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
Search in scripts for "ai"
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.
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^^ 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
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∞
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!
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)
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 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.
Vdubus Divergence Wave Pattern Generator V1The Vdubus Divergence Wave Theory
10 years in the making & now finally thanks to AI I have attempted to put my Trading strategy & logic into a visual representation of how I analyse and project market using Core price action & MacD. Enjoy :)
A Proprietary Structural & Momentum Confluence SystemPart 1: The Strategic Concept1. The Core Philosophy: "Geometry + Physics"Traditional technical analysis often fails because traders confuse location with timing.Geometry (Price Patterns): Tells us WHERE the market is likely to reverse (e.g., at a resistance level or harmonic D-point).Physics (Momentum): Tells us WHEN the energy driving the trend has actually shifted. The Vdubus Theory posits that a trade should never be taken based on Geometry alone. A valid signal requires a specific, fractal decay in momentum—a "Handshake" between price structure and energy exhaustion.2. The 3-Wave Momentum Filter (The Engine)Most traders look for simple divergence (2 points). The Vdubus Theory demands a 3-Wave Structure to confirm the true state of the market.A. The Standard Reversal (Exhaustion)This is the "Safe" entry, catching the slow death of a trend.Wave 1 $\rightarrow$ 2 (The Warning): Price pushes higher, but momentum is lower (Standard Divergence). This signals that the trend is tapping the brakes.Wave 2 $\rightarrow$ 3 (The Confirmation): Price pushes to a final extreme (often a stop-hunt), but momentum is flat or lower than Wave 2 ("No Divergence").The Logic: This confirms that the buyers have expended all remaining energy. The engine is dead.
B. The Climax Reversal (The Trap)This is the "Aggressive" entry, catching V-shape reversals.Wave 1 $\rightarrow$ 2 (The Bait): Price pushes higher, and momentum is Stronger/Higher (No Divergence). This sucks in retail traders who believe the trend is accelerating.Wave 2 $\rightarrow$ 3 (The Snap): Price pushes again, but momentum suddenly collapses (Divergence).The Logic: A "Strong to Weak" shift. The market traps traders with a show of strength before hitting a "concrete wall" of limit orders.C. The Predator (The Trend Continuation)The Logic: Trends rarely move in straight lines. The "Predator" looks for Hidden Divergence during a pullback.The Signal: Price makes a Higher Low (Trend Structure Intact), but Momentum makes a Lower Low (Oversold Trap). This signals the end of the correction and the resumption of the main trend.3. The "Clean Path" PrincipleA trade is only valid if there is no opposing force. If you are looking to Sell (Bearish Reversal), the opposing Bullish momentum must be weak or neutral. If the "Enemy" is strong, the trade is skipped.
Part 2: The Indicator Breakdown
Tool Name: Vdubus Divergence Wave Pattern Generator V1
This script automates your analysis by combining ZigZag Pattern Recognition (Geometry) with your Custom MACD Logic (Physics).
1. The "Golden" Settings
The physics engine is tuned to your specific discovery:
Fast Length: 8
Slow Length: 21
Signal Length: 5
Lookback: 3 (Sensitive enough to catch the exact pivot points).
2. Signal Generation Logic
The indicator scans for four distinct setups. Here is the exact logic code translated into English:
Signal 1: Standard Reversal (Green/Red Pattern)
Geometry: The ZigZag algorithm identifies a 5-point structure (X-A-B-C-D), such as a Gartley, Bat, or Butterfly.
Physics Check:
Finds the last 3 momentum peaks matching the price highs.
Rule: Momentum Peak 2 must be < Peak 1 (Divergence).
Rule: Momentum Peak 3 must be <= Peak 2 (Confirmation/No Div).
Output: Draws the colored pattern and labels it (e.g., "Bearish Gartley (Exhaustion)").
Signal 2: Climax Reversal (Orange Pattern)
Geometry: Identifies the same 5-point structures.
Physics Check:
Rule: Momentum Peak 2 is >= Peak 1 (Strength/No Div).
Rule: Momentum Peak 3 is < Peak 2 (Sudden Failure/Div).
Output: Draws the pattern in Orange labeled "⚠️ CLIMAX REVERSAL". This is your "Trap" detector.
Signal 3: Rounded Top/Bottom (Navy/Maroon Label)
Geometry: Price is compressing or rounding over.
Physics Check:
Scans for 4 consecutive waves of momentum decay.
Rule: Peak 1 > Peak 2 > Peak 3 > Peak 4.
Output: Places a label indicating a "Multi-Wave Decay," identifying turns that don't have sharp pivots.
Signal 4: The Predator (Purple Pattern)
Geometry: Identifies a trend pullback (Higher Low for Buys).
Physics Check:
Rule: Momentum makes a Lower Low while Price makes a Higher Low (Hidden Divergence).
Output: Draws a Purple pattern labeled "🦖 PREDATOR" to signal trend continuation.
3. The Confluence Dashboard
Located in the corner of the screen, this provides a final "Safety Check."
Logic: It compares the absolute value (strength) of the most recent Bearish Momentum Peak vs. the most recent Bullish Momentum Low.
Output:
Green (Bulls Strong): Buying pressure is dominant. Safe to Buy, Dangerous to Sell.
Red (Bears Strong): Selling pressure is dominant. Safe to Sell, Dangerous to Buy.
Grey (Neutral): Forces are balanced.
Summary of Potential
This system solves the "Trader's Dilemma" of entering too early or too late. By waiting for the 3rd Wave, you effectively filter out the market noise and only commit capital when the opposing side has structurally and physically collapsed. It transforms trading from a guessing game into a disciplined execution of identifying Geometric Exhaustion.
Logic 1 / PREVIOUS DIVERGENCE PROJECTS future TREND BREAKS / Reversals *Not in script*
Logic 2 / Wave 1 to 2 = Divergence / Wave 2 to 3 = NO divergence = Signal
Reverse logic: Wave 1 to 2 = NO Divergence / Wave 2 to 3 = Divergence = Signal
MACD Forecast Colorful [DiFlip]MACD Forecast Colorful
The Future of Predictive MACD — is one of the most advanced and customizable MACD indicators ever published on TradingView. Built on the classic MACD foundation, this upgraded version integrates statistical forecasting through linear regression to anticipate future movements — not just react to the past.
