SpiralGrinder Ultimate Trading System SpiralGrinder Ultimate Trading System
SpiralGrinder Ultimate (SGU) is a unique type of Trading System dedicated for leverage-trading BTC on Bitmex platform. Since it's highly customized to give statistically reliable signals based exclusively on BTC/USD Perpetual Swaps BITMEX chart BITMEX:XBTUSD , using it with other BTC charts will give usable, but less reliable signals!
SpiralGrinder’s Ultimate first iteration was SpiralSwinger V1 indicator released in march 2019, since then much has been changed, different algos were developed and then thrown into the bin, until after 6 months of intensive work current version was developed, backtested on XBT/USD Perpetual Inverse Swap Contract chart from Bitmex exchange on whole chart history from late 2015 until January 2020, on these timeframes – 1d, 12h, 8h, 6h, 4h, 3h, 2h, 90m, 1h.
Indicator algo is based on idea of price being a so called "fractal" - when same price action patterns occur over and over from time to time on different timeframes be it 1D, 4h, 1h or even 15m! Every time a particular timeframe (TF) has suitable volatility and price action is exhibiting wave structure with distinct highs and lows there will be a situations when high probability trade setups are possible. To predict those recurrent situations SGU tracks more than 30 parameters (godmode oscillator and some it’s experimental derivatives, historical volatility coefficients, some time-based variables, ATR-based Trend lines, regular divergences… etc) comparing them against each other, so when “all stars are aligned” based on statistical model built into its algo and when price has enough potential to move in particular direction reaching some measured move target a SIGNAL to enter position is generated.
Theoretical True Winrate of this indicator is around 60%, while practical is somewhat under 50%. True Winrate is a percentage of trades that reached PREDICTED target be it 1R or 20R prediction, instead of just being a common winrate (used by most traders) - percentage of all profitable trades even though many of them didn’t reach initially predicted targets. True WinRate is tied to a signal generating algo implemented in SGU and cannot be changed unless a new more sophisticated algo is found by the developer of this indicator and is implemented in future updates!
Main User Interface of SGU consists of many elements that are developed to help manage trades more efficiently without any emotional impact on decision making process. Apart from obvious Long/Short signals there are also predicted targets that should be hit with some probability for every given signal, suggested stop loss levels corresponding to predicted RR. There are 4 ATR-based trendlines that help determine trend bias on current timeframe and to set intermediate take profit points on the way toward target, also there are indicators of regular divergences to show us weakness during uptrends and downtrends, also there are special warnings included when price closes behind particularly important ATR line with strength enough to continue further it’s movement in initial direction. Also there are 2 candle color-based systems available: one of monitoring how overbought or oversold is price on current TF, second is created to tell us overall trend sentiment - how strong is movement of price in particular direction.
Since price could move in the same fashion during prolonged periods of time there could be a particular TF when signals will be absent till price volatility and oscillator readings doesn’t change its character and become favorable (become synchronized with price action) for signals to be generated. That’s why this indicator should be monitored on multiple TFs at once – you’ll never know on which TF next signal will appear. There will be a multiple signals going on parallel at the SAME TIME, simultaneously in DIFFERENT DIRECTIONS: for example swing long trade based on signal from 12h TF, while having a scalp short at the same time based on 1h chart. Exploring this kind of optimized multi-tasking could be done only by splitting bankroll on multiple accounts registered on Bitmex platform.
Suggested timeframes to monitor for potential signals are empirically chosen that their round multiples should give 24H or 1440m=(24h x 60m) : 12h x 2 = 24h, 3h x 8 = 24h, 144m x 10 = 1440m=24h.
Therefore main timeframes are: 1D, 12h, 8h, 6h, 4h, 3h, 2h, 90m and 1h.
Additional timeframes to watch are: 288m, 144m, and 72m.
Timeframes under 1h aren’t tested yet, but could be traded with additional caution: 45m, 36m and 30m.
To track effectively all signals generated by SGU one should have at least PRO subscription plan paid on TradingView as this allows to use non-standard timeframes and maximum of 10 server-side alerts on price/indicators necessary to work with this indicator.
To do in near future: add volume weighted macd with custom settings as an additional confluence in algo to increase average win rate of signals.
Attention! Past performance of this indicator is not indicative of future results!
For those interested to dig deeper into logic behind using SGU a full 20-page pdf user manual is available for download here: drive.google.com
To gain free test access just write me a DM.
Cari dalam skrip untuk "algo"
(16) DRAGON-X VS-148The Dragon is an experimental indicator that is currently still under development. I called this indicator the Dragon because, not unlike the movie and book; “How to train your Dragon”, you must adjust or dial in this indicator (train it) to get good entry/exit signals out of it, for each individual equity you want to examine. That is not nearly as convenient as all of my other indicators, but the extra work can be worth the effort. The benefits of this indicator are its responsive nature and it forecasting ability. In the inputs the algorithm allows you to select a forecasting option. Forecasting in this instance merely means shifting the resulting indicator projections forward by altering the algorithm to be looser. It can fairly accurately forecast 1 to 3 bars forward. The more forward you set the adjustment the less accurate it becomes. John Ehlers was the first person to transform Dr. Voss’s algorithm into an equity trading indicatory. His observations about forecasting are important. While the Voss filter “can’t it really look into the future, it can provide signals in advance of signals used by other traders – and that may be enough to create a successful trading edge.”
As the image below demonstrates the Dragon does indeed get you into and our of trades in advance of even our best indicator, Genie-Cycles, shown below the Dragon.
The second issue regarding this indicator is, it’s not easy to understand the rational behind it. The Dragon filter is a direct derivative of the Voss Predictive Filter. Dr. Voss describes this filter as “A filter for universal real-time prediction of band-limited signals” This algorithm was developed to provide greater resolution and insight into a wide class of signals generated by deterministic or stochastic systems. It attempts to remove group and phase delays from the Weighted Moving Average output. One of Dr. Voss’s fields of endeavor is working to make MRI images clearer. This is done by extracting the first harmonic of the output using a bandpass filter and then applying a "negative-delay" formula to it. Forecasting financial time series is regarded as one of the most challenging applications of time series prediction due to their dynamic nature.
We have more information on our website describing this indicator as well as three links to reference articles that describe the scientific concept underpinning this indicator.
In the image below, the Dragon Indicator is plotted below the price chart so you can see the correlation between the two. If you examine the last two entry signals you can clearly see that the Dragon flags an entry position very early in the turning point transition shift. Actually, at points in the chart that do not in any way look like the end of the last down leg of the cycle. This get you into a trade before most of the rest of the other market competitors.
We consider the Dragon to still be under development. It requires a narrow band width of input data, for the output to generate reliably accurate signals. Market data has unlimited bandwidth.
Our future development of this indicator will take two center of gravity filters and first narrow that resulting bandwidth by utilizing a pass band filter. We will than use this data as an input to the Voss algorithm. We will advise all of our user when this updated version is available. Currentely this experimental version is only available to our unlimited members.
Access this Genie indicator for your Tradingview account, through our web site. (Links Below) This will provide you with additional educational information and reference articles, videos, input and setting options and trading strategies this indicator excels in.
JERK UP {LM.Alerts Edition} (D)This is the " LONGS-MANAGEMENT Alerts " {LM.Alerts} Edition of JERK UP to enable auto-trading via alerts signaling.
Only the long-signals, generated from the underlying JERK UP algorithm, is used in this strategy-alerts script, with my latest risk-exit (collect gains) and stop-limit algorithms, as well as a bear-market filter, implemented.
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Since {LM.Alerts} engine only focuses on trading and managing longs, a bear-market filter is implemented base on the FUSIONGAPS indicator.
The FUSIONGAPS algorithm signals local bull or bear market phases, and then disables trades conditionally to reduce the chances of having to take losses during a local bear market phase (since the short-signals are not traded).
Enabling the different (Fastest >> Slowest) FUSIONGAPS levels (e.g. 50/15, 100/50, 200/50, 200/100, etc) activates the use of each of these levels to decide the local bull/bear market phases.
So in summary, the {LM.Alerts} algorithm trades up a bullish-hill, taking profits along the way; but stops all trading activity when the market is rolling down a bearish-hill; and then once a local bull-phase is detected again, it resumes trading, etc.
Note: To trade on both bullish and bearish phases, {LM.Alerts} scripts can be applied on an inverse-chart (i.e. 0-BTCUSD) for shorts.
The {LM.Alerts} engine will be ported to my other more powerful trade-signaling scripts in the future.
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FUSIONGAPS V5
Note: In no way is this intended as a financial/investment/trading advice. You are responsible for your own investment decisions and/or trades.
~JuniAiko
(=^~^=)v~
MTF Improved Schaff Trend Cycle IndicatorThis is my cutting edge "Improved Schaff Trend Cycle Indicator" that I radically modified for all assets, not just Forex. Just when you may have thought it was the end of the evolutionary line for Schaff trend cycle indicators, it's not! It's actually two different modified Schaff trend cycle tandem algorithms combined making this a very versatile multicator. Members obtaining Invite-Only access, I might suggest using two of these for increased situational awareness. The creator of "Schaff Trend Cycle", Doug Schaff, a pioneer in Forex analytic trading tools, was really on the right track decades ago when he created the original indicator. At the time of this release, my original free to use formulation shown on the very bottom above is highly popular with members on TV, and in my opinion, one of my most favored indicators I have published so far. Well, this is the NEW and IMPROVED version with reduced lag...
Modifications included are rescaling the range from 0/100 to +/-1.0, employing reversion to the mean principles Dr. John Ehlers elaborates about. The thresholds are set to +/-0.8, nothing significant about those numbers at all, be forewarned! One characteristic about these formulations is that I was able to reduce the lag in many cases. While both are more reactive than the original Schaff trend cycle indicator, often in downward trends, one has the ability to hug the -1.0 line more having an occasional propensity to anticipate false bottoms when significant divergences between the two occur. This is one capability in an indicator I have for so long tried to achieve without any success until now. Also in positive trends, these formulations are more effective when encountering detected peaks/tops without the inherent lag the original formulation had. Both are typically in agreement when an opportune selling exit point is commencing. These characteristics are displayed above on top of the original formulation shown on the bottom.
Another most notable feature I have been including recently is the multiple time frame (MTF) features in the indicator "Settings". The indicator accommodates selectable second-based time frames. This is my third PSv4.0 script to accommodate seconds in MTF adequately. Be forewarned, second-based time frames are currently for Premium subscribers only, until such time in the future when the prerogative of TV might change. I will continue adding second-based time frames to my other indicators where I feel it is beneficial to the indicator.
I.P.O.C.S.: "Initial Public Offering Clean Start" proprietary technology. I figured it's time to more accurately describe this tech starting with this novel indicator. Many of my other indicators already possess this capability. It allows suitable plotting from day one, minute one of IPO, remedying visually delayed signal analysis. It's basically accurate plotting from the very first bar (bar_index==0) on Tradingview. If you don't know what this is, most people don't, go back to the VERY beginning of any stock on the "All" chart and compare it to other similar indicators. What's so special about this? It is extremely difficult to get a healthy plot from bar_index==0 on any platform. However, I have become exceedingly talented performing this feat in most cases but not all depending on the algorithm. This indicator is a successful accomplishment implementing IPOCS. It's inherent value is predominantly for IPO traders who in the past have had to wait 20, 50, and 150 bars before they obtain a precise indicator measurement for the simplest of algorithms in order to make a properly informed decision to potentially invest in an asset. How is this achieved? It's a highly protected secret of mine... but I will say I rarely use Pine built-in functions at all. When I do, I use them scarcely due to currently existing Pine language limitations.
Anyhow, this supersedes my "Enhanced Schaff Trend Cycle Indicator" by far. For those of you who obtain this indicator, enjoy the POWER of Schaff renewed!
Features List Includes:
I.P.O.C.S.(Initial Public Offering Clean Start) Technology
Enable/disable dark background for enhanced visibility
MTF adjustments/selections
Typical Schaff adjustments
"Display Trends" selection to show both trends or each one independently
"Line Width" adjustment for increased line visibility
Ranges and thresholds are enable/disable capable
Upper threshold adjustment
Lower threshold adjustment
Adjustable centered medial zone
This is not a freely available indicator, FYI. To witness my Pine poetry in action, properly negotiated requests for unlimited access, per indicator, may ONLY be obtained by direct contact with me using TV's "Private Chats" or by "Message" hidden in my member name above. The comments section below is solely just for commenting and other remarks, ideas, compliments, etc... regarding only this indicator, not others. If you do have any questions or comments regarding this indicator, I will consider your inquiries, thoughts, and concepts presented below in the comments section, when time provides it. When my indicators achieve more prevalent use by TV members, I will implement more ideas when they present themselves as worthy additions. As always, "Like" it if you simply just like it with a proper thumbs up, and also return to my scripts list occasionally for additional postings. Have a profitable future everyone!
Enhanced Instantaneous Cycle Period - Dr. John EhlersThis is my first public release of detector code entitled "Enhanced Instantaneous Cycle Period" for PSv4.0 I built many months ago. Be forewarned, this is not an indicator, this is a detector to be used by ADVANCED developers to build futuristic indicators in Pine. The origins of this script come from a document by Dr. John Ehlers entitled "SIGNAL ANALYSIS CONCEPTS". You may find this using the NSA's reverse search engine "goggles", as I call it. John Ehlers' MESA used this measurement to establish the data window for analysis for MESA Cycle computations. So... does any developer wish to emulate MESA Cycle now??
I decided to take instantaneous cycle period to another level of novel attainability in this public release of source code with the following methods, if you are curious how I ENHANCED it. Firstly I reduced the delay of accurate measurement from bar_index==0 by quite a few bars closer to IPO. Secondarily, I provided a limit of 6 for a minimum instantaneous cycle period. At bar_index==0, it would provide a period of 0 wrecking many algorithms from the start. I also increased the instantaneous cycle period's maximum value to 80 from 50, providing a window of 6-80 for the instantaneous cycle period value window limits. Thirdly, I replaced the internal EMA with another algorithm. It reduces the lag while extracting a floating point number, for algorithms that will accept that, compared to a sluggish ordinary EMA return. You will see the excessive EMA delay with adding plot(ema(ICP,7)) as it was originally designed. Lastly it's in one simple function for reusability in a nice little package comprising of less than 40 lines of code. I hope I explained that adequately enough and gave you the reader a glimpse of the "Power of Pine" combined with ingenuity.
Be forewarned again, that most of Pine's built-in functions will not accept a floating-point number or dynamic integers for the "length" of it's calculation. You will have to emulate the built-in functions by creating Pine based custom functions, and I assure you, this is very possible in many cases, but not all without array support. You may use int(ICP) to extract an integer from the smoothICP return variable, which may be favorable compared to the choppiness/ringing if ICP alone.
This is commonly what my dense intricate code looks like behind the veil. If you are wondering why there is barely any notation, that's because the notation is in the variable naming and this is intended primarily for ADVANCED developers too. It does contain lines of code that explore techniques in Pine that may be applicable in other Pine projects for those learning or wishing to excel with Pine.
Showcased in the chart below is my free to use "Enhanced Schaff Trend Cycle Indicator", having a common appeal to TV users frequently. If you do have any questions or comments regarding this indicator, I will consider your inquiries, thoughts, and ideas presented below in the comments section, when time provides it. As always, "Like" it if you simply just like it with a proper thumbs up, and also return to my scripts list occasionally for additional postings. Have a profitable future everyone!
