TTM Squeeze Value OscillatorThis indicator is specifically designed for use with TradingView's Stock Screener, not for chart analysis. It provides numerical values and binary signals that allow traders to efficiently scan stocks for specific TTM Squeeze conditions, momentum patterns, and EMA alignments.
What It Does
The TTM Squeeze Value Oscillator converts the popular TTM Squeeze indicator into a screenable format by outputting specific numerical values and binary signals (1 or 0) that can be filtered in TradingView's screener tool.
Key Features
1. TTM Squeeze Compression Levels
Value 0: Low Compression (Black) - Bollinger Bands inside outer Keltner Channels
Value 1: Mid Compression (Red) - Bollinger Bands inside middle Keltner Channels
Value 2: High Compression (Orange) - Bollinger Bands inside inner Keltner Channels
Value 3: Squeeze Fired (Green) - Bollinger Bands outside Keltner Channels
2. Momentum Analysis
Four distinct momentum conditions based on TTM Squeeze methodology:
Buy Momentum Increasing - Positive momentum growing stronger
Buy Momentum Decreasing - Positive momentum weakening
Sell Momentum Increasing - Negative momentum growing stronger
Sell Momentum Decreasing - Negative momentum weakening
3. EMA Stacking Analysis
Three EMA alignment patterns using 8, 21, and 48 period EMAs:
EMA Stacked Bullish - 8 EMA > 21 EMA > 48 EMA (uptrend alignment)
EMA Stacked Bearish - 8 EMA < 21 EMA < 48 EMA (downtrend alignment)
EMA Mixed - EMAs not in clear bullish or bearish alignment
4. Consecutive Day Counters
Tracks how many consecutive days each squeeze condition has persisted:
Low Compression Days
Mid Compression Days
High Compression Days
Squeeze Fired Days
5. Combined Signal Analysis
Pre-calculated combinations of squeeze conditions with momentum:
All squeeze levels combined with all four momentum conditions
16 total combined signals for advanced screening
Cerca negli script per "binary"
[Kpt-Ahab] Poor Mans Orderflow SimulatorScript Description – Poor Mans Orderflow Simulator
Purpose of the Script
This script simulates a simplified order flow approach ("Poor Man's Orderflow") without access to actual Bid/Ask data. The goal is to detect, quantify, and visualize patterns such as absorption, impulsive moves, and structured re-entry behaviors.
Calculation Logic
Absorption Candles
A candle is classified as "absorption" if:
The ratio of body size to full candle range is below a defined threshold,
Volume is significantly higher than the average of the last N periods,
The candle direction is negative (for long absorption) or positive (for short absorption).
These conditions define a candle with high activity but minimal price movement in the respective direction.
Impulse Candles
A candle is classified as "impulse" if:
The body-to-range ratio is high (indicating a strong directional move),
Volume exceeds the average significantly,
The price closes in the direction of the candle body (bullish or bearish).
Additionally, the average range of previous candles serves as a minimum benchmark for the impulse.
Cluster Detection
A cluster is detected when:
A minimum number of absorption candles is counted within a defined lookback period,
Either the long or short version of the absorption logic is used,
The result is a binary condition: cluster active or inactive.
Entry Signals (Re-entry)
An entry signal is generated when:
One or more absorption candles occurred in the last two bars,
A pullback against the direction of absorption occurs,
The current candle shows a directional move confirmed by a close in the expected direction.
These re-entry signals are evaluated separately for long and short scenarios.
Cluster-Confirmed Signals
A separate signal is generated when a valid re-entry setup occurs while a cluster is active. This represents a combined logic condition.
Alert Logic
The script provides a multi-layer alert framework:
Signal selection (Alertmode):
The user defines which signal type should trigger an alert (e.g. re-entry only, cluster only, combination, or impulse).
Optional filter (Filtermode):
A secondary filter limits alerts to cases where an additional condition (e.g. absorption cluster) is active.
Signal output:
As a simple binary value (+1 / –1) for classic alerts,
Or via an encoded Multibit signal, compatible with other modules in the djmad ecosystem.
These alerts are intended for integration with external systems or for use within platform-native visual or automation features.
Fuzzy SMA Trend Analyzer (experimental)[FibonacciFlux]Fuzzy SMA Trend Analyzer (Normalized): Advanced Market Trend Detection Using Fuzzy Logic Theory
Elevate your technical analysis with institutional-grade fuzzy logic implementation
Research Genesis & Conceptual Framework
This indicator represents the culmination of extensive research into applying fuzzy logic theory to financial markets. While traditional technical indicators often produce binary outcomes, market conditions exist on a continuous spectrum. The Fuzzy SMA Trend Analyzer addresses this limitation by implementing a sophisticated fuzzy logic system that captures the nuanced, multi-dimensional nature of market trends.
Core Fuzzy Logic Principles
At the heart of this indicator lies fuzzy logic theory - a mathematical framework designed to handle imprecision and uncertainty:
// Improved fuzzy_triangle function with guard clauses for NA and invalid parameters.
fuzzy_triangle(val, left, center, right) =>
if na(val) or na(left) or na(center) or na(right) or left > center or center > right // Guard checks
0.0
else if left == center and center == right // Crisp set (single point)
val == center ? 1.0 : 0.0
else if left == center // Left-shoulder shape (ramp down from 1 at center to 0 at right)
val >= right ? 0.0 : val <= center ? 1.0 : (right - val) / (right - center)
else if center == right // Right-shoulder shape (ramp up from 0 at left to 1 at center)
val <= left ? 0.0 : val >= center ? 1.0 : (val - left) / (center - left)
else // Standard triangle
math.max(0.0, math.min((val - left) / (center - left), (right - val) / (right - center)))
This implementation of triangular membership functions enables the indicator to transform crisp numerical values into degrees of membership in linguistic variables like "Large Positive" or "Small Negative," creating a more nuanced representation of market conditions.
Dynamic Percentile Normalization
A critical innovation in this indicator is the implementation of percentile-based normalization for SMA deviation:
// ----- Deviation Scale Estimation using Percentile -----
// Calculate the percentile rank of the *absolute* deviation over the lookback period.
// This gives an estimate of the 'typical maximum' deviation magnitude recently.
diff_abs_percentile = ta.percentile_linear_interpolation(math.abs(raw_diff), normLookback, percRank) + 1e-10
// ----- Normalize the Raw Deviation -----
// Divide the raw deviation by the estimated 'typical max' magnitude.
normalized_diff = raw_diff / diff_abs_percentile
// ----- Clamp the Normalized Deviation -----
normalized_diff_clamped = math.max(-3.0, math.min(3.0, normalized_diff))
This percentile normalization approach creates a self-adapting system that automatically calibrates to different assets and market regimes. Rather than using fixed thresholds, the indicator dynamically adjusts based on recent volatility patterns, significantly enhancing signal quality across diverse market environments.
Multi-Factor Fuzzy Rule System
The indicator implements a comprehensive fuzzy rule system that evaluates multiple technical factors:
SMA Deviation (Normalized): Measures price displacement from the Simple Moving Average
Rate of Change (ROC): Captures price momentum over a specified period
Relative Strength Index (RSI): Assesses overbought/oversold conditions
These factors are processed through a sophisticated fuzzy inference system with linguistic variables:
// ----- 3.1 Fuzzy Sets for Normalized Deviation -----
diffN_LP := fuzzy_triangle(normalized_diff_clamped, 0.7, 1.5, 3.0) // Large Positive (around/above percentile)
diffN_SP := fuzzy_triangle(normalized_diff_clamped, 0.1, 0.5, 0.9) // Small Positive
diffN_NZ := fuzzy_triangle(normalized_diff_clamped, -0.2, 0.0, 0.2) // Near Zero
diffN_SN := fuzzy_triangle(normalized_diff_clamped, -0.9, -0.5, -0.1) // Small Negative
diffN_LN := fuzzy_triangle(normalized_diff_clamped, -3.0, -1.5, -0.7) // Large Negative (around/below percentile)
// ----- 3.2 Fuzzy Sets for ROC -----
roc_HN := fuzzy_triangle(roc_val, -8.0, -5.0, -2.0)
roc_WN := fuzzy_triangle(roc_val, -3.0, -1.0, -0.1)
roc_NZ := fuzzy_triangle(roc_val, -0.3, 0.0, 0.3)
roc_WP := fuzzy_triangle(roc_val, 0.1, 1.0, 3.0)
roc_HP := fuzzy_triangle(roc_val, 2.0, 5.0, 8.0)
// ----- 3.3 Fuzzy Sets for RSI -----
rsi_L := fuzzy_triangle(rsi_val, 0.0, 25.0, 40.0)
rsi_M := fuzzy_triangle(rsi_val, 35.0, 50.0, 65.0)
rsi_H := fuzzy_triangle(rsi_val, 60.0, 75.0, 100.0)
Advanced Fuzzy Inference Rules
The indicator employs a comprehensive set of fuzzy rules that encode expert knowledge about market behavior:
// --- Fuzzy Rules using Normalized Deviation (diffN_*) ---
cond1 = math.min(diffN_LP, roc_HP, math.max(rsi_M, rsi_H)) // Strong Bullish: Large pos dev, strong pos roc, rsi ok
strength_SB := math.max(strength_SB, cond1)
cond2 = math.min(diffN_SP, roc_WP, rsi_M) // Weak Bullish: Small pos dev, weak pos roc, rsi mid
strength_WB := math.max(strength_WB, cond2)
cond3 = math.min(diffN_SP, roc_NZ, rsi_H) // Weakening Bullish: Small pos dev, flat roc, rsi high
strength_N := math.max(strength_N, cond3 * 0.6) // More neutral
strength_WB := math.max(strength_WB, cond3 * 0.2) // Less weak bullish
This rule system evaluates multiple conditions simultaneously, weighting them by their degree of membership to produce a comprehensive trend assessment. The rules are designed to identify various market conditions including strong trends, weakening trends, potential reversals, and neutral consolidations.
