Short Selling EMA Cross (By Coinrule)BINANCE:AVAXUSDT
This short selling script works best in periods of downtrends and general bearish market conditions, with the ultimate goal to sell as the the price decreases further and buy back before a rebound.
This script can work well on coins you are planning to hodl for long-term and works especially well whilst using an automated bot that can execute your trades for you. It allows you to hedge your investment by allocating a % of your coins to trade with, whilst not risking your entire holding. This mitigates unrealised losses from hodling as it provides additional cash from the profits made. You can then choose to to hodl this cash, or use it to reinvest when the market reaches attractive buying levels.
Entry
The exponential moving average ( EMA ) 20 and EMA 50 have been used for the variables determining the entry to the short. EMAs can operate better than simple moving averages due to the additional weighting placed on the most recent data points, whereas simple moving averages weight all the data the same. This means that price is tracked more closely and the most recent volatile moves can be captured and exploited more efficiently using EMAs.
Our backtesting data revealed that the most profitable timeframe was the 30-minute timeframe, this also enabled a good frequency of trades and high profitability.
A fast (shorter term) exponential moving average , in this strategy the EMA 20, crossing under a slow (longer term) moving average, in this example the EMA 50, signals the price of an asset has started to trend to the downside, as the most recent data signals price is declining compared to earlier data. The entry acts on this principle and executes when the EMA 20 crosses under the EMA 50.
Enter Short: EMA 20 crosses under EMA 50.
Exit
This script utilises a take profit and stop loss for the exit. The take profit is set at -8% and the stop loss is set at +16% from the entry price. This would normally be a poor trade due to the risk:reward equalling 0.5. However, when looking at the backtesting data, the high profitability of the strategy (93.33%) leads to increased confidence and showcases the high probability of success according to historical data.
The take profit (-8%) and the stop loss (+16%) of the strategy are widely placed to ensure the move is captured without being stopped out due to relief rallies. The stop loss also plays a role of mitigating losses and minimising risk of being stuck in a short position once there has been a fundamental trend reversal and the market has become bullish .
Exit Short: -8% price decrease from entry price.
OR
Exit Short: +16% price increase from entry price.
Tip: Research what coins have consistent and large token unlocks / highly inflationary tokenomics, and target these during bear markets to short as they will most likely have substantial selling pressure that outweighs demand - leading to declining prices.
The strategy assumes each order is using 30% of the available coins to make the results more realistic and to simulate you only ran this strategy on 30% of your holdings. A trading fee of 0.1% is also taken into account and is aligned to the base fee applied on Binance.
The backtesting data was recorded from December 1st 2021, just as the market was beginning its downtrend. We therefore recommend analysing the market conditions prior to utilising this strategy as it operates best on weak coins during downtrends and bearish conditions.
Поиск скриптов по запросу "backtesting"
Last Available Bar InfoLibrary "Last_Available_Bar_Info"
getLastBarTimeStamp()
getAvailableBars()
This simple library is built with an aim of getting the last available bar information for the chart. This returns a constant value that doesn't change on bar change.
For backtesting with accurate results on non standard charts, it will be helpful. (Especially if you are using non standard charts like Renko Chart).
Methods
getLastBarTimeStamp()
: Returns Timestamp of the last available bar (Constant)
getAvailableBars()
:Returns Number of Available Bars on the chart (Constant)
Example
import paragjyoti2012/Last_Available_Bar_Info/v1 as LastBarInfo
last_bar_timestamp=LastBarInfo.getLastBarTimeStamp()
no_of_bars=LastBarInfo.getAvailableBars()
If you are using Renko Charts, for backtesting, it's necesary to filter out the historical bars that are not of this timeframe.
In Renko charts, once the available bars of the current timeframe (based on your Tradingview active plan) are exhausted,
previous bars are filled in with historical bars of higher timeframe. Which is detrimental for backtesting, and it leads to unrealistic results.
To get the actual number of bars available of that timeframe, you should use this security function to get the timestamp for the last (real) bar available.
tf=timeframe.period
real_available_bars = request.security(syminfo.ticker, tf , LastBarInfo.getAvailableBars() , lookahead = barmerge.lookahead_off)
last_available_bar_timestamp = request.security(syminfo.ticker, tf , LastBarInfo.getLastBarTimeStamp() , lookahead = barmerge.lookahead_off)
Financial Astrology Crypto ML Daily TrendThis daily trend indicator is based on financial astrology cycles detected with advanced machine learning techniques for the crypto-currencies research portfolio: ADA, BAT, BNB, BTC, DASH, EOS, ETC, ETH, LINK, LTC, XLM, XMR, XRP, ZEC and ZRX. The daily price trend is forecasted through this planets cycles (angular aspects, speed, declination), fast ones are based on Moon, Mercury, Venus and Sun and Mid term cycles are based on Mars, Vesta and Ceres. The combination of all this cycles produce a daily price trend prediction that is encoded into a PineScript array using binary format "0 or 1" that represent sell and buy signals respectively. The indicator provides signals since 2021-01-01 to 2022-12-31, the past months signals purpose is to support backtesting of the indicator combined with other technical indicator entries like MAs, RSI or Stochastic. For future predictions besides 2022 a machine learning models re-train phase will be required.
The resolution of this indicator is 1D, you can tune a parameter where you can determine how many future bars of daily trend are plotted and adjust an hours shift to anticipate future signals into current bar in order to produce a leading indicator effect to anticipate the trend changes with some hours of anticipation. Combined with technical analysis indicators this daily trend is very powerful because can help to produce approximately 60% of profitable signals based on the backtesting results. You can look at our open source Github repositories to validate accuracy using the backtesting strategies we have implemented in Jesse Crypto Trading Framework as proof of concept of the predictive potential of this indicator. Alternatively, we have implemented a PineScript strategy that use this indicator, just consider that we are pending to do signals update to the period July 2021 to December 2022: This strategy have accumulated more than 110 likes and many traders have validated the predictive power of Financial Astrology.
DISCLAIMER: This indicator is experimental and don’t provide financial or investment advice, the main purpose is to demonstrate the predictive power of financial astrology. Any allocation of funds following the documented machine learning model prediction is a high-risk endeavour and it’s the users responsibility to practice healthy risk management according to your situation.
[laoowai]BNB_USDT_3m_3Commas_Bollinger_MACD_RSI_StrategyBNB_USDT _3m
Release Notes:
Time: 3min
Pair: BNB_USDT
Use: {{strategy.order.alert_message}}
What's the difference with 3Commas Bollinger Strategy by tedwardd:
1. Initial capital: 1210 USDT (10$ Base order / 400$*3 Safety order), if you will change, please change JUST safety order volume or number of safety orders 2-3
2. Using just 2(3) safety order (original script 4)
3. More high-performance strategy for BNB_USDT
4. Using MACD to sell order (original script take profit by scale), thanks Drun30 .
5. Using RSI to analyze the market conditions.
Need to change:
bot_id = input(title="3Commas Bot ID", defval=" YOUR DATA ")
email_token = input(title="Bot Email Token", defval=" YOUR DATA ")
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FAQ copy from tedwardd
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This strategy is intended for use as a way of backtesting various parameters available on 3commas.
The primary inputs for the strategy are:
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// USER INPUTS
Short MA Window - The length of the Short moving average
Long MA Window - The length of the Long moving average
Upper Band Offset - The offset to use for the upper bollinger offset
Lower Band Offset - The offset to use for the lower bollinger offset
Long Stop Loss % - The stop loss percentage to test
Long Take Profit % - The Take profit percentage to test
Initial SO Deviation % - The price deviation percentage required to place to first safety order
Safety Order Vol Step % - The volume scale to test
3Commas Bot ID - (self-explanatory)
Bot Email Token - Found in the deal start message for your bot (see link in the previous section for details)
3Commas Bot Trading Pair - The pair to include for composite bot start deals (should match the format of 3commas, not TradingView IE. USDT_BTC not BTCUSDT )
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Start Date, Month, Year and End Date, Month, and Year all apply to the backtesting window. By default, it will use as much data as it can give the current period select (there is less historical data available for periods below 1H) back as far as 2016 (there appears to be no historical data on Trading view much before this). If you would like to test a different period of time, just change these values accordingly.
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Composite bot using a Bollinger band type trading strategy. While its primary intention is to provide users a way of backtesting bot parameters, it can also be used to trigger a deal start by either using the {{strategy.order.alert_message}} field in your alert and providing the bot details in the configuration screen for the strategy or by including the usual deal start message provided by 3commas.
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Original script:
1. 3Commas Bollinger Strategy by tedwardd
2. Momentum Strategy ( BTC /USDT; 1h) - MACD (with source code) by Drun30
3Commas Bollinger StrategyThis strategy is intended for use as a way of backtesting various parameters available on 3commas.io composite bot using a bollinger band type trading strategy. While it's primary intention is to provide users a way of backtesting bot parameters, it can also be used to trigger a deal start by either using the {{strategy.open.alert_message}} field in your alert and providing the bot details in the configuration screen for the strategy or by including the usual deal start message provided by 3commas. You can find more information about how to do this from help.3commas.io
The primary inputs for the strategy are:
// USER INPUTS
Short MA Window - The length of the Short moving average
Long MA Window - The length of the Long moving average
Upper Band Offset - The offset to use for the upper bollinger offset
Lower Band Offset - The offset to use for the lower bollinger offset
Long Stop Loss % - The stop loss percentage to test
Long Take Profit % - The Take profit percentage to test
Initial SO Deviation % - The price deviation percentage required to place to first safety order
Safety Order Vol Step % - The volume scale to test
3Commas Bot ID - (self explanatory)
Bot Email Token - Found in the deal start message for your bot (see link in previous section for details)
3Commas Bot Trading Pair - The pair to include for composite bot start deals (should match format of 3commas, not TradingView IE. USDT_BTC not BTCUSDT)
Start Date, Month, Year and End Date, Month and Year all apply to the backtesting window. By default it will use as much data as it can given the current period select (there is less historical data available for periods below 1H) back as far as 2016 (there appears to be no historical data on Trading view much before this). If you would like to test a different period of time, just change these values accordingly.
Known Issues
Currently there are a couple of issues with this strategy that you should be aware of. I may fix them at some point in the future but they don't really bug me so this is more for informational purposes than a promise that they may one day be fixed.
Does not test trailing take profit
Number of safety orders and Safety Order Step Scale are currently not user configurable (must edit source code)
Using the user configuration to generate deal start message assumes you are triggering a composite bot, not a simple bot.
Efficient Work [LucF]█ OVERVIEW
Efficient Work measures the ratio of price movement from close to close ( resulting work ) over the distance traveled to the high and low before settling down at the close ( total work ). The closer the two values are, the more Efficient Work approaches its maximum value of +1 for an up move or -1 for a down move. When price does not change, Efficient Work is zero.
Higher values of Efficient Work indicate more efficient price travel between the close of two successive bars, which I interpret to be more significant, regardless of the move's amplitude. Because it measures the direction and strength of price changes rather than their amplitude, Efficient Work may be thought of as a sentiment indicator.
█ CONCEPTS
This oscillator's design stems from a few key concepts.
Relative Levels
Other than the centerline, relative rather than absolute levels are used to identify levels of interest. Accordingly, no fixed levels correspond to overbought/oversold conditions. Relative levels of interest are identified using:
• A Donchian channel (historical highs/lows).
• The oscillator's position relative to higher timeframe values.
• Oscillator levels following points in time where a divergence is identified.
Higher timeframes
Two progressively higher timeframes are used to calculate larger-context values for the oscillator. The rationale underlying the use of timeframes higher than the chart's is that, while they change less frequently than the values calculated at the chart's resolution, they are more meaningful because more work (trader activity) is required to calculate them. Combining the immediacy of values calculated at the chart's resolution to higher timeframe values achieves a compromise between responsiveness and reliability.
Divergences as points of interest rather than directional clues
A very simple interpretation of what constitutes a divergence is used. A divergence is defined as a discrepancy between any bar's direction and the direction of the signal line on that same bar. No attempt is made to attribute a directional bias to divergences when they occur. Instead, the oscillator's level is saved and subsequent movement of the oscillator relative to the saved level is what determines the bullish/bearish state of the oscillator.
Conservative coloring scheme
Several additive coloring conditions allow the bull/bear coloring of the oscillator's main line to be restricted to specific areas meeting all the selected conditions. The concept is built on the premise that most of the time, an oscillator's value should be viewed as mere noise, and that somewhat like price, it only occasionally conveys actionable information.
█ FEATURES
Plots
• Three lines can be plotted. They are named Main line , Line 2 and Line 3 . You decide which calculation to use for each line:
• The oscillator's value at the chart's resolution.
• The oscillator's value at a medium timeframe higher than the chart's resolution.
• The oscillator's value at the highest timeframe.
• An aggregate line calculated using a weighed average of the three previous lines (see the Aggregate Weights section of Inputs to configure the weights).
• The coloring conditions, divergence levels and the Hi/Lo channel always apply to the Main line, whichever calculation you decide to use for it.
• The color of lines 2 and 3 are fixed but can be set in the "Colors" section of Inputs.
• You can change the thickness of each line.
• When the aggregate line is displayed, higher timeframe values are only used in its calculation when they become available in the chart's history,
otherwise the aggregate line would appear much later on the chart. To indicate when each higher timeframe value becomes available,
a small label appears near the centerline.
• Divergences can be shown as small dots on the centerline.
• Divergence levels can be shown. The level and fill are determined by the oscillator's position relative to the last saved divergence level.
• Bull/bear markers can be displayed. They occur whenever a new bull/bear state is determined by the "Main Line Coloring Conditions".
• The Hi/Lo (Donchian) channel can be displayed, and its period defined.
• The background can display the state of any one of 11 different conditions.
• The resolutions used for the higher timeframes can be displayed to the right of the last bar's value.
• Four key values are always displayed in the Data Window (fourth icon down to the right of your chart):
oscillator values for the chart, medium and highest timeframes, and the oscillator's instant value before it is averaged.
Main Line Coloring Conditions
• Nine different conditions can be selected to determine the bull/bear coloring of the main line. All conditions set to "ON" must be met to determine the bull/bear state.
• A volatility state can also be used to filter the conditions.
• When the coloring conditions and the filter do not allow for a bull/bear state to be determined, the neutral color is used.
Signal
• Seven different averages can be used to calculate the average of the oscillator's value.
• The average's period can be set. A period of one will show the instant value of the oscillator,
provided you don't use linear regression or the Hull MA as they do not work with a period of one.
• An external signal can be used as the oscillator's instant value. If an already averaged external value is used, set the period to one in this indicator.
• For the cases where an external signal is used, a centerline value can be set.
Higher Timeframes
• The two higher timeframes are named Medium timeframe and Highest timeframe . They can be determined using one of three methods:
• Auto-steps: the higher timeframes are determined using the chart's resolution. If the chart uses a seconds resolution, for example,
the medium and highest resolutions will be 15 and 60 minutes.
• Multiples: the timeframes are calculated using a multiple of the chart's resolution, which you can set.
• Fixed: the set timeframes do not change with the chart's resolution.
Repainting
• Repainting can be controlled separately for the chart's value and the higher timeframe values.
• The default is a repainting chart value and non-repainting higher timeframe values. The Aggregate line will thus repaint by default,
as it uses the chart's value along with the higher timeframes values.
Aggregate Weights
• The weight of each component of the Aggregate line can be set.
• The default is equal weights for the three components, meaning that the chart's value accounts for one third of the weight in the Aggregate.
High Volatility
• This provides control over the volatility filter used in the Main line's coloring conditions and the background display.
• Volatility is determined to be high when the short-term ATR is greater than the long-term ATR.
Colors
• You can define your own colors for all of the oscillator's plots.
• The default colors will perform well on both white and black chart backgrounds.
Alerts
• An alert can be defined for the script. The alert will trigger whenever a bull/bear marker appears in the indicator's display.
The particular combination of coloring conditions and the display of bull/bear markers when you create the alert will thus determine when the alert triggers.
Once the alerts are created, subsequent changes to the conditions controlling the display of markers will not affect the existing alert(s).
• You can create multiple alerts from this script, each triggering on different conditions.
Backtesting & Trading Engine Signal Line
• An invisible plot named "BTE Signal" is provided. It can be used as an entry signal when connected to the PineCoders Backtesting & Trading Engine as an external input.
It will generate an entry whenever a marker is displayed.
█ NOTES
• I do not know for sure if the calculations in Efficient Work are original. I apologize if they are not.
• Because this version of Efficient Work only has access to OHLC information, it cannot measure the total distance traveled through all of a bar's ticks, but the indicator nonetheless behaves in a manner consistent with the intentions underlying its design.
For Pine coders
This code was written using the following standards:
• The PineCoders Coding Conventions for Pine .
• A modified version of the PineCoders MTF Oscillator Framework and MTF Selection Framework .
MTF Oscillator Framework [PineCoders]This framework allows Pine coders to quickly build a complete multi-timeframe oscillator from any calculation producing values around a centerline, whether the values are bounded or not. Insert your calculation in the script and you have a ready-to-publish MTF Oscillator offering a plethora of presentation options and features.
█ HOW TO USE THE FRAMEWORK
1 — Insert your calculation in the `f_signal()` function at the top of the "Helper Functions" section of the script.
2 — Change the script's name in the `study()` declaration statement and the `alertcondition()` text in the last part of the "Plots" section.
3 — Adapt the default value used to initialize the CENTERLINE constant in the script's "Constants" section.
4 — If you want to publish the script, copy/paste the following description in your new publication's description and replace the "OVERVIEW" section with a description of your calculations.
5 — Voilà!
═════════════════════════════════════════════════════════════════════════
█ OVERVIEW
This oscillator calculates a directional value of True Range. When a bar is up, the positive value of True Range is used. A negative value is used when the bar is down. When there is no movement during the bar, a zero value is generated, even if True Range is different than zero. Because the unit of measure of True Range is price, the oscillator is unbounded (it does not have fixed upper/lower bounds).
True Range can be used as a metric for volatility, but by using a signed value, this oscillator will show the directional bias of progressively increasing/decreasing volatility, which can make it more useful than an always positive value of True Range.
The True Range calculation appeared for the first time in J. Welles Wilder's New Concepts in Technical Trading Systems book published in 1978. Wilder's objective was to provide a reliable measure of the effective movement—or range—between two bars, to measure volatility. True Range is also the building block used to calculate ATR (Average True Range), which calculates the average of True Range values over a given period using the `rma` averaging method—the same used in the calculation of another of Wilder's remarkable creations: RSI.
█ CONCEPTS
This oscillator's design stems from a few key concepts.
Relative Levels
Other than the centerline, relative rather than absolute levels are used to identify levels of interest. Accordingly, no fixed levels correspond to overbought/oversold conditions. Relative levels of interest are identified using:
• A Donchian channel (historical highs/lows).
• The oscillator's position relative to higher timeframe values.
• Oscillator levels following points in time where a divergence is identified.
Higher timeframes
Two progressively higher timeframes are used to calculate larger-context values for the oscillator. The rationale underlying the use of timeframes higher than the chart's is that, while they change less frequently than the values calculated at the chart's resolution, they are more meaningful because more work (trader activity) is required to calculate them. Combining the immediacy of values calculated at the chart's resolution to higher timeframe values achieves a compromise between responsiveness and reliability.
Divergences as points of interest rather than directional clues
A very simple interpretation of what constitutes a divergence is used. A divergence is defined as a discrepancy between any bar's direction and the direction of the signal line on that same bar. No attempt is made to attribute a directional bias to divergences when they occur. Instead, the oscillator's level is saved and subsequent movement of the oscillator relative to the saved level is what determines the bullish/bearish state of the oscillator.
Conservative coloring scheme
Several additive coloring conditions allow the bull/bear coloring of the oscillator's main line to be restricted to specific areas meeting all the selected conditions. The concept is built on the premise that most of the time, an oscillator's value should be viewed as mere noise, and that somewhat like price, it only occasionally conveys actionable information.
█ FEATURES
Plots
• Three lines can be plotted. They are named Main line , Line 2 and Line 3 . You decide which calculation to use for each line:
• The oscillator's value at the chart's resolution.
• The oscillator's value at a medium timeframe higher than the chart's resolution.
• The oscillator's value at the highest timeframe.
• An aggregate line calculated using a weighed average of the three previous lines (see the Aggregate Weights section of Inputs to configure the weights).
• The coloring conditions, divergence levels and the Hi/Lo channel always apply to the Main line, whichever calculation you decide to use for it.
• The color of lines 2 and 3 are fixed but can be set in the "Colors" section of Inputs.
• You can change the thickness of each line.
• When the aggregate line is displayed, higher timeframe values are only used in its calculation when they become available in the chart's history,
otherwise the aggregate line would appear much later on the chart. To indicate when each higher timeframe value becomes available,
a small label appears near the centerline.
• Divergences can be shown as small dots on the centerline.
• Divergence levels can be shown. The level and fill are determined by the oscillator's position relative to the last saved divergence level.
• Bull/bear markers can be displayed. They occur whenever a new bull/bear state is determined by the "Main Line Coloring Conditions".
• The Hi/Lo (Donchian) channel can be displayed, and its period defined.
• The background can display the state of any one of 11 different conditions.
• The resolutions used for the higher timeframes can be displayed to the right of the last bar's value.
• Four key values are always displayed in the Data Window (fourth icon down to the right of your chart):
oscillator values for the chart, medium and highest timeframes, and the oscillator's instant value before it is averaged.
Main Line Coloring Conditions
• Nine different conditions can be selected to determine the bull/bear coloring of the main line. All conditions set to "ON" must be met to determine the bull/bear state.
• A volatility state can also be used to filter the conditions.
• When the coloring conditions and the filter do not allow for a bull/bear state to be determined, the neutral color is used.
Signal
• Seven different averages can be used to calculate the average of the oscillator's value.
• The average's period can be set. A period of one will show the instant value of the oscillator,
provided you don't use linear regression or the Hull MA as they do not work with a period of one.
• An external signal can be used as the oscillator's instant value. If an already averaged external value is used, set the period to one in this indicator.
• For the cases where an external signal is used, a centerline value can be set.
Higher Timeframes
• The two higher timeframes are named Medium timeframe and Highest timeframe . They can be determined using one of three methods:
• Auto-steps: the higher timeframes are determined using the chart's resolution. If the chart uses a seconds resolution, for example,
the medium and highest resolutions will be 15 and 60 minutes.
• Multiples: the timeframes are calculated using a multiple of the chart's resolution, which you can set.
• Fixed: the set timeframes do not change with the chart's resolution.
Repainting
• Repainting can be controlled separately for the chart's value and the higher timeframe values.
• The default is a repainting chart value and non-repainting higher timeframe values. The Aggregate line will thus repaint by default,
as it uses the chart's value along with the higher timeframes values.
Aggregate Weights
• The weight of each component of the Aggregate line can be set.
• The default is equal weights for the three components, meaning that the chart's value accounts for one third of the weight in the Aggregate.
High Volatility
• This provides control over the volatility filter used in the Main line's coloring conditions and the background display.
• Volatility is determined to be high when the short-term ATR is greater than the long-term ATR.
Colors
• You can define your own colors for all of the oscillator's plots.
• The default colors will perform well on both white and black chart backgrounds.
Alerts
• An alert can be defined for the script. The alert will trigger whenever a bull/bear marker appears in the indicator's display.
The particular combination of coloring conditions and the display of bull/bear markers when you create the alert will thus determine when the alert triggers.
Once the alerts are created, subsequent changes to the conditions controlling the display of markers will not affect the existing alert(s).
• You can create multiple alerts from this script, each triggering on different conditions.
Backtesting & Trading Engine Signal Line
• An invisible plot named "BTE Signal" is provided. It can be used as an entry signal when connected to the PineCoders Backtesting & Trading Engine as an external input.
It will generate an entry whenever a marker is displayed.
Look first. Then leap.
