Impulse MACD buy OwlPixelDescription:
The Impulse MACD Buy Indicator, developed by OwlPixel, is a powerful trading tool for traders using TradingView's Pine Script version 5. This indicator aims to provide valuable insights for identifying potential buy signals in the market using the popular MACD (Moving Average Convergence Divergence) oscillator.
Key Features:
MACD Analysis: The indicator displays the MACD line (blue) and the signal line (orange) on the chart, helping traders assess the momentum and trend direction of an asset.
Impulse Histo: The Impulse Histo (blue histogram) visualizes the difference between the MACD line and the signal line, making it easier to spot changes in market strength and potential trend reversals.
Impulse MACD CD Signal: This histogram (maroon color) highlights the divergence between the Impulse Histo and the signal line, providing further insights into trend shifts.
Background Boxes: The indicator features three rows of different colored background boxes that represent distinct market conditions - an uptrend (light green), a downtrend (light red), and a neutral trend (light yellow).
Crossover Points: Buy signals are marked with green circles when the MACD line crosses above the signal line, suggesting potential entry points for long positions.
Demand and Supply Bars: The demand (lime/green) and supply (red/orange) bars are intensified, aiding traders in identifying possible reversal areas.
Stop Loss and Take Profit:
The Impulse MACD Buy Indicator automatically calculates Stop Loss (SL) and Take Profit (TP) levels for buy signals. The SL level is set at the highest of the last three candles, while the TP level is determined by a user-defined percentage of the closing price. This information helps traders manage risk and optimize their profit potential.
Usage:
Apply the Impulse MACD Buy Indicator to your TradingView chart by copying the provided Pine Script into the Pine Editor.
Configure the input parameters, such as the MA Length and Signal Length, to suit your trading preferences.
Observe the MACD line, signal line, and histograms to gain insights into market momentum and trends.
Identify buy signals when the MACD line crosses above the signal line, signaled by green circles.
Utilize the provided Stop Loss and Take Profit levels for risk management and exit strategies.
Please note that this indicator is for informational purposes only and should be used in conjunction with other analysis techniques to make well-informed trading decisions. Happy trading!
在脚本中搜索"demand"
Liquidity Voids (FVG) [LuxAlgo]The Liquidity Voids (FVG) indicator is designed to detect liquidity voids/imbalances derived from the fair value gaps and highlight the distribution of the liquidity voids at specific price levels.
Fair value gaps and liquidity voids are both indicators of sell-side and buy-side imbalance in trading. The only difference is how they are represented in the trading chart. Liquidity voids occur when the price moves sharply in one direction forming long-range candles that have little trading activity, whilst a fair value is a gap in price.
🔶 USAGE
Liquidity can help you to determine where the price is likely to head next. In conjunction with higher timeframe market structure, and supply and demand, liquidity can give you insights into potential price movement. It's essential to practice using liquidity alongside trend analysis and supply and demand to read market conditions effectively.
The peculiar thing about liquidity voids is that they almost always fill up. And by “filling”, we mean the price returns to the origin of the gap. The reason for this is that during the gap, an imbalance is created in the asset that has to be made up for. The erasure of this gap is what we call the filling of the void. And while some voids waste no time in filling, some others take multiple periods before they get filled.
🔶 SETTINGS
The script takes into account user-defined parameters and detects the liquidity voids based on them, where detailed usage for each user-defined input parameter in indicator settings is provided with the related input's tooltip.
🔹 Liquidity Detection
Liquidity Voids Threshold: Act as a filter while detecting the Liquidity Voids. When set to 0 basically means no filtering is applied, increasing the value causes the script to check the width of the void compared to a fixed-length ATR value
Bullish: Color customization option for Bullish Liquidity Voids
Bearish: Color customization option for Bearish Liquidity Voids
Labels: Toggles the visibility of the Liquidity Void label
Filled Liquidity Voids: Toggles the visibility of the Filled Liquidity Voids
🔹 Display Options
Mode: Controls the lookback length of detection and visualization
# Bars: Lookback length customization, in case Mode is set to Present
🔶 RELATED SCRIPTS
Buyside-Sellside-Liquidity
Fair-Value-Gaps
Volume Spread Analysis Candle PatternsVolume Spread Analysis (VSA) is a methodology used in trading and investing to analyze the relationship between volume, price spread, and price movement in financial markets. It was developed by Richard Wyckoff, a prominent trader and market observer.
The core principle of VSA is that changes in volume can provide insights into the strength or weakness of price movements and indicate the intentions of market participants. By examining the interplay between volume and price, traders aim to identify the behavior of smart money (informed institutional investors) versus less-informed market participants.
Key concepts in Volume Spread Analysis include:
1. Volume: VSA places significant emphasis on volume as a leading indicator. It suggests that changes in volume precede price movements and can provide clues about the market's sentiment.
2. Spread: The spread refers to the price range between the high and low of a given trading period (e.g., a candlestick or bar). VSA considers the relationship between volume and spread to gauge the strength of price action.
3. Upthrust and Springs: These are VSA candle patterns that indicate potential market reversals. An upthrust occurs when prices briefly move above a resistance level but fail to sustain the upward momentum. Springs, on the other hand, happen when prices briefly dip below a support level but quickly rebound.
4. No Demand and No Supply: These patterns suggest a lack of interest or participation from buyers (no demand) or sellers (no supply) at a particular price level. These conditions may foreshadow a potential price reversal or consolidation.
5. Hidden Buying and Selling: Hidden buying occurs when prices close near the high of a bar, indicating the presence of buyers even though the market appears weak. Hidden selling is the opposite, where prices close near the low of a bar, suggesting the presence of sellers despite apparent strength.
By combining these VSA concepts with other technical analysis tools, traders seek to identify potential trading opportunities with favorable risk-reward ratios. VSA can be applied to various financial markets, including stocks, futures, forex, and cryptocurrencies.
It's important to note that while VSA provides a framework for analyzing volume and price, its interpretation and application require experience, skill, and subjective judgment. Traders often use VSA in conjunction with other technical indicators and chart patterns to make well-informed trading decisions.
Multi-Timeframe High Low (@JP7FX)Multi-Timeframe High Low Levels (@JP7FX)
This Price Action indicator displays high and low levels from a selected timeframe on your current chart.
These levels COULD represent areas of potential liquidity, providing key price points where traders can target entries, reversals, or continuation trades.
Key Features:
Display high and low levels from a selected timeframe.
Customize line width, colors for high and low levels, and label text color.
Enable or disable the display of high levels, low levels, and labels.
Receive alerts when the price takes out high or low levels.
How to use:
It is important to note that using this indicator on it's own is not advisable. Instead, it should be combined with other tools and analysis for a more comprehensive trading strategy.
Possibly look to use my MTF Supply and Demand Indicator to look for zones to trade from at these levels?
If the price breaks above a high level, you might consider entering a long position, with the expectation that the price will continue to rise. Conversely, if the price breaks below a low level, you may think about entering a short position, anticipating further downward movement.
On the other hand, you can also use high or low levels to look for reversal trades, as these areas can represent attractive liquidity zones.
By identifying these key price points, you could take advantage of potential market reversals and capitalise on new trading opportunities.
Always remember to use this indicator in conjunction with other technical analysis tools for the best results.
Additionally, you can enable alerts to notify you when the price takes out high or low levels, helping you stay informed about significant price movements.
This indicator could be a valuable tool for traders looking to identify key price points for potential trading opportunities.
As always with the markets, Trade Safe :)
Paradigm Trades_VPA Swing IndicatorThe indicator is designed to identify specific patterns in price and volume movements that can signal potential trading opportunities. It does this by calculating several conditions based on the current bar's price and volume movements.
The code defines five conditions: Narrow Spread Up Bar, Wide Spread Down Bar, No Demand Bar, No Selling Bar, and Churning. These conditions are then plotted on the chart using specific shapes and colors. The code also includes alert conditions for each of the signals, which can be used to generate alerts for traders when a particular pattern is identified.
The VPA Swing Indicator can be used as part of a swing trading strategy to identify potential buy or sell signals. For example, a Narrow Spread Up Bar may indicate bullish momentum, while a Wide Spread Down Bar may indicate bearish momentum. Traders can use these signals to make informed trading decisions and manage their risk accordingly.
Legend:
Spread Up Bar: This is a bullish bar with a small spread, indicating a lack of selling pressure and strong buying activity.
Wide Spread Down Bar: This is a bearish bar with a large spread, indicating strong selling pressure and weak buying activity.
No Demand Bar: This is a bearish bar with a small spread and low volume, indicating a lack of buying interest and the smart money selling off their positions.
No Selling Bar: This is a bullish bar with a small spread and low volume, indicating a lack of selling interest and the smart money buying up positions.
Churning: This is a sideways market with narrow spread bars and low volume, indicating the smart money is distributing shares to the retail traders.
Rich Robin Index, The Crypto Fear & Greed Index with RSI Trend The Relative Strength Index (RSI) is a technical indicator based on price movements that is used to determine whether a particular asset is overbought or oversold. It measures the ratio of rising to falling prices over a certain period of time.
The Fear & Greed Index, on the other hand, is a composite index that tracks the sentiment of the crypto market. It is based on seven indicators, each of which measures a different aspect of market behavior. These indicators are: Safe Haven Demand, Stock Price Breadth, Market Momentum, Stock Price Strength, Put and Call Options, Junk Bond Demand, and Market Volatility.
The combination of the RSI and the Fear & Greed Index can provide valuable insights for crypto traders. The RSI can help identify overbought and oversold conditions, while the Fear & Greed Index can give an overall sense of the sentiment in the market. Together, they can provide a more complete picture of the market conditions. For example, if the RSI is indicating that an asset is overbought, but the Fear & Greed Index is showing that the market is still in a state of fear, it may be a good time to sell. On the other hand, if the RSI is indicating that an asset is oversold, but the Fear & Greed Index is showing that the market is in a state of greed, it may be a good time to buy.
Overall, the combination of the RSI and the Fear & Greed Index can provide useful information for traders to make more informed decisions, by giving a sense of the market conditions, and providing a way to identify overbought and oversold conditions.
4 main Stablecoin MarketCapThis indicator summarized 4 main stablecoin marketcap (USDT, USDC , BUSD, DAI).
