frequency and fine tuning the neutral meaning
• Volume labels with custom size and color
• Tracking price dot to be able to see the current price when you hide your default candlesticks or bars
█ SETTINGS
Click here or set any impar period to see the HELP INFO : show the HELP INFO, if it is activated the indicator will not plot
PERIOD SIZE (max 2880 min) : waindrop size in minutes, default 60, max 2880 to allow the first half of a 48H period as a full session volume profile
BARS : show the central and vwap bars, default true
Central bars : show the central bars, default true
VWAP bars : show the left and right vwap bars, default true
Bars pixels : width of the bars in pixels, default 2
Bars color mode : bars color behavior
• BARS : gets the color from the 'Bars color' option on the settings panel
• HISTOGRAM : gets the color from the Bearish/Bullish/Neutral Histogram color options from the settings panel
Bars color : color for the central and vwap bars, default white
HISTOGRAM show the volume histogram, default true
Execution window (x24H) : last 24H periods where the volume funcionality will be plotted, default 5
Ticks per bar (max 50) : width in ticks of each histogram bar, default 2
Updates per period : number of times the histogram will update
• ONE : update at the last bar of the period
• TWO : update at the last bar of each half period
• FOUR : slice the period in 4 quarters and updates at the last bar of each of them
• EACH BAR : updates at the close of each bar
Pixels per bar : width in pixels of each histogram bar, default 4
Neutral Treshold (ticks) : delta in ticks between left and right vwaps to identify a waindrop as neutral, default 0
Bearish Histogram color : histogram color when right vwap is lower than left vwap, default red
Bullish Histogram color : histogram color when right vwap is higher than left vwap, default green
Neutral Histogram color : histogram color when the delta between right and left vwaps is equal or lower than the Neutral treshold, default blue
VOLUME LABELS : show volume labels
Volume labels color : color for the volume labels, default white
Volume Labels size : text size for the volume labels, choose between AUTO, TINY, SMALL, NORMAL or LARGE, default TINY
TRACK PRICE : show a yellow ball tracking the last price, default true
█ LIMITS
This indicator only works on intraday charts (minutes only) up to 12H (720m), the lower chart timeframe you can use is 1m
This indicator needs price, time and volume to work, it will not work on an index (there is no volume), the execution will not be allowed
The histogram (volume profile) can be plotted on 24H sessions as limit but you can plot several 24H sessions
█ ERRORS AND PERFORMANCE
Depending on the choosed settings, the script performance will be highly affected and it will experience errors
Two of the more common errors it can throw are:
• Calculation takes too long to execute
• Loop takes too long
The indicator performance is highly related to the underlying volatility (tick wise), the script takes each candlestick or bar and for each tick in it stores the price and volume, if the ticker in your chart has thousands and thousands of ticks per bar the indicator will throw an error for sure, it can not calculate in time such amount of ticks.
What all of that means? Simply put, this will throw error on the BITCOIN pair BTCUSD (high volatility with tick size 0.01) because it has too many ticks per bar, but lucky you it will work just fine on the futures contract BTC1! (tick size 5) because it has a lot less ticks per bar
There are some options you can fine tune to boost the script performance, the more demanding option in terms of resources consumption is Updates per period , by default is maxed out so lowering this setting will improve the performance in a high way.
If you wanna know more about how to improve the script performance, read the HELP INFO accessible from the settings panel
█ HOW-TO SETUP
The basic parameters to adjust are Period size , Ticks per bar and Pixels per bar
• Period size is the main setting, defines the waindrop size, to get a better looking histogram set bigger period and smaller chart timeframe
• Ticks per bar is the tricky one, adjust it differently for each underlying (ticker) volatility wise, for some you will need a low value, for others a high one.
To get a more accurate histogram set it as lower as you can (min value is 1)
• Pixels per bar allows you to adjust the width of each histogram bar, with it you can adjust the blank space between them or allow overlaping
You must play with these three parameters until you obtain the desired histogram: smoother, sharper, etc...
These are some of the different kind of charts you can setup thru the settings:
• Balanced Waindrops (default): charts with waindrops where the two halfs are of same size.
This is the default chart, just select a period (30m, 60m, 120m, 240m, pick your poison), adjust the histogram ticks and pixels and watch
• Unbalanced Waindrops: chart with waindrops where the two halfs are of different sizes.