Volume + MA5 & MA10This Volume + MA5 & MA10 (Technical Volume Trend Analysis)
The Volume + MA5 & MA10 indicator provides a precise view of market participation and volume momentum by combining raw volume data with two moving averages (MA5 and MA10). It’s designed for traders who rely on volume-based confirmation to validate price movements, breakouts, and trend reversals.
🔍 Overview
This indicator displays volume bars alongside two smooth volume averages — MA5 (short-term) and MA10 (medium-term) — making it easier to detect shifts in market activity.
When the short-term average crosses above or below the long-term average, it signals a potential change in trading intensity or market sentiment.
⚙️ Key Features
Dual Volume Moving Averages (MA5 & MA10) for short- and medium-term analysis.
Dynamic Bar Coloring based on whether current volume exceeds MA5 or MA10.
Crossover Detection with visual markers for MA5/MA10 intersections.
Alert Conditions to notify you of significant volume trend shifts.
Fully customizable appearance and smoothing options.
📊 How to Interpret
MA5 > MA10 → Increasing short-term volume activity (strengthening momentum).
MA5 < MA10 → Decreasing short-term volume (weakening participation).
Rising volume with price → Confirms trend strength.
Falling volume with rising/falling price → Suggests potential reversal or reduced conviction.
💡 Applications
Confirm breakouts and trend continuations.
Identify momentum divergences between price and volume.
Filter out low-volume or weak-trend setups.
Combine with RSI, MACD, or moving averages for enhanced signal validation.
✅ Advantages
Simple yet powerful structure for clean visual analysis.
Works across all timeframes and markets (crypto, stocks, forex, indices).
No repainting — reliable for both live and historical backtesting.
Use Volume + MA5 & MA10 to strengthen your technical analysis and gain a deeper understanding of how market participation drives price trends.
4-Hour Range Scalping [v6.3]User Guide: 4-Hour Range Scalping Strategy
Hello! Here is the guide for the Pine Script strategy. Please read it carefully to get the best results.
📈 This script automates the "4-Hour Range Scalping Strategy" from the video.
The main idea is that the first four hours of a major trading day (like New York) set up a "trap zone." The strategy waits for the price to break out of this zone and then fail, giving us a signal that the breakout was false and the price is likely to reverse.
Here’s the simple logic:
Define the Range: It precisely calculates the highest high and lowest low during the first four hours of the selected trading session (e.g., 00:00 to 04:00 New York Time).
Wait for a Breakout: It then monitors the 5-minute chart for a price breakout where a candle fully closes outside of this established range.
Identify the Reversal: The trade trigger occurs when the price fails to continue its breakout and a subsequent 5-minute candle closes back inside the range. This signals a potential reversal or "failed breakout."
Execute the Trade:
]A Short (Sell) trade is triggered after a failed breakout above the range high.
A Long (Buy) trade is triggered after a failed breakout below the range low.
Manage the Risk: The Stop Loss is automatically placed at the peak (for shorts) or trough (for longs) of the breakout move, and the Take Profit is set to a default 2:1 Risk/Reward Ratio.
How to Use the Script (Step-by-Step) ⚙️
Follow these instructions to get it running perfectly.
1. Set Your Chart Timeframe This is the most important step. The strategy is designed to run on a 5-minute (5m) chart. Open your TradingView chart and make sure the timeframe is set to "5m".
2. Add the Script to Your Chart Open the Pine Editor tab at the bottom of TradingView, paste the entire script, and click the "Add to chart" button.
3. Configure the Settings On your chart, find the strategy's name (e.g., "4-Hour Range Scalping ") and click the gear icon ⚙️ to open its settings.
Trading Session: Choose the session for the range. New York is the default and the one from the video.
Risk/Reward Ratio: The default is 2.0, meaning your potential profit is twice your potential loss. You can adjust this to test other targets.
Backtesting Period: To see how the strategy performed on all historical data, go to the "Strategy Tester" panel, click its own gear icon ⚙️, and uncheck the boxes for "Start Date" and "End Date."
4. Understand the Visuals on Your Chart
Blue Background Area: This is the 4-hour calculation window. The script is identifying the day's high and low during this time. No trades will ever happen here.
Red Line (Range High): The highest price of the 4-hour window. This is the upper boundary of the "trap zone."
Green Line (Range Low): The lowest price of the 4-hour window. This is the lower boundary.
Green Triangle (▲): Shows where a Long (Buy) trade was entered.
Red Triangle (▼): Shows where a Short (Sell) trade was entered.
A Very Important Note on Timezones 🕒
This is critical for you in the Philippines (PHT).
The script is based on the New York session, which is 12 hours behind you. Your TradingView chart will still show your local time, but the script works on NY time in the background.
The New York "day" begins at 12:00 PM (Noon) your time.
The script's blue calculation window will be from 12:00 PM to 4:00 PM your local time.
The red and green range lines will appear on your chart only after 4:00 PM your time.
So, if you look at your chart in the morning or early afternoon, you will not see today's range yet. This is normal! The script is just waiting for the New York session to start.
How to Set Up Trade Alerts 🔔
You can have TradingView send you a notification whenever the script enters a trade.
Click the "Alert" button (looks like a clock) in the right-hand toolbar of TradingView.
In the "Condition" dropdown, select the name of the script (e.g., "4-Hour Range Scalping...").
You will then see two options: "Long Signal" and "Short Signal".
Select one (e.g., "Long Signal") and configure how you want to be notified (e.g., "Notify on app").
Click "Create". Repeat the process to create an alert for the other signal.
⚠️ Important Disclosure
For Educational and Research Purposes Only.
This script and all accompanying information are provided for educational and research purposes only. The strategy demonstrated is a technical concept and should not be misconstrued as financial, investment, legal, or tax advice.
Trading financial markets involves substantial risk and is not suitable for every investor. There is a possibility that you could sustain a loss of some or all of your initial investment. Therefore, you should not invest money that you cannot afford to lose.
Past performance is not indicative of future results. The backtesting results shown by this script are historical and do not guarantee future performance. Market conditions are constantly changing.
By using this script, you acknowledge that you are solely responsible for any and all trading decisions you make. You should conduct your own thorough research and, if necessary, seek advice from an independent financial advisor before making any investment decisions. The creators of this script assume no liability for any of your trading results.
Portfolio Strategy TesterThe Portfolio Strategy Tester is an institutional-grade backtesting framework that evaluates the performance of trend-following strategies on multi-asset portfolios. It enables users to construct custom portfolios of up to 30 assets and apply moving average crossover strategies across individual holdings. The model features a clear, color-coded table that provides a side-by-side comparison between the buy-and-hold portfolio and the portfolio using the risk management strategy, offering a comprehensive assessment of both approaches relative to the benchmark.
Portfolios are constructed by entering each ticker symbol in the menu, assigning its respective weight, and reviewing the total sum of individual weights displayed at the top left of the table. For strategy selection, users can choose between Exponential Moving Average (EMA), Simple Moving Average (SMA), Wilder’s Moving Average (RMA), Weighted Moving Average (WMA), Moving Average Convergence Divergence (MACD), and Volume-Weighted Moving Average (VWMA). Moving average lengths are defined in the menu and apply only to strategy-enabled assets.
To accurately replicate real-world portfolio conditions, users can choose between daily, weekly, monthly, or quarterly rebalancing frequencies and decide whether cash is held or redistributed. Daily rebalancing maintains constant portfolio weights, while longer intervals allow natural drift. When cash positions are not allowed, capital from bearish assets is automatically redistributed proportionally among bullish assets, ensuring the portfolio remains fully invested at all times. The table displays a comprehensive set of widely used institutional-grade performance metrics:
CAGR = Compounded annual growth rate of returns.
Volatility = Annualized standard deviation of returns.
Sharpe = CAGR per unit of annualized standard deviation.
Sortino = CAGR per unit of annualized downside deviation.
Calmar = CAGR relative to maximum drawdown.
Max DD = Largest peak-to-trough decline in value.
Beta (β) = Sensitivity of returns relative to benchmark returns.
Alpha (α) = Excess annualized risk-adjusted returns relative to benchmark.
Upside = Ratio of average return to benchmark return on up days.
Downside = Ratio of average return to benchmark return on down days.
Tracking = Annualized standard deviation of returns versus benchmark.
Turnover = Average sum of absolute changes in weights per year.
Cumulative returns are displayed on each label as the total percentage gain from the selected start date, with green indicating positive returns and red indicating negative returns. In the table, baseline metrics serve as the benchmark reference and are always gray. For portfolio metrics, green indicates outperformance relative to the baseline, while red indicates underperformance relative to the baseline. For strategy metrics, green indicates outperformance relative to both the baseline and the portfolio, red indicates underperformance relative to both, and gray indicates underperformance relative to either the baseline or portfolio. Metrics such as Volatility, Tracking Error, and Turnover ratio are always displayed in gray as they serve as descriptive measures.
In summary, the Portfolio Strategy Tester is a comprehensive backtesting tool designed to help investors evaluate different trend-following strategies on custom portfolios. It enables real-world simulation of both active and passive investment approaches and provides a full set of standard institutional-grade performance metrics to support data-driven comparisons. While results are based on historical performance, the model serves as a powerful portfolio management and research framework for developing, validating, and refining systematic investment strategies.
Curved Radius Supertrend [BOSWaves]Curved Radius Supertrend — Adaptive Parabolic Trend Framework with Dynamic Acceleration Geometry
Overview
The Curved Radius Supertrend introduces an evolution of the classic Supertrend indicator - engineered with a dynamic curvature engine that replaces rigid ATR bands with parabolic, radius-based motion. Traditional Supertrend systems rely on static band displacement, reacting linearly to volatility and often lagging behind emerging price acceleration. The Curved Radius Supertend model redefines this by integrating controlled acceleration and curvature geometry, allowing the trend bands to adapt fluidly to both velocity and duration of price movement.
The result is a smoother, more organic trend flow that visually captures the momentum curve of price action - not just its direction. Instead of sharp pivots or whipsaws, traders experience a structurally curved trajectory that mirrors real market inertia. This makes it particularly effective for identifying sustained directional phases, detecting early trend rotations, and filtering out noise that plagues standard Supertrend methodologies.
Unlike conventional band-following systems, the Curved Radius framework is time-reactive and velocity-aware, providing a nuanced signal structure that blends geometric precision with volatility sensitivity.
Theoretical Foundation
The Curved Radius Supertrend draws from the intersection of mathematical curvature dynamics and adaptive volatility processing. Standard Supertrend algorithms extend from Average True Range (ATR) envelopes - a linear measure of volatility that moves proportionally with price deviation. However, markets do not expand or contract linearly. Trend velocity typically accelerates and decelerates in nonlinear arcs, forming natural parabolas across price phases.
By embedding a radius-based acceleration function, the indicator models this natural behavior. The core variable, radiusStrength, controls how aggressively curvature accelerates over time. Instead of simply following price distance, the band now evolves according to temporal acceleration - each bar contributes incremental velocity, bending the trend line into a radius-like curve.
This structural design allows the indicator to anticipate rather than just respond to price action, capturing momentum transitions as curved accelerations rather than binary flips. In practice, this eliminates the stutter effect typical of standard Supertrends and replaces it with fluid directional motion that better reflects actual trend geometry.
How It Works
The Curved Radius Supertrend is constructed through a multi-stage process designed to balance price responsiveness with geometric stability:
1. Baseline Supertrend Core
The framework begins with a standard ATR-derived upper and lower band calculation. These define the volatility envelope that constrains potential price zones. Directional bias is determined through crossover logic - prices above the lower band confirm an uptrend, while prices below the upper band confirm a downtrend.
2. Curvature Acceleration Engine
Once a trend direction is established, a curvature engine is activated. This system uses radiusStrength as a coefficient to simulate acceleration per bar, incrementally increasing velocity over time. The result is a parabolic displacement from the anchor price (the price level at trend change), creating a curved motion path that dynamically widens or tightens as the trend matures.
Mathematically, this acceleration behaves quadratically - each new bar compounds the previous velocity, forming an exponential rate of displacement that resembles curved inertia.
3. Adaptive Smoothing Layer
After the radius curve is applied, a smoothing stage (defined by the smoothness parameter) uses a simple moving average to regulate curve noise. This ensures visual coherence without sacrificing responsiveness, producing flowing arcs rather than jagged band steps.
4. Directional Visualization and Outer Envelope
Directional state (bullish or bearish) dictates both the color gradient and band displacement. An outer envelope is plotted one ATR beyond the curved band, creating a layered trend visualization that shows the extent of volatility expansion.
5. Signal Events and Alerts
Each directional transition triggers a 'BUY' or 'SELL' signal, clearly labeling phase shifts in market structure. Alerts are built in for automation and backtesting.
Interpretation
The Curved Radius Supertrend reframes how traders visualize and confirm trends. Instead of simply plotting a trailing stop, it maps the dynamic curvature of trend development.
Uptrend Phases : The band curves upward with increasing acceleration, reflecting the market’s growing directional velocity. As curvature steepens, conviction strengthens.
Downtrend Phases : The band bends downward in a mirrored acceleration pattern, indicating sustained bearish momentum.
Trend Change Points : When the direction flips and a new anchor point forms, the curve resets - providing a clean, early visual confirmation of structural reversal.
Smoothing and Radius Interplay : A lower radius strength produces a tighter, more reactive curve ideal for scalping or short timeframes. Higher values generate broad, sweeping arcs optimized for swing or positional analysis.
Visually, this curvature system translates market inertia into shape - revealing how trends bend, accelerate, and ultimately exhaust.
Strategy Integration
The Curved Radius Supertrend is versatile enough to integrate seamlessly into multiple trading frameworks:
Trend Following : Use BUY/SELL flips to identify emerging directional bias. Strong curvature continuation confirms sustained momentum.
Momentum Entry Filtering : Combine with oscillators or volume tools to filter entries only when the curve slope accelerates (high momentum conditions).
Pullback and Re-entry Timing : The smooth curvature of the radius band allows traders to identify shallow retracements without premature exits. The band acts as a dynamic, self-adjusting support/resistance arc.
Volatility Compression and Expansion : Flattening curvature indicates volatility compression - a potential pre-breakout zone. Rapid re-steepening signals expansion and directional conviction.
Stop Placement Framework : The curved band can serve as a volatility-adjusted trailing stop. Because the curve reflects acceleration, it adapts naturally to market rhythm - widening during momentum surges and tightening during stagnation.
Technical Implementation Details
Curved Radius Engine : Parabolic acceleration algorithm that applies quadratic velocity based on bar count and radiusStrength.
Anchor Logic : Resets curvature at each trend change, establishing a new reference base for directional acceleration.
Smoothing Layer : SMA-based curve smoothing for noise reduction.
Outer Envelope : ATR-derived band offset visualizing volatility extension.
Directional Coloring : Candle and band coloration tied to current trend state.
Signal Engine : Built-in BUY/SELL markers and alert conditions for automation or script integration.
Optimal Application Parameters
Timeframe Guidance :
1-5 min (Scalping) : 0.08–0.12 radius strength, minimal smoothing for rapid responsiveness.
15 min : 0.12–0.15 radius strength for intraday trends.
1H : 0.15–0.18 radius strength for structured short-term swing setups.
4H : 0.18–0.22 radius strength for macro-trend shaping.
Daily : 0.20–0.25 radius strength for broad directional curves.
Weekly : 0.25–0.30 radius strength for smooth macro-level cycles.
The suggested radius strength ranges provide general structural guidance. Optimal values may vary across assets and volatility regimes, and should be refined through empirical testing to account for instrument-specific behavior and prevailing market conditions.
Asset Guidance :
Cryptocurrency : Higher radius and multiplier values to stabilize high-volatility environments.
Forex : Midrange settings (0.12-0.18) for clean curvature transitions.
Equities : Balanced curvature for trending sectors or momentum rotation setups.
Indices/Futures : Moderate radius values (0.15-0.22) to capture cyclical macro swings.
Performance Characteristics
High Effectiveness :
Trending environments with directional expansion.
Markets exhibiting clean momentum arcs and low structural noise.
Reduced Effectiveness :
Range-bound or low-volatility conditions with repeated false flips.
Ultra-short-term timeframes (<1m) where curvature acceleration overshoots.
Integration Guidelines
Confluence Framework : Combine with structure tools (order blocks, BOS, liquidity zones) for entry validation.
Risk Management : Trail stops along the curved band rather than fixed points to align with adaptive market geometry.
Multi-Timeframe Confirmation : Use higher timeframe curvature as a trend filter and lower timeframe curvature for execution timing.
Curve Compression Awareness : Treat flattening arcs as potential exhaustion zones - ideal for scaling out or reducing exposure.
Disclaimer
The Curved Radius Supertrend is a geometric trend model designed for professional traders and analysts. It is not a predictive system or a guaranteed profit method. Its performance depends on correct parameter calibration and sound risk management. BOSWaves recommends using it as part of a comprehensive analytical framework, incorporating volume, liquidity, and structural context to validate directional signals.
Moving Average Trend Strategy V2.1 — With Stop Loss and Add Posi**Strategy Feature Description:**
---
### **Entry Logic:**
* When **MA7** crosses **MA15**, and the distance between **MA15** and **MA99** is less than **0.5%**
* When **MA15** crosses **MA99**, and the distance between **MA7** and **MA15** is less than **0.5%**
* When the distance among all three MAs (**MA7**, **MA15**, **MA99**) is less than **0.5%** (adjustable via parameters)
---
### **Capital Management:**
* Initial capital: **$100**
* Each position uses **15%** of total capital
* Opens **both long and short positions simultaneously** (dual-direction mode)
---
### **Risk Control:**
* **Long position stop-loss:** Entry price − 2%
* **Short position stop-loss:** Entry price + 2%
* Uses a **five-level take-profit grid**:
* Every 5% profit → close 20% of position
* Any pending take-profit orders are automatically canceled when stop-loss triggers
---
### **Visualization Features:**
* Real-time display of the three moving averages
* Chart annotations for entry signal points
* All trade signals and performance can be viewed through **TradingView backtest reports**
---
### **Notes:**
* Parameters can be adjusted based on the volatility of the instrument (historical backtesting is recommended first)
* Dual-direction positions may generate **hedging costs** — recommended for low-fee markets
* Real trading must consider **exchange minimum order size limits**
* Suggest enabling a **volume filter mechanism** (extension interface already reserved)
* Always perform **historical backtesting and parameter optimization** in TradingView before connecting to live trading systems
Swing AURORA v4.0 — Refined Trend Signals### Swing Algo v4.0 — Refined Trend Signals
#### Overview
Swing Algo v4.0 is an advanced technical indicator designed for TradingView, built to detect trend changes and provide actionable buy/sell signals in various market conditions. It combines multiple technical elements like moving averages, ADX for trend strength, Stochastic RSI for timing, and RSI divergence for confirmation, all while adapting to different timeframes through auto-tuning. This indicator overlays on your chart, highlighting trend regimes with background colors, displaying buy/sell labels (including "strong" variants), and offering early "potential" signals for proactive trading decisions. It's suitable for swing trading, trend following, or as a filter for other strategies across forex, stocks, crypto, and other assets.
#### Purpose
The primary goal of Swing Algo v4.0 is to help traders identify high-probability trend reversals and continuations early, reducing noise and false signals. It aims to provide clear, non-repainting signals that align with market structure, volatility, and momentum. By incorporating filters like higher timeframe (HTF) alignment, bias EMAs, and divergence, it refines entries for better accuracy. The indicator emphasizes balanced performance across aggressive, balanced, and conservative modes, making it versatile for both novice and experienced traders seeking to optimize their decision-making process.
#### What It Indicates
- **Trend Regimes (Background Coloring)**: The chart background changes color to reflect the current market regime:
- **Green (Intense for strong uptrends, faded when cooling)**: Indicates bullish trends where price is above the baseline and EMAs are aligned upward.
- **Red/Maroon (Intense maroon for strong downtrends, faded red when cooling)**: Signals bearish trends with price below the baseline and downward EMA alignment.
- **Faded Yellow**: Marks "no-trade" zones or potential trend changes, where conditions are choppy, weak, or neutral (e.g., low ADX, near baseline, or low volatility).
- **Buy/Sell Signals**: Labels appear on the chart for confirmed entries:
- "BUY" or "STRONG BUY" for bullish signals (strong variants require higher scores and optional divergence).
- "SELL" or "STRONG SELL" for bearish signals.
- **Potential Signals**: Early warnings like "Potential BUY" or "Potential SELL" appear before full confirmation, allowing traders to anticipate moves (confirmed after a few bars based on the trigger window).
- **Divergence Marks**: Small "DIV↑" (bullish) or "DIV↓" (bearish) labels highlight RSI divergences on pivots, adding confluence for strong signals.
- **Lines**: Optional plots for baseline (teal), EMA13/21 (lime/red based on crossover), providing visual trend context.
Signals are anchored either to the current bar or confirmed pivots, ensuring alignment with price action. The indicator avoids repainting by confirming on close if enabled.
#### Key Parameters and Customization
Swing Algo v4.0 offers minimal yet efficient parameters for fine-tuning, with defaults optimized for common use cases. Most can be auto-tuned based on timeframe for simplicity:
- **Confirm on Close (no repaint)**: Boolean (default: true) – Ensures signals don't repaint by waiting for bar confirmation.
- **Auto-tune by Timeframe**: Boolean (default: true) – Automatically adjusts lengths and sensitivity for 5-15m, 30-60m, 2-4h, or higher frames.
- **Mode**: String (options: Aggressive, Balanced , Conservative) – Controls signal thresholds; Aggressive for more signals, Conservative for fewer but higher-quality ones.
- **Signal Anchor**: String (options: Pivot (divLB) , Current bar) – Places labels on confirmed pivots or the current bar.
- **Trigger Window (bars)**: Integer (default: 3) – Window for signal timing; auto-tuned if enabled.
- **Baseline Type**: String (options: HMA , EMA, ALMA) – Core trend line; lengths auto-tune (e.g., 55 for short frames).
- **Use Bias EMA Filter**: Boolean (default: false) – Adds a long-term EMA for trend bias.
- **Use HTF Filter**: Boolean (default: false) – Aligns with higher timeframe (auto or manual like 60m, 240m, D); override for stricter scoring.
- **Sensitivity (10–90)**: Integer (default: 55) – Adjusts ADX threshold for trend detection; higher = more sensitive.
- **Use RSI-Stoch Trigger**: Boolean (default: true) – Enables Stochastic RSI for entry timing; customizable lengths, smooths, and levels.
- **Use RSI Divergence for STRONG**: Boolean (default: true) – Requires divergence for strong signals; pivot lookback (default: 5).
- **Visual Options**: Booleans for background regime, labels, divergence marks, and lines (all default: true).
These parameters are grouped for ease, with tooltips in TradingView for quick reference. Start with defaults and tweak based on backtesting.
#### How It Works
At its core, Swing Algo v4.0 calculates a baseline (e.g., HMA) to define the trend direction. It then scores potential buys/sells using factors like:
- **Trend Strength**: ADX above a dynamic threshold, combined with EMA crossovers (13/21) and slope analysis.
- **Volatility/Volume**: Bollinger/Keltner squeeze exits, volume z-score, and ATR filters to avoid choppy markets.
- **Timing**: Stochastic RSI crossovers or micro-timing via DEMA/TEMA for precise entries.
- **Filters**: Bias EMA, HTF alignment, gap from baseline, and no-trade zones (weak ADX, near baseline, low vol).
- **Divergence**: RSI pivots confirm strong signals.
- **Scoring**: Buy/sell scores (min 3-5 based on mode) trigger labels only when all gates pass, with early "potential" detection for foresight.
The algorithm processes these in real-time, auto-adapting to timeframe for efficiency. Signals flip only on direction changes to prevent over-trading. For best results, use on liquid assets and combine with risk management.
