[GYTS-CE] Signal Provider | WaveTrend 4D with QMCWaveTrend 4D with Quantile Median Crosses (Community Edition)
🌸 " 📡 Signal Provider" in GoemonYae Trading System (GYTS) 🌸
WaveTrend 4D (WT4D) is an extension of the incredible WaveTrend 3D (2022, Justin Dehorty) . This oscillator elevates the classic WaveTrend by integrating advanced mathematical models for a multi-dimensional view of market momentum, capturing subtle shifts and trends that traditional indicators might miss. Each oscillator layer uses a combination of normalised derivatives, hyperbolic tangent transformations, and dual-pole filtering (John Ehlers' SuperSmoother), providing normalised and smooth signals with minimised lag.
The name "WaveTrend 4D" is derived from the usage of 4 dimensions, representing different frequencies or timeframes. Next to the "fast", "normal" and "slow" frequency, the fourth frequency is called "lethargic" (very slow). This gives the opportunity utilise more dimensions without having abundant signals, since we quantify and filter the quality of signals.
WT4D strives to help discriminating high-quality signals from the indicator by introducing the Gradient Divergence Measure (GDM) and Quantile Median Crosses (QMC). For simplicity, speed and focus, this particular indicator includes only the QMC part. Check the other 🤲Community Edition of this indicator that focuses on the GDM. For QMC, see below for more information.
🌸 --- QUANTILE MEDIAN CROSSES (QMC) --- 🌸
💮 Introduction
--
A powerful approach when working with WaveTrend is to use the frequencies' crossings of the median (zero) line. This would signify a continuation of the reversal. However, not all of those crossings would be trades with a high probability of success. For this reason, we strive to only consider reversals after the most strong trends start to show weakness. We call these reversals the "Quantile Median Crosses" (QMC), deriving the name from the used methodology.
💮 Methodology
--
To find these "most strong trends", we calculate the integral ("the area") of a frequency between all historical median crosses, and take an upper quantile of those integrals. This means that when the frequency is crossing the median in a period of consolidation, the areas between those crosses would be small. But if there was a strong momentum, and the frequency would separate itself significantly from the median and would do so for a long time, its area would be large.
So after considering all the past integrals, we take the upper quantile of those (i.e. sort all integrals and for example take the top 5%) and if the latest trend's integral was in this upper quantile, it is considered "significant". Hence, the name "quantile" in the name "Quantile Median Cross".
💮 QMC on the Oscillator
--
The QMC is shown as a label "🔴" above the median or with "🟢" below the median. The normal frequency has a "bronze" colour, the slow frequency "silver" and the lethargic is "gold". In addition to the labels, there are also diamond shapes in the same colour drawn on the median in the oscillator. This represents the previous median crossing, and helps the user to see between which two points the integral is calculated.
🌸 --- GOEMONYAE TRADING SYSTEM --- 🌸
As previously mentioned, this indicator is a 📡 Signal Provider, part of the suite of the GoemonYae Trading System (🤲 Community Edition). The greatest value comes from connecting multiple 📡 Signal Providers to the 🧬 Flux Composer to find confluence between signals. Contrary to most other indicators that connect with each other, the signals that are passed are not just binary signals ("buy" or "sell") but pass the actual GDM and QMC values. This gives the opportunity in the 🧬 Flux Composer to more accurately use multiple signals with different strengths to finally give an overall signal. On its turn, the Flux Composer can be connected to the GYTS "🎼 Order Orchestrator" for backtesting and trade automation.
Cerca negli script per "binary"
Timeframe Continuity Oscillator [TFO]This indicator is used to visualize timeframe continuity - a core concept of "The Strat" - along with some added logic for potential range limiters.
When discussing timeframe continuity, typically we are evaluating several timeframes to see if price is trading above or below the current open of each respective timeframe. If we are concerned with the 15m, 4h, and 1D for example, and price is trading above the current open of each of those timeframes, we can say that we have full timeframe continuity (FTFC) up. Conversely, if price is trading below the current open of each of those timeframes, we can say that we have FTFC down.
We can visualize this with an oscillator of sorts, where the zero line is anchored to the open price of the highest timeframe that we're concerned with. Using the prior example, this would be the 1D timeframe. As long as price is above the current 1D open, it is impossible to have FTFC down; and as long as price is below the current 1D open, it is impossible to have FTFC up. This is why we base the oscillator's values off of the highest timeframe's open (the values are simply how far price has traded from this open) - any value greater than zero tells us that there is potential to have FTFC up, and any value less than zero tells us that there is potential to have FTFC down.
There are a few ways we chose to visualize this data. First, we can choose the "Binary" option which simply uses one solid bullish color above the zero line, and one solid bearish color below the zero line.
Second, we can choose the "Gradient" option to help describe whether we have FTFC up or down. Values above the zero line will be a mix of the bullish color and mid color, where the mid color indicates no timeframe continuity up and the bullish color indicates FTFC up - sort of like a color spectrum of timeframe continuity to describe how many timeframes are in agreement. Similarly, values below the zero line will be a mix of the bearish color and the mid color, where the mid color again indicates no timeframe continuity down and the bearish color indicates FTFC down.
Lastly, we can choose the "FTFC Only" option which will only color the histogram bars as bullish if there is FTFC up, or bearish if there is FTFC down.
One more feature that we added is these upper and lower bands that aim to help describe the potential upper and lower limits that price may travel, relative to the highest timeframe's open. This is done by taking the standard deviation of some defined lookback period, for example, 2 standard deviations of the previous 10 weeks, assuming 1W is the highest timeframe enabled.
The concept is similar to that of an ADR (average daily range) as it can be used to estimate maximum range extensions for the largest timeframe. The arrows you see are plotted once the value exceeds either band - alerts can be enabled for these events as well through any alert() function call.
Machine Learning: Multiple Logistic Regression
Multiple Logistic Regression Indicator
The Logistic Regression Indicator for TradingView is a versatile tool that employs multiple logistic regression based on various technical indicators to generate potential buy and sell signals. By utilizing key indicators such as RSI, CCI, DMI, Aroon, EMA, and SuperTrend, the indicator aims to provide a systematic approach to decision-making in financial markets.
How It Works:
Technical Indicators:
The script uses multiple technical indicators such as RSI, CCI, DMI, Aroon, EMA, and SuperTrend as input variables for the logistic regression model.
These indicators are normalized to create categorical variables, providing a consistent scale for the model.
Logistic Regression:
The logistic regression function is applied to the normalized input variables (x1 to x6) with user-defined coefficients (b0 to b6).
The logistic regression model predicts the probability of a binary outcome, with values closer to 1 indicating a bullish signal and values closer to 0 indicating a bearish signal.
Loss Function (Cross-Entropy Loss):
The cross-entropy loss function is calculated to quantify the difference between the predicted probability and the actual outcome.
The goal is to minimize this loss, which essentially measures the model's accuracy.
// Error Function (cross-entropy loss)
loss(y, p) =>
-y * math.log(p) - (1 - y) * math.log(1 - p)
// y - depended variable
// p - multiple logistic regression
Gradient Descent:
Gradient descent is an optimization algorithm used to minimize the loss function by adjusting the weights of the logistic regression model.
