Intelligent Moving Average Private AccessNote: This indicator is intended for those who have been granted private access and may be more frequently updated than the previous versions.
Introduction
This indicator uses machine learning (Artificial Intelligence) to solve a real human problem.
The Moving Average is the most used indicator on the planet, yet no one really knows what pair of moving average lengths works best in combination with each other.
A reason for this is because no two moving averages are always going to be the best on every instrument, time-frame, and at any given point in time.
The " Intelligent Moving Average " solves the moving average problem by adapting the period length to match the most profitable combination of moving averages in real time.
How does the Intelligent Moving Average work?
The artificial intelligence that operates these moving average lengths was created by an algorithm that tests every single combination across the entire chart history of an instrument for maximum profitability in real-time.
No matter what happens, the combination of these moving averages will be the most profitable.
Can we learn from the Intelligent Moving Average?
There are many lessons to be learned from the Intelligent Moving Average. Most will come with time as it is still a new concept.
Adopting the usefulness of this AI will change how we perceive moving averages to work.
Limitations
Ultimately, there are no limiting factors within the range of combinations that has been programmed. The moving averages will operate normally, but may change lengths in unexpected ways - maybe it knows something we don't?
Thresholds
The range of moving average lengths is between 5 to 40.
Additional coverage resulted in TradingView server errors.
Future Updates!
This indicator will be maintained and many updates will come in the near future! Stay tuned.
View the documentation on this indicator here: www.kenzing.com
在脚本中搜索"ai"
Well Rounded Moving AverageIntroduction
There are tons of filters, way to many, and some of them are redundant in the sense they produce the same results as others. The task to find an optimal filter is still a big challenge among technical analysis and engineering, a good filter is the Kalman filter who is one of the more precise filters out there. The optimal filter theorem state that : The optimal estimator has the form of a linear observer , this in short mean that an optimal filter must use measurements of the inputs and outputs, and this is what does the Kalman filter. I have tried myself to Kalman filters with more or less success as well as understanding optimality by studying Linear–quadratic–Gaussian control, i failed to get a complete understanding of those subjects but today i present a moving average filter (WRMA) constructed with all the knowledge i have in control theory and who aim to provide a very well response to market price, this mean low lag for fast decision timing and low overshoots for better precision.
Construction
An good filter must use information about its output, this is what exponential smoothing is about, simple exponential smoothing (EMA) is close to a simple moving average and can be defined as :
output = output(1) + α(input - output(1))
where α (alpha) is a smoothing constant, typically equal to 2/(Period+1) for the EMA.
This approach can be further developed by introducing more smoothing constants and output control (See double/triple exponential smoothing - alpha-beta filter) .
The moving average i propose will use only one smoothing constant, and is described as follow :
a = nz(a ) + alpha*nz(A )
b = nz(b ) + alpha*nz(B )
y = ema(a + b,p1)
A = src - y
B = src - ema(y,p2)
The filter is divided into two components a and b (more terms can add more control/effects if chosen well) , a adjust itself to the output error and is responsive while b is independent of the output and is mainly smoother, adding those components together create an output y , A is the output error and B is the error of an exponential moving average.
Comparison
There are a lot of low-lag filters out there, but the overshoots they induce in order to reduce lag is not a great effect. The first comparison is with a least square moving average, a moving average who fit a line in a price window of period length .
Lsma in blue and WRMA in red with both length = 100 . The lsma is a bit smoother but induce terrible overshoots
ZLMA in blue and WRMA in red with both length = 100 . The lag difference between each moving average is really low while VWRMA is way more precise.
Hull MA in blue and WRMA in red with both length = 100 . The Hull MA have similar overshoots than the LSMA.
Reduced overshoots moving average (ROMA) in blue and WRMA in red with both length = 100 . ROMA is an indicator i have made to reduce the overshoots of a LSMA, but at the end WRMA still reduce way more the overshoots while being smoother and having similar lag.
I have added a smoother version, just activate the extra smooth option in the indicator settings window. Here the result with length = 200 :
This result is a little bit similar to a 2 order Butterworth filter. Our filter have more overshoots which in this case could be useful to reduce the error with edges since other low pass filters tend to smooth their amplitude thus reducing edge estimation precision.
Conclusions
I have presented a well rounded filter in term of smoothness/stability and reactivity. Try to add more terms to have different results, you could maybe end up with interesting results, if its the case share them with the community :)
As for control theory i have seen neural networks integrated to Kalman flters which leaded to great accuracy, AI is everywhere and promise to be a game a changer in real time data smoothing. So i asked myself if it was possible for a neural networks to develop pinescript indicators, if yes then i could be replaced by AI ? Brrr how frightening.
Thanks for reading :)
Optimised Volume-weighted Moving AverageAbout
This tool measures the profitability of every volume-weighted moving average length combination for the entire history of the instrument that it is applied,
and only displays the most profitable combination in real-time which means that this indicator is fully functional for trading.
The Optimised Volume-weighted Moving Average can be tested using a Volume-weighted Moving Average Strategy and the Strategy Tester panel on any instrument or time-stamp. It will always display the lengths of the most profitable exponential moving average lengths at the current moment in time.
This can be used on its own, or paired with the Intelligent Volume-weighted Moving Average (AI) for a better understanding of the indicators movements.
The Intelligent Volume-weighted Moving Average (AI) uses this tool as a predictive method for machine learning.
Optimised Exponential Moving AverageAbout
This tool measures the profitability of every exponential moving average length combination for the entire history of the instrument that it is applied and only displays the most profitable combination in real-time meaning that this indicator is fully functional for trading.
The Optimised Exponential Moving Average can be tested using an Exponential Moving Average strategy and the Strategy Tester panel on any instrument or time-stamp. It will always display the lengths of the most profitable exponential moving average lengths at the current moment in time.
This can be used on its own, or paired with the Intelligent Exponential Moving Average (AI) for a better understanding of the indicators movements.
The Intelligent Exponential Moving Average (AI) uses this tool as a predictive method for machine learning.
ALT Risk Strategy with Fear & Greed + ISM PMI📊 Overview
This advanced crypto trading strategy combines multiple macro indicators to identify optimal buy and sell zones for altcoins. It tracks the relationship between altcoin performance versus Bitcoin (ALT/BTC pairs) while incorporating broader market sentiment and economic data to generate risk-adjusted entry and exit signals.
