Uptrick: Dynamic AMA RSI Indicator### **Uptrick: Dynamic AMA RSI Indicator**
**Overview:**
The **Uptrick: Dynamic AMA RSI Indicator** is an advanced technical analysis tool designed for traders who seek to optimize their trading strategies by combining adaptive moving averages with the Relative Strength Index (RSI). This indicator dynamically adjusts to market conditions, offering a nuanced approach to trend detection and momentum analysis. By leveraging the Adaptive Moving Average (AMA) and Fast Adaptive Moving Average (FAMA), along with RSI-based overbought and oversold signals, traders can better identify entry and exit points with higher precision and reduced noise.
**Key Components:**
1. **Source Input:**
- The source input is the price data that forms the basis of all calculations. Typically set to the closing price, traders can customize this to other price metrics such as open, high, low, or even the output of another indicator. This flexibility allows the **Uptrick** indicator to be tailored to a wide range of trading strategies.
2. **Adaptive Moving Average (AMA):**
- The AMA is a moving average that adapts its sensitivity based on the dominant market cycle. This adaptation allows the AMA to respond swiftly to significant price movements while smoothing out minor fluctuations, making it particularly effective in trending markets. The AMA adjusts its responsiveness dynamically using a calculated phase adjustment from the dominant cycle, ensuring it remains responsive to the current market environment without being overly reactive to market noise.
3. **Fast Adaptive Moving Average (FAMA):**
- The FAMA is a more sensitive version of the AMA, designed to react faster to price changes. It serves as a signal line in the crossover strategy, highlighting shorter-term trends. The interaction between the AMA and FAMA forms the core of the signal generation, with crossovers between these lines indicating potential buy or sell opportunities.
4. **Relative Strength Index (RSI):**
- The RSI is a momentum oscillator that measures the speed and change of price movements, providing insights into whether an asset is overbought or oversold. In the **Uptrick** indicator, the RSI is used to confirm the validity of crossover signals between the AMA and FAMA, adding an additional layer of reliability to the trading signals.
**Indicator Logic:**
1. **Dominant Cycle Calculation:**
- The indicator starts by calculating the dominant market cycle using a smoothed price series. This involves applying exponential moving averages to a series of price differences, extracting cycle components, and determining the instantaneous phase of the cycle. This phase is then adjusted to provide a phase adjustment factor, which plays a critical role in determining the adaptive alpha.
2. **Adaptive Alpha Calculation:**
- The adaptive alpha, a key feature of the AMA, is computed based on the fast and slow limits set by the trader. This alpha is clamped within these limits to ensure the AMA remains appropriately sensitive to market conditions. The dynamic adjustment of alpha allows the AMA to be highly responsive in volatile markets and more conservative in stable markets.
3. **Crossover Detection:**
- The indicator generates trading signals based on crossovers between the AMA and FAMA:
- **CrossUp:** When the AMA crosses above the FAMA, it indicates a potential bullish trend, suggesting a buy opportunity.
- **CrossDown:** When the AMA crosses below the FAMA, it signals a potential bearish trend, indicating a sell opportunity.
4. **RSI Confirmation:**
- To enhance the reliability of these crossover signals, the indicator uses the RSI to confirm overbought and oversold conditions:
- **Buy Signal:** A buy signal is generated only when the AMA crosses above the FAMA and the RSI confirms an oversold condition, ensuring that the signal aligns with a momentum reversal from a low point.
- **Sell Signal:** A sell signal is triggered when the AMA crosses below the FAMA and the RSI confirms an overbought condition, indicating a momentum reversal from a high point.
5. **Signal Management:**
- To prevent signal redundancy during strong trends, the indicator tracks the last generated signal (buy or sell) and ensures that the next signal is only issued when there is a genuine reversal in trend direction.
6. **Signal Visualization:**
- **Buy Signals:** The indicator plots a "BUY" label below the bar when a buy signal is generated, using a green color to clearly mark the entry point.
- **Sell Signals:** A "SELL" label is plotted above the bar when a sell signal is detected, marked in red to indicate an exit or shorting opportunity.
- **Bar Coloring (Optional):** Traders have the option to enable bar coloring, where green bars indicate a bullish trend (AMA above FAMA) and red bars indicate a bearish trend (AMA below FAMA), providing a visual representation of the market’s direction.
**Customization Options:**
- **Source:** Traders can select the price data input that best suits their strategy (e.g., close, open, high, low, or custom indicators).
- **Fast Limit:** Adjustable sensitivity for the fast response of the AMA, allowing traders to tailor the indicator to different market conditions.
- **Slow Limit:** Sets the slower boundary for the AMA’s sensitivity, providing stability in less volatile markets.
- **RSI Length:** The period for the RSI calculation can be adjusted to fit different trading timeframes.
- **Overbought/Oversold Levels:** These thresholds can be customized to define the RSI levels that trigger buy or sell confirmations.
- **Enable Bar Colors:** Traders can choose whether to enable bar coloring based on the AMA/FAMA relationship, enhancing visual clarity.
**How Different Traders Can Use the Indicator:**
1. **Day Traders:**
- **Uptrick: Dynamic AMA RSI Indicator** is highly effective for day traders who need to make quick decisions in fast-moving markets. The adaptive nature of the AMA and FAMA allows the indicator to respond rapidly to intraday price swings. Day traders can use the buy and sell signals generated by the crossover and RSI confirmation to time their entries and exits with greater precision, minimizing exposure to false signals often prevalent in high-frequency trading environments.
2. **Swing Traders:**
- Swing traders can benefit from the indicator’s ability to identify and confirm trend reversals over several days or weeks. By adjusting the RSI length and sensitivity limits, swing traders can fine-tune the indicator to catch longer-term price movements, helping them to ride trends and maximize profits over medium-term trades. The dual confirmation of crossovers with RSI ensures that swing traders enter trades that have a higher probability of success.
3. **Position Traders:**
- For position traders who hold trades over longer periods, the **Uptrick** indicator offers a reliable method to stay in trades that align with the dominant trend while avoiding premature exits. By adjusting the slow limit and extending the RSI length, position traders can smooth out the indicator’s sensitivity, allowing them to focus on major market shifts rather than short-term volatility. The bar coloring feature also provides a clear visual indication of the overall trend, aiding in trade management decisions.
4. **Scalpers:**
- Scalpers, who seek to profit from small price movements, can use the fast responsiveness of the FAMA in conjunction with the RSI to identify micro-trends within larger market moves. The indicator’s ability to adapt quickly to changing conditions makes it a valuable tool for scalpers looking to execute numerous trades in a short period, capturing profits from minor price fluctuations while avoiding prolonged exposure.
5. **Algorithmic Traders:**
- Algorithmic traders can incorporate the **Uptrick** indicator into automated trading systems. The precise crossover signals combined with RSI confirmation provide clear and actionable rules that can be coded into algorithms. The adaptive nature of the indicator ensures that it can be used across different market conditions and timeframes, making it a versatile component of algorithmic strategies.
**Usage:**
The **Uptrick: Dynamic AMA RSI Indicator** is a versatile tool that can be integrated into various trading strategies, from short-term day trading to long-term investing. Its ability to adapt to changing market conditions and provide clear buy and sell signals makes it an invaluable asset for traders seeking to improve their trading performance. Whether used as a standalone indicator or in conjunction with other technical tools, **Uptrick** offers a dynamic approach to market analysis, helping traders to navigate the complexities of financial markets with greater confidence.
**Conclusion:**
The **Uptrick: Dynamic AMA RSI Indicator** offers a comprehensive and adaptable solution for traders across different styles and timeframes. By combining the strengths of adaptive moving averages with RSI confirmation, it delivers robust signals that help traders capitalize on market trends while minimizing the risk of false signals. This indicator is a powerful addition to any trader’s toolkit, enabling them to make informed decisions with greater precision and confidence. Whether you're a day trader, swing trader, or long-term investor, the **Uptrick** indicator can enhance your trading strategy and improve your market outcomes.
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ICT Power Of Three | Flux Charts💎 GENERAL OVERVIEW
Introducing our new ICT Power Of Three Indicator! This indicator is built around the ICT's "Power Of Three" strategy. This strategy makes use of these 3 key smart money concepts : Accumulation, Manipulation and Distribution. Each step is explained in detail within this write-up. For more information about the process, check the "HOW DOES IT WORK" section.
Features of the new ICT Power Of Three Indicator :
Implementation of ICT's Power Of Three Strategy
Different Algorithm Modes
Customizable Execution Settings
Customizable Backtesting Dashboard
Alerts for Buy, Sell, TP & SL Signals
📌 HOW DOES IT WORK ?
The "Power Of Three" comes from these three keywords "Accumulation, Manipulation and Distribution". Here is a brief explanation of each keyword :
Accumulation -> Accumulation phase is when the smart money accumulate their positions in a fixed range. This phase indicates price stability, generally meaning that the price constantly switches between up & down trend between a low and a high pivot point. When the indicator detects an accumulation zone, the Power Of Three strategy begins.
Manipulation -> When the smart money needs to increase their position sizes, they need retail traders' positions for liquidity. So, they manipulate the market into the opposite direction of their intended direction. This will result in retail traders opening positions the way that the smart money intended them to do, creating liquidity. After this step, the real move that the smart money intended begins.
Distribution -> This is when the real intention of the smart money comes into action. With the new liquidity thanks to the manipulation phase, the smart money add their positions towards the opposite direction of the retail mindset. The purpose of this indicator is to detect the accumulation and manipulation phases, and help the trader move towards the same direction as the smart money for their trades.
Detection Methods Of The Indicator :
Accumulation -> The indicator detects accumulation zones as explained step-by-step :
1. Draw two lines from the lowest point and the highest point of the latest X bars.
2. If the (high line - low line) is lower than Average True Range (ATR) * accumulationConstant
3. After the condition is validated, an accumulation zone is detected. The accumulation zone will be invalidated and manipulation phase will begin when the range is broken.
Manipulation -> If the accumulation range is broken, check if the current bar closes / wicks above the (high line + ATR * manipulationConstant) or below the (low line - ATR * manipulationConstant). If the condition is met, the indicator detects a manipulation zone.
Distribution -> The purpose of this indicator is to try to foresee the distribution zone, so instead of a detection, after the manipulation zone is detected the indicator automatically create a "shadow" distribution zone towards the opposite direction of the freshly detected manipulation zone. This shadow distribution zone comes with a take-profit and stop-loss layout, customizable by the trader in the settings.
The X bars, accumulationConstant and manipulationConstant are subject to change with the "Algorithm Mode" setting. Read the "Settings" section for more information.
This indicator follows these steps and inform you step by step by plotting them in your chart.
🚩UNIQUENESS
This indicator is an all-in-one suite for the ICT's Power Of Three concept. It's capable of plotting the strategy, giving signals, a backtesting dashboard and alerts feature. Different and customizable algorithm modes will help the trader fine-tune the indicator for the asset they are currently trading. The backtesting dashboard allows you to see how your settings perform in the current ticker. You can also set up alerts to get informed when the strategy is executable for different tickers.
⚙️SETTINGS
1. General Configuration
Algorithm Mode -> The indicator offers 3 different detection algorithm modes according to your needs. Here is the explanation of each mode.
a) Small Manipulation
This mode has the default bar length for the accumulation detection, but a lower manipulation constant, meaning that slighter imbalances in the price action can be detected as manipulation. This setting can be useful on tickers that have lower liquidity, thus can be manipulated easier.
b) Big Manipulation
This mode has the default bar length for the accumulation detection, but a higher manipulation constant, meaning that heavier imbalances on the price action are required in order to detect manipulation zones. This setting can be useful on tickers that have higher liquidity, thus can be manipulated harder.
c) Short Accumulation
This mode has a ~70% lower bar length requirement for accumulation zone detection, and the default manipulation constant. This setting can be useful on tickers that are highly volatile and do not enter accumulation phases too often.
Breakout Method -> If "Close" is selected, bar close price will be taken into calculation when Accumulation & Manipulation zone invalidation. If "Wick" is selected, a wick will be enough to validate the corresponding zone.
2. TP / SL
TP / SL Method -> If "Fixed" is selected, you can adjust the TP / SL ratios from the settings below. If "Dynamic" is selected, the TP / SL zones will be auto-determined by the algorithm.
