Relative volume zone + Smart Order Flow Dynamic S/ROverview:
The Relative Volume Zone + Smart Order Flow with Dynamic S/R indicator is designed to help traders identify key trading opportunities by combining multiple technical components. This script integrates relative volume analysis, order flow detection, VWAP, RSI filtering, and dynamic support and resistance levels to offer a comprehensive view of the market conditions. It is particularly effective on shorter timeframes (M5, M15), making it suitable for scalping and day trading strategies.
Key Components:
1. Relative Volume Zones:
• The script calculates the relative volume by comparing the current volume with the average volume over a defined lookback period (volLookback). When the relative volume exceeds a specified multiplier (volMultiplier), it indicates a high volume zone, signaling potential accumulation or distribution areas.
• Purpose: Identifies high-volume trading zones that may act as significant support or resistance, indicating possible entry or exit points.
2. Smart Order Flow Analysis:
• The indicator uses Volume Delta (the difference between buying and selling volume) and a Cumulative Delta to detect order imbalances in the market.
• Order Imbalance is identified using a moving average of the Volume Delta (orderImbalance), which helps highlight hidden buying or selling pressure.
• Purpose: Reveals market sentiment by showing whether buyers or sellers dominate the market, aiding in the identification of trend reversals or continuations.
3. VWAP (Volume Weighted Average Price):
• VWAP is calculated over a default daily length (vwapLength) to show the average price a security has traded at throughout the day, based on both volume and price.
• Purpose: Provides insight into the fair value of the asset, indicating whether the market is in an accumulation or distribution phase.
4. RSI (Relative Strength Index) Filter:
• RSI is used to filter buy and sell signals, preventing trades in overbought or oversold conditions. It is calculated using a specified period (rsiPeriod).
• Purpose: Reduces false signals and improves trade accuracy by only allowing trades when RSI conditions align with volume and order flow signals.
5. Dynamic Support and Resistance Levels:
• The script dynamically plots support and resistance levels based on recent swing highs and lows (swingLookback).
• Purpose: Identifies potential reversal zones where price action may change direction, allowing for more precise entry and exit points.
How It Works:
• Buy Signal:
A buy signal is generated when:
• The price enters a high-volume zone.
• The price crosses above a 5-period moving average.
• The cumulative delta shows more buying pressure (cumulativeDelta > SMA of cumulativeDelta).
• The RSI is below 70 (not in overbought conditions).
• Sell Signal:
A sell signal is generated when:
• The price enters a high-volume zone.
• The price crosses below a 5-period moving average.
• The cumulative delta shows more selling pressure (cumulativeDelta < SMA of cumulativeDelta).
• The RSI is above 30 (not in oversold conditions).
• Dynamic Support and Resistance Lines:
Drawn based on recent swing highs and lows, these lines provide context for potential price reversals or breakouts.
• VWAP and Order Imbalance Lines:
Plotted to show the average traded price and highlight order flow shifts, helping to validate buy/sell signals.
How to Use:
1. Apply the Indicator:
Add the script to your chart and adjust the settings to match your trading style and preferred timeframe (optimized for M5/M15).
2. Interpret the Signals:
Use the buy and sell signals in conjunction with dynamic support/resistance, VWAP, and order imbalance lines to identify high-probability trade setups.
3. Monitor Alerts:
Set alerts for significant order flow events to receive notifications when there is a positive or negative order imbalance, indicating potential market shifts.
What Makes It Unique:
This script is unique because it combines multiple market analysis tools — relative volume zones, smart order flow, VWAP, RSI filtering, and dynamic support/resistance — to provide a well-rounded, multi-dimensional view of the market. This integration allows traders to make more informed decisions by validating signals across various indicators, enhancing overall trading accuracy and effectiveness.
在脚本中搜索"accumulation"
Rocket RSI from John EhlersWhat is Rocket RSI
Welles Wilder's original description of the relative strength index (RSI) in his 1978 New Concepts In Technical Trading Systems specified a calculation period of 14 days. This requirement led him on a 40-year quest to find the right length of data for calculating indicators and trading strategy rules. Many technicians touched on RSI and explained its applications. In this study we will obtain a more flexible and easier to interpret formulation (of the indicator). We will also estimate the algorithm to properly handle a statistical approach to technical analysis. Start with RSI Here is the original definition of the RSI indicator:
RSI = 100 - 100 / (1 + RS)
RS = Average gain from downtime over the specified time period / Average loss from downtime over the specified time period My first observation is that the factor of 100 is insignificant. Second, there is no need for averages because we take the ratio of closes (CU) to closes (CD) and if we accumulate the wins and losses independently, the averages emerge. Therefore We will only accumulate CU and CD. He can then write the RSI equation as:
RSI = 1 – 1 / (1 + CU / CD)
If he use a little algebra to put everything on a common denominator on the right side of the equation, the indicator equation becomes:
RSI = CU / (CU + CD)
In this formulation, if CU accumulation is zero, the RSI value is zero, and if CD accumulation is zero, the RSI value is 1. If you reduce the price action to its primitive level as a sine wave, it is easy to see that this RSI only has CU going from valley to peak and only CD going from peak to valley. This RSI follows the shape of the sine wave between these two limits. However, the sine wave oscillates between -1 and +1, not between 0 and +1. If we multiply the above equation by 2 and then subtract 1, we can make the RSI have the same swing limits as the sine wave. the product is as follows:
RSI = 2*CU / (CU + CD) – 1
Again, using a little algebra to put the right-hand side of the equation on a common denominator, the equation develops like this:
MyRSI = (CU – CD) / (CU + CD)
Again, the vertical scale of the RocketRSI indicator is in standard deviations. For example, -2 means it is two standard deviations below the mean. Since exceeding two standard deviations in the Gaussian probability distribution occurs in only 2.4% of the results
Because we are using the momentum of the dominant cycle period, the spike where the indicator falls below -2 provides a surgically precise timing signal to enter a long position. Similarly, exceeding the +2 standard deviation level is a timing signal to exit a long position or return to a short position. Therefore using the RocketRSI indicator is relatively intuitive. The only concern is whether a dominant cycle is present in the data, setting the indicator to half the dominant cycle period, and whether smoothing causes lag.
DETERMINING CYCLICAL TURNING POINTS
When you insert the chart you see an example of what the RocketRSI indicator looks like. Here you see that RocketRSI precisely displays cyclical turning points as statistical events. Cator can be applied. I used RS Length 10 because according to Ehlers, stocks and stock indexes usually have a more or less monthly cycle (about 20 bars). A cursory examination of Figure 2 shows that negative increases in the indicator correspond to excellent buying opportunities, while positive increases correspond to excellent selling opportunities. Exceeding +/- 2 on the indicator scale indicates that a cyclical reversal is a high probability event.
Asset Rotation ApertureAsset Rotation Aperture visualizes volume accumulation momentum, of multiple assets, side by side.
It's a surgical, multi-purpose leading indicator of price, market narratives and capital rotation.
Each colored line represents the rolling cumulative volume (or open interest) of an asset, index, narrative, or symbol equation. Normalized to each other, relative to each other.
This enables Asset Rotation Aperture to visualize assets and narratives with dramatically different market caps (and therefore different volume accumulation profiles), side by side.
METRIC CONSTRUCTION
Asset Rotation Aperture is a fork of Money Flow Index, like a centered On Balance Volume.
Modified to more effectively lead price, smoothed to more clearly visualize with clarity, and recursively printed.
SYMBOL SELECTION
I highly recommend selecting symbols from exchanges that dominate volume for the asset(s) you're visualizing.
For crypto, this typically means Binance pairs.
Keep the exchange consistent across symbols whenever possible.
To construct an index / narrative, use the following formula format:
(BINANCE:UNIUSDT*BINANCE:SNXUSDT*BINANCE:AAVEUSDT*BINANCE:CRVUSDT)^(1/4)
THE Y AXIS
The Y axis represents the asset's percentage of volume accumulated, relative to its norm AND relative to other assets.
It's a made up figure, and I recommend ignoring it.
A visual scan of the data viz is more effective than studying any Y-axis output.
The Flash-Strategy with Minervini Stage Analysis QualifierThe Flash-Strategy (Momentum-RSI, EMA-crossover, ATR) with Minervini Stage Analysis Qualifier
Introduction
Welcome to a comprehensive guide on a cutting-edge trading strategy I've developed, designed for the modern trader seeking an edge in today's dynamic markets. This strategy, which I've honed through my years of experience in the trading arena, stands out for its unique blend of technical analysis and market intuition, tailored specifically for use on the TradingView platform.
As a trader with a deep passion for the financial markets, my journey began several years ago, driven by a relentless pursuit of a trading methodology that is both effective and adaptable. My background in trading spans various market conditions and asset classes, providing me with a rich tapestry of experiences from which to draw. This strategy is the culmination of that journey, embodying the lessons learned and insights gained along the way.
The cornerstone of this strategy lies in its ability to generate precise long signals in a Stage 2 uptrend and equally accurate short signals in a Stage 4 downtrend. This approach is rooted in the principles of trend following and momentum trading, harnessing the power of key indicators such as the Momentum-RSI, EMA Crossover, and Average True Range (ATR). What sets this strategy apart is its meticulous design, which allows it to adapt to the ever-changing market conditions, providing traders with a robust tool for navigating both bullish and bearish scenarios.
