PINE LIBRARY
DafePatternLib
DafePatternLib: The Neural Pattern Recognition & Reinforcement Learning Engine
This is not a pattern library. This is an artificial trading brain. It doesn't just find patterns; it learns, adapts, and evolves based on their performance in the live market.
█ CHAPTER 1: THE PHILOSOPHY - BEYOND STATIC RULES, INTO DYNAMIC LEARNING
For decades, chart pattern analysis has been trapped in a static, rigid paradigm. An indicator is coded to find a "Bullish Engulfing" or a "Head and Shoulders," and it will signal that pattern with the same blind confidence every single time, regardless of whether that pattern has been consistently failing for the past month. It has no memory, no intelligence, no ability to adapt. It is a dumb machine executing a fixed command.
The DafePatternLib was created to shatter this paradigm. It is built on a powerful, revolutionary philosophy borrowed from the world of artificial intelligence: Reinforcement Learning. This library is not just a collection of pattern detection functions; it is a complete, self-optimizing neural framework. It doesn't just find patterns; it tracks their outcomes. It remembers what works and what doesn't. Over time, it learns to amplify the signals of high-probability patterns and silence the noise from those that are failing in the current market regime.
This is not a black box. It is an open-source, observable learning system. It is a "Neural Edition" because, like a biological brain, it strengthens and weakens its own "synaptic" connections based on positive and negative feedback, evolving into a tool that is uniquely adapted to the specific personality of the asset you are trading.
█ CHAPTER 2: THE CORE INNOVATIONS - WHAT MAKES THIS A "NEURAL" LIBRARY?
This library introduces several concepts previously unseen in the TradingView ecosystem, creating a truly next-generation analytical tool.
Reinforcement Learning Engine: The brain of the system. Every time a high-confidence pattern is detected, it is logged into an "Active Memory." The library then tracks the outcome of that pattern against its projected stop and target. If the pattern is successful, the "synaptic weight" for that entire category of patterns is strengthened. If it fails, the weight is weakened. This is a continuous feedback loop of performance-driven adaptation.
Synaptic Plasticity (Learning Rate): You have direct control over the brain's "plasticity"—its ability to learn. A high plasticity allows it to adapt very quickly to changing market conditions, while a lower plasticity creates a more stable, long-term learning model.
Dynamic Volatility Scaling (DVS): Markets are not static; they breathe. DVS is a proprietary function that calculates a real-time volatility scalar by comparing the current ATR to its historical average. This scalar is then used to automatically adjust the lookback periods and sensitivity of all relevant pattern detection engines. In high-volatility environments, the engines look for larger, more significant patterns. In low-volatility, they tighten their focus to find smaller, more subtle setups.
Neural Confidence Score: The output of this library is not a simple "true/false" signal. Every detected pattern comes with two confidence scores:
Raw Confidence: The original, static confidence level based on the pattern's textbook definition.
Net Confidence: The AI-adjusted score. This is the Raw Confidence × Learned Bias. A pattern that has been performing well will see its confidence amplified (e.g., 70% raw → 95% net). A pattern that has been failing will see its confidence diminished (e.g., 70% raw → 45% net).
Intelligent Filtering: The learning system is not just for scoring. If the learned bias for a particular pattern category (e.g., "Candle") drops below a certain threshold (e.g., 0.8), the library will automatically begin to filter out those signals, treating them as unreliable noise until their performance improves.
█ CHAPTER 3: THE ANATOMY OF THE AI - HOW IT THINKS
The library's intelligence is built on a clear, observable architecture.
The NeuralWeights (The Brain)
This is the central data structure that holds the system's "memory." It is a simple object that stores a single floating-point number—a weight or "bias"—for each of the five major categories of pattern analysis. It is initialized with neutral weights of 1.0 for all categories.
w_candle: For candlestick patterns.
w_harmonic: For harmonic patterns.
w_structure: For market structure patterns (e.g., BOS/CHoCH).
w_geometry: For classic geometric patterns (e.g., flags, wedges).
w_vsa: For Volume Spread Analysis patterns.