With a total of 22 fully configurable long and short entry conditions, visual enhancements, and full automation support, this indicator is designed for serious traders seeking an analytical edge.
⯁ Real-Time MACD Forecasting
For the first time, a public MACD script combines the classic structure of MACD with predictive analytics powered by linear regression. Instead of simply responding to current values, this tool projects the MACD line, signal line, and histogram n bars into the future, allowing you to trade with foresight rather than hindsight.
⯁ Fully Customizable
This indicator is built for flexibility. It includes 22 entry conditions, all of which are fully configurable. Each condition can be turned on/off, chained using AND/OR logic, and adapted to your trading model.
Whether you're building a rules-based quant system, automating alerts, or refining discretionary signals, MACD Forecast Colorful gives you full control over how signals are generated, displayed, and triggered.
⯁ With MACD Forecast Colorful, you can:
• Detect MACD crossovers before they happen.
• Anticipate trend reversals with greater precision.
• React earlier than traditional indicators.
• Gain a powerful edge in both discretionary and automated strategies.
• This isn’t just smarter MACD — it’s predictive momentum intelligence.
⯁ Scientifically Powered by Linear Regression
MACD Forecast Colorful is the first public MACD indicator to apply least-squares predictive modeling to MACD behavior — effectively introducing machine learning logic into a time-tested tool.
It uses statistical regression to analyze historical behavior of the MACD and project future trajectories. The result is a forward-shifted MACD forecast that can detect upcoming crossovers and divergences before they appear on the chart.
⯁ Linear Regression: Technical Foundation
Linear regression is a statistical method that models the relationship between a dependent variable (y) and one or more independent variables (x). The basic formula for simple linear regression is:
y = β₀ + β₁x + ε
Where:
y = predicted variable (e.g., future MACD value)
x = independent variable (e.g., bar index)
β₀ = intercept
β₁ = slope
ε = random error (residual)
The regression model calculates β₀ and β₁ using the least squares method, minimizing the sum of squared prediction errors to produce the best-fit line through historical values. This line is then extended forward, generating a forecast based on recent price momentum.
⯁ Least Squares Estimation
The regression coefficients are computed with the following formulas:
β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²)
β₀ = ȳ - β₁x̄
Where:
Σ denotes summation; x̄ and ȳ are the means of x and y; and i ranges from 1 to n (number of observations). These equations produce the best linear unbiased estimator under the Gauss–Markov assumptions — constant variance (homoscedasticity) and a linear relationship between variables.
⯁ Regression in Machine Learning
Linear regression is a foundational model in supervised learning. Its ability to provide precise, explainable, and fast forecasts makes it critical in AI systems and quantitative analysis.
Applying linear regression to MACD forecasting is the equivalent of injecting artificial intelligence into one of the most widely used momentum tools in trading.
⯁ Visual Interpretation
Picture the MACD values over time like this:
Time →
MACD →
A regression line is fitted to recent MACD values, then projected forward n periods. The result is a predictive trajectory that can cross over the real MACD or signal line — offering an early-warning system for trend shifts and momentum changes.
The indicator plots both current MACD and forecasted MACD, allowing you to visually compare short-term future behavior against historical movement.
⯁ Scientific Concepts Used
Linear Regression: models the relationship between variables using a straight line.
Least Squares Method: minimizes squared prediction errors for best-fit.
Time-Series Forecasting: projects future data based on past patterns.
Supervised Learning: predictive modeling using labeled inputs.
Statistical Smoothing: filters noise to highlight trends.
⯁ Why This Indicator Is Revolutionary
First open-source MACD with real-time predictive modeling.
Scientifically grounded with linear regression logic.
Automatable through TradingView alerts and bots.
Smart signal generation using forecasted crossovers.
Highly customizable with 22 buy/sell conditions.
Enhanced visuals with background (bgcolor) and area fill (fill) support.
This isn’t just an update — it’s the next evolution of MACD forecasting.
⯁ Example of simple linear regression with one independent variable
This example demonstrates how a basic linear regression works when there is only one independent variable influencing the dependent variable. This type of model is used to identify a direct relationship between two variables.
⯁ In linear regression, observations (red) are considered the result of random deviations (green) from an underlying relationship (blue) between a dependent variable (y) and an independent variable (x)
This concept illustrates that sampled data points rarely align perfectly with the true trend line. Instead, each observed point represents the combination of the true underlying relationship and a random error component.
⯁ Visualizing heteroscedasticity in a scatterplot with 100 random fitted values using Matlab
Heteroscedasticity occurs when the variance of the errors is not constant across the range of fitted values. This visualization highlights how the spread of data can change unpredictably, which is an important factor in evaluating the validity of regression models.
⯁ The datasets in Anscombe’s quartet were designed to have nearly the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but look very different when plotted
This classic example shows that summary statistics alone can be misleading. Even with identical numerical metrics, the datasets display completely different patterns, emphasizing the importance of visual inspection when interpreting a model.
⯁ Result of fitting a set of data points with a quadratic function
This example illustrates how a second-degree polynomial model can better fit certain datasets that do not follow a linear trend. The resulting curve reflects the true shape of the data more accurately than a straight line.
⯁ What is the MACD?
The Moving Average Convergence Divergence (MACD) is a technical analysis indicator developed by Gerald Appel. It measures the relationship between two moving averages of a security’s price to identify changes in momentum, direction, and strength of a trend. The MACD is composed of three components: the MACD line, the signal line, and the histogram.
⯁ How to use the MACD?
The MACD is calculated by subtracting the 26-period Exponential Moving Average (EMA) from the 12-period EMA. A 9-period EMA of the MACD line, called the signal line, is then plotted on top of the MACD line. The MACD histogram represents the difference between the MACD line and the signal line.
Here are the primary signals generated by the MACD:
• Bullish Crossover: When the MACD line crosses above the signal line, indicating a potential buy signal.
• Bearish Crossover: When the MACD line crosses below the signal line, indicating a potential sell signal.
• Divergence: When the price of the security diverges from the MACD, suggesting a potential reversal.
• Overbought/Oversold Conditions: Indicated by the MACD line moving far away from the signal line, though this is less common than in oscillators like the RSI.
⯁ How to use MACD forecast?
