Honest Volatility Grid [Honestcowboy]The Honest Volatility Grid is an attempt at creating a robust grid trading strategy but without standard levels.
Normal grid systems use price levels like 1.01;1.02;1.03;1.04... and place an order at each of these levels. In this program instead we create a grid using keltner channels using a long term moving average.
🟦 IS THIS EVEN USEFUL?
The idea is to have a more fluid style of trading where levels expand and follow price and do not stick to precreated levels. This however also makes each closed trade different instead of using fixed take profit levels. In this strategy a take profit level can even be a loss. It is useful as a strategy because it works in a different way than most strategies, making it a good tool to diversify a portfolio of trading strategies.
🟦 STRATEGY
There are 10 levels below the moving average and 10 above the moving average. For each side of the moving average the strategy uses 1 to 3 orders maximum (3 shorts at top, 3 longs at bottom). For instance you buy at level 2 below moving average and you increase position size when level 6 is reached (a cheaper price) in order to spread risks.
By default the strategy exits all trades when the moving average is reached, this makes it a mean reversion strategy. It is specifically designed for the forex market as these in my experience exhibit a lot of ranging behaviour on all the timeframes below daily.
There is also a stop loss at the outer band by default, in case price moves too far from the mean.
What are the risks?
In case price decides to stay below the moving average and never reaches the outer band one trade can create a very substantial loss, as the bands will keep following price and are not at a fixed level.
Explanation of default parameters
By default the strategy uses a starting capital of 25000$, this is realistic for retail traders.
Lot sizes at each level are set to minimum lot size 0.01, there is no reason for the default to be risky, if you want to risk more or increase equity curve increase the number at your own risk.
Slippage set to 20 points: that's a normal 2 pip slippage you will find on brokers.
Fill limit assumtion 20 points: so it takes 2 pips to confirm a fill, normal forex spread.
Commission is set to 0.00005 per contract: this means that for each contract traded there is a 5$ or whatever base currency pair has as commission. The number is set to 0.00005 because pinescript does not know that 1 contract is 100000 units. So we divide the number by 100000 to get a realistic commission.
The script will also multiply lot size by 100000 because pinescript does not know that lots are 100000 units in forex.
Extra safety limit
Normally the script uses strategy.exit() to exit trades at TP or SL. But because these are created 1 bar after a limit or stop order is filled in pinescript. There are strategy.orders set at the outer boundaries of the script to hedge against that risk. These get deleted bar after the first order is filled. Purely to counteract news bars or huge spikes in price messing up backtest.
🟦 VISUAL GOODIES
I've added a market profile feature to the edge of the grid. This so you can see in which grid zone market has been the most over X bars in the past. Some traders may wish to only turn on the strategy whenever the market profile displays specific characteristics (ranging market for instance).
These simply count how many times a high, low, or close price has been in each zone for X bars in the past. it's these purple boxes at the right side of the chart.
🟦 Script can be fully automated to MT5
There are risk settings in lot sizes or % for alerts and symbol settings provided at the bottom of the indicator. The script will send alert to MT5 broker trying to mimic the execution that happens on tradingview. There are always delays when using a bridge to MT5 broker and there could be errors so be mindful of that. This script sends alerts in format so they can be read by tradingview.to which is a bridge between the platforms.
Use the all alert function calls feature when setting up alerts and make sure you provide the right webhook if you want to use this approach.
Almost every setting in this indicator has a tooltip added to it. So if any setting is not clear hover over the (?) icon on the right of the setting.
볼래틸리티
Adaptive Kalman Trend Filter (Zeiierman)█ Overview
The Adaptive Kalman Trend Filter indicator is an advanced trend-following tool designed to help traders accurately identify market trends. Utilizing the Kalman Filter—a statistical algorithm rooted in control theory and signal processing—this indicator adapts to changing market conditions, smoothing price data to filter out noise. By focusing on state vector-based calculations, it dynamically adjusts trend and range measurements, making it an excellent tool for both trend-following and range-based trading strategies. The indicator's adaptive nature is enhanced by options for volatility adjustment and three unique Kalman filter models, each tailored for different market conditions.
█ How It Works
The Kalman Filter works by maintaining a model of the market state through matrices that represent state variables, error covariances, and measurement uncertainties. Here’s how each component plays a role in calculating the indicator’s trend:
⚪ State Vector (X): The state vector is a two-dimensional array where each element represents a market property. The first element is an estimate of the true price, while the second element represents the rate of change or trend in that price. This vector is updated iteratively with each new price, maintaining an ongoing estimate of both price and trend direction.
⚪ Covariance Matrix (P): The covariance matrix represents the uncertainty in the state vector’s estimates. It continuously adapts to changing conditions, representing how much error we expect in our trend and price estimates. Lower covariance values suggest higher confidence in the estimates, while higher values indicate less certainty, often due to market volatility.
⚪ Process Noise (Q): The process noise matrix (Q) is used to account for uncertainties in price movements that aren’t explained by historical trends. By allowing some degree of randomness, it enables the Kalman Filter to remain responsive to new data without overreacting to minor fluctuations. This noise is particularly useful in smoothing out price movements in highly volatile markets.
⚪ Measurement Noise (R): Measurement noise is an external input representing the reliability of each new price observation. In this indicator, it is represented by the setting Measurement Noise and determines how much weight is given to each new price point. Higher measurement noise makes the indicator less reactive to recent prices, smoothing the trend further.
⚪ Update Equations:
Prediction: The state vector and covariance matrix are first projected forward using a state transition matrix (F), which includes market estimates based on past data. This gives a “predicted” state before the next actual price is known.
Kalman Gain Calculation: The Kalman gain is calculated by comparing the predicted state with the actual price, balancing between the covariance matrix and measurement noise. This gain determines how much of the observed price should influence the state vector.
Correction: The observed price is then compared to the predicted price, and the state vector is updated using this Kalman gain. The updated covariance matrix reflects any adjustment in uncertainty based on the latest data.
█ Three Kalman Filter Models
Standard Model: Assumes that market fluctuations follow a linear progression without external adjustments. It is best suited for stable markets.
Volume Adjusted Model: Adjusts the filter sensitivity based on trading volume. High-volume periods result in stronger trends, making this model suitable for volume-driven assets.
Parkinson Adjusted Model: Uses the Parkinson estimator, accounting for volatility through high-low price ranges, making it effective in markets with high intraday fluctuations.
These models enable traders to choose a filter that aligns with current market conditions, enhancing trend accuracy and responsiveness.
█ Trend Strength
The Trend Strength provides a visual representation of the current trend's strength as a percentage based on oscillator calculations from the Kalman filter. This table divides trend strength into color-coded segments, helping traders quickly assess whether the market is strongly trending or nearing a reversal point. A high trend strength percentage indicates a robust trend, while a low percentage suggests weakening momentum or consolidation.
█ Trend Range
The Trend Range section evaluates the market's directional movement over a specified lookback period, highlighting areas where price oscillations indicate a trend. This calculation assesses how prices vary within the range, offering an indication of trend stability or the likelihood of reversals. By adjusting the trend range setting, traders can fine-tune the indicator’s sensitivity to longer or shorter trends.
█ Sigma Bands
The Sigma Bands in the indicator are based on statistical standard deviations (sigma levels), which act as dynamic support and resistance zones. These bands are calculated using the Kalman Filter's trend estimates and adjusted for volatility (if enabled). The bands expand and contract according to market volatility, providing a unique visualization of price boundaries. In high-volatility periods, the bands widen, offering better protection against false breakouts. During low volatility, the bands narrow, closely tracking price movements. Traders can use these sigma bands to spot potential entry and exit points, aiming for reversion trades or trend continuation setups.
Trend Based
Volatility Based
█ How to Use
Trend Following:
When the Kalman Filter is green, it signals a bullish trend, and when it’s red, it indicates a bearish trend. The Sigma Cloud provides additional insights into trend strength. In a strong bullish trend, the cloud remains below the Kalman Filter line, while in a strong bearish trend, the cloud stays above it. Expansion and contraction of the Sigma Cloud indicate market momentum changes. Rapid expansion suggests an impulsive move, which could either signal the continuation of the trend or be an early sign of a possible trend reversal.
Mean Reversion: Watch for prices touching the upper or lower sigma bands, which often act as dynamic support and resistance.
Volatility Breakouts: Enable volatility-adjusted sigma bands. During high volatility, watch for price movements that extend beyond the bands as potential breakout signals.
Trend Continuation: When the Kalman Filter line aligns with a high trend strength, it signals a continuation in that direction.
█ Settings
Measurement Noise: Adjusts how sensitive the indicator is to price changes. Higher values smooth out fluctuations but delay reaction, while lower values increase sensitivity to short-term changes.
Kalman Filter Model: Choose between the standard, volume-adjusted, and Parkinson-adjusted models based on market conditions.
Band Sigma: Sets the standard deviation used for calculating the sigma bands, directly affecting the width of the dynamic support and resistance.
Volatility Adjusted Bands: Enables bands to dynamically adapt to volatility, increasing their effectiveness in fluctuating markets.
Trend Strength: Defines the lookback period for trend strength calculation. Shorter periods result in more responsive trend strength readings, while longer periods smooth out the calculation.
Trend Range: Specifies the lookback period for the trend range, affecting the assessment of trend stability over time.
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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!
Fractional Accumulation Distribution Strategy🔹 INTRODUCTION:
As traders and investors, we often find ourselves searching for ways to maximize our market positioning—trying to capture the best price, manage risk, and adapt to ever-changing volatility. Through years of working with a variety of traders and investors, a common theme emerged: the most successful market participants were those who accumulated positions strategically over time, rather than relying on one-off, rigid entry points. However, even the best of them struggled to consistently time their entries and exits for optimal results.
That's why I created the Fractional Accumulation/Distribution Strategy (FADS)—an adaptable solution designed to dynamically adjust position sizing and entry points based on changing market conditions, enabling both passive and active market participants to optimize their approach.
The FADS trading strategy combines volatility-based trend detection and adaptive position scaling to maximize profitability across varied market conditions. By using the price ranges from higher timeframes, FADS pinpoints extreme demand and supply zones with a high statistical probability of reversal, making it effective in both high and low volatility environments. By applying adjustable threshold settings, users can focus on meaningful price movements to reduce unnecessary trades. Adaptive position scaling further enhances this approach by adjusting position sizes based on entry level distances, allowing for strategic position building that balances risk and reward in uncertain markets. This systematic scaling begins with smaller positions, expanding as the trend solidifies, creating a refined, robust trading experience.
🔹 FEATURES:
Multi-Timeframe Volatility-Based Trend Detection
Accumulation/Distribution Level Filter
Customizable Period for Highest/Lowest Prices Capture
Adjustable Sensitivity & Frequency in Positioning
Broad control settings of Strategy
Adaptive Position Scaling
🔹 SETTINGS:
Volatility : Determines trading range based on market volatility . Highest range value number of periods.
