Bitpanda Coinrule TemplateThis strategy for Bitpanda on the Coinrule platform utilises 3 different conditions that have to be met to buy and 1 condition to sell. This strategy works best on the ETH/EUR pair on the 4 hour timescale.
In order for the strategy to enter the trade it must meet all of the conditions listed below.
ENTRY
RSI increases by 5
RSI is lower than 70
MA9 crosses above MA50
EXIT
MA50 crosses above MA9
This strategy works well on LINK/EUR on the 1 day timeframe, MIOTA/EUR on the 2 hour timeframe, BTC/EUR on the 4 hour timeframe and BEST/EUR on the 1 day timeframe (and 4h).
Back tested from 1 January 2020.
The strategy assumes each order is using 30% of the available coins to make the results more realistic and to simulate you only ran this strategy on 30% of your holdings. A trading fee of 0.1% is also taken into account and is aligned to the base fee applied on Binance.
"2020年+国债收益率" için komut dosyalarını ara
Green Line Breakout (GLB) - Public UseNOTE: This is public use - open source version of GLB published by me in Sep 2020. As Trading View is not allow unprotect script already shared, I am sharing it for anyone to use the script and make a copy.
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This is an implementation of Green Line Breakout ( GLB ) which is popularized by Eric Wish through his Wishing Wealth Blog.
GLB indicator looks at a monthly chart for a stock that hit a new all time high recently and draw a green horizontal line at the highest price reached at any month, that has not been surpassed for at least 3 months.
In other words, this method finds stock that reached an all-time high and has then rested for at least three months. When a stock moves through the green line or is above its last green line, it is an indication of strong buying interest.
Read more about how to use the indicator in Wishing Wealth Blog.
Usage Explanation:
1. Set the time frame to Monthly for a stock and automatically a green dashed line appears based on the calculation explained above
2. If no GLB found for a stock, then green line appears at 0.0
2. If you set any other time frame other than Monthly, no Green Dashed line shown
Chanu Delta StrategyThis strategy is built on the Chanu Delta Indicator, which indicates the strength of the Bitcoin market. When the Chanu Delta Indicator hits “Delta_bull” and “Delta_bear” and closes the candle, long and short signals are triggered respectively. The example shown on the screen is a default setting optimized for a 4-hour candlestick strategy based on the Bybit BTCUSDT futures market. For the 15-minute candle, "Delta_bull=32", "Delta_bear=-31", "Source=hlc3" are best. You can use it by adjusting the setting value and modifying it to suit you.
If you use this strategy in conjunction with the Chanu Delta Indicator, it is convenient to anticipate alert signals in advance. Since the Chanu Delta Indicator represents the price difference based on the Bybit BTCUSDT futures market, backtesting is possible from March 2020.
Random Entries Work!" tHe MaRkEtS aRe RaNdOm ", say moron academics.
The purpose of this study is to show that most markets are NOT random! Most markets show a clear bias where we can make such easy money, that a random number generator can do it.
=== HOW THE INDICATOR WORKS ===
The study will randomly enter the market
The study will randomly exit the market if in a trade
You can choose a Long Only, Short Only, or Bidirectional strategy
=== DEFAULT VALUES AND THEIR LOGIC ===
Percent Chance to Enter Per Bar: 10%
Percent Chance to Exit Per Bar: 3%
Direction: Long Only
Commission: 0
Each bar has a 10% chance to enter the market. Each bar has a 3% to exit the market . It will only enter long.
I included zero commission for simplification. It's a good exercise to include a commission/slippage to see just how much trading fees take from you.
=== TIPS ===
Increasing "Percent Chance to Exit" will shorten the time in a trade. You can see the "Avg # Bars In Trade" go down as you increase. If "Percent Chance to Exit" is too high, the study won't be in the market long enough to catch any movement, possibly exiting on the same bar most of the time.
If you're getting the red screen, that means the strategy lost so much money it went broke. Try reducing the percent equity on the Properties tab.
Switch the start year to avoid/minimize black swan events like the covid drop in 2020.
