RBF Kijun Trend System [InvestorUnknown]The RBF Kijun Trend System utilizes advanced mathematical techniques, including the Radial Basis Function (RBF) kernel and Kijun-Sen calculations, to provide traders with a smoother trend-following experience and reduce the impact of noise in price data. This indicator also incorporates ATR to dynamically adjust smoothing and further minimize false signals.
Radial Basis Function (RBF) Kernel Smoothing
The RBF kernel is a mathematical method used to smooth the price series. By calculating weights based on the distance between data points, the RBF kernel ensures smoother transitions and a more refined representation of the price trend.
The RBF Kernel Weighted Moving Average is computed using the formula:
f_rbf_kernel(x, xi, sigma) =>
math.exp(-(math.pow(x - xi, 2)) / (2 * math.pow(sigma, 2)))
The smoothed price is then calculated as a weighted sum of past prices, using the RBF kernel weights:
f_rbf_weighted_average(src, kernel_len, sigma) =>
float total_weight = 0.0
float weighted_sum = 0.0
// Compute weights and sum for the weighted average
for i = 0 to kernel_len - 1
weight = f_rbf_kernel(kernel_len - 1, i, sigma)
total_weight := total_weight + weight
weighted_sum := weighted_sum + (src * weight)
// Check to avoid division by zero
total_weight != 0 ? weighted_sum / total_weight : na
Kijun-Sen Calculation
The Kijun-Sen, a component of Ichimoku analysis, is used here to further establish trends. The Kijun-Sen is computed as the average of the highest high and the lowest low over a specified period (default: 14 periods).
This Kijun-Sen calculation is based on the RBF-smoothed price to ensure smoother and more accurate trend detection.
f_kijun_sen(len, source) =>
math.avg(ta.lowest(source, len), ta.highest(source, len))
ATR-Adjusted RBF and Kijun-Sen
To mitigate false signals caused by price volatility, the indicator features ATR-adjusted versions of both the RBF smoothed price and Kijun-Sen.
The ATR multiplier is used to create upper and lower bounds around these lines, providing dynamic thresholds that account for market volatility.
Neutral State and Trend Continuation
This indicator can interpret a neutral state, where the signal is neither bullish nor bearish. By default, the indicator is set to interpret a neutral state as a continuation of the previous trend, though this can be adjusted to treat it as a truly neutral state.
Users can configure this setting using the signal_str input:
simple string signal_str = input.string("Continuation of Previous Trend", "Treat 0 State As", options = , group = G1)
Visual difference between "Neutral" (Bottom) and "Continuation of Previous Trend" (Top). Click on the picture to see it in full size.
Customizable Inputs and Settings:
Source Selection: Choose the input source for calculations (open, high, low, close, etc.).
Kernel Length and Sigma: Adjust the RBF kernel parameters to change the smoothing effect.
Kijun Length: Customize the lookback period for Kijun-Sen.
ATR Length and Multiplier: Modify these settings to adapt to market volatility.
Backtesting and Performance Metrics
The indicator includes a Backtest Mode, allowing users to evaluate the performance of the strategy using historical data. In Backtest Mode, a performance metrics table is generated, comparing the strategy's results to a simple buy-and-hold approach. Key metrics include mean returns, standard deviation, Sharpe ratio, and more.
Equity Calculation: The indicator calculates equity performance based on signals, comparing it against the buy-and-hold strategy.
Performance Metrics Table: Detailed performance analysis, including probabilities of positive, neutral, and negative returns.
Alerts
To keep traders informed, the indicator supports alerts for significant trend shifts:
// - - - - - ALERTS - - - - - //{
alert_source = sig
bool long_alert = ta.crossover (intrabar ? alert_source : alert_source , 0)
bool short_alert = ta.crossunder(intrabar ? alert_source : alert_source , 0)
alertcondition(long_alert, "LONG (RBF Kijun Trend System)", "RBF Kijun Trend System flipped ⬆LONG⬆")
alertcondition(short_alert, "SHORT (RBF Kijun Trend System)", "RBF Kijun Trend System flipped ⬇Short⬇")
//}
Important Notes
Calibration Needed: The default settings provided are not optimized and are intended for demonstration purposes only. Traders should adjust parameters to fit their trading style and market conditions.
Neutral State Interpretation: Users should carefully choose whether to treat the neutral state as a continuation or a separate signal.
Backtest Results: Historical performance is not indicative of future results. Market conditions change, and past trends may not recur.
Cerca negli script per "backtesting"
Adaptive MA Crossover with ATR-Based Risk MarkersDescription:
The Cross MA Entry Indicator with ATR-Based Stop-Loss and Take-Profit Markers is a powerful tool designed to help traders identify trend-following opportunities while managing risk effectively. By combining customizable moving average (MA) crossovers with ATR-based stop-loss (SL) and take-profit (TP) markers, this indicator provides a complete entry and risk management framework in a single script.
Unique Features:
1. Versatile Moving Average Combinations: The indicator allows users to select from four types of moving averages—SMA, EMA, DEMA, and TEMA—for both fast and slow lines, enabling a variety of crossover configurations. This flexibility helps traders tailor entry signals to specific trading strategies, asset types, or market conditions, enhancing the adaptability of the indicator across different styles and preferences.
2. ATR-Based Dynamic Risk Management: Leveraging the Average True Range (ATR), the indicator dynamically calculates stop-loss and take-profit levels based on market volatility. This approach adjusts to changing market conditions, making it more responsive and reliable for setting realistic, volatility-based risk parameters.
3. Customizable Risk/Reward Ratio: Users can define their preferred risk/reward ratio (e.g., 2:1, 3:1) to tailor take-profit levels relative to stop-loss distances. This feature empowers traders to align trades with their individual risk management strategies and objectives, while maintaining consistency and discipline in execution.
4. Streamlined Visualization of Entry and Risk Levels: Upon a crossover, the indicator places discrete markers at the calculated SL and TP levels, avoiding clutter while providing traders with an immediate view of potential risk and reward. Small dots represent SL and TP levels, offering a clean, clear display of critical decision points.
How to Use:
1. Entry Signals from MA Crossovers: This indicator generates entry signals when the selected moving averages cross, with green markers indicating long entries and red markers indicating short entries. The customizable MA selection enables traders to optimize crossover signals for various timeframes and asset classes.
2. Integrated Risk Markers: SL and TP levels are shown as small dots at the crossover point, based on the ATR multiplier and risk/reward ratio settings. These markers allow traders to quickly visualize the defined risk and potential reward for each entry.
This indicator offers a comprehensive solution for trend-following strategies by combining entry signals with adaptive risk management. Suitable for multiple timeframes, it allows for backtesting and adjustments to ATR and risk/reward parameters for improved alignment with individual trading goals. As with all strategies, thorough testing is recommended to ensure compatibility with your trading approach.
Demo GPT - Day Trading Scalping StrategyOverview:
This strategy is designed for day trading and scalping, utilizing a combination of technical indicators, candlestick patterns, and volume analysis to determine entry and exit points. It focuses on capturing short-term price movements while ensuring that trades are executed under specific market conditions.
Key Components:
Technical Indicators Used:
Exponential Moving Average (EMA): The strategy uses the 20-period EMA to identify the trend direction. The EMA smooths out price data, helping traders make more informed decisions about potential buy or sell signals.
Volume Weighted Average Price (VWAP): VWAP is used to measure the average price a security has traded at throughout the day, based on both volume and price. This indicator helps assess whether the current price is above or below the average trading price.
Camarilla Pivot Points: The strategy calculates four levels of Camarilla pivots (S2, S3, R2, R3) based on the highest and lowest prices over the last 14 daily candles. These levels act as potential support and resistance zones, guiding entry and exit decisions.
Candlestick Analysis:
Buy Condition: A buy signal is triggered when:
The first candle (previous candle) is green (close > open).
The second candle (current candle) is also green and opens above the first candle.
The volume of the current candle exceeds the 20-period moving average of volume, indicating strong buying interest.
Sell Condition: A sell signal is triggered when:
The first candle is red (close < open).
The second candle opens below the first red candle.
The volume of the current candle also exceeds the 20-period moving average of volume, indicating strong selling pressure.
Position Management:
The strategy enters a long position (buy) when the buy condition is met and closes the long position when the sell condition is met. This approach aims to capture upward momentum while avoiding extended exposure to downside risks.
Trading Settings:
Capital Management: The strategy uses 100% of available capital for each trade, allowing for maximum exposure to potential gains.
Commission and Slippage: The script includes settings for a commission rate of 0.1% and slippage of 3, accounting for trading costs and potential price changes during order execution.
Date Filtering: The strategy allows users to set a start date (January 1, 2018) and an end date (December 31, 2069) for trade execution, providing flexibility in backtesting and live trading.
Visualization:
The script plots the 20 EMA, VWAP, and the Camarilla pivot levels on the chart for visual reference.
Buy and sell signals are visually represented with shapes on the chart, making it easy to identify potential trade opportunities at a glance.
Volume is plotted in a separate pane to assess trading activity, and a horizontal line at zero provides a reference point.
Summary:
This Day Trading Scalping Strategy is designed to exploit short-term price movements by using a combination of EMAs, VWAP, and Camarilla pivot levels, alongside candlestick patterns and volume analysis. It is well-suited for traders looking to make quick trades based on real-time market conditions while maintaining a disciplined approach to entry and exit points. The strategy is highly visual, allowing traders to quickly assess market conditions and make informed trading decisions.
Feel free to modify or adjust any aspects of the strategy according to your specific trading goals or preferences!
Engulfing Pattern & Impulse [UAlgo]The Engulfing Pattern & Impulse is a tool designed for technical traders who utilize price action and volume analysis to assess market trends and potential reversals. This indicator identifies two powerful trading signals: Engulfing Patterns and Volume Impulses, which are essential components for evaluating potential bullish or bearish market momentum.
Engulfing Patterns are classic candlestick formations often associated with reversals or trend continuations, depending on the overall trend context. This indicator highlights both bullish and bearish engulfing patterns based on configurable criteria such as trend detection settings, comparison with average body size, and a customizable body multiplier for validation. The Volume Impulse feature signals moments of significant volume compared to historical levels, which often precede substantial price movements. Together, these features provide traders with a versatile tool for better timing entry and exit points.
The indicator also offers an adaptive trend detection system, allowing traders to choose from multiple methods (e.g., SMA50 or SMA50/SMA200 combinations) to assess the trend context, making it ideal for various market conditions.
🔶Key Features
Engulfing Pattern Detection: Identifies bullish and bearish engulfing patterns with customizable parameters, including body length and average size comparison.
Configurable trend basis: Choose between SMA50 or SMA50 with SMA200 to define trend direction.
Body size multiplier: Adjust the size threshold for valid engulfing patterns, providing flexibility based on market conditions.
Volume Impulse Signal: Highlights volume spikes that meet or exceed a specified multiplier, which can indicate increased buying or selling interest.
Customizable volume period and multiplier: Allows you to tailor the volume impulse detection based on the instrument’s average volume behavior.
Trend Detection Options: Select different trend detection methods to suit various trading styles and instruments.
SMA50-based detection: Classifies the trend based on the position of price relative to the 50-period SMA.
SMA50 and SMA200 combination: Incorporates a dual-moving average approach, classifying trends based on the relationship between price, SMA50, and SMA200.
Enhanced Visualization: Distinguishes bullish and bearish signals with customizable colors, providing clear and immediate visual cues for easy interpretation.