The update_brain() Function (The Learning Process)
This is the core of the reinforcement learning loop. When a pattern from the ActiveMemory is resolved (as a win or a loss), this function is called. If the pattern was a "win," it applies a small, positive adjustment (the plasticity value) to the corresponding weight in the brain. If it was a "loss," it applies a negative adjustment. The weights are constrained between 0.5 (maximum distrust) and 2.0 (maximum trust), preventing runaway feedback loops.
The manage_memory() Function (The Short-Term Memory)
This function is the AI's hippocampus. It maintains an array of ActiveMemory objects, tracking up to 50 recent, high-confidence signals. On every bar, it checks each active pattern to see if its target or stop has been hit. If a pattern resolves, it triggers the update_brain() function with the outcome and removes the pattern from memory. If a pattern does not resolve within a set number of bars (e.g., 50), it is considered "expired" and is treated as a minor loss, teaching the AI to distrust patterns that lead to nowhere.
The scan_neural_universe() Function (The Master Controller)
This is the main exported function that you will call from your indicator. It is the AI's "consciousness." On every bar, it performs a sequence of high-level actions:
It calculates the current Dynamic Volatility Scalar (DVS).
It runs all of its built-in pattern detection engines (VSA, Geometry, Candles, etc.), feeding them the DVS to ensure they are adapted to the current market volatility.
It identifies the single "best" active pattern for the current bar based on its raw confidence score.
It passes this "best" pattern to the manage_memory() function to be tracked and to trigger learning from any previously resolved patterns.
It retrieves the current learned bias for the "best" pattern's category from the brain.
It calculates the final net_confidence by multiplying the raw confidence by the learned bias.
It performs a final check, intelligently filtering out the signal if its learned bias is too low.
It returns the final, neurally-enhanced PatternResult object to your indicator.
█ CHAPTER 4: A GUIDE FOR DEVELOPERS - INTEGRATING THE BRAIN
I have designed the DafePatternLib to be both incredibly powerful and remarkably simple to integrate into your own scripts.
Import the Library: Add the following line to the top of your script (replace YourUsername with your TradingView username):
import DskyzInvestments/DafePatternLib/1 as pattern
Call the Scanner: On every bar, simply call the main scanning function. The library handles everything else internally—the DVS calculation, the multi-pattern scanning, the memory management, and the reinforcement learning.
pattern.PatternResult signal = pattern.scan_neural_universe()
Use the Result: The signal object now contains all the intelligence you need. Check if a pattern is active, and if so, use its properties to draw your signals and alerts. You can choose to display the raw_confidence vs. the net_confidence to give your users a direct view of the AI's learning process.
if signal.is_active
label.new(bar_index, signal.entry, "AI Conf: " + str.tostring(signal.net_confidence, "#") + "%")
With just these few lines, you have integrated a self-learning, self-optimizing, multi-pattern recognition engine into your indicator.
// ═══════════════════════════════════════════════════════════
// INSTRUCTIONS FOR DEVELOPERS:
// ───────────────────────────────────────────────────────────
1. Import the library at the top of your indicator script:
import YourUsername/DafePatternLib/1 as pattern
2. Copy the entire "INPUTS TEMPLATE" section below and paste it into your indicator's code.
This will create the complete user settings panel for controlling the AI.
3. Copy the "USAGE EXAMPLE" section and adapt it to your script's logic.
This shows how to initialize the brain, call the scanner, and use the results.
