The real breakout indicator CCI + Money Flow + Buy / SellComponents of the indicator
1. CCI (Commodity Channel Index)
The CCI component measures the deviation of the price from its statistical average. It is used to identify overbought or oversold conditions and is integrated into the trend logic to determine potential trend reversals. High values may indicate overbought conditions, while low values could signify oversold situations.
Detailed
The CCI (Commodity Channel Index) used in "The Real Breakout Indicator Hawk" is an enhanced version compared to the traditional CCI, offering several advantages:
1. Weighting and Smoothing Mechanism
In this version, the CCI values are weighted and smoothed using custom parameters (c1, c2, c3), which allows for greater flexibility in adjusting the sensitivity of the CCI to market conditions. This smoothing reduces noise and provides clearer signals compared to the standard CCI, which can be prone to whipsaws in volatile markets.
2. Multi-level Calculation
The indicator uses an array-based approach to calculate multiple variations of CCI values (with p as the parameter for different levels of calculation), which is then combined to create a more robust signal. This multi-level approach allows for capturing different market cycles, unlike the traditional CCI that only uses a single period for calculation.
3. Integration with Moving Averages and Trend Detection
Unlike the original CCI, which is often used in isolation, this version integrates with the trend detection logic by combining it with moving averages and money flow. The enhanced CCI contributes to the broader trend analysis, ensuring that buy/sell signals are not just based on CCI overbought/oversold levels but also validated by moving averages and slope calculations.
4. Trend-Weighted CCI
This version adds weight to recent price action trends, making it more adaptive to current market momentum. The CCI values are influenced by recent high and low prices, adding a trend-following aspect that is missing from the original CCI, which treats all price deviations equally.
This image of EURAD shows for example that when CCI component is green a strong trend is detected which can hold for up to 10 days in this example, ideal for swing trades;
EURAUD 2H
5. Improved Overbought/Oversold Detection
The script incorporates a dynamic overbought/oversold detection zone based on the enhanced CCI. It accounts for market volatility, allowing it to adjust its thresholds (such as the 200 level) more effectively in different market environments. This makes the enhanced CCI better suited for varying market conditions compared to the fixed thresholds of the original CCI.
You can see that the red diamond signal is generated at the absolute top of the price range after which price started to reverse, the detection is based on a cross over value together with Money Flow strength
BTCUSDT 2H
6. Strong Buy/Sell Confirmation
The enhanced CCI works in tandem with other components like Money Flow and Moving Averages to confirm buy or sell signals. This cross-validation makes the indicator less reliant on CCI alone and ensures that the signals generated are stronger and less prone to false positives, which is a common issue with the standalone CCI.
The green diamond buy signal in a strong downtrend is mostly a short retrace of price before continuing down further, yo can use this as an entry signal after the bounce up into an FVG for example. However when price is at a support, meaning price is not moving down further and this occurs this could be a potential reversal signal as shown on the right side on the chart below. FVG is not respected, retested and price continues up.
BTCUSDT 2H
Summary:
In summary, the enhanced CCI in this indicator improves over the original CCI by providing better noise reduction, multi-level analysis, trend integration, and adaptability to different market conditions. These improvements lead to more reliable and actionable trading signals.
2. Money Flow (MF) www.tradingview.com
The Money Flow component tracks the flow of capital in and out of an asset. Positive values indicate strong buying pressure, while negative values show selling pressure. This is smoothed to avoid noise and is used to confirm strong buy or sell conditions.
The Money Flow (MF) in "The Real Breakout Indicator Hawk" measures the flow of capital into or out of an asset, helping to assess the underlying buying or selling pressure in the market.
1. Positive Money Flow (Buying Pressure)
When the MF is positive, it indicates that more money is flowing into the asset, which suggests strong buying interest. This helps confirm that a price increase or breakout to the upside is supported by demand.
2. Negative Money Flow (Selling Pressure)
A negative MF indicates that capital is leaving the asset, reflecting selling pressure. This is a sign that the market is under bearish conditions, and prices are likely to decline or break down.
3. Confirmation of Buy and Sell Signals
The MF is used to confirm buy and sell signals generated by other components of the indicator. When the MF aligns with other bullish signals, it strengthens the buy condition, and similarly, when the MF shows strong selling pressure, it reinforces a sell signal.
4. Filtering Noise
The MF is smoothed to filter out noise, ensuring that only significant movements in buying or selling pressure are considered. This helps avoid false signals and makes the MF a reliable tool for detecting true market strength.
5. Range Sensitivity
The MF operates within defined ranges, ensuring that buy or sell signals are only triggered when the flow of money is strong enough, adding precision to signal generation.
In summary, the Money Flow component is crucial for validating market direction, enhancing signal reliability, and helping traders make more informed decisions based on the underlying capital movement in the market.
3. Moving Averages (MA)
Multiple types of moving averages (SMA, EMA, HMA, etc.) are used to smooth price action and highlight the trend direction. The script supports different types of moving averages, and their slopes are calculated to assist in identifying changes in trend momentum.
The Moving Averages (MA) section of "The Real Breakout Indicator Hawk" plays a critical role in smoothing price data, identifying trends, and generating buy/sell signals. Here’s a breakdown of what it does and how you can use it effectively without diving into the script:
1. Moving Average Types
This section allows the user to choose from different types of moving averages, each with unique characteristics:
SMA (Simple Moving Average): Takes the average of closing prices over a specific period. It’s slower and better suited for detecting long-term trends.
EMA (Exponential Moving Average): Gives more weight to recent prices, making it more responsive to new price action and suitable for short-term trading.
HMA (Hull Moving Average): A smoother and faster moving average, useful for reducing lag in fast-moving markets.
LVMA (Linear Weighted Moving Average): Places the most weight on recent prices, making it even more responsive than EMA.
Alma (Arnaud Legoux Moving Average): A smoother version that reduces noise while maintaining responsiveness to recent price action.
2. Smoothing and Trend Detection
The moving average smooths out price data to remove small fluctuations and focuses on the overall trend. When prices are trading above the moving average, it suggests that the market is in an uptrend. When prices are below the moving average, it indicates a downtrend.
3. Trend Confirmation
The moving average serves as a confirmation tool. When the price crosses above the moving average, it could signal the start of a bullish trend, and when the price crosses below, it may indicate the beginning of a bearish trend.
4. Buy and Sell Signals
Buy Signal: The system detects a buy signal when:
The moving average crosses above 0, indicating a potential upward momentum.
Other indicators like Money Flow and CCI align to confirm the trend.
Sell Signal: A sell signal is triggered when:
The moving average crosses below 0, signaling a potential downtrend.
This signal is further validated by other components such as Money Flow and CCI to reduce false signals.
5. Using Moving Averages in Trading
Crossover Strategy: One of the simplest ways to use moving averages is by employing a crossover strategy. For instance:
When the shorter-term moving average (e.g., 20-period) crosses above a longer-term moving average (e.g., 50-period), this is a bullish crossover, indicating a buy signal.
Conversely, when the shorter-term moving average crosses below the longer-term moving average, this is a bearish crossover, indicating a sell signal.
Trend Following: If you’re trading with the trend, you can use a moving average to stay in the trade as long as the price remains above (for long positions) or below (for short positions) the moving average.
Support and Resistance: Moving averages can also act as dynamic support or resistance levels. For example, in an uptrend, the CCI might bounce off the moving average, offering a good entry point for a long position. In a downtrend, the moving average could act as resistance where prices may reverse, offering a shorting opportunity.
To use the MA section effectively:
Choose the right type of moving average based on your trading style (e.g., use EMA for faster response or SMA for long-term trends).
Watch for crossovers as buy/sell signals, especially in combination with other indicators.
Follow the trend by observing whether the price is above or below the moving average.
Use the moving average as a dynamic support/resistance level to find optimal entry/exit points.
This approach makes the moving average a versatile tool for identifying trends, refining entry and exit points, and confirming overall market direction.
an example when MA crosses below 0, keep in mind that when it it starts curving up and turning green there is a reversal brewing, this could take time...
BTCUSDT 2H
4. Buy Signals
Buy signals are generated when the moving average crosses up, and the Money Flow and other trend-based conditions are met, including CCI levels confirming the strength of the breakout. Additionally, slope calculations and other momentum indicators provide extra confirmation for entries.
5. Sell Signals
Sell signals occur when the moving average crosses down, combined with negative Money Flow, confirming downward pressure. Other trend-based conditions, including the CCI, must also align to validate the signal, and slope calculations ensure that momentum is on the sell side.
6. Slope and Trend Detection
The script includes calculations for the slope of price action over a lookback period to measure trend strength and direction. The slope is normalized to help identify when the market is gaining or losing momentum. This slope is used in conjunction with the moving averages and Money Flow to give more accurate trend signals.
The Slope and Trend Detection component in "The Real Breakout Indicator Hawk" is designed to measure the direction and strength of the market’s trend by calculating the slope of the price action over a specific period. This helps to identify whether the market is gaining or losing momentum, and it is a key element in refining buy/sell signals.
Here’s how the Slope and Trend Detection works and how you can use it effectively without diving into the script:
1. Slope Calculation
Slope is essentially the rate of change of the moving average (or price) over a given number of bars. It measures how steeply the price is moving up or down.
The script calculates the slope by measuring the difference between the moving average over a defined number of bars (e.g., 12 bars in this case). A larger slope indicates a stronger trend, while a smaller slope suggests a weaker or consolidating trend.
2. Normalized Slope
The slope is normalized, meaning it is adjusted to fall within a range that makes it easier to compare across different time frames and markets. This normalization helps to gauge whether the slope is strong or weak relative to historical data.
Positive slopes (above 0) indicate an uptrend or rising price momentum, while negative slopes (below 0) indicate a downtrend or falling price momentum.
3. Trend Detection
The slope of the moving average is used to detect the current trend:
If the slope is positive, the market is in an uptrend.
If the slope is negative, the market is in a downtrend.
The stronger the slope (the steeper it is), the stronger the trend. A small slope indicates a weak trend or consolidation.
4. Slope Thresholds
The system uses thresholds to determine the significance of the slope. These thresholds are set as upper and lower bounds:
Upper Threshold: If the slope exceeds this threshold, the trend is considered strong, and it could trigger a buy signal.
Lower Threshold: If the slope falls below this threshold (into the negative range), it indicates a strong downtrend, and it could trigger a sell signal.
These thresholds help filter out weak or false signals that occur in sideways or low-momentum markets.
5. Positive and Negative Slope Arrays
The system keeps track of both positive and negative slopes over a defined lookback period (e.g., 500 bars). By storing these values, it creates a historical context that helps to assess the current slope in relation to past price movements.
It calculates the standard deviation and the average of these slopes to dynamically adjust the thresholds for each market condition, making the trend detection more adaptive to different types of assets or market phases.
6. Using Slope and Trend Detection in Trading
Buy Signal with Positive Slope: When the slope is positive and exceeds a certain threshold, it confirms that the market is in a strong uptrend. This can be used as a signal to enter a long position or add to existing long trades.
Sell Signal with Negative Slope: When the slope turns negative and falls below the lower threshold, it signals a strong downtrend, indicating a potential short-selling opportunity or the time to exit long positions.
Avoiding Flat Markets: If the slope remains close to zero (neither strongly positive nor negative), it suggests a lack of clear trend or a consolidating market. In these conditions, it might be better to avoid taking new trades or use additional filters to confirm signals.
7. Slope-Based Trend Strength Indicator
You can also use the slope as a measure of trend strength:
Strong Trend: When the slope is steep (either positive or negative), it indicates strong momentum, and you can be more confident in holding a trade in that direction.
Weak Trend or Consolidation: When the slope is flat, it indicates weak price momentum, which may signal a period of consolidation or indecision in the market.
8. Visual Representation
The slope is often visually represented as a gradient or line that fluctuates around a central point (usually zero). Positive values are shown in one color (e.g., green for an uptrend), while negative values are shown in another color (e.g., red for a downtrend). This allows traders to quickly identify the current trend direction and its strength.
Summary:
To use Slope and Trend Detection effectively:
Monitor the slope to determine the trend direction (positive = uptrend, negative = downtrend).
Look for thresholds to identify strong trends. For instance, a steep positive slope signals a strong uptrend, while a steep negative slope signals a strong downtrend.
Use slope changes to confirm buy/sell signals. For example, if you receive a buy signal and the slope is positive and increasing, it confirms that momentum is behind the trade.
Avoid low-slope periods when the slope is close to zero, indicating a lack of trend or sideways market conditions.
This approach helps traders stay on the right side of the trend while avoiding periods of low momentum, enhancing the accuracy of trade signals.
7. Banker Fund Flow Trend
This component identifies potential large institutional moves by tracking specific patterns in price and volume data. When the institutional or "banker" entry or exit conditions are met, it highlights these moments with candles and generates alerts.
The Banker Fund Flow Trend in "The Real Breakout Indicator Hawk" helps detect the flow of institutional (or "smart money") into and out of the market by tracking price trends and large player activity. It uses red and yellow candles to signal when institutional money is influencing the market.
Key Points:
Yellow Candles (Banker Entry):
A yellow candle is plotted when institutional money starts flowing into the market.
This signals a potential buy opportunity, as large market players are likely pushing prices upward.
Red Candles (Banker Exit):
A red candle appears when institutional money starts exiting the market.
This is a signal to consider selling or exiting long positions, as institutional selling could drive prices lower.
Usage:
Yellow candles: Use these as signals to enter long trades or add to existing positions, confirming upward momentum driven by institutional buyers.
Red candles: Treat these as signals to exit long trades or consider short positions, as institutional selling may lead to further downside.
BTCUSDT 2H
The yellow and red candles provide clear, actionable signals for aligning trades with institutional flows, ensuring you’re following the "smart money."
8. Dynamic Buy/Sell Calculations
A dynamic component is designed to refine the buy and sell signals further based on additional conditions like price patterns, volatility, and Money Flow. This ensures that signals are more responsive to changing market conditions.
The Dynamic Buy/Sell Calculations in "The Real Breakout Indicator Hawk" are designed to refine entry and exit points for trades by using additional conditions beyond simple crossovers. These calculations adapt to the current market conditions, making them more responsive to changes in volatility, trend strength, and momentum.
Key Features:
Dynamic Buy Calculation:
The indicator generates a buy signal when multiple conditions align. These conditions include the money flow (MF) being within a favorable range, the moving average (MA) confirming upward momentum, and the CCI and other trend components indicating strength.
This makes the buy signal more reliable, as it considers multiple aspects of market behavior (price, momentum, and money flow) to avoid false entries.
Dynamic Sell Calculation:
Similarly, the sell signal is triggered when the dynamic conditions indicate downward momentum.
This includes:
The moving average crossing down.
Negative money flow, suggesting selling pressure.
Other trend signals confirming a bearish move.
The dynamic nature of these conditions ensures that sell signals are only generated when there’s a high probability of continued downside movement.
Adaptive to Market Conditions:
The dynamic nature of these calculations means that the buy/sell signals adapt to market changes, like volatility spikes or sudden trend reversals. Instead of relying on static conditions, the system adjusts to current price movements and volatility.
Avoiding Noise:
By adding multiple filters like MF thresholds, slope, and moving averages, the dynamic calculations help reduce false signals that occur in noisy, sideways markets. This helps traders avoid entering trades during periods of low momentum or unclear trends.
How to Use:
Buy Signals: Use these signals to enter long trades when the dynamic conditions align, confirming that upward momentum is strong and backed by institutional flows.
BTCUSDT 2H
Aqua marker/cross signals (price manipulation/continuation)
BTCUSDT 2H
Sell Signals: Use the sell signals to exit long positions or enter short trades when the market shows signs of bearish momentum, confirmed by multiple conditions like MA crossovers and negative money flow.
BTCUSDT 2H
In summary, the Dynamic Buy/Sell Calculations provide a more sophisticated approach to generating trade signals by combining various trend and momentum indicators, helping traders make more informed decisions in different market conditions.
This part of the code is identifying two key trading signals: moments to buy and moments to sell based on the behavior of a calculated trend line.
Buy Condition:
The system looks for a situation where the trend has been moving downward but has started to reverse upward. Specifically, it checks if the trend was declining a little while ago, then stopped falling, and is now starting to rise. If these conditions are met and the trend is still below a certain level, the system considers this a possible time to buy.
Sell Condition:
The opposite happens for selling. The system monitors for a situation where the trend has been moving upward but starts to turn downward. It checks if the trend was rising, leveled off, and now seems to be starting to fall. If these conditions are met and the trend is above a certain level, this could indicate a good time to sell.
Visual Markers:
To help the user easily see these signals on a chart, the system places symbols at specific points. A marker appears on the chart where the conditions for buying or selling are met, allowing the trader to quickly spot potential entry or exit points in the market.
In summary, this logic is designed to detect possible changes in trend direction and signal appropriate times to consider buying or selling, with clear visual markers on the chart for quick identification.
9. Alerts for Buy and Sell
The indicator provides built-in alert conditions for both buy and sell signals. When these conditions are met, the system generates alerts, making it suitable for automated monitoring.
Each of these components works together to detect potential breakout opportunities, trend continuations, and reversals, making the indicator suitable for both short-term and long-term trading strategies.
ค้นหาในสคริปต์สำหรับ "the script"
[ADOL_]Trend_Oscillators_MTF
ENG) Trend_Oscillator_MTF
introduction)
This is a trend analyzer implemented in the form of an oscillator.
An oscillator is a technical analysis tool that identifies the direction of market trends and determines the time period. Making it an oscillator means creating range. By setting the upper and lower limits like this, the unlimited expansion area that can appear on the chart is limited. As a limited area is created, we can identify oversold and overbought areas, which is good for checking momentum.
Through oscillatorization, you can find overbought, oversold, and current trend areas.
It adopts MTF and is a simple but functional indicator.
To use multiple time frames, use the timeframe.multiplier function.
A table was created using the table.new function, and various information windows were installed on the right side of the chart.
I hope this can be a destination for many travelers looking for good landmarks.
- 8 types of moving averages can be selected (in addition to independently developed moving averages), trend area display, signal display, up to 3 multi-time chart overlapping functions, information table display, volatility and whipsaw search, and alerts are possible.
- You can set various time zones in Timeframe. With three timeframes, you can check the conditions overlapping time at a glance.
principle)
Set up two moving averages with different speeds and make the relative difference.
Create the speed difference between the two moving averages using methods such as over = crossover(fast, slow) and under = crossunder(fast, slow).
The point at which the difference in relative speed decreases is where the possibility of inflection is high. Through the cross code, you can find out when the speed difference becomes 0.
