Three Bar Reversal Pattern [LuxAlgo]The Three Bar Reversal Pattern indicator identifies and highlights three bar reversal patterns on the user price chart.
The script also provides an option for incorporating various trend indicators used to filter out detected signals, allowing them to enhance their accuracy and help obtain a more comprehensive analysis.
🔶 USAGE
The script automates the detection of three-bar reversal patterns and provides a clear, visually identifiable signal for potential trend reversals.
When a reversal chart pattern is confirmed and price action aligns with the pattern, the pattern's boundaries are extended, forming levels, with the upper boundary often acting as a resistance and the lower boundary as a support.
The script allows users to filter patterns based on a specific trend direction detected by multiple trend indicators. Users can choose to view patterns that are either aligned with the detected trend or opposite to it.
Included trend indicators are: Moving Average Cloud, Supertrend, and Donchian Channels.
🔶 DETAILS
The three-bar reversal pattern is a technical analysis pattern that signals a potential reversal in the prevailing trend. The pattern consists of three consecutive bar formations:
First Bar and Second Bar: 2 consecutive of the same sentiment, representing the prevailing trend in the market.
Third Bar: Confirms the reversal by closing beyond the high or low of the first bar, signaling a potential change in market sentiment.
Various types of three-bar reversal patterns are documented. The script supports two main types:
Normal Pattern: Detects three-bar reversal patterns without requiring the third bar closing price to surpass the high (bullish pattern) or low (bearish pattern) of the first bar. It identifies basic formations signaling potential trend reversals.
Enhanced Pattern: Specifically identifies three-bar reversal patterns where the third bar closing price surpasses the high (bullish pattern) or low (bearish pattern) of the first bar. This type provides a more selective signal for stronger trend reversals.
🔶 SETTINGS
Pattern Type: Users can choose the type of 3-bar reversal patterns to detect: Normal, Enhanced, or All. "Normal" detects patterns that do not necessarily surpass the high/low of the first bar. "Enhanced" detects patterns where the third bar surpasses the high/low of the first bar. "All" detects both Normal and Enhanced patterns.
Derived Support and Resistance: Toggles the visibility of the support and resistance levels/zones.
🔹 Trend Filtering
Filtering: Allows users to filter patterns based on the trend indicators: Moving Average Cloud, Supertrend, and Donchian Channels. The "Aligned" option only detects patterns that align with the trend and conversely, the "Opposite" option detects patterns that go against the trend.
🔹 Trend Indicator Settings
Moving Average Cloud: Allows traders to choose the type of moving averages (SMA, EMA, HMA, etc.) and set the lengths for fast and slow-moving averages.
Supertrend: Options to set the ATR length and factor for Supertrend.
Donchian Channels: Option to set the length for the channel calculation.
🔶 RELATED SCRIPTS
Reversal-Candlestick-Structure .
Reversal-Signals .
Recherche dans les scripts pour "indicators"
Descriptive Backtesting Framework (DBF)As the name suggests, this is a backtesting framework made to offer full backtesting functionality to any custom indicator in a visually descriptive way.
Any trade taken will be very clear to visualize on the chart and the equity line will be updated live allowing us to use the REPLAY feature to view the strategy performing in real time.
Stops and Targets will also get draw on the chart with labels and tooltips and there will be a table on the top right corner displaying lots of descriptive metrics to measure your strategy's performance.
IF YOU DECIDE TO USE THIS FRAMEWORK, PLEASE READ **EVERYTHING** BELOW
HOW TO USE IT
Step 1 - Insert Your Strategy Indicators:
Inside this framework's code, right at the beginning, you will find a dedicated section where you can manually insert any set of indicators you desire.
Just replace the example code in there with your own strategy indicators.
Step 2 - Specify The Conditions To Take Trades:
After that, there will be another section where you need to specify your strategy's conditions to enter and exit trades.
When met, those conditions will fire the trading signals to the trading engine inside the framework.
If you don't wish to use some of the available signals, please just assign false to the signal.
DO NOT DELETE THE SIGNAL VARIABLES
Step 3 - Specify Entry/Exit Prices, Stops & Targets:
Finally you'll reach the last section where you'll be able to specify entry/exit prices as well as add stops and targets.
On most cases, it's easier and more reliable to just use the close price to enter and exit trades.
If you decide to use the open price instead, please remember to change step 2 so that trades are taken on the open price of the next candle and not the present one to avoid the look ahead bias.
Stops and targets can be set in any way you want.
Also, please don't forget to update the spread. If your broker uses commissions instead of spreads or a combination of both, you'll need to manually incorporate those costs in this step.
And that's it! That's all you have to do.
Below this section you'll now see a sign warning you about not making any changes to the code below.
From here on, the framework will take care of executing the trades and calculating the performance metrics for you and making sure all calculations are consistent.
VISUAL FEATURES:
Price candles get painted according to the current trade.
They will be blue during long trades, purple on shorts and white when no trade is on.
When the framework receives the signals to start or close a trade, it will display those signals as shapes on the upper and lower limits of the chart:
DIAMOND: represents a signal to open a trade, the trade direction is represented by the shape's color;
CROSS: means a stop loss was triggered;
FLAG: means a take profit was triggered;
CIRCLE: means an exit trade signal was fired;
Hovering the mouse over the trade labels will reveal:
Asset Quantity;
Entry/Exit Prices;
Stops & Targets;
Trade Profit;
Profit As Percentage Of Trade Volume;
**Please note that there's a limit as to how many labels can be drawn on the chart at once.**
If you which to see labels from the beginning of the chart, you'll probably need to use the replay feature.
PERFORMANCE TABLE:
The performance table displays several performance metrics to evaluate the strategy.
All the performance metrics here are calculated by the framework. It does not uses the oficial pine script strategy tester.
All metrics are calculated in real time. If using the replay feature, they will be updated up to the last played bar.
Here are the available metrics and their definition:
INITIAL EQUITY: the initial amount of money we had when the strategy started, obviously...;
CURRENT EQUITY: the amount of money we have now. If using the replay feature, it will show the current equity up to the last bar played. The number on it's right side shows how many times our equity has been multiplied from it's initial value;
TRADE COUNT: how many trades were taken;
WIN COUNT: how many of those trades were wins. The percentage at the right side is the strategy WIN RATE;
AVG GAIN PER TRADE: the average percentage gain per trade. Very small values can indicate a fragile strategy that can behave in unexpected ways under high volatility conditions;
AVG GAIN PER WIN: the average percentage gain of trades that were profitable;
AVG GAIN PER LOSS: the average percentage loss on trades that were not profitable;
EQUITY MAX DD: the maximum drawdown experienced by our equity during the entire strategy backtest;
TRADE MAX DD: the maximum drawdown experienced by our equity after one single trade;
AVG MONTHLY RETURN: the compound monthly return that our strategy was able to create during the backtested period;
AVG ANNUAL RETURN: this is the strategy's CAGR (compound annual growth rate);
ELAPSED MONTHS: number of months since the backtest started;
RISK/REWARD RATIO: shows how profitable the strategy is for the amount of risk it takes. Values above 1 are very good (and rare). This is calculated as follows: (Avg Annual Return) / mod(Equity Max DD). Where mod() is the same as math.abs();
AVAILABLE SETTINGS:
SPREAD: specify your broker's asset spread
ENABLE LONGS / SHORTS: you can keep both enable or chose to take trades in only one direction
MINIMUM BARS CLOSED: to avoid trading before indicators such as a slow moving average have had time to populate, you can manually set the number of bars to wait before allowing trades.
INITIAL EQUITY: you can specify your starting equity
EXPOSURE: is the percentage of equity you wish to risk per trade. When using stops, the strategy will automatically calculate your position size to match the exposure with the stop distance. If you are not using stops then your trade volume will be the percentage of equity specified here. 100 means you'll enter trades with all your equity and 200 means you'll use a 2x leverage.
MAX LEVERAGE ALLOWED: In some situations a short stop distance can create huge levels of leverage. If you want to limit leverage to a maximum value you can set it here.
SEVERAL PLOTTING OPTIONS: You'll be able to specify which of the framework visuals you wish to see drawn on the chart.
FRAMEWORK **LIMITATIONS**:
When stop and target are both triggered in the same candle, this framework isn't able to enter faster timeframes to check which one was triggered first, so it will take the pessimistic assumption and annul the take profit signal;
This framework doesn't support pyramiding;
This framework doesn't support both long and short positions to be active at the same time. So for example, if a short signal is received while a long trade is open, the framework will close the long trade and then open a short trade;
FINAL CONSIDERATIONS:
I've been using this framework for a good time and I find it's better to use and easier to analyze a strategy's performance then relying on the oficial pine script strategy tester. However, I CANNOT GUARANTEE IT TO BE BUG FREE.
**PLEASE PERFORM A MANUAL BACKTEST BEFORE USING ANY STRATEGY WITH REAL MONEY**
Combined IndicatorSummary
This custom Pine Script combines three main indicators into one, each with its own functionalities and visual cues. It provides a comprehensive approach to trend analysis by integrating short-term, medium-term, and long-term indicators. Each part of the indicator can be toggled on or off independently to suit the trader’s needs.
Part 1: EMA 14 and EMA 200
Purpose: This part of the indicator is designed to identify short-term and long-term trends using Exponential Moving Averages (EMA). It helps traders spot potential entry and exit points based on the relationship between short-term and long-term moving averages.
Visuals:
• EMA 14: Plotted in blue (#2962ff)
• EMA 200: Plotted in red (#f23645)
Signals:
• Long Signal: Generated when EMA 14 crosses above EMA 200, indicating a potential upward trend.
• Short Signal: Generated when EMA 14 crosses below EMA 200, indicating a potential downward trend.
Usage: Toggle this part on or off using the checkbox input to focus on short-term vs. long-term trends.
Part 2: EMA 9 and SMA 20
Purpose: This part combines Exponential and Simple Moving Averages to provide a medium-term trend analysis. It helps smooth out price data and identify potential trend reversals and continuation patterns.
Visuals:
• EMA 9: Plotted in green
• SMA 20: Plotted in dark red
Usage: Toggle this part on or off using the checkbox input to focus on medium-term trends and price smoothing.
Part 3: Golden Cross and Death Cross
Purpose: This part identifies long-term bullish and bearish market conditions using the 50-day and 200-day Simple Moving Averages (SMA). It highlights major trend changes that can inform long-term investment decisions.
Visuals:
• 50-day SMA: Plotted in gold (#ffe600)
• 200-day SMA: Plotted in black
Signals:
• Golden Cross: Generated when the 50-day SMA crosses above the 200-day SMA, indicating a potential long-term upward trend.
• Death Cross: Generated when the 50-day SMA crosses below the 200-day SMA, indicating a potential long-term downward trend.
Usage: Toggle this part on or off using the checkbox input to focus on long-term trend changes.
How to Use
1. Enable/Disable Indicators: Use the checkboxes provided in the input settings to enable or disable each part of the indicator according to your analysis needs.
2. Interpret Signals: Look for crossover events to determine potential entry and exit points based on the relationship between the moving averages.
3. Visual Confirmation: Use the color-coded lines and shape markers on the chart to visually confirm signals and trends.
4. Customize Settings: Adjust the lengths of the EMAs and SMAs in the input settings to suit your trading strategy and the specific asset you are analyzing.