NOTICE: Copy pasting bandits who may be having nefarious thoughts, DO NOT attempt this, because this may violate Tradingview's terms, conditions and/or house rules. "WE" are always watching the TV community vigilantly for mischievous behaviors and actions that exploit well intended authors for the purpose of increasing brownie points in reputation scores. Hiding behind a "protected" wall may not protect you from investigation and account penalization by TV staff. Be respectful, and don't just throw an ma() in there branding it as "your" gizmo. Fair enough? Alrighty then... I firmly believe in "innovating" future state-of-the-art indicators, and please contact me if you wish to do so.
Bold Plot-v5A non multi time frame indicator script that includes different algorithms in order to create signals. All signals are created upon new candle open. Never re-paints. When initial entry achieved, it follows the trend and creates different RE-entry/TP/Safety Exit signals depending price movement. It is a release candidate version and still under development.
Changes in v5:
- Take Profit algorithm severely enhanced.
- New Safe Exit algorithm integrated. Safety Exit signals are being created if no take profit signals achieved after an initial entry or re-entry and safety exit algorithm senses a price movement change opposite to recent position.
- Re-Entry algorithm severely enhanced.
Zentrading Trend Follower_v1.1For more information on how to use and how to subscribe please visit
www.zentrading.co
Our ZenTrend Follower is designed to get you into trends in a safe an risk averse manner. It does not only provide you with buy and sell signals forcing you to either react quickly or miss the trade. Rather, our algorithm detects when a trend setup is active and plots a breakout level where you can enter the trade. This also makes it easy for you to scan many assets quickly: All you need to do is see if the indicator has detected a setup, if not, move on!
To ensure that you capture the trend, the indicator indicator shows you where to place your stop loss as the trend progresses. We will also show you a few other simple ways to exit the trades at higher profit levels in the detailed manual you receive after purchasing the indicator.
The shaded areas on the chart indicate that a trade setup has been detected by the algorithm: Green for bullish setups, red for bearish setups. The blue dots are the breakout level, if the price breaks this level the trade is entered. (as you can see on the chart, they can sometimes move towards the price!) Red crosses are plotted as your trailing stop loss, if price breaks the stop loss the trade is closed.
Jurik Moving Average🧠 Jurik Moving Average (JMA) — Ultra-Smooth Adaptive Trend Filter
This indicator implements a precise non-repainting recreation of the Jurik Moving Average (JMA) — a high-performance smoothing algorithm known for its ability to reduce lag while preserving rapid response to price changes. It is ideal for traders seeking a responsive yet stable line for trend detection, dynamic support/resistance, or signal generation.
⚙️ Core Functionality
At its heart, this indicator replicates the JMA logic as described in the original Jurik documentation, including:
✅ Adaptive smoothing based on price volatility.
✅ Variable phase shifting for forward/backward displacement.
✅ Power curve smoothing for dynamic control over responsiveness vs. smoothness.
✅ Volatility-aware band generation (optional).
The core algorithm uses a Kalman-style recursive filter with dynamic coefficients, adjusting to market conditions in real-time. Unlike traditional MAs (EMA, WMA, etc.), this implementation uses:
A volatility-normalized momentum engine to track price deviation (Kv factor).
A recursive double-smoothing mechanism for noise suppression without lag.
📈 Inputs
Length: Controls the base smoothing period.
Phase Shift: Moves the curve forward or backward in time (−100 to +100), for signal anticipation or lag removal.
Smoothing Power: Adjusts the sensitivity to price changes. Higher = smoother, lower = faster reaction.
Source: Any input (close, hl2, etc.) to apply the filter on.
Bands (optional): Dynamically generated adaptive envelopes based on real-time volatility.
🎯 How to Use
Use the JMA line as a trend-following tool or dynamic support/resistance.
Apply crossovers with price or other indicators for entries/exits.
Enable the bands to observe overbought/oversold zones or potential breakout areas.
Adjust phase to suit leading (anticipatory) or lagging (confirmation) strategies.
This tool is particularly suitable for:
Scalpers looking for precision in fast markets.
Swing traders filtering noise from signals.
Algorithmic systems needing high-fidelity moving averages.
Liquidity Trap Zones [PhenLabs]📊 Liquidity Trap Zones
Version: PineScript™ v6
📌 Description
The goal of the Liquidity Trap Zones indicator is to try and help traders identify areas where market liquidity appears abundant but is actually thin or artificial, helping traders avoid potential fake outs and false breakouts. This advanced indicator analyzes the relationship between price wicks and volume to detect “mirage” zones where large price movements occur on low volume, indicating potential liquidity traps.
By highlighting these deceptive zones on your charts, the indicator helps traders recognize where institutional players might be creating artificial liquidity to trap retail traders. This enables more informed decision-making and better risk management when approaching key price levels.
🚀 Points of Innovation
Mirage Score Algorithm: Proprietary calculation that normalizes wick size relative to volume and average bar size
Dynamic Zone Creation: Automatically generates gradient-filled zones at trap locations with ATR-based sizing
Intelligent Zone Management: Maintains clean charts by limiting displayed zones and auto-updating existing ones
Scale-Invariant Design: Works across all assets and timeframes with intelligent normalization
Real-Time Detection: Identifies trap zones as they form, not after the fact
Volume-Adjusted Analysis: Incorporates tick volume when available for more accurate detection
🔧 Core Components
Mirage Score Calculator: Analyzes the ratio of price wicks to volume, normalized by average bar size
ATR-Based Filter: Ensures only significant price movements are considered for trap zone creation
EMA Smoothing: Reduces noise in the mirage score for clearer signals
Gradient Zone Renderer: Creates visually distinct zones with multiple opacity levels for better visibility
🔥 Key Features
Real-Time Trap Detection: Identifies liquidity mirages as they develop during live trading
Dynamic Zone Sizing: Adjusts zone height based on current market volatility (ATR)
Smart Zone Management: Automatically maintains a clean chart by limiting the number of displayed zones
Customizable Sensitivity: Fine-tune detection parameters for different market conditions
Visual Clarity: Gradient-filled zones with distinct borders for easy identification
Status Line Display: Shows current mirage score and threshold for quick reference
🎨 Visualization
Gradient Trap Zones: Purple gradient boxes with darker centers indicating trap strength
Mirage Score Line: Orange line in status area showing current liquidity quality
Threshold Reference: Gray line showing your configured detection threshold
Extended Zone Display: Zones automatically extend forward as new bars form
📖 Usage Guidelines
Detection Settings
Smoothing Length (EMA) - Default: 10 - Range: 1-50 - Description: Controls responsiveness of mirage score. Lower values make detection more sensitive to recent price action
Mirage Threshold - Default: 5.0 - Range: 0.1-20.0 - Description: Score above this level triggers trap zone creation. Higher values reduce false positives but may miss subtle traps
Filter Settings
ATR Length for Range Filter - Default: 14 - Range: 1-50 - Description: Period for volatility calculation. Standard 14 works well for most timeframes
ATR Multiplier - Default: 1.0 - Range: 0.0-5.0 - Description: Minimum bar range as multiple of ATR. Higher values filter out smaller moves
Display Settings
Zone Height Multiplier - Default: 0.5 - Range: 0.1-2.0 - Description: Controls trap zone height relative to ATR. Adjust for visual preference
Max Trap Zones - Default: 5 - Range: 1-20 - Description: Maximum zones displayed before oldest are removed. Balance clarity vs. history
✅ Best Use Cases
Identifying potential fakeout levels before entering trades
Confirming support/resistance quality by checking for liquidity traps
Avoiding stop-loss placement in trap zones where sweeps are likely
Timing entries after trap zones are cleared
Scalping opportunities when price approaches known trap zones
⚠️ Limitations
Requires volume data - less effective on instruments without reliable volume
May generate false signals during news events or genuine volume spikes
Not a standalone system - combine with price action and other indicators
Zone creation is based on historical data - future price behavior not guaranteed
💡 What Makes This Unique
First indicator to specifically target liquidity mirages using wick-to-volume analysis
Proprietary normalization ensures consistent performance across all markets
Visual gradient design makes trap zones immediately recognizable
Combines multiple volatility and volume metrics for robust detection
🔬 How It Works
1. Wick Analysis: Calculates upper and lower wicks for each bar. Normalizes by average bar size to ensure scale independence
2. Mirage Score Calculation: Divides total wick size by volume to identify thin liquidity. Applies EMA smoothing to reduce noise. Scales result for optimal visibility
3. Zone Creation: Triggers when smoothed score crosses threshold. Creates gradient boxes centered on trap bar. Sizes zones based on current ATR for market-appropriate scaling
💡 Note: Liquidity Trap Zones works best when combined with traditional support/resistance analysis and volume profile indicators. The zones highlight areas of deceptive liquidity but should not be the sole factor in trading decisions. Always use proper risk management and confirm signals with price action.
Signal Quality Validator - Lite# Signal Quality Validator Lite - Technical Documentation
## Introduction
The Signal Quality Validator (SQV) Lite represents a comprehensive approach to technical signal validation, designed to evaluate trading opportunities through multi-dimensional market analysis. This indicator provides traders with objective quality assessments for their entry signals across various market conditions and timeframes.
## Core Architecture
### Component-Based Validation System
SQV Lite employs five fundamental market components, each contributing weighted scores to produce a final quality assessment. The system analyzes multiple market dimensions simultaneously to provide comprehensive signal validation.
Each component uses proprietary algorithms to evaluate specific market conditions:
- Directional bias and strength assessment
- Market participation and flow analysis
- Price acceleration patterns
- Key technical level identification
- Optimal volatility conditions
The final score represents a weighted combination of all components, with thresholds adjusted for different market conditions and timeframes.
## Scoring Methodology
### Quality Grades
- **Grade A+ (90-100)**: Exceptional setup quality with maximum component confluence
- **Grade A (80-89)**: High-quality signals suitable for full position sizing
- **Grade B (65-79)**: Acceptable signals meeting minimum validation criteria
- **Grade C (<65)**: Substandard conditions, signal rejected
### Timeframe Profiles
Pre-configured profiles optimize component weights and thresholds:
| Profile | Typical Use Case | Min/High/Perfect Scores |
|---------|------------------|------------------------|
| 1-5 min | Scalping | 60/75/85 |
| 15-30 min | Day Trading | 65/80/90 |
| 1H-4H | Intraday Swing | 70/85/95 |
| Daily+ | Position Trading | 75/88/95 |
| Custom | User Defined | Configurable |
## Integration Guide
### Standalone Usage
1. Add SQV Lite to your chart
2. Select appropriate timeframe profile
3. Monitor real-time quality grades on signal bars
4. Use dashboard for current market assessment
### Bidirectional Strategy Integration
SQV Lite supports complete two-way communication with your custom strategies, enabling sophisticated signal validation workflows.
#### Step 1: Setting Up Your Strategy to Send Signals
In your custom strategy/indicator, export your signals as plots:
```pinescript
//@version=6
indicator("My Custom Strategy", overlay=true)
// Your signal logic
longSignal = ta.crossover(ema9, ema21) // Example
shortSignal = ta.crossunder(ema9, ema21) // Example
// CRITICAL: Export signals for SQV to read
// Use display=display.none to hide the plots
plot(longSignal ? 1 : 0, "Long Signal Output", display=display.none)
plot(shortSignal ? 1 : 0, "Short Signal Output", display=display.none)
```
#### Step 2: Configure SQV Lite to Receive Signals
1. Add SQV Lite to the same chart as your strategy
2. In SQV Lite settings, enable "Use External Signals"
3. Click on "External Long Signal Source" and select your strategy's "Long Signal Output"
4. Click on "External Short Signal Source" and select your strategy's "Short Signal Output"
#### Step 3: Import SQV Validation Back to Your Strategy
Complete the bidirectional flow by importing SQV's validation results:
```pinescript
//@version=6
strategy("My Strategy with SQV Integration", overlay=true)
// Import SQV validation results
sqvScore = input.source(close, "SQV Score Source", group="SQV Integration")
sqvLongValid = input.source(close, "SQV Long Valid Source", group="SQV Integration")
sqvShortValid = input.source(close, "SQV Short Valid Source", group="SQV Integration")
sqvTradingMode = input.source(close, "SQV Trading Mode", group="SQV Integration")
// Your original signals
longSignal = ta.crossover(ema9, ema21)
shortSignal = ta.crossunder(ema9, ema21)
// Export for SQV
plot(longSignal ? 1 : 0, "Long Signal Output", display=display.none)
plot(shortSignal ? 1 : 0, "Short Signal Output", display=display.none)
// Use SQV validation in entry logic
if longSignal and sqvLongValid > 0
strategy.entry("Long", strategy.long)
// Optional: Use sqvScore for position sizing
if shortSignal and sqvShortValid > 0
strategy.entry("Short", strategy.short)
```
#### Step 4: Complete Integration Setup
After adding both scripts to your chart:
1. In your strategy settings → SQV Integration:
- Set "SQV Score Source" → Select SQV Lite: SQV Score
- Set "SQV Long Valid Source" → Select SQV Lite: SQV Long Valid
- Set "SQV Short Valid Source" → Select SQV Lite: SQV Short Valid
2. In SQV Lite settings → Signal Import:
- Enable "Use External Signals"
- Set "External Long Signal Source" → Select Your Strategy: Long Signal Output
- Set "External Short Signal Source" → Select Your Strategy: Short Signal Output
### Available Data Exports from SQV
```pinescript
// Core validation data
plot(currentTotalScore, "SQV Score", display=display.none) // 0-100
plot(sqvLongValid ? 1 : 0, "SQV Long Valid", display=display.none) // 0 or 1
plot(sqvShortValid ? 1 : 0, "SQV Short Valid", display=display.none) // 0 or 1
// Component scores for advanced usage
plot(currentTrendScore, "SQV Trend Score", display=display.none)
plot(currentVolumeScore, "SQV Volume Score", display=display.none)
plot(currentMomentumScore, "SQV Momentum Score", display=display.none)
plot(currentStructureScore, "SQV Structure Score", display=display.none)
plot(currentVolatilityScore, "SQV Volatility Score", display=display.none)
// Additional data
plot(orderFlowDelta, "SQV Order Flow Delta", display=display.none)
plot(tradingMode == "Long" ? 1 : tradingMode == "Short" ? -1 : 0, "SQV Trading Mode", display=display.none)
```
### Advanced Integration Examples
#### Example 1: Quality-Based Position Sizing
```pinescript
// In your strategy
sqvScore = input.source(close, "SQV Score Source", group="SQV Integration")
// Dynamic position sizing based on signal quality
positionSize = sqvScore >= 90 ? 3 : // A+ quality = 3 units
sqvScore >= 80 ? 2 : // A quality = 2 units
sqvScore >= 65 ? 1 : 0 // B quality = 1 unit
if longSignal and sqvLongValid > 0 and positionSize > 0
strategy.entry("Long", strategy.long, qty=positionSize)
```
#### Example 2: Filtering by Component Scores
```pinescript
// Import individual components
sqvTrend = input.source(close, "SQV Trend Score", group="SQV Integration")
sqvVolume = input.source(close, "SQV Volume Score", group="SQV Integration")
sqvMomentum = input.source(close, "SQV Momentum Score", group="SQV Integration")
// Custom filtering logic
strongTrend = sqvTrend > 80
goodVolume = sqvVolume > 70
strongSetup = strongTrend and goodVolume
if longSignal and sqvLongValid > 0 and strongSetup
strategy.entry("Strong Long", strategy.long)
```
#### Example 3: Order Flow Integration
```pinescript
// Import order flow data
sqvOrderFlow = input.source(close, "SQV Order Flow Delta", group="SQV Integration")
// Use order flow for additional confirmation
bullishFlow = sqvOrderFlow > 100 // Significant buying pressure
bearishFlow = sqvOrderFlow < -100 // Significant selling pressure
if longSignal and sqvLongValid > 0 and bullishFlow
strategy.entry("Long+Flow", strategy.long)
```
### Visual Feedback Configuration
#### Label Display Modes
1. **Autonomous Mode** (standalone testing):
- Enable "Show Labels Without Signals"
- Labels appear on every bar where score >= minimum threshold
- Useful for initial testing without strategy integration
2. **Signal Mode** (production use):
- Disable "Show Labels Without Signals"
- Enable "Use External Signals"
- Labels appear ONLY when your strategy generates signals
- Prevents chart clutter, shows validation exactly when needed
#### Troubleshooting Integration
**Common Issues:**
1. **Labels not appearing:**
- Verify "Use External Signals" is enabled
- Check signal sources are properly connected
- Ensure your strategy is actually generating signals (add visible plots temporarily)
2. **Wrong source selection:**
- Source dropdowns should show your indicator/strategy name
- Each output plot should be visible in the dropdown
- If not visible, check plot titles in your strategy
3. **Validation always failing:**
- Check Trading Mode matches your signal types
- Verify minimum score thresholds aren't too high
- Use Autonomous Mode to test if SQV is working properly
### Best Practices
1. **Always use `display=display.none`** for communication plots to keep charts clean
2. **Name your plots clearly** for easy identification in source dropdowns
3. **Test in Autonomous Mode first** to understand SQV behavior
4. **Use consistent signal logic** - ensure signals are binary (1 or 0)
5. **Consider adding a small delay** between signal and entry for validation processing
### Complete Integration Template
Here's a full template for a strategy with complete SQV integration:
```pinescript
//@version=6
strategy("Complete SQV Integration Template", overlay=true)
// ========== SQV Integration Inputs ==========
sqvScore = input.source(close, "SQV Score Source", group="SQV Integration")
sqvLongValid = input.source(close, "SQV Long Valid Source", group="SQV Integration")
sqvShortValid = input.source(close, "SQV Short Valid Source", group="SQV Integration")
sqvOrderFlow = input.source(close, "SQV Order Flow Delta", group="SQV Integration")
// ========== Strategy Parameters ==========
emaFast = input.int(9, "Fast EMA")
emaSlow = input.int(21, "Slow EMA")
useQualitySizing = input.bool(true, "Use Quality-Based Sizing")
// ========== Indicators ==========
ema1 = ta.ema(close, emaFast)
ema2 = ta.ema(close, emaSlow)
// ========== Signal Logic ==========
longSignal = ta.crossover(ema1, ema2)
shortSignal = ta.crossunder(ema1, ema2)
// ========== Export Signals to SQV ==========
plot(longSignal ? 1 : 0, "Long Signal Output", display=display.none)
plot(shortSignal ? 1 : 0, "Short Signal Output", display=display.none)
// ========== Position Sizing ==========
baseSize = 1
qualityMultiplier = useQualitySizing ?
