AndyB Combined ATR and Outside BarsAndyB Combined ATR and Outside Bars Indicator
This indicator integrates ATR (Average True Range) with outside bars to provide traders with a unique perspective on market momentum and volatility. By focusing exclusively on ATR and outside bars, this tool highlights key price action events that may signal trend continuation, reversals, or points of interest (POIs) for potential entry or exit points.
What Makes It Unique: This indicator stands out by combining two powerful concepts:
ATR Bars: ATR is a common volatility measure, but this indicator leverages it to detect price movements that exceed expected volatility ranges. When the price closes above or below the ATR-based thresholds, the indicator generates signals to help traders spot potential momentum shifts.
Outside Bars: Outside bars are price action patterns where a candle’s high surpasses the previous candle’s high, and its low dips below the previous candle’s low. These bars often indicate heightened volatility and can be a strong indicator of potential breakouts or reversals.
The combination of ATR and outside bars provides a unique approach to volatility-based trading, giving traders insight into market dynamics that may not be evident through standard trend or momentum indicators.
How to Use It:
With Price Action or Moving Averages: This indicator is most effective when used alongside price action analysis or moving averages. Traders should look for higher highs and lower lows to confirm potential long or short signals.
Momentum and Reversal Detection: The indicator can be used to confirm momentum in a given direction, flagging potential reversal points or continuation signals based on the ATR’s volatility thresholds and outside bar price patterns.
This indicator is designed to focus purely on ATR-based signals and outside bar patterns, offering traders a clear and concise tool for analyzing market volatility and price action in one simplified indicator.
Pesquisar nos scripts por "averages"
[blackcat] L2 Twisted Pair IndicatorOn the grand stage of the financial market, every trader is looking for a partner who can lead them to dance the tango well. The "Twisted Pair" indicator is that partner who dances gracefully in the market fluctuations. It weaves the rhythm of the market with two lines, helping traders to find the rhythm in the market's dance floor.
Imagine when the market is as calm as water, the "Twisted Pair" is like two ribbons tightly intertwined. They almost overlap on the chart, as if whispering: "Now, let's enjoy these quiet dance steps." This is the market consolidation period, the price fluctuation is not significant, traders can relax and slowly savor every detail of the market.
Now, let's describe the market logic of this code in natural language:
- **HJ_1**: This is the foundation of the market dance steps, by calculating the average price and trading volume, setting the tone for the market rhythm.
- **HJ_2** and **HJ_3**: These two lines are the arms of the dance partner, they help traders identify the long-term trend of the market through smoothing.
- **HJ_4**: This is a magnifying glass for market sentiment, it reveals the tension and excitement of the market by calculating the short-term deviation of the price.
- **A7** and **A9**: These two lines are the guide to the dance steps, they separate when the market volatility increases, guiding the traders in the right direction.
- **WATCH**: This is the signal light of the dance, when the two lines overlap, the market is calm; when they separate, the market is active.
The "Twisted Pair" indicator is like a carefully choreographed dance, it allows traders to find their own rhythm in the market dance floor, whether in a calm slow dance or a passionate tango. Remember, the market is always changing, and the "Twisted Pair" is the perfect dance partner that can lead you to dance out brilliant steps.
The script of this "Twisted Pair" uses three different types of moving averages: EMA (Exponential Moving Average), DEMA (Double EMA), and TEMA (Triple EMA). These types can be selected by the user through exchange input.
Here are the main functions of this code:
1. Defined the DEMA and TEMA functions: These two functions are used to calculate the corresponding moving averages. EMA is the exponential moving average, which is a special type of moving average that gives more weight to recent data. In the first paragraph, ema1 is the EMA of "length", and ema2 is the EMA of ema1. DEMA is 2 times of ema1 minus ema2.
2. Let users choose to use EMA, DEMA or TEMA: This part of the code provides an option for users to choose which type of moving average they want to use.
3. Defined an algorithm called "Twisted Pair algorithm": This part of the code defines a complex algorithm to calculate a value called "HJ". This algorithm involves various complex calculations and applications of EMA, DEMA, TEMA.
4. Plotting charts: The following code is used to plot charts on Tradingview. It uses the plot function to draw lines, the plotcandle function to draw candle (K-line) charts, and yellow and red to represent different conditions.
5. Specify colors: The last two lines of code use yellow and red K-line charts to represent the conditions of HJ_7. If the conditions of HJ_7 are met, the color of the K-line chart will change to the corresponding color.
HTF Trend Filter - Dynamic SmoothingSummary of the HTF Trend Filter
The Higher Time Frame (HTF) Trend Filter is a cutting-edge tool crafted for traders who want to scan moving average trend lines time efficiently. At its core, it harnesses the power of dynamic smoothing to present a sleek moving average line regardless of the time frame you’re on. Here's a glimpse of the advantages you unlock with the HTF trend filter:
Dynamic Smoother: Ever been irked by jagged lines on your chart? With the dynamic smoother, those days are gone. The smoother streamlines HTF moving average line on your current lower time frame chart.
Time Efficiency: Time is of the essence in trading. With this tool, you can nimbly toggle between time charts without the hassle of readjusting input parameters, ensuring your screening process remains unhindered.
Features of the Script
Variety of Moving Averages: The script caters to different trading styles by offering a plethora of moving average types, ranging from the classic SMA and EMA to the innovative Hull and McGinley Dynamic MAs.
Dynamic Smoothing: This is the script's pièce de résistance. The dynamic smoothing factor is ingeniously derived by taking the ratio of minutes of the higher time frame to the current time frame. This ensures the moving average remains fluid and consistent across different time frames, eliminating the common pitfalls of jagged moving averages.
Reversal Indicators: It includes a reversal indicator. Green circles pinpoint the start of a potential uptrend, while red ones signify a potential downtrend.
Customizable Alerts: To ensure you never miss a beat, the script is equipped with customizable alert conditions.
Trading Idea
The essence of trading lies in confirming assumptions and validating trends. The HTF Trend Dynamic Smoother positions itself as a potential game-changer in this domain. One could consider using the HTF trend dynamic smoother as a supplementary confirmation tool alongside other primary indicators. For instance, if you're plotting a moving average on a lower time frame, toggling the HTF smoother can offer a broader perspective of the trend from a higher time frame. By ensuring alignment between these perspectives, you could potentially trade with increased confidence, reinforcing your lower time frame strategies with higher time frame confirmations. It's worth noting, however, that while this method can offer additional layers of information and validation, it doesn't replace due diligence. Every trade decision should be the culmination of thorough analysis, and no tool should be solely relied upon for decision-making.
Limitations
While the HTF Trend Filter is an exceptional tool, like all tools, it has its constraints. Lower Time Frame Dependency: For the indicator to function optimally, it's paramount to ensure that the time frame open is always lower (or equal) than the one selected in the input parameters. This limitation is crucial to remember as the dynamic smoother's accuracy hinges on this condition.
In conclusion, the HTF Trend Filter - Dynamic Smoothing is a remarkable blend of innovation and efficiency, tailored for traders who demand fast screening of higher time frame MA trends. Due to it simplistic design it gives a user-friendly experience. However, always remember the golden rule of trading: utilize tools as part of a comprehensive strategy, never in isolation.
Moving Average Rainbow (Stormer)This strategy is based and shown by trader and investor Alexandre Wolwacz "Stormer".
Overview
The strategy uses 12 moving averages (default EMA) to identify trends and generate trading signals opening positions.
Allowing to select the type of moving average and length to be used.
The conditions includes relationship between moving averages, the position of the current price relative to the moving averages, and the occurrence of certain price patterns.
Calculation
The mean moving averages is calculated by adding all the 12 moving averages and dividing by 12, the value is used to help to identify trend and possible condition to open position.
The 12 moving averages is spliced by 3 ranges, initial range (moving average lines 1 to 4), middle range (moving average lines 5 to 8) and end range (moving average lines 9 to 12). These ranges helps to identify potential trend and market turn over.
The moving average touch price is a relationship between the low price (uptrend) or high price (downtrend) with the moving average lines, it identifies where the price (low/high) has reached the the moving average line. Fetching the value to help for opening position, set stop loss and take profit.
Since the stop loss is based and set from the previous moving average touch price value, when position is about to be open and setting the stop loss value, there is a verification to check both current and previous moving average touch price to recalculate the stop loss value.
The turnover trend checks for a possible market turnover event, setting up a new profit target, this setting when enabled is to be helpful when a turnover occurs against the position to exit position with some profit based on highest high price if long or lowest low price if short.
The turnover signal is similar to turnover trend. The difference is that when this setting is enabled and it triggers, it simply exit the current position and opens up a reverse position, long goes short and short goes long. And there is an complement optional that checks current price exit profitable.
Entry Position
Long Position:
Price is higher than the mean moving averages. Meaning possible uptrend.
The lines of the middle range from the moving averages are in increasing order. Meaning possible uptrend.
The current high pierced up previous high.
Fetch the previous value of the moving average touch price. Meaning the low price has touched one of the moving average lines, which that value is conditioning to open position.
Short Position:
Price is lower than the mean moving averages. Meaning possible downtrend.
The lines of the middle range from the moving averages are in decreasing order. Meaning possible downtrend.
The current low pierced down previous low.
Fetch the previous value of the moving average touch price. Meaning the high price has touched one of the moving average lines, which that value is conditioning to open position.
Risk Management
Stop Loss:
The stop loss is based from the previous moving average touch price value, high price for short and low price for long or occurs an verification to check for both current and previous moving average touch price value and a recalculation is done to set the stop loss.
Take Profit:
According to the author, the profit target should be at least 1:1.6 the risk, so to have the strategy mathematically positive.
The profit target is configured input, can be increased or decreased.
It calculates the take profit based on the price of the stop loss with the profit target input.
Turnover Trend
Long Position:
The moving averages initial range lines signals a possible market turnover. Meaning long might be going short.
Fetches the highest high hit since the opening of the position, setting that value to the new profit target.
Short Position:
The moving averages initial range lines signals a possible market turnover. Meaning short might be going long.
Fetches the lowest low hit since the opening of the position, setting that value to the new profit target.
GKD-C Nyquist Moving Average (NMA) MACD [Loxx]Giga Kaleidoscope GKD-C Nyquist Moving Average (NMA) MACD is a Confirmation module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ Giga Kaleidoscope Modularized Trading System
What is Loxx's "Giga Kaleidoscope Modularized Trading System"?
The Giga Kaleidoscope Modularized Trading System is a trading system built on the philosophy of the NNFX (No Nonsense Forex) algorithmic trading.
What is the NNFX algorithmic trading strategy?
The NNFX (No-Nonsense Forex) trading system is a comprehensive approach to Forex trading that is designed to simplify the process and remove the confusion and complexity that often surrounds trading. The system was developed by a Forex trader who goes by the pseudonym "VP" and has gained a significant following in the Forex community.
The NNFX trading system is based on a set of rules and guidelines that help traders make objective and informed decisions. These rules cover all aspects of trading, including market analysis, trade entry, stop loss placement, and trade management.
Here are the main components of the NNFX trading system:
1. Trading Philosophy: The NNFX trading system is based on the idea that successful trading requires a comprehensive understanding of the market, objective analysis, and strict risk management. The system aims to remove subjective elements from trading and focuses on objective rules and guidelines.
2. Technical Analysis: The NNFX trading system relies heavily on technical analysis and uses a range of indicators to identify high-probability trading opportunities. The system uses a combination of trend-following and mean-reverting strategies to identify trades.
3. Market Structure: The NNFX trading system emphasizes the importance of understanding the market structure, including price action, support and resistance levels, and market cycles. The system uses a range of tools to identify the market structure, including trend lines, channels, and moving averages.
4. Trade Entry: The NNFX trading system has strict rules for trade entry. The system uses a combination of technical indicators to identify high-probability trades, and traders must meet specific criteria to enter a trade.
5. Stop Loss Placement: The NNFX trading system places a significant emphasis on risk management and requires traders to place a stop loss order on every trade. The system uses a combination of technical analysis and market structure to determine the appropriate stop loss level.
6. Trade Management: The NNFX trading system has specific rules for managing open trades. The system aims to minimize risk and maximize profit by using a combination of trailing stops, take profit levels, and position sizing.
Overall, the NNFX trading system is designed to be a straightforward and easy-to-follow approach to Forex trading that can be applied by traders of all skill levels.
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v1.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data between modules. Data is passed between each module as described below:
GKD-B => GKD-V => GKD-C(1) => GKD-C(2) => GKD-C(Continuation) => GKD-E => GKD-BT
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Strategy with 1-3 take profits, trailing stop loss, multiple types of PnL volatility, and 2 backtesting styles
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Nyquist Moving Average (NMA) MACD as shown on the chart above
Confirmation 2: Williams Percent Range
Continuation: Fisher Transform
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD protocol chain.
Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm)
Standard Entry
1. GKD-C Confirmation 1 Signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Continuation Entry
1. Standard Entry, Baseline Entry, or Pullback; entry triggered previously
2. GKD-B Baseline hasn't crossed since entry signal trigger
3. GKD-C Confirmation Continuation Indicator signals
4. GKD-C Confirmation 1 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 2 agrees
1-Candle Rule Standard Entry
1. GKD-C Confirmation 1 signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
1-Candle Rule Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume Agrees
1-Candle Rule Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close)
2. GKD-B Volatility/Volume agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
PullBack Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is beyond 1.0x Volatility of Baseline
Next Candle:
1. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
2. GKD-C Confirmation 1 agrees
3. GKD-C Confirmation 2 agrees
4. GKD-V Volatility/Volume Agrees
]█ Setting up the GKD
The GKD system involves chaining indicators together. These are the steps to set this up.
Use a GKD-C indicator alone on a chart
1. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Simple"
Use a GKD-V indicator alone on a chart
**nothing, it's already useable on the chart without any settings changes
Use a GKD-B indicator alone on a chart
**nothing, it's already useable on the chart without any settings changes
Baseline (Baseline, Backtest)
1. Import the GKD-B Baseline into the GKD-BT Backtest: "Input into Volatility/Volume or Backtest (Baseline testing)"
2. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Baseline"
Volatility/Volume (Volatility/Volume, Backte st)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Solo"
2. Inside the GKD-V indicator, change the "Signal Type" setting to "Crossing" (neither traditional nor both can be backtested)
3. Import the GKD-V indicator into the GKD-BT Backtest: "Input into C1 or Backtest"
4. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Volatility/Volume"
5. Inside the GKD-BT Backtest, a) change the setting "Backtest Type" to "Trading" if using a directional GKD-V indicator; or, b) change the setting "Backtest Type" to "Full" if using a directional or non-directional GKD-V indicator (non-directional GKD-V can only test Longs and Shorts separately)
6. If "Backtest Type" is set to "Full": Inside the GKD-BT Backtest, change the setting "Backtest Side" to "Long" or "Short
7. If "Backtest Type" is set to "Full": To allow the system to open multiple orders at one time so you test all Longs or Shorts, open the GKD-BT Backtest, click the tab "Properties" and then insert a value of something like 10 orders into the "Pyramiding" settings. This will allow 10 orders to be opened at one time which should be enough to catch all possible Longs or Shorts.