// ═══════════════════════════════════════════════════════════
// INPUT GROUPS
// ═══════════════════════════════════════════════════════════
string G_AI_ENGINE = "══════════ 🧠 NEURAL ENGINE ══════════"
string G_AI_PATTERNS = "══════════ 🔬 PATTERN SELECTION ══════════"
string G_AI_VISUALS = "══════════ 🎨 VISUALS & SIGNALS ══════════"
string G_AI_DASH = "══════════ 📋 BRAIN STATE DASHBOARD ══════════"
string G_AI_ALERTS = "══════════ 🔔 ALERTS ══════════"
// ═══════════════════════════════════════════════════════════
// NEURAL ENGINE CONTROLS
// ═══════════════════════════════════════════════════════════
bool i_enable_ai = input.bool(true, "✨ Enable Neural Pattern Engine", group = G_AI_ENGINE,
tooltip="Master switch to enable or disable the entire pattern recognition and learning system.")
float i_plasticity = input.float(0.03, "Synaptic Plasticity (Learning Rate)", minval=0.01, maxval=0.1, step=0.01, group = G_AI_ENGINE,
tooltip="Controls how quickly the AI adapts to pattern performance.\n\n" +
"• Low (0.01-0.02): Slow, stable learning. Good for long-term adaptation.\n" +
"• Medium (0.03-0.05): Balanced adaptation (Recommended).\n" +
"• High (0.06-0.10): Fast, aggressive learning. Adapts quickly to new market regimes but can be more volatile.")
float i_filter_threshold = input.float(0.8, "Neural Filter Threshold", minval=0.5, maxval=1.0, step=0.05, group = G_AI_ENGINE,
tooltip="The AI will automatically hide (filter) signals from any pattern category whose learned 'Bias' falls below this value. Set to 0.5 to disable filtering.")
// ═══════════════════════════════════════════════════════════
// PATTERN SELECTION
// ═══════════════════════════════════════════════════════════
bool i_scan_candles = input.bool(true, "🕯️ Candlestick Patterns", group = G_AI_PATTERNS, inline="row1")
bool i_scan_vsa = input.bool(true, "📦 Volume Spread Analysis", group = G_AI_PATTERNS, inline="row1")
bool i_scan_geometry = input.bool(true, "📐 Geometric Patterns", group = G_AI_PATTERNS, inline="row2")
bool i_scan_structure = input.bool(true, "📈 Market Structure (SMC)", group = G_AI_PATTERNS, inline="row2")
bool i_scan_harmonic = input.bool(false, "🦋 Harmonic Setups (Experimental)", group = G_AI_PATTERNS, inline="row3",
tooltip="Harmonic detection is simplified and experimental. Enable for additional confluence but use with caution.")
// ═══════════════════════════════════════════════════════════
// VISUALS & SIGNALS
// ═══════════════════════════════════════════════════════════
bool i_show_signals = input.bool(true, "Show Pattern Signals on Chart", group = G_AI_VISUALS)
color i_bull_color = input.color(#00FF88, "Bullish Signal Color", group = G_AI_VISUALS, inline="colors")
color i_bear_color = input.color(#FF0055, "Bearish Signal Color", group = G_AI_VISUALS, inline="colors")
string i_signal_size = input.string("Small", "Signal Size", options=["Tiny", "Small", "Normal", "Large"], group = G_AI_VISUALS)
//══════════════════════════════════════════════════════
// BRAIN STATE DASHBOARD
//══════════════════════════════════════════════════════
bool i_show_dashboard = input.bool(true, "Show Brain State Dashboard", group = G_AI_DASH)
string i_dash_position = input.string("Bottom Right", "Position", options=["Top Right", "Top Left", "Bottom Right", "Bottom Left"], group = G_AI_DASH)
string i_dash_size = input.string("Small", "Size", options=["Small", "Normal", "Large"], group = G_AI_DASH)
// ══════════════════════════════════════════════════════════
// ALERTS
// ═══════════════════════════════════════════════════════════
bool i_enable_alerts = input.bool(true, "Enable All Pattern Alerts", group = G_AI_ALERTS)
int i_alert_min_confidence = input.int(75, "Min Neural Confidence to Alert (%)", minval=50, maxval=100, group = G_AI_ALERTS)
█ DEVELOPMENT PHILOSOPHY
The DafePatternLib was born from a vision to bring the principles of modern AI to the world of technical analysis on TradingView. We believe that an indicator should not be a static, lifeless tool. It should be a dynamic, intelligent partner that learns and adapts alongside the trader. This library is an open-source framework designed to empower developers to build the next generation of smart indicators, moving beyond fixed rules and into the realm of adaptive, performance-driven intelligence.