It is given that most of the transactions of cryptocurrencies are traded by stablecoins, and USDT, USDC , BUSD and DAI shared 90%+ of the stablecoins market capacity. Therefore, by summarizing these 4 main stablecoins total market capacity, can reflect the overall demand power.
When the indicator goes up, it is expected that the overall market demand will increase.
When the indicator goes down, it is expected that the cryptocurrencies market might be in a recession.
This indicator could be more useful in a longer timeframe, day-trade or even shorter might not be the suitable timeframe.
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V2 update
Separate 4 sectors and shadowed in different colors for 4 different stablecoins for more accurated view.
Support/Resistant Zone (Simple)The concepts of trading level support and resistance are undoubtedly two of the most highly discussed attributes of technical analysis.
Support is a price level where a downtrend can be expected to pause due to a concentration of demand or buying interest. As the price of assets or securities drops, demand for the shares increases, thus forming the support line. Meanwhile, resistance zones arise due to selling interest when prices have increased.
There are many ways to identify support and resistance zones. This indicator is a simple method to identify them. Support/Resistant zones will draw basing on the size of the wick for candles, which are Pivots High/Low before.
Volume Price Trend with Divergence and Pivot Points The volume price trend indicator is used to determine the balance between a security’s demand and supply. The percentage change in the share price trend shows the relative supply or demand of a particular security, while volume indicates the force behind the trend. The VPT indicator is similar to the on-balance volume (OBV) indicator in that it measures cumulative volume and provides traders with information about a security’s money flow.
This is Volume Price Trend or VPT recalculated to be an Oscillator, a Divergence hunter was added, also Pivot Points and Alerts.
VPT is considered a "leading indicator" - in contrast to a "lagging indicator" just as Moving Averages it does not show a confirmation what already happened, but it shows what can happen in the future. For example: The chart is climbing while the VPT oscillator is slowly declining, gets weaker and weaker, maybe even prints bearish divergences? That means that a reversal might be occurring soon. Leading indicators are best paired with Stop and Resistance Lines, general Trendlines , Fib Retracements etc...Your chart is approaching a very important Resistance Trendline but the VPT shows a very positive signal? That means there is a high probability that the Resistance is going to be pushed though and becomes Support in the future.
What are those circles?
-These are Divergences. Red for Regular-Bearish. Orange for Hidden-Bearish. Green for Regular-Bullish. Aqua for Hidden-Bullish.
What are those triangles?
- These are Pivots . They show when the VPT oscillator might reverse, this is important to know because many times the price action follows this move.
Please keep in mind that this indicator is a tool and not a strategy, do not blindly trade signals, do your own research first! Use this indicator in conjunction with other indicators to get multiple confirmations.
Freedom of MovementFreedom of Movement Indicator
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In “Evidence-Based Support & Resistance” article, author Melvin Dickover introduces two new indicators to help traders note support and resistance areas by identifying supply and demand pools. Here you can find the support-resistance technical indicator called "Freedom of Movement".
The indicator takes into account price-volume behavior in order to detect points where movement of price is suddenly restricted, the possible supply and demand pools. These points are also marked by Defended Price Lines (DPLs).
DPLs are horizontal lines that run across the chart at levels defined by following conditions:
* Overlapping bars: If the indicator spike (i.e., indicator is above 2.0 or a custom value) corresponds to a price bar overlapping the previous one, the previous close can be used as the DPL value.
* Very large bars: If the indicator spike corresponds to a price bar of a large size, use its close price as the DPL value.
* Gapping bars: If the indicator spike corresponds to a price bar gapping from the previous bar, the DPL value will depend on the gap size. Small gaps can be ignored: the author suggests using the previous close as the DPL value. When the gap is big, the close of the latter bar is used instead.
* Clustering spikes: If the indicator spikes come in clusters, use the extreme close or open price of the bar corresponding to the last or next to last spike in cluster.
DPLs can be used as support and resistance levels. In order confirm and refine them, FoM (Freedom of Movement) is used along with the Relative Volume Indicator (RVI), which you can find here:
Clustering spikes provide the strongest DPLs while isolated spikes can be used to confirm and refine those provided by the RVI. Coincidence of spikes of the two indicator can be considered a sign of greater strength of the DPL.
More info:
S&C magazine, April 2014.
Trading Psychology - Fear & Greed Index by DGTPsychology of a Market Cycle - Where are we in the cycle?
Before proceeding with the question "where", let's first have a quick look at "What is market psychology?"
Market psychology is the idea that the movements of a market reflect the emotional state of its participants. It is one of the main topics of behavioral economics - an interdisciplinary field that investigates the various factors that precede economic decisions. Many believe that emotions are the main driving force behind the shifts of financial markets and that the overall fluctuating investor sentiment is what creates the so-called psychological market cycles - which is also dynamic.
Stages of Investor Emotions:
* Optimism – A positive outlook encourages us about the future, leading us to buy stocks.
* Excitement – Having seen some of our initial ideas work, we begin considering what our market success could allow us to accomplish.
* Thrill – At this point we investors cannot believe our success and begin to comment on how smart we are.
* Euphoria – This marks the point of maximum financial risk. Having seen every decision result in quick, easy profits, we begin to ignore risk and expect every trade to become profitable.
* Anxiety – For the first time the market moves against us. Having never stared at unrealized losses, we tell ourselves we are long-term investors and that all our ideas will eventually work.
* Denial – When markets have not rebounded, yet we do not know how to respond, we begin denying either that we made poor choices or that things will not improve shortly.
* Fear – The market realities become confusing. We believe the stocks we own will never move in our favor.
* Desperation – Not knowing how to act, we grasp at any idea that will allow us to get back to breakeven.
* Panic – Having exhausted all ideas, we are at a loss for what to do next.
* Capitulation – Deciding our portfolio will never increase again, we sell all our stocks to avoid any future losses.
* Despondency – After exiting the markets we do not want to buy stocks ever again. This often marks the moment of greatest financial opportunity.
* Depression – Not knowing how we could be so foolish, we are left trying to understand our actions.
* Hope – Eventually we return to the realization that markets move in cycles, and we begin looking for our next opportunity.
* Relief – Having bought a stock that turned profitable, we renew our faith that there is a future in investing.
It's hard to predict with certainty where we exactly are in the market cycle, we can only make an educated guess as to the rough stage based on data available. And here comes the study "Trading Psychology - Fear & Greed Index"
Factors taken into account in this study include:
1-Price Momentum : Price Divergence/Convergence versus its Slow Moving Average
2-Strenght : Rate of Return (RoR) also called Return on Investment (ROI) is a performance measure used to evaluate the efficiency of an investment, net gain or loss of an investment over a specified time period, the rate of change in price movement over a period of time to help investors determine the strength
3-Money Flow : Chaikin Money Flow (CMF) is a technical analysis indicator used to measure Money Flow Volume over a set period of time. CMF can be used as a way to further quantify changes in buying and selling pressure and can help to anticipate future changes and therefore trading opportunities. CMF calculations is based on Accumulation/Distribution
4-Market Volatility : CBOE Volatility Index (VIX), the Volatility Index, or VIX, is a real-time market index that represents the market's expectation of 30-day forward-looking volatility. Derived from the price inputs of the S&P 500 index options, it provides a measure of market risk and investors' sentiments. It is also known by other names like "Fear Gauge" or "Fear Index." Investors, research analysts and portfolio managers look to VIX values as a way to measure market risk, fear and stress before they take investment decisions
5-Safe Haven Demand : in this study GOLD demand is assumed
What to look for :
*Fear and Greed Index as explained above,
*Divergencies
Tool tip of the label displayed provides details of references
Conclusion:
As investors, we always get caught up in the day to day price movements, and lose sight of the bigger picture. The biggest crashes happen not when investors are cautious and fearful, it's when they're euphoric and expecting financial instruments to continue going higher. So as we continue investing, don’t forget to stop and ask yourself, where in the chart do you think we are right now? The Market Psychology Cycle shines light on how emotions evolve, fear and greed index can come in handy, provided that it is not the only tool used to make investment decisions. It is easy to look back at market cycles and recognize how the overall psychology changed. Analyzing previous data makes it obvious what actions and decisions would have been the most profitable. However, it is much harder to understand how the market is changing as it goes - and even harder to predict what comes next. Many investors use technical analysis (TA) to attempt to anticipate where the market is likely to go. Investors are advised to keep tabs on fear for potential buying the dips opportunities and view periods of greed as a potential indicator that financial instruments might be overvalued.
Warren Buffett's quote, buy when others are fearful, and sell when others are greedy
Trading success is all about following your trading strategy and the indicators should fit within your trading strategy, and not to be traded upon solely
Disclaimer : The script is for informational and educational purposes only. Use of the script does not constitute professional and/or financial advice. You alone have the sole responsibility of evaluating the script output and risks associated with the use of the script. In exchange for using the script, you agree not to hold dgtrd TradingView user liable for any possible claim for damages arising from any decision you make based on use of the script
RedK_AvgMoneyFlow Oscillator v1This is a compact & simple study that tracks the short-term average price change and the (average) volume associated with it, to generate a very clear signal when a change of buying/selling flow is detected. these buy/sell cycles can happen within a longer "demand / trend-up" or "supply / trend down" phases as we know.
this concept is a bit different from MFI or CMF. The math we use here is simpler, and more "relative" and short-term focused, deliberately.
how does it work
===============
once the average price change and the average volumes are calculated for the specified length, we then turn that into a +100/-100 oscillator format - using the stoch() function - which helps to generate a clearly identifiable unambiguous signal (crossing the zero line up or down) that help traders (mainly with entries)
-- the stoch() function also makes the oscillator "relative" to the specified period length, meaning, we can be in a uptrend (demand mode) and the MFO is showing flow "out" (negative) - that's specific to the short-term period - and that's exactly what i was trying to see
- the thinking here is that the best spot to go long is when the existing selling has been depleted and no more supply exists (during an uptrend), and vice verca.
- other stuff: i use WMA() throughout the script -- and we apply a smoothing for the final plot. keep smoothing to a minimum to avoid unnecessary lag in the signals
- the signal should be considered *after* a bar is fully closed.