Do you trade futures and want to plot a waindrop with the first half for the overnight session and the second half for the cash session? you got it;
just adjust the period to 1860 for any CME ticker (like ES1! for example) adjust the histogram ticks and pixels and watch
• Full Session Volume Profile: chart with waindrops where only the first half plots.
Do you use Volume profile to analize the market? Lucky you, now you can trick this one to plot it, just try a period of 780 on SPY, 2760 on ES1!, or 2880 on EURUSD
remember to adjust the histogram ticks and pixels for each underlying
• Only Bars: charts with only central and vwap bars plotted, simply deactivate the histogram and volume labels
• Only Histogram: charts with only the histogram plotted (volume profile charts), simply deactivate the bars and volume labels
• Only Volume: charts with only the raw volume numbers plotted, simply deactivate the bars and histogram
If you wanna know more about custom full session periods for different asset classes, read the HELP INFO accessible from the settings panel
EXAMPLES
Full Session Volume Profile on MES 5m chart:
Full Session Unbalanced Waindrop on MNQ 2m chart (left side Overnight session, right side Cash Session):
The following examples will have the exact same charts but on four different tickers representing a futures contract, a forex pair, an etf and a stock.
We are doing this to be able to see the different parameters we need for plotting the same kind of chart on different assets
The chart composition is as follows:
• Left side: Volume Labels chart (period 10)
• Upper Right side: Waindrops (period 60)
• Lower Right side: Full Session Volume Profile
The first example will specify the main parameters, the rest of the charts will have only the differences
MES :
• Left: Period size: 10, Bars: uncheck, Histogram: uncheck, Execution window: 1, Ticks per bar: 2, Updates per period: EACH BAR,
Pixels per bar: 4, Volume labels: check, Track price: check
• Upper Right: Period size: 60, Bars: check, Bars color mode: HISTOGRAM, Histogram: check, Execution window: 2, Ticks per bar: 2,
Updates per period: EACH BAR, Pixels per bar: 4, Volume labels: uncheck, Track price: check
• Lower Right: Period size: 2760, Bars: uncheck, Histogram: check, Execution window: 1, Ticks per bar: 1, Updates per period: EACH BAR,
Pixels per bar: 2, Volume labels: uncheck, Track price: check
EURUSD :
• Upper Right: Ticks per bar: 10
• Lower Right: Period size: 2880, Ticks per bar: 1, Pixels per bar: 1
SPY :
• Left: Ticks per bar: 3
• Upper Right: Ticks per bar: 5, Pixels per bar: 3
• Lower Right: Period size: 780, Ticks per bar: 2, Pixels per bar: 2
AAPL :
• Left: Ticks per bar: 2
• Upper Right: Ticks per bar: 6, Pixels per bar: 3
• Lower Right: Period size: 780, Ticks per bar: 1, Pixels per bar: 2
█ THANKS TO
PineCoders for all they do, all the tools and help they provide and their involvement in making a better community
scarf for the idea of coding a waindrops like indicator, I did not know something like that existed at all
All the Pine Coders, Pine Pros and Pine Wizards, people who share their work and knowledge for the sake of it and helping others, I'm very grateful indeed
I'm learning at each step of the way from you all, thanks for this awesome community;
Opensource and shared knowledge: this is the way! (said with canned voice from inside my helmet :D)
█ NOTE
This description was formatted following THIS guidelines
═════════════════════════════════════════════════════════════════════════
I sincerely hope you enjoy reading and using this work as much as I enjoyed developing it :D
GOOD LUCK AND HAPPY TRADING!
EulerMethod: DeltaEN
Shows the Integral Volume Delta (IVD)
It is a detailed OBV. Each bar sums up the volume for bars of a shorter timeframe.
For example, inside a 1M bar, every 12h bar is added up, and inside a 1h bar, every 1min bar is added. Thus, a conditional volume delta inside the bar is obtained.
The indicator for each bar shows the volume of purchases (positive), sales (negative) and the difference — IVD
The delta histogram is thicker than the volume histograms
Settings detalisation
M — 6 hours, 12 hours and 1 day for the M timeframe (720 by default)
W — 4 hours, 6 hours and 12 hours for the W timeframe (240 by default)
D — 30 minutes, 1 hour and 2 hours for the D timeframe (60 by default)
H — 1 minute, 5 minutes and 15 minutes for timeframes [1h, D) (default is 1)
For timeframes of 15m and less, the calculation is carried out by minute bars
VSA mode
The classic OBV adds volume to the cumulative sum under the condition Сlose (n) > Close (n-1) and subtracts it under the condition Close (n) < Close (n-1)
When VSA mode is disabled, all volumes are summed up under these conditions.