Factor : Adjusts the width of the Accumulation & Distribution bands separately. The Level Filter feature offers customizable triggering bands, allowing users to fine-tune the initiation point for the Accumulation/Distribution sequence. This flexibility enables traders to align entries more precisely with market conditions, setting optimal thresholds for initiating trade chains, whether in accumulating positions during uptrends or distributing in downtrends.
Lowest : Choose the price source (e.g., Close, Low). Number of bars considered when determining the lowest price level. Selecting the checkbox generate a signal when the price crosses below the previous lowest value for calculating the lowest value used for trade signals.
Highest : Choose the price source (e.g., Close, High). Number of bars considered when determining the highest price levels. Selecting the checkbox generate a signal when the price crosses above the previous highest value for calculating the highest value used for trade signals.
Accumulation Spread : Adjusts the buying frequency sensitivity by setting the distance between entries based on personal risk tolerance. Larger values for less frequent buys; smaller values for more frequent buys.
Distribution Spread : Adjusts the selling frequency sensitivity by setting the distance between exits based on reward preference. Larger values for less frequent sells; smaller values for more frequent sells.
Percentage of Capital Allocation : Sets the portion of total capital used for the initial trade in a strategy. It sets the scale for subsequent trades during accumulation phase.
🔹 APPLICATIONS:
❖ Accumulation and Distribution Phases
Early entries are avoided by initiating accumulation only after a trend reversal is confirmed and price breaks below long-term range.
Position sizes are determined by the distance between consecutive trades, smaller distance results in smaller position sizes and vice versa.
Average position cost is reduced by accumulating larger positions at the lower prices, potentially resulting in improved profitability.
Early exits are avoided by initiating distribution only after trend reversal is confirmed and price breaks above long-term range.
The pace of distribution can be tracked by the violet line that represents average positions during distribution phase
❖ Use Cases (Different than default setting input is used for illustration purposes)
If the starting point of accumulation starts too high for the risk preference, Accumulation Level Filter can be lowered by increasing the 🟢 threshold Factor.
If the starting point of distribution is too low for the reward preference, the Distribution Level Filter can be raised by increasing the 🔴 threshold Factor.
In lower timeframes, positions during the accumulation phase could be purchased at higher levels relative to prior entry positions. To optimize for this, consider extending the period used to capture the lowest prices. Similarly, during the distribution phase, increasing the period for identifying higher prices can improve accuracy.
🔹 Strategy Properties:
Adjusting properties within the script settings is recommended to align with specific accounts and trading platforms, ensuring realistic strategy results.
Balance (default): $100,000
Initial Order Size: 1% of the default balance
Commission: 0.1%
Slippage: 5 Ticks
Backtesting: Backtested using TradingView’s built-in strategy testing tool with default commission rates of 0.1% and slippage of 5 ticks. It reflects average market conditions for Apple Inc. (APPL) on 1-hour timeframe
Disclaimers: Commission and slippage varies with market conditions and brokerage policies. The assumed value may not represent all trading environments.
PAST PERFORMANCE DOESN’T GUARANTEE FUTURE RESULTS!
Disclaimer: Please remember that past performance may not be indicative of future results. Due to various factors, including changing market conditions, the strategy may no longer perform as well as in historical backtesting. This post and the script don’t provide any financial advice.
This invite-only script is being published as part of my commitment to developing tools that align with TradingView’s community standards. Access requests will be reviewed carefully after the script passes TradingView's moderation process.
ICT Setup 03 [TradingFinder] Judas Swing NY 9:30am + CHoCH/FVG🔵 Introduction
Judas Swing is an advanced trading setup designed to identify false price movements early in the trading day. This advanced trading strategy operates on the principle that major market players, or "smart money," drive price in a certain direction during the early hours to mislead smaller traders.
This deceptive movement attracts liquidity at specific levels, allowing larger players to execute primary trades in the opposite direction, ultimately causing the price to return to its true path.
The Judas Swing setup functions within two primary time frames, tailored separately for Forex and Stock markets. In the Forex market, the setup uses the 8:15 to 8:30 AM window to identify the high and low points, followed by the 8:30 to 8:45 AM frame to execute the Judas move and identify the CISD Level break, where Order Block and Fair Value Gap (FVG) zones are subsequently detected.
In the Stock market, these time frames shift to 9:15 to 9:30 AM for identifying highs and lows and 9:30 to 9:45 AM for executing the Judas move and CISD Level break.
Concepts such as Order Block and Fair Value Gap (FVG) are crucial in this setup. An Order Block represents a chart region with a high volume of buy or sell orders placed by major financial institutions, marking significant levels where price reacts.
Fair Value Gap (FVG) refers to areas where price has moved rapidly without balance between supply and demand, highlighting zones of potential price action and future liquidity.
Bullish Setup :
Bearish Setup :
🔵 How to Use
The Judas Swing setup enables traders to pinpoint entry and exit points by utilizing Order Block and FVG concepts, helping them align with liquidity-driven moves orchestrated by smart money. This setup applies two distinct time frames for Forex and Stocks to capture early deceptive movements, offering traders optimized entry or exit moments.
🟣 Bullish Setup
In the Bullish Judas Swing setup, the first step is to identify High and Low points within the initial time frame. These levels serve as key points where price may react, forming the basis for analyzing the setup and assisting traders in anticipating future market shifts.
In the second time frame, a critical stage of the bullish setup begins. During this phase, the price may create a false break or Fake Break below the low level, a deceptive move by major players to absorb liquidity. This false move often causes smaller traders to enter positions incorrectly. After this fake-out, the price reverses upward, breaking the CISD Level, a critical point in the market structure, signaling a potential bullish trend.
Upon breaking the CISD Level and reversing upward, the indicator identifies both the Order Block and Fair Value Gap (FVG). The Order Block is an area where major players typically place large buy orders, signaling potential price support. Meanwhile, the FVG marks a region of supply-demand imbalance, signaling areas where price might react.
Ultimately, after these key zones are identified, a trader may open a buy position if the price reaches one of these critical areas—Order Block or FVG—and reacts positively. Trading at these levels enhances the chance of success due to liquidity absorption and support from smart money, marking an opportune time for entering a long position.
🟣 Bearish Setup
In the Bearish Judas Swing setup, analysis begins with marking the High and Low levels in the initial time frame. These levels serve as key zones where price could react, helping to signal possible trend reversals. Identifying these levels is essential for locating significant bearish zones and positioning traders to capitalize on downward movements.
In the second time frame, the primary bearish setup unfolds. During this stage, price may exhibit a Fake Break above the high, causing a brief move upward and misleading smaller traders into incorrect positions. After this false move, the price typically returns downward, breaking the CISD Level—a crucial bearish trend indicator.
With the CISD Level broken and a bearish trend confirmed, the indicator identifies the Order Block and Fair Value Gap (FVG). The Bearish Order Block is a region where smart money places significant sell orders, prompting a negative price reaction. The FVG denotes an area of supply-demand imbalance, signifying potential selling pressure.
When the price reaches one of these critical areas—the Bearish Order Block or FVG—and reacts downward, a trader may initiate a sell position. Entering trades at these levels, due to increased selling pressure and liquidity absorption, offers traders an advantage in profiting from price declines.
🔵 Settings
Market : The indicator allows users to choose between Forex and Stocks, automatically adjusting the time frames for the "Opening Range" and "Trading Permit" accordingly: Forex: 8:15–8:30 AM for identifying High and Low points, and 8:30–8:45 AM for capturing the Judas move and CISD Level break. Stocks: 9:15–9:30 AM for identifying High and Low points, and 9:30–9:45 AM for executing the Judas move and CISD Level break.
Refine Order Block : Enables finer adjustments to Order Block levels for more accurate price responses.
Mitigation Level OB : Allows users to set specific reaction points within an Order Block, including: Proximal: Closest level to the current price. 50% OB: Midpoint of the Order Block. Distal: Farthest level from the current price.
FVG Filter : The Judas Swing indicator includes a filter for Fair Value Gap (FVG), allowing different filtering based on FVG width: FVG Filter Type: Can be set to "Very Aggressive," "Aggressive," "Defensive," or "Very Defensive." Higher defensiveness narrows the FVG width, focusing on narrower gaps.
Mitigation Level FVG : Like the Order Block, you can set price reaction levels for FVG with options such as Proximal, 50% OB, and Distal.
CISD : The Bar Back Check option enables traders to specify the number of past candles checked for identifying the CISD Level, enhancing CISD Level accuracy on the chart.
🔵 Conclusion
The Judas Swing indicator helps traders spot reliable trading opportunities by detecting false price movements and key levels such as Order Block and FVG. With a focus on early market movements, this tool allows traders to align with major market participants, selecting entry and exit points with greater precision, thereby reducing trading risks.
Its extensive customization options enable adjustments for various market types and trading conditions, giving traders the flexibility to optimize their strategies. Based on ICT techniques and liquidity analysis, this indicator can be highly effective for those seeking precision in their entry points.
Overall, Judas Swing empowers traders to capitalize on significant market movements by leveraging price volatility. Offering precise and dependable signals, this tool presents an excellent opportunity for enhancing trading accuracy and improving performance
Percentile Momentum IndicatorInput Parameters:
lengthPercentile: Defines the period used to calculate the percentile values (default: 30).
lengthMomentum: Defines the period for calculating the Rate of Change (ROC) momentum (default: 10).
Core Logic:
Rate of Change (ROC): The script calculates the ROC of the closing price over the specified period (lengthMomentum).
Percentile Calculations: The script calculates two key percentiles:
percentile_upper (80th percentile of the high prices)
percentile_lower (20th percentile of the low prices)
Percentile Average: An average of the upper and lower percentiles is calculated (avg_percentile).
Trade Signals:
Buy Signal: Triggered when the ROC is positive, the close is above the percentile_lower, and the close is above the avg_percentile.
Sell Signal: Triggered when the ROC is negative, the close is below the percentile_upper, and the close is below the avg_percentile.
Trade State Management:
The script uses a binary state: 1 for long (buy) and -1 for short (sell).
The trade state is updated based on buy or sell signals.
Bar Coloring:
Bars are colored dynamically based on the trade state:
Green for long (buy signal).
Red for short (sell signal).
The same color is applied to the percentile and average percentile lines for visual consistency.
BarRange StrategyHello,
This is a long-only, volatility-based strategy that analyzes the range of the previous bar (high - low).
If the most recent bar’s range exceeds a threshold based on the last X bars, a trade is initiated.
You can customize the lookback period, threshold value, and exit type.
