intraday_bondsStatistics for assisting with intraday bond trading, using five minute periods and one hour ranges. There are two tables, a volatility table and a correlation table. The correlation table shows the correlation of five minute returns (absolute) between the four different bond contracts that trade on the CME. The volatility table shows for each contract:
- The current realized volatility, based on the previous one hour of realized volatility. This figure is annualized for easy comparison with options contracts.
- The current realized volatility's z-score, based on all available data.
- The tick range of an "N" standard deviation move over one hour. Choose "N" using the stdevs input.
- The previous hour's true range (high - low).
The ranges are expressed in ticks.
Buscar en scripts para "文华财经tick价格"
Intrabar OBV/PVTI got this idea from @fikira's script Intrabar-Price-Volume-Change-experimental
The indicator calculates OBV and PVT based on ticks. Since, the indicator relies on live ticks, it only starts execution after it is put on the charts. The script can be useful in analysing intraday buy and sell pressure. Details are color coded based on the values.
Data is presented in simple tabular format.
Formula for OBV and PVT can be found here:
www.investopedia.com
www.investopedia.com
Super Multi Trend [Salty]This script uses the 5, 8, 13, 21, 34 low, 34 close, 34 high, and 55 EMAs in comparison to each other to gauge momentum and trend strength for the current ticker. Additionally, it provides the ability to compare to 3 additional tickers at the same time (Uncheck boxes in settings to hide if desired). For the Super Trend Row darker colors are more bearish than lighter colors, and consequently lighter colors are more bullish than darker colors. Yellow indicates a neutral or choppy market. Fully stacked EMAs are shown with a Light Green (Lime) color for the bullish condition, and Dark Red for the bearish condition.
SQV CrossThis strategy is used to find tickers that do well when SPY and QQQ are up and VIX is down. This uses EMA's on the user defined resolution to define direction of each ticker. Trades are entered upon crossover. EMAs are user defined as well.
Volume Prism RibbonNASDAQ:SPWR
The purpose of this script is to give insight into the volume action. The relative volume is calculated (based on 400 ticks) with the volume of down days (close-close <0) being given a negative value. This function is then summed over 100 ticks. WMA's are used to generate a rainbow ribbon who's color order is easily recognized buy all of us. Watch and Warning points are added using crossover points. I find it to be a good supplement to my favorite Buy/Sell indicator. In addition to the wrapping of the ribbon, pay attention to where the zero line is as well.
Technical Analysis Consulting Table (TACT)Inspired by Tradingview's own "Technical Analysis Summary", I present to you a table with analogous logic.
You can track any ticker you want, no matter your chart. You can even have multiple tables to track multiple tickers. By default it tracks the Total Crypto Cap.
You can change the resolution you want to track. By default it is the same as the chart.
You can position the table to whichever corner of the chart you want. By default it draws in the bottom right corner.
Background colors and text size can be adjusted.
Indicators Used:
Oscillators
RSI(14)
STOCH(14, 3, 3)
CCI(20)
ADX(14)
AO
Momentum(10)
MACD(12, 26)
STOCH RSI(3, 3, 14, 14)
%R(14)
Bull Bear Power
UO(7,14,28)
Moving Averages
EMA(5)
SMA(5)
EMA(10)
SMA(10)
EMA(20)
SMA(20)
EMA(30)
SMA(30)
EMA(50)
SMA(50)
EMA(100)
SMA(100)
EMA(200)
SMA(200)
Ichimoku Cloud(9, 26, 52, 26)
VMWA(20)
HMA(9)
Pivots
Traditional
Fibonacci
Camarilla
Woodie
WARNING: I have observed up to a couple of seconds of signal jitter/delay, so use it with caution in very small resolutions (1s to 1m).
I hope you enjoy this and good luck with your trading. Suggestions and feedback are most welcome.
Relative Strength RatioAbout this indicator:
This indicator goes under many different names such as Relative Strength Ratio, Comparative Strength, Relative Strength Comparison (RSC) etc.
It compares the strength of the current asset to another asset of your choice and displays the relative strength of the current asset. (Not RSI)
There are several comparative indicators published already here on TV but I have added some unique features that I think is really useful.
When to use it:
This is useful when you want to compare for example a stock with its sector to find out if the stock is a leadership stock in that sector or main index.
You could also use it to compare the sector to its index etc.
If you want to compare for example a stock to both its index and its sector at the same time I suggest you add 2 instances of this indicator.
In many trading systems you are supposed find the strongest tickers by identifying "layers of strength" like 1. The strongest asset class that money is flowing into (like stocks, commodities, bonds, etc.) 2. Find the strongest sectors. 3. Find the stocks outperforming those sectors. 4. Find some kind of entry signal there.
How it works:
The indicator calculates an "RS line" (Relative Strength) by dividing the current ticker with a ticker of your choice. This creates a ratio or pair similar to how currencies are related to each others like USDGBP (dollar against pound). The RS line is black by default so you might want to change its color if you use a dark theme.
If the RS line is moving up it means your current asset is stronger than the asset you are comparing it with and vice versa.
Use the simple moving average to quickly see long term strength trends.
Features
RS line
Shows the relative strength of the current asset.
SMA
This Simple Moving Average has directional coloring; green when it is angled up and red when it is angled down. You can customise the period in the settings. If you don't want the color change just make both up/down color the same.
Relative market label
This displays the market you are currently comparing with as a label next to the last data point.
Because I tried stocks in 2 different countries I want to know which index I am comparing with and this label saves me from going into the settings to check. I find it to be a life saver!
Please let me know if you find any problems or if you have any ideas for improvement
Difference over barsDescription:
One of my followers asked about an indicator that shows the difference between the open and a previous close and didn't find one so I wrote this one. This is similar to a momentum indicator except it offers more flexibility. While the standard momentum indicator calculates a difference between current close and a previous close (sometimes customizable to work on open, high or lows instead of close), this allows to mix and match between open, high, low and close. It also offers multiple kinds of moving averages.
Settings:
Current point of reference
Previous point of reference
Difference over how many bars?
How it works:
The indicator calculates the difference between the current point of reference and a previous (n-bars back) point of reference (where n is given by the "Difference over how many bars?").
How to use it:
find historical support lines like the 0.68 line in the cart above where in the past the indicator tends to bounce back; similarly find resistance lines like the -0.75 line in the chart (which servers as a resistance line both for the main indicator line and its moving average )
look for convergence between the price and the indicator; for example, if the price is going up and the indicator is going down a change in the price direction may be coming soon
look for the indicator crossing its moving average: moving up will signify an up trend and vice-versa
since the difference between the open and previous close (which is what the blue line in the chart shows) since to go up to 0.68 (the upper horizontal line) and down to -0.75 (the lower horizontal line) most of the time, one strategy, using options, is to to buy, right before the close of a trading day, a "long iron butterfly": buy-to-open (BTO) both a call and a put at the strike price and sell-to-open (STO) a call at a strike of around $0.68 more and sell to open a put at a strike of around $0.75 less. The STO legs should be for the next expiration and the BTO legs for the next expiration after that. This way the STO will decrease their time value faster than the BTO legs if the price stays flat (which plays to your advantage) and the BTO legs may make profit if the next day it opens away from the price at which the ticker closed the previous day (when the position was opened). The most profit is when it moves right up to one of the STO legs. This position would normally be closed next day at opening. The percentage of profit it makes is low compared to other strategies but also the percentage of the total cost at risk is also low which could potentially allow a trader to increase the lot and thus, in the end, the total profit amount may be comparable to other strategies.
