GRG/RGR Signal, MA, Ranges and PivotsThis indicator is a combination of several indicators.
It is a combination of two of my indicators which I solely use for trading
1. EMA 10-20-50-200, Pivots and Previous Day/Week/Month range
2. 3/4-Bar GRG / RGR Pattern (Conditional 4th Candle)
You can use them individually if you already have some of them or just use this one. Belive me when I say, this is all you need, along with market structure knowlege and even if you don’t have that, this indicator has been doing wonders for me. This is all I use. I do not use anything else.
**Note - Do checkout the indicators individually as I have added valuable information in the comment section.
It contains the following,
1. 10 EMA/SMA - configurable
2. 20 EMA/SMA - configurable
3. 50 EMA/SMA - configurable
4. 200 EMA/SMA - configurable
5. Previous Day's Range - configurable
6. Previous Week's Range - configurable
7. Previous Month's Range - configurable
8. Pivots - configurable
9. Buy Sell Signal - configurable
The Moving Averages
It is a very important combination and using it correctly with price action will strengthen your entries and exits.
The ema's or sma's added are the most powerful ones and they do definitely act as support and resistance.
The Daily/Weekly/Monthly Ranges
The Daily/Weekly/Monthly ranges are extremely important for any trader and should be used for targets and reversals.
Pivots
Pivots can provide support and resistance level. R5 and S5 can be used to check for over stretched conditions. You can customise them however you like. It is a full pivot indicator.
It is defaulted to show R5 and S5 only to reduce noise in the chart but it can be customised.
The 3/4 RGR or GRG Signal Generator
Combined with a 3/4 RGR or GRG setup can be all a trader needs.
You don't need complex strategies and SMC concepts to trade. Simple EMAs, ranges and RGR/GRG setup is the most winning combination.
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
1. 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.
2. Here the buyers defeated the sellers.
3. 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.
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.
5. But for a more optimised entry I have added an option to use candle’s highs and lows instead of open and close. This reduces lot of noise and provides us with more precise entry. This setting is turned on by default.
6. I have restricted it to 4 candles and that is all that is needed. More than that is a longer sideways market.
7. I call it the +-+ or GRG pattern or Green-Red-Green or Buyer-Seller-Buyer or Seller defeated or just Buyer pattern.
8. Stop loss can be candle 2's mid for safe traders (that includes me) or candle 2's body low for risky traders.
9. 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
1. 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.
2. 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.
3. 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.
4. But for a more optimised entry I have added an option to use candle’s highs and lows instead of open and close. This reduces lot of noise and provides us with more precise entry. This setting is turned on by default.
5. I have restricted it to 4 candles and that is all that is needed. More than that is a longer sideways market.
6. I call it the -+- or RGR pattern or Red-Green-Red or Seller-Buyer-Seller or Buyer defeated or just Seller pattern.
7. Stop loss can be candle 2's mid for safe traders ( that includes me) or candle 2's body high for risky traders.
8. 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.
Combining Indicators and Signal
Combining these indicators with GRG/RGR signal can be very powerful and can provide big moves.
1. MA crossover and Signal - This is very powerful and provides a very big move. Trades can be held for longer. If after taking the trade we notice that the MA crossover has happened then trades can be held for higher targets.
2. Pivots and Signal - Pivots and add a support or resistance point. Take profits on these points. R5/S5 are over streched conditions so we can start looking for reversal signals and ignore other signals
3. Intraday Range - first 1, 5, 15 min of the day - Sideways days is when price will stay in these ranges. You can take profits at these ranges or if the range is broken and we get a signal, then it can mean that the direction will be sustained.
4. Previous Day/Week/Month Ranges - These can be used as Take Profit points if the price is moving towards them after getting the signal. If the range is broken and we get a signal then it can be a strong signal. They can also be used as reversal points if a strong signal is generated.
Important Settings
1. Include 4th Candle Confirmation - 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.
2. Bars to check (default 10) - 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.
3. Use Candle High/Low for confirmation instead of Candle Open/Close - More optimized entry and noise reduction. This option is now defaulted to false.
4. Show Green-Red-Green (bull) signals - Show only bull entries. Useful when I have a predefined view i.e, I know market is going to go up today.
5. Show Red-Green-Red (bear) signals - Show only bear entries. Useful when I have a predefined view i.e, I know market is going to go down today.
6. 3rd candle should be a Strong candle before considering 4th candle - This will enforce additional logic in 4 candle setup that the 3rd candle is the candle in our direction of breakout. This means something like GRGG is mandatory, which is still the default behaviour. If disabled, the 3rd candle can be any candle and 4th candle will act as our breakout candle. This behaviour has led to breakouts and breakdowns as times, hence I added this as a separate feature. Vice-versa for a RGGR.
For a 4 candle setup till now we were expecting GRGG or RGRR but we can let the system ignore the 3rd candle completely if needed.
This will result in additional signals.
7. Three intraday ranges added for index and stock traders - 1 min, 5 min and 15 min ranges will be displayed. These are disabled by default except 15 min. These are very important ranges and in sideways days the price will usually move within the 15 min. A breakout of this range and a positive signal can be a very powerful setup.
Safe traders can avoid taking a trade in this range as it can lead to fakeouts.
The line style, width, color and opacity are configurable.
Pointers/Golden Rules
1. 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.
2. 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.
3. Trades need to be taken on previous 2 candle's body high or low combined and not the wicks.
4. 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.
5. Hold trades for longer targets and don't panic.
6. If last 3-4 days have been sideways then there is a good probability that today will be trending so we can hold our trade for longer targets. Inverse is true when the market has been trending for 2-3 days then volatility followed by sideways is coming (DOW theory). Target to hold the trade for whole day and not exit till the day closes.
7. In general avoid trading in the middle of the day for index and stocks. Divide the day into 3 parts and avoid the middle.
8. Use Support/Resistance, 10, 20, 50, 200 EMA/SMA, Gaps, Whole/Round numbers(very imp) for identifying targets.
9. Trail your SL.
10. For indexes I would use 5 min and 15 min timeframe and at times 10 mins.
11. 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.
12. If a GRG or RGR pattern appears on a daily timeframe then this is our time to go big.
13. Minimum Risk to Reward should be 1:2 and for longer targets can be 1:4 to 1:10.
14. Trade with small lot size. Money management will happen automatically.
15. With small lot size and correct Risk-Reward we can be very profitable. Don't trade with big lot size.
16. Stay in the market for longer and collect points not money.
17. Very imp - Watch market and learn to generate a market view.
18. Very imp - Only 3 type of candles are needed in trading -
Strong Bullish (Big Green candle), Strong Bearish (Big Red candle),
Hammer (it is Strong Bullish), Inverse Hammer (it is Strong Bearish)
and Doji (indecision or confusion).
If on daily timeframe I see Strong Bullish candle previous day then I am biased to the upside the next day, if I see Strong Bearish candle the previous day then I am biased to the downside the next day, if I see Doji on the previous day then I am cautious the next day, if there are back to back Dojis forming in daily or weekly then I am preparing for big move so time to go big once I get the signal.
19. Most Important Candlestick pattern - Bullish and Bearish Engulfing
20. The only Chart patterns I need -
a) Falling Wedge/Channel Bullish Pattern Uptrend or Bull Flag - Buying - Forming over a couple days for intraday and forming over a couple of weeks for swing
b) Falling Wedge/Channel Bullish Pattern Downtrend or Falling Channel - Buying
c) Rising Wedge Bearish Pattern Uptrend or Rising Channel - Selling
d) Rising Wedge Bearish Pattern Downtrend or Bear flag - Selling
e) Head and Shoulder - Over a longer period not for intraday. In 15 min takes few days and for swing 1hr or 4h or daily can take few days
f) M and W pattern - Reversal Patterns - They form within the above 4 patterns, usually resulting in the break of trend line
21. How Gaps work -
a) Small Gap up in Uptrend - Market can fill the gap and reverse. The perception is that people are buying. If previous day candle was Strong Bullish then market view is up.
b) Big Gap up in Uptrend - Not news driven - Profit booking will come but may not fill the entire gap
c) Big Gap up in Uptrend - News driven, war related, tax, interest rate - Market can keep going up without stopping.
c) Flat opening in Uptrend - Big chance of market going up. If previous day candle was Strong Bullish then view is upwards, if it was Doji then still upwards.
d) Gap down in Uptrend - Market is surprised. After going down initially it can go up
e) Small Gap down in Downtrend - Market can fill the gap and keep moving down. If previous day candle was Strong Bearish then view is still down.
f) Flat opening in Downtrend - View is down, short today.
g) Big Gap down in Downtrend - Profit booking and foolish buying will come but market view is still down.
h) Gap down with News - Volatility, sideways then down.
i) Gap Up in Downtrend - Can move up - Price can move up during 2/3rd of the day and End of the day revert and close in red.
22. Go big on bearish days for option traders. Puts are better bought and Calls are better sold.
23. Cluster of green signals can lead to bigger move on the upside and vice versa for red signals.
24. Most of this is what I learned from successful traders (from the top 2%) only the indicator is mine.
In den Scripts nach "GOLD" suchen
RSI Scalping Gold (XAUUSD) - v5Displays the EMA9 and SMA20 to identify the trend.
Colors the area between the two averages to better visualize the equilibrium zone.
Displays green (buy) and red (sell) arrows aligned with the candles.
The RSI is calculated but hidden from the main chart (you can activate it by checking “Display on chart” in the settings).
MTF Market Bias+ (Smart Multi-Timeframe Trend Dashboard)The MTF Market Bias+ indicator provides a clear, data-driven view of market direction across multiple timeframes — from scalper to swing trader level.
It automatically calculates the bullish / bearish / neutral bias for each selected timeframe using various configurable methods such as EMA slope, price vs EMA, or EMA50 vs EMA200.
This tool gives you an instant overview of market alignment and helps you identify when lower and higher timeframes are in sync — the most powerful condition for high-probability trades.