#### Disclaimer
This indicator is for educational and informational purposes only and does not constitute financial advice, investment recommendations, or trading signals. Trading involves significant risk of loss and is not suitable for all investors. Past performance is not indicative of future results. Always backtest the indicator on your preferred assets and timeframes, and consult a qualified financial advisor before making any trading decisions. The author assumes no liability for any losses incurred from using this script. Use at your own risk.
Turtle Strategy - Triple EMA Trend with ADX and ATRDescription
The Triple EMA Trend strategy is a directional momentum system built on the alignment of three exponential moving averages and a strong ADX confirmation filter. It is designed to capture established trends while maintaining disciplined risk management through ATR-based stops and targets.
Core Logic
The system activates only under high-trend conditions, defined by the Average Directional Index (ADX) exceeding a configurable threshold (default: 43).
A bullish setup occurs when the short-term EMA is above the mid-term EMA, which in turn is above the long-term EMA, and price trades above the fastest EMA.
A bearish setup is the mirror condition.
Execution Rules
Entry:
• Long when ADX confirms trend strength and EMA alignment is bullish.
• Short when ADX confirms trend strength and EMA alignment is bearish.
Exit:
• Stop Loss: 1.8 × ATR below (for longs) or above (for shorts) the entry price.
• Take Profit: 3.3 × ATR in the direction of the trade.
Both parameters are configurable.
Additional Features
• Start/end date inputs for controlled backtesting.
• Selective activation of long or short trades.
• Built-in commission and position sizing (percent of equity).
• Full visual representation of EMAs, ADX, stop-loss, and target levels.
This strategy emphasizes clean trend participation, strict entry qualification, and consistent reward-to-risk structure. Ideal for swing or medium-term testing across trending assets.
T3 ATR [DCAUT]█ T3 ATR
📊 ORIGINALITY & INNOVATION
The T3 ATR indicator represents an important enhancement to the traditional Average True Range (ATR) indicator by incorporating the T3 (Tilson Triple Exponential Moving Average) smoothing algorithm. While standard ATR uses fixed RMA (Running Moving Average) smoothing, T3 ATR introduces a configurable volume factor parameter that allows traders to adjust the smoothing characteristics from highly responsive to heavily smoothed output.
This innovation addresses a fundamental limitation of traditional ATR: the inability to adapt smoothing behavior without changing the calculation period. With T3 ATR, traders can maintain a consistent ATR period while adjusting the responsiveness through the volume factor, making the indicator adaptable to different trading styles, market conditions, and timeframes through a single unified implementation.
The T3 algorithm's triple exponential smoothing with volume factor control provides improved signal quality by reducing noise while maintaining better responsiveness compared to traditional smoothing methods. This makes T3 ATR particularly valuable for traders who need to adapt their volatility measurement approach to varying market conditions without switching between multiple indicator configurations.
📐 MATHEMATICAL FOUNDATION
The T3 ATR calculation process involves two distinct stages:
Stage 1: True Range Calculation
The True Range (TR) is calculated using the standard formula:
TR = max(high - low, |high - close |, |low - close |)
This captures the greatest of the current bar's range, the gap from the previous close to the current high, or the gap from the previous close to the current low, providing a comprehensive measure of price movement that accounts for gaps and limit moves.
Stage 2: T3 Smoothing Application
The True Range values are then smoothed using the T3 algorithm, which applies six exponential moving averages in succession:
First Layer: e1 = EMA(TR, period), e2 = EMA(e1, period)
Second Layer: e3 = EMA(e2, period), e4 = EMA(e3, period)
Third Layer: e5 = EMA(e4, period), e6 = EMA(e5, period)
Final Calculation: T3 = c1×e6 + c2×e5 + c3×e4 + c4×e3
The coefficients (c1, c2, c3, c4) are derived from the volume factor (VF) parameter:
a = VF / 2
c1 = -a³
c2 = 3a² + 3a³
c3 = -6a² - 3a - 3a³
c4 = 1 + 3a + a³ + 3a²
The volume factor parameter (0.0 to 1.0) controls the weighting of these coefficients, directly affecting the balance between responsiveness and smoothness:
Lower VF values (approaching 0.0): Coefficients favor recent data, resulting in faster response to volatility changes with minimal lag but potentially more noise
Higher VF values (approaching 1.0): Coefficients distribute weight more evenly across the smoothing layers, producing smoother output with reduced noise but slightly increased lag
📊 COMPREHENSIVE SIGNAL ANALYSIS
Volatility Level Interpretation:
High Absolute Values: Indicate strong price movements and elevated market activity, suggesting larger position risks and wider stop-loss requirements, often associated with trending markets or significant news events
Low Absolute Values: Indicate subdued price movements and quiet market conditions, suggesting smaller position risks and tighter stop-loss opportunities, often associated with consolidation phases or low-volume periods
Rapid Increases: Sharp spikes in T3 ATR often signal the beginning of significant price moves or market regime changes, providing early warning of increased trading risk
Sustained High Levels: Extended periods of elevated T3 ATR indicate sustained trending conditions with persistent volatility, suitable for trend-following strategies
Sustained Low Levels: Extended periods of low T3 ATR indicate range-bound conditions with suppressed volatility, suitable for mean-reversion strategies
Volume Factor Impact on Signals:
Low VF Settings (0.0-0.3): Produce responsive signals that quickly capture volatility changes, suitable for short-term trading but may generate more frequent color changes during minor fluctuations
Medium VF Settings (0.4-0.7): Provide balanced signal quality with moderate responsiveness, filtering out minor noise while capturing significant volatility changes, suitable for swing trading
High VF Settings (0.8-1.0): Generate smooth, stable signals that filter out most noise and focus on major volatility trends, suitable for position trading and long-term analysis
🎯 STRATEGIC APPLICATIONS
Position Sizing Strategy:
Determine your risk per trade (e.g., 1% of account capital - adjust based on your risk tolerance and experience)
Decide your stop-loss distance multiplier (e.g., 2.0x T3 ATR - this varies by market and strategy, test different values)
Calculate stop-loss distance: Stop Distance = Multiplier × Current T3 ATR
Calculate position size: Position Size = (Account × Risk %) / Stop Distance
Example: $10,000 account, 1% risk, T3 ATR = 50 points, 2x multiplier → Position Size = ($10,000 × 0.01) / (2 × 50) = $100 / 100 points = 1 unit per point
Important: The ATR multiplier (1.5x - 3.0x) should be determined through backtesting for your specific instrument and strategy - using inappropriate multipliers may result in stops that are too tight (frequent stop-outs) or too wide (excessive losses)
Adjust the volume factor to match your trading style: lower VF for responsive stop distances in short-term trading, higher VF for stable stop distances in position trading
Dynamic Stop-Loss Placement:
Determine your risk tolerance multiplier (typically 1.5x to 3.0x T3 ATR)
For long positions: Set stop-loss at entry price minus (multiplier × current T3 ATR value)
For short positions: Set stop-loss at entry price plus (multiplier × current T3 ATR value)
Trail stop-losses by recalculating based on current T3 ATR as the trade progresses
Adjust the volume factor based on desired stop-loss stability: higher VF for less frequent adjustments, lower VF for more adaptive stops
Market Regime Identification:
Calculate a reference volatility level using a longer-period moving average of T3 ATR (e.g., 50-period SMA)
High Volatility Regime: Current T3 ATR significantly above reference (e.g., 120%+) - favor trend-following strategies, breakout trades, and wider targets
Normal Volatility Regime: Current T3 ATR near reference (e.g., 80-120%) - employ standard trading strategies appropriate for prevailing market structure
Low Volatility Regime: Current T3 ATR significantly below reference (e.g., <80%) - favor mean-reversion strategies, range trading, and prepare for potential volatility expansion
Monitor T3 ATR trend direction and compare current values to recent history to identify regime transitions early
Risk Management Implementation:
Establish your maximum portfolio heat (total risk across all positions, typically 2-6% of capital)
For each position: Calculate position size using the formula Position Size = (Account × Individual Risk %) / (ATR Multiplier × Current T3 ATR)
When T3 ATR increases: Position sizes automatically decrease (same risk %, larger stop distance = smaller position)
When T3 ATR decreases: Position sizes automatically increase (same risk %, smaller stop distance = larger position)
This approach maintains constant dollar risk per trade regardless of market volatility changes
Use consistent volume factor settings across all positions to ensure uniform risk measurement
📋 DETAILED PARAMETER CONFIGURATION
ATR Length Parameter:
Default Setting: 14 periods
This is the standard ATR calculation period established by Welles Wilder, providing balanced volatility measurement that captures both short-term fluctuations and medium-term trends across most markets and timeframes
Selection Principles:
Shorter periods increase sensitivity to recent volatility changes and respond faster to market shifts, but may produce less stable readings
Longer periods emphasize sustained volatility trends and filter out short-term noise, but respond more slowly to genuine regime changes
The optimal period depends on your holding time, trading frequency, and the typical volatility cycle of your instrument
Consider the timeframe you trade: Intraday traders typically use shorter periods, swing traders use intermediate periods, position traders use longer periods
Practical Approach:
Start with the default 14 periods and observe how well it captures volatility patterns relevant to your trading decisions
If ATR seems too reactive to minor price movements: Increase the period until volatility readings better reflect meaningful market changes
If ATR lags behind obvious volatility shifts that affect your trades: Decrease the period for faster response
Match the period roughly to your typical holding time - if you hold positions for N bars, consider ATR periods in a similar range
Test different periods using historical data for your specific instrument and strategy before committing to live trading
T3 Volume Factor Parameter:
Default Setting: 0.7
This setting provides a reasonable balance between responsiveness and smoothness for most market conditions and trading styles
Understanding the Volume Factor:
Lower values (closer to 0.0) reduce smoothing, allowing T3 ATR to respond more quickly to volatility changes but with less noise filtering
Higher values (closer to 1.0) increase smoothing, producing more stable readings that focus on sustained volatility trends but respond more slowly
The trade-off is between immediacy and stability - there is no universally optimal setting
Selection Principles:
Match to your decision speed: If you need to react quickly to volatility changes for entries/exits, use lower VF; if you're making longer-term risk assessments, use higher VF
Match to market character: Noisier, choppier markets may benefit from higher VF for clearer signals; cleaner trending markets may work well with lower VF for faster response
Match to your preference: Some traders prefer responsive indicators even with occasional false signals, others prefer stable indicators even with some delay
Practical Adjustment Guidelines:
Start with default 0.7 and observe how T3 ATR behavior aligns with your trading needs over multiple sessions
If readings seem too unstable or noisy for your decisions: Try increasing VF toward 0.9-1.0 for heavier smoothing
If the indicator lags too much behind volatility changes you care about: Try decreasing VF toward 0.3-0.5 for faster response
Make meaningful adjustments (0.2-0.3 changes) rather than small increments - subtle differences are often imperceptible in practice
Test adjustments in simulation or paper trading before applying to live positions
📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES
Responsiveness Characteristics:
The T3 smoothing algorithm provides improved responsiveness compared to traditional RMA smoothing used in standard ATR. The triple exponential design with volume factor control allows the indicator to respond more quickly to genuine volatility changes while maintaining the ability to filter noise through appropriate VF settings. This results in earlier detection of volatility regime changes compared to standard ATR, particularly valuable for risk management and position sizing adjustments.
Signal Stability:
Unlike simple smoothing methods that may produce erratic signals during transitional periods, T3 ATR's multi-layer exponential smoothing provides more stable signal progression. The volume factor parameter allows traders to tune signal stability to their preference, with higher VF settings producing remarkably smooth volatility profiles that help avoid overreaction to temporary market fluctuations.
Comparison with Standard ATR:
Adaptability: T3 ATR allows adjustment of smoothing characteristics through the volume factor without changing the ATR period, whereas standard ATR requires changing the period length to alter responsiveness, potentially affecting the fundamental volatility measurement
Lag Reduction: At lower volume factor settings, T3 ATR responds more quickly to volatility changes than standard ATR with equivalent periods, providing earlier signals for risk management adjustments
Noise Filtering: At higher volume factor settings, T3 ATR provides superior noise filtering compared to standard ATR, producing cleaner signals for long-term analysis without sacrificing volatility measurement accuracy
Flexibility: A single T3 ATR configuration can serve multiple trading styles by adjusting only the volume factor, while standard ATR typically requires multiple instances with different periods for different trading applications
Suitable Use Cases:
T3 ATR is well-suited for the following scenarios:
Dynamic Risk Management: When position sizing and stop-loss placement need to adapt quickly to changing volatility conditions
Multi-Style Trading: When a single volatility indicator must serve different trading approaches (day trading, swing trading, position trading)
Volatile Markets: When standard ATR produces too many false volatility signals during choppy conditions
Systematic Trading: When algorithmic systems require a single, configurable volatility input that can be optimized for different instruments
Market Regime Analysis: When clear identification of volatility expansion and contraction phases is critical for strategy selection
Known Limitations:
Like all technical indicators, T3 ATR has limitations that users should understand:
Historical Nature: T3 ATR is calculated from historical price data and cannot predict future volatility with certainty
Smoothing Trade-offs: The volume factor setting involves a trade-off between responsiveness and smoothness - no single setting is optimal for all market conditions
Extreme Events: During unprecedented market events or gaps, T3 ATR may not immediately reflect the full scope of volatility until sufficient data is processed
Relative Measurement: T3 ATR values are most meaningful in relative context (compared to recent history) rather than as absolute thresholds
Market Context Required: T3 ATR measures volatility magnitude but does not indicate price direction or trend quality - it should be used in conjunction with directional analysis
Performance Expectations:
T3 ATR is designed to help traders measure and adapt to changing market volatility conditions. When properly configured and applied:
It can help reduce position risk during volatile periods through appropriate position sizing
It can help identify optimal times for more aggressive position sizing during stable periods
It can improve stop-loss placement by adapting to current market conditions
It can assist in strategy selection by identifying volatility regimes
However, volatility measurement alone does not guarantee profitable trading. T3 ATR should be integrated into a comprehensive trading approach that includes directional analysis, proper risk management, and sound trading psychology.
USAGE NOTES
This indicator is designed for technical analysis and educational purposes. T3 ATR provides adaptive volatility measurement but has limitations and should not be used as the sole basis for trading decisions. The indicator measures historical volatility patterns, and past volatility characteristics do not guarantee future volatility behavior. Market conditions can change rapidly, and extreme events may produce volatility readings that fall outside historical norms.
Traders should combine T3 ATR with directional analysis tools, support/resistance analysis, and other technical indicators to form a complete trading strategy. Proper backtesting and forward testing with appropriate risk management is essential before applying T3 ATR-based strategies to live trading. The volume factor parameter should be optimized for specific instruments and trading styles through careful testing rather than assuming default settings are optimal for all applications.
RSI Bollinger Bands [DCAUT]█ RSI Bollinger Bands
📊 ORIGINALITY & INNOVATION
The RSI Bollinger Bands indicator represents a meaningful advancement in momentum analysis by combining two proven technical tools: the Relative Strength Index (RSI) and Bollinger Bands. This combination addresses a significant limitation in traditional RSI analysis - the use of fixed overbought/oversold thresholds (typically 70/30) that fail to adapt to changing market volatility conditions.
Core Innovation:
Rather than relying on static threshold levels, this indicator applies Bollinger Bands statistical analysis directly to RSI values, creating dynamic zones that automatically adjust based on recent momentum volatility. This approach helps reduce false signals during low volatility periods while remaining sensitive to genuine extremes during high volatility conditions.
Key Enhancements Over Traditional RSI:
Dynamic Thresholds: Overbought/oversold zones adapt to market conditions automatically, eliminating the need for manual threshold adjustments across different instruments and timeframes
Volatility Context: Band width provides immediate visual feedback about momentum volatility, helping traders distinguish between stable trends and erratic movements
Reduced False Signals: During ranging markets, narrower bands filter out minor RSI fluctuations that would trigger traditional fixed-threshold signals
Breakout Preparation: Band squeeze patterns (similar to price-based BB) signal potential momentum regime changes before they occur
Self-Referencing Analysis: By measuring RSI against its own statistical behavior rather than arbitrary levels, the indicator provides more relevant context
📐 MATHEMATICAL FOUNDATION
Two-Stage Calculation Process:
Stage 1: RSI Calculation
RSI = 100 - (100 / (1 + RS))
where RS = Average Gain / Average Loss over specified period
The RSI normalizes price momentum into a bounded 0-100 scale, making it ideal for statistical band analysis.
Stage 2: Bollinger Bands on RSI
Basis = MA(RSI, BB Length)
Upper Band = Basis + (StdDev(RSI, BB Length) × Multiplier)
Lower Band = Basis - (StdDev(RSI, BB Length) × Multiplier)
Band Width = Upper Band - Lower Band
The Bollinger Bands measure RSI's standard deviation from its own moving average, creating statistically-derived dynamic zones.
Statistical Interpretation:
Under normal distribution assumptions with default 2.0 multiplier, approximately 95% of RSI values should fall within the bands
Band touches represent statistically significant momentum extremes relative to recent behavior
Band width expansion indicates increasing momentum volatility (strengthening trend or increasing uncertainty)
Band width contraction signals momentum consolidation and potential regime change preparation
📊 COMPREHENSIVE SIGNAL ANALYSIS
Visual Color Signals:
This indicator features dynamic color fills that highlight extreme momentum conditions:
Green Fill (Above Upper Band):
Appears when RSI breaks above the upper band, indicating exceptionally strong bullish momentum
Represents dynamic overbought zone - not necessarily a reversal signal but a warning of extreme conditions
In strong uptrends, green fills can persist as RSI "rides the band" - this indicates sustained momentum strength
Exit of green zone (RSI falling back below upper band) often signals initial momentum weakening
Red Fill (Below Lower Band):
Appears when RSI breaks below the lower band, indicating exceptionally weak bearish momentum
Represents dynamic oversold zone - potential reversal or continuation signal depending on trend context
In strong downtrends, red fills can persist as RSI "rides the band" - this indicates sustained selling pressure
Exit of red zone (RSI rising back above lower band) often signals initial momentum recovery
Position-Based Signals:
Upper Band Interactions:
RSI Touching Upper Band: Dynamic overbought condition - momentum is extremely strong relative to recent volatility, potential exhaustion or continuation depending on trend context
RSI Riding Upper Band: Sustained strong momentum, often seen in powerful trends, not necessarily an immediate reversal signal but warrants monitoring for exhaustion
RSI Crossing Below Upper Band: Initial momentum weakening signal, particularly significant if accompanied by price divergence
Lower Band Interactions:
RSI Touching Lower Band: Dynamic oversold condition - momentum is extremely weak relative to recent volatility, potential reversal or continuation of downtrend
RSI Riding Lower Band: Sustained weak momentum, common in strong downtrends, monitor for potential exhaustion
RSI Crossing Above Lower Band: Initial momentum strengthening signal, early indication of potential reversal or consolidation
Basis Line Signals:
RSI Above Basis: Bullish momentum regime - upward pressure dominant
RSI Below Basis: Bearish momentum regime - downward pressure dominant
Basis Crossovers: Momentum regime shifts, more significant when accompanied by band width changes
RSI Oscillating Around Basis: Balanced momentum, often indicates ranging market conditions
Volatility-Based Signals:
Band Width Patterns:
Narrow Bands (Squeeze): Momentum volatility compression, often precedes significant directional moves, similar to price coiling patterns
Expanding Bands: Increasing momentum volatility, indicates trend acceleration or growing uncertainty
Narrowest Band in 100 Bars: Extreme compression alert, high probability of upcoming volatility expansion
Advanced Pattern Recognition:
Divergence Analysis:
Bullish Divergence: Price makes lower lows while RSI touches or stays above previous lower band touch, suggests downward momentum weakening
Bearish Divergence: Price makes higher highs while RSI touches or stays below previous upper band touch, suggests upward momentum weakening
Hidden Bullish: Price makes higher lows while RSI makes lower lows at the lower band, indicates strong underlying bullish momentum
Hidden Bearish: Price makes lower highs while RSI makes higher highs at the upper band, indicates strong underlying bearish momentum
Band Walk Patterns:
Upper Band Walk: RSI consistently touching or staying near upper band indicates exceptionally strong trend, wait for clear break below basis before considering reversal
Lower Band Walk: RSI consistently at lower band signals very weak momentum, requires break above basis for reversal confirmation
🎯 STRATEGIC APPLICATIONS
Strategy 1: Mean Reversion Trading
Setup Conditions:
Market Type: Ranging or choppy markets with no clear directional trend
Timeframe: Works best on lower timeframes (5m-1H) or during consolidation phases
Band Characteristic: Normal to narrow band width
Entry Rules:
Long Entry: RSI touches or crosses below lower band, wait for RSI to start rising back toward basis before entry
Short Entry: RSI touches or crosses above upper band, wait for RSI to start falling back toward basis before entry
Confirmation: Use price action confirmation (candlestick reversal patterns) at band touches
Exit Rules:
Target: RSI returns to basis line or opposite band
Stop Loss: Fixed percentage or below recent swing low/high
Time Stop: Exit if position not profitable within expected timeframe
Strategy 2: Trend Continuation Trading
Setup Conditions:
Market Type: Clear trending market with higher highs/lower lows
Timeframe: Medium to higher timeframes (1H-Daily)
Band Characteristic: Expanding or wide bands indicating strong momentum
Entry Rules:
Long Entry in Uptrend: Wait for RSI to pull back to basis line or slightly below, enter when RSI starts rising again
Short Entry in Downtrend: Wait for RSI to rally to basis line or slightly above, enter when RSI starts falling again
Avoid Counter-Trend: Do not fade RSI at bands during strong trends (band walk patterns)
Exit Rules:
Trailing Stop: Move stop to break-even when RSI reaches opposite band
Trend Break: Exit when RSI crosses basis against trend direction with conviction
Band Squeeze: Reduce position size when bands start narrowing significantly
Strategy 3: Breakout Preparation
Setup Conditions:
Market Type: Consolidating market after significant move or at key technical levels
Timeframe: Any timeframe, but longer timeframes provide more reliable breakouts
Band Characteristic: Narrowest band width in recent 100 bars (squeeze alert)
Preparation Phase:
Identify band squeeze condition (bands at multi-period narrowest point)
Monitor price action for consolidation patterns (triangles, rectangles, flags)
Prepare bracket orders for both directions
Wait for band expansion to begin
Entry Execution:
Breakout Confirmation: Enter in direction of RSI band breakout (RSI breaks above upper band or below lower band)
Price Confirmation: Ensure price also breaks corresponding technical level
Volume Confirmation: Look for volume expansion supporting the breakout
Risk Management:
Stop Loss: Place beyond consolidation pattern opposite extreme
Position Sizing: Use smaller size due to false breakout risk
Quick Exit: Exit immediately if RSI returns inside bands within 1-3 bars
Strategy 4: Multi-Timeframe Analysis
Timeframe Selection:
Higher Timeframe: Daily or 4H for trend context
Trading Timeframe: 1H or 15m for entry signals
Confirmation Timeframe: 5m or 1m for precise entry timing
Analysis Process:
Trend Identification: Check higher timeframe RSI position relative to bands, trade only in direction of higher timeframe momentum
Setup Formation: Wait for trading timeframe RSI to show pullback to basis in trending direction
Entry Timing: Use confirmation timeframe RSI band touch or crossover for precise entry
Alignment Confirmation: All timeframes should show RSI moving in same direction for highest probability setups
📋 DETAILED PARAMETER CONFIGURATION
RSI Source:
Close (Default): Standard price point, balances responsiveness and reliability
HL2: Reduces noise from intrabar volatility, provides smoother RSI values
HLC3 or OHLC4: Further smoothing for very choppy markets, slower to respond but more stable
Volume-Weighted: Consider using VWAP or volume-weighted prices for additional liquidity context
RSI Length Parameter:
Shorter Periods (5-10): More responsive but generates more signals, suitable for scalping or very active trading, higher noise level
Standard (14): Default and most widely used setting, proven balance between responsiveness and reliability, recommended starting point