=== FINDINGS ===
Most markets lose money with a "Random" direction strategy.
Most markets lose ALL money with a "Short Only" strategy.
Most markets make money with a "Long Only" strategy.
Try this strategy on: Bitcoin (BTCUSD) and the NASDAQ (QQQ).
There are two popular memes right now: "Bitcoin to the moon" and "Stocks only go up". Both are seemingly true. Bitcoin was the best performing asset of the 2010's, gaining several billion percent in gains. The stock market is on a 100 year long uptrend. Why? BECAUSE FIAT CURRENCIES ALWAYS GO DOWN! This is inflation. If we measure the market in terms of others assets instead of fiat, the Long Only strategy doesn't work anymore (or works less well).
Try this strategy on: Bitcoin/GLD (BTCUSD/GLD), the Eurodollar (EURUSD), and the S&P 500 measured in gold (SPY/GLD).
Bitcoin measured in gold (BTCUSD/GLD) still works with a Long Only strategy because Bitcoin increased in value over both USD and gold.
The Eurodollar (EURUSD) generally loses money no matter what, especially if you add any commission. This makes sense as they are both fiat currencies with similar inflation schedules.
Gold and the S&P 500 have gained roughly the same amount since ~2000. Some years will show better results for a long strategy, while others will favor a short strategy. Now look at just SPY or GLD (which are both measured in USD by default!) and you'll see the same trend again: a Long Only strategy crushes even when entering and exiting randomly.
=== " JUST TELL ME WHAT TO DO, YOU NERD! " ===
Bulls always win and Bears always lose because fiat currencies go to zero.
You're not underperforming a random number generator, are you?
Bitcoin S2F(X)This indicator shows the BTCUSD price based on the S2F Model by PlanB.
We can see not only the S2F(Stock-to-Flow) but also the S2FX(Stock-to-Flow Cross Asset) model announced in 2020.
█ Overview
In this model, bitcoin is treated as comparable to commodities such as gold .
These commodities are known as "store of value" commodities because they retain their value over time due to their relative scarcity.
Bitcoins are scarce.
The number of coins in existence is limited, and the rate of supply is at an all-time low because mining the 2.2 million outstanding coins that have yet to be mined requires a lot of power and computing power.
The Stock-to-flow ratio is used to evaluate the current stock of a commodity (the total amount currently available) versus the flow of new production (the amount mined in a given year).
The higher this ratio, the more scarce the commodity is and the more valuable it is as a store of value.
█ How To View
On the above chart price is overlaid on top of the S2F(X) line. We can see that price has continued to follow the stock-to-flow of Bitcoin over time. By observing the S2F(X) line, we can expect to be able to predict where the price will go.
The coloured circles on the price line of this chart show the number of days until the next Bitcoin halving event. This is an event where the reward for mining new blocks is halved, meaning miners receive 50% fewer bitcoins for verifying transactions. Bitcoin halvings are scheduled to occur every 210,000 blocks until the maximum supply of 21 million bitcoins has been generated by the network. That makes stock-to-flow ratio (scarcity) higher so in theory price should go up.
The stock-to-flow line on this chart incorporates a 463-day average into the model to smooth out the changes caused in the market by the halving events.
I recommend using this indicator on a weekly or monthly basis for BITSTAMP:BTCUSD .
█ Reference Script
Bitcoin Stock to Flow Multiple by yomofoV
rocketLaunchI wanted to see if I could programmatically identify the conditions I saw just before Bitcoin broke its all-time high end of 2020. The signal picks up several rocket launch moments prior to launching which is quite cool. It also picks up a few false starts, however. In any case, I would have loved to be stopped out on those false starts but been there for all the starts this thing picks up.
It could probably use more confirmatory elements such as trailing conditions and volume perhaps?
BINANCE:BTCUSDTPERP
Let it snow... [QuantNomad]It's almost the end of 2020. If you don't have any snow outside but still you want some Christmas mood - feel free to use my indicator.
TradingView added a possibility to use up to 500 labels, so I decided to create something fun and completely useless.