🎯 Core Methodology
Base Risk Metric (65% weight):
MACD Momentum (5%): Normalized trend strength on weekly ALT/BTC pair
RSI (60%): Relative strength indicating overbought/oversold conditions
Price Deviation (35%): Distance from 150-period moving average
Fear & Greed Index (20% weight):
Analyzes market sentiment using multiple factors:
Price momentum and rate of return
Money flow and volume analysis
Volatility metrics (crypto: BVOL24H, traditional: VIX)
Dominance indicators (crypto: BTC.D, traditional: Gold)
Two modes: Crypto-focused or Traditional markets
Customizable smoothing and weighting
US ISM PMI Integration (15% weight):
Manufacturing economic indicator (contraction vs expansion)
PMI < 50 = Economic weakness = Better crypto buying opportunities
PMI > 50 = Economic strength = Risk-on environment
Configurable offset to lead/lag the signal
Daily data smoothed over customizable period
💰 Trading Logic
Tiered Buy System:
Level 1 (Risk < 70): Initial entry with conservative amount
Level 2 (Risk < 50): Double down as risk decreases
Level 3 (Risk < 30): Maximum accumulation at extreme lows
All purchases customizable by dollar amount
Tiered Sell System:
Level 1 (Risk > 70): Take partial profits (default 25%)
Level 2 (Risk > 85): Continue scaling out (default 35%)
Level 3 (Risk > 100): Final exit (default 40%)
Sells reset when new buys occur (can re-accumulate)
⚙️ Key Features
Multi-Asset Support: ETH, SOL, ADA, LINK, UNI, XRP, DOGE, AVAX, MATIC, RENDER, or custom
Exchange Selection: Works with Binance, Coinbase, Kraken, Bitfinex, Bybit
3Commas Integration: Optional webhook alerts for automated bot trading
Visual Risk Zones: Color-coded indicator (green/lime/yellow/orange/red/maroon)
Real-time Info Table: Displays current risk metric, F&G index, PMI value, weights, and position status
Flexible Weighting: Adjust influence of each component (Base/F&G/PMI)
Weekly Timeframe: Reduces noise and focuses on macro trends
📈 Use Cases
DCA Strategy: Dollar-cost averaging with intelligent timing
Swing Trading: Catching major market cycles (weeks to months)
Risk Management: Exit before major downturns, enter during fear
Macro Trading: Align crypto positions with economic conditions
Bot Automation: Connect to 3Commas for hands-free execution
🎓 Credits & Attribution
Original Concept & Base Risk Metric:
Inspired by community-developed ALT/BTC risk oscillators
Fear & Greed methodology adapted from crypto market sentiment research
Enhancements & Integration:
ISM PMI integration and weighting system
Multi-indicator combination framework
Tiered buy/sell logic with reset mechanism
3Commas webhook integration
Development:
Primary Development: Claude AI (Anthropic)
Collaboration & Testing: User feedback and iteration
Pine Script Implementation: TradingView v5
⚠️ Disclaimer
This strategy is for educational and informational purposes only. Past performance does not guarantee future results. Cryptocurrency trading involves substantial risk of loss. Always conduct your own research and consider your risk tolerance before trading. The strategy uses lagging indicators (weekly timeframe) which may not react quickly to sudden market changes.
🔧 Recommended Settings
For better performance than default conservative settings:
Increase buy amounts: Try $50/$75/$100 for more meaningful positions
Adjust thresholds: Consider 40/60/80 for more frequent entries
Test different weights: Experiment with F&G and PMI influence
Optimize for your asset: Different cryptos may require different parameters
Version: 1.0
Last Updated: December 2025
Compatible With: TradingView Pine Script v5
VIX Term Structure Pro [v7.0 Enhanced]# VIX Term Structure Pro v7.0
[! (img.shields.io)](www.tradingview.com)
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**Professional VIX-based Market Sentiment & Timing Indicator**
专业的 VIX 市场情绪与择时指标
---
## 🌟 Overview / 概述
VIX Term Structure Pro is an advanced multi-factor market timing indicator that analyzes the VIX futures term structure, volatility regime, and market breadth to generate actionable buy/sell signals.
VIX Term Structure Pro 是一款高级多因子市场择时指标，通过分析 VIX 期货期限结构、波动率区间及市场广度，生成可操作的买卖信号。
---
## 🚀 Key Features / 核心功能
### 📊 Multi-Factor Scoring System / 多因子评分系统
- **Term Structure Z-Score**: Measures deviation from historical mean / 期限结构 Z 分数：衡量与历史均值的偏离
- **VIX/VX1 Basis**: Spot premium detection for panic signals / VIX 现货溢价：恐慌信号检测
- **Contango Analysis**: Futures curve shape insights / 期货升水分析
- **SKEW Integration**: Options skew for tail risk / SKEW 整合：尾部风险监测
- **Put/Call Ratio**: Sentiment extremes / 看跌/看涨比率：情绪极端
- **VVIX Support**: Volatility of volatility (optional) / VVIX 支持：波动率的波动率
### 🎯 Three-Tier Signal System / 三级信号系统
| Signal | Score | Description |
|--------|-------|-------------|
| 🚨 **CRASH BUY** | ≥ 6 | Extreme panic, rare opportunity / 极端恐慌，罕见机会 |
| 🟢 **STRONG BUY** | ≥ 5 | Multi-factor confluence / 多因子共振 |
| 🟡 **BUY DIP** | ≥ 4 | Accumulate on weakness / 逢低吸纳 |
| 🟠 **SELL/HEDGE** | ≤ -2 | Consider reducing risk / 考虑减仓对冲 |
| 🔴 **STRONG SELL** | ≤ -5 | Strong bearish signals / 强烈看跌信号 |
| 🔥 **EUPHORIA SELL** | ≤ -6 | Extreme greed, sell signal / 极度贪婪，卖出信号 |
### 📈 Dashboard Indicators / 仪表盘指标解读
| Indicator | Bullish 🟢 | Bearish 🔴 |
|-----------|------------|------------|
| Overall Bias | STRONG BUY / BUY DIP | STRONG SELL / SELL/HEDGE |
| AI Score | ≥ 5 (Extreme Fear) | ≤ -5 (Extreme Greed) |
| Market Trend | 🟢SPX 🟢NDX (Above MA200) | 🔴SPX 🔴NDX (Below MA200) |
| VIX Regime | LOW VOL (<15) | HIGH VOL (>25) |
| Term Struct Z | < -2.0 (Panic) | > 2.0 (Complacency) |
---
## ⚙️ Configuration / 配置选项
### 📡 Data Sources / 数据源
- **VIX Symbol**: Default `CBOE:VIX` (Alternative: `TVC:VIX`)
- **Put/Call Ratio**: Default `INDEX:CPCI` (Index P/C)
- **Timeframe**: Daily (stable) or Chart (real-time)
### ⚠️ Strategy Mode / 策略模式
- **High (Scalping)**: Sensitive, for short-term trades / 高敏感，短线
- **Normal (Swing)**: Balanced approach / 平衡模式
- **Low (Trend/Safe)**: Conservative, trend-following / 保守，趋势跟踪
### 🔬 Backtest Mode / 回测模式
- **OFF (Real-time)**: Shows current day data, suitable for live monitoring / 显示当日数据，适合实盘监控
- **ON (Historical)**: Uses only confirmed data, avoids look-ahead bias / 仅使用已确认数据，避免未来函数
---
## 📖 Usage Guide / 使用指南
### Best Practices / 最佳实践
1. **Apply to SPX/SPY/QQQ daily charts** for optimal signal accuracy
在 SPX/SPY/QQQ 日线图上使用，信号准确度最佳
2. **Wait for next trading day** to execute signals (signals trigger on daily close)
信号触发后在下一交易日执行（信号基于日线收盘）
3. **Use in conjunction with price action** for confirmation
结合价格走势确认信号
4. **Enable Market Trend Filter** (MA200) for safer entries in uncertain markets
开启趋势过滤（MA200）以在不确定市场中更安全入场
### Signal Interpretation / 信号解读
```
🚨 CRASH BUY (Score ≥ 6)
→ Rare extreme panic event
→ Historical average return: significant positive over 2 months
→ Consider aggressive positioning
🟢 STRONG BUY (Score ≥ 5)
→ Multiple indicators align
→ Historical average return: positive over 1 month
→ Consider building positions
🟡 BUY DIP (Score ≥ 4)
→ Moderate fear detected
→ Suitable for adding to existing positions
→ Filtered out in bear markets if Trend Filter is ON
```
---
## 📊 Historical Statistics / 历史统计
The indicator tracks signal frequency and average subsequent returns:
- **CRASH BUY**: 40-day return period (~2 months)
- **STRONG BUY**: 20-day return period (~1 month)
- **BUY DIP**: 10-day return period (~2 weeks)
指标追踪信号频率和后续平均收益，可在仪表盘中查看历史统计。
---
## 🔔 Alerts / 警报
Built-in alert conditions with cooldown mechanism to prevent spam:
| Alert | Condition |
|-------|-----------|
| Crash Buy Alert | Score ≥ 6, extreme panic |
| Strong Buy Alert | Score ≥ 5, multi-factor confluence |
| Buy Dip Alert | Score ≥ threshold |
| Euphoria Sell Alert | Score ≤ -6, extreme greed |
| Strong Sell Alert | Score ≤ -5 |
| VIX Basis Panic | VIX spot premium spike |
---
## 📋 Changelog / 更新日志
### v7.0 (Current)
- ✨ Three-tier buy/sell signal system
- 📊 Signal statistics with average return tracking
- 🔬 Backtest Mode toggle for historical testing
- 🎨 Configurable ±1 Z-Score reference lines
- ⚡ Modular scoring functions
- 🛡️ Dual index trend display (SPX + NDX)
- 📱 Compact & Full dashboard modes
---
## ⚠️ Disclaimer / 免责声明
**English:**
This indicator is for educational and informational purposes only. It does not constitute financial advice. Past performance does not guarantee future results. Always do your own research and consider your risk tolerance before trading.
