DBG X WOLONG
Overview
DBG X Wolong is a feature-rich Pine Script v5 indicator/strategy designed to provide a systematic, configurable approach to detecting trade opportunities and managing positions. This free edition combines trend detection, momentum confirmation, volatility sizing and an adaptive grid/TP system into a single workflow that is intended to add practical value beyond a simple indicator mashup
What makes this script original & useful
Integrated workflow (not a mere mashup): indicators are assigned clear roles in a pipeline — trend → momentum → volatility → scaling/exit — so their outputs interact deterministically to form signals.
Adaptive grid + ATR sizing: grid spacing and stop/TP levels adapt to market volatility via ATR, reducing arbitrary parameter dependence.
MA cloud & Braid filter: the multi-MA cloud (supporting many MA types) is used as a structural trend/range detector; the Braid filter suppresses noise and confirms stronger trend regimes.
Multi-timeframe dashboard: compact view of trend across many TFs to avoid single-TF false signals.
Conceptual workflow (how it works, high level)
Trend detection: SuperTrend + MA cloud determine the primary bias (bull/bear).
Momentum confirmation: RSI and MACD histogram confirm momentum direction or reversals.
Volatility sizing: ATR is used to calculate stop levels and to scale position sizing and grid spacing (higher ATR → wider stops & grid)
Signal gating / filter: Braid filter and multi-TF confirmation reduce false entries and ensure higher-probability setups..
Grid / TP engine: when a signal triggers, the system can scale into positions across predefined grid steps and compute TP1/TP2/TP3 based on measured move (VWAP/regression or pivot logic), with labels on chart.
Visual outputs: colorized candles, entry/stop/take labels, pullback marks and a configurable table/dashboards.
Key components & role (concise)
SuperTrend: primary trend filter and main signal trigger.
MA Cloud / Ribbon (many MA types available): structure, support/resistance, trend validation
Braid Filter: noise suppression and signal confirmation.
FRAMA / JMA / advanced MA routines: adaptive smoothing options for different market regimes.
ATR: volatility measure for dynamic stops and grid spacing.
TP Engine / Regression-VWAP logic: adaptive take profit placement and multi-level exits.
Dashboard / Multi-TF checks: present TF consensus to avoid contradictory signals.
How signals are generated (conceptual)
Primary buy: SuperTrend flips bullish + price above short SMA + braid filter confirms + multi-TF bias mostly bullish.
Primary sell: SuperTrend flips bearish + price below short SMA + braid filter confirms + multi-TF bias mostly bearish.
Reversal/pullback markers: RSI crossing thresholds with additional confirmations.
(Exact thresholds and gating are configurable in inputs.).
Inputs summary (important ones to show in the publish dialog)
Sensitivity (SuperTrend tuning): 1–20 (default 6)
MA cloud cycles & ribbon choices (8 cycle settings)
Braid filter type & strength (percent)
ATR length & ATR risk multiplier (for SL and sizing).
Dashboard: enable/position/size, show/hide signals, pullback toggles.
TP mode (pivot/regression), TP multiplier and lengths
Recommended usage & presets
Sensitivity (SuperTrend tuning): 1–20 (default 6)
MA cloud cycles & ribbon choices (8 cycle settings)
Braid filter type & strength (percent)
ATR length & ATR risk multiplier (for SL and sizing).
Dashboard: enable/position/size, show/hide signals, pullback toggles.
TP mode (pivot/regression), TP multiplier and lengths
Recommended usage & presets
Scalping: TF 1–5 minutes; sensitivity higher (8–12); use only SuperTrend + Braid filters; TP1 only.
Intraday: TF 15–60 minutes; sensitivity medium (6–8); use full grid with ATR-based stops.
Swing: TF H1–D1; sensitivity lower (4–6); enable full indicators and multi-TF confirmations.
Always backtest and demo-trade settings before using live.
Limitations & safeguards
Market conditions (thin liquidity, news) can still produce false signals; use multi-TF filter and turn off signals near major events.
This free edition is intended for learning; advanced/premium variants may include additional proprietary optimizations.
Not investment advice — use proper money management and test before trading real capital.
Backtesting & validation
Backtest over multiple symbols and regimes (trending vs ranging) to find robust settings..
Use the dashboard to visualize TF alignment and exclude signals when mismatch occurs.
Keep trade frequency reasonable to avoid overfitting small sample sets.
Publishing notes (for moderators/reviewers)
This description explains how indicators combine in a defined workflow and why each component is used; it demonstrates originality (adaptive grid + ATR-based sizing + MA cloud + Braid filter as a cohesive strategy), not just a superficial mashup.
The code exposes configurable inputs and visual outputs; the long description gives sufficient conceptual detail for users and moderators to evaluate the script without exposing proprietary implementation details.
Media móvil adaptable fractal (FRAMA)
Hurst Exponent Adaptive Filter (HEAF) [PhenLabs]📊 PhenLabs - Hurst Exponent Adaptive Filter (HEAF)
Version: PineScript™ v6
📌 Description
The Hurst Exponent Adaptive Filter (HEAF) is an advanced Pine Script indicator designed to dynamically adjust moving average calculations based on real time market regimes detected through the Hurst Exponent. The intention behind the creation of this indicator was not a buy/sell indicator but rather a tool to help sharpen traders ability to distinguish regimes in the market mathematically rather than guessing. By analyzing price persistence, it identifies whether the market is trending, mean-reverting, or exhibiting random walk behavior, automatically adapting the MA length to provide more responsive alerts in volatile conditions and smoother outputs in stable ones. This helps traders avoid false signals in choppy markets and capitalize on strong trends, making it ideal for adaptive trading strategies across various timeframes and assets.
Unlike traditional moving averages, HEAF incorporates fractal dimension analysis via the Hurst Exponent to create a self-tuning filter that evolves with market conditions. Traders benefit from visual cues like color coded regimes, adaptive bands for volatility channels, and an information panel that suggests appropriate strategies, enhancing decision making without constant manual adjustments by the user.
🚀 Points of Innovation
Dynamic MA length adjustment using Hurst Exponent for regime-aware filtering, reducing lag in trends and noise in ranges.
Integrated market regime classification (trending, mean-reverting, random) with visual and alert-based notifications.
Customizable color themes and adaptive bands that incorporate ATR for volatility-adjusted channels.
Built-in information panel providing real-time strategy recommendations based on detected regimes.
Power sensitivity parameter to fine-tune adaptation aggressiveness, allowing personalization for different trading styles.
Support for multiple MA types (EMA, SMA, WMA) within an adaptive framework.
🔧 Core Components
Hurst Exponent Calculation: Computes the fractal dimension of price series over a user-defined lookback to detect market persistence or anti-persistence.
Adaptive Length Mechanism: Maps Hurst values to MA lengths between minimum and maximum bounds, using a power function for sensitivity control.
Moving Average Engine: Applies the chosen MA type (EMA, SMA, or WMA) to the adaptive length for the core filter line.
Adaptive Bands: Creates upper and lower channels using ATR multiplied by a band factor, scaled to the current adaptive length.
Regime Detection: Classifies market state with thresholds (e.g., >0.55 for trending) and triggers alerts on regime changes.
Visualization System: Includes gradient fills, regime-colored MA lines, and an info panel for at-a-glance insights.
🔥 Key Features
Regime-Adaptive Filtering: Automatically shortens MA in mean-reverting markets for quick responses and lengthens it in trends for smoother signals, helping traders stay aligned with market dynamics.
Custom Alerts: Notifies on regime shifts and band breakouts, enabling timely strategy adjustments like switching to trend-following in bullish regimes.
Visual Enhancements: Color-coded MA lines, gradient band fills, and an optional info panel that displays market state and trading tips, improving chart readability.
Flexible Settings: Adjustable lookback, min/max lengths, sensitivity power, MA type, and themes to suit various assets and timeframes.
Band Breakout Signals: Highlights potential overbought/oversold conditions via ATR-based channels, useful for entry/exit timing.
🎨 Visualization
Main Adaptive MA Line: Plotted with regime-based colors (e.g., green for trending) to visually indicate market state and filter position relative to price.
Adaptive Bands: Upper and lower lines with gradient fills between them, showing volatility channels that widen in random regimes and tighten in trends.
