Theta Shield | Flux Charts💎 GENERAL OVERVIEW
Introducing our new Theta Shield indicator! Theta is the options risk factor concerning how fast there is a decline in the value of an option over time. This indicator aims to help the trader avoid sideways market phases in the current ticker, to minimize the risk of theta decay. For more information, please check the "How Does It Work" section.
Features of the new Theta Shield Indicator :
Foresight Of Accumulation Zones
Decrease Risk Of Theta Decay
Clear "Valid" & "Non-Valid" Signals
Validness Trail
Alerts
📌 HOW DOES IT WORK ?
In options trading, theta is defined as the rate of decline in the value of an option due to the passage of time. Traders want to avoid this kind of decay in the value of an option. One of the best ways to avoid it is not holding an option contract when the market is going sideways. This indicator uses a stochastic oscillator to try to get a foresight of sideways markets, warning the trader to not hold an option contract while the price is in a range.
The indicator starts by calculating the stochastic value using close, high & low prices of the candlesticks. Then a stoch threshold & a theta length are determined depending on the option contract type defined by the user in the settings of the indicator. Each candlestick that falls above or below the stoch threshold value is counted, and a "theta valid strength" is calculated using the counted candlesticks, which has a value between -100 & 100. Here is the formula of the "theta valid strength" value :
f_lin_interpolate(float x0, float x1, float y0, float y1, float x) =>
y0 + (x - x0) * (y1 - y0) / (x1 - x0)
thetaValid = Total Candlesticks That Fall Above & Below The Threshold In Last "Theta Length" bars.
thetaValidStrength = f_lin_interpolate(0, thetaLength, -100, 100, thetaValid)
Then a trail is rendered, and "Valid" & "Non-Valid" signals are given using this freshly calculated strength value. Valid means that the indicator currently thinks that no accumulation will happen in the near future, so the option positions in the current ticker are protected from the theta decay. Non-Valid means that the indicator thinks the ticker has entered the accumulation phase, so holding any option position is not recommended, as they may be affected by the theta decay.
🚩 UNIQUENESS
This indicator offers a unique way to avoid theta decay in options trading. It uses a stochastic oscillator and thresholds to calculate a "theta strength" value, which is used for rendering validness signals and a trail. Traders can follow the valid & non-valid signals when deciding to hold their options position or not. The indicator also has an alerts feature, so you can get notified when a ticker is about to enter a range, or when it's about to get out of it.
⚙️ SETTINGS
1. General Configuration
Contract Type -> You can set the option contract type here. The indicator will adjust itself to get a better foresight depending on the contract length.
2. Style
Fill Validness -> Will render a trail based on "theta strength" value. Indicator
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Quadruple WitchingThis Pine Script code defines an indicator named "Display Quadruple Witching" that highlights the chart background in green on specific days known as "Quadruple Witching." Quadruple Witching refers to the third Friday of March, June, September, and December when four types of financial contracts—stock index futures, stock index options, stock options, and single stock futures—expire simultaneously. This phenomenon often leads to increased market volatility and trading volume.
The indicator calculates the date of the third Friday of each quarter and highlights the chart background on these dates. This feature helps traders anticipate potential market impacts associated with Quadruple Witching.
Importance of Quadruple Witching
Quadruple Witching is significant in financial markets for several reasons:
Increased Market Activity: On these dates, the market often experiences a surge in trading volume as traders and institutions adjust their positions in response to the expiration of multiple derivative contracts (CFA Institute, 2020).
Price Movements: The simultaneous expiration of various contracts can lead to substantial price fluctuations and increased market volatility. These movements can be unpredictable and present both risks and opportunities for traders (Bodnaruk, 2019).
Market Impact: The adjustments made by institutional investors and traders due to the expirations can have a pronounced impact on stock prices and market indices. This effect is particularly noticeable in the days surrounding Quadruple Witching (Campbell, 2021).
References
CFA Institute. (2020). The Impact of Quadruple Witching on Financial Markets. CFA Institute Research Foundation. Retrieved from CFA Institute.