Solo Confirmation Simple (Confirmation, Backtest)
1. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Simple"
1. Import the GKD-C indicator into the GKD-BT Backtest: "Input into Backtest"
2. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Solo Confirmation Simple"
Solo Confirmation Complex without Exits (Baseline, Volatility/Volume, Confirmation, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Complex"
4. Import the GKD-V indicator into the GKD-C indicator: "Input into C1 or Backtest"
5. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full wo/ Exits"
6. Import the GKD-C into the GKD-BT Backtest: "Input into Exit"
Solo Confirmation Complex with Exits (Baseline, Volatility/Volume, Confirmation, Exit, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Complex"
4. Import the GKD-V indicator into the GKD-C indicator: "Input into C1 or Backtest"
5. Import the GKD-C indicator into the GKD-E indicator: "Input into Exit"
6. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full w/ Exits"
7. Import the GKD-E into the GKD-BT Backtest: "Input into Backtest"
Full GKD without Exits (Baseline, Volatility/Volume, Confirmation 1, Confirmation 2, Continuation, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C 1 indicator, change the "Confirmation Type" setting to "Confirmation 1"
4. Import the GKD-V indicator into the GKD-C 1 indicator: "Input into C1 or Backtest"
5. Inside the GKD-C 2 indicator, change the "Confirmation Type" setting to "Confirmation 2"
6. Import the GKD-C 1 indicator into the GKD-C 2 indicator: "Input into C2"
7. Inside the GKD-C Continuation indicator, change the "Confirmation Type" setting to "Continuation"
8. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full wo/ Exits"
9. Import the GKD-E into the GKD-BT Backtest: "Input into Exit"
Full GKD with Exits (Baseline, Volatility/Volume, Confirmation 1, Confirmation 2, Continuation, Exit, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C 1 indicator, change the "Confirmation Type" setting to "Confirmation 1"
4. Import the GKD-V indicator into the GKD-C 1 indicator: "Input into C1 or Backtest"
5. Inside the GKD-C 2 indicator, change the "Confirmation Type" setting to "Confirmation 2"
6. Import the GKD-C 1 indicator into the GKD-C 2 indicator: "Input into C2"
7. Inside the GKD-C Continuation indicator, change the "Confirmation Type" setting to "Continuation"
8. Import the GKD-C Continuation indicator into the GKD-E indicator: "Input into Exit"
9. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full w/ Exits"
10. Import the GKD-E into the GKD-BT Backtest: "Input into Backtest"
Baseline + Volatility/Volume (Baseline, Volatility/Volume, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Baseline + Volatility/Volume"
2. Inside the GKD-V indicator, make sure the "Signal Type" setting is set to "Traditional"
3. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
4. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Baseline + Volatility/Volume"
5. Import the GKD-V into the GKD-BT Backtest: "Input into C1 or Backtest"
6. Inside the GKD-BT Backtest, change the setting "Backtest Type" to "Full". For this backtest, you must test Longs and Shorts separately
7. To allow the system to open multiple orders at one time so you can test all Longs or Shorts, open the GKD-BT Backtest, click the tab "Properties" and then insert a value of something like 10 orders into the "Pyramiding" settings. This will allow 10 orders to be opened at one time which should be enough to catch all possible Longs or Shorts.
█ GKD-C Nyquist Moving Average (NMA) MACD
What is the Nyquist Moving Average (NMA) MACD?
The Nyquist Moving Average (NMA) is a lesser-known type of moving average in the field of technical analysis. It is designed to reduce lag and improve the responsiveness of moving averages. The name "Nyquist" likely originates from the Nyquist-Shannon sampling theorem, which states that a continuous-time signal can be accurately reconstructed from its discrete samples if the sampling rate is at least twice the highest frequency component of the original signal. The NMA tries to apply this concept to moving averages by reducing the time lag and improving the signal's fidelity to the underlying price movement.
The Nyquist Moving Average is calculated using a primary period (nper) and a secondary period (smper). A lambda value is determined by the ratio of nper to smper, and if lambda is less than 2, it is adjusted to meet the Nyquist-Shannon sampling theorem's requirements. Then, an alpha value is calculated using lambda and nper. Two moving averages are calculated using the specified type (e.g., simple, exponential, etc.), source data, and the primary and secondary periods. The final NMA is obtained using the alpha value and the two moving averages.
While the NMA is not as popular or widely used as other types of moving averages, like the Simple Moving Average (SMA) or the Exponential Moving Average (EMA), it can still be a valuable tool for technical analysts and traders looking for a more responsive moving average with reduced lag.
As with any technical indicator, it is essential to use the Nyquist Moving Average in conjunction with other indicators and analysis methods to maximize its effectiveness and reduce the risk of false signals.
What is the MACD?
The Moving Average Convergence Divergence (MACD) is a widely used technical analysis indicator developed by Gerald Appel in the late 1970s. The MACD is a momentum oscillator that helps traders identify changes in the strength, direction, and duration of a market trend. It is calculated using exponential moving averages (EMAs) and can provide buy and sell signals based on the interaction between the MACD line, the signal line, and the MACD histogram.
The MACD consists of three components:
1. MACD Line: The MACD line is calculated by subtracting a longer-period EMA from a shorter-period EMA. Typically, a 26-day EMA is subtracted from a 12-day EMA. The resulting line represents the difference between the two EMAs and highlights the momentum of the underlying asset.
2. Signal Line: The signal line is a 9-day EMA of the MACD line. It is used to smooth out the fluctuations of the MACD line and generate trading signals based on crossovers between the MACD line and the signal line.
3. MACD Histogram: The MACD histogram is a visual representation of the difference between the MACD line and the signal line. It is plotted as vertical bars above or below the zero line, with bars above the zero line indicating positive momentum and bars below the zero line indicating negative momentum.
The MACD generates trading signals based on the interaction between the MACD line, the signal line, and the MACD histogram:
1. Bullish Crossover: A buy signal is generated when the MACD line crosses above the signal line, indicating that the short-term momentum is stronger than the long-term momentum and suggesting a potential upward move in the asset's price.
2. Bearish Crossover: A sell signal is generated when the MACD line crosses below the signal line, indicating that the short-term momentum is weaker than the long-term momentum and suggesting a potential downward move in the asset's price.
3. Histogram Divergence: The MACD histogram can also provide signals based on divergences between the histogram and the asset's price. A bullish divergence occurs when the asset's price forms a lower low while the histogram forms a higher low, suggesting a potential reversal to the upside. A bearish divergence occurs when the asset's price forms a higher high while the histogram forms a lower high, suggesting a potential reversal to the downside.
It is essential to use the MACD in conjunction with other technical analysis tools and methods to increase the reliability of the signals and reduce the risk of false signals. Additionally, the MACD is a lagging indicator, meaning it is based on historical data and may not always provide timely signals.
What is the Nyquist Moving Average (NMA) MACD
This indicator uses the Nyquist Moving averge in the MACD caculation instead of the typical EMA.
Requirements
Inputs
Confirmation 1: GKD-V Volatility / Volume indicator
Confirmation 2: GKD-C Confirmation indicator
Continuation: GKD-C Confirmation indicator
Solo Confirmation Simple: GKD-B Baseline
Solo Confirmation Complex: GKD-V Volatility / Volume indicator
Solo Confirmation Super Complex: GKD-V Volatility / Volume indicator
Stacked 1: None
Stacked 2+: GKD-C, GKD-V, or GKD-B Stacked 1
Outputs
Confirmation 1: GKD-C Confirmation 2 indicator
Confirmation 2: GKD-C Continuation indicator
Continuation: GKD-E Exit indicator
Solo Confirmation Simple: GKD-BT Backtest
Solo Confirmation Complex: GKD-BT Backtest or GKD-E Exit indicator
Solo Confirmation Super Complex: GKD-C Continuation indicator
Stacked 1: GKD-C, GKD-V, or GKD-B Stacked 2+
Stacked 2+: GKD-C, GKD-V, or GKD-B Stacked 2+ or GKD-BT Backtest
Additional features will be added in future releases.
+ Awesome OscillatorHi again. I have another indicator that I think is pretty neat.
I had the idea of creating an Awesome Oscillator for my Ultimate MA, just to see what kind of signals it might produce. If you're not familiar with my UMA you should go take a look at it, but essentially it is just an average of eight different length MAs, and if you're not familiar with the Awesome Oscillator, it is simply a comparison of the gap between two different moving averages (traditionally a 5 and 34 SMA) plotted as a histogram below the price chart. The two UMAs I was comparing in this version of the AO were the Hull and Simple. It looked okay, but I thought due to the nature of the movements of these MAs, that it was necessary to add something to this indicator in order to validate its creation and make it truly useful
I came to the idea of simply comparing the closing price of the asset on the chart to both the Awesome Oscillator moving averages. What this effectively does is gives you a representation of the moving averages on the chart (assuming you are using those same MAs) as an oscillator below the chart, enabling you to remove the moving averages from your price chart (obviously if you so choose). For me, I like this because fewer things on the chart makes it easier for me to see the price action and structure of the market clearly, or add something like a tWAP or two.
So, like, "how exactly would I use this indicator?"" you're probably asking.
First off: the Awesome Oscillator. By default it is a faintly shaded area, and is the least obvious part of the indicator.
Second: the plotted line. This is what I call the baseline (if you're familiar with NNFX, then you know what this is). It's basically your bias moving average (this means it defines, based on its lookback or length, whether momentum is bullish, bearish or ranging). In the case of the oscillator though, the ZERO line represents the baseline, and the oscillating line represents price in relation to it. If the line is above the zero line then price is above the moving average, and vice versa if it's below. The farther from the center line the baseline price is the greater the volatility,
Third: the histogram. This is the faster moving average, and same rules apply to it as your baseline. You can think of your fast moving average as a trade entry trigger, or an exit. It shows more immediate momentum shifts.
What's interesting about the relationships of all three of these things is that you don't actually NEED all three displayed. Because the Awesome Oscillator is a relation of your two moving averages, and the baseline and histogram are representational of the price relative to those two moving averages, you will notice that when the histogram (fast MA) flips up or down is the same exact time that the baseline price dips into the AO. The AO is effectively a moving average on that. So you can run this with just the AO and Baseline, or just the Baseline and fast MA histogram. To get started, I might recommend keeping your moving averages that you use on the chart just so you can see how this indicator works.
Both the fast MA and Baseline will show nice divergences (divergence indicator is added if you want to use it). And I've added Donchian Channels as upper and lower bounds that act neatly as support or resistance (especially effective if you're using my UMA with Bollinger Bands, or Magic Carpet Bands).
I've also done the usual colored candles thing, which gives you another great reason to get the moving averages off your chart. There are of course alerts for conditions that one might need to be alerted to as well.
Below are some images of different ways you might set these up using the default moving average/baseline settings. In all of these I've left the moving averages on the price chart (with the addition of a 233 SMA) so you can see the relationship between the indicators.
Right here is the indicator set up with just the awesome oscillator and baseline price. Gives a cleaner overall look. You can see that every time the baseline crosses the awesome oscillator is when price crosses the 8 SMA. Candle colors are based on if candle closes above baseline or below.
This is the indicator set up without the awesome oscillator. Here you can see candle closes over the 8 SMA (fast moving average) are shown by the histogram. Candle coloring is still the same as the above image.
This image looks identical to the first, except that the candle coloring is different. This time it is based on the 8 SMA (same as the baseline entering the awesome oscillator).
And the final example image. This one depicts the awesome oscillator and the fast moving average histogram. Candle coloring is based on the awesome oscillator. This can be a great way to visualize momentum because the awesome oscillator is depicting the crossing of the moving averages. A lot of people poo-poo moving average crosses, but I'd say they're wrong. Well, they're right and wrong. Depends on the MAs you're using. The power in moving average crosses is in their ability to show bullish or bearish momentum (or ranging behavior if they continually cross over each other). If you're using slow moving averages, then crosses are often very late (hence so many people who don't know saying, "but moving average crosses are too laggy". Here you might try changing these and having the baseline be faster than the UMA, and actually plot on chart the UMA (or some other moving average). These are just some thoughts.
Anyway, I hope this indicator proves useful to you all. I think for anyone looking to look at price action a bit more, but is used to using moving averages, this could be a really useful indicator. Most oscillating indicators (if not all) are built around moving averages, but they're never explained in such a way as I'm explaining how this one works (I don't think). I think knowing this could help many traders come to a deeper understanding of what the indicator they're using is actually doing.
On Chart Anticipated Moving Average Crossover IndicatorIntroducing the on chart moving average crossover indicator.
This is my On Chart Pinescript implementation of the Anticipated Simple Moving Average Crossover idea.
This indicator plots 6 user defined moving averages.
It also plots the 5 price levels required on the next close to cross a user selected moving average with the 5 other user defined moving averages
It also gives signals of anticipated moving average crosses as arrows on chart and also as tradingview alerts with a very high degree of accuracy
Much respect to the creator of the original idea Mr. Dimitris Tsokakis
Moving Averages
A moving average simplifies price data by smoothing it out by averaging closing prices and creating one flowing line which makes seeing the trend easier.
Moving averages can work well in strong trending conditions, but poorly in choppy or ranging conditions.
Adjusting the time frame can remedy this problem temporarily, although at some point, these issues are likely to occur regardless of the time frame chosen for the moving average(s).
While Exponential moving averages react quicker to price changes than simple moving averages. In some cases, this may be good, and in others, it may cause false signals.
Moving averages with a shorter look back period (20 days, for example) will also respond quicker to price changes than an average with a longer look back period (200 days).
Trading Strategies — Moving Average Crossovers
Moving average crossovers are a popular strategy for both entries and exits. MAs can also highlight areas of potential support or resistance.
The first type is a price crossover, which is when the price crosses above or below a moving average to signal a potential change in trend.
Another strategy is to apply two moving averages to a chart: one longer and one shorter.
When the shorter-term MA crosses above the longer-term MA, it's a buy signal, as it indicates that the trend is shifting up. This is known as a "golden cross."
