Gabriel's Squeeze Momentum PRO📌 Gabriel’s Squeeze Momentum PRO
A full-spectrum market compression, momentum, and seasonality suite engineered for cycle-aware traders.
🚀 What Is It?
Gabriel’s Squeeze Momentum PRO is an advanced trading indicator that detects volatility compression, calculates adaptive momentum, and reveals hidden seasonal opportunities. It builds on and transcends the traditional SQZMOM by incorporating spectral filters (Ehlers/MESA), Goertzel transforms, Pivot reversal logic, and optional seasonality overlays based on rolling-year returns. The script adapts to all timeframes and asset classes—stocks, futures, crypto, and forex.
🔍 Key Modules
🔸 1. Dynamic Squeeze Detection (RAFA Framework)
Identifies 5 squeeze types: Wide (🟠), Normal (🔴), Narrow (🟡), Very Narrow (🟣), and Fired (🟢).
Uses adaptive Bollinger Band and Keltner Channel thresholds unique to each timeframe (15m to 1M).
BB multiplier is adjusted dynamically via Goertzel and RMS-volatility signals.
Comes with RAFA alerts: Ready (compression), Aim (Jurik trigger), and Fire (breakout).
🔸 2. Adaptive Momentum Engine
Core momentum line: Linear regression of mid-price deviation from SMA + highest/lowest mean.
Signal line: Jurik Moving Average (JMA) with adaptive phasing and power smoothing.
Multiple normalization modes:
Unbounded (raw)
Min-Max (0–100)
RSX-based (centered -50 * 2)
Standard Deviations (via Butterworth/EMA RMS)
Optional Directional Momentum Mode: highlights histogram slope/angle with four-tier color coding.
🔸 3. MESA-Based Dynamic Bands
Calculates dominant fast and slow cycles via Maximum Entropy Spectral Analysis.
Computes a composite cyclic memory and percentile-based overbought/oversold levels.
Enables dynamic OS/OB bands that adjust with the market rhythm.
🔸 4. Multi-Timeframe MA Ribbon
Fully customizable ribbon with 5 MA slots per timeframe.
Supports 10 MA types: SMA, EMA, WMA, VWMA, RMA, DEMA, TEMA, LSMA, KAMA, TRAMA.
Includes Symmetrical MA smoothing via ta.swma() for visual consistency across volatile markets.
Optional trend coloring and ribbon overlays.
🔸 5. Goertzel + RMS-Filtered ROC
Rate of change line for momentum differentials with scaling multiplier.
Option to use Goertzel frequency detection to dynamically adjust the adaptive length.
📈 Additional Features
🔹 Williams VIX Fix Integration
Includes both standard and Inverse WVF for top/bottom detection.
Highlights both Aggressive (AE) and Filtered (FE) entry/exit zones.
Alerts and optional OBV-based squeeze dots included.
Useful for spotting reversals, early volatility expansions, and sentiment shifts.
🔹 Grab Bar System
Inspired by Michael Covel's trend-following logic.
Colors bars based on EMA(34) or RMA(28) channels to visually identify entry zones.
Overlayed trend direction markers on bar close.
🔹 Reversal Signal Lines
Plots DM-style pivot projections on momentum crossovers with configurable MA length.
Color-coded bullish and bearish setups.
🧠 Seasonality Toolkit (Seasonax Mode)
📅 Year-Based Return Modeling
Aggregates historical price returns per calendar year.
Supports 4 independent lookback periods (e.g., 5y, 10y, 15y, 30y).
Automatically filters outliers via IQR method (customizable factor setting).
📉 Detrending Options
Choose from:
Off: Raw seasonal trend
Linear: Removes regression slope
MA: Removes centered moving average
🎯 Entry/Exit Highlights
Highlights the most bullish/bearish seasonal windows using rolling return ranges.
Labels best seasonal entry and exit points on the chart.
🧰 Visual Grid & Legend
Clean grid overlay with monthly divisions.
Inline legend with custom line styles, sizes, and colors for each year set.