Practical Application
• Short-Term Trading: Use the EMA 14 and EMA 200 signals to identify quick trend changes.
• Medium-Term Trading: Use the EMA 9 and SMA 20 to capture medium-term trends and reversals.
• Long-Term Investing: Monitor the Golden Cross and Death Cross signals to make decisions based on long-term trend changes.
Example of Unique Features
• Integrated Toggle System: Allows users to enable or disable specific parts of the indicator to customize their analysis.
• Multi-Tier Trend Analysis: Combines short-term, medium-term, and long-term indicators to provide a comprehensive view of the market.
GT 5.1 Strategy═════════════════════════════════════════════════════════════════════════
█ OVERVIEW
People often look an indicator in their technical analysis to enter a position. We may also need to look at the signals of one or more indicators to verify the signals given by some indicators. In this context, I developed a strategy to test whether it really works by choosing some of the indicators that capture trend changes with the same characteristics. Also, since the subject is to catch the trend change, I thought it would be right to include an indicator using the heikin ashi logic. By averaging and smoothing the market noise, Heiken Ashi makes it easier to detect the direction of the trend helps to see possible reversal points on the chart. However, it should be noted that Heiken Ashi is a lagging indicator.
I picked 5 different indicators (but their purpose are similar) and combined them to produce buy and sell signals based on your choice(not repaint). First of all let's get some information about our indicators. So you will understand me why i picked these indicators and what is the meaning of their signals.
1 — Coral Trend Indicator by LazyBear
Coral Trend Indicator is a linear combination of moving averages, all obtained by a triple or higher order exponential smoothing. The indicator comes with a trend indication which is based on the normalized slope of the plot. the usage of this indicator is simple. When the color of the line is green that means the market is in uptrend. But when the color is red that means the market is in downtrend.
As you see the original indicator it is simple to find is it in uptrend or downtrend.
So i added a code to find when the color of the line change. When it turns green to red my script giving sell signals, when it turns red to green it gives buy signals.
I hide the candles to show you more clearly what is happening when you choose only Coral Strategy. But sometimes it is not enough only using itself. Even if green dots turn to red it continues in uptrend. So we need a to look another indicator to approve our signal.
2 — SSL channel by ErwinBeckers
Known as the SSL , the Semaphore Signal Level channel is an indicator that combines moving averages to provide you with a clear visual signal of price movement dynamics. In short, it's designed to show you when a price trend is forming. This indicator creates a band by calculating the high and low values according to the determined period. Simply if you decide 10 as period, it calculates a 10-period moving average on the latest 10 highs. Calculate a 10-period moving average on the latest 10 lows. If the price falls below the low band, the downtrend begins, if the price closes above the high band, the uptrend begins. Lets look the original form of indicator and learn how it using.
If the red line is below and the green band is above, it means that we are in uptrend, and if it is on the opposite side, it means that we are in downtrend. Therefore, it would be logical to enter a position where the trend has changed. So i added a code to find when the crossover has occured.
As you see in my strategy, it gives you signals when the trend has changed. But sometimes it is not enough only using this indicator itself. So lets look 2 indicator together in one chart.
Look circle SSL is saying it is in downtrend but Coral is saying it has entered in uptrend. if we just look to coral signal it can misleads us. So it can be better to look another indicator for validating our signals.
3 — Heikin Ashi RSI Oscillator by JayRogers
The Heikin-Ashi technique is used by technical traders to identify a given trend more easily. Heikin-Ashi has a smoother look because it is essentially taking an average of the movement. There is a tendency with Heikin-Ashi for the candles to stay red during a downtrend and green during an uptrend, whereas normal candlesticks alternate color even if the price is moving dominantly in one direction. This indicator actually recalculates the RSI indicator with the logic of heikin ashi. Due to smoothing, the bars are formed with a slight lag, reflecting the trend rather than the exact price movement. So lets look the original version to understand more clearly. If red bars turn to green bars it means uptrend may begin, if green bars turn to red it means downtrend may begin.
As you see HARSI giving lots of signal some of them is really good but some of them are not very well. Because it gives so much signals Now i will change time period and lets look same chart again.
Now results are better because of heikin ashi's logic. it is not suitable for day traders, it gives more accurate result when using the time period is longer. But it can be useful to use this indicator in short time periods using with other indicators. So you may catch the trend changes more accurately.
4 — MACD DEMA by ToFFF
This indicator uses a double EMA and MACD algorithm to analyze the direction of the trend. Though it might seem a tough task to manage the trades with the help of MACD DEMA once you know how the proper way to interpret the signal lines, it will be an easy task.
This indicator also smoothens the signal lines with the time series algorithm which eventually makes the higher time frame important. So, expecting better results in the lower time frame can result in big losses as the data reading from the MACD DEMA will not be accurate. In order to understand the function of this indicator, you have to know the functions of the EMA also.
The exponential moving average tends to give more priority to the recent price changes. So, expecting better results when the volatility is very high is a very risky approach to trade the market. Moreover, the MACD has some lagging issues compared to the EMA, so it is super important to use a trading method that focuses on the higher time frame only. What does MACD 12 26 Close 9 mean? When the DEMA-9 crosses above the MACD(12,26), this is considered a bearish signal. It means the trend in the stock – its magnitude and/or momentum – is starting to shift course. When the MACD(12,26) crosses above the DEMA-9, this is considered a bullish signal. Lets see this indicator on Chart.
When the blue line crossover red line it is good time to buy. As you see from the chart i put arrows where the crossover are appeared.
When the red line crossover blue line it is good time to sell or exit from position.
5 — WaveTrend Oscillator by LazyBear
This is a technical indicator that creates high and low bands between two values. It then creates a trend indicator that draws waves with highs and lows within these boundaries. WaveTrend is a widely used indicator for finding direction of an asset.
Calculation period: number of candles used to calculate WaveTrend, defaults to 10. Averaging period: number of candles used to average WaveTrend, defaults to 21.
As you see in chart when the lines crossover occured my strategy gives buy or sell signals.
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█ HOW TO USE
I hope you understand how the indicators I mentioned above work and what they are used for. Now, I will explain in detail how to use the strategy I have created.
When you enter the settings section, you will see 5 types of indicators. If you want to use the signals of the indicators, simply tick the box next to the indicators. Also, under each option there is an area where you can set the "lookback". This setting is a field that will make the signals overlap when you select more than one option. If you are going to trade with only one option, you should make sure that this field is 0. Otherwise, it may continue to generate as many signals as you choose.
Lets see in chart for easy understanding.
As you see chart, if i chose only HARSI with lookback 0 (HARSI and CORAL should be 1 minumum because of algorithm-we looking 1 bar before, others 0 because we are looking crossovers), it will give signals only when harsı bar's color changed. But when i changed Lookback as 7 it will be like this in chart.
Now i will choose 2 indicator with settings of their lookback 0.
As you see it will give signals when both of them occurs same time. But HARSI is an indicator giving very early signal so we can enter position 5-6 bars after the first bar color change. So i will change HARSI Lookback settings as 7. Lets look what happens when we use lookback option.
So it wil be useful to change lookback settings to find best signals in each time period and in each symbol. But it shouldnt be too high. Because you can be late to catch trend's starting.
this is an image of MACD and WAVE trend used and lookback option are both 6.
Now lets see an example with 3 options are chosen with lookback option 11-1-5
Now lets talk about indicators settings. After strategy options you will see each indicators settings, you can change their settings as you desired. So each indicators signal will be changed according to your adjustment.
I left strategy options with default settings. You can change it manually as if you want.
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█ LIMITATIONS: Don't rely on non-standard charts results. For example Heikin Ashi is a technical analysis method used with the traditional candlestick chart.Heikin Ashi vs. Candlestick Chart: The decisive visual difference between Heikin Ashi and the traditional chart is that Heikin Ashi flattens the traditional candlestick chart using a modified formula.
The primary advantage of Heikin Ashi is that it makes the chart more reader-friendly and helps users identify and analyze trends .
Because Heikin Ashi provides averaged price information rather than real-time price and reacts slowly to volatility — not suitable for scalpers and high-frequency traders. I added HARSI indicator as a supportive signal because it is useful with using CORAL and SSL channel indicators. If you change your candle types to Heikin Ashi , your profit will change in good way but dont rely on it.
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█ THANKS:
Special thanks to authors of the scripts that i used.
@LazyBear and @ErwinBeckers and @JayRogers and @ToFFF
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█ DISCLAIMER
Any trade decisions you make are entirely your own responsibility.
Accumulation/Distribution Level (ADL) [Cyrus c|:D]This indicator shows Accumulation/Distribution level which can be used for confirming trends or reversals (via divergence). It is an alternative to Chaikin's Accumulation/Distribution Line (ADL) and On Balance Volume (OBV) indicators. It can also replicate PVT and OBV via options in the input menu.
Here is a comparison of four related indicators:
OBV is too simple and has serious flaws as explained in PVT's wiki.
Chaikin's ADL is a broken indicator as can be seen in the chart below:
A/D Level addresses the flaws in these two indicators. It simply sums up portions of the volume that contributes to price change. These portions are visualized in dark green and red on "Accumulation/Distribution Volume (ADV)" indicator. This can also be achieved by ADV indicator if you are nerd enough.
PS: There is Williams A/D as well which is also a broken indicator.
indicator CalibrationIndicator Calibration - Multi-Indicator Consensus System
Overview
Indicator Calibration is a powerful consensus-based trading indicator that leverages the MyIndicatorLibrary (NormalizedIndicators) to combine multiple trend-following indicators into a single, actionable signal. By averaging the normalized outputs of up to 8 different trend indicators, this tool provides traders with a clear consensus view of market direction, reducing noise and false signals inherent in single-indicator approaches.
The indicator outputs a value between -1 (strong bearish) and +1 (strong bullish), with 0 representing a neutral market state. This creates an intuitive, easy-to-read oscillator that synthesizes multiple analytical perspectives into one coherent signal.