Meanwhile, when the shorter-term MA crosses below the longer-term MA, it's a sell signal, as it indicates that the trend is shifting down. This is known as a "dead/death cross."
MA and MA Cross Strategy Disadvantages
Moving averages are calculated based on historical data, and while this may appear predictive nothing about the calculation is predictive in nature.
Moving averages are always based on historical data and simply show the average price over a certain time period.
Therefore, results using moving averages can be quite random.
At times, the market seems to respect MA support/resistance and trade signals, and at other times, it shows these indicators no respect.
One major problem is that, if the price action becomes choppy, the price may swing back and forth, generating multiple trend reversal or trade signals.
When this occurs, it's best to step aside or utilize another indicator to help clarify the trend.
The same thing can occur with MA crossovers when the MAs get "tangled up" for a period of time during periods of consolidation, triggering multiple losing trades.
Ensure you use a robust risk management system to avoid getting "Chopped Up" or "Whip Sawed" during these periods.
Trend Thrust Indicator - RafkaThis indicator defines the impact of volume on the volume-weighted moving average, emphasizing trends with greater volume.
What determines a security’s value? Price is the agreement to exchange despite the possible disagreement in value. Price is the conviction, emotion, and volition of investors. It is not a constant but is influenced by information, opinions, and emotions over time. Volume represents this degree of conviction and is the embodiment of information and opinions flowing through investor channels. It is the asymmetry between the volume being forced through supply (offers) and demand (bids) that facilitates price change. Quantifying the extent of asymmetry between price trends and the corresponding volume flows is a primary objective of volume analysis. Volume analysis research reveals that volume often leads price but may also be used to confirm the present price trend.
Trend thrust indicator
The trend thrust indicator (TTI), an enhanced version of the volume-weighted moving average convergence/divergence (VW-Macd) indicator, was introduced in Buff Pelz Dormeier's book 'Investing With Volume Analysis'. The TTI uses a volume multiplier in unique ways to exaggerate the impact of volume on volume-weighted moving averages. Like the VW-Macd, the TTI uses volume-weighted moving averages as opposed to exponential moving averages. Volume-weighted averages weigh closing prices proportionally to the volume traded during each time period, so the TTI gives greater emphasis to those price trends with greater volume and less emphasis to time periods with lighter volume. In the February 2001 issue of Stocks & Commodities, I showed that volume-weighted moving averages (Buff averages, or Vwmas) improve responsiveness while increasing reliability of simple moving averages.
Like the Macd and VW-Macd, the TTI calculates a spread by subtracting the short (fast) average from the long (slow) average. This spread combined with a volume multiplier creates the Buff spread
Market Analysis Assistant This indicator uniquely maps and interprets key market conditions using Moving Averages, MACD, RSI, and Bollinger Bands. Unlike traditional indicators that only display visual signals, this tool provides written analysis directly on your chart as soon as specific conditions are met. This feature makes it easier to understand the market’s current state and anticipate potential moves.
Why Moving Averages? Moving Averages are essential for identifying the overall trend of the market. By analyzing the 200, 20, and 9-period Moving Averages, this indicator helps traders quickly determine whether the market is in an uptrend, downtrend, or sideways phase. The integration of multiple averages offers a comprehensive view, allowing for more accurate trend identification.
Why MACD? The MACD is a powerful tool for spotting trend reversals and momentum shifts. By monitoring MACD crossovers, divergences, and the position of the MACD line relative to the zero line, this indicator helps you identify potential changes in the trend direction before they fully develop, giving you a critical edge.
Why RSI? RSI is crucial for understanding the market's overbought and oversold conditions. By tracking RSI levels and its crossover with its moving average, this indicator provides early warnings for potential trend reversals or continuations, helping you time your entries and exits more effectively.
Why Bollinger Bands? Bollinger Bands are used to measure market volatility and identify breakout opportunities. By analyzing the price’s relationship with the upper and lower bands, this indicator helps traders spot potential overbought or oversold conditions, as well as possible breakout scenarios, offering a clear view of market dynamics.
Trend Identification (getTrend()): Detects whether the market is in an uptrend, downtrend, or sideways phase by analyzing the position of the price relative to the 200, 20, and 9-period moving averages.
MACD Analysis (analyzeMACD()): Identifies potential trend reversals or continuations through MACD divergence, crossovers, and the MACD signal line's position relative to the zero line.
RSI Monitoring (analyzeRSI()): Detects overbought and oversold conditions and anticipates trend continuation or corrections based on RSI crossings with its moving average.
Trap Zone Detection (analyzeTrapZone()): Highlights areas of potential price consolidation between the 20 and 200-period moving averages, indicating possible breakouts.
Bollinger Bands Analysis (analyzeBollingerBands()): Analyzes the price’s relationship with Bollinger Bands to identify overbought/oversold conditions, breakouts, and potential trend continuations or correction.
Fibonacci retracement will also check the moment the price tests a monthly or daily weekly Fibonacci retracement
What Makes This Indicator Unique?
This indicator stands out by transforming complex technical analysis into clear, written insights directly on your chart. As soon as specific conditions are met—such as a MACD crossover or an RSI overbought/oversold level—this tool immediately displays a written summary of the event, helping traders to quickly understand and act on market developments.
How to Use My Indicator:
The indicator is designed to provide detailed, real-time market condition analysis using Moving Averages, MACD, RSI, and Bollinger Bands. When certain market conditions are met, such as the price testing a specific moving average or the MACD indicating a potential reversal, the indicator displays this information in written form directly on the chart, in both English and Portuguese.
How to Interpret the Displayed Information:
The information displayed by the indicator can be used for:
Identifying Support and Resistance: The indicator can help identify when the price is testing an important support or resistance level, such as a moving average or a Fibonacci level, allowing the user to decide whether to enter or exit a position.
Trend Detection: If the indicator shows that the price is above the 200, 20, and 9-period moving averages, this may be a sign of an uptrend, indicating that the user should consider maintaining or opening buy positions.
Correction Signals: When the MACD indicates a potential correction, the user may decide to protect their profits by adjusting stops or even exiting the position to avoid losses.
Identifying Overbought/Oversold Conditions: Based on the RSI, the indicator can alert to overbought or oversold conditions, helping the user avoid entering a trade at an unfavorable time.
Example of Use:
the indicator shows several important pieces of information, such as:
"US100 Price is at the 50.0% Fibonacci level (Last Monthly)."
This suggests that the price is testing a significant Fibonacci level, which could be a point of reversal or continuation. A trader can use this information to adjust their entry or exit strategy.
"DXY RSI below 30: Indication of oversold condition"
This indicates that the DXY is in an oversold condition, which might suggest an upcoming bullish reversal. A trader could consider this when trading DXY-related assets.
"Bullish Trend: Price is above the 200, 20, and 9-period moving averages."
This confirms an uptrend, giving the user more confidence to hold long positions.
Availability:
This indicator is available in two languages: English and Portuguese. It is ideal for traders who prefer analysis in English as well as those who prefer it in Portuguese, making it a versatile and accessible tool for traders from different backgrounds
Este indicador mapeia e interpreta de forma única as principais condições de mercado utilizando Médias Móveis, MACD, RSI e Bandas de Bollinger. Ao contrário dos indicadores tradicionais que apenas exibem sinais visuais, esta ferramenta oferece uma análise escrita diretamente no seu gráfico assim que determinadas condições são atendidas. Isso facilita o entendimento do estado atual do mercado e a antecipação de possíveis movimentos.
Por que Médias Móveis? As Médias Móveis são essenciais para identificar a tendência geral do mercado. Ao analisar as Médias Móveis de 200, 20 e 9 períodos, este indicador ajuda os traders a determinarem rapidamente se o mercado está em tendência de alta, baixa ou em fase lateral. A integração de múltiplas médias oferece uma visão abrangente, permitindo uma identificação mais precisa das tendências.
Por que MACD? O MACD é uma ferramenta poderosa para identificar reversões de tendência e mudanças de momentum. Monitorando os cruzamentos do MACD, divergências e a posição da linha MACD em relação à linha zero, este indicador ajuda você a identificar possíveis mudanças na direção da tendência antes que elas se desenvolvam completamente, dando-lhe uma vantagem crítica.
Por que RSI? O RSI é crucial para entender as condições de sobrecompra e sobrevenda do mercado. Acompanhando os níveis do RSI e seu cruzamento com sua média móvel, este indicador fornece avisos antecipados para possíveis reversões ou continuações de tendência, ajudando você a cronometrar suas entradas e saídas de forma mais eficaz.
Por que Bandas de Bollinger? As Bandas de Bollinger são usadas para medir a volatilidade do mercado e identificar oportunidades de rompimento. Ao analisar a relação do preço com as bandas superior e inferior, este indicador ajuda os traders a identificar condições de sobrecompra ou sobrevenda, bem como possíveis cenários de rompimento, oferecendo uma visão clara da dinâmica do mercado.
Identificação de Tendências (getTrend()): Detecta se o mercado está em tendência de alta, baixa ou em fase lateral, analisando a posição do preço em relação às médias móveis de 200, 20 e 9 períodos.
Análise de MACD (analyzeMACD()): Identifica possíveis reversões ou continuações de tendência através de divergências do MACD, cruzamentos, e a posição da linha de sinal do MACD em relação à linha zero.
Monitoramento do RSI (analyzeRSI()): Detecta condições de sobrecompra e sobrevenda e antecipa a continuação da tendência ou correções com base nos cruzamentos do RSI com sua média móvel.
Detecção de Zona de Armadilha (analyzeTrapZone()): Destaca áreas de possível consolidação de preços entre as médias móveis de 20 e 200 períodos, indicando possíveis rompimentos.
Análise das Bandas de Bollinger (analyzeBollingerBands()): Analisa a relação do preço com as Bandas de Bollinger para identificar condições de sobrecompra/sobrevenda, rompimentos e possíveis continuações de tendência ou correção.
A retração de Fibonacci também verificará o momento em que o preço testa uma retração de Fibonacci semanal mensal ou diária
O que Torna Este Indicador Único?
Este indicador se destaca por transformar análises técnicas complexas em insights escritos claros diretamente no seu gráfico. Assim que condições específicas são atendidas—como um cruzamento do MACD ou um nível de sobrecompra/sobrevenda do RSI—esta ferramenta exibe imediatamente um resumo escrito do evento, ajudando os traders a entenderem e agirem rapidamente sobre as mudanças do mercado.
Como Utilizar o Meu Indicador:
O indicador foi desenvolvido para oferecer uma análise detalhada e em tempo real das condições de mercado, utilizando os conceitos de Médias Móveis, MACD, RSI e Bandas de Bollinger. Quando certas condições de mercado são atingidas, como o preço testando uma média móvel específica ou o MACD indicando uma possível reversão, o indicador exibe essas informações de forma escrita diretamente no gráfico, em inglês e português.
Como Interpretar as Informações Exibidas:
As informações exibidas pelo indicador podem ser usadas para:
Identificação de Suportes e Resistências: O indicador pode ajudar a identificar quando o preço está testando um nível de suporte ou resistência importante, como uma média móvel ou um nível de Fibonacci, permitindo ao usuário decidir se deve entrar ou sair de uma posição.
Detecção de Tendências: Se o indicador mostra que o preço está acima das médias móveis de 200, 20 e 9 períodos, isso pode ser um sinal de uma tendência de alta, indicando que o usuário deve considerar manter ou abrir posições de compra.
Sinais de Correção: Quando o MACD indica uma possível correção, o usuário pode decidir proteger seus lucros ajustando os stops ou até mesmo saindo da posição para evitar perdas.
Identificação de Condições de Sobrecompra/Sobrevenda: Com base no RSI, o indicador pode alertar sobre condições de sobrecompra ou sobrevenda, ajudando o usuário a evitar entrar em uma operação em um momento desfavorável.
Exemplo de Utilização:
o indicador mostra várias informações importantes, como:
"O preço do US100 está no nível de Fibonacci de 50,0% (mês passado)."
Isso sugere que o preço está testando um nível significativo de Fibonacci, o que pode ser um ponto de reversão ou continuação. Um trader pode usar essa informação para ajustar sua estratégia de entrada ou saída.
DXY RSI abaixo de 30: Indicação de condição de sobrevenda"
Isso indica que o DXY está em uma condição de sobrevenda, o que pode sugerir uma reversão de alta em breve. Um trader pode considerar isso ao fazer operações relacionadas ao DXY.
"Tendência de alta: o preço está acima das médias móveis de 200, 20 e 9 períodos."
Isso confirma uma tendência de alta, dando ao usuário mais confiança para manter posições longas.
Disponibilidade:
Este indicador está disponível em dois idiomas: inglês e português. Ele é ideal tanto para traders que preferem análises em inglês quanto para aqueles que preferem em português. Isso o torna uma ferramenta versátil e acessível para traders de diferentes origens.
Alxuse Supertrend 4EMA Buy and Sell for tutorialAll abilities of Supertrend, moreover :
Drawing 4 EMA band & the ability to change values, change colors, turn on/off show.
Sends Signal Sell and Buy in multi timeframe.
The ability used in the alert section and create customized alerts.
To receive valid alerts the replay section , the timeframe of the chart must be the same as the timeframe of the indicator.
Supertrend with a simple EMA Filter can improve the performance of the signals during a strong trend.
For detecting the continuation of the downward and upward trend we can use 4 EMA colors.
In the upward trend , the EMA lines are in order of green, blue, red, yellow from bottom to top.
In the downward trend, the EMA lines are in order of yellow, red, blue, green from bottom to top.
How it works:
x1 = MA1 < MA2 and MA2 < MA3 and MA3 < MA4 and ta.crossunder(MA3, MA4)
x2 = MA1 < MA2 and MA2 < MA3 and MA3 < MA4 and ta.crossunder(MA2, MA3)
x3 = MA1 < MA2 and MA2 < MA3 and MA3 < MA4 and ta.crossunder(MA1, MA2)
y1 = MA4 < MA3 and MA3 < MA2 and MA2 < MA1 and ta.crossover(MA3, MA4)
y2 = MA4 < MA3 and MA3 < MA2 and MA2 < MA1 and ta.crossover(MA2, MA3)
y3 = MA4 < MA3 and MA3 < MA2 and MA2 < MA1 and ta.crossover(MA1, MA2)
Red triangle = x1 or x2 or x3
Green triangle = y1 or y2 or y3
Long = BUY signal and followed by a Green triangle
Exit Long = SELL signal
Short = SELL signal and followed by a Red triangle
Exit Short = BUY signal
It is also possible to get help from the Stochastic RSI and MACD indicators for confirmation.
For receiving a signal with these two conditions or more conditions, i am making a video tutorial that I will release soon.