Custom label colors: Allows you to set distinct colors for bullish, bearish, and neutral signals for quick identification.
Pattern filtering: Enable or disable specific patterns (Bullish, Bearish, or Both) based on your trading preferences.
🔶 Interpreting Indicator
Bullish Engulfing Pattern: Indicates a potential bullish reversal in a downtrend. This signal occurs when a white candlestick with a body size exceeding a specified multiplier completely engulfs the previous black candlestick. The pattern will display a “BE” label below the candle if it meets the criteria, signaling potential upward momentum.
Bearish Engulfing Pattern: Indicates a potential bearish reversal in an uptrend. A black candlestick with a body size exceeding the specified multiplier fully engulfs the previous white candlestick, signaling possible downward movement. The “BE” label appears above the candle to denote this pattern.
Volume Impulse Up: Displays a “VI” label below the candle when the volume surpasses the defined multiplier, and the price closes higher than it opened, indicating strong upward buying interest.
Volume Impulse Down: Displays a “VI” label above the candle when the volume meets or exceeds the specified threshold, and the price closes lower than it opened, signaling strong selling pressure.
Indicator uses the SMA50 and SMA200 to determine trend direction due to their popularity in technical analysis as indicators of medium- and long-term trends. The SMA50 reflects the average price over the past 50 periods, providing insight into intermediate trends, while the SMA200 is often used to identify the broader trend direction. These SMAs help traders quickly assess whether the market is in an uptrend, downtrend, or consolidation phase, enhancing decision-making for both short-term and long-term strategies.
🔶 Disclaimer
Use with Caution: This indicator is provided for educational and informational purposes only and should not be considered as financial advice. Users should exercise caution and perform their own analysis before making trading decisions based on the indicator's signals.
Not Financial Advice: The information provided by this indicator does not constitute financial advice, and the creator (UAlgo) shall not be held responsible for any trading losses incurred as a result of using this indicator.
Backtesting Recommended: Traders are encouraged to backtest the indicator thoroughly on historical data before using it in live trading to assess its performance and suitability for their trading strategies.
Risk Management: Trading involves inherent risks, and users should implement proper risk management strategies, including but not limited to stop-loss orders and position sizing, to mitigate potential losses.
No Guarantees: The accuracy and reliability of the indicator's signals cannot be guaranteed, as they are based on historical price data and past performance may not be indicative of future results.
Z-Score RSI StrategyOverview
The Z-Score RSI Indicator is an experimental take on momentum analysis. By applying the Relative Strength Index (RSI) to a Z-score of price data, it measures how far prices deviate from their mean, scaled by standard deviation. This isn’t your traditional use of RSI, which is typically based on price data alone. Nevertheless, this unconventional approach can yield unique insights into market trends and potential reversals.
Theory and Interpretation
The RSI calculates the balance between average gains and losses over a set period, outputting values from 0 to 100. Typically, people look at the overbought or oversold levels to identify momentum extremes that might be likely to lead to a reversal. However, I’ve often found that RSI can be effective for trend-following when observing the crossover of its moving average with the midline or the crossover of the RSI with its own moving average. These crossovers can provide useful trend signals in various market conditions.
By combining RSI with a Z-score of price, this indicator estimates the relative strength of the price’s distance from its mean. Positive Z-score trends may signal a potential for higher-than-average prices in the near future (scaled by the standard deviation), while negative trends suggest the opposite. Essentially, when the Z-Score RSI indicates a trend, it reflects that the Z-score (the distance between the average and current price) is likely to continue moving in the trend’s direction. Generally, this signals a potential price movement, though it’s important to note that this could also occur if there’s a shift in the mean or standard deviation, rather than a meaningful change in price itself.
While the Z-Score RSI could be an insightful addition to a comprehensive trading system, it should be interpreted carefully. Mean shifts may validate the indicator’s predictions without necessarily indicating any notable price change, meaning it’s best used in tandem with other indicators or strategies.
Recommendations
Before putting this indicator to use, conduct thorough backtesting and avoid overfitting. The added parameters allow fine-tuning to fit various assets, but be careful not to optimize purely for the highest historical returns. Doing so may create an overly tailored strategy that performs well in backtests but fails in live markets. Keep it balanced and look for robust performance across multiple scenarios, as overfitting is likely to lead to disappointing real-world results.
SuperATR 7-Step Profit - Strategy [presentTrading] Long time no see!
█ Introduction and How It Is Different
The SuperATR 7-Step Profit Strategy is a multi-layered trading approach that integrates adaptive Average True Range (ATR) calculations with momentum-based trend detection. What sets this strategy apart is its sophisticated 7-step take-profit mechanism, which combines four ATR-based exit levels and three fixed percentage levels. This hybrid approach allows traders to dynamically adjust to market volatility while systematically capturing profits in both long and short market positions.
Traditional trading strategies often rely on static indicators or single-layered exit strategies, which may not adapt well to changing market conditions. The SuperATR 7-Step Profit Strategy addresses this limitation by:
- Using Adaptive ATR: Enhances the standard ATR by making it responsive to current market momentum.
- Incorporating Momentum-Based Trend Detection: Identifies stronger trends with higher probability of continuation.
- Employing a Multi-Step Take-Profit System: Allows for gradual profit-taking at predetermined levels, optimizing returns while minimizing risk.
BTCUSD 6hr Performance
█ Strategy, How It Works: Detailed Explanation
The strategy revolves around detecting strong market trends and capitalizing on them using an adaptive ATR and momentum indicators. Below is a detailed breakdown of each component of the strategy.
🔶 1. True Range Calculation with Enhanced Volatility Detection
The True Range (TR) measures market volatility by considering the most significant price movements. The enhanced TR is calculated as:
TR = Max
Where:
High and Low are the current bar's high and low prices.
Previous Close is the closing price of the previous bar.
Abs denotes the absolute value.
Max selects the maximum value among the three calculations.
🔶 2. Momentum Factor Calculation
To make the ATR adaptive, the strategy incorporates a Momentum Factor (MF), which adjusts the ATR based on recent price movements.
Momentum = Close - Close
Stdev_Close = Standard Deviation of Close over n periods
Normalized_Momentum = Momentum / Stdev_Close (if Stdev_Close ≠ 0)
Momentum_Factor = Abs(Normalized_Momentum)
Where:
Close is the current closing price.
n is the momentum_period, a user-defined input (default is 7).
Standard Deviation measures the dispersion of closing prices over n periods.
Abs ensures the momentum factor is always positive.
🔶 3. Adaptive ATR Calculation
The Adaptive ATR (AATR) adjusts the traditional ATR based on the Momentum Factor, making it more responsive during volatile periods and smoother during consolidation.
Short_ATR = SMA(True Range, short_period)
Long_ATR = SMA(True Range, long_period)
Adaptive_ATR = /
Where:
SMA is the Simple Moving Average.
short_period and long_period are user-defined inputs (defaults are 3 and 7, respectively).
🔶 4. Trend Strength Calculation
The strategy quantifies the strength of the trend to filter out weak signals.
Price_Change = Close - Close
ATR_Multiple = Price_Change / Adaptive_ATR (if Adaptive_ATR ≠ 0)
Trend_Strength = SMA(ATR_Multiple, n)
🔶 5. Trend Signal Determination
If (Short_MA > Long_MA) AND (Trend_Strength > Trend_Strength_Threshold):
Trend_Signal = 1 (Strong Uptrend)
Elif (Short_MA < Long_MA) AND (Trend_Strength < -Trend_Strength_Threshold):
Trend_Signal = -1 (Strong Downtrend)
Else:
Trend_Signal = 0 (No Clear Trend)
🔶 6. Trend Confirmation with Price Action
Adaptive_ATR_SMA = SMA(Adaptive_ATR, atr_sma_period)
If (Trend_Signal == 1) AND (Close > Short_MA) AND (Adaptive_ATR > Adaptive_ATR_SMA):
Trend_Confirmed = True
Elif (Trend_Signal == -1) AND (Close < Short_MA) AND (Adaptive_ATR > Adaptive_ATR_SMA):
Trend_Confirmed = True
Else:
Trend_Confirmed = False
Local Performance
🔶 7. Multi-Step Take-Profit Mechanism
The strategy employs a 7-step take-profit system
█ Trade Direction
The SuperATR 7-Step Profit Strategy is designed to work in both long and short market conditions. By identifying strong uptrends and downtrends, it allows traders to capitalize on price movements in either direction.
Long Trades: Initiated when the market shows strong upward momentum and the trend is confirmed.
Short Trades: Initiated when the market exhibits strong downward momentum and the trend is confirmed.
█ Usage
To implement the SuperATR 7-Step Profit Strategy:
1. Configure the Strategy Parameters:
- Adjust the short_period, long_period, and momentum_period to match the desired sensitivity.
- Set the trend_strength_threshold to control how strong a trend must be before acting.
2. Set Up the Multi-Step Take-Profit Levels:
- Define ATR multipliers and fixed percentage levels according to risk tolerance and profit goals.
- Specify the percentage of the position to close at each level.
3. Apply the Strategy to a Chart:
- Use the strategy on instruments and timeframes where it has been tested and optimized.
- Monitor the positions and adjust parameters as needed based on performance.
4. Backtest and Optimize:
- Utilize TradingView's backtesting features to evaluate historical performance.
- Adjust the default settings to optimize for different market conditions.
█ Default Settings
Understanding default settings is crucial for optimal performance.
Short Period (3): Affects the responsiveness of the short-term MA.
Effect: Lower values increase sensitivity but may produce more false signals.
Long Period (7): Determines the trend baseline.
Effect: Higher values reduce noise but may delay signals.
Momentum Period (7): Influences adaptive ATR and trend strength.
Effect: Shorter periods react quicker to price changes.
Trend Strength Threshold (0.5): Filters out weaker trends.
Effect: Higher thresholds yield fewer but stronger signals.
ATR Multipliers: Set distances for ATR-based exits.
Effect: Larger multipliers aim for bigger moves but may reduce hit rate.
Fixed TP Levels (%): Control profit-taking on smaller moves.
Effect: Adjusting these levels affects how quickly profits are realized.
Exit Percentages: Determine how much of the position is closed at each TP level.
Effect: Higher percentages reduce exposure faster, affecting risk and reward.
Adjusting these variables allows you to tailor the strategy to different market conditions and personal risk preferences.
By integrating adaptive indicators and a multi-tiered exit strategy, the SuperATR 7-Step Profit Strategy offers a versatile tool for traders seeking to navigate varying market conditions effectively. Understanding and adjusting the key parameters enables traders to harness the full potential of this strategy.
Range Detection [No Repaint]DETECTS RANGE EARLY
Using Confirmed Data:
All calculations now use to reference the previous completed candle
Signals are only generated based on completed candles
Range state is stored and confirmed before displaying
Key Changes to Prevent Repainting:
ATR calculations use previous candle data
Bollinger Bands calculate from previous closes
Price range checks use previous highs and lows
Range state is confirmed before displaying
How to Verify No Repainting:
Signals will only appear after a candle closes
Historical signals will remain unchanged
Alerts will only trigger on confirmed changes
This means:
The indicator will be slightly delayed (one candle)
But signals will be more reliable
Historical analysis will be accurate
Backtesting results will match real-time performance
Usage Tips with No-Repaint Version:
Wait for candle close before acting on signals
Use the confirmed range state for decision making
Consider the one-candle delay in your strategy timing
Alerts will only trigger on confirmed condition changes
Would you like me to:
Add a parameter to choose between real-time and no-repaint modes?
