Adaptive Signal Oscillator (ASO)📘 Adaptive Signal Oscillator (ASO)
A fully dynamic, self-calibrating oscillator that adapts to any asset or timeframe by optimizing for real-time signal stability and volatility structure — without relying on static parameters or hardcoded thresholds.
🔍 Overview
The Adaptive Signal Oscillator (ASO) is a next-generation technical analysis tool designed to provide context-aware long/short signals across crypto, equities, or forex markets. Unlike traditional oscillators (RSI, Stochastics, MACD), ASO requires no manual tuning of lookback periods or overbought/oversold zones — it self-optimizes based on current market behavior.
🧠 How It Works
✅ 1. Dynamic Lookback Optimization
ASO evaluates a range of lookback lengths between user-defined minLen and maxLen. For each length, it calculates the standard deviation of returns and finds the one with the least volatility change (i.e., the most stable structure). This length is dynamically assigned as bestLen, recalculated on every bar.
✅ 2. Multi-Layer Signal Composition
Four independent signal layers are computed using bestLen:
RSI Layer: Measures relative price strength via a custom dynamic RSI.
Z-Score Layer: Standardized deviation of price from its mean.
Volatility Layer: Standard deviation of log or percent returns.
Price Position Layer: Current price percentile within the lookback window.
Each of these layers is transformed into a percentile score scaled to the range .
✅ 3. Volatility-Based Weighting
The standard deviation (volatility) of each signal layer is computed. Less volatile layers are weighted more heavily, ensuring the final composite signal prioritizes stable, consistent inputs.
Weights are normalized and combined to form a composite score, representing a dynamically blended, noise-weighted signal across the four layers.
✅ 4. Optional Adaptive Smoothing
A boolean toggle lets users apply smoothing to the final score. The smoothing window scales proportionally to bestLen, preserving adaptiveness even during trend transitions.
✅ 5. Percentile-Based Thresholding
Rather than using arbitrary fixed thresholds, ASO converts the composite score into a ranked percentile. Long/short signals are then generated based on user-defined percentile bands, adapting naturally to each asset’s behavior.
📈 Interpreting ASO
Score > Threshold → Strong long signal (highlighted in aqua).
Score < Threshold → Strong short signal (highlighted in fuchsia).
Crossing h_thresh (e.g., 0) → Neutral-to-bias change; useful for early trend cues.
The background and label update in real time to reflect the current regime and bestLen.
⚙️ Inputs
minLen, maxLen, step: Define the search range for optimal lookback length.
retMethod: Choose between log or percent return calculations.
threshHigh, threshLow: Define signal zones using percentiles.
smooth: Enable dynamic score smoothing.
h_thresh: Midline crossover zone for directional context.
⚠️ Disclaimer
This tool is designed for exploratory and educational purposes only. It does not offer financial advice or trading recommendations. Past performance is not indicative of future results.
Always consult a licensed financial advisor before making investment decisions.
Statistics
Turtle Trading System (Full Version)The turtle trader strategy by Richard Dennis, buys from breakouts and uses volatility for sizing. Accurate on most asset classes, best on Gold.
GCM Price Based ColorIndicator Name:
GCM Price Based Color Indicator
Detailed Description:
The GCM Price Based Color Indicator is a unique tool designed to help traders spot potential "pump" events in the market. Unlike traditional Volume Rate of Change (VROC) indicators, this script is conditional: it calculates a VROC value only when both the average volume and the price are increasing. This focus helps filter out volume surges that don't accompany immediate price appreciation, highlighting more relevant "pump" signals.
Key Features & Calculation Logic:
Conditional Volume Rate of Change (VROC):
It first calculates a Simple Moving Average (SMA) of the volume over a user-defined length (lookback period).
It then checks two conditions:
Is the current SMA volume greater than the previous bar's SMA volume (i.e., volumeIncreasing)?
Is the current close price greater than the previous bar's close price (i.e., valueIncreasing)?
Only if both volume Increasing AND value Increasing are true, a VROC value is calculated as (current _ MA _ volume - previous _ MA _ volume) * (100 / previous _ MA _ volume). Otherwise, the VROC for that bar is 0.
Historical Normalization:
The raw VROC value is then normalized against its own historical maximum value observed since the indicator was applied. This scaling brings all VROC values into a common 0-100 range.
Why is this important? Normalization makes the indicator's readings comparable across different assets (e.g., high-volume vs. low-volume stocks/cryptos) and different timeframes, making it easier to interpret the strength of a "pump" relative to its own past.
Dynamic Plot Color (Price-Based):
The plot line's color itself provides an immediate visual cue about the current bar's price action:
Green: close is greater than close (price is up for the current bar).
Red: close is less than close (price is down for the current bar).
Grey: close is equal to close (price is flat for the current bar).
Important Note: The plot color reflects the price movement of the current bar, not the magnitude of the VROC Normalized value itself. This means you can have a high vrocNormalized value (indicating a strong conditional volume surge) but a red plot color if the very next bar's price closes lower, providing a multi-faceted view.
Thresholds & Alerts:
Two horizontal lines (small Pump Threshold and big Pump Threshold) are plotted to visually mark significant levels of normalized pump strength.
Customizable alerts can be set up to notify you when VROC Normalized reaches or exceeds these thresholds, helping you catch potential pump events in real-time.
How to Use It:
Identify Potential Pumps: Look for upward spikes in the VROC Normalized line. Higher spikes indicate stronger pump signals (i.e., a larger increase in average volume coinciding with an increasing price).
Monitor Thresholds: Pay attention when the VROC Normalized line crosses above your small Pump Threshold or big Pump Threshold. These are configurable levels to suit different assets and trading styles.