Suggested Use
==============
i suggest you use this in combination with other indicators that can show the overall short-term and long-term bias (for example, i use the Ribbon here for that) - and take only entry signals in the same direction - a signal to go long, for example, would be when the bias / trend is up *and* the MFO crosses the zero line *going up* .. you may need to wait for that setup to show before you hit the trigger.
another benefit here, is that MFO will also detect strengths and weaknesses - when we see diversion with price movement. this shows couple of times in the example below
Please Note
============
i do not do short-term trading / scalping - those who do, i hope may find this useful - if you decide to use it and you do find it useful, please post feedback here for the common learning
Good luck!
Gap driven intraday trade (better in 15 Min chart)// Based on yesterday's High, Low, today's open, and Bollinger Band (20) in current minute chart,
// Defined intraday Trading opportunity: Stop, Entry, T0, Target (S.E.T.T)
// Back test in 60, 30, 15, 5 Min charts with SPY, QQQ, XOP, AAPL, TSLA, NVDA, UAL
// In 60 and 30 min chart, the stop and target are too big. 5 min is too small.
// 15 min Chart is the best time frame for this strategy;
// -------------------------------------------------------------------------------
// There will be Four lines in this study:
// 1. Entry Line,
// 1.1 Green Color line to Buy, If today's open price above Yesterday's High, and current price below BB upper line.
// 1.2 Red Color line to Short, if today's open price below Yesterday's Low, and current above BB Lower line.
//
// 2. Black line to show initial stop, one ATR in current min chart;
//
// 3. Blue Line (T0) to show where trader can move stop to make even, one ATR in current min chart;
//
// 4. Orange Line to show initial target, Three ATR in current min chart;
//
// Trading opportunity:
// If Entry line is green color, Set stop buy order at today's Open;
// Whenever price is below the green line, Prepare to buy;
//
// If Entry line is Red color, Set Stop short at today's Open;
// Whenever price is above the red line, Prepare to short;
//
// Initial Stop: One ATR in min chart;
// Initial T0: One ATR in min chart;
// Initial Target: Three ATR in min chart;
// Initial RRR: Reward Risk Ratio = 3:1;
//
// Maintain: Once the position moves to T0, Move stop to "Make even + Lunch (such as, Entry + $0.10)";
// Allow to move target bigger, such as, next demand/supply zone;
// When near target or demand/supply zone or near Market close, move stop tightly;
//
// Close position: Limit order filled, or near Market Close, or trendline break;
//
// Key Step: Move stop to "Make even" after T0, Do not turn winner to loser;
// Willing to "in and out" many times in one day, and trade the same direction, same price again and again.
//
// Basic trading platform requests:
// To use this strategy, user needs to:
// 1. Scan Stocks Before market open:
// Prepare a watch list for top 10 ETF and Top 90 stocks which are most actively traded.
// Stock might be limited by price range, Beta, optionable, ...
// Before market open, Run a scan for these stocks, find which has GAP and inside BB;
// create watch list for that day.
//
// 2. Attach OSO and OCO orders:
// User needs to Send Entry, Stop (loss), and limit (target) orders at one time;
// Order Send order ( OSO ): Entry order sends Stop order and limit order;
// Order Cancel order ( OCO ): Stop order and limit order, when one is filled, it will cancel the other instantly;
Dynamic Support and ResistanceSupport is a price level where a downtrend can be expected to pause due to a concentration of demand or buying interest. As the price of assets or securities drops, demand for the shares increases, thus forming the support line.
Meanwhile, resistance zones arise due to selling interest when prices have increased.s their name implies, dynamic support and resistance levels change their level with each new price-tick.To draw dynamic support and resistance levels, traders usually use moving averages which are automatically drawn by your trading platform. The 200-day exponential moving average (EMA), 100-day EMA, and 20-30-40-50-day EMA are very popular dynamic support and resistance levels.also in some references Williams Fractal level used for dynamic support and resistance levels. and it also includes other support and resistance levels that are projected based on the pivot point calculation. All these levels help traders see where the price could experience support or resistance. Similarly, if the price moves through these levels it lets the trader know the price is trending in that direction.
VPT and Heiken Ashi Candles MTFThe volume price trend indicator is used to determine the balance between a security’s demand and supply. The percentage change in the share price trend shows the relative supply or demand of a particular security, while volume indicates the force behind the trend. The VPT indicator is similar to the on-balance volume (OBV) indicator in that it measures cumulative volume and provides traders with information about a security’s money flow
So we put the VPT and add HA candles with non repainting MTF , the crossing up or down of the VPT over candles create the signals
since VPT tend to overshoot you can smooth it with Leni..(just give the smoothing of the length this stupid name:) )
alerts inside
just example of play with MTF and the smooth of VPT
Katana_Fox RSI Pro - Advanced Momentum Indicator with Clear BUOverview:
Connors RSI Pro is a sophisticated enhancement of the classic Connors RSI indicator, designed for traders who demand professional-grade tools. This premium version combines multiple momentum components with intelligent signaling and beautiful visualization to give you an edge in the markets.
Key Features:
🎯 Clear BUY/SELL Signal System
BUY signals in green when CRSI crosses above oversold level
SELL signals in red when CRSI crosses below overbought level
Clean, professional labels that are easy to read
Customizable overbought/oversold levels (70/30 default)
🎨 Professional Visualization
Modern color scheme that adapts to market conditions
Customizable background fills for better readability
Smooth, easy-to-read line plotting
⚡ Enhanced Calculations
Triple-component momentum analysis (RSI, UpDown RSI, Percent Rank)
EMA smoothing for reduced noise and false signals
Configurable lengths for each component
🔔 Advanced Alert System
4 distinct alert conditions for various market scenarios
Compatible with TradingView's native alert system
Perfect for automated trading strategies
Input Parameters:
RSI Length (3): Period for standard RSI calculation
UpDown Length (2): Period for UpDown RSI component
ROC Length (100): Period for Rate of Change percentile ranking
Signal Alerts: Toggle BUY/SELL signals on/off
Custom Colors: Choose between classic and modern color schemes
Trading Signals:
BUY (Green Label): Bullish signal when CRSI crosses above oversold level
SELL (Red Label): Bearish signal when CRSI crosses below overbought level
Background Colors: Visual zones indicating momentum strength
Ideal For:
Swing traders seeking momentum reversals
Day traders looking for overbought/oversold conditions
Algorithmic traders needing reliable signals
Technical analysts wanting multi-timeframe confirmation
How to Use:
Oversold Bounce: Enter long when CRSI shows BUY signal above 30
Overbought Rejection: Enter short when CRSI shows SELL signal below 70
Trend Confirmation: Use the 50-level crossover for trend direction
Divergence Trading: Look for price/indicator divergences at extremes
Upgrade your trading arsenal with Connors RSI Pro - where professional analytics meet clear trading signals!
No Supply (Low-Volume Down Bars) — IdoThis indicator flags classic Wyckoff/VSA “No Supply (NS)” events—down bars that print on unusually low volume, suggesting a lack of sellers rather than strong selling pressure. NS often appears near support, LPS, or within re-accumulation ranges as a test before continuation higher.
Signal definition (configurable):
Down bar: choose Close < PrevClose or Close < Open.
Low volume: Volume < SMA(Volume, len) × threshold (e.g., 0.7).
Optional volume lower than the prior two bars (reduces noise).
Optional narrow spread: range (H–L) below its average.
Optional close position: close in the upper half of the bar.
Optional trend filter: only mark NS above or below an EMA (or any).
Optional wide-bar exclusion: skip unusually wide bars.
Visuals & outputs
Blue dot below each NS bar (optional bar tint).
Separate pane showing Relative Volume (vol / volSMA) to gauge effort.
Built-in alertcondition to trigger notifications when NS prints.
Inputs (high level)
lenVol: Volume SMA length.
ratioVol: Volume threshold vs. average (e.g., 0.7 = 70%).
usePrev2: Require volume below each of the prior two bars.
useNarrow + lenRange + ratioRange: Narrow-bar filter.
useClosePos + minClosePos: Close in upper portion of the bar.
downBarMode: Define “down bar” logic.
trendFiltOn, trendLen, trendSide: EMA trend filter.
useWideFilter, lenRangeWide, wideThreshold: Skip wide bars.
How to use (Wyckoff/VSA context)
Treat NS as a test of supply: price dips, but volume is light and close holds up.
Stronger when it prints near support/LPS within a re-accumulation structure.
Confirmation (recommended): within 1–3 bars, see demand—e.g., break above the NS high with expanding volume (above average or above the prior two bars). Many traders place a buy-stop just above the NS high; common stops are below the NS low or the most recent swing low.
Scanning tip
TradingView’s stock screener can’t consume Pine directly.
Use a Watchlist Custom Column that reports “bars since NS” to sort symbols (0 = NS on the latest bar). A companion column script is provided separately.
Notes & limitations
Works on any timeframe (intraday/daily/weekly), but context matters.
Expect false positives around news, gaps, or illiquid symbols—combine with structure (trend, S/R, phases) and risk management.
© moshel — Educational use only; not financial advice.
TTM Squeeze Screener [Pineify]TTM Squeeze Screener for Multiple Crypto Assets and Timeframes
This advanced TradingView Pine script, TTM Squeeze Screener, helps traders scan multiple crypto symbols and timeframes simultaneously, unlocking new dimensions in momentum and volatility analysis.
Key Features
Screen up to 8 crypto symbols across 4 different timeframes in one pane
TTM Squeeze indicator detects volatility contraction and expansion (“squeeze”) phases
Momentum filter reveals potential breakout direction and strength
Visual screener table for intuitive multi-asset monitoring
Fully customizable for symbols and timeframes
How It Works
The heart of this screener is the TTM Squeeze algorithm—a hybrid volatility and momentum indicator leveraging Bollinger Bands, Keltner Channels, and linear momentum analysis. The script checks whether Bollinger Bands are “squeezed” inside Keltner Channels, flagging periods of low volatility primed for expansion. Once a squeeze is released, the included momentum calculation suggests the likely breakout direction.
For each selected symbol and timeframe, the screener runs the TTM Squeeze logic, outputs “SQUEEZE” or “NO SQZ”, and tags momentum values. A table layout organizes the results, allowing rapid pattern recognition across symbols.
Trading Ideas and Insights
Spot multi-symbol volatility clusters—ideal for finding synchronized market moves
Assess breakout potential and direction before entering trades
Scalping and swing trading decisions are enhanced by cross-timeframe momentum filtering
Portfolio managers can quickly identify which assets are about to move
How Multiple Indicators Work Together
This screener unites three essential concepts:
Bollinger Bands : Measure volatility using standard deviation of price
Keltner Channels : Define expected price range based on average true range (ATR)
Momentum : Linear regression calculation to evaluate the direction and intensity after a squeeze
By combining these, the indicator not only signals when volatility compresses and releases, but also adds directional context—filtering false signals and helping traders time entries and exits more precisely.
