Forecast OscillatorGeneral Overview
The Forecast Oscillator Plus (FOSC+) is not just another oscillator. It is an advanced quantitative analysis tool developed to bridge the gap left by traditional momentum indicators (like RSI or Stochastic) which often suffer from "lag" or remain pinned in extreme zones during strong trends.
This "Plus" version has been specifically engineered and optimized for high-velocity scalping and day-trading on assets like NAS100 (Nasdaq) and XAUUSD (Gold) using ultra-short timeframes (1-min, 5-min).
🛡️ Why is FOSC+ Different?
1. Linear Regression Intelligence
At the heart of this script is a powerful Linear Regression (LinReg) engine. Instead of comparing price to a simple average, FOSC+ calculates the percentage deviation between the current price and its predicted theoretical trajectory. This allows the indicator to identify not just if the price is "high" or "low," but if it is abnormally distanced from its current trend, signaling an imminent Mean Reversion.
2. Adaptive Dynamic Bands (Volatility-Adjusted)
A major weakness of classic oscillators is the use of fixed levels (e.g., 80/20). FOSC+ utilizes Standard Deviation to generate overbought and oversold zones that "breathe" with the market.
During high volatility, the bands expand to filter out noise and premature entries.
During low volatility, they tighten to capture precise turning points.
3. Institutional Volume Filter (Anti-Fakeout)
To succeed in the Nasdaq market, you must follow the "Smart Money." This script integrates a Volume Spike Filter. A signal (Buy/Sell) is only triggered if the current candle's volume is significantly higher than its moving average (adjustable multiplier). This ensures you only enter trades backed by real institutional strength.
4. Algo-Ready for PineConnector
The code has been structured for seamless automation. With built-in EMA smoothing to reduce 1-minute "market chatter," the signals are clean and sharp, minimizing execution errors when sending orders to MetaTrader 5 via PineConnector.
📈 Technical Trading Guide
Buy Signals (Green Triangle): Occur when the oscillator crosses above the dynamic oversold band OR crosses back above the zero line, provided that volume confirms the impulse.
Sell Signals (Red Triangle): Occur when the oscillator crosses below the dynamic overbought band OR breaks below the zero line from above, with volume confirmation.
Momentum Histogram: The colored columns indicate acceleration strength. Excellent for Trailing Stops: as long as the histogram is growing, the momentum is in your favor!.
⚙️ Recommended Parameters
Length (14): The "Sweet Spot" for balancing reactivity and reliability.
Smooth Len (4): Essential for 1-min charts to eliminate micro-fluctuations without adding lag.
Volume Mult (1.15): Filters out the bottom 15% of volume to keep only significant candles.
⚠️ Stress-Tested for Real Conditions
This script has been rigorously backtested with Slippage settings ranging from 10 to 25 points. Even under difficult market conditions with high spreads, the indicator maintains a positive expectancy, making it a premier tool for traders using Standard or Raw accounts.
Индикаторы и стратегии
Trend & Volume indicatorThis is a Trend, Entry, Exit, and Risk-assessment indicator built around the NNFX (No-Nonsense Forex) methodology, intended primarily for Daily timeframes.
It combines:
A Baseline trend filter
Signal Wave (formerly SSL) lines for continuation and exits
ATR-based volatility analysis
Dynamic risk assessment
Visual trade context (colors, channels, alerts, table)
The indicator does not auto-trade. It provides decision support.
Core Components
1. Baseline (Trend Filter)
Uses a configurable moving average (HMA, EMA, JMA, McGinley, etc.)
Acts as the primary trend direction filter
Includes an optional volatility channel around the baseline
Trend logic:
Above baseline → bullish bias
Below baseline → bearish bias
Inside channel → neutral / ranging
This follows NNFX’s rule: Only trade in the direction of the baseline.
2. Signal Wave 1 (Trend Confirmation)
A high/low MA-based line (SW1)
Changes direction only when price clearly breaks structure
Confirms:
Trend continuation
Momentum agreement with baseline
3. Signal Wave 2 (Continuation Entries)
Short-length MA-based signal (SW2)
Uses ATR-distance logic to detect valid pullbacks
Signals continuation entries only when volatility criteria are met
Prevents chasing price during overextended moves
4. Exit Line (Trade Management)
Independent MA-based exit line
Generates:
Exit Long when price crosses below
Exit Short when price crosses above
Designed to:
Protect profits
Avoid emotional exits
Volatility & ATR Logic
ATR is smoothed using selectable methods (RMA, SMA, EMA, WMA)
ATR bands show:
Expected price range
Overextension zones
Used for:
Entry qualification
Continuation logic
False breakout detection
Risk Assessment System
ATR Percentile
Compares current ATR to historical volatility
Classifies risk as:
Low
Normal
High
Dynamic Color Fading
High volatility → more transparent colors
Low volatility → solid colors
Gives instant visual risk feedback
Entry Quality Filters
Entry Distance
Measures how far price is from the baseline:
Near → ideal
Extended → caution
Far → high risk
False Breakout Detection
Flags candles:
Larger than 1× ATR
Still within baseline range
Helps avoid trap trades
Visual Elements
Colored candles reflect trend + risk
Baseline channel fill changes with market bias
Diamond markers highlight risky candles
Optional ATR bands
Risk table showing:
Risk level
Entry distance
Volatility percentile
Current ATR
Alerts Included
The script provides alerts for:
Exit signals
Continuation entries
Baseline breaks
High-risk entries
False breakouts
Extreme volatility conditions
All alerts are context-aware, not raw crossovers.
Best Use Case
✔ Daily timeframe
✔ Trend-following strategies
✔ NNFX-style system building
✔ Discretionary traders who want structure, not automation
KJ Sessions : Asia/London/US + OverlapKJ Sessions : Asia/London/US + Overlap.
times are set as per dubai time.
USDC/USDT PremiumUSDC/USDT Premium Index
Overview
This indicator tracks the premium or discount of USD Coin (USDC) relative to Tether (USDT) using data from Binance. It serves as a barometer for sentiment within the stablecoin market. A premium on USDC often suggests a flight to quality or higher demand for a stablecoin perceived as more transparent and regulated.
Key Features
•
Premium Calculation: The premium is calculated as (USDC/USDT Price - 1) * 100 to represent the deviation from parity in basis points. For example, a value of 0.1 means USDC is trading at a 0.1% premium to USDT (i.e., a price of 1.001).
•
Dynamic Coloring: The indicator's line color changes based on its position relative to a moving average (MA):
•
Green: The premium is currently above its moving average, suggesting bullish momentum for USDC.
•
Red: The premium is below its moving average, indicating bearish momentum.
•
Zero Line: A zero line is plotted to clearly distinguish between a premium (above zero) and a discount (below zero).
•
Customizable MA: You can adjust the moving average period and type (SMA, EMA, etc.) to fine-tune the indicator's sensitivity.
How to Use
1.
Gauge Stablecoin Sentiment: A rising premium (green line) can indicate that traders are favoring USDC over USDT, which might happen during times of market uncertainty or concerns about USDT's reserves.
2.
Identify Shifts in Momentum: Look for the color to flip from red to green as a sign that the USDC premium is gaining strength. A flip from green to red may signal a weakening trend.
3.
Spot Extremes: Extreme deviations from the zero line can signal market stress or significant capital flows between the two major stablecoins.