Observe Plot Color: The line color provides crucial context. A high VROC Normalized (strong pump signal) with a green line indicates current price momentum is still positive. If VROC Normalized is high but the line turns red, it might suggest the initial pump is losing steam or experiencing a pullback.
Combine with Other Tools: This indicator is best used in conjunction with other technical analysis tools (e.g., support/resistance, trend lines, other momentum indicators) for confirmation and a more holistic trading strategy.
Indicator Inputs:
Lookback period (1 - 4999) (default: 420): This length determines the period for the Simple Moving Average (SMA) of volume. A higher value will smooth the volume average more, reacting slower, while a lower value will make it more reactive. Adjust based on the timeframe and asset volatility.
Big Pump Threshold (0.01 - 99.99) (default: 10.0): The normalized VROC Normalized level that signifies a "Big Pump." When VROC Normalized reaches or exceeds this level, an alert can be triggered.
Small Pump Threshold (0.01 - 99.99) (default: 0.5): The normalized VROC Normalized level that signifies a "Small Pump." This is a lower threshold for earlier or less significant pump activity.
Alerts:
Small Pump: Triggers when VROC Normalized crosses above or equals the small Pump Threshold.
Big Pump: Triggers when VROC Normalized crosses above or equals the big Pump Threshold.
Best Practices & Considerations:
Timeframes: The indicator can be used on various timeframes, but its effectiveness may vary. Experiment to find what works best for your chosen asset and trading style.
Volatility: Highly volatile assets might require different threshold settings compared to less volatile ones.
Lag: Due to the use of a Simple Moving Average (SMA) for volume, there will be some inherent lag in the calculation.
Normalization Start: The historic Max for normalization starts with a default value of 10.0. For the very first bars, or if there hasn't been a significant VROC yet, the VROC Normalized might behave differently until a true historical maximum VROC establishes itself.
Not Financial Advice: This indicator is a tool for analysis and does not constitute financial advice. Always perform your own research and manage your risk.
Flux Capacitor (FC)# Flux Capacitor
**A volume-weighted, outlier-resistant momentum oscillator designed to expose hidden directional pressure from institutional participants.**
---
### Why "Flux Capacitor"?
The name pays homage to the fictional energy core in *Back to the Future* — an invisible engine that powers movement. Similarly, this indicator detects whether price movement is being powered by real market participation (volume) or if it's coasting without conviction.
---
### Methodology
The Flux Capacitor fuses three statistical layers:
- **Normalized Momentum**: `(Close – Open) / ATR`
Controls for raw price size and volatility.
- **Volume Scaling**:
Amplifies the effect of price moves that occur with elevated volume.
- **Robust Normalization**:
- *Winsorization* caps outlier spikes.
- *MAD-Z scoring* normalizes the signal across assets (crypto, futures, stocks).
- This produces consistent scaling across timeframes and symbols.
The result is a smooth oscillator that reliably indicates **liquidity-backed momentum** — not just price movement.
---
### Signal Events
- **Divergence (D)**: Price makes higher highs or lower lows, but Flux does not.
- **Absorption (A)**: Candle shows high volume and small body, while Flux opposes the candle direction — indicates smart money stepping in.
- **Compression (◆)**: High volume with low momentum — potential breakout zone.
- **Zero-Cross**: Indicates directional regime flip.
- **Flux Acceleration**: Histogram shows pressure rate of change.
- **Regime Background**: Color fades with weakening trend conviction.
All signals are color-coded and visually compact for easy pattern recognition.
---
### Interpreting Divergence & Absorption Correctly
Signal strength improves significantly when it appears **in the correct zone**:
#### Divergence:
| Signal | Zone | Meaning | Strength |
|--------|------------|------------------------------------------|--------------|
| Green D | Below 0 | Bullish reversal forming in weakness | **Strong** |
| Green D | Above 0 | Bullish, but less convincing | Moderate |
| Red D | Above 0 | Bearish reversal forming in strength | **Strong** |
| Red D | Below 0 | Bearish continuation — low warning value | Weak |
#### Absorption:
| Signal | Zone | Meaning | Strength |
|--------|------------|-----------------------------------------|--------------|
| Green A | Below 0 | Buyers absorbing panic-selling | **Strong** |
| Green A | Above 0 | Support continuation | Moderate |
| Red A | Above 0 | Sellers absorbing FOMO buying | **Strong** |
| Red A | Below 0 | Trend continuation — not actionable | Weak |
Look for **absorption or divergence signals in “enemy territory”** for the most actionable entries.
---
### Reducing Visual Footprint
If your chart shows a long line of numbers across the top of the Flux Capacitor pane (e.g. "FC 14 20 9 ... Bottom Right"), it’s due to TradingView’s *status line input display*.
**To fix this**:
Right-click the indicator pane → **Settings** → **Status Line** tab → uncheck “Show Indicator Arguments”.
This frees up vertical space so top-edge signals (like red `D` or yellow `◆`) remain visible and unobstructed.
---
### Features
- Original MAD-Z based momentum design
- True volume-based divergence and absorption logic
- Built-in alerts for all signal types
- Works across timeframes (1-min to weekly)
- Minimalist, responsive layout
- 25+ customizable parameters
- No future leaks, no repainting
---
### Usage Scenarios
- **Trend confirmation**: Flux > 0 confirms bullish trend strength
- **Reversal detection**: Divergence or absorption in opposite territory = high-probability reversal
- **Breakout anticipation**: Compression signal inside range often precedes directional move
- **Momentum shifts**: Watch for zero-crosses + flux acceleration spikes
---
### ⚠ Visual Note for BTC, ETH, Crude Oil & Futures
These high-priced or rapidly accelerating instruments can visually compress any linear oscillator. You may notice the Flux Capacitor’s line appears "flat" or muted on these assets — especially over long lookbacks.