Unique Aspects
Multi-symbol, multi-timeframe architecture—optimized for crypto traders and market scanners
Advanced table visualization—see all signals at a glance, minimizing cognitive overload
Modular calculation functions—easy to adapt and extend for other asset classes or strategies
Real-time, low-latency screening—built for actionable alerts on fast-moving markets
How to Use
Add the script to a TradingView chart (works on custom layouts)
Select up to 8 symbols and 4 timeframes using input fields (defaults to BTCUSD, ETHUSD, etc.)
Monitor the screener table; “SQUEEZE” highlights assets in potential breakout phase
Use momentum values to judge if the squeeze is likely bullish or bearish
Combine screener insights with manual chart analysis for optimal results
Customization
Symbols: Easily set any ticker for deep market scanning
Timeframes: Adjust to match your trading horizon (scalping, swing, long-term)
Indicator parameters: Refine Bollinger/Keltner/Momentum settings for sensitivity
Visuals: Personalize table layout, color codes, and formatting for clarity
Conclusion
In summary, the TTM Squeeze Screener is a robust, original TradingView indicator designed for crypto traders who demand a sophisticated multi-symbol, multi-timeframe edge. Its combination of volatility and momentum analytics makes it ideal for catching explosive breakouts, managing risk, and scanning the market efficiently. Whether you’re a scalper or swing trader, this screener provides the insights needed to stay ahead of the curve.
Relative Volume (RVOL) [JopAlgo]Relative Volume (RVOL) — “Filter Fakes, Ride Real Moves”
What it does:
Shows how today’s volume compares to its own average.
RVOL = current volume ÷ SMA(volume, length)
RVOL > cutoff → participation above normal (green)
RVOL < cutoff → participation below normal (red)
Use it to confirm breaks, filter entries, and avoid chasing moves fueled by thin volume.
Read it in 5 seconds
Above/Below the cutoff line (white) = high/low participation now.
Spikes through the cutoff on a break = real interest.
Dry-ups (well below cutoff) into support/resistance = good risk for mean-revert or pullback entries.
If you remember one rule: don’t chase a breakout with RVOL under the cutoff.
Simple playbook (copy this)
Breakout confirmation
Break at VAH/LVN/structure and RVOL > cutoff → take the retest that holds.
If RVOL stays below cutoff on the break → likely fake; wait for reclaim.
Pullback in trend
Trend up, price pulls to AVWAP / VAL / MA cluster with RVOL below cutoff → take the bounce when price turns; add if RVOL rises on the resume.
Fade the exhaustion
Into resistance, huge RVOL spike but no follow-through (long wick, CVD Absorption) → look for the fail back inside value.
Do less in chop
When RVOL hugs below cutoff all session, expect range; trade edges only.
Timeframe guide
1–5m (scalps): Signals are frequent. Keep cutoff ≥ 1.5; demand RVOL on breaks.
15m–1H (intraday): Sweet spot. cutoff 1.5–2.0 is a solid filter.
2H–4H (swing): Look for clustered bars > cutoff during expansions; dry-ups flag pullback entries.
1D+: Use RVOL to separate true trend days from drift.
Settings that matter
Length (default 14):
Shorter = reacts faster; Longer = smoother baseline.
Intraday: 14–20
Swing/Daily: 20–30
Cutoff (default 1.0):
Set the bar for “real” volume.
Conservative confirmation: 1.5–2.0
For slower pairs/timeframes: 1.2–1.5
Tune by scrolling back and marking where good breaks happened.
Color logic: green above cutoff, red below—no surprises.
Best combos (kept simple)
Volume Profile v3.2 : Confirm breaks of VAH/VAL/LVNs with RVOL > cutoff; target POC/HVNs.
Anchored VWAP : Reclaims/rejections with RVOL > cutoff stick more often.
CVDv1 :
Yes: RVOL high and CVD Alignment OK and no Absorption → higher-quality move.
No: RVOL high but Absorption red → don’t chase; look for fail/reclaim.
Pattern cheat sheet
Trend day: RVOL stays > cutoff on pushes; pullbacks show RVOL dip, then re-expand.
False break: Price pokes level, RVOL < cutoff, quick give-back.
Accumulation: Series of low-RVOL bars compressing under a level → watch for the first RVOL pop to go.
Exhaustion wick: RVOL spike + long wick into resistance/support → likely trap unless next bar accepts.
Notes & pitfalls
Exchange volume varies (crypto): use the same feed you trade and calibrate cutoff there.
RVOL ≠ direction: it’s participation. Always pair with location, structure, and flow.
Quick defaults to start
Length: 20
Cutoff: 1.5 (intraday) / 1.8–2.0 (for stricter confirmation)
Process: Level → RVOL above/below cutoff → CVD quality → Execute with structure-based risk
Mini-disclaimer
Educational tool, not financial advice. Test first, size sensibly, and always anchor decisions to levels, flow, and risk.
Multi Doji Detector v1 [JopAlgo]Multi Doji Detector v1 — fast pattern pings with real filters
What it does (one line):
Marks reversal/indecision candles (Doji family) and impulse candles (Engulfing, Hammer/Inverted Hammer), with optional ATR and volume filters so you don’t chase every wiggle.
Why it’s useful:
Candles tell you when the auction pauses or flips. This tool spots those moments, but only matters at a level. Use it to time entries at Volume Profile edges and AVWAP—not as a standalone signal.
What you’ll see on the chart
Doji family
Standard Doji (▲ blue above bar)
Dragonfly Doji (● green below bar)
Gravestone Doji (● red above bar)
Long-Legged Doji (▲ orange above bar)
Engulfing
Bullish Engulfing (⬆ teal below bar)
Bearish Engulfing (⬇ purple above bar)
Hammer set
Hammer (⬆ lime below bar)
Inverted Hammer (⬆ fuchsia below bar)
Shapes = heads-up. Your trade still needs location, flow, and a risk plan.
How to use it (the simple playbook)
Location first
Work at Volume Profile v3.2 levels (VAH/VAL/POC/LVNs) or Anchored VWAP.
No level, no trade.
Flow check (optional but strong)
Use CVDv1: take signals only when Alignment = OK and Absorption ≠ red against your idea.
Pattern = timing
At support (VAL/AVWAP): Bullish Engulfing or Hammer, or a Dragonfly/Standard Doji that gets follow-through up.
At resistance (VAH/AVWAP): Bearish Engulfing, Inverted Hammer, or Gravestone/Standard Doji with follow-through down.
Confirm the bar
Let the signal close. If the next bar rejects the idea, stand down.
Timeframe guidance
1–5m (scalps): Many marks. Keep ATR/volume filters ON. Only take signals at VA edges / Session AVWAP.
15m–1H (intraday): Cleanest. Best combo of signal quality and frequency.
2H–4H (swing): Fewer, stronger signals. Ideal for AVWAP/Composite VP reclaims.
1D+ (position): Use as a heads-up at weekly levels; wait for intraday confirmation to enter.
Entries, exits, risk (quick rules)
Entry:
Long: Bullish Engulfing / Hammer / bullish Doji at support, next bar holds above signal’s high or prints follow-through.
Short: Bearish Engulfing / Inverted Hammer / bearish Doji at resistance, next bar holds below signal’s low or follows through.
Stop:
Longs: below signal low or structure under the level.
Shorts: above signal high or structure over the level.
Targets:
Aim for POC/HVNs or obvious swings. Don’t use the symbol alone as a target.
Pass:
Signals mid-range (no level), or against CVDv1 (Absorption), or when ATR is tiny (fake pokes).
Settings that actually matter
Doji mode
Use Percentage-Based: compares body to full candle range (default 5%).
Off = fixed definition (body < 10% of range).
Tip: If you get too many dojis, lower the %; if too few, raise it slightly.
Engulfing filters
ATR Length (default 14) + Min Size (ATR): require real body expansion.
Volume confirmation: ON = demand above-average volume; reduces noise.
Hammer filters
Wick-to-Body Ratio: default 2.5×; increase for stricter hammers.
ATR Filter: minimum candle size; blocks tiny “toy” candles.
Volume confirmation: ON = better reliability.
Alerts
Toggle Doji Alerts on if you want all doji pings; engulfing/hammer alerts are always available.
Pattern cheatsheet (what they mean at a level)
Standard Doji: indecision → wait for directional close next bar.
Dragonfly (at support): buyers rejected lows → look for long on follow-through.
Gravestone (at resistance): sellers rejected highs → look for short on follow-through.
Long-Legged Doji: big fight → only trade it at a level and with the next bar confirming.
Bullish Engulfing: fresh control shift to buyers; best after a drive into support.
Bearish Engulfing: fresh control shift to sellers; best after a pop into resistance.
Hammer: capitulation then rescue; strongest when the low sweeps a level and closes back above.
Inverted Hammer: rejection from above; needs downside follow-through to matter.
Best combos (kept simple)
Volume Profile v3.2 : Signals at VAH/VAL/LVNs. Use POC/HVNs for targets.
Anchored VWAP : Reclaims/rejections get much better with a matching candle signal.
CVDv1 : Take signals with flow (ALIGN OK, no Absorption). If Absorption flashes red against your signal, skip it.
Common mistakes this prevents
Taking a “pretty” candle in the middle of nowhere.
Shorting every Gravestone in a real uptrend (ATR expanding, CVD strong).
Ignoring size: Engulfing/Hammer without ATR/volume often fail.
Entering before close: half of false signals vanish by the close.
Practical defaults to start
Doji: Percentage-based ON, 5%
Engulfing: Min size 1.0 ATR, Volume confirm ON
Hammer: Wick/Body 2.5×, ATR filter 0.5, Volume confirm ON
Timeframes: 15m–1H for most assets; 2H–4H for swing
Quick disclaimer
Educational tool, not financial advice. Patterns are timers, not trades by themselves. Always pair with location, flow, and risk.
Dynamic Equity Allocation Model"Cash is Trash"? Not Always. Here's Why Science Beats Guesswork.