Interpretation
•
Green Line (Premium > MA): Suggests that the short-term trend for the USDC premium is positive and strengthening.
•
Red Line (Premium < MA): Suggests that the short-term trend is negative, with USDC's value declining relative to its recent average against USDT.
•
Above Zero Line: USDC is trading at a premium to USDT.
•
Below Zero Line: USDC is trading at a discount to USDT.
This tool provides a nuanced view of the stablecoin ecosystem, helping traders understand capital flows and risk appetite. It is most effective when used to complement a broader market analysis strategy.
Institutional Volatility Expansion & Liquidity Thresholds (IVEL)Overview
The IVEL Engine is an institutional-grade volatility modeling tool designed to identify the mathematical boundaries of price delivery. Unlike retail oscillators that use fixed scales, this script utilizes dynamic ATR-based multiples to map Institutional Premium and Discount zones in real-time.
How to Use
To maximize the effectiveness of the IVEL Engine, traders should focus on Price Delivery at the extreme thresholds:
Identifying Institutional Premium (Short Setup) : When price expands into the Upper Red Zone, it has reached a mathematical exhaustion point. Seek short-side entries when price shows signs of rejection from this level back toward the Fair Value Baseline.
Identifying Institutional Discount (Long Setup) : When price reaches the Lower Green Zone, it is considered "cheap" by institutional algorithms. Look for long-side absorption or accumulation patterns within this zone.
Mean Reversion Targets: The Fair Value Baseline (Center Line) acts as the primary magnetic target. Successful trades taken at the outer thresholds should use the baseline as the first objective for profit-taking.
Alerts & Execution Strategy
The IVEL Engine is designed for automated monitoring so you don't have to watch the screen 24/7. To set up your execution workflow:
Set the Alert : Right-click the indicator and select "Add Alert." Set the condition to "Price Crossing Institutional Premium" (Upper Red) or "Price Crossing Institutional Discount" (Lower Green).
Wait for the Hit : Do not market-enter as soon as the alert fires. The alert tells you price has entered a High-Probability Liquidity Zone.
Confirm the Rejection : Once alerted, drop down to a lower timeframe (e.g., 5m or 15m) and look for a "Shift in Market Structure" or an SMT Divergence.
Execute : Enter once the rejection is confirmed, targeting the Fair Value Baseline as your primary TP1.
Methodology
The script anchors to an EMA-based baseline and projects expansion bands that adapt to current market conditions.
Value Area : The blue inner region where the majority of trading volume occurs.
Liquidity Exhaustion : The red and green outer regions where the probability of "Smart Money" reversal is highest.
Bar Count & EMABar Count & EMA Indicator
A clean and lightweight indicator designed for intraday price action traders.
Features:
1. Bar Count
Displays bar numbers only on 3-minute and 5-minute timeframes
Works during Regular Trading Hours (RTH) only
Shows bar 1 and multiples of 3 (3, 6, 9, 12, 15...)
Color-coded for key bars: Bar 18 & 48 (Red), Bar 6 (Light Green), Multiples of 12 (Sky Blue), Others (Gray)
2. EMA 20
Simple 20-period Exponential Moving Average
Customizable source, length, offset, and color
Why these specific timeframes?
5-Minute Chart (US Markets):
Bar 6, 12, 18, 24... represent 30-min, 1-hour, 1.5-hour intervals
Bar 18 and 48 often mark significant intraday turning points
Best for: ES, NQ, SPY, QQQ
3-Minute Chart (China A-Share Markets):
Bar 10, 20, 30... represent 30-min, 1-hour, 1.5-hour intervals
Designed for CSI 1000 Index Futures (IM) and other China futures
Helps track the 4-hour trading session rhythm (9:30-11:30, 13:00-15:00)
Why Bar Count Matters:
Tracking bar numbers helps traders identify market rhythm, timing cycles, and potential reversal zones throughout the trading session.
Session Liquidity Sweep + Trend ConfirmationThis strategy aims to capture high-probability intraday trades by combining liquidity sweeps with a trend confirmation filter. It is designed for traders who want a systematic approach to trade breakouts during specific market sessions while controlling risk with ATR-based stops.
How it Works:
Session Filter: Trades are only considered during a defined session (default 9:30 - 11:00). This helps avoid low-volume periods that can lead to false signals.
Trend Confirmation: The strategy uses a 50-period EMA to identify the market trend. Long trades are only taken in an uptrend, and short trades in a downtrend.
Liquidity Sweep Detection:
A long entry occurs when price dips below the prior N-bar low but closes back above it, indicating a potential liquidity sweep that stops being triggered before the trend continues upward.
A short entry occurs when price spikes above the prior N-bar high but closes below it, signaling a potential sweep of stops before the downward trend resumes.
ATR-Based Risk Management:
Stop loss is calculated using the Average True Range (ATR) multiplied by a configurable factor (default 1.5).
Take profit is set based on a risk-reward ratio (default 2.5x).
Position Sizing: Default position size is 5% of equity per trade, making it suitable for risk-conscious trading.
Inputs:
Session Start/End (HHMM)
Liquidity Lookback Period (number of bars to define prior high/low)
ATR Length for stop calculation
ATR Stop Multiplier
Risk-Reward Ratio
EMA Trend Filter Length
Visuals:
Prior Liquidity High (red)
Prior Liquidity Low (green)
EMA Trend (blue)
Why Use This Strategy:
Captures stop-hunt moves often triggered by larger market participants.
Only trades with trend confirmation, reducing false signals.
Provides automatic ATR-based stop loss and take profit for consistent risk management.
Easy to adjust session time, ATR, EMA length, and risk-reward to suit your trading style.
Important Notes:
Assumes 0.05% commission and 1-pip slippage. Adjust according to your broker.
Not financial advice; intended for educational, backtesting, or paper trading purposes.
Always test strategies thoroughly before applying to live accounts.
Squeeze Indicator Squeeze Indicator is a volatility-focused indicator designed to identify periods of compression and the early transition into expansion.
It measures Bollinger Band Width (BBW) using a 20-period Bollinger Band to quantify how tightly price is coiling, then smooths BBW with a 16-period SMA and a faster 8-period EMA to distinguish structural compression from short-term changes in volatility.
The BBW itself is displayed as a subtle grey filled area to emphasize relative contraction and expansion, while a squeeze condition is highlighted whenever BBW falls below both its SMA and EMA, signaling an environment where volatility is suppressed and a directional move is more likely to follow.
Crossovers of the EMA above or below the SMA provide early warnings of volatility expansion or renewed compression, making the indicator especially useful for timing breakouts, anticipating regime shifts from range to trend, and managing options strategies that depend on changes in volatility rather than price direction.
Hazmeed HTF Candles Aligned)HTF Candles Overlay (v6, Aligned + Accurate Wicks)
This indicator overlays higher timeframe candles on your current chart.
It allows you to visually compare HTF price action directly on your lower timeframe chart without switching timeframes.
