Sunil BB Blast Heikin Ashi StrategySunil BB Blast Heikin Ashi Strategy
The Sunil BB Blast Heikin Ashi Strategy is a trend-following trading strategy that combines Bollinger Bands with Heikin-Ashi candles for precise market entries and exits. It aims to capitalize on price volatility while ensuring controlled risk through dynamic stop-loss and take-profit levels based on a user-defined Risk-to-Reward Ratio (RRR).
Key Features:
Trading Window:
The strategy operates within a user-defined time window (e.g., from 09:20 to 15:00) to align with market hours or other preferred trading sessions.
Trade Direction:
Users can select between Long Only, Short Only, or Long/Short trade directions, allowing flexibility depending on market conditions.
Bollinger Bands:
Bollinger Bands are used to identify potential breakout or breakdown zones. The strategy enters trades when price breaks through the upper or lower Bollinger Band, indicating a possible trend continuation.
Heikin-Ashi Candles:
Heikin-Ashi candles help smooth price action and filter out market noise. The strategy uses these candles to confirm trend direction and improve entry accuracy.
Risk Management (Risk-to-Reward Ratio):
The strategy automatically adjusts the take-profit (TP) level and stop-loss (SL) based on the selected Risk-to-Reward Ratio (RRR). This ensures that trades are risk-managed effectively.
Automated Alerts and Webhooks:
The strategy includes automated alerts for trade entries and exits. Users can set up JSON webhooks for external execution or trading automation.
Active Position Tracking:
The strategy tracks whether there is an active position (long or short) and only exits when price hits the pre-defined SL or TP levels.
Exit Conditions:
The strategy exits positions when either the take-profit (TP) or stop-loss (SL) levels are hit, ensuring risk management is adhered to.
Default Settings:
Trading Window:
09:20-15:00
This setting confines the strategy to the specified hours, ensuring trading only occurs during active market hours.
Strategy Direction:
Default: Long/Short
This allows for both long and short trades depending on market conditions. You can select "Long Only" or "Short Only" if you prefer to trade in one direction.
Bollinger Band Length (bbLength):
Default: 19
Length of the moving average used to calculate the Bollinger Bands.
Bollinger Band Multiplier (bbMultiplier):
Default: 2.0
Multiplier used to calculate the upper and lower bands. A higher multiplier increases the width of the bands, leading to fewer but more significant trades.
Take Profit Multiplier (tpMultiplier):
Default: 2.0
Multiplier used to determine the take-profit level based on the calculated stop-loss. This ensures that the profit target aligns with the selected Risk-to-Reward Ratio.
Risk-to-Reward Ratio (RRR):
Default: 1.0
The ratio used to calculate the take-profit relative to the stop-loss. A higher RRR means larger profit targets.
Trade Automation (JSON Webhooks):
Allows for integration with external systems for automated execution:
Long Entry JSON: Customizable entry condition for long positions.
Long Exit JSON: Customizable exit condition for long positions.
Short Entry JSON: Customizable entry condition for short positions.
Short Exit JSON: Customizable exit condition for short positions.
Entry Logic:
Long Entry:
The strategy enters a long position when:
The Heikin-Ashi candle shows a bullish trend (green close > open).
The price is above the upper Bollinger Band, signaling a breakout.
The previous candle also closed higher than it opened.
Short Entry:
The strategy enters a short position when:
The Heikin-Ashi candle shows a bearish trend (red close < open).
The price is below the lower Bollinger Band, signaling a breakdown.
The previous candle also closed lower than it opened.
Exit Logic:
Take-Profit (TP):
The take-profit level is calculated as a multiple of the distance between the entry price and the stop-loss level, determined by the selected Risk-to-Reward Ratio (RRR).
Stop-Loss (SL):
The stop-loss is placed at the opposite Bollinger Band level (lower for long positions, upper for short positions).
Exit Trigger:
The strategy exits a trade when either the take-profit or stop-loss level is hit.
Plotting and Visuals:
The Heikin-Ashi candles are displayed on the chart, with green candles for uptrends and red candles for downtrends.
Bollinger Bands (upper, lower, and basis) are plotted for visual reference.
Entry points for long and short trades are marked with green and red labels below and above bars, respectively.
Strategy Alerts:
Alerts are triggered when:
A long entry condition is met.
A short entry condition is met.
A trade exits (either via take-profit or stop-loss).
These alerts can be used to trigger notifications or webhook events for automated trading systems.
Notes:
The strategy is designed for use on intraday charts but can be applied to any timeframe.
It is highly customizable, allowing for tailored risk management and trading windows.
The Sunil BB Blast Heikin Ashi Strategy combines two powerful technical analysis tools (Bollinger Bands and Heikin-Ashi candles) with strong risk management, making it suitable for both beginners and experienced traders.
Feebacks are welcome from the users.
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Market Flow Volatility Oscillator (AiBitcoinTrend)The Market Flow Volatility Oscillator (AiBitcoinTrend) is a cutting-edge technical analysis tool designed to evaluate and classify market volatility regimes. By leveraging Gaussian filtering and clustering techniques, this indicator provides traders with clear insights into periods of high and low volatility, helping them adapt their strategies to evolving market conditions. Built for precision and clarity, it combines advanced mathematical models with intuitive visual feedback to identify trends and volatility shifts effectively.
👽 How the Indicator Works
👾 Volatility Classification with Gaussian Filtering
The indicator detects volatility levels by applying Gaussian filters to the price series. Gaussian filters smooth out noise while preserving significant price movements. Traders can adjust the smoothing levels using sigma parameters, enabling greater flexibility:
Low Sigma: Emphasizes short-term volatility.
High Sigma: Captures broader trends with reduced sensitivity to small fluctuations.
👾 Clustering Algorithm for Regime Detection
The core of this indicator is its clustering model, which classifies market conditions into two distinct regimes:
Low Volatility Regime: Calm periods with reduced market activity.
High Volatility Regime: Intense periods with heightened price movements.
The clustering process works as follows:
A rolling window of data is analyzed to calculate the standard deviation of price returns.
Two cluster centers are initialized using the 25th and 75th percentiles of the data distribution.
Each price volatility value is assigned to the nearest cluster based on its distance to the centers.
The cluster centers are refined iteratively, providing an accurate and adaptive classification.
👾 Oscillator Generation with Slope R-Values
The indicator computes Gaussian filter slopes to generate oscillators that visualize trends:
Oscillator Low: Captures low-frequency market behavior.
Oscillator High: Tracks high-frequency, faster-changing trends.
The slope is measured using the R-value of the linear regression fit, scaled and adjusted for easier interpretation.
👽 Applications
👾 Trend Trading
When the oscillator rises above 0.5, it signals potential bullish momentum, while dips below 0.5 suggest bearish sentiment.
👾 Pullback Detection
When the oscillator peaks, especially in overbought or oversold zones, provide early warnings of potential reversals.
👽 Indicator Settings
👾 Oscillator Settings
Sigma Low/High: Controls the smoothness of the oscillators.
Smaller Values: React faster to price changes but introduce more noise.
Larger Values: Provide smoother signals with longer-term insights.
👾 Window Size and Refit Interval
Window Size: Defines the rolling period for cluster and volatility calculations.
Shorter windows: adapt faster to market changes.
Longer windows: produce stable, reliable classifications.
Disclaimer: This information is for entertainment purposes only and does not constitute financial advice. Please consult with a qualified financial advisor before making any investment decisions.
40 Ticker Cross-Sectional Z-Scores [BackQuant]40 Ticker Cross-Sectional Z-Scores
BackQuant’s 40 Ticker Cross-Sectional Z-Scores is a powerful portfolio management strategy that analyzes the relative performance of up to 40 different assets, comparing them on a cross-sectional basis to identify the top and bottom performers. This indicator computes Z-scores for each asset based on their log returns and evaluates them relative to the mean and standard deviation over a rolling window. The Z-scores represent how far an asset's return deviates from the average, and these values are used to rank the assets, allowing for dynamic asset allocation based on performance.
By focusing on the strongest-performing assets and avoiding the weakest, this strategy aims to enhance returns while managing risk. Additionally, by adjusting for standard deviations, the system offers a risk-adjusted method of ranking assets, making it suitable for traders who want to dynamically allocate capital based on performance metrics rather than just price movements.
Key Features
1. Cross-Sectional Z-Score Calculation:
The system calculates Z-scores for 40 different assets, evaluating their log returns against the mean and standard deviation over a rolling window. This enables users to assess the relative performance of each asset dynamically, highlighting which assets are performing better or worse compared to their historical norms. The Z-score is a useful statistical tool for identifying outliers in asset performance.
2. Asset Ranking and Allocation:
The system ranks assets based on their Z-scores and allocates capital to the top performers. It identifies the top and bottom assets, and traders can allocate capital to the top-performing assets, ensuring that their portfolio is aligned with the best performers. Conversely, the bottom assets are removed from the portfolio, reducing exposure to underperforming assets.
3. Rolling Window for Mean and Standard Deviation Calculations:
The Z-scores are calculated based on rolling means and standard deviations, making the system adaptive to changing market conditions. This rolling calculation window allows the strategy to adjust to recent performance trends and minimize the impact of outdated data.
4. Mean and Standard Deviation Visualization:
The script provides real-time visualizations of the mean (x̄) and standard deviation (σ) of asset returns, helping traders quickly identify trends and volatility in their portfolio. These visual indicators are useful for understanding the current market environment and making more informed allocation decisions.
5. Top & Bottom Performer Tables:
The system generates tables that display the top and bottom performers, ranked by their Z-scores. Traders can quickly see which assets are outperforming and underperforming. These tables provide clear and actionable insights, helping traders make informed decisions about which assets to include in their portfolio.
6. Customizable Parameters:
The strategy allows traders to customize several key parameters, including:
Rolling Calculation Window: Set the window size for the rolling mean and standard deviation calculations.
Top & Bottom Tickers: Choose how many of the top and bottom assets to display and allocate capital to.
Table Orientation: Select between vertical or horizontal table formats to suit the user’s preference.
7. Forward Test & Out-of-Sample Testing:
The system includes out-of-sample forward tests, ensuring that the strategy is evaluated based on real-time performance, not just historical data. This forward testing approach helps validate the robustness of the strategy in dynamic market conditions.
8. Visual Feedback and Alerts:
The system provides visual feedback on the current asset rankings and allocations, with dynamic labels and plots on the chart. Additionally, users receive alerts when allocations change, keeping them informed of important adjustments.
9. Risk Management via Z-Scores and Std Dev:
The system’s approach to asset selection is based on Z-scores, which normalize performance relative to the historical mean. By incorporating standard deviation, it accounts for the volatility and risk associated with each asset. This allows for more precise risk management and portfolio construction.
10. Note on Mean Reversion Strategy:
If you take the inverse of the signals provided by this indicator, the strategy can be used for mean-reversion rather than trend-following. This would involve buying the underperforming assets and selling the outperforming ones. However, it's important to note that this approach does not work well with highly correlated assets, as the relationship between the assets could result in the same directional movement, undermining the effectiveness of the mean-reversion strategy.
References
www.uts.edu.au
onlinelibrary.wiley.com
www.cmegroup.com
Final Thoughts
The 40 Ticker Cross-Sectional Z-Scores strategy offers a data-driven approach to portfolio management, dynamically allocating capital based on the relative performance of assets. By using Z-scores and standard deviations, this strategy ensures that capital is directed to the strongest performers while avoiding weaker assets, ultimately improving the risk-adjusted returns of the portfolio. Whether you’re focused on trend-following or looking to explore mean-reversion strategies, this flexible system can be tailored to suit your investment goals.
Poisson Projection of Price Levels### **Poisson Projection of Price Levels**
**Overview:**
The *Poisson Projection of Price Levels* is a cutting-edge technical indicator designed to identify and visualize potential support and resistance levels based on historical price interactions. By leveraging the Poisson distribution, this tool dynamically adjusts the significance of each price level's past "touches" to project future interactions with varying degrees of probability. This probabilistic approach offers traders a nuanced view of where price levels may hold or react in upcoming bars, enhancing both analysis and trading strategies.
---
**🔍 **Math & Methodology**
1. **Strata Levels:**
- **Definition:** Strata are horizontal lines spaced evenly around the current closing price.
- **Calculation:**
\
where \(i\) ranges from 0 to \(\text{Strata Count} - 1\).
2. **Forecast Iterations:**
- **Structure:** The indicator projects five forecast iterations into the future, each spaced by a Fibonacci sequence of bars: 2, 3, 5, 8, and 13 bars ahead. This spacing is inspired by the Fibonacci sequence, which is prevalent in financial market analysis for identifying key levels.
- **Purpose:** Each iteration represents a distinct forecast point where the price may interact with the strata, allowing for a multi-step projection of potential price levels.
3. **Touch Counting:**
- **Definition:** A "touch" occurs when the closing price of a bar is within half the increment of a stratum level.
- **Process:** For each stratum and each forecast iteration, the indicator counts the number of touches within a specified lookback window (e.g., 80 bars), offset by the forecasted position. This ensures that each iteration's touch count is independent and contextually relevant to its forecast horizon.
- **Adjustment:** Each forecast iteration analyzes a unique segment of the lookback window, offset by its forecasted position to ensure independent probability calculations.
4. **Poisson Probability Calculation:**
- **Formula:**
\
\
- **Interpretation:** \(p(k=1)\) represents the probability of exactly one touch occurring within the lookback window for each stratum and iteration.
- **Application:** This probability is used to determine the transparency of each stratum line, where higher probabilities result in more opaque (less transparent) lines, indicating stronger historical significance.
5. **Transparency Mapping:**
- **Calculation:**
\
- **Purpose:** Maps the Poisson probability to a visual transparency level, enhancing the readability of significant strata levels.
- **Outcome:** Strata with higher probabilities (more historical touches) appear more opaque, while those with lower probabilities appear fainter.
---
**📊 **Comparability to Standard Techniques**
1. **Support and Resistance Levels:**
- **Traditional Approach:** Traders identify support and resistance based on historical price reversals, pivot points, or psychological price levels.
- **Poisson Projection:** Automates and quantifies this process by statistically analyzing the frequency of price interactions with specific levels, providing a probabilistic measure of significance.
2. **Statistical Modeling:**
- **Standard Models:** Techniques like Moving Averages, Bollinger Bands, or Fibonacci Retracements offer dynamic and rule-based levels but lack direct probabilistic interpretation.
- **Poisson Projection:** Introduces a discrete event probability framework, offering a unique blend of statistical rigor and visual clarity that complements traditional indicators.
3. **Event-Based Analysis:**
- **Financial Industry Practices:** Event studies and high-frequency trading models often use Poisson processes to model order arrivals or price jumps.
- **Indicator Application:** While not identical, the use of Poisson probabilities in this indicator draws inspiration from event-based modeling, applying it to the context of price level interactions.
---
**💡 **Strengths & Advantages**
1. **Innovative Visualization:**
- Combines statistical probability with traditional support/resistance visualization, offering a fresh perspective on price level significance.
2. **Dynamic Adaptability:**
- Parameters like strata increment, lookback window, and probability threshold are user-defined, allowing customization across different markets and timeframes.
3. **Independent Probability Calculations:**
- Each forecast iteration calculates its own Poisson probability, ensuring that projections are contextually relevant and independent of other iterations.
4. **Clear Visual Cues:**
- Transparency-based coloring intuitively highlights significant price levels, making it easier for traders to identify key areas of interest at a glance.
---
**⚠️ **Limitations & Considerations**
1. **Poisson Assumptions:**
- Assumes that touches occur independently and at a constant average rate (\(\lambda\)), which may not always align with market realities characterized by trends and volatility clustering.
2. **Computational Intensity:**
- Managing multiple iterations and strata can be resource-intensive, potentially affecting performance on lower-powered devices or with very high lookback windows.
3. **Interpretation Complexity:**
- While transparency offers visual clarity, understanding the underlying probability calculations requires a basic grasp of Poisson statistics, which may be a barrier for some traders.
---
**📢 **How to Use It**
1. **Add to TradingView:**
- Open TradingView and navigate to the Pine Script Editor.
- Paste the script above and click **Add to Chart**.
2. **Configure Inputs:**
- **Strata Increment:** Set the desired price step between strata (e.g., `0.1` for 10 cents).
- **Lookback Window:** Define how many past bars to consider for calculating Poisson probabilities (e.g., `80`).
- **Probability Transparency Threshold (%):** Set the threshold percentage to map probabilities to line transparency (e.g., `25%`).
3. **Understand the Forecast Iterations:**
- The indicator projects five forecast points into the future at bar spacings of 2, 3, 5, 8, and 13 bars ahead.
- Each iteration independently calculates its Poisson probability based on the touch counts within its specific lookback window offset by its forecasted position.
4. **Interpret the Visualization:**
- **Opaque Lines:** Indicate higher Poisson probabilities, suggesting historically significant price levels that are more likely to interact again.
- **Fainter Lines:** Represent lower probabilities, indicating less historically significant levels that may be less likely to interact.
- **Forecast Spacing:** The spacing of 2, 3, 5, 8, and 13 bars ahead aligns with Fibonacci principles, offering a natural progression in forecast horizons.
5. **Apply to Trading Strategies:**
- **Support/Resistance Identification:** Use the opaque lines as potential support and resistance levels for placing trades.
- **Entry and Exit Points:** Anticipate price interactions at forecasted levels to plan strategic entries and exits.
- **Risk Management:** Utilize the transparency mapping to determine where to place stop-loss and take-profit orders based on the probability of price interactions.
6. **Customize as Needed:**
- Adjust the **Strata Increment** to fit different price ranges or volatility levels.
- Modify the **Lookback Window** to capture more or fewer historical touches, adapting to different timeframes or market conditions.
- Tweak the **Probability Transparency Threshold** to control the sensitivity of transparency mapping to Poisson probabilities.
**📈 **Practical Applications**
1. **Identifying Key Levels:**
- Quickly visualize which price levels have historically had significant interactions, aiding in the identification of potential support and resistance zones.
2. **Forecasting Price Reactions:**
- Use the forecast iterations to anticipate where price may interact in the near future, assisting in planning entry and exit points.
3. **Risk Management:**
- Determine areas of high probability for price reversals or consolidations, enabling better placement of stop-loss and take-profit orders.
4. **Market Analysis:**
- Assess the strength of market levels over different forecast horizons, providing a multi-layered understanding of market structure.
---
**🔗 **Conclusion**
The *Poisson Projection of Price Levels* bridges the gap between statistical modeling and traditional technical analysis, offering traders a sophisticated tool to quantify and visualize the significance of price levels. By integrating Poisson probabilities with dynamic transparency mapping, this indicator provides a unique and insightful perspective on potential support and resistance zones, enhancing both analysis and trading strategies.
---
**📞 **Contact:**
For support or inquiries, please contact me on TradingView!
---
**📢 **Join the Conversation!**
Have questions, feedback, or suggestions for further enhancements? Feel free to comment below or reach out directly. Your input helps refine and evolve this tool to better serve the trading community.