Add visual indicators for pending vs confirmed signals?
Modify the sensitivity of the range detection?
Supertrend StrategyThe Supertrend Strategy was created based on the Supertrend and Relative Strength Index (RSI) indicators, widely respected tools in technical analysis. This strategy combines these two indicators to capture market trends with precision and reliability, looking for optimizing exit levels at oversold or overbought price levels.
The Supertrend indicator identifies trend direction based on price and volatility by using the Average True Range (ATR). The ATR measures market volatility by calculating the average range between an asset’s high and low prices over a set period. It provides insight into price fluctuations, with higher ATR values indicating increased volatility and lower values suggesting stability. The Supertrend Indicator plots a line above or below the price, signaling potential buy or sell opportunities: when the price closes above the Supertrend line, an uptrend is indicated, while a close below the line suggests a downtrend. This line shifts as price movements and volatility levels change, acting as both a trailing stop loss and trend confirmation.
To enhance the Supertrend strategy, the Relative Strength Index (RSI) has been added as an exit criterion. As a momentum oscillator, the RSI indicates overbought (usually above 70) or oversold (usually below 30) conditions. This integration allows trades to close when the asset is overbought or oversold, capturing gains before a possible reversal, even if the percentage take profit level has not been reached. This mechanism aims to prevent losses due to market reversals before the Supertrend signal changes.
### Key Features
1. **Entry criteria**:
- The strategy uses the Supertrend indicator calculated by adding or subtracting a multiple of the ATR from the closing price, depending on the trend direction.
- When the price crosses above the Supertrend line, the strategy signals a long (buy) entry. Conversely, when the price crosses below, it signals a short (sell) entry.
- The strategy performs a reversal if there is an open position and a change in the direction of the supertrend occurs
2. **Exit criteria**:
- Take profit of 30% (default) on the average position price.
- Oversold (≤ 5) or overbought (≥ 95) RSI
- Reversal when there is a change in direction of the Supertrend
3. **No Repainting**:
- This strategy is not subject to repainting, as long as the timeframe configured on your chart is the same as the supertrend timeframe .
4. **Position Sizing by Equity and risk management**:
- This strategy has a default configuration to operate with 35% of the equity. At the time of opening the position, the supertrend line is typically positioned at about 12 to 16% of the entry price. This way, the strategy is putting at risk about 16% of 35% of equity, that is, around 5.6% of equity for each trade. The percentage of equity can be adjusted by the user according to their risk management.
5. **Backtest results**:
- This strategy was subjected to deep backtesting and operations in replay mode, including transaction fees of 0.12%, and slippage of 5 ticks.
- The past results in deep backtest and replay mode were compatible and profitable (Variable results depending on the take profit used, supertrend and RSI parameters). However, it should be noted that few operations were evaluated, since the currency in question has been created for a short time and the frequency of operations is relatively small.
- Past results are no guarantee of future results. The strategy's backtest results may even be due to overfitting with past data.
Default Settings
Chart timeframe: 2h
Supertrend Factor: 3.42
ATR period: 14
Supertrend timeframe: 2 h
RSI timeframe: 15 min
RSI Lenght: 5 min
RSI Upper limit: 95
RSI Lower Limit: 5
Take Profit: 30%
BYBIT:1000000MOGUSDT.P
Advanced Multi-Seasonality StrategyThe Multi-Seasonality Strategy is a trading system based on seasonal market patterns. Seasonality refers to recurring market trends driven by predictable calendar-based events. These patterns emerge due to economic cycles, corporate activities (e.g., earnings reports), and investor behavior around specific times of the year. Studies have shown that such effects can influence asset prices over defined periods, leading to opportunities for traders who exploit these patterns (Hirshleifer, 2001; Bouman & Jacobsen, 2002).
How the Strategy Works:
The strategy allows the user to define four distinct periods within a calendar year. For each period, the trader selects:
Entry Date (Month and Day): The date to enter the trade.
Holding Period: The number of trading days to remain in the trade after the entry.
Trade Direction: Whether to take a long or short position during that period.
The system is designed with flexibility, enabling the user to activate or deactivate each of the four periods. The idea is to take advantage of seasonal patterns, such as buying during historically strong periods and selling during weaker ones. A well-known example is the "Sell in May and Go Away" phenomenon, which suggests that stock returns are higher from November to April and weaker from May to October (Bouman & Jacobsen, 2002).
Seasonality in Financial Markets:
Seasonal effects have been documented across different asset classes and markets:
Equities: Stock markets tend to exhibit higher returns during certain months, such as the "January effect," where prices rise after year-end tax-loss selling (Haugen & Lakonishok, 1987).
Commodities: Agricultural commodities often follow seasonal planting and harvesting cycles, which impact supply and demand patterns (Fama & French, 1987).
Forex: Currency pairs may show strength or weakness during specific quarters based on macroeconomic factors, such as fiscal year-end flows or central bank policy decisions.
Scientific Basis:
Research shows that market anomalies like seasonality are linked to behavioral biases and institutional practices. For example, investors may respond to tax incentives at the end of the year, and companies may engage in window dressing (Haugen & Lakonishok, 1987). Additionally, macroeconomic factors, such as monetary policy shifts and holiday trading volumes, can also contribute to predictable seasonal trends (Bouman & Jacobsen, 2002).
Risks of Seasonal Trading:
While the strategy seeks to exploit predictable patterns, there are inherent risks:
Market Changes: Seasonal effects observed in the past may weaken or disappear as market conditions evolve. Increased algorithmic trading, globalization, and policy changes can reduce the reliability of historical patterns (Lo, 2004).
Overfitting: One of the risks in seasonal trading is overfitting the strategy to historical data. A pattern that worked in the past may not necessarily work in the future, especially if it was based on random chance or external factors that no longer apply (Sullivan, Timmermann, & White, 1999).
Liquidity and Volatility: Trading during specific periods may expose the trader to low liquidity, especially around holidays or earnings seasons, leading to slippage and larger-than-expected price swings.
Economic and Geopolitical Shocks: External events such as pandemics, wars, or political instability can disrupt seasonal patterns, leading to unexpected market behavior.
Conclusion:
The Multi-Seasonality Strategy capitalizes on the predictable nature of certain calendar-based patterns in financial markets. By entering and exiting trades based on well-established seasonal effects, traders can potentially capture short-term profits. However, caution is necessary, as market dynamics can change, and seasonal patterns are not guaranteed to persist. Rigorous backtesting, combined with risk management practices, is essential to successfully implementing this strategy.
References:
Bouman, S., & Jacobsen, B. (2002). The Halloween Indicator, "Sell in May and Go Away": Another Puzzle. American Economic Review, 92(5), 1618-1635.
Fama, E. F., & French, K. R. (1987). Commodity Futures Prices: Some Evidence on Forecast Power, Premiums, and the Theory of Storage. Journal of Business, 60(1), 55-73.
Haugen, R. A., & Lakonishok, J. (1987). The Incredible January Effect: The Stock Market's Unsolved Mystery. Dow Jones-Irwin.
Hirshleifer, D. (2001). Investor Psychology and Asset Pricing. Journal of Finance, 56(4), 1533-1597.
Lo, A. W. (2004). The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective. Journal of Portfolio Management, 30(5), 15-29.
Sullivan, R., Timmermann, A., & White, H. (1999). Data-Snooping, Technical Trading Rule Performance, and the Bootstrap. Journal of Finance, 54(5), 1647-1691.
This strategy harnesses the power of seasonality but requires careful consideration of the risks and potential changes in market behavior over time.
CPR by NKDCentral Pivot Range (CPR) Trading Strategy:
The Central Pivot Range (CPR) is a widely-used tool in technical analysis, helping traders pinpoint potential support and resistance levels in the market. By using the CPR effectively, traders can better gauge market trends and determine favorable entry and exit points. This guide explores how the CPR works, outlines its calculation, and describes how traders can enhance their strategies using an extended 10-line version of CPR.
What Really Central Pivot Range (CPR) is?
At its core, the CPR consists of three key lines:
Pivot Point (PP) – The central line, calculated as the average of the previous day’s high, low, and closing prices.
Upper Range (R1) – Positioned above the Pivot Point, acting as a potential ceiling where price may face resistance.
Lower Range (S1) – Found below the Pivot Point, serving as a potential floor where price might find support.
Advanced traders often expand on the traditional three-line CPR by adding extra levels above and below the pivot, creating up to a 10-line system. This extended CPR allows for a more nuanced understanding of the market and helps identify more detailed trading opportunities.
Applying CPR for Trading Success
1. How CPR is Calculation
The CPR relies on the previous day's high (H), low (L), and close (C) prices to create its structure:
Pivot Point (PP) = (H + L + C) / 3
First Resistance (R1) = (2 * PP) - L
First Support (S1) = (2 * PP) - H
Additional resistance levels (R2, R3) and support levels (S2, S3) are calculated by adding or subtracting multiples of the previous day’s price range (H - L) from the Pivot Point.
2. Recognizing the Market Trend
To effectively trade using CPR, it’s essential to first determine whether the market is trending up (bullish) or down (bearish). In an upward-trending market, traders focus on buying at support levels, while in a downward market, they look to sell near resistance.
3. Finding Ideal Entry Points
Traders often look to enter trades when price approaches key levels within the CPR range. Support levels (S1, S2) offer buying opportunities, while resistance levels (R1, R2) provide selling opportunities. These points are considered potential reversal zones, where price may bounce or reverse direction.
4. Managing Risk with Stop-Loss Orders
Proper risk management is crucial in any trading strategy. A stop-loss should be set slightly beyond the support level for buy positions and above the resistance level for sell positions, ensuring that losses are contained if the market moves against the trader’s position.
5. Determining Profit Targets
Profit targets are typically set based on the distance between entry points and the next support or resistance level. Many traders apply a risk-reward ratio, aiming for larger potential profits compared to the potential losses. However, if the next resistance and support level is far then middle levels are used for targets (i.e. 50% of R1 and R2)
6. Confirmation Through Other Indicators
While CPR provides strong support and resistance levels, traders often use additional indicators to confirm potential trade setups. Indicators such as moving averages can
help validate the signals provided by the CPR.
7. Monitoring Price Action At CPR Levels
Constantly monitoring price movement near CPR levels is essential. If the price fails to break through a resistance level (R1) or holds firm at support (S1), it can offer cues on when to exit or adjust a trade. However, a strong price break past these levels often signals a continued trend.
8. Trading Breakouts with CPR
When the price breaks above resistance or below support with strong momentum, it may signal a potential breakout. Traders can capitalize on these movements by entering positions in the direction of the breakout, ideally confirmed by volume or other technical indicators.
9. Adapting to Changing Market Conditions
CPR should be used in the context of broader market influences, such as economic reports, news events, or geopolitical shifts. These factors can dramatically affect market direction and how price reacts to CPR levels, making it important to stay informed about external market conditions.
10. Practice and Backtesting for Improvements
Like any trading tool, the CPR requires practice. Traders are encouraged to backtest their strategies on historical price data to get a better sense of how CPR works in different market environments. Continuous analysis and practice help improve decision-making and strategy refinement.
The Advantages of Using a 10-Line CPR System
An extended 10-line CPR system—comprising up to five resistance and five support levels—provides more granular control and insight into market movements. This expanded view helps traders better gauge trends and identify more opportunities for entry and exit. Key benefits include:
R2, S2 Levels: These act as secondary resistance or support zones, giving traders additional opportunities to refine their trade entries and exits.