Supertrend
Definition
Supertrend is a trend-following indicator based on Average True Range (ATR). The calculation of its single line combines trend detection and volatility. It can be used to detect changes in trend direction and to position stops.
The basics
The Supertrend is a trend-following indicator. It is overlaid on the main chart and their plots indicate the current trend. A Supertrend can be used with varying periods (daily, weekly, intraday etc.) and on varying instruments.
The Supertrend has several inputs that you can adjust to match your trading strategy. Adjusting these settings allows you to make the indicator more or less sensitive to price changes.
For the Supertrend inputs, you can adjust atrLength and multiplier:
the atrLength setting is the lookback length for the ATR calculation;
multiplier is what the ATR is multiplied by to offset the bands from price.
When the price falls below the indicator curve, it turns red and indicates a downtrend. Conversely, when the price rises above the curve, the indicator turns green and indicates an uptrend. After each close above or below Supertrend, a new trend appears.
Summary
The Supertrend helps you make the right trading decisions. However, there are times when it generates false signals. Therefore, it is best to use the right combination of several indicators. Like any other indicator, Supertrend works best when used with other indicators such as MACD, Parabolic SAR, or RSI.
Exponential Moving Average
Definition
The Exponential Moving Average (EMA) is a specific type of moving average that points towards the importance of the most recent data and information from the market. The Exponential Moving Average is just like it’s name says - it’s exponential, weighting the most recent prices more than the less recent prices. The EMA can be compared and contrasted with the simple moving average.
Similar to other moving averages, the EMA is a technical indicator that produces buy and sell signals based on data that shows evidence of divergence and crossovers from general and historical averages. Additionally, the EMA tries to amplify the importance that the most recent data points play in a calculation.
It is common to use more than one EMA length at once, to provide more in-depth and focused data. For example, by choosing 10-day and 200-day moving averages, a trader is able to determine more from the results in a long-term trade, than a trader who is only analyzing one EMA length.
It’s best to use the EMA when for trending markets, as it shows uptrends and downtrends when a market is strong and weak, respectively. An experienced trader will know to look both at the line the EMA projects, as well as the rate of change that comes from each bar as it moves to the next data point. Analyzing these points and data streams correctly will help the trader determine when they should buy, sell, or switch investments from bearish to bullish or vice versa.
Short-term averages, on the other hand, is a different story when analyzing Exponential Moving Average data. It is most common for traders to quote and utilize 12- and 26-day EMAs in the short-term. This is because they are used to create specific indicators. Look into Moving Average Convergence Divergence (MACD) for more information. Similarly, the 50- and 200-day moving averages are most common for analyzing long-term trends.
Moving averages can be very useful for traders using technical analysis for profit. It is important to identify and realize, however, their shortcomings, as all moving averages tend to suffer from recurring lag. It is difficult to modify the moving average to work in your favor at times, often having the preferred time to enter or exit the market pass before the moving average even shows changes in the trend or price movement for that matter.
All of this is true, however, the EMA strives to make this easier for traders. The EMA is unique because it places more emphasis on the most recent data. Therefore, price movement and trend reversals or changes are closely monitored, allowing for the EMA to react quicker than other moving averages.
Limitations
Although using the Exponential Moving Average has a lot of advantages when analyzing market trends, it is also uncertain whether or not the use of most recent data points truly affects technical and market analysis. In addition, the EMA relies on historical data as its basis for operating and because news, events, and other information can change rapidly the indicator can misinterpret this information by weighting the current prices higher than when the event actually occurred.
Summary
The Exponential Moving Average (EMA) is a moving average and technical indicator that reflects and projects the most recent data and information from the market to a trader and relies on a base of historical data. It is one of many different types of moving averages and has an easily calculable formula.
The added features to the indicator are made for training, it is advisable to use it with caution in tradings.
Adaptive MA-Bollinger HistogramVisualize two of your favorite moving averages in a fun new way.
This script calculates the distance (or difference) between the price and two moving averages of your choosing and then creates two histograms.
The two histograms are plotted inversely, so if the price is over both moving averages, one will be positive above the centerline while the other still positive will be below the centerline.
(In a future update you will have the option to have them both positive at the same time)
Next, what it does is apply Bollinger Bands (optional) to each of the histograms.
This creates a very interesting effect that can highlight areas of interest you may miss with other indicators.
You have plenty of options for coloring, the type of moving average, Bollinger Band length, and toggling features on and off.
Give it a few minutes of your time to study, and see what information you can learn from watching this indicator by comparing it with the chart.
Here is a full user guide:
Adaptive MA-Bollinger Histogram Indicator User Guide
Welcome to the user guide for the **Adaptive MA-Bollinger Histogram** indicator. This custom indicator is designed to help traders analyze trends and potential reversals in a financial instrument's price movements. The indicator combines two Moving Averages (MA) and Bollinger Bands to provide valuable insights into market conditions.
### Indicator Overview
The Adaptive MA-Bollinger Histogram indicator comprises the following components:
1. **Moving Averages (MA1 and MA2):** The indicator uses two moving averages, namely MA1 and MA2, to track different time periods. MA1 has a user-defined length (default: 50) and MA2 has a longer user-defined length (default: 100). These moving averages can be calculated using different methods such as Simple Moving Average (SMA), Exponential Moving Average (EMA), Weighted Moving Average (WMA), Volume Weighted Moving Average (VWMA), or Smoothed Moving Average (RMA).
2. **Histograms:** The indicator displays histograms based on the differences between the price source and the respective moving averages. Positive values of the histogram for MA1 are plotted in one color (default: green), while negative values are plotted in another color (default: red). Similarly, positive values of the histogram for MA2 are plotted in one color (default: blue), while negative values are plotted in another color (default: yellow). It's important to note that the histogram for MA1 is plotted positively, while the histogram for MA2 is plotted inversely.
3. **Bollinger Bands:** The indicator also features Bollinger Bands calculated based on the differences between the price source and the respective moving averages (dist1 and dist2). Bollinger Bands consist of three lines: the middle band, upper band, and lower band. These bands help visualize the potential volatility and overbought/oversold levels of the instrument's price.
### Understanding the Indicator
- **Histograms:** The histograms highlight the divergence between the price and the two moving averages. When the histogram for MA1 is positive, it indicates that the price is above the MA1. Conversely, when the histogram for MA1 is negative, it suggests that the price is below the MA1. Similarly, the histogram for MA2 is plotted inversely.
- **Bollinger Bands:** The Bollinger Bands consist of three lines. The middle band represents the moving average (MA1 or MA2), while the upper and lower bands are calculated based on the standard deviation of the differences between the price source and the moving average. The bands expand during periods of higher volatility and contract during periods of lower volatility.
### Possible Trading Ideas
1. **Trend Confirmation:** When the histograms for both MA1 and MA2 are consistently positive, it may indicate a strong bullish trend. Conversely, when both histograms are consistently negative, it may suggest a strong bearish trend.
2. **Divergence:** Divergence between price and the histograms could signal potential reversals. For example, if the price is making new highs while the histogram is declining, it might indicate a bearish divergence and a possible upcoming trend reversal.
3. **Bollinger Bands Squeeze:** A narrowing of the Bollinger Bands indicates lower volatility and often precedes a significant price movement. Traders might consider a potential breakout trade when the bands start to expand again.
4. **Overbought/Oversold Levels:** Prices touching or exceeding the upper Bollinger Band could suggest overbought conditions, while prices touching or falling below the lower Bollinger Band could indicate oversold conditions. Traders might look for reversals or corrections in such scenarios.
### Customization
- You can adjust the parameters such as MA lengths, Bollinger Bands length, width, and colors to suit your preferences and trading strategy.
### Conclusion
The **Adaptive MA-Bollinger Histogram** indicator provides a comprehensive view of price trends, divergences, and potential reversal points. Traders can use the information from this indicator to make informed decisions in their trading strategies. However, like any technical tool, it's recommended to combine this indicator with other forms of analysis and risk management techniques for optimal results.
Universal MA Trend(Republishing in Open source)
Hello traders,
Many existing moving average indicators have not been satisfactory in terms of the number, types, and length adjustments of moving averages.
Feeling the inconvenience, I created a moving average indicator and collected numerous famous moving averages.
Fortunately, there was a PineCoder "andre_007" who had already compiled various Moving Averages,
so I was able to find a new Moving Average and combine it with the indicator. Here is the link below
Among these, for the JMA, which has not been publicly disclosed, I utilized the source code from TradingView Wizard everget:
For VIDYA, I also used everget's source code:
And also MAMA / FAMA Coded from Pinescript Wizard everget :
Ehlers MESA Adaptive Moving Averages (MAMA & FAMA)
For Frama, I used the code from nemozny's source code :
Thanks to all these Pinecoders.
---
By using these excellent moving averages together, I found that the simultaneous Up/Down changes of various moving averages with different characteristics tend to be maintained for quite a long time.
Therefore, this indicator not only collects various moving averages but also displays areas with simultaneous trends as background.
An example can be found here:
Furthermore, to prevent the up/down changes of the moving averages due to factors like whipsaws, a smoothing filter has been introduced.
And Also, Alert is able when trend changes.
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(오픈소스화 후 재발행)
안녕하세요 트레이더여러분.
기존의 이동평균선 지표들은, 이동평균선의 갯수, 종류, 길이조절 등에서 만족스럽지 못한 점들이 많았습니다.
불편함을 느끼고 직접 이동평균선 지표를 만들면서, 유명한 수 많은 이동평균선들을 모았습니다.
그리고 이미 이러한 수많은 이동평균선을 손수 모아서 정리해주신 고마우신 파인코더(andere_007 님)가 있어서, 그 분의 코드를 많이 이용했습니다. 링크는 아래와 같습니다.
이 중 소스가 공개되지 않은 이동평균선 중 JMA는 트레이딩뷰 위자드이신 everget의 소스코드를 이용했습니다.
VIDYA 역시 everget의 소스코드를 이용했습니다.
MAMA와 FAMA의 코드 역시 everget님의 코드를 가져왔습니다.
Ehlers MESA Adaptive Moving Averages (MAMA & FAMA)
Frama는 nemozny님의 코드를 이용했습니다.
의 코드를 이용했습니다.
이 자리를 빌어 위의 파인코더님들께 감사의 말씀을 전합니다.
---
이러한 좋은 이동평균선을 모아서 사용해보니, 다양한 특성을 갖고 있는 이동평균선의 동시적인 Up/Down 변화는 꽤 오랫동안 유지된다는 점을 발견했습니다.
그래서 이 지표는, 위의 여러가지 이동평균선을 모아놓은 것 뿐만 아니라,
그것에서 동시적인 트랜드가 나오는 곳을 배경화면으로 표시해두었습니다.
예시는 다음과 같습니다.
나아가 휩쏘 등으로 이동평균선의 up/down이 바뀌는 것을 막고자, Smoothing 필터도 도입했습니다.
또한 트랜드가 바뀔 때 얼러트가 울리도록, 얼러트 기능을 설정해놓을 수 있게 해놓았으며, 현재 이동평균선과 상태를 보기 쉽도록 테이블을 만들어놓았습니다.
R-squared Adaptive T3 [Loxx]R-squared Adaptive T3 is an R-squared adaptive version of Tilson's T3 moving average. This adaptivity was originally proposed by mladen on various forex forums. This is considered experimental but shows how to use r-squared adapting methods to moving averages. In theory, the T3 is a six-pole non-linear Kalman filter.
What is the T3 moving average?
Better Moving Averages Tim Tillson
November 1, 1998
Tim Tillson is a software project manager at Hewlett-Packard, with degrees in Mathematics and Computer Science. He has privately traded options and equities for 15 years.
Introduction
"Digital filtering includes the process of smoothing, predicting, differentiating, integrating, separation of signals, and removal of noise from a signal. Thus many people who do such things are actually using digital filters without realizing that they are; being unacquainted with the theory, they neither understand what they have done nor the possibilities of what they might have done."
This quote from R. W. Hamming applies to the vast majority of indicators in technical analysis. Moving averages, be they simple, weighted, or exponential, are lowpass filters; low frequency components in the signal pass through with little attenuation, while high frequencies are severely reduced.
"Oscillator" type indicators (such as MACD, Momentum, Relative Strength Index) are another type of digital filter called a differentiator.
Tushar Chande has observed that many popular oscillators are highly correlated, which is sensible because they are trying to measure the rate of change of the underlying time series, i.e., are trying to be the first and second derivatives we all learned about in Calculus.
We use moving averages (lowpass filters) in technical analysis to remove the random noise from a time series, to discern the underlying trend or to determine prices at which we will take action. A perfect moving average would have two attributes:
It would be smooth, not sensitive to random noise in the underlying time series. Another way of saying this is that its derivative would not spuriously alternate between positive and negative values.
It would not lag behind the time series it is computed from. Lag, of course, produces late buy or sell signals that kill profits.
The only way one can compute a perfect moving average is to have knowledge of the future, and if we had that, we would buy one lottery ticket a week rather than trade!
Having said this, we can still improve on the conventional simple, weighted, or exponential moving averages. Here's how:
Two Interesting Moving Averages
We will examine two benchmark moving averages based on Linear Regression analysis.
In both cases, a Linear Regression line of length n is fitted to price data.
I call the first moving average ILRS, which stands for Integral of Linear Regression Slope. One simply integrates the slope of a linear regression line as it is successively fitted in a moving window of length n across the data, with the constant of integration being a simple moving average of the first n points. Put another way, the derivative of ILRS is the linear regression slope. Note that ILRS is not the same as a SMA (simple moving average) of length n, which is actually the midpoint of the linear regression line as it moves across the data.
We can measure the lag of moving averages with respect to a linear trend by computing how they behave when the input is a line with unit slope. Both SMA(n) and ILRS(n) have lag of n/2, but ILRS is much smoother than SMA.
Our second benchmark moving average is well known, called EPMA or End Point Moving Average. It is the endpoint of the linear regression line of length n as it is fitted across the data. EPMA hugs the data more closely than a simple or exponential moving average of the same length. The price we pay for this is that it is much noisier (less smooth) than ILRS, and it also has the annoying property that it overshoots the data when linear trends are present.
However, EPMA has a lag of 0 with respect to linear input! This makes sense because a linear regression line will fit linear input perfectly, and the endpoint of the LR line will be on the input line.
These two moving averages frame the tradeoffs that we are facing. On one extreme we have ILRS, which is very smooth and has considerable phase lag. EPMA has 0 phase lag, but is too noisy and overshoots. We would like to construct a better moving average which is as smooth as ILRS, but runs closer to where EPMA lies, without the overshoot.