Snowflakes suppose to fall nicely, but labels are not regularly updated by TradingView. If you know how to make it better - let me know )
For the best experience use Dark Theme and play the "Let it snow" song )
Merry Christmas & Happy New Year!
FIR Trend Filter (Sawtooth and Square Waves)Experimental script!
Using sigma approximation with Sine wave to form Sawtooth and Square waves, for a Finite Impulse Response filter.
Higher harmonics make the sawtooth or square wave more "exact", at the expense of more computation. It also makes the filter more "sensitive". I wouldn't exceed 100, but you're the boss.
The default number of harmonics is 20. The length is 20, too. Why? Because we are currently in 2020. Silly, I know.
Feel free to play around with the settings and tune it to your liking.
How to use it is pretty straight forward: Green is trend-up and red is trend-down.
Credit to alexgrover for the template.
Probability of ATR Index (On-chart) [racer8]This indicator is an on-chart version of my other indicator called Probability of ATR Index (PAI) that was published on October 16th 2020.
PAI is an indicator I created that tells you the probability of current price moving a specified ATR distance over a specified number of periods into the future. It takes into account 4 variables: the ATR & the standard deviation of price, and the 2 parameters: ATR distance and # bars (time).
The formula is very complex so I will not be able to explain it without confusion arising.
The reason I created this PAI was because the other PAI does not show you levels. This one plots the price levels that correspond to your specified ATR distance. So it makes it easier for options traders to set their strangle or condor.
Enjoy 😀
Session High and Session LowI have heard many people ask for a script that will identify the high and low of a specific session. So, I made one.
Important Note: This indicator has to be set up properly or you will get an error. Important things to note are the length of the range and the session definition. The idea is that you would set it up for what's relevant to your trading. Going too far back in the chart history will cause errors. Setting the session for a time that is not on the chart can cause errors. If you set it to look farther back than there are bars to display, you may get an error. What I've found is that if you get an error, you just need to change the settings to reflect available data and it will be able to compile the script. At the time of its publishing, the default range start is set to 10/01/2020. If you're looking at this years later, you'll probably have to set the range to something more recent.
Features:
Plot or Lines:
Using Plot (displayed), the indicator will track the high/low from the end of the session into the next session. Then at the start of the next session, it will start tracking the high/low of that session until its end, then track that high/low until the start of the next session then reset.
Using lines, it will extend horizontal lines to the right indefinitely. The number of sessions back that the lines apply to is a user-defined number of sessions. There are limits to the number of lines that can be cast on a chart (roughly 40-50). So, the maximum number of sessions you can apply the lines to is the last 21 sessions (42 lines total). That gets really noisy though so I can't imagine that is a limiting factor.
Colors:
You can change the background color and its transparency, as well as turn the background color on or off.
You can change the highs and lows colors
You can adjust the line width to your preference
Session Length:
You can use a continuous session covering any user-defined period (provided its not tooooo many candles back)
You can define the session length for intraday
You can exclude weekends
Display Options:
You can adjust the colors, transparency, and linewidth
You can display the plotline or horizontal lines
You can show/hide the background color.
You can change how many sessions back the horizontal lines will track
Let me know if there's anything this script is missing or if you run into any issues that I might be able to help resolve.
Here's what it looks like with Lines for the last 5 sessions and different background color.
Profit Maximizer PMaxPMax is a brand new indicator developed by KivancOzbilgic in earlier 2020.
It's a combination of two trailing stop loss indicators;
One is Anıl Özekşi's MOST (Moving Stop Loss) Indicator
and the other one is well known ATR based SuperTrend.
Both MOST and SuperTrend Indicators are very good at trend following systems but conversely their performance is not bright in sideways market conditions like most of the other indicators.
Profit Maximizer - PMax tries to solve this problem. PMax combines the powerful sides of MOST (Moving Average Trend Changer) and SuperTrend (ATR price detection) in one indicator.
Backtest and optimization results of PMax are far better when compared to its ancestors MOST and SuperTrend. It reduces the number of false signals in sideways and give more reliable trade signals.