> **This does not affect signal validity.** Divergence, absorption, and compression triggers still fire based on underlying logic — only the line’s amplitude appears reduced due to scaling constraints.
---
### Disclaimer
This indicator is for educational purposes only. It is not trading advice. Past results do not guarantee future performance. Use in combination with your own risk management and analysis.
TUFAN Hacim / Fiyat SkoruCalculates long, mid, and short-term volume averages along with volume volatility.
Scores volume strength based on volume trend slope and price changes.
Adjusts the score based on the relationship between price and its 50, 100, and 150-day moving averages.
Combines and normalizes these factors into a final volume/price score.
Triggers an alert when the score is 3 or higher and displays the score with color coding on the chart.
Lọc nhiễu MACD [VNFlow]Contact and discuss to use advanced tools to support your trading strategy
Email: hasobin@outlook.com
Phone: 0373885338
See you,
Luma DCA Tracker (BTC)Luma DCA Tracker (BTC) – User Guide
Function
This indicator simulates a regular Bitcoin investment strategy (Dollar Cost Averaging). It calculates and visualizes:
Accumulated BTC amount
Average entry price
Total amount invested
Current portfolio value
Profit/loss in absolute and percentage terms
Settings
Investment per interval
Fixed amount to be invested at each interval (e.g., 100 USD)
Start date
The date when DCA simulation begins
Investment interval
Choose between:
daily, weekly, every 14 days, or monthly
Show investment data
Displays additional chart lines (total invested, value, profit, etc.)
Chart Elements
Orange line: Average DCA entry price
Grey dots: Entry points based on selected interval
Info box (bottom left): Live summary of all key values
Notes
Purchases are simulated at the closing price of each interval
No fees, slippage, or taxes are included
The indicator is a simulation only and not linked to an actual portfolio
Volume Velocity by TenozenA new indicator is here!
Volume Velocity (VV) is designed to help identify whether the market is likely to trend or move sideways. When VV drops below 0, it suggests weak volume momentum, which often leads to choppy or range-bound price action. Conversely, a positive VV typically signals a stronger market drive, potentially indicating trend formation. (Strong reminder! Volume data quality is crucial! I suggest using volume data from futures market!)
VV is calculated using Kalman filter concepts; adapting to the state, covariance, and predictions over time, using matrix-based calculations.
To be honest, it's quite a complex method, and I’m still learning as I go. The current version is based on a translated formula that I personaly modified into Pine Script.
I’d really appreciate any insights or ideas you guys might have! Maybe there’s something I missed or a new approach I could try! Ciao!
Retail PositioningTest indicator for positioning should show how the retailers are positioned.
Indicator is still under construction, with the Pine.seed it should be possible to show how much of the retailers are positioned on the forex pairs.
Normalized Volume & True RangeThis indicator solves a fundamental challenge that traders face when trying to analyze volume and volatility together on their charts. Traditionally, volume and price volatility exist on completely different scales, making direct comparison nearly impossible. Volume might range from thousands to millions of shares, while volatility percentages typically stay within single digits. This indicator brings both measurements onto a unified scale from 0 to 100 percent, allowing you to see their relationship clearly for the first time.
The core innovation lies in the normalization process, which automatically calculates appropriate scaling factors for both volume and volatility based on their historical statistical properties. Rather than using arbitrary fixed scales that might work for one stock but fail for another, this system adapts to each instrument's unique characteristics. The indicator establishes baseline averages for both measurements and then uses statistical analysis to determine reasonable maximum values, ensuring that extreme outliers don't distort the overall picture.
You can choose from three different volatility calculation methods depending on your analytical preferences. The "Body" option measures the distance between opening and closing prices, focusing on the actual trading range that matters most for price action. The "High/Low" method captures the full daily range including wicks and shadows, giving you a complete picture of intraday volatility. The "Close/Close" approach compares consecutive closing prices, which can be particularly useful for identifying gaps and overnight price movements.
The indicator displays volume as colored columns that match your candlestick colors, making it intuitive to see whether high volume occurred during up moves or down moves. Volatility appears as a gray histogram, providing a clean background reference that doesn't interfere with volume interpretation. Both measurements are clipped at 100 percent, which represents their calculated maximum normal values, so any readings near this level indicate unusually high activity in either volume or volatility.
The baseline reference line shows you what "normal" volume looks like for the current instrument, helping you quickly identify when trading activity is above or below average. Optional moving averages for both volume and volatility are available if you prefer smoothed trend analysis over raw daily values. The entire system updates in real-time as new data arrives, continuously refining its statistical calculations to maintain accuracy as market conditions evolve.
This two-in-one indicator provides a straightforward way to examine how price movements relate to trading volume by presenting both measurements on the same normalized scale, making it easier to spot patterns and relationships that might otherwise remain hidden when analyzing these metrics separately.
Technical Strength Index (TSI)📘 TSI with Dynamic Bands – Technical Strength Index
The TSI with Dynamic Bands is a multi-factor indicator designed to measure the statistical strength and structure of a trend. It combines several quantitative metrics into a single, normalized score between 0 and 1, allowing traders to assess the technical quality of market moves and detect overbought/oversold conditions with adaptive precision.
🧠 Core Components
This indicator draws from the StatMetrics library, blending:
📈 Trend Persistence: via the Hurst exponent, indicating whether price action is mean-reverting or trending.
📉 Risk-Adjusted Volatility: via the inverted , rewarding smoother, less erratic price movement.
🚀 Momentum Strength: using a combination of directional momentum and Z-score–normalized returns.