🔍 Core Features
✅ Multi-Timeframe Bias Dashboard: Visual table showing bullish/bearish sentiment across your chosen timeframes (from 3m to 1W).
⚙️ Customizable Methods: Choose between
EMA Slope (default) → detects trend direction by EMA momentum
Price vs EMA → shows short-term strength or weakness
EMA50 vs EMA200 → classic golden cross vs death cross structure
🎨 Configurable Colors, Size & Layout: Adjust background, text, and label sizes for any chart style.
📊 Summary Row: Displays the majority trend (bullish, bearish, or neutral) with real-time score.
🧩 Adaptive Background Mode (optional): Automatically colors your chart background according to overall bias.
💡 Method Info Panel: Clearly shows which method and parameters are active (e.g. “EMA Slope | EMA=50”).
📈 How to Use
Add the indicator to your chart.
Select the timeframes you want to monitor (e.g. 3m, 5m, 15m, 1h, 4h, D, W).
Watch for alignment between lower and higher timeframes:
When all turn green → strong bullish alignment → consider longs.
When all turn red → strong bearish alignment → consider shorts.
Mixed colors indicate consolidation or correction phases.
Combine it with your favorite Fair Value Gap, CHOCH/BOS, or Liquidity Sweep strategy to significantly improve trade timing and confidence.
🧩 Author’s Note
This indicator is designed for traders who want fast, visual confirmation of multi-timeframe structure without cluttering their charts.
It’s simple, lightweight, and highly adaptable — whether you’re scalping on 3-minute charts or swing trading daily candles.
Kalman Filter [DCAUT]█ Kalman Filter
📊 ORIGINALITY & INNOVATION
The Kalman Filter represents an important adaptation of aerospace signal processing technology to financial market analysis. Originally developed by Rudolf E. Kalman in 1960 for navigation and guidance systems, this implementation brings the algorithm's noise reduction capabilities to price trend analysis.
This implementation addresses a common challenge in technical analysis: the trade-off between smoothness and responsiveness. Traditional moving averages must choose between being smooth (with increased lag) or responsive (with increased noise). The Kalman Filter improves upon this limitation through its recursive estimation approach, which continuously balances historical trend information with current price data based on configurable noise parameters.
The key advancement lies in the algorithm's adaptive weighting mechanism. Rather than applying fixed weights to historical data like conventional moving averages, the Kalman Filter dynamically adjusts its trust between the predicted trend and observed prices. This allows it to provide smoother signals during stable periods while maintaining responsiveness during genuine trend changes, helping to reduce whipsaws in ranging markets while not missing significant price movements.
📐 MATHEMATICAL FOUNDATION
The Kalman Filter operates through a two-phase recursive process:
Prediction Phase:
The algorithm first predicts the next state based on the previous estimate:
State Prediction: Estimates the next value based on current trend
Error Covariance Prediction: Calculates uncertainty in the prediction
Update Phase:
Then updates the prediction based on new price observations:
Kalman Gain Calculation: Determines the weight given to new measurements
State Update: Combines prediction with observation based on calculated gain
Error Covariance Update: Adjusts uncertainty estimate for next iteration
Core Parameters:
Process Noise (Q): Represents uncertainty in the trend model itself. Higher values indicate the trend can change more rapidly, making the filter more responsive to price changes.
Measurement Noise (R): Represents uncertainty in price observations. Higher values indicate less trust in individual price points, resulting in smoother output.
Kalman Gain Formula:
The Kalman Gain determines how much weight to give new observations versus predictions:
K = P(k|k-1) / (P(k|k-1) + R)
Where:
K is the Kalman Gain (0 to 1)
P(k|k-1) is the predicted error covariance
R is the measurement noise parameter
When K approaches 1, the filter trusts new measurements more (responsive).
When K approaches 0, the filter trusts its prediction more (smooth).
This dynamic adjustment mechanism allows the filter to adapt to changing market conditions automatically, providing an advantage over fixed-weight moving averages.
📊 COMPREHENSIVE SIGNAL ANALYSIS
Visual Trend Indication:
The Kalman Filter line provides color-coded trend information:
Green Line: Indicates the filter value is rising, suggesting upward price momentum
Red Line: Indicates the filter value is falling, suggesting downward price momentum
Gray Line: Indicates sideways movement with no clear directional bias
Crossover Signals:
Price-filter crossovers generate trading signals:
Golden Cross: Price crosses above the Kalman Filter line, suggests potential bullish momentum development, may indicate a favorable environment for long positions, filter will naturally turn green as it adapts to price moving higher
Death Cross: Price crosses below the Kalman Filter line, suggests potential bearish momentum development, may indicate consideration for position reduction or shorts, filter will naturally turn red as it adapts to price moving lower
Trend Confirmation:
The filter serves as a dynamic trend baseline:
Price Consistently Above Filter: Confirms established uptrend
Price Consistently Below Filter: Confirms established downtrend
Frequent Crossovers: Suggests ranging or choppy market conditions
Signal Reliability Factors:
Signal quality varies based on market conditions:
Higher reliability in trending markets with sustained directional moves
Lower reliability in choppy, range-bound conditions with frequent reversals
Parameter adjustment can help adapt to different market volatility levels
🎯 STRATEGIC APPLICATIONS
Trend Following Strategy:
Use the Kalman Filter as a dynamic trend baseline:
Enter long positions when price crosses above the filter
Enter short positions when price crosses below the filter
Exit when price crosses back through the filter in the opposite direction
Monitor filter slope (color) for trend strength confirmation
Dynamic Support/Resistance:
The filter can act as a moving support or resistance level:
In uptrends: Filter often provides dynamic support for pullbacks
In downtrends: Filter often provides dynamic resistance for bounces
Price rejections from the filter can offer entry opportunities in trend direction
Filter breaches may signal potential trend reversals
Multi-Timeframe Analysis:
Combine Kalman Filters across different timeframes:
Higher timeframe filter identifies primary trend direction
Lower timeframe filter provides precise entry and exit timing
Trade only in direction of higher timeframe trend for better probability
Use lower timeframe crossovers for position entry/exit within major trend
Volatility-Adjusted Configuration:
Adapt parameters to match market conditions:
Low Volatility Markets (Forex majors, stable stocks): Use lower process noise for stability, use lower measurement noise for sensitivity
Medium Volatility Markets (Most equities): Process noise default (0.05) provides balanced performance, measurement noise default (1.0) for general-purpose filtering
High Volatility Markets (Cryptocurrencies, volatile stocks): Use higher process noise for responsiveness, use higher measurement noise for noise reduction
Risk Management Integration:
Use filter as a trailing stop-loss level in trending markets
Tighten stops when price moves significantly away from filter (overextension)
Wider stops in early trend formation when filter is just establishing direction
Consider position sizing based on distance between price and filter
📋 DETAILED PARAMETER CONFIGURATION
Source Selection:
Determines which price data feeds the algorithm:
OHLC4 (default): Uses average of open, high, low, close for balanced representation
Close: Focuses purely on closing prices for end-of-period analysis
HL2: Uses midpoint of high and low for range-based analysis
HLC3: Typical price, gives more weight to closing price
HLCC4: Weighted close price, emphasizes closing values
Process Noise (Q) - Adaptation Speed Control:
This parameter controls how quickly the filter adapts to changes:
Technical Meaning:
Represents uncertainty in the underlying trend model
Higher values allow the estimated trend to change more rapidly
Lower values assume the trend is more stable and slow-changing
Practical Impact:
Lower Values: Produces very smooth output with minimal noise, slower to respond to genuine trend changes, best for long-term trend identification, reduces false signals in choppy markets
Medium Values: Balanced responsiveness and smoothness, suitable for swing trading applications, default (0.05) works well for most markets
Higher Values: More responsive to price changes, may produce more false signals in ranging markets, better for short-term trading and day trading, captures trend changes earlier, adjust freely based on market characteristics
Measurement Noise (R) - Smoothing Control:
This parameter controls how much the filter trusts individual price observations:
Technical Meaning:
Represents uncertainty in price measurements
Higher values indicate less trust in individual price points
Lower values make each price observation more influential
Practical Impact:
Lower Values: More reactive to each price change, less smoothing with more noise in output, may produce choppy signals
Medium Values: Balanced smoothing and responsiveness, default (1.0) provides general-purpose filtering
Higher Values: Heavy smoothing for very noisy markets, reduces whipsaws significantly but increases lag in trend change detection, best for cryptocurrency and highly volatile assets, can use larger values for extreme smoothing
Parameter Interaction:
The ratio between Process Noise and Measurement Noise determines overall behavior:
High Q / Low R: Very responsive, minimal smoothing
Low Q / High R: Very smooth, maximum lag reduction
Balanced Q and R: Middle ground for most applications
Optimization Guidelines:
Start with default values (Q=0.05, R=1.0)
If too many false signals: Increase R or decrease Q
If missing trend changes: Decrease R or increase Q
Test across different market conditions before live use
Consider different settings for different timeframes
📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES
Comparison with Traditional Moving Averages:
Versus Simple Moving Average (SMA):
The Kalman Filter typically responds faster to genuine trend changes
Produces smoother output than SMA of comparable length
Better noise reduction in ranging markets
More configurable for different market conditions
Versus Exponential Moving Average (EMA):
Similar responsiveness but with better noise filtering
Less prone to whipsaws in choppy conditions
More adaptable through dual parameter control (Q and R)
Can be tuned to match or exceed EMA responsiveness while maintaining smoothness
Versus Hull Moving Average (HMA):
Different noise reduction approach (recursive estimation vs. weighted calculation)
Kalman Filter offers more intuitive parameter adjustment
Both reduce lag effectively, but through different mechanisms
Kalman Filter may handle sudden volatility changes more gracefully
Response Characteristics:
Lag Time: Moderate and configurable through parameter adjustment
Noise Reduction: Good to excellent, particularly in volatile conditions
Trend Detection: Effective across multiple timeframes
False Signal Rate: Typically lower than simple moving averages in ranging markets
Computational Efficiency: Efficient recursive calculation suitable for real-time use
Optimal Use Cases:
Markets with mixed trending and ranging periods
Assets with moderate to high volatility requiring noise filtering
Multi-timeframe analysis requiring consistent methodology
Systematic trading strategies needing reliable trend identification
Situations requiring balance between responsiveness and smoothness
Known Limitations:
Parameters require adjustment for different market volatility levels
May still produce false signals during extreme choppy conditions
No single parameter set works optimally for all market conditions
Requires complementary indicators for comprehensive analysis
Historical performance characteristics may not persist in changing market conditions
USAGE NOTES
This indicator is designed for technical analysis and educational purposes. The Kalman Filter's effectiveness varies with market conditions, tending to perform better in markets with clear trending phases interrupted by consolidation. Like all technical indicators, it has limitations and should not be used as the sole basis for trading decisions, but rather as part of a comprehensive trading approach.
