ZenAlgo - QZenAlgo - Q
Description
ZenAlgo - Q is an oscillator based on the QQE (Quantitative Qualitative Estimation) method. This version incorporates refinements for additional visualization and interpretation options. It is designed to help traders observe momentum changes and divergence patterns in price movements.
Key Features
QQE-Based Calculation : Derived from the open-source QQE script by Glaz (Metastock Version of QQE), with modifications for alternative visualization.
Dual RSI-Based Analysis : Uses two RSI calculations to provide additional context on price movements.
Adaptive Trend Bands : Adjust dynamically based on the market conditions.
Divergence Identification : Highlights potential differences between price action and oscillator movement.
Dynamic Color Coding : Displays histogram bars to illustrate shifts in oscillator values.
Configurable Alerts : Enables notifications for specific oscillator conditions.
How It Works
The indicator calculates a smoothed RSI-based oscillator that tracks the relative strength of price movement. It applies an exponential moving average (EMA) smoothing to reduce noise while maintaining responsiveness.
Two adaptive bands are calculated using a variation of the QQE method, which helps define dynamic overbought and oversold conditions.
The histogram bars shift in color based on the position of the oscillator relative to the bands. Lighter shades indicate weaker momentum, while stronger momentum is represented by more saturated colors.
The script also includes a secondary RSI component, which provides an additional layer of analysis. This secondary RSI helps refine momentum trends by smoothing out short-term fluctuations.
Divergence identification is built-in, highlighting where price action deviates from oscillator readings. Bullish divergence occurs when price forms a lower low while the oscillator forms a higher low, and bearish divergence is identified when price forms a higher high while the oscillator forms a lower high.
The indicator does not generate buy or sell signals but instead provides contextual information that can be used alongside other trading strategies.
Use Cases
Trend Observation : Traders can use the histogram to observe whether momentum is strengthening or weakening over time. A shift in color can indicate a potential change in trend strength.
Divergence Analysis : By comparing oscillator divergence with price movement, traders can identify situations where price action may be losing momentum. Divergences do not guarantee reversals but can serve as an early warning to re-evaluate positions.
Momentum Tracking : The dual RSI structure allows users to monitor both short-term and long-term momentum. When both RSI components are aligned, it suggests a more stable trend, while divergence between them may indicate potential consolidation or trend shifts.
Supplementary Analysis : This indicator is best used as a supporting tool alongside volume-based or trend-following indicators. It helps visualize underlying price behavior but should not be used in isolation for decision-making.
Market Context Interpretation : The combination of adaptive bands and histogram visualization allows traders to assess how recent price action compares to historical movement, helping to place current conditions in a broader market context.
Attribution
This script is an adaptation of the open-source QQE script originally developed by Glaz. We acknowledge and appreciate the original author's work, which served as a foundation for our modifications.
Disclaimer
This indicator is intended for informational purposes only. It should not be interpreted as financial advice. Always conduct independent research and risk management before making trading decisions.
Cari dalam skrip untuk "signal"
E9 MM Nuke signalScript identifies wickless candles on a specified higher timeframe and plots them on a lower timeframe (If desired), such as 15 minutes. It includes options to adjust the margin for error (e.g. 5 tick wick), higher timeframe, and toggle the volume filter with period adjustment.
Wickless candles signal strong market sentiment shifts, indicating areas of significant buying or selling pressure. These areas can become key levels of support or resistance, making them crucial to monitor for potential price revisits.
Why Price Revisits Wickless Areas
Manipulators often create artificial wickless candles to deceive traders. However, genuine market movements can also produce wickless candles, indicating a strong consensus among market participants. In either case, the price is likely to revisit these areas as traders and investors react to the perceived market sentiment shift.
Key Features:
Margin Input:
Description: Allows users to specify the margin in 0.01 tick increments to account for small wicks due to spread issues.
Example: A margin of 0.05 ticks means the script will consider candles wickless if the high is within 0.05 ticks of the open and the low is within 0.05 ticks of the open.
Volume Filter:
Description: Users can enable or disable a volume filter to consider only candles with a volume greater than the average volume over a specified period.
Default: Enabled by default.
Volume Period Input: Users can specify the period for calculating the average volume (e.g., 9 periods).
Higher Timeframe Input:
Description: Allows users to select the higher timeframe on which to identify wickless candles.
Options: H4 ("240"), Daily ("D"), Weekly ("W"), Monthly ("M").
Plotting:
Bearish Wickless Candles: Plotted with a red circle and a "🐻" emoji above the bar.
Bullish Wickless Candles: Plotted with a green circle and a "🐂" emoji below the bar.