**中文：**
本指标仅供教育和信息参考，不构成投资建议。过往表现不代表未来收益。交易前请自行研究并评估风险承受能力。
---
## 📄 License / 许可证
MIT License - Feel free to use, modify, and share.
---
## 🤝 Contributing / 贡献
Issues and pull requests are welcome!
欢迎提交问题和贡献代码！
---
**Made with ❤️ for the trading community**
**为交易社区用心打造**
Ultimate Adaptive RSIUltimate Adaptive RSI
RSI That Adapts to Any Market
This isn't your grandpa's RSI. It dynamically adjusts its sensitivity based on market conditions—smoother in trends, responsive in ranges.
Traditional RSI fails in strong trends and changing volatility. UA-RSI fixes both by adapting its sensitivity in real-time, giving you reliable signals whether the market is trending, ranging, or transitioning between regimes.
How It Adapts:
Smart Pre-Smoothing: Uses Efficiency Ratio to detect trend strength and automatically lengthens/shortens its smoothing window.
Dominant Cycle Detection: Matches its internal period to the market's actual rhythm.
Dynamic Bands: RMS-based overbought/oversold levels that expand/contract with volatility.
Smoothing Stack: ALMA pre-smoothing → Ultimate Smoother → Jurik filter creates the cleanest RSI you've ever seen.
Trade Signals:
Buy: RSI crosses above lower band or midline + price confirms
Sell: RSI crosses below upper band or midline + price confirms
Bands expand in high volatility → wait for deeper extremes
Bands contract in low volatility → take earlier signals
Signal line for crossover entries
Adaptive smoothing = fewer false signals in trends
Day trading: Use 1.0 band multiplier
Swing trading: Use 1.2-1.5 multiplier
Ranging markets: Lower multiplier to 0.8
Trending markets: Raise multiplier to 1.5+
Bands widen in volatility = wait for deeper extremes
Bands tighten in calm markets = take earlier signals
Never trade RSI alone - always wait for price confirmation
online Moment-Based Adaptive Detection🙏🏻 oMBAD (online Moment-Based Adaptive Detection): adaptive anomaly || outlier || novelty detection, higher-order standardized moments; at O(1) time complexity
For TradingView users: this entity would truly unleash its true potential for you ‘only’ if you work with tick-based & seconds-based resolutions, otherwise I recommend to keep using original non-online MBAD . Otherwise it may only help with a much faster backtesting & strategy development processes.
...
Main features :
O(1) time complexity: the whole method works @ O(1) time complexity, it’s lighting fast and cheap
HFT-ready: frequency, amount and magnitude of data points are irrelevant
Axiomatic: no need to optimize or to provide arbitrary hyperparameters, adaptive thresholds are completely data-driven and based on combination of higher-order central moments
Accepts weights: the method can gain additional information by accepting weights (e.g. volume weighting)
Example use cases for high-frequency trading:
Ordeflow analysis: can be applied on non-aggregated flow of market orders to gauge its imbalance and momentum
Liquidity provision: can be applied to high-resolution || tick data to place and dynamically adjust prices of limit orders
ML-based signals: online estimates of higher-order central moments can be used as features & in further feature engineering for trading signal generation
Operation & control: can be applied on PnL stream of your strategy for immediate returns analysis and equity control
Abstract:
This method is the online version of originally O(n) MBAD (Moment-Based Adaptive Detection) . It uses higher-order central & standardized moments to naturally estimate data’s extremums using all data while not touching order-statistics (i.e. current min and max) at all. By the same principles it also estimates “ever-possible” values given the data-generating process stays the same.
This online version achieves reduced time complexity to O(1) by using weighted exponential smoothing, and in particular is based on Pebay et al (2008) work, which provides mathematically correct results for the moments, and is numerically stable, unlike the raw sum-based estimates of moments.
Additionally, I provide adjustments for non-continuous lattice geometry of orderbooks, and correct re-quantization math, allowing to artificially increase the native tick size.
The guidelines of how to adjust alpha (smoothing parameter of exponential smoothing) in order to completely match certain types of moving averages, or to minimize errors with ones when it’s impossible to match; are also provided.
Mathematical correctness of the realization was verified experimentally by observing the exact match with the original non-recursive MBAD in expanding window mode, and confirmed by 2 AI agents independently. Both weighted and non-weighted versions were tested successfully.
...
^^ On micro level with moving window size 1
^^ With artificial tick size increase, moving window size 64
^^ Expanding window mode anchored to session start
^^ Demonstrates numerical stability even on very large inputs
...
∞
BTC DCA Risk Metric StrategyBTC DCA Risk Strategy - Automated Dollar Cost Averaging with 3Commas Integration
Overview
This strategy combines the proven Oakley Wood Risk Metric with an intelligent tiered Dollar Cost Averaging (DCA) system, designed to help traders systematically accumulate Bitcoin during periods of low risk and take profits during high-risk conditions.
Key Features
📊 Multi-Component Risk Assessment
4-Year SMA Deviation: Measures Bitcoin's distance from its long-term mean
20-Week MA Analysis: Tracks medium-term momentum shifts
50-Day/50-Week MA Ratio: Captures short-to-medium term trend strength
All metrics are normalized by time to account for Bitcoin's maturing market dynamics
💰 3-Tier DCA Buy System
Level 1 (Low Risk): Conservative entry with base allocation
Level 2 (Lower Risk): Increased allocation as opportunity improves
Level 3 (Extreme Low Risk): Maximum allocation during rare buying opportunities
Buys execute every bar while risk remains below thresholds, enabling true DCA accumulation
📈 Progressive Profit Taking
Sell Level 1: Take initial profits as risk increases
Sell Level 2: Scale out further positions during elevated risk
Sell Level 3: Final exit during extreme market conditions
Sell levels automatically reset when new buy signals occur, allowing flexible re-entry
🤖 3Commas Integration
Fully automated webhook alerts for Custom Signal Bots
JSON payloads formatted per 3Commas API specifications
Supports multiple exchanges (Binance, Coinbase, Kraken, Gemini, Bybit)
Configurable quote currency (USD, USDT, BUSD)
How It Works
The strategy calculates a composite risk metric (0-1 scale):
0.0-0.2: Extreme buying opportunity (green zone)
0.2-0.5: Favorable accumulation range (yellow zone)
0.5-0.8: Neutral to cautious territory (orange zone)
0.8-1.0+: High risk, profit-taking zone (red zone)
Buy Logic: As risk decreases, position sizes increase automatically. If risk drops from L1 to L3 threshold, the strategy combines all three tier allocations for maximum exposure.
Sell Logic: Sequential profit-taking ensures you capture gains progressively. The system won't advance to Sell L2 until L1 completes, preventing premature full exits.
