RSI and Bollinger Bands Screener [deepakks444]Indicator Overview
The indicator is designed to help traders identify potential long signals by combining the Relative Strength Index (RSI) and Bollinger Bands across multiple timeframes. This combination allows traders to leverage the strengths of both indicators to make more informed trading decisions.
Understanding RSI
What is RSI?
The Relative Strength Index (RSI) is a momentum oscillator that measures the speed and change of price movements. Developed by J. Welles Wilder Jr. for stocks and forex trading, the RSI is primarily used to identify overbought or oversold conditions in an asset.
How RSI Works:
Calculation: The RSI is calculated using the average gains and losses over a specified period, typically 14 periods.
Range: The RSI oscillates between 0 and 100.
Interpretation:
Key Features of RSI:
Momentum Indicator: RSI helps identify the momentum of price movements.
Divergences: RSI can show divergences, where the price makes a higher high, but the RSI makes a lower high, indicating potential reversals.
Trend Identification: RSI can also help identify trends. In an uptrend, the RSI tends to stay above 50, and in a downtrend, it tends to stay below 50.
Understanding Bollinger Bands
What is Bollinger Bands?
Bollinger Bands are a type of trading band or envelope plotted two standard deviations (positively and negatively) away from a simple moving average (SMA) of a price. Developed by financial analyst John Bollinger, Bollinger Bands consist of three lines:
Upper Band: SMA + (Standard Deviation × Multiplier)
Middle Band (Basis): SMA
Lower Band: SMA - (Standard Deviation × Multiplier)
How Bollinger Bands Work:
Volatility Measure: Bollinger Bands measure the volatility of the market. When the bands are wide, it indicates high volatility, and when the bands are narrow, it indicates low volatility.
Price Movement: The price tends to revert to the mean (middle band) after touching the upper or lower bands.
Support and Resistance: The upper and lower bands can act as dynamic support and resistance levels.
Key Features of Bollinger Bands:
Volatility Indicator: Bollinger Bands help traders understand the volatility of the market.
Mean Reversion: Prices tend to revert to the mean (middle band) after touching the bands.
Squeeze: A Bollinger Band Squeeze occurs when the bands narrow significantly, indicating low volatility and a potential breakout.
Combining RSI and Bollinger Bands
Strategy Overview:
The strategy aims to identify potential long signals by combining RSI and Bollinger Bands across multiple timeframes. The key conditions are:
RSI Crossing Above 60: The RSI should cross above 60 on the 15-minute timeframe.
RSI Above 60 on Higher Timeframes: The RSI should already be above 60 on the hourly and daily timeframes.
Price Above 20MA or Walking on Upper Bollinger Band: The price should be above the 20-period moving average of the Bollinger Bands or walking on the upper Bollinger Band.
Strategy Details:
RSI Calculation:
Calculate the RSI for the 15-minute, 1-hour, and 1-day timeframes.
Check if the RSI crosses above 60 on the 15-minute timeframe.
Ensure the RSI is above 60 on the 1-hour and 1-day timeframes.
Bollinger Bands Calculation:
Calculate the Bollinger Bands using a 20-period moving average and 2 standard deviations.
Check if the price is above the 20-period moving average or walking on the upper Bollinger Band.
Entry and Exit Signals:
Long Signal: When all the above conditions are met, consider a long entry.
Exit: Exit the trade when the price crosses below the 20-period moving average or the stop-loss is hit.
Example Usage
Setup:
Add the indicator to your TradingView chart.
Configure the inputs as per your requirements.
Monitoring:
Look for the long signal on the chart.
Ensure that the RSI is above 60 on the 15-minute, 1-hour, and 1-day timeframes.
Check that the price is above the 20-period moving average or walking on the upper Bollinger Band.
Trading:
Enter a long position when the criteria are met.
Set a stop-loss below the low of the recent 15-minute candle or based on your risk management rules.
Monitor the trade and exit when the RSI returns below 60 on any of the timeframes or when the price crosses below the 20-period moving average.
House Rules Compliance
No Financial Advice: This strategy is for educational purposes only and should not be construed as financial advice.
Risk Management: Always use proper risk management techniques, including stop-loss orders and position sizing.
Past Performance: Past performance is not indicative of future results. Always conduct your own research and analysis.
TradingView Guidelines: Ensure that any shared scripts or strategies comply with TradingView's terms of service and community guidelines.
Conclusion
This strategy combines RSI and Bollinger Bands across multiple timeframes to identify potential long signals. By ensuring that the RSI is above 60 on higher timeframes and that the price is above the 20-period moving average or walking on the upper Bollinger Band, traders can make more informed decisions. Always remember to conduct thorough research and use proper risk management techniques.
Pesquisar nos scripts por "机械革命无界15+时不时闪屏"
Structure Pilot Vision [Wang Indicators]Built and refined with Dave Teaches, the HTF Vision Pro supercharges the trader, providing them with the tools to approach price with a layered analysis.
Providing the trader the instruments to put on the spotlight significant zones to anticipate price deliveries
HTF CANDLE VISION
Displays up to 3 series of HTF Candles
Shows candlesticks from a higher time frame (e.g., daily, 4-hour, weekly) on a lower time frame chart (e.g., 1-hour, 15-minute). This allows traders to simultaneously observe both short-term and long-term market dynamics.
Customizable Time Frames: Users can select any higher time frame to overlay on the current chart. Common time frames include daily, weekly, and monthly candles, but other custom time frames can also be used.
Color Coding: The HTF candles are color-coded for easy differentiation from the lower time frame candles. Users can customize colors to suit their preferences.
Open, High, Low, Close (OHLC) Representation: The indicator displays the full candlestick pattern for the chosen HTF, including the open, high, low, and close values. This helps traders easily identify key price levels and trends.
Settings :
Number of candles
Space between the chart and the HTF candles
Space between candles sets
Size : from Tiny (2x regular candle size) to Large (x8 regular candle size)
Space between candles
Colors of candles, borders and wicks
Incorporating a Higher Time Frame (HTF) candle into your Lower Time Frame (LTF) chart can be immensely beneficial for traders looking to enhance their analysis and decision-making process.
Use Cases for HTF Candles on LTF Charts:
Trend Confirmation:
Use Case: A trader might be looking at a 15-minute chart (LTF) but wants to confirm if the short-term trends align with the daily trend (HTF). Plotting a daily candle on the 15-minute chart helps visualize whether the short-term movements are part of a broader, longer-term trend.
Support and Resistance Identification:
Use Case: By plotting a weekly candle on a daily chart, traders can quickly identify levels that have acted as significant support or resistance in the past on the higher time frame, which might not be as visible or influential on the daily chart alone.
Entry and Exit Points Enhancement:
Use Case: When preparing to enter a trade based on a 1-hour chart, overlaying a 4-hour candle can provide insights into potential reversal points or continuation patterns that are more significant on the higher time frame, thus refining entry and exit strategies.
Volatility and Breakout Analysis:
Use Case: Seeing how a single HTF candle (like a monthly candle on a weekly chart) closes can give traders an idea of the market's volatility or the strength behind breakouts. A long wick on the HTF candle might suggest a rejected breakout or a potential reversal.
Risk Management:
Use Case: Using an HTF candle can help set more informed stop-loss levels. For instance, if a trader uses a 4-hour candle on a 1-hour chart, they might place their stop-loss just beyond the low of the HTF candle, assuming this represents a significant level of support or resistance.
Contextual Trading Decisions:
Use Case: For scalpers or day traders, understanding where the current price action sits within the context of a higher timeframe can lead to better decision-making. For instance, trading within an HTF consolidation range might suggest less aggressive moves, while being near the top or bottom of such a range might indicate potential for larger movements.
Market Sentiment Analysis:
Use Case: The color (red for bearish, green for bullish) and size of the HTF candle can give a quick visual cue of the market sentiment over that period, helping traders assess whether they are going with or against the broader market flow.
Swing Trading:
Use Case: Swing traders might plot a weekly candle on a daily chart to align their trades with the direction of the weekly trend, ensuring they're not fighting the broader market momentum.
Educational and Visual Reference:
Use Case: For educational purposes, having an HTF candle overlay can serve as a visual reminder for students or new traders about how price movements on different time frames can influence each other, aiding in teaching concepts like "the trend is your friend."
Wang use cases :
The way it is intended to be used is as follow
If you trade the 1 min chart and have a set of 5 min HTF candles plotted on your charts it could be used as follow :
As long as the 5 min keep providing close below the last 5 min candle if you're short you're safe ... if the 5 min candle stop closing below the last ones and start giving up-close you should consider closing your trade
Another use of HTF Candle is to find fractals responsible (up or down internal mouv before the breakout that creates a new zone). This fractal acts as supply and demand zone responsible for maintening the trend or for a reversal.
See examples below :
These fractals are interesting zones because they often cause the price to react, so following a flip in the fractal, you can take a short in bearish zones and a long in bullish zones. Fractals are easier to detect thanks to the HTF candles function, and allow you to enter positions with greater confidence. They can be used in the same way as the 70%, 50% and 30% interest zones, or they can be used simultaneously.
Use with zones :
▫️ VERTICAL BARS VISION ▫️
The vertical bars provide a view of market fractality: on a low time frame chart, they show the size of a candle in a higher time frame, and thus give a better understanding of the price fractality essential to the strategy we use.
Example :
For your information, when you modify data in the vertical bars or HTF candles parameters, the two are synchronized automatically.
The Vertical HTF Candle Closures Indicator is a simple yet effective tool that helps traders visually track the closing times of higher time frame (HTF) candles (such as 4H, 1H, 15M) on a lower time frame chart (e.g., 1-minute).
This feature plots vertical lines on the chart at the exact closure time of each selected HTF, allowing traders to quickly recognize key moments when the HTF candles close, or better yet when we trade above / below the last one and reverse ''sweepy sweepy'' .
Its more like a vertical and more micro visualisation than the HTF Candles.
Wang usage :
its a great tool to be able to reverse engineer what's in a HTFcandle precisely its a good combination with HTF candle projections to train the eyes of the traders about Whats is inside a candle that formed on the higher time frame
Limitation & know issues :
The chart may become cluttered with too many lines if multiple time frames are selected. Adjusting the line style or disabling certain time frames can help reduce visual noise.
On low time frame (<30s), some bar may notshow exactly on time (e.g : in 10sec timeframe, the 15min bar can be displayed at 01:15:10 instead of 01:15:00).
Because of the data provider and the interpreter of Trading View, if there is not data for a candle, Trading view just "skip" the candle. Sometime, those skip are on the candle that goes to 15min, 1 hour or 4 hour. As this is a Trading View issue. There is pretty much nothing we can do.
Some users may experience vertical bars at 1am, 5am, 9am ... instead of 0am, 4am, 8am ... That is because of the difference between the Timezone set on the chart and the timezone of the market they trade. Vertical bar will always refer to the symbol displayed
Opening Range Breakout [UkutaLabs]█ OVERVIEW
The Opening Range Breakout is a powerful trading tool that indicates a strong range based on the high and low of the first fifteen or thirty minutes after market open. This range serves as a potential area of Support or Resistance that traders should be aware of during their trading. Because of this, the Opening Range Breakout is a versatile trading tool that can be included in a wide variety of trading strategies.
The aim of this script is to simplify the trading experience of users by automatically identifying and displaying price levels that they should be aware of.
█ USAGE
When the New York Market opens each day, the script will automatically identify and label the opening range in real time. The user can control whether the script measures the first 15 or 30 minutes of each trading day to fit each trader’s trading style.
Because there tends to be a spike in volume during this period, the range that is identified can serve as a powerful indication of overall market strength. Once the price breaks out of this range, it then can be used as an area of support or resistance depending on the direction of the breakout.
█ SETTINGS
Configuration
• Show Labels: Determines whether labels are drawn within the range.
• Display Mode: Determines the number of days the script should load.
Range Settings
• 15 Minute: Determines whether or not the 15 minute range is drawn.
• 15 Minute Color: Determines the color of the 15 minute range and labels.
• 30 Minute: Determines whether or not the 30 minute range is drawn.
• 30 Minute Color: Determines the color of the 30 minute range and labels.
EMA Scalping StrategyEMA Slope Indicator Overview:
The indicator plots two exponential moving averages (EMAs) on the chart: a 9-period EMA and a 15-period EMA.
It visually represents the EMAs on the chart and highlights instances where the slope of each EMA exceeds a certain threshold (approximately 30 degrees).
Scalping Strategy:
Using the EMA Slope Indicator on a 5-minute timeframe for scalping can be effective, but it requires adjustments to account for the shorter time horizon.
Trend Identification: Look for instances where the 9-period EMA is above the 15-period EMA. This indicates an uptrend. Conversely, if the 9-period EMA is below the 15-period EMA, it suggests a downtrend.