(sqvScore >= 90 ? 3 : sqvScore >= 80 ? 2 : 1) : 1
positionSize = baseSize * qualityMultiplier
// ========== Entry Logic with SQV Validation ==========
if longSignal and sqvLongValid > 0
strategy.entry("Long", strategy.long, qty=positionSize)
if shortSignal and sqvShortValid > 0
strategy.entry("Short", strategy.short, qty=positionSize)
// ========== Exit Logic ==========
if strategy.position_size > 0 and shortSignal
strategy.close("Long")
if strategy.position_size < 0 and longSignal
strategy.close("Short")
// ========== Visual Feedback ==========
plotshape(longSignal and sqvLongValid > 0, "Valid Long",
location=location.belowbar, color=color.green, style=shape.triangleup)
plotshape(shortSignal and sqvShortValid > 0, "Valid Short",
location=location.abovebar, color=color.red, style=shape.triangledown)
```
This template provides everything needed for professional bidirectional integration between your custom strategy and SQV Lite.
## Order Flow Analysis
The integrated Order Flow system automatically adapts to market conditions, providing intelligent analysis of buying and selling pressure. The system handles various market scenarios including low liquidity and minimal price movement conditions through advanced algorithms.
## Visual Interface
### Signal Labels
Displays three-line information blocks:
- Grade designation (A+, A, B, C)
- Numerical quality score
- Order flow direction and magnitude
### Dashboard Elements
- **Profile Display**: Active configuration and thresholds
- **Score Visualization**: Real-time quality assessment
- **Flow Indicator**: Directional bias representation
- **Status Monitor**: Ready/Wait signal state
### Customization Options
- Label distance adjustment (0.5-3.0x ATR)
- Profile selection and custom configuration
- Component weight modifications (Custom mode)
- Threshold adjustments for different market conditions
## Trading Mode Selection
Three operational modes accommodate different trading styles:
- **Long Only**: Validates bullish signals exclusively
- **Short Only**: Validates bearish signals exclusively
- **Both**: Bi-directional signal validation
## Performance Considerations
SQV Lite maintains computational efficiency through:
- Optimized calculation cycles
- Selective component updates
- Efficient data structure usage
- Minimal redundant processing
---
## Feature Comparison: SQV Lite vs Full Version
### Core Components
| Component | SQV Lite | SQV Full | Details |
|-----------|----------|----------|---------|
| **Trend Analysis** | ✅ Full | ✅ Full | Professional trend evaluation |
| **Volume Dynamics** | ✅ Full | ✅ Full | Advanced volume analysis |
| **Momentum Assessment** | ✅ Full | ✅ Full | Multi-factor momentum |
| **Market Structure** | ✅ Basic | ✅ Enhanced | Key level detection |
| **Volatility Filter** | ✅ Full | ✅ Full | Risk-adjusted filtering |
| **Performance Analytics** | ❌ | ✅ | Real-time performance tracking |
| **Impulse Detection** | ❌ | ✅ | Advanced signal filtering |
### Advanced Features
| Feature | SQV Lite | SQV Full | Benefits |
|---------|----------|----------|----------|
| **Multi-Timeframe Analysis** | ❌ | ✅ | Higher timeframe confirmation |
| **Dynamic Position Sizing** | ❌ | ✅ Automatic | Dynamic size optimization |
| **Auto Mode** | ❌ | ✅ | Self-optimizing system |
| **Advanced Profiling** | ❌ | ✅ | Market depth analysis |
| **Recovery Mode** | ❌ | ✅ | Adaptive drawdown handling |
| **Statistical Validation** | ❌ | ✅ | Confidence-based filtering |
### Profiles & Configuration
| Feature | SQV Lite | SQV Full |
|---------|----------|----------|
| **Timeframe Profiles** | 5 | 8 |
| **Available Profiles** | 1-5m, 15-30m, 1-4H, Daily+, Custom | All Lite + ES, NQ, Auto |
| **Custom Weights** | ✅ Manual | ✅ Manual + Auto-optimization |
| **Threshold Adjustment** | ✅ | ✅ Enhanced |
### Visual Interface
| Feature | SQV Lite | SQV Full |
|---------|----------|----------|
| **Dashboard Styles** | 1 (Standard) | 4 (Multiple layouts) |
| **Signal Labels** | ✅ Basic | ✅ Enhanced with sizing |
| **Advanced Visualizations** | ❌ | ✅ |
| **Component Breakdown** | ❌ | ✅ Detailed view |
| **Performance Display** | ❌ | ✅ Live statistics |
| **Debug Mode** | ❌ | ✅ |
### Integration Capabilities
| Feature | SQV Lite | SQV Full |
|---------|----------|----------|
| **Script Type** | Indicator | Strategy |
| **Signal Import** | ✅ | Via strategy conditions |
| **Data Export** | ✅ All via plots | Internal to strategy |
| **Bidirectional Flow** | ✅ Full support | One-way (strategy-based) |
### Risk Management
| Feature | SQV Lite | SQV Full |
|---------|----------|----------|
| **Position Sizing** | Manual | ✅ Automatic |
| **Quality-Based Sizing** | Via integration | ✅ Built-in |
| **Performance Adjustment** | ❌ | ✅ |
| **Risk Grade System** | ❌ | ✅ Risk grading system |
| **Statistical Filtering** | ❌ | ✅ |
### Market Analysis
| Feature | SQV Lite | SQV Full |
|---------|----------|----------|
| **Order Flow Analysis** | ✅ Automatic | ✅ Advanced |
| **Market Manipulation Detection** | ❌ | ✅ |
| **Multi-Timeframe Validation** | ❌ | ✅ |
| **Advanced Momentum Analysis** | Basic | ✅ Enhanced |
| **Market Regime Adaptation** | Basic | ✅ Full Auto Mode |
### Summary
| Aspect | SQV Lite | SQV Full |
|--------|----------|----------|
| **Best For** | Signal validation, integration with custom strategies | Complete trading system with built-in strategy |
| **Learning Curve** | Easy | Moderate |
| **Customization** | High (via integration) | Very High (all parameters) |
| **Price** | Free | $29/month |
---
## SQV Bridge System
### Overview
The SQV Bridge System allows you to connect any TradingView indicator or strategy with the Signal Quality Validator (SQV) system. This enables you to add professional-grade signal validation to your existing trading tools without modifying their code.
### System Components
1. **SQV Lite** (Required) - The core validation engine
2. **Bridge** (Choose one):
- **Indicator Bridge** - For visual signals and alerts
- **Strategy Bridge** - For automated backtesting and trading
3. **Your Trading Tool** - Any indicator or strategy that generates signals
---
## SQV Indicator Bridge
### //@version=6
### indicator("SQV Indicator Bridge", overlay=true)
### Purpose
The Indicator Bridge displays validated entry signals on your chart. It receives signals from any indicator and validation from SQV Lite, showing only high-quality trade opportunities.
### Features
- Visual labels for validated signals
- Customizable appearance (size, color, position)
- Alert capabilities
- Hidden signal exports for other tools
### Setup Instructions
1. **Add Your Indicator**
- Apply your trading indicator to the chart
- Note which plots contain long/short signals
2. **Add SQV Lite**
- Add SQV Lite indicator to the same chart
- Configure SQV settings as needed
3. **Add Indicator Bridge**
- Add "SQV Indicator Bridge" to chart
- Connect the sources:
- Long Signal Source → Your indicator's long signal
- Short Signal Source → Your indicator's short signal
- SQV Long Valid → From SQV Lite
- SQV Short Valid → From SQV Lite
- SQV Score → From SQV Lite (for alerts)
### Configuration Options
#### Visual Settings
- **Show Labels**: Toggle signal labels on/off
- **Label Offset**: Distance from candles (0-5 ATR)
- **Label Size**: Tiny, Small, or Normal
- **Colors**: Customize long/short colors
#### Alerts
- Enable/disable alert notifications
- Alerts include SQV score in message
### Example Code (Add to Your Indicator)
```pinescript
// Export signals from your indicator
plot(longCondition ? 1 : 0, "Long Signal", display=display.none)
plot(shortCondition ? 1 : 0, "Short Signal", display=display.none)
```
### Complete Indicator Bridge Code
```pinescript
//@version=6
indicator("SQV Indicator Bridge", overlay=true)
// ===================================================================
// SQV INDICATOR BRIDGE - CLEAN VERSION
// Version 1.0
//
// Simple bridge that shows validated entry signals.
// Receives signals from any indicator and validation from SQV Lite.
//
// SETUP:
// 1. Add your indicator to chart
// 2. Add SQV Lite to chart
// 3. Add this bridge
// 4. Connect sources in settings
// ===================================================================
// ===================================================================
// INPUT SOURCES
// ===================================================================
// From your indicator
longSignal = input.source(close, "Long Signal Source", group="Signal Sources",
tooltip="Select Long Signal from your indicator")
shortSignal = input.source(close, "Short Signal Source", group="Signal Sources",
tooltip="Select Short Signal from your indicator")
// From SQV Lite
sqvLongValid = input.source(close, "SQV Long Valid", group="SQV Sources",
tooltip="Select 'SQV Long Valid' from SQV Lite")
sqvShortValid = input.source(close, "SQV Short Valid", group="SQV Sources",
tooltip="Select 'SQV Short Valid' from SQV Lite")
sqvScore = input.source(close, "SQV Score", group="SQV Sources",
tooltip="Select 'SQV Score' from SQV Lite (for alerts)")
// ===================================================================
// SETTINGS
// ===================================================================
showLabels = input.bool(true, "Show Labels", group="Visual")
labelOffset = input.float(0.0, "Label Offset (ATR)", minval=0.0, maxval=5.0, step=0.5, group="Visual",
tooltip="0 = Labels at candle edges, higher = further away")
labelSize = input.string("small", "Label Size", options= , group="Visual")
longColor = input.color(color.green, "Long Color", group="Visual")
shortColor = input.color(color.red, "Short Color", group="Visual")
enableAlerts = input.bool(false, "Enable Alerts", group="Alerts")
// ===================================================================
// MAIN LOGIC
// ===================================================================
// Calculate offset
atr = ta.atr(14)
offset = labelOffset > 0 ? atr * labelOffset : 0
// Check for validated signals
hasValidLong = longSignal > 0 and sqvLongValid > 0 and barstate.isconfirmed
hasValidShort = shortSignal > 0 and sqvShortValid > 0 and barstate.isconfirmed
// Show labels
if showLabels
if hasValidLong
label.new(bar_index, low - offset, "LONG",
style=label.style_label_up,
color=longColor,
textcolor=color.white,
size=labelSize == "tiny" ? size.tiny :
labelSize == "small" ? size.small : size.normal)
if hasValidShort
label.new(bar_index, high + offset, "SHORT",
style=label.style_label_down,
color=shortColor,
textcolor=color.white,
size=labelSize == "tiny" ? size.tiny :
labelSize == "small" ? size.small : size.normal)
// Alerts
if enableAlerts
if hasValidLong
alert("Long Signal Validated | Score: " + str.tostring(sqvScore, "#"), alert.freq_once_per_bar_close)
if hasValidShort
alert("Short Signal Validated | Score: " + str.tostring(sqvScore, "#"), alert.freq_once_per_bar_close)
// Hidden exports
plot(hasValidLong ? 1 : 0, "Valid Long", display=display.none)
plot(hasValidShort ? 1 : 0, "Valid Short", display=display.none)
```
---
## SQV Strategy Bridge
### //@version=6
### strategy("SQV Strategy Bridge", overlay=true, ...)
### Purpose
The Strategy Bridge executes trades with SQV validation, enabling backtesting and live trading with quality-filtered signals. It can receive position sizing, stop loss, and take profit levels from your strategy.