This strategy was born out of a desire to create a trading system that is not only highly effective in identifying potential trade setups but also straightforward enough to be implemented by traders of varying skill levels. It's a reflection of my belief that successful trading hinges on clarity, precision, and disciplined execution. Whether you are a seasoned trader or just beginning your journey, this guide aims to provide you with a comprehensive understanding of how to harness the full potential of this strategy in your trading endeavors.
In the following sections, we will delve deeper into the mechanics of the strategy, its implementation, and how to make the most out of its features. Join me as we explore the nuances of a strategy that is designed to elevate your trading to the next level.
Stage-Specific Signal Generation
A distinctive feature of this trading strategy is its focus on generating long signals exclusively during Stage 2 uptrends and short signals during Stage 4 downtrends. This approach is based on the widely recognized market cycle theory, which divides the market into four stages: Stage 1 (accumulation), Stage 2 (uptrend), Stage 3 (distribution), and Stage 4 (downtrend). By aligning the signal generation with these specific stages, the strategy aims to capitalize on the most dynamic and clear-cut market movements, thereby enhancing the potential for profitable trades.
1. Long Signals in Stage 2 Uptrends
• Characteristics of Stage 2: Stage 2 is characterized by a strong uptrend, where prices are consistently rising. This stage typically follows a period of accumulation (Stage 1) and is marked by increased investor interest and bullish sentiment in the market.
• Criteria for Long Signal Generation: Long signals are generated during this stage when the technical indicators align with the characteristics of a Stage 2 uptrend.
• Rationale for Stage-Specific Signals: By focusing on Stage 2 for long trades, the strategy seeks to enter positions during the phase of strong upward momentum, thus riding the wave of rising prices and investor optimism. This stage-specific approach minimizes exposure to less predictable market phases, like the consolidation in Stage 1 or the indecision in Stage 3.
2. Short Signals in Stage 4 Downtrends
• Characteristics of Stage 4: Stage 4 is identified by a pronounced downtrend, with declining prices indicating prevailing bearish sentiment. This stage typically follows the distribution phase (Stage 3) and is characterized by increasing selling pressure.
• Criteria for Short Signal Generation: Short signals are generated in this stage when the indicators reflect a strong bearish trend.
• Rationale for Stage-Specific Signals: Targeting Stage 4 for shorting capitalizes on the market's downward momentum. This tactic aligns with the natural market cycle, allowing traders to exploit the downward price movements effectively. By doing so, the strategy avoids the potential pitfalls of shorting during the early or late stages of the market cycle, where trends are less defined and more susceptible to reversals.
In conclusion, the strategy’s emphasis on stage-specific signal generation is a testament to its sophisticated understanding of market dynamics. By tailoring the long and short signals to Stages 2 and 4, respectively, it leverages the most compelling phases of the market cycle, offering traders a clear and structured approach to aligning their trades with dominant market trends.
Strategy Overview
At the heart of this trading strategy is a philosophy centered around capturing market momentum and trend efficiency. The core objective is to identify and capitalize on clear uptrends and downtrends, thereby allowing traders to position themselves in sync with the market's prevailing direction. This approach is grounded in the belief that aligning trades with these dominant market forces can lead to more consistent and profitable outcomes.
The strategy is built on three foundational components, each playing a critical role in the decision-making process:
1. Momentum-RSI (Relative Strength Index): The Momentum-RSI is a pivotal element of this strategy. It's an enhanced version of the traditional RSI, fine-tuned to better capture the strength and velocity of market trends. By measuring the speed and change of price movements, the Momentum-RSI provides invaluable insights into whether a market is potentially overbought or oversold, suggesting possible entry and exit points. This indicator is especially effective in filtering out noise and focusing on substantial market moves.
2. EMA (Exponential Moving Average) Crossover: The EMA Crossover is a crucial component for trend identification. This strategy employs two EMAs with different timeframes to determine the market trend. When the shorter-term EMA crosses above the longer-term EMA, it signals an emerging uptrend, suggesting a potential long entry. Conversely, a crossover below indicates a possible downtrend, hinting at a short entry opportunity. This simple yet powerful tool is key in confirming trend directions and timing market entries.
3. ATR (Average True Range): The ATR is instrumental in assessing market volatility. This indicator helps in understanding the average range of price movements over a given period, thus providing a sense of how much a market might move on a typical day. In this strategy, the ATR is used to adjust stop-loss levels and to gauge the potential risk and reward of trades. It allows for more informed decisions by aligning trade management techniques with the current volatility conditions.
The synergy of these three components – the Momentum-RSI, EMA Crossover, and ATR – creates a robust framework for this trading strategy. By combining momentum analysis, trend identification, and volatility assessment, the strategy offers a comprehensive approach to navigating the markets. Whether it's capturing a strong trend in its early stages or identifying a potential reversal, this strategy aims to provide traders with the tools and insights needed to make well-informed, strategically sound trading decisions.
Detailed Component Analysis
The efficacy of this trading strategy hinges on the synergistic functioning of its three key components: the Momentum-RSI, EMA Crossover, and Average True Range (ATR). Each component brings a unique perspective to the strategy, contributing to a well-rounded approach to market analysis.
1. Momentum-RSI (Relative Strength Index)
• Definition and Function: The Momentum-RSI is a modified version of the classic Relative Strength Index. While the traditional RSI measures the velocity and magnitude of directional price movements, the Momentum-RSI amplifies aspects that reflect trend strength and momentum.
• Significance in Identifying Trend Strength: This indicator excels in identifying the strength behind a market's move. A high Momentum-RSI value typically indicates strong bullish momentum, suggesting the potential continuation of an uptrend. Conversely, a low Momentum-RSI value signals strong bearish momentum, possibly indicative of an ongoing downtrend.
• Application in Strategy: In this strategy, the Momentum-RSI is used to gauge the underlying strength of market trends. It helps in filtering out minor fluctuations and focusing on significant movements, providing a clearer picture of the market's true momentum.
2. EMA (Exponential Moving Average) Crossover
• Definition and Function: The EMA Crossover component utilizes two exponential moving averages of different timeframes. Unlike simple moving averages, EMAs give more weight to recent prices, making them more responsive to new information.
• Contribution to Market Direction: The interaction between the short-term and long-term EMAs is key to determining market direction. A crossover of the shorter EMA above the longer EMA is an indicator of an emerging uptrend, while a crossover below signals a developing downtrend.
• Application in Strategy: The EMA Crossover serves as a trend confirmation tool. It provides a clear, visual representation of the market's direction, aiding in the decision-making process for entering long or short positions. This component ensures that trades are aligned with the prevailing market trend, a crucial factor for the success of the strategy.
3. ATR (Average True Range)
• Definition and Function: The ATR is an indicator that measures market volatility by calculating the average range between the high and low prices over a specified period.
• Role in Assessing Market Volatility: The ATR provides insights into the typical market movement within a given timeframe, offering a measure of the market's volatility. Higher ATR values indicate increased volatility, while lower values suggest a calmer market environment.
• Application in Strategy: Within this strategy, the ATR is instrumental in tailoring risk management techniques, particularly in setting stop-loss levels. By accounting for the market's volatility, the ATR ensures that stop-loss orders are placed at levels that are neither too tight (risking premature exits) nor too loose (exposing to excessive risk).
In summary, the combination of Momentum-RSI, EMA Crossover, and ATR in this trading strategy provides a comprehensive toolkit for market analysis. The Momentum-RSI identifies the strength of market trends, the EMA Crossover confirms the market direction, and the ATR guides in risk management by assessing volatility. Together, these components form the backbone of a strategy designed to navigate the complexities of the financial markets effectively.
1. Signal Generation Process
• Combining Indicators: The strategy operates by synthesizing signals from the Momentum-RSI, EMA Crossover, and ATR indicators. Each indicator serves a specific purpose: the Momentum-RSI gauges trend momentum, the EMA Crossover identifies the trend direction, and the ATR assesses the market’s volatility.
• Criteria for Signal Validation: For a signal to be considered valid, it must meet specific criteria set by each of the three indicators. This multi-layered approach ensures that signals are not only based on one aspect of market behavior but are a result of a comprehensive analysis.
2. Conditions for Long Positions
• Uptrend Confirmation: A long position signal is generated when the shorter-term EMA crosses above the longer-term EMA, indicating an uptrend.
• Momentum-RSI Alignment: Alongside the EMA crossover, the Momentum-RSI should indicate strong bullish momentum. This is typically represented by the Momentum-RSI being at a high level, confirming the strength of the uptrend.
• ATR Consideration: The ATR is used to fine-tune the entry point and set an appropriate stop-loss level. In a low volatility scenario, as indicated by the ATR, the stop-loss can be set tighter, closer to the entry point.
3. Conditions for Short Positions
• Downtrend Confirmation: Conversely, a short position signal is indicated when the shorter-term EMA crosses below the longer-term EMA, signaling a downtrend.