The script iteratively updates the weights (b1 to b6) based on the negative gradient of the loss function with respect to each weight.
// Adjusting model weights using gradient descent
b1 -= lr * (p + loss) * x1
b2 -= lr * (p + loss) * x2
b3 -= lr * (p + loss) * x3
b4 -= lr * (p + loss) * x4
b5 -= lr * (p + loss) * x5
b6 -= lr * (p + loss) * x6
// lr - learning rate or step of learning
// p - multiple logistic regression
// x_n - variables
Learning Rate:
The learning rate (lr) determines the step size in the weight adjustment process. It prevents the algorithm from overshooting the minimum of the loss function.
Users can set the learning rate to control the speed and stability of the optimization process.
Visualization:
The script visualizes the output of the logistic regression model by coloring the SMA.
Arrows are plotted at crossover and crossunder points, indicating potential buy and sell signals.
Lables are showing logistic regression values from 1 to 0 above and below bars
Table Display:
A table is displayed on the chart, providing real-time information about the input variables, their values, and the learned coefficients.
This allows traders to monitor the model's interpretation of the technical indicators and observe how the coefficients change over time.
How to Use:
Parameter Adjustment:
Users can adjust the length of technical indicators (rsi_length, cci_length, etc.) and the Z score length based on their preference and market characteristics.
Set the initial values for the regression coefficients (b0 to b6) and the learning rate (lr) according to your trading strategy.
Signal Interpretation:
Buy signals are indicated by an upward arrow (▲), and sell signals are indicated by a downward arrow (▼).
The color-coded SMA provides a visual representation of the logistic regression output by color.
Table Information:
Monitor the table for real-time information on the input variables, their values, and the learned coefficients.
Keep an eye on the learning rate to ensure a balance between model adjustment speed and stability.
Backtesting and Validation:
Before using the script in live trading, conduct thorough backtesting to evaluate its performance under different market conditions.
Validate the model against historical data to ensure its reliability.
TimeSeriesRecurrencePlotLibrary "TimeSeriesRecurrencePlot"
In descriptive statistics and chaos theory, a recurrence plot (RP) is a plot showing, for each moment i i in time, the times at which the state of a dynamical system returns to the previous state at `i`, i.e., when the phase space trajectory visits roughly the same area in the phase space as at time `j`.
```
A recurrence plot (RP) is a graphical representation used in the analysis of time series data and dynamical systems. It visualizes recurring states or events over time by transforming the original time series into a binary matrix, where each element represents whether two consecutive points are above or below a specified threshold. The resulting Recurrence Plot Matrix reveals patterns, structures, and correlations within the data while providing insights into underlying mechanisms of complex systems.
```
~starling7b
___
Reference:
en.wikipedia.org
github.com
github.com
github.com
github.com
juliadynamics.github.io
distance_matrix(series1, series2, max_freq, norm)
Generate distance matrix between two series.
Parameters:
series1 (float) : Source series 1.
series2 (float) : Source series 2.
max_freq (int) : Maximum frequency to inpect or the size of the generated matrix.
norm (string) : Norm of the distance metric, default=`euclidean`, options=`euclidean`, `manhattan`, `max`.
Returns: Matrix with distance values.
method normalize_distance(M)
Normalizes a matrix within its Min-Max range.
Namespace types: matrix
Parameters:
M (matrix) : Source matrix.
Returns: Normalized matrix.
method threshold(M, threshold)
Updates the matrix with the condition `M(i,j) > threshold ? 1 : 0`.
Namespace types: matrix
Parameters:
M (matrix) : Source matrix.
threshold (float)
Returns: Cross matrix.
rolling_window(a, b, sample_size)
An experimental alternative method to plot a recurrence_plot.
Parameters:
a (array) : Array with data.
b (array) : Array with data.
sample_size (int)
Returns: Recurrence_plot matrix.
Trend Direction & Levels IdentifierOverview : Trend Direction & Levels Identifier (TDLI) provides you with two lines - Resistance/Support line (RSLine) and Trend Line. These two lines form a channel which is filled with a colour showing current market direction, which also prints Bullish or Bearish text. Trend Line calculation is similar but follows different approach than Super Trend indicator. RSLine calculation is done using EMA and dynamic ATR.
How does this work?
Firstly understand Supertrend - The Supertrend indicator is a freely available technical analysis tool that helps traders identify the direction of the trend . It is based on the concept of volatility, and it provides a simple way to identify whether the current market trend is bullish or bearish.
Here's a basic explanation of the Supertrend indicator's logic and how it is commonly used:
Supertrend Indicator Logic:
Calculation of Average True Range (ATR) : The first step involves calculating the Average True Range (ATR) over a specified period. ATR measures market volatility by considering the average range between the high and low prices over a given number of periods.
Multiplier Factor : A multiplier factor is then applied to the ATR. The multiplier is usually set by the trader or analyst and determines the sensitivity of the Supertrend to changes in volatility.
Calculation of Upper and Lower Bands:The Supertrend indicator calculates two bands - an upper band and a lower band.
Upper Band (UB) = High price - (Multiplier * ATR)
Lower Band (LB) = Low price + (Multiplier * ATR)
Determining Trend Direction : If the current market price is above the Upper Band, the Supertrend suggests a bearish trend (sell signal). If the current market price is below the Lower Band, the Supertrend suggests a bullish trend (buy signal).
Now, Let's understand how we use this logic with some modification to build our Trend line -
Let's break down the key differences:
1. Calculation of Trend Switch Points:
- Supertrend: The Supertrend indicator primarily relies on the Average True Range (ATR) to calculate the volatility of the market. It then determines trend direction based on whether the closing price is above or below the Supertrend line.
- Our Trend: We use a modified ATR for volatility measurement (ATR / x), our code introduces modifications in the calculation of trend switch points. It incorporates moving averages (SMA - Simple Moving Averages) to define high and low prices, adding a dynamic element to the identification of trend reversal points.
2. Trend States and Switch Logic:
- Supertrend: The Supertrend generally has two states: uptrend or downtrend. It switches its state when the closing price crosses the Supertrend line.
- Our Trend: Our code introduces an additional variable, which is not binary (0 or 1) but rather represents the state of the trend (0 for uptrend, 1 for downtrend). The indicator uses a more complex logic involving previous trend states and moving averages to determine trend switches.
So, our trend line incorporates additional elements such as moving averages, dynamic amplitude, and channel deviation to modify the Supertrend logic and provide a more nuanced and visually informative representation of market trends. These modifications offer traders more flexibility in adapting the indicator to different market conditions and trading preferences.
Remember the underlying logic is of Supertrend which is freely available to all.
Another line is RSLine, lets dive into its logic and calculation -
Average True Range (ATR) Calculation : Calculates the Average True Range, a measure of market volatility. The ATR can be dynamically adjusted based on user preference.
Chande Momentum Oscillator (CMO) and Variable (VAR) Calculation : Calculates the CMO, which measures momentum, and uses it to compute the VAR value. This introduces an adaptive element to the indicator.
Other Moving Averages : Calculates various moving averages, including Wilder's Moving Average (WWMA), Zero-Lag Exponential Moving Average (ZLEMA), and Time Series Forecast (TSF), providing different perspectives on trend direction.