For exits, you can choose to exit after X bars or when the close price exceeds the previous bar’s high.
The strategy is designed for instruments with a long-term upward-sloping curves, such as ES1! or NQ1!. It may not perform well on other instruments.
Commissions are set to $2.50 per side ($5.00 per round trip).
Recommended timeframes are 1h and higher. With adjustments to the lookback period and threshold, it could potentially achieve similar results on lower timeframes as well.
Old Price OscillatorThe Old Price Oscillator (OPO) is a momentum indicator widely used by traders and analysts to gauge the direction and strength of price trends. It works by calculating the difference between two moving averages—a shorter-term moving average and a longer-term moving average—of a security’s price. This difference is plotted as an oscillating line, helping traders visualize the momentum and determine when price reversals or continuations might occur. Typically, when the oscillator value is positive, the price is trending upwards, suggesting potential buy signals; conversely, when the oscillator turns negative, it indicates downward momentum, which could signal a potential sell.
The OPO is similar to other oscillators, like the Moving Average Convergence Divergence (MACD), in that it uses moving averages to smooth out price fluctuations and clarify trends. Traders often customize the length of the short- and long-term moving averages to better suit specific assets or market conditions. Generally, this indicator is especially useful in markets that exhibit clear trends. However, it may generate false signals during sideways or highly volatile periods, so many traders combine the OPO with other technical indicators or filters to improve accuracy.
Z Value AlertZ Value Alert analyzes daily price movements by evaluating fluctuations relative to historical volatility. It calculates the daily percentage change in the closing price, the average of this change over 252 days, and the standard deviation. Using these values, a Z-Score is calculated, indicating how much the current price change deviates from the historical range of fluctuations.
The user can set a threshold in standard deviations (Z-Score). When the absolute Z-Score exceeds this threshold, a significant movement is detected, indicating increased volatility. The Z-Score is visualized as a histogram, and an alert can be triggered when a significant movement occurs.
The number of trading days used to calculate historical volatility is adjustable, allowing the Sigma Move Alert to be tailored to various trading strategies and analysis periods.
Additionally, a dropdown option for the calculation method is available in the input menu, allowing the user to select between:
Normal: Calculates the percentage change in closing prices without using the logarithm.
Logarithmic: Uses the natural logarithm of daily returns. This method is particularly suitable for longer timeframes and scientific analyses, as logarithmic returns are additive.
These comprehensive features allow for precise customization of the Sigma Move Alert to individual needs and specific market conditions.
Optimus trader Optimus Trader
Indicator Description:
The Optimus Trader indicator is designed for technical traders looking for entry and exit points in financial markets. It combines signals based on volume, moving averages, VWAP (Volume Weighted Average Price), as well as the recognition of candlestick patterns such as Pin Bar and Inside Bars. This indicator helps identify opportune moments to buy or sell based on trends, volumes, and recent liquidity zones.
Parameters and Features:
1. Simple Moving Average (MA) and VWAP:
- Optimus Trader uses a 50-period simple moving average to determine the underlying trend. It also includes VWAP for precise price analysis based on traded volumes.
- These two indicators help identify whether the market is in an uptrend or downtrend, enhancing the reliability of buy and sell signals.
2. Volume :
- To avoid false signals, a volume threshold is set using a 20-period moving average, adjusted to 1.2 times the average volume. This filters signals by considering only high-volume periods, indicating heightened market interest.
3. Candlestick Pattern Recognition:
- Pin Bar: This sought-after candlestick pattern is detected for both bullish and bearish setups. A bullish or bearish *Pin Bar* often signals a possible reversal or continuation.
- *Inside Bar*: This price compression pattern is also detected, indicating a zone of indecision before a potential movement.
4. Trend:
- An uptrend is confirmed when the price is above the MA and VWAP, while a downtrend is identified when the price is below both indicators.
5. Liquidity Zones:
- Optimus Trader includes an approximate liquidity zone detection feature. By identifying recent support and resistance levels, the indicator detects if the price is near these zones. This feature strengthens the relevance of buy or sell signals.
6. Buy and Sell Signals:
- Buy: A buy signal is generated when the indicator detects a bullish *Pin Bar* or *Inside Bar* in an uptrend with high volume, and the price is close to a liquidity zone.
- Sell: A sell signal is generated when a bearish *Pin Bar* or *Inside Bar* is detected in a downtrend with high volume, and the price is near a liquidity zone.
Signal Display:
The signals are visible directly on the chart:
- A "BUY" label in green is displayed below the bar for buy signals.
- A "SELL" label in red is displayed above the bar for sell signals.
Summary:
This indicator is intended for traders seeking precise entry and exit points by integrating trend analysis, volume, and candlestick patterns. With liquidity zones, *Optimus Trader* helps minimize false signals, providing clear and accurate alerts.
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This description can be directly added to TradingView to help users quickly understand the features and logic of this indicator.
Smooth Price Oscillator [BigBeluga]The Smooth Price Oscillator by BigBeluga leverages John Ehlers' SuperSmoother filter to produce a clear and smooth oscillator for identifying market trends and mean reversion points. By filtering price data over two distinct periods, this indicator effectively removes noise, allowing traders to focus on significant signals without the clutter of market fluctuations.
🔵 KEY FEATURES & USAGE
● SuperSmoother-Based Oscillator:
This oscillator uses Ehlers' SuperSmoother filter, applied to two different periods, to create a smooth output that highlights price momentum and reduces market noise. The dual-period application enables a comparison of long-term and short-term price movements, making it suitable for both trend-following and reversion strategies.
// @function SuperSmoother filter based on Ehlers Filter
// @param price (float) The price series to be smoothed
// @param period (int) The smoothing period
// @returns Smoothed price
method smoother_F(float price, int period) =>
float step = 2.0 * math.pi / period
float a1 = math.exp(-math.sqrt(2) * math.pi / period)
float b1 = 2 * a1 * math.cos(math.sqrt(2) * step / period)
float c2 = b1
float c3 = -a1 * a1
float c1 = 1 - c2 - c3
float smoothed = 0.0
smoothed := bar_index >= 4
? c1 * (price + price ) / 2 + c2 * smoothed + c3 * smoothed
: price
smoothed
● Mean Reversion Signals:
The indicator identifies two types of mean reversion signals:
Simple Mean Reversion Signals: Triggered when the oscillator moves between thresholds of 1 and Overbought or between thresholds -1 and Ovesold, providing additional reversion opportunities. These signals are useful for capturing shorter-term corrections in trending markets.
Strong Mean Reversion Signals: Triggered when the oscillator above the overbought (upper band) or below oversold (lower band) thresholds, indicating a strong reversal point. These signals are marked with a "+" symbol on the chart for clear visibility.
Both types of signals are plotted on the oscillator and the main chart, helping traders to quickly identify potential trade entries or exits.
● Dynamic Bands and Thresholds:
The oscillator includes overbought and oversold bands based on a dynamically calculated standard deviation and EMA. These bands provide visual boundaries for identifying extreme price conditions, helping traders anticipate potential reversals at these levels.
● Real-Time Labels:
Labels are displayed at key thresholds and bands to indicate the oscillator’s status: "Overbought," "Oversold," and "Neutral". Mean reversion signals are also displayed on the main chart, providing an at-a-glance summary of current indicator conditions.
● Customizable Threshold Levels:
Traders can adjust the primary threshold and smoothing length according to their trading style. A higher threshold can reduce signal frequency, while a lower setting will provide more sensitivity to market reversals.
The Smooth Price Oscillator by BigBeluga is a refined, noise-filtered indicator designed to highlight mean reversion points with enhanced clarity. By providing both strong and simple reversion signals, as well as dynamic overbought/oversold bands, this tool allows traders to spot potential reversals and trend continuations with ease. Its dual representation on the oscillator and the main price chart offers flexibility and precision for any trading strategy focused on capturing cyclical market movements.
Weighted CG Oscillator with ATRATR-Weighted CG Oscillator
The ATR-Weighted CG Oscillator is an enhanced version of the Center of Gravity (CG) Oscillator, originally developed by John Ehlers . By adding the Average True Range (ATR) to dynamically adjust the oscillator’s values based on market volatility, this indicator aims to make trend signals more responsive to price changes, offering an adaptive tool for trend analysis.
Functionality Overview :
The CG Oscillator, a classic trend-following indicator, has been modified here to incorporate the ATR for improved context and adaptability in different market conditions. The indicator calculates the CG Oscillator and scales it by dividing the ATR by the closing price to normalize for volatility. This creates a “weighted” CG Oscillator that generates more contextually relevant signals. A colored line shows green for long signals (above the long threshold), red for short signals (below the short threshold), and gray for neutral conditions.
Input Parameters :
CGO Length : Sets the period of the CG Oscillator calculation.
ATR Length : Determines the period of the ATR calculation. Longer periods smooth out the volatility impact.
Long Threshold : The threshold that triggers a long signal; a long (green) signal occurs when the weighted CG Oscillator crosses above this level.
Short Threshold : The threshold that triggers a short signal; a short (red) signal occurs when the weighted CG Oscillator crosses below this level.
Source : Specifies the data source for CG Oscillator calculations, with the default set to the closing price.
Recommended Use :
This indicator is designed to be an adaptive tool, not your sole resource. To ensure its effectiveness, it’s essential to backtest the indicator on your chosen asset over your preferred timeframe. Market dynamics vary, so testing the indicator’s parameters—especially the thresholds—will allow you to find the settings that best suit your strategy. While the default values work well for some scenarios, customizing the settings will help align the indicator with your unique trading style and the asset’s characteristics.
Momentum TrackerTo screen for momentum movers, one can filter for stocks that have made a noticeable move over a set period—this initial move defines the momentum or swing move. From this list of candidates, we can create a watchlist by selecting those showing a momentum pause, such as a pullback or consolidation, which later could set up for a continuation.
This Momentum Tracker Indicator serves as a study tool to visualize when stocks historically met these momentum conditions. It marks on the chart where a stock would have appeared on the screener, allowing us to review past momentum patterns and screener requirements.
Indicator Calculation
Bullish Momentum: Price is above the lowest point within the lookback period by the specified threshold percentage.
Bearish Momentum: Price is below the highest point within the lookback period by the specified threshold percentage.
The tool is customizable in terms of lookback period and percentage threshold to accommodate different trading styles and timeframes, allowing us to set criteria that align with specific hold times and momentum requirements.
DIVERGENCE SPOT X P.FUTURES (INVERTED VERSION) [GUSLM]Many asked me to change the positive position x negative(of "DIVERGENCE SPOT X P.FUTURES"). being maybe no intuitive for some coins and situations....
So, Now on this version you are going to have UP moves for Upwards from derivatives ( p. Futures with Higher prices than Spot prices), and Dowwards for Negative Futures derivatives. ( it will match the future funding rates probably)
The "pushs" now are in the oposite direction.
