Smoothed SuperTrend with VWAP Confirmation [CHE] Smoothed SuperTrend with Automated Optimization and VWAP Confirmation
Overview
The "Smoothed SuperTrend with VWAP Confirmation" is an advanced technical analysis indicator designed for precise trend identification and trading signal generation. This script integrates a smoothed version of the popular SuperTrend indicator with an additional layer of confirmation using the Volume-Weighted Average Price (VWAP). The combination of these two elements offers traders a powerful tool for identifying optimal entry and exit points in the market.
Key Features
1. Smoothed SuperTrend
- Super Smoother Algorithm: The SuperTrend in this script is not just a regular one; it is enhanced by the Super Smoother filter, which reduces market noise and provides more reliable trend signals.
- Customizable Parameters: Traders can adjust three different sets of SuperTrend parameters (factor and ATR length), allowing them to tailor the indicator to their specific trading strategies.
- Automatic Optimization: The script automatically evaluates the performance of each SuperTrend parameter set and selects the one with the best cumulative performance. This selection process can be set to pick either the best or the worst performing parameter set, depending on the trader's preference.
2. VWAP Confirmation
- Precise Trend Confirmation: Once the best-performing SuperTrend is identified, the script further refines the signals by using VWAP as a confirmation tool. VWAP is a highly respected indicator in the trading community, often used to assess the true average price of an asset.
- Long and Short Signal Generation: The script generates Long and Short signals only when the price action is confirmed by both the SuperTrend and VWAP. For a Long signal, the price must be above the VWAP, and for a Short signal, it must be below the VWAP. This dual confirmation ensures higher accuracy and reduces the likelihood of false signals.
3. Visual and Informative Labels
- Signal Labels: Upon confirmation of a trend reversal by both the SuperTrend and VWAP, the script plots clear labels on the chart, indicating confirmed Long or Short signals. These labels are customizable in terms of color, text, and size, ensuring they fit seamlessly into any chart setup.
- Best Parameters Display: At the close of the most recent bar, the script displays a label that provides detailed information about the best-performing SuperTrend parameters and their cumulative performance. This feature keeps traders informed about which settings are currently most effective.
Input Customization Options
1. Super Smoother Length
- Traders can define the length of the Super Smoother filter, which is used to smooth both price data and ATR (Average True Range) values. This input allows traders to control the sensitivity of the indicator, with shorter lengths providing faster responses and longer lengths offering smoother trends.
2. SuperTrend Parameters
- Factor: For each of the three SuperTrends, traders can set a unique factor that determines the distance of the SuperTrend bands from the average price. A higher factor results in wider bands and fewer signals, while a lower factor results in narrower bands and more signals.
- ATR Length: Traders can also specify the length of the ATR used in each SuperTrend calculation. A longer ATR period captures broader market volatility, while a shorter period focuses on more immediate price movements.
3. Label Settings
- Label Colors: The script allows full customization of label colors for Long and Short signals, ensuring that they match the trader’s chart aesthetics.
- Label Text Colors and Sizes: Traders can adjust the text color and size of the labels for Long, Short, and information labels, allowing them to prioritize visibility and readability on their charts.
4. Performance Selection Mode
- Best or Worst Performer: This input allows traders to select whether the script should optimize for the best or worst performing SuperTrend parameter set. This flexibility is useful in different market conditions, where a trader might want to analyze either the strongest trend or focus on a contrarian strategy.
5. VWAP Calculation
- The script automatically recalculates the VWAP based on trend changes, ensuring that the confirmation signals are as accurate and relevant as possible to the current market context.
Important Note
This script is designed to provide more accurate trend signals and confirmations, but like all technical indicators, it should not be used in isolation. It is recommended to use this tool as part of a broader trading strategy, including proper risk management and consideration of fundamental market conditions.
Conclusion
The "Smoothed SuperTrend with VWAP Confirmation" script is an innovative trading tool that combines the strengths of the SuperTrend and VWAP indicators. By integrating smoothing techniques and automatic parameter optimization, this indicator provides traders with more accurate and reliable trend signals. The added confirmation by VWAP further enhances the precision of the entry and exit points, making it an excellent choice for traders looking to improve their technical analysis and trading outcomes. This tool is especially valuable for those who prefer customizable inputs and a systematic approach to trading, ensuring that the indicator adapts to various market conditions and individual trading styles.
Best regards
Chervolino
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Price Action Smart Money Concepts [BigBeluga]THE SMART MONEY CONCEPTS Toolkit
The Smart Money Concepts [ BigBeluga ] is a comprehensive toolkit built around the principles of "smart money" behavior, which refers to the actions and strategies of institutional investors.
The Smart Money Concepts Toolkit brings together a suite of advanced indicators that are all interconnected and built around a unified concept: understanding and trading like institutional investors, or "smart money." These indicators are not just randomly chosen tools; they are features of a single overarching framework, which is why having them all in one place creates such a powerful system.
This all-in-one toolkit provides the user with a unique experience by automating most of the basic and advanced concepts on the chart, saving them time and improving their trading ideas.
Real-time market structure analysis simplifies complex trends by pinpointing key support, resistance, and breakout levels.
Advanced order block analysis leverages detailed volume data to pinpoint high-demand zones, revealing internal market sentiment and predicting potential reversals. This analysis utilizes bid/ask zones to provide supply/demand insights, empowering informed trading decisions.
Imbalance Concepts (FVG and Breakers) allows traders to identify potential market weaknesses and areas where price might be attracted to fill the gap, creating opportunities for entry and exit.
Swing failure patterns help traders identify potential entry points and rejection zones based on price swings.
Liquidity Concepts, our advanced liquidity algorithm, pinpoints high-impact events, allowing you to predict market shifts, strong price reactions, and potential stop-loss hunting zones. This gives traders an edge to make informed trading decisions based on liquidity dynamics.
🔵 FEATURES
The indicator has quite a lot of features that are provided below:
Swing market structure
Internal market structure
Mapping structure
Adjustable market structure
Strong/Weak H&L
Sweep
Volumetric Order block / Breakers
Fair Value Gaps / Breakers (multi-timeframe)
Swing Failure Patterns (multi-timeframe)
Deviation area
Equal H&L
Liquidity Prints
Buyside & Sellside
Sweep Area
Highs and Lows (multi-timeframe)
🔵 BASIC DEMONSTRATION OF ALL FEATURES
1. MARKET STRUCTURE
The preceding image illustrates the market structure functionality within the Smart Money Concepts indicator.
➤ Solid lines: These represent the core indicator's internal structure, forming the foundation for most other components. They visually depict the overall market direction and identify major reversal points marked by significant price movements (denoted as 'x').
➤ Internal Structure: These represent an alternative internal structure with the potential to drive more rapid market shifts. This is particularly relevant when a significant gap exists in the established swing structure, specifically between the Break of Structure (BOS) and the most recent Change of High/Low (CHoCH). Identifying these formations can offer opportunities for quicker entries and potential short-term reversals.
➤ Sweeps (x): These signify potential turning points in the market where liquidity is removed from the structure. This suggests a possible trend reversal and presents crucial entry opportunities. Sweeps are identified within both swing and internal structures, providing valuable insights for informed trading decisions.
➤ Mapping structure: A tool that automatically identifies and connects significant price highs and lows, creating a zig-zag pattern. It visualizes market structure, highlights trends, support/resistance levels, and potential breakouts. Helps traders quickly grasp price action patterns and make informed decisions.
➤ Color-coded candles based on market structure: These colors visually represent the underlying market structure, making it easier for traders to quickly identify trends.
➤ Extreme H&L: It visualizes market structure with extreme high and lows, which gives perspective for macro Market Structure.
2. VOLUMETRIC ORDER BLOCKS
Order blocks are specific areas on a financial chart where significant buying or selling activity has occurred. These are not just simple zones; they contain valuable information about market dynamics. Within each of these order blocks, volume bars represent the actual buying and selling activity that took place. These volume bars offer deeper insights into the strength of the order block by showing how much buying or selling power is concentrated in that specific zone.
Additionally, these order blocks can be transformed into Breaker Blocks. When an order block fails—meaning the price breaks through this zone without reversing—it becomes a breaker block. Breaker blocks are particularly useful for trading breakouts, as they signal that the market has shifted beyond a previously established zone, offering opportunities for traders to enter in the direction of the breakout.
Here's a breakdown:
➤ Bear Order Blocks (Red): These are zones where a lot of selling happened. Traders see these areas as places where sellers were strong, pushing the price down. When the price returns to these zones, it might face resistance and drop again.
➤ Bull Order Blocks (Green): These are zones where a lot of buying happened. Traders see these areas as places where buyers were strong, pushing the price up. When the price returns to these zones, it might find support and rise again.
These Order Blocks help traders identify potential areas for entering or exiting trades based on past market activity. The volume bars inside blocks show the amount of trading activity that occurred in these blocks, giving an idea of the strength of buying or selling pressure.
➤ Breaker Block: When an order block fails, meaning the price breaks through this zone without reversing, it becomes a breaker block. This indicates a significant shift in market liquidity and structure.
➤ A bearish breaker block occurs after a bullish order block fails. This typically happens when there's an upward trend, and a certain level that was expected to support the market's rise instead gives way, leading to a sharp decline. This decline indicates that sellers have overcome the buyers, absorbing liquidity and shifting the sentiment from bullish to bearish.
Conversely, a bullish breaker block is formed from the failure of a bearish order block. In a downtrend, when a level that was expected to act as resistance is breached, and the price shoots up, it signifies that buyers have taken control, overpowering the sellers.
3. FAIR VALUE GAPS:
A fair value gap (FVG), also referred to as an imbalance, is an essential concept in Smart Money trading. It highlights the supply and demand dynamics. This gap arises when there's a notable difference between the volume of buy and sell orders. FVGs can be found across various asset classes, including forex, commodities, stocks, and cryptocurrencies.
FVGs in this toolkit have the ability to detect raids of FVG which helps to identify potential price reversals.
Mitigation option helps to change from what source FVGs will be identified: Close, Wicks or AVG.
4. SWING FAILURE PATTERN (SFP):
The Swing Failure Pattern is a liquidity engineering pattern, generally used to fill large orders. This means, the SFP generally occurs when larger players push the price into liquidity pockets with the sole objective of filling their own positions.
SFP is a technical analysis tool designed to identify potential market reversals. It works by detecting instances where the price briefly breaks a previous high or low but fails to maintain that breakout, quickly reversing direction.
How it works:
Pattern Detection: The indicator scans for price movements that breach recent highs or lows.
Reversal Confirmation: If the price quickly reverses after breaching these levels, it's identified as an SFP.