This library is designed to be a tool for that discipline. By providing an objective, data-driven, and self-correcting analysis of patterns, it helps to remove the emotional guesswork and second-guessing that plagues so many traders, allowing you to act with the cold, calculated confidence of a machine.
█ A NOTE TO USERS & DISCLAIMER
THIS IS A LIBRARY FOR DEVELOPERS: This script does nothing on its own. It is a powerful engine that must be imported and used by other indicator developers in their own scripts.
THE AI LEARNS, IT DOES NOT PREDICT: The reinforcement learning is based on the recent historical performance of patterns. It is a powerful statistical edge, but it is not a crystal ball. Past performance does not guarantee future results.
ALL TRADING INVOLVES RISK: The patterns and confidence scores are for informational and educational purposes only. Always use proper risk management.
**Please be aware that this is a library script and has no visual output on its own. The charts, signals, and dashboards shown in the images were created with a separate demonstration indicator that utilizes this library's powerful pattern recognition and learning engine.
"The key to trading success is emotional discipline. If intelligence were the key, there would be a lot more people making money trading."
— Victor Sperandeo, Market Wizard
Taking you to school. - Dskyz, Create with DAFE
This is not a pattern library. This is an artificial trading brain. It doesn't just find patterns; it learns, adapts, and evolves based on their performance in the live market.
█ CHAPTER 1: THE PHILOSOPHY - BEYOND STATIC RULES, INTO DYNAMIC LEARNING
For decades, chart pattern analysis has been trapped in a static, rigid paradigm. An indicator is coded to find a "Bullish Engulfing" or a "Head and Shoulders," and it will signal that pattern with the same blind confidence every single time, regardless of whether that pattern has been consistently failing for the past month. It has no memory, no intelligence, no ability to adapt. It is a dumb machine executing a fixed command.
The DafePatternLib was created to shatter this paradigm. It is built on a powerful, revolutionary philosophy borrowed from the world of artificial intelligence: Reinforcement Learning. This library is not just a collection of pattern detection functions; it is a complete, self-optimizing neural framework. It doesn't just find patterns; it tracks their outcomes. It remembers what works and what doesn't. Over time, it learns to amplify the signals of high-probability patterns and silence the noise from those that are failing in the current market regime.
This is not a black box. It is an open-source, observable learning system. It is a "Neural Edition" because, like a biological brain, it strengthens and weakens its own "synaptic" connections based on positive and negative feedback, evolving into a tool that is uniquely adapted to the specific personality of the asset you are trading.
█ CHAPTER 2: THE CORE INNOVATIONS - WHAT MAKES THIS A "NEURAL" LIBRARY?
This library introduces several concepts previously unseen in the TradingView ecosystem, creating a truly next-generation analytical tool.
Reinforcement Learning Engine: The brain of the system. Every time a high-confidence pattern is detected, it is logged into an "Active Memory." The library then tracks the outcome of that pattern against its projected stop and target. If the pattern is successful, the "synaptic weight" for that entire category of patterns is strengthened. If it fails, the weight is weakened. This is a continuous feedback loop of performance-driven adaptation.
Synaptic Plasticity (Learning Rate): You have direct control over the brain's "plasticity"—its ability to learn. A high plasticity allows it to adapt very quickly to changing market conditions, while a lower plasticity creates a more stable, long-term learning model.
Dynamic Volatility Scaling (DVS): Markets are not static; they breathe. DVS is a proprietary function that calculates a real-time volatility scalar by comparing the current ATR to its historical average. This scalar is then used to automatically adjust the lookback periods and sensitivity of all relevant pattern detection engines. In high-volatility environments, the engines look for larger, more significant patterns. In low-volatility, they tighten their focus to find smaller, more subtle setups.
Neural Confidence Score: The output of this library is not a simple "true/false" signal. Every detected pattern comes with two confidence scores:
Raw Confidence: The original, static confidence level based on the pattern's textbook definition.