Slope Analysis: Pay attention to the slope of each EMA. When the slope of both EMAs is steep (exceeds 30 degrees), it signals a strong trend. This can be a favorable condition for scalping as it suggests potential momentum.
Entry Points:
For Long (Buy) Positions: Consider entering a long position when both EMAs are sloping upwards strongly (exceeding 30 degrees) and the 9-period EMA is above the 15-period EMA. Look for entry points when price retraces to the EMAs or when there's a bullish candlestick pattern.
For Short (Sell) Positions: Look for opportunities to enter short positions when both EMAs are sloping downwards strongly (exceeding -30 degrees) and the 9-period EMA is below the 15-period EMA. Similar to long positions, consider entering on retracements or bearish candlestick patterns.
Exit Strategy: Use tight stop-loss orders to manage risk, and aim for small, quick profits. Since scalping involves short-term trading, consider exiting positions when the momentum starts to weaken or when the price reaches a predetermined profit target.
Risk Management:
Scalping involves high-frequency trading with smaller profit targets, so it's crucial to implement strict risk management practices. This includes setting stop-loss orders to limit potential losses and not risking more than a small percentage of your trading capital on each trade.
Backtesting and Optimization:
Before implementing the strategy in live trading, backtest it on historical data to assess its performance under various market conditions. You may also consider optimizing the strategy parameters (e.g., EMA lengths) to maximize its effectiveness.
Continuous Monitoring:
Keep a close eye on market conditions and adjust your strategy accordingly. Market dynamics can change rapidly, so adaptability is key to successful scalping.
ICT Silver Bullet with signals
The "ICT Silver Bullet with signals" indicator (inspired from the lectures of "The Inner Circle Trader" (ICT)),
goes a step further than the ICT Silver Bullet publication, which I made for LuxAlgo :
• uses HTF candles
• instant drawing of Support & Resistance (S/R) lines when price retraces into FVG
• NWOG - NDOG S/R lines
• signals
The Silver Bullet (SB) window which is a specific 1-hour interval where a Fair Value Gap (FVG) pattern can be formed.
When price goes back to the FVG, without breaking it, Support & Resistance lines will be drawn immediately.
There are 3 different Silver Bullet windows (New York local time):
The London Open Silver Bullet (03 AM — 04 AM ~ 03:00 — 04:00)
The AM Session Silver Bullet (10 AM — 11 AM ~ 10:00 — 11:00)
The PM Session Silver Bullet (02 PM — 03 PM ~ 14:00 — 15:00)
🔶 USAGE
This technique can visualise potential support/resistance lines, which can be used as targets.
The script contains 2 main components:
• forming of a Fair Value Gap (FVG)
• drawing support/resistance (S/R) lines
🔹 Forming of FVG
When HTF candles forms an FVG, the FVG will be drawn at the end (close) of the last HTF candle.
To make it easier to visualise the 2 HTF candles that form the FVG, you can enable
• SHOW -> HTF candles
During the SB session, when a FVG is broken, the FVG will be removed, together with its S/R lines.
The same goes if price did not retrace into FVG at the last bar of the SB session
Only exception is when "Remove broken FVG's" is disabled.
In this case a FVG can be broken, as long as price bounces back before the end of the SB session, it will remain to be visible:
🔹 Drawing support/resistance lines
S/R target lines are drawn immediately when price retraces into the FVG.
They will remain updated until they are broken (target hit)
Potential S/R lines are formed by:
• previous swings (swing settings (left-right)
• New Week Opening Gap (NWOG): close on Friday - weekly open
• New Day Opening Gap (NWOG): close previous day - current daily open
Only non-broken lines are included.
Broken =
• minimum of open and close below potential S/R line
• maximum of open and close above potential S/R line
NDOG lines are coloured fuchsia (as in the ICT lectures), NWOG are coloured white (darkmode) or black (lightmode ~ ICT lectures)
Swing line colour can be set as desired.
Here S/R includes NDOG lines:
The same situation, with "Extend Target-lines to their source" enabled:
Here with NWOG lines:
This publication contains a "Minimum Trade Framework (mTFW)", which represents the best-case expected price delivery, this is not your actual trade entry - exit range.
• 40 ticks for index futures or indices
• 15 pips for Forex pairs
The minimum distance (if applicable) can be shown by enabling "Show" - "Minimum Trade Framework" -> blue arrow from close to mTFW
Potential S/R lines needs to be higher (bullish) or lower (bearish) than mTFW.
🔶 SETTINGS
(check USAGE for deeper insights and explanation)
🔹 Only last x bars: when enabled, the script will do most of the calculations at these last x candles, potentially this can speeds calculations.
🔹 Swing settings (left-right): Sets the length, which will set the lookback period/sensitivity of the ZigZag patterns (which directs the trend and points for S/R lines)
🔹 FVG
HTF (minutes): 1-15 minutes.
• When the chart TF is equal of higher, calculations are based on current TF.
• Chart TF > 15 minutes will give the warning: "Please use a timeframe <= 15 minutes".
Remove broken FVG's: when enabled the script will remove FVG (+ associated S/R lines) immediately when FVG is broken at opposite direction.
FVG's still will be automatically removed at the end of the SB session, when there is no retrace, together with associated S/R lines,...
~ trend: Only include FVG in the same direction as the current trend
Note -> when set 'right' (swing setting) rather high ( > 3), he trend change will be delayed as well (default 'right' max 5)
Extend: extend FVG to max right side of SB session
🔹 Targets – support/resistance
Extend Target-lines to their source: extend lines to their origin
Colours (Swing S/R lines)
🔹 Show
SB session: show lines and labels of SB session (+ colour)
• Labels can be disabled separately in the 'Style' section, colour is set at the 'Inputs' section
Trend : Show trend (ZigZag, coloured ~ trend)
HTF candles: Show the 2 HTF candles that form the FVG
Minimum Trade Framework: blue arrow (if applicable)
🔶 ALERTS
There are 4 signals provided (bullish/bearish):
FVG Formed
FVG Retrace
Target reached
FVG cancelled
You can choose between dynamic alerts - only 1 alert needs to be set for all signals, or you can set specific alerts as desired.
💜 PURPLE BARS 😈
• Since TradingView has chosen to give away our precious Purple coloured Wizard Badge, bars are coloured purple 😊😉
Code Unity 1.0Bitcoin 15 minutes strategy.
Bitcoin 15 minutes strategy.
Bitcoin 15 minutes strategy.
Bitcoin 15 minutes strategy.
Bitcoin 15 minutes strategy.
Multi HMA Lines by NB(ENG)
The Hull Moving Average (HMA) line responds quickly to volatile markets,
sometimes it provides more accurate information than the Exponancital Moving Average (EMA).
In particular, the 200 HMA line is easy to decide the overall trend of the market,
and it serves the basis entry position.
So I made indicator that provides these HMA lines into various periods so that they can be checked in one.
In addition, a custom TimeFrame HMA line function has been added so that you can check
not only the TimeFrame that meets your trading standards, but also the HMA of the other TimeFrame that you custome sets.
For example, if you want to see the 200 HMA of the 60-minute bar, you can select and set the different TimeFrame in the Multi TF section below.
For reference, 200 HMA at the 15-minute bar is the same value as 50 HMA at the 1-hour bar, so as shown in the following chart,
I use 4 HMA lines at the 15-minute bar : 20 HMA, 50 HMA, 200 HMA, and 200 HMA from 60-minute TimeFrame.
We hope it will help you in your trading. :)
(KOR)
HMA(Hull Moving Average) 라인은 변동성이 심한 시장에 빠르게 반응하며,
때때로 EMA(Exponancital Moving Average)보다 더 정확한 정보를 제공하곤 합니다.
특히 200HMA 라인은 시장의 전반적인 추세를 판단하기에 용이하며,
큰 틀에서의 포지션 진입 근거의 기반이 됩니다.
이러한 HMA 라인을 다양한 기간으로 나누어 하나의 지표에서 확인 할 수 있도록 만들어 보았습니다.
아울러, 자신의 매매 기준에 맞는 타임 프레임은 물론, 다른 타임 프레임의 HMA도 확인 할 수 있도록
커스텀 타임 프레임 HMA 라인 기능을 추가로 넣었습니다.
예를 들어, 15분 타임 프레임이 본인 매매 기준표이지만, 60분 봉의 200 HMA도 보고 싶다면
밑의 Multi TF 항목에서 해당 타임 프레임을 선택 후 설정하시면 됩니다.
참고로 15분 봉에서의 200 HMA은 1시간 봉에서의 50 HMA과 동일한 값이므로 저는 다음 차트 그림과 같이
15분 봉에서 20 HMA, 50 HMA, 200 HMA, 그리고 1시간 봉에서 200 HMA 이렇게 4개의 라인을 참고 하고 있습니다.
여러분 거래에 도움이 되기를 바랍니다. :)
Cowabunga System from babypips.comPlease do read the information below as well, especially if you are new to Forex.
The Cowabunga System is a type of Mechanical Trading System that filters trades based on the trend of the 4 hour chart with EMAs and some other familiar indicators (RSI, Stochastics and MACD) while entering trades base on 15 minute chart.
I have coded (quite amateurishly) the basic system onto a 15 minute chart (the 4 hour settings are coded as well). The author says the system is to be traded off the 15 minute chart with the 4 hour chart only as a reference for trend direction.
4 Hour Chart Settings
5 EMA
10 EMA
Stochastics (10,3,3)
RSI (9)
Then we move onto the 15 minute chart, where he gives us the trade entry rules.
15 Minute Chart Settings
5 EMA
10 EMA
Stochastics (10,3,3)
RSI (9)
MACD (12,26,9)
Entry Rules - long entry rules used, obviously reverse these for shorting.
1. EMA must cross above the 10 EMA.
2. RSI must be greater than 50 and not overbought.
3. Stochastic must be headed up and not be in overbought territory.
4. MACD histogram must go from negative to positive OR be negative and start to increase in value.
What I did.
1. Set the RSI and Stochastic levels to avoid entries when they indicate overbought conditions for long and oversold conditions for short (80 and 20 levels).
2. Users can input specific times they want to backtest.
3. User's can configure profit targets, trailing stops and stops. Default is set it to was 100 pips profit target with a 40 pip trailing stop. (Note, when you are changing these values, please note that each pip is worth 10, so 100 pips is entered as 1000.)
The Cowabunga System from babypips.com is another popular and active system. The author, Pip Surfer, continues to post wins and losses with this system. It shows there is a lot of honesty and integrity with this system if the author keeps up to date even 10 years later and is not afraid of sharing the times the system causes losses.
As an example of this, here is post he shared just last week . It's almost like a journal, he gives specific times and reasons why he entered, lets the readers know when he was stopped out, etc. I think that what he does is equally important as his system.
To read more about this system, visit the thread on babypips.com, click here.
Luxy Flexible Moving AveragesUltra-lightweight moving average suite supporting six calculation methods (EMA, SMA, WMA, VWMA, RMA, HMA).
Overview
Luxy Flexible Moving Averages is a performance-optimized indicator designed for traders who need clean, reliable moving average lines without the overhead of complex calculations or unnecessary features. This indicator prioritizes speed and visual clarity, making it ideal for traders who run multiple indicators simultaneously or work on lower-powered devices.
Unlike traditional moving average indicators that calculate all lines regardless of whether they are enabled, Luxy only processes the moving averages you actually need, resulting in near-instantaneous chart loading times.
What Makes This Different
The primary design philosophy behind Luxy Flexible Moving Averages is efficiency without compromise. The indicator includes four independently configurable moving average lines, each supporting six different calculation methods. Every calculation is conditionally executed, meaning that disabled lines consume zero processing power. This approach delivers exceptional performance even when paired with resource-intensive indicators like volume profiles, market structure tools, or custom scanners.
Features
The indicator provides four distinct moving average lines, each fully customizable:
Fast MA is typically used for short-term momentum and quick directional changes. Traders often configure this as an EMA with lengths between 5 and 20 bars, depending on their trading timeframe.
Medium MA serves as a middle-ground reference, often used to identify the intermediate trend or as a dynamic support and resistance level. This line commonly uses EMA or SMA calculations with lengths between 10 and 50bars.
Medium-Long MA acts as a visual bridge between short-term noise and long-term structure. Many traders disable this line entirely if they prefer a cleaner chart, but it can be useful for identifying larger trend phases. Typical configurations use SMA or RMA with lengths between 50 and one 150 bars.
Long MA represents the dominant trend or bias. This is often configured as a 200 period SMA, which is a widely-watched level across most markets and timeframes. Alternatively, traders may use RMA for a smoother visual appearance.
Each line supports six calculation methods:
EMA (Exponential Moving Average) applies exponentially decreasing weights to older prices, making it highly responsive to recent price action. This is the preferred method for momentum-based strategies and short-term trading.
SMA (Simple Moving Average ) treats all prices equally within the lookback period, resulting in a smoother line that is less reactive to sudden price spikes. This is commonly used for identifying long-term trends.