Every retail trader knows the frustration: you draw support and resistance lines, you spot patterns, you follow market gurus on social media—and still, when the next bear market hits, your portfolio bleeds red. Meanwhile, institutional investors seem to navigate market turbulence with ease, preserving capital when markets crash and participating when they rally. What's their secret?
The answer isn't insider information or access to exotic derivatives. It's systematic, scientifically validated decision-making. While most retail traders rely on subjective chart analysis and emotional reactions, professional portfolio managers use quantitative models that remove emotion from the equation and process multiple streams of market information simultaneously.
This document presents exactly such a system—not a proprietary black box available only to hedge funds, but a fully transparent, academically grounded framework that any serious investor can understand and apply. The Dynamic Equity Allocation Model (DEAM) synthesizes decades of financial research from Nobel laureates and leading academics into a practical tool for tactical asset allocation.
Stop drawing colorful lines on your chart and start thinking like a quant. This isn't about predicting where the market goes next week—it's about systematically adjusting your risk exposure based on what the data actually tells you. When valuations scream danger, when volatility spikes, when credit markets freeze, when multiple warning signals align—that's when cash isn't trash. That's when cash saves your portfolio.
The irony of "cash is trash" rhetoric is that it ignores timing. Yes, being 100% cash for decades would be disastrous. But being 100% equities through every crisis is equally foolish. The sophisticated approach is dynamic: aggressive when conditions favor risk-taking, defensive when they don't. This model shows you how to make that decision systematically, not emotionally.
Whether you're managing your own retirement portfolio or seeking to understand how institutional allocation strategies work, this comprehensive analysis provides the theoretical foundation, mathematical implementation, and practical guidance to elevate your investment approach from amateur to professional.
The choice is yours: keep hoping your chart patterns work out, or start using the same quantitative methods that professionals rely on. The tools are here. The research is cited. The methodology is explained. All you need to do is read, understand, and apply.
The Dynamic Equity Allocation Model (DEAM) is a quantitative framework for systematic allocation between equities and cash, grounded in modern portfolio theory and empirical market research. The model integrates five scientifically validated dimensions of market analysis—market regime, risk metrics, valuation, sentiment, and macroeconomic conditions—to generate dynamic allocation recommendations ranging from 0% to 100% equity exposure. This work documents the theoretical foundations, mathematical implementation, and practical application of this multi-factor approach.
1. Introduction and Theoretical Background
1.1 The Limitations of Static Portfolio Allocation
Traditional portfolio theory, as formulated by Markowitz (1952) in his seminal work "Portfolio Selection," assumes an optimal static allocation where investors distribute their wealth across asset classes according to their risk aversion. This approach rests on the assumption that returns and risks remain constant over time. However, empirical research demonstrates that this assumption does not hold in reality. Fama and French (1989) showed that expected returns vary over time and correlate with macroeconomic variables such as the spread between long-term and short-term interest rates. Campbell and Shiller (1988) demonstrated that the price-earnings ratio possesses predictive power for future stock returns, providing a foundation for dynamic allocation strategies.
The academic literature on tactical asset allocation has evolved considerably over recent decades. Ilmanen (2011) argues in "Expected Returns" that investors can improve their risk-adjusted returns by considering valuation levels, business cycles, and market sentiment. The Dynamic Equity Allocation Model presented here builds on this research tradition and operationalizes these insights into a practically applicable allocation framework.
1.2 Multi-Factor Approaches in Asset Allocation
Modern financial research has shown that different factors capture distinct aspects of market dynamics and together provide a more robust picture of market conditions than individual indicators. Ross (1976) developed the Arbitrage Pricing Theory, a model that employs multiple factors to explain security returns. Following this multi-factor philosophy, DEAM integrates five complementary analytical dimensions, each tapping different information sources and collectively enabling comprehensive market understanding.
2. Data Foundation and Data Quality
2.1 Data Sources Used
The model draws its data exclusively from publicly available market data via the TradingView platform. This transparency and accessibility is a significant advantage over proprietary models that rely on non-public data. The data foundation encompasses several categories of market information, each capturing specific aspects of market dynamics.
First, price data for the S&P 500 Index is obtained through the SPDR S&P 500 ETF (ticker: SPY). The use of a highly liquid ETF instead of the index itself has practical reasons, as ETF data is available in real-time and reflects actual tradability. In addition to closing prices, high, low, and volume data are captured, which are required for calculating advanced volatility measures.
Fundamental corporate metrics are retrieved via TradingView's Financial Data API. These include earnings per share, price-to-earnings ratio, return on equity, debt-to-equity ratio, dividend yield, and share buyback yield. Cochrane (2011) emphasizes in "Presidential Address: Discount Rates" the central importance of valuation metrics for forecasting future returns, making these fundamental data a cornerstone of the model.
Volatility indicators are represented by the CBOE Volatility Index (VIX) and related metrics. The VIX, often referred to as the market's "fear gauge," measures the implied volatility of S&P 500 index options and serves as a proxy for market participants' risk perception. Whaley (2000) describes in "The Investor Fear Gauge" the construction and interpretation of the VIX and its use as a sentiment indicator.
Macroeconomic data includes yield curve information through US Treasury bonds of various maturities and credit risk premiums through the spread between high-yield bonds and risk-free government bonds. These variables capture the macroeconomic conditions and financing conditions relevant for equity valuation. Estrella and Hardouvelis (1991) showed that the shape of the yield curve has predictive power for future economic activity, justifying the inclusion of these data.
2.2 Handling Missing Data
A practical problem when working with financial data is dealing with missing or unavailable values. The model implements a fallback system where a plausible historical average value is stored for each fundamental metric. When current data is unavailable for a specific point in time, this fallback value is used. This approach ensures that the model remains functional even during temporary data outages and avoids systematic biases from missing data. The use of average values as fallback is conservative, as it generates neither overly optimistic nor pessimistic signals.
3. Component 1: Market Regime Detection
3.1 The Concept of Market Regimes
The idea that financial markets exist in different "regimes" or states that differ in their statistical properties has a long tradition in financial science. Hamilton (1989) developed regime-switching models that allow distinguishing between different market states with different return and volatility characteristics. The practical application of this theory consists of identifying the current market state and adjusting portfolio allocation accordingly.
DEAM classifies market regimes using a scoring system that considers three main dimensions: trend strength, volatility level, and drawdown depth. This multidimensional view is more robust than focusing on individual indicators, as it captures various facets of market dynamics. Classification occurs into six distinct regimes: Strong Bull, Bull Market, Neutral, Correction, Bear Market, and Crisis.
3.2 Trend Analysis Through Moving Averages
Moving averages are among the oldest and most widely used technical indicators and have also received attention in academic literature. Brock, Lakonishok, and LeBaron (1992) examined in "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns" the profitability of trading rules based on moving averages and found evidence for their predictive power, although later studies questioned the robustness of these results when considering transaction costs.
The model calculates three moving averages with different time windows: a 20-day average (approximately one trading month), a 50-day average (approximately one quarter), and a 200-day average (approximately one trading year). The relationship of the current price to these averages and the relationship of the averages to each other provide information about trend strength and direction. When the price trades above all three averages and the short-term average is above the long-term, this indicates an established uptrend. The model assigns points based on these constellations, with longer-term trends weighted more heavily as they are considered more persistent.
3.3 Volatility Regimes
Volatility, understood as the standard deviation of returns, is a central concept of financial theory and serves as the primary risk measure. However, research has shown that volatility is not constant but changes over time and occurs in clusters—a phenomenon first documented by Mandelbrot (1963) and later formalized through ARCH and GARCH models (Engle, 1982; Bollerslev, 1986).
DEAM calculates volatility not only through the classic method of return standard deviation but also uses more advanced estimators such as the Parkinson estimator and the Garman-Klass estimator. These methods utilize intraday information (high and low prices) and are more efficient than simple close-to-close volatility estimators. The Parkinson estimator (Parkinson, 1980) uses the range between high and low of a trading day and is based on the recognition that this information reveals more about true volatility than just the closing price difference. The Garman-Klass estimator (Garman and Klass, 1980) extends this approach by additionally considering opening and closing prices.
The calculated volatility is annualized by multiplying it by the square root of 252 (the average number of trading days per year), enabling standardized comparability. The model compares current volatility with the VIX, the implied volatility from option prices. A low VIX (below 15) signals market comfort and increases the regime score, while a high VIX (above 35) indicates market stress and reduces the score. This interpretation follows the empirical observation that elevated volatility is typically associated with falling markets (Schwert, 1989).
3.4 Drawdown Analysis
A drawdown refers to the percentage decline from the highest point (peak) to the lowest point (trough) during a specific period. This metric is psychologically significant for investors as it represents the maximum loss experienced. Calmar (1991) developed the Calmar Ratio, which relates return to maximum drawdown, underscoring the practical relevance of this metric.
The model calculates current drawdown as the percentage distance from the highest price of the last 252 trading days (one year). A drawdown below 3% is considered negligible and maximally increases the regime score. As drawdown increases, the score decreases progressively, with drawdowns above 20% classified as severe and indicating a crisis or bear market regime. These thresholds are empirically motivated by historical market cycles, in which corrections typically encompassed 5-10% drawdowns, bear markets 20-30%, and crises over 30%.
3.5 Regime Classification
Final regime classification occurs through aggregation of scores from trend (40% weight), volatility (30%), and drawdown (30%). The higher weighting of trend reflects the empirical observation that trend-following strategies have historically delivered robust results (Moskowitz, Ooi, and Pedersen, 2012). A total score above 80 signals a strong bull market with established uptrend, low volatility, and minimal losses. At a score below 10, a crisis situation exists requiring defensive positioning. The six regime categories enable a differentiated allocation strategy that not only distinguishes binarily between bullish and bearish but allows gradual gradations.
4. Component 2: Risk-Based Allocation
4.1 Volatility Targeting as Risk Management Approach
The concept of volatility targeting is based on the idea that investors should maximize not returns but risk-adjusted returns. Sharpe (1966, 1994) defined with the Sharpe Ratio the fundamental concept of return per unit of risk, measured as volatility. Volatility targeting goes a step further and adjusts portfolio allocation to achieve constant target volatility. This means that in times of low market volatility, equity allocation is increased, and in times of high volatility, it is reduced.