R3, S3 Levels: Provide an even wider range for identifying reversals or trend continuations in more volatile markets.
Flexibility: The broader range of levels allows traders to adapt to changing market conditions and make more precise decisions based on market momentum.
So in Essential:
The Central Pivot Range is a valuable tool for traders looking to identify critical price levels in the market. By providing a clear framework for identifying potential support and resistance zones, it helps traders make informed decisions about entering and exiting trades. However, it’s important to combine CPR with sound risk management and additional confirmation through other technical indicators for the best results.
Although no trading tool guarantees success, the CPR, when used effectively and combined with practice, can significantly enhance a trader’s ability to navigate market fluctuations.
Parent Session Sweeps + Alert Killzone Ranges with Parent Session Sweep
Key Features:
1. Multiple Session Support: The script tracks three major trading sessions - Asia, London, and New York. Users can customize the timing of these sessions.
2. Killzone Visualization: The strategy visually represents each session's range, either as filled boxes or lines, allowing traders to easily identify key price levels.
3. Parent Session Logic: The core of the strategy revolves around identifying a "parent" session - a session that encompasses the range of the following session. This parent session becomes the basis for potential trade setups.
4. Sweep and Reclaim Setups: The strategy looks for price movements that sweep (break above or below) the parent session's high or low, followed by a reclaim of that level. This price action often indicates a potential reversal.
5. Risk-Reward Filtering: Each potential setup is evaluated based on a user-defined minimum risk-reward ratio, ensuring that only high-quality trade opportunities are considered.
6. Candle Close Filter: An optional filter that checks the characteristics of the candle that reclaims the parent session level, adding an extra layer of confirmation to the setup.
7. Performance Tracking: The strategy keeps track of bullish and bearish setup success rates, providing valuable feedback on its performance over time.
8. Visual Aids: The script draws lines to mark the parent session's high and low, making it easy for traders to identify key levels.
How It Works:
1. The script continuously monitors price action across the defined sessions.
2. When a session fully contains the range of the next session, it's identified as a potential parent session.
3. The strategy then waits for price to sweep either the high or low of this parent session.
4. If a sweep occurs, it looks for a reclaim of the swept level within the parameters set by the user.
5. If a valid setup is identified, the script generates an alert and places a trade (if backtesting or running live).
6. The strategy continues to monitor the trade for either reaching the target (opposite level of the parent session) or hitting the stop loss.
Considerations for Signals:
- Sweep: A break of the parent session's high or low.
- Reclaim: A close back inside the parent session range after a sweep.
- Candle Characteristics: Optional filter for the reclaim candle (e.g., bullish candle for long setups).
- Risk-Reward: Each setup must meet or exceed the user-defined minimum risk-reward ratio.
- Session Timing: The strategy is sensitive to the defined session times, which should be set according to the trader's preferred time zone.
This strategy aims to capitalize on institutional order flow and liquidity patterns in the forex market, providing traders with a systematic approach to identifying potential reversal points with favorable risk-reward profiles.
Momentum Nexus Oscillator [UAlgo]The "Momentum Nexus Oscillator " indicator is a comprehensive momentum-based tool designed to provide traders with visual cues on market conditions using multiple oscillators. By combining four popular technical indicators—RSI (Relative Strength Index), VZO (Volume Zone Oscillator), MFI (Money Flow Index), and CCI (Commodity Channel Index)—this heatmap offers a holistic view of the market's momentum.
The indicator plots two lines: one representing the current chart’s combined momentum score and the other representing a higher timeframe’s (HTF) score, if enabled. Through smooth gradient color transitions and easy-to-read signals, the Momentum Nexus Heatmap allows traders to easily identify potential trend reversals or continuation patterns.
Traders can use this tool to detect overbought or oversold conditions, helping them anticipate possible long or short trade opportunities. The option to use a higher timeframe enhances the flexibility of the indicator for longer-term trend analysis.
🔶 Key Features
Multi-Oscillator Approach: Combines four popular momentum oscillators (RSI, VZO, MFI, and CCI) to generate a weighted score, providing a comprehensive picture of market momentum.
Dynamic Color Heatmap: Utilizes a smooth gradient transition between bullish and bearish colors, reflecting market momentum across different thresholds.
Higher Timeframe (HTF) Compatibility: Includes an optional higher timeframe input that displays a separate score line based on the same momentum metrics, allowing for multi-timeframe analysis.
Customizable Parameters: Adjustable RSI, VZO, MFI, and CCI lengths, as well as overbought and oversold levels, to match the trader’s strategy or preference.
Signal Alerts: Built-in alert conditions for both the current chart and higher timeframe scores, notifying traders when long or short entry signals are triggered.
Buy/Sell Signals: Displays visual signals (▲ and ▼) on the chart when combined scores reach overbought or oversold levels, providing clear entry cues.
User-Friendly Visualization: The heatmap is separated into four sections representing each indicator, providing a transparent view of how each contributes to the overall momentum score.
🔶 Interpreting Indicator:
Combined Score
The indicator generates a combined score by weighing the individual contributions of RSI, VZO, MFI, and CCI. This score ranges from 0 to 100 and is plotted as a line on the chart. Lower values suggest potential oversold conditions, while higher values indicate overbought conditions.
Color Heatmap
The indicator divides the combined score into four distinct sections, each representing one of the underlying momentum oscillators (RSI, VZO, MFI, and CCI). Bullish (greenish) colors indicate upward momentum, while bearish (grayish) colors suggest downward momentum.
Long/Short Signals
When the combined score drops below the oversold threshold (default is 26), a long signal (▲) is displayed on the chart, indicating a potential buying opportunity.
When the combined score exceeds the overbought threshold (default is 74), a short signal (▼) is shown, signaling a potential sell or short opportunity.
Higher Timeframe Analysis
If enabled, the indicator also plots a line representing the combined score for a higher timeframe. This can be used to align lower timeframe trades with the broader trend of a higher timeframe, providing added confirmation.
Signals for long and short entries are also plotted for the higher timeframe when its combined score reaches overbought or oversold levels.
🔶Purpose of Using Multiple Technical Indicators
The combination of RSI, VZO, MFI, and CCI in the Momentum Nexus Heatmap provides a comprehensive approach to analyzing market momentum by leveraging the unique strengths of each indicator. This multi-indicator method minimizes the limitations of using just one tool, resulting in more reliable signals and a clearer understanding of market conditions.
RSI (Relative Strength Index)
RSI contributes by measuring the strength and speed of recent price movements. It helps identify overbought or oversold levels, signaling potential trend reversals or corrections. Its simplicity and effectiveness make it one of the most widely used indicators in technical analysis, contributing to momentum assessment in a straightforward manner.
VZO (Volume Zone Oscillator)
VZO adds the critical element of volume to the analysis. By assessing whether price movements are supported by significant volume, VZO distinguishes between price changes that are driven by real market conviction and those that might be short-lived. It helps validate the strength of a trend or alert the trader to potential weakness when price moves are unsupported by volume.
MFI (Money Flow Index)
MFI enhances the analysis by combining price and volume to gauge money flow into and out of an asset. This indicator provides insight into the participation of large players in the market, showing if money is pouring into or exiting the asset. MFI acts as a volume-weighted version of RSI, giving more weight to volume shifts and helping traders understand the sustainability of price trends.
CCI (Commodity Channel Index)
CCI contributes by measuring how far the price deviates from its statistical average. This helps in identifying extreme conditions where the market might be overextended in either direction. CCI is especially useful for spotting trend reversals or continuations, particularly during market extremes, and for identifying divergence signals.
🔶 Disclaimer
Use with Caution: This indicator is provided for educational and informational purposes only and should not be considered as financial advice. Users should exercise caution and perform their own analysis before making trading decisions based on the indicator's signals.
Not Financial Advice: The information provided by this indicator does not constitute financial advice, and the creator (UAlgo) shall not be held responsible for any trading losses incurred as a result of using this indicator.
Backtesting Recommended: Traders are encouraged to backtest the indicator thoroughly on historical data before using it in live trading to assess its performance and suitability for their trading strategies.
Risk Management: Trading involves inherent risks, and users should implement proper risk management strategies, including but not limited to stop-loss orders and position sizing, to mitigate potential losses.
No Guarantees: The accuracy and reliability of the indicator's signals cannot be guaranteed, as they are based on historical price data and past performance may not be indicative of future results.
BRT MACD CustomBRT MACD Custom — Adaptive and Flexible MACD for Multi-Timeframe Analysis
The BRT MACD Custom is an advanced version of the traditional MACD indicator, offering additional flexibility and adaptability for multi-timeframe trading. This custom script allows traders to adjust the calculation parameters for MACD to suit their specific trading strategy, timeframe, and market conditions.
Key Features
Multi-Timeframe Support
Unlike the standard MACD, this indicator lets you choose a specific timeframe (different from the chart timeframe) for calculating MACD values. This feature provides more flexibility in analyzing market trends on multiple timeframes without changing the main chart.
Example: You can analyze MACD on a 15-minute timeframe even when your chart is set to 1-minute, giving you broader market insights.
Customizable EMA and Signal Settings
Users can adjust the fast and slow EMA lengths as well as the signal smoothing to better align with their preferred trading strategies. The script allows switching between the two popular types of moving averages — SMA or EMA — for both the MACD and the signal line.
Volatility-Based Adaptive EMA
The script includes an adaptive mechanism for EMA calculation. When the selected timeframe closes, the indicator dynamically adjusts the calculation, ensuring the MACD values respond quickly to market volatility. This makes the indicator more reactive compared to static MACD implementations.
Shift Options for MACD, Signal, and Histogram
The indicator allows shifting the MACD, signal line, and histogram values by one or more bars. This can be useful for backtesting and simulating strategies where you anticipate future price movements.
Signal Alerts for Long and Short Trades
The script generates visual signals when certain conditions are met, indicating potential long or short trade opportunities. These signals are based on MACD and histogram crossovers:
Long Signal: Triggered when MACD is above the signal line and both are rising.
Short Signal: Triggered when MACD is below the signal line and both are falling.
Custom Plotting
The MACD line, signal line, and histogram are plotted on the chart for easy visualization. The histogram changes colors to reflect positive or negative momentum:
Green shades when MACD is above the signal line.
Red shades when MACD is below the signal line.
Applications in Trading
The BRT MACD Custom is ideal for traders who need flexibility in their technical analysis. Its multi-timeframe capabilities and customizable moving averages make it suitable for day trading, swing trading, and long-term investing across a variety of markets.
Scalping: Use the 1-minute or 5-minute timeframe to identify short-term trends while calculating MACD on a higher timeframe such as 15 or 30 minutes.
Swing Trading: Apply the indicator on 1-hour or 4-hour charts to detect mid-term trends.
Long-Term Investing: Analyze daily or weekly charts with longer EMA periods to confirm market direction before making large investments.
The Bar Counter Trend Reversal Strategy [TradeDots]Overview
The Bar Counter Trend Reversal Strategy is designed to identify potential counter-trend reversal points in the market after a series of consecutive rising or falling bars.
By analyzing price movements in conjunction with optional volume confirmation and channel bands (Bollinger Bands or Keltner Channels), this strategy aims to detect overbought or oversold conditions where a trend reversal may occur.
🔹How it Works
Consecutive Price Movements
Rising Bars: The strategy detects when there are a specified number of consecutive rising bars (No. of Rises).
Falling Bars: Similarly, it identifies a specified number of consecutive falling bars (No. of Falls).