Main Moving Average (MAvg) Calculation : Computes the main moving average based on EMA and length.
Stop Level Calculation : Determines stop levels for both long and short positions. The levels are influenced by the moving average (MAvg) and ATR, with an option to normalize them.
The Stop Levels forms the RSLine which acts as either resistance or support based on market direction.
Lets see how the indicator tells you probable market direction -
Direction Identification : Identifies the current trend direction (uptrend or downtrend) based on the relationship between the moving average and the previous stop level. It also prints Bullish or Bearish on chart based on crossovers and crossunders between the Trend Line and the RSLine.
Fill Coloring for Highlighting : It Fills the area between the Trend Line and RSLine with either green or red color to visually emphasize the trend direction. The colors change based on whether the Trend Line and MAvg is above or below the stop levels.
So there are 3 major things -
1. RSLine - Uses EMA and dynamic ATR to calculate stop levels. This acts as support or resistance to current trend. It is always red in colour.
2. Trend Line - Unlike Super Trend this Trend Line calculation uses a combination of highest high, lowest low, and EMA of a fixed range of candles to determine trend changes. It uses a fixed amplitude for calculating the highest high, lowest low, and EMA values, but it doesn't incorporate dynamic volatility adjustments like ATR. Its colour varies from red to green based on calculation.
3. Channel Colour - Channel colour is decided based on crossover of Trend Line and RSLine, if Trend Line crosses RSLine from bottom then channel colour becomes green, similarly red colour is calculated.
How to use this?
Refer this snapshot for content below -
1. Once a crossover happens between Trend Line and RSLine, bullish / bearish text is printed with change in colour of channel. RSLine acts as support/resistance.
2. Look for colour of Trend Line - when it matches channel colour, it means favourable direction is that colour (green - long, red - short)
3. Remember any ongoing trend can reverse any second, so follow price action for better results.
Preferred Timeframe : It works best in 5 minute timeframe, but can also be used in other time frames.
Reason to use these two lines ?
The Trend Line tells current trend direction using a line which keeps changing colours, for double confirmation we use the RSline and channel colour which is calculated using Trend line and RSLine crossover. When both Trend line and RSLine channel colour is same that gives a more solid confirmation (not 100%) of a trend
Why it is worth paying for :
As mentioned earlier this indicator is built over freely available Supertrend and EMA indicators. The modifications which we have done for better calculation and visualisation makes it worth.
The indicator may be considered valuable for traders who appreciate a visual representation of market trend direction and important stop levels. Normal indicators like supertrend just shows a line which gives you idea about the trend but our indicator apart from telling trend direction tells important levels and provide a channel filled with current trend direction significance which helps in following trend precisely.
1. The customization options and visual clarity could enhance decision-making for those who prefer a more tailored approach.
2. Traders willing to pay for this indicator may find it useful in complementing their existing analysis and strategy.
Although one should understand using premium indicator doesnt mean it will generate magic results, if you know price action and risk management properly then only consider trying our indicator else practice trading on free indicators first.
IMPORTANT : As Stock markets are dynamic in nature, no indicator is a magic indicator which will give you 100% accurate results on one click. You still have to use price action for best results.
DISCLAIMER : This indicator isn't a get rich quick scheme, neither it can provide 100% accurate results. It is meant to be used as an aid to Price Action Trading and proper risk management.
BTC Supply in Profits and Losses (BTCSPL) [AlgoAlpha]Description:
🚨The BTC Supply in Profits and Losses (BTCSPL) indicator, developed by AlgoAlpha, offers traders insights into the distribution of INDEX:BTCUSD addresses between profits and losses based on INDEX:BTCUSD on-chain data.
Features:
🔶Alpha Decay Adjustment: The indicator provides the option to adjust the data against Alpha Decay, this compensates for the reduction in clarity of the signal over time.
🔶Rolling Change Display: The indicator enables the display of the rolling change in the distribution of Bitcoin addresses between profits and losses, aiding in identifying shifts in market sentiment.
🔶BTCSPL Value Score: The indicator optionally displays a value score ranging from -1 to 1, traders can use this to carry out strategic dollar cost averaging and reverse dollar cost averaging based on the implied value of bitcoin.
🔶Reversal Signals: The indicator gives long-term reversal signals denoted as "▲" and "▼" for the price of bitcoin based on oversold and overbought conditions of the BTCSPL.
🔶Moving Average Visualization: Traders can choose to display a moving average line, allowing for better trend identification.
How to Use ☝️ (summary):
Alpha Decay Adjustment: Toggle this option to enable or disable Alpha Decay adjustment for a normalized representation of the data.
Moving Average: Toggle this option to show or hide the moving average line, helping traders identify trends.
Short-Term Trend: Enable this option to display the short-term trend based on the Aroon indicator.
Rolling Change: Choose this option to visualize the rolling change in the distribution between profits and losses.
BTCSPL Value Score: Activate this option to show the BTCSPL value score, ranging from -1 to 1, 1 implies that bitcoin is extremely cheap(buy) and -1 implies bitcoin is extremely expensive(sell).
Reversal Signals: Gives binary buy and sell signals for the long term
IU Probability CalculatorHow This Script Works:
1. This script calculate the probability of price reaching a user-defined price level within one candle with the help Normal Distribution Probability Table.
2. Normal Distribution Probability Table is use for calculating probability of events, it's very powerful for calculation of probability and this script is fully based on that table.
3. It takes the Average True Range value or Standard Deviation value of past user-defined length bar.
4. After that it take this formula z = ( price_level - close ) / (ATR or Standard Deviation) and return the value for z, for the bearish side it take z = (close - price level) / (ATR or Standard Deviation ) formula.
5. Once we have the z it look into Normal Distribution Probability Table and match the value.
6. Now the value of z is multiple buy 100 in order to make it look in percentage term.
7. After that this script subtract the final value with 100 because probability always comes under 100%
8. finally we plot the probability at the bottom of the chart the red line indicates "The probability of price not reaching that price level", While the green line indicates "Probability of price Reaching that level " .
9. This script will work fine for both of the directions
How This Is Useful For The User:
1. With this script user can know the probability of price reaching the certain level within one candle for both Directions .
2. This is useful while creating options hedging strategies
3. This can be helpful for deciding stop loss level.
4. It's useful for scalpers for managing their traders and it can be use by binary option traders.
Math NeuronThis open source script uses the mathematical rules of a classic two-input neuron with two weights and one bias(x * w1 + y*w2 + b).
The two inputs are the rsi (length 14) of close and volume, The result that we try to anticipate is the development of a pivot high or a pivot low (high or low candle are the max or min of the previous n° )
The activation function is sigmoid(binary results).
Liquidity Levels/Voids (VP) [LuxAlgo]The Liquidity Levels/Voids (VP) is a script designed to detect liquidity voids & levels by measuring traded volume at all price levels on the market between two swing points and highlighting the distribution of the liquidity voids & levels at specific price levels.
🔶 USAGE
Liquidity is a fundamental market force that shapes the trajectory of assets.
The creation of a liquidity level comes as a result of an initial imbalance of supply/demand, which forms what we know as a swing high or swing low. As more players take positions in the market, these are levels that market participants will use as a historical reference to place their stops. When the levels are then re-tested, a decision will be made. The binary outcome here can be a breakout of the level or a reversal back to the mean.
