Zerg range filter credit to Kivanc turkish pinecoder for base indicator i reworked with chatgpt and some common sense
this indicator similar to the ADX but i think its better visually to keep you out of market conditions that are unfavorable.
i made original indicator to work in a 0-100 enviroment (before it was a zero middle line oscillator) and added background coloring that has a lower and higher threshold setting. i also added a smoothing moving average. this will trigger threshold levels (not the core oscillator)
above higher level would indicate trending market conditions and its purple. these are the areas where you might want to buy low period moving average bounces like 10 or 21 ema
lower band will paint indicator background blue and its cold, meaning range bound trade ideas are likely play out better. selling resistance and buying horizontal supports for example.
you are encourage to play with lookback period and change thresholds until you find something that works for your trading.
on the picture above it illustrates how i intended its usage.
it also shows divergences which was not intended but also a function.
you can also observe as the oscillator likes to coil up into a tight range (horizontal or a wedge formation) and when these break their trendlines explosive moves are incoming usually.
if you have a trading system and can generate a lot of signals but want to filter out some loser trades this could be the indicator you were looking for.
i hope this will be inline with community guidelines. my other publishing got removed unfortunately
Recherche dans les scripts pour "chatgpt"
Schwarzman Custom ORB with Box DisplayIndicator Overview
The Schwarzman Custom ORB (Opening Range Breakout) Indicator is a fully self-developed script designed for traders who utilize opening range breakout strategies. This indicator allows users to customize their ORB settings, apply them to historical price data, and visually connect multiple ORBs to analyze past performance. The goal is to provide traders with a tool to backtest and refine their breakout strategies based on historical ORB data.
How the Indicator Works
1️⃣ User-Defined ORB Settings
• The user selects a custom start time (hour and minute) for the ORB.
• The user defines a duration (e.g., 15 minutes, 30 minutes, etc.) for the ORB period.
• A timezone offset is included to adjust for different market sessions.
2️⃣ ORB High and Low Calculation
• The script records the highest and lowest prices within the selected ORB time window.
• The recorded values remain static after the ORB period ends, ensuring accurate range plotting.
3️⃣ Historical ORB Visualization
• Instead of only showing a single ORB for the current session, this indicator connects multiple ORBs across past data.
• This allows traders to visually analyze previous breakout performance.
• The plotted ORBs remain fixed and do not repaint, ensuring an accurate backtesting experience.
4️⃣ Stepline Visualization & Range Filling
• The high and low ORB levels are displayed using stepline plots to maintain clear horizontal levels.
• A shaded box is applied between the ORB high and low for better visualization.
Use Cases & Strategy Application
📌 Backtesting Historical ORBs – See how past ORBs performed under different market conditions.
📌 Custom ORB Settings – Adjust the start time and duration for different trading sessions.
📌 Multi-ORB Analysis – Connect ORBs over multiple trading days to study trends and breakouts.
📌 Breakout Strategy Optimization – Use the historical ORB connections to refine entry and exit points.
This indicator is particularly useful for day traders, scalpers, and breakout traders looking for a data-driven approach to trading.
Indicator Development & Transparency Statement
As a trader, I have tested various ORB (Opening Range Breakout) indicators available in the TradingView community. Through these experiences, I aimed to develop a version that best fits my own trading needs and strategy.
This script is a self-developed ORB tool, created from scratch while drawing inspiration from the concept of opening range breakouts, which is widely used in trading. Since I initially coded in Pine Script v4, I used ChatGPT to help refine and migrate the script to Pine Script v6 to ensure compatibility with the latest TradingView features. However, the core logic, structure, and customization were entirely designed and implemented based on my own approach.
I am making this indicator public not to violate any TradingView guidelines but to share my work with the trading community and provide a tool that can help others analyze ORB-based strategies. If there are any compliance concerns, I am open to adjusting the script accordingly, but I want to clarify that this is not a copy of any existing ORB script—it is a custom-built indicator tailored to my own trading preferences.
I appreciate the opportunity to contribute to the community and would welcome any specific feedback from TradingView regarding rule compliance.
Best regards,
Janko S. (Schwarzman)
Appeal to TradingView
Dear TradingView Team,
This script is 100% self-developed and does not copy or replicate any third-party code. It is a customized ORB tool designed for traders who wish to backtest and analyze opening range breakout strategies over multiple sessions. We kindly request specific clarification regarding which exact line(s) of code violate TradingView’s guidelines. If there are any compliance concerns, we are happy to adjust the script accordingly.
