EngineerBuySellHighRiskThis TradingView indicator script is designed to identify various trading signals based on price action and the 5-period Exponential Moving Average (EMA), providing traders with insights into potential buy and sell opportunities. The script generates signals under the following categories:
Buy Signals
Regular Buy Signal: Identified when the entire previous candle (Candle 1) is below the 5 EMA, and the following candle (Candle 2) has a higher high compared to Candle 1 and closes higher than its opening price (indicating a green candle). This signal suggests a potential upward momentum as the price moves above the recent lows and the 5 EMA, indicating a buying opportunity.
High-Risk Buy Signal: Similar to the regular buy signal, but it specifically targets scenarios where Candle 1's high is exactly on the 5 EMA. Candle 2 must either have a higher high than Candle 1 or touch the 5 EMA, and it must close higher than its opening price. This signal indicates a potential for an upward trend continuation but is considered higher risk due to the price's proximity to the 5 EMA.
High Buy Risk Signal: This signal is generated under the same conditions as the regular buy signal regarding the position of Candle 1 relative to the 5 EMA and the requirement for Candle 2 to have a higher high. However, it allows for Candle 2 to close lower than its opening price (indicating a red candle), broadening the criteria for a buy signal. This modification acknowledges the potential for buying opportunities even in cases where Candle 2 closes down, assuming the price still shows upward momentum compared to Candle 1.
Sell Signals
Sell Signal: Generated when Candle 1 is entirely above the 5 EMA, and the following candle (Candle 2) has a lower low compared to Candle 1 and closes lower than its opening price (indicating a red candle). This setup suggests a potential downward trend, signaling a selling or shorting opportunity.
High Risk Sell Signal: This signal is for scenarios where Candle 1 is above the 5 EMA, and Candle 2's low is lower than Candle 1's low, but unlike the standard sell signal, it allows Candle 2 to close higher than its opening price (indicating a green candle). It signifies a potential downward price movement but with increased risk due to the mixed signal from Candle 2's close.
Stop-Loss Levels
Buy Stop-Loss Level: For buy signals, the stop-loss is set at the low of Candle 1, providing a risk management level to minimize potential losses if the market moves against the trade.
Sell Stop-Loss Level: For sell signals, the stop-loss is set at the high of Candle 1, serving as a risk management tool to protect against unfavorable price movements after entering a short position.
Visualization
The script uses different colors and labels to distinguish between the types of signals, making it easier for traders to identify and act upon these trading opportunities. It plots the 5 EMA for reference, providing context for the price action relative to this moving average. This script aims to offer a comprehensive toolkit for traders looking for nuanced entry and exit points based on short-term price movements and momentum relative to the 5 EMA.
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Backtest any Indicator v5Happy Trade,
here you get the opportunity to backtest any of your indicators like a strategy without converting them into a strategy. You can choose to go long or go short and detailed time filters. Further more you can set the take profit and stop loss, initial capital, quantity per trade and set the exchange fees. You get an overall result table and even a detailed, scroll-able table with all trades. In the Image 1 you see the provided info tables about all Trades and the Result Summary. Further more every trade is marked by a background color, Labels and Levels. An opening Label with the trade direction and trade number. A closing Label again with the trade number, the trades profit in % and the total amount of $ after all past trades. A green line for the take profit level and a red line for the stop loss.
Image 1
Example
For this description we choose the Stochastic RSI indicator from TradingView as it is. In Image 2 is shown the performance of it with decent settings.
Timeframe=45, BTCUSD, 2023-08-01 - 2023-10-20
Stoch RSI: k=30, d=40, RSI-length=140, stoch-length=140
Backtest any Indicator: input signal=Stoch RSI, goLong, take profit=9.1%, stop loss=2.5%, start capital=1000$, qty=5%, fee=0.1%, no Session Filter
Image 2
Usage
1) You need to know the name of the boolean (or integer) variable of your indicator which hold the buy condition. Lets say that this boolean variable is called BUY. If this BUY variable is not plotted on the chart you simply add the following code line at the end of your pine script.
For boolean (true/false) BUY variables use this:
plot(BUY ? 1:0,'Your buy condition hold in that variable BUY',display = display.data_window)
And in case your script's BUY variable is an integer or float then use instate the following code line:
plot(BUY ,'Your buy condition hold in that variable BUY',display = display.data_window)
2) Probably the name of this BUY variable in your indicator is not BUY. Simply replace in the code line above the BUY with the name of your script's trade condition variable.
3) Save your changed Indicator script.
4) Then add this 'Backtest any Indicator' script to the chart ...
5) and go to the settings of it. Choose under "Settings -> Buy Signal" your Indicator. So in the example above choose .
The form is usually: ' : BUY'. Then you see something like Image 2
6) Decide which trade direction the BUY signal should trigger. A go Long or a go Short by set the hook or not.
Now you have a backtest of your Indicator without converting it into a strategy. You may change the setting of your Indicator to the best results and setup the following strategy settings like Time- and Session Filter, Stop Loss, Take Profit etc. More of it below in the section Settings Menu.
Appereance
In the Image 2 you see on the right side the List of Trades . To scroll down you go into the settings again and decrease the scroll value. So you can see all trades that have happened before. In case there is an open trade you will find it at the last position of the list.
Every Long trade is green back grounded while Short trades are red.
Every trade begins with a label that show goLong or goShort and its number. And ends with another label again with its number, Profit in % and the resulting total amount of cash.
If activated you further see the Take Profit as a green line and the Stop Loss as a orange line. In the settings you can set their percentage above or below the entry price.
You also see the Result Summary below. Here you find the usual stats of a strategy of all closed trades. The profit after total amount of fees , amount of trades, Profit Factor and the total amount of fees .
Settings Menu
In the settings menu you will find the following high-lighted sections. Most of the settings have a question mark on their right side. Move over it with the cursor to read specific explanation.
Input Signal of your Indicator: Under Buy you set the trade signal of your Indicator. And under Target you set the value when a trade should happen. In the Example with the Stochastic RSI above we used 20. Below you can set the trade direction, let it be go short when hooked or go long when unhooked.
Trade Settings & List of Trades: Take Profit set the target price of any trade. Stop Loss set the price to step out when a trade goes the wrong direction. Check mark the List of Trades to see any single trade with their stats. In case that there are more trades as fits in the list you can scroll down the list by decrease the value Scroll .
Time Filter: You can set a Start Time or deactivate it by leave it unhooked. The same with End Time .
Session Filter: here you can choose to activate it on weekly base. Which days of the week should be trading and those without. And also on daily base from which time on and until trade are possible. Outside of all times and sessions there will be no new trades if activated.
Invest Settings: here you can choose the amount of cash to start with. The Quantity percentage define for every trade how much of the cash should be invested and the Fee percentage which have to be payed every trade. Open position and closing position.
Other Announcements
This Backtest script don't use the strategy functions of TradingView. It is programmed as an indicator. All trades get executed at candle closing. This script use the functionality "Indicator-on-Indicator" from TradingView.
Conclusion
So now it is your turn, take your promising indicators and connect it to that Backtest script. With it you get a fast impression of how successful your indicator will trade. You don't have to relay on coders who maybe add cheating code lines. Further more you can check with the Time Filter under which market condition you indicator perform the best or not so well. Also with the Session Filter you can sort out repeating good market conditions for your indicator. Even you can check with the GoShort XOR GoLong check mark the trade signals of you indicator in opposite trade direction with one click. And compare your indicators under the same conditions and get the results just after 2 clicks. Thanks to the in-build fee setting you get an impression how much a 0.1% fee cost you in total.
Cheers
Targets For Overlay Indicators [LuxAlgo]The Targets For Overlay Indicators is a useful utility tool able to display targets during crossings made between the price and external indicators on the user chart. Users can display a series of two targets, one for crossover events and another one for crossunder event.
Alerts are included for the occurrence of a new target as well as for reached targets.
🔶 USAGE
In order for targets to be displayed users need to select an appropriate input source from the "Source" drop-down input setting. In the example above we apply the indicator to a volatility stop.
This can also easily be done by adding the "Targets For Overlay Indicators" script on the VStop indicator directly.
Targets can help users determine the price limit where the price might start deviating from an indication given by one or multiple indicators. In the context of trading, targets can help secure profits/reduce losses of a trade, as such this tool can be useful to evaluate/determine user take profits/stop losses.
Due to these essentially being horizontal levels, they can also serve as potential support/resistances, with breakouts potentially confirming new trends.
Users might be interested in obtaining new targets once one is reached, this can be done by enabling "New Target When Reached" in the target logic setting section, resulting in more frequent targets.
Lastly, users can restrict new target creation until current ones are reached. This can result in fewer and longer-term targets, with a higher reach rate.
🔹 Examples
The indicator can be applied to many overlay indicators that naturally produce crosses with the price, such as moving average, trailing stops, bands...etc.
Users can use trailing stops such as the SuperTrend or VStop to more easily create clean targets. Do note that certain SuperTrend scripts separate the upper and lower extremities of the SuperTrend into two different plot, which cannot be used with this tool, you may use the provided SuperTrend script below to have a compatible version with our tool:
//@version=5
indicator("SuperTrend", overlay = true)
factor = input.float(3, 'Factor', minval = 0)
atrLen = input.int(10, 'ATR Length', minval = 1)
= ta.supertrend(factor, atrLen)
plot(spt, 'SuperTrend', dir != dir ? na : dir < 0 ? #089981 : #f23645, 2)
plot(spt, 'Circles', dir > dir ? #f23645 : dir < dir ? #089981 : na, 3, plot.style_circles)
Using moving averages can produce more targets than other overlay indicators.
Users can apply the tool twice when using bands or any overlay indicator returning two outputs, using crossover targets for obtaining targets using the upper band as source and crossunder targets for targets using the lower band. We can also use the Trendlines with breaks indicator as example:
🔹 Dashboard
A dashboard is displayed on the top right of the chart, displaying the amount, reach rate of targets 1/2, and total amount.
This dashboard can be useful to evaluate the selected target distances relative to the selected conditions, with a higher reach rate suggesting the distance of the targets from the price allows them to be reached.
🔶 SETTINGS
Source: Indicator source used to create targets. Targets are created when the closing price crosses the specified source.
Show Target Labels: Display target labels on the chart.
Candle Coloring: Apply candle coloring based on the most recent active target.
🔹 Target
Crossover and Crossunder targets use the same settings below:
Show Target: Determines if the target is displayed or not.
Above Price Target: If selected, will create targets above the closing price.
Wait Until Reached: When enabled will not create a new target until an existing one is reached.
New Target When Reached: Will create a new target when an existing one is reached.
Evaluate Wicks: Will use high/low prices to determine if a target is reached. Unselecting this setting will use the closing price.