Notes:
The indicator in the chart above comes with the standard options. For a more standard momentum indicator set both the current and previous reference point to the same OHLC value (such as "close").
The 0.68 and -0.75 levels are for open/close (current/previous point of reference) for ticker INTC. Obviously, other tickers will likely have other levels and you will have to find those yourself. If you use INTC but use other combination of current and previous reference points, they will have different levels as well.
Bitcoin CME Gaps [NeoButane]Simple script that checks for gaps in price from CME. tickerid(x, y, sess) doesn't seem to be applying correctly for the ticker specified at the moment so there are a couple of 'gaps' peppered on lower timeframes.
Gaps are legitimate price levels to look as a support or resistance. The theory is that volume needs to be gap filled, but I currently believe it's an easy entry/exit trade for those who can move the market. I don't think there is sound analysis behind the why, but it is real.
QuantNomad - Simple Custom Screener in PineScriptQuite often I need to run screeners with the custom condition, but unfortunately, in TradingView it's impossible.
I created an example script to show how you can create a simple custom screener in Pine Script on your own.
It's not very good, it requires some manual adjustments, it can be improved in some ways, but I think it might work for some tasks.
What do you think? Do you have a better way to implement custom screeners in TradingView?
To run your own conditions you need to implement them in:
customFunc() function and for every ticker you want to include in your search add 2 lines like these with newly defined variable:
s1 = security('BTCUSD', '1', customFunc())
and
scr_label := s1 ? scr_label + 'BTCUSD\n' : scr_label
I'm not sure that it will work well for more than a few dozen tickers.
But I hope it will be helpful for you.
And remember:
Past performance does not guarantee future results.
Chiki-Poki BFXLS Longs Shorts Abs Normalized Volume Pro by RRBChiki-Poki BFXLS Longs vs Shorts Absolute Normalized Volume Value Pro by RagingRocketBull 2018
Version 1.0
This indicator displays Longs vs Shorts in a side by side graph, shows volume's absolute price value and normalized volume of Longs/Shorts for the current asset. This allows for more accurate L/S comparisons (like a log scale for volume) since volume on spot exchanges (Bitstamp, Bitfinex, Coinbase etc) is measured in coins traded, not USD traded. Similarly, L/S is usually the amount of coins in open L/S positions, not their total USD value. On Bitmex and other futures exchanges volume is measured in USD traded, so you don't need to apply the Volume Absolute Price Value checkbox to compare L/S. You should always check first whether your source is measured in coins or USD.
Chiki-Poki BFXLS primarily uses *SHORTS/LONGS feeds from Bitfinex for the current crypto asset, but you can specify custom L/S source tickers instead.
This 2-in-1 works both in the Main Chart and in the indicator pane below. You can switch between Main/Sub Window panes using RMB on the indicator's name and selecting Move To/Pane Above/Below.
This indicator doesn't use volume of the current asset. It uses L/S ticker's OHLC as a source for SHORTS/LONGS volumes instead. Essentially L/S => L/S Volume == L/S
Features:
- Display Longs vs Shorts side by side graph for the current crypto asset, i.e. for BTCUSD - BTCUSDLONGS/BTCUSDSHORTS, for ETHUSD - ETHUSDLONGS/ETHUSDSHORTS etc.
- Use custom OHLC ticker sources for Longs/Shorts from different exchanges/crypto assets with/without exchange prefix.
- Plot Longs/Shorts as lines or candles
- Show/Hide L/S, Diff, MAs, ATH/ATL
- Use Longs/Shorts Volume Absolute Price Value (Price * L/S Volume) instead of Coins Traded in open L/S positions to compare total L/S value/capitalization
- Normalize L/S Volume using Price / Price MA / L/S Volume MA
- Supports any existing type of MA: SMA, EMA, WMA, HMA etc
- Volume Absolute Price Value / Normalize also works on candles
- Oscillator mode with negative axis (works in both Main Chart/Subwindow panes).
- Highlight L/S Volume spikes above L/S MAs in both lines/oscillator.
- Change L/S MA color based on a number of last rising/falling L/S bars, colorize candles
- Display L/S volume as 1000s, mlns, or blns using alpha multiplier
1. based on BFXLS Longs vs Shorts and Compare Style, uses plot*, security and custom hma functions
2. swma has a fixed length = 4, alma and linreg have additional offset and smoothing params
Notes:
- Make sure that Left Price Scale shows up with Auto Fit Data enabled. You can reattach indicator to a different scale in Style.
- It is not recommended to switch modes multiple times due to TradingView's scale reattachment bugs. You should switch between Main Chart and Sub Window only once.
- When the USD price of an asset is lower you can trade more coins but capitalization value won't be as significant as when there are less coins for a higher price. Same goes for Shorts/Longs.
Current ATH in shorts doesn't trigger a squeeze because its total value is now far less than before and we are in a bear market where it's normal to have a higher number of shorts.
- You should always subtract Hedged L/S from L/S because hedged positions are temporary - used to preserve the value of the main position in the opposite direction and should be disregarded as such.
- Low margin rates increase the probability of a move in an underlying direction because it is cheaper. High margin rates => the market is anticipating a move in this direction, thus a more expensive rate. Sudden 5-10x rate raises imply a possible reversal soon. high - 0.1%, avg - 0.01-0.02%, low - 0.001-0.005%
You can also check out:
- BFXLS Longs/Shorts on BFXData
- Bitfinex L/S margin rates and Hedged L/S on datamish
- Bitmex L/S on Coinfarm.online
ec tEST cODE FOR pERCENT DIFERENCE ////////////////////////////////////////////////////////////
// Copyright by HPotter v1.0 04/04/2015
// Percent difference between price and MA
////////////////////////////////////////////////////////////
study(title="Percent difference between price and MA")
source = close
useCurrentRes = input(true, title="Use Current Chart Resolution?")
resCustom = input(title="Use Different Timeframe? Uncheck Box Above", type=resolution, defval="60")
smd = input(true, title="Show MacD & Signal Line? Also Turn Off Dots Below")
sd = input(true, title="Show Dots When MacD Crosses Signal Line?")
sh = input(true, title="Show Histogram?")
macd_colorChange = input(true,title="Change MacD Line Color-Signal Line Cross?")
hist_colorChange = input(true,title="MacD Histogram 4 Colors?")