➤ SFP Display:
Bullish SFP: Marked with a green symbol when price drops below a recent low before reversing upwards.
Bearish SFP: Marked with a red symbol when price rises above a recent high before reversing downwards.
➤ Deviation Levels: After detecting an SFP, the indicator projects white lines showing potential price deviation:
For bullish SFPs, the deviation line appears above the current price.
For bearish SFPs, the deviation line appears below the current price.
These deviation levels can serve as a potential trading opportunity or areas where the reversal might lose momentum.
With Volume Threshold and Filtering of SFP traders can adjust their trading style:
Volume Threshold: This setting allows traders to filter SFPs based on the volume of the reversal candle. By setting a higher volume threshold, traders can focus on potentially more significant reversals that are backed by higher trading activity.
SFP Filtering: This feature enables traders to filter SFP detection. It includes parameters such as:
5. LIQUIDITY CONCEPTS:
➤ Equal Lows (EQL) and Equal Highs (EQH) are important concepts in liquidity-based trading.
EQL: A series of two or more swing lows that occur at approximately the same price level.
EQH: A series of two or more swing highs that occur at approximately the same price level.
EQLs and EQHs are seen as potential liquidity pools where a large number of stop loss orders or limit orders may be clustered. They can be used as potential reverse points for trades.
This multi-period feature allows traders to select less and more significant EQL and EQH:
➤ Liquidity wicks:
Liquidity wicks are a minor representation of a stop-loss hunt during the retracement of a pivot point:
➤ Buy and Sell side liquidity:
The buy side liquidity represents a concentration of potential buy orders below the current price level. When price moves into this area, it can lead to increased buying pressure due to the execution of these orders.
The sell side liquidity indicates a pool of potential sell orders below the current price level. Price movement into this area can result in increased selling pressure as these orders are executed.
➤ Sweep Liquidation Zones:
Sweep Liquidation Zones are crucial for understanding market structure and potential future price movements. They provide insights into areas where significant market participants have been forced out of their positions, potentially setting up new trading opportunities.
🔵 USAGE & EXAMPLES
The core principle behind the success of this toolkit lies in identifying "confluence." This refers to the convergence of multiple trading indicators all signaling the same information at a specific point or area. By seeking such alignment, traders can significantly enhance the likelihood of successful trades.
MS + OBs
The chart illustrates a highly bullish setup where the price is rejecting from a bullish order block (POC), while simultaneously forming a bullish Swing Failure Pattern (SFP). This occurs after an internal structure change, marked by a bullish Change of Character (CHoCH). The price broke through a bearish order block, transforming it into a breaker block, further confirming the bullish momentum.
The combination of these elements—bullish order blocks, SFP, and CHoCH—creates a powerful bullish signal, reinforcing the potential for upward movement in the market.
SFP + Bear OB
This chart above displays a bearish setup with a high probability of a price move lower. The price is currently rejecting from a bear order block, which represents a key resistance area where significant selling pressure has previously occurred. A Swing Failure Pattern (SFP) has also formed near this bear order block, indicating that the price briefly attempted to break above a recent high but failed to sustain that upward movement. This failure suggests that buyers are losing momentum, and the market could be preparing for a move to the downside.
Additionally, we can toggle on the Deviation Area in the SFP section to highlight potential levels where price deviation might occur. These deviation areas represent zones where the price is likely to react after the Swing Failure Pattern:
BUY – SELL sides + EQL
The chart showcases a bullish setup with a high probability of price breaking out of the current sell-side resistance level. The market structure indicates a formation of Equal Lows (EQL), which often suggests a build-up of liquidity that could drive the price higher.
The presence of strong buy-side pressure (69%), indicated by the green zone at the bottom, reinforces this bullish outlook. This area represents a key support zone where buyers are outpacing sellers, providing the foundation for a potential upward breakout.
EQL + Bull ChoCh
This chart illustrates a potential bullish setup, driven by the formation of Equal Lows (EQL) followed by a bullish Change of Character (CHoCH). The presence of Equal Lows often signals a liquidity build-up, which can lead to a reversal when combined with additional bullish signals.
Liquidity grab + Bull ChoCh + FVGs
This chart demonstrates a strong bullish scenario, where several important market dynamics are at play. The price begins its upward momentum from Liquidity grab following a bullish Change of Character (CHoCH), signaling the transition from a bearish phase to a bullish one.
As the price progresses, it performs liquidity grabs, which serve to gather the necessary fuel for further movement. These liquidity grabs often occur before significant price surges, as large market participants exploit these areas to accumulate positions before pushing the price higher.
The chart also highlights a market imbalance area, showing strong momentum as the price moves swiftly through this zone.
In this examples, we see how the combination of multiple “smart money” tools helps identify a potential trade opportunities. This is just one of the many scenarios that traders can spot using this toolkit. Other combinations—such as order blocks, liquidity grabs, fair value gaps, and Swing Failure Patterns (SFPs)—can also be layered on top of these concepts to further refine your trading strategy.
🔵 SETTINGS
Window: limit calculation period
Swing: limit drawing function
Mapping structure: show structural points
Algorithmic Logic: (Extreme-Adjusted) Use max high/low or pivot point calculation
Algorithmic loopback: pivot point look back
Show Last: Amount of Order block to display
Hide Overlap: hide overlapping order blocks
Construction: Size of the order blocks
Fair value gaps: Choose between normal FVG or Breaker FVG
Mitigation: (close - wick - avg) point to mitigate the order block/imbalance
SFP lookback: find a higher / lower point to improve accuracy
Threshold: remove less relevant SFP
Equal H&L: (short-mid-long term) display longer term
Liquidity Prints: Shows wicks of candles where liquidity was grabbed
Sweep Area: Identify Sweep Liquidation areas
By combining these indicators in one toolkit, traders are equipped with a comprehensive suite of tools that address every angle of the Smart Money Concept. Instead of relying on disparate tools spread across various platforms, having them integrated into a single, cohesive system allows traders to easily see confluence and make more informed trading decisions.
Trend Strength | Flux Charts💎 GENERAL OVERVIEW
Introducing the new Trend Strength indicator! Latest trends and their strengths play an important role for traders. This indicator aims to make trend and strength detection much easier by coloring candlesticks based on the current strength of trend. More info about the process in the "How Does It Work" section.
Features of the new Trend Strength Indicator :
3 Trend Detection Algorithms Combined (RSI, Supertrend & EMA Cross)
Fully Customizable Algorithm
Strength Labels
Customizable Colors For Bullish, Neutral & Bearish Trends
📌 HOW DOES IT WORK ?
This indicator uses three different methods of trend detection and combines them all into one value. First, the RSI is calculated. The RSI outputs a value between 0 & 100, which this indicator maps into -100 <-> 100. Let this value be named RSI. Then, the Supertrend is calculated. Let SPR be -1 if the calculated Supertrend is bearish, and 1 if it's bullish. After that, latest EMA Cross is calculated. This is done by checking the distance between the two EMA's adjusted by the user. Let EMADiff = EMA1 - EMA2. Then EMADiff is mapped from -ATR * 2 <-> ATR * 2 to -100 <-> 100.
Then a Total Strength (TS) is calculated by given formula : RSI * 0.5 + SPR * 0.2 + EMADiff * 0.3
The TS value is between -100 <-> 100, -100 being fully bearish, 0 being true neutral and 100 being fully bullish.
Then the Total Strength is converted into a color adjusted by the user. The candlesticks in the chart will be presented with the calculated color.
If the Labels setting is enabled, each time the trend changes direction a label will appear indicating the new direction. The latest candlestick will always show the current trend with a label.
EMA = Exponential Moving Average
RSI = Relative Strength Index
ATR = Average True Range
🚩 UNIQUENESS
The main point that differentiates this indicator from others is it's simplicity and customization options. The indicator interprets trend and strength detection in it's own way, combining 3 different well-known trend detection methods: RSI, Supertrend & EMA Cross into one simple method. The algorithm is fully customizable and all styling options are adjustable for the user's liking.
⚙️ SETTINGS
1. General Configuration
Detection Length -> This setting determines the amount of candlesticks the indicator will look for trend detection. Higher settings may help the indicator find longer trends, while lower settings will help with finding smaller trends.
Smoothing -> Higher settings will result in longer periods of time required for trend to change direction from bullish to bearish and vice versa.
EMA Lengths -> You can enter two EMA Lengths here, the second one must be longer than the first one. When the shorter one crosses under the longer one, this will be a bearish sign, and if it crosses above it will be a bullish sign for the indicator.
Labels -> Enables / Disables trend strength labels.
SpectrumLibrary "Spectrum"
This library includes spectrum analysis tools such as the Fast Fourier Transform (FFT).
method toComplex(data, polar)
Creates an array of complex type objects from a float type array.
Namespace types: array
Parameters:
data (array) : The float type array of input data.
polar (bool) : Initialization coordinates; the default is false (cartesian).
Returns: The complex type array of converted data.
method sAdd(data, value, end, start, step)
Performs scalar addition of a given float type array and a simple float value.
Namespace types: array
Parameters:
data (array) : The float type array of input data.
value (float) : The simple float type value to be added.
end (int) : The last index of the input array (exclusive) on which the operation is performed.
start (int) : The first index of the input array (inclusive) on which the operation is performed; the default value is 0.
step (int) : The step by which the function iterates over the input data array between the specified boundaries; the default value is 1.
Returns: The modified input array.
method sMult(data, value, end, start, step)
Performs scalar multiplication of a given float type array and a simple float value.
Namespace types: array
Parameters:
data (array) : The float type array of input data.
value (float) : The simple float type value to be added.
end (int) : The last index of the input array (exclusive) on which the operation is performed.
start (int) : The first index of the input array (inclusive) on which the operation is performed; the default value is 0.
step (int) : The step by which the function iterates over the input data array between the specified boundaries; the default value is 1.
Returns: The modified input array.
method eMult(data, data02, end, start, step)
Performs elementwise multiplication of two given complex type arrays.
Namespace types: array
Parameters:
data (array type from RezzaHmt/Complex/1) : the first complex type array of input data.
data02 (array type from RezzaHmt/Complex/1) : The second complex type array of input data.
end (int) : The last index of the input arrays (exclusive) on which the operation is performed.
start (int) : The first index of the input arrays (inclusive) on which the operation is performed; the default value is 0.
step (int) : The step by which the function iterates over the input data array between the specified boundaries; the default value is 1.
Returns: The modified first input array.
method eCon(data, end, start, step)
Performs elementwise conjugation on a given complex type array.
Namespace types: array
Parameters:
data (array type from RezzaHmt/Complex/1) : The complex type array of input data.
end (int) : The last index of the input array (exclusive) on which the operation is performed.
start (int) : The first index of the input array (inclusive) on which the operation is performed; the default value is 0.
step (int) : The step by which the function iterates over the input data array between the specified boundaries; the default value is 1.