Longer Periods (21-30): Smoother momentum measurement, fewer but potentially more reliable signals, better for swing trading or position trading
Optimization Note: Test across different market regimes, optimal length often varies by instrument volatility characteristics
RSI MA Type Parameter:
RMA (Default): Wilder's original smoothing method, provides traditional RSI behavior with balanced lag, most widely recognized and tested, recommended for standard technical analysis
EMA: Exponential smoothing gives more weight to recent values, faster response to momentum changes, suitable for active trading and trending markets, reduces lag compared to RMA
SMA: Simple average treats all periods equally, smoothest output with highest lag, best for filtering noise in choppy markets, useful for long-term position analysis
WMA: Weighted average emphasizes recent data less aggressively than EMA, middle ground between SMA and EMA characteristics, balanced responsiveness for swing trading
Advanced Options: Full access to 25+ moving average types including HMA (reduced lag), DEMA/TEMA (enhanced responsiveness), KAMA/FRAMA (adaptive behavior), T3 (smoothness), Kalman Filter (optimal estimation)
Selection Guide: RMA for traditional analysis and backtesting consistency, EMA for faster signals in trending markets, SMA for stability in ranging markets, adaptive types (KAMA/FRAMA) for varying volatility regimes
BB Length Parameter:
Short Length (10-15): Tighter bands that react quickly to RSI changes, more frequent band touches, suitable for active trading styles
Standard (20): Balanced approach providing meaningful statistical context without excessive lag
Long Length (30-50): Smoother bands that filter minor RSI fluctuations, captures only significant momentum extremes, fewer but higher quality signals
Relationship to RSI Length: Consider BB Length greater than RSI Length for cleaner signals
BB MA Type Parameter:
SMA (Default): Standard Bollinger Bands calculation using simple moving average for basis line, treats all periods equally, widely recognized and tested approach
EMA: Exponential smoothing for basis line gives more weight to recent RSI values, creates more responsive bands that adapt faster to momentum changes, suitable for trending markets
RMA: Wilder's smoothing provides consistent behavior aligned with traditional RSI when using RMA for both RSI and BB calculations
WMA: Weighted average for basis line balances recent emphasis with historical context, middle ground between SMA and EMA responsiveness
Advanced Options: Full access to 25+ moving average types for basis calculation, including HMA (reduced lag), DEMA/TEMA (enhanced responsiveness), KAMA/FRAMA (adaptive to volatility changes)
Selection Guide: SMA for standard Bollinger Bands behavior and backtesting consistency, EMA for faster band adaptation in dynamic markets, matching RSI MA type creates unified smoothing behavior
BB Multiplier Parameter:
Conservative (1.5-1.8): Tighter bands resulting in more frequent touches, useful in low volatility environments, higher signal frequency but potentially more false signals
Standard (2.0): Default setting representing approximately 95% confidence interval under normal distribution, widely accepted statistical threshold
Aggressive (2.5-3.0): Wider bands capturing only extreme momentum conditions, fewer but potentially more significant signals, reduces false signals in high volatility
Adaptive Approach: Consider adjusting multiplier based on instrument characteristics, lower multiplier for stable instruments, higher for volatile instruments
Parameter Optimization Workflow:
Start with default parameters (RSI:14, BB:20, Mult:2.0)
Test across representative sample period including different market regimes
Adjust RSI length based on desired responsiveness vs stability tradeoff
Tune BB length to match your typical holding period
Modify multiplier to achieve desired signal frequency
Validate on out-of-sample data to avoid overfitting
Document optimal parameters for different instruments and timeframes
Reference Levels Display:
Enabled (Default): Shows traditional 30/50/70 levels for comparison with dynamic bands, helps visualize the adaptive advantage
Disabled: Cleaner chart focusing purely on dynamic zones, reduces visual clutter for experienced users
Educational Value: Keeping reference levels visible helps understand how dynamic bands differ from fixed thresholds across varying market conditions
📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES
Comparison with Traditional RSI:
Fixed Threshold RSI Limitations:
In ranging low-volatility markets: RSI rarely reaches 70/30, missing tradable extremes
In trending high-volatility markets: RSI frequently breaks through 70/30, generating excessive false reversal signals
Across different instruments: Same thresholds applied to volatile crypto and stable forex pairs produce inconsistent results
Threshold Adjustment Problem: Manually changing thresholds for different conditions is subjective and lagging
RSI Bollinger Bands Advantages:
Automatic Adaptation: Bands adjust to current volatility regime without manual intervention
Consistent Logic: Same statistical approach works across different instruments and timeframes
Reduced False Signals: Band width filtering helps distinguish meaningful extremes from noise
Additional Information: Band width provides volatility context missing in standard RSI
Objective Extremes: Statistical basis (standard deviations) provides objective extreme definition
Comparison with Price-Based Bollinger Bands:
Price BB Characteristics:
Measures absolute price volatility
Affected by large price gaps and outliers
Band position relative to price not normalized
Difficult to compare across different price scales
RSI BB Advantages:
Normalized Scale: RSI's 0-100 bounds make band interpretation consistent across all instruments
Momentum Focus: Directly measures momentum extremes rather than price extremes
Reduced Gap Impact: RSI calculation smooths price gaps impact on band calculations
Comparable Analysis: Same RSI BB appearance across stocks, forex, crypto enables consistent strategy application
Performance Characteristics:
Signal Quality:
Higher Signal-to-Noise Ratio: Dynamic bands help filter RSI oscillations that don't represent meaningful extremes
Context-Aware Alerts: Band width provides volatility context helping traders adjust position sizing and stop placement
Reduced Whipsaws: During consolidations, narrower bands prevent premature signals from minor RSI movements
Responsiveness:
Adaptive Lag: Band calculation introduces some lag, but this lag is adaptive to current conditions rather than fixed
Faster Than Manual Adjustment: Automatic band adjustment is faster than trader's ability to manually modify thresholds
Balanced Approach: Combines RSI's inherent momentum lag with BB's statistical smoothing for stable yet responsive signals
Versatility:
Multi-Strategy Application: Supports both mean reversion (ranging markets) and trend continuation (trending markets) approaches
Universal Instrument Coverage: Works effectively across equities, forex, commodities, cryptocurrencies without parameter changes
Timeframe Agnostic: Same interpretation applies from 1-minute charts to monthly charts
Limitations and Considerations:
Known Limitations:
Dual Lag Effect: Combines RSI's momentum lag with BB's statistical lag, making it less suitable for very short-term scalping
Requires Volatility History: Needs sufficient bars for BB calculation, less effective immediately after major regime changes
Statistical Assumptions: Assumes RSI values are somewhat normally distributed, extreme trending conditions may violate this
Not a Standalone System: Like all indicators, should be combined with price action analysis and risk management
Optimal Use Cases:
Best for swing trading and position trading timeframes
Most effective in markets with alternating volatility regimes
Ideal for traders who use multiple instruments and timeframes
Suitable for systematic trading approaches requiring consistent logic
Suboptimal Conditions:
Very low timeframes (< 5 minutes) where lag becomes problematic
Instruments with extreme volatility spikes (gap-prone markets)
Markets in strong persistent trends where mean reversion rarely occurs
Periods immediately following major structural changes (new trading regime)
USAGE NOTES
This indicator is designed for technical analysis and educational purposes to help traders understand the interaction between momentum measurement and statistical volatility bands. The RSI Bollinger Bands has limitations and should not be used as the sole basis for trading decisions.
Important Considerations:
No Predictive Guarantee: Past band touches and patterns do not guarantee future price behavior
Market Regime Dependency: Indicator performance varies significantly between trending and ranging market conditions
Complementary Analysis Required: Should be used alongside price action, support/resistance levels, and fundamental analysis
Risk Management Essential: Always use proper position sizing, stop losses, and risk controls regardless of signal quality
Parameter Sensitivity: Different instruments and timeframes may require parameter optimization for optimal results
Continuous Monitoring: Band characteristics change with market conditions, requiring ongoing assessment
Recommended Supporting Analysis:
Price structure analysis (support/resistance, trend lines)
Volume confirmation for breakout signals
Multiple timeframe alignment
Market context awareness (news events, session times)
Correlation analysis with related instruments
The indicator aims to provide adaptive momentum analysis that adjusts to changing market volatility, but traders must apply sound judgment, proper risk management, and comprehensive market analysis in their decision-making process.
Keltner Channel Enhanced [DCAUT]█ Keltner Channel Enhanced
📊 ORIGINALITY & INNOVATION
The Keltner Channel Enhanced represents an important advancement over standard Keltner Channel implementations by introducing dual flexibility in moving average selection for both the middle band and ATR calculation. While traditional Keltner Channels typically use EMA for the middle band and RMA (Wilder's smoothing) for ATR, this enhanced version provides access to 25+ moving average algorithms for both components, enabling traders to fine-tune the indicator's behavior to match specific market characteristics and trading approaches.
Key Advancements:
Dual MA Algorithm Flexibility: Independent selection of moving average types for middle band (25+ options) and ATR smoothing (25+ options), allowing optimization of both trend identification and volatility measurement separately
Enhanced Trend Sensitivity: Ability to use faster algorithms (HMA, T3) for middle band while maintaining stable volatility measurement with traditional ATR smoothing, or vice versa for different trading strategies
Adaptive Volatility Measurement: Choice of ATR smoothing algorithm affects channel responsiveness to volatility changes, from highly reactive (SMA, EMA) to smoothly adaptive (RMA, TEMA)
Comprehensive Alert System: Five distinct alert conditions covering breakouts, trend changes, and volatility expansion, enabling automated monitoring without constant chart observation
Multi-Timeframe Compatibility: Works effectively across all timeframes from intraday scalping to long-term position trading, with independent optimization of trend and volatility components
This implementation addresses key limitations of standard Keltner Channels: fixed EMA/RMA combination may not suit all market conditions or trading styles. By decoupling the trend component from volatility measurement and allowing independent algorithm selection, traders can create highly customized configurations for specific instruments and market phases.
📐 MATHEMATICAL FOUNDATION
Keltner Channel Enhanced uses a three-component calculation system that combines a flexible moving average middle band with ATR-based (Average True Range) upper and lower channels, creating volatility-adjusted trend-following bands.
Core Calculation Process:
1. Middle Band (Basis) Calculation:
The basis line is calculated using the selected moving average algorithm applied to the price source over the specified period:
basis = ma(source, length, maType)
Supported algorithms include EMA (standard choice, trend-biased), SMA (balanced and symmetric), HMA (reduced lag), WMA, VWMA, TEMA, T3, KAMA, and 17+ others.
2. Average True Range (ATR) Calculation:
ATR measures market volatility by calculating the average of true ranges over the specified period:
trueRange = max(high - low, abs(high - close ), abs(low - close ))
atrValue = ma(trueRange, atrLength, atrMaType)
ATR smoothing algorithm significantly affects channel behavior, with options including RMA (standard, very smooth), SMA (moderate smoothness), EMA (fast adaptation), TEMA (smooth yet responsive), and others.
3. Channel Calculation:
Upper and lower channels are positioned at specified multiples of ATR from the basis:
upperChannel = basis + (multiplier × atrValue)
lowerChannel = basis - (multiplier × atrValue)
Standard multiplier is 2.0, providing channels that dynamically adjust width based on market volatility.
Keltner Channel vs. Bollinger Bands - Key Differences:
While both indicators create volatility-based channels, they use fundamentally different volatility measures:
Keltner Channel (ATR-based):
Uses Average True Range to measure actual price movement volatility
Incorporates gaps and limit moves through true range calculation
More stable in trending markets, less prone to extreme compression
Better reflects intraday volatility and trading range
Typically fewer band touches, making touches more significant
More suitable for trend-following strategies
Bollinger Bands (Standard Deviation-based):
Uses statistical standard deviation to measure price dispersion
Based on closing prices only, doesn't account for intraday range
Can compress significantly during consolidation (squeeze patterns)
More touches in ranging markets
Better suited for mean-reversion strategies
Provides statistical probability framework (95% within 2 standard deviations)
Algorithm Combination Effects:
The interaction between middle band MA type and ATR MA type creates different indicator characteristics:
Trend-Focused Configuration (Fast MA + Slow ATR): Middle band uses HMA/EMA/T3, ATR uses RMA/TEMA, quick trend changes with stable channel width, suitable for trend-following
Volatility-Focused Configuration (Slow MA + Fast ATR): Middle band uses SMA/WMA, ATR uses EMA/SMA, stable trend with dynamic channel width, suitable for volatility trading
Balanced Configuration (Standard EMA/RMA): Classic Keltner Channel behavior, time-tested combination, suitable for general-purpose trend following
Adaptive Configuration (KAMA + KAMA): Self-adjusting indicator responding to efficiency ratio, suitable for markets with varying trend strength and volatility regimes
📊 COMPREHENSIVE SIGNAL ANALYSIS
Keltner Channel Enhanced provides multiple signal categories optimized for trend-following and breakout strategies.
Channel Position Signals:
Upper Channel Interaction:
Price Touching Upper Channel: Strong bullish momentum, price moving more than typical volatility range suggests, potential continuation signal in established uptrends
Price Breaking Above Upper Channel: Exceptional strength, price exceeding normal volatility expectations, consider adding to long positions or tightening trailing stops
Price Riding Upper Channel: Sustained strong uptrend, characteristic of powerful bull moves, stay with trend and avoid premature profit-taking
Price Rejection at Upper Channel: Momentum exhaustion signal, consider profit-taking on longs or waiting for pullback to middle band for reentry
Lower Channel Interaction:
Price Touching Lower Channel: Strong bearish momentum, price moving more than typical volatility range suggests, potential continuation signal in established downtrends
Price Breaking Below Lower Channel: Exceptional weakness, price exceeding normal volatility expectations, consider adding to short positions or protecting against further downside
Price Riding Lower Channel: Sustained strong downtrend, characteristic of powerful bear moves, stay with trend and avoid premature covering
Price Rejection at Lower Channel: Momentum exhaustion signal, consider covering shorts or waiting for bounce to middle band for reentry
Middle Band (Basis) Signals:
Trend Direction Confirmation:
Price Above Basis: Bullish trend bias, middle band acts as dynamic support in uptrends, consider long positions or holding existing longs
Price Below Basis: Bearish trend bias, middle band acts as dynamic resistance in downtrends, consider short positions or avoiding longs
Price Crossing Above Basis: Potential trend change from bearish to bullish, early signal to establish long positions
Price Crossing Below Basis: Potential trend change from bullish to bearish, early signal to establish short positions or exit longs
Pullback Trading Strategy:
Uptrend Pullback: Price pulls back from upper channel to middle band, finds support, and resumes upward, ideal long entry point
Downtrend Bounce: Price bounces from lower channel to middle band, meets resistance, and resumes downward, ideal short entry point
Basis Test: Strong trends often show price respecting the middle band as support/resistance on pullbacks
Failed Test: Price breaking through middle band against trend direction signals potential reversal
Volatility-Based Signals:
Narrow Channels (Low Volatility):
Consolidation Phase: Channels contract during periods of reduced volatility and directionless price action
Breakout Preparation: Narrow channels often precede significant directional moves as volatility cycles
Trading Approach: Reduce position sizes, wait for breakout confirmation, avoid range-bound strategies within channels
Breakout Direction: Monitor for price breaking decisively outside channel range with expanding width
Wide Channels (High Volatility):
Trending Phase: Channels expand during strong directional moves and increased volatility
Momentum Confirmation: Wide channels confirm genuine trend with substantial volatility backing
Trading Approach: Trend-following strategies excel, wider stops necessary, mean-reversion strategies risky
Exhaustion Signs: Extreme channel width (historical highs) may signal approaching consolidation or reversal
Advanced Pattern Recognition:
Channel Walking Pattern:
Upper Channel Walk: Price consistently touches or exceeds upper channel while staying above basis, very strong uptrend signal, hold longs aggressively
Lower Channel Walk: Price consistently touches or exceeds lower channel while staying below basis, very strong downtrend signal, hold shorts aggressively
Basis Support/Resistance: During channel walks, price typically uses middle band as support/resistance on minor pullbacks
Pattern Break: Price crossing basis during channel walk signals potential trend exhaustion
Squeeze and Release Pattern:
Squeeze Phase: Channels narrow significantly, price consolidates near middle band, volatility contracts
Direction Clues: Watch for price positioning relative to basis during squeeze (above = bullish bias, below = bearish bias)
Release Trigger: Price breaking outside narrow channel range with expanding width confirms breakout
Follow-Through: Measure squeeze height and project from breakout point for initial profit targets
Channel Expansion Pattern:
Breakout Confirmation: Rapid channel widening confirms volatility increase and genuine trend establishment
Entry Timing: Enter positions early in expansion phase before trend becomes overextended
Risk Management: Use channel width to size stops appropriately, wider channels require wider stops
Basis Bounce Pattern:
Clean Bounce: Price touches middle band and immediately reverses, confirms trend strength and entry opportunity
Multiple Bounces: Repeated basis bounces indicate strong, sustainable trend
Bounce Failure: Price penetrating basis signals weakening trend and potential reversal
Divergence Analysis:
Price/Channel Divergence: Price makes new high/low while staying within channel (not reaching outer band), suggests momentum weakening
Width/Price Divergence: Price breaks to new extremes but channel width contracts, suggests move lacks conviction
Reversal Signal: Divergences often precede trend reversals or significant consolidation periods
Multi-Timeframe Analysis:
Keltner Channels work particularly well in multi-timeframe trend-following approaches:
Three-Timeframe Alignment:
Higher Timeframe (Weekly/Daily): Identify major trend direction, note price position relative to basis and channels
Intermediate Timeframe (Daily/4H): Identify pullback opportunities within higher timeframe trend
Lower Timeframe (4H/1H): Time precise entries when price touches middle band or lower channel (in uptrends) with rejection
Optimal Entry Conditions:
Best Long Entries: Higher timeframe in uptrend (price above basis), intermediate timeframe pulls back to basis, lower timeframe shows rejection at middle band or lower channel
Best Short Entries: Higher timeframe in downtrend (price below basis), intermediate timeframe bounces to basis, lower timeframe shows rejection at middle band or upper channel
Risk Management: Use higher timeframe channel width to set position sizing, stops below/above higher timeframe channels
🎯 STRATEGIC APPLICATIONS
Keltner Channel Enhanced excels in trend-following and breakout strategies across different market conditions.
Trend Following Strategy:
Setup Requirements:
Identify established trend with price consistently on one side of basis line
Wait for pullback to middle band (basis) or brief penetration through it
Confirm trend resumption with price rejection at basis and move back toward outer channel
Enter in trend direction with stop beyond basis line
Entry Rules:
Uptrend Entry:
Price pulls back from upper channel to middle band, shows support at basis (bullish candlestick, momentum divergence)
Enter long on rejection/bounce from basis with stop 1-2 ATR below basis
Aggressive: Enter on first touch; Conservative: Wait for confirmation candle
Downtrend Entry:
Price bounces from lower channel to middle band, shows resistance at basis (bearish candlestick, momentum divergence)
Enter short on rejection/reversal from basis with stop 1-2 ATR above basis
Aggressive: Enter on first touch; Conservative: Wait for confirmation candle
Trend Management:
Trailing Stop: Use basis line as dynamic trailing stop, exit if price closes beyond basis against position
Profit Taking: Take partial profits at opposite channel, move stops to basis
Position Additions: Add to winners on subsequent basis bounces if trend intact
Breakout Strategy:
Setup Requirements:
Identify consolidation period with contracting channel width
Monitor price action near middle band with reduced volatility
Wait for decisive breakout beyond channel range with expanding width
Enter in breakout direction after confirmation
Breakout Confirmation:
Price breaks clearly outside channel (upper for longs, lower for shorts), channel width begins expanding from contracted state
Volume increases significantly on breakout (if using volume analysis)
Price sustains outside channel for multiple bars without immediate reversal
Entry Approaches:
Aggressive: Enter on initial break with stop at opposite channel or basis, use smaller position size
Conservative: Wait for pullback to broken channel level, enter on rejection and resumption, tighter stop
Volatility-Based Position Sizing:
Adjust position sizing based on channel width (ATR-based volatility):
Wide Channels (High ATR): Reduce position size as stops must be wider, calculate position size using ATR-based risk calculation: Risk / (Stop Distance in ATR × ATR Value)
Narrow Channels (Low ATR): Increase position size as stops can be tighter, be cautious of impending volatility expansion
ATR-Based Risk Management: Use ATR-based risk calculations, position size = 0.01 × Capital / (2 × ATR), use multiples of ATR (1-2 ATR) for adaptive stops
Algorithm Selection Guidelines:
Different market conditions benefit from different algorithm combinations:
Strong Trending Markets: Middle band use EMA or HMA, ATR use RMA, capture trends quickly while maintaining stable channel width
Choppy/Ranging Markets: Middle band use SMA or WMA, ATR use SMA or WMA, avoid false trend signals while identifying genuine reversals
Volatile Markets: Middle band and ATR both use KAMA or FRAMA, self-adjusting to changing market conditions reduces manual optimization
Breakout Trading: Middle band use SMA, ATR use EMA or SMA, stable trend with dynamic channels highlights volatility expansion early
Scalping/Day Trading: Middle band use HMA or T3, ATR use EMA or TEMA, both components respond quickly
Position Trading: Middle band use EMA/TEMA/T3, ATR use RMA or TEMA, filter out noise for long-term trend-following
📋 DETAILED PARAMETER CONFIGURATION
Understanding and optimizing parameters is essential for adapting Keltner Channel Enhanced to specific trading approaches.
Source Parameter:
Close (Most Common): Uses closing price, reflects daily settlement, best for end-of-day analysis and position trading, standard choice
HL2 (Median Price): Smooths out closing bias, better represents full daily range in volatile markets, good for swing trading
HLC3 (Typical Price): Gives more weight to close while including full range, popular for intraday applications, slightly more responsive than HL2
OHLC4 (Average Price): Most comprehensive price representation, smoothest option, good for gap-prone markets or highly volatile instruments
Length Parameter:
Controls the lookback period for middle band (basis) calculation:
Short Periods (10-15): Very responsive to price changes, suitable for day trading and scalping, higher false signal rate
Standard Period (20 - Default): Represents approximately one month of trading, good balance between responsiveness and stability, suitable for swing and position trading
Medium Periods (30-50): Smoother trend identification, fewer false signals, better for position trading and longer holding periods
Long Periods (50+): Very smooth, identifies major trends only, minimal false signals but significant lag, suitable for long-term investment
Optimization by Timeframe: 1-15 minute charts use 10-20 period, 30-60 minute charts use 20-30 period, 4-hour to daily charts use 20-40 period, weekly charts use 20-30 weeks.
ATR Length Parameter:
Controls the lookback period for Average True Range calculation, affecting channel width:
Short ATR Periods (5-10): Very responsive to recent volatility changes, standard is 10 (Keltner's original specification), may be too reactive in whipsaw conditions
Standard ATR Period (10 - Default): Chester Keltner's original specification, good balance between responsiveness and stability, most widely used
Medium ATR Periods (14-20): Smoother channel width, ATR 14 aligns with Wilder's original ATR specification, good for position trading
Long ATR Periods (20+): Very smooth channel width, suitable for long-term trend-following
Length vs. ATR Length Relationship: Equal values (20/20) provide balanced responsiveness, longer ATR (20/14) gives more stable channel width, shorter ATR (20/10) is standard configuration, much shorter ATR (20/5) creates very dynamic channels.
Multiplier Parameter:
Controls channel width by setting ATR multiples:
Lower Values (1.0-1.5): Tighter channels with frequent price touches, more trading signals, higher false signal rate, better for range-bound and mean-reversion strategies
Standard Value (2.0 - Default): Chester Keltner's recommended setting, good balance between signal frequency and reliability, suitable for both trending and ranging strategies
Higher Values (2.5-3.0): Wider channels with less frequent touches, fewer but potentially higher-quality signals, better for strong trending markets
Market-Specific Optimization: High volatility markets (crypto, small-caps) use 2.5-3.0 multiplier, medium volatility markets (major forex, large-caps) use 2.0 multiplier, low volatility markets (bonds, utilities) use 1.5-2.0 multiplier.
