HA Reversal StrategyCertainly! Here's a detailed **description (elaboration)** for the **"HA Candle Test"** (i.e., the Heikin Ashi strategy script I just gave you):
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### 📌 **Script Name**: HA Candle Test
### 📖 **Description**:
This script visualizes **Heikin Ashi candles** and identifies **trend reversal signals** using classic momentum candle behavior — particularly the appearance of **no-wick candles**, which are known to reflect strong directional pressure in Heikin Ashi charts.
It aims to **capture high-probability trend reversals** with minimal noise, relying on the natural smoothing behavior of Heikin Ashi candles.
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### ✅ **Buy Signal Conditions**:
* At least **two consecutive red Heikin Ashi candles** (indicating a short-term downtrend).
* Followed by a **green Heikin Ashi candle** that has **no lower wick** (i.e., open == low).
* This suggests that **buyers have taken full control**, with no push from sellers — a potential start of an uptrend.
📍 **Interpreted as**: “Market was selling off, but now buyers stepped in strongly — time to consider buying.”
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### ✅ **Sell Signal Conditions**:
* At least **two consecutive green Heikin Ashi candles** (short-term uptrend).
* Followed by a **red Heikin Ashi candle** that has **no upper wick** (i.e., open == high).
* This implies **sellers are dominating**, with no attempt from buyers to push higher — possible start of a downtrend.
📍 **Interpreted as**: “Market was rallying, but sellers just took over decisively — time to consider selling.”
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### 📊 **Visual Aids Included**:
* Plots **Heikin Ashi candles** on your main chart for clarity.
* Uses **Buy** and **Sell** label markers (green & red) at signal points.
* Compatible with any timeframe — higher timeframes typically yield stronger signals.
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### 💡 **Suggested Use**:
* Combine with **support/resistance**, **volume**, or **trend filters** for more robust setups.
* Works well on **1H, 4H, and Daily charts** in trending markets.
* Can be used manually or turned into an automated strategy for backtesting or alerts.
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Would you like this script packaged as a **strategy()** for backtesting, or would you like me to add **alerts** so you can get notified in real-time when signals appear?
指標和策略
TSE USD Upper LimitThis script calculates and displays the daily upper price limit for a Tokyo Stock Exchange (TSE) stock based on the official JPX limit table. The limit is determined from the previous session’s closing price and displayed as a fixed horizontal line on the current chart. Ideal for tracking regulatory price caps and identifying squeeze scenarios.
GER40 BIAS Forecast [ML-Based]🎯 Purpose:
This indicator provides a daily directional bias (LONG / SHORT / FLAT) for the German DAX40 index (GER40) using a statistically optimized scoring model, developed with 6 years of historical data and verified through machine learning analysis.
🧠 How the Score Works (ML-derived):
Each trading day receives a bias score (0–3) for both long and short setups, based on these 3 factors from the daily candle:
Condition Long Score Logic Short Score Logic
1. Candle Direction Close > Open → +1 Close < Open → +1
2. VWAP Slope VWAP > VWAP → +1 VWAP < VWAP → +1
3. Volatility Strength Range > SMA(20) → +1 Close < Yesterday's Low → +1 (Rejection)
➡️ A score of 2 or more triggers a Long or Short Bias for the day.
These scoring rules are derived from a machine learning model trained on 6 years of DAX data, identifying the most predictive features for directional follow-through.
📘 Bias Interpretation:
Score Result Daily Bias Background Color
Long Score ≥ 2 LONG Green
Short Score ≥ 2 SHORT Red
Both < 2 FLAT Gray
📍 Indicator Features:
🎨 Background coloring to visualize daily bias directly on intraday charts
🔢 Optional score labels (e.g. “Long: 2 | Short: 1”) per calendar day
📈 VWAP line plotted for additional intraday context
❌ Entry signals removed – this version focuses solely on forecasting directional bias
💡 Use Case:
Morning planning aid
Filtering for high-probability intraday setups
Combining with session-based entry systems
ALMA Trend-boxThis indicator uses the ALMA (Arnaud Legoux Moving Average) – a special type of moving average that provides a smoother and more responsive trend line. Based on the slope (angle) of the ALMA line and the price position relative to it, the indicator:
Colors candles in three different ways (to reflect market structure),
Plots the ALMA line on the chart,
Detects consolidation and highlights it with blue candles, background shading, and horizontal "box" lines.
📘 Candle Colors – How to Interpret Them
Candle Color Meaning Interpretation
🟩 Green Uptrend ALMA is sloping upward and price is above ALMA – look for buying opportunities.
🟥 Red Downtrend ALMA is sloping downward and price is below ALMA – look for selling opportunities.
🔵 Blue Sideways (Consolidation) Weak or neutral trend – market is moving sideways or accumulating.
🔵 What Do Blue Candles and the “Trend-box” Mean?
Blue candles represent consolidation periods, which occur when:
The slope of the ALMA line is less than ±40°, indicating a lack of strong trend,
The price behavior is not consistent with the direction of the slope (e.g., price is below ALMA even though ALMA is pointing upward).
During this time:
Blue candles and a blue background appear to visually highlight the consolidation,
Two dashed horizontal lines (a “box”) are drawn at the high and low of the consolidation range.