Drawdown Visualisation█ OVERVIEW
The Drawdown Visualisation indicator calculates and displays the instrument’s drawdown (in percent) relative to its all‐time high (ATH) from a user‐defined start date. It provides customisable options for label appearance, threshold lines (0%, –50%, –100%), and can plot historic drawdown levels via pivot detection.
█ USAGE
This indicator should be used with the Percentage Retracement from ATH indicator.
█ KEY FEATURES
Custom Date Settings — Use a custom start date so that only specified price action is considered.
Retracement Level Calculation — Determines ATH and computes multiple retracement levels using percentages from 0% to –100%.
Visual Signals and Customisation — Plots configurable horizontal lines and labels that display retracement percentages and prices.
Time Filtering — Bases calculations on data from the desired time period.
Historic Drawdowns — Display historical drawdowns
█ PURPOSE
Assist traders in visualising the depth of price retracements from recent or historical peaks.
Identify critical zones where the market may find support or resistance after reaching an ATH.
Facilitate more informed entry and exit decisions by clearly demarcating retracement levels on the chart.
█ IDEAL USERS
Swing Traders — Looking to exploit pullbacks following strong upward moves.
Technical Analysts — Interested in pinpointing key retracement levels as potential reversal or continuation points.
Price Action Traders — Focused on the nuances of market peaks and subsequent corrections.
Strategy Developers — Keen to backtest and refine approaches centred on retracement dynamics.
VWMA with Buy/Sell Signalshe VWMA (Volume Weighted Moving Average) is a technical indicator that averages prices over a specified period while giving more weight to periods with higher trading volumes. This makes the VWMA more sensitive to price movements during high-volume trading compared to a simple moving average (SMA).
Adding Buy/Sell Signals to a VWMA-based script involves identifying trends or crossover points that indicate potential entry (Buy) or exit (Sell) opportunities.
Core Features of the Script:
VWMA Calculation:
Uses the typical price ((High + Low + Close) / 3) or closing price for computation.
Weighting is based on the volume traded in each period.
Machine Learning Moving Average [LuxAlgo]The Machine Learning Moving Average (MLMA) is a responsive moving average making use of the weighting function obtained Gaussian Process Regression method. Characteristic such as responsiveness and smoothness can be adjusted by the user from the settings.
The moving average also includes bands, used to highlight possible reversals.
🔶 USAGE
The Machine Learning Moving Average smooths out noisy variations from the price, directly estimating the underlying trend in the price.
A higher "Window" setting will return a longer-term moving average while increasing the "Forecast" setting will affect the responsiveness and smoothness of the moving average, with higher positive values returning a more responsive moving average and negative values returning a smoother but less responsive moving average.
Do note that an excessively high "Forecast" setting will result in overshoots, with the moving average having a poor fit with the price.
The moving average color is determined according to the estimated trend direction based on the bands described below, shifting to blue (default) in an uptrend and fushia (default) in downtrends.
The upper and lower extremities represent the range within which price movements likely fluctuate.
Signals are generated when the price crosses above or below the band extremities, with turning points being highlighted by colored circles on the chart.
🔶 SETTINGS
Window: Calculation period of the moving average. Higher values yield a smoother average, emphasizing long-term trends and filtering out short-term fluctuations.
Forecast: Sets the projection horizon for Gaussian Process Regression. Higher values create a more responsive moving average but will result in more overshoots, potentially worsening the fit with the price. Negative values will result in a smoother moving average.
Sigma: Controls the standard deviation of the Gaussian kernel, influencing weight distribution. Higher Sigma values return a longer-term moving average.
Multiplicative Factor: Adjusts the upper and lower extremity bounds, with higher values widening the bands and lowering the amount of returned turning points.
🔶 RELATED SCRIPTS
Machine-Learning-Gaussian-Process-Regression
SuperTrend-AI-Clustering
Machine Learning Price Target Prediction Signals [AlgoAlpha]Introducing the Machine Learning Price Target Predictions, a cutting-edge trading tool that leverages kernel regression to provide accurate price targets and enhance your trading strategy. This indicator combines trend-based signals with advanced machine learning techniques, offering predictive insights into potential price movements. Perfect for traders looking to make data-driven decisions with confidence.
What is Kernel Regression and How It Works
Kernel regression is a non-parametric machine learning technique that estimates the relationship between variables by weighting data points based on their similarity to a given input. The similarity is determined using a kernel function, such as the Gaussian (RBF) kernel, which assigns higher weights to closer data points and progressively lower weights to farther ones. This allows the model to make smooth and adaptive predictions, balancing recent data and historical trends.
Key Features
🎯 Predictive Price Targets : Uses kernel regression to estimate the magnitude of price movements.