Price vs. MA Fills: Color-coded areas between price and MA (e.g., bullish green above MA in trending modes) for quick trend strength assessment.
Information Panel: Top-right table displaying current regime (e.g., "Trending Market") and strategy suggestions like "Follow trends" or "Trade ranges."
📖 Usage Guidelines
Core Settings
Hurst Lookback Period
Default: 100
Range: 20-500
Description: Sets the period for Hurst Exponent calculation; longer values provide more stable regime detection but may lag, while shorter ones are more responsive to recent changes.
Minimum MA Length
Default: 10
Range: 5-50
Description: Defines the shortest possible adaptive MA length, ideal for fast responses in mean-reverting conditions.
Maximum MA Length
Default: 200
Range: 50-500
Description: Sets the longest adaptive MA length for smoothing in strong trends; adjust based on asset volatility.
Sensitivity Power
Default: 2.0
Range: 1.0-5.0
Description: Controls how aggressively the length adapts to Hurst changes; higher values make it more sensitive to regime shifts.
MA Type
Default: EMA
Options: EMA, SMA, WMA
Description: Chooses the moving average calculation method; EMA is more responsive, while SMA/WMA offer different weighting.
🖼️ Visual Settings
Show Adaptive Bands
Default: True
Description: Toggles visibility of upper/lower bands for volatility channels.
Band Multiplier
Default: 1.5
Range: 0.5-3.0
Description: Scales band width using ATR; higher values create wider channels for conservative signals.
Show Information Panel
Default: True
Description: Displays regime info and strategy tips in a top-right panel.
MA Line Width
Default: 2
Range: 1-5
Description: Adjusts thickness of the main MA line for better visibility.
Color Theme
Default: Blue
Options: Blue, Classic, Dark Purple, Vibrant
Description: Selects color scheme for MA, bands, and fills to match user preferences.
🚨 Alert Settings
Enable Alerts
Default: True
Description: Activates notifications for regime changes and band breakouts.
✅ Best Use Cases
Trend-Following Strategies: In detected trending regimes, use the adaptive MA as a trailing stop or entry filter for momentum trades.
Range Trading: During mean-reverting periods, monitor band breakouts for buying dips or selling rallies within channels.
Risk Management in Random Markets: Reduce exposure when random walk is detected, using tight stops suggested in the info panel.
Multi-Timeframe Analysis: Apply on higher timeframes for regime confirmation, then drill down to lower ones for entries.
Volatility-Based Entries: Use upper/lower band crossovers as signals in adaptive channels for overbought/oversold trades.
⚠️ Limitations
Lagging in Transitions: Regime detection may delay during rapid market shifts, requiring confirmation from other tools.
Not a Standalone System: Best used in conjunction with other indicators; random regimes can lead to whipsaws if traded aggressively.
Parameter Sensitivity: Optimal settings vary by asset and timeframe, necessitating backtesting.
💡 What Makes This Unique
Hurst-Driven Adaptation: Unlike static MAs, it uses fractal analysis to self-tune, providing regime-specific filtering that's rare in standard indicators.
Integrated Strategy Guidance: The info panel offers actionable tips tied to regimes, bridging analysis and execution.
Multi-Regime Visualization: Combines adaptive bands, colored fills, and alerts in one tool for comprehensive market state awareness.
🔬 How It Works
Hurst Exponent Computation:
Calculates log returns over the lookback period to derive the rescaled range (R/S) ratio.
Normalizes to a 0-1 value, where >0.55 indicates trending, <0.45 mean-reverting, and in-between random.
Length Adaptation:
Maps normalized Hurst to an MA length via a power function, clamping between min and max.
Applies the selected MA type to close prices using this dynamic length.
Visualization and Signals:
Plots the MA with regime colors, adds ATR-based bands, and fills areas for trend strength.
Triggers alerts on regime changes or band crosses, with the info panel suggesting strategies like momentum riding in trends.
💡 Note:
For optimal results, backtest settings on your preferred assets and combine with volume or momentum indicators. Remember, no indicator guarantees profits—use with proper risk management. Access premium features and support at PhenLabs.
Uptrick: FRAMA Matrix RSIUptrick: FRAMA Matrix RSI
Introduction
The Uptrick: FRAMA Matrix RSI is a momentum-based indicator that integrates the Relative Strength Index (RSI) with the Fractal Adaptive Moving Average (FRAMA). By applying FRAMA's adaptive smoothing to RSI—and further refining it with a Zero-Lag Moving Average (ZLMA)—this script creates a refined and reliable momentum oscillator. The indicator now includes enhanced divergence detection, potential reversal signals, customizable buy/sell signal options, an internal stats table, and a fully customizable bar coloring system for an enhanced visual trading experience.
Why Combine RSI with FRAMA
Traditional RSI is a well-known momentum indicator but has several limitations. It is highly sensitive to price fluctuations, often generating false signals in choppy or volatile markets. FRAMA, in contrast, adapts dynamically to price changes by adjusting its smoothing factor based on market conditions.
By integrating FRAMA into RSI calculations, this indicator reduces noise while preserving RSI's ability to track momentum, adapts to volatility by reducing lag in trending markets and smoothing out choppiness in ranging conditions, enhances trend-following capability for more reliable momentum shifts, and refines overbought and oversold signals by adjusting to the current market structure.
With the new enhancements, such as a manual alpha input, noise filtering, divergence detection, and multiple buy/sell signal options, the indicator offers even greater flexibility and precision for traders. This combination improves the standard RSI by making it more adaptive and responsive to market changes.
Originality
This indicator is unique because it applies FRAMA's adaptive smoothing technique to RSI, creating a dynamic momentum oscillator that adjusts to different market conditions. Many traditional RSI-based indicators either use fixed smoothing methods like exponential moving averages or employ basic RSI calculations without adjusting for volatility.
This script stands out by integrating several elements, including the fractal dimension-based smoothing of FRAMA to reduce noise while retaining responsiveness, the use of Zero-Lag Moving Average smoothing to enhance trend sensitivity and reduce lag, divergence detection to highlight mismatches between price action and RSI momentum, a noise filter and manual alpha option to prevent minor fluctuations from generating false signals, customizable buy/sell signal options that let traders choose between ZLMA-based or FRAMA RSI-based signals, an internal stats table displaying real-time FRAMA calculations such as fractal dimension and the adaptive alpha factor, and a fully customizable bar coloring system to visually distinguish bullish, bearish, and neutral conditions.
Features
Adaptive FRAMA RSI
The indicator applies FRAMA to RSI values, making the momentum oscillator adaptive to volatility while filtering out noise. Unlike a traditional RSI that reacts equally to all price movements, FRAMA RSI adjusts its smoothing factor based on market structure, making it more effective for identifying true momentum shifts.
Zero-Lag Moving Average (ZLMA)
A smoothing technique that minimizes lag while preserving the responsiveness of price movements. It is applied to the FRAMA RSI to further refine signals and ensure smoother trend detection.
Bullish and Bearish Threshold Crossovers
This system compares FRAMA RSI to a user-defined threshold (default is 50). When FRAMA RSI moves above the threshold, it indicates bullish momentum, while movement below signals bearish conditions. The enhanced noise filter ensures that only significant moves trigger signals.
Noise Filter and Manual Alpha
A new noise filter input prevents tiny fluctuations from triggering false signals. In addition, a manual alpha option allows traders to override the automatically computed smoothing factor with a custom value, providing extra control over the indicator’s sensitivity.
Divergence Detection
The indicator identifies divergence patterns by comparing FRAMA RSI pivots to price action. Bullish divergence occurs when price makes a lower low while FRAMA RSI makes a higher low, and bearish divergence occurs when price makes a higher high while FRAMA RSI makes a lower high. These signals can help traders anticipate potential reversals.
Reversal Signals
Labels appear on the chart when FRAMA RSI confirms classic RSI overbought (70) or oversold (30) conditions, providing visual cues for potential trend reversals.
Buy and Sell Signal Options
Traders can now choose between two signal-generation methods. ZLMA-based signals trigger when the ZLMA of FRAMA RSI crosses key overbought (70) or oversold (30) levels, while FRAMA RSI-based signals trigger when FRAMA RSI itself crosses these levels. This added flexibility allows users to tailor the indicator to their preferred trading style.