MA Type Parameter (Middle Band):
Critical selection that determines trend identification characteristics:
EMA (Exponential Moving Average - Default): Standard Keltner Channel choice, Chester Keltner's original specification, emphasizes recent prices, faster response to trend changes, suitable for all timeframes
SMA (Simple Moving Average): Equal weighting of all data points, no directional bias, slower than EMA, better for ranging markets and mean-reversion
HMA (Hull Moving Average): Minimal lag with smooth output, excellent for fast trend identification, best for day trading and scalping
TEMA (Triple Exponential Moving Average): Advanced smoothing with reduced lag, responsive to trends while filtering noise, suitable for volatile markets
T3 (Tillson T3): Very smooth with minimal lag, excellent for established trend identification, suitable for position trading
KAMA (Kaufman Adaptive Moving Average): Automatically adjusts speed based on market efficiency, slow in ranging markets, fast in trends, suitable for markets with varying conditions
ATR MA Type Parameter:
Determines how Average True Range is smoothed, affecting channel width stability:
RMA (Wilder's Smoothing - Default): J. Welles Wilder's original ATR smoothing method, very smooth, slow to adapt to volatility changes, provides stable channel width
SMA (Simple Moving Average): Equal weighting, moderate smoothness, faster response to volatility changes than RMA, more dynamic channel width
EMA (Exponential Moving Average): Emphasizes recent volatility, quick adaptation to new volatility regimes, very responsive channel width changes
TEMA (Triple Exponential Moving Average): Smooth yet responsive, good balance for varying volatility, suitable for most trading styles
Parameter Combination Strategies:
Conservative Trend-Following: Length 30/ATR Length 20/Multiplier 2.5, MA Type EMA or TEMA/ATR MA Type RMA, smooth trend with stable wide channels, suitable for position trading
Standard Balanced Approach: Length 20/ATR Length 10/Multiplier 2.0, MA Type EMA/ATR MA Type RMA, classic Keltner Channel configuration, suitable for general purpose swing trading
Aggressive Day Trading: Length 10-15/ATR Length 5-7/Multiplier 1.5-2.0, MA Type HMA or EMA/ATR MA Type EMA or SMA, fast trend with dynamic channels, suitable for scalping and day trading
Breakout Specialist: Length 20-30/ATR Length 5-10/Multiplier 2.0, MA Type SMA or WMA/ATR MA Type EMA or SMA, stable trend with responsive channel width
Adaptive All-Conditions: Length 20/ATR Length 10/Multiplier 2.0, MA Type KAMA or FRAMA/ATR MA Type KAMA or TEMA, self-adjusting to market conditions
Offset Parameter:
Controls horizontal positioning of channels on chart. Positive values shift channels to the right (future) for visual projection, negative values shift left (past) for historical analysis, zero (default) aligns with current price bars for real-time signal analysis. Offset affects only visual display, not alert conditions or actual calculations.
📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES
Keltner Channel Enhanced provides improvements over standard implementations while maintaining proven effectiveness.
Response Characteristics:
Standard EMA/RMA Configuration: Moderate trend lag (approximately 0.4 × length periods), smooth and stable channel width from RMA smoothing, good balance for most market conditions
Fast HMA/EMA Configuration: Approximately 60% reduction in trend lag compared to EMA, responsive channel width from EMA ATR smoothing, suitable for quick trend changes and breakouts
Adaptive KAMA/KAMA Configuration: Variable lag based on market efficiency, automatic adjustment to trending vs. ranging conditions, self-optimizing behavior reduces manual intervention
Comparison with Traditional Keltner Channels:
Enhanced Version Advantages:
Dual Algorithm Flexibility: Independent MA selection for trend and volatility vs. fixed EMA/RMA, separate tuning of trend responsiveness and channel stability
Market Adaptation: Choose configurations optimized for specific instruments and conditions, customize for scalping, swing, or position trading preferences
Comprehensive Alerts: Enhanced alert system including channel expansion detection
Traditional Version Advantages:
Simplicity: Fewer parameters, easier to understand and implement
Standardization: Fixed EMA/RMA combination ensures consistency across users
Research Base: Decades of backtesting and research on standard configuration
When to Use Enhanced Version: Trading multiple instruments with different characteristics, switching between trending and ranging markets, employing different strategies, algorithm-based trading systems requiring customization, seeking optimization for specific trading style and timeframe.
When to Use Standard Version: Beginning traders learning Keltner Channel concepts, following published research or trading systems, preferring simplicity and standardization, wanting to avoid optimization and curve-fitting risks.
Performance Across Market Conditions:
Strong Trending Markets: EMA or HMA basis with RMA or TEMA ATR smoothing provides quicker trend identification, pullbacks to basis offer excellent entry opportunities
Choppy/Ranging Markets: SMA or WMA basis with RMA ATR smoothing and lower multipliers, channel bounce strategies work well, avoid false breakouts
Volatile Markets: KAMA or FRAMA with EMA or TEMA, adaptive algorithms excel by automatic adjustment, wider multipliers (2.5-3.0) accommodate large price swings
Low Volatility/Consolidation: Channels narrow significantly indicating consolidation, algorithm choice less impactful, focus on detecting channel width contraction for breakout preparation
Keltner Channel vs. Bollinger Bands - Usage Comparison:
Favor Keltner Channels When: Trend-following is primary strategy, trading volatile instruments with gaps, want ATR-based volatility measurement, prefer fewer higher-quality channel touches, seeking stable channel width during trends.
Favor Bollinger Bands When: Mean-reversion is primary strategy, trading instruments with limited gaps, want statistical framework based on standard deviation, need squeeze patterns for breakout identification, prefer more frequent trading opportunities.
Use Both Together: Bollinger Band squeeze + Keltner Channel breakout is powerful combination, price outside Bollinger Bands but inside Keltner Channels indicates moderate signal, price outside both indicates very strong signal, Bollinger Bands for entries and Keltner Channels for trend confirmation.
Limitations and Considerations:
General Limitations:
Lagging Indicator: All moving averages lag price, even with reduced-lag algorithms
Trend-Dependent: Works best in trending markets, less effective in choppy conditions
No Direction Prediction: Indicates volatility and deviation, not future direction, requires confirmation
Enhanced Version Specific Considerations:
Optimization Risk: More parameters increase risk of curve-fitting historical data
Complexity: Additional choices may overwhelm beginning traders
Backtesting Challenges: Different algorithms produce different historical results
Mitigation Strategies:
Use Confirmation: Combine with momentum indicators (RSI, MACD), volume, or price action
Test Parameter Robustness: Ensure parameters work across range of values, not just optimized ones
Multi-Timeframe Analysis: Confirm signals across different timeframes
Proper Risk Management: Use appropriate position sizing and stops
Start Simple: Begin with standard EMA/RMA before exploring alternatives
Optimal Usage Recommendations:
For Maximum Effectiveness:
Start with standard EMA/RMA configuration to understand classic behavior
Experiment with alternatives on demo account or paper trading
Match algorithm combination to market condition and trading style
Use channel width analysis to identify market phases
Combine with complementary indicators for confirmation
Implement strict risk management using ATR-based position sizing
Focus on high-quality setups rather than trading every signal
Respect the trend: trade with basis direction for higher probability
Complementary Indicators:
RSI or Stochastic: Confirm momentum at channel extremes
MACD: Confirm trend direction and momentum shifts
Volume: Validate breakouts and trend strength
ADX: Measure trend strength, avoid Keltner signals in weak trends
Support/Resistance: Combine with traditional levels for high-probability setups
Bollinger Bands: Use together for enhanced breakout and volatility analysis
USAGE NOTES
This indicator is designed for technical analysis and educational purposes. Keltner Channel Enhanced has limitations and should not be used as the sole basis for trading decisions. While the flexible moving average selection for both trend and volatility components provides valuable adaptability across different market conditions, algorithm performance varies with market conditions, and past characteristics do not guarantee future results.
Key considerations:
Always use multiple forms of analysis and confirmation before entering trades
Backtest any parameter combination thoroughly before live trading
Be aware that optimization can lead to curve-fitting if not done carefully
Start with standard EMA/RMA settings and adjust only when specific conditions warrant
Understand that no moving average algorithm can eliminate lag entirely
Consider market regime (trending, ranging, volatile) when selecting parameters
Use ATR-based position sizing and risk management on every trade
Keltner Channels work best in trending markets, less effective in choppy conditions
Respect the trend direction indicated by price position relative to basis line
The enhanced flexibility of dual algorithm selection provides powerful tools for adaptation but requires responsible use, thorough understanding of how different algorithms behave under various market conditions, and disciplined risk management.
T3 [DCAUT]█ T3
📊 INDICATOR OVERVIEW
The T3 Moving Average is a smoothing indicator developed by Tim Tillson and published in Technical Analysis of Stocks & Commodities magazine (January 1998). The algorithm applies Generalized DEMA (Double Exponential Moving Average) recursively three times, creating a six-pole filtering effect that aims to balance noise reduction with responsiveness while minimizing lag relative to price changes.
📐 MATHEMATICAL FOUNDATION
Generalized DEMA (GD) Function:
The core building block is the Generalized DEMA function, which combines two exponential moving averages with weights controlled by the volume factor:
GD(input, v) = EMA(input) × (1 + v) - EMA(EMA(input)) × v
Where v is the volume factor parameter (default 0.7). This weighted combination reduces lag while maintaining smoothness by extrapolating beyond the first EMA using the double-smoothed EMA as a reference.
T3 Calculation Process:
T3 applies the GD function three times recursively:
T3 = GD(GD(GD(Price, v), v), v)
This triple nesting creates a six-pole smoothing effect (each GD applies two EMA operations, resulting in 2 × 3 = 6 total EMA calculations). The cascading refinement progressively filters noise while preserving trend information.
Step-by-Step Breakdown:
First GD application: GD1 = EMA(Price) × (1 + v) - EMA(EMA(Price)) × v - Creates initial smoothed series with lag reduction
Second GD application: GD2 = EMA(GD1) × (1 + v) - EMA(EMA(GD1)) × v - Further refines the smoothing while maintaining responsiveness
Third GD application: T3 = EMA(GD2) × (1 + v) - EMA(EMA(GD2)) × v - Final refinement produces the T3 output
Volume Factor Impact:
The volume factor (v) is the key parameter controlling the balance between smoothness and responsiveness. Tim Tillson recommended v = 0.7 as the optimal default value.
Lower volume factors (v closer to 0.0): Increase the extrapolation effect, making T3 more responsive to price changes but potentially more sensitive to noise.
Higher volume factors (v closer to 1.0): Reduce the extrapolation effect, producing smoother output with less sensitivity to short-term fluctuations but slightly more lag.
The recursive application of the volume factor through three GD stages creates a nonlinear filtering effect that achieves superior lag reduction compared to traditional moving averages of equivalent smoothness.
📊 SIGNAL INTERPRETATION
Trend Direction Signals:
Green Line (T3 Rising): Smoothed trend line is rising, may indicate uptrend, consider bullish opportunities when confirmed by other factors
Red Line (T3 Falling): Smoothed trend line is falling, may indicate downtrend, consider bearish opportunities when confirmed by other factors
Gray Line (T3 Flat): Smoothed trend line is flat, indicates unclear trend or consolidation phase
Price Crossover Signals:
Price Crosses Above T3: Price breaks above smoothed trend line, may be bullish signal, requires confirmation from other indicators
Price Crosses Below T3: Price breaks below smoothed trend line, may be bearish signal, requires confirmation from other indicators
Price Position Relative to T3: Price sustained above T3 may indicate uptrend, sustained below may indicate downtrend
Supporting Analysis Signals:
T3 Slope Angle: Steeper slopes indicate stronger trend momentum, flatter slopes suggest weakening trends
Price Deviation: Significant price separation from T3 may indicate overextension, watch for pullback or reversal
Dynamic Support/Resistance: T3 line can serve as dynamic support (in uptrends) or resistance (in downtrends) reference
🎯 STRATEGIC APPLICATIONS
Common Usage Patterns:
The T3 Moving Average can be incorporated into trading analysis in various ways. These represent common approaches used by market participants, though effectiveness varies by market conditions and requires individual testing:
Trend Filtering:
T3 can be used as a trend filter by observing the relationship between price and the T3 line. The color-coded slope (green for rising, red for falling, gray for sideways) provides visual feedback about the current trend direction of the smoothed series.
Price Crossover Analysis:
Some traders monitor crossovers between price and the T3 line as potential indication points. When price crosses the T3 line, it may suggest a change in the relationship between current price action and the smoothed trend.
Multi-Timeframe Observation:
T3 can be applied to multiple timeframes simultaneously. Observing alignment or divergence between different timeframe T3 indicators may provide context about trend consistency across time scales.
Dynamic Reference Level:
The T3 line can serve as a dynamic reference level for price action analysis. Price distance from T3, price reactions when approaching T3, and the behavior of price relative to the T3 line can all be incorporated into market analysis frameworks.
Application Considerations:
Any trading application should be thoroughly tested on historical data before implementation
T3 performance characteristics vary across different market conditions and asset types
The indicator provides smoothed trend information but does not predict future price movements
Combining T3 with other analytical tools and market context improves analysis quality
Risk management practices remain essential regardless of the analytical approach used
📋 DETAILED PARAMETER CONFIGURATION
Source Selection:
Close Price (Default): Standard choice for end-of-period trend analysis, reduces intrabar noise
HL2 (High+Low)/2: Provides balanced view of price action, considers full bar range
HLC3 or OHLC4: Incorporates more price information, may provide smoother results
Selection Impact: Different sources affect signal timing and smoothness characteristics
Length Configuration:
Shorter periods: More responsive, faster reaction, frequent signals, but higher false signal risk in choppy markets
Longer periods: Smoother output, fewer signals, better for long-term trends, but slower response
Default 14 periods is a common baseline, but optimal length varies by asset, timeframe, and market conditions
Parameter selection should be determined through backtesting rather than general recommendations
Volume Factor Configuration:
Lower values (closer to 0.0): Increase responsiveness but also noise sensitivity
Higher values (closer to 1.0): Increase smoothness but slightly more lag
Default 0.7 (Tim Tillson's recommendation) provides good balance for most applications
Optimal value depends on signal frequency versus reliability preference, test for specific use case
Parameter Optimization Approach:
There are no universal "best" parameter values - optimal settings depend on the specific asset, timeframe, market regime, and trading strategy
Start with default values (Length: 14, Volume Factor: 0.7) and adjust based on observed performance in your target market
Conduct systematic backtesting across different market conditions to evaluate parameter sensitivity
Consider that parameters optimized for historical data may not perform identically in future market conditions
Monitor performance and be prepared to adjust parameters as market characteristics evolve
📈 DESIGN FEATURES & MARKET ADAPTATION
Algorithm Design Features:
Simple Moving Average (SMA): Equal weighting across lookback period
Exponential Moving Average (EMA): Exponentially decreasing weights on historical prices
T3 Moving Average: Recursive Generalized DEMA with adjustable volume factor
Market Condition Adaptation:
Trending markets: Smoothed indicators generally align more closely with sustained directional movement
Ranging markets: All moving averages may generate more crossover signals during non-trending periods
Volatile conditions: Higher smoothing parameters reduce short-term sensitivity but increase lag
Indicator behavior relative to market conditions should be evaluated for specific applications
USAGE NOTES
This indicator is designed for technical analysis and educational purposes. The T3 Moving Average has limitations and should not be used as the sole basis for trading decisions. Like all trend-following indicators, its performance varies with market conditions, and past signal characteristics do not guarantee future results.
Key Points:
T3 is a lagging indicator that responds to price changes rather than predicting future movements
Signals should be confirmed with other technical tools and market context
Parameters should be optimized for specific market and timeframe
Risk management and position sizing are essential
Market regime changes can affect indicator effectiveness
Test strategies thoroughly on historical data before live implementation
Consider broader market context and fundamental factors
FirstStrike Long 200 - Daily Trend Rider [KedArc Quant]Strategy Description
FirstStrike Long 200 is a disciplined, long-only momentum strategy designed for daily "strike-first" entries in trending markets. It scans for RSI momentum above a customizable trigger (default 50), confirmed by EMA trend filters, and limits you to *exactly one trade per day* to avoid overtrading. It uses ATR for dynamic risk management (1.5x stop, 2:1 RR target) and optional trailing stops to ride winners. Backtested with realistic commissions and sizing, it prioritizes low drawdowns (<1% max in tests) over aggressive gains—ideal for swing traders seeking quality setups in bull runs.
Why It's Different from Other Strategies
Unlike generic RSI crossover bots or EMA ribbon mashups that spam signals and bleed in chop, FirstStrike enforces a "one-and-done" daily gate, blending precision momentum (RSI modes with grace/sustain) with robust filters (volume, sessions, rearm dips).
How It Helps Traders
- Reduces Emotional Trading: One entry/day forces discipline—miss a setup? Wait for tomorrow. Perfect for busy pros avoiding screen fatigue.
- Adapts to Regimes: Switch modes for trends ("Cross+Grace") vs. ranges ("Any bar")—boosts win rates 5-10% in backtests on high-beta names like .
- Risk-First Design: ATR scales stops to vol capping DD at 0.2% while targeting 2R winners. Trailing option locks +3-5% runs without early exits.
- Quick Insights: Labels/alerts flag entries with RSI values; bgcolor highlights signals for visual scanning. Helps spot "first-strike" edges in uptrends, filtering ~60% noise.
Why This Is Not a Mashup
This isn't a Frankenstein of off-the-shelf indicators—while it uses standard RSI/EMA/ATR (core Pine primitives), the innovation lies in:
- Custom Trigger Engine: Switchable modes (e.g., "Cross+Grace+Sustain" requires post-cross hold) prevent perpetual signals, unlike basic `ta.crossover()`.
- Daily Rearm Gate: Resets eligibility only after a dip (if enabled), tying momentum to mean-reversion—original logic not found in common scripts.
- Per-Day Isolation: `var` vars + `ta.change(time("D"))` ensure zero pyramiding/overlaps, beyond simple session filters.
All formulae are derived in-house for "first-strike" (early RSI pops in trends), not copied from public repos.
Input Configurations
Let's break down every input in the FirstStrike Long 200 strategy. These settings let you tweak the strategy like a dashboard—start with defaults for quick testing,
then adjust based on your asset or timeframe (5m for intraday). They're grouped logically to keep things organized, and most have tooltips in the script for quick reminders.
RSI / Trigger Group: The Heart of Momentum Detection
This is where the magic starts—the strategy hunts for "upward energy" using RSI (Relative Strength Index), a tool that measures if a stock is overbought (too hot) or oversold (too cold) on a 0-100 scale.
- RSI Length: How many bars (candles) back to calculate RSI. Default is 14, like a 14-day window for daily charts. Shorter (e.g., 9) makes it snappier for fast markets; longer (21) smooths out noise but misses quick turns.
- Trigger Level (RSI >= this): The key RSI value where the strategy says, "Go time!" Default 50 means enter when RSI crosses or holds above the neutral midline. Why is this trigger required? It acts as your "green light" filter—without it, you'd enter on every tiny price wiggle, leading to endless losers. RSI above this shows building buyer power, avoiding weak or sideways moves. It's essential for quality over quantity, especially in one-trade-per-day setups.
- Trigger Mode: Picks how strict the RSI signal must be. Options: "Cross only" (exact RSI crossover above trigger—super precise, fewer trades); "Cross+Grace" (crossover or within a grace window after—gives a second chance); "Cross+Grace+Sustain" (crossover/grace plus RSI holding steady for bars—best for steady climbs); "Any bar >= trigger" (looser, any bar above—more opportunities but riskier in chop). Start with "Any bar" for trends, switch to "Cross only" for caution.
- Grace Window (bars after cross): If mode allows, how many bars post-RSI-cross you can still enter if RSI dips but recovers. Default 30 (about 2.5 hours on 5m). Zero means no wiggle room—pure precision.
- Sustain Bars (RSI >= trigger): In sustain mode, how many straight bars RSI must stay above trigger. Default 3 ensures it's not a fluke spike.
- Require RSI Dip Below Rearm Before Any Entry?: A yes/no toggle. If on, the strategy "rearms" only after RSI dips below a low level (like a breather), preventing back-to-back signals in overextended rallies.
- Rearm Level (if requireDip=true): The dip threshold for rearming. Default 45—RSI must go below this to reset eligibility. Lower (30) for deeper pullbacks in volatile stocks.
For the trigger level itself, presets matter a lot—default 50 is neutral and versatile for broad trends. Bump to 55-60 for "strong momentum only" (fewer but higher-win trades, great in bull runs like tech surges); drop to 40-45 for "early bird" catches in recoveries (more signals but watch for fakes in ranges). The optimize hint (40-60) lets you test these in TradingView to match your risk—higher presets cut noise by 20-30% in backtests.
Trend / Filters Group: Keeping You on the Right Side of the Market
These EMAs (Exponential Moving Averages) act like guardrails, ensuring you only long in uptrends.
- EMA (Fast) Confirmation: Short-term EMA for price action. Default 20 periods—price must be above this for "recent strength." Shorter (10) reacts faster to intraday pops.
- EMA (Trend Filter): Long-term EMA for big-picture trend. Default 200 (classic "above the 200-day" rule)—price above it confirms bull market. Minimum 50 to avoid over-smoothing.
Optional Hour Window Group: Timing Your Strikes
Avoid bad hours like lunch lulls or after-hours tricks.
- Restrict by Session?: Yes/no for using exact market hours. Default off.
- Session (e.g., 0930-1600 for NYSE): Time string like "0930-1600" for open to close. Auto-skips pre/post-market noise.
- Restrict by Hour Range?: Fallback yes/no for simple hours. Default off.
- Start Hour / End Hour: Clock times (0-23). Defaults 9-15 ET—focus on peak volume.
Volume Filter Group: No Volume, No Party
Confirms conviction—big moves need big participation.
- Require Volume > SMA?: Yes/no toggle. Default off—only fires on above-average volume.
- Volume SMA Length: Periods for the average. Default 20—compares current bar to recent norm.
Risk / Exits Group: Protecting and Profiting Smartly
Dynamic stops based on volatility (ATR = Average True Range) keep things realistic.
- ATR Length: Bars for ATR calc. Default 14—measures recent "wiggle room" in price.
- ATR Stop Multiplier: How far below entry for stop-loss. Default 1.5x ATR—gives breathing space without huge risk
- Take-Profit R Multiple: Reward target as multiple of risk. Default 2.0 (2:1 ratio)—aims for twice your stop distance.
- Use Trailing Stop?: Yes/no for profit-locking trail. Default off—activates after entry.
- Trailing ATR Multiplier: Trail distance. Default 2.0x ATR—looser than initial stop to let winners run.
These inputs make the strategy plug-and-play: Defaults work out-of-box for trending stocks, but tweak RSI trigger/modes first for your style.
Always backtest changes—small shifts can flip a 40% win rate to 50%+!
Outputs (Visuals & Alerts):
- Plots: Blue EMA200 (trend line), Orange EMA20 (price filter), Green dashed entry price.
- Labels: Green "LONG" arrow with RSI value on entries.
- Background: Light green highlight on signal bars.
- Alerts: "FirstStrike Long Entry" fires on conditions (integrates with TradingView notifications).
Entry-Exit Logic
Entry (Long Only, One Per Day):
1. Daily Reset: New day clears trade gate and (if required) rearm status.
2. Filters Pass: Time/session OK + Close > EMA200 (trend) + Close > EMA20 (price) + Volume > SMA (if enabled) + Rearmed (dip below rearm if toggled).