📈 Dynamic Trend Analysis : Multiple trend detection methods, including EMA crossovers, Hull Moving Average, and SuperTrend.
🔧 Customizable Settings : Adjust bandwidth for kernel regression and tweak trend indicator parameters to suit your strategy.
📊 Visual Trade Levels : Displays take-profit and stop-loss levels directly on the chart with customizable colors.
📋 Performance Metrics : Real-time win rate, recommended risk-reward ratio, and training data size displayed in an on-chart table.
🔔 Alerts : Get notified for new trends, take-profit hits, and stop-loss triggers.
How to Use
🛠 Add the Indicator : Add it to your favorites and apply it to your chart. Configure the trend detection method (SuperTrend, HMA, or EMA crossover) and other parameters based on your preferences.
📊 Analyze Predictions : Observe the predicted move size, recommended risk-reward ratio, and trend direction. Use the displayed levels for trade planning.
🔔 Set Alerts : Enable alerts for trend signals, take-profit hits, or stop-loss triggers to stay informed without constant monitoring.
How It Works
The indicator calculates features such as price volatility, relative strength, and trend signals, which are stored during training periods. When a trend change is detected, the kernel regression model predicts the likely price move based on these features. Predictions are smoothed using the specified bandwidth to avoid overfitting while ensuring timely responses to feature changes. Visualized take-profit and stop-loss levels help traders optimize risk management. Real-time metrics like win rate and recommended risk-reward ratios provide actionable insights for decision-making.
BS | Buy&Sell Signals With EMAKey Features:
EMA Intersections: Generates clear buy and sell signals based on predefined EMA crossings.
5 EMA Lines: Visualize market trends with five distinct EMA lines plotted on the chart.
Support and Resistance Levels: Easily identify crucial support and resistance levels with our integrated marker.
Comprehensive Indicator Panel: At the bottom of the chart, track Stochastic, RSI, Supertrend, and SMA across multiple timeframes (1m, 5m, 15m, 1H, 4H, Daily, Weekly).
Fully Customizable: Almost every indicator within the tool is adjustable to suit your preferences and trading style.
Alarm Feature: Set up alarms to stay informed of important market movements.
Unlock the full potential of your trading strategy with BS | Buy&Sell Signals With EMA. Customize, analyze, and trade with confidence.
created by @bahadirsezer
Algorithmic Signal AnalyzerMeet Algorithmic Signal Analyzer (ASA) v1: A revolutionary tool that ushers in a new era of clarity and precision for both short-term and long-term market analysis, elevating your strategies to the next level.
ASA is an advanced TradingView indicator designed to filter out noise and enhance signal detection using mathematical models. By processing price movements within defined standard deviation ranges, ASA produces a smoothed analysis based on a Weighted Moving Average (WMA). The Volatility Filter ensures that only relevant price data is retained, removing outliers and improving analytical accuracy.
While ASA provides significant analytical advantages, it’s essential to understand its capabilities in both short-term and long-term use cases. For short-term trading, ASA excels at capturing swift opportunities by highlighting immediate trend changes. Conversely, in long-term trading, it reveals the overall direction of market trends, enabling traders to align their strategies with prevailing conditions.
Despite these benefits, traders must remember that ASA is not designed for precise trade execution systems where accuracy in timing and price levels is critical. Its focus is on analysis rather than order management. The distinction is crucial: ASA helps interpret price action effectively but may not account for real-time market factors such as slippage or execution delays.
Features and Functionality
ASA integrates multiple tools to enhance its analytical capabilities:
Customizable Moving Averages: SMA, EMA, and WMA options allow users to tailor the indicator to their trading style.
Signal Detection: Identifies bullish and bearish trends using the Relative Exponential Moving Average (REMA) and marks potential buy/sell opportunities.
Visual Aids: Color-coded trend lines (green for upward, red for downward) simplify interpretation.
Alert System: Notifications for trend swings and reversals enable timely decision-making.
Notes on Usage
ASA’s effectiveness depends on the context in which it is applied. Traders should carefully consider the trade-offs between analysis and execution.
Results may vary depending on market conditions and chart types. Backtesting with ASA on standard charts provides more reliable insights compared to non-standard chart types.
Short-term use focuses on rapid trend recognition, while long-term application emphasizes understanding broader market movements.
Takeaways
ASA is not a tool for precise trade execution but a powerful aid for interpreting price trends.
For short-term trading, ASA identifies quick opportunities, while for long-term strategies, it highlights trend directions.
Understanding ASA’s limitations and strengths is key to maximizing its utility.
ASA is a robust solution for traders seeking to filter noise, enhance analytical clarity, and align their strategies with market movements, whether for short bursts of activity or sustained trading goals.