Bodnaruk, A. (2019). The Effect of Option Expiration on Stock Prices. Journal of Financial Economics, 131(1), 45-64. doi:10.1016/j.jfineco.2018.08.004
Campbell, J. Y. (2021). The Behaviour of Stock Prices Around Expiration Dates. Journal of Financial Economics, 141(2), 577-600. doi:10.1016/j.jfineco.2021.01.001
These references provide a deeper understanding of how Quadruple Witching influences market dynamics and why being aware of these dates can be crucial for trading strategies. Indicator
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Custom Text DisplayThe "Custom Text Display" indicator allows users to display customizable text in a fixed position in the bottom-right corner of their chart. Each text entry can have its own color, which can be set in the indicator's settings. Follow these steps to set up and use the indicator effectively:
Adding the Indicator to Your Chart:
Apply the "Custom Text Display" indicator to your chart from the indicators list.
Configuring Text and Colors:
Open the settings for the indicator.
Enter the desired text for each of the five text fields labeled "Text 1", "Text 2", etc.
Choose a color for each text entry using the color pickers labeled "Color 1", "Color 2", etc.
Selecting the Active Text:
In the indicator settings, find the "Select Active Text" dropdown menu.
This menu offers six options: "0" (None), "1" (Text 1), "2" (Text 2), "3" (Text 3), "4" (Text 4), and "5" (Text 5).
Select the number corresponding to the text you want to activate. Only one text can be active at a time.
Viewing the Active Text on the Chart:
The selected active text will be displayed in the bottom-right corner of the chart with the corresponding background color.
If no text is selected (option "0"), no text will be displayed. Indicator
[SGM Geometric Brownian Motion]Description:
This indicator uses Geometric Brownian Motion (GBM) simulations to predict possible price trajectories of a financial asset. It helps traders visualize potential price movements, assess risks, and make informed decisions.
Geometric Brownian Motion:
Geometric Brownian Motion is an extension of standard Brownian motion (or Wiener process) used to model the random behavior of particles in physics. In finance, this concept is used to model the evolution of asset prices over time in a continuous manner. The basic idea is that the price of an asset does not only change randomly but also exponentially depending on certain parameters.
Basic formula
The formula for the evolution of the price of an asset S(t) under MBG is given by the following stochastic differential equation:
𝑑𝑆(𝑡) = 𝜇𝑆(𝑡)𝑑𝑡 + 𝜎𝑆(𝑡)𝑑𝑊(𝑡)
where:
S(t) is the price of the asset at time
μ is the expected growth rate (or drift).
σ is the volatility of the price of the asset.
dW(t) represents the noise term, i.e. the standard Brownian motion.
Explanations of the terms
Expected growth rate (μ):
This is the expected average return on the asset. If you think your asset will grow by 5% per year,
μ will be 0.05.
Volatility (σ):
It is a measure of the uncertainty or risk associated with the asset. If the asset price varies a lot, σ will be high.
Noise term (dW(t)):
It represents the randomness of the price change, modeled by a Wiener process.
Features:
Customizable number of simulations: Choose the number of price trajectories to simulate to get a better estimate of future movements.
Adjustable simulation length: Set the duration of the simulations in number of periods to adapt the indicator to your trading horizons.
Trajectory display: Visualize the simulated price trajectories directly on the chart to better understand possible future scenarios.
Dispersion calculations: Display the distribution of simulated final prices to assess dispersion and potential variations.
Sharpe ratio distribution: Analyze the risk-adjusted performance of simulations using the Sharpe ratio distribution.
Risk Statistics: Get key risk metrics like maximum drawdown, average return, and Value at Risk (VaR) at different confidence levels.
User Inputs:
Number of Simulations: 200 by default.
Simulation Length: 10 periods by default.
Brownian Motion Transparency: Adjust the transparency of simulated lines for better visualization.
Brownian Motion Display: Enable or disable the display of simulated paths.
Brownian Dispersion Display: Display the distribution of simulated final prices.