Configuration
Risk Metric Parameters:
All calculations use Bitcoin price data (any BTC chart works)
Time-normalized formulas adapt to market maturity
No manual parameter tuning required
Buy Settings:
Set risk thresholds for each tier (default: 0.20, 0.10, 0.00)
Define dollar amounts per tier (default: $10, $15, $20)
Fully customizable to your risk tolerance and capital
Sell Settings:
Configure risk thresholds for profit-taking (default: 1.00, 1.50, 2.00)
Set percentage of position to sell at each level (default: 25%, 35%, 40%)
3Commas Setup:
Create a Custom Signal Bot in 3Commas
Copy Bot UUID and Secret Token into strategy inputs
Enable 3Commas Alerts checkbox
Create TradingView alert: Condition → "alert() function calls only", Webhook → api.3commas.io
Backtesting Results
Strengths:
Systematically buys dips without emotion
Averages down during extended bear markets
Captures explosive bull run profits through tiered exits
Pyramiding (1000 max orders) allows true DCA behavior
Considerations:
Requires sufficient capital for multiple buys during prolonged downtrends
Backtest on Daily timeframe for most reliable signals
Past performance does not guarantee future results
Visual Design
The indicator pane displays:
Color-coded risk metric line: Changes from white→red→orange→yellow→green as risk decreases
Background zones: Green (buy), yellow (hold), red (sell) areas
Dashed threshold lines: Clear visual markers for each buy/sell level
Entry/Exit labels: Green buy labels and orange/red sell labels mark all trades
Credits
Original Risk Metric: Oakley Wood
Strategy Development & 3Commas Integration: Claude AI (Anthropic)
Modifications: pommesUNDwurst
Disclaimer
This strategy is for educational and informational purposes only. Cryptocurrency trading carries substantial risk of loss. Always conduct your own research and never invest more than you can afford to lose. The authors are not financial advisors and assume no responsibility for trading decisions made using this tool.
Market Dynamics - Backtest Engine [NeuraAlgo]Market Dynamics – Backtest Engine
Market Dynamics – Backtest Engine is an advanced research-grade trading framework engineered by NeuraAlgo.
🔹 Core Engine – Dynamic Trend Model
The strategy leverages the NeuraAlgo – Market Dynamics indicator as its foundation, providing intelligent insights to guide trading decisions. It is designed to automatically identify the optimal settings for the NeuraAlgo – Market Dynamics indicator, helping traders fine-tune their strategy for maximum efficiency, accuracy, and profitability. This engine dynamically adapts to market conditions, ensuring your strategy stays optimized in real-time.
🔹 Optimization Engine
A built-in optimization module allows automatic testing of:
Winrate-focused configurations
Profit-focused configurations
Sensitivity ranges
Step sizes
Main Entry, Main Filter, Feature Filter, and Risk Manager categories
This enables rapid identification of optimal parameters similar to a lightweight AI optimizer.
This Backtesting + Auto Optimization Engine includes an integrated optimizer that automatically tests sensitivity ranges:
Maximize Winrate
Maximize Profits
Optimize Main Entries, Risk Manager, or Feature Filters
Users can set:
start sensitivity
step size
parameter category
The engine autonomously computes which parameter delivers the strongest performance.
🔹 How To Use
1. Identify the Parameters
First, you need to know which indicator parameters can be optimized. For the NeuraAlgo – Market Dynamics indicator, these might include:
Trend sensitivity
Smoothing periods
Threshold values for bullish/bearish signals
These parameters are the inputs your engine will test.
2. Define a Range
For each parameter, define a range of values to test. Example:
Sensitivity: 2 → 10
Trend period: 14 → 50
Threshold: 0.1 → 1.0
The more granular the range, the more precise the optimization—but it will also take longer.
3. Run Backtest Optimization
Attach the strategy to a chart.
Select optimization mode in your engine (or set the range for each parameter).
Start the backtest: the engine will simulate trades for every combination of parameter values.
The system will automatically record key metrics for each run:
Net profit
Win rate
Profit factor
Max drawdown
4. Analyze the Results
After the backtest, your engine will display a results table or chart showing performance for each parameter combination. Look for:
Highest net profit
Highest win rate
Or a combination depending on your strategy goals
Some engines will highlight the “best” parameter set automatically.
5. Apply Optimal Settings
Once identified:
Select the best-performing parameter values.
Apply them to your live strategy or paper trade.
Optionally, forward test to confirm they work on unseen market data.
Congratulations! The setup is now optimized.
🔹 Conclusion
The backtest optimization process helps you find the best parameter values for the NeuraAlgo – Market Dynamics indicator by systematically testing different settings and measuring their performance. By analyzing metrics like net profit, win rate, and drawdown, you can select optimized parameters that are more likely to perform consistently in real trading. Proper optimization ensures your strategy is data-driven, adaptable, and reduces guesswork, giving you a stronger edge in the market.
🐋 MACRO POSITION TRADER - Quarterly Alignment 💎Disclaimer: This tool is an alignment filter and educational resource, not financial advice. Backtest and use proper risk management. Past performance does not guarantee future returns.
so the idea behind this one came from an experience i had when i first started learning how to trade. dont laugh at me but i was the guy to buy into those stupid AI get rich quick schemes or the first person to buy the "golden indicator" just to find out that it was a scam. Its also to help traders place trades they can hold for months with high confidence and not have to sit in front of charts all day, and to also scale up quickly with small accounts confidently. and basically what it does is gives an alert once the 3 mo the 6 mo and the 12 mo tfs all align with eachother and gives the option to toggle on or off the 1 mo tf as well for extra confidence. Enter on the 5M–15M after a sweep + CHOCH in the direction of the aligned 1M–12M bias. that simple just continue to keep watching key levels mabey take profit 1-2 weeks and jump back in scaling up if desired..easy way to combine any small account size.
Perfect balance of:
low risk
high R:R
optimal precision
minimal chop
best sweep/CHOCH clarity
hope you guys enjoy this one.
EMA Crossover CandlesEMA Crossover Candles
This indicator colors your chart candles based on the relationship between two Exponential Moving Averages (EMAs).
How It Works
Green Candles - When the Fast EMA is above the Slow EMA, indicating bullish momentum
Red Candles - When the Fast EMA is below the Slow EMA, indicating bearish momentum
Settings
Source - The price data used for EMA calculations (default: close)
Fast Length - Period for the fast EMA (default: 5)
Slow Length - Period for the slow EMA (default: 10)
How To Use
This indicator provides a quick visual reference for trend direction. Green candles suggest the short-term trend is bullish, while red candles suggest bearish conditions. This can help you:
Identify trend direction at a glance
Filter trades in the direction of the trend
Spot potential trend changes when candle colors shift
Tips
Adjust the Fast and Slow Length settings to match your trading timeframe
Shorter periods = more responsive but more false signals
Longer periods = smoother but slower to react to trend changes
Consider hiding default candles in Chart Settings for a cleaner look
Note: This indicator is for informational purposes only and should not be used as the sole basis for trading decisions. Always use proper risk management and consider combining with other forms of analysis.
Feel free to modify this to match your style or add any additional details you'd like to include.Claude is AI and can make mistakes. Please double-check responses. Opus 4.5
SM Screener — Alert Engine (Tiered)🔥 Momentum Radar — Powered by My Premium All-In-One Signal Engine
This isn’t just another screener.
This is the official early-warning radar that plugs directly into my Premium All-In-One Buy/Sell Signal Tool.
The Premium version is where the real executions happen — the legitimate Buy and Sell signals, trend flips, squeeze confirmations, BOS/CHOCH tracking, and high-accuracy momentum logic.
But this?
This is the scanner that tells you where to look BEFORE the big move happens.
If the Premium tool is the weapon…
this screener is the radar locking onto targets.