Defuzzification Process
The final step transforms the fuzzy result back into a crisp numerical value representing the overall trend strength:
// --- Step 6: Defuzzification ---
denominator = strength_SB + strength_WB + strength_N + strength_WBe + strength_SBe
if denominator > 1e-10 // Use small epsilon instead of != 0.0 for float comparison
fuzzyTrendScore := (strength_SB * STRONG_BULL +
strength_WB * WEAK_BULL +
strength_N * NEUTRAL +
strength_WBe * WEAK_BEAR +
strength_SBe * STRONG_BEAR) / denominator
The resulting FuzzyTrendScore ranges from -1 (strong bearish) to +1 (strong bullish), providing a smooth, continuous evaluation of market conditions that avoids the abrupt signal changes common in traditional indicators.
Advanced Visualization with Rainbow Gradient
The indicator incorporates sophisticated visualization using a rainbow gradient coloring system:
// Normalize score to for gradient function
normalizedScore = na(fuzzyTrendScore) ? 0.5 : math.max(0.0, math.min(1.0, (fuzzyTrendScore + 1) / 2))
// Get the color based on gradient setting and normalized score
final_color = get_gradient(normalizedScore, gradient_type)
This color-coding system provides intuitive visual feedback, with color intensity reflecting trend strength and direction. The gradient can be customized between Red-to-Green or Red-to-Blue configurations based on user preference.
Practical Applications
The Fuzzy SMA Trend Analyzer excels in several key applications:
Trend Identification: Precisely identifies market trend direction and strength with nuanced gradation
Market Regime Detection: Distinguishes between trending markets and consolidation phases
Divergence Analysis: Highlights potential reversals when price action and fuzzy trend score diverge
Filter for Trading Systems: Provides high-quality trend filtering for other trading strategies
Risk Management: Offers early warning of potential trend weakening or reversal
Parameter Customization
The indicator offers extensive customization options:
SMA Length: Adjusts the baseline moving average period
ROC Length: Controls momentum sensitivity
RSI Length: Configures overbought/oversold sensitivity
Normalization Lookback: Determines the adaptive calculation window for percentile normalization
Percentile Rank: Sets the statistical threshold for deviation normalization
Gradient Type: Selects the preferred color scheme for visualization
These parameters enable fine-tuning to specific market conditions, trading styles, and timeframes.
Acknowledgments
The rainbow gradient visualization component draws inspiration from LuxAlgo's "Rainbow Adaptive RSI" (used under CC BY-NC-SA 4.0 license). This implementation of fuzzy logic in technical analysis builds upon Fermi estimation principles to overcome the inherent limitations of crisp binary indicators.
This indicator is shared under Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.
Remember that past performance does not guarantee future results. Always conduct thorough testing before implementing any technical indicator in live trading.
Neural Pulse System [Alpha Extract]Neural Pulse System (NPS)
The Neural Pulse System (NPS) is a custom technical indicator that analyzes price action through a probabilistic lens, offering a dynamic view of bullish and bearish tendencies.
Unlike traditional binary classification models, NPS employs Ordinary Least Squares (OLS) regression with dynamically computed coefficients to produce a smooth probability output ranging from -1 to 1.
Paired with ATR-based bands, this indicator provides an intuitive and volatility-aware approach to trend analysis.
🔶 CALCULATION
The Neural Pulse System utilizes OLS regression to compute probabilities of bullish or bearish price action while incorporating ATR-based bands for volatility context:
Dynamic Coefficients: Coefficients are recalculated in real-time and scaled up to ensure the regression adapts to evolving market conditions.
Ordinary Least Squares (OLS): Uses OLS regression instead of gradient descent for more precise and efficient coefficient estimation.
ATR Bands: Smoothed Average True Range (ATR) bands serve as dynamic boundaries, framing the regression within market volatility.
Probability Output: Instead of a binary result, the output is a continuous probability curve (-1 to 1), helping traders gauge the strength of bullish or bearish momentum.
Formula:
OLS Regression = Line of best fit minimizing squared errors
Probability Signal = Transformed regression output scaled to -1 (bearish) to 1 (bullish)
ATR Bands = Smoothed Average True Range (ATR) to frame price movements within market volatility
🔶 DETAILS
📊 Visual Features:
Probability Curve: Smooth probability signal ranging from -1 (bearish) to 1 (bullish)
ATR Bands: Price action is constrained within volatility bands, preventing extreme deviations
Color-Coded Signals:
Blue to Green: Increasing probability of bullish momentum
Orange to Red: Increasing probability of bearish momentum
Interpretation:
Bullish Bias: Probability output consistently above 0 suggests a bullish trend.
Bearish Bias: Probability output consistently below 0 indicates bearish pressure.
Reversals: Extreme values near -1 or 1, followed by a move toward 0, may signal potential trend reversals.
🔶 EXAMPLES
📌 Trend Identification: Use the probability output to gauge trend direction.
📌Example: On a 1-hour chart, NPS moves from -0.5 to 0.8 as price breaks resistance, signaling a bullish trend.
Reversal Signals: Watch for probability extremes near -1 or 1 followed by a reversal toward 0.
Example: NPS hits 0.9, price touches the upper ATR band, then both retreat—indicating a potential pullback.
📌 Example snapshots:
Volatility Context: ATR bands help assess whether price action aligns with typical market conditions.
Example: During low volatility, the probability signal hovers near 0, and ATR bands tighten, suggesting a potential breakout.
🔶 SETTINGS
Customization Options:
ATR Period – Defines lookback length for ATR calculation (shorter = more responsive, longer = smoother).
ATR Multiplier – Adjusts band width for better volatility capture.
Regression Length – Controls how many bars feed into the coefficient calculation (longer = smoother, shorter = more reactive).
Scaling Factor – Adjusts the strength of regression coefficients.
Output Smoothing – Option to apply a moving average for a cleaner probability curve
MTF Signal XpertMTF Signal Xpert – Detailed Description
Overview:
MTF Signal Xpert is a proprietary, open‑source trading signal indicator that fuses multiple technical analysis methods into one cohesive strategy. Developed after rigorous backtesting and extensive research, this advanced tool is designed to deliver clear BUY and SELL signals by analyzing trend, momentum, and volatility across various timeframes. Its integrated approach not only enhances signal reliability but also incorporates dynamic risk management, helping traders protect their capital while navigating complex market conditions.
Detailed Explanation of How It Works:
Trend Detection via Moving Averages
Dual Moving Averages:
MTF Signal Xpert computes two moving averages—a fast MA and a slow MA—with the flexibility to choose from Simple (SMA), Exponential (EMA), or Hull (HMA) methods. This dual-MA system helps identify the prevailing market trend by contrasting short-term momentum with longer-term trends.
Crossover Logic:
A BUY signal is initiated when the fast MA crosses above the slow MA, coupled with the condition that the current price is above the lower Bollinger Band. This suggests that the market may be emerging from a lower price region. Conversely, a SELL signal is generated when the fast MA crosses below the slow MA and the price is below the upper Bollinger Band, indicating potential bearish pressure.
Recent Crossover Confirmation:
To ensure that signals reflect current market dynamics, the script tracks the number of bars since the moving average crossover event. Only crossovers that occur within a user-defined “candle confirmation” period are considered, which helps filter out outdated signals and improves overall signal accuracy.
Volatility and Price Extremes with Bollinger Bands
Calculation of Bands:
Bollinger Bands are calculated using a 20‑period simple moving average as the central basis, with the upper and lower bands derived from a standard deviation multiplier. This creates dynamic boundaries that adjust according to recent market volatility.
Signal Reinforcement:
For BUY signals, the condition that the price is above the lower Bollinger Band suggests an undervalued market condition, while for SELL signals, the price falling below the upper Bollinger Band reinforces the bearish bias. This volatility context adds depth to the moving average crossover signals.
Momentum Confirmation Using Multiple Oscillators
RSI (Relative Strength Index):
The RSI is computed over 14 periods to determine if the market is in an overbought or oversold state. Only readings within an optimal range (defined by user inputs) validate the signal, ensuring that entries are made during balanced conditions.
MACD (Moving Average Convergence Divergence):
The MACD line is compared with its signal line to assess momentum. A bullish scenario is confirmed when the MACD line is above the signal line, while a bearish scenario is indicated when it is below, thus adding another layer of confirmation.