Look at the DIVERGENCE SPOT X P.FUTURES script for a better view about it.
For instance:
This version is better for normal coin and market - where the derivatives go in the direction of the price and the coin will have a positive FR(funding) when going up, and maybe sometimes negatives when going down.
The First non inverted version: is better for manipulated coins, where you have pushs and pulls, to try to build a negative funding while hold longs positions. it will go up with negative FR. - Shorters paying the longs and being liquidated in the way..
But you can chose one and adaptd to use only one fot both situations, only need to take a look on the market and define whats going on with the books and prices moves.
Enhanced Keltner TrendThe Enhanced Keltner Trend (EKT) indicator builds on the classic Keltner Channel, using volatility to define potential trend channels around a central moving average. It combines customizable volatility measures moving average, giving traders flexibility to adapt the trend channel to various market conditions.
How It Works?
MA Calculation:
A user-defined moving average forms the central line (or price basis) of the Keltner Channel.
Channel Width:
The width of the Keltner Channel depends on market volatility.
You can choose between two methods for measuring the volatility:
ATR-based Width: Uses the Average True Range (ATR) with customizable periods and multipliers.
Price Range Width: Uses the high and low price range over a defined period.
Trend Signal:
The trend is evaluated by price in relation to the Keltner Channel:
Bullish Trend (Blue Line): When the price crosses above the upper band, it signals upward momentum.
Bearish Trend (Orange Line): When the price crosses below the lower band, it signals downward momentum.
What Is Unique?
This Enhanced version of the Keltner Trend is for investors who want to have more control over the Keltner's channels calculation, so they can calibrate it to provide them more alpha when combined with other Technical Indicators.
Use ATR: Gives the user the choice to use the ATR for the channel width calculation, or use the default High - Low over specified period.
ATR Period: Users can modify ATR length to calculate the channels width (Volatility).
ATR Multiplier: Users can fine-tune how much of the volatility they want to factor into the channels, providing more control over the final calculation.
MA Period: Smoothing period for the Moving Averages.
MA Type: Choosing from different Moving Averages types providing different smoothing types.
Setting Alerts:
Built-in alerts for trend detection:
Bullish Trend: When price crosses the upper band, it signals a Bullish Signal (Blue Color)
Bearish Trend: When price crosses the lower band, it signals a Bearish Signal (Orange Color)
Credits to @jaggedsoft , it's a modified version of his.
Volatility FinderVolatility Finder / Daily Range.
This indicator will measure the Average amount of Pips/Points movement of price, over an X amount of time.
This is often referred to as "Forex Volatility" Most pairs have different amounts of volatility. Exotics pairs are considered very volatile, Forex Majors is less volatile.
So this Indicator, will measure the amount of ADR/Average Daily Range.
Average amount of Pips/Points of movement, within a specific period of time, and tell you that.
- In the settings, you can choose how many days you want the indicator to measure from, and it will tell you the average amount of pips, based on the average movement on those days.
The Default setting is set to 90 days/3 months.
IMPORTANT:
To see the number the indicator tells you, you have to RIGHT-click up in the Left-side corner, where you see the Pair you have open on your Chart. And make sure to Enable "INDICATOR VALUES". Then if you However over the Indicator area, where the indicators you have open. You will see the number that the indicator has found. Based on the Settings you have set in the Settings Menu.
* One applicable way to use this information is if you are inside a trade, and price has moved past the Daily Range. It could be less probable it will continue in the same direction when it has Met the Daily Range.
* Another is to use this, to find pairs that you might want to trade. If the Average Price movement over the time you input, is High, you can use this information to help you decide if this pair is to Volatile for you to consider trading, or if it moving to slow for you.
It's very accurate, if you want to compare, you can go to 3rd party websites like
Mataf / mataf.net/en/forex/tools/volatility
Investing.com / investing.com/tools/forex-volatility-calculator
VolbandsThe Volbands indicator dynamically plots upper and lower volatility bands based on implied daily moves derived from volatility indices. This tool provides a visual forecast of the next trading day's price range, helping traders anticipate potential price movement boundaries.
Key Features:
1. Auto-Detect Volatility Index: Volbands automatically detects the appropriate volatility index based on the current symbol. For example, it uses the VIX for S&P 500, VXN for Nasdaq 100, and custom indexes like VXAPL for Apple. Users can also manually select a specific volatility index if preferred.
2. Projected Bands:
- The indicator plots the projected upper and lower bands for the next trading day using the implied move from the volatility index.
- Displays today’s projected bands as a reference and overlays next day’s bands with a slight offset, visually indicating the anticipated range.
3. Dynamic Updates: The indicator updates automatically as new bars are added, ensuring that users have up-to-date projections based on the latest volatility data.
4. Highlighting Extreme Price Action: Candles that close outside of the projected bands are colored in yellow, highlighting moments of higher-than-expected volatility.
5. Informative Table: A customizable table displays relevant information, including:
- The selected or auto-detected volatility index
- Implied daily move percentage
- Projected upper and lower levels
Potential Applications:
- Risk Management: The Volbands indicator can help traders set more informed stop-loss and take-profit levels based on volatility-driven price projections.
- Identifying Overbought/Oversold Conditions: Price movement outside the projected bands may indicate overbought or oversold conditions, potentially signaling trade opportunities.
-Enhancing Entry and Exit Points: The projected bands act as soft support and resistance levels, assisting traders in timing entries and exits in anticipation of volatility-driven price reactions.
Future Enhancements:
Potential improvements to expand functionality could include:
- Additional Volatility Indices: Expanding coverage to include more assets and volatility indices.
- Alerts: Setting alerts for when prices breach the projected bands, enabling traders to react quickly to unexpected price movements.
- Customization of Bands: Adding options for users to adjust the implied move percentage, creating customized bands that reflect individual trading strategies.
This indicator combines implied volatility with price action, offering valuable insights to traders on expected price ranges and volatility.
GeoMean+The Geometric Moving Average (GMA) with Sigma Bands is a technical indicator that combines trend following and volatility measurement. The blue center line represents the GMA, while the upper and lower bands (light blue) show price volatility using standard deviations (sigma). Traders can use this indicator for both trend following and mean reversion strategies. For trend following, enter long when price crosses above the GMA and short when it crosses below, using the bands as profit targets. For mean reversion, look for buying opportunities when price touches the lower band and selling opportunities at the upper band, with the GMA as your profit target. The indicator includes alerts for band touches and crosses, providing real-time notifications with symbol, timeframe, current price, and band level information. The default 100-period setting works well for daily charts, but can be adjusted shorter (20-50) for intraday trading or longer (200+) for position trading. Wider bands indicate higher volatility (use smaller positions), while narrower bands suggest lower volatility (larger positions possible). For best results, confirm signals with volume and avoid trading against strong trends. Stop losses can be placed beyond the touched band or at the GMA line, depending on your risk tolerance.
Nasan Hull-smoothed envelope The Nasan Hull-Smoothed Envelope indicator is a sophisticated overlay designed to track price movement within an adaptive "envelope." It dynamically adjusts to market volatility and trend strength, using a series of smoothing and volatility-correction techniques. Here's a detailed breakdown of its components, from the input settings to the calculated visual elements:
Inputs
look_back_length (500):
Defines the lookback period for calculating intraday volatility (IDV), smoothing it over time. A higher value means the indicator considers a longer historical range for volatility calculations.
sl (50):
Sets the smoothing length for the Hull Moving Average (HMA). The HMA smooths various lines, creating a balance between sensitivity and stability in trend signals.
mp (1.5):
Multiplier for IDV, scaling the volatility impact on the envelope. A higher multiplier widens the envelope to accommodate higher volatility, while a lower one tightens it.
p (0.625):
Weight factor that determines the balance between extremes (highest high and lowest low) and averages (sma of high and sma of low) in the high/low calculations. A higher p gives more weight to extremes, making the envelope more responsive to abrupt market changes.
Volatility Calculation (IDV)
The Intraday Volatility (IDV) metric represents the average volatility per bar as an exponentially smoothed ratio of the high-low range to the close price. This is calculated over the look_back_length period, providing a base volatility value which is then scaled by mp. The IDV enables the envelope to dynamically widen or narrow with market volatility, making it sensitive to current market conditions.
Composite High and Low Bands
The high and low bands define the upper and lower bounds of the envelope.
High Calculation
a_high:
Uses a multi-period approach to capture the highest highs over several intervals (5, 8, 13, 21, and 34 bars). Averaging these highs provides a more stable reference for the high end of the envelope, capturing both immediate and recent peak values.
b_high:
Computes the average of shorter simple moving averages (5, 8, and 13 bars) of the high prices, smoothing out fluctuations in the recent highs. This generates a balanced view of high price trends.
high_c:
Combines a_high and b_high using the weight p. This blend creates a composite high that balances between recent peaks and smoothed averages, making the upper envelope boundary adaptive to short-term price shifts.
Low Calculation
a_low and b_low:
Similar to the high calculation, these capture extreme lows and smooth low values over the same intervals. This approach creates a stable and adaptive lower bound for the envelope.
low_c:
Combines a_low and b_low using the weight p, resulting in a composite low that adjusts to price fluctuations while maintaining a stable trend line.
Volatility-Adjusted Bands
The final composite high (c_high) and composite low (c_low) bands are adjusted using IDV, which accounts for intraday volatility. When volatility is high, the bands expand; when it’s low, they contract, providing a visual representation of volatility-adjusted price bounds.
Basis Line
The basis line is a Hull Moving Average (HMA) of the average of c_high and c_low. The HMA is known for its smoothness and responsiveness, making the basis line a central trend indicator. The color of the basis line changes:
Green when the basis line is increasing.
Red when the basis line is decreasing.
This color-coded basis line serves as a quick visual reference for trend direction.
Short-Term Trend Strength Block
This component analyzes recent price action to assess short-term bullish and bearish momentum.
Conditions (green, red, green1, red1):
These are binary conditions that categorize price movements as bullish or bearish based on the close compared to the open and the close’s relationship with the exponential moving average (EMA). This separation helps capture different types of strength (above/below EMA) and different bullish or bearish patterns.
Composite Trend Strength Values:
Each of the bullish and bearish counts (above and below the EMA) is normalized, resulting in the following values:
green_EMAup_a and red_EMAup_a for bullish and bearish strength above the EMA.
green_EMAdown_a and red_EMAdown_a for bullish and bearish strength below the EMA.
Trend Strength (t_s):
This calculated metric combines the normalized trend strengths with extra weight to conditions above the EMA, giving more relevance to trends that have momentum behind them.