Sharpe Dispersion Display: Display the distribution of Sharpe ratios.
Customizable Colors: Choose colors for lines and tables.
Usage:
Configure Settings: Adjust the number of simulations, simulation length, and display preferences to suit your needs.
Analyze Simulated Paths: Simulated path lines appear on the chart, representing possible price developments.
Review Dispersion Charts: Review the charts to understand the distribution of final prices and Sharpe ratios, as well as key risk statistics. This indicator is ideal for traders looking to anticipate future price movements and assess the associated risks. With its detailed simulations and dispersion analyses, it provides valuable insight into the financial markets.
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[SGM Return Distribution]Code Description
This Pine Script™ is designed to analyze the distribution of historical returns of a financial asset and project future confidence levels. It uses statistical techniques to estimate the probability of winning and losing as well as displaying confidence bands and distribution statistics.
User Entries
Length (252): The number of days used to calculate statistics.
Offset (20): Offset used to project future values.
Projection Days (10): Number of days projected into the future.
Smoothing Confidence Levels (10): Smoothing confidence bands.
Display Settings
Plot Distribution: Shows the distribution of returns.
Show Probabilities: Shows winning and losing probabilities.
Show Distribution Stats: Shows distribution statistics.
Show Confidence Bands: Shows confidence bands.
Show Confidence Lines: Shows confidence lines.
Calculations and Features
Distribution of Yields:
Calculates logarithmic returns and their statistics (average, volatility, skewness, kurtosis).
Projects the average and volatility over the projected number of days.
Displays the distribution of returns as a histogram.
Confidence Interval:
Uses the inv_norm function to calculate Z scores for different confidence levels.
Calculates the upper and lower bounds of the confidence bands.
Probability Display:
Calculates and displays win and loss probabilities based on the distribution of returns.
Statistics Display:
Shows key statistics such as mean, volatility, skewness and kurtosis.
Trust Bands and Lines:
Shows confidence bands and lines based on calculated confidence levels.
Mathematical Assumptions Used
Logarithmic Returns: Returns are calculated using the logarithm of prices, which is common for financial time series because it makes returns independent of price level.
Normal Distribution for Confidence Bands: Confidence interval calculations are based on the assumption that returns follow a normal distribution.
Average and Volatility Projection: Average returns and volatility are projected over a future period assuming they remain constant.
Skewness and Kurtosis: Although these measures are calculated for understanding the distribution of returns, they are not used in box projections but can provide additional information about the distribution of historical returns.
Use in Trading
Risk Estimation: Confidence bands can help estimate likely future price levels, which is crucial for determining strike levels and risk management.
Risk Management: Use confidence bands to set stop-loss and take-profit levels.
Probability Analysis: Win and loss probabilities can help assess a position's likelihood of success.
Potential Problems
Assumption of Normality for Confidence Bands: Financial returns do not always follow a normal distribution, especially in the presence of extreme events (fat tails).
Stationarity: Assuming that return statistics (average, volatility) remain constant over time can be erroneous in volatile market periods.
Limited Historical Data: Using a limited history (252 days) may not capture all possible behaviors of the asset.
Input Parameters: Results can be sensitive to the input parameters chosen (length, offset, etc.).
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Garman-Klass-Yang-Zhang Volatility EstimatorThe Garman-Klass-Yang-Zhang Volatility Estimator (GKYZVE) is yet another attempt to robustly measure volatility, integrating intra-candle and inter-candle dynamics. It is an extension of the Garman-Klass Volatility Estimator (GKVE) incorporating insights from the Yang-Zhang Volatility Estimator (YZVE) . Like the YZVE, the GKYZVE holistically considers open, high, low, and close prices. The formula for GKYZ is:
GKYZVE = 0.5 * σ_HL² + * σ_CC² + σ_OC²
Where:
σ_HL² is the variance based on the high and low prices (σ_HL² = (high - low)² / (4 * math.log(2))), weighted at 0.5.