PMax is easy to determine the trend and can be used in any type of markets and instruments. It does not repaint.
The first parameter in the PMax indicator set by the three parameters is the period/length of ATR.
The second Parameter is the Multiplier of ATR which would be useful to set the value of distance from the built in Moving Average.
I personally think the most important parameter is the Moving Average Length and type.
PMax will be much sensitive to trend movements if Moving Average Length is smaller. And vice versa, will be less sensitive when it is longer.
As the period increases it will become less sensitive to little trends and price actions.
In this way, your choice of period, will be closely related to which of the sort of trends you are interested in.
We are under the effect of the uptrend in cases where the Moving Average is above PMax;
conversely under the influence of a downward trend, when the Moving Average is below PMax.
Built in Moving Average type defaultly set as EMA but users can choose from 8 different Moving Average types like:
SMA : Simple Moving Average
EMA : Exponential Movin Average
WMA : Weighted Moving Average
TMA : Triangular Moving Average
VAR : Variable Index Dynamic Moving Average aka VIDYA
WWMA : Welles Wilder's Moving Average
ZLEMA : Zero Lag Exponential Moving Average
TSF : True Strength Force
Tip: In sideways VAR would be a good choice
You can use PMax default alarms and Buy Sell signals like:
1-
BUY when Moving Average crosses above PMax
SELL when Moving Average crosses under PMax
2-
BUY when prices jumps over PMax line.
SELL when prices go under PMax line.
Monster Breakout Index V2Brief Description:
Monster Breakout Index V2 is a the successor to Monster Breakout Index, an indicator I published on May 13, 2020.
Like it's predecessor, MBI V2 gives high quality signals and is incredibly robust at preventing you from trading sideways/consolidating markets.
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Interpreting Signals:
Green = Buy
Red = Sell
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Calculation:
1) Calculate the median price of each bar over n periods. Determine the highest & lowest medians.
2) Current bar's high > highest median? -----Yes = Buy signal
3) Current bar's low < lowest median? -------Yes = Sell signal
Note: Occasionally, the indicator will simultaneously produce both a buy & sell signal. Because of this, it is recommended you use at least one other indicator in conjunction with this one...OR alternatively, ignore this double signal.
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Enjoy ;)
BV's MACD SIGNAL TESTERHello ladies and gentlemen,
Today, as you may have seen in the title, I have coded a strategy to determine once and for all if MACD could make you money in 2020.
So, at the end of this video, you will know which MACD strategy will bring you the most money.
Spoiler alert: we've hit the 90% WinRAte mark on the Euro New Zealand Dollar chart.
I've seen a lot of videos of people testing different MACD signals, some up to 100 times.
But In my opinion, all traders must rely on statistics to put all the odds on their side and good statistics require a lot more data.
The algorithm I'm showing you tests each signal one by one over a 3 year period and on 28 different graphs.
That way we are sure that we have encountered all possible market behavior.
From phases of congestion to major trends or even the effects of COVID-19
I use the ATR to determine my Stop Loss and Take Profits. The Stop Loss is placed at 1.5 times the ATR, the Take Profit is placed at 1 time the ATR.
If my Take Profit is hit, I take 50% of the profits and let the position run by moving my Stop Loss to Zero.
This way, the position can no longer be a losing position.
If you are not familiar with this practice, I invite you to study the "Scaling out" video from the NoNonsenseForex channel.
BV's Trading Journal.
FundCandlesV1sloth288FundCandlesV1sloth288 is an indicator I decided to put together so I can track how funds are doing on $GVT Genesis Vision.
Using a standard MACD or RSI indicator you can change source to use the FundsCandles values to determine if its a good time to enter or exit different funds on the platform.
What you need to know...
Currently all securities need to pair the same, (USD / BTC ).
Security 01, 02, 03 etc etc to maximum of 10 need to be in "BINANCE:LINKUSD" format.
Manually need to input circulating supply from CMC to get the proper ratios for index.
Allocation is the % of the funds exposure to said security.
Inputting the values does not track previous reallocation's, the whole chart will be if the history of the fund was using up to date settings.