When the VSA approximation is turned on, the volume per bar of detail is divided by the factor (Close - Low) / (High - Low)
That is, it takes into account the spread per bar and closing relative to the spread. VSA is enabled by default
A/D mode
Shows the cumulative Accumulation / Distribution Index
The delta of the detail bar is multiplied by (High + Low + Close) / 3 bars, the result is added to the cumulative sum
No additional price conversions required due to integral summation
Index line view is customizable
EM Delta does not receive intermediate values in real time.
To see the result, wait until the bar closes or switch to a smaller timeframe
RU
Показывает Интегральную Дельту Объёма (ИДО)
Представляет собой детализированный OBV. В каждом баре суммируется объём за бары меньшего таймфрейма.
Например, внутри 1М-бара суммируется каждый 12h-бар, а внутри 1h — каждый 1m-бар. Таким образом получается условная дельта объёма внутри бара
Индикатор на каждый бар показывает объём покупок (положительный), объём продаж (отрицательный) и разницу — ИДО
Гистограмма дельты толще гистограмм объёмов
Настройки детализации внутри бара
M — 6 часов, 12 часов и 1 день для таймфрейма M (по-умолчанию 720)
W — 4 часа, 6 часов и 12 часов для таймфрейма W (по-умолчанию 240)
D — 30 минут, 1 час и 2 часа для таймфрейма D (по-умолчанию 60)
H — 1 минута, 5 минут и 15 минут для таймфреймов [1h, D) (по-умолчанию 1)
Для таймфреймов 15m и меньше расчёт ведётся по минутным барам
Режим VSA
Классический OBV прибавляет объём к кумулятивной сумме при условии Сlose(n) > Close(n-1) и отнимает при условии Close(n) < Close(n-1)
При отключении режима VSA все объёмы суммируются по этим условиям
При включённой VSA-аппроксимации объём за бар детализации делится по фактору (Close - Low) / (High - Low)
То есть учитывает спред за бар и закрытие относительно спреда. По-умолчанию режим VSA включен
Режим A/D
Показывает кумулятивный индекс Накопления/Распределения
Дельта бара детализации умножается на (High + Low + Close) / 3 бара, результат прибавляется к кумулятивной сумме
Дополнительные преобразования цены не требуются ввиду интегрального суммирования
Вид линии индекса настраивается
EM Delta не получает промежуточные значения в реальном времени.
Чтобы увидеть результат, дождитесь закрытия бара или перейдите на меньший таймфрейм
ICHIMOKU MTFMultiple Time Frame Version of Ichimoku Kinko Hyo Indicator.
Created in 1940's by Goichi Hosoda withe the help of University students in Japan.
Ichimoku is one of the best trend following indicators that works nearly perfect in all markets and time frames.
Ichimoku is originally an built in indicator in Tradingview but there are some problems like:
the indicator hast 5 lines but you can change only 4 parameters in the settings menu of Tradingview Charts which you could only control 3 of the lines effectively. A second problem is that Tradingview preferred to use English titles for the ICHIMOKU lines instead of giving them the most common original Japanese ones. (So I rewrite the indicator)
Kijun Sen (blue line): Also called standard line or base line, this is calculated by averaging the highest high and the lowest low for the past 26 periods.
Tenkan Sen (red line): This is also known as the turning line and is derived by averaging the highest high and the lowest low for the past nine periods.
Chikou Span (Plum line): This is called the lagging line. It is today’s closing price plotted 26 periods behind.
Senkou SpanA (green line): The first Senkou line is calculated by averaging the Tenkan Sen and the Kijun Sen and plotted 26 periods ahead.
Senkou SpanB (purple line):
The second Senkou line is determined by averaging the highest high and the lowest low for the past 52 periods and plotted 26 periods ahead.