3. Trigger Fires: RSI >= trigger via selected mode (e.g., crossover + grace window).
4. Execute: Enter long at close; set daily flag to block repeats.
Exit:
- Stop-Loss: Entry - (ATR * 1.5) – dynamic, vol-scaled.
- Take-Profit: Entry + (Risk * 2.0) – fixed RR.
- Trailing (Optional): Activates post-entry; trails at Close - (ATR * 2.0), updating on each bar for trend extension.
No shorts or hedging—pure long bias.
Formulae Used
- RSI: `ta.rsi(close, rsiLen)` – Standard 14-period momentum oscillator (0-100).
- EMAs: `ta.ema(close, len)` – Exponential moving averages for trend/price filters.
- ATR: `ta.atr(atrLen)` – True range average for stop sizing: Stop = Entry - (ATR * mult).
- Volume SMA: `ta.sma(volume, volLen)` – Simple average for relative strength filter.
- Grace Window: `bar_index - lastCrossBarIndex <= graceBars` – Counts bars since RSI crossover.
- Sustain: `ta.barssince(rsi < trigger) >= sustainBars` – Consecutive bars above threshold.
- Session Check: `time(timeframe.period, sessionStr) != 0` – TradingView's built-in session validator.
- Risk Distance: `riskPS = entry - stop; TP = entry + (riskPS * RR)` – Asymmetric reward calc.
FAQ
Q: Why only one trade/day?
A: Prevents revenge trading in volatile sessions . Backtests show it cuts losers by 20-30% vs. multi-entry bots.
Q: Does it work on all assets/timeframes?
A: Best for trending stocks/indices on 5m-1H. Test on crypto/forex with wider ATR mult (2.0+).
Q: How to optimize?
A: Use TradingView's optimizer on RSI trigger (40-60) and EMA fast (10-30). Aim for PF >1.0 over 1Y data.
Q: Alerts don't fire—why?
A: Ensure `alertcondition` is enabled in script settings. Test with "Any alert() function calls only."
Q: Trailing stop too loose?
A: Tune `trailMult` to 1.5 for tighter; it activates alongside fixed TP/SL for hybrid protection.
Glossary
- Grace Window: Post-RSI-cross period (bars) where entry still allowed if RSI holds trigger.
- Rearm Dip: Optional pullback below a low RSI level (e.g., 45) to "reset" eligibility after signals.
- Profit Factor (PF): Gross profit / gross loss—>1.0 means winners outweigh losers.
- R Multiple: Risk units (e.g., 2R = 2x stop distance as target).
- Sustain Bars: Consecutive bars RSI stays >= trigger for mode confirmation.
Recommendations
- Backtest First: Run on your symbols (/) over 6-12M; tweak RSI to 55 for +5% win rate.
- Live Use: Start paper trading with `useSession=true` and `useVol=true` to filter noise.
- Pairs Well With: Higher TF (daily) for bias; add ADX (>25) filter for strong trends (code snippet in prior chats).
- Risk Note: 10% sizing suits $100k+ accounts; scale down for smaller. Not financial advice—past performance ≠ future.
- Publish Tip: Add tags like "momentum," "RSI," "long-only" on TradingView for visibility.
Strategy Properties & Backtesting Setup
FirstStrike Long 200 is configured with conservative, realistic backtesting parameters to ensure reliable performance simulations. These settings prioritize capital preservation and transparency, making it suitable for both novice and experienced traders testing on stocks.
Initial Capital
$100,000 Standard starting equity for portfolio-level testing; scales well for retail accounts. Adjust lower (e.g., $10k) for smaller simulations.
Base Currency
Default (USD) Aligns with most US equities (e.g., NASDAQ symbols); auto-converts for other assets.
Order Size
1 (Quantity) Fixed share contracts for simplicity—e.g., buys 1 share per trade. For % of equity, switch to "Percent of Equity" in strategy code.
Pyramiding
0 Orders No additional entries on open positions; enforces strict one-trade-per-day discipline to avoid overexposure.
Commission
0.1% Realistic broker fee (e.g., Interactive Brokers tier); factors in round-trip costs without over-penalizing winners.
Verify Price for Limit Orders
0 Ticks No slippage delay on TPs—assumes ideal fills for historical accuracy.
Slippage
0 Ticks Zero assumed slippage for clean backtests; real-world trading may add 1-2 ticks on volatile opens.
These defaults yield low drawdowns (<0.3% max in tests) while capturing trend edges. For live trading, enable slippage (1-3 ticks) to mimic execution gaps. Always forward-test before deploying!
⚠️ Disclaimer
This script is provided for educational purposes only.
Past performance does not guarantee future results.
Trading involves risk, and users should exercise caution and use proper risk management when applying this strategy.
Dynamic Equity Allocation Model"Cash is Trash"? Not Always. Here's Why Science Beats Guesswork.
Every retail trader knows the frustration: you draw support and resistance lines, you spot patterns, you follow market gurus on social media—and still, when the next bear market hits, your portfolio bleeds red. Meanwhile, institutional investors seem to navigate market turbulence with ease, preserving capital when markets crash and participating when they rally. What's their secret?
The answer isn't insider information or access to exotic derivatives. It's systematic, scientifically validated decision-making. While most retail traders rely on subjective chart analysis and emotional reactions, professional portfolio managers use quantitative models that remove emotion from the equation and process multiple streams of market information simultaneously.
This document presents exactly such a system—not a proprietary black box available only to hedge funds, but a fully transparent, academically grounded framework that any serious investor can understand and apply. The Dynamic Equity Allocation Model (DEAM) synthesizes decades of financial research from Nobel laureates and leading academics into a practical tool for tactical asset allocation.
Stop drawing colorful lines on your chart and start thinking like a quant. This isn't about predicting where the market goes next week—it's about systematically adjusting your risk exposure based on what the data actually tells you. When valuations scream danger, when volatility spikes, when credit markets freeze, when multiple warning signals align—that's when cash isn't trash. That's when cash saves your portfolio.
The irony of "cash is trash" rhetoric is that it ignores timing. Yes, being 100% cash for decades would be disastrous. But being 100% equities through every crisis is equally foolish. The sophisticated approach is dynamic: aggressive when conditions favor risk-taking, defensive when they don't. This model shows you how to make that decision systematically, not emotionally.
Whether you're managing your own retirement portfolio or seeking to understand how institutional allocation strategies work, this comprehensive analysis provides the theoretical foundation, mathematical implementation, and practical guidance to elevate your investment approach from amateur to professional.
The choice is yours: keep hoping your chart patterns work out, or start using the same quantitative methods that professionals rely on. The tools are here. The research is cited. The methodology is explained. All you need to do is read, understand, and apply.
The Dynamic Equity Allocation Model (DEAM) is a quantitative framework for systematic allocation between equities and cash, grounded in modern portfolio theory and empirical market research. The model integrates five scientifically validated dimensions of market analysis—market regime, risk metrics, valuation, sentiment, and macroeconomic conditions—to generate dynamic allocation recommendations ranging from 0% to 100% equity exposure. This work documents the theoretical foundations, mathematical implementation, and practical application of this multi-factor approach.
1. Introduction and Theoretical Background
1.1 The Limitations of Static Portfolio Allocation
Traditional portfolio theory, as formulated by Markowitz (1952) in his seminal work "Portfolio Selection," assumes an optimal static allocation where investors distribute their wealth across asset classes according to their risk aversion. This approach rests on the assumption that returns and risks remain constant over time. However, empirical research demonstrates that this assumption does not hold in reality. Fama and French (1989) showed that expected returns vary over time and correlate with macroeconomic variables such as the spread between long-term and short-term interest rates. Campbell and Shiller (1988) demonstrated that the price-earnings ratio possesses predictive power for future stock returns, providing a foundation for dynamic allocation strategies.
The academic literature on tactical asset allocation has evolved considerably over recent decades. Ilmanen (2011) argues in "Expected Returns" that investors can improve their risk-adjusted returns by considering valuation levels, business cycles, and market sentiment. The Dynamic Equity Allocation Model presented here builds on this research tradition and operationalizes these insights into a practically applicable allocation framework.
1.2 Multi-Factor Approaches in Asset Allocation
Modern financial research has shown that different factors capture distinct aspects of market dynamics and together provide a more robust picture of market conditions than individual indicators. Ross (1976) developed the Arbitrage Pricing Theory, a model that employs multiple factors to explain security returns. Following this multi-factor philosophy, DEAM integrates five complementary analytical dimensions, each tapping different information sources and collectively enabling comprehensive market understanding.
2. Data Foundation and Data Quality
2.1 Data Sources Used
The model draws its data exclusively from publicly available market data via the TradingView platform. This transparency and accessibility is a significant advantage over proprietary models that rely on non-public data. The data foundation encompasses several categories of market information, each capturing specific aspects of market dynamics.
First, price data for the S&P 500 Index is obtained through the SPDR S&P 500 ETF (ticker: SPY). The use of a highly liquid ETF instead of the index itself has practical reasons, as ETF data is available in real-time and reflects actual tradability. In addition to closing prices, high, low, and volume data are captured, which are required for calculating advanced volatility measures.
Fundamental corporate metrics are retrieved via TradingView's Financial Data API. These include earnings per share, price-to-earnings ratio, return on equity, debt-to-equity ratio, dividend yield, and share buyback yield. Cochrane (2011) emphasizes in "Presidential Address: Discount Rates" the central importance of valuation metrics for forecasting future returns, making these fundamental data a cornerstone of the model.
Volatility indicators are represented by the CBOE Volatility Index (VIX) and related metrics. The VIX, often referred to as the market's "fear gauge," measures the implied volatility of S&P 500 index options and serves as a proxy for market participants' risk perception. Whaley (2000) describes in "The Investor Fear Gauge" the construction and interpretation of the VIX and its use as a sentiment indicator.
Macroeconomic data includes yield curve information through US Treasury bonds of various maturities and credit risk premiums through the spread between high-yield bonds and risk-free government bonds. These variables capture the macroeconomic conditions and financing conditions relevant for equity valuation. Estrella and Hardouvelis (1991) showed that the shape of the yield curve has predictive power for future economic activity, justifying the inclusion of these data.
2.2 Handling Missing Data
A practical problem when working with financial data is dealing with missing or unavailable values. The model implements a fallback system where a plausible historical average value is stored for each fundamental metric. When current data is unavailable for a specific point in time, this fallback value is used. This approach ensures that the model remains functional even during temporary data outages and avoids systematic biases from missing data. The use of average values as fallback is conservative, as it generates neither overly optimistic nor pessimistic signals.
3. Component 1: Market Regime Detection
3.1 The Concept of Market Regimes
The idea that financial markets exist in different "regimes" or states that differ in their statistical properties has a long tradition in financial science. Hamilton (1989) developed regime-switching models that allow distinguishing between different market states with different return and volatility characteristics. The practical application of this theory consists of identifying the current market state and adjusting portfolio allocation accordingly.
DEAM classifies market regimes using a scoring system that considers three main dimensions: trend strength, volatility level, and drawdown depth. This multidimensional view is more robust than focusing on individual indicators, as it captures various facets of market dynamics. Classification occurs into six distinct regimes: Strong Bull, Bull Market, Neutral, Correction, Bear Market, and Crisis.
3.2 Trend Analysis Through Moving Averages
Moving averages are among the oldest and most widely used technical indicators and have also received attention in academic literature. Brock, Lakonishok, and LeBaron (1992) examined in "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns" the profitability of trading rules based on moving averages and found evidence for their predictive power, although later studies questioned the robustness of these results when considering transaction costs.
The model calculates three moving averages with different time windows: a 20-day average (approximately one trading month), a 50-day average (approximately one quarter), and a 200-day average (approximately one trading year). The relationship of the current price to these averages and the relationship of the averages to each other provide information about trend strength and direction. When the price trades above all three averages and the short-term average is above the long-term, this indicates an established uptrend. The model assigns points based on these constellations, with longer-term trends weighted more heavily as they are considered more persistent.
3.3 Volatility Regimes
Volatility, understood as the standard deviation of returns, is a central concept of financial theory and serves as the primary risk measure. However, research has shown that volatility is not constant but changes over time and occurs in clusters—a phenomenon first documented by Mandelbrot (1963) and later formalized through ARCH and GARCH models (Engle, 1982; Bollerslev, 1986).
DEAM calculates volatility not only through the classic method of return standard deviation but also uses more advanced estimators such as the Parkinson estimator and the Garman-Klass estimator. These methods utilize intraday information (high and low prices) and are more efficient than simple close-to-close volatility estimators. The Parkinson estimator (Parkinson, 1980) uses the range between high and low of a trading day and is based on the recognition that this information reveals more about true volatility than just the closing price difference. The Garman-Klass estimator (Garman and Klass, 1980) extends this approach by additionally considering opening and closing prices.
The calculated volatility is annualized by multiplying it by the square root of 252 (the average number of trading days per year), enabling standardized comparability. The model compares current volatility with the VIX, the implied volatility from option prices. A low VIX (below 15) signals market comfort and increases the regime score, while a high VIX (above 35) indicates market stress and reduces the score. This interpretation follows the empirical observation that elevated volatility is typically associated with falling markets (Schwert, 1989).
3.4 Drawdown Analysis
A drawdown refers to the percentage decline from the highest point (peak) to the lowest point (trough) during a specific period. This metric is psychologically significant for investors as it represents the maximum loss experienced. Calmar (1991) developed the Calmar Ratio, which relates return to maximum drawdown, underscoring the practical relevance of this metric.
The model calculates current drawdown as the percentage distance from the highest price of the last 252 trading days (one year). A drawdown below 3% is considered negligible and maximally increases the regime score. As drawdown increases, the score decreases progressively, with drawdowns above 20% classified as severe and indicating a crisis or bear market regime. These thresholds are empirically motivated by historical market cycles, in which corrections typically encompassed 5-10% drawdowns, bear markets 20-30%, and crises over 30%.
3.5 Regime Classification
Final regime classification occurs through aggregation of scores from trend (40% weight), volatility (30%), and drawdown (30%). The higher weighting of trend reflects the empirical observation that trend-following strategies have historically delivered robust results (Moskowitz, Ooi, and Pedersen, 2012). A total score above 80 signals a strong bull market with established uptrend, low volatility, and minimal losses. At a score below 10, a crisis situation exists requiring defensive positioning. The six regime categories enable a differentiated allocation strategy that not only distinguishes binarily between bullish and bearish but allows gradual gradations.
4. Component 2: Risk-Based Allocation
4.1 Volatility Targeting as Risk Management Approach
The concept of volatility targeting is based on the idea that investors should maximize not returns but risk-adjusted returns. Sharpe (1966, 1994) defined with the Sharpe Ratio the fundamental concept of return per unit of risk, measured as volatility. Volatility targeting goes a step further and adjusts portfolio allocation to achieve constant target volatility. This means that in times of low market volatility, equity allocation is increased, and in times of high volatility, it is reduced.
Moreira and Muir (2017) showed in "Volatility-Managed Portfolios" that strategies that adjust their exposure based on volatility forecasts achieve higher Sharpe Ratios than passive buy-and-hold strategies. DEAM implements this principle by defining a target portfolio volatility (default 12% annualized) and adjusting equity allocation to achieve it. The mathematical foundation is simple: if market volatility is 20% and target volatility is 12%, equity allocation should be 60% (12/20 = 0.6), with the remaining 40% held in cash with zero volatility.
4.2 Market Volatility Calculation
Estimating current market volatility is central to the risk-based allocation approach. The model uses several volatility estimators in parallel and selects the higher value between traditional close-to-close volatility and the Parkinson estimator. This conservative choice ensures the model does not underestimate true volatility, which could lead to excessive risk exposure.
Traditional volatility calculation uses logarithmic returns, as these have mathematically advantageous properties (additive linkage over multiple periods). The logarithmic return is calculated as ln(P_t / P_{t-1}), where P_t is the price at time t. The standard deviation of these returns over a rolling 20-trading-day window is then multiplied by √252 to obtain annualized volatility. This annualization is based on the assumption of independently identically distributed returns, which is an idealization but widely accepted in practice.
The Parkinson estimator uses additional information from the trading range (High minus Low) of each day. The formula is: σ_P = (1/√(4ln2)) × √(1/n × Σln²(H_i/L_i)) × √252, where H_i and L_i are high and low prices. Under ideal conditions, this estimator is approximately five times more efficient than the close-to-close estimator (Parkinson, 1980), as it uses more information per observation.
4.3 Drawdown-Based Position Size Adjustment
In addition to volatility targeting, the model implements drawdown-based risk control. The logic is that deep market declines often signal further losses and therefore justify exposure reduction. This behavior corresponds with the concept of path-dependent risk tolerance: investors who have already suffered losses are typically less willing to take additional risk (Kahneman and Tversky, 1979).
The model defines a maximum portfolio drawdown as a target parameter (default 15%). Since portfolio volatility and portfolio drawdown are proportional to equity allocation (assuming cash has neither volatility nor drawdown), allocation-based control is possible. For example, if the market exhibits a 25% drawdown and target portfolio drawdown is 15%, equity allocation should be at most 60% (15/25).
4.4 Dynamic Risk Adjustment
An advanced feature of DEAM is dynamic adjustment of risk-based allocation through a feedback mechanism. The model continuously estimates what actual portfolio volatility and portfolio drawdown would result at the current allocation. If risk utilization (ratio of actual to target risk) exceeds 1.0, allocation is reduced by an adjustment factor that grows exponentially with overutilization. This implements a form of dynamic feedback that avoids overexposure.
Mathematically, a risk adjustment factor r_adjust is calculated: if risk utilization u > 1, then r_adjust = exp(-0.5 × (u - 1)). This exponential function ensures that moderate overutilization is gently corrected, while strong overutilization triggers drastic reductions. The factor 0.5 in the exponent was empirically calibrated to achieve a balanced ratio between sensitivity and stability.
5. Component 3: Valuation Analysis
5.1 Theoretical Foundations of Fundamental Valuation
DEAM's valuation component is based on the fundamental premise that the intrinsic value of a security is determined by its future cash flows and that deviations between market price and intrinsic value are eventually corrected. Graham and Dodd (1934) established in "Security Analysis" the basic principles of fundamental analysis that remain relevant today. Translated into modern portfolio context, this means that markets with high valuation metrics (high price-earnings ratios) should have lower expected returns than cheaply valued markets.
Campbell and Shiller (1988) developed the Cyclically Adjusted P/E Ratio (CAPE), which smooths earnings over a full business cycle. Their empirical analysis showed that this ratio has significant predictive power for 10-year returns. Asness, Moskowitz, and Pedersen (2013) demonstrated in "Value and Momentum Everywhere" that value effects exist not only in individual stocks but also in asset classes and markets.
5.2 Equity Risk Premium as Central Valuation Metric
The Equity Risk Premium (ERP) is defined as the expected excess return of stocks over risk-free government bonds. It is the theoretical heart of valuation analysis, as it represents the compensation investors demand for bearing equity risk. Damodaran (2012) discusses in "Equity Risk Premiums: Determinants, Estimation and Implications" various methods for ERP estimation.
DEAM calculates ERP not through a single method but combines four complementary approaches with different weights. This multi-method strategy increases estimation robustness and avoids dependence on single, potentially erroneous inputs.
The first method (35% weight) uses earnings yield, calculated as 1/P/E or directly from operating earnings data, and subtracts the 10-year Treasury yield. This method follows Fed Model logic (Yardeni, 2003), although this model has theoretical weaknesses as it does not consistently treat inflation (Asness, 2003).
The second method (30% weight) extends earnings yield by share buyback yield. Share buybacks are a form of capital return to shareholders and increase value per share. Boudoukh et al. (2007) showed in "The Total Shareholder Yield" that the sum of dividend yield and buyback yield is a better predictor of future returns than dividend yield alone.
The third method (20% weight) implements the Gordon Growth Model (Gordon, 1962), which models stock value as the sum of discounted future dividends. Under constant growth g assumption: Expected Return = Dividend Yield + g. The model estimates sustainable growth as g = ROE × (1 - Payout Ratio), where ROE is return on equity and payout ratio is the ratio of dividends to earnings. This formula follows from equity theory: unretained earnings are reinvested at ROE and generate additional earnings growth.
The fourth method (15% weight) combines total shareholder yield (Dividend + Buybacks) with implied growth derived from revenue growth. This method considers that companies with strong revenue growth should generate higher future earnings, even if current valuations do not yet fully reflect this.
The final ERP is the weighted average of these four methods. A high ERP (above 4%) signals attractive valuations and increases the valuation score to 95 out of 100 possible points. A negative ERP, where stocks have lower expected returns than bonds, results in a minimal score of 10.
5.3 Quality Adjustments to Valuation
Valuation metrics alone can be misleading if not interpreted in the context of company quality. A company with a low P/E may be cheap or fundamentally problematic. The model therefore implements quality adjustments based on growth, profitability, and capital structure.
Revenue growth above 10% annually adds 10 points to the valuation score, moderate growth above 5% adds 5 points. This adjustment reflects that growth has independent value (Modigliani and Miller, 1961, extended by later growth theory). Net margin above 15% signals pricing power and operational efficiency and increases the score by 5 points, while low margins below 8% indicate competitive pressure and subtract 5 points.
Return on equity (ROE) above 20% characterizes outstanding capital efficiency and increases the score by 5 points. Piotroski (2000) showed in "Value Investing: The Use of Historical Financial Statement Information" that fundamental quality signals such as high ROE can improve the performance of value strategies.
Capital structure is evaluated through the debt-to-equity ratio. A conservative ratio below 1.0 multiplies the valuation score by 1.2, while high leverage above 2.0 applies a multiplier of 0.8. This adjustment reflects that high debt constrains financial flexibility and can become problematic in crisis times (Korteweg, 2010).
6. Component 4: Sentiment Analysis
6.1 The Role of Sentiment in Financial Markets
Investor sentiment, defined as the collective psychological attitude of market participants, influences asset prices independently of fundamental data. Baker and Wurgler (2006, 2007) developed a sentiment index and showed that periods of high sentiment are followed by overvaluations that later correct. This insight justifies integrating a sentiment component into allocation decisions.
Sentiment is difficult to measure directly but can be proxied through market indicators. The VIX is the most widely used sentiment indicator, as it aggregates implied volatility from option prices. High VIX values reflect elevated uncertainty and risk aversion, while low values signal market comfort. Whaley (2009) refers to the VIX as the "Investor Fear Gauge" and documents its role as a contrarian indicator: extremely high values typically occur at market bottoms, while low values occur at tops.
6.2 VIX-Based Sentiment Assessment
DEAM uses statistical normalization of the VIX by calculating the Z-score: z = (VIX_current - VIX_average) / VIX_standard_deviation. The Z-score indicates how many standard deviations the current VIX is from the historical average. This approach is more robust than absolute thresholds, as it adapts to the average volatility level, which can vary over longer periods.
A Z-score below -1.5 (VIX is 1.5 standard deviations below average) signals exceptionally low risk perception and adds 40 points to the sentiment score. This may seem counterintuitive—shouldn't low fear be bullish? However, the logic follows the contrarian principle: when no one is afraid, everyone is already invested, and there is limited further upside potential (Zweig, 1973). Conversely, a Z-score above 1.5 (extreme fear) adds -40 points, reflecting market panic but simultaneously suggesting potential buying opportunities.
6.3 VIX Term Structure as Sentiment Signal
The VIX term structure provides additional sentiment information. Normally, the VIX trades in contango, meaning longer-term VIX futures have higher prices than short-term. This reflects that short-term volatility is currently known, while long-term volatility is more uncertain and carries a risk premium. The model compares the VIX with VIX9D (9-day volatility) and identifies backwardation (VIX > 1.05 × VIX9D) and steep backwardation (VIX > 1.15 × VIX9D).
