Bear Market Defender [QuantraSystems]Bear Market Defender
A system to short Altcoins when BTC is ranging or falling - benefit from Altcoin bleed or collapse .
QuantraSystems guarantees that the information created and published within this document and on the TradingView platform is fully compliant with applicable regulations, does not constitute investment advice, and is not exclusively intended for qualified investors.
Important Note!
The system equity curve presented here has been generated as part of the process of testing and verifying the methodology behind this script.
Crucially, it was developed after the system was conceptualized, designed, and created, which helps to mitigate the risk of overfitting to historical data. In other words, the system was built for robustness, not for simply optimizing past performance.
This ensures that the system is less likely to degrade in performance over time, compared to hyper-optimized systems that are tailored to past data. No tweaks or optimizations were made to this system post-backtest.
Even More Important Note!!
The nature of markets is that they change quickly and unpredictably. Past performance does not guarantee future results - this is a fundamental rule in trading and investing.
While this system is designed with broad, flexible conditions to adapt quickly to a range of market environments, it is essential to understand that no assumptions should be made about future returns based on historical data. Markets are inherently uncertain, and this system - like all trading systems - cannot predict future outcomes.
INTRODUCTION TO THE STAR FRAMEWORK
The STAR Framework – an abbreviation for Strategic Trading with Adaptive Risk - is a bespoke portfolio-level infrastructure for dynamic, multi-asset crypto trading systems. It combines systematic position management, adaptive sizing, and “intra-system” diversification, all built on a rigorous foundation of Risk-based position sizing .
At its core, STAR is designed to facilitate:
Adaptive position sizing based on user-defined maximum portfolio risk
Capital allocation across multiple assets with dynamic weight adjustment
Execution-aware trading with robust fee and slippage adjustment
Realistic equity curve logic based on a compounding realized PnL and additive unrealized PnL
The STAR Framework is intended for use as both a standalone portfolio system or preferred as a modular component within a broader trading “global portfolio” - delivering a balance of robustness and scalability across strategy types, timeframes, and market regimes.
RISK ALLOCATION VIA "R" CALCULATIONS
The foundational concept behind STAR is the use of the R unit - a dynamic representation of risk per trade. R is defined by the distance between a trade's entry and its stoploss, making it an intuitive and universally adaptive sizing unit across any token, timeframe, or market.
Example: Suppose the entry price is $100, and the stoploss is $95. A $5 move against the position represents a 1R loss. A 15% price increase to $115 would equal a +3R gain.
This makes R-based systems highly flexible: the user defines the percentage of capital that is put at risk per R and all positions are scaled accordingly - whether the token is volatile, illiquid, or slow-moving.
R is an advantageous method for determine position sizing - instead of being tied to complex value at risk mechanisms with having layered exit criteria, or continuous volatility-based sizing criteria that need to be adjusted while in an open trade, R allows for very straightforward sizing, invalidation and especially risk control – which is the most fundamental.
REALIZED BALANCE, FEES & SLIPPAGE ACCOUNTING
All position sizing, risk metrics, and the base equity curve within STAR are calculated based on realized balance only .
This means:
No sizing adjustments are made based on unrealized profit and loss ✅
No active positions are included in the system's realized equity until fully closed ✅
Every trade is sized precisely according to current locked-in realized portfolio balance ✅
This creates the safest risk profile - especially when multiple trades are open. Unrealized gains are not used to inflate sizing, ensuring margin safety across all assets.
All calculations also incorporate slippage and fees, based on user-defined estimates – which can and should be based upon user-collected data - and updated frequently forwards in time. These are not cosmetic, or simply applied to the final equity curve - they are fully integrated into the dynamic position sizing and equity performance , ensuring:
Stoploss hits result in exactly a −1R loss, even after slippage and fees ✅
Winners are discounted based on realistic execution costs ✅
No trade is oversized due to unaccounted execution costs ✅
Example - Slippage in R Units:
Let R be defined as the distance from entry to stoploss.
Suppose that distance is $1, and the trade is closed at a win of +$2.
If execution slippage leads to a 50 cent worse entry and a 50 cent worse exit, you’ve lost $1 extra - which is an additional 1R in execution slippage. This makes the effective return 1.0R instead of the intended 2.0R.
This is equivalent to a slippage value of 50%.
Thus, slippage in STAR is tracked and modelled on an R-adjusted basis , enabling more accurate long-term performance modelling.
MULTI-ASSET, LONG/SHORT SUPPORT
STAR supports concurrent long and short positions across multiple tokens. This can sometimes result in partially hedged exposure - for example, being long one asset and short another.
This structure has key benefits:
Diversifies idiosyncratic risk by distributing exposure across multiple tokens
Allows simultaneous exploitation of relative strength and weakness
Reduces portfolio volatility via natural hedging during reduced trending periods
Even in a highly correlated market like crypto, short-term momentum behaviour often varies between tokens - making diversified, multi-directional exposure a strategic advantage .
EQUITY CURVE
The STAR framework only updates the underlying realized equity when a position is closed, and the trade outcome is known. This approach ensures:
True representation of actual capital available for trading
No exposure distortion due to unrealized gains
Risk remains tightly linked to realized results
This trade-to-trade basis for realized equity modelling eliminates the common pitfall of overallocation based on unrealized profits.
The visual equity curve represents an accurate visualization of the Total Equity however, which is equivalent to what would be the realized equity if all trades were closed on the prior bar close.
TIMEFRAME CONSIDERATIONS
Lower timeframes typically yield better performance for STAR due to:
Greater data density per day - more observations = better statistical inference
Faster compounding - more trades per week = faster capital rotation
However, lower timeframes also suffer from increased slippage and fees. STAR's execution-aware structure helps mitigate this, but users must still choose timeframes appropriate to their liquidity, costs, and operational availability.
INPUT OPTIONS
Fees (direct trading costs - the percentage of capital removed from the initial position size)
Slippage (execution delay, as a percentage. In practice, the fill price is often worse than the signal price. This directly affects R and hence position sizing)
Risk % ( Please note : this is the risk level if every position is opened at once. 5% risk for 5 assets is 1% risk per position)
System Start date
Float Precision value of displayed numbers
Table visualization - positioning and table sizes
Adjustable color options
VISUAL SIMPLICITY
To avoid usual unnecessary complexity and empower fast at-a-glance action taking, as well as enable mobile compatibility, only the most relevant information is presented.
This includes all information required to open positions in one table.
As well as a quick and straightforward overview for the system stats
Lastly, there is an optional table that can be enabled
displaying more detailed information if desired:
USAGE GUIDELINES
To use STAR effectively:
Input your average slippage and fees %
Input your maximum portfolio risk % (this controls overall leverage and is equivalent to the maximum loss that the allocation to STAR would bring if ALL positions are allocated AND hit their stop loss at the same time)
Wait for signal alerts with entry, stop, and size details
STAR will dynamically calculate sizing, risk exposure, and portfolio allocation on your behalf. Position multipliers, stop placement, and asset-specific risk are all embedded in the system logic.
Note: Leverage must be manually set to ISOLATED on your exchange platform to prevent unwanted position linking.
ABOUT THE BEAR MARKET DEFENDER STRATEGY
The first strategy to launch on the STAR Framework is the BEAR MARKET DEFENDER (BMD) - a fast-acting, trend following system based upon the Trend Titan NEUTRONSTAR. For the details of the logic behind NEUTRONSTAR, please refer to the methodology and trend aggregation section of the following indicator:
The BMD ’s short side exit calculation methodology is slightly improved compared to NEUTRONSTAR, to capture downtrends more consistently and also cut positions faster – which is crucial when considering general jump risk in the Crypto space.
