Rational Qaudratic Kernel Elder Force Index Introduction:
The Rational Quadratic Kernel Elder Force Index is a versatile and mathematically sophisticated technical indicator that enhances the traditional Elder Force Index (EFI) by applying a rational quadratic kernel smoothing technique. This advanced regression method is designed to provide traders with a more adaptive and accurate tool for measuring the strength behind price movements, incorporating the influence of volume. This indicator does not predict future price movements, but it offers a clearer view of market dynamics through its advanced smoothing mechanism.
Key Features:
Elder Force Index Foundation:
The core of this indicator is built on the Elder Force Index, a popular tool developed by Dr. Alexander Elder. The EFI is a momentum indicator that calculates the strength of price movements by combining both price change and volume.
Rational Quadratic Kernel Smoothing: The indicator incorporates a rational quadratic kernel for smoothing, providing traders with a more refined view of price action trends. This kernel is highly adjustable based on user inputs, allowing for flexible tuning to suit individual strategies.
Adaptive Time Frame Weighting:
Through the adjustable parameter Relative Weighting (r), traders can modify the influence of different time frames. Lower values give more weight to longer time frames, while higher values make the behavior resemble that of a Gaussian kernel.
Dynamic Visualization:
The indicator visually displays the smoothed force index as a color-coded line. It dynamically changes color based on market conditions—green when the smoothed force index is positive and red when it is negative—providing a clear and easy-to-read signal for traders.
How It Works:
The Elder Force Index is calculated by multiplying the price change between two bars with the volume of the current bar.
A rational quadratic kernel regression is applied to this raw EFI data. The smoothing process provides a more stable and reliable signal by reducing noise, particularly in volatile markets.
The user-defined parameters, such as the length of the smoothing window and the relative weighting factor, allow traders to customize the indicator to suit their specific trading style or asset class.
User Inputs:
Length:
Sets the smoothing window for the kernel regression. A longer length results in more significant smoothing.
Relative Weighting (r):
Controls the influence of different time frames in the smoothing process. A smaller value emphasizes longer-term data, while a higher value makes it behave more like a traditional Gaussian kernel.
Source:
Select the price source (default is the closing price) for the calculations.
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Volume-Price PercentileDescription:
The "Volume-Price Percentile Live" indicator is designed to provide real-time analysis of the relationship between volume percentiles and price percentiles on any given timeframe. This tool helps traders assess market activity by comparing how current volume levels rank relative to historical volume data and how current price movements (specifically high-low ranges) rank relative to historical price data. The indicator visualizes the ratio of volume percentile to price percentile as a histogram, allowing traders to gauge the relative strength of volume against price movements in real time.
Functionality:
Volume Percentile: Calculates the percentile rank of the current volume within a user-defined rolling period (default is 30 bars). This percentile indicates where the current volume stands in comparison to historical volumes over the specified period.
Price Percentile: Calculates the percentile rank of the current candle's high-low difference within a user-defined rolling period (default is 30 bars). This percentile reflects the current price movement's strength relative to past movements over the specified period.
Percentile Ratio (VP Ratio): The indicator plots the ratio of the volume percentile to the price percentile. This ratio helps identify periods when volume is significantly higher or lower relative to price movement, providing insights into potential market imbalances or strength.
Real-Time Data: By fetching data from a lower timeframe (e.g., 1-minute), the indicator updates continuously within the current timeframe, offering live, intra-candle updates. This ensures that traders can see the histogram change in real-time as new data becomes available, without waiting for the current candle to close.
How to Use:
Adding the Indicator: To use this indicator, add it to your chart on TradingView by selecting it from the Indicators list once it is published publicly.
Setting Parameters:
Volume Period Length: This input sets the rolling window length for calculating the volume percentile (default is 30). You can adjust it based on the desired sensitivity or historical period relevance.
Candle Period Length: This input sets the rolling window length for calculating the price percentile based on the high-low difference of candles (default is 30). Adjust this to match your trading style or analysis period.
Interpreting the Histogram:
The histogram represents the volume percentile divided by the price percentile.
Above 1: A value greater than 1 indicates that volume is relatively strong compared to price movement, which may suggest high activity or potential accumulation/distribution phases.
Below 1: A value less than 1 suggests that price movement is relatively stronger than volume, indicating potential weakness in volume relative to price moves.
Near 1: Values close to 1 suggest a balanced relationship between volume and price movement.
Application: Use this indicator to identify potential breakout or breakdown scenarios, assess the strength of price movements, and confirm trends. When volume percentile consistently leads price percentile, it might signal sustained interest and support for the current price trend. Conversely, if volume percentile lags significantly, it might warn of potential trend weakness.
Best Practices:
Multiple Timeframe Analysis: While the indicator provides real-time updates on any timeframe, consider using it alongside higher timeframe analysis to confirm trends and volume behavior across different periods.
Customization: Adjust the period lengths based on the asset’s typical volume and price behavior, as well as your trading strategy (e.g., short-term scalping vs. long-term trend following).
Complement with Other Indicators: Use this indicator in conjunction with other volume-based tools, trend indicators, or momentum oscillators to gain a comprehensive view of market dynamics.
Gaussian Kernel Smoothing EMAGaussian Kernel Smoothing EMA
The Gaussian Kernel Smoothing EMA integrates the exponential moving average with kernel smoothing techniques to refine the trend tool. Kernel smoothing is a non-parametric technique used to estimate a smooth curve from a set of data points. It is particularly useful in reducing noise and capturing the underlying structure of data. The smoothed value at each point is calculated as a weighted average of neighboring points, with the weights determined by a kernel function.
The Gaussian kernel is a popular choice in kernel smoothing due to its properties of being smooth, symmetric, and having infinite support. This function gives higher weights to data points closer to the target point and lower weights to those further away, resulting in a smooth and continuous estimate. Since price isn't normally distributed a logarithmic transformation is performed to remove most of its skewness to be able to fit the Gaussian kernel.
This indicator also has a bandwidth, which in kernel smoothing controls the width of the window over which the smoothing is performed. It determines how much influence nearby data points have on the smoothed value. In this indicator, the bandwidth is dynamically adjusted based on the standard deviation of the log-transformed prices so that the smoothing adapts to the underlying variability and potential volatility.
Bandwidth Factor: The bandwidth factor in this indicator is used to adjust the degree of the smoothing applied to the MA. In kernel smoothing, Bandwidth controls the width of the window over which the smoothing is applied. It determines how many data points around a central point are considered when calculating a smooth value. A smaller bandwidth results in less smoothing, while a larger bandwidth smooths out more noise, leading to a broader, more general trend.
Support line based on RSIThis indicator builds a support line using the stock price and RSI.
Inputs:
1. Time window for the RSI:
the time window the RSI is calculated with, usually it's 14 but in here I recommend 30.
2. offset by percentage:
just adding or subtructing some percentage of the result, some stocks need a bit of offset to work
3. stability:
the higher it is the less the RSI effects the graph. for realy high stability the indicator the the stock price will be realy close.
formula: (close*(100-newRSI)/50)*(100+offset)/100
when:
newRSI = (RSI + (50 * stability1))/(stability+1)
recommended usage:
Usually, if the indicator becomes higher than the price, (the price lowers). the stock will go up again to around the last price where they met.
so, for example, if the stock price was 20 and going down. while the indicator was 18 and going up, then they met at 19 and later the indicator became 20 while the stock fell to 18. most chances are that the stock will come back to 19 where they met and at the same time the indicator will also get to 19.