The MACD Forecast is built on the same foundation as the classic MACD, but with predictive capabilities.
Step 1 — Spot Predicted Crossovers:
Watch for forecasted bullish or bearish crossovers. These signals anticipate when the MACD line will cross the signal line in the future, letting you prepare trades before the move.
Step 2 — Confirm with Histogram Projection:
Use the projected histogram to validate momentum direction. A rising histogram signals strengthening bullish momentum, while a falling projection points to weakening or bearish conditions.
Step 3 — Combine with Multi-Timeframe Analysis:
Use forecasts across multiple timeframes to confirm signal strength (e.g., a 1h forecast aligned with a 4h forecast).
Step 4 — Set Entry Conditions & Automation:
Customize your buy/sell rules with the 20 forecast-based conditions and enable automation for bots or alerts.
Step 5 — Trade Ahead of the Market:
By preparing for future momentum shifts instead of reacting to the past, you’ll always stay one step ahead of lagging traders.
📈 BUY
🍟 Signal Validity: The signal will remain valid for X bars.
🍟 Signal Sequence: Configurable as AND or OR.
🍟 MACD > Signal Smoothing
🍟 MACD < Signal Smoothing
🍟 Histogram > 0
🍟 Histogram < 0
🍟 Histogram Positive
🍟 Histogram Negative
🍟 MACD > 0
🍟 MACD < 0
🍟 Signal > 0
🍟 Signal < 0
🍟 MACD > Histogram
🍟 MACD < Histogram
🍟 Signal > Histogram
🍟 Signal < Histogram
🍟 MACD (Crossover) Signal
🍟 MACD (Crossunder) Signal
🍟 MACD (Crossover) 0
🍟 MACD (Crossunder) 0
🍟 Signal (Crossover) 0
🍟 Signal (Crossunder) 0
🔮 MACD (Crossover) Signal Forecast
🔮 MACD (Crossunder) Signal Forecast
📉 SELL
🍟 Signal Validity: The signal will remain valid for X bars.
🍟 Signal Sequence: Configurable as AND or OR.
🍟 MACD > Signal Smoothing
🍟 MACD < Signal Smoothing
🍟 Histogram > 0
🍟 Histogram < 0
🍟 Histogram Positive
🍟 Histogram Negative
🍟 MACD > 0
🍟 MACD < 0
🍟 Signal > 0
🍟 Signal < 0
🍟 MACD > Histogram
🍟 MACD < Histogram
🍟 Signal > Histogram
🍟 Signal < Histogram
🍟 MACD (Crossover) Signal
🍟 MACD (Crossunder) Signal
🍟 MACD (Crossover) 0
🍟 MACD (Crossunder) 0
🍟 Signal (Crossover) 0
🍟 Signal (Crossunder) 0
🔮 MACD (Crossover) Signal Forecast
🔮 MACD (Crossunder) Signal Forecast
🤖 Automation
All BUY and SELL conditions can be automated using TradingView alerts. Every configurable condition can trigger alerts suitable for fully automated or semi-automated strategies.
⯁ Unique Features
Linear Regression: (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Table of Conditions: BUY/SELL
Conditions Label: BUY/SELL
Plot Labels in the graph above: BUY/SELL
Automate & Monitor Signals/Alerts: BUY/SELL
Background Colors: "bgcolor"
Background Colors: "fill"
Linear Regression (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Table of Conditions: BUY/SELL
Conditions Label: BUY/SELL
Plot Labels in the graph above: BUY/SELL
Automate & Monitor Signals/Alerts: BUY/SELL
Background Colors: "bgcolor"
Background Colors: "fill"
YaS-IN Multi-Timeframe RSI AnalyzerYAS-IN MULTI-TIMEFRAME RSI ANALYZER
📊 OVERVIEW
YaS-IN (Yield and Signal Indicator) is an advanced RSI-based trading tool that analyzes multiple timeframe RSI data (14, 25, 100 periods) to identify 5 key market scenarios with confirmation from volume, MACD, and ATR indicators.
🎯 KEY FEATURES
1. MULTI-TIMEFRAME RSI ANALYSIS
RSI 14: Short-term momentum
RSI 25: Medium-term trend
RSI 100: Long-term structural trend
2. 5 MARKET SCENARIOS
Trend Start (New trend confirmation)
Trend Continuation (Healthy uptrend)
Trend End (Overbought, reversal imminent)
Dip Buy Opportunity (Oversold, bounce expected)
Structural Turn (Major trend change)
3. CONFIRMATION SYSTEM
Volume: Above/below average confirmation
MACD: Momentum and crossover confirmation
ATR: Volatility confirmation
4. VISUAL TABLE DISPLAY
Real-time color-coded table showing:
Current RSI values
Active scenarios
Confirmation status
Scenario colors
🔧 HOW IT WORKS
SCENARIO DETECTION
The indicator analyzes RSI values against predefined thresholds to identify which market scenario is currently active.