🚀 What It Actually Does
It monitors every ticker on your chart and fires alerts the moment a symbol starts showing:
✔ Early momentum ignition
✔ Rising relative volume
✔ Trend pressure shifting
✔ Volatility expansion
✔ Early squeeze build-up
✔ Clustered signal behavior
✔ High-tier conviction score
These alerts tell you exactly which tickers to pull up in your Premium tool so you can inspect the chart deeper with full confirmation.
If you're serious about catching explosive moves, this combo is unreal.
💥 Designed for Traders Who Want the Monster Moves
This system is built for the same plays that create legends — the massive momentum runners and wild squeezes like the $4 → $400+ SMX eruption.
The goal is simple:
**Find the move early.
Confirm it with the Premium tool.
Then ride it with confidence.**
⚡ Alert Engine That Feels Like Insider Info
Every alert is laser-targeted:
🔥 Early Interest — tells you something is heating up
🔥 Entry Signal — means the ticker is firing on all cylinders
🔥 Volume bursts
🔥 Momentum flips
🔥 High conviction score
🔥 Trend strength alignment
You get notified instantly so you never miss the tickers entering “potential explosion mode.”
Perfect for:
→ Custom automation
→ Watchlist building
📈 A Complete Momentum Ecosystem
This isn’t a standalone indicator — it’s part of a full ecosystem:
1️⃣ The Premium All-In-One Tool (master)
→ Generates true Buy/Sell signals
→ Full trend model
→ Squeeze engine
→ Premium/discount logic
→ Volume & volatility confirmation
→ BOS/CHOCH structure tracking
2️⃣ THIS Screener Engine (scanner)
→ Alerts you which tickers deserve attention
→ Filters out noise
→ Points you to the potential runners
→ Helps you never miss the early setups
Together, they’re unreal.
⭐ Follow for More
This is only one piece of a growing suite of professional-grade tools I’m publishing.
If you want:
🔥 More scanners
🔥 Predictive momentum engines
🔥 AI-grade alert logic
🔥 My official Premium trading toolkit
Hit Follow — new releases drop frequently.
Trade smart.
Trade fast.
And catch the ones everyone else regrets missing.
Swing Aurora v7.0 — The ExecutionerSwing Aurora v7.0 — The Executioner
Swing Aurora v7.0 is a multi-engine swing trading framework that combines trend-following, momentum, HTF confluence and SMC/Fibonacci structure in one script.
This version moves from a rigid gate logic to a scoring + state machine engine, so you can see not only if there is a signal, but how strong that signal really is.
🧠 1. Scoring Engine – A-Grade & B-Grade Signals
Instead of a single if (all conditions == true) check, v7.0 builds a score on every bar:
Trend score – position vs Baseline, slope, Supertrend direction.
Momentum score – MACD, RSI-Stoch triggers, ADX, local HH/LL.
HTF score – alignment with higher timeframe Baseline, Bias EMA, EMAs and RSI.
Confluence flags – divergences, ST flip/retest, SMC zones, VDub context.
Results:
A-Grade (Strong) signals → high score, strong trend + momentum + HTF alignment.
B-Grade (Speculative) signals → early/partial setups, clearly marked as higher risk.
You no longer lose good entries just because one minor filter disagrees, but you can clearly distinguish high-quality setups from speculative ones.
🔁 2. Strict Trade Cycle – State Machine
v7.0 uses a simple state machine:
0 = Flat, 1 = Long, -1 = Short.
When you are Long, the script only looks for exits or reversals, not new BUY entries.
Same for Short.
This enforces a clean, disciplined flow:
BUY → Hold → EXIT → wait for next setup, without label spam or conflicting signals while already in a position.
🛡️ 3. Quality Gates & Anti-FOMO Filters
To avoid buying local tops or chopping yourself to death:
RSI Gate – blocks BUY when RSI is already overbought (and vice-versa for SELL).
ATR Over-Extension filter – no entries when price is too far from the Baseline (parabolic moves).
No-Trade / Chop zone – combines ADX, ATR vs ATR-slow, distance to Baseline, Bollinger/Keltner squeeze and volume behavior.
Volume Gate – requires a real volume spike, not just random price wiggle.
Supertrend Gate – entries are synchronized with ST (flip / early / retest — configurable).
HTF Guardrails – optional: blocks entries against the dominant HTF regime.
📈 4. Visual Layer: Trend Map, Labels & Gradient
BUY/SELL labels with confidence percentage.
Background gradient based on trend direction and strength (ADX).
EMA 13/21 + Baseline with dynamic bull/bear colors.
Optional mini-legend showing: TS / RSI / ADX / HTF status at a glance.
🧩 5. Divergences, VDub & Macro Map
Full divergence engine (classic + hidden) on a basket of indicators (RSI, MACD, CCI, OBV, etc.), with optional lines and count labels.
VDub levels & signals – “smart levels” (solid/dotted) and add-on BUY/SELL signals filtered by market regime.
HTF Macro Map – higher timeframe Baseline, Bias EMA, fast EMAs, RSI and slope, using an auto or user-selected higher TF.
🧱 6. SMC Zones & Fibonacci (v7.0 Logic)
The SMC / Fibo component was refined so it is not hard-wired to the current bar’s entry signal:
Automatic HH / HL / LH / LL market structure labelling.
Demand / Supply zones:
derived from BOS with ATR buffer,
auto-update bar-by-bar,
auto-delete when broken or after a user-defined lifetime.
Fibonacci range:
built from the latest valid swing-high / swing-low,
shows 0 / 0.382 / 0.5 / 0.618 / 1 / 1.618 levels plus equilibrium line,
persists while the range is valid (independent of being in a trade).
AI zone boost (v7.0) – optional: zone opacity adapts dynamically to the underlying confidence score, highlighting higher-quality areas.
⚙️ 7. Modes & Configuration
Modes: Aggressive / Balanced / Conservative – adjust score thresholds and confidence requirements.
Risk & Quality: slope filter, min ATR distance, strict anti-chop, volume gate, HTF guardrails.
Visual toggles: labels on/off, baseline & EMAs, gradient, mini-legend, SMC boxes, Fibonacci.
This script does not trade for you – it provides a structured, consistent framework for reading trend, momentum and structure, plus graded signals so you can execute your own risk management and strategy.
Disclaimer
This script is provided strictly for educational and research purposes. It does not constitute financial advice, investment recommendation or any guarantee of profit. Historical performance, backtests and chart examples do not ensure future results.
Always use your own risk management rules, test the script on multiple instruments and timeframes, and never trade with money you cannot afford to lose. The author and contributors accept no responsibility for any trading decisions made based on this indicator.
Advanced Volume & Price Heatmap (Fixed)Work in Progress. Used AI to help me code. Not really sure it worked very well. I need to run it through Cursor and make it cleaner and better.
Obsidian Flux Matrix# Obsidian Flux Matrix | JackOfAllTrades
Made with my Senior Level AI Pine Script v6 coding bot for the community!
Narrative Overview
Obsidian Flux Matrix (OFM) is an open-source Pine Script v6 study that fuses social sentiment, higher timeframe trend bias, fair-value-gap detection, liquidity raids, VWAP gravitation, session profiling, and a diagnostic HUD. The layout keeps the obsidian palette so critical overlays stay readable without overwhelming a price chart.
Purpose & Scope
OFM focuses on actionable structure rather than marketing claims. It documents every driver that powers its confluence engine so reviewers understand what triggers each visual.
Core Analytical Pillars
1. Social Pulse Engine
Sentiment Webhook Feed: Accepts normalized scores (-1 to +1). Signals only arm when the EMA-smoothed value exceeds the `sentimentMin` input (0.35 by default).
Volume Confirmation: Requires local volume > 30-bar average × `volSpikeMult` (default 2.0) before sentiment flags.
EMA Cross Validation: Fast EMA 8 crossing above/below slow EMA 21 keeps momentum aligned with flow.