🎯 Core Concept
Consensus Trading Philosophy
Rather than relying on a single indicator that may give conflicting or premature signals, Indicator Calibration employs a democratic voting system where multiple indicators contribute their normalized opinion:
Each enabled indicator votes: +1 (bullish), -1 (bearish), or 0 (neutral)
The votes are averaged to create a consensus signal
Strong consensus (closer to ±1) indicates high agreement among indicators
Weak consensus (closer to 0) indicates market indecision or transition
Key Benefits
Reduced False Signals: Multiple indicators must agree before strong signals appear
Noise Filtering: Individual indicator quirks are smoothed out by averaging
Customizable: Enable/disable indicators and adjust parameters to suit your trading style
Universal Application: Works across all timeframes and asset classes
Clear Visualization: Simple line oscillator with clear bull/bear zones
📊 Included Indicators
The system can utilize up to 8 normalized trend-following indicators from the library:
1. BBPct - Bollinger Bands Percent
Parameters: Length (default: 20), Factor (default: 2)
Type: Stationary oscillator
Strength: Mean reversion and volatility detection
2. NorosTrendRibbonEMA
Parameters: Length (default: 20)
Type: Non-stationary trend follower
Strength: Breakout detection with momentum confirmation
3. RSI - Relative Strength Index
Parameters: Length (default: 9), SMA Length (default: 4)
Type: Stationary momentum oscillator
Strength: Overbought/oversold with smoothing
4. Vidya - Variable Index Dynamic Average
Parameters: Length (default: 30), History Length (default: 9)
Type: Adaptive moving average
Strength: Volatility-adjusted trend following
5. HullSuite
Parameters: Length (default: 55), Multiplier (default: 1)
Type: Fast-response moving average
Strength: Low-lag trend identification
6. TrendContinuation
Parameters: MA Length 1 (default: 50), MA Length 2 (default: 25)
Type: Dual HMA system
Strength: Trend quality assessment with neutral states
7. LeonidasTrendFollowingSystem
Parameters: Short Length (default: 21), Key Length (default: 10)
Type: Dual EMA crossover
Strength: Simple, reliable trend tracking
8. TRAMA - Trend Regularity Adaptive Moving Average
Parameters: Length (default: 50)
Type: Adaptive trend follower
Strength: Adjusts to trend stability
⚙️ Input Parameters
Source Settings
Source: Choose your price input (default: close)
Can be modified to: open, high, low, close, hl2, hlc3, ohlc4, hlcc4
Indicator Selection
Each indicator can be enabled or disabled via checkboxes:
use_bbpct: Enable/disable Bollinger Bands Percent
use_noros: Enable/disable Noro's Trend Ribbon
use_rsi: Enable/disable RSI
use_vidya: Enable/disable VIDYA
use_hull: Enable/disable Hull Suite
use_trendcon: Enable/disable Trend Continuation
use_leonidas: Enable/disable Leonidas System
use_trama: Enable/disable TRAMA
Parameter Customization
Each indicator has its own parameter group where you can fine-tune:
val 1: Primary period/length parameter
val 2: Secondary parameter (multiplier, smoothing, etc.)
📈 Signal Interpretation
Output Line (Orange)
The main output oscillates between -1 and +1:
+1.0 to +0.5: Strong bullish consensus (all or most indicators agree on uptrend)
+0.5 to +0.2: Moderate bullish bias (bullish indicators outnumber bearish)
+0.2 to -0.2: Neutral zone (mixed signals or transition phase)
-0.2 to -0.5: Moderate bearish bias (bearish indicators outnumber bullish)
-0.5 to -1.0: Strong bearish consensus (all or most indicators agree on downtrend)
Reference Lines
Green line (+1): Maximum bullish consensus
Red line (-1): Maximum bearish consensus
Gray line (0): Neutral midpoint
💡 Trading Strategies
Strategy 1: Consensus Threshold Trading
Entry Rules:
- Long: Output crosses above +0.5 (strong bullish consensus)
- Short: Output crosses below -0.5 (strong bearish consensus)
Exit Rules:
- Exit Long: Output crosses below 0 (consensus lost)
- Exit Short: Output crosses above 0 (consensus lost)
Strategy 2: Zero-Line Crossover
Entry Rules:
- Long: Output crosses above 0 (bullish shift in consensus)
- Short: Output crosses below 0 (bearish shift in consensus)
Exit Rules:
- Exit on opposite crossover
Strategy 3: Divergence Trading
Look for divergences between:
- Price making higher highs while indicator makes lower highs (bearish divergence)
- Price making lower lows while indicator makes higher lows (bullish divergence)
Strategy 4: Extreme Reading Reversal
Entry Rules:
- Long: Output reaches -0.8 or below (extreme bearish consensus = potential reversal)
- Short: Output reaches +0.8 or above (extreme bullish consensus = potential reversal)
Use with caution - best combined with other reversal signals
🔧 Optimization Tips
For Trending Markets
Enable trend-following indicators: Noro's, VIDYA, Hull Suite, Leonidas
Use higher threshold levels (±0.6) to filter out minor retracements
Increase indicator periods for smoother signals
For Range-Bound Markets
Enable oscillators: BBPct, RSI
Use zero-line crossovers for entries
Decrease indicator periods for faster response
For Volatile Markets
Enable adaptive indicators: VIDYA, TRAMA
Use wider threshold levels to avoid whipsaws
Consider disabling fast indicators that may overreact
Custom Calibration Process
Start with all indicators enabled using default parameters
Backtest on your chosen timeframe and asset
Identify which indicators produce the most false signals
Disable or adjust parameters for problematic indicators
Test different threshold levels for entry/exit
Validate on out-of-sample data
📊 Visual Guide
Color Scheme
Orange Line: Main consensus output
Green Horizontal: Bullish extreme (+1)
Red Horizontal: Bearish extreme (-1)
Gray Horizontal: Neutral zone (0)
Reading the Chart
Line above 0: Net bullish sentiment
Line below 0: Net bearish sentiment
Line near extremes: Strong consensus
Line fluctuating near 0: Indecision or transition
Smooth line movement: Stable consensus
Erratic line movement: Conflicting signals
⚠️ Important Considerations
Lag Characteristics
This is a lagging indicator by design (consensus takes time to form)
Best used for trend confirmation rather than early entry
May miss the first portion of strong moves
Reduces false entries at the cost of delayed entries
Number of Active Indicators
More indicators = smoother but slower signals
Fewer indicators = faster but potentially noisier signals
Minimum recommended: 4 indicators for reliable consensus
Optimal: 6-8 indicators for balanced performance
Market Conditions
Best: Strong trending markets (up or down)
Good: Volatile markets with clear directional moves
Poor: Choppy, sideways markets with no clear trend
Worst: Low-volume, range-bound conditions
Complementary Tools
Consider combining with:
Volume analysis for confirmation
Support/resistance levels for entry/exit points
Market structure analysis (higher timeframe trends)
Risk management tools (ATR-based stops)
🎓 Example Use Cases
Swing Trading
Timeframe: Daily or 4H
Enable: All 8 indicators with default parameters
Entry: Consensus > +0.5 or < -0.5
Hold: Until consensus reverses to opposite extreme
Day Trading
Timeframe: 15m or 1H
Enable: Faster indicators (RSI, BBPct, Noro's, Hull Suite)
Entry: Zero-line crossover with volume confirmation
Exit: Opposite crossover or profit target
Position Trading
Timeframe: Weekly or Daily
Enable: Slower indicators (TRAMA, VIDYA, Trend Continuation)
Entry: Strong consensus (±0.7) with higher timeframe confirmation
Hold: Months until consensus weakens significantly
🔬 Technical Details
Calculation Method
1. Each enabled indicator calculates its normalized signal (-1, 0, or +1)
2. All active signals are stored in an array
3. Array.avg() computes the arithmetic mean
4. Result is plotted as a continuous line
Output Range
Theoretical: -1.0 to +1.0
Practical: Typically ranges between -0.8 to +0.8
Rare: All indicators perfectly aligned at ±1.0
Performance
Lightweight calculation (simple averaging)
No repainting (all indicators are non-repainting)
Compatible with all Pine Script features
Works on all TradingView plans
📋 License
This code is subject to the Mozilla Public License 2.0 at mozilla.org
🚀 Quick Start Guide
Add to Chart: Apply indicator to your chart
Choose Timeframe: Select appropriate timeframe for your trading style
Enable Indicators: Start with all 8 enabled
Observe Behavior: Watch how consensus forms during different market conditions
Calibrate: Adjust parameters and indicator selection based on observations
Backtest: Validate your settings on historical data
Trade: Apply with proper risk management
🎯 Key Takeaways
✅ Consensus beats individual indicators - Multiple perspectives reduce errors
✅ Customizable to your style - Enable/disable and tune to preference
✅ Simple interpretation - One line tells the story
✅ Works across markets - Stocks, crypto, forex, commodities
✅ Reduces emotional trading - Clear, objective signal generation
✅ Professional-grade - Built on proven technical analysis principles
Indicator Calibration transforms complex multi-indicator analysis into a single, actionable signal. By harnessing the collective wisdom of multiple proven trend-following systems, traders gain a powerful edge in identifying high-probability trade setups while filtering out market noise.
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).
References
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Diodato 'All Stars Align' SignalDescription:
This indicator is an overlay that plots the "All Stars Align" buy signal from Chris Diodato's 2019 CMT paper, "Making The Most Of Panic." It is designed to identify high-conviction, short-term buying opportunities by requiring a confluence of both price-based momentum and market-internal weakness.
What It Is
This script works entirely in the background, calculating three separate indicators: the 14-day Slow Stochastic, the Short-Term Capitulation Oscillator (STCO), and the 3-DMA of % Declining Issues. It then plots a signal directly on the main price chart only when the specific "All Stars Align" conditions are met.
How to Interpret
A green cross (+) appears below a price bar when a high-conviction buy signal is generated. This signal triggers only when two primary conditions are true:
The 14-day Slow Stochastic is in "oversold" territory (e.g., below 20).
AND at least one of the market internal indicators shows a state of panic:
Either the STCO is oversold (e.g., below 140).
Or the 3-DMA % Declines shows a panic spike (e.g., above 65).
This confluence signifies a potential exhaustion of sellers and can mark an opportune moment to look for entries.
Settings
Trigger Thresholds: You can customize the exact levels that define an "oversold" or "panic" state for each of the three underlying indicators.
Data Sources: Allows toggling the use of "Unchanged" data for the background calculations.
Stochastic Settings: You can adjust the parameters for the Slow Stochastic calculation.
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.
Liquidity Sweep with EMAThis Pine Script indicator helps traders identify potential market reversals based on liquidity sweeps, where the price moves through the previous candle's low or high and then closes above or below the previous candle's wick. These are often seen as significant market moves or liquidity grabs before a potential reversal or continuation.
The indicator is also equipped with an EMA (Exponential Moving Average) as an optional visual aid to give traders a sense of the prevailing trend, though it is not used as part of the signal generation logic.
Key Features:
Liquidity Sweep Detection:
Bullish Sweep: Triggered when the current candle sweeps below the low of the previous candle and then closes above the high of the previous candle. This indicates a potential market reversal to the upside after the liquidity sweep.
Bearish Sweep: Triggered when the current candle sweeps above the high of the previous candle and then closes below the low of the previous candle. This indicates a potential market reversal to the downside after the liquidity sweep.
EMA:
The EMA (50) is plotted on the chart for visual trend guidance. While it is not used to confirm the signals, it can help traders see if the market is in a general uptrend or downtrend.
Signal Presentation:
Buy Signal: The indicator will plot a green upward arrow below the candle when a bullish liquidity sweep is detected.
Sell Signal: The indicator will plot a red downward arrow above the candle when a bearish liquidity sweep is detected.
Timeframe Filter:
The indicator only generates signals on the following timeframes: 30-minute, 1-hour, 4-hour, and Daily. This helps to ensure the sweeps are significant and likely to result in meaningful price moves.
Alerts:
Alerts can be set up for both bullish and bearish sweep signals, so traders can be notified when these events occur.
Customizable:
EMA Length: The length of the Exponential Moving Average (EMA) can be adjusted. By default, it is set to 50, but you can modify this to fit your trading strategy.
Show EMA Option: You can toggle whether or not to display the EMA line on the chart.
How It Works:
The indicator looks for price action patterns where the current candle sweeps through the high or low of the previous candle and closes beyond the previous wick.
