Lanczos CandlesThis indicator reconstructs price action using Lanczos resampling, incorporating lower timeframe data to create a more detailed representation of market movements. Traditional candle aggregation on higher timeframes tends to lose some price action detail - this indicator attempts to preserve more of that information through mathematical resampling.
The indicator samples price data from a lower timeframe and uses the Lanczos algorithm, a mathematical method commonly used in signal processing and image resampling, to reconstruct the price series at the chart's timeframe. The process helps maintain price movements that might otherwise be smoothed out in regular candle aggregation.
The main settings allow you to select the source timeframe for sampling, adjust the Lanczos filter width to balance smoothness versus detail preservation, and optionally enable Heikin Ashi calculation. The filter width parameter (default: 3) affects how aggressive the smoothing is - higher values produce smoother results while lower values retain more of the original variation.
This approach can be useful for technical analysis when you want to work with higher timeframes while maintaining awareness of significant price movements that occurred within those candles. The optional Heikin Ashi mode can help visualize trends in the resampled data.
The indicator works best when there's a clear ratio between your chart timeframe and the source timeframe (for example, using 1-minute data to build 5-minute candles).
Cari dalam skrip untuk "algo"
Smart DCA Strategy (Public)INSPIRATION
While Dollar Cost Averaging (DCA) is a popular and stress-free investment approach, I noticed an opportunity for enhancement. Standard DCA involves buying consistently, regardless of market conditions, which can sometimes mean missing out on optimal investment opportunities. This led me to develop the Smart DCA Strategy – a 'set and forget' method like traditional DCA, but with an intelligent twist to boost its effectiveness.
The goal was to build something more profitable than a standard DCA strategy so it was equally important that this indicator could backtest its own results in an A/B test manner against the regular DCA strategy.
WHY IS IT SMART?
The key to this strategy is its dynamic approach: buying aggressively when the market shows signs of being oversold, and sitting on the sidelines when it's not. This approach aims to optimize entry points, enhancing the potential for better returns while maintaining the simplicity and low stress of DCA.
WHAT THIS STRATEGY IS, AND IS NOT
This is an investment style strategy. It is designed to improve upon the common standard DCA investment strategy. It is therefore NOT a day trading strategy. Feel free to experiment with various timeframes, but it was designed to be used on a daily timeframe and that's how I recommend it to be used.
You may also go months without any buy signals during bull markets, but remember that is exactly the point of the strategy - to keep your buying power on the sidelines until the markets have significantly pulled back. You need to be patient and trust in the historical backtesting you have performed.
HOW IT WORKS
The Smart DCA Strategy leverages a creative approach to using Moving Averages to identify the most opportune moments to buy. A trigger occurs when a daily candle, in its entirety including the high wick, closes below the threshold line or box plotted on the chart. The indicator is designed to facilitate both backtesting and live trading.
HOW TO USE
Settings:
The input parameters for tuning have been intentionally simplified in an effort to prevent users falling into the overfitting trap.
The main control is the Buying strictness scale setting. Setting this to a lower value will provide more buying days (less strict) while higher values mean less buying days (more strict). In my testing I've found level 9 to provide good all round results.
Validation days is a setting to prevent triggering entries until the asset has spent a given number of days (candles) in the overbought state. Increasing this makes entries stricter. I've found 0 to give the best results across most assets.
In the backtest settings you can also configure how much to buy for each day an entry triggers. Blind buy size is the amount you would buy every day in a standard DCA strategy. Smart buy size is the amount you would buy each day a Smart DCA entry is triggered.
You can also experiment with backtesting your strategy over different historical datasets by using the Start date and End date settings. The results table will not calculate for any trades outside what you've set in the date range settings.
Backtesting:
When backtesting you should use the results table on the top right to tune and optimise the results of your strategy. As with all backtests, be careful to avoid overfitting the parameters. It's better to have a setup which works well across many currencies and historical periods than a setup which is excellent on one dataset but bad on most others. This gives a much higher probability that it will be effective when you move to live trading.
The results table provides a clear visual representation as to which strategy, standard or smart, is more profitable for the given dataset. You will notice the columns are dynamically coloured red and green. Their colour changes based on which strategy is more profitable in the A/B style backtest - green wins, red loses. The key metrics to focus on are GOA (Gain on Account) and Avg Cost.
Live Trading:
After you've finished backtesting you can proceed with configuring your alerts for live trading.
But first, you need to estimate the amount you should buy on each Smart DCA entry. We can use the Total invested row in the results table to calculate this. Assuming we're looking to trade on
BTCUSD
Decide how much USD you would spend each day to buy BTC if you were using a standard DCA strategy. Lets say that is $5 per day
Enter that USD amount in the Blind buy size settings box
Check the Blind Buy column in the results table. If we set the backtest date range to the last 10 years, we would expect the amount spent on blind buys over 10 years to be $18,250 given $5 each day
Next we need to tweak the value of the Smart buy size parameter in setting to get it as close as we can to the Total Invested amount for Blind Buy
By following this approach it means we will invest roughly the same amount into our Smart DCA strategy as we would have into a standard DCA strategy over any given time period.
After you have calculated the Smart buy size, you can go ahead and set up alerts on Smart DCA buy triggers.
BOT AUTOMATION
In an effort to maintain the 'set and forget' stress-free benefits of a standard DCA strategy, I have set my personal Smart DCA Strategy up to be automated. The bot runs on AWS and I have a fully functional project for the bot on my GitHub account. Just reach out if you would like me to point you towards it. You can also hook this into any other 3rd party trade automation system of your choice using the pre-configured alerts within the indicator.
PLANNED FUTURE DEVELOPMENTS
Currently this is purely an accumulation strategy. It does not have any sell signals right now but I have ideas on how I will build upon it to incorporate an algorithm for selling. The strategy should gradually offload profits in bull markets which generates more USD which gives more buying power to rinse and repeat the same process in the next cycle only with a bigger starting capital. Watch this space!
MARKETS
Crypto:
This strategy has been specifically built to work on the crypto markets. It has been developed, backtested and tuned against crypto markets and I personally only run it on crypto markets to accumulate more of the coins I believe in for the long term. In the section below I will provide some backtest results from some of the top crypto assets.
Stocks:
I've found it is generally more profitable than a standard DCA strategy on the majority of stocks, however the results proved to be a lot more impressive on crypto. This is mainly due to the volatility and cycles found in crypto markets. The strategy makes its profits from capitalising on pullbacks in price. Good stocks on the other hand tend to move up and to the right with less significant pullbacks, therefore giving this strategy less opportunity to flourish.
Forex:
As this is an accumulation style investment strategy, I do not recommend that you use it to trade Forex.
For more info about this strategy including backtest results, please see the full description on the invite only version of this strategy named "Smart DCA Strategy"
Market StructureThis is an advanced, non-repainting Market Structure indicator that provides a robust framework for understanding market dynamics across any timeframe and instrument.
Key Features:
- Non-repainting market structure detection using swing highs/lows
- Clear identification of internal and general market structure levels
- Breakout threshold system for structure adjustments
- Integrated multi-timeframe compatibility
- Rich selection of 30+ moving average types, from basic to advanced adaptive variants
What Makes It Different:
Unlike most market structure indicators that repaint or modify past signals, this implementation uses a fixed-length lookback period to identify genuine swing points.
This means once a structure level or pivot is identified, it stays permanent - providing reliable signals for analysis and trading decisions.
The indicator combines two layers of market structure:
1. Internal Structure (lighter lines) - More sensitive to local price action
2. General Structure (darker lines) - Shows broader market context
Technical Details:
- Uses advanced pivot detection algorithm with customizable swing size
- Implements consecutive break counting for structure adjustments
- Supports both close and high/low price levels for breakout detection
- Includes offset option for better visual alignment
- Each structure break is validated against multiple conditions to prevent false signals
Offset on:
Offset off:
Moving Averages Library:
Includes comprehensive selection of moving averages, from traditional to advanced adaptive types:
- Basic: SMA, EMA, WMA, VWMA
- Advanced: KAMA, ALMA, VIDYA, FRAMA
- Specialized: Hull MA, Ehlers Filter Series
- Adaptive: JMA, RPMA, and many more
Perfect for:
- Price action analysis
- Trend direction confirmation
- Support/resistance identification
- Market structure trading strategies
- Multiple timeframe analysis
This open-source tool is designed to help traders better understand market dynamics and make more informed trading decisions. Feel free to use, modify, and enhance it for your trading needs.
DemaRSI StrategyThis is a repost to a old script that cant be updated anymore, the request was made on Feb, 27, 2016.
Here's a engaging description for the tradingview script:
**DemaRSI Strategy: A Proven Trading System**
Join thousands of traders who have already experienced the power of this highly effective strategy. The DemaRSI system combines two powerful indicators - DEMA (Double Exponential Moving Average) and RSI (Relative Strength Index) - to generate profitable trades with minimal risk.