Algorithm performance varies with market conditions, and past characteristics do not guarantee future results. Always test thoroughly with different parameter settings across various market conditions before using in live trading. No technical indicator can predict future price movements with certainty, and all trading involves risk of loss.
Global Risk Terminal – Multi-Asset Macro Sentiment IndicatorDescription:
The Global Risk Terminal is a sophisticated macro sentiment indicator that synthesizes signals from three key cross-asset relationships to produce a single, actionable risk appetite score. It is designed to help traders and investors identify whether global markets are in a risk-on (growth-seeking) or risk-off (defensive) regime. The indicator analyzes the behavior of commodities, equities, bonds, and currencies to generate a comprehensive view of market conditions.
Indicator Output:
The Global Risk Terminal produces a normalized risk score ranging from -1 to +1:
Positive values indicate risk-on conditions (growth assets favored)
Negative values indicate risk-off conditions (safe-haven assets favored)
Core Components:
Growth Pulse (Copper to Gold Ratio, HG/GC)
Purpose: Measures investor preference for industrial growth versus safe-haven assets.
Interpretation:
Rising ratio → Copper outperforming gold → Risk-on environment
Falling ratio → Gold outperforming copper → Risk-off environment
Flat ratio → Transitional market phase
Technical Implementation: Dual moving average slope method (fast MA default 20, slow MA default 40). Positive slope = +1, negative slope = -1, flat slope = 0
Equity Rotation (Russell 2000 to S&P 500 Ratio, RTY/ES)
Purpose: Tracks rotation between small-cap and large-cap equities, revealing market risk appetite.
Interpretation:
Rising ratio → Small-caps outperforming → Strong risk-on
Falling ratio → Large-caps outperforming → Defensive positioning
Technical Implementation: Dual moving average slope method (same as Growth Pulse)
Flow Gauge (10-Year Treasury to US Dollar Index, ZN/DXY)
Purpose: Captures liquidity conditions and cross-asset capital flows.
Interpretation:
Rising ratio → Treasury prices rising or USD weakening → Liquidity expansion, risk-on environment
Falling ratio → Treasury prices falling or USD strengthening → Liquidity contraction, risk-off environment
Technical Implementation: Dual moving average slope method
Composite Risk Score Calculation:
Analyze each component for trend using dual moving averages
Assign signal values: +1 (risk-on), -1 (risk-off), 0 (neutral)
Average the three signals:
Risk Score = (Growth Pulse + Equity Rotation + Flow Gauge) / 3
Optional smoothing with exponential moving average (default 3 periods) to reduce noise
Interpreting the Risk Score:
+0.66 to +1.0: Full risk-on – favor cyclical sectors, small-caps, growth strategies
+0.33 to +0.66: Moderate risk-on – mostly bullish environment, watch for fading momentum
-0.33 to +0.33: Neutral/transition – markets in flux, signals mixed, exercise caution
-0.66 to -0.33: Cautious risk-off – favor defensive sectors, reduce high-beta exposure
-1.0 to -0.66: Full risk-off – strong defensive positioning, prioritize safe-haven assets
How to Use the Global Risk Terminal to Frame Trades:
Aligning Trades with Market Regime
Risk-On (+0.33 and above): Look for buying opportunities in cyclical stocks, high-beta equities, commodities, and emerging markets. Use long entries for swing trades or intraday positions, following confirmed price action.
Risk-Off (-0.33 and below): Shift focus to defensive sectors, large-cap quality stocks, U.S. Treasuries, and safe-haven currencies. Prefer short entries or reduced exposure in risky assets.
Entry and Exit Framing
Use the risk score as a macro filter before executing trades:
Example: The risk score is +0.7 (strong risk-on). Prefer long positions in equities or commodities that are showing bullish confirmation on your regular chart.
Conversely, if the risk score is -0.7 (strong risk-off), avoid aggressive longs and consider short or defensive trades.
Watch for threshold crossings (+/-0.33, +/-0.66) as potential inflection points for adjusting position size, stop-loss levels, or sector rotation.
Confirming Trade Decisions
Combine the Global Risk Terminal with price action, volume, and trend indicators:
If equities rally but the risk score is declining, this may indicate a fragile rally driven by few leaders—trade cautiously.
If equities fall but the risk score is rising, consider counter-trend entries or buying dips.
Risk Management and Position Sizing
Strong alignment across components → increase position size and hold with wider stops
Mixed or neutral signals → reduce exposure, tighten stops, or avoid new trades
Defensive regimes → rotate into stable, low-volatility assets and increase cash buffer
Framing Trades Across Timeframes
Use the indicator as a strategic guide rather than a precise timing tool. Even without the MTF table:
Daily trend alignment → Guide swing trade bias
Shorter timeframe price action → Refine entry points and stop placement
Example: Daily chart shows +0.6 risk score → identify high-probability long setups using intraday technical patterns (breakouts, trend continuation).
Sector and Asset Rotation
Risk-On: Focus on cyclical sectors (financials, industrials, materials, energy), small-caps, high-beta instruments
Risk-Off: Focus on defensive sectors (utilities, consumer staples, healthcare), large-caps, safe-haven instruments
Alert Integration
Set alerts on the risk score to notify you when markets move from neutral to risk-on or risk-off regimes. Use these alerts to plan entries, exits, or portfolio adjustments in advance.
Customization Options:
Moving Average Length (5–100): Adjust sensitivity of trend detection
Score Smoothing (1–10): Reduce noise or see raw risk score
Visual Themes: Six preset themes (Cyber, Ocean, Sunset, Monochrome, Matrix, Custom)
Display Options: Show or hide component dashboards, main header, risk level lines, gradient fill, and component signals
Label Size: Tiny, Small, Normal, Large
Alert Conditions:
Risk score crosses above +0.66 → Strong risk-on
Risk score crosses below -0.66 → Strong risk-off
Risk score crosses zero → Neutral line
Risk score crosses above +0.33 → Moderate risk-on
Risk score crosses below -0.33 → Moderate risk-off
Data Sources:
HG1! – Copper Futures (COMEX)
GC1! – Gold Futures (COMEX)
RTY1! – Russell 2000 E-mini Futures (CME)
ES1! – S&P 500 E-mini Futures (CME)
ZN1! – 10-Year U.S. Treasury Note Futures (CBOT)
DXY – U.S. Dollar Index (ICE)
Notes and Limitations:
Works best during clear macro regimes and aligned trends
Use with price action, volume, and other technical tools
Not a standalone trading system; serves as a macro context filter
Equal weighting assumes all three components are equally important, but market conditions may vary
Past performance does not guarantee future results
Conclusion:
The Global Risk Terminal consolidates complex cross-asset signals into a simple, actionable score that informs market regime, portfolio positioning, sector rotation, and trading decisions. Its user-friendly layout and extensive customization options make it suitable for traders of all experience levels seeking macro-driven insights. By framing trades around risk score thresholds and combining macro context with tactical execution, traders can identify higher-probability opportunities and optimize position sizing, entries, and exits across a wide range of market conditions.
FVG Scanner ProFVG Scanner Pro — Smart Fair Value Gap Detector (with HTF context & proximity alerts)
What it does
FVG Scanner Pro automatically finds Fair Value Gaps (FVGs) on your current chart and (optionally) on a higher timeframe (HTF), draws them as color-coded zones, and notifies you when price comes close to a gap boundary using an ADR-based proximity trigger and (optional) volume confirmation. It’s designed for ICT-style gap trading, confluence building, and clean visual execution.
How it works:
FVG definition
* Bullish FVG (gap up): low > high (the current candle’s low is above the high 2 bars ago).
* Bearish FVG (gap down): high < low (the current candle’s high is below the low 2 bars ago).
* Gaps smaller than your Min FVG Size (%) are ignored. (Gap size = (top-bottom)/bottom * 100.)
Higher-timeframe logic (auto-selected)
The script auto picks a sensible HTF:
1–5m → 15m, 15m → 1H, 1H → 4H, 4H → 1D, 1D → 1W, 1W → 1M, small 1M → 3M, big ≥3M → 12M.
You can display HTF FVGs and even filter so current-TF FVGs only show when they overlap an HTF gap.
Proximity alerts (ADR-based)
The script computes ADR on the current chart timeframe over a user-set lookback (default 20 bars).
An alert fires when price moves toward the closest actionable boundary and comes within ADR × Multiplier:
Bullish: price moving down, within distance of the bottom of a bullish FVG.
Bearish: price moving up, within distance of the top of a bearish FVG.
Yellow ▲/▼ markers show where a proximity alert triggered.
Volume filter (optional)
Require volume to be greater than SMA(20) × multiplier to accept a newly formed FVG.
Lifecycle
Each gap remains active for Extend FVG Box (Bars) bars.
You can delete the box after fill, or keep filled gaps visible as gray zones, or hide them.