### Features
- Automated trade execution with SQV validation
- Dynamic position sizing support
- Stop loss and take profit integration
- Position status display
- Alert system for trade notifications
### Setup Instructions
1. **Prepare Your Strategy**
- Export required values as plots (see examples below)
- Ensure signals are clear (1 for entry, 0 for no signal)
2. **Add SQV Lite**
- Add SQV Lite indicator to the chart
- Configure validation parameters
3. **Add Strategy Bridge**
- Add "SQV Strategy Bridge" to chart
- Connect all required sources
### Source Connections
#### Required Sources
- **Long Signal Source** → Your strategy's long signal
- **Short Signal Source** → Your strategy's short signal
- **SQV Long Valid** → From SQV Lite
- **SQV Short Valid** → From SQV Lite
- **SQV Score** → From SQV Lite
#### Optional Sources (Advanced)
- **Position Size Source** → Dynamic position sizing
- **Long/Short Stop Loss** → Stop loss prices
- **Long/Short Take Profit** → Take profit prices
### Configuration Options
#### Position Management
- **Use Position Size from Strategy**: Enable dynamic sizing
- **Default Position Size %**: Fallback size (0.1-100%)
#### Risk Management
- **Use Stop Loss from Strategy**: Enable dynamic stops
- **Use Take Profit from Strategy**: Enable dynamic targets
### Example Code (Add to Your Strategy)
```pinescript
// Basic signal export
plot(buySignal ? 1 : 0, "Long Signal", display=display.none)
plot(sellSignal ? 1 : 0, "Short Signal", display=display.none)
// Advanced exports (optional)
// Position size (0.1 = 10% of equity)
plot(myPositionSize, "Position Size Output", display=display.none)
// Stop loss prices
plot(longStopPrice, "Long Stop Price", display=display.none)
plot(shortStopPrice, "Short Stop Price", display=display.none)
// Take profit prices
plot(longTPPrice, "Long TP Price", display=display.none)
plot(shortTPPrice, "Short TP Price", display=display.none)
```
### Complete Strategy Bridge Code
```pinescript
//@version=6
strategy("SQV Strategy Bridge",
overlay=true,
initial_capital=10000,
default_qty_type=strategy.percent_of_equity,
default_qty_value=10,
commission_type=strategy.commission.percent,
commission_value=0.1)
// ===================================================================
// SQV STRATEGY BRIDGE - SIMPLE VERSION
// Version 1.0
//
// Receives everything from your strategy:
// - Signals (when to trade)
// - Position size (how much to trade)
// - Stop loss levels (optional)
// - Take profit levels (optional)
//
// Bridge only executes trades with SQV validation
// ===================================================================
// ===================================================================
// SIGNAL SOURCES
// ===================================================================
longSignal = input.source(close, "Long Signal Source", group="Signal Sources",
tooltip="Connect to Long Signal from your strategy")
shortSignal = input.source(close, "Short Signal Source", group="Signal Sources",
tooltip="Connect to Short Signal from your strategy")
// ===================================================================
// SQV SOURCES
// ===================================================================
sqvLongValid = input.source(close, "SQV Long Valid", group="SQV Sources")
sqvShortValid = input.source(close, "SQV Short Valid", group="SQV Sources")
sqvScore = input.source(close, "SQV Score", group="SQV Sources")
// ===================================================================
// POSITION SIZE SOURCES (FROM YOUR STRATEGY)
// ===================================================================
usePositionFromStrategy = input.bool(false, "Use Position Size from Strategy", group="Position")
positionSizeSource = input.source(close, "Position Size Source", group="Position",
tooltip="Your strategy should export position size (% or fixed quantity)")
defaultPositionSize = input.float(10, "Default Position Size %", minval=0.1, maxval=100, group="Position",
tooltip="Used if 'Use Position Size from Strategy' is disabled")
// ===================================================================
// STOP LOSS SOURCES (FROM YOUR STRATEGY)
// ===================================================================
useStopFromStrategy = input.bool(false, "Use Stop Loss from Strategy", group="Risk Management")
longStopSource = input.source(close, "Long Stop Loss Price", group="Risk Management",
tooltip="Your strategy should export exact stop price for longs")
shortStopSource = input.source(close, "Short Stop Loss Price", group="Risk Management",
tooltip="Your strategy should export exact stop price for shorts")
// ===================================================================
// TAKE PROFIT SOURCES (FROM YOUR STRATEGY)
// ===================================================================
useTakeProfitFromStrategy = input.bool(false, "Use Take Profit from Strategy", group="Risk Management")
longTakeProfitSource = input.source(close, "Long Take Profit Price", group="Risk Management",
tooltip="Your strategy should export exact TP price for longs")
shortTakeProfitSource = input.source(close, "Short Take Profit Price", group="Risk Management",
tooltip="Your strategy should export exact TP price for shorts")
// ===================================================================
// ALERTS
// ===================================================================
enableAlerts = input.bool(true, "Enable Alerts", group="Alerts")
// ===================================================================
// TRADING LOGIC
// ===================================================================
// Check signals with SQV validation
hasLongSignal = longSignal > 0 and sqvLongValid > 0 and barstate.isconfirmed
hasShortSignal = shortSignal > 0 and sqvShortValid > 0 and barstate.isconfirmed
// Position state
inLong = strategy.position_size > 0
inShort = strategy.position_size < 0
// Get position size
getPositionSize() =>
if usePositionFromStrategy and positionSizeSource > 0
positionSizeSource
else
defaultPositionSize / 100
// LONG ENTRY
if hasLongSignal and not inLong
if inShort
strategy.close("Short")
qty = getPositionSize()
strategy.entry("Long", strategy.long, qty=qty)
// Set exit orders if provided by strategy
if useStopFromStrategy or useTakeProfitFromStrategy
stopPrice = useStopFromStrategy and longStopSource > 0 ? longStopSource : na
tpPrice = useTakeProfitFromStrategy and longTakeProfitSource > 0 ? longTakeProfitSource : na
if not na(stopPrice) or not na(tpPrice)
strategy.exit("Long Exit", "Long", stop=stopPrice, limit=tpPrice)
if enableAlerts
alert("Long Entry | Score: " + str.tostring(sqvScore, "#"), alert.freq_once_per_bar_close)
// SHORT ENTRY
if hasShortSignal and not inShort
if inLong
strategy.close("Long")
qty = getPositionSize()
strategy.entry("Short", strategy.short, qty=qty)
// Set exit orders if provided by strategy
if useStopFromStrategy or useTakeProfitFromStrategy
stopPrice = useStopFromStrategy and shortStopSource > 0 ? shortStopSource : na
tpPrice = useTakeProfitFromStrategy and shortTakeProfitSource > 0 ? shortTakeProfitSource : na
if not na(stopPrice) or not na(tpPrice)
strategy.exit("Short Exit", "Short", stop=stopPrice, limit=tpPrice)
if enableAlerts
alert("Short Entry | Score: " + str.tostring(sqvScore, "#"), alert.freq_once_per_bar_close)
// ===================================================================
// POSITION INFO
// ===================================================================
var label infoLabel = label.new(bar_index, high, "", style=label.style_label_left)
if barstate.islast
posText = "Bridge Status\n"
posText := inLong ? posText + "Position: LONG\n" : inShort ? posText + "Position: SHORT\n" : posText + "Position: FLAT\n"
posText := "SQV Score: " + str.tostring(sqvScore, "#")
label.set_xy(infoLabel, bar_index + 1, high)
label.set_text(infoLabel, posText)
label.set_color(infoLabel, inLong ? color.new(color.green, 80) : inShort ? color.new(color.red, 80) : color.new(color.gray, 80))
label.set_textcolor(infoLabel, color.white)
// ===================================================================
// HOW TO EXPORT FROM YOUR STRATEGY:
//
// // In your strategy, export these values:
//
// // Position size (% as decimal: 0.1 = 10%, or fixed: 0.2 = 0.2 BTC)
// plot(myPositionSize, "Position Size Output", display=display.none)
//
// // Stop loss prices
// plot(longStopPrice, "Long Stop Price", display=display.none)
// plot(shortStopPrice, "Short Stop Price", display=display.none)
//
// // Take profit prices
// plot(longTPPrice, "Long TP Price", display=display.none)
// plot(shortTPPrice, "Short TP Price", display=display.none)
// ===================================================================
```
---
## Quick Start Guide
### For Indicators (Visual Signals)
1. Add these three indicators in order:
- Your trading indicator
- SQV Lite
- SQV Indicator Bridge
2. In Bridge settings, connect:
- Signal sources from your indicator
- Validation sources from SQV Lite
3. Adjust visual settings to preference
### For Strategies (Automated Trading)
1. Modify your strategy to export signals:
```pinescript
plot(longSignal ? 1 : 0, "Long Signal", display=display.none)
plot(shortSignal ? 1 : 0, "Short Signal", display=display.none)
```
2. Add to chart:
- Your modified strategy (as indicator)
- SQV Lite
- SQV Strategy Bridge
3. Connect all sources in Bridge settings
4. Run backtest or enable live trading
---
## Tips & Best Practices
### Signal Quality
- SQV validates signals based on 5 market components (7 in full version)
- Only signals with sufficient quality score pass validation
- Adjust SQV settings to match your trading style
### Position Sizing
- Default sizing uses percentage of equity
- Advanced users can export dynamic sizing from strategy
- Size based on signal quality or market conditions
### Risk Management
- Always use stop losses (manual or from strategy)
- Consider using SQV's quality score for position sizing
- Monitor win rate and Sharpe ratio in SQV dashboard (full version)
### Troubleshooting
- **No signals showing**: Check source connections
- **Too few signals**: Lower SQV minimum score
- **Too many signals**: Increase SQV requirements
- **Backtest issues**: Ensure strategy calculations match
---
## Example Setups
### Simple Moving Average Cross + SQV
```pinescript
// In your indicator
ma_fast = ta.sma(close, 20)
ma_slow = ta.sma(close, 50)
longSignal = ta.crossover(ma_fast, ma_slow)
shortSignal = ta.crossunder(ma_fast, ma_slow)
plot(longSignal ? 1 : 0, "Long Signal", display=display.none)
plot(shortSignal ? 1 : 0, "Short Signal", display=display.none)
```
### RSI Strategy with Dynamic Stops
```pinescript
// In your strategy
rsi = ta.rsi(close, 14)
longSignal = rsi < 30
shortSignal = rsi > 70
// Dynamic stops based on ATR
atr = ta.atr(14)
longStop = close - (atr * 2)
shortStop = close + (atr * 2)
// Export everything
plot(longSignal ? 1 : 0, "Long Signal", display=display.none)
plot(shortSignal ? 1 : 0, "Short Signal", display=display.none)
plot(longStop, "Long Stop Price", display=display.none)
plot(shortStop, "Short Stop Price", display=display.none)
```
---
## Advanced Features
### Multi-Timeframe Validation
SQV automatically checks higher timeframes for confluence, improving signal reliability (Full version only).
### Adaptive Profiles
Use "Auto" profile in SQV for dynamic parameter adjustment based on market conditions (Full version only).
### Performance Tracking
SQV tracks win rate, Sharpe ratio, and other metrics to ensure consistent performance (Full version only).
### Order Flow Analysis
Validates signals using volume delta and buying/selling pressure (included in Lite version).
---
## Upgrade to SQV Full Version
### Enhanced Capabilities in Full Version
The complete SQV system extends validation capabilities with advanced components:
#### 🎯 **Performance Analytics Component**
- Real-time Sharpe Ratio calculation
- Win rate tracking with confidence intervals
- Risk-adjusted performance metrics
- Adaptive threshold adjustments
#### ⚡ **Impulse Detection with Trap Analysis**
- Advanced momentum surge detection
- Market manipulation identification
- False breakout filtering
- Volume/price divergence analysis
#### 📊 **Multi-Timeframe Confluence**
- Three-timeframe trend alignment
- Higher timeframe confirmation requirements
- Confluence strength scoring
- Directional bias validation
#### 🎰 **Dynamic Position Sizing**
- Automatic position multipliers based on signal quality
- Grade A+ signals (90+) = Maximum multiplier
- Grade A signals (80-89) = Scaled multiplier
- Grade B signals (65-79) = Base position size
- Risk-adjusted position management
- Sharpe-influenced adjustments
#### 🔄 **Auto Mode**
- Market-adaptive parameter optimization
- Dynamic weight redistribution
- Volatility-based threshold adjustments
- Self-calibrating component settings
#### 📈 **Volume Profile Integration**
- Point of Control (POC) identification
- Value Area analysis (VAH/VAL)
- Profile-based support/resistance
- Volume distribution visualization
#### 🛡️ **Recovery Mode**
- Drawdown detection and adaptation
- Conservative validation during recovery
- Gradual threshold normalization
- Performance-based re-engagement
#### 📊 **Extended Visualizations**
- Multiple dashboard layouts
- Component breakdown displays
- Performance statistics panels
- Risk grade assessments
### Why Upgrade?
While SQV Lite provides robust signal validation, the Full Version transforms your trading with:
- **Automated risk management** through dynamic sizing
- **Superior signal filtering** via Impulse and MTF components
- **Performance optimization** with real-time analytics
- **Market adaptation** through Auto Mode
- **Additional dashboard layouts** for complete market insight
The Full Version includes everything in Lite plus seven additional premium components.
---
## 💰 **SQV Full Version Pricing**
### **Monthly Subscription: $29/month**
Get instant access to the complete Signal Quality Validator system with all premium features:
- ✅ All 7 additional advanced components
- ✅ Automatic position sizing optimization
- ✅ Performance analytics & Sharpe tracking
- ✅ Impulse detection with trap analysis
- ✅ Multi-timeframe confluence validation
- ✅ Auto Mode with self-optimization
- ✅ Recovery mode for drawdown management
- ✅ 4 dashboard layouts
- ✅ Lifetime updates included
- ✅ Priority support
**The automatic position sizing feature alone can pay for months of subscription with a single properly-sized winning trade.**
### 📩 **How to Subscribe**
To get access to SQV Full Version:
1. **Send me a DM** on TradingView
2. **Include your TradingView username/ID** in the message
3. Receive payment instructions and access upon confirmation
*Your TradingView ID is required to grant access to the private indicator.*
### 🔧 **Custom Integration Services**
**Need direct integration into your Pine Script strategy?**
For traders requiring seamless library-based integration without the 500-bar limitation:
- Full backtesting on complete price history
- Zero signal delay
- Custom parameter optimization
- Private library implementation
**📩 DM me for custom integration pricing and details**
---
## Support and Updates
- Both bridges are regularly updated
- SQV Lite receives regular maintenance updates
- For technical questions or feature requests, please reach out through TradingView's messaging system
- Check for new features and improvements in the script descriptions
## Disclaimer
Signal Quality Validator provides technical analysis assistance only. All trading decisions remain the sole responsibility of the user. Past performance does not guarantee future results. Trade responsibly and within your risk tolerance.
*Note: This system is designed for educational purposes. Always test thoroughly before live trading.*
TrEx H/L Trendlines [ETPINVEST]TrEx H/L Trendlines - User Guide
🎯 WHAT IS THIS INDICATOR?
TrEx H/L Trendlines is a professional indicator for automatic search and display of price extremes with trendline construction.
🔍 WHAT IS IT USED FOR?
Main tasks:
Extreme identification - automatic search for significant price highs and lows
Trend analysis - building trendlines between found extremes
Reversal point detection - identifying potential zones of direction change
Structural analysis - understanding the internal structure of price movement
Who is it suitable for:
📈 Swing traders - for identifying key turning points
⚡ Day traders - for analyzing intraday structure
🎯 Scalpers - for precise local extreme identification
📊 Analysts - for structural market analysis
⚙️ HOW DOES THE ALGORITHM WORK?
1. Extreme search
The indicator uses a complex algorithm to find significant highs and lows through strictly defined candle combinations.