Awesome Oscillator (AO):
The AO measures the difference between short-term and long-term simple moving averages of the median price. Positive AO values support BUY signals, while negative values back SELL signals, offering additional momentum insight.
ADX (Average Directional Index):
The ADX quantifies trend strength. MTF Signal Xpert only considers signals when the ADX value exceeds a specified threshold, ensuring that trades are taken in strongly trending markets.
Optional Stochastic Oscillator:
An optional stochastic oscillator filter can be enabled to further refine signals. It checks for overbought conditions (supporting SELL signals) or oversold conditions (supporting BUY signals), thus reducing ambiguity.
Multi-Timeframe Verification
Higher Timeframe Filter:
To align short-term signals with broader market trends, the script calculates an EMA on a higher timeframe as specified by the user. This multi-timeframe approach helps ensure that signals on the primary chart are consistent with the overall trend, thereby reducing false signals.
Dynamic Risk Management with ATR
ATR-Based Calculations:
The Average True Range (ATR) is used to measure current market volatility. This value is multiplied by a user-defined factor to dynamically determine stop loss (SL) and take profit (TP) levels, adapting to changing market conditions.
Visual SL/TP Markers:
The calculated SL and TP levels are plotted on the chart as distinct colored dots, enabling traders to quickly identify recommended exit points.
Optional Trailing Stop:
An optional trailing stop feature is available, which adjusts the stop loss as the trade moves favorably, helping to lock in profits while protecting against sudden reversals.
Risk/Reward Ratio Calculation:
MTF Signal Xpert computes a risk/reward ratio based on the dynamic SL and TP levels. This quantitative measure allows traders to assess whether the potential reward justifies the risk associated with a trade.
Condition Weighting and Signal Scoring
Binary Condition Checks:
Each technical condition—ranging from moving average crossovers, Bollinger Band positioning, and RSI range to MACD, AO, ADX, and volume filters—is assigned a binary score (1 if met, 0 if not).
Cumulative Scoring:
These individual scores are summed to generate cumulative bullish and bearish scores, quantifying the overall strength of the signal and providing traders with an objective measure of its viability.
Detailed Signal Explanation:
A comprehensive explanation string is generated, outlining which conditions contributed to the current BUY or SELL signal. This explanation is displayed on an on‑chart dashboard, offering transparency and clarity into the signal generation process.
On-Chart Visualizations and Debug Information
Chart Elements:
The indicator plots all key components—moving averages, Bollinger Bands, SL and TP markers—directly on the chart, providing a clear visual framework for understanding market conditions.
Combined Dashboard:
A dedicated dashboard displays key metrics such as RSI, ADX, and the bullish/bearish scores, alongside a detailed explanation of the current signal. This consolidated view allows traders to quickly grasp the underlying logic.
Debug Table (Optional):
For advanced users, an optional debug table is available. This table breaks down each individual condition, indicating which criteria were met or not met, thus aiding in further analysis and strategy refinement.
Mashup Justification and Originality
MTF Signal Xpert is more than just an aggregation of existing indicators—it is an original synthesis designed to address real-world trading complexities. Here’s how its components work together:
Integrated Trend, Volatility, and Momentum Analysis:
By combining moving averages, Bollinger Bands, and multiple oscillators (RSI, MACD, AO, ADX, and an optional stochastic), the indicator captures diverse market dynamics. Each component reinforces the others, reducing noise and filtering out false signals.
Multi-Timeframe Analysis:
The inclusion of a higher timeframe filter aligns short-term signals with longer-term trends, enhancing overall reliability and reducing the potential for contradictory signals.
Adaptive Risk Management:
Dynamic stop loss and take profit levels, determined using ATR, ensure that the risk management strategy adapts to current market conditions. The optional trailing stop further refines this approach, protecting profits as the market evolves.
Quantitative Signal Scoring:
The condition weighting system provides an objective measure of signal strength, giving traders clear insight into how each technical component contributes to the final decision.
How to Use MTF Signal Xpert:
Input Customization:
Adjust the moving average type and period settings, ATR multipliers, and oscillator thresholds to align with your trading style and the specific market conditions.
Enable or disable the optional stochastic oscillator and trailing stop based on your preference.
Interpreting the Signals:
When a BUY or SELL signal appears, refer to the on‑chart dashboard, which displays key metrics (e.g., RSI, ADX, bullish/bearish scores) along with a detailed breakdown of the conditions that triggered the signal.
Review the SL and TP markers on the chart to understand the associated risk/reward setup.
Risk Management:
Use the dynamically calculated stop loss and take profit levels as guidelines for setting your exit points.
Evaluate the provided risk/reward ratio to ensure that the potential reward justifies the risk before entering a trade.
Debugging and Verification:
Advanced users can enable the debug table to see a condition-by-condition breakdown of the signal generation process, helping refine the strategy and deepen understanding of market dynamics.
Disclaimer:
MTF Signal Xpert is intended for educational and analytical purposes only. Although it is based on robust technical analysis methods and has undergone extensive backtesting, past performance is not indicative of future results. Traders should employ proper risk management and adjust the settings to suit their financial circumstances and risk tolerance.
MTF Signal Xpert represents a comprehensive, original approach to trading signal generation. By blending trend detection, volatility assessment, momentum analysis, multi-timeframe alignment, and adaptive risk management into one integrated system, it provides traders with actionable signals and the transparency needed to understand the logic behind them.
Risk Matrix [QuantraSystems]Risk Matrix
The Risk Matrix is a sophisticated tool that aggregates a variety of fundamental inputs, primarily external (non-crypto) market data is used to assess investor risk appetite. By combining external macroeconomic factors and proxies for liquidity data with specific signals from the cryptomarket - the Risk Matrix provides a holistic view of market risk conditions. These insights are designed to help traders and investors make informed decisions on when to adopt a risk-on or risk-off approach.
Core Concept
The Risk Matrix functions as a dynamic risk assessment tool that integrates both fundamental and technical market indicators to generate an aggregated Z-score. This score helps traders to identify where the market is in a risk-off or risk-on state, The system provides both binary risk signals and a more nuanced “risk seasonality” mode for deeper analysis.
Key Features
Global Liquidity Aggregate - The Liquidity score is a custom measure of global liquidity, built by combining a variety of traditional financial metrics. These include data from central bank balance sheets, reverse repo operations and credit availability. This data is sourced from organizations such as the U.S. Federal Reserve, the European Central Bank, and the People’s Bank of China. The purpose of this aggregate is to gauge how much liquidity is available in the global financial system - which often correlates with risk sentiment. Rising liquidity tends to boost risk-on appetite, while liquidity contractions signal increased caution (risk-off) in the markets. The data sources used in this global liquidity aggregate include:
- U.S. Commercial Bank Credit data
- Federal Reserve balance sheet and reverse repo operations
- Liquidity from major central banks including the Fed, Bank of Japan, ECB, and PBoC
- Asset performance from major global financial indices such as the S&P 500, TLT, DXY (U.S. Dollar Index), MOVE (bond market volatility), and commodities like gold and oil.
Other key Z-scores (measured individually) - The Risk Matrix also incorporates other major Z-scores that represent different facets of the financial markets:
- Collateral Risk - A measure of US bond volatility, where higher values indicate higher interest rate risk - leading to potential market instability and cautious market behaviors.
- Stablecoin Dominance - The dominance of stablecoins in the crypto markets - which can signal risk aversion the total capital allocated to stables increases relative to other cryptocurrencies.
- US Currency Strength - The U.S. Dollar Index Z-score reflects currency market strength, with higher values typically indicating risk aversion as investors sell more volatile assets and flock to the dollar.
- Trans-pacific Monetary Bias - Signals capital flow and monetary trends that link between the East and West, heavily influencing global risk sentiment.
- Total - A measure of the total cryptocurrency market cap, signaling broader risk sentiment with the crypto market.
Neural Network Synthesis - The NNSYNTH component adds a machine learning inspired layer to the Risk Matrix. This custom indicator synthesizes inputs from various technical indicators (such as RSI, MACD, Bollinger Bands, and others) to generate a composite signal that reflects the health of the cryptomarket. While highly complex in its design, the NNSYNTH ultimately helps detect market shifts early by synthesizing multiple signals into one cohesive output. This score is particularly useful for gauging momentum and identifying potential turning points in market trends. Because the NNSYNTH is a closed source indicator, and it is included here, the Risk Matrix by extension is a closed source indicator.
How it Works
Z-score Aggregation - The Risk Matrix computes a final risk score by aggregating several Z-scores from different asset classes and data sources, all of which contribute proportionally to the overall market risk assessment. Each input is equally weighted - normalization allows for direct comparisons across global liquidity trends, currency fluctuations, bond market volatility and crypto market conditions. Furthermore, this system employs multi-calibration aggregation - where each individual matrix is itself an aggregate of multiple Z-scores derived from various timeframes. This ensures that each matrix captures a distinct average across different time horizons before being combined into the overall Risk Matrix. This layered, multi timeframe approach enhances the precision and robustness of the final Z-score.