---
**Happy Trading!** 🚀
Quick scan for signal🙏🏻 Hey TV, this is QSFS, following:
^^ Quick scan for drift (QSFD)
^^ Quick scan for cycles (QSFC)
As mentioned before, ML trading is all about spotting any kind of non-randomness, and this metric (along with 2 previously posted) gonna help ya'll do it fast. This one will show you whether your time series possibly exhibits mean-reverting / consistent / noisy behavior, that can be later confirmed or denied by more sophisticated tools. This metric is O(n) in windowed mode and O(1) if calculated incrementally on each data update, so you can scan Ks of datasets w/o worrying about melting da ice.
^^ windowed mode
Now the post will be divided into several sections, and a couple of things I guess you’ve never seen or thought about in your life:
1) About Efficiency Ratios posted there on TV;
Some of you might say this is the Efficiency Ratio you’ve seen in Perry's book. Firstly, I can assure you that neither me nor Perry, just as X amount of quants all over the world and who knows who else, would say smth like, "I invented it," lol. This is just a thing you R&D when you need it. Secondly, I invite you (and mods & admin as well) to take a lil glimpse at the following screenshot:
^^ not cool...
So basically, all the Efficiency Ratios that were copypasted to our platform suffer the same bug: dudes don’t know how indexing works in Pine Script. I mean, it’s ok, I been doing the same mistakes as well, but loxx, cmon bro, you... If you guys ever read it, the lines 20 and 22 in da code are dedicated to you xD
2) About the metric;
This supports both moving window mode when Length > 0 and all-data expanding window mode when Length < 1, calculating incrementally from the very first data point in the series: O(n) on history, O(1) on live updates.
Now, why do I SQRT transform the result? This is a natural action since the metric (being a ratio in essence) is bounded between 0 and 1, so it can be modeled with a beta distribution. When you SQRT transform it, it still stays beta (think what happens when you apply a square root to 0.01 or 0.99), but it becomes symmetric around its typical value and starts to follow a bell-shaped curve. This can be easily checked with a normality test or by applying a set of percentiles and seeing the distances between them are almost equal.
Then I noticed that on different moving window sizes, the typical value of the metric seems to slide: higher window sizes lead to lower typical values across the moving windows. Turned out this can be modeled the same way confidence intervals are made. Lines 34 and 35 explain it all, I guess. You can see smth alike on an autocorrelogram. These two match the mean & mean + 1 stdev applied to the metric. This way, we’ve just magically received data to estimate alpha and beta parameters of the beta distribution using the method of moments. Having alpha and beta, we can now estimate everything further. Btw, there’s an alternative parameterization for beta distributions based on data length.
Now what you’ll see next is... u guys actually have no idea how deep and unrealistically minimalistic the underlying math principles are here.
I’m sure I’m not the only one in the universe who figured it out, but the thing is, it’s nowhere online or offline. By calculating higher-order moments & combining them, you can find natural adaptive thresholds that can later be used for anomaly detection/control applications for any data. No hardcoded thresholds, purely data-driven. Imma come back to this in one of the next drops, but the truest ones can already see it in this code. This way we get dem thresholds.
Your main thresholds are: basis, upper, and lower deviations. You can follow the common logic I’ve described in my previous scripts on how to use them. You just register an event when the metric goes higher/lower than a certain threshold based on what you’re looking for. Then you take the time series and confirm a certain behavior you were looking for by using an appropriate stat test. Or just run a certain strategy.
To avoid numerous triggers when the metric jitters around a threshold, you can follow this logic: forget about one threshold if touched, until another threshold is touched.
In general, when the metric gets higher than certain thresholds, like upper deviation, it means the signal is stronger than noise. You confirm it with a more sophisticated tool & run momentum strategies if drift is in place, or volatility strategies if there’s no drift in place. Otherwise, you confirm & run ~ mean-reverting strategies, regardless of whether there’s drift or not. Just don’t operate against the trend—hedge otherwise.
3) Flex;
Extension and limit thresholds based on distribution moments gonna be discussed properly later, but now you can see this:
^^ magic
Look at the thresholds—adaptive and dynamic. Do you see any optimizations? No ML, no DL, closed-form solution, but how? Just a formula based on a couple of variables? Maybe it’s just how the Universe works, but how can you know if you don’t understand how fundamentally numbers 3 and 15 are related to the normal distribution? Hm, why do they always say 3 sigmas but can’t say why? Maybe you can be different and say why?
This is the primordial power of statistical modeling.
4) Thanks;
I really wanna dedicate this to Charlotte de Witte & Marion Di Napoli, and their new track "Sanctum." It really gets you connected to the Source—I had it in my soul when I was doing all this ∞
Tensor Market Analysis Engine (TMAE)# Tensor Market Analysis Engine (TMAE)
## Advanced Multi-Dimensional Mathematical Analysis System
*Where Quantum Mathematics Meets Market Structure*
---
## 🎓 THEORETICAL FOUNDATION
The Tensor Market Analysis Engine represents a revolutionary synthesis of three cutting-edge mathematical frameworks that have never before been combined for comprehensive market analysis. This indicator transcends traditional technical analysis by implementing advanced mathematical concepts from quantum mechanics, information theory, and fractal geometry.
### 🌊 Multi-Dimensional Volatility with Jump Detection
**Hawkes Process Implementation:**
The TMAE employs a sophisticated Hawkes process approximation for detecting self-exciting market jumps. Unlike traditional volatility measures that treat price movements as independent events, the Hawkes process recognizes that market shocks cluster and exhibit memory effects.
**Mathematical Foundation:**
```
Intensity λ(t) = μ + Σ α(t - Tᵢ)
```
Where market jumps at times Tᵢ increase the probability of future jumps through the decay function α, controlled by the Hawkes Decay parameter (0.5-0.99).
**Mahalanobis Distance Calculation:**
The engine calculates volatility jumps using multi-dimensional Mahalanobis distance across up to 5 volatility dimensions:
- **Dimension 1:** Price volatility (standard deviation of returns)
- **Dimension 2:** Volume volatility (normalized volume fluctuations)
- **Dimension 3:** Range volatility (high-low spread variations)
- **Dimension 4:** Correlation volatility (price-volume relationship changes)
- **Dimension 5:** Microstructure volatility (intrabar positioning analysis)
This creates a volatility state vector that captures market behavior impossible to detect with traditional single-dimensional approaches.
### 📐 Hurst Exponent Regime Detection
**Fractal Market Hypothesis Integration:**
The TMAE implements advanced Rescaled Range (R/S) analysis to calculate the Hurst exponent in real-time, providing dynamic regime classification:
- **H > 0.6:** Trending (persistent) markets - momentum strategies optimal
- **H < 0.4:** Mean-reverting (anti-persistent) markets - contrarian strategies optimal
- **H ≈ 0.5:** Random walk markets - breakout strategies preferred
**Adaptive R/S Analysis:**
Unlike static implementations, the TMAE uses adaptive windowing that adjusts to market conditions:
```
H = log(R/S) / log(n)
```
Where R is the range of cumulative deviations and S is the standard deviation over period n.
**Dynamic Regime Classification:**
The system employs hysteresis to prevent regime flipping, requiring sustained Hurst values before regime changes are confirmed. This prevents false signals during transitional periods.
### 🔄 Transfer Entropy Analysis
**Information Flow Quantification:**
Transfer entropy measures the directional flow of information between price and volume, revealing lead-lag relationships that indicate future price movements:
```
TE(X→Y) = Σ p(yₜ₊₁, yₜ, xₜ) log
```
**Causality Detection:**
- **Volume → Price:** Indicates accumulation/distribution phases
- **Price → Volume:** Suggests retail participation or momentum chasing
- **Balanced Flow:** Market equilibrium or transition periods
The system analyzes multiple lag periods (2-20 bars) to capture both immediate and structural information flows.
---
## 🔧 COMPREHENSIVE INPUT SYSTEM
### Core Parameters Group
**Primary Analysis Window (10-100, Default: 50)**
The fundamental lookback period affecting all calculations. Optimization by timeframe:
- **1-5 minute charts:** 20-30 (rapid adaptation to micro-movements)
- **15 minute-1 hour:** 30-50 (balanced responsiveness and stability)
- **4 hour-daily:** 50-100 (smooth signals, reduced noise)
- **Asset-specific:** Cryptocurrency 20-35, Stocks 35-50, Forex 40-60
**Signal Sensitivity (0.1-2.0, Default: 0.7)**
Master control affecting all threshold calculations:
- **Conservative (0.3-0.6):** High-quality signals only, fewer false positives
- **Balanced (0.7-1.0):** Optimal risk-reward ratio for most trading styles
- **Aggressive (1.1-2.0):** Maximum signal frequency, requires careful filtering
**Signal Generation Mode:**
- **Aggressive:** Any component signals (highest frequency)
- **Confluence:** 2+ components agree (balanced approach)
- **Conservative:** All 3 components align (highest quality)
### Volatility Jump Detection Group
**Volatility Dimensions (2-5, Default: 3)**
Determines the mathematical space complexity:
- **2D:** Price + Volume volatility (suitable for clean markets)
- **3D:** + Range volatility (optimal for most conditions)
- **4D:** + Correlation volatility (advanced multi-asset analysis)
- **5D:** + Microstructure volatility (maximum sensitivity)
**Jump Detection Threshold (1.5-4.0σ, Default: 3.0σ)**
Standard deviations required for volatility jump classification:
- **Cryptocurrency:** 2.0-2.5σ (naturally volatile)
- **Stock Indices:** 2.5-3.0σ (moderate volatility)
- **Forex Major Pairs:** 3.0-3.5σ (typically stable)
- **Commodities:** 2.0-3.0σ (varies by commodity)
**Jump Clustering Decay (0.5-0.99, Default: 0.85)**
Hawkes process memory parameter:
- **0.5-0.7:** Fast decay (jumps treated as independent)
- **0.8-0.9:** Moderate clustering (realistic market behavior)
- **0.95-0.99:** Strong clustering (crisis/event-driven markets)
### Hurst Exponent Analysis Group
**Calculation Method Options:**
- **Classic R/S:** Original Rescaled Range (fast, simple)
- **Adaptive R/S:** Dynamic windowing (recommended for trading)
- **DFA:** Detrended Fluctuation Analysis (best for noisy data)
**Trending Threshold (0.55-0.8, Default: 0.60)**
Hurst value defining persistent market behavior:
- **0.55-0.60:** Weak trend persistence
- **0.65-0.70:** Clear trending behavior
- **0.75-0.80:** Strong momentum regimes
**Mean Reversion Threshold (0.2-0.45, Default: 0.40)**
Hurst value defining anti-persistent behavior:
- **0.35-0.45:** Weak mean reversion
- **0.25-0.35:** Clear ranging behavior
- **0.15-0.25:** Strong reversion tendency
### Transfer Entropy Parameters Group
**Information Flow Analysis:**
- **Price-Volume:** Classic flow analysis for accumulation/distribution
- **Price-Volatility:** Risk flow analysis for sentiment shifts
- **Multi-Timeframe:** Cross-timeframe causality detection
**Maximum Lag (2-20, Default: 5)**
Causality detection window:
- **2-5 bars:** Immediate causality (scalping)
- **5-10 bars:** Short-term flow (day trading)
- **10-20 bars:** Structural flow (swing trading)
**Significance Threshold (0.05-0.3, Default: 0.15)**
Minimum entropy for signal generation:
- **0.05-0.10:** Detect subtle information flows
- **0.10-0.20:** Clear causality only
- **0.20-0.30:** Very strong flows only
---
## 🎨 ADVANCED VISUAL SYSTEM
### Tensor Volatility Field Visualization
**Five-Layer Resonance Bands:**
The tensor field creates dynamic support/resistance zones that expand and contract based on mathematical field strength:
- **Core Layer (Purple):** Primary tensor field with highest intensity
- **Layer 2 (Neutral):** Secondary mathematical resonance
- **Layer 3 (Info Blue):** Tertiary harmonic frequencies
- **Layer 4 (Warning Gold):** Outer field boundaries
- **Layer 5 (Success Green):** Maximum field extension
**Field Strength Calculation:**
```
Field Strength = min(3.0, Mahalanobis Distance × Tensor Intensity)
```
The field amplitude adjusts to ATR and mathematical distance, creating dynamic zones that respond to market volatility.
**Radiation Line Network:**
During active tensor states, the system projects directional radiation lines showing field energy distribution:
- **8 Directional Rays:** Complete angular coverage
- **Tapering Segments:** Progressive transparency for natural visual flow
- **Pulse Effects:** Enhanced visualization during volatility jumps
### Dimensional Portal System
**Portal Mathematics:**
Dimensional portals visualize regime transitions using category theory principles:
- **Green Portals (◉):** Trending regime detection (appear below price for support)
- **Red Portals (◎):** Mean-reverting regime (appear above price for resistance)
- **Yellow Portals (○):** Random walk regime (neutral positioning)
**Tensor Trail Effects:**
Each portal generates 8 trailing particles showing mathematical momentum:
- **Large Particles (●):** Strong mathematical signal
- **Medium Particles (◦):** Moderate signal strength
- **Small Particles (·):** Weak signal continuation
- **Micro Particles (˙):** Signal dissipation
### Information Flow Streams
**Particle Stream Visualization:**
Transfer entropy creates flowing particle streams indicating information direction:
- **Upward Streams:** Volume leading price (accumulation phases)
- **Downward Streams:** Price leading volume (distribution phases)
- **Stream Density:** Proportional to information flow strength
**15-Particle Evolution:**
Each stream contains 15 particles with progressive sizing and transparency, creating natural flow visualization that makes information transfer immediately apparent.
### Fractal Matrix Grid System
**Multi-Timeframe Fractal Levels:**
The system calculates and displays fractal highs/lows across five Fibonacci periods:
- **8-Period:** Short-term fractal structure
- **13-Period:** Intermediate-term patterns
- **21-Period:** Primary swing levels
- **34-Period:** Major structural levels
- **55-Period:** Long-term fractal boundaries
**Triple-Layer Visualization:**
Each fractal level uses three-layer rendering:
- **Shadow Layer:** Widest, darkest foundation (width 5)
- **Glow Layer:** Medium white core line (width 3)
- **Tensor Layer:** Dotted mathematical overlay (width 1)
**Intelligent Labeling System:**
Smart spacing prevents label overlap using ATR-based minimum distances. Labels include:
- **Fractal Period:** Time-based identification
- **Topological Class:** Mathematical complexity rating (0, I, II, III)
- **Price Level:** Exact fractal price
- **Mahalanobis Distance:** Current mathematical field strength
- **Hurst Exponent:** Current regime classification
- **Anomaly Indicators:** Visual strength representations (○ ◐ ● ⚡)
### Wick Pressure Analysis
**Rejection Level Mathematics:**
The system analyzes candle wick patterns to project future pressure zones:
- **Upper Wick Analysis:** Identifies selling pressure and resistance zones
- **Lower Wick Analysis:** Identifies buying pressure and support zones
- **Pressure Projection:** Extends lines forward based on mathematical probability
**Multi-Layer Glow Effects:**
Wick pressure lines use progressive transparency (1-8 layers) creating natural glow effects that make pressure zones immediately visible without cluttering the chart.
### Enhanced Regime Background
**Dynamic Intensity Mapping:**
Background colors reflect mathematical regime strength:
- **Deep Transparency (98% alpha):** Subtle regime indication
- **Pulse Intensity:** Based on regime strength calculation
- **Color Coding:** Green (trending), Red (mean-reverting), Neutral (random)
**Smoothing Integration:**
Regime changes incorporate 10-bar smoothing to prevent background flicker while maintaining responsiveness to genuine regime shifts.
### Color Scheme System
**Six Professional Themes:**
- **Dark (Default):** Professional trading environment optimization
- **Light:** High ambient light conditions
- **Classic:** Traditional technical analysis appearance
- **Neon:** High-contrast visibility for active trading
- **Neutral:** Minimal distraction focus
- **Bright:** Maximum visibility for complex setups
Each theme maintains mathematical accuracy while optimizing visual clarity for different trading environments and personal preferences.