Liquidity voids are sudden price changes that occur in the market when the price jumps from one level to another with little trading activity (low volume), creating an imbalance in price. The price tends to fill or retest the liquidity voids area, and traders understand at which price level institutional players have been active.
Liquidity voids are a valuable concept in trading, as they provide insights about where many orders were injected, creating this inefficiency in the market. The price tends to restore the balance.
🔶 SETTINGS
The script takes into account user-defined parameters and detects the liquidity voids based on them, where detailed usage for each user-defined input parameter in indicator settings is provided with the related input's tooltip.
🔹 Liquidity Levels / Voids
Liquidity Levels/Voids: Color customization option for Unfilled Liquidity Levels/Voids.
Detection Length: Lookback period used for the calculation of Swing Levels.
Threshold %: Threshold used for the calculation of the Liquidity Levels & Voids.
Sensitivity: Adjusts the number of levels between two swing points, as a result, the height of a level is determined, and then based on the above-given threshold the level is checked if it matches the liquidity level/void conditions.
Filled Liquidity Levels/Voids: Toggles the visibility of the Filled Liquidity Levels/Voids and color customization option for Filled Liquidity Levels/Voids.
🔹 Other Features
Swing Highs/Lows: Toggles the visibility of the Swing Levels, where tooltips present statistical information, such as price, price change, and cumulative volume between the two swing levels detected based on the detection length specified above, Coloring options to customize swing low and swing high label colors, and Size option to adjust the size of the labels.
🔹 Display Options
Mode: Controls the lookback length of detection and visualization.
# Bars: Lookback length customization, in case Mode is set to Present.
🔶 RELATED SCRIPTS
Liquidity-Voids-FVG
Buyside-Sellside-Liquidity
Swing-Volume-Profiles
Flux Charts SFX Algo (Premium)Flux Charts SFX Algo indicator is a comprehensive and sophisticated all-in-one toolkit designed to cater to all the technical analysis needs of traders. Developed and designed by Russell W., head developer at Flux Charts.
The Flux Charts SFX Algo indicator stands apart with its unique ability to seamlessly integrate with various forms of technical analysis, while also offering the option to function as a standalone toolkit adaptable to any trading style. The indicator has been designed to take into account the dynamic nature of market conditions, ensuring that every feature included remains relevant, reliable, and effective.
Traders have countless possibilities when utilizing this indicator, allowing for the exploration and analysis of an array of cutting-edge features over time. This enables traders to selectively employ the features that align best with their individual trading styles and build a personal trading strategy.
The Flux Charts SFX Algo indicator is set to revolutionize the way traders approach technical analysis, providing them with the tools and insights needed to navigate complex financial markets with confidence and precision.
Flux Charts SFX Algo works in all markets (stocks, crypto, forex, futures, bonds, options, etc) and has many features including:
Buy signals (Not to be followed blindly)
Sell signals (Not to be followed blindly)
Buy & Sell Signal Ratings (Higher rating doesn't necessarily mean a "better" signal)
Algorithm Weighting Customization
Algorithm Sensitivity Customization
Algorithm Signal Strength Filter
Take Profit signals
Take Profit Retest signals
Take Profit Level Optimization
Trend Candle Coloring
Volatility Bands
+ more
What it does
The indicator uses an Adjusted Weighted majority algorithm to generate "buy" and "sell" signals. The algorithm takes into account several market metrics and weights them based on their recent performance. How far back the algorithm checks is based on the “Time Weighting” setting. This allows users to choose between having more data points or having more recency bias within the algorithm, but less data to decipher.
How it works and what differentiates it
There are many popular strategies in the market all of which go in and out of successful periods. The SFX algorithm effectively uses popular indicators or "experts" and weights them using a period decided through the "Time Weighting" Setting. The "experts" include popular indicators that cover Momenutmn, ATR trends, and EMA trends. Adjusted Weighted Majority typically weighs only through binary events however the SFX also uses a dynamic system to punish larger losses. The total weighting is then used to confirm a signal is agreeing with the most successful "experts" or indicators within the time period. This effectively will filter poor signals during periods of underperformance compared to other indicators and the converse during performant periods.
This weighting algorithm was inspired by the Princeton University lecture "Multiplicative Weight Algorithm" by Sanjeev Arora!
Usage
CME_MINI:ES1! 3 minute timeframe, July 7 2023.
Indicator Settings: (Sensitivity: 70, Signal Strength: 40, Time Weighting: Recent Trends)
The star-rated signals show the strength of the signals based on our weighting system
The colored candles (green & red) simplify the market into basic uptrends/downtrends
The volatility bands show areas of potential reversals
The volatility bands also show potential breakouts (Tight bands = consolidation, which could lead to an impulsive move)
The take profit signals suggest areas where profits should be taken in a trade
Settings and their Usage
Algorithm Settings Explained
Sensitivity determines how frequently signals appear. A higher sensitivity would lead to more frequent signals (Buy & Sell) appearing on your chart
Signal Strength helps filter out low-rated signals based on our Stochastic Weighting Algorithm. A higher signal strength will lead to fewer signals on your chart. A higher-rated signal doesn't necessarily make it a better signal than a lower-rated signal.
Time Weighting allows you to choose how much historic data you want the indicator to use when interpreting data for the signals. There are three options to choose from including:
- Recent Trends
- Mixed Trends
- Longterm Trends
Using the "Recent Trends" option will only use recent market data when looking at the market metrics our algorithm uses for generating "Buy" and "Sell" signals. Thus, there will be a recency bias which means the metrics the algorithm is weighing more heavily have recently performed well.
Using the "Longterm Trends" option will use more historic market data when looking at the market metrics our algorithm uses. This will give more data points for the algorithm to use, but it won't count for recent performances, but rather an overall performance in the past. Thus, if one metric has been doing poorly recently, it will still receive the same weight, even though it was performing well at the start of our lookback period for data.
Using the "Mixed Trends" option will give you a choice that is in between these two options. This will give you a good balance between having enough data points for market metrics, while also sustaining a good bit of market recency bias.
Liquidation Ranges + Volume/OI Dots [Kioseff Trading]Hello!
Introducing a multi-faceted indicator "Liquidation Ranges + Volume Dots" - this indicator replicates the volume dot tools found on various charting platforms and populates a liquidation range on crypto assets!
Features
Volume/OI dots populated according to user settings
Size of volume/OI dots corresponds to degree of abnormality
Naked level volume dots
Fixed range capabilities for volume/OI dots
Visible time range capabilities for volume/OI dots
Lower timeframe data used to discover iceberg orders (estimated using 1-minute data)
S/R lines drawn at high volume/OI areas
Liquidation ranges for crypto assets (10x - 100x)
Liquidation ranges are calculated using a popular crypto exchange's method
# of violations of liquidation ranges are recorded and presented in table
Pertinent high volume/OI price areas are recorded and presented in table
Personalized coloring for volume/OI dots
Net shorts / net long for the price range recorded
Lines shows reflecting net short & net long increases/decreases
Configurable volume/OI heatmap (displayed between liquidation ranges)
And some more (:
Liquidation Range
The liquidation range component of the indicator uses a popular crypto exchange's calculation (for liquidation ranges) to populate the chart for where 10x - 100x leverage orders are stopped out.