These components are normalized and averaged into the TSI line.
🎯 Features
TSI Line: Composite score of trend quality (0 = weak/noise, 1 = strong/structured).
Dynamic Bands: Mean ± 1 standard deviation envelopes provide adaptive context.
Overbought/Oversold Detection: Based on a rolling quantile (e.g. 90th/10th percentile of TSI history).
Signal Strength Bar (optional): Measures how statistically extreme the current TSI value is, helping validate confidence in trade setups.
Dynamic Color Cues: Background and bar gradients help visually identify statistically significant zones.
📈 How to Use
Look for overbought (red background) or oversold (green background) conditions as potential reversal zones.
Confirm trend strength with the optional signal strength bar — stronger values suggest higher signal confidence.
Use the TSI line and context bands to filter out noisy ranges and focus on structured price moves.
⚙️ Inputs
Lookback Period: Controls the smoothing and window size for statistical calculations.
Overbought/Oversold Quantiles: Adjust the thresholds for signal zones.
Plot Signal Strength: Enable or disable the signal confidence bar.
Overlay Signal Strength: Show signal strength in the same panel (compact) or not (cleaner TSI-only view).
🛠 Example Use Cases
Mean reversion traders identifying reversal zones with statistical backing
Momentum/Trend traders confirming structure before entries
Quantitative dashboards or multi-asset screening tools
⚠️ Disclaimer
This script is for educational and informational purposes only. It does not constitute financial advice or a recommendation to buy or sell any financial instrument.
This AI is not a financial advisor; please consult your financial advisor for personalized advice.
Open Price multi TimeframeMulti Open Price Lines
© 2025 Unikryptonian. Licensed under MIT.
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**What it does**
This indicator plots the opening price of up to four user-selectable higher timeframes directly on your current chart. You can toggle between showing only the current period’s open or the full historical line.
**Inputs**
• Select Timeframe 1/2/3/4 (e.g. 5m, 30m, 1h, 4h, 1D)
• Show only current open? (On/Off for each timeframe)
**How to use**
1. Add the indicator to any chart (recommended timeframe ≥ lowest selected TF).
2. In Settings ► Inputs, choose your desired timeframes.
3. Tick “Show only current open” to hide past lines and see only the latest open price extended to the right.
4. Untick to display the full historical open-price line.
**Changelog**
• v1.0 (2025-06-11): Initial release with multi-TF support.
**Disclaimer**
• For educational purposes only.
• Not financial advice—use at your own risk.
Market Strength Buy Sell Indicator [TradeDots]A specialized tool designed to assist traders in evaluating market conditions through a multifaceted analysis of relative performance, beta-adjusted returns, momentum, and volume—allowing you to identify optimal points for long or short trades. By integrating multiple benchmarks (default S&P 500) and percentile-based thresholds, the script provides clear, actionable insights suitable for both day trading and higher-level timeframe assessments.
📝 HOW IT WORKS
1. Multi-Factor Composite Score
Relative Performance (RS Ratio): Compares your asset’s performance to a chosen benchmark (default: SPY). Values above 1.0 indicate outperformance, while below 1.0 suggest underperformance.
Beta-Adjusted Returns: Checks the ticker’s excess movement relative to expected market-related moves. This helps distinguish pure “alpha” from broad market effects.
Volume & Correlation: Volume spikes often confirm the momentum behind a move, while correlation measures how closely the asset tracks or diverges from its benchmark.
These components merge into a 0–100 composite score. Scores above 50 frequently imply bullish strength; drops below 50 often point to underperformance—potentially flagging short opportunities.
2. Intraday & Day Trading Focus
Monitoring Below 50: During the trading day, the script calculates live data against the benchmark, offering an intraday-sensitive composite score. A dip under 50 may indicate a short bias for that session, especially when accompanied by high volume or momentum shifts.
3. Higher Timeframe Monitoring
Daily Strategies: On daily or weekly charts, the script reveals overall relative strength or weakness compared to the S&P 500. This higher-level perspective helps form broader trading biases—crucial for swing or position trades spanning multiple days.
Long/Short Thresholds: Persistent readings above 50 on a daily chart typically reinforce a long bias, while consistent dips below 50 can sustain a short or cautious outlook.
4. Pair Trading Applications
Custom Benchmark Selection: By setting a specific ticker pair as your benchmark instead of the default S&P 500, you can identify spread trading opportunities between two correlated assets. This allows you to go long the outperforming asset while shorting the underperforming one when the spread reaches extreme levels.
4. Color-Coded Signals & Alerts
Visual Zones (25–75): Color-coded bands highlight strong outperformance (above 75) or pronounced underperformance (below 25).
Alerts on Strong Shifts: Automatic alerts can notify you of sudden entries or exits from bullish or bearish zones, so you can potentially act on new market information without delay.
⚙️ HOW TO USE
1. Select Your Timeframe: For scalping or day trading, lower intervals (e.g., 5-minute) offer immediate data resets at the session’s start. For multi-day insight, daily or weekly charts reveal broader performance trends.
2. Watch Key Levels Around 50: Intraday dips under 50 may be a cue to consider short trades, while bounces above 50 can confirm renewed strength.
3. Assess Benchmark Relationships: Compare your asset’s score and signals to the broader market. A stock falling below its pair’s relative strength line might lag overall market momentum.
4. Combine Tools & Validate: This script excels when integrated with other technical analysis methods (e.g., support/resistance, chart patterns) and fundamental factors for a holistic market view.
❗ LIMITATIONS
No Direction Guarantee: The indicator identifies relative strength but does not guarantee directional price moves.
Delayed Updates: Since calculations update after each bar close, sudden intrabar changes may not immediately reflect.