📌 The Trend-box helps you visually spot ranging markets, which often precede strong breakouts.
📈 How to Use This Indicator in Practice
Trend Following Strategy:
When candles are green → consider long trades.
When candles are red → consider short trades.
Use additional indicators (like RSI, MACD, or volume) to confirm entries.
Breakout Trading:
When blue candles and the box appear, wait for the price to:
Break above the box → potential long breakout.
Break below the box → potential short breakout.
You can set pending orders (buy stop/sell stop) just outside the box range.
Avoiding Choppy Entries:
Blue candles signal uncertainty – avoid entering impulsively during this time. Wait for trend confirmation.
⚙️ Adjustable Settings
ALMA Length – controls how quickly the moving average reacts.
Slope Threshold – determines the minimum angle required to define a trend.
Candle Colors – fully customizable (green/red/blue by default).
✅ Conclusion
ALMA Trend-box is a powerful visual tool for identifying:
Trending conditions (bullish or bearish),
Sideways markets (consolidation),
Breakout setups with clearly marked zones.
It works well on its own or as part of a larger trading system. Blue candles tell you to be patient, while transitions into green/red candles indicate developing trends.
10 EMA -3*ATRThis custom indicator plots the line calculated as 10-period Exponential Moving Average (EMA) minus 3 times the 14-period Average True Range (ATR). It helps traders identify dynamic support levels or pullback zones during strong trends by adjusting for market volatility. A falling line may signal increasing volatility or weakening momentum, while a rising line may indicate strengthening trend stability. Suitable for trend-following strategies and volatility-aware entries.
Darren - Engulfing + MACD CrossDarren – Engulfing + MACD Cross
Overall Behavior
Identify an engulfing candle (bullish or bearish).
Wait up to windowBars bars for the corresponding MACD crossover (bullish engulfing → MACD cross up; bearish engulfing → MACD cross down).
If the crossover occurs within that window, trigger an entry (long or short) and close any opposite open trade.
Inputs
• macdFast (default 12): length of MACD fast EMA
• macdSlow (default 26): length of MACD slow EMA
• macdSignal (default 9): length of MACD signal line
• windowBars (default 3): maximum bars allowed between an engulfing candle and a MACD crossover
Indicators
• macdLine and signalLine are calculated using ta.macd(close, macdFast, macdSlow, macdSignal)
• macdHist = macdLine – signalLine, plotted as columns (green when ≥ 0, red when < 0)
Engulfing Pattern Detection
• Bullish engulfing (bullEngulfing) is true when the previous candle is bearish (close < open ), the current candle is bullish (close > open), and the current body fully engulfs the previous body (open < close and close > open ).
• Bearish engulfing (bearEngulfing) is the inverse: previous candle bullish, current candle bearish, and current body fully engulfs the prior body.
MACD Crossover Detection
• macdCrossUp is true when macdLine crosses above signalLine.
• macdCrossDown is true when macdLine crosses below signalLine.
Timing Logic
• barsSinceBull = ta.barssince(bullEngulfing) returns number of bars since the last bullish engulfing.
• barsSinceBear = ta.barssince(bearEngulfing) returns number of bars since the last bearish engulfing.
• longCondition occurs if a MACD cross up happens within windowBars bars of a bullish engulfing (barsSinceBull ≤ windowBars and macdCrossUp).
• shortCondition occurs if a MACD cross down happens within windowBars bars of a bearish engulfing (barsSinceBear ≤ windowBars and macdCrossDown).
Chart Markers
• “Bull” label below bar whenever bullEngulfing is true.
• “Bear” label above bar whenever bearEngulfing is true.
• Small “Up” ▲ below bar when macdCrossUp is true.
• Small “Down” ▼ above bar when macdCrossDown is true.
• Triangle ▲ below bar for Long Entry (longCondition).
• Triangle ▼ above bar for Short Entry (shortCondition).
Entry & Exit Rules
• On longCondition: enter “Long”, and close any existing “Short” position.
• On shortCondition: enter “Short”, and close any existing “Long” position.
Forex Session Levels + Dashboard (AEST)This is the only indicator you will EVER need for the breakthrough and retest strategy.
Follow me on IG for more trading tips!
@LiviuPircalabu10
Forex Session Levels + Dashboard (AEST)This is the only indicator you will EVER need on the breakout and retest strategy.
Follow me on IG:
@liviupircalabu10
Patrick BTC Exponential ModelA test of an exp reversion model, WIP
BTC(t) = a × e^(r × t)
Where:
- BTC(t) = Bitcoin price at time t
- a = Anchor coefficient (fitted parameter)
- r = Growth rate per year (fitted parameter)
- t = Years since anchor date
- e = Euler's number (2.71828...)
Credit to @GallantCryptoYT (but based on, no implication of endorsement either way)
Enhanced MFI Divergence with Pivot SignalsEnhanced MFI Divergence with Pivot Signals
This custom Pine Script indicator identifies bullish and bearish divergences between price action and the Money Flow Index (MFI), enhancing the trader's ability to spot potential reversal zones with visual clarity and optional confirmation filters.
📊 Key Features:
🔹 MFI Divergence Detection
The script detects:
Bullish divergence when price forms a lower low but MFI forms a higher low.