**Key Features:**
* **Trend-Following**: Our algorithm identifies strong trends using a combination of DEMA and RSI, allowing you to ride the waves of market momentum.
* **Risk Management**: The system includes built-in stop-loss and take-profit levels, ensuring that your gains are protected and losses are minimized.
* **Session-Based Trading**: Trade during specific sessions only (e.g., London or New York) for even more targeted results.
* **Customizable Settings**: Adjust the length of moving averages, RSI periods, and other parameters to suit your trading style.
**What You'll Get:**
* A comprehensive strategy that can be used with any broker or platform
* Easy-to-use interface with customizable settings
* Real-time performance metrics and backtesting capabilities
**Start Trading Like a Pro Today!**
This script is designed for intermediate to advanced traders who want to take their trading game to the next level. With its robust risk management features, this strategy can help you achieve consistent profits in various market conditions.
**Disclaimer:** This script is not intended as investment advice and should be used at your own discretion. Trading carries inherent risks, and losses are possible.
~Llama3
Alternative Price [OmegaTools]The Alternative Price script is a sophisticated and flexible indicator designed to redefine how traders visualize and interpret price data. By offering multiple unique charting modes, robust customization options, and advanced features, this tool provides a comprehensive alternative to traditional price charts. It is particularly useful for identifying market trends, detecting patterns, and simplifying complex data into actionable insights.
This script is highly versatile, allowing users to choose from five distinct charting modes: Candles, Line, Channel, Renko, and Bubbles. Each mode serves a unique purpose and presents price information in an innovative way. When using this script, it is strongly recommended to hide the platform’s default price candles or chart data. Doing so will eliminate redundancy and provide a clearer and more focused view of the alternative price visualization.
The Candles mode offers a traditional candlestick charting style but with added flexibility. Users can choose to enable smoothed opens or smoothed closes, which adjust the way the open and close prices are calculated. When smoothed opens are enabled, the opening price is computed as the average of the actual open price and the closing prices of the previous two bars. This creates a more gradual representation of price transitions, particularly useful in markets prone to sudden spikes or irregularities. Similarly, smoothed closes modify the closing price by averaging it with the previous close, the high-low midpoint, and an exponential moving average of the high-low-close mean. This technique filters out noise, making trends and price momentum easier to identify.
In the Line mode, the script displays a simple line chart that connects the smoothed closing prices. This mode is ideal for traders who prefer minimalism or need to focus on the overall trend without the distraction of individual bar details. The Channel mode builds upon this by plotting additional lines representing the highs and lows of each bar. The resulting visualization resembles a price corridor that helps identify support and resistance zones or price compression areas.
The Renko mode introduces a more advanced and noise-filtering method of visualizing price movements. Renko charts, constructed using the ATR (Average True Range) as a baseline, display blocks that represent a specific price range. The script dynamically calculates the size of these blocks based on ATR, with separate thresholds for upward and downward movements. This makes Renko mode particularly effective for identifying sustained trends while ignoring minor price fluctuations. Additionally, the open and close values of Renko blocks can be smoothed to further refine the visualization.
The Bubbles mode represents price activity using circles or bubbles whose size corresponds to relative volume. This mode provides a quick and intuitive way to assess market participation at different price levels. Larger bubbles indicate higher trading volumes, while smaller bubbles highlight periods of lower activity. This visualization is particularly valuable in understanding the relationship between price movements and market liquidity.
The coloring of candles and other chart elements is a core feature of this script. Users can select between two color modes: Normal and Volume. In Normal mode, bullish candles are displayed in the user-defined bullish color, while bearish candles use the bearish color. Neutral elements, such as midpoints or undecided price movements, are shaded with a neutral color. In Volume mode, the candle colors are dynamically adjusted based on trading volume. A gradient color scale is applied, where the intensity of the bullish or bearish colors reflects the volume for that particular bar. This feature allows traders to visually identify periods of heightened activity and associate them with specific price movements.
Engulfing patterns, a popular technical analysis tool, are automatically detected and marked on the chart when the corresponding setting is enabled. The script identifies long engulfing patterns, where the current bar's range completely encompasses the previous bar’s range and indicates a potential bullish reversal. Similarly, short engulfing patterns are identified where the current bar fully engulfs the previous bar in the opposite direction, suggesting a bearish reversal. These patterns are visually highlighted with circular markers to draw the trader’s attention.
Each feature and mode is highly customizable. The colors for bullish, bearish, and neutral movements can be personalized, and the thresholds for patterns or smoothing can be fine-tuned to match specific trading strategies. The script's ability to toggle between various modes makes it adaptable to different market conditions and analysis preferences.
In summary, the Alternative Price script is a comprehensive tool that redefines the way traders view price charts. By offering multiple visualization modes, customizable features, and advanced detection algorithms, it provides a powerful way to uncover market trends, volume relationships, and significant patterns. The recommendation to hide default chart elements ensures that the focus remains on this innovative tool, enhancing its usability and clarity. This script empowers traders to gain deeper insights into market behavior and make informed trading decisions, all while maintaining a clean and visually appealing chart layout.
Keep in mind that some of the modes of this indicator might not reflect the actual closing price of the underlying asset, before opening a trade, check carefully the actual price!
Adaptive Supertrend with Dynamic Optimization [EdgeTerminal]The Enhanced Adaptive Supertrend represents a significant evolution of the traditional Supertrend indicator, incorporating advanced mathematical optimization, dynamic volatility adjustment, intelligent signal filtering, reduced noise and false positives.
Key Features
Dynamic volatility-adjusted bands
Self-optimizing multiplier
Intelligent signal filtering system
Cooldown period to prevent signal clustering
Clear buy/sell signals with optimal positioning
Smooth trend visualization
RSI and MACD integration for confirmation
Performance-based optimization
Dynamic Band Calculation
Dynamic Band Calculation automatically adapts to market volatility, generates wider bands in volatile periods, reducing false signals. It also generates tighter bands in stable periods, capturing smaller moves and smooth transitions between different volatility regimes.
RSI Integration
The RSI and MACD play multiple crucial roles in the Adaptive Supertrend.
It first helps with momentum factor calculation. This dynamically adjusts band width based on momentum conditions. When the RSI is oversold, bands widen by 20% to prevent false signals during strong downtrends and provide more room for price movements in extreme conditions.
When the RSI is overbought, brands tighten by 20% and they become more sensitive to potential reversals to help catch trend changes earlier.
This reduces false signals in strong trends, helps detect potential reversals earlier than the usual, create adaptive band width based on market conditions and finally, better protection against whipsaws.
MACD Integration
The MACD in this supertrend indicator serves as a trend confirmation tool. The idea is to use MACD crossovers to confirm trend changes to reduce false trend change signals and enhance the signal quality.
For this to become a signal, MACD crossovers must align with price movement to help filter out weak or false signals, which acts as an additional layer of trend confirmation.
Additionally, MACD line position relative to signal line indicates trend strength, helps maintain positions in strong trends and assists in early detection of trend weakening.
Momentum Integration
Momentum Integration prevents false signals in extreme conditions, It adjusts dynamic bands based on market momentum, improves trend confirmation in strong moves and reduces whipsaws during consolidations.
Improved signals
There are a few systems to generate better signals, allowing for generally faster signals compared to original supertrend, such as:
Enforced cooldown period between signals
Prevents signal clustering
Clearer entry/exit points
Reduced false signals during choppy markets
Performance Optimization
This script implements a Sharpe ratio-inspired optimization algorithm to balance returns against risk, penalize large drawdowns, adapt parameters in real-time and improve risk-adjusted performance
Parameter Settings
ATR Period: 10 (default) - adjust based on timeframe
Initial Multiplier: 3.0 (default) - will self-optimize
Optimization Period: 50 (default) - longer periods for more stability
Smoothing Period: 3 (default) - adjust for signal smoothness
Best Practices
Use on multiple timeframes for confirmation
Allow the optimization process to run for at least 50 bars
Monitor the adaptive multiplier for trend strength indication
Consider RSI and MACD alignment for stronger signals
MultiLayer Awesome Oscillator Saucer Strategy [Skyrexio]Overview
MultiLayer Awesome Oscillator Saucer Strategy leverages the combination of Awesome Oscillator (AO), Williams Alligator, Williams Fractals and Exponential Moving Average (EMA) to obtain the high probability long setups. Moreover, strategy uses multi trades system, adding funds to long position if it considered that current trend has likely became stronger. Awesome Oscillator is used for creating signals, while Alligator and Fractal are used in conjunction as an approximation of short-term trend to filter them. At the same time EMA (default EMA's period = 100) is used as high probability long-term trend filter to open long trades only if it considers current price action as an uptrend. More information in "Methodology" and "Justification of Methodology" paragraphs. The strategy opens only long trades.
