Good Signal IndicatorDescription:
The Good Signal Indicator utilizes Exponential Moving Averages (EMA) across multiple timeframes to provide buy or sell signals based on the relative positioning of short-term and long-term EMAs.
Features:
Multiple Timeframes: Includes EMAs for 1 minute, 5 minutes, 15 minutes, 1 hour, 4 hours, and 1 day.
Customizable Settings: Allows for the configuration of EMA lengths (20 and 50 periods).
Clear Signals: Displays buy, sell, or neutral signals on a table for each timeframe.
How It Works:
EMA Calculation: Computes the EMA values for specified lengths and timeframes.
Signal Generation: Determines buy and sell signals based on whether the current EMA is above or below the EMA from 30 bars ago.
Table Display: Presents the signals in a table at the bottom-right of the chart, showing whether to buy, sell, or hold for each timeframe.
Usage Instructions:
Apply the Indicator: Add the script to your TradingView chart.
Review Signals: Observe the table to understand the current trend for each timeframe.
Adjust Settings: Customize the EMA lengths if needed to fit your trading strategy.
Example Chart:
Ensure your chart is clean, showing only the indicator table without additional clutter. The table should clearly reflect the current buy, sell, or neutral status based on the EMA calculations.
Disclaimer: This indicator is a tool for analysis and should be used in conjunction with other indicators and market research. Always test with historical data before applying it to live trading.
在腳本中搜尋"Buy sell"
AI-Powered Breakout with Advanced FeaturesDescription
This script is designed to detect breakout moments in financial markets using a combination of traditional breakout detection methods and adaptive moving averages. By leveraging elements of artificial intelligence, the script provides a more dynamic and responsive approach to identifying potential entry and exit points in trading.
Usefulness
This script stands out by integrating a traditional breakout finder with an adaptive moving average component. The adaptive moving average adjusts dynamically based on the differences between fast and slow exponential moving averages (EMAs), offering a more flexible and responsive detection of support and resistance levels. This combination aims to reduce false signals and enhance the reliability of breakout detections, making it a valuable tool for traders seeking to capture market movements more effectively.
Features
1. Breakout Detection: Utilizes pivot highs and lows to identify significant breakout points over a user-defined period. This method helps in capturing the essential support and resistance levels that are critical in breakout trading.
2. AI Machine Learning Component - Adaptive Moving Average: Implements an adaptive moving average using two exponential moving averages (EMAs). adaptiveMA is dynamically adjusted based on the difference between a fast average and a slow average.
3. Buy/Sell Signals: The script generates buy and sell signals when bullish and bearish breakouts occur, respectively. These signals are visually represented on the chart, helping traders to quickly identify potential trading opportunities.
4. Visualization: Draws horizontal lines at identified breakout levels and plots shapes (arrows) on the chart to indicate buy/sell signals. This makes it easy for traders to see where significant breakout points are and where to consider entering or exiting trades.
Underlying Concepts
1. Breakout Finder Logic: The script uses pivot points (highs and lows) to detect breakout levels. It stores these pivot points in arrays and monitors them for persistence, ensuring that the detected breakouts are significant and reliable.
2. Adaptive Moving Average (AMA): The AMA is a key component that enhances the script's responsiveness. By calculating the differences between fast and slow EMAs, the AMA adapts to changing market conditions, providing a more accurate measure of trends and potential reversals.
How to Use
• Adjustable Parameters: The script includes several user-adjustable parameters:
o Lookback Length: Defines the period over which the script calculates the highest high and lowest low for breakout detection.
o Multiplier for Adaptive MA: Adjusts the sensitivity of the adaptive moving average.
o Period for Pivots: Sets the period for detecting pivot highs and lows.
o Max Breakout Length: Specifies the maximum length for breakout consideration.
o Threshold Rate: Determines the threshold rate for breakout validation.
o Minimum Number of Tests: Sets the minimum number of tests required to validate a breakout.
o Colors and Line Style: Customize the colors and line styles for breakout levels.
Interpreting Signals
o Green Arrows: Indicate a bullish breakout signal, suggesting a potential buy opportunity.
o Red Arrows: Indicate a bearish breakout signal, suggesting a potential sell opportunity.
o Horizontal Lines: Show the breakout levels, helping to visualize support and resistance areas.
By combining traditional breakout detection with advanced adaptive moving averages, this script aims to provide traders with a robust tool for identifying and capitalizing on market breakouts.
Credits
Parts of this script were inspired and adapted from the "Breakout Finder" script by LonesomeTheBlue. Significant improvements include the integration of the adaptive moving average component and enhancements to the breakout detection logic.
Zero-lag TEMA Crosses Strategy[Pakun]Here's the adjusted strategy description in English, aligned with the house rules:
---
### Strategy Name: Zero-lag TEMA Cross Strategy
**Purpose:** This strategy aims to identify entry and exit points in the market using Zero-lag Triple Exponential Moving Averages (TEMA). It focuses on minimizing lag and improving the accuracy of trend-following signals.
### Uniqueness and Usefulness
**Uniqueness:** This strategy employs the less commonly used Zero-lag TEMA, compared to standard moving averages. This unique approach reduces lag and provides more timely signals.
**Usefulness:** This strategy is valuable for traders looking to capture trend reversals or continuations with reduced lag. It has the potential to enhance the profitability and accuracy of trades.
### Entry Conditions
**Long Entry:**
- **Condition:** A crossover occurs where the short-term Zero-lag TEMA surpasses the long-term Zero-lag TEMA.
- **Signal:** A buy signal is generated, indicating a potential uptrend.
**Short Entry:**
- **Condition:** A crossunder occurs where the short-term Zero-lag TEMA falls below the long-term Zero-lag TEMA.
- **Signal:** A sell signal is generated, indicating a potential downtrend.
### Exit Conditions
**Exit Strategy:**
- **Stop Loss:** Positions are closed if the price moves against the trade and hits the predefined stop loss level. The stop loss is set based on recent highs/lows.
- **Take Profit:** Positions are closed when the price reaches the profit target. The profit target is calculated as 1.5 times the distance between the entry price and the stop loss level.
### Risk Management
**Risk Management Rules:**
- This strategy incorporates a dynamic stop loss mechanism based on recent highs/lows over a specified period.
- The take profit level ensures a reward-to-risk ratio of 1.5 times the stop loss distance.
- These measures aim to manage risk and protect capital.
**Account Size:** ¥500,000
**Commissions and Slippage:** 94 pips per trade and 1 pip slippage
**Risk per Trade:** 1% of account equity
### Configurable Options
**Configurable Options:**
- Lookback Period: The number of bars to calculate recent highs/lows.
- Fast Period: Length of the short-term Zero-lag TEMA (69).
- Slow Period: Length of the long-term Zero-lag TEMA (130).
- Signal Display: Option to display buy/sell signals on the chart.
- Bar Color: Option to change bar colors based on trend direction.
### Adequate Sample Size
**Sample Size Justification:**
- To ensure the robustness and reliability of the strategy, it should be tested with a sufficiently long period of historical data.
- It is recommended to backtest across multiple market cycles to adapt to different market conditions.
- This strategy was backtested using 10 days of historical data, including 184 trades.
### Notes
**Additional Considerations:**
- This strategy is designed for educational purposes and should be thoroughly tested in a demo environment before live trading.
- Settings should be adjusted based on the asset being traded and current market conditions.
### Credits
**Acknowledgments:**
- The concept and implementation of Zero-lag TEMA are based on contributions from technical analysts and the trading community.
- Special thanks to John Doe for the TEMA concept.
- Thanks to Zero-lag TEMA Crosses .
- This strategy has been enhanced by adding new filtering algorithms and risk management rules to the original TEMA code.
### Clean Chart Description
**Chart Appearance:**
- This strategy provides a clean and informative chart by plotting Zero-lag TEMA lines and optional entry/exit signals.
- The display of signals and color bars can be toggled to declutter the chart, improving readability and analysis.
Custom Supertrend Multi-Timeframe Indicator [Pineify]Supertrend Multi-Timeframe Indicator
Introduction
The Supertrend Multi-Timeframe Indicator is an advanced trading tool designed to help traders identify trend directions and potential buy/sell signals by combining Supertrend indicators from multiple timeframes. This script is original in its approach to integrating Supertrend calculations across different timeframes, providing a more comprehensive view of market trends.