Returns: The modified input array.
method zeros(length)
Creates a complex type array of zeros.
Namespace types: series int, simple int, input int, const int
Parameters:
length (int) : The size of array to be created.
method bitReverse(data)
Rearranges a complex type array based on the bit-reverse permutations of its size after zero-padding.
Namespace types: array
Parameters:
data (array type from RezzaHmt/Complex/1) : The complex type array of input data.
Returns: The modified input array.
method R2FFT(data, inverse)
Calculates Fourier Transform of a time series using Cooley-Tukey Radix-2 Decimation in Time FFT algorithm, wikipedia.org
Namespace types: array
Parameters:
data (array type from RezzaHmt/Complex/1) : The complex type array of input data.
inverse (int) : Set to -1 for FFT and to 1 for iFFT.
Returns: The modified input array containing the FFT result.
method LBFFT(data, inverse)
Calculates Fourier Transform of a time series using Leo Bluestein's FFT algorithm, wikipedia.org This function is nearly 4 times slower than the R2FFT function in practice.
Namespace types: array
Parameters:
data (array type from RezzaHmt/Complex/1) : The complex type array of input data.
inverse (int) : Set to -1 for FFT and to 1 for iFFT.
Returns: The modified input array containing the FFT result.
method DFT(data, inverse)
This is the original DFT algorithm. It is not suggested to be used regularly.
Namespace types: array
Parameters:
data (array type from RezzaHmt/Complex/1) : The complex type array of input data.
inverse (int) : Set to -1 for DFT and to 1 for iDFT.
Returns: The complex type array of DFT result.
Gaussian Weighted Moving Average with Forecast [CHE]Presentation for TradingView: Gaussian Weighted Moving Average with Forecast
Introduction
Welcome to our presentation on the "Gaussian Weighted Moving Average with Forecast" (GWMA). This script, written in Pine Script™, offers an enhanced method for analyzing and predicting price movements on TradingView. The script combines Gaussian Weighted Moving Averages and polynomial regression to provide accurate and customizable forecasts.
Overview
Title: Gaussian Weighted Moving Average with Forecast
Author: chervolino
License: Mozilla Public License 2.0
Main Features
1. Gaussian Weighted Moving Average (GWMA):
- Calculates a weighted moving average using a Gaussian weighting function.
- Parameters for length and standard deviation allow fine-tuning of the smoothing effect.
2. Polynomial Regression with Forecast:
- Creates a model to predict future price movements.
- Adjustable length and degree of polynomial regression.
- Option to extrapolate predictions and visualize them.
3. Visual Representation:
- Uses lines and colors to depict trend changes.
- Customizable colors for upward and downward trends.
Input Parameters
Length: Length of the moving average (default: 50)
Standard Deviation: Standard deviation for Gaussian weighting (default: 10.0)
Width: Width of the plotted lines (default: 1)
Colors: Customizable colors for upward and downward trends
Forecast Length: Length of the forecast period (default: 20)
Extrapolate Length: Length of the extrapolation (default: 50)
Polynomial Degree: Degree of the polynomial regression (default: 3)
Lock Forecast: Option to lock and stabilize the forecast
Core Algorithms
1. Gaussian Weight Calculation:
gaussian_weight(x, std_dev) =>
1 / (std_dev * math.sqrt(2 * math.pi)) * math.exp(-0.5 * math.pow(x / std_dev, 2))
2. GWMA Calculation:
calculate_gwma(length, std_dev) =>
// Algorithm to calculate the weighted moving average
3. Initialize Lines for Polynomial Regression:
initialize_lines_array(extrapolate, length) =>
// Initialize array lines
4. Create Design Matrix for Polynomial Regression:
get_design_matrix(length, degree) =>
// Create the design matrix
5. Calculate and Plot Polynomial Regression:
calculate_polynomial_regression(src, length, degree, extrapolate, lines_arr, lock, width, upward_color, downward_color) =>
// Algorithm to calculate polynomial regression and plot the forecast
Combining Indicators: Originality and Usefulness
The combination of Gaussian Weighted Moving Average and polynomial regression provides traders with a robust tool for trend analysis and prediction. The GWMA smooths out price data while emphasizing recent prices, making it sensitive to short-term trends. Polynomial regression, on the other hand, offers a mathematical approach to model and forecast future prices based on historical data. By integrating these two methodologies, traders can achieve a more comprehensive view of market trends and potential future movements, making the tool highly valuable for decision-making.
Explanation for Users
Most TradingView users are not familiar with Pine Script, so a clear description is essential for understanding how to use the script.
Gaussian Weighted Moving Average (GWMA): This indicator calculates a moving average using Gaussian weights, which gives more importance to recent prices. The length and standard deviation parameters allow users to control the sensitivity and smoothness of the average.
Polynomial Regression with Forecast: This feature uses polynomial regression to model the price trend and predict future movements. Users can adjust the length of the historical data used, the degree of the polynomial, and the length of the forecast. The script plots these predictions, making it easier for traders to visualize potential future price paths.
Visualization of Results
1. GWMA Plotting:
plot(gaussian_ma_result, title="GWMA", color=line_color, linewidth=width_input)
2. Forecast Extrapolation:
plot(forecast_val, 'Extrapolation', offset=extrapolate_setting, linewidth=width_input, style=plot.style_circles)
Conclusion
The "Gaussian Weighted Moving Average with Forecast" script provides a powerful tool for analyzing and predicting price movements on TradingView. By combining Gaussian weighting and polynomial regression, it offers a precise and customizable method for trend analysis and forecasting.
Thank you for your attention! For any questions or further information, please feel free to reach out.
Venit A.I Trading V1RSI indicatorThis indicator is designed to provide buy and sell signals based on the Relative Strength Index (RSI). Here's a breakdown of its components and functionality:
1. **Input Parameters**:
- `Period`: This parameter allows the user to adjust the period used in calculating the RSI.
- `Upper Threshold` and `Lower Threshold`: These parameters define the overbought and oversold levels for the RSI.
- `Imverse Algorithm`: This parameter allows the user to toggle between different algorithms for generating buy and sell signals.
- `Show Lines`: This parameter toggles the visibility of lines on the chart indicating buy and sell signals.
- `Show Labels`: This parameter toggles the visibility of labels on the chart indicating buy and sell signals.
2. **RSI Calculation**:
- The RSI is calculated using the specified period (`myPeriod`), typically representing the closing prices of the asset.
3. **Buy and Sell Conditions**:
- Buy conditions are determined based on whether the RSI crosses below the lower threshold (`myThresholdDn`), indicating potential oversold conditions.
- Sell conditions are determined based on whether the RSI crosses above the upper threshold (`myThresholdUp`), indicating potential overbought conditions.
- The choice of buy and sell conditions can be toggled using the `Imverse Algorithm` parameter.
4. **Position Tracking**:
- The indicator maintains a variable `myPosition` to track the current position (buy or sell) based on the generated signals.
- If a buy signal occurs (`buy` condition is true), `myPosition` is set to 0. If a sell signal occurs (`sell` condition is true) or the previous position was a buy, `myPosition` is set to 1. Otherwise, `myPosition` remains unchanged.
5. **Visualization**:
- Buy and sell signals are plotted on the chart using shapes (`plotshape`) based on the `myLineToggle` and `myLabelToggle` parameters.
- Lines are drawn on the chart to visually represent buy and sell signals.
- Labels are placed on the chart indicating buy and sell signals.
6. **Alerts**:
- The indicator provides alerts for buy and sell signals using the `alertcondition` function.
Overall, this indicator aims to provide traders with signals based on RSI movements, helping them identify potential buying and selling opportunities in the market. The flexibility in parameters allows users to customize the indicator based on their trading preferences and strategies.
Fair Value Gap Screener | Flux Charts💎 GENERAL OVERVIEW
Introducing our new Fair Value Gap Screener! This screener can provide information about the latest Fair Value Gaps in up to 5 tickers. You can also customize the algorithm that finds the Fair Value Gaps and the styling of the screener.
Features of the new Fair Value Gap (FVG) Screener :
Find Latest Fair Value Gaps Accross 5 Tickers
Shows Their Information Of :
Latest Status
Number Of Retests
Consumption Percent
Bullish & Bearish Volume
Customizable Algoritm / Styling
📌 HOW DOES IT WORK ?
A Fair Value Gap generally occur when there is an imbalance in the market. They can be detected by specific formations within the chart. This screener then finds Fair Value Gaps accross 5 different tickers, and shows the latest information about them.
Status ->
Far -> The current price is far away from the FVG.
Approaching ⬆️/⬇️ -> The current price is approaching the FVG, and the direction it's approaching from.
Inside -> The price is currently inside the FVG.
Retests -> Retest means the price tried to invalidate the FVG, but failed to do so. Here you can see how many times the price retested the FVG.
Consumed -> FVGs get consumed when a Close / Wick enters the FVG zone. For example, if the price hits the middle of the FVG zone, the zone is considered 50% consumed.
Bullish / Bearish Volume -> Bullish & Bearish volume of a FVG is calculated by analyzing the bars that formed it. For example in a bullish FVG, the bullish volume is the total volume of the first 2 bars forming the FVG, and the bearish volume is the volume of the 3rd bar that forms it.
🚩UNIQUENESS
This screener can detect latest Fair Value Gaps and give information about them for up to 5 tickers. This saves the user time by showing them all in a dashboard at the same time. The screener also uniquely shows information about the number of retests and the consumed percent of the FVG, as well as it's bullish & bearish volume. We believe that this extra information will help you spot reliable FVGs easier.
⚙️SETTINGS
1. Tickers
You can set up to 5 tickers for the screener to scan Fair Value Gaps here. You can also enable / disable them and set their individual timeframes.
2. General Configuration
Zone Invalidation -> Select between Wick & Close price for FVG Zone Invalidation.
Zone Filtering -> With "Average Range" selected, algorithm will find FVG zones in comparison with average range of last bars in the chart. With the "Volume Threshold" option, you may select a Volume Threshold % to spot FVGs with a larger total volume than average.
FVG Detection -> With the "Same Type" option, all 3 bars that formed the FVG should be the same type. (Bullish / Bearish). If the "All" option is selected, bar types may vary between Bullish / Bearish.
Detection Sensitivity -> You may select between Low, Normal or High FVG detection sensitivity. This will essentially determine the size of the spotted FVGs, with lower sensitivies resulting in spotting bigger FVGs, and higher sensitivies resulting in spotting all sizes of FVGs.