res = useCurrentRes ? period : resCustom
fastLength = input(12, minval=1), slowLength=input(26,minval=1)
signalLength=input(9,minval=1)
fastMA = ema(source, fastLength)
slowMA = ema(source, slowLength)
Length = input(9, minval=1)
Length2= input(36,minval=1)
Length3= input(81,minval=1)
AveragePrice= input(9,minval=1)
Length5= input(3,minval=1)
xSMA = (sma(close, Length)+sma(close, Length2)+sma(close, Length3))/3
pSAM=sma(close, AveragePrice)
nRes = (pSAM - xSMA) * 100 / close
signalnRes = sma(nRes, signalLength)
macd = nRes
signal = sma(macd, signalLength)
hist = macd - signal
outMacD = security(tickerid, res, macd)
outSignal = security(tickerid, res, signal)
outHist = security(tickerid, res, hist)
histA_IsUp = outHist > outHist and outHist > 0
histA_IsDown = outHist < outHist and outHist > 0
histB_IsDown = outHist < outHist and outHist <= 0
histB_IsUp = outHist > outHist and outHist <= 0
//MacD Color Definitions
macd_IsAbove = outMacD >= outSignal
macd_IsBelow = outMacD < outSignal
plot_color = hist_colorChange ? histA_IsUp ? aqua : histA_IsDown ? blue : histB_IsDown ? red : histB_IsUp ? maroon :yellow :gray
macd_color = macd_colorChange ? macd_IsAbove ? lime : red : red
signal_color = macd_colorChange ? macd_IsAbove ? yellow : yellow : lime
circleYPosition = outSignal
// MA COLOR DEFINITION
maColor = change(nRes)>0 ? green : change(nRes)<0 ? red : na
mA_IsAbove = nRes> 0
mA_IsBelow = nRes< 0
plot( nRes, color=maColor, style=line, title="MMA", linewidth=2)
//plot(smd and signalnRes ? signalnRes : na, title="Signal Line", color=signal_color, style=line ,linewidth=2)
//plot(smd and outMacD ? outMacD : na, title="MACD", color=macd_color, linewidth=4)
//plot(smd and outSignal ? outSignal : na, title="Signal Line", color=signal_color, style=line ,linewidth=2)
//plot(sh and outHist ? outHist : na, title="Histogram", color=plot_color, style=histogram, linewidth=4)
plot(sd and cross(outMacD, outSignal) ? circleYPosition : na, title="Cross", style=circles, linewidth=4, color=macd_color)
hline(0, '0 Line', linestyle=solid, linewidth=2, color=white)
//////ALERT cONDITION////
src = input(close)
ma_1 = sma(src, 20)
ma_2 = sma(src, 10)
c = cross(ma_1, ma_2)
alertcondition(c, title='Red crosses blue', message='Red and blue have crossed!')
d = cross(outMacD, outSignal)
alertcondition(d, title='GOING DOWN', message='SELL!')
//
//e = cross(outSignal, outMacD)
//alertcondition(E, title='GOING UP', message='BUY!')
BTC World Price: Multi-Exchange VWAPBTC World Price: Multi-Exchange VWAP
__________________________
WHAT IT DOES
What you see above are not Bitmex candles, but this indicator's.
Bitcoin is listed on multiple exchanges. Many people have called for a single global index that would quote BTC price and volume across all exchanges: this script is such a virtual aggregate (formerly: Multi-Listed , Volume-Weighted Average Price ).
It will, independently for each tick, for any time-frame:
- Quote the price (O, H, L, C) and volume from Bitfinex (USD), Binance (USDT), bitFlyer (Yen), Bithumb (S. Korean Won), Coinbase (USD), Kraken (EUR) and even Bitmex (USD Contracts).
- Weight each price with the corresponding volume of the exchange.
- Quote the FOREX conversion rate in USD for each currency (USDJPY etc.)
- Finally return global average price (candles) in USD.
- Additionally provide (H+L)/2 etc. values.
No more "on Coinbase this" or "on Bitstamp that", you've now got a global overview!
See CoinMarketCap: Markets for reference. I've included alternative exchanges in the comments at the top of the script.
__________________________
HOW TO USE IT
Basically just add it to your chart and use the indicator's candles instead of the chart's main ticker.
By default, BTC World Price will display candles only, but you can also display OHLC & averages (in whichever style you want).
You may indeed want to hide the main symbol (top-left corner, click the 'eye' button next to its name), or switch it to something else than candles/bars (e.g. line).
Make sure "Scale Price Chart Only" is disabled if you want to use the auto-zoom feature. (if other indicators are messing your zoom, you can try to select "Line with Breaks" or "Area with Breaks" to allow these to overflow from the main window)
By clicking the triangle next to the indicator's name, you can select "Visual Order" (e.g "Bring to Front").
You can select regular Candles or Heikin-Ashi in Options.
In the Format > Inputs tab, you can select which exchanges to quote. By default, all of them are enabled.
The script also exposes the following typical values to the backend, which you can use as Price Source for other indicators: (e.g. MA, RSI, in their "Format > Input" tab)
Open Price (grey)
High Price (green)
Low Price (red)
Close Price (white)
(H + L)/2 (light blue)
(H + L + C)/3 (blue)
(O + H + L + C)/4 (purple)
They are all hidden by default (by means of maximum transparency).
In the Format > Style tab, you can change their color, transparency and style (line, area, etc), as well as uncheck Candles and Wicks to hide these.
If you are using "Indicator Last Value" and want to clear the clutter from all these values, simply uncheck them in Style. They will still be available as Price Source for other indicators.
You can also choose to scale it to the left, right (default) or "screen" (no scaling).
Once you're satisfied with your Style, you may click "Default"> "Save as default" in the botton-left. Everytime you load the indicator, it will look the same. ("Reset Settings" will reset to the script's defaults)
__________________________
Please leave feedback below in comments or pm me directly for bugs and suggestions.
SMART4TRADER-Margin ZONEIndicator based on marginal zones (according to Mityukov Sergey). In open source.
Formula for calculating the margin:
Margin size / cost tick * minimum price change
Example:
EURUSD = 2100 $ / 6.25 $ * 0.00005 points = 0.01680 points
....
For currency pairs where USD is in the first place it is necessary to write so that the indicator is taken away from zero
Iff (ticker == "USDCAD", (0- (950/5 * 0.00005)),
//////////////////////////////////////////////////////////////////////////////////////////////////
Индикатор на основе маржинальных зон (по Митюкову Сергею). В открытом исходном коде.
Формула рассчета маржи:
размер маржи / стоимость тика * минимальное ценовое изменение
Пример:
EURUSD = 2100 $ / 6.25 $ * 0.00005 points = 0.01680 points
....
Для валютных пар где USD стоит на первом месте нужно писать так, чтобы показатель отнимался от нуля
iff (ticker=="USDCAD", (0-(950/5*0.00005)),
//////////////////////////////////////////////////////////////////////////////////////////////////
Convert Yuan value symbols to USDIGNORE PREVIOUS SCRIPT/POST (titled: "yuan normiz")
If you like to look add symbols that are valued in China's Yuan and want to convert them to USD accurately then this is the perfect script for you.
"I'm not sure if this script is for me. Does my setup apply here?"
If either of these resemble your chart setup then this is for you:
Example 1: You have COINBASE:BTCUSD on your main chart often add to compare Bitstamp:btcusd and Okcoin:btccny.