PERSONALLY I ADVISE YOU TO USE ICHIMOKU WITH DEAFULT LENGTHS (9,26,26,52,26) IN ORDER FOR STOCK MARKETS AND FOREX MARKETS
FOR CRYPTO YOU'D BETTER USE:
10,30,30,60,30 OR 20,60,60,120,60
THE TRICKY THING IS THAT KEEPING THE 1-3-3-6-3 RATIO CONSTANT IS NECESSARY
Here's a link of my Youtube video explaining ICHIMOKU but unfortunately only in TURKISH:
www.youtube.com
Developed by: Goichi Hosoda
Here's the link to a complete list of all my indicators:
tr.tradingview.com
Ichimoku kullanımı anlattığım detaylı video serisini linkten izleyebilirsiniz:
www.youtube.com
İndikatörü geliştiren: Goichi Hosoda
ICHIMOKU Kinko Hyo by KIVANC fr3762Created in 1940's by Goichi Hosoda withe the help of University students in Japan.
Ichimoku is one of the best trend following indicators that works nearly perfect in all markets and time frames.
Ichimoku is originally an built in indicator in Tradingview but there are some problems like:
the indicator hast 5 lines but you can change only 4 parameters in the settings menu of Tradingview Charts which you could only control 3 of the lines effectively. A second problem is that Tradingview preferred to use English titles for the ICHIMOKU lines instead of giving them the most common original Japanese ones. (So I rewrite the indicator)
Kijun Sen (blue line): Also called standard line or base line, this is calculated by averaging the highest high and the lowest low for the past 26 periods.
Tenkan Sen (red line): This is also known as the turning line and is derived by averaging the highest high and the lowest low for the past nine periods.
Chikou Span (Plum line): This is called the lagging line. It is today’s closing price plotted 26 periods behind.
Senkou SpanA (green line): The first Senkou line is calculated by averaging the Tenkan Sen and the Kijun Sen and plotted 26 periods ahead.
Senkou SpanB (purple line):
The second Senkou line is determined by averaging the highest high and the lowest low for the past 52 periods and plotted 26 periods ahead.
PERSONALLY I ADVISE YOU TO USE ICHIMOKU WITH DEAFULT LENGTHS (9,26,26,52,26) IN ORDER FOR STOCK MARKETS AND FOREX MARKETS
FOR CRYPTO YOU'D BETTER USE:
10,30,30,60,30 OR 20,60,60,120,60
THE TRICKY THING IS THAT KEEPING THE 1-3-3-6-3 RATIO CONSTANT IS NECESSARY
Here's a link of my Youtube video explaining ICHIMOKU but unfortunately only in TURKISH:
www.youtube.com
Developed by: Goichi Hosoda
Money Flow Index + AlertsThis study is based on the work of TV user Beasley Savage ( ) and all credit goes to them.
Changes I've made:
1. Added a visual symbol of an overbought/oversold threshold cross in the form of a red/green circle, respectively. Sometimes it can be hard to see when a cross actually occurs, and if your scaling isn't set up properly you can get misleading visuals. This way removes all doubt. Bear in mind they aren't meant as trading signals, so DO NOT use them as such. Research the MFI if you're unsure, but I use them as an early warning and that particular market/stock is added to my watchlist.
2. Added 60/40 lines as the MFI respects these incredibly well in trends. E.g. in a solid uptrend the MFI won't go below 40, and vice versa. Use the idea of support and resistance levels on the indicator and it'll be a great help. I've coloured the zones. Strong uptrends should stay above 60, strong downtrends should stay below 40. The zone in between 40-60 I've called the transition zone. MFI often stays here in consolidation periods, and in the last leg of a cycle/trend the MFI will often drop into this zone after being above 60 or below 40. This is a great sign that you should get out and start looking to reverse your position. Hopefully it helps to spot divergences as well.
3. Added alerts based on an overbought/oversold cross. Also added an alert for when either condition is triggered, so hopefully that's useful for those struggling with low alert limits. Feel free to change the overbought/oversold levels, the alerts + crossover visual are set to adapt.
Like any indicator, don't use this one alone. It works best paired with indicators/techniques that contradict it. You'll often see a OB/OS cross, and price will continue on it's way for many weeks more. But MFI is a great tool for identifying upcoming trend changes.
Any queries please comment or PM me.
Cheers,
RJR
PT Magic - BTC/ETH Trigger Trend ChangesThis Script shows you how BTC/ETH Percentage change in trends affect coins you trade.