Unique Features
No fixed stop-loss and take profit: Instead of fixed stop-loss level strategy utilizes technical condition obtained by Fractals and Alligator to identify when current uptrend is likely to be over (more information in "Methodology" and "Justification of Methodology" paragraphs)
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Multilayer trades opening system: strategy uses only 10% of capital in every trade and open up to 5 trades at the same time if script consider current trend as strong one.
Short and long term trend trade filters: strategy uses EMA as high probability long-term trend filter and Alligator and Fractal combination as a short-term one.
Methodology
The strategy opens long trade when the following price met the conditions:
1. Price closed above EMA (by default, period = 100). Crossover is not obligatory.
2. Combination of Alligator and Williams Fractals shall consider current trend as an upward (all details in "Justification of Methodology" paragraph)
3. Awesome Oscillator shall create the "Saucer" long signal (all details in "Justification of Methodology" paragraph). Buy stop order is placed one tick above the candle's high of last created "Saucer signal".
4. If price reaches the order price, long position is opened with 10% of capital.
5. If currently we have opened position and price creates and hit the order price of another one "Saucer" signal another one long position will be added to the previous with another one 10% of capital. Strategy allows to open up to 5 long trades simultaneously.
6. If combination of Alligator and Williams Fractals shall consider current trend has been changed from up to downtrend, all long trades will be closed, no matter how many trades has been opened.
Script also has additional visuals. If second long trade has been opened simultaneously the Alligator's teeth line is plotted with the green color. Also for every trade in a row from 2 to 5 the label "Buy More" is also plotted just below the teeth line. With every next simultaneously opened trade the green color of the space between teeth and price became less transparent.
Strategy settings
In the inputs window user can setup strategy setting: EMA Length (by default = 100, period of EMA, used for long-term trend filtering EMA calculation). User can choose the optimal parameters during backtesting on certain price chart.
Justification of Methodology
Let's go through all concepts used in this strategy to understand how they works together. Let's start from the easies one, the EMA. Let's briefly explain what is EMA. The Exponential Moving Average (EMA) is a type of moving average that gives more weight to recent prices, making it more responsive to current price changes compared to the Simple Moving Average (SMA). It is commonly used in technical analysis to identify trends and generate buy or sell signals. It can be calculated with the following steps:
1.Calculate the Smoothing Multiplier:
Multiplier = 2 / (n + 1), Where n is the number of periods.
2. EMA Calculation
EMA = (Current Price) × Multiplier + (Previous EMA) × (1 − Multiplier)
In this strategy uses EMA an initial long term trend filter. It allows to open long trades only if price close above EMA (by default 50 period). It increases the probability of taking long trades only in the direction of the trend.
Let's go to the next, short-term trend filter which consists of Alligator and Fractals. Let's briefly explain what do these indicators means. The Williams Alligator, developed by Bill Williams, is a technical indicator designed to spot trends and potential market reversals. It uses three smoothed moving averages, referred to as the jaw, teeth, and lips:
Jaw (Blue Line): The slowest of the three, based on a 13-period smoothed moving average shifted 8 bars ahead.
Teeth (Red Line): The medium-speed line, derived from an 8-period smoothed moving average shifted 5 bars forward.
Lips (Green Line): The fastest line, calculated using a 5-period smoothed moving average shifted 3 bars forward.
When these lines diverge and are properly aligned, the "alligator" is considered "awake," signaling a strong trend. Conversely, when the lines overlap or intertwine, the "alligator" is "asleep," indicating a range-bound or sideways market. This indicator assists traders in identifying when to act on or avoid trades.
The Williams Fractals, another tool introduced by Bill Williams, are used to pinpoint potential reversal points on a price chart. A fractal forms when there are at least five consecutive bars, with the middle bar displaying the highest high (for an up fractal) or the lowest low (for a down fractal), relative to the two bars on either side.
Key Points:
Up Fractal: Occurs when the middle bar has a higher high than the two preceding and two following bars, suggesting a potential downward reversal.
Down Fractal: Happens when the middle bar shows a lower low than the surrounding two bars, hinting at a possible upward reversal.
Traders often combine fractals with other indicators to confirm trends or reversals, improving the accuracy of trading decisions.
How we use their combination in this strategy? Let’s consider an uptrend example. A breakout above an up fractal can be interpreted as a bullish signal, indicating a high likelihood that an uptrend is beginning. Here's the reasoning: an up fractal represents a potential shift in market behavior. When the fractal forms, it reflects a pullback caused by traders selling, creating a temporary high. However, if the price manages to return to that fractal’s high and break through it, it suggests the market has "changed its mind" and a bullish trend is likely emerging.
The moment of the breakout marks the potential transition to an uptrend. It’s crucial to note that this breakout must occur above the Alligator's teeth line. If it happens below, the breakout isn’t valid, and the downtrend may still persist. The same logic applies inversely for down fractals in a downtrend scenario.
So, if last up fractal breakout was higher, than Alligator's teeth and it happened after last down fractal breakdown below teeth, algorithm considered current trend as an uptrend. During this uptrend long trades can be opened if signal was flashed. If during the uptrend price breaks down the down fractal below teeth line, strategy considered that uptrend is finished with the high probability and strategy closes all current long trades. This combination is used as a short term trend filter increasing the probability of opening profitable long trades in addition to EMA filter, described above.
Now let's talk about Awesome Oscillator's "Sauser" signals. Briefly explain what is the Awesome Oscillator. The Awesome Oscillator (AO), created by Bill Williams, is a momentum-based indicator that evaluates market momentum by comparing recent price activity to a broader historical context. It assists traders in identifying potential trend reversals and gauging trend strength.
AO = SMA5(Median Price) − SMA34(Median Price)
where:
Median Price = (High + Low) / 2
SMA5 = 5-period Simple Moving Average of the Median Price
SMA 34 = 34-period Simple Moving Average of the Median Price
Now we know what is AO, but what is the "Saucer" signal? This concept was introduced by Bill Williams, let's briefly explain it and how it's used by this strategy. Initially, this type of signal is a combination of the following AO bars: we need 3 bars in a row, the first one shall be higher than the second, the third bar also shall be higher, than second. All three bars shall be above the zero line of AO. The price bar, which corresponds to third "saucer's" bar is our signal bar. Strategy places buy stop order one tick above the price bar which corresponds to signal bar.
After that we can have the following scenarios.
Price hit the order on the next candle in this case strategy opened long with this price.
Price doesn't hit the order price, the next candle set lower low. If current AO bar is increasing buy stop order changes by the script to the high of this new bar plus one tick. This procedure repeats until price finally hit buy order or current AO bar become decreasing. In the second case buy order cancelled and strategy wait for the next "Saucer" signal.
If long trades has been opened strategy use all the next signals until number of trades doesn't exceed 5. All trades are closed when the trend changes to downtrend according to combination of Alligator and Fractals described above.
Why we use "Saucer" signals? If AO above the zero line there is a high probability that price now is in uptrend if we take into account our two trend filters. When we see the decreasing bars on AO and it's above zero it's likely can be considered as a pullback on the uptrend. When we see the stop of AO decreasing and the first increasing bar has been printed there is a high probability that this local pull back is finished and strategy open long trade in the likely direction of a main trend.
Why strategy use only 10% per signal? Sometimes we can see the false signals which appears on sideways. Not risking that much script use only 10% per signal. If the first long trade has been open and price continue going up and our trend approximation by Alligator and Fractals is uptrend, strategy add another one 10% of capital to every next saucer signal while number of active trades no more than 5. This capital allocation allows to take part in long trades when current uptrend is likely to be strong and use only 10% of capital when there is a high probability of sideways.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2024.11.25. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 10%
Maximum Single Position Loss: -5.10%
Maximum Single Profit: +22.80%
Net Profit: +2838.58 USDT (+28.39%)
Total Trades: 107 (42.99% win rate)
Profit Factor: 3.364
Maximum Accumulated Loss: 373.43 USDT (-2.98%)
Average Profit per Trade: 26.53 USDT (+2.40%)
Average Trade Duration: 78 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 3h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
Kalman Trend Strength Index (K-TSI)The Kalman Trend Strength Index (K-TSI) is an innovative technical indicator that combines the Kalman filter with correlation analysis to measure trend strength in financial markets. This sophisticated tool aims to provide traders with a more refined method for trend analysis and market dynamics interpretation.
The use of the Kalman filter is a key feature of the K-TSI. This advanced algorithm is renowned for its ability to extract meaningful signals from noisy data. In financial markets, this translates to smoothing out price action while maintaining responsiveness to genuine market movements. By applying the Kalman filter to price data before performing correlation analysis, the K-TSI potentially offers more stable and reliable trend signals.
The synergy between the Kalman-filtered price data and correlation analysis creates an oscillator that attempts to capture market dynamics more effectively. The correlation component contributes by measuring the strength and consistency of price movements relative to time, while the Kalman filter adds robustness by reducing the impact of market noise. Basing these calculations on Kalman-filtered data may help reduce false signals and provide a clearer picture of underlying market trends.