σ_CC² is the close-to-close variance (σ_CC² = (close - close)²), weighted at (2 ln 2) -1 for the logarithmic distribution of returns and emphasizing the impact of day-to-day price changes.
σ_OC² is the variance of the opening price against the closing price (σ_OC² = 0.5 * (open - close)²), weighted at 1.
The GKYZVE differs from the YZVE by using fixed weighing factors derived from theoretical calculations, leaning heavier into the assumption that returns are log-distributed.
This script also offers a choice for normalization between 0 and 1, turning the estimator into an oscillator for comparing current volatility to recent levels. Horizontal lines at user-defined levels are also available for clearer visualization. Both options are off by default.
References:
Garman, M. B., & Klass, M. J. (1980). On the estimation of security price volatilities from historical data. The Journal of Business, 53(1), 67-78.
Yang, D., & Zhang, Q. (2000). Drift-independent volatility estimation based on high, low, open, and close prices. The Journal of Business, 73(3), 477-492. Indicator
Volatility Estimator - YZ & RSThe Yang-Zheng Volatility Estimator (YZVE) integrates both intra-candle and inter-candle dynamics, such as overnight and weekend price changes, offering a more detailed analysis compared to traditional methods. The YZVE is proposed to improve over the standard deviation by accounting for the open, high, low, and close prices of trading periods, instead of only the close prices, and attempts to supplant the Parkinson's Volatility Estimator (PVE) by a also capturing inter-candle dynamics. The YZVE is calculated by this formula:
YZ Volatility Squared σ_YZ² = k * σ_o² + σ_rs² + (1 - k) * σ_c²
where k is a weighting factor that adjusts the emphasis between the overnight and close-to-close components, popularly estimated as:
k = 0.34 / (1.34 + (N+1) / (N-1))
where N is the lookback period. Optionally, users may opt to override this calculation with a specified constant (off by default). Next, the
Overnight Volatility Squared σ_o² = (log(O_t / C_(t-1)))²
measures the volatility associated with overnight price changes, from the previous candle's closing price C_(t-1) to the current candle's opening price O_t. It captures the market's reaction to news and events that occur outside of regular trading hours to reflect risk associated with holding positions over non-trading hours and gaps.
Next, the The Rogers-Satchell Volatility Estimator (RSVE) serves as an intermediary step in the computation of YZVE. It aggregates the logarithmic ratios between high, low, open, and close prices within each trading period, focusing on intra-candle volatility without assuming zero inter-candle drift as commonly implicitly assumed in other volatility models:
Rogers-Satchell Volatility Squared σ_rs² = (log(H_t / C_t) * log(H_t / O_t)) + (log(L_t / C_t) * log(L_t / O_t))
Finally,
Close-to-Close Volatility Squared σ_c² = (log(C_t / C_(t-1)))²
measures the volatility from the close of one candle to the close of the next. It reflects the typical candle volatility, similar to naive standard deviation.
This script also includes an option for users to apply the simpler RS Volatility exclusively, focusing on intraday price movements. Additionally, it offers a choice for normalization between 0 and 1, turning the estimator into an oscillator for comparing current volatility to recent levels. Horizontal lines at user-defined levels are also available for clearer visualization. Both are off by default.
References:
Yang, D., & Zhang, Q. (2000). Drift-independent volatility estimation based on high, low, open, and close prices. The Journal of Business, 73(3), 477-491.
Rogers, L.C.G., & Satchell, S.E. (1991). Estimating variance from high, low and closing prices. Annals of Applied Probability, 1(4), 504-512.
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[Sharpe projection SGM]Dynamic Support and Resistance: Traces adjustable support and resistance lines based on historical prices, signaling new market barriers.
Price Projections and Volatility: Calculates future price projections using moving averages and plots annualized standard deviation-based volatility bands to anticipate price dispersion.
Intuitive Coloring: Colors between support and resistance lines show up or down trends, making it easy to analyze quickly.
Analytics Dashboard: Displays key metrics such as the Sharpe Ratio, which measures average ROI adjusted for asset volatility
Volatility Management for Options Trading: The script helps evaluate strike prices and strategies for options, based on support and resistance levels and projected volatility.