Example 2: You have SPY or SPX (or DJIA etc) as your main chart but like to add other composites to compare like SSE(Shanghai Stock Exchange index) to your main chart.
This takes the symbol of your choice (default is BTCCHINA:BTCCNY) that is expressed in Yuan and divides it by the corresponding value of IDC's USDCNH ticker. Not the last value of USDCNH, but the respective tick mark----BTCCNY's close 3 months ago is divided by USDCNH's close 3 months ago.
VWAP + Volume Spikes See Where Smart Money ExhaustsVolume tells the truth. VWAP tells the bias. This script shows both — live.
If you trade intraday momentum, reversals, or liquidity sweeps, this indicator is built for you.
It shows where volume spikes hit extreme levels, anchored around VWAP and its dynamic bands, so you can instantly spot capitulation or hidden absorption.
🎯 What This Indicator Does
✅ Plots VWAP — session-anchored, updates automatically
✅ Adds dynamic VWAP bands — standard deviation envelopes showing volatility context
✅ Highlights volume spikes — colored candles + background for abnormal prints
✅ Includes alerts — “Volume Spike”, “VWAP Cross”, or a combined alert with direction
✅ Clean visual design — instantly readable in fast markets
It’s your visual orderflow radar — whether you’re trading gold, indices, or small caps.
🔍 Why It Works
Institutions build and unwind positions around VWAP.
Retail often chases volume… this script shows you when that volume becomes too extreme.
A spike above VWAP near resistance? → Likely distribution.
A spike below VWAP near support? → Likely capitulation.
Combine volume exhaustion + VWAP context, and you’ll see market turning points form before most indicators react.
⚙️ Inputs You Can Tune
Bands lookback: adjusts how reactive the VWAP bands are
Band width (σ): set how tight or wide your deviation envelope is
Volume baseline length: controls how “abnormal” a spike must be
Spike threshold: multiplier vs. average volume
Toggle color-coding, bands, and labels
Default settings work well across 1m–15m intraday charts and 1h–4h swing frames.
💡 How Traders Use It
1️⃣ Fade Parabolics:
When a green spike candle pierces upper VWAP band on high volume → smart money unloading.
Look for rejection and short into VWAP.
2️⃣ Catch Capitulations:
When a red spike candle dumps below lower VWAP band → panic selling.
Watch for stabilization and long back to VWAP.
3️⃣ VWAP Rotation Plays:
Alerts for price crossing VWAP help you spot shift in intraday control.
Above VWAP = buyers in charge.
Below VWAP = sellers in charge.
🧠 Best Practices
Pair it with Volume Profile or Delta/Flow tools to confirm exhaustion.
Don’t chase — wait for spike confirmation + reversal candle.
Use it on liquid tickers (NASDAQ, SPY, GOLD, BTC, etc.).
Great for Dux-style small-cap shorts or index pullbacks.
🔔 Alerts Ready
Choose from:
Volume Spike (single-bar explosion)
VWAP Cross Up/Down (trend shift confirmation)
One Combined Alert (any signal, includes ticker, price, and volume)
Set once — get real-time push notifications, Telegram, or webhook signals.
📊 My Favorite Setups
US100 / NASDAQ: fade rallies above VWAP + spike
Gold / Silver: trade reversals from VWAP bands
Small caps: short back-side after volume climax
ES, DAX, Oil: scalp VWAP rotation with confluence
❤️ Support This Work
I release free and premium scripts weekly — combining smart money concepts, VWAP tools, and volume analytics.
👉 Follow me on TradingView for more indicators and setups.
👉 Comment “🔥” if you want me to post the multi-timeframe VWAP + Volume Pressure version next.
👉 Share this with your team — it helps the community grow.
BB SPY Mean Reversion Investment StrategySummary
Mean reversion first, continuation second. This strategy targets equities and ETFs on daily timeframes. It waits for price to revert from a Bollinger location with candle and EMA agreement, then manages risk with ATR based exits. Uniqueness comes from two elements working together. One, an adaptive band multiplier driven by volatility of volatility that expands or contracts the envelope as conditions change. Two, a bias memory that re arms the same direction after any stop, target, or time exit until a true opposite signal appears. Add it to a clean chart, use the markers and levels, and select on bar close for conservative alerts. Shapes can move while the bar is open and settle on close.
Scope and intent
• Markets. Currently adapted for SPY, needs to be optimized for other assets
• Timeframes. Daily primary. Other frames are possible but not the default
• Default demo. SPY on daily
• Purpose. Trade mean reversion entries that can chain into a longer swing by splitting holds into ATR or time segments
Originality and usefulness
• Novelty. Adaptive band width from volatility of volatility plus a persistent bias array that keeps the original direction alive across sequential entries until an opposite setup is confirmed
• Failure modes mitigated. False starts in chop are reduced by candle color and EMA location. Missed continuation after a take profit or stop is addressed by the re arm engine. Oversized envelopes during quiet regimes are avoided by the adaptive multiplier
• Testability. Every module has Inputs and visible levels so users can see why a suggestion appears
• Portable yardstick. All risk and targets are expressed in ATR units
Method overview in plain language
The engine measures where price sits relative to Bollinger bands, confirms with candle color and EMA location, requires ADX for shorts(in our case long close since we use it currently as long only), and optionally requires a trend or mean reversion regime using band width percent rank and basis slope. Risk uses ATR for stop, target, and optional breakeven. A small array stores the last confirmed direction. While flat, the engine keeps a pending order in that direction. The array flips only when a true opposite setup appears.
Base measures
• Range basis. True Range smoothed over a user defined ATR Length
• Return basis. Not required
Components
• Bollinger envelope. SMA length and standard deviation multiplier. Entry is based on cross of close through the band with location bias
• Candle and EMA filter. Close relative to open and close relative to EMA align direction
• ADX gate for shorts. Requires minimum trend strength for short trades
• Adaptive multiplier. Band width scales using volatility of volatility so envelopes breathe with conditions
• Regime gate optional. Band width percent rank and basis slope identify trend or mean reversion regimes
• Risk manager. ATR stop, ATR target, optional breakeven, optional time exit
• Bias memory. Array stores last confirmed direction and re arms entries while flat
Fusion rule
Minimum satisfied gates count style. All required gates must be true. Optional gates are controlled in Inputs. Bias memory never overrides an opposite confirmed setup.