Bearish divergence when price forms a higher high but MFI forms a lower high.
🔹 Pivot-Based Logic
To ensure high-confidence signals, the script uses pivot point logic to mark local highs and lows on both price and MFI. This avoids noise and focuses only on meaningful swing points.
🔹 Optional Confirmation Filter
You can enable a filter that checks if MFI is above 50 during bullish divergence (implying buying pressure) and below 50 for bearish divergence (implying selling pressure), adding an extra layer of confirmation.
🔹 Signal Markers
Signals are visually displayed on the chart using colored triangles:
Green triangle up for bullish divergence
Red triangle down for bearish divergence
🔹 Background Color Shading
The background is optionally shaded green or red based on MFI’s relationship to its smoothed WMA, helping you visually interpret trend bias.
🔹 Pivot Point Debugging Tools
Circles and crosses mark pivot points on price and MFI for debugging and visual clarity.
🔹 Alerts Ready
Real-time alerts notify you instantly when a bullish or bearish MFI divergence occurs, allowing for quick decision-making.
⚙️ How It Helps
This indicator is designed to help traders:
Anticipate price reversals by identifying hidden strength or weakness in momentum,
Avoid false breakouts,
Confirm entries or exits based on volume-weighted momentum divergence.
It works especially well when used alongside trend-following tools like moving averages, support/resistance zones, or additional volume indicators.
40 Ticker Cross-Sectional Z-Scores [BackQuant]40 Ticker Cross-Sectional Z-Scores
BackQuant’s 40 Ticker Cross-Sectional Z-Scores is a powerful portfolio management strategy that analyzes the relative performance of up to 40 different assets, comparing them on a cross-sectional basis to identify the top and bottom performers. This indicator computes Z-scores for each asset based on their log returns and evaluates them relative to the mean and standard deviation over a rolling window. The Z-scores represent how far an asset's return deviates from the average, and these values are used to rank the assets, allowing for dynamic asset allocation based on performance.
By focusing on the strongest-performing assets and avoiding the weakest, this strategy aims to enhance returns while managing risk. Additionally, by adjusting for standard deviations, the system offers a risk-adjusted method of ranking assets, making it suitable for traders who want to dynamically allocate capital based on performance metrics rather than just price movements.
Key Features
1. Cross-Sectional Z-Score Calculation:
The system calculates Z-scores for 40 different assets, evaluating their log returns against the mean and standard deviation over a rolling window. This enables users to assess the relative performance of each asset dynamically, highlighting which assets are performing better or worse compared to their historical norms. The Z-score is a useful statistical tool for identifying outliers in asset performance.
2. Asset Ranking and Allocation:
The system ranks assets based on their Z-scores and allocates capital to the top performers. It identifies the top and bottom assets, and traders can allocate capital to the top-performing assets, ensuring that their portfolio is aligned with the best performers. Conversely, the bottom assets are removed from the portfolio, reducing exposure to underperforming assets.
3. Rolling Window for Mean and Standard Deviation Calculations:
The Z-scores are calculated based on rolling means and standard deviations, making the system adaptive to changing market conditions. This rolling calculation window allows the strategy to adjust to recent performance trends and minimize the impact of outdated data.
4. Mean and Standard Deviation Visualization:
The script provides real-time visualizations of the mean (x̄) and standard deviation (σ) of asset returns, helping traders quickly identify trends and volatility in their portfolio. These visual indicators are useful for understanding the current market environment and making more informed allocation decisions.
5. Top & Bottom Performer Tables:
The system generates tables that display the top and bottom performers, ranked by their Z-scores. Traders can quickly see which assets are outperforming and underperforming. These tables provide clear and actionable insights, helping traders make informed decisions about which assets to include in their portfolio.
6. Customizable Parameters:
The strategy allows traders to customize several key parameters, including:
Rolling Calculation Window: Set the window size for the rolling mean and standard deviation calculations.
Top & Bottom Tickers: Choose how many of the top and bottom assets to display and allocate capital to.
Table Orientation: Select between vertical or horizontal table formats to suit the user’s preference.
7. Forward Test & Out-of-Sample Testing:
The system includes out-of-sample forward tests, ensuring that the strategy is evaluated based on real-time performance, not just historical data. This forward testing approach helps validate the robustness of the strategy in dynamic market conditions.
8. Visual Feedback and Alerts:
The system provides visual feedback on the current asset rankings and allocations, with dynamic labels and plots on the chart. Additionally, users receive alerts when allocations change, keeping them informed of important adjustments.
9. Risk Management via Z-Scores and Std Dev:
The system’s approach to asset selection is based on Z-scores, which normalize performance relative to the historical mean. By incorporating standard deviation, it accounts for the volatility and risk associated with each asset. This allows for more precise risk management and portfolio construction.
10. Note on Mean Reversion Strategy:
If you take the inverse of the signals provided by this indicator, the strategy can be used for mean-reversion rather than trend-following. This would involve buying the underperforming assets and selling the outperforming ones. However, it's important to note that this approach does not work well with highly correlated assets, as the relationship between the assets could result in the same directional movement, undermining the effectiveness of the mean-reversion strategy.