CONFIRMATION STATUS
Each scenario is validated against three confirmation indicators:
✅ CONFIRMED: 2+ indicators confirm
🔶 PARTIAL: 1 indicator confirms
⚠️ WARNING: 1 indicator contradicts
⚠️ DIVERGENT: 2+ indicators contradict
➖ NEUTRAL: No clear signal
TABLE COLORS
Green: Active bullish scenario
Blue: Active continuation scenario
Red: Active bearish scenario
Orange: Active dip buy scenario
Purple: Active structural turn
Gray: Inactive scenario
⚙️ CUSTOMIZATION OPTIONS
1. RSI PERIODS
Adjust RSI calculation periods (14, 25, 100 default)
2. CONFIRMATION INDICATORS
Toggle Volume/MACD/ATR confirmation on/off
Adjust volume threshold multiplier
Set ATR change percentage
3. TABLE SETTINGS
Position: 6 different screen positions
Size: Small/Medium/Large text
Colors: Custom text and background
Opacity: Background transparency
4. VISUAL OPTIONS
Show/hide chart label
Customize text colors
Adjust table transparency
📈 OPTIMAL TIMEFRAMES
BEST PERFORMANCE
1-Hour: Optimal balance for most traders
4-Hour: Excellent for swing trading
Daily: Good for position trading
GOOD PERFORMANCE
30-Minute: Short-term swing trading
15-Minute: Precise entry timing
Weekly: Long-term analysis
NOT RECOMMENDED
1-5 Minute: Too much noise
Monthly: Too slow for active trading
🎮 USAGE GUIDE
FOR BEGINNERS
Add indicator to 4-hour chart
Watch table for 1-2 days
Trade only "✅ CONFIRMED" scenarios
Use 1-hour chart for entry confirmation
FOR INTERMEDIATE TRADERS
Use multi-timeframe analysis:
4-hour: Main trend direction
1-hour: Confirmation signals
30-minute: Entry timing
Look for scenario consistency across timeframes
Use divergence warnings for risk management
FOR ADVANCED TRADERS
Combine with other technical analysis
Adjust parameters for specific markets
Use alerts for automated notifications
Backtest different parameter combinations
📊 INTERPRETING RESULTS
STRONG SIGNALS
Multiple "✅ CONFIRMED" scenarios
Consistent signals across timeframes
High volume + MACD confirmation
WEAK SIGNALS
"🔶 PARTIAL" or "➖ NEUTRAL" status
Contradictory indicators
Low volume during signals
WARNING SIGNALS
"⚠️ WARNING" or "⚠️ DIVERGENT" status
Indicator divergence
ATR showing low volatility during moves
🔔 ALERT SYSTEM
4 TYPES OF ALERTS
Divergence Detected: Indicators contradict scenarios
Strong Confirmation: Multiple indicators confirm
Confirmed Trend End: Trend reversal with confirmation
Confirmed Dip Buy: Oversold bounce with confirmation
💡 TRADING STRATEGIES
TREND FOLLOWING
Enter on "Trend Start ✅ CONFIRMED"
Add on "Trend Continuation ✅ CONFIRMED"
Exit on "Trend End ✅ CONFIRMED"
MEAN REVERSION
Enter on "Dip Buy ✅ CONFIRMED"
Exit on RSI returning to normal levels
Use ATR for stop loss placement
BREAKOUT TRADING
Watch for "Structural Turn ✅ CONFIRMED"
Enter on confirmation of new trend
Use volume confirmation for validity
⚠️ RISK MANAGEMENT
POSITION SIZING
"✅ CONFIRMED": Full position
"🔶 PARTIAL": Half position
"⚠️ WARNING": Quarter position or avoid
"⚠️ DIVERGENT": No position
STOP LOSS SUGGESTIONS
Based on ATR value (2x ATR recommended)
Adjust for timeframe (tighter on lower TFs)
Consider scenario type (wider for structural turns)
📚 EDUCATIONAL VALUE
LEARN MARKET CYCLES
Understand different market phases
Recognize trend transitions
Identify overbought/oversold conditions
IMPROVE TIMING
Better entry/exit points
Reduced false signals
Improved risk/reward ratios
🚀 BENEFITS
Clear Visualization: All data in one table
Multi-Indicator Confirmation: Reduces false signals
Customizable: Adapt to any trading style
Educational: Helps understand market dynamics
Versatile: Works across multiple timeframes
📝 PUBLISHING NOTES
When publishing this indicator:
Name: YaS-IN Multi-Timeframe RSI Analyzer
Category: Momentum/Volume Indicators
Access Type: Open Source
Tags: RSI, Multi-Timeframe, Volume, MACD, ATR, Scanner
Description: Include this complete documentation
Preview Images: Show table on different charts
Video Tutorial: Demonstrate multi-timeframe usage
🔄 UPDATES & SUPPORT
For updates, improvements, or support:
Check TradingView script page
Join community discussions
Share backtest results
Suggest new features
Happy Trading with YaS-IN! 🚀
This response is AI-generated, for reference only.
Intraday Fibonacci Retracement Golden pocket for scalping# Intraday Fibonacci Retracement Golden pocket for scalping
## Overview
This advanced Pine Script indicator provides dynamic Fibonacci retracement levels specifically designed for intraday trading. Using proprietary AI-powered algorithms, the script automatically identifies optimal high and low reference points to generate precise Fibonacci levels that adapt in real-time throughout the trading day.
## Key Features
### 🎯 Dynamic Level Generation
- **Intelligent Auto-Detection**: Advanced algorithm automatically identifies key price levels using machine learning-based pattern recognition
- **Real-Time Updates**: Fibonacci levels dynamically adjust as new highs or lows are established during the session
- **Seven Core Levels**: 0% (LOD), 23.6%, 38.2%, 50%, 61.8%, 78.6%, and 100% (HOD)
### 📊 Visual Customization
- **Individual Level Control**: Show or hide any Fibonacci level independently
- **Custom Color Schemes**: Assign unique colors to each retracement level for easy identification
- **Adjustable Line Width**: Choose line thickness from 1-5 pixels for optimal chart clarity
- **Professional Labeling**: Each level displays both percentage and exact price value
### 🏆 Golden Zone Highlighting
- **Automated Zone Detection**: Automatically highlights the critical 50%-61.8% retracement zone
- **Visual Emphasis**: Shaded area between these key levels for quick visual reference
- **Customizable Transparency**: Adjust the golden zone color and opacity to match your chart theme
### 🔧 Flexible Configuration Options
#### Label Management
- **Master Toggle**: Instantly show or hide all labels with a single switch
- **Individual Label Control**: Selective visibility for each Fibonacci level label
- **Custom Label Colors**: Choose distinct colors for each label to match your trading style
- **Price Display Format**: Labels show percentage and corresponding price level
#### Level Visibility
Independent toggles for each retracement level:
- 0% (Low of Day)
- 23.6% Retracement
- 38.2% Retracement
- 50% Retracement (Midpoint)
- 61.8% Retracement (Golden Ratio)
- 78.6% Retracement
- 100% (High of Day)
### 📈 Trading Applications
**Support & Resistance**
- Identify potential reversal zones
- Spot key support and resistance levels
- Plan entry and exit points
**Price Targets**
- Set realistic profit targets based on Fibonacci extensions
- Identify potential pullback levels in trending markets
**Risk Management**
- Place stop losses at strategic Fibonacci levels
- Calculate risk-to-reward ratios using multiple levels
**Golden Zone Strategy**
- Focus on the 50%-61.8% zone for high-probability trade setups
- The golden ratio area often acts as a strong confluence zone
### 🔔 Built-in Alert System
Pre-configured alert conditions for critical price level crossings:
- 38.2% level cross
- 50% level cross (equilibrium)
- 61.8% level cross (golden ratio)
### 💡 Best Practices
**Optimal Usage**
- Works on all intraday timeframes (1min, 5min, 15min, 30min, 1hour)
- Most effective during active trading sessions
- Combine with volume analysis for confirmation
- Use alongside other technical indicators for confluence
**Chart Setup Tips**
- Adjust colors to ensure levels are visible against your chart background
- Use thicker lines on higher timeframes for better visibility
- Enable only the levels most relevant to your trading strategy
- Customize label colors to differentiate between key levels quickly
## Technical Specifications
**Performance Features**
- Maximum 500 lines supported for extensive historical analysis
- Maximum 500 labels for comprehensive price level identification
- Optimized calculations for minimal chart lag
- Real-time updates with every price tick
**Compatibility**
- Pine Script Version 6
- Compatible with all TradingView chart types
- Works across all markets (Stocks, Forex, Crypto, Futures, Options)
- Supports all timeframes from 1-minute to daily
## Installation & Setup
1. Copy the script code into TradingView Pine Editor
2. Click "Add to Chart" to apply the indicator
3. Access settings via the indicator's gear icon
4. Customize colors, labels, and visibility options to your preference
5. Save your configuration as a default template for future use
## Advanced Configuration
**For Clean Charts**
- Disable labels for a minimalist view
- Show only 50% and 61.8% levels for focused trading
- Use muted colors with higher transparency
**For Detailed Analysis**
- Enable all levels and labels
- Use high-contrast colors for each level
- Increase line width for emphasis
**For Specific Strategies**
- Mean reversion traders: Focus on 38.2%, 50%, 61.8%
- Breakout traders: Monitor 0% and 100% levels closely
- Scalpers: Use golden zone exclusively with tight stops
## Algorithm Intelligence
The indicator employs sophisticated algorithms to:
- Automatically calculate optimal reference points
- Adapt to changing market conditions
- Filter out noise and false signals
- Provide consistent, reliable level placement
This ensures that traders receive accurate, actionable Fibonacci levels without manual intervention or subjective placement decisions.
🎁 Free Trial Access
Interested in trying this indicator?
I'm offering a ONE MONTH FREE TRIAL to help you experience the power of dynamic Fibonacci levels in your trading.
To request your trial access:
Send me a Direct Message (DM) on TradingView
Include "Fib Trial Request" in your message
I'll respond with access instructions within 24 hours
This trial includes:
✅ Full access to all indicator features
✅ All customization options unlocked
✅ Priority support during trial period
✅ Setup assistance and configuration help
Don't miss this opportunity to enhance your intraday trading with professional-grade Fibonacci analysis!
📞 Author's Notes
For questions, feedback, or trial access requests, feel free to reach out via DM. I'm committed to helping traders succeed and continuously improving this tool based on user feedback.
Happy Trading!
---
**Disclaimer**: This indicator is a technical analysis tool. Past performance does not guarantee future results. Always use proper risk management and combine with other forms of analysis for trading decisions.
Jericho AI ScalperThis indicator is designed for use on Nifty and Sensex Options 1-minute chart.
A trade entry is valid only if the very next candle breaks above the high of the signal candle.
If the next candle fails to break that high, the setup becomes invalid and no trade should be taken.
Based on historical observations, a 1:1 risk-reward ratio is recommended; however, market conditions can change, and results may vary.
This indicator is intended strictly for educational and research purposes, helping traders understand market structure and candle-based momentum behavior.
It does not offer financial advice or guarantee profits. Please conduct your own analysis and consult a licensed financial professional when required.
ADX Forecast Colorful [DiFlip]ADX Forecast Colorful
Introducing one of the most advanced ADX indicators available — a fully customizable analytical tool that integrates forward-looking forecasting capabilities. ADX Forecast Colorful is a scientific evolution of the classic ADX, designed to anticipate future trend strength using linear regression. Instead of merely reacting to historical data, this indicator projects the future behavior of the ADX, giving traders a strategic edge in trend analysis.
⯁ Real-Time ADX Forecasting
For the first time, a public ADX indicator incorporates linear regression (least squares method) to forecast the future behavior of ADX. This breakthrough approach enables traders to anticipate trend strength changes based on historical momentum. By applying linear regression to the ADX, the indicator plots a projected trendline n periods ahead — helping users make more accurate and timely trading decisions.
⯁ Highly Customizable
The indicator adapts seamlessly to any trading style. It offers a total of 26 long entry conditions and 26 short entry conditions, making it one of the most configurable ADX tools on TradingView. Each condition is fully adjustable, enabling the creation of statistical, quantitative, and automated strategies. You maintain full control over the signals to align perfectly with your system.
⯁ Innovative and Science-Based
This is the first public ADX indicator to apply least-squares predictive modeling to ADX dynamics. Technically, it embeds machine learning logic into a traditional trend-strength indicator. Using linear regression as a predictive engine adds powerful statistical rigor to the ADX, turning it into an intelligent, forward-looking signal generator.
⯁ Scientific Foundation: Linear Regression
Linear regression is a fundamental method in statistics and machine learning used to model the relationship between a dependent variable y and one or more independent variables x. The basic formula for simple linear regression is:
y = β₀ + β₁x + ε
Where:
y = predicted value (e.g., future ADX)
x = explanatory variable (e.g., bar index or time)
β₀ = intercept
β₁ = slope (rate of change)
ε = random error term
The goal is to estimate β₀ and β₁ by minimizing the sum of squared errors. This is achieved using the least squares method, ensuring the best linear fit to historical data. Once the coefficients are calculated, the model extends the regression line forward, generating the ADX projection based on recent trends.
⯁ Least Squares Estimation
To minimize the error, the regression coefficients are calculated as:
β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²)
β₀ = ȳ - β₁x̄
Where:
Σ = summation
x̄ and ȳ = means of x and y
i ranges from 1 to n (number of data points)
These formulas provide the best linear unbiased estimator under Gauss-Markov conditions — assuming constant variance and linearity.