- For ETH market please change BTC in Setting for ETH
What you need it PT Magic:
{
"Name": "BTC1h", // UNIQUE market trend name (to be referenced by your triggers below)
"Platform": "CoinMarketCap", // Platform to grab prices from (Allowed values are: CoinMarketCap, Exchange)
"MaxMarkets": 1, // Number of markets/pairs to analyze sorted by 24h volume
"TrendMinutes": 60 // Number of minutes to build a trend (1440 = 24h, 720 = 12h, 60 = 1h)
},
{
"Name": "ETH1h", // UNIQUE market trend name (to be referenced by your triggers below)
"Platform": "CoinMarketCap", // Platform to grab prices from (Allowed values are: CoinMarketCap, Exchange)
"MaxMarkets": 2, // Number of markets/pairs to analyze sorted by 24h volume
"IgnoredMarkets": "BTC",
"TrendMinutes": 60 // Number of minutes to build a trend (1440 = 24h, 720 = 12h, 60 = 1h)
},
VWAP forex Yesterday Hi/Low update fix This script is an updte fix of an earlier script that stopped functioning when TradingView updated Pine script. This script plots Forex (24 hour session) VWAP, yesterday's high, low, open and close (HLOC),
the day before's HLOC -
Also plots higher timeframe 20 emas
1 minute 5, 15, 60 period 20 ema
5 minute 15, 60 period 20 ema
15 minute 60, 120 , 240 period 20 ema
60 minute 120, 240 period 20 ema
120 minute 240, D period 20 ema
240 minute D period 20 ema
Also signals inside bars (high is less than or equal to the previous bar's high and the low is greater than or equal to the previous low) the : true inside bars have a maroon triangle below the bar as well as a ">" above the bar.
If subsequest bars are inside the last bar before the last true inside bar they also are marked with an ">"
This is probably a slight variation from the way Leaf_West plots the inside bars.
It appears that he marks all bars that are inside the original bar until one a bar has a high or low
outside the original bar. But I would need to see an example on his charts.
The Time Session Glitch and the Fix FX_IDC, COINBASE and BITSTAMP:
The script will correctly default to 1700 hrs to 1700hrs EDT/EST session for FXCM.
Strangely some securities appear to erroneously start their session at 1200 hrs ie. My guess is that they are somehow tied to GMT+0 instead of New York time (GMT+5). See this for yourself by selecting EURUSD using the FXCM exchange (FX:EURUSD) and then EURUSD from the IDC exchange (FX_IDC:EURUSD). The FX-IDC session opening range starts 5 hours before it actually should at 1700 hrs EDT/EST. To correct for this I have implemented an automatic fix (default) and a user selected "5 hour time shift adjust. ment needed on some securities".
There is also a 4 hour time shift button which might be necessary when New York reverts from Eastern Standard Time to Eastern Daylight Time (1 hour difference) in March (and then back again in November). In the default auto adjust mode you will need to select the 1 hour time shift. That is if this glitch still exists at that time.
I have looked at other scripts, other than my own and where the script is available, that need to use information about the opening bar and all have the same time shift issue
COINBASE and BITSTAMP open at 0000 hours GMT. Since I use lines instead of circles or crosses I had to make a small adjustment to plot the lines correctly.
If it needs work let me know.
Jayy
VWAP forex Yesterday Hi/Low switchThis script plots VWAP, yesterday's high, low, open and close (HLOC), the day before's HLOC -
Also plots higher timeframe 20 emas including:
1 minute 5, 15, 60 period 20 ema
5 minute 15, 60 period 20 ema
15 minute 60, 120 , 240 period 20 ema
60 minute 120, 240 period 20 ema
120 minute 240, D period 20 ema
240 minute D period 20 ema
Also signals inside bars (high is less than or equal to the previous
bar's high and the low is greater than or equal to the previous low) the : true inside bars have a maroon triangle below the bar as well as a ">" above the bar.
If subsequent bars are inside the last bar before the last true inside bar they also are marked with an ">"
If you have suggestions let me know.
Jayy
HeikenAshi[1]This is the alert script so you can automate this strategy using AutoView:
Make sure to use
crossing down value 0.9 once per bar (on condition) for this.