Momentum Alignment: Multi-timeframe momentum composite must agree (positive for longs, negative for shorts).
2. Peer Momentum Heatmap
Multi-Timeframe Blend: RSI + Stoch RSI fetched via request.security() on 1H/4H/1D by default.
Composite Scoring: Each timeframe votes +1/-1/0; totals are clamped between -3 and +3.
Intraday Readability: Configurable band thickness (1-5) so scalpers see context without losing space.
Dynamic Opacity: Stronger agreement boosts column opacity for quick bias checks.
3. Trend & Displacement Framework
Dual EMA Ribbon: Cyan/magenta ribbon highlights immediate posture.
HTF Bias: A higher-timeframe EMA (default 55 on 4H) sets macro direction.
Displacement Score: Body-to-ATR ratio (>1.4 default) detects impulses that seed FVGs or VWAP raids.
ATR Normalization: All thresholds float with volatility so the study adapts to assets and regimes.
4. Intelligent Fair Value Gap (FVG) System
Gap Detection: Three-candle logic (bullish: low > high ; bearish: high < low ) with ATR-sized minimums (0.15 × ATR default).
Overlap Prevention: Price-range checks stop redundant boxes.
Spacing Control: `fvgMinSpacing` (default 5) avoids stacking from the same impulse.
Storage Caps: Max three FVGs per side unless the user widens the limit.
Session Awareness: Kill zone filters keep taps focused on London/NY if desired.
Auto Cleanup: Boxes delete when price closes beyond their invalidation level.
5. VWAP Magnet + Liquidity Raid Engine
Session or Rolling VWAP: Toggle resets to match intraday or rolling preferences.
Equal High/Low Scanner: Looks back 20 bars by default for liquidity pools.
Displacement Filter: ATR multiplier ensures raids represent genuine liquidity sweeps.
Mean Reversion Focus: Signals fire when price displaces back toward VWAP following a raid.
6. Session Range Breakout System
Initial Balance Tracking: First N bars (15 default) define the session box.
Breakout Logic: Requires simultaneous liquidity spikes, nearby FVG activity, and supportive momentum.
Z-Score Volume Filter: >1.5σ by default to filter noisy moves.
7. Lifestyle Liquidity Scanner
Volume Z-Scores: 50-bar baseline highlights statistically significant spikes.
Smart Money Footprints: Bottom-of-chart squares color-code buy vs sell participation.
Panel Memory: HUD logs the last five raid timestamps, direction, and normalized size.
8. Risk Matrix & Diagnostic HUD
HUD Structure: Table in the top-right summarizes HTF bias, sentiment, momentum, range state, liquidity memory, and current risk references.
Signal Tags: Aggregates SPS, FVG, VWAP, Range, and Liquidity states into a compact string.
Risk Metrics: Swing-based stops (5-bar lookback) + ATR targets (1.5× default) keep risk transparent.
Signal Families & Alerts
Social Pulse (SPS): Volume-confirmed sentiment alignment; triangle markers with “SPS”.
Kill-Zone FVG: Session + HTF alignment + FVG tap; arrow markers plus SL/TP labels.
Local FVG: Captures local reversals when HTF bias has not flipped yet.
VWAP Raid: Equal-high/low raids that snap toward VWAP; “VWAP” label markers.
Range Breakout: Initial balance violations with liquidity and imbalance confirmation; circle markers.
Liquidity Spike: Z-score spikes ≥ threshold; square markers along the baseline.
Visual Design & Customization
Theme Palette: Primary background RGB (12,6,24). Accent shading RGB (26,10,48). Long accents RGB (88,174,255). Short accents RGB (219,109,255).
Stylized Candles: Optional overlay using theme colors.
Signal Toggles: Independently enable markers, heatmap, and diagnostics.
Label Spacing: Auto-spacing enforces ≥4-bar gaps to prevent text overlap.
Customization & Workflow Notes
Adjust ATR/FVG thresholds when volatility shifts.
Re-anchor sentiment to your webhook cadence; EMA smoothing (default 5) dampens noise.
Reposition the HUD by editing the `table.new` coordinates.
Use multiples of the chart timeframe for HTF requests to minimize load.
Session inputs accept exchange-local time; align them to your market.
Performance & Compliance
Pure Pine v6: Single-line statements, no `lookahead_on`.
Resource Safe: Arrays trimmed, boxes limited, `request.security` cached.
Repaint Awareness: Signals confirm on close; alerts mirror on-chart logic.
Runtime Safety: Arrays/loops guard against `na`.
Use Cases
Measure when social sentiment aligns with structure.
Plan ICT-style intraday rebalances around session-specific FVG taps.
Fade VWAP raids when displacement shows exhaustion.
Watch initial balance breaks backed by statistical volume.
Keep risk/target references anchored in ATR logic.
Signal Logic Snapshot
Social Pulse Long/Short: `sentimentEMA` gated by `sentimentMin`, `volSpike`, EMA 8/21 cross, and `momoComposite` sign agreement. Keeps hype tied to structural follow-through.
Kill-Zone FVG Long/Short: Requires session filter, HTF EMA bias alignment, and an active FVG tap (`bullFvgTap` / `bearFvgTap`). Labels include swing stops + ATR targets pulled from `swingLookback` and `liqTargetMultiple`.
Local FVG Long/Short: Uses `localBullish` / `localBearish` heuristics (EMA slope, displacement, sequential closes) to surface intraday reversals even when HTF bias has not flipped.
VWAP Raids: Detect equal-high/equal-low sweeps (`raidHigh`, `raidLow`) that revert toward `sessionVwap` or rolling VWAP when displacement exceeds `vwapAlertDisplace`.
Range Breakouts: Combine `rangeComplete`, breakout confirmation, liquidity spikes, and nearby FVG activity for statistically backed initial balance breaks.
Liquidity Spikes: Volume Z-score > `zScoreThreshold` logs direction, size, and timestamp for the HUD and optional review workflows.
Session Logic & VWAP Handling
Kill zone + NY session inputs use TradingView’s session strings; `f_inSession()` drives both visual shading and whether FVG taps are tradeable when `killZoneOnly` is true.
Session VWAP resets using cumulative price × volume sums that restart when the daily timestamp changes; rolling VWAP falls back to `ta.vwap(hlc3)` for instruments where daily resets are less relevant.
Initial balance box (`rangeBars` input) locks once complete, extends forward, and stays on chart to contextualize later liquidity raids or breakouts.
Parameter Reference
Trend: `emaFastLen`, `emaSlowLen`, `htfResolution`, `htfEmaLen`, `showEmaRibbon`, `showHtfBiasLine`.
Momentum: `tf1`, `tf2`, `tf3`, `rsiLen`, `stochLen`, `stochSmooth`, `heatmapHeight`.
Volume/Liquidity: `volLookback`, `volSpikeMult`, `zScoreLen`, `zScoreThreshold`, `equalLookback`.
VWAP & Sessions: `vwapMode`, `showVwapLine`, `vwapAlertDisplace`, `killSession`, `nySession`, `showSessionShade`, `rangeBars`.
FVG/Risk: `fvgMinTicks`, `fvgLookback`, `fvgMinSpacing`, `killZoneOnly`, `liqTargetMultiple`, `swingLookback`.
Visualization Toggles: `showSignalMarkers`, `showHeatmapBand`, `showInfoPanel`, `showStylizedCandles`.
Workflow Recipes
Kill-Zone Continuation: During the defined kill session, look for `killFvgLong` or `killFvgShort` arrows that line up with `sentimentValid` and positive `momoComposite`. Use the HUD’s risk readout to confirm SL/TP distances before entering.
VWAP Raid Fade: Outside kill zone, track `raidToVwapLong/Short`. Confirm the candle body exceeds the displacement multiplier, and price crosses back toward VWAP before considering reversions.