Simply crossing the moving average is easy. To fine-tune the speed difference, it is necessary to re-establish the relationship between functions.
Painting the green and red areas is designed to be painted when the three time frames overlap.
Using the code of fill(fast, slow, color = fast>= slow? color.green: color.red, transp = 80, title = "fillcolor")
You can color and distinguish areas.
MA: You can select the MA_type. This is a necessary option because the profit/loss ratio for each item varies depending on the type of moving average.
Start: The starting value to set the oscillator range.
End: This is the last value to set the oscillator range.
Lenght: This is the number of candles used to calculate the calculation formula in the oscillator.
Timeframe: Set the time to overlap with up to 3 time frames.
repaint: You can choose whether to apply repaint. The default is OFF.
The coding for repaint settings for the indicator was written using the recommended method recommended by TradingView.
reference :
security(syminfo.tickerid, tf, src)
Trading method)
With the Multi-Time-Frame (MTF) function, the time zone set in the indicator is displayed the same in any chart time zone.
The repaint problem that occurred when using MTF was resolved by referring to TradingView's recommended code.
User can decide whether to repaint or not. The default is OFF.
- signal
Buy and Sell signals are displayed when there are 3 stacks. Even if there is no triple overlap, you can decide to buy or sell at the point where the short-term line and long-term line intersect.
Entry is determined through Buy and Sell signals, and exit is determined through BL (BuyLoss) and SL (SellLoss).
BL and SL can also be applied as entry.
You can judge overlap by the color of the lines. When two conditions overlap, it is orange, and when one condition overlaps, it is blue.
- Divergence
Divergence is a signal that arises from a discrepancy between the oscillator and the actual price.
Divergence can be identified because the range is set with conditions that have upper and lower limits.
- trend line
As shown in the picture, draw a downward trend line connecting the high points in the same area.
As shown in the picture, an upward trend line is drawn connecting the low points in the same area.
It can be used to view trend line breakout points that candles cannot display.
- Find a property for sale by amplitude
When the low point in the red area and the high point in the green area occur, the difference is regarded as one amplitude and the range is set.
Here, one amplitude becomes a pattern value that can go up or down, and this pattern value acts as support/resistance. It was developed in a unique way that is different from traditional methods and has a high standard of accuracy. This works best when using that indicator. Use 1, 2, 3, or 4 multiples of the amplitude range.
A multiple of 2 is a position with a high probability of a retracement.
- Whipsaw & volatility search section
Whipsaw refers to a trick that causes frequent trading in a convergence zone or confuses the trend in the opposite direction before it occurs. Whip saws are usually seen as having technical limitations that are difficult to overcome.
To overcome this problem, the indicator was created to define a section where whipsaw and volatility can appear. If a whipsaw & volatility indicator section occurs, a big move may occur later.
Alert)
Buy, Sell, BuyLoss, SellLoss, Whipsaw alert
Disclaimer)
Scripts are for informational and educational purposes only. Use of the script does not constitute professional and/or financial advice. You are solely responsible for evaluating the risks associated with your script output and use of the script.
KOR) 트렌드_오실레이터_MTF
소개)
이것은 오실레이터 형태로 구현된 트렌드 분석기 입니다.
오실레이터는 시장의 추세방향을 확인하고 기간을 결정하는 기술적 분석 도구입니다. 오실레이터로 만드는 것은 범위가 생기는 것을 의미합니다. 이렇게 상한과 하한을 정함으로써, 차트에서 나타날 수 있는 무제한적인 확장영역이 제한됩니다. 제한된 영역이 만들어짐에 따라 우리는 과매도와 과매수 구간을 식별할 수 있게 되며, 모멘텀을 확인하기 좋습니다.
오실레이터화를 통해, 과매수와 과매도, 현재의 트렌드 영역을 잘 찾을 수 있습니다.
MTF를 채택했으며, 단순하지만, 기능적으로 훌륭한 지표입니다.
멀티타임프레임을 사용하기 위해 timeframe.multiplier 함수를 사용합니다.
table.new 함수를 사용하여 table을 만들고, 차트 우측에 여러가지 정보창을 갖췄습니다.
좋은 지표를 찾는 많은 여행자들에게 이곳이 종착지가 될 수 있기를 바랍니다.
- 이평선 종류 8종 선택(독자적으로 개발한 이평선 추가), 추세영역표시, 시그널 표기, 최대 3개 멀티타임차트 중첩기능, 정보테이블 표시, 변동성과 휩쏘찾기, 얼러트가 가능합니다.
- Timeframe에서 다양한 시간대를 설정할 수 있습니다. 3개의 Timeframe을 통해 시간을 중첩한 조건을 한눈에 확인할 수 있습니다.
원리)
속도가 다른 두 개의 이평선을 설정하고 상대적인 차이를 만듭니다.
over = crossover(fast, slow) , under = crossunder(fast, slow) 와 같은 방법으로 두개의 이평선의 속도차이를 만듭니다.
상대적 속도의 차이가 줄어드는 시점은 변곡의 가능성이 높은 자리입니다. cross code를 통해 속도차가 0이 되는 시점을 알 수 있습니다.
단순히 이평선을 교차하는 것은 쉽습니다. 세밀하게 속도차이를 조정하는데 함수간의 관계를 다시 설정할 필요가 있습니다.
초록색과 빨간색의 영역을 칠하는 것은 3가지 타임프레임이 중첩될 때 칠하도록 만들어졌습니다.
fill(fast, slow, color = fast>= slow? color.green: color.red, transp = 80, title = "fillcolor") 의 코드를 사용하여
영역을 색칠하고 구분할 수 있습니다.
MA : MA_유형을 선택할 수 있습니다. 이평선의 종류에 따라 종목당 손익비가 달라지므로 꼭 필요한 옵션입니다.
Start : 오실레이터 범위를 설정할 시작값입니다.
End : 오실레이터 범위를 설정할 마지막값입니다.
Lenght : 오실레이터에서 계산식을 산출하기 위한 캔들의 개수입니다.
Timeframe : 최대 3개의 타임프레임으로 중첩할 시간을 설정합니다.
repaint : 리페인팅을 적용할지 선택할 수 있습니다. 기본값은 OFF 입니다.
해당 지표의 리페인트 설정에 관한 코딩은 트레이딩뷰에서 권장하는 추천 방법으로 작성되었습니다.
참고 :
security(syminfo.tickerid, tf, src)
매매방법)
Multi-Time-Frame(MTF) 기능으로 지표에서 설정한 시간대가 어느 차트 시간대에서나 동일하게 표시됩니다.
MTF 사용시 발생하는 리페인트 문제는 트레이딩뷰의 권장코드를 참고하여 해결했습니다.
사용자가 리페인트 여부를 결정할 수 있습니다. 기본값은 OFF 입니다.
- 시그널
시그널의 Buy와 Sell은 3중첩일 경우 표시됩니다. 3중첩이 아니라도 단기선과 장기선이 교차되는 시점에서 매매를 결정할 수 있습니다.
Buy와 Sell 시그널에서 진입을 결정하고 BL(BuyLoss)와 SL(SellLoss) 에서 exit를 결정합니다.
BL과 SL을 진입으로 응용할 수도 있습니다.
라인의 컬러로 중첩을 판단할 수 있습니다. 2개의 조건이 중첩되면 오렌지, 1개의 조건이 중첩되면 블루컬러입니다.
- 다이버전스
다이버전스는 오실레이터와 실제 가격의 불일치에서 발생하는 신호입니다.
상한과 하한이 있는 조건으로 범위를 설정하였기 때문에 다이버전스를 식별가능합니다.
- 추세선
그림과 같이 같은 영역의 고점을 이어 하락추세선을 긋습니다.
그림과 같이 같은 영역의 저점을 이어 상승추세선을 긋습니다.
캔들이 표시할 수 없는 추세선돌파 지점을 볼 수 있게 활용가능합니다.
- 진폭으로 매물대 찾기
빨간색 영역의 저점과 초록색 영역의 고점이 발생할 때, 그 차이를 하나의 진폭으로 보고 범위를 설정합니다.
여기서 하나의 진폭은 위나 아래로 갈 수 있는 패턴값이 되며, 이 패턴값은 지지/저항으로 작용합니다. 전통적인 방식에 없는 독창적인 방식으로 개발된 것으로 정확성 높은 기준입니다. 이것은 해당 지표를 사용할 때 가장 잘 맞습니다. 진폭 범위의 1배수,2배수,3배수,4배수 자리를 사용합니다.
2배수 자리는 다시 돌아오는 되돌림 확률이 높은 위치입니다.
- 휩쏘&변동성 찾기 구간
휩쏘는 수렴구간에서 잦은 매매를 유발하거나, 추세가 발생하기 전에 반대방향으로 혼란을 주는 속임수를 의미합니다. 휩쏘는 보통 극복하기 어려운 기술적 한계로 여겨집니다.
해당지표에서는 이를 극복하기 위해 휩쏘와 변동성이 나타날 수 있는 구간을 정의하도록 만들었습니다. 휩쏘&변동성 표시 구간이 발생하면 이후 큰 움직임이 발생할 수 있습니다.
얼러트)
Buy, Sell, BuyLoss, SellLoss, Whipsaw alert
면책조항)
스크립트는 정보 제공 및 교육 목적으로만 사용됩니다. 스크립트의 사용은 전문적 및/또는 재정적 조언으로 간주되지 않습니다. 스크립트 출력 및 스크립트 사용과 관련된 위험을 평가하는 책임은 전적으로 귀하에게 있습니다.
Liquidity Heatmap [BigBeluga]The Liquidity Heatmap is an indicator designed to spot possible resting liquidity or potential stop loss using volume or Open interest.
The Open interest is the total number of outstanding derivative contracts for an asset—such as options or futures—that have not been settled. Open interest keeps track of every open position in a particular contract rather than tracking the total volume traded.
The Volume is the total quantity of shares or contracts traded for the current timeframe.
🔶 HOW IT WORKS
Based on the user choice between Volume or OI, the idea is the same for both.
On each candle, we add the data (volume or OI) below or above (long or short) that should be the hypothetical liquidation levels; More color of the liquidity level = more reaction when the price goes through it.
Gradient color is calculated between an average of 2 points that the user can select. For example: 500, and the script will take the average of the highest data between 500 and 250 (half of the user's choice), and the gradient will be based on that.
If we take volume as an example, a big volume spike will mean a lot of long or short activity in that candle. A liquidity level will be displayed below/above the set leverage (4.5 = 20x leverage as an example) so when the price revisits that zone, all the 20x leverage should be liquidated.
Huge volume = a lot of activity
Huge OI = a lot of positions opened
More volume / OI will result in a stronger color that will generate a stronger reaction.
🔶 ROUTE
Here's an example of a route for long liquidity:
Enable the filter = consider only green candles.
Set the leverage to 4.5 (20x).
Choose Data = Volume.
Process:
A green candle is formed.
A liquidity level is established.
The level is placed below to simulate the 20x leverage.
Color is applied, considering the average volume within the chosen area.
Route completed.
🔶 FEATURE
Possibility to change the color of both long and short liquidity
Manual opacity value
Manual opacity average
Leverage
Autopilot - set a good average automatically of the opacity value
Enable both long or short liquidity visualization
Filtering - grab only red/green candle of the corresponding side or grab every candle
Data - nzVolume - Volume - nzOI - OI
🔶 TIPS
Since the limit of the line is 500, it's best to plot 2 scripts: one with only long and another with only short.
🔶 CONCLUSION
The liquidity levels are an interesting way to think about possible levels, and those are not real levels.
Vola2vola Volatility indicatorHello everyone!
For those who remember vola2vola volatility script, we are excited to bring it back within the Myfractalrange Tradingview account!
As you know, Volatility is very important to assets and many people use it to trade. This tool automate the calculation of the volatility of every asset as well as provide an estimated value of its "Trend" and "Trade".
The idea in this script is to allow users to have an idea of the current volatility regime of the asset he is monitoring: Is its volatility Bullish or Bearish Trend, Bearish or Bullish Trade? Is its volatility compressed to a previous minimum value? Is it about to experience a spike in volatility? Let's dig together into how this tool works and how you could integrate it into your trading shall we?
What are the data provided by the script, let see one by one:
- Volatility: The value of what vola2vola calls the "synthetic" volatility of the asset is calculated using a custom formula based on the VIXFIX formula. Default colour is blue
- Trade : Trade is generated using an arbitrary and fixed look back period, it acts as a short-term trend. It will give the user the possibility to know if the volatility of the asset is still trending short-term or not. Default colour is black
- Trend: Trend is also generated using an arbitrary and fixed look back period (20 times the one used for Trade), it acts as a longer-term trend. It works the same way as Trade and will give the user the possibility to know if the volatility of the asset is trending a longer-term basis or not. Default colours are: red when the Trend of the volatility of the asset is Bearish and green when the Trend of the volatility of the asset is Bullish
- 52-weeks high & low: Based on the highest and lowest value of Volatility in the past 52 weeks, a 52-weeks high and a 52-weeks low will be marked. These values usually acts as Resistance and Support for volatility. Default colour is black and they are in dotted lines
Here are some of the questions you need to know the answer to before using this script:
- How do you define a "Bullish/Bearish volatility Trade"? Volatility is Bullish Trade is when Volatility is above Trade and it is Bearish Trade when volatility is below Trade
- How do you define a "Bullish/Bearish volatility Trend"? Volatility is Bullish Trend is when Volatility is above Trend and it is Bearish Trend when volatility is below Trend
- On which time frame should i use this script? You want to use the Daily time frame. Although, for short term moves in the volatility space, users could monitor the Hourly timeframe
Understanding the volatility of an asset, along with the bullish or bearish nature of its Trade and Trend, is crucial for investors. Assets with decreasing volatility tend to appreciate in value, while those with increasing volatility tend to depreciate. Therefore, we recommend investors be aware of the volatility situation of the asset they are holding in their portfolio.
Here are the different scenarios that you will encounter on a Daily timeframe and how to interpret them:
- Volatility is below Trade & Trend and Volatility is Bearish Trade and Trend: It is the most Bullish set up for the price of an asset
- Volatility is above Trade & Trend and Volatility is Bullish Trade and Trend: It is the most Bearish set up for the price of an asset
- Any other set up suggests uncertainty, caution is therefore recommended
These are some cases that you could experience while using this script:
1) Bearish Volatility set up on a daily timeframe:
In this example using SPY, when its Volatility is Bearish Trend on a daily timeframe, the price of SPY tends to appreciate
2) Bullish Volatility set up on a daily timeframe:
In this example using SPY, when its Volatility is Bullish Trend on a daily timeframe, the price of SPY tends to depreciate
We hope that you will find these explanations useful, please contact us by private message for access.
Enjoy!
DISCLAIMER: No sharing, copying, reselling, modifying, or any other forms of use are authorised. This script is strictly for individual use and educational purposes only. This is not financial or investment advice. Investments are always made at your own risk and are based on your personal judgement. Myfractalrange is not responsible for any losses you may incur. Please invest wisely.
Ema Short Long Indicator[CHE]█ CONCEPTS
This Pine Script is an EMA Short Long indicator that displays the crossing EMA lines on the chart. The indicator uses three exponential moving averages (EMAs) to generate the buy and sell signals. The EMA lines are plotted as green (uptrend) and red (downtrend) lines. When the green line is above the white signal line, the indicator generates a buy signal, when the green line is below the white signal line, the indicator generates a sell signal. Arrows are also displayed marking the buy and sell signals. There is also an option to allow indicator repainting or not. Finally, users can also set alerts to be alerted to potential trading opportunities.
Note: please do not disable "time frame gaps". Allows to calculate the indicator on a Timeframe (TF) different from that of the chart Time window. The TF should ideally be higher than the charts to provide a broader perspective than
the TF of the chart. Using TFs lower than the chart's will deliver fragmentary results, since only the last value of intrabar is displayed (multiple values cannot be displayed for a single chart bar). The Gaps setting determines the behavior when the TF is higher than the TF of the chart. If 'gaps' is checked, higher TF values only come in and are interconnected on the diagram when the higher TF completed. This has the advantage of avoidance Real-time epainting. If Gaps is not enabled, Gaps are filled with the last higher TF value calculated, which will not produce a repaint Values on historical bars but repaint values realtime.
█ HOW TO USE IT
Load the indicator on an active chart (see the Help Center if you don't know how).
Time period
By default, the script uses an auto-stepping mechanism to adjust the time period of its moving window to the chart's timeframe. The following table shows chart timeframes and the corresponding time period used by the script. When the chart's timeframe is less than or equal to the timeframe in the first column, the second column's time period is used to calculate the Ema Short Long Indicator :
Chart Time
timeframe period
1min 🠆 1H
5min 🠆 4H
1H 🠆 1D
4H 🠆 3D
12H 🠆 1W
1D 🠆 1M
1W 🠆 3M
█ DESCRIPTION
The script begins by setting up the chart indicator with a short title, "ESLI", and enabling it as an overlay. It then initializes several variables for time conversions, to be used later in the script.
The timeStep_translate() function converts the timeframe of the chart into a string representing a larger time interval, based on the number of seconds in the timeframe. The resulting string is used to label the horizontal axis of the chart.
Next, the script defines several input variables that can be modified by the user. These include the colors of the EMA lines and the signals, whether or not the indicator is allowed to repaint (i.e. update past values based on future data), and the number of periods used to calculate the EMA and signal lines.
The f_security() function calls the request.security() function to fetch data from the specified security and timeframe, and is used to calculate the EMA and signal lines using the ta.ema() function. The clo variable is assigned the closing price data, adjusted for repainting and timeframe.
The EMA line is calculated using a weighted average of the EMA over the specified period and two times that period, as well as three times that period, divided by six. The signal line is calculated as the EMA of the EMA line over the specified period.
The col_css variable sets the color of the EMA line based on whether it is currently above or below the signal line. The script then plots the EMA and signal lines, and uses the plotshape() function to indicate long and short signals based on the crossovers and crossunders of the EMA and signal lines.
Finally, the script sets up alert conditions using the alertcondition() function to notify the user when a long or short signal is generated, including information about the symbol and closing price.
█ SPECIAL THANKS
Special thanks to LOXX, I wanted to take a moment to express my gratitude for his valuable input in the EMA calculation. His insights and expertise have greatly helped me in improving my Pine Script coding skills. Thanks to his suggestion, I was able to better understand the EMA formula and implement it effectively in my script.
Your generosity in sharing your knowledge and experience is truly appreciated. It is through collaboration and exchanging ideas that we can all grow and become better in our craft.