In stocks that are unstable, like NVDA. this indicator can be used to see the trend and avoid the unstability of the stock.
Fear/Greed Zone Reversals [UAlgo]The "Fear/Greed Zone Reversals " indicator is a custom technical analysis tool designed for TradingView, aimed at identifying potential reversal points in the market based on sentiment zones characterized by fear and greed. This indicator utilizes a combination of moving averages, standard deviations, and price action to detect when the market transitions from extreme fear to greed or vice versa. By identifying these critical turning points, traders can gain insights into potential buy or sell opportunities.
🔶 Key Features
Customizable Moving Averages: The indicator allows users to select from various types of moving averages (SMA, EMA, WMA, VWMA, HMA) for both fear and greed zone calculations, enabling flexible adaptation to different trading strategies.
Fear Zone Settings:
Fear Source: Select the price data point (e.g., close, high, low) used for Fear Zone calculations.
Fear Period: This defines the lookback window for calculating the Fear Zone deviation.
Fear Stdev Period: This sets the period used to calculate the standard deviation of the Fear Zone deviation.
Greed Zone Settings:
Greed Source: Select the price data point (e.g., close, high, low) used for Greed Zone calculations.
Greed Period: This defines the lookback window for calculating the Greed Zone deviation.
Greed Stdev Period: This sets the period used to calculate the standard deviation of the Greed Zone deviation.
Alert Conditions: Integrated alert conditions notify traders in real-time when a reversal in the fear or greed zone is detected, allowing for timely decision-making.
🔶 Interpreting Indicator
Greed Zone: A Greed Zone is highlighted when the price deviates significantly above the chosen moving average. This suggests market sentiment might be leaning towards greed, potentially indicating a selling opportunity.
Fear Zone Reversal: A Fear Zone is highlighted when the price deviates significantly below the chosen moving average of the selected price source. This suggests market sentiment might be leaning towards fear, potentially indicating a buying opportunity. When the indicator identifies a reversal from a fear zone, it suggests that the market is transitioning from a period of intense selling pressure to a more neutral or potentially bullish state. This is typically indicated by an upward arrow (▲) on the chart, signaling a potential buy opportunity. The fear zone is characterized by high price volatility and overselling, making it a crucial point for traders to consider entering the market.
Greed Zone Reversal: Conversely, a Greed Zone is highlighted when the price deviates significantly above the chosen moving average. This suggests market sentiment might be leaning towards greed, potentially indicating a selling opportunity. When the indicator detects a reversal from a greed zone, it indicates that the market may be moving from an overbought condition back to a more neutral or bearish state. This is marked by a downward arrow (▼) on the chart, suggesting a potential sell opportunity. The greed zone is often associated with overconfidence and high buying activity, which can precede a market correction.
🔶 Why offer multiple moving average types?
By providing various moving average types (SMA, EMA, WMA, VWMA, HMA) , the indicator offers greater flexibility for traders to tailor the indicator to their specific trading strategies and market preferences. Different moving averages react differently to price data and can produce varying signals.
SMA (Simple Moving Average): Provides an equal weighting to all data points within the specified period.
EMA (Exponential Moving Average): Gives more weight to recent data points, making it more responsive to price changes.
WMA (Weighted Moving Average): Allows for custom weighting of data points, providing more flexibility in the calculation.
VWMA (Volume Weighted Moving Average): Considers both price and volume data, giving more weight to periods with higher trading volume.
HMA (Hull Moving Average): A combination of weighted moving averages designed to reduce lag and provide a smoother curve.
Offering multiple options allows traders to:
Experiment: Traders can try different moving averages to see which one produces the most accurate signals for their specific market.
Adapt to different market conditions: Different market conditions may require different moving average types. For example, a fast-moving market might benefit from a faster moving average like an EMA, while a slower-moving market might be better suited to a slower moving average like an SMA.
Personalize: Traders can choose the moving average that best aligns with their personal trading style and risk tolerance.
In essence, providing a variety of moving average types empowers traders to create a more personalized and effective trading experience.
🔶 Disclaimer
Use with Caution: This indicator is provided for educational and informational purposes only and should not be considered as financial advice. Users should exercise caution and perform their own analysis before making trading decisions based on the indicator's signals.
Not Financial Advice: The information provided by this indicator does not constitute financial advice, and the creator (UAlgo) shall not be held responsible for any trading losses incurred as a result of using this indicator.
Backtesting Recommended: Traders are encouraged to backtest the indicator thoroughly on historical data before using it in live trading to assess its performance and suitability for their trading strategies.
Risk Management: Trading involves inherent risks, and users should implement proper risk management strategies, including but not limited to stop-loss orders and position sizing, to mitigate potential losses.
No Guarantees: The accuracy and reliability of the indicator's signals cannot be guaranteed, as they are based on historical price data and past performance may not be indicative of future results.
US Market Real Value Adjusted for CPI and Dollar IndexUS Market Real Value Adjusted for CPI and Dollar Index
Provides quick access to this formula: (SP:SPX+NASDAQ_DLY:IXIC+TVC:DJI+CAPITALCOM:RTY)/4/(ECONOMICS:USCPI*TVC:DXY*100)
Overview:
This indicator provides a dynamic view of the US stock market's real value, adjusted for inflation and currency strength. It combines major stock indices including the S&P 500, NASDAQ, Dow Jones, and Russell 2000, and adjusts the composite index using the US Consumer Price Index (CPI) and the US Dollar Index (DXY). This adjustment helps to reveal the true market performance, stripped of inflationary effects and currency valuation changes.
Key Features:
Composite Index Calculation: Averages the prices of SPX, IXIC, DJI, and RTY to create a broad market overview.
Inflation Adjustment: Uses the CPI to adjust for the effects of inflation, ensuring that the real value changes in the stock market are highlighted.
Currency Strength Adjustment: Applies the DXY to account for fluctuations in the strength of the US dollar, providing insights into how currency variations impact market valuation.
Dynamic Base Calculation: Utilizes a rolling window to dynamically update base values, allowing for continuous reassessment of the market’s adjusted value as new data becomes available.
This indicator provides:
Real Value Insights: By adjusting for both inflation and currency strength, this indicator offers a more accurate measure of the underlying market conditions.
Dynamic Updates: With a rolling window approach, the indicator continually adapts, providing up-to-date information.
Strategic Decisions: Helps in identifying true market growth or decline periods, aiding in strategic investment planning.
Usage:
To use this indicator, simply add it to your chart, and it will automatically display the adjusted composite index. This index can be particularly useful for investors looking to understand underlying market trends beyond nominal price movements, helping in making more informed investment decisions when comparing certain tickers to an average of the major US stock market indexes, adjusted for inflation and the strength of the US dollar.
Example Use Case:
A typical use case might involve comparing periods of high inflation to see how the overall US stock market performed in real terms, not just nominal terms. This can indicate whether the market growth was genuine or merely a reflection of inflation. By comparing this result to an average of these major indexes without adjusting for inflation or currency strength changes, you can see how significantly these forces can impact real gains or losses.
test - ClassificationTensor-Based Classification Experiment
This innovative script represents an experimental foray into classification techniques, specifically designed to analyze returns within a compact time frame. By leveraging tensor-based analytics, it generates a comprehensive table that visually illustrates the distribution of counts across both current and historical bars, providing valuable insights into market patterns.