Signal rule
• Long setup when close crosses up through the lower band, the bar closes green, and close is above the long EMA
• Short setup when close crosses down through the upper band, the bar closes red, close is below the short EMA, and ADX is above the minimum
• While flat the model keeps a pending order in the stored direction until a true opposite setup appears
• IN LONG or IN SHORT describes states between entry and exit
What you will see on the chart
• Markers for Long and Short setups
• Exit markers from ATR or time rules
• Reference levels for entry, stop, and target
• Bollinger bands and optional adaptive bands
Inputs with guidance
Setup
• Signal timeframe. Uses the chart timeframe
• Invert direction optional. Flips long and short
Logic
• BB Length. Typical 10 to 50. Higher smooths more
• BB Mult. Typical 1.0 to 2.5. Higher widens entries
• EMA Length long. Typical 10 to 50
• EMA Length short. Typical 5 to 30
• ADX Minimum for short. Typical 15 to 35
Filters
• Regime Type. none or trend or mean reversion
• Rank Lookback. Typical 100 to 300
• Basis Slope Length and Threshold. Larger values reduce false trends
Risk
• ATR Length. Typical 10 to 21
• ATR Stop Mult. Typical 1.0 to 3.0
• ATR Take Profit Mult. Typical 2.0 to 5.0
• Breakeven Trigger R. Move stop to entry after the chosen multiple
• Time Exit. Minimum bars and extension when profit exceeds a fraction of ATR
Bias and rearm
• Bias flips kept. Array depth
• Keep rearm when flat. Maintain a pending order while flat
UI
• Show markers and levels. Clean defaults
Usage recipes
Alerts update in real time and can change while the bar forms. Select on bar close for conservative workflows.
Properties visible in this publication
• Initial capital 25000
• Base currency USD
• If any higher timeframe calls are enabled, request.security uses lookahead off
• Commission 0.03 percent
• Slippage 3 ticks
• Default order size method Percent of equity with value 5
• Pyramiding 0
• Process orders on close On
• Bar magnifier Off
• Recalculate after order is filled Off
• Calc on every tick Off
Realism and responsible publication
No performance claims. Costs and fills vary by venue. Shapes can move intrabar and settle on close. Strategies use standard candles only.
Honest limitations and failure modes
High impact releases and thin liquidity can break assumptions. Gap heavy symbols may require larger ATR. Very quiet regimes can reduce contrast in the mean reversion signal. If stop and target can both be touched inside one bar, outcome follows the TradingView order model for that bar path.
Regimes with extreme one sided trend and very low volatility can reduce mean reversion edges. Results vary by symbol and venue. Past results never guarantee future outcomes.
Open source reuse and credits
None.
Backtest realism
Costs are realistic for liquid equities. Sizing does not exceed five percent per trade by default. Any departure should be justified by the user.
If you got any questions please le me know
cd_correlation_analys_Cxcd_correlation_analys_Cx
General:
This indicator is designed for correlation analysis by classifying stocks (487 in total) and indices (14 in total) traded on Borsa İstanbul (BIST) on a sectoral basis.
Tradingview's sector classifications (20) have been strictly adhered to for sector grouping.
Depending on user preference, the analysis can be performed within sectors, between sectors, or manually (single asset).
Let me express my gratitude to the code author, @fikira, beforehand; you will find the reason for my thanks in the context.
Details:
First, let's briefly mention how this indicator could have been prepared using the classic method before going into details.
Classically, assets could be divided into groups of forty (40), and the analysis could be performed using the built-in function:
ta.correlation(source1, source2, length) → series float.
I chose sectoral classification because I believe there would be a higher probability of assets moving together, rather than using fixed-number classes.
In this case, 21 arrays were formed with the following number of elements:
(3, 11, 21, 60, 29, 20, 12, 3, 31, 5, 10, 11, 6, 48, 73, 62, 16, 19, 13, 34 and indices (14)).
However, you might have noticed that some arrays have more than 40 elements. This is exactly where @Fikira's indicator came to the rescue. When I examined their excellent indicator, I saw that it could process 120 assets in a single operation. (I believe this was the first limit overrun; thanks again.)
It was amazing to see that data for 3 pairs could be called in a single request using a special method.
You can find the details here:
When I adapted it for BIST, I found it sufficient to call data for 2 pairs instead of 3 in a single go. Since asset prices are regular and have 2 decimal places, I used a fixed multiplier of $10^8$ and a fixed decimal count of 2 in Fikira's formulas.
With this method, the (high, low, open, close) values became accessible for each asset.
The summary up to this point is that instead of the ready-made formula + groups of 40, I used variable-sized groups and the method I will detail now.
Correlation/harmony/co-movement between assets provides advantages to market participants. Coherent assets are expected to rise or fall simultaneously.
Therefore, to convert co-movement into a mathematical value, I defined the possible movements of the current candle relative to the previous candle bar over a certain period (user-defined). These are:
Up := high > high and low > low
Down := high < high and low < low
Inside := high <= high and low >= low
Outside := high >= high and low <= low and NOT Inside.
Ignore := high = low = open = close
If both assets performed the same movement, 1 was added to the tracking counter.
If (Up-Up), (Down-Down), (Inside-Inside), or (Outside-Outside), then counter := counter + 1.
If the period length is 100 and the counter is 75, it means there is 75% co-movement.
Corr = counter / period ($75/100$)
Average = ta.sma(Corr, 100) is obtained.
The highest coefficients recorded in the array are presented to the user in a table.
From the user menu options, the user can choose to compare:
• With assets in its own sector
• With assets in the selected sector
• By activating the confirmation box and manually entering a single asset for comparison.
Table display options can be adjusted from the Settings tab.
In the attached examples:
Results for AKBNK stock from the Finance sector compared with GARAN stock from the same sector:
Timeframe: Daily, Period: 50 => Harmony 76% (They performed the same movement in 38 out of 50 bars)
Comment: Opposite movements at swing high and low levels may indicate a change in the direction of the price flow (SMT).
Looking at ASELS from the Electronic Technology sector over the last 30 daily candles, they performed the same movements by 40% with XU100, 73.3% (22/30) with XUTEK (Technology Index), and 86.9% according to the averages.
Comment: It is more appropriate to follow ASELS stock with XUTEK (Technology index) instead of the general index (XU100). Opposite movements at swing high and low levels may indicate a change in the direction of the price flow (SMT).
Again, when ASELS stock is taken on H1 instead of daily, and the length is 100 instead of 30, the harmony rate is seen to be 87%.
Please share your thoughts and criticisms regarding the indicator, which I prepared with a bit of an educational purpose specifically for BIST.
Happy trading.
TriAnchor Elastic Reversion US Market SPY and QQQ adaptedSummary in one paragraph
Mean-reversion strategy for liquid ETFs, index futures, large-cap equities, and major crypto on intraday to daily timeframes. It waits for three anchored VWAP stretches to become statistically extreme, aligns with bar-shape and breadth, and fades the move. Originality comes from fusing daily, weekly, and monthly AVWAP distances into a single ATR-normalized energy percentile, then gating with a robust Z-score and a session-safe gap filter.