Target Distance From Price: Controls the distance of a target from the price. Can be determined in currencies/points, percentages, ATR multiples, or ticks.
buy/sell signals with Support/Resistance (InvestYourAsset) 📣The present indicator is a MACD based buy/sell signals indicator with support and resistance, that can be used to identify potential buy and sell signals in a security's price.
📣It is based on the MACD (Moving Average Convergence Divergence) indicator, which is a momentum indicator that shows the relationship between two moving averages of a security's price.
📣 The indicator also plots support and resistance levels, which can be used to confirm buy and sell signals. The support and resistance can also be used as a stoploss for existing position.
👉 To use the indicator, simply add it to your trading chart. The indicator will plot three sections:
📈 Price and Signals: This section plots the security's price and the MACD buy and sell signals.
📈 MACD Oscillator: This section plots the MACD oscillator, which is a histogram that shows the difference between the two moving averages.
📈 Moving Averages: This section plots the two moving averages that the MACD oscillator is based on.
📈 Support and Resistance: This section plots support and resistance levels, which are calculated based on the security's recent price action.
👉 To identify buy and sell signals, you can look for the following:
📈 Buy signal: When shorter Moving Average crosses over longer Moving Average.
📈 Sell signal: When shorter moving average crosses under longer moving average.
📈 You can also look for divergences between the MACD oscillator and the security's price. A divergence occurs when the MACD oscillator is moving in one direction, but the security's price is moving in the opposite direction. Divergences can be a sign of a potential trend reversal.
👉 To confirm buy and sell signals, you can look for support and resistance levels take a look at below snapshot. If a buy signal occurs at a support level, it is a stronger signal than if it occurs at a random price level. Similarly, if a sell signal occurs at a resistance level, it is a stronger signal than if it occurs at a random price level.
⚡ Here is a example of how to use the indicator to identify buy signal:
☑ Add the indicator to your trading chart.
☑Look for a buy signal when short MA crosses over Long MA.
☑Look for the buy signal to occur at a support level.
☑Enter a long position at the next candle.
☑Place a stop loss order below the support level.
☑Take profit when the MACD line crosses below the signal line, or when the security reaches a resistance level.
⚡ Here is an example of how to use the indicator to identify a sell signal:
☑Add the indicator to your trading chart.
☑Look for a sell signal, when shorter moving average crosses under longer moving average.
☑Look for the sell signal to occur at a resistance level.
☑Enter a short position at the next candle.
☑Place a stop loss order above the resistance level.
☑Take profit when the MACD line crosses above the signal line, or when the security reaches a support level.
✅Things to consider while using the indicator:
📈Look for buy signals in an uptrend and sell signals in a downtrend. This will increase the likelihood of your trades being successful.
📈Place your stop losses below the previous swing low or support for buy signals and above the previous swing high or resistance for sell signals. This will help to limit your losses if the trade goes against you.
📈Consider taking profits at key resistance and support levels. This will help you to lock in your profits and avoid giving them back to the market.
Follow us for timely updates regarding indicators that we may publish in future and give it a like if you appreciate the indicator.
DTR & ATR
Description
This ATR and DTR label is update of Existing Label provided by © ssksubam
Please See Notes on original Script Here :
Original Code is not mine but I have done few code changes which I believe will help everyone who are looking to add more labels together and save space on the chart
ATR & DTR Script is very helpful for Day Traders as I will explain in detail bellow
Following are changes I have incorporated
Previous Label took more space on the charts with Header and Footer.
I removed the Header and moved both DTR vs ATR descriptions on the same line, saving space on the chart.
I updated the code to remove => signs, which are self-explanatory as I will explain below.
I made the label in 1 single compact line for maximum space efficiency and aesthetics.
These changes improve the content's clarity and conciseness while optimizing space on the charts. If you have any further requests or need additional assistance, feel free to let me know!
What Does DTR Signify?
Stock ATR stands for Average True Range, which is a technical indicator used in trading and investment analysis. The Average True Range measures the volatility of a stock over a given period of time. It provides insights into the price movement and potential price ranges of the stock.
The ATR is calculated as the average of the true ranges over a specific number of periods. The true range is the greatest of the following three values:
The difference between the current high and the current low.
The absolute value of the difference between the current high and the previous close.
The absolute value of the difference between the current low and the previous close.
Traders and investors use ATR to assess the potential risk and reward of a stock. A higher ATR value indicates higher volatility and larger price swings, while a lower ATR value suggests lower volatility and smaller price movements. By understanding the ATR, traders can set appropriate stop-loss levels and make informed decisions about position sizing and risk management.
It's important to note that the ATR is not a directional indicator like moving averages or oscillators. Instead, it provides a measure of volatility, helping traders adapt their strategies to suit the current market conditions.
What Does ATR Signify?
The Average True Range (ATR) signifies the level of volatility or price variability in a particular financial asset, such as a stock, currency pair, or commodity, over a specific period of time. It provides valuable information to traders and investors regarding the potential risk and reward associated with the asset.
Here are the key significances of ATR:
Volatility Measurement: ATR measures the average price range between high and low prices over a specified timeframe. Higher ATR values indicate greater volatility, while lower values suggest lower volatility. Traders use this information to gauge the potential price movements and adjust their strategies accordingly.
Risk Assessment: A higher ATR value implies larger price swings, indicating increased market uncertainty and risk. Traders can use ATR to set appropriate stop-loss levels and manage risk by adjusting position sizes based on the current volatility.
Trend Strength: ATR can also be used to assess the strength of a trend. In an uptrend or downtrend, ATR tends to increase, indicating a more powerful price movement. Conversely, a declining ATR might signify a weakening trend or a consolidation period.
Range-Bound Market Identification: In a range-bound or sideways market, the ATR value tends to be relatively low, reflecting the lack of significant price movements. This information can be helpful for range-trading strategies.
Volatility Breakouts: Traders often use ATR to identify potential breakouts from consolidation patterns. When the ATR value expands significantly, it may indicate the beginning of a new trend or a breakout move.
Comparison between Assets: ATR allows traders to compare the volatility of different
How to use DTR & ATR for Trading
Using Average True Range (ATR) and Daily Trading Range (DTR) can be beneficial for day trading to assess potential price movements, manage risk, and identify trading opportunities. Here's how you can use both indicators effectively:
Calculate ATR and DTR: First, calculate the ATR and DTR values for the asset you are interested in trading. ATR is the average of true ranges over a specified period (e.g., 14 days), while DTR is the difference between the high and low prices of a single trading day.
Assess Volatility: Compare the ATR and DTR values to understand the current volatility of the asset. Higher values indicate increased volatility, while lower values suggest reduced volatility.
Setting Stop-Loss: Use ATR to set appropriate stop-loss levels. For example, you might decide to set your stop-loss a certain number of ATR points away from your entry point. This approach allows you to factor in market volatility when determining your risk tolerance.
Identify Trading Range: Analyze DTR to determine the typical daily price range of the asset. This information can help you identify potential support and resistance levels, which are essential for day trading strategies such as breakout or range trading.
Breakout Strategies: ATR can assist in identifying potential breakout opportunities. When ATR values increase significantly, it suggests an expansion in volatility, which may indicate an upcoming breakout from a trading range. Look for breakouts above resistance or below support levels with higher than usual ATR values.
Scalping Strategies: For scalping strategies, where traders aim to profit from small price movements within a single trading session, knowing the typical DTR can help set reasonable profit targets and stop-loss levels.
Confirming Trend Strength: In day trading, you may encounter short-term trends. Use ATR to assess the strength of these trends. If the ATR is rising, it suggests a strong trend, while a declining ATR may indicate a weakening trend or potential reversal.
Risk Management: Both ATR and DTR can aid in risk management. Determine your position size based on the current ATR value to align it with your risk tolerance. Additionally, understanding the DTR can help you avoid overtrading during periods of low volatility.
Combine with Other Indicators: ATR and DTR work well when used in conjunction with other technical indicators like moving averages, Bollinger Bands, or RSI. Combining multiple indicators can provide a mor
Inside candle (Inside Bar) Strategy- by smartanuThe Inside Candle strategy is a popular price action trading strategy that can be used to trade in a variety of markets. Here's how you can trade the Inside Candle strategy using the Pine script code provided:
1. Identify an Inside Candle: Look for a candlestick pattern where the current candle is completely engulfed within the previous candle's high and low. This is known as an Inside Candle.
2. Enter a Long Position: If an Inside Candle is identified, enter a long position at the open of the next candle using the Pine script code provided.
3. Set Stop Loss and Take Profit: Set a stop loss at a reasonable level to limit your potential losses if the trade goes against you. Set a take profit at a reasonable level to take profit when the price reaches the desired level.
4. Manage the Trade: Monitor the trade closely and adjust the stop loss and take profit levels if necessary. You can use the Pine script code to automatically exit the trade when the stop loss or take profit level is hit.
5. Exit the Trade: Exit the trade when the price reaches the take profit level or the stop loss level is hit.
It's important to note that the Inside Candle strategy is just one of many strategies that traders use to trade the markets. It's important to perform your own analysis and use additional indicators before making any trades. Additionally, it's important to practice proper risk management techniques and never risk more than you can afford to lose.
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.
BB Signal v2.1 [ABA Invest]About
This signal appears based on 2nd candle break out of Bollinger Bands (called Momentum) with additional EMA 50 and EMA 200 as trend filters. so the concept is to take advantage of candle breakout by following trends.
How to use
Buy: When signal 'Buy' appears (following trend of upper timeframe)
Recommended stop loss: previous swing low
Sell: When signal 'Sell' appears (following trend of upper timeframe)
Recommended stop loss: previous swing high
Rules
1. use a good risk-reward ratio (minimum 1.5)
2. Please do backtest before using this signal
3. Don't always take every signal (must know when to stop)
Indicators Combination Framework v3 IND [DTU]Hello All,
This script is a framework to analyze and see the results by combine selected indicators for (long, short, longexit, shortexit) conditions.
I was designed this for beginners and users to facilitate to see effects of the technical indicators combinations on the chart WITH NO CODE
You can improve your strategies according the results of this system by connecting the framework to a strategy framework/template such as Pinecoder, Benson, daveatt or custom.