ZLMA:
FRAMA:
Customizable Alerts
Alerts notify traders when FRAMA RSI crosses key levels, divergence signals occur, reversal conditions are met, or buy/sell signals trigger. This ensures that important trading events are not missed.
Fully Customizable Bar Coloring System
Users can color bars based on different conditions, enhancing visual clarity. Bar coloring modes include: FRAMA RSI threshold (bars change color based on whether FRAMA RSI is above or below the threshold), ZLMA crossover (bars change when ZLMA crosses overbought or oversold levels), buy/sell signals (bars change when official signals trigger), divergence (bars highlight when bullish or bearish divergence is detected), and reversals (bars indicate when RSI reaches overbought or oversold conditions confirmed by FRAMA RSI). The system also remembers the last applied bar color, ensuring a smooth visual transition.
Input Parameters and Features
Core Inputs
RSI Length (default: 14) defines the period for RSI calculations.
FRAMA Lookback (default: 16) determines the length for the FRAMA smoothing function.
RSI Bull Threshold (default: 50) sets the level above which the market is considered bullish and below which it is bearish.
Noise Filter (default: 1.0) ensures that small fluctuations do not trigger false bullish or bearish signals.
Additional Features
Show Bull and Bear Alerts (default: true) enables notifications when FRAMA RSI crosses the threshold.
Enable Divergence Detection (default: false) highlights bullish and bearish divergences based on price and FRAMA RSI pivots.
Show Potential Reversal Signals (default: false) identifies overbought (70) and oversold (30) levels as possible trend reversal points.
Buy and Sell Signal Option (default: ZLMA) allows traders to choose between ZLMA-based signals or FRAMA RSI-based signals for trade entry.
ZLMA Enhancements
ZLMA Length (default: 14) determines the period for the Zero-Lag Moving Average applied to FRAMA RSI.
Visualization Options
Show Internal Stats Table (default: false) displays real-time FRAMA calculations, including fractal dimension and the adaptive alpha smoothing factor.
Show Threshold FRAMA Signals (default: false) plots buy and sell labels when FRAMA RSI crosses the threshold level.
How It Works
FRAMA Calculation
FRAMA dynamically adjusts smoothing based on the price fractal dimension. The alpha smoothing factor is derived from the fractal dimension or can be set manually to maintain responsiveness.
RSI with FRAMA Smoothing
RSI is calculated using the user-defined lookback period. FRAMA is then applied to the RSI to make it more adaptive to volatility. Optionally, ZLMA is applied to further refine the signals and reduce lag.
Bullish and Bearish Threshold Crosses
A bullish condition occurs when FRAMA RSI crosses above the threshold, while a bearish condition occurs when it falls below. The noise filter ensures that only significant trend shifts generate signals.
Buy and Sell Signal Options
Traders can choose between ZLMA crossovers or FRAMA RSI crossovers as the basis for buy and sell signals, offering flexibility in trade entry timing.
Divergence Detection
The indicator identifies divergences where price action and FRAMA RSI momentum do not align, potentially signaling upcoming reversals.
Reversal Signal Labels
When classic RSI overbought or oversold levels are confirmed by FRAMA RSI conditions, reversal labels are added on the chart to highlight potential exhaustion points.
Bar Coloring System
Bars are dynamically colored based on various conditions such as RSI thresholds, ZLMA crossovers, buy/sell signals, divergence, and reversals, allowing traders to quickly interpret market sentiment.
Alerts and Internal Stats
Customizable alerts notify traders of key events, and an optional internal stats table displays real-time calculations (fractal dimension, alpha value, and RSI values) to help users understand the underlying dynamics of the indicator.
Summary
The Uptrick: FRAMA Matrix RSI offers an enhanced approach to momentum analysis by combining RSI with adaptive FRAMA smoothing and additional layers of signal refinement. The indicator now includes adaptive RSI smoothing to reduce noise and improve responsiveness, Zero-Lag Moving Average filtering to minimize lag, divergence and reversal detection to identify potential turning points, customizable buy/sell signal options that let traders choose between different signal methodologies, a fully customizable bar coloring system to visually distinguish market conditions, and an internal stats table for real-time insight into FRAMA calculation parameters.
Whether used for trend confirmation, divergence detection, or momentum-based strategies, this indicator provides a powerful and adaptive approach to trading.
Disclaimer
This script is for informational and educational purposes only. Trading involves risk, and past performance does not guarantee future results. Always conduct proper research and consult with a financial advisor before making trading decisions.
FRAMA Channel [BigBeluga]This is a trend-following indicator that utilizes the Fractal Adaptive Moving Average (FRAMA) to create a dynamic channel around the price. The FRAMA Channel helps identify uptrends, downtrends, and ranging markets by examining the relationship between the price and the channel's boundaries. It also marks trend changes with arrows, optionally displaying either price values or average volume at these key points.
🔵 IDEA
The core idea behind the FRAMA Channel indicator is to use the fractal nature of markets to adapt to different market conditions. By creating a channel around the FRAMA line, it not only tracks price trends but also adapts its sensitivity based on market volatility. When the price crosses the upper or lower bands of the channel, it signals a potential shift in trend direction. If the price remains within the channel and crosses over the upper or lower bands without a breakout, the market is likely in a ranging phase with low momentum. This adaptive approach makes the FRAMA Channel effective in both trending and ranging market environments.
🔵 KEY FEATURES & USAGE
◉ Dynamic FRAMA Channel with Trend Signals:
The FRAMA Channel uses a fractal-based moving average to create an adaptive channel around the price. When the price crosses above the upper band, it signals an uptrend and plots an upward arrow with the price (or average volume) value. Conversely, when the price crosses below the lower band, it signals a downtrend and marks the point with a downward arrow. This dynamic adaptation to market conditions helps traders identify key trend shifts effectively.
◉ Ranging Market Detection:
If the price remains within the channel, and only the high crosses the upper band or the low crosses the lower band, the indicator identifies a ranging market with low momentum. In this case, the channel turns gray, signaling a neutral trend. This is particularly useful for avoiding false signals during periods of market consolidation.
◉ Color-Coded Candles and Channel Bands:
Candles and channel bands are color-coded to reflect the current trend direction. Green indicates an upward trend, blue shows a downward trend, and gray signals a neutral or ranging market. This visual representation makes it easy to identify the market condition at a glance, helping traders make informed decisions quickly.
◉ Customizable Display of Price or Average Volume:
On trend change signals, the indicator allows users to choose whether to display the price at the point of trend change or the average volume of 10 bars. This flexibility enables traders to focus on the information that is most relevant to their strategy, whether it's the exact price entery or the volume context of the market shift. Displaying the average volume allows to see the strength of the trend change.
Price Data:
Average Volume of points:
🔵 CUSTOMIZATION
Length & Bands Distance: Adjust the length for the FRAMA calculation to control the sensitivity of the channel. A shorter length makes the channel more reactive to price changes, while a longer length smooths it out. The Bands Distance setting determines how far the bands are from the FRAMA line, helping to define the breakout and ranging conditions.
Signals Data: Choose between displaying the price or the average volume on trend change arrows. This allows traders to focus on either the exact price level of trend change or the market volume context.
Color Settings: Customize the colors for upward momentum, downward momentum, and neutral states to suit your charting preferences. You can also toggle whether to color the candles based on the momentum for a clearer visual of the trend direction.
The FRAMA Channel indicator adapts to market conditions, providing a versatile tool for identifying trends and ranging markets with clear visual cues.
Machine Learning: VWAP [YinYangAlgorithms]Machine Learning: VWAP aims to use Machine Learning to Identify the best location to Anchor the VWAP at. Rather than using a traditional fixed length or simply adjusting based on a Date / Time; by applying Machine Learning we may hope to identify crucial areas which make sense to reset the VWAP and start anew. VWAP’s may act similar to a Bollinger Band in the sense that they help to identify both Overbought and Oversold Price locations based on previous movements and help to identify how far the price may move within the current Trend. However, unlike Bollinger Bands, VWAPs have the ability to parabolically get quite spaced out and also reset. For this reason, the price may never actually go from the Lower to the Upper and vice versa (when very spaced out; when the Upper and Lower zones are narrow, it may bounce between the two). The reason for this is due to how the anchor location is calculated and in this specific Indicator, how it changes anchors based on price movement calculated within Machine Learning.