This script provides exact signals that, with suitable additional indicators, provide very good results.
Best regards
Chervolino
Strat AssistantStrat Assistant
This script will help you follow the strat. While other collections of scripts exist to do similar functionality, the idea of this (work in progress) is to be a one stop shop for all things strat that will evolve over time. Fairly new to the strat and pine script. The script is for informational purposes only. Please do you due diligence.
Features:
=Candle numbering: will number candles underneath based on the prior candle. 1 for an inside bar 2 for a directional bar (up or down) and 3 for an outside bar.
=Candle coloring: will highlight candles. Yellow for an inside candle, magenta for an outside candle, red for a 2 down candle, green for a 2 up candle. It will not modify the outside border of the candle so you can still see green if the open was lower than the close or red if the close was below open.
=Candle shape: will place an arrow up if the 2 candle is a directional UP and arrows down if the 2 candle is a directional DOWN. It will display red if it's bearish and green if it's bullish.
=Strat combos: will provide a text description of all currently applicable strat combinations if they are active at the top right of the chart. It will display red if it's bearish and green if it's bullish.
=Actionable signals: will provide text description of actionable signals if they are active on the bottom right of the chart. Inside bar if the bar is inside the prior bar, the color of this signal will be blue (shows better on white background). Hammer will be 75% of the candle is at the bottom and the open and close are above the 75% of the wick. Hammers will display green for bullish. Shooters are just the opposite of hammers, 75% of the wick is at the top and the open and close are below 75% of the wick. Shooters will display at red for bearish.
=Time Frame Continuity: will provide time frame continuity across 15m, 30m, Hour, Day, Week, Quarter, Year with green arrows up if the close is above the open for the given time frame, or red arrows down if the close is below the open for the given time frame. This will also look to determine if the time frame is applicable based on what time frame the user selects as well as ensures history exists for the given time frame.
Backlog / Work in progress:
=Opacity for time frame continuity
=Line indicators (or maybe just a label) for highs and lows of previous periods (hour, day, week, quarter)
=Alert conditions
=User input for various indicators
The MATRIX: Ultimate Crypto Position Strategy (Alert Version)Welcome back everyone,
It's been a while since our last post. In recent months we have worked on all kinds of projects, but more on that later. In the meantime, we also received a lot of positive feedback about our original 'The MATRIX: Ultimate Crypto Position Strategy' script.
One of the many requests was whether we could release an alert version.
However, since Pinescript does not provide the alert functionality in a strategy type script, it had to be converted to a study type script. Besides that, we have also added a stop-loss functionality. This release has the same internal algorithm as the original 'The MATRIX: Ultimate Crypto Position Strategy' script. But instead of showing back test results, this script provides the functionality to add alerts that can notify the user via email / pop-up / sms / app once a signal is given! You must manually add these alerts via TradingView. If you need help setting up these alerts, feel free to ask in the comment box or send us a dm.
***The script is invite-only, message us to get script access***
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The MATRIX: Ultimate Crypto Position Strategy should be used as follows:
• The trading strategy was designed and optimized for trading cryptocurrencies only ; furthermore it works best on established high market cap cryptocurrencies that have a clear trend such as:
BTCUSD
ETHUSD
LTCUSD
XRMUSD
EOSUSD
ADAUSD
DASHUSD
ETCUSD
• The trading strategy is based on swing/position methodology. The script must therefore be used on daily timeframe candles only (1D).
• Use USD trading pairs only (e.g. use ETHUSD instead of the ETHBTC) since the individual trend is captured more effectively and therefore gives better results.
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The MATRIX:Ultimate Crypto Position Strategy is based on the following indicators:
• Ichimoku Cloud ; acts as the leading indicator.
• Volume ; without strong volume , a market move is not valid.
• MACD and Vortex ; both being used as confirmation indicators.
• Choppiness index ; avoids trading in choppy markets.
• Bullish/ Bearish Regular Divergences in combination with RSI to spot tops and bottoms.
• Simple and Exponential Moving Averages ; prêvents trading against the trend.
The trading strategy is easy to use, trend based and without repainting, meaning once a signal has been made it is permanent and that no future data is used in the decision making. It detects the trend and filters out market noise based on more than 10 technical indicators. ONLY when all indicators align with each other the algorithm prints a BUY or SELL signal. The trading strategy provides high probability trading signals and minimizes risk! This script aims to capture the profit from longer term trending moves and by doing so filters out non-substantial trends and avoids the associated risks with these trades.
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The MATRIX: Ultimate Crypto Position Strategy has the following features:
• ALERTS can be enabled which can notify the user through email/popup/app once a signal is given.
• Automatically generated Buy / Sell alerts in the form of a label.
• NO Repaint once candle is closed.
• SAFEGUARD ; custom built-in security prevẹnts trading when the price is out of equilibrium.
• Customizable Display for the Ichimoku cloud indicator display.
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Reminder: Use this trading strategy at your own risk and trade responsibly. We are not responsible for any financial loss using this strategy.
***The script is invite-only, message us to get script access***
McGinley Dynamic (Improved) - John R. McGinley, Jr.For all the McGinley enthusiasts out there, this is my improved version of the "McGinley Dynamic", originally formulated and publicized in 1990 by John R. McGinley, Jr. Prior to this release, I recently had an encounter with a member request regarding the reliability and stability of the general algorithm. Years ago, I attempted to discover the root of it's inconsistency, but success was not possible until now. Being no stranger to a good old fashioned computational crisis, I revisited it with considerable contemplation.
I discovered a lack of constraints in the formulation that either caused the algorithm to implode to near zero and zero OR it could explosively enlarge to near infinite values during unusual price action volatility conditions, occurring on different time frames. A numeric E-notation in a moving average doesn't mean a stock just shot up in excess of a few quintillion in value from just "10ish" moments ago. Anyone experienced with the usual McGinley Dynamic, has probably encountered this with dynamically dramatic surprises in their chart, destroying it's usability.
Well, I believe I have found an answer to this dilemma of 'susceptibility to miscalculation', to provide what is most likely McGinley's whole hearted intention. It required upgrading the formulation with two constraints applied to it using min/max() functions. Let me explain why below.
When using base numbers with an exponent to the power of four, some miniature numbers smaller than one can numerically collapse to near 0 values, or even 0.0 itself. A denominator of zero will always give any computational device a horribly bad day, not to mention the developer. Let this be an EASY lesson in computational division, I often entertainingly express to others. You have heard the terminology "$#|T happens!🙂" right? In the programming realm, "AnyNumber/0.0 CAN happen!🤪" too, and it happens "A LOT" unexpectedly, even when it's highly improbable. On the other hand, numbers a bit larger than 2 with the power of four can tremendously expand rapidly to the numeric limits of 64-bit processing, generating ginormous spikes on a chart.
The ephemeral presence of one OR both of those potentials now has a combined satisfactory remedy, AND you as TV members now have it, endowed with the ever evolving "Power of Pine". Oh yeah, this one plots from bar_index==0 too. It also has experimental settings tweaks to play with, that may reveal untapped potential of this formulation. This function now has gain of function capabilities, NOT to be confused with viral gain of function enhancements from reckless BSL-4 leaking laboratories that need to be eternally abolished from this planet. Although, I do have hopes this imd() function has the potential to go viral. I believe this improved function may have utility in the future by developers of the TradingView community. You have the source, and use it wisely...
I included an generic ema() plot for a basic comparison, ultimately unveiling some of this algorithm's unique characteristics differing on a variety of time frames. Also another unconstrained function is included to display some the disparities of having no limitations on a divisor in the calculation. I strongly advise against the use of umd() in any published script. There is simply just no reason to even ponder using it. I also included notes in the script to warn against this. It's funny now, but some folks don't always read/understand my advisories... You have been warned!
NOTICE: You have absolute freedom to use this source code any way you see fit within your new Pine projects, and that includes TV themselves. You don't have to ask for my permission to reuse this improved function in your published scripts, simply because I have better things to do than answer requests for the reuse of this simplistic imd() function. Sufficient accreditation regarding this script and compliance with "TV's House Rules" regarding code reuse, is as easy as copying the entire function as is. Fair enough? Good! I have a backlog of "computational crises" to contend with, including another one during the writing of this elaborate description.
When available time provides itself, I will consider your inquiries, thoughts, and concepts presented below in the comments section, should you have any questions or comments regarding this indicator. When my indicators achieve more prevalent use by TV members, I may implement more ideas when they present themselves as worthy additions. Have a profitable future everyone!
Trend with ADX/EMA - Buy & Sell SignalsThis script is designed to help traders make buy and sell decisions based on trend analysis using two key methods: ADX (Average Directional Index) and EMA (Exponential Moving Averages). Here's a breakdown in simple terms:
What Does It Do?
Identifies the Trend's Strength and Direction:
Uses the ADX indicator to determine how strong the trend is.
Compares two lines (DI+ and DI−) to identify whether the trend is moving up or down.
Generates Buy and Sell Signals:
Uses two EMAs (a fast one and a slow one) to check when the price crosses key levels, signaling a possible buy or sell opportunity.
Plots visual indicators (arrows and labels) for easy interpretation.
Color-Codes the Chart:
Highlights the background in green when the trend is bullish (uptrend).
Highlights the background in red when the trend is bearish (downtrend).
Alerts the User:
Creates alerts when specific conditions for buying or selling are met.
Key Components:
1. ADX (Trend Strength & Direction)
What is ADX?
ADX measures how strong the trend is (not the direction). Higher ADX means a stronger trend.
It also calculates two lines:
DI+: Measures upward movement strength.
DI−: Measures downward movement strength.
How It Works in the Script:
If DI+ is greater than DI−, it’s a bullish trend (upward).
If DI− is greater than DI+, it’s a bearish trend (downward).
The background turns green for an uptrend and red for a downtrend.
2. EMA (Buy and Sell Decisions)
What is EMA?
EMA is a moving average that gives more weight to recent prices. It’s used to smooth out price fluctuations.
How It Works in the Script:
The script calculates two EMAs:
Fast EMA (short-term average): Reacts quickly to price changes.
Slow EMA (long-term average): Reacts slower and shows overall trends.
When the Fast EMA crosses above the Slow EMA, it’s a signal to Buy.
When the Fast EMA crosses below the Slow EMA, it’s a signal to Sell.
These signals are marked on the chart as "Buy" and "Sell" labels.
3. Buy and Sell Alerts
The script sets up alerts for the user:
Buy Alert: When a crossover indicates a bullish signal.
Sell Alert: When a crossunder indicates a bearish signal.
Visual Elements on the Chart:
Background Colors:
Green: When the DI+ line indicates an uptrend.
Red: When the DI− line indicates a downtrend.
EMA Lines:
Green Line: Fast EMA.
Red Line: Slow EMA.
Buy/Sell Labels:
"Buy" label: Shown when the Fast EMA crosses above the Slow EMA.
"Sell" label: Shown when the Fast EMA crosses below the Slow EMA.
Why Use This Script?
Trend Analysis: Helps you quickly identify the strength and direction of the market trend.
Buy/Sell Signals: Gives clear signals to enter or exit trades based on trend and EMA crossovers.
Custom Alerts: Ensures you never miss a trading opportunity by notifying you when conditions are met.
Visual Simplicity: Makes it easy to interpret trading signals with color-coded backgrounds and labeled arrows.
Buy&Sell Hollow CandlesThe Hollow Candles Script is a type of candlestick analysis script designed to highlight the following:
Purpose of the Script: This script provides the user with buy and sell signals based on candlesticks that show an upward or downward reversal.
Mechanism of the Script: When a hollow (unfilled) red candle appears, it signals a potential entry, provided that this candle is at a low point, following a series of red candles with higher volume than previous days. Similarly, it gives a sell signal when a green candle appears at a peak with high sell volume surpassing that of prior days. However, the appearance of these candles alone should not prompt an immediate buy or sell; you should wait for a confirming candle to validate the signal.
Sideways Movement Caution: If these signals appear during a sideways or flat trend, it is not advisable to proceed with buying or selling.
Chart Insights: The chart demonstrates certain buy and sell operations along with some non-ideal signals where decision-making should be based on fundamental analytical experience.
Value at Risk [OmegaTools]The "Value at Risk" (VaR) indicator is a powerful financial risk management tool that helps traders estimate the potential losses in a portfolio over a specified period of time, given a certain level of confidence. VaR is widely used by financial institutions, traders, and risk managers to assess the probability of portfolio losses in both normal and volatile market conditions. This TradingView script implements a comprehensive VaR calculation using several models, allowing users to visualize different risk scenarios and adjust their trading strategies accordingly.
Concept of Value at Risk
Value at Risk (VaR) is a statistical technique used to measure the likelihood of losses in a portfolio or financial asset due to market risks. In essence, it answers the question: "What is the maximum potential loss that could occur in a given portfolio over a specific time horizon, with a certain confidence level?" For instance, if a portfolio has a one-day 95% VaR of $10,000, it means that there is a 95% chance the portfolio will not lose more than $10,000 in a single day. Conversely, there is a 5% chance of losing more than $10,000. VaR is a key risk management tool for portfolio managers and traders because it quantifies potential losses in monetary terms, allowing for better-informed decision-making.
There are several ways to calculate VaR, and this indicator script incorporates three of the most commonly used models:
Historical VaR: This approach uses historical returns to estimate potential losses. It is based purely on past price data, assuming that the past distribution of returns is indicative of future risks.
Variance-Covariance VaR: This model assumes that asset returns follow a normal distribution and that the risk can be summarized using the mean and standard deviation of past returns. It is a parametric method that is widely used in financial risk management.
Exponentially Weighted Moving Average (EWMA) VaR: In this model, recent data points are given more weight than older data. This dynamic approach allows the VaR estimation to react more quickly to changes in market volatility, which is particularly useful during periods of market stress. This model uses the Exponential Weighted Moving Average Volatility Model.
How the Script Works
The script starts by offering users a set of customizable input settings. The first input allows the user to choose between two main calculation modes: "All" or "OCT" (Only Current Timeframe). In the "All" mode, the script calculates VaR using all available methodologies—Historical, Variance-Covariance, and EWMA—providing a comprehensive risk overview. The "OCT" mode narrows the calculation to the current timeframe, which can be particularly useful for intraday traders who need a more focused view of risk.
The next input is the lookback window, which defines the number of historical periods used to calculate VaR. Commonly used lookback periods include 21 days (approximately one month), 63 days (about three months), and 252 days (roughly one year), with the script supporting up to 504 days for more extended historical analysis. A longer lookback period provides a more comprehensive picture of risk but may be less responsive to recent market conditions.
The confidence level is another important setting in the script. This represents the probability that the loss will not exceed the VaR estimate. Standard confidence levels are 90%, 95%, and 99%. A higher confidence level results in a more conservative risk estimate, meaning that the calculated VaR will reflect a more extreme loss scenario.
In addition to these core settings, the script allows users to customize the visual appearance of the indicator. For example, traders can choose different colors for "Bullish" (Risk On), "Bearish" (Risk Off), and "Neutral" phases, as well as colors for highlighting "Breaks" in the data, where returns exceed the calculated VaR. These visual cues make it easy to identify periods of heightened risk at a glance.
The actual VaR calculation is broken down into several models, starting with the Historical VaR calculation. This is done by computing the logarithmic returns of the asset's closing prices and then using linear interpolation to determine the percentile corresponding to the desired confidence level. This percentile represents the potential loss in the asset over the lookback period.
Next, the script calculates Variance-Covariance VaR using the mean and standard deviation of the historical returns. The standard deviation is multiplied by a z-score corresponding to the chosen confidence level (e.g., 1.645 for 95% confidence), and the resulting value is subtracted from the mean return to arrive at the VaR estimate.
The EWMA VaR model uses the EWMA for the sigma parameter, the standard deviation, obtaining a specific dynamic in the volatility. It is particularly useful in volatile markets where recent price behavior is more indicative of future risk than older data.
For traders interested in intraday risk management, the script provides several methods to adjust VaR calculations for lower timeframes. By using intraday returns and scaling them according to the chosen timeframe, the script provides a dynamic view of risk throughout the trading day. This is especially important for short-term traders who need to manage their exposure during high-volatility periods within the same day. The script also incorporates an EWMA model for intraday data, which gives greater weight to the most recent intraday price movements.
In addition to calculating VaR, the script also attempts to detect periods where the asset's returns exceed the estimated VaR threshold, referred to as "Breaks." When the returns breach the VaR limit, the script highlights these instances on the chart, allowing traders to quickly identify periods of extreme risk. The script also calculates the average of these breaks and displays it for comparison, helping traders understand how frequently these high-risk periods occur.
The script further visualizes the risk scenario using a risk phase classification system. Depending on the level of risk, the script categorizes the market as either "Risk On," "Risk Off," or "Risk Neutral." In "Risk On" mode, the market is considered bullish, and the indicator displays a green background. In "Risk Off" mode, the market is bearish, and the background turns red. If the market is neither strongly bullish nor bearish, the background turns neutral, signaling a balanced risk environment.
Traders can customize whether they want to see this risk phase background, along with toggling the display of the various VaR models, the intraday methods, and the break signals. This flexibility allows traders to tailor the indicator to their specific needs, whether they are day traders looking for quick intraday insights or longer-term investors focused on historical risk analysis.
The "Risk On" and "Risk Off" phases calculated by this Value at Risk (VaR) script introduce a novel approach to market risk assessment, offering traders an advanced toolset to gauge market sentiment and potential risk levels dynamically. These risk phases are built on a combination of traditional VaR methodologies and proprietary logic to create a more responsive and intuitive way to manage exposure in both normal and volatile market conditions. This method of classifying market conditions into "Risk On," "Risk Off," or "Risk Neutral" is not something that has been traditionally associated with VaR, making it a groundbreaking addition to this indicator.
How the "Risk On" and "Risk Off" Phases Are Calculated
In typical VaR implementations, the focus is on calculating the potential losses at a given confidence level without providing an overall market outlook. This script, however, introduces a unique risk classification system that takes the output of various VaR models and translates it into actionable signals for traders, marking whether the market is in a Risk On, Risk Off, or Risk Neutral phase.
The Risk On and Risk Off phases are primarily determined by comparing the current returns of the asset to the average VaR calculated across several different methods, including Historical VaR, Variance-Covariance VaR, and EWMA VaR. Here's how the process works:
1. Threshold Setting and Effect Calculation: The script first computes the average VaR using the selected models. It then checks whether the current returns (expressed as a negative value to signify loss) exceed the average VaR value. If the current returns surpass the calculated VaR threshold, this indicates that the actual market risk is higher than expected, signaling a potential shift in market conditions.