A easy way to attempt this is to split the difference, i.e. use (ILRS(n)+EPMA(n))/2. This will give us a moving average (call it IE/2) which runs in between the two, has phase lag of n/4 but still inherits considerable noise from EPMA. IE/2 is inspirational, however. Can we build something that is comparable, but smoother? Figure 1 shows ILRS, EPMA, and IE/2.
Filter Techniques
Any thoughtful student of filter theory (or resolute experimenter) will have noticed that you can improve the smoothness of a filter by running it through itself multiple times, at the cost of increasing phase lag.
There is a complementary technique (called twicing by J.W. Tukey) which can be used to improve phase lag. If L stands for the operation of running data through a low pass filter, then twicing can be described by:
L' = L(time series) + L(time series - L(time series))
That is, we add a moving average of the difference between the input and the moving average to the moving average. This is algebraically equivalent to:
2L-L(L)
This is the Double Exponential Moving Average or DEMA, popularized by Patrick Mulloy in TASAC (January/February 1994).
In our taxonomy, DEMA has some phase lag (although it exponentially approaches 0) and is somewhat noisy, comparable to IE/2 indicator.
We will use these two techniques to construct our better moving average, after we explore the first one a little more closely.
Fixing Overshoot
An n-day EMA has smoothing constant alpha=2/(n+1) and a lag of (n-1)/2.
Thus EMA(3) has lag 1, and EMA(11) has lag 5. Figure 2 shows that, if I am willing to incur 5 days of lag, I get a smoother moving average if I run EMA(3) through itself 5 times than if I just take EMA(11) once.
This suggests that if EPMA and DEMA have 0 or low lag, why not run fast versions (eg DEMA(3)) through themselves many times to achieve a smooth result? The problem is that multiple runs though these filters increase their tendency to overshoot the data, giving an unusable result. This is because the amplitude response of DEMA and EPMA is greater than 1 at certain frequencies, giving a gain of much greater than 1 at these frequencies when run though themselves multiple times. Figure 3 shows DEMA(7) and EPMA(7) run through themselves 3 times. DEMA^3 has serious overshoot, and EPMA^3 is terrible.
The solution to the overshoot problem is to recall what we are doing with twicing:
DEMA(n) = EMA(n) + EMA(time series - EMA(n))
The second term is adding, in effect, a smooth version of the derivative to the EMA to achieve DEMA. The derivative term determines how hot the moving average's response to linear trends will be. We need to simply turn down the volume to achieve our basic building block:
EMA(n) + EMA(time series - EMA(n))*.7;
This is algebraically the same as:
EMA(n)*1.7-EMA(EMA(n))*.7;
I have chosen .7 as my volume factor, but the general formula (which I call "Generalized Dema") is:
GD(n,v) = EMA(n)*(1+v)-EMA(EMA(n))*v,
Where v ranges between 0 and 1. When v=0, GD is just an EMA, and when v=1, GD is DEMA. In between, GD is a cooler DEMA. By using a value for v less than 1 (I like .7), we cure the multiple DEMA overshoot problem, at the cost of accepting some additional phase delay. Now we can run GD through itself multiple times to define a new, smoother moving average T3 that does not overshoot the data:
T3(n) = GD(GD(GD(n)))
In filter theory parlance, T3 is a six-pole non-linear Kalman filter. Kalman filters are ones which use the error (in this case (time series - EMA(n)) to correct themselves. In Technical Analysis, these are called Adaptive Moving Averages; they track the time series more aggressively when it is making large moves.
Included:
Bar coloring
Signals
Alerts
Loxx's Expanded Source Types
Overlay Indicators (EMAs, SMAs, Ichimoku & Bollinger Bands)This is a combination of popular overlay indicators that are used for dynamic support and resistance, trade targets and trend strength.
Included are:
-> 6 Exponential Moving Averages
-> 6 Simple Moving Averages
-> Ichimoku Cloud
-> Bollinger Bands
-> There is also a weekend background marker ideal for cryptocurrency trading
Using all these indicators in conjunction with each other provide great confluence and confidence in trades and price targets.
An explanation of each indicator is listed below.
What Is an Exponential Moving Average (EMA)?
"An exponential moving average (EMA) is a type of moving average (MA) that places a greater weight and significance on the most recent data points. The exponential moving average is also referred to as the exponentially weighted moving average. An exponentially weighted moving average reacts more significantly to recent price changes than a simple moving average (SMA), which applies an equal weight to all observations in the period.
What Does the Exponential Moving Average Tell You?
The 12- and 26-day exponential moving averages (EMAs) are often the most quoted and analyzed short-term averages. The 12- and 26-day are used to create indicators like the moving average convergence divergence (MACD) and the percentage price oscillator (PPO). In general, the 50- and 200-day EMAs are used as indicators for long-term trends. When a stock price crosses its 200-day moving average, it is a technical signal that a reversal has occurred.
Traders who employ technical analysis find moving averages very useful and insightful when applied correctly. However, they also realize that these signals can create havoc when used improperly or misinterpreted. All the moving averages commonly used in technical analysis are, by their very nature, lagging indicators."
Source: www.investopedia.com
Popular EMA lookback periods include fibonacci numbers and round numbers such as the 100 or 200. The default values of the EMAs in this indicator are the most widely used, specifically for cryptocurrency but they also work very well with traditional.
EMAs are normally used in conjunction with Simple Moving Averages.
" What Is Simple Moving Average (SMA)?
A simple moving average (SMA) calculates the average of a selected range of prices, usually closing prices, by the number of periods in that range.
Simple Moving Average vs. Exponential Moving Average
The major difference between an exponential moving average (EMA) and a simple moving average is the sensitivity each one shows to changes in the data used in its calculation. More specifically, the EMA gives a higher weighting to recent prices, while the SMA assigns an equal weighting to all values."
Source: www.investopedia.com
In this indicator, I've included 6 popular moving averages that are commonly used. Most traders will find specific settings for their own personal trading style.
Along with the EMA and SMA, another indicator that is good for finding confluence between these two is the Ichimoku Cloud.
" What is the Ichimoku Cloud?
The Ichimoku Cloud is a collection of technical indicators that show support and resistance levels, as well as momentum and trend direction. It does this by taking multiple averages and plotting them on the chart. It also uses these figures to compute a "cloud" which attempts to forecast where the price may find support or resistance in the future.
The Ichimoku cloud was developed by Goichi Hosoda, a Japanese journalist, and published in the late 1960s.1 It provides more data points than the standard candlestick chart. While it seems complicated at first glance, those familiar with how to read the charts often find it easy to understand with well-defined trading signals."
More info can be seen here: www.investopedia.com
I have changed the default settings on the Ichimoku to suit cryptocurrency trading (as cryptocurrency is usually fast and thus require slightly longer lookbacks) to 20 60 120 30.
Along with the Ichimoku, I like to use Bollinger Bands to not only find confluence for support and resistance but for price discovery targets and trend strength.
" What Is a Bollinger Band®?
A Bollinger Band® is a technical analysis tool defined by a set of trendlines plotted two standard deviations (positively and negatively) away from a simple moving average (SMA) of a security's price, but which can be adjusted to user preferences.
Bollinger Bands® were developed and copyrighted by famous technical trader John Bollinger, designed to discover opportunities that give investors a higher probability of properly identifying when an asset is oversold or overbought."
This article goes into great detail of the complexities of using the Bollinger band and how to use it.
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This indicator combines all these powerful indicators into one so that it is easier to input different settings, turn specific tools on or off and can be easily customised.
MACD Enhanced [DCAUT]█ MACD Enhanced
📊 ORIGINALITY & INNOVATION
The MACD Enhanced represents a significant improvement over traditional MACD implementations. While Gerald Appel's original MACD from the 1970s was limited to exponential moving averages (EMA), this enhanced version expands algorithmic options by supporting 21 different moving average calculations for both the main MACD line and signal line independently.
This improvement addresses an important limitation of traditional MACD: the inability to adapt the indicator's mathematical foundation to different market conditions. By allowing traders to select from algorithms ranging from simple moving averages (SMA) for stability to advanced adaptive filters like Kalman Filter for noise reduction, this implementation changes MACD from a fixed-algorithm tool into a flexible instrument that can be adjusted for specific market environments and trading strategies.
The enhanced histogram visualization system uses a four-color gradient that helps communicate momentum strength and direction more clearly than traditional single-color histograms.
📐 MATHEMATICAL FOUNDATION
The core calculation maintains the proven MACD formula: Fast MA(source, fastLength) - Slow MA(source, slowLength), but extends it with algorithmic flexibility. The signal line applies the selected smoothing algorithm to the MACD line over the specified signal period, while the histogram represents the difference between MACD and signal lines.
Available Algorithms:
The implementation supports a comprehensive spectrum of technical analysis algorithms:
Basic Averages: SMA (arithmetic mean), EMA (exponential weighting), RMA (Wilder's smoothing), WMA (linear weighting)
Advanced Averages: HMA (Hull's low-lag), VWMA (volume-weighted), ALMA (Arnaud Legoux adaptive)
Mathematical Filters: LSMA (least squares regression), DEMA (double exponential), TEMA (triple exponential), ZLEMA (zero-lag exponential)
Adaptive Systems: T3 (Tillson T3), FRAMA (fractal adaptive), KAMA (Kaufman adaptive), MCGINLEY_DYNAMIC (reactive to volatility)
Signal Processing: ULTIMATE_SMOOTHER (low-pass filter), LAGUERRE_FILTER (four-pole IIR), SUPER_SMOOTHER (two-pole Butterworth), KALMAN_FILTER (state-space estimation)
Specialized: TMA (triangular moving average), LAGUERRE_BINOMIAL_FILTER (binomial smoothing)
Each algorithm responds differently to price action, allowing traders to match the indicator's behavior to market characteristics: trending markets benefit from responsive algorithms like EMA or HMA, while ranging markets require stable algorithms like SMA or RMA.
📊 COMPREHENSIVE SIGNAL ANALYSIS
Histogram Interpretation:
Positive Values: Indicate bullish momentum when MACD line exceeds signal line, suggesting upward price pressure and potential buying opportunities
Negative Values: Reflect bearish momentum when MACD line falls below signal line, indicating downward pressure and potential selling opportunities
Zero Line Crosses: MACD crossing above zero suggests transition to bullish bias, while crossing below indicates bearish bias shift
Momentum Changes: Rising histogram (regardless of positive/negative) signals accelerating momentum in the current direction, while declining histogram warns of momentum deceleration
Advanced Signal Recognition:
Divergences: Price making new highs/lows while MACD fails to confirm often precedes trend reversals
Convergence Patterns: MACD line approaching signal line suggests impending crossover and potential trade setup
Histogram Peaks: Extreme histogram values often mark momentum exhaustion points and potential reversal zones
🎯 STRATEGIC APPLICATIONS
Comprehensive Trend Confirmation Strategies:
Primary Trend Validation Protocol:
Identify primary trend direction using higher timeframe (4H or Daily) MACD position relative to zero line
Confirm trend strength by analyzing histogram progression: consistent expansion indicates strong momentum, contraction suggests weakening
Use secondary confirmation from MACD line angle: steep angles (>45°) indicate strong trends, shallow angles suggest consolidation
Validate with price structure: trending markets show consistent higher highs/higher lows (uptrend) or lower highs/lower lows (downtrend)
Entry Timing Techniques:
Pullback Entries in Uptrends: Wait for MACD histogram to decline toward zero line without crossing, then enter on histogram expansion with MACD line still above zero
Breakout Confirmations: Use MACD line crossing above zero as confirmation of upward breakouts from consolidation patterns
Continuation Signals: Look for MACD line re-acceleration (steepening angle) after brief consolidation periods as trend continuation signals
Advanced Divergence Trading Systems:
Regular Divergence Recognition:
Bullish Regular Divergence: Price creates lower lows while MACD line forms higher lows. This pattern is traditionally considered a potential upward reversal signal, but should be combined with other confirmation signals
Bearish Regular Divergence: Price makes higher highs while MACD shows lower highs. This pattern is traditionally considered a potential downward reversal signal, but trading decisions should incorporate proper risk management
Hidden Divergence Strategies:
Bullish Hidden Divergence: Price shows higher lows while MACD displays lower lows, indicating trend continuation potential. Use for adding to existing long positions during pullbacks
Bearish Hidden Divergence: Price creates lower highs while MACD forms higher highs, suggesting downtrend continuation. Optimal for adding to short positions during bear market rallies
Multi-Timeframe Coordination Framework:
Three-Timeframe Analysis Structure:
Primary Timeframe (Daily): Determine overall market bias and major trend direction. Only trade in alignment with daily MACD direction
Secondary Timeframe (4H): Identify intermediate trend changes and major entry opportunities. Use for position sizing decisions
Execution Timeframe (1H): Precise entry and exit timing. Look for MACD line crossovers that align with higher timeframe bias
Timeframe Synchronization Rules:
Daily MACD above zero + 4H MACD rising = Strong uptrend context for long positions
Daily MACD below zero + 4H MACD declining = Strong downtrend context for short positions
Conflicting signals between timeframes = Wait for alignment or use smaller position sizes
1H MACD signals only valid when aligned with both higher timeframes
Algorithm Considerations by Market Type:
Trending Markets: Responsive algorithms like EMA, HMA may be considered, but effectiveness should be tested for specific market conditions
Volatile Markets: Noise-reducing algorithms like KALMAN_FILTER, SUPER_SMOOTHER may help reduce false signals, though results vary by market
Range-Bound Markets: Stability-focused algorithms like SMA, RMA may provide smoother signals, but individual testing is required
Short Timeframes: Low-lag algorithms like ZLEMA, T3 theoretically respond faster but may also increase noise
Important Note: All algorithm choices and parameter settings should be thoroughly backtested and validated based on specific trading strategies, market conditions, and individual risk tolerance. Different market environments and trading styles may require different configuration approaches.
📋 DETAILED PARAMETER CONFIGURATION
Comprehensive Source Selection Strategy:
Price Source Analysis and Optimization:
Close Price (Default): Most commonly used, reflects final market sentiment of each period. Best for end-of-day analysis, swing trading, daily/weekly timeframes. Advantages: widely accepted standard, good for backtesting comparisons. Disadvantages: ignores intraday price action, may miss important highs/lows
HL2 (High+Low)/2: Midpoint of the trading range, reduces impact of opening gaps and closing spikes. Best for volatile markets, gap-prone assets, forex markets. Calculation impact: smoother MACD signals, reduced noise from price spikes. Optimal when asset shows frequent gaps, high volatility during specific sessions
HLC3 (High+Low+Close)/3: Weighted average emphasizing the close while including range information. Best for balanced analysis, most asset classes, medium-term trading. Mathematical effect: 33% weight to high/low, 33% to close, provides compromise between close and HL2. Use when standard close is too noisy but HL2 is too smooth
OHLC4 (Open+High+Low+Close)/4: True average of all price points, most comprehensive view. Best for complete price representation, algorithmic trading, statistical analysis. Considerations: includes opening sentiment, smoothest of all options but potentially less responsive. Optimal for markets with significant opening moves, comprehensive trend analysis
Parameter Configuration Principles:
Important Note: Different moving average algorithms have distinct mathematical characteristics and response patterns. The same parameter settings may produce vastly different results when using different algorithms. When switching algorithms, parameter settings should be re-evaluated and tested for appropriateness.