Volume Confirmation (Optional)
When enabled, the strategy checks for increasing volume during the consecutive price movements, adding an extra layer of confirmation to the potential reversal signal.
Channel Confirmation (Optional)
Channel Type: Choose between Bollinger Bands ("BB") or Keltner Channels ("KC").
Channel Interaction: The strategy checks if the price interacts with the upper or lower channel lines: For short signals, it looks for price moving above the upper channel line. For long signals, it looks for price moving below the lower channel line.
Customization:
No. of Rises/Falls: Set the number of consecutive bars required to trigger a signal.
Volume Confirmation: Enable or disable volume as a confirmation factor.
Channel Confirmation: Enable or disable channel bands as a confirmation factor.
Channel Settings: Adjust the length and multiplier for the Bollinger Bands or Keltner Channels.
Visual Indicators:
Entry Signals: Triangles plotted on the chart indicate potential entry points:
Green upward triangle for long entries.
Red downward triangle for short entries.
Channel Bands: The upper and lower bands are plotted for visual reference.
Strategy Parameters:
Initial Capital: $10,000.
Position Sizing: 80% of equity per trade.
Commission: 0.01% per trade to simulate realistic trading costs.
🔹Usage
Set up the number of Rises/Falls and choose whether if you want to use channel indicators and volume as the confirmation.
Monitor the chart for triangles indicating potential entry points.
Consider the context of the overall market trend and other technical factors.
Backtesting and Optimization:
Use TradingView's Strategy Tester to evaluate performance.
Adjust parameters to optimize results for different market conditions.
🔹 Considerations and Recommendations
Risk Management:
The strategy does not include built-in stop-loss or take-profit levels. It's recommended to implement your own risk management techniques.
Market Conditions:
Performance may vary in different market environments. Testing and adjustments are advised when applying the strategy to new instruments or timeframes.
No Guarantee of Future Results:
Past performance is not indicative of future results. Always perform due diligence and consider the risks involved in trading.
Volumetric Volatility Breaker Blocks [UAlgo]The "Volumetric Volatility Breaker Blocks " indicator is designed for traders who want a comprehensive understanding of market volatility combined with volume analysis. This indicator provides a clear visualization of significant volatility areas (or blocks), characterized by price movements that exceed a specific volatility threshold, as calculated using the ATR (Average True Range). The concept is enhanced by integrating volume-based insights, offering a view of market activity that helps users to recognize when significant price changes are being supported by an appropriate level of market participation.
The indicator calculates breaker blocks for both bullish and bearish market conditions, providing distinct visual elements that identify periods of high volatility and substantial volume divergence. The focus on both volume and volatility makes this tool versatile, allowing traders to assess the strength of price movements as well as areas where price might break above or below previously established levels.
It supports adjustable parameters, such as volatility length, smoothness factor, and volume display, allowing traders to fine-tune the indicator according to their trading strategy and market environment. The highlighted breaker blocks assist in identifying zones of potential price reversal or continuation, which can be critical for making informed trading decisions.
🔶 Key Features
Volatility-Based Block Identification: The indicator uses the Average True Range (ATR) to determine the volatility of the market. When the ATR exceeds a specified threshold (smooth ATR multiplied by a user-defined multiplier), it highlights these areas as volatility blocks. The idea is to mark periods where price activity is significantly divergent from normal conditions, which often signals market opportunities.
Volume Integrated Analysis: In addition to tracking volatility, the indicator incorporates volume data, allowing traders to see the amount of activity that occurs during these high-volatility periods. This helps in identifying whether a price movement is likely sustainable or whether it lacks market support.
User Adjustable Parameters: The indicator offers customization options for the volatility length (using ATR), smooth length, and multiplier for sensitivity adjustment. These settings enable users to modify the indicator’s responsiveness to market conditions.
The option to display the last few volatility blocks allows traders to manage clutter on their charts and focus only on the most recent significant data.
Mitigation Method: Users can select between different mitigation methods ("Close" or "Wick") to determine how blocks are broken. This adds an extra layer of adaptability, allowing traders to modify the indicator's response based on different price action strategies.
Dynamic Visual Representation: The indicator dynamically draws boxes for volatility blocks and shades them according to market direction, with split areas showing the bullish and bearish strength contributions. It also provides percentage volume for each block, helping traders understand the relative market participation during these moves.
🔶 Interpreting the Indicator
Identifying High Volatility Areas: When a new volatility block appears, it signifies that the market is experiencing higher-than-usual volatility, driven by increased ATR values. Traders should pay attention to these blocks, as they often indicate that a significant price move is occurring. Bullish blocks suggest upward pressure, whereas bearish blocks indicate downward pressure.
Volume Insights: The volume associated with each volatility block provides an insight into how much market participation accompanies these moves. Higher volume within a block implies that the market is actively supporting the price change, which may be a sign of continuation. Low volume suggests that the movement may lack the strength to persist.
Bullish vs. Bearish Strength Analysis: Each block is split into bullish and bearish strength, giving a clearer picture of what’s happening within the volatility period. If the bullish portion dominates, it indicates strong upward sentiment during that period. Conversely, if the bearish side is more prominent, there is more selling pressure. This breakdown helps in understanding intra-block market dynamics.
Volume Percentage Display: The indicator also displays the volume percentage in each block, which provides context for the strength of the move relative to recent market activity. Higher percentages mean more market engagement, which could confirm the legitimacy of a trend or a significant breakout.
🔶 Disclaimer
Use with Caution: This indicator is provided for educational and informational purposes only and should not be considered as financial advice. Users should exercise caution and perform their own analysis before making trading decisions based on the indicator's signals.
Not Financial Advice: The information provided by this indicator does not constitute financial advice, and the creator (UAlgo) shall not be held responsible for any trading losses incurred as a result of using this indicator.
Backtesting Recommended: Traders are encouraged to backtest the indicator thoroughly on historical data before using it in live trading to assess its performance and suitability for their trading strategies.
Risk Management: Trading involves inherent risks, and users should implement proper risk management strategies, including but not limited to stop-loss orders and position sizing, to mitigate potential losses.
No Guarantees: The accuracy and reliability of the indicator's signals cannot be guaranteed, as they are based on historical price data and past performance may not be indicative of future results.
D_Rock's MA IndicatorD_Rock's Moving Average Indicator
This is an indicator version of my strategy linked here
**Overview:**
The basic concept of this indicator is to generate a signal when a faster/shorter length moving average crosses over (for Longs) or crosses under (for Shorts) a medium/longer length moving average. All of which are customizable. This indicator can work on any timeframe, however the daily is the timeframe used for the default settings and screenshots, as it was designed to be a multi-day swing strategy. Once a signal has been confirmed with a candle close, based on user options, the strategy is to enter the trade on the open of the next candle.
The crossover strategy is nothing new to trading, but what can make this strategy unique and helpful, is the addition of further confirmation points before a signal is generated along with the ability to show multiple moving averages on the chart if you choose. Each moving average pair can also be turned into a "cloud" instead of the traditional lines, for additional viewing preferences. Just about everything visual can be toggled on/off as well.
This indicator is a Trend (MA) indicator with optional confirmation points using a Momentum (MACD) indicator. While a Volume-based indicator is not shown here, one could consider using their favorite from that category to further compliment the signal idea.
If you would like to see the backtesting results for your favorite moving average crossover/under, please see my strategy version linked here .
Shoutout given to Ripster's Clouds Indicator as pieces of that code were taken and modified to create both the Cloud visualization effects, and the Moving Average Pair Plots that are implemented in this strategy.
MOVING AVERAGE OPTIONS
Select between and change the length & type of up to 5 pairs (10 total) of moving averages
The "Show Cloud-x" option will display a fill color between the "a" and "b" pairs
All moving averages lines can be toggled on/off in the "Style" tab, as well as adjusting their colors.
Visualization features do not affect calculations, meaning you could have all or nothing on the chart and the strategy will still produce results
SIGNAL CHOICES
Choose the fast/shorter length MA and the medium/longer length MA to determine the entry signal
CONFIRMATION OPTIONS
Both of these have customizable values and can be toggled on/off
A candle close over a slower/much longer length moving average
An additional cross-over (cross-under for Shorts) on the MACD indicator using default MACD values. While the MACD indicator is not necessary to have on the chart, it can help to add that for visualization. The calculations will perform whether the indicator is on the chart or not.
ADDITIONAL PLOTS
MACD (Moving Average Convergence/Divergence):
- The MACD is an optional confirmation indicator for this strategy.
- Plotting the indicator is not necessary for the strategy to work, but it can be helpful to visually see the status and position of the MACD if this feature is enabled in the strategy
- This helps to identify if there is also momentum behind the entry signal
Hma Swing Points | viResearchHma Swing Points | viResearch
Conceptual Foundation and Innovation
The "Hma Swing Points" script introduces a simple yet effective method for identifying key swing points in the market using Hull Moving Averages (HMA). The Hull Moving Average is a faster and smoother alternative to traditional moving averages, making it ideal for detecting significant price swings. By applying HMA to both high and low prices, the script identifies swing highs and lows, providing traders with visual cues for potential trend reversals or continuations. This approach helps traders recognize turning points in the market with minimal lag, allowing for more precise entries and exits.
Technical Composition and Calculation
This script uses two Hull Moving Averages—one for the high prices and another for the low prices. These HMAs offer smoother trend detection while filtering out market noise. The script identifies the highest and lowest HMA values over a user-defined lookback period to determine the swing high and swing low points. Long signals are generated when the current HMA of the highs matches the highest value within the lookback period, while short signals are generated when the HMA of the lows matches the lowest value. These signals are plotted on the chart, and alerts can be set to notify the trader of possible entry or exit points.
Features and User Inputs
The script offers several customizable inputs to adjust its sensitivity and behavior according to the trader’s preferences. The lookback period defines the number of bars used to calculate the highest and lowest HMA values, allowing traders to control how responsive the script is to price changes. The length of the Hull Moving Average can also be modified, giving traders flexibility in smoothing the indicator. Additionally, optional bar color settings provide visual cues, with bullish and bearish trends highlighted. Alerts are included to notify traders when long or short swing points are detected, ensuring they are informed even when not actively monitoring the chart.
Practical Applications
The "Hma Swing Points" script is useful for traders who aim to identify critical market turning points and potential reversals. It is especially effective in trending markets where price swings present trading opportunities. Traders can use the script to detect reversals by spotting swing points that indicate a possible shift from bullish to bearish trends, or vice versa. The script also helps confirm ongoing trends by showing the strength of swings, allowing traders to make informed decisions about entering or exiting trades. Its ability to mark precise swing points enhances trade timing, helping traders optimize their entries and exits.
Advantages and Strategic Value
The script offers a streamlined approach to detecting swing points with the speed and smoothness of the Hull Moving Average. This makes it easier to filter out false signals and noise, improving the accuracy of trend identification. The customizable inputs allow traders to tailor the script for different assets and market conditions, making it versatile for various trading styles. By highlighting key swing points, the script provides traders with clear visual signals for potential reversals and trend confirmations, enhancing their ability to follow and act on market movements.
Summary and Usage Tips
Incorporating the "Hma Swing Points" script into a trading strategy helps traders identify market reversals and continuation points more effectively. Adjusting the lookback period and HMA length ensures the script adapts to different assets and market conditions. The alert system ensures traders don’t miss key swing points. As always, backtesting is important to evaluate the script’s performance under various market conditions, and past results may not guarantee future outcomes.