For the alert Message if you're using AutoView:
Long GBPUSD
c=order b=short
c=position b=short l=200 t=market
b=long q=0.01 l=200 t=market tp=60 sl=60
Short GBPUSD
c=order b=long
c=position b=long l=200 t=market
b=short q=0.01 l=200 t=market tp=60 sl=60
FirstStrike Long 200 - Daily Trend Rider [KedArc Quant]Strategy Description
FirstStrike Long 200 is a disciplined, long-only momentum strategy designed for daily "strike-first" entries in trending markets. It scans for RSI momentum above a customizable trigger (default 50), confirmed by EMA trend filters, and limits you to *exactly one trade per day* to avoid overtrading. It uses ATR for dynamic risk management (1.5x stop, 2:1 RR target) and optional trailing stops to ride winners. Backtested with realistic commissions and sizing, it prioritizes low drawdowns (<1% max in tests) over aggressive gains—ideal for swing traders seeking quality setups in bull runs.
Why It's Different from Other Strategies
Unlike generic RSI crossover bots or EMA ribbon mashups that spam signals and bleed in chop, FirstStrike enforces a "one-and-done" daily gate, blending precision momentum (RSI modes with grace/sustain) with robust filters (volume, sessions, rearm dips).
How It Helps Traders
- Reduces Emotional Trading: One entry/day forces discipline—miss a setup? Wait for tomorrow. Perfect for busy pros avoiding screen fatigue.
- Adapts to Regimes: Switch modes for trends ("Cross+Grace") vs. ranges ("Any bar")—boosts win rates 5-10% in backtests on high-beta names like .
- Risk-First Design: ATR scales stops to vol capping DD at 0.2% while targeting 2R winners. Trailing option locks +3-5% runs without early exits.
- Quick Insights: Labels/alerts flag entries with RSI values; bgcolor highlights signals for visual scanning. Helps spot "first-strike" edges in uptrends, filtering ~60% noise.
Why This Is Not a Mashup
This isn't a Frankenstein of off-the-shelf indicators—while it uses standard RSI/EMA/ATR (core Pine primitives), the innovation lies in:
- Custom Trigger Engine: Switchable modes (e.g., "Cross+Grace+Sustain" requires post-cross hold) prevent perpetual signals, unlike basic `ta.crossover()`.
- Daily Rearm Gate: Resets eligibility only after a dip (if enabled), tying momentum to mean-reversion—original logic not found in common scripts.
- Per-Day Isolation: `var` vars + `ta.change(time("D"))` ensure zero pyramiding/overlaps, beyond simple session filters.
All formulae are derived in-house for "first-strike" (early RSI pops in trends), not copied from public repos.
Input Configurations
Let's break down every input in the FirstStrike Long 200 strategy. These settings let you tweak the strategy like a dashboard—start with defaults for quick testing,
then adjust based on your asset or timeframe (5m for intraday). They're grouped logically to keep things organized, and most have tooltips in the script for quick reminders.
RSI / Trigger Group: The Heart of Momentum Detection
This is where the magic starts—the strategy hunts for "upward energy" using RSI (Relative Strength Index), a tool that measures if a stock is overbought (too hot) or oversold (too cold) on a 0-100 scale.
- RSI Length: How many bars (candles) back to calculate RSI. Default is 14, like a 14-day window for daily charts. Shorter (e.g., 9) makes it snappier for fast markets; longer (21) smooths out noise but misses quick turns.
- Trigger Level (RSI >= this): The key RSI value where the strategy says, "Go time!" Default 50 means enter when RSI crosses or holds above the neutral midline. Why is this trigger required? It acts as your "green light" filter—without it, you'd enter on every tiny price wiggle, leading to endless losers. RSI above this shows building buyer power, avoiding weak or sideways moves. It's essential for quality over quantity, especially in one-trade-per-day setups.
- Trigger Mode: Picks how strict the RSI signal must be. Options: "Cross only" (exact RSI crossover above trigger—super precise, fewer trades); "Cross+Grace" (crossover or within a grace window after—gives a second chance); "Cross+Grace+Sustain" (crossover/grace plus RSI holding steady for bars—best for steady climbs); "Any bar >= trigger" (looser, any bar above—more opportunities but riskier in chop). Start with "Any bar" for trends, switch to "Cross only" for caution.
- Grace Window (bars after cross): If mode allows, how many bars post-RSI-cross you can still enter if RSI dips but recovers. Default 30 (about 2.5 hours on 5m). Zero means no wiggle room—pure precision.