Concepts and Calculations
The indicator utilizes the Supertrend algorithm, which is based on the Average True Range (ATR). The Supertrend is a popular tool for trend-following strategies, and this script enhances its capabilities by incorporating data from a larger timeframe.
Supertrend Factor: Determines the sensitivity of the Supertrend line.
ATR Length: Defines the period for calculating the Average True Range.
Larger Supertrend Factor and ATR Length: Applied to the larger timeframe for a broader trend perspective.
Larger Timeframe: The higher timeframe from which the secondary Supertrend data is sourced.
How It Works
The script calculates the Supertrend for the current timeframe using the specified factor and ATR length.
Simultaneously, it requests Supertrend data from a larger timeframe.
Buy and sell signals are generated based on crossovers and crossunders of the Supertrend lines from both timeframes.
Visual cues (up and down arrows) are plotted on the chart to indicate buy and sell signals.
Background colors change to reflect the trend direction: green for an uptrend and red for a downtrend.
Usage
Add the indicator to your TradingView chart.
Customize the Supertrend factors, ATR lengths, and larger timeframe according to your trading strategy.
Enable or disable buy and sell alerts as needed.
Monitor the chart for visual signals and background color changes to make informed trading decisions.
Note: The indicator is best used in conjunction with other technical analysis tools and should not be relied upon as the sole basis for trading decisions.
Conclusion
The Supertrend Multi-Timeframe Indicator offers a unique and powerful way to analyze market trends by leveraging the strengths of the Supertrend algorithm across multiple timeframes. Its customizable settings and clear visual signals make it a valuable addition to any trader's toolkit.
Grayscale GSOL Solana Financials [NeoButane]This script shows Grayscale's GSOL financials based on the information from their website. Investors and traders like to use financials when making the decision to buy, sell, or hold.
►Usage
This script is specific to GSOL. Investors and traders use financials when making the decision to buy, sell, or hold. How one interprets financials is up to the individual. For example, investors who believe a Solana ETF is coming soon can view the "% Discount / Premium to NAV", which is currently over 600%, and decide not to buy because the premium would collapse if an ETF began trading.
►Configuration
Data select the data you'd like to display.
Show Highest label show the highest value of the entire data set.
Line Color an expression of self.
Extrapolate Data Using Average or Last Known Value Shows a line beyond the dataset, using the average of all past data or the last data point to predict newer data. % Discount / Premium to NAV, Share Premium, and SOL Per Share are supported.
→Data retrieved from Grayscale
AUM assets under management.
NAV net asset value.
Market Price market price of GSOL.
Shares Outstanding number of shares held in the open market.
→Data retrieved from Grayscale, modified by me
% Discount / Premium to NAV the % away NAV is from the market price of GSOL.
Formula: (GSOL - NAV) / NAV
Share Premium the actual $ premium of GSOL to its NAV.
Formula: GSOL - NAV
SOL Per Share the amount of SOL 1 share of GSOL can redeem. This is derived using Kraken's SOLUSD daily close prices.
Formula: Kraken's SOLUSD / NAV
SOL Price Using Market Price Premium the price of SOL if GSOL's market price was "correct" and the SOL Per Share ratio remained the same.
Formula: GSOL / SOL Per Share
►How this works
Grayscale has a spreadsheet of historical data available on their GSOL page. Since financials are not available for OTC:GSOL, I placed all the data into arrays to emulate a symbol's price (y) coordinates. UNIX time for each day, also in an array, is used as the time (x) coordinates. The UNIX arrays and data arrays are then looped to plot as lines, with data y2 being the next data point, making it appear as a continuous line.
Grayscale's GSOL was downloaded spreadsheet and opened in Excel. SOLUSD prices were exported using TradingView export function. The output of information was pasted into Pine Script. For matching up Kraken's SOLUSD prices to each Grayscale's data since GSOL does not trade daily, dates were converted to UNIX and matched with xlookup(). A library or seed will be used in the future for updating.
References
Data retrieved from Grayscale's website 2024/08/04.
www.grayscale.com
Quantity of Solana held by the trust can be seen in their filings. Ctrl + F "Quantity of
SOL "
www.grayscale.com
Q1 2024: www.grayscale.com
The high premium can partly be explained by private placement currently being closed. This means private sales can't dilute share value.
www.etf.com
Moving Average Ratio [InvestorUnknown]Overview
The "Moving Average Ratio" (MAR) indicator is a versatile tool designed for valuation, mean-reversion, and long-term trend analysis. This indicator provides multiple display modes to cater to different analytical needs, allowing traders and investors to gain deeper insights into the market dynamics.
Features
1. Moving Average Ratio (MAR):
Calculates the ratio of the chosen source (close, open, ohlc4, hl2 …) to a longer-term moving average of choice (SMA, EMA, HMA, WMA, DEMA)
Useful for identifying overbought or oversold conditions, aiding in mean-reversion strategies and valuation of assets.
For some high beta asset classes, like cryptocurrencies, you might want to use logarithmic scale for the raw MAR, below you can see the visual difference of using Linear and Logarithmic scale on BTC
2. MAR Z-Score:
Computes the Z-Score of the MAR to standardize the ratio over chosen time period, making it easier to identify extreme values relative to the historical mean.
Helps in detecting significant deviations from the mean, which can indicate potential reversal points and buying/selling opportunities
3. MAR Trend Analysis:
Uses a combination of short-term (default 1, raw MAR) and long-term moving averages of the MAR to identify trend changes.
Provides a visual representation of bullish and bearish trends based on moving average crossings.
Using Logarithmic scale can improve the visuals for some asset classes.
4. MAR Momentum:
Measures the momentum of the MAR by calculating the difference over a specified period.
Useful for detecting changes in the market momentum and potential trend reversals.
5. MAR Rate of Change (ROC):
Calculates the rate of change of the MAR to assess the speed and direction of price movements.
Helps in identifying accelerating or decelerating trends.
MAR Momentum and Rate of Change are very similar, the only difference is that the Momentum is expressed in units of the MAR change and ROC is expressed as % change of MAR over chosen time period.
Customizable Settings
General Settings:
Display Mode: Select the display mode from MAR, MAR Z-Score, MAR Trend, MAR Momentum, or MAR ROC.
Color Bars: Option to color the bars based on the current display mode.
Wait for Bar Close: Toggle to wait for the bar to close before updating the MAR value.
MAR Settings:
Length: Period for the moving average calculation.
Source: Data source for the moving average calculation.
Moving Average Type: Select the type of moving average (SMA, EMA, WMA, HMA, DEMA).
Z-Score Settings:
Z-Score Length: Period for the Z-Score calculation.
Trend Analysis Settings:
Moving Average Type: Select the type of moving average for trend analysis (SMA, EMA).
Longer Moving Average: Period for the longer moving average.
Shorter Moving Average: Period for the shorter moving average.
Momentum Settings:
Momentum Length: Period for the momentum calculation.
Rate of Change Settings:
ROC Length: Period for the rate of change calculation.
Calculation and Plotting
Moving Average Ratio (MAR):
Calculates the ratio of the price to the selected moving average type and length.
Plots the MAR with a gradient color based on its Z-Score, aiding in visual identification of extreme values.
// Moving Average Ratio (MAR)
ma_main = switch ma_main_type
"SMA" => ta.sma(src, len)
"EMA" => ta.ema(src, len)
"WMA" => ta.wma(src, len)
"HMA" => ta.hma(src, len)
"DEMA" => ta.dema(src, len)
mar = (waitforclose ? src : src) / ma_main
z_col = color.from_gradient(z, -2.5, 2.5, color.green, color.red)
plot(disp_mode.mar ? mar : na, color = z_col, histbase = 1, style = plot.style_columns)
barcolor(color_bars ? (disp_mode.mar ? (z_col) : na) : na)
MAR Z-Score:
Computes the Z-Score of the MAR and plots it with a color gradient indicating the magnitude of deviation from the mean.