Dynamic Sentiment RSI [UAlgo]The Dynamic Sentiment RSI is a technical analysis tool that combines the classic RSI (Relative Strength Index) concept with dynamic sentiment analysis, offering traders enhanced insights into market conditions. Unlike the traditional RSI, this indicator integrates volume weighting, sentiment factors, and smoothing features to provide a more nuanced view of momentum and potential market reversals. It is designed to assist traders in detecting overbought/oversold conditions, momentum shifts, and to generate potential buy or sell signals using crossover and crossunder techniques. By dynamically adjusting based on sentiment and volume factors, this RSI offers better adaptability to varying market conditions, making it suitable for different trading styles and timeframes.
This tool is particularly helpful for traders who wish to explore not only price movement but also the underlying market sentiment, offering a more comprehensive approach to momentum analysis. The sentiment factor amplifies the RSI's sensitivity to price shifts, making it easier to detect early signals of market reversals or the continuation of a trend.
🔶 Key Features
Dynamic Sentiment Calculation: The indicator incorporates a "Sentiment Factor" that adjusts the RSI length dynamically based on a multiplier, helping traders better understand market sentiment at different time intervals.
Volume Weighting: When enabled, the RSI calculations are weighted by volume, allowing traders to give more importance to price movements with higher trading volume, which may provide more accurate signals.
Smoothing Feature: A customizable smoothing period is applied to the RSI to help filter out noise and make the signal smoother. This feature is particularly useful for traders who prefer to focus on long-term trends while minimizing false signals.
Step Size Customization: A "Step Size" input allows users to round the sentiment RSI to predefined intervals, making the results easier to interpret and act upon. This feature allows you to focus on significant sentiment changes and ignore minor fluctuations.
Crossover/Crossunder Alerts: The indicator includes crossover and crossunder signals on the zero-line, helping traders identify potential buy and sell opportunities as the smoothed RSI crosses these levels.
The indicator offers a clear visual display with multiple color-coded lines and areas:
Sentiment RSI: Plotted as an area chart, color-coded based on sentiment strength.
Raw RSI: A purple line representing the raw adjusted RSI.
Smoothed RSI: A dynamic line, color-coded aqua or orange based on its position relative to the zero line.
Buy/Sell Signals: Triangle shapes are plotted at crossovers and crossunders, providing clear entry and exit points.
🔶 Interpreting the Indicator
Sentiment RSI
-This line represents the sentiment-adjusted RSI, where the higher the value, the stronger the bullish sentiment, and the lower the value, the stronger the bearish sentiment. It is rounded to step intervals, making it easier to detect significant shifts in sentiment.
- A positive sentiment RSI (above 0) suggests bullish market conditions, while a negative sentiment RSI (below 0) suggests bearish conditions.
Smoothed RSI
The smoothed RSI helps reduce noise and shows the trend more clearly.
Crossovers of the zero line are significant:
- Crossover above zero: Indicates that bullish momentum is building, potentially signaling a buying opportunity.
- Crossunder below zero: Signals a shift towards bearish momentum, potentially indicating a sell signal.
Traders should look for these crossovers in conjunction with other signals for more accurate entry/exit points.
Raw RSI (Adjusted)
The raw adjusted RSI offers a less smoothed, more responsive version of the RSI. While it may be noisier, it provides early signals of market reversals and trends.
Crossover/Crossunder Signals
- When the smoothed RSI crosses above the zero line, a "Signal Up" triangle appears, indicating a potential buying opportunity.
- When the smoothed RSI crosses below the zero line, a "Signal Down" triangle appears, signaling a potential sell opportunity.
These signals help traders time their entries and exits by identifying momentum shifts.
Volume Weighting (Optional)
- If volume weighting is enabled, the RSI will give more weight to periods of higher trading volume, making the signals more reliable when the market is highly active.
Strong Up/Down Levels (40/-40)
- These dotted lines represent extreme sentiment levels. When the sentiment RSI reaches 40 or -40, the market may be nearing an overbought or oversold condition, respectively. This could be a signal for traders to prepare for potential reversals or shifts in momentum.
By combining the various components of this indicator, traders can gain a comprehensive view of market sentiment and price action, helping them make more informed trading decisions. The combination of sentiment factors, volume weighting, and smoothing makes this indicator highly flexible and suitable for a variety of trading strategies.
🔶 Disclaimer
Use with Caution: This indicator is provided for educational and informational purposes only and should not be considered as financial advice. Users should exercise caution and perform their own analysis before making trading decisions based on the indicator's signals.
Not Financial Advice: The information provided by this indicator does not constitute financial advice, and the creator (UAlgo) shall not be held responsible for any trading losses incurred as a result of using this indicator.
Backtesting Recommended: Traders are encouraged to backtest the indicator thoroughly on historical data before using it in live trading to assess its performance and suitability for their trading strategies.
Risk Management: Trading involves inherent risks, and users should implement proper risk management strategies, including but not limited to stop-loss orders and position sizing, to mitigate potential losses.
No Guarantees: The accuracy and reliability of the indicator's signals cannot be guaranteed, as they are based on historical price data and past performance may not be indicative of future results.
Options Series - Ichimoku Cloud and HalfTrend
The provided script combines two powerful technical indicators, Ichimoku Cloud and HalfTrend, to create a hybrid trading tool. Here's an analysis of the key components and how they work together:
Ichimoku Cloud and HalfTrend
⭐ 1. Indicator Title and Settings:
The script sets the title as "Options Series - Ichimoku Cloud and HalfTrend" and uses the overlay=true option to display the indicators directly on the price chart.
⭐ 2. Color Definitions:
Several colors are defined for later use:
Green and Red for different types of candles and signals.
Fluorescent Colors for highlighting significant trends or changes in market conditions.
⭐ 3. Ichimoku Cloud Setup:
The Ichimoku Cloud is a comprehensive indicator used to identify support, resistance, and trend direction. Here’s how the script configures it:
Conversion Periods, Base Periods, Lagging Span 2 Periods, and Displacement are customizable via input options, giving flexibility to adjust Ichimoku settings based on different market conditions.
The function donchian(len) calculates the Donchian Channel average, which is used to define the Conversion Line and Base Line. The crossover of these lines is crucial in determining bullish or bearish trends.
Color Logic for Kijun Cross: If the Conversion Line is above the Base Line, the trend is bullish (green color), while a bearish trend is indicated by red. A neutral condition is marked with orange.
⭐ 4. HalfTrend Indicator Setup:
The HalfTrend indicator detects trend reversals based on high/low price deviations from a moving average:
Amplitude and Channel Deviation inputs allow users to control the sensitivity of the indicator.
showArrows and showChannels toggle the display of buy/sell arrows and trend channels.
maxLowPrice and minHighPrice variables are initialized to track significant high/low points during the trend, used to confirm trend reversals.
⭐ 5. ATR and Trend Calculations:
The Average True Range (ATR) is used to calculate the volatility-based channels. The script calculates atr2 and uses this to create atrHigh and atrLow for plotting the channel.
The trend detection logic is as follows:
When the trend is upward, the script seeks confirmation by comparing the high moving average with previous lows, signaling a continuation of the uptrend if it holds.
Conversely, a downtrend is confirmed when the low moving average exceeds previous highs.
⭐ 6. Customized Candle Coloring:
A custom color scheme is applied to candles based on a combination of trend direction and Ichimoku Cloud signals:
GreenFluorescent for strong bullish conditions where price is above the HalfTrend line, and the Conversion Line is above the Base Line.
RedFluorescent for strong bearish conditions, with price below the HalfTrend line and Conversion Line below the Base Line.
Gray for neutral or indecisive conditions.
⭐ 7. Plots and Shapes:
The script plots various elements:
HalfTrend Line: The main trendline is plotted in either green (buy) or red (sell), with adjustable line width.
Ichimoku Base Line: This is plotted with the dynamic color based on crossovers.
Buy/Sell Arrows: These are drawn on the chart when valid buy/sell conditions are met.
Custom Candles: The script overrides default chart candles with custom-colored candles based on the previously discussed logic.
⭐ 8. Improvements:
Optimization: Parameters like the amplitude, channel deviation, and Ichimoku periods can be fine-tuned based on backtesting results to maximize performance for specific assets or timeframes.
Alerts: The script could be enhanced by adding alert conditions for real-time buy/sell notifications, leveraging alertcondition() in Pine Script.
In summary, this script merges two trend-following techniques for a multi-faceted view of the market, using visual cues and trendline logic to provide a robust trading tool.
🚀 Conclusion:
Trend-Following System: The combination of Ichimoku Cloud and HalfTrend provides a comprehensive view of both long-term trends (via Ichimoku) and shorter-term reversals (via HalfTrend).
Visual Signals: The script includes clear visual signals (arrows and custom-colored candles) to help traders quickly spot buy/sell opportunities.
Dynamic Customization: Through user inputs, this indicator can be tailored to different market conditions, making it versatile.
$TUBR: Stop Loss IndicatorATR-Based Stop Loss Indicator for TradingView by The Ultimate Bull Run Community: TUBR
**Overview**
The ATR-Based Stop Loss Indicator is a custom tool designed for traders using TradingView. It helps you determine optimal stop loss levels by leveraging the Average True Range (ATR), a popular measure of market volatility. By adapting to current market conditions, this indicator aims to minimize premature stop-outs and enhance your risk management strategy.
---
**Key Features**
- **Dynamic Stop Loss Levels**: Calculates stop loss prices based on the ATR, providing both long and short stop loss suggestions.
- **Customizable Parameters**: Adjust the ATR period, multiplier, and smoothing method to suit your trading style and the specific instrument you're trading.
- **Visual Aids**: Plots stop loss lines directly on your chart for easy visualization.
- **Alerts and Notifications** (Optional): Set up alerts to notify you when the price approaches or hits your stop loss levels.
---
**Understanding the Indicator**
1. **Average True Range (ATR)**:
- **What It Is**: ATR measures market volatility by calculating the average range between high and low prices over a specified period.
- **Why It's Useful**: A higher ATR indicates higher volatility, which can help you set stop losses that accommodate market fluctuations.
2. **ATR Multiplier**:
- **Purpose**: Determines how far your stop loss is placed from the current price based on the ATR.
- **Example**: An ATR multiplier of 1.5 means the stop loss is set at 1.5 times the ATR away from the current price.
3. **Smoothing Methods**:
- **Options**: Choose from RMA (default), SMA, EMA, WMA, or Hull MA.
- **Effect**: Different smoothing methods can make the ATR more responsive or smoother, affecting where the stop loss is placed.
---
**How the Indicator Works**
- **Long Stop Loss Calculation**:
- **Formula**: `Long Stop Loss = Close Price - (ATR * ATR Multiplier)`
- **Purpose**: For long positions, the stop loss is set below the current price to protect against downside risk.
- **Short Stop Loss Calculation**:
- **Formula**: `Short Stop Loss = Close Price + (ATR * ATR Multiplier)`
- **Purpose**: For short positions, the stop loss is set above the current price to protect against upside risk.
- **Plotting on the Chart**:
- **Green Line**: Represents the suggested stop loss level for long positions.
- **Red Line**: Represents the suggested stop loss level for short positions.
---
**How to Use the Indicator**
1. **Adding the Indicator to Your Chart**:
- **Step 1**: Copy the PineScript code of the indicator.
- **Step 2**: In TradingView, click on **Pine Editor** at the bottom of the platform.
- **Step 3**: Paste the code into the editor and click **Add to Chart**.
- **Step 4**: The indicator will appear on your chart with the default settings.