A notable aspect of the K-TSI is its normalization process. This approach adjusts the indicator's values to a standardized range (-1 to 1), allowing for consistent interpretation across different market conditions and timeframes. This flexibility, combined with the noise-reduction properties of the Kalman filter, positions the K-TSI as a potentially useful tool for various market environments.
In practice, traders might find that the K-TSI offers several potential benefits:
Smoother trend identification, which could aid in detecting the start and end of trends more accurately.
Possibly reduced false signals, particularly in choppy or volatile markets.
Potential for improved trend strength assessment, which might lead to more confident trading decisions.
Consistent performance across different timeframes, due to the adaptive nature of the Kalman filter and the normalization process.
The K-TSI's visual representation as a color-coded histogram further enhances its utility. The changing colors and intensities provide an intuitive way to gauge both the direction and strength of trends, making it easier for traders to quickly assess market conditions.
While the K-TSI builds upon existing concepts in technical analysis, its integration of the Kalman filter with correlation analysis offers traders an interesting tool for market analysis. It represents an attempt to address common challenges in technical analysis, such as noise reduction and trend strength quantification.
As with any technical indicator, the K-TSI should be used as part of a broader trading strategy rather than in isolation. Its effectiveness will depend on how well it aligns with a trader's individual approach and market conditions. For traders looking to explore a more refined trend strength oscillator, the Kalman Trend Strength Index could be a worthwhile addition to their analytical toolkit.
Kalman Synergy Oscillator (KSO)The Kalman Synergy Oscillator (KSO) is an innovative technical indicator that combines the Kalman filter with two well-established momentum oscillators: the Relative Strength Index (RSI) and Williams %R. This combination aims to provide traders with a more refined tool for market analysis.
The use of the Kalman filter is a key feature of the KSO. This sophisticated algorithm is known for its ability to extract meaningful signals from noisy data. In financial markets, this translates to smoothing out price action while maintaining responsiveness to genuine market movements. By applying the Kalman filter to price data before calculating the RSI and Williams %R, the KSO potentially offers more stable and reliable signals.
The synergy between the Kalman-filtered price data and the two momentum indicators creates an oscillator that attempts to capture market dynamics more effectively. The RSI contributes its strength in measuring the magnitude and speed of price movements, while Williams %R adds sensitivity to overbought and oversold conditions. Basing these calculations on Kalman-filtered data may help reduce false signals and provide a clearer picture of underlying market trends.
A notable aspect of the KSO is its dynamic weighting system. This approach adjusts the relative importance of the RSI and Williams %R based on their current strengths, allowing the indicator to emphasize the most relevant information as market conditions change. This flexibility, combined with the noise-reduction properties of the Kalman filter, positions the KSO as a potentially useful tool for different market conditions.
In practice, traders might find that the KSO offers several potential benefits:
Smoother oscillator movements, which could aid in trend identification and reversal detection.
Possibly reduced whipsaws, particularly in choppy or volatile markets.
Potential for improved divergence detection, which might lead to more timely reversal signals.
Consistent performance across different timeframes, due to the adaptive nature of the Kalman filter.
While the KSO builds upon existing concepts in technical analysis, its integration of the Kalman filter with traditional momentum indicators offers traders an interesting tool for market analysis. It represents an attempt to address common challenges in technical analysis, such as noise reduction and false signal minimization.
As with any technical indicator, the KSO should be used as part of a broader trading strategy rather than in isolation. Its effectiveness will depend on how well it aligns with a trader's individual approach and market conditions. For traders looking to explore a more refined momentum oscillator, the Kalman Synergy Oscillator could be a worthwhile addition to their analytical toolkit.
Hybrid Triple Exponential Smoothing🙏🏻 TV, I present you HTES aka Hybrid Triple Exponential Smoothing, designed by Holt & Winters in the US, assembled by me in Saint P. I apply exponential smoothing individually to the data itself, then to residuals from the fitted values, and lastly to one-point forecast (OPF) errors, hence 'hybrid'. At the same time, the method is a closed-form solution and purely online, no need to make any recalculations & optimize anything, so the method is O(1).
^^ historical OPFs and one-point forecasting interval plotted instead of fitted values and prediction interval
Before the How-to, first let me tell you some non-obvious things about Triple Exponential smoothing (and about Exponential Smoothing in general) that not many catch. Expo smoothing seems very straightforward and obvious, but if you look deeper...
1) The whole point of exponential smoothing is its incremental/online nature, and its O(1) algorithm complexity, making it dope for high-frequency streaming data that is also univariate and has no weights. Consequently:
- Any hybrid models that involve expo smoothing and any type of ML models like gradient boosting applied to residuals rarely make much sense business-wise: if you have resources to boost the residuals, you prolly have resources to use something instead of expo smoothing;
- It also concerns the fashion of using optimizers to pick smoothing parameters; honestly, if you use this approach, you have to retrain on each datapoint, which is crazy in a streaming context. If you're not in a streaming context, why expo smoothing? What makes more sense is either picking smoothing parameters once, guided by exogenous info, or using dynamic ones calculated in a minimalistic and elegant way (more on that in further drops).
2) No matter how 'right' you choose the smoothing parameters, all the resulting components (level, trend, seasonal) are not pure; each of them contains a bit of info from the other components, this is just how non-sequential expo smoothing works. You gotta know this if you wanna use expo smoothing to decompose your time series into separate components. The only pure component there, lol, is the residuals;
3) Given what I've just said, treating the level (that does contain trend and seasonal components partially) as the resulting fit is a mistake. The resulting fit is level (l) + trend (b) + seasonal (s). And from this fit, you calculate residuals;
4) The residuals component is not some kind of bad thing; it is simply the component that contains info you consciously decide not to include in your model for whatever reason;
5) Forecasting Errors and Residuals from fitted values are 2 different things. The former are deltas between the forecasts you've made and actual values you've observed, the latter are simply differences between actual datapoints and in-sample fitted values;
6) Residuals are used for in-sample prediction intervals, errors for out-of-sample forecasting intervals;
7) Choosing between single, double, or triple expo smoothing should not be based exclusively on the nature of your data, but on what you need to do as well. For example:
- If you have trending seasonal data and you wanna do forecasting exclusively within the expo smoothing framework, then yes, you need Triple Exponential Smoothing;
- If you wanna use prediction intervals for generating trend-trading signals and you disregard seasonality, then you need single (simple) expo smoothing, even on trending data. Otherwise, the trend component will be included in your model's fitted values → prediction intervals.
8) Kind of not non-obvious, but when you put one smoothing parameter to zero, you basically disregard this component. E.g., in triple expo smoothing, when you put gamma and beta to zero, you basically end up with single exponential smoothing.
^^ data smoothing, beta and gamma zeroed out, forecasting steps = 0
About the implementation
* I use a simple power transform that results in a log transform with lambda = 0 instead of the mainstream-used transformers (if you put lambda on 2 in Box-Cox, you won't get a power of 2 transform)
* Separate set of smoothing parameters for data, residuals, and errors smoothing
* Separate band multipliers for residuals and errors
* Both typical error and typical residuals get multiplied by math.sqrt(math.pi / 2) in order to approach standard deviation so you can ~use Z values and get more or less corresponding probabilities
* In script settings → style, you can switch on/off plotting of many things that get calculated internally:
- You can visualize separate components (just remember they are not pure);
- You can switch off fit and switch on OPF plotting;
- You can plot residuals and their exponentially smoothed typical value to pick the smoothing parameters for both data and residuals;
- Or you might plot errors and play with data smoothing parameters to minimize them (consult SAE aka Sum of Absolute Errors plot);
^^ nuff said
More ideas on how to use the thing
1) Use Double Exponential Smoothing (data gamma = 0) to detrend your time series for further processing (Fourier likes at least weakly stationary data);
2) Put single expo smoothing on your strategy/subaccount equity chart (data alpha = data beta = 0), set prediction interval deviation multiplier to 1, run your strat live on simulator, start executing on real market when equity on simulator hits upper deviation (prediction interval), stop trading if equity hits lower deviation on simulator. Basically, let the strat always run on simulator, but send real orders to a real market when the strat is successful on your simulator;
3) Set up the model to minimize one-point forecasting errors, put error forecasting steps to 1, now you're doing nowcasting;
4) Forecast noisy trending sine waves for fun.
^^ nuff said 2
All Good TV ∞
libTFLibrary "libTF"
libTF: Find higher/lower TF automatically
This library to find higher/lower TF from current timeframe(timeframe.period) for Pine Script version6(or higher).
Basic Algorithm
Using a timeframe scale Array and timeframe.in_seconds() function to find higher/lower timeframe.
Return value is na if could not find TF in the timeframe scale.
The timeframe scale could be changed by the parameter 'scale'(CSV).
How to use
1. Set higher/lower TF
higher()/lower() function returns higher/lower TF.