---
## 📊 INSTITUTIONAL-GRADE DASHBOARD
### Tensor Field Status Section
**Field Strength Display:**
Real-time Mahalanobis distance calculation with dynamic emoji indicators:
- **⚡ (Lightning):** Extreme field strength (>1.5× threshold)
- **● (Solid Circle):** Strong field activity (>1.0× threshold)
- **○ (Open Circle):** Normal field state
**Signal Quality Rating:**
Democratic algorithm assessment:
- **ELITE:** All 3 components aligned (highest probability)
- **STRONG:** 2 components aligned (good probability)
- **GOOD:** 1 component active (moderate probability)
- **WEAK:** No clear component signals
**Threshold and Anomaly Monitoring:**
- **Threshold Display:** Current mathematical threshold setting
- **Anomaly Level (0-100%):** Combined volatility and volume spike measurement
- **>70%:** High anomaly (red warning)
- **30-70%:** Moderate anomaly (orange caution)
- **<30%:** Normal conditions (green confirmation)
### Tensor State Analysis Section
**Mathematical State Classification:**
- **↑ BULL (Tensor State +1):** Trending regime with bullish bias
- **↓ BEAR (Tensor State -1):** Mean-reverting regime with bearish bias
- **◈ SUPER (Tensor State 0):** Random walk regime (neutral)
**Visual State Gauge:**
Five-circle progression showing tensor field polarity:
- **🟢🟢🟢⚪⚪:** Strong bullish mathematical alignment
- **⚪⚪🟡⚪⚪:** Neutral/transitional state
- **⚪⚪🔴🔴🔴:** Strong bearish mathematical alignment
**Trend Direction and Phase Analysis:**
- **📈 BULL / 📉 BEAR / ➡️ NEUTRAL:** Primary trend classification
- **🌪️ CHAOS:** Extreme information flow (>2.0 flow strength)
- **⚡ ACTIVE:** Strong information flow (1.0-2.0 flow strength)
- **😴 CALM:** Low information flow (<1.0 flow strength)
### Trading Signals Section
**Real-Time Signal Status:**
- **🟢 ACTIVE / ⚪ INACTIVE:** Long signal availability
- **🔴 ACTIVE / ⚪ INACTIVE:** Short signal availability
- **Components (X/3):** Active algorithmic components
- **Mode Display:** Current signal generation mode
**Signal Strength Visualization:**
Color-coded component count:
- **Green:** 3/3 components (maximum confidence)
- **Aqua:** 2/3 components (good confidence)
- **Orange:** 1/3 components (moderate confidence)
- **Gray:** 0/3 components (no signals)
### Performance Metrics Section
**Win Rate Monitoring:**
Estimated win rates based on signal quality with emoji indicators:
- **🔥 (Fire):** ≥60% estimated win rate
- **👍 (Thumbs Up):** 45-59% estimated win rate
- **⚠️ (Warning):** <45% estimated win rate
**Mathematical Metrics:**
- **Hurst Exponent:** Real-time fractal dimension (0.000-1.000)
- **Information Flow:** Volume/price leading indicators
- **📊 VOL:** Volume leading price (accumulation/distribution)
- **💰 PRICE:** Price leading volume (momentum/speculation)
- **➖ NONE:** Balanced information flow
- **Volatility Classification:**
- **🔥 HIGH:** Above 1.5× jump threshold
- **📊 NORM:** Normal volatility range
- **😴 LOW:** Below 0.5× jump threshold
### Market Structure Section (Large Dashboard)
**Regime Classification:**
- **📈 TREND:** Hurst >0.6, momentum strategies optimal
- **🔄 REVERT:** Hurst <0.4, contrarian strategies optimal
- **🎲 RANDOM:** Hurst ≈0.5, breakout strategies preferred
**Mathematical Field Analysis:**
- **Dimensions:** Current volatility space complexity (2D-5D)
- **Hawkes λ (Lambda):** Self-exciting jump intensity (0.00-1.00)
- **Jump Status:** 🚨 JUMP (active) / ✅ NORM (normal)
### Settings Summary Section (Large Dashboard)
**Active Configuration Display:**
- **Sensitivity:** Current master sensitivity setting
- **Lookback:** Primary analysis window
- **Theme:** Active color scheme
- **Method:** Hurst calculation method (Classic R/S, Adaptive R/S, DFA)
**Dashboard Sizing Options:**
- **Small:** Essential metrics only (mobile/small screens)
- **Normal:** Balanced information density (standard desktop)
- **Large:** Maximum detail (multi-monitor setups)
**Position Options:**
- **Top Right:** Standard placement (avoids price action)
- **Top Left:** Wide chart optimization
- **Bottom Right:** Recent price focus (scalping)
- **Bottom Left:** Maximum price visibility (swing trading)
---
## 🎯 SIGNAL GENERATION LOGIC
### Multi-Component Convergence System
**Component Signal Architecture:**
The TMAE generates signals through sophisticated component analysis rather than simple threshold crossing:
**Volatility Component:**
- **Jump Detection:** Mahalanobis distance threshold breach
- **Hawkes Intensity:** Self-exciting process activation (>0.2)
- **Multi-dimensional:** Considers all volatility dimensions simultaneously
**Hurst Regime Component:**
- **Trending Markets:** Price above SMA-20 with positive momentum
- **Mean-Reverting Markets:** Price at Bollinger Band extremes
- **Random Markets:** Bollinger squeeze breakouts with directional confirmation
**Transfer Entropy Component:**
- **Volume Leadership:** Information flow from volume to price
- **Volume Spike:** Volume 110%+ above 20-period average
- **Flow Significance:** Above entropy threshold with directional bias
### Democratic Signal Weighting
**Signal Mode Implementation:**
- **Aggressive Mode:** Any single component triggers signal
- **Confluence Mode:** Minimum 2 components must agree
- **Conservative Mode:** All 3 components must align
**Momentum Confirmation:**
All signals require momentum confirmation:
- **Long Signals:** RSI >50 AND price >EMA-9
- **Short Signals:** RSI <50 AND price 0.6):**
- **Increase Sensitivity:** Catch momentum continuation
- **Lower Mean Reversion Threshold:** Avoid counter-trend signals
- **Emphasize Volume Leadership:** Institutional accumulation/distribution
- **Tensor Field Focus:** Use expansion for trend continuation
- **Signal Mode:** Aggressive or Confluence for trend following
**Range-Bound Markets (Hurst <0.4):**
- **Decrease Sensitivity:** Avoid false breakouts
- **Lower Trending Threshold:** Quick regime recognition
- **Focus on Price Leadership:** Retail sentiment extremes
- **Fractal Grid Emphasis:** Support/resistance trading
- **Signal Mode:** Conservative for high-probability reversals
**Volatile Markets (High Jump Frequency):**
- **Increase Hawkes Decay:** Recognize event clustering
- **Higher Jump Threshold:** Avoid noise signals
- **Maximum Dimensions:** Capture full volatility complexity
- **Reduce Position Sizing:** Risk management adaptation
- **Enhanced Visuals:** Maximum information for rapid decisions
**Low Volatility Markets (Low Jump Frequency):**
- **Decrease Jump Threshold:** Capture subtle movements
- **Lower Hawkes Decay:** Treat moves as independent
- **Reduce Dimensions:** Simplify analysis
- **Increase Position Sizing:** Capitalize on compressed volatility
- **Minimal Visuals:** Reduce distraction in quiet markets
---
## 🚀 ADVANCED TRADING STRATEGIES
### The Mathematical Convergence Method
**Entry Protocol:**
1. **Fractal Grid Approach:** Monitor price approaching significant fractal levels
2. **Tensor Field Confirmation:** Verify field expansion supporting direction
3. **Portal Signal:** Wait for dimensional portal appearance
4. **ELITE/STRONG Quality:** Only trade highest quality mathematical signals
5. **Component Consensus:** Confirm 2+ components agree in Confluence mode
**Example Implementation:**
- Price approaching 21-period fractal high
- Tensor field expanding upward (bullish mathematical alignment)
- Green portal appears below price (trending regime confirmation)
- ELITE quality signal with 3/3 components active
- Enter long position with stop below fractal level
**Risk Management:**
- **Stop Placement:** Below/above fractal level that generated signal
- **Position Sizing:** Based on Mahalanobis distance (higher distance = smaller size)
- **Profit Targets:** Next fractal level or tensor field resistance
### The Regime Transition Strategy
**Regime Change Detection:**
1. **Monitor Hurst Exponent:** Watch for persistent moves above/below thresholds
2. **Portal Color Change:** Regime transitions show different portal colors
3. **Background Intensity:** Increasing regime background intensity
4. **Mathematical Confirmation:** Wait for regime confirmation (hysteresis)
**Trading Implementation:**
- **Trending Transitions:** Trade momentum breakouts, follow trend
- **Mean Reversion Transitions:** Trade range boundaries, fade extremes
- **Random Transitions:** Trade breakouts with tight stops
**Advanced Techniques:**
- **Multi-Timeframe:** Confirm regime on higher timeframe
- **Early Entry:** Enter on regime transition rather than confirmation
- **Regime Strength:** Larger positions during strong regime signals
### The Information Flow Momentum Strategy
**Flow Detection Protocol:**
1. **Monitor Transfer Entropy:** Watch for significant information flow shifts
2. **Volume Leadership:** Strong edge when volume leads price
3. **Flow Acceleration:** Increasing flow strength indicates momentum
4. **Directional Confirmation:** Ensure flow aligns with intended trade direction
**Entry Signals:**
- **Volume → Price Flow:** Enter during accumulation/distribution phases
- **Price → Volume Flow:** Enter on momentum confirmation breaks
- **Flow Reversal:** Counter-trend entries when flow reverses
**Optimization:**
- **Scalping:** Use immediate flow detection (2-5 bar lag)
- **Swing Trading:** Use structural flow (10-20 bar lag)
- **Multi-Asset:** Compare flow between correlated assets
### The Tensor Field Expansion Strategy
**Field Mathematics:**
The tensor field expansion indicates mathematical pressure building in market structure:
**Expansion Phases:**
1. **Compression:** Field contracts, volatility decreases
2. **Tension Building:** Mathematical pressure accumulates
3. **Expansion:** Field expands rapidly with directional movement
4. **Resolution:** Field stabilizes at new equilibrium
**Trading Applications:**
- **Compression Trading:** Prepare for breakout during field contraction
- **Expansion Following:** Trade direction of field expansion
- **Reversion Trading:** Fade extreme field expansion
- **Multi-Dimensional:** Consider all field layers for confirmation
### The Hawkes Process Event Strategy
**Self-Exciting Jump Trading:**
Understanding that market shocks cluster and create follow-on opportunities:
**Jump Sequence Analysis:**
1. **Initial Jump:** First volatility jump detected
2. **Clustering Phase:** Hawkes intensity remains elevated
3. **Follow-On Opportunities:** Additional jumps more likely
4. **Decay Period:** Intensity gradually decreases
**Implementation:**
- **Jump Confirmation:** Wait for mathematical jump confirmation
- **Direction Assessment:** Use other components for direction
- **Clustering Trades:** Trade subsequent moves during high intensity
- **Decay Exit:** Exit positions as Hawkes intensity decays
### The Fractal Confluence System
**Multi-Timeframe Fractal Analysis:**
Combining fractal levels across different periods for high-probability zones:
**Confluence Zones:**
- **Double Confluence:** 2 fractal levels align
- **Triple Confluence:** 3+ fractal levels cluster
- **Mathematical Confirmation:** Tensor field supports the level
- **Information Flow:** Transfer entropy confirms direction
**Trading Protocol:**
1. **Identify Confluence:** Find 2+ fractal levels within 1 ATR
2. **Mathematical Support:** Verify tensor field alignment
3. **Signal Quality:** Wait for STRONG or ELITE signal
4. **Risk Definition:** Use fractal level for stop placement
5. **Profit Targeting:** Next major fractal confluence zone
---
## ⚠️ COMPREHENSIVE RISK MANAGEMENT
### Mathematical Position Sizing
**Mahalanobis Distance Integration:**
Position size should inversely correlate with mathematical field strength:
```
Position Size = Base Size × (Threshold / Mahalanobis Distance)
```
**Risk Scaling Matrix:**
- **Low Field Strength (<2.0):** Standard position sizing
- **Moderate Field Strength (2.0-3.0):** 75% position sizing
- **High Field Strength (3.0-4.0):** 50% position sizing
- **Extreme Field Strength (>4.0):** 25% position sizing or no trade
### Signal Quality Risk Adjustment
**Quality-Based Position Sizing:**
- **ELITE Signals:** 100% of planned position size
- **STRONG Signals:** 75% of planned position size
- **GOOD Signals:** 50% of planned position size
- **WEAK Signals:** No position or paper trading only
**Component Agreement Scaling:**
- **3/3 Components:** Full position size
- **2/3 Components:** 75% position size
- **1/3 Components:** 50% position size or skip trade
### Regime-Adaptive Risk Management
**Trending Market Risk:**
- **Wider Stops:** Allow for trend continuation
- **Trend Following:** Trade with regime direction
- **Higher Position Size:** Trend probability advantage
- **Momentum Stops:** Trail stops based on momentum indicators
**Mean-Reverting Market Risk:**
- **Tighter Stops:** Quick exits on trend continuation
- **Contrarian Positioning:** Trade against extremes
- **Smaller Position Size:** Higher reversal failure rate
- **Level-Based Stops:** Use fractal levels for stops
**Random Market Risk:**
- **Breakout Focus:** Trade only clear breakouts
- **Tight Initial Stops:** Quick exit if breakout fails
- **Reduced Frequency:** Skip marginal setups
- **Range-Based Targets:** Profit targets at range boundaries
### Volatility-Adaptive Risk Controls
**High Volatility Periods:**
- **Reduced Position Size:** Account for wider price swings
- **Wider Stops:** Avoid noise-based exits
- **Lower Frequency:** Skip marginal setups
- **Faster Exits:** Take profits more quickly
**Low Volatility Periods:**
- **Standard Position Size:** Normal risk parameters
- **Tighter Stops:** Take advantage of compressed ranges
- **Higher Frequency:** Trade more setups
- **Extended Targets:** Allow for compressed volatility expansion
### Multi-Timeframe Risk Alignment
**Higher Timeframe Trend:**
- **With Trend:** Standard or increased position size
- **Against Trend:** Reduced position size or skip
- **Neutral Trend:** Standard position size with tight management
**Risk Hierarchy:**
1. **Primary:** Current timeframe signal quality
2. **Secondary:** Higher timeframe trend alignment
3. **Tertiary:** Mathematical field strength
4. **Quaternary:** Market regime classification
---
## 📚 EDUCATIONAL VALUE AND MATHEMATICAL CONCEPTS
### Advanced Mathematical Concepts
**Tensor Analysis in Markets:**
The TMAE introduces traders to tensor analysis, a branch of mathematics typically reserved for physics and advanced engineering. Tensors provide a framework for understanding multi-dimensional market relationships that scalar and vector analysis cannot capture.
**Information Theory Applications:**
Transfer entropy implementation teaches traders about information flow in markets, a concept from information theory that quantifies directional causality between variables. This provides intuition about market microstructure and participant behavior.
**Fractal Geometry in Trading:**
The Hurst exponent calculation exposes traders to fractal geometry concepts, helping understand that markets exhibit self-similar patterns across multiple timeframes. This mathematical insight transforms how traders view market structure.
**Stochastic Process Theory:**
The Hawkes process implementation introduces concepts from stochastic process theory, specifically self-exciting point processes. This provides mathematical framework for understanding why market events cluster and exhibit memory effects.
### Learning Progressive Complexity
**Beginner Mathematical Concepts:**
- **Volatility Dimensions:** Understanding multi-dimensional analysis
- **Regime Classification:** Learning market personality types
- **Signal Democracy:** Algorithmic consensus building
- **Visual Mathematics:** Interpreting mathematical concepts visually
**Intermediate Mathematical Applications:**
- **Mahalanobis Distance:** Statistical distance in multi-dimensional space
- **Rescaled Range Analysis:** Fractal dimension measurement
- **Information Entropy:** Quantifying uncertainty and causality
- **Field Theory:** Understanding mathematical fields in market context
**Advanced Mathematical Integration:**
- **Tensor Field Dynamics:** Multi-dimensional market force analysis
- **Stochastic Self-Excitation:** Event clustering and memory effects
- **Categorical Composition:** Mathematical signal combination theory
- **Topological Market Analysis:** Understanding market shape and connectivity
### Practical Mathematical Intuition
**Developing Market Mathematics Intuition:**
The TMAE serves as a bridge between abstract mathematical concepts and practical trading applications. Traders develop intuitive understanding of:
- **How markets exhibit mathematical structure beneath apparent randomness**
- **Why multi-dimensional analysis reveals patterns invisible to single-variable approaches**
- **How information flows through markets in measurable, predictable ways**
- **Why mathematical models provide probabilistic edges rather than certainties**
---
## 🔬 IMPLEMENTATION AND OPTIMIZATION
### Getting Started Protocol
**Phase 1: Observation (Week 1)**
1. **Apply with defaults:** Use standard settings on your primary trading timeframe
2. **Study visual elements:** Learn to interpret tensor fields, portals, and streams
3. **Monitor dashboard:** Observe how metrics change with market conditions
4. **No trading:** Focus entirely on pattern recognition and understanding
**Phase 2: Pattern Recognition (Week 2-3)**
1. **Identify signal patterns:** Note what market conditions produce different signal qualities
2. **Regime correlation:** Observe how Hurst regimes affect signal performance
3. **Visual confirmation:** Learn to read tensor field expansion and portal signals
4. **Component analysis:** Understand which components drive signals in different markets
**Phase 3: Parameter Optimization (Week 4-5)**
1. **Asset-specific tuning:** Adjust parameters for your specific trading instrument
2. **Timeframe optimization:** Fine-tune for your preferred trading timeframe
3. **Sensitivity adjustment:** Balance signal frequency with quality
4. **Visual customization:** Optimize colors and intensity for your trading environment
**Phase 4: Live Implementation (Week 6+)**
1. **Paper trading:** Test signals with hypothetical trades
2. **Small position sizing:** Begin with minimal risk during learning phase
3. **Performance tracking:** Monitor actual vs. expected signal performance
4. **Continuous optimization:** Refine settings based on real performance data
### Performance Monitoring System
**Signal Quality Tracking:**
- **ELITE Signal Win Rate:** Track highest quality signals separately
- **Component Performance:** Monitor which components provide best signals
- **Regime Performance:** Analyze performance across different market regimes
- **Timeframe Analysis:** Compare performance across different session times
**Mathematical Metric Correlation:**
- **Field Strength vs. Performance:** Higher field strength should correlate with better performance
- **Component Agreement vs. Win Rate:** More component agreement should improve win rates
- **Regime Alignment vs. Success:** Trading with mathematical regime should outperform
### Continuous Optimization Process
**Monthly Review Protocol:**
1. **Performance Analysis:** Review win rates, profit factors, and maximum drawdown
2. **Parameter Assessment:** Evaluate if current settings remain optimal
3. **Market Adaptation:** Adjust for changes in market character or volatility
4. **Component Weighting:** Consider if certain components should receive more/less emphasis
**Quarterly Deep Analysis:**
1. **Mathematical Model Validation:** Verify that mathematical relationships remain valid
2. **Regime Distribution:** Analyze time spent in different market regimes
3. **Signal Evolution:** Track how signal characteristics change over time
4. **Correlation Analysis:** Monitor correlations between different mathematical components
---
## 🌟 UNIQUE INNOVATIONS AND CONTRIBUTIONS
### Revolutionary Mathematical Integration
**First-Ever Implementations:**
1. **Multi-Dimensional Volatility Tensor:** First indicator to implement true tensor analysis for market volatility
2. **Real-Time Hawkes Process:** First trading implementation of self-exciting point processes
3. **Transfer Entropy Trading Signals:** First practical application of information theory for trade generation
4. **Democratic Component Voting:** First algorithmic consensus system for signal generation
5. **Fractal-Projected Signal Quality:** First system to predict signal quality at future price levels
### Advanced Visualization Innovations
**Mathematical Visualization Breakthroughs:**
- **Tensor Field Radiation:** Visual representation of mathematical field energy
- **Dimensional Portal System:** Category theory visualization for regime transitions
- **Information Flow Streams:** Real-time visual display of market information transfer
- **Multi-Layer Fractal Grid:** Intelligent spacing and projection system
- **Regime Intensity Mapping:** Dynamic background showing mathematical regime strength
### Practical Trading Innovations
**Trading System Advances:**
- **Quality-Weighted Signal Generation:** Signals rated by mathematical confidence
- **Regime-Adaptive Strategy Selection:** Automatic strategy optimization based on market personality
- **Anti-Spam Signal Protection:** Mathematical prevention of signal clustering
- **Component Performance Tracking:** Real-time monitoring of algorithmic component success
- **Field-Strength Position Sizing:** Mathematical volatility integration for risk management
---
## ⚖️ RESPONSIBLE USAGE AND LIMITATIONS
### Mathematical Model Limitations
**Understanding Model Boundaries:**
While the TMAE implements sophisticated mathematical concepts, traders must understand fundamental limitations:
- **Markets Are Not Purely Mathematical:** Human psychology, news events, and fundamental factors create unpredictable elements
- **Past Performance Limitations:** Mathematical relationships that worked historically may not persist indefinitely
- **Model Risk:** Complex models can fail during unprecedented market conditions
- **Overfitting Potential:** Highly optimized parameters may not generalize to future market conditions
### Proper Implementation Guidelines
**Risk Management Requirements:**
- **Never Risk More Than 2% Per Trade:** Regardless of signal quality
- **Diversification Mandatory:** Don't rely solely on mathematical signals
- **Position Sizing Discipline:** Use mathematical field strength for sizing, not confidence
- **Stop Loss Non-Negotiable:** Every trade must have predefined risk parameters
**Realistic Expectations:**
- **Mathematical Edge, Not Certainty:** The indicator provides probabilistic advantages, not guaranteed outcomes
- **Learning Curve Required:** Complex mathematical concepts require time to master
- **Market Adaptation Necessary:** Parameters must evolve with changing market conditions
- **Continuous Education Important:** Understanding underlying mathematics improves application
### Ethical Trading Considerations
**Market Impact Awareness:**
- **Information Asymmetry:** Advanced mathematical analysis may provide advantages over other market participants
- **Position Size Responsibility:** Large positions based on mathematical signals can impact market structure
- **Sharing Knowledge:** Consider educational contributions to trading community
- **Fair Market Participation:** Use mathematical advantages responsibly within market framework
### Professional Development Path
**Skill Development Sequence:**
1. **Basic Mathematical Literacy:** Understand fundamental concepts before advanced application
2. **Risk Management Mastery:** Develop disciplined risk control before relying on complex signals
3. **Market Psychology Understanding:** Combine mathematical analysis with behavioral market insights
4. **Continuous Learning:** Stay updated on mathematical finance developments and market evolution
---
## 🔮 CONCLUSION
The Tensor Market Analysis Engine represents a quantum leap forward in technical analysis, successfully bridging the gap between advanced pure mathematics and practical trading applications. By integrating multi-dimensional volatility analysis, fractal market theory, and information flow dynamics, the TMAE reveals market structure invisible to conventional analysis while maintaining visual clarity and practical usability.