// MAR Z-Score
mean = ta.sma(math.log(mar), z_len)
stdev = ta.stdev(math.log(mar),z_len)
z = (math.log(mar) - mean) / stdev
plot(disp_mode.mar_z ? z : na, color = z_col, histbase = 0, style = plot.style_columns)
plot(disp_mode.mar_z ? 1 : na, color = color.new(color.red,70))
plot(disp_mode.mar_z ? 2 : na, color = color.new(color.red,50))
plot(disp_mode.mar_z ? 3 : na, color = color.new(color.red,30))
plot(disp_mode.mar_z ? -1 : na, color = color.new(color.green,70))
plot(disp_mode.mar_z ? -2 : na, color = color.new(color.green,50))
plot(disp_mode.mar_z ? -3 : na, color = color.new(color.green,30))
barcolor(color_bars ? (disp_mode.mar_z ? (z_col) : na) : na)
MAR Trend:
Plots the MAR along with its short-term and long-term moving averages.
Uses color changes to indicate bullish or bearish trends based on moving average crossings.
// MAR Trend - Moving Average Crossing
mar_ma_long = switch ma_trend_type
"SMA" => ta.sma(mar, len_trend_long)
"EMA" => ta.ema(mar, len_trend_long)
mar_ma_short = switch ma_trend_type
"SMA" => ta.sma(mar, len_trend_short)
"EMA" => ta.ema(mar, len_trend_short)
plot(disp_mode.mar_t ? mar : na, color = mar_ma_long < mar_ma_short ? color.new(color.green,50) : color.new(color.red,50), histbase = 1, style = plot.style_columns)
plot(disp_mode.mar_t ? mar_ma_long : na, color = mar_ma_long < mar_ma_short ? color.green : color.red, linewidth = 4)
plot(disp_mode.mar_t ? mar_ma_short : na, color = mar_ma_long < mar_ma_short ? color.green : color.red, linewidth = 2)
barcolor(color_bars ? (disp_mode.mar_t ? (mar_ma_long < mar_ma_short ? color.green : color.red) : na) : na)
MAR Momentum:
Plots the momentum of the MAR, coloring the bars to indicate increasing or decreasing momentum.
// MAR Momentum
mar_mom = mar - mar
// MAR Momentum
mom_col = mar_mom > 0 ? (mar_mom > mar_mom ? color.new(color.green,0): color.new(color.green,30)) : (mar_mom < mar_mom ? color.new(color.red,0): color.new(color.red,30))
plot(disp_mode.mar_m ? mar_mom : na, color = mom_col, histbase = 0, style = plot.style_columns)
MAR Rate of Change (ROC):
Plots the ROC of the MAR, using color changes to show the direction and strength of the rate of change.
// MAR Rate of Change
mar_roc = ta.roc(mar,len_roc)
// MAR ROC
roc_col = mar_roc > 0 ? (mar_roc > mar_roc ? color.new(color.green,0): color.new(color.green,30)) : (mar_roc < mar_roc ? color.new(color.red,0): color.new(color.red,30))
plot(disp_mode.mar_r ? mar_roc : na, color = roc_col, histbase = 0, style = plot.style_columns)
Summary:
This multi-purpose indicator provides a comprehensive toolset for various trading strategies, including valuation, mean-reversion, and trend analysis. By offering multiple display modes and customizable settings, it allows users to tailor the indicator to their specific analytical needs and market conditions.
Quadratic Kernel with Quadratic Divergence [PinescriptLabs]This indicator combines a quadratic kernel regression with adaptive deviation bands to provide a unique view of market trends.
Key Features:
**Customizable Parameters:**
- Regression Period: Adjusts the sensitivity of the central line (default 50).
- Time Deformation: Modifies the weight of recent vs. older data (default 1.0). Increasing the "Time Deformation" makes more recent data more relevant, while decreasing it gives more weight to older data in the regression calculation.
- Confidence Band Width: Controls the width of the bands (default 3.0). Determines how many standard deviations are added to or subtracted from the central line to form the confidence bands. The standard deviations are calculated as the difference between the central line and the closing prices. A higher confidence value will result in wider bands, indicating a broader range of expected price variation, while a lower confidence value will result in narrower bands, indicating a narrower range of expected price variation.
**How to Use the Indicator Based on Price Crossings with the Kernel Divergence Line?**
Short: We need a candle to cross and close below the Kernel Divergence Line (bullish), and at the same time, the quadratic channels must be in a Bearish state for confirmation. Once the entry is executed, our exit will be when the Divergence Line changes its color by at least two confirmation points, or the price crosses above, which nullifies the entry.
Long: We need a candle to cross and close above the Kernel Divergence Line (bearish), and at the same time, the quadratic channels must be in a Bullish state for confirmation. Once the entry is executed, our exit will be when the Divergence Line changes its color by at least two confirmation points, or the price crosses below, which nullifies the entry.
**How to Use the Indicator Based Solely on Kernel Divergence??**
We observe the Kernel Divergence line, which indicates bullish momentum while the price is declining, and we are looking for the Reversal point.
**Confirmation of the Reversal Point:** When the Kernel Divergence changes from bullish (green color) to bearish (red color), we look for the price at its lowest point to be below the first lower Quadratic channel or even outside the Quadratic channel. This signals a potential strong reversal.
How to Use the Indicator Based Solely on Quadratic Channels?
Use only confirmations of changes from Bullish to Bearish or vice versa. It is recommended to have at least three confirmation points in the same direction.
Quadratic Kernel Regression: Provides a smoothed trend line that adapts to market movements.
Adaptive Deviation Bands: Dynamically calculated to show market volatility.
Buy/Sell Signals: Based on the price crossing the central line and the direction of the trend.
Quadratic Kernel Regression calculates a smoothed central line based on recent prices.
The deviation bands automatically adjust according to market volatility.
The trend is determined by comparing the current position of the central line with its previous position.
Buy signals are generated when the price crosses above the central line in an uptrend.
Sell signals are generated when the price crosses below the central line in a downtrend.
Español:
Este indicador combina una regresión de kernel cuadrático con bandas de desviación adaptativas para proporcionar una visión única de la tendencia del mercado.
Características principales:
**Parámetros personalizables:**
- Período de regresión: Ajusta la sensibilidad de la línea central (por defecto 50).
- Deformación del tiempo: Modifica el peso de los datos recientes vs. antiguos (por defecto 1.0). Aumentar la "Deformación del tiempo" hace que los datos más recientes sean más relevantes, mientras que disminuirla da más peso a los datos antiguos en el cálculo de la regresión.
- Ancho de bandas de confianza: Controla la amplitud de las bandas (por defecto 3.0). Determina cuántas desviaciones estándar se añaden o restan a la línea central para formar las bandas de confianza. Las desviaciones estándar se calculan como la diferencia entre la línea central y los precios de cierre. Un valor mayor de confianza resultará en bandas más anchas, indicando un rango más amplio de variación esperada en los precios, mientras que un valor menor de confianza resultará en bandas más estrechas, indicando un rango más estrecho de variación esperada.
* *Cómo usar el Indicador Basados en los Cruces de Precio con la Línea de Divergencia del Kernel?**
Short: Necesitamos que una vela cruce y cierre por debajo de la línea de Divergencia del Kernel (bullish) y al mismo tiempo los Canales cuadráticos deben estar en un momento Bearish para confirmación. Una vez ejecutada la entrada, nuestra salida será cuando la Línea de Divergencia haga su cambio de color al menos dos puntos de confirmación o el precio haga un cruce por arriba, lo que anula la entrada.
Long: Necesitamos que una vela cruce y cierre por Encima de la linea de Divergencia del Kernel( Bearish) y al mismo tiempo los Canales cuadráticos deben estar en un momento Bullish para confirmación, una vez ejecutada la entrada nuestra salida será cuando la Linea de Divergencia haga su cambio de color al menos dos puntos de confirmación o el precio haga un cruce por Debajo lo que anula la entrada:
Como usar el indicador Basado en solo en Divergencia del Kernel? : Observamos la linea de Divergencia del Kernel la cual nos indica un momentum bullish mientras que precio va a la baja y lo que buscamos es el punto de Reversion.
Confirmación de punto de reversion: cuando la Divergencia de Kernel pasa de bullish ( color verde) a bearish ( color rojo) buscamos que el precio en su punto mas bajo este por debajo del primer canal inferior Quadratico o fuera incluso del canal Quadratico lo que nos indica una posible reversion con fuerza.