2. Break Analysis: In addition to monitoring whether returns exceed the average VaR, the script counts the number of instances within the lookback period where this breach occurs. This is referred to as the "break effect." For each period in the lookback window, the script checks whether the returns surpass the calculated VaR threshold and increments a counter. The percentage of periods where this breach occurs is then calculated as the "effect" or break percentage.
3. Dual Effect Check (if "Double" Risk Scenario is selected): When the user chooses the "Double" risk scenario mode, the script performs two layers of analysis. First, it calculates the effect of returns exceeding the VaR threshold for the current timeframe. Then, it calculates the effect for the lower intraday timeframe as well. Both effects are compared to the user-defined confidence level (e.g., 95%). If both effects exceed the confidence level, the market is deemed to be in a high-risk situation, thus triggering a Risk Off phase. If both effects fall below the confidence level, the market is classified as Risk On.
4. Risk Phases Determination: The final risk phase is determined by analyzing these effects in relation to the confidence level:
- Risk On: If the calculated effect of breaks is lower than the confidence level (e.g., fewer than 5% of periods show returns exceeding the VaR threshold for a 95% confidence level), the market is considered to be in a relatively safe state, and the script signals a "Risk On" phase. This is indicative of bullish conditions where the potential for extreme loss is minimal.
- Risk Off: If the break effect exceeds the confidence level (e.g., more than 5% of periods show returns breaching the VaR threshold), the market is deemed to be in a high-risk state, and the script signals a "Risk Off" phase. This indicates bearish market conditions where the likelihood of significant losses is higher.
- Risk Neutral: If the break effect hovers near the confidence level or if there is no clear trend indicating a shift toward either extreme, the market is classified as "Risk Neutral." In this phase, neither bulls nor bears are dominant, and traders should remain cautious.
The phase color that the script uses helps visualize these risk phases. The background will turn green in Risk On conditions, red in Risk Off conditions, and gray in Risk Neutral phases, providing immediate visual feedback on market risk. In addition to this, when the "Double" risk scenario is selected, the background will only turn green or red if both the current and intraday timeframes confirm the respective risk phase. This double-checking process ensures that traders are only given a strong signal when both longer-term and short-term risks align, reducing the likelihood of false signals.
A New Way of Using Value at Risk
This innovative Risk On/Risk Off classification, based on the interaction between VaR thresholds and market returns, represents a significant departure from the traditional use of Value at Risk as a pure risk measurement tool. Typically, VaR is employed as a backward-looking measure of risk, providing a static estimate of potential losses over a given timeframe with no immediate actionable feedback on current market conditions. This script, however, dynamically interprets VaR results to create a forward-looking, real-time signal that informs traders whether they are operating in a favorable (Risk On) or unfavorable (Risk Off) environment.
By incorporating the "break effect" analysis and allowing users to view the VaR breaches as a percentage of past occurrences, the script adds a predictive element that can be used to time market entries and exits more effectively. This **dual-layer risk analysis**, particularly when using the "Double" scenario mode, adds further granularity by considering both current timeframe and intraday risks. Traders can therefore make more informed decisions not just based on historical risk data, but on how the market is behaving in real-time relative to those risk benchmarks.
This approach transforms the VaR indicator from a risk monitoring tool into a decision-making system that helps identify favorable trading opportunities while alerting users to potential market downturns. It provides a more holistic view of market conditions by combining both statistical risk measurement and intuitive phase-based market analysis. This level of integration between VaR methodologies and real-time signal generation has not been widely seen in the world of trading indicators, marking this script as a cutting-edge tool for risk management and market sentiment analysis.
I would like to express my sincere gratitude to @skewedzeta for his invaluable contribution to the final script. From generating fresh ideas to applying his expertise in reviewing the formula, his support has been instrumental in refining the outcome.
TrendYFriend Description
This script is designed for automatic trendline plotting and generating alerts for key market events: retests and trendline breakouts. Using trendlines is one of the core methods of technical analysis, helping traders to identify the current market trend and open positions in its direction. The script is based on detecting pivot points and connecting them with trendlines, which helps visualize important support and resistance levels.
Importance of Trading with the Trend
Trend trading is one of the most reliable and time-tested approaches in trading. The main principle is that a trend is more likely to continue than to reverse. Following the trend allows traders to enter positions when the probability of further movement in the direction of the trend is high. By trading with the trend, traders can capture prolonged market movements, reducing risk and increasing profit potential.
Opening Positions from Trendlines
Trendlines help identify key levels from which price may either bounce or break through. Upward trendlines serve as dynamic support levels, while downward lines act as resistance levels. It’s important to understand that trendline retests can provide a signal to enter trades in the direction of the primary trend. Conversely, a trendline breakout may signal a trend reversal or correction, which is also an important trading signal.
Main Features of the Script:
1. **Automatic Trendline Drawing** — connecting key pivot points and displaying upward and downward trends on the chart.
2. **Alerts for Retests and Breakouts** — generating signals when the price touches (retest) or breaks through a trendline.
- **Retest of Uptrend Line** — a signal of a potential bounce from support and continuation of the upward trend.
- **Retest of Downtrend Line** — a signal of a potential bounce from resistance in a downward trend.
- **Breakout of Uptrend Line** — a signal of a potential reversal or correction of the upward trend.
- **Breakout of Downtrend Line** — a signal of a potential reversal or continuation of the downward trend.
How to Use the Script:
1. Apply the script to the chart.
2. When an alert triggers, pay attention to the current market situation and verify if the signal aligns with your trading strategy.
3. Open positions in the direction of the trend during retests, or exit trades if a trendline breakout occurs.
Jason's Simple Moving Averages WaveUnderstanding the Script:
Purpose: This script identifies potential trend direction and momentum using a moving average and wave amplitude calculation. It shows a green line when the price is trending upwards and a red line when trending downwards.
Strategy: This script doesn't provide a complete trading strategy. It's an indicator designed to be used alongside other tools.
Parameters: You can adjust the "Moving Average Length" input to change the sensitivity of the indicator. A shorter length will react quicker to price changes, while a longer length will be smoother but less responsive.
How to Use it:
Load the Script: In TradingView, navigate to the indicator creation section and paste the provided script code.
Adjust Parameters: Set the "Moving Average Length" based on your preferred timeframe and trading style.
Combine with Other Tools: Use the indicator along with other technical indicators or price action analysis to confirm potential entry and exit points for trades.
Here are some additional points to consider:
Crossovers: You could look for buy signals when the price crosses above the green line and sell signals when it crosses below the red line. However, these can be prone to false signals.
Divergence: Look for divergences between the price movement and the wave indicator. For example, a rising price with a falling wave could indicate overbought conditions and a potential reversal.
Confirmation: Don't rely solely on this indicator. Use it alongside other confirmations from price action, volume analysis, or other indicators to identify higher probability trades.
Important Note:
ET's FlagsPurpose:
This Pine Script is designed for the TradingView platform to identify and visually highlight specific technical chart patterns known as "Bull Flags" and "Bear Flags" on financial charts. These patterns are significant in trading as they can indicate potential continuation trends after a brief consolidation. The script includes mechanisms to manage signal frequency through a cooldown period, ensuring that the trading signals are not excessively frequent and are easier to interpret.
Functionality:
Input Parameters:
flagpole_length: Defines the number of bars to consider when identifying the initial surge in price, known as the flagpole.
flag_length: Determines the number of bars over which the flag itself is identified, representing a period of consolidation.
percent_change: Sets the minimum percentage change required to validate the presence of a flagpole.
cooldown_period: Specifies the number of bars to wait before another flag can be identified, reducing the risk of overlapping signals.
Percentage Change Calculation:
The script calculates the percentage change between two price points using a helper function percentChange(start, end). This function is crucial for determining whether the price movement within the specified flagpole_length meets the threshold set by percent_change, thus qualifying as a potential flagpole.
Flagpole Identification:
Bull Flagpole: Identified by finding the lowest close price over the flagpole_length and determining if the subsequent price rise meets or exceeds the specified percent_change.
Bear Flagpole: Identified by finding the highest close price over the flagpole_length and checking if the subsequent price drop is sufficient as per the percent_change.
Flag Identification:
After identifying a flagpole, the script assesses if the price action within the next flag_length bars consolidates in a manner that fits a flag pattern. This involves checking if the price fluctuation stays within the bounds set by the percent_change.
Signal Plotting:
If a bull or bear flag pattern is confirmed, and the cooldown period has passed since the last flag of the same type was identified, the script plots a visual shape on the chart:
Green shapes below the price bar for Bull Flags.
Red shapes above the price bar for Bear Flags.
Line Drawing:
For enhanced visualization, the script draws lines at the high and low prices of the flag during its formation period. This visually represents the consolidation phase of the flag pattern.
Debugging Labels:
The script optionally displays labels at the flag formation points, showing the exact percentage change achieved during the flagpole formation. This feature aids users in understanding why a particular segment of the price chart was identified as a flag.
Compliance and Usage:
This script does not automate trading but provides visual aids and potential signals based on historical price analysis. It adheres to TradingView's scripting policies by only accessing publicly available price data and user-defined parameters without executing trades or accessing any external data.
Conclusion:
This Pine Script is a powerful tool for traders who follow technical analysis, offering a clear, automated way to spot potential continuation patterns in the markets they monitor. By emphasizing visual clarity and reducing signal redundancy through cooldown periods, the script enhances decision-making processes for chart analysis on TradingView.
Smart Money Analysis with Golden/Death Cross [YourTradingSensei]Description of the script "Smart Money Analysis with Golden/Death Cross":
This TradingView script is designed for market analysis based on the concept of "Smart Money" and includes the detection of Golden Cross and Death Cross signals.
Key features of the script:
Moving Averages (SMA):
Two moving averages are calculated: a short-term (50 periods) and a long-term (200 periods).
The intersections of these moving averages are used to determine Golden Cross and Death Cross signals.
High Volume:
The current trading volume is analyzed.
Periods of high volume are identified when the current volume exceeds the average volume by a specified multiplier.
Support and Resistance Levels:
Key support and resistance levels are determined based on the highest and lowest prices over a specified period.
Buy and Sell Signals:
Buy and sell signals are generated based on moving average crossovers, high volume, and the closing price relative to key levels.
Golden Cross and Death Cross:
A Golden Cross occurs when the short-term moving average crosses above the long-term moving average.
A Death Cross occurs when the short-term moving average crosses below the long-term moving average.
These signals are displayed on the chart with text color changes for better visualization.
Using the script:
The script helps traders visualize key signals and levels, aiding in making informed trading decisions based on the behavior of major market players and technical analysis.
Custom candle lighting(CCL) © 2024 by YourTradingSensei is licensed under CC BY-NC-SA 4.0. To view a copy of this license.
Johnny's Moving Average RibbonProps to Madrid for creating the original script: Madrid Moving Average Ribbon.
All I did was upgrade it to pinescript v5 and added a few changes to the script.
Features and Functionality
Moving Average Types: The indicator offers a choice between exponential moving averages (EMAs) and simple moving averages (SMAs), allowing users to select the type that best fits their trading strategy.
Dynamic Color Coding: Each moving average line within the ribbon changes color based on its direction and position relative to a reference moving average, providing visual cues for market sentiment and trend strength.
Lime Green: Indicates an uptrend and potential long positions, shown when a moving average is rising and above the longer-term reference MA.
Maroon: Suggests caution for long positions or potential short reentry points, displayed when a moving average is rising but below the reference MA.
Ruby Red: Represents a downtrend, suitable for short positions, shown when a moving average is falling and below the reference MA.
Green: Signals potential reentry points for downtrends or warnings for uptrend reversals, displayed when a moving average is falling but above the reference MA.
Usage and Application
Trend Identification: Traders can quickly ascertain the market's direction at a glance by observing the predominant color of the ribbon and its orientation.
Trade Entry and Exit Points: The color transitions within the ribbon can signal potential entry or exit points, with changes from green to lime or red to maroon indicating shifts in market momentum.
Customization: Users have the flexibility to toggle between exponential and simple moving averages, allowing for a tailored analytical approach that aligns with their individual trading preferences.
Technical Specifications
The ribbon consists of multiple moving averages calculated over different periods, typically ranging from shorter to longer-term intervals to capture various aspects of market behavior.
The color dynamics are determined by comparing each moving average to a reference point, often a longer-term moving average within the ribbon, to assess the relative trend strength and direction.
Liquidations Meter [LuxAlgo]The Liquidation Meter aims to gauge the momentum of the bar, identify the strength of the bulls and bears, and more importantly identify probable exhaustion/reversals by measuring probable liquidations.
🔶 USAGE
This tool includes many features related to the concept of liquidation. The two core ones are the liquidation meter and liquidation price calculator, highlighted below.
🔹 Liquidation Meter
The liquidation meter presents liquidations on the price chart by measuring the highest leverage value of longs and shorts that have been potentially liquidated on the last chart bar, hence allowing traders to:
gauge the momentum of the bar.
identify the strength of the bulls and bears.
identify probable reversal/exhaustion points.
Liquidation of low-leveraged positions can be indicative of exhaustion.
🔹 Liquidation Price Calculator
A liquidation price calculator might come in handy when you need to calculate at what price level your leveraged position in Crypto, Forex, Stocks, or any other asset class gets liquidated to add a protective stop to mitigate risk. Monitoring an open position gets easier if the trader can calculate the total risk in order for them to choose the right amount of margin and leverage.
Liquidation price is the distance from the trader's entry price to the price where trader's leveraged position gets liquidated due to a loss. As the leverage is increased, the distance from trader's entry price to the liquidation price shrinks.
While you have one or several trades open you can quickly check their liquidation levels and determine which one of the trades is closest to their liquidation price.
If you are a day trader that uses leverage and you want to know which trade has the best outlook you can calculate the liquidation price to see which one of the trades looks best.
🔹 Dashboard
The bar statistics option enables measuring and presenting trading activity, volatility, and probable liquidations for the last chart bar.
🔶 DETAILS
It's important to note that liquidation price calculator tool uses a formula to calculate the liquidation price based on the entry price + leverage ratio.
Other factors such as leveraged fees, position size, and other interest payments have been excluded since they are variables that don’t directly affect the level of liquidation of a leveraged position.
The calculator also assumes that traders are using an isolated margin for one single position and does not take into consideration the additional margin they might have in their account.
🔹Liquidation price formula
the liquidation distance in percentage = 100 / leverage ratio
the liquidation distance in price = current asset price x the liquidation distance in percentage
the liquidation price (longs) = current asset price – the liquidation distance in price
the liquidation price (shorts) = current asset price + the liquidation distance in price
or simply
the liquidation price (longs) = entry price * (1 – 1 / leverage ratio)
the liquidation price (shorts) = entry price * (1 + 1 / leverage ratio)
Example:
Let’s say that you are trading a leverage ratio of 1:20. The first step is to calculate the distance to your liquidation point in percentage.
the liquidation distance in percentage = 100 / 20 = 5%
Now you know that your liquidation price is 5% away from your entry price. Let's calculate 5% below and above the entry price of the asset you are currently trading. As an example, we assume that you are trading bitcoin which is currently priced at $35000.
the liquidation distance in price = $35000 x 0.05 = $1750
Finally, calculate liquidation prices.
the liquidation price (longs) = $35000 – $1750 = $33250
the liquidation price (short) = $35000 + $1750 = $36750
In this example, short liquidation price is $36750 and long liquidation price is $33250.
🔹How leverage ratio affects the liquidation price
The entry price is the starting point of the calculation and it is from here that the liquidation price is calculated, where the leverage ratio has a direct impact on the liquidation price since the more you borrow the less “wiggle-room” your trade has.
An increase in leverage will subsequently reduce the distance to full liquidation. On the contrary, choosing a lower leverage ratio will give the position more room to move on.
🔶 SETTINGS
🔹Liquidations Meter
Base Price: The option where to set the reference/base price.
🔹Liquidation Price Calculator
Liquidation Price Calculator: Toggles the visibility of the calculator. Details and assumptions made during the calculations are stated in the tooltip of the option.
Entry Price: The option where to set the entry price, a value of 0 will use the current closing price. Details are given in the tooltip of the option.
Leverage: The option where to set the leverage value.
Show Calculated Liquidation Prices on the Chart: Toggles the visibility of the liquidation prices on the price chart.
🔹Dashboard
Show Bar Statistics: Toggles the visibility of the last bar statistics.
🔹Others
Liquidations Meter Text Size: Liquidations Meter text size.
Liquidations Meter Offset: Liquidations Meter offset.
Dashboard/Calculator Placement: Dashboard/calculator position on the chart.
Dashboard/Calculator Text Size: Dashboard text size.
🔶 RELATED SCRIPTS
Here are some of the scripts that are related to the liquidation and liquidity concept, for more and other conceptual scripts you are kindly invited to visit LuxAlgo-Scripts .
Liquidation-Levels
Liquidations-Real-Time
Buyside-Sellside-Liquidity
[blackcat] L1 T3 MA Lite Version
Tilson T3 Moving Average (T3MA) is a type of moving average line designed to reduce lag and improve the accuracy of trend identification. It is based on a combination of multiple smoothed moving averages, with each subsequent smoothed moving average having a higher weight than the previous one. The T3MA formula includes three different smoothing coefficients and a volume coefficient or volatility coefficient, which can be adjusted according to user preferences. T3MA is commonly used by traders and investors to identify trends and generate trading signals.
The calculation method for T3MA requires the use of exponential moving averages (EMA). In Pine scripts in the TradingView community, over 90% of them use the EMA function to calculate T3MA. Specifically, in Pine scripts, it is necessary to define the length and volatility coefficient of T3MA, then calculate three different lengths of EMA separately. Next, three constants need to be calculated that are related to volatility. Finally, the weighted average value of the three EMAs and three constants is added together to obtain the value of T3MA. If you want to customize the length and volatility of T3MA, you just need to modify the parameters in the code. Overall, T3MA is a very useful technical indicator that can help traders better understand market trends and improve trading efficiency.
The improved version introduced today mainly addresses my perception that traditional T3 algorithms are too redundant with high computational complexity leading to delayed reactions. Therefore, I have developed a lightweight version called L1 T3 MA Lite Version. This doesn't bring about any qualitative changes; it simply makes adjustments in terms of computational resources and response speed. To illustrate its advantages compared with traditional T3 MA indicators, I will provide a comparison using Everget's script from TradingView community blogger everget.