Please let us know the precise rules or community guidelines that were violated so we can make the necessary modifications.
🚀 Summary
✔ Fully Custom & Self-Developed – No copied or third-party code.
✔ Innovative Feature – Connects past ORBs for strategy backtesting.
✔ Transparent & Compliant – Requesting exact details on any potential rule violations.
Noteworthy CandlesticksThis indicator identifies noteworthy candlestick formations by analyzing the proportions of a candlestick’s body, wicks, and shadows in relation to its total range. It highlights specific patterns to assist in analyzing potential market activity.
Key Features
Lower Wick Candles: It identifies candlesticks with long lower wicks, which may indicate price rejection at lower levels.
Upper Wick Candles: It detects candlesticks with long upper wicks, which may indicate price rejection at higher levels.
Doji Candles: It recognizes candlesticks with small bodies relative to their range, often associated with market indecision.
Definitions of Wicks and Shadows
In traditional candlestick charting, the terms wick and shadow are interchangeable, referring to the thin lines extending above and below the candlestick's body. However, this indicator uses distinct definitions:
Wicks represent the distance from the edges of the candlestick body (the open and close prices) to the high or low.
Shadows measure the distance from the closing price to the high or low of the candlestick.
By distinguishing between wicks and shadows, the indicator provides separate insights into price extremes (wicks) and price behavior around the close (shadows).
How It Works
The indicator evaluates each candlestick against user-defined thresholds for wick and shadow length. It compares the overall range of the candlestick to the ATR (Average True Range) to ensure patterns are relevant in the context of recent volatility.
Candlesticks with noteworthy lower wicks are marked with a symbol below the bar.
Candlesticks with noteworthy upper wicks are marked with a symbol above the bar.
Doji candles are marked with symbols both above and below the bar.
Applications
This indicator can assist traders in identifying potential areas of price reversal, rejection, or indecision. It can also provide additional context when used alongside other technical tools like volume analysis, trendlines, or support and resistance levels.
Acknowledgment
This description was written by ChatGPT to facilitate the understanding of the indicator's features and functionality.
BTC Slayer 9000 - Relative Risk-adjusted performanceBTC Slayer 9000: Relative Risk-Adjusted Performance
Dear friends and fellow traders,
I am pleased to introduce the BTC Slayer 9000, a script designed to provide clear insights into risk-adjusted performance relative to a benchmark. Whether you're navigating the volatile world of cryptocurrencies or exploring opportunities in stocks, this tool helps you make informed decisions by comparing assets against your chosen benchmark.
What Does It Do?
This indicator is based on the Ulcer Index (UI), a metric that measures downside risk. It calculates the Ulcer Performance Index (UPI), which combines returns and downside risk, and compares it to a benchmark (like BTC/USDT, SPY500, or any trading pair).
The result is the Relative UPI (RUPI):
Positive RUPI (green area): The asset's risk-adjusted performance is better than the benchmark.
Negative RUPI (red area): The asset's risk-adjusted performance is worse than the benchmark.
Why Use It?
Risk vs. Reward: See if the extra risk of an asset is justified by its returns.
Customizable Benchmark: Compare any asset against BTC, SPY500, or another chart.
Dynamic Insights: Quickly identify outperforming assets for long positions and underperformers for potential shorts.
How to Use:
Inputs:
Adjust the lookback period to set the time frame for analysis. 720 Period is meant to represent 30 days. I like to use 168 period because I do not hold trades for long.
Choose your comparison chart (e.g., BTC/USDT, SPY500, AAPL, etc.).
Interpretation:
Green Area Above 0: The asset offers better risk-adjusted returns than the benchmark.
Red Area Below 0: The benchmark is a safer or more rewarding option.
Perfect for All Traders
Whether you:
Trade Cryptocurrencies: Compare altcoins to BTC.
Invest in Stocks: Compare individual stocks to indices like SPY500.
Evaluate Portfolio Options: Decide between assets like AAPL or TSLA.
This indicator equips you with a systematic way to evaluate "Is the extra risk worth it?".
The script was compiled in Collaboration with ChatGPT
Linear Regression Channel Screener [Daveatt]Hello traders
First and foremost, I want to extend a huge thank you to @LonesomeTheBlue for his exceptional Linear Regression Channel indicator that served as the foundation for this screener.
Original work can be found here:
Overview
This project demonstrates how to transform any open-source indicator into a powerful multi-asset screener.
The principles shown here can be applied to virtually any indicator you find interesting.