The image above depicts features corresponding to net shorts and net longs.
The image above shows features corresponding to liquidation zones for the underlying coin.
The image above shows the option to display volume/oi delta at the time the corresponding grid was traded at.
The image above shows an instance of using the "fixed range" feature for the script.
*The average price of the range is calculated to project liquidation zones.
*Heatmap is calculated using OI (or volume) delta.
Huge thank you to Pine Wizard @DonovanWall for his range filter code!
Price ranges are automatically detected using his calculation (:
Volume / OI Dots
Similar to other charting platforms, the volume/OI dots component of the indicator distinguishes "abnormal" changes in volume/OI; the detected price area is subsequently identified on the chart.
The detection method uses percent rank and calculates on the last bar of the chart. The "agelessness" of detection is contingent on user settings.
The image above shows volume dots in action; the size of each volume dot corresponds to the amount of volume at the price area.
Smaller dots = lower volume
Larger dots = higher volume
The image above exemplifies the highest aggression setting for volume/OI dot detection.
The table oriented top-right shows the highest volume areas (discovered on the 1-minute chart) for the calculated period.
The open interest change and corresponding price level are also shown. Results are listed in descending order but can also be listed in order of occurrence (most relevant).
Additionally, you can use the visible time range feature to detect volume dots.
The feature shows and explains how the visible range feature works. You select how many levels you want to detect and the script will detect the selected number of levels.
For instance, if I select to show 20 levels, the script will find the 20 highest volume/OI change price areas and distinguish them.
The image above shows a narrower price range.
The image above shows the same price range; however, the script is detecting the highest OI change price areas instead of volume.
* You can also set a fixed range with this feature
* Naked levels can be used
Additionally, you can select for the script to show only the highest volume/ OI change price area for each bar. When active, the script will successively identify the highest volume / OI change price area for the most recent bars.
Naked Levels
The image above shows and explains how naked levels can be detected when using the script.
And that's pretty much it!
Of course, there're a few more features you can check out when you use the script that haven't been explained here (:
Thank you again to @DonovanWall
Thank you to @Trendoscope for his binary insertion sort library (:
Thank you to @PineCoders for their time library
Thank you for checking this out!
DarkWaveColorThemesLibrary "DarkWaveColorThemes"
Description:
A simple, binary color-theming library that provides you with easy-access 'bullish and bearish' colors which you can use to make your indicators more aesthetically pleasing. These color themes were developed to help the community make indicators look excellent with ease.
Functions:
1. getThemeColor(themeName, colorType)
Description:
This function returns a color (either a 'Bullish' or 'Bearish' color, depending on your 'colorType' parameter input) according to the theme you have supplied as the 'themeName' parameter.
Parameters:
themeName (string) : Specify the theme you want to reference. Options include: 'DarkWave', 'Synthwave', 'DarkWave Crypto', 'Crystal Pool', 'Aquafarer', 'Mystic Armor', 'Futurist', 'Electric Zest', 'Stealth Ride', 'Long Trader', 'Short Trader', 'Emerald Glow', 'Gold Heist', 'Floral', 'Cobalt Twilight', and 'Sunrise'.
colorType (string) : Specify which color you want to reference from the theme. Options include: 'Bullish' and 'Bearish'.
Returns:
Your specified color type according to your specified theme.
Arbitrary Price Point Probability (APPP)The "Arbitrary Price Point Probability" indicator is designed to calculate the probability of a given price point occurring within a certain range of prices. The indicator uses statistical analysis to determine the likelihood of a specific price point appearing based on the market data.
The indicator works by taking the input price, which is the price point for which the probability is being calculated. The indicator then calculates the mean and standard deviation of the prices over a certain period specified by the user. The length of the period for calculating the mean and standard deviation is also specified by the user.
Once the mean and standard deviation have been calculated, the indicator uses them to calculate the probability of the input price point occurring within the range of prices over the specified period. The indicator does this by calculating the z-score, which is the number of standard deviations between the input price point and the mean price. The z-score is then used to calculate the probability using a t-distribution probability density function.
The t-distribution probability density function used by the indicator is a mathematical function that describes the likelihood of obtaining a particular value from a t-distribution. A t-distribution is a statistical distribution used when the sample size is small, and the population standard deviation is unknown.
The indicator also uses a binary search algorithm to find the t-value for a given confidence level. The t-value is the number of standard deviations from the mean at which the confidence interval is set. The confidence level is set by the user, and the default value is 99%.
Overall, the "Arbitrary Price Point Probability" indicator is a useful tool for traders who want to determine the probability of a particular price point occurring within a certain range of prices. The indicator can be used in conjunction with other technical analysis tools to make more informed trading decisions.
Challenge training (journal)Dynamic trading journal with equity curve display. Detailed results with prop firm objectives, editable, $/month estimation, possibility to compare two strategies.
one line in parameter = one trading day. 20 days max.
For each trading day, specify : The number of trades, the number of SL, the number of total winning RR.
A table at the bottom right summarizes the days and performances during the backtest in order to have an idea of the current performance.
The bottom left table summarizes the overall performance with some key information.
Depending on the number of days traded, a monthly "salary" is deducted, taking into account the prop firm commission.
there is the possibility to define a "Type" for each trading day, 1 or 2. It allows to compare in a binary way, example for type 1: when the high time frame structure is doing well and I am confident for scalping, otherwise type 2.
Again: type 1: SL shorter by 50%, type 2: normal SL etc..
the button "separate 1 and 2" allows to display two additional equity curves : type 1 and type 2. It allows to have a quick visual comparison on the impact of our parameter studied in our backtest on our performance. at the scale of the main equity curve
All the conditions to succeed in the challenge are adjustable in the parameters. The drawdown calculation has been simplified - in order not to have to put 80 trades in the parameters window, I have gathered them by "day", and pessimistically, we consider first the stoplosses and then the take profits, simplifying the performances of the day into "one losing trade" and "one winning trade" (graphically). It is a good compromise between quantity and quality.
Use "A random day trading" indicator to spice up your training.
I hope this will be useful for you to track your performance !
MACD Optimizer Pro [Kioseff Trading]Massive update! This script now includes 12 different moving averages and 30+ built-in technical indicators to enhance your trading strategy optimization! (:
This script (MACD Optimizer Pro) allows the user to optimize and test hundreds of MACD strategies, simultaneously, in under 40 seconds. Of course, theoretically, an unlimited number of trading strategies can be tested with the MACD Optimizer Pro. After the optimization period - the MACD Optimizer Pro will show the most profitable MACD strategy or, should you choose, the highest win-rate MACD strategy or the most-efficient MACD strategy!
Optimization results can be backtested and verified using the native TradingView backtester - which is included in the MACD Optimizer Pro - and made easy to use! This feature makes settings alerts a simple practice!
Features
Test hundreds of MACD strategies, simultaneously, in under 40 seconds.
Optimize long MACD strategies and short MACD strategies.
12 different built-in moving averages included to improve your MACD strategy.
30+ built-in technical indicators to improve your MACD strategy.