Range Break Monitor: After the initial balance locks, mark `rangeBreakLong/Short` circles only when the momentum band is >0 or <0 respectively and a fresh FVG box sits near price.
Liquidity Spike Review: When the HUD shows “Liquidity” timestamps, hover the plotted squares at chart bottom to see whether spikes were buy/sell oriented and if local FVGs formed immediately after.
Metadata
Author: officialjackofalltrades
Platform: TradingView (Pine Script v6)
Category: Sentiment + Liquidity Intelligence
Hope you Enjoy!
Pharma vs Market Monthly Returns (XLV vs SPY)A Bloomberg-style pharma momentum indicator built for TradingView.
This script recreates the “Pharma Index Monthly Returns” chart highlighted by Jordi Visser in his Youtube video — offering a clean, accessible poor man’s Bloomberg version of sector-rotation analysis for users without institutional data feeds.
Features
• XLV monthly returns (absolute mode)
• XLV vs SPY relative monthly returns (market-neutral mode)
• Top 5 strongest months ★ (momentum spikes)
• Top 5 weakest months ★ (capitulation signals)
• Optional 6-month rolling momentum line (regime trend)
• Full history from 1998 (XLV inception)
Use Cases
Ideal for tracking pharma/healthcare sector regimes, macro rotations, biotech cycles, and timing asymmetric entries in innovation themes (AI-pharma, computational drug discovery, biotech moonshots, etc.).
The Quantum Leap: Renko + ML(Note: This indicator uses the BackQuant & SuperTrend which takes a 4-5 seconds to load)
This strategy uses the following indicators (please see source code)
Synthetic Renko: Ignores time and focuses purely on price movement to detect clear trend reversals (Red-to-Green).
ATR (Average True Range): Measures volatility to calculate the Renko brick sizes and SuperTrend sensitivity.
Adaptive SuperTrend: A trend filter that uses volatility clustering to confirm if the market is currently in a "Bearish" state.
RSI (Relative Strength Index): A momentum gauge ensuring the asset is "Oversold" (exhausted) before we consider a setup.
Monthly Pivots: Horizontal support lines based on last month's data acting as price "floors" (S1, S2, S3).
SMA (Simple Moving Average): A 100-bar average ensuring we are strictly buying below the long-term mean (deep value).
BackQuant (KNN): A Machine Learning engine that compares current data to historical patterns to predict immediate momentum.
This is a sophisticated, multi-stage strategy script. It combines "Old School" price action (Renko) with "New School" Machine Learning (KNN and Clustering).
Here is the high-level summary of how we will break this down:
Topic 1: The "Bottom Hunter" Setup. How the script uses Renko bricks and aggressive filtering (SuperTrend, SMA, RSI, Pivots) to find a potential market bottom.
Topic 2: The ML Engine (BackQuant & SuperTrend). How the script uses K-Nearest Neighbors (KNN) to predict momentum and Volatility Clustering to adjust the SuperTrend.
Topic 3: The "Leap" Execution. How the script synchronizes the Setup (Topic 1) with the ML Trigger (Topic 2) using a time window.
Topic 1: The "Bottom Hunter" Setup
This script is designed as a Mean Reversion strategy (often called "catching a falling knife" or "bottom fishing"). It is trying to find the exact moment a downtrend stops and reverses.
Most strategies buy when price is above the 200 SMA or above the SuperTrend. This script does the exact opposite.
The Logic:
Renko Bricks: It simulates Renko bricks internally (without changing your chart view). It waits for a specific pattern: A Red Brick followed immediately by a Green Brick (a reversal).
The "Bearish" Filters: To generate a "WATCH" signal, the following must be true:
Price < SuperTrend: The market must officially be in a downtrend.
Price < SMA: Long-term trend is down.
Price < Monthly Pivot: Price is deeply discounted.
RSI < Threshold: The asset is oversold (exhausted).
Recommended Settings for daily signals for Stocks :
Confirmation : 10. (How many bars after Renko Buy signal the AI has to identify a bullish move).
Percentage : 2 (This is the Renko bar size. This represents 2% move.)
SMA: 100 (Signal must be found below 100 SMA)
Price must be below: PIVOT (This is the monthly Pivot levels)
A.I. 👑 Optimus Prime [RubiXalgo]A.I. OPTIMUS PRIME — RUBIK’S ALGO EDITION (2025)
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
Imagine a Rubik’s Cube spinning inside another Rubik’s Cube.
The outer cube = Supply / Demand structure
The inner cube = Trend / xTrend (price + volume momentum)
While speed-cubers solve cubes blindfolded and while juggling,
the tiny hand movements they make are eerily similar to real market microstructure.
This indicator tries to visualize that analogy using heavy Kalman filtering,
k-Nearest-Neighbors regression, LOWESS smoothing, dynamic volume delta,
and machine-learning-driven color gradients — all wrapped in a clean visual language.
Features
• Dual Kalman “Rubik” trend lines (fast + slow) with adaptive noise models
• AI candle coloring (optional) using trend-angle + momentum gradients
• Dynamic Linear Regression Volume Profile (slanted VPOC channel)
• Volume Profit-Trend polyline (walk-forward volume delta prediction)
• Liquidation / Target window with automatic stop-loss & 3 take-profit levels
• Up to 5 multi-timeframe moving averages (SMA/DEMA/TEMA/VWMA) + trend table
• All calculations use dynamic scaling (VSQC lookback) so the same settings stay relevant
across timeframes and assets
How to trade it (simple version)
• Green fast + slow line → bullish bias
• Red fast + slow line → bearish bias
• Green liquidation window + green volume polyline → high-probability long setup
• Red liquidation window + red volume polyline → high-probability short setup
• Targets are drawn automatically — aim for Target 2 or 3 (3:1+ RR typical)
Educational note
This script is shared for learning and experimentation purposes only.
It is not financial advice. Trading involves risk. Test thoroughly on demo before live use.
Credits & inspiration
Heavily inspired by Zeiierman, ChartPrime, LuxAlgo, BigBeluga, DeltaSeek,
and many open-source Pine coders. Special thanks to the entire TradingView community.
© 2025 StupidBitcoin — Open source under Mozilla Public License 2.0 + CC BY-NC-SA 4.0
Feel free to fork, improve, and share — just keep the credits.
↦ (Paste the full working code here — the one you already have, starting with string X7K9P = ... and ending with the last plot)
- Legal & fair-use footer (keeps it clean and TV-compliant)
Disclaimer
This script is published for educational purposes only.
It is not investment advice. Use at your own risk.
License
Mozilla Public License 2.0 — mozilla.org
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 — creativecommons.org
// Enjoy the cube.
// StupidBitcoin — 2025
A.I. 👑 Market Cipher EZ🚀 A.I. Market Cipher EZ – “Rubik’s Algo” 2025 Edition
by StupidBitcoin | Built with love & Grok’s help
Imagine a Rubik’s Cube that solves itself while the market moves — every twist and turn instantly reflected in color.
That’s exactly what this indicator does.
Two animated Rubik’s Cubes (Figure 1 & Figure 2) symbolize the dual-layer intelligence inside:
- The outer cube = Supply / Demand / Bull vs Bear forces
- The inner cube = Price / Volume / Trend (xTrend) constantly rotating to find equilibrium
The result? A living, breathing, self-adapting color language that removes noise, bias, and lag — turning complex market physics into simple visual signals even a beginner can trade confidently.