The difference between these two scripts for calculating T3 Moving Average lies in their implementation methods. The first script (Everget) uses a more complex calculation formula, which requires calculating three different lengths of EMA and computing three constants based on volatility. Finally, they are weighted averaged to obtain T3MA. This complex calculation formula can enhance the sensitivity of the T3MA indicator, thereby better identifying price trends. On the other hand, the second script (Blackcat1402) uses a relatively simple calculation formula that only requires calculating three different lengths of EMA and computing three constants based on volatility. Finally, they are weighted averaged to obtain T3MA as well. This simple calculation formula reduces computational complexity and speeds up calculations. Both have slightly different effects and calculation methods; users can choose the script that suits their needs.
In summary, T3 Moving Average is a very useful technical indicator that can help traders better understand market trends and improve trading efficiency. Users can choose scripts suitable for themselves according to their needs and flexibly adjust the length and volatility coefficient of T3MA to adapt to different markets.
Goertzel Cycle Composite Wave [Loxx]As the financial markets become increasingly complex and data-driven, traders and analysts must leverage powerful tools to gain insights and make informed decisions. One such tool is the Goertzel Cycle Composite Wave indicator, a sophisticated technical analysis indicator that helps identify cyclical patterns in financial data. This powerful tool is capable of detecting cyclical patterns in financial data, helping traders to make better predictions and optimize their trading strategies. With its unique combination of mathematical algorithms and advanced charting capabilities, this indicator has the potential to revolutionize the way we approach financial modeling and trading.
*** To decrease the load time of this indicator, only XX many bars back will render to the chart. You can control this value with the setting "Number of Bars to Render". This doesn't have anything to do with repainting or the indicator being endpointed***
█ Brief Overview of the Goertzel Cycle Composite Wave
The Goertzel Cycle Composite Wave is a sophisticated technical analysis tool that utilizes the Goertzel algorithm to analyze and visualize cyclical components within a financial time series. By identifying these cycles and their characteristics, the indicator aims to provide valuable insights into the market's underlying price movements, which could potentially be used for making informed trading decisions.
The Goertzel Cycle Composite Wave is considered a non-repainting and endpointed indicator. This means that once a value has been calculated for a specific bar, that value will not change in subsequent bars, and the indicator is designed to have a clear start and end point. This is an important characteristic for indicators used in technical analysis, as it allows traders to make informed decisions based on historical data without the risk of hindsight bias or future changes in the indicator's values. This means traders can use this indicator trading purposes.
The repainting version of this indicator with forecasting, cycle selection/elimination options, and data output table can be found here:
Goertzel Browser
The primary purpose of this indicator is to:
1. Detect and analyze the dominant cycles present in the price data.
2. Reconstruct and visualize the composite wave based on the detected cycles.
To achieve this, the indicator performs several tasks:
1. Detrending the price data: The indicator preprocesses the price data using various detrending techniques, such as Hodrick-Prescott filters, zero-lag moving averages, and linear regression, to remove the underlying trend and focus on the cyclical components.
2. Applying the Goertzel algorithm: The indicator applies the Goertzel algorithm to the detrended price data, identifying the dominant cycles and their characteristics, such as amplitude, phase, and cycle strength.
3. Constructing the composite wave: The indicator reconstructs the composite wave by combining the detected cycles, either by using a user-defined list of cycles or by selecting the top N cycles based on their amplitude or cycle strength.
4. Visualizing the composite wave: The indicator plots the composite wave, using solid lines for the cycles. The color of the lines indicates whether the wave is increasing or decreasing.
This indicator is a powerful tool that employs the Goertzel algorithm to analyze and visualize the cyclical components within a financial time series. By providing insights into the underlying price movements, the indicator aims to assist traders in making more informed decisions.
█ What is the Goertzel Algorithm?
The Goertzel algorithm, named after Gerald Goertzel, is a digital signal processing technique that is used to efficiently compute individual terms of the Discrete Fourier Transform (DFT). It was first introduced in 1958, and since then, it has found various applications in the fields of engineering, mathematics, and physics.
The Goertzel algorithm is primarily used to detect specific frequency components within a digital signal, making it particularly useful in applications where only a few frequency components are of interest. The algorithm is computationally efficient, as it requires fewer calculations than the Fast Fourier Transform (FFT) when detecting a small number of frequency components. This efficiency makes the Goertzel algorithm a popular choice in applications such as:
1. Telecommunications: The Goertzel algorithm is used for decoding Dual-Tone Multi-Frequency (DTMF) signals, which are the tones generated when pressing buttons on a telephone keypad. By identifying specific frequency components, the algorithm can accurately determine which button has been pressed.
2. Audio processing: The algorithm can be used to detect specific pitches or harmonics in an audio signal, making it useful in applications like pitch detection and tuning musical instruments.
3. Vibration analysis: In the field of mechanical engineering, the Goertzel algorithm can be applied to analyze vibrations in rotating machinery, helping to identify faulty components or signs of wear.
4. Power system analysis: The algorithm can be used to measure harmonic content in power systems, allowing engineers to assess power quality and detect potential issues.
The Goertzel algorithm is used in these applications because it offers several advantages over other methods, such as the FFT:
1. Computational efficiency: The Goertzel algorithm requires fewer calculations when detecting a small number of frequency components, making it more computationally efficient than the FFT in these cases.
2. Real-time analysis: The algorithm can be implemented in a streaming fashion, allowing for real-time analysis of signals, which is crucial in applications like telecommunications and audio processing.
3. Memory efficiency: The Goertzel algorithm requires less memory than the FFT, as it only computes the frequency components of interest.
4. Precision: The algorithm is less susceptible to numerical errors compared to the FFT, ensuring more accurate results in applications where precision is essential.
The Goertzel algorithm is an efficient digital signal processing technique that is primarily used to detect specific frequency components within a signal. Its computational efficiency, real-time capabilities, and precision make it an attractive choice for various applications, including telecommunications, audio processing, vibration analysis, and power system analysis. The algorithm has been widely adopted since its introduction in 1958 and continues to be an essential tool in the fields of engineering, mathematics, and physics.
█ Goertzel Algorithm in Quantitative Finance: In-Depth Analysis and Applications
The Goertzel algorithm, initially designed for signal processing in telecommunications, has gained significant traction in the financial industry due to its efficient frequency detection capabilities. In quantitative finance, the Goertzel algorithm has been utilized for uncovering hidden market cycles, developing data-driven trading strategies, and optimizing risk management. This section delves deeper into the applications of the Goertzel algorithm in finance, particularly within the context of quantitative trading and analysis.
Unveiling Hidden Market Cycles:
Market cycles are prevalent in financial markets and arise from various factors, such as economic conditions, investor psychology, and market participant behavior. The Goertzel algorithm's ability to detect and isolate specific frequencies in price data helps trader analysts identify hidden market cycles that may otherwise go unnoticed. By examining the amplitude, phase, and periodicity of each cycle, traders can better understand the underlying market structure and dynamics, enabling them to develop more informed and effective trading strategies.
Developing Quantitative Trading Strategies:
The Goertzel algorithm's versatility allows traders to incorporate its insights into a wide range of trading strategies. By identifying the dominant market cycles in a financial instrument's price data, traders can create data-driven strategies that capitalize on the cyclical nature of markets.
For instance, a trader may develop a mean-reversion strategy that takes advantage of the identified cycles. By establishing positions when the price deviates from the predicted cycle, the trader can profit from the subsequent reversion to the cycle's mean. Similarly, a momentum-based strategy could be designed to exploit the persistence of a dominant cycle by entering positions that align with the cycle's direction.
Enhancing Risk Management:
The Goertzel algorithm plays a vital role in risk management for quantitative strategies. By analyzing the cyclical components of a financial instrument's price data, traders can gain insights into the potential risks associated with their trading strategies.
By monitoring the amplitude and phase of dominant cycles, a trader can detect changes in market dynamics that may pose risks to their positions. For example, a sudden increase in amplitude may indicate heightened volatility, prompting the trader to adjust position sizing or employ hedging techniques to protect their portfolio. Additionally, changes in phase alignment could signal a potential shift in market sentiment, necessitating adjustments to the trading strategy.
Expanding Quantitative Toolkits:
Traders can augment the Goertzel algorithm's insights by combining it with other quantitative techniques, creating a more comprehensive and sophisticated analysis framework. For example, machine learning algorithms, such as neural networks or support vector machines, could be trained on features extracted from the Goertzel algorithm to predict future price movements more accurately.
Furthermore, the Goertzel algorithm can be integrated with other technical analysis tools, such as moving averages or oscillators, to enhance their effectiveness. By applying these tools to the identified cycles, traders can generate more robust and reliable trading signals.
The Goertzel algorithm offers invaluable benefits to quantitative finance practitioners by uncovering hidden market cycles, aiding in the development of data-driven trading strategies, and improving risk management. By leveraging the insights provided by the Goertzel algorithm and integrating it with other quantitative techniques, traders can gain a deeper understanding of market dynamics and devise more effective trading strategies.
█ Indicator Inputs
src: This is the source data for the analysis, typically the closing price of the financial instrument.
detrendornot: This input determines the method used for detrending the source data. Detrending is the process of removing the underlying trend from the data to focus on the cyclical components.
The available options are:
hpsmthdt: Detrend using Hodrick-Prescott filter centered moving average.
zlagsmthdt: Detrend using zero-lag moving average centered moving average.
logZlagRegression: Detrend using logarithmic zero-lag linear regression.
hpsmth: Detrend using Hodrick-Prescott filter.
zlagsmth: Detrend using zero-lag moving average.
DT_HPper1 and DT_HPper2: These inputs define the period range for the Hodrick-Prescott filter centered moving average when detrendornot is set to hpsmthdt.
DT_ZLper1 and DT_ZLper2: These inputs define the period range for the zero-lag moving average centered moving average when detrendornot is set to zlagsmthdt.
DT_RegZLsmoothPer: This input defines the period for the zero-lag moving average used in logarithmic zero-lag linear regression when detrendornot is set to logZlagRegression.
HPsmoothPer: This input defines the period for the Hodrick-Prescott filter when detrendornot is set to hpsmth.
ZLMAsmoothPer: This input defines the period for the zero-lag moving average when detrendornot is set to zlagsmth.
MaxPer: This input sets the maximum period for the Goertzel algorithm to search for cycles.
squaredAmp: This boolean input determines whether the amplitude should be squared in the Goertzel algorithm.
useAddition: This boolean input determines whether the Goertzel algorithm should use addition for combining the cycles.
useCosine: This boolean input determines whether the Goertzel algorithm should use cosine waves instead of sine waves.
UseCycleStrength: This boolean input determines whether the Goertzel algorithm should compute the cycle strength, which is a normalized measure of the cycle's amplitude.
WindowSizePast: These inputs define the window size for the composite wave.
FilterBartels: This boolean input determines whether Bartel's test should be applied to filter out non-significant cycles.
BartNoCycles: This input sets the number of cycles to be used in Bartel's test.
BartSmoothPer: This input sets the period for the moving average used in Bartel's test.
BartSigLimit: This input sets the significance limit for Bartel's test, below which cycles are considered insignificant.
SortBartels: This boolean input determines whether the cycles should be sorted by their Bartel's test results.
StartAtCycle: This input determines the starting index for selecting the top N cycles when UseCycleList is set to false. This allows you to skip a certain number of cycles from the top before selecting the desired number of cycles.
UseTopCycles: This input sets the number of top cycles to use for constructing the composite wave when UseCycleList is set to false. The cycles are ranked based on their amplitudes or cycle strengths, depending on the UseCycleStrength input.
SubtractNoise: This boolean input determines whether to subtract the noise (remaining cycles) from the composite wave. If set to true, the composite wave will only include the top N cycles specified by UseTopCycles.
█ Exploring Auxiliary Functions
The following functions demonstrate advanced techniques for analyzing financial markets, including zero-lag moving averages, Bartels probability, detrending, and Hodrick-Prescott filtering. This section examines each function in detail, explaining their purpose, methodology, and applications in finance. We will examine how each function contributes to the overall performance and effectiveness of the indicator and how they work together to create a powerful analytical tool.
Zero-Lag Moving Average:
The zero-lag moving average function is designed to minimize the lag typically associated with moving averages. This is achieved through a two-step weighted linear regression process that emphasizes more recent data points. The function calculates a linearly weighted moving average (LWMA) on the input data and then applies another LWMA on the result. By doing this, the function creates a moving average that closely follows the price action, reducing the lag and improving the responsiveness of the indicator.
The zero-lag moving average function is used in the indicator to provide a responsive, low-lag smoothing of the input data. This function helps reduce the noise and fluctuations in the data, making it easier to identify and analyze underlying trends and patterns. By minimizing the lag associated with traditional moving averages, this function allows the indicator to react more quickly to changes in market conditions, providing timely signals and improving the overall effectiveness of the indicator.
Bartels Probability:
The Bartels probability function calculates the probability of a given cycle being significant in a time series. It uses a mathematical test called the Bartels test to assess the significance of cycles detected in the data. The function calculates coefficients for each detected cycle and computes an average amplitude and an expected amplitude. By comparing these values, the Bartels probability is derived, indicating the likelihood of a cycle's significance. This information can help in identifying and analyzing dominant cycles in financial markets.
The Bartels probability function is incorporated into the indicator to assess the significance of detected cycles in the input data. By calculating the Bartels probability for each cycle, the indicator can prioritize the most significant cycles and focus on the market dynamics that are most relevant to the current trading environment. This function enhances the indicator's ability to identify dominant market cycles, improving its predictive power and aiding in the development of effective trading strategies.
Detrend Logarithmic Zero-Lag Regression:
The detrend logarithmic zero-lag regression function is used for detrending data while minimizing lag. It combines a zero-lag moving average with a linear regression detrending method. The function first calculates the zero-lag moving average of the logarithm of input data and then applies a linear regression to remove the trend. By detrending the data, the function isolates the cyclical components, making it easier to analyze and interpret the underlying market dynamics.
The detrend logarithmic zero-lag regression function is used in the indicator to isolate the cyclical components of the input data. By detrending the data, the function enables the indicator to focus on the cyclical movements in the market, making it easier to analyze and interpret market dynamics. This function is essential for identifying cyclical patterns and understanding the interactions between different market cycles, which can inform trading decisions and enhance overall market understanding.
Bartels Cycle Significance Test:
The Bartels cycle significance test is a function that combines the Bartels probability function and the detrend logarithmic zero-lag regression function to assess the significance of detected cycles. The function calculates the Bartels probability for each cycle and stores the results in an array. By analyzing the probability values, traders and analysts can identify the most significant cycles in the data, which can be used to develop trading strategies and improve market understanding.
The Bartels cycle significance test function is integrated into the indicator to provide a comprehensive analysis of the significance of detected cycles. By combining the Bartels probability function and the detrend logarithmic zero-lag regression function, this test evaluates the significance of each cycle and stores the results in an array. The indicator can then use this information to prioritize the most significant cycles and focus on the most relevant market dynamics. This function enhances the indicator's ability to identify and analyze dominant market cycles, providing valuable insights for trading and market analysis.
Hodrick-Prescott Filter:
The Hodrick-Prescott filter is a popular technique used to separate the trend and cyclical components of a time series. The function applies a smoothing parameter to the input data and calculates a smoothed series using a two-sided filter. This smoothed series represents the trend component, which can be subtracted from the original data to obtain the cyclical component. The Hodrick-Prescott filter is commonly used in economics and finance to analyze economic data and financial market trends.
The Hodrick-Prescott filter is incorporated into the indicator to separate the trend and cyclical components of the input data. By applying the filter to the data, the indicator can isolate the trend component, which can be used to analyze long-term market trends and inform trading decisions. Additionally, the cyclical component can be used to identify shorter-term market dynamics and provide insights into potential trading opportunities. The inclusion of the Hodrick-Prescott filter adds another layer of analysis to the indicator, making it more versatile and comprehensive.
Detrending Options: Detrend Centered Moving Average:
The detrend centered moving average function provides different detrending methods, including the Hodrick-Prescott filter and the zero-lag moving average, based on the selected detrending method. The function calculates two sets of smoothed values using the chosen method and subtracts one set from the other to obtain a detrended series. By offering multiple detrending options, this function allows traders and analysts to select the most appropriate method for their specific needs and preferences.
The detrend centered moving average function is integrated into the indicator to provide users with multiple detrending options, including the Hodrick-Prescott filter and the zero-lag moving average. By offering multiple detrending methods, the indicator allows users to customize the analysis to their specific needs and preferences, enhancing the indicator's overall utility and adaptability. This function ensures that the indicator can cater to a wide range of trading styles and objectives, making it a valuable tool for a diverse group of market participants.
The auxiliary functions functions discussed in this section demonstrate the power and versatility of mathematical techniques in analyzing financial markets. By understanding and implementing these functions, traders and analysts can gain valuable insights into market dynamics, improve their trading strategies, and make more informed decisions. The combination of zero-lag moving averages, Bartels probability, detrending methods, and the Hodrick-Prescott filter provides a comprehensive toolkit for analyzing and interpreting financial data. The integration of advanced functions in a financial indicator creates a powerful and versatile analytical tool that can provide valuable insights into financial markets. By combining the zero-lag moving average,
█ In-Depth Analysis of the Goertzel Cycle Composite Wave Code
The Goertzel Cycle Composite Wave code is an implementation of the Goertzel Algorithm, an efficient technique to perform spectral analysis on a signal. The code is designed to detect and analyze dominant cycles within a given financial market data set. This section will provide an extremely detailed explanation of the code, its structure, functions, and intended purpose.
Function signature and input parameters:
The Goertzel Cycle Composite Wave function accepts numerous input parameters for customization, including source data (src), the current bar (forBar), sample size (samplesize), period (per), squared amplitude flag (squaredAmp), addition flag (useAddition), cosine flag (useCosine), cycle strength flag (UseCycleStrength), past sizes (WindowSizePast), Bartels filter flag (FilterBartels), Bartels-related parameters (BartNoCycles, BartSmoothPer, BartSigLimit), sorting flag (SortBartels), and output buffers (goeWorkPast, cyclebuffer, amplitudebuffer, phasebuffer, cycleBartelsBuffer).
Initializing variables and arrays:
The code initializes several float arrays (goeWork1, goeWork2, goeWork3, goeWork4) with the same length as twice the period (2 * per). These arrays store intermediate results during the execution of the algorithm.
Preprocessing input data:
The input data (src) undergoes preprocessing to remove linear trends. This step enhances the algorithm's ability to focus on cyclical components in the data. The linear trend is calculated by finding the slope between the first and last values of the input data within the sample.