This Indicator changes the anchor if the Low < Lowest Low of a length of X and likewise if the High > Highest High of a length of X. This logic is applied within a Machine Learning standpoint that likewise amplifies this Lookback Length by adding a Machine Learning Length to it and increasing the lookback length even further.
Due to how the anchor for this VWAP changes, you may notice that the Basis Line (Orange) may act as a Trend Identifier. When the Price is above the basis line, it may represent a bullish trend; and likewise it may represent a bearish trend when below it. You may also notice what may happen is when the trend occurs, it may push all the way to the Upper or Lower levels of this VWAP. It may then proceed to move horizontally until the VWAP expands more and it may gain more movement; or it may correct back to the Basis Line. If it corrects back to the basis line, what may happen is it either uses the Basis Line as a Support and continues in its current direction, or it will change the VWAP anchor and start anew.
Tutorial:
If we zoom in on the most recent VWAP we can see how it expands. Expansion may be caused by time but generally it may be caused by price movement and volume. Exponential Price movement causes the VWAP to expand, even if there are corrections to it. However, please note Volume adds a large weighted factor to the calculation; hence Volume Weighted Average Price (VWAP).
If you refer to the white circle in the example above; you’ll be able to see that the VWAP expanded even while the price was correcting to the Basis line. This happens due to exponential movement which holds high volume. If you look at the volume below the white circle, you’ll notice it was very large; however even though there was exponential price movement after the white circle, since the volume was low, the VWAP didn’t expand much more than it already had.
There may be times where both Volume and Price movement isn’t significant enough to cause much of an expansion. During this time it may be considered to be in a state of consolidation. While looking at this example, you may also notice the color switch from red to green to red. The color of the VWAP is related to the movement of the Basis line (Orange middle line). When the current basis is > the basis of the previous bar the color of the VWAP is green, and when the current basis is < the basis of the previous bar, the color of the VWAP is red. The color may help you gauge the current directional movement the price is facing within the VWAP.
You may have noticed there are signals within this Indicator. These signals are composed of Green and Red Triangles which represent potential Bullish and Bearish momentum changes. The Momentum changes happen when the Signal Type:
The High/Low or Close (You pick in settings)
Crosses one of the locations within the VWAP.
Bullish Momentum change signals occur when :
Signal Type crosses OVER the Basis
Signal Type crosses OVER the lower level
Bearish Momentum change signals occur when:
Signal Type crosses UNDER the Basis
Signal Type Crosses UNDER the upper level
These signals may represent locations where momentum may occur in the direction of these signals. For these reasons there are also alerts available to be set up for them.
If you refer to the two circles within the example above, you may see that when the close goes above the basis line, how it mat represents bullish momentum. Likewise if it corrects back to the basis and the basis acts as a support, it may continue its bullish momentum back to the upper levels again. However, if you refer to the red circle, you’ll see if the basis fails to act as a support, it may then start to correct all the way to the lower levels, or depending on how expanded the VWAP is, it may just reset its anchor due to such drastic movement.
You also have the ability to disable Machine Learning by setting ‘Machine Learning Type’ to ‘None’. If this is done, it will go off whether you have it set to:
Bullish
Bearish
Neutral
For the type of VWAP you want to see. In this example above we have it set to ‘Bullish’. Non Machine Learning VWAP are still calculated using the same logic of if low < lowest low over length of X and if high > highest high over length of X.
Non Machine Learning VWAP’s change much quicker but may also allow the price to correct from one side to the other without changing VWAP Anchor. They may be useful for breaking up a trend into smaller pieces after momentum may have changed.
Above is an example of how the Non Machine Learning VWAP looks like when in Bearish. As you can see based on if it is Bullish or Bearish is how it favors the trend to be and may likewise dictate when it changes the Anchor.
When set to neutral however, the Anchor may change quite quickly. This results in a still useful VWAP to help dictate possible zones that the price may move within, but they’re also much tighter zones that may not expand the same way.
We will conclude this Tutorial here, hopefully this gives you some insight as to why and how Machine Learning VWAPs may be useful; as well as how to use them.
Settings:
VWAP:
VWAP Type: Type of VWAP. You can favor specific direction changes or let it be Neutral where there is even weight to both. Please note, these do not apply to the Machine Learning VWAP.
Source: VWAP Source. By default VWAP usually uses HLC3; however OHLC4 may help by providing more data.
Lookback Length: The Length of this VWAP when it comes to seeing if the current High > Highest of this length; or if the current Low is < Lowest of this length.
Standard VWAP Multiplier: This multiplier is applied only to the Standard VWMA. This is when 'Machine Learning Type' is set to 'None'.
Machine Learning:
Use Rational Quadratics: Rationalizing our source may be beneficial for usage within ML calculations.
Signal Type: Bullish and Bearish Signals are when the price crosses over/under the basis, as well as the Upper and Lower levels. These may act as indicators to where price movement may occur.
Machine Learning Type: Are we using a Simple ML Average, KNN Mean Average, KNN Exponential Average or None?
KNN Distance Type: We need to check if distance is within the KNN Min/Max distance, which distance checks are we using.
Machine Learning Length: How far back is our Machine Learning going to keep data for.
k-Nearest Neighbour (KNN) Length: How many k-Nearest Neighbours will we account for?
Fast ML Data Length: What is our Fast ML Length? This is used with our Slow Length to create our KNN Distance.
Slow ML Data Length: What is our Slow ML Length? This is used with our Fast Length to create our KNN Distance.
If you have any questions, comments, ideas or concerns please don't hesitate to contact us.
HAPPY TRADING!
Machine Learning: Optimal RSI [YinYangAlgorithms]This Indicator, will rate multiple different lengths of RSIs to determine which RSI to RSI MA cross produced the highest profit within the lookback span. This ‘Optimal RSI’ is then passed back, and if toggled will then be thrown into a Machine Learning calculation. You have the option to Filter RSI and RSI MA’s within the Machine Learning calculation. What this does is, only other Optimal RSI’s which are in the same bullish or bearish direction (is the RSI above or below the RSI MA) will be added to the calculation.
You can either (by default) use a Simple Average; which is essentially just a Mean of all the Optimal RSI’s with a length of Machine Learning. Or, you can opt to use a k-Nearest Neighbour (KNN) calculation which takes a Fast and Slow Speed. We essentially turn the Optimal RSI into a MA with different lengths and then compare the distance between the two within our KNN Function.
RSI may very well be one of the most used Indicators for identifying crucial Overbought and Oversold locations. Not only that but when it crosses its Moving Average (MA) line it may also indicate good locations to Buy and Sell. Many traders simply use the RSI with the standard length (14), however, does that mean this is the best length?
By using the length of the top performing RSI and then applying some Machine Learning logic to it, we hope to create what may be a more accurate, smooth, optimal, RSI.
Tutorial:
This is a pretty zoomed out Perspective of what the Indicator looks like with its default settings (except with Bollinger Bands and Signals disabled). If you look at the Tables above, you’ll notice, currently the Top Performing RSI Length is 13 with an Optimal Profit % of: 1.00054973. On its default settings, what it does is Scan X amount of RSI Lengths and checks for when the RSI and RSI MA cross each other. It then records the profitability of each cross to identify which length produced the overall highest crossing profitability. Whichever length produces the highest profit is then the RSI length that is used in the plots, until another length takes its place. This may result in what we deem to be the ‘Optimal RSI’ as it is an adaptive RSI which changes based on performance.
In our next example, we changed the ‘Optimal RSI Type’ from ‘All Crossings’ to ‘Extremity Crossings’. If you compare the last two examples to each other, you’ll notice some similarities, but overall they’re quite different. The reason why is, the Optimal RSI is calculated differently. When using ‘All Crossings’ everytime the RSI and RSI MA cross, we evaluate it for profit (short and long). However, with ‘Extremity Crossings’, we only evaluate it when the RSI crosses over the RSI MA and RSI <= 40 or RSI crosses under the RSI MA and RSI >= 60. We conclude the crossing when it crosses back on its opposite of the extremity, and that is how it finds its Optimal RSI.
The way we determine the Optimal RSI is crucial to calculating which length is currently optimal.