• Momentum-RSI Confirmation: The Momentum-RSI should reflect strong bearish momentum, usually seen when the Momentum-RSI is at a low level. This confirms the bearish strength of the market.
• ATR Application: The ATR again plays a role in determining the stop-loss level for the short position. Higher volatility, as indicated by a higher ATR, would warrant a wider stop-loss to accommodate larger market swings.
By adhering to these mechanics, the strategy aims to ensure that each trade is entered with a high probability of success, aligning with the market’s current momentum and trend. The integration of these indicators allows for a holistic market analysis, providing traders with clear and actionable signals for both entering and exiting trades.
Customizable Parameters in the Strategy
Flexibility and adaptability are key features of this trading strategy, achieved through a range of customizable parameters. These parameters allow traders to tailor the strategy to their individual trading style, risk tolerance, and specific market conditions. By adjusting these parameters, users can fine-tune the strategy to optimize its performance and align it with their unique trading objectives. Below are the primary parameters that can be customized within the strategy:
1. Momentum-RSI Settings
• Period: The lookback period for the Momentum-RSI can be adjusted. A shorter period makes the indicator more sensitive to recent price changes, while a longer period smoothens the RSI line, offering a broader view of the momentum.
• Overbought/Oversold Thresholds: Users can set their own overbought and oversold levels, which can help in identifying extreme market conditions more precisely according to their trading approach.
2. EMA Crossover Settings
• Timeframes for EMAs: The strategy uses two EMAs with different timeframes. Traders can modify these timeframes, choosing shorter periods for a more responsive approach or longer periods for a more conservative one.
• Source Data: The choice of price data (close, open, high, low) used in calculating the EMAs can be varied depending on the trader’s preference.
3. ATR Settings
• Lookback Period: Adjusting the lookback period for the ATR impacts how the indicator measures volatility. A longer period may provide a more stable but less responsive measure, while a shorter period offers quicker but potentially more erratic readings.
• Multiplier for Stop-Loss Calculation: This parameter allows traders to set how aggressively or conservatively they want their stop-loss to be in relation to the ATR value.
Here are the standard settings:
sᴛᴀɢᴇ ᴀɴᴀʏʟsɪsStage analysis is a technical analysis approach that involves categorizing a stock's price movements into different stages to help traders and investors make more informed decisions. It was popularized by Stan Weinstein in his book, "Secrets for Profiting in Bull and Bear Markets." The stages are used to identify the overall trend and to time entries and exits in the market. Here's an explanation of the typical stages in stage analysis:
1. **Stage 1: Accumulation Phase**
- In this stage, the stock is in a downtrend or has been trading sideways for an extended period.
- Volume is relatively low, indicating that institutions and smart money may be quietly accumulating shares.
- The stock may test and hold support levels, showing signs of stability.
- The goal for traders in this stage is to identify the potential for a trend reversal.
2. **Stage 2: Markup (Bull Market) Phase**
- This is the stage where the stock starts a significant uptrend.
- Volume increases as institutional and retail investors become more interested in the stock.
- Technical indicators like moving averages and trendlines confirm the uptrend.
- Traders and investors look for buying opportunities during pullbacks or consolidations within the uptrend.
3. **Stage 3: Distribution Phase**
- In this stage, the stock's price begins to show signs of weakness.
- Volume might decrease as institutions and smart money start selling their positions.
- The stock may start forming a trading range or exhibit bearish chart patterns.
- Traders should consider taking profits or reducing exposure to the stock as it may enter a downtrend.
4. **Stage 4: Markdown (Bear Market) Phase**
- This is the stage where the stock enters a significant downtrend.
- Volume may remain elevated as selling pressure dominates.
- Technical indicators confirm the downtrend.
- Traders and investors should avoid buying the stock and may consider short-selling or staying on the sidelines.
Stage analysis helps traders and investors make decisions based on the current stage of a stock's price movement. The goal is to enter during the accumulation phase or early in the markup phase and exit during the distribution phase or before the markdown phase to maximize profits and minimize losses.
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try to just show the Stage number in a table, but always double check for yourself
Volume With ColorVolume with color helps to quickly identify accumulation or distribution.
An accumulation day is an up day with volume greater than a user selected average.
A distribution day is a down day with volume greater than a user selected average.
This indicator will highlight those days by changing the volume bar colors for an easy visual.
ADX W. Wilders(DI+, DI-, DX, ADXR, Equilibrium Point)The reason for publishing the script was the lack of display of important components in the standard ADX indicator, such as DI+, DI-, DX , ADXR, and the absence of a choice of methods for calculating moving averages in the indicator.
According to the book by the author of the ADX indicator, W. Wilder, the indicator components were calculated using the SMA formula, however, the RMA moving average is used in the code of the built-in indicator in TradingView, which shows excellent results, but this is not a classic calculation method. In addition to SMA and RMA, there are also EMA , HMA , WMA , VWMA moving averages to choose from. Added the ability to display lines ADX , ADXR , DX , DI+, DI- and Equilibrium points (when DI+ and DI- are equal or intersect).
ADX Trading Rules
1. Trade the intersections of DI+ and DI-
2. Extreme Point Rule(EPR). EPR is formed when DI+, DI- (Equilibrium point) crosses, forming a trend reversal point at the extremum of the current bar. In the example on the ADX RMA chart, the DI- line is above DI+. Being in a short position at the reverse intersection of the DI- and DI + lines, it is necessary to take the high price of the crossing bar for the reversal point, upon breakdown of which, turn to long. In this example, the breakdown did not take place and the short position remained active, despite the intersection of the DI+ lines over DI-. This rule is an excellent filter that removes unnecessary transactions in the trading system.
3. DI+ > ADX and DI- > ADX. Stop trading trend-following systems.
4. If ADXR > 25, the trading system will be profitable. With ADXR < 20, trend-following systems need to stop trading. Many mistakenly use ADX values instead of ADXR . The author explicitly pointed to ADXR in his book.
5. Equilibrium Point - balance points. The accumulation of these points on the chart means the presence of a flat in the market. Accumulation often appears on a declining ADX after a top has been established on the ADX indicator. The smaller the distance between the points, the less significant movements occurred in the market.
6. For intraday trading of cryptocurrencies use can the following ADX settings:
DI Length = 100
ADX Smoothing = 14
MA Type = VWMA
Flat Zone = 30
P.S. Fragment from an interview with W. Wilder:
OH: You are probably best known for inventing the Relative Strength Index ( RSI ), Average Directional Index ( ADX ) and Average True Range (ATR). Which of these is the most powerful tool for a trader?
WW: The ADX .
OH: Is it the indicator you are most proud of?
WW: I guess so.
Directional Movement IndexADX is an oscillating indicator, displayed as a single line, ranging from 0 to 100, it only indicates the strength of the trend and does not indicate its direction. In other words, the ADX is non-directional, meaning that it measures the strength of a trend, but doesn’t distinguish between uptrend and downtrends. So, during a strong uptrend, the ADX rises and during a strong downtrend, the ADX also rises.
Here is how you correctly read what ADX is saying about the market. Here are 5 aspects regarding the interpretation of the ADX:
1- When ADX is above 25, trend strength is strong. Usually, once the ADX gets above 25 this signals the beginning of a trend. Big moves (upwards or downwards) tend to happen when ADX is right around this number. You can experiment with this number, some traders that want faster signals, tend to use a 20 threshold when trading with the ADX.
2- When ADX is below 25, traders must avoid trend trading strategies as the market is in accumulation or distribution phase. So, when we see the ADX line below 20 or 25 level, we forget about trend following strategies and we apply strategies suitable for a ranging market.
3- When ADX is above 25 and Positive Directional Movement Indicator (+DMI) is above the Negative Directional Movement Indicator (-DMI). ADX measures the strength of an uptrend. The crossover between the 2 Directional Movement Indicator, as the ADX line is well above 25 can result in an excellent bullish move.
4- The Positive Directional Movement Indicator (+DMI) should be above the Negative Directional Movement and the ADX should be above 25 signals for a strong upward trend for long opportunities. When ADX is above 25 and Positive Directional Movement Indicator is below the Negative Directional Movement Indicator, ADX measures the strength of a downtrend and short opportunities.
5- Values over 50 of the ADX indicate a very strong trend
There are pros and cons of ADX.
So, why is the ADX useful for traders: First, is excellent at quantifying trend strength. Also, it allows traders to see the strength of bulls and bears at the same time. It is good at filtering out trades, during accumulation periods and is good at identifying trending conditions.
But the ADX also has its limitations. The most important disadvantage is the fact that ADX is a lagging indicator that follows the price, so we must be very careful when we apply this indicator, because we might miss the inception of the trend and join it when it’s nearly over.
Also, it offers many false signals when used on shorter time frames, so it’s advisable to trade it on higher time frames Also, the ADX does not contain all of the data necessary a for proper analysis of price action, so it must be used in combination with other tools or indicators.
Now that we fully covered the good and the bad regarding ADX, let’s see how it is used in a trading strategy.
The trading strategy involves a DMI crossover, confirmed by ADX above consolidation threshold. If +DMI crossover, we take long position and if -DMI crosses over, we take a short position.