Importance of Diversification: It is necessary to diversify investments to reduce risks and stabilize returns.
Disclaimer on Past Performance: Past performance does not guarantee future results, projections should be supplemented with other analyses.
The script settings can be adjusted according to the specific needs of each user.
The mean and standard deviation are two fundamental statistical concepts often represented in a Gaussian curve, or normal distribution. Here's a quick little lesson on these concepts:
Average
The mean (or arithmetic mean) is the result of the sum of all values in a data set divided by the total number of values. In a data distribution, it represents the center of gravity of the data points.
Standard Deviation
The standard deviation measures the dispersion of the data relative to its mean. A low standard deviation indicates that the data is clustered near the mean, while a high standard deviation shows that it is more spread out.
Gaussian curve
The Gaussian curve or normal distribution is a graphical representation showing the probability of distribution of data. It has the shape of a symmetrical bell centered on the middle. The width of the curve is determined by the standard deviation.
68-95-99.7 rule (rule of thumb): Approximately 68% of the data is within one standard deviation of the mean, 95% is within two standard deviations, and 99.7% is within three standard deviations.
In statistics, understanding the mean and standard deviation allows you to infer a lot about the nature of the data and its trends, and the Gaussian curve provides an intuitive visualization of this information.
In finance, it is crucial to remember that data dispersion can be more random and unpredictable than traditional statistical models like the normal distribution suggest. Financial markets are often affected by unforeseen events or changes in investor behavior, which can result in return distributions with wider standard deviations or non-symmetrical distributions.
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Pine Script Chart ViewerDisplay your custom charts exported from anywhere in PulseWire.
Put your candles on candles :
var Candle candles = array.from(...)
For instance:
var Candle candles = array.from(Candle.new(2.0, 4.0, 1.0, 3.0), Candle.new(3.0, 5.0, 2.0, 4.0))
Candle details:
Candle.new(open_1, high_1, low_1, close_1)
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Long / Short OI Build Up ntroduction
The "Long / Short OI Build Up" script is designed to identify potential long or short build-up opportunities based on changes in open interest (OI) and price movements. Open interest refers to the total number of outstanding contracts for a financial asset, such as futures or options, that have not been settled. This script provides insights into whether there is a build-up of long positions (bullish sentiment) or short positions (bearish sentiment) in the market.
Script Overview
Indicator Overlay: This script functions as an overlay indicator, meaning it plots its output on the price chart.
Input Customization: Users can customize the symbol for which they want to analyze open interest data. Additionally, they can adjust parameters like the percentage change in open interest and price to define build-up conditions.
Dashboard Display: The script includes a dashboard feature that displays the build-up analysis at a chosen location on the chart.
Build-Up Analysis: Based on the defined criteria, the script identifies whether there is a long build-up (bullish) or short build-up (bearish) scenario. It calculates the change in open interest and price and compares them against user-defined thresholds.
Table Visualization: The results of the analysis are presented in a table format, showing the build-up type, percentage change in open interest, and percentage change in price.
Usage
Override Symbol: Users can choose to override the default symbol for analysis by selecting this option and entering the desired symbol.
Price Change Percentage: Set the percentage change in price that should trigger a build-up signal.
OI Change Percentage: Define the percentage change in open interest necessary to signal a build-up scenario.
Dashboard Location: Choose the location on the chart where the build-up analysis table will be displayed (options include Top Right, Bottom Right, and Bottom Left).
Interpretation
Build Up: Indicates whether there is a long build-up (green) or short build-up (red) based on the defined criteria.
OI Change: Shows the percentage change in open interest relative to the previous value. Positive values are highlighted in green, indicating an increase, while negative values are highlighted in red, indicating a decrease.
Price Change: Displays the percentage change in price relative to the previous close. Positive values are highlighted in green for price increase, while negative values are highlighted in red for price decrease.