2. **Adjusting the Settings**:
- **ATR Period**:
- **Definition**: Number of periods over which the ATR is calculated.
- **Adjustment**: Increase for a smoother ATR; decrease for a more responsive ATR.
- **ATR Multiplier**:
- **Definition**: Factor by which the ATR is multiplied to set the stop loss distance.
- **Adjustment**: Increase to widen the stop loss (less likely to be hit); decrease to tighten the stop loss.
- **Smoothing Method**:
- **Options**: RMA, SMA, EMA, WMA, Hull MA.
- **Adjustment**: Experiment to see which method aligns best with your trading strategy.
- **Display Options**:
- **Show Long Stop Loss**: Toggle to display or hide the long stop loss line.
- **Show Short Stop Loss**: Toggle to display or hide the short stop loss line.
3. **Interpreting the Indicator**:
- **Long Positions**:
- **Action**: Set your stop loss at the value indicated by the green line when entering a long trade.
- **Short Positions**:
- **Action**: Set your stop loss at the value indicated by the red line when entering a short trade.
- **Adjusting Stop Losses**:
- **Trailing Stops**: You may choose to adjust your stop loss over time, moving it in the direction of your trade as the ATR-based stop loss levels change.
4. **Implementing in Your Trading Strategy**:
- **Risk Management**:
- **Position Sizing**: Use the stop loss distance to calculate your position size based on your risk tolerance.
- **Consistency**: Apply the same settings consistently to maintain discipline.
- **Combining with Other Indicators**:
- **Enhance Decision-Making**: Use in conjunction with trend indicators, support and resistance levels, or other technical analysis tools.
- **Alerts Setup** (If included in the code):
- **Purpose**: Receive notifications when the price approaches or hits your stop loss level.
- **Configuration**: Set up alerts in TradingView based on the alert conditions defined in the indicator.
---
**Benefits of Using This Indicator**
- **Adaptive Risk Management**: By accounting for current market volatility, the indicator helps prevent setting stop losses that are too tight or too wide.
- **Minimize Premature Stop-Outs**: Reduces the likelihood of being stopped out due to normal price fluctuations.
- **Flexibility**: Customizable settings allow you to tailor the indicator to different trading instruments and timeframes.
- **Visualization**: Clear visual representation of stop loss levels aids in quick decision-making.
---
**Things to Consider**
- **Market Conditions**:
- **High Volatility**: Be cautious as ATR values—and thus stop loss distances—can widen, increasing potential losses.
- **Low Volatility**: Tighter stop losses may increase the chance of being stopped out by minor price movements.
- **Backtesting and Optimization**:
- **Historical Analysis**: Test the indicator on past data to evaluate its effectiveness and adjust settings accordingly.
- **Continuous Improvement**: Regularly reassess and fine-tune the parameters to adapt to changing market conditions.
- **Risk Per Trade**:
- **Alignment with Risk Tolerance**: Ensure the stop loss level keeps potential losses within your acceptable risk per trade (e.g., 1-2% of your trading capital).
- **Emotional Discipline**:
- **Stick to Your Plan**: Avoid making impulsive changes to your stop loss levels based on emotions rather than analysis.
---
**Example Usage Scenario**
1. **Setting Up a Long Trade**:
- **Entry Price**: $100
- **ATR Value**: $2
- **ATR Multiplier**: 1.5
- **Calculated Stop Loss**: $100 - ($2 * 1.5) = $97
- **Action**: Place a stop loss order at $97.
2. **During the Trade**:
- **Price Increases to $105**
- **ATR Remains at $2**
- **New Stop Loss Level**: $105 - ($2 * 1.5) = $102
- **Action**: Move your stop loss up to $102 to lock in profits.
---
**Final Tips**
- **Documentation**: Keep a trading journal to record your trades, stop loss levels, and observations for future reference.
- **Education**: Continuously educate yourself on risk management and technical analysis to enhance your trading skills.
- **Support**: Engage with trading communities or seek professional advice if you're unsure about implementing the indicator effectively.
---
**Conclusion**
The ATR-Based Stop Loss Indicator is a valuable tool for traders looking to enhance their risk management by setting stop losses that adapt to market volatility. By integrating this indicator into your trading routine, you can improve your ability to protect capital and potentially increase profitability. Remember to use it as part of a comprehensive trading strategy, and always adhere to sound risk management principles.
---
**How to Access the Indicator**
To start using the ATR-Based Stop Loss Indicator, follow these steps:
1. **Obtain the Code**: Copy the PineScript code provided for the indicator.
2. **Create a New Indicator in TradingView**:
- Open TradingView and navigate to the **Pine Editor**.
- Paste the code into the editor.
- Click **Save** and give your indicator a name.
3. **Add to Chart**: Click **Add to Chart** to apply the indicator to your current chart.
4. **Customize Settings**: Adjust the input parameters to suit your preferences and start integrating the indicator into your trading strategy.
---
**Disclaimer**
Trading involves significant risk, and it's possible to lose all your capital. The ATR-Based Stop Loss Indicator is a tool to aid in decision-making but does not guarantee profits or prevent losses. Always conduct your own analysis and consider seeking advice from a financial professional before making trading decisions.
Mean Reversion Cloud (Ornstein-Uhlenbeck) // AlgoFyreThe Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator detects mean-reversion opportunities by applying the Ornstein-Uhlenbeck process. It calculates a dynamic mean using an Exponential Weighted Moving Average, surrounded by volatility bands, signaling potential buy/sell points when prices deviate.
TABLE OF CONTENTS
🔶 ORIGINALITY
🔸Adaptive Mean Calculation
🔸Volatility-Based Cloud
🔸Speed of Reversion (θ)
🔶 FUNCTIONALITY
🔸Dynamic Mean and Volatility Bands
🞘 How it works
🞘 How to calculate
🞘 Code extract
🔸Visualization via Table and Plotshapes
🞘 Table Overview
🞘 Plotshapes Explanation
🞘 Code extract
🔶 INSTRUCTIONS
🔸Step-by-Step Guidelines
🞘 Setting Up the Indicator
🞘 Understanding What to Look For on the Chart
🞘 Possible Entry Signals
🞘 Possible Take Profit Strategies
🞘 Possible Stop-Loss Levels
🞘 Additional Tips
🔸Customize settings
🔶 CONCLUSION
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🔶 ORIGINALITY The Mean Reversion Cloud (Ornstein-Uhlenbeck) is a unique indicator that applies the Ornstein-Uhlenbeck stochastic process to identify mean-reverting behavior in asset prices. Unlike traditional moving average-based indicators, this model uses an Exponentially Weighted Moving Average (EWMA) to calculate the long-term mean, dynamically adjusting to recent price movements while still considering all historical data. It also incorporates volatility bands, providing a "cloud" that visually highlights overbought or oversold conditions. By calculating the speed of mean reversion (θ) through the autocorrelation of log returns, this indicator offers traders a more nuanced and mathematically robust tool for identifying mean-reversion opportunities. These innovations make it especially useful for markets that exhibit range-bound characteristics, offering timely buy and sell signals based on statistical deviations from the mean.
🔸Adaptive Mean Calculation Traditional MA indicators use fixed lengths, which can lead to lagging signals or over-sensitivity in volatile markets. The Mean Reversion Cloud uses an Exponentially Weighted Moving Average (EWMA), which adapts to price movements by dynamically adjusting its calculation, offering a more responsive mean.
🔸Volatility-Based Cloud Unlike simple moving averages that only plot a single line, the Mean Reversion Cloud surrounds the dynamic mean with volatility bands. These bands, based on standard deviations, provide traders with a visual cue of when prices are statistically likely to revert, highlighting potential reversal zones.
🔸Speed of Reversion (θ) The indicator goes beyond price averages by calculating the speed at which the price reverts to the mean (θ), using the autocorrelation of log returns. This gives traders an additional tool for estimating the likelihood and timing of mean reversion, making the signals more reliable in practice.
🔶 FUNCTIONALITY The Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator is designed to detect potential mean-reversion opportunities in asset prices by applying the Ornstein-Uhlenbeck stochastic process. It calculates a dynamic mean through the Exponentially Weighted Moving Average (EWMA) and plots volatility bands based on the standard deviation of the asset's price over a specified period. These bands create a "cloud" that represents expected price fluctuations, helping traders to identify overbought or oversold conditions. By calculating the speed of reversion (θ) from the autocorrelation of log returns, the indicator offers a more refined way of assessing how quickly prices may revert to the mean. Additionally, the inclusion of volatility provides a comprehensive view of market conditions, allowing for more accurate buy and sell signals.
Let's dive into the details:
🔸Dynamic Mean and Volatility Bands The dynamic mean (μ) is calculated using the EWMA, giving more weight to recent prices but considering all historical data. This process closely resembles the Ornstein-Uhlenbeck (OU) process, which models the tendency of a stochastic variable (such as price) to revert to its mean over time. Volatility bands are plotted around the mean using standard deviation, forming the "cloud" that signals overbought or oversold conditions. The cloud adapts dynamically to price fluctuations and market volatility, making it a versatile tool for mean-reversion strategies. 🞘 How it works Step one: Calculate the dynamic mean (μ) The Ornstein-Uhlenbeck process describes how a variable, such as an asset's price, tends to revert to a long-term mean while subject to random fluctuations. In this indicator, the EWMA is used to compute the dynamic mean (μ), mimicking the mean-reverting behavior of the OU process. Use the EWMA formula to compute a weighted mean that adjusts to recent price movements. Assign exponentially decreasing weights to older data while giving more emphasis to current prices. Step two: Plot volatility bands Calculate the standard deviation of the price over a user-defined period to determine market volatility. Position the upper and lower bands around the mean by adding and subtracting a multiple of the standard deviation. 🞘 How to calculate Exponential Weighted Moving Average (EWMA)
The EWMA dynamically adjusts to recent price movements:
mu_t = lambda * mu_{t-1} + (1 - lambda) * P_t
Where mu_t is the mean at time t, lambda is the decay factor, and P_t is the price at time t. The higher the decay factor, the more weight is given to recent data.
Autocorrelation (ρ) and Standard Deviation (σ)
To measure mean reversion speed and volatility: rho = correlation(log(close), log(close ), length) Where rho is the autocorrelation of log returns over a specified period.
To calculate volatility:
sigma = stdev(close, length)
Where sigma is the standard deviation of the asset's closing price over a specified length.
Upper and Lower Bands
The upper and lower bands are calculated as follows:
upper_band = mu + (threshold * sigma)
lower_band = mu - (threshold * sigma)
Where threshold is a multiplier for the standard deviation, usually set to 2. These bands represent the range within which the price is expected to fluctuate, based on current volatility and the mean.
🞘 Code extract // Calculate Returns
returns = math.log(close / close )
// Calculate Long-Term Mean (μ) using EWMA over the entire dataset
var float ewma_mu = na // Initialize ewma_mu as 'na'
ewma_mu := na(ewma_mu ) ? close : decay_factor * ewma_mu + (1 - decay_factor) * close
mu = ewma_mu
// Calculate Autocorrelation at Lag 1
rho1 = ta.correlation(returns, returns , corr_length)
// Ensure rho1 is within valid range to avoid errors
rho1 := na(rho1) or rho1 <= 0 ? 0.0001 : rho1
// Calculate Speed of Mean Reversion (θ)
theta = -math.log(rho1)
// Calculate Volatility (σ)
sigma = ta.stdev(close, corr_length)
// Calculate Upper and Lower Bands
upper_band = mu + threshold * sigma
lower_band = mu - threshold * sigma
🔸Visualization via Table and Plotshapes
The table shows key statistics such as the current value of the dynamic mean (μ), the number of times the price has crossed the upper or lower bands, and the consecutive number of bars that the price has remained in an overbought or oversold state.