Risk -> The risk you're willing to take if "Dynamic" TP / SL Method is selected. Higher risk usually means a better winrate at the cost of losing more if the strategy fails. This setting is has a crucial effect on the performance of the indicator, as different tickers may have different volatility so the indicator may have increased performance when this setting is correctly adjusted.
3. Visuals
Show Zones -> Enables / Disables rendering of Accumulation (yellow) and Manipulation (red) zones.
KNN OscillatorOverview
The KNN Oscillator is an advanced technical analysis tool designed to help traders identify potential trend reversals and market momentum. Using the K-Nearest Neighbors (KNN) algorithm, this oscillator normalizes KNN values to create a dynamic and responsive indicator. The oscillator line changes color to reflect the market sentiment, providing clear visual cues for trading decisions.
Key Features
Dynamic Color Oscillator: The line changes color based on the oscillator value – green for positive, red for negative, and grey for neutral.
Advanced KNN Algorithm: Utilizes the K-Nearest Neighbors algorithm for precise trend detection.
Normalized Values: Ensures the oscillator values are normalized to align with the stock price range, making it applicable to various assets.
Easy Integration: Can be easily added to any TradingView chart for enhanced analysis.
How It Works
The KNN Oscillator leverages the K-Nearest Neighbors algorithm to calculate the average distance of the nearest neighbors over a specified period. These values are then normalized to match the stock price range, ensuring they are comparable across different assets. The oscillator value is derived by taking the difference between the normalized KNN values and the source price. The line's color changes dynamically to provide an immediate visual indication of the market's state:
Green: Positive values indicate upward momentum.
Red: Negative values indicate downward momentum.
Grey: Neutral values indicate a stable or consolidating market.
Usage Instructions
Trend Reversal Detection: Use the color changes to identify potential trend reversals. A shift from red to green suggests a bullish reversal, while a shift from green to red indicates a bearish reversal.
Momentum Analysis: The oscillator's value and color help gauge market momentum. Strong positive values (green) indicate strong upward momentum, while strong negative values (red) indicate strong downward momentum.
Market Sentiment: The dynamic color changes provide an easy-to-understand visual representation of market sentiment, helping traders make informed decisions quickly.
Confirmation Tool: Use the KNN Oscillator in conjunction with other technical indicators to confirm signals and improve the accuracy of your trades.
Scalability: Applicable to various timeframes and asset classes, making it a versatile tool for all types of traders.
Tri-State SupertrendTri-State Supertrend: Buy, Sell, Range
( Credits: Based on "Pivot Point Supertrend" by LonesomeTheBlue.)
Tri-State Supertrend incorporates a range filter into a supertrend algorithm.
So in addition to the Buy and Sell states, we now also have a Range state.
This avoids the typical "whipsaw" problem: During a range, a standard supertrend algorithm will fire Buy and Sell signals in rapid succession. These signals are all false signals as they lead to losing positions when acted on.
In this case, a tri-state supertrend will go into Range mode and stay in this mode until price exits the range and a new trend begins.
I used Pivot Point Supertrend by LonesomeTheBlue as a starting point for this script because I believe LonesomeTheBlue's version is superior to the classic Supertrend algorithm.
This indicator has two additional parameters over Pivot Point Supertrend:
A flag to turn the range filter on or off
A range size threshold in percent
With that last parameter, you can define what a range is. The best value will depend on the asset you are trading.
Also, there are two new display options.
"Show (non-) trendline for ranges" - determines whether to draw the "trendline" inside of a range. Seeing as there is no trend in a range, this is usually just visual noise.
"Show suppressed signals" - allows you to see the Buy/Sell signals that were skipped by the range filter.
How to use Tri-State Supertrend in a strategy
You can use the Buy and Sell signals to enter positions as you would with a normal supertrend. Adding stop loss, trailing stop etc. is of course encouraged and very helpful. But what to do when the Range signal appears?
I currently run a strategy on LDO based on Tri-State Supertrend which appears to be profitable. (It will quite likely be open sourced at some point, but it is not released yet.)
In that strategy, I experimented with different actions being taken when the Range state is entered:
Continue: Just keep last position open during the range
Close: Close the last position when entering range
Reversal: During the range, execute the OPPOSITE of each signal (sell on "buy", buy on "sell")
In the backtest, it transpired that "Continue" was the most profitable option for this strategy.
How ranges are detected
The mechanism is pretty simple: During each Buy or Sell trend, we record price movement, specifically, the furthest move in the trend direction that was encountered (expressed as a percentage).
When a new signal is issued, the algorithm checks whether this value (for the last trend) is below the range size set by the user. If yes, we enter Range mode.
The same logic is used to exit Range mode. This check is performed on every bar in a range, so we can enter a buy or sell as early as possible.
I found that this simple logic works astonishingly well in practice.
Pros/cons of the range filter
A range filter is an incredibly useful addition to a supertrend and will most likely boost your profits.
You will see at most one false signal at the beginning of each range (because it takes a bit of time to detect the range); after that, no more false signals will appear over the range's entire duration. So this is a huge advantage.
There is essentially only one small price you have to pay:
When a range ends, the first Buy/Sell signal you get will be delayed over the regular supertrend's signal. This is, again, because the algorithm needs some time to detect that the range has ended. If you select a range size of, say, 1%, you will essentially lose 1% of profit in each range because of this delay.
In practice, it is very likely that the benefits of a range filter outweigh its cost. Ranges can last quite some time, equating to many false signals that the range filter will completely eliminate (all except for the first one, as explained above).
You have to do your own tests though :)
Smart Money Concepts Probability (Expo)█ Overview
The Smart Money Concept Probability (Expo) is an indicator developed to track the actions of institutional investors, commonly known as "smart money." This tool calculates the likelihood of smart money being actively engaged in buying or selling within the market, referred to as the "smart money order flow."
The indicator measures the probability of three key events: Change of Character ( CHoCH ), Shift in Market Structure ( SMS ), and Break of Structure ( BMS ). These probabilities are displayed as percentages alongside their respective levels, providing a straightforward and immediate understanding of the likelihood of smart money order flow.
Finally, the backtested results are shown in a table, which gives traders an understanding of the historical performance of the current order flow direction.
█ Calculations
The algorithm individually computes the likelihood of the events ( CHoCH , SMS , and BMS ). A positive score is assigned for events where the price successfully breaks through the level with the highest probability, and a negative score when the price fails to do so. By doing so, the algorithm determines the probability of each event occurring and calculates the total profitability derived from all the events.
█ Example
In this case, we have an 85% probability that the price will break above the upper range and make a new Break Of Structure and only a 16.36% probability that the price will break below the lower range and make a Change Of Character.
█ Settings
The Structure Period sets the pivot period to use when calculating the market structure.
The Structure Response sets how responsive the market structure should be. A low value returns a more responsive structure. A high value returns a less responsive structure.
█ How to use
This indicator is a perfect tool for anyone that wants to understand the probability of a Change of Character ( CHoCH ), Shift in Market Structure ( SMS ), and Break of Structure ( BMS )
The insights provided by this tool help traders gain an understanding of the smart money order flow direction, which can be used to determine the market trend.
█ Any Alert function call
An alert is sent when the price breaks the upper or lower range, and you can select what should be included in the alert. You can enable the following options:
Ticker ID
Timeframe
Probability percentage
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Breakout Probability (Expo)█ Overview
Breakout Probability is a valuable indicator that calculates the probability of a new high or low and displays it as a level with its percentage. The probability of a new high and low is backtested, and the results are shown in a table— a simple way to understand the next candle's likelihood of a new high or low. In addition, the indicator displays an additional four levels above and under the candle with the probability of hitting these levels.
The indicator helps traders to understand the likelihood of the next candle's direction, which can be used to set your trading bias.
█ Calculations
The algorithm calculates all the green and red candles separately depending on whether the previous candle was red or green and assigns scores if one or more lines were reached. The algorithm then calculates how many candles reached those levels in history and displays it as a percentage value on each line.
█ Example
In this example, the previous candlestick was green; we can see that a new high has been hit 72.82% of the time and the low only 28.29%. In this case, a new high was made.
█ Settings
Percentage Step
The space between the levels can be adjusted with a percentage step. 1% means that each level is located 1% above/under the previous one.
Disable 0.00% values
If a level got a 0% likelihood of being hit, the level is not displayed as default. Enable the option if you want to see all levels regardless of their values.
Number of Lines
Set the number of levels you want to display.
Show Statistic Panel
Enable this option if you want to display the backtest statistics for that a new high or low is made. (Only if the first levels have been reached or not)
█ Any Alert function call
An alert is sent on candle open, and you can select what should be included in the alert. You can enable the following options:
Ticker ID
Bias
Probability percentage
The first level high and low price
█ How to use
This indicator is a perfect tool for anyone that wants to understand the probability of a breakout and the likelihood that set levels are hit.
The indicator can be used for setting a stop loss based on where the price is most likely not to reach.
The indicator can help traders to set their bias based on probability. For example, look at the daily or a higher timeframe to get your trading bias, then go to a lower timeframe and look for setups in that direction.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
FATL, SATL, RFTL, & RSTL Digital Signal Filter Smoother [Loxx]FATL, SATL, RFTL, & RSTL Digital Signal Filter (DSP) Smoother is is a baseline indicator with DSP processed source inputs
What are digital indicators: distinctions from standard tools, types of filters.
To date, dozens of technical analysis indicators have been developed: trend instruments, oscillators, etc. Most of them use the method of averaging historical data, which is considered crude. But there is another group of tools - digital indicators developed on the basis of mathematical methods of spectral analysis. Their formula allows the trader to filter price noise accurately and exclude occasional surges, making the forecast more effective in comparison with conventional indicators. In this review, you will learn about their distinctions, advantages, types of digital indicators and examples of strategies based on them.
Two non-standard strategies based on digital indicators
Basic technical analysis indicators built into most platforms are based on mathematical formulas. These formulas are a reflection of market behavior in past periods. In other words, these indicators are built based on patterns that were discovered as a result of statistical analysis, which allows one to predict further trend movement to some extent. But there is also a group of indicators called digital indicators. They are developed using mathematical analysis and are an algorithmic spectral system called ATCF (Adaptive Trend & Cycles Following). In this article, I will tell you more about the components of this system, describe the differences between digital and regular indicators, and give examples of 2 strategies with indicator templates.
ATCF - Market Spectrum Analysis Method
There is a theory according to which the market is chaotic and unpredictable, i.e. it cannot be accurately analyzed. After all, no one can tell how traders will react to certain news, or whether some large investor will want to play against the market like George Soros did with the Bank of England. But there is another theory: many general market trends are logical, and have a rationale, causes and effects. The economy is undulating, which means it can be described by mathematical methods.
Digital indicators are defined as a group of algorithms for assessing the market situation, which are based exclusively on mathematical methods. They differ from standard indicators by the form of analysis display. They display certain values: price, smoothed price, volumes. Many standard indicators are built on the basis of filtering the minute significant price fluctuations with the help of moving averages and their variations. But we can hardly call the MA a good filter, because digital indicators that use spectral filters make it possible to do a more accurate calculation.
Simply put, digital indicators are technical analysis tools in which spectral filters are used to filter out price noise instead of moving averages.
The display of traditional indicators is lines, areas, and channels. Digital indicators can be displayed both in the form of lines and in digital form (a set of numbers in columns, any data in a text field, etc.). The digital display of the data is more like an additional source of statistics; for trading, a standard visual linear chart view is used.
All digital models belong to the category of spectral analysis of the market situation. In conventional technical indicators, price indications are averaged over a fixed period of time, which gives a rather rough result. The use of spectral analysis allows us to increase trading efficiency due to the fact that digital indicators use a statistical data set of past periods, which is converted into a “frequency” of the market (period of fluctuations).