Candles are re-colored for easy demonstration of uptrend, downtrend and consolidation periods.
Green candles – ADX > Consolidation Threshold and +DMI > -DMI
Red candles – ADX > Consolidation Threshold and +DMI < -DMI
Black candles – ADX < Consolidation Threshold
Repaint – This is a non-repainting strategy - All the signals are generated at candle closing. All the calculations are made on previous candle’s open, high, low, close. No request security function is used. No data is being used from higher time frame. Trade exit uses close function instead of exit to avoid limit orders. Only one long trade at a time (no pyramiding) is allowed.
Strategy Time frame – D (To filter out false signals, higher time frame is recommended)
Strategy For – Swing Traders
Assets – Cryptocurrencies + Stocks
BTC Active1Y holders: OnchainUse this Indicator in The Weekly timeframe
This indicator is based on "Percent of Supply Last Active 1+ Years Ago".
This is so important indicator that shows " The percent of circulating supply that has not moved in at least 1 year."
It can show the situation of the holders who have been holding their coins for more than a year. When this indicator starts to decline, it means that the price has risen so much that the holders are selling their coins. When this indicator starts to increase, it means that the number of coins held has been increasing for more than a year. This is because the price is too low for investors.
This indicator can be used to indicate accumulation and distribution areas. When the indicator enters the overlow area (red) it means that the distribution is happening
When the indicator enters the overhigh range (blue), it means that accumulation is taking place by the holders
Intraday Power 3 VisualDescription
This indicator draws a dynamic "Open High Low Close" type visual on intraday charts so the trader can easily keep track of the daily/weekly movement. This indicator was inspired by the Inner Circle Trader’s (ICT) “Power 3” concept, which is Accumulation, Manipulation, and Distribution of price on a daily timeframe.
Visual
This indicator plots the chosen timeframes opening price along with a live line for the current price. This makes it very easy to identify the daily/weekly range along it’s open. And the user can combine this indicator with my other indicator “Futures Exchange Sessions” to plot the midnight EST & 8:30 AM EST lines to get a great summation of over night price action.
Inputs and Style
In the Input section the user can dynamically switch between Daily and Weekly timeframes. Built in ability to move the entire Visual to the right makes preventing indicator overlap a breeze. All of the lines can be configured: color, style, and width. Independently toggle ON/OFF the Power 3 labels (Accumulation, Manipulation, Range Extension, Distribution) and can change labels color. The labels dynamically move and switch positions based upon bear or bull daily/weekly range.
Special Notes
The Futures market is open 23/5. It is closed everyday for 1-hour at 5pm EST and closed over the weekends. Because this Intraday Power 3 Visual is drawing in the 'future' on the users TradingView chart, when the visual is close or in a time when the market is closed, the visual doesn't behave properly. This is because TradingView doesn't display times when the Market is closed, thus the drawings cannot be displayed during those times. There is nothing wrong with the script. Please wait until the Market is open and the visual will be drawn normally.
This indicator is intended for use in the Futures Market
Adaptive Average Vortex Index [lastguru]As a longtime fan of ADX, looking at Vortex Indicator I often wondered, where is the third line. I have rarely seen that anybody is calculating it. So, here it is: Average Vortex Index - an ADX calculated from Vortex Indicator. I interpret it similarly to the ADX indicator: higher values show stronger trend. If you discover other interpretation or have suggestions, comments are welcome.
Both VI+ and VI- lines are also drawn. As I use adaptive length calculation in my other scripts (based on the libraries I've developed and published), I have also included the possibility to have an adaptive length here, so if you hate the idea of calculating ADX from VI, you can disable that line and just look at the adaptive Vortex Indicator.
Note that as with all my oscillators, all the lines here are renormalized to -1..1 range unlike the original Vortex Indicator computation. To do that for VI+ and VI- lines, I subtract 1 from their values. It does not change the shape or the amplitude of the lines.
Adaptation algorithms are roughly subdivided in two categories: classic Length Adaptations and Cycle Estimators (they are also implemented in separate libraries), all are selected in Adaptation dropdown. Length Adaptation used in the Adaptive Moving Averages and the Adaptive Oscillators try to follow price movements and accelerate/decelerate accordingly (usually quite rapidly with a huge range). Cycle Estimators, on the other hand, try to measure the cycle period of the current market, which does not reflect price movement or the rate of change (the rate of change may also differ depending on the cycle phase, but the cycle period itself usually changes slowly).
VIDYA - based on VIDYA algorithm. The period oscillates from the Lower Bound up (slow)
VIDYA-RS - based on Vitali Apirine's modification of VIDYA algorithm (he calls it Relative Strength Moving Average). The period oscillates from the Upper Bound down (fast)
Kaufman Efficiency Scaling - based on Efficiency Ratio calculation originally used in KAMA
Fractal Adaptation - based on FRAMA by John F. Ehlers
MESA MAMA Cycle - based on MESA Adaptive Moving Average by John F. Ehlers
Pearson Autocorrelation* - based on Pearson Autocorrelation Periodogram by John F. Ehlers
DFT Cycle* - based on Discrete Fourier Transform Spectrum estimator by John F. Ehlers
Phase Accumulation* - based on Dominant Cycle from Phase Accumulation by John F. Ehlers
Length Adaptation usually take two parameters: Bound From (lower bound) and To (upper bound). These are the limits for Adaptation values. Note that the Cycle Estimators marked with asterisks(*) are very computationally intensive, so the bounds should not be set much higher than 50, otherwise you may receive a timeout error (also, it does not seem to be a useful thing to do, but you may correct me if I'm wrong).
The Cycle Estimators marked with asterisks(*) also have 3 checkboxes: HP (Highpass Filter), SS (Super Smoother) and HW (Hann Window). These enable or disable their internal prefilters, which are recommended by their author - John F. Ehlers . I do not know, which combination works best, so you can experiment.
If no Adaptation is selected ( None option), you can set Length directly. If an Adaptation is selected, then Cycle multiplier can be set.
The oscillator also has the option to configure the internal smoothing function with Window setting. By default, RMA is used (like in ADX calculation). Fast Default option is using half the length for smoothing. Triangle , Hamming and Hann Window algorithms are some better smoothers suggested by John F. Ehlers.
After the oscillator a Moving Average can be applied. The following Moving Averages are included: SMA , RMA, EMA , HMA , VWMA , 2-pole Super Smoother, 3-pole Super Smoother, Filt11, Triangle Window, Hamming Window, Hann Window, Lowpass, DSSS.
Postfilter options are applied last:
Stochastic - Stochastic
Super Smooth Stochastic - Super Smooth Stochastic (part of MESA Stochastic ) by John F. Ehlers
Inverse Fisher Transform - Inverse Fisher Transform
Noise Elimination Technology - a simplified Kendall correlation algorithm "Noise Elimination Technology" by John F. Ehlers
Momentum - momentum (derivative)
Except for Inverse Fisher Transform , all Postfilter algorithms can have Length parameter. If it is not specified (set to 0), then the calculated Slow MA Length is used. If Filter/MA Length is less than 2 or Postfilter Length is less than 1, they are calculated as a multiplier of the calculated oscillator length.
More information on the algorithms is given in the code for the libraries used. I am also very grateful to other TradingView community members (they are also mentioned in the library code) without whom this script would not have been possible.
Adaptive Oscillator constructor [lastguru]Adaptive Oscillators use the same principle as Adaptive Moving Averages. This is an experiment to separate length generation from oscillators, offering multiple alternatives to be combined. Some of the combinations are widely known, some are not. Note that all Oscillators here are normalized to -1..1 range. This indicator is based on my previously published public libraries and also serve as a usage demonstration for them. I will try to expand the collection (suggestions are welcome), however it is not meant as an encyclopaedic resource , so you are encouraged to experiment yourself: by looking on the source code of this indicator, I am sure you will see how trivial it is to use the provided libraries and expand them with your own ideas and combinations. I give no recommendation on what settings to use, but if you find some useful setting, combination or application ideas (or bugs in my code), I would be happy to read about them in the comments section.
The indicator works in three stages: Prefiltering, Length Adaptation and Oscillators.
Prefiltering is a fast smoothing to get rid of high-frequency (2, 3 or 4 bar) noise.
Adaptation algorithms are roughly subdivided in two categories: classic Length Adaptations and Cycle Estimators (they are also implemented in separate libraries), all are selected in Adaptation dropdown. Length Adaptation used in the Adaptive Moving Averages and the Adaptive Oscillators try to follow price movements and accelerate/decelerate accordingly (usually quite rapidly with a huge range). Cycle Estimators, on the other hand, try to measure the cycle period of the current market, which does not reflect price movement or the rate of change (the rate of change may also differ depending on the cycle phase, but the cycle period itself usually changes slowly).