Default timeframe scale is "1, 5, 15, 60, 240, 1D, 1M, 3M, 12M".
example:
htf1 = higher()
htf2 = higher(htf1)
ltf1 = lower()
ltf2 = lower(ltf1)
2. Set higher/lower TF using your timeframe scale
The timeframe scale could be changed by the parameter.
example:
myscale="1,60,1D,1M,12M"
htf1 = higher(timeframe.period,myscale)
htf2 = higher(htf1,myscale)
ltf1 = lower(timeframe.period,myscale)
ltf2 = lower(ltf1,myscale)
3. How to use with request.*() function
na value is set if no higher/lower TF in timeframe scale.
It returns current timeframe's value, when na value as timeframe parameter in request.*().
As bellow, if it should be na when timeframe is na.
example:
return_value_request_htf1 = na(htf1)?na:request.security(syminfo.tickerid,htf1,timeframe.period)
return_value_request_ltf1 = na(ltf1)?na:request.security(syminfo.tickerid,ltf1,timeframe.period)
higher(tf, scale)
higher: find higher TF from TF string.
Parameters:
tf (string) : default value is timeframe.period.
scale (string) : TF scale in CSV. default is "1,5,15,60,240,1D,1W,1M,3M,12M".
Returns: higher TF string.
lower(tf, scale)
lower: find lower TF from TF string.
Parameters:
tf (string) : default value is timeframe.period.
scale (string) : TF scale in CSV. defalut is "1,5,15,60,240,1D,1W,1M,3M,12M".
Returns: lower TF string.
Abnormal volume [VG]🪙 INTRODUCTION
This technical indicator helps identify and highlight large volume clusters on the chart.
Abnormal volume refers to unusually large accumulations of volume over short time intervals. Such clusters appear when the amount of assets bought or sold significantly exceeds typical volumes for a specific asset over a given period. These patterns can indicate significant events or intentions of market participants.
Reasons for abnormal volume clusters:
Institutional investments :
Large investment funds and banks may buy or sell significant volumes of assets to rebalance their portfolios.
Impact of news and events :
Important news (e.g., mergers, bankruptcies, management changes) can trigger large-scale buying or selling of assets.
Market manipulation :
Big players may execute large trades to artificially create demand or supply for an asset, affecting its price in the short term.
Insider trading :
Abnormal volumes may signal that someone with insider information has started buying or selling assets in anticipation of future events that could impact the price.
What do abnormal volume clusters mean for traders?
A signal of potential price changes :
High trading volumes are often accompanied by sharp price movements. An increase in volume during price growth might indicate rising interest in the asset, while an increase during a decline could signal a sell-off.
Potential entry or exit points :
For short-term traders, abnormal trades can serve as signals to enter or exit positions. For example, a large volume growth accompanied by a breakout of a key level might be seen as a buy signal.
Caution due to potential manipulation :
Abnormal trades don’t always lead to expected outcomes. Sometimes, they are part of a price manipulation strategy, so it’s essential to consider the broader context and confirm with other signals.
🪙 USAGE
This indicator doesn’t provide trading signals, entry points, or actionable recommendations.
Instead, it simplifies tracking market dynamics and highlights unusual activity worth considering during analysis.
After adding the indicator to the chart, you only need to configure two parameters: the threshold value that determines what constitutes a significant volume cluster and the period over which volumes are aggregated for comparison against the threshold.
It’s recommended to use the shortest available period, as this helps more precisely identify the prevailing volume direction (since this depends on price changes, not trade direction).
The threshold value can be fine-tuned by switching the chart’s timeframe to match the selected period, observing of the significant volume increase on the classic volume histogram, and noting the corresponding market reactions. This allows for selecting a threshold that highlights early signs of impactful trading events on higher timeframes.
Let’s look at an example in the screenshot:
Once the parameters are set, you can also enable an alert to trigger whenever a new volume cluster appears, simplifying event tracking.
Note: in the current version of the indicator, the alert will be triggered only once per bar on the chart at the first detected cluster of abnormal volume.
🪙 IMPLEMENTATION
Technically, the script retrieves volume data from a lower timeframe and estimates whether the volume was primarily generated by buyers or sellers based on price movements.
The lower resolution timeframe is determined as follows:
if the settings base period is less than 1 minute, then the data timeframe will be equal to 1 second
if the settings base period is equals 1 minute or more, then the data timeframe will be equal to 1 minute
The algorithm checks whether the price increased or decreased at each point. If the price rose, the volume is presumed to be driven by buyers and marked as buy volume; otherwise, it’s marked as sell volume.
The total volume at each point is then checked against the user-defined threshold. If the volume exceeds the threshold, a corresponding circle is drawn on the chart, and an alert is generated if created.
The size of the visual representation is proportional to the most recent maximum volume and follows the rules below:
Percentage of max volume -> Volume cluster size
less than 25% -> Tiny
25% to 50% -> Small
50% to 75% -> Normal
75% to 100% -> Large
100% or more -> Huge
🪙 SETTINGS
The indicator is designed to be as simple and minimalist as possible, making configuration effortless. There are only two core parameters, with additional options to customize the colors of volume clusters based on their type.
Trade volume threshold
Defines the volume level above which a cluster is considered significant and displayed on the chart as a circle. The size of the circle depends on the proportion of the current volume relative to the most recent maximum over the chosen period.
Trades base period
Specifies the period for aggregating trade volumes to determine whether they qualify as abnormal. The significance level is set using the Trade volume threshold parameter.
Buy/Sell trades
Allows you to set the colors for abnormal volume circles based on the price direction during cluster formation.
🪙 CONCLUSION
Abnormal volume clusters are always a critical indicator requiring attention and analysis, but they are not a guaranteed predictor of trend changes.
Trend Flow Line (TFL)The Trend Flow Line (TFL) is a versatile moving average indicator that dynamically adjusts to trends using a combination of Hull and Weighted Moving Averages, with optional color coding for bullish and bearish trends.
Introduction
The Trend Flow Line (TFL) is a powerful indicator designed to help traders identify and follow market trends with precision. It combines multiple moving average techniques to create a responsive yet smooth trendline. Whether you're a beginner or an experienced trader, the TFL can enhance your chart analysis by highlighting key price movements and trends.
Detailed Description
The Trend Flow Line (TFL) goes beyond traditional moving averages by leveraging a hybrid approach to calculate trends.
Here's how it works:
.........
Combination of Hull and Weighted Moving Averages
The TFL integrates the Hull Moving Average (HMA), known for its fast responsiveness, and the Double Weighted Moving Average (DWMA), which offers smooth transitions.
The HMA is adjusted dynamically based on the user-defined length, ensuring adaptability to various trading styles and timeframes.
.....
Dynamic Smoothing
The TFL calculates its value by averaging the HMA and DWMA, creating a balanced line that responds to market fluctuations without excessive noise.
This balance makes it ideal for identifying both short-term reversals and long-term trends.
.....
Customizable Features
Timeframe: Analyze the indicator on custom timeframes, independent of the chart's current timeframe.
Color Coding: Optional color settings visually differentiate bullish (uptrend) and bearish (downtrend) phases.
Line Width: Adjust the line thickness to suit your chart preferences.
Color Smoothness: Fine-tune how quickly the color changes to reflect trend shifts, providing a visual cue for potential reversals.
The TFL's algorithm ensures a blend of precision and adaptability, making it suitable for any market or trading strategy.
.........
The Trend Flow Line (TFL) is an essential tool for traders looking to stay ahead of market trends while maintaining a clear and visually intuitive charting experience. It combines HMA and DWMA for trend sensitivity and smoothness.
Zigzag3 -Invincible3Description:
Zigzag3 - Invincible3 is a powerful and flexible support and resistance indicator for TradingView. Utilizing an enhanced ZigZag algorithm and Dow Theory principles, it detects price pivots, higher highs (HH), lower highs (LH), higher lows (HL), and lower lows (LL). The indicator draws lines and labels to visualize these pivots, making it easier to identify market structure, trends, and potential reversal points.
The Length input allows traders to control the sensitivity of pivot detection.
Support and Resistance Lines:
Displays dotted and solid SR lines based on significant pivots to highlight key market zones.
Option to extend support/resistance lines dynamically with real-time progression for the latest pivot.
Labels for Dow Theory Points:
Mark higher highs, lower highs, higher lows, and lower lows with customizable colors.
Identifies market direction and potential breakout levels with visual clarity.
ZigZag Line Visualization:
Toggle the ZigZag lines to connect pivots for a better understanding of price movement.
Dynamic Dotted Line Progression:
A dotted line extends in real-time from the most recent significant pivot point, aiding in quick analysis.
This indicator is ideal for traders looking to analyze market structure, identify trends, and spot potential reversals. It can be used as a standalone tool or in combination with other strategies for enhanced precision.
ORB Screener with Trailing SLThis is an extension to our already published script ORB with ATR Trailing SL indicator
Many people requested to add screener to the existing indicator but since it's slowing down the performance heavily, we decided to add this as a separate screener.