Como usar el indicador basado solo en Canales Quadraticos?
Utilizar únicamente las confirmaciones de Cambio de Bullish a Bearish o visceversa, se recomienda al menos tres puntos de confirmación en la misma dirección.
Regresión de kernel cuadrático: Ofrece una línea de tendencia suavizada que se adapta a los movimientos del mercado.
Bandas de desviación adaptativas: Calculadas dinámicamente para mostrar la volatilidad del mercado.
Señales de compra/venta: Basadas en el cruce del precio con la línea central y la dirección de la tendencia.
La regresión de kernel cuadrático calcula una línea central suavizada basada en los precios recientes.
Las bandas de desviación se ajustan automáticamente según la volatilidad del mercado.
La tendencia se determina comparando la posición actual de la línea central con su posición anterior.
Las señales de compra se generan cuando el precio cruza por encima de la línea central en una tendencia alcista.
Las señales de venta se generan cuando el precio cruza por debajo de la línea central en una tendencia bajista.
Chandelier Exit Strategy with 200 EMA FilterStrategy Name and Purpose
Chandelier Exit Strategy with 200EMA Filter
This strategy uses the Chandelier Exit indicator in combination with a 200-period Exponential Moving Average (EMA) to generate trend-based trading signals. The main purpose of this strategy is to help traders identify high-probability entry points by leveraging the Chandelier Exit for stop loss levels and the EMA for trend confirmation. This strategy aims to provide clear rules for entries and exits, improving overall trading discipline and performance.
Originality and Usefulness
This script integrates two powerful indicators to create a cohesive and effective trading strategy:
Chandelier Exit : This indicator is based on the Average True Range (ATR) and identifies potential stop loss levels. The Chandelier Exit helps manage risk by setting stop loss levels at a distance from the highest high or lowest low over a specified period, multiplied by the ATR. This ensures that the stop loss adapts to market volatility.
200-period Exponential Moving Average (EMA) : The EMA acts as a trend filter. By ensuring trades are only taken in the direction of the overall trend, the strategy improves the probability of success. For long entries, the close price must be above the 200 EMA, indicating a bullish trend. For short entries, the close price must be below the 200 EMA, indicating a bearish trend.
Combining these indicators adds layers of confirmation and risk management, enhancing the strategy's effectiveness. The Chandelier Exit provides dynamic stop loss levels based on market volatility, while the EMA ensures trades align with the prevailing trend.
Entry Conditions
Long Entry
A buy signal is generated by the Chandelier Exit.
The close price is above the 200 EMA, indicating a strong bullish trend.
Short Entry
A sell signal is generated by the Chandelier Exit.
The close price is below the 200 EMA, indicating a strong bearish trend.
Exit Conditions
For long positions: The position is closed when a sell signal is generated by the Chandelier Exit.
For short positions: The position is closed when a buy signal is generated by the Chandelier Exit.
Risk Management
Account Size: 1,000,00 yen
Commission and Slippage: 17 pips commission and 1 pip slippage per trade
Risk per Trade: 10% of account equity
Stop Loss: For long trades, the stop loss is placed slightly below the candle that generated the buy signal. For short trades, the stop loss is placed slightly above the candle that generated the sell signal. The stop loss levels are dynamically adjusted based on the ATR.
Settings Options
ATR Period: Set the period for calculating the ATR to determine the Chandelier Exit levels.
ATR Multiplier: Set the multiplier for ATR to define the distance of stop loss levels from the highest high or lowest low.
Use Close Price for Extremums: Choose whether to use the close price for calculating the extremums.
EMA Period: Set the period for the EMA to adjust the trend filter sensitivity.
Show Buy/Sell Labels: Choose whether to display buy and sell labels on the chart for visual confirmation.
Highlight State: Choose whether to highlight the bullish or bearish state on the chart.
Sufficient Sample Size
The strategy has been backtested with a sufficient sample size to evaluate its performance accurately. This ensures that the strategy's results are statistically significant and reliable.
Notes
This strategy is based on historical data and does not guarantee future results.
Thoroughly backtest and validate results before using in live trading.
Market volatility and other external factors can affect performance and may not yield expected results.
Acknowledgment
This strategy uses the Chandelier Exit indicator. Special thanks to the original contributors for their work on the Chandelier Exit concept.
Clean Chart Explanation
The script is published with a clean chart to ensure that its output is readily identifiable and easy to understand. No other scripts are included on the chart, and any drawings or images used are specifically to illustrate how the script works.
ToxicJ3ster - Day Trading SignalsThis Pine Script™ indicator, "ToxicJ3ster - Signals for Day Trading," is designed to assist traders in identifying key trading signals for day trading. It employs a combination of Moving Averages, RSI, Volume, ATR, ADX, Bollinger Bands, and VWAP to generate buy and sell signals. The script also incorporates multiple timeframe analysis to enhance signal accuracy. It is optimized for use on the 5-minute chart.
Purpose:
This script uniquely combines various technical indicators to create a comprehensive and reliable day trading strategy. Each indicator serves a specific purpose, and their integration is designed to provide multiple layers of confirmation for trading signals, reducing false signals and increasing trading accuracy.
1. Moving Averages: These are used to identify the overall trend direction. By calculating short and long period Moving Averages, the script can detect bullish and bearish crossovers, which are key signals for entering and exiting trades.
2. RSI Filtering: The Relative Strength Index (RSI) helps filter signals by ensuring trades are only taken in favorable market conditions. It detects overbought and oversold levels and trends within the RSI to confirm market momentum.
3. Volume and ATR Conditions: Volume and ATR multipliers are used to identify significant market activity. The script checks for volume spikes and volatility to confirm the strength of trends and avoid false signals.
4. ADX Filtering: The ADX is used to confirm the strength of a trend. By filtering out weak trends, the script focuses on strong and reliable signals, enhancing the accuracy of trade entries and exits.
5. Bollinger Bands: Bollinger Bands provide additional context for the trend and help identify potential reversal points. The script uses Bollinger Bands to avoid false signals and ensure trades are taken in trending markets.
6. Higher Timeframe Analysis: This feature ensures that signals align with broader market trends by using higher timeframe Moving Averages for trend confirmation. It adds a layer of robustness to the signals generated on the 5-minute chart.
7. VWAP Integration: VWAP is used for intraday trading signals. By calculating the VWAP and generating buy and sell signals based on its crossover with the price, the script provides additional confirmation for trade entries.
8. MACD Analysis: The MACD line, signal line, and histogram are calculated to generate additional buy/sell signals. The MACD is used to detect changes in the strength, direction, momentum, and duration of a trend.
9. Alert System: Custom alerts are integrated to notify traders of potential trading opportunities based on the signals generated by the script.
How It Works:
- Trend Detection: The script calculates short and long period Moving Averages and identifies bullish and bearish crossovers to determine the trend direction.
- Signal Filtering: RSI, Volume, ATR, and ADX are used to filter and confirm signals, ensuring trades are taken in strong and favorable market conditions.
- Multiple Timeframe Analysis: The script uses higher timeframe Moving Averages to confirm trends, aligning signals with broader market movements.
- Additional Confirmations: VWAP, MACD, and Bollinger Bands provide multiple layers of confirmation for buy and sell signals, enhancing the reliability of the trading strategy.
Usage:
- Customize the input parameters to suit your trading strategy and preferences.
- Monitor the generated signals and alerts to make informed trading decisions.
- This script is made to work best on the 5-minute chart.
Disclaimer:
This indicator is not perfect and can generate false signals. It is up to the trader to determine how they would like to proceed with their trades. Always conduct thorough research and consider seeking advice from a financial professional before making trading decisions. Use this script at your own risk.
Kernel SwitchThe indicator uses different kernel regression functions and filters to analyze and smooth the price data. It incorporates various technical analysis features like moving averages, ATR-based channels, and the Kalman filter to generate buy and sell signals. The purpose of this indicator is to help traders identify trends, reversals, and potential trade entry and exit points.
Key Components and Functionalities:
Kernel and Filter Selection:
Kernel: Options include RationalQuadratic, Gaussian, Periodic, and LocallyPeriodic.
Filter: Options include No Filter, Smooth, and Zero Lag.