These patterns are often seen as potential traps, where the price initially moves in one direction (sweeping the liquidity) and then quickly reverses, making them important for traders who want to catch reversals or breakouts after a liquidity sweep.
The EMA (50) gives a general trend direction but doesn't directly affect the trade signals. It serves as a visual reference for trend analysis.
Potential Use Cases:
Reversal Trading: Traders can use this indicator to catch reversals after a liquidity sweep. The green upward arrows may indicate a bullish reversal, while the red downward arrows may indicate a bearish reversal.
Trend Trading: The EMA can help traders gauge the overall market trend. If the price is above the EMA, the market may be in an uptrend, and traders may focus on bullish sweeps. Conversely, if the price is below the EMA, the market may be in a downtrend, and traders may focus on bearish sweeps.
Confirmation with Other Indicators: Although the EMA is not used to confirm signals in this script, it can be combined with other indicators (like RSI, Volume, or MACD) to enhance the accuracy of your trades.
Final Thoughts:
This script is designed to identify liquidity sweeps and price reversals based on price action alone, without relying on complex indicators. The optional EMA serves as a helpful tool for understanding the overall market trend. It’s ideal for traders looking to spot potential reversal points after significant price sweeps and is suitable for multiple timeframes (30m, 1h, 4h, Daily).
You can use this description to help potential users understand the functionality of your indicator when publishing it on TradingView or selling it as an invite-only script. Let me know if you need any adjustments or further details!
N Bar Reversal Detector [LuxAlgo]The N Bar Reversal Detector is designed to detect and highlight N-bar reversal patterns in user charts, where N represents the length of the candle sequence used to detect the patterns. The script incorporates various trend indicators to filter out detected signals and offers a range of customizable settings to fit different trading strategies.
🔶 USAGE
The N-bar reversal pattern extends the popular 3-bar reversal pattern. While the 3-bar reversal pattern involves identifying a sequence of three bars signaling a potential trend reversal, the N-bar reversal pattern builds on this concept by incorporating additional bars based on user settings. This provides a more comprehensive indication of potential trend reversals. The script automates the identification of these patterns and generates clear, visually distinct signals to highlight potential trend changes.
When a reversal chart pattern is confirmed and aligns with the price action, the pattern's boundaries are extended to create levels. The upper boundary serves as resistance, while the lower boundary acts as support.
The script allows users to filter patterns based on the trend direction identified by various trend indicators. Users can choose to view patterns that align with the detected trend or those that are contrary to it.
🔶 DETAILS
🔹 The N-bar Reversal Pattern
The N-bar reversal pattern is a technical analysis tool designed to signal potential trend reversals in the market. It consists of N consecutive bars, with the first N-1 bars used to identify the prevailing trend and the Nth bar confirming the reversal. Here’s a detailed look at the pattern:
Bullish Reversal : In a bullish reversal setup, the first bar is the highest among the first N-1 bars, indicating a prevailing downtrend. Most of the remaining bars in this sequence should be bearish (closing lower than where they opened), reinforcing the existing downward momentum. The Nth (most recent) bar confirms a bullish reversal if its high price is higher than the high of the first bar in the sequence (standard pattern). For a stronger signal, the closing price of the Nth bar should also be higher than the high of the first bar.
Bearish Reversal : In a bearish reversal setup, the first bar is the lowest among the first N-1 bars, indicating a prevailing uptrend. Most of the remaining bars in this sequence should be bullish (closing higher than where they opened), reinforcing the existing upward momentum. The Nth bar confirms a bearish reversal if its low price is lower than the low of the first bar in the sequence (standard pattern). For a stronger signal, the closing price of the Nth bar should also be lower than the low of the first bar.
🔹 Min Percentage of Required Candles
This parameter specifies the minimum percentage of candles that must be bullish (for a bearish reversal) or bearish (for a bullish reversal) among the first N-1 candles in a pattern. For higher values of N, it becomes more challenging for all of the first N-1 candles to be consistently bullish or bearish. By setting a percentage value, P, users can adjust the requirement so that only a minimum of P percent of the first N-1 candles need to meet the bullish or bearish condition. This allows for greater flexibility in pattern recognition, accommodating variations in market conditions.
🔶 SETTINGS
Pattern Type: Users can choose the type of the N-bar reversal patterns to detect: Normal, Enhanced, or All. "Normal" detects patterns that do not necessarily surpass the high/low of the first bar. "Enhanced" detects patterns where the last bar surpasses the high/low of the first bar. "All" detects both Normal and Enhanced patterns.
Reversal Pattern Sequence Length: Specifies the number of candles (N) in the sequence used to identify a reversal pattern.
Min Percentage of Required Candles: Sets the minimum percentage of the first N-1 candles that must be bullish (for a bearish reversal) or bearish (for a bullish reversal) to qualify as a valid reversal pattern.
Derived Support and Resistance: Toggles the visibility of the support and resistance levels/zones.
🔹 Trend Filtering
Filtering: Allows users to filter patterns based on the trend indicators: Moving Average Cloud, Supertrend, and Donchian Channels. The "Aligned" option only detects patterns that align with the trend and conversely, the "Opposite" option detects patterns that go against the trend.
🔹 Trend Indicator Settings
Moving Average Cloud: Allows traders to choose the type of moving averages (SMA, EMA, HMA, etc.) and set the lengths for fast and slow moving averages.
Supertrend: Options to set the ATR length and factor for Supertrend.
Donchian Channels: Option to set the length for the channel calculation.
🔶 RELATED SCRIPTS
Reversal-Candlestick-Structure.
Reversal-Signals.
MACD of Relative Strenght StrategyMACD Relative Strenght Strategy :
INTRODUCTION :
This strategy is based on two well-known indicators: MACD and Relative Strenght (RS). By coupling them, we obtain powerful buy signals. In fact, the special feature of this strategy is that it creates an indicator from an indicator. Thus, we construct a MACD whose source is the value of the RS. The strategy only takes buy signals, ignoring SHORT signals as they are mostly losers. There's also a money management method enabling us to reinvest part of the profits or reduce the size of orders in the event of substantial losses.
RELATIVE STRENGHT :
RS is an indicator that measures the anomaly between momentum and the assumption of market efficiency. It is used by professionals and is one of the most robust indicators. The idea is to own assets that do better than average, based on their past performance. We calculate RS using this formula :
RS = close/highest_high(RS_Length)
Where highest_high(RS_Length) = highest value of the high over a user-defined time period (which is the RS_Length).
We can thus situate the current price in relation to its highest price over this user-defined period.
MACD (Moving Average Convergence - Divergence) :
This is one of the best-known indicators, measuring the distance between two exponential moving averages : one fast and one slower. A wide distance indicates fast momentum and vice versa. We'll plot the value of this distance and call this line macdline. The MACD uses a third moving average with a lower period than the first two. This last moving average will give a signal when it crosses the macdline. It is therefore constructed using the values of the macdline as its source.
It's important to note that the first two MAs are constructed using RS values as their source. So we've just built an indicator of an indicator. This kind of method is very powerful because it is rarely used and brings value to the strategy.
PARAMETERS :
RS Length : Relative Strength length i.e. the number of candles back to find the highest high and compare the current price with this high. Default is 300.
MACD Fast Length : Relative Strength fast EMA length used to plot the MACD. Default is 14.
MACD Slow Length : Relative Strength slow EMA length used to plot the MACD. Default is 26.
MACD Signal Smoothing : Macdline SMA length used to plot the MACD. Default is 10.
Max risk per trade (in %) : The maximum loss a trade can incur (in percentage of the trade value). Default is 8%.
Fixed Ratio : This is the amount of gain or loss at which the order quantity is changed. Default is 400, meaning that for each $400 gain or loss, the order size is increased or decreased by a user-selected amount.
Increasing Order Amount : This is the amount to be added to or subtracted from orders when the fixed ratio is reached. The default is $200, which means that for every $400 gain, $200 is reinvested in the strategy. On the other hand, for every $400 loss, the order size is reduced by $200.
Initial capital : $1000
Fees : Interactive Broker fees apply to this strategy. They are set at 0.18% of the trade value.
Slippage : 3 ticks or $0.03 per trade. Corresponds to the latency time between the moment the signal is received and the moment the order is executed by the broker.
Important : A bot has been used to test the different parameters and determine which ones maximize return while limiting drawdown. This strategy is the most optimal on BITSTAMP:ETHUSD in 8h timeframe with the parameters set by default.
ENTER RULES :
The entry rules are very simple : we open a long position when the MACD value turns positive. You are therefore LONG when the MACD is green.
EXIT RULES :
We exit a position (whether losing or winning) when the MACD becomes negative, i.e. turns red.
RISK MANAGEMENT :
This strategy can incur losses, so it's important to manage our risks well. If the position is losing and has incurred a loss of -8%, our stop loss is activated to limit losses.
MONEY MANAGEMENT :
The fixed ratio method was used to manage our gains and losses. For each gain of an amount equal to the value of the fixed ratio, we increase the order size by a value defined by the user in the "Increasing order amount" parameter. Similarly, each time we lose an amount equal to the value of the fixed ratio, we decrease the order size by the same user-defined value. This strategy increases both performance and drawdown.
Enjoy the strategy and don't forget to take the trade :)
Enhanced Parabolic SAR + EMA 200 + MACD SignalsParabolic SAR + EMA 200 + MACD Signals Indicator is a popular technical analysis tool used by traders to identify potential entry and exit points in the market. It combines three widely used indicators: Parabolic SAR, EMA 200, and MACD.
The Parabolic SAR indicator helps determine potential price reversals. It places dots above or below the price chart to indicate the direction of the trend. When the dots are below the price, it suggests an upward trend, and when they are above the price, it indicates a downward trend.
The EMA 200 (Exponential Moving Average 200) is a moving average that gives more weight to recent price data. It is often used as a significant support or resistance level. Traders consider the price to be in an uptrend if it is above the EMA 200 and in a downtrend if it is below the EMA 200.
The MACD (Moving Average Convergence Divergence) is a trend-following momentum indicator that calculates the difference between two exponential moving averages. It consists of a MACD line and a signal line. When the MACD line crosses above the signal line, it generates a bullish signal, indicating a potential buying opportunity. Conversely, when the MACD line crosses below the signal line, it generates a bearish signal, suggesting a potential selling opportunity.
To use the MACD-Parabolic SAR-EMA200 Indicator for trading, you can follow these guidelines:
Buy conditions:
1. The price should be above the EMA 200.
2. The Parabolic SAR should indicate an upward trend (dots below the price).
3. The MACD delta (the difference between the MACD line and the signal line) should be positive.
Sell conditions:
1. The price should be below the EMA 200.
2. The Parabolic SAR should indicate a downward trend (dots above the price).
3. The MACD delta should be negative.
By combining these three indicators, traders can gain additional confirmation of the overall trend direction and make more informed trading decisions. However, it's important to note that no indicator guarantees successful trades, and it's always advisable to use additional analysis and risk management techniques in conjunction with technical indicators.
Trend Correlation HeatmapHello everyone!
I am excited to release my trend correlation heatmap, or trend heatmap for short.
Per usual, I think its important to explain the theory before we get into the use of the indicator, so let's get into the theory!