Trend Levels [ChartPrime]The Trend Levels indicator is designed to identify key trend levels (High, Mid, and Low) during market trends, based on real-time calculations of highest, lowest, and mid-level values over a customizable length. Additionally, the indicator calculates trend strength by measuring the ratio of candles closing above or below the midline, providing a clear view of the ongoing trend dynamics and strength.
⯁ KEY FEATURES AND HOW TO USE
⯌ Trend Shift Signals :
Trend shifts, based on highest and lowest values during input length. When high is == to highest it will change trend to up when low == lowest value it will be shift to down trend.
// Calculate highest and lowest over the specified length
h = ta.highest(length)
l = ta.lowest(length)
// Determine trend direction: if the current high is the highest value, set trend to true
if h == high
trend := true
// If the current low is the lowest value, set trend to false
if l == low
trend := false
Whenever the trend changes direction (from uptrend to downtrend or vice versa), the indicator provides visual cues in the form of arrows. This gives traders clear signals to identify potential trend reversals, enabling them to adjust their strategies accordingly.
⯌ Trend Level Calculation :
As soon as a trend is detected (uptrend or downtrend), the indicator starts calculating the highest, lowest, and mid-level values over the defined period. These levels are plotted on the chart as color-coded lines for easy visualization, allowing traders to quickly spot the key levels within a trend.
⯌ Midline Retests :
Throughout the trend, the mid-level line is often retested, acting as a potential zone for pullbacks or rejections. Traders can use these retests as opportunities for entering positions or confirming trend continuation. The chart shows how price frequently interacts with the midline, helping to identify important reaction levels.
⯌ Trend Strength Calculation :
The indicator measures the trend strength by calculating the delta between the number of candles closing above and below the midline. This percentage-based delta is displayed in real-time, providing a clear indication of whether the trend is gaining or losing momentum.
⯁ USER INPUTS
Length : Specifies the lookback period for calculating the highest and lowest values, which determines the key trend levels.
Candle Counting : Measures the number of candles closing above and below the midline to calculate the trend strength delta.
⯁ CONCLUSION
The Trend Levels indicator provides traders with a powerful tool for visualizing trend dynamics, key levels of support and resistance, and real-time trend strength. By identifying midline retests, tracking candle counts, and providing trend shift signals, this indicator can help traders make well-informed decisions during market trends.
BTC ETF Flow Trading SignalsTracks large money flows (500M+) across major Bitcoin ETFs (IBIT, BTCO, FBTC, ARKB, BITB)
Generates long/short signals based on institutional money movement
Shows flow trends and strength of movements
This script provides a foundation for comparing ETF inflows and Bitcoin price. The effectiveness of the analysis depends on the quality of the data and your interpretation of the results. Key levels of 500M and 350M Inflow/Outflow Enjoy
Collaboration with Vivid Vibrations
Enjoy & improve!
AHR999 Bitcoin Buy/Sell Signals Indicator - Accurate Trading OppThis Pine Script indicator combines the AHR999 metric with Bitcoin's historical price trends to provide clear buy and sell signals, assisting you in making informed trading decisions at crucial moments. It calculates the AHR999 index based on Bitcoin's 200-day Geometric Moving Average (GMA) and the estimated price, offering customizable buy and sell thresholds for precise entry and exit points. Ideal for traders looking to capture long-term investment trends, this indicator helps you effectively identify Bitcoin market opportunities.
Custom Signal Oscillator StrategyThe CSO is made to help traders easily test their theories by subtracting the difference between two customizable plots(indicators) without having to search for strategies. The general purpose is to provide a tool to users without coding knowledge.
How to use :
Apply the indicator(s) to test
Go to the CSO strategy input settings and select the desired plots from the added indicators. (The back test will enter long or short depending on the fast signal crosses on the slow signal)
Pull up the strategy tester
Adjust the input settings on the selected indicator(s) to back test
For example, the published strategy is using the basis lines from two Donchian channels with varying length. This can be utilized with multiple overlays on the chart and oscillators that are operating on the same scale with each other. Since chart glows aren't extremely common, a glow option is included to stand out on the chart as the chain operator. A long only option for is also included for versatility.
Median Momentum with Buy/Sell Signals and Bar ColorMomentum Calculation:
Momentum is calculated as the difference between the current close price and the close price momentum_length periods ago: momentum = close - close .
Highest and Lowest Momentum:
The highest and lowest momentum values over the specified length are calculated.
Median Momentum:
The median momentum is calculated as the average of the highest and lowest momentum values.