Note: This screener does NOT plot the chart and so you want to have both plotting and screener, use both scripts together.
Overview:
The ORB Screener is a TradingView indicator designed to assist traders in identifying breakout opportunities based on the Opening Range Breakout (ORB) strategy. It features multi-symbol screening, customizable session timeframes, and a detailed table for quick visual reference and stock scanning.
The ORB Screener utilizes the ORB strategy to calculate breakout levels for multiple symbols. It identifies the high and low during a specified session (e.g., first 5 minutes after market open) and provides insights on whether the price is above the high (bullish), below the low (bearish), or between the range (neutral).
Additionally, the script calculates and displays the RSI values for each symbol, aiding traders in assessing momentum alongside breakout status.
Note: One can add up to 40 symbols for screening the stocks.
Key Features and Inputs:
ORB Session Time: Define a specific timeframe (e.g., "0915-0920") during which the ORB high and low are calculated. This serves as the foundation for identifying breakouts.
Multi-Symbol Screening: Screen up to 40 symbols at once, enabling you to monitor multiple opportunities without switching charts.
Breakout Validation:
Select from two methods for confirming a breakout: Close (based on closing prices) or Touch (based on intraday highs/lows).
Breakout Status Indicators:
Above High: Indicates a current bullish breakout when the price exceeds the ORB high.
Below Low: Indicates a current bearish breakout when the price falls below the ORB low.
Between Range: Indicates no breakout (price remains within the range).
RSI Integration : Calculates the RSI for each symbol to help traders evaluate momentum alongside breakout signals.
Customizable Table Display:
Position: Place the data table at the top, middle, or bottom of the chart and align it left, center, or right.
Size: Choose from multiple table size options for optimal visibility (Auto, Huge, Large, Normal, Small, Tiny).
Visual Feedback:
Green Background: Indicates a breakout happened at least once above the ORB high.
Red Background: Indicates a breakout happened at least once below the ORB low.
Gray Background: Indicates price is within the ORB range.
Adaptive Squeeze Momentum StrategyThe Adaptive Squeeze Momentum Strategy is a versatile trading algorithm designed to capitalize on periods of low volatility that often precede significant price movements. By integrating multiple technical indicators and customizable settings, this strategy aims to identify optimal entry and exit points for both long and short positions.
Key Features:
Long/Short Trade Control:
Toggle Options: Easily enable or disable long and short trades according to your trading preferences or market conditions.
Flexible Application: Adapt the strategy for bullish, bearish, or neutral market outlooks.
Squeeze Detection Mechanism:
Bollinger Bands and Keltner Channels: Utilizes the convergence of Bollinger Bands inside Keltner Channels to detect "squeeze" conditions, indicating a potential breakout.
Dynamic Squeeze Length: Calculates the average squeeze duration to adapt to changing market volatility.
Momentum Analysis:
Linear Regression: Applies linear regression to price changes over a specified momentum length to gauge the strength and direction of momentum.
Dynamic Thresholds: Sets momentum thresholds based on standard deviations, allowing for adaptive sensitivity to market movements.
Momentum Multiplier: Adjustable setting to fine-tune the aggressiveness of momentum detection.
Trend Filtering:
Exponential Moving Average (EMA): Implements a trend filter using an EMA to align trades with the prevailing market direction.
Customizable Length: Adjust the EMA length to suit different trading timeframes and assets.
Relative Strength Index (RSI) Filtering:
Overbought/Oversold Signals: Incorporates RSI to avoid entering trades during overextended market conditions.
Adjustable Levels: Set your own RSI oversold and overbought thresholds for personalized signal generation.
Advanced Risk Management:
ATR-Based Stop Loss and Take Profit:
Adaptive Levels: Uses the Average True Range (ATR) to set stop loss and take profit points that adjust to market volatility.
Custom Multipliers: Modify ATR multipliers for both stop loss and take profit to control risk and reward ratios.
Minimum Volatility Filter: Ensures trades are only taken when market volatility exceeds a user-defined minimum, avoiding periods of low activity.
Time-Based Exit:
Holding Period Multiplier: Defines a maximum holding period based on the momentum length to reduce exposure to adverse movements.
Automatic Position Closure: Closes positions after the specified holding period is reached.
Session Filtering:
Trading Session Control: Limits trading to predefined market hours, helping to avoid illiquid periods.
Custom Session Times: Set your preferred trading session to match market openings, closings, or specific timeframes.
Visualization Tools:
Indicator Plots: Displays Bollinger Bands, Keltner Channels, and trend EMA on the chart for visual analysis.
Squeeze Signals: Marks squeeze conditions on the chart, providing clear visual cues for potential trade setups.
Customization Options:
Indicator Parameters: Fine-tune lengths and multipliers for Bollinger Bands, Keltner Channels, momentum calculation, and ATR.
Entry Filters: Choose to use trend and RSI filters to refine trade entries based on your strategy.
Risk Management Settings: Adjust stop loss, take profit, and holding periods to match your risk tolerance.
Trade Direction Control: Enable or disable long and short trades independently to align with your market strategy or compliance requirements.
Time Settings: Modify the trading session times and enable or disable the time filter as needed.
Use Cases:
Trend Traders: Benefit from aligning entries with the broader market trend while capturing breakout movements.
Swing Traders: Exploit periods of low volatility leading to significant price swings.
Risk-Averse Traders: Utilize advanced risk management features to protect capital and manage exposure.
Disclaimer:
This strategy is a tool to assist in trading decisions and should be used in conjunction with other analyses and risk management practices. Past performance is not indicative of future results. Always test the strategy thoroughly and adjust settings to suit your specific trading style and market conditions.
Advanced Pivot Manipulation SuperTrend - Consolidation ZoneHere’s the description translated into English for your TradingView publication:
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Advanced Pivot Manipulation SuperTrend - Consolidation Zone
Description :
This advanced indicator combines multiple technical tools to provide a comprehensive analysis of trends, key levels, and consolidation zones. Ideal for traders seeking to spot opportunities while avoiding the traps of flat markets, it helps you better understand market dynamics and improve your trading decisions.
Key Features:
1.
Dynamic SuperTrend with Pivot Points:
- An enhanced SuperTrend algorithm based on pivot points for more precise trend tracking.
- Thresholds (Up/Dn) are dynamically adjusted using ATR (Average True Range) for improved volatility adaptation.
2. Consolidation Zones:
- Automatically identifies periods when the market moves within a narrow range (1% by default).
- Consolidation zones are visually highlighted to help avoid risky trades.
3. Dynamic Support and Resistance:
- Automatically calculates support and resistance levels based on a rolling period (configurable).
- These levels serve as key references for potential breakouts or trend reversals.
4. Advanced Detection Tools:
- Includes a volume multiplier and shadow-to-body ratio to signal unusual or potentially manipulated moves (e.g., spoofing).
5. Intuitive Visuals:
- SuperTrend lines are color-coded to indicate bullish (green) or bearish (red) trends.
- Semi-transparent lines mark support and resistance levels, and red backgrounds indicate consolidation zones.
Customizable Parameters:
- Pivot Point Period: Adjust the period for detecting pivot highs and lows.
- ATR Factor and Period: Control the sensitivity of the SuperTrend indicator.
- Lookback Period for S/R: Define the duration for calculating support and resistance levels.
- Volume Multiplier and Shadow/Body Ratio: Configure thresholds for detecting high volumes or anomalies in candlestick patterns.
How to Use:
- Easily identify dominant trends using the SuperTrend.
- Spot consolidation zones to avoid inefficient trades or prepare breakout strategies.
- Use support and resistance levels as reference points for placing orders or adjusting risk management.
Target Audience:
- Intraday and swing traders.
- Anyone looking for a comprehensive and customizable indicator to effectively analyze volatile markets.
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Notes:
The indicator is fully customizable to suit your needs and strategies. Feel free to experiment with the parameters to maximize its effectiveness according to your trading style.
Keywords: SuperTrend, Support and Resistance, Consolidation, Pivot Points, Trends, ATR, Advanced Trading.
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This description highlights the indicator’s strengths and is designed to appeal to the TradingView community.
B-Xtrender Simplified-BUY/SELL print Thanks to @puppytherapy for creating the original B-Xtrender indicator, available at this link: B-Xtrender by Puppytherapy.
This is a modified version of the original script, which now includes Buy and Sell arrows directly plotted on the chart for clear entry signals. The core logic of the indicator remains intact, with enhancements for simplicity and usability.
Overview:
The B-Xtrender Simplified indicator is a trend-following tool designed to identify potential buy and sell opportunities based on momentum and trend confluence. It combines short-term and long-term RSI-based oscillators with exponential moving averages to detect trend shifts and provide clear, actionable signals on the chart.
This simplified version focuses on clean visuals and provides buy and sell signals using arrows, ensuring an uncluttered chart. The indicator is suitable for traders who want a straightforward tool to assist with entry signals in trending markets.