Iterative calculation of Goertzel coefficients:
The core of the Goertzel Cycle Composite Wave algorithm lies in the iterative calculation of Goertzel coefficients for each frequency bin. These coefficients represent the spectral content of the input data at different frequencies. The code iterates through the range of frequencies, calculating the Goertzel coefficients using a nested loop structure.
Cycle strength computation:
The code calculates the cycle strength based on the Goertzel coefficients. This is an optional step, controlled by the UseCycleStrength flag. The cycle strength provides information on the relative influence of each cycle on the data per bar, considering both amplitude and cycle length. The algorithm computes the cycle strength either by squaring the amplitude (controlled by squaredAmp flag) or using the actual amplitude values.
Phase calculation:
The Goertzel Cycle Composite Wave code computes the phase of each cycle, which represents the position of the cycle within the input data. The phase is calculated using the arctangent function (math.atan) based on the ratio of the imaginary and real components of the Goertzel coefficients.
Peak detection and cycle extraction:
The algorithm performs peak detection on the computed amplitudes or cycle strengths to identify dominant cycles. It stores the detected cycles in the cyclebuffer array, along with their corresponding amplitudes and phases in the amplitudebuffer and phasebuffer arrays, respectively.
Sorting cycles by amplitude or cycle strength:
The code sorts the detected cycles based on their amplitude or cycle strength in descending order. This allows the algorithm to prioritize cycles with the most significant impact on the input data.
Bartels cycle significance test:
If the FilterBartels flag is set, the code performs a Bartels cycle significance test on the detected cycles. This test determines the statistical significance of each cycle and filters out the insignificant cycles. The significant cycles are stored in the cycleBartelsBuffer array. If the SortBartels flag is set, the code sorts the significant cycles based on their Bartels significance values.
Waveform calculation:
The Goertzel Cycle Composite Wave code calculates the waveform of the significant cycles for specified time windows. The windows are defined by the WindowSizePast parameters, respectively. The algorithm uses either cosine or sine functions (controlled by the useCosine flag) to calculate the waveforms for each cycle. The useAddition flag determines whether the waveforms should be added or subtracted.
Storing waveforms in a matrix:
The calculated waveforms for the cycle is stored in the matrix - goeWorkPast. This matrix holds the waveforms for the specified time windows. Each row in the matrix represents a time window position, and each column corresponds to a cycle.
Returning the number of cycles:
The Goertzel Cycle Composite Wave function returns the total number of detected cycles (number_of_cycles) after processing the input data. This information can be used to further analyze the results or to visualize the detected cycles.
The Goertzel Cycle Composite Wave code is a comprehensive implementation of the Goertzel Algorithm, specifically designed for detecting and analyzing dominant cycles within financial market data. The code offers a high level of customization, allowing users to fine-tune the algorithm based on their specific needs. The Goertzel Cycle Composite Wave's combination of preprocessing, iterative calculations, cycle extraction, sorting, significance testing, and waveform calculation makes it a powerful tool for understanding cyclical components in financial data.
█ Generating and Visualizing Composite Waveform
The indicator calculates and visualizes the composite waveform for specified time windows based on the detected cycles. Here's a detailed explanation of this process:
Updating WindowSizePast:
The WindowSizePast is updated to ensure they are at least twice the MaxPer (maximum period).
Initializing matrices and arrays:
The matrix goeWorkPast is initialized to store the Goertzel results for specified time windows. Multiple arrays are also initialized to store cycle, amplitude, phase, and Bartels information.
Preparing the source data (srcVal) array:
The source data is copied into an array, srcVal, and detrended using one of the selected methods (hpsmthdt, zlagsmthdt, logZlagRegression, hpsmth, or zlagsmth).
Goertzel function call:
The Goertzel function is called to analyze the detrended source data and extract cycle information. The output, number_of_cycles, contains the number of detected cycles.
Initializing arrays for waveforms:
The goertzel array is initialized to store the endpoint Goertzel.
Calculating composite waveform (goertzel array):
The composite waveform is calculated by summing the selected cycles (either from the user-defined cycle list or the top cycles) and optionally subtracting the noise component.
Drawing composite waveform (pvlines):
The composite waveform is drawn on the chart using solid lines. The color of the lines is determined by the direction of the waveform (green for upward, red for downward).
To summarize, this indicator generates a composite waveform based on the detected cycles in the financial data. It calculates the composite waveforms and visualizes them on the chart using colored lines.
█ Enhancing the Goertzel Algorithm-Based Script for Financial Modeling and Trading
The Goertzel algorithm-based script for detecting dominant cycles in financial data is a powerful tool for financial modeling and trading. It provides valuable insights into the past behavior of these cycles. However, as with any algorithm, there is always room for improvement. This section discusses potential enhancements to the existing script to make it even more robust and versatile for financial modeling, general trading, advanced trading, and high-frequency finance trading.
Enhancements for Financial Modeling
Data preprocessing: One way to improve the script's performance for financial modeling is to introduce more advanced data preprocessing techniques. This could include removing outliers, handling missing data, and normalizing the data to ensure consistent and accurate results.
Additional detrending and smoothing methods: Incorporating more sophisticated detrending and smoothing techniques, such as wavelet transform or empirical mode decomposition, can help improve the script's ability to accurately identify cycles and trends in the data.
Machine learning integration: Integrating machine learning techniques, such as artificial neural networks or support vector machines, can help enhance the script's predictive capabilities, leading to more accurate financial models.
Enhancements for General and Advanced Trading
Customizable indicator integration: Allowing users to integrate their own technical indicators can help improve the script's effectiveness for both general and advanced trading. By enabling the combination of the dominant cycle information with other technical analysis tools, traders can develop more comprehensive trading strategies.
Risk management and position sizing: Incorporating risk management and position sizing functionality into the script can help traders better manage their trades and control potential losses. This can be achieved by calculating the optimal position size based on the user's risk tolerance and account size.
Multi-timeframe analysis: Enhancing the script to perform multi-timeframe analysis can provide traders with a more holistic view of market trends and cycles. By identifying dominant cycles on different timeframes, traders can gain insights into the potential confluence of cycles and make better-informed trading decisions.
Enhancements for High-Frequency Finance Trading
Algorithm optimization: To ensure the script's suitability for high-frequency finance trading, optimizing the algorithm for faster execution is crucial. This can be achieved by employing efficient data structures and refining the calculation methods to minimize computational complexity.
Real-time data streaming: Integrating real-time data streaming capabilities into the script can help high-frequency traders react to market changes more quickly. By continuously updating the cycle information based on real-time market data, traders can adapt their strategies accordingly and capitalize on short-term market fluctuations.
Order execution and trade management: To fully leverage the script's capabilities for high-frequency trading, implementing functionality for automated order execution and trade management is essential. This can include features such as stop-loss and take-profit orders, trailing stops, and automated trade exit strategies.
While the existing Goertzel algorithm-based script is a valuable tool for detecting dominant cycles in financial data, there are several potential enhancements that can make it even more powerful for financial modeling, general trading, advanced trading, and high-frequency finance trading. By incorporating these improvements, the script can become a more versatile and effective tool for traders and financial analysts alike.
█ Understanding the Limitations of the Goertzel Algorithm
While the Goertzel algorithm-based script for detecting dominant cycles in financial data provides valuable insights, it is important to be aware of its limitations and drawbacks. Some of the key drawbacks of this indicator are:
Lagging nature:
As with many other technical indicators, the Goertzel algorithm-based script can suffer from lagging effects, meaning that it may not immediately react to real-time market changes. This lag can lead to late entries and exits, potentially resulting in reduced profitability or increased losses.
Parameter sensitivity:
The performance of the script can be sensitive to the chosen parameters, such as the detrending methods, smoothing techniques, and cycle detection settings. Improper parameter selection may lead to inaccurate cycle detection or increased false signals, which can negatively impact trading performance.
Complexity:
The Goertzel algorithm itself is relatively complex, making it difficult for novice traders or those unfamiliar with the concept of cycle analysis to fully understand and effectively utilize the script. This complexity can also make it challenging to optimize the script for specific trading styles or market conditions.
Overfitting risk:
As with any data-driven approach, there is a risk of overfitting when using the Goertzel algorithm-based script. Overfitting occurs when a model becomes too specific to the historical data it was trained on, leading to poor performance on new, unseen data. This can result in misleading signals and reduced trading performance.
Limited applicability:
The Goertzel algorithm-based script may not be suitable for all markets, trading styles, or timeframes. Its effectiveness in detecting cycles may be limited in certain market conditions, such as during periods of extreme volatility or low liquidity.
While the Goertzel algorithm-based script offers valuable insights into dominant cycles in financial data, it is essential to consider its drawbacks and limitations when incorporating it into a trading strategy. Traders should always use the script in conjunction with other technical and fundamental analysis tools, as well as proper risk management, to make well-informed trading decisions.
█ Interpreting Results
The Goertzel Cycle Composite Wave indicator can be interpreted by analyzing the plotted lines. The indicator plots two lines: composite waves. The composite wave represents the composite wave of the price data.
The composite wave line displays a solid line, with green indicating a bullish trend and red indicating a bearish trend.
Interpreting the Goertzel Cycle Composite Wave indicator involves identifying the trend of the composite wave lines and matching them with the corresponding bullish or bearish color.
█ Conclusion
The Goertzel Cycle Composite Wave indicator is a powerful tool for identifying and analyzing cyclical patterns in financial markets. Its ability to detect multiple cycles of varying frequencies and strengths make it a valuable addition to any trader's technical analysis toolkit. However, it is important to keep in mind that the Goertzel Cycle Composite Wave indicator should be used in conjunction with other technical analysis tools and fundamental analysis to achieve the best results. With continued refinement and development, the Goertzel Cycle Composite Wave indicator has the potential to become a highly effective tool for financial modeling, general trading, advanced trading, and high-frequency finance trading. Its accuracy and versatility make it a promising candidate for further research and development.
█ Footnotes
What is the Bartels Test for Cycle Significance?
The Bartels Cycle Significance Test is a statistical method that determines whether the peaks and troughs of a time series are statistically significant. The test is named after its inventor, George Bartels, who developed it in the mid-20th century.
The Bartels test is designed to analyze the cyclical components of a time series, which can help traders and analysts identify trends and cycles in financial markets. The test calculates a Bartels statistic, which measures the degree of non-randomness or autocorrelation in the time series.
The Bartels statistic is calculated by first splitting the time series into two halves and calculating the range of the peaks and troughs in each half. The test then compares these ranges using a t-test, which measures the significance of the difference between the two ranges.
If the Bartels statistic is greater than a critical value, it indicates that the peaks and troughs in the time series are non-random and that there is a significant cyclical component to the data. Conversely, if the Bartels statistic is less than the critical value, it suggests that the peaks and troughs are random and that there is no significant cyclical component.
The Bartels Cycle Significance Test is particularly useful in financial analysis because it can help traders and analysts identify significant cycles in asset prices, which can in turn inform investment decisions. However, it is important to note that the test is not perfect and can produce false signals in certain situations, particularly in noisy or volatile markets. Therefore, it is always recommended to use the test in conjunction with other technical and fundamental indicators to confirm trends and cycles.
Deep-dive into the Hodrick-Prescott Fitler
The Hodrick-Prescott (HP) filter is a statistical tool used in economics and finance to separate a time series into two components: a trend component and a cyclical component. It is a powerful tool for identifying long-term trends in economic and financial data and is widely used by economists, central banks, and financial institutions around the world.
The HP filter was first introduced in the 1990s by economists Robert Hodrick and Edward Prescott. It is a simple, two-parameter filter that separates a time series into a trend component and a cyclical component. The trend component represents the long-term behavior of the data, while the cyclical component captures the shorter-term fluctuations around the trend.
The HP filter works by minimizing the following objective function:
Minimize: (Sum of Squared Deviations) + λ (Sum of Squared Second Differences)
Where:
1. The first term represents the deviation of the data from the trend.
2. The second term represents the smoothness of the trend.
3. λ is a smoothing parameter that determines the degree of smoothness of the trend.
The smoothing parameter λ is typically set to a value between 100 and 1600, depending on the frequency of the data. Higher values of λ lead to a smoother trend, while lower values lead to a more volatile trend.
The HP filter has several advantages over other smoothing techniques. It is a non-parametric method, meaning that it does not make any assumptions about the underlying distribution of the data. It also allows for easy comparison of trends across different time series and can be used with data of any frequency.
However, the HP filter also has some limitations. It assumes that the trend is a smooth function, which may not be the case in some situations. It can also be sensitive to changes in the smoothing parameter λ, which may result in different trends for the same data. Additionally, the filter may produce unrealistic trends for very short time series.
Despite these limitations, the HP filter remains a valuable tool for analyzing economic and financial data. It is widely used by central banks and financial institutions to monitor long-term trends in the economy, and it can be used to identify turning points in the business cycle. The filter can also be used to analyze asset prices, exchange rates, and other financial variables.
The Hodrick-Prescott filter is a powerful tool for analyzing economic and financial data. It separates a time series into a trend component and a cyclical component, allowing for easy identification of long-term trends and turning points in the business cycle. While it has some limitations, it remains a valuable tool for economists, central banks, and financial institutions around the world.
Goertzel Browser [Loxx]As the financial markets become increasingly complex and data-driven, traders and analysts must leverage powerful tools to gain insights and make informed decisions. One such tool is the Goertzel Browser indicator, a sophisticated technical analysis indicator that helps identify cyclical patterns in financial data. This powerful tool is capable of detecting cyclical patterns in financial data, helping traders to make better predictions and optimize their trading strategies. With its unique combination of mathematical algorithms and advanced charting capabilities, this indicator has the potential to revolutionize the way we approach financial modeling and trading.
█ Brief Overview of the Goertzel Browser
The Goertzel Browser is a sophisticated technical analysis tool that utilizes the Goertzel algorithm to analyze and visualize cyclical components within a financial time series. By identifying these cycles and their characteristics, the indicator aims to provide valuable insights into the market's underlying price movements, which could potentially be used for making informed trading decisions.
The primary purpose of this indicator is to:
1. Detect and analyze the dominant cycles present in the price data.
2. Reconstruct and visualize the composite wave based on the detected cycles.
3. Project the composite wave into the future, providing a potential roadmap for upcoming price movements.
To achieve this, the indicator performs several tasks:
1. Detrending the price data: The indicator preprocesses the price data using various detrending techniques, such as Hodrick-Prescott filters, zero-lag moving averages, and linear regression, to remove the underlying trend and focus on the cyclical components.
2. Applying the Goertzel algorithm: The indicator applies the Goertzel algorithm to the detrended price data, identifying the dominant cycles and their characteristics, such as amplitude, phase, and cycle strength.
3. Constructing the composite wave: The indicator reconstructs the composite wave by combining the detected cycles, either by using a user-defined list of cycles or by selecting the top N cycles based on their amplitude or cycle strength.
4. Visualizing the composite wave: The indicator plots the composite wave, using solid lines for the past and dotted lines for the future projections. The color of the lines indicates whether the wave is increasing or decreasing.
5. Displaying cycle information: The indicator provides a table that displays detailed information about the detected cycles, including their rank, period, Bartel's test results, amplitude, and phase.
This indicator is a powerful tool that employs the Goertzel algorithm to analyze and visualize the cyclical components within a financial time series. By providing insights into the underlying price movements and their potential future trajectory, the indicator aims to assist traders in making more informed decisions.
█ What is the Goertzel Algorithm?
The Goertzel algorithm, named after Gerald Goertzel, is a digital signal processing technique that is used to efficiently compute individual terms of the Discrete Fourier Transform (DFT). It was first introduced in 1958, and since then, it has found various applications in the fields of engineering, mathematics, and physics.
The Goertzel algorithm is primarily used to detect specific frequency components within a digital signal, making it particularly useful in applications where only a few frequency components are of interest. The algorithm is computationally efficient, as it requires fewer calculations than the Fast Fourier Transform (FFT) when detecting a small number of frequency components. This efficiency makes the Goertzel algorithm a popular choice in applications such as:
1. Telecommunications: The Goertzel algorithm is used for decoding Dual-Tone Multi-Frequency (DTMF) signals, which are the tones generated when pressing buttons on a telephone keypad. By identifying specific frequency components, the algorithm can accurately determine which button has been pressed.
2. Audio processing: The algorithm can be used to detect specific pitches or harmonics in an audio signal, making it useful in applications like pitch detection and tuning musical instruments.
3. Vibration analysis: In the field of mechanical engineering, the Goertzel algorithm can be applied to analyze vibrations in rotating machinery, helping to identify faulty components or signs of wear.
4. Power system analysis: The algorithm can be used to measure harmonic content in power systems, allowing engineers to assess power quality and detect potential issues.
The Goertzel algorithm is used in these applications because it offers several advantages over other methods, such as the FFT:
1. Computational efficiency: The Goertzel algorithm requires fewer calculations when detecting a small number of frequency components, making it more computationally efficient than the FFT in these cases.
2. Real-time analysis: The algorithm can be implemented in a streaming fashion, allowing for real-time analysis of signals, which is crucial in applications like telecommunications and audio processing.
3. Memory efficiency: The Goertzel algorithm requires less memory than the FFT, as it only computes the frequency components of interest.
4. Precision: The algorithm is less susceptible to numerical errors compared to the FFT, ensuring more accurate results in applications where precision is essential.
The Goertzel algorithm is an efficient digital signal processing technique that is primarily used to detect specific frequency components within a signal. Its computational efficiency, real-time capabilities, and precision make it an attractive choice for various applications, including telecommunications, audio processing, vibration analysis, and power system analysis. The algorithm has been widely adopted since its introduction in 1958 and continues to be an essential tool in the fields of engineering, mathematics, and physics.
█ Goertzel Algorithm in Quantitative Finance: In-Depth Analysis and Applications
The Goertzel algorithm, initially designed for signal processing in telecommunications, has gained significant traction in the financial industry due to its efficient frequency detection capabilities. In quantitative finance, the Goertzel algorithm has been utilized for uncovering hidden market cycles, developing data-driven trading strategies, and optimizing risk management. This section delves deeper into the applications of the Goertzel algorithm in finance, particularly within the context of quantitative trading and analysis.
Unveiling Hidden Market Cycles:
Market cycles are prevalent in financial markets and arise from various factors, such as economic conditions, investor psychology, and market participant behavior. The Goertzel algorithm's ability to detect and isolate specific frequencies in price data helps trader analysts identify hidden market cycles that may otherwise go unnoticed. By examining the amplitude, phase, and periodicity of each cycle, traders can better understand the underlying market structure and dynamics, enabling them to develop more informed and effective trading strategies.