WMA (Weighted Moving Average) applies linearly decreasing weights, offering a middle ground between EMA and SMA. It responds faster than SMA but with less sensitivity than EMA.
VWMA (Volume-Weighted Moving Average) incorporates volume data into the calculation, giving more weight to bars with higher trading activity. This method is particularly useful in liquid markets where volume represents genuine participation.
RMA (Relative Moving Average, also known as Wilder's Smoothing) is a variant of EMA with a slower response curve. It is commonly used in oscillators like RSI and ADX, and provides very smooth trend lines on charts.
HMA (Hull Moving Average) is designed to reduce lag while maintaining smoothness. It is the most responsive option available in this indicator but can produce more false signals during choppy or sideways markets.
How It Works
The indicator operates on a conditional calculation model. When you load the indicator, it checks which moving average lines are enabled via the input settings. Only the enabled lines are calculated on each bar, and disabled lines are assigned a not-applicable value, preventing any processing overhead.
Each moving average is calculated using native TradingView functions, ensuring maximum compatibility and reliability across all asset classes and timeframes. The indicator does not use any security calls, loops, or external data requests, which are common sources of performance degradation in more complex indicators.
Recommended Configurations
The optimal moving average configuration depends on your trading style and timeframe. Below are general guidelines based on common trading approaches.
Scalping (1 minute to 5 minute charts)
Scalpers require fast-reacting moving averages that can identify micro-trends and momentum shifts within seconds. The recommended configuration prioritizes EMA or HMA for all lines, with very short lengths to capture quick moves.
For the Fast MA, use EMA with a length between 5 and 8. This line should react almost immediately to price changes and helps confirm entry timing during breakouts or pullbacks.
For the Medium MA , use EMA with a length between 10 and 15. This serves as your primary directional filter. When price is above this line, you look for long opportunities. When below, you look for shorts.
The Medium-Long MA is often disabled in scalping setups to reduce visual noise. If used, configure it as SMA between 40 and 80 to provide context on the broader 5-minute or 15-minute trend.
The Long MA can be set to SMA with a length between 100 and 150, or simply disabled. On very short timeframes, this line often provides more historical context than real-time utility.
Day Trading (5 minute to 1 hour charts)
Day traders benefit from a balanced approach that filters out noise while remaining responsive to intraday volatility. A common configuration combines EMA for short-term lines and SMA for long-term structure.
For the Fast MA , use EMA with a length between 8 and 12. This captures momentum without overreacting to every minor price swing.
For the Medium MA , use EMA with a length between 12 and 21. This is often used as a dynamic support or resistance level during trending sessions.
For the Medium-Long MA , configure SMA or RMA between 60 and one 120. This line helps identify whether the intraday trend aligns with the broader daily bias.
The Long MA is typically set to SMA with a length of 200. This is a critical level that many institutional traders watch, and price reactions around this line are often significant.
Swing Trading (4 hour to daily charts)
Swing traders operate on longer timeframes and need moving averages that filter out daily noise while highlighting multi-day or multi-week trends. SMA and RMA are commonly preferred for their smoothness, though EMA can be used for faster momentum entries.
For the Fast MA , use EMA or SMA with a length between 10 and 20. This line helps time entries during pullbacks within the larger trend.
For the Medium MA , use EMA or SMA with a length between 20 and 34. This often serves as a key decision point for whether a pullback is likely to reverse or continue.
For the Medium-Long MA , configure SMA between 100 and 180. This provides visual context on the broader weekly trend and can act as a significant support or resistance zone.
The Long MA should be SMA with a length of 200 or higher. On daily charts, the two-hundred-day moving average is one of the most widely-referenced indicators in global markets, and price behavior around this level is often predictable.
Using Moving Averages for Trend Identification
Moving averages are primarily used to determine trend direction and strength. The relationship between price and the moving average lines provides insight into market structure.
When price is trading above a moving average, the trend is generally considered bullish on that timeframe. When price is below, the trend is bearish. The steeper the slope of the moving average, the stronger the trend. A flat moving average indicates consolidation or a potential trend change.
Crossovers between moving averages are commonly used as trend confirmation signals. When a faster moving average crosses above a slower moving average, this suggests increasing bullish momentum. When the faster line crosses below, it suggests increasing bearish momentum. However, crossovers should not be used in isolation, as they can produce false signals during sideways markets.
Many traders use moving averages as dynamic support and resistance levels. During uptrends, price often pulls back to a key moving average before resuming higher. During downtrends, price often rallies to a moving average before resuming lower. These levels can be used to plan entries, exits, or stop-loss placement.
Multi-Time Frame Momentum PredictorFifteen-minute candle forming:
- Minute 1-15: Analyze one-minute candles
- Minute 14:30: Evaluate conditions
- Minute 14:45: Make decision
- Minute 14:59: Execute order if criteria are met
3/4-Bar GRG / RGR Pattern (Conditional 4th Candle)This indicator can be used to identify the Green-Red-Green or Red-Green-Red pattern.
It is a price action indicator where a price action which identifies the defeat of buyers and sellers.
If the buyers comprehensively defeat the sellers then the price moves up and if the sellers defeat the buyers then the price moves down.
In my trading experience this is what defines the price movement.
It is a 3 or 4 candle pattern, beyond that i.e, 5 or more candles could mean a very sideways market and unnecessary signal generation.
How does it work?
Upside/Green signal
Say candle 1 is Green, which means buyers stepped in, then candle 2 is Red or a Doji, that means sellers brought the price down. Then if candle 3 is forming to be Green and breaks the closing of the 1st candle and opening of the 2nd candle, then a green arrow will appear and that is the place where you want to take your trade.
Here the buyers defeated the sellers.
Sometimes candle 3 falls short but candle 4 breaks candle 1's closing and candle 2's opening price. We can enter on candle 4.
Important - We need to enter the trade as soon as the price moves above the candle 1 and 2's body and should not wait for the 3rd or 4th candle to close. Ignore wicks.
I have restricted it to 4 candles and that is all that is needed. More than that is a longer sideways market.
I call it the +-+ or GRG pattern.
Stop loss can be candle 2's mid for safe traders (that includes me) or candle 2's body low for risky traders.
Back testing suggests that body low will be useless and result in more points in loss because for the bigger move this point will not be touched, so why not get out faster.
Downside/Red signal
Say candle 1 is Red, which means sellers stepped in, then candle 2 is Green or a Doji, that means buyers took the price up. Then if candle 3 is forming to be Red and breaks the closing of the 1st candle and opening of the 2nd candle then a Red arrow will appear and that is the place where you want to take your trade.
Sometimes candle 3 falls short but candle 4 breaks candle 1's closing and candle 2's opening price. We can enter on candle 4.
We need to enter the trade as soon as the price moves below the candle 1 and 2's body and should not wait for the 3rd or 4th candle to close.
I have restricted it to 4 candles and that is all that is needed. More than that is a longer sideways market.
I call it the -+- or RGR pattern.
Stop loss can be candle 2's mid for safe traders ( that includes me) or candle 2's body high for risky traders.
Back testing suggests that body high will be useless and result in more points in loss because for the bigger move this point will not be touched, so why not get out faster.
Important Settings
You can enable or disable the 4th candle signal to avoid the noise, but at times I have noticed that the 4th candle gives a very strong signal or I can say that the strong signal falls on the 4th candle. This is mostly a coincidence.
You can also configure how many previous bars should the signal be generated for. 10 to 30 is good enough. To backtest increase it to 2000 or 5000 for example.
Rest are self explanatory.
Pointers
If after taking the trade, the next candle moves in your direction and closes strong bullish or bearish, then move SL to break even and after that you can trail it.
If a upside trade hits SL and immediately a down side trade signal is generated on the next candle then take it. Vice versa is true.
Trades need to be taken on previous 2 candle's body high or low combined and not the wicks.
The most losses a trader takes is on a sideways day and because in our strategy the stop loss is so small that even on a sideways day we'll get out with a little profit or worst break even.
Hold targets for longer targets and don't panic.
If last 3-4 days have been sideways then there is a good probability that day will be trending so we can hold our trade for longer targets. Target to hold the trade for whole day and not exit till the day closes.
In general avoid trading in the middle of the day for index and stocks. Divide the day into 3 parts and avoid the middle.
Use Support/Resistance, 10, 20, 50, 200 EMA/SMA, Gaps, Whole/Round numbers(very imp) for identifying targets.
Trail your SL.
For indexes I would use 5 min and 15 min timeframe.
For commodities and crypto we can use higher timeframe as well. Look for signals during volatile time durations and avoid trading the whole day. Signal usually gives good targets on those times.
If a GRG or RGR pattern appears on a daily timeframe then this is our time to go big.
Minimum Risk to Reward should be 1:2 and for longer targets can be 1:4 to 1:10.
Trade with small lot size. Money management will happen automatically.
With small lot size and correct Risk-Re ward we can be very profitable. Don't trade with big lot size.
Stay in the market for longer and collect points not money.
Very imp - Watch market and learn to generate a market view.
Very imp - Only 4 candles are needed in trading - strong bullish, strong bearish, hammer, inverse hammer and doji.
Go big on bearish days for option traders. Puts are better bought and Calls are better sold.
Cluster of green signals can lead to bigger move on the upside and vice versa for red signals.
Most of this is what I learned from successful traders (from the top 2%) only the indicator is mine.
Crypto Scalping Strategy - High Win Rategrok first try. I used grok to create a scalping strategy that is automated for crypto scalp trading on 5-15 min intervals
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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dabilThe strategy is probably to go short or long with the trend depending on the case, but if all time units 1 minute then 3 minutes then 5 minutes then 15 minutes then 1 hour all show the same direction, but first the 1 hour must be bullish in which the 1 hour candle closes above the previous one, for example if the trend is bearish then the market wants to change direction, then a 1 hour bullish close must then be followed by a 1 hour bearish close below the bullish candle, then another bullish candle must shoot above the previous bullish candle, then 15 minutes also shoot above the previous 15 bullish candles, then 1 and 2...3.5. Then I can rise with the market by only covering the last 15 bullish candles with my stop loss, if my SL is 50 pips then I want 100 pips and then I'm out.
Yelober - Market Internal direction+ Key levelsYelober – Market Internals + Key Levels is a focused intraday trading tool that helps you spot high-probability price direction by anchoring decisions to structure that matters: yesterday’s RTH High/Low, today’s pre-market High/Low, and a fast Value Area/POC from the prior session. Paired with a compact market internals dashboard (NYSE/NASDAQ UVOL vs. DVOL ratios, VOLD slopes, TICK/TICKQ momentum, and optional VIX trend), it gives you a real-time read on breadth so you can choose which direction to trade, when to enter (breaks, retests, or fades at PMH/PML/VAH/VAL/POC), and how to plan exits as internals confirm or deteriorate. On top of these intraday decision benefits, it also allows traders—in a very subtle but powerful way—to keep an eye on the VIX and immediately recognize significant spikes or sharp decreases that should be factored in before entering a trade, or used as a quick signal to modify an existing position. In short: clear levels for the chart, live internals for the context, and a smarter, rules-based path to execution.
# Yelober – Market Internals + Key Levels
*A TradingView indicator for session key levels + real‑time market internals (NYSE/NASDAQ TICK, UVOL/DVOL/VOLD, and VIX).*
**Script name in Pine:** `Yelober - Market Internal direction+ Key levels` (Pine v6)
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## 1) What this indicator does
**Purpose:** Help intraday traders quickly find high‑probability reaction zones and read market internals momentum without switching charts. It overlays yesterday/today’s **automatic price levels** on your active chart and shows a **market breadth table** that summarizes NYSE/NASDAQ buying pressure and TICK direction, with an optional VIX trend read.
### Key features at a glance
* **Automatic Price Levels (overlay on chart)**
* Yesterday’s High/Low of Day (**yHoD**, **yLoD**)
* Extended Hours High/Low (**yEHH**, **yEHL**) across yesterday AH + today pre‑market
* Today’s Pre‑Market High/Low (**PMH**, **PML**)
* Yesterday’s **Value Area High/Low** (**VAH/VAL**) and **Point of Control (POC)** computed from a volume profile of yesterday’s **regular session**
* Smart de‑duplication:
* Shows **only the higher** of (yEHH vs PMH) and **only the lower** of (yEHL vs PML) to avoid redundant bands
* **Market Breadth Table (on‑chart table)**
* **NYSE ratio** = UVOL/DVOL (signed) with **VOLD slope** from session open
* **NASDAQ ratio** = UVOLQ/DVOLQ (signed) with **VOLDQ slope** from session open
* **TICK** and **TICKQ**: live cumulative ratio and short‑term slope
* **VIX** (optional): current value + slope over a configurable lookback/timeframe
* Color‑coded trends with sensible thresholds and optional normalization
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## 2) How to use it (trader workflow)
1. **Mark your reaction zones**
* Watch **yHoD/yLoD**, **PMH/PML**, and **VAH/VAL/POC** for first touches, break/retest, and failure tests.