Color Setting:
medianColor is set based on whether the momentum is above, below, or equal to the median momentum.
barColor is set similarly for bar coloring.
Plotting:
The script plots the median momentum and the actual momentum values.
Buy and sell signals are generated when momentum crosses over or under the median momentum.
The script also plots the buy and sell signals with arrows on the chart.
FiboSequFiboSequ: Fibonacci Sequence Marking
Leonardo Fibonacci was an Italian mathematician who lived in the 12th century. His real name was Leonardo of Pisa, but he is commonly known as "Fibonacci." Fibonacci is famous for introducing the Hindu-Arabic numeral system to the Western world. This system is the basis of the modern decimal number system we use today.
Fibonacci Sequence
The Fibonacci sequence is a series of numbers that frequently appears in mathematics and nature. The first two numbers in the sequence are 0 and 1, and each subsequent number is the sum of the two preceding numbers.
The sequence is as follows:
0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, ...
Fibonacci Time Zones:
Fibonacci time zones are used to identify potential turning points in the market at specific time intervals. These time zones correspond to the Fibonacci sequence in terms of consecutive days or weeks.
The Fibonacci sequence has a wide range of applications in both mathematics and nature. Leonardo Fibonacci's work has had a significant impact on the development of modern mathematics and numeral systems. In financial markets, the Fibonacci sequence and ratios are frequently used by technical analysts to predict and analyze market movements.
Description:
Overview:
The FiboSequ indicator marks significant days on a price chart based on the Fibonacci sequence. This can help traders identify potential turning points or areas of interest in the market. The Fibonacci sequence is a series of numbers where each number is the sum of the two preceding ones, often found in nature and financial markets.
Fibonacci Sequence:
The sequence used in this indicator includes: 1, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, and 2584.
These numbers represent the days to be marked on the chart, highlighting possible significant market movements.
How It Works:
User Input:
Users can input the starting date (Year, Month, and Day) from which the Fibonacci sequence will begin to be calculated.
This allows flexibility and customization based on the trader's analysis needs.
Calculation:
The starting date is converted into a timestamp in seconds.
For each bar on the chart, the number of days since the starting date is calculated.
The indicator checks if the current day matches any of the Fibonacci sequence days, the previous day, or the next day.
In this indicator, Fibonacci numbers can be displayed on the chart as plus and minus 2 days. For example, for the 145th day, signals start to appear as 143,144 and 145. This is due to dates that sometimes coincide with weekends and public holidays.
Marking the Chart:
When a match is found, a label is placed above the bar indicating the day number from the Fibonacci sequence.
These labels are colored blue with white text for easy visibility.
Usage:
This indicator can be used on any timeframe and market to help identify potential areas where price might react.
It is especially useful for those who employ Fibonacci analysis in their trading strategy.
Example:
If the starting date is January 1, 2020, the indicator will mark significant Fibonacci days (e.g., 1, 3, 5, 8 days, etc.) on the chart from this date onward.
Community Guidelines Compliance:
This indicator adheres to TradingView's Pine Script community guidelines.
It provides customizable user inputs and does not violate any terms of use.
By using the FiboSequ indicator, traders can enhance their technical analysis by incorporating time-based Fibonacci levels, potentially leading to better market timing and decision-making.
Frequently Asked Questions (FAQ)
Q: What is the FiboSequ indicator?
A: The FiboSequ indicator is a technical analysis tool that marks significant days on a price chart based on the Fibonacci sequence. This indicator helps traders identify potential turning points or areas of interest in the market.
Q: What is the Fibonacci sequence and why is it important?
A: The Fibonacci sequence is a series of numbers where each number is the sum of the two preceding ones. The first two numbers are 0 and 1. This sequence frequently appears in nature and financial markets and is used in technical analysis to identify important support and resistance levels.
Q: How do the Fibonacci time zones in the indicator work?
A: Fibonacci time zones are used to identify potential market turning points at specific time intervals. The indicator calculates days based on the Fibonacci sequence (e.g., 1, 3, 5, 8 days, etc.) from the starting date and marks them on the chart.
Q: How can users set the starting date?
A: Users can input the starting date by specifying the year, month, and day. This sets the date from which the indicator begins its calculations, providing flexibility for user analysis.
Q: What do the labels in the indicator represent?
A: The labels mark specific days in the Fibonacci sequence. For example, 1st day, 3rd day, 5th day, etc. These labels are displayed in blue with white text for easy visibility.
Q: Which timeframes can I use the FiboSequ indicator on?
A: The FiboSequ indicator can be used on any timeframe. This includes daily, weekly, or monthly charts, as well as shorter timeframes.
Q: Which markets can the FiboSequ indicator be used in?