How to Transform an Indicator into a Screener
Step 1: Identify the Core Logic
First, identify the main calculations of the indicator.
In our case, it's the Linear Regression
Channel calculation:
get_channel(src, len) =>
mid = math.sum(src, len) / len
slope = ta.linreg(src, len, 0) - ta.linreg(src, len, 1)
intercept = mid - slope * math.floor(len / 2) + (1 - len % 2) / 2 * slope
endy = intercept + slope * (len - 1)
dev = 0.0
for x = 0 to len - 1 by 1
dev := dev + math.pow(src - (slope * (len - x) + intercept), 2)
dev
dev := math.sqrt(dev / len)
Step 2: Use request.security()
Pass the function to request.security() to analyze multiple assets:
= request.security(sym, timeframe.period, get_channel(src, len))
Step 3: Scale to Multiple Assets
PineScript allows up to 40 request.security() calls, letting you monitor up to 40 assets simultaneously.
Features of This Screener
The screener provides real-time trend detection for each monitored asset, giving you instant insights into market movements.
It displays each asset's position relative to its middle regression line, helping you understand price momentum.
The data is presented in a clean, organized table with color-coded trends for easy interpretation.
At its core, the screener performs trend detection based on regression slope calculations, clearly indicating whether an asset is in a bullish or bearish trend.
Each asset's price is tracked relative to its middle regression line, providing additional context about trend strength.
The color-coded visual feedback makes it easy to spot changes at a glance.
Built-in alerts notify you instantly when any asset experiences a trend change, ensuring you never miss important market moves.
Customization Tips
You can easily expand the screener by adding more symbols to the symbols array, adapting it to your watchlist.
The regression parameters can be adjusted to match your preferred trading timeframes and sensitivity.
The alert system is already configured to notify you of trend changes, but you can customize the alert messages and conditions to your needs.
Limitations
While powerful, the screener is bound by PineScript's limitation of 40 security calls, capping the maximum number of monitored assets.
Using AI to Help With Conversion
An interesting tip:
You can use AI tools to help convert single-asset indicators to screeners.
Simply provide the original code and ask for assistance in transforming it into a screener format. While the AI output might need some syntax adjustments, it can handle much of the heavy lifting in the conversion process.
Prompt (example) : " Please make a pinescript version 5 screener out of this indicator below or in attachment to scan 20 instruments "
I prefer Claude AI (Opus model) over ChatGPT for pinescript.
Conclusion
This screener transformation technique opens up endless possibilities for market analysis.
By following these steps, you can convert any indicator into a powerful multi-asset scanner, enhancing your trading toolkit significantly.
Remember: The power of a screener lies not just in monitoring multiple assets, but in applying consistent analysis across your entire watchlist in real-time.
Feel free to fork and modify this screener for your own needs.
Happy trading! 🚀📈
Daveatt
Monthly Day Long Strategy with VIX and Risk ManagementThis trading strategy is designed to open long positions on a specific day of the month, with the conditions for entry and exit based on the VIX index and additional risk management techniques. The strategy includes stop-loss and take-profit features to manage risk and lock in profits.
Inputs:
Entry Day of the Month (entry_day): Specifies which day of the month to consider for initiating a trade. The default value is the 27th.
Hold Duration (Days) (hold_duration_days): Defines how many days to hold the position after opening. The default value is 4 days.
VIX Threshold (vix_threshold): Sets the maximum acceptable value for the VIX index to consider an entry. If the VIX is below this threshold, it signals a potential trade. The default value is 20.0.
Stop Loss (%) (stop_loss_percentage): Determines the percentage below the entry price where the stop-loss will be triggered. The default value is 2.0%.
Take Profit (%) (take_profit_percentage): Sets the percentage above the entry price where the take-profit will be triggered. The default value is 5.0%.
Functions:
next_weekday(date): Adjusts the entry date to the next Monday if it falls on a weekend (Saturday or Sunday). This ensures trades do not occur on non-trading days.
Logic:
Entry Conditions:
Date Check: Opens a long position if the current date matches the adjusted entry date (the 27th or the next Monday if the 27th falls on a weekend).
VIX Filter: The VIX index value must be below the specified threshold (e.g., 20.0) to consider an entry.
Exit Conditions:
Time-Based Exit: Closes the position after the hold duration of 4 days.
Stop-Loss: Automatically closes the position if the price drops to a level that is a specified percentage below the entry price (e.g., 2.0%).