Runs as a strategy script - profit factor, PnL , win-rate, number of trades, max drawdown, equity curve and other pertinent statistics shown.
Alerts
Optimize any MACD setting
Profit targets, trailing stops, fixed stop losses, and a binary MACD strategy can all be tested.
Strategies can be optimized for highest win rate, highest net profit, most efficient profit.
Limit orders can be simulated.
External indicators can be used for optimization i.e. your own, custom-built indicator, an indicator from your favorite author, or almost any publicly available
TradingView indicator.
Date range for optimization and backtesting are configurable.
Explanation
The image above shows a list of configurations for the optimizer. You can
You can test hundreds of different MACD settings in under 40 seconds on any timeframe, asset, etc.
The image above shows additional settings to filter the outcome of your optimization testing. Additionally, you can test an unlimited number of profit targets and stop losses!
You can add one of several built-in TradingView indicators to filter trade entries.
The image above shows all built-in moving averages and TradingView indicators that can be incorporated into your MACD strategy.
Additionally, you can add your own, custom indicator to the optimization test, your favorite indicator by your favorite author or almost any publicly available indicator on TradingView.
The image above shows the settings section in which you can implement this feature.
The image above shows an example of the custom indicator feature! In this instance, I am using the public indicator titled "Self-Optimizing" RSI and requiring it to measure below a level prior to entry! Almost any custom indicator, your favorite indicator, etc. is compatible with this feature!
The MACD Optimizer has improved user friendliness over previous versions. The optimizer can be as simple or complex as you'd like - capable of handling both "easy" and "difficult" tasks at your discretion.
Additionally, you can configure the optimizer to prioritize MACD strategies that earn profit most efficiently!
The image above shows this feature in action.
You can also configure the optimizer to prioritize MACD strategies that achieve the highest win rate!
The image above shows this feature in action.
Instructions
The instructions below show a rudimentary approach to using the optimizer.
1. Build your strategy in the settings.
You should also disable the "Run a Backtest" feature to improve load times during optimization.
The image above shows my custom strategy settings.
Now that you've got some data on your chart - you should try "Freezing" the "Smoothing" setting for MACD . When doing this, the optimizer will test hundreds of MACD settings with a fixed "Smoothing" setting. Try using the best "Smoothing" setting you were able to find for your initial testing.
2. Take the best "Smoothing" setting and test various MACD and Signal Lengths.
The image above shows me configuring the MACD Optimizer to test different MACD line lengths and Signal line lengths with a fixed "smoothing" setting.
From the results, we can see that there are better MACD settings than what was shown in our initial test!
With this information we can execute a TradingView backtest.
3. Execute a TradingView Backtest.
You must enable the "Run a Backtest" feature to perform a TradingView backtest. Additionally, it's advised to enable the "STOP OPTIMIZATION" feature when performing a TradingView backtest. Enabling this feature will improve load times for the backtest to only a few seconds (since the optimizer won't look for the best setting when this feature is enabled).
The image above shows completion of the process!
From here, you can perform further testing, set alerts, etc.
Backtest Settings Shown
Initial Capital: The initial capital used for the shown backtests is $3,500 USD. Set the initial capital to replicate your true starting capital (: PnL for the MACD strategies (listed in table) is calculated using a starting capital of $10,000 USD.
Slippage: The slippage settings for the displayed backtest was set to 2 ticks.
Commission: Commission was adjusted to 0.1%.
Verify Price for Limit Orders was set to 2 ticks.
Optimization
Trading system optimization is immensely advantageous when executed with prudence.
Technical-oriented, mechanical trading systems work when a valid correlation is methodical to the extent that an objective, precisely-defined ruleset can consistently exploit it. If no such correlation exists, or a technical-oriented system is erroneously designed to exploit an illusory correlation (absent predictive utility), the trading system will fail.
Evaluate results practically and test parameters rigorously after discovery. Simply mining the best-performing parameters and immediately trading them is unlikely a winning strategy. Put as much effort into testing strong-performing parameters and building an accompanying system as you would any other trading strategy. Automated optimization involves curve fitting - it's the responsibility of the trader to validate a replicable sequence or correlation and the trading system that exploits it.
Thanks for checking this out!
Delta-Agnostic Correlation Coefficient (alt)Calculate a sort of correlation between two symbols based only on the sign of their changes, regardless of the amplitude of price change.
When positive, the two symbols tend to move together. When negative, the symbols move in opposite directions.
Since there is no significance calculation, and that the result is binary, keep in mind that correlation will always tend to go towards 1 or -1 even when there is no correlation. To reduce this issue, an EMA or SMA is applied to smooth out transitions: SMA smoothes over the selected length period but adds lag, whereas EMA smoothes amplitude without any additional lag. Hence, to know if the correlation is true or not, try to look at the amplitude and the number of consecutive days the correlation is maintained (both quantities are related), because when the correlation is spurious, it will tend to switch more or less alternatively between 1 and -1 and hence will hover around 0, whereas if the correlation is true, it will get further away from 0 and closer to 1 or -1.
In addition, since there is some time lag for the correlation to switch sign, the area is colored to know the current candle's correlation, regardless of past data's correlation: blue is a positive correlation (1), yellow is negative. The coloring can allow to know a trend reversal early on, but it's noisy.
Finally, symbols with closing days are better accounted for, with the correlation set to 0 on closed days (e.g., on week-ends), and the area is then colored in gray to signal that there is no new correlation data.
This is an improved fork over the original indicator by alexjvale, please show him some love if you like this work:
Candlestick Pattern Criteria and Analysis Indicator█ OVERVIEW
Define, then locate the presence of a candle that fits a specific criteria. Run a basic calculation on what happens after such a candle occurs.
Here, I’m not giving you an edge, but I’m giving you a clear way to find one.
IMPORTANT NOTE: PLEASE READ:
THE INDICATOR WILL ALWAYS INITIALLY LOAD WITH A RUNTIME ERROR. WHEN INITIALLY LOADED THERE NO CRITERIA SELECTED.
If you do not select a criteria or run a search for a criteria that doesn’t exist, you will get a runtime error. If you want to force the chart to load anyway, enable the debug panel at the bottom of the settings menu.
Who this is for:
- People who want to engage in TradingView for tedious and challenging data analysis related to candlestick measurement and occurrence rate and signal bar relationships with subsequent bars. People who don’t know but want to figure out what a strong bullish bar or a strong bearish bar is.
Who this is not for:
- People who want to be told by an indicator what is good or bad or buy or sell. Also, not for people that don’t have any clear idea on what they think is a strong bullish bar or a strong bearish bar and aren’t willing to put in the work.
Recommendation: Use on the candle resolution that accurately reflects your typical holding period. If you typically hold a trade for 3 weeks, use 3W candles. If you hold a trade for 3 minutes, use 3m candles.
Tldr; Read the tool tips and everything above this line. Let me know any issues that arise or questions you have.
█ CONCEPTS
Many trading styles indicate that a certain candle construct implies a bearish or bullish future for price. That said, it is also common to add to that idea that the context matters. Of course, this is how you end up with all manner of candlestick patterns accounting for thousands of pages of literature. No matter the context though, we can distill a discretionary trader's decision to take a trade based on one very basic premise: “A trader decides to take a trade on the basis of the rightmost candle's construction and what he/she believes that candle construct implies about the future price.” This indicator vets that trader’s theory in the most basic way possible. It finds the instances of any candle construction and takes a look at what happens on the next bar. This current bar is our “Signal Bar.”