Validation - verification of compliance with strict mathematical conditions
2. Filtering and alternation
Found extremes undergo additional processing:
Selection of strongest - choosing the most significant extremes in each zone
Ensuring alternation - correct sequence of highs and lows
Time sorting - chronological ordering
3. Trendline construction
Based on filtered extremes, connecting lines are built:
Sequential connection - linking all extremes in order
Trend visualization - displaying overall movement direction
Structure analysis - understanding internal movement waves
🛠️ DETAILED SETTINGS DESCRIPTION
📊 Extremes
Show Extremes
Purpose: Enable/disable extreme display
Default: Enabled
Upper Extreme Color
Purpose: Color of upper extreme markers (highs)
Default: Red
Lower Extreme Color
Purpose: Color of lower extreme markers (lows)
Default: Green
Analysis Depth
Range: 50-300 bars
Default: 200
Purpose: Depth of historical analysis for extreme search
Timeframe recommendations:
- M1-M5: 100-150 bars
- M15-H1: 150-250 bars
- H4-D1: 200-300 bars
📈 Trendline
Show Trendline
Purpose: Enable/disable trendline display
Default: Enabled
Trendline Color
Purpose: Color of trendlines connecting extremes
Default: Yellow
Trendline Width
Range: 1-5 pixels
Default: 1
Purpose: Thickness of trendlines
📈 PRACTICAL USAGE TIPS
🎯 Trading strategies
1. Trading from extremes
✅ Buy signal:
Price approaches lower extreme (green marker)
Reversal pattern forms on lower timeframe
Confirmation by volume or other indicators
✅ Sell signal:
Price approaches upper extreme (red marker)
Reversal pattern forms on lower timeframe
Confirmation by additional signals
2. Trend structure analysis
✅ Uptrend:
Sequential higher highs and lows
Trendlines directed upward
Each new extreme higher than previous
✅ Downtrend:
Sequential lower highs and lows
Trendlines directed downward
Each new extreme lower than previous
3. Trend reversal identification
⚠️ Reversal signals:
Violation of extreme sequence
Change in trendline slope
Formation of divergences with oscillators
💡 Settings optimization
For scalping (M1-M5):
Analysis Depth: 100-150
Focus on fresh extremes
For day trading (M15-H1):
Analysis Depth: 150-200
Balance between history and relevance
For swing trading (H4-D1):
Analysis Depth: 200-300
Maximum analysis depth
🔍 Additional techniques
Combining with other tools:
Oscillators - finding divergences at extremes
TrEx S/R Levels - applying support and resistance levels
⚠️ IMPORTANT FEATURES
✅ Advantages:
Automation - no manual extreme search required
Mathematical precision - strict selection algorithms
Universality - works on any assets and timeframes
Ease of use - intuitive interface
Trend analysis - automatic structure construction
Real-time updates - on each candle close
⚠️ Limitations:
Requires history - needs minimum 50 bars for operation
Lag - extremes determined after their formation
🎯 CONCLUSION
TrEx H/L Trendlines is a powerful tool for automatic analysis of extremes and trend structure of the market. The indicator is perfect for studying price behavior and can serve as a foundation for developing trading strategies.
Smarter Money Flow Divergence Detector [PhenLabs]📊 Smarter Money Flow Divergence Detector
Version: PineScript™ v6
📌 Description
SMFD was developed to help give you guys a better ability to “read” what is going on behind the scenes without directly having access to that level of data. SMFD is an enhanced divergence detection indicator that identifies money flow patterns from advanced volume analysis and price action correspondence. The detection portion of this indicator combines intelligent money flow calculations with multi timeframe volume analysis to help you see hidden accumulation and distribution phases before major price movements occur.
The indicator measures institutional trading activity by looking at volume surges, price volume dynamics, and the factors of momentum to construct an overall picture of market sentiment. It’s built to assist traders in identifying high probability entries by identifying if smart money is positioning against price action.
🚀 Points of Innovation
● Advanced Smart Money Flow algorithm with volume spike detection and large trade weighting
● Multi timeframe volume analysis for enhanced institutional activity detection
● Dynamic overbought/oversold zones that adapt to current market conditions
● Enhanced divergence detection with pivot confirmation and strength validation
● Color themes with customizable visual styling options
● Real time institutional bias tracking through accumulation/distribution analysis
🔧 Core Components
● Smart Money Flow Calculation: Combines price momentum, volume expansion, and VWAP analysis
● Institutional Bias Oscillator: Tracks accumulation/distribution patterns with volume pressure analysis
● Enhanced Divergence Engine: Detects bullish/bearish divergences with multiple confirmation factors
● Dynamic Zone Detection: Automatically adjusts overbought/oversold levels based on market volatility
● Volume Pressure Analysis: Measures buying vs selling pressure over configurable periods
● Multi factor Signal System: Generates entries with trend alignment and strength validation
🔥 Key Features
● Smart Money Flow Period: Configurable calculation period for institutional activity detection
● Volume Spike Threshold: Adjustable multiplier for detecting unusual institutional volume
● Large Trade Weight: Emphasis factor for high volume periods in flow calculations
● Pivot Detection: Customizable lookback period for accurate divergence identification
● Signal Sensitivity: Three tier system (Conservative/Medium/Aggressive) for signal generation
● Themes: Four color schemes optimized for different chart backgrounds
🎨 Visualization
● Main Oscillator: Line, Area, or Histogram display styles with dynamic color coding
● Institutional Bias Line: Real time tracking of accumulation/distribution phases
● Dynamic Zones: Adaptive overbought/oversold boundaries with gradient fills
● Divergence Lines: Automatic drawing of bullish/bearish divergence connections
● Entry Signals: Clear BUY/SELL labels with signal strength indicators
● Information Panel: Real time statistics and status updates in customizable positions
📖 Usage Guidelines
Algorithm Settings
● Smart Money Flow Period
○ Default: 20
○ Range: 5-100
○ Description: Controls the calculation period for institutional flow analysis.
Higher values provide smoother signals but reduce responsiveness to recent activity
● Volume Spike Threshold
○ Default: 1.8
○ Range: 1.0-5.0
○ Description: Multiplier for detecting unusual volume activity indicating institutional participation. Higher values require more extreme volume for detection
● Large Trade Weight
○ Default: 2.5
○ Range: 1.5-5.0
○ Description: Weight applied to high volume periods in smart money calculations. Increases emphasis on institutional sized transactions
Divergence Detection
● Pivot Detection Period
○ Default: 12
○ Range: 5-50
○ Description: Bars to analyze for pivot high/low identification.
Affects divergence accuracy and signal frequency
● Minimum Divergence Strength
○ Default: 0.25
○ Range: 0.1-1.0
○ Description: Required price change percentage for valid divergence patterns.
Higher values filter out weaker signals
✅ Best Use Cases
● Trading with intraday to daily timeframes for institutional position identification
● Confirming trend reversals when divergences align with support/resistance levels
● Entry timing in trending markets when institutional bias supports the direction
● Risk management by avoiding trades against strong institutional positioning
● Multi timeframe analysis combining short term signals with longer term bias
⚠️ Limitations
● Requires sufficient volume for accurate institutional detection in low volume markets
● Divergence signals may have false positives during highly volatile news events
● Best performance on liquid markets with consistent institutional participation
● Lagging nature of volume based calculations may delay signal generation
● Effectiveness reduced during low participation holiday periods
💡 What Makes This Unique
● Multi Factor Analysis: Combines volume, price, and momentum for comprehensive institutional detection
● Adaptive Zones: Dynamic overbought/oversold levels that adjust to market conditions
● Volume Intelligence: Advanced algorithms identify institutional sized transactions
● Professional Visualization: Multiple display styles with customizable themes
● Confirmation System: Multiple validation layers reduce false signal generation
🔬 How It Works
1. Volume Analysis Phase:
● Analyzes current volume against historical averages to identify institutional activity
● Applies multi timeframe analysis for enhanced detection accuracy
● Calculates volume pressure through buying vs selling momentum
2. Smart Money Flow Calculation:
● Combines typical price with volume weighted analysis
● Applies institutional trade weighting for high volume periods
● Generates directional flow based on price momentum and volume expansion
3. Divergence Detection Process:
● Identifies pivot highs/lows in both price and indicator values
● Validates divergence strength against minimum threshold requirements
● Confirms signals through multiple technical factors before generation
💡 Note: This indicator works best when combined with proper risk management and position sizing. The institutional bias component helps identify market sentiment shifts, while divergence signals provide specific entry opportunities. For optimal results, use on liquid markets with consistent institutional participation and combine with additional technical analysis methods.
Faster Heikin AshiFaster Heikin Ashi
The Faster Heikin Ashi improves traditional Heikin Ashi candles by introducing advanced weighting mechanisms and lag reduction techniques. While maintaining the price smoothing benefits of standard Heikin Ashi, this enhanced version delivers faster signals and responsiveness.
Key Features
Unified Responsiveness Control
Single parameter (0.1 - 1.0) controls all responsiveness aspects
Eliminates conflicting settings found in other enhanced HA indicators
Intuitive scaling from conservative (0.1) to highly responsive (1.0)
Advanced Weighted Calculations
Smart Close Weighting: Close prices receive 2-3x more influence for faster trend detection
Dynamic OHLC Processing: All price components are intelligently weighted based on responsiveness setting
Balanced High/Low Emphasis: Maintains price level accuracy while improving speed
Enhanced Open Calculation
Transition Speed: Open prices "catch up" to market movements faster
Lag Reduction Algorithm: Eliminates the typical delay in Heikin Ashi open calculations
Smooth Integration: Maintains visual continuity while improving responsiveness
Four-Color Scheme
- 🟢 **Lime**: Strong bullish momentum
- 🔴 **Red**: Strong bearish momentum
- 🟢 **Green**: Moderate bullish
- 🔴 **Maroon**: Moderate bearish
How It Works
Traditional Heikin Ashi smooths price action but often lags behind real market movements. This enhanced version:
1. Weights price components based on their predictive value
2. Accelerates trend transitions through advanced open calculations
3. Scales all enhancements through a single responsiveness parameter
4. Maintains smoothing benefits while reducing lag
Responsiveness (0.1 - 1.0)
0.1 - 0.3: Conservative, maximum smoothing
0.4 - 0.6: Balanced, good for swing trading and trend following
0.7 - 1.0: Aggressive, fast signals, suitable for scalping and active trading
Identical Candles Detector [Premium]Identical Candles Detector
Advanced pattern recognition for consecutive similar candles
Description
This professional-grade indicator detects sequences of nearly identical candles, a pattern often signaling consolidation before significant breakouts. Unlike basic similarity detectors, it employs a weighted comparison system evaluating both candle bodies and wicks with adjustable tolerance.
Key Features:
Smart Comparison Algorithm: Weighs body vs. wick importance (adjustable 0-100%)
Directional Filtering: Optional same-direction requirement for bullish/bearish consistency
Statistical Backtesting: Tracks historical pattern success rates in real-time
Future Projection: Analyzes post-pattern performance with customizable lookahead
Visual Highlighting: Clear pattern marking with optional performance statistics
How It Works:
Calculates weighted candle size (body + wicks)
Compares consecutive candles within user-defined tolerance
Verifies directional consistency when enabled
Evaluates future price action for statistical significance
Usage Guidelines:
Best used on 15m-4h timeframes for swing trading
Combine with volume confirmation for higher probability signals
Tighten tolerance (3-5%) for more precise patterns
Use minimum pattern distance to avoid over-crowding
Technical Notes:
Safe historical access prevents lookback errors
Comprehensive input validation ensures stable operation
Memory-efficient implementation supports long backtests
Why This Stands Out:
While simple candle patterns are common, this tool adds:
Quantitative similarity measurement
Configurable component weighting
Built-in performance analytics
Professional-grade alert conditions
Note: This is not a standalone trading system. Always use with proper risk management and confirmation from other indicators.
Lorentzian Classification - Advanced Trading DashboardLorentzian Classification - Relativistic Market Analysis
A Journey from Theory to Trading Reality
What began as fascination with Einstein's relativity and Lorentzian geometry has evolved into a practical trading tool that bridges theoretical physics and market dynamics. This indicator represents months of wrestling with complex mathematical concepts, debugging intricate algorithms, and transforming abstract theory into actionable trading signals.
The Theoretical Foundation
Lorentzian Distance in Market Space
Traditional Euclidean distance treats all feature differences equally, but markets don't behave uniformly. Lorentzian distance, borrowed from spacetime geometry, provides a more nuanced similarity measure:
d(x,y) = Σ ln(1 + |xi - yi|)
This logarithmic formulation naturally handles:
Scale invariance: Large price moves don't overwhelm small but significant patterns
Outlier robustness: Extreme values are dampened rather than dominating
Non-linear relationships: Captures market behavior better than linear metrics
K-Nearest Neighbors with Relativistic Weighting
The algorithm searches historical market states for patterns similar to current conditions. Each neighbor receives weight inversely proportional to its Lorentzian distance:
w = 1 / (1 + distance)
This creates a "gravitational" effect where closer patterns have stronger influence on predictions.
The Implementation Challenge
Creating meaningful market features required extensive experimentation:
Price Features: Multi-timeframe momentum (1, 2, 3, 5, 8 bar lookbacks) Volume Features: Relative volume analysis against 20-period average
Volatility Features: ATR and Bollinger Band width normalization Momentum Features: RSI deviation from neutral and MACD/price ratio
Each feature undergoes min-max normalization to ensure equal weighting in distance calculations.
The Prediction Mechanism
For each current market state:
Feature Vector Construction: 12-dimensional representation of market conditions
Historical Search: Scan lookback period for similar patterns using Lorentzian distance
Neighbor Selection: Identify K nearest historical matches
Outcome Analysis: Examine what happened N bars after each match
Weighted Prediction: Combine outcomes using distance-based weights
Confidence Calculation: Measure agreement between neighbors
Technical Hurdles Overcome
Array Management: Complex indexing to prevent look-ahead bias
Distance Calculations: Optimizing nested loops for performance
Memory Constraints: Balancing lookback depth with computational limits
Signal Filtering: Preventing clustering of identical signals
Advanced Dashboard System
Main Control Panel
The primary dashboard provides real-time market intelligence:
Signal Status: Current prediction with confidence percentage
Neighbor Analysis: How many historical patterns match current conditions
Market Regime: Trend strength, volatility, and volume analysis
Temporal Context: Real-time updates with timestamp
Performance Analytics
Comprehensive tracking system monitors:
Win Rate: Percentage of successful predictions
Signal Count: Total predictions generated
Streak Analysis: Current winning/losing sequence
Drawdown Monitoring: Maximum equity decline
Sharpe Approximation: Risk-adjusted performance estimate
Risk Assessment Panel
Multi-dimensional risk analysis:
RSI Positioning: Overbought/oversold conditions
ATR Percentage: Current volatility relative to price
Bollinger Position: Price location within volatility bands
MACD Alignment: Momentum confirmation
Confidence Heatmap
Visual representation of prediction reliability:
Historical Confidence: Last 10 periods of prediction certainty
Strength Analysis: Magnitude of prediction values over time
Pattern Recognition: Color-coded confidence levels for quick assessment
Input Parameters Deep Dive
Core Algorithm Settings
K Nearest Neighbors (1-20): More neighbors create smoother but less responsive signals. Optimal range 5-8 for most markets.
Historical Lookback (50-500): Deeper history improves pattern recognition but reduces adaptability. 100-200 bars optimal for most timeframes.
Feature Window (5-30): Longer windows capture more context but reduce sensitivity. Match to your trading timeframe.
Feature Selection
Price Changes: Essential for momentum and reversal detection Volume Profile: Critical for institutional activity recognition Volatility Measures: Key for regime change detection Momentum Indicators: Vital for trend confirmation
Signal Generation
Prediction Horizon (1-20): How far ahead to predict. Shorter horizons for scalping, longer for swing trading.
Signal Threshold (0.5-0.9): Confidence required for signal generation. Higher values reduce false signals but may miss opportunities.
Smoothing (1-10): EMA applied to raw predictions. More smoothing reduces noise but increases lag.
Visual Design Philosophy
Color Themes
Professional: Corporate blue/red for institutional environments Neon: Cyberpunk cyan/magenta for modern aesthetics
Matrix: Green/red hacker-inspired palette Classic: Traditional trading colors
Information Hierarchy
The dashboard system prioritizes information by importance:
Primary Signals: Largest, most prominent display
Confidence Metrics: Secondary but clearly visible
Supporting Data: Detailed but unobtrusive
Historical Context: Available but not distracting
Trading Applications
Signal Interpretation
Long Signals: Prediction > threshold with high confidence
Look for volume confirmation
- Check trend alignment
- Verify support levels
Short Signals: Prediction < -threshold with high confidence
Confirm with resistance levels
- Check for distribution patterns
- Verify momentum divergence
- Market Regime Adaptation
Trending Markets: Higher confidence in directional signals
Ranging Markets: Focus on reversal signals at extremes
Volatile Markets: Require higher confidence thresholds
Low Volume: Reduce position sizes, increase caution
Risk Management Integration
Confidence-Based Sizing: Larger positions for higher confidence signals
Regime-Aware Stops: Wider stops in volatile regimes
Multi-Timeframe Confirmation: Align signals across timeframes
Volume Confirmation: Require volume support for major signals
Originality and Innovation
This indicator represents genuine innovation in several areas:
Mathematical Approach
First application of Lorentzian geometry to market pattern recognition. Unlike Euclidean-based systems, this naturally handles market non-linearities.