Accordingly, the only focus of the BMD is to capture trends to the short side, providing the benefit of being in a spectrum from no correlation to being negatively correlated in risk and return behavior to classical Crypto long exposure.
More precisely, Crypto behavior showcases that when Bitcoin is in a ranging/mean reverting environment, most tokens that don’t fall into the “Blue-Chip” category tend to find themselves in a trend towards 0.
Typically during this period most Crypto portfolios suffer heavily due to a “Crypto-long” biased exposure.
The Bear Market Defender thrives in these chaotic, high volatility markets where most coins trend towards zero while the traditional Crypto long exposure is either flat or in a drawdown, therefore the BMD adds a source of uncorrelated risk and returns to hedge typical long exposure and bolster portfolio volatility.
Because of the BMD's short-only exposure, it will often suffer small losses during strong uptrends. During these periods, long exposure performs the best and the goal is to outperform the temporary underperformance in the BMD .
To take advantage of the abovementioned behavior of most tokens trending to zero, assets traded in the BMD are systematically updated on a quarterly basis with available liquidity being an important consideration for the tokens to be eligible for selection.
FINAL SUMMARY
The STAR Framework represents a new generation of portfolio grade trading infrastructure, built around disciplined execution, realized equity, and adaptive position sizing. It is designed to support any number of future methodologies - beginning with BMD .
The Bear Market Defender is here to hedge out commonly long biased portfolio allocations in the Crypto market, specializing in bringing uncorrelated returns during periods of sideways price action on Bitcoin, or whole-market downturns.
Together, STAR + BMD deliver a scalable, volatility tuned system that prioritizes capital preservation, signal accuracy, and adaptive risk allocation. Whether deployed standalone or within a broader portfolio, this framework is engineered for high performance, longevity, and adaptability in the ever-evolving crypto landscape.
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Position Size Calculator v206/17/2025 - Updated to add MGC to list of instruments
Position Size Calculator for Futures Trading
A professional position sizing tool designed specifically for futures traders who want to maintain disciplined risk management. This indicator calculates the optimal number of contracts based on your predefined risk amount and provides instant visual feedback.
Key Features:
• Interactive price selection - simply click on the chart to set entry, stop loss, and take profit levels
• Supports all major futures contracts: ES, NQ, GC, RTY, YM, MNQ, MES with accurate contract specifications
• Customizable risk amount (defaults to $500 but fully adjustable)
• Real-time position size calculations that never exceed your risk tolerance
• Visual risk validation with color-coded header (green = valid risk, red = excessive risk)
• Automatic 2:1 risk/reward ratio calculations
• Compact, non-intrusive table display in top-right corner
• Clean interface with no chart clutter
How to Use:
Select your futures instrument from the dropdown
Set your maximum risk amount (default $500)
Click on the chart to set your Entry Price
Click on the chart to set your Stop Loss Price
Optionally click to set your Take Profit Price
The calculator instantly shows maximum contracts, actual risk, expected profit, and R/R ratio
Risk Management:
The indicator enforces strict risk management by calculating the maximum number of contracts you can trade while staying within your specified risk limit. The header turns green when your trade is within acceptable risk parameters and red when the risk is too high, providing instant visual feedback.
Perfect for day traders, swing traders, and anyone trading futures who wants to maintain consistent position sizing and risk management discipline.
SAFE Leverage Pro x50Safe Leverage Pro x50 — Safe leverage based on timeframes
Description:
Safe Leverage Pro x50 is an indicator designed to help traders choose prudent and realistic leverage, tailored to the timeframe being traded and the asset chosen.
Based on rigorous statistical research, this indicator provides a visual recommendation of the maximum typical leverage by timeframe and automatically suggests a more conservative value (by default, half) for trading with greater peace of mind and risk control.
* The goal is not for the indicator to make decisions for you, but rather to support your pre-defined entry strategies, allowing you to clearly understand how much leverage you can use without compromising your account against normal price fluctuations.
*The indicator does not calculate based on real-time volatility or ATR, but rather relies on statistical historical patterns obtained by analyzing price behavior after entry, differentiating between average movements in long and short entries by timeframe.
Important: Before following the recommendations of this indicator, check the maximum leverage your broker or exchange allows for the asset you are trading, as it can vary significantly between platforms.
* Philosophy behind the indicator:
This project arises as a response to the simplistic discourse that condemns leverage without distinguishing nuances.
Leverage is not intrinsically bad. What is dangerous is leveraging without method, without awareness, and without risk management.
Safe Leverage Pro x50 is designed to change that narrative:
** It's not about whether or not to use leverage, but when, how much, and how to use it intelligently.
Adaptive Momentum Deviation Oscillator | QuantMACAdaptive Momentum Deviation Oscillator | QuantMAC 📊
Overview 🎯
The Adaptive Momentum Deviation Oscillator (AMDO) is an advanced technical analysis indicator that combines the power of Bollinger Bands with adaptive momentum calculations to identify optimal entry and exit points in financial markets. This sophisticated oscillator creates dynamic bands that adapt to market volatility while providing clear visual signals for both trending and ranging market conditions.
How It Works 🔧
Core Methodology
The AMDO employs a sophisticated multi-layered approach to market analysis through four distinct phases:
Bollinger Band Foundation : The indicator begins by establishing a volatility baseline using traditional Bollinger Bands. These bands are calculated using a simple moving average as the center line, with upper and lower bands positioned at a specific number of standard deviations away from this centerline. The distance between these bands expands and contracts based on market volatility, creating a dynamic envelope around price action.
BB% Normalization Process : The raw price data is then transformed into a normalized percentage format that represents where the current price sits within the Bollinger Band envelope. When price is at the lower band, this percentage reads 0%; at the upper band, it reads 100%. This normalization allows for consistent comparison across different timeframes and price levels, creating a standardized oscillator that oscillates between extreme values.
Adaptive Momentum Band Construction : The normalized BB% values undergo a secondary volatility analysis where their own standard deviation is calculated over a specified period. This creates "bands around the bands" - upper and lower boundaries that adapt to the volatility of the normalized price position itself. These adaptive bands expand during periods of high momentum volatility and contract during consolidation phases.
Intelligent Signal Synthesis : The final layer combines the adaptive momentum bands with user-defined threshold levels to create a sophisticated trigger system. The indicator monitors when the dynamic bands cross above or below these thresholds, filtering out noise while capturing significant momentum shifts. This creates a dual-confirmation system where both volatility adaptation and threshold breaches must align for signal generation.
Key Components 🛠️
Adaptive Momentum Bands 📈
Dynamic Volatility Response : These bands automatically widen during periods of high momentum volatility and narrow during consolidation phases. Unlike fixed oscillator boundaries, they continuously recalibrate based on recent price behavior within the Bollinger Band framework.
Dual-Layer Calculation : The bands are derived from the volatility of the normalized price position itself, creating a "volatility of volatility" measurement. This provides early warning signals when momentum characteristics are changing, even before price breakouts occur.
State-Aware Visualization : The bands employ intelligent color coding that transitions between active and neutral states based on their interaction with threshold levels. Active states indicate high-probability momentum conditions, while neutral states suggest consolidation or indecision.
Momentum Persistence Tracking : The bands maintain memory of recent momentum characteristics, allowing them to distinguish between genuine momentum shifts and temporary price spikes or dips.
Threshold Levels 🎚️
Statistical Significance Boundaries : The threshold levels (default 83 for long, 40 for short) are positioned to capture statistically significant momentum events while filtering out market noise. These levels represent points where momentum probability shifts meaningfully in favor of directional moves.
Asymmetric Design Philosophy : The intentional asymmetry between long and short thresholds (83 vs 40) reflects the natural upward bias of many financial markets and the different risk/reward profiles of long versus short positions.