Core Engine (all running live):
• Multi-stage Kalman Filters (standard / volume-adjusted / Parkinson volatility modes)
• k-Nearest-Neighbour (k-NN) machine-learning clustering
• Dynamic VSQC scaling (the “fast Rubik”) + ultra-smooth slow Rubik
• Zero-lag Gaussian + Chebyshev filtering
• AI-driven Stochastic Money Flow % oscillator (3 % – 120 % range)
• Volume imbalance “Vector Recovery Zones” & momentum “Bounce Boxes”
• Real-time color gradients (Classic red/green or Crypto teal/purple themes)
What you actually see on the chart:
- Fast & Slow dynamic trend lines (the “speed lanes”) painted in intelligent gradients
- Stochastic Money Flow % label on every bar (green < 31 % = oversold rocket fuel | red > 69 % red = overbought rejection)
- Bollinger Width % label (optional)
- Vector Recovery Boxes (volume magnets)
- Bull/Bear Bounce Boxes (support & resistance with wick pressure)
- Market-structure squares below bars (green = bullish structure, red = bearish, yellow = neutral)
- Kalman Target marker on current bar (reduces fakeouts)
Top confirmed setups (3:1+ RR):
Longs → Green % label (< 31 %) + price on fast green line + green recovery/bounce box
Shorts → Red % label (> 69 %) + price on slow red line + red recovery/bounce box
Breakouts → Green % + fast line breakout + green structure squares
Breakdowns → Red % + slow line breakdown + red structure squares
All inputs are carefully preset with the developer’s recommended values (lookback 9 / max length 188 / accelerator 4.4 / k = 63) — just load and trade. Tweak only if you really know what you’re doing.
Disclaimer
For educational purposes only. Not financial advice. Use at your own risk. Past performance ≠ future results.
License
Released under CC BY-NC-SA 4.0 + Mozilla Public License 2.0 – free to use, study, modify and share non-commercially with attribution.
Enjoy the colors. May your trends be strong and your drawdowns short.
© 2025 Rubik’s Algo – All Rights Reserved
BTC Fear & Greed Incremental StrategyIMPORTANT: READ SETUP GUIDE BELOW OR IT WON'T WORK
# BTC Fear & Greed Incremental Strategy — TradeMaster AI (Pure BTC Stack)
## Strategy Overview
This advanced Bitcoin accumulation strategy is designed for long-term hodlers who want to systematically take profits during greed cycles and accumulate during fear periods, while preserving their core BTC position. Unlike traditional strategies that start with cash, this approach begins with a specified BTC allocation, making it perfect for existing Bitcoin holders who want to optimize their stack management.
## Key Features
### 🎯 **Pure BTC Stack Mode**
- Start with any amount of BTC (configurable)
- Strategy manages your existing stack, not new purchases
- Perfect for hodlers who want to optimize without timing markets
### 📊 **Fear & Greed Integration**
- Uses market sentiment data to drive buy/sell decisions
- Configurable thresholds for greed (selling) and fear (buying) triggers
- Automatic validation to ensure proper 0-100 scale data source
### 🐂 **Bull Year Optimization**
- Smart quarterly selling during bull market years (2017, 2021, 2025)
- Q1: 1% sells, Q2: 2% sells, Q3/Q4: 5% sells (configurable)
- **NO SELLING** during non-bull years - pure accumulation mode
- Preserves BTC during early bull phases, maximizes profits at peaks
### 🐻 **Bear Market Intelligence**
- Multi-regime detection: Bull, Early Bear, Deep Bear, Early Bull
- Different buying strategies based on market conditions
- Enhanced buying during deep bear markets with configurable multipliers
- Visual regime backgrounds for easy market condition identification
### 🛡️ **Risk Management**
- Minimum BTC allocation floor (prevents selling entire stack)
- Configurable position sizing for all trades
- Multiple safety checks and validation
### 📈 **Advanced Visualization**
- Clean 0-100 scale with 2 decimal precision
- Three main indicators: BTC Allocation %, Fear & Greed Index, BTC Holdings
- Real-time portfolio tracking with cash position display
- Enhanced info table showing all key metrics
## How to Use
### **Step 1: Setup**
1. Add the strategy to your BTC/USD chart (daily timeframe recommended)
2. **CRITICAL**: In settings, change the "Fear & Greed Source" from "close" to a proper 0-100 Fear & Greed indicator
---------------
I recommend Crypto Fear & Greed Index by TIA_Technology indicator
When selecting source with this indicator, look for "Crypto Fear and Greed Index:Index"
---------------
3. Set your "Starting BTC Quantity" to match your actual holdings
4. Configure your preferred "Start Date" (when you want the strategy to begin)
### **Step 2: Configure Bull Year Logic**
- Enable "Bull Year Logic" (default: enabled)
- Adjust quarterly sell percentages:
- Q1 (Jan-Mar): 1% (conservative early bull)
- Q2 (Apr-Jun): 2% (moderate mid bull)
- Q3/Q4 (Jul-Dec): 5% (aggressive peak targeting)
- Add future bull years to the list as needed
### **Step 3: Fine-tune Thresholds**
- **Greed Threshold**: 80 (sell when F&G > 80)
- **Fear Threshold**: 20 (buy when F&G < 20 in bull markets)
- **Deep Bear Fear Threshold**: 25 (enhanced buying in bear markets)
- Adjust based on your risk tolerance
### **Step 4: Risk Management**
- Set "Minimum BTC Allocation %" (default 20%) - prevents selling entire stack
- Configure sell/buy percentages based on your position size
- Enable bear market filters for enhanced timing
### **Step 5: Monitor Performance**
- **Orange Line**: Your BTC allocation percentage (target: fluctuate between 20-100%)
- **Blue Line**: Actual BTC holdings (should preserve core position)
- **Pink Line**: Fear & Greed Index (drives all decisions)
- **Table**: Real-time portfolio metrics including cash position
## Reading the Indicators
### **BTC Allocation Percentage (Orange Line)**
- **100%**: All portfolio in BTC, no cash available for buying
- **80%**: 80% BTC, 20% cash ready for fear buying
- **20%**: Minimum allocation, maximum cash position
### **Trading Signals**
- **Green Buy Signals**: Appear during fear periods with available cash
- **Red Sell Signals**: Appear during greed periods in bull years only
- **No Signals**: Either allocation limits reached or non-bull year
## Strategy Logic
### **Bull Years (2017, 2021, 2025)**
- Q1: Conservative 1% sells (preserve stack for later)
- Q2: Moderate 2% sells (gradual profit taking)
- Q3/Q4: Aggressive 5% sells (peak targeting)
- Fear buying active (accumulate on dips)
### **Non-Bull Years**
- **Zero selling** - pure accumulation mode
- Enhanced fear buying during bear markets
- Focus on rebuilding stack for next bull cycle
## Important Notes
- **This is not financial advice** - backtest thoroughly before use
- Designed for **long-term holders** (4+ year cycles)
- **Requires proper Fear & Greed data source** - validate in settings
- Best used on **daily timeframe** for major trend following
- **Cash calculations**: Use allocation % and BTC holdings to calculate available cash: `Cash = (Total Portfolio × (1 - Allocation%/100))`
## Risk Disclaimer
This strategy involves active trading and position management. Past performance does not guarantee future results. Always do your own research and never invest more than you can afford to lose. The strategy is designed for educational purposes and long-term Bitcoin accumulation thesis.
---
*Developed by Sol_Crypto for the Bitcoin community. Happy stacking! 🚀*
Sky Eye AI 體驗版至12/15體驗版至12/15
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Assisted with entry and exit point planning and position drawing to accelerate your learning.
You only need to know what this position represents before you can further study and analyze technical indicators.
To learn more, you can join us at DC
Sky Eye TRADE AI DC: discord.gg
輔助 規劃進出場 位子畫線 幫助你加速學習
只需要知道這個位子是甚麼在去加強研究 技術分析 即可
想學習更多可以到DC一起學習
DC: discord.gg
Assisted with entry and exit point planning and position drawing to accelerate your learning.
You only need to know what this position represents before you can further study and analyze technical indicators.
To learn more, you can join us at DC.