Market-Specific Behaviors: Some assets or unusual market conditions may deviate from typical benchmarks, weakening signal reliability.
Past ≠ Future: High or low relative strength in the past may not predict continued performance.
RISK DISCLAIMER
All forms of trading and investing involve risk, including the possible loss of principal. This indicator analyzes relative performance but cannot assure profits or eliminate losses. Past performance of any strategy does not guarantee future results. Always combine analysis with proper risk management and your broader trading plan. Consult a licensed financial advisor if you are unsure of your individual risk tolerance or investment objectives.
Multi Asset Comparative📊 Multi Asset Comparative – Compare Baskets of Cryptos Visually
This indicator allows you to compare the performance of two groups of cryptocurrencies (or any assets) over time, using a clean and intuitive chart.
Instead of looking at each asset separately, this tool gives you a global view by showing how one group performs relative to another — all displayed in the form of candlesticks.
🧠 What This Tool Is For
Markets constantly shift, and capital rotates between sectors or tokens. This script helps you visually track those shifts by answering a key question:
"Is this group of assets getting stronger or weaker compared to another group?"
For example:
Compare altcoins vs Bitcoin
Track the DeFi sector vs Ethereum
Analyze your custom portfolio vs the market
Spot moments when money flows from majors to smaller caps, or vice versa
🧩 How It Works (Simplified)
You select two groups of assets:
Group 1 (up to 20 assets) — the one you want to analyze
Group 2 (up to 5 assets) — your comparison baseline
The indicator then creates a single line of candles that represents the performance of Group 1 compared to Group 2. If the candles go up, it means Group 1 is gaining strength over Group 2. If the candles go down, it's losing ground.
This lets you see market dynamics in one glance, instead of switching charts or running calculations manually.
🚀 Why It's Unique
Unlike many indicators that just show data from one asset, this one provides a bird's-eye view of multiple assets at once — condensed into a simple visual ratio.
It’s:
Customizable (you choose the assets)
Visual and intuitive (no need to interpret tables or formulas)
Actionable (helps with trend confirmation, macro views, and market rotation)
Whether you're a swing trader, a macro analyst, or building your own strategy, this tool can help you spot opportunities hidden in plain sight.
✅ How to Use It
Choose your two groups of assets (e.g., altcoins vs BTC/ETH)
Watch the direction of the candles:
Uptrend = Group 1 gaining strength over Group 2
Downtrend = Group 1 weakening
Use it to confirm market shifts, anticipate rotations, or analyze sector strength
Altseason HunterAltseason Hunter is an early warning indicator for potential altcoin seasons in the cryptocurrency market.
It compares Bitcoin Dominance (BTC.D) and Altcoin Dominance (TOTAL3.D), and generates a signal when Bitcoin Dominance is in a downtrend while Altcoin Dominance is in an uptrend.
A green triangle appears when these conditions are met, indicating that altcoins are starting to outperform Bitcoin in terms of market share.
This tool helps traders anticipate shifts in market cycles, but it does not provide direct buy or sell signals. Use for informational and educational purposes only.
Developed by Kriptomist.
Approximate Entropy Zones [PhenLabs]Version: PineScript™ v6
Description
This indicator identifies periods of market complexity and randomness by calculating the Approximate Entropy (ApEn) of price action. As the movement of the market becomes complex, it means the current trend is losing steam and a reversal or consolidation is likely near. The indicator plots high-entropy periods as zones on your chart, providing a graphical suggestion to anticipate a potential market direction change. This indicator is designed to help traders identify favorable times to get in or out of a trade by highlighting when the market is in a state of disarray.
Points of Innovation
Advanced Complexity Analysis: Instead of relying on traditional momentum or trend indicators, this tool uses Approximate Entropy to quantify the unpredictability of price movements.
Dynamic Zone Creation: It automatically plots zones on the chart during periods of high entropy, providing a clear and intuitive visual guide.
Customizable Sensitivity: Users can fine-tune the ‘Entropy Threshold’ to adjust how frequently zones appear, allowing for calibration to different assets and timeframes.
Time-Based Zone Expiration: Zones can be set to expire after a specific time, keeping the chart clean and relevant.
Built-in Zone Size Filter: Excludes zones that form on excessively large candles, filtering out noise from extreme volatility events.
On-Chart Calibration Guide: A persistent note on the chart provides simple instructions for adjusting the entropy threshold, making it easy for users to optimize the indicator’s performance.
Core Components
Approximate Entropy (ApEn) Calculation: The core of the indicator, which measures the complexity or randomness of the price data.
Zone Plotting: Creates visual boxes on the chart when the calculated ApEn value exceeds a user-defined threshold.
Dynamic Zone Management: Manages the lifecycle of the zones, from creation to expiration, ensuring the chart remains uncluttered.
Customizable Settings: A comprehensive set of inputs that allow users to control the indicator’s sensitivity, appearance, and time-based behavior.
Key Features
Identifies Potential Reversals: The high-entropy zones can signal that a trend is nearing its end, giving traders an early warning.
Works on Any Timeframe: The indicator can be applied to any chart timeframe, from minutes to days.
Customizable Appearance: Users can change the color and transparency of the zones to match their chart’s theme.
Informative Labels: Each zone can display the calculated entropy value and the direction of the candle on which it formed.
Visualization
Entropy Zones: Shaded boxes that appear on the chart, highlighting candles with high complexity.
Zone Labels: Text within each zone that displays the ApEn value and a directional arrow (e.g., “0.525 ↑”).
Calibration Note: A small table in the top-right corner of the chart with instructions for adjusting the indicator’s sensitivity.