Fourier theory provides the following spectral ranging of the trend duration:
low frequency range (0-4) - a reflection of a long trend of 2 months or more
medium frequency range (5-40) - the trend lasts 10-60 days, thus it is referred to as a correction
high frequency range (41-130) - price noise that lasts for several days
The ATCF algorithm is built on the basis of spectral analysis and includes a set of indicators created using digital filters. Its consists of indicators and filters:
FATL: Built on the basis of a low-frequency digital trend filter
SATL: Built on the basis of a low-frequency digital trend filter of a different order
RFTL: High frequency trend line
RSTL: Low frequency trend line
Inclucded:
4 DSP filters
Bar coloring
Keltner channels with variety ranges and smoothing functions
Bollinger bands
40 Smoothing filters
33 souce types
Variable channels
Ehlers Autocorrelation Periodogram [Loxx]Ehlers Autocorrelation Periodogram contains two versions of Ehlers Autocorrelation Periodogram Algorithm. This indicator is meant to supplement adaptive cycle indicators that myself and others have published on Trading View, will continue to publish on Trading View. These are fast-loading, low-overhead, streamlined, exact replicas of Ehlers' work without any other adjustments or inputs.
Versions:
- 2013, Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers
- 2016, TASC September, "Measuring Market Cycles"
Description
The Ehlers Autocorrelation study is a technical indicator used in the calculation of John F. Ehlers’s Autocorrelation Periodogram. Its main purpose is to eliminate noise from the price data, reduce effects of the “spectral dilation” phenomenon, and reveal dominant cycle periods. The spectral dilation has been discussed in several studies by John F. Ehlers; for more information on this, refer to sources in the "Further Reading" section.
As the first step, Autocorrelation uses Mr. Ehlers’s previous installment, Ehlers Roofing Filter, in order to enhance the signal-to-noise ratio and neutralize the spectral dilation. This filter is based on aerospace analog filters and when applied to market data, it attempts to only pass spectral components whose periods are between 10 and 48 bars.
Autocorrelation is then applied to the filtered data: as its name implies, this function correlates the data with itself a certain period back. As with other correlation techniques, the value of +1 would signify the perfect correlation and -1, the perfect anti-correlation.
Using values of Autocorrelation in Thermo Mode may help you reveal the cycle periods within which the data is best correlated (or anti-correlated) with itself. Those periods are displayed in the extreme colors (orange) while areas of intermediate colors mark periods of less useful cycles.
What is an adaptive cycle, and what is the Autocorrelation Periodogram Algorithm?
From his Ehlers' book mentioned above, page 135:
"Adaptive filters can have several different meanings. For example, Perry Kaufman’s adaptive moving average ( KAMA ) and Tushar Chande’s variable index dynamic average ( VIDYA ) adapt to changes in volatility . By definition, these filters are reactive to price changes, and therefore they close the barn door after the horse is gone.The adaptive filters discussed in this chapter are the familiar Stochastic , relative strength index ( RSI ), commodity channel index ( CCI ), and band-pass filter.The key parameter in each case is the look-back period used to calculate the indicator.This look-back period is commonly a fixed value. However, since the measured cycle period is changing, as we have seen in previous chapters, it makes sense to adapt these indicators to the measured cycle period. When tradable market cycles are observed, they tend to persist for a short while.Therefore, by tuning the indicators to the measure cycle period they are optimized for current conditions and can even have predictive characteristics.
The dominant cycle period is measured using the Autocorrelation Periodogram Algorithm. That dominant cycle dynamically sets the look-back period for the indicators. I employ my own streamlined computation for the indicators that provide smoother and easier to interpret outputs than traditional methods. Further, the indicator codes have been modified to remove the effects of spectral dilation.This basically creates a whole new set of indicators for your trading arsenal."
How to use this indicator
The point of the Ehlers Autocorrelation Periodogram Algorithm is to dynamically set a period between a minimum and a maximum period length. While I leave the exact explanation of the mechanic to Dr. Ehlers’s book, for all practical intents and purposes, in my opinion, the punchline of this method is to attempt to remove a massive source of overfitting from trading system creation–namely specifying a look-back period. SMA of 50 days? 100 days? 200 days? Well, theoretically, this algorithm takes that possibility of overfitting out of your hands. Simply, specify an upper and lower bound for your look-back, and it does the rest. In addition, this indicator tells you when its best to use adaptive cycle inputs for your other indicators.
Usage Example 1
Let's say you're using "Adaptive Qualitative Quantitative Estimation (QQE) ". This indicator has the option of adaptive cycle inputs. When the "Ehlers Autocorrelation Periodogram " shows a period of high correlation that adaptive cycle inputs work best during that period.
Usage Example 2
Check where the dominant cycle line lines, grab that output number and inject it into your other standard indicators for the length input.
Bogdan Ciocoiu - Code runnerDescription
The Code Runner is a hybrid indicator that leverages other pre-configured, integrated open-source algorithms to help traders spot regular and continuation divergences.
The Code Runner specialises in integrating some of the most popular oscillators well known for their accuracy when scalping using divergence strategies.
Uniqueness
The Code Runner stands out as a one-stop-shop pack of oscillator algorithms that traders can further customise to spot divergences.
The indicator's uniqueness stands from its capability to recast each algorithm to apply to the same scale. This feature is achieved by manually adjusting the outputs of each algorithm to fit on a scale between +100 and -100.
Another benefit of the Code Runner comes from its standardisation of outputs, mainly consisting of lines. Showing lines enables traders to draw potential regular and continuation divergences quickly.
The indicator has been pre-configured to support scalping at 1-5 minutes.
Open-source
The Code Runner uses the following open-source scripts and algorithms:
www.tradingview.com
www.tradingview.com
www.tradingview.com
www.tradingview.com
www.tradingview.com
www.tradingview.com
www.tradingview.com
www.tradingview.com
These algorithms are available in the public domain either in TradingView space or outside (given their popularity in the financial markets industry).
Machine Learning: kNN-based StrategykNN-based Strategy (FX and Crypto)
Description:
This strategy uses a classic machine learning algorithm - k Nearest Neighbours (kNN) - to let you find a prediction for the next (tomorrow's, next month's, etc.) market move. Being an unsupervised machine learning algorithm, kNN is one of the most simple learning algorithms.
To do a prediction of the next market move, the kNN algorithm uses the historic data, collected in 3 arrays - feature1, feature2 and directions, - and finds the k-nearest
neighbours of the current indicator(s) values.
The two dimensional kNN algorithm just has a look on what has happened in the past when the two indicators had a similar level. It then looks at the k nearest neighbours,
sees their state and thus classifies the current point.
The kNN algorithm offers a framework to test all kinds of indicators easily to see if they have got any *predictive value*. One can easily add cog, wpr and others.
Note: TradingViews's playback feature helps to see this strategy in action.
Warning: Signals ARE repainting.
Style tags: Trend Following, Trend Analysis
Asset class: Equities, Futures, ETFs, Currencies and Commodities
Dataset: FX Minutes/Hours+++/Days
Relative Strength(RSMK) + Perks - Markos KatsanosIf you are desperately looking for a novel RSI, this isn't that. This is another lesser known novel species of indicator. Hot off the press, in multiple stunning color schemes, I present my version of "Relative Strength (RSMK)" employing PSv4.0, originally formulated by Markos Katsanos for TASC - March 2020 Traders Tips. This indicator is used to compare performance of an asset to a market index of your choosing. I included the S&P 500 index along side the Dow Jones and the NASDAQ indices selectively by an input() in "Settings". You may comparatively analyze other global market indices by adapting the code, if you are skilled enough in Pine to do so.
With this contribution to the Tradingview community, also included is MY twin algorithmic formulation of "Comparative Relative Strength" as a supplementary companion indicator. They are eerily similar, so I decided to include it. You may easily disable my algorithm within the indicator "Settings". I do hope you may find both of them useful. Configurations are displayed above in multiple scenarios that should be suitable for most traders.
As always, I have included advanced Pine programming techniques that conform to proper "Pine Etiquette". For those of you who are newcomers to Pine Script, this script may also help you understand advanced programming techniques in Pine and how they may be utilized in a most effective manner. Utilizing the "Power of Pine", I included the maximum amount of features I could surmise in an ultra small yet powerful package, being less than a 60 line implementation at initial release.
Unfortunately, there are so many Pine mastery techniques included, I don't have time to write about all of them. I will have to let you discover them for yourself, excluding the following Pine "Tricks and Tips" described next. Of notable mention with this release, I have "overwritten" the Pine built-in function ema(). You may overwrite other built-in functions too. If you weren't aware of this Pine capability, you now know! Just heed caution when doing so to ensure your replacement algorithms are 100% sound. My ema() will also accept a floating point number for the period having ultimate adjustability. Yep, you heard all of that properly. Pine is becoming more impressive than `impressive` was originally thought of...
Features List Includes:
Dark Background - Easily disabled in indicator Settings->Style for "Light" charts or with Pine commenting
AND much, much more... You have the source!
The comments section below is solely just for commenting and other remarks, ideas, compliments, etc... regarding only this indicator, not others. When available time provides itself, I will consider your inquiries, thoughts, and concepts presented below in the comments section, should you have any questions or comments regarding this indicator. When my indicators achieve more prevalent use by TV members, I may implement more ideas when they present themselves as worthy additions. As always, "Like" it if you simply just like it with a proper thumbs up, and also return to my scripts list occasionally for additional postings. Have a profitable future everyone!
MACD, backtest 2015+ only, cut in half and doubledThis is only a slight modification to the existing "MACD Strategy" strategy plugin!
found the default MACD strategy to be lacking, although impressive for its simplicity. I added "year>2014" to the IF buy/sell conditions so it will only backtest from 2015 and beyond ** .
I also had a problem with the standard MACD trading late, per se. To that end I modified the inputs for fast/slow/signal to double. Example: my defaults are 10, 21, 10 so I put 20, 42, 20 in. This has the effect of making a 30min interval the same as 1 hour at 10,21,10. So if you want to backtest at 4hr, you would set your time interval to 2hr on the main chart. This is a handy way to make shorter time periods more useful even regardless of strategy/testing, since you can view 15min with alot less noise but a better response.
Used on BTCCNY OKcoin, with the chart set at 45 min (so really 90min in the strategy) this gave me a percent profitable of 42% and a profit factor of 1.998 on 189 trades.
Personally, I like to set the length/signals to 30,63,30. Meaning you need to triple the time, it allows for much better use of shorter time periods and the backtests are remarkably profitable. (i.e. 15min chart view = 45min on script, 30min= 1.5hr on script)
** If you want more specific time periods you need to try plugging in different bar values: replace "year" with "n" and "2014" with "5500". The bars are based on unix time I believe so you will need to play around with the number for n, with n being the numbers of bars.
Quantum Reversal# 🧠 Quantum Reversal
## **Quantitative Mean Reversion Framework**
This algorithmic trading system employs **statistical mean reversion theory** combined with **adaptive volatility modeling** to capitalize on Bitcoin's inherent price oscillations around its statistical mean. The strategy integrates multiple technical indicators through a **multi-layered signal processing architecture**.
---
## ⚡ **Core Technical Architecture**
### 📊 **Statistical Foundation**
- **Bollinger Band Mean Reversion Model**: Utilizes 20-period moving average with 2.2 standard deviation bands for volatility-adjusted entry signals
- **Adaptive Volatility Threshold**: Dynamic standard deviation multiplier accounts for Bitcoin's heteroscedastic volatility patterns
- **Price Action Confluence**: Entry triggered when price breaches lower volatility band, indicating statistical oversold conditions
### 🔬 **Momentum Analysis Layer**
- **RSI Oscillator Integration**: 14-period Relative Strength Index with modified oversold threshold at 45
- **Signal Smoothing Algorithm**: 5-period simple moving average applied to RSI reduces noise and false signals
- **Momentum Divergence Detection**: Captures mean reversion opportunities when momentum indicators show oversold readings
### ⚙️ **Entry Logic Architecture**
```
Entry Condition = (Price ≤ Lower_BB) OR (Smoothed_RSI < 45)
```
- **Dual-Condition Framework**: Either statistical price deviation OR momentum oversold condition triggers entry
- **Boolean Logic Gate**: OR-based entry system increases signal frequency while maintaining statistical validity
- **Position Sizing**: Fixed 10% equity allocation per trade for consistent risk exposure
### 🎯 **Exit Strategy Optimization**
- **Profit-Lock Mechanism**: Positions only closed when showing positive unrealized P&L
- **Trend Continuation Logic**: Allows winning trades to run until momentum exhaustion
- **Dynamic Exit Timing**: No fixed profit targets - exits based on profitability state rather than arbitrary levels
---
## 📈 **Statistical Properties**
### **Risk Management Framework**
- **Long-Only Exposure**: Eliminates short-squeeze risk inherent in cryptocurrency markets
- **Mean Reversion Bias**: Exploits Bitcoin's tendency to revert to statistical mean after extreme moves
- **Position Management**: Single position limit prevents over-leveraging
### **Signal Processing Characteristics**
- **Noise Reduction**: SMA smoothing on RSI eliminates high-frequency oscillations
- **Volatility Adaptation**: Bollinger Bands automatically adjust to changing market volatility
- **Multi-Timeframe Coherence**: Indicators operate on consistent timeframe for signal alignment
---
## 🔧 **Parameter Configuration**
| Technical Parameter | Value | Statistical Significance |
|-------------------|-------|-------------------------|
| Bollinger Period | 20 | Standard statistical lookback for volatility calculation |
| Std Dev Multiplier | 2.2 | Optimized for Bitcoin's volatility distribution (95.4% confidence interval) |
| RSI Period | 14 | Traditional momentum oscillator period |
| RSI Threshold | 45 | Modified oversold level accounting for Bitcoin's momentum characteristics |
| Smoothing Period | 5 | Noise reduction filter for momentum signals |
---
## 📊 **Algorithmic Advantages**
✅ **Statistical Edge**: Exploits documented mean reversion tendency in Bitcoin markets
✅ **Volatility Adaptation**: Dynamic bands adjust to changing market conditions
✅ **Signal Confluence**: Multiple indicator confirmation reduces false positives
✅ **Momentum Integration**: RSI smoothing improves signal quality and timing
✅ **Risk-Controlled Exposure**: Systematic position sizing and long-only bias
---
## 🔬 **Mathematical Foundation**
The strategy leverages **Bollinger Band theory** (developed by John Bollinger) which assumes that prices tend to revert to the mean after extreme deviations. The RSI component adds **momentum confirmation** to the statistical price deviation signal.