Take-Profit: Closes the position if the price rises to a level that is a specified percentage above the entry price (e.g., 5.0%).
Plots:
VIX Plot: Displays the VIX index on the chart for visual reference.
VIX Threshold Line: A horizontal line representing the VIX threshold value.
Summary:
The strategy aims to take advantage of specific entry days while filtering trades based on VIX levels to ensure market conditions are favorable. Risk management is enhanced through stop-loss and take-profit settings, which help in controlling potential losses and securing profits. The strategy ensures trades are only made on trading days and not on weekends, adjusting automatically to the next Monday if needed.
ChatGPT kann Fehler machen. Überprüfe wichtige Informationen.
Retest Confirm Point TibbuCreating a "Retest Confirm Point" indicator that generates buy and sell signals involves defining criteria to confirm that a price retest is valid before issuing a trade signal. This generally requires identifying a key level (such as support, resistance, or a trendline), detecting a retest of this level, and then confirming the validity of the retest.
Here’s a Pine Script example to help you create such an indicator. This script identifies and confirms retests of previous highs and lows, and generates buy and sell signals based on those retests: Explanation:
Recent High and Low:
The script identifies the highest and lowest prices over a specified lookback period.
These levels are plotted on the chart as reference points.
Retest Conditions:
Retest High: The closing price is within a buffer range around the recent high.
Retest Low: The closing price is within a buffer range around the recent low.
Confirmation:
Confirm High: The closing price reaches a new high over a set number of bars after the retest condition.
Confirm Low: The closing price reaches a new low over a set number of bars after the retest condition.
Signals:
Buy Signal: Issued when a confirmed retest of the recent high occurs.
Sell Signal: Issued when a confirmed retest of the recent low occurs.
Customization:
Lookback Period: Adjust to determine the historical range for finding recent highs and lows.
Confirmation Bars: Change the number of bars used to confirm the retest.
Retest Buffer: Adjust the percentage buffer to fine-tune the retest conditions.
Testing and Optimization:
Backtest: Always backtest the strategy on historical data to ensure it behaves as expected.
Adjust Parameters: Modify parameters based on the asset, timeframe, and market conditions.
Feel free to modify this script further based on your specific trading strategy and needs. If you need help with any additional features or further customization, let me know!
ChatGPT can make mistakes. Check important info.
OrderBlock Trend (CISD)OrderBlock Trend (CISD) Indicator
Overview:
The "OrderBlock Trend (CISD)" AKA: change in state of delivery by ICT inner circle trader this indicator is designed to help traders identify and visualize market trends based on higher timeframe candle behavior. This script leverages the concept of order blocks, which are price levels where significant buying or selling activity has occurred, to signal potential trend reversals or continuations. By analyzing bullish and bearish order blocks on a higher timeframe, the indicator provides visual cues and statistical insights into the market's current trend dynamics.
Key Features:
Higher Timeframe Analysis: The indicator uses a higher timeframe (e.g., Daily) to assess the trend direction based on the open and close prices of candles. This approach helps in identifying more significant and reliable trend changes, filtering out noise from lower timeframes.
Bullish and Bearish Order Blocks: The script detects the first bullish or bearish candle on the selected higher timeframe and uses these candles as reference points (order blocks) to determine the trend direction. A bullish trend is indicated when the current price is above the last bearish order block's open price, and a bearish trend is indicated when the price is below the last bullish order block's open price.
Visual Trend Indication: The indicator visually represents the trend using background colors and plot shapes:
A green background and a square shape above the bars indicate a bullish trend.
A red background and a square shape above the bars indicate a bearish trend.
Candle Count and Statistics: The script keeps track of the number of up and down candles during bullish and bearish trends, providing percentages of up and down candles in each trend. This data is displayed in a table, giving traders a quick overview of market sentiment during each trend phase.
User Customization: The higher timeframe can be adjusted according to the trader's preference, allowing flexibility in trend analysis based on different time horizons.
Concepts and Calculations:
The "OrderBlock Trend (CISD)" indicator is based on the concept of order blocks, a key area where institutional traders are believed to place large orders, creating significant support or resistance levels. By identifying these blocks on a higher timeframe, the indicator aims to highlight potential trend reversals or continuations. The use of higher timeframe data helps filter out minor fluctuations and focus on more meaningful price movements.
The candle count and percentage calculations provide additional context, allowing traders to understand the proportion of bullish or bearish candles within each trend. This information can be useful for assessing the strength and consistency of a trend.