Enhanced Trend Strength
avg_movement:
Calculates the average absolute price movement over the short_term_length, providing a measurement of recent price activity that scales with volatility.
enhanced_t_s:
Multiplies t_s by avg_movement, creating an enhanced trend strength value that reflects both directional strength and the magnitude of recent price movement.
min and max:
Minimum and maximum percentile thresholds, respectively, based on enhanced_t_s for controlling the color gradient in the fill area.
Fill Area
The fill area between plot_c_high and plot_c_low is color-coded based on the enhanced trend strength (enhanced_t_s):
Gradient color transitions from blue to green based on the strength level, with blue representing weaker trends and green indicating stronger trends.
This visual fill provides an at-a-glance assessment of trend strength across the envelope, with color shifts highlighting momentum shifts.
Summary
The indicator’s purpose is to offer an adaptive price envelope that reflects real-time market volatility and trend strength. Here’s what each component contributes:
Basis Line: A trend-following line in the center that adjusts color based on trend direction.
Envelope (c_high, c_low): Adapts to volatility by expanding and contracting based on IDV, giving traders a responsive view of expected price bounds.
Fill Area: A color-gradient region representing trend strength within the envelope, helping traders easily identify momentum changes.
Overall, this tool helps to identify trend direction, market volatility, and strength of price movements, allowing for more informed decisions based on visual cues around price boundaries and trend momentum.
Advanced Bitcoin Trend Following StrategyTitle: Bitcoin Multi-Factor Trend Following Strategy
Description:
The Bitcoin Multi-Factor Trend Following Strategy is designed for traders seeking a robust, multi-factor approach to trend following in Bitcoin markets. This script combines technical indicators and statistical methods to identify trend directions, optimize entry and exit points, and manage position sizing based on volatility and leverage constraints. Key features of the strategy include:
Multi-Indicator Trend Forecasting:
This strategy employs three trend forecasting methods: range, exponential moving average (EMA), and Bollinger Bands. Each method can be independently enabled or disabled, giving traders flexibility in how trends are identified and followed.
Range Forecast : Calculates forecast based on the range (high and low) of recent prices, with optional smoothing via a Kalman filter to reduce noise.
EMA Spread Forecast : Utilizes the spread between fast and slow EMAs to gauge the trend’s strength, adjusted for volatility.
Bollinger Band Forecast : Measures the proximity of price to Bollinger Band levels to assess trend intensity.
Kalman Filter for Smoothing:
The Kalman filter is applied to price data for smoother trend estimation, particularly within the range forecast. This allows the strategy to reduce noise and focus on more reliable price signals.
Volatility-Adjusted Position Sizing:
The strategy incorporates volatility targeting to dynamically adjust position sizes based on current market conditions. Traders can set an annualized volatility target to control the risk level, with position size scaled accordingly to maintain consistent risk exposure. A maximum leverage cap ensures that position sizes do not exceed a user-defined threshold, offering an additional layer of risk control.
Dynamic Entry and Exit Points:
Entry and exit points are based on customizable thresholds that determine trend strength and are sensitive to market volatility. The script monitors changes in forecast values and automatically adjusts trades to capitalize on emerging trends or exit weakening ones. The strategy includes an option to close all open positions when trend forecasts fall below defined thresholds, ensuring an automated approach to risk management.
Backtesting and Performance Metrics:
To support strategy optimization, the script includes a backtest mode that calculates key performance metrics such as Sharpe Ratio, Buy & Hold profit, Strategy profit, Win rate, and other metrics. These metrics are displayed in a summary table directly on the chart, providing real-time insight into the strategy’s historical performance compared to a buy-and-hold approach.
Configurable Time and Date Range:
Users can specify start and end dates for the backtest period, allowing for focused backtesting over any desired timeframe. This feature enables in-depth analysis of performance across varying market conditions.
Use Case:
This strategy is best suited for experienced traders who wish to apply a structured trend-following approach in Bitcoin or other high-volatility assets. It is highly customizable, making it adaptable to various market conditions and trading styles. The combination of trend forecasting methods, volatility targeting, and automatic leverage control offers a balanced approach to capturing long-term trends while managing risk.
Parameters:
Entry Threshold: Adjusts the sensitivity of the entry point for trends. Lower values make the strategy more reactive.
Annual Volatility Target: Controls the risk level by targeting a specific annualized volatility percentage.
Max Leverage: Caps the allowable leverage for each trade.
Forecast Activations: Toggles to enable or disable the use of range, EMA, and Bollinger forecasts.
Date Range: Allows users to define the start and end dates for testing the strategy.
Notes:
This strategy is designed for educational purposes and requires thorough backtesting and optimization before live trading. Real-time performance may vary, and additional risk management practices are advised.
License:
This script is subject to the terms of the Mozilla Public License 2.0.
Trend Trader-RemasteredThe script was originally coded in 2018 with Pine Script version 3, and it was in invite only status. It has been updated and optimised for Pine Script v5 and made completely open source.
Overview
The Trend Trader-Remastered is a refined and highly sophisticated implementation of the Parabolic SAR designed to create strategic buy and sell entry signals, alongside precision take profit and re-entry signals based on marked Bill Williams (BW) fractals. Built with a deep emphasis on clarity and accuracy, this indicator ensures that only relevant and meaningful signals are generated, eliminating any unnecessary entries or exits.
Key Features
1) Parabolic SAR-Based Entry Signals:
This indicator leverages an advanced implementation of the Parabolic SAR to create clear buy and sell position entry signals.
The Parabolic SAR detects potential trend shifts, helping traders make timely entries in trending markets.
These entries are strategically aligned to maximise trend-following opportunities and minimise whipsaw trades, providing an effective approach for trend traders.
2) Take Profit and Re-Entry Signals with BW Fractals:
The indicator goes beyond simple entry and exit signals by integrating BW Fractal-based take profit and re-entry signals.
Relevant Signal Generation: The indicator maintains strict criteria for signal relevance, ensuring that a re-entry signal is only generated if there has been a preceding take profit signal in the respective position. This prevents any misleading or premature re-entry signals.
Progressive Take Profit Signals: The script generates multiple take profit signals sequentially in alignment with prior take profit levels. For instance, in a buy position initiated at a price of 100, the first take profit might occur at 110. Any subsequent take profit signals will then occur at prices greater than 110, ensuring they are "in favour" of the original position's trajectory and previous take profits.
3) Consistent Trend-Following Structure:
This design allows the Trend Trader-Remastered to continue signaling take profit opportunities as the trend advances. The indicator only generates take profit signals in alignment with previous ones, supporting a systematic and profit-maximising strategy.
This structure helps traders maintain positions effectively, securing incremental profits as the trend progresses.
4) Customisability and Usability:
Adjustable Parameters: Users can configure key settings, including sensitivity to the Parabolic SAR and fractal identification. This allows flexibility to fine-tune the indicator according to different market conditions or trading styles.
User-Friendly Alerts: The indicator provides clear visual signals on the chart, along with optional alerts to notify traders of new buy, sell, take profit, or re-entry opportunities in real-time.
Normal Price Indicator by KirillPOHEnglish:
Normal Price Indicator is a technical indicator designed to analyze market prices and find normal price levels, as well as the upper and lower boundaries of the normal price area for a given period of time. The indicator is designed for traders and analysts who want to track price movements and identify potential levels for buying or selling based on statistical calculations.
This indicator calculates three main lines:
- The Normal price (Median Price) — the line showing the median of prices for the selected period.
- Upper Bound — a line located at a certain distance from the normal price, based on the standard deviation.
- Lower Bound — a line also located based on the standard deviation from the normal price.
In addition, the indicator can highlight areas on the chart when the price goes beyond these boundaries, which can be a signal to traders about possible important levels.
Main Features:
- Normal price: It is calculated as the median of prices for a given period of time, which helps to track the typical price value on the chart.
- Upper and lower bounds: These limits are calculated as the average price ± (multiplier * standard deviation), which allows you to take into account market fluctuations and set a price range in which the price is considered "normal".
- Adaptation to the scale of the graph: The lines of the indicator adjust correctly to changes in the scale of the chart, while maintaining a link to price levels. They are always displayed in the current position, no matter how much you increase or decrease the graph.
- Zone allocation: The indicator also allows you to highlight areas on the chart where the price is above the upper limit or below the lower limit, which may signal unusual market conditions.
How to use the indicator:
1. The normal price (Median Price): This is the main line of the indicator, which shows the central price level on the chart for the selected period. It helps traders keep track of the standard market level and determine if the current price is within that range.
2. Upper and lower borders: These lines are used to identify potential deviations from the normal price zone. If the closing price turns out to be above the upper limit or below the lower one, this may indicate strong market movements or potential reversals. For example:
- The price above the upper limit may signal a strong bullish trend.
- The price below the lower limit may indicate a bearish trend or a strong correction.
3. Areas on the graph: The indicator highlights the background when the price is above the upper limit (the area is colored green) or below the lower limit (the area is colored red). These visual cues can help traders quickly identify deviations.
Settings :
- Period: The period for calculating the median, standard deviation, and upper/lower bounds. It is usually set to 14, but can be changed depending on the user's needs.
is the multiplier for the standard deviation: This parameter allows you to adjust how much the upper and lower limits will deviate from the normal price. The standard value is 2, which corresponds to two standard deviations, but can be adjusted to suit your needs.
Application:
Traders can use the indicator to analyze market levels and make decisions about entering or exiting the market. Analysts can use the indicator to identify normal price ranges and deviations, which allows them to more accurately predict market trends and potential pivot points.
This indicator is not a signal for trading, but rather a tool for analyzing the market and price levels. It should be used in combination with other indicators and analysis methods for more accurate trading decisions.
Russia:
Normal Price Indicator — это технический индикатор, предназначенный для анализа рыночных цен и нахождения нормальных ценовых уровней, а также верхних и нижних границ нормальной ценовой области за заданный период времени. Индикатор предназначен для трейдеров и аналитиков, которые хотят отслеживать ценовые движения и выявлять потенциальные уровни для покупки или продажи, основываясь на статистических расчетах.
Этот индикатор рассчитывает три основные линии:
- Нормальная цена (Median Price) — линия, отображающая медиану цен за выбранный период.
- Верхняя граница (Upper Bound) — линия, находящаяся на определённом расстоянии от нормальной цены, основанная на стандартном отклонении.
- Нижняя граница (Lower Bound) — линия, также расположенная на основе стандартного отклонения от нормальной цены.
Кроме того, индикатор может выделять области на графике, когда цена выходит за пределы этих границ, что может быть сигналом для трейдеров о возможных важных уровнях.
Основные особенности:
- Нормальная цена: Вычисляется как медиана цен за заданный период времени, что помогает отследить типичное значение цены на графике.