This is enhanced version of my previous indicator "Indicators & Conditions Test Framework "
Currently there are 93 indicators (23 newly added) connected over library. You can also import an External Indicator or add Custom indicator (In the source)
It is possible to change it from Indicator to strategy (simple one) by just remarking strategy parts in the source code and see real time profit of your combinations
Feel free to change or use it in your source
Special thanks goes to Pine wizards: Trading view (built-in Indicators), @Rodrigo, @midtownsk8rguy, @Lazybear, @Daveatt and others for their open source codes and contributions
SIMPLE USAGE
1. SETTING: Show Alerts= True (To see your entries and Exists)
2. Define your Indicators (ex: INDICATOR1: ema(close,14), INDICATOR2: ema(close,21), INDICATOR3: ema(close,200)
3. Define Your Combinations for long & Short Conditions
a. For Long: (INDICATOR1 crossover INDICATOR2) AND (INDICATOR3 < close)
b. For Short: (INDICATOR1 crossunder INDICATOR2) AND (INDICATOR3 > close)
4. Select Strategy/template (Import strategy to chart) that you export your signals from the list
5. Analyze the best profit by changing Indicators values
SOME INDICATORS DETAILS
Each Indicator includes:
- Factorization : Converting the selected indicator to Double, triple Quadruple such as EMA to DEMA, TEMA QEMA
- Log : Simple or log10 can be used for calculation on function entries
- Plot Type : You can overlay the indicator on the chart (such ema) or you can use stochastic/Percentrank approach to display in the variable hlines range
- Extended Parametes : You can use default parameters or you can use extended (P1,P2) parameters regarding to indicator type and your choice
- Color : You can define indicator color and line properties
- Smooth : you can enable swma smooth
- indicators : you can select one of the 93 function like ema(),rsi().. to define your indicator
- Source : you can select from already defined indicators (IND1-4), External Indicator (EXT), Custom Indicator (CUST), and other sources (close, open...)
CONDITION DETAILS
- There are are 4 type of conditions, long entry, short entry, long exit, short exit.
- Each condition are built up from 4 combinations that joined with "AND" & "OR" operators
- You can see the results by enabling show alerts check box
- If you only wants to enter long entry and long exit, just fill these conditions
- If "close on opposite" checkbox selected on settings, long entry will be closed on short entry and vice versa
COMBINATIONS DETAILS
- There are 4 combinations that joined with "AND" & "OR" operators for each condition
- combinations are built up from compare 1st entry with 2nd one by using operator
- 1st and 2nd entries includes already defined indicators (IND1-5), External Indicator (EXT), Custom Indicator (CUST), and other sources (close, open...)
- Operators are comparison values such as >,<, crossover,...
- 2nd entry include "VALUE" parameter that will use to compare 1st indicator with value area
- If 2nd indicator selected different than "VALUE", value are will mean previous value of the selection. (ex: value area= 2, 2nd entry=close, means close )
- Selecting "NONE" for the 1st entry will disable calculation of current and following combinations
JOINS DETAILS
- Each combination will join wiht the following one with the JOIN (AND, OR) operator (if the following one is not equal "NONE")
CUSTOM INDICATOR
- Custom Indicator defines harcoded in the source code.
- You can call it with "CUST" in the Indicator definition source or combination entries source
- You can change or implement your custom indicator by updating the source code
EXTERNAL INDICATOR
- You can import an external indicator by selecting it from the ext source.
- External Indicator should be already imported to the chart and it have an plot function to output its signal
EXPORTING SIGNAL
- You can export your result to an already defined strategy template such as Pine coders, Benson, Daveatt Strategy templates
- Or you can define your custom export for other future strategy templates
ALERTS
- By enabling show alerts checkbox, you can see long entry exits on the bottom, and short entry exits aon the top of the chart
ADDITIONAL INFO
- You can see all off the inputs descriptions in the tooltips. (You can also see the previous version for details)
- Availability to set start, end dates
- Minimize repainting by using security function options (Secure, Semi Secure, Repaint)
- Availability of use timeframes
-
Version 3 INDICATORS LIST (More to be added):
▼▼▼ OVERLAY INDICATORS ▼▼▼
alma(src,len,offset=0.85,sigma=6).-------Arnaud Legoux Moving Average
ama(src,len,fast=14,slow=100).-----------Adjusted Moving Average
accdist().-------------------------------Accumulation/distribution index.
cma(src,len).----------------------------Corrective Moving average
dema(src,len).---------------------------Double EMA (Same as EMA with 2 factor)
ema(src,len).----------------------------Exponential Moving Average
gmma(src,len).---------------------------Geometric Mean Moving Average
highest(src,len).------------------------Highest value for a given number of bars back.
hl2ma(src,len).--------------------------higest lowest moving average
hma(src,len).----------------------------Hull Moving Average.
lagAdapt(src,len,perclen=5,fperc=50).----Ehlers Adaptive Laguerre filter
lagAdaptV(src,len,perclen=5,fperc=50).---Ehlers Adaptive Laguerre filter variation
laguerre(src,len).-----------------------Ehlers Laguerre filter
lesrcp(src,len).-------------------------lowest exponential esrcpanding moving line
lexp(src,len).---------------------------lowest exponential expanding moving line
linreg(src,len,loffset=1).---------------Linear regression
lowest(src,len).-------------------------Lovest value for a given number of bars back.
mcginley(src, len.-----------------------McGinley Dynamic adjusts for market speed shifts, which sets it apart from other moving averages, in addition to providing clear moving average lines
percntl(src,len).------------------------percentile nearest rank. Calculates percentile using method of Nearest Rank.
percntli(src,len).-----------------------percentile linear interpolation. Calculates percentile using method of linear interpolation between the two nearest ranks.
previous(src,len).-----------------------Previous n (len) value of the source
pivothigh(src,BarsLeft=len,BarsRight=2).-Previous pivot high. src=src, BarsLeft=len, BarsRight=p1=2
pivotlow(src,BarsLeft=len,BarsRight=2).--Previous pivot low. src=src, BarsLeft=len, BarsRight=p1=2
rema(src,len).---------------------------Range EMA (REMA)
rma(src,len).----------------------------Moving average used in RSI. It is the exponentially weighted moving average with alpha = 1 / length.
sar(start=len, inc=0.02, max=0.02).------Parabolic SAR (parabolic stop and reverse) is a method to find potential reversals in the market price direction of traded goods.start=len, inc=p1, max=p2. ex: sar(0.02, 0.02, 0.02)
sma(src,len).----------------------------Smoothed Moving Average
smma(src,len).---------------------------Smoothed Moving Average
super2(src,len).-------------------------Ehlers super smoother, 2 pole
super3(src,len).-------------------------Ehlers super smoother, 3 pole
supertrend(src,len,period=3).------------Supertrend indicator
swma(src,len).---------------------------Sine-Weighted Moving Average
tema(src,len).---------------------------Triple EMA (Same as EMA with 3 factor)
tma(src,len).----------------------------Triangular Moving Average
vida(src,len).---------------------------Variable Index Dynamic Average
vwma(src,len).---------------------------Volume Weigted Moving Average
volstop(src,len,atrfactor=2).------------Volatility Stop is a technical indicator that is used by traders to help place effective stop-losses. atrfactor=p1
wma(src,len).----------------------------Weigted Moving Average
vwap(src_).------------------------------Volume Weighted Average Price (VWAP) is used to measure the average price weighted by volume
▼▼▼ NON OVERLAY INDICATORS ▼▼
adx(dilen=len, adxlen=14, adxtype=0).----adx. The Average Directional Index (ADX) is a used to determine the strength of a trend. len=>dilen, p1=adxlen (default=14), p2=adxtype 0:ADX, 1:+DI, 2:-DI (def:0)
angle(src,len).--------------------------angle of the series (Use its Input as another indicator output)
aroon(len,dir=0).------------------------aroon indicator. Aroons major function is to identify new trends as they happen.p1 = dir: 0=mid (default), 1=upper, 2=lower
atr(src,len).----------------------------average true range. RMA of true range.
awesome(fast=len=5,slow=34,type=0).------Awesome Oscilator is an indicator used to measure market momentum. defaults : fast=len= 5, p1=slow=34, p2=type: 0=Awesome, 1=difference
bbr(src,len,mult=1).---------------------bollinger %%
bbw(src,len,mult=2).---------------------Bollinger Bands Width. The Bollinger Band Width is the difference between the upper and the lower Bollinger Bands divided by the middle band.
cci(src,len).----------------------------commodity channel index
cctbbo(src,len).-------------------------CCT Bollinger Band Oscilator
change(src,len).-------------------------A.K.A. Momentum. Difference between current value and previous, source - source . is most commonly referred to as a rate and measures the acceleration of the price and/or volume of a security
cmf(len=20).-----------------------------Chaikin Money Flow Indicator used to measure Money Flow Volume over a set period of time. Default use is len=20
cmo(src,len).----------------------------Chande Momentum Oscillator. Calculates the difference between the sum of recent gains and the sum of recent losses and then divides the result by the sum of all price movement over the same period.
cog(src,len).----------------------------The cog (center of gravity) is an indicator based on statistics and the Fibonacci golden ratio.
copcurve(src,len).-----------------------Coppock Curve. was originally developed by Edwin Sedge Coppock (Barrons Magazine, October 1962).
correl(src,len).-------------------------Correlation coefficient. Describes the degree to which two series tend to deviate from their ta.sma values.
count(src,len).--------------------------green avg - red avg
cti(src,len).----------------------------Ehler s Correlation Trend Indicator by
dev(src,len).----------------------------ta.dev() Measure of difference between the series and its ta.sma
dpo(len).--------------------------------Detrended Price OScilator is used to remove trend from price.
efi(len).--------------------------------Elders Force Index (EFI) measures the power behind a price movement using price and volume.
eom(len=14,div=10000).-------------------Ease of Movement.It is designed to measure the relationship between price and volume.p1 = div: 10000= (default)
falling(src,len).------------------------ta.falling() Test if the `source` series is now falling for `length` bars long. (Use its Input as another indicator output)
fisher(len).-----------------------------Fisher Transform is a technical indicator that converts price to Gaussian normal distribution and signals when prices move significantly by referencing recent price data
histvol(len).----------------------------Historical volatility is a statistical measure used to analyze the general dispersion of security or market index returns for a specified period of time.
kcr(src,len,mult=2).---------------------Keltner Channels Range
kcw(src,len,mult=2).---------------------ta.kcw(). Keltner Channels Width. The Keltner Channels Width is the difference between the upper and the lower Keltner Channels divided by the middle channel.
klinger(type=len).-----------------------Klinger oscillator aims to identify money flow’s long-term trend. type=len: 0:Oscilator 1:signal
macd(src,len).---------------------------MACD (Moving Average Convergence/Divergence)
mfi(src,len).----------------------------Money Flow Index s a tool used for measuring buying and selling pressure
msi(len=10).-----------------------------Mass Index (def=10) is used to examine the differences between high and low stock prices over a specific period of time
nvi().-----------------------------------Negative Volume Index
obv().-----------------------------------On Balance Volume
pvi().-----------------------------------Positive Volume Index
pvt().-----------------------------------Price Volume Trend
ranges(src,upper=len, lower=-5).---------ranges of the source. src=src, upper=len, v1:lower=upper . returns: -1 source=upper otherwise 0
rising(src,len).-------------------------ta.rising() Test if the `source` series is now rising for `length` bars long. (Use its Input as another indicator output)
roc(src,len).----------------------------Rate of Change
rsi(src,len).----------------------------Relative strength Index
rvi(src,len).----------------------------The Relative Volatility Index (RVI) is calculated much like the RSI, although it uses high and low price standard deviation instead of the RSI’s method of absolute change in price.
smi_osc(src,len,fast=5, slow=34).--------smi Oscillator
smi_sig(src,len,fast=5, slow=34).--------smi Signal
stc(src,len,fast=23,slow=50).------------Schaff Trend Cycle (STC) detects up and down trends long before the MACD. Code imported from
stdev(src,len).--------------------------Standart deviation
trix(src,len) .--------------------------the rate of change of a triple exponentially smoothed moving average.
tsi(src,len).----------------------------The True Strength Index indicator is a momentum oscillator designed to detect, confirm or visualize the strength of a trend.
ultimateOsc(len.-------------------------Ultimate Oscillator indicator (UO) indicator is a technical analysis tool used to measure momentum across three varying timeframes
variance(src,len).-----------------------ta.variance(). Variance is the expectation of the squared deviation of a series from its mean (ta.sma), and it informally measures how far a set of numbers are spread out from their mean.
willprc(src,len).------------------------Williams %R
wad().-----------------------------------Williams Accumulation/Distribution.
wvad().----------------------------------Williams Variable Accumulation/Distribution.