Length Parameter Considerations:
Fast Length (Default 12): Shorter periods provide faster response but may increase noise and false signals, longer periods offer more stable signals but slower response, different algorithms respond differently to the same parameters and may require adjustment
Slow Length (Default 26): Should maintain a reasonable proportional relationship with fast length, different timeframes may require different parameter configurations, algorithm characteristics influence optimal length settings
Signal Length (Default 9): Shorter lengths produce more frequent crossovers but may increase false signals, longer lengths provide better signal confirmation but slower response, should be adjusted based on trading style and chosen algorithm characteristics
Comprehensive Algorithm Selection Framework:
MACD Line Algorithm Decision Matrix:
EMA (Standard Choice): Mathematical properties: exponential weighting, recent price emphasis. Best for general use, traditional MACD behavior, backtesting compatibility. Performance characteristics: good balance of speed and smoothness, widely understood behavior
SMA (Stability Focus): Equal weighting of all periods, maximum smoothness. Best for ranging markets, noise reduction, conservative trading. Trade-offs: slower signal generation, reduced sensitivity to recent price changes
HMA (Speed Optimized): Hull Moving Average, designed for reduced lag. Best for trending markets, quick reversals, active trading. Technical advantage: square root period weighting, faster trend detection. Caution: can be more sensitive to noise
KAMA (Adaptive): Kaufman Adaptive MA, adjusts smoothing based on market efficiency. Best for varying market conditions, algorithmic trading. Mechanism: fast smoothing in trends, slow smoothing in sideways markets. Complexity: requires understanding of efficiency ratio
Signal Line Algorithm Optimization Strategies:
Matching Strategy: Use same algorithm for both MACD and signal lines. Benefits: consistent mathematical properties, predictable behavior. Best when backtesting historical strategies, maintaining traditional MACD characteristics
Contrast Strategy: Use different algorithms for optimization. Common combinations: MACD=EMA, Signal=SMA for smoother crossovers, MACD=HMA, Signal=RMA for balanced speed/stability, Advanced: MACD=KAMA, Signal=T3 for adaptive behavior with smooth signals
Market Regime Adaptation: Trending markets: both fast algorithms (EMA/HMA), Volatile markets: MACD=KALMAN_FILTER, Signal=SUPER_SMOOTHER, Range-bound: both slow algorithms (SMA/RMA)
Parameter Sensitivity Considerations:
Impact of Parameter Changes:
Length Parameter Sensitivity: Small parameter adjustments can significantly affect signal timing, while larger adjustments may fundamentally change indicator behavior characteristics
Algorithm Sensitivity: Different algorithms produce different signal characteristics. Thoroughly test the impact on your trading strategy before switching algorithms
Combined Effects: Changing multiple parameters simultaneously can create unexpected effects. Recommendation: adjust parameters one at a time and thoroughly test each change
📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES
Response Characteristics by Algorithm:
Fastest Response: ZLEMA, HMA, T3 - minimal lag but higher noise
Balanced Performance: EMA, DEMA, TEMA - good trade-off between speed and stability
Highest Stability: SMA, RMA, TMA - reduced noise but increased lag
Adaptive Behavior: KAMA, FRAMA, MCGINLEY_DYNAMIC - automatically adjust to market conditions
Noise Filtering Capabilities:
Advanced algorithms like KALMAN_FILTER and SUPER_SMOOTHER help reduce false signals compared to traditional EMA-based MACD. Noise-reducing algorithms can provide more stable signals in volatile market conditions, though results will vary based on market conditions and parameter settings.
Market Condition Adaptability:
Unlike fixed-algorithm MACD, this enhanced version allows real-time optimization. Trending markets benefit from responsive algorithms (EMA, HMA), while ranging markets perform better with stable algorithms (SMA, RMA). The ability to switch algorithms without changing indicators provides greater flexibility.
Comparative Performance vs Traditional MACD:
Algorithm Flexibility: 21 algorithms vs 1 fixed EMA
Signal Quality: Reduced false signals through noise filtering algorithms
Market Adaptability: Optimizable for any market condition vs fixed behavior
Customization Options: Independent algorithm selection for MACD and signal lines vs forced matching
Professional Features: Advanced color coding, multiple alert conditions, comprehensive parameter control
USAGE NOTES
This indicator is designed for technical analysis and educational purposes. Like all technical indicators, it has limitations and should not be used as the sole basis for trading decisions. Algorithm performance varies with market conditions, and past characteristics do not guarantee future results. Always combine with proper risk management and thorough strategy testing.
Stochastic Enhanced [DCAUT]█ Stochastic Enhanced
📊 ORIGINALITY & INNOVATION
The Stochastic Enhanced indicator builds upon George Lane's classic momentum oscillator (developed in the late 1950s) by providing comprehensive smoothing algorithm flexibility. While traditional implementations limit users to Simple Moving Average (SMA) smoothing, this enhanced version offers 21 advanced smoothing algorithms, allowing traders to optimize the indicator's characteristics for different market conditions and trading styles.
Key Improvements:
Extended from single SMA smoothing to 21 professional-grade algorithms including adaptive filters (KAMA, FRAMA), zero-lag methods (ZLEMA, T3), and advanced digital filters (Kalman, Laguerre)
Maintains backward compatibility with traditional Stochastic calculations through SMA default setting
Unified smoothing algorithm applies to both %K and %D lines for consistent signal processing characteristics
Enhanced visual feedback with clear color distinction and background fill highlighting for intuitive signal recognition
Comprehensive alert system covering crossovers and zone entries for systematic trade management
Differentiation from Traditional Stochastic:
Traditional Stochastic indicators use fixed SMA smoothing, which introduces consistent lag regardless of market volatility. This enhanced version addresses the limitation by offering adaptive algorithms that adjust to market conditions (KAMA, FRAMA), reduce lag without sacrificing smoothness (ZLEMA, T3, HMA), or provide superior noise filtering (Kalman Filter, Laguerre filters). The flexibility helps traders balance responsiveness and stability according to their specific needs.
📐 MATHEMATICAL FOUNDATION
Core Stochastic Calculation:
The Stochastic Oscillator measures the position of the current close relative to the high-low range over a specified period:
Step 1: Raw %K Calculation
%K_raw = 100 × (Close - Lowest Low) / (Highest High - Lowest Low)
Where:
Close = Current closing price
Lowest Low = Lowest low over the %K Length period
Highest High = Highest high over the %K Length period
Result ranges from 0 (close at period low) to 100 (close at period high)
Step 2: Smoothed %K Calculation
%K = MA(%K_raw, K Smoothing Period, MA Type)
Where:
MA = Selected moving average algorithm (SMA, EMA, etc.)
K Smoothing = 1 for Fast Stochastic, 3+ for Slow Stochastic
Traditional Fast Stochastic uses %K_raw directly without smoothing
Step 3: Signal Line %D Calculation
%D = MA(%K, D Smoothing Period, MA Type)
Where:
%D acts as a signal line and moving average of %K
D Smoothing typically set to 3 periods in traditional implementations
Both %K and %D use the same MA algorithm for consistent behavior
Available Smoothing Algorithms (21 Options):
Standard Moving Averages:
SMA (Simple): Equal-weighted average, traditional default, consistent lag characteristics
EMA (Exponential): Recent price emphasis, faster response to changes, exponential decay weighting
RMA (Rolling/Wilder's): Smoothed average used in RSI, less reactive than EMA
WMA (Weighted): Linear weighting favoring recent data, moderate responsiveness
VWMA (Volume-Weighted): Incorporates volume data, reflects market participation intensity
Advanced Moving Averages:
HMA (Hull): Reduced lag with smoothness, uses weighted moving averages and square root period
ALMA (Arnaud Legoux): Gaussian distribution weighting, minimal lag with good noise reduction
LSMA (Least Squares): Linear regression based, fits trend line to data points
DEMA (Double Exponential): Reduced lag compared to EMA, uses double smoothing technique
TEMA (Triple Exponential): Further lag reduction, triple smoothing with lag compensation
ZLEMA (Zero-Lag Exponential): Lag elimination attempt using error correction, very responsive
TMA (Triangular): Double-smoothed SMA, very smooth but slower response
Adaptive & Intelligent Filters:
T3 (Tilson T3): Six-pass exponential smoothing with volume factor adjustment, excellent smoothness
FRAMA (Fractal Adaptive): Adapts to market fractal dimension, faster in trends, slower in ranges
KAMA (Kaufman Adaptive): Efficiency ratio based adaptation, responds to volatility changes
McGinley Dynamic: Self-adjusting mechanism following price more accurately, reduced whipsaws
Kalman Filter: Optimal estimation algorithm from aerospace engineering, dynamic noise filtering
Advanced Digital Filters:
Ultimate Smoother: Advanced digital filter design, superior noise rejection with minimal lag
Laguerre Filter: Time-domain filter with N-order implementation, adjustable lag characteristics
Laguerre Binomial Filter: 6-pole Laguerre filter, extremely smooth output for long-term analysis
Super Smoother: Butterworth filter implementation, removes high-frequency noise effectively
📊 COMPREHENSIVE SIGNAL ANALYSIS
Absolute Level Interpretation (%K Line):
%K Above 80: Overbought condition, price near period high, potential reversal or pullback zone, caution for new long entries
%K in 70-80 Range: Strong upward momentum, bullish trend confirmation, uptrend likely continuing
%K in 50-70 Range: Moderate bullish momentum, neutral to positive outlook, consolidation or mild uptrend
%K in 30-50 Range: Moderate bearish momentum, neutral to negative outlook, consolidation or mild downtrend
%K in 20-30 Range: Strong downward momentum, bearish trend confirmation, downtrend likely continuing
%K Below 20: Oversold condition, price near period low, potential bounce or reversal zone, caution for new short entries
Crossover Signal Analysis:
%K Crosses Above %D (Bullish Cross): Momentum shifting bullish, faster line overtakes slower signal, consider long entry especially in oversold zone, strongest when occurring below 20 level
%K Crosses Below %D (Bearish Cross): Momentum shifting bearish, faster line falls below slower signal, consider short entry especially in overbought zone, strongest when occurring above 80 level
Crossover in Midrange (40-60): Less reliable signals, often in choppy sideways markets, require additional confirmation from trend or volume analysis
Multiple Failed Crosses: Indicates ranging market or choppy conditions, reduce position sizes or avoid trading until clear directional move
Advanced Divergence Patterns (%K Line vs Price):
Bullish Divergence: Price makes lower low while %K makes higher low, indicates weakening bearish momentum, potential trend reversal upward, more reliable when %K in oversold zone
Bearish Divergence: Price makes higher high while %K makes lower high, indicates weakening bullish momentum, potential trend reversal downward, more reliable when %K in overbought zone
Hidden Bullish Divergence: Price makes higher low while %K makes lower low, indicates trend continuation in uptrend, bullish trend strength confirmation
Hidden Bearish Divergence: Price makes lower high while %K makes higher high, indicates trend continuation in downtrend, bearish trend strength confirmation
Momentum Strength Analysis (%K Line Slope):
Steep %K Slope: Rapid momentum change, strong directional conviction, potential for extended moves but also increased reversal risk
Gradual %K Slope: Steady momentum development, sustainable trends more likely, lower probability of sharp reversals
Flat or Horizontal %K: Momentum stalling, potential reversal or consolidation ahead, wait for directional break before committing
%K Oscillation Within Range: Indicates ranging market, sideways price action, better suited for range-trading strategies than trend following
🎯 STRATEGIC APPLICATIONS
Mean Reversion Strategy (Range-Bound Markets):
Identify ranging market conditions using price action or Bollinger Bands
Wait for Stochastic to reach extreme zones (above 80 for overbought, below 20 for oversold)
Enter counter-trend position when %K crosses %D in extreme zone (sell on bearish cross above 80, buy on bullish cross below 20)
Set profit targets near opposite extreme or midline (50 level)
Use tight stop-loss above recent swing high/low to protect against breakout scenarios
Exit when Stochastic reaches opposite extreme or %K crosses %D in opposite direction
Trend Following with Momentum Confirmation:
Identify primary trend direction using higher timeframe analysis or moving averages
Wait for Stochastic pullback to oversold zone (<20) in uptrend or overbought zone (>80) in downtrend
Enter in trend direction when %K crosses %D confirming momentum shift (bullish cross in uptrend, bearish cross in downtrend)
Use wider stops to accommodate normal trend volatility
Add to position on subsequent pullbacks showing similar Stochastic pattern
Exit when Stochastic shows opposite extreme with failed cross or bearish/bullish divergence
Divergence-Based Reversal Strategy:
Scan for divergence between price and Stochastic at swing highs/lows
Confirm divergence with at least two price pivots showing divergent Stochastic readings
Wait for %K to cross %D in direction of anticipated reversal as entry trigger
Enter position in divergence direction with stop beyond recent swing extreme
Target profit at key support/resistance levels or Fibonacci retracements
Scale out as Stochastic reaches opposite extreme zone
Multi-Timeframe Momentum Alignment:
Analyze Stochastic on higher timeframe (4H or Daily) for primary trend bias
Switch to lower timeframe (1H or 15M) for precise entry timing
Only take trades where lower timeframe Stochastic signal aligns with higher timeframe momentum direction
Higher timeframe Stochastic in bullish zone (>50) = only take long entries on lower timeframe
Higher timeframe Stochastic in bearish zone (<50) = only take short entries on lower timeframe
Exit when lower timeframe shows counter-signal or higher timeframe momentum reverses
Zone Transition Strategy:
Monitor Stochastic for transitions between zones (oversold to neutral, neutral to overbought, etc.)