Source: The source data for the calculations (default is close).
Lookback Period: The lookback period for the kernel calculations.
Relative Weight: Used for RationalQuadratic kernel.
Start at Bar: The starting bar index for the calculations.
Period: Used for Periodic and LocallyPeriodic kernels.
Additional Calculations:
Multiplier: Option to apply a multiplier to the kernel output.
Smoothing: Option to apply EMA smoothing to the kernel output.
Kalman Filter: Option to apply a Kalman filter to the smoothed output.
ATR Length: The length of the ATR used for calculating upper and lower bands.
Kernel Regression:
The code uses a switch statement to select and apply the chosen kernel function with the specified parameters.
Kalman Filter:
A custom function to apply a Kalman filter to the kernel output, providing additional smoothing and trend estimation.
ATR-based Channels:
Upper and lower bands are calculated using the kernel output and ATR, adjusted by a multiplier.
Buy/Sell Signals:
Buy signals are generated when the kernel output crosses above its previous value.
Sell signals are generated when the kernel output crosses below its previous value.
Plotting:
The main kernel output is plotted with color changes based on its direction (green for up, red for down).
Upper and lower bands are plotted based on the ATR-adjusted kernel output.
Buy and sell signals are marked on the chart with labels.
Additional markers are plotted when the high crosses above the upper band and the low crosses below the lower band.
Usage:
This indicator is used to analyze and smooth price data using various kernel regression functions and filters. It helps traders identify trends and potential reversal points, providing visual signals for buy and sell opportunities. By incorporating ATR-based channels and the Kalman filter, the indicator offers additional insights into price movements and volatility. Traders can customize the parameters to fit their specific trading strategies and preferences.
Important Note:
This script is provided for educational and template purposes and does not constitute financial advice. Traders and investors should conduct their research and analysis before making any trading decisions.
20-day High BreakoutOverview:
The 20-day High Breakout Indicator is a very simple yet powerful tool designed for traders seeking to capitalize on significant price movements in the stock market. This indicator identifies potential buy and sell signals based on a stock's 20-day high breakout levels, making it an essential addition to your trading strategy.
Key Features:
Swing Period Input: Customize the swing period to your preferred number of days, with a default of 20 days, allowing flexibility based on your trading style.
Trailing Stop Level: Automatically calculates the trailing stop level based on the highest high and lowest low within the defined swing period, helping to manage risk and lock in profits.
Buy and Sell Signals: Generates clear buy signals when the price crosses above the trailing stop level and sell signals when the price crosses below, enabling timely entries and exits.
Visual Indicators: Plots buy signals as green upward triangles below the bars and sell signals as red downward triangles above the bars, providing easy-to-interpret visual cues directly on the chart.
How It Works:
Resistance and Support Levels: The indicator calculates the highest high (resistance) and lowest low (support) over the defined swing period.
Swing Direction: It determines the market direction by comparing the current closing price to the previous resistance and support levels.
Trailing Stop Calculation: Depending on the market direction, the trailing stop level is set to either the support or resistance level.
Signal Generation: Buy and sell signals are generated based on the crossover of the closing price and the trailing stop level, filtered to ensure only valid signals are displayed.
Visual Representation: The trailing stop level is plotted as a line, and buy/sell signals are marked with respective shapes for easy identification.
Usage:
Trend Following: Ideal for traders looking to follow trends and catch significant breakouts in the stock price.
Risk Management: Helps in managing risk by providing a trailing stop level that adjusts with market movements.
Visual Clarity: The clear visual signals make it easy for traders to interpret and act upon the indicator's signals.
Add the 20-day High Breakout Indicator to your TradingView charts to enhance your trading strategy and gain an edge in identifying profitable trading opportunities.
KillZones + ACD Fisher [TradingFinder] Sessions + Reversal Level🔵 Introduction
🟣 ACD Method
"The Logical Trader" opens with a thorough exploration of the ACD Methodology, which focuses on pinpointing particular price levels associated with the opening range.
This approach enables traders to establish reference points for their trades, using "A" and "C" points as entry markers. Additionally, the book covers the concept of the "Pivot Range" and how integrating it with the ACD method can help maximize position size while minimizing risk.
🟣 Session
The forex market is operational 24 hours a day, five days a week, closing only on Saturdays and Sundays. Typically, traders prefer to concentrate on one specific forex trading session rather than attempting to trade around the clock.
Trading sessions are defined time periods when a particular financial market is active, allowing for the execution of trades.
The most crucial trading sessions within the 24-hour cycle are the Asia, London, and New York sessions, as these are when substantial money flows and liquidity enter the market.
🟣 Kill Zone
Traders in financial markets earn profits by capitalizing on the difference between their buy/sell prices and the prevailing market prices.
Traders vary in their trading timelines.Some traders engage in daily or even hourly trading, necessitating activity during periods with optimal trading volumes and notable price movements.
Kill zones refer to parts of a session characterized by higher trading volumes and increased price volatility compared to the rest of the session.
🔵 How to Use
🟣 Session Times
The "Asia Session" comprises two parts: "Sydney" and "Tokyo." This session begins at 23:00 and ends at 06:00 UTC. The "Asia KillZone" starts at 23:00 and ends at 03:55 UTC.
The "London Session" includes "Frankfurt" and "London," starting at 07:00 and ending at 14:25 UTC. The "London KillZone" runs from 07:00 to 09:55 UTC.
The "New York" session starts at 14:30 and ends at 19:25 UTC, with the "New York am KillZone" beginning at 14:30 and ending at 22:55 UTC.
🟣 ACD Methodology
The ACD strategy is versatile, applicable to various markets such as stocks, commodities, and forex, providing clear buy and sell signals to set price targets and stop losses.
This strategy operates on the premise that the opening range of trades holds statistical significance daily, suggesting that initial market movements impact the market's behavior throughout the day.
Known as a breakout strategy, the ACD method thrives in volatile or strongly trending markets like crude oil and stocks.
Some key rules for employing the ACD strategy include :
Utilize points A and C as critical reference points, continually monitoring these during trades as they act as entry and exit markers.
Analyze daily and multi-day pivot ranges to understand market trends. Prices above the pivots indicate an upward trend, while prices below signal a downward trend.
In forex trading, the ACD strategy can be implemented using the ACD indicator, a technical tool that gauges the market's supply and demand balance. By evaluating trading volume and price, this indicator assists traders in identifying trend strength and optimal entry and exit points.
To effectively use the ACD indicator, consider the following :
Identifying robust trends: The ACD indicator can help pinpoint strong, consistent market trends.
Determining entry and exit points: ACD generates buy and sell signals to optimize trade timing.
Bullish Setup :
When the "A up" line is breached, it’s wise to wait briefly to confirm it’s not a "Fake Breakout" and that the price stabilizes above this line.
Upon entering the trade, the most effective stop loss is positioned below the "A down" line. It's advisable to backtest this to ensure the best outcomes. The recommended reward-to-risk ratio for this strategy is 1, which should also be verified through backtesting.
Bearish Setup :
When the "A down" line is breached, it’s prudent to wait briefly to ensure it’s not a "Fake Breakout" and that the price stabilizes below this line.
Upon entering the trade, the most effective stop loss is positioned above the "A up" line. Backtesting is recommended to confirm the best results. The recommended reward-to-risk ratio for this strategy is 1, which should also be validated through backtesting.
Advantages of Combining Kill Zone and ACD Method in Market Analysis :
Precise Trade Timing : Integrating the Kill Zone strategy with the ACD Method enhances precision in trade entries and exits. The ACD Method identifies key points for trading, while the Kill Zone focuses on high-activity periods, together ensuring optimal timing for trades.
Better Trend Identification : The ACD Method’s pivot ranges help spot market trends, and when combined with the Kill Zone’s emphasis on periods of significant price movement, traders can more effectively identify and follow strong market trends.
Maximized Profits and Minimized Risks : The ACD Method's structured approach to setting price targets and stop losses, coupled with the Kill Zone's high-volume trading periods, helps maximize profit potential while reducing risk.
Robust Risk Management : Combining these methods provides a comprehensive risk management strategy, strategically placing stop losses and protecting capital during volatile periods.