⚙️ Customization Highlights
Feature Options / Notes
Normalization Unbounded, Min-Max, RSX, Standard Deviation
MA Ribbon Enable/disable, Symmetry smoothing, full color & type customization
Momentum Direction Mode Directional histogram vs. baseline coloring
Reversal Logic Toggle per timeframe with custom JMA length
Cauchy Smoothing Gamma adjustable (0.1–6), optionally volume-weighted
Goertzel Filtering For adaptive momentum length and rate of change signal scaling
Timeframe Logic Fully adapts thresholds, lengths, and styles based on current chart timeframe
Seasonality Mode Custom lookbacks, overlays, trend removal, best/worst windows
📊 Alerts Included
🔔 Momentum Crossovers: Bullish/Bearish Reversals
🔔 Squeeze States: Wide, Normal, Narrow, Very Narrow, and Fired
🔔 WVF Events: Raw, Aggressive, Filtered, Inverted (Top Detection)
🔔 New Month + EOM Warnings: Seasonality-aware shift alerts
✅ Use Cases
Use Case How It Helps
🔹 Squeeze Breakout Trader Detects compression zones and high-probability breakouts
🔹 Cycle-Based Swing Trader Uses MESA filters + band dynamics to time pullbacks and mean reversion
🔹 Volatility Strategist Tracks multi-tier squeeze states across intraday to monthly charts
🔹 Seasonal Analyst Highlights best/worst periods using historical seasonality and anomaly logic
🔹 Reversal Sniper Uses signal cross + DM-pivots for precise reversal line placement
🎓 Advanced Math Behind It
Spectral Analysis: MESA (John Ehlers), Goertzel Transform
High/Low-Pass Filtering: 2-pole Butterworth + Super Smoother
Momentum Deviation: Linear regression + SMA + Cauchy-weighted midlines
Cyclic Band Percentiles: Rolling histograms, percentile mapping
Seasonal Aggregation: Rolling years + IQR outlier pruning
Volatility Proxy: RMS + adaptive deviation = signal-agnostic band precision
Bayesianprobability
Gabriel's Squeeze Momentum PRO“Gabriel’s Squeeze Momentum PRO” is a next-generation evolution of the classic SQZMOM concept. It layers multiple John Ehlers filters, Jurik smoothing, adaptive cycle-detection, and a Cauchy-weighted price filter on top of the familiar Bollinger-Band-inside-Keltner-Channel squeeze logic. The goal is to pinpoint volatility contractions and immediately gauge whether forthcoming expansion is likely to break bullish or bearish—while screening out noise, lag, and regime shifts across any symbol or timeframe.
1 · What the script plots
Plot What it represents Why it matters
Momentum line (teal/red) Price-de-trended linear-regression of a Cauchy-filtered source, optionally normalized. Measures directional thrust during / after a squeeze.
Signal line (white JMA) Jurik moving average of the momentum line. Smooth trigger for crossovers / reversals.
Squeeze dots (blue, black, red, yellow, purple, green) Real-time volatility state: No squeeze → Wide → Normal → Narrow → Very Narrow → Fired. Helps anticipate explosive moves as BB exits KC.
Cyclic RSI bands (cyan / fuchsia) Dynamic overbought / oversold bands derived by MESA dominant-cycle analysis. Contextualizes momentum extremes—no fixed 70/30.
Rate-of-Change (optional) (orange / blue shading) ROC of the momentum-signal spread, scaled. Highlights acceleration / deceleration.
Reversal guide lines (optional colored rays) Drawn when momentum crosses its JMA and reversal-mode is on. Visual confirmation of early trend change.
2 · Key engine components
Cauchy PDF-weighted moving average
Creates a heavy-tailed weighting curve; center bars dominate while still capturing fat-tail outliers—excellent for choppy instruments or volume-weighting (Volume weighted?).
Butterworth High-Pass & Super-Smoother Low-Pass
Strip out drift, then smooth what’s left. This isolates true cyclic motion before momentum is computed.
Fast RMS normalizer
Converts the band-pass output into a unit-scale “power” reading—vital for adaptive thresholds.
Goertzel + MESA dominant-cycle
Auto-detects fast & slow cycles, then blends them to size overbought / oversold bands and to set the adaptiveLength (if Use Adaptive Length? is enabled).
Jurik RSX & JMA
Provide ultra-low-lag smoothing for momentum and for reversal detection.