Color legend
Current-TF Bullish: Pink/Magenta box
Current-TF Bearish: Cyan/Turquoise box
HTF Bullish: Gold box
HTF Bearish: Orange box
Filled (if shown): Gray box
Alert markers: Yellow ▲ (bullish), Yellow ▼ (bearish)
Inputs (what to tweak)
Show FVGs: Bullish / Bearish / Both
Max Bars Back to Find FVG: collection window & cleanup guard
Extend FVG Box (Bars): how long a zone stays tradable/active
Min FVG Size (%): ignore micro gaps
Delete Box After Fill & Show Filled FVGs: choose how you want completed gaps handled
Show Alert Markers: show/hide the yellow proximity arrows
Show Higher Timeframe FVG: overlay HTF gaps (auto TF)
HTF Filter: only display current-TF gaps that overlap an HTF gap
ADR Lookback & Proximity Multiplier: tune alert sensitivity to your market & timeframe
Volume Filter & Volume > MA Multiple: require above-average volume for new gaps
Built-in alerts (ready to use)
Create alerts in TradingView (⚠️ “Once per bar” or “Once per bar close”, your choice) and select from:
🟢 Bullish FVG Proximity — price approaching a bullish gap bottom
🔴 Bearish FVG Proximity — price approaching a bearish gap top
✅ New Bullish FVG Formed
⚠️ New Bearish FVG Formed
The alert messages include the symbol and price; proximity markers are also plotted on chart.
Tips & best practices
Use FVGs with market structure (break of structure, swing points), order blocks, or liquidity pools for confluence.
On very low timeframes, raise Min FVG Size and/or lower Max Bars Back to reduce noise and keep things fast.
Extend FVG Box controls how long a zone is considered valid; align it with your holding horizon (scalp vs swing).
Information panel (top-right)
Shows your mode, current HTF, number of gaps in memory, active bull/bear counts, and current-TF ADR.
Ajay R5.41🔻 Ajay Gold 3H Power Indicator 🔻
Precision-Based Smart Sell System for Gold (XAU/USD)
💡 Overview
This indicator is specifically designed for Gold (XAU/USD) and delivers best results on the 3-Hour Timeframe (3H TF).
It is a Smart Money Logic-based Sell Confirmation System, combining institutional structure and candle behavior to generate highly accurate bearish signals.
⚙️ Technical Foundation
The indicator uses multiple advanced confirmations:
📉 EMA Trend Filter → Confirms downtrend
💪 RSI Overbought Rejection → Momentum reversal signal
📊 MACD Bearish Cross → Confirms trend strength
🕯️ Bearish Candle Structure → Price action validation
When all conditions align, a clear 🔻 Sell Signal is plotted on the chart.
💎 Hidden Feature
This indicator includes a hidden feature that activates only when the correct market structure forms.
It helps reduce false signals and increases accuracy without being visible on the chart — fully automated internal logic.
📆 Recommended Settings
Symbol: XAU/USD (Gold)
Timeframe: 3-Hour (3H)
Market: Forex / Commodity
Mode: Sell-Only Confirmation Indicator
Performance: Best precision and consistency on 3H TF
📈 How to Use
Select XAU/USD on chart and set 3H timeframe.
Add the indicator to the chart.
Wait for the 🔻 Sell Signal and confirm the market structure after candle close.
Take entry according to your risk management.
⚠️ Disclaimer
This indicator is for educational and analytical purposes only.
No system is 100% accurate — always backtest and demo trade before using in real trading.
💬 Credits
Developed by Ajay Sahu (India)
Based on Institutional & Smart Money Logic
Best results on 3H TF
Hidden Algorithm for XAU/USD traders
XAUUSD/SPX with SMA(48)📊 Gold vs S&P 500 | XAUUSD/SPX Ratio with SMA (48) – Full Pine Script Breakdown
In this video, we build and explain a custom Pine Script that plots the Gold to S&P 500 ratio (XAUUSD/SPX) along with a 48-period Simple Moving Average (SMA).
This ratio helps us analyze how Gold is performing against equities and whether smart money is shifting from risk assets (stocks) to safe haven (gold).
🔧 What’s Included in the Script:
✅ Live ratio of XAUUSD (Gold) / SPX (S&P 500)
✅ 48-period SMA for trend analysis
✅ Clean visual chart in a separate pane
✅ Pine Script v5 compatible
🧠 Why This Matters:
Tracking the XAUUSD/SPX ratio gives deeper insight into macro trends, inflation hedge behavior, and market sentiment.
A rising ratio can signal weakness in equities and strength in precious metals — a key trend for long-term investors and macro traders.
Multi-Signal IndikatorHier ist eine professionelle Beschreibung für deinen Indikator auf Englisch:
Multi-Signal Trading Indicator - Complete Market Analysis
This comprehensive trading indicator combines multiple technical analysis tools into one powerful dashboard, providing traders with all essential market information at a glance.
Key Features:
Trend Analysis: Three EMAs (9, 21, 50) with automatic trend detection and Golden/Death Cross signals
Momentum Indicators: RSI with overbought/oversold zones and visual alerts
Trend Strength: ADX indicator with DI+ and DI- showing the power of bullish and bearish movements
Market Fear Gauge: VIX (Volatility Index) integration displaying market sentiment from calm to panic levels
Volume Confirmation: Smart volume analysis comparing current activity against 20-period average
Support & Resistance: Automatic pivot point detection with dynamic S/R lines
Buy/Sell Signals: Combined signals only trigger when trend, RSI, and volume align perfectly
Visual Dashboard: Color-coded info panel showing all metrics in real-time with intuitive emoji indicators
Perfect for: Day traders, swing traders, and investors who want a complete market overview without cluttering their charts with multiple indicators.
Customizable settings allow you to adjust all parameters to match your trading style.
BRC High/Low + Retest + Sweep🧭 Overview
The BRC System (Break–Retest–Claim) is a structured breakout-retest strategy that automatically identifies new highs or lows, confirms liquidity sweeps, and highlights high-probability reclaim zones. It supports both long and short setups with adaptive zone shading and full-session awareness.
⚙️ Core Features
✅ Dual-Side Logic: Detects both bullish (Break–Retest–Reclaim) and bearish (Breakdown–Retest–Reclaim) setups.
✅ Liquidity Sweep Mode: Captures false breakouts (sweep-and-reclaim) for advanced liquidity-based trading.
✅ Adaptive Shading:
🟩 Green — Long bias
🟥 Red — Short bias
⬜ Grey — Neutral (weak ADX)
✅ EMA + ADX Trend Filters: Confirms direction using higher-timeframe momentum.
✅ Configurable Profiles: Pre-tuned for Gold day-trades and EUR/USD swings (customizable mode included).
✅ Compact Dashboard: Shows active profile, trend timeframe, ADX, bias direction, and win/loss stats for the last N trades.
✅ Abbreviated Labels (toggle): RL = Retest Long | SL = Sweep Long | RS = Retest Short | SS = Sweep Short.
✅ Dynamic Zones: Automatically updates breakout-retest areas with visual boxes extending forward.
📊 How It Works
Detects a new swing high/low breakout within a chosen lookback range.
Waits for retest of the broken level (or reclaim after liquidity sweep).
Confirms entry when body closes in trend direction + ADX/EMA filters pass.
Tracks outcomes with auto-calculated win % dashboard.
💡 Best Use
Use on Gold (XAUUSD) for intraday scalps or EUR/USD for swing trades.
Works across timeframes — best visual clarity on M15–H4.
Integrate with your risk-reward or alert-triggered execution system.
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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Macro Momentum – 4-Theme, Vol Target, RebalanceMacro Momentum — 4-Theme, Vol Target, Rebalance
Purpose. A macro-aware strategy that blends four economic “themes”—Business Cycle, Trade/USD, Monetary Policy, and Risk Sentiment—into a single, smoothed Composite signal. It then:
gates entries/exits with hysteresis bands,
enforces optional regime filters (200-day bias), and
sizes the position via volatility targeting with caps for long/short exposure.
It’s designed to run on any chart (index, ETF, futures, single stocks) while reading external macro proxies on a chosen Signal Timeframe.
How it works (high level)
Build four theme signals from robust macro proxies:
Business Cycle: XLI/XLU and Copper/Gold momentum, confirmed by the chart’s price vs a long SMA (default 200D).
Trade / USD: DXY momentum (sign-flipped so a rising USD is bearish for risk assets).
Monetary Policy: 10Y–2Y curve slope momentum and 10Y yield trend (steepening & falling 10Y = risk-on; rising 10Y = risk-off).
Risk Sentiment: VIX momentum (bearish if higher) and HYG/IEF momentum (bullish if credit outperforms duration).
Normalize & de-noise.
Optional Winsorization (MAD or stdev) clamps outliers over a lookback window.
Optional Z-score → tanh mapping compresses to ~ for stable weighting.
Theme lines are SMA-smoothed; the final Composite is LSMA-smoothed (linreg).
Decide direction with hysteresis.
Enter/hold long when Composite ≥ Entry Band; enter/hold short when Composite ≤ −Entry Band.
Exit bands are tighter than entry bands to avoid whipsaws.
Apply regime & direction constraints.
Optional Long-only above 200MA (chart symbol) and/or Short-only below 200MA.
Global Direction control (Long / Short / Both) and Invert switch.
Size via volatility targeting.
Realized close-to-close vol is annualized (choose 9-5 or 24/7 market profile).
Target exposure = TargetVol / RealizedVol, capped by Max Long/Max Short multipliers.
Quantity is computed from equity; futures are rounded to whole contracts.
Rebalance cadence & execution.
Trades are placed on Weekly / Monthly / Quarterly rebalance bars or when the sign of exposure flips.
Optional ATR stop/TP for single-stock style risk management.
Inputs you’ll actually tweak
General
Signal Timeframe: Where macro is sampled (e.g., D/W).
Rebalance Frequency: Weekly / Monthly / Quarterly.
ROC & SMA lengths: Defaults for theme momentum and the 200D regime filter.
Normalization: Z-score (tanh) on/off.
Winsorization
Toggle, lookback, multiplier, MAD vs Stdev.
Risk / Sizing
Target Annualized Vol & Realized Vol Lookback.
Direction (Long/Short/Both) and Invert.