Values on the right is the Marketcap of the fund.
Standard settings is of Oracle Basket on the platform made by Somnium Funds as of Aug 13 2020.
Next update will be after GV includes traditional stocks onto the platform for managers to diversify their current allocations into them.
Realized Volatility IIR Filters with BandsDISCLAIMER:
The Following indicator/code IS NOT intended to be a formal investment advice or recommendation by the author, nor should be construed as such. Users will be fully responsible by their use regarding their own trading vehicles/assets.
The following indicator was made for NON LUCRATIVE ACTIVITIES and must remain as is following TradingView's regulations. Use of indicator and their code are published by Invitation Only for work and knowledge sharing. All access granted over it, their use, copy or re-use should mention authorship(s) and origin(s).
WARNING NOTICE!
THE INCLUDED FUNCTION MUST BE CONSIDERED AS TESTING. The models included in the indicator have been taken from open sources on the web and some of them has been modified by the author, problems could occur at diverse data sceneries.
WHAT'S THIS...?
Work derived by previous own research for study:
This is mainly an INFINITE IMPULSE RESPONSE FILTERING INDICATOR , it's purpose is to catch trend given by the nature of lag given by a VOLATILITY ESTIMATION ALGORITHM as it's coefficient. It provides as well an INFINITE IMPULSE RESPONSE DEVIATION FILTER that uses the same coefficients of the main filter to plot deviation bands as an auxiliary tool.
The given Filter based indicator provides my own Multi Volatility-Estimators Function with only 3 models:
ELASTIC VOLUME WEIGHTED VOLATILITY : This is a Modified Daigler & Padungsaksawasdi "Volume Weighted Volatility" as on DOI: 10.1504/IJBAAF.2018.089423 but with Elastic Volume Weighted Moving Average instead of VWAP (intraday) for faster (but inaccurate) calculation. A future version is planned on the way using intra-bar inspection for intraday timeframe as described in original paper.
GARMAN & KLASS / YANG-ZANG EXTENSION : As one of the best range based (OHLC) with open gaps inclusion in a single bar.
PETER MARTIN'S ULCER INDEX : This is a better approach to measure realized volatility than standard deviation of log returns given it's proven convex risk metric for DrawDowns as shown in Chekhlov et al. (2005) . Regarding this particular model, I take a different approach to use it as coefficient feed: Given that the UI only takes in consideration DrawDawns, I code myself the inverse of this to compute Draw-Ups as well and use both of them to filter minimums volatility levels in order to create a SLOW version of the IIR filter, and maximums of both to calculate as FAST variation. This approach can be used as a better proxy instead of any other common moving average given that with NO COMPOUND IN TIME AT ALL (N=1) or only using as long as N=3 bars of compund, the filter can catch a trend easily, making the indicator nearly a NON PARAMETRIC FILTER.
NOTES:
This version DO NOT INCLUDE ALERTS.
This version DO NOT INCLUDE STRATEGY: ALL Feedback welcome.
DERIVED WORK:
Incremental calculation of weighted mean and variance by Tony Finch (fanf2@cam. ac .uk) (dot@dotat.at), 2009.
Volume weighted volatility: empirical evidence for a new realised volatility measure by Chaiyuth Padungsaksawasdi & Robert T. Daigler, 2018.
Basic DSP Tips & Trics by TradingView user @alexgrover
CHEERS!
@XeL_Arjona 2020.
Ehler's Reflex Indicator ( + MTF & Adaptive )Implementation of Ehler's Reflex Indicator from TASC Feb 2020.
Optional MTF and fixed/adaptive length based on one of Ehler's cycle measurements.
Optional settings for his recommended 2 bar averaging, can apply the averaging to either/and source ie (close + close ) / 2, the output of the smoothing filter portion of the calculation or the final indicator output.
Green/Red : Reflex/Cycle
Aqua/Purple : Trend
SMU Price Volume Noise V1This Script show the price volume movement for different time frame. As you can see large buy/sell has significantly increased before the crash or 2018 and similar pattern is developing for 2019/2020. In shorter time frame, the chart shows daily movement of big volume of Buy/Sell and the low volume period appears as a noise. The idea is to look ta the volume price noise to distinguish big market moves from small side line or low volume movement. Fell free to expand on this idea.