Usage Guidelines
Entropy Analysis
Source: The price data used for the ApEn calculation. (Default: close)
Lookback Length: The number of bars used in the ApEn calculation. (Default: 20, Range: 10-50)
Embedding Dimension (m): The length of patterns to be compared; a standard value for financial data. (Default: 2)
Tolerance Multiplier (r): Adjusts the tolerance for pattern matching; a larger value makes matching more lenient. (Default: 0.2)
Entropy Threshold: The ApEn value that must be exceeded to plot a zone. Increase this if too many zones appear; decrease it if too few appear. (Default: 0.525)
Time Settings
Analysis Timeframe: How long a zone remains on the chart after it forms. (Default: 1D)
Custom Period (Bars): The zone’s lifespan in bars if “Analysis Timeframe” is set to “Custom”. (Default: 1000)
Zone Settings
Zone Fill Color: The color of the entropy zones. (Default: #21f38a with 80% transparency)
Maximum Zone Size %: Filters out zones on candles that are larger than this percentage of their low price. (Default: 0.5)
Display Options
Show Entropy Label: Toggles the visibility of the text label inside each zone. (Default: true)
Label Text Position: The horizontal alignment of the text label. (Default: Right)
Show Calibration Note: Toggles the visibility of the calibration note in the corner of the chart. (Default: true)
Best Use Cases
Trend Reversal Trading: Identifying when a strong trend is likely to reverse or pause.
Breakout Confirmation: Using the absence of high entropy to confirm the strength of a breakout.
Ranging Market Identification: Periods of high entropy can indicate that a market is transitioning into a sideways or choppy phase.
Limitations
Not a Standalone Signal: This indicator should be used in conjunction with other forms of analysis to confirm trading signals.
Lagging Nature: Like all indicators based on historical data, ApEn is a lagging measure and does not predict future price movements with certainty.
Calibration Required: The effectiveness of the indicator is highly dependent on the “Entropy Threshold” setting, which needs to be adjusted for different assets and timeframes.
What Makes This Unique
Quantifies Complexity: It provides a numerical measure of market complexity, offering a different perspective than traditional indicators.
Clear Visual Cues: The zones make it easy to see when the market is in a state of high unpredictability.
User-Friendly Design: With features like the on-chart calibration note, the indicator is designed to be easy to use and optimize.
How It Works
Calculate Standard Deviation: The indicator first calculates the standard deviation of the source price data over a specified lookback period.
Calculate Phi: It then calculates a value called “phi” for two different pattern lengths (embedding dimensions ‘m’ and ‘m+1’). This involves comparing sequences of data points to see how many are “similar” within a certain tolerance (determined by the standard deviation and the ‘r’ multiplier).
Calculate ApEn: The Approximate Entropy is the difference between the two phi values. A higher ApEn value indicates greater irregularity and unpredictability in the data.
Plot Zones: If the calculated ApEn exceeds the user-defined ‘Entropy Threshold’, a zone is plotted on the chart.
Note: The “Entropy Threshold” is the most important setting to adjust. If you see too many zones, increase the threshold. If you see too few, decrease it.
Local Extremes by Hour (Fractal Period = 12)An indicator to backtest the percentage of William Fractals extremes per hour of day
SmartPhase Analyzer📝 SmartPhase Analyzer – Composite Market Regime Classifier
SmartPhase Analyzer is an adaptive regime classification tool that scores market conditions using a customizable set of statistical indicators. It blends multiple normalized metrics into a composite score, which is dynamically evaluated against rolling statistical thresholds to determine the current market regime.
✅ Features:
Composite score calculated from 13+ toggleable statistical indicators:
Sharpe, Sortino, Omega, Alpha, Beta, CV, R², Entropy, Drawdown, Z-Score, PLF, SRI, and Momentum Rank
Uses dynamic thresholds (mean ± std deviation) to classify regime states:
🟢 BULL – Strongly bullish
🟩 ACCUM – Mildly bullish
⚪ NEUTRAL – Sideways
🟧 DISTRIB – Mildly bearish
🔴 BEAR – Strongly bearish
Color-coded histogram for composite score clarity
Real-time regime label plotted on chart
Benchmark-aware metrics (Alpha, Beta, etc.)
Modular design using the StatMetrics library by RWCS_LTD
🧠 How to Use:
Enable/disable metrics in the settings panel to customize your composite model
Use the composite histogram and regime background for discretionary or systematic analysis
⚠️ Disclaimer:
This indicator is for educational and informational purposes only. It does not constitute financial advice or a trading recommendation. Always consult your financial advisor before making investment decisions.
StatMetricsLibrary "StatMetrics"
A utility library for common statistical indicators and ratios used in technical analysis.
Includes Z-Score, correlation, PLF, SRI, Sharpe, Sortino, Omega ratios, and normalization tools.