Net Confidence: The AI-adjusted score. This is the Raw Confidence × Learned Bias. A pattern that has been performing well will see its confidence amplified (e.g., 70% raw → 95% net). A pattern that has been failing will see its confidence diminished (e.g., 70% raw → 45% net).
Intelligent Filtering: The learning system is not just for scoring. If the learned bias for a particular pattern category (e.g., "Candle") drops below a certain threshold (e.g., 0.8), the library will automatically begin to filter out those signals, treating them as unreliable noise until their performance improves.
█ CHAPTER 3: THE ANATOMY OF THE AI - HOW IT THINKS
The library's intelligence is built on a clear, observable architecture.
The NeuralWeights (The Brain)
This is the central data structure that holds the system's "memory." It is a simple object that stores a single floating-point number—a weight or "bias"—for each of the five major categories of pattern analysis. It is initialized with neutral weights of 1.0 for all categories.
w_candle: For candlestick patterns.
w_harmonic: For harmonic patterns.
w_structure: For market structure patterns (e.g., BOS/CHoCH).
w_geometry: For classic geometric patterns (e.g., flags, wedges).
w_vsa: For Volume Spread Analysis patterns.
The update_brain() Function (The Learning Process)
This is the core of the reinforcement learning loop. When a pattern from the ActiveMemory is resolved (as a win or a loss), this function is called. If the pattern was a "win," it applies a small, positive adjustment (the plasticity value) to the corresponding weight in the brain. If it was a "loss," it applies a negative adjustment. The weights are constrained between 0.5 (maximum distrust) and 2.0 (maximum trust), preventing runaway feedback loops.
The manage_memory() Function (The Short-Term Memory)
This function is the AI's hippocampus. It maintains an array of ActiveMemory objects, tracking up to 50 recent, high-confidence signals. On every bar, it checks each active pattern to see if its target or stop has been hit. If a pattern resolves, it triggers the update_brain() function with the outcome and removes the pattern from memory. If a pattern does not resolve within a set number of bars (e.g., 50), it is considered "expired" and is treated as a minor loss, teaching the AI to distrust patterns that lead to nowhere.
The scan_neural_universe() Function (The Master Controller)
This is the main exported function that you will call from your indicator. It is the AI's "consciousness." On every bar, it performs a sequence of high-level actions:
It calculates the current Dynamic Volatility Scalar (DVS).
It runs all of its built-in pattern detection engines (VSA, Geometry, Candles, etc.), feeding them the DVS to ensure they are adapted to the current market volatility.
It identifies the single "best" active pattern for the current bar based on its raw confidence score.
It passes this "best" pattern to the manage_memory() function to be tracked and to trigger learning from any previously resolved patterns.
It retrieves the current learned bias for the "best" pattern's category from the brain.
It calculates the final net_confidence by multiplying the raw confidence by the learned bias.
It performs a final check, intelligently filtering out the signal if its learned bias is too low.
It returns the final, neurally-enhanced PatternResult object to your indicator.
█ CHAPTER 4: A GUIDE FOR DEVELOPERS - INTEGRATING THE BRAIN
I have designed the DafePatternLib to be both incredibly powerful and remarkably simple to integrate into your own scripts.
Import the Library: Add the following line to the top of your script (replace YourUsername with your TradingView username):
import DskyzInvestments/DafePatternLib/1 as pattern
Call the Scanner: On every bar, simply call the main scanning function. The library handles everything else internally—the DVS calculation, the multi-pattern scanning, the memory management, and the reinforcement learning.
pattern.PatternResult signal = pattern.scan_neural_universe()
Use the Result: The signal object now contains all the intelligence you need. Check if a pattern is active, and if so, use its properties to draw your signals and alerts. You can choose to display the raw_confidence vs. the net_confidence to give your users a direct view of the AI's learning process.
if signal.is_active
label.new(bar_index, signal.entry, "AI Conf: " + str.tostring(signal.net_confidence, "#") + "%")
With just these few lines, you have integrated a self-learning, self-optimizing, multi-pattern recognition engine into your indicator.