⭐ Features
Displays Higher Timeframe (HTF) candles on the current chart
Fully aligned to HTF candle start time
Option to show accurate wicks
Supports configurable:
HTF timeframe (e.g., 1D, 4H, 1W)
Number of HTF candles displayed
Candle width (in bars)
Bull/Bear colors and wick color
Needle Below 20, Sub-chart## 指标名称
**单针下20副图（Needle Below 20, Sub-chart）**
## 一句话概述
在**中周期保持强势**的背景下，用短周期的“快速降温”来定位**强势回撤/错杀**，并额外标记**极端超卖（双线归零）**的情绪极值窗口。
---
## 指标逻辑与构成
本指标包含两条强弱线（随机指标风格的区间归一化）：
* **短期线（默认 3）**
反映近短周期内价格在区间的位置，敏感度高，用于捕捉短线回撤与情绪快速降温。
* **长期线（默认 21）**
反映中周期强弱分布，用作“趋势/强势背景”的过滤。
并提供两类柱状提示（STICK）：
1. **双线归零（默认：短期≤6 且 长期≤6）**
代表“情绪极端恐慌/极端超卖”的窗口，更多用于观察**反弹可能性**与“风险释放阶段”。
2. **单针下20（默认：短期≤20 且 长期≥80）**
代表“中周期强势未破 + 短线急跌/下探”的典型回撤信号，用于寻找趋势交易中的**回撤介入候选**。
图中绘制 **80/20** 参考线，帮助快速判断分位区域。
---
## 使用方法（推荐工作流）
### 1）先定“背景”：只在你认可的趋势环境使用
本指标本质是“**强势回撤定位器**”，并不负责替你判断大盘/板块/个股是否处于可交易趋势。建议搭配以下任一类过滤器：
* **趋势过滤（强烈推荐）**
* 价格位于中长期均线之上（例：MA50/MA200 上方）
* 或者你自己的趋势线/多空线系统显示多头趋势（如：快线>慢线、结构未破）
* **结构过滤（强烈推荐）**
* 回撤仍然在关键支撑之上（前高、趋势线、箱体上沿回踩等）
* 避免在明显“破位下跌”的结构中把信号当作抄底依据
### 2）信号触发后的处理：不要“看到就买”，而是“进入观察与触发”
* **单针下20（黄柱）**：
作为“候选提示”，下一步关注是否出现：
* 回踩关键位后的止跌K线（锤子线/吞没/放量止跌等）
* 次日/后续重新站回关键位或出现趋势延续确认
* 量价关系转好（缩量回撤、放量反转等）
* **双线归零（红柱）**：
更偏“情绪极值”提示，常见用途是：
* 提醒你风险已经释放到极端区域，观察是否出现反弹结构
* 不建议无过滤硬抄底；更适合与“结构止跌/大盘企稳”一起使用
---
## 风险控制与止损思路（示例，不构成建议）
以下仅提供“如何把信号落到可执行”的框架示例：
* **入场触发（示例）**
* 黄柱出现后：等待价格在关键位止跌，或出现确认K线再介入
* 分批：先小仓试错，确认后再加
* **止损锚点（示例）**
* 关键支撑位下破（趋势线/前低/箱体下沿）
* 或“信号触发后的反弹失败又破前低”
* **仓位建议（原则）**
* 把仓位大小与止损距离联动：止损距离越大，仓位越小
* 避免单次信号重仓；这类信号更适合“低风险试错 + 确认加仓”的趋势回撤逻辑
---
## 参数说明与调参建议
### 1）为什么是 3 / 21？
* **3**：强调“短期情绪/回撤”的敏感度，适合捕捉快速下探
* **21**：近似一个月交易日，刻画中周期强弱背景，适合作为“强势过滤”
如果你交易周期更短（更偏日内/隔日）：
* 可考虑 **短期 2–5**、长期 **13–34** 区间做测试。
如果你交易周期更长（偏波段/中线）：
* 可考虑 **短期 5–8**、长期 **34–55**。
### 2）为什么阈值是 6 / 20 / 80？
* **6**：更接近“极端”区域，用于标记情绪极值（双线归零）
* **20/80**：经典分位阈值，表示低位/高位区域，用于区分“短弱/长强”的错位状态
### 3）如何调参更贴合你的市场与标的？
建议按“信号密度—胜率—回撤”三者权衡：
* 信号太多：
* 降低“长期≥”阈值的触发频率（如 80→85）
* 或提高“短期≤”门槛的严苛度（20→15）
* 或把长期周期加长（21→34）
* 信号太少：
* 放宽阈值（长期 80→75、短期 20→25）
* 或缩短长期周期（21→13）
调参务必结合你常交易的品种波动特征，建议在同一市场同一类标的上做一致性回测/复盘。
---
## 免责声明
本脚本仅用于教育与研究目的，展示一种技术分析可视化方法，不构成任何形式的投资建议、交易建议或收益承诺。市场有风险，交易需谨慎。使用者应基于自身风险承受能力独立决策，并对交易结果自行负责。作者不对任何因使用本脚本导致的直接或间接损失承担责任。
---
## 致谢与来源
* **策略/思路来源**：B站 UP 主 **z哥** 的相关分享与讲解。
* **实现说明**：本脚本为 TradingView（Pine Script）版本的复现与可视化实现，便于在 TradingView 环境中使用与研究。
* 如原作者对公开引用有额外要求，请以原作者说明为准；若有侵权或需要修改归因方式，请联系我调整/下架相关描述。
下面给你补齐一份**英文版（可直接用于 TradingView 发布页）**，并与中文版结构对齐，符合社区常见写法（Overview / How it works / How to use / Inputs / Risk / Credits）。
---
## Title
**Needle Below 20 (Sub-chart) — TDX Style Recreation**
## Overview
This indicator is an auxiliary tool designed to spot **sharp short-term pullbacks (shakeouts) within a strong mid-term regime**, and to highlight **extreme oversold “panic” zones**. It is best used as a **candidate filter and timing aid** inside a broader trend-following framework, rather than as a standalone buy/sell signal.
## How it works
The script plots two normalized strength lines (stochastic-style normalization over a lookback window):
* **Short-term line (default: 3 bars)**
Captures fast sentiment cooling and short-term compression (high sensitivity).
* **Long-term line (default: 21 bars)**
Represents the mid-cycle regime strength, used as a context filter.
It also provides two stick/column signals:
1. **Double-Line Near-Zero (Red stick)**
Triggered when **Short-term ≤ 6 AND Long-term ≤ 6** (defaults).
This typically represents an **extreme oversold / capitulation-like** window, often used to monitor potential technical rebounds (confirmation recommended).
2. **Needle Below 20 (Yellow stick)**
Triggered when **Short-term ≤ 20 AND Long-term ≥ 80** (defaults).
This is commonly interpreted as a **strong regime intact + sudden short-term dump/pullback**, useful for spotting potential **trend pullback re-entry candidates**.
Reference levels **80/20** are drawn for quick zone reading.
### How to use (recommended workflow)
1. **Define the regime first (strongly recommended)**
This indicator does not decide whether a market is tradable. Use a trend/structure filter, for example:
* Price above a medium/long MA (e.g., MA50/MA200), or your own trend model
* Structure not broken (support holds, pullback into a valid support zone)
2. **Treat signals as “watchlist triggers,” not instant entries**
* **Yellow stick (Needle Below 20):**
After it prints, look for confirmation such as:
* A hold/reclaim of a key level (prior high, trendline, range top retest, etc.)
* A reversal candle or continuation confirmation
* Constructive volume/price behavior (e.g., pullback on lighter volume, rebound with demand)
* **Red stick (Double-Line Near-Zero):**
Best viewed as an **extreme sentiment/oversold marker**. Avoid blind bottom-fishing; combine with structure stabilization and broader market context.