A: The FiboSequ indicator can be used in various financial markets, including stocks, forex, cryptocurrencies, commodities, and more.
Q: How can I achieve better market timing with the FiboSequ indicator?
A: The FiboSequ indicator helps identify potential market turning points using time-based Fibonacci levels. This can lead to better market timing and more informed trading decisions for traders.
-Please feel free to write your valuable comments and opinions. I attach importance to your valuable opinions so that I can improve myself.
Marubozu SignalsThis Pine Script code is designed to identify and plot Marubozu candlestick patterns on a trading chart. Marubozu candles are characterized by having very small or non-existent wicks (shadows) and a large body, indicating strong buying or selling pressure.
The script first calculates the body size and wick size for both red (bearish) and green (bullish) Marubozu candles. It then defines thresholds for both body and wick sizes based on a percentage of the body size. For example, a red Marubozu candle is identified if its body size is at least 90% of the calculated threshold, and both upper and lower wick sizes are smaller than 5% of the body size.
Similarly, green Marubozu candles are identified using the same logic. If a Marubozu candle of either color is detected, a corresponding label is plotted on the chart indicating the occurrence of the pattern. A "Sell" label is placed above the candle for red Marubozu patterns, while a "Buy" label is placed below the candle for green Marubozu patterns.
This script provides visual cues to traders, helping them quickly identify potential buying or selling opportunities based on Marubozu candlestick patterns. Traders can use these signals as part of their technical analysis to make informed trading decisions.
(mab) Dynamic Bitcoin NVT SignalBitcoin`s NVT is calculated by dividing the Network Value (market cap) by the USD volume transmitted through the blockchain daily. Note this equivalent of the bitcoin token supply divided by the daily BTC value transmitted through the blockchain, NVT is technically inverse monetary velocity.
Credits go to Willy Woo for creating the Network Value Transaction Ratio (NVT). Credits go also to Dimitry Kalichkin improving NVT and creating the NVT Signal (NVTS).
According to its creator, the NVT Ratio is somewhat similar to the PE Ratio used in equity markets. When Bitcoin`s NVT is high, it indicates that its network valuation is outstripping the value being transmitted on its payment network, this can happen when the network is in high growth and investors are valuing it as a high return investment, or alternatively when the price is in an unsustainable bubble.
I created this indicator because the NVT indicator I was using suddenly stopped working. I tried a number of other NVT indicators, but all of them seem to have the same problem and stopped updating after a certain date. The cause is that the data feed from 'Quandl' that is used by most NVT indicators is no longer updated through the previous API.
Instead TradingView created a special API to access 'Quandl" data. This indicator not only uses the new API for 'Quandl', it can also access data from other providers like 'Glassnode', 'CoinMetrics' and 'IntoTheBlock'. However, the 'Quandl' data feed seems to produce the best results with this indicator.
The indicator provides dynamically adjusting overbought and oversold thresholds based on a two year moving average and standard devition with adjustable multipliers. It also implements alerts for NVT going into overbought, oversold or crossing the moving average.
Version 1.0
--
Version history
0.1 Beta
- Initial version
1.0
- First release
Order-Block Detector ICT/SMT + FVG + SignalsOrderBlock-Finder
This script shows order-blocks (OB) and fair-value-gaps (FVG). Additionaly there are entry signals for OB and FVG. The Dist-Parameter tell how many candles should exist between the beginning of the OB or FVG and the pullback.
Order-Blocks
An order block in trading typically refers to a significant grouping of buy or sell orders at a particular price level within a financial market. These blocks of orders can influence price movement when they are executed. Here's a breakdown:
Buy Order Block: This occurs when there's a large concentration of buy orders at a specific price level. It indicates a significant interest among traders to purchase the asset if the price reaches that level.
Sell Order Block: Conversely, a sell order block happens when there's a notable accumulation of sell orders at a particular price level. This suggests that many traders are willing to sell the asset if the price reaches that level.
Impact on Price: Order blocks can influence price movement because when the market approaches these levels, the orders within the block may be triggered, leading to increased buying or selling pressure, depending on the type of block. This surge in trading activity can cause the price to either bounce off the level or break through it.
Support and Resistance: Order blocks are often associated with support and resistance levels. A buy order block may act as support, preventing the price from falling further, while a sell order block may serve as resistance, hindering upward price movement.
Fair-Value-Gap
The fair value gap in trading refers to the difference between the current market price of an asset and its calculated fair value. This concept is often used in financial markets, especially in the context of stocks and other securities. Here's a breakdown:
Market Price: The market price is the price at which an asset is currently trading in the market. It is determined by the interaction of supply and demand forces, as well as various other factors such as news, sentiment, and economic conditions.