DNA GRAVITY PRICE V1 PINESCRIPTLABSWe can observe that this indicator displays the range within which the asset fluctuates around the average price, and its behavior depends on the parameters of amplitude and angular frequency. "price_mas" is a measure calculated as part of the indicator. It is derived by adding an adjusted amplitude (A_mas) multiplied by the cosine of the combination of angular frequency (w), time, and a phase shift (phi) to the average price (P0). This calculated value oscillates around the actual asset price and is used to identify potential turning points and the range where the price has established itself within the specified lookback period.
2.- At its core, the indicator utilizes the innovative concept of 'price_mas,' a calculated metric visualized in three essential colors: green to indicate low levels, blue for medium levels, and red for high levels. These colors reflect the position of the price in relation to a range determined by historical highs and lows.
In the context of the "DNA GRAVITY PRICE V1 " indicator, low, medium, and high levels specifically refer to the calculated value of 'price_mas,' which is a derived measure within the indicator. They do not directly refer to the actual asset price but rather to a calculated value that the indicator uses to analyze and predict the behavior of the asset's price.
This algorithm stands out for its ability to capture the 'strength' of the price through the 'price_mas' zones. Once the price exits the zones marked by the 'price_mas' (red, blue, and green plots), it tends to return with significant force.
Buy & Sell Signals:
Buy Signal: If the price and the Donchian lines cross above the high threshold, visually represented by red diamonds, it indicates a strong bullish momentum. This not only shows that the price is rising but also that the trend is strong enough to push the Donchian lines, which represent price extremes over a certain period, above the threshold. This convergence of movements, marked by the crossing over the red diamonds, suggests a higher probability of the bullish trend continuing.
Sell Signal: Similarly, if the price and the Donchian lines fall below the low threshold, visualized as green diamonds, this signals a significant bearish momentum. The simultaneous decline of the price and the Donchian lines below this threshold, marked by the green diamonds, indicates that not only is the price decreasing, but the bearish trend is strong enough to influence the price extremes calculated by the Donchian lines.
Configuration:
-The "Initial Dynamic Length of MAS Price" parameter controls the smoothness and sensitivity of the indicator. A high value smooths the Simple Moving Average (SMA), making the indicator less responsive to short-term price fluctuations. On the other hand, a low value makes the indicator more sensitive to short-term price fluctuations, generating faster and more volatile signals
-This parameter, "MAS Amplitude Percentage," determines the amplitude as a percentage. Increasing the Initial Dynamic Price will result in a larger amplitude relative to the price, leading to wider ranges for the indicator. Decreasing this value will have the opposite effect, reducing the amplitude relative to the price. Increasing "A_mas_pct" can make signals more extreme and less frequent, while decreasing it will make signals smoother and more frequent.
-This parameter, "Angular Frequency of MAS," affects the frequency of oscillations in the calculation of the "Initial Dynamic Price." A higher value of "w" will make the oscillations faster and more frequent, which means that the indicator will be more responsive to abrupt price changes. Conversely, a lower value will make the oscillations slower and smoother, making the indicator less sensitive to rapid price changes. Modifying ""Angular Frequency of MAS,"" directly impacts the frequency of oscillations in the indicator.
Español:
Podemos observar que este indicador muestra el rango en el cual el activo fluctúa alrededor del precio promedio y su comportamiento depende de los parámetros de amplitud y frecuencia angular. "price_mas" es una medida calculada como parte del indicador. Se deriva al sumar una amplitud ajustada (A_mas) multiplicada por el coseno de la combinación de frecuencia angular (w), tiempo y un desplazamiento de fase (phi) al precio promedio (P0). Este valor calculado oscila alrededor del precio real del activo y se utiliza para identificar posibles puntos de giro y el rango donde el precio se ha establecido dentro del período de búsqueda especificado.
En su núcleo, el indicador utiliza el innovador concepto de 'price_mas', una métrica calculada visualizada en tres colores esenciales: verde para indicar niveles bajos, azul para niveles medios y rojo para niveles altos. Estos colores reflejan la posición del precio en relación con un rango determinado por los máximos y mínimos históricos.
En el contexto del indicador "DNA GRAVITY PRICE V1", los niveles bajos, medios y altos se refieren específicamente al valor calculado de 'price_mas', que es una medida derivada dentro del indicador. No se refieren directamente al precio real del activo, sino a un valor calculado que el indicador utiliza para analizar y predecir el comportamiento del precio del activo.
Este algoritmo se destaca por su capacidad para capturar la 'fortaleza' del precio a través de las zonas de 'price_mas'. Una vez que el precio sale de las zonas marcadas por 'price_mas' (trazas rojas, azules y verdes), tiende a regresar con una fuerza significativa. Este comportamiento es crucial para los operadores, ya que proporciona oportunidades tanto para capitalizar las retracciones de precios como para anticipar posibles cambios de tendencia.
Señales de Compra y Venta:
Señal de Compra: Si el precio y las líneas Donchian cruzan por encima del umbral alto, visualmente representado por diamantes rojos, indica un fuerte impulso alcista. Esto no solo muestra que el precio está aumentando, sino que la tendencia es lo suficientemente fuerte como para empujar las líneas Donchian, que representan los extremos de precio durante un período determinado, por encima del umbral. Esta convergencia de movimientos, marcada por el cruce sobre los diamantes rojos, sugiere una mayor probabilidad de que la tendencia alcista continúe.
Señal de Venta: De manera similar, si el precio y las líneas Donchian caen por debajo del umbral bajo, visualizado como diamantes verdes, esto señala un fuerte impulso bajista. La caída simultánea del precio y las líneas Donchian por debajo de este umbral, marcada por los diamantes verdes, indica que no solo el precio está disminuyendo, sino que la tendencia bajista es lo suficientemente fuerte como para influir en los extremos de precio calculados por las líneas Donchian.
Configuración:
El parámetro "Longitud Dinámica Inicial de MAS Price" controla la suavidad y la sensibilidad del indicador. Un valor alto suaviza el Promedio Móvil Simple (SMA), lo que hace que el indicador sea menos sensible a las fluctuaciones de precio a corto plazo. Por otro lado, un valor bajo hace que el indicador sea más sensible a las fluctuaciones de precio a corto plazo, generando señales más rápidas y volátiles.
Este parámetro, "Porcentaje de Amplitud de MAS," determina la amplitud como un porcentaje. Aumentar el valor de "Longitud Dinámica Inicial de MAS Price" dará como resultado una amplitud más grande en relación con el precio, lo que conducirá a rangos más amplios para el indicador. Disminuir este valor tendrá el efecto contrario, reduciendo la amplitud en relación con el precio. Aumentar "Porcentaje de A_mas" puede hacer que las señales sean más extremas y menos frecuentes, mientras que disminuirlo hará que las señales sean más suaves y más frecuentes.
Este parámetro, "Frecuencia Angular de MAS," afecta la frecuencia de las oscilaciones en el cálculo del "Precio Móvil Simple Inicial." Un valor más alto de "w" hará que las oscilaciones sean más rápidas y frecuentes, lo que significa que el indicador será más receptivo a cambios abruptos en el precio. Por otro lado, un valor más bajo hará que las oscilaciones sean más lentas y suaves, haciendo que el indicador sea menos sensible a cambios rápidos en el precio. Modificar "Frecuencia Angular de MAS" afecta directamente la frecuencia de las oscilaciones en el indicador.
KNN Regression [SS]Another indicator release, I know.
But note, this isn't intended to be a stand-alone indicator, this is just a functional addition for those who program Machine Learning algorithms in Pinescript! There isn't enough content here to merit creating a library for (it's only 1 function), but it's a really useful function for those who like machine learning and Nearest Known Neighbour Algos (or KNN).
About the indicator:
This indicator creates a function to perform KNN-based regression.
In contrast to traditional linear regression, KNN-based regression has the following advantages over linear regression:
Advantages of KNN Regression vs. Linear Regression:
🎯 Non-linearity: KNN is a non-parametric method, meaning it makes no assumptions about the underlying data distribution. This allows it to capture non-linear relationships between features and the target variable.
🎯Simple Implementation: KNN is conceptually simple and easy to understand. It doesn't require the estimation of parameters, making it straightforward to implement.
🎯Robust to Outliers: KNN is less sensitive to outliers compared to linear regression. Outliers can have a significant impact on linear regression models, but KNN tends to be less affected.
Disadvantages of KNN Regression vs. Linear Regression:
🎯 Resource Intensive for Computation: Because KNN operates on identifying the nearest neighbors in a dataset, each new instance has to be searched for and identified within the dataset, vs. linear regression which can create a coefficient-based model and draw from the coefficient for each new data point.
🎯Curse of Dimensionality: KNN performance can degrade with an increasing number of features, leading to a "curse of dimensionality." This is because, in high-dimensional spaces, the concept of proximity becomes less meaningful.
🎯Sensitive to Noise: KNN can be sensitive to noisy data, as it relies on the local neighborhood for predictions. Noisy or irrelevant features may affect its performance.
Which is better?
I am very biased, coming from a statistics background. I will always love linear regression and will always prefer it over KNN. But depending on what you want to accomplish, KNN makes sense. If you are using highly skewed data or data that you cannot identify linearity in, KNN is probably preferable.
However, if you require precise estimations of ranges and outliers, such as creating co-integration models, I would advise sticking with linear regression. However, out of curiosity, I exported the function into a separate dummy indicator and pulled in data from QQQ to predict SPY close, and the results are actually very admirable:
And plotted with showing the standard error variance:
Pretty impressive, I must say I was a little shocked, it's really giving linear regression a run for its money. In school I was taught LinReg is the gold standard for modeling, nothing else compares. So as with most things in trading, this is challenging some biases of mine ;).
Functionality of the function
I have permitted 3 types of KNN regression. Traditional KNN regression, as I understand it, revolves around clustering. ( Clustering refers to identifying a cluster, normally 3, of identical cases and averaging out the Dependent variable in each of those cases) . Clustering is great, but when you are working with a finite dataset, identifying exact matches for 2 or 3 clusters can be challenging when you are only looking back at 500 candles or 1000 candles, etc.
So to accommodate this, I have added a functionality to clustering called "Tolerance". And it allows you to set a tolerance level for your Euclidean distance parameters. As a default, I have tested this with a default of 0.5 and it has worked great and no need to change even when working with large numbers such as NQ and ES1!.
However, I have added 2 additional regression types that can be done with KNN.
#1 One is a regression by the last IDENTICAL instance, which will find the most recent instance of a similar Independent variable and pull the Dependent variable from that instance. Or
#2 Average from all IDENTICAL instances.
Using the function
The code has the instructions for integrating the function into your own code, the parameters, and such, so I won't exhaust you with the boring details about that here.