In this next example we have zoomed in a bit, and have the full default settings on. Now we have signals (which you can set alerts for), for when the RSI and RSI MA cross (green is bullish and red is bearish). We also have our Optimal RSI Bollinger Bands enabled here too. These bands allow you to see where there may be Support and Resistance within the RSI at levels that aren’t static; such as 30 and 70. The length the RSI Bollinger Bands use is the Optimal RSI Length, allowing it to likewise change in correlation to the Optimal RSI.
In the example above, we’ve zoomed out as far as the Optimal RSI Bollinger Bands go. You’ll notice, the Bollinger Bands may act as Support and Resistance locations within and outside of the RSI Mid zone (30-70). In the next example we will highlight these areas so they may be easier to see.
Circled above, you may see how many times the Optimal RSI faced Support and Resistance locations on the Bollinger Bands. These Bollinger Bands may give a second location for Support and Resistance. The key Support and Resistance may still be the 30/50/70, however the Bollinger Bands allows us to have a more adaptive, moving form of Support and Resistance. This helps to show where it may ‘bounce’ if it surpasses any of the static levels (30/50/70).
Due to the fact that this Indicator may take a long time to execute and it can throw errors for such, we have added a Setting called: Adjust Optimal RSI Lookback and RSI Count. This settings will automatically modify the Optimal RSI Lookback Length and the RSI Count based on the Time Frame you are on and the Bar Indexes that are within. For instance, if we switch to the 1 Hour Time Frame, it will adjust the length from 200->90 and RSI Count from 30->20. If this wasn’t adjusted, the Indicator would Timeout.
You may however, change the Setting ‘Adjust Optimal RSI Lookback and RSI Count’ to ‘Manual’ from ‘Auto’. This will give you control over the ‘Optimal RSI Lookback Length’ and ‘RSI Count’ within the Settings. Please note, it will likely take some “fine tuning” to find working settings without the Indicator timing out, but there are definitely times you can find better settings than our ‘Auto’ will create; especially on higher Time Frames. The Minimum our ‘Auto’ will create is:
Optimal RSI Lookback Length: 90
RSI Count: 20
The Maximum it will create is:
Optimal RSI Lookback Length: 200
RSI Count: 30
If there isn’t much bar index history, for instance, if you’re on the 1 Day and the pair is BTC/USDT you’ll get < 4000 Bar Indexes worth of data. For this reason it is possible to manually increase the settings to say:
Optimal RSI Lookback Length: 500
RSI Count: 50
But, please note, if you make it too high, it may also lead to inaccuracies.
We will conclude our Tutorial here, hopefully this has given you some insight as to how calculating our Optimal RSI and then using it within Machine Learning may create a more adaptive RSI.
Settings:
Optimal RSI:
Show Crossing Signals: Display signals where the RSI and RSI Cross.
Show Tables: Display Information Tables to show information like, Optimal RSI Length, Best Profit, New Optimal RSI Lookback Length and New RSI Count.
Show Bollinger Bands: Show RSI Bollinger Bands. These bands work like the TDI Indicator, except its length changes as it uses the current RSI Optimal Length.
Optimal RSI Type: This is how we calculate our Optimal RSI. Do we use all RSI and RSI MA Crossings or just when it crosses within the Extremities.
Adjust Optimal RSI Lookback and RSI Count: Auto means the script will automatically adjust the Optimal RSI Lookback Length and RSI Count based on the current Time Frame and Bar Index's on chart. This will attempt to stop the script from 'Taking too long to Execute'. Manual means you have full control of the Optimal RSI Lookback Length and RSI Count.
Optimal RSI Lookback Length: How far back are we looking to see which RSI length is optimal? Please note the more bars the lower this needs to be. For instance with BTC/USDT you can use 500 here on 1D but only 200 for 15 Minutes; otherwise it will timeout.
RSI Count: How many lengths are we checking? For instance, if our 'RSI Minimum Length' is 4 and this is 30, the valid RSI lengths we check is 4-34.
RSI Minimum Length: What is the RSI length we start our scans at? We are capped with RSI Count otherwise it will cause the Indicator to timeout, so we don't want to waste any processing power on irrelevant lengths.
RSI MA Length: What length are we using to calculate the optimal RSI cross' and likewise plot our RSI MA with?
Extremity Crossings RSI Backup Length: When there is no Optimal RSI (if using Extremity Crossings), which RSI should we use instead?
Machine Learning:
Use Rational Quadratics: Rationalizing our Close may be beneficial for usage within ML calculations.
Filter RSI and RSI MA: Should we filter the RSI's before usage in ML calculations? Essentially should we only use RSI data that are of the same type as our Optimal RSI? For instance if our Optimal RSI is Bullish (RSI > RSI MA), should we only use ML RSI's that are likewise bullish?
Machine Learning Type: Are we using a Simple ML Average, KNN Mean Average, KNN Exponential Average or None?
KNN Distance Type: We need to check if distance is within the KNN Min/Max distance, which distance checks are we using.
Machine Learning Length: How far back is our Machine Learning going to keep data for.
k-Nearest Neighbour (KNN) Length: How many k-Nearest Neighbours will we account for?
Fast ML Data Length: What is our Fast ML Length? This is used with our Slow Length to create our KNN Distance.
Slow ML Data Length: What is our Slow ML Length? This is used with our Fast Length to create our KNN Distance.
If you have any questions, comments, ideas or concerns please don't hesitate to contact us.
HAPPY TRADING!
Machine Learning: SuperTrend Strategy TP/SL [YinYangAlgorithms]The SuperTrend is a very useful Indicator to display when trends have shifted based on the Average True Range (ATR). Its underlying ideology is to calculate the ATR using a fixed length and then multiply it by a factor to calculate the SuperTrend +/-. When the close crosses the SuperTrend it changes direction.
This Strategy features the Traditional SuperTrend Calculations with Machine Learning (ML) and Take Profit / Stop Loss applied to it. Using ML on the SuperTrend allows for the ability to sort data from previous SuperTrend calculations. We can filter the data so only previous SuperTrends that follow the same direction and are within the distance bounds of our k-Nearest Neighbour (KNN) will be added and then averaged. This average can either be achieved using a Mean or with an Exponential calculation which puts added weight on the initial source. Take Profits and Stop Losses are then added to the ML SuperTrend so it may capitalize on Momentum changes meanwhile remaining in the Trend during consolidation.
By applying Machine Learning logic and adding a Take Profit and Stop Loss to the Traditional SuperTrend, we may enhance its underlying calculations with potential to withhold the trend better. The main purpose of this Strategy is to minimize losses and false trend changes while maximizing gains. This may be achieved by quick reversals of trends where strategic small losses are taken before a large trend occurs with hopes of potentially occurring large gain. Due to this logic, the Win/Loss ratio of this Strategy may be quite poor as it may take many small marginal losses where there is consolidation. However, it may also take large gains and capitalize on strong momentum movements.
Tutorial:
In this example above, we can get an idea of what the default settings may achieve when there is momentum. It focuses on attempting to hit the Trailing Take Profit which moves in accord with the SuperTrend just with a multiplier added. When momentum occurs it helps push the SuperTrend within it, which on its own may act as a smaller Trailing Take Profit of its own accord.
We’ve highlighted some key points from the last example to better emphasize how it works. As you can see, the White Circle is where profit was taken from the ML SuperTrend simply from it attempting to switch to a Bullish (Buy) Trend. However, that was rejected almost immediately and we went back to our Bearish (Sell) Trend that ended up resulting in our Take Profit being hit (Yellow Circle). This Strategy aims to not only capitalize on the small profits from SuperTrend to SuperTrend but to also capitalize when the Momentum is so strong that the price moves X% away from the SuperTrend and is able to hit the Take Profit location. This Take Profit addition to this Strategy is crucial as momentum may change state shortly after such drastic price movements; and if we were to simply wait for it to come back to the SuperTrend, we may lose out on lots of potential profit.
If you refer to the Yellow Circle in this example, you’ll notice what was talked about in the Summary/Overview above. During periods of consolidation when there is little momentum and price movement and we don’t have any Stop Loss activated, you may see ‘Signal Flashing’. Signal Flashing is when there are Buy and Sell signals that keep switching back and forth. During this time you may be taking small losses. This is a normal part of this Strategy. When a signal has finally been confirmed by Momentum, is when this Strategy shines and may produce the profit you desire.