How to Use:
Select the Higher Timeframe: Choose the higher timeframe (e.g., Daily) that best suits your trading strategy. The default setting is "D" (Daily), but it can be adjusted to other timeframes as needed.
Interpret the Trend Signals:
A green background indicates a bullish trend, while a red background indicates a bearish trend. The corresponding square shapes above the bars reinforce these signals.
Use the information on the proportion of up and down candles during each trend to gauge the trend's strength and consistency.
Trading Decisions: The indicator can be used in conjunction with other technical analysis tools and indicators to make informed trading decisions. It is particularly useful for identifying trend reversals and potential entry or exit points based on the behavior of higher timeframe order blocks.
Customization and Optimization: Experiment with different higher timeframes and settings to optimize the indicator for your specific trading style and preferences.
Conclusion:
The "OrderBlock Trend (CISD)" indicator offers a comprehensive approach to trend analysis, combining the power of higher timeframe order blocks with clear visual cues and statistical insights. By understanding the underlying concepts and utilizing the provided features, traders can enhance their trend detection and decision-making processes in the markets.
Disclaimer:
This indicator is intended for educational purposes and should be used in conjunction with other analysis methods. Always perform your own research and risk management before making trading decisions.
Some known bugs when you switch to lower timeframe while using daily timeframe data it didn't use the daily candle close to establish the trend change but your current time frame If some of you know how to fix it that would be great if you help me to I would try my best to fix this in the future :) credit to ChatGPT 4o
Noise Area Indicator with Gap AdjustmentsThis version of the Noise Area Pine Script, developed with the assistance of ChatGPT, includes adjustments for opening gaps to better account for overnight price changes that affect the market open. This Pine Script is designed to provide traders with a dynamic visualization of the Noise Area based on the volatility of the last 14 trading days. It calculates the upper and lower boundaries using the daily opening price, representing typical price movements relative to the open. This helps identify significant deviations, potentially indicating the start of a trend.
Features:
Captures and adjusts for gaps between the previous day's close and the current day's open, allowing for more precise trend analysis.
Sets the Noise Area boundaries using both the daily opening price and the previous day's closing price, ensuring that sudden market moves are adequately considered.
Measures deviations in price from the opening, averaged over the last 14 days to calculate absolute movements.
Plots upper and lower boundaries on the chart, providing a visual guide for traders to assess market volatility.
Includes a dynamically plotted daily opening price, serving as a consistent reference point for market open conditions.
Usage:
This indicator is particularly useful for day traders and short-term traders who need to understand intraday volatility and pinpoint potential breakout points, aiding in the strategic planning of entry and exit points based on historical volatility patterns relative to the daily open (with gap adjustments).
Backtest Strategy Optimizer Adapter - Supertrend ExampleSample Code
This is a sample code for my Backtest Strategy Optimizer Adapter library.
You can find the library at:
Backtest Strategy Optimizer Tester
With this indicator, you will be able to run one or multiple backtests with different variables (combinations). For example, you can run dozens of backtests of Supertrend at once with an increment factor of 0.1, or whatever you prefer. This way, you can easily grab the most profitable settings and use them in your strategy. The chart above shows different color plots, each indicating a profit backtest equal to tradingview backtesting system. This code uses my backtest library, available in my profile.
Below the code you should edit yourself
You can use ChatGPT or write a python script to autogenerate code for you.
// #################################################################
// # ENTRIES AND EXITS
// #################################################################
// You can use the link and code in the description to create
// your code for the desired number of entries / exits.
// #################################################################
// AUTO GENERATED CODE
// ▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼▼
= ti.supertrend(10, 0.1)
= ti.supertrend(10, 0.2)
= ti.supertrend(10, 0.3)
= ti.supertrend(10, 0.4)
// 005 etc...
pnl_001 = backtest.profit(date_start, date_end, entry_001, exit_001)
pnl_002 = backtest.profit(date_start, date_end, entry_002, exit_002)
pnl_003 = backtest.profit(date_start, date_end, entry_003, exit_003)
pnl_004 = backtest.profit(date_start, date_end, entry_004, exit_004)
plot(pnl_001, title='0.1', color=backtest.color(001))
plot(pnl_002, title='0.2', color=backtest.color(002))
plot(pnl_003, title='0.3', color=backtest.color(003))
plot(pnl_004, title='0.4', color=backtest.color(004))
// Make sure you set the correct array size.