⯁ Linear Regression in Machine Learning
Linear regression is a foundational algorithm in supervised learning. Its power in producing quantitative predictions makes it essential in AI systems, predictive analytics, time-series forecasting, and automated trading. Applying it to the ADX essentially places an intelligent forecasting engine inside a classic trend tool.
⯁ Visual Interpretation
Imagine an ADX time series like this:
Time →
ADX →
The regression line smooths these values and projects them n periods forward, creating a predictive trajectory. This forecasted ADX line can intersect with the actual ADX, offering smarter buy and sell signals.
⯁ Summary of Scientific Concepts
Linear Regression: Models variable relationships with a straight line.
Least Squares: Minimizes prediction errors for best fit.
Time-Series Forecasting: Predicts future values using historical data.
Supervised Learning: Trains models to predict outcomes from inputs.
Statistical Smoothing: Reduces noise and highlights underlying trends.
⯁ Why This Indicator Is Revolutionary
Scientifically grounded: Based on rigorous statistical theory.
Unprecedented: First public ADX using least-squares forecast modeling.
Smart: Uses machine learning logic.
Forward-Looking: Generates predictive, not just reactive, signals.
Customizable: Flexible for any strategy or timeframe.
⯁ Conclusion
By merging ADX and linear regression, this indicator enables traders to predict market momentum rather than merely follow it. ADX Forecast Colorful is not just another indicator — it’s a scientific leap forward in technical analysis. With 26 fully configurable entry conditions and smart forecasting, this open-source tool is built for creating cutting-edge quantitative strategies.
⯁ Example of simple linear regression with one independent variable
This example demonstrates how a basic linear regression works when there is only one independent variable influencing the dependent variable. This type of model is used to identify a direct relationship between two variables.
⯁ In linear regression, observations (red) are considered the result of random deviations (green) from an underlying relationship (blue) between a dependent variable (y) and an independent variable (x)
This concept illustrates that sampled data points rarely align perfectly with the true trend line. Instead, each observed point represents the combination of the true underlying relationship and a random error component.
⯁ Visualizing heteroscedasticity in a scatterplot with 100 random fitted values using Matlab
Heteroscedasticity occurs when the variance of the errors is not constant across the range of fitted values. This visualization highlights how the spread of data can change unpredictably, which is an important factor in evaluating the validity of regression models.
⯁ The datasets in Anscombe’s quartet were designed to have nearly the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but look very different when plotted
This classic example shows that summary statistics alone can be misleading. Even with identical numerical metrics, the datasets display completely different patterns, emphasizing the importance of visual inspection when interpreting a model.
⯁ Result of fitting a set of data points with a quadratic function
This example illustrates how a second-degree polynomial model can better fit certain datasets that do not follow a linear trend. The resulting curve reflects the true shape of the data more accurately than a straight line.
⯁ What is the ADX?
The Average Directional Index (ADX) is a technical analysis indicator developed by J. Welles Wilder. It measures the strength of a trend in a market, regardless of whether the trend is up or down.
The ADX is an integral part of the Directional Movement System, which also includes the Plus Directional Indicator (+DI) and the Minus Directional Indicator (-DI). By combining these components, the ADX provides a comprehensive view of market trend strength.
⯁ How to use the ADX?
The ADX is calculated based on the moving average of the price range expansion over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and has three main zones:
Strong Trend: When the ADX is above 25, indicating a strong trend.
Weak Trend: When the ADX is below 20, indicating a weak or non-existent trend.
Neutral Zone: Between 20 and 25, where the trend strength is unclear.
⯁ Entry Conditions
Each condition below is fully configurable and can be combined to build precise trading logic.
📈 BUY
🅰️ Signal Validity: The signal will remain valid for X bars .
🅰️ Signal Sequence: Configurable as AND or OR .
🅰️ +DI > -DI
🅰️ +DI < -DI
🅰️ +DI > ADX
🅰️ +DI < ADX
🅰️ -DI > ADX
🅰️ -DI < ADX
🅰️ ADX > Threshold
🅰️ ADX < Threshold
🅰️ +DI > Threshold
🅰️ +DI < Threshold
🅰️ -DI > Threshold
🅰️ -DI < Threshold
🅰️ +DI (Crossover) -DI
🅰️ +DI (Crossunder) -DI
🅰️ +DI (Crossover) ADX
🅰️ +DI (Crossunder) ADX
🅰️ +DI (Crossover) Threshold
🅰️ +DI (Crossunder) Threshold
🅰️ -DI (Crossover) ADX
🅰️ -DI (Crossunder) ADX
🅰️ -DI (Crossover) Threshold
🅰️ -DI (Crossunder) Threshold
🔮 +DI (Crossover) -DI Forecast
🔮 +DI (Crossunder) -DI Forecast
🔮 ADX (Crossover) +DI Forecast
🔮 ADX (Crossunder) +DI Forecast
📉 SELL
🅰️ Signal Validity: The signal will remain valid for X bars .
🅰️ Signal Sequence: Configurable as AND or OR .
🅰️ +DI > -DI
🅰️ +DI < -DI
🅰️ +DI > ADX
🅰️ +DI < ADX
🅰️ -DI > ADX
🅰️ -DI < ADX
🅰️ ADX > Threshold
🅰️ ADX < Threshold
🅰️ +DI > Threshold
🅰️ +DI < Threshold
🅰️ -DI > Threshold
🅰️ -DI < Threshold
🅰️ +DI (Crossover) -DI
🅰️ +DI (Crossunder) -DI
🅰️ +DI (Crossover) ADX
🅰️ +DI (Crossunder) ADX
🅰️ +DI (Crossover) Threshold
🅰️ +DI (Crossunder) Threshold
🅰️ -DI (Crossover) ADX
🅰️ -DI (Crossunder) ADX
🅰️ -DI (Crossover) Threshold
🅰️ -DI (Crossunder) Threshold
🔮 +DI (Crossover) -DI Forecast
🔮 +DI (Crossunder) -DI Forecast
🔮 ADX (Crossover) +DI Forecast
🔮 ADX (Crossunder) +DI Forecast
🤖 Automation
All BUY and SELL conditions are compatible with TradingView alerts, making them ideal for fully or semi-automated systems.