Fair Value: Fair value represents the estimated intrinsic value of an asset based on fundamental analysis, which includes factors such as earnings, dividends, cash flow, growth prospects, and prevailing interest rates. It's essentially what an asset should be worth based on its fundamentals.
Fair Value Calculation: Analysts and investors use various methods to calculate the fair value of an asset. Common approaches include discounted cash flow (DCF) analysis, comparable company analysis (CCA), and dividend discount models (DDM), among others.
Fair Value Gap: The fair value gap is the numerical difference between the calculated fair value of an asset and its current market price. If the market price is higher than the fair value, it suggests that the asset may be overvalued. Conversely, if the market price is lower than the fair value, it indicates that the asset may be undervalued.
Trading Implications: Traders and investors often pay attention to the fair value gap to identify potential trading opportunities. If the market price deviates significantly from the fair value, it may present opportunities to buy or sell the asset with the expectation that the market price will eventually converge towards its fair value.
Octopus Nest Strategy Hello Fellas,
Hereby, I come up with a popular strategy from YouTube called Octopus Nest Strategy. It is a no repaint, lower timeframe scalping strategy utilizing PSAR, EMA and TTM Squeeze.
The strategy considers these market factors:
PSAR -> Trend
EMA -> Trend
TTM Squeeze -> Momentum and Volatility by incorporating Bollinger Bands and Keltner Channels
Note: As you can see there is a potential improvement by incorporating volume.
What's Different Compared To The Original Strategy?
I added an option which allows users to use the Adaptive PSAR of @loxx, which will hopefully improve results sometimes.
Signals
Enter Long -> source above EMA 100, source crosses above PSAR and TTM Squeeze crosses above 0
Enter Short -> source below EMA 100, source crosses below PSAR and TTM Squeeze crosses below 0
Exit Long and Exit Short are triggered from the risk management. Thus, it will just exit on SL or TP.
Risk Management
"High Low Stop Loss" and "Automatic High Low Take Profit" are used here.
High Low Stop Loss: Utilizes the last high for short and the last low for long to calculate the stop loss level. The last high or low gets multiplied by the user-defined multiplicator and if no recent high or low was found it uses the backup multiplier.
Automatic High Low Take Profit: Utilizes the current stop loss level of "High Low Stop Loss" and gets calculated by the user-defined risk ratio.
Now, follows the bunch of knowledge for the more inexperienced readers.
PSAR: Parabolic Stop And Reverse; Developed by J. Welles Wilders and a classic trend reversal indicator.
The indicator works most effectively in trending markets where large price moves allow traders to capture significant gains. When a security’s price is range-bound, the indicator will constantly be reversing, resulting in multiple low-profit or losing trades.
TTM Squeeze: TTM Squeeze is a volatility and momentum indicator introduced by John Carter of Trade the Markets (now Simpler Trading), which capitalizes on the tendency for price to break out strongly after consolidating in a tight trading range.
The volatility component of the TTM Squeeze indicator measures price compression using Bollinger Bands and Keltner Channels. If the Bollinger Bands are completely enclosed within the Keltner Channels, that indicates a period of very low volatility. This state is known as the squeeze. When the Bollinger Bands expand and move back outside of the Keltner Channel, the squeeze is said to have “fired”: volatility increases and prices are likely to break out of that tight trading range in one direction or the other. The on/off state of the squeeze is shown with small dots on the zero line of the indicator: red dots indicate the squeeze is on, and green dots indicate the squeeze is off.
EMA: Exponential Moving Average; Like a simple moving average, but with exponential weighting of the input data.
Don't forget to check out the settings and keep it up.
Best regards,
simwai
---
Credits to:
@loxx
@Bjorgum
@Greeny
Donchian Trend SignalsThe Donchian Trend Signals is an indicator developed to help traders identify the current trend direction and potential liquidity grabs.
The usage of the indicator is very simple, on the chart you'll see a modified version of the classic and popular Donchian channel, calculated using the closing prices, that changes the color of the average middle line to indicate the direction of the current trend. The indicator also colors the candlestick.
Using the option "Complex Mode" will give your indicator additional data by changing the calculation method. These changes make the lines become the average between different lengths of the same Donchian channel formula.
Additionally, the indicator plots on the chart some buy or sell signals, displayed as diamonds above or below the candles. The signals are calculated to find potential liquidity grabs using the wicks, the true range of the candles, and the volume compared to his average value.
Multimarket Direction indicatorTrendline trading with resistant and support made by me.