**Statistical Basis:**
- Mean reversion follows the principle that extreme price deviations from the moving average are temporary
- The 2.2 standard deviation multiplier captures approximately 97.2% of price movements under normal distribution
- RSI momentum smoothing reduces noise inherent in oscillator calculations
---
## ⚠️ **Risk Considerations**
This algorithm is designed for traders with understanding of **quantitative finance principles** and **cryptocurrency market dynamics**. The strategy assumes mean-reverting behavior which may not persist during trending market phases. Proper risk management and position sizing are essential.
---
## 🎯 **Implementation Notes**
- **Market Regime Awareness**: Most effective in ranging/consolidating markets
- **Volatility Sensitivity**: Performance may vary during extreme volatility events
- **Backtesting Recommended**: Historical performance analysis advised before live implementation
- **Capital Allocation**: 10% per trade sizing assumes diversified portfolio approach
---
**Engineered for quantitative traders seeking systematic mean reversion exposure in Bitcoin markets through statistically-grounded technical analysis.**
Institutional Volume Profile# Institutional Volume Profile (IVP) - Advanced Volume Analysis Indicator
## Overview
The Institutional Volume Profile (IVP) is a sophisticated technical analysis tool that combines traditional volume profile analysis with institutional volume detection algorithms. This indicator helps traders identify key price levels where significant institutional activity has occurred, providing insights into market structure and potential support/resistance zones.
## Key Features
### 🎯 Volume Profile Analysis
- **Point of Control (POC)**: Identifies the price level with the highest volume activity
- **Value Area**: Highlights the price range containing a specified percentage (default 70%) of total volume
- **Multi-Row Distribution**: Displays volume distribution across 10-50 price levels for detailed analysis
- **Customizable Period**: Analyze volume profiles over 10-500 bars
### 🏛️ Institutional Volume Detection
- **Pocket Pivot Volume (PPV)**: Detects bullish institutional buying when up-volume exceeds recent down-volume peaks
- **Pivot Negative Volume (PNV)**: Identifies bearish institutional selling when down-volume exceeds recent up-volume peaks
- **Accumulation Detection**: Spots potential accumulation phases with high volume and narrow price ranges
- **Distribution Analysis**: Identifies distribution patterns with high volume but minimal price movement
### 🎨 Visual Customization Options
- **Multiple Color Schemes**: Heat Map, Institutional, Monochrome, and Rainbow themes
- **Bar Styles**: Solid, Gradient, Outlined, and 3D Effect rendering
- **Volume Intensity Display**: Visual intensity based on volume magnitude
- **Flexible Positioning**: Left or right side profile placement
- **Current Price Highlighting**: Real-time price level indication
### 📊 Advanced Visual Features
- **Volume Labels**: Display volume amounts at key price levels
- **Gradient Effects**: Multi-step gradient rendering for enhanced visibility
- **3D Styling**: Shadow effects for professional appearance
- **Opacity Control**: Adjustable transparency (10-100%)
- **Border Customization**: Configurable border width and styling
## How It Works
### Volume Distribution Algorithm
The indicator analyzes each bar within the specified period and distributes its volume proportionally across the price levels it touches. This creates an accurate representation of where trading activity has been concentrated.
### Institutional Detection Logic
- **PPV Trigger**: Current up-bar volume > highest down-volume in lookback period + above volume MA
- **PNV Trigger**: Current down-bar volume > highest up-volume in lookback period + above volume MA
- **Accumulation**: High volume + narrow range + bullish close
- **Distribution**: Very high volume + minimal price movement
### Value Area Calculation
Starting from the POC, the algorithm expands both upward and downward, adding volume until reaching the specified percentage of total volume (default 70%).
## Configuration Parameters
### Profile Settings
- **Profile Period**: 10-500 bars (default: 50)
- **Number of Rows**: 10-50 levels (default: 24)
- **Profile Width**: 10-100% of screen (default: 30%)
- **Value Area %**: 50-90% (default: 70%)
### Institutional Analysis
- **PPV Lookback Days**: 5-20 periods (default: 10)
- **Volume MA Length**: 10-200 periods (default: 50)
- **Institutional Threshold**: 1.0-2.0x multiplier (default: 1.2)
### Visual Controls
- **Bar Style**: Solid, Gradient, Outlined, 3D Effect
- **Color Scheme**: Heat Map, Institutional, Monochrome, Rainbow
- **Profile Position**: Left or Right side
- **Opacity**: 10-100%
- **Show Labels**: Volume amount display toggle
## Interpretation Guide
### Volume Profile Elements
- **Thick Horizontal Bars**: High volume nodes (strong support/resistance)
- **Thin Horizontal Bars**: Low volume nodes (weak levels)
- **White Line (POC)**: Strongest support/resistance level
- **Blue Highlighted Area**: Value Area (fair value zone)
### Institutional Signals
- **Blue Triangles (PPV)**: Bullish institutional buying detected
- **Orange Triangles (PNV)**: Bearish institutional selling detected
- **Color-Coded Bars**: Different colors indicate institutional activity types
### Color Scheme Meanings
- **Heat Map**: Red (high volume) → Orange → Yellow → Gray (low volume)
- **Institutional**: Blue (PPV), Orange (PNV), Aqua (Accumulation), Yellow (Distribution)
- **Monochrome**: Grayscale intensity based on volume
- **Rainbow**: Color-coded by price level position
## Trading Applications
### Support and Resistance
- POC acts as dynamic support/resistance
- High volume nodes indicate strong price levels
- Low volume areas suggest potential breakout zones
### Institutional Activity
- PPV above Value Area: Strong bullish signal
- PNV below Value Area: Strong bearish signal
- Accumulation patterns: Potential upward breakouts
- Distribution patterns: Potential downward pressure
### Market Structure Analysis
- Value Area defines fair value range
- Profile shape indicates market sentiment
- Volume gaps suggest potential price targets
## Alert Conditions
- PPV Detection at current price level
- PNV Detection at current price level
- PPV above Value Area (strong bullish)
- PNV below Value Area (strong bearish)
## Best Practices
1. Use multiple timeframes for confirmation
2. Combine with price action analysis
3. Pay attention to volume context (above/below average)
4. Monitor institutional signals near key levels
5. Consider overall market conditions
## Technical Notes
- Maximum 500 boxes and 100 labels for optimal performance
- Real-time calculations update on each bar close
- Historical analysis uses complete bar data
- Compatible with all TradingView chart types and timeframes
---
*This indicator is designed for educational and informational purposes. Always combine with other analysis methods and risk management strategies.*
DisplayUtilitiesLibrary "DisplayUtilities"
Display utilities for color management and visual presentation
get_direction_color(direction, up_excessive, up_normal, neutral, down_normal, down_excessive)
Get candle color based on direction and color scheme
Parameters:
direction (int) : Direction value (-2, -1, 0, 1, 2)
up_excessive (color) : Color for +2 direction
up_normal (color) : Color for +1 direction
neutral (color) : Color for 0 direction
down_normal (color) : Color for -1 direction
down_excessive (color) : Color for -2 direction
Returns: Appropriate color for the direction
get_candle_paint_directions(paint_opt, body_dir, bar_dir, breakout_dir, combined_dir)
Get candle directions for different painting algorithms
Parameters:
paint_opt (string) : Painting option algorithm
body_dir (int) : Body direction
bar_dir (int) : Bar direction
breakout_dir (int) : Breakout direction
combined_dir (int) : Combined direction
Returns:
get_bias_paint_directions(paint_bias, unified_dir)
Get paint directions based on bias filter
Parameters:
paint_bias (string) : Paint bias option ("All", "Bull Bias", "Bear Bias")
unified_dir (int) : Unified direction
Returns: Directions for two plotcandle series
get_transparency_levels(sf_filtered, fade_option, fade_opacity)
Calculate transparency levels for strength factor filtering
Parameters:
sf_filtered (bool) : Is strength factor filtered
fade_option (string) : Fade option ("Disabled", "Fade Candle", "Do Not Fade Wick", "Do Not Fade Wick and Border")
fade_opacity (int) : Fade opacity percentage
Returns:
get_strength_factor_filter(filter_option, individual_filters)
Generate strength factor filter conditions
Parameters:
filter_option (string) : Filter option string
individual_filters (map) : Map of individual filter conditions
Returns: Boolean filter result
get_signal_bar_condition(signal_option, individual_filters)
Generate signal bar conditions (inverted filters)
Parameters:
signal_option (string) : Signal bar option string
individual_filters (map) : Map of individual filter conditions
Returns: Boolean signal bar result
get_zscore_signal_condition(z_signal_option, z_filters)
Get Z-score signal bar conditions
Parameters:
z_signal_option (string) : Z-score signal option
z_filters (map) : Map of Z-score filters
Returns: Boolean Z-score signal condition
get_standard_colors()
Create a standard color scheme for directions
Returns: Standard color set
apply_zscore_modification(original_dir, z_filtered)
Modify directions for Z-score excess display
Parameters:
original_dir (int) : Original direction
z_filtered (bool) : Is Z-score filtered (shows excess)
Returns: Modified direction (doubled if excess detected)
get_default_fade_colors()
Get default fade colors for strength factor overlay
Returns: Default colors for TV overlay
should_paint_candles(paint_algo)
Check if paint algorithm should show candles
Parameters:
paint_algo (string) : Paint algorithm option
Returns: True if algorithm should display candles
get_signal_bar_char(signal_type, is_bullish)
Get signal bar character based on signal type
Parameters:
signal_type (string) : Signal type ("strength_factor" or "zscore")
is_bullish (bool) : Direction is bullish
Returns: Character and location for plotchar
get_signal_bar_color(signal_type, is_bullish)
Get signal bar colors
Parameters:
signal_type (string) : Signal type ("strength_factor" or "zscore")
is_bullish (bool) : Direction is bullish
Returns: Signal bar color
Why EMA Isn't What You Think It IsMany new traders adopt the Exponential Moving Average (EMA) believing it's simply a "better Simple Moving Average (SMA)". This common misconception leads to fundamental misunderstandings about how EMA works and when to use it.
EMA and SMA differ at their core. SMA use a window of finite number of data points, giving equal weight to each data point in the calculation period. This makes SMA a Finite Impulse Response (FIR) filter in signal processing terms. Remember that FIR means that "all that we need is the 'period' number of data points" to calculate the filter value. Anything beyond the given period is not relevant to FIR filters – much like how a security camera with 14-day storage automatically overwrites older footage, making last month's activity completely invisible regardless of how important it might have been.