⯁ Unique Features
Linear Regression: (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Plot Labels in the Graph Above: BUY/SELL
Automate and Monitor Signals/Alerts: BUY/SELL
Background Colors: "bgcolor"
Background Colors: "fill"
Linear Regression (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Table of Conditions: BUY/SELL
Conditions Label: BUY/SELL
Plot Labels in the graph above: BUY/SELL
Automate & Monitor Signals/Alerts: BUY/SELL
Background Colors: "bgcolor"
Background Colors: "fill"
RSI Forecast Colorful [DiFlip]RSI Forecast Colorful
Introducing one of the most complete RSI indicators available — a highly customizable analytical tool that integrates advanced prediction capabilities. RSI Forecast Colorful is an evolution of the classic RSI, designed to anticipate potential future RSI movements using linear regression. Instead of simply reacting to historical data, this indicator provides a statistical projection of the RSI’s future behavior, offering a forward-looking view of market conditions.
⯁ Real-Time RSI Forecasting
For the first time, a public RSI indicator integrates linear regression (least squares method) to forecast the RSI’s future behavior. This innovative approach allows traders to anticipate market movements based on historical trends. By applying Linear Regression to the RSI, the indicator displays a projected trendline n periods ahead, helping traders make more informed buy or sell decisions.
⯁ Highly Customizable
The indicator is fully adaptable to any trading style. Dozens of parameters can be optimized to match your system. All 28 long and short entry conditions are selectable and configurable, allowing the construction of quantitative, statistical, and automated trading models. Full control over signals ensures precise alignment with your strategy.
⯁ Innovative and Science-Based
This is the first public RSI indicator to apply least-squares predictive modeling to RSI calculations. Technically, it incorporates machine-learning logic into a classic indicator. Using Linear Regression embeds strong statistical foundations into RSI forecasting, making this tool especially valuable for traders seeking quantitative and analytical advantages.
⯁ Scientific Foundation: Linear Regression
Linear regression is a fundamental statistical method that models the relationship between a dependent variable y and one or more independent variables x. The general formula for simple linear regression is:
y = β₀ + β₁x + ε
where:
y = predicted variable (e.g., future RSI value)
x = explanatory variable (e.g., bar index or time)
β₀ = intercept (value of y when x = 0)
β₁ = slope (rate of change of y relative to x)
ε = random error term
The goal is to estimate β₀ and β₁ by minimizing the sum of squared errors. This is achieved using the least squares method, ensuring the best linear fit to historical data. Once the coefficients are calculated, the model extends the regression line forward, generating the RSI projection based on recent trends.
⯁ Least Squares Estimation
To minimize the error between predicted and observed values, we use the formulas:
β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²)
β₀ = ȳ - β₁x̄
Σ denotes summation; x̄ and ȳ are the means of x and y; and i ranges from 1 to n (number of observations). These equations produce the best linear unbiased estimator under the Gauss–Markov assumptions — constant variance (homoscedasticity) and a linear relationship between variables.
⯁ Linear Regression in Machine Learning
Linear regression is a foundational component of supervised learning. Its simplicity and precision in numerical prediction make it essential in AI, predictive algorithms, and time-series forecasting. Applying regression to RSI is akin to embedding artificial intelligence inside a classic indicator, adding a new analytical dimension.
⯁ Visual Interpretation
Imagine a time series of RSI values like this:
Time →
RSI →
The regression line smooths these historical values and projects itself n periods forward, creating a predictive trajectory. This projected RSI line can cross the actual RSI, generating sophisticated entry and exit signals. In summary, the RSI Forecast Colorful indicator provides both the current RSI and the forecasted RSI, allowing comparison between past and future trend behavior.
⯁ Summary of Scientific Concepts Used
Linear Regression: Models relationships between variables using a straight line.
Least Squares: Minimizes squared prediction errors for optimal fit.
Time-Series Forecasting: Predicts future values from historical patterns.
Supervised Learning: Predictive modeling based on known output values.
Statistical Smoothing: Reduces noise to highlight underlying trends.
⯁ Why This Indicator Is Revolutionary
Scientifically grounded: Built on statistical and mathematical theory.
First of its kind: The first public RSI with least-squares predictive modeling.
Intelligent: Incorporates machine-learning logic into RSI interpretation.
Forward-looking: Generates predictive, not just reactive, signals.
Customizable: Exceptionally flexible for any strategic framework.
⯁ Conclusion
By combining RSI and linear regression, the RSI Forecast Colorful allows traders to predict market momentum rather than simply follow it. It's not just another indicator: it's a scientific advancement in technical analysis technology. Offering 28 configurable entry conditions and advanced signals, this open-source indicator paves the way for innovative quantitative systems.
⯁ Example of simple linear regression with one independent variable
This example demonstrates how a basic linear regression works when there is only one independent variable influencing the dependent variable. This type of model is used to identify a direct relationship between two variables.
⯁ In linear regression, observations (red) are considered the result of random deviations (green) from an underlying relationship (blue) between a dependent variable (y) and an independent variable (x)
This concept illustrates that sampled data points rarely align perfectly with the true trend line. Instead, each observed point represents the combination of the true underlying relationship and a random error component.
⯁ Visualizing heteroscedasticity in a scatterplot with 100 random fitted values using Matlab
Heteroscedasticity occurs when the variance of the errors is not constant across the range of fitted values. This visualization highlights how the spread of data can change unpredictably, which is an important factor in evaluating the validity of regression models.
⯁ The datasets in Anscombe’s quartet were designed to have nearly the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but look very different when plotted
This classic example shows that summary statistics alone can be misleading. Even with identical numerical metrics, the datasets display completely different patterns, emphasizing the importance of visual inspection when interpreting a model.
⯁ Result of fitting a set of data points with a quadratic function
This example illustrates how a second-degree polynomial model can better fit certain datasets that do not follow a linear trend. The resulting curve reflects the true shape of the data more accurately than a straight line.
⯁ What Is RSI?
The RSI (Relative Strength Index) is a technical indicator developed by J. Welles Wilder. It measures the velocity and magnitude of recent price movements to identify overbought and oversold conditions. The RSI ranges from 0 to 100 and is commonly used to identify potential reversals and evaluate trend strength.
⯁ How RSI Works
RSI is calculated from average gains and losses over a set period (commonly 14 bars) and plotted on a 0–100 scale. It consists of three key zones:
Overbought: RSI above 70 may signal an overbought market.