- Sustain Bars (RSI >= trigger): In sustain mode, how many straight bars RSI must stay above trigger. Default 3 ensures it's not a fluke spike.
- Require RSI Dip Below Rearm Before Any Entry?: A yes/no toggle. If on, the strategy "rearms" only after RSI dips below a low level (like a breather), preventing back-to-back signals in overextended rallies.
- Rearm Level (if requireDip=true): The dip threshold for rearming. Default 45—RSI must go below this to reset eligibility. Lower (30) for deeper pullbacks in volatile stocks.
For the trigger level itself, presets matter a lot—default 50 is neutral and versatile for broad trends. Bump to 55-60 for "strong momentum only" (fewer but higher-win trades, great in bull runs like tech surges); drop to 40-45 for "early bird" catches in recoveries (more signals but watch for fakes in ranges). The optimize hint (40-60) lets you test these in TradingView to match your risk—higher presets cut noise by 20-30% in backtests.
Trend / Filters Group: Keeping You on the Right Side of the Market
These EMAs (Exponential Moving Averages) act like guardrails, ensuring you only long in uptrends.
- EMA (Fast) Confirmation: Short-term EMA for price action. Default 20 periods—price must be above this for "recent strength." Shorter (10) reacts faster to intraday pops.
- EMA (Trend Filter): Long-term EMA for big-picture trend. Default 200 (classic "above the 200-day" rule)—price above it confirms bull market. Minimum 50 to avoid over-smoothing.
Optional Hour Window Group: Timing Your Strikes
Avoid bad hours like lunch lulls or after-hours tricks.
- Restrict by Session?: Yes/no for using exact market hours. Default off.
- Session (e.g., 0930-1600 for NYSE): Time string like "0930-1600" for open to close. Auto-skips pre/post-market noise.
- Restrict by Hour Range?: Fallback yes/no for simple hours. Default off.
- Start Hour / End Hour: Clock times (0-23). Defaults 9-15 ET—focus on peak volume.
Volume Filter Group: No Volume, No Party
Confirms conviction—big moves need big participation.
- Require Volume > SMA?: Yes/no toggle. Default off—only fires on above-average volume.
- Volume SMA Length: Periods for the average. Default 20—compares current bar to recent norm.
Risk / Exits Group: Protecting and Profiting Smartly
Dynamic stops based on volatility (ATR = Average True Range) keep things realistic.
- ATR Length: Bars for ATR calc. Default 14—measures recent "wiggle room" in price.
- ATR Stop Multiplier: How far below entry for stop-loss. Default 1.5x ATR—gives breathing space without huge risk
- Take-Profit R Multiple: Reward target as multiple of risk. Default 2.0 (2:1 ratio)—aims for twice your stop distance.
- Use Trailing Stop?: Yes/no for profit-locking trail. Default off—activates after entry.
- Trailing ATR Multiplier: Trail distance. Default 2.0x ATR—looser than initial stop to let winners run.
These inputs make the strategy plug-and-play: Defaults work out-of-box for trending stocks, but tweak RSI trigger/modes first for your style.
Always backtest changes—small shifts can flip a 40% win rate to 50%+!
Outputs (Visuals & Alerts):
- Plots: Blue EMA200 (trend line), Orange EMA20 (price filter), Green dashed entry price.
- Labels: Green "LONG" arrow with RSI value on entries.
- Background: Light green highlight on signal bars.
- Alerts: "FirstStrike Long Entry" fires on conditions (integrates with TradingView notifications).
Entry-Exit Logic
Entry (Long Only, One Per Day):
1. Daily Reset: New day clears trade gate and (if required) rearm status.
2. Filters Pass: Time/session OK + Close > EMA200 (trend) + Close > EMA20 (price) + Volume > SMA (if enabled) + Rearmed (dip below rearm if toggled).
3. Trigger Fires: RSI >= trigger via selected mode (e.g., crossover + grace window).
4. Execute: Enter long at close; set daily flag to block repeats.
Exit:
- Stop-Loss: Entry - (ATR * 1.5) – dynamic, vol-scaled.
- Take-Profit: Entry + (Risk * 2.0) – fixed RR.
- Trailing (Optional): Activates post-entry; trails at Close - (ATR * 2.0), updating on each bar for trend extension.