// ═══════════════════════════════════════════════════════════
// INSTRUCTIONS FOR DEVELOPERS:
// ───────────────────────────────────────────────────────────
1. Import the library at the top of your indicator script:
import YourUsername/DafePatternLib/1 as pattern
2. Copy the entire "INPUTS TEMPLATE" section below and paste it into your indicator's code.
This will create the complete user settings panel for controlling the AI.
3. Copy the "USAGE EXAMPLE" section and adapt it to your script's logic.
This shows how to initialize the brain, call the scanner, and use the results.
// ═══════════════════════════════════════════════════════════
// INPUT GROUPS
// ═══════════════════════════════════════════════════════════
string G_AI_ENGINE = "══════════ 🧠 NEURAL ENGINE ══════════"
string G_AI_PATTERNS = "══════════ 🔬 PATTERN SELECTION ══════════"
string G_AI_VISUALS = "══════════ 🎨 VISUALS & SIGNALS ══════════"
string G_AI_DASH = "══════════ 📋 BRAIN STATE DASHBOARD ══════════"
string G_AI_ALERTS = "══════════ 🔔 ALERTS ══════════"
// ═══════════════════════════════════════════════════════════
// NEURAL ENGINE CONTROLS
// ═══════════════════════════════════════════════════════════
bool i_enable_ai = input.bool(true, "✨ Enable Neural Pattern Engine", group = G_AI_ENGINE,
tooltip="Master switch to enable or disable the entire pattern recognition and learning system.")
float i_plasticity = input.float(0.03, "Synaptic Plasticity (Learning Rate)", minval=0.01, maxval=0.1, step=0.01, group = G_AI_ENGINE,
tooltip="Controls how quickly the AI adapts to pattern performance.\n\n" +
"• Low (0.01-0.02): Slow, stable learning. Good for long-term adaptation.\n" +
"• Medium (0.03-0.05): Balanced adaptation (Recommended).\n" +
"• High (0.06-0.10): Fast, aggressive learning. Adapts quickly to new market regimes but can be more volatile.")
float i_filter_threshold = input.float(0.8, "Neural Filter Threshold", minval=0.5, maxval=1.0, step=0.05, group = G_AI_ENGINE,
tooltip="The AI will automatically hide (filter) signals from any pattern category whose learned 'Bias' falls below this value. Set to 0.5 to disable filtering.")
// ═══════════════════════════════════════════════════════════
// PATTERN SELECTION
// ═══════════════════════════════════════════════════════════
bool i_scan_candles = input.bool(true, "🕯️ Candlestick Patterns", group = G_AI_PATTERNS, inline="row1")
bool i_scan_vsa = input.bool(true, "📦 Volume Spread Analysis", group = G_AI_PATTERNS, inline="row1")
bool i_scan_geometry = input.bool(true, "📐 Geometric Patterns", group = G_AI_PATTERNS, inline="row2")
bool i_scan_structure = input.bool(true, "📈 Market Structure (SMC)", group = G_AI_PATTERNS, inline="row2")
bool i_scan_harmonic = input.bool(false, "🦋 Harmonic Setups (Experimental)", group = G_AI_PATTERNS, inline="row3",
tooltip="Harmonic detection is simplified and experimental. Enable for additional confluence but use with caution.")