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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Hosoda’s CloudsMany investors aim to develop trading systems with a high win rate, mistakenly associating it with substantial profits. In reality, high returns are typically achieved through greater exposure to market trends, which inevitably lowers the win rate due to increased risk and more volatile conditions.
The system I present, called “Hosoda’s Clouds” in honor of Goichi Hosoda , the creator of the Ichimoku Kinko Hyo indicator, is likely one of the first profitable systems many traders will encounter. Designed to capture trends, it performs best in markets with clear directional movements and is less suitable for range-bound markets like Forex, which often exhibit lateral price action.
This system is not recommended for low timeframes, such as minute charts, due to the random and emotionally driven nature of price movements in those periods. For a deeper exploration of this topic, I recommend reading my article “Timeframe is Everything”, which discusses the critical importance of selecting the appropriate timeframe.
I suggest testing and applying the “Hosoda’s Clouds” strategy on assets with a strong trending nature and a proven track record of performance. Ideal markets include Tesla (1-hour, 4-hour, and daily), BTC/USDT (daily), SPY (daily), and XAU/USD (daily), as these have consistently shown clear directional trends over time.
Commissions and Configuration
Commissions can be adjusted in the system’s settings to suit individual needs. For evaluating the effectiveness of “Hosoda’s Clouds,” I’ve used a standard commission of $1 per order as a baseline, though this can be modified in the code to accommodate different brokers or preferences.
The margin per trade is set to $1,000 by default, but users are encouraged to experiment with different margin settings in the configuration to match their trading style.
Rules of the “Hosoda’s Clouds” System (Bullish Strategy)
This strategy is designed to capture trending movements in bullish markets using the Ichimoku Kinko Hyo indicator. The rules are as follows:
Long Entry: A long position is triggered when the Tenkan-sen crosses above the Kijun-sen below the Ichimoku cloud, identifying potential reversals or bounces in a bearish context.
Stop Loss (SL): Placed at the low of the candle 12 bars prior to the entry candle. This setting has proven optimal in my tests, but it can be adjusted in the code based on risk tolerance.
Take Profit (TP): The position is closed when the Tenkan-sen crosses below the bottom of the Ichimoku cloud (the minimum of Senkou Span A and Senkou Span B).
Notes on the Code
margin_long=0: Ideal for strategies requiring a fixed position size, particularly useful for manual entries or testing with a constant capital allocation.
margin_long=100: Recommended for high-frequency systems where positions are closed quickly, simulating gradual growth based on realized profits and reflecting real-world broker constraints.
System Performance
The following performance metrics account for $1 per order commissions and were tested on the specified assets and timeframes:
Tesla (H1)
Trades: 148
Win Rate: 29.05%
Period: Jan 2, 2014 – Jan 6, 2020 (+172%)
Simple Annual Growth Rate: +34.3%
Trades: 130
Win Rate: 30.77%
Period: Jan 2, 2020 – Sep 24, 2025 (+858.90%)
Simple Annual Growth Rate: +150.7%
Tesla (H4)
Trades: 102
Win Rate: 32.35%
Period: Jun 29, 2010 – Sep 24, 2025 (+11,356.36%)
Simple Annual Growth Rate: +758.5%
Tesla (Daily)
Trades: 56
Win Rate: 35.71%
Period: Jun 29, 2010 – Sep 24, 2025 (+3,166.64%)
Simple Annual Growth Rate: +211.5%
BTC/USDT (Daily)
Trades: 44
Win Rate: 31.82%
Period: Sep 30, 2017 – Sep 24, 2025 (+2,592.23%)
Simple Annual Growth Rate: +324.8%
SPY (Daily)
Trades: 81
Win Rate: 37.04%
Period: Jan 23, 1993 – Sep 24, 2025 (+476.90%)
Simple Annual Growth Rate: +14.3%
XAU/USD (Daily)
Trades: 216
Win Rate: 32.87%
Period: Jan 6, 1833 – Sep 24, 2025 (+5,241.73%)
Simple Annual Growth Rate: +27.1%
SPX (Daily)
Trades: 217
Win Rate: 38.25%
Period: Feb 1, 1871 – Sep 24, 2025 (+16,791.02%)
Simple Annual Growth Rate: +108.1%
Conclusion
With the “ Hosoda’s Clouds ” strategy, I aim to showcase the potential of technical analysis to generate consistent profits in trending markets, challenging recent doubts about its effectiveness. My goal is for this system to serve as both a practical tool for traders and a source of inspiration for the trading community I deeply respect. I hope it encourages the creation of new strategies, fosters creativity in technical analysis, and empowers traders to approach the markets with confidence and discipline.