References
www.uts.edu.au
onlinelibrary.wiley.com
www.cmegroup.com
Final Thoughts
The 40 Ticker Cross-Sectional Z-Scores strategy offers a data-driven approach to portfolio management, dynamically allocating capital based on the relative performance of assets. By using Z-scores and standard deviations, this strategy ensures that capital is directed to the strongest performers while avoiding weaker assets, ultimately improving the risk-adjusted returns of the portfolio. Whether you’re focused on trend-following or looking to explore mean-reversion strategies, this flexible system can be tailored to suit your investment goals.
Psychological Levels + Buffer ZonesThis indicator automatically draws major (100-pip) and minor (50-pip) psychological levels on your Forex chart, along with optional buffer zones for smarter trade entries. Zones help you visually capture breakouts, retests, and fakeouts. Includes:
Major & minor psych levels
Adjustable buffer zones (±0.1%, etc.)
Customizable zone color & transparency
Optional ATR trailing lines for trend confirmation
Perfect for scalpers, breakout traders, and zone-based strategies.
Wavelet Filter with Adaptive Upsampling [BackQuant]Wavelet Filter with Adaptive Upsampling
The Wavelet Filter with Adaptive Upsampling is an advanced filtering and signal reconstruction tool designed to enhance the analysis of financial time series data. It combines wavelet transforms with adaptive upsampling techniques to filter and reconstruct price data, making it ideal for capturing subtle market movements and enhancing trend detection. This system uses high-pass and low-pass filters to decompose the price series into different frequency components, applying adaptive thresholding to eliminate noise and preserve relevant signal information.
Shout out to Loxx for the Least Squares fitting of trigonometric series and Quinn and Fernandes algorithm for finding frequency
www.tradingview.com
Key Features
1. Frequency Decomposition with High-Pass and Low-Pass Filters:
The indicator decomposes the input time series using high-pass and low-pass filters to separate the high-frequency (detail) and low-frequency (trend) components of the data. This decomposition allows for a more accurate analysis of underlying trends, while mitigating the impact of noise.
2. Soft Thresholding for Noise Reduction:
A soft thresholding function is applied to the high-frequency component, allowing for the reduction of noise while retaining significant market signals. This function adjusts the coefficients of the high-frequency data, removing small fluctuations and leaving only the essential price movements.
3. Adaptive Upsampling Process:
The upsampling process in this script can be customized using different methods: sinusoidal upsampling, advanced upsampling, and simple upsampling. Each method serves a unique purpose:
Sinusoidal Upsample uses a sine wave to interpolate between data points, providing a smooth transition.
Advanced Upsample utilizes a Quinn-Fernandes algorithm to estimate frequency and apply more sophisticated interpolation techniques, adapting to the market’s cyclical behavior.
Simple Upsample linearly interpolates between data points, providing a basic upsampling technique for less complex analysis.
4. Reconstruction of Filtered Signal:
The indicator reconstructs the filtered signal by summing the high and low-frequency components after upsampling. This allows for a detailed yet smooth representation of the original time series, which can be used for analyzing underlying trends in the market.
5. Visualization of Reconstructed Data:
The reconstructed series is plotted, showing how the upsampling and filtering process enhances the clarity of the price movements. Additionally, the script provides the option to visualize the log returns of the reconstructed series as a histogram, with positive returns shown in green and negative returns in red.
6. Cumulative Series and Trend Detection:
A cumulative series is plotted to visualize the compounded effect of the filtered and reconstructed data. This feature helps traders track the overall performance of the asset over time, identifying whether the asset is following a sustained upward or downward trend.
7. Adaptive Thresholding and Noise Estimation:
The system estimates the noise level in the high-frequency component and applies an adaptive thresholding process based on the standard deviation of the downsampled data. This ensures that only significant price movements are retained, further refining the trend analysis.
8. Customizable Parameters for Flexibility:
Users can customize the following parameters to adjust the behavior of the indicator:
Frequency and Phase Shift: Control the periodicity of the wavelet transformation and the phase of the upsampling function.
Upsample Factor: Adjust the level of interpolation applied during the upsampling process.
Smoothing Period: Determine the length of time used to smooth the signal, helping to filter out short-term fluctuations.
References
Enhancing Cross-Sectional Currency Strategies with Context-Aware Learning to Rank
arxiv.org
Daubechies Wavelet - Wikipedia
en.wikipedia.org
Quinn Fernandes Fourier Transform of Filtered Price by Loxx
Note on Usage for Mean-Reversion Strategy
This indicator is primarily designed for trend-following strategies. However, by taking the inverse of the signals, it can be adapted for mean-reversion strategies. This involves buying underperforming assets and selling outperforming ones. Caution: This method may not work effectively with highly correlated assets, as the price movements between correlated assets tend to mirror each other, limiting the effectiveness of mean-reversion strategies.
Final Thoughts
The Wavelet Filter with Adaptive Upsampling is a powerful tool for traders seeking to improve their understanding of market trends and noise. By using advanced wavelet decomposition and adaptive upsampling, this system offers a clearer, more refined picture of price movements, enhancing trend-following strategies. It’s particularly useful for detecting subtle shifts in market momentum and reconstructing price data in a way that removes noise, providing more accurate insights into market conditions.