No shorts or hedging—pure long bias.
Formulae Used
- RSI: `ta.rsi(close, rsiLen)` – Standard 14-period momentum oscillator (0-100).
- EMAs: `ta.ema(close, len)` – Exponential moving averages for trend/price filters.
- ATR: `ta.atr(atrLen)` – True range average for stop sizing: Stop = Entry - (ATR * mult).
- Volume SMA: `ta.sma(volume, volLen)` – Simple average for relative strength filter.
- Grace Window: `bar_index - lastCrossBarIndex <= graceBars` – Counts bars since RSI crossover.
- Sustain: `ta.barssince(rsi < trigger) >= sustainBars` – Consecutive bars above threshold.
- Session Check: `time(timeframe.period, sessionStr) != 0` – TradingView's built-in session validator.
- Risk Distance: `riskPS = entry - stop; TP = entry + (riskPS * RR)` – Asymmetric reward calc.
FAQ
Q: Why only one trade/day?
A: Prevents revenge trading in volatile sessions . Backtests show it cuts losers by 20-30% vs. multi-entry bots.
Q: Does it work on all assets/timeframes?
A: Best for trending stocks/indices on 5m-1H. Test on crypto/forex with wider ATR mult (2.0+).
Q: How to optimize?
A: Use TradingView's optimizer on RSI trigger (40-60) and EMA fast (10-30). Aim for PF >1.0 over 1Y data.
Q: Alerts don't fire—why?
A: Ensure `alertcondition` is enabled in script settings. Test with "Any alert() function calls only."
Q: Trailing stop too loose?
A: Tune `trailMult` to 1.5 for tighter; it activates alongside fixed TP/SL for hybrid protection.
Glossary
- Grace Window: Post-RSI-cross period (bars) where entry still allowed if RSI holds trigger.
- Rearm Dip: Optional pullback below a low RSI level (e.g., 45) to "reset" eligibility after signals.
- Profit Factor (PF): Gross profit / gross loss—>1.0 means winners outweigh losers.
- R Multiple: Risk units (e.g., 2R = 2x stop distance as target).
- Sustain Bars: Consecutive bars RSI stays >= trigger for mode confirmation.
Recommendations
- Backtest First: Run on your symbols (/) over 6-12M; tweak RSI to 55 for +5% win rate.
- Live Use: Start paper trading with `useSession=true` and `useVol=true` to filter noise.
- Pairs Well With: Higher TF (daily) for bias; add ADX (>25) filter for strong trends (code snippet in prior chats).
- Risk Note: 10% sizing suits $100k+ accounts; scale down for smaller. Not financial advice—past performance ≠ future.
- Publish Tip: Add tags like "momentum," "RSI," "long-only" on TradingView for visibility.
Strategy Properties & Backtesting Setup
FirstStrike Long 200 is configured with conservative, realistic backtesting parameters to ensure reliable performance simulations. These settings prioritize capital preservation and transparency, making it suitable for both novice and experienced traders testing on stocks.
Initial Capital
$100,000 Standard starting equity for portfolio-level testing; scales well for retail accounts. Adjust lower (e.g., $10k) for smaller simulations.
Base Currency
Default (USD) Aligns with most US equities (e.g., NASDAQ symbols); auto-converts for other assets.
Order Size
1 (Quantity) Fixed share contracts for simplicity—e.g., buys 1 share per trade. For % of equity, switch to "Percent of Equity" in strategy code.
Pyramiding
0 Orders No additional entries on open positions; enforces strict one-trade-per-day discipline to avoid overexposure.
Commission
0.1% Realistic broker fee (e.g., Interactive Brokers tier); factors in round-trip costs without over-penalizing winners.
Verify Price for Limit Orders
0 Ticks No slippage delay on TPs—assumes ideal fills for historical accuracy.
Slippage
0 Ticks Zero assumed slippage for clean backtests; real-world trading may add 1-2 ticks on volatile opens.
These defaults yield low drawdowns (<0.3% max in tests) while capturing trend edges. For live trading, enable slippage (1-3 ticks) to mimic execution gaps. Always forward-test before deploying!
⚠️ Disclaimer
This script is provided for educational purposes only.
Past performance does not guarantee future results.
Trading involves risk, and users should exercise caution and use proper risk management when applying this strategy.
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).
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