EMA, however, is an Infinite Impulse Response (IIR) filter. It uses ALL historical data, with each past price having a diminishing - but never zero - influence on the calculated value. This creates an EMA response that extends infinitely into the past—not just for the last N periods. IIR filters cannot be precise if we give them only a 'period' number of data to work on - they will be off-target significantly due to lack of context, like trying to understand Game of Thrones by watching only the final season and wondering why everyone's so upset about that dragon lady going full pyromaniac.
If we only consider a number of data points equal to the EMA's period, we are capturing no more than 86.5% of the total weight of the EMA calculation. Relying on he period window alone (the warm-up period) will provide only 1 - (1 / e^2) weights, which is approximately 1−0.1353 = 0.8647 = 86.5%. That's like claiming you've read a book when you've skipped the first few chapters – technically, you got most of it, but you probably miss some crucial early context.
▶️ What is period in EMA used for?
What does a period parameter really mean for EMA? When we select a 15-period EMA, we're not selecting a window of 15 data points as with an SMA. Instead, we are using that number to calculate a decay factor (α) that determines how quickly older data loses influence in EMA result. Every trader knows EMA calculation: α = 1 / (1+period) – or at least every trader claims to know this while secretly checking the formula when they need it.
Thinking in terms of "period" seriously restricts EMA. The α parameter can be - should be! - any value between 0.0 and 1.0, offering infinite tuning possibilities of the indicator. When we limit ourselves to whole-number periods that we use in FIR indicators, we can only access a small subset of possible IIR calculations – it's like having access to the entire RGB color spectrum with 16.7 million possible colors but stubbornly sticking to the 8 basic crayons in a child's first art set because the coloring book only mentioned those by name.
For example:
Period 10 → alpha = 0.1818
Period 11 → alpha = 0.1667
What about wanting an alpha of 0.17, which might yield superior returns in your strategy that uses EMA? No whole-number period can provide this! Direct α parameterization offers more precision, much like how an analog tuner lets you find the perfect radio frequency while digital presets force you to choose only from predetermined stations, potentially missing the clearest signal sitting right between channels.
Sidenote: the choice of α = 1 / (1+period) is just a convention from 1970s, probably started by J. Welles Wilder, who popularized the use of the 14-day EMA. It was designed to create an approximate equivalence between EMA and SMA over the same number of periods, even thought SMA needs a period window (as it is FIR filter) and EMA doesn't. In reality, the decay factor α in EMA should be allowed any valye between 0.0 and 1.0, not just some discrete values derived from an integer-based period! Algorithmic systems should find the best α decay for EMA directly, allowing the system to fine-tune at will and not through conversion of integer period to float α decay – though this might put a few traditionalist traders into early retirement. Well, to prevent that, most traditionalist implementations of EMA only use period and no alpha at all. Heaven forbid we disturb people who print their charts on paper, draw trendlines with rulers, and insist the market "feels different" since computers do algotrading!
▶️ Calculating EMAs Efficiently
The standard textbook formula for EMA is:
EMA = CurrentPrice × alpha + PreviousEMA × (1 - alpha)
But did you know that a more efficient version exists, once you apply a tiny bit of high school algebra:
EMA = alpha × (CurrentPrice - PreviousEMA) + PreviousEMA
The first one requires three operations: 2 multiplications + 1 addition. The second one also requires three ops: 1 multiplication + 1 addition + 1 subtraction.
That's pathetic, you say? Not worth implementing? In most computational models, multiplications cost much more than additions/subtractions – much like how ordering dessert costs more than asking for a water refill at restaurants.
Relative CPU cost of float operations :
Addition/Subtraction: ~1 cycle
Multiplication: ~5 cycles (depending on precision and architecture)
Now you see the difference? 2 * 5 + 1 = 11 against 5 + 1 + 1 = 7. That is ≈ 36.36% efficiency gain just by swapping formulas around! And making your high school math teacher proud enough to finally put your test on the refrigerator.
▶️ The Warmup Problem: how to start the EMA sequence right
How do we calculate the first EMA value when there's no previous EMA available? Let's see some possible options used throughout the history:
Start with zero : EMA(0) = 0. This creates stupidly large distortion until enough bars pass for the horrible effect to diminish – like starting a trading account with zero balance but backdating a year of missed trades, then watching your balance struggle to climb out of a phantom debt for months.
Start with first price : EMA(0) = first price. This is better than starting with zero, but still causes initial distortion that will be extra-bad if the first price is an outlier – like forming your entire opinion of a stock based solely on its IPO day price, then wondering why your model is tanking for weeks afterward.
Use SMA for warmup : This is the tradition from the pencil-and-paper era of technical analysis – when calculators were luxury items and "algorithmic trading" meant your broker had neat handwriting. We first calculate an SMA over the initial period, then kickstart the EMA with this average value. It's widely used due to tradition, not merit, creating a mathematical Frankenstein that uses an FIR filter (SMA) during the initial period before abruptly switching to an IIR filter (EMA). This methodology is so aesthetically offensive (abrupt kink on the transition from SMA to EMA) that charting platforms hide these early values entirely, pretending EMA simply doesn't exist until the warmup period passes – the technical analysis equivalent of sweeping dust under the rug.
Use WMA for warmup : This one was never popular because it is harder to calculate with a pencil - compared to using simple SMA for warmup. Weighted Moving Average provides a much better approximation of a starting value as its linear descending profile is much closer to the EMA's decay profile.
These methods all share one problem: they produce inaccurate initial values that traders often hide or discard, much like how hedge funds conveniently report awesome performance "since strategy inception" only after their disastrous first quarter has been surgically removed from the track record.
▶️ A Better Way to start EMA: Decaying compensation
Think of it this way: An ideal EMA uses an infinite history of prices, but we only have data starting from a specific point. This creates a problem - our EMA starts with an incorrect assumption that all previous prices were all zero, all close, or all average – like trying to write someone's biography but only having information about their life since last Tuesday.
But there is a better way. It requires more than high school math comprehension and is more computationally intensive, but is mathematically correct and numerically stable. This approach involves compensating calculated EMA values for the "phantom data" that would have existed before our first price point.
Here's how phantom data compensation works:
We start our normal EMA calculation:
EMA_today = EMA_yesterday + α × (Price_today - EMA_yesterday)
But we add a correction factor that adjusts for the missing history:
Correction = 1 at the start
Correction = Correction × (1-α) after each calculation
We then apply this correction:
True_EMA = Raw_EMA / (1-Correction)
This correction factor starts at 1 (full compensation effect) and gets exponentially smaller with each new price bar. After enough data points, the correction becomes so small (i.e., below 0.0000000001) that we can stop applying it as it is no longer relevant.
Let's see how this works in practice:
For the first price bar:
Raw_EMA = 0
Correction = 1
True_EMA = Price (since 0 ÷ (1-1) is undefined, we use the first price)
For the second price bar:
Raw_EMA = α × (Price_2 - 0) + 0 = α × Price_2
Correction = 1 × (1-α) = (1-α)
True_EMA = α × Price_2 ÷ (1-(1-α)) = Price_2
For the third price bar:
Raw_EMA updates using the standard formula
Correction = (1-α) × (1-α) = (1-α)²
True_EMA = Raw_EMA ÷ (1-(1-α)²)
With each new price, the correction factor shrinks exponentially. After about -log₁₀(1e-10)/log₁₀(1-α) bars, the correction becomes negligible, and our EMA calculation matches what we would get if we had infinite historical data.
This approach provides accurate EMA values from the very first calculation. There's no need to use SMA for warmup or discard early values before output converges - EMA is mathematically correct from first value, ready to party without the awkward warmup phase.
Here is Pine Script 6 implementation of EMA that can take alpha parameter directly (or period if desired), returns valid values from the start, is resilient to dirty input values, uses decaying compensator instead of SMA, and uses the least amount of computational cycles possible.
// Enhanced EMA function with proper initialization and efficient calculation
ema(series float source, simple int period=0, simple float alpha=0)=>
// Input validation - one of alpha or period must be provided
if alpha<=0 and period<=0
runtime.error("Alpha or period must be provided")
// Calculate alpha from period if alpha not directly specified
float a = alpha > 0 ? alpha : 2.0 / math.max(period, 1)
// Initialize variables for EMA calculation
var float ema = na // Stores raw EMA value
var float result = na // Stores final corrected EMA
var float e = 1.0 // Decay compensation factor
var bool warmup = true // Flag for warmup phase
if not na(source)
if na(ema)
// First value case - initialize EMA to zero
// (we'll correct this immediately with the compensation)
ema := 0
result := source
else
// Standard EMA calculation (optimized formula)
ema := a * (source - ema) + ema
if warmup
// During warmup phase, apply decay compensation
e *= (1-a) // Update decay factor
float c = 1.0 / (1.0 - e) // Calculate correction multiplier
result := c * ema // Apply correction
// Stop warmup phase when correction becomes negligible
if e <= 1e-10
warmup := false
else
// After warmup, EMA operates without correction
result := ema
result // Return the properly compensated EMA value
▶️ CONCLUSION
EMA isn't just a "better SMA"—it is a fundamentally different tool, like how a submarine differs from a sailboat – both float, but the similarities end there. EMA responds to inputs differently, weighs historical data differently, and requires different initialization techniques.
By understanding these differences, traders can make more informed decisions about when and how to use EMA in trading strategies. And as EMA is embedded in so many other complex and compound indicators and strategies, if system uses tainted and inferior EMA calculatiomn, it is doing a disservice to all derivative indicators too – like building a skyscraper on a foundation of Jell-O.
The next time you add an EMA to your chart, remember: you're not just looking at a "faster moving average." You're using an INFINITE IMPULSE RESPONSE filter that carries the echo of all previous price actions, properly weighted to help make better trading decisions.
EMA done right might significantly improve the quality of all signals, strategies, and trades that rely on EMA somewhere deep in its algorithmic bowels – proving once again that math skills are indeed useful after high school, no matter what your guidance counselor told you.
Smarter Money Concepts - OBs [PhenLabs]📊 Smarter Money Concepts - OBs
Version: PineScript™ v6
📌 Description
Smarter Money Concepts - OBs (Order Blocks) is an advanced technical analysis tool designed to identify and visualize institutional order zones on your charts. Order blocks represent significant areas of liquidity where smart money has entered positions before major moves. By tracking these zones, traders can anticipate potential reversals, continuations, and key reaction points in price action.
This indicator incorporates volume filtering technology to identify only the most significant order blocks, eliminating low-quality signals and focusing on areas where institutional participation is likely present. The combination of price structure analysis and volume confirmation provides traders with high-probability zones that may attract future price action for tests, rejections, or breakouts.
🚀 Points of Innovation
Volume-Filtered Block Detection : Identifies only order blocks formed with significant volume, focusing on areas with institutional participation
Advanced Break of Structure Logic : Uses sophisticated price action analysis to detect legitimate market structure breaks preceding order blocks
Dynamic Block Management : Intelligently tracks, extends, and removes order blocks based on price interaction and time-based expiration
Structure Recognition System : Employs technical analysis algorithms to find significant swing points for accurate order block identification
Dual Directional Tracking : Simultaneously monitors both bullish and bearish order blocks for comprehensive market structure analysis
🔧 Core Components
Order Block Detection : Identifies institutional entry zones by analyzing price action before significant breaks of structure, capturing where smart money has likely positioned before moves.
Volume Filtering Algorithm : Calculates relative volume compared to a moving average to qualify only order blocks formed with significant market participation, eliminating noise.
Structure Break Recognition : Uses price action analysis to detect legitimate breaks of market structure, ensuring order blocks are identified only at significant market turning points.
Dynamic Block Management : Continuously monitors price interaction with existing blocks, extending, maintaining, or removing them based on current market behavior.
🔥 Key Features
Volume-Based Filtering : Filter out insignificant blocks by requiring a minimum volume threshold, focusing only on zones with likely institutional activity
Visual Block Highlighting : Color-coded boxes clearly mark bullish and bearish order blocks with customizable appearance
Flexible Mitigation Options : Choose between “Wick” or “Close” methods for determining when a block has been tested or mitigated
Scan Range Adjustment : Customize how far back the indicator looks for structure points to adapt to different market conditions and timeframes
Break Source Selection : Configure which price component (close, open, high, low) is used to determine structure breaks for precise block identification
🎨 Visualization
Bullish Order Blocks : Blue-colored rectangles highlighting zones where bullish institutional orders were likely placed before upward moves, representing potential support areas.