Enter long when Stochastic crosses above 20 (exiting oversold), signaling momentum shift from bearish to neutral/bullish
Enter short when Stochastic crosses below 80 (exiting overbought), signaling momentum shift from bullish to neutral/bearish
Use zone midpoint (50) as dynamic support/resistance for position management
Trail stops as Stochastic advances through favorable zones
Exit when Stochastic fails to maintain momentum and reverses back into prior zone
📋 DETAILED PARAMETER CONFIGURATION
%K Length (Default: 14):
Lower Values (5-9): Highly sensitive to price changes, generates more frequent signals, increased false signals in choppy markets, suitable for very short-term trading and scalping
Standard Values (10-14): Balanced sensitivity and reliability, traditional default (14) widely used,适合 swing trading and intraday strategies
Higher Values (15-21): Reduced sensitivity, smoother oscillations, fewer but potentially more reliable signals, better for position trading and lower timeframe noise reduction
Very High Values (21+): Slow response, long-term momentum measurement, fewer trading signals, suitable for weekly or monthly analysis
%K Smoothing (Default: 3):
Value 1: Fast Stochastic, uses raw %K calculation without additional smoothing, most responsive to price changes, generates earliest signals with higher noise
Value 3: Slow Stochastic (default), traditional smoothing level, reduces false signals while maintaining good responsiveness, widely accepted standard
Values 5-7: Very slow response, extremely smooth oscillations, significantly reduced whipsaws but delayed entry/exit timing
Recommendation: Default value 3 suits most trading scenarios, active short-term traders may use 1, conservative long-term positions use 5+
%D Smoothing (Default: 3):
Lower Values (1-2): Signal line closely follows %K, frequent crossover signals, useful for active trading but requires strict filtering
Standard Value (3): Traditional setting providing balanced signal line behavior, optimal for most trading applications
Higher Values (4-7): Smoother signal line, fewer crossover signals, reduced whipsaws but slower confirmation, better for trend trading
Very High Values (8+): Signal line becomes slow-moving reference, crossovers rare and highly significant, suitable for long-term position changes only
Smoothing Type Algorithm Selection:
For Trending Markets:
ZLEMA, DEMA, TEMA: Reduced lag for faster trend entry, quick response to momentum shifts, suitable for strong directional moves
HMA, ALMA: Good balance of smoothness and responsiveness, effective for clean trend following without excessive noise
EMA: Classic choice for trending markets, faster than SMA while maintaining reasonable stability
For Ranging/Choppy Markets:
Kalman Filter, Super Smoother: Superior noise filtering, reduces false signals in sideways action, helps identify genuine reversal points
Laguerre Filters: Smooth oscillations with adjustable lag, excellent for mean reversion strategies in ranges
T3, TMA: Very smooth output, filters out market noise effectively, clearer extreme zone identification
For Adaptive Market Conditions:
KAMA: Automatically adjusts to market efficiency, fast in trends and slow in congestion, reduces whipsaws during transitions
FRAMA: Adapts to fractal market structure, responsive during directional moves, conservative during uncertainty
McGinley Dynamic: Self-adjusting smoothing, follows price naturally, minimizes lag in trending markets while filtering noise in ranges
For Conservative Long-Term Analysis:
SMA: Traditional choice, predictable behavior, widely understood characteristics
RMA (Wilder's): Smooth oscillations, reduced sensitivity to outliers, consistent behavior across market conditions
Laguerre Binomial Filter: Extremely smooth output, ideal for weekly/monthly timeframe analysis, eliminates short-term noise completely
Source Selection:
Close (Default): Standard choice using closing prices, most common and widely tested
HLC3 or OHLC4: Incorporates more price information, reduces impact of sudden spikes or gaps, smoother oscillator behavior
HL2: Midpoint of high-low range, emphasizes intrabar volatility, useful for markets with wide intraday ranges
Custom Source: Can use other indicators as input (e.g., Heikin Ashi close, smoothed price), creates derivative momentum indicators
📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES
Responsiveness Characteristics:
Traditional SMA-Based Stochastic:
Fixed lag regardless of market conditions, consistent delay of approximately (K Smoothing + D Smoothing) / 2 periods
Equal treatment of trending and ranging markets, no adaptation to volatility changes
Predictable behavior but suboptimal in varying market regimes
Enhanced Version with Adaptive Algorithms:
KAMA and FRAMA reduce lag by up to 40-60% in strong trends compared to SMA while maintaining similar smoothness in ranges
ZLEMA and T3 provide near-zero lag characteristics for early entry signals with acceptable noise levels
Kalman Filter and Super Smoother offer superior noise rejection, reducing false signals in choppy conditions by estimations of 30-50% compared to SMA
Performance improvements vary by algorithm selection and market conditions
Signal Quality Improvements:
Adaptive algorithms help reduce whipsaw trades in ranging markets by adjusting sensitivity dynamically
Advanced filters (Kalman, Laguerre, Super Smoother) provide clearer extreme zone readings for mean reversion strategies
Zero-lag methods (ZLEMA, DEMA, TEMA) generate earlier crossover signals in trending markets for improved entry timing
Smoother algorithms (T3, Laguerre Binomial) reduce false extreme zone touches for more reliable overbought/oversold signals
Comparison with Standard Implementations:
Versus Basic Stochastic: Enhanced version offers 21 smoothing options versus single SMA, allowing optimization for specific market characteristics and trading styles
Versus RSI: Stochastic provides range-bound measurement (0-100) with clear extreme zones, RSI measures momentum speed, Stochastic offers clearer visual overbought/oversold identification
Versus MACD: Stochastic bounded oscillator suitable for mean reversion, MACD unbounded indicator better for trend strength, Stochastic excels in range-bound and oscillating markets
Versus CCI: Stochastic has fixed bounds (0-100) for consistent interpretation, CCI unbounded with variable extremes, Stochastic provides more standardized extreme readings across different instruments
Flexibility Advantages:
Single indicator adaptable to multiple strategies through algorithm selection rather than requiring different indicator variants
Ability to optimize smoothing characteristics for specific instruments (e.g., smoother for crypto volatility, faster for forex trends)
Multi-timeframe analysis with consistent algorithm across timeframes for coherent momentum picture
Backtesting capability with algorithm as optimization parameter for strategy development
Limitations and Considerations:
Increased complexity from multiple algorithm choices may lead to over-optimization if parameters are curve-fitted to historical data
Adaptive algorithms (KAMA, FRAMA) have adjustment periods during market regime changes where signals may be less reliable
Zero-lag algorithms sacrifice some smoothness for responsiveness, potentially increasing noise sensitivity in very choppy conditions
Performance characteristics vary significantly across algorithms, requiring understanding and testing before live implementation
Like all oscillators, Stochastic can remain in extreme zones for extended periods during strong trends, generating premature reversal signals
USAGE NOTES
This indicator is designed for technical analysis and educational purposes to provide traders with enhanced flexibility in momentum analysis. The Stochastic Oscillator has limitations and should not be used as the sole basis for trading decisions.
Important Considerations:
Algorithm performance varies with market conditions - no single smoothing method is optimal for all scenarios
Extreme zone signals (overbought/oversold) indicate potential reversal areas but not guaranteed turning points, especially in strong trends
Crossover signals may generate false entries during sideways choppy markets regardless of smoothing algorithm
Divergence patterns require confirmation from price action or additional indicators before trading
Past indicator characteristics and backtested results do not guarantee future performance
Always combine Stochastic analysis with proper risk management, position sizing, and multi-indicator confirmation
Test selected algorithm on historical data of specific instrument and timeframe before live trading
Market regime changes may require algorithm adjustment for optimal performance
The enhanced smoothing options are intended to provide tools for optimizing the indicator's behavior to match individual trading styles and market characteristics, not to create a perfect predictive tool. Responsible usage includes understanding the mathematical properties of selected algorithms and their appropriate application contexts.
ANDROMEDA - TrendSyncANDROMEDA - TrendSync
Pedro Canto - Portfolio Manager | CGA/CGE
OVERVIEW
Trend Sync is a multi-layered trend-following indicator designed to help traders identify high-probability trend continuation setups while avoiding low-quality entries caused by overbought or oversold market conditions.
This indicator combines the power of Moving Averages (MA), MACD , and a visual RSI-based filter to validate both trend direction and timing for entries. It's goal is simple: filter out noise and highlight only the most technically relevant buy and sell signals based on objective momentum and trend criteria.
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WALKTHROUGH
This indicator is built for traders seeking to operate in the direction of established trends. It's core principle is to identify and validate current trend conditions, and then signal entry opportunities during pullbacks to key moving averages.
Trend identification is achieved through the alignment of two moving averages. When these MAs are crossed and angled in the same direction, they confirm that a trend is in progress. To double-confirm trend direction, the MACD histogram is used—only. When both the MAs and MACD are aligned in the same direction, then the trend is considered valid.
Once all trend criteria are met, a dynamic coloring system is activated to visually reinforce the trend across the candles and moving averages.
To avoid poor entries during market exhaustion, an RSI-based filter is used. This short-term RSI highlights overbought or oversold zones, helping traders filter trades in extreme price conditions.
Only when the trend is validated and price pulls back to one of the MAs will a buy/sell signal be triggered, aligning momentum, price action and timing into a single actionable setup.
This combination ensures that each component plays a specific role:
i) Moving Averages define the trend
ii) MACD validates it
iii) RSI filters noise
iv) Intrabar price action triggers entries
This synchronism helps improve decision-making and entry timing, especially for swing and intraday traders.
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USE CASES
- Identifying trend continuation setups
- Filtering false signals during consolidation phases
- Avoiding trades in overbought or oversold zones
- Enhancing entry timing for both swing and intraday strategies
- Providing visual confirmation of trend strength and momentum alignment
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KEY FEATURES
1. Dual Moving Average Setup
The indicator allows full customization of two moving averages (MA1 and MA2), supporting both EMA and SMA types. The slope of the longer MA (MA2) acts as an essential trend filter, ensuring signals are only generated when the market shows clear directional bias.
2. MACD Histogram Trend Confirmation
A classic MACD Histogram calculation is used to validate the momentum of the prevailing trend.
- Bullish Trend: Histogram > 0
- Bearish Trend: Histogram < 0
This step filters out counter-trend signals and ensures trades are aligned with momentum.
3. Intrabar Price Trigger
Unlike standard crossover systems, this indicator waits for intrabar price action to trigger entries:
- Buy Signal: Price crosses below one of the MAs during an uptrend (dip-buy logic)
- Sell Signal: Price crosses above one of the MAs during a downtrend (rally-sell logic)
This intrabar trigger improves entry timing and helps capture retracement-based opportunities.
4. RSI Visual Filter
A short-term RSI is plotted and color-coded to visually highlight overbought and oversold conditions, acting as a discretionary filter for users to avoid low-probability trades during exhaustion points.
5. Dynamic Coloring System
Bar Colors:
- Blue: Bullish trend
- Red: Bearish trend
- Orange: RSI Overbought/Oversold zones
MA Colors:
- Blue for bullish conditions
- Red for bearish conditions
- Gray for neutral/no-trend phases
6. Signal Markers and Alerts
Clear visual buy and sell markers are plotted directly on the chart.
Additionally, the indicator includes real-time alerts for both Buy and Sell signals, helping traders stay informed even when away from the screen.
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INPUTS AND CUSTOMIZATION OPTIONS
- Moving Average Types: EMA or SMA for both MA1 and MA2.
- MACD Settings: Customizable fast, slow, and signal periods.
- RSI Settings: Source, length, and overbought/oversold levels fully adjustable.
- Color Customization: Adjust RSI zone colors to suit your chart theme.
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DISCLAIMER
This indicator is a technical analysis tool designed for educational and informational purposes only. It should not be used as a standalone trading system. Always combine it with sound risk management, price action analysis, and, where applicable, fundamental context.
Past performance does not guarantee future results.
Super Arma Institucional PRO v6.3Super Arma Institucional PRO v6.3
Description
Super Arma Institucional PRO v6.3 is a multifunctional indicator designed for traders looking for a clear and objective analysis of the market, focusing on trends, key price levels and high liquidity zones. It combines three essential elements: moving averages (EMA 20, SMA 50, EMA 200), dynamic support and resistance, and volume-based liquidity zones. This integration offers an institutional view of the market, ideal for identifying strategic entry and exit points.
How it Works
Moving Averages:
EMA 20 (orange): Sensitive to short-term movements, ideal for capturing fast trends.
SMA 50 (blue): Represents the medium-term trend, smoothing out fluctuations.
EMA 200 (red): Indicates the long-term trend, used as a reference for the general market bias.
Support and Resistance: Calculated based on the highest and lowest prices over a defined period (default: 20 bars). These dynamic levels help identify zones where the price may encounter barriers or supports.
Liquidity Zones: Purple rectangles are drawn in areas of significantly above-average volume, indicating regions where large market participants (institutional) may be active. These zones are useful for anticipating price movements or order absorption.
Purpose
The indicator was developed to provide a clean and institutional view of the market, combining classic tools (moving averages and support/resistance) with modern liquidity analysis. It is ideal for traders operating swing trading or position trading strategies, allowing to identify:
Short, medium and long-term trends.
Key support and resistance levels to plan entries and exits.
High liquidity zones where institutional orders can influence the price.
Settings
Show EMA 20 (true): Enables/disables the 20-period EMA.
Show SMA 50 (true): Enables/disables the 50-period SMA.
Show EMA 200 (true): Enables/disables the 200-period EMA.
Support/Resistance Period (20): Sets the period for calculating support and resistance levels.
Liquidity Sensitivity (20): Period for calculating the average volume.
Minimum Liquidity Factor (1.5): Multiplier of the average volume to identify high liquidity zones.
How to Use
Moving Averages:
Crossovers between the EMA 20 and SMA 50 may indicate short/medium-term trend changes.
The EMA 200 serves as a reference for the long-term bias (above = bullish, below = bearish).
Support and Resistance: Use the red (resistance) and green (support) lines to identify reversal or consolidation zones.
Liquidity Zones: The purple rectangles highlight areas of high volume, where the price may react (reversal or breakout). Consider these zones to place orders or manage risks.
Adjust the parameters according to the asset and timeframe to optimize the analysis.
Notes
The chart should be configured only with this indicator to ensure clarity.
Use on timeframes such as 1 hour, 4 hours or daily for better visualization of liquidity zones and support/resistance levels.
Avoid adding other indicators to the chart to keep the script output easily identifiable.
The indicator is designed to be clean, without explicit buy/sell signals, following an institutional approach.
This indicator is perfect for traders who want a visually clear and powerful tool to trade based on trends, key levels and institutional behavior.
[blackcat] L3 Dynamic CrossOVERVIEW
The L3 Dynamic Cross indicator is a powerful tool designed to assist traders in identifying potential buy and sell opportunities through the use of dynamic moving averages. This versatile script offers a wide range of customizable options, allowing users to tailor the moving averages to their specific needs and preferences. By providing clear visual cues and generating precise crossover signals, it helps traders make informed decisions about market trends and potential entry/exit points 📈💹.