3 · Input groups and how to tune them
Group Why change it Tips
Normalization (Unbounded / Min-Max / Standard Deviations) Puts momentum & signal on the scale that best suits the asset. Crypto / small-caps: StdDev (handles volatility).
FX / indices: Min-Max or leave unbounded for raw juice.
Cauchy Distribution Tailors the Cauchy filter. Gamma ↓ (0.1-0.4) ⇒ faster / riskier. Use Adaptive Length pairs it with MESA cycle length for auto speed control.
Rate of Change Visual momentum acceleration. Leave off (Show Rate of Change = false) if you want a cleaner pane.
Momentum Colors / Directional Momentum? Switch between classic SQZMOM coloring and trend-biased histogram. Turn on when you prefer “green-gets-greener / red-gets-redder” style signals.
Squeeze Colors & Thresholds Fine-tune what “wide / normal / narrow” mean. Larger assets (SPX, BTC-Perp): raise the thresholds a touch. Thin or low-ATR symbols: lower them.
Multi-Time-frame blocks (1 h, 4 h, D, W, M) Pre-sets for BB/KC length, squeeze thresholds, and reversal MA length per TF. The script auto-detects the chart timeframe and loads the matching row—just adjust each block once.
Reversal Signals Whether to draw vertical rays on momentum crossovers. Use on swing-trading timeframes (≥1 h) to catch early momentum flips.
4 · How to read & trade it
Scan for purple / yellow / red dots
These indicate Very-Narrow, Narrow, and Normal squeezes—markets are coiling.
Wait for a fired squeeze (green dot)
BB has pushed outside KC; volatility is expanding. Momentum direction often dictates breakout bias.
Check momentum relative to zero & signal
Bullish setup: Momentum > 0 and crossing above signal. Bearish setup: Momentum < 0 and crossing below signal. Alerts “Bullish / Bearish Trend Reversal” are raised here if enabled.
Validate with cyclic bands
If momentum launches from near the lower cyan band, bullish moves are higher-probability (symmetrical for upper fuchsia band).
Confirm trend strength
Directional-momentum histogram keeps turning brighter in trend direction; ROC is above zero and rising.
Manage the trade
First target = prior squeeze mid-range or recent swing high/low.
Consider scaling out when momentum weakens (histogram fades) or reverses through signal line.
Optional: draw the reversal rays to highlight exit zones automatically.
5 · Practical workflows
Scalpers (1-5 min)
Uncheck Use Adaptive Length, set main Length to 10-12, Gamma to 0.3.
Use ROC for ultra-fast divergences.
Treat Normal squeezes (red) as tradable; ignore Wide. Healthy Volume is ideal.
Swing traders (1 h – 4 h)
Keep default adaptive length; enable 1-H/4-H reversal blocks.
Trade only after Very-Narrow/Narrow squeezes; ride until weekly/daily reversal ray prints.
Position / Trend followers (Daily+)
Raise Wide/Normal thresholds a bit (e.g., 2.2 / 1.7).
Momentum normalization = Standard Deviations to filter regime shifts.
Combine with higher-timeframe MTF panel or moving-average ribbons.
6 · Built-in alert catalog
Alert name Fires when Typical action
🟢 Fired Squeeze Green dot appears (vol expansion already under way) Stay in trend or add on pullbacks.
🟠 Low / 🔴 Normal / 🟡 Tight / 🟣 Very Tight Respective squeeze engages Get your watch-list ready; plan trades.
🐂 Bullish / 🐻 Bearish Trend Reversal Momentum crosses signal in requested direction Entry / exit / scale adds.
Set alerts on “Once Per Bar Close” for reliable signals.
7 · Best practices & caveats
Context is king – Use higher-timeframe structure (support/resistance, VWAP, market profile) to avoid false breakouts.
Data quality – On illiquid symbols, consider turning volume weighting off (pre-market gaps distort results).
Normalization choice – Mixing different normalizations across charts can confuse muscle memory; pick one style per asset class.
Lag vs. noise – If entries feel late, lower Gamma or disable adaptive length. If too jumpy, increase Length or choose Standard-Deviation normalization.
Not a stand-alone holy grail – Combine with risk management (ATR-based stops, Kelly-fraction sizing) and confirm with price action.