But essentially, it exports 3, float variables, the Result, the Correlation, and the simplified R2.
As this is KNN regression, there are no coefficients, slopes, or intercepts and you do not need to test for linearity before applying it.
Also, the output can be a bit choppy, so I tend to like to throw in a bit of smoothing using the ta.sma function at a deault of 14.
For example, here is SPY from QQQ smoothed as a 14 SMA:
And it is unsmoothed:
It seems relatively similar but it does make a bit of an aesthetic difference. And if you are doing it over 14, there is no data loss and it is still quite reactive to changes in data.
And that's it! Hopefully you enjoy and find some interesting uses for this function in your own scripts :-).
Safe trades everyone!
IPDA Standard Deviations [DexterLab x TFO x toodegrees]> Introduction and Acknowledgements
The IPDA Standard Deviations tool encompasses the Time and price relationship as studied by @TraderDext3r .
I am not the creator of this Theory, and I do not hold the answers to all the questions you may have; I suggest you to study it from Dexter's tweets, videos, and material.
This tool was born from a collaboration between @TraderDext3r, @tradeforopp and I, with the objective of bringing a comprehensive IPDA Standard Deviations tool to Tradingview.
> Tool Description
This is purely a graphical aid for traders to be able to quickly determine Fractal IPDA Time Windows, and trace the potential Standard Deviations of the moves at their respective high and low extremes.
The disruptive value of this tool is that it allows traders to save Time by automatically adapting the Time Windows based on the current chart's Timeframe, as well as providing customizations to filter and focus on the appropriate Standard Deviations.
> IPDA Standard Deviations by TraderDext3r
The underlying idea is based on the Interbank Price Delivery Algorithm's lookback windows on the daily chart as taught by the Inner Circle Trader:
IPDA looks at the past three months of price action to determine how to deliver price in the future.
Additionally, the ICT concept of projecting specific manipulation moves prior to large displacement upwards/downwards is used to navigate and interpret the priorly mentioned displacement move. We pay attention to specific Standard Deviations based on the current environment and overall narrative.
Dexter being one of the most prominent Inner Circle Trader students, harnessed the fractal nature of price to derive fractal IPDA Lookback Time Windows for lower Timeframes, and studied the behaviour of price at specific Deviations.
For Example:
The -1 to -2 area can initiate an algorithmic retracement before continuation.
The -2 to -2.5 area can initiate an algorithmic retracement before continuation, or a Smart Money Reversal.
The -4 area should be seen as the ultimate objective, or the level at which the displacement will slow down.
Given that these ideas stem from ICT's concepts themselves, they are to be used hand in hand with all other ICT Concepts (PD Array Matrix, PO3, Institutional Price Levels, ...).
> Fractal IPDA Time Windows
The IPDA Lookbacks Types identified by Dexter are as follows:
Monthly – 1D Chart: one widow per Month, highlighting the past three Months.
Weekly – 4H to 8H Chart: one window per Week, highlighting the past three Weeks.
Daily – 15m to 1H Chart: one window per Day, highlighting the past three Days.
Intraday – 1m to 5m Chart: one window per 4 Hours highlighting the past 12 Hours.
Inside these three respective Time Windows, the extreme High and Low will be identified, as well as the prior opposing short term market structure point. These represent the anchors for the Standard Deviation Projections.
> Tool Settings
The User is able to plot any type of Standard Deviation they want by inputting them in the settings, in their own line of the text box. They will always be plotted from the Time Windows extremes.
As previously mentioned, the User is also able to define their own Timeframe intervals for the respective IPDA Lookback Types. The specific Timeframes on which the different Lookback Types are plotted are edge-inclusive. In case of an overlap, the higher Timeframe Lookback will be prioritized.
Finally the User is able to filter and remove Standard Deviations in two ways:
"Remove Once Invalidated" will automatically delete a Deviation once its outer anchor extreme is traded through.
Manual Toggles will allow to remove the Upward or Downward Deviation of each Time Window at the discretion of the User.
Major shoutout to Dexter and TFO for their Time, it was a pleasure to collaborate and create this tool with them.
GLGT!
Machine Learning Momentum Oscillator [ChartPrime]The Machine Learning Momentum Oscillator brings together the K-Nearest Neighbors (KNN) algorithm and the predictive strength of the Tactical Sector Indicator (TSI) Momentum. This unique oscillator not only uses the insights from TSI Momentum but also taps into the power of machine learning therefore being designed to give traders a more comprehensive view of market momentum.
At its core, the Machine Learning Momentum Oscillator blends TSI Momentum with the capabilities of the KNN algorithm. Introducing KNN logic allows for better handling of noise in the data set. The TSI Momentum is known for understanding how strong trends are and which direction they're headed, and now, with the added layer of machine learning, we're able to offer a deeper perspective on market trends. This is a fairly classical when it comes to visuals and trading.
Green bars show the trader when the asset is in an uptrend. On the flip side, red bars mean things are heading down, signaling a bearish movement driven by selling pressure. These color cues make it easier to catch the sentiment and direction of the market in a glance.
Yellow boxes are also displayed by the oscillator. These boxes highlight potential turning points or peaks. When the market comes close to these points, they can provide a heads-up about the possibility of changes in momentum or even a trend reversal, helping a trader make informed choices quickly. These can be looked at as possible reversal areas simply put.
Settings:
Users can adjust the number of neighbours in the KNN algorithm and choose the periods they prefer for analysis. This way, the tool becomes a part of a trader's strategy, adapting to different market conditions as they see fit. Users can also adjust the smoothing used by the oscillator via the smoothing input.
SimilarityMeasuresLibrary "SimilarityMeasures"
Similarity measures are statistical methods used to quantify the distance between different data sets
or strings. There are various types of similarity measures, including those that compare:
- data points (SSD, Euclidean, Manhattan, Minkowski, Chebyshev, Correlation, Cosine, Camberra, MAE, MSE, Lorentzian, Intersection, Penrose Shape, Meehl),
- strings (Edit(Levenshtein), Lee, Hamming, Jaro),
- probability distributions (Mahalanobis, Fidelity, Bhattacharyya, Hellinger),
- sets (Kumar Hassebrook, Jaccard, Sorensen, Chi Square).
---
These measures are used in various fields such as data analysis, machine learning, and pattern recognition. They
help to compare and analyze similarities and differences between different data sets or strings, which
can be useful for making predictions, classifications, and decisions.
---
References:
en.wikipedia.org
cran.r-project.org
numerics.mathdotnet.com
github.com
github.com
github.com
Encyclopedia of Distances, doi.org
ssd(p, q)
Sum of squared difference for N dimensions.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Measure of distance that calculates the squared euclidean distance.
euclidean(p, q)
Euclidean distance for N dimensions.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Measure of distance that calculates the straight-line (or Euclidean).
manhattan(p, q)
Manhattan distance for N dimensions.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Measure of absolute differences between both points.
minkowski(p, q, p_value)
Minkowsky Distance for N dimensions.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
p_value (float) : `float` P value, default=1.0(1: manhatan, 2: euclidean), does not support chebychev.
Returns: Measure of similarity in the normed vector space.
chebyshev(p, q)
Chebyshev distance for N dimensions.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Measure of maximum absolute difference.
correlation(p, q)
Correlation distance for N dimensions.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Measure of maximum absolute difference.
cosine(p, q)
Cosine distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
Returns: The Cosine distance between vectors `p` and `q`.
---
angiogenesis.dkfz.de
camberra(p, q)
Camberra distance for N dimensions.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Weighted measure of absolute differences between both points.
mae(p, q)
Mean absolute error is a normalized version of the sum of absolute difference (manhattan).
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Mean absolute error of vectors `p` and `q`.
mse(p, q)
Mean squared error is a normalized version of the sum of squared difference.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Mean squared error of vectors `p` and `q`.
lorentzian(p, q)
Lorentzian distance between provided vectors.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Lorentzian distance of vectors `p` and `q`.
---
angiogenesis.dkfz.de
intersection(p, q)
Intersection distance between provided vectors.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Intersection distance of vectors `p` and `q`.
---
angiogenesis.dkfz.de
penrose(p, q)
Penrose Shape distance between provided vectors.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Penrose shape distance of vectors `p` and `q`.
---
angiogenesis.dkfz.de
meehl(p, q)
Meehl distance between provided vectors.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Meehl distance of vectors `p` and `q`.
---
angiogenesis.dkfz.de
edit(x, y)
Edit (aka Levenshtein) distance for indexed strings.
Parameters:
x (int ) : `array` Indexed array.
y (int ) : `array` Indexed array.
Returns: Number of deletions, insertions, or substitutions required to transform source string into target string.
---
generated description:
The Edit distance is a measure of similarity used to compare two strings. It is defined as the minimum number of
operations (insertions, deletions, or substitutions) required to transform one string into another. The operations
are performed on the characters of the strings, and the cost of each operation depends on the specific algorithm
used.
The Edit distance is widely used in various applications such as spell checking, text similarity, and machine
translation. It can also be used for other purposes like finding the closest match between two strings or
identifying the common prefixes or suffixes between them.
---
github.com
www.red-gate.com
planetcalc.com
lee(x, y, dsize)
Distance between two indexed strings of equal length.
Parameters:
x (int ) : `array` Indexed array.
y (int ) : `array` Indexed array.
dsize (int) : `int` Dictionary size.
Returns: Distance between two strings by accounting for dictionary size.
---
www.johndcook.com
hamming(x, y)
Distance between two indexed strings of equal length.
Parameters:
x (int ) : `array` Indexed array.
y (int ) : `array` Indexed array.
Returns: Length of different components on both sequences.
---
en.wikipedia.org
jaro(x, y)
Distance between two indexed strings.
Parameters:
x (int ) : `array` Indexed array.
y (int ) : `array` Indexed array.
Returns: Measure of two strings' similarity: the higher the value, the more similar the strings are.
The score is normalized such that `0` equates to no similarities and `1` is an exact match.
---
rosettacode.org
mahalanobis(p, q, VI)
Mahalanobis distance between two vectors with population inverse covariance matrix.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
VI (matrix) : `matrix` Inverse of the covariance matrix.
Returns: The mahalanobis distance between vectors `p` and `q`.
---
people.revoledu.com
stat.ethz.ch
docs.scipy.org
fidelity(p, q)
Fidelity distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
Returns: The Bhattacharyya Coefficient between vectors `p` and `q`.
---
en.wikipedia.org
bhattacharyya(p, q)
Bhattacharyya distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
Returns: The Bhattacharyya distance between vectors `p` and `q`.
---
en.wikipedia.org
hellinger(p, q)
Hellinger distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
Returns: The hellinger distance between vectors `p` and `q`.