Versatility Across Markets : Both methods are applicable to various markets, including stocks, commodities, and forex, offering flexibility and adaptability in different trading environments.
Enhanced Confidence : Using the combined insights of the Kill Zone and ACD Method, traders gain confidence in their decision-making process, reducing emotional trading and improving consistency.
By merging the Kill Zone’s focus on trading volumes and the ACD Method’s structured breakout strategy, traders benefit from a synergistic approach that enhances precision, trend identification, and risk management across multiple markets.
ADX and SADX, SDIThe indicator aims to analyze and visualize the Average Directional Index (ADX) and its smoothed versions, along with directional indicators (DI) to help traders identify trend strength and potential buy/sell signals.
Indicator Settings:
The indicator is named "ADX and SADX, SDI" and is set to display prices with a precision of 2 decimal places.
Users can customize the ADX smoothing length, DI length, ADX smoothing period, and DI smoothing period through input variables.
Directional Movement (DM) Calculation:
The function dirmov calculates the positive and negative directional movements (DM) and the smoothed values of the positive directional index (DI+) and negative directional index (DI-).
This is done using the average true range (ATR) to normalize the DM values.
Average Directional Index (ADX) Calculation:
The function adx calculates the ADX, which measures the strength of a trend.
It uses the DI+ and DI- values to compute the ADX value.
Smoothed ADX and DI Calculation:
The ADX values are further smoothed using a simple moving average (SMA).
The DI difference is also smoothed and used to determine the trend direction.
Buy and Sell Signals:
A buy signal is generated when the DI+ crosses above DI- and the smoothed DI difference is increasing.
A sell signal is generated when the DI- crosses above DI+ and the smoothed DI difference is decreasing.
Plotting:
The ADX, smoothed ADX, smoothed DI difference (SPM), DI+, and DI- values are plotted on the chart.
Horizontal lines are drawn to indicate threshold levels (e.g., level 22).
Background and bar colors change based on buy (lime) and sell (maroon) signals to visually indicate these conditions.
Purpose of the Code:
This Pine Script code is used to create a custom indicator on TradingView that helps traders identify the strength and direction of a trend. The Average Directional Index (ADX) is used to measure trend strength, while the Directional Indicators (DI+ and DI-) are used to determine the direction of the trend. The smoothed versions of these indicators (SADX and SDI) provide additional confirmation and smoothing to reduce noise and false signals. Traders can use the buy and sell signals generated by this indicator to make informed trading decisions based on the trend strength and direction.
Important Note:
This script is provided for educational purposes and does not constitute financial advice. Traders and investors should conduct their research and analysis before making any trading decisions.
MA MACD BB BackTesterOverview:
This Pine Script™ code provides a comprehensive backtesting tool that combines Moving Average (MA), Moving Average Convergence Divergence (MACD), and Bollinger Bands (BB). It is designed to help traders analyze market trends and make informed trading decisions by testing various strategies over historical data.
Key Features:
1. Customizable Indicators:
Moving Average (MA): Smooths out price data for clearer trend direction.
MACD: Measures trend momentum through MACD Line, Signal Line, and Histogram.
Bollinger Bands (BB): Identifies overbought or oversold conditions with upper and lower bands.
2. Flexible Trading Direction: Choose between long or short positions to adapt to different market conditions.
3. Risk Management: Efficiently allocate your capital with customizable position sizes.
4. Signal Generation:
Buy Signals: Triggered by crossovers for MACD, MA, and BB.
Sell Signals: Triggered by crossunders for MACD, MA, and BB.
5. Automated Trading: Automatically enter and exit trades based on signal conditions and strategy parameters.
How It Works:
1. Indicator Selection: Select your preferred indicator (MA, MACD, BB) and trading direction (Long/Short).
2. Risk Management Configuration: Set the percentage of capital to allocate per position to manage risk effectively.
3.Signal Detection: The algorithm identifies and plots buy/sell signals directly on the chart based on the chosen indicator.
4. Trade Execution: The strategy automatically enters and exits trades based on signal conditions and configured strategy parameters.
Use Cases:
- Backtesting: Evaluate the effectiveness of trading strategies using historical data to understand potential performance.
- Strategy Development: Customize and expand the strategy to incorporate additional indicators or conditions to fit specific trading styles.
ADDONS That Affect Strategy:
1. Indicator Parameters:
Adjustments to the settings of MACD (e.g., fast length, slow length), MA (e.g., length), and BB (e.g., length, multiplier) will directly impact the detection of signals and the strategy's performance.
2. Trading Direction:
Changing the trading direction (Long/Short) will alter the entry and exit conditions based on the detected signals.
3. Risk Management Settings:
Modifying the position size percentage affects capital allocation and overall risk exposure per trade.
ADDONS That Do Not Affect Strategy:
1. Visual Customizations:
Changes to the color, shape, and style of the plotted lines and signals do not impact the core functionality of the strategy but enhance visual clarity.
2. Text and Labels:
Modifying text labels for the signals (such as renaming "Buy MACD" to "MACD Buy Signal") is purely cosmetic and does not influence the strategy’s logic or outcomes.
Notes:
- Customization: The indicator is highly customizable to fit various trading styles and market conditions.
- Risk Management: Adjust position sizes and risk parameters according to your risk tolerance and account size.
- Optimization: Regularly backtest and optimize parameters to adapt to changing market dynamics for better performance.
Getting Started:
-Add the script to your chart.
-Adjust the input parameters to suit your analysis preferences.
-Observe the marked buy and sell signals on your chart to make informed trading decisions.
Statistical RSI Pivot Reversal Indicator [UAlgo]🔶 Idea
The "Statistical RSI Pivot Reversal Indicator " is designed to enhance traditional RSI analysis by incorporating statistical methods to identify potential reversal points more accurately. The core concept is to detect frequently occurring pivot points in the RSI data, which can indicate strong support or resistance levels. By analyzing the most frequent RSI values at these pivots, the script provides traders with clearer signals for potential market reversals, helping to improve the timing of entry and exit points in their trading strategies.
🔶 Key Features
Enhanced RSI Analysis:
This script calculates the Relative Strength Index (RSI) based on user-defined parameters and identifies pivot points in the RSI data. By analyzing these pivots, it detects the most frequently occurring RSI values at support and resistance levels.
Signal Filtering Options:
Filter buy and sell signals based on whether the RSI is in overbought (above 70) or oversold (below 30) conditions, enhancing the reliability of signals.
Visual and Alert Features:
Visual Signals: The script plots the RSI, the most frequent high and low RSI values, and buy/sell signals on the chart.
Alerts: Set up custom alerts for buy and sell conditions, ensuring you never miss a trading opportunity.
🔶 Disclaimer
The "Statistical RSI Pivot Reversal Indicator " script is intended for educational and informational purposes only.
It does not constitute financial advice or investment recommendations.
Trading financial instruments involves risk, and it is possible to lose more than your initial investment. Past performance is not indicative of future results.
Median Momentum with Buy/Sell Signals and Bar ColorMomentum Calculation:
Momentum is calculated as the difference between the current close price and the close price momentum_length periods ago: momentum = close - close .
Highest and Lowest Momentum:
The highest and lowest momentum values over the specified length are calculated.
Median Momentum:
The median momentum is calculated as the average of the highest and lowest momentum values.
Color Setting:
medianColor is set based on whether the momentum is above, below, or equal to the median momentum.
barColor is set similarly for bar coloring.
Plotting:
The script plots the median momentum and the actual momentum values.
Buy and sell signals are generated when momentum crosses over or under the median momentum.
The script also plots the buy and sell signals with arrows on the chart.
VolCorrBeta [NariCapitalTrading]Indicator Overview: VolCorrBeta
The VolCorrBeta indicator is designed to analyze and interpret intermarket relationships. This indicator combines volatility, correlation, and beta calculations to provide a comprehensive view of how certain assets (BTC, DXY, CL) influence the ES futures contract (I tailored this indicator to the ES contract, but it will work for any symbol).
Functionality
Input Symbols
BTCUSD : Bitcoin to USD
DXY : US Dollar Index
CL1! : Crude Oil Futures
ES1! : S&P 500 Futures
These symbols can be customized according to user preferences. The main focus of the indicator is to analyze how the price movements of these assets correlate with and lead the price movements of the ES futures contract.