Harness the script’s adaptive filtering, multi-TF presets, and rich alert suite to spot compression, time breakouts, and stay on the right side of momentum—whether you’re scalping ES futures or swing-trading alt-coins.
Naive Bayes Candlestick Pattern Classifier v1.1 BETAAn intermezzo on why i made this script publication..
A : Candlestick Pattern took hours to backtest, why not using Machine Learning techniques?
B : Machine Learning, no that's gonna be really heavy bro!
A : Not really, because we use Naive Bayes.
B : The simplest, yet powerful machine learning algorithm to separate (a.k.a classify) multivariate data.
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Hello, everyone!
After deep research in extracting meaningful information from the market, I ended up building this powerful machine learning indicator based on the evolution of Bayesian Statistics. This indicator not only leverages the simplicity of Naive Bayes but also extends its application to candlestick pattern analysis, making it an invaluable tool for traders who are looking to enhance their technical analysis without spending countless hours manually backtesting each pattern on each market!.
What most interesting part is actually after learning all of likely useless methods like fibonacci, supply and demand, volume profile, etc. We always ended up back to basic like support and resistance and candlestick patterns, but with a slight twist on strategy algorithm design and statistical approach. Thus, the only reason why i made this, because i exactly know that you guys will ended up in this position as time goes by.
The essence of this indicator lies in its ability to automate the recognition and statistical evaluation of various candlestick patterns. Traditionally, traders have relied on visual inspection and manual backtesting to determine the effectiveness of patterns like Bullish Engulfing, Bearish Engulfing, Harami variations, Hammer formations, and even more complex multi-candle patterns such as Three White Soldiers, Three Black Crows, Dark Cloud Cover, and Piercing Pattern. However, these conventional methods are both time-consuming and prone to subjective bias.
To address these challenges, I employed Naive Bayes—a probabilistic classifier that, despite its simplicity, offers robust performance in various domains. Naive Bayes assumes that each feature is independent of the others given the class label, which, although a strong assumption, works remarkably well in practice, especially when the dataset is large like market data and the feature space is high-dimensional. In our case, each candlestick pattern acts as a feature that can be statistically evaluated based on its historical performance. The indicator calculates a probability that a given pattern will lead to a price reversal, by comparing the pattern’s close price to the highest or lowest price achieved in a lookahead window.
One of the standout features of this script is its flexibility. Each candlestick pattern is not only coded into the system but also comes with individual toggles to enable or disable them based on your trading strategy. This means you can choose to focus on single-candle patterns like Bullish Engulfing or more complex multi-candle formations such as Three White Soldiers, without modifying the core code. The built-in customization options allow you to adjust colors and labels for each pattern, giving you the freedom to tailor the visual output to your preference. This level of customization ensures that the indicator integrates seamlessly into your existing TradingView setup.
Moreover, the indicator isn’t just about pattern recognition—it also incorporates outcome-based learning. Every time a pattern is detected, it looks ahead a predefined number of bars to evaluate if the expected reversal actually materialized. This outcome is then stored in arrays, and over time, the script dynamically calculates the probability of success for each pattern. These probabilities are presented in a real-time updating table on your chart, which shows not only the percentage probability but also the count of historical occurrences. With this information at your fingertips, you can quickly gauge the reliability of each pattern in your chosen market and timeframe.
Another significant advantage of this approach is its speed and efficiency. While more complex machine learning models like neural networks might require heavy computational resources and longer training times, the Naive Bayes classifier in this script is lightweight, instantaneous and can be updated on the fly with each new bar. This real-time capability is essential for modern traders who need to make quick decisions in fast-paced markets.
Furthermore, by automating the process of backtesting, the indicator frees up your time to focus on other aspects of trading strategy development. Instead of manually analyzing hundreds or even thousands of candles, you can rely on the statistical power of Naive Bayes to provide you with insights on which patterns are most likely to result in profitable moves. This not only enhances your efficiency but also helps to eliminate the cognitive biases that often plague manual analysis.
In summary, this indicator represents a fusion of traditional candlestick analysis with modern machine learning techniques. It harnesses the simplicity and effectiveness of Naive Bayes to deliver a dynamic, real-time evaluation of various candlestick patterns. Whether you are a seasoned trader looking to refine your technical analysis or a beginner eager to understand market dynamics, this tool offers a powerful, customizable, and efficient solution. Welcome to a new era where advanced statistical methods meet practical trading insights—happy trading and may your patterns always be in your favor!