// The amount of tests + 1 (e.g. 4 tests you set it to 5)
var results_list = array.new_string(5)
if (ta.change(pnl_001))
array.set(results_list, 0, str.tostring(pnl_001) + '|0.1')
if (ta.change(pnl_002))
array.set(results_list, 1, str.tostring(pnl_002) + '|0.2')
if (ta.change(pnl_003))
array.set(results_list, 2, str.tostring(pnl_003) + '|0.3')
if (ta.change(pnl_004))
array.set(results_list, 3, str.tostring(pnl_004) + '|0.4')
// ▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲▲
// AUTO GENERATED CODE
// #################################################################
Noa: Z-distance from VWAP with Kalman Smoother
Title: Noa: Z-distance from VWAP with Kalman Smoother
Description:
The "Z-distance from VWAP with Kalman Smoother" is a tool constructed on the premise that price evolves in distinct stages: normal or extreme trends (upward or downward) and transitional periods, termed as 'flips'. The Volume Weighted Average Price (VWAP) serves as a benchmark, representing the market's expectation of a fair value over a given time frame. However, since each stock trades on its unique price scale, direct comparisons are not feasible. This script introduces a standardized method, using the Z-score from the VWAP, to understand and compare these relationships across diverse scales.
Core Principles:
Stages of Price Movement:
- Prices don't move purely randomly; while they contain a random element, they oscillate in discernible patterns or stages—either maintaining a trend (normal or extreme) or undergoing transition (flip).
- VWAP as Fair Value: VWAP offers a dynamic representation of what the market perceives as fair value for a stock over a specific period.
- Standardizing Price Relations: Given the varied scales at which different stocks trade, a model was imperative to standardize these relations. The Z-score from the VWAP fulfills this role, offering a normalized measure of how far the price deviates from its perceived fair value.
Features:
Z-score Levels:
The indicator demarcates various stages of price movements, offering clarity on potential overbought or oversold conditions.
- Extreme Up Trend: Indicated when the Z-score surpasses the upper limit.
- Normal Up Trend: Represented when the Z-score lies between the flip upper and the upper limit.
- Transition (Flip): Recognized when the Z-score oscillates within the flip range.
- Normal Down Trend: Denoted when the Z-score is between the flip lower and the lower limit.
- Extreme Down Trend: Marked when the Z-score falls below the lower limit.
Visual Aids:
- Color-coded regions between specific Z-score levels and the Z-score plot itself elucidate the current market state.
- Kalman Filter: By incorporating a Kalman filter, the indicator offers a less noisy and smoother representation of the Z-score, enhancing its interpretability.
Usage:
Trend Analysis:
- The Z-score states and the color-coded plot facilitate a nuanced understanding of the prevailing market trend.
- Potential Reversal Points: Extremely positive or negative Z-scores might hint at impending reversals.
- Buy/Sell Signals: Z-score's interactions with the flip level can be interpreted as potential trading signals.
Example (for illustration purposes only):
AAPL since April 2022: The stock exited from a normal uptrend and transitioned potentially towards a downtrend. By the end of April, AAPL flipped twice before transitioning to a normal downtrend. By early May, the stock moved into an aggressive downtrend. Market buyers were able to counter this downtrend by June, but selling pressure persisted, pushing the stock back into an aggressive downtrend. By the end of June, buyers halted the aggressive selling and transitioned the stock from an aggressive to normal downtrend, then to a flip, and finally to a normal uptrend by the end of August. AAPL briefly peaked into an aggressive uptrend before being pressured back to a normal downtrend. The rest of 2022 saw AAPL attempting several short-lived uptrend flips. However, 2023 brought a change, with AAPL flipping into a normal uptrend by the end of January, maintaining it until August of that year.
Credits:
This script, inspired by Z distance from VWAP by LazyBear and Kalman Smoother by alexgrover, was revamped and enriched by nord-ouestadvisors to embed these core principles and heighten its usability. A special acknowledgment to ChatGPT by OpenAI for the guidance.
Normal Distribution CurveThis Normal Distribution Curve is designed to overlay a simple normal distribution curve on top of any TradingView indicator. This curve represents a probability distribution for a given dataset and can be used to gain insights into the likelihood of various data levels occurring within a specified range, providing traders and investors with a clear visualization of the distribution of values within a specific dataset. With the only inputs being the variable source and plot colour, I think this is by far the simplest and most intuitive iteration of any statistical analysis based indicator I've seen here!