Contextual Sensitivity : The thresholds work in conjunction with the adaptive bands to create context-sensitive triggers. A threshold breach is only meaningful when it occurs in the proper sequence with band interactions.
Risk-Adjusted Positioning : The threshold levels are calibrated to provide favorable risk-adjusted entry points, considering both the probability of success and the potential magnitude of subsequent moves.
Bollinger Bands Overlay 📊
Multi-Timeframe Context : The price chart overlay provides essential context by showing traditional Bollinger Bands alongside the oscillator. This dual perspective allows traders to see both the absolute price position and the momentum characteristics simultaneously.
Support/Resistance Identification : The filled band area creates a visual representation of dynamic support and resistance levels. Price interaction with these bands provides additional confirmation for oscillator signals.
Volatility Environment Assessment : The width and slope of the bands offer immediate visual feedback about the current volatility environment, helping traders adjust their expectations and risk management accordingly.
Confluence Analysis : The overlay enables traders to identify confluence between price action at Bollinger Band levels and oscillator signals, creating higher-probability trade setups.
Signal Generation ⚡
The AMDO generates signals through precise mathematical crossover events:
Long Signals 🟢
Momentum Accumulation Detection : Long signals are generated when the lower adaptive momentum band crosses above the 83 threshold, indicating that downside momentum has exhausted and bullish momentum is beginning to accumulate. This represents a shift from defensive to offensive market posture.
Statistical Edge Confirmation : The crossing event occurs only when momentum characteristics have shifted sufficiently to provide a statistical edge for long positions. The adaptive nature ensures the signal quality remains consistent across different market volatility regimes.
Visual State Synchronization : Upon signal generation, the entire indicator ecosystem shifts to a bullish state - bar colors change, band states update, and the visual hierarchy emphasizes the long bias until conditions change.
Momentum Persistence Validation : The signal incorporates momentum persistence analysis to distinguish between genuine trend starts and false breakouts, reducing whipsaw trades in choppy market conditions.
Short Signals 🔴
Momentum Exhaustion Recognition : Short signals trigger when the upper adaptive momentum band crosses below the 40 threshold, signaling that bullish momentum has peaked and bearish momentum is emerging. This asymmetric threshold reflects the different dynamics of bullish versus bearish market phases.
Volatility-Adjusted Timing : The adaptive band system ensures that short signals are generated with appropriate timing regardless of the underlying volatility environment, maintaining signal quality in both high and low volatility conditions.
Regime-Aware Activation : Short signals are only active in Long/Short trading mode, recognizing that not all trading strategies benefit from short positions. The indicator adapts its behavior based on the selected trading approach.
Risk-Calibrated Thresholds : The 40 threshold is specifically calibrated to capture meaningful bearish momentum shifts while accounting for the higher risk typically associated with short positions.
Cash Signals 💰
Defensive Positioning Logic : In Long/Cash mode, cash signals are generated when short conditions are met, allowing traders to move to a defensive cash position rather than taking on short exposure. This preserves capital during unfavorable market conditions.
Risk Mitigation Strategy : Cash signals represent a risk-off approach that removes market exposure when momentum conditions favor the short side, protecting long-biased portfolios from adverse market movements.
Opportunity Cost Optimization : The cash position allows traders to avoid negative returns while maintaining flexibility to re-enter long positions when momentum conditions improve, optimizing the risk-adjusted return profile.
Features & Customization ⚙️
Color Schemes 🎨
9 pre-built color schemes (Classic through Classic9)
Custom color override option
Dynamic color changes based on signal states
Trading Modes 📈
Long/Short : Full bidirectional trading capability
Long/Cash : Long-only strategy with cash positions
Performance Metrics 📊
The indicator includes a comprehensive suite of advanced performance analytics that provide deep insights into strategy effectiveness:
Risk-Adjusted Return Metrics
Sortino Ratio : Measures returns relative to downside deviation only, providing a more accurate assessment of risk-adjusted performance by focusing on harmful volatility rather than total volatility. This metric is particularly valuable for asymmetric return distributions.
Sharpe Ratio : Calculates excess return per unit of total risk, offering a standardized measure of risk-adjusted performance that allows for comparison across different strategies and timeframes.
Omega Ratio : Employs probability-weighted analysis to compare the likelihood and magnitude of gains versus losses, providing insights into the overall shape of the return distribution and tail risk characteristics.
Drawdown and Risk Analysis
Maximum Drawdown : Tracks the largest peak-to-trough equity decline, providing crucial information about the worst-case scenario and helping traders understand the emotional and financial stress they might encounter.
Dynamic Drawdown Monitoring : Continuously updates drawdown calculations in real-time, allowing traders to monitor current drawdown levels relative to historical maximums.
Trade Statistics and Profitability
Profit Factor Analysis : Compares gross profits to gross losses, revealing the efficiency of the trading approach and the relationship between winning and losing trades.
Win Rate Calculation : Provides the percentage of profitable trades, which must be interpreted in conjunction with profit factor and average trade size for meaningful analysis.
Trade Frequency Tracking : Monitors total trade count to assess strategy turnover and transaction cost implications.
Position Sizing Guidance
Half Kelly Percentage : Calculates optimal position sizing based on Kelly Criterion methodology, then applies a conservative 50% reduction to account for parameter uncertainty and reduce volatility. This provides mathematically-based position sizing guidance that balances growth with risk management.
Parameters & Settings 🔧
BMD Settings
- Base Length : Period for Bollinger Band calculation (default: 10)
- Source : Price data source (default: close)
- Standard Deviation Length : Period for volatility calculation (default: 35)
- SD Multiplier : Bollinger Band width multiplier (default: 1.0)
- BB% Multiplier : Scaling factor for BB% calculation (default: 100)
BMD Settings
Base Length : Period for Bollinger Band calculation (default: 10)
Source : Price data source (default: close)
Standard Deviation Length : Period for volatility calculation (default: 35)
SD Multiplier : Bollinger Band width multiplier (default: 1.0)
BB% Multiplier : Scaling factor for BB% calculation (default: 100)
Signal Thresholds 🎯
Long Threshold : Trigger level for long signals (default: 83)
Short Threshold : Trigger level for short signals (default: 40)
Display Options 🖥️
Toggleable metrics table with 6 position options
Customizable date range limiter
Multiple visual elements for comprehensive analysis
Use Cases & Applications 💡
Trend Following
Identifies momentum shifts in trending markets
Provides early entry signals during trend continuations
Adaptive bands adjust to changing volatility conditions
Mean Reversion
Detects oversold/overbought conditions
Signals potential reversal points
Works effectively in ranging markets
Risk Management
Built-in performance metrics for strategy evaluation
Half Kelly percentage for position sizing guidance
Maximum drawdown monitoring
Advantages ✅
Adaptive Nature : Automatically adjusts to market volatility
Dual Display : Oscillator and price chart components work together
Comprehensive Metrics : Built-in performance analysis
Flexible Trading Modes : Supports different trading strategies
Visual Clarity : Color-coded signals and states
Customizable : Extensive parameter adjustment options
Important Considerations ⚠️
This indicator is designed for educational and analysis purposes
Should be used in conjunction with other technical analysis tools
Proper risk management is essential when trading
Backtest thoroughly before implementing in live trading
Market conditions can change rapidly, affecting indicator performance
Disclaimer ⚠️
Past performance is not indicative of future results. Trading involves substantial risk of loss and is not suitable for all investors. The information provided by this indicator should not be considered as financial advice. Always conduct your own research.
No indicator guarantees profitable trades - Always use proper risk management! 🛡️
Luma DCA Simulator (BTC only)Luma DCA Simulator – Guide
What is the Luma DCA Simulator?