## Risk management (examples, not financial advice)
* **Entry trigger (example):** scale in after confirmation rather than buying the first signal
* **Stop reference (example):** below the key support / prior swing low / structural invalidation level
* **Position sizing principle:** size positions based on stop distance; larger stop = smaller size
## Inputs / Parameters
* **Lookbacks (3 / 21):**
* 3 bars: short-term sensitivity for fast pullback detection
* 21 bars: mid-cycle regime context (roughly one trading month)
Suggested adjustments:
* Shorter-term trading: try **2–5** (short) and **13–34** (long)
* Longer swing trading: try **5–8** (short) and **34–55** (long)
* **Thresholds (6 / 20 / 80):**
* 6: “extreme” zone for near-zero panic marker
* 20/80: classic zone thresholds for low/high regime separation
If signals are too frequent:
* tighten thresholds (e.g., long ≥ 80 → 85, short ≤ 20 → 15), or lengthen long lookback (21 → 34)
If signals are too rare:
* loosen thresholds (e.g., long ≥ 80 → 75, short ≤ 20 → 25), or shorten long lookback (21 → 13)
## Disclaimer
This script is provided for **educational and research purposes only**. It does **not** constitute financial advice, investment recommendations, or any guarantee of performance. Trading involves risk. You are solely responsible for your decisions and outcomes.
## Credits / Attribution
* **Concept origin:** Inspired by the Bilibili creator **“z哥”**.
* **Implementation:** This is a TradingView (Pine Script) recreation for visualization and study.
If the original creator has specific attribution requirements, please follow the creator’s instructions. If any changes are needed, attribution text can be updated accordingly.
## Citation snippet
> Concept inspired by Bilibili creator “z哥”. Pine Script recreation for educational/research use.
Neeson bitcoin Dynamic ATR Trailing SystemNeeson bitcoin Dynamic ATR Trailing System: A Comprehensive Guide to Volatility-Adaptive Trend Following
Introduction
The Dynamic ATR Trailing System (DATR-TS) represents a sophisticated approach to trend following that transcends conventional moving average or breakout-based methodologies. Unlike standard trend-following systems that rely on price pattern recognition or fixed parameter oscillators, this system operates on the principle of volatility-adjusted position management—a nuanced approach that dynamically adapts to changing market conditions rather than imposing rigid rules on market behavior.
Originality and Innovation
Distinct Methodological Approach
What sets DATR-TS apart from hundreds of existing trend-following systems is its dual-layered conditional execution framework. While most trend-following systems fall into one of three broad categories—moving average crossovers, channel breakouts, or momentum oscillators—this system belongs to the more specialized category of volatility-normalized trailing stop systems.
Key Original Contributions:
Volatility-Threshold Signal Filtering: Most trend systems generate signals continuously, leading to overtrading during low-volatility periods. DATR-TS implements a proprietary volatility filter that requires minimum market movement before generating signals, effectively separating high-probatility trend opportunities from market noise.
Self-Contained Position State Management: Unlike traditional systems that require external position tracking, DATR-TS maintains an internal position state that prevents contradictory signals and creates a closed-loop decision framework.
Dynamic Risk Parameter Adjustment: The system doesn't use fixed percentage stops or rigid ATR multiples. Instead, it implements a responsive adjustment mechanism that widens stops during high volatility and tightens them during low volatility, creating an optimal balance between risk protection and opportunity capture.
Trader-Centric Visualization Philosophy: Beyond mere signal generation, the system provides a comprehensive visual feedback system designed to align with human cognitive patterns, reducing emotional decision-making through consistent color coding and information hierarchy.
Technical Implementation and Functionality
Core Operational Mechanism
DATR-TS implements a volatility-adjusted trend persistence model that operates on the principle that trending markets exhibit characteristic volatility signatures. The system specifically targets medium-term directional movements (typically lasting 5-20 days) rather than short-term scalping opportunities or long-term position trades.
The Four-Pillar Architecture:
Volatility Measurement and Normalization
Calculates Average True Range (ATR) over a user-defined period
Converts absolute volatility to percentage terms relative to price
Compares current volatility against user-defined thresholds to filter suboptimal conditions
Dynamic Trailing Stop Algorithm
Establishes an initial stop distance based on current volatility
Implements a four-state adjustment mechanism that responds to price action
Maintains stop position during trend continuation while allowing for trend reversal detection
Conditional Signal Generation
Generates entry signals only when price action meets both directional and volatility criteria
Produces exit signals based on trailing stop penetration
Incorporates position state awareness to prevent conflicting signals
Comprehensive Feedback System
Provides multi-layer visual information including dynamic stop lines, signal labels, and color-coded price action
Displays real-time metrics through an integrated dashboard
Offers configurable visualization options for different trading styles
Specific Trend-Following Methodology
DATR-TS employs a volatility-normalized trailing stop breakout approach, which differs significantly from common trend identification methods:
Not a moving average crossover system (like MACD or traditional MA crosses)
Not a channel breakout system (like Bollinger Band or Donchian Channel breaks)
Not a momentum oscillator system (like RSI or Stochastic trend following)
Not a price pattern recognition system (like head-and-shoulders or triangle breaks)
Instead, it belongs to the more specialized category of volatility-adjusted stop-and-reverse systems that:
Wait for market volatility to reach actionable levels
Establish positions when price confirms directional bias through stop penetration
Manage risk dynamically based on evolving market conditions
Exit positions when the trend exhausts itself through stop violation
Practical Application and Usage
Market Environment Optimization
Ideal Conditions:
Trending markets with sustained directional movement
Medium volatility environments (neither excessively calm nor chaotic)
Timeframes: 4-hour to daily charts for optimal signal quality
Instruments: Forex majors, commodity futures, equity indices
Suboptimal Conditions:
Ranging or consolidating markets
Extreme volatility events or news-driven spikes
Very short timeframes (below 1-hour)
Illiquid or highly manipulated instruments
Parameter Configuration Strategy
Core Parameter Philosophy:
ATR Length (Default: 21 periods)
Controls the system's memory of volatility
Shorter lengths increase sensitivity but may cause overtrading
Longer lengths provide smoother signals but may lag during volatility shifts
ATR Multiplier (Default: 6.3x)
Determines the initial risk buffer
Lower values (4-5x) create tighter stops for conservative trading
Higher values (6-8x) allow for larger trends but increase drawdown risk
Volatility Threshold (Default: 1.5%)
Filters out low-quality trading environments
Adjust based on market characteristics (higher for volatile markets)
Acts as a quality control mechanism for signals
Trading Workflow and Execution
Signal Interpretation and Action:
Entry Protocol:
Wait for BLUE "BUY" signal label appearance
Confirm volatility conditions meet threshold requirements
Enter long position at market or next reasonable opportunity
Set initial stop at displayed dynamic stop level
Position Management:
Monitor dynamic stop line for position adjustment
Allow profits to run while stop protects capital
No manual adjustment required—system manages stop automatically
Exit Protocol:
Exit on ORANGE "SELL" signal label appearance
Alternative exit if price hits dynamic stop level
System will generate new entry signal if conditions warrant re-entry
Risk Management Integration:
Position sizing based on distance to dynamic stop
Volatility filter prevents trades during unfavorable conditions
Clear visual feedback on current risk exposure
Built-in protection against overtrading
Philosophical Foundation and Market Theory
Core Trading Principles
DATR-TS embodies several foundational market principles:
Volatility Defines Opportunity
Markets don't trend continuously—they alternate between trending and ranging phases
Volatility provides the energy for trends to develop and sustain
By measuring and filtering volatility, we can focus on high-probability trend phases
Risk Should Be Proportional
Fixed percentage stops ignore market context
Dynamic stops that adjust with volatility provide more appropriate risk management
Position sizing should reflect current market conditions, not arbitrary rules
Simplicity Through Sophistication
Complex systems often fail in real-world conditions
A simple core algorithm with intelligent filtering outperforms complex multi-indicator approaches
Clear visual feedback reduces cognitive load and emotional interference
Trends Persist Until Proven Otherwise
Markets exhibit momentum characteristics
Once a trend establishes itself, it tends to continue
The trailing stop methodology captures this persistence while providing exit mechanisms
Mathematical and Statistical Foundation
The system operates on several statistical market observations:
Volatility Clustering Phenomenon
High volatility periods tend to follow high volatility periods
Low volatility periods tend to follow low volatility periods
By filtering for adequate volatility, we increase the probability of capturing meaningful trends
Trend Magnitude Distribution
Most trends are small to medium in magnitude
Very large trends are rare but account for disproportionate returns
The dynamic stop methodology allows capture of varying trend magnitudes
Autocorrelation in Price Movements
Price movements exhibit short-term positive autocorrelation during trends
This persistence allows trailing stops to capture continued movement
The system leverages this characteristic without requiring explicit autocorrelation calculation
Performance Characteristics and Expectations
Typical System Behavior
Signal Frequency:
Low to moderate signal generation (prevents overtrading)
Signals concentrated during trending market phases
Extended periods without signals during ranging conditions
Risk-Reward Profile:
Win rate typically 40-60% in trending conditions
Average win larger than average loss
Risk-reward ratios of 1:2 to 1:3 achievable
Drawdown Patterns:
Controlled through volatility adjustment
Larger drawdowns during extended ranging periods
Recovery typically follows when trending conditions resume
Comparison with Alternative Approaches
Versus Moving Average Systems:
Less prone to whipsaws during ranging markets
Better adaptation to changing volatility conditions
Clearer exit signals through stop levels
Versus Channel Breakout Systems:
More responsive to emerging trends
Lower false breakout probability
Dynamic risk adjustment rather than fixed parameters
Versus Momentum Oscillator Systems:
Better trend persistence capture
Less susceptible to overbought/oversold false signals
Clearer position management rules
Educational Value and Skill Development
Learning Opportunities
DATR-TS serves as more than just a trading tool—it provides educational value through:
Market Condition Awareness
Teaches traders to distinguish between trending and ranging markets
Develops understanding of volatility's role in trading opportunities
Encourages patience and selectivity in trade execution
Risk Management Discipline
Demonstrates dynamic position sizing principles
Illustrates the importance of adaptive stops
Reinforces the concept of risk-adjusted returns
Psychological Skill Development
Reduces emotional trading through clear rules
Builds patience through conditional execution
Develops discipline through systematic approach
Customization and Evolution
The system provides a foundation for further development:
Beginner Level:
Use default parameters for initial learning
Focus on signal recognition and execution discipline
Develop understanding of system behavior across market conditions
Intermediate Level:
Adjust parameters based on specific market characteristics
Combine with complementary analysis techniques
Develop personal variations based on trading style
Advanced Level:
Integrate with portfolio management systems
Develop automated execution frameworks
Create derivative systems for specialized applications
Conclusion: The Modern Trend-Following Paradigm
The Dynamic ATR Trailing System represents a significant evolution in trend-following methodology. By moving beyond simple price pattern recognition or fixed parameter oscillators, it embraces the complex reality of financial markets where volatility, trend persistence, and risk management interact dynamically.
This system doesn't claim to predict market direction or identify tops and bottoms. Instead, it provides a systematic framework for participating in trends when they emerge, managing risk appropriately as conditions change, and preserving capital during unfavorable environments.
For traders seeking a methodology that combines mathematical rigor with practical execution, adapts to changing market conditions rather than fighting against them, and provides clear, actionable information without cognitive overload, DATR-TS offers a sophisticated yet accessible approach to modern trend following.
The true value lies not in any single signal or parameter setting, but in the comprehensive philosophy of volatility-aware, risk-adjusted, conditionally-executed trend participation that the system embodies—a philosophy that aligns with how markets actually behave rather than how we might wish them to behave.
Trailing Stoploss % BasedA minimalistic trend-following indicator that plots a single trailing line based on a user-defined percentage using price highs and lows.
The line:
Trails price in trends
Moves only in the direction of the trend
Flattens when price is not making new highs or lows
Acts as support in uptrends and resistance in downtrends
Useful on all instruments and all timeframes for clean trend tracking and trailing stop management.
XAUUSD Lot Size Calculator1. What This Indicator Does
This tool is a Visual Risk Management System. Instead of using a calculator on your phone or switching tabs, it allows you to calculate the exact lot size for your trade directly on the TradingView chart by dragging lines.
It automates the math for:
Lot Size: How big your position should be to risk exactly X% of your account.
Take Profit: Where your target should be based on your Risk-to-Reward ratio.
Safety Checks: It warns you if your stop loss is too tight for the minimum lot size (0.01).
2. Visual Features
🔴 The Red Line (Stop Loss): This is your interactive line. You can grab it with your mouse and drag it to your desired invalidation point (e.g., below a support wick).
🟢 The Green Line (Take Profit): This line moves automatically. You cannot drag it. It calculates where your Take Profit must be to satisfy your Risk:Reward ratio (Default 1:1) based on where you placed the Red line.
⚫ The Info Table: A high-contrast black box in the corner that displays your calculated Lot Size, Risk amount, and Trade direction (Long/Short).
3. How to Use It (Step-by-Step)
Step 1: Initial Setup
When you first add the indicator to the chart, you need to tell it about your account:
Double-click the Black Table (or the Red Line) to open Settings.
Inputs Tab:
Account Balance: Enter your current trading balance (e.g., 10,000).
Risk %: Enter how much you want to lose per trade (e.g., 1.0%).
Contract Size: Keep this at 100 for Gold (XAUUSD) or standard Forex pairs.
Risk : Reward Ratio: Set your target (e.g., 1.0 for 1:1, or 2.0 for 1:2).
Step 2: Planning a Trade
Look at the chart and identify where you want to enter (current price) and where you want your Stop Loss.
Find the Red Line on your chart. (If you don't see it, go to Settings and change "Stop Loss Level" to a price near the current candle).
Click and Drag the Red Line to your specific Stop Loss price.
Step 3: Reading the Signals
Direction: If you drag the Red Line below the price, the table shows LONG. If you drag it above, it shows SHORT.
Lot Size: Read the big green number in the table (e.g., 0.55). This is the exact lot size you should enter in your broker.
TP Target: Look at the Green Line on the chart. That is your exit price.
Step 4: The "Orange Warning"
If you place your Stop Loss very close to the entry, or if your account is small, the math might suggest a lot size smaller than is possible (e.g., 0.004).
The table text will turn ORANGE.
The Lot Size will stick to 0.01 (the minimum).
The "Risk ($)" row will show you the actual risk. (Example: Instead of risking your desired $100, you might be forced to risk $105 because you can't trade smaller than 0.01 lots).