Moreira and Muir (2017) showed in "Volatility-Managed Portfolios" that strategies that adjust their exposure based on volatility forecasts achieve higher Sharpe Ratios than passive buy-and-hold strategies. DEAM implements this principle by defining a target portfolio volatility (default 12% annualized) and adjusting equity allocation to achieve it. The mathematical foundation is simple: if market volatility is 20% and target volatility is 12%, equity allocation should be 60% (12/20 = 0.6), with the remaining 40% held in cash with zero volatility.
4.2 Market Volatility Calculation
Estimating current market volatility is central to the risk-based allocation approach. The model uses several volatility estimators in parallel and selects the higher value between traditional close-to-close volatility and the Parkinson estimator. This conservative choice ensures the model does not underestimate true volatility, which could lead to excessive risk exposure.
Traditional volatility calculation uses logarithmic returns, as these have mathematically advantageous properties (additive linkage over multiple periods). The logarithmic return is calculated as ln(P_t / P_{t-1}), where P_t is the price at time t. The standard deviation of these returns over a rolling 20-trading-day window is then multiplied by √252 to obtain annualized volatility. This annualization is based on the assumption of independently identically distributed returns, which is an idealization but widely accepted in practice.
The Parkinson estimator uses additional information from the trading range (High minus Low) of each day. The formula is: σ_P = (1/√(4ln2)) × √(1/n × Σln²(H_i/L_i)) × √252, where H_i and L_i are high and low prices. Under ideal conditions, this estimator is approximately five times more efficient than the close-to-close estimator (Parkinson, 1980), as it uses more information per observation.
4.3 Drawdown-Based Position Size Adjustment
In addition to volatility targeting, the model implements drawdown-based risk control. The logic is that deep market declines often signal further losses and therefore justify exposure reduction. This behavior corresponds with the concept of path-dependent risk tolerance: investors who have already suffered losses are typically less willing to take additional risk (Kahneman and Tversky, 1979).
The model defines a maximum portfolio drawdown as a target parameter (default 15%). Since portfolio volatility and portfolio drawdown are proportional to equity allocation (assuming cash has neither volatility nor drawdown), allocation-based control is possible. For example, if the market exhibits a 25% drawdown and target portfolio drawdown is 15%, equity allocation should be at most 60% (15/25).
4.4 Dynamic Risk Adjustment
An advanced feature of DEAM is dynamic adjustment of risk-based allocation through a feedback mechanism. The model continuously estimates what actual portfolio volatility and portfolio drawdown would result at the current allocation. If risk utilization (ratio of actual to target risk) exceeds 1.0, allocation is reduced by an adjustment factor that grows exponentially with overutilization. This implements a form of dynamic feedback that avoids overexposure.
Mathematically, a risk adjustment factor r_adjust is calculated: if risk utilization u > 1, then r_adjust = exp(-0.5 × (u - 1)). This exponential function ensures that moderate overutilization is gently corrected, while strong overutilization triggers drastic reductions. The factor 0.5 in the exponent was empirically calibrated to achieve a balanced ratio between sensitivity and stability.
5. Component 3: Valuation Analysis
5.1 Theoretical Foundations of Fundamental Valuation
DEAM's valuation component is based on the fundamental premise that the intrinsic value of a security is determined by its future cash flows and that deviations between market price and intrinsic value are eventually corrected. Graham and Dodd (1934) established in "Security Analysis" the basic principles of fundamental analysis that remain relevant today. Translated into modern portfolio context, this means that markets with high valuation metrics (high price-earnings ratios) should have lower expected returns than cheaply valued markets.
Campbell and Shiller (1988) developed the Cyclically Adjusted P/E Ratio (CAPE), which smooths earnings over a full business cycle. Their empirical analysis showed that this ratio has significant predictive power for 10-year returns. Asness, Moskowitz, and Pedersen (2013) demonstrated in "Value and Momentum Everywhere" that value effects exist not only in individual stocks but also in asset classes and markets.
5.2 Equity Risk Premium as Central Valuation Metric
The Equity Risk Premium (ERP) is defined as the expected excess return of stocks over risk-free government bonds. It is the theoretical heart of valuation analysis, as it represents the compensation investors demand for bearing equity risk. Damodaran (2012) discusses in "Equity Risk Premiums: Determinants, Estimation and Implications" various methods for ERP estimation.
DEAM calculates ERP not through a single method but combines four complementary approaches with different weights. This multi-method strategy increases estimation robustness and avoids dependence on single, potentially erroneous inputs.
The first method (35% weight) uses earnings yield, calculated as 1/P/E or directly from operating earnings data, and subtracts the 10-year Treasury yield. This method follows Fed Model logic (Yardeni, 2003), although this model has theoretical weaknesses as it does not consistently treat inflation (Asness, 2003).
The second method (30% weight) extends earnings yield by share buyback yield. Share buybacks are a form of capital return to shareholders and increase value per share. Boudoukh et al. (2007) showed in "The Total Shareholder Yield" that the sum of dividend yield and buyback yield is a better predictor of future returns than dividend yield alone.
The third method (20% weight) implements the Gordon Growth Model (Gordon, 1962), which models stock value as the sum of discounted future dividends. Under constant growth g assumption: Expected Return = Dividend Yield + g. The model estimates sustainable growth as g = ROE × (1 - Payout Ratio), where ROE is return on equity and payout ratio is the ratio of dividends to earnings. This formula follows from equity theory: unretained earnings are reinvested at ROE and generate additional earnings growth.
The fourth method (15% weight) combines total shareholder yield (Dividend + Buybacks) with implied growth derived from revenue growth. This method considers that companies with strong revenue growth should generate higher future earnings, even if current valuations do not yet fully reflect this.
The final ERP is the weighted average of these four methods. A high ERP (above 4%) signals attractive valuations and increases the valuation score to 95 out of 100 possible points. A negative ERP, where stocks have lower expected returns than bonds, results in a minimal score of 10.
5.3 Quality Adjustments to Valuation
Valuation metrics alone can be misleading if not interpreted in the context of company quality. A company with a low P/E may be cheap or fundamentally problematic. The model therefore implements quality adjustments based on growth, profitability, and capital structure.
Revenue growth above 10% annually adds 10 points to the valuation score, moderate growth above 5% adds 5 points. This adjustment reflects that growth has independent value (Modigliani and Miller, 1961, extended by later growth theory). Net margin above 15% signals pricing power and operational efficiency and increases the score by 5 points, while low margins below 8% indicate competitive pressure and subtract 5 points.
Return on equity (ROE) above 20% characterizes outstanding capital efficiency and increases the score by 5 points. Piotroski (2000) showed in "Value Investing: The Use of Historical Financial Statement Information" that fundamental quality signals such as high ROE can improve the performance of value strategies.
Capital structure is evaluated through the debt-to-equity ratio. A conservative ratio below 1.0 multiplies the valuation score by 1.2, while high leverage above 2.0 applies a multiplier of 0.8. This adjustment reflects that high debt constrains financial flexibility and can become problematic in crisis times (Korteweg, 2010).
6. Component 4: Sentiment Analysis
6.1 The Role of Sentiment in Financial Markets
Investor sentiment, defined as the collective psychological attitude of market participants, influences asset prices independently of fundamental data. Baker and Wurgler (2006, 2007) developed a sentiment index and showed that periods of high sentiment are followed by overvaluations that later correct. This insight justifies integrating a sentiment component into allocation decisions.
Sentiment is difficult to measure directly but can be proxied through market indicators. The VIX is the most widely used sentiment indicator, as it aggregates implied volatility from option prices. High VIX values reflect elevated uncertainty and risk aversion, while low values signal market comfort. Whaley (2009) refers to the VIX as the "Investor Fear Gauge" and documents its role as a contrarian indicator: extremely high values typically occur at market bottoms, while low values occur at tops.
6.2 VIX-Based Sentiment Assessment
DEAM uses statistical normalization of the VIX by calculating the Z-score: z = (VIX_current - VIX_average) / VIX_standard_deviation. The Z-score indicates how many standard deviations the current VIX is from the historical average. This approach is more robust than absolute thresholds, as it adapts to the average volatility level, which can vary over longer periods.
A Z-score below -1.5 (VIX is 1.5 standard deviations below average) signals exceptionally low risk perception and adds 40 points to the sentiment score. This may seem counterintuitive—shouldn't low fear be bullish? However, the logic follows the contrarian principle: when no one is afraid, everyone is already invested, and there is limited further upside potential (Zweig, 1973). Conversely, a Z-score above 1.5 (extreme fear) adds -40 points, reflecting market panic but simultaneously suggesting potential buying opportunities.
6.3 VIX Term Structure as Sentiment Signal
The VIX term structure provides additional sentiment information. Normally, the VIX trades in contango, meaning longer-term VIX futures have higher prices than short-term. This reflects that short-term volatility is currently known, while long-term volatility is more uncertain and carries a risk premium. The model compares the VIX with VIX9D (9-day volatility) and identifies backwardation (VIX > 1.05 × VIX9D) and steep backwardation (VIX > 1.15 × VIX9D).
Backwardation occurs when short-term implied volatility is higher than longer-term, which typically happens during market stress. Investors anticipate immediate turbulence but expect calming. Psychologically, this reflects acute fear. The model subtracts 15 points for backwardation and 30 for steep backwardation, as these constellations signal elevated risk. Simon and Wiggins (2001) analyzed the VIX futures curve and showed that backwardation is associated with market declines.
6.4 Safe-Haven Flows
During crisis times, investors flee from risky assets into safe havens: gold, US dollar, and Japanese yen. This "flight to quality" is a sentiment signal. The model calculates the performance of these assets relative to stocks over the last 20 trading days. When gold or the dollar strongly rise while stocks fall, this indicates elevated risk aversion.
The safe-haven component is calculated as the difference between safe-haven performance and stock performance. Positive values (safe havens outperform) subtract up to 20 points from the sentiment score, negative values (stocks outperform) add up to 10 points. The asymmetric treatment (larger deduction for risk-off than bonus for risk-on) reflects that risk-off movements are typically sharper and more informative than risk-on phases.
Baur and Lucey (2010) examined safe-haven properties of gold and showed that gold indeed exhibits negative correlation with stocks during extreme market movements, confirming its role as crisis protection.