---
en.wikipedia.org
jamesmccaffrey.wordpress.com
kumar_hassebrook(p, q)
Kumar Hassebrook distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
Returns: The Kumar Hassebrook distance between vectors `p` and `q`.
---
github.com
jaccard(p, q)
Jaccard distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
Returns: The Jaccard distance between vectors `p` and `q`.
---
github.com
sorensen(p, q)
Sorensen distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
Returns: The Sorensen distance between vectors `p` and `q`.
---
people.revoledu.com
chi_square(p, q, eps)
Chi Square distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
eps (float)
Returns: The Chi Square distance between vectors `p` and `q`.
---
uw.pressbooks.pub
stats.stackexchange.com
www.itl.nist.gov
kulczynsky(p, q, eps)
Kulczynsky distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
eps (float)
Returns: The Kulczynsky distance between vectors `p` and `q`.
---
github.com
[blackcat] L1 TradingView Array and Series ConversionsLevel 1
Background
It just so happens that I need some functions that can convert between the Series data type and the Array data type.
Function
Series is a unique data type of TradingView. By operating Series data, the algorithm can be simplified, which is very convenient. However, in high-level languages, Array is a basic data type that provides great flexibility and can be used to develop advanced algorithms. This is why TradingView introduces the Array data type. This script simply demonstrates how to convert between these two data types.
s2a function: Convert a TV series into an array.
a2s function: Convert an array into a TV series
Finally, Courtesy of Electrified for his "Average Lib":
Remarks
Feedbacks are appreciated.
Discrete Fourier Transform Overlay [wbburgin]The discrete Fourier transform (DFT) overlay uses a discrete Fourier transform algorithm to identify trend direction. This is a simpler interpretation that only uses the magnitude of the first frequency component obtained from the DFT algorithm, but can be useful for visualization purposes. I haven't seen many Fourier scripts on TradingView that actually have the magnitude plotted on the chart (some have lines, for instance, but that makes it difficult to look into the past or to see previous lines).
About the Discrete Fourier Transform
The DFT is a mathematical transformation that decomposes a time-domain signal into its constituent frequency components. By applying the DFT to OHLC data, we can interpret the periodicities and trends present in the market. I've designed the overlay so that you can choose your source for the Fourier transform, as well as the length.
Settings and Configuration
The "Fourier Period" is the transform length of the DFT algorithm. This input indicates the number of data points considered for the DFT calculation. For example, if this input is set to 20, the DFT will be performed on the most recent 20 data points of the input series. The transform length affects the resolution and accuracy of the frequency analysis. A shorter transform length may provide a broader frequency range but with less detail, while a longer transform length can provide finer frequency resolution but may be computationally more intensive (I recommend using under 100 - anything above that might take too much time to load on the platform).
The "Fourier X Series" is the source you want the Fourier transform to be applied to. I have it set in default to the close.
"Kernel Smoothing" is the bar-start of the rational quadratic kernel used to smooth the frequency component. Think of it just like a normal moving average if you are unfamiliar with the concept, it functions similarly to the "length" value of a moving average.
Aggregate Medians [wbburgin]This indicator recursively finds the average of all high/low medians under your chosen length. This can be very, very helpful for analyzing trends where a moving average or a normal median would produce a bunch of false signals.
Settings:
The "Length" setting is the maximum median that you want the algorithm to add into the sum. The "Start at Period" setting is the the minimum median that you want the algorithm to take into account. Starting at a higher period means that the faster, more sensitive medians of lower lengths are not included, and will smooth out your curve.
I haven't seen many recursive algorithms on TradingView so feel free to use this script as inspiration for any of your ideas. In theory, you can essentially replace the median function with any other function - a moving average, a supertrend, or anything else.
The start must be lower than the length, because this is a sum from the start to the length of all medians in between.
MLExtensionsLibrary "MLExtensions"
normalizeDeriv(src, quadraticMeanLength)
Returns the smoothed hyperbolic tangent of the input series.
Parameters:
src : The input series (i.e., the first-order derivative for price).
quadraticMeanLength : The length of the quadratic mean (RMS).
Returns: nDeriv The normalized derivative of the input series.
normalize(src, min, max)
Rescales a source value with an unbounded range to a target range.
Parameters:
src : The input series
min : The minimum value of the unbounded range
max : The maximum value of the unbounded range
Returns: The normalized series
rescale(src, oldMin, oldMax, newMin, newMax)
Rescales a source value with a bounded range to anther bounded range
Parameters:
src : The input series
oldMin : The minimum value of the range to rescale from
oldMax : The maximum value of the range to rescale from
newMin : The minimum value of the range to rescale to
newMax : The maximum value of the range to rescale to
Returns: The rescaled series
color_green(prediction)
Assigns varying shades of the color green based on the KNN classification
Parameters:
prediction : Value (int|float) of the prediction
Returns: color
color_red(prediction)
Assigns varying shades of the color red based on the KNN classification
Parameters:
prediction : Value of the prediction
Returns: color
tanh(src)
Returns the the hyperbolic tangent of the input series. The sigmoid-like hyperbolic tangent function is used to compress the input to a value between -1 and 1.
Parameters:
src : The input series (i.e., the normalized derivative).
Returns: tanh The hyperbolic tangent of the input series.
dualPoleFilter(src, lookback)
Returns the smoothed hyperbolic tangent of the input series.
Parameters:
src : The input series (i.e., the hyperbolic tangent).
lookback : The lookback window for the smoothing.
Returns: filter The smoothed hyperbolic tangent of the input series.
tanhTransform(src, smoothingFrequency, quadraticMeanLength)
Returns the tanh transform of the input series.
Parameters:
src : The input series (i.e., the result of the tanh calculation).
smoothingFrequency
quadraticMeanLength
Returns: signal The smoothed hyperbolic tangent transform of the input series.
n_rsi(src, n1, n2)
Returns the normalized RSI ideal for use in ML algorithms.
Parameters:
src : The input series (i.e., the result of the RSI calculation).
n1 : The length of the RSI.
n2 : The smoothing length of the RSI.
Returns: signal The normalized RSI.
n_cci(src, n1, n2)
Returns the normalized CCI ideal for use in ML algorithms.
Parameters:
src : The input series (i.e., the result of the CCI calculation).
n1 : The length of the CCI.
n2 : The smoothing length of the CCI.
Returns: signal The normalized CCI.
n_wt(src, n1, n2)
Returns the normalized WaveTrend Classic series ideal for use in ML algorithms.
Parameters:
src : The input series (i.e., the result of the WaveTrend Classic calculation).
n1
n2
Returns: signal The normalized WaveTrend Classic series.
n_adx(highSrc, lowSrc, closeSrc, n1)
Returns the normalized ADX ideal for use in ML algorithms.
Parameters:
highSrc : The input series for the high price.
lowSrc : The input series for the low price.
closeSrc : The input series for the close price.
n1 : The length of the ADX.
regime_filter(src, threshold, useRegimeFilter)
Parameters:
src
threshold
useRegimeFilter
filter_adx(src, length, adxThreshold, useAdxFilter)
filter_adx
Parameters:
src : The source series.
length : The length of the ADX.
adxThreshold : The ADX threshold.
useAdxFilter : Whether to use the ADX filter.
Returns: The ADX.
filter_volatility(minLength, maxLength, useVolatilityFilter)
filter_volatility
Parameters:
minLength : The minimum length of the ATR.
maxLength : The maximum length of the ATR.
useVolatilityFilter : Whether to use the volatility filter.
Returns: Boolean indicating whether or not to let the signal pass through the filter.
backtest(high, low, open, startLongTrade, endLongTrade, startShortTrade, endShortTrade, isStopLossHit, maxBarsBackIndex, thisBarIndex)
Performs a basic backtest using the specified parameters and conditions.
Parameters:
high : The input series for the high price.
low : The input series for the low price.
open : The input series for the open price.
startLongTrade : The series of conditions that indicate the start of a long trade.`
endLongTrade : The series of conditions that indicate the end of a long trade.
startShortTrade : The series of conditions that indicate the start of a short trade.
endShortTrade : The series of conditions that indicate the end of a short trade.
isStopLossHit : The stop loss hit indicator.
maxBarsBackIndex : The maximum number of bars to go back in the backtest.
thisBarIndex : The current bar index.
Returns: A tuple containing backtest values
init_table()
init_table()
Returns: tbl The backtest results.
update_table(tbl, tradeStatsHeader, totalTrades, totalWins, totalLosses, winLossRatio, winrate, stopLosses)
update_table(tbl, tradeStats)
Parameters:
tbl : The backtest results table.
tradeStatsHeader : The trade stats header.
totalTrades : The total number of trades.
totalWins : The total number of wins.
totalLosses : The total number of losses.
winLossRatio : The win loss ratio.
winrate : The winrate.
stopLosses : The total number of stop losses.
Returns: Updated backtest results table.
WaveTrend 3D█ OVERVIEW
WaveTrend 3D (WT3D) is a novel implementation of the famous WaveTrend (WT) indicator and has been completely redesigned from the ground up to address some of the inherent shortcomings associated with the traditional WT algorithm.
█ BACKGROUND
The WaveTrend (WT) indicator has become a widely popular tool for traders in recent years. WT was first ported to PineScript in 2014 by the user @LazyBear, and since then, it has ascended to become one of the Top 5 most popular scripts on TradingView.
The WT algorithm appears to have origins in a lesser-known proprietary algorithm called Trading Channel Index (TCI), created by AIQ Systems in 1986 as an integral part of their commercial software suite, TradingExpert Pro. The software’s reference manual states that “TCI identifies changes in price direction” and is “an adaptation of Donald R. Lambert’s Commodity Channel Index (CCI)”, which was introduced to the world six years earlier in 1980. Interestingly, a vestige of this early beginning can still be seen in the source code of LazyBear’s script, where the final EMA calculation is stored in an intermediate variable called “tci” in the code.
█ IMPLEMENTATION DETAILS
WaveTrend 3D is an alternative implementation of WaveTrend that directly addresses some of the known shortcomings of the indicator, including its unbounded extremes, susceptibility to whipsaw, and lack of insight into other timeframes.
In the canonical WT approach, an exponential moving average (EMA) for a given lookback window is used to assess the variability between price and two other EMAs relative to a second lookback window. Since the difference between the average price and its associated EMA is essentially unbounded, an arbitrary scaling factor of 0.015 is typically applied as a crude form of rescaling but still fails to capture 20-30% of values between the range of -100 to 100. Additionally, the trigger signal for the final EMA (i.e., TCI) crossover-based oscillator is a four-bar simple moving average (SMA), which further contributes to the net lag accumulated by the consecutive EMA calculations in the previous steps.