The script's primary objective is to classify returns over a small window, using this information to inform trading decisions. The output table showcases a normal distribution of count values for each bar in the lookback period, allowing traders to gain a deeper understanding of market behavior and identify potential opportunities.
Key Features:
Experimental classification approach utilizing tensor-based analytics
Compact time frame analysis (small window)
Comprehensive table displaying return counts across current and historical bars
Normal distribution visualization for better insight into market patterns
By exploring this script, traders can gain a deeper understanding of the underlying dynamics driving market movements and develop more effective trading strategies.
Market Sentiment Fear and Greed [AlgoAlpha]Unleash the power of sentiment analysis with the Market Sentiment Fear and Greed Indicator! 📈💡 This tool provides insights into market sentiment, helping you make informed trading decisions. Let's dive into its key features and how it works. 🚀✨
Key Features 🎯
🧠 Sentiment Analysis : Calculates market sentiment using volume and price data. 📊
📅 Customizable Lookback Window : Adjust the lookback period to fine-tune sensitivity. 🔧
🎨 Bullish and Bearish Colors : Visualize trends with customizable colors. 🟢🔴
🚀 Impulse Detection : Identifies bullish and bearish impulses for trend confirmation. 🔍
📉 Normalized Sentiment Index : Offers a normalized view of market sentiment. 📊
🔔 Alerts : Set alerts for key sentiment changes and trend impulses. 🚨
🟢🔴 Table Visualization : Displays sentiment strength using a gradient color table. 🗂️
How to Use 📖
Maximize your trading potential with this indicator by following these steps:
🔍 Add the Indicator : Search for "Market Sentiment Fear and Greed " in TradingView's Indicators & Strategies. Customize settings like the lookback window and trend breakout threshold to suit your trading strategy.
📊 Monitor Sentiment : Watch the sentiment gauge and plot changes to detect market sentiment shifts. Use the Normalized Sentiment Index for a more balanced view.
🚨 Set Alerts : Enable alerts for sentiment flips and trend impulses to stay ahead of market movements.
How It Works ⚙️
The indicator calculates market sentiment by averaging the volume and closing prices over a user-defined lookback period, creating a sentiment score. It differentiates between bullish and bearish sentiment by evaluating whether the closing price is higher or lower than the opening price, summing the respective volumes. The true sentiment is determined by comparing these summed values, with a positive score indicating bullish sentiment and a negative score indicating bearish sentiment. The indicator further normalizes this sentiment score by dividing it by the EMA of the highest high minus the lowest low over double the lookback period, ensuring values are constrained between -1 and 1. Bullish and bearish impulses are identified using Hull Moving Averages (HMA) of the positive and negative sentiments, respectively. When these impulses exceed a calculated threshold based on the standard deviation of the sentiment, it indicates a significant trend change. The script also includes a gradient color table to visually represent the strength of sentiment, and customizable alerts to notify users of key sentiment changes and trend impulses.
Unlock deeper insights into market sentiment and elevate your trading strategy with the Market Sentiment Fear and Greed Indicator! 📈✨
BB Position CalculatorPosition Size Calculator Instructions
Overview
The Position Size Calculator is designed to help traders automatically determine the appropriate lot size based on the dollar amount they are willing to risk. It includes features for automatic lot sizing, fixed lot risk calculations, take profit calculations (both automatic and fixed), max run-up, and max drawdown. Calculated values are displayed in ticks, points, and USD.
Key Features
• Automatic Lot Sizing: Automatically calculates lot size based on the amount of money you are willing to risk.
• Fixed Lot Risk Calculations: Provides risk calculations for fixed lot sizes.
• Take Profit Calculations: Offers both automatic and fixed take profit calculations.
• Max Run-Up and Max Drawdown: Monitors and displays the maximum run-up and drawdown of your trade.
• Detailed Metrics: Displays all calculated values in ticks, points, and USD.
Setup Instructions
1. Add and Remove for Each Position: The calculator is designed to be added to your chart for each new position. Once your preferences are set the first time, save them as your default to retain your settings for future use.
2. Adding the Indicator to Favorites:
• Use the TradingView keyboard shortcut “/” then type “pos.”
• Use the arrow key to select the Position Size Calculator and press enter.
• Close the indicator selection pop-up.
3. Setting the Trigger Price:
• A blue pop-up labeled “SET TRIGGER PRICE” will appear at the bottom of the chart.
• Click on the chart at the price level where you want to enter the trade.
4. Setting the Stop Loss:
• The pop-up will change to “SET STOP LOSS.”
• Click on the chart at the price level where your stop loss will be set.
5. Setting the Take Profit:
• The pop-up will change to “SET TAKE PROFIT.”
• Click on the chart at the price level where you want to take profit. If you have selected the option to overwrite with a set risk/reward ratio (R:R), the calculation will use this price level.
6. Setting the Trade Window Start:
• The pop-up will change to “SET TRADE WINDOW START.”
• Click on the bar in time where you want the indicator to start monitoring for price to trigger the position.
7. Adjusting the Position:
• Clicking on any part of the indicator will display draggable lines, allowing you to fine-tune the position that was previously plotted by the first four chart clicks.
Additional Notes
• Compatibility: This calculator has only been tested with futures trading.
• Customization: Once your preferences are set, save them as your default to make setup quicker for future trades.
• Support: If you have any questions or feature requests, please feel free to reach out.
Holding ValueTrading view requires me to add a larger description:
Short done by me: Show your holding value at each candlestick
Chatgpt:
This Pine Script plugin is designed to help traders and investors visualize the current value of their holdings in USD directly on TradingView charts. The plugin calculates the total value of the specified holdings based on the closing price of each candle and displays this value dynamically, formatted to show in thousands (k) or millions (m) with a maximum of two decimal places.
Features
Holdings Input: Allows the user to input the amount of the asset they hold.
Dynamic Calculation: Calculates the current value of holdings based on the closing price of each candle.
Formatted Display: Formats the value in thousands or millions with up to two decimal places.
Chart Label: Displays the formatted value as a label on the chart.
Data Window Display: Uses plotchar to display the current value in the data window without plotting it directly on the chart.
Distance from MA (%)Purpose:
This indicator calculates and plots the distance in percentage between the current price and a specified moving average. The distance is displayed in a separate window below the main price chart.
Features:
Configurable Moving Average Period: You can set the period for the moving average calculation.
Multiple Moving Average Methods: The indicator supports various moving average methods, including Simple Moving Average (SMA), Exponential Moving Average (EMA), Weighted Moving Average (WMA), and Volume Weighted Moving Average (VWMA).
Applied Price Selection: You can choose which price to use for the moving average calculation (e.g., close, open, high, low, etc.).
Parameters:
MA Period: The number of periods to use for the moving average calculation.
MA Method: The type of moving average to use (SMA, EMA, WMA, VWMA).
Applied Price: The price used for the moving average calculation.
Calculation:
Moving Average Calculation:
Depending on the selected method, the indicator calculates the moving average (MA) value for each bar using the specified period and applied price.
Distance in Percentage:
The distance is calculated as the difference between the current price and the moving average value, divided by the moving average value, and then multiplied by 100 to convert it to a percentage.