Scope and intent
• Markets: SPY QQQ IWM NDX large caps liquid futures liquid crypto
• Timeframes: 5 min to 1 day
• Default demo: SPY on 60 min
• Purpose: fade stretched moves only when multi-anchor context and breadth agree
• Limits: strategy uses standard candles for signals and orders only
Originality and usefulness
• Unique fusion: tri-anchor AVWAP energy percentile plus robust Z of close plus shape-in-range gate plus breadth Z of SPY QQQ IWM
• Failure mode addressed: chasing extended moves and fading during index-wide thrusts
• Testability: each component is an input and visible in orders list via L and S tags
• Portable yardstick: distances are ATR-normalized so thresholds transfer across symbols
• Open source: method and implementation are disclosed for community review
Method overview in plain language
Base measures
• Range basis: ATR(length = atr_len) as the normalization unit
• Return basis: not used directly; we use rank statistics for stability
Components
• Tri-Anchor Energy: squared distances of price from daily, weekly, monthly AVWAPs, each divided by ATR, then summed and ranked to a percentile over base_len
• Robust Z of Close: median and MAD based Z to avoid outliers
• Shape Gate: position of close inside bar range to require capitulation for longs and exhaustion for shorts
• Breadth Gate: average robust Z of SPY QQQ IWM to avoid fading when the tape is one-sided
• Gap Shock: skip signals after large session gaps
Fusion rule
• All required gates must be true: Energy ≥ energy_trig_prc, |Robust Z| ≥ z_trig, Shape satisfied, Breadth confirmed, Gap filter clear
Signal rule
• Long: energy extreme, Z negative beyond threshold, close near bar low, breadth Z ≤ −breadth_z_ok
• Short: energy extreme, Z positive beyond threshold, close near bar high, breadth Z ≥ +breadth_z_ok
What you will see on the chart
• Standard strategy arrows for entries and exits
• Optional short-side brackets: ATR stop and ATR take profit if enabled
Inputs with guidance
Setup
• Base length: window for percentile ranks and medians. Typical 40 to 80. Longer smooths, shorter reacts.
• ATR length: normalization unit. Typical 10 to 20. Higher reduces noise.
• VWAP band stdev: volatility bands for anchors. Typical 2.0 to 4.0.
• Robust Z window: 40 to 100. Larger for stability.
• Robust Z entry magnitude: 1.2 to 2.2. Higher means stronger extremes only.
• Energy percentile trigger: 90 to 99.5. Higher limits signals to rare stretches.
• Bar close in range gate long: 0.05 to 0.25. Larger requires deeper capitulation for longs.
Regime and Breadth
• Use breadth gate: on when trading indices or broad ETFs.
• Breadth Z confirm magnitude: 0.8 to 1.8. Higher avoids fighting thrusts.
• Gap shock percent: 1.0 to 5.0. Larger allows more gaps to trade.
Risk — Short only
• Enable short SL TP: on to bracket shorts.
• Short ATR stop mult: 1.0 to 3.0.
• Short ATR take profit mult: 1.0 to 6.0.
Properties visible in this publication
• Initial capital: 25000USD
• Default order size: Percent of total equity 3%
• Pyramiding: 0
• Commission: 0.03 percent
• Slippage: 5 ticks
• Process orders on close: OFF
• Bar magnifier: OFF
• Recalculate after order is filled: OFF
• Calc on every tick: OFF
• request.security lookahead off where used
Realism and responsible publication
• No performance claims. Past results never guarantee future outcomes
• Fills and slippage vary by venue
• Shapes can move during bar formation and settle on close
• Standard candles only for strategies
Honest limitations and failure modes
• Economic releases or very thin liquidity can overwhelm mean-reversion logic
• Heavy gap regimes may require larger gap filter or TR-based tuning
• Very quiet regimes reduce signal contrast; extend windows or raise thresholds
Open source reuse and credits
• None
Strategy notice
Orders are simulated by TradingView on standard candles. request.security uses lookahead off where applicable. Non-standard charts are not supported for execution.
Entries and exits
• Entry logic: as in Signal rule above
• Exit logic: short side optional ATR stop and ATR take profit via brackets; long side closes on opposite setup
• Risk model: ATR-based brackets on shorts when enabled
• Tie handling: stop first when both could be touched inside one bar
Dataset and sample size
• Test across your visible history. For robust inference prefer 100 plus trades.
Aurum DCX AVE Gold and Silver StrategySummary in one paragraph
Aurum DCX AVE is a volatility break strategy for gold and silver on intraday and swing timeframes. It aligns a new Directional Convexity Index with an Adaptive Volatility Envelope and an optional USD/DXY bias so trades appear only when direction quality and expansion agree. It is original because it fuses three pieces rarely combined in one model for metals: a convexity aware trend strength score, a percentile based envelope that widens with regime heat, and an intermarket DXY filter.
Scope and intent
• Markets. Gold and silver futures or spot, other liquid commodities, major indices
• Timeframes. Five minutes to one day. Defaults to 30min for swing pace
• Default demo used in this publication. TVC:GOLD on 30m
• Purpose. Enter confirmed volatility breaks while muting chop using regime heat and USD bias
• Limits. This is a strategy. Orders are simulated on standard candles only
Originality and usefulness
• Unique fusion. DCX combines DI strength with path efficiency and curvature. AVE blends ATR with a high TR percentile and widens with DCX heat. DXY adds an intermarket bias
• Failure mode addressed. False starts inside compression and unconfirmed breakouts during USD swings
• Testability. Each component has a named input. Entry names L and S are visible in the list of trades
• Portable yardstick. Weekly ATR for stops and R multiples for targets
• Open source. Method and implementation are disclosed for community review
Method overview in plain language
You score direction quality with DCX, size an adaptive envelope with a blend of ATR and a high TR percentile, and only allow breaks that clear the band while DCX is above a heat threshold in the same direction. An optional DXY filter favors long when USD weakens and short when USD strengthens. Orders are bracketed with a Weekly ATR stop and an R multiple target, with optional trailing to the envelope.