You may be wondering, what causes these jagged like patterns in the SuperTrend? It's due to the ML logic, and it may be a little confusing, but essentially what is happening is the Fast Moving SuperTrend and the Slow Moving SuperTrend are creating KNN Min and Max distances that are extreme due to (usually) parabolic movement. This causes fewer values to be added to and averaged within the ML and causes less smooth and more exponential drastic movements. This is completely normal, and one of the perks of using k-Nearest Neighbor for ML calculations. If you don’t know, the Min and Max Distance allowed is derived from the most recent(0 index of data array) to KNN Length. So only SuperTrend values that exhibit distances within these Min/Max will be allowed into the average.
Since the KNN ML logic can cause these exponential movements in the SuperTrend, they likewise affect its Take Profit. The Take Profit may benefit from this movement like displayed in the example above which helped it claim profit before then exhibiting upwards movement.
By default our Stop Loss Multiplier is kept quite low at 0.0000025. Keeping it low may help to reduce some Signal Flashing while not taking extra losses more so than not using it at all. However, if we increase it even more to say 0.005 like is shown in the example above. It can really help the trend keep momentum. Please note, although previous results don’t imply future results, at 0.0000025 Stop Loss we are currently exhibiting 69.27% profit while at 0.005 Stop Loss we are exhibiting 33.54% profit. This just goes to show that although there may be less Signal Flashing, it may not result in more profit.
We will conclude our Tutorial here. Hopefully this has given you some insight as to how Machine Learning, combined with Trailing Take Profit and Stop Loss may have positive effects on the SuperTrend when turned into a Strategy.
Settings:
SuperTrend:
ATR Length: ATR Length used to create the Original Supertrend.
Factor: Multiplier used to create the Original Supertrend.
Stop Loss Multiplier: 0 = Don't use Stop Loss. Stop loss can be useful for helping to prevent false signals but also may result in more loss when hit and less profit when switching trends.
Take Profit Multiplier: Take Profits can be useful within the Supertrend Strategy to stop the price reverting all the way to the Stop Loss once it's been profitable.
Machine Learning:
Only Factor Same Trend Direction: Very useful for ensuring that data used in KNN is not manipulated by different SuperTrend Directional data. Please note, it doesn't affect KNN Exponential.
Rationalized Source Type: Should we Rationalize only a specific source, All or None?
Machine Learning Type: Are we using a Simple ML Average, KNN Mean Average, KNN Exponential Average or None?
Machine Learning Smoothing Type: How should we smooth our Fast and Slow ML Datas to be used in our KNN Distance calculation? SMA, EMA or VWMA?
KNN Distance Type: We need to check if distance is within the KNN Min/Max distance, which distance checks are we using.
Machine Learning Length: How far back is our Machine Learning going to keep data for.
k-Nearest Neighbour (KNN) Length: How many k-Nearest Neighbours will we account for?
Fast ML Data Length: What is our Fast ML Length?? This is used with our Slow Length to create our KNN Distance.
Slow ML Data Length: What is our Slow ML Length?? This is used with our Fast Length to create our KNN Distance.
If you have any questions, comments, ideas or concerns please don't hesitate to contact us.
HAPPY TRADING!
Machine Learning: Trend Lines [YinYangAlgorithms]Trend lines have always been a key indicator that may help predict many different types of price movements. They have been well known to create different types of formations such as: Pennants, Channels, Flags and Wedges. The type of formation they create is based on how the formation was created and the angle it was created. For instance, if there was a strong price increase and then there is a Wedge where both end points meet, this is considered a Bull Pennant. The formations Trend Lines create may be powerful tools that can help predict current Support and Resistance and also Future Momentum changes. However, not all Trend Lines will create formations, and alone they may stand as strong Support and Resistance locations on the Vertical.
The purpose of this Indicator is to apply Machine Learning logic to a Traditional Trend Line Calculation, and therefore allowing a new approach to a modern indicator of high usage. The results of such are quite interesting and goes to show the impacts a simple KNN Machine Learning model can have on Traditional Indicators.
Tutorial:
There are a few different settings within this Indicator. Many will greatly impact the results and if any are changed, lots will need ‘Fine Tuning’. So let's discuss the main toggles that have great effects and what they do before discussing the lengths. Currently in this example above we have the Indicator at its Default Settings. In this example, you can see how the Trend Lines act as key Support and Resistance locations. Due note, Support and Resistance are a relative term, as is their color. What starts off as Support or Resistance may change when the price crosses over / under them.
In the example above we have zoomed in and circled locations that exhibited markers of Support and Resistance along the Trend Lines. These Trend Lines are all created using the Default Settings. As you can see from the example above; just because it is a Green Upwards Trend Line, doesn’t mean it’s a Support Line. Support and Resistance is always shifting on Trend Lines based on the prices location relative to them.
We won’t go through all the Formations Trend Lines make, but the example above, we can see the Trend Lines formed a Downward Channel. Channels are when there are two parallel downwards Trend Lines that are at a relatively similar angle. This means that they won’t ever meet. What may happen when the price is within these channels, is it may bounce between the upper and lower bounds. These Channels may drive the price upwards or downwards, depending on if it is in an Upwards or Downwards Channel.
If you refer to the example above, you’ll notice that the Trend Lines are formed like traditional Trend Lines. They don’t stem from current Highs and Lows but rather Machine Learning Highs and Lows. More often than not, the Machine Learning approach to Trend Lines cause their start point and angle to be quite different than a Traditional Trend Line. Due to this, it may help predict Support and Resistance locations at are more uncommon and therefore can be quite useful.
In the example above we have turned off the toggle in Settings ‘Use Exponential Data Average’. This Settings uses a custom Exponential Data Average of the KNN rather than simply averaging the KNN. By Default it is enabled, but as you can see when it is disabled it may create some pretty strong lasting Trend Lines. This is why we advise you ZOOM OUT AS FAR AS YOU CAN. Trend Lines are only displayed when you’ve zoomed out far enough that their Start Point is visible.
As you can see in this example above, there were 3 major Upward Trend Lines created in 2020 that have had a major impact on Support and Resistance Locations within the last year. Lets zoom in and get a closer look.
We have zoomed in for this example above, and circled some of the major Support and Resistance locations that these Upward Trend Lines may have had a major impact on.
Please note, these Machine Learning Trend Lines aren’t a ‘One Size Fits All’ kind of thing. They are completely customizable within the Settings, so that you can get a tailored experience based on what Pair and Time Frame you are trading on.
When any values are changed within the Settings, you’ll likely need to ‘Fine Tune’ the rest of the settings until your desired result is met. By default the modifiable lengths within the Settings are:
Machine Learning Length: 50
KNN Length:5
Fast ML Data Length: 5
Slow ML Data Length: 30
For example, let's toggle ‘Use Exponential Data Averages’ back on and change ‘Fast ML Data Length’ from 5 to 20 and ‘Slow ML Data Length’ from 30 to 50.
As you can in the example above, all of the lines have changed. Although there are still some strong Support Locations created by the Upwards Trend Lines.
We will conclude our Tutorial here. Hopefully you’ve learned how to use Machine Learning Trend Lines and will be able to now see some more unorthodox Support and Resistance locations on the Vertical.
Settings:
Use Machine Learning Sources: If disabled Traditional Trend line sources (High and Low) will be used rather than Rational Quadratics.
Use KNN Distance Sorting: You can disable this if you wish to not have the Machine Learning Data sorted using KNN. If disabled trend line logic will be Traditional.
Use Exponential Data Average: This Settings uses a custom Exponential Data Average of the KNN rather than simply averaging the KNN.
Machine Learning Length: How strong is our Machine Learning Memory? Please note, when this value is too high the data is almost 'too' much and can lead to poor results.
K-Nearest Neighbour (KNN) Length: How many K-Nearest Neighbours are allowed with our Distance Clustering? Please note, too high or too low may lead to poor results.
Fast ML Data Length: Fast and Slow speed needs to be adjusted properly to see results. 3/5/7 all seem to work well for Fast.
Slow ML Data Length: Fast and Slow speed needs to be adjusted properly to see results. 20 - 50 all seem to work well for Slow.
If you have any questions, comments, ideas or concerns please don't hesitate to contact us.
HAPPY TRADING!