Oversold: RSI below 30 may signal an oversold market.
Neutral Zone: RSI between 30 and 70, indicating no extreme condition.
These zones help identify potential price reversals and confirm trend strength.
⯁ Entry Conditions
All conditions below are fully customizable and allow detailed control over entry signal creation.
📈 BUY
🧲 Signal Validity: Signal remains valid for X bars.
🧲 Signal Logic: Configurable using AND or OR.
🧲 RSI > Upper
🧲 RSI < Upper
🧲 RSI > Lower
🧲 RSI < Lower
🧲 RSI > Middle
🧲 RSI < Middle
🧲 RSI > MA
🧲 RSI < MA
🧲 MA > Upper
🧲 MA < Upper
🧲 MA > Lower
🧲 MA < Lower
🧲 RSI (Crossover) Upper
🧲 RSI (Crossunder) Upper
🧲 RSI (Crossover) Lower
🧲 RSI (Crossunder) Lower
🧲 RSI (Crossover) Middle
🧲 RSI (Crossunder) Middle
🧲 RSI (Crossover) MA
🧲 RSI (Crossunder) MA
🧲 MA (Crossover)Upper
🧲 MA (Crossunder)Upper
🧲 MA (Crossover) Lower
🧲 MA (Crossunder) Lower
🧲 RSI Bullish Divergence
🧲 RSI Bearish Divergence
🔮 RSI (Crossover) Forecast MA
🔮 RSI (Crossunder) Forecast MA
📉 SELL
🧲 Signal Validity: Signal remains valid for X bars.
🧲 Signal Logic: Configurable using AND or OR.
🧲 RSI > Upper
🧲 RSI < Upper
🧲 RSI > Lower
🧲 RSI < Lower
🧲 RSI > Middle
🧲 RSI < Middle
🧲 RSI > MA
🧲 RSI < MA
🧲 MA > Upper
🧲 MA < Upper
🧲 MA > Lower
🧲 MA < Lower
🧲 RSI (Crossover) Upper
🧲 RSI (Crossunder) Upper
🧲 RSI (Crossover) Lower
🧲 RSI (Crossunder) Lower
🧲 RSI (Crossover) Middle
🧲 RSI (Crossunder) Middle
🧲 RSI (Crossover) MA
🧲 RSI (Crossunder) MA
🧲 MA (Crossover)Upper
🧲 MA (Crossunder)Upper
🧲 MA (Crossover) Lower
🧲 MA (Crossunder) Lower
🧲 RSI Bullish Divergence
🧲 RSI Bearish Divergence
🔮 RSI (Crossover) Forecast MA
🔮 RSI (Crossunder) Forecast MA
🤖 Automation
All BUY and SELL conditions can be automated using TradingView alerts. Every configurable condition can trigger alerts suitable for fully automated or semi-automated strategies.
⯁ Unique Features
Linear Regression Forecast
Signal Validity: Keep signals active for X bars
Signal Logic: AND/OR configuration
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Chart Labels: BUY/SELL markers above price
Automation & Alerts: BUY/SELL
Background Colors: bgcolor
Fill Colors: fill
Linear Regression Forecast
Signal Validity: Keep signals active for X bars
Signal Logic: AND/OR configuration
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Chart Labels: BUY/SELL markers above price
Automation & Alerts: BUY/SELL
Background Colors: bgcolor
Fill Colors: fill
XAU DOMINION AI This script is a technical analysis tool that helps traders visualize market structure and signals.
It should be used with proper risk management.
This script does not guarantee accuracy or profit, and is only for educational use.
Buy/Sell Signals [WynTrader]Hello dear Friend
Here is a new version ( B-S_251121_wt ) of my Buy/Sell Signals indicator.
Some calculation updates and useful enhancements have been applied.
Concepts
This Buy/Sell Signals indicator generates Buy/Sell signals as accurately as possible, identifying trend changes. Compared to other tools that detect trend shifts, this one is simple, easy to use, and demonstrates its efficiency on its own.
- Its features are carefully designed to minimize false signals while ensuring optimal signal placement.
- The Table results allow you to quickly evaluate signal performance, both on their own and compared to a Buy & Hold strategy.
- The Table calculations are fully synchronized with the visible chart (WYSIWYG – What You See Is What You Get). You can also scroll the chart across different date ranges to see how a stock or product performs under various market conditions.
- Seeing Buy/Sell signals on a chart is appealing, but assessing their performance in a Table makes it even more convincing. And without running a full backtest, you can get a clear overview of overall performance immediately.
Features
This indicator generates Buy/Sell signals using:
- Fast and Slow Moving Averages (adjustable).
- Bollinger Bands (adjustable).
- Filters (optional, adjustable) to refine signals, including : Bollinger Bands Lookback Trend Filter; High-Low vs Candle Range Threshold %; Distance from Fast and Slow MAs Threshold %.
- Results are displayed in a Table on the chart, based on the currently visible start and end dates.
Functionality
- The indicator aims to confirm trend changes through timely Buy/Sell signals.
- It uses two Moving Averages and Bollinger Bands, combined with filters such as BB Lookback, -- The variable settings have been tested with a mix of manual and AI testing to find the optimal configuration. You can adjust the variables to suit your goals.
- The design is simple, with clear parameters and instant readability of Buy/Sell Signals on the chart and in the Table results, without complex interpretation needed.
- It works effectively by requiring both trend confirmation and volatility control management.
- Signals are timed to be as accurate as possible, avoiding futile weak or false ones.
- A Table shows the effectiveness of the signals on the current visible chart, providing immediate, realistic feedback. The Buy & Hold strategy results are also included for comparison with the Buy/Sell swing strategy. The Buy & Hold results start from the first Buy signal to ensure a fair comparison.
- Changing the parameters instantly updates the Table, giving a quick, at-a-glance performance check.
Caution
- No technical tool is perfect; it cannot predict disasters, wars, or the actions of large fund managers or short sellers.
- After testing thousands of TradingView indicators over 24 years, I’ve found none to be 100% accurate all the time.
- This Buy/Sell Signals indicator may outperform some others but is still not perfect.
So, just be aware, and don’t be fooled by this tool.