Performance Metrics With Bracketed Rebalacing [BackQuant]Performance Metrics With Bracketed Rebalancing
The Performance Metrics With Bracketed Rebalancing script offers a robust method for assessing portfolio performance, integrating advanced portfolio metrics with different rebalancing strategies. With a focus on adaptability, the script allows traders to monitor and adjust portfolio weights, equity, and other key financial metrics dynamically. This script provides a versatile approach for evaluating different trading strategies, considering factors like risk-adjusted returns, volatility, and the impact of portfolio rebalancing.
Please take the time to read the following:
Key Features and Benefits of Portfolio Methods
Bracketed Rebalancing:
Bracketed Rebalancing is an advanced strategy designed to trigger portfolio adjustments when an asset's weight surpasses a predefined threshold. This approach minimizes overexposure to any single asset while maintaining flexibility in response to market changes. The strategy is particularly beneficial for mitigating risks that arise from significant asset weight fluctuations. The following image illustrates how this method reacts when asset weights cross the threshold:
Daily Rebalancing:
Unlike the bracketed method, Daily Rebalancing adjusts portfolio weights every trading day, ensuring consistent asset allocation. This method aims for a more even distribution of portfolio weights, making it a suitable option for traders who prefer less sensitivity to individual asset volatility. Here's an example of Daily Rebalancing in action:
No Rebalancing:
For traders who prefer a passive approach, the "No Rebalancing" option allows the portfolio to remain static, without any adjustments to asset weights. This method may appeal to long-term investors or those who believe in the inherent stability of their selected assets. Here’s how the portfolio looks when no rebalancing is applied:
Portfolio Weights Visualization:
One of the standout features of this script is the visual representation of portfolio weights. With adjustable settings, users can track the current allocation of assets in real-time, making it easier to analyze shifts and trends. The following image shows the real-time weight distribution across three assets:
Rolling Drawdown Plot:
Managing drawdown risk is a critical aspect of portfolio management. The Rolling Drawdown Plot visually tracks the drawdown over time, helping traders monitor the risk exposure and performance relative to the peak equity levels. This feature is essential for assessing the portfolio's resilience during market downturns:
Daily Portfolio Returns:
Tracking daily returns is crucial for evaluating the short-term performance of the portfolio. The script allows users to plot daily portfolio returns to gain insights into daily profit or loss, helping traders stay updated on their portfolio’s progress:
Performance Metrics
Net Profit (%):
This metric represents the total return on investment as a percentage of the initial capital. A positive net profit indicates that the portfolio has gained value over the evaluation period, while a negative value suggests a loss. It's a fundamental indicator of overall portfolio performance.
Maximum Drawdown (Max DD):
Maximum Drawdown measures the largest peak-to-trough decline in portfolio value during a specified period. It quantifies the most significant loss an investor would have experienced if they had invested at the highest point and sold at the lowest point within the timeframe. A smaller Max DD indicates better risk management and less exposure to significant losses.
Annual Mean Returns (% p/y):
This metric calculates the average annual return of the portfolio over the evaluation period. It provides insight into the portfolio's ability to generate returns on an annual basis, aiding in performance comparison with other investment opportunities.
Annual Standard Deviation of Returns (% p/y):
This measure indicates the volatility of the portfolio's returns on an annual basis. A higher standard deviation signifies greater variability in returns, implying higher risk, while a lower value suggests more stable returns.
Variance:
Variance is the square of the standard deviation and provides a measure of the dispersion of returns. It helps in understanding the degree of risk associated with the portfolio's returns.
Sortino Ratio:
The Sortino Ratio is a variation of the Sharpe Ratio that only considers downside risk, focusing on negative volatility. It is calculated as the difference between the portfolio's return and the minimum acceptable return (MAR), divided by the downside deviation. A higher Sortino Ratio indicates better risk-adjusted performance, emphasizing the importance of avoiding negative returns.
Sharpe Ratio:
The Sharpe Ratio measures the portfolio's excess return per unit of total risk, as represented by standard deviation. It is calculated by subtracting the risk-free rate from the portfolio's return and dividing by the standard deviation of the portfolio's excess return. A higher Sharpe Ratio indicates more favorable risk-adjusted returns.
Omega Ratio:
The Omega Ratio evaluates the probability of achieving returns above a certain threshold relative to the probability of experiencing returns below that threshold. It is calculated by dividing the cumulative probability of positive returns by the cumulative probability of negative returns. An Omega Ratio greater than 1 indicates a higher likelihood of achieving favorable returns.
Gain-to-Pain Ratio:
The Gain-to-Pain Ratio measures the return per unit of risk, focusing on the magnitude of gains relative to the severity of losses. It is calculated by dividing the total gains by the total losses experienced during the evaluation period. A higher ratio suggests a more favorable balance between reward and risk.
www.linkedin.com
Compound Annual Growth Rate (CAGR) (% p/y):
CAGR represents the mean annual growth rate of the portfolio over a specified period, assuming the investment has been compounding over that time. It provides a smoothed annual rate of growth, eliminating the effects of volatility and offering a clearer picture of long-term performance.
Portfolio Alpha (% p/y):
Portfolio Alpha measures the portfolio's performance relative to a benchmark index, adjusting for risk. It is calculated using the Capital Asset Pricing Model (CAPM) and represents the excess return of the portfolio over the expected return based on its beta and the benchmark's performance. A positive alpha indicates outperformance, while a negative alpha suggests underperformance.