Note : On this current released beta version, you must manually adjust reversal percentage move based on each market. Further updates may include automated best range detection and probability.
Dual Bayesian For Loop [QuantAlgo]Discover the power of probabilistic investing and trading with Dual Bayesian For Loop by QuantAlgo , a cutting-edge technical indicator that brings statistical rigor to trend analysis. By merging advanced Bayesian statistics with adaptive market scanning, this tool transforms complex probability calculations into clear, actionable signals—perfect for both data-driven traders seeking statistical edge and investors who value probability-based confirmation!
🟢 Core Architecture
At its heart, this indicator employs an adaptive dual-timeframe Bayesian framework with flexible scanning capabilities. It utilizes a configurable loop start parameter that lets you fine-tune how recent price action influences probability calculations. By combining adaptive scanning with short-term and long-term Bayesian probabilities, the indicator creates a sophisticated yet clear framework for trend identification that dynamically adjusts to market conditions.
🟢 Technical Foundation
The indicator builds on three innovative components:
Adaptive Loop Scanner: Dynamically evaluates price relationships with adjustable start points for precise control over historical analysis
Bayesian Probability Engine: Transforms market movements into probability scores through statistical modeling
Dual Timeframe Integration: Merges immediate market reactions with broader probability trends through custom smoothing
🟢 Key Features & Signals
The Adaptive Dual Bayesian For Loop transforms complex calculations into clear visual signals:
Binary probability signal displaying definitive trend direction
Dynamic color-coding system for instant trend recognition
Strategic L/S markers at key probability reversals
Customizable bar coloring based on probability trends
Comprehensive alert system for probability-based shifts
🟢 Practical Usage Tips
Here's how you can get the most out of the Dual Bayesian For Loop :
1/ Setup:
Add the indicator to your TradingView chart by clicking on the star icon to add it to your favorites ⭐️
Start with default source for balanced price representation
Use standard length for probability calculations
Begin with Loop Start at 1 for complete price analysis
Start with default Loop Lookback at 70 for reliable sampling size
2/ Signal Interpretation:
Monitor probability transitions across the 50% threshold (0 line)
Watch for convergence of short and long-term probabilities
Use L/S markers for potential trade signals
Monitor bar colors for additional trend confirmation
Configure alerts for significant trend crossovers and reversals, ensuring you can act on market movements promptly, even when you’re not actively monitoring the charts
🟢 Pro Tips
Fine-tune loop parameters for optimal sensitivity:
→ Lower Loop Start (1-5) for more reactive analysis
→ Higher Loop Start (5-10) to filter out noise
Adjust probability calculation period:
→ Shorter lengths (5-10) for aggressive signals
→ Longer lengths (15-30) for trend confirmation
Strategy Enhancement:
→ Compare signals across multiple timeframes
→ Combine with volume for trade validation
→ Use with support/resistance levels for entry timing
→ Integrate other technical tools for even more comprehensive analysis
Bayesian Bias OscillatorWhat is a Bayes Estimator?
Bayesian estimation, or Bayesian inference, is a statistical method for estimating unknown parameters of a probability distribution based on observed data and prior knowledge about those parameters. At first , you will need a prior probability distribution, which is a prior belief about the distribution of the parameter that you are interested in estimating. This distribution represents your initial beliefs or knowledge about the parameter value before observing any data. Second , you need a likelihood function, which represents the probability of observing the data given different values of the parameter. This function quantifies how well different parameter values explain the observed data. Then , you will need a posterior probability distribution by combining the prior distribution and the likelihood function to obtain the posterior distribution of the parameter. The posterior distribution represents the updated belief about the parameter value after observing the data.
Bayesian Bias Oscillator
This tool calculates the Bayes bias of returns, which are directional probabilities that provide insight on the "trend" of the market or the directional bias of returns. It comes with two outputs: the default one, which is the Z-Score of the Bayes Bias, and the regular raw probability, which can be switched on in the settings of the indicator.