Developing Quantitative Trading Strategies:
The Goertzel algorithm's versatility allows traders to incorporate its insights into a wide range of trading strategies. By identifying the dominant market cycles in a financial instrument's price data, traders can create data-driven strategies that capitalize on the cyclical nature of markets.
For instance, a trader may develop a mean-reversion strategy that takes advantage of the identified cycles. By establishing positions when the price deviates from the predicted cycle, the trader can profit from the subsequent reversion to the cycle's mean. Similarly, a momentum-based strategy could be designed to exploit the persistence of a dominant cycle by entering positions that align with the cycle's direction.
Enhancing Risk Management:
The Goertzel algorithm plays a vital role in risk management for quantitative strategies. By analyzing the cyclical components of a financial instrument's price data, traders can gain insights into the potential risks associated with their trading strategies.
By monitoring the amplitude and phase of dominant cycles, a trader can detect changes in market dynamics that may pose risks to their positions. For example, a sudden increase in amplitude may indicate heightened volatility, prompting the trader to adjust position sizing or employ hedging techniques to protect their portfolio. Additionally, changes in phase alignment could signal a potential shift in market sentiment, necessitating adjustments to the trading strategy.
Expanding Quantitative Toolkits:
Traders can augment the Goertzel algorithm's insights by combining it with other quantitative techniques, creating a more comprehensive and sophisticated analysis framework. For example, machine learning algorithms, such as neural networks or support vector machines, could be trained on features extracted from the Goertzel algorithm to predict future price movements more accurately.
Furthermore, the Goertzel algorithm can be integrated with other technical analysis tools, such as moving averages or oscillators, to enhance their effectiveness. By applying these tools to the identified cycles, traders can generate more robust and reliable trading signals.
The Goertzel algorithm offers invaluable benefits to quantitative finance practitioners by uncovering hidden market cycles, aiding in the development of data-driven trading strategies, and improving risk management. By leveraging the insights provided by the Goertzel algorithm and integrating it with other quantitative techniques, traders can gain a deeper understanding of market dynamics and devise more effective trading strategies.
█ Indicator Inputs
src: This is the source data for the analysis, typically the closing price of the financial instrument.
detrendornot: This input determines the method used for detrending the source data. Detrending is the process of removing the underlying trend from the data to focus on the cyclical components.
The available options are:
hpsmthdt: Detrend using Hodrick-Prescott filter centered moving average.
zlagsmthdt: Detrend using zero-lag moving average centered moving average.
logZlagRegression: Detrend using logarithmic zero-lag linear regression.
hpsmth: Detrend using Hodrick-Prescott filter.
zlagsmth: Detrend using zero-lag moving average.
DT_HPper1 and DT_HPper2: These inputs define the period range for the Hodrick-Prescott filter centered moving average when detrendornot is set to hpsmthdt.
DT_ZLper1 and DT_ZLper2: These inputs define the period range for the zero-lag moving average centered moving average when detrendornot is set to zlagsmthdt.
DT_RegZLsmoothPer: This input defines the period for the zero-lag moving average used in logarithmic zero-lag linear regression when detrendornot is set to logZlagRegression.
HPsmoothPer: This input defines the period for the Hodrick-Prescott filter when detrendornot is set to hpsmth.
ZLMAsmoothPer: This input defines the period for the zero-lag moving average when detrendornot is set to zlagsmth.
MaxPer: This input sets the maximum period for the Goertzel algorithm to search for cycles.
squaredAmp: This boolean input determines whether the amplitude should be squared in the Goertzel algorithm.
useAddition: This boolean input determines whether the Goertzel algorithm should use addition for combining the cycles.
useCosine: This boolean input determines whether the Goertzel algorithm should use cosine waves instead of sine waves.
UseCycleStrength: This boolean input determines whether the Goertzel algorithm should compute the cycle strength, which is a normalized measure of the cycle's amplitude.
WindowSizePast and WindowSizeFuture: These inputs define the window size for past and future projections of the composite wave.
FilterBartels: This boolean input determines whether Bartel's test should be applied to filter out non-significant cycles.
BartNoCycles: This input sets the number of cycles to be used in Bartel's test.
BartSmoothPer: This input sets the period for the moving average used in Bartel's test.
BartSigLimit: This input sets the significance limit for Bartel's test, below which cycles are considered insignificant.
SortBartels: This boolean input determines whether the cycles should be sorted by their Bartel's test results.
UseCycleList: This boolean input determines whether a user-defined list of cycles should be used for constructing the composite wave. If set to false, the top N cycles will be used.
Cycle1, Cycle2, Cycle3, Cycle4, and Cycle5: These inputs define the user-defined list of cycles when 'UseCycleList' is set to true. If using a user-defined list, each of these inputs represents the period of a specific cycle to include in the composite wave.
StartAtCycle: This input determines the starting index for selecting the top N cycles when UseCycleList is set to false. This allows you to skip a certain number of cycles from the top before selecting the desired number of cycles.
UseTopCycles: This input sets the number of top cycles to use for constructing the composite wave when UseCycleList is set to false. The cycles are ranked based on their amplitudes or cycle strengths, depending on the UseCycleStrength input.
SubtractNoise: This boolean input determines whether to subtract the noise (remaining cycles) from the composite wave. If set to true, the composite wave will only include the top N cycles specified by UseTopCycles.
█ Exploring Auxiliary Functions
The following functions demonstrate advanced techniques for analyzing financial markets, including zero-lag moving averages, Bartels probability, detrending, and Hodrick-Prescott filtering. This section examines each function in detail, explaining their purpose, methodology, and applications in finance. We will examine how each function contributes to the overall performance and effectiveness of the indicator and how they work together to create a powerful analytical tool.
Zero-Lag Moving Average:
The zero-lag moving average function is designed to minimize the lag typically associated with moving averages. This is achieved through a two-step weighted linear regression process that emphasizes more recent data points. The function calculates a linearly weighted moving average (LWMA) on the input data and then applies another LWMA on the result. By doing this, the function creates a moving average that closely follows the price action, reducing the lag and improving the responsiveness of the indicator.
The zero-lag moving average function is used in the indicator to provide a responsive, low-lag smoothing of the input data. This function helps reduce the noise and fluctuations in the data, making it easier to identify and analyze underlying trends and patterns. By minimizing the lag associated with traditional moving averages, this function allows the indicator to react more quickly to changes in market conditions, providing timely signals and improving the overall effectiveness of the indicator.
Bartels Probability:
The Bartels probability function calculates the probability of a given cycle being significant in a time series. It uses a mathematical test called the Bartels test to assess the significance of cycles detected in the data. The function calculates coefficients for each detected cycle and computes an average amplitude and an expected amplitude. By comparing these values, the Bartels probability is derived, indicating the likelihood of a cycle's significance. This information can help in identifying and analyzing dominant cycles in financial markets.
The Bartels probability function is incorporated into the indicator to assess the significance of detected cycles in the input data. By calculating the Bartels probability for each cycle, the indicator can prioritize the most significant cycles and focus on the market dynamics that are most relevant to the current trading environment. This function enhances the indicator's ability to identify dominant market cycles, improving its predictive power and aiding in the development of effective trading strategies.
Detrend Logarithmic Zero-Lag Regression:
The detrend logarithmic zero-lag regression function is used for detrending data while minimizing lag. It combines a zero-lag moving average with a linear regression detrending method. The function first calculates the zero-lag moving average of the logarithm of input data and then applies a linear regression to remove the trend. By detrending the data, the function isolates the cyclical components, making it easier to analyze and interpret the underlying market dynamics.
The detrend logarithmic zero-lag regression function is used in the indicator to isolate the cyclical components of the input data. By detrending the data, the function enables the indicator to focus on the cyclical movements in the market, making it easier to analyze and interpret market dynamics. This function is essential for identifying cyclical patterns and understanding the interactions between different market cycles, which can inform trading decisions and enhance overall market understanding.
Bartels Cycle Significance Test:
The Bartels cycle significance test is a function that combines the Bartels probability function and the detrend logarithmic zero-lag regression function to assess the significance of detected cycles. The function calculates the Bartels probability for each cycle and stores the results in an array. By analyzing the probability values, traders and analysts can identify the most significant cycles in the data, which can be used to develop trading strategies and improve market understanding.
The Bartels cycle significance test function is integrated into the indicator to provide a comprehensive analysis of the significance of detected cycles. By combining the Bartels probability function and the detrend logarithmic zero-lag regression function, this test evaluates the significance of each cycle and stores the results in an array. The indicator can then use this information to prioritize the most significant cycles and focus on the most relevant market dynamics. This function enhances the indicator's ability to identify and analyze dominant market cycles, providing valuable insights for trading and market analysis.
Hodrick-Prescott Filter:
The Hodrick-Prescott filter is a popular technique used to separate the trend and cyclical components of a time series. The function applies a smoothing parameter to the input data and calculates a smoothed series using a two-sided filter. This smoothed series represents the trend component, which can be subtracted from the original data to obtain the cyclical component. The Hodrick-Prescott filter is commonly used in economics and finance to analyze economic data and financial market trends.
The Hodrick-Prescott filter is incorporated into the indicator to separate the trend and cyclical components of the input data. By applying the filter to the data, the indicator can isolate the trend component, which can be used to analyze long-term market trends and inform trading decisions. Additionally, the cyclical component can be used to identify shorter-term market dynamics and provide insights into potential trading opportunities. The inclusion of the Hodrick-Prescott filter adds another layer of analysis to the indicator, making it more versatile and comprehensive.
Detrending Options: Detrend Centered Moving Average:
The detrend centered moving average function provides different detrending methods, including the Hodrick-Prescott filter and the zero-lag moving average, based on the selected detrending method. The function calculates two sets of smoothed values using the chosen method and subtracts one set from the other to obtain a detrended series. By offering multiple detrending options, this function allows traders and analysts to select the most appropriate method for their specific needs and preferences.
The detrend centered moving average function is integrated into the indicator to provide users with multiple detrending options, including the Hodrick-Prescott filter and the zero-lag moving average. By offering multiple detrending methods, the indicator allows users to customize the analysis to their specific needs and preferences, enhancing the indicator's overall utility and adaptability. This function ensures that the indicator can cater to a wide range of trading styles and objectives, making it a valuable tool for a diverse group of market participants.
The auxiliary functions functions discussed in this section demonstrate the power and versatility of mathematical techniques in analyzing financial markets. By understanding and implementing these functions, traders and analysts can gain valuable insights into market dynamics, improve their trading strategies, and make more informed decisions. The combination of zero-lag moving averages, Bartels probability, detrending methods, and the Hodrick-Prescott filter provides a comprehensive toolkit for analyzing and interpreting financial data. The integration of advanced functions in a financial indicator creates a powerful and versatile analytical tool that can provide valuable insights into financial markets. By combining the zero-lag moving average,
█ In-Depth Analysis of the Goertzel Browser Code
The Goertzel Browser code is an implementation of the Goertzel Algorithm, an efficient technique to perform spectral analysis on a signal. The code is designed to detect and analyze dominant cycles within a given financial market data set. This section will provide an extremely detailed explanation of the code, its structure, functions, and intended purpose.
Function signature and input parameters:
The Goertzel Browser function accepts numerous input parameters for customization, including source data (src), the current bar (forBar), sample size (samplesize), period (per), squared amplitude flag (squaredAmp), addition flag (useAddition), cosine flag (useCosine), cycle strength flag (UseCycleStrength), past and future window sizes (WindowSizePast, WindowSizeFuture), Bartels filter flag (FilterBartels), Bartels-related parameters (BartNoCycles, BartSmoothPer, BartSigLimit), sorting flag (SortBartels), and output buffers (goeWorkPast, goeWorkFuture, cyclebuffer, amplitudebuffer, phasebuffer, cycleBartelsBuffer).
Initializing variables and arrays:
The code initializes several float arrays (goeWork1, goeWork2, goeWork3, goeWork4) with the same length as twice the period (2 * per). These arrays store intermediate results during the execution of the algorithm.
Preprocessing input data:
The input data (src) undergoes preprocessing to remove linear trends. This step enhances the algorithm's ability to focus on cyclical components in the data. The linear trend is calculated by finding the slope between the first and last values of the input data within the sample.
Iterative calculation of Goertzel coefficients:
The core of the Goertzel Browser algorithm lies in the iterative calculation of Goertzel coefficients for each frequency bin. These coefficients represent the spectral content of the input data at different frequencies. The code iterates through the range of frequencies, calculating the Goertzel coefficients using a nested loop structure.
Cycle strength computation:
The code calculates the cycle strength based on the Goertzel coefficients. This is an optional step, controlled by the UseCycleStrength flag. The cycle strength provides information on the relative influence of each cycle on the data per bar, considering both amplitude and cycle length. The algorithm computes the cycle strength either by squaring the amplitude (controlled by squaredAmp flag) or using the actual amplitude values.
Phase calculation:
The Goertzel Browser code computes the phase of each cycle, which represents the position of the cycle within the input data. The phase is calculated using the arctangent function (math.atan) based on the ratio of the imaginary and real components of the Goertzel coefficients.
Peak detection and cycle extraction:
The algorithm performs peak detection on the computed amplitudes or cycle strengths to identify dominant cycles. It stores the detected cycles in the cyclebuffer array, along with their corresponding amplitudes and phases in the amplitudebuffer and phasebuffer arrays, respectively.
Sorting cycles by amplitude or cycle strength:
The code sorts the detected cycles based on their amplitude or cycle strength in descending order. This allows the algorithm to prioritize cycles with the most significant impact on the input data.
Bartels cycle significance test:
If the FilterBartels flag is set, the code performs a Bartels cycle significance test on the detected cycles. This test determines the statistical significance of each cycle and filters out the insignificant cycles. The significant cycles are stored in the cycleBartelsBuffer array. If the SortBartels flag is set, the code sorts the significant cycles based on their Bartels significance values.
Waveform calculation:
The Goertzel Browser code calculates the waveform of the significant cycles for both past and future time windows. The past and future windows are defined by the WindowSizePast and WindowSizeFuture parameters, respectively. The algorithm uses either cosine or sine functions (controlled by the useCosine flag) to calculate the waveforms for each cycle. The useAddition flag determines whether the waveforms should be added or subtracted.
Storing waveforms in matrices:
The calculated waveforms for each cycle are stored in two matrices - goeWorkPast and goeWorkFuture. These matrices hold the waveforms for the past and future time windows, respectively. Each row in the matrices represents a time window position, and each column corresponds to a cycle.
Returning the number of cycles:
The Goertzel Browser function returns the total number of detected cycles (number_of_cycles) after processing the input data. This information can be used to further analyze the results or to visualize the detected cycles.
The Goertzel Browser code is a comprehensive implementation of the Goertzel Algorithm, specifically designed for detecting and analyzing dominant cycles within financial market data. The code offers a high level of customization, allowing users to fine-tune the algorithm based on their specific needs. The Goertzel Browser's combination of preprocessing, iterative calculations, cycle extraction, sorting, significance testing, and waveform calculation makes it a powerful tool for understanding cyclical components in financial data.
█ Generating and Visualizing Composite Waveform
The indicator calculates and visualizes the composite waveform for both past and future time windows based on the detected cycles. Here's a detailed explanation of this process:
Updating WindowSizePast and WindowSizeFuture:
The WindowSizePast and WindowSizeFuture are updated to ensure they are at least twice the MaxPer (maximum period).
Initializing matrices and arrays:
Two matrices, goeWorkPast and goeWorkFuture, are initialized to store the Goertzel results for past and future time windows. Multiple arrays are also initialized to store cycle, amplitude, phase, and Bartels information.
Preparing the source data (srcVal) array:
The source data is copied into an array, srcVal, and detrended using one of the selected methods (hpsmthdt, zlagsmthdt, logZlagRegression, hpsmth, or zlagsmth).
Goertzel function call:
The Goertzel function is called to analyze the detrended source data and extract cycle information. The output, number_of_cycles, contains the number of detected cycles.
Initializing arrays for past and future waveforms:
Three arrays, epgoertzel, goertzel, and goertzelFuture, are initialized to store the endpoint Goertzel, non-endpoint Goertzel, and future Goertzel projections, respectively.
Calculating composite waveform for past bars (goertzel array):
The past composite waveform is calculated by summing the selected cycles (either from the user-defined cycle list or the top cycles) and optionally subtracting the noise component.
Calculating composite waveform for future bars (goertzelFuture array):
The future composite waveform is calculated in a similar way as the past composite waveform.
Drawing past composite waveform (pvlines):
The past composite waveform is drawn on the chart using solid lines. The color of the lines is determined by the direction of the waveform (green for upward, red for downward).
Drawing future composite waveform (fvlines):
The future composite waveform is drawn on the chart using dotted lines. The color of the lines is determined by the direction of the waveform (fuchsia for upward, yellow for downward).
Displaying cycle information in a table (table3):
A table is created to display the cycle information, including the rank, period, Bartel value, amplitude (or cycle strength), and phase of each detected cycle.
Filling the table with cycle information:
The indicator iterates through the detected cycles and retrieves the relevant information (period, amplitude, phase, and Bartel value) from the corresponding arrays. It then fills the table with this information, displaying the values up to six decimal places.
To summarize, this indicator generates a composite waveform based on the detected cycles in the financial data. It calculates the composite waveforms for both past and future time windows and visualizes them on the chart using colored lines. Additionally, it displays detailed cycle information in a table, including the rank, period, Bartel value, amplitude (or cycle strength), and phase of each detected cycle.
█ Enhancing the Goertzel Algorithm-Based Script for Financial Modeling and Trading
The Goertzel algorithm-based script for detecting dominant cycles in financial data is a powerful tool for financial modeling and trading. It provides valuable insights into the past behavior of these cycles and potential future impact. However, as with any algorithm, there is always room for improvement. This section discusses potential enhancements to the existing script to make it even more robust and versatile for financial modeling, general trading, advanced trading, and high-frequency finance trading.
Enhancements for Financial Modeling
Data preprocessing: One way to improve the script's performance for financial modeling is to introduce more advanced data preprocessing techniques. This could include removing outliers, handling missing data, and normalizing the data to ensure consistent and accurate results.
Additional detrending and smoothing methods: Incorporating more sophisticated detrending and smoothing techniques, such as wavelet transform or empirical mode decomposition, can help improve the script's ability to accurately identify cycles and trends in the data.
Machine learning integration: Integrating machine learning techniques, such as artificial neural networks or support vector machines, can help enhance the script's predictive capabilities, leading to more accurate financial models.
Enhancements for General and Advanced Trading
Customizable indicator integration: Allowing users to integrate their own technical indicators can help improve the script's effectiveness for both general and advanced trading. By enabling the combination of the dominant cycle information with other technical analysis tools, traders can develop more comprehensive trading strategies.