Plotshapes (diamonds) are used to signal buy and sell opportunities. A green diamond below the price suggests a buy signal when the price crosses below the lower band, and a red diamond above the price indicates a sell signal when the price crosses above the upper band.
The table and plotshapes provide a comprehensive visualization, combining both statistical and actionable information to aid decision-making.
🞘 Code extract // Reset consecutive_bars when price crosses the mean
var consecutive_bars = 0
if (close < mu and close >= mu) or (close > mu and close <= mu)
consecutive_bars := 0
else if math.abs(deviation) > 0
consecutive_bars := math.min(consecutive_bars + 1, dev_length)
transparency = math.max(0, math.min(100, 100 - (consecutive_bars * 100 / dev_length)))
🔶 INSTRUCTIONS
The Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator can be set up by adding it to your TradingView chart and configuring parameters such as the decay factor, autocorrelation length, and volatility threshold to suit current market conditions. Look for price crossovers and deviations from the calculated mean for potential entry signals. Use the upper and lower bands as dynamic support/resistance levels for setting take profit and stop-loss orders. Combining this indicator with additional trend-following or momentum-based indicators can improve signal accuracy. Adjust settings for better mean-reversion detection and risk management.
🔸Step-by-Step Guidelines
🞘 Setting Up the Indicator
Adding the Indicator to the Chart:
Go to your TradingView chart.
Click on the "Indicators" button at the top.
Search for "Mean Reversion Cloud (Ornstein-Uhlenbeck)" in the indicators list.
Click on the indicator to add it to your chart.
Configuring the Indicator:
Open the indicator settings by clicking on the gear icon next to its name on the chart.
Decay Factor: Adjust the decay factor (λ) to control the responsiveness of the mean calculation. A higher value prioritizes recent data.
Autocorrelation Length: Set the autocorrelation length (θ) for calculating the speed of mean reversion. Longer lengths consider more historical data.
Threshold: Define the number of standard deviations for the upper and lower bands to determine how far price must deviate to trigger a signal.
Chart Setup:
Select the appropriate timeframe (e.g., 1-hour, daily) based on your trading strategy.
Consider using other indicators such as RSI or MACD to confirm buy and sell signals.
🞘 Understanding What to Look For on the Chart
Indicator Behavior:
Observe how the price interacts with the dynamic mean and volatility bands. The price staying within the bands suggests mean-reverting behavior, while crossing the bands signals potential entry points.
The indicator calculates overbought/oversold conditions based on deviation from the mean, highlighted by color-coded cloud areas on the chart.
Crossovers and Deviation:
Look for crossovers between the price and the mean (μ) or the bands. A bullish crossover occurs when the price crosses below the lower band, signaling a potential buying opportunity.
A bearish crossover occurs when the price crosses above the upper band, suggesting a potential sell signal.
Deviations from the mean indicate market extremes. A large deviation indicates that the price is far from the mean, suggesting a potential reversal.
Slope and Direction:
Pay attention to the slope of the mean (μ). A rising slope suggests bullish market conditions, while a declining slope signals a bearish market.
The steepness of the slope can indicate the strength of the mean-reversion trend.
🞘 Possible Entry Signals
Bullish Entry:
Crossover Entry: Enter a long position when the price crosses below the lower band with a positive deviation from the mean.
Confirmation Entry: Use additional indicators like RSI (above 50) or increasing volume to confirm the bullish signal.
Bearish Entry:
Crossover Entry: Enter a short position when the price crosses above the upper band with a negative deviation from the mean.
Confirmation Entry: Look for RSI (below 50) or decreasing volume to confirm the bearish signal.
Deviation Confirmation:
Enter trades when the deviation from the mean is significant, indicating that the price has strayed far from its expected value and is likely to revert.
🞘 Possible Take Profit Strategies
Static Take Profit Levels:
Set predefined take profit levels based on historical volatility, using the upper and lower bands as guides.
Place take profit orders near recent support/resistance levels, ensuring you're capitalizing on the mean-reversion behavior.
Trailing Stop Loss:
Use a trailing stop based on a percentage of the price deviation from the mean to lock in profits as the trend progresses.
Adjust the trailing stop dynamically along the calculated bands to protect profits as the price returns to the mean.
Deviation-Based Exits:
Exit when the deviation from the mean starts to decrease, signaling that the price is returning to its equilibrium.
🞘 Possible Stop-Loss Levels
Initial Stop Loss:
Place an initial stop loss outside the lower band (for long positions) or above the upper band (for short positions) to protect against excessive deviations.
Use a volatility-based buffer to avoid getting stopped out during normal price fluctuations.
Dynamic Stop Loss:
Move the stop loss closer to the mean as the price converges back towards equilibrium, reducing risk.
Adjust the stop loss dynamically along the bands to account for sudden market movements.
🞘 Additional Tips
Combine with Other Indicators:
Enhance your strategy by combining the Mean Reversion Cloud with momentum indicators like MACD, RSI, or Bollinger Bands to confirm market conditions.
Backtesting and Practice:
Backtest the indicator on historical data to understand how it performs in various market environments.
Practice using the indicator on a demo account before implementing it in live trading.
Market Awareness:
Keep an eye on market news and events that might cause extreme price movements. The indicator reacts to price data and might not account for news-driven events that can cause large deviations.
🔸Customize settings 🞘 Decay Factor (λ): Defines the weight assigned to recent price data in the calculation of the mean. A value closer to 1 places more emphasis on recent prices, while lower values create a smoother, more lagging mean.
🞘 Autocorrelation Length (θ): Sets the period for calculating the speed of mean reversion and volatility. Longer lengths capture more historical data, providing smoother calculations, while shorter lengths make the indicator more responsive.
🞘 Threshold (σ): Specifies the number of standard deviations used to create the upper and lower bands. Higher thresholds widen the bands, producing fewer signals, while lower thresholds tighten the bands for more frequent signals.
🞘 Max Gradient Length (γ): Determines the maximum number of consecutive bars for calculating the deviation gradient. This setting impacts the transparency of the plotted bands based on the length of deviation from the mean.
🔶 CONCLUSION
The Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator offers a sophisticated approach to identifying mean-reversion opportunities by applying the Ornstein-Uhlenbeck stochastic process. This dynamic indicator calculates a responsive mean using an Exponentially Weighted Moving Average (EWMA) and plots volatility-based bands to highlight overbought and oversold conditions. By incorporating advanced statistical measures like autocorrelation and standard deviation, traders can better assess market extremes and potential reversals. The indicator’s ability to adapt to price behavior makes it a versatile tool for traders focused on both short-term price deviations and longer-term mean-reversion strategies. With its unique blend of statistical rigor and visual clarity, the Mean Reversion Cloud provides an invaluable tool for understanding and capitalizing on market inefficiencies.
Killzones And Macros LibraryKillzones & Macros Library for Trading Sessions
This Pine Script library is designed to help traders identify and act during high-volatility trading windows, commonly referred to as "Killzones." These are specific times during the day when institutional traders are most active, resulting in increased liquidity and price movement. The library provides boolean fields that return true when the current time falls within one of the killzones or macroeconomic event windows, allowing for enhanced trade timing and precision.
Killzones Include:
London Open, New York Open, Midnight Open, London Lunch, New York PM, and more.
Capture high-volume periods like Power Hour, Equities Open, and Asian Range.
Macros:
Identify key moments like London 02:33, New York 08:50, and other significant times aligned with market movements or events.
This library is perfect for integrating into your custom strategies, backtesting, or setting alerts for optimal trade execution during major trading sessions and events.
Hyperbolic Tangent SuperTrend [InvestorUnknown]The Hyperbolic Tangent SuperTrend (HTST) is designed for technical analysis, particularly in markets with assets that have lower prices or price ratios. This indicator leverages the Hyperbolic Tangent Moving Average (HTMA), a custom moving average calculated using the hyperbolic tangent function, to smooth price data and reduce the impact of short-term volatility.
Hyperbolic Tangent Moving Average (HTMA):
The indicator's core uses a hyperbolic tangent function to calculate a smoothed average of the price. The HTMA provides enhanced trend-following capabilities by dampening the impact of sharp price swings and maintaining a focus on long-term market movements.
The hyperbolic tangent function (tanh) is commonly used in mathematical fields like calculus, machine learning and signal processing due to its properties of “squashing” inputs into a range between -1 and 1. The function provides a non-linear transformation that can reduce the impact of extreme values while retaining a certain level of smoothness.
tanh(x) =>
e_x = math.exp(x)
e_neg_x = math.exp(-x)
(e_x - e_neg_x) / (e_x + e_neg_x)
The HTMA is calculated by taking the difference between the price and its simple moving average (SMA), applying a multiplier to control sensitivity, and then transforming it using the hyperbolic tangent function.
htma(src, len, mul) =>
tanh_src = tanh((src - ta.sma(src, len)) * mul) * ta.stdev(src, len) + ta.sma(src, len)
htma = ta.sma(tanh_src, len)
Important Note: The Hyperbolic Tangent function becomes less accurate with very high prices. For assets priced above 100,000, the results may deteriorate, and for prices exceeding 1 million, the function may stop functioning properly. Therefore, this indicator is better suited for assets with lower prices or lower price ratios.
SuperTrend Calculation:
In addition to the HTMA, the indicator includes an Average True Range (ATR)-based SuperTrend calculation, which helps identify uptrends and downtrends in the market. The SuperTrend is adjusted dynamically using the HTMA to avoid false signals in fast-moving markets.
The ATR period and multiplier are customizable, allowing users to fine-tune the sensitivity of the trend signals.
pine_supertrend(src, calc_price, atrPeriod, factor) =>
atr = ta.atr(atrPeriod)
upperBand = src + factor * atr
lowerBand = src - factor * atr
prevLowerBand = nz(lowerBand )
prevUpperBand = nz(upperBand )
lowerBand := lowerBand > prevLowerBand or calc_price < prevLowerBand ? lowerBand : prevLowerBand
upperBand := upperBand < prevUpperBand or calc_price > prevUpperBand ? upperBand : prevUpperBand
int _direction = na
float superTrend = na
prevSuperTrend = superTrend
if na(atr )
_direction := 1
else if prevSuperTrend == prevUpperBand
_direction := calc_price > upperBand ? -1 : 1
else
_direction := calc_price < lowerBand ? 1 : -1
superTrend := _direction == -1 ? lowerBand : upperBand
Inbuilt Backtest Mode:
The HTST includes an inbuilt backtest mode that enables users to test the indicator's performance against historical data, similar to TradingView strategies.
The backtest mode allows you to compare the performance of different indicator settings with a simple buy and hold strategy to assess its effectiveness in different market conditions.
Hint Table for Display Modes:
The indicator includes a Hint Table that recommends the best pane to use for different display modes. For example, it suggests using the "Overlay" mode in the same pane as the price action, while the "Backtest Mode" is better suited for a separate pane. This ensures a more organized and clear visual experience.
The Hint Table appears as a small table at the bottom of the chart with easy-to-follow recommendations, ensuring the best setup for both visual clarity and indicator functionality.
With these features, the Hyperbolic Tangent SuperTrend Indicator offers traders a versatile and customizable tool for analyzing price trends while providing additional functionalities like backtesting and display mode hints for optimal usability.