Traders can quickly assess how data clusters around the mean in a bell curve and easily see the percentile frequency of the data; or perhaps with both and upper and lower peaks identify likely periods of upcoming volatility or mean reversion. Facilitating the identification of outliers was my main purpose when creating this tool, I believed fixed values for upper/lower bounds within most indicators are too static and do not dynamically fit the vastly different movements of all assets and timeframes - and being able to easily understand the spread of information simplifies the process of identifying key regions to take action.
The curve's tails, representing the extreme percentiles, can help identify outliers and potential areas of price reversal or trend acceleration. For example using the RSI which typically has static levels of 70 and 30, which will be breached considerably more on a less liquid or more volatile asset and therefore reduce the actionable effectiveness of the indicator, likewise for an asset with little to no directional volatility failing to ever reach this overbought/oversold areas. It makes considerably more sense to look for the top/bottom 5% or 10% levels of outlying data which are automatically calculated with this indicator, and may be a noticeable distance from the 70 and 30 values, as regions to be observing for your investing.
This normal distribution curve employs percentile linear interpolation to calculate the distribution. This interpolation technique considers the nearest data points and calculates the price values between them. This process ensures a smooth curve that accurately represents the probability distribution, even for percentiles not directly present in the original dataset; and applicable to any asset regardless of timeframe. The lookback period is set to a value of 5000 which should ensure ample data is taken into calculation and consideration without surpassing any TradingView constraints and limitations, for datasets smaller than this the indicator will adjust the length to just include all data. The labels providing the percentile and average levels can also be removed in the style tab if preferred.
Additionally, as an unplanned benefit is its applicability to the underlying price data as well as any derived indicators. Turning it into something comparable to a volume profile indicator but based on the time an assets price was within a specific range as opposed to the volume. This can therefore be used as a tool for identifying potential support and resistance zones, as well as areas that mark market inefficiencies as price rapidly accelerated through. This may then give a cleaner outlook as it eliminates the potential drawbacks of volume based profiles that maybe don't collate all exchange data or are misrepresented due to large unforeseen increases/decreases underlying capital inflows/outflows.
Thanks to @ALifeToMake, @Bjorgum, vgladkov on stackoverflow (and possibly some chatGPT!) for all the assistance in bringing this indicator to life. I really hope every user can find some use from this and help bring a unique and data driven perspective to their decision making. And make sure to please share any original implementaions of this tool too! If you've managed to apply this to the average price change once you've entered your position to better manage your trade management, or maybe overlaying on an implied volatility indicator to identify potential options arbitrage opportunities; let me know! And of course if anyone has any issues, questions, queries or requests please feel free to reach out! Thanks and enjoy.
Shifted EMAsJa verschobene EMAS halt lol.
Oder wie ChatGPT sagen würde:
The "Shifted EMAs" indicator on TradingView is a customizable tool that displays three Exponential Moving Averages (EMAs) on the chart. Users can adjust the EMA lengths and apply vertical shifts to the EMAs, enabling flexible analysis of trends and potential support/resistance levels. Each EMA is represented with distinct colors for easy differentiation, providing traders with valuable insights into price movements and aiding in making well-informed trading decisions.
Smoothing R-Squared ComparisonIntroduction
Heyo guys, here I made a comparison between my favorised smoothing algorithms.
I chose the R-Squared value as rating factor to accomplish the comparison.
The indicator is non-repainting.
Description
In technical analysis, traders often use moving averages to smooth out the noise in price data and identify trends. While moving averages are a useful tool, they can also obscure important information about the underlying relationship between the price and the smoothed price.
One way to evaluate this relationship is by calculating the R-squared value, which represents the proportion of the variance in the price that can be explained by the smoothed price in a linear regression model.
This PineScript code implements a smoothing R-squared comparison indicator.
It provides a comparison of different smoothing techniques such as Kalman filter, T3, JMA, EMA, SMA, Super Smoother and some special combinations of them.
The Kalman filter is a mathematical algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement.
The input parameters for the Kalman filter include the process noise covariance and the measurement noise covariance, which help to adjust the sensitivity of the filter to changes in the input data.
The T3 smoothing technique is a popular method used in technical analysis to remove noise from a signal.
The input parameters for the T3 smoothing method include the length of the window used for smoothing, the type of smoothing used (Normal or New), and the smoothing factor used to adjust the sensitivity to changes in the input data.
The JMA smoothing technique is another popular method used in technical analysis to remove noise from a signal.
The input parameters for the JMA smoothing method include the length of the window used for smoothing, the phase used to shift the input data before applying the smoothing algorithm, and the power used to adjust the sensitivity of the JMA to changes in the input data.