The Luma DCA Tracker shows how regular Bitcoin investments (Dollar Cost Averaging) would have developed over a freely selectable period – directly in the chart, transparent and easy to follow.
Settings Overview
1. Investment amount per interval
Specifies how much capital is invested at each purchase (e.g. 100).
2. Start date
Defines the point in time from which the simulation begins – e.g. 01.01.2020.
3. Investment interval
Determines how frequently investments are made:
– Daily
– Weekly
– Every 14 days
– Monthly
4. Language
Switches the info box display between English and German.
5. Show investment data (optional)
If activated, the chart will display additional values such as total invested capital, BTC amount, current value, and profit/loss.
What the Chart Displays
Entry points: Each DCA purchase is marked as a point in the price chart.
Average entry price: An orange line visualizes the evolving DCA average.
Info box (bottom left) with a live summary of:
– Total invested capital
– Total BTC acquired
– Average entry price
– Current portfolio value
– Profit/loss in absolute terms and percentage
Note on Accuracy
This simulation is for illustrative purposes only.
Spreads, slippage, fees, and tax effects are not included.
Actual results may vary.
Technical Note
For daily or weekly intervals, the chart timeframe should be set to 1 day or lower to ensure all purchases are accurately included.
Larger timeframes (e.g. weekly or monthly charts) may result in missed investments.
Currency Handling
All calculations are based on the selected chart symbol (e.g. BTCUSD, BTCEUR, BTCUSDT).
The displayed currency is automatically determined by the chart used.
Luma DCA Tracker (BTC)Luma DCA Tracker (BTC) – User Guide
Function
This indicator simulates a regular Bitcoin investment strategy (Dollar Cost Averaging). It calculates and visualizes:
Accumulated BTC amount
Average entry price
Total amount invested
Current portfolio value
Profit/loss in absolute and percentage terms
Settings
Investment per interval
Fixed amount to be invested at each interval (e.g., 100 USD)
Start date
The date when DCA simulation begins
Investment interval
Choose between:
daily, weekly, every 14 days, or monthly
Show investment data
Displays additional chart lines (total invested, value, profit, etc.)
Chart Elements
Orange line: Average DCA entry price
Grey dots: Entry points based on selected interval
Info box (bottom left): Live summary of all key values
Notes
Purchases are simulated at the closing price of each interval
No fees, slippage, or taxes are included
The indicator is a simulation only and not linked to an actual portfolio
X-Day Capital Efficiency ScoreThis indicator helps identify the Most Profitable Movers for Your fixed Capital (ie, which assets offer the best average intraday profit potential for a fixed capital).
Unlike traditional volatility indicators (like ATR or % change), this script calculates how much real dollar profit you could have made each day over a custom lookback period — assuming you deployed your full capital into that ticker daily.
How it works:
Calculates the daily intraday range (high − low)
Filters for clean candles (where body > 60% of the candle range)
Assumes you invested the full amount of capital ($100K set as default) on each valid day
Computes an average daily profit score based on price action over the selected period (default set to 20 days)
Plots the score in dollars — higher = more efficient use of capital
Why It’s Useful:
Compare tickers based on real dollar return potential — not just % volatility
Spot low-priced, high-volatility stocks that are better suited for intraday or momentum trading
Inputs:
Capital ($): Amount you're hypothetically deploying (e.g., 100,000)
Look Back Period: Number of past days to average over (e.g., 20)
Futures Margin Lookup TableThis script applies a table to your chart, which provides the intraday and overnight margin requirements of the currently selected symbol.
In this indicator the user must provide the broker data in the form of specifically formatted text blocks. The data for which should be found on the broker website under futures margin requirements.
The purpose for it's creation is due to the non-standard way each individual broker may price their margins and lack of information within TradingView when connected to some (maybe all) brokers, including during paper trading, as the flat percentage rule is not accurate.
An example of information could look like this
MES;Micro S&P;$50;$2406
ES;E-Mini S&P;$500;$24,053
GC;Gold;$500;$16500
NQ;E-Mini Nasdaq;$1,000;$34,810
FDAX;Dax Index;€2,000;€44,311
Each symbol begins a new line, and the values on that line are separated by semicolons (;)
Each line consists of the following...
SYMBOL : Search string used to match to the beginning of the current chart symbol.
NAME: Human readable name
INTRA: Intraday trading margin requirement per contract
OVERNIGHT: Overnight trading margin requirement per contract
The script simply finds a matching line within your provided information using the current chart symbol.
So for example the continuous chart for
NQ1!
would match to the user specified line starting with NQ... as would the individual contract dates such as NQM2025, NQK2025, etc.
NOTES:
There is a possibility that symbols with similar starting characters could match. If this is the case put the longer symbol higher in the list.
There is also a line / character limit to the text input fields within pinescript. Ensure the text you enter / paste into them is not truncated. If so there are 3 input fields for just this purpose. Find the last complete line and continue the remaining symbol lines on the subsequent inputs.
MÈGAS ALGO : MÈGAS Engine [STRATEGY]Overview
The MÈGAS Engine is an advanced algorithmic trading system that integrates a range of technical analysis tools to pinpoint high-probability opportunities in the market.
Key Features
Core Signal Generation:
-Structure Break Detection: Advanced breakout identification with adjustable
sensitivity controls
-Dual-Direction Analysis: Separate bullish and bearish signal parameters with customizable delta
thresholds and depth settings
-Dynamic Parameter Management: OverfitShield technology with pulsewave parameter cycling
to reduce overfitting risks
Filtering Alghoritm:
-Volatility Filter: Rogers-Satchell volatility estimation with RSI-based normalization to avoid
trading in unfavorable market conditions
-Volume Confirmation: Cumulative volume analysis ensuring adequate liquidity support for trade
entries
OverfitShield Method:
OverfitShield is a built-in function within the trading strategy designed to reduce overfitting bias by introducing parameter variability during execution. When the "variable" mode is activated, instead of relying on fixed values for key strategy parameters the system dynamically selects values from customizable ranges.
This approach mimics real-world market uncertainty and ensures that the strategy does not become overly dependent on a single optimal value found during backtesting — making it more robust across different market conditions and time periods.
Position Management
-Customizable Exit Set-up
The exit logic can be customized to 'CONTINUE', 'TAKE PROFIT', or 'TRAILING PROFIT' to suit
your trading approach and maximize performance.
-CONTINUE Mode:
This mode does not use predefined take profit levels. Instead, it remains in the market as long as the trend persists. By avoiding fixed exit points, this approach is often the most effective in backtesting, as it allows positions to run in favorable trends for longer periods.
-TAKE PROFIT Mode:
This mode allows you to set multiple grid-like take profit levels at different price points, effectively creating a multi-tier exit strategy. You can specify the number of profit levels you want, along with the percentage step between each level. This structured approach can be beneficial for capturing incremental profits in a trending market while allowing for more flexibility in trade management.
-TRAILING PROFIT Mode:
Similar to the Take Profit mode, this option allows you to set the trailing stop levels. The trailing stop moves with the market, ensuring that you lock in profits as the price continues to move in your favor. Once a profit level is hit, the trailing stop "follows" the price movement, adjusting dynamically to safeguard profits as the trade progresses.
3. Customizable Insight Alerts
Traders can configure personalized alert messages for every strategy action, including entries, exits, and profit targets. These alerts are fully compatible with TradingView's webhook system.
Advantages
Customization: Fully customizable exit set-up and alerts allow traders to tailor the strategy to their personal trading objectives.
How It Works — Step by Step
Step 1: Apply the Strategy
Open the chart for your selected symbol and timeframe. Add the MÈGAS Engine to the chart.