FEATURES
Multiple Moving Average Types:
Simple Moving Average (SMA): Provides a straightforward average of prices over a specified period.
Exponential Moving Average (EMA): Gives more weight to recent prices, making it responsive to new information.
Weighted Moving Average (WMA): Assigns weights to all prices within the look-back period, giving more importance to recent prices.
Volume Weighted Moving Average (VWMA): Incorporates volume data to provide a more accurate representation of price movements.
Smoothed Moving Average (SMMA): Averages out fluctuations to create a smoother trend line.
Double Exponential Moving Average (DEMA): Reduces lag by applying two layers of exponential smoothing.
Triple Exponential Moving Average (TEMA): Further reduces lag with three layers of exponential smoothing.
Hull Moving Average (HullMA): Combines weighted moving averages to minimize lag and noise.
Super Smoother Moving Average (SSMA): Uses a sophisticated algorithm to smooth out price data while preserving trend direction.
Zero-Lag Exponential Moving Average (ZEMA): Eliminates lag entirely by adjusting the calculation method.
Triangular Moving Average (TMA): Applies a double smoothing process to reduce volatility and enhance trend identification.
Customizable Parameters:
Length: Adjust the period for both fast and slow moving averages to match your trading style.
Source: Select different price sources such as close, open, high, or low for more nuanced analysis.
Visual Representation:
Fast MA: Displayed as a green line representing shorter-term trends.
Slow MA: Shown as a red line indicating longer-term trends.
Crossover Signals:
Generate buy ('BUY') and sell ('SELL') labels based on crossover events between the fast and slow moving averages 🏷️.
Clear visual cues help traders quickly identify potential entry and exit points.
Alert Functionality:
Receive real-time notifications when crossover conditions are met, ensuring timely action 🔔.
Customizable alert messages for personalized trading strategies.
Advanced Trade Management:
Support for pyramiding levels allows traders to manage multiple positions effectively.
Fine-tune your risk management by setting the number of allowed trades per signal.
HOW TO USE
Adding the Indicator:
Open your TradingView chart and go to the indicators list.
Search for L3 Dynamic Cross and add it to your chart.
Configuring Settings:
Choose your desired Moving Average Type from the dropdown menu.
Adjust the Fast MA Length and Slow MA Length according to your trading timeframe.
Select appropriate Price Sources for both fast and slow moving averages.
Monitoring Signals:
Observe the plotted lines on the chart to track short-term and long-term trends.
Look for buy and sell labels that indicate potential trade opportunities.
Setting Up Alerts:
Enable alerts based on crossover conditions to receive instant notifications.
Customize alert messages to suit your trading plan.
Managing Positions:
Utilize the pyramiding feature to handle multiple entries and exits efficiently.
Keep track of your position sizes relative to the defined pyramiding levels.
Combining with Other Tools:
Integrate this indicator with other technical analysis tools for confirmation.
Use additional filters like volume, RSI, or MACD to enhance decision-making accuracy.
LIMITATIONS
Market Conditions: The effectiveness of the indicator may vary in highly volatile or sideways markets. Be cautious during periods of low liquidity or sudden price spikes 🌪️.
Parameter Sensitivity: Different moving average types and lengths can produce varying results. Experiment with settings to find what works best for your asset class and timeframe.
False Signals: Like any technical indicator, false signals can occur. Always confirm signals with other forms of analysis before executing trades.
NOTES
Historical Data: Ensure you have enough historical data loaded into your chart for accurate moving average calculations.
Backtesting: Thoroughly backtest the indicator on various assets and timeframes using demo accounts before deploying it in live trading environments 🔍.
Customization: Feel free to adjust colors, line widths, and label styles to better fit your chart aesthetics and personal preferences.
EXAMPLE STRATEGIES
Trend Following: Use the indicator to ride trends by entering positions when the fast MA crosses above/below the slow MA and exiting when the opposite occurs.
Mean Reversion: Identify overbought/oversold conditions by combining the indicator with oscillators like RSI or Stochastic. Enter counter-trend positions when the moving averages diverge significantly from the mean.
Scalping: Apply tight moving average settings to capture small, quick profits in intraday trading. Combine with volume indicators to filter out weak signals.
Advanced MVRV Trend AnalyzerThe "Advanced MVRV Trend Analyzer" is a sophisticated trading tool designed for the TradingView platform that enhances traditional Market Value to Realized Value (MVRV) analysis. It provides a multi-timeframe perspective of market valuation dynamics by comparing the current market price to the realized price across short-term, mid-term, and long-term cohorts. This indicator is particularly useful for cryptocurrency traders and investors who seek deeper insights into potential overvaluation or undervaluation conditions in the market.
Key Features
Multiple Timeframes:
Analyzes market conditions across three distinct timeframes: short-term (14 days), mid-term (50 days), and long-term (200 days).
Moving Averages: Includes moving averages for each MVRV ratio to smooth out short-term fluctuations and highlight longer-term trends.
Dynamic Thresholds: Provides dynamic color-coded backgrounds that highlight overvalued and undervalued market conditions based on predefined thresholds.
How to Use
Adding the Indicator:
Open your TradingView chart.
Click on "Indicators" at the top of your screen.
Search for "Advanced MVRV Trend Analyzer" and add it to your chart.
Interpreting the Indicator:
MVRV Lines: Each of the three MVRV lines (short-term, mid-term, long-term) reflects how much higher or lower the current market price is compared to the average price at which coins were last moved. A value above 1 indicates that the current price is higher than the realized price, suggesting overvaluation. Conversely, a value below 1 suggests undervaluation.
Moving Averages: The moving averages of the MVRV ratios help identify the underlying trend. If the MVRV line deviates significantly from its moving average, it might indicate a potential reversal or continuation of the current trend.
Color-coded Backgrounds:
Red background indicates an overvalued condition where the MVRV ratio exceeds 1.5, suggesting caution as the market may be overheated.
Green background indicates an undervalued condition where the MVRV ratio is below 0.5, potentially signaling a buying opportunity.
Trading Strategies:
Overvalued Zones: Consider taking profits or setting stop-loss orders when the indicator shows a prolonged red background, especially if supported by other bearish signals.
Undervalued Zones: Look for buying opportunities when the indicator shows a prolonged green background, especially if other bullish signals are present.
Combining with Other Indicators:
Enhance your analysis by combining the "Advanced MVRV Trend Analyzer" with other technical indicators such as RSI, MACD, or volume-based tools to confirm trends and signals.
Conclusion
The "Advanced MVRV Trend Analyzer" offers a nuanced view of market dynamics, providing traders with valuable insights into when a market may be approaching extremes. By utilizing this indicator, traders can better time their entries and exits, manage risk, and align their strategies with underlying market trends.
Simplest Strategy Crossover with Labels Buy/Sell to $1000This Pine Script code, titled Custom Moving Average Crossover with Labels, is a trading indicator developed for the TradingView platform. It enables traders to visualize potential buy and sell signals based on the crossover of two moving averages, offering customizable settings for enhanced flexibility. Here’s a breakdown of its key features:
Key Features
User-Defined Moving Averages:
The script includes two moving averages: a fast and a slow one. Users can adjust the periods of each average (default values are 10 for the fast MA and 100 for the slow MA), allowing them to adapt the indicator to various market conditions and trading styles.
Time-Restricted Signal Validity:
The indicator includes settings for active trading hours, defined in UTC time. Users specify a start and end hour, making it possible to limit buy and sell signals to certain times of the day. This is especially useful for traders who wish to avoid signals outside their preferred trading hours or during periods of high volatility.
Crossover-Based Buy and Sell Signals:
Buy Signal: A "Buy" label is triggered and displayed when the fast moving average crosses above the slow moving average within the user-defined trading hours, signifying a potential upward trend.
Sell Signal: A "Sell" label is generated when the fast moving average crosses below the slow moving average, indicating a possible downtrend. Labels are displayed on the chart, color-coded for easy identification: green for buys and red for sells.
Profit Target Labels (+100 Points):
After each buy or sell entry, the indicator tracks price movements. When the price increases by 100 points from a buy entry or decreases by 100 points from a sell entry, a +100 label appears to signify a 100-point movement.
These labels serve as checkpoints to help traders assess performance and decide on further actions, such as taking profits or adjusting stop losses.
Visual Customization:
The moving averages are color-coded (blue for fast MA, red for slow MA) for easy distinction, and label text appears in white to enhance visibility against various chart backgrounds.
Benefits for Traders
Efficient Trade Identification: The moving average crossover combined with time-based restrictions allows traders to capture key market trends within chosen hours.
Clear Profit Checkpoints: The +100 point label alerts traders to significant price movement, useful for those looking for set profit targets.
Flexibility: Customizable inputs give users control over the indicator’s behavior, making it suitable for both day trading and swing trading.
This indicator is designed for traders looking to enhance their technical analysis with reliable, user-defined buy/sell signals, helping to increase confidence and improve trade timing based on objective data.
Multi-Sector Trend AnalysisThis script, titled "Multi-Sector Trend Analysis: Track Sector Momentum and Trends," is designed to assist traders and investors in monitoring multiple sectors of the stock market simultaneously. It leverages technical analysis by incorporating trend detection and momentum indicators like moving averages and the Relative Strength Index (RSI) to offer insights into the price action of various market sectors.
Core Features:
1. Sector-Based Analysis: The script covers 20 major sectors from the NSE (National Stock Exchange) such as Auto, Banking, Energy, FMCG, IT, Pharma, and others. Users can customize which sectors they wish to analyze using the available input fields.
Technical Indicators: The script uses two core technical indicators to detect trends and momentum:
2. Moving Averages: The script calculates both fast and slow exponential moving averages (EMAs). These are critical for identifying short- and long-term price trends and crossovers, helping detect shifts in momentum.
3. Relative Strength Index (RSI): A well-known momentum indicator that shows whether a stock is overbought or oversold. This script uses a 14-period RSI to gauge the strength of each sector.
4. Trend Detection: The script identifies whether the current market trend is "Up" or "Down" based on the relationship between the fast and slow EMAs (i.e., whether the fast EMA is above or below the slow EMA). It highlights this trend visually in a table format, allowing quick and easy trend recognition.
5. Gain/Loss Tracking: This feature calculates the percentage gain or loss since the last EMA crossover (a key point in trend change), giving users a sense of how much the price has moved since the trend shifted.
6. Customizable Table for Display: The script displays the analyzed data in a table format, where users can view each sector's:
Symbol
Trend (Up or Down)
RSI Value
Gain/Loss Since the Last EMA Crossover
This table is customizable in terms of size and color theme (dark or light), providing flexibility in presentation for different charting styles.
How It Works:
Sector Selection: Users can input up to 20 different sector symbols for analysis.
Moving Averages: Users can define the period lengths for both the fast and slow EMAs to suit their trading strategies.
Table Options: Choose between different table sizes and opt for a dark theme to enhance the visual appearance on charts.
How to Use:
Select the symbols (sectors) that you want to track. The script includes pre-configured symbols for major sectors on the NSE, but you can modify these to suit your needs.
Adjust the fast and slow EMA lengths to your preference. A common setting would be 3 for the fast EMA and 4 for the slow EMA, but more conservative traders might opt for higher values.
Customize the table size and theme based on your preference, whether you want a compact table or a larger one for easier readability.
Why Use This Script:
This script is ideal for traders looking to:
Monitor multiple market sectors simultaneously.
Identify key trends across sectors quickly.
Understand momentum and detect potential reversals through RSI and EMA crossovers.
Stay informed on sector performance using a clear visual table that tracks gains or losses.
By using this script, traders can gain better insights into sector-based trading strategies, improve their sector rotation tactics, and stay informed about the broader market environment. It provides a powerful yet easy-to-use tool for both beginner and advanced traders.
AHS_MicroGaps_ReversalAttempts V1.0MicroGaps + ReversalAttempts Indicator
Summary:
The "MicroGaps + ReversalAttempts" is a custom TradingView indicator that identifies and visualizes micro gaps, detects reversal attempts after gaps, and optionally plots moving averages and the first-hour trading range. The indicator plots micro gaps as boxes on the chart and marks potential reversal attempts with arrows.
Features:
1. Micro Gaps**:
- Show Micro Gaps: Toggle to display micro gaps on the chart.
- Max Number of Gaps: Limit the number of gaps displayed.
- Minimal Deviation: Set the minimum size for detected gaps as a percentage of the average high-low range for the last 14 bars.
- Limit Max Gap Trail Length: Limit the maximum length (in bars) that a gap is tracked.
2. Gap Colors:
- Up Gaps: Set the border and background colors for upward gaps.
- Down Gaps: Set the border and background colors for downward gaps.
3. Reversal Attempts:
- 1st Reversal Attempt Bulls: Color for the first bullish reversal attempt after a micro gap.
- 1st Reversal Attempt Bears: Color for the first bearish reversal attempt after a micro gap.
4. Moving Averages:
- EMA 1 and EMA 2: Set lengths, colors, and toggle visibility for two EMAs.
- Show EMA Cross: Option to display markers where the EMAs cross each other.
5. First Hour Box:
- Show First Hour Box: Toggle to display the first hour's high and low range as a shaded area.
- Activate First Hour High/Low View: Toggle to highlight the first hour high/low range.
Usage:
1. Adding the Indicator:
- Add the "MicroGaps + ReversalAttempts" indicator to your TradingView chart.
2. Configuring Inputs:
- Micro Gaps: Enable the "Show Micro Gaps" option to visualize gaps.
- Set Parameters: Adjust the parameters for the maximum number of gaps, minimal deviation, and gap trail length.
- Gap Colors: Customize the colors for upward and downward gaps.
- Reversal Attempts: Set colors for the first bullish and bearish reversal attempts.
3. Using the Visuals:
- Micro Gaps: Gaps are displayed as colored boxes on the chart.
- Reversal Attempts: Arrows are plotted above/below the bars to indicate potential reversal attempts after gaps.
- Moving Averages: Configure the lengths and colors of two EMAs and optionally display markers at their crossover points.
- First Hour Box: Enable and customize the first hour high/low range visualization.
4. Alerts:
- Alerts are triggered for the appearance and closure of gaps. Configure these alerts as needed for your trading strategy.
5. First Hour Range:
- The first hour's trading range can be highlighted, providing visual cues about potential support and resistance levels during the session.
This indicator helps traders identify significant market events like micro gaps and reversal attempts, supplemented with moving averages and first-hour range visualization for a comprehensive analysis tool.