* Expect increased responsiveness when multiple levels cluster (e.g., PMH ≈ VAH ≈ daily pivot).
2. **Read the breadth panel for context**
* **NYSE/NASDAQ ratio** (>1 = more up‑volume than down‑volume; <−1 = down‑dominant). Strong green across both favors long setups; red favors short setups.
* **VOLD slopes** (NYSE & NASDAQ): positive and accelerating → broadening participation; negative → persistent pressure.
* **TICK/TICKQ**: cumulative ratio and **slope arrows** (↗ / ↘ / →). Use the slope to gauge **near‑term thrust or fade**.
* **VIX slope**: rising VIX (red) often coincides with risk‑off; falling VIX (green) with risk‑on.
3. **Confluence = higher confidence**
* Example: Price reclaims **PMH** while **NYSE/NASDAQ ratios** print green and **TICK slopes** point ↗ — consider break‑and‑go; if VIX slope is ↘, that adds risk‑on confidence.
* Example: Price rejects **VAH** while **VOLD slopes** roll negative and VIX ↗ — consider fade/reversal.
4. **Risk management**
* Place stops just beyond key levels tested; if breadth flips, tighten or exit.
> **Timeframes:** Works best on 1–15m charts for intraday. Value Area is computed from **yesterday’s RTH**; choose a smaller calculation timeframe (e.g., 5–15m) for stable profiles.
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## 3) Inputs & settings (what each option controls)
### Global Style
* **Enable all automatic price levels**: master toggle for yHoD/yLoD, yEHH/yEHL, PMH/PML, VAH/VAL/POC.
* **Line style/width**: applies to all drawn levels.
* **Label size/style** and **label color linking**: use the same color as the line or override with a global label color.
* **Maximum bars lookback**: how far the script scans to build yesterday metrics (performance‑sensitive).
### Value Area / Volume Profile
* **Enable Value Area calculations** *(on by default)*: computes yesterday’s **POC**, **VAH**, **VAL** from a simplified intraday volume profile built from yesterday’s **regular session bars**.
* **Max Volume Profile Points** *(default 50)*: lower values = faster; higher = more precise.
* **Value Area Calculation Timeframe** *(default 15)*: the security timeframe used when collecting yesterday’s highs/lows/volumes.
### Individual Level Toggles & Colors
* **yHoD / yLoD** (yesterday high/low)
* **yEHH / yEHL** (yesterday AH + today pre‑market extremes)
* **PMH / PML** (today pre‑market extremes)
* **VAH / VAL / POC** (yesterday RTH value area + point of control)
### Market Breadth Panel
* **Show NYSE / NASDAQ / VIX**: choose which series to display in the table.
* **Table Position / Size / Background Color**: UI placement and legibility.
* **Slope Averaging Periods** *(default 5)*: number of recent TICK/TICKQ ratio points used in slope calculation.
* **Candles for Rate** *(default 10)* & **Normalize Rate**: VIX slope calculation as % change between `now` and `n` candles ago; normalize divides by `n`.
* **VIX Timeframe**: optionally compute VIX on a higher TF (e.g., 15, 30, 60) for a smoother regime read.
* **Volume Normalization** (NYSE & NASDAQ): display VOLD slopes scaled to `tens/thousands/millions/10th millions` for readable magnitudes; color thresholds adapt to your choice.
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## 4) Data sources & definitions
* **UVOL/VOLD (NYSE)** and **UVOLQ/DVOLQ/VOLDQ (NASDAQ)** via `request.security()`
* **Ratio** = `UVOL/DVOL` (signed; negative when down‑volume dominates)
* **VOLD slope** ≈ `(VOLD_now − VOLD_open) / bars_since_open`, then normalized per your setting
* **TICK/TICKQ**: cumulative sum of prints this session with **positives vs negatives ratio**, plus a simple linear regression **slope** of the last `N` ratio values
* **VIX**: value and slope across a user‑selected timeframe and lookback
* **Sessions (EST/EDT)**
* **Regular:** 09:30–16:00
* **Pre‑Market:** 04:00–09:30
* **After Hours:** 16:00–20:00
* **Extended‑hours extremes** combine **yesterday AH** + **today PM**
> **Note:** All session checks are done with TradingView’s `time(…,"America/New_York")` context. If your broker’s RTH differs (e.g., futures), adjust expectations accordingly.
---
## 5) How the algorithms work (plain English)
### A) Key Levels
* **Yesterday’s RTH High/Low**: scans yesterday’s bars within 09:30–16:00 and records the extremes + bar indices.
* **Extended Hours**: scans yesterday AH and today PM to get **yEHH/yEHL**. Script shows **either yEHH or PMH** (whichever is **higher**) and **either yEHL or PML** (whichever is **lower**) to avoid duplicate bands stacked together.
* **Value Area & POC (RTH only)**
* Build a coarse volume profile with `Max Volume Profile Points` buckets across the price range formed by yesterday’s RTH bars.
* Distribute each bar’s volume uniformly across the buckets it spans (fast approximation to keep Pine within execution limits).
* **POC** = bucket with max volume. **VA** expands from POC outward until **70%** of cumulative volume is enclosed → yields **VAH/VAL**.
### B) Market Breadth Table
* **NYSE/NASDAQ Ratio**: signed UVOL/DVOL with basic coloring.
* **VOLD Slopes**: from session open to current, normalized to human‑readable units; colors flip green/red based on thresholds that map to your normalization setting (e.g., ±2M for NYSE, ±3.5×10M for NASDAQ).
* **TICK/TICKQ Slope**: linear regression over the last `N` ratio points → **↗ / → / ↘** with the rounded slope value.
* **VIX Slope**: % change between now and `n` candles ago (optionally divided by `n`). Red when rising beyond threshold; green when falling.
---
## 6) Recommended presets
* **Stocks (liquid, intraday)**
* Value Area **ON**, `Max Volume Points` = **40–60**, **Timeframe** = **5–15**
* Breadth: show **NYSE & NASDAQ & VIX**, `Slope periods` = **5–8**, `Candles for rate` = **10–20**, **Normalize VIX** = **ON**
* **Index futures / very high‑volume symbols**
* If you see Pine timeouts, set `Max Volume Points` = **20–40** or temporarily **disable Value Area**.
* Keep breadth panel **ON** (it’s light). Consider **VIX timeframe = 15/30** for regime clarity.
---
## 7) Tips, edge cases & performance
* **Performance:** The volume profile is capped (`maxBarsToProcess ≤ 500` and bucketed) to keep it responsive. If you experience slowdowns, reduce `Max Volume Points`, `Maximum bars lookback`, or disable Value Area.
* **Redundant lines:** The script **intentionally suppresses** PMH/PML when yEHH/yEHL are more extreme, and vice‑versa.
* **Label visibility:** Use `Label style = none` if you only want clean lines and read values from the right‑end labels.
* **Futures/RTH differences:** Value Area is from **yesterday’s RTH** only; for 24h instruments the RTH period may not reflect overnight structure.
* **Session transitions:** PMH/PML tracking stops as soon as RTH starts; values persist as static levels for the session.
---
## 8) Known limitations
* Uses public TradingView symbols: `UVOL`, `VOLD`, `UVOLQ`, `DVOLQ`, `VOLDQ`, `TICK`, `TICKQ`, `VIX`. If your data plan or region limits any symbol, the corresponding table rows may show `na`.
* The VA/POC approximation assumes uniform distribution of each bar’s volume across its high–low. That’s fast but not a tick‑level profile.
* Works best on US equities with standard NY session; alternative sessions may need code changes.
---
## 9) Troubleshooting
* **“Script is too slow / timed out”** → Lower `Max Volume Points`, lower `Maximum bars lookback`, or toggle **OFF** `Enable Value Area calculations` for that instrument.
* **Missing breadth values** → Ensure the symbols above load on your account; try reloading chart or switching timeframes once.
* **Overlapping labels** → Set `Label style = none` or reduce label size.
---
## 10) Version / license / contribution
* **Version:** Initial public release (Pine v6).
* **Author:** © yelober
* **License:** Free for community use and enhancement. Please keep author credit.
* **Contributing:** Open PRs/ideas: presets, alert conditions, multi‑day VA composites, optional mid‑value (`(VAH+VAL)/2`), session filter for futures, and alertable state machine for breadth regime transitions.
---
## 11) Quick start (TL;DR)
1. Add the indicator and **keep default settings**.
2. Trade **reactions** at yHoD/yLoD/PMH/PML/VAH/VAL/POC.
3. Use the **breadth table**: look for **green ratios + ↗ slopes** (risk‑on) or **red ratios + ↘ slopes** (risk‑off). Check **VIX** slope for confirmation.
4. Manage risk around levels; when breadth flips against you, tighten or exit.
---
### Changelog (public)
* **v1.0:** First community release with automatic RTH levels, VA/POC approximation, breadth dashboard (NYSE/NASDAQ/TICK/TICKQ/VIX) with normalization and adaptive color thresholds.
ST-Stochastic DashboardST-Stochastic Dashboard: User Manual & Functionality
1. Introduction
The ST-Stochastic Dashboard is a comprehensive tool designed for traders who utilize the Stochastic Oscillator. It combines two key features into a single indicator:
A standard, fully customizable Stochastic Oscillator plotted directly on your chart.
A powerful Multi-Timeframe (MTF) Dashboard that shows the status of the Stochastic %K value across three different timeframes of your choice.
This allows you to analyze momentum on your current timeframe while simultaneously monitoring for confluence or divergence on higher or lower timeframes, all without leaving your chart.
Disclaimer: In accordance with TradingView's House Rules, this document describes the technical functionality of the indicator. It is not financial advice. The indicator provides data based on user-defined parameters; all trading decisions are the sole responsibility of the user. Past performance is not indicative of future results.
2. How It Works (Functionality)
The indicator is divided into two main components:
A. The Main Stochastic Indicator (Chart Pane)
This is the visual representation of the Stochastic Oscillator for the chart's current timeframe.
%K Line (Blue): This is the main line of the oscillator. It shows the current closing price in relation to the high-low range over a user-defined period. A high value means the price is closing near the top of its recent range; a low value means it's closing near the bottom.
%D Line (Black): This is the signal line, which is a moving average of the %K line. It is used to smooth out the %K line and generate trading signals.
Overbought Zone (Red Area): By default, this zone is above the 75 level. When the Stochastic lines are in this area, it indicates that the asset may be "overbought," meaning the price is trading near the peak of its recent price range.
Oversold Zone (Blue Area): By default, this zone is below the 25 level. When the Stochastic lines are in this area, it indicates that the asset may be "oversold," meaning the price is trading near the bottom of its recent price range.
Crossover Signals:
Buy Signal (Blue Up Triangle): A blue triangle appears below the candles when the %K line crosses above the Oversold line (e.g., from 24 to 26). This suggests a potential shift from bearish to bullish momentum.
Sell Signal (Red Down Triangle): A red triangle appears above the candles when the %K line crosses below the Overbought line (e.g., from 76 to 74). This suggests a potential shift from bullish to bearish momentum.
B. The Multi-Timeframe Dashboard (Table on Chart)
This is the informational table that appears on your chart. Its purpose is to give you a quick, at-a-glance summary of the Stochastic's condition on other timeframes.
Function: The script uses TradingView's request.security() function to pull the %K value from three other timeframes that you specify in the settings.
Efficiency: The table is designed to update only on the last (most recent) bar (barstate.islast) to ensure the script runs efficiently and does not slow down your chart.
Columns:
Timeframe: Displays the timeframe you have selected (e.g., '5', '15', '60').
Stoch %K: Shows the current numerical value of the %K line for that specific timeframe, rounded to two decimal places.
Status: Interprets the %K value and displays a clear status:
OVERBOUGHT (Red Background): The %K value is above the "Upper Line" setting.
OVERSOLD (Blue Background): The %K value is below the "Lower Line" setting.
NEUTRAL (Black/Dark Background): The %K value is between the Overbought and Oversold levels.
3. Settings / Parameters in Detail
You can access these settings by clicking the "Settings" (cogwheel) icon on the indicator name.
Stochastic Settings
This group controls the behavior and appearance of the main Stochastic indicator plotted in the pane.
Stochastic Period (length)
Description: This is the lookback period used to calculate the Stochastic Oscillator. It defines the number of past bars to consider for the high-low range.
Default: 9
%K Smoothing (smoothK)
Description: This is the moving average period used to smooth the raw Stochastic value, creating the %K line. A higher value results in a smoother, less sensitive line.
Default: 3
%D Smoothing (smoothD)
Description: This is the moving average period applied to the %K line to create the %D (signal) line. A higher value creates a smoother signal line that lags further behind the %K line.