- Верхняя и нижняя границы: Эти границы рассчитываются как средняя цена ± (множитель * стандартное отклонение), что позволяет учитывать рыночные колебания и задавать диапазон цен, в котором цена считается "нормальной".
- Адаптация под масштаб графика: Линии индикатора корректно подстраиваются под изменения масштаба графика, сохраняя привязку к уровням цен. Они всегда отображаются в актуальном положении, независимо от того, насколько вы увеличиваете или уменьшаете график.
- Выделение зон: Индикатор также позволяет выделять области на графике, где цена находится выше верхней границы или ниже нижней границы, что может сигнализировать о необычных рыночных условиях.
Как использовать индикатор:
1. Нормальная цена (Median Price): Это основная линия индикатора, которая показывает центральный уровень цен на графике за выбранный период. Она помогает трейдерам отслеживать стандартный рыночный уровень и определять, находится ли текущая цена в пределах этого диапазона.
2. Верхняя и нижняя границы: Эти линии используются для выявления потенциальных отклонений от нормальной ценовой зоны. Если цена закрытия оказывается выше верхней границы или ниже нижней, это может свидетельствовать о сильных движениях на рынке или потенциальных разворотах. Например:
- Цена выше верхней границы может сигнализировать о сильном бычьем тренде.
- Цена ниже нижней границы может указывать на медвежий тренд или сильную коррекцию.
3. Области на графике: Индикатор выделяет фон, когда цена находится выше верхней границы (область окрашивается в зелёный) или ниже нижней границы (область окрашивается в красный). Эти визуальные подсказки могут помочь трейдерам быстро выявить отклонения.
Параметры настройки :
- Период: Период для расчета медианы, стандартного отклонения и верхних/нижних границ. Обычно устанавливается на 14, но может быть изменён в зависимости от потребностей пользователя.
- Множитель для стандартного отклонения: Этот параметр позволяет настроить, насколько сильно будут отступать верхняя и нижняя границы от нормальной цены. Стандартное значение — 2, что соответствует двум стандартным отклонениям, но можно настроить под свои нужды.
Применение:
Трейдеры могут использовать индикатор для анализа рыночных уровней и принятия решений о входе или выходе на рынок. Аналитики могут использовать индикатор для выявления нормальных диапазонов цен и отклонений, что позволяет более точно прогнозировать рыночные тренды и потенциальные точки разворота.
Этот индикатор не является сигналом для торговли, а скорее инструментом для анализа рынка и ценовых уровней. Его стоит использовать в комплексе с другими индикаторами и методами анализа для более точных торговых решений.
AI x Meme Impulse Tracker [QuantraSystems]AI x Meme Impulse Tracker
Quantra Systems guarantees that the information created and published within this document and on the Tradingview platform is fully compliant with applicable regulations, does not constitute investment advice, and is not exclusively intended for qualified investors.
Important Note!
The system equity curve presented here has been generated as part of the process of testing and verifying the methodology behind this script.
Crucially, it was developed after the system was conceptualized, designed, and created, which helps to mitigate the risk of overfitting to historical data. In other words, the system was built for robustness, not for simply optimizing past performance.
This ensures that the system is less likely to degrade in performance over time, compared to hyper-optimized systems that are tailored to past data. No tweaks or optimizations were made to this system post-backtest.
Even More Important Note!!
The nature of markets is that they change quickly and unpredictably. Past performance does not guarantee future results - this is a fundamental rule in trading and investing.
While this system is designed with broad, flexible conditions to adapt quickly to a range of market environments, it is essential to understand that no assumptions should be made about future returns based on historical data. Markets are inherently uncertain, and this system - like all trading systems - cannot predict future outcomes.
Introduction
The AI x Meme Impulse Tracker is a cutting-edge, fast-acting rotational algorithm designed to capitalize on the strength of assets within pre-selected categories. Using a custom function built on top of the RSI Pulsar, the system measures momentum through impulses rather than traditional trend following methods. This allows for swifter reallocations based on short bursts of strength.
This system focuses on precision and agility - making it highly adaptable in volatile markets. The strategy is built around three independent asset categories - with allocations only made to the strongest asset in each - ensuring that capital movement (in particular between blockchains) is kept to a minimum for efficiency purposes while maintaining exposure to the highest performing tokens.
Legend
Token Inputs:
The Impulse Tracker is designed with dynamic asset selection - allowing traders to customize the inputs for each category. This feature enables flexible system management, as the number of active tokens within each category can be adjusted at any time. Whether the user chooses the default of 13 tokens per category, or fewer, the system will automatically recalibrate. This ensures that all calculations, from relative strength to individual performance assessments, adjust as required. Disabled tokens are treated by the system as if they don’t exist - seamlessly updating performance metrics and the Impulse Tracker’s allocation behavior to maintain the highest level of efficiency and accuracy.
System Equity Curve:
The Impulse Tracker plots both the rotational system’s equity and the Buy-and-Hold (or ‘HODL’) benchmark of Bitcoin for comparison. While the HODL approach allocates the entire portfolio to Bitcoin and functions as an index to compare to, the Impulse Tracker dynamically allocates based on strength impulses within the chosen tokens and categories. The system equity curve is representative of adding an equal capital split between the strongest assets of each category. The relative strength system does handle ‘ties’ of strength - in this situation multiple tokens from a single category can be included in the final equity curve, with the allocated weight to that category split between the tied assets.
TABLES:
Equity Stats:
This table is held in Quantra System's typical UI design language. It offers a comprehensive snapshot of the system’s performance, with key metrics organized to help traders quickly assess both short-term and cumulative results. The left side provides details on individual asset performance, while the right side presents a comparison of the system’s risk-adjusted metrics against a simple BTC Hodl strategy.
The leftmost column of the Equity Stats table showcases performance indicators for the system’s current allocations. This provides quick identification of the current strongest tokens, based on confirmed and non-repainting data as soon as the current opens and the last bar closes.
The right-hand side compares the performance differences between the system and Hodl profits, both on a cumulative basis and analyzing only the previous bar. The total number of position changes is also tracked in this table - an important metric when calculating total slippage and should be used to determine how ‘hands-on’ the strategy will be on the current timeframe.
The lower part of the table highlights a direct comparison of the AI x Memes Impulse strategy with buy-and-hold Bitcoin. The risk adjusted performance ratios, Sharpe, Sortino and Omega, are shown side by side, as well as the maximum drawdown experienced by both strategies within the set testing window.
Screener Table:
This table provides a detailed breakdown of the performance for each asset that has been the strongest in its category at some point and thus received an allocation. The table tracks several key metrics for each asset - including returns, volatility, Sharpe ratio, Sortino ratio, Omega ratio, and maximum drawdown. It also displays the signals for both current and previous periods, as well as the assets weight in the theoretical portfolio. Assets that have never received a signal are also included, giving traders an overview of which assets have contributed to the portfolio's performance and which have not played a role so far.
The position changes cell also offers important insights, as it shows the frequency of not just total position changes, but also rebalancing events.
Detailed Slippage Table:
The Detailed Slippage Table provides a comprehensive breakdown of the calculated slippage and fees incurred throughout the strategy’s operations. It contains several key metrics that give traders a granular view of the costs associated with executing the system:
Selected Slippage - Displays the current slippage rate, as defined in the input menu.
Removal Slippage - This accounts for any slippage or fees incurred when removing an allocation from a token.
Reallocation Slippage - Tracks the slippage or fees when reallocating capital to existing positions.
Addition Slippage - Measures the slippage or fees incurred when allocating capital to new tokens.
Final Slippage - Is the sum of all the individual slippage points and provides a quick view of the total slippage accounted for by the system.
The table is also divided into two columns:
Last Transaction Slippage + Fees - Displays any slippage or fees incurred based on position changes within the current bar.
Total Slippage + Fees - Shows the cumulative slippage and fees incurred since the portfolio’s selected start date.
Visual Customization:
Several customizable features are included within the input menu to enhance user experience. These include custom color palettes, both preloaded and user-selectable. This allows traders to personalize the visual appearance of the tables, ensuring clarity and consistency with their preferred interface themes and background coloring.
Additionally, users can adjust both the position and sizes of all the tables - enabling complete tailoring to the trader’s layout and specific viewing preferences and screen configurations. This level of customization ensures a more intuitive and flexible interaction with the system’s data.
Core Features and Methodologies
Advanced Risk Management - A Unique Filtering Approach:
The Equity Curve Activation Filter introduces an innovative way to dynamically manage capital allocation, aligning with periods of market trend strength. This filter is rooted in the understanding that markets move cyclically - altering between periods trending and mean-reverting periods. This cycle is especially pronounced in the crypto markets, where strong uptrends are often followed by prolonged periods of sideways movements or corrections as participants take profits and momentum fades.
The Cyclical Nature of Markets and Trend Following:
Financial markets do not trend indefinitely. Each uptrend or downtrend, whether over high and low timeframes, tends to culminate in a phase where momentum exhausts - leading to the sideways or corrective phases. This cycle results from the natural dynamics of market participants: during extended trends, more participants jump in, riding the momentum until profit taking causes the trend to slow down or reverse. This cyclical behavior occurs across all timeframes and in all markets - making it essential to adapt trading strategies in attempt to minimize losses during less favorable conditions.
In a trend following system, profitability often mirrors this cyclical pattern. Trend following strategies thrive when markets are moving directionally, capturing gains as price moves with strength in a single direction. However in phases where the market chops sideways, trend following strategies will usually experience drawdowns and reduced returns due to the impersistent nature of any trends. This fluctuation in trend following profitability can actually serve as one of the best coincident indicators of broader market regime change - when profitability begins to fade, it often signals a transition to drawn out unfavorable trend trading conditions.
The Equity Curve as a Market Signal
Within the Impulse Tracker, a continuous equity curve is calculated based upon the system's allocation to the strongest tokens. This equity curve effectively tracks the system’s performance under all market conditions. However, instead of solely relying on the direct performance of the selected tokens, the system applies additional filters to analyze the trend strength of this equity curve itself.
In the same way you only want to purchase an asset that is moving up in price, you only want to allocate capital to a strategy whose equity curve is trending upwards!
The Equity Curve Activation Filter consistently monitors the trend of this equity curve through various filter indicators, such as the “Wave Pendulum Trend”, the “Quasar QSM” and the “MAQSM” (an aggregate of multiple types of averages). These filters help determine whether the equity curve is trending upwards, signaling a favorable period for trend following. When the equity curve is in a positive trend, capital is allocated to the system as normal - allowing it to capture gains during favorable market conditions, Conversely, when the trend weakens and the equity curves begins to stagnate or decline, the activation filter shifts the system into a “cash” positions - temporarily halting allocations in order to prevent market exposure during choppy or mean reverting phases.