Portfolio Beta:
Portfolio Beta assesses the portfolio's sensitivity to market movements, indicating its exposure to systematic risk. A beta greater than 1 suggests the portfolio is more volatile than the market, while a beta less than 1 indicates lower volatility. Beta is used to understand the portfolio's potential for gains or losses in relation to market fluctuations.
Skewness of Returns:
Skewness measures the asymmetry of the return distribution. A positive skew indicates a distribution with a long right tail, suggesting more frequent small losses and fewer large gains. A negative skew indicates a long left tail, implying more frequent small gains and fewer large losses. Understanding skewness helps in assessing the likelihood of extreme outcomes.
Value at Risk (VaR) 95th Percentile:
VaR at the 95th percentile estimates the maximum potential loss over a specified period, given a 95% confidence level. It provides a threshold value such that there is a 95% probability that the portfolio will not experience a loss greater than this amount.
Conditional Value at Risk (CVaR):
CVaR, also known as Expected Shortfall, measures the average loss exceeding the VaR threshold. It provides insight into the tail risk of the portfolio, indicating the expected loss in the worst-case scenarios beyond the VaR level.
These metrics collectively offer a comprehensive view of the portfolio's performance, risk exposure, and efficiency. By analyzing these indicators, investors can make informed decisions, balancing potential returns with acceptable levels of risk.
Conclusion
The Performance Metrics With Bracketed Rebalancing script provides a comprehensive framework for evaluating and optimizing portfolio performance. By integrating advanced metrics, adaptive rebalancing strategies, and visual analytics, it empowers traders to make informed decisions in managing their investment portfolios. However, it's crucial to consider the implications of rebalancing strategies, as academic research indicates that predictable rebalancing can lead to market impact costs. Therefore, adopting flexible and less predictable rebalancing approaches may enhance portfolio performance and reduce associated costs.
FX 2025 - Triple EMA Entrada y Cierre ÚnicaCruce de emas 9/21/50, se recomienda combinar con los indicadores de volumen y macd
EMA 9: Represents very short-term price movement.
EMA 21: Smoother and shows short-term trend.
EMA 50: Reflects the medium-term trend.
Common signals:
Bullish crossover: When the EMA 9 crosses above the EMA 21 (and preferably also the EMA 50), it’s seen as a buy signal.
Bearish crossover: When the EMA 9 crosses below the EMA 21 or 50, it may signal a sell or correction.
TSE EUR Upper LimitThis indicator calculates and displays the daily upper price limit for a Tokyo Stock Exchange (TSE) stock based on the official JPX limit table. The limit is determined from the previous session’s closing price and displayed as a fixed horizontal line on the current chart. Ideal for tracking regulatory price caps and identifying squeeze scenarios.
_CM_MacD_Ult_MTF_V2.1//------New V2 Update 07-28-2021----------
//Thanks to @SKTennis for help in Updating code to V2
//Added Groups to Settings Pane.
//Added Color Plots to Settings Pane
//Switched MTF Logic to turn ON/OFF automatically w/ TradingView's Built in Feature
//Updated Color Transparency plots to work in future update
//Added Ability to Turn ON/OFF Show MacD & Signal Line
//Added Ability to Turn ON/OFF Show Histogram
//Added Ability to Change MACD Line Colors Based on Trend
//Added Ability to Highlight Price Bars Based on Trend
//Added Alerts to Settings Pane.
//Customized how Alerts work. Must keep Checked in Settings Pane, and...
//When you go to Alerts Panel, Change Symbol to Indicator (CM_Ult_MacD_MTF_V2)
//Customized Alerts to Show Symbol, TimeFrame, Closing Price, MACD Crosses Up & MACD Crosses Down Signals in Alert
//Alerts are Pre-Set to only Alert on Bar Close
//------New V2.1 Update 08-03-2021----------
//Added back in ability to show Dots when MACD Crosses.
//Added Ability to Change Plot Widths in Settings Pane
//Added in Alert Feature where Cross Up if above 0 or cross down if below 0 (OFF By Default) user Request. @creid58
//FIXED - Plot Orders to Default what Plots are on top of each other
//FIXED - Two of the histogrm colors were backwrds
//------New V2.1 Update 12-07-2021----------
//Updated to PineScript V5
//------Minor Update 02-16-2022----------
//Per user request...Increased the Maxval for Signal Smoothing
//Next Add in Plot Types to Settings Pane.
//Next Add in more Moving Average types.