### Mathematical Innovation Legacy
This indicator establishes new paradigms in technical analysis:
- **Tensor analysis for market volatility understanding**
- **Stochastic self-excitation for event clustering prediction**
- **Information theory for causality-based trade generation**
- **Democratic algorithmic consensus for signal quality enhancement**
- **Mathematical field visualization for intuitive market understanding**
### Practical Trading Revolution
Beyond mathematical innovation, the TMAE transforms practical trading:
- **Quality-rated signals replace binary buy/sell decisions**
- **Regime-adaptive strategies automatically optimize for market personality**
- **Multi-dimensional risk management integrates mathematical volatility measures**
- **Visual mathematical concepts make complex analysis immediately interpretable**
- **Educational value creates lasting improvement in trading understanding**
### Future-Proof Design
The mathematical foundations ensure lasting relevance:
- **Universal mathematical principles transcend market evolution**
- **Multi-dimensional analysis adapts to new market structures**
- **Regime detection automatically adjusts to changing market personalities**
- **Component democracy allows for future algorithmic additions**
- **Mathematical visualization scales with increasing market complexity**
### Commitment to Excellence
The TMAE represents more than an indicator—it embodies a philosophy of bringing rigorous mathematical analysis to trading while maintaining practical utility and visual elegance. Every component, from the multi-dimensional tensor fields to the democratic signal generation, reflects a commitment to mathematical accuracy, trading practicality, and educational value.
### Trading with Mathematical Precision
In an era where markets grow increasingly complex and computational, the TMAE provides traders with mathematical tools previously available only to institutional quantitative research teams. Yet unlike academic mathematical models, the TMAE translates complex concepts into intuitive visual representations and practical trading signals.
By combining the mathematical rigor of tensor analysis, the statistical power of multi-dimensional volatility modeling, and the information-theoretic insights of transfer entropy, traders gain unprecedented insight into market structure and dynamics.
### Final Perspective
Markets, like nature, exhibit profound mathematical beauty beneath apparent chaos. The Tensor Market Analysis Engine serves as a mathematical lens that reveals this hidden order, transforming how traders perceive and interact with market structure.
Through mathematical precision, visual elegance, and practical utility, the TMAE empowers traders to see beyond the noise and trade with the confidence that comes from understanding the mathematical principles governing market behavior.
Trade with mathematical insight. Trade with the power of tensors. Trade with the TMAE.
*"In mathematics, you don't understand things. You just get used to them." - John von Neumann*
*With the TMAE, mathematical market understanding becomes not just possible, but intuitive.*
— Dskyz, Trade with insight. Trade with anticipation.
Gold Opening 15-Min ORB INDICATOR by AdéThis indicator is designed for trading Gold (XAUUSD) during the first 15 minutes of major market openings: Asian, European, and US sessions. It highlights these key time windows, plots the high and low ranges of each session, and generates breakout-based buy/sell signals. Ideal for traders focusing on volatility at market opens.
Features:Session Windows:
Asian: 1:00–1:15 AM Barcelona time (23:00–23:15 UTC, CEST-adjusted).
European: 9:00–9:15 AM Barcelona time (07:00–07:15 UTC).
US: 3:30–3:45 PM Barcelona time (13:30–13:45 UTC).
Marked with yellow (Asian), green (Europe), and blue (US) triangles below bars.
High/Low Ranges:Plots horizontal lines showing the highest high and lowest low of each session’s first 15 minutes.Lines appear after each session ends and persist until the next day, color-coded to match the sessions.Breakout Signals:Buy (Long): Triggers when the closing price breaks above the highest high of the previous 5 bars during a session window (lime triangle above bar).Sell (Short): Triggers when the closing price breaks below the lowest low of the previous 5 bars during a session window (red triangle below bar).
Signals are restricted to the 15-minute session periods for focused trading.Usage:Timeframe: Optimized for 1-minute XAUUSD charts.Timezone: Set your chart to UTC for accurate session timing (script uses UTC internally, based on Barcelona CEST, UTC+2 in April).Strategy:
Use buy/sell signals for breakout trades during volatile market opens, with session ranges as support/resistance levels.Customization: Adjust the lookback variable (default: 5) to tweak signal sensitivity.Notes:Tested for April 2025 (CEST, UTC+2).
Adjust timestamp values if using outside daylight saving time (CET, UTC+1) or for different broker timezones.Best for scalping or short-term trades during high-volatility periods. Combine with other indicators for confirmation if desired.How to Use:Apply to a 1-minute XAUUSD chart.Watch for session markers (triangles) and breakout signals during the 15-minute windows.Use the high/low lines to gauge potential breakout targets or reversals.
ICT Macro Zone Boxes w/ Individual H/L Tracking v3.1ICT Macro Zones (Grey Box Version
This indicator dynamically highlights key intraday time-based macro sessions using a clean, minimalistic grey box overlay, helping traders align with institutional trading cycles. Inspired by ICT (Inner Circle Trader) concepts, it tracks real-time highs and lows for each session and optionally extends the zone box after the session ends — making it a precision tool for intraday setups, order flow analysis, and macro-level liquidity sweeps.
### 🔍 **What It Does**
- Plots **six predefined macro sessions** used in Smart Money Concepts:
- AM Macro (09:50–10:10)
- London Close (10:50–11:10)
- Lunch Macro (11:30–13:30)
- PM Macro (14:50–15:10)
- London SB (03:00–04:00)
- PM SB (15:00–16:00)
- Each zone:
- **Tracks high and low dynamically** throughout the session.
- **Draws a consistent grey shaded box** to visualize price boundaries.
- **Displays a label** at the first bar of the session (optional).
- **Optionally extends** the box to the right after the session closes.
### 🧠 **How It Works**
- Uses Pine Script arrays to define each session’s time window, label, and color.
- Detects session entry using `time()` within a New York timezone context.
- High/Low values are updated per bar inside the session window.
- Once a session ends, the box is optionally closed and fixed in place.
- All visual zones use a standardized grey tone for clarity and consistency across charts.
### 🛠️ **Settings**
- **Shade Zone High→Low:** Enable/disable the grey macro box.
- **Extend Box After Session:** Keep the zone visible after it ends.
- **Show Entry Label:** Display a label at the start of each session.
### 🎯 **Why This Script is Unique**
Unlike basic session markers or colored backgrounds, this tool:
- Focuses on **macro moments of liquidity and reversal**, not just open/close times.
- Uses **per-session logic** to individually track price behavior inside key time windows.
- Supports **real-time high/low tracking and clean zone drawing**, ideal for Smart Money and ICT-style strategies.
Perfect — based on your list, here's a **bundle-style description** that not only explains the function of each script but also shows how they **work together** in a Smart Money/ICT workflow. This kind of cross-script explanation is exactly what TradingView wants to see to justify closed-source mashups or interdependent tools.
---
📚 ICT SMC Toolkit — Script Integration Guide
This set of advanced Smart Money Concept (SMC) tools is designed for traders who follow ICT-based methodologies, combining liquidity theory, time-based precision, and engineered confluences for high-probability trades. Each indicator is optimized to work both independently and synergistically, forming a comprehensive trading framework.
---
First FVG Custom Time Range
**Purpose:**
Plots the **first Fair Value Gap (FVG)** that appears within a defined session (e.g., NY Kill Zone, Custom range). Includes optional retest alerts.
**Best Used With:**
- Use with **ICT Macro Zones (Grey Box Version)** to isolate FVGs during high-probability times like AM Macro or PM SB.
- Combine with **Liquidity Levels** to assess whether FVGs form near swing points or liquidity voids.
---
ICT SMC Liquidity Grabs and OB s
**Purpose:**
Detects **liquidity grabs** (stop hunts above/below swing highs/lows) and **bullish/bearish order blocks**. Includes optional Fibonacci OTE levels for sniper entries.
**Best Used With:**
- Use with **ICT Turtle Soup (Reversal)** for confirmation after a liquidity grab.
- Combine with **Macro Zones** to catch order blocks forming inside timed macro windows.
- Match with **Smart Swing Levels** to confirm structure breaks before entry.
ICT SMC Liquidity Levels (Smart Swing Lows)
**Purpose:**
Automatically marks swing highs/lows based on user-defined lookbacks. Tracks whether those levels have been breached or respected.
**Best Used With:**
- Combine with **Turtle Soup** to detect if a swing level was swept, then reversed.
- Use with **Liquidity Grabs** to confirm a grab occurred at a meaningful structural point.
- Align with **Macro Zones** to understand when liquidity events occur within macro session timing.
ICT Turtle Soup (Liquidity Reversal)
**Purpose:**
Implements the classic ICT Turtle Soup model. Looks for swing failure and quick reversals after a liquidity sweep — ideal for catching traps.
Best Used With:
- Confirm with **Liquidity Grabs + OBs** to identify institutional activity at the reversal point.
- Use **Liquidity Levels** to ensure the reversal is happening at valid previous swing highs/lows.
- Amplify probability when pattern appears during **Macro Zones** or near the **First FVG**.
ICT Turtle Soup Ultimate V2
**Purpose:**
An enhanced, multi-layer version of the Turtle Soup setup that includes built-in liquidity checks, OTE levels, structure validation, and customizable visual output.
**Best Used With:**
- Use as an **entry signal generator** when other indicators (e.g., OBs, liquidity grabs) are aligned.
- Pair with **Macro Zones** for high-precision timing.
- Combine with **First FVG** to anticipate price rebalancing before explosive moves.
---
## 🧠 Workflow Example:
1. **Start with Macro Zones** to focus only on institutional trading windows.
2. Look for **Liquidity Grabs or Swing Sweeps** around key highs/lows.
3. Check for a **Turtle Soup Reversal** or **Order Block Reaction** near that level.
4. Confirm confluence with a **Fair Value Gap**.
5. Execute using the **OTE level** from the Liquidity Grabs + OB script.
---
Let me know which script you want to publish first — I’ll tailor its **individual TradingView description** and flag its ideal **“Best Used With” partners** to help users see the value in your ecosystem.
Bullish Reversal Bar Strategy [Skyrexio]Overview
Bullish Reversal Bar Strategy leverages the combination of candlestick pattern Bullish Reversal Bar (description in Methodology and Justification of Methodology), Williams Alligator indicator and Williams Fractals to create the high probability setups. Candlestick pattern is used for the entering into trade, while the combination of Williams Alligator and Fractals is used for the trend approximation as close condition. Strategy uses only long trades.
Unique Features
No fixed stop-loss and take profit: Instead of fixed stop-loss level strategy utilizes technical condition obtained by Fractals and Alligator or the candlestick pattern invalidation to identify when current uptrend is likely to be over (more information in "Methodology" and "Justification of Methodology" paragraphs)
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Trend Trade Filter: strategy uses Alligator and Fractal combination as high probability trend filter.
Methodology
The strategy opens long trade when the following price met the conditions:
1.Current candle's high shall be below the Williams Alligator's lines (Jaw, Lips, Teeth)(all details in "Justification of Methodology" paragraph)
2.Price shall create the candlestick pattern "Bullish Reversal Bar". Optionally if MFI and AO filters are enabled current candle shall have the decreasing AO and at least one of three recent bars shall have the squat state on the MFI (all details in "Justification of Methodology" paragraph)
3.If price breaks through the high of the candle marked as the "Bullish Reversal Bar" the long trade is open at the price one tick above the candle's high
4.Initial stop loss is placed at the Bullish Reversal Bar's candle's low
5.If price hit the Bullish Reversal Bar's low before hitting the entry price potential trade is cancelled
6.If trade is active and initial stop loss has not been hit, trade is closed when the combination of Alligator and Williams Fractals shall consider current trend change from upward to downward.
Strategy settings
In the inputs window user can setup strategy setting:
Enable MFI (if true trades are filtered using Market Facilitation Index (MFI) condition all details in "Justification of Methodology" paragraph), by default = false)
Enable AO (if true trades are filtered using Awesome Oscillator (AO) condition all details in "Justification of Methodology" paragraph), by default = false)
Justification of Methodology
Let's explore the key concepts of this strategy and understand how they work together. The first and key concept is the Bullish Reversal Bar candlestick pattern. This is just the single bar pattern. The rules are simple:
Candle shall be closed in it's upper half
High of this candle shall be below all three Alligator's lines (Jaw, Lips, Teeth)
Next, let’s discuss the short-term trend filter, which combines the Williams Alligator and Williams Fractals. Williams Alligator
Developed by Bill Williams, the Alligator is a technical indicator that identifies trends and potential market reversals. It consists of three smoothed moving averages:
Jaw (Blue Line): The slowest of the three, based on a 13-period smoothed moving average shifted 8 bars ahead.
Teeth (Red Line): The medium-speed line, derived from an 8-period smoothed moving average shifted 5 bars forward.
Lips (Green Line): The fastest line, calculated using a 5-period smoothed moving average shifted 3 bars forward.
When the lines diverge and align in order, the "Alligator" is "awake," signaling a strong trend. When the lines overlap or intertwine, the "Alligator" is "asleep," indicating a range-bound or sideways market. This indicator helps traders determine when to enter or avoid trades.
Fractals, another tool by Bill Williams, help identify potential reversal points on a price chart. A fractal forms over at least five consecutive bars, with the middle bar showing either:
Up Fractal: Occurs when the middle bar has a higher high than the two preceding and two following bars, suggesting a potential downward reversal.
Down Fractal: Happens when the middle bar shows a lower low than the surrounding two bars, hinting at a possible upward reversal.
Traders often use fractals alongside other indicators to confirm trends or reversals, enhancing decision-making accuracy.
How do these tools work together in this strategy? Let’s consider an example of an uptrend.
When the price breaks above an up fractal, it signals a potential bullish trend. This occurs because the up fractal represents a shift in market behavior, where a temporary high was formed due to selling pressure. If the price revisits this level and breaks through, it suggests the market sentiment has turned bullish.
The breakout must occur above the Alligator’s teeth line to confirm the trend. A breakout below the teeth is considered invalid, and the downtrend might still persist. Conversely, in a downtrend, the same logic applies with down fractals.
How we can use all these indicators in this strategy? This strategy is a counter trend one. Candle's high shall be below all Alligator's lines. During this market stage the bullish reversal bar candlestick pattern shall be printed. This bar during the downtrend is a high probability setup for the potential reversal to the upside: bulls were able to close the price in the upper half of a candle. The breaking of its high is a high probability signal that trend change is confirmed and script opens long trade. If market continues going down and break down the bullish reversal bar's low potential trend change has been invalidated and strategy close long trade.
If market really reversed and started moving to the upside strategy waits for the trend change form the downtrend to the uptrend according to approximation of Alligator and Fractals combination. If this change happens strategy close the trade. This approach helps to stay in the long trade while the uptrend continuation is likely and close it if there is a high probability of the uptrend finish.
Optionally users can enable MFI and AO filters. First of all, let's briefly explain what are these two indicators. The Awesome Oscillator (AO), created by Bill Williams, is a momentum-based indicator that evaluates market momentum by comparing recent price activity to a broader historical context. It assists traders in identifying potential trend reversals and gauging trend strength.
AO = SMA5(Median Price) − SMA34(Median Price)
where:
Median Price = (High + Low) / 2
SMA5 = 5-period Simple Moving Average of the Median Price
SMA 34 = 34-period Simple Moving Average of the Median Price
This indicator is filtering signals in the following way: if current AO bar is decreasing this candle can be interpreted as a bullish reversal bar. This logic is applicable because initially this strategy is a trend reversal, it is searching for the high probability setup against the current trend. Decreasing AO is the additional high probability filter of a downtrend.
Let's briefly look what is MFI. The Market Facilitation Index (MFI) is a technical indicator that measures the price movement per unit of volume, helping traders gauge the efficiency of price movement in relation to trading volume. Here's how you can calculate it:
MFI = (High−Low)/Volume
MFI can be used in combination with volume, so we can divide 4 states. Bill Williams introduced these to help traders interpret the interaction between volume and price movement. Here’s a quick summary:
Green Window (Increased MFI & Increased Volume): Indicates strong momentum with both price and volume increasing. Often a sign of trend continuation, as both buying and selling interest are rising.
Fake Window (Increased MFI & Decreased Volume): Shows that price is moving but with lower volume, suggesting weak support for the trend. This can signal a potential end of the current trend.
Squat Window (Decreased MFI & Increased Volume): Shows high volume but little price movement, indicating a tug-of-war between buyers and sellers. This often precedes a breakout as the pressure builds.
Fade Window (Decreased MFI & Decreased Volume): Indicates a lack of interest from both buyers and sellers, leading to lower momentum. This typically happens in range-bound markets and may signal consolidation before a new move.
For our purposes we are interested in squat bars. This is the sign that volume cannot move the price easily. This type of bar increases the probability of trend reversal. In this indicator we added to enable the MFI filter of reversal bars. If potential reversal bar or two preceding bars have squat state this bar can be interpret as a reversal one.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2024.12.31. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 50%
Maximum Single Position Loss: -5.29%
Maximum Single Profit: +29.99%
Net Profit: +5472.66 USDT (+54.73%)
Total Trades: 103 (33.98% win rate)
Profit Factor: 1.634
Maximum Accumulated Loss: 1231.15 USDT (-8.32%)
Average Profit per Trade: 53.13 USDT (+0.94%)
Average Trade Duration: 76 hours
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 4h ETH/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
(MA-EWMA) with ChannelsHamming Windowed Volume-Weighted Bidirectional Momentum-Adaptive Exponential Weighted Moving Average
This script is an advanced financial indicator that calculates a Hamming Windowed Volume-Weighted Bidirectional Momentum-Adaptive Exponential Weighted Moving Average (MA-EWMA). It adapts dynamically to market conditions, adjusting key parameters like lookback period, momentum length, and volatility sensitivity based on price volatility.
Key Components:
Dynamic Adjustments: The indicator adjusts its lookback and momentum length using the ATR (Average True Range), making it more responsive to volatile markets.
Volume Weighting: It incorporates volume data, weighting the moving average based on the volume activity, adding further sensitivity to price movement.