Formula: Distance %=(Applied Price−MA ValueMA Value)×100Distance %=(MA ValueApplied Price−MA Value)×100
Plotting:
The indicator plots the calculated distance in percentage as a line in a separate window below the main chart. The plot is colored red and has a linewidth of 2 for better visibility.
MathTransformLibrary "MathTransform"
Auxiliary functions for transforming data using mathematical and statistical methods
scaler_zscore(x, lookback_window)
Calculates Z-Score normalization of a series.
Parameters:
x (float) : : floating point series to normalize
lookback_window (int) : : lookback period for calculating mean and standard deviation
Returns: Z-Score normalized series
scaler_min_max(x, lookback_window, min_val, max_val, empiric_min, empiric_max, empiric_mid)
Performs Min-Max scaling of a series within a given window, user-defined bounds, and optional midpoint
Parameters:
x (float) : : floating point series to transform
lookback_window (int) : : int : optional lookback window size to consider for scaling.
min_val (float) : : float : minimum value of the scaled range. Default is 0.0.
max_val (float) : : float : maximum value of the scaled range. Default is 1.0.
empiric_min (float) : : float : user-defined minimum value of the input data. This means that the output could exceed the `min_val` bound if there is data in `x` lesser than `empiric_min`. If na, it's calculated from `x` and `lookback_window`.
empiric_max (float) : : float : user-defined maximum value of the input data. This means that the output could exceed the `max_val` bound if there is data in `x` greater than `empiric_max`. If na, it's calculated from `x` and `lookback_window`.
empiric_mid (float) : : float : user-defined midpoint value of the input data. If na, it's calculated from `empiric_min` and `empiric_max`.
Returns: rescaled series
log(x, base)
Applies logarithmic transformation to a value, base can be user-defined.
Parameters:
x (float) : : floating point value to transform
base (float) : : logarithmic base, must be greater than 0
Returns: logarithm of the value to the given base, if x <= 0, returns logarithm of 1 to the given base
exp(x, base)
Applies exponential transformation to a value, base can be user-defined.
Parameters:
x (float) : : floating point value to transform
base (float) : : base of the exponentiation, must be greater than 0
Returns: the result of raising the base to the power of the value
power(x, exponent)
Applies power transformation to a value, exponent can be user-defined.
Parameters:
x (float) : : floating point value to transform
exponent (float) : : exponent for the transformation
Returns: the value raised to the given exponent, preserving the sign of the original value
tanh(x, scale)
The hyperbolic tangent is the ratio of the hyperbolic sine and hyperbolic cosine. It limits an output to a range of −1 to 1.
Parameters:
x (float) : : floating point series
scale (float)
sigmoid(x, scale, offset)
Applies the sigmoid function to a series.
Parameters:
x (float) : : floating point series to transform
scale (float) : : scaling factor for the sigmoid function
offset (float) : : offset for the sigmoid function
Returns: transformed series using the sigmoid function
sigmoid_double(x, scale, offset)
Applies a double sigmoid function to a series, handling positive and negative values differently.
Parameters:
x (float) : : floating point series to transform
scale (float) : : scaling factor for the sigmoid function
offset (float) : : offset for the sigmoid function
Returns: transformed series using the double sigmoid function
logistic_decay(a, b, c, t)
Calculates logistic decay based on given parameters.
Parameters:
a (float) : : parameter affecting the steepness of the curve
b (float) : : parameter affecting the direction of the decay
c (float) : : the upper bound of the function's output
t (float) : : time variable
Returns: value of the logistic decay function at time t
Volatility DashboardThis indicator calculates and displays volatility metrics for a specified number of bars (rolling window) on a TradingView chart. It can be customized to display information in English or Thai and can position the dashboard at various locations on the chart.
Inputs
Language: Users can choose between English ("ENG") and Thai ("TH") for the dashboard's language.
Dashboard Position: Users can specify where the dashboard should appear on the chart. Options include various positions such as "Bottom Right", "Top Center", etc.
Calculation Method: Currently, the script supports "High-Low" for volatility calculation. This method calculates the difference between the highest and lowest prices within a specified timeframe.
Bars: Number of bars used to calculate the volatility.
Display Logic
Fills the islast_vol_points array with the calculated volatility points.
Sets the table cells with headers and corresponding values:
=> Highest Volatility: The maximum value in the islast_vol_points array
=> Mean Volatility: The average value in the islast_vol_points array,
=> Lowest Volatility: The minimum value in the islast_vol_points array, Number of Bars: The rolling window size.
Rolling Volatility Indicator
Description :
The Rolling Volatility indicator calculates the volatility of an asset's price movements over a specified period. It measures the degree of variation in the price series over time, providing insights into the market's potential for price fluctuations.
This indicator utilizes a rolling window approach, computing the volatility by analyzing the logarithmic returns of the asset's price. The user-defined length parameter determines the timeframe for the volatility calculation.
How to Use :
Adjust the "Length" parameter to set the rolling window period for volatility calculation.
Ajust "trading_days" for the sampling period, this is the total number of trading days (usually 252 days for stocks and 365 for crypto)
Higher values for the length parameter will result in a smoother, longer-term view of volatility, while lower values will provide a more reactive, shorter-term perspective.
Volatility levels can assist in identifying periods of increased market activity or potential price changes. Higher volatility may suggest increased risk and potential opportunities, while lower volatility might indicate periods of reduced market activity.
Key Features :
Customizable length parameter for adjusting the calculation period and trading days such that it can also be applied to stock market or any markets.
Visual representation of volatility with a plotted line on the chart.
The Rolling Volatility indicator can be a valuable tool for traders and analysts seeking insights into market volatility trends, aiding in decision-making processes and risk management strategies.
MacroTrend VisionThe "MacroTrend Vision" indicator is crafted with a singular goal – to provide traders with a quick and insightful snapshot of a country's global index. Seamlessly combining macroeconomic and technical perspectives, this tool is designed for those seeking a straightforward yet comprehensive overview. Let's explore the key features that make the "MacroTrend Vision" a valuable asset for traders looking to grasp both the big-picture economic context and technical nuances.
1. Long-Term Vision with Weekly Periods:
Gain a genuine long-term perspective with the ability to process 2500 weekly periods. This feature ensures a holistic understanding of global indices from both macroeconomic and technical viewpoints.
2. Composite Leading Indicator (CLI) Conditions:
Integrate both macroeconomic trends and technical signals through Composite Leading Indicator (CLI) conditions derived from the Relative Strength Index (RSI), offering a comprehensive outlook for informed decision-making.
3. Deviation Bands for Volatility Analysis:
Refine market analysis with strategically integrated deviation bands (0.2 and 0.4) based on smoothed linear regression. Anticipate volatility and potential trend shifts, aligning macro and technical insights.
4. Logarithmic Scale Transformation:
Enhance precision in understanding price movements with a logarithmic scale transformation, especially beneficial for assets with exponential growth patterns.
5. Separated Window for Easy Navigation:
Streamline your analysis with a user-friendly design – a separated window allowing easy navigation through different symbols without altering indicator settings.
6. Alert System for CLI Conditions:
Stay informed about critical shifts with an alert system for both long and close conditions based on the RSI of the CLI. Even during periods of limited chart monitoring, this feature keeps you connected to macroeconomic and technical changes.