Default: 6
Lower Line (Oversold) (ul)
Description: This sets the threshold for the oversold condition. When the %K line is below this value, the dashboard will show "OVERSOLD". It is also the level the %K line must cross above to trigger a Buy Signal triangle.
Default: 25
Upper Line (Overbought) (ll)
Description: This sets the threshold for the overbought condition. When the %K line is above this value, the dashboard will show "OVERBOUGHT". It is also the level the %K line must cross below to trigger a Sell Signal triangle.
Default: 75
Dashboard Settings
This group controls the data and appearance of the multi-timeframe table.
Timeframe 1 (tf1)
Description: The first timeframe to be displayed in the dashboard.
Default: 5 (5 minutes)
Timeframe 2 (tf2)
Description: The second timeframe to be displayed in the dashboard.
Default: 15 (15 minutes)
Timeframe 3 (tf3)
Description: The third timeframe to be displayed in the dashboard.
Default: 60 (1 hour)
Dashboard Position (table_pos)
Description: Allows you to select where the dashboard table will appear on your chart.
Options: top_right, top_left, bottom_right, bottom_left
Default: bottom_right
4. How to Use & Interpret
Configuration: Adjust the Stochastic Settings to match your trading strategy. The default values (9, 3, 6) are common, but feel free to experiment. Set the Dashboard Settings to the timeframes that are most relevant to your analysis (e.g., your entry timeframe, a medium-term timeframe, and a long-term trend timeframe).
Analysis with the Dashboard: The primary strength of this tool is confluence. Look for situations where multiple timeframes align. For example:
If the dashboard shows OVERSOLD on the 15-minute, 60-minute, and your current 5-minute chart, a subsequent Buy Signal on your 5-minute chart may carry more weight.
Conversely, if your 5-minute chart shows OVERSOLD but the 60-minute chart is strongly OVERBOUGHT, it could indicate that you are looking at a minor pullback in a larger downtrend.
Interpreting States:
Overbought is not an automatic "sell" signal. It simply means momentum has been strong to the upside, and the price is near its recent peak. It could signal a potential reversal, but the price can also remain overbought for extended periods in a strong uptrend.
Oversold is not an automatic "buy" signal. It means momentum has been strong to the downside. While it can signal a potential bounce, prices can remain oversold for a long time in a strong downtrend.
Use the signals and dashboard states as a source of information to complement your overall trading strategy, which should include other forms of analysis such as price action, support/resistance levels, or other indicators.
Trading Macro Windows by BW v2
Trading Macros by BW: Integrating ICT Concepts for Session Analysis
This indicator combines two key Inner Circle Trader (ICT) concepts—Change in State of Delivery (CISD) or Inverted Fair Value Gap (IFVG) signals with Macro Time Windows—to provide a unified tool for analyzing intraday price action, particularly during Pacific Time (PT) sessions. Rather than simply merging existing scripts, this integration creates a cohesive visual framework that highlights how macro consolidation periods interact with potential reversal or continuation signals like CISD or IFVG. By overlaying macro candle styling and borders on the chart alongside selectable signal lines, traders can better contextualize setups within ICT's macro narrative, where price often manipulates liquidity during these windows before displacing toward higher-timeframe objectives.
Core Components and How They Work Together:
Macro Time Windows (Inspired by ICT's Macro Periods):
ICT emphasizes "macro" as 30-minute windows (e.g., 06:45–07:15 PT, 07:45–08:15 PT, up to 11:45–12:15 PT) where price tends to consolidate, sweep liquidity, or form key structures like Fair Value Gaps (FVGs). These periods set the stage for the session's directional bias.
The indicator styles candles within these windows using a user-defined color for wicks, borders, and bodies (translucent for visibility). This visual emphasis helps traders focus on activity inside macros, where reversals or continuations often originate.
Borders are drawn as vertical lines at the start and end of each window (with a +5 minute buffer to capture related activity), using a dotted style by default. This creates a "study zone" that encapsulates macro events, allowing traders to assess if price is respecting or violating these zones in alignment with broader ICT models like the Power of 3 (AMD cycle).
Toggle: "Macro Candles Enabled" (default: true) – Turn off to disable styling and borders if focusing solely on signals.
CISD or IFVG Signals (Selectable Mode):
Mode Selection: Choose between "Change in the State of Delivery" (CISD) or "IFVG" (default: IFVG). Both detect shifts in market delivery during specific 30-minute slices (15–45 or 17–45 minutes past the hour in PT sessions).
CISD Mode: Based on ICT's definition of a sudden directional shift, this identifies aggressive displacements after sweeping recent highs/lows. It uses a rolling reference high/low over 6 bars, checks for sweeps (penetrating by at least 2 ticks in the last 2-3 bars), reclamation (closing beyond the reference with at least 50% body), and displacement (50% of prior range or an immediate FVG of 6+ ticks). Signals plot a horizontal line from the close, extending 24 bars right, labeled "CISD."
IFVG Mode: Focuses on Inverted Fair Value Gaps, where a bullish FVG (low > high by 13+ ticks) forms but is inverted (closed below) in the same slice, signaling bearish intent (or vice versa). This targets violations against opposing liquidity, often leading to raids on external ranges. Signals plot similarly, labeled "IFVG."
Shared Logic: Both modes enforce a 55-bar cooldown to prevent clustering, operate only during PT sessions (06:30–13:00), and use tick-based thresholds for precision across instruments. The integration with macros allows traders to see if signals occur within or at the edges of macro windows, enhancing confirmation—for example, a CISD inside a macro might indicate a manipulated reversal toward the session's true objective.
Toggle: "Signals Enabled" (default: true) – Turn off to hide all signal lines and labels, isolating the macro visualization.
How Components Interact:
Macro windows provide the "narrative context" (consolidation/manipulation), while CISD/IFVG signals detect the "delivery shift" (displacement). Together, they form a mashup that justifies publication: isolated signals can be noisy, but when filtered by macro periods, they align with ICT's session model. For instance, an IFVG inversion during a macro might confirm a liquidity sweep before targeting PD arrays or order blocks.
No external dependencies; all calculations are self-contained using Pine's built-in functions like ta.highest/lowest for references and time-based sessions for windows.
Usage Guidelines:
Apply to intraday charts (e.g., 1-5 min) or stocks during PT hours.
Look for confluence: A bull IFVG signal post-macro low sweep might target the next macro high or daily bias.
Customize colors/styles for signals (solid/dashed/dotted lines) and macros to suit your chart.
Backtest in replay mode to observe how macros frame signals—e.g., price often respects macro borders as S/R.
Limitations: Timezone-fixed to PT (America/Los_Angeles); signals are directional hints, not trade entries. Combine with ICT tools like order blocks or liquidity pools for full setups.
This script draws from community ICT implementations but refines them into a single, purpose-built tool for macro-driven trading, reducing chart clutter while emphasizing interconnected concepts. Feedback welcome!
Ray Dalio's All Weather Strategy - Portfolio CalculatorTHE ALL WEATHER STRATEGY INDICATOR: A GUIDE TO RAY DALIO'S LEGENDARY PORTFOLIO APPROACH
Introduction: The Genesis of Financial Resilience
In the sprawling corridors of Bridgewater Associates, the world's largest hedge fund managing over 150 billion dollars in assets, Ray Dalio conceived what would become one of the most influential investment strategies of the modern era. The All Weather Strategy, born from decades of market observation and rigorous backtesting, represents a paradigm shift from traditional portfolio construction methods that have dominated Wall Street since Harry Markowitz's seminal work on Modern Portfolio Theory in 1952.
Unlike conventional approaches that chase returns through market timing or stock picking, the All Weather Strategy embraces a fundamental truth that has humbled countless investors throughout history: nobody can consistently predict the future direction of markets. Instead of fighting this uncertainty, Dalio's approach harnesses it, creating a portfolio designed to perform reasonably well across all economic environments, hence the evocative name "All Weather."
The strategy emerged from Bridgewater's extensive research into economic cycles and asset class behavior, culminating in what Dalio describes as "the Holy Grail of investing" in his bestselling book "Principles" (Dalio, 2017). This Holy Grail isn't about achieving spectacular returns, but rather about achieving consistent, risk-adjusted returns that compound steadily over time, much like the tortoise defeating the hare in Aesop's timeless fable.
HISTORICAL DEVELOPMENT AND EVOLUTION
The All Weather Strategy's origins trace back to the tumultuous economic periods of the 1970s and 1980s, when traditional portfolio construction methods proved inadequate for navigating simultaneous inflation and recession. Raymond Thomas Dalio, born in 1949 in Queens, New York, founded Bridgewater Associates from his Manhattan apartment in 1975, initially focusing on currency and fixed-income consulting for corporate clients.
Dalio's early experiences during the 1970s stagflation period profoundly shaped his investment philosophy. Unlike many of his contemporaries who viewed inflation and deflation as opposing forces, Dalio recognized that both conditions could coexist with either economic growth or contraction, creating four distinct economic environments rather than the traditional two-factor models that dominated academic finance.
The conceptual breakthrough came in the late 1980s when Dalio began systematically analyzing asset class performance across different economic regimes. Working with a small team of researchers, Bridgewater developed sophisticated models that decomposed economic conditions into growth and inflation components, then mapped historical asset class returns against these regimes. This research revealed that traditional portfolio construction, heavily weighted toward stocks and bonds, left investors vulnerable to specific economic scenarios.
The formal All Weather Strategy emerged in 1996 when Bridgewater was approached by a wealthy family seeking a portfolio that could protect their wealth across various economic conditions without requiring active management or market timing. Unlike Bridgewater's flagship Pure Alpha fund, which relied on active trading and leverage, the All Weather approach needed to be completely passive and unleveraged while still providing adequate diversification.
Dalio and his team spent months developing and testing various allocation schemes, ultimately settling on the 30/40/15/7.5/7.5 framework that balances risk contributions rather than dollar amounts. This approach was revolutionary because it focused on risk budgeting—ensuring that no single asset class dominated the portfolio's risk profile—rather than the traditional approach of equal dollar allocations or market-cap weighting.
The strategy's first institutional implementation began in 1996 with a family office client, followed by gradual expansion to other wealthy families and eventually institutional investors. By 2005, Bridgewater was managing over $15 billion in All Weather assets, making it one of the largest systematic strategy implementations in institutional investing.
The 2008 financial crisis provided the ultimate test of the All Weather methodology. While the S&P 500 declined by 37% and many hedge funds suffered double-digit losses, the All Weather strategy generated positive returns, validating Dalio's risk-balancing approach. This performance during extreme market stress attracted significant institutional attention, leading to rapid asset growth in subsequent years.
The strategy's theoretical foundations evolved throughout the 2000s as Bridgewater's research team, led by co-chief investment officers Greg Jensen and Bob Prince, refined the economic framework and incorporated insights from behavioral economics and complexity theory. Their research, published in numerous institutional white papers, demonstrated that traditional portfolio optimization methods consistently underperformed simpler risk-balanced approaches across various time periods and market conditions.
Academic validation came through partnerships with leading business schools and collaboration with prominent economists. The strategy's risk parity principles influenced an entire generation of institutional investors, leading to the creation of numerous risk parity funds managing hundreds of billions in aggregate assets.
In recent years, the democratization of sophisticated financial tools has made All Weather-style investing accessible to individual investors through ETFs and systematic platforms. The availability of high-quality, low-cost ETFs covering each required asset class has eliminated many of the barriers that previously limited sophisticated portfolio construction to institutional investors.
The development of advanced portfolio management software and platforms like TradingView has further democratized access to institutional-quality analytics and implementation tools. The All Weather Strategy Indicator represents the culmination of this trend, providing individual investors with capabilities that previously required teams of portfolio managers and risk analysts.
Understanding the Four Economic Seasons
The All Weather Strategy's theoretical foundation rests on Dalio's observation that all economic environments can be characterized by two primary variables: economic growth and inflation. These variables create four distinct "economic seasons," each favoring different asset classes. Rising growth benefits stocks and commodities, while falling growth favors bonds. Rising inflation helps commodities and inflation-protected securities, while falling inflation benefits nominal bonds and stocks.
This framework, detailed extensively in Bridgewater's research papers from the 1990s, suggests that by holding assets that perform well in each economic season, an investor can create a portfolio that remains resilient regardless of which season unfolds. The elegance lies not in predicting which season will occur, but in being prepared for all of them simultaneously.
Academic research supports this multi-environment approach. Ang and Bekaert (2002) demonstrated that regime changes in economic conditions significantly impact asset returns, while Fama and French (2004) showed that different asset classes exhibit varying sensitivities to economic factors. The All Weather Strategy essentially operationalizes these academic insights into a practical investment framework.