█ GUIDE
I said that we vet the theory in the most basic way possible. But, in truth, this indicator is very complex as a result of there being thousands of ways to define a ‘strong’ candle. And you get to define things on a very granular level with this indicator.
Features:
1. Candle Highlighting
When the user’s criteria is met, the candle is highlighted on the chart.
The following candle is highlighted based on whether it breaks out, breaks down, or is an inside bar.
2. User-Defined Criteria
Criteria that you define include:
Candle Type: Bull bars, Bear bars, or both
Candle Attributes
Average Size based on Standard Deviation or Average of all potential bars in price history
Search within a specific price range
Search within a specific time range
Clarify time range using defined sessions and with or without weekends
3. Strike Lines on Candle
Often you want to know how price reacts when it gets back to a certain candle. Also it might be true that candle types cluster in a price region. This can be identified visually by adding lines that extend right on candles that fit the criteria.
4. User-Defined Context
Labeled “Alternative Criteria,” this facet of the script allows the user to take the context provided from another indicator and import it into the indicator to use as a overriding criteria. To account for the fact that the external indicator must be imported as a float value, true (criteria of external indicator is met) must be imported as 1 and false (criteria of external indicator is not met) as 0. Basically a binary Boolean. This can be used to create context, such as in the case of a traditional fractal, or can be used to pair with other signals.
If you know how to code in Pinescript, you can save a copy and simply add your own code to the section indicated in the code and set your bull and bear variables accordingly and the code should compile just fine with no further editing needed.
Included with the script to maximize out-of-the-box functionality, there is preloaded as alternative criteria a code snippet. The criteria is met on the bull side when the current candle close breaks out above the prior candle high. The bear criteria is met when the close breaks below the prior candle. When Alternate Criteria is run by itself, this is the only criteria set and bars are highlighted when it is true. You can qualify these candles by adding additional attributes that you think would fit well.
Using Alternative Criteria, you are essentially setting a filter for the rest of the criteria.
5. Extensive Read Out in the Data Window (right side bar pop out window).
As you can see in the thumbnail, there is pasted a copy of the Data Window Dialogue. I am doubtful I can get the thumbnail to load up perfectly aligned. Its hard to get all these data points in here. It may be better suited for a table at this point. Let me know what you think.
The primary, but not exclusive, purpose of what is in the Data Window is to talk about how often your criteria happens and what happens on the next bar. There are a lot of pieces to this.
Red = Values pertaining to the size of the current bar only
Blue = Values pertaining or related to the total number of signals
Green = Values pertaining to the signal bars themselves, including their measurements
Purple = Values pertaining to bullish bars that happen after the signal bar
Fuchsia = Values pertaining to bearish bars that happen after the signal bar
Lime = Last four rows which are your percentage occurrence vs total signals percentages
The best way I can explain how to understand parts you don’t understand otherwise in the data window is search the title of the row in the code using ‘ctrl+f’ and look at it and see if it makes more sense.
█ [b}Available Candle Attributes
Candle attributes can be used in any combination. They include:
[*}Bodies
[*}High/Low Range
[*}Upper Wick
[*}Lower Wick
[*}Average Size
[*}Alternative Criteria
Criteria will evaluate each attribute independently. If none is set for a particular attribute it is bypassed.
Criteria Quantity can be in Ticks, Points, or Percentage. For percentage keep in mind if using anything involving the candle range will not work well with percentage.
Criteria Operators are “Greater Than,” “Less Than,” and “Threshold.” Threshold means within a range of two numbers.
█ Problems with this methodology and opportunities for future development:
#1 This kind of work is hard.
If you know what you’re doing you might be able to find success changing out the inputs for loops and logging results in arrays or matrices, but to manually go through and test various criteria is a lot of work. However, it is rewarding. At the time of publication in early Oct 2022, you will quickly find that you get MUCH more follow through on bear bars than bull bars. That should be obvious because we’re in the middle of a bear market, but you can still work with the parameters and contextual inputs to determine what maximizes your probability. I’ve found configurations that yield 70% probability across the full series of bars. That’s an edge. That means that 70% of the time, when this criteria is met, the next bar puts you in profit.
#2 The script is VERY heavy.
Takes an eternity to load. But, give it a break, it’s doing a heck of a lot! There is 10 unique arrays in here and a loop that is a bit heavy but gives us the debug window.
#3 If you don’t have a clear idea its hard to know where to start.
There are a lot of levers to pull on in this script. Knowing which ones are useful and meaningful is very challenging. Combine that with long load times… its not great.
#4 Your brain is the only thing that can optimize your results because the criteria come from your mind.
Machine learning would be much more useful here, but for now, you are the machine. Learn.
#5 You can’t save your settings.
So, when you find a good combo, you’ll have to write it down elsewhere for future reference. It would be nice if we could save templates on custom indicators like we can on some of the built in drawing tools, but I’ve had no success in that. So, I recommend screenshotting your settings and saving them in Notion.so or some other solid record keeping database. Then you can go back and retrieve those settings.
#6 no way to export these results into conditions that can be copy/pasted into another script.
Copy/Paste of labels or tables would be the best feature ever at this point. Because you could take the criteria and put it in a label, copy it and drop it into another strategy script or something. But… men can dream.
█ Opportunities to PineCoders Learn:
1. In this script I’m importing libraries, showing some of my libraries functionality. Hopefully that gives you some ideas on how to use them too.
The price displacement library (which I love!)
Creative and conventional ways of using debug()
how to display arrays and matrices on charts
I didn’t call in the library that holds the backtesting function. But, also demonstrating, you can always pull the library up and just copy/paste the function out of there and into your script. That’s fine to do a lot of the time.
2. I am using REALLY complicated logic in this script (at least for me). I included extensive descriptions of this ? : logic in the text of the script. I also did my best to bracket () my logic groups to demonstrate how they fit together, both for you and my future self.
3. The breakout, built-in, “alternative criteria” is actually a small bit of genius built in there if you want to take the time to understand that block of code and think about some of the larger implications of the method deployed.
As always, a big thank you to TradingView and the Pinescript community, the Pinescript pros who have mentored me, and all of you who I am privileged to help in their Pinescripting journey.
"Those who stay will become champions" - Bo Schembechler
Probability Effort Scalper [PES]Probability Effort Scalper
Indicator is made of Two Basic Component
1. Probability Distribution Filter
2. Cumulative Effort Volumes
What is a Probability Distribution Filter ?
A filter which segregate the outcomes of any experiment into binary score of momentum based probabilities, so the filter is actually acting as a classifier to classify the probability of future occurrence of any event { in this case Stock prices going up / going down } { Long/ Short / Exit } by Binomial fitting method.
So the script uses Predictive Differential Filter, for filtering out the probability distribution, it actually uses differential calculations on binomial models.