Bradley Industries IndicatorThe Bradley Industries Indicator is a confluence-based trading system designed to identify early trend impulses while filtering out late or low-probability entries.
It combines four independent indicators, each measuring a different market dimension, and only generates a primary signal when all four align on the same bar.
The philosophy of the system is simple:
Enter only when structure, momentum, volatility, and directional flow agree at the start of a move.
4 Period Momentum Composite IndicatorThe 4‑Period Momentum Indicator blends four lookback windows (1m, 3m, 6m, 12m) into a single zero‑centered momentum line. The value recalculates from whatever candle you anchor on, giving you full control when scrolling through historical price action. Positive readings reflect upward momentum, negative readings show weakness, and zero‑line crossovers highlight potential trend shifts. Designed for multi‑timeframe use and ETF relative‑strength comparison.
John Trade AlertsImagine you are watching a ball bounce up and down on a graph.
This script is like a set of rules that says:
When to start playing
When to stop playing
When you got some prize levels
and it yells to you (alerts) when those things happen.
The main ideas
Breakout Buy (ball jumps high)
There is a line drawn high on the chart called the breakout level.
If the price (the ball) closes above that line, and some extra “good conditions” are true (enough volume, uptrend, etc.),
the script says: “We entered a Breakout trade now.”
Pullback Buy (ball dips into a box)
There is a zone (a small box) between a low line and a high line: the pullback zone.
If the price closes inside that zone, and the pullback looks “healthy” (not too much volume, still above a moving average, etc.),
the script says: “We entered a Pullback trade now.”
Stops (when to get out if it goes wrong)
For each entry type (Breakout or Pullback), there is a red stop line under the price.
If the price falls below that stop line, the script says:
“Stop hit, we’re out of the trade.”
Hard Support / Invalidation (big no‑no level)
There is a special hard support line.
The script also looks at the 1‑hour chart in the background.
If a 1‑hour candle closes below that hard support, it says:
“Hard invalidation – idea is broken, get out.”
Targets (prize levels)
Above the current price there are several orange lines: Target 1, 2, 3A, 3B, 4A, 4B.
If the price goes up and crosses one of these lines, the script says:
“Target X reached!”
Trend and Volume “health checks”
It checks if the short‑term average price (SMA20) is going up → “uptrend.”
It can check if price is above a long‑term average (SMA200).
For breakouts, it checks if volume is stronger than usual (good push).
For pullbacks, it prefers quieter than usual volume (calm dip).
It can also check an Anchored VWAP line (a special average price from a chosen starting time) and only trade if price is above that too.
Remembering if you are “in a trade”
The script keeps a little memory:
Are we currently in a position (inPos) or not?
Was it a Breakout or a Pullback entry?
What is our entry price and active stop?
When it gets a new entry signal, it turns inPos to true, picks the right stop, and draws that stop line.
When a stop or hard invalidation happens, it sets inPos to false again.
It can also “forget” and reset at the start of a new trading day if you want.
Alerts
When:
you get a Breakout entry
or a Pullback entry
or a Stop is hit
or the hard support is broken on 1‑hour
or a Target is reached
the script sends a message you can use in TradingView alerts (pop‑ups, email, webhook, etc.).
Things you see on the chart
Teal line: Breakout level
Green lines: Pullback zone low & high
Red line: Active stop (only when you’re “in” a trade)
Orange lines: Targets 1, 2, 3A, 3B, 4A, 4B
Blue line: Anchored VWAP (if you turn it on)
Purple faint line: SMA20 (short‑term trend)
Gray faint line: SMA200 (long‑term trend)
Little label near the last bar that says:
if you’re IN or Flat
which type of entry (Breakout/Pullback)
what your current stop is
So in kid words:
It draws important lines on the chart.
It watches the price move like a ball.
When the ball does something special (jump above, fall below, hit a prize line),
it shouts to you with alerts.
It remembers if you’re in the game or not, and where your safety line (stop) is.
Ichimoku Multi-BG System by Pranojit Dey (Exact Alignment)It shows trend of different levels with the help of Ichimoku, VWAP, SMA and Pivot. Use it as a strong confluence for any entry. Lets trade guys...
Volume Variance SuppressionVolume Variance Suppression Indicator
This indicator measures the variance of traded volume over a rolling window to detect periods of participation compression.
When volume variance falls below a defined threshold, it signals:
Reduced initiative order flow
Dominance of passive liquidity
Market balance / consolidation rather than trend
These suppression phases often precede volatility expansion, failed auctions, or impulsive moves, as liquidity builds and positioning becomes crowded.
The indicator is not directional and should be used as a market state filter, not a standalone signal. It helps distinguish balance vs expansion regimes and improves trade selection by aligning strategies with the current microstructural environment.
Risk Manager & ATR TS Strategy📌 Overview
This script is not a simple indicator mashup. It is a Risk & Trade Planning Engine that combines a strategy-based signal generator with a snapshot-based risk, sizing, and expectancy model. It is designed to support real trading decisions, not just to generate cosmetic signals or overfitted backtests.
The core idea is to separate market logic from risk logic, evaluating each trade only at the moment it becomes actionable using fixed reference points that do not change afterward.
🎯 What makes this script original Unlike most tools that merely combine indicators or visualize entries, this script introduces several non-standard design choices:
Snapshot-based risk sizing (The "Time Machine" logic).
Expected Value (EV) calculation in both Money and R-multiples.
Kelly Criterion applied with weighted multi-target logic.
Strict architectural separation between the signal engine and the risk engine.
Decision-oriented dashboard instead of decorative plots.
These components are not merged for convenience; they are architecturally dependent on each other.
🧠 Conceptual Architecture
1️⃣ Signal Engine (Market Context) The signal engine is based on an ATR Trailing Stop system combined with trend regime filters (ADX and Choppiness Index). Its only responsibility is to answer one question: "Is this a valid directional opportunity right now?" It does not manage risk; it only identifies the opportunity.
2️⃣ Snapshot Logic (Key Design Choice) When a valid signal occurs, the script captures a Snapshot of the Entry price, Initial Stop-Loss, and Risk Distance. This snapshot is frozen at signal time. It is never updated, even if the trailing stop moves later. This avoids the most common error in TradingView scripts: recalculating position size using a moving stop, which falsifies the risk data.
3️⃣ Risk Engine (Sizing & Control) Using the snapshot values, the script computes:
Monetary risk per trade (capped at your user-defined max).
Position size derived from the fixed stop distance.
Effective leverage (informational).
4️⃣ Multi-Target Reward Model Instead of assuming a single take-profit, the script supports multiple targets with user-defined probability weights. From this, it derives a Weighted Risk/Reward Ratio, which feeds directly into the EV and Kelly calculations.
5️⃣ Expected Value (EV) in Money & R The script calculates EV in your account currency (real impact) and normalized in R-multiples (statistical quality). This allows you to compare trade quality across different assets and timeframes objectively.
6️⃣ Kelly Criterion (Conservative) The Kelly Criterion is applied using the weighted reward model and is always subordinated to your hard risk cap. If Kelly suggests a negative value, the script advises "NO TRADE". It is used as a filter, not a leverage amplifier.
📊 Dashboard & Alerts The on-chart dashboard summarizes everything you need at the moment of the signal:
Risk % and Position Size
Expected Value (Money + R)
Kelly Suggestion
Signal Strength
Alerts are triggered once per signal (on bar close) using snapshot data, ensuring no repainting and no spam.