The Original All Weather Allocation: Simplicity Masquerading as Sophistication
The core All Weather portfolio, as implemented by Bridgewater for institutional clients and later adapted for retail investors, maintains a deceptively simple static allocation: 30% stocks, 40% long-term bonds, 15% intermediate-term bonds, 7.5% commodities, and 7.5% Treasury Inflation-Protected Securities (TIPS). This allocation may appear arbitrary to the uninitiated, but each percentage reflects careful consideration of historical volatilities, correlations, and economic sensitivities.
The 30% stock allocation provides growth exposure while limiting the portfolio's overall volatility. Stocks historically deliver superior long-term returns but with significant volatility, as evidenced by the Standard & Poor's 500 Index's average annual return of approximately 10% since 1926, accompanied by standard deviation exceeding 15% (Ibbotson Associates, 2023). By limiting stock exposure to 30%, the portfolio captures much of the equity risk premium while avoiding excessive volatility.
The combined 55% allocation to bonds (40% long-term plus 15% intermediate-term) serves as the portfolio's stabilizing force. Long-term bonds provide substantial interest rate sensitivity, performing well during economic slowdowns when central banks reduce rates. Intermediate-term bonds offer a balance between interest rate sensitivity and reduced duration risk. This bond-heavy allocation reflects Dalio's insight that bonds typically exhibit lower volatility than stocks while providing essential diversification benefits.
The 7.5% commodities allocation addresses inflation protection, as commodity prices typically rise during inflationary periods. Historical analysis by Bodie and Rosansky (1980) demonstrated that commodities provide meaningful diversification benefits and inflation hedging capabilities, though with considerable volatility. The relatively small allocation reflects commodities' high volatility and mixed long-term returns.
Finally, the 7.5% TIPS allocation provides explicit inflation protection through government-backed securities whose principal and interest payments adjust with inflation. Introduced by the U.S. Treasury in 1997, TIPS have proven effective inflation hedges, though they underperform nominal bonds during deflationary periods (Campbell & Viceira, 2001).
Historical Performance: The Evidence Speaks
Analyzing the All Weather Strategy's historical performance reveals both its strengths and limitations. Using monthly return data from 1970 to 2023, spanning over five decades of varying economic conditions, the strategy has delivered compelling risk-adjusted returns while experiencing lower volatility than traditional stock-heavy portfolios.
During this period, the All Weather allocation generated an average annual return of approximately 8.2%, compared to 10.5% for the S&P 500 Index. However, the strategy's annual volatility measured just 9.1%, substantially lower than the S&P 500's 15.8% volatility. This translated to a Sharpe ratio of 0.67 for the All Weather Strategy versus 0.54 for the S&P 500, indicating superior risk-adjusted performance.
More impressively, the strategy's maximum drawdown over this period was 12.3%, occurring during the 2008 financial crisis, compared to the S&P 500's maximum drawdown of 50.9% during the same period. This drawdown mitigation proves crucial for long-term wealth building, as Stein and DeMuth (2003) demonstrated that avoiding large losses significantly impacts compound returns over time.
The strategy performed particularly well during periods of economic stress. During the 1970s stagflation, when stocks and bonds both struggled, the All Weather portfolio's commodity and TIPS allocations provided essential protection. Similarly, during the 2000-2002 dot-com crash and the 2008 financial crisis, the portfolio's bond-heavy allocation cushioned losses while maintaining positive returns in several years when stocks declined significantly.
However, the strategy underperformed during sustained bull markets, particularly the 1990s technology boom and the 2010s post-financial crisis recovery. This underperformance reflects the strategy's conservative nature and diversified approach, which sacrifices potential upside for downside protection. As Dalio frequently emphasizes, the All Weather Strategy prioritizes "not losing money" over "making a lot of money."
Implementing the All Weather Strategy: A Practical Guide
The All Weather Strategy Indicator transforms Dalio's institutional-grade approach into an accessible tool for individual investors. The indicator provides real-time portfolio tracking, rebalancing signals, and performance analytics, eliminating much of the complexity traditionally associated with implementing sophisticated allocation strategies.
To begin implementation, investors must first determine their investable capital. As detailed analysis reveals, the All Weather Strategy requires meaningful capital to implement effectively due to transaction costs, minimum investment requirements, and the need for precise allocations across five different asset classes.
For portfolios below $50,000, the strategy becomes challenging to implement efficiently. Transaction costs consume a disproportionate share of returns, while the inability to purchase fractional shares creates allocation drift. Consider an investor with $25,000 attempting to allocate 7.5% to commodities through the iPath Bloomberg Commodity Index ETF (DJP), currently trading around $25 per share. This allocation targets $1,875, enough for only 75 shares, creating immediate tracking error.
At $50,000, implementation becomes feasible but not optimal. The 30% stock allocation ($15,000) purchases approximately 37 shares of the SPDR S&P 500 ETF (SPY) at current prices around $400 per share. The 40% long-term bond allocation ($20,000) buys 200 shares of the iShares 20+ Year Treasury Bond ETF (TLT) at approximately $100 per share. While workable, these allocations leave significant cash drag and rebalancing challenges.
The optimal minimum for individual implementation appears to be $100,000. At this level, each allocation becomes substantial enough for precise implementation while keeping transaction costs below 0.4% annually. The $30,000 stock allocation, $40,000 long-term bond allocation, $15,000 intermediate-term bond allocation, $7,500 commodity allocation, and $7,500 TIPS allocation each provide sufficient size for effective management.
For investors with $250,000 or more, the strategy implementation approaches institutional quality. Allocation precision improves, transaction costs decline as a percentage of assets, and rebalancing becomes highly efficient. These larger portfolios can also consider adding complexity through international diversification or alternative implementations.
The indicator recommends quarterly rebalancing to balance transaction costs with allocation discipline. Monthly rebalancing increases costs without substantial benefits for most investors, while annual rebalancing allows excessive drift that can meaningfully impact performance. Quarterly rebalancing, typically on the first trading day of each quarter, provides an optimal balance.
Understanding the Indicator's Functionality
The All Weather Strategy Indicator operates as a comprehensive portfolio management system, providing multiple analytical layers that professional money managers typically reserve for institutional clients. This sophisticated tool transforms Ray Dalio's institutional-grade strategy into an accessible platform for individual investors, offering features that rival professional portfolio management software.
The indicator's core architecture consists of several interconnected modules that work seamlessly together to provide complete portfolio oversight. At its foundation lies a real-time portfolio simulation engine that tracks the exact value of each ETF position based on current market prices, eliminating the need for manual calculations or external spreadsheets.
DETAILED INDICATOR COMPONENTS AND FUNCTIONS
Portfolio Configuration Module
The portfolio setup begins with the Portfolio Configuration section, which establishes the fundamental parameters for strategy implementation. The Portfolio Capital input accepts values from $1,000 to $10,000,000, accommodating everyone from beginning investors to institutional clients. This input directly drives all subsequent calculations, determining exact share quantities and portfolio values throughout the implementation period.
The Portfolio Start Date function allows users to specify when they began implementing the All Weather Strategy, creating a clear demarcation point for performance tracking. This feature proves essential for investors who want to track their actual implementation against theoretical performance, providing realistic assessment of strategy effectiveness including timing differences and implementation costs.
Rebalancing Frequency settings offer two options: Monthly and Quarterly. While monthly rebalancing provides more precise allocation control, quarterly rebalancing typically proves more cost-effective for most investors due to reduced transaction costs. The indicator automatically detects the first trading day of each period, ensuring rebalancing occurs at optimal times regardless of weekends, holidays, or market closures.
The Rebalancing Threshold parameter, adjustable from 0.5% to 10%, determines when allocation drift triggers rebalancing recommendations. Conservative settings like 1-2% maintain tight allocation control but increase trading frequency, while wider thresholds like 3-5% reduce trading costs but allow greater allocation drift. This flexibility accommodates different risk tolerances and cost structures.
Visual Display System
The Show All Weather Calculator toggle controls the main dashboard visibility, allowing users to focus on chart visualization when detailed metrics aren't needed. When enabled, this comprehensive dashboard displays current portfolio value, individual ETF allocations, target versus actual weights, rebalancing status, and performance metrics in a professionally formatted table.
Economic Environment Display provides context about current market conditions based on growth and inflation indicators. While simplified compared to Bridgewater's sophisticated regime detection, this feature helps users understand which economic "season" currently prevails and which asset classes should theoretically benefit.
Rebalancing Signals illuminate when portfolio drift exceeds user-defined thresholds, highlighting specific ETFs that require adjustment. These signals use color coding to indicate urgency: green for balanced allocations, yellow for moderate drift, and red for significant deviations requiring immediate attention.
Advanced Label System
The rebalancing label system represents one of the indicator's most innovative features, providing three distinct detail levels to accommodate different user needs and experience levels. The "None" setting displays simple symbols marking portfolio start and rebalancing events without cluttering the chart with text. This minimal approach suits experienced investors who understand the implications of each symbol.
"Basic" label mode shows essential information including portfolio values at each rebalancing point, enabling quick assessment of strategy performance over time. These labels display "START $X" for portfolio initiation and "RBL $Y" for rebalancing events, providing clear performance tracking without overwhelming detail.
"Detailed" labels provide comprehensive trading instructions including exact buy and sell quantities for each ETF. These labels might display "RBL $125,000 BUY 15 SPY SELL 25 TLT BUY 8 IEF NO TRADES DJP SELL 12 SCHP" providing complete implementation guidance. This feature essentially transforms the indicator into a personal portfolio manager, eliminating guesswork about exact trades required.
Professional Color Themes
Eight professionally designed color themes adapt the indicator's appearance to different aesthetic preferences and market analysis styles. The "Gold" theme reflects traditional wealth management aesthetics, while "EdgeTools" provides modern professional appearance. "Behavioral" uses psychologically informed colors that reinforce disciplined decision-making, while "Quant" employs high-contrast combinations favored by quantitative analysts.
"Ocean," "Fire," "Matrix," and "Arctic" themes provide distinctive visual identities for traders who prefer unique chart aesthetics. Each theme automatically adjusts for dark or light mode optimization, ensuring optimal readability across different TradingView configurations.
Real-Time Portfolio Tracking
The portfolio simulation engine continuously tracks five separate ETF positions: SPY for stocks, TLT for long-term bonds, IEF for intermediate-term bonds, DJP for commodities, and SCHP for TIPS. Each position's value updates in real-time based on current market prices, providing instant feedback about portfolio performance and allocation drift.
Current share calculations determine exact holdings based on the most recent rebalancing, while target shares reflect optimal allocation based on current portfolio value. Trade calculations show precisely how many shares to buy or sell during rebalancing, eliminating manual calculations and potential errors.
Performance Analytics Suite
The indicator's performance measurement capabilities rival professional portfolio analysis software. Sharpe ratio calculations incorporate current risk-free rates obtained from Treasury yield data, providing accurate risk-adjusted performance assessment. Volatility measurements use rolling periods to capture changing market conditions while maintaining statistical significance.
Portfolio return calculations track both absolute and relative performance, comparing the All Weather implementation against individual asset classes and benchmark indices. These metrics update continuously, providing real-time assessment of strategy effectiveness and implementation quality.
Data Quality Monitoring
Sophisticated data quality checks ensure reliable indicator operation across different market conditions and potential data interruptions. The system monitors all five ETF price feeds plus economic data sources, providing quality scores that alert users to potential data issues that might affect calculations.
When data quality degrades, the indicator automatically switches to fallback values or alternative data sources, maintaining functionality during temporary market data interruptions. This robust design ensures consistent operation even during volatile market conditions when data feeds occasionally experience disruptions.
Risk Management and Behavioral Considerations
Despite its sophisticated design, the All Weather Strategy faces behavioral challenges that have derailed countless well-intentioned investment plans. The strategy's conservative nature means it will underperform growth stocks during bull markets, potentially by substantial margins. Maintaining discipline during these periods requires understanding that the strategy optimizes for risk-adjusted returns over absolute returns.
Behavioral finance research by Kahneman and Tversky (1979) demonstrates that investors feel losses approximately twice as intensely as equivalent gains. This loss aversion creates powerful psychological pressure to abandon defensive strategies during bull markets when aggressive portfolios appear more attractive. The All Weather Strategy's bond-heavy allocation will seem overly conservative when technology stocks double in value, as occurred repeatedly during the 2010s.
Conversely, the strategy's defensive characteristics provide psychological comfort during market stress. When stocks crash 30-50%, as they periodically do, the All Weather portfolio's modest losses feel manageable rather than catastrophic. This emotional stability enables investors to maintain their investment discipline when others capitulate, often at the worst possible times.
Rebalancing discipline presents another behavioral challenge. Selling winners to buy losers contradicts natural human tendencies but remains essential for the strategy's success. When stocks have outperformed bonds for several quarters, rebalancing requires selling high-performing stock positions to purchase seemingly stagnant bond positions. This action feels counterintuitive but captures the strategy's systematic approach to risk management.