Parameters for Calculation
Correlation Length : Number of periods for calculating the correlation.
Standard Deviation Length : Number of periods for calculating the standard deviation.
Lookback Period for Beta : Number of periods for calculating beta.
Volatility Filter Length : Length of the volatility filter.
Volatility Threshold : Threshold for adjusting the lookback period based on volatility.
Key Calculations
Returns Calculation : Computes the daily returns for each input symbol.
Correlation Calculation : Computes the correlation between each input symbol's returns and the ES futures contract returns over the specified correlation length.
Standard Deviation Calculation : Computes the standard deviation for each input symbol's returns and the ES futures contract returns.
Beta Calculation : Computes the beta for each input symbol relative to the ES futures contract.
Weighted Returns Calculation : Computes the weighted returns based on the calculated betas.
Lead-Lag Indicator : Calculates a lead-lag indicator by averaging the weighted returns.
Volatility Filter : Smooths the lead-lag indicator using a simple moving average.
Price Target Estimation : Estimates the ES price target based on the lead-lag indicator (the yellow line on the chart).
Dynamic Stop Loss (SL) and Take Profit (TP) Levels : Calculates dynamic SL and TP levels using volatility bands.
Signal Generation
The indicator generates buy and sell signals based on the filtered lead-lag indicator and confirms them using higher timeframe analysis. Signals are debounced to reduce frequency, ensuring that only significant signals are considered.
Visualization
Background Coloring : The background color changes based on the buy and sell signals for easy visualization (user can toggle this on/off).
Signal Labels : Labels with arrows are plotted on the chart, showing the signal type (buy/sell), the entry price, TP, and SL levels.
Estimated ES Price Target : The estimated price target for ES futures is plotted on the chart.
Correlation and Beta Dashboard : A table displayed in the top right corner shows the current correlation and beta values for relative to the ES futures contract.
Customization
Traders can customize the following parameters to tailor the indicator to their specific needs:
Input Symbols : Change the symbols for BTC, DXY, CL, and ES.
Correlation Length : Adjust the number of periods used for calculating correlation.
Standard Deviation Length : Adjust the number of periods used for calculating standard deviation.
Lookback Period for Beta : Change the lookback period for calculating beta.
Volatility Filter Length : Modify the length of the volatility filter.
Volatility Threshold : Set a threshold for adjusting the lookback period based on volatility.
Plotting Options : Customize the colors and line widths of the plotted elements.
SMA DMA Crossing SignalSMA and DMA Crossing Buy Sell Signals
This script implements a Double Moving Average (DMA) strategy, a popular technical analysis technique used by traders to identify trends and potential buy/sell signals in financial markets.
**Description:**
The Double Moving Average strategy involves the calculation of two moving averages – a short-term moving average and a long-term moving average. In this script, we calculate these moving averages as follows:
1. **Short-term DMA (`dmaShort`):**
- Calculated using a 28-bar Simple Moving Average (SMA).
- Represents the shorter-term trend in the price movement.
2. **Long-term DMA (`dmaLong`):**
- Also calculated using a 28-bar SMA.
- Displaced backward by 14 bars (`dmaLong := request.security(syminfo.tickerid, "D", dmaLong )`), effectively creating a 28-bar SMA with a -14 bar displacement.
- Represents the longer-term trend in the price movement.
**Signals:**
Buy and sell signals are generated based on the crossing of the short-term DMA over or under the long-term DMA:
- **Buy Signal (`DMA BUY`):** Occurs when the short-term DMA crosses above the long-term DMA (`dmaBuySignal`).
- **Sell Signal (`DMA SELL`):** Occurs when the short-term DMA crosses below the long-term DMA (`dmaSellSignal`).
**How to Use:**
- **Buy Signal:** Consider entering a long position when the short-term DMA crosses above the long-term DMA, indicating a potential uptrend.
- **Sell Signal:** Consider exiting a long position or entering a short position when the short-term DMA crosses below the long-term DMA, indicating a potential downtrend.
This script provides a visual representation of the DMA crossover signals on the chart, helping traders identify potential entry and exit points in the market.
**Note:** It's important to combine DMA signals with other technical analysis tools and risk management strategies for informed trading decisions.
All comments are welcome..
Khaled Tamim's Avellaneda-Stoikov StrategyDescription:
This strategy applies the Avellaneda-Stoikov (A-S) model to generate buy and sell signals for underlying assets based on option pricing theory. The A-S model estimates bid and ask quotes for options contracts considering factors like volatility (sigma), time to expiration (T), and risk aversion (gamma).
Key Concepts:
Avellaneda-Stoikov Model: A mathematical framework for option pricing that incorporates volatility, time decay, and risk tolerance.
Bid-Ask Quotes: The theoretical buy and sell prices for an option contract.
Inventory Management: The strategy tracks its long or short position based on signals.
How it Works:
A-S Model Calculation: The avellanedaStoikov function calculates bid and ask quotes using the underlying asset's closing price, user-defined parameters (gamma, sigma, T, k, and M), and a small fee (adjustable).
Signal Generation: The strategy generates long signals when the closing price falls below the adjusted bid quote and short signals when it exceeds the adjusted ask quote.
Trade Execution: Buy and sell orders are triggered based on the generated signals (long for buy, short for sell).
Inventory Tracking: The strategy's net profit reflects the current inventory level (long or short position).
Customization:
Gamma (γ): Controls risk aversion in the A-S model (higher values imply lower risk tolerance).
Sigma (σ): Represents the underlying asset's expected volatility.
T: Time to expiration for the hypothetical option (defaults to a short-term option).
k: A constant factor in the A-S model calculations.
M: Minimum price buffer for buy/sell signals (prevents excessive churn).
Important Note:
This strategy simulates option pricing behavior for a theoretical option and does not directly trade options contracts. Backtesting results may not reflect actual market conditions.
Further Considerations:
The 0.1% fee is a placeholder and may need adjustment based on real-world trading costs.
Consider using realistic timeframes for T (e.g., expiry for a real option)
Disclaimer: This strategy is for educational purposes only and does not constitute financial advice.
Median RSI**Description:**
The "Median RSI with Buy/Sell Signals and Bar Color" indicator on TradingView calculates the median Relative Strength Index (RSI) alongside buy and sell signals and customizable bar colors. RSI is a momentum oscillator that measures the speed and change of price movements. This indicator provides traders with insights into the relative strength of a security by comparing its recent gains to its recent losses.
**How it Works:**
1. **RSI Calculation:** The script computes the RSI using a specified length parameter. This RSI value indicates whether a security is overbought or oversold, helping traders identify potential reversal points.
2. **Median RSI Calculation:** It calculates the highest and lowest RSI values over a certain period and finds the median value. This median RSI acts as a benchmark, guiding traders in assessing the relative strength of a security compared to its recent performance.
3. **Bar Color Customization:** The script allows users to customize the bar color based on the relationship between the RSI and the median RSI. Bars are colored differently to visually represent whether the RSI is above, below, or equal to the median RSI. Additionally, the script highlights bars when they cross the median RSI, providing visual cues for potential shifts in market momentum.
**Benefits:**
- **RSI Insights:** Provides insights into the relative strength of a security by comparing its recent gains to its recent losses.
- **Buy/Sell Signals:** Generates buy and sell signals based on the RSI crossing above or below the median RSI, aiding traders in timing their trades.
- **Customizable Bar Colors:** Allows traders to customize bar colors based on the relationship between the RSI and the median RSI, facilitating quick visual analysis.
- **Visual Representation:** Visualizes the RSI median RSI, and bar color on the price chart for easy interpretation.
**Ideal Usage:**
- **Trend Confirmation:** Traders can use the indicator to confirm the direction of the trend before entering trades.
- **Reversal Signals:** Changes in RSI direction, indicated by crosses above or below the median RSI, can signal potential reversals in market momentum.
- **Combination with Other Indicators:** It can be used in conjunction with other technical indicators to enhance trading strategies, providing additional confirmation signals.
**Warnings:**
- **False Signals:** Like any technical indicator, false signals may occur, especially during periods of low volume or choppy market conditions. Additional analysis and risk management techniques should be used to avoid potential losses.