Feature Engineering
Sophisticated multi-dimensional feature space combining price, volume, volatility, and momentum in normalized form.
Visualization System
Professional-grade dashboard system providing comprehensive market intelligence in intuitive format.
Performance Tracking
Real-time performance analytics typically found only in institutional trading systems.
Development Journey
Creating this indicator involved overcoming numerous technical challenges:
Mathematical Complexity: Translating theoretical concepts into practical code
Performance Optimization: Balancing accuracy with computational efficiency
User Interface Design: Making complex data accessible and actionable
Signal Quality: Filtering noise while maintaining responsiveness
The result is a tool that brings institutional-grade analytics to individual traders while maintaining the theoretical rigor of its mathematical foundation.
Best Practices
- Parameter Optimization
- Start with default settings and adjust based on:
Market Characteristics: Volatile vs. stable
Trading Timeframe: Scalping vs. swing trading
Risk Tolerance: Conservative vs. aggressive
Signal Confirmation
Never trade on Lorentzian signals alone:
Price Action: Confirm with support/resistance
Volume: Verify with volume analysis
Multiple Timeframes: Check higher timeframe alignment
Market Context: Consider overall market conditions
Risk Management
Position Sizing: Scale with confidence levels
Stop Losses: Adapt to market volatility
Profit Targets: Based on historical performance
Maximum Risk: Never exceed 2-3% per trade
Disclaimer
This indicator is for educational and research purposes only. It does not constitute financial advice or guarantee profitable trading results. The Lorentzian classification system reveals market patterns but cannot predict future price movements with certainty. Always use proper risk management, conduct your own analysis, and never risk more than you can afford to lose.
Market dynamics are inherently uncertain, and past performance does not guarantee future results. This tool should be used as part of a comprehensive trading strategy, not as a standalone solution.
Bringing the elegance of relativistic geometry to market analysis through sophisticated pattern recognition and intuitive visualization.
Thank you for sharing the idea. You're more than a follower, you're a leader!
@vasanthgautham1221
Trade with precision. Trade with insight.
— Dskyz , for DAFE Trading Systems
PhenLabs - Market Fluid Dynamics📊 Market Fluid Dynamics -
Version: PineScript™ v6
📌 Description
The Market Fluid Dynamics - Phen indicator is a new thinking regarding market analysis by modeling price action, volume, and volatility using a fluid system. It attempts to offer traders control over more profound market forces, such as momentum (speed), resistance (thickness), and buying/selling pressure. By visualizing such dynamics, the script allows the traders to decide on the prevailing market flow, its power, likely continuations, and zones of calmness and chaos, and thereby allows improved decision-making.
This measure avoids the usual difficulty of reconciling multiple, often contradictory, market indications by including them within a single overarching model. It moves beyond traditional binary indicators by providing a multi-dimensional view of market behavior, employing fluid dynamic analogs to describe complex interactions in an accessible manner.
🚀 Points of Innovation
Integrated Fluid Dynamics Model: Combines velocity, viscosity, pressure, and turbulence into a single indicator.
Normalized Metrics: Uses ATR and other normalization techniques for consistent readings across different assets and timeframes.
Dynamic Flow Visualization: Main flow line changes color and intensity based on direction and strength.
Turbulence Background: Visually represents market stability with a gradient background, from calm to turbulent.
Comprehensive Dashboard: Provides an at-a-glance summary of key fluid dynamic metrics.
Multi-Layer Smoothing: Employs several layers of EMA smoothing for a clearer, more responsive main flow line.
🔧 Core Components
Velocity Component: Measures price momentum (first derivative of price), normalized by ATR. It indicates the speed and direction of price changes.
Viscosity Component: Represents market resistance to price changes, derived from ATR relative to its historical average. Higher viscosity suggests it’s harder for prices to move.
Pressure Component: Quantifies the force created by volume and price range (close - open), normalized by ATR. It reflects buying or selling pressure.
Turbulence Detection: Calculates a Reynolds number equivalent to identify market stability, ranging from laminar (stable) to turbulent (chaotic).
Main Flow Indicator: Combines the above components, applying sensitivity and smoothing, to generate a primary signal of market direction and strength.
🔥 Key Features
Advanced Smoothing Algorithm: Utilizes multiple EMA layers on the raw flow calculation for a fluid and responsive main flow line, reducing noise while maintaining sensitivity.
Gradient Flow Coloring: The main flow line dynamically changes color from light to deep blue for bullish flow and light to deep red for bearish flow, with intensity reflecting flow strength. This provides an immediate visual cue of market sentiment and momentum.
Turbulence Level Background: The chart background changes color based on calculated turbulence (from calm gray to vibrant orange), offering an intuitive understanding of market stability and potential for erratic price action.
Informative Dashboard: A customizable on-screen table displays critical metrics like Flow State, Flow Strength, Market Viscosity, Turbulence, Pressure Force, Flow Acceleration, and Flow Continuity, allowing traders to quickly assess current market conditions.
Configurable Lookback and Sensitivity: Users can adjust the base lookback period for calculations and the sensitivity of the flow to viscosity, tailoring the indicator to different trading styles and market conditions.
Alert Conditions: Pre-defined alerts for flow direction changes (positive/negative crossover of zero line) and detection of high turbulence states.
🎨 Visualization
Main Flow Line: A smoothed line plotted below the main chart, colored blue for bullish flow and red for bearish flow. The intensity of the color (light to dark) indicates the strength of the flow. This line crossing the zero line can signal a change in market direction.
Zero Line: A dotted horizontal line at the zero level, serving as a baseline to gauge whether the market flow is positive (bullish) or negative (bearish).
Turbulence Background: The indicator pane’s background color changes based on the calculated turbulence level. A calm, almost transparent gray indicates low turbulence (laminar flow), while a more vibrant, semi-transparent orange signifies high turbulence. This helps traders visually assess market stability.
Dashboard Table: An optional table displayed on the chart, showing key metrics like ‘Flow State’, ‘Flow Strength’, ‘Market Viscosity’, ‘Turbulence’, ‘Pressure Force’, ‘Flow Acceleration’, and ‘Flow Continuity’ with their current values and qualitative descriptions (e.g., ‘Bullish Flow’, ‘Laminar (Stable)’).
📖 Usage Guidelines
Setting Categories
Show Dashboard - Default: true; Range: true/false; Description: Toggles the visibility of the Market Fluid Dynamics dashboard on the chart. Enable to see key metrics at a glance.
Base Lookback Period - Default: 14; Range: 5 - (no upper limit, practical limits apply); Description: Sets the primary lookback period for core calculations like velocity, ATR, and volume SMA. Shorter periods make the indicator more sensitive to recent price action, while longer periods provide a smoother, slower signal.
Flow Sensitivity - Default: 0.5; Range: 0.1 - 1.0 (step 0.1); Description: Adjusts how much the market viscosity dampens the raw flow. A lower value means viscosity has less impact (flow is more sensitive to raw velocity/pressure), while a higher value means viscosity has a greater dampening effect.
Flow Smoothing - Default: 5; Range: 1 - 20; Description: Controls the length of the EMA smoothing applied to the main flow line. Higher values result in a smoother flow line but with more lag; lower values make it more responsive but potentially noisier.
Dashboard Position - Default: ‘Top Right’; Range: ‘Top Right’, ‘Top Left’, ‘Bottom Right’, ‘Bottom Left’, ‘Middle Right’, ‘Middle Left’; Description: Determines the placement of the dashboard on the chart.
Header Size - Default: ‘Normal’; Range: ‘Tiny’, ‘Small’, ‘Normal’, ‘Large’, ‘Huge’; Description: Sets the text size for the dashboard header.
Values Size - Default: ‘Small’; Range: ‘Tiny’, ‘Small’, ‘Normal’, ‘Large’; Description: Sets the text size for the metric values in the dashboard.
✅ Best Use Cases
Trend Identification: Identifying the dominant market flow (bullish or bearish) and its strength to trade in the direction of the prevailing trend.
Momentum Confirmation: Using the flow strength and acceleration to confirm the conviction behind price movements.
Volatility Assessment: Utilizing the turbulence metric to gauge market stability, helping to adjust position sizing or avoid choppy conditions.
Reversal Spotting: Watching for divergences between price and flow, or crossovers of the main flow line above/below the zero line, as potential reversal signals, especially when combined with changes in pressure or viscosity.
Swing Trading: Leveraging the smoothed flow line to capture medium-term market swings, entering when flow aligns with the desired trade direction and exiting when flow weakens or reverses.
Intraday Scalping: Using shorter lookback periods and higher sensitivity to identify quick shifts in flow and turbulence for short-term trading opportunities, particularly in liquid markets.
⚠️ Limitations
Lagging Nature: Like many indicators based on moving averages and lookback periods, the main flow line can lag behind rapid price changes, potentially leading to delayed signals.
Whipsaws in Ranging Markets: During periods of low volatility or sideways price action (high viscosity, low flow strength), the indicator might produce frequent buy/sell signals (whipsaws) as the flow oscillates around the zero line.
Not a Standalone System: While comprehensive, it should be used in conjunction with other forms of analysis (e.g., price action, support/resistance levels, other indicators) and not as a sole basis for trading decisions.
Subjectivity in Interpretation: While the dashboard provides quantitative values, the interpretation of “strong” flow, “high” turbulence, or “significant” acceleration can still have a subjective element depending on the trader’s strategy and risk tolerance.
💡 What Makes This Unique
Fluid Dynamics Analogy: Its core strength lies in translating complex market interactions into an intuitive fluid dynamics framework, making concepts like momentum, resistance, and pressure easier to visualize and understand.
Market View: Instead of focusing on a single aspect (like just momentum or just volatility), it integrates multiple factors (velocity, viscosity, pressure, turbulence) to provide a more comprehensive picture of market conditions.
Adaptive Visualization: The dynamic coloring of the flow line and the turbulence background provide immediate, adaptive visual feedback that changes with market conditions.
🔬 How It Works
Price Velocity Calculation: The indicator first calculates price velocity by measuring the rate of change of the closing price over a given ‘lookback’ period. The raw velocity is then normalized by the Average True Range (ATR) of the same lookback period. Normalization enables comparison of momentum between assets or timeframes by scaling for volatility. This is the direction and speed of initial price movement.
Viscosity Calculation: Market ‘viscosity’ or resistance to price movement is determined by looking at the current ATR relative to its longer-term average (SMA of ATR over lookback * 2). The further the current ATR is above its average, the lower the viscosity (less resistance to price movement), and vice-versa. The script inverts this relationship and bounds it so that rising viscosity means more resistance.
Pressure Force Measurement: A ‘pressure’ variable is calculated as a function of the ratio of current volume to its simple moving average, multiplied by the price range (close - open) and normalized by ATR. This is designed to measure the force behind price movement created by volume and intraday price thrusts. This pressure is smoothed by an EMA.
Turbulence State Evaluation: A equivalent ‘Reynolds number’ is calculated by dividing the absolute normalized velocity by the viscosity. This is the proclivity of the market to move in a chaotic or orderly fashion. This ‘reynoldsValue’ is smoothed with an EMA to get the ‘turbulenceState’, which indicates if the market is laminar (stable), transitional, or turbulent.
Main Flow Derivation: The ‘rawFlow’ is calculated by taking the normalized velocity, dampening its impact based on the ‘viscosity’ and user-input ‘sensitivity’, and orienting it by the sign of the smoothed ‘pressureSmooth’. The ‘rawFlow’ is then put through multiple layers of exponential moving average (EMA) smoothing (with ‘smoothingLength’ and derived values) to reach the final ‘mainFlow’ line. The extensive smoothing is designed to give a smooth and clear visualization of the overall market direction and magnitude.
Dashboard Metrics Compilation: Additional metrics like flow acceleration (derivative of mainFlow), and flow continuity (correlation between close and volume) are calculated. All primary components (Flow State, Strength, Viscosity, Turbulence, Pressure, Acceleration, Continuity) are then presented in a user-configurable dashboard for ease of monitoring.
💡 Note:
The “Market Fluid Dynamics - Phen” indicator is designed to offer a unique perspective on market behavior by applying principles from fluid dynamics. It’s most effective when used to understand the underlying forces driving price rather than as a direct buy/sell signal generator in isolation. Experiment with the settings, particularly the ‘Base Lookback Period’, ‘Flow Sensitivity’, and ‘Flow Smoothing’, to find what best suits your trading style and the specific asset you are analyzing. Always combine its insights with robust risk management practices.
[blackcat] L1 Net Volume DifferenceOVERVIEW
The L1 Net Volume Difference indicator serves as an advanced analytical tool designed to provide traders with deep insights into market sentiment by examining the differential between buying and selling volumes over precise timeframes. By leveraging these volume dynamics, it helps identify trends and potential reversal points more accurately, thereby supporting well-informed decision-making processes. The key focus lies in dissecting intraday changes that reflect short-term market behavior, offering critical input for both swing and day traders alike. 📊
Key benefits encompass:
• Precise calculation of net volume differences grounded in real-time data.
• Interactive visualization elements enhancing interpretability effortlessly.
• Real-time generation of buy/sell signals driven by dynamic volume shifts.
TECHNICAL ANALYSIS COMPONENTS
📉 Volume Accumulation Mechanisms:
Monitors cumulative buy/sell volumes derived from comparative closing prices.
Periodically resets accumulation counters aligning with predefined intervals (e.g., 5-minute bars).
Facilitates identification of directional biases reflecting underlying market forces accurately.
🕵️♂️ Sentiment Detection Algorithms:
Employs proprietary logic distinguishing between bullish/bearish sentiments dynamically.
Ensures consistent adherence to predefined statistical protocols maintaining accuracy.
Supports adaptive thresholds adjusting sensitivities based on changing market conditions flexibly.
🎯 Dynamic Signal Generation:
Detects transitions indicating dominance shifts between buyers/sellers promptly.
Triggers timely alerts enabling swift reactions to evolving market dynamics effectively.
Integrates conditional logic reinforcing signal validity minimizing erroneous activations.
INDICATOR FUNCTIONALITY
🔢 Core Algorithms:
Utilizes moving averages along with standardized deviation formulas generating precise net volume measurements.
Implements Arithmetic Mean Line Algorithm (AMLA) smoothing techniques improving interpretability.
Ensures consistent alignment with established statistical principles preserving fidelity.
🖱️ User Interface Elements:
Dedicated plots displaying real-time net volume markers facilitating swift decision-making.
Context-sensitive color coding distinguishing positive/negative deviations intuitively.
Background shading highlighting proximity to key threshold activations enhancing visibility.
STRATEGY IMPLEMENTATION
✅ Entry Conditions:
Confirm bullish/bearish setups validated through multiple confirmatory signals.
Validate entry decisions considering concurrent market sentiment factors.
Assess alignment between net volume readings and broader trend directions ensuring coherence.
🚫 Exit Mechanisms:
Trigger exits upon hitting predetermined thresholds derived from historical analyses.
Monitor continuous breaches signifying potential trend reversals promptly executing closures.