// ═══════════════════════════════════════════════════════════
// VISUALS & SIGNALS
// ═══════════════════════════════════════════════════════════
bool i_show_signals = input.bool(true, "Show Pattern Signals on Chart", group = G_AI_VISUALS)
color i_bull_color = input.color(#00FF88, "Bullish Signal Color", group = G_AI_VISUALS, inline="colors")
color i_bear_color = input.color(#FF0055, "Bearish Signal Color", group = G_AI_VISUALS, inline="colors")
string i_signal_size = input.string("Small", "Signal Size", options=["Tiny", "Small", "Normal", "Large"], group = G_AI_VISUALS)
//══════════════════════════════════════════════════════
// BRAIN STATE DASHBOARD
//══════════════════════════════════════════════════════
bool i_show_dashboard = input.bool(true, "Show Brain State Dashboard", group = G_AI_DASH)
string i_dash_position = input.string("Bottom Right", "Position", options=["Top Right", "Top Left", "Bottom Right", "Bottom Left"], group = G_AI_DASH)
string i_dash_size = input.string("Small", "Size", options=["Small", "Normal", "Large"], group = G_AI_DASH)
// ══════════════════════════════════════════════════════════
// ALERTS
// ═══════════════════════════════════════════════════════════
bool i_enable_alerts = input.bool(true, "Enable All Pattern Alerts", group = G_AI_ALERTS)
int i_alert_min_confidence = input.int(75, "Min Neural Confidence to Alert (%)", minval=50, maxval=100, group = G_AI_ALERTS)
█ DEVELOPMENT PHILOSOPHY
The DafePatternLib was born from a vision to bring the principles of modern AI to the world of technical analysis on TradingView. We believe that an indicator should not be a static, lifeless tool. It should be a dynamic, intelligent partner that learns and adapts alongside the trader. This library is an open-source framework designed to empower developers to build the next generation of smart indicators, moving beyond fixed rules and into the realm of adaptive, performance-driven intelligence.
This library is designed to be a tool for that discipline. By providing an objective, data-driven, and self-correcting analysis of patterns, it helps to remove the emotional guesswork and second-guessing that plagues so many traders, allowing you to act with the cold, calculated confidence of a machine.
█ A NOTE TO USERS & DISCLAIMER
THIS IS A LIBRARY FOR DEVELOPERS: This script does nothing on its own. It is a powerful engine that must be imported and used by other indicator developers in their own scripts.
THE AI LEARNS, IT DOES NOT PREDICT: The reinforcement learning is based on the recent historical performance of patterns. It is a powerful statistical edge, but it is not a crystal ball. Past performance does not guarantee future results.
ALL TRADING INVOLVES RISK: The patterns and confidence scores are for informational and educational purposes only. Always use proper risk management.
**Please be aware that this is a library script and has no visual output on its own. The charts, signals, and dashboards shown in the images were created with a separate demonstration indicator that utilizes this library's powerful pattern recognition and learning engine.
"The key to trading success is emotional discipline. If intelligence were the key, there would be a lot more people making money trading."
— Victor Sperandeo, Market Wizard
Taking you to school. - Dskyz, Create with DAFE
Libreria Pine
Nello spirito di TradingView, l'autore ha pubblicato questo codice Pine come libreria open source affinché altri programmatori della nostra comunità possano riutilizzarlo. Complimenti all'autore! È possibile utilizzare questa libreria privatamente o in altre pubblicazioni open source, ma il riutilizzo di questo codice nelle pubblicazioni è soggetto al Regolamento.
Empowering everyday traders and DAFE Trading Systems
DAFETradingSystems.com
DAFETradingSystems.com
Declinazione di responsabilità
Le informazioni e le pubblicazioni non sono intese come, e non costituiscono, consulenza o raccomandazioni finanziarie, di investimento, di trading o di altro tipo fornite o approvate da TradingView. Per ulteriori informazioni, consultare i Termini di utilizzo.
Libreria Pine
Nello spirito di TradingView, l'autore ha pubblicato questo codice Pine come libreria open source affinché altri programmatori della nostra comunità possano riutilizzarlo. Complimenti all'autore! È possibile utilizzare questa libreria privatamente o in altre pubblicazioni open source, ma il riutilizzo di questo codice nelle pubblicazioni è soggetto al Regolamento.
Empowering everyday traders and DAFE Trading Systems
DAFETradingSystems.com
DAFETradingSystems.com
Declinazione di responsabilità
Le informazioni e le pubblicazioni non sono intese come, e non costituiscono, consulenza o raccomandazioni finanziarie, di investimento, di trading o di altro tipo fornite o approvate da TradingView. Per ulteriori informazioni, consultare i Termini di utilizzo.