Tax considerations add complexity for taxable accounts. Frequent rebalancing generates taxable events that can erode after-tax returns, particularly for high-income investors facing elevated capital gains rates. Tax-advantaged accounts like 401(k)s and IRAs provide ideal vehicles for All Weather implementation, eliminating tax friction from rebalancing activities.
Capital Requirements and Cost Analysis
Comprehensive cost analysis reveals the capital requirements for effective All Weather implementation. Annual expenses include management fees for each ETF, transaction costs from rebalancing, and bid-ask spreads from trading less liquid securities.
ETF expense ratios vary significantly across asset classes. The SPDR S&P 500 ETF charges 0.09% annually, while the iShares 20+ Year Treasury Bond ETF charges 0.20%. The iShares 7-10 Year Treasury Bond ETF charges 0.15%, the Schwab US TIPS ETF charges 0.05%, and the iPath Bloomberg Commodity Index ETF charges 0.75%. Weighted by the All Weather allocations, total expense ratios average approximately 0.19% annually.
Transaction costs depend heavily on broker selection and account size. Premium brokers like Interactive Brokers charge $1-2 per trade, resulting in $20-40 annually for quarterly rebalancing. Discount brokers may charge higher per-trade fees but offer commission-free ETF trading for selected funds. Zero-commission brokers eliminate explicit trading costs but often impose wider bid-ask spreads that function as hidden fees.
Bid-ask spreads represent the difference between buying and selling prices for each security. Highly liquid ETFs like SPY maintain spreads of 1-2 basis points, while less liquid commodity ETFs may exhibit spreads of 5-10 basis points. These costs accumulate through rebalancing activities, typically totaling 10-15 basis points annually.
For a $100,000 portfolio, total annual costs including expense ratios, transaction fees, and spreads typically range from 0.35% to 0.45%, or $350-450 annually. These costs decline as a percentage of assets as portfolio size increases, reaching approximately 0.25% for portfolios exceeding $250,000.
Comparing costs to potential benefits reveals the strategy's value proposition. Historical analysis suggests the All Weather approach reduces portfolio volatility by 35-40% compared to stock-heavy allocations while maintaining competitive returns. This volatility reduction provides substantial value during market stress, potentially preventing behavioral mistakes that destroy long-term wealth.
Alternative Implementations and Customizations
While the original All Weather allocation provides an excellent starting point, investors may consider modifications based on personal circumstances, market conditions, or geographic considerations. International diversification represents one potential enhancement, adding exposure to developed and emerging market bonds and equities.
Geographic customization becomes important for non-US investors. European investors might replace US Treasury bonds with German Bunds or broader European government bond indices. Currency hedging decisions add complexity but may reduce volatility for investors whose spending occurs in non-dollar currencies.
Tax-location strategies optimize after-tax returns by placing tax-inefficient assets in tax-advantaged accounts while holding tax-efficient assets in taxable accounts. TIPS and commodity ETFs generate ordinary income taxed at higher rates, making them candidates for retirement account placement. Stock ETFs generate qualified dividends and long-term capital gains taxed at lower rates, making them suitable for taxable accounts.
Some investors prefer implementing the bond allocation through individual Treasury securities rather than ETFs, eliminating management fees while gaining precise maturity control. Treasury auctions provide access to new securities without bid-ask spreads, though this approach requires more sophisticated portfolio management.
Factor-based implementations replace broad market ETFs with factor-tilted alternatives. Value-tilted stock ETFs, quality-focused bond ETFs, or momentum-based commodity indices may enhance returns while maintaining the All Weather framework's diversification benefits. However, these modifications introduce additional complexity and potential tracking error.
Conclusion: Embracing the Long Game
The All Weather Strategy represents more than an investment approach; it embodies a philosophy of financial resilience that prioritizes sustainable wealth building over speculative gains. In an investment landscape increasingly dominated by algorithmic trading, meme stocks, and cryptocurrency volatility, Dalio's methodical approach offers a refreshing alternative grounded in economic theory and historical evidence.
The strategy's greatest strength lies not in its potential for extraordinary returns, but in its capacity to deliver reasonable returns across diverse economic environments while protecting capital during market stress. This characteristic becomes increasingly valuable as investors approach or enter retirement, when portfolio preservation assumes greater importance than aggressive growth.
Implementation requires discipline, adequate capital, and realistic expectations. The strategy will underperform growth-oriented approaches during bull markets while providing superior downside protection during bear markets. Investors must embrace this trade-off consciously, understanding that the strategy optimizes for long-term wealth building rather than short-term performance.
The All Weather Strategy Indicator democratizes access to institutional-quality portfolio management, providing individual investors with tools previously available only to wealthy families and institutions. By automating allocation tracking, rebalancing signals, and performance analysis, the indicator removes much of the complexity that has historically limited sophisticated strategy implementation.
For investors seeking a systematic, evidence-based approach to long-term wealth building, the All Weather Strategy provides a compelling framework. Its emphasis on diversification, risk management, and behavioral discipline aligns with the fundamental principles that have created lasting wealth throughout financial history. While the strategy may not generate headlines or inspire cocktail party conversations, it offers something more valuable: a reliable path toward financial security across all economic seasons.
As Dalio himself notes, "The biggest mistake investors make is to believe that what happened in the recent past is likely to persist, and they design their portfolios accordingly." The All Weather Strategy's enduring appeal lies in its rejection of this recency bias, instead embracing the uncertainty of markets while positioning for success regardless of which economic season unfolds.
STEP-BY-STEP INDICATOR SETUP GUIDE
Setting up the All Weather Strategy Indicator requires careful attention to each configuration parameter to ensure optimal implementation. This comprehensive setup guide walks through every setting and explains its impact on strategy performance.
Initial Setup Process
Begin by adding the indicator to your TradingView chart. Search for "Ray Dalio's All Weather Strategy" in the indicator library and apply it to any chart. The indicator operates independently of the underlying chart symbol, drawing data directly from the five required ETFs regardless of which security appears on the chart.
Portfolio Configuration Settings
Start with the Portfolio Capital input, which drives all subsequent calculations. Enter your exact investable capital, ranging from $1,000 to $10,000,000. This input determines share quantities, trade recommendations, and performance calculations. Conservative recommendations suggest minimum capitals of $50,000 for basic implementation or $100,000 for optimal precision.
Select your Portfolio Start Date carefully, as this establishes the baseline for all performance calculations. Choose the date when you actually began implementing the All Weather Strategy, not when you first learned about it. This date should reflect when you first purchased ETFs according to the target allocation, creating realistic performance tracking.
Choose your Rebalancing Frequency based on your cost structure and precision preferences. Monthly rebalancing provides tighter allocation control but increases transaction costs. Quarterly rebalancing offers the optimal balance for most investors between allocation precision and cost control. The indicator automatically detects appropriate trading days regardless of your selection.
Set the Rebalancing Threshold based on your tolerance for allocation drift and transaction costs. Conservative investors preferring tight control should use 1-2% thresholds, while cost-conscious investors may prefer 3-5% thresholds. Lower thresholds maintain more precise allocations but trigger more frequent trading.
Display Configuration Options
Enable Show All Weather Calculator to display the comprehensive dashboard containing portfolio values, allocations, and performance metrics. This dashboard provides essential information for portfolio management and should remain enabled for most users.
Show Economic Environment displays current economic regime classification based on growth and inflation indicators. While simplified compared to Bridgewater's sophisticated models, this feature provides useful context for understanding current market conditions.
Show Rebalancing Signals highlights when portfolio allocations drift beyond your threshold settings. These signals use color coding to indicate urgency levels, helping prioritize rebalancing activities.
Advanced Label Customization
Configure Show Rebalancing Labels based on your need for chart annotations. These labels mark important portfolio events and can provide valuable historical context, though they may clutter charts during extended time periods.
Select appropriate Label Detail Levels based on your experience and information needs. "None" provides minimal symbols suitable for experienced users. "Basic" shows portfolio values at key events. "Detailed" provides complete trading instructions including exact share quantities for each ETF.
Appearance Customization
Choose Color Themes based on your aesthetic preferences and trading style. "Gold" reflects traditional wealth management appearance, while "EdgeTools" provides modern professional styling. "Behavioral" uses psychologically informed colors that reinforce disciplined decision-making.
Enable Dark Mode Optimization if using TradingView's dark theme for optimal readability and contrast. This setting automatically adjusts all colors and transparency levels for the selected theme.
Set Main Line Width based on your chart resolution and visual preferences. Higher width values provide clearer allocation lines but may overwhelm smaller charts. Most users prefer width settings of 2-3 for optimal visibility.
Troubleshooting Common Setup Issues
If the indicator displays "Data not available" messages, verify that all five ETFs (SPY, TLT, IEF, DJP, SCHP) have valid price data on your selected timeframe. The indicator requires daily data availability for all components.
When rebalancing signals seem inconsistent, check your threshold settings and ensure sufficient time has passed since the last rebalancing event. The indicator only triggers signals on designated rebalancing days (first trading day of each period) when drift exceeds threshold levels.
If labels appear at unexpected chart locations, verify that your chart displays percentage values rather than price values. The indicator forces percentage formatting and 0-40% scaling for optimal allocation visualization.
COMPREHENSIVE BIBLIOGRAPHY AND FURTHER READING
PRIMARY SOURCES AND RAY DALIO WORKS
Dalio, R. (2017). Principles: Life and work. New York: Simon & Schuster.
Dalio, R. (2018). A template for understanding big debt crises. Bridgewater Associates.
Dalio, R. (2021). Principles for dealing with the changing world order: Why nations succeed and fail. New York: Simon & Schuster.
BRIDGEWATER ASSOCIATES RESEARCH PAPERS
Jensen, G., Kertesz, A. & Prince, B. (2010). All Weather strategy: Bridgewater's approach to portfolio construction. Bridgewater Associates Research.
Prince, B. (2011). An in-depth look at the investment logic behind the All Weather strategy. Bridgewater Associates Daily Observations.
Bridgewater Associates. (2015). Risk parity in the context of larger portfolio construction. Institutional Research.
ACADEMIC RESEARCH ON RISK PARITY AND PORTFOLIO CONSTRUCTION
Ang, A. & Bekaert, G. (2002). International asset allocation with regime shifts. The Review of Financial Studies, 15(4), 1137-1187.
Bodie, Z. & Rosansky, V. I. (1980). Risk and return in commodity futures. Financial Analysts Journal, 36(3), 27-39.
Campbell, J. Y. & Viceira, L. M. (2001). Who should buy long-term bonds? American Economic Review, 91(1), 99-127.
Clarke, R., De Silva, H. & Thorley, S. (2013). Risk parity, maximum diversification, and minimum variance: An analytic perspective. Journal of Portfolio Management, 39(3), 39-53.
Fama, E. F. & French, K. R. (2004). The capital asset pricing model: Theory and evidence. Journal of Economic Perspectives, 18(3), 25-46.
BEHAVIORAL FINANCE AND IMPLEMENTATION CHALLENGES
Kahneman, D. & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-292.
Thaler, R. H. & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. New Haven: Yale University Press.
Montier, J. (2007). Behavioural investing: A practitioner's guide to applying behavioural finance. Chichester: John Wiley & Sons.
MODERN PORTFOLIO THEORY AND QUANTITATIVE METHODS
Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91.
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425-442.
Black, F. & Litterman, R. (1992). Global portfolio optimization. Financial Analysts Journal, 48(5), 28-43.
PRACTICAL IMPLEMENTATION AND ETF ANALYSIS
Gastineau, G. L. (2010). The exchange-traded funds manual. 2nd ed. Hoboken: John Wiley & Sons.
Poterba, J. M. & Shoven, J. B. (2002). Exchange-traded funds: A new investment option for taxable investors. American Economic Review, 92(2), 422-427.
Israelsen, C. L. (2005). A refinement to the Sharpe ratio and information ratio. Journal of Asset Management, 5(6), 423-427.
ECONOMIC CYCLE ANALYSIS AND ASSET CLASS RESEARCH
Ilmanen, A. (2011). Expected returns: An investor's guide to harvesting market rewards. Chichester: John Wiley & Sons.
Swensen, D. F. (2009). Pioneering portfolio management: An unconventional approach to institutional investment. Rev. ed. New York: Free Press.
Siegel, J. J. (2014). Stocks for the long run: The definitive guide to financial market returns & long-term investment strategies. 5th ed. New York: McGraw-Hill Education.
RISK MANAGEMENT AND ALTERNATIVE STRATEGIES
Taleb, N. N. (2007). The black swan: The impact of the highly improbable. New York: Random House.
Lowenstein, R. (2000). When genius failed: The rise and fall of Long-Term Capital Management. New York: Random House.
Stein, D. M. & DeMuth, P. (2003). Systematic withdrawal from retirement portfolios: The impact of asset allocation decisions on portfolio longevity. AAII Journal, 25(7), 8-12.