HISTORY
v3.01
ADD: 23 new indicators added to indicators list from the library. Current Total number of Indicators are 93. (to be continued to adding)
ADD: 2 more Parameters (P1,P2) for indicator calculation added. Par:(Use Defaults) uses only indicator(Source, Length) with library's default parameters. Par:(Use Extra Parameters P1,P2) use indicator(Source,Length,p1,p2) with additional parameters if indicator needs.
ADD: log calculation (simple, log10) option added on indicator function entries
ADD: New Output Signals added for compatibility on exporting condition signals to different Strategy templates.
ADD: Alerts Added according to conditions results
UPD: Indicator source inputs now display with indicators descriptions
UPD: Most off the source code rearranged and some functions moved to the new library. Now system work like a little bit frontend/backend
UPD: Performance improvement made on factorization and other source code
UPD: Input GUI rearranged
UPD: Tooltips corrected
REM: Extended indicators removed
UPD: IND1-IND4 added to indicator data source. Now it is possible to create new indicators with the previously defined indicators value. ex: IND1=ema(close,14) and IND2=rsi(IND1,20) means IND2=rsi(ema(close,14),20)
UPD: Custom Indicator (CUST) added to indicator data source and Combination Indicator source.
UPD: Volume added to indicator data source and Combination Indicator source.
REM: Custom indicators removed and only one custom indicator left
REM: Plot Type "Org. Range (-1,1)" removed
UPD: angle, rising, falling type operators moved to indicator library
stop out or margin call price levelsAbstract
This script finds the potential stop out or margin call price levels without considering timeframe.
This script computes stop out or margin call price levels that over leveraged positions buy cheap enough.
You can use this indicator to follow stop hunters.
Introduction
Stop hunting exists because of benefit conflict.
When most of retails traders are in the correct direction, big banks do not want to buy more expensive then retail traders.
Therefore, Big banks create sell pressure to make retail traders misunderstand their trade decisions are wrong.
When retail traders decide to cut loss, it is time big banks buy cheaper.
Many courses recommend average true range as a stop loss reference.
However, in different timeframe, average true ranges are different.
Therefore, we need to have a method to measure potential stop hunting levels which is not relative to timeframe.
There is a method because there are observable levels where over leveraged positions must cut loss.
For example, for a 100 leverage position, its margin call level is 0.005, so 0.005 drawdown is a potential stop hunting level.
Computing
For buy
potential stop out or margin call price level = low * ( 1 - ratio )
For sell
potential stop out or margin call price level = high * ( 1 + ratio )
Parameters
There are 4 levels available for adjusting.
The default values are :
(1) 0.001 : 500 leverage , 50% margin call
(2) 0.0025 : 200 leverage , 50% margin call
(3) 0.005 : 100 leverage , 50% margin call
(4) 0.010 : 50 leverage , 50% margin call
Usage
(1) Find an oversold price level. If you don't want to use an another indicator, you can use previous low.
(2) Memorize their stop out or margin call price levels of that level.
(2) Consider buy near those levels.
(3) If there are quick price rejection near those levels, better entry opportunities.
(4) Take profit and stop loss : you decide.
Conclusion
This script can find potential stop out or margin call price levels that over leveraged positions buy cheap enough.
If you are bored, you can consider find 100 signals you are interested in and share your observation.
Reference
Brokers, leverage and margin call threshold
mForex - 3 Moving Average - Parabolic SARScript identifies the uptrend and downtrend according to the average price of the MA and PSAR lines.
Rules of sale:
- Open short positions when:
MA12 cuts MA26 and MA52 from the top.
PSAR dots on the price line
- Stop loss:
At the dots of PSAR and use Trailing Stop facing up
On the nearest peak
- Profit-taking: Corresponding ratio of R: R (1: 2)
- Open long positions when:
MA12 cuts MA26 and MA52 from the bottom.
PSAR dots below the price line
- Stop loss:
At the dots of PSAR and use Trailing Stop facing down
Below the nearest peak
- Profit-taking: Corresponding ratio of R: R (1: 2)
Customizable MACD (how to detect a strong convergence)Helloooo traders
I wondered once if a MACD was based on an EMA/EMA/SMA or SMA/SMA/EMA (or WHATEVA/WHATEVA/WHATEVA).
Seems they're so many alternatives out there.
I decided to empower my audience more by choosing the type of moving averages you want for your MACD.
More options doesn't always mean better performance - but who knows - some might find a config that they like with it for their favorite asset/timeframe.
I added also a multi-timeframe component because I'm a nice guy ^^
Convergence is my BEST friend
An oscillator (like MACD) is to measure how strong a momentum is - generally, traders use those indicators to confirm a trend.
So understand that a MACD (or any other indicator not based on convergence ) won't likely be sufficient for doing great on the market.
Combined with your favorite indicator, however, you may get great results.
My indicators fav cocktail is mixing :
1) an oscillator (momentum confirmation)
2) a trendline/key level break (momentum confirmation)
3) adding-up on a different trading method but still converging with the first entry.
The reason I'm deep with convergence detection is because I'm obsessed with removing those fakeout signals. You know which ones I'm talking about :)
Those trades when the market goes sideways but our capital goes South (pun 100% intended) - 2 days later, the price hasn't changed much but some lost some capital due to fees, being overexposed, buying the top/selling the bottom of a range they didn't identify.
It's publicly known that ranges are the worst traders' enemy. It's boring, not fun, and .... end up moving in the direction we expected when we go to sleep or outside.
NO ONE/BROKER/EX-GF is tracking your computer - I checked also for mine as it happened for me way too often in the past.
I surely preferred blaming a few external unknown conditions than improving my TA back in the days #bad #dave
But my backtest sir...
Our backtests show what they're being told to show . A backtest without a stop-loss/hard exit logic will show incredible results.
Then trying that backtest with live trading is like in the Matrix movie - discovering the real world is tough and we must choose between the blue pill (learning how to evaluate properly risk/opportunity caught) and the red pill (increasing the position sizing, not setting a stop loss, holding the positions hoping for the best)
Last few words
Convergences aren't invented because it's cool to mix indicators with others. (it is actually and even fun)
They're created to remove most of the fakeouts . For those that can't be removed - a strong risk management would cut most of the remaining potential big losses.
No system works 100% of the time - so a convergence system needs a back-up plan in case the converged signal is wrong (could be stop-loss, hard exit, reducing position sizing, ...)
Wishing you the BEST and happy beginning of your week
Daveatt
ATR based Pivots mcbwHey everyone this is an exciting new script I have prepared for you.
I was reading an old forex bulletin article some time ago when I came across this: solar.murty.net (or you can download the full bulletin with lots of other good articles here: www.forexfactory.com).
You can already buy this for metatrader (www.mql5.com) so I figured to make it for free for tradingview.
This bulletin suggested that you can reasonably predict daily volatility by adding or subtracting multiples of the daily ATR to the daily opening. Using this you can choose multiples to use as price targets and alternatively as stop losses. For example, if you already have a sense of market direction you can buy at market open place a stop loss at - 1 daily ATR and a profit target at + 3 ATRs for a risk to reward ratio of 3. If you are looking for smaller/quicker moves with a ratio of 3 you can have a stop loss at -0.25 ATR and a take profit at +0.75 ATR.
Alternatively this article also suggests to use this method to catch volatility breakouts. If price is higher than the + 1 ATR area then you can safely assume it will be going to the +2 ATR area so you can put a buy stop at + 1 ATR with a profit target at + 2 ATR with a stop loss at +0.5 ATR to catch a volatility breakout with a risk to reward ratio of 2!
Even further there are methods that you can use with ATRs of multiple window sizes, for example by opening two copies of this indicator and measuring recent volatility with a 1 week window and long term volatility within a 1 month window. If the short term volatility is crossing the long term volatility then there is a high probability chance that even more price movement will occur.
However I have found that this method is good for more than daily volatility , it can also be used to measure weekly volatility , and monthly volatility and use these multiples as good long term price targets.
To select if you want daily, weekly, or monthly values of the ATR of volatility you're using go to the settings and click on the options in the "Opening period". The default window of the ATR here is 14 periods, but you can change this if you want to in "ATR period". Most importantly you are able to select which multiples of the ATR you would like to use in the settings in "ATR multiple 1" which is the green line, "ATR multiple 2" which is the blue line, and "ATR multiple 3" which is the purple line. You can select any values you want to put in these, the choice of 0.25, 0.5, and 1 is not special, some people use fibonacci numbers here or simply 0.33, 0.66, and 0.99.
Repainting issue: This script uses the daily value of the Average True Range (ATR), which measures the volatility that is happening today. If price becomes more volatile then the value of the ATR can increase throughout the day, but it can never decrease. What this means is that the ATR based pivots are able to expand away from the opening price, which should not affect the trades that you take based on these areas. If you base your take profit on one of these ATR multiples and the daily volatility increase this means that your take profit area will be closer to your entry than the ATR multiple. Meaning that your trades will be more conservative.
While this all may sound very technical it is super intuitive, throw this on your chart and play around with it :)
Happy trading!