🔍 How this is NOT a mashup Each component exists because another component depends on it. Snapshot logic is required for valid risk sizing; Risk sizing is required for EV normalization; Weighted RR is required for meaningful Kelly. Removing any part breaks the system’s logic.
📘 How to use
Choose your account size and risk parameters in the settings.
Configure your stop logic and reward targets.
Wait for a valid signal.
Evaluate the dashboard: Decide if the trade quality (EV, R, Risk) justifies participation.
⚖️ Open-Source Notice This script is published under the Mozilla Public License 2.0 (MPL-2.0). It does not copy or replicate any single public script. Standard concepts (ATR, ADX) are used as building blocks, but the architecture and calculations are original.
🚫 Disclaimer This script is a planning and evaluation engine designed to help traders think in terms of risk, expectancy, and discipline. It does not guarantee profitability.
✅ Summary This is a professional-grade framework built to answer one core question: “Is this trade worth taking, given my risk and my expectations?” Not every signal is a trade, and not every trade deserves capital. This script helps you make that distinction.
MLMatrixLibOverview
MLMatrixLib is a comprehensive Pine Script v6 library implementing machine learning algorithms using native matrix operations. This library provides traders and developers with a toolkit of statistical and ML methods for building quantitative trading systems, performing data analysis, and creating adaptive indicators.
How It Works
The library leverages Pine Script's native matrix type to perform efficient linear algebra operations. Each algorithm is implemented from first principles, using matrix decomposition, iterative optimization, and statistical estimation techniques. All functions are designed for numerical stability with careful handling of edge cases.
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Library Contents (34 Sections)
Section 1: Utility Functions & Matrix Operations
Core building blocks including:
• identity(n) - Creates n×n identity matrix
• diagonal(values) - Creates diagonal matrix from array
• ones(rows, cols) / zeros(rows, cols) - Matrix constructors
• frobeniusNorm(m) / l1Norm(m) - Matrix norm calculations
• hadamard(m1, m2) - Element-wise multiplication
• columnMeans(m) / rowMeans(m) - Statistical aggregations
• standardize(m) - Z-score normalization (zero mean, unit variance)
• minMaxNormalize(m) - Scale values to range
• fitStandardScaler(m) / fitMinMaxScaler(m) - Reusable scaler parameters
• addBiasColumn(m) - Prepend column of ones for regression
• arrayMedian(arr) / arrayPercentile(arr, p) - Array statistics
Section 2: Activation Functions
Numerically stable implementations:
• sigmoid(x) / sigmoidMatrix(m) - Logistic function with overflow protection
• tanhActivation(x) / tanhMatrix(m) - Hyperbolic tangent
• relu(x) / reluMatrix(m) - Rectified Linear Unit
• leakyRelu(x, alpha) - Leaky ReLU with configurable slope
• elu(x, alpha) - Exponential Linear Unit
• Derivatives for backpropagation: sigmoidDerivative, tanhDerivative, reluDerivative
Section 3: Linear Regression (OLS)
Ordinary Least Squares implementation using the normal equation (X'X)⁻¹X'y:
• fitLinearRegression(X, y) - Fits model, returns coefficients, R², standard error
• fitSimpleLinearRegression(x, y) - Single-variable regression
• predictLinear(model, X) - Generate predictions
• predictionInterval(model, X, confidence) - Confidence intervals using t-distribution
• Model type stores: coefficients, R-squared, residuals, standard error
Section 4: Weighted Linear Regression
Generalized least squares with observation weights:
• fitWeightedLinearRegression(X, y, weights) - Solves (X'WX)⁻¹X'Wy
• Useful for downweighting outliers or emphasizing recent data
Section 5: Polynomial Regression
Fits polynomials of arbitrary degree:
• fitPolynomialRegression(x, y, degree) - Constructs Vandermonde matrix
• predictPolynomial(model, x) - Evaluate polynomial at points
Section 6: Ridge Regression (L2 Regularization)
Adds penalty term λ||β||² to prevent overfitting:
• fitRidgeRegression(X, y, lambda) - Solves (X'X + λI)⁻¹X'y
• Lambda parameter controls regularization strength
Section 7: LASSO Regression (L1 Regularization)
Coordinate descent algorithm for sparse solutions:
• fitLassoRegression(X, y, lambda, maxIter, tolerance) - Iterative soft-thresholding
• Produces sparse coefficients by driving some to exactly zero
• softThreshold(x, lambda) - Core shrinkage operator
Section 8: Elastic Net (L1 + L2 Regularization)
Combines LASSO and Ridge penalties:
• fitElasticNet(X, y, lambda, alpha, maxIter, tolerance)
• Alpha balances L1 vs L2: alpha=1 is LASSO, alpha=0 is Ridge
Section 9: Huber Robust Regression
Iteratively Reweighted Least Squares (IRLS) for outlier resistance:
• fitHuberRegression(X, y, delta, maxIter, tolerance)
• Delta parameter defines transition between L1 and L2 loss
• Downweights observations with large residuals
Section 10: Quantile Regression
Estimates conditional quantiles using linear programming approximation:
• fitQuantileRegression(X, y, tau, maxIter, tolerance)
• Tau specifies quantile (0.5 = median, 0.25 = lower quartile, etc.)