The core idea behind WT3D is to replace the EMA-based crossover system with modern Digital Signal Processing techniques. By assuming that price action adheres approximately to a Gaussian distribution, it is possible to sidestep the scaling nightmare associated with unbounded price differentials of the original WaveTrend method by focusing instead on the alteration of the underlying Probability Distribution Function (PDF) of the input series. Furthermore, using a signal processing filter such as a Butterworth Filter, we can eliminate the need for consecutive exponential moving averages along with the associated lag they bring.
Ideally, it is convenient to have the resulting probability distribution oscillate between the values of -1 and 1, with the zero line serving as a median. With this objective in mind, it is possible to borrow a common technique from the field of Machine Learning that uses a sigmoid-like activation function to transform our data set of interest. One such function is the hyperbolic tangent function (tanh), which is often used as an activation function in the hidden layers of neural networks due to its unique property of ensuring the values stay between -1 and 1. By taking the first-order derivative of our input series and normalizing it using the quadratic mean, the tanh function performs a high-quality redistribution of the input signal into the desired range of -1 to 1. Finally, using a dual-pole filter such as the Butterworth Filter popularized by John Ehlers, excessive market noise can be filtered out, leaving behind a crisp moving average with minimal lag.
Furthermore, WT3D expands upon the original functionality of WT by providing:
First-class support for multi-timeframe (MTF) analysis
Kernel-based regression for trend reversal confirmation
Various options for signal smoothing and transformation
A unique mode for visualizing an input series as a symmetrical, three-dimensional waveform useful for pattern identification and cycle-related analysis
█ SETTINGS
This is a summary of the settings used in the script listed in roughly the order in which they appear. By default, all default colors are from Google's TensorFlow framework and are considered to be colorblind safe.
Source: The input series. Usually, it is the close or average price, but it can be any series.
Use Mirror: Whether to display a mirror image of the source series; for visualizing the series as a 3D waveform similar to a soundwave.
Use EMA: Whether to use an exponential moving average of the input series.
EMA Length: The length of the exponential moving average.
Use COG: Whether to use the center of gravity of the input series.
COG Length: The length of the center of gravity.
Speed to Emphasize: The target speed to emphasize.
Width: The width of the emphasized line.
Display Kernel Moving Average: Whether to display the kernel moving average of the signal. Like PCA, an unsupervised Machine Learning technique whereby neighboring vectors are projected onto the Principal Component.
Display Kernel Signal: Whether to display the kernel estimator for the emphasized line. Like the Kernel MA, it can show underlying shifts in bias within a more significant trend by the colors reflected on the ribbon itself.
Show Oscillator Lines: Whether to show the oscillator lines.
Offset: The offset of the emphasized oscillator plots.
Fast Length: The length scale factor for the fast oscillator.
Fast Smoothing: The smoothing scale factor for the fast oscillator.
Normal Length: The length scale factor for the normal oscillator.
Normal Smoothing: The smoothing scale factor for the normal frequency.
Slow Length: The length scale factor for the slow oscillator.
Slow Smoothing: The smoothing scale factor for the slow frequency.
Divergence Threshold: The number of bars for the divergence to be considered significant.
Trigger Wave Percent Size: How big the current wave should be relative to the previous wave.
Background Area Transparency Factor: Transparency factor for the background area.
Foreground Area Transparency Factor: Transparency factor for the foreground area.
Background Line Transparency Factor: Transparency factor for the background line.
Foreground Line Transparency Factor: Transparency factor for the foreground line.
Custom Transparency: Transparency of the custom colors.
Total Gradient Steps: The maximum amount of steps supported for a gradient calculation is 256.
Fast Bullish Color: The color of the fast bullish line.
Normal Bullish Color: The color of the normal bullish line.
Slow Bullish Color: The color of the slow bullish line.
Fast Bearish Color: The color of the fast bearish line.
Normal Bearish Color: The color of the normal bearish line.
Slow Bearish Color: The color of the slow bearish line.
Bullish Divergence Signals: The color of the bullish divergence signals.
Bearish Divergence Signals: The color of the bearish divergence signals.
█ ACKNOWLEDGEMENTS
@LazyBear - For authoring the original WaveTrend port on TradingView
@PineCoders - For the beautiful color gradient framework used in this indicator
@veryfid - For the inspiration of using mirrored signals for cycle analysis and using multiple lookback windows as proxies for other timeframes
Improved Chaikin Money FlowChaikin Money Flow is a well-known Indicator for gauging buying/selling pressure. Marc Chaikin intended this to be used on the daily timeframe to capture the behavior of price action at or near the daily close when larger-scale actors influence the market. The calculation is straight forward as described within the built-in TradingView "CMF" indicator:
1. Period Money Flow Multiplier = ((Close - Low) - (High - Close)) /(High - Low)
2. Period Money Flow Volume = Period Money Flow Multiplier x Volume for the Period
3. Chaikin Money Flow = 21 Period Sum of Money Flow Volume / 21 Period Sum of Volume
There is, however, a problem with this algorithm: it does not account for daily gaps in price action. This leads to the indicator sometimes moving out-of-sync with price action and/or an under-emphasis of the magnitude change of the indicator relative to the change in price action. This is a significant problem for someone trying to read divergences against an underlying.
Note: I have never seen a published attempt to improve this indicator which is why I decided that there had to be a way to do it.
In order to mitigate this issue, I have taken the basic script provided by TradingView and made a key modification. If the open of a candle is outside the range of the previous candle, then the close of the previous candle is used as the "high" for the current candle (in the case of a gap down) or the "low" for the current candle (in the case of a gap up). However, if the close of the current candle exceeds the previous close, highs and lows for the current candle are calculated as normal. I believe this accounts for gaps in price action without significantly altering the original intent of the indicator.
I have made four other minor tweaks:
1. Default style is color coded area above and below the Zero Line
2. Range scaled to +/-100 instead of +/-1 (displays better on graph)
3. Set timeframe to Daily (as that is the timeframe for which this indicator was intended by Chaikin)
4. Length defaults to 21 (which is what Chaikin uses)
Extreme Trend Reversal Points [HeWhoMustNotBeNamed]Using moving average crossover for identifying the change in trend is very common. However, this method can give lots of false signals during the ranging markets. In this algorithm, we try to find the extreme trend by looking at fully aligned multi-level moving averages and only look at moving average crossover when market is in the extreme trend - either bullish or bearish. These points can mean long term downtrend or can also cause a small pullback before trend continuation. In this discussion, we will also check how to handle different scenarios.
🎲 Components
🎯 Recursive Multi Level Moving Averages
Multi level moving average here refers to applying moving average on top of base moving average on multiple levels. For example,
Level 1 SMA = SMA(source, length)
Level 2 SMA = SMA(Level 1 SMA, length)
Level 3 SMA = SMA(Level 2 SMA, length)
..
..
..
Level n SMA = SMA(Level (n-1) SMA, length)
In this script, user can select how many levels of moving averages need to be calculated. This is achieved through " recursive moving average " algorithm. Requirement for building such algorithm was initially raised by @loxx
While I was able to develop them in minimal code with the help of some of the existing libraries built on arrays and matrix , I also thought why not extend this to find something interesting.
Note that since we are using variable levels - we will not be able to plot all the levels of moving average. (This is because plotting cannot be done in the loop). Hence, we are using lines to display the latest moving average levels in front of the last candle. Lines are color coded in such a way that least numbered levels are greener and higher levels are redder.
🎯 Finding the trend and range
Strength of fully aligned moving average is calculated based on position of each level with respect to other levels.
For example, in a complete uptrend, we can find
source > L(1)MA > L(2)MA > L(3)MA ...... > L(n-1)MA > L(n)MA
Similarly in a complete downtrend, we can find
source < L(1)MA < L(2)MA < L(3)MA ...... < L(n-1)MA < L(n)MA
Hence, the strength of trend here is calculated based on relative positions of each levels. Due to this, value of strength can range from 0 to Level*(Level-1)/2
0 represents the complete downtrend
Level*(Level-1)/2 represents the complete uptrend.
Range and Extreme Range are calculated based on the percentile from median. The brackets are defined as per input parameters - Range Percentile and Extreme Range Percentile by using Percentile History as reference length.
Moving average plot is color coded to display the trend strength.
Green - Extreme Bullish
Lime - Bullish
Silver - range
Orange - Bearish
Red - Extreme Bearish
🎯 Finding the trend reversal
Possible trend reversals are when price crosses the moving average while in complete trend with all the moving averages fully aligned. Triangle marks are placed in such locations which can help observe the probable trend reversal points. But, there are possibilities of trend overriding these levels. An example of such thing, we can see here:
In order to overcome this problem, we can employ few techniques.
1. After the signal, wait for trend reversal (moving average plot color to turn silver) before placing your order.
2. Place stop orders on immediate pivot levels or support resistance points instead of opening market order. This way, we can also place an order in the direction of trend. Whichever side the price breaks out, will be the direction to trade.
3. Look for other confirmations such as extremely bullish and bearish candles before placing the orders.
🎯 An example of using stop orders
Let us take this scenario where there is a signal on possible reversal from complete uptrend.
Create a box joining high and low pivots at reasonable distance. You can also chose to add 1 ATR additional distance from pivots.
Use the top of the box as stop-entry for long and bottom as stop-entry for short. The other ends of the box can become stop-losses for each side.
After few bars, we can see that few more signals are plotted but, the price is still within the box. There are some candles which touched the top of the box. But, the candlestick patterns did not represent bullishness on those instances. If you have placed stop orders, these orders would have already filled in. In that case, just wait for position to hit either stop or target.
For bullish side, targets can be placed at certain risk reward levels. In this case, we just use 1:1 for bullish (trend side) and 1:1.5 for bearish side (reversal side)
In this case, price hit the target without any issue:
Wait for next reversal signal to appear before placing another order :)
American Approximation Bjerksund & Stensland 2002 [Loxx]American Approximation Bjerksund & Stensland 2002 is an American Options pricing model. This indicator also includes numerical greeks. You can compare the output of the American Approximation to the Black-Scholes-Merton value on the output of the options panel.
The Bjerksund & Stensland (2002) Approximation
The Bjerksund and Stensland (2002) approximation divides the time to maturity into two parts, each with a separate flat exercise boundary. It is thus a straightforward generalization of the Bjerksund-Stensland 1993 algorithm. The method is fast and efficient and should be more accurate than the Barone-Adesi and Whaley (1987) and the Bjerksund and Stensland (1993b) approximations. The algorithm requires an accurate cumulative bivariate normal approximation. Several approximations that are described in the literature are not sufficiently accurate, but the Genze algorithm works.