Trend is your friendThis indicator evaluates the trend based on crosses of two McGinley moving averages. It paints candles accordingly (it does not repaint), so you can see what the indicator is saying more clearly and stay in your trade until you see a period of consolidation or a reversal. You can control how far away those moving averages need to be for you to consider it a trend. If this distance is not met candles color is not changed and it shows you that the market is in a period of consolidation. I also added visualization of RSI, so you can have an easier time finding appropriate profit targets. For stop loss I would recommend placing it a couple points above or below the previous high / low that is located above / below you final target for entry. You can also use a certain percentage that works for you. I tried adding a stop loss based on ATR, but I did not like the results. Using market structure is a better choice in my opinion.
Here is a basic trading strategy for the default settings:
Wait for the indicator to start printing a series of green or red candles. After that you can enter a long or a short around moving averages. Another valid place to entry is the specific RSI zone. If we are in an uptrend buying when RSI is oversold can be beneficial as you expect market to recover. I do not recommend changing RSI from 14. Vice versa for the downtrend. It gives you an edge as you know at what price RSI will be oversold and allows you to place trades in advance. Pretty neat! You need to realize that no indicator or strategy can give you an exact entry. There will always be some margin of error. What I wanted to say is that if there is a strong trend up and you buy around your key moving averages and when RSI is oversold you entered in good places and there is a pretty good chance you will make money.
Time frame settings:
If you want to use tighter stop losses I would recommend sticking to 15m. Do not go lower. It is not worth the stress. 1h and 4h seems to be very good as well, but expect your stop losses to be wider. What I personally tend to do is display 15m, 30m and 1h and compare it. Think of it as a short, mid and long term. That way you can see things little bit better.
Examples:
1H chart BTC
4h chart EUR / USD
1D chart NASDAQ
15m chart BTC (Daytrading)
That last chart shows that even if you were longing while the trend was about to change you still had a good chance to close it with a little profit and switch to short easily. The default settings is what has worked the best for me. Feel free to change them as you see fit and do not forget to let me know if you find something that works better :)
Notes:
Either disable wick display or change it to a neutral color like gray for both green and red candles. Unfortunately pine script does not allow wick painting, so if you have red / green wicks it will look terrible. If RSI visualization makes your candles look too small you can go to settings and disable the display of individual RSI levels. You will still be able to see the zones, but the scale won't be affected.
Position Calculator---
# Position Calculator
Calculates the optimal position size with a fixed profit/loss ratio based on opening, stop-loss, and take-profit levels. Determines the direction of the position based on the opening and stop-loss settings.
Initial use requires manual setting of opening, take-profit, and stop-loss. Afterward, you can manually drag the price line to set values and the system will automatically calculate position information.
---
# 仓位计算器
通过开仓、止损、止盈计算固定盈亏比适合的开仓数量，根据开仓和止损判断开仓方向。
首次使用需要手动设置开仓、止盈、止损，之后可以手动拖拽价格线设置值然后自动计算仓位信息。
Realtime Position CalculatorRisk management is the single most important factor in trading success. This indicator automates the process of position sizing in real-time based on your account risk and a dynamic technical Stop Loss. It eliminates the need for manual calculations and helps you execute trades faster while adhering to strict risk management rules.
How it Works
The indicator visually places a Stop Loss line based on recent market structure (Highs/Lows) and instantly calculates the required position size (Contracts/Lots) to match your defined monetary risk.
1. Dynamic Stop Loss : It identifies the highest high (for Shorts) or lowest low (for Longs) over a user-defined lookback period.
2. Position Calculation : It calculates the distance between the current price and the Stop Loss level.
3. Formula : Contract Size = Risk Amount / (Distance * Point Value)
4. Actual vs. Target Risk : Because of the rounding, the script calculates and displays the Actual Risk (e.g., $95) alongside your Target Risk (e.g., $100), so you know exactly what is at stake.
Key Features
Real-time Calculation : Updates instantly as price moves.
Copy Trading Support : Includes an "Account Multiplier" setting. If you trade 10 accounts via a copy trader, set the multiplier to 10. The indicator will show the total contract size needed across all accounts.
Point Value Support : Works for Stocks/Crypto (Point Value = 1) and Futures (e.g., ES = 50, NQ = 20).
Customizable UI : Toggle specific data on/off in the label (e.g., hide price, show only contracts). Adjustable label offset to keep the chart clean.
Settings Guide
Trade Direction : Toggle between Long and Short setups. Add the indicator two times and set another for Longs and another for Shorts so you can see both direction at the same time.
Risk Amount : Your max risk in currency (e.g., $100).
Lookback : How many bars back to look for the SL pivot (e.g., 10 bars).
Point Value : Crucial for Futures. Use 1.0 for Crypto/Stocks. Use tick value/point value for futures (e.g., 50 for ES).
Account Multiplier : Multiply the position size for multiple accounts.
Label Offset : Move the information label to the right to avoid overlapping with price action.
Disclaimer
This tool is for informational and educational purposes only. Always verify calculations manually before executing trades. Past performance is not indicative of future results.
Simulated Liquidation Heatmap [QuantAlgo]🟢 Overview
This indicator visualizes where clusters of stop-loss orders and liquidation levels are likely located, displayed as a 'heatmap'. It's based on the concept of market structure liquidity: large groups of stop orders tend to gather around obvious technical levels (like swing highs and lows), and these pools of orders often attract price movement from institutional traders. The indicator uses a fractal-based algorithm to identify these high-probability liquidation zones and displays them as dynamic, color-coded boxes.
The key feature is the thermal color gradient, which indicates the freshness (age) and therefore the relative relevance of the liquidity zone. Hot colors (e.g., Red/Yellow) represent fresh clusters that have just formed, suggesting strong and immediate liquidity interest. Cold colors (e.g., Blue/Purple) represent aged or decaying clusters that are becoming less relevant over time. This visualization allows traders to anticipate potential liquidity sweeps (stop hunts) and understand areas of significant retail and institutional positioning.
🟢 Key Features
1. Liquidity Zone Heatmap
The core function is the identification of swing high and swing low price points using a user-defined Lookback period. These points are where retail traders are statistically most likely to place their stop-loss orders. The indicator simulates the clustering of these orders by drawing a zone (box) around the detected swing point, with the vertical size controlled by the Stop/Liquidation Zone Width (%) setting.
▶ Cluster Lookback: Defines the sensitivity of swing point detection. Lower values detect frequent, minor zones (scalping/intraday); higher values detect major, stronger swing points (swing trading).
▶ Zone Width (%): Sets the percentage range above and below the swing point where stops are simulated to cluster, accounting for slippage and typical stop placement spread.
▶ Liquidity Decay: Zones gradually fade in color intensity and are eventually removed after the user-defined Liquidity Decay Period (Bars), ensuring the heatmap only displays relevant, current liquidity areas.
▶ Round Number Filter: An optional filter that limits the display to liquidity zones occurring only at psychologically significant round numbers (e.g., $100, $1,500.00), which typically attract higher concentrations of orders.
2. Thermal Color Gradient
The heatmap's color is a direct function of the zone's age, providing a visual proxy for immediate relevance.
▶ Freshness: Newly created zones are displayed in the Hot Color (high relevance).
▶ Decay: As bars pass, the zone color transitions along the gradient toward the Cold Color and increased transparency (lower relevance), until it is removed entirely.
▶ Color Schemes: Multiple pre-configured and custom color schemes are available to optimize the visualization for different chart themes and color preferences.
3. Liquidity Heat Thermometer
An optional visual thermometer is displayed on the chart to provide an instant, overall assessment of the current liquidation heat level in the immediate vicinity of the price.
▶ Calculation: The thermometer calculates an aggregate heat score based on the age and proximity of all liquidity zones within a user-defined Zone Detection Range (%) of the current price.
▶ Visual Feedback: A marker (triangle) points to the corresponding level on the thermometer's color gradient (Hot to Cold). A high reading indicates price is close to fresh, dense stop clusters, suggesting high volatility or an imminent liquidity sweep is probable. A low reading indicates price is in a low-density or aged liquidity area.
▶ Customization: The thermometer's resolution, position, and text size are fully customizable for optimal chart placement and readability.
🟢 Practical Applications
▶ Anticipate Sweeps: Prioritize trading in the direction of Hot (fresh) liquidity zones. For example, a hot low-side zone suggests strong sell-side liquidity (stop-losses) is available for large buyers to sweep.
▶ Filter Noise: Use the Round Number Filter to focus only on the highest probability liquidation zones, which are often at clean, psychological price levels.
▶ Validate Entries: Combine the Heat Thermometer with price action analysis. A rising heat level indicates increasing proximity to a major stop cluster, signaling a potential turn or an aggressive market move to sweep those stops.
▶ Risk Management: Understand that price often acts dynamically around these zones. High heat levels imply high risk/reward setups; stops should be placed strategically beyond the defined Liquidation Zone Width.
▶ Multi-Timeframe Context: Higher timeframes (e.g., Daily, 4-Hour) often reveal more significant, major liquidity zones. Use this indicator on lower timeframes (e.g., 5-min, 15-min) for execution, but prioritize zones that align with higher-timeframe structures.