Machine Learning : Cosine Similarity & Euclidean DistanceIntroduction:
This script implements a comprehensive trading strategy that adheres to the established rules and guidelines of housing trading. It leverages advanced machine learning techniques and incorporates customised moving averages, including the Conceptive Price Moving Average (CPMA), to provide accurate signals for informed trading decisions in the housing market. Additionally, signal processing techniques such as Lorentzian, Euclidean distance, Cosine similarity, Know sure thing, Rational Quadratic, and sigmoid transformation are utilised to enhance the signal quality and improve trading accuracy.
Features:
Market Analysis: The script utilizes advanced machine learning methods such as Lorentzian, Euclidean distance, and Cosine similarity to analyse market conditions. These techniques measure the similarity and distance between data points, enabling more precise signal identification and enhancing trading decisions.
Cosine similarity:
Cosine similarity is a measure used to determine the similarity between two vectors, typically in a high-dimensional space. It calculates the cosine of the angle between the vectors, indicating the degree of similarity or dissimilarity.
In the context of trading or signal processing, cosine similarity can be employed to compare the similarity between different data points or signals. The vectors in this case represent the numerical representations of the data points or signals.
Cosine similarity ranges from -1 to 1, with 1 indicating perfect similarity, 0 indicating no similarity, and -1 indicating perfect dissimilarity. A higher cosine similarity value suggests a closer match between the vectors, implying that the signals or data points share similar characteristics.
Lorentzian Classification:
Lorentzian classification is a machine learning algorithm used for classification tasks. It is based on the Lorentzian distance metric, which measures the similarity or dissimilarity between two data points. The Lorentzian distance takes into account the shape of the data distribution and can handle outliers better than other distance metrics.
Euclidean Distance:
Euclidean distance is a distance metric widely used in mathematics and machine learning. It calculates the straight-line distance between two points in Euclidean space. In two-dimensional space, the Euclidean distance between two points (x1, y1) and (x2, y2) is calculated using the formula sqrt((x2 - x1)^2 + (y2 - y1)^2).
Dynamic Time Windows: The script incorporates a dynamic time window function that allows users to define specific time ranges for trading. It checks if the current time falls within the specified window to execute the relevant trading signals.
Custom Moving Averages: The script includes the CPMA, a powerful moving average calculation. Unlike traditional moving averages, the CPMA provides improved support and resistance levels by considering multiple price types and employing a combination of Exponential Moving Averages (EMAs) and Simple Moving Averages (SMAs). Its adaptive nature ensures responsiveness to changes in price trends.
Signal Processing Techniques: The script applies signal processing techniques such as Know sure thing, Rational Quadratic, and sigmoid transformation to enhance the quality of the generated signals. These techniques improve the accuracy and reliability of the trading signals, aiding in making well-informed trading decisions.
Trade Statistics and Metrics: The script provides comprehensive trade statistics and metrics, including total wins, losses, win rate, win-loss ratio, and early signal flips. These metrics offer valuable insights into the performance and effectiveness of the trading strategy.
Usage:
Configuring Time Windows: Users can customize the time windows by specifying the start and finish time ranges according to their trading preferences and local market conditions.
Signal Interpretation: The script generates long and short signals based on the analysis, custom moving averages, and signal processing techniques. Users should pay attention to these signals and take appropriate action, such as entering or exiting trades, depending on their trading strategies.
Trade Statistics: The script continuously tracks and updates trade statistics, providing users with a clear overview of their trading performance. These statistics help users assess the effectiveness of the strategy and make informed decisions.
Conclusion:
With its adherence to housing trading rules, advanced machine learning methods, customized moving averages like the CPMA, and signal processing techniques such as Lorentzian, Euclidean distance, Cosine similarity, Know sure thing, Rational Quadratic, and sigmoid transformation, this script offers users a powerful tool for housing market analysis and trading. By leveraging the provided signals, time windows, and trade statistics, users can enhance their trading strategies and improve their overall trading performance.
Disclaimer:
Please note that while this script incorporates established tradingview housing rules, advanced machine learning techniques, customized moving averages, and signal processing techniques, it should be used for informational purposes only. Users are advised to conduct their own analysis and exercise caution when making trading decisions. The script's performance may vary based on market conditions, user settings, and the accuracy of the machine learning methods and signal processing techniques. The trading platform and developers are not responsible for any financial losses incurred while using this script.
By publishing this script on the platform, traders can benefit from its professional presentation, clear instructions, and the utilisation of advanced machine learning techniques, customised moving averages, and signal processing techniques for enhanced trading signals and accuracy.
I extend my gratitude to TradingView, LUX ALGO, and JDEHORTY for their invaluable contributions to the trading community. Their innovative scripts, meticulous coding patterns, and insightful ideas have profoundly enriched traders' strategies, including my own.
FRAMA & CPMA Strategy [CSM]The script is an advanced technical analysis tool specifically designed for trading in financial markets, with a particular focus on the BankNifty market. It utilizes two powerful indicators: the Fractal Adaptive Moving Average (FRAMA) and the CPMA (Conceptive Price Moving Average), which is similar to the well-known Chande Momentum Oscillator (CMO) with Center of Gravity (COG) bands.
The FRAMA is a dynamic moving average that adapts to changing market conditions, providing traders with a more precise representation of price movements. The CMO is an oscillator that measures momentum in the market, helping traders identify potential entry and exit points. The COG bands are a technical indicator used to identify potential support and resistance levels in the market.
Custom functions are included in the script to calculate the FRAMA and CSM_CPMA indicators, with the FRAMA function calculating the value of the FRAMA indicator based on user-specified parameters of length and multiplier, while the CSM_CPMA function calculates the value of the CMO with COG bands indicator based on the user-specified parameters of length and various price types.
The script also includes trailing profit and stop loss functions, which while not meeting expectations, have been backtested with a success rate of over 90%, making the script a valuable tool for traders.
Overall, the script provides traders with a comprehensive technical analysis tool for analyzing cryptocurrency markets and making informed trading decisions. Traders can improve their success rate and overall profitability by using smaller targets with trailing profit and minimizing losses. Feedback is always welcome, and the script can be improved for future use. Special thanks go to Tradingview for providing inbuilt functions that are utilized in the script.
FRAMA and Candlestick Patterns [CSM]FRAMA (Fractal Adaptive Moving Average) is a technical analysis indicator that adapts its smoothing period according to the market's volatility, allowing it to provide accurate signals in all market conditions. This indicator script plots the FRAMA on a chart and generates buy and sell signals based on the FRAMA and candlestick patterns. It also includes an option to filter signals based on bullish and bearish engulfing patterns.
To detect candlestick patterns, the script imports the "BankNifty_CSM" library from the creator's public library on TradingView. The FRAMA calculation is done using a loop that iterates over the last "length" number of bars, with the smoothing factor adjusted based on the "fracDim" parameter.
The buy and sell signals are generated based on the position of the current price relative to the FRAMA line. If the "engulfing" parameter is set to true, the signals are further filtered based on bullish and bearish engulfing patterns.
Overall, this script combines various technical indicators and candlestick pattern recognition to provide a complete trading strategy. However, as with any trading strategy, it should be thoroughly backtested and evaluated before using it in a live trading environment.
AI-Bank-Nifty Tech AnalysisThis code is a TradingView indicator that analyzes the Bank Nifty index of the Indian stock market. It uses various inputs to customize the indicator's appearance and analysis, such as enabling analysis based on the chart's timeframe, detecting bullish and bearish engulfing candles, and setting the table position and style.
The code imports an external script called BankNifty_CSM, which likely contains functions that calculate technical indicators such as the RSI, MACD, VWAP, and more. The code then defines several table cell colors and other styling parameters.
Next, the code defines a table to display the technical analysis of eight bank stocks in the Bank Nifty index. It then defines a function called get_BankComponent_Details that takes a stock symbol as input, requests the stock's OHLCV data, and calculates several technical indicators using the imported CSM_BankNifty functions.
The code also defines two functions called get_EngulfingBullish_Detection and get_EngulfingBearish_Detection to detect bullish and bearish engulfing candles.
Finally, the code calculates the technical analysis for each bank stock using the get_BankComponent_Details function and displays the results in the table. If the engulfing input is enabled, the code also checks for bullish and bearish engulfing candles and displays buy/sell signals accordingly.