Risk-On / Risk-Off Mode - The system’s binary mode provides a clear Risk On and Off signal. This nature of this signal is determined by the behavior of the Z-score relative to the midline, or Standard Deviation Bands, depending on specific conditions:
Risk-On is signaled when the aggregated final Z-score crosses above 0. However, in extreme oversold conditions, Risk-On can trigger early if the upper standard deviation band falls below the zero line. In such cases, the Risk-On signal is triggered when the z-score crosses the upper standard deviation band - without waiting to cross the midline.
Risk-Off is signaled when the final Z-score moves below 0. Similarly, Risk-Off can also be triggered early if the lower standard deviation band rises above the midline. In this instance, Risk-Off is triggered when the Z-score crosses below the lower band.
Risk Seasonality Mode - This mode offers a more gradual transition between risk states, measuring the change in the Z-score to visualize the shifts in risk appetite over time. It's useful for traders seeking to understand broader market cycles and risk phases. The seasonality view breaks down the market into the following phases:
Risk-On - High risk appetite where risk/cyclical markets are generally bullish.
Weakening - Markets showing signs of cooling off, here the higher beta assets tend to sell off first.
Risk-Off - Investors pull back, and bearish sentiment prevails.
Recovery - Signs of bottoming out, potential for market re-entry.
Component Matrices - Each individual Z-score is visualized as part of the component matrices - scaled to a 3 Sigma range. These component matrices allow traders to view how each data source is contributing to the overall risk assessment in real time - offering transparency and granularity.
Visuals and UI
Main Risk Matrix - The aggregated Z-Score is displayed saliently in the main risk matrix. Traders and investors can quickly see what season the Risk Matrix is signaling and adjust their strategies accordingly.
Overview Table - A detailed overview table shows the current confirmed Z-scores for each component, along with values from 2, and 3 bars back. This helps traders spot trends and the rate of change (RoC) between signals, offering additional insights for shorter-term risk management.
Customizability - Users can customize the visual elements of the matrix, including color palettes, table sizes, and positions. This allows for optimal integration into any trader’s existing workspace.
Usage Summary
The Risk Matrix is an incredibly versatile tool. It is especially valuable as a means of achieving a cross-market view of risk, incorporating both crypto-specific and macroeconomic factors. Some key use cases include:
Adjusting Capital Allocation Based on Risk Seasons - Traders can use the Risk Matrix to adjust their capital allocation dynamically. During Risk-On periods, they might increase exposure to long positions, capitalizing on stronger market conditions. Conversely, during Risk-Off periods, traders could reduce or hedge long positions and potentially scale up short positions or move into safer assets.
Complementing Other Trading Systems - The Risk Matrix can work alongside other technical systems to provide context to market moves. For instance, a trend-following strategy might suggest an entry, but the Risk Matrix could be used to verify whether the broader market conditions support this trade. If the Matrix is in a Risk-Off period, a trader might opt for more conservative trade sizes or avoid the trade entirely.
This flexibility allows traders to adjust their strategies and portfolio risk dynamically, enhancing decision making based on broader market conditions - as indicated by external macroeconomic factors, liquidity, and risk sentiment.
Important Note
The Risk Matrix always uses the most up-to-date data available, ensuring analysis reflects the latest market conditions and macroeconomic inputs. In rare cases, governments or financial institutions revise past data - and the Risk Matrix will adjust accordingly. This behavior can only be seen in the Liquidity Matrix. and can affect the final score. While this is uncommon, it highlights the benefit of using a system that adapts in real-time, incorporating the most accurate and current information to enhance decision making processes.
[GYTS-CE] Signal Provider | WaveTrend 4D with GDMWaveTrend 4D with Gradient Divergence Measure (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 GDM part. Check the other 🤲Community Edition of this indicator that focuses on the QMC. For GDM, see below for more information.
🌸 --- GRADIENT DIVERGENCE MEASURE (GDM) --- 🌸
💮 Introduction
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The GDM dynamically calculates a composite measure based on multiple factors. Unlike traditional binary divergence indicators, GDM employs a continuous value system to capture the nuanced dynamics of market behaviour. This methodology allows traders and analysts to assess the potency of divergence signals with greater precision, facilitating more informed decision-making processes.
💮 Methodology
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The GDM is calculated using a composite formula that integrates various market dynamics. At its core, it consists of six components listed below, each weighted to optimize the indicator's responsiveness to market conditions:
The magnitude of relative change between waves -- A larger difference between the waves, i.e. lower high or higher low could signify a stronger divergence.
The absolute value of the latest wave -- The strength of the latest wave provides insight into the extremity of the market conditions.
Slope of the divergence -- The slope between the two points of divergence essentially measures the rate of change in the frequency\'s value over time. It captures both the direction and the steepness of the indicator’s move between two waves.
The magnitude of relative change of the price -- A divergence means that the oscillator shows an opposite pattern than price action. Thus, if the price makes a significantly higher high or lower low, but the indicator does not, this discrepancy can be used to measure the divergence strength. This components measures the price's extrema during the crosses of the indicator's waves.
Higher timeframe's frequency trend -- Similarly, instead of looking at the price directly, this component measures the more general trend of the price by using the higher timeframe frequency (i.e. the slow frequency when looking at divergences of the normal frequency).
Time duration -- Lastly, the time duration between the two points of a divergence can also be an important factor. A divergence that spans over a longer period might indicate a more significant market sentiment shift.
💮 Tuning the GDM
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The 6 components discussed above are not independent, e.g. the slope is actually the result of the magnitude between waves, the absolute value and time duration. However, the default GDM is carefully tuned to include all these features without being too sensitive to outliers.
This makes this indicator very user-friendly. The only core parameter is the the "sensitivity". This controls the extent of normalisation between signals, and essentially affects how often strong GDMs appear. At the conservative end (higher sensitivity), the strong GDMs are less frequent but are relatively significant, while with a lower sensitivity the strong GDMs appear more frequent.
💮 GDM on the Oscillator
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The GDMs are represented by triangles and their value represents the strength. A value close to `1` signifies a strong bearish divergence and thus a possible reversal of continuation of a downtrend. Similarly, a value close to `-1` signifies a strong bullish divergence.
Note that there are two colour sets which can be enabled and disabled. One uses crosses between the fast and normal frequencies (with the slow frequency acting as the price trend with which there should be an opposite interaction -- hence a "divergence"). Similarly, crosses between the normal and slow frequencies (with the lethargic (the most slow) frequency acting as the price trend) are used to find divergences on a higher timeframe.
Another handy feature is a threshold to more strikingly visualise "strong" GDMs.
🌸 --- 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.
[GYTS-Pro] Signal Provider | WaveTrend 4D with GDM + QMCWaveTrend 4D with GDM + QMC (Professional 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 a normalised and smooth signals.
WT4D strives to help discriminating high-quality signals from the indicator by introducing the Gradient Divergence Measure (GDM) and Quantile Median Crosses (QMC) -- see below for more information.
WaveTrend 4D is a "📡 Signal Provider" in the 🌸 GoemonYae Trading System (GYTS) 🌸. Multiple 📡 Signal Providers connect to a GYTS "🧬 Flux Composer" to find confluence. On its turn, the Flux Composer can be connected to the GYTS "🎼 Order Orchestrator" for backtesting and trade automation. However, WaveTrend 4D is a wonderful indicator on its own as well.
🌸 --- MAIN FEATURES --- 🌸
- The focus is on two type of signals: divergences between the overall trend and the waves (GDM) and the weakening of strong trends (QMC)
- The name "WaveTrend 4D" is derived from the usage of 4 dimensions, representing different frequencies or timeframes. This gives the opportunity to use 2 sets of 3 frequencies to find divergences. Next to the "fast", "normal" and "slow" frequency, the fourth frequency is called "lethargic" (very slow).
- High probability trading involves diligently determining the significance of signals. For this purpose, a novel "Gradient Divergence Measure" (GDM) is developed to signify the strength of divergence signals and are drawn as triangles next to the divergence circles.
- Another and powerful approach is to use the frequencies' crossing of the median (zero) line. We seek to only signal reversals after a significant trend, and call this the "Quantile Median Crosses" (QMC).
More information the GDM and QMC and details of all features are described below.
🌸 --- GRADIENT DIVERGENCE MEASURE (GDM) --- 🌸
💮 Introduction
--
The GDM dynamically calculates a composite measure based on multiple factors. Unlike traditional binary divergence indicators, GDM employs a continuous value system to capture the nuanced dynamics of market behaviour. This methodology allows traders and analysts to assess the potency of divergence signals with greater precision, facilitating more informed decision-making processes.
💮 Methodology
--
The GDM is calculated using a composite formula that integrates various market dynamics. At its core, it consists of six components listed below, each weighted to optimize the indicator's responsiveness to market conditions:
The magnitude of relative change between waves -- A larger difference between the waves, i.e. lower high or higher low could signify a stronger divergence.
The absolute value of the latest wave -- The strength of the latest wave provides insight into the extremity of the market conditions.
Slope of the divergence -- The slope between the two points of divergence essentially measures the rate of change in the frequency\'s value over time. It captures both the direction and the steepness of the indicator’s move between two waves.