Deviation Rate Crash SignalDescription
This indicator provides entry signals for contrarian trades that aim to capture rebounds after sharp declines, such as during market crashes.
A signal is triggered when the deviation rate from the 25-day moving average falls below -25% (default setting). On the chart, a red circle is displayed below the candlestick to indicate the signal.
Backtest (2000–2024, Nikkei 225 stocks):
Win rate: 64.73%
Payoff ratio: 1.141
Probability of ruin: 0.0% (with proper risk control)
Trading Rules (Long only):
Entry: Market buy at next day’s open when the closing price is 25% or more below the 25-day MA.
Exit: Market sell at next day’s open when:
The closing price is 10% above the entry price (take profit), or
The closing price is 10% below the entry price (stop loss), or
40 days have passed since entry.
Notes:
This indicator is tuned for crisis periods (e.g., 2008 Lehman Shock, 2011 Great East Japan Earthquake, 2020 COVID-19 crash, 2024 Yen carry trade reversal).
In normal market conditions, signals will be rare.
Pine Screener BETA Support:
Add this indicator to your favorites and scan with long condition = true.
Screener results display both the MA deviation rate and current price.
When multiple signals occur, use the deviation rate as a reference to prioritize setups.
説明
このインジケーターは、暴落時など短期間で急落した銘柄のリバウンドを狙う逆張りトレードのエントリーシグナルを提供します。
25日移動平均線からの乖離率が -25% を下回ったときにシグナルが点灯します（初期設定）。シグナルはメインチャートのローソク足の下に赤い丸印で表示されます。
バックテスト結果（2000～2024年、日経225銘柄）:
勝率: 64.73%
ペイオフレシオ: 1.141
破産確率: 0.0%（適切なリスク管理を行った場合）
トレードルール（買いのみ）:
エントリー: 終値が25日移動平均線から25%以上下方乖離した場合、翌日の寄り付きで成行買い。
手仕舞い: 翌日の寄り付きで成行売り（以下のいずれかの条件を満たした場合）
終値が買値より10%以上上昇（利確）
終値が買値より10%以上下落（損切り）
エントリーから40日経過
注意点:
このインジケーターは、2008年リーマンショック、2011年東日本大震災、2020年コロナショック、2024年円キャリートレード巻き戻しショックなど、危機的局面で効果を発揮するように調整されています。
通常の相場ではシグナルはほとんど出現しません。
Pine Screener BETA 対応:
このインジケーターをお気に入り登録し、long condition = true をフィルター条件にしてスキャンしてください。
スクリーナー結果には移動平均乖離率と現在値が表示されます。
シグナルが同時に多数出現した場合は、移動平均乖離率を参考に優先順位をつけてください。
Weekly VwapsThe Weekly Vwaps indicator lets you plot weekly Volume-Weighted Average Price (VWAP) lines for up to six months of your choosing, with years ranging from 2020 to 2050. It’s a focused tool pulled straight from the weekly VWAP section of the Advanced VWAP Calendar indicator, keeping all the same controls and look but expanded to handle more months. You can use it alongside the original indicator if you need extra weekly VWAPs (up to 30 lines total) or run it on its own for a clean, dedicated setup.