Max long/short exposure caps.
Advanced Thresholds
Theme/Composite smoothing lengths.
Entry/Exit bands (hysteresis).
Regime / Execution
Long-only above 200MA, Short-only below 200MA.
Stops/TP (optional)
ATR length and SL/TP multiples.
Theme Weights
Per-theme scalars so you can push/pull emphasis (e.g., overweight Policy during rate cycles).
Macro Proxies
Symbols for each theme (XLI, XLU, HG1!, GC1!, DXY, US10Y, US02Y, VIX, HYG, IEF). Swap to alternatives as needed (e.g., UUP for DXY).
Signals & logic (under the hood)
Business Cycle = ½ ROC(XLI/XLU) + ½ ROC(Copper/Gold), then confirmed by (price > 200SMA ? +1 : −1).
Trade / USD = −ROC(DXY).
Monetary Policy = 0.6·ROC(10Y–2Y) − 0.4·ROC(10Y).
Risk Sentiment = −0.6·ROC(VIX) + 0.4·ROC(HYG/IEF).
Each theme → (optional Winsor) → (robust z or scaled ROC) → tanh → SMA smoothing.
Composite = weighted average → LSMA smoothing → compare to bands → dir ∈ {−1,0,+1}.
Rebalance & flips. Orders fire on your chosen cadence or when the sign of exposure changes.
Position size. exposure = clamp(TargetVol / realizedVol, maxLong/Short) × dir.
Note: The script also exposes Gross Exposure (% equity) and Signed Exposure (× equity) as diagnostics. These can help you audit how vol-targeting and caps translate into sizing over time.
Visuals & alerts
Composite line + columns (color/intensity reflect direction & strength).
Entry/Exit bands with green/red fills for quick polarity reads.
Hidden plots for each Theme if you want to show them.
Optional rebalance labels (direction, gross & signed exposure, σ).
Background heatmap keyed to Composite.
Alerts
Enter/Inc LONG when Composite crosses up (and on rebalance bars).
Enter/Inc SHORT when Composite crosses down (and on rebalance bars).
Exit to FLAT when Composite returns toward neutral (and on rebalance bars).
Practical tips
Start higher timeframes. Daily signals with Monthly rebalance are a good baseline; weekly signals with quarterly rebalances are even cleaner.
Tune Entry/Exit bands before anything else. Wider bands = fewer trades and less noise.
Weights reflect regime. If policy dominates markets, raise Monetary Policy weight; if credit stress drives moves, raise Risk Sentiment.
Proxies are swappable. Use UUP for USD, or futures-continuous symbols that match your data plan.
Futures vs ETFs. Quantity auto-rounds for futures; ETFs accept fractional shares. Check contract multipliers when interpreting exposure.
Caveats
Macro proxies can repaint at the selected signal timeframe as higher-TF bars form; that’s intentional for macro sampling, but test live.
Vol targeting assumes reasonably stationary realized vol over the lookback; if markets regime-shift, revisit volLook and targetVol.
If you disable normalization/winsorization, themes can become spikier; expect more hysteresis band crossings.
What to change first (quick start)
Set Signal Timeframe = D, Rebalance = Monthly, Z-score on, Winsor on (MAD).
Entry/Exit bands: 0.25 / 0.12 (defaults), then nudge until trade count and turnover feel right.
TargetVol: try 10% for diversified indices; lower for single stocks, higher for vol-sell strategies.
Leave weights = 1.0 until you’ve inspected the four theme lines; then tilt deliberately.
8 SMA Bands (Points)The "8 SMA Bands (Points)" indicator creates a set of eight Simple Moving Average (SMA) bands with adjustable offsets, overlaid on a price chart.
Here’s a breakdown:
Purpose: It tracks price trends using multiple SMAs of varying lengths (default 25, 50, 100, 200, 400, 800, 1600 periods) and adds upper and lower bands around each SMA based on point offsets, helping identify potential support, resistance, and trend strength.
Key Components:
SMAs: Eight SMAs are calculated using closing prices with lengths ranging from 25 to 1600 periods. Each SMA is plotted with a distinct color and line thickness (e.g., MA 1 is blue, MA 8 is white with thicker lines).
Bands: For each SMA, upper and lower bands are created by adding or subtracting a point-based offset (suggestions are to use default Murray Math based numbers e.g., 0.305176 for MA 1, 39.062528 for MA 8) multiplied by a global multiplier (default 1.0). These offsets define the band width and are customizable.
Customization: Users can adjust SMA lengths, offset points, colors, and the global multiplier via input settings grouped by each MA.
Visuals: SMAs are plotted as solid lines with increasing thickness for longer periods (e.g., MA 6–8 use thicker lines or circles).
Bands are plotted as semi-transparent lines matching the SMA color, with longer-term bands (MA 6–7) using a different style for emphasis.
Usage: The indicator helps traders visualize trend direction (upward if price is above most SMAs, downward if below) and potential reversal zones where price interacts with band boundaries.
The flattening or crossing of bands can signal momentum shifts. The coming together of multiple envelope tops/bottoms can signal reversal zones of various degrees based on how many envelopes come together. More envelopes converging mean a more significant top or bottom.
This indicator is particularly useful for identifying multi-timeframe trends and volatility zones on assets like Gold Futures, with flexibility to fine-tune based on market conditions.
LA - Opening Price based Previous day Range PivotThis "LA - Opening Price based Previous day Range Pivot" indicator is a custom technical analysis tool designed for Trading View charts. It plots support and resistance levels (often referred to as pivots or ranges) based on the current opening price combined with the previous period's trading range. The "previous period" can be daily, weekly, or monthly, making it a multi-timeframe tool. These levels are projected using Fibonacci-inspired multipliers to create potential breakout or reversal zones.
The core idea is inspired by concepts like the Opening Range Breakout (ORB) strategy or Fibonacci pivots, but it's customized here to use a dynamic range calculation (the maximum of several absolute price differences) rather than a simple high-low range. This makes it more robust for volatile markets. Levels are symmetric above (resistance) and below (support) the opening price, helping traders identify potential entry/exit points, stop-losses, or targets. This will be useful when there is a gap-up/down as in Nifty/Sensex .
Purpose of the Indicator:
To visualize potential support/resistance zones for the current trading session based on the opening price and historical range data. This helps traders anticipate price movements, such as breakouts above resistance or bounces off support
Use Cases:
Intraday Trading: On lower timeframes (e.g., 5-min or 15-min charts), it shows daily levels for short-term trades.
Swing Trading: On higher timeframes (e.g., hourly or daily), it displays weekly/monthly levels for longer holds.
Range Identification: The filled bands highlight "zones" where price might consolidate or reverse.
Conditional Display: Levels only appear on appropriate timeframes (e.g., daily levels on intraday charts <60min), preventing clutter.
Theoretical Basis: It builds on pivot point theory, where the opening price acts as a central pivot. Multipliers (e.g., 0.618 for Fibonacci golden ratio) project levels, assuming price often respects these ratios due to market psychology.
How Calculations Work
Let's dive into the math with examples. Assume a stock with:
Current daily open (cdo) = $100
Previous daily high (pdh) = $105, low (pdl) = $95, close (pdc) = $102, close 2 days ago (pdc2) = $98
Step 1: Dynamic Range Calculation (var_d2):
This is the max of:
|pdh - pdc2| = |105 - 98| = 7
|pdl - pdc2| = |95 - 98| = 3
|pdh - pdl| = |105 - 95| = 10 (previous day range)
|pdh - cdo| = |105 - 100| = 5
|pdl - cdo| = |95 - 100| = 5
|pdc - cdo| = |102 - 100| = 2
|pdc2 - cdo| = |98 - 100| = 2
Max = 10 (so range = 10). This ensures the range accounts for gaps and extended moves, not just high-low.
Step 2: Level Projections:
Resistance (above open): Open + (Range * Multiplier)
dre6 = 100 + (10 * 1.5) = 115
dre5 = 100 + (10 * 1.27) ≈ 112.7
... down to dre0 = 100 + (10 * 0.1) = 101
dre50 = 100 + (10 * 0.5) = 105 (midpoint)
Support (below open): Open - (Range * Multiplier)
dsu0 = 100 - (10 * 0.1) = 99
... up to dsu6 = 100 - (10 * 1.5) = 85
Without Indicator
With Indicator
Pros and Cons
Pros:
Multi-Timeframe Flexibility: Seamlessly integrates daily, weekly, and monthly levels, useful for aligning short-term trades with longer trends (e.g., intraday breakout confirmed by weekly support).
Dynamic Range Calculation: Unlike standard pivots (just (H+L+C)/3), it uses max of multiple diffs, capturing gaps/volatility better—great for stocks with overnight moves.
Customizable via Inputs: Users can toggle levels, adjust multipliers, or change timeframes without editing code. Inline inputs keep the UI clean.
Visual Aids: Filled bands make zones obvious; conditional colors highlight "tight" vs. "wide" ranges (e.g., for volatility assessment).
Fibonacci Integration: Levels based on proven ratios, appealing to technical traders. Symmetric supports/resistances simplify strategy building (e.g., buy at support, sell at resistance).
No Repainting: Uses historical data with lookahead, so levels are fixed once calculated—reliable for back-testing.
Cons:
Chart Clutter: With all toggles on, 50+ plots/fills can overwhelm the chart, especially on mobile or small screens. Requires manual disabling.
Complexity for Beginners: Many inputs and calculations; without understanding fib ratios or range logic, it might confuse new users.
Performance Overhead: On low timeframes (e.g., 1-min), fetching higher TF data multiple times could lag, especially with many symbols or back-tests.
Assumes Volatility Persistence: Relies on previous range projecting future moves; in low-vol markets (e.g., sideways trends), levels may be irrelevant or too wide/narrow.
No Alerts or Signals: Purely visual; no built-in buy/sell alerts or crossover conditions—users must add separately.
Hardcoded Styles/Colors: Limited customization without code edits (e.g., can't change line styles via inputs).