Chande (Price) - based on Chande's Dynamic Momentum Index (CDMI or DYMOI), which is dynamic RSI with this length
Chande (Volume) - a variant of Chande's algorithm, where volume is used instead of price
VIDYA - based on VIDYA algorithm. The period oscillates from the Lower Bound up (slow)
VIDYA-RS - based on Vitali Apirine's modification of VIDYA algorithm (he calls it Relative Strength Moving Average). The period oscillates from the Upper Bound down (fast)
Kaufman Efficiency Scaling - based on Efficiency Ratio calculation originally used in KAMA
Deviation Scaling - based on DSSS by John F. Ehlers
Median Average - based on Median Average Adaptive Filter by John F. Ehlers
Fractal Adaptation - based on FRAMA by John F. Ehlers
MESA MAMA Alpha - based on MESA Adaptive Moving Average by John F. Ehlers
MESA MAMA Cycle - based on MESA Adaptive Moving Average by John F. Ehlers , but unlike Alpha calculation, this adaptation estimates cycle period
Pearson Autocorrelation* - based on Pearson Autocorrelation Periodogram by John F. Ehlers
DFT Cycle* - based on Discrete Fourier Transform Spectrum estimator by John F. Ehlers
Phase Accumulation* - based on Dominant Cycle from Phase Accumulation by John F. Ehlers
Length Adaptation usually take two parameters: Bound From (lower bound) and To (upper bound). These are the limits for Adaptation values. Note that the Cycle Estimators marked with asterisks(*) are very computationally intensive, so the bounds should not be set much higher than 50, otherwise you may receive a timeout error (also, it does not seem to be a useful thing to do, but you may correct me if I'm wrong).
The Cycle Estimators marked with asterisks(*) also have 3 checkboxes: HP (Highpass Filter), SS (Super Smoother) and HW (Hann Window). These enable or disable their internal prefilters, which are recommended by their author - John F. Ehlers . I do not know, which combination works best, so you can experiment.
Chande's Adaptations also have 3 additional parameters: SD Length (lookback length of Standard deviation), Smooth (smoothing length of Standard deviation) and Power ( exponent of the length adaptation - lower is smaller variation). These are internal tweaks for the calculation.
Oscillators section offer you a choice of Oscillator algorithms:
Stochastic - Stochastic
Super Smooth Stochastic - Super Smooth Stochastic (part of MESA Stochastic) by John F. Ehlers
CMO - Chande Momentum Oscillator
RSI - Relative Strength Index
Volume-scaled RSI - my own version of RSI. It scales price movements by the proportion of RMS of volume
Momentum RSI - RSI of price momentum
Rocket RSI - inspired by RocketRSI by John F. Ehlers (not an exact implementation)
MFI - Money Flow Index
LRSI - Laguerre RSI by John F. Ehlers
LRSI with Fractal Energy - a combo oscillator that uses Fractal Energy to tune LRSI gamma
Fractal Energy - Fractal Energy or Choppiness Index by E. W. Dreiss
Efficiency ratio - based on Kaufman Adaptive Moving Average calculation
DMI - Directional Movement Index (only ADX is drawn)
Fast DMI - same as DMI, but without secondary smoothing
If no Adaptation is selected (None option), you can set Length directly. If an Adaptation is selected, then Cycle multiplier can be set.
Before an Oscillator, a High Pass filter may be executed to remove cyclic components longer than the provided Highpass Length (no High Pass filter, if Highpass Length = 0). Both before and after the Oscillator a Moving Average can be applied. The following Moving Averages are included: SMA, RMA, EMA, HMA , VWMA, 2-pole Super Smoother, 3-pole Super Smoother, Filt11, Triangle Window, Hamming Window, Hann Window, Lowpass, DSSS. For more details on these Moving Averages, you can check my other Adaptive Constructor indicator:
The Oscillator output may be renormalized and postprocessed with the following Normalization algorithms:
Stochastic - Stochastic
Super Smooth Stochastic - Super Smooth Stochastic (part of MESA Stochastic) by John F. Ehlers
Inverse Fisher Transform - Inverse Fisher Transform
Noise Elimination Technology - a simplified Kendall correlation algorithm "Noise Elimination Technology" by John F. Ehlers
Except for Inverse Fisher Transform, all Normalization algorithms can have Length parameter. If it is not specified (set to 0), then the calculated Oscillator length is used.
More information on the algorithms is given in the code for the libraries used. I am also very grateful to other TradingView community members (they are also mentioned in the library code) without whom this script would not have been possible.
Adaptive MA constructor [lastguru]Adaptive Moving Averages are nothing new, however most of them use EMA as their MA of choice once the preferred smoothing length is determined. I have decided to make an experiment and separate length generation from smoothing, offering multiple alternatives to be combined. Some of the combinations are widely known, some are not. This indicator is based on my previously published public libraries and also serve as a usage demonstration for them. I will try to expand the collection (suggestions are welcome), however it is not meant as an encyclopaedic resource, so you are encouraged to experiment yourself: by looking on the source code of this indicator, I am sure you will see how trivial it is to use the provided libraries and expand them with your own ideas and combinations. I give no recommendation on what settings to use, but if you find some useful setting, combination or application ideas (or bugs in my code), I would be happy to read about them in the comments section.
The indicator works in three stages: Prefiltering, Length Adaptation and Moving Averages.
Prefiltering is a fast smoothing to get rid of high-frequency (2, 3 or 4 bar) noise.
Adaptation algorithms are roughly subdivided in two categories: classic Length Adaptations and Cycle Estimators (they are also implemented in separate libraries), all are selected in Adaptation dropdown. Length Adaptation used in the Adaptive Moving Averages and the Adaptive Oscillators try to follow price movements and accelerate/decelerate accordingly (usually quite rapidly with a huge range). Cycle Estimators, on the other hand, try to measure the cycle period of the current market, which does not reflect price movement or the rate of change (the rate of change may also differ depending on the cycle phase, but the cycle period itself usually changes slowly).
Chande (Price) - based on Chande's Dynamic Momentum Index (CDMI or DYMOI), which is dynamic RSI with this length
Chande (Volume) - a variant of Chande's algorithm, where volume is used instead of price
VIDYA - based on VIDYA algorithm. The period oscillates from the Lower Bound up (slow)
VIDYA-RS - based on Vitali Apirine's modification of VIDYA algorithm (he calls it Relative Strength Moving Average). The period oscillates from the Upper Bound down (fast)
Kaufman Efficiency Scaling - based on Efficiency Ratio calculation originally used in KAMA
Deviation Scaling - based on DSSS by John F. Ehlers
Median Average - based on Median Average Adaptive Filter by John F. Ehlers
Fractal Adaptation - based on FRAMA by John F. Ehlers
MESA MAMA Alpha - based on MESA Adaptive Moving Average by John F. Ehlers
MESA MAMA Cycle - based on MESA Adaptive Moving Average by John F. Ehlers, but unlike Alpha calculation, this adaptation estimates cycle period
Pearson Autocorrelation* - based on Pearson Autocorrelation Periodogram by John F. Ehlers
DFT Cycle* - based on Discrete Fourier Transform Spectrum estimator by John F. Ehlers
Phase Accumulation* - based on Dominant Cycle from Phase Accumulation by John F. Ehlers
Length Adaptation usually take two parameters: Bound From (lower bound) and To (upper bound). These are the limits for Adaptation values. Note that the Cycle Estimators marked with asterisks(*) are very computationally intensive, so the bounds should not be set much higher than 50, otherwise you may receive a timeout error (also, it does not seem to be a useful thing to do, but you may correct me if I'm wrong).
The Cycle Estimators marked with asterisks(*) also have 3 checkboxes: HP (Highpass Filter), SS (Super Smoother) and HW (Hann Window). These enable or disable their internal prefilters, which are recommended by their author - John F. Ehlers. I do not know, which combination works best, so you can experiment.
Chande's Adaptations also have 3 additional parameters: SD Length (lookback length of Standard deviation), Smooth (smoothing length of Standard deviation) and Power (exponent of the length adaptation - lower is smaller variation). These are internal tweaks for the calculation.
Length Adaptaton section offer you a choice of Moving Average algorithms. Most of the Adaptations are originally used with EMA, so this is a good starting point for exploration.
SMA - Simple Moving Average
RMA - Running Moving Average
EMA - Exponential Moving Average
HMA - Hull Moving Average
VWMA - Volume Weighted Moving Average
2-pole Super Smoother - 2-pole Super Smoother by John F. Ehlers
3-pole Super Smoother - 3-pole Super Smoother by John F. Ehlers
Filt11 -a variant of 2-pole Super Smoother with error averaging for zero-lag response by John F. Ehlers
Triangle Window - Triangle Window Filter by John F. Ehlers
Hamming Window - Hamming Window Filter by John F. Ehlers
Hann Window - Hann Window Filter by John F. Ehlers
Lowpass - removes cyclic components shorter than length (Price - Highpass)
DSSS - Derivation Scaled Super Smoother by John F. Ehlers
There are two Moving Averages that are drown on the chart, so length for both needs to be selected. If no Adaptation is selected ( None option), you can set Fast Length and Slow Length directly. If an Adaptation is selected, then Cycle multiplier can be selected for Fast and Slow MA.
More information on the algorithms is given in the code for the libraries used. I am also very grateful to other TradingView community members (they are also mentioned in the library code) without whom this script would not have been possible.