The FRAMA stands for "Fractal Adaptive Moving Average," which is a type of moving average that adjusts its smoothing factor based on the fractal dimension of the price data. The fractal dimension reflects self-similarity at different scales. The FRAMA uses this property to adapt to the scale of price movements, capturing short-term and long-term trends while minimizing lag. The FRAMA was developed by John F. Ehlers and is commonly used by traders and analysts in technical analysis to identify trends and generate buy and sell signals. I tried to create this indicator in Pine.
In this context, "RS" stands for "Relative Strength," which is a technical indicator that compares the performance of a particular stock or market sector against a benchmark index.
The "Alligator" is a technical analysis tool that consists of three smoothed moving averages. Introduced by Bill Williams in his book "Trading Chaos," the three lines are called the Jaw, Teeth, and Lips of the Alligator. The Alligator indicator helps traders identify the trend direction and its strength, as well as potential entry and exit points. When the three lines are intertwined or close to each other, it indicates a range-bound market, while a divergence between them indicates a trending market. The position of the price in relation to the Alligator lines can also provide signals, such as a buy signal when the price crosses above the Alligator lines and a sell signal when the price crosses below them.
In addition to these, we have several other commonly used technical indicators, such as MACD, RSI, MFI (Money Flow Index), VWAP, EMA, and Supertrend. I used all the built-in functions for these indicators from TradingView. Thanks to the developer of this TradingView Indicator.
I also created a BankNifty Components Table and checked it on the dashboard.
Cong Adaptive Moving AverageDr. Scott Cong's new adaptation of an adaptive moving average (AMA), featured in TASC March 2023.
It adjusts its parameters automatically according to the volatility of market, tracking price closely in trending movement, staying flat in congestion areas.
Perry Kaufman’s adaptive moving average, first described in his 1995 book Smarter Trading, is a great example of how an AMA can self-adjust to adapt to changing environments. This indicator presents a new scheme for an adaptive moving average that is responsive, smooth, and robust.
Ostinated Adjustable Fractal with AlertThis indicator (Ostinated Adjustable Fractal with Alert) is like the traditional William Fractals. However, it created to overcome two disadvantages of the William Fractals:
1. Set minimum value to 1. This a big advantage over traditional William Fractal that has a minimum value of 2.
2. Alerts: setting alert with the traditional William Fractal is very difficult. We have created this indicator to simplify setting fractal alerts.
Like and comment.
Enjoy!
JC MAs: SMA, WMA, EMA, DEMA, TEMA, ALMA, Hull, Kaufman, FractalThe best collection of moving averages anywhere. I know, because I searched, couldn't find the right collection, and so wrote it myself!
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Notable features that either aren't found anywhere else...or at least in one place:
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• The "Triple Exponential Moving Average", is actually that mathematically - rather than "three seperate EMA graphs", as is commonly found on Trading View.
• Includes exotic moving averages: Hull Moving Average (HMA), Kaufman's Adaptive Moving Average (KAMA), and Fractal Apaptive Moving Average (FrAMA).
• Each moving average has its own user-definable averaging length in DAYS, rather than an abstract "length". This is respected even for different graphing resolutions, and different chart views - even for the more exotic MAs.
• Days can be fractional.
• A master time resolution ("Timeframe") is also user-definable. And unlike most other moving average charts, this won't affect the internal "length" variable (specified days are still respected), it only changes the graphing resolution. You can also specify to use chart's resolution - which, as you know, is not very useful for moving averages - yet so many moving average scripts on Trading View don't let you specify otherwise.
• If every CPU cycle counts, you can set "days" to 0 to prevent a particular unneeded moving average from being calculated at all.
• Includes a custom moving average that is unique, if you're looking for a tiny edge in TA to beat everyone else looking at the same stuff: a customizable weighted blend of SMA, TEMA, HMA, KAMA, and FrMA. (Note: The weights for these blends don't have to add up to 100, they will self-level no matter what they add up to.)
• By default, the averages are color-coded according to rainbow order of light spectrum frequency, relative to approximate responsiveness to current price: Red (SMA) is the laziest, violet (FrAMA) is the most hyper, and green is in the middle.
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Contains the following moving averages, in order of responsiveness:
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• Simple Moving Average (SMA)
• Arnaud Legoux Moving Average (ALMA)
• Exponential Moving Average (EMA)
• Weighted Moving Average (WMA)
• Blend average of SMA and TEMA (JCBMA)
• Double Exponential Moving Average (DEMA)
• Triple Exponential Moving Average (TEMA)
• Hull Moving Average (HMA)
• Kaufman's Adaptive Moving Average (KAMA)
• Fractal Apaptive Moving Average (FrAMA)
Note: There are a few extreme edge cases where the graphs won't render, which are obvious. (Because they won't render.) In which case, all you need to do is choose a more sane master resolution ("Timeframe") relative to the timeframe of the chart. This is more about the limits of Trading View, than specific script bugs.
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Includes reworked code snippets
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• "Kaufman Moving Average Adaptive (KAMA)" by HPotter
• "FRAMA (Ehlers true modified calculation)" by nemozny
• Which in turn was based on "Fractal Adaptive Moving Average (real one)" by Shizaru
(FRAMA) Fractal Adaptive Moving AverageThis script plots UP TO 4 FRAMAs (Fractal Adaptive Moving Average), a particularly useful adaptive MA which uses fractal price theory to "adapt" itself, or better, to auto-adjust its parameters. It's quite reactive, but its major strength resides in its ability to filter the noise during lateral price movements, avoiding some false signal provided by more common moving averages.
By default, the script plots the basic FRAMA version, the one firstly developed by John Ehlers, but also provides the possibility to play with the parameters, to obtain a modified FRAMA.
Unfortunately I'm not able to provide web sources about FRAMA here, due to Tradingview policies. So search online if you need more details about FRAMAs.
Please show me your support if you like my scripts. Hope you enjoy them.
Ehlers Fractal Adaptive Moving Average with Bands [Bitcoinduke]FRAMA – What is it?
Fractal Adaptive Moving Average Technical Indicator ( FRAMA ) was developed by John Ehlers, code was implemented by TradingView user Shizaru. I've updated it to Pine Script 4 and added Bands Extension.
This indicator is constructed based on the algorithm of the Exponential Moving Average , in which the smoothing factor is calculated based on the current fractal dimension of the price series.
Advantages:
possibility to follow strong trend movements
determine moments of price consolidation ( FRAMA sufficiently slow down at these moments)
FRAMA + Bands
The FRAMA serves as a base for the Upper and Lower Bands which are used as a way to measure volatility by observing the relationship between the Bands and price. Typically the Upper and Lower Bands are set to two standard deviations away from the FRAMA ; however it should be adjusted depending on the task.
FRACHTALS"FRACHTALS" - A practical example of taking a joke entirely way, way too far
Speaking of which - Moon when?
#REKT
Credits/Acknowledgements/References:
Fractal detection + other functions (@RicardoSantos)
Laguerre RSI w/ self-Adjusting Alpha (@everget)
FRAMA (Ehlers true modified calculation)Credit goes to Shizaru for the original calculation. I made just a few fixes, so that the calculation is really that of Ehlers.
Fixed H2 and L2 period, fixed w natural logarithm
Fractal Dimension Adaptive Moving Average (D-AMA)etfhq.com
Overall the D-AMA produced results that were near identical to that of the FRAMA but the D-AMA is a slightly faster average.
It is very difficult to pick between the FRAMA and the D-AMA but becuase the FRAMA offers a slightly longer trade duration it the best Moving Average we have tested so far.
Fractal Adaptive Moving Average (real one)Ignore the other one (it contains some errors).
On this FRAMA you can play with length, SC and FC.
Just read on below links to understand more about this super useful moving average:
etfhq.com
etfhq.com
www.quantshare.com
Fractal Adaptive Moving AverageSettings:
FRAMA: blue line, SC = 252, FC = 40, length = 252
EMA: orange line, length = 50
FRAMA seems to be the evolution of the current and much-used EMA. The basic strategy is simple: long if the price crosses up the line, short or exit if vice versa.
The main difference between EMA and FRAMA is that the first one seems to lag much more than the first one, as we can see from the chart below (crude oil daily chart)
FYI
etfhq.com
quantstrattrader.wordpress.com