//See Video for Detailed Overview
//@version=5
indicator(title="_CM_MacD_Ult_MTF_V2.1", shorttitle="_CM_Ult_MacD_MTF_V2.1")
//Plot Inputs
res = input.timeframe("", "Indicator TimeFrame")
fast_length = input.int(title="Fast Length", defval=12)
slow_length = input.int(title="Slow Length", defval=26)
src = input.source(title="Source", defval=close)
signal_length = input.int(title="Signal Smoothing", minval = 1, maxval = 999, defval = 9)
sma_source = input.string(title="Oscillator MA Type", defval="EMA", options= )
sma_signal = input.string(title="Signal Line MA Type", defval="EMA", options= )
// Show Plots T/F
show_macd = input.bool(true, title="Show MACD Lines", group="Show Plots?", inline="SP10")
show_macd_LW = input.int(3, minval=0, maxval=5, title = "MACD Width", group="Show Plots?", inline="SP11")
show_signal_LW= input.int(2, minval=0, maxval=5, title = "Signal Width", group="Show Plots?", inline="SP11")
show_Hist = input.bool(true, title="Show Histogram", group="Show Plots?", inline="SP20")
show_hist_LW = input.int(5, minval=0, maxval=5, title = "-- Width", group="Show Plots?", inline="SP20")
show_trend = input.bool(true, title = "Show MACD Lines w/ Trend Color", group="Show Plots?", inline="SP30")
show_HB = input.bool(false, title="Show Highlight Price Bars", group="Show Plots?", inline="SP40")
show_cross = input.bool(false, title = "Show BackGround on Cross", group="Show Plots?", inline="SP50")
show_dots = input.bool(true, title = "Show Circle on Cross", group="Show Plots?", inline="SP60")
show_dots_LW = input.int(5, minval=0, maxval=5, title = "-- Width", group="Show Plots?", inline="SP60")
//show_trend = input(true, title = "Colors MACD Lines w/ Trend Color", group="Show Plots?", inline="SP5")
// MACD Lines colors
col_macd = input.color(#FF6D00, "MACD Line ", group="Color Settings", inline="CS1")
col_signal = input.color(#2962FF, "Signal Line ", group="Color Settings", inline="CS1")
col_trnd_Up = input.color(#4BAF4F, "Trend Up ", group="Color Settings", inline="CS2")
col_trnd_Dn = input.color(#B71D1C, "Trend Down ", group="Color Settings", inline="CS2")
// Histogram Colors
col_grow_above = input.color(#26A69A, "Above Grow", group="Histogram Colors", inline="Hist10")
col_fall_above = input.color(#B2DFDB, "Fall", group="Histogram Colors", inline="Hist10")
col_grow_below = input.color(#FF5252, "Below Grow", group="Histogram Colors", inline="Hist20")
col_fall_below = input.color(#FFCDD2, "Fall", group="Histogram Colors", inline="Hist20")
// Alerts T/F Inputs
alert_Long = input.bool(true, title = "MACD Cross Up", group = "Alerts", inline="Alert10")
alert_Short = input.bool(true, title = "MACD Cross Dn", group = "Alerts", inline="Alert10")
alert_Long_A = input.bool(false, title = "MACD Cross Up & > 0", group = "Alerts", inline="Alert20")
alert_Short_B = input.bool(false, title = "MACD Cross Dn & < 0", group = "Alerts", inline="Alert20")
// Calculating
fast_ma = request.security(syminfo.tickerid, res, sma_source == "SMA" ? ta.sma(src, fast_length) : ta.ema(src, fast_length))
slow_ma = request.security(syminfo.tickerid, res, sma_source == "SMA" ? ta.sma(src, slow_length) : ta.ema(src, slow_length))
macd = fast_ma - slow_ma
signal = request.security(syminfo.tickerid, res, sma_signal == "SMA" ? ta.sma(macd, signal_length) : ta.ema(macd, signal_length))
hist = macd - signal
// MACD Trend and Cross Up/Down conditions
trend_up = macd > signal
trend_dn = macd < signal
cross_UP = signal >= macd and signal < macd
cross_DN = signal <= macd and signal > macd
cross_UP_A = (signal >= macd and signal < macd) and macd > 0
cross_DN_B = (signal <= macd and signal > macd) and macd < 0
// Condition that changes Color of MACD Line if Show Trend is turned on..
trend_col = show_trend and trend_up ? col_trnd_Up : trend_up ? col_macd : show_trend and trend_dn ? col_trnd_Dn: trend_dn ? col_macd : na
//Var Statements for Histogram Color Change
var bool histA_IsUp = false
var bool histA_IsDown = false
var bool histB_IsDown = false
var bool histB_IsUp = false
histA_IsUp := hist == hist ? histA_IsUp : hist > hist and hist > 0
histA_IsDown := hist == hist ? histA_IsDown : hist < hist and hist > 0
histB_IsDown := hist == hist ? histB_IsDown : hist < hist and hist <= 0
histB_IsUp := hist == hist ? histB_IsUp : hist > hist and hist <= 0
hist_col = histA_IsUp ? col_grow_above : histA_IsDown ? col_fall_above : histB_IsDown ? col_grow_below : histB_IsUp ? col_fall_below :color.silver
// Plot Statements
//Background Color
bgcolor(show_cross and cross_UP ? col_trnd_Up : na, editable=false)
bgcolor(show_cross and cross_DN ? col_trnd_Dn : na, editable=false)
//Highlight Price Bars
barcolor(show_HB and trend_up ? col_trnd_Up : na, title="Trend Up", offset = 0, editable=false)
barcolor(show_HB and trend_dn ? col_trnd_Dn : na, title="Trend Dn", offset = 0, editable=false)
//Regular Plots
plot(show_Hist and hist ? hist : na, title="Histogram", style=plot.style_columns, color=color.new(hist_col ,0),linewidth=show_hist_LW)