Bidirectional Momentum: It calculates upward and downward momentum separately, using these values to determine the directional weighting of the moving average.
Hamming Window: This technique smooths the price data by applying a Hamming window, which helps to reduce noise in the data and enhances the accuracy of the moving average.
Channels: Instead of plotting a single line, the script creates dynamic channels, providing more context for support and resistance levels based on the market's behavior.
The result is a highly adaptive and sophisticated moving average indicator that responds dynamically to both price momentum and volume trends.
chrono_utilsLibrary "chrono_utils"
Collection of objects and common functions that are related to datetime windows session days and time
ranges. The main purpose of this library is to handle time-related functionality and make it easy to reason about a
future bar checking if it will be part of a predefined session and/or inside a datetime window. All existing session
functionality I found in the documentation e.g. "not na(time(timeframe, session, timezone))" are not suitable for
strategy scripts, since the execution of the orders is delayed by one bar, due to the script execution happening at
the bar close. Moreover, a history operator with a negative value that looks forward is not allowed in any pinescript
expression. So, a prediction for the next bar using the bars_back argument of "time()"" and "time_close()" was
necessary. Thus, I created this library to overcome this small but very important limitation. In the meantime, I
added useful functionality to handle session-based behavior. An interesting utility that emerged from this
development is the data anomaly detection where a comparison between the prediction and the actual value is happening.
If those two values are different then a data inconsistency happened between the prediction bar and the actual bar
(probably due to a holiday, half session day, a timezone change etc..)
exTimezone(timezone)
exTimezone - Convert extended timezone to timezone string
Parameters:
timezone (simple string) : - The timezone or a special string
Returns: string representing the timezone
nameOfDay(day)
nameOfDay - Convert the day id into a short nameOfDay
Parameters:
day (int) : - The day id to convert
Returns: - The short name of the day
today()
today - Get the day id of this day
Returns: - The day id
nthDayAfter(day, n)
nthDayAfter - Get the day id of n days after the given day
Parameters:
day (int) : - The day id of the reference day
n (int) : - The number of days to go forward
Returns: - The day id of the day that is n days after the reference day
nextDayAfter(day)
nextDayAfter - Get the day id of next day after the given day
Parameters:
day (int) : - The day id of the reference day
Returns: - The day id of the next day after the reference day
nthDayBefore(day, n)
nthDayBefore - Get the day id of n days before the given day
Parameters:
day (int) : - The day id of the reference day
n (int) : - The number of days to go forward
Returns: - The day id of the day that is n days before the reference day
prevDayBefore(day)
prevDayBefore - Get the day id of previous day before the given day
Parameters:
day (int) : - The day id of the reference day
Returns: - The day id of the previous day before the reference day
tomorrow()
tomorrow - Get the day id of the next day
Returns: - The next day day id
normalize(num, min, max)
normalizeHour - Check if number is inthe range of
Parameters:
num (int)
min (int)
max (int)
Returns: - The normalized number
normalizeHour(hourInDay)
normalizeHour - Check if hour is valid and return a noralized hour range from
Parameters:
hourInDay (int)
Returns: - The normalized hour
normalizeMinute(minuteInHour)
normalizeMinute - Check if minute is valid and return a noralized minute from
Parameters:
minuteInHour (int)
Returns: - The normalized minute
monthInMilliseconds(mon)
monthInMilliseconds - Calculate the miliseconds in one bar of the timeframe
Parameters:
mon (int) : - The month of reference to get the miliseconds
Returns: - The number of milliseconds of the month
barInMilliseconds()
barInMilliseconds - Calculate the miliseconds in one bar of the timeframe
Returns: - The number of milliseconds in one bar
method to_string(this)
to_string - Formats the time window into a human-readable string
Namespace types: DateTimeWindow
Parameters:
this (DateTimeWindow) : - The time window object with the from and to datetimes
Returns: - The string of the time window
method to_string(this)
to_string - Formats the session days into a human-readable string with short day names
Namespace types: SessionDays
Parameters:
this (SessionDays) : - The session days object with the day selection
Returns: - The string of the session day short names
method to_string(this)
to_string - Formats the session time into a human-readable string
Namespace types: SessionTime
Parameters:
this (SessionTime) : - The session time object with the hour and minute of the time of the day
Returns: - The string of the session time
method to_string(this)
to_string - Formats the session time into a human-readable string
Namespace types: SessionTimeRange
Parameters:
this (SessionTimeRange) : - The session time range object with the start and end time of the daily session
Returns: - The string of the session time
method to_string(this)
to_string - Formats the session into a human-readable string
Namespace types: Session
Parameters:
this (Session) : - The session object with the day and the time range selection
Returns: - The string of the session
method init(this, fromDateTime, toDateTime)
init - Initialize the time window object from boolean values of each session day
Namespace types: DateTimeWindow
Parameters:
this (DateTimeWindow) : - The time window object that will hold the from and to datetimes
fromDateTime (int) : - The starting datetime of the time window
toDateTime (int) : - The ending datetime of the time window
Returns: - The time window object
method init(this, refTimezone, chTimezone, fromDateTime, toDateTime)
init - Initialize the time window object from boolean values of each session day
Namespace types: DateTimeWindow
Parameters:
this (DateTimeWindow) : - The time window object that will hold the from and to datetimes
refTimezone (simple string) : - The timezone of reference of the 'from' and 'to' dates
chTimezone (simple string) : - The target timezone to convert the 'from' and 'to' dates
fromDateTime (int) : - The starting datetime of the time window
toDateTime (int) : - The ending datetime of the time window
Returns: - The time window object
method init(this, sun, mon, tue, wed, thu, fri, sat)
init - Initialize the session days object from boolean values of each session day
Namespace types: SessionDays
Parameters:
this (SessionDays) : - The session days object that will hold the day selection
sun (bool) : - Is Sunday a trading day?
mon (bool) : - Is Monday a trading day?
tue (bool) : - Is Tuesday a trading day?
wed (bool) : - Is Wednesday a trading day?
thu (bool) : - Is Thursday a trading day?
fri (bool) : - Is Friday a trading day?
sat (bool) : - Is Saturday a trading day?
Returns: - The session days object
method init(this, unixTime)
init - Initialize the object from the hour and minute of the session time in exchange timezone (syminfo.timezone)
Namespace types: SessionTime
Parameters:
this (SessionTime) : - The session time object with the hour and minute of the time of the day
unixTime (int) : - The unix time
Returns: - The session time object
method init(this, hourInDay, minuteInHour)
init - Initialize the object from the hour and minute of the session time in exchange timezone (syminfo.timezone)
Namespace types: SessionTime
Parameters:
this (SessionTime) : - The session time object with the hour and minute of the time of the day
hourInDay (int) : - The hour of the time
minuteInHour (int) : - The minute of the time
Returns: - The session time object
method init(this, hourInDay, minuteInHour, refTimezone)
init - Initialize the object from the hour and minute of the session time
Namespace types: SessionTime
Parameters:
this (SessionTime) : - The session time object with the hour and minute of the time of the day
hourInDay (int) : - The hour of the time
minuteInHour (int) : - The minute of the time
refTimezone (string) : - The timezone of reference of the 'hour' and 'minute'
Returns: - The session time object
method init(this, startTime, endTime)
init - Initialize the object from the start and end session time in exchange timezone (syminfo.timezone)
Namespace types: SessionTimeRange
Parameters:
this (SessionTimeRange) : - The session time range object that will hold the start and end time of the daily session
startTime (SessionTime) : - The time the session begins
endTime (SessionTime) : - The time the session ends
Returns: - The session time range object
method init(this, startTimeHour, startTimeMinute, endTimeHour, endTimeMinute, refTimezone)
init - Initialize the object from the start and end session time
Namespace types: SessionTimeRange
Parameters:
this (SessionTimeRange) : - The session time range object that will hold the start and end time of the daily session
startTimeHour (int) : - The time hour the session begins
startTimeMinute (int) : - The time minute the session begins
endTimeHour (int) : - The time hour the session ends
endTimeMinute (int) : - The time minute the session ends
refTimezone (string)
Returns: - The session time range object
method init(this, days, timeRanges)
init - Initialize the session object from session days and time range
Namespace types: Session
Parameters:
this (Session) : - The session object that will hold the day and the time range selection
days (SessionDays) : - The session days object that defines the days the session is happening
timeRanges (array) : - The array of all the session time ranges during a session day
Returns: - The session object
method init(this, days, timeRanges, names, colors)
init - Initialize the session object from session days and time range
Namespace types: SessionView
Parameters:
this (SessionView) : - The session view object that will hold the session, the names and the color selections
days (SessionDays) : - The session days object that defines the days the session is happening
timeRanges (array) : - The array of all the session time ranges during a session day
names (array) : - The array of the names of the sessions
colors (array) : - The array of the colors of the sessions
Returns: - The session object
method get_size_in_secs(this)
get_size_in_secs - Count the seconds from start to end in the given timeframe
Namespace types: DateTimeWindow
Parameters:
this (DateTimeWindow) : - The time window object with the from and to datetimes
Returns: - The number of seconds inside the time widow for the given timeframe
method get_size_in_secs(this)
get_size_in_secs - Calculate the seconds inside the session
Namespace types: SessionTimeRange
Parameters:
this (SessionTimeRange) : - The session time range object with the start and end time of the daily session
Returns: - The number of seconds inside the session
method get_size_in_bars(this)
get_size_in_bars - Count the bars from start to end in the given timeframe
Namespace types: DateTimeWindow
Parameters:
this (DateTimeWindow) : - The time window object with the from and to datetimes
Returns: - The number of bars inside the time widow for the given timeframe
method get_size_in_bars(this)
get_size_in_bars - Calculate the bars inside the session
Namespace types: SessionTimeRange
Parameters:
this (SessionTimeRange) : - The session time range object with the start and end time of the daily session
Returns: - The number of bars inside the session for the given timeframe
method is_bar_included(this, offset_forward)
is_bar_included - Check if the given bar is between the start and end dates of the window
Namespace types: DateTimeWindow
Parameters:
this (DateTimeWindow) : - The time window object with the from and to datetimes
offset_forward (simple int) : - The number of bars forward. Default is 1
Returns: - Whether the current bar is inside the datetime window
method is_bar_included(this, offset_forward)
is_bar_included - Check if the given bar is inside the session as defined by the input params (what "not na(time(timeframe.period, this.to_sess_string()) )" should return if you could write it
Namespace types: Session
Parameters:
this (Session) : - The session with the day and the time range selection
offset_forward (simple int) : - The bar forward to check if it is between the from and to datetimes. Default is 1
Returns: - Whether the current time is inside the session
method to_sess_string(this)
to_sess_string - Formats the session days into a session string with day ids
Namespace types: SessionDays
Parameters:
this (SessionDays) : - The session days object
Returns: - The string of the session day ids
method to_sess_string(this)
to_sess_string - Formats the session time into a session string
Namespace types: SessionTime
Parameters:
this (SessionTime) : - The session time object with the hour and minute of the time of the day
Returns: - The string of the session time
method to_sess_string(this)
to_sess_string - Formats the session time into a session string
Namespace types: SessionTimeRange
Parameters:
this (SessionTimeRange) : - The session time range object with the start and end time of the daily session
Returns: - The string of the session time
method to_sess_string(this)
to_sess_string - Formats the session into a session string
Namespace types: Session
Parameters:
this (Session) : - The session object with the day and the time range selection
Returns: - The string of the session
method from_sess_string(this, sess)
from_sess_string - Initialize the session days object from the session string
Namespace types: SessionDays
Parameters:
this (SessionDays) : - The session days object that will hold the day selection
sess (string) : - The session string part that represents the days
Returns: - The session days object
method from_sess_string(this, sess)
from_sess_string - Initialize the session time object from the session string in exchange timezone (syminfo.timezone)
Namespace types: SessionTime
Parameters:
this (SessionTime) : - The session time object that will hold the hour and minute of the time
sess (string) : - The session string part that represents the time HHmm
Returns: - The session time object
method from_sess_string(this, sess, refTimezone)
from_sess_string - Initialize the session time object from the session string
Namespace types: SessionTime
Parameters:
this (SessionTime) : - The session time object that will hold the hour and minute of the time
sess (string) : - The session string part that represents the time HHmm
refTimezone (simple string) : - The timezone of reference of the 'hour' and 'minute'
Returns: - The session time object
method from_sess_string(this, sess)
from_sess_string - Initialize the session time range object from the session string in exchange timezone (syminfo.timezone)
Namespace types: SessionTimeRange
Parameters:
this (SessionTimeRange) : - The session time range object that will hold the start and end time of the daily session
sess (string) : - The session string part that represents the time range HHmm-HHmm
Returns: - The session time range object
method from_sess_string(this, sess, refTimezone)
from_sess_string - Initialize the session time range object from the session string
Namespace types: SessionTimeRange
Parameters:
this (SessionTimeRange) : - The session time range object that will hold the start and end time of the daily session
sess (string) : - The session string part that represents the time range HHmm-HHmm
refTimezone (simple string) : - The timezone of reference of the time ranges
Returns: - The session time range object
method from_sess_string(this, sess)
from_sess_string - Initialize the session object from the session string in exchange timezone (syminfo.timezone)
Namespace types: Session
Parameters:
this (Session) : - The session object that will hold the day and the time range selection
sess (string) : - The session string that represents the session HHmm-HHmm,HHmm-HHmm:ddddddd
Returns: - The session time range object
method from_sess_string(this, sess, refTimezone)
from_sess_string - Initialize the session object from the session string
Namespace types: Session
Parameters:
this (Session) : - The session object that will hold the day and the time range selection
sess (string) : - The session string that represents the session HHmm-HHmm,HHmm-HHmm:ddddddd
refTimezone (simple string) : - The timezone of reference of the time ranges
Returns: - The session time range object
method nth_day_after(this, day, n)
nth_day_after - The nth day after the given day that is a session day (true) in the object
Namespace types: SessionDays
Parameters:
this (SessionDays) : - The session days object with the day selection
day (int) : - The day id of the reference day
n (int) : - The number of days after
Returns: - The day id of the nth session day of the week after the given day
method nth_day_before(this, day, n)
nth_day_before - The nth day before the given day that is a session day (true) in the object
Namespace types: SessionDays
Parameters:
this (SessionDays) : - The session days object with the day selection
day (int) : - The day id of the reference day
n (int) : - The number of days after
Returns: - The day id of the nth session day of the week before the given day
method next_day(this)
next_day - The next day that is a session day (true) in the object
Namespace types: SessionDays
Parameters:
this (SessionDays) : - The session days object with the day selection
Returns: - The day id of the next session day of the week
method previous_day(this)
previous_day - The previous day that is session day (true) in the object
Namespace types: SessionDays
Parameters:
this (SessionDays) : - The session days object with the day selection
Returns: - The day id of the previous session day of the week
method get_sec_in_day(this)
get_sec_in_day - Count the seconds since the start of the day this session time represents
Namespace types: SessionTime
Parameters:
this (SessionTime) : - The session time object with the hour and minute of the time of the day
Returns: - The number of seconds passed from the start of the day until that session time
method get_ms_in_day(this)
get_ms_in_day - Count the milliseconds since the start of the day this session time represents
Namespace types: SessionTime
Parameters:
this (SessionTime) : - The session time object with the hour and minute of the time of the day
Returns: - The number of milliseconds passed from the start of the day until that session time
method is_day_included(this, day)
is_day_included - Check if the given day is inside the session days
Namespace types: SessionDays
Parameters:
this (SessionDays) : - The session days object with the day selection
day (int) : - The day to check if it is a trading day
Returns: - Whether the current day is included in the session days
DateTimeWindow
DateTimeWindow - Object that represents a datetime window with a beginning and an end
Fields:
fromDateTime (series int) : - The beginning of the datetime window
toDateTime (series int) : - The end of the datetime window
SessionDays
SessionDays - Object that represent the trading days of the week
Fields:
days (map) : - The map that contains all days of the week and their session flag
SessionTime
SessionTime - Object that represents the time (hour and minutes)
Fields:
hourInDay (series int) : - The hour of the day that ranges from 0 to 24
minuteInHour (series int) : - The minute of the hour that ranges from 0 to 59
minuteInDay (series int) : - The minute of the day that ranges from 0 to 1440. They will be calculated based on hourInDay and minuteInHour when method is called
SessionTimeRange
SessionTimeRange - Object that represents a range that extends from the start to the end time
Fields:
startTime (SessionTime) : - The beginning of the time range
endTime (SessionTime) : - The end of the time range
isOvernight (series bool) : - Whether or not this is an overnight time range
Session
Session - Object that represents a session
Fields:
days (SessionDays) : - The map of the trading days
timeRanges (array) : - The array with all time ranges of the session during the trading days
SessionView
SessionView - Object that visualize a session
Fields:
sess (Session) : - The Session object to be visualized
names (array) : - The names of the session time ranges
colors (array) : - The colors of the session time ranges
chrono_utilsLibrary "chrono_utils"
Collection of objects and common functions that are related to datetime windows session days and time
ranges. The main purpose of this library is to handle time-related functionality and make it easy to reason about a
future bar and see if it is part of a predefined user session and/or inside a datetime window. All existing session
functions I found in the documentation e.g. "not na(time(timeframe, session, timezone))" are not suitable for
strategies, since the execution of the orders is delayed by one bar due to the execution happening at the bar close.
So a prediction for the next bar is necessary. Moreover, a history operator with a negative value is not allowed e.g.