Backwardation occurs when short-term implied volatility is higher than longer-term, which typically happens during market stress. Investors anticipate immediate turbulence but expect calming. Psychologically, this reflects acute fear. The model subtracts 15 points for backwardation and 30 for steep backwardation, as these constellations signal elevated risk. Simon and Wiggins (2001) analyzed the VIX futures curve and showed that backwardation is associated with market declines.
6.4 Safe-Haven Flows
During crisis times, investors flee from risky assets into safe havens: gold, US dollar, and Japanese yen. This "flight to quality" is a sentiment signal. The model calculates the performance of these assets relative to stocks over the last 20 trading days. When gold or the dollar strongly rise while stocks fall, this indicates elevated risk aversion.
The safe-haven component is calculated as the difference between safe-haven performance and stock performance. Positive values (safe havens outperform) subtract up to 20 points from the sentiment score, negative values (stocks outperform) add up to 10 points. The asymmetric treatment (larger deduction for risk-off than bonus for risk-on) reflects that risk-off movements are typically sharper and more informative than risk-on phases.
Baur and Lucey (2010) examined safe-haven properties of gold and showed that gold indeed exhibits negative correlation with stocks during extreme market movements, confirming its role as crisis protection.
7. Component 5: Macroeconomic Analysis
7.1 The Yield Curve as Economic Indicator
The yield curve, represented as yields of government bonds of various maturities, contains aggregated expectations about future interest rates, inflation, and economic growth. The slope of the yield curve has remarkable predictive power for recessions. Estrella and Mishkin (1998) showed that an inverted yield curve (short-term rates higher than long-term) predicts recessions with high reliability. This is because inverted curves reflect restrictive monetary policy: the central bank raises short-term rates to combat inflation, dampening economic activity.
DEAM calculates two spread measures: the 2-year-minus-10-year spread and the 3-month-minus-10-year spread. A steep, positive curve (spreads above 1.5% and 2% respectively) signals healthy growth expectations and generates the maximum yield curve score of 40 points. A flat curve (spreads near zero) reduces the score to 20 points. An inverted curve (negative spreads) is particularly alarming and results in only 10 points.
The choice of two different spreads increases analysis robustness. The 2-10 spread is most established in academic literature, while the 3M-10Y spread is often considered more sensitive, as the 3-month rate directly reflects current monetary policy (Ang, Piazzesi, and Wei, 2006).
7.2 Credit Conditions and Spreads
Credit spreads—the yield difference between risky corporate bonds and safe government bonds—reflect risk perception in the credit market. Gilchrist and Zakrajšek (2012) constructed an "Excess Bond Premium" that measures the component of credit spreads not explained by fundamentals and showed this is a predictor of future economic activity and stock returns.
The model approximates credit spread by comparing the yield of high-yield bond ETFs (HYG) with investment-grade bond ETFs (LQD). A narrow spread below 200 basis points signals healthy credit conditions and risk appetite, contributing 30 points to the macro score. Very wide spreads above 1000 basis points (as during the 2008 financial crisis) signal credit crunch and generate zero points.
Additionally, the model evaluates whether "flight to quality" is occurring, identified through strong performance of Treasury bonds (TLT) with simultaneous weakness in high-yield bonds. This constellation indicates elevated risk aversion and reduces the credit conditions score.
7.3 Financial Stability at Corporate Level
While the yield curve and credit spreads reflect macroeconomic conditions, financial stability evaluates the health of companies themselves. The model uses the aggregated debt-to-equity ratio and return on equity of the S&P 500 as proxies for corporate health.
A low leverage level below 0.5 combined with high ROE above 15% signals robust corporate balance sheets and generates 20 points. This combination is particularly valuable as it represents both defensive strength (low debt means crisis resistance) and offensive strength (high ROE means earnings power). High leverage above 1.5 generates only 5 points, as it implies vulnerability to interest rate increases and recessions.
Korteweg (2010) showed in "The Net Benefits to Leverage" that optimal debt maximizes firm value, but excessive debt increases distress costs. At the aggregated market level, high debt indicates fragilities that can become problematic during stress phases.
8. Component 6: Crisis Detection
8.1 The Need for Systematic Crisis Detection
Financial crises are rare but extremely impactful events that suspend normal statistical relationships. During normal market volatility, diversified portfolios and traditional risk management approaches function, but during systemic crises, seemingly independent assets suddenly correlate strongly, and losses exceed historical expectations (Longin and Solnik, 2001). This justifies a separate crisis detection mechanism that operates independently of regular allocation components.
Reinhart and Rogoff (2009) documented in "This Time Is Different: Eight Centuries of Financial Folly" recurring patterns in financial crises: extreme volatility, massive drawdowns, credit market dysfunction, and asset price collapse. DEAM operationalizes these patterns into quantifiable crisis indicators.
8.2 Multi-Signal Crisis Identification
The model uses a counter-based approach where various stress signals are identified and aggregated. This methodology is more robust than relying on a single indicator, as true crises typically occur simultaneously across multiple dimensions. A single signal may be a false alarm, but the simultaneous presence of multiple signals increases confidence.
The first indicator is a VIX above the crisis threshold (default 40), adding one point. A VIX above 60 (as in 2008 and March 2020) adds two additional points, as such extreme values are historically very rare. This tiered approach captures the intensity of volatility.
The second indicator is market drawdown. A drawdown above 15% adds one point, as corrections of this magnitude can be potential harbingers of larger crises. A drawdown above 25% adds another point, as historical bear markets typically encompass 25-40% drawdowns.
The third indicator is credit market spreads above 500 basis points, adding one point. Such wide spreads occur only during significant credit market disruptions, as in 2008 during the Lehman crisis.
The fourth indicator identifies simultaneous losses in stocks and bonds. Normally, Treasury bonds act as a hedge against equity risk (negative correlation), but when both fall simultaneously, this indicates systemic liquidity problems or inflation/stagflation fears. The model checks whether both SPY and TLT have fallen more than 10% and 5% respectively over 5 trading days, adding two points.
The fifth indicator is a volume spike combined with negative returns. Extreme trading volumes (above twice the 20-day average) with falling prices signal panic selling. This adds one point.
A crisis situation is diagnosed when at least 3 indicators trigger, a severe crisis at 5 or more indicators. These thresholds were calibrated through historical backtesting to identify true crises (2008, 2020) without generating excessive false alarms.
8.3 Crisis-Based Allocation Override
When a crisis is detected, the system overrides the normal allocation recommendation and caps equity allocation at maximum 25%. In a severe crisis, the cap is set at 10%. This drastic defensive posture follows the empirical observation that crises typically require time to develop and that early reduction can avoid substantial losses (Faber, 2007).
This override logic implements a "safety first" principle: in situations of existential danger to the portfolio, capital preservation becomes the top priority. Roy (1952) formalized this approach in "Safety First and the Holding of Assets," arguing that investors should primarily minimize ruin probability.
9. Integration and Final Allocation Calculation
9.1 Component Weighting
The final allocation recommendation emerges through weighted aggregation of the five components. The standard weighting is: Market Regime 35%, Risk Management 25%, Valuation 20%, Sentiment 15%, Macro 5%. These weights reflect both theoretical considerations and empirical backtesting results.
The highest weighting of market regime is based on evidence that trend-following and momentum strategies have delivered robust results across various asset classes and time periods (Moskowitz, Ooi, and Pedersen, 2012). Current market momentum is highly informative for the near future, although it provides no information about long-term expectations.
The substantial weighting of risk management (25%) follows from the central importance of risk control. Wealth preservation is the foundation of long-term wealth creation, and systematic risk management is demonstrably value-creating (Moreira and Muir, 2017).
The valuation component receives 20% weight, based on the long-term mean reversion of valuation metrics. While valuation has limited short-term predictive power (bull and bear markets can begin at any valuation), the long-term relationship between valuation and returns is robustly documented (Campbell and Shiller, 1988).
Sentiment (15%) and Macro (5%) receive lower weights, as these factors are subtler and harder to measure. Sentiment is valuable as a contrarian indicator at extremes but less informative in normal ranges. Macro variables such as the yield curve have strong predictive power for recessions, but the transmission from recessions to stock market performance is complex and temporally variable.
9.2 Model Type Adjustments
DEAM allows users to choose between four model types: Conservative, Balanced, Aggressive, and Adaptive. This choice modifies the final allocation through additive adjustments.
Conservative mode subtracts 10 percentage points from allocation, resulting in consistently more cautious positioning. This is suitable for risk-averse investors or those with limited investment horizons. Aggressive mode adds 10 percentage points, suitable for risk-tolerant investors with long horizons.
Adaptive mode implements procyclical adjustment based on short-term momentum: if the market has risen more than 5% in the last 20 days, 5 percentage points are added; if it has declined more than 5%, 5 points are subtracted. This logic follows the observation that short-term momentum persists (Jegadeesh and Titman, 1993), but the moderate size of adjustment avoids excessive timing bets.
Balanced mode makes no adjustment and uses raw model output. This neutral setting is suitable for investors who wish to trust model recommendations unchanged.
9.3 Smoothing and Stability
The allocation resulting from aggregation undergoes final smoothing through a simple moving average over 3 periods. This smoothing is crucial for model practicality, as it reduces frequent trading and thus transaction costs. Without smoothing, the model could fluctuate between adjacent allocations with every small input change.
The choice of 3 periods as smoothing window is a compromise between responsiveness and stability. Longer smoothing would excessively delay signals and impede response to true regime changes. Shorter or no smoothing would allow too much noise. Empirical tests showed that 3-period smoothing offers an optimal ratio between these goals.
10. Visualization and Interpretation
10.1 Main Output: Equity Allocation
DEAM's primary output is a time series from 0 to 100 representing the recommended percentage allocation to equities. This representation is intuitive: 100% means full investment in stocks (specifically: an S&P 500 ETF), 0% means complete cash position, and intermediate values correspond to mixed portfolios. A value of 60% means, for example: invest 60% of wealth in SPY, hold 40% in money market instruments or cash.
The time series is color-coded to enable quick visual interpretation. Green shades represent high allocations (above 80%, bullish), red shades low allocations (below 20%, bearish), and neutral colors middle allocations. The chart background is dynamically colored based on the signal, enhancing readability in different market phases.
10.2 Dashboard Metrics
A tabular dashboard presents key metrics compactly. This includes current allocation, cash allocation (complement), an aggregated signal (BULLISH/NEUTRAL/BEARISH), current market regime, VIX level, market drawdown, and crisis status.
Additionally, fundamental metrics are displayed: P/E Ratio, Equity Risk Premium, Return on Equity, Debt-to-Equity Ratio, and Total Shareholder Yield. This transparency allows users to understand model decisions and form their own assessments.
Component scores (Regime, Risk, Valuation, Sentiment, Macro) are also displayed, each normalized on a 0-100 scale. This shows which factors primarily drive the current recommendation. If, for example, the Risk score is very low (20) while other scores are moderate (50-60), this indicates that risk management considerations are pulling allocation down.
10.3 Component Breakdown (Optional)
Advanced users can display individual components as separate lines in the chart. This enables analysis of component dynamics: do all components move synchronously, or are there divergences? Divergences can be particularly informative. If, for example, the market regime is bullish (high score) but the valuation component is very negative, this signals an overbought market not fundamentally supported—a classic "bubble warning."
This feature is disabled by default to keep the chart clean but can be activated for deeper analysis.
10.4 Confidence Bands
The model optionally displays uncertainty bands around the main allocation line. These are calculated as ±1 standard deviation of allocation over a rolling 20-period window. Wide bands indicate high volatility of model recommendations, suggesting uncertain market conditions. Narrow bands indicate stable recommendations.
This visualization implements a concept of epistemic uncertainty—uncertainty about the model estimate itself, not just market volatility. In phases where various indicators send conflicting signals, the allocation recommendation becomes more volatile, manifesting in wider bands. Users can understand this as a warning to act more cautiously or consult alternative information sources.
11. Alert System
11.1 Allocation Alerts
DEAM implements an alert system that notifies users of significant events. Allocation alerts trigger when smoothed allocation crosses certain thresholds. An alert is generated when allocation reaches 80% (from below), signaling strong bullish conditions. Another alert triggers when allocation falls to 20%, indicating defensive positioning.
These thresholds are not arbitrary but correspond with boundaries between model regimes. An allocation of 80% roughly corresponds to a clear bull market regime, while 20% corresponds to a bear market regime. Alerts at these points are therefore informative about fundamental regime shifts.
11.2 Crisis Alerts
Separate alerts trigger upon detection of crisis and severe crisis. These alerts have highest priority as they signal large risks. A crisis alert should prompt investors to review their portfolio and potentially take defensive measures beyond the automatic model recommendation (e.g., hedging through put options, rebalancing to more defensive sectors).
11.3 Regime Change Alerts
An alert triggers upon change of market regime (e.g., from Neutral to Correction, or from Bull Market to Strong Bull). Regime changes are highly informative events that typically entail substantial allocation changes. These alerts enable investors to proactively respond to changes in market dynamics.
11.4 Risk Breach Alerts
A specialized alert triggers when actual portfolio risk utilization exceeds target parameters by 20%. This is a warning signal that the risk management system is reaching its limits, possibly because market volatility is rising faster than allocation can be reduced. In such situations, investors should consider manual interventions.
12. Practical Application and Limitations
12.1 Portfolio Implementation
DEAM generates a recommendation for allocation between equities (S&P 500) and cash. Implementation by an investor can take various forms. The most direct method is using an S&P 500 ETF (e.g., SPY, VOO) for equity allocation and a money market fund or savings account for cash allocation.
A rebalancing strategy is required to synchronize actual allocation with model recommendation. Two approaches are possible: (1) rule-based rebalancing at every 10% deviation between actual and target, or (2) time-based monthly rebalancing. Both have trade-offs between responsiveness and transaction costs. Empirical evidence (Jaconetti, Kinniry, and Zilbering, 2010) suggests rebalancing frequency has moderate impact on performance, and investors should optimize based on their transaction costs.
12.2 Adaptation to Individual Preferences
The model offers numerous adjustment parameters. Component weights can be modified if investors place more or less belief in certain factors. A fundamentally-oriented investor might increase valuation weight, while a technical trader might increase regime weight.
Risk target parameters (target volatility, max drawdown) should be adapted to individual risk tolerance. Younger investors with long investment horizons can choose higher target volatility (15-18%), while retirees may prefer lower volatility (8-10%). This adjustment systematically shifts average equity allocation.
Crisis thresholds can be adjusted based on preference for sensitivity versus specificity of crisis detection. Lower thresholds (e.g., VIX > 35 instead of 40) increase sensitivity (more crises are detected) but reduce specificity (more false alarms). Higher thresholds have the reverse effect.
12.3 Limitations and Disclaimers
DEAM is based on historical relationships between indicators and market performance. There is no guarantee these relationships will persist in the future. Structural changes in markets (e.g., through regulation, technology, or central bank policy) can break established patterns. This is the fundamental problem of induction in financial science (Taleb, 2007).
The model is optimized for US equities (S&P 500). Application to other markets (international stocks, bonds, commodities) would require recalibration. The indicators and thresholds are specific to the statistical properties of the US equity market.
The model cannot eliminate losses. Even with perfect crisis prediction, an investor following the model would lose money in bear markets—just less than a buy-and-hold investor. The goal is risk-adjusted performance improvement, not risk elimination.
Transaction costs are not modeled. In practice, spreads, commissions, and taxes reduce net returns. Frequent trading can cause substantial costs. Model smoothing helps minimize this, but users should consider their specific cost situation.
The model reacts to information; it does not anticipate it. During sudden shocks (e.g., 9/11, COVID-19 lockdowns), the model can only react after price movements, not before. This limitation is inherent to all reactive systems.
12.4 Relationship to Other Strategies
DEAM is a tactical asset allocation approach and should be viewed as a complement, not replacement, for strategic asset allocation. Brinson, Hood, and Beebower (1986) showed in their influential study "Determinants of Portfolio Performance" that strategic asset allocation (long-term policy allocation) explains the majority of portfolio performance, but this leaves room for tactical adjustments based on market timing.
The model can be combined with value and momentum strategies at the individual stock level. While DEAM controls overall market exposure, within-equity decisions can be optimized through stock-picking models. This separation between strategic (market exposure) and tactical (stock selection) levels follows classical portfolio theory.
The model does not replace diversification across asset classes. A complete portfolio should also include bonds, international stocks, real estate, and alternative investments. DEAM addresses only the US equity allocation decision within a broader portfolio.
13. Scientific Foundation and Evaluation
13.1 Theoretical Consistency
DEAM's components are based on established financial theory and empirical evidence. The market regime component follows from regime-switching models (Hamilton, 1989) and trend-following literature. The risk management component implements volatility targeting (Moreira and Muir, 2017) and modern portfolio theory (Markowitz, 1952). The valuation component is based on discounted cash flow theory and empirical value research (Campbell and Shiller, 1988; Fama and French, 1992). The sentiment component integrates behavioral finance (Baker and Wurgler, 2006). The macro component uses established business cycle indicators (Estrella and Mishkin, 1998).
This theoretical grounding distinguishes DEAM from purely data-mining-based approaches that identify patterns without causal theory. Theory-guided models have greater probability of functioning out-of-sample, as they are based on fundamental mechanisms, not random correlations (Lo and MacKinlay, 1990).
13.2 Empirical Validation
While this document does not present detailed backtest analysis, it should be noted that rigorous validation of a tactical asset allocation model should include several elements:
In-sample testing establishes whether the model functions at all in the data on which it was calibrated. Out-of-sample testing is crucial: the model should be tested in time periods not used for development. Walk-forward analysis, where the model is successively trained on rolling windows and tested in the next window, approximates real implementation.
Performance metrics should be risk-adjusted. Pure return consideration is misleading, as higher returns often only compensate for higher risk. Sharpe Ratio, Sortino Ratio, Calmar Ratio, and Maximum Drawdown are relevant metrics. Comparison with benchmarks (Buy-and-Hold S&P 500, 60/40 Stock/Bond portfolio) contextualizes performance.
Robustness checks test sensitivity to parameter variation. If the model only functions at specific parameter settings, this indicates overfitting. Robust models show consistent performance over a range of plausible parameters.
13.3 Comparison with Existing Literature
DEAM fits into the broader literature on tactical asset allocation. Faber (2007) presented a simple momentum-based timing system that goes long when the market is above its 10-month average, otherwise cash. This simple system avoided large drawdowns in bear markets. DEAM can be understood as a sophistication of this approach that integrates multiple information sources.
Ilmanen (2011) discusses various timing factors in "Expected Returns" and argues for multi-factor approaches. DEAM operationalizes this philosophy. Asness, Moskowitz, and Pedersen (2013) showed that value and momentum effects work across asset classes, justifying cross-asset application of regime and valuation signals.
Ang (2014) emphasizes in "Asset Management: A Systematic Approach to Factor Investing" the importance of systematic, rule-based approaches over discretionary decisions. DEAM is fully systematic and eliminates emotional biases that plague individual investors (overconfidence, hindsight bias, loss aversion).
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Trend Fib Zone Bounce (TFZB) [KedArc Quant]Description:
Trend Fib Zone Bounce (TFZB) trades with the latest confirmed Supply/Demand zone using a single, configurable Fib pullback (0.3/0.5/0.6). Trade only in the direction of the most recent zone and use a single, configurable fib level for pullback entries.
• Detects market structure via confirmed swing highs/lows using a rolling window.
• Draws Supply/Demand zones (bearish/bullish rectangles) from the latest MSS (CHOCH or BOS) event.
• Computes intra zone Fib guide rails and keeps them extended in real time.
• Triggers BUY only inside bullish zones and SELL only inside bearish zones when price touches the selected fib and closes back beyond it (bounce confirmation).
• Optional labels print BULL/BEAR + fib next to the triangle markers.
What it does
Finds structure using confirmed swing highs/lows (you choose the confirmation length).
Builds the latest zone (bullish = demand, bearish = supply) after a CHOCH/BOS event.
Draws intra-zone “guide rails” (Fib lines) and extends them live.
Signals only with the trend of that zone:
BUY inside a bullish zone when price tags the selected Fib and closes back above it.
SELL inside a bearish zone when price tags the selected Fib and closes back below it.
Optional labels print BULL/BEAR + Fib next to triangles for quick context
Why this is different
Most “zone + fib + signal” tools bolt together several indicators, or fire counter-trend signals because they don’t fully respect structure. TFZB is intentionally minimal:
Single bias source: the latest confirmed zone defines direction; nothing else overrides it.
Single entry rule: one Fib bounce (0.3/0.5/0.6 selectable) inside that zone—no counter-trend trades by design.
Clean visuals: you can show only the most recent zone, clamp overlap, and keep just the rails that matter.
Deterministic & transparent: every plot/label comes from the code you see—no external series or hidden smoothing
How it helps traders
Cuts decision noise: you always know the bias and the only entry that matters right now.
Forces discipline: if price isn’t inside the active zone, you don’t trade.
Adapts to volatility: pick 0.3 in strong trends, 0.5 as the default, 0.6 in chop.
Non-repainting zones: swings are confirmed after Structure Length bars, then used to build zones that extend forward (they don’t “teleport” later)
How it works (details)
*Structure confirmation
A swing high/low is only confirmed after Structure Length bars have elapsed; the dot is plotted back on the original bar using offset. Expect a confirmation delay of about Structure Length × timeframe.
*Zone creation
After a CHOCH/BOS (momentum shift / break of prior swing), TFZB draws the new Supply/Demand zone from the swing anchors and sets it active.
*Fib guide rails
Inside the active zone TFZB projects up to five Fib lines (defaults: 0.3 / 0.5 / 0.7) and extends them as time passes.
*Entry logic (with-trend only)
BUY: bar’s low ≤ fib and close > fib inside a bullish zone.
SELL: bar’s high ≥ fib and close < fib inside a bearish zone.
*Optionally restrict to one signal per zone to avoid over-trading.
(Optional) Aggressive confirm-bar entry
When do the swing dots print?
* The code confirms a swing only after `structureLen` bars have elapsed since that candidate high/low.
* On a 5-min chart with `structureLen = 10`, that’s about 50 minutes later.
* When the swing confirms, the script plots the dot back on the original bar (via `offset = -structureLen`). So you *see* the dot on the old bar, but it only appears on the chart once the confirming bar arrives.
> Practical takeaway: expect swing markers to appear roughly `structureLen × timeframe` later. Zones and signals are built from those confirmed swings.
Best timeframe for this Indicator
Use the timeframe that matches your holding period and the noise level of the instrument:
* Intraday :
* 5m or 15m are the sweet spots.
* Suggested `structureLen`:
* 5m: 10–14 (confirmation delay \~50–70 min)
* 15m: 8–10 (confirmation delay \~2–2.5 hours)
* Keep Entry Fib at 0.5 to start; try 0.3 in strong trends, 0.6 in chop.
* Tip: avoid the first 10–15 minutes after the open; let the initial volatility set the early structure.
* Swing/overnight:
* 1h or 4h.
* `structureLen`:
* 1h: 6–10 (6–10 hours confirmation)
* 4h: 5–8 (20–32 hours confirmation)
* 1m scalping: not recommended here—the confirmation lag relative to the noise makes zones less reliable.
Inputs (all groups)
Structure
• Show Swing Points (structureTog)
o Plots small dots on the bar where a swing point is confirmed (offset back by Structure Length).
• Structure Length (structureLen)
o Lookback used to confirm swing highs/lows and determine local structure. Higher = fewer, stronger swings; lower = more reactive.
Zones
• Show Last (zoneDispNum)
o Maximum number of zones kept on the chart when Display All Zones is off.
• Display All Zones (dispAll)
o If on, ignores Show Last and keeps all zones/levels.
• Zone Display (zoneFilter): Bullish Only / Bearish Only / Both
o Filters which zone types are drawn and eligible for signals.
• Clean Up Level Overlap (noOverlap)
o Prevents fib lines from overlapping when a new zone starts near the previous one (clamps line start/end times for readability).