The magnitude of relative change of the price -- A divergence means that the oscillator shows an opposite pattern than price action. Thus, if the price makes a significantly higher high or lower low, but the indicator does not, this discrepancy can be used to measure the divergence strength. This components measures the price's extrema during the crosses of the indicator's waves.
Higher timeframe's frequency trend -- Similarly, instead of looking at the price directly, this component measures the more general trend of the price by using the higher timeframe frequency (i.e. the slow frequency when looking at divergences of the normal frequency).
Time duration -- Lastly, the time duration between the two points of a divergence can also be a factor. A divergence that spans over a longer period might indicate a more significant market sentiment shift.
Note that these 6 components are not independent, e.g. the slope is actually the result of the magnitude between waves, the absolute value and time duration. However, the default GDM is carefully tuned to include all these features without being too sensitive to outliers.
💮 Tuning the GDM
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At the same time, different people have different ideas of what factors are important to denote a "strong" divergence. For this reason, in the 🧰 Professional Edition of this indicator, as opposed to the 🤲 Community Edition, the user can select between different "GDM profiles" that resemble a certain approach:
Upon initiating the GDM indicator, users are prompted to select one of six distinct profiles. Each profile adjusts the indicator’s parameters to optimize performance under different market scenarios:
balanced : Offers a general approach, with a balanced assessment of market conditions without specific focus on any one aspect.
regular divergence : Emphasises price action, ideal for identifying classical divergence patterns where price and momentum diverge.
wavetrend focus : Minimises the influence of price action, concentrating on the WaveTrend oscillator’s behaviour for trend analysis.
short-term waves : Prioritises the slope of the waves, targeting traders interested in short-term market movements and potential inflection points.
long-term waves : Extends the analysis period, focusing on longer-term market trends and wave duration for strategic positioning.
overbought/oversold : Highlights extreme conditions in market valuation, useful for identifying potential reversal points from overbought or oversold levels.
The 🎩 Ultimate Edition takes it a step further and gives full freedom to dial in weights for each of the 6 components. The GDM formula is set up in such way to accommodate ease of use and react logically to these parameters. Having said that, the default GDM calculation should be more than sufficient for most cases.
Another way of tuning the GDM is to dial in the "sensitivity". This controls the extent of normalisation between signals, and essentially affects how often strong GDMs appear. At the conservative end (higher sensitivity), the strong GDMs are less frequent but are relatively significant, while with a lower sensitivity the strong GDMs appear more frequent.
💮 GDM on the Oscillator
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Coming back to the indicator, the GDMs are represented by triangles and their value represents the strength. A value close to `1` signifies a strong bearish divergence and thus a possible reversal of continuation of a downtrend. Similarly, a value close to `-1` signifies a strong bullish divergence.
Note that there are two colour sets which can be enabled and disabled. One uses crosses between the fast and normal frequencies (with the slow frequency acting as the price trend with which there should be an opposite interaction -- "divergence"). Similarly, crosses between the normal and slow frequencies (with the lethargic (the most slow) frequency acting as the price trend) are used to find divergences on a higher timeframe.
🌸 --- QUANTILE MEDIAN CROSSES (QMC) --- 🌸
💮 Introduction
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A different and powerful approach is to use the frequencies' crossing of the median (zero) line. This would signify a continuation of the reversal. However, also here, not all of those crossings would be trades with a high probability of success. For this reason, we seek to only consider reversals after the most strong trends start to show weakness. We call these reversals the "Quantile Median Crosses" (QMC), derived from the methodology.
💮 Methodology
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To find this "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 series is crossing the median in often (consolidation), the ares between those crosses would be small. But if there was a strong momentum, and the series 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 integral 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"
💮 Tuning the QMC
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The QMC is easily tuned by its "sensitivity". This basically represents a set of quantile bounds for the normal, slow and lethargic series. We have set these 3 parameters for each sensitivity profile after careful testing. The 🎩 Ultimate Edition gives full control for each quantile bound.
💮 QMC on the Oscillator
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The QMC is shown as a label "🔴" above the median or with "🟢" below the median. In the 🎩 Ultimate Edition, the user instead sees the exact quantile and the number of samples. 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.
🌸 --- DETAILED FEATURES --- 🌸
As discussed, at its core, the main signals are the Gradient Divergence Signals (GDM) and Quantile Median Crosses (QMC). However, there are more very powerful features that this 📡 Signal Provider can include. Below is a list of all features and we differentiate the availability of a feature per 📡 Signal Provider version by using these icons: 🤲 Community Edition; 🧰 Professional Edition; 🎩 Ultimate Edition.
Before going into the features, there are two important aspects to note: As this is a 📡 Signal Provider, it can be connected to the GYTS 🧬 Flux Composer and this is possible for each edition (i.e. the 🤲 Community Edition 📡 Signal Composer works with the 🤲 Community Edition 🧬 Flux Composer, and the same holds for the 🧰 Professional and 🎩 Ultimate Editions). 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.
The second important aspect is that for the 🤲 Community Edition, there are two versions of this 📡 Signal Provider: one that has the GDM feature and another the QMC feature. Besides that, the list below depicts a fairly complete overview of all the features across different versions:
( 🤲 🧰 🎩 ) Four Dimensions -- All four dimensions are available for each edition. The input data can also be transformed with an EMA or CoG as in the original WaveTrend 3D.
( -- 🧰 🎩 ) Both GDM and QMC -- Only the Pro and Ult versions include both the GDM and QMC in one indicator
( 🤲 🧰 🎩 ) Custom indicator name -- There's an option to give a name to the indicator which will be displayed on the chart. On its own, it might not be helpful, but in the GoemonYae Trading System (GYTS) suite, it helps to identify the different Signal Providers.
( 🤲 🧰 🎩 ) Visual improvements -- As in the original WaveTrend 3D, there are various ways the indicator can be displayed, including emphasising a certain frequency, a "mirror mode" and separating each frequency. We have expanded on some of these options. For example, the divergences, GDMs and QMCs are also displayed when the frequencies are separated, the mirror mode works with the emphasised frequency, there are more options to control the width of the emphasised frequency and each frequency can be enabled or disabled.
( 🤲 🧰 🎩 ) Support for HTF -- The indicator works on higher timeframes than the current chart and all parameters and calculations are scaled accordingly.
( __ 🧰 🎩 ) Support for other tickers -- There is also an option to select another ticker than the current chart. This especially makes sense in the 🌸 GYTS suite 🌸, where multiple Signal Providers are combined to find confluence. For example, a common approach is to use a certain ETF (or BTC in crypto) on a higher timeframe as filter to determine overall market direction.
( __ __ 🎩 ) Disable "only true divergences" -- In the Ultimate Edition, less signals can be filtered out when disabling looking at the third frequency. In general, this is not the best idea but it can be helpful when filtering signals with other means.
( __ 🧰 __ ) GDM profiles -- As mentioned, the GDM is carefully tuned and we consider it an excellent method to signify the strength of a divergence. Therefore, the standard calculation in the Community Edition is sufficient. Nevertheless, the Pro Edition has profiles (as previously described) so the user can select how (s)he feels a "strong divergence" should be.
( __ __ 🎩 ) GDM weights -- Full control over the weights of the 6 components of the GDM instead of using the profiles. The GDM algorithm is set up in such way that this is possible in an intuitive way.
( __ __ 🎩 ) Disable asymmetric GDM calculation -- Calculate the bullish and bearish GDMs independently (asymmetric calculation) or normalise them altogether (symmetric calculation). This can sometimes be helpful to filter out weaker GDMs depending on market conditions.
( 🤲 🧰 🎩 ) QMC calculation -- Using the QMC is possible in all versions, and each of the Normal, Slow and Lethargic frequencies can be toggled on and off.
( __ 🧰 __ ) QMC sensitivity -- Similar to the GDM profiles, in the Pro version there are presets to make the sensitivity higher (and thus get more signals) or lower.
( __ __ 🎩 ) QMC quantile threshold -- Instead of the sensitivity presets, in the Ult Edition the quantile threshold for each frequency is set. The user also sees the actual quantile and number of samples in the label
( 🤲 🧰 🎩 ) WaveTrend 4D settings -- Possibility to adjust the core WaveTrend settings
( 🤲 🧰 🎩 ) Alerts -- When alerts are enabled, TradingView will notify when there is a bullish/bearish strong GDM (i.e. within the zone) and a bullish/bearish QMC.
PhiSmoother Moving Average Ribbon [ChartPrime]DSP FILTRATION PRIMER:
DSP (Digital Signal Processing) filtration plays a critical role with financial indication analysis, involving the application of digital filters to extract actionable insights from data. Its primary trading purpose is to distinguish and isolate relevant signals separate from market noise, allowing traders to enhance focus on underlying trends and patterns. By smoothing out price data, DSP filters aid with trend detection, facilitating the formulation of more effective trading techniques.
Additionally, DSP filtration can play an impactful role with detecting support and resistance levels within financial movements. By filtering out noise and emphasizing significant price movements, identifying key levels for entry and exit points become more apparent. Furthermore, DSP methods are instrumental in measuring market volatility, enabling traders to assess volatility levels with improved accuracy.