zscore(src, len)
Calculates the Z-score of a series
Parameters:
src (float) : The input price or series (e.g., close)
len (simple int) : The lookback period for mean and standard deviation
Returns: Z-score: number of standard deviations the input is from the mean
corr(x, y, len)
Computes Pearson correlation coefficient between two series
Parameters:
x (float) : First series
y (float) : Second series
len (simple int) : Lookback period
Returns: Correlation coefficient between -1 and 1
plf(src, longLen, shortLen, smoothLen)
Calculates the Price Lag Factor (PLF) as the difference between long and short Z-scores, normalized and smoothed
Parameters:
src (float) : Source series (e.g., close)
longLen (simple int) : Long Z-score period
shortLen (simple int) : Short Z-score period
smoothLen (simple int) : Hull MA smoothing length
Returns: Smoothed and normalized PLF oscillator
sri(signal, len)
Computes the Statistical Reliability Index (SRI) based on trend persistence
Parameters:
signal (float) : A price or signal series (e.g., smoothed PLF)
len (simple int) : Lookback period for smoothing and deviation
Returns: Normalized trend reliability score
sharpe(src, len)
Calculates the Sharpe Ratio over a period
Parameters:
src (float) : Price series (e.g., close)
len (simple int) : Lookback period
Returns: Sharpe ratio value
sortino(src, len)
Calculates the Sortino Ratio over a period, using only downside volatility
Parameters:
src (float) : Price series
len (simple int) : Lookback period
Returns: Sortino ratio value
omega(src, len)
Calculates the Omega Ratio as the ratio of upside to downside return area
Parameters:
src (float) : Price series
len (simple int) : Lookback period
Returns: Omega ratio value
beta(asset, benchmark, len)
Calculates beta coefficient of asset vs benchmark using rolling covariance
Parameters:
asset (float) : Series of the asset (e.g., close)
benchmark (float) : Series of the benchmark (e.g., SPX close)
len (simple int) : Lookback window
Returns: Beta value (slope of linear regression)
alpha(asset, benchmark, len)
Calculates rolling alpha of an asset relative to a benchmark
Parameters:
asset (float) : Series of the asset (e.g., close)
benchmark (float) : Series of the benchmark (e.g., SPX close)
len (simple int) : Lookback window
Returns: Alpha value (excess return not explained by Beta exposure)
skew(x, len)
Computes skewness of a return series
Parameters:
x (float) : Input series (e.g., returns)
len (simple int) : Lookback period
Returns: Skewness value
kurtosis(x, len)
Computes kurtosis of a return series
Parameters:
x (float) : Input series (e.g., returns)
len (simple int) : Lookback period
Returns: Kurtosis value
cv(x, len)
Calculates Coefficient of Variation
Parameters:
x (float) : Input series (e.g., returns or prices)
len (simple int) : Lookback period
Returns: CV value
autocorr(x, len)
Calculates autocorrelation with 1-lag
Parameters:
x (float) : Series to test
len (simple int) : Lookback window
Returns: Autocorrelation at lag 1
stderr(x, len)
Calculates rolling standard error of a series
Parameters:
x (float) : Input series
len (simple int) : Lookback window
Returns: Standard error (std dev / sqrt(n))
info_ratio(asset, benchmark, len)
Calculates the Information Ratio
Parameters:
asset (float) : Asset price series
benchmark (float) : Benchmark price series
len (simple int) : Lookback period
Returns: Information ratio (alpha / tracking error)
tracking_error(asset, benchmark, len)
Measures deviation from benchmark (Tracking Error)
Parameters:
asset (float) : Asset return series
benchmark (float) : Benchmark return series
len (simple int) : Lookback window
Returns: Tracking error value
max_drawdown(x, len)
Computes maximum drawdown over a rolling window
Parameters:
x (float) : Price series
len (simple int) : Lookback window
Returns: Rolling max drawdown percentage (as a negative value)
zscore_signal(z, ob, os)
Converts Z-score into a 3-level signal
Parameters:
z (float) : Z-score series
ob (float) : Overbought threshold
os (float) : Oversold threshold
Returns: -1, 0, or 1 depending on signal state
r_squared(x, y, len)
Calculates rolling R-squared (coefficient of determination)
Parameters:
x (float) : Asset returns
y (float) : Benchmark returns
len (simple int) : Lookback window
Returns: R-squared value (0 to 1)
entropy(x, len)
Approximates Shannon entropy using log returns
Parameters:
x (float) : Price series
len (simple int) : Lookback period
Returns: Approximate entropy
zreversal(z)
Detects Z-score reversals to the mean
Parameters:
z (float) : Z-score series
Returns: +1 on upward reversal, -1 on downward
momentum_rank(x, len)
Calculates relative momentum strength
Parameters:
x (float) : Price series
len (simple int) : Lookback window
Returns: Proportion of lookback where current price is higher
normalize(x, len)
Normalizes a series to a 0–1 range over a period
Parameters:
x (float) : The input series
len (simple int) : Lookback period
Returns: Normalized value between 0 and 1
composite_score(score1, score2, score3)
Combines multiple normalized scores into a composite score
Parameters:
score1 (float)
score2 (float)
score3 (float)
Returns: Average composite score
Candle Overlap DegreeThis indicator gives the ratio of max(0, min High - max Low) to (max High - min Low) over n-day.
TitanGrid L/S SuperEngineTitanGrid L/S SuperEngine
Experimental Trend-Aligned Grid Signal Engine for Long & Short Execution
🔹 Overview
TitanGrid is an advanced, real-time signal engine built around a tactical grid structure.
It manages Long and Short trades using trend-aligned entries, layered scaling, and partial exits.
Unlike traditional strategy() -based scripts, TitanGrid runs as an indicator() , but includes its own full internal simulation engine.
This allows it to track capital, equity, PnL, risk exposure, and trade performance bar-by-bar — effectively simulating a custom backtest, while remaining compatible with real-time alert-based execution systems.
The concept was born from the fusion of two prior systems:
Assassin’s Grid (grid-based execution and structure) + Super 8 (trend-filtering, smart capital logic), both developed under the AssassinsGrid framework.
🔹 Disclaimer
This is an experimental tool intended for research, testing, and educational use.
It does not provide guaranteed outcomes and should not be interpreted as financial advice.
Use with demo or simulated accounts before considering live deployment.
🔹 Execution Logic
Trend direction is filtered through a custom SuperTrend engine. Once confirmed:
• Long entries trigger on pullbacks, exiting progressively as price moves up
• Short entries trigger on rallies, exiting as price declines
Grid levels are spaced by configurable percentage width, and entries scale dynamically.