How It Works: Six Month Groups: Pick any six months (e.g., Jan 2020, Sep 2025, or Jul 2040) and enable up to five weekly VWAPs per month (W1–W5), starting from Monday midnight.
Default Setup: Loads with September 2025 VWAPs turned on, with other months (August–April 2025) off but ready to enable. All default to 2025.
Customization: Toggle all weeks in a month or pick specific ones. Adjust label sizes (tiny to huge) and line widths (1–5). Colors are teal, fuchsia, red, green, and yellow/orange for weeks 1–5, with clear labels like “W1 Sep 2025 123.45”.
Label Control: A “Show All Labels” switch lets you hide labels to keep your chart tidy.
Intraday Only: Works on intraday timeframes (e.g., 5-minute, 1-hour) for accurate VWAPs.
Why Use It: Add to Advanced VWAP Calendar: If the original’s two-month limit isn’t enough, this adds six more months of weekly VWAPs for deeper analysis.
Standalone Option: Perfect if you only want weekly VWAPs without other features, with flexibility to pick any months and years.
User-Friendly: Ready to go with September 2025 enabled, easy to tweak for past or future data.
Get Started: Add it to your TradingView chart, and September 2025 VWAPs will show up instantly. Adjust months, years, or toggles in the settings to focus on what you need. Test it on intraday charts and use the label toggle to manage clutter. Great for traders wanting precise, customizable weekly VWAPs!
Lunar calendar day Crypto Trading StrategyLunar calendar day Crypto Trading Strategy
This strategy explores the potential impact of the lunar calendar on cryptocurrency price cycles.
It implements a simple but unconventional rule:
Buy on the 5th day of each lunar month
Sell on the 26th day of the lunar month
No trades between January 1 (solar) and Lunar New Year’s Day (holiday buffer period)
Research background
Several academic studies have investigated the influence of lunar cycles on financial markets. Their findings suggest:
Returns tend to be higher around the full moon compared to the new moon.
Periods between the full moon and the waning phase often show stronger average returns than the waxing phase.
This strategy combines those observations into a practical implementation by testing fixed entry (lunar day 5) and exit (lunar day 26) points, while excluding the transition period from solar New Year to Lunar New Year, effectively capturing mid-month lunar effects.
How it works
The script includes a custom lunar date calculation function, reconstructing lunar months and days for each year (2020–2026).
On lunar day 5, the strategy opens a long position with 100% of equity.
On lunar day 26, the strategy closes the position.
No trades are executed between Jan 1 and Lunar New Year’s Day.
All trades include:
Commission: 0.1%
Slippage: 3 ticks
Position sizing uses the entire equity (100%) for simplicity, but this is not recommended for live trading.
Why this is original
Unlike mashups of built-in indicators, this script:
Implements a full lunar calendar system inside Pine Script.
Translates academic findings on lunar effects into an applied backtest.
Adds a realistic trading filter (holiday gap) based on cultural/seasonal calendar rules.
Provides researchers and traders with a framework to explore non-traditional, time-based signals.
Notes
This is an experimental, research-oriented strategy, not financial advice.
Results are highly dependent on the chosen period (2020–2026).
Using 100% equity per trade is for simplification only and is not a viable money management practice.
The purpose is to investigate whether cyclical patterns linked to lunar time can provide any statistical edge in ETHUSDT.
Jackson Hole Meetings - Lines and LabelsThis TradingView Pine Script indicator marks the dates of the Federal Reserve’s annual Jackson Hole Economic Symposium meetings on your chart. For each meeting date from 2020 through 2025, it draws a red dashed vertical line directly on the corresponding daily bar. Additionally, it places a label above the bar indicating the year of the meeting (e.g., "JH 2025").
Features:
Marks all known Jackson Hole meeting dates from 2020 to 2025.
Draws a vertical dashed line on each meeting day for clear visual identification.
Displays a label above the candle with the meeting year.
Works best on daily timeframe charts.
Helps traders quickly spot potential market-moving events related to Jackson Hole meetings.
Use this tool to visually correlate price action with these key Federal Reserve events and enhance your trading analysis.