Nifty Scalping System by Rakesh Sharma🎯 What This Indicator Does:
Core Features:
✅ Fast Entry/Exit Signals - Quick BUY/SELL labels on chart
✅ 3 Signal Modes:
Aggressive - More signals, faster entries
Moderate - Balanced (Recommended)
Conservative - Fewer but high-quality signals
✅ Automatic Target & Stop Loss - Plotted on chart as soon as you enter
✅ Time Filter - Only trades during your specified hours (9:20 AM - 3:15 PM default)
✅ Trade Statistics - Win rate, W/L ratio tracked automatically
✅ Live Dashboard - Shows trend, RSI, VWAP position, current trade status
Indicators Used:
📊 3 EMAs (9, 21, 50) - Trend direction
📈 Supertrend - Primary trend filter
💪 RSI - Momentum & overbought/oversold
💜 VWAP - Intraday support/resistance
📉 ATR - Dynamic stop loss & targets
📊 Volume - Confirmation of moves
⚙️ Best Settings for Nifty/Bank Nifty:
For 5-Minute Charts (Most Popular):
Signal Mode: Moderate
Target R:R: 1.5 (1:1.5 risk-reward)
Time Filter: 9:20 AM to 3:15 PM
For 3-Minute Charts (More Scalps):
Signal Mode: Aggressive
Target R:R: 1.0 (quick exits)
Time Filter: 9:20 AM to 3:15 PM
For 15-Minute Charts (Swing Scalping):
Signal Mode: Conservative
Target R:R: 2.0 (bigger targets)
Time Filter: 9:30 AM to 3:00 PM
💡 How to Use:
Step 1: Setup
Add indicator to 5-min Nifty or Bank Nifty chart
Choose your Signal Mode (start with Moderate)
Set Risk:Reward (1.5 is balanced)
Enable Time Filter (avoid first 10 mins)
Step 2: Trading
BUY Signal appears = Go LONG
Green label shows entry price
Green line = Target
Red line = Stop Loss
SELL Signal appears = Go SHORT
Red label shows entry price
Green line = Target
Red line = Stop Loss
Exit automatically when Target or SL is hit
Step 3: Risk Management
Automatic SL based on ATR (volatility)
Adjustable R:R ratio
Never trade outside session hours
🎯 Trading Rules (Important!):
✅ Take the Trade When:
Signal appears during trading session
Dashboard shows strong trend
Volume spike present
Price above/below VWAP (for buy/sell)
❌ Avoid Trading When:
First 10 minutes (9:15-9:25 AM)
Last 15 minutes (3:15-3:30 PM)
Dashboard shows "SIDEWAYS"
Major news events
📊 Dashboard Explained:
FieldWhat It MeansModeYour current signal sensitivityTrendOverall market directionRSIOverbought/Oversold/NeutralPrice vs VWAPAbove = Bullish, Below = BearishCurrent TradeShows if you're in a positionSessionTrading time active or notWin RateYour success %
🚀 Pro Tips for Nifty/Bank Nifty:
Best Timeframe: 5-minute chart
Best Time: 9:30 AM - 2:30 PM (avoid opening/closing rushes)
Risk per Trade: 1-2% of capital max
Follow the Trend: Take only BUY in uptrend, SELL in downtrend
Use Alerts: Set alerts so you don't miss signals
Start Small: Paper trade first with 1 lot
⚡ Quick Start Guide:
For Bank Nifty (5-min chart):
1. Signal Mode: Moderate
2. Target R:R: 1.5
3. Trading Hours: 9:20 AM - 3:15 PM
4. Watch for 3-5 signals per day
5. Average 30-50 points per trade
For Nifty 50 (5-min chart):
1. Signal Mode: Moderate
2. Target R:R: 1.5
3. Trading Hours: 9:20 AM - 3:15 PM
4. Watch for 3-5 signals per day
5. Average 15-30 points per trade
📈 Expected Performance:
Conservative Mode: 2-4 trades/day, 65-70% win rate
Moderate Mode: 4-8 trades/day, 55-65% win rate
Aggressive Mode: 8-15 trades/day, 45-55% win rate
This is a complete scalping system, Rakesh! All you need to do is:
Add to chart
Wait for signals
Follow the targets/stop losses
Track your stats
Ready to test it? Let me know if you want any adjustments! 🎯💰Claude can make mistakes. Please double-check responses.
Dark Vector ScalpingThe Dark Vector Scalping indicator is a high-frequency trend-following system designed specifically to capture rapid momentum shifts in the market. It combines a staircase-style breakout logic with volatility-adjusted trailing stops to define market direction.
While the underlying math is robust enough for various asset classes, this specific configuration is optimized for scalping operations on 1-minute and 5-minute timeframes. It aims to filter out the "noise" common in lower timeframes while reacting quickly to genuine breakouts.
Core Components
1. The Apex Engine (Staircase Logic) Unlike traditional moving averages that curve with price, this engine uses a "hard" breakout logic. It looks back at a specific number of bars (Sensitivity) to find the highest highs and lowest lows.
Bullish Flip: Occurs when the price closes below the calculated low of the previous trend.
Bearish Flip: Occurs when the price closes above the calculated high of the previous trend.
Trailing Stop: Once a trend is established, a trailing stop line is drawn. This line only moves in the direction of the trend (up for bullish, down for bearish) and never retraces, acting as a ratchet to lock in paper profits.
2. Volatility Normalization To prevent getting stopped out by random market noise (scam wicks), the indicator calculates the Average True Range (ATR). It multiplies this volatility metric by a user-defined deviation factor to determine exactly how far the stop line should be from the current price action.
3. The Hull Moving Average (HMA) Filter The script includes an optional 50-period Hull Moving Average. The HMA is known for being extremely fast and smooth, reducing lag compared to standard moving averages.
Visual Reference: You can plot the line to see the overall macro trend.
Hard Filter: You can enable a "Safety Filter" in the settings. If enabled, the system will only generate Buy signals if the price is above the HMA, and Sell signals if the price is below the HMA.
4. The Dashboard A data panel is located on the chart (customizable position) to provide instant numerical data without needing to calculate levels manually. It displays the current trend state, the exact price of the trailing stop, and the status of the HMA filter.
Settings & Configuration
Sensitivity (Lookback)
Default: 5
This is the primary setting for the Apex Engine. A setting of 5 is the "sweet spot" for 1-minute and 5-minute charts. It allows the system to react very quickly to sudden volume spikes. Increasing this number (e.g., to 10) will make the signals slower and more conservative.
Stop Deviation
Default: 3.0
This controls the "breathing room" for the trade. A value of 3.0 allows for standard volatility on minute charts without triggering a premature exit. Lowering this to 2.0 will result in tighter stops but more false signals.
HMA Filter
Use HMA as Filter? (Default: OFF):
When OFF, the system signals purely on price action breakouts (fastest).
When ON, the system waits for the price to align with the 50-period HMA before signaling (safest, but may delay entry).
How to Interpret Visuals
Candle Colors
Teal/Green: The market is in a Bullish regime.
Red/Pink: The market is in a Bearish regime.
The Line
The solid stepped line represents the hard invalidation point. If price closes beyond this line, the trend is considered over.
Diamond Signals
Light Green Diamond (Below Bar): Confirmed Buy Signal. A new bullish trend has started.
Light Red/Pink Diamond (Above Bar): Confirmed Sell Signal. A new bearish trend has started.
Trading Strategy Guide
The Scalp Entry
Ensure you are on a 1-minute or 5-minute timeframe.
Wait for a signal Diamond to close. Do not enter while the bar is still forming, as the signal may repaint (disappear) if the price retraces before the close.
Long Entry: Enter when a Green Diamond appears and the candle turns Teal.
Short Entry: Enter when a Red Diamond appears and the candle turns Red.
Risk Management
Stop Loss: Your invalidation level is the "Apex Stop" line. You can place your hard stop loss slightly beyond this line.
Take Profit: Because this is a trend-following system, it is often best to hold until the candle color changes, or to take profit at fixed Risk:Reward ratios (e.g., 1:1.5 or 1:2).
The HMA Nuance If you find the market is "choppy" (moving sideways), enable the "Use HMA as Filter" option in the settings. This will force the system to ignore signals that are counter-trend to the longer-term momentum.
Disclaimer
The information provided by the "Dark Vector Scalping" indicator and this accompanying guide is for educational and informational purposes only. It does not constitute financial, investment, or trading advice. Trading cryptocurrencies, stocks, and forex involves a high level of risk and may not be suitable for all investors. You could lose some or all of your initial investment.
Viprasol Elite Flow Pro - Premium Order Flow & Trend System═══════════════════════════════════════════════════════════════
🔥 VIPRASOL ELITE FLOW PRO
Professional Order Flow & Trend Detection System
═══════════════════════════════════════════════════════════════
📊 WHAT IS THIS INDICATOR?
Viprasol Elite Flow Pro is a comprehensive trading system that combines institutional order flow analysis with adaptive trend detection. Unlike basic indicators, this tool identifies high-probability setups by analyzing where smart money is likely positioning, while filtering signals through multiple confirmation layers.
This indicator is designed for traders who want to:
✓ Identify premium (supply) and discount (demand) zones automatically
✓ Detect trend direction with adaptive cloud technology
✓ Spot high-volume rejection points before major moves
✓ Filter low-quality signals with intelligent confirmation logic
✓ Track market strength in real-time via elite dashboard
═══════════════════════════════════════════════════════════════
🎯 CORE FEATURES
═══════════════════════════════════════════════════════════════
1️⃣ ELITE TREND ENGINE
• Adaptive Moving Average system (Fast/Adaptive/Smooth modes)
• Dynamic trend cloud that expands/contracts with volatility
• Real-time trend state tracking (Bullish/Bearish/Ranging)
• Trend strength meter (0-10 scale)
• ATR-based volatility adjustments
2️⃣ ORDER FLOW DETECTION
• Automatic Premium Zone (Supply) identification
• Automatic Discount Zone (Demand) identification
• Smart zone extension - zones remain valid until broken
• Zone rejection detection with price action confirmation
• Customizable zone strength (5-30 bars lookback)
3️⃣ VOLUME INTELLIGENCE
• Volume spike detection (configurable threshold)
• Climax bar identification (exhaustion signals)
• Volume filter for signal validation
• Institutional activity detection
4️⃣ SMART SIGNAL SYSTEM
• 3 Signal Modes: Aggressive, Balanced, Conservative
• Multi-layer confirmation logic
• Automatic profit targets (2:1 risk-reward)
• Stop loss suggestions based on ATR
• Prevents overtrading with bars-since-signal filter
5️⃣ ELITE DASHBOARD (HUD)
• Real-time trend direction and strength
• Volume status monitoring
• Active zones counter
• Market volatility gauge
• Current signal status
• 4 positioning options, compact mode available
6️⃣ PREMIUM STYLING
• 4 Professional color themes (Cyber/Gold/Ocean/Fire)
• Adjustable transparency and label sizes
• Clean, institutional-grade visuals
• Optimized for all chart types
═══════════════════════════════════════════════════════════════
📖 HOW TO USE THIS INDICATOR
═══════════════════════════════════════════════════════════════
STEP 1: TREND IDENTIFICATION
→ Green Cloud = Bullish trend - look for LONG opportunities
→ Red Cloud = Bearish trend - look for SHORT opportunities
→ Purple Cloud = Ranging - wait for breakout or fade extremes
STEP 2: ZONE ANALYSIS
→ PREMIUM (Red) zones = Potential resistance/supply areas
→ DISCOUNT (Green) zones = Potential support/demand areas
→ Price rejecting from zones = high-probability setups
STEP 3: SIGNAL CONFIRMATION
→ Wait for "LONG" or "SHORT" labels to appear
→ Check dashboard for trend strength (Moderate/Strong preferred)
→ Confirm volume status is "HIGH" or "CLIMAX"
→ Entry: Enter when label appears
→ Stop Loss: Use dotted line (1 ATR away)
→ Take Profit: Use dashed line (2 ATR away)
STEP 4: RISK MANAGEMENT
→ Never risk more than 1-2% per trade
→ Use the provided stop loss levels
→ Trail stops as price moves in your favor
→ Avoid trading during low volatility periods
═══════════════════════════════════════════════════════════════
⚙️ RECOMMENDED SETTINGS
═══════════════════════════════════════════════════════════════
FOR SCALPING (1M - 5M):
- Trend Type: Fast
- Sensitivity: 15
- Signal Mode: Aggressive
- Zone Strength: 8
FOR DAY TRADING (15M - 1H):
- Trend Type: Adaptive
- Sensitivity: 21 (default)
- Signal Mode: Balanced
- Zone Strength: 12 (default)
FOR SWING TRADING (4H - Daily):
- Trend Type: Smooth
- Sensitivity: 34
- Signal Mode: Conservative
- Zone Strength: 20
BEST MARKETS:
✓ Crypto (BTC, ETH, major altcoins)
✓ Forex (Major pairs: EUR/USD, GBP/USD)
✓ Indices (S&P 500, NASDAQ, DAX)
✓ High-liquidity stocks
═══════════════════════════════════════════════════════════════
🎓 UNDERSTANDING THE METHODOLOGY
═══════════════════════════════════════════════════════════════
This indicator is built on three core concepts:
1. ORDER FLOW THEORY
Markets move between premium (expensive) and discount (cheap) zones. Smart money accumulates in discount zones and distributes in premium zones. This indicator identifies these zones automatically.