Timing Allocation With Market Conditions
This unique filtering approach ensures that the system is primarily active during periods when market trends are most supportive. By aligning capital allocations with the uptrend in trend following profitability, the system is designed to enter during periods of strong momentum and move to cash when momentum with the equity curve wanes. This approach reduces the risk of overtrading in less favorable conditions and preserves capital for the next favorable trend.
In essence the Equity Curve Allocation Filter serves as a dynamic risk management layer that leverages the cyclicality of trend following profitability in order to navigate shifting market phases.
Sensitivity and Signal Responsiveness:
The Quasar Sensitivity Setting allows users to fine-tune the system’s responsiveness to asset signals. High sensitivity settings lead to quicker position changes, making the system highly reactive to short term strength impulses. This is especially useful in fast moving markets where token strength can shift rapidly. The Sensitive setting might be more applicable to higher volatility or lower market cap assets - as the increased volatility increases the necessity of faster position cutting in order to front run the crowd. Of course - a balanced approach is ideal, as if the signals are too fast there will be too many whips and false signals. (And extra fees + slippage!)
The benefit of this script is because of the advanced slippage calculations, false signals are sufficiently punished (unlike systems without fees or slippage) - so it will become immediately apparent if the false signals have a significantly detrimental impact on the system’s equity curve.
Asset specific signals within each category are re-evaluated after the close of each bar to ensure that capital is always allocated to the highest performing asset. If a token’s momentum begins to fade the system swiftly reallocates to the next strongest asset within that category.
Category Filter - Allocates only to the Strongest Asset per group
One of the core innovations of the AI x Meme Impulse Tracker is the customizable Category Filter, which ensures that only the strongest-performing asset within each predefined group receives capital allocation. This approach not only increases the precision of asset selection but also allows traders to tailor the system to specific token narratives or categories. Sectors can include trending themes such as high-attention meme tokens, AI-driven tokens, or even categorize assets by blockchain ecosystems like Ethereum, Solana, or Base chain. This flexibility enables users to align their strategies with the latest market narratives or to optimize for specific groups, focusing on high-beta tokens within well defined sectors for a more targeted exposure. By keeping the focus on category leaders, the system avoids diluting its impact across underperforming assets, thereby maximizing capital efficiency and reducing unnecessary trading costs.
Dynamic Asset Reallocation:
Dynamic reallocation ensures that the system remains nimble and adapts to changing market conditions. Unlike slower systems, the Quasar method continually monitors for changes in asset strength and reallocates capital accordingly - ensuring that the system is always positioned in the highest performing assets within each category.
Position Changes and Slippage:
The Impulse Tracker places a strong emphasis on realistic simulation, prioritizing accuracy over inflated backtest results. This approach ensures that slippage is accounted for in a more aggressive manner than what may be experienced in real-world execution.
Each position change within the system - whether it’s buying, selling, reallocating, or rebalancing between assets - incurs slippage. Slippage is applied to both ends of every transaction: when a position is entered and exited, and when reallocating capital from one token to another. This dynamic behavior is further enhanced by a customizable slippage/fees input, allowing users to simulate realistic transaction costs based on their own market conditions and execution behaviors.
The slippage model works by applying a weighted slippage to the equity curve, taking into account the actual amount of capital being moved. Slippage is not applied in a blanket manner but rather in proportion to the allocation changes. For example, if the system reallocates from a single 100% position to two 50% allocations, slippage will be applied to the 50% removed from the first asset and the 50% added to the new asset, resulting in a 1x slippage multiplier.
This process becomes more granular when multiple assets are involved. For instance, if reallocating from two 50% positions to three 33% positions, slippage will be incurred on each of the changes, but at a reduced rate (⅔ x slippage), reflecting the smaller percentage of portfolio equity being moved. The slippage model accounts for all types of allocation shifts, whether increasing or decreasing the number of tokens held, providing a realistic assessment of system costs.
Here are some detailed examples to illustrate how slippage is calculated based on different scenarios:
100% → 50% / 50%: 1x slippage applied to both position changes (2 allocation changes).
50% / 50% → 33% / 33% / 33%: ⅔ x slippage multiplier applied across 3 allocation changes.
33% / 33% / 33% → 100%: 4/3 x slippage multiplier applied across 3 allocation changes.
In practice, not every position change will be rebalanced perfectly, leading to a lower number of transactions and lower costs in practice. Additionally, with the use of limit orders, a trader can easily reduce the costs of entering a position, as well as ensuring a competitive entry price.
By simulating slippage in this granular manner, the system captures the absolute maximum level of fees and slippage, in order to ensure that backtest results lean towards an underrepresentation - opposed to inflated results compared with practical execution.
A Special Note on Slippage
In the image above, the system has been applied to four different timeframes - 20h, 15h, 10h, and 5h - using identical settings and a selected slippage amount of 2%. By isolating a recent trend leg, we can illustrate an important concept: while the 15h timeframe is more profitable than the 20h timeframe, this difference stems from a core trading principle. Lower timeframes typically provide more data points and allow for quicker entries and exits in a robust system. This often results in reduced downside and compounding of gains.
However, slippage, fees, and execution constraints are limiting factors, especially in volatile, low-cap cryptocurrencies. Although lower timeframes can improve performance by increasing trade frequency, each trade incurs heavy slippage costs that accumulate - impacting the portfolio’s capital at a compounding rate. In this example, the chosen slippage rate of 2% per trade is designed to reflect the realistic trading costs, emphasizing how lower timeframe trading comes at the cost of increased slippage and fees
Finding the optimal balance between timeframe and slippage impact requires careful consideration of factors such as portfolio size, liquidity of selected tokens, execution speed, and the fee rate of the exchange you execute trades on.
Equity Curve and Performance Calculations
To provide a benchmark, the script also generates a Buy-and-Hold (or "HODL") equity curve that represents a complete allocation to Bitcoin. This allows users to easily compare the performance of the dynamic rotation system with that more traditional benchmark strategy.
The script tracks key performance metrics for both the dynamic portfolio and the HODL strategy, including:
Sharpe Ratio
The Sharpe Ratio is a key metric that evaluates a portfolio’s risk-adjusted return by comparing its ‘excess’ return to its volatility. Traditionally, the Sharpe Ratio measures returns relative to a risk-free rate. However, in our system’s calculation, we omit the risk-free rate and instead measure returns above a benchmark of 0%. This adjustment provides a more universal comparison, especially in the context of highly volatile assets like cryptocurrencies, where a traditional risk-free benchmark, such as the usual 3-month T-bills, is often irrelevant or too distant from the realities of the crypto market.
By using 0% as the baseline, we focus purely on the strategy's ability to generate raw returns in the face of market risk, which makes it easier to compare performance across different strategies or asset classes. In an environment like cryptocurrency, where volatility can be extreme, the importance of relative return against a highly volatile backdrop outweighs comparisons to a risk-free rate that bears little resemblance to the risk profile of digital assets.
Sortino Ratio
The Sortino Ratio improves upon the Sharpe Ratio by specifically targeting downside risk and leaves the upside potential untouched. In contrast to the Sharpe Ratio (which penalizes both upside and downside volatility), the Sortino Ratio focuses only on negative return deviations. This makes it a more suitable metric for evaluating strategies like the AI x Meme Impulse Tracker - that aim to minimize drawdowns without restricting upside capture. By measuring returns relative to a 0% baseline, the Sortino ratio provides a clearer assessment of how well the system generates gains while avoiding substantial losses in highly volatile markets like crypto.
Omega Ratio
The Omega Ratio is calculated as the ratio of gains to losses across all return thresholds, providing a more complete view of how the system balances upside and downside risk even compared to the Sortino Ratio. While it achieves a similar outcome to the Sortino Ratio by emphasizing the system's ability to capture gains while limiting losses, it is technically a mathematically superior method. However, we include both the Omega and Sortino ratios in our metric table, as the Sortino Ratio remains more widely recognized and commonly understood by traders and investors of all levels.
Usage Summary:
While the backtests in this description are generated as if a trader held a portfolio of just the strongest tokens, this was mainly designed as a method of logical verification and not a recommended investment strategy. In practice, this system can be used in multiple ways.
It can be used as above, or as a factor in forming part of a broader asset selection system, or even a method of filtering tokens by strength in order to inform a day trader which tokens might be optimal to look for long-only trading setups on an intrabar timeframe.
Final Summary:
The AI x Meme Impulse Tracker is a powerful algorithm that leverages a unique strength and impulse based approach to asset allocation within high beta token categories. Built with a robust risk management framework, the system’s Equity Curve Activation Filter dynamically manages capital exposure based on the cyclical nature of market trends, minimizing exposure during weaker phases.
With highly customizable settings, the Impulse Tracker enables precise capital allocation to only the strongest assets, informed by real-time metrics and rigorous slippage modeling in order to provide the best view of historical profitability. This adaptable design, coupled with advanced performance analytics, makes it a versatile tool for traders seeking an edge in fast moving and volatile crypto markets.
Trend Titan Neutronstar [QuantraSystems]Trend Titan NEUTRONSTAR
Credits
The Trend Titan NEUTRONSTAR is a comprehensive aggregation of nearly 100 unique indicators and custom combinations, primarily developed from unique and public domain code.
We'd like to thank our TradingView community members: @IkKeOmar for allowing us to add his well-built "Normalized KAMA Oscillator" and "Adaptive Trend Lines " indicators to the aggregation, as well as @DojiEmoji for his valuable "Drift Study (Inspired by Monte Carlo Simulations with BM)".
Introduction
The Trend Titan NEUTRONSTAR is a robust trend following algorithm meticulously crafted to meet the demands of crypto investors. Designed with a multi layered aggregation approach, NEUTRONSTAR excels in navigating the unique volatility and rapid shifts of the cryptocurrency market. By stacking and refining a variety of carefully selected indicators, it combines their individual strengths while reducing the impact of noise or false signals. This "aggregation of aggregators" approach enables NEUTRONSTAR to produce a consistently reliable trend signal across assets and timeframes, making it an exceptional tool for investors focused on medium to long term market positioning.
NEUTRONSTAR ’s powerful trend following capabilities provide investors with straightforward, data driven analysis. It signals when tokens exhibit sustained upward momentum and systematically removes allocations from assets showing signs of weakness. This structure aids investors in recognizing peak market phases. In fact, one of NEUTRONSTAR ’s most valuable applications is its potential to help investors time exits near the peak of bull markets. This aims to maximize gains while mitigating exposure to downturns.
Ultimately, NEUTRONSTAR equips investors with a high precision, adaptable framework for strategic decision making. It offers robust support to identify strong trends, manage risk, and navigate the dynamic crypto market landscape.