Basic Assumptions:
That the Stock prices are in semi-strong efficiency
That the Stock prices follow up the Binomial Distribution
What is Cumulative Effort Volume
Effort Volume estimation is the process of predicting the most realistic amount of Volume Required to Push the Prices up or down, Its a group estimation model,
works on law of effort vs results and estimates the flow of the prices, (same as fluid dynamics), it's basically used to justify the harmony and Divergence occurrence in probability distribution.
How to use the Indicator
Simple Concept :
{ Signal candle = candle with a Triangle mark }
Long on the High of the Long Signal Candle,
Short on the Low of the Short Signal Candle
Exit on the Candle where "X" is present
For Long / Buy Signals {refer image below}
For Short / Sell Signals {refer image below}
Provisions for Alerts
Listed below are the Types of Alerts :
BUY SIGNAL
SELL SIGNAL
BOTH BUY/SELL SIGNAL
ALL STOP / EXIT SIGNALS
EXIT FROM LONG
EXIT FROM SHORT
What Securities will it work upon ?
The indicator works on every liquid security : stocks, futures, futures of indexes, forex, crypto : Having a Volume Informations provided by tradingview
Since the Indicator uses Volume Effort Estimation, The securities that you can apply the indicator on should be liquid
How to Get Access
Just Private Message me, would be happy to help you out !
Do not use comment box for asking for access, use it only for constructive feedbacks
HexLibrary "Hex"
Hex String Utility
intToHex(_n)
helper Binary half octet to hex character
Parameters:
_n : Digits to convert
fromDigits(_input, _buffer)
Digits to Hex String output
Parameters:
_input : Integer Input
_buffer : Number of 0's to pad Hex with
Returns: string output hex character value buffered to desired length (00-ff default)
FunctionKellyCriterionLibrary "FunctionKellyCriterion"
Kelly criterion methods.
the kelly criterion helps with the decision of how much one should invest in
a asset as long as you know the odds and expected return of said asset.
simplified(win_p, rr)
simplified version of the kelly criterion formula.
Parameters:
win_p : float, probability of winning.
rr : float, reward to risk rate.
Returns: float, optimal fraction to risk.
usage:
simplified(0.55, 1.0)
partial(win_p, loss_p, win_rr, loss_rr)
general form of the kelly criterion formula.
Parameters:
win_p : float, probability of the investment returns a positive outcome.
loss_p : float, probability of the investment returns a negative outcome.
win_rr : float, reward on a positive outcome.
loss_rr : float, reward on a negative outcome.
Returns: float, optimal fraction to risk.
usage:
partial(0.6, 0.4, 0.6, 0.1)
from_returns(returns)
Calculate the fraction to invest from a array of returns.
Parameters:
returns : array trade/asset/strategy returns.
Returns: float, optimal fraction to risk.
usage:
from_returns(array.from(0.1,0.2,0.1,-0.1,-0.05,0.05))
final_f(fraction, max_expected_loss)
Final fraction, eg. if fraction is 0.2 and expected max loss is 10%
then you should size your position as 0.2/0.1=2 (leverage, 200% position size).
Parameters:
fraction : float, aproximate percent fraction invested.
max_expected_loss : float, maximum expected percent on a loss (ex 10% = 0.1).
Returns: float, final fraction to invest.
usage:
final_f(0.2, 0.5)
hpr(fraction, trade, biggest_loss)
Holding Period Return function
Parameters:
fraction : float, aproximate percent fraction invested.
trade : float, profit or loss in a trade.
biggest_loss : float, value of the biggest loss on record.
Returns: float, multiplier of effect on equity so that a win of 5% is 1.05 and loss of 5% is 0.95.
usage:
hpr(fraction=0.05, trade=0.1, biggest_loss=-0.2)
twr(returns, rr, eps)
Terminal Wealth Relative, returns a multiplier that can be applied
to the initial capital that leadds to the final balance.
Parameters:
returns : array, list of trade returns.
rr : float , reward to risk rate.
eps : float , minimum resolution to void zero division.
Returns: float, optimal fraction to invest.
usage:
twr(returns=array.from(0.1,-0.2,0.3), rr=0.6)
ghpr(returns, rr, eps)
Geometric mean Holding Period Return, represents the average multiple made on the stake.
Parameters:
returns : array, list of trade returns.
rr : float , reward to risk rate.
eps : float , minimum resolution to void zero division.
Returns: float, multiplier of effect on equity so that a win of 5% is 1.05 and loss of 5% is 0.95.
usage:
ghpr(returns=array.from(0.1,-0.2,0.3), rr=0.6)
run_coin_simulation(fraction, initial_capital, n_series, n_periods)
run multiple coin flipping (binary outcome) simulations.
Parameters:
fraction : float, fraction of capital to bet.
initial_capital : float, capital at the start of simulation.
n_series : int , number of simulation series.
n_periods : int , number of periods in each simulation series.
Returns: matrix(n_series, n_periods), matrix with simulation results per row.
usage:
run_coin_simulation(fraction=0.1)
run_asset_simulation(returns, fraction, initial_capital)
run a simulation over provided returns.
Parameters:
returns : array, trade, asset or strategy percent returns.
fraction : float , fraction of capital to bet.
initial_capital : float , capital at the start of simulation.
Returns: array, array with simulation results.
usage:
run_asset_simulation(returns=array.from(0.1,-0.2,0.-3,0.4), fraction=0.1)
strategy_win_probability()
calculate strategy() current probability of positive outcome in a trade.
strategy_avg_won()
calculate strategy() current average won on a trade with positive outcome.
strategy_avg_loss()
calculate strategy() current average lost on a trade with negative outcome.
Overloaded Volume-Canddle v1This indicator will detect the candle has volume "too strong" base on "n" previous candle.
The Yellow Line is avg volume base n candle previous.
The Red line show over power volume of canlde.
Important : This indicator can use for forex, but i recommend it for binary options only.
Technimentals S&P Weighted FlowThis script runs a proprietary money flow algorithm three times with different user defined inputs on the subsectors of the S&P, weights their outputs directly according to their weighting in the S&P and then plots the cumulative total of the 33 outputs as a single line which overlays the chart.
The algorithm works by measuring relative volatility on each candlestick compared to the previous candlestick and compares that with it's smoothed recent volatility . This produces a binary (signum) output which is then weighted and accumulated.
The script is designed for use on shorter term timeframes. I do not recommend using this indicator on the daily timeframe or higher unless you lower the timeframe setting inside the script itself. The reason for this is that the signals it provides are often very slow and require zooming the chart out to get enough context to interpret the signals. For example, using this indicator on the one minute timeframe may produce signals several days out, or more!
Technimentals NDX Weighted FlowThis script runs a proprietary money flow algorithm three times with different user defined inputs on the top 20 components of the NDX, weights their outputs directly according to their weighting in the NDX and then plots the cumulative total of the 60 outputs as a single line which overlays the chart.
The algorithm works by measuring relative volatility on each candlestick compared to the previous candlestick and compares that with it's smoothed recent volatility . This produces a binary (signum) output which is then weighted and accumulated.
The script is designed for use on shorter term timeframes. I do not recommend using this indicator on the daily timeframe or higher unless you lower the timeframe setting inside the script itself. The reason for this is that the signals it provides are often very slow and require zooming the chart out to get enough context to interpret the signals. For example, using this indicator on the one minute timeframe may produce signals several days out.