DominantCycleCollection of Dominant Cycle estimators. Length adaptation used in the Adaptive Moving Averages and the Adaptive Oscillators try to follow price movements and accelerate/decelerate accordingly (usually quite rapidly with a huge range). Cycle estimators, on the other hand, try to measure the cycle period of the current market, which does not reflect price movement or the rate of change (the rate of change may also differ depending on the cycle phase, but the cycle period itself usually changes slowly). This collection may become encyclopaedic, so if you have any working cycle estimator, drop me a line in the comments below. Suggestions are welcome. Currently included estimators are based on the work of John F. Ehlers
mamaPeriod(src, dynLow, dynHigh) MESA Adaptation - MAMA Cycle
Parameters:
src : Series to use
dynLow : Lower bound for the dynamic length
dynHigh : Upper bound for the dynamic length
Returns: Calculated period
Based on MESA Adaptive Moving Average by John F. Ehlers
Performs Hilbert Transform Homodyne Discriminator cycle measurement
Unlike MAMA Alpha function (in LengthAdaptation library), this does not compute phase rate of change
Introduced in the September 2001 issue of Stocks and Commodities
Inspired by the @everget implementation:
Inspired by the @anoojpatel implementation:
paPeriod(src, dynLow, dynHigh, preHP, preSS, preHP) Pearson Autocorrelation
Parameters:
src : Series to use
dynLow : Lower bound for the dynamic length
dynHigh : Upper bound for the dynamic length
preHP : Use High Pass prefilter (default)
preSS : Use Super Smoother prefilter (default)
preHP : Use Hann Windowing prefilter
Returns: Calculated period
Based on Pearson Autocorrelation Periodogram by John F. Ehlers
Introduced in the September 2016 issue of Stocks and Commodities
Inspired by the @blackcat1402 implementation:
Inspired by the @rumpypumpydumpy implementation:
Corrected many errors, and made small speed optimizations, so this could be the best implementation to date (still slow, though, so may revisit in future)
High Pass and Super Smoother prefilters are used in the original implementation
dftPeriod(src, dynLow, dynHigh, preHP, preSS, preHP) Discrete Fourier Transform
Parameters:
src : Series to use
dynLow : Lower bound for the dynamic length
dynHigh : Upper bound for the dynamic length
preHP : Use High Pass prefilter (default)
preSS : Use Super Smoother prefilter (default)
preHP : Use Hann Windowing prefilter
Returns: Calculated period
Based on Spectrum from Discrete Fourier Transform by John F. Ehlers
Inspired by the @blackcat1402 implementation:
High Pass, Super Smoother and Hann Windowing prefilters are used in the original implementation
phasePeriod(src, dynLow, dynHigh, preHP, preSS, preHP) Phase Accumulation
Parameters:
src : Series to use
dynLow : Lower bound for the dynamic length
dynHigh : Upper bound for the dynamic length
preHP : Use High Pass prefilter (default)
preSS : Use Super Smoother prefilter (default)
preHP : Use Hamm Windowing prefilter
Returns: Calculated period
Based on Dominant Cycle from Phase Accumulation by John F. Ehlers
High Pass and Super Smoother prefilters are used in the original implementation
doAdapt(type, src, len, dynLow, dynHigh, chandeSDLen, chandeSmooth, chandePower, preHP, preSS, preHP) Execute a particular Length Adaptation or Dominant Cycle Estimator from the list
Parameters:
type : Length Adaptation or Dominant Cycle Estimator type to use
src : Series to use
len : Reference lookback length
dynLow : Lower bound for the dynamic length
dynHigh : Upper bound for the dynamic length
chandeSDLen : Lookback length of Standard deviation for Chande's Dynamic Length
chandeSmooth : Smoothing length of Standard deviation for Chande's Dynamic Length
chandePower : Exponent of the length adaptation for Chande's Dynamic Length (lower is smaller variation)
preHP : Use High Pass prefilter for the Estimators that support it (default)
preSS : Use Super Smoother prefilter for the Estimators that support it (default)
preHP : Use Hann Windowing prefilter for the Estimators that support it
Returns: Calculated period (float, not limited)
doEstimate(type, src, dynLow, dynHigh, preHP, preSS, preHP) Execute a particular Dominant Cycle Estimator from the list
Parameters:
type : Dominant Cycle Estimator type to use
src : Series to use
dynLow : Lower bound for the dynamic length
dynHigh : Upper bound for the dynamic length
preHP : Use High Pass prefilter for the Estimators that support it (default)
preSS : Use Super Smoother prefilter for the Estimators that support it (default)
preHP : Use Hann Windowing prefilter for the Estimators that support it
Returns: Calculated period (float, not limited)
Jake Bernstein - Moving Average ChannelWe all know that moving averages, in particular, moving averages of closing prices tend to be highly inaccurate indicators and frequently miss major tops and bottoms. In backtesting, they tend to be accurate some 30 to 40% of the time which is to my way of thinking unacceptable. On the contrary moving averages of opens versus closes for highs versus lows, when used properly avoid the drawbacks of closing moving averages, particularly when combined with a trigger. Shown above is my moving average channel method which uses the 57 SMA of Williams accumulation distribution as a setup or trigger. As shown by the arrows two consecutive price bars completely below the MA channel low and triggered by Williams below SMA constitutes a sell signal. Conversely, two consecutive price bars or more above the moving average channel high accompanied by Williams above its moving average constitutes a sell trigger. The moving average channel high, the red line is a 10 period Moving average of highs. The Moving average channel low, the green line is an 8 period Moving average of the low. There are at least a dozen applications of this methodology including its ability to spot trend changes, support, resistance, swing trades, market strength, market weakness, and more. I will post some of these additional uses of the moving average channel as they present themselves. Do note that in this chart there were two instances above the moving average channel high but these were not triggered by Williams AD and therefore the trend remains down for the duration of this chart. The methodology associated with my MAC is completely rules-based and works in any timeframe. Thank you my friend Larry Williams for developing your excellent version of accumulation-distribution
Baekdoo multi OverSold OverBuy colored CandleHi forks,
I'm trader Baekdoosan who trading Equity from South Korea. This Baekdoo multi OverSold OverBuy colored candle will give you the idea of
multiple indicators in one shot with colored candle. Those indicators tell us that oversold or overbuy statistically. For the color, you can freely change
based on your comfort. For me, in Korea white candle has red color and black candle has blue color. So somewhat confusing for you. Anyway you can
easily modify color in the script. Please refer this line.
barcolor(open<close and result_pos == 4 ? color.new(color.red, 0) : open<close and result_pos == 3 ? color.new(color.red, 25) : open<close and result_pos == 2 ? color.new(color.red, 50) : open<close and result_pos == 1? color.new(color.red, 75) : na)
you can see I put different transparency at color.new() function with color code. Let me divide and conquer to explain for up candle
white candle and black candle.
1. White candle
with 4 oversold signal case with white candle tells us it is almost reached real bottom and try to rebound. In this case, I put vivid color (no transparency) on the candle. And all 4 signal case, I put text on "OverSold". It will not happen frequently. Then 2 approaches can be made.
(a) short term approach
You can buy on this time. and you set stop loss with open price. This is mainly aimed for technical rebound.
(b) long term approach
You can accumulate based on your budget with 5 times dividing. At that day might not be the very bottom but those period will most probably real bottom. You can put more weight on latter buy. Let say, 1 : 1.25 : 1.5 : 1.75 : 2.5. So for example, if you have $8,000 to investigate then, buy $1,000 and then $1,250, $1,500, accordingly. If price rebound then don't adding weight on accumulation but with the first amount that you buy(i.e., $1,000 with above example). With this approach, you will not have much stress and you will get profit well. If this is grand bottom case, then you can HODL this long term. What you needs is stick to the plan. :)
with 3 signals the color is less vivid, 2 signals is much less vivid, accordingly.
2. Black candle
The approaches are opposite to above. The signal will tells us for 4 overBuy signals, then vivid blue candle will be shown. Our strategy is distribute to sell. Please do not sell in one shot. As Newton said, "I can calculate the motions of the heavenly bodies, but not the madness of the people". Strong buy phase, we don't know how far will it go. But indicators will tell us it is quite overSold situation. So what I can suggest you is sell it 10% to 20% on resistance price, and put 50% of lower than certain support price. Remember, accumulation and distribution will always better than one shot trading if you want to survive long time on this war field.
Hope this will help your trading on equity as well as crypto. I didn't try it on futures. Best of luck all of you. Gazua~!
CMT's ProGo indicatorThis is an experiment. I've never traded with it and won't tell you to. The nuances of how effective this is have yet to be seen.
Shoutout to @BillionaireLau, who very recently posted Larry William's original ProGo indicator. I hypothesized that a few minor changes to values and operations would allow for greater utility and responsiveness. I believe this has been achieved. What we're looking at here appears to offer a new means of spotting divergences. Have fun. To quote BillionaireLau regarding the nature of this indicator:
"ProGo, created by Larry William, (earlier than 2002), is a 2 line graph using daily data.
1. Professional Line (color orange) is a professional Accumulation/Distribution line is constructed by using the change from today's open to today's close.