Risk management and position sizing: Incorporating risk management and position sizing functionality into the script can help traders better manage their trades and control potential losses. This can be achieved by calculating the optimal position size based on the user's risk tolerance and account size.
Multi-timeframe analysis: Enhancing the script to perform multi-timeframe analysis can provide traders with a more holistic view of market trends and cycles. By identifying dominant cycles on different timeframes, traders can gain insights into the potential confluence of cycles and make better-informed trading decisions.
Enhancements for High-Frequency Finance Trading
Algorithm optimization: To ensure the script's suitability for high-frequency finance trading, optimizing the algorithm for faster execution is crucial. This can be achieved by employing efficient data structures and refining the calculation methods to minimize computational complexity.
Real-time data streaming: Integrating real-time data streaming capabilities into the script can help high-frequency traders react to market changes more quickly. By continuously updating the cycle information based on real-time market data, traders can adapt their strategies accordingly and capitalize on short-term market fluctuations.
Order execution and trade management: To fully leverage the script's capabilities for high-frequency trading, implementing functionality for automated order execution and trade management is essential. This can include features such as stop-loss and take-profit orders, trailing stops, and automated trade exit strategies.
While the existing Goertzel algorithm-based script is a valuable tool for detecting dominant cycles in financial data, there are several potential enhancements that can make it even more powerful for financial modeling, general trading, advanced trading, and high-frequency finance trading. By incorporating these improvements, the script can become a more versatile and effective tool for traders and financial analysts alike.
█ Understanding the Limitations of the Goertzel Algorithm
While the Goertzel algorithm-based script for detecting dominant cycles in financial data provides valuable insights, it is important to be aware of its limitations and drawbacks. Some of the key drawbacks of this indicator are:
Lagging nature:
As with many other technical indicators, the Goertzel algorithm-based script can suffer from lagging effects, meaning that it may not immediately react to real-time market changes. This lag can lead to late entries and exits, potentially resulting in reduced profitability or increased losses.
Parameter sensitivity:
The performance of the script can be sensitive to the chosen parameters, such as the detrending methods, smoothing techniques, and cycle detection settings. Improper parameter selection may lead to inaccurate cycle detection or increased false signals, which can negatively impact trading performance.
Complexity:
The Goertzel algorithm itself is relatively complex, making it difficult for novice traders or those unfamiliar with the concept of cycle analysis to fully understand and effectively utilize the script. This complexity can also make it challenging to optimize the script for specific trading styles or market conditions.
Overfitting risk:
As with any data-driven approach, there is a risk of overfitting when using the Goertzel algorithm-based script. Overfitting occurs when a model becomes too specific to the historical data it was trained on, leading to poor performance on new, unseen data. This can result in misleading signals and reduced trading performance.
No guarantee of future performance: While the script can provide insights into past cycles and potential future trends, it is important to remember that past performance does not guarantee future results. Market conditions can change, and relying solely on the script's predictions without considering other factors may lead to poor trading decisions.
Limited applicability: The Goertzel algorithm-based script may not be suitable for all markets, trading styles, or timeframes. Its effectiveness in detecting cycles may be limited in certain market conditions, such as during periods of extreme volatility or low liquidity.
While the Goertzel algorithm-based script offers valuable insights into dominant cycles in financial data, it is essential to consider its drawbacks and limitations when incorporating it into a trading strategy. Traders should always use the script in conjunction with other technical and fundamental analysis tools, as well as proper risk management, to make well-informed trading decisions.
█ Interpreting Results
The Goertzel Browser indicator can be interpreted by analyzing the plotted lines and the table presented alongside them. The indicator plots two lines: past and future composite waves. The past composite wave represents the composite wave of the past price data, and the future composite wave represents the projected composite wave for the next period.
The past composite wave line displays a solid line, with green indicating a bullish trend and red indicating a bearish trend. On the other hand, the future composite wave line is a dotted line with fuchsia indicating a bullish trend and yellow indicating a bearish trend.
The table presented alongside the indicator shows the top cycles with their corresponding rank, period, Bartels, amplitude or cycle strength, and phase. The amplitude is a measure of the strength of the cycle, while the phase is the position of the cycle within the data series.
Interpreting the Goertzel Browser indicator involves identifying the trend of the past and future composite wave lines and matching them with the corresponding bullish or bearish color. Additionally, traders can identify the top cycles with the highest amplitude or cycle strength and utilize them in conjunction with other technical indicators and fundamental analysis for trading decisions.
This indicator is considered a repainting indicator because the value of the indicator is calculated based on the past price data. As new price data becomes available, the indicator's value is recalculated, potentially causing the indicator's past values to change. This can create a false impression of the indicator's performance, as it may appear to have provided a profitable trading signal in the past when, in fact, that signal did not exist at the time.
The Goertzel indicator is also non-endpointed, meaning that it is not calculated up to the current bar or candle. Instead, it uses a fixed amount of historical data to calculate its values, which can make it difficult to use for real-time trading decisions. For example, if the indicator uses 100 bars of historical data to make its calculations, it cannot provide a signal until the current bar has closed and become part of the historical data. This can result in missed trading opportunities or delayed signals.
█ Conclusion
The Goertzel Browser indicator is a powerful tool for identifying and analyzing cyclical patterns in financial markets. Its ability to detect multiple cycles of varying frequencies and strengths make it a valuable addition to any trader's technical analysis toolkit. However, it is important to keep in mind that the Goertzel Browser indicator should be used in conjunction with other technical analysis tools and fundamental analysis to achieve the best results. With continued refinement and development, the Goertzel Browser indicator has the potential to become a highly effective tool for financial modeling, general trading, advanced trading, and high-frequency finance trading. Its accuracy and versatility make it a promising candidate for further research and development.
█ Footnotes
What is the Bartels Test for Cycle Significance?
The Bartels Cycle Significance Test is a statistical method that determines whether the peaks and troughs of a time series are statistically significant. The test is named after its inventor, George Bartels, who developed it in the mid-20th century.
The Bartels test is designed to analyze the cyclical components of a time series, which can help traders and analysts identify trends and cycles in financial markets. The test calculates a Bartels statistic, which measures the degree of non-randomness or autocorrelation in the time series.
The Bartels statistic is calculated by first splitting the time series into two halves and calculating the range of the peaks and troughs in each half. The test then compares these ranges using a t-test, which measures the significance of the difference between the two ranges.
If the Bartels statistic is greater than a critical value, it indicates that the peaks and troughs in the time series are non-random and that there is a significant cyclical component to the data. Conversely, if the Bartels statistic is less than the critical value, it suggests that the peaks and troughs are random and that there is no significant cyclical component.
The Bartels Cycle Significance Test is particularly useful in financial analysis because it can help traders and analysts identify significant cycles in asset prices, which can in turn inform investment decisions. However, it is important to note that the test is not perfect and can produce false signals in certain situations, particularly in noisy or volatile markets. Therefore, it is always recommended to use the test in conjunction with other technical and fundamental indicators to confirm trends and cycles.
Deep-dive into the Hodrick-Prescott Fitler
The Hodrick-Prescott (HP) filter is a statistical tool used in economics and finance to separate a time series into two components: a trend component and a cyclical component. It is a powerful tool for identifying long-term trends in economic and financial data and is widely used by economists, central banks, and financial institutions around the world.
The HP filter was first introduced in the 1990s by economists Robert Hodrick and Edward Prescott. It is a simple, two-parameter filter that separates a time series into a trend component and a cyclical component. The trend component represents the long-term behavior of the data, while the cyclical component captures the shorter-term fluctuations around the trend.
The HP filter works by minimizing the following objective function:
Minimize: (Sum of Squared Deviations) + λ (Sum of Squared Second Differences)
Where:
The first term represents the deviation of the data from the trend.
The second term represents the smoothness of the trend.
λ is a smoothing parameter that determines the degree of smoothness of the trend.
The smoothing parameter λ is typically set to a value between 100 and 1600, depending on the frequency of the data. Higher values of λ lead to a smoother trend, while lower values lead to a more volatile trend.
The HP filter has several advantages over other smoothing techniques. It is a non-parametric method, meaning that it does not make any assumptions about the underlying distribution of the data. It also allows for easy comparison of trends across different time series and can be used with data of any frequency.
However, the HP filter also has some limitations. It assumes that the trend is a smooth function, which may not be the case in some situations. It can also be sensitive to changes in the smoothing parameter λ, which may result in different trends for the same data. Additionally, the filter may produce unrealistic trends for very short time series.
Despite these limitations, the HP filter remains a valuable tool for analyzing economic and financial data. It is widely used by central banks and financial institutions to monitor long-term trends in the economy, and it can be used to identify turning points in the business cycle. The filter can also be used to analyze asset prices, exchange rates, and other financial variables.
The Hodrick-Prescott filter is a powerful tool for analyzing economic and financial data. It separates a time series into a trend component and a cyclical component, allowing for easy identification of long-term trends and turning points in the business cycle. While it has some limitations, it remains a valuable tool for economists, central banks, and financial institutions around the world.
Crossover Alerts for Yesterday O/H/L/C , Today Vwap [Zero54]This is a very simple script/indicator that trigger alerts every time the script triggers the following conditions.
1) Script crosses yesterday's (previous day's) high
2) Script crosses yesterday's (previous day's) low
3) Script crosses yesterday's (previous day's) open
4) Script crosses yesterday's (previous day's) close
5) Script crosses today's vwap.
I developed this to keep track of the scripts I follow and I find it useful. Hope you will find it useful too.
Steps to use:
1) Open the ticker for which you want to set the alerts.
2) Add this indicator to the chart.
3) Right Click on the text and set choose "Add Alert"
4) After you have done with setting up the alert, feel free to remove the indicator from the chart. It is not necessary for the indicator to be added in the chart in order for it to work.
5) Repeat 1-4 for all the scripts for which you want to set the alerts.
Be advised: During market open, if you have set alerts for multiple scripts, a tsunami of alerts may be triggered.
If you like this alert indicator, please like/boost it. Feel free to re-use this code however you may wish to. Cheers!
Nifty and Bank Nifty Dashboard V2This shows a performance glance of Dow and major Constituents of NSE:NIFTY or NSE:BANKNIFTY . This is an enhancement to the Bank nifty dashboard published earlier.
Usage
• Customizable Table and Style settings
• Customizable Indicator Settings
• Customizable Time frame of Indicators in Table. Can change to higher or lower TF other than the chart time frame
• Customizable Input symbols. Can modify with the Scripts you want to track.
• The Last row will be the current script viewed in charts.
• Can enable or disable indicators on the chart like ST, SMA, VWAP.
• Strong Volume Indication at bottom based on the average volume inputs for Nifty, Bank Nifty and for other stocks volume > 20 ma(volume)
• Displays bank nifty stocks if Bank nifty is the open chart else it will display top Nifty Stocks.
• This will help to monitor the performance of various scripts.
• Can change the stock list according to usage/Index.
• It will show all the symbols if Additional Symbols is selected.
Buy-Sell Signal
• Volume > Average Volume, it Shows #
• ST – Buy - Price > Super trend (10,2) and vice versa
• SMA – Buy - Price > MA and vice versa
• RSI – Buy – RSI > 50, Sell – RSI < 40
• ADX: Buy - ADX > 25, DMI+ Above DMI - and vice versa
• Previous day High low is not considered for buy or sell score calculation. This is just for additional observation.
• ATR will be highlighted when change > 0.75 of the average true range of daily price.
Strong colours will be shown for respective boxes when some additional conditions satisfy.
Style settings
Dashboard Location: Location of the dashboard on the chart
Dashboard Size: The size of the dashboard on the chart
Text/Frame Color: Determines the colour of the frame grid as well as the text colour
Bullish Cell Color: Determines the colour of cell associated with a rising indicator direction
Bearish Cell Color: Determines the colour of cell associated with a decreasing indicator direction
Cell Transparency: Transparency of each cell
Technical Ratings█ OVERVIEW
This indicator calculates TradingView's well-known "Strong Buy", "Buy", "Neutral", "Sell" or "Strong Sell" states using the aggregate biases of 26 different technical indicators.
█ FEATURES
Differences with the built-in version
• You can adjust the weight of the Oscillators and MAs components of the rating here.
• The built-in version produces values matching the states displayed in the "Technicals" ratings gauge; this one does not always, where weighting is used.
• A strategy version is also available as a built-in; this script is an indicator—not a strategy.
• This indicator will show a slightly different vertical scale, as it does not use a fixed scale like the built-in.
• This version allows control over repainting of the signal when you do not use a higher timeframe. Higher timeframe (HTF) information from this version does not repaint.
• You can configure markers on signal breaches of configurable levels, or on advances declines of the signal.
The indicator's settings allow you to:
• Choose the timeframe you want calculations to be made on.
• When not using a HTF, you can select a repainting or non-repainting signal.
• When using both MAs and Oscillators groups to calculate the rating, you can vary the weight of each group in the calculation. The default is 50/50.
Because the MAs group uses longer periods for some of its components, its value is not as jumpy as the Oscillators value.
Increasing the weight of the MAs group will thus have a calming effect on the signal.
• Alerts can be created on the indicator using the conditions configured to control the display of markers.
Display
The calculated rating is displayed as columns, but you can change the style in the inputs. The color of the signal can be one of three colors: bull, bear, or neutral. You can choose from a few presets, or check one and edit its color. The color is determined from the rating's value. Between 0.1 and -0.1 it is in the neutral color. Above/below 0.1/-0.1 it will appear in the bull/bear color. The intensity of the bull/bear color is determined by cumulative advances/declines in the rating. It is capped to 5, so there are five intensities for each of the bull/bear colors.
The "Strong Buy", "Buy", "Neutral", "Sell" or "Strong Sell" state of the last calculated value is displayed to the right of the last bar for each of the three groups: All, MAs and Oscillators. The first value always reflects your selection in the "Rating uses" field and is the one used to display the signal. A "Strong Buy" or "Strong Sell" state appears when the signal is above/below the 0.5/-0.5 level. A "Buy" or "Sell" state appears when the signal is above/below the 0.1/-0.1 level. The "Neutral" state appears when the signal is between 0.1 and -0.1 inclusively.
Five levels are always displayed: 0.5 and 0.1 in the bull color, zero in the neutral color, and -0.1 and - 0.5 in the bull color.
The levels that can be used to determine the breaches displaying long/short markers will only be visible when their respective long/short markers are turned on in the "Direction" input. The levels appear as a bright dotted line in bull/bear colors. You can control both levels separately through the "Longs Level" and "Shorts Level" inputs.
If you specify a higher timeframe that is not greater than the chart's timeframe, an error message will appear and the indicator's background will turn red, as it doesn't make sense to use a lower timeframe than the chart's.
Markers
Markers are small triangles that appear at the bottom and top of the indicator's pane. The marker settings define the conditions that will trigger an alert when you configure an alert on the indicator. You can:
• Choose if you want long, short or both long and short markers.
• Determine the signal level and/or the number of cumulative advances/declines in the signal which must be reached for either a long or short marker to appear.
Reminder: the number of advances/declines is also what controls the brightness of the plotted signal.
• Decide if you want to restrict markers to ones that alternate between longs and shorts, if you are displaying both directions.
This helps to minimize the number of markers, e.g., only the first long marker will be displayed, and then no more long markers will appear until a short comes in, then a long, etc.
Alerts
When you create an alert from this indicator, that alert will trigger whenever your marker conditions are confirmed. Before creating your alert, configure the makers so they reflect the conditions you want your alert to trigger on.
The script uses the alert() function, which entails that you select the "Any alert() function call" condition from the "Create Alert" dialog box when creating alerts on the script. The alert messages can be configured in the inputs. You can safely disregard the warning popup that appears when you create alerts from this script. Alerts will not repaint. Markers will appear, and thus alerts will trigger, at the opening of the bar following the confirmation of the marker condition. Markers will never disappear from the bar once they appear.
Repainting
This indicator uses a two-pronged approach to control repainting. The repainting of the displayed signal is controlled through the "Repainting" field in the script's inputs. This only applies when you have "Same as chart" selected in the "Timeframe" field, as higher timeframe data never repaints. Regardless of that setting, markers and thus alerts never repaint.
When using the chart's timeframe, choosing a non-repainting signal makes the signal one bar late, so that it only displays a value once the bar it was calculated has elapsed. When using a higher timeframe, new values are only displayed once the higher timeframe completes.
Because the markers never repaint, their logic adapts to the repainting setting used for the signal. When the signal repaints, markers will only appear at the close of a realtime bar. When the signal does not repaint (or if you use a higher timeframe), alerts will appear at the beginning of the realtime bar, since they are calculated on values that already do not repaint.
█ CALCULATIONS
The indicator calculates the aggregate value of two groups of indicators: moving averages and oscillators.
The "MAs" group is comprised of 15 different components:
• Six Simple Moving Averages of periods 10, 20, 30, 50, 100 and 200
• Six Exponential Moving Averages of the same periods
• A Hull Moving Average of period 9
• A Volume-weighed Moving Average of period 20
• Ichimoku
The "Oscillators" group includes 11 components:
• RSI
• Stochastic
• CCI
• ADX
• Awesome Oscillator
• Momentum
• MACD
• Stochastic RSI
• Wiliams %R
• Bull Bear Power
• Ultimate Oscillator
The state of each group's components is evaluated to a +1/0/-1 value corresponding to its bull/neutral/bear bias. The resulting value for each of the two groups are then averaged to produce the overall value for the indicator, which oscillates between +1 and -1. The complete conditions used in the calculations are documented in the Help Center .
█ NOTES
Accuracy
When comparing values to the other versions of the Rating, make sure you are comparing similar timeframes, as the "Technicals" gauge in the chart's right pane, for example, uses a 1D timeframe by default.
For coders
We use a handy characteristic of array.avg() which, contrary to avg() , does not return na when one of the averaged values is na . It will average only the array elements which are not na . This is useful in the context where the functions used to calculate the bull/neutral/bear bias for each component used in the rating include special checks to return na whenever the dataset does not yet contain enough data to provide reliable values. This way, components gradually kick in the calculations as the script calculates on more and more historical data.
We also use the new `group` and `tooltip` parameters to input() , as well as dynamic color generation of different transparencies from the bull/bear/neutral colors selected by the user.
Our script was written using the PineCoders Coding Conventions for Pine .
The description was formatted using the techniques explained in the How We Write and Format Script Descriptions PineCoders publication.
Bits and pieces were lifted from the PineCoders' MTF Selection Framework .
Look first. Then leap.