Key Components:
Short-Term Oscillator:
Measures short-term momentum using the RSI of the difference between two EMAs (Short - L1 and Short - L2).
Provides early signals for trend reversals and momentum shifts.
Long-Term Oscillator:
Evaluates broader trend strength using the RSI of a single EMA (Long - L1).
Acts as a filter to ensure signals align with the overall market trend.
Smoothed Oscillator (T3):
Applies a smoothing algorithm to the short-term oscillator to reduce noise.
Ensures the signals are more reliable and less prone to false alarms.
How It Works:
Buy Signal (Green Arrow Below Candles):
Triggered when:
The short-term oscillator is above 0 (indicating upward momentum).
The smoothed short-term oscillator (maShortTermXtrender) is rising (momentum confirmation).
The long-term oscillator is above 0 (trend confirmation).
Sell Signal (Red Arrow Above Candles):
Triggered when:
The short-term oscillator is below 0 (indicating downward momentum).
The smoothed short-term oscillator (maShortTermXtrender) is falling (momentum confirmation).
The long-term oscillator is below 0 (trend confirmation).
Alerts:
Buy and sell signals generate alerts for traders to take immediate action when conditions are met.
Customization Options:
Short-Term Parameters:
Short - L1, Short - L2, Short - L3 control the responsiveness of the short-term oscillator.
Long-Term Parameters:
Long - L1, Long - L2 adjust the sensitivity of the long-term trend filter.
Default values ensure the indicator works effectively in most market conditions, but they can be fine-tuned for specific instruments or timeframes.
Strengths:
Clarity: Uses clean buy/sell arrows for visual simplicity.
Confluence-Based: Ensures alignment between short-term momentum and long-term trend before signaling.
Real-Time Alerts: Alerts for both buy and sell signals allow for timely decision-making.
Usage Tips:
Confirm Trend:
Use the indicator on a higher timeframe (e.g., 1-hour or daily) to confirm the overall trend direction.
Combine with Other Tools:
Enhance accuracy by combining the indicator with support/resistance levels, volume analysis, or other technical indicators.
Risk Management:
Always use stop-loss orders to protect against adverse market movements.
Maintain a risk-to-reward ratio of at least 1:2.
Ideal For:
Traders seeking clear and straightforward entry signals.
Trend-following strategies in liquid markets.
Beginners who want an easy-to-interpret tool for identifying momentum-based trades.
This simplified version of the B-Xtrender retains the original power of the indicator while focusing on clean visuals and actionable signals for trend-following traders.
Silver Bullet ICT Strategy [TradingFinder] 10-11 AM NY Time +FVG🔵 Introduction
The ICT Silver Bullet trading strategy is a precise, time-based algorithmic approach that relies on Fair Value Gaps and Liquidity to identify high-probability trade setups. The strategy primarily focuses on the New York AM Session from 10:00 AM to 11:00 AM, leveraging heightened market activity within this critical window to capture short-term trading opportunities.
As an intraday strategy, it is most effective on lower timeframes, with ICT recommending a 15-minute chart or lower. While experienced traders often utilize 1-minute to 5-minute charts, beginners may find the 1-minute timeframe more manageable for applying this strategy.
This approach specifically targets quick trades, designed to take advantage of market movements within tight one-hour windows. By narrowing its focus, the Silver Bullet offers a streamlined and efficient method for traders to capitalize on liquidity shifts and price imbalances with precision.
In the fast-paced world of forex trading, the ability to identify market manipulation and false price movements is crucial for traders aiming to stay ahead of the curve. The Silver Bullet Indicator simplifies this process by integrating ICT principles such as liquidity traps, Order Blocks, and Fair Value Gaps (FVG).
These concepts form the foundation of a tool designed to mimic the strategies of institutional players, empowering traders to align their trades with the "smart money." By transforming complex market dynamics into actionable insights, the Silver Bullet Indicator provides a powerful framework for short-term trading success
Silver Bullet Bullish Setup :
Silver Bullet Bearish Setup :
🔵 How to Use
The Silver Bullet Indicator is a specialized tool that operates within the critical time windows of 9:00-10:00 and 10:00-11:00 in the forex market. Its design incorporates key principles from ICT (Inner Circle Trader) methodology, focusing on concepts such as liquidity traps, CISD Levels, Order Blocks, and Fair Value Gaps (FVG) to provide precise and actionable trade setups.
🟣 Bullish Setup
In a bullish setup, the indicator starts by marking the high and low of the session, serving as critical reference points for liquidity. A typical sequence involves a liquidity grab below the low, where the price manipulates retail traders into selling positions by breaching a key support level.
This movement is often orchestrated by smart money to accumulate buy orders. Following this liquidity grab, a market structure shift (MSS) occurs, signaled by the price breaking the CISD Level—a confirmation of bullish intent. The indicator then highlights an Order Block near the CISD Level, representing the zone where institutional buying is concentrated.
Additionally, it identifies a Fair Value Gap, which acts as a high-probability area for price retracement and trade entry. Traders can confidently take long positions when the price revisits these zones, targeting the next significant liquidity pool or resistance level.
Bullish Setup in CAPITALCOM:US100 :
🟣 Bearish Setup
Conversely, in a bearish setup, the price manipulates liquidity by creating a false breakout above the high of the session. This move entices retail traders into long positions, allowing institutional players to enter sell orders.
Once the price reverses direction and breaches the CISD Level to the downside, a change of character (CHOCH) becomes evident, confirming a bearish market structure. The indicator highlights an Order Block near this level, indicating the origin of the institutional sell orders, along with an associated FVG, which represents an imbalance zone likely to be revisited before the price continues downward.
By entering short positions when the price retraces to these levels, traders align their strategies with the anticipated continuation of bearish momentum, targeting nearby liquidity voids or support zones.
Bearish Setup in OANDA:XAUUSD :
🔵 Settings
Refine Order Block : Enables finer adjustments to Order Block levels for more accurate price responses.
Mitigation Level OB : Allows users to set specific reaction points within an Order Block, including: Proximal: Closest level to the current price. 50% OB: Midpoint of the Order Block. Distal: Farthest level from the current price.
FVG Filter : The Judas Swing indicator includes a filter for Fair Value Gap (FVG), allowing different filtering based on FVG width: FVG Filter Type: Can be set to "Very Aggressive," "Aggressive," "Defensive," or "Very Defensive." Higher defensiveness narrows the FVG width, focusing on narrower gaps.
Mitigation Level FVG : Like the Order Block, you can set price reaction levels for FVG with options such as Proximal, 50% OB, and Distal.
CISD : The Bar Back Check option enables traders to specify the number of past candles checked for identifying the CISD Level, enhancing CISD Level accuracy on the chart.
🔵 Conclusion
The Silver Bullet Indicator is a cutting-edge tool designed specifically for forex traders who aim to leverage market dynamics during critical liquidity windows. By focusing on the highly active 9:00-10:00 and 10:00-11:00 timeframes, the indicator simplifies complex market concepts such as liquidity traps, Order Blocks, Fair Value Gaps (FVG), and CISD Levels, transforming them into actionable insights.
What sets the Silver Bullet Indicator apart is its precision in detecting false breakouts and market structure shifts (MSS), enabling traders to align their strategies with institutional activity. The visual clarity of its signals, including color-coded zones and directional arrows, ensures that both novice and experienced traders can easily interpret and apply its findings in real-time.
By integrating ICT principles, the indicator empowers traders to identify high-probability entry and exit points, minimize risk, and optimize trade execution. Whether you are capturing short-term price movements or navigating complex market conditions, the Silver Bullet Indicator offers a robust framework to enhance your trading performance.
Ultimately, this tool is more than just an indicator; it is a strategic ally for traders who seek to decode the movements of smart money and capitalize on institutional strategies. With the Silver Bullet Indicator, traders can approach the market with greater confidence, precision, and profitability.
Williams Fractals for ExtremesThis script, written in Pine Script (version 5), implements an indicator for the automatic detection and visualization of fractal extremes on the price chart. The core algorithm is based on Bill Williams' fractal theory and identifies local highs and lows, which are often used to determine potential reversal points and support/resistance levels in the market.
### Key Features:
#### Fractal Detection:
- The indicator identifies a fractal high if the middle candle in a sequence of five candles (two on the left and two on the right) has the highest value.
- A fractal low is identified if the middle candle in the same type of five-candle sequence has the lowest value.
#### Extreme Visualization:
- Fractal highs are displayed as red dots on the chart, signaling potential local peaks.
- Fractal lows are shown as green dots, indicating local troughs.
### Usage:
- The indicator is designed for use across all timeframes and can be applied to both cryptocurrency and traditional financial markets.
- Highlighted points allow traders to quickly spot key levels, aiding in identifying potential zones for trade entry or exit.
### Application in Trading:
#### Identifying Key Levels:
- Fractal highs and lows can serve as resistance and support levels. A breakout beyond a fractal in either direction may signal a continuation of movement in that direction.