2. ADAPTIVE TREND FOLLOWING
Unlike fixed-period moving averages, the Elite Trend Engine adjusts to current market volatility, providing more accurate trend signals in both trending and ranging conditions.
3. CONFLUENCE-BASED ENTRIES
Signals only trigger when multiple conditions align:
- Price in correct zone (premium for shorts, discount for longs)
- Trend confirmation (cloud color matches direction)
- Volume validation (spike or climax present)
- Price action strength (strong rejection candles)
This multi-layer approach dramatically reduces false signals.
═══════════════════════════════════════════════════════════════
🔔 ALERT SETUP
═══════════════════════════════════════════════════════════════
This indicator includes 5 alert types:
1. Long Signal → Triggers when buy conditions met
2. Short Signal → Triggers when sell conditions met
3. Volume Climax → Warns of pot
Red to Green / Green to Red Tracker# Red to Green / Green to Red Tracker - Quick Reference
## Core Concept
```
PRIOR CLOSE = Yesterday's closing price = The "zero line" for today
Above Prior Close = 🟢 GREEN (profitable for yesterday's buyers)
Below Prior Close = 🔴 RED (losing for yesterday's buyers)
```
---
## The Two Key Moves
### 🟢 Red to Green (R2G)
```
OPEN: Below prior close (RED)
↓
CROSS: Price moves above prior close
↓
RESULT: Now GREEN - Bullish signal
```
**Why it matters:**
- Bears who shorted get squeezed
- Creates FOMO buying
- Momentum often continues
---
### 🔴 Green to Red (G2R)
```
OPEN: Above prior close (GREEN)
↓
CROSS: Price moves below prior close
↓
RESULT: Now RED - Bearish signal
```
**Why it matters:**
- Longs who bought get trapped
- Triggers stop losses
- Panic selling follows
---
## Signals Explained
| Signal | Shape | Location | Meaning |
|--------|-------|----------|---------|
| R2G | ▲ Green Triangle | Below bar | Crossed to green |
| G2R | ▼ Red Triangle | Above bar | Crossed to red |
---
## Level Lines
| Line | Color | Style | What It Is |
|------|-------|-------|------------|
| Prior Close | Orange | Solid | KEY R2G/G2R level |
| Prior High | Green | Dashed | Yesterday's high |
| Prior Low | Red | Dashed | Yesterday's low |
| Today Open | White | Dotted | Gap reference |
---
## Info Table Reference
| Field | What It Shows |
|-------|---------------|
| Status | 🟢 GREEN / 🔴 RED / ⚪ FLAT |
| Day Change | % change from prior close |
| Prior Close | The key level price |
| Distance | How far from prior close |
| Opened | Did today open green or red |
| R2G | R2G status + price if triggered |
| G2R | G2R status + price if triggered |
| Rel Vol | Current relative volume |
| Prior High | Yesterday's high + distance |
| Prior Low | Yesterday's low + distance |
---
## Trading R2G (Long Setup)
### Entry Checklist
- Stock opened RED (below prior close)
- R2G cross signal triggered (green triangle)
- Volume confirmation (1.5x+ preferred, 2x+ ideal)
- Price holding above prior close
- Overall market not tanking
### Entry Method
1. **Aggressive:** Enter immediately on R2G cross
2. **Conservative:** Wait for pullback to prior close (now support)
### Stop Loss
- Below the R2G cross candle low
- OR below prior close (tighter)
### Target
- Prior day high (first target)
- 2:1 risk-reward minimum
---
## Trading G2R (Short Setup)
### Entry Checklist
- Stock opened GREEN (above prior close)
- G2R cross signal triggered (red triangle)
- Volume confirmation
- Price staying below prior close
- Overall market not ripping
### Entry Method
1. **Aggressive:** Enter immediately on G2R cross
2. **Conservative:** Wait for bounce to prior close (now resistance)
### Stop Loss
- Above the G2R cross candle high
- OR above prior close (tighter)
### Target
- Prior day low (first target)
- Gap fill (if gapped up)
---
## Signal Quality
### High Quality R2G ✓
- Opened significantly red (-2% or more)
- Strong volume on cross (2x+)
- First R2G of the day
- Market trending up
- News catalyst present
### Low Quality R2G ✗
- Opened barely red (-0.5%)
- Low volume cross
- Multiple R2G/G2R already today (choppy)
- Fighting market direction
- No clear catalyst
---
## Common Patterns
### Clean R2G (Best)
```
Open red → Steady climb → Cross prior close → Continue higher
```
### Failed R2G (Avoid/Exit)
```
Open red → Cross to green → Immediately fail back to red
```
### Choppy R2G/G2R (Avoid)
```
Multiple crosses back and forth = Indecision, no clear direction
```
---
## First Cross Rule
**The FIRST R2G or G2R of the day is usually the most significant.**
Why?
- Catches traders off guard
- Largest reaction from market
- Sets tone for rest of day
If you miss the first cross, be more selective on subsequent crosses.
---
## Volume Guide
| Rel Volume | Quality | Action |
|------------|---------|--------|
| < 1.0x | Weak | Skip or small size |
| 1.0-1.5x | Average | Standard position |
| 1.5-2.0x | Good | Full position |
| 2.0x+ | Strong | High conviction |
---
## Settings Recommendations
### Default (Balanced)
```
Require Opposite Open: ON
Require Volume: ON (1.5x)
Candle Close Confirm: OFF
Min Cross %: 0
```
### Conservative (Fewer, Better Signals)
```
Require Opposite Open: ON
Require Volume: ON (2.0x)
Candle Close Confirm: ON
Min Cross %: 0.5
```
### Aggressive (More Signals)
```
Require Opposite Open: OFF
Require Volume: OFF
Candle Close Confirm: OFF
Min Cross %: 0
```
---
## Alert Setup
### Essential Alerts
1. **First R2G of Day** - Highest value alert
2. **R2G with Strong Volume** - High conviction
### How to Set
1. Right-click chart → Add Alert
2. Condition: R2G/G2R Tracker
3. Select alert type
4. Set notification method
---
## Combining with Other Indicators
| Indicator | How to Use |
|-----------|------------|
| **Gap & Go** | R2G on gap-down stock = strong reversal |
| **Bull Flag** | Look for bull flag after R2G confirmation |
| **Float Rotation** | R2G + high rotation = explosive potential |
| **VWAP** | R2G above VWAP = strongest setup |
---
## Common Mistakes
❌ **Chasing late R2G**
- If price is already 3-5% green, you missed the move
- Wait for pullback or next setup
❌ **Ignoring volume**
- Low volume R2G often fails
- Always check relative volume
❌ **Fighting the market**
- R2G in a tanking market often fails
- G2R in a ripping market often fails
❌ **No stop loss**
- Failed R2G can reverse hard
- Always have a defined stop
❌ **Overtrading choppy stocks**
- Multiple R2G/G2R = no clear direction
- Skip stocks that keep crossing back and forth
---
## Quick Decision Framework
```
1. Did it open opposite color? (Red for R2G, Green for G2R)
- NO → Lower probability, be cautious
- YES → Continue
2. Is volume confirming? (1.5x+ relative volume)
- NO → Skip or small size
- YES → Continue
3. Is this the first cross of the day?
- YES → Higher probability
- NO → Be more selective
4. Is market direction supportive?
- NO → Skip
- YES → Take the trade
5. Can you define risk? (Clear stop level)
- NO → Skip
- YES → Execute
```
---
## Key Takeaways
1. **Prior close is THE key level** - everyone watches it
2. **First cross matters most** - sets daily tone
3. **Volume confirms** - low volume crosses often fail
4. **Failed crosses reverse hard** - always use stops
5. **Don't overtrade choppy action** - multiple crosses = stay out
---
Happy Trading! 🟢🔴
ULTIMATE ORDER FLOW SYSTEM🔥 ULTIMATE ORDER FLOW SYSTEM
Overview
This comprehensive order flow analysis tool combines **Volume Profile**, **Cumulative Delta**, and **Large Order Detection** to identify high-probability trading setups. The script analyzes institutional order flow patterns and volume distribution to pinpoint key levels where price is likely to react.
📊 Core Components & Methodology
🔥 ULTIMATE ORDER FLOW SYSTEM
Overview
This comprehensive order flow analysis tool combines Volume Profile, Cumulative Delta, and Large Order Detection to identify high-probability trading setups. The script analyzes institutional order flow patterns and volume distribution to pinpoint key levels where price is likely to react.
________________________________________
📊 Core Components & Methodology
1. Volume Profile Analysis
The script constructs a horizontal volume profile by:
• Dividing the price range into configurable rows (default: 20)
• Accumulating volume at each price level over a lookback period (default: 50 bars)
• Separating buy volume (green bars close > open) from sell volume (red bars)
• Identifying three critical levels:
o POC (Point of Control): Price level with highest traded volume - acts as a strong magnet
o VAH/VAL (Value Area High/Low): Contains 70% of total volume - defines fair value zone
o HVN (High Volume Nodes): Resistance zones where institutions accumulated positions
o LVN (Low Volume Nodes): Thin zones that price moves through quickly - ideal targets
Why This Matters: Institutional traders leave footprints through volume. HVN zones show where large players defended levels, making them reliable support/resistance.
________________________________________
2. Cumulative Delta (Order Flow)
Tracks the running total of buying vs selling pressure:
• Bar Delta: Difference between buy and sell volume per candle
• Cumulative Delta: Sum of all bar deltas - shows net directional pressure
• Delta Moving Average: Smoothed delta (20-period) to identify trend
• Delta Divergences:
o Bullish: Price makes lower low, but delta makes higher low (absorption at bottom)
o Bearish: Price makes higher high, but delta makes lower high (exhaustion at top)
How It Works: When cumulative delta trends up while price consolidates, it signals accumulation. Delta divergences reveal when smart money is positioned opposite to retail expectations.