The theory:
So what is a correlation?
Correlation is the relationship one variable has to another. Correlations are the basis of everything I do as a quantitative trader. From the correlation between the same variables (i.e. autocorrelation), the correlation between other variables (i.e. VIX and SPY, SPY High and SPY Low, DXY and ES1! close, etc.) and, as well, the correlation between price and time (time series correlation).
This may sound very familiar to you, especially if you are a user, observer or follower of my ideas and/or indicators. Ninety-five percent of my indicators are a function of one of those three things. Whether it be a time series based indicator (i.e.my time series indicator), whether it be autocorrelation (my autoregressive cloud indicator or my autocorrelation oscillator) or whether it be regressive in nature (i.e. my SPY Volume weighted close, or even my expected move which uses averages in lieu of regressive approaches but is foundational in regression principles. Or even my VIX oscillator which relies on the premise of correlations between tickers.) So correlation is extremely important to me and while its true I am more of a regression trader than anything, I would argue that I am more of a correlation trader, because correlations are the backbone of how I develop math models of stocks.
What I am trying to stress here is the importance of correlations. They really truly are foundational to any type of quantitative analysis for stocks. And as such, understanding the current relationship a stock has to time is pivotal for any meaningful analysis to be conducted.
So what is correlation to time and what does it tell us?
Correlation to time, otherwise known and commonly referred to as "Time Series", is the relationship a ticker's price has to the passing of time. It is displayed in the traditional Pearson Correlation Coefficient or R value and can be any value from -1 (strong negative relationship, i.e. a strong downtrend) to + 1 (i.e. a strong positive relationship, i.e. a strong uptrend). The higher or lower the value the stronger the up or downtrend is.
As such, correlation to time tells us two very important things. These are:
a) The direction of the stock; and
b) The strength of the trend.
Let's take a look at an example:
Above we have a chart of QQQ. We can see a trendline that seems to fit well. The questions we ask as traders are:
1. What is the likelihood QQQ breaks down from this trendline?
2. What is the likelihood QQQ continues up?
3. What is the likelihood QQQ does a false breakdown?
There are numerous mathematical approaches we can take to answer these questions. For example, 1 and 2 can be answered by use of a Cumulative Distribution Density analysis (CDDA) or even a linear or loglinear regression analysis and 3 can be answered, more or less, with a linear regression analysis and standard error ascertainment, or even just a general comparison using a data science approach (such as cosine similarity or Manhattan distance).
But, the reality is, all 3 of these questions can be visualized, at least in some way, by simply looking at the correlation to time. Let's look at this chart again, this time with the correlation heatmap applied:
If we look at the indicator we can see some pivotal things. These are:
1. We have 4, very strong uptrends that span both higher AND lower timeframes. We have a strong uptrend of 0.96 on the 5 minute, 50 candle period. We have a strong uptrend at the 300 candle lookback period on the 1 minute, we have a strong uptrend on the 100 day lookback on the daily timeframe period and we have a strong uptrend on the 5 minute on the 500 candle lookback period.
2. By comparison, we have 3 downtrends, all of which have correlations less than the 4 uptrends. All of the downtrends have a correlation above -0.8 (which we would want lower than -0.8 to be very strong), and all of the uptrends are greater than + 0.80.
3. We can also see that the uptrends are not confined to the smaller timeframes. We have multiple uptrends on multiple timeframes and both short term (50 to 100 candles) and long term (up to 500 candles).
4. The overall trend is strengthening to the upside manifested by a positive Max Change and a Positive Min change (to be discussed later more in-depth).
With this, we can see that QQQ is actually very strong and likely will continue at least some upside. If we let this play out:
We continued up, had one test and then bounced.
Now, I want to specify, this indicator is not a panacea for all trading. And in relation to the 3 questions posed, they are best answered, at least quantitatively, not only by correlation but also by the aforementioned methods (CDDA, etc.) but correlation will help you get a feel for the strength or weakness present with a stock.
What are some tangible applications of the indicator?
For me, this indicator is used in many ways. Let me outline some ways I generally apply this indicator in my day and swing trading:
1. Gauging the strength of the stock: The indictor tells you the most prevalent behavior of the stock. Are there more downtrends than uptrends present? Are the downtrends present on the larger timeframes vs uptrends on the shorter indicating a possible bullish reversal? or vice versa? Are the trends strengthening or weakening? All of these things can be visualized with the indicator.
2. Setting parameters for other indicators: If you trade EMAs or SMAs, you may have a "one size fits all" approach. However, its actually better to adjust your EMA or SMA length to the actual trend itself. Take a look at this:
This is QQQ on the 1 hour with the 200 EMA with 200 standard deviation bands added. If we look at the heatmap, we can see, yes indeed 200 has a fairly strong uptrend correlation of 0.70. But the strongest hourly uptrend is actually at 400 candles, with a correlation of 0.91. So what happens if we change the EMA length and standard deviation to 400? This:
The exact areas are circled and colour coded. You can see, the 400 offers more of a better reference point of supports and resistances as well as a better overall trend fit. And this is why I never advocate for getting married to a specific EMA. If you are an EMA 200 lover or 21 or 51, know that these are not always the best depending on the trend and situation.
Components of the indicator:
Ah okay, now for the boring stuff. Let's go over the functionality of the indicator. I tried to keep it simple, so it is pretty straight forward. If we open the menu here are our options:
We have the ability to toggle whichever timeframes we want. We also have the ability to toggle on or off the legend that displays the colour codes and the Max and Min highest change.
Max and Min highest change: The max and min highest change simply display the change in correlation over the previous 14 candles. An increasing Max change means that the Max trend is strengthening. If we see an increasing Max change and an increasing Min change (the Min correlation is moving up), this means the stock is bullish. Why? Because the min (i.e. ideally a big negative number) is going up closer to the positives. Therefore, the downtrend is weakening.
If we see both the Max and Min declining (red), that means the uptrend is weakening and downtrend is strengthening. Here are some examples:
Final Thoughts:
And that is the indicator and the theory behind the indicator.
In a nutshell, to summarize, the indicator simply tracks the correlation of a ticker to time on multiple timeframes. This will allow you to make judgements about strength, sentiment and also help you adjust which tools and timeframes you are using to perform your analyses.
As well, to make the indicator more user friendly, I tried to make the colours distinctively different. I was going to do different shades but it was a little difficult to visualize. As such, I have included a toggle-able legend with a breakdown of the colour codes!
That's it my friends, I hope you find it useful!
Safe trades and leave your questions, comments and feedback below!
MA, MATR, ChEx | All in One - 4CR CUPIn trade position setup, we always need to determine the market structure and manage the position sizing in a short period of decision time. Indicators such as moving average, initial stop loss and trailing stop loss are always helpful.
This indicator put all these handy tools into a single toolkit, which includes the following price action and risk management indicators:
MA - Moving Average
MATR - Moving Average less Average True Range
ChEx - Chandelier Exit
This script further enhances the setting so that you can easily customize the indicators.
For both the Moving Averages and the Moving Average less Average True Range , you can pick a type of moving average which suits your analysis style from a list of commonly used moving average formulations: namely, EMA , HMA , RMA, SMA and WMA , where EMA is selected as default.
The Moving Average less Average True Range , MATR, is usually applied as a reference to set the initial stop loss whenever opening a new position.
The abbreviation, MATR, is picked, so that this can serve as a handy reminder of a very good trading framework as elaborates as below:
M – Market Structure
A – Area of Value
T – Trigger
R – Risk Management (aka. Exit Strategy)
BTC World Price: Multi-Exchange VWAPBTC World Price: Multi-Exchange VWAP
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WHAT IT DOES
What you see above are not Bitmex candles, but this indicator's.
Bitcoin is listed on multiple exchanges. Many people have called for a single global index that would quote BTC price and volume across all exchanges: this script is such a virtual aggregate (formerly: Multi-Listed , Volume-Weighted Average Price ).
It will, independently for each tick, for any time-frame:
- Quote the price (O, H, L, C) and volume from Bitfinex (USD), Binance (USDT), bitFlyer (Yen), Bithumb (S. Korean Won), Coinbase (USD), Kraken (EUR) and even Bitmex (USD Contracts).
- Weight each price with the corresponding volume of the exchange.
- Quote the FOREX conversion rate in USD for each currency (USDJPY etc.)
- Finally return global average price (candles) in USD.
- Additionally provide (H+L)/2 etc. values.
No more "on Coinbase this" or "on Bitstamp that", you've now got a global overview!
See CoinMarketCap: Markets for reference. I've included alternative exchanges in the comments at the top of the script.
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HOW TO USE IT
Basically just add it to your chart and use the indicator's candles instead of the chart's main ticker.
By default, BTC World Price will display candles only, but you can also display OHLC & averages (in whichever style you want).
You may indeed want to hide the main symbol (top-left corner, click the 'eye' button next to its name), or switch it to something else than candles/bars (e.g. line).
Make sure "Scale Price Chart Only" is disabled if you want to use the auto-zoom feature. (if other indicators are messing your zoom, you can try to select "Line with Breaks" or "Area with Breaks" to allow these to overflow from the main window)
By clicking the triangle next to the indicator's name, you can select "Visual Order" (e.g "Bring to Front").
You can select regular Candles or Heikin-Ashi in Options.
In the Format > Inputs tab, you can select which exchanges to quote. By default, all of them are enabled.
The script also exposes the following typical values to the backend, which you can use as Price Source for other indicators: (e.g. MA, RSI, in their "Format > Input" tab)
Open Price (grey)
High Price (green)
Low Price (red)
Close Price (white)
(H + L)/2 (light blue)
(H + L + C)/3 (blue)
(O + H + L + C)/4 (purple)
They are all hidden by default (by means of maximum transparency).
In the Format > Style tab, you can change their color, transparency and style (line, area, etc), as well as uncheck Candles and Wicks to hide these.
If you are using "Indicator Last Value" and want to clear the clutter from all these values, simply uncheck them in Style. They will still be available as Price Source for other indicators.
You can also choose to scale it to the left, right (default) or "screen" (no scaling).
Once you're satisfied with your Style, you may click "Default"> "Save as default" in the botton-left. Everytime you load the indicator, it will look the same. ("Reset Settings" will reset to the script's defaults)
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Please leave feedback below in comments or pm me directly for bugs and suggestions.
: Volume Zone Oscillator & Price Zone Oscillator LB Update JRMThis is a simple update of Lazy Bear's " Indicators: Volume Zone Indicator & Price Zone Indicator" Script. PZO plots on the same indicator. The horizontal plot lines are taken primarily from two articles by Wahalil and Steckler "In The Volume Zone" May 2011, Stocks and Commodities and "Entering The Price Zone"June 2011, Stocks and Commodities. With both indicators on the same plot it is easier to see divergences between the indicators. I did add a plot line at 80 and -80 as well because that is getting into truly extreme price/volume territory where one might contemplate a close your eyes and sell or cover particularly if confirmed at a higher time frame with the expectation of some type of corrective move..
The inputs and plot lines can be edited as per Lazy Bear's original script and follows the original format. Many thanks to Lazy Bear.