#### Finding Reversal Points:
- Fractal extremes indicate potential market reversals, making them useful in counter-trend trading strategies.
#### Adaptability to Market Conditions:
- The indicator updates dynamically with the appearance of new candles, providing traders with real-time fractal extreme levels.
### Settings and Parameters:
- In its current version, the script does not include customizable settings as it implements the standard concept of Williams' fractals.
Time Change Indicator-AYNETDetailed Scientific Explanation of the Time Change Indicator Code
This Pine Script code implements a financial indicator designed to measure and visualize the percentage change in the closing price of an asset over a specified timeframe. It uses historical data to calculate changes and displays them as a histogram for intuitive analysis. Below is a comprehensive scientific breakdown of the code:
1. User Inputs
The script begins by defining user-configurable parameters, enabling flexibility in analysis:
timeframe: The user selects the timeframe for measuring price changes (e.g., 1 hour, 1 day). This determines the granularity of the analysis.
positive_color and negative_color: Users choose the colors for positive and negative changes, enhancing visual interpretation.
2. Data Retrieval
The script employs request.security to fetch closing price data (close) for the specified timeframe. This function ensures that the indicator adapts to different timeframes, providing consistent results regardless of the chart's base timeframe.
Current Closing Price (current_close):
current_close
=
request.security(syminfo.tickerid, timeframe, close)
current_close=request.security(syminfo.tickerid, timeframe, close)
Retrieves the closing price for the defined timeframe.
Previous Closing Price (prev_close): The script uses a variable (prev_close) to store the previous closing price. This variable is updated dynamically as new data is processed.
3. Price Change Calculation
The script calculates both the absolute and percentage change in closing price:
Absolute Price Change (price_change):
price_change
=
current_close
−
prev_close
price_change=current_close−prev_close
Measures the difference between the current and previous closing prices.
Percentage Change (percent_change):
percent_change
=
price_change
prev_close
×
100
percent_change=
prev_close
price_change
×100
Normalizes the change relative to the previous closing price, making it easier to compare changes across different assets or timeframes.
4. Conditional Logic for Visualization
The script uses a conditional statement to determine the color of each histogram bar:
Positive Change: If price_change > 0, the bar is assigned the user-defined positive_color.
Negative Change: If price_change < 0, the bar is assigned the negative_color.
This differentiation provides a clear visual cue for understanding price movement direction.
5. Visualization
The script visualizes the percentage change using a histogram and enhances the chart with dynamic labels:
Histogram (plot.style_histogram):
Each bar represents the percentage change for a given timeframe.
Bars above the zero line indicate positive changes, while bars below the zero line indicate negative changes.
Zero Line (hline(0)): A reference line at zero provides a baseline for interpreting changes.
Dynamic Labels (label.new):
Each bar is annotated with its exact percentage change value.
The label's position and color correspond to the bar, improving clarity.
6. Algorithmic Flow
Data Fetching: Retrieve the current and previous closing prices for the specified timeframe.
Change Calculation: Compute the absolute and percentage changes between the two prices.
Bar Coloring: Determine the color of the histogram bar based on the change's direction.
Plotting: Visualize the changes as a histogram and add labels for precise data representation.
7. Applications
This indicator has several practical applications in financial analysis:
Volatility Analysis: By visualizing percentage changes, traders can assess the volatility of an asset over specific timeframes.
Trend Identification: Positive and negative bars highlight periods of upward or downward momentum.
Cross-Asset Comparison: Normalized percentage changes enable the comparison of price movements across different assets, regardless of their nominal values.
Market Sentiment: Persistent positive or negative changes may indicate prevailing bullish or bearish sentiment.
8. Scientific Relevance
This script applies fundamental principles of data visualization and time-series analysis:
Statistical Normalization: Percentage change provides a scale-invariant metric for comparing price movements.
Dynamic Data Processing: By updating the prev_close variable with real-time data, the script adapts to new market conditions.
Visual Communication: The use of color and labels improves the interpretability of quantitative data.
Conclusion
This indicator combines advanced Pine Script functions with robust financial analysis techniques to create an effective tool for evaluating price changes. It is highly adaptable, providing users with the ability to tailor the analysis to their specific needs. If additional features, such as smoothing or multi-timeframe analysis, are required, the code can be further extended.
Kalman Based VWAP [EdgeTerminal]Kalman VWAP is a different take on volume-weighted average price (VWAP) indicator where we enhance the results with Kalman filtering and dynamic wave visualization for a more smooth and improved trend identification and volatility analysis.
A little bit about Kalman Filter:
Kalman filtering (also known as linear quadratic estimation) is an algorithm that uses a series of measurements observed over time, including statistical noise and other inaccuracies, to produce estimates of unknown variables that tend to be more accurate than those based on a single measurement, by estimating a joint probability distribution over the variables for each time-step. The filter is constructed as a mean squared error minimiser, but an alternative derivation of the filter is also provided showing how the filter relates to maximum likelihood statistics
This indicator combines:
Volume-Weighted Average Price (VWAP) for institutional price levels
Kalman filtering for noise reduction and trend smoothing
Dynamic wave visualization for volatility zones
This creates a robust indicator that helps traders identify trends, support/resistance zones, and potential reversal points with high precision.
What makes this even more special is the fact that we use open price as a data source instead of usual close price. This allows you to tune the indicator more accurately when back testing it and generally get results that are closer to real time market data.
The math:
In case if you're interested in the math of this indicator, the indicator employs a state-space Kalman filter model:
State Equation: x_t = x_{t-1} + w_t
Measurement Equation: z_t = x_t + v_t
x_t is the filtered VWAP state
w_t is process noise ~ N(0, Q)
v_t is measurement noise ~ N(0, R)
z_t is the traditional VWAP measurement
The Kalman filter recursively updates through:
Prediction: x̂_t|t-1 = x̂_{t-1}
Update: x̂_t = x̂_t|t-1 + K_t(z_t - x̂_t|t-1)
Where K_t is the Kalman gain, optimally balancing between prediction and measurement.
Input Parameters
Measurement Noise: Controls signal smoothing (0.0001 to 1.0)
Process Noise: Adjusts trend responsiveness (0.0001 to 1.0)
Wave Size: Multiplier for volatility bands (0.1 to 5.0)
Trend Lookback: Period for trend determination (1 to 100)
Bull/Bear Colors: Customizable color schemes
Application:
I recommend using this along other indicators. This is best used for assets that don't have a close time, such as BTC but can be used with anything as long as the data is there.
With default settings, this works better for swing trades but you can adjust it for day trading as well, by adjusting the lookback and also process noise.
Volume Flow ConfluenceVolume Flow Confluence (CMF-KVO Integration)
Core Function:
The Volume Flow Confluence Indicator combines two volume-analysis methods: Chaikin Money Flow (CMF) and the Klinger Volume Oscillator (KVO). It displays a histogram only when both indicators align in their respective signals.
Signal States:
• Green Bars: CMF is positive (> 0) and KVO is above its signal line
• Red Bars: CMF is negative (< 0) and KVO is below its signal line
• No Bars: When indicators disagree
Technical Components:
Chaikin Money Flow (CMF):
Measures the relationship between volume and price location within the trading range:
• Calculates money flow volume using close position relative to high/low range
• Aggregates and normalizes over specified period
• Default period: 20
Klinger Volume Oscillator (KVO):
Evaluates volume in relation to price movement:
• Tracks trend changes using HLC3
• Applies volume force calculation
• Uses two EMAs (34/55) with a signal line (13)
Practical Applications:
1. Signal Identification
- New colored bars after blank periods show new agreement between indicators
- Color intensity differentiates new signals from continuations
- Blank spaces indicate lack of agreement
2. Trend Analysis
- Consecutive colored bars show continued indicator agreement
- Transitions between colors or to blank spaces show changing conditions
- Can be used alongside other technical analysis tools
3. Risk Considerations
- Signals are not predictive of future price movement
- Should be used as one of multiple analysis tools
- Effectiveness may vary across different markets and timeframes
Technical Specifications:
Core Algorithm
CMF = Σ(((C - L) - (H - C))/(H - L) × V)n / Σ(V)n
KVO = EMA(VF, 34) - EMA(VF, 55)
Where VF = V × |2(dm/cm) - 1| × sign(Δhlc3)
Signal Line = EMA(KVO, 13)
Signal Logic
Long: CMF > 0 AND KVO > Signal
Short: CMF < 0 AND KVO < Signal
Neutral: All other conditions
Parameters
CMF Length = 20
KVO Fast = 34
KVO Slow = 55
KVO Signal = 13
Volume = Regular/Actual Volume
Data Requirements
Price Data: OHLC
Volume Data: Required
Minimum History: 55 bars
Recommended Timeframe: ≥ 1H
Credits:
• Marc Chaikin - Original CMF development
• Stephen Klinger - Original KVO development
• Alex Orekhov (everget) - CMF script implementation
• nj_guy72 - KVO script implementation