Bearish Order Blocks : Red-colored rectangles highlighting zones where bearish institutional orders were likely placed before downward moves, representing potential resistance areas.
Block Extension : Order blocks extend to the right of the chart, providing clear visualization of these significant zones as price continues to develop.
📖 Usage Guidelines
Order Block Settings
Scan Range : Default: 25. Defines how many bars the indicator scans to determine significant structure points for order block identification.
Bull Break Price Source : Default: Close. Determines which price component is used to detect bullish breaks of structure.
Bear Break Price Source : Default: Close. Determines which price component is used to detect bearish breaks of structure.
Visual Settings
Bullish Blocks Color : Default: Blue with 85% transparency. Controls the appearance of bullish order blocks.
Bearish Blocks Color : Default: Red with 85% transparency. Controls the appearance of bearish order blocks.
General Options
Block Mitigation Method : Default: Wick, Options: Wick, Close. Determines how block mitigation is calculated - “Wick” uses high/low values while “Close” uses close values for more conservative mitigation criteria.
Remove Filled Blocks : Default: Disabled. When enabled, order blocks are removed once they’ve been mitigated by price action.
Volume Filter
Volume Filter Enabled : Default: Enabled. When activated, only shows order blocks formed with significant volume relative to recent average.
Volume SMA Period : Default: 15, Range: 1-50. Number of periods used to calculate the average volume baseline.
Min. Volume Ratio : Default: 1.5, Range: 0.5-10.0. Minimum volume ratio compared to average required to display an order block; higher values filter out more blocks.
✅ Best Use Cases
Identifying high-probability support and resistance zones for trade entries and exits
Finding optimal stop-loss placement behind significant order blocks
Detecting potential reversal areas where price may react after extended moves
Confirming breakout trades when price clears major order blocks
Building a comprehensive market structure map for medium to long-term trading decisions
Pinpointing areas where smart money may have positioned before major market moves
⚠️ Limitations
Most effective on higher timeframes (1H and above) where institutional activity is more clearly defined
Can generate multiple signals in choppy market conditions, requiring additional filtering
Volume filtering relies on accurate volume data, which may be less reliable for some securities
Recent market structure changes may invalidate older order blocks not yet automatically removed
Block identification is based on historical price action and may not predict future behavior with certainty
💡 What Makes This Unique
Volume Intelligence : Unlike basic order block indicators, this script incorporates volume analysis to identify only the most significant institutional zones, focusing on quality over quantity.
Structural Precision : Uses sophisticated break of structure algorithms to identify true market turning points, going beyond simple price pattern recognition.
Dynamic Block Management : Implements automatic block tracking, extension, and cleanup to maintain a clean and relevant chart display without manual intervention.
Institutional Focus : Designed specifically to highlight areas where smart money has likely positioned, helping retail traders align with institutional perspectives rather than retail noise.
🔬 How It Works
1. Structure Identification Process :
The indicator continuously scans price action to identify significant swing points and structure levels within the specified range, establishing a foundation for order block recognition.
2. Break Detection :
When price breaks an established structure level (crossing below a significant low for bearish breaks or above a significant high for bullish breaks), the indicator marks this as a potential zone for order block formation.
3. Volume Qualification :
For each potential order block, the algorithm calculates the relative volume compared to the configured period average. Only blocks formed with volume exceeding the minimum ratio threshold are displayed.
4. Block Creation and Management :
Valid order blocks are created, tracked, and managed as price continues to develop. Blocks extend to the right of the chart until they are either mitigated by price action or expire after the designated timeframe.
5. Continuous Monitoring :
The indicator constantly evaluates price interaction with existing blocks, determining when blocks have been tested, mitigated, or invalidated, and updates the visual representation accordingly.
💡 Note:
Order Blocks represent areas where institutional traders have likely established positions and may defend these zones during future price visits. For optimal results, use this indicator in conjunction with other confluent factors such as key support/resistance levels, trendlines, or additional confirmation indicators. The most reliable signals typically occur on higher timeframes where institutional activity is most prominent. Start with the default settings and adjust parameters gradually to match your specific trading instrument and style.
Adaptive Kalman Trend Filter (Zeiierman)█ Overview
The Adaptive Kalman Trend Filter indicator is an advanced trend-following tool designed to help traders accurately identify market trends. Utilizing the Kalman Filter—a statistical algorithm rooted in control theory and signal processing—this indicator adapts to changing market conditions, smoothing price data to filter out noise. By focusing on state vector-based calculations, it dynamically adjusts trend and range measurements, making it an excellent tool for both trend-following and range-based trading strategies. The indicator's adaptive nature is enhanced by options for volatility adjustment and three unique Kalman filter models, each tailored for different market conditions.
█ How It Works
The Kalman Filter works by maintaining a model of the market state through matrices that represent state variables, error covariances, and measurement uncertainties. Here’s how each component plays a role in calculating the indicator’s trend:
⚪ State Vector (X): The state vector is a two-dimensional array where each element represents a market property. The first element is an estimate of the true price, while the second element represents the rate of change or trend in that price. This vector is updated iteratively with each new price, maintaining an ongoing estimate of both price and trend direction.
⚪ Covariance Matrix (P): The covariance matrix represents the uncertainty in the state vector’s estimates. It continuously adapts to changing conditions, representing how much error we expect in our trend and price estimates. Lower covariance values suggest higher confidence in the estimates, while higher values indicate less certainty, often due to market volatility.
⚪ Process Noise (Q): The process noise matrix (Q) is used to account for uncertainties in price movements that aren’t explained by historical trends. By allowing some degree of randomness, it enables the Kalman Filter to remain responsive to new data without overreacting to minor fluctuations. This noise is particularly useful in smoothing out price movements in highly volatile markets.
⚪ Measurement Noise (R): Measurement noise is an external input representing the reliability of each new price observation. In this indicator, it is represented by the setting Measurement Noise and determines how much weight is given to each new price point. Higher measurement noise makes the indicator less reactive to recent prices, smoothing the trend further.
⚪ Update Equations:
Prediction: The state vector and covariance matrix are first projected forward using a state transition matrix (F), which includes market estimates based on past data. This gives a “predicted” state before the next actual price is known.
Kalman Gain Calculation: The Kalman gain is calculated by comparing the predicted state with the actual price, balancing between the covariance matrix and measurement noise. This gain determines how much of the observed price should influence the state vector.
Correction: The observed price is then compared to the predicted price, and the state vector is updated using this Kalman gain. The updated covariance matrix reflects any adjustment in uncertainty based on the latest data.
█ Three Kalman Filter Models
Standard Model: Assumes that market fluctuations follow a linear progression without external adjustments. It is best suited for stable markets.
Volume Adjusted Model: Adjusts the filter sensitivity based on trading volume. High-volume periods result in stronger trends, making this model suitable for volume-driven assets.
Parkinson Adjusted Model: Uses the Parkinson estimator, accounting for volatility through high-low price ranges, making it effective in markets with high intraday fluctuations.
These models enable traders to choose a filter that aligns with current market conditions, enhancing trend accuracy and responsiveness.
█ Trend Strength
The Trend Strength provides a visual representation of the current trend's strength as a percentage based on oscillator calculations from the Kalman filter. This table divides trend strength into color-coded segments, helping traders quickly assess whether the market is strongly trending or nearing a reversal point. A high trend strength percentage indicates a robust trend, while a low percentage suggests weakening momentum or consolidation.
█ Trend Range
The Trend Range section evaluates the market's directional movement over a specified lookback period, highlighting areas where price oscillations indicate a trend. This calculation assesses how prices vary within the range, offering an indication of trend stability or the likelihood of reversals. By adjusting the trend range setting, traders can fine-tune the indicator’s sensitivity to longer or shorter trends.
█ Sigma Bands
The Sigma Bands in the indicator are based on statistical standard deviations (sigma levels), which act as dynamic support and resistance zones. These bands are calculated using the Kalman Filter's trend estimates and adjusted for volatility (if enabled). The bands expand and contract according to market volatility, providing a unique visualization of price boundaries. In high-volatility periods, the bands widen, offering better protection against false breakouts. During low volatility, the bands narrow, closely tracking price movements. Traders can use these sigma bands to spot potential entry and exit points, aiming for reversion trades or trend continuation setups.
Trend Based
Volatility Based
█ How to Use
Trend Following:
When the Kalman Filter is green, it signals a bullish trend, and when it’s red, it indicates a bearish trend. The Sigma Cloud provides additional insights into trend strength. In a strong bullish trend, the cloud remains below the Kalman Filter line, while in a strong bearish trend, the cloud stays above it. Expansion and contraction of the Sigma Cloud indicate market momentum changes. Rapid expansion suggests an impulsive move, which could either signal the continuation of the trend or be an early sign of a possible trend reversal.
Mean Reversion: Watch for prices touching the upper or lower sigma bands, which often act as dynamic support and resistance.
Volatility Breakouts: Enable volatility-adjusted sigma bands. During high volatility, watch for price movements that extend beyond the bands as potential breakout signals.
Trend Continuation: When the Kalman Filter line aligns with a high trend strength, it signals a continuation in that direction.
█ Settings
Measurement Noise: Adjusts how sensitive the indicator is to price changes. Higher values smooth out fluctuations but delay reaction, while lower values increase sensitivity to short-term changes.
Kalman Filter Model: Choose between the standard, volume-adjusted, and Parkinson-adjusted models based on market conditions.
Band Sigma: Sets the standard deviation used for calculating the sigma bands, directly affecting the width of the dynamic support and resistance.
Volatility Adjusted Bands: Enables bands to dynamically adapt to volatility, increasing their effectiveness in fluctuating markets.
Trend Strength: Defines the lookback period for trend strength calculation. Shorter periods result in more responsive trend strength readings, while longer periods smooth out the calculation.
Trend Range: Specifies the lookback period for the trend range, affecting the assessment of trend stability over time.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Multiple Naked LevelsPURPOSE OF THE INDICATOR
This indicator autogenerates and displays naked levels and gaps of multiple types collected into one simple and easy to use indicator.
VALUE PROPOSITION OF THE INDICATOR AND HOW IT IS ORIGINAL AND USEFUL
1) CONVENIENCE : The purpose of this indicator is to offer traders with one coherent and robust indicator providing useful, valuable, and often used levels - in one place.
2) CLUSTERS OF CONFLUENCES : With this indicator it is easy to identify levels and zones on the chart with multiple confluences increasing the likelihood of a potential reversal zone.
THE TYPES OF LEVELS AND GAPS INCLUDED IN THE INDICATOR
The types of levels include the following:
1) PIVOT levels (Daily/Weekly/Monthly) depicted in the chart as: dnPIV, wnPIV, mnPIV.
2) POC (Point of Control) levels (Daily/Weekly/Monthly) depicted in the chart as: dnPoC, wnPoC, mnPoC.
3) VAH/VAL STD 1 levels (Value Area High/Low with 1 std) (Daily/Weekly/Monthly) depicted in the chart as: dnVAH1/dnVAL1, wnVAH1/wnVAL1, mnVAH1/mnVAL1
4) VAH/VAL STD 2 levels (Value Area High/Low with 2 std) (Daily/Weekly/Monthly) depicted in the chart as: dnVAH2/dnVAL2, wnVAH2/wnVAL2, mnVAH1/mnVAL2
5) FAIR VALUE GAPS (Daily/Weekly/Monthly) depicted in the chart as: dnFVG, wnFVG, mnFVG.
6) CME GAPS (Daily) depicted in the chart as: dnCME.
7) EQUILIBRIUM levels (Daily/Weekly/Monthly) depicted in the chart as dnEQ, wnEQ, mnEQ.
HOW-TO ACTIVATE LEVEL TYPES AND TIMEFRAMES AND HOW-TO USE THE INDICATOR
You can simply choose which of the levels to be activated and displayed by clicking on the desired radio button in the settings menu.
You can locate the settings menu by clicking into the Object Tree window, left-click on the Multiple Naked Levels and select Settings.
You will then get a menu of different level types and timeframes. Click the checkboxes for the level types and timeframes that you want to display on the chart.
You can then go into the chart and check out which naked levels that have appeared. You can then use those levels as part of your technical analysis.
The levels displayed on the chart can serve as additional confluences or as part of your overall technical analysis and indicators.