Quantum Market Analyzer X7Quantum Market Analyzer X7 - Complete Study Guide
Table of Contents
1. Overview
2. Indicator Components
3. Signal Interpretation
4. Live Market Analysis Guide
5. Best Practices
6. Limitations and Considerations
7. Risk Disclaimer
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Overview
The Quantum Market Analyzer X7 is a comprehensive multi-timeframe technical analysis indicator that combines traditional and modern analytical methods. It aggregates signals from multiple technical indicators across seven key analysis categories to provide traders with a consolidated view of market sentiment and potential trading opportunities.
Key Features:
• Multi-Indicator Analysis: Combines 20+ technical indicators
• Real-Time Dashboard: Professional interface with customizable display
• Signal Aggregation: Weighted scoring system for overall market sentiment
• Advanced Analytics: Includes Order Block detection, Supertrend, and Volume analysis
• Visual Progress Indicators: Easy-to-read progress bars for signal strength
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Indicator Components
1. Oscillators Section
Purpose: Identifies overbought/oversold conditions and momentum changes
Included Indicators:
• RSI (14): Relative Strength Index - momentum oscillator
• Stochastic (14): Compares closing price to price range
• CCI (20): Commodity Channel Index - cycle identification
• Williams %R (14): Momentum indicator similar to Stochastic
• MACD (12,26,9): Moving Average Convergence Divergence
• Momentum (10): Rate of price change
• ROC (9): Rate of Change
• Bollinger Bands (20,2): Volatility-based indicator
Signal Interpretation:
• Strong Buy (6+ points): Multiple oscillators indicate oversold conditions
• Buy (2-5 points): Moderate bullish momentum
• Neutral (-1 to 1 points): Balanced conditions
• Sell (-2 to -5 points): Moderate bearish momentum
• Strong Sell (-6+ points): Multiple oscillators indicate overbought conditions
2. Moving Averages Section
Purpose: Determines trend direction and strength
Included Indicators:
• SMA: 10, 20, 50, 100, 200 periods
• EMA: 10, 20, 50 periods
Signal Logic:
• Price >2% above MA = Strong Buy (+2)
• Price above MA = Buy (+1)
• Price below MA = Sell (-1)
• Price >2% below MA = Strong Sell (-2)
Signal Interpretation:
• Strong Buy (6+ points): Price well above multiple MAs, strong uptrend
• Buy (2-5 points): Price above most MAs, bullish trend
• Neutral (-1 to 1 points): Mixed MA signals, consolidation
• Sell (-2 to -5 points): Price below most MAs, bearish trend
• Strong Sell (-6+ points): Price well below multiple MAs, strong downtrend
3. Order Block Analysis
Purpose: Identifies institutional support/resistance levels and breakouts
How It Works:
• Detects historical levels where large orders were placed
• Monitors price behavior around these levels
• Identifies breakouts from established order blocks
Signal Types:
• BULLISH BRK (+2): Breakout above resistance order block
• BEARISH BRK (-2): Breakdown below support order block
• ABOVE SUP (+1): Price holding above support
• BELOW RES (-1): Price rejected at resistance
• NEUTRAL (0): No significant order block interaction
4. Supertrend Analysis
Purpose: Trend following indicator based on Average True Range
Parameters:
• ATR Period: 10 (default)
• ATR Multiplier: 6.0 (default)
Signal Types:
• BULLISH (+2): Price above Supertrend line
• BEARISH (-2): Price below Supertrend line
• NEUTRAL (0): Transition period
5. Trendline/Channel Analysis
Purpose: Identifies trend channels and breakout patterns
Components:
• Dynamic trendline calculation using pivot points
• Channel width based on historical volatility
• Breakout detection algorithm
Signal Types:
• UPPER BRK (+2): Breakout above upper channel
• LOWER BRK (-2): Breakdown below lower channel
• ABOVE MID (+1): Price above channel midline
• BELOW MID (-1): Price below channel midline
6. Volume Analysis
Purpose: Confirms price movements with volume data
Components:
• Volume spikes detection
• On Balance Volume (OBV)
• Volume Price Trend (VPT)
• Money Flow Index (MFI)
• Accumulation/Distribution Line
Signal Calculation: Multiple volume indicators are combined to determine institutional activity and confirm price movements.
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Signal Interpretation
Overall Summary Signals
The indicator aggregates all component signals into an overall market sentiment:
Signal Score Range Interpretation Action
STRONG BUY 10+ Overwhelming bullish consensus Consider long positions
BUY 4-9 Moderate to strong bullish bias Look for long opportunities
NEUTRAL -3 to 3 Mixed signals, consolidation Wait for clearer direction
SELL -4 to -9 Moderate to strong bearish bias Look for short opportunities
STRONG SELL -10+ Overwhelming bearish consensus Consider short positions
Progress Bar Interpretation
• Filled bars indicate signal strength
• Green bars: Bullish signals
• Red bars: Bearish signals
• More filled bars = stronger conviction
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Live Market Analysis Guide
Step 1: Initial Assessment
1. Check Overall Summary: Start with the main signal
2. Verify with Component Analysis: Ensure signals align
3. Look for Divergences: Identify conflicting signals
Step 2: Timeframe Analysis
1. Set Appropriate Timeframe: Use 1H for intraday, 4H/1D for swing trading
2. Multi-Timeframe Confirmation: Check higher timeframes for trend context
3. Entry Timing: Use lower timeframes for precise entry points
Step 3: Signal Confirmation Process.
For Buy Signals:
1. Oscillators: Look for oversold conditions (RSI <30, Stoch <20)
2. Moving Averages: Price should be above key MAs
3. Order Blocks: Confirm bounce from support levels
4. Volume: Check for accumulation patterns
5. Supertrend: Ensure bullish trend alignment.
For Sell Signals:
1. Oscillators: Look for overbought conditions (RSI >70, Stoch >80)
2. Moving Averages: Price should be below key MAs
3. Order Blocks: Confirm rejection at resistance levels
4. Volume: Check for distribution patterns
5. Supertrend: Ensure bearish trend alignment.
Step 4: Risk Management Integration
1. Signal Strength Assessment: Stronger signals = larger position size
2. Stop Loss Placement: Use Order Block levels for stops
3. Take Profit Targets: Based on channel analysis and resistance levels
4. Position Sizing: Adjust based on signal confidence
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Best Practices
Entry Strategies
1. High Conviction Entries: Wait for STRONG BUY/SELL signals
2. Confluence Trading: Look for multiple components aligning
3. Breakout Trading: Use Order Block and Trendline breakouts
4. Trend Following: Align with Supertrend direction.
Risk Management
1. Never Risk More Than 2% Per Trade: Regardless of signal strength
2. Use Stop Losses: Place at invalidation levels
3. Scale Positions: Stronger signals warrant larger (but still controlled) positions
4. Diversification: Don't rely solely on one indicator.
Market Conditions
1. Trending Markets: Focus on Supertrend and MA signals
2. Range-Bound Markets: Emphasize Oscillator and Order Block signals
3. High Volatility: Reduce position sizes, widen stops
4. Low Volume: Be cautious of breakout signals.
Common Mistakes to Avoid
1. Signal Chasing: Don't enter after signals have already moved significantly
2. Ignoring Context: Consider overall market conditions
3. Overtrading: Wait for high-quality setups
4. Poor Risk Management: Always use appropriate position sizing
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Limitations and Considerations
Technical Limitations
1. Lagging Nature: All technical indicators are based on historical data
2. False Signals: No indicator is 100% accurate
3. Market Regime Changes: Indicators may perform differently in various market conditions
4. Whipsaws: Possible in choppy, sideways markets.
Optimal Use Cases
1. Trending Markets: Performs best in clear trending environments
2. Medium to High Volatility: Requires sufficient price movement for signals
3. Liquid Markets: Works best with adequate volume and tight spreads
4. Multiple Timeframe Analysis: Most effective when used across different timeframes.
When to Use Caution
1. Major News Events: Fundamental analysis may override technical signals
2. Market Opens/Closes: Higher volatility can create false signals
3. Low Volume Periods: Signals may be less reliable
4. Holiday Trading: Reduced participation affects signal quality
________________________________________
Risk Disclaimer
IMPORTANT LEGAL DISCLAIMER FROM aiTrendview
WARNING: TRADING INVOLVES SUBSTANTIAL RISK OF LOSS
This Quantum Market Analyzer X7 indicator ("the Indicator") is provided for educational and informational purposes only. By using this indicator, you acknowledge and agree to the following terms:
No Investment Advice
• The Indicator does NOT constitute investment advice, financial advice, or trading recommendations
• All signals generated are based on historical price data and mathematical calculations
• Past performance does not guarantee future results
• No representation is made that any account will achieve profits or losses similar to those shown.
Risk Acknowledgment
• TRADING CARRIES SUBSTANTIAL RISK: You may lose some or all of your invested capital
• LEVERAGE AMPLIFIES RISK: Margin trading can result in losses exceeding your initial investment
• MARKET VOLATILITY: Financial markets are inherently unpredictable and volatile
• TECHNICAL ANALYSIS LIMITATIONS: No technical indicator is infallible or guarantees profitable trades.
User Responsibility
• YOU ARE SOLELY RESPONSIBLE for all trading decisions and their consequences
• CONDUCT YOUR OWN RESEARCH: Always perform independent analysis before making trading decisions
• CONSULT PROFESSIONALS: Seek advice from qualified financial advisors
• RISK MANAGEMENT: Implement appropriate risk management strategies
No Warranties
• The Indicator is provided "AS IS" without warranties of any kind
• aiTrendview makes no representations about the accuracy, reliability, or suitability of the Indicator
• Technical glitches, data feed issues, or calculation errors may occur
• The Indicator may not work as expected in all market conditions.
Limitation of Liability
• aiTrendview SHALL NOT BE LIABLE for any direct, indirect, incidental, or consequential damages
• This includes but is not limited to: trading losses, missed opportunities, data inaccuracies, or system failures
• MAXIMUM LIABILITY is limited to the amount paid for the indicator (if any)
Code Usage and Distribution
• This indicator is published on TradingView in accordance with TradingView's house rules
• UNAUTHORIZED MODIFICATION or redistribution of this code is prohibited
• Users may not claim ownership of this intellectual property
• Commercial use requires explicit written permission from aiTrendview.
Compliance and Regulations
• VERIFY LOCAL REGULATIONS: Ensure compliance with your jurisdiction's trading laws
• Some trading strategies may not be suitable for all investors
• Tax implications of trading are your responsibility
• Report trading activities as required by law
Specific Risk Factors
1. False Signals: The Indicator may generate incorrect buy/sell signals
2. Market Gaps: Overnight gaps can invalidate technical analysis
3. Fundamental Events: News and economic data can override technical signals
4. Liquidity Risk: Some markets may have insufficient liquidity
5. Technology Risk: Platform failures or connectivity issues may prevent order execution.
Professional Trading Warning
• THIS IS NOT PROFESSIONAL TRADING SOFTWARE: Not intended for institutional or professional trading
• NO REGULATORY APPROVAL: This indicator has not been approved by any financial regulatory authority
• EDUCATIONAL PURPOSE: Designed primarily for learning technical analysis concepts
FINAL WARNING
NEVER INVEST MONEY YOU CANNOT AFFORD TO LOSE
Trading financial instruments involves significant risk. The majority of retail traders lose money. Before using this indicator in live trading:
1. Practice on paper/demo accounts extensively
2. Start with small position sizes
3. Develop a comprehensive trading plan
4. Implement strict risk management rules
5. Continuously educate yourself about market dynamics
By using the Quantum Market Analyzer X7, you acknowledge that you have read, understood, and agree to this disclaimer. You assume full responsibility for all trading decisions and their outcomes.