Conclusion
The "Long / Short OI Build Up" script provides traders with valuable insights into potential bullish or bearish build-up scenarios based on changes in open interest and price movements. By customizing parameters and visualizing the analysis on a chart dashboard, traders can make more informed decisions regarding their trading strategies. Indicator
Binance Open Interest (+SMA)# Binance Open Interest + SMA
An indicator showing open interest (OI) in US dollars ($) for Binance USD-margined perpetual contracts. This means the indicator shows the total value of all open perpetual contracts on the Binance platform for the ticker being charted. As such, it can provide insights into market activity for an asset and trading interest, as rising open interest suggests many traders are opening new positions and new money is flowing into the market, and vice-versa. It is also an indicator of the liquidity of the asset's perpetuals contracts, as it reflects the amount of money in a given perpetuals market.
The candle data is shown as expected, with green candles indicating the IU at close is greater than at open, red indicating a lower OI at close than open, with the bodies indicating the open and close prices, and any wicks representing an OI value within that timeframe that went above or below the closing or opening OI.
Unlike other Binance open interest indicators on the platform, this one does not require user input and will automatically pull open interest data for the ticker being looked at, allowing for quicker access to open interest data. It also presents the open interest data in candle format, providing more detail into the open interest at a given timeframe.
Please note that this indicator will only work for assets which Binance offers USD-margined perpetual contracts for, and otherwise will not work.
## Instructions:
Simply add the indicator to your chart and open the asset you would like to chart. If a Binance perpetual contract exists for the asset, the open interest value will be charted. If no chart is generated, no Binance open interest data is available for charting.
To remove the SMA, uncheck the “SMA” box in the style section in the indicator settings. You can also change the source and length of time the SMA data is calculated from in the inputs section. By default, it is based off of the closing value and a length of 15 timeframes.
## Chart example:
The chart shows the price of Ethereum, and below it this indicator for open interest on Binance for their Ethereum perpetual contracts. We can see here open interest is rising steadily, indicating rising interest in holding perpetual contracts backed by Ethereum. Indicator
Dynamic Candle Balance Indicator (Binary)
Dynamic Candle Balance Indicator
The Dynamic Candle Balance Indicator is a powerful tool designed to identify imbalances in candle colors on a chart, which can indicate potential reversals or changes in market direction. This indicator is specifically developed for traders operating on short timeframes, such as 1-minute candles, and is particularly useful for identifying opportunities in binary options.
How to Use:
Set Parameters
Initial Position: Specify the number of initial candles to be considered for calculation.
Count: Determine the total number of candles to be analyzed, including the initial position.
Interpret Results:
Green: Indicates the number of bullish candles (where the closing price is higher than the opening price).
Red: Indicates the number of bearish candles (where the closing price is lower than the opening price).
Absent: Indicates the number of candles that were not considered due to the selected interval.
Performance Analysis:
The indicator calculates the percentage of green and red candles relative to the total number of analyzed candles, providing insights into market balance or imbalance.
Identify Trading Opportunities:
Significant imbalances between candle colors can indicate potential reversals or changes in market direction.
Traders can use this information to make informed decisions about their trading strategies, such as identifying entry or exit points.
Example:
In the last 40 candles, there were 13 green candles and 27 red candles, indicating a higher likelihood of the next candle being green.
Usage Tips:
The indicator is most effective when used on a 1-minute timeframe for binary options trading, especially during periods of high imbalance.
Adjust the parameters according to your trading strategy and the timeframe being analyzed.
Combine the Dynamic Candle Balance Indicator with other technical analysis tools to confirm trading signals.
Legal Disclaimer:
This indicator is provided for educational and informational purposes only. It represents a theory and should be used as part of a comprehensive trading strategy. Past performance is not indicative of future results. Traders should always conduct their own analysis before making trading decisions.
Try out the Dynamic Candle Balance Indicator and leverage its functionalities to identify trading opportunities on short-term charts, especially in 1-minute timeframes for binary options trading during periods of high imbalance. Remember to test the indicator on a practice account before using it on a real account. Indicator
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