In essence, the "MacroTrend Vision" is your go-to tool for a balanced view, simplifying the complexities of global indices with a blend of macroeconomic insights and technical clarity.
Range Detector [LuxAlgo]The Range Detector indicator aims to detect and highlight intervals where prices are ranging. The extremities of the ranges are highlighted in real-time, with breakouts being indicated by the color changes of the extremities.
🔶 USAGE
Ranging prices are defined by a period of stationarity, that is where prices move within a specific range.
Detecting ranging markets is a common task performed manually by traders. Price breaking one of the extremities of a range can be indicative of a new trend, with an uptrend if price breaks the upper range extremity, and a downtrend if price breaks the lower range extremity.
Ranges are highlighted as zones and are set retrospectively, that is the starting point of a range is offset in the past. The exact moment a range is detected is highlighted by a gray background color. The average between the maximum/minimum of a zone is also highlighted as a dotted line and is also set retrospectively.
The range extremities are set in real-time, blue extremities indicate the range extremities were not broken, green extremities indicate that price broke the upper range extremity, while red extremities indicate price broke the lower range extremity.
Extremities are extended until a new range is detected, allowing past ranges extremities can be used as future support/resistances.
🔶 DETAILS
The detection algorithm used to detect ranges tests if all the prices within a user-set window are all within two extremities. These extremities are determined by the mean of the detection window plus/minus an ATR value.
When a new range is detected, the script checks if this new range overlaps with a previously detected range, if this is the case, both ranges are merged into one; updating the extremities of the previous range.
This can be observed with the real-time extremities changing within a highlighted zone.
🔶 SETTINGS
Minimum Range Length: Minimum amount of bars needed to detect a range.
Range Width: Multiplicative factor for the ATR used to detect new ranges. Lower values detect ranges with a lower width. Using higher values might return false positives.
ATR Length: ATR length used to determine the range width.
Histogram-based price zonesThis indicator provides a new approach to creating price zones that can be used as support and resistance. The approach does not use pivot points or Fibonacci levels. Instead, it uses the frequency of occurence of local maxima and minima to determine zones of interest where price often changed direction.
The algorithm is as follows:
- Gather price data from the last Lookback trading periods
- Calculate rolling minima and rolling maxima along the price points with window size Window size
- Build a histogram from the rolling extrema which are binned into different zones. The number of bins and therefore the width of a zone can be adjusted with the parameter Zone width factor
- Select only the top fullest bins. The number of bins selected for plotting can be controlled with Zone multiplier
The result are a number of boxes that appear on the chart which mark levels of interest to watch for. You can combine multiple instances of this indicator on different settings to find zones that are very relevant.
Shown as an example is the Nasdaq 100 futures ( NQ1! ) on the D timeframe with levels built from the last 100 periods with default settings. The boxes are the only output of the indicator, no signals are created.
Supertrend Targets [ChartPrime]The Supertrend Targets indicator combines the concepts of trend-following with dynamic volatility-based target levels. It takes core simple and classical concepts and provides actionable insights. The core of this indicator revolves around the "Supertrend" algorithm, which essentially uses the Average True Range (ATR) and a multiplier to determine if the price of a financial instrument is in an uptrend or downtrend. The indicator generates various plot points on the trading chart, which traders can use to make informed trading decisions.
Users can set several input parameters such as the source price, custom levels, multiplier scale, length of the average true range, and the window length. Traders can also opt to enable a table that shows numeric target data by percentiles, risk ratio, take profit and stop loss points.
The generated plots and fills on the chart represent various levels of potential gains and drawdowns, acting as potential targets for taking profit or stopping losses. These include the 25th, 50th, 75th, 90th, and 100th percentiles, which are adjustable by scale. There are also plots for average gain and drawdown levels, enhanced by standard deviation curves if enabled.
The Supertrend line indicators are color-coded for ease of understanding: blue for bullish performance and orange for bearish performance. The "Center Line" represents the point at which traders might consider entering a position.
Lastly, the script presents a summary table (when enabled) at the right side of the chart displaying numeric data of the plotted targets. This data provides additional insights on the risk-reward balance for each percentile, helping traders to execute their strategies more effectively.
Here's a comprehensive breakdown of its functionalities and features:
Inputs:
Source: Determines the price series type (e.g., Close, Open, High, Low, etc.).
Show Trailing Stop: Option to display the trailing stop on the chart.
Levels: Sets the number of target levels you want to display. Can range from -5 to 5.
Scale: A scaling factor for adjusting targets, can be between 1 to 100.
Window Length: Length for the target computation, determines how many bars will be considered.
Unique: Ensures every data point used in calculations is unique.
Multiplier: Multiplier for the ATR (Average True Range) to compute the SuperTrend.
ATR Length: Period for the ATR computation.
Custom Level: Allows users to set their own levels using various statistics like Average, Average + STDEV, Percentile, or can be disabled.
Percent Rank: Determines the percentile rank for targeting.
Enable Table: Enables or disables a table display.
Methods:
Flag: Identifies bullish and bearish trend reversals.
Target Percent: Determines the expected price movement (both gains and drawdowns) based on historical trend reversals.
Value Percent: Computes the percentage difference between the current price and the entry price during trend reversals.
Plots:
Multiple target lines are plotted on the chart to visualize potential gain and drawdown levels. These levels are adjusted based on user settings. Additionally, the main Supertrend line is plotted to indicate the prevailing trend direction.
Gain Levels: Target levels which show potential upside from the current price.
Drawdown Levels: Target levels which represent potential downside from the current price.
SuperTrend Line: A line that adjusts based on price volatility and trend direction, acting as a dynamic support or resistance.
In conclusion, the "Supertrend Targets " indicator is a powerful tool that combines the principle of trend-following with dynamic targets, providing traders with insights into potential future price movements. The range of customization options allows traders to adapt the indicator to different trading strategies and market conditions.
RiskToolsLibrary "RiskTools"
Provides functions for calculating risk metrics
pctDrop(start, result)
Calculates what is the percentage drop from a reference price
Parameters:
start (float) : Starting price before the drop occurred
result (float) : Resulting price to which the percentage drop occurred
Returns: Percentage drop from "start" to "result"
priceBeforeDrop(pctDrop, result)
Calculates a starting price given a resulting price and a percentage drop to that price
Parameters:
pctDrop (float) : Percentage drop
result (float) : Resulting price to which the percentage drop occurred
Returns: The starting price from which a percentage drop "pctDrop" gave a "result"
dropzone(price, masource, malength, window, zonesize)
Calculates drop zone as an integer representing some multiple of the "zoning size"
Parameters:
price (float) : The current price from which you want to calculate the drop zone
masource (float) : The source series used in the SMA calculation from which the floor price is determined
malength (simple int) : The length used in the SMA calculation from which the floor price is determined
window (simple int) : The lookback period from which to calculate the floor price
zonesize (simple int)
Returns: The zone identifier as a multiple of the zone size. A value of zero or less is translated to the first zone.
OBV Oscillator Volume FilterOBV Oscillator Volume Filter
Introduction
The On-Balance Volume (OBV) is a widely-used technical indicator that aims to relate price and volume in trading. Price and volume are two of the most basic and yet crucial concepts in price movement. Together, they can reveal a lot about the instruments trends and the market's sentiment. This On Balance Volume (OBV) Oscillator incorporates enhanced features like a volume filter using a rolling window to detect outliers in accumulated volume, making it an advanced and more refined version of the standard OBV.