Contact: For questions about this disclaimer or the indicator, contact aiTrendview through official TradingView channels only.
________________________________________
This study guide and indicator are published on TradingView in compliance with TradingView's community guidelines and house rules. All users must adhere to TradingView's terms of service when using this indicator.
Document Version: 1.0
Publisher: aiTrendview
________________________________________
Disclaimer
The content provided in this blog post is for educational and training purposes only. It is not intended to be, and should not be construed as, financial, investment, or trading advice. All charting and technical analysis examples are for illustrative purposes. Trading and investing in financial markets involve substantial risk of loss and are not suitable for every individual. Before making any financial decisions, you should consult with a qualified financial professional to assess your personal financial situation.
LibVolmLibrary "LibVolm"
This library provides a collection of core functions for volume and
money flow analysis. It offers implementations of several classic
volume-based indicators, with a focus on flexibility
for applications like multi-timeframe and session-based analysis.
Key Features:
1. **Suite of Classic Volume Indicators:** Includes standard
implementations of several foundational indicators:
- **On Balance Volume (`obv`):** A momentum indicator that
accumulates volume based on price direction.
- **Accumulation/Distribution Line (`adLine`):** Measures cumulative
money flow using the close's position within the bar's range.
- **Chaikin Money Flow (`cmf`):** An oscillator version of the ADL
that measures money flow over a specified lookback period.
2. **Anchored/Resettable Indicators:** The library includes flexible,
resettable indicators ideal for cyclical analysis:
- **Anchored VWAP (`vwap`):** Calculates a Volume Weighted Average
Price that can be reset on any user-defined `reset` condition.
It returns both the VWAP and the number of bars (`prdBars`) in
the current period.
- **Resettable CVD (`cvd`):** Computes a Cumulative Volume Delta
that can be reset on a custom `reset` anchor. The function
also tracks and returns the highest (`hi`) and lowest (`lo`)
delta values reached within the current period.
(Note: The delta sign is determined by a specific logic:
it first checks close vs. open, then close vs. prior
close, and persists the last non-zero sign).
3. **Volume Sanitization:** All functions that use the built-in
`volume` variable automatically sanitize it via an internal
function. This process replaces `na` values with 0 and ensures
no negative volume values are used, providing stable calculations.
---
**DISCLAIMER**
This library is provided "AS IS" and for informational and
educational purposes only. It does not constitute financial,
investment, or trading advice.
The author assumes no liability for any errors, inaccuracies,
or omissions in the code. Using this library to build
trading indicators or strategies is entirely at your own risk.
As a developer using this library, you are solely responsible
for the rigorous testing, validation, and performance of any
scripts you create based on these functions. The author shall
not be held liable for any financial losses incurred directly
or indirectly from the use of this library or any scripts
derived from it.
obv(price)
Calculates the On Balance Volume (OBV) cumulative indicator.
Parameters:
price (float) : series float Source price series, typically the close.
Returns: series float Cumulative OBV value.
adLine()
Computes the Accumulation/Distribution Line (AD Line).
Returns: series float Cumulative AD Line value.
cmf(length)
Computes Chaikin Money Flow (CMF).
Parameters:
length (int) : series int Lookback length for the CMF calculation.
Returns: series float CMF value.
vwap(price, reset)
Calculates an anchored Volume Weighted Average Price (VWAP).
Parameters:
price (float) : series float Source price series (usually *close*).
reset (bool) : series bool A signal that is *true* on the bar where the
accumulation should be reset.
Returns:
vwap series float The calculated Volume Weighted Average Price for the current period.
prdBars series int The number of bars that have passed since the last reset.
cvd(reset)
Calculates a resettable, cumulative Volume Delta (CVD).
It accumulates volume delta and tracks its high/low range. The
accumulation is reset to zero whenever the `reset` condition is true.
This is useful for session-based analysis, intra-bar calculations,
or any other custom-anchored accumulation.
Parameters:
reset (bool) : series bool A signal that is *true* on the bar where the
accumulation should be reset.
Returns:
cum series float The current cumulative volume delta.
hi series float The highest peak the cumulative delta has reached in the current period.
lo series float The lowest trough the cumulative delta has reached in the current period.
Universal Valuation | Lyro RSUniversal Valuation
⚠️Disclaimer: This indicator is a tool for technical analysis and does not provide guaranteed results. It should be used in conjunction with other analysis methods and proper risk management practices. The creators of this indicator are not responsible for any financial decisions made based on its signals.
Overview
The Universal Valuation indicator helps identify whether the market is undervalued/cheap or overvalued/expensive. And another mode this indicator offers is This cutting-edge tool works flawlessly ACROSS ALL TIMEFRAMES & TICKERS/CHARTS.
By combining regular TradingView indicators & some of our valuation indicators basic/simple with advanced statistical functions, this indicator offers a powerful, universal valuation tool.
Key Features
INPUTS: The Universal Valuation indicator offers flexibility through its customizable input sections. The "Indicator Settings" let you adjust lengths for the raw indicators and statistical functions. The "Signals" section defines thresholds for background color changes, helping you visually spot key market moments. The "Colors" section allows you to pick from pre-defined schemes or personalize colors for better clarity. Lastly, the "Tables" section gives you full control over the UV table’s size and positioning, including options to overlay it on the chart or place it in the allocated space.
A DEEPER INSIGHT: This indicator is built around three distinct categories: "UVM Andromeda," "UVM Sentinel," and "UVM Nexus." Each category has three different drivers. The statistical function powering this indicator is the Z-score. The Z-score is an incredibly powerful tool that helps determine if the market is overvalued/expensive or undervalued/cheap, offering critical insights for traders."
Plotting: The plotted value represents the average of all the drivers. In other words, it is the combined average of all 9 Z-scored indicators, providing a balanced and comprehensive market valuation.
What is Z-score? & Why does this system use it?
Z-score is an advanced statistical function used to measure how far a value deviates from the average in a data set. The formula for Z-score is: (x - h) / o, where x is the observed value, h is the average (mean) of the data set, and o is the standard deviation.
This system uses the Z-score because it helps determine whether the market is overvalued or undervalued based on historical data and how we apply the calculation. By measuring how far a value deviates from the average, the Z-score provides a clearer and more objective valuation of market conditions. In our case, a Z-score of -3 indicates an undervalued market, while a Z-score of 3 signals an overvalued market.
UVM Andromeda:
UVM stands for Universal Valuation Model, which is the core of this indicator. Andromeda, one of the most stunning galaxies in the universe, inspired by its name. We chose this name because a powerful indicator should not only be effective but also visually appealing.
You might be wondering what drives UVM Andromeda. The three key drivers are Price, RSI, and ROC. These indicators are pre-defined, while the "Indicator Settings" allow you to adjust the length of the Z-score calculation, refining how the model analyzes market conditions.
UVM Sentinel:
Sentinel, refers to a guard or watchman, someone or something that keeps watch and provides protection. In our case this name refers to a model that actively observes market conditions, acting as a vigilant tool that signals important shifts in valuation.
Wondering what drives UVM Sentinel? The three key drivers are BB%, CCI, and Crosby. While these indicators are simple on their own, applying our Z-score function elevates them to a whole new level, enhancing their ability to detect market conditions with greater accuracy.
UVM Nexus:
We chose the name Nexus simply because it sounds cool—there’s no deeper meaning behind it for us. However, the word itself does have a meaning; it refers to a connection or link between multiple things.
The three key drivers for UVM Nexus are the Sharpe, Sortino, and Omega ratios. These are all asset performance metrics, but by applying the Z-score, we transform them into powerful valuation indicators/drivers, giving you a deeper insight into market conditions.
Why do we use 9 different indicators instead of 1?
That's a great question, and the answer is quite simple. Think of it like this: if you have one super soldier, and they miss a shot, it’s game over. But if you have many soldiers, even if one misses, the others can step in and take the shot. The strength of using multiple indicators lies in their collective power – if one misses, the others still provide valuable insights, making the overall system more reliable.
Final Thoughts:
In our Universal Valuation indicator, you have the flexibility to customize it however you like using our inputs. The system is divided into three distinct categories, with each category containing three indicators. The value plotted on the chart is the average of all nine indicators. We apply the Z-score, an advanced statistical function, to each of these nine indicators. The final plotted average is the average of all the Z-scores, giving you a comprehensive and refined market valuation. This indicator can work on any timeframe & chart ticker.
Boxes_PlotIn the world of data visualization, heatmaps are an invaluable tool for understanding complex datasets. They use color gradients to represent the values of individual data points, allowing users to quickly identify patterns, trends, and outliers in their data. In this post, we will delve into the history of heatmaps, and then discuss how its implemented.
The "Boxes_Plot" library is a powerful and versatile tool for visualizing multiple indicators on a trading chart using colored boxes, commonly known as heatmaps. These heatmaps provide a user-friendly and efficient method for analyzing the performance and trends of various indicators simultaneously. The library can be customized to display multiple charts, adjust the number of rows, and set the appropriate offset for proper spacing. This allows traders to gain insights into the market and make informed decisions.
Heatmaps with cells are interesting and useful for several reasons. Firstly, they allow for the visualization of large datasets in a compact and organized manner. This is especially beneficial when working with multiple indicators, as it enables traders to easily compare and contrast their performance. Secondly, heatmaps provide a clear and intuitive representation of the data, making it easier for traders to identify trends and patterns. Finally, heatmaps offer a visually appealing way to present complex information, which can help to engage and maintain the interest of traders.
History of Heatmaps
The concept of heatmaps can be traced back to the 19th century when French cartographer and sociologist Charles Joseph Minard used color gradients to visualize statistical data. He is well-known for his 1869 map, which depicted Napoleon's disastrous Russian campaign of 1812 using a color gradient to represent the dwindling size of Napoleon's army.
In the 20th century, heatmaps gained popularity in the fields of biology and genetics, where they were used to visualize gene expression data. In the early 2000s, heatmaps found their way into the world of finance, where they are now used to display stock market data, such as price, volume, and performance.
The boxes_plot function in the library expects a normalized value from 0 to 100 as input. Normalizing the data ensures that all values are on a consistent scale, making it easier to compare different indicators. The function also allows for easy customization, enabling users to adjust the number of rows displayed, the size of the boxes, and the offset for proper spacing.
One of the key features of the library is its ability to automatically scale the chart to the screen. This ensures that the heatmap remains clear and visible, regardless of the size or resolution of the user's monitor. This functionality is essential for traders who may be using various devices and screen sizes, as it enables them to easily access and interpret the heatmap without needing to make manual adjustments.