`not na(time(timeframe, session, timezone) )` expression is not valid. Thus, I created this library to overcome
this small but very important limitation. In the meantime, I added useful functionality to handle session-based
behavior. An interesting utility that emerged from this development is data anomaly detection where a comparison
between the prediction and the actual value is happening. If those two values are different then a data inconsistency
happens between the prediction bar and the actual bar (probably due to a holiday or half session day etc..)
exTimezone(timezone)
exTimezone - Convert extended timezone to timezone string
Parameters:
timezone (simple string) : - The timezone or a special string
Returns: string representing the timezone
nameOfDay(day)
nameOfDay - Convert the day id into a short nameOfDay
Parameters:
day (int) : - The day id to convert
Returns: - The short name of the day
today()
today - Get the day id of this day
Returns: - The day id
nthDayAfter(day, n)
nthDayAfter - Get the day id of n days after the given day
Parameters:
day (int) : - The day id of the reference day
n (int) : - The number of days to go forward
Returns: - The day id of the day that is n days after the reference day
nextDayAfter(day)
nextDayAfter - Get the day id of next day after the given day
Parameters:
day (int) : - The day id of the reference day
Returns: - The day id of the next day after the reference day
nthDayBefore(day, n)
nthDayBefore - Get the day id of n days before the given day
Parameters:
day (int) : - The day id of the reference day
n (int) : - The number of days to go forward
Returns: - The day id of the day that is n days before the reference day
prevDayBefore(day)
prevDayBefore - Get the day id of previous day before the given day
Parameters:
day (int) : - The day id of the reference day
Returns: - The day id of the previous day before the reference day
tomorrow()
tomorrow - Get the day id of the next day
Returns: - The next day day id
normalize(num, min, max)
normalizeHour - Check if number is inthe range of
Parameters:
num (int)
min (int)
max (int)
Returns: - The normalized number
normalizeHour(hourInDay)
normalizeHour - Check if hour is valid and return a noralized hour range from
Parameters:
hourInDay (int)
Returns: - The normalized hour
normalizeMinute(minuteInHour)
normalizeMinute - Check if minute is valid and return a noralized minute from
Parameters:
minuteInHour (int)
Returns: - The normalized minute
monthInMilliseconds(mon)
monthInMilliseconds - Calculate the miliseconds in one bar of the timeframe
Parameters:
mon (int) : - The month of reference to get the miliseconds
Returns: - The number of milliseconds of the month
barInMilliseconds()
barInMilliseconds - Calculate the miliseconds in one bar of the timeframe
Returns: - The number of milliseconds in one bar
method init(this, fromDateTime, toDateTime)
init - Initialize the time window object from boolean values of each session day
Namespace types: DateTimeWindow
Parameters:
this (DateTimeWindow) : - The time window object that will hold the from and to datetimes
fromDateTime (int) : - The starting datetime of the time window
toDateTime (int) : - The ending datetime of the time window
Returns: - The time window object
method init(this, refTimezone, chTimezone, fromDateTime, toDateTime)
init - Initialize the time window object from boolean values of each session day
Namespace types: DateTimeWindow
Parameters:
this (DateTimeWindow) : - The time window object that will hold the from and to datetimes
refTimezone (simple string) : - The timezone of reference of the 'from' and 'to' dates
chTimezone (simple string) : - The target timezone to convert the 'from' and 'to' dates
fromDateTime (int) : - The starting datetime of the time window
toDateTime (int) : - The ending datetime of the time window
Returns: - The time window object
method init(this, sun, mon, tue, wed, thu, fri, sat)
init - Initialize the session days object from boolean values of each session day
Namespace types: SessionDays
Parameters:
this (SessionDays) : - The session days object that will hold the day selection
sun (bool) : - Is Sunday a trading day?
mon (bool) : - Is Monday a trading day?
tue (bool) : - Is Tuesday a trading day?
wed (bool) : - Is Wednesday a trading day?
thu (bool) : - Is Thursday a trading day?
fri (bool) : - Is Friday a trading day?
sat (bool) : - Is Saturday a trading day?
Returns: - The session days objectfrom_chart
method init(this, unixTime)
init - Initialize the object from the hour and minute of the session time in exchange timezone (syminfo.timezone)
Namespace types: SessionTime
Parameters:
this (SessionTime) : - The session time object with the hour and minute of the time of the day
unixTime (int) : - The unix time
Returns: - The session time object
method init(this, hourInDay, minuteInHour)
init - Initialize the object from the hour and minute of the session time in exchange timezone (syminfo.timezone)
Namespace types: SessionTime
Parameters:
this (SessionTime) : - The session time object with the hour and minute of the time of the day
hourInDay (int) : - The hour of the time
minuteInHour (int) : - The minute of the time
Returns: - The session time object
method init(this, hourInDay, minuteInHour, refTimezone)
init - Initialize the object from the hour and minute of the session time
Namespace types: SessionTime
Parameters:
this (SessionTime) : - The session time object with the hour and minute of the time of the day
hourInDay (int) : - The hour of the time
minuteInHour (int) : - The minute of the time
refTimezone (string) : - The timezone of reference of the 'hour' and 'minute'
Returns: - The session time object
method init(this, startTime, endTime)
init - Initialize the object from the start and end session time in exchange timezone (syminfo.timezone)
Namespace types: SessionTimeRange
Parameters:
this (SessionTimeRange) : - The session time range object that will hold the start and end time of the daily session
startTime (SessionTime) : - The time the session begins
endTime (SessionTime) : - The time the session ends
Returns: - The session time range object
method init(this, startTimeHour, startTimeMinute, endTimeHour, endTimeMinute, refTimezone)
init - Initialize the object from the start and end session time
Namespace types: SessionTimeRange
Parameters:
this (SessionTimeRange) : - The session time range object that will hold the start and end time of the daily session
startTimeHour (int) : - The time hour the session begins
startTimeMinute (int) : - The time minute the session begins
endTimeHour (int) : - The time hour the session ends
endTimeMinute (int) : - The time minute the session ends
refTimezone (string)
Returns: - The session time range object
method init(this, days, timeRanges)
init - Initialize the user session object from session days and time range
Namespace types: UserSession
Parameters:
this (UserSession) : - The user-defined session object that will hold the day and the time range selection
days (SessionDays) : - The session days object that defines the days the session is happening
timeRanges (SessionTimeRange ) : - The array of all the session time ranges during a session day
Returns: - The user session object
method to_string(this)
to_string - Formats the time window into a human-readable string
Namespace types: DateTimeWindow
Parameters:
this (DateTimeWindow) : - The time window object with the from and to datetimes
Returns: - The string of the time window
method to_string(this)
to_string - Formats the session days into a human-readable string with short day names
Namespace types: SessionDays
Parameters:
this (SessionDays) : - The session days object with the day selection
Returns: - The string of the session day short names
method to_string(this)
to_string - Formats the session time into a human-readable string
Namespace types: SessionTime
Parameters:
this (SessionTime) : - The session time object with the hour and minute of the time of the day
Returns: - The string of the session time
method to_string(this)
to_string - Formats the session time into a human-readable string
Namespace types: SessionTimeRange
Parameters:
this (SessionTimeRange) : - The session time range object with the start and end time of the daily session
Returns: - The string of the session time
method to_string(this)
to_string - Formats the user session into a human-readable string
Namespace types: UserSession
Parameters:
this (UserSession) : - The user-defined session object with the day and the time range selection
Returns: - The string of the user session
method to_string(this)
to_string - Formats the bar into a human-readable string
Namespace types: Bar
Parameters:
this (Bar) : - The bar object with the open and close times
Returns: - The string of the bar times
method to_string(this)
to_string - Formats the chart session into a human-readable string
Namespace types: ChartSession
Parameters:
this (ChartSession) : - The chart session object that contains the days and the time range shown in the chart
Returns: - The string of the chart session
method get_size_in_secs(this)
get_size_in_secs - Count the seconds from start to end in the given timeframe
Namespace types: DateTimeWindow
Parameters:
this (DateTimeWindow) : - The time window object with the from and to datetimes
Returns: - The number of seconds inside the time widow for the given timeframe
method get_size_in_secs(this)
get_size_in_secs - Calculate the seconds inside the session
Namespace types: SessionTimeRange
Parameters:
this (SessionTimeRange) : - The session time range object with the start and end time of the daily session
Returns: - The number of seconds inside the session
method get_size_in_bars(this)
get_size_in_bars - Count the bars from start to end in the given timeframe
Namespace types: DateTimeWindow
Parameters:
this (DateTimeWindow) : - The time window object with the from and to datetimes
Returns: - The number of bars inside the time widow for the given timeframe
method get_size_in_bars(this)
get_size_in_bars - Calculate the bars inside the session
Namespace types: SessionTimeRange
Parameters:
this (SessionTimeRange) : - The session time range object with the start and end time of the daily session
Returns: - The number of bars inside the session for the given timeframe
method from_chart(this)
from_chart - Initialize the session days object from the chart
Namespace types: SessionDays
Parameters:
this (SessionDays) : - The session days object that will hold the day selection
Returns: - The user session object
method from_chart(this)
from_chart - Initialize the session time range object from the chart
Namespace types: SessionTimeRange
Parameters:
this (SessionTimeRange) : - The session time range object that will hold the start and end time of the daily session
Returns: - The session time range object
method from_chart(this)
from_chart - Initialize the session object from the chart
Namespace types: ChartSession
Parameters:
this (ChartSession) : - The chart session object that will hold the days and the time range shown in the chart
Returns: - The chart session object
method to_sess_string(this)
to_sess_string - Formats the session days into a session string with day ids
Namespace types: SessionDays
Parameters:
this (SessionDays) : - The session days object
Returns: - The string of the session day ids
method to_sess_string(this)
to_sess_string - Formats the session time into a session string
Namespace types: SessionTime
Parameters:
this (SessionTime) : - The session time object with the hour and minute of the time of the day
Returns: - The string of the session time
method to_sess_string(this)
to_sess_string - Formats the session time into a session string
Namespace types: SessionTimeRange
Parameters:
this (SessionTimeRange) : - The session time range object with the start and end time of the daily session
Returns: - The string of the session time
method to_sess_string(this)
to_sess_string - Formats the user session into a session string
Namespace types: UserSession
Parameters:
this (UserSession) : - The user-defined session object with the day and the time range selection
Returns: - The string of the user session
method to_sess_string(this)
to_sess_string - Formats the chart session into a session string
Namespace types: ChartSession
Parameters:
this (ChartSession) : - The chart session object that contains the days and the time range shown in the chart
Returns: - The string of the chart session
method from_sess_string(this, sess)
from_sess_string - Initialize the session days object from the session string
Namespace types: SessionDays
Parameters:
this (SessionDays) : - The session days object that will hold the day selection
sess (string) : - The session string part that represents the days
Returns: - The session days object
method from_sess_string(this, sess)
from_sess_string - Initialize the session time object from the session string in exchange timezone (syminfo.timezone)
Namespace types: SessionTime
Parameters:
this (SessionTime) : - The session time object that will hold the hour and minute of the time
sess (string) : - The session string part that represents the time HHmm
Returns: - The session time object
method from_sess_string(this, sess, refTimezone)
from_sess_string - Initialize the session time object from the session string
Namespace types: SessionTime
Parameters:
this (SessionTime) : - The session time object that will hold the hour and minute of the time
sess (string) : - The session string part that represents the time HHmm
refTimezone (simple string) : - The timezone of reference of the 'hour' and 'minute'
Returns: - The session time object
method from_sess_string(this, sess)
from_sess_string - Initialize the session time range object from the session string in exchange timezone (syminfo.timezone)
Namespace types: SessionTimeRange
Parameters:
this (SessionTimeRange) : - The session time range object that will hold the start and end time of the daily session
sess (string) : - The session string part that represents the time range HHmm-HHmm
Returns: - The session time range object
method from_sess_string(this, sess, refTimezone)
from_sess_string - Initialize the session time range object from the session string
Namespace types: SessionTimeRange
Parameters:
this (SessionTimeRange) : - The session time range object that will hold the start and end time of the daily session
sess (string) : - The session string part that represents the time range HHmm-HHmm
refTimezone (simple string) : - The timezone of reference of the time ranges
Returns: - The session time range object
method from_sess_string(this, sess)
from_sess_string - Initialize the user session object from the session string in exchange timezone (syminfo.timezone)
Namespace types: UserSession
Parameters:
this (UserSession) : - The user-defined session object that will hold the day and the time range selection
sess (string) : - The session string that represents the user session HHmm-HHmm,HHmm-HHmm:ddddddd
Returns: - The session time range object
method from_sess_string(this, sess, refTimezone)
from_sess_string - Initialize the user session object from the session string
Namespace types: UserSession
Parameters:
this (UserSession) : - The user-defined session object that will hold the day and the time range selection
sess (string) : - The session string that represents the user session HHmm-HHmm,HHmm-HHmm:ddddddd
refTimezone (simple string) : - The timezone of reference of the time ranges
Returns: - The session time range object
method nth_day_after(this, day, n)
nth_day_after - The nth day after the given day that is a session day (true) in the object
Namespace types: SessionDays
Parameters:
this (SessionDays) : - The session days object with the day selection
day (int) : - The day id of the reference day
n (int) : - The number of days after
Returns: - The day id of the nth session day of the week after the given day
method nth_day_before(this, day, n)
nth_day_before - The nth day before the given day that is a session day (true) in the object
Namespace types: SessionDays
Parameters:
this (SessionDays) : - The session days object with the day selection
day (int) : - The day id of the reference day
n (int) : - The number of days after
Returns: - The day id of the nth session day of the week before the given day
method next_day(this)
next_day - The next day that is a session day (true) in the object
Namespace types: SessionDays
Parameters:
this (SessionDays) : - The session days object with the day selection
Returns: - The day id of the next session day of the week
method previous_day(this)
previous_day - The previous day that is session day (true) in the object
Namespace types: SessionDays
Parameters:
this (SessionDays) : - The session days object with the day selection
Returns: - The day id of the previous session day of the week
method get_sec_in_day(this)
get_sec_in_day - Count the seconds since the start of the day this session time represents
Namespace types: SessionTime
Parameters:
this (SessionTime) : - The session time object with the hour and minute of the time of the day
Returns: - The number of seconds passed from the start of the day until that session time
method get_ms_in_day(this)
get_ms_in_day - Count the milliseconds since the start of the day this session time represents
Namespace types: SessionTime
Parameters:
this (SessionTime) : - The session time object with the hour and minute of the time of the day
Returns: - The number of milliseconds passed from the start of the day until that session time
method eq(this, other)
eq - Compare two bars
Namespace types: Bar
Parameters:
this (Bar) : - The bar object with the open and close times
other (Bar) : - The bar object to compare with
Returns: - Whether this bar is equal to the other one
method get_open_time(this)
get_open_time - The open time object
Namespace types: Bar
Parameters:
this (Bar) : - The bar object with the open and close times
Returns: - The open time object
method get_close_time(this)
get_close_time - The close time object
Namespace types: Bar
Parameters:
this (Bar) : - The bar object with the open and close times
Returns: - The close time object
method get_time_range(this)
get_time_range - Get the time range of the bar
Namespace types: Bar
Parameters:
this (Bar) : - The bar object with the open and close times
Returns: - The time range that the bar is in
getBarNow()
getBarNow - Get the current bar object with time and time_close timestamps
Returns: - The current bar
getFixedBarNow()
getFixedBarNow - Get the current bar with fixed width defined by the timeframe. Note: There are case like SPX 15min timeframe where the last session bar is only 10min. This will return a bar of 15 minutes
Returns: - The current bar
method is_in_window(this, win)
is_in_window - Check if the given bar is between the start and end dates of the window
Namespace types: Bar
Parameters:
this (Bar) : - The bar to check if it is between the from and to datetimes of the window
win (DateTimeWindow) : - The time window object with the from and to datetimes
Returns: - Whether the current bar is inside the datetime window
method is_in_timerange(this, rng)
is_in_timerange - Check if the given bar is inside the session time range
Namespace types: Bar
Parameters:
this (Bar) : - The bar to check if it is between the from and to datetimes
rng (SessionTimeRange) : - The session time range object with the start and end time of the daily session
Returns: - Whether the bar is inside the session time range and if this part of the next trading day
method is_in_days(this, days)
is_in_days - Check if the given bar is inside the session days
Namespace types: Bar
Parameters:
this (Bar) : - The bar to check if its day is a trading day
days (SessionDays) : - The session days object with the day selection
Returns: - Whether the current bar day is inside the session
method is_in_session(this, sess)
is_in_session - Check if the given bar is inside the session as defined by the input params (what "not na(time(timeframe.period, this.to_sess_string()) )" should return if you could write it
Namespace types: Bar
Parameters:
this (Bar) : - The bar to check if it is between the from and to datetimes
sess (UserSession) : - The user-defined session object with the day and the time range selection
Returns: - Whether the current time is inside the session
method next_bar(this, offsetBars)
next_bar - Predicts the next bars open and close time based on the charts session
Namespace types: ChartSession
Parameters:
this (ChartSession) : - The chart session object that contains the days and the time range shown in the chart
offsetBars (simple int) : - The number of bars forward
Returns: - Whether the current time is inside the session
DateTimeWindow
DateTimeWindow - Object that represents a datetime window with a beginning and an end
Fields:
fromDateTime (series int) : - The beginning of the datetime window
toDateTime (series int) : - The end of the datetime window
SessionDays
SessionDays - Object that represent the trading days of the week
Fields:
days (map) : - The map that contains all days of the week and their session flag
SessionTime
SessionTime - Object that represents the time (hour and minutes)
Fields:
hourInDay (series int) : - The hour of the day that ranges from 0 to 24
minuteInHour (series int) : - The minute of the hour that ranges from 0 to 59
minuteInDay (series int) : - The minute of the day that ranges from 0 to 1440. They will be calculated based on hourInDay and minuteInHour when method is called
SessionTimeRange
SessionTimeRange - Object that represents a range that extends from the start to the end time
Fields:
startTime (SessionTime) : - The beginning of the time range
endTime (SessionTime) : - The end of the time range
isOvernight (series bool) : - Whether or not this is an overnight time range
UserSession
UserSession - Object that represents a user-defined session
Fields:
days (SessionDays) : - The map of the user-defined trading days
timeRanges (SessionTimeRange ) : - The array with all time ranges of the user-defined session during the trading days
Bar
Bar - Object that represents the bars' open and close times
Fields:
openUnixTime (series int) : - The open time of the bar
closeUnixTime (series int) : - The close time of the bar
chartDayOfWeek (series int)
ChartSession
ChartSession - Object that represents the default session that is shown in the chart
Fields:
days (SessionDays) : - A map with the trading days shown in the chart
timeRange (SessionTimeRange) : - The time range of the session during a trading day
isFinalized (series bool)
Relative Volume (rVol), Better Volume, Average Volume ComparisonThis is the best version of relative volume you can find a claim which is based on the logical soundness of its calculation.
I have amalgamated various volume analysis into one synergistic script. I wasn't going to opensource it. But, as one of the lucky few winners of TradingClue 2. I felt obligated to give something back to the community.
Relative volume traditionally compares current volume to prior bar volume or SMA of volume. This has drawbacks. The question of relative volume is "Volume relative to what?" In the traditional scripts you'll find it displays current volume relative to the last number of bars. But, is that the best way to compare volume. On a daily chart, possibly. On a daily chart this can work because your units of time are uniform. Each day represents a full cycle of volume. However, on an intraday chart? Not so much.
Example: If you have a lookback of 9 on an hourly chart in a 24 hour market, you are then comparing the average volume from Midnight - 9 AM to the 9 AM volume. What do you think you'll find? Well at 9:30 when NY exchanges open the volume should be consistently and predictably higher. But though rVol is high relative to the lookback period, its actually just average or maybe even below average compared to prior NY session opens. But prior NY session opens are not included in the lookback and thus ignored.
This problem is the most visibly noticed when looking at the volume on a CME futures chart or some equivalent. In a 24 hour market, such as crypto, there are website's like skew can show you the volume disparity from time of day. This led me to believe that the traditional rVol calculation was insufficient. A better way to calculate it would be to compare the 9:30 am 30m bar today to the last week's worth of 9:30 am 30m bars. Then I could know whether today's volume at 9:30 am today is high or low based on prior 9:30 am bars. This seems to be a superior method on an intraday basis and is clearly superior in markets with irregular volume
This led me to other problems, such as markets that are open for less than 24 hours and holiday hours on traditional market exchanges. How can I know that the script is accurately looking at the correct prior relevant bars. I've created and/or adapted solutions to all those problems and these calculations and code snippets thus have value that extend beyond this rVol script for other pinecoders.