The EMA and SMA techniques are also popular methods used in technical analysis to remove noise from a signal.
The input parameters for the EMA and SMA techniques include the length of the window used for smoothing.
The indicator displays a comparison of the R-squared values for each smoothing technique, which provides an indication of how well the technique is fitting the data.
Higher R-squared values indicate a better fit. By adjusting the input parameters for each smoothing technique, the user can compare the effectiveness of different techniques in removing noise from the input data.
Usage
You can use it to find the best fitting smoothing method for the timeframe you usually use.
Just apply it on your preferred timeframe and look for the highlighted table cell.
Conclusion
It seems like the T3 works best on timeframes under 4H.
There's where I am active, so I will use this one more in the future.
Thank you for checking this out. Enjoy your day and leave me a like or comment. 🧙♂️
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Credits to:
▪@loxx – T3
▪@balipour – Super Smoother
▪ChatGPT – Wrote 80 % of this article and helped with the research
Local Model Kalman Market ModeIntroduction
Heyo guys, I made a new (repainting) indicator called Local Model Kalman Market Mode.
I created it, because I wanted a reliable market mode filter for a potential mean-reversion strategy (e. g. BB Scalping).
On the screenshot you can see an example of how to use it in a BB strategy.
E.g. you would enter long when you have bullish divergence, price is under lower BB, price is under PoC and this indicator here shows range-bound market phase.
You would exit long on cross of the middle band.
Description
The indicator attempts to model the underlying market using different local models (i.e., trending, range-bound, and choppy) and combines them using the T3 Six Pole Kalman Filter to generate an overall estimate of the market.
The Fisher Transform is applied on the price to reach a Gaussian distribution, which increases the accuracy of the indicator itself.
The script first defines state variables for each local model, which include trend direction, trend strength, upper and lower bounds of the range, volatility of the range, level of choppiness, and strength of noise.
Then, likelihood functions are defined for each local model based on the state variables.
Next, the script calculates weights for each local model based on their likelihoods and uses them to calculate state variables for the overall estimate.
Finally, the script combines the state variables using the T3 Six Pole Kalman Filter to generate the overall estimate of the market, which is plotted in blue.
Fundamental Knowledge
To understand the explanation of the indicator and the script, there are a few fundamental concepts that you need to know:
Market: A market is a place where buyers and sellers come together to exchange goods or services.
In the context of trading, the market refers to the exchange where financial instruments such as stocks, currencies, and commodities are bought and sold.
Local models: Local models are statistical models that attempt to capture the characteristics of a particular market regime.
For example, a trending market may have different characteristics than a range-bound market or a choppy market.
The indicator uses different local models to capture the different market regimes.
Trend direction and strength: The trend direction refers to the direction in which the market is moving, either up or down.
The trend strength refers to the magnitude of the trend and how likely it is to continue.
Range-bound market: A range-bound market is a market where prices are trading within a specific range, with a clear upper and lower bound.
Choppiness: Choppiness refers to the degree of irregularity in price movements, often seen in sideways or range-bound markets.
Volatility: Volatility refers to the degree of variation in the price of an asset over time. High volatility implies larger price swings, while low volatility implies smaller price swings.
Kalman filter: A Kalman filter is a mathematical algorithm used to estimate an unknown variable from a series of noisy measurements.
In the context of the indicator, the Kalman filter is used to generate an overall estimate of the market by combining the local models.
T3 Six Pole Kalman Filter: The T3 Six Pole Kalman Filter is a specific type of Kalman filter that is used to smooth and filter time-series data, such as the price data of a financial instrument.
Fisher Transform: The Fisher Transform is a mathematical formula used to transform any probability distribution into a Gaussian normal distribution. It is commonly used in technical analysis to transform non-Gaussian indicators into ones that are more suitable for statistical analysis.
By understanding these fundamental concepts, you should have a basic understanding of how the indicator works and how it generates an overall estimate of the market.
Usage
You can use this indicator on every timeframe.
Users can customize the parameters of the T3 Six Pole Kalman Filter (T3 length, alpha, beta, gamma, and delta) using input functions.
Try out different parameter combinations and use the one you like most.
Thank you for checking this out. Leave me a comment or boost the script, when you wanna support me! 👌
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Credits to:
▪@HPotter - Fisher Transform
▪@loxx - T3
▪ChatGPT - Helped me to make the research for this indicator and helped to build the core algorithm.