Interpreting the OBV Indicator
The primary function of the OBV is to accumulate volume. In simpler terms:
When the market closes higher than the previous candle, all of that candle's volume is considered 'up-volume'.
Conversely, when the market closes lower than the previous day, all of that candle's volume is considered 'down-volume'.
A rising OBV suggests that volume is being accumulated, indicating bullish market sentiment. A declining OBV, on the other hand, points to a bearish sentiment.
Features of the Script
1. Moving Averages Selection:
The script provides users with the option to select among six types of moving averages (EMA, DEMA, TEMA, SMA, WMA, HMA) to calculate the OBV. This feature offers flexibility and enables traders to choose an MA type they're most comfortable with or find the most effective.
2. Smoothing Option:
To reduce the inherent noise in the indicator, there's an option to apply smoothing. It uses a Simple Moving Average (SMA) to produce a clearer signal, making it easier for traders to interpret and respond to. If you don't want to use smoothing, just simply change the input length of smoothing to 1 in the settings.
3. Outlier Detection:
One of the standout features is the use of a rolling window to detect volume outliers. This ensures that the OBV only reacts to significant volume changes and isn't overly influenced by random spikes or drops. The volume filter is calculated based on a % of the highest OBV volume of X number of bars back. Users can adjust the time (# bars) and the sensitivity (%) of the volume filter. A longer timeperiode (# bars) and a higher % (sensitivity) in the settings result to less signals presented by the indicator.
4. Divergence Detection:
The script automatically highlights both regular and hidden divergences on the chart. Divergences can be a powerful signal of potential price reversals. This feature aids traders in spotting potential buy or sell opportunities based on divergences between price and OBV.
Regular Bullish Divergence: When the price makes lower lows, but the OBV makes higher lows.
Hidden Bullish Divergence: When the price makes higher lows, but the OBV makes lower lows.
Regular Bearish Divergence: When the price makes higher highs, but the OBV makes lower highs.
Hidden Bearish Divergence: When the price makes lower highs, but the OBV makes higher highs.
5. Alerts for Trend Reversals:
The script incorporates alerts that notify traders when the OBV indicates potential trend reversals. This feature can be instrumental in catching early entries or exits.
Disclaimer
It's crucial to understand that no single indicator should be used in isolation. To increase the probability of making accurate market predictions, always use the OBV Oscillator in conjunction with other indicators and tools. Remember that all trading involves risk, and it's possible to lose your invested capital. Always seek advice from a financial advisor before making any trading decisions. By enhancing the OBV with features like the volume filter, multiple MA types, smoothing, and divergence detection, this script becomes a potent tool in a trader's arsenal. Use it wisely, and always ensure to maintain proper risk management.
Candles In Row (Expo)█ Overview
The Candles In Row (Expo) indicator is a powerful tool designed to track and visualize sequences of consecutive candlesticks in a price chart. Whether you're looking to gauge momentum or determine the prevailing trend, this indicator offers versatile functionality tailored to the needs of active traders. The Candles In Row indicator can be an integral part of a multi-timeframe trading strategy, allowing traders to understand market momentum, and set trading bias. By recognizing the patterns and likelihood of future price movements, traders can make more informed decisions and align their trades with the overall market direction.
█ How to use
The indicator enhances traders' understanding of the consecutive candle patterns, helping them to uncover trends and momentum. Consecutive candles in the same direction may indicate a strong trend. The Candles In Row indicator can be an essential tool for traders employing a multiple timeframes strategy.
Analyzing a Higher Timeframe:
Understanding Momentum: By analyzing consecutive green or red candles in a higher timeframe, traders can identify the prevailing momentum in the market. A series of green candles would suggest an upward trend, while a series of red candles would indicate a downward trend.
Predicting Next Candle: The indicator's predictive feature calculates the likelihood of the next candle being green or red based on historical patterns. This probability helps traders gauge the potential continuation of the trend.
Setting the Trading Bias: If the likelihood of the next candle being green is high, the trader may decide to focus on long (buy) opportunities. Conversely, if the likelihood of the next candle being red is high, the trader may look for short (sell) opportunities.
In this example, we are using the Heikin Ashi candles.
Moving to a Lower Timeframe:
Finding Entry Points: Once the trading bias is set based on the higher timeframe analysis, traders can switch to a lower timeframe to look for entry points in the direction of the bias. For example, if the higher timeframe suggests a high likelihood of a green candle, traders may look for buy opportunities in the lower timeframe.
Combining Timeframes for a Comprehensive Strategy:
Confirmation and Alignment: By analyzing the higher timeframe and confirming the direction in the lower timeframe, traders can ensure that they are trading in alignment with the broader trend.
Avoiding False Signals: By using a higher timeframe to set the trading bias and a lower timeframe to find entries, traders can avoid false signals and whipsaws that might be present in a single timeframe analysis.
█ Settings
Price Input Selection: Choose between regular open and close prices or Heikin Ashi candles as the basis for calculation.
Data Window Control: Decide between displaying the full data window or only the active data. You can also enable a counter that keeps track of the number of candles.
Alert Configuration: Set the desired number and color of consecutive candles that must occur in a row to trigger an alert.
Table Display Customization: Customize the location and size of the display table according to your preferences.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
AI Trend Navigator [K-Neighbor]█ Overview
In the evolving landscape of trading and investment, the demand for sophisticated and reliable tools is ever-growing. The AI Trend Navigator is an indicator designed to meet this demand, providing valuable insights into market trends and potential future price movements. The AI Trend Navigator indicator is designed to predict market trends using the k-Nearest Neighbors (KNN) classifier.
By intelligently analyzing recent price actions and emphasizing similar values, it helps traders to navigate complex market conditions with confidence. It provides an advanced way to analyze trends, offering potentially more accurate predictions compared to simpler trend-following methods.
█ Calculations
KNN Moving Average Calculation: The core of the algorithm is a KNN Moving Average that computes the mean of the 'k' closest values to a target within a specified window size. It does this by iterating through the window, calculating the absolute differences between the target and each value, and then finding the mean of the closest values. The target and value are selected based on user preferences (e.g., using the VWAP or Volatility as a target).
KNN Classifier Function: This function applies the k-nearest neighbor algorithm to classify the price action into positive, negative, or neutral trends. It looks at the nearest 'k' bars, calculates the Euclidean distance between them, and categorizes them based on the relative movement. It then returns the prediction based on the highest count of positive, negative, or neutral categories.
█ How to use
Traders can use this indicator to identify potential trend directions in different markets.
Spotting Trends: Traders can use the KNN Moving Average to identify the underlying trend of an asset. By focusing on the k closest values, this component of the indicator offers a clearer view of the trend direction, filtering out market noise.
Trend Confirmation: The KNN Classifier component can confirm existing trends by predicting the future price direction. By aligning predictions with current trends, traders can gain more confidence in their trading decisions.
█ Settings
PriceValue: This determines the type of price input used for distance calculation in the KNN algorithm.
hl2: Uses the average of the high and low prices.
VWAP: Uses the Volume Weighted Average Price.
VWAP: Uses the Volume Weighted Average Price.