________________________________________
3. Large Order Detection
Identifies institutional-sized orders in real-time:
• Compares current bar volume to 20-period moving average
• Flags orders exceeding 2.5x average volume (configurable multiplier)
• Distinguishes bullish (green circles below) vs bearish (red circles above) large orders
Rationale: Sudden volume spikes at key levels indicate institutional participation - the "fuel" needed for breakouts or reversals.
________________________________________
🎯 Trading Signal Logic
Combined Setup Criteria
The script generates SHORT and LONG signals when multiple conditions align:
SHORT Signal Requirements:
1. Price reaches an HVN resistance zone (within 0.2%)
2. Large sell order detected (volume spike + red candle)
3. Cumulative delta is bearish OR bearish divergence present
4. 10-bar cooldown between signals (prevents overtrading)
LONG Signal Requirements:
1. Price reaches an HVN support zone
2. Large buy order detected (volume spike + green candle)
3. Cumulative delta is bullish OR bullish divergence present
4. 10-bar cooldown enforced
________________________________________
🔧 Customization Options
Setting - Purpose - Recommendation
Volume Profile Rows - Granularity of level detection - 20 (balanced)
Lookback Period - Historical data analyzed - 50 bars (intraday), 200 (swing)
Large Order Multiplier - Sensitivity to volume spikes - 2.5x (standard), 3.5x (conservative)
HVN Threshold - Resistance zone detection - 1.3 (default)
LVN Threshold - Target zone identification - 0.6 (default)
Divergence Lookback - Pivot detection period - 5 bars (responsive)
________________________________________
📈 Dashboard Indicators
The real-time panel displays:
• POC: Current Point of Control price
• Location: Whether price is at HVN resistance
• Orders: Current large buy/sell activity
• Cumulative Δ: Net order flow value + trend direction
• Divergence: Active bullish/bearish divergences
• Bar Strength: % of candle volume that's directional (>65% = strong)
• SETUP: Current trade signal (LONG/SHORT/WAIT)
________________________________________
🎨 Visual System
• Yellow POC Line: Highest volume level - primary pivot
• Blue Value Area Box: Fair value zone (VAH to VAL)
• Red HVN Zones: Resistance/support from institutional accumulation
• Green LVN Zones: Low-liquidity targets for quick moves
• Volume Bars: Green (buy pressure) vs Red (sell pressure) distribution
• Triangles: LONG (green up) and SHORT (red down) entry signals
• Diamonds: Divergence warnings (cyan=bullish, fuchsia=bearish)
________________________________________
💡 How This Script Is Unique
Unlike standalone volume profile or delta indicators, this script:
1. Synthesizes three complementary methods - volume structure, order flow momentum, and liquidity detection
2. Requires multi-factor confirmation - signals only trigger when price, volume, and delta align at key zones
3. Adapts to market regime - delta filters ensure you're trading with the dominant order flow direction
4. Provides context, not just signals - the dashboard helps you understand why a setup is forming
________________________________________
⚙️ Best Practices
Timeframes:
• 5-15 min: Scalping (use 30-50 bar lookback)
• 1-4 hour: Swing trading (use 100-200 bar lookback)
Risk Management:
• Enter on signal candle close
• Stop loss: Beyond nearest HVN/LVN zone
• Target 1: Next LVN level
• Target 2: Opposite value area boundary
Filters:
• Avoid signals during major news events
• Require bar delta strength >65% for aggressive entries
• Wait for delta MA cross confirmation in ranging markets
________________________________________
🚨 Alerts Available
• Long Setup Trigger
• Short Setup Trigger
• Bullish/Bearish Divergence Detection
• Large Buy/Sell Order Execution
________________________________________
📚 Educational Context
This methodology is based on principles used by professional order flow traders:
• Market Profile Theory: Volume distribution reveals fair value
• Tape Reading: Large orders show institutional intent
• Auction Theory: Price seeks areas of liquidity imbalance (LVN zones)
The script automates pattern recognition that discretionary traders spend years learning to identify manually.
________________________________________
⚠️ Disclaimer
This indicator is a trading tool, not a trading system. It identifies high-probability setups based on order flow analysis but requires proper risk management, market context, and trader discretion. Past performance does not guarantee future results.
________________________________________
Version: 6 (Pine Script)
Type: Overlay + Separate Pane (Delta Panel)
Resource Usage: Moderate (500 bars history, 500 lines/boxes)
________________________________________
For questions or support, please comment below. If you find this script valuable, please boost and favorite! 🚀
1. Volume Profile Analysis
The script constructs a horizontal volume profile by:
- Dividing the price range into configurable rows (default: 20)
- Accumulating volume at each price level over a lookback period (default: 50 bars)
- Separating buy volume (green bars close > open) from sell volume (red bars)
- Identifying three critical levels:
- POC (Point of Control): Price level with highest traded volume - acts as a strong magnet
- VAH/VAL (Value Area High/Low): Contains 70% of total volume - defines fair value zone
- HVN (High Volume Nodes): Resistance zones where institutions accumulated positions
- LVN (Low Volume Nodes): Thin zones that price moves through quickly - ideal targets
Why This Matters: Institutional traders leave footprints through volume. HVN zones show where large players defended levels, making them reliable support/resistance.
---
2. Cumulative Delta (Order Flow)
Tracks the running total of buying vs selling pressure:
- **Bar Delta**: Difference between buy and sell volume per candle
- **Cumulative Delta**: Sum of all bar deltas - shows net directional pressure
- **Delta Moving Average**: Smoothed delta (20-period) to identify trend
- **Delta Divergences**:
- **Bullish**: Price makes lower low, but delta makes higher low (absorption at bottom)
- **Bearish**: Price makes higher high, but delta makes lower high (exhaustion at top)
**How It Works**: When cumulative delta trends up while price consolidates, it signals accumulation. Delta divergences reveal when smart money is positioned opposite to retail expectations.
---
### 3. **Large Order Detection**
Identifies **institutional-sized orders** in real-time:
- Compares current bar volume to 20-period moving average
- Flags orders exceeding 2.5x average volume (configurable multiplier)
- Distinguishes bullish (green circles below) vs bearish (red circles above) large orders
**Rationale**: Sudden volume spikes at key levels indicate institutional participation - the "fuel" needed for breakouts or reversals.
---
## 🎯 Trading Signal Logic
### Combined Setup Criteria
The script generates **SHORT** and **LONG** signals when multiple conditions align:
**SHORT Signal Requirements:**
1. Price reaches an HVN resistance zone (within 0.2%)
2. Large sell order detected (volume spike + red candle)
3. Cumulative delta is bearish OR bearish divergence present
4. 10-bar cooldown between signals (prevents overtrading)
**LONG Signal Requirements:**
1. Price reaches an HVN support zone
2. Large buy order detected (volume spike + green candle)
3. Cumulative delta is bullish OR bullish divergence present
4. 10-bar cooldown enforced
---
## 🔧 Customization Options
| Setting | Purpose | Recommendation |
|---------|---------|----------------|
| **Volume Profile Rows** | Granularity of level detection | 20 (balanced) |
| **Lookback Period** | Historical data analyzed | 50 bars (intraday), 200 (swing) |
| **Large Order Multiplier** | Sensitivity to volume spikes | 2.5x (standard), 3.5x (conservative) |
| **HVN Threshold** | Resistance zone detection | 1.3 (default) |
| **LVN Threshold** | Target zone identification | 0.6 (default) |
| **Divergence Lookback** | Pivot detection period | 5 bars (responsive) |
---
## 📈 Dashboard Indicators
The real-time panel displays:
- **POC**: Current Point of Control price
- **Location**: Whether price is at HVN resistance
- **Orders**: Current large buy/sell activity
- **Cumulative Δ**: Net order flow value + trend direction
- **Divergence**: Active bullish/bearish divergences
- **Bar Strength**: % of candle volume that's directional (>65% = strong)
- **SETUP**: Current trade signal (LONG/SHORT/WAIT)
---
## 🎨 Visual System
- **Yellow POC Line**: Highest volume level - primary pivot
- **Blue Value Area Box**: Fair value zone (VAH to VAL)
- **Red HVN Zones**: Resistance/support from institutional accumulation
- **Green LVN Zones**: Low-liquidity targets for quick moves
- **Volume Bars**: Green (buy pressure) vs Red (sell pressure) distribution
- **Triangles**: LONG (green up) and SHORT (red down) entry signals
- **Diamonds**: Divergence warnings (cyan=bullish, fuchsia=bearish)
---
## 💡 How This Script Is Unique
Unlike standalone volume profile or delta indicators, this script:
1. **Synthesizes three complementary methods** - volume structure, order flow momentum, and liquidity detection
2. **Requires multi-factor confirmation** - signals only trigger when price, volume, and delta align at key zones
3. **Adapts to market regime** - delta filters ensure you're trading with the dominant order flow direction
4. **Provides context, not just signals** - the dashboard helps you understand *why* a setup is forming
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## ⚙️ Best Practices
**Timeframes:**
- 5-15 min: Scalping (use 30-50 bar lookback)
- 1-4 hour: Swing trading (use 100-200 bar lookback)
**Risk Management:**
- Enter on signal candle close
- Stop loss: Beyond nearest HVN/LVN zone
- Target 1: Next LVN level
- Target 2: Opposite value area boundary
**Filters:**
- Avoid signals during major news events
- Require bar delta strength >65% for aggressive entries
- Wait for delta MA cross confirmation in ranging markets
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## 🚨 Alerts Available
- Long Setup Trigger
- Short Setup Trigger
- Bullish/Bearish Divergence Detection
- Large Buy/Sell Order Execution
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## 📚 Educational Context
This methodology is based on principles used by professional order flow traders:
- **Market Profile Theory**: Volume distribution reveals fair value
- **Tape Reading**: Large orders show institutional intent
- **Auction Theory**: Price seeks areas of liquidity imbalance (LVN zones)
The script automates pattern recognition that discretionary traders spend years learning to identify manually.
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## ⚠️ Disclaimer
This indicator is a **trading tool, not a trading system**. It identifies high-probability setups based on order flow analysis but requires proper risk management, market context, and trader discretion. Past performance does not guarantee future results.
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**Version**: 6 (Pine Script)
**Type**: Overlay + Separate Pane (Delta Panel)
**Resource Usage**: Moderate (500 bars history, 500 lines/boxes)
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*For questions or support, please comment below. If you find this script valuable, please boost and favorite!* 🚀