plot(show_macd and signal ? signal : na, title="Signal", color=color.new(col_signal, 0), style=plot.style_line ,linewidth=show_signal_LW)
plot(show_macd and macd ? macd : na, title="MACD", color=color.new(trend_col, 0), style=plot.style_line ,linewidth=show_macd_LW)
hline(0, title="0 Line", color=color.new(color.gray, 0), linestyle=hline.style_dashed, linewidth=1, editable=false)
plot(show_dots and cross_UP ? macd : na, title="Dots", color=color.new(trend_col ,0), style=plot.style_circles, linewidth=show_dots_LW, editable=false)
plot(show_dots and cross_DN ? macd : na, title="Dots", color=color.new(trend_col ,0), style=plot.style_circles, linewidth=show_dots_LW, editable=false)
//Alerts
if alert_Long and cross_UP
alert("Symbol = (" + syminfo.tickerid + ") TimeFrame = (" + timeframe.period + ") Current Price (" + str.tostring(close) + ") MACD Crosses Up.", alert.freq_once_per_bar_close)
if alert_Short and cross_DN
alert("Symbol = (" + syminfo.tickerid + ") TimeFrame = (" + timeframe.period + ") Current Price (" + str.tostring(close) + ") MACD Crosses Down.", alert.freq_once_per_bar_close)
//Alerts - Stricter Condition - Only Alerts When MACD Crosses UP & MACD > 0 -- Crosses Down & MACD < 0
if alert_Long_A and cross_UP_A
alert("Symbol = (" + syminfo.tickerid + ") TimeFrame = (" + timeframe.period + ") Current Price (" + str.tostring(close) + ") MACD > 0 And Crosses Up.", alert.freq_once_per_bar_close)
if alert_Short_B and cross_DN_B
alert("Symbol = (" + syminfo.tickerid + ") TimeFrame = (" + timeframe.period + ") Current Price (" + str.tostring(close) + ") MACD < 0 And Crosses Down.", alert.freq_once_per_bar_close)
//End Code
CODEX#33CODEX#33 is a dynamic EMA-based system designed to visualize trend strength, volatility, and key market zones. It includes:
5 customizable EMAs (13, 21, 50, 200, 800)
Optional labels with future offset to keep charts clean
An EMA 50-based volatility cloud using standard deviation
Full control over visibility, colors, and label display
Built for clean execution and easy visual tracking of momentum shifts across all timeframes.
Simplicity is key!
Session Time MarkersAdds colored time markers on your 1 min, 5 min chart.
9:30am = White
11:00am = Blue
12:00 noon = Yellow
2:00pm = Purple
3:00pm = Orange
HTF 3rd Weekly High/LowThis indicator plots horizontal lines for the high and low of a selected past weekly candle, allowing traders to visualize higher time frame (HTF) structure on lower time frame charts (e.g., 1H, 4H, etc.).
Features:
Custom Weekly Range Selection: Use the dropdown to choose which weekly candle to reference — from the current week (0) to up to five weeks back.
Clean Horizontal Lines: High and low levels of the selected week are drawn as persistent horizontal lines.
Automatic Text Labels: Labels like Week-3H and Week-3L are shown on the right side of the chart, matching the week selected.
Customization:
Line colors
Line width and style (solid, dotted, dashed)
Text label offset
Automatic Refresh: Levels and labels are redrawn at the start of each new week to stay current with your selection.
ML Super Divergence + ADX ConfirmationThe "ML Super Divergence + ADX Confirmation" indicator is a powerful tool designed for technical traders who want to combine machine learning-inspired divergence signals with the ADX (Average Directional Index) trend strength indicator for more reliable trade signals.
This script is written in Pine Script v6 and is suitable for use on TradingView charts. It visually highlights potential buy and sell opportunities and also generates alerts when conditions are met.
📈 Core Components:
1. ADX System (Average Directional Index):
Measures trend strength, not direction.
Calculated using a 14-period (user-configurable) setting.
DI+ (Positive Directional Index) and DI− (Negative Directional Index) are used to gauge bullish and bearish pressure.
Signals are only valid when ADX > threshold (e.g., 20), ensuring that the trend is strong enough to act upon.
2. Simulated ML Super Divergence Signal:
This is a placeholder for actual ML-based divergence detection.
For demonstration, it simulates:
Bullish divergence: when RSI crosses above 30 and OBV (On-Balance Volume) is rising.
Bearish divergence: when RSI crosses below 70 and OBV is falling.
This approximation helps visualize how real ML divergence signals could be integrated.
✅ Buy Signal Conditions:
A buy signal is plotted when:
A bullish divergence is detected.
DI+ is greater than DI− (i.e., bullish directional strength).
ADX is above the minimum threshold (confirming a strong trend).
📍 Visual cue: Green upward triangle below the price bar.
🔔 Alert Triggered: "Buy Signal: ML Super Divergence + DI+ + ADX Confirmation"
❌ Sell Signal Conditions:
A sell signal is plotted when:
A bearish divergence is detected.
DI− is greater than DI+ (i.e., bearish directional strength).
ADX is above the threshold.
📍 Visual cue: Red dot above the price bar.
🔔 Alert Triggered: "Sell Signal: ML Super Divergence + DI- + ADX Confirmation"