In summary, DSP filtration techniques are versatile tools for traders and analysts, enhancing decision-making processes in financial markets. By mitigating noise and highlighting relevant signals, DSP filtration improves the overall quality of trading analysis, ultimately leading to better conclusions for market participants.
APPLYING FIR FILTERS:
FIR (Finite Impulse Response) filters are indispensable tools in the realm of financial analysis, particularly for trend identification and characterization within market data. These filters effectively smooth out price fluctuations and noise, enabling traders to discern underlying trends with greater fidelity. By applying FIR filters to price data, robust trading strategies can be developed with grounded trend-following principles, enhancing their ability to capitalize on market movements.
Moreover, FIR filter applications extend into wide-ranging utility within various fields, one being vital for informed decision-making in analysis. These filters help identify critical price levels where assets may tend to stall or reverse direction, providing traders with valuable insights to aid with identification of optimal entry and exit points within their indicator arsenal. FIRs are undoubtedly a cornerstone to modern trading innovation.
Additionally, FIR filters aid in volatility measurement and analysis, allowing traders to gauge market volatility accurately and adjust their risk management approaches accordingly. By incorporating FIR filters into their analytical arsenal, traders can improve the quality of their decision-making processes and achieve better trading outcomes when contending with highly dynamic market conditions.
INTRODUCTORY DEBUT:
ChartPrime's " PhiSmoother Moving Average Ribbon " indicator aims to mark a significant advancement in technical analysis methodology by removing unwanted fluctuations and disturbances while minimizing phase disturbance and lag. This indicator introduces PhiSmoother, a powerful FIR filter in it's own right comparable to Ehlers' SuperSmoother.
PhiSmoother leverages a custom tailored FIR filter to smooth out price fluctuations by mitigating aliasing noise problematic to identification of underlying trends with accuracy. With adjustable parameters such as phase control, traders can fine-tune the indicator to suit their specific analytical needs, providing a flexible and customizable solution.
Mathemagically, PhiSmoother incorporates various color coding preferences, enabling traders to visualize trends more effectively on a volatile landscape. Whether utilizing progression, chameleon, or binary color schemes, you can more fluidly interpret market dynamics and make informed visual decisions regarding entry and exit points based on color-coded plotting.
The indicator's alert system further enhances its utility by providing notifications of specifically chosen filter crossings. Traders can customize alert modes and messages while ensuring they stay informed about potential opportunities aligned with their trading style.
Overall, the "PhiSmoother Moving Average Ribbon" visually stands out as a revolutionary mechanism for technical analysis, offering traders a comprehensive solution for trend identification, visualization, and alerting within financial markets to achieve advantageous outcomes.
NOTEWORTHY SETTINGS FEATURES:
Price Source Selection - The indicator offers flexibility in choosing the price source for analysis. Traders can select from multiple options.
Phase Control Parameter - One of the notable standout features of this indicator is the phase control parameter. Traders can fine-tune the phase or lag of the indicator to adapt it to different market conditions or timeframes. This feature enables optimization of the indicator's responsiveness to price movements and align it with their specific trading tactics.
Coloring Preferences - Another magical setting is the coloring features, one being "Chameleon Color Magic". Traders can customize the color scheme of the indicator based on their visual preferences or to improve interpretation. The indicator offers options such as progression, chameleon, or binary color schemes, all having versatility to dynamically visualize market trends and patterns. Two colors may be specifically chosen to reduce overlay indicator interference while also contrasting for your visual acuity.
Alert Controls - The indicator provides diverse alert controls to manage alerts for specific market events, depending on their trading preferences.
Alertable Crossings: Receive an alert based on selectable predefined crossovers between moving average neighbors
Customizable Alert Messages: Traders can personalize alert messages with preferred information details
Alert Frequency Control: The frequency of alerts is adjustable for maximum control of timely notifications
Weighted Average Volume Depth [QuantraSystems]Weighted Average Volume Depth
Introduction
The Weighted Average Volume Depth (𝓦𝓐𝓥𝓓) indicator is calibrated to provide extensive insights, calculated using volumetric price action and volume depth, and provides dynamic adjustments based upon historical volatility.
This indicator is a valuable asset for traders and investors, aiming to capture trends, measure dynamic volatility, and provide market reversion analysis in a systematic way.
Legend
Volumetric Top Cap: Plotted at y = 0, this line represents the probabilistic maximum value, or ‘cap’ for the signal line. It is colored using a binary color scheme, and indicates the dominant trend direction - green for an uptrend and purple for a downtrend.
Base Line: Calculated using a volume-weighted volatility measurement, this line is used as the benchmark to calculate momentum in the 𝓦𝓐𝓥𝓓 indicator.
Signal Line: The signal line represents the volume and volatility weighted measurements, and oscillates between the Base Line and Top Cap. Its position between these levels provides the depth of insights available in this script.
When the signal line is remaining in close proximity to the base line, this is indicative of a low volatility market environment. These periods are also reflected as muted bar coloring when the ‘Trend Intensity’ setting is enabled.
Conversely, when the signal line approaches, or even breaks above the Top Cap, this is characteristic of an unsustainable trending action - and probabilistically speaking, a reversion or consolation is likely to occur at these levels.
Highlighting: When this setting is enabled, background coloring is applied when the Signal Line breaks above the Top Cap. This highlights green as an oversold zone, and purple as an overbought zone.
Reversal Signals: When price begins to reverse from a zone of overextension, a signal is plotted when this reversion occurs from a high probability zone.
Circle - Shows a possible bullish reversal.
Cross - Shows a possible bearish reversal.
Case Study
In the above image, we showcase three distinct trades in short succession, showcasing the 𝓦𝓐𝓥𝓓’s speed and accuracy under the right conditions.
The first long trade was initiated upon receiving a bullish reversal signal. The trade was then closed after the price experienced a sharp upwards movement - and an overbought signal was indicated by the purple shading.
The second, short trade was entered on the next bar, after a bearish reversal signal was printed by the indicator (a white cross). Similarly, this trade was closed upon the oversold signal.
Once again, a reversal signal was indicated by the 𝓦𝓐𝓥𝓓 indicator. This time a bullish signal (a white circle), and hence a long position was opened. However, this trade was held until a negative trend confirmation (signaled by the Top Cap’s shift in color). This makes apparent the indicator’s flexible nature, and showcases the multiple signaling types available for traders to use.
Recommended Settings
The optimal settings for the 𝓦𝓐𝓥𝓓 indicator will vary upon the chosen asset’s average level volatility, as well as the timeframe it is applied to.
Due to increased volatility levels on lower timeframes, it is recommended to increase the 'Top Cap Multiplier' to take into account the increased frequency of false signals found in these trading environments. The same can be said when used on highly volatile assets - a trader will likely benefit from using a higher 'Top Cap Multiplier.'
On more price-stable assets, as well as any asset on higher timeframes, there is merit to tightening the length of the 'Top Cap Multiplier,' due to the slower nature of price action.
Methodology
The 𝓦𝓐𝓥𝓓 starts with calculating the volume weighted average price and the volume weighted variance - which is the expectation of the squared deviation of a variable from its mean, giving insights into the distribution of trading volume.
Using the volume weighted variance, a standard deviation value is calculated based on user input. This value acts as the ‘Volumetric Top Cap’ - seen in the 𝓦𝓐𝓥𝓓 indicator window as the zero line.
The signal line is calculated as the difference between the current price and the theoretical upper or lower VWAP deviation bands. This line acts as the trigger for identifying prevailing trends and high probability reversal points.
The base line serves as a reference point for historical momentum. It is calculated using an exponential moving average of the lowest signal line values over a defined lookback period. This baseline helps in assessing whether the current momentum is high or low relative to historical norms.
Notes
Bar coloring can be turned off - especially useful when stacking multiple indicators as recommended, or set to 'Trend Intensity,' or 'Binary Trend' (which reflects the top cap coloring).
It is always recommended to never rely on a single indicator - and instead build and test multiple strategies utilizing more than one indicator as confirmation.
Pattern Probability with EMA FilterThe provided code is a custom indicator that identifies specific price patterns on a chart and uses a 14-period Exponential Moving Average (EMA) as a filter to display only certain patterns based on the EMA trend direction. These code identifies patterns display them as upward and downward arrows indicates potential price corrections and short term trend reversals in the direction of the arrow. Use with indicators such as RSI that inform overbought and oversold condition to add reliability and confluence.
Code Explanation:
The code first calculates three values 'a', 'b', and 'c' based on the difference between the current high, low, and close prices, respectively, and their respective previous moving average values.
Binary values are then assigned to 'a', 'b', and 'c', where each value is set to 1 if it's greater than 0, and 0 otherwise.
The 'pattern_type' is determined based on the binary values of 'a', 'b', and 'c', combining them into a single number (ranging from 0 to 7) to represent different price patterns.
The code calculates a 14-period Exponential Moving Average (EMA) of the closing price.
It determines the EMA trend direction by comparing the current EMA value with the previous EMA value, setting 'ema_going_up' to true if the EMA is going up and 'ema_going_down' to true if the EMA is going down.
The indicator then plots arrows on the chart for specific pattern_type values while considering the EMA trend direction as a filter. It displays different colored arrows for each pattern_type.