7. Component 5: Macroeconomic Analysis
7.1 The Yield Curve as Economic Indicator
The yield curve, represented as yields of government bonds of various maturities, contains aggregated expectations about future interest rates, inflation, and economic growth. The slope of the yield curve has remarkable predictive power for recessions. Estrella and Mishkin (1998) showed that an inverted yield curve (short-term rates higher than long-term) predicts recessions with high reliability. This is because inverted curves reflect restrictive monetary policy: the central bank raises short-term rates to combat inflation, dampening economic activity.
DEAM calculates two spread measures: the 2-year-minus-10-year spread and the 3-month-minus-10-year spread. A steep, positive curve (spreads above 1.5% and 2% respectively) signals healthy growth expectations and generates the maximum yield curve score of 40 points. A flat curve (spreads near zero) reduces the score to 20 points. An inverted curve (negative spreads) is particularly alarming and results in only 10 points.
The choice of two different spreads increases analysis robustness. The 2-10 spread is most established in academic literature, while the 3M-10Y spread is often considered more sensitive, as the 3-month rate directly reflects current monetary policy (Ang, Piazzesi, and Wei, 2006).
7.2 Credit Conditions and Spreads
Credit spreads—the yield difference between risky corporate bonds and safe government bonds—reflect risk perception in the credit market. Gilchrist and Zakrajšek (2012) constructed an "Excess Bond Premium" that measures the component of credit spreads not explained by fundamentals and showed this is a predictor of future economic activity and stock returns.
The model approximates credit spread by comparing the yield of high-yield bond ETFs (HYG) with investment-grade bond ETFs (LQD). A narrow spread below 200 basis points signals healthy credit conditions and risk appetite, contributing 30 points to the macro score. Very wide spreads above 1000 basis points (as during the 2008 financial crisis) signal credit crunch and generate zero points.
Additionally, the model evaluates whether "flight to quality" is occurring, identified through strong performance of Treasury bonds (TLT) with simultaneous weakness in high-yield bonds. This constellation indicates elevated risk aversion and reduces the credit conditions score.
7.3 Financial Stability at Corporate Level
While the yield curve and credit spreads reflect macroeconomic conditions, financial stability evaluates the health of companies themselves. The model uses the aggregated debt-to-equity ratio and return on equity of the S&P 500 as proxies for corporate health.
A low leverage level below 0.5 combined with high ROE above 15% signals robust corporate balance sheets and generates 20 points. This combination is particularly valuable as it represents both defensive strength (low debt means crisis resistance) and offensive strength (high ROE means earnings power). High leverage above 1.5 generates only 5 points, as it implies vulnerability to interest rate increases and recessions.
Korteweg (2010) showed in "The Net Benefits to Leverage" that optimal debt maximizes firm value, but excessive debt increases distress costs. At the aggregated market level, high debt indicates fragilities that can become problematic during stress phases.
8. Component 6: Crisis Detection
8.1 The Need for Systematic Crisis Detection
Financial crises are rare but extremely impactful events that suspend normal statistical relationships. During normal market volatility, diversified portfolios and traditional risk management approaches function, but during systemic crises, seemingly independent assets suddenly correlate strongly, and losses exceed historical expectations (Longin and Solnik, 2001). This justifies a separate crisis detection mechanism that operates independently of regular allocation components.
Reinhart and Rogoff (2009) documented in "This Time Is Different: Eight Centuries of Financial Folly" recurring patterns in financial crises: extreme volatility, massive drawdowns, credit market dysfunction, and asset price collapse. DEAM operationalizes these patterns into quantifiable crisis indicators.
8.2 Multi-Signal Crisis Identification
The model uses a counter-based approach where various stress signals are identified and aggregated. This methodology is more robust than relying on a single indicator, as true crises typically occur simultaneously across multiple dimensions. A single signal may be a false alarm, but the simultaneous presence of multiple signals increases confidence.
The first indicator is a VIX above the crisis threshold (default 40), adding one point. A VIX above 60 (as in 2008 and March 2020) adds two additional points, as such extreme values are historically very rare. This tiered approach captures the intensity of volatility.
The second indicator is market drawdown. A drawdown above 15% adds one point, as corrections of this magnitude can be potential harbingers of larger crises. A drawdown above 25% adds another point, as historical bear markets typically encompass 25-40% drawdowns.
The third indicator is credit market spreads above 500 basis points, adding one point. Such wide spreads occur only during significant credit market disruptions, as in 2008 during the Lehman crisis.
The fourth indicator identifies simultaneous losses in stocks and bonds. Normally, Treasury bonds act as a hedge against equity risk (negative correlation), but when both fall simultaneously, this indicates systemic liquidity problems or inflation/stagflation fears. The model checks whether both SPY and TLT have fallen more than 10% and 5% respectively over 5 trading days, adding two points.
The fifth indicator is a volume spike combined with negative returns. Extreme trading volumes (above twice the 20-day average) with falling prices signal panic selling. This adds one point.
A crisis situation is diagnosed when at least 3 indicators trigger, a severe crisis at 5 or more indicators. These thresholds were calibrated through historical backtesting to identify true crises (2008, 2020) without generating excessive false alarms.
8.3 Crisis-Based Allocation Override
When a crisis is detected, the system overrides the normal allocation recommendation and caps equity allocation at maximum 25%. In a severe crisis, the cap is set at 10%. This drastic defensive posture follows the empirical observation that crises typically require time to develop and that early reduction can avoid substantial losses (Faber, 2007).
This override logic implements a "safety first" principle: in situations of existential danger to the portfolio, capital preservation becomes the top priority. Roy (1952) formalized this approach in "Safety First and the Holding of Assets," arguing that investors should primarily minimize ruin probability.
9. Integration and Final Allocation Calculation
9.1 Component Weighting
The final allocation recommendation emerges through weighted aggregation of the five components. The standard weighting is: Market Regime 35%, Risk Management 25%, Valuation 20%, Sentiment 15%, Macro 5%. These weights reflect both theoretical considerations and empirical backtesting results.
The highest weighting of market regime is based on evidence that trend-following and momentum strategies have delivered robust results across various asset classes and time periods (Moskowitz, Ooi, and Pedersen, 2012). Current market momentum is highly informative for the near future, although it provides no information about long-term expectations.
The substantial weighting of risk management (25%) follows from the central importance of risk control. Wealth preservation is the foundation of long-term wealth creation, and systematic risk management is demonstrably value-creating (Moreira and Muir, 2017).
The valuation component receives 20% weight, based on the long-term mean reversion of valuation metrics. While valuation has limited short-term predictive power (bull and bear markets can begin at any valuation), the long-term relationship between valuation and returns is robustly documented (Campbell and Shiller, 1988).
Sentiment (15%) and Macro (5%) receive lower weights, as these factors are subtler and harder to measure. Sentiment is valuable as a contrarian indicator at extremes but less informative in normal ranges. Macro variables such as the yield curve have strong predictive power for recessions, but the transmission from recessions to stock market performance is complex and temporally variable.
9.2 Model Type Adjustments
DEAM allows users to choose between four model types: Conservative, Balanced, Aggressive, and Adaptive. This choice modifies the final allocation through additive adjustments.
Conservative mode subtracts 10 percentage points from allocation, resulting in consistently more cautious positioning. This is suitable for risk-averse investors or those with limited investment horizons. Aggressive mode adds 10 percentage points, suitable for risk-tolerant investors with long horizons.
Adaptive mode implements procyclical adjustment based on short-term momentum: if the market has risen more than 5% in the last 20 days, 5 percentage points are added; if it has declined more than 5%, 5 points are subtracted. This logic follows the observation that short-term momentum persists (Jegadeesh and Titman, 1993), but the moderate size of adjustment avoids excessive timing bets.
Balanced mode makes no adjustment and uses raw model output. This neutral setting is suitable for investors who wish to trust model recommendations unchanged.
9.3 Smoothing and Stability
The allocation resulting from aggregation undergoes final smoothing through a simple moving average over 3 periods. This smoothing is crucial for model practicality, as it reduces frequent trading and thus transaction costs. Without smoothing, the model could fluctuate between adjacent allocations with every small input change.
The choice of 3 periods as smoothing window is a compromise between responsiveness and stability. Longer smoothing would excessively delay signals and impede response to true regime changes. Shorter or no smoothing would allow too much noise. Empirical tests showed that 3-period smoothing offers an optimal ratio between these goals.
10. Visualization and Interpretation
10.1 Main Output: Equity Allocation
DEAM's primary output is a time series from 0 to 100 representing the recommended percentage allocation to equities. This representation is intuitive: 100% means full investment in stocks (specifically: an S&P 500 ETF), 0% means complete cash position, and intermediate values correspond to mixed portfolios. A value of 60% means, for example: invest 60% of wealth in SPY, hold 40% in money market instruments or cash.
The time series is color-coded to enable quick visual interpretation. Green shades represent high allocations (above 80%, bullish), red shades low allocations (below 20%, bearish), and neutral colors middle allocations. The chart background is dynamically colored based on the signal, enhancing readability in different market phases.
10.2 Dashboard Metrics
A tabular dashboard presents key metrics compactly. This includes current allocation, cash allocation (complement), an aggregated signal (BULLISH/NEUTRAL/BEARISH), current market regime, VIX level, market drawdown, and crisis status.
Additionally, fundamental metrics are displayed: P/E Ratio, Equity Risk Premium, Return on Equity, Debt-to-Equity Ratio, and Total Shareholder Yield. This transparency allows users to understand model decisions and form their own assessments.
Component scores (Regime, Risk, Valuation, Sentiment, Macro) are also displayed, each normalized on a 0-100 scale. This shows which factors primarily drive the current recommendation. If, for example, the Risk score is very low (20) while other scores are moderate (50-60), this indicates that risk management considerations are pulling allocation down.
10.3 Component Breakdown (Optional)
Advanced users can display individual components as separate lines in the chart. This enables analysis of component dynamics: do all components move synchronously, or are there divergences? Divergences can be particularly informative. If, for example, the market regime is bullish (high score) but the valuation component is very negative, this signals an overbought market not fundamentally supported—a classic "bubble warning."
This feature is disabled by default to keep the chart clean but can be activated for deeper analysis.
10.4 Confidence Bands
The model optionally displays uncertainty bands around the main allocation line. These are calculated as ±1 standard deviation of allocation over a rolling 20-period window. Wide bands indicate high volatility of model recommendations, suggesting uncertain market conditions. Narrow bands indicate stable recommendations.