Also, not optimized for non-stock assets (e.g., forex with 24/7 trading).
In summary, this is a versatile pivot tool for range-based trading based on Opening price, excelling in volatile markets but requiring some setup. If you're using it, start with defaults on a daily chart and toggle off unnecessary levels.
MAMA [DCAUT]█ MAMA (MESA Adaptive Moving Average)
📊 OVERVIEW
The MESA Adaptive Moving Average (MAMA) represents an advanced implementation of John F. Ehlers' adaptive moving average system using the Hilbert Transform Discriminator. This indicator automatically adjusts to market cycles, providing superior responsiveness compared to traditional fixed-period moving averages while maintaining smoothness.
MAMA dynamically calculates two lines: the fast-adapting MAMA line and the following FAMA (Following Adaptive Moving Average) line. The system's core strength lies in its ability to automatically detect and adapt to the dominant market cycle, reducing lag during trending periods while providing stability during consolidation phases.
🎯 CORE CONCEPTS
Signal Interpretation:
• MAMA above FAMA: Indicates bullish trend momentum with the fast line leading upward movement
• MAMA below FAMA: Suggests bearish trend momentum with the fast line leading downward movement
• Golden Cross: MAMA crossing above FAMA signals potential upward momentum shift
• Death Cross: MAMA crossing below FAMA indicates potential downward momentum shift
• Line Convergence: MAMA and FAMA approaching each other suggests trend consolidation or potential reversal
Primary Applications:
• Trend Following: Enhanced responsiveness to trend changes compared to traditional moving averages
• Crossover Signals: MAMA/FAMA crossovers for identifying potential entry and exit points
• Cycle Analysis: Automatic adaptation to market's dominant cycle characteristics
• Reduced Lag: Minimized delay in trend detection while maintaining signal smoothness
📐 MATHEMATICAL FOUNDATION
Hilbert Transform Discriminator Technology:
The MAMA system employs John F. Ehlers' Hilbert Transform Discriminator, a sophisticated signal processing technique borrowed from telecommunications engineering. The Hilbert Transform creates a complex representation of the price series by generating a 90-degree phase-shifted version of the original signal, enabling precise cycle measurement.
The discriminator analyzes the instantaneous phase relationships between the original price series and its Hilbert Transform counterpart. This mathematical relationship reveals the dominant cycle period present in the market data at each point in time, forming the foundation for adaptive smoothing.
Instantaneous Period Calculation:
The algorithm computes the instantaneous period using the arctangent of the ratio between the Hilbert Transform and the original price series. This calculation produces a real-time measurement of the market's dominant cycle, typically ranging from short-term noise cycles to longer-term trend cycles.
The instantaneous period measurement undergoes additional smoothing to prevent erratic behavior from single-bar anomalies. This smoothed period value becomes the basis for calculating the adaptive alpha coefficient that controls the moving average's responsiveness.
Dynamic Alpha Coefficient System:
The adaptive alpha calculation represents the core mathematical innovation of MAMA. The alpha coefficient is derived from the instantaneous period measurement and constrained within the user-defined fast and slow limits.
The mathematical relationship converts the measured cycle period into an appropriate smoothing factor: shorter detected cycles result in higher alpha values (increased responsiveness), while longer cycles produce lower alpha values (increased stability). This creates an automatic adaptation mechanism that responds to changing market conditions.
MAMA/FAMA Calculation Process:
The MAMA line applies the dynamically calculated alpha coefficient to an exponential moving average formula: MAMA = alpha × Price + (1 - alpha) × MAMA . The FAMA line then applies a secondary smoothing operation to the MAMA line, creating a following average that provides confirmation signals.
This dual-line approach ensures that the fast-adapting MAMA line captures trend changes quickly, while the FAMA line offers a smoother confirmation signal, reducing the likelihood of acting on temporary price fluctuations.
Cycle Detection Mechanism:
The underlying cycle detection employs quadrature components derived from the Hilbert Transform to measure both amplitude and phase characteristics of price movements. This allows the system to distinguish between genuine trend changes and temporary price noise, automatically adjusting the smoothing intensity accordingly.
The mathematical framework ensures that during strong trending periods with clear directional movement, the algorithm reduces smoothing to minimize lag. Conversely, during consolidation phases with mixed signals, increased smoothing helps filter out false breakouts and whipsaws.
📋 PARAMETER CONFIGURATION
Source Selection Strategy:
• HL2 (High+Low)/2 (Default): Recommended for cycle analysis as it represents the midpoint of each period's trading range, reducing impact of opening gaps and closing spikes
• Close Price: Traditional choice reflecting final market sentiment, suitable for end-of-day analysis
• HLC3 (High+Low+Close)/3: Balanced approach incorporating range information with closing emphasis
• OHLC4 (Open+High+Low+Close)/4: Most comprehensive price representation for complete market view
Fast Limit Configuration (Default 0.5):
Controls the maximum responsiveness of the adaptive system. Higher values increase sensitivity to recent price changes but may introduce more noise. This parameter sets the upper bound for the dynamic alpha calculation.
Slow Limit Configuration (Default 0.05):
Determines the minimum responsiveness, providing stability during uncertain market conditions. Lower values increase smoothing but may cause delayed signals. This parameter sets the lower bound for the dynamic alpha calculation.
Parameter Relationship Considerations:
The fast and slow limits work together to define the adaptive range. The wider the range between these limits, the more dramatic the adaptation between trending and consolidating market conditions. Different market characteristics may benefit from different parameter configurations, requiring individual testing and validation.
📊 COLOR CODING SYSTEM
Line Visualization:
• Green Line (MAMA): The fast-adapting moving average that responds quickly to price changes
• Red Line (FAMA): The following adaptive moving average that provides confirmation signals
The fixed color scheme provides consistent visual identification of each line, enabling clear differentiation between the fast-adapting MAMA and the following FAMA throughout all market conditions.
💡 CORE VALUE PROPOSITION
Advantages Over Traditional Moving Averages:
• Cycle Adaptation: Automatically adjusts to market's dominant cycle rather than using fixed periods
• Reduced Lag: Faster response to genuine trend changes while filtering market noise
• Mathematical Foundation: Based on advanced signal processing techniques from telecommunications engineering
• Dual-Line System: Provides both fast adaptation (MAMA) and confirmation (FAMA) in one indicator
Comparative Performance Characteristics:
Unlike fixed-period moving averages that apply the same smoothing regardless of market conditions, MAMA adapts its behavior based on current market cycle characteristics. This may help reduce whipsaws during consolidation periods while maintaining responsiveness during trending phases.
Usage Considerations:
This indicator is designed for technical analysis purposes. The adaptive nature means that parameter optimization should consider the specific characteristics of the asset and timeframe being analyzed. Like all technical indicators, MAMA should be used as part of a comprehensive analysis approach rather than as a standalone signal generator.
Alert Functionality:
The indicator includes alert conditions for MAMA/FAMA crossovers, enabling automated notification of potential momentum shifts. These alerts can assist in timing analysis but should be combined with other forms of market analysis for decision-making purposes.
Global M2 Money SupplyThis indicator calculates and plots an aggregated estimate of the Global M2 money supply, expressed in U.S. dollar terms. It combines M2 data from major economies and regions—including the U.S., Eurozone, Canada, the U.K., Switzerland, China, Japan, India, Brazil, and others—and adjusts each by its respective FX rate to USD. By summing these series, the script provides a broad view of worldwide liquidity conditions in one line.
A user-defined offset in days allows you to shift the global M2 line forward or backward, making it easier to visually compare liquidity trends against asset prices such as Bitcoin, gold, or equities. This tool is designed for traders and macro observers who want to study how global money supply growth or contraction correlates with financial markets over time.
이 지표는 전 세계 주요 국가와 지역의 M2 통화량을 달러 기준으로 합산하여 글로벌 유동성 지표로 보여줍니다. 미국, 유로존, 캐나다, 영국, 스위스, 중국, 일본, 인도, 브라질 등 여러 지역의 M2 데이터를 각 통화의 환율(USD 환산)로 조정한 뒤 합산해 하나의 흐름으로 표현합니다. 이를 통해 글로벌 차원의 통화 공급 변화를 한눈에 파악할 수 있습니다.
또한 사용자가 지정한 일 단위 오프셋 기능을 통해 글로벌 M2 라인을 앞뒤로 이동시켜, 비트코인·금·주식 등 다양한 자산 가격과의 시차적 관계를 직관적으로 비교할 수 있습니다. 거시경제 환경과 자산시장 간의 상관성을 연구하거나 시장 유동성 추이를 모니터링하려는 투자자에게 유용한 도구입니다.
AI Trading Alerts v6 — SL/TP + Confidence + Panel (Fixed)Overview
This Pine Script is designed to identify high-probability trading opportunities in Forex, commodities, and crypto markets. It combines EMA trend filters, RSI, and Stochastic RSI, with automatic stop-loss (SL) & take-profit (TP) suggestions, and provides a confidence panel to quickly assess the trade setup strength.
It also includes TradingView alert conditions so you can set up notifications for Long/Short setups and EMA crosses.
⚙️ Features
EMA Trend Filter
Uses EMA 50, 100, 200 for trend confirmation.
Bull trend = EMA50 > EMA100 > EMA200
Bear trend = EMA50 < EMA100 < EMA200
RSI Filter
Bullish trades require RSI > 50
Bearish trades require RSI < 50
Stochastic RSI Filter
Prevents entries during overbought/oversold extremes.
Bullish entry only if %K and %D < 80
Bearish entry only if %K and %D > 20
EMA Proximity Check
Price must be near EMA50 (within ATR × adjustable multiplier).
Signals
Continuation Signals:
Long if all bullish conditions align.
Short if all bearish conditions align.
Cross Events:
Long Cross when price crosses above EMA50 in bull trend.
Short Cross when price crosses below EMA50 in bear trend.
Automatic SL/TP Suggestions
SL size adjusts depending on asset:
Gold/Silver (XAU/XAG): 5 pts
Bitcoin/Ethereum: 100 pts
FX pairs (default): 20 pts
TP = SL × Risk:Reward ratio (default 1:2).