CONTEMPORARY DEVELOPMENTS AND FUTURE DIRECTIONS
Asness, C. S., Frazzini, A. & Pedersen, L. H. (2012). Leverage aversion and risk parity. Financial Analysts Journal, 68(1), 47-59.
Roncalli, T. (2013). Introduction to risk parity and budgeting. Boca Raton: CRC Press.
Ibbotson Associates. (2023). Stocks, bonds, bills, and inflation 2023 yearbook. Chicago: Morningstar.
PERIODICALS AND ONGOING RESEARCH
Journal of Portfolio Management - Quarterly publication featuring cutting-edge research on portfolio construction and risk management
Financial Analysts Journal - Bi-monthly publication of the CFA Institute with practical investment research
Bridgewater Associates Daily Observations - Regular market commentary and research from the creators of the All Weather Strategy
RECOMMENDED READING SEQUENCE
For investors new to the All Weather Strategy, begin with Dalio's "Principles" for philosophical foundation, then proceed to the Bridgewater research papers for technical details. Supplement with Markowitz's original portfolio theory work and behavioral finance literature from Kahneman and Tversky.
Intermediate students should focus on academic papers by Ang & Bekaert on regime shifts, Clarke et al. on risk parity methods, and Ilmanen's comprehensive analysis of expected returns across asset classes.
Advanced practitioners will benefit from Roncalli's technical treatment of risk parity mathematics, Asness et al.'s academic critique of leverage aversion, and ongoing research in the Journal of Portfolio Management.
Options Strategy V2.0📈 Options Strategy V2.0 – Intraday Reversal-Resilient Momentum System
Overview:
This strategy is designed specifically for intraday SPY, TSLA, MSFT, etc. options trading (0DTE or 1DTE), using high-probability signals derived from a confluence of technical indicators: EMA crossovers, RSI thresholds, ATR-based risk control, and volume spikes. The strategy aims to capture strong directional moves while avoiding overtrading, thanks to a built-in cooldown logic and optional time/session filters.
⚙️ Core Concept
The strategy executes trades only in the direction of the prevailing trend, determined by short- and long-term Exponential Moving Averages (EMA). Entry signals are generated when the Relative Strength Index (RSI) confirms momentum in the direction of the trend, and volume spikes suggest institutional activity.
To increase adaptability and user control, it includes a highly customizable parameter set for both long and short entries independently.
📌 Key Features
✅ Trend-Following Logic
Long entries are only allowed when EMA(short) > EMA(long)
Short entries are only allowed when EMA(short) < EMA(long)
✅ RSI Confirmation
Long: Requires RSI crossover above a configurable threshold
Short: Requires RSI crossunder below a configurable threshold
Optional rejection filters: Entry blocked above/below specific RSI extremes
✅ Volume Spike Filter
Confirms institutional participation by comparing current volume to an average multiplied by a user-defined factor.
✅ ATR-Based Risk Management
Both Stop Loss (SL) and Take Profit (TP) are dynamically calculated using ATR × a multiplier.
TP/SL ratio is fully configurable.
✅ Cooldown Control
After every trade, the system waits for a set number of bars before allowing new entries.
This prevents overtrading and increases signal quality.
Optionally, cooldown is ignored for reversal trades, ensuring the system can react immediately to a confirmed trend change.
✅ Candle Body Filter (Noise Control)
Avoids trades on candles with too small bodies relative to wicks (often noise or indecision candles).
✅ VWAP Confirmation (Optional)
Ensures price is trading above VWAP for long entries, or below for short entries.
✅ Time & Session Filters
Trades only during regular market hours (09:30–16:00 EST).
No-trade zone (e.g., 14:15–15:45 EST) to avoid low-liquidity traps or late-day whipsaws.
✅ End-of-Day Auto Close
All open positions are force-closed at 15:55 EST, protecting against overnight risk (especially relevant for 0DTE options).
📊 Visual Aids
EMA plots show trend direction
VWAP line provides real-time mean-reversion context
Stop Loss and Take Profit lines appear dynamically with each trade
Alerts notify of entry signals and exit triggers
🔧 Customization Panel
Nearly every element of the strategy can be tailored:
EMA lengths (short and long, for both sides)
RSI thresholds and length
ATR length, SL multiplier, and TP/SL ratio
Volume spike sensitivity
Minimum EMA distance filter
Candle body ratio filter
Session restrictions
Cooldown logic (duration + reversal exception)
This makes the strategy extremely versatile, allowing both conservative and aggressive configurations depending on the trader’s profile and the market context.
📌 Example Use Case: SPY Options (0DTE or 1DTE)
This system was designed and tested specifically for SPY and other intraday options trading, where:
Delta is around 0.50 or higher
Trades are short-lived (often 1–5 candles)
You aim to trade 1–3 signals per day, filtering out weak entries
🚫 Important Notes
It is not a scalping strategy; it relies on confirmed breakouts with trend support
No pyramiding or re-entries without cooldown to preserve risk integrity
Should be used with real-time alerts and manual broker execution
📈 Alerts Included
📈 Long Entry Signal
📉 Short Entry Signal
⚠️ Auto-closed all positions at 15:55 EST
✅ Proven Settings – Real Trades + Backtest Results
The current version of the strategy includes the optimal settings I’ve arrived at through extensive backtesting, as well as 3 months of real trading with consistent profitability. These results reflect real-world execution under live market conditions using 0DTE SPY options, with disciplined trade management and risk control.
🧠 Final Thoughts
Options Strategy V2.0 is a robust, highly tunable intraday strategy that blends momentum, trend-following, and volume confirmation. It is ideal for disciplined traders focused on SPY or other 0DTE/1DTE options, and it includes guardrails to reduce false signals and improve execution timing.
Perfect for those who seek precision, flexibility, and risk-defined setups—not blind automation.
TimezoneFormatIANAUTCLibrary "TimezoneFormatIANAUTC"
Provides either the full IANA timezone identifier or the corresponding UTC offset for TradingView’s built-in variables and functions.
tz(_tzname, _format)
Parameters:
_tzname (string) : "London", "New York", "Istanbul", "+1:00", "-03:00" etc.
_format (string) : "IANA" or "UTC"
Returns: "Europe/London", "America/New York", "UTC+1:00"
Example Code
import ARrowofTime/TimezoneFormatIANAUTC/1 as libtz
sesTZInput = input.string(defval = "Singapore", title = "Timezone")
example1 = libtz.tz("London", "IANA") // Return Europe/London
example2 = libtz.tz("London", "UTC") // Return UTC+1:00
example3 = libtz.tz("UTC+5", "IANA") // Return UTC+5:00
example4 = libtz.tz("UTC+4:30", "UTC") // Return UTC+4:30
example5 = libtz.tz(sesTZInput, "IANA") // Return Asia/Singapore
example6 = libtz.tz(sesTZInput, "UTC") // Return UTC+8:00
sesTime1 = time("","1300-1700", example1) // returns the UNIX time of the current bar in session time or na
sesTime2 = time("","1300-1700", example2) // returns the UNIX time of the current bar in session time or na
sesTime3 = time("","1300-1700", example3) // returns the UNIX time of the current bar in session time or na
sesTime4 = time("","1300-1700", example4) // returns the UNIX time of the current bar in session time or na
sesTime5 = time("","1300-1700", example5) // returns the UNIX time of the current bar in session time or na
sesTime6 = time("","1300-1700", example6) // returns the UNIX time of the current bar in session time or na
Parameter Format Guide
This section explains how to properly format the parameters for the tz(_tzname, _format) function.
_tzname (string) must be either;
A valid timezone name exactly as it appears in the chart’s lower-right corner (e.g. New York, London).
A valid UTC offset in ±H:MM or ±HH:MM format. Hours: 0–14 (zero-padded or not, e.g. +1:30, +01:30, -0:00). Minutes: Must be 00, 15, 30, or 45
examples;
"New York" → ✅ Valid chart label
"London" → ✅ Valid chart label
"Berlin" → ✅ Valid chart label
"America/New York" → ❌ Invalid chart label. (Use "New York" instead)
"+1:30" → ✅ Valid offset with single-digit hour
"+01:30" → ✅ Valid offset with zero-padded hour
"-05:00" → ✅ Valid negative offset
"-0:00" → ✅ Valid zero offset
"+1:1" → ❌ Invalid (minute must be 00, 15, 30, or 45)
"+2:50" → ❌ Invalid (minute must be 00, 15, 30, or 45)
"+15:00" → ❌ Invalid (hour must be 14 or below)
_tztype (string) must be either;
"IANA" → returns full IANA timezone identifier (e.g. "Europe/London"). When a time function call uses an IANA time zone identifier for its timezone argument, its calculations adjust automatically for historical and future changes to the specified region’s observed time, such as daylight saving time (DST) and updates to time zone boundaries, instead of using a fixed offset from UTC.
"UTC" → returns UTC offset string (e.g. "UTC+01:00")
Relative Measured Volatility (RMV)RMV • Volume-Sensitive Consolidation Indicator
A lightweight Pine Script that highlights true low-volatility, low-volume bars in a single squeeze measure.
What it does
Calculates each bar’s raw High-Low range.
Down-weights bars where volume is below its 30-day average, emphasizing genuine quiet periods.
Normalizes the result over the prior 15 bars (excluding the current bar), scaling from 0 (tightest) to 100 (most volatile).
Draws the series as a step plot, shades true “tight” bars below the user threshold, and marks sustained squeezes with a small arrow.
Key inputs
Lookback (bars): Number of bars to use for normalization (default 15).
Tight Threshold: RMV value under which a bar is considered squeezed (default 15).
Volume SMA Period: Period for the volume moving average benchmark (default 30).
How it works
Raw range: barRange = high - low
Volume ratio: volRatio = min(volume / sma(volume,30), 1)
Weighted range: vwRange = barRange * volRatio
Rolling min/max (prior 15 bars): exclude today so a new low immediately registers a 0.
Normalize: rmv = clamp(100 * (vwRange - min) / (max - min), 0, 100)
Visualization & signals
Step line for exact bar-by-bar values.
Shaded background when RMV < threshold.
Consecutive-bar filter ensures arrows only appear when tightness lasts at least two bars, cutting noise.
Why use it
Quickly spot consolidation zones that combine narrow price action with genuine dry volume—ideal for swing entries ahead of breakouts.
Opening Range Breakout🧭 Overview
The Open Range Breakout (ORB) indicator is designed to capture and display the initial price range of the trading day (typically the first 15 minutes), and help traders identify breakout opportunities beyond this range. This is a popular strategy among intraday and momentum traders.
🔧 Features
📊 ORB High/Low Lines
Plots horizontal lines for the session’s high and low
🟩 Breakout Zones
Background highlights when price breaks above or below the range
🏷️ Breakout Labels
Text labels marking breakout events
🧭 Session Control
Customizable session input (default: 09:15–09:30 IST)
📍 ORB Line Labels
Text labels anchored to the ORB high and low lines (aligned right)
🔔 Alerts
Configurable alerts for breakout events
⚙️ Adjustable Settings
Show/hide background, labels, session window, etc.
⏱️ Session Logic
• The ORB range is calculated during a defined session window (default: 09:15–09:30).
• During this window, the highest high and lowest low are recorded as ORB High and ORB Low.
📈 Breakout Detection
• Breakout Above: Triggered when price crosses above the ORB High.
• Breakout Below: Triggered when price crosses below the ORB Low.
• Each breakout can trigger:
• A background highlight (green/red)
• A text label (“Breakout ↑” / “Breakout ↓”)
• An optional alert
🔔 Alerts
Two built-in alert conditions:
1. Breakout Above ORB High
• Message: "🔼 Price broke above ORB High: {{close}}"
2. Breakout Below ORB Low
• Message: "🔽 Price broke below ORB Low: {{close}}"
You can create alerts in TradingView by selecting these from the Add Alert window.
📌 Best Use Cases
• Intraday momentum trading
• Breakout and scalping strategies
• First 15-minute range traders (NSE, BSE markets)
NY ORB + Fakeout Detector🗽 NY ORB + Fakeout Detector
This indicator automatically plots the New York Opening Range (ORB) based on the first 15 minutes of the NY session (15:30–15:45 CEST / 13:30–13:45 UTC) and detects potential fakeouts (false breakouts).
🔍 Key Features:
✅ Plots ORB high and low based on the 15-minute NY open range
✅ Automatically detects fake breakouts (price wicks beyond the box but closes back inside)
✅ Visual markers:
🔺 "Fake ↑" if a fake breakout occurs above the range
🔻 "Fake ↓" if a fake breakout occurs below the range
✅ Gray background highlights the ORB session window
✅ Designed for scalping and short-term breakout strategies
🧠 Best For:
Intraday traders looking for NY volatility setups
Scalpers using ORB-based entries
Traders seeking early-session fakeout traps to avoid false signals
Those combining with EMA 12/21, volume, or other confluence tools