Effect: Changing this input will modify the reference values used in the KNN classification, potentially altering the predictions.
TargetValue: This sets the target variable that the KNN classification will attempt to predict.
Price Action: Uses the moving average of the closing price.
VWAP: Uses the Volume Weighted Average Price.
Volatility: Uses the Average True Range (ATR).
Effect: Selecting different targets will affect what the KNN is trying to predict, altering the nature and intent of the predictions.
Number of Closest Values: Defines how many closest values will be considered when calculating the mean for the KNN Moving Average.
Effect: Increasing this value makes the algorithm consider more nearest neighbors, smoothing the indicator and potentially making it less reactive. Decreasing this value may make the indicator more sensitive but possibly more prone to noise.
Neighbors: This sets the number of neighbors that will be considered for the KNN Classifier part of the algorithm.
Effect: Adjusting the number of neighbors affects the sensitivity and smoothness of the KNN classifier.
Smoothing Period: Defines the smoothing period for the moving average used in the KNN classifier.
Effect: Increasing this value would make the KNN Moving Average smoother, potentially reducing noise. Decreasing it would make the indicator more reactive but possibly more prone to false signals.
█ What is K-Nearest Neighbors (K-NN) algorithm?
At its core, the K-NN algorithm recognizes patterns within market data and analyzes the relationships and similarities between data points. By considering the 'K' most similar instances (or neighbors) within a dataset, it predicts future price movements based on historical trends. The K-Nearest Neighbors (K-NN) algorithm is a type of instance-based or non-generalizing learning. While K-NN is considered a relatively simple machine-learning technique, it falls under the AI umbrella.
We can classify the K-Nearest Neighbors (K-NN) algorithm as a form of artificial intelligence (AI), and here's why:
Machine Learning Component: K-NN is a type of machine learning algorithm, and machine learning is a subset of AI. Machine learning is about building algorithms that allow computers to learn from and make predictions or decisions based on data. Since K-NN falls under this category, it is aligned with the principles of AI.
Instance-Based Learning: K-NN is an instance-based learning algorithm. This means that it makes decisions based on the entire training dataset rather than deriving a discriminative function from the dataset. It looks at the 'K' most similar instances (neighbors) when making a prediction, hence adapting to new information if the dataset changes. This adaptability is a hallmark of intelligent systems.
Pattern Recognition: The core of K-NN's functionality is recognizing patterns within data. It identifies relationships and similarities between data points, something akin to human pattern recognition, a key aspect of intelligence.
Classification and Regression: K-NN can be used for both classification and regression tasks, two fundamental problems in machine learning and AI. The indicator code is used for trend classification, a predictive task that aligns with the goals of AI.
Simplicity Doesn't Exclude AI: While K-NN is often considered a simpler algorithm compared to deep learning models, simplicity does not exclude something from being AI. Many AI systems are built on simple rules and can be combined or scaled to create complex behavior.
No Explicit Model Building: Unlike traditional statistical methods, K-NN does not build an explicit model during training. Instead, it waits until a prediction is required and then looks at the 'K' nearest neighbors from the training data to make that prediction. This lazy learning approach is another aspect of machine learning, part of the broader AI field.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
CandlestickPatternsLibrary "CandlestickPatterns"
This library provides a wide range of candlestick patterns, and available for user to call each pattern individually. It's a comprehensive and common tool designed for traders seeking to raise their technical analysis, and it may help users identify key turning of price action in financial instruments. Credit to public technical “*All Candlestick Patterns*” indicator.
abandonedBaby(order, d1)
The "Abandoned Baby" candlestick pattern is a bullish/bearish pattern consists of three candles.
Parameters:
order (simple string) : (simple string) Pattern order type "bull" or "bear".
d1 (simple float) : (simple float) Previous candle's body percentage out of candle range. Optional argument, default is 5.
darkCloudCover(c1, n)
The "Dark Cloud Cover" is a bearish pattern consists of two candles.
Parameters:
c1 (simple bool) : (simple bool) Previous candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
doji(d0)
The "Doji" is neither bullish or bearish consists of one candles.
Parameters:
d0 (simple float) : (simple float) Current candle's body percentage out of candle range. Optional argument, default is 5.
dojiStar(order, c1, n, d0)
The "Doji Star" is a bullish/bearish pattern consists of two candles.
Parameters:
order (simple string) : (simple string) Pattern order type "bull" or "bear" .
c1 (simple bool) : (simple bool) Previous candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
d0 (simple float) : (simple float) Current candle's body percentage out of candle range. Optional argument, default is 5.
downsideTasukiGap(c2, c1, n)
The "Downside Tasuki Gap" is a bearish pattern consists of three candles.
Parameters:
c2 (simple bool) : (simple bool) Before previous candle's body must be higher than average. Optional argument, default is true.
c1 (simple bool) : (simple bool) Previous candle's body must be lower than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
dragonflyDoji(d0)
The "Dragon Fly Doji" is a bullish pattern consists of one candle.
Parameters:
d0 (simple float) : (simple float) Current candle's body percentage out of candle range. Optional argument, default is 5.
engulfing(order, c1, c0, n)
The "Engulfing" is a bullish/bearish pattern consists of two candles.
Parameters:
order (simple string) : (simple string) Pattern order type "bull" or "bear".
c1 (simple bool) : (simple bool) Previous candle's body must be lower than average. Optional argument, default is true.
c0 (simple bool) : (simple bool) Current candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
eveningDojiStar(c2, c0, d1, n)
The "Evening Doji Star" is a bearish pattern consists of three candles.
Parameters:
c2 (simple bool) : (simple bool) Before previous candle's body must be higher than average, default is true.
c0 (simple bool) : (simple bool) Current candle's body must be higher than average. Optional argument, default is true.
d1 (simple float) : (simple float) Previous candle's body percentage out of candle range. Optional argument, default is 5.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
eveningStar(c2, c1, c0, n)
The "Evening Star" is a bearish pattern consists of three candles.
Parameters:
c2 (simple bool) : (simple bool) Before previous candle's body must be higher than average. Optional argument, default is true.
c1 (simple bool) : (simple bool) Previous candle's body must be lower than average. Optional argument, default is true.
c0 (simple bool) : (simple bool) Current candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
fallingThreeMethods(c4, c3, c2, c1, c0, n)
The "Falling Three Methods" is a bearish pattern consists of five candles.
Parameters:
c4 (simple bool) : (simple bool) 5th candle ago body must be higher than average. Optional argument, default is true.
c3 (simple bool) : (simple bool) 4th candle ago body must be lower than average. Optional argument, default is true.
c2 (simple bool) : (simple bool) 3rd candle ago body must be lower than average. Optional argument, default is true.
c1 (simple bool) : (simple bool) 2nd candle ago body must be lower than average. Optional argument, default is true.
c0 (simple bool) : (simple bool) Current candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
Returns: (bool)
fallingWindow()
The "Falling Window" is a bearish pattern consists of two candles.
gravestoneDoji(d0)
The "Gravestone Doji" is a bearish pattern consists of one candle.
Parameters:
d0 (simple float) : (simple float) Current candle's body percentage out of candle range. Optional argument, default is 5.
hammer(c0, n)
The "Hammer" is a bullish pattern consists of one candle.
Parameters:
c0 (simple bool) : (simple bool) Current candle's body must be lower than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
hangingMan(c0, n)
The "Hanging Man" is a bearish pattern consists of one candle.