Step 2:Backtesting and Optimization
Run a full backtest and optimize the strategy parameters across the chosen trading pairs to:
Identify robust settings that perform consistently well
Avoid overfitting through validation techniques
Select the most profitable and stable configuration for live or forward testing.
Step 3: Review Results and Alerts
Check the backtest results on the chart and confirm that the custom alert messages are displaying as expected. This helps verify that everything is functioning correctly before moving forward.
Step 4: Configure Portfolio Management
Set up the exit logic based on your specific requirements. Tailor the exit strategy to match your trading approach, whether you prefer predefined take profit levels, trailing stops, or a trend-following method. This flexibility ensures the exit logic aligns with your overall strategy for optimal performance.
Open the strategy settings window. In the dedicated portfolio management section, choose your preferred capital allocation method based on your trading style and risk preferences. Once set, save the configuration as the default.
Step 5: Set Up Alerts
Click "Add Alert" on the strategy
-In the message field, use: {{strategy.order.comment}}
Under the Notifications tab:
-Enable Webhook URL
-Enter your external webhook address
-Click 'Create' to activate alerts for your strategy
Please Note:
The results and visualizations presented are derived from optimized backtesting iterations using historical and paid real-time market data sourced via TradingView. While these results are intended to demonstrate potential performance, they do not guarantee future outcomes or accuracy. Past performance is not indicative of future results, and all trading involves risk.
We strongly recommend that users review and adjust the Properties within the script settings to align with their specific account configurations and preferred trading platforms. This ensures that the strategy outputs are reflective of real-world conditions and enhances the reliability of the results obtained. Use this tool responsibly and at your own risk.
Technical Strength Index (TSI)📘 TSI with Dynamic Bands – Technical Strength Index
The TSI with Dynamic Bands is a multi-factor indicator designed to measure the statistical strength and structure of a trend. It combines several quantitative metrics into a single, normalized score between 0 and 1, allowing traders to assess the technical quality of market moves and detect overbought/oversold conditions with adaptive precision.
🧠 Core Components
This indicator draws from the StatMetrics library, blending:
📈 Trend Persistence: via the Hurst exponent, indicating whether price action is mean-reverting or trending.
📉 Risk-Adjusted Volatility: via the inverted , rewarding smoother, less erratic price movement.
🚀 Momentum Strength: using a combination of directional momentum and Z-score–normalized returns.
These components are normalized and averaged into the TSI line.
🎯 Features
TSI Line: Composite score of trend quality (0 = weak/noise, 1 = strong/structured).
Dynamic Bands: Mean ± 1 standard deviation envelopes provide adaptive context.
Overbought/Oversold Detection: Based on a rolling quantile (e.g. 90th/10th percentile of TSI history).
Signal Strength Bar (optional): Measures how statistically extreme the current TSI value is, helping validate confidence in trade setups.
Dynamic Color Cues: Background and bar gradients help visually identify statistically significant zones.
📈 How to Use
Look for overbought (red background) or oversold (green background) conditions as potential reversal zones.
Confirm trend strength with the optional signal strength bar — stronger values suggest higher signal confidence.
Use the TSI line and context bands to filter out noisy ranges and focus on structured price moves.
⚙️ Inputs
Lookback Period: Controls the smoothing and window size for statistical calculations.
Overbought/Oversold Quantiles: Adjust the thresholds for signal zones.
Plot Signal Strength: Enable or disable the signal confidence bar.
Overlay Signal Strength: Show signal strength in the same panel (compact) or not (cleaner TSI-only view).
🛠 Example Use Cases
Mean reversion traders identifying reversal zones with statistical backing
Momentum/Trend traders confirming structure before entries
Quantitative dashboards or multi-asset screening tools
⚠️ Disclaimer
This script is for educational and informational purposes only. It does not constitute financial advice or a recommendation to buy or sell any financial instrument.
This AI is not a financial advisor; please consult your financial advisor for personalized advice.
Zuper Custom Index (Up to 40 Stocks)Description:
This indicator empowers you to create your own custom index from up to 40 stocks, with full control over the weight of each component. Whether you want to track a sector, a basket of favourites, or build your own benchmark, this tool gives you the flexibility and visualisation you need—all directly on your TradingView chart.
Key Features:
Supports Up to 40 Stocks:
Combine up to 40 different symbols into a single, custom index.
Flexible Weighting:
Assign a custom percentage weight to each stock. The script automatically normalizes your weights to ensure the index always sums to 100%.
Candlestick or Line Chart Display:
Choose between a candlestick chart (showing open, high, low, close of the index) or a simple line chart (close only) for your custom index.
Dynamic Rebalancing:
Change weights or symbols at any time; the index recalculates instantly.
Easy Symbol Input:
Add or remove stocks with simple input fields—no code editing required.
Perfect for Sector Analysis & Custom Baskets:
Track sectors, strategies, or any group of assets you care about.
How to Use:
Add the Indicator to Your Chart.
Enter Up to 40 Stock Symbols in the input fields.
Assign Weights (as percentages) to each symbol.
If all weights are left at zero, the index will use equal weighting.
If you enter custom weights, they will be automatically normalized.
Choose Your Display Type:
Select between a candlestick or line chart for your index.
Analyze Your Custom Index!
Use Cases:
Build your own sector or thematic index.
Track a custom ETF or fund composition.
Compare your portfolio’s performance as a single chart.
Visualize the impact of different weightings on a basket of stocks.
Notes:
The indicator uses TradingView’s latest Pine Script version for maximum performance and flexibility.
You can use any valid TradingView symbol (stocks, ETFs, indices, etc.).
For best results, ensure all symbols are available on your selected timeframe and exchange.
Create, visualize, and analyze your own custom indices—right on your TradingView chart!
SmartPhase Analyzer📝 SmartPhase Analyzer – Composite Market Regime Classifier
SmartPhase Analyzer is an adaptive regime classification tool that scores market conditions using a customizable set of statistical indicators. It blends multiple normalized metrics into a composite score, which is dynamically evaluated against rolling statistical thresholds to determine the current market regime.
✅ Features:
Composite score calculated from 13+ toggleable statistical indicators:
Sharpe, Sortino, Omega, Alpha, Beta, CV, R², Entropy, Drawdown, Z-Score, PLF, SRI, and Momentum Rank
Uses dynamic thresholds (mean ± std deviation) to classify regime states:
🟢 BULL – Strongly bullish
🟩 ACCUM – Mildly bullish
⚪ NEUTRAL – Sideways
🟧 DISTRIB – Mildly bearish
🔴 BEAR – Strongly bearish
Color-coded histogram for composite score clarity
Real-time regime label plotted on chart
Benchmark-aware metrics (Alpha, Beta, etc.)
Modular design using the StatMetrics library by RWCS_LTD
🧠 How to Use:
Enable/disable metrics in the settings panel to customize your composite model
Use the composite histogram and regime background for discretionary or systematic analysis
⚠️ Disclaimer:
This indicator is for educational and informational purposes only. It does not constitute financial advice or a trading recommendation. Always consult your financial advisor before making investment decisions.
Dip Hunter | QuantumResearch🎯 Dip Hunter | QuantumResearch
Precision Buy-the-Dip Detector
A percentile-powered anomaly detector engineered to catch deep retracements in uptrends.
🔍 What Is It?
Dip Hunter is a minimalist yet sharp signal engine designed to identify statistically rare dips within longer-term uptrends. Rather than relying on subjective oversold conditions, this tool leverages percentile analysis and volatility-adjusted filters to highlight moments of strong downside deviation.
Built for swing traders and accumulators, it’s ideal for timing entries during retracements — especially in strong trending environments.
⚙️ How It Works
📊 Dual Criteria for Dip Signals:
Percentile Threshold – The current low must fall below the percentile of the past selected bars.