With over a year of rigorous forward testing and live trading, NEUTRONSTAR demonstrates remarkable robustness and effectiveness, maintaining its performance without succumbing to overfitting. The system has been purposefully designed to avoid unnecessary optimization to past data, ensuring it can adapt as market conditions evolve. By focusing on aggregating valuable trend signals rather than tuning to historical performance, the NEUTRONSTAR serves as a reliable universal trend following system that aligns with the natural market cycles of growth and correction.
Core Methodology
The foundation of the NEUTRONSTAR lies in its multi aggregated structure, where five custom developed trend models are combined to capture the dominant market direction. Each of these aggregates has been carefully crafted with a specific trend signaling period in mind, allowing it to adapt seamlessly across various timeframes and asset classes. Here’s a breakdown of the key components:
FLARE - The original Quantra Signaling Matrix (QSM) model, best suited for timeframes above 12 hours. It forms the foundation of long term trend detection, providing stable signals.
FLAREV2 - A refined and more sophisticated model that performs well across both high and low timeframes, adding a layer of adaptability to the system.
NEBULA - An advanced model combining FLARE and FLAREV2. NEBULA brings the advantages of both components together, enhancing reliability and capturing smoother, more accurate trends.
SPARK - A high speed trend aggregator based on the QSM Universal model. It focuses on fast moving trends, providing early signals of potential shifts.
SUNBURST - A balanced aggregate that combines elements of SPARK and FLARE, confirming SPARK’s signals while minimizing false positives.
Each of these models contributes its own unique perspective on market movement. By layering fast, medium, and slower trend following signals, NEUTRONSTAR can confirm strong trends while filtering out shorter term noise. The result is a comprehensive tool that signals clear market direction with minimized false signals.
A Unique Approach to Trend Aggregation
One of the defining characteristics of NEUTRONSTAR is its deliberate choice to avoid perfectly time coherent indicators within its aggregation. In simpler terms, NEUTRONSTAR purposefully incorporates trend following indicators with slightly different signal periods, rather than synchronizing all components to a single signaling period. This choice brings significant benefits in terms of diversification, adaptability, and robustness of the overall trend signal.
When aggregating multiple trend following components, if all indicators were perfectly time coherent - meaning they responded to market changes in exactly the same way and over the time periods - the resulting signal would effectively be no different from a single trend following indicator. This uniformity would limit the system’s ability to capture a variety of market conditions, leaving it vulnerable to the same noise or false signals that any single indicator might encounter. Instead, NEUTRONSTAR leverages a balanced mix of indicators with varied timing: some fast, some slower, and some in the medium range. This choice allows the system to extract the unique strengths of each component, creating a combined signal that is stronger and more reliable than any single indicator.
By incorporating different signal periods, NEUTRONSTAR achieves what can be thought of as a form of edge accumulation. The fast components within NEUTRONSTAR , for example, are highly sensitive to quick shifts in market direction. These indicators excel at identifying early trend signals, enabling NEUTRONSTAR to react swiftly to emerging momentum. However, these fast indicators alone would be prone to reacting to market noise, potentially generating too many premature signals. This is where the medium term indicators come into play. These components operate with a slower reaction time, filtering out the short term fluctuations and confirming the direction of the trend established by the faster indicators. The combination of these varying signal speeds results in a balanced, adaptive response to market changes.
This approach also allows NEUTRONSTAR to adapt to different market regimes seamlessly. In fast moving, volatile markets, the faster indicators provide an early alert to potential trend shifts, while the slower components offer a stabilizing influence, preventing overreaction to temporary noise. Conversely, in steadier or trending markets, the medium and slower indicators sustain the trend signal, reducing the likelihood of premature exits. This flexible design enhances NEUTRONSTAR ’s ability to operate effectively across multiple asset classes and timeframes, from short term fluctuations to longer term market cycles.
The result is a powerful, multi-layered trend following tool that remains adaptive, capturing the benefits of both fast and medium paced reactions without becoming overly sensitive to short term noise. This unique aggregation methodology also supports NEUTRONSTAR ’s robustness, reducing the risk of overfitting to historical data and ensuring that the system can perform reliably in forward testing and live trading environments. The slightly staggered signal periods provide a greater degree of resilience, making NEUTRONSTAR a dependable choice for traders looking to capitalize on sustained trends while minimizing exposure during periods of market uncertainty.
In summary, the lack of perfect time coherence among NEUTRONSTAR ’s sub components is not a flaw - but a deliberate, robust design choice.
Risk Management through Market Mode Analysis
An essential part of NEUTRONSTAR is its ability to assess the market's underlying behavior and adapt accordingly. It employs a Market Mode Analysis mechanism that identifies when the market is either in a “Trending State” or a “Mean Reverting State.” When enough confidence is established that the market is trending, the system confirms and signals a “Trending State,” which is optimal for maintaining positions in the direction of the trend. Conversely, if there’s insufficient confidence, it labels the market as “Mean Reverting,” alerting traders to potentially avoid trend trades during likely sideways movement.
This distinction is particularly valuable in crypto, where asset prices often oscillate between aggressive trends and consolidation periods. The Market Mode Analysis keeps traders aligned with the broader market conditions, minimizing exposure during periods of potential whipsaws and maximizing gains during sustained trends.
Zero Overfitting: Design and Testing for Real World Resilience
Unlike many trend following indicators that rely heavily on backtesting and optimization, NEUTRONSTAR was built to perform well in forward testing and live trading without post design adjustments. Over a year of live market exposure has all but proven its robustness, with the system’s methodology focused on universal applicability and simplicity rather than curve fitting to past data. This approach ensures the aggregator remains effective across different market cycles and maintains relevance as new data unfolds.
By avoiding overfitting, NEUTRONSTAR is inherently more resistant to the common issue of strategy degradation over time, making it a valuable tool for traders seeking reliable market analysis you can trust for the long term.
Settings and Customization Options
To accommodate a range of trading styles and market conditions, NEUTRONSTAR includes adjustable settings that allow for fine tuning sensitivity and signal generation:
Calculation Method - Users can choose between calculating the NEUTRONSTAR score based on aggregated scores or by using the state of individual aggregates (long, neutral, short). The score method provides faster signals with slightly more noise, while the state based approach offers a smoother signal.
Sensitivity Threshold - This setting adjusts the system’s sensitivity, defining the width of the neutral zone. Higher thresholds reduce sensitivity, allowing for a broader range of volatility before triggering a trend reversal.
Market Regime Sensitivity - A sensitivity adjustment, ranging from 0 to 100, that affects the sensitivity of the sub components in market regime calculation.
These settings offer flexibility for users to tailor NEUTRONSTAR to their specific needs, whether for medium term investment strategies or shorter term trading setups.
Visualization and Legend
For intuitive usability, NEUTRONSTAR uses color coded bar overlays to indicate trend direction:
Green - indicates an uptrend.
Gray - signals a neutral or transition phase.
Purple - denotes a downtrend.
An optional background color can be enabled for market mode visualization, indicating the overall market state as either trending or mean reverting. This feature allows traders to assess trend direction and strength at a glance, simplifying decision making.
Additional Metrics Table
To support strategic decision making, NEUTRONSTAR includes an additional metrics table for in depth analysis:
Performance Ratios - Sharpe, Sortino, and Omega ratios assess the asset’s risk adjusted returns.
Volatility Insights - Provides an average volatility measure, valuable for understanding market stability.
Beta Measurement - Calculates asset beta against BTC, offering insight into asset volatility in the context of the broader market.
These metrics provide deeper insights into individual asset behavior, supporting more informed trend based allocations. The table is fully customizable, allowing traders to adjust the position and size for a seamless integration into their workspace.
Final Summary
The Trend Titan NEUTRONSTAR indicator is a powerful and resilient trend following system for crypto markets, built with a unique aggregation of high performance models to deliver dependable, noise reduced trend signals. Its robust design, free from overfitting, ensures adaptability across various assets and timeframes. With customizable sensitivity settings, intuitive color coded visualization, and an advanced risk metrics table, NEUTRONSTAR provides traders with a comprehensive tool for identifying and riding profitable trends, while safeguarding capital during unfavorable market phases.
RSI and Dev Advanced Volatility IndexEnglish Explanation of the "RSI and Dev Advanced Volatility Index" Pine Script Code
Understanding the Code
Purpose:
This Pine Script code creates a custom indicator that combines the Relative Strength Index (RSI) and Deviation (DEV) to provide insights into market volatility.
Key Components:
* Deviation (DEV): Calculates the difference between the closing price and the 10-period simple moving average. This measures the extent to which the price deviates from its recent average, indicating volatility.
* RSI: The traditional RSI is then applied to the calculated deviations. This helps to smooth the data and identify overbought or oversold conditions in terms of volatility.
Calculation Steps:
* Deviation Calculation: The difference between the closing price and its 10-period simple moving average is calculated.
* RSI Calculation: The RSI is calculated on the deviations, providing a measure of the speed and change of volatility relative to recent volatility changes.
* Plotting:
* The RSI of the deviations is plotted on the chart.
* Horizontal lines are plotted at 50, 0, and 110 to visually represent different volatility zones.
* The area between the lines is filled with color to highlight low and high volatility regions.
Interpretation and Usage
* Volatility Analysis:
* High Volatility: When the RSI is above 50, it indicates high volatility, suggesting the market might be in a consolidation or trend reversal phase.
* Low Volatility: When the RSI is below 50, it indicates low volatility, suggesting a relatively calm market.
* Trading Signals:
* Buy Signal: When the RSI crosses above 50 from below, it might signal increasing volatility, which could be a buying opportunity.
* Sell Signal: When the RSI crosses below 50 from above, it might signal decreasing volatility, which could be a selling opportunity.
* Risk Management:
* By monitoring volatility, traders can better manage their risk. During periods of high volatility, traders might reduce their position size or adopt more conservative strategies.
Advantages
* Comprehensive: Combines RSI and DEV for a more holistic view of volatility.
* Sensitivity: Quickly responds to changes in market volatility.
* Visual Clarity: Color-coded zones provide a clear visual representation of different volatility levels.
Limitations
* Parameter Sensitivity: The indicator's performance is sensitive to parameter changes, such as the lookback period for the moving average.
* Lag: Like most technical indicators, it has some lag and might not capture every market movement.
* Not Predictive: It can only indicate current and past volatility, not future movements.
Summary
This custom indicator offers a valuable tool for analyzing market volatility. By combining RSI and DEV, it provides a more nuanced perspective on price fluctuations. However, it should be used in conjunction with other technical indicators and fundamental analysis for more robust trading decisions.
Key points to remember:
* Higher RSI values indicate higher volatility.
* Lower RSI values indicate lower volatility.
* Crossovers of the RSI line above or below 50 can provide potential trading signals.
* The indicator should be used in conjunction with other analysis tools for a more complete picture of the market.