This visualization implements a concept of epistemic uncertainty—uncertainty about the model estimate itself, not just market volatility. In phases where various indicators send conflicting signals, the allocation recommendation becomes more volatile, manifesting in wider bands. Users can understand this as a warning to act more cautiously or consult alternative information sources.
11. Alert System
11.1 Allocation Alerts
DEAM implements an alert system that notifies users of significant events. Allocation alerts trigger when smoothed allocation crosses certain thresholds. An alert is generated when allocation reaches 80% (from below), signaling strong bullish conditions. Another alert triggers when allocation falls to 20%, indicating defensive positioning.
These thresholds are not arbitrary but correspond with boundaries between model regimes. An allocation of 80% roughly corresponds to a clear bull market regime, while 20% corresponds to a bear market regime. Alerts at these points are therefore informative about fundamental regime shifts.
11.2 Crisis Alerts
Separate alerts trigger upon detection of crisis and severe crisis. These alerts have highest priority as they signal large risks. A crisis alert should prompt investors to review their portfolio and potentially take defensive measures beyond the automatic model recommendation (e.g., hedging through put options, rebalancing to more defensive sectors).
11.3 Regime Change Alerts
An alert triggers upon change of market regime (e.g., from Neutral to Correction, or from Bull Market to Strong Bull). Regime changes are highly informative events that typically entail substantial allocation changes. These alerts enable investors to proactively respond to changes in market dynamics.
11.4 Risk Breach Alerts
A specialized alert triggers when actual portfolio risk utilization exceeds target parameters by 20%. This is a warning signal that the risk management system is reaching its limits, possibly because market volatility is rising faster than allocation can be reduced. In such situations, investors should consider manual interventions.
12. Practical Application and Limitations
12.1 Portfolio Implementation
DEAM generates a recommendation for allocation between equities (S&P 500) and cash. Implementation by an investor can take various forms. The most direct method is using an S&P 500 ETF (e.g., SPY, VOO) for equity allocation and a money market fund or savings account for cash allocation.
A rebalancing strategy is required to synchronize actual allocation with model recommendation. Two approaches are possible: (1) rule-based rebalancing at every 10% deviation between actual and target, or (2) time-based monthly rebalancing. Both have trade-offs between responsiveness and transaction costs. Empirical evidence (Jaconetti, Kinniry, and Zilbering, 2010) suggests rebalancing frequency has moderate impact on performance, and investors should optimize based on their transaction costs.
12.2 Adaptation to Individual Preferences
The model offers numerous adjustment parameters. Component weights can be modified if investors place more or less belief in certain factors. A fundamentally-oriented investor might increase valuation weight, while a technical trader might increase regime weight.
Risk target parameters (target volatility, max drawdown) should be adapted to individual risk tolerance. Younger investors with long investment horizons can choose higher target volatility (15-18%), while retirees may prefer lower volatility (8-10%). This adjustment systematically shifts average equity allocation.
Crisis thresholds can be adjusted based on preference for sensitivity versus specificity of crisis detection. Lower thresholds (e.g., VIX > 35 instead of 40) increase sensitivity (more crises are detected) but reduce specificity (more false alarms). Higher thresholds have the reverse effect.
12.3 Limitations and Disclaimers
DEAM is based on historical relationships between indicators and market performance. There is no guarantee these relationships will persist in the future. Structural changes in markets (e.g., through regulation, technology, or central bank policy) can break established patterns. This is the fundamental problem of induction in financial science (Taleb, 2007).
The model is optimized for US equities (S&P 500). Application to other markets (international stocks, bonds, commodities) would require recalibration. The indicators and thresholds are specific to the statistical properties of the US equity market.
The model cannot eliminate losses. Even with perfect crisis prediction, an investor following the model would lose money in bear markets—just less than a buy-and-hold investor. The goal is risk-adjusted performance improvement, not risk elimination.
Transaction costs are not modeled. In practice, spreads, commissions, and taxes reduce net returns. Frequent trading can cause substantial costs. Model smoothing helps minimize this, but users should consider their specific cost situation.
The model reacts to information; it does not anticipate it. During sudden shocks (e.g., 9/11, COVID-19 lockdowns), the model can only react after price movements, not before. This limitation is inherent to all reactive systems.
12.4 Relationship to Other Strategies
DEAM is a tactical asset allocation approach and should be viewed as a complement, not replacement, for strategic asset allocation. Brinson, Hood, and Beebower (1986) showed in their influential study "Determinants of Portfolio Performance" that strategic asset allocation (long-term policy allocation) explains the majority of portfolio performance, but this leaves room for tactical adjustments based on market timing.
The model can be combined with value and momentum strategies at the individual stock level. While DEAM controls overall market exposure, within-equity decisions can be optimized through stock-picking models. This separation between strategic (market exposure) and tactical (stock selection) levels follows classical portfolio theory.
The model does not replace diversification across asset classes. A complete portfolio should also include bonds, international stocks, real estate, and alternative investments. DEAM addresses only the US equity allocation decision within a broader portfolio.
13. Scientific Foundation and Evaluation
13.1 Theoretical Consistency
DEAM's components are based on established financial theory and empirical evidence. The market regime component follows from regime-switching models (Hamilton, 1989) and trend-following literature. The risk management component implements volatility targeting (Moreira and Muir, 2017) and modern portfolio theory (Markowitz, 1952). The valuation component is based on discounted cash flow theory and empirical value research (Campbell and Shiller, 1988; Fama and French, 1992). The sentiment component integrates behavioral finance (Baker and Wurgler, 2006). The macro component uses established business cycle indicators (Estrella and Mishkin, 1998).
This theoretical grounding distinguishes DEAM from purely data-mining-based approaches that identify patterns without causal theory. Theory-guided models have greater probability of functioning out-of-sample, as they are based on fundamental mechanisms, not random correlations (Lo and MacKinlay, 1990).
13.2 Empirical Validation
While this document does not present detailed backtest analysis, it should be noted that rigorous validation of a tactical asset allocation model should include several elements:
In-sample testing establishes whether the model functions at all in the data on which it was calibrated. Out-of-sample testing is crucial: the model should be tested in time periods not used for development. Walk-forward analysis, where the model is successively trained on rolling windows and tested in the next window, approximates real implementation.
Performance metrics should be risk-adjusted. Pure return consideration is misleading, as higher returns often only compensate for higher risk. Sharpe Ratio, Sortino Ratio, Calmar Ratio, and Maximum Drawdown are relevant metrics. Comparison with benchmarks (Buy-and-Hold S&P 500, 60/40 Stock/Bond portfolio) contextualizes performance.
Robustness checks test sensitivity to parameter variation. If the model only functions at specific parameter settings, this indicates overfitting. Robust models show consistent performance over a range of plausible parameters.
13.3 Comparison with Existing Literature
DEAM fits into the broader literature on tactical asset allocation. Faber (2007) presented a simple momentum-based timing system that goes long when the market is above its 10-month average, otherwise cash. This simple system avoided large drawdowns in bear markets. DEAM can be understood as a sophistication of this approach that integrates multiple information sources.
Ilmanen (2011) discusses various timing factors in "Expected Returns" and argues for multi-factor approaches. DEAM operationalizes this philosophy. Asness, Moskowitz, and Pedersen (2013) showed that value and momentum effects work across asset classes, justifying cross-asset application of regime and valuation signals.
Ang (2014) emphasizes in "Asset Management: A Systematic Approach to Factor Investing" the importance of systematic, rule-based approaches over discretionary decisions. DEAM is fully systematic and eliminates emotional biases that plague individual investors (overconfidence, hindsight bias, loss aversion).
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FEI: Futures Entry Identifier📘 FEI: Futures Entry Identifier
FEI is a modular, futures-grade entry engine designed for precision trading across GC1!, MNQ1!, ES1!, and related contracts. It combines manual SVP structure, CHoCH detection, and Colby-style candle strength filters to identify high-probability long and short entries.
🔧 Features
• Manual SVP inputs (VAH, VAL, POC)
• Symbol-aware filters for micro vs standard contracts
• Multi-timeframe signal logic (3m, 5m, 10m, 15m, 30m)
• CHoCH detection with optional engulfing filter (default off)
• FRVP entry zone plotting after CHoCH confirmation
• Candle coloring on CHoCH trigger
• Session-aware logic (ETH default, optional RTH-only)
• Narratable visuals and audit-safe alerts
🧭 How to Use
1. Input VAH, VAL, and POC manually
2. Select signal timeframe (e.g. 3m or 5m)
3. Watch for CHoCH (white candle = structural shift)
4. Entry line plots at top/bottom of recent range
5. Long/short markers appear when SVP + candle strength align
6. Toggle RTH-only mode if needed
🌟 Why It’s Unique
FEI is built for traders who demand clarity, structure, and precision. Every signal is narratable, audit-safe, and resolution-aware—ideal for futures overlays and sniper-grade entries.
Trend/Range Composite (Single-Line) v1.4🔹 Step 1: Add it to your chart
Copy the whole script.
In TradingView → Pine Editor → paste it.
Click Add to chart.
It will show a white line in a subwindow, plus thresholds at 40 and 60, and a colored background.
Optional: You’ll see a status box (top-right of chart) with details like ADX, ATR, slope, etc.
🔹 Step 2: Understand the Score
The indicator compresses all signals into a 0–100 “Trend Strength Score”:
≥ 60 = TREND (teal background)
→ Market is trending, consider trend strategies like vertical spreads, runners, breakouts.
≤ 40 = RANGE (orange background)
→ Market is choppy/sideways, consider range strategies like butterflies, condors, mean-reversion fades.
40–60 = MIXED (gray background)
→ Indecision / chop. Best to reduce size or wait for clarity.
🔹 Step 3: Use with Your Trading Plan
Intraday (5m, 15m, 30m)
Score < 40 → play support/resistance bounces, fade extremes.
Score > 60 → play momentum breakouts or pullback continuations.
Daily chart
Good for swing context (is this month trending or just chopping?).
🔹 Step 4: Alerts
You can set TradingView alerts:
Cross above 60 → market entering trend mode.
Cross below 40 → market entering range mode.
Useful if you don’t want to watch constantly.
🔹 Step 5: Confirm with Price Levels
The score tells you “trend vs range”, but you still need levels:
If score < 40 → mark PDH / PDL (previous day high/low), VAH/VAL, VWAP. Expect rejections/fades.
If score > 60 → watch for breakouts beyond PDH/PDL or supply/demand zones.