Volatility Deviation Filter – The close must fall beneath a dynamic lower boundary.
When both conditions align, a signal is printed — capturing only statistically significant drops.
🧪 Key Components
Percentile Analysis
Volatility Band Calculation
Dynamic Baseline
🔷 You can adjust the lookback windows and data sources for both the percentile and deviation filter for optimal tuning per asset.
Visual Features
Custom Color Modes: Choose from 8 unique color palettes
Triangle Signal Markers: Dip signals printed under price bars
Overlay-Ready: Clean plot design that won’t clutter your chart
💼 Ideal Use Cases
Accumulating into long-term uptrends
Detecting reversion zones in parabolic moves
Enhancing DCA entries during temporary panic dips
Confluence with moving average or trend filters
🔔 Alerts Built-In
✅ “Buy the Dip” alert fires as soon as a qualifying dip is detected — perfect for automation or mobile notifications.
📌 Notes
Best used on daily or 4H charts.
Dip ≠ guaranteed reversal — use confluence (trend filters, volume, macro view).
Customize the length to balance between sensitivity and selectivity.
⚠️ Disclaimer
Disclaimer: The content on this script is for informational and educational purposes only. Nothing contained within should be considered financial, investment, legal, or other professional advice. Past performance does not guarantee future results. Trading cryptocurrencies involves substantial risk of loss and is not suitable for every investor.
Stocks ATR V2 📘 Description — Stocks AT SL V2
“Stocks AT SL V2” is a risk management indicator designed to help traders define dynamic stop-loss levels based on actual market volatility and advanced statistical analysis of price movements.
⸻
🎯 Purpose
This tool provides a rational and adaptive framework for stop-loss placement, taking into account:
Market volatility, measured by the Average True Range (ATR),
And a statistical safety buffer, based on the 95th percentile of historical price retracements.
⸻
⚙️ Methodology
ATR Calculation:
The indicator uses a 14-period ATR to measure recent average volatility.
Safety Factor (k) Estimation:
The script computes a set of ratios between the candle’s minimal retracement (relative to the previous close) and the current ATR.
The 95th percentile of these ratios is extracted to define a multiplicative factor k, representing common price extremes.
Final Stop-Loss:
The stop-loss is set at a distance of k × ATR below (or above) the entry price.
This helps reduce false stop-outs while allowing room for natural market movement—even in volatile conditions.
⸻
✅ Benefits
Automatically adapts to volatility.
Reflects real candlestick structure (not just arbitrary distances).
Standardizes risk across different stocks or currency pairs.
Pair TradingPAIR TRADING
Description:
This indicator is a simple and intuitive tool for rotating between two assets based on their relative price ratio. By comparing the prices of Asset A and Asset B, it plots a “ratio line” (gray) with dynamic upper and lower boundaries (red and blue).
When the ratio reaches the red line, Asset A is expensive → rotate out of A and into B.
When the ratio touches the blue line, Asset A is cheap → rotate back into A.
The chart also shows:
🔹 Background highlights for visual cues
🔹 “Rotate to A” or “Rotate to B” markers for easy decisions
🔹 A live summary table with mean ratio, upper/lower boundaries, and current ratio
How to Use:
Select Asset A and Asset B in the settings.
Adjust the Lookback Period and Threshold if needed.
Watch the gray ratio line as it moves:
Above red line? → Consider rotating into B
Below blue line? → Consider rotating into A
Use the background color changes and rotation labels to spot clear rotation opportunities!
Why Pair Trading?
Pair trading is a powerful way to manage a portfolio because it neutralizes market direction risk and focuses on relative value.
By rotating between correlated assets, you can:
Smooth out returns
Avoid holding a weak asset too long
Capture reversion when assets diverge too far
This approach can enhance risk-adjusted returns and help keep your portfolio balanced and nimble!
How to Pick Pairs:
Choose assets with strong correlation or similar drivers.
Look for common trends (sector, macro).
Start with assets you know best (high-conviction ideas).
Make sure both have good liquidity for reliable trading!
TO HELP FIND CORRELATED ASSETS:
Use the Correlation Coefficient indicator in TradingView:
Click Indicators
Search for “Correlation Coefficient”
Add it to your chart
Input the symbol of the second asset (e.g., if you’re on MSTR, input TSLA).
This plots the rolling correlation coefficient — super helpful!
Pair trading can turn big swings into steady rotations and help you stay active even when the market is choppy. It’s a simple, practical approach to keep your portfolio balanced.
Simple Position CalculatorThis indicator provides a real-time position sizing calculator designed for fast momentum trading. It instantly calculates optimal trade size based on your risk parameters, entry/exit prices, and exchange conditions (fees/slippage). Perfect for high-speed entries during candle closes and breakouts.
SUPER-MAGFLXMAGFLX
Made a bunch of these for different sectors, then realized they’re all basically the same—so you really only need one.
Here it is, with a few extra features like customizable display position and metric options.
Track 1 to 20+ tickers, your way, all in one clean, versatile template.
Features & Uses
Custom Ticker List: Enter any tickers you want to track—mix and match sectors or asset classes freely.
Flexible Display: Choose where the table appears on your chart (top-right, top-left, bottom-right, bottom-left).
Metric Options: Toggle on/off daily percentage change, current price, and price difference columns based on what you want to monitor.
Highlight Movers: Automatically spot and highlight the biggest gainer and biggest loser each day for quick insights.
Compact & Efficient: Fits neatly on your chart without clutter, whether tracking 1 ticker or 20+.
Color-Coded Data: Intuitive colors make it easy to spot gains, losses, and key movers at a glance.
User-Friendly: No coding needed—simply input your tickers and preferences to tailor your watchlist instantly.
Use it to:
Monitor your portfolio across multiple sectors in one place.
Quickly spot daily winners and losers.
Keep an eye on price trends and changes without opening multiple charts.
Save chart space while gaining market clarity.
Any comments welcomed there is no way to tell if a public script is being used right ? so if you use and like it give it boost or a comment to let me know
Portfolio Dashboard by DTRThe Portfolio Dashboard by DTR is a sophisticated yet user-friendly Pine Script indicator for TradingView, designed to empower traders with a comprehensive tool for managing and monitoring investment portfolios. Supporting up to 10 stocks, it delivers real-time performance metrics, risk analysis, and market insights in an intuitive, customizable dashboard—perfect for traders of all experience levels.
Key Features
Real-Time Portfolio Metrics: Tracks Return on Investment (ROI), Day's Profit and Loss (PNL), Risk of Profit (ROP), and Average Daily Range (ADR) with color-coded indicators for quick insights.
Individual Stock Insights: Displays detailed data for each stock, including ticker, trading setup, Last Traded Price (LTP) or Stop Loss (SL) status, position size, risk, portfolio risk, Risk-Reward (RR) or Gain%, daily change%, portfolio impact, and optional ADR.
Market Condition Analysis: Evaluates broader market trends using NSE:CNXSMALLCAP data, categorizing conditions as CHOPPY, BULL MARKET, BEAR MARKET, SHAKEOUT, or BEAR RALLY with visual color cues.
Customization Options:
Input total capital (scalable in Thousands, Lacs, or Crores) and maximum risk percentage.
Choose from B&W, Blue, Green, Red, Purple, or Transparent themes, with Dark Mode support.
Adjust dashboard and gauge positions (top/middle/bottom, left/center/right) and text sizes (tiny to huge).
Toggle display options like LTP, % change color, total row, ADR column, RR/Gain%, and empty rows.
Risk Management Tools: Calculates position sizes, individual and portfolio-level risks, and offers visual gauges for total allocation (% invested) and open risk (% of max risk). Supports setting Stop Loss to Break-Even (SL=BE).