Confidence Score (0–4)
Based on conditions met (trend, RSI, Stoch, EMA proximity).
Labels:
Strongest (4/4)
Strong (3/4)
Medium (2/4)
Low (1/4)
Visual Panel on Chart
Shows ✅/❌ for each condition (trend, RSI, Stoch, EMA proximity, signal now).
Confidence row with color-coded strength.
Alerts
Long Setup
Short Setup
Long Cross
Short Cross
🖥️ How to Use
1. Add the Script
Open TradingView → Pine Editor.
Paste the full script.
Click Add to chart.
Save as "AI Trading Alerts v6 — SL/TP + Confidence + Panel".
2. Configure Inputs
EMA Lengths: Default 50/100/200 (works well for swing trading).
RSI Length: 14 (standard).
Stochastic Length/K/D: Default 14/3/3.
Risk:Reward Ratio: Default 2.0 (can change to 1.5, 3.0, etc.).
EMA Proximity Threshold: Default 0.20 × ATR (adjust to be stricter/looser).
3. Read the Panel
Top-right of chart, you’ll see ✅ or ❌ for:
Trend → Are EMAs aligned?
RSI → Above 50 (bull) or below 50 (bear)?
Stoch OK → Not extreme?
Near EMA50 → Close enough to EMA50?
Above/Below OK → Price position vs. EMA50 matches trend?
Signal Now → Entry triggered?
Confidence row:
🟢 Green = Strongest
🟩 Light green = Strong
🟧 Orange = Medium
🟨 Yellow = Low
⬜ Gray = None
4. Alerts Setup
Go to TradingView Alerts (⏰ icon).
Choose the script under “Condition”.
Select alert type:
Long Setup
Short Setup
Long Cross
Short Cross
Set notification method (popup, sound, email, mobile).
Click Create.
Now TradingView will notify you automatically when signals appear.
5. Example Workflow
Wait for Confidence = Strong/Strongest.
Check if market session supports volatility (e.g., XAU in London/NY).
Review SL/TP suggestions:
Long → Entry: current price, SL: close - risk_pts, TP: close + risk_pts × RR.
Short → Entry: current price, SL: close + risk_pts, TP: close - risk_pts × RR.
Adjust based on your own price action analysis.
📊 Best Practices
Use on H1 + D1 combo → align higher timeframe bias with intraday entries.
Risk only 1–2% of account per trade (position sizing required).
Filter with market sessions (Asia, Europe, US).
Strongest signals work best with trending pairs (e.g., XAUUSD, USDJPY, BTCUSD).
Multi-Symbol Volatility Tracker with Range DetectionMulti-Symbol Volatility Tracker with Range Detection
🎯 Main Purpose:
This indicator is specifically designed for scalpers to quickly identify symbols with high volatility that are currently in ranging conditions . It helps you spot the perfect opportunities for buying at lows and selling at highs repeatedly within the same trading session.
📊 Table Data Explanation:
The indicator displays a comprehensive table with 5 columns for 4 major symbols (GOLD, SILVER, NASDAQ, SP500):
SYMBOL: The trading instrument being analyzed
VOLATILITY: Color-coded volatility levels (NORMAL/HIGH/EXTREME) based on ATR values
Last Candle %: The percentage range of the most recent 5-minute candle
Last 5 Candle Avg %: Average percentage range over the last 5 candles
RANGE: Shows "YES" (blue) or "NO" (gray) indicating if the symbol is currently ranging
🔍 How to Identify Trading Opportunities:
Look for symbols that combine these characteristics:
RANGE column shows "YES" (highlighted in blue) - This means the symbol is moving sideways, perfect for range trading
VOLATILITY shows "HIGH" or "EXTREME" - Ensures there's enough movement for profitable scalping
Higher candlestick percentages - Indicates larger candle ranges, meaning more profit potential per trade
⚡ Optimal Usage:
Best Timeframe: Works optimally on 5-minute charts where the ranging patterns are most reliable for scalping
Trading Strategy: When you find a symbol with "YES" in the RANGE column, switch to that symbol and look for opportunities to buy near the lows and sell near the highs of the ranging pattern
Risk Management: Higher volatility symbols offer more profit potential but require tighter risk management
⚙️ Settings:
ATR Length: Adjusts the Average True Range calculation period (default: 14)
Range Sensitivity: Fine-tune range detection sensitivity (0.1-2.0, lower = more sensitive)
💡 Pro Tips:
The indicator updates in real-time, so monitor for symbols switching from "NO" to "YES" in the RANGE column
Combine HIGH/EXTREME volatility with RANGE: YES for the most profitable scalping setups
Use the candlestick percentages to gauge potential profit per trade - higher percentages mean more movement
The algorithm uses advanced statistical analysis including standard deviation, linear regression slopes, and range efficiency to accurately detect ranging conditions
Perfect for day traders and scalpers who want to quickly identify which symbols offer the best ranging opportunities for consistent buy-low, sell-high strategies.
All Levels This script draws key price levels on your chart, including:
• Previous Day (PD): High, Low, Close
• Day Before Yesterday (DBY): High, Low, Close
• Pre-Market (PM): High and Low
• Today’s levels: High, Low, Open, Close
• Current bar levels: High, Low, Open, Close
Each level is displayed as a horizontal line with a label showing the level value.
It works on any timeframe, including 1-minute charts, and automatically updates as new bars form.
⸻
2. Features
1. Custom Colors
Each type of level has its own color, declared as a const color. For example:
• Previous Day High = red
• Today’s Close = gold
• Pre-Market High = fuchsia
2. Right-Extending Lines
All horizontal levels extend to the right, so you always see them on the chart.
3. Persistent Labels
Every line has a label at the right side showing its name and price. For example:
• PDH 422
• TODL 415.5
4. Dynamic Updates
The script updates automatically whenever a new bar forms, so levels stay accurate.
5. Session-Based Pre-Market
You can define the pre-market session (default “04:00–09:30 EST”). The script calculates the high and low of this session only.
6. Checkbox Inputs
You can enable/disable entire groups of levels:
• Previous Day
• Day Before Yesterday
• Pre-Market
• Today
• Current bar
Pairs Trading Scanner [BackQuant]Pairs Trading Scanner
What it is
This scanner analyzes the relationship between your chart symbol and a chosen pair symbol in real time. It builds a normalized “spread” between them, tracks how tightly they move together (correlation), converts the spread into a Z-Score (how far from typical it is), and then prints clear LONG / SHORT / EXIT prompts plus an at-a-glance dashboard with the numbers that matter.
Why pairs at all?
Markets co-move. When two assets are statistically related, their relationship (the spread) tends to oscillate around a mean.
Pairs trading doesn’t require calling overall market direction you trade the relative mispricing between two instruments.
This scanner gives you a robust, visual way to find those dislocations, size their significance, and structure the trade.
How it works (plain English)
Step 1 Pick a partner: Select the Pair Symbol to compare against your chart symbol. The tool fetches synchronized prices for both.
Step 2 Build a spread: Choose a Spread Method that defines “relative value” (e.g., Log Spread, Price Ratio, Return Difference, Price Difference). Each lens highlights a different flavor of divergence.
Step 3 Validate relationship: A rolling Correlation checks if the pair is moving together enough to be tradable. If correlation is weak, the scanner stands down.
Step 4 Standardize & score: The spread is normalized (mean & variability over a lookback) to form a Z-Score . Large absolute Z means “stretched,” small means “near fair.”
Step 5 Signals: When the Z-Score crosses user-defined thresholds with sufficient correlation , entries print:
LONG = long chart symbol / short pair symbol,
SHORT = short chart symbol / long pair symbol,
EXIT = mean reversion into the exit zone or correlation failure.
Core concepts (the three pillars)
Spread Method Your definition of “distance” between the two series.
Guidance:
Log Spread: Focuses on proportional differences; robust when prices live on different scales.
Price Ratio: Classic relative value; good when you care about “X per Y.”
Return Difference: Emphasizes recent performance gaps; nimble for momentum-to-mean plays.
Price Difference: Straight subtraction; intuitive for similar-scale assets (e.g., two ETFs).
Correlation A rolling score of co-movement. The scanner requires it to be above your Min Correlation before acting, so you’re not trading random divergence.
Z-Score “How abnormal is today’s spread?” Positive = chart richer than pair; negative = cheaper. Thresholds define entries/exits with transparent, statistical context.
What you’ll see on the chart
Correlation plot (blue line) with a dashed Min Correlation guide. Above the line = green zone for signals; below = hands off.
Z-Score plot (white line) with colored, dashed Entry bands and dotted Exit bands. Zero line for mean.
Normalized spread (yellow) for a quick “shape read” of recent divergence swings.
Signal markers :
LONG (green label) when Z < –Entry and corr OK,
SHORT (red label) when Z > +Entry and corr OK,
EXIT (gray label) when Z returns inside the Exit band or correlation drops below the floor.
Background tint for active state (faint green for long-spread stance, faint red for short-spread stance).
The two built-in dashboards
Statistics Table (top-right)
Pair Symbol Your chosen partner.
Correlation Live value vs. your minimum.
Z-Score How stretched the spread is now.
Current / Pair Prices Real-time anchors.
Signal State NEUTRAL / LONG / SHORT.
Price Ratio Context for ratio-style setups.
Analysis Table (bottom-right)
Avg Correlation Typical co-movement level over your window.
Max |Z| The recent extremes of dislocation.
Spread Volatility How “lively” the spread has been.
Trade Signal A human-readable prompt (e.g., “LONG A / SHORT B” or “NO TRADE” / “LOW CORRELATION”).
Risk Level LOW / MEDIUM / HIGH based on current stretch (absolute Z).
Signals logic (plain English)
Entry (LONG): The spread is unusually negative (chart cheaper vs pair) and correlation is healthy. Expect mean reversion upward in the spread: long chart, short pair.