🔹 Stop Loss Mechanism
TitanGrid uses a dual-layer stop system:
• A static stop per entry, placed at a fixed percentage distance matching the grid width
• A trend reversal exit that closes the entire position if price crosses the SuperTrend in the opposite direction
Stops are triggered once per cycle, ensuring predictable and capital-aware behavior.
🔹 Key Features
• Dual-side grid logic (Long-only, Short-only, or Both)
• SuperTrend filtering to enforce directional bias
• Adjustable grid spacing, scaling, and sizing
• Static and dynamic stop-loss logic
• Partial exits and reset conditions
• Webhook-ready alerts (browser-based automation compatible)
• Internal simulation of equity, PnL, fees, and liquidation levels
• Real-time dashboard for full transparency
🔹 Best Use Cases
TitanGrid performs best in structured or mean-reverting environments.
It is especially well-suited to assets with the behavioral profile of ETH — reactive, trend-intraday, and prone to clean pullback formations.
While adaptable to multiple timeframes, it shows strongest performance on the 15-minute chart , offering a balance of signal frequency and directional clarity.
🔹 License
Published under the Mozilla Public License 2.0 .
You are free to study, adapt, and extend this script.
🔹 Panel Reference
The real-time dashboard displays performance metrics, capital state, and position behavior:
• Asset Type – Automatically detects the instrument class (e.g., Crypto, Stock, Forex) from symbol metadata
• Equity – Total simulated capital: realized PnL + floating PnL + remaining cash
• Available Cash – Capital not currently allocated to any position
• Used Margin – Capital locked in open trades, based on position size and leverage
• Net Profit – Realized gain/loss after commissions and fees
• Raw Net Profit – Gross result before trading costs
• Floating PnL – Unrealized profit or loss from active positions
• ROI – Return on initial capital, including realized and floating PnL. Leverage directly impacts this metric, amplifying both gains and losses relative to account size.
• Long/Short Size & Avg Price – Open position sizes and volume-weighted average entry prices
• Leverage & Liquidation – Simulated effective leverage and projected liquidation level
• Hold – Best-performing hold side (Long or Short) over the session
• Hold Efficiency – Performance efficiency during holding phases, relative to capital used
• Profit Factor – Ratio of gross profits to gross losses (realized)
• Payoff Ratio – Average profit per win / average loss per loss
• Win Rate – Percent of profitable closes (including partial exits)
• Expectancy – Net average result per closed trade
• Max Drawdown – Largest recorded drop in equity during the session
• Commission Paid – Simulated trading costs: maker, taker, funding
• Long / Short Trades – Count of entry signals per side
• Time Trading – Number of bars spent in active positions
• Volume / Month – Extrapolated 30-day trading volume estimate
• Min Capital – Lowest equity level recorded during the session
🔹 Reference Ranges by Strategy Type
Use the following metrics as reference depending on the trading style:
Grid / Mean Reversion
• Profit Factor: 1.2 – 2.0
• Payoff Ratio: 0.5 – 1.2
• Win Rate: 50% – 70% (based on partial exits)
• Expectancy: 0.05% – 0.25%
• Drawdown: Moderate to high
• Commission Impact: High
Trend-Following
• Profit Factor: 1.5 – 3.0
• Payoff Ratio: 1.5 – 3.5
• Win Rate: 30% – 50%
• Expectancy: 0.3% – 1.0%
• Drawdown: Low to moderate
Scalping / High-Frequency
• Profit Factor: 1.1 – 1.6
• Payoff Ratio: 0.3 – 0.8
• Win Rate: 80% – 95%
• Expectancy: 0.01% – 0.05%
• Volume / Month: Very high
Breakout Strategies
• Profit Factor: 1.4 – 2.2
• Payoff Ratio: 1.2 – 2.0
• Win Rate: 35% – 60%
• Expectancy: 0.2% – 0.6%
• Drawdown: Can be sharp after failed breakouts
🔹 Note on Performance Simulation
TitanGrid includes internal accounting of fees, slippage, and funding costs.
While its logic is designed for precision and capital efficiency, performance is naturally affected by exchange commissions.
In frictionless environments (e.g., zero-fee simulation), its high-frequency logic could — in theory — extract substantial micro-edges from the market.
However, real-world conditions introduce limits, and all results should be interpreted accordingly.
Candle Range % vs 8-Candle AvgCandle % Indicator – Measure Candle Strength by Range %
**Overview:**
The *Candle % Indicator* helps traders visually and analytically gauge the strength or significance of a price candle relative to its recent historical context. This is particularly useful for detecting breakout moves, volatility shifts, or overextended candles that may signal exhaustion.
**What It Does:**
* Calculates the **percentage range** of the current candle compared to the **average range of the past N candles**.
* Highlights candles that exceed a user-defined threshold (e.g., 150% of the average range).
* Useful for **filtering out extreme candles** that might represent anomalies or unsustainable moves.
* Can be combined with other strategies (like EMA crossovers, support/resistance breaks, etc.) to improve signal quality.
**Use Case Examples:**
***Filter out fakeouts** in breakout strategies by ignoring candles that are overly large and may revert.
***Volatility control**: Avoid entries when market conditions are erratic.
**Confluence**: Combine with EMA or RSI signals for refined entries.
**How to Read:**
* If a candle is larger than the average range by more than the set percentage (default 150%), it's flagged (e.g., no entry signal or optional visual marker).
* Ideal for intraday, swing, or algorithmic trading setups.
**Customizable Inputs:**
**Lookback Period**: Number of previous candles to calculate the average range.
**% Threshold**: Maximum percentage a candle can exceed the average before being filtered or marked.