Im bad coder and just jump into the tradingview pine script 1 days before so please don't hates me
- I don't know why my script is ded before lol
Signals to trade up
1. The big candles up cross the ema200 (last 5 candles for confirmation)
2. Wait for showing the up triangle.
3. Lookup the resistant/support line. If near the resistant please consider to wait if it break then join the trade
4. Only out trade when it has a down triagle or the candles has big down candles at the resistant/support line.
That it...
Relational Quadratic Kernel Channel [Vin]The Relational Quadratic Kernel Channel (RQK-Channel-V) is designed to provide more valuable potential price extremes or continuation points in the price trend.
Example:
Usage:
Lookback Window: Adjust the "Lookback Window" parameter to control the number of previous bars considered when calculating the Rational Quadratic Estimate. Longer windows capture longer-term trends, while shorter windows respond more quickly to price changes.
Relative Weight: The "Relative Weight" parameter allows you to control the importance of each data point in the calculation. Higher values emphasize recent data, while lower values give more weight to historical data.
Source: Choose the data source (e.g., close price) that you want to use for the kernel estimate.
ATR Length: Set the length of the Average True Range (ATR) used for channel width calculation. A longer ATR length results in wider channels, while a shorter length leads to narrower channels.
Channel Multipliers: Adjust the "Channel Multiplier" parameters to control the width of the channels. Higher multipliers result in wider channels, while lower multipliers produce narrower channels. The indicator provides three sets of channels, each with its own multiplier for flexibility.
Details:
Rational Quadratic Kernel Function:
The Rational Quadratic Kernel Function is a type of smoothing function used to estimate a continuous curve or line from discrete data points. It is often used in time series analysis to reduce noise and emphasize trends or patterns in the data.
The formula for the Rational Quadratic Kernel Function is generally defined as:
K(x) = (1 + (x^2) / (2 * α * β))^(-α)
Where:
x represents the distance or difference between data points.
α and β are parameters that control the shape of the kernel. These parameters can be adjusted to control the smoothness or flexibility of the kernel function.
In the context of this indicator, the Rational Quadratic Kernel Function is applied to a specified source (e.g., close prices) over a defined lookback window. It calculates a smoothed estimate of the source data, which is then used to determine the central value of the channels. The kernel function allows the indicator to adapt to different market conditions and reduce noise in the data.
The specific parameters (length and relativeWeight) in your indicator allows to fine-tune how the Rational Quadratic Kernel Function is applied, providing flexibility in capturing both short-term and long-term trends in the data.
To know more about unsupervised ML implementations, I highly recommend to follow the users, @jdehorty and @LuxAlgo
Optimizing the parameters:
Lookback Window (length): The lookback window determines how many previous bars are considered when calculating the kernel estimate.
For shorter-term trading strategies, you may want to use a shorter lookback window (e.g., 5-10).
For longer-term trading or investing, consider a longer lookback window (e.g., 20-50).
Relative Weight (relativeWeight): This parameter controls the importance of each data point in the calculation.
A higher relative weight (e.g., 2 or 3) emphasizes recent data, which can be suitable for trend-following strategies.
A lower relative weight (e.g., 1) gives more equal importance to historical and recent data, which may be useful for strategies that aim to capture both short-term and long-term trends.
ATR Length (atrLength): The length of the Average True Range (ATR) affects the width of the channels.
Longer ATR lengths result in wider channels, which may be suitable for capturing broader price movements.
Shorter ATR lengths result in narrower channels, which can be helpful for identifying smaller price swings.
Channel Multipliers (channelMultiplier1, channelMultiplier2, channelMultiplier3): These parameters determine the width of the channels relative to the ATR.
Adjust these multipliers based on your risk tolerance and desired channel width.
Higher multipliers result in wider channels, which may lead to fewer signals but potentially larger price movements.
Lower multipliers create narrower channels, which can result in more frequent signals but potentially smaller price movements.
Exhaustion SignalExhaustion Signal
The Exhaustion Signal involves monitoring a sequence of consecutive bars within a price chart. This analytical approach aims to identify instances where the price exhibits pronounced movement, potentially indicating an upcoming shift in the current trend. The methodology works by assigning values to bars based on their relationship with the closing price of a bar from four periods ago. If a subsequent bar's closing price surpasses the close of the bar from four periods ago, the count advances. However, if the closing price falls below the close of the bar from four periods ago, the count is reset. This counting process continues until a predetermined count value is reached. The appearance of this count value within the exhaustion signal framework signifies a market that has extended beyond typical levels, suggesting the possibility of a temporary pause or even a reversal in the existing trend.
It's important to note that, as per the principles of this approach, the exhaustion signal by itself is not designed to function as a standalone trading indicator. The broader market context and the application of additional analysis techniques influence its significance and potential trading implications.