2. The Public Line (color blue) is done by creating a public accumulation/distribution line that shows the change from yesterdays close to today's open.
The graph is an index of the previous close to open +/- values (public) and then taking a 14 day average which is plotted against a 14 day average of the +/- values of the open to close(pro).
Background color:
Green colored area is where "pro" line crossover line, and the "pro" line is also positive."
William's ProGo indicatorProGo, created by Larry William, (earlier than 2002), is a 2 line graph using daily data.
1. Professional Line (color orange) is a professional Accumulation/Distribution line is constructed by using the change from today's open to today's close.
2. The Public Line (color blue) is done by creating a public accumulation/distribution line that shows the change from yesterdays close to today's open.
The graph is an index of the previous close to open +/- values (public) and then taking a 14 day average which is plotted against a 14 day average of the +/- values of the open to close(pro).
Background color:
Green colored area is where "pro" line crossover "amatuers" line, and the "pro" line is also positive.
Created this for literature review.
Combo Backtest 123 Reversal & Smoothed Williams ADThis is combo strategies for get a cumulative signal.
First strategy
This System was created from the Book "How I Tripled My Money In The
Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies.
The strategy buys at market, if close price is higher than the previous close
during 2 days and the meaning of 9-days Stochastic Slow Oscillator is lower than 50.
The strategy sells at market, if close price is lower than the previous close price
during 2 days and the meaning of 9-days Stochastic Fast Oscillator is higher than 50.
Second strategy
Accumulation is a term used to describe a market controlled by buyers;
whereas distribution is defined by a market controlled by sellers.
Williams recommends trading this indicator based on divergences:
Distribution of the security is indicated when the security is making
a new high and the A/D indicator is failing to make a new high. Sell.
Accumulation of the security is indicated when the security is making
a new low and the A/D indicator is failing to make a new low. Buy.
WARNING:
- For purpose educate only
- This script to change bars colors.
Function Square WaveThis is a script to draw a square wave on the chart, with an indicator for current price.
Markets undergoing Dow Jones or Wyckoff Accumulation/Distribution cycles tend to move in such waves, and if the period of the cycles are detected, a signal for accumulation/distribution phases can be created as an early warning.
Useful inputs:
- Average True Range as the wave height.
- Assumed Wave period as the wave duration.
I divided the current price wave by 2 to make the indicator more visually friendly.
GLHF
- DPT
Combo Backtest 123 Reversal & Klinger Volume Oscillator This is combo strategies for get a cumulative signal.
First strategy
This System was created from the Book "How I Tripled My Money In The
Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies.
The strategy buys at market, if close price is higher than the previous close
during 2 days and the meaning of 9-days Stochastic Slow Oscillator is lower than 50.
The strategy sells at market, if close price is lower than the previous close price
during 2 days and the meaning of 9-days Stochastic Fast Oscillator is higher than 50.
Second strategy
The Klinger Oscillator (KO) was developed by Stephen J. Klinger. Learning
from prior research on volume by such well-known technicians as Joseph Granville,
Larry Williams, and Marc Chaikin, Mr. Klinger set out to develop a volume-based
indicator to help in both short- and long-term analysis.
The KO was developed with two seemingly opposite goals in mind: to be sensitive
enough to signal short-term tops and bottoms, yet accurate enough to reflect the
long-term flow of money into and out of a security.
The KO is based on the following tenets:
Price range (i.e. High - Low) is a measure of movement and volume is the force behind
the movement. The sum of High + Low + Close defines a trend. Accumulation occurs when
today's sum is greater than the previous day's. Conversely, distribution occurs when
today's sum is less than the previous day's. When the sums are equal, the existing trend
is maintained.
Volume produces continuous intra-day changes in price reflecting buying and selling pressure.
The KO quantifies the difference between the number of shares being accumulated and distributed
each day as "volume force". A strong, rising volume force should accompany an uptrend and then
gradually contract over time during the latter stages of the uptrend and the early stages of
the following downtrend. This should be followed by a rising volume force reflecting some
accumulation before a bottom develops.
WARNING:
- For purpose educate only
- This script to change bars colors.
[blackcat] L2 Ehlers Phase Accumulator Cycle Period MeasurerLevel: 2
Background
John F. Ehlers introuced Phase Accumulation technique of cycle period measurement in his "Rocket Science for Traders" chapter 7. It is perhaps the easiest to comprehend. In this technique, John Ehlers measures the phase at each sample by taking the arctangent of the ratio of the Quadrature component to the In-phase component. A delta phase is generated by taking the difference of the phase between successive samples. At each sample Dr. Ehlers then looks backward, adding up the delta phases. When the sum of the delta phases reaches 360 degrees (2*pi in tradingview), we must have passed through one full cycle, on average. The process is repeated for each new sample.
Function
blackcat L2 Ehlers Phase Accumulator Cycle Period Measurer is used to measure Dominant Cycle (DC). This is one of John Ehlers three major methods to measure DC. The Phase Accumulation method of cycle measurement always uses one full cycle’s worth of historical data. This is both an advantage and disadvantage. The advantage is the lag in obtaining the answer scales directly with the cycle period. That is, the measurement of a short cycle period has less lag than the measurement of a longer cycle period. However, the number of samples used in making the measurement means the averaging period is variable with cycle period. Longer averaging reduces the noise level compared to the signal. Therefore, shorter cycle periods necessarily have a higher output Signal-to-Noise Ratio (SNR).
Key Signal
Smooth --> 4 bar WMA w/ 1 bar lag
Detrender --> The amplitude response of a minimum-length HT can be improved by adjusting the filter coefficients by
trial and error. HT does not allow DC component at zero frequency for transformation. So, Detrender is used to remove DC component/ trend component.
Q1 --> Quadrature phase signal
I1 --> In-phase signal
Period --> Dominant Cycle in bars
Pros and Cons
100% John F. Ehlers definition translation of original work, even variable names are the same. This help readers who would like to use pine to read his book. If you had read his works, then you will be quite familiar with my code style.
Remarks
The 2nd script for Blackcat1402 John F. Ehlers Week publication.
Readme
In real life, I am a prolific inventor. I have successfully applied for more than 60 international and regional patents in the past 12 years. But in the past two years or so, I have tried to transfer my creativity to the development of trading strategies. Tradingview is the ideal platform for me. I am selecting and contributing some of the hundreds of scripts to publish in Tradingview community. Welcome everyone to interact with me to discuss these interesting pine scripts.
The scripts posted are categorized into 5 levels according to my efforts or manhours put into these works.
Level 1 : interesting script snippets or distinctive improvement from classic indicators or strategy. Level 1 scripts can usually appear in more complex indicators as a function module or element.
Level 2 : composite indicator/strategy. By selecting or combining several independent or dependent functions or sub indicators in proper way, the composite script exhibits a resonance phenomenon which can filter out noise or fake trading signal to enhance trading confidence level.
Level 3 : comprehensive indicator/strategy. They are simple trading systems based on my strategies. They are commonly containing several or all of entry signal, close signal, stop loss, take profit, re-entry, risk management, and position sizing techniques. Even some interesting fundamental and mass psychological aspects are incorporated.
Level 4 : script snippets or functions that do not disclose source code. Interesting element that can reveal market laws and work as raw material for indicators and strategies. If you find Level 1~2 scripts are helpful, Level 4 is a private version that took me far more efforts to develop.
Level 5 : indicator/strategy that do not disclose source code. private version of Level 3 script with my accumulated script processing skills or a large number of custom functions. I had a private function library built in past two years. Level 5 scripts use many of them to achieve private trading strategy.
MACD-X, More Than MACD by DGTMoving Average Convergence Divergence – MACD
The most popular indicator used in technical analysis, the moving average convergence divergence (MACD), created by Gerald Appel. MACD is a trend-following momentum indicator, designed to reveal changes in the strength, direction, momentum, and duration of a trend in a financial instrument’s price
Historical evolution of MACD,
- Gerald Appel created the MACD line,
- Thomas Aspray added the histogram feature to MACD
- Giorgos E. Siligardos created a leader of MACD
MACD employs two Moving Averages of varying lengths (which are lagging indicators) to identify trend direction and duration. Then, MACD takes the difference in values between those two Moving Averages (MACD Line) and an EMA of those Moving Averages (Signal Line) and plots that difference between the two lines as a histogram which oscillates above and below a center Zero Line. The histogram is used as a good indication of a security's momentum.
Mathematically expressed as;
macd = ma(source, fast_length) – ma(source, slow_length)
signal = ma(macd, signal_length)
histogram = macd – signal
where exponential moving average (ema) is in common use as a moving average (ma)
fast_length = 12
slow_length = 26
signal_length = 9
The MACD indicator is typically good for identifying three types of basic signals ;
Signal Line Crossovers
A Signal Line Crossover is the most common signal produced by the MACD. On the occasions where the MACD Line crosses above or below the Signal Line, that can signify a potentially strong move. The standard interpretation of such an event is a recommendation to buy if the MACD line crosses up through the Signal Line (a "bullish" crossover), or to sell if it crosses down through the Signal Line (a "bearish" crossover). These events are taken as indications that the trend in the financial instrument is about to accelerate in the direction of the crossover.