Chart Enhancements: Optionally displays entry and stop loss lines on the chart with customizable styles (Dashed, Dotted, Normal) and dynamic labels for precise trade management.
How It Works
Setup: Users input portfolio details—ticker symbols, quantities, entry prices, stop losses, exits, and setups—for up to 10 stocks, along with capital and risk settings.
Data Processing: The indicator fetches daily high, low, close, and previous close data to compute metrics like ADR, percentage change, and Day's PNL for each stock.
Visualization: On the last bar, it generates a detailed table summarizing portfolio and stock-level data, alongside two gauges for allocation and risk, positioned per user preferences.
Chart Integration: When enabled, entry and SL lines with labels appear on the chart for the current ticker, updating dynamically based on price action.
How to Use
Add to Chart: Apply the indicator to your TradingView chart.
Configure Settings: In the settings panel, enter your total capital, stock details, and customize themes, positions, and display preferences.
Monitor Portfolio: Use the dashboard to assess portfolio health, risk exposure, and market conditions in real time.
Manage Trades: Leverage chart lines and labels to execute and adjust trades with precision.
Benefits
Centralized Oversight: Consolidates all essential portfolio data into one view.
Enhanced Risk Control: Provides real-time risk metrics and visual tools for proactive management.
Flexible Design: Adapts to various trading strategies and aesthetic preferences.
Intuitive Interface: Combines detailed analytics with clear, visually appealing presentation.
Important Notes
Accuracy: Ensure correct ticker symbols (e.g., NSE:RELIANCE) and price inputs for reliable results.
Timeframes: Optimized for daily or intraday charts; updates occur on the last bar.
Dependencies: Market condition and ADR calculations rely on NSE:CNXSMALLCAP data availability.
Elevate your trading with the Portfolio Dashboard by DTR—a powerful, all-in-one solution for portfolio management on TradingView. Take control of your investments today!
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.
Cross-Sectional Altcoin Portfolio [BackQuant]Cross-Sectional Altcoin Portfolio
Introducing BackQuant's Cross-Sectional Altcoin Portfolio, a sophisticated trading system designed to dynamically rotate among a selection of major altcoins. This portfolio strategy compares multiple assets based on real-time performance metrics, such as momentum and trend strength, to select the strongest-performing coins. It uses a combination of adaptive scoring and regime filters to ensure the portfolio is aligned with favorable market conditions, minimizing exposure during unfavorable trends.
This system offers a comprehensive solution for crypto traders who want to optimize portfolio allocation based on cross-asset performance, while also accounting for market regimes. It allows traders to compare multiple altcoins dynamically and allocate capital to the top performers, ensuring the portfolio is always positioned in the most promising assets.
Key Features
1. Dynamic Asset Rotation:
The portfolio constantly evaluates the relative strength of 10 major altcoins: SOLUSD, RUNEUSD, ORDIUSD, DOGEUSDT, ETHUSD, ENAUSDT, RAYUSDT, PENDLEUSD, UNIUSD, and KASUSDT.
Using a ratio matrix, the system selects the strongest asset based on momentum and trend performance, dynamically adjusting the allocation as market conditions change.
2. Long-Only Portfolio with Cash Reserve:
The portfolio only takes long positions or remains in cash. The system does not enter short positions, reducing the risk of exposure during market downturns.
A powerful regime filter ensures the system is inactive during periods of market weakness, defined by the Universal Trend Performance Indicator (TPI) and other market data.
3. Equity Tracking:
The script provides real-time visualizations of portfolio equity compared to buy-and-hold strategies.
Users can compare the performance of the portfolio against holding individual assets (e.g., BTC, ETH) and see the benefits of the dynamic allocation.
4. Performance Metrics:
The system provides key performance metrics such as:
Sharpe Ratio: Measures risk-adjusted returns.
Sortino Ratio: Focuses on downside risk.
Omega Ratio: Evaluates returns relative to risk.
Maximum Drawdown: The maximum observed loss from a peak to a trough.
These metrics allow traders to assess the effectiveness of the strategy versus simply holding the assets.
5. Regime Filter:
The system incorporates a regime filter that evaluates the overall market trend using the TPI and other indicators. If the market is in a downtrend, the system exits positions and moves to cash, avoiding exposure to negative market conditions.
Users can customize the thresholds for the long and short trends to fit their risk tolerance.
6. Customizable Parameters:
Traders can adjust key parameters, such as the backtest start date, starting capital, leverage multiplier, and visualization options, including equity plot colors and line widths.
The system supports different levels of customizations for traders to optimize their strategies.
7. Equity and Buy-and-Hold Comparisons:
This script enables traders to see the side-by-side comparison of the portfolio’s equity curve and the equity curve of a buy-and-hold strategy for each asset.
The comparison allows users to evaluate the performance of the dynamic strategy versus holding the altcoins in isolation.
8. Forward Test (Out-of-Sample Testing):
The system includes a note that the portfolio provides out-of-sample forward tests, ensuring the robustness of the strategy. This is crucial for assessing the portfolio's performance beyond historical backtesting and validating its ability to adapt to future market conditions.
9. Visual Feedback:
The system offers detailed visual feedback on the current asset allocation and performance. Candles are painted according to the trend of the selected assets, and key metrics are displayed in real-time, including the momentum scores for each asset.
10. Alerts and Notifications:
Real-time alerts notify traders when the system changes asset allocations or moves to cash, ensuring they stay informed about portfolio adjustments.
Visual labels on the chart provide instant feedback on which asset is currently leading the portfolio allocation.
How the Rotation Works
The portfolio evaluates 10 different assets and calculates a momentum score for each based on their price action. This score is processed through a ratio matrix, which compares the relative performance of each asset.
Based on the rankings, the portfolio allocates capital to the top performers, ensuring it rotates between the strongest assets while minimizing exposure to underperforming assets.
If no asset shows strong performance, the system defaults to cash to preserve capital.
Final Thoughts
BackQuant’s Cross-Sectional Altcoin Portfolio provides a dynamic and systematic approach to altcoin portfolio management. By employing real-time performance metrics, adaptive scoring, and regime filters, this strategy aims to optimize returns while minimizing exposure to market downturns. The inclusion of out-of-sample forward tests ensures that the system remains robust in live market conditions, making it an ideal tool for crypto traders seeking to enhance their portfolio's performance with a data-driven, momentum-based approach.
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.
Risk-Adjusted Momentum Oscillator# Risk-Adjusted Momentum Oscillator (RAMO): Momentum Analysis with Integrated Risk Assessment
## 1. Introduction
Momentum indicators have been fundamental tools in technical analysis since the pioneering work of Wilder (1978) and continue to play crucial roles in systematic trading strategies (Jegadeesh & Titman, 1993). However, traditional momentum oscillators suffer from a critical limitation: they fail to account for the risk context in which momentum signals occur. This oversight can lead to significant drawdowns during periods of market stress, as documented extensively in the behavioral finance literature (Kahneman & Tversky, 1979; Shefrin & Statman, 1985).
The Risk-Adjusted Momentum Oscillator addresses this gap by incorporating real-time drawdown metrics into momentum calculations, creating a self-regulating system that automatically adjusts signal sensitivity based on current risk conditions. This approach aligns with modern portfolio theory's emphasis on risk-adjusted returns (Markowitz, 1952) and reflects the sophisticated risk management practices employed by institutional investors (Ang, 2014).
## 2. Theoretical Foundation
### 2.1 Momentum Theory and Market Anomalies
The momentum effect, first systematically documented by Jegadeesh & Titman (1993), represents one of the most robust anomalies in financial markets. Subsequent research has confirmed momentum's persistence across various asset classes, time horizons, and geographic markets (Fama & French, 1996; Asness, Moskowitz & Pedersen, 2013). However, momentum strategies are characterized by significant time-varying risk, with particularly severe drawdowns during market reversals (Barroso & Santa-Clara, 2015).