Fib Levels
Each row controls whether a fib is drawn and how it looks:
• Toggle (f1Tog…f5Tog): Show/hide a given fib line.
• Level (f1Lvl…f5Lvl): Numeric ratio in . Defaults active: 0.3, 0.5, 0.7 (0 and 1 off by default).
• Line Style (f1Style…f5Style): Solid / Dashed / Dotted.
• Bull/Bear Colors (f#BullColor, f#BearColor): Per-fib color in bullish vs bearish zones.
Style
• Structure Color: Dot color for confirmed swing points.
• Bullish Zone Color / Bearish Zone Color: Rectangle fills (transparent by default).
Signals
• Entry Fib for Signals (entryFibSel): Choose 0.3, 0.5 (default), or 0.6 as the trigger line.
• Show Buy/Sell Signals (showSignals): Toggles triangle markers on/off.
• One Signal Per Zone (oneSignalPerZone): If on, suppresses additional entries within the same zone after the first trigger.
• Show Signal Text Labels (Bull/Bear + Fib) (showSignalLabels): Adds a small label next to each triangle showing zone bias and the fib used (e.g., BULL 0.5 or BEAR 0.3).
How TFZB decides signals
With trend only:
• BUY
1. Latest active zone is bullish.
2. Current bar’s close is inside the zone (between top and bottom).
3. The bar’s low ≤ selected fib and it closes > selected fib (bounce).
• SELL
1. Latest active zone is bearish.
2. Current bar’s close is inside the zone.
3. The bar’s high ≥ selected fib and it closes < selected fib.
Markers & labels
• BUY: triangle up below the bar; optional label “BULL 0.x” above it.
• SELL: triangle down above the bar; optional label “BEAR 0.x” below it.
Right-Panel Swing Log (Table)
What it is
A compact, auto-updating log of the most recent Swing High/Low events, printed in the top-right of the chart.
It helps you see when a pivot formed, when it was confirmed, and at what price—so you know the earliest bar a zone-based signal could have appeared.
Columns
Type – Swing High or Swing Low.
Date – Calendar date of the swing bar (follows the chart’s timezone).
Swing @ – Time of the original swing bar (where the dot is drawn).
Confirm @ – Time of the bar that confirmed that swing (≈ Structure Length × timeframe after the swing). This is also the earliest moment a new zone/entry can be considered.
Price – The swing price (high for SH, low for SL).
Why it’s useful
Clarity on repaint/confirmation: shows the natural delay between a swing forming and being usable—no guessing.
Planning & journaling: quick reference of today’s pivots and prices for notes/backtesting.
Scanning intraday: glance to see if you already have a confirmed zone (and therefore valid fib-bounce entries), or if you’re still waiting.
Context for signals: if a fib-bounce triangle appears before the time listed in Confirm @, it’s not a valid trade (you were too early).
Settings (Inputs → Logging)
Log swing times / Show table – turn the table on/off.
Rows to keep – how many recent entries to display.
Show labels on swing bar – optional tags on the chart (“Swing High 11:45”, “Confirm SH 14:15”) that match the table.
Recommended defaults
• Structure Length: 10–20 for intraday; 20–40 for swing.
• Entry Fib for Signals: 0.5 to start; try 0.3 in stronger trends and 0.6 in choppier markets.
• One Signal Per Zone: ON (prevents over trading).
• Zone Display: Both.
• Fib Lines: Keep 0.3/0.5/0.7 on; turn on 0 and 1 only if you need anchors.
Alerts
Two alert conditions are available:
• BUY signal – fires when a with trend bullish bounce at the selected fib occurs inside a bullish zone.
• SELL signal – fires when a with trend bearish bounce at the selected fib occurs inside a bearish zone.
Create alerts from the chart’s Alerts panel and select the desired condition. Use Once Per Bar Close to avoid intrabar flicker.
Notes & tips
• Swing dots are confirmed only after Structure Length bars, so they plot back in time; zones built from these confirmed swings do not repaint (though they extend as new bars form).
• If you don’t see a BUY where you expect one, check: (1) Is the active zone bullish? (2) Did the candle’s low actually pierce the selected fib and close above it? (3) Is One Signal Per Zone suppressing a second entry?
• You can hide visual clutter by reducing Show Last to 1–3 while keeping Display All Zones off.
Glossary
• CHOCH (Change of Character): A shift where price breaks beyond the last opposite swing while local momentum flips.
• BOS (Break of Structure): A cleaner break beyond the prior swing level in the current momentum direction.
• MSS: Either CHOCH or BOS – any event that spawns a new zone.
Extension ideas (optional)
• Add fib extensions (1.272 / 1.618) for target lines.
• Zone quality score using ATR normalization to filter weak impulses.
• HTF filter to only accept zones aligned with a higher timeframe trend.
⚠️ Disclaimer This script is provided for educational purposes only.
Past performance does not guarantee future results.
Trading involves risk, and users should exercise caution and use proper risk management when applying this strategy.
BOCS Channel Scalper Strategy - Automated Mean Reversion System# BOCS Channel Scalper Strategy - Automated Mean Reversion System
## WHAT THIS STRATEGY DOES:
This is an automated mean reversion trading strategy that identifies consolidation channels through volatility analysis and executes scalp trades when price enters entry zones near channel boundaries. Unlike breakout strategies, this system assumes price will revert to the channel mean, taking profits as price bounces back from extremes. Position sizing is fully customizable with three methods: fixed contracts, percentage of equity, or fixed dollar amount. Stop losses are placed just outside channel boundaries with take profits calculated either as fixed points or as a percentage of channel range.
## KEY DIFFERENCE FROM ORIGINAL BOCS:
**This strategy is designed for traders seeking higher trade frequency.** The original BOCS indicator trades breakouts OUTSIDE channels, waiting for price to escape consolidation before entering. This scalper version trades mean reversion INSIDE channels, entering when price reaches channel extremes and betting on a bounce back to center. The result is significantly more trading opportunities:
- **Original BOCS**: 1-3 signals per channel (only on breakout)
- **Scalper Version**: 5-15+ signals per channel (every touch of entry zones)
- **Trade Style**: Mean reversion vs trend following
- **Hold Time**: Seconds to minutes vs minutes to hours
- **Best Markets**: Ranging/choppy conditions vs trending breakouts
This makes the scalper ideal for active day traders who want continuous opportunities within consolidation zones rather than waiting for breakout confirmation. However, increased trade frequency also means higher commission costs and requires tighter risk management.
## TECHNICAL METHODOLOGY:
### Price Normalization Process:
The strategy normalizes price data to create consistent volatility measurements across different instruments and price levels. It calculates the highest high and lowest low over a user-defined lookback period (default 100 bars). Current close price is normalized using: (close - lowest_low) / (highest_high - lowest_low), producing values between 0 and 1 for standardized volatility analysis.
### Volatility Detection:
A 14-period standard deviation is applied to the normalized price series to measure price deviation from the mean. Higher standard deviation values indicate volatility expansion; lower values indicate consolidation. The strategy uses ta.highestbars() and ta.lowestbars() to identify when volatility peaks and troughs occur over the detection period (default 14 bars).
### Channel Formation Logic:
When volatility crosses from a high level to a low level (ta.crossover(upper, lower)), a consolidation phase begins. The strategy tracks the highest and lowest prices during this period, which become the channel boundaries. Minimum duration of 10+ bars is required to filter out brief volatility spikes. Channels are rendered as box objects with defined upper and lower boundaries, with colored zones indicating entry areas.
### Entry Signal Generation:
The strategy uses immediate touch-based entry logic. Entry zones are defined as a percentage from channel edges (default 20%):
- **Long Entry Zone**: Bottom 20% of channel (bottomBound + channelRange × 0.2)
- **Short Entry Zone**: Top 20% of channel (topBound - channelRange × 0.2)
Long signals trigger when candle low touches or enters the long entry zone. Short signals trigger when candle high touches or enters the short entry zone. This captures mean reversion opportunities as price reaches channel extremes.
### Cooldown Filter:
An optional cooldown period (measured in bars) prevents signal spam by enforcing minimum spacing between consecutive signals. If cooldown is set to 3 bars, no new long signal will fire until 3 bars after the previous long signal. Long and short cooldowns are tracked independently, allowing both directions to signal within the same period.
### ATR Volatility Filter:
The strategy includes a multi-timeframe ATR filter to avoid trading during low-volatility conditions. Using request.security(), it fetches ATR values from a specified timeframe (e.g., 1-minute ATR while trading on 5-minute charts). The filter compares current ATR to a user-defined minimum threshold:
- If ATR ≥ threshold: Trading enabled
- If ATR < threshold: No signals fire
This prevents entries during dead zones where mean reversion is unreliable due to insufficient price movement.
### Take Profit Calculation:
Two TP methods are available:
**Fixed Points Mode**:
- Long TP = Entry + (TP_Ticks × syminfo.mintick)
- Short TP = Entry - (TP_Ticks × syminfo.mintick)
**Channel Percentage Mode**:
- Long TP = Entry + (ChannelRange × TP_Percent)
- Short TP = Entry - (ChannelRange × TP_Percent)
Default 50% targets the channel midline, a natural mean reversion target. Larger percentages aim for opposite channel edge.
### Stop Loss Placement:
Stop losses are placed just outside the channel boundary by a user-defined tick offset:
- Long SL = ChannelBottom - (SL_Offset_Ticks × syminfo.mintick)
- Short SL = ChannelTop + (SL_Offset_Ticks × syminfo.mintick)
This logic assumes channel breaks invalidate the mean reversion thesis. If price breaks through, the range is no longer valid and position exits.
### Trade Execution Logic:
When entry conditions are met (price in zone, cooldown satisfied, ATR filter passed, no existing position):
1. Calculate entry price at zone boundary
2. Calculate TP and SL based on selected method
3. Execute strategy.entry() with calculated position size
4. Place strategy.exit() with TP limit and SL stop orders
5. Update info table with active trade details
The strategy enforces one position at a time by checking strategy.position_size == 0 before entry.
### Channel Breakout Management:
Channels are removed when price closes more than 10 ticks outside boundaries. This tolerance prevents premature channel deletion from minor breaks or wicks, allowing the mean reversion setup to persist through small boundary violations.
### Position Sizing System:
Three methods calculate position size:
**Fixed Contracts**:
- Uses exact contract quantity specified in settings
- Best for futures traders (e.g., "trade 2 NQ contracts")
**Percentage of Equity**:
- position_size = (strategy.equity × equity_pct / 100) / close
- Dynamically scales with account growth
**Cash Amount**:
- position_size = cash_amount / close
- Maintains consistent dollar exposure regardless of price
## INPUT PARAMETERS:
### Position Sizing:
- **Position Size Type**: Choose Fixed Contracts, % of Equity, or Cash Amount
- **Number of Contracts**: Fixed quantity per trade (1-1000)
- **% of Equity**: Percentage of account to allocate (1-100%)
- **Cash Amount**: Dollar value per position ($100+)
### Channel Settings:
- **Nested Channels**: Allow multiple overlapping channels vs single channel
- **Normalization Length**: Lookback for high/low calculation (1-500, default 100)
- **Box Detection Length**: Period for volatility detection (1-100, default 14)
### Scalping Settings:
- **Enable Long Scalps**: Toggle long entries on/off
- **Enable Short Scalps**: Toggle short entries on/off
- **Entry Zone % from Edge**: Size of entry zone (5-50%, default 20%)
- **SL Offset (Ticks)**: Distance beyond channel for stop (1+, default 5)
- **Cooldown Period (Bars)**: Minimum spacing between signals (0 = no cooldown)
### ATR Filter:
- **Enable ATR Filter**: Toggle volatility filter on/off
- **ATR Timeframe**: Source timeframe for ATR (1, 5, 15, 60 min, etc.)
- **ATR Length**: Smoothing period (1-100, default 14)
- **Min ATR Value**: Threshold for trade enablement (0.1+, default 10.0)
### Take Profit Settings:
- **TP Method**: Choose Fixed Points or % of Channel
- **TP Fixed (Ticks)**: Static distance in ticks (1+, default 30)
- **TP % of Channel**: Dynamic target as channel percentage (10-100%, default 50%)
### Appearance:
- **Show Entry Zones**: Toggle zone labels on channels
- **Show Info Table**: Display real-time strategy status
- **Table Position**: Corner placement (Top Left/Right, Bottom Left/Right)
- **Color Settings**: Customize long/short/TP/SL colors
## VISUAL INDICATORS:
- **Channel boxes** with semi-transparent fill showing consolidation zones
- **Colored entry zones** labeled "LONG ZONE ▲" and "SHORT ZONE ▼"
- **Entry signal arrows** below/above bars marking long/short entries
- **Active TP/SL lines** with emoji labels (⊕ Entry, 🎯 TP, 🛑 SL)
- **Info table** showing position status, channel state, last signal, entry/TP/SL prices, and ATR status
## HOW TO USE:
### For 1-3 Minute Scalping (NQ/ES):
- ATR Timeframe: "1" (1-minute)
- ATR Min Value: 10.0 (for NQ), adjust per instrument
- Entry Zone %: 20-25%
- TP Method: Fixed Points, 20-40 ticks
- SL Offset: 5-10 ticks
- Cooldown: 2-3 bars
- Position Size: 1-2 contracts
### For 5-15 Minute Day Trading:
- ATR Timeframe: "5" or match chart
- ATR Min Value: Adjust to instrument (test 8-15 for NQ)
- Entry Zone %: 20-30%
- TP Method: % of Channel, 40-60%
- SL Offset: 5-10 ticks
- Cooldown: 3-5 bars
- Position Size: Fixed contracts or 5-10% equity
### For 30-60 Minute Swing Scalping:
- ATR Timeframe: "15" or "30"
- ATR Min Value: Lower threshold for broader market
- Entry Zone %: 25-35%
- TP Method: % of Channel, 50-70%
- SL Offset: 10-15 ticks
- Cooldown: 5+ bars or disable
- Position Size: % of equity recommended
## BACKTEST CONSIDERATIONS:
- Strategy performs best in ranging, mean-reverting markets
- Strong trending markets produce more stop losses as price breaks channels
- ATR filter significantly reduces trade count but improves quality during low volatility
- Cooldown period trades signal quantity for signal quality
- Commission and slippage materially impact sub-5-minute timeframe performance
- Shorter timeframes require tighter entry zones (15-20%) to catch quick reversions
- % of Channel TP adapts better to varying channel sizes than fixed points
- Fixed contract sizing recommended for consistent risk per trade in futures
**Backtesting Parameters Used**: This strategy was developed and tested using realistic commission and slippage values to provide accurate performance expectations. Recommended settings: Commission of $1.40 per side (typical for NQ futures through discount brokers), slippage of 2 ticks to account for execution delays on fast-moving scalp entries. These values reflect real-world trading costs that active scalpers will encounter. Backtest results without proper cost simulation will significantly overstate profitability.
## COMPATIBLE MARKETS:
Works on any instrument with price data including stock indices (NQ, ES, YM, RTY), individual stocks, forex pairs (EUR/USD, GBP/USD), cryptocurrency (BTC, ETH), and commodities. Volume-based features require data feed with volume information but are optional for core functionality.
## KNOWN LIMITATIONS:
- Immediate touch entry can fire multiple times in choppy zones without adequate cooldown
- Channel deletion at 10-tick breaks may be too aggressive or lenient depending on instrument tick size
- ATR filter from lower timeframes requires higher-tier TradingView subscription (request.security limitation)
- Mean reversion logic fails in strong breakout scenarios leading to stop loss hits
- Position sizing via % of equity or cash amount calculates based on close price, may differ from actual fill price
- No partial closing capability - full position exits at TP or SL only
- Strategy does not account for gap openings or overnight holds
## RISK DISCLOSURE:
Trading involves substantial risk of loss. Past performance does not guarantee future results. This strategy is for educational purposes and backtesting only. Mean reversion strategies can experience extended drawdowns during trending markets. Stop losses may not fill at intended levels during extreme volatility or gaps. Thoroughly test on historical data and paper trade before risking real capital. Use appropriate position sizing and never risk more than you can afford to lose. Consider consulting a licensed financial advisor before making trading decisions. Automated trading systems can malfunction - monitor all live positions actively.
## ACKNOWLEDGMENT & CREDITS:
This strategy is built upon the channel detection methodology created by **AlgoAlpha** in the "Smart Money Breakout Channels" indicator. Full credit and appreciation to AlgoAlpha for pioneering the normalized volatility approach to identifying consolidation patterns. The core channel formation logic using normalized price standard deviation is AlgoAlpha's original contribution to the TradingView community.
Enhancements to the original concept include: mean reversion entry logic (vs breakout), immediate touch-based signals, multi-timeframe ATR volatility filtering, flexible position sizing (fixed/percentage/cash), cooldown period filtering, dual TP methods (fixed points vs channel percentage), automated strategy execution with exit management, and real-time position monitoring table.
RSI ROC Signals with Price Action# RSI ROC Signals with Price Action
## Overview
The RSI ROC (Rate of Change) Signals indicator is an advanced momentum-based trading system that combines RSI velocity analysis with price action confirmation to generate high-probability buy and sell signals. This indicator goes beyond traditional RSI analysis by measuring the speed of RSI changes and requiring price confirmation before triggering signals.
## Core Concept: RSI Rate of Change (ROC)
### What is RSI ROC?
RSI ROC measures the **velocity** or **acceleration** of the RSI indicator, providing insights into momentum shifts before they become apparent in traditional RSI readings.
**Formula**: `RSI ROC = ((Current RSI - Previous RSI) / Previous RSI) × 100`
### Why RSI ROC is Superior to Standard RSI:
1. **Early Momentum Detection**: Identifies momentum shifts before RSI reaches traditional overbought/oversold levels
2. **Velocity Analysis**: Measures the speed of momentum changes, not just absolute levels
3. **Reduced False Signals**: Filters out weak momentum moves that don't sustain
4. **Dynamic Thresholds**: Adapts to market volatility rather than using fixed RSI levels
5. **Leading Indicator**: Provides earlier signals compared to traditional RSI crossovers
## Signal Generation Logic
### 🟢 Buy Signal Process (3-Stage System):
#### Stage 1: Trigger Activation
- **RSI ROC** > threshold (default 7%) - RSI accelerating upward
- **Price ROC** > 0 - Price moving higher
- Records the **trigger high** (highest point during trigger)
#### Stage 2: Invalidation Check
- Signal invalidated if **RSI ROC** drops below negative threshold
- Prevents false signals during momentum reversals
#### Stage 3: Confirmation
- **Price breaks above trigger high** - Price action confirmation
- **Current candle is green** (close > open) - Bullish price action
- **State alternation** - Ensures no consecutive duplicate signals
### 🔴 Sell Signal Process (3-Stage System):
#### Stage 1: Trigger Activation
- **RSI ROC** < negative threshold (default -7%) - RSI accelerating downward
- **Price ROC** < 0 - Price moving lower
- Records the **trigger low** (lowest point during trigger)
#### Stage 2: Invalidation Check
- Signal invalidated if **RSI ROC** rises above positive threshold
- Prevents false signals during momentum reversals
#### Stage 3: Confirmation
- **Price breaks below trigger low** - Price action confirmation
- **Current candle is red** (close < open) - Bearish price action
- **State alternation** - Ensures no consecutive duplicate signals
## Key Features
### 🎯 **Smart Signal Management**
- **State Alternation**: Prevents signal clustering by alternating between buy/sell states
- **Trigger Invalidation**: Automatically cancels weak signals that lose momentum
- **Price Confirmation**: Requires actual price breakouts, not just momentum shifts
- **No Repainting**: Signals are confirmed and won't disappear or change
### ⚙️ **Customizable Parameters**
#### **RSI Length (Default: 14)**
- Standard RSI calculation period
- Shorter periods = more sensitive to price changes
- Longer periods = smoother, less noisy signals
#### **Lookback Period (Default: 1)**
- Period for ROC calculations
- 1 = compares to previous bar (most responsive)
- Higher values = smoother momentum detection
#### **RSI ROC Threshold (Default: 7%)**
- Minimum RSI velocity required for signal trigger
- Lower values = more signals, potentially more noise
- Higher values = fewer but higher-quality signals
### 📊 **Visual Signals**
- **Green Arrow Up**: Buy signal below price bar
- **Red Arrow Down**: Sell signal above price bar
- **Clean Chart**: No additional lines or oscillators cluttering the view
- **Size Options**: Customizable arrow sizes for visibility preferences
## Advantages Over Traditional Indicators
### vs. Standard RSI:
✅ **Earlier Signals**: Detects momentum changes before RSI reaches extremes
✅ **Dynamic Thresholds**: Adapts to market conditions vs. fixed 30/70 levels
✅ **Velocity Focus**: Measures momentum speed, not just position
✅ **Better Timing**: Combines momentum with price action confirmation
### vs. Moving Average Crossovers:
✅ **Leading vs. Lagging**: RSI ROC is forward-looking vs. backward-looking MAs
✅ **Volatility Adaptive**: Automatically adjusts to market volatility
✅ **Fewer Whipsaws**: Built-in invalidation logic reduces false signals
✅ **Momentum Focus**: Captures acceleration, not just direction changes
### vs. MACD:
✅ **Price-Normalized**: RSI ROC works consistently across different price ranges
✅ **Simpler Logic**: Clear trigger/confirmation process vs. complex crossovers
✅ **Built-in Filters**: Automatic signal quality control
✅ **State Management**: Prevents over-trading through alternation logic
## Trading Applications
### 📈 **Trend Following**
- Use in trending markets to catch momentum continuations
- Combine with trend filters for directional bias
- Excellent for breakout strategies
### 🔄 **Swing Trading**
- Ideal timeframes: 4H, Daily, Weekly
- Captures major momentum shifts
- Perfect for position entries/exits
### ⚡ **Scalping (Advanced Users)**
- Lower timeframes: 1m, 5m, 15m
- Reduce threshold for more frequent signals
- Combine with volume confirmation
### 🎯 **Momentum Strategies**
- Perfect for momentum-based trading systems
- Identifies acceleration phases in trends
- Complements breakout and continuation patterns
## Optimization Guidelines
### **Conservative Settings (Lower Risk)**
- RSI Length: 21
- ROC Threshold: 10%
- Lookback: 2
### **Standard Settings (Balanced)**
- RSI Length: 14 (default)
- ROC Threshold: 7% (default)
- Lookback: 1 (default)
### **Aggressive Settings (Higher Frequency)**
- RSI Length: 7
- ROC Threshold: 5%
- Lookback: 1
## Best Practices
### 🎯 **Entry Strategy**
1. Wait for signal arrow confirmation
2. Consider market context (trend, support/resistance)
3. Use proper position sizing based on volatility
4. Set stop-loss below/above trigger levels
### 🛡️ **Risk Management**
1. **Stop Loss**: Place beyond trigger high/low levels
2. **Position Sizing**: Use 1-2% risk per trade
3. **Market Context**: Avoid counter-trend signals in strong trends
4. **Time Filters**: Consider avoiding signals near major news events
### 📊 **Backtesting Recommendations**
1. Test on multiple timeframes and instruments
2. Analyze win rate vs. average win/loss ratio
3. Consider transaction costs in backtesting
4. Optimize threshold values for different market conditions
## Technical Specifications
- **Pine Script Version**: v6
- **Signal Type**: Non-repainting, confirmed signals
- **Calculation Basis**: RSI velocity with price action confirmation
- **Update Frequency**: Real-time on bar close
- **Memory Management**: Efficient state tracking with minimal resource usage
## Ideal For:
- **Momentum Traders**: Captures acceleration phases
- **Swing Traders**: Medium-term position entries/exits
- **Breakout Traders**: Confirms momentum behind breakouts
- **System Traders**: Mechanical signal generation with clear rules
This indicator represents a significant evolution in momentum analysis, combining the reliability of RSI with the precision of rate-of-change analysis and the confirmation of price action. It's designed for traders who want sophisticated momentum detection with built-in quality controls.