Execute partial/total closes contingent upon cumulative loss limits preserving capital efficiently.
PARAMETER CONFIGURATIONS
🎯 Optimization Guidelines:
Reset Interval: Governs responsiveness versus stability balancing sensitivity/stability.
Price Source: Dictates primary data series driving volume calculations selecting relevant inputs accurately.
💬 Customization Recommendations:
Commence with baseline defaults; iteratively refine parameters isolating individual impacts.
Evaluate adjustments independently prior to combined modifications minimizing disruptions.
Prioritize minimizing erroneous trigger occurrences first optimizing signal fidelity.
Sustain balanced risk-reward profiles irrespective of chosen settings upholding disciplined approaches.
ADVANCED RISK MANAGEMENT
🛡️ Proactive Risk Mitigation Techniques:
Enforce strict compliance with pre-defined maximum leverage constraints adhering strictly to guidelines.
Mandatorily apply trailing stop-loss orders conforming to script outputs reinforcing discipline.
Allocate positions proportionately relative to available capital reserves managing exposures prudently.
Conduct periodic reviews gauging strategy effectiveness rigorously identifying areas needing refinement.
⚠️ Potential Pitfalls & Solutions:
Address frequent violations arising during heightened volatility phases necessitating manual interventions judiciously.
Manage false alerts warranting immediate attention avoiding adverse consequences systematically.
Prepare contingency plans mitigating margin call possibilities preparing proactive responses effectively.
Continuously assess automated system reliability amidst fluctuating conditions ensuring seamless functionality.
PERFORMANCE AUDITS & REFINEMENTS
🔍 Critical Evaluation Metrics:
Assess win percentages consistently across diverse trading instruments gauging reliability.
Calculate average profit ratios per successful execution measuring profitability efficiency accurately.
Measure peak drawdown durations alongside associated magnitudes evaluating downside risks comprehensively.
Analyze signal generation frequencies revealing hidden patterns potentially skewing outcomes uncovering systematic biases.
📈 Historical Data Analysis Tools:
Maintain comprehensive records capturing every triggered event meticulously documenting results.
Compare realized profits/losses against backtested simulations benchmarking actual vs expected performances accurately.
Identify recurrent systematic errors demanding corrective actions implementing iterative refinements steadily.
Document evolving performance metrics tracking progress dynamically addressing identified shortcomings proactively.
PROBLEM SOLVING ADVICE
🔧 Frequent Encountered Challenges:
Unpredictable behaviors emerging within thinly traded markets requiring filtration processes.
Latency issues manifesting during abrupt price fluctuations causing missed opportunities.
Overfitted models yielding suboptimal results post-extensive tuning demanding recalibrations.
Inaccuracies stemming from incomplete/inaccurate data feeds necessitating verification procedures.
💡 Effective Resolution Pathways:
Exclude low-liquidity assets prone to erratic movements enhancing signal integrity.
Introduce buffer intervals safeguarding major news/event impacts mitigating distortions effectively.
Limit ongoing optimization attempts preventing model degradation maintaining optimal performance levels consistently.
Verify reliable connections ensuring uninterrupted data flows guaranteeing accurate interpretations reliably.
USER ENGAGEMENT SEGMENT
🤝 Community Contributions Welcome
Highly encourage active participation sharing experiences & recommendations!
THANKS
Heartfelt acknowledgment extends to all developers contributing invaluable insights about volume-based trading methodologies! ✨
BollingerBands MTF | AlchimistOfCrypto🌌 Bollinger Bands – Unveiling Market Volatility Fields 🌌
"The Bollinger Bands, reimagined through quantum mechanics principles, visualizes the probabilistic distribution of price movements within a multi-dimensional volatility field. This indicator employs principles from wave function mathematics where standard deviation creates probabilistic boundaries, similar to electron cloud models in quantum physics. Our implementation features algorithmically enhanced visualization derived from extensive mathematical modeling, creating a dynamic representation of volatility compression and expansion cycles with adaptive glow effects that highlight the critical moments where volatility phase transitions occur."
📊 Professional Trading Application
The Bollinger Bands Quantum transcends traditional volatility measurement with a sophisticated gradient illumination system that reveals the underlying structure of market volatility fields. Scientifically calibrated for multiple timeframes and featuring eight distinct visual themes, it enables traders to perceive volatility contractions and expansions with unprecedented clarity.
⚙️ Indicator Configuration
- Volatility Field Parameters 📏
Python-optimized settings for specific market conditions:
- Period: 20 (default) - The quantum time window for volatility calculation
- StdDev Multiplier: 2.0 - The probabilistic boundary coefficient
- MA Type: SMA/EMA/VWMA/WMA/RMA - The quantum field smoothing algorithm
- Visual Theming 🎨
Eight scientifically designed visual palettes optimized for volatility pattern recognition:
- Neon (default): High-contrast green/red scheme enhancing volatility transition visibility
- Cyan-Magenta: Vibrant palette for maximum volatility boundary distinction
- Yellow-Purple: Complementary colors for enhanced compression/expansion detection
- Specialized themes (Green-Red, Forest Green, Blue Ocean, Orange-Red, Grayscale): Each calibrated for different market environments
- Opacity Control 🔍
- Variable transparency system (0-100) allowing seamless integration with price action
- Adaptive glow effect that intensifies during volatility phase transitions
- Quantum field visualization that reveals the probabilistic nature of price movements
🚀 How to Use
1. Select Visualization Parameters ⏰: Adjust period and standard deviation to match market conditions
2. Choose MA Type 🎚️: Select the appropriate smoothing algorithm for your trading strategy
3. Select Visual Theme 🌈: Choose a color scheme that enhances your personal pattern recognition
4. Adjust Opacity 🔎: Fine-tune visualization intensity to complement your chart analysis
5. Identify Volatility Phases ✅: Monitor band width to detect compression (pre-breakout) and expansion (trend)
6. Trade with Precision 🛡️: Enter during band contraction for breakouts, or trade mean reversion using band boundaries
7. Manage Risk Dynamically 🔐: Use band width as volatility-based position sizing parameter
Chan Theory - Chanlun UltraChan Theory -Chanlun Ultra
Overview
This script is based on the core technical framework of Chan Theory, transforming complex market fluctuations into a multi-layered, quantifiable structural analysis system. Through real-time dynamic computation, it automatically parses key components in price movements such as fractals, pens, segments, and pivot zones. Integrated with momentum analysis and trading signal alerts, it provides traders with comprehensive market insights from micro to macro perspectives. The core distinction of Chan Theory from traditional technical indicators lies in its rigorous recursive logic and human-centric market philosophy. This script faithfully restores Chan Theory's essence of "using Zen to resolve market complexity," decomposing spiral price movements into an orderly trading decision system.
Technical Principles
This indicator implements the complete recognition process from candlesticks to fractals, pens, segments, and pivot zones using pure Pine Script under Chan Theory's framework. Core technical implementations include:
1. Candlestick Containment Processing
Employs specific algorithms to handle candlestick containment relationships, eliminating random noise:
In uptrends: Select the higher high and higher low values
In downtrends: Select the lower high and lower low values
Ensure complete elimination of containment through recursive processing
2. Fractal Identification System
Performs strict fractal judgment on processed candlesticks:
Top Fractal: The middle candlestick's high is higher than both adjacent candlesticks
Bottom Fractal: The middle candlestick's low is lower than both adjacent candlesticks
Validate fractal effectiveness via the filterOperateType function
3. Pen Construction Mechanism & Type Selection
Connects valid top/bottom fractals to form pen structures, offering four pen types:
Classic Pen: Traditional Chan Theory definition, strictly following classic rules
Optimized Pen: Enhanced algorithm for short-term volatility recognition
4K Pen: Builds pens based on fractals formed by at least 4 candlesticks (improves stability)
Strict Pen: Employs the most stringent validation conditions for reliability
4. Segment Partitioning Algorithm
Applies segment rules to pen sequences with three modes:
- Dynamic Real-time Progressive Correction: Adjusts forming segments continuously with new data
- Strict Mode: Fully complies with Chan Theory definitions
- Extension Mode: Flexible handling of trend developments
5. Pivot Zone Recognition Technology
Identifies pen-level and segment-level pivot zones
Calculates pivot zone price ranges and time durations
Analyzes pivot zone evolution characteristics
Supports display of pivot zones across different levels
Trading Signal System & Filters
Trading Signal Filtering System
This indicator provides comprehensive filtering functions:
Fractal Validity Filter: Verifies fractal patterns and post-fractal developments
Basic Fractal Filter: Eliminates non-compliant fractals through basic feature checks
Type I MACD Divergence Filter: Enhances Type I signal reliability via MACD divergence analysis
Type II Signal Filter: Custom conditions for Type II signals
-False Signal Trap Avoidance: Detects and bypasses deceptive price patterns
Chan Theory Trading Signal Principles
Type I Signals (Trend Reversals)
Principle: Forms when price makes new highs/lows with weakening internal momentum (divergence)
Identification: Compares structural features of adjacent same-direction pens
Application: Early trend reversal signals for swing trading
Type II Signals (Pullback Entries)
Principle: Occurs during retracements as sub-level reversal signals
Identification: Determined by pivot zone support/resistance and fractal combinations
Application: Optimal positions for pullback trades with controlled risk
Type III Signals (Breakout Confirmations)
Principle: Confirms pivot zone breakouts
Identification: Price breaks prior pivot zone boundaries with valid fractals
Application: Trend continuation signals for trend-following strategies
Indicator Features
Multi-Level Structural Analysis
Distinguishes structures across levels via level parameters
Higher-level trends guide lower-level operations
Implements cross-level collaborative logic
Displays sub-level pivot zones
Structural Visualization
Pens: Displayed per selected pen type
Segments: Rendered according to chosen segment mode
Pivot Zones: Color gradients indicate consolidation strength
Technical Implementation
Data Structure Design
Pen Object: Stores direction, timestamps, and price attributes
Segment Object: Manages segments and constituent pens
Pivot Object: Defines pivot zone ranges and characteristics
Grade Object: Organizes analysis results across levels
User Guide
Parameter Settings
Pen Type: Classic/Optimized/4K/Strict (adapt to analysis needs)
Segment Mode: Dynamic/Strict/Extension (match trading strategies)
Signal Filters: Enable/disable specific filters
Pivot Display: Toggle sub-level pivot zones
Divergence Settings: Configure types (regular/hidden) and display styles
Strategy Settings: Set trading rules linked to signals
Strategy Configuration
Follow Segments: Trade in alignment with segment direction
Signal Participation: Enable/disable Type I/II/III signals
Signal Conditions: Require signals to appear post-pivot zone formation
Prevent Early Entries:
Type I signals require ≥1 pivot zone or 5 pens
Type II Safety Control: Participate only if Type III signals are absent
Practical Recommendations
Select pen types/segment modes per market conditions
Adjust filters for different instruments and timeframes
Enhance accuracy through multi-level analysis
Confirm Type I signals with divergence indicators
Choose strategy parameters aligned with risk tolerance
Value Proposition
Systematizes Chan Theory into computable structures
Multiple pen/segment methods adapt to diverse markets
Advanced filtering significantly improves signal quality (historically validated)
Multi-level analysis provides holistic market insights
This tool is for technical analysis only. It does not constitute investment advice. Users must exercise independent judgment based on personal risk tolerance and objectives.
概述
本脚本基于缠论核心技术框架，将复杂的市场波动转化为多层次、可量化的结构分析系统。通过实时动态演算，自动解析价格走势中的分型、笔、线段、中枢等核心组件，并融合动量分析与交易信号预警功能，为交易者提供从微观到宏观的全方位市场透视。缠论区别于传统技术指标的核心在于其严格的递归逻辑与人性化市场哲学，本脚本忠实还原缠论"以禅破缠"的思想精髓，将螺旋缠绕的价格运动分解为有序的交易决策体系。
技术原理
本指标基于缠论技术分析框架，通过纯Pine Script实现了从K线到分型、笔、线段和中枢的完整识别流程。核心技术实现包括：
1. K线包含处理
采用特定算法处理K线包含关系，消除随机波动干扰：
- 上涨趋势中取高点高值、低点高值
- 下跌趋势中取高点低值、低点低值
- 通过递归处理确保包含关系完全消除
2. 分型识别系统
在处理后的K线基础上实现严格的分型判断：
- 顶分型：中间K线高点高于两侧K线
- 底分型：中间K线低点低于两侧K线
- 通过`filterOperateType`函数实现分型有效性验证
3. 笔的构建机制与类型选择
连接有效顶底分型形成笔结构，提供四种笔类型选择：
- **老笔**：传统缠论笔定义，严格遵循经典规则
- **新笔**：优化算法，增强对短期波动的识别能力
- **4K**：基于至少4根K线形成的分型构建笔，提高稳定性
- **严笔**：采用最严格的条件验证，确保形成的笔结构可靠
4. 线段划分算法
基于笔序列应用线段划分规则，支持三种线段模式：
- **当下延伸后修正**：实时计算当前形成中的线段，并随新数据更新修正
- **严格模式**：要求线段完全符合缠论定义，减少假信号
- **延伸模式**：更灵活地处理线段延伸情况，适合趋势分析
5. 中枢识别技术
- 实现笔中枢和线段中枢识别
- 计算中枢价格区间与时间范围
- 分析中枢演变特征
- 支持显示不同级别中枢功能
买卖点系统与过滤机制
买卖点过滤系统
本指标提供全面的买卖点过滤功能：
- **买卖点分型过滤**：检验分型形态有效性，验证分型后续发展
- **买卖点分型基础过滤**：针对分型基本特征进行验证，排除不合格分型
- **1买卖macd背驰过滤**：通过MACD判断背驰情况，提高一类买卖点可靠性
- **2买卖点过滤**：专门针对二类买卖点的过滤条件
- **防狼术**：避免陷阱式买卖点，提高交易安全性
缠论买卖点原理
1. **一类买卖点**
- 原理：基于趋势背驰原理，当价格创新高/低但内部结构力度减弱时形成
- 识别方法：通过比较相邻同向笔的结构特征判断力度变化
- 应用：提供趋势可能反转的早期信号，适合波段操作
2. **二类买卖点**
- 原理：发生在回调过程中，属于次级别转折信号
- 识别方法：通过中枢支撑位与分型组合判断
- 应用：回调买入或做空的较佳位置，风险相对可控
3. **三类买卖点**
- 原理：中枢突破确认信号
- 识别方法：价格突破前中枢边界并形成有效分型
- 应用：趋势延续的确认信号，适合追踪趋势
指标特点
多级别结构分析
本指标支持多级别联动分析：
- 通过级别参数区分不同级别结构
- 高级别趋势指导低级别操作
- 实现级别间的协同判断逻辑
- 支持显示次级别中枢功能
结构可视化
- 笔结构：根据选择的笔类型显示
- 线段结构：按照选定的线段模式呈现
- 中枢区域：颜色渐变标识不同强度
技术实现说明
数据结构设计
指标设计了完整的面向对象结构：
- Pen结构：存储笔的方向、时间、价格等属性
- Segment结构：管理线段及其组成笔
- Pivot结构：表示中枢范围和特性
- Grade结构：区分不同级别的分析结果
使用指南
参数设置
- 笔的类型：选择老笔、新笔、4K或严笔以适应不同分析需求
- 线段模式：根据交易策略选择合适的线段计算方式
- 买卖点过滤：根据需要启用不同的过滤机制
- 中枢显示：选择是否显示次级别中枢
- 背离设置：选择背离类型、显示方式和样式
- 策略设置：配置与买卖点相关的交易策略选项
策略应用配置
- 跟随线段：根据线段方向进行交易
- 买卖点参与设置：可选择性参与一类、二类和三类买卖点
- 买卖点条件限制：可设置买卖点需要在中枢形成后出现
- 防止过早进场：可要求一类买卖点至少出现一个中枢后或至少5笔后才参与
- 二类买卖点安全性控制：可选择仅在未出现三类买卖点的情况下参与
实际应用建议
- 结合市场环境选择合适的笔类型和线段模式
- 针对不同品种和时间周期调整过滤设置
- 通过多级别分析提高判断准确性
- 使用背离指标确认一类买卖点的有效性
- 根据策略风格选择适合的策略配置参数
技术特点与价值
本指标通过系统化实现缠论结构分析，提供了一种客观的技术分析工具。它的核心价值在于：
1. 将复杂的缠论理论系统化为可计算的结构
2. 提供多种笔、线段判断方法以适应不同市场环境
3. 完善的买卖点过滤系统大幅提高信号质量
4. 多级别联动分析提供全面市场视角
*本指标仅提供技术分析参考，不构成投资建议。用户应根据自身风险承受能力和投资目标进行判断。*
Machine Learning RSI ║ BullVisionOverview:
Introducing the Machine Learning RSI with KNN Adaptation – a cutting-edge momentum indicator that blends the classic Relative Strength Index (RSI) with machine learning principles. By leveraging K-Nearest Neighbors (KNN), this indicator aims at identifying historical patterns that resemble current market behavior and uses this context to refine RSI readings with enhanced sensitivity and responsiveness.