In order to back-test the impact of the different naked levels you can also enable tapped levels to be depicted on the chart. Do this by toggling the 'Show tapped levels' checkbox.
Keep in mind however that Trading View can not shom more than 500 lines and text boxes so the indocator will not be able to give you the complete history back to the start for long duration assets.
In order to clean up the charts a little bit there are two additional settings that can be used in the Settings menu:
- Selecting the price range (%) from the current price to be included in the chart. The default is 25%. That means that all levels below or above 20% will not be displayed. You can set this level yourself from 0 up to 100%.
- Selecting the minimum gap size to include on the chart. The default is 1%. That means that all gaps/ranges below 1% in price difference will not be displayed on the chart. You can set the minimum gap size yourself.
BASIC DESCRIPTION OF THE INNER WORKINGS OF THE INDICTATOR
The way the indicator works is that it calculates and identifies all levels from the list of levels type and timeframes above. The indicator then adds this level to a list of untapped levels.
Then for each bar after, it checks if the level has been tapped. If the level has been tapped or a gap/range completely filled, this level is removed from the list so that the levels displayed in the end are only naked/untapped levels.
Below is a descrition of each of the level types and how it is caluclated (algorithm):
PIVOT
Daily, Weekly and Monthly levels in trading refer to significant price points that traders monitor within the context of a single trading day. These levels can provide insights into market behavior and help traders make informed decisions regarding entry and exit points.
Traders often use D/W/M levels to set entry and exit points for trades. For example, entering long positions near support (daily close) or selling near resistance (daily close).
Daily levels are used to set stop-loss orders. Placing stops just below the daily close for long positions or above the daily close for short positions can help manage risk.
The relationship between price movement and daily levels provides insights into market sentiment. For instance, if the price fails to break above the daily high, it may signify bearish sentiment, while a strong breakout can indicate bullish sentiment.
The way these levels are calculated in this indicator is based on finding pivots in the chart on D/W/M timeframe. The level is then set to previous D/W/M close = current D/W/M open.
In addition, when price is going up previous D/W/M open must be smaller than previous D/W/M close and current D/W/M close must be smaller than the current D/W/M open. When price is going down the opposite.
POINT OF CONTROL
The Point of Control (POC) is a key concept in volume profile analysis, which is commonly used in trading.
It represents the price level at which the highest volume of trading occurred during a specific period.
The POC is derived from the volume traded at various price levels over a defined time frame. In this indicator the timeframes are Daily, Weekly, and Montly.
It identifies the price level where the most trades took place, indicating strong interest and activity from traders at that price.
The POC often acts as a significant support or resistance level. If the price approaches the POC from above, it may act as a support level, while if approached from below, it can serve as a resistance level. Traders monitor the POC to gauge potential reversals or breakouts.
The way the POC is calculated in this indicator is by an approximation by analysing intrabars for the respective timeperiod (D/W/M), assigning the volume for each intrabar into the price-bins that the intrabar covers and finally identifying the bin with the highest aggregated volume.
The POC is the price in the middle of this bin.
The indicator uses a sample space for intrabars on the Daily timeframe of 15 minutes, 35 minutes for the Weekly timeframe, and 140 minutes for the Monthly timeframe.
The indicator has predefined the size of the bins to 0.2% of the price at the range low. That implies that the precision of the calulated POC og VAH/VAL is within 0.2%.
This reduction of precision is a tradeoff for performance and speed of the indicator.
This also implies that the bigger the difference from range high prices to range low prices the more bins the algorithm will iterate over. This is typically the case when calculating the monthly volume profile levels and especially high volatility assets such as alt coins.
Sometimes the number of iterations becomes too big for Trading View to handle. In these cases the bin size will be increased even more to reduce the number of iterations.
In such cases the bin size might increase by a factor of 2-3 decreasing the accuracy of the Volume Profile levels.
Anyway, since these Volume Profile levels are approximations and since precision is traded for performance the user should consider the Volume profile levels(POC, VAH, VAL) as zones rather than pin point accurate levels.
VALUE AREA HIGH/LOW STD1/STD2
The Value Area High (VAH) and Value Area Low (VAL) are important concepts in volume profile analysis, helping traders understand price levels where the majority of trading activity occurs for a given period.
The Value Area High/Low is the upper/lower boundary of the value area, representing the highest price level at which a certain percentage of the total trading volume occurred within a specified period.
The VAH/VAL indicates the price point above/below which the majority of trading activity is considered less valuable. It can serve as a potential resistance/support level, as prices above/below this level may experience selling/buying pressure from traders who view the price as overvalued/undervalued
In this indicator the timeframes are Daily, Weekly, and Monthly. This indicator provides two boundaries that can be selected in the menu.
The first boundary is 70% of the total volume (=1 standard deviation from mean). The second boundary is 95% of the total volume (=2 standard deviation from mean).
The way VAH/VAL is calculated is based on the same algorithm as for the POC.
However instead of identifying the bin with the highest volume, we start from range low and sum up the volume for each bin until the aggregated volume = 30%/70% for VAL1/VAH1 and aggregated volume = 5%/95% for VAL2/VAH2.
Then we simply set the VAL/VAH equal to the low of the respective bin.
FAIR VALUE GAPS
Fair Value Gaps (FVG) is a concept primarily used in technical analysis and price action trading, particularly within the context of futures and forex markets. They refer to areas on a price chart where there is a noticeable lack of trading activity, often highlighted by a significant price movement away from a previous level without trading occurring in between.
FVGs represent price levels where the market has moved significantly without any meaningful trading occurring. This can be seen as a "gap" on the price chart, where the price jumps from one level to another, often due to a rapid market reaction to news, events, or other factors.
These gaps typically appear when prices rise or fall quickly, creating a space on the chart where no transactions have taken place. For example, if a stock opens sharply higher and there are no trades at the prices in between the two levels, it creates a gap. The areas within these gaps can be areas of liquidity that the market may return to “fill” later on.
FVGs highlight inefficiencies in pricing and can indicate areas where the market may correct itself. When the market moves rapidly, it may leave behind price levels that traders eventually revisit to establish fair value.
Traders often watch for these gaps as potential reversal or continuation points. Many traders believe that price will eventually “fill” the gap, meaning it will return to those price levels, providing potential entry or exit points.
This indicator calculate FVGs on three different timeframes, Daily, Weekly and Montly.
In this indicator the FVGs are identified by looking for a three-candle pattern on a chart, signalling a discrete imbalance in order volume that prompts a quick price adjustment. These gaps reflect moments where the market sentiment strongly leans towards buying or selling yet lacks the opposite orders to maintain price stability.
The indicator sets the gap to the difference from the high of the first bar to the low of the third bar when price is moving up or from the low of the first bar to the high of the third bar when price is moving down.
CME GAPS (BTC only)
CME gaps refer to price discrepancies that can occur in charts for futures contracts traded on the Chicago Mercantile Exchange (CME). These gaps typically arise from the fact that many futures markets, including those on the CME, operate nearly 24 hours a day but may have significant price movements during periods when the market is closed.
CME gaps occur when there is a difference between the closing price of a futures contract on one trading day and the opening price on the following trading day. This difference can create a "gap" on the price chart.
Opening Gaps: These usually happen when the market opens significantly higher or lower than the previous day's close, often influenced by news, economic data releases, or other market events occurring during non-trading hours.
Gaps can result from reactions to major announcements or developments, such as earnings reports, geopolitical events, or changes in economic indicators, leading to rapid price movements.
The importance of CME Gaps in Trading is the potential for Filling Gaps: Many traders believe that prices often "fill" gaps, meaning that prices may return to the gap area to establish fair value.
This can create potential trading opportunities based on the expectation of gap filling. Gaps can act as significant support or resistance levels. Traders monitor these levels to identify potential reversal points in price action.
The way the gap is identified in this indicator is by checking if current open is higher than previous bar close when price is moving up or if current open is lower than previous day close when price is moving down.
EQUILIBRIUM
Equilibrium in finance and trading refers to a state where supply and demand in a market balance each other, resulting in stable prices. It is a key concept in various economic and trading contexts. Here’s a concise description:
Market Equilibrium occurs when the quantity of a good or service supplied equals the quantity demanded at a specific price level. At this point, there is no inherent pressure for the price to change, as buyers and sellers are in agreement.
Equilibrium Price is the price at which the market is in equilibrium. It reflects the point where the supply curve intersects the demand curve on a graph. At the equilibrium price, the market clears, meaning there are no surplus goods or shortages.
In this indicator the equilibrium level is calculated simply by finding the midpoint of the Daily, Weekly, and Montly candles respectively.
NOTES
1) Performance. The algorithms are quite resource intensive and the time it takes the indicator to calculate all the levels could be 5 seconds or more, depending on the number of bars in the chart and especially if Montly Volume Profile levels are selected (POC, VAH or VAL).
2) Levels displayed vs the selected chart timeframe. On a timeframe smaller than the daily TF - both Daily, Weekly, and Monthly levels will be displayed. On a timeframe bigger than the daily TF but smaller than the weekly TF - the Weekly and Monthly levels will be display but not the Daily levels. On a timeframe bigger than the weekly TF but smaller than the monthly TF - only the Monthly levels will be displayed. Not Daily and Weekly.
CREDITS
The core algorithm for calculating the POC levels is based on the indicator "Naked Intrabar POC" developed by rumpypumpydumpy (https:www.tradingview.com/u/rumpypumpydumpy/).
The "Naked intrabar POC" indicator calculates the POC on the current chart timeframe.
This indicator (Multiple Naked Levels) adds two new features:
1) It calculates the POC on three specific timeframes, the Daily, Weekly, and Monthly timeframes - not only the current chart timeframe.
2) It adds functionaly by calculating the VAL and VAH of the volume profile on the Daily, Weekly, Monthly timeframes .
[Pandora] Vast Volatility Treasure TroveINTRODUCTION:
Volatility enthusiasts, prepare for VICTORY on this day of July 4th, 2024! This is my "Vast Volatility Treasure Trove," intended mostly for educational purposes, yet these functions will also exhibit versatility when combined with other algorithms to garner statistical excellence. Once again, I am now ripping the lid off of Pandora's box... of volatility. Inside this script is a 'vast' collection of volatility estimators, reflecting the indicators name. Whether you are a seasoned trader destined to navigate financial strife or an eagerly curious learner, this script offers a comprehensive toolkit for a broad spectrum of volatility analysis. Enjoy your journey through the realm of market volatility with this code!
WHAT IS MARKET VOLATILITY?:
Market volatility refers to various fluctuations in the value of a financial market or asset over a period of time, often characterized by occasional rapid and significant deviations in price. During periods of greater market volatility, evolving conditions of prices can move rapidly in either direction, creating uncertainty for investors with results of sharp declines as well as rapid gains. However, market volatility is a typical aspect expected in financial markets that can also present opportunities for informed decision-making and potential benefits from the price flux.
SCRIPT INTENTION:
Volatility is assuredly omnipresent, waxing and waning in magnitude, and some readers have every intention of studying and/or measuring it. This script serves as an all-in-one armada of volatility estimators for TradingView members. I set out to provide a diverse set of tools to analyze and interpret market volatility, offering volatile insights, and aid with the development of robust trading indicators and strategies.
In today's fast-paced financial markets, understanding and quantifying volatility is informative for both seasoned traders and novice investors. This script is designed to empower users by equipping them with a comprehensive suite of volatility estimators. Each function within this script has been meticulously crafted to address various aspects of volatility, from traditional methods like Garman-Klass and Parkinson to more advanced techniques like Yang-Zhang and my custom experimental algorithms.
Ultimately, this script is more than just a collection of functions. It is a gateway to a deeper understanding of market volatility and a valuable resource for anyone committed to mastering the complexities of financial markets.
SCRIPT CONTENTS:
This script includes a variety of functions designed to measure and analyze market volatility. Where applicable, an input checkbox option provides an unbiased/biased estimate. Below is a brief description of each function in the original order they appear as code upon first publish:
Parkinson Volatility - Estimates volatility emphasizing the high and low range movements.
Alternate Parkinson Volatility - Simpler version of the original Parkinson Volatility that I realized.
Garman-Klass Volatility - Estimates volatility based on high, low, open, and close prices using a formula that adjusts for biases in price dynamics.