Zero Line Crossovers
Zero Line Crossovers occur when the MACD Line crossed the Zero Line and either becomes positive (above 0) or negative (below 0). A change from positive to negative MACD is interpreted as "bearish", and from negative to positive as "bullish". Zero crossovers provide evidence of a change in the direction of a trend but less confirmation of its momentum than a signal line crossover
Divergence
Divergence is another signal created by the MACD. Simply, divergence occurs when the MACD and actual price are not in agreement. A "positive divergence" or "bullish divergence" occurs when the price makes a new low but the MACD does not confirm with a new low of its own. A "negative divergence" or "bearish divergence" occurs when the price makes a new high but the MACD does not confirm with a new high of its own. A divergence with respect to price may occur on the MACD line and/or the MACD Histogram
Moving Average Crossovers , another hidden signal that MACD Indicator identifies
Many traders will watch for a short-term moving average to cross above a longer-term moving average and use this to signal increasing upward momentum. This bullish crossover suggests that the price has recently been rising at a faster rate than it has in the past, so it is a common technical buy sign. Conversely, a short-term moving average crossing below a longer-term average is used to illustrate that the asset's price has been moving downward at a faster rate and that it may be a good time to sell.
Moving Average Crossovers in reality is Zero Line Crossovers, the value of the MACD indicator is equal to zero each time the two moving averages cross over each other. For easy interpretation by trades, Zero Line Crossovers are simply described as positive or negative MACD
False signals
Like any forecasting algorithm, the MACD can generate false signals. A false positive, for example, would be a bullish crossover followed by a sudden decline in a financial instrument. A false negative would be a situation where there is bearish crossover, yet the financial instrument accelerated suddenly upwards
What is “MACD-X” and Why it is “More Than MACD”
In its simples form, MACD-X implements variety of different calculation techniques applied to obtain MACD Line, ability to use of variety of different sources , including Volume related sources, and can be plotted along with MACD in the same window and all those features are available and presented within a single indicator, MACD-X
Different calculation techniques lead to different values for MACD Line, as will further discuss below, and as a consequence the signal line and the histogram values will differentiate accordingly. Mathematical calculation of both signal line and the histogram remain the same.
Main features of MACD-X ;
1- Introduces different proven techniques applied on MACD calculation , such as MACD-Histogram, MACD-Leader and MACD-Source, besides the traditional MACD (MACD-TRADITIONAL)
• MACD-Traditional , by Gerald Appel
It is the MACD that we know, stated as traditional just to avoid confusion with other techniques used with this study
• MACD-Histogram , by Thomas Aspray
The MACD-Histogram measures the distance between MACD and its signal line (the 9-day EMA of MACD). Aspray developed the MACD-Histogram to anticipate signal line crossovers in MACD. Because MACD uses moving averages and moving averages lag price, signal line crossovers can come late and affect the reward-to-risk ratio of a trade. Bullish or bearish divergences in the MACD-Histogram can alert chartists to an imminent signal line crossover in MACD
The MACD-Histogram represents the difference between MACD and its 9-day EMA, the signal line. Mathematically,
macdx = macd - ma(macd, signal_length)
Aspray's contribution served as a way to anticipate (and therefore cut down on lag) possible MACD crossovers which are a fundamental part of the indicator.
Here come a question, what if repeat the same calculations once more (macdh2 = macdh - ma(macdh, signal_length), will it be even better, this question will remain to be tested
• MACD-Leader , by Giorgos E. Siligardos, PhD
MACD Leader has the ability to lead MACD at critical situations. Almost all smoothing methods encounter in technical analysis are based on a relative-weighted sum of past prices, and the Leader is no exception. The concealed weights of MACD Leader are such that more relative weight is used in the more recent prices than the respective weights used by the components of MACD. In effect, the Leader expresses more changes in average price dynamics for the recent price movement than MACD, thus eventually leading MACD, especially when significant trend changes are about to take place.
Siligardos creates two less-laggard moving averages indicators in its formula using the same periods as follows
Indicator1 = ma(source, fast_length) + ma(source - ma(source, fast_length), fast_length)
Indicator2 = ma(source, slow_length) + ma(source - ma(source, slow_length), slow_length)
and then take the difference:
Indicator1 - Indicator2
The result is a new MACD Leader indicator
macdx = macd + ma(source - fast_ma, fast_length) - ma(source - slow_ma, slow_length)
• MACD-Source , a custom experimental interpretation of mine ,
MACD Source, presents an application of MACD that evaluates Source/MA Ratio, relatively with less lag, as a basis for MACD Line, also can be expressed as source convergence/divergence to its moving average. Among the various techniques for removing the lag between price and moving average (MA) of the price, one in particular stands out: the addition to the moving average of a portion of the difference between the price and MA. MACD Source, is based on signal length mean of the difference between Source and average value of shot length and long length moving average of the source (Source/MA Ratio), where the source is actual value and hence no lag and relatively less lag with the average value of moving average of the source . Mathematically expressed as,
macdx = ma(source - avg( ma(source, fast_length), ma(source, slow_length) ), signal_length)
MACD Source provides relatively early crossovers comparing to MACD and better momentum direction indications, assuming the lengths are set to same values
For further details, you are invited to check the following two studies, where the first seeds were sown of the MACD-Source idea
Price Distance to its Moving Averages study, adapts the idea of “Prices high above the moving average (MA) or low below it are likely to be remedied in the future by a reverse price movement", presented in an article by Denis Alajbeg, Zoran Bubas and Dina Vasic published in International Journal of Economics, Commerce and Management
First MACD like interpretation comes with the second study named as “ P-MACD ”, where P stands for price, P-MACD study attempts to display relationship between Price and its 20 and 200-period moving average. Calculations with P-MACD were based on price distance (convergence/divergence) to its 200-period moving average, and moving average convergence/divergence of 20-period moving average to 200-period moving average of price.
Now as explained above, MACD Source is a one adapted with traditional MACD, where Source stands for Price, Volume Indicator etc, any source applicable with MACD concept
2- Allows usage of variety of different sources, including Volume related indicators
The most common usage of Source for MACD calculation is close value of the financial instruments price. As an experimental approach, this study will allow source to be selected as one of the following series;
• Current Close Price (close)
• Average of High, Low, and Close Price (hlc3)
• On Balance Volume (obv)
• Accumulation Distribution (accdist)
• Price Volume Trend (pvt)
Where,
-Current Close Price and Average of High, Low, and Close Price are price actions of the financial instrument
- Accumulation Distribution is a volume based indicator designed to measure underlying supply and demand
- On Balance Volume (OBV) , is a momentum indicator that measures positive and negative volume flow
- Price Volume Trend (PVT) is a momentum based indicator used to measure money flow
3- Can be plotted along with MACD in the same window using the same scaling
Default setting of MACD-X will display MACD-Source with Current Close Price as a source and traditional MACD can be plotted eighter as a companion of MACD-X or can be selected to be plotted alone.
Applying both will add ability to compare, or use as a confirmation of one other
In case, traditional MACD Is plotted along with MACD-X to avoid misinterpreting, the lines plotted, the area between MACD-X Line and Signal-X Line is highlighted automatically, even if the highlight option not selected. Otherwise highlight will be applied only if that option selected
4- 4C Histogram
Histogram is plotted with four colors to emphasize the momentum and direction
5- Customizable
Additional to ability of selecting Calculation Method, Source, plotting along with MACD, there are few other option that allows users to customize the MACD-X indicator
Lengths are configurable, default values are set as 12, 26, 9 respectively for fast, slow and smoothing length. Setting lengths to 8,21,5 respectively Is worth checking, slower length moving averages will lead to less lag and earlier reaction to price actions but yet requires a caution and back testing before applying
Highlight the area between MACD-X Line and Signal-X Line, with colors emphasising the direction
Label can be added to display Calculation Method, Source and Length settings, the aim of this label is to server only as a reminder to trades to be aware of settings while they are occupied with charts, analysis etc.
Here comes another question, which is of more importance having the reminder or having the indicators with multi timeframe feature? Build-in Multi Time Frame features of Pine is not supported when labels and lines introduced in the script, there are other methods but brings complexity. To be studied further, this version will be with labels for time being.
Epilogue
MACD-X is an alternative variant of MACD, the insight/signals provided by MACD are also applicable to MACD-X with early and clear warnings for the changes in the trend.
If MACD is essential to your analysis, then it is my guess that after using the MACD-X for a while and familiarizing yourself with its unique character and personality, you will make it an inseparable companion to other indicators in your charts.
The various signals generated by MACD/MACD-X are easily interpreted and very few indicators in technical analysis have proved to be more reliable than the MACD, and this relatively simple indicator can quickly be incorporated into any short-term trading strategy
Disclaimer : Trading success is all about following your trading strategy and the indicators should fit within your trading strategy, and not to be traded upon solely
The script is for informational and educational purposes only. Use of the script does not constitutes professional and/or financial advice. You alone the sole responsibility of evaluating the script output and risks associated with the use of the script. In exchange for using the script, you agree not to hold dgtrd TradingView user liable for any possible claim for damages arising from any decision you make based on use of the script