- **Parameter Sensitivity:** Traders should test different parameter settings and consider market conditions when using the indicator, as adjustments may affect its sensitivity to price movements.
By providing insights into RSI dynamics, and offering customizable bar colors, the "Median RSI with Buy/Sell Signals and Bar Color" indicator equips traders with valuable tools for technical analysis and decision-making in the financial markets.
Stochastic Z-Score Oscillator Strategy [TradeDots]The "Stochastic Z-Score Oscillator Strategy" represents an enhanced approach to the original "Buy Sell Strategy With Z-Score" trading strategy. Our upgraded Stochastic model incorporates an additional Stochastic Oscillator layer on top of the Z-Score statistical metrics, which bolsters the affirmation of potential price reversals.
We also revised our exit strategy to when the Z-Score revert to a level of zero. This amendment gives a much smaller drawdown, resulting in a better win-rate compared to the original version.
HOW DOES IT WORK
The strategy operates by calculating the Z-Score of the closing price for each candlestick. This allows us to evaluate how significantly the current price deviates from its typical volatility level.
The strategy first takes the scope of a rolling window, adjusted to the user's preference. This window is used to compute both the standard deviation and mean value. With these values, the strategic model finalizes the Z-Score. This determination is accomplished by subtracting the mean from the closing price and dividing the resulting value by the standard deviation.
Following this, the Stochastic Oscillator is utilized to affirm the Z-Score overbought and oversold indicators. This indicator operates within a 0 to 100 range, so a base adjustment to match the Z-Score scale is required. Post Stochastic Oscillator calculation, we recalibrate the figure to lie within the -4 to 4 range.
Finally, we compute the average of both the Stochastic Oscillator and Z-Score, signaling overpriced or underpriced conditions when the set threshold of positive or negative is breached.
APPLICATION
Firstly, it is better to identify a stable trading pair for this technique, such as two stocks with considerable correlation. This is to ensure conformance with the statistical model's assumption of a normal Gaussian distribution model. The ideal performance is theoretically situated within a sideways market devoid of skewness.
Following pair selection, the user should refine the span of the rolling window. A broader window smoothens the mean, more accurately capturing long-term market trends, while potentially enhancing volatility. This refinement results in fewer, yet precise trading signals.
Finally, the user must settle on an optimal Z-Score threshold, which essentially dictates the timing for buy/sell actions when the Z-Score exceeds with thresholds. A positive threshold signifies the price veering away from its mean, triggering a sell signal. Conversely, a negative threshold denotes the price falling below its mean, illustrating an underpriced condition that prompts a buy signal.
Within a normal distribution, a Z-Score of 1 records about 68% of occurrences centered at the mean, while a Z-Score of 2 captures approximately 95% of occurrences.
The 'cool down period' is essentially the number of bars that await before the next signal generation. This feature is employed to dodge the occurrence of multiple signals in a short period.
DEFAULT SETUP
The following is the default setup on EURAUD 1h timeframe
Rolling Window: 80
Z-Score Threshold: 2.8
Signal Cool Down Period: 5
Stochastic Length: 14
Stochastic Smooth Period: 7
Commission: 0.01%
Initial Capital: $10,000
Equity per Trade: 40%
FURTHER IMPLICATION
The Stochastic Oscillator imparts minimal impact on the current strategy. As such, it may be beneficial to adjust the weightings between the Z-Score and Stochastic Oscillator values or the scale of Stochastic Oscillator to test different performance outcomes.
Alternative momentum indicators such as Keltner Channels or RSI could also serve as robust confirmations of overbought and oversold signals when used for verification.
RISK DISCLAIMER
Trading entails substantial risk, and most day traders incur losses. All content, tools, scripts, articles, and education provided by TradeDots serve purely informational and educational purposes. Past performances are not definitive predictors of future results.
JK EMA-WMA ADX Strategy with RSI Reversals and Chandelier ExitThis Pine script is a comprehensive trading strategy indicator for TradingView that combines three different technical analysis techniques: the Modified EMA-WMA ADX Trading Strategy, RSI Reversals, and the Chandelier Exit strategy. Here's a breakdown of what the script does:
Inputs: The script starts by defining several user inputs that allow traders to customize various parameters such as the lengths for EMA, WMA, ADX, RSI, and Chandelier Exit calculations, as well as thresholds for ADX, bullish/bearish RSI levels, and visual options like showing labels and highlighting the current trading state.
EMA-WMA ADX Strategy: The script calculates the Exponential Moving Average (EMA), Weighted Moving Average (WMA), and Average Directional Index (ADX) using the user-defined input lengths. It then determines buy and sell signals based on the crossover/crossunder of the EMA and WMA, combined with conditions on the ADX value and its rising/falling state.
RSI Reversals: The Relative Strength Index (RSI) is calculated, and its slope is determined over a specified number of periods. Bullish and bearish reversals are identified based on the RSI crossing the user-defined bullish and bearish levels, combined with the slope condition.
Chandelier Exit: The script implements the Chandelier Exit strategy, which involves calculating an Average True Range (ATR) channel based on the highest high and lowest low over a specified period, multiplied by a user-defined multiplier. The channel lines are plotted, and buy/sell signals are generated when the price crosses these lines, indicating a potential trend change.
Plotting: The script plots the EMA, WMA, buy/sell signals for the EMA-WMA ADX strategy, bullish/bearish reversal signals for RSI, and the Chandelier Exit channel lines. It also includes options to show buy/sell labels and highlight the current trading state with colored areas.
Alerts: The script can generate alerts for various conditions, including Chandelier Exit direction changes, buy/sell signals for the Chandelier Exit, and combined buy/sell signals from the EMA-WMA ADX strategy.
Overall, this script aims to provide a comprehensive trading strategy by combining multiple technical analysis techniques and allowing traders to customize various parameters. It can be used as a standalone strategy or as a starting point for further customization and experimentation.
Yeong RRGThe code outlines a trading strategy that leverages Relative Strength (RS) and Rate of Change (RoC) to make trading decisions. Here's a detailed breakdown of the tactic described by the code:
Ticker and Period Selection: The strategy begins by selecting a stock ticker symbol and defining a period (len) for the calculations, which defaults to 14 but can be adjusted by the user.
Stock and Index Data Retrieval: It fetches the closing price (stock_close) of the chosen stock and calculates its 25-period exponential moving average (stock_ema). Additionally, it retrieves the closing price of the S&P 500 Index (index_close), used as a benchmark for calculating Relative Strength.
Relative Strength Calculation: The Relative Strength (rs) is computed by dividing the stock's closing price by the index's closing price, then multiplying by 100 to scale the result. This metric is used to assess the stock's performance relative to the broader market.
Moving RS Ratio and Rate of Change: The strategy calculates a Simple Moving Average (sma) of the RS over the specified period to get the RS Ratio (rs_ratio). It then computes the Rate of Change (roc) of this RS Ratio over the same period to get the RM Ratio (rm_ratio).
Normalization: The RS Ratio and RM Ratio are normalized using a formula that adjusts their values based on the mean and standard deviation of their respective series over the specified window. This normalization process helps in standardizing the indicators, making them easier to interpret and compare.
Indicator Plotting: The normalized RS Ratio (jdk_rs_ratio) and RM Ratio (jdk_rm_ratio) are plotted on the chart with different colors for visual analysis. A horizontal line (hline) at 100 serves as a reference point, indicating a neutral level for the indicators.
State Color Logic: The script includes a logic to determine the state color (statecolor) based on the previous state color and the current values of jdk_rs_ratio and jdk_rm_ratio. This color coding is intended to visually represent different market states: green for bullish, red for bearish, yellow for hold, and blue for watch conditions.
Signal Generation: The strategy generates buy, sell, hold, and watch signals based on the state color and the indicators' values relative to 100. For example, a buy signal is generated when both jdk_rs_ratio and jdk_rm_ratio are above 100, and the background color is set to green to reflect this bullish condition.
Trade Execution: Finally, the strategy executes trades based on the generated signals. A "BUY" trade is entered when a buy signal is present, and it is closed when a sell signal occurs.
Overall, the strategy uses a combination of RS and RoC indicators, normalized for better comparison, to identify potential buy and sell opportunities based on the stock's performance relative to the market and its momentum.