Rogers-Satchell-Yoon Volatility #1 - Estimates volatility based on logarithmic differences between high, low, open, and close values.
Rogers-Satchell-Yoon Volatility #2 - Similar estimate to Rogers-Satchell with the same result via an alternate formulation of volatility.
Yang-Zhang Volatility - An advanced volatility estimate combining both strengths of the Garman-Klass and Rogers-Satchell estimators, with weights determined by an alpha parameter.
Yang-Zhang (Modified) Volatility - My experimental modification slightly different from the Yang-Zhang formula with improved computational efficiency.
Selectable Volatility - Basic customizable volatility calculation based on the logarithmic difference between selected numerator and denominator prices (e.g., open, high, low, close).
Close-to-Close Volatility - Estimates volatility using the logarithmic difference between consecutive closing prices. Specifically applicable to data sources without open, high, and low prices.
Open-to-Close Volatility - (Overnight Volatility): Estimates volatility based on the logarithmic difference between the opening price and the last closing price emphasizing overnight gaps.
Hilo Volatility - Estimates volatility using a method similar to Parkinson's method, which considers the logarithm of the high and low prices.
Vantage Volatility - My experimental custom 'vantage' method to estimate volatility similar to Yang-Zhang, which incorporates various factors (Alpha, Beta, Gamma) to generate a weighted logarithmic calculation. This may be a volatility advantage or disadvantage, hence it's name.
Schwert Volatility - Estimates volatility based on arithmetic returns.
Historical Volatility - Estimates volatility considering logarithmic returns.
Annualized Historical Volatility - Estimates annualized volatility using logarithmic returns, adjusted for the number of trading days in a year.
If I omitted any other known varieties, detailed requests for future consideration can be made below for their inclusion into this script within future versions...
BONUS ALGORITHMS:
This script also includes several experimental and bonus functions that push the boundaries of volatility analysis as I understand it. These functions are designed to provide additional insights and also are my ideal notions for traders looking to explore other methods of volatility measurement.
VOLATILITY APPLICATIONS:
Volatility estimators serve a common role across various facets of trading and financial analysis, offering insights into market behavior. These tools are already in instrumental with enhancing risk management practices by providing a deeper understanding of market dynamics and the inherent uncertainty in asset prices. With volatility estimators, traders can effectively quantifying market risk and adjust their strategies accordingly, optimizing portfolio performance and mitigating potential losses. Additionally, volatility estimations may serve as indication for detecting overbought or oversold market conditions, offering probabilistic insights that could inform strategic decisions at turning points. This script
distinctly offers a variety of volatility estimators to navigate intricate financial terrains with informed judgment to address challenges of strategic planning.
CODE REUSE:
You don't have to ask for my permission to use/reuse these functions in your published scripts, simply because I have better things to do than answer requests for the reuse of these functions.
Notice: Unfortunately, I will not provide any integration support into member's projects at all. I have my own projects that require way too much of my day already.
ICT Concept [TradingFinder] Order Block | FVG | Liquidity Sweeps🔵 Introduction
The "ICT" style is one of the subsets of "Price Action" technical analysis. ICT is a method created by "Michael Huddleston", a professional forex trader and experienced mentor. The acronym ICT stands for "Inner Circle Trader".
The main objective of the ICT trading strategy is to combine "Price Action" and the concept of "Smart Money" to identify optimal entry points into trades. However, finding suitable entry points is not the only strength of this approach. With the ICT style, traders can better understand price behavior and adapt their trading approach to market structure accordingly.
Numerous concepts are discussed in this style, but the key practical concepts for trading in financial markets include "Order Block," "Liquidity," and "FVG".
🔵 How to Use
🟣Order Block
Order blocks are a specific type of "Supply and Demand" zones formed when a series of orders are placed in a block. These orders could be created by banks or other major players. Banks typically execute large orders in blocks during their trading sessions. If they were to enter the market directly with a small quantity, significant price movements would occur before the orders are fully executed, resulting in less profit. To avoid this, they divide their orders into smaller, manageable positions. Traders should look for "buy" opportunities in "demand order blocks" areas and "sell" opportunities in "supply order blocks".
🟣Liquidity
These levels are where traders aim to exit their trades. "Market Makers" or smart money usually collects or distributes their trading positions near levels where many retail traders have placed their "Stop Loss" orders. When the liquidity resulting from these losses is collected, the price often reverses direction.
A "Stop Hunt" is a move designed to neutralize liquidity generated by triggered stop losses. Banks often use significant news events to trigger stop hunts and acquire the liquidity released in the market. If, for example, they intend to execute heavy buy orders, they encourage others to sell through stop hunts.
As a result, if there is liquidity in the market before reaching the order block region, the credibility of that order block is higher. Conversely, if liquidity is near the order block, meaning the price reaches the order block before reaching the liquidity area, the credibility of that order block is lower.
🟣FVG (Fair Value Gap)
To identify the "Fair Value Gap" on the chart, one must analyze candle by candle. Focus on candles with large bodies, examining one candle and the one before it. The candles before and after this central candle should have long shadows, and their bodies should not overlap with the body of the central candle. The distance between the shadows of the first and third candles is called the FVG range.
These zone function in two ways :
•Supply and Demand zone: In this case, the price reacts to these zone, and its trend reverses.
•Liquidity zone: In this scenario, the price "fills" the zone and then reaches the order block.
Important Note: In most cases, FVG zone with very small width act as supply and demand zone, while zone with a significant width act as liquidity zone, absorbing the price.
🔵 Setting
🟣Order Block
Refine Order Block : When the option for refining order blocks is Off, the supply and demand zones encompass the entire length of the order block (from Low to High) in their standard state and remain unaltered. On the option for refining order blocks triggers the improvement of supply and demand zones using the error correction algorithm.
Refine Type : The enhancement of order blocks via the error correction algorithm can be executed through two methods: Defensive and Aggressive. In the Aggressive approach, the widest possible range is taken into account for order blocks.
Show High Levels : If major high levels are to be displayed, set the option for showing high level to Yes.
Show Low Levels : If major low levels are to be displayed, set the option for showing low level to Yes.
Show Last Support : If showing the last support is desired, set the option for showing last support to Yes.
Show Last Resistance : If showing the last resistance is desired, set the option for showing last resistance to Yes.
🟣 FVG
FVG Filter : When FVG filtering is activated, the number of FVG areas undergoes filtration based on the specified algorithm.
FVG Filter Types :
1. Very Aggressive : Apart from the initial condition, an additional condition is introduced. For an upward FVG, the maximum price of the last candle should exceed the maximum price of the middle candle. Similarly, for a downward FVG, the minimum price of the last candle should be lower than the minimum price of the middle candle. This mode eliminates a minimal number of FVGs.
2. Aggressive : In addition to the conditions of the Very Aggressive mode, this mode considers the size of the middle candle; it should not be small. Consequently, a larger number of FVGs are eliminated in this mode.
3. Defensive : Alongside the conditions of the Very Aggressive mode, this mode takes into account the size of the middle candle, which should be relatively large with the majority of it comprising the body. Furthermore, to identify upward FVGs, the second and third candles must be positive, whereas for downward FVGs, the second and third candles must be negative. This mode filters out a considerable number of FVGs, retaining only those of suitable quality.
4. Very Defensive : In addition to the conditions of the Defensive mode, the first and third candles should not be very small-bodied doji candles. This mode filters out the majority of FVGs, leaving only the highest quality ones. Show Demand FVG: Enables the display of demand-related boxes, which can be toggled between off and on. Show Supply FVG: Enables the display of supply-related boxes along the path, which can also be toggled between off and on.
🟣 Liquidity
Statics Liquidity Line Sensitivity : A value ranging from 0 to 0.4. Increasing this value reduces the sensitivity of the "Statics Liquidity Line Detection" function and increases the number of identified lines. The default value is 0.3.
Dynamics Liquidity Line Sensitivity : A value ranging from 0.4 to 1.95. Increasing this value enhances the sensitivity of the "Dynamics Liquidity Line Detection" function and decreases the number of identified lines. The default value is 1.
Statics Period Pivot : Default value is set to 8. By adjusting this value, you can specify the period for static liquidity line pivots.
Dynamics Period Pivot : Default value is set to 3. By adjusting this value, you can specify the period for dynamic liquidity line pivots.
You can activate or deactivate liquidity lines as necessary using the buttons labeled "Show Statics High Liquidity Line," "Show Statics Low Liquidity Line," "Show Dynamics High Liquidity Line," and "Show Dynamics Low Liquidity Line".
Double AI Super Trend Trading - Strategy [PresentTrading]█ Introduction and How It is Different
The Double AI Super Trend Trading Strategy is a cutting-edge approach that leverages the power of not one, but two AI algorithms, in tandem with the SuperTrend technical indicator. The strategy aims to provide traders with enhanced precision in market entry and exit points. It is designed to adapt to market conditions dynamically, offering the flexibility to trade in both bullish and bearish markets.
*The KNN part is mainly referred from @Zeiierman.
BTCUSD 8hr performance
ETHUSD 8hr performance
█ Strategy, How It Works: Detailed Explanation
1. SuperTrend Calculation
The SuperTrend is a popular indicator that captures market trends through a combination of the Volume-Weighted Moving Average (VWMA) and the Average True Range (ATR). This strategy utilizes two sets of SuperTrend calculations with varying lengths and factors to capture both short-term and long-term market trends.
2. KNN Algorithm
The strategy employs k-Nearest Neighbors (KNN) algorithms, which are supervised machine learning models. Two sets of KNN algorithms are used, each focused on different lengths of historical data and number of neighbors. The KNN algorithms classify the current SuperTrend data point as bullish or bearish based on the weighted sum of the labels of the k closest historical data points.
3. Signal Generation
Based on the KNN classifications and the SuperTrend indicator, the strategy generates signals for the start of a new trend and the continuation of an existing trend.
4. Trading Logic
The strategy uses these signals to enter long or short positions. It also incorporates dynamic trailing stops for exit conditions.
Local picture
█ Trade Direction
The strategy allows traders to specify their trading direction: long, short, or both. This enables the strategy to be versatile and adapt to various market conditions.
█ Usage
ToolTips: Comprehensive tooltips are provided for each parameter to guide the user through the customization process.
Inputs: Traders can customize numerous parameters including the number of neighbors in KNN, ATR multiplier, and types of moving averages.
Plotting: The strategy also provides visual cues on the chart to indicate bullish or bearish trends.
Order Execution: Based on the generated signals, the strategy will execute buy or sell orders automatically.
█ Default Settings
The default settings are configured to offer a balanced approach suitable for most scenarios:
Initial Capital: $10,000
Default Quantity Type: 10% of equity
Commission: 0.1%
Slippage: 1
Currency: USD
These settings can be modified to suit various trading styles and asset classes.
Fibonacci Structure & Trend Channel (Expo)█ Overview
The Fibonacci Structure & Trend Channel (Expo) is designed to identify trend direction and potential reversal levels and offer insights into price structure based on Fibonacci ratios. The algorithm plots a Fibonacci channel, making it easier for traders to identify potential retracement points. Additionally, the Fibonacci market structure is plotted to enhance traders' understanding of the underlying order flow.
█ How to Use
Identify Trends
Use the plotted Fibonacci Trend Line to identify the direction of the market trend. A green line typically signifies a bullish trend, while a red line signifies a bearish trend.
Retracement Levels
The plotted Fibonacci levels can act as potential support or resistance levels. Look for price action signs at these levels for entry or exit points.
Channel Trading
If you enable the Fibonacci channel, the upper and lower bounds can act as overbought or oversold levels.
Market Structure
The plotted Fibonacci market structure serves as a valuable tool for dissecting the underlying order flow and gauging the strength or weakness of a trend. By analyzing these structures, traders can identify key levels where supply and demand intersect, which often act as pivotal points for trend reversals or accelerations. This visual representation simplifies complex market dynamics. Whether you're looking to catch a new trend early or seeking confirmation for a potential reversal, understanding the market structure plotted by the Fibonacci ratios can provide actionable insights for various trading strategies.
Use the Table
The information table can provide quick insights into the current trend and when it started.
█ Settings
The Fibonacci settings allow traders to specify the Fibonacci retracement levels that will be used to calculate the trend and its channel.
The Fibonacci Structure Trend Channel structure settings enable traders to fine-tune how the indicator identifies and plots the underlying price structure.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