The 14-period EMA is also plotted on the chart, with the color changing to green when the EMA is going up and red when the EMA is going down.
Concept:
pattern_type = 0: H- L- C- (Downward trend continuation) - Indicates a continuation of the downward trend, suggesting further losses ahead.
pattern_type = 1: H- L- C+ (Likely trend change: Downwards to upwards) - Implies the upward trend or price movement change.
pattern_type = 2: H- L+ C- (Likely trend change: Upwards to downwards) - Suggests a potential reversal from an uptrend to a downtrend, but further confirmation is needed.
pattern_type = 3: H- L+ C+ (Trend uncertainty: Potential reversal) - Indicates uncertainty in the trend, potential for a reversal, but further price action confirmation is required.
pattern_type = 4: H+ L- C- (Downward trend continuation with lower volatility) - Suggests the downward trend may continue, but with reduced price swings or lower volatility.
pattern_type = 5: H+ L- C+ (Likely trend change: Downwards to upwards) - Implies a potential reversal from a downtrend to an uptrend, with buying interest increasing.
(pattern_type = 6: H+ L+ C- (Likely trend change: Upwards to downwards) - Suggests a potential reversal from an uptrend to a downtrend, with selling pressure increasing.
pattern_type = 7: H+ L+ C+ (Upward trend continuation) - Indicates a continuation of the upward trend, suggesting further gains ahead.
In the US market, when analyzing a 15-minute chart, we observe the following proportions of the different pattern_type occurrences: The code will plot the low frequency patterns (P1 - P6)
P0 (H- L- C-): 37.60%
P1 (H- L- C+): 3.60%
P2 (H- L+ C-): 3.10%
P3 (H- L+ C+): 3.40%
P4 (H+ L- C-): 2.90%
P5 (H+ L- C+): 2.70%
P6 (H+ L+ C-): 3.50%
P7 (H+ L+ C+): 43.50%
When analyzing higher time frames, such as daily or weekly charts, the occurrence of these patterns is expected to be even lower, but they may carry more significant implications due to their rarity and potential impact on longer-term trends.
TMA Legacy - "The Arty"This is a script based on the original "The Arty" indicator by PhoenixBinary.
The Phoenix Binary community and the TMA community built this version to be public code for the community for further use and revision after the reported passing of Phoenix Binary (The community extends our condolences to Phoenix's family).
The intended uses are the same as the original but some calculations are different and may not act or signal the same as the original.
Description of the indicator from original posting.
This indicator was inspired by Arty and Christy .
TMA-LegacyThis is a script based on the original TMA- RSI Divergence indicator by PhoenixBinary.
The Phoenix Binary community and the TMA community built this version to be public code for the community for further use and revision after the reported passing of Phoenix Binary (The community extends our condolences to Phoenix's family.
The intended uses are the same as the original but some calculations are different and may not act or signal the same as the original.
Description of the indicator from original posting.
This indicator was inspired by Arty and Christy .
█ COMPONENTS
Here is a brief overview of the indicator from the original posting:
1 — RSI Divergence
Arty uses the RSI divergence as a tool to find entry points and possible reversals. He doesn't use the traditional overbought/oversold. He uses a 50 line. This indicator includes a 50 line and a floating 50 line.
The floating 50 line is a multi-timeframe smoothed moving average . Price is not linear, therefore, your 50 line shouldn't be either.
The RSI line is using a dynamic color algo that shows current control of the market as well as possible turning points in the market.
2 — Smoothed RSI Divergence
The Smoothed RSI Divergence is a slower RSI with different calculations to smooth out the RSI line. This gives a different perspective of price action and more of a long term perspective of the trend. When crosses of the floating 50 line up with the traditional RSI crossing floating 50.
3 — Momentum Divergence
This one will take a little bit of time to master. But, once you master this, and combined with the other two, damn these entries get downright lethal!
Evan Cabral Binary Strategy 2 / Lobowass Binary SignalThe script contains 1 Bollinger band with 2 different deviations also incorporated with a maximum and minimum of 30 minutes in green and red and a maximum and minimum of 4 hours in fuchsia. Also a 200 period EMA .
The red arrows appear whenever the stochastic , the DMI-stochastic and the two stochastics RSI are overbought and the low of the candle is breaking the bollnger band.
The green arrows appear whenever the stochastic , the DMI-stochastic and the two stochastics RSI are oversold and the high of the candle is breaking the bollnger band.
What makes this script unique is the combination of different indicators to give a buy or sell signal made by Edrul_Alejandro.
Indicator parameters:
Stochastic = (14,3,3) / (80,20)
Stochastic RSI 1 = (3,3,14,14) / (80,20)
Stochastic RSI 2 = (3,3,6,6) / (80,20)
Stochastic DMI = (10.3) / (90.10)
Bollinger band 1 = (34,2.0)
Bollinge band 2 = (34,2.5)
EMA = 200
Machine Learning: Perceptron-based strategyPerceptron-based strategy
Description:
The Learning Perceptron is the simplest possible artificial neural network (ANN), consisting of just a single neuron and capable of learning a certain class of binary classification problems. The idea behind ANNs is that by selecting good values for the weight parameters (and the bias), the ANN can model the relationships between the inputs and some target.
Generally, ANN neurons receive a number of inputs, weight each of those inputs, sum the weights, and then transform that sum using a special function called an activation function. The output of that activation function is then either used as the prediction (in a single neuron model) or is combined with the outputs of other neurons for further use in more complex models.
The purpose of the activation function is to take the input signal (that’s the weighted sum of the inputs and the bias) and turn it into an output signal. Think of this activation function as firing (activating) the neuron when it returns 1, and doing nothing when it returns 0. This sort of computation is accomplished with a function called step function: f(z) = {1 if z > 0 else 0}. This function then transforms any weighted sum of the inputs and converts it into a binary output (either 1 or 0). The trick to making this useful is finding (learning) a set of weights that lead to good predictions using this activation function.
Training our perceptron is simply a matter of initializing the weights to zero (or random value) and then implementing the perceptron learning rule, which just updates the weights based on the error of each observation with the current weights. This has the effect of moving the classifier’s decision boundary in the direction that would have helped it classify the last observation correctly. This is achieved via a for loop which iterates over each observation, making a prediction of each observation, calculating the error of that prediction and then updating the weights accordingly. In this way, weights are gradually updated until they converge. Each sweep through the training data is called an epoch.
In this script the perceptron is retrained on each new bar trying to classify this bar by drawing the moving average curve above or below the bar.
This script was tested with BTCUSD, USDJPY, and EURUSD.
Note: TradingViews's playback feature helps to see this strategy in action.
Warning: Signals ARE repainting.
Style tags: Trend Following, Trend Analysis
Asset class: Equities, Futures, ETFs, Currencies and Commodities
Dataset: FX Minutes/Hours+/Days
Relative Falling three Methods IndicatorAbstract
This script measure the related speed between rising and falling.
This script can replace binary Falling Three Methods detector and, report continuous value and estimate potential trend direction.
My suggestion of using this script is combining it with trading emotion.
Introduction
Falling Three Methods (F3M) is a candlestick pattern.
Many trading courses say traders can regard it as predicting falling will continue.
However, it is not easy to see perfect Falling Three Methods pattern from charts.
Therefore, we need an alternative method to measure it.
We can use the observation that falling is faster than rising during those time.
When falling is faster than rising, some long ( buy , call , higher , upper ) position owners may worry the price will fall very much suddenly.
When rising is faster than falling, some traders may worry they may miss buy opportunities.
Computing Related Falling Three Methods Indicator
(1) The value of rising and falling
In this script, open price is replaced with previous close price.
If the previous price is equal to the close price, than both rising and falling are equal to high-low.
If the previous price is lower than the close price, than the falling value becomes smaller, high-close+previous-low.
If the previous price is higher than the close price, than the rising value becomes smaller, high-previous+close-low.
(2) Area of value (aov)
Area of value is equal to highest-lowest. The previous close price is included.
(3) Compute weight and filter noise
We need a threshold for the noise filter. The default setting is aov/length, where length means how many days are counted.
When a rising or falling value <= threshold, it is not counted.
When a rising or falling value > threshold, the counted value = original value - threshold
and its weight = min ( counted value , threshold )
(4) compute speed
Rising speed = sum ( counted rising value ) / sum ( rising weight )
Falling speed = sum ( counted falling value ) / sum ( falling weight )
(5) Final result
Final result = Rising speed / ( Rising speed + Falling speed ) * 100 - 50
I move the middle level to 0 because 0 axis is always visible unless you cannot see negative values or you cannot see positive values.
Parameters
Length : how many days are counted. The default value is 16 just because 16=4*4, using binary characteristic.
Multi : the multiplier of noise threshold. Threshold applied = default threshold * multi
src : current not used
Conclusion
Related Falling Three Methods Indicator can measure the related speed between rising and falling.
I hope this indicator can help us to evaluate the possibility of trend continue or reversal and potential breakout direction.
After all, we care how trading emotion control the price movement and therefore we can take advantage to it.