Entry (SHORT): The spread is unusually positive (chart richer vs pair) and correlation is healthy. Expect mean reversion downward in the spread: short chart, long pair.
Exit: The spread relaxes back toward normal (inside your exit band), or correlation deteriorates (relationship no longer trusted).
A quick, repeatable workflow
1) Choose your pair in context (same sector/theme or known macro link). Think: “Do these two plausibly co-move?”
2) Pick a spread lens that matches your narrative (ratio for relative value, returns for short-term performance gaps, etc.).
3) Confirm correlation is above your floor no corr, no trade.
4) Wait for a stretch (Z beyond Entry band) and a printed LONG / SHORT .
5) Manage to the mean (EXIT band) or correlation failure; let the scanners’ state/labels keep you honest.
Settings that matter (and why)
Spread Method Defines the “mispricing” you care about.
Correlation Period Longer = steadier regime read, shorter = snappier to regime change.
Z-Score Period The window that defines “normal” for the spread; it sets the yardstick.
Use Percentage Returns Normalizes series when using return-based logic; keep on for mixed-scale assets.
Entry / Exit Thresholds Set your stretch and your target reversion zone. Wider entries = rarer but stronger signals.
Minimum Correlation The gatekeeper. Raising it favors quality over quantity.
Choosing pairs (practical cheat sheet)
Same family: two index ETFs, two oil-linked names, two gold miners, two L1 tokens.
Hedge & proxy: stock vs. sector ETF, BTC vs. BTC index, WTI vs. energy ETF.
Cross-venue or cross-listing: instruments that are functionally the same exposure but price differently intraday.
Reading the cues like a pro
Divergence shape: The yellow normalized spread helps you see rhythm fast spike and snap-back versus slow grind.
Corr-first discipline: Don’t fight the “Min Correlation” line. Good pairs trading starts with a relationship you can trust.
Exit humility: When Z re-centers, let the EXIT do its job. The edge is the journey to the mean, not overstaying it.
Frequently asked (quick answers)
“Long/Short means what exactly?”
LONG = long the chart symbol and short the pair symbol.
SHORT = short the chart symbol and long the pair symbol.
“Do I need same price scales?” No. The spread methods normalize in different ways; choose the one that fits your use case (log/ratio are great for mixed scales).
“What if correlation falls mid-trade?” The scanner will neutralize the state and print EXIT . Relationship first; trade second.
Field notes & patterns
Snap-back days: After a one-sided session, return-difference spreads often flag cleaner intraday mean reversions.
Macro rotations: Ratio spreads shine during sector re-weights (e.g., value vs. growth ETFs); look for steady corr + elevated |Z|.
Event bleed-through: If one symbol reacts to news and its partner lags, Z often flags a high-quality, short-horizon re-centering.
Display controls at a glance
Show Statistics Table Live state & key numbers, top-right.
Show Analysis Table Context/risk read, bottom-right.
Show Correlation / Spread / Z-Score Toggle the sub-charts you want visible.
Show Entry/Exit Signals Turn markers on/off as needed.
Coloring Adjust Long/Short/Neutral and correlation line colors to match your theme.
Alerts (ready to route to your workflow)
Pairs Long Entry Z falls through the long threshold with correlation above minimum.
Pairs Short Entry Z rises through the short threshold with correlation above minimum.
Pairs Trade Exit Z returns to neutral or the relationship fails your correlation floor.
Correlation Breakdown Rolling correlation crosses your minimum; relationship caution.
Final notes
The scanner is designed to keep you systematic: require relationship (correlation), quantify dislocation (Z-Score), act when stretched, stand down when it normalizes or the relationship degrades. It’s a full, visual loop for relative-value trading that stays out of your way when it should and gets loud only when the numbers line up.
Apex Edge Sentinel - Stop Loss HUDApex Edge – ATR Sentinel Stop Loss HUD
The Apex Edge – ATR Sentinel is a complete stop-loss intelligence system built as a clean, always-on HUD.
It delivers institutional-level risk guidance by calculating and displaying live ATR-based stop levels for both long and short trades at multiple risk tolerances.
Forget cluttered charts and repainting lines — Sentinel gives you a clear stop-loss reference panel that updates dynamically with every bar.
✅ Features
• Triple ATR Multipliers
User-defined (e.g. x1.5 / x2.0 / x2.5). Compare tight, medium, and wide stops instantly.
• Dual-Side SL Levels
Both Long and Short safe stop prices displayed side by side. No more guessing trend
bias.
• ATR Transparency
HUD shows ATR(length) so you always know the calculation basis. Default = 14, adjustable
to your style.
• ATR Regime Meter
Detects volatility conditions (LOW / NORMAL / HIGH) by comparing ATR to its SMA. Helps
you avoid over-tight stops in high-volatility markets.
• Tick-Aware Rounding
Stop levels auto-rounded to the instrument’s tick size (Gold = 0.10, FX = 0.0001, indices =
whole points).
Custom HUD Design
• Location: Top/Bottom, Left/Right
• Sizes: Compact / Medium / Large (desktop or mobile)
• Opacity control (25% default Apex styling)
How to Use
1. Load Sentinel on your chart.
2. Check the HUD:
• ATR(14): 2.6 → base volatility measure.
• x1.5 / x2.0 / x2.5 → instant SL levels for both long & short trades.
3. Before entering a trade → decide which multiplier matches your style (tight scalper vs wider swing).
4. Manually place your SL at the level displayed in the HUD.
Sentinel works as both:
• A pre-trade check (is ATR stop too wide for my RR?).
• A live risk compass (updated stop levels every bar).
Why Apex Sentinel?
Most ATR stop indicators clutter charts with lagging lines or repainting trails. Sentinel strips it back to what matters:
• The numbers.
• The risk levels.
• The context.
It’s a pure stop-loss HUD, designed for serious traders who want clarity, discipline, and instant reference points across any market or timeframe.
Notes
• This is a HUD-only system (no automatic SL line). Traders manually apply the SL level
shown in the panel.
• Defaults: ATR(14), multipliers 1.5 / 2.0 / 2.5. Adjust to your trading style.
• Best used on intraday pairs like XAUUSD, EURUSD, indices, but works universally.
Apex Edge Philosophy: Clean. Smart. Institutional.
No clutter. No gimmicks. Just precision tools for modern markets.
Futures Playbook: VWAP + OR + Cross-Asset TellsFutures Playbook: VWAP + OR + Cross-Asset Tells (with Trade Messages + Coach Panel)
This all-in-one futures trading toolkit combines Opening Range (OR) levels, VWAP, and cross-asset signals to help traders quickly read intraday structure, manage execution, and filter noise.
Core Features
• Opening Range (OR):
• Customizable OR window with High/Low and Midpoint.
• Automatic shading of the OR zone.
• VWAP & Bands:
• Built-in or session-anchored VWAP.
• Optional standard deviation bands for context.
• Cross-Asset Tells:
• Live reads on US 10Y yield, DXY, Crude, and Gold.
• Regime detection: rates risk, USD strength, energy softness, and real-rate easing.
• Confirmations:
• Volume vs. moving average filter.
• Cumulative delta with smoothing.
• ATR-based chop filter to avoid low-quality trends.
Trade Messages + Coach Panel
• Trade Messages (labels): Automatic on-chart prompts for OR completion, VWAP reclaim/loss, long/short setups, and EU close flows.
• Coach Panel (table): Real-time dashboard with regime context, directional bias, execution notes, risk reminders, and key levels (ORH, ORL, VWAP).
Alerts
• OR breakout (long/short with confirmations).
• VWAP reclaim or loss.
• 10Y yield crossing risk threshold.
Use Case
Designed for futures traders and scalpers who rely on VWAP + OR dynamics and need cross-asset confirmation before committing to trades. Great for structuring entries, managing risk, and filtering market noise throughout the session.
Mongoose Global Conflict Risk Index v1Overview
The Mongoose Global Conflict Risk Index v1 is a multi-asset composite indicator designed to track the early pricing of geopolitical stress and potential conflict risk across global markets. By combining signals from safe havens, volatility indices, energy markets, and emerging market equities, the index provides a normalized 0–10 score with clear bias classifications (Neutral, Caution, Elevated, High, Shock).
This tool is not predictive of headlines but captures when markets are clustering around conflict-sensitive assets before events are widely recognized.
Methodology
The indicator calculates rolling rate-of-change z-scores for eight conflict-sensitive assets:
Gold (XAUUSD) – classic safe haven
US Dollar Index (DXY) – global reserve currency flows
VIX (Equity Volatility) – S&P 500 implied volatility
OVX (Crude Oil Volatility Index) – energy stress gauge
Crude Oil (CL1!) – WTI front contract
Natural Gas (NG1!) – energy security proxy, especially Europe
EEM (Emerging Markets ETF) – global risk capital flight
FXI (China ETF) – Asia/China proxy risk
Rules:
Safe havens and vol indices trigger when z-score > threshold.
Energy triggers when z-score > threshold.
Risk assets trigger when z-score < –threshold.
Each trigger is assigned a weight, summed, normalized, and scaled 0–10.
Bias classification:
0–2: Neutral
2–4: Caution
4–6: Elevated
6–8: High
8–10: Conflict Risk-On
How to Use
Timeframes:
Daily (1D) for strategic signals and early warnings.
4H for event shocks (missiles, sanctions, sudden escalations).
Weekly (1W) for sustained trends and macro build-ups.
What to Look For:
A single trigger (for example, Gold ON) may be noise.
A cluster of 2–3 triggers across Gold, USD, VIX, and Energy often marks early stress pricing.
Elevated readings (>4) = caution; High (>6) = rotation into havens; Shock (>8) = market conviction of conflict risk.
Practical Application:
Monitor as a heatmap of global stress.
Combine with fundamental or headline tracking.
Use alert conditions at ≥4, ≥6, ≥8 for systematic monitoring.
Notes
This indicator is for informational and educational purposes only.
It is not financial advice and should be used in conjunction with other analysis methods.