Parameters:
c0 (simple bool) : (simple bool) Current candle's body must be lower than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
haramiCross(order, c1, n)
The "Harami Cross" candlestick pattern is a bullish/bearish pattern consists of two candles.
Parameters:
order (string) : (simple string) Pattern order type "bull" or "bear".
c1 (simple bool) : (simple bool) Previous candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
harami(order, c1, c0, n)
The "Harami" candlestick pattern is a bullish/bearish pattern consists of two candles.
Parameters:
order (string) : (simple string) Pattern order type "bull" or "bear"
c1 (simple bool) : (simple bool) Previous candle's body must be higher than average. Optional argument, default is true.
c0 (simple bool) : (simple bool) Current candle's body must be lower than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
invertedHammer(c0, n)
The "Inverted Hammer" is a bullish pattern consists of one candle.
Parameters:
c0 (simple bool) : (simple bool) Current candle's body must be lower than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
kicking(order, c1, c0, n)
The "Kicking" candlestick pattern is a bullish/bearish pattern consists of two candles.
Parameters:
order (string) : (simple string) Pattern order type "bull" or "bear"
c1 (simple bool) : (simple bool) Previous candle's body must be higher than average. Optional argument, default is true.
c0 (simple bool) : (simple bool) Current candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
longLowerShadow(l0)
The "Long Lower Shadow" candlestick pattern is a bullish pattern consists of one candles.
Parameters:
l0 (simple float) : (simple float) Current candle's lower wick min percentage out of candle range. Optional argument, default is 75.
longUpperShadow(u0)
The "Long Upper Shadow" candlestick pattern is a bearish pattern consists of one candles.
Parameters:
u0 (simple float) : (simple float) Current candle's upper wick min percentage out of candle range. Optional argument, default is 75.
marubozuBlack(c0, n)
The "Marubozu Black" candlestick pattern is a bearish pattern consists of one candles.
Parameters:
c0 (simple bool) : (simple bool) Current candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
marubozuWhite(c0, n)
The "Marubozu White" candlestick pattern is a bullish pattern consists of one candles.
Parameters:
c0 (simple bool) : (simple bool) Current candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
morningDojiStar(c2, d1, c0, n)
The "Morning Doji Star" candlestick pattern is a bullish pattern consists of three candles.
Parameters:
c2 (simple bool) : (simple bool) Before previous candle's body must be higher than average. Optional argument, default is true.
d1 (simple float) : (simple float) Previous candle's body percentage out of candle range. Optional argument, default is 5.
c0 (simple bool) : (simple bool) Current candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
morningStar(c2, c1, c0, n)
The "Morning Star" candlestick pattern is a bullish pattern consists of three candles.
Parameters:
c2 (simple bool) : (simple bool) Before previous candle's body must be higher than average. Optional argument, default is true.
c1 (simple bool) : (simple bool) Previous candle's body must be lower than average. Optional argument, default is true.
c0 (simple bool) : (simple bool) Cuurent candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
onNeck(c1, c0, n)
The "On Neck" candlestick pattern is a bearish pattern consists of two candles.
Parameters:
c1 (simple bool) : (simple bool) Previous candle's body must be higher than average. Optional argument, default is true.
c0 (simple bool) : (simple bool) Cuurent candle's body must be lower than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
piercing(c1, n)
The "Piercing" candlestick pattern is a bullish pattern consists of two candles.
Parameters:
c1 (simple bool) : (simple bool) Previous candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
risingThreeMethods(c4, c3, c2, c1, c0, n)
The "Rising Three Methods" candlestick pattern is a bullish pattern consists of five candles.
Parameters:
c4 (simple bool) : (simple bool) 5th candle ago body must be higher than average. Optional argument, default is true.
c3 (simple bool) : (simple bool) 4th candle ago body must be Lower than average. Optional argument, default is true.
c2 (simple bool) : (simple bool) 3rd candle ago body must be Lower than average. Optional argument, default is true.
c1 (simple bool) : (simple bool) 2nd candle ago body must be Lower than average. Optional argument, default is true.
c0 (simple bool) : (simple bool) Current candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
risingWindow()
The "Rising Window" candlestick pattern is a bullish pattern consists of two candle.
shootingStar(c0, n)
The "Shooting Star" candlestick pattern is a bearish pattern consists of one candle.
Parameters:
c0 (simple bool) : (simple bool) Current candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
spinningTopBlack(l0, u0)
The "Spinning Top Black" is neither bullish or bearish.
Parameters:
l0 (simple float) : (simple float) Current candle's lower wick min percentage out of candle range. Optional argument, default is 34.
u0 (simple float) : (simple float) Current candle's upper wick min percentage out of candle range. Optional argument, default is 34.
spinningTopWhite(l0, u0)
The "Spinning Top White" is neither bullish or bearish.
Parameters:
l0 (simple float) : (simple float) Current candle's lower wick min percentage out of candle range. Optional argument, default is 34.
u0 (simple float) : (simple float) Current candle's upper wick min percentage out of candle range. Optional argument, default is 34.
threeBlackCrows(c2, c1, c0, n)
The "Three Black Crows" candlestick pattern is a bearish pattern consists of three candles.
Parameters:
c2 (simple bool) : (simple bool) Before previous candle's body must be higher than average. Optional argument, default is true.
c1 (simple bool) : (simple bool) Previous candle's body must be higher than average. Optional argument, default is true.
c0 (simple bool) : (simple bool) Cuurent candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
threeWhiteSoldiers(c2, c1, c0, n)
The "Three White Soldiers" candlestick pattern is a bullish pattern consists of three candles.
Parameters:
c2 (simple bool) : (simple bool) Before previous candle's body must be higher than average. Optional argument, default is true.
c1 (simple bool) : (simple bool) Previous candle's body must be higher than average. Optional argument, default is true.
c0 (simple bool) : (simple bool) Cuurent candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
triStar(order, d2, d1, d0)
The "Tri Star" candlestick pattern is a bullish/bearish pattern consists of three candles.
Parameters:
order (simple string) : (simple string) Pattern order type "bull" or "bear".
d2 (simple float) : (simple float) Before previous candle's body percentage out of candle range. Optional argument, default is 5.
d1 (simple float) : (simple float) Previous candle's body percentage out of candle range. Optional argument, default is 5.
d0 (simple float) : (simple float) Current candle's body percentage out of candle range. Optional argument, default is 5.
tweezerBottom(c1, n)
The "Tweezer Bottom" candlestick pattern is a bullish pattern consists of two candles.
Parameters:
c1 (simple bool) : (simple bool) Previous candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
tweezerTop(c1, n)
The "Tweezer Top" candlestick pattern is a bearish pattern consists of two candles.
Parameters:
c1 (simple bool) : (simple bool) Previous candle's body must be higher than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
upsideTasukiGap(c2, c1, n)
The "Tri Star" candlestick pattern is a bullish pattern consists of three candles.
Parameters:
c2 (simple bool) : (simple bool) Before Previous candle's body must be higher than average. Optional argument, default is true.
c1 (simple bool) : (simple bool) Previous candle's body must be lower than average. Optional argument, default is true.
n (simple int) : (simple int) Length of average candle's body. Optional argument, default is 14.