Reference
How to trade with Falling Three Methods pattern
How to trade with Related Strength Indicator
Dex Atomic for nadexThis script utilizes two major concepts of price action.
1. Support and resistance - shown as the colored lines.
2. Dynamic price action movement - shown as the arrows on the chart.
This script is best utilized with the Nadex (north american derivative exchange ) system
Their exchange utilizes a derivative timed statement of true or false.
eurusd will be > the 1.0987 at 2pm you will either agree or disagree
You can learn more from their website.
A sell signal
Spotting the red arrow for direction
use the closest Higher support and resistance line as the statement price you would look for as a binary on nadex
A buy signals
use the closest lower support and resistance line as the statement price you would look for as a binary on nadex
These signals represented as arrows are a perceived notion of direction for a short period of time.
There are three times frames our systems work for
a 1 min time frame is used for a 5 min statement on nadex
a 5 min time frame is used for a one hour statement on nadex
a 15 in time frame is used for a 2 hour statement on nadex
Nadex uses ten pairs as well for forex, but also has indices , commodities and bitcoin statements as well. This script be used for those as well.
This script has an availability to be used as a forex trading potential with a litlle creative thought. but I wold suggest to just stay with the derivative exchange.
4 in 1 Stoch Indicators as used by HG (Stoch, SRSIx2, DMIStoch)By using this indicator you can better view the Stoch indicators used by this strategy which are:
- Stochastic (14,3,3)
- Stochastic RSI (14,14,3,3)
- Stochastic RSI (6,6,3,3)
- DMI Stochastic
This is best used alongside:
- Evan Cabral binary strategy 2
- Binary with Temito
The analisis is:
- When all lines in the indicator are above or below the overbough/oversold lines
- When the bollinger bands are broken
- A support or resistance is reached
That means a change of Trend.
Sifo's DMIHelps for better entries with my strategy (link bellow) on binary trades and some swing trades 1H-4H (10-100 pips).
Edge-Preserving FilterIntroduction
Edge-preserving smoothing is often used in image processing in order to preserve edge information while filtering the remaining signal. I introduce two concepts in this indicator, edge preservation and an adaptive cumulative average allowing for fast edge-signal transition with period increase over time. This filter have nothing to do with classic filters for image processing, those filters use kernels convolution and are most of the time in a spatial domain.
Edge Detection Method
We want to minimize smoothing when an edge is detected, so our first goal is to detect an edge. An edge will be considered as being a peak or a valley, if you recall there is one of my indicator who aim to detect peaks and valley (reference at the bottom of the post) , since this estimation return binary outputs we will use it to tell our filter when to stop filtering.
Filtering Increase By Using Multi Steps Cumulative Average
The edge detection is a binary output, using a exponential smoothing could be possible and certainly more efficient but i wanted instead to try using a cumulative average approach because it smooth more and is a bit more original to use an adaptive architecture using something else than exponential averaging. A cumulative average is defined as the sum of the price and the previous value of the cumulative average and then this result is divided by n with n = number of data points. You could say that a cumulative average is a moving average with a linear increasing period.
So lets call CMA our cumulative average and n our divisor. When an edge is detected CMA = close price and n = 1 , else n is equal to previous n+1 and the CMA act as a normal cumulative average by summing its previous values with the price and dividing the sum by n until a new edge is detected, so there is a "no filtering state" and a "filtering state" with linear period increase transition, this is why its multi-steps.
The Filter
The filter have two parameters, a length parameter and a smooth parameter, length refer to the edge detection sensitivity, small values will detect short terms edges while higher values will detect more long terms edges. Smooth is directly related to the edge detection method, high values of smooth can avoid the detection of some edges.
smooth = 200
smooth = 50
smooth = 3
Conclusion
Preserving the price edges can be useful when it come to allow for reactivity during important price points, such filter can help with moving average crossover methods or can be used as a source for other indicators making those directly dependent of the edge detection.
Rsi with a period of 200 and our filter as source, will cross triggers line when an edge is detected
Feel free to share suggestions ! Thanks for reading !
References
Peak/Valley estimator used for the detection of edges in price.
SuperR V.2Hi,
This is the same indicator but now it's more accurate. you can use it with 1- minute binary options trading. Please note that SuperR is not for FX or stock trading expect binary options. With this system, we're using 2 step of martingale.
And my indicator is for sale. once you buy it you will get permission to access indicator. as simple as that.
Mail me, if you are interested. SuperR indicator is priced at $150. once-off fee.
For more information
Drop me a mail, id is jogadiyahemlata786@gmail.com ( Ensure spelling before you send it out )
(This is officile annocenment )
SPG Fx Volume IndicatorThis indicator is for Forex intraday trading and works best for binary 5 minute contracts but will work for Contracts up to 2 hours in length.
The "SPG - FOREX BINARY INTRA" indicator is a companion for this one. It will give confirmation of the entry signals that this will show you.
This script is broken up into 4 parts
Confidence Cloud/Background Color
This will indicate the current bull/bear trend and if your entering a position - the strength of the direction of that bar will be reflected by the background color behind that bar.
Green - Bull Trend
Red - Bear Trend
Yellow - Transition/unsure
Small BUY/SELL arrows and Green/Red triangles
On the bottom of the chat are the main entry indicators
Green/Red Triangles are a strong entry signal
Green/Red Tringles and a Buy/Sell arrow is a very strong entry signal
Buy/Sell Pressure
The histogram indicated the buy/sell pressure for the bar – This indicated in which direction the bar moved the most – This is mostly for a future “rating” on a position that was taken and perhaps can drive an indication when to Hold or exit the contract.
Green/Red arrows and Xs labeled OS, OB or X
These are located on the top of this indicator and aren’t necessarily actionable indicators but are meant to indicate overbought or oversold conditions and transitions when prices are moving out of those states.
The indicators may correlate with entry signals but watch for the Xs and do not enter a trade on a transition.
PRO MomentumINVITE ONLY SCRIPT:
FEATURES:
As its name suggests, PRO Momentum provides non-subjective momentum analysis to traders through automatic pattern detections, covering a wide range of statistically relevant structures in both ranging and trending contexts. Our goal was to provide a professional grade risk management tool capable of providing various signals, which guide the trader in its decision to engage or not in a certain price area filtered by Framework. Nevertheless, both indicators are complex tools requiring extensive learning. To support students in their journey, there is a wide open online community of users in our Discord channel, providing peer-to-peer assistance to progress with the strategy as well as tutored courses.
OUTPUTS:
To share a brief description of the PRO Momentum functioning, we will scroll through the major set of outputs that are presented to the user. Please note that the indicator is meant to assist from Junior to Senior expertise, to achieve this we have set different base templates right into the indicators. To keep this description simple, we will present the outputs you’ll see with the beginner setup:
Momentum Signals: As shown on the chart, there are multiple types of output signals, each corresponding to different momentum patterns. Detailed documentation is available on our website for those seeking in-depth information. Here's a high-level overview: The patterns are divided into three categories, each represented by different colors. Blue and Red signals are used in trending contexts, Gray signals are for ranging contexts, and dark-colored signals are exclusive to reversal contexts, suitable for more experienced traders. Momentum signals are binary outputs, making it easy for users to set alerts. The indicator includes built-in alerts for these groups to streamline the process. However, it’s crucial to remember that momentum signals are not standalone trading signals. The Framework indicator must first filter interesting prices and identify the context. Only then should traders use momentum signals to adjust risk.
Sinewave Oscillators: Cyclical analysis is a critical aspect of professional risk management. Markets naturally oscillate, and significant statistical probabilities can be derived from cycle studies. We use a custom-modified version of Ehlers’ sinewave methodology. Cyclical analysis, while somewhat predictive, scans past prices to predict probable future states. Since markets are inherently unpredictable, cycle analysis is used as a confirmation signal in our strategy. Essentially, we filter out all momentum signals that occur outside favorable cyclical conditions. Bearish signals are only exploited if the sinewave is in the top area of the oscillator, and vice-versa for bullish signals.
GENERAL STRATEGY:
Overall, the PRO Strategy combines two “core” indicators, Framework and Momentum. Framework is plotted on the main chart section as an overlay, it is definitely the most important as it guides the user through the hard process of filtering prices and timeframes that are suitable for technical analysis. On the other hand, PRO Momentum is on a separate oscillator tab under the chart section, it will study the momentum and cyclical structure, also offering automated pattern detection. Ultimately, our strategy is based on collecting and processing non-subjective rules, emanating from the indicators outputs. Essentially, this means that the indicator actually takes care of producing all the necessary binary outputs, leaving you with the remaining task of combining them correctly following the strategy’s patterns.
RISK LIMITATION:
Even if we provide automated momentum signal detection, there is no “one-click” or "easy-win” solution, the user still needs to carefully review the elements. When applicable pattern rules are confirmed, the user will gather risk-limitation information from both indicators (breakout targets, price confirmations, momentum and cyclical coordination) and decide whether or not to trade according to its own risk profile. If so, the position sizing, stop-loss positioning, risk management and profit targets will all be defined according to the same indicator’s outputs. This effectively suppresses most behavioral and personal biases the trader could introduce, creating a stable and statistical risk management structure aiming for a durable profitability.