Base measures
• Range basis. True Range and ATR over user windows. A high TR percentile captures expansion tails used by AVE
• Return basis. Not required
Components
• Directional Convexity Index DCX. Measures directional strength with DX, multiplies by path efficiency, blends a curvature term from acceleration, scales to 0 to 100, and uses a rise window
• Adaptive Volatility Envelope AVE. Midline ALMA or HMA or EMA plus bands sized by a blend of ATR and a high TR percentile. The blend weight follows volatility of volatility. Band width widens with DCX heat
• DXY Bias optional. Daily EMA trend of DXY. Long bias when USD weakens. Short bias when USD strengthens
• Risk block. Initial stop equals Weekly ATR times a multiplier. Target equals an R multiple of the initial risk. Optional trailing to AVE band
Fusion rule
• All gates must pass. DCX above threshold and rising. Directional lead agrees. Price breaks the AVE band in the same direction. DXY bias agrees when enabled
Signal rule
• Long. Close above AVE upper and DCX above threshold and DCX rising and plus DI leads and DXY bias is bearish
• Short. Close below AVE lower and DCX above threshold and DCX falling and minus DI leads and DXY bias is bullish
• Exit and flip. Bracket exit at stop or target. Optional trailing to AVE band
Inputs with guidance
Setup
• Symbol. Default TVC:GOLD (Correlation Asset for internal logic)
• Signal timeframe. Blank follows the chart
• Confirm timeframe. Default 1 day used by the bias block
Directional Convexity Index
• DCX window. Typical 10 to 21. Higher filters more. Lower reacts earlier
• DCX rise bars. Typical 3 to 6. Higher demands continuation
• DCX entry threshold. Typical 15 to 35. Higher avoids soft moves
• Efficiency floor. Typical 0.02 to 0.06. Stability in quiet tape
• Convexity weight 0..1. Typical 0.25 to 0.50. Higher gives curvature more influence
Adaptive Volatility Envelope
• AVE window. Typical 24 to 48. Higher smooths more
• Midline type. ALMA or HMA or EMA per preference
• TR percentile 0..100. Typical 75 to 90. Higher favors only strong expansions
• Vol of vol reference. Typical 0.05 to 0.30. Controls how much the percentile term weighs against ATR
• Base envelope mult. Typical 1.4 to 2.2. Width of bands
• Regime adapt 0..1. Typical 0.6 to 0.95. How much DCX heat widens or narrows the bands
Intermarket Bias
• Use DXY bias. Default ON
• DXY timeframe. Default 1 day
• DXY trend window. Typical 10 to 50
Risk
• Risk percent per trade. Reporting field. Keep live risk near one to two percent
• Weekly ATR. Default 14. Basis for stops
• Stop ATR weekly mult. Typical 1.5 to 3.0
• Take profit R multiple. Typical 1.5 to 3.0
• Trail with AVE band. Optional. OFF by default
Properties visible in this publication
• Initial capital. 20000
• Base currency. USD
• request.security lookahead off everywhere
• Commission. 0.03 percent
• Slippage. 5 ticks
• Default order size method percent of equity with value 3% of the total capital available
• Pyramiding 0
• Process orders on close ON
• Bar magnifier ON
• Recalculate after order is filled OFF
• Calc on every tick OFF
Realism and responsible publication
• No performance claims. Past results never guarantee future outcomes
• Shapes can move while a bar forms and settle on close
• Strategies use standard candles for signals and orders only
Honest limitations and failure modes
• Economic releases and thin liquidity can break assumptions behind the expansion logic
• Gap heavy symbols may prefer a longer ATR window
• Very quiet regimes can reduce signal contrast. Consider higher DCX thresholds or wider bands
• Session time follows the exchange of the chart and can change symbol to symbol
• Symbol sensitivity is expected. Use the gates and length inputs to find stable settings
Open source reuse and credits
• None
Mode
Public open source. Source is visible and free to reuse within TradingView House Rules
Legal
Education and research only. Not investment advice. You are responsible for your decisions. Test on historical data and in simulation before any live use. Use realistic costs.
FluxGate Daily Swing StrategySummary in one paragraph
FluxGate treats long and short as different ecosystems. It runs two independent engines so the long side can be bold when the tape rewards upside persistence while the short side can stay selective when downside is messy. The core reads three directional drivers from price geometry then removes overlap before gating with clean path checks. The complementary risk module anchors stop distance to a higher timeframe ATR so a unit means the same thing on SPY and BTC. It can add take profit breakeven and an ATR trail that only activates after the trade earns it. If a stop is hit the strategy can re enter in the same direction on the next bar with a daily retry cap that you control. Add it to a clean chart. Use defaults to see the intended behavior. For conservative workflows evaluate on bar close.
Scope and intent
• Markets. Large cap equities and liquid ETFs major FX pairs US index futures and liquid crypto pairs
• Timeframes. From one minute to daily
• Default demo in this publication. SPY on one day timeframe
• Purpose. Reduce false starts without missing sustained trends by fusing independent drivers and suppressing activity when the path is noisy
• Limits. This is a strategy. Orders are simulated on standard candles. Non standard chart types are not supported for execution
Originality and usefulness
• Unique fusion. FluxGate extracts three drivers that look at price from different angles. Direction measures slope of a smoothed guide and scales by realized volatility so a point of slope does not mean a different thing on different symbols. Persistence looks at short sign agreement to reward series of closes that keep direction. Curvature measures the second difference of a local fit to wake up during convex pushes. These three are then orthonormalized so a strong reading in one does not double count through another.
• Gates that matter. Efficiency ratio prefers direct paths over treadmills. Entropy turns up versus down frequency into an information read. Light fractal cohesion punishes wrinkly paths. Together they slow the system in chop and allow it to open up when the path is clean.
• Separate long and short engines. Threshold tilts adapt to the skew of score excursions. That lets long engage earlier when upside distribution supports it and keeps short cautious where downside surprise and venue frictions are common.
• Practical risk behavior. Stops are ATR anchored on a higher timeframe so the unit is portable. Take profit is expressed in R so two R means the same concept across symbols. Breakeven and trailing only activate after a chosen R so early noise does not squeeze a good entry. Re entry after stop lets the system try again without you babysitting the chart.
• Testability. Every major window and the aggression controls live in Inputs. There is no hidden magic number.
Method overview in plain language
Base measures
• Return basis. Natural log of close over prior close for stability and easy aggregation through time. Realized volatility is the standard deviation of returns over a moving window.
• Range basis for risk. ATR computed on a higher timeframe anchor such as day week or month. That anchor is steady across venues and avoids chasing chart specific quirks.
Components
• Directional intensity. Use an EMA of typical price as a guide. Take the day to day slope as raw direction. Divide by realized volatility to get a unit free measure. Soft clip to keep outliers from dominating.
• Persistence. Encode whether each bar closed up or down. Measure short sign agreement so a string of higher closes scores better than a jittery sequence. This favors push continuity without guessing tops or bottoms.
• Curvature. Fit a short linear regression and compute the second difference of the fitted series. Strong curvature flags acceleration that slope alone may miss.
• Efficiency gate. Compare net move to path length over a gate window. Values near one indicate direct paths. Values near zero indicate treadmill behavior.
• Entropy gate. Convert up versus down frequency into a probability of direction. High entropy means coin toss. The gate narrows there.
• Fractal cohesion. A light read of path wrinkliness relative to span. Lower cohesion reduces the urge to act.
• Phase assist. Map price inside a recent channel to a small signed bias that grows with confidence. This helps entries lean toward the right half of the channel without becoming a breakout rule.
• Shock control. Compare short volatility to long volatility. When short term volatility spikes the shock gate temporarily damps activity so the system waits for pressure to normalize.