C = alpha2*S^B - alpha2*phi(S, t1, B, I2, I2)
+ phi(S, t1, I2, I2) - phi(S, t1, I, I1, I2)
- X*phi(S, t1, 0, I2, I2) + X*phi(S, t1, 0, I1, I2)
+ alpha1*phi(X, t1, B, I1, I2) - alpha1*psi*St, T, B, I1, I2, I1, t1)
+ psi(S, T, 1, I1, I2, I1, t1) - psi(S, T, 1, X, I2, I1, t1)
- X*psi(S, T, 0, I1, I2, I1, t1) + psi(S, T, 0 ,X, I2, I1, t1)
where
alpha1 = (I1 - X)*I1^-B
alpha2 = (I2 - X)*I2^-B
B = (1/2 - b/v^2) + ((b/v^2 - 1/2)^2 + 2*(r/v^2))^0.5
The function psi(S, T, y, H, I) is given by
psi(S, T, gamma, H, I) = e^lambda * S^gamma * (N(-d) - (I/S)^k * N(-d2))
d = (log(S/H) + (b + (gamma - 1/2) * v^2) * T) / (v * T^0.5)
d2 = (log(I^2/(S*H)) + (b + (gamma - 1/2) * v^2) * T) / (v * T^0.5)
lambda = -r + gamma * b + 1/2 * gamma * (gamma - 1) * v^2
k = 2*b/v^2 + (2 * gamma - 1)
and the trigger price I is defined as
I1 = B0 + (B(+infi) - B0) * (1 - e^h1)
I2 = B0 + (B(+infi) - B0) * (1 - e^h2)
h1 = -(b*t1 + 2*v*t1^0.5) * (X^2 / ((B(+infi) - B0))*B0)
h2 = -(b*T + 2*v*T^0.5) * (X^2 / ((B(+infi) - B0))*B0)
t1 = 1/2 * (5^0.5 - 1) * T
B(+infi) = (B / (B - 1)) * X
B0 = max(X, (r / (r - b)) * X)
Moreover, the function psi(S, T, gamma, H, I2, I1, t1) is given by
psi(S, T, gamma, H, I2, I1, t1, r, b, v) = e^(lambda * T) * S^gamma * (M(-e1, -f1, rho) - (I2/S)^k * M(-e2, -f2, rho)
- (I1/S)^k * M(-e3, -f3, -rho) + (I1/I2)^k * M(-e4, -f4, -rho))
where (see screenshot for e and f values)
b=r options on non-dividend paying stock
b=r-q options on stock or index paying a dividend yield of q
b=0 options on futures
b=r-rf currency options (where rf is the rate in the second currency)
Inputs
S = Stock price.
K = Strike price of option.
T = Time to expiration in years.
r = Risk-free rate
c = Cost of Carry
V = Variance of the underlying asset price
cnd1(x) = Cumulative Normal Distribution
cbnd3(x) = Cumulative Bivariate Normal Distribution
nd(x) = Standard Normal Density Function
convertingToCCRate(r, cmp ) = Rate compounder
Numerical Greeks or Greeks by Finite Difference
Analytical Greeks are the standard approach to estimating Delta, Gamma etc... That is what we typically use when we can derive from closed form solutions. Normally, these are well-defined and available in text books. Previously, we relied on closed form solutions for the call or put formulae differentiated with respect to the Black Scholes parameters. When Greeks formulae are difficult to develop or tease out, we can alternatively employ numerical Greeks - sometimes referred to finite difference approximations. A key advantage of numerical Greeks relates to their estimation independent of deriving mathematical Greeks. This could be important when we examine American options where there may not technically exist an exact closed form solution that is straightforward to work with. (via VinegarHill FinanceLabs)
Things to know
Only works on the daily timeframe and for the current source price.
You can adjust the text size to fit the screen
Nadaraya-Watson CombineThis is a combination of the Lux Algo Nadaraya-Watson Estimator and Envelope. Please note the repainting issue.
In addition, I've added a plot of the actual values of the current barstate of
the Nadaraya-Watson windows as they are computed (lines 92-95). It only plots values for the current data at
each time update. It is interesting to compare the trajectory of the end points of the Estimator and
Envelope to the smoothing function at each time update. Due to the kernel smoothing at each update the
history is lost at each update (repaint).
I've added a feature to allow adjustment to the kernel smoothing algorithm as suggested by thomsonraja (line 59).
The settings and usage are repeated from Lux Algo below.
Settings
Window Size: Determines the number of recent price observations to be used to fit the Nadaraya-Watson Estimator.
Bandwidth: Controls the degree of smoothness of the envelopes , with higher values returning smoother results.
Mult: Controls the envelope width.
Src: Input source of the indicator.
Kernel power: See line 59, adjusts the exponential power (powh) as suggested by thomsonraja
Kernel denominator: See line 59, adjusts the denominator (den) as suggested by thomsonraja
Usage
This tool outlines extremes made by the prices within the selected window size.
This is achieved by estimating the underlying trend in the price using kernel smoothing,
calculating the mean absolute deviations from it, and adding/subtracting it
from the estimated underlying trend.
I repeat Lux Algo's caution: 'we do not recommend this tool to be used alone
or solely for real time applications.'
End-Pointed SSA of Normalized Price Corridor [Loxx]End-Pointed SSA of Normalized Price Corridor is an end-pointed SSA of normalized input price to output a smoothed normalized oscillator of price. Corridors are added in attempt to decipher larger trend direction of price. These corridor trend lines are based on highs and lows of price. Due to the SSA algorithm, this indicator takes some time load on the chat, so be patient. You can adjust the lag parameter downward to speed up the indicator load time but this will also degrade the signal. There are many different ways to use this indicator. It is also Renko chart friendly.
An example of emerging trends (these do not repaint)
What is Singular Spectrum Analysis ( SSA )?
Singular spectrum analysis ( SSA ) is a technique of time series analysis and forecasting. It combines elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing. SSA aims at decomposing the original series into a sum of a small number of interpretable components such as a slowly varying trend, oscillatory components and a ‘structureless’ noise. It is based on the singular value decomposition ( SVD ) of a specific matrix constructed upon the time series. Neither a parametric model nor stationarity-type conditions have to be assumed for the time series. This makes SSA a model-free method and hence enables SSA to have a very wide range of applicability.
For our purposes here, we are only concerned with the "Caterpillar" SSA . This methodology was developed in the former Soviet Union independently (the ‘iron curtain effect’) of the mainstream SSA . The main difference between the main-stream SSA and the "Caterpillar" SSA is not in the algorithmic details but rather in the assumptions and in the emphasis in the study of SSA properties. To apply the mainstream SSA , one often needs to assume some kind of stationarity of the time series and think in terms of the "signal plus noise" model (where the noise is often assumed to be ‘red’). In the "Caterpillar" SSA , the main methodological stress is on separability (of one component of the series from another one) and neither the assumption of stationarity nor the model in the form "signal plus noise" are required.
"Caterpillar" SSA
The basic "Caterpillar" SSA algorithm for analyzing one-dimensional time series consists of:
Transformation of the one-dimensional time series to the trajectory matrix by means of a delay procedure (this gives the name to the whole technique);
Singular Value Decomposition of the trajectory matrix;
Reconstruction of the original time series based on a number of selected eigenvectors.
This decomposition initializes forecasting procedures for both the original time series and its components. The method can be naturally extended to multidimensional time series and to image processing.
The method is a powerful and useful tool of time series analysis in meteorology, hydrology, geophysics, climatology and, according to our experience, in economics, biology, physics, medicine and other sciences; that is, where short and long, one-dimensional and multidimensional, stationary and non-stationary, almost deterministic and noisy time series are to be analyzed.
Included
Bar coloring
Signals
Alerts
Loxx's Expanded Source Types
End-pointed SSA of Williams %R [Loxx]End-pointed SSA of Williams %R is an indicator that runes Williams %R SSA calculation through a Singular Spectrum Analysis (SSA) algorithm to derive a smoother final output. The reduction in noise from the traditional Williams %R is significant.
What is Williams %R?
Williams %R , also known as the Williams Percent Range, is a type of momentum indicator that moves between 0 and -100 and measures overbought and oversold levels. The Williams %R may be used to find entry and exit points in the market. The indicator is very similar to the Stochastic oscillator and is used in the same way. It was developed by Larry Williams and it compares a stock’s closing price to the high-low range over a specific period, typically 14 days or periods.
What is Singular Spectrum Analysis ( SSA )?
Singular spectrum analysis ( SSA ) is a technique of time series analysis and forecasting. It combines elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing. SSA aims at decomposing the original series into a sum of a small number of interpretable components such as a slowly varying trend, oscillatory components and a ‘structureless’ noise. It is based on the singular value decomposition ( SVD ) of a specific matrix constructed upon the time series. Neither a parametric model nor stationarity-type conditions have to be assumed for the time series. This makes SSA a model-free method and hence enables SSA to have a very wide range of applicability.
For our purposes here, we are only concerned with the "Caterpillar" SSA . This methodology was developed in the former Soviet Union independently (the ‘iron curtain effect’) of the mainstream SSA . The main difference between the main-stream SSA and the "Caterpillar" SSA is not in the algorithmic details but rather in the assumptions and in the emphasis in the study of SSA properties. To apply the mainstream SSA , one often needs to assume some kind of stationarity of the time series and think in terms of the "signal plus noise" model (where the noise is often assumed to be ‘red’). In the "Caterpillar" SSA , the main methodological stress is on separability (of one component of the series from another one) and neither the assumption of stationarity nor the model in the form "signal plus noise" are required.
"Caterpillar" SSA
The basic "Caterpillar" SSA algorithm for analyzing one-dimensional time series consists of:
Transformation of the one-dimensional time series to the trajectory matrix by means of a delay procedure (this gives the name to the whole technique);
Singular Value Decomposition of the trajectory matrix;
Reconstruction of the original time series based on a number of selected eigenvectors.
This decomposition initializes forecasting procedures for both the original time series and its components. The method can be naturally extended to multidimensional time series and to image processing.
The method is a powerful and useful tool of time series analysis in meteorology, hydrology, geophysics, climatology and, according to our experience, in economics, biology, physics, medicine and other sciences; that is, where short and long, one-dimensional and multidimensional, stationary and non-stationary, almost deterministic and noisy time series are to be analyzed.
Included:
Bar coloring
[*Alerts
[*Signals
[*Loxx's Expanded Source Types
Related Williams %R Indicators
Williams %R on Chart w/ Dynamic Zones
Williams %R w/ Bollinger Bands
Intermediate Williams %R w/ Discontinued Signal Lines
Related SSA Indicators
End-pointed SSA of FDASMA
End-pointed SSA of Normalized Price Oscillator