Section 11: Logistic Regression (Binary Classification)
Gradient descent optimization of cross-entropy loss:
• fitLogisticRegression(X, y, learningRate, maxIter, tolerance)
• predictProbability(model, X) - Returns probabilities
• predictClass(model, X, threshold) - Returns binary predictions
Section 12: Linear SVM (Support Vector Machine)
Sub-gradient descent with hinge loss:
• fitLinearSVM(X, y, C, learningRate, maxIter)
• C parameter controls regularization (higher = harder margin)
• predictSVM(model, X) - Returns class predictions
Section 13: Recursive Least Squares (RLS)
Online learning with exponential forgetting:
• createRLSState(nFeatures, lambda, delta) - Initialize state
• updateRLS(state, x, y) - Update with new observation
• Lambda is forgetting factor (0.95-0.99 typical)
• Useful for adaptive indicators that update incrementally
Section 14: Covariance and Correlation
Matrix statistics:
• covarianceMatrix(m) - Sample covariance
• correlationMatrix(m) - Pearson correlations
• pearsonCorrelation(x, y) - Single correlation coefficient
• spearmanCorrelation(x, y) - Rank-based correlation
Section 15: Principal Component Analysis (PCA)
Dimensionality reduction via eigendecomposition:
• fitPCA(X, nComponents) - Power iteration method
• transformPCA(X, model) - Project data onto principal components
• Returns components, explained variance, and mean
Section 16: K-Means Clustering
Lloyd's algorithm with k-means++ initialization:
• fitKMeans(X, k, maxIter, tolerance) - Cluster data points
• predictCluster(model, X) - Assign new points to clusters
• withinClusterVariance(model) - Measure cluster compactness
Section 17: Gaussian Mixture Model (GMM)
Expectation-Maximization algorithm:
• fitGMM(X, k, maxIter, tolerance) - Soft clustering with probabilities
• predictProbaGMM(model, X) - Returns membership probabilities
• Models data as mixture of Gaussian distributions
Section 18: Kalman Filter
Linear state estimation:
• createKalman1D(processNoise, measurementNoise, ...) - 1D filter
• createKalman2D(processNoise, measurementNoise) - Position + velocity tracking
• kalmanStep(state, measurement) - Predict-update cycle
• Optimal filtering for noisy measurements
Section 19: K-Nearest Neighbors (KNN)
Instance-based learning:
• fitKNN(X, y) - Store training data
• predictKNN(model, X, k) - Classify by majority vote
• predictKNNRegression(model, X, k) - Average of k neighbors
• predictKNNWeighted(model, X, k) - Distance-weighted voting
Section 20: Neural Network (Feedforward)
Multi-layer perceptron:
• createNeuralNetwork(architecture) - Define layer sizes
• trainNeuralNetwork(nn, X, y, learningRate, epochs) - Backpropagation
• predictNN(nn, X) - Forward pass
• Supports configurable hidden layers
Section 21: Naive Bayes Classifier
Gaussian Naive Bayes:
• fitNaiveBayes(X, y) - Estimate class-conditional distributions
• predictNaiveBayes(model, X) - Maximum a posteriori classification
• Assumes feature independence given class
Section 22: Anomaly Detection
Statistical outlier detection:
• fitAnomalyDetector(X, contamination) - Mahalanobis distance-based
• detectAnomalies(model, X) - Returns anomaly scores
• isAnomaly(model, X, threshold) - Binary classification
Section 23: Dynamic Time Warping (DTW)
Time series similarity:
• dtw(series1, series2) - Compute DTW distance
• Handles sequences of different lengths
• Useful for pattern matching
Section 24: Markov Chain / Regime Detection
Discrete state transitions:
• fitMarkovChain(states, nStates) - Estimate transition matrix
• predictNextState(transitionMatrix, currentState) - Most likely next state
• stationaryDistribution(transitionMatrix) - Long-run probabilities
Section 25: Hidden Markov Model (Simple)
Baum-Welch algorithm:
• fitHMM(observations, nStates, maxIter) - EM training
• viterbi(model, observations) - Most likely state sequence
• Useful for regime detection
Section 26: Exponential Smoothing & Holt-Winters
Time series smoothing:
• exponentialSmooth(data, alpha) - Simple exponential smoothing
• holtWinters(data, alpha, beta, gamma, seasonLength) - Triple smoothing
• Captures trend and seasonality
Section 27: Entropy and Information Theory
Information measures:
• entropy(probabilities) - Shannon entropy in bits
• conditionalEntropy(jointProbs, marginalProbs) - H(X|Y)
• mutualInformation(probsX, probsY, jointProbs) - I(X;Y)
• kldivergence(p, q) - Kullback-Leibler divergence
Section 28: Hurst Exponent
Long-range dependence measure:
• hurstExponent(data) - R/S analysis
• H < 0.5: mean-reverting, H = 0.5: random walk, H > 0.5: trending
Section 29: Change Detection (CUSUM)
Cumulative sum control chart:
• cusumChangeDetection(data, threshold, drift) - Detect regime changes
• cusumOnline(value, prevCusumPos, prevCusumNeg, target, drift) - Streaming version
Section 30: Autocorrelation
Serial dependence analysis:
• autocorrelation(data, maxLag) - ACF for all lags
• partialAutocorrelation(data, maxLag) - PACF via Durbin-Levinson
• Useful for time series model identification
Section 31: Ensemble Methods
Model combination:
• baggingPredict(models, X) - Average predictions
• votingClassify(models, X) - Majority vote
• Improves robustness through aggregation
Section 32: Model Evaluation Metrics
Performance assessment:
• mse(actual, predicted) / rmse / mae / mape - Regression metrics
• accuracy(actual, predicted) - Classification accuracy
• precision / recall / f1Score - Binary classification metrics
• confusionMatrix(actual, predicted, nClasses) - Multi-class evaluation
• rSquared(actual, predicted) / adjustedRSquared - Goodness of fit
Section 33: Cross-Validation
Model validation:
• trainTestSplit(X, y, trainRatio) - Random split
• Foundation for walk-forward validation
Section 34: Trading Convenience Functions
Trading-specific utilities:
• priceMatrix(length) - OHLC data as matrix
• logReturns(length) - Log return series
• rollingSlope(src, length) - Linear trend strength
• kalmanFilter(src, processNoise, measurementNoise) - Filtered price
• kalmanFilter2D(src, ...) - Price with velocity estimate
• adaptiveMA(src, sensitivity) - Kalman-based adaptive moving average
• volAdjMomentum(src, length) - Volatility-normalized momentum
• detectSRLevels(length, nLevels) - K-means based S/R detection
• buildFeatures(src, lengths) - Multi-timeframe feature construction
• technicalFeatures(length) - Standard indicator feature set (RSI, MACD, BB, ATR, etc.)
• lagFeatures(src, lags) - Time-lagged features
• sharpeRatio(returns) - Risk-adjusted return measure
• sortinoRatio(returns) - Downside risk-adjusted return
• maxDrawdown(equity) - Maximum peak-to-trough decline
• calmarRatio(returns, equity) - Return/drawdown ratio
• kellyCriterion(winRate, avgWin, avgLoss) - Optimal position sizing
• fractionalKelly(...) - Conservative Kelly sizing
• rollingBeta(assetReturns, benchmarkReturns) - Market exposure
• fractalDimension(data) - Market complexity measure
---
Usage Example
```
import YourUsername/MLMatrixLib/1 as ml
// Create feature matrix
matrix X = ml.priceMatrix(50)
X := ml.standardize(X)
// Fit linear regression
ml.LinearRegressionModel model = ml.fitLinearRegression(X, y)
float prediction = ml.predictLinear(model, X_new)
// Kalman filter for smoothing
float smoothedPrice = ml.kalmanFilter(close, 0.01, 1.0)
// Detect support/resistance levels
array levels = ml.detectSRLevels(100, 3)
// K-means clustering for regime detection
ml.KMeansModel km = ml.fitKMeans(features, 3)
int cluster = ml.predictCluster(km, newFeature)
```
---
Technical Notes
• All matrix operations use Pine Script's native matrix type
• Numerical stability ensured through:
- Clamping exponential arguments to prevent overflow
- Division by zero protection with epsilon thresholds
- Iterative algorithms with convergence tolerance
• Designed for bar-by-bar execution in Pine Script's event-driven model
• Compatible with Pine Script v6
---
Disclaimer
This library provides mathematical tools for quantitative analysis. It does not constitute financial advice. Past performance of any algorithm does not guarantee future results. Users are responsible for validating models on their specific use cases and understanding the limitations of each method.
Performance Table: Standard DCA | Last 6-12-24-48MThis indicator visualizes Standard Dollar-Cost Averaging (DCA) performance across multiple time horizons (6M, 12M, 24M, 48M).
It summarizes invested capital, current portfolio value, net profit, and return percentage in a compact table, allowing quick comparison of short- and long-term DCA outcomes.
Designed for long-term investors, it helps evaluate how consistent periodic investments perform over time without relying on market timing.
The indicator is asset-agnostic and works on any symbol supported by TradingView.
Key use cases:
Long-term portfolio tracking
DCA strategy validation
Performance comparison across periods
Educational and analytical purposes
This tool focuses on clarity and realism, avoiding over-optimization and short-term noise.
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I hope this table helps investors better understand long-term DCA performance.
Feedback and suggestions for improvement are always welcome.