Fusion rule
• Normalize the three drivers after removing overlap
• Blend with weights that adapt to your aggression input
• Multiply by the gates to respect path quality
• Smooth just enough to avoid jitter while keeping timing responsive
• Compute an adaptive mean and deviation of the score and set separate long and short thresholds with a small tilt informed by skew sign
• The result is one long score and one short score that can cross their thresholds at different times for the same tape which is a feature not a bug
Signal rule
• A long suggestion appears when the long score crosses above its long threshold while all gates are active
• A short suggestion appears when the short score crosses below its short threshold while all gates are active
• If any required gate is missing the state is wait
• When a position is open the status is in long or in short until the complementary risk engine exits or your entry mode closes and flips
Inputs with guidance
Setup Long
• Base length Long. Master window for the long engine. Typical range twenty four to eighty. Raising it improves selectivity and reduces trade count. Lowering it reacts faster but can increase noise
• Aggression Long. Zero to one. Higher values make thresholds more permissive and shorten smoothing
Setup Short
• Base length Short. Master window for the short engine. Typical range twenty eight to ninety six
• Aggression Short. Zero to one. Lower values keep shorts conservative which is often useful on upward drifting symbols
Entries and UI
• Entry mode. Both or Long only or Short only
Complementary risk engine
• Enable risk engine. Turns on bracket exits while keeping your signal logic untouched
• ATR anchor timeframe. Day Week or Month. This sets the structural unit of stop distance
• ATR length. Default fourteen
• Stop multiple. Default one point five times the anchor ATR
• Use take profit. On by default
• Take profit in R. Default two R
• Breakeven trigger in R. Default one R
Usage recipes
Intraday trend focus
• Entry mode Both
• ATR anchor Week
• Aggression Long zero point five Aggression Short zero point three
• Stop multiple one point five Take profit two R
• Expect fewer trades that stick to directional pushes and skip treadmill noise
Intraday mean reversion focus
• Session windows optional if you add them in your copy
• ATR anchor Day
• Lower aggression both sides
• Breakeven later and trailing later so the first bounce has room
• This favors fade entries that still convert into trends when the path stays clean
Swing continuation
• Signal timeframe four hours or one day
• Confirm timeframe one day if you choose to include bias
• ATR anchor Week or Month
• Larger base windows and a steady two R target
• This accepts fewer entries and aims for larger holds
Properties visible in this publication
• Initial capital 25.000
• Base currency USD
• Default order size percent of equity value three - 3% of the total capital
• Pyramiding zero
• Commission zero point zero three percent - 0.03% of total capital
• Slippage five ticks
• Process orders on close off
• Recalculate after order is filled off
• Calc on every tick off
• Bar magnifier off
• Any request security calls use lookahead off everywhere
Realism and responsible publication
• No performance promises. Past results never guarantee future outcomes
• Fills and slippage vary by venue and feed
• Strategies run on standard candles only
• Shapes can update while a bar is forming and settle on close
• Keep risk per trade sensible. Around one percent is typical for study. Above five to ten percent is rarely sustainable
Honest limitations and failure modes
• Sudden news and thin liquidity can break assumptions behind entropy and cohesion reads
• Gap heavy symbols often behave better with a True Range basis for risk than a simple range
• Very quiet regimes can reduce score contrast. Consider longer windows or higher thresholds when markets sleep
• Session windows follow the exchange time of the chart if you add them
• If stop and target can both be inside a single bar this strategy prefers stop first to keep accounting conservative
Open source reuse and credits
• No reused open source beyond public domain building blocks such as ATR EMA and linear regression concepts
Legal
Education and research only. Not investment advice. You are responsible for your decisions. Test on history and in simulation with realistic costs
Dollar Volume Ownership Gauge Dollar Volume Ownership Gauge (DVOG)
By: Mando4_27
Version: 1.0 — Pine Script® v6
Overview
The Dollar Volume Ownership Gauge (DVOG) is designed to measure the intensity of real money participation behind each price bar.
Instead of tracking raw share volume, this tool converts every bar’s trading activity into dollar volume (price × volume) and highlights the transition points where institutional capital begins to take control of a move.
DVOG’s mission is simple:
Show when the crowd is trading vs. when the institutions are buying control.
Core Concept
Most retail traders focus on share count (volume) — but institutions think in dollar exposure.
A small-cap printing a 1-million-share candle at $1 is very different from a 1-million-share candle at $10.
DVOG normalizes this by displaying total traded dollar value per bar, then color-codes and alerts when the volume of money crosses key thresholds.
This exposes the exact moments when ownership is shifting — often before major breakouts, reclaims, or exhaustion reversals.
How It Works
Dollar Volume Calculation
Each candle’s dollar volume is computed as close × volume.
Data is aggregated from the 5-minute timeframe regardless of your current chart, allowing consistent institutional-flow detection on any resolution.
Threshold Logic
Two customizable levels define interest zones:
$500K Threshold → Early or moderate institutional attention.
$1M Threshold → High-conviction or aggressive accumulation.
Both levels can be edited to fit different market caps or trading styles.
Bar Coloring Scheme
Red = Dollar Volume ≥ $1,000,000 → Significant institutional activity / control bar.
Green = Dollar Volume ≥ $500,000 and < $1,000,000 → Emerging accumulation / transition bar.
Black = Below $500,000 → Retail or low-interest zone.
(Colors are intentionally inverted from standard expectation: when volume intensity spikes, the bar turns hotter in tone.)
Plot Display
Histogram style plot displays 5-minute aggregated dollar volume per bar.
Dotted reference lines mark $500K and $1M levels, with live right-hand labels for quick reading.
Optional debug label shows current bar’s dollar value, closing price, and raw volume for transparency.
Alerts & Conditions
DVOG includes three alert triggers for hands-off monitoring:
Alert Name Trigger Message Purpose
Green Bar Alert – Dollar Volume ≥ $500K When dollar volume first crosses $500K “Institutional interest starting on ” Signals early money entering.
Dollar Volume ≥ $500K Same as above, configurable “Early institutional interest detected…” Broad alert option.
Dollar Volume ≥ $1M When dollar volume first crosses $1M “Significant money flow detected…” Indicates heavy institutional presence or ignition bar.
You can enable or disable alerts via checkbox inputs, allowing you to monitor just the levels that fit your style.
Interpretation & Use Cases
Identify Institutional “Ignition” Points:
Watch for sudden green or red DVOG bars after long low-volume consolidation — these often precede explosive continuation moves.
Confirm Breakouts & Reclaims:
If price reclaims a key level (HOD, neckline, or coil top) and DVOG flashes green/red, odds strongly favor follow-through.
Spot Trap Exhaustion:
After a flush or low-volume fade, the first strong green/red DVOG bar can mark the institutional reclaim — the moment retail control ends.
Filter Noise:
Ignore standard volume spikes. DVOG only reacts when dollar ownership materially changes hands, not when small traders churn shares.
Customization
Setting Default Description
$500K Threshold 500,000 Lower limit for “Green” institutional attention.
$1M Threshold 1,000,000 Upper limit for “Red” heavy institutional control.
Show Alerts ✅ Enable or disable global alerts.
Alert on Green Bars ✅ Toggle only the $500K crossover alerts.
Adjust thresholds to match the liquidity of your preferred tickers — for example, micro-caps may use $100K/$300K, while large-caps might use $5M/$20M.
Reading the Output
Black baseline = Noise / retail chop.
First Green bar = Smart money starts building position.
Red bar(s) = Ownership shift confirmed — institutions active.
Flat-to-rising pattern in DVOG = Sustained accumulation; often aligns with strong trend continuation.
Summary
DVOG transforms raw volume into actionable context — showing you when capital, not hype, is moving.
It’s particularly effective for:
Momentum and breakout traders
Liquidity trap reclaims (Kuiper-style setups)
Identifying early ignition bars before halts
Confirming frontside strength in micro-caps
Use DVOG as your ownership radar — the visual cue for when the market stops being retail and starts being real.
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.
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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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