Unlike traditional RSI models, which treat every market environment the same, this version adapts in real-time based on how similar past conditions evolved, offering an analytical edge without relying on predictive assumptions.
Key Features:
🔁 KNN-Based RSI Refinement
This indicator uses a machine learning algorithm (K-Nearest Neighbors) to compare current RSI and price action characteristics to similar historical conditions. The resulting RSI is weighted accordingly, producing a dynamically adjusted value that reflects historical context.
📈 Multi-Feature Similarity Analysis
Pattern similarity is calculated using up to five customizable features:
RSI level
RSI momentum
Volatility
Linear regression slope
Price momentum
Users can adjust how many features are used to tailor the behavior of the KNN logic.
🧠 Machine Learning Weight Control
The influence of the machine learning model on the final RSI output can be fine-tuned using a simple slider. This lets you blend traditional RSI and machine learning-enhanced RSI to suit your preferred level of adaptation.
🎛️ Adaptive Filtering
Additional smoothing options (Kalman Filter, ALMA, Double EMA) can be applied to the RSI, offering better visual clarity and helping to reduce noise in high-frequency environments.
🎨 Visual & Accessibility Settings
Custom color palettes, including support for color vision deficiencies, ensure that trend coloring remains readable for all users. A built-in neon mode adds high-contrast visuals to improve RSI visibility across dark or light themes.
How It Works:
Similarity Matching with KNN:
At each candle, the current RSI and optional market characteristics are compared to historical bars using a KNN search. The algorithm selects the closest matches and averages their RSI values, weighted by similarity. The more similar the pattern, the greater its influence.
Feature-Based Weighting:
Similarity is determined using normalized values of the selected features, which gives a more refined result than RSI alone. You can choose to use only 1 (RSI) or up to all 5 features for deeper analysis.
Filtering & Blending:
After the machine learning-enhanced RSI is calculated, it can be optionally smoothed using advanced filters to suppress short-term noise or sharp spikes. This makes it easier to evaluate RSI signals in different volatility regimes.
Parameters Explained:
📊 RSI Settings:
Set the base RSI length and select your preferred smoothing method from 10+ moving average types (e.g., EMA, ALMA, TEMA).
🧠 Machine Learning Controls:
Enable or disable the KNN engine
Select how many nearest neighbors to compare (K)
Choose the number of features used in similarity detection
Control how much the machine learning engine affects the RSI calculation
🔍 Filtering Options:
Enable one of several advanced smoothing techniques (Kalman Filter, ALMA, Double EMA) to adjust the indicator’s reactivity and stability.
📏 Threshold Levels:
Define static overbought/oversold boundaries or reference dynamically adjusted thresholds based on historical context identified by the KNN algorithm.
🎨 Visual Enhancements:
Select between trend-following or impulse coloring styles. Customize color palettes to accommodate different types of color blindness. Enable neon-style effects for visual clarity.
Use Cases:
Swing & Trend Traders
Can use the indicator to explore how current RSI readings compare to similar market phases, helping to assess trend strength or potential turning points.
Intraday Traders
Benefit from adjustable filters and fast-reacting smoothing to reduce noise in shorter timeframes while retaining contextual relevance.
Discretionary Analysts
Use the adaptive OB/OS thresholds and visual cues to supplement broader confluence zones or market structure analysis.
Customization Tips:
Higher Volatility Periods: Use more neighbors and enable filtering to reduce noise.
Lower Volatility Markets: Use fewer features and disable filtering for quicker RSI adaptation.
Deeper Contextual Analysis: Increase KNN lookback and raise the feature count to refine pattern recognition.
Accessibility Needs: Switch to Deuteranopia or Monochrome mode for clearer visuals in specific color vision conditions.
Final Thoughts:
The Machine Learning RSI combines familiar momentum logic with statistical context derived from historical similarity analysis. It does not attempt to predict price action but rather contextualizes RSI behavior with added nuance. This makes it a valuable tool for those looking to elevate traditional RSI workflows with adaptive, research-driven enhancements.
Stochastic and MACD HistogramStochastic-MACD Fusion Histogram (concept)
How It Works:
This indicator combines Stochastic Oscillator and MACD Histogram to create a unique momentum-tracking histogram. It blends stochastic-based overbought/oversold levels with MACD-based trend strength, helping traders identify potential reversals and trend momentum more effectively.
Stochastic Component: Measures where the price is relative to its recent range, highlighting overbought/oversold conditions.
MACD Component: Captures momentum shifts by calculating the difference between two EMAs and a signal line.
Fusion Algorithm: The MACD histogram is normalized and combined with the Stochastic %K using a weighted formula (60% Stoch, 40% MACD) to smooth fluctuations and improve signal clarity.
Usage:
Histogram Colors:
Blue / SkyBlue: Positive momentum increasing.
Red / LightRed: Negative momentum increasing.
Levels:
Overbought (>30): Potential selling pressure.
Oversold (<-30): Potential buying pressure.
Zero Line: Momentum shift zone.
Notes:
Best to combine it with others indicators for trend confirmation, like Moving Average, MACD, etc.
This indicator is good for quick entry/exit in futures market, from few seconds up to minutes.
It works well on 5 minutes candle. Regular Hours works better.
To sell wait for histogram to go OVER overbought level, once the first candle reach BELOW the overbought level hit sell. Same strategy for buy when it hits oversold level. Make sure you won't use the indicator alone.
Invictus📝 Invictus – Probabilistic Trading Indicator
🔍 1. General Introduction
Invictus is a technical trading indicator designed to support traders by identifying potential buy and sell signals through a probabilistic and adaptive analytical approach. It aims to enhance the analytical process rather than provide explicit trading recommendations. The indicator integrates multiple analytical components—price pattern detection, momentum analysis (RSI), dynamic trend lines (Kalman Line), and volatility bands (ATR)—to offer traders a structured and contextual framework for making informed decisions.
Invictus does not guarantee profitable outcomes but seeks to enhance analytical clarity and support cautious decision-making through multiple validation layers.
⚙️ 2. Main Components
🌊 2.1. Price Pattern Detection
Invictus identifies potential market shifts by analyzing specific candlestick sequences:
Bearish Patterns (Sell): Detected when consecutive candles close below their openings, indicating increased selling pressure.
Bullish Patterns (Buy): Detected when consecutive candles close above their openings, suggesting increased buying interest.
These patterns provide historical insights rather than absolute predictions for market movements.
⚡ 2.2. Momentum Confirmation (RSI)
To improve signal clarity, Invictus employs the Relative Strength Index (RSI):
Buy Signal: RSI below a predefined threshold (e.g., 30), signaling potential oversold conditions.
Sell Signal: RSI above a threshold (e.g., 70), signaling potential overbought conditions.
RSI acts exclusively as an additional validation filter to reduce, though not eliminate, false signals derived solely from price patterns.
🌀 2.3. Kalman Dynamic Line
The Kalman Dynamic Line smooths price action and dynamically tracks trends using a Kalman filter algorithm:
Noise Reduction: Minimizes minor price fluctuations.
Trend Direction Indicator: Line slope visually represents bullish or bearish market bias.
Adaptive Support/Resistance: Adjusts continuously to market conditions.
Volatility Sensitivity: Adjustments use ATR to scale proportionally with market volatility.
This adaptive dynamic line provides clear context, aiding traders by filtering short-term volatility.
📊 2.4. Volatility Bands (ATR-based)
ATR-based volatility bands define potential breakout zones and market extremes dynamically:
Upper/Lower Bands: Positioned relative to the Kalman Line based on ATR (volatility multiplier).
Volatility Zones: Highlight potential areas of trend continuation or reversal due to significant price movements.
These bands assist traders in visually assessing significant market movements and reducing the focus on minor fluctuations.
🧠 3. Component Interaction and Validation Logic
Invictus is designed to enhance analytical clarity by integrating multiple technical components, requiring independent confirmations before signals may be considered as potentially actionable
🔗 Step 1: Pattern + RSI Validation
Initial identification of price patterns.
Signal validation through RSI conditions (oversold/overbought).
🔗 Step 2: Trend Alignment (Kalman Line)
Validated signals undergo further assessment with respect to the Kalman Dynamic Line.
Buy signals require price action above the Kalman Line; sell signals require price action below.
🔗 Step 3: Volatility Confirmation (ATR Bands)
Price action must penetrate and close beyond the corresponding volatility band.
Ensures signals align with adequate market volatility and momentum.
🔄 4. Comprehensive Decision-Making Flow
Identify price patterns (initial indication).
Confirm momentum via RSI.
Verify trend alignment using the Kalman Line.
Confirm adequate volatility via ATR bands.
💡 5. Practical Example (Buy Scenario)
Invictus signals a potential buy scenario.
Trader waits for the price to cross above the Kalman Line.
Entry consideration occurs only after a confirmed close above the upper ATR volatility band.
⚠️ 6. Important Limitations
Do not rely solely on Invictus signals; always perform broader market analysis.
Invictus performs optimally in trending markets; exercise caution in sideways or range-bound markets.
Always evaluate broader market context and the dominant trend before making decisions.
📝 7. Risk Management & Responsible Trading
Invictus serves as an analytical support tool, not a guarantee of market outcomes:
Set prudent stop-loss levels.
Apply conservative leverage, especially in volatile conditions.
Conduct thorough backtesting and practice on a demo account before live trading.
⚠️ Disclaimer: Trading involves significant risks. Invictus generates signals based on historical and technical analysis. Past performance is not indicative of future results. Responsible trading practices are strongly advised.
💡 8. Final Considerations
Invictus provides an analytical framework integrating various supportive technical methodologies designed to enhance decision-making and comprehensive analysis. Its multi-layered validation process encourages disciplined analysis and informed decision-making without implying any guarantees of profitability.
Traders should incorporate Invictus within broader strategic frameworks, consistently applying disciplined risk management and thorough market analysis.
Wall Street Ai**Wall Street Ai – Advanced Technical Indicator for Market Analysis**
**Overview**
Wall Street Ai is an advanced, AI-powered technical indicator meticulously engineered to provide traders with in-depth market analysis and insight. By leveraging state-of-the-art artificial intelligence algorithms and comprehensive historical price data, Wall Street Ai is designed to identify significant market turning points and key price levels. Its sophisticated analytical framework enables traders to uncover potential shifts in market momentum, assisting in the formulation of strategic trading decisions while maintaining the highest standards of objectivity and reliability.
**Key Features**
- **Intelligent Pattern Recognition:**
Wall Street Ai employs advanced machine learning techniques to analyze historical price movements and detect recurring patterns. This capability allows it to differentiate between typical market noise and meaningful signals indicative of potential trend reversals.
- **Robust Noise Reduction:**
The indicator incorporates a refined volatility filtering system that minimizes the impact of minor price fluctuations. By isolating significant price movements, it ensures that the analytical output focuses on substantial market shifts rather than ephemeral variations.
- **Customizable Analytical Parameters:**
With a wide range of adjustable settings, Wall Street Ai can be fine-tuned to align with diverse trading strategies and risk appetites. Traders can modify sensitivity, threshold levels, and other critical parameters to optimize the indicator’s performance under various market conditions.
- **Comprehensive Data Analysis:**
By harnessing the power of artificial intelligence, Wall Street Ai performs a deep analysis of historical data, identifying statistically significant highs and lows. This analysis not only reflects past market behavior but also provides valuable insights into potential future turning points, thereby enhancing the predictive aspect of your trading strategy.
- **Adaptive Market Insights:**
The indicator’s dynamic algorithm continuously adjusts to current market conditions, adapting its analysis based on real-time data inputs. This adaptive quality ensures that the indicator remains relevant and effective across different market environments, whether the market is trending strongly, consolidating, or experiencing volatility.
- **Objective and Reliable Analysis:**
Wall Street Ai is built on a foundation of robust statistical methods and rigorous data validation. Its outputs are designed to be objective and free from any exaggerated claims, ensuring that traders receive a clear, unbiased view of market conditions.
**How It Works**
Wall Street Ai integrates advanced AI and deep learning methodologies to analyze a vast array of historical price data. Its core algorithm identifies and evaluates critical market levels by detecting patterns that have historically preceded significant market movements. By filtering out non-essential fluctuations, the indicator emphasizes key price extremes and trend changes that are likely to impact market behavior. The system’s adaptive nature allows it to recalibrate its analytical parameters in response to evolving market dynamics, providing a consistently reliable framework for market analysis.
**Usage Recommendations**
- **Optimal Timeframes:**
For the most effective application, it is recommended to utilize Wall Street Ai on higher timeframe charts, such as hourly (H1) or higher. This approach enhances the clarity of the detected patterns and provides a more comprehensive view of long-term market trends.
- **Market Versatility:**
Wall Street Ai is versatile and can be applied across a broad range of financial markets, including Forex, indices, commodities, cryptocurrencies, and equities. Its adaptable design ensures consistent performance regardless of the asset class being analyzed.
- **Complementary Analytical Tools:**
While Wall Street Ai provides profound insights into market behavior, it is best utilized in combination with other analytical tools and techniques. Integrating its analysis with additional indicators—such as trend lines, support/resistance levels, or momentum oscillators—can further refine your trading strategy and enhance decision-making.
- **Strategy Testing and Optimization:**
Traders are encouraged to test Wall Street Ai extensively in a simulated trading environment before deploying it in live markets. This allows for thorough calibration of its settings according to individual trading styles and risk management strategies, ensuring optimal performance across diverse market conditions.
**Risk Management and Best Practices**
Wall Street Ai is intended to serve as an analytical tool that supports informed trading decisions. However, as with any technical indicator, its outputs should be interpreted as part of a comprehensive trading strategy that includes robust risk management practices. Traders should continuously validate the indicator’s findings with additional analysis and maintain a disciplined approach to position sizing and risk control. Regular review and adjustment of trading strategies in response to market changes are essential to mitigate potential losses.
**Conclusion**
Wall Street Ai offers a cutting-edge, AI-driven approach to technical analysis, empowering traders with detailed market insights and the ability to identify potential turning points with precision. Its intelligent pattern recognition, adaptive analytical capabilities, and extensive noise reduction make it a valuable asset for both experienced traders and those new to market analysis. By integrating Wall Street Ai into your trading toolkit, you can enhance your understanding of market dynamics and develop a more robust, data-driven trading strategy—all while adhering to the highest standards of analytical integrity and performance.