In order to create a heatmap using the boxes_plot function, users need to supply several parameters:
1. Source: An array of floating-point values representing the indicator values to display.
2. Name: An array of strings representing the names of the indicators.
3. Boxes_per_row: The number of boxes to display per row.
4. Offset (optional): An integer to offset the boxes horizontally (default: 0).
5. Scale (optional): A floating-point value to scale the size of the boxes (default: 1).
The library also includes a gradient function (grad) that is used to generate the colors for the heatmap. This function is responsible for determining the appropriate color based on the value of the indicator, with higher values typically represented by warmer colors such as red and lower values by cooler colors such as blue.
Implementing Heatmaps as a Pine Script Library
In this section, we'll explore how to create a Pine Script library that can be used to generate heatmaps for various indicators on the TradingView platform. The library utilizes colored boxes to represent the values of multiple indicators, making it simple to visualize complex data.
We'll now go over the key components of the code:
grad(src) function: This function takes an integer input 'src' and returns a color based on a predefined color gradient. The gradient ranges from dark blue (#1500FF) for low values to dark red (#FF0000) for high values.
boxes_plot() function: This is the main function of the library, and it takes the following parameters:
source: an array of floating-point values representing the indicator values to display
name: an array of strings representing the names of the indicators
boxes_per_row: the number of boxes to display per row
offset (optional): an integer to offset the boxes horizontally (default: 0)
scale (optional): a floating-point value to scale the size of the boxes (default: 1)
The function first calculates the screen size and unit size based on the visible chart area. Then, it creates an array of box objects representing each data point. Each box is assigned a color based on the value of the data point using the grad() function. The boxes are then plotted on the chart using the box.new() function.
Example Usage:
In the example provided in the source code, we use the Relative Strength Index (RSI) and the Stochastic Oscillator as the input data for the heatmap. We create two arrays, 'data_1' containing the RSI and Stochastic Oscillator values, and 'data_names_1' containing the names of the indicators. We then call the 'boxes_plot()' function with these arrays, specifying the desired number of boxes per row, offset, and scale.
Conclusion
Heatmaps are a versatile and powerful data visualization tool with a rich history, spanning multiple fields of study. By implementing a heatmap library in Pine Script, we can enhance the capabilities of the TradingView platform, making it easier for users to visualize and understand complex financial data. The provided library can be easily customized and extended to suit various use cases and can be a valuable addition to any trader's toolbox.
Library "Boxes_Plot"
boxes_plot(source, name, boxes_per_row, offset, scale)
Parameters:
source (float ) : - an array of floating-point values representing the indicator values to display
name (string ) : - an array of strings representing the names of the indicators
boxes_per_row (int) : - the number of boxes to display per row
offset (int) : - an optional integer to offset the boxes horizontally (default: 0)
scale (float) : - an optional floating-point value to scale the size of the boxes (default: 1)
Radar RiderThe Radar Rider indicator is a powerful tool that combines multiple technical indicators into a single spider plot, providing traders with a comprehensive view of market conditions. This article will delve into the workings of each built-in indicator and their arrangement within the spider plot. To better understand the structure of the script, let's first examine some of the primary functions and how they are utilized in the script.
Normalize Function: normalize(close, len)
The normalize function takes the close price and a length as arguments and normalizes the price data by scaling it between 0 and 1, making it easier to compare different indicators.
Exponential Moving Average (EMA) Filter: bes(source, alpha)
The EMA filter is used to smooth out data using an exponential moving average, with the given alpha value defining the level of smoothing. This helps reduce noise and enhance the trend-following characteristics of the indicators.
Maximum and Minimum Functions: max(src) and min(src)
These functions find the maximum and minimum values of the input data over a certain period, respectively. These values are used in the normalization process and can help identify extreme conditions in the market.
Min-Max Function: min_max(src)
The min-max function scales the input data between 0 and 100 by dividing the difference between the data point and the minimum value by the range between the maximum and minimum values. This standardizes the data, making it easier to compare across different indicators.
Slope Function: slope(source, length, n_len, pre_smoothing = 0.15, post_smoothing = 0.7)
The slope function calculates the slope of a given data source over a specified length, and then normalizes it using the provided normalization length. Pre-smoothing and post-smoothing values can be adjusted to control the level of smoothing applied to the data before and after calculating the slope.
Percent Function: percent(x, y)
The percent function calculates the percentage difference between two values, x and y. This is useful for comparing the relative change in different indicators.
In the given code, there are multiple indicators included. Here, we will discuss each of them in detail.
EMA Diff:
The Exponential Moving Average (EMA) Diff is the difference between two EMA values of different lengths. The EMA is a type of moving average that gives more weight to recent data points. The EMA Diff helps traders identify trends and potential trend reversals. In the code, the EMA Diff is calculated using the ema_diff() function, which takes length, close, filter, and len_norm as parameters.
Percent Rank EMA Diff:
The Percent Rank EMA Diff is the percentage rank of the EMA Diff within a given range. It helps traders identify overbought or oversold conditions in the market. In the code, the Percent Rank EMA Diff is calculated using the percent_rank_ema_diff() function, which takes length, close, filter, and len_norm as parameters.
EMA Diff Longer:
The EMA Diff Longer is the difference between two EMA values of different lengths, similar to EMA Diff but with a longer period. In the code, the EMA Diff Longer is calculated using the ema_diff_longer() function, which takes length, close, filter, and len_norm as parameters.
RSI Filter:
The Relative Strength Index (RSI) is a momentum oscillator that measures the speed and change of price movements. The RSI Filter is the RSI value passed through a filter to smooth out the data. In the code, the RSI Filter is calculated using the rsi_filter() function, which takes length, close, and filter as parameters.
RSI Diff Normalized:
The RSI Diff Normalized is the normalized value of the derivative of the RSI. It helps traders identify potential trend reversals in the market. In the code, the RSI Diff Normalized is calculated using the rsi_diff_normalized() function, which takes length, close, filter, len_mad, and len_norm as parameters.
Z Score:
The Z Score is a statistical measurement that describes a value's relationship to the mean of a group of values. In the context of the code, the Z Score is calculated for the closing price of a security. The z_score() function takes length, close, filter, and len_norm as parameters.
EMA Normalized:
The EMA Normalized is the normalized value of the EMA, which helps traders identify trends and potential trend reversals in the market. In the code, the EMA Normalized is calculated using the ema_normalized() function, which takes length, close, filter, and len_norm as parameters.
WMA Volume Normalized:
The Weighted Moving Average (WMA) Volume Normalized is the normalized value of the WMA of the volume. It helps traders identify volume trends and potential trend reversals in the market. In the code, the WMA Volume Normalized is calculated using the wma_volume_normalized() function, which takes length, volume, filter, and len_norm as parameters.
EMA Close Diff Normalized:
The EMA Close Diff Normalized is the normalized value of the derivative of the EMA of the closing price. It helps traders identify potential trend reversals in the market. In the code, the EMA Close Diff Normalized is calculated using the ema_close_diff_normalized() function, which takes length, close, filter, len_mad, and len_norm as parameters.
Momentum Normalized:
The Momentum Normalized is the normalized value of the momentum, which measures the rate of change of a security's price. It helps traders identify trends and potential trend reversals in the market. In the code, the Momentum Normalized is calculated using the momentum_normalized() function, which takes length, close, filter, and len_norm as parameters.
Slope Normalized:
The Slope Normalized is the normalized value of the slope, which measures the rate of change of a security's price over a specified period. It helps traders identify trends and potential trend reversals in the market. In the code, the Slope Normalized is calculated using the slope_normalized() function, which takes length, close, filter, and len_norm as parameters.
Trend Intensity:
Trend Intensity is a measure of the strength of a security's price trend. It is based on the difference between the average of price increases and the average of price decreases over a given period. The trend_intensity() function in the code calculates the Trend Intensity by taking length, close, filter, and len_norm as parameters.
Volatility Ratio:
The Volatility Ratio is a measure of the volatility of a security's price, calculated as the ratio of the True Range (TR) to the Exponential Moving Average (EMA) of the TR. The volatility_ratio() function in the code calculates the Volatility Ratio by taking length, high, low, close, and filter as parameters.
Commodity Channel Index (CCI):
The Commodity Channel Index (CCI) is a momentum-based oscillator used to help determine when an investment vehicle is reaching a condition of being overbought or oversold. The CCI is calculated as the difference between the mean price of a security and its moving average, divided by the mean absolute deviation (MAD) of the mean price. In the code, the CCI is calculated using the cci() function, which takes length, high, low, close, and filter as parameters.
These indicators are combined in the code to create a comprehensive trading strategy that considers multiple factors such as trend strength, momentum, volatility, and overbought/oversold conditions. The combined analysis provided by these indicators can help traders make informed decisions and improve their chances of success in the market.
The Radar Rider indicator is a powerful tool that combines multiple technical indicators into a single, easy-to-read visualization. By understanding the inner workings of each built-in indicator and their arrangement within the spider plot, traders can better interpret market conditions and make informed trading decisions.
ERDAL SARIDAS Visual RSIOne-stop shop for all your divergence needs, including:
(1) A single metric for divergence strength across multiple indicators.
(2) Labels that make it easy to spot where the truly strong divergence is by showing the overall divergence strength value along with the number of divergent indicators. Hovering over the label shows a breakdown of each divergent indicator and its individual divergence strength value.
(3) Fully customizable, including inputs for pivot lengths, divergence types, and weights for every component of the divergence strength calculation. This allows you to quickly and easily optimize the output for any chart. Don't worry, the default settings will have you covered if you're not interested in what's going on under the hood.
The Divergence Strength Calculation:
The total divergence strength value is the sum of the divergence strengths of all indicators for which divergence was detected at a given bar. Each indicator's individual divergence strength is comprised of two basic components: (1) |ΔPrice| - the magnitude of the change in price over the divergence period (pivot-to-pivot), and (2) |ΔIndicator| - the magnitude of the change in indicator value over the divergence period.
Because different indicators' scales and volatility can vary greatly, the Δ values are expressed in terms of standard deviation to ensure that the values are meaningful and equitable across all indicators and assets/instruments/currency pairs, etc:
|ΔIndicator| = |indicator_value_1 - indicator_value_2| / 2 * StDev(indicator_series,100)
Calculation Weights:
All components of the calculation are weighted and can be modified on the Inputs page in settings (weights are simply multipliers). For example, if you think hidden divergence should carry less weight than regular divergence, you can assign it a lesser weight. Or if you think RSI divergence is worth more than OBV divergence, you can adjust their weights accordingly. List of weights:
Regular divergence weight - default = 1
Hidden divergence weight - default = 1
ΔPrice weight - default = 0.5 (multiplied by the ΔPrice component)
ΔIndicator weight - default = 1.5 (multiplied by the ΔIndicator component)
RSI weight - default = 1.1
OBV weight - default = 0.8
MACD weight - default = 0.9
STOCH weight - default = 0.9
Development for additional indicators is ongoing, as is research into the optimal weight configuration(s).
Other Inputs:
Pivot lengths - specify the number of bars before and after each pivot high/low to consider it a valid candidate for divergence.
Lookback bars and Lookback pivots - specify the number of bars or the number of pivots to look back across.
Price sources - specify separate price sources for bullish and bearish divergence
Display settings - specify how lines and labels should display, including which divergence strength values should show the largest labels. Include/exclude specific divergence types and indicators.
Please report any bugs, or let me know if you have any enhancement suggestions or requests for additional indicators.