The Z-Score output value doesn't tell you the probability, but it does tell you how much of a standard deviation the value is from the mean. It uses both probabilities, the probability of a positive return and the probability of a negative return, which is just (1 - probability of a positive return).
The probability output value shows you the raw probability of a positive return vs. the probability of a negative return. The probability is the value of each line plotted (blue is the probability of a positive return, and purple is the probability of a negative return).
Bayesian predictive leading indicator--------- ENGLISH ---------
This is a predictive indicator ( leading indicator ) that uses Bayes' formula to calculate the conditional probability of price increases given the angular coefficient. The indicator calculates the angular coefficient and its regression and uses it to predict prices.
Bayes' theorem is a fundamental result of probability theory and is used to calculate the probability of a cause causing the verified event. In other words, for our indicator, Bayes' theorem is used to calculate the conditional probability of one event (price event in this case) with respect to another event by calculating the probabilities of the two events (past price) and the conditional probability of the second event (future price) with respect to the first event.
The red line represents the angular coefficient. The blue line represents the normalized expected price. Finally, the yellow line represents the conditional probability that the price will increase or decrease.
How to use it. In addition to the convenient histogram, which follows the angular coefficient, another practical operational application might be to go long when the blue line is above the red and yellow lines. Conversely short when the blue is below the red and yellow.
When the yellow line passes above all others, a reversal in the long direction is imminent and vice versa.
The extent of the reversal depends on how far the yellow line will be away in price from the other 2 lines.
This indicator is in its embryonic state and updates will follow to make it more graphically readable, add alerts, etc.
Stay tuned! Leave a boost and comment or write to me if you wish.
--------- ITALIANO ---------
Questo è un indicatore predittivo ( leading indicator ) che utilizza la formula di Bayes per calcolare la probabilità condizionata che il prezzo aumenti dato il coefficiente angolare. L’indicatore calcola il coefficiente angolare e la sua regressione e lo utilizza per prevedere i prezzi.
Il teorema di Bayes è un risultato fondamentale della teoria della probabilità e viene impiegato per calcolare la probabilità di una causa che ha provocato l’evento verificato. In altre parole, per il nostro indicatore, il teorema di Bayes serve per calcolare la probabilità condizionata di un evento (di prezzo in questo caso) rispetto a un altro evento, calcolando le probabilità dei due eventi (prezzo passato) e la probabilità condizionata del secondo evento (prezzo futuro) rispetto al primo.
La linea rossa rappresenta il coefficiente angolare. La linea blu rappresenta il prezzo previsto normalizzato. Infine la linea gialla rappresenta la probabilità condizionata che il prezzo aumenti o diminuisca.
Come si usa? Oltre al comodo istogramma, che segue il coefficiente angolare, un'altra applicazione operativa pratica potrebbe essere di andare long quando la linea blu è sopra la linea rossa e gialla. Viceversa short quando la blu è sotto la rossa e la gialla.
Quando la linea gialla passa sopra tutte le altre è imminente un'inversione in direzione long e viceversa.
L'entità dell'inversione dipende da quanto la linea gialla sarà distante di prezzo dalle altre 2 linee.
Questo indicatore è al suo stato embrionale e seguiranno aggiornamenti per renderlo graficamente più leggibile, aggiungere alert, ecc.
Stay tuned! Lascia un boost e commenta o scrivimi se desideri.
Probability Oscillator (Expo)█ Overview
The Probability Oscillator uses a Bayesian approach to measure the probability of a price movement and trend continuation. This approach considers the prior probability of a price movement and the current market conditions to identify trends, sentiment, momentum, and retracements.
█ How does the indicator work?
The Probability Oscillator is based on the idea of Bayesian probability , which is a way of using existing data to make predictions about the likelihood of an event occurring. This indicator uses the Bayesian probability model to analyze past trading activity and calculate the probability of a trend continuing. This function also considers the prior probability of a price movement and the current market conditions to analyze the likelihood of a retracement.
█ How to use
Investors can use this indicator to measure the market sentiment and the strength/direction of a trend. It does also give insights into momentum moves and retracements.
█ Indicator Customization
The user can change the trend approaches and input source as well as adjust the overbought and oversold areas to make the calculation more sensitive to retracements.
The user can change the sensitivity of the momentum function to adjust it only to identify the most significant momentum moves.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!