The Script
This rVol script looks back at the bars of the same time period on the viewing timeframe. So, as we said, the last 9:30 bars. Averages those, then divides the: . The result is a percentage expressed as x.xxx. Thus 1.0 mean current volume is equal to average volume. Below 1.0 is below the average and above 1.0 is above the average.
This information can be viewed on its own. But there are more levels of analysis added to it.
Above the bars are signals that correlate to the "Better Volume Indicator" developed by, I believe, the folks at emini-watch and originally adapted to pinescript by LazyBear. The interpretation of these symbols are in a table on the right of the indicator.
The volume bars can also be colored. The color is defined by the relationship between the average of the rVol outputs and the current volume. The "Average rVol" so to speak. The color coding is also defined by a legend in the table on the right.
These can be researched by you to determine how to best interpret these signals. I originally got these ideas and solid details on how to use the analysis from a fellow out there, PlanTheTrade.
I hope you find some value in the code and in the information that the indicator presents. And I'd like to thank the TradingView team for producing the most innovative and user friendly charting package on the market.
(p.s. Better Volume is provides better information with a longer lookback value than the default imo)
Credit for certain code sections and ideas is due to:
LazyBear - Better Volume
Grimmolf (From GitHub) - Logic for Loop rVol
R4Rocket - The idea for my rVol 1 calculation
And I can't find the guy who had the idea for the multiples of volume to the average. Tag him if you know him
Final Note: I'd like to leave a couple of clues of my own for fellow seekers of trading infamy.
Indicators: indicators are like anemometers (The things that measure windspeed). People talk bad about them all the time because they're "lagging." Well, you can't tell what the windspeed is unless the wind is blowing. anemometers are lagging indicators of wind. But forecasters still rely on them. You would use an indicator, which I would define as a instrument of measure, to tell you the windspeed of the markets. Conversely, when people talk positively about indicators they say "This one is great and this one is terrible." This is like a farmer saying "Shovels are great, but rakes are horrible." There are certain tools that have certain functions and every good tool has a purpose for a specific job. So the next time someone shares their opinion with you about indicators. Just smile and nod, realizing one day they'll learn... hopefully before they go broke.
How to forecast: Prediction is accomplished by analyzing the behavior of instruments of measure to aggregate data (using your anemometer). The data is then assembled into a predictive model based on the measurements observed (a trading system). That predictive model is tested against reality for it's veracity (backtesting). If the model is predictive, you can optimize your decision making by creating parameter sets around the prediction that are synergistic with the implications of the prediction (risk, stop loss, target, scaling, pyramiding etc).
<3
REVELATIONS (VoVix - PoC) REVELATIONS (VoVix - POC): True Regime Detection Before the Move
Let’s not sugarcoat it: Most strategies on TradingView are recycled—RSI, MACD, OBV, CCI, Stochastics. They all lag. No matter how many overlays you stack, every one of these “standard” indicators fires after the move is underway. The retail crowd almost always gets in late. That’s never been enough for my team, for DAFE, or for anyone who’s traded enough to know the real edge vanishes by the time the masses react.
How is this different?
REVELATIONS (VoVix - POC) was engineered from raw principle, structured to detect pre-move regime change—before standard technicals even light up. We built, tested, and refined VoVix to answer one hard question:
What if you could see the spike before the trend?
Here’s what sets this system apart, line-by-line:
o True volatility-of-volatility mathematics: It’s not just "ATR of ATR" or noise smoothing. VoVix uses normalized, multi-timeframe v-vol spikes, instantly detecting orderbook stress and "outlier" market events—before the chart shows them as trends.
o Purist regime clustering: Every trade is enabled only during coordinated, multi-filter regime stress. No more signals in meaningless chop.
o Nonlinear entry logic: No trade is ever sent just for a “good enough” condition. Every entry fires only if every requirement is aligned—local extremes, super-spike threshold, regime index, higher timeframe, all must trigger in sync.
o Adaptive position size: Your contracts scale up with event strength. Tiny size during nominal moves, max leverage during true regime breaks—never guesswork, never static exposure.
o All exits governed by regime decay logic: Trades are closed not just on price targets but at the precise moment the market regime exhausts—the hardest part of systemic trading, now solved.
How this destroys the lag:
Standard indicators (RSI, MACD, OBV, CCI, and even most “momentum” overlays) simply tell you what already happened. VoVix triggers as price structure transitions—anyone running these generic scripts will trade behind the move while VoVix gets in as stress emerges. Real alpha comes from anticipation, not confirmation.
The visuals only show what matters:
Top right, you get a live, live quant dashboard—regime index, current position size, real-time performance (Sharpe, Sortino, win rate, and wins). Bottom right: a VoVix "engine bar" that adapts live with regime stress. Everything you see is a direct function of logic driving this edge—no cosmetics, no fake momentum.
Inputs/Signals—explained carefully for clarity:
o ATR Fast Length & ATR Slow Length:
These are the heart of VoVix’s regime sensing. Fast ATR reacts to sharp volatility; Slow ATR is stability baseline. Lower Fast = reacts to every twitch; higher Slow = requires more persistent, “real” regime shifts.
Tip: If you want more signals or faster markets, lower ATR Fast. To eliminate noise, raise ATR Slow.
o ATR StdDev Window: Smoothing for volatility-of-volatility normalization. Lower = more jumpy, higher = only the cleanest spikes trigger.
Tip: Shorten for “jumpy” assets, raise for indices/futures.
o Base Spike Threshold: Think of this as your “minimum event strength.” If the current move isn’t volatile enough (normalized), no signal.
Tip: Higher = only biggest moves matter. Lower for more signals but more potential noise.
o Super Spike Multiplier: The “are you sure?” test—entry only when the current spike is this multiple above local average.
Tip: Raise for ultra-selective/swing-trading; lower for more active style.
Regime & MultiTF:
o Regime Window (Bars):
How many bars to scan for regime cluster “events.” Short for turbo markets, long for big swings/trends only.
o Regime Event Count: Only trade when this many spikes occur within the Regime Window—filters for real stress, not isolated ticks.
Tip: Raise to only ever trade during true breakouts/crashes.
o Local Window for Extremes:
How many bars to check that a spike is a local max.
Tip: Raise to demand only true, “clearest” local regime events; lower for early triggers.
o HTF Confirm:
Higher timeframe regime confirmation (like 45m on an intraday chart). Ensures any event you act on is visible in the broader context.
Tip: Use higher timeframes for only major moves; lower for scalping or fast regimes.
Adaptive Sizing:
o Max Contracts (Adaptive): The largest size your system will ever scale to, even on extreme event.
Tip: Lower for small accounts/conservative risk; raise on big accounts or when you're willing to go big only on outlier events.
o Min Contracts (Adaptive): The “toe-in-the-water.” Smallest possible trade.
Tip: Set as low as your broker/exchange allows for safety, or higher if you want to always have meaningful skin in the game.
Trade Management:
o Stop %: Tightness of your stop-loss relative to entry. Lower for tighter/safer, higher for more breathing room at cost of greater drawdown.
o Take Profit %: How much you'll hold out for on a win. Lower = more scalps. Higher = only run with the best.
o Decay Exit Sensitivity Buffer: Regime index must dip this far below the trading threshold before you exit for “regime decay.”
Tip: 0 = exit as soon as stress fails, higher = exits only on stronger confirmation regime is over.
o Bars Decay Must Persist to Exit: How long must decay be present before system closes—set higher to avoid quick fades and whipsaws.
Backtest Settings
Initial capital: $10,000
Commission: Conservative, realistic roundtrip cost:
15–20 per contract (including slippage per side) I set this to $25
Slippage: 3 ticks per trade
Symbol: CME_MINI:NQ1!
Timeframe: 1 min (but works on all timeframes)
Order size: Adaptive, 1–3 contracts
No pyramiding, no hidden DCA
Why these settings?
These settings are intentionally strict and realistic, reflecting the true costs and risks of live trading. The 10,000 account size is accessible for most retail traders. 25/contract including 3 ticks of slippage are on the high side for NQ, ensuring the strategy is not curve-fit to perfect fills. If it works here, it will work in real conditions.
Tip: Set to 1 for instant regime exit; raise for extra confirmation (less whipsaw risk, exits held longer).
________________________________________
Bottom line: Tune the sensitivity, selectivity, and risk of REVELATIONS by these inputs. Raise thresholds and windows for only the best, most powerful signals (institutional style); lower for activity (scalpers, fast cryptos, signals in constant motion). Sizing is always adaptive—never static or martingale. Exits are always based on both price and regime health. Every input is there for your control, not to sell “complexity.” Use with discipline, and make it your own.
This strategy is not just a technical achievement: It’s a statement about trading smarter, not just more.
* I went back through the code to make sure no the strategy would not suffer from repainting, forward looking, or any frowned upon loopholes.
Disclaimer:
Trading is risky and carries the risk of substantial loss. Do not use funds you aren’t prepared to lose. This is for research and informational purposes only, not financial advice. Backtest, paper trade, and know your risk before going live. Past performance is not a guarantee of future results.
Expect more: We’ll keep pushing the standard, keep evolving the bar until “quant” actually means something in the public code space.
Use with clarity, use with discipline, and always trade your edge.
— Dskyz , for DAFE Trading Systems
Prometheus Topological Persistent EntropyPersistence Entropy falls under the branch of math topology. Topology is a study of shapes as they twist and contort. It can be useful in the context of markets to determine how volatile they may be and different from the past.
The key idea is to create a persistence diagram from these log return segments. The persistence diagram tracks the "birth" and "death" of price features:
A birth occurs when a new price pattern or feature emerges in the data.
A death occurs when that pattern disappears.
By comparing prices within each segment, the script tracks how long specific price features persist before they die out. The lifetime of each feature (difference between death and birth) represents how robust or fleeting the pattern is. Persistent price features tend to reflect stable trends, while shorter-lived features indicate volatility.
Entropy Calculation: The lifetimes of these patterns are then used to compute the entropy of the system. Entropy, in this case, measures the amount of disorder or randomness in the price movements. The more varied the lifetimes, the higher the entropy, indicating a more volatile market. If the price patterns exhibit longer, more consistent lifetimes, the entropy is lower, signaling a more stable market.
Calculation:
We start by getting log returns for a user defined look back value. In the compute_persistent_entropy function we separate the overall log returns into windows. We then compute persistence diagrams of the windows. It tracks the birth and death of price patterns to see how persistent they are. Then we calculate the entropy of the windows.
After we go through that process we get an array of entropies, we then smooth it by taking the sum of all of them and dividing it by how many we have so the indicator can function better.
// Calculate log returns
log_returns = array.new()
for i = 1 to lgr_lkb
array.push(log_returns, math.log(close / close ))
// Function to compute a simplified persistence diagram
compute_persistence_diagram(segment) =>
n = array.size(segment)
lifetimes = array.new()
for i = 0 to n - 1
for j = i + 1 to n - 1
birth = array.get(segment, i)
death = array.get(segment, j-1)
if birth != death
array.push(lifetimes, math.abs(death - birth))
lifetimes
// Function to compute entropy of a list of values
compute_entropy(values) =>
n = array.size(values)
if n == 0
0.0
else
freq_map = map.new()
total_sum = 0.0
for i = 0 to n - 1
value = array.get(values, i)
//freq_map := freq_map.get(value, 0.0) + 1
map.put(freq_map, value, value + 1)
total_sum += 1
entropy = 0.0
for in freq_map
p = count / total_sum
entropy -= p * math.log(p)
entropy
compute_persistent_entropy(log_returns, window_size) =>
n = (lgr_lkb) - (2 * window_size) + 1
entropies = array.new()
for i = 0 to n - 1
segment1 = array.new()
segment2 = array.new()
for j = 0 to window_size - 1
array.push(segment1, array.get(log_returns, i + j))
array.push(segment2, array.get(log_returns, i + window_size + j))
dgm1 = compute_persistence_diagram(segment1)
dgm2 = compute_persistence_diagram(segment2)
combined_diagram = array.concat(dgm1, dgm2)
entropy = compute_entropy(combined_diagram)
array.push(entropies, entropy)
entropies
//---------------------------------------------
//---------------PE----------------------------
//---------------------------------------------
// Calculate Persistent Entropy
entropies = compute_persistent_entropy(log_returns, window_size)
smooth_pe = array.sum(entropies) / array.size(entropies)
This image illustrates how the indicator works for traders. The purple line is the actual indicator value. The line that changes from green to red is a SMA of the indicator value, we use this to determine bullish or bearish. When the smoothed persistence entropy is above it’s SMA that signals bearishness.
The indicator tends to look prettier on higher time frames, we see NASDAQ:TSLA on a 4 hour here and below we see it on the 5 minute.
On a lower time frame it looks a little weird but still functions the same way.
Prometheus encourages users to use indicators as tools along with their own discretion. No indicator is 100% accurate. We encourage comments about requested features and criticism.
Harmonic Rolling VWAP (Zeiierman)█ Overview
The Harmonic Rolling VWAP (Zeiierman) indicator combines the concept of the Rolling Volume Weighted Average Price (VWAP) with advanced harmonic analysis using Discrete Fourier Transform (DFT). This innovative indicator aims to provide traders with a dynamic view of price action, capturing both the volume-weighted price and underlying harmonic patterns. By leveraging this combination, traders can gain deeper insights into market trends and potential reversal points.
█ How It Works
The Harmonic Rolling VWAP calculates the rolling VWAP over a specified window of bars, giving more weight to periods with higher trading volume. This VWAP is then subjected to harmonic analysis using the Discrete Fourier Transform (DFT), which decomposes the VWAP into its frequency components.
Key Components:
Rolling VWAP (RVWAP): A moving average that gives more weight to higher volume periods, calculated over a user-defined window.
True Range (TR): Measures volatility by comparing the current high and low prices, considering the previous close price.
Discrete Fourier Transform (DFT): Analyzes the harmonic patterns within the RVWAP by decomposing it into its frequency components.
Standard Deviation Bands: These bands provide a visual representation of price volatility around the RVWAP, helping traders identify potential overbought or oversold conditions.
█ How to Use
Identify Trends: The RVWAP line helps in identifying the underlying trend by smoothing out short-term price fluctuations and focusing on volume-weighted prices.
Assess Volatility: The standard deviation bands around the RVWAP give a clear view of price volatility, helping traders identify potential breakout or breakdown points.
Find Entry and Exit Points: Traders can look for entries when the price is near the lower bands in an uptrend or near the upper bands in a downtrend. Exits can be considered when the price approaches the opposite bands or shows harmonic divergence.
█ Settings
VWAP Source: Defines the price data used for VWAP calculations. The source input defines the price data used for calculations. This setting affects the VWAP calculations and the resulting bands.
Window: Sets the number of bars used for the rolling calculations. The window input sets the number of bars used for the rolling calculations. A larger window smooths the VWAP and standard deviation bands, making the indicator less sensitive to short-term price fluctuations. A smaller window makes the indicator more responsive to recent price changes.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Nick_OS RangesUNDERSTANDING THE SCRIPT:
TIMEFRAME RESOLUTION:
* You have the option to choose Daily , Weekly , or Monthly
LOOKBACK WINDOW:
* This number represents how far back you want the data to pull from
- Example: "250" would represent the past 250 Days, Weeks, or Months depending on what is selected in the Timeframe Resolution
RANGE 1 nth (Gray lines):
* This number represents the range of the nth biggest day, week, or month in the Lookback Window
- Example: "30" would represent the range of the 30th biggest day in the past 250 days. (If the Lookback Window is "250")
RANGE 2 nth (Blue lines):
* This number represents the range of the nth biggest day, week, or month in the Lookback Window
- Example: "10" would represent the range of the 10th biggest day in the past 250 days. (If the Lookback Window is "250")
RANGE 3 nth (Pink lines):
* This number represents the range of the nth biggest day, week, or month in the Lookback Window
- Example: "3" would represent the range of the 3rd biggest day in the past 250 days. (If the Lookback Window is "250")
YELLOW LINES:
* The yellow lines are the average percentage move of the inputted number in the Lookback Window
SUGGESTED INPUTS:
FOR DAILY:
Lookback Window: 250
Range 1 nth: 30
Range 2 nth: 10
Range 3 nth: 3
FOR WEEKLY:
Lookback Window: 50
Range 1 nth: 10
Range 2 nth: 5
Range 3 nth: 2
FOR MONTHLY:
Lookback Window: 12
Range 1 nth: 3
Range 2 nth: 2
Range 3 nth: 1
TIMEFRAMES TO USE (If You Have TradingView Premium):
Daily: 5 minute timeframe and higher (15 minute timeframe and higher for Futures)
Weekly: 15 minute timeframe and higher
Monthly: Daily timeframe and higher (Monthly still has issues)
TIMEFRAMES TO USE (If You DO NOT Have TradingView Premium):
Daily: 15 minute timeframe and higher
Weekly: 30 minute timeframe and higher
Monthly: Daily timeframe and higher (Monthly still has issues)
IMPORTANT RELATED NOTE:
If you decide to use a higher Lookback Window, the ranges might be off and the timeframes listed above might not apply
ISSUES THAT MIGHT BE RESOLVED IN THE FUTURE
1. If it is a shortened week (No Monday or Friday), then the Weekly Ranges will show the same ranges as last week
2. Monthly ranges will change based on any timeframe used
C&B Auto MK5C&B Auto MK5.2ema BullBear
Overview
The C&B Auto MK5.2ema BullBear is a versatile Pine Script indicator designed to help traders identify bullish and bearish market conditions across various timeframes. It combines Exponential Moving Averages (EMAs), Relative Strength Index (RSI), Average True Range (ATR), and customizable time filters to generate actionable signals. The indicator overlays on the price chart, displaying EMAs, a dynamic cloud, scaled RSI levels, bull/bear signals, and market condition labels, making it suitable for swing trading, day trading, or scalping in trending or volatile markets.
What It Does
This indicator generates bull and bear signals based on the interaction of two EMAs, filtered by RSI thresholds, ATR-based volatility, a 50/200 EMA trend filter, and user-defined time windows. It adapts to market volatility by adjusting EMA lengths and RSI thresholds. A dynamic cloud highlights trend direction or neutral zones, with candlestick coloring in neutral conditions. Market condition labels (current and historical) provide real-time trend and volatility context, displayed above the chart.
How It Works
The indicator uses the following components:
EMAs: Two EMAs (short and long) are calculated on a user-selected timeframe (1, 5, 15, 30, or 60 minutes). Their crossover or crossunder triggers potential bull/bear signals. EMA lengths adjust based on volatility (e.g., 10/20 for volatile markets, 5/10 for non-volatile).
Dynamic Cloud: The area between the EMAs forms a cloud, colored green for bullish trends, red for bearish trends, or a user-defined color (default yellow) for neutral zones (when EMAs are close, determined by an ATR-based threshold). Users can widen the cloud for visibility.
RSI Filter: RSI is scaled to price levels and plotted on the chart (optional). Signals are filtered to ensure RSI is within volatility-adjusted bull/bear thresholds and not in overbought/oversold zones.
ATR Volatility Filter: An optional filter ensures signals occur during sufficient volatility (ATR(14) > SMA(ATR, 20)).
50/200 EMA Trend Filter: An optional filter restricts bull signals to bullish trends (50 EMA > 200 EMA) and bear signals to bearish trends (50 EMA < 200 EMA).
Time Filter: Signals are restricted to a user-defined UTC time window (default 9:00–15:00), aligning with active trading sessions.
Market Condition Labels: Labels above the chart display the current trend (Bullish, Bearish, Neutral) and optionally volatility (e.g., “Bullish Volatile”). Up to two historical labels persist for a user-defined number of bars (default 5) to show recent trend changes.
Visual Aids: Bull signals appear as green triangles/labels below the bar, bear signals as red triangles/labels above. Candlesticks in neutral zones are colored (default yellow).
The indicator ensures compatibility with standard chart types (e.g., candlestick or bar charts) to produce realistic signals, avoiding non-standard types like Heikin Ashi or Renko.
How to Use It
Add to Chart: Apply the indicator to a candlestick or bar chart on TradingView.
Configure Settings:
Timeframe: Choose a timeframe (1, 5, 15, 30, or 60 minutes) to match your trading style.
Filters:
Enable/disable the ATR volatility filter to focus on high-volatility periods.
Enable/disable the 50/200 EMA trend filter to align signals with the broader trend.
Enable the time filter and set custom UTC hours/minutes (default 9:00–15:00).
Cloud Settings: Adjust the cloud width, neutral zone threshold, color, and transparency.
EMA Colors: Use default trend-based colors or set custom colors for short/long EMAs.
RSI Display: Toggle the scaled RSI and its thresholds, with customizable colors.
Signal Settings: Toggle bull/bear labels and set signal colors.
Market Condition Labels: Toggle current/historical labels, include/exclude volatility, and adjust decay period.
Interpret Signals:
Bull Signal: A green triangle or “Bull” label below the bar indicates potential bullish momentum (EMA crossover, RSI above bull threshold, within time window, passing filters).
Bear Signal: A red triangle or “Bear” label above the bar indicates potential bearish momentum (EMA crossunder, RSI below bear threshold, within time window, passing filters).
Neutral Zone: Yellow candlesticks and cloud (if enabled) suggest a lack of clear trend; consider range-bound strategies or avoid trading.
Market Condition Labels: Check labels above the chart for real-time trend (Bullish, Bearish, Neutral) and volatility status to confirm market context.
Monitor Context: Use the cloud, RSI, and labels to assess trend strength and volatility before acting on signals.
Unique Features
Volatility-Adaptive EMAs: Automatically adjusts EMA lengths based on ATR to suit volatile or non-volatile markets, reducing manual configuration.
Neutral Zone Detection: Uses an ATR-based threshold to identify low-trend periods, helping traders avoid choppy markets.
Scaled RSI Visualization: Plots RSI and thresholds directly on the price chart, simplifying momentum analysis relative to price.
Flexible Time Filtering: Supports precise UTC-based trading windows, ideal for day traders targeting specific sessions.
Historical Market Labels: Displays recent trend changes (up to two) with a decay period, providing context for market shifts.
50/200 EMA Trend Filter: Aligns signals with the broader market trend, enhancing signal reliability.
Notes
Use on standard candlestick or bar charts to ensure accurate signals.
Test the indicator on a demo account to optimize settings for your market and timeframe.
Combine with other analysis (e.g., support/resistance, volume) for better decision-making.
The indicator is not a standalone system; use it as part of a broader trading strategy.
Limitations
Signals may lag in fast-moving markets due to EMA-based calculations.
Neutral zone detection may vary in extremely volatile or illiquid markets.
Time filters are UTC-based; ensure your platform’s timezone settings align.
This indicator is designed for traders seeking a customizable, trend-following tool that adapts to volatility and provides clear visual cues with robust filtering for bullish and bearish market conditions.
Adaptive Freedom Machine w/labelsAdaptive Freedom Machine w/ Labels
Overview
The Adaptive Freedom Machine w/ Labels is a versatile Pine Script indicator designed to assist traders in identifying buy and sell opportunities across various market conditions (trending, ranging, or volatile). It combines Exponential Moving Averages (EMAs), Relative Strength Index (RSI), Average True Range (ATR), and customizable time filters to generate actionable signals. The indicator overlays on the price chart, displaying EMAs, a dynamic cloud, scaled RSI levels, buy/sell signals, and market condition labels, making it suitable for swing trading, day trading, or scalping.
What It Does
This indicator generates buy and sell signals based on the interaction of two EMAs, filtered by RSI thresholds, ATR-based volatility, and user-defined time windows. It adapts to the selected market condition by adjusting EMA lengths, RSI thresholds, and trading hours. A dynamic cloud highlights trend direction or neutral zones, and candlestick bodies are colored in neutral conditions for clarity. A table displays real-time trend and volatility status.
How It Works
The indicator uses the following components:
EMAs: Two EMAs (short and long) are calculated on a user-selected timeframe (1, 5, 15, 30, or 60 minutes). Their crossover or crossunder generates potential buy/sell signals, with lengths adjusted based on the market condition (e.g., longer EMAs for trending markets, shorter for ranging).
Dynamic Cloud: The area between the EMAs forms a cloud, colored green for uptrends, red for downtrends, or a user-defined color (default yellow) for neutral zones (when EMAs are close, determined by an ATR-based threshold). Users can widen the cloud for visibility.
RSI Filter: RSI is scaled to price levels and plotted on the chart (optional). Signals are filtered to ensure RSI is within user-defined buy/sell thresholds and not in overbought/oversold zones, with thresholds tailored to the market condition.
ATR Volatility Filter: An optional filter ensures signals occur during sufficient volatility (ATR(14) > SMA(ATR, 20)).
Time Filter: Signals are restricted to a user-defined or market-specific time window (e.g., 10:00–15:00 UTC for volatile markets), with an option for custom hours.
Visual Aids: Buy/sell signals appear as green triangles (buy) or red triangles (sell). Candlesticks in neutral zones are colored (default yellow). A table in the top-right corner shows the current trend (Uptrend, Downtrend, Neutral) and volatility (High or Low).
The indicator ensures compatibility with standard chart types (e.g., candlestick charts) to produce realistic signals, avoiding non-standard types like Heikin Ashi or Renko.
How to Use It
Add to Chart: Apply the indicator to a candlestick or bar chart on TradingView.
Configure Settings:
Timeframe: Choose a timeframe (1, 5, 15, 30, or 60 minutes) to align with your trading style.
Market Condition: Select one market condition (Trending, Ranging, or Volatile). Volatile is the default if none is selected. Only one condition can be active.
Filters:
Enable/disable the ATR volatility filter to trade only in high-volatility periods.
Enable the time filter and choose default hours (specific to the market condition) or set custom UTC hours.
Cloud Settings: Adjust the cloud width, neutral zone threshold, and color. Enable/disable the neutral cloud.
RSI Display: Toggle the scaled RSI and its thresholds on the chart.
Interpret Signals:
Buy Signal: A green triangle below the bar indicates a potential long entry (EMA crossover, RSI above buy threshold, within time window, and passing volatility filter).
Sell Signal: A red triangle above the bar indicates a potential short entry (EMA crossunder, RSI below sell threshold, within time window, and passing volatility filter).
Neutral Zone: Yellow candlesticks and cloud (if enabled) suggest a lack of clear trend; avoid trading or use for range-bound strategies.
Monitor the Table: Check the top-right table for real-time trend (Uptrend, Downtrend, Neutral) and volatility (High or Low) to confirm market context.
Unique Features
Adaptive Parameters: Automatically adjusts EMA lengths, RSI thresholds, and trading hours based on the selected market condition, reducing manual tweaking.
Neutral Zone Detection: Uses an ATR-based threshold to identify low-trend periods, helping traders avoid choppy markets.
Scaled RSI Visualization: Plots RSI and thresholds directly on the price chart, making it easier to assess momentum relative to price action.
Flexible Time Filtering: Supports both default and custom UTC-based trading windows, ideal for day traders targeting specific sessions.
Dynamic Cloud: Enhances trend visualization with customizable width and neutral zone coloring, improving readability.
Notes
Use on standard candlestick or bar charts to ensure realistic signals.
Test the indicator on a demo account to understand its behavior in your chosen market and timeframe.
Adjust settings to match your trading strategy, but avoid over-optimizing for past data.
The indicator is not a standalone system; combine it with other analysis (e.g., support/resistance, news events) for better results.
Limitations
Signals may lag in fast-moving markets due to EMA-based calculations.
Neutral zone detection may vary in extremely volatile or illiquid markets.
Time filters are UTC-based; ensure your platform’s timezone settings align.
This indicator is designed for traders seeking a customizable, trend-following tool that adapts to different market environments while providing clear visual cues and robust filtering.
Stochastic Z-Score Oscillator Strategy [TradeDots]The "Stochastic Z-Score Oscillator Strategy" represents an enhanced approach to the original "Buy Sell Strategy With Z-Score" trading strategy. Our upgraded Stochastic model incorporates an additional Stochastic Oscillator layer on top of the Z-Score statistical metrics, which bolsters the affirmation of potential price reversals.
We also revised our exit strategy to when the Z-Score revert to a level of zero. This amendment gives a much smaller drawdown, resulting in a better win-rate compared to the original version.
HOW DOES IT WORK
The strategy operates by calculating the Z-Score of the closing price for each candlestick. This allows us to evaluate how significantly the current price deviates from its typical volatility level.
The strategy first takes the scope of a rolling window, adjusted to the user's preference. This window is used to compute both the standard deviation and mean value. With these values, the strategic model finalizes the Z-Score. This determination is accomplished by subtracting the mean from the closing price and dividing the resulting value by the standard deviation.
Following this, the Stochastic Oscillator is utilized to affirm the Z-Score overbought and oversold indicators. This indicator operates within a 0 to 100 range, so a base adjustment to match the Z-Score scale is required. Post Stochastic Oscillator calculation, we recalibrate the figure to lie within the -4 to 4 range.
Finally, we compute the average of both the Stochastic Oscillator and Z-Score, signaling overpriced or underpriced conditions when the set threshold of positive or negative is breached.
APPLICATION
Firstly, it is better to identify a stable trading pair for this technique, such as two stocks with considerable correlation. This is to ensure conformance with the statistical model's assumption of a normal Gaussian distribution model. The ideal performance is theoretically situated within a sideways market devoid of skewness.
Following pair selection, the user should refine the span of the rolling window. A broader window smoothens the mean, more accurately capturing long-term market trends, while potentially enhancing volatility. This refinement results in fewer, yet precise trading signals.
Finally, the user must settle on an optimal Z-Score threshold, which essentially dictates the timing for buy/sell actions when the Z-Score exceeds with thresholds. A positive threshold signifies the price veering away from its mean, triggering a sell signal. Conversely, a negative threshold denotes the price falling below its mean, illustrating an underpriced condition that prompts a buy signal.
Within a normal distribution, a Z-Score of 1 records about 68% of occurrences centered at the mean, while a Z-Score of 2 captures approximately 95% of occurrences.
The 'cool down period' is essentially the number of bars that await before the next signal generation. This feature is employed to dodge the occurrence of multiple signals in a short period.
DEFAULT SETUP
The following is the default setup on EURAUD 1h timeframe
Rolling Window: 80
Z-Score Threshold: 2.8
Signal Cool Down Period: 5
Stochastic Length: 14
Stochastic Smooth Period: 7
Commission: 0.01%
Initial Capital: $10,000
Equity per Trade: 40%
FURTHER IMPLICATION
The Stochastic Oscillator imparts minimal impact on the current strategy. As such, it may be beneficial to adjust the weightings between the Z-Score and Stochastic Oscillator values or the scale of Stochastic Oscillator to test different performance outcomes.
Alternative momentum indicators such as Keltner Channels or RSI could also serve as robust confirmations of overbought and oversold signals when used for verification.
RISK DISCLAIMER
Trading entails substantial risk, and most day traders incur losses. All content, tools, scripts, articles, and education provided by TradeDots serve purely informational and educational purposes. Past performances are not definitive predictors of future results.
Buy Sell Strategy With Z-Score [TradeDots]The "Buy Sell Strategy With Z-Score" is a trading strategy that harnesses Z-Score statistical metrics to identify potential pricing reversals, for opportunistic buying and selling opportunities.
HOW DOES IT WORK
The strategy operates by calculating the Z-Score of the closing price for each candlestick. This allows us to evaluate how significantly the current price deviates from its typical volatility level.
The strategy first takes the scope of a rolling window, adjusted to the user's preference. This window is used to compute both the standard deviation and mean value. With these values, the strategic model finalizes the Z-Score. This determination is accomplished by subtracting the mean from the closing price and dividing the resulting value by the standard deviation.
This approach provides an estimation of the price's departure from its traditional trajectory, thereby identifying market conditions conducive to an asset being overpriced or underpriced.
APPLICATION
Firstly, it is better to identify a stable trading pair for this technique, such as two stocks with considerable correlation. This is to ensure conformance with the statistical model's assumption of a normal Gaussian distribution model. The ideal performance is theoretically situated within a sideways market devoid of skewness.
Following pair selection, the user should refine the span of the rolling window. A broader window smoothens the mean, more accurately capturing long-term market trends, while potentially enhancing volatility. This refinement results in fewer, yet precise trading signals.
Finally, the user must settle on an optimal Z-Score threshold, which essentially dictates the timing for buy/sell actions when the Z-Score exceeds with thresholds. A positive threshold signifies the price veering away from its mean, triggering a sell signal. Conversely, a negative threshold denotes the price falling below its mean, illustrating an underpriced condition that prompts a buy signal.
Within a normal distribution, a Z-Score of 1 records about 68% of occurrences centered at the mean, while a Z-Score of 2 captures approximately 95% of occurrences.
The 'cool down period' is essentially the number of bars that await before the next signal generation. This feature is employed to dodge the occurrence of multiple signals in a short period.
DEFAULT SETUP
The following is the default setup on EURUSD 1h timeframe
Rolling Window: 80
Z-Score Threshold: 2.8
Signal Cool Down Period: 5
Commission: 0.03%
Initial Capital: $10,000
Equity per Trade: 30%
RISK DISCLAIMER
Trading entails substantial risk, and most day traders incur losses. All content, tools, scripts, articles, and education provided by TradeDots serve purely informational and educational purposes. Past performances are not definitive predictors of future results.
STD/C-Filtered, Truncated Taylor Family FIR Filter [Loxx]STD/C-Filtered, Truncated Taylor Family FIR Filter is a FIR Digital Filter that uses Truncated Taylor Family of Windows. Taylor functions are obtained by adding a weighted-cosine series to a constant (called a pedestal). A simpler form of these functions can be obtained by dropping some of the higher-order terms in the Taylor series expansion. If all other terms, except for the first two significant ones, are dropped, a truncated Taylor function is obtained. This is a generalized window that is expressed as:
(1 + K) / 2 + (1 - K) / 2 * math.cos(2.0 * math.pi *n / N) where 0 ≤ |n| ≤ N/2
Here k can take the values in the range 0≤k≤1. We note that the Hann 0 ≤ |n| ≤ window is a special case of the truncated Taylor family with k = 0 and Rectangular 0 ≤ |n| ≤ window (SMA) is a special case of the truncated Taylor family with k = 1.
Truncated Taylor Family of Windows amplitudes for this indicator with K = 0.5
This indicator also includes Standard Deviation and Clutter filtering.
What is a Standard Devaition Filter?
If price or output or both don't move more than the (standard deviation) * multiplier then the trend stays the previous bar trend. This will appear on the chart as "stepping" of the moving average line. This works similar to Super Trend or Parabolic SAR but is a more naive technique of filtering.
What is a Clutter Filter?
For our purposes here, this is a filter that compares the slope of the trading filter output to a threshold to determine whether to shift trends. If the slope is up but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. If the slope is down but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. Alternatively if either up or down slope exceeds the threshold then the trend turns green for up and red for down. Fro demonstration purposes, an EMA is used as the moving average. This acts to reduce the noise in the signal.
Included
Bar coloring
Loxx's Expanded Source Types
Signals
Alerts
ArraysAssorted🟩 OVERVIEW
This library provides utility methods for working with arrays in Pine Script. The first method finds extreme values (highest/lowest) within a rolling lookback window and returns both the value and its position. I might extend the library for other ad-hoc methods I use to work with arrays.
🟩 HOW TO USE
Pine Script libraries contain reusable code for importing into indicators. You do not need to copy any code out of here. Just import the library and call the method you want.
For example, for version 1 of this library, import it like this:
import SimpleCryptoLife/ArraysAssorted/1
See the EXAMPLE USAGE sections within the library for examples of calling the methods.
You do not need permission to use Pine libraries in your open-source scripts.
However, you do need explicit permission to reuse code from a Pine Script library’s functions in a public protected or invite-only publication .
In any case, credit the author in your description. It is also good form to credit in open-source comments.
For more information on libraries and incorporating them into your scripts, see the Libraries section of the Pine Script User Manual.
🟩 METHOD 1: m_getHighestLowestFloat()
Finds the highest or lowest float value from an array. Simple enough. It also returns the index of the value as an offset from the end of the array.
• It works with rolling lookback windows, so you can find extremes within the last N elements
• It includes an offset parameter to skip recent elements if needed
• It handles edge cases like empty arrays and invalid ranges gracefully
• It can find either the first or last occurrence of the extreme value
We also export two enums whose sole purpose is to look pretty as method arguments.
method m_getHighestLowestFloat(_self, _highestLowest, _lookbackBars, _offset, _firstLastType)
Namespace types: array
This method finds the highest or lowest value in a float array within a rolling lookback window, and returns the value along with the offset (number of elements back from the end of the array) of its first or last occurrence.
Parameters:
_self (array) : The array of float values to search for extremes.
_highestLowest (HighestLowest) : Whether to search for the highest or lowest value. Use the enum value HighestLowest.highest or HighestLowest.lowest.
_lookbackBars (int) : The number of array elements to include in the rolling lookback window. Must be positive. Note: Array elements only correspond to bars if the consuming script always adds exactly one element on consecutive bars.
_offset (int) : The number of array elements back from the end of the array to start the lookback window. A value of zero means no offset. The _offset parameter offsets both the beginning and end of the range.
_firstLastType (FirstLast) : Whether to return the offset of the first (lowest index) or last (highest index) occurrence of the extreme value. Use FirstLast.first or FirstLast.last.
Returns: (tuple) A tuple containing the highest or lowest value and its offset -- the number of elements back from the end of the array. If not found, returns . NOTE: The _offsetFromEndOfArray value is not affected by the _offset parameter. In other words, it is not the offset from the end of the range but from the end of the array. This number may or may not have any relation to the number of *bars* back, depending on how the array is populated. The calling code needs to figure that out.
EXPORTED ENUMS
HighestLowest
Whether to return the highest value or lowest value in the range.
• highest : Find the highest value in the specified range
• lowest : Find the lowest value in the specified range
FirstLast
Whether to return the first (lowest index) or last (highest index) occurrence of the extreme value.
• first : Return the offset of the first occurrence of the extreme value
• last : Return the offset of the last occurrence of the extreme value