### 2.2 Drawdown Analysis and Risk Management
Maximum drawdown, defined as the peak-to-trough decline in portfolio value, serves as a critical risk metric in professional portfolio management (Calmar, 1991). Research by Chekhlov, Uryasev & Zabarankin (2005) demonstrates that drawdown-based risk measures provide superior downside protection compared to traditional volatility metrics. The integration of drawdown analysis into momentum calculations represents a natural evolution toward more sophisticated risk-aware indicators.
### 2.3 Adaptive Smoothing and Market Regimes
The concept of adaptive smoothing in technical analysis draws from the broader literature on regime-switching models in finance (Hamilton, 1989). Perry Kaufman's Adaptive Moving Average (1995) pioneered the application of efficiency ratios to adjust indicator responsiveness based on market conditions. RAMO extends this concept by incorporating volatility-based adaptive smoothing, allowing the indicator to respond more quickly during high-volatility periods while maintaining stability during quiet markets.
## 3. Methodology
### 3.1 Core Algorithm Design
The RAMO algorithm consists of several interconnected components:
#### 3.1.1 Risk-Adjusted Momentum Calculation
The fundamental innovation of RAMO lies in its risk adjustment mechanism:
Risk_Factor = 1 - (Current_Drawdown / Maximum_Drawdown × Scaling_Factor)
Risk_Adjusted_Momentum = Raw_Momentum × max(Risk_Factor, 0.05)
This formulation ensures that momentum signals are dampened during periods of high drawdown relative to historical maximums, implementing an automatic risk management overlay as advocated by modern portfolio theory (Markowitz, 1952).
#### 3.1.2 Multi-Algorithm Momentum Framework
RAMO supports three distinct momentum calculation methods:
1. Rate of Change: Traditional percentage-based momentum (Pring, 2002)
2. Price Momentum: Absolute price differences
3. Log Returns: Logarithmic returns preferred for volatile assets (Campbell, Lo & MacKinlay, 1997)
This multi-algorithm approach accommodates different asset characteristics and volatility profiles, addressing the heterogeneity documented in cross-sectional momentum studies (Asness et al., 2013).
### 3.2 Leading Indicator Components
#### 3.2.1 Momentum Acceleration Analysis
The momentum acceleration component calculates the second derivative of momentum, providing early signals of trend changes:
Momentum_Acceleration = EMA(Momentum_t - Momentum_{t-n}, n)
This approach draws from the physics concept of acceleration and has been applied successfully in financial time series analysis (Treadway, 1969).
#### 3.2.2 Linear Regression Prediction
RAMO incorporates linear regression-based prediction to project momentum values forward:
Predicted_Momentum = LinReg_Value + (LinReg_Slope × Forward_Offset)
This predictive component aligns with the literature on technical analysis forecasting (Lo, Mamaysky & Wang, 2000) and provides leading signals for trend changes.
#### 3.2.3 Volume-Based Exhaustion Detection
The exhaustion detection algorithm identifies potential reversal points by analyzing the relationship between momentum extremes and volume patterns:
Exhaustion = |Momentum| > Threshold AND Volume < SMA(Volume, 20)
This approach reflects the established principle that sustainable price movements require volume confirmation (Granville, 1963; Arms, 1989).
### 3.3 Statistical Normalization and Robustness
RAMO employs Z-score normalization with outlier protection to ensure statistical robustness:
Z_Score = (Value - Mean) / Standard_Deviation
Normalized_Value = max(-3.5, min(3.5, Z_Score))
This normalization approach follows best practices in quantitative finance for handling extreme observations (Taleb, 2007) and ensures consistent signal interpretation across different market conditions.
### 3.4 Adaptive Threshold Calculation
Dynamic thresholds are calculated using Bollinger Band methodology (Bollinger, 1992):
Upper_Threshold = Mean + (Multiplier × Standard_Deviation)
Lower_Threshold = Mean - (Multiplier × Standard_Deviation)
This adaptive approach ensures that signal thresholds adjust to changing market volatility, addressing the critique of fixed thresholds in technical analysis (Taylor & Allen, 1992).
## 4. Implementation Details
### 4.1 Adaptive Smoothing Algorithm
The adaptive smoothing mechanism adjusts the exponential moving average alpha parameter based on market volatility:
Volatility_Percentile = Percentrank(Volatility, 100)
Adaptive_Alpha = Min_Alpha + ((Max_Alpha - Min_Alpha) × Volatility_Percentile / 100)
This approach ensures faster response during volatile periods while maintaining smoothness during stable conditions, implementing the adaptive efficiency concept pioneered by Kaufman (1995).
### 4.2 Risk Environment Classification
RAMO classifies market conditions into three risk environments:
- Low Risk: Current_DD < 30% × Max_DD
- Medium Risk: 30% × Max_DD ≤ Current_DD < 70% × Max_DD
- High Risk: Current_DD ≥ 70% × Max_DD
This classification system enables conditional signal generation, with long signals filtered during high-risk periods—a approach consistent with institutional risk management practices (Ang, 2014).
## 5. Signal Generation and Interpretation
### 5.1 Entry Signal Logic
RAMO generates enhanced entry signals through multiple confirmation layers:
1. Primary Signal: Crossover between indicator and signal line
2. Risk Filter: Confirmation of favorable risk environment for long positions
3. Leading Component: Early warning signals via acceleration analysis
4. Exhaustion Filter: Volume-based reversal detection
This multi-layered approach addresses the false signal problem common in traditional technical indicators (Brock, Lakonishok & LeBaron, 1992).
### 5.2 Divergence Analysis
RAMO incorporates both traditional and leading divergence detection:
- Traditional Divergence: Price and indicator divergence over 3-5 periods
- Slope Divergence: Momentum slope versus price direction
- Acceleration Divergence: Changes in momentum acceleration
This comprehensive divergence analysis framework draws from Elliott Wave theory (Prechter & Frost, 1978) and momentum divergence literature (Murphy, 1999).
## 6. Empirical Advantages and Applications
### 6.1 Risk-Adjusted Performance
The risk adjustment mechanism addresses the fundamental criticism of momentum strategies: their tendency to experience severe drawdowns during market reversals (Daniel & Moskowitz, 2016). By automatically reducing position sizing during high-drawdown periods, RAMO implements a form of dynamic hedging consistent with portfolio insurance concepts (Leland, 1980).
### 6.2 Regime Awareness
RAMO's adaptive components enable regime-aware signal generation, addressing the regime-switching behavior documented in financial markets (Hamilton, 1989; Guidolin, 2011). The indicator automatically adjusts its parameters based on market volatility and risk conditions, providing more reliable signals across different market environments.
### 6.3 Institutional Applications
The sophisticated risk management overlay makes RAMO particularly suitable for institutional applications where drawdown control is paramount. The indicator's design philosophy aligns with the risk budgeting approaches used by hedge funds and institutional investors (Roncalli, 2013).
## 7. Limitations and Future Research
### 7.1 Parameter Sensitivity
Like all technical indicators, RAMO's performance depends on parameter selection. While default parameters are optimized for broad market applications, asset-specific calibration may enhance performance. Future research should examine optimal parameter selection across different asset classes and market conditions.
### 7.2 Market Microstructure Considerations
RAMO's effectiveness may vary across different market microstructure environments. High-frequency trading and algorithmic market making have fundamentally altered market dynamics (Aldridge, 2013), potentially affecting momentum indicator performance.
### 7.3 Transaction Cost Integration
Future enhancements could incorporate transaction cost analysis to provide net-return-based signals, addressing the implementation shortfall documented in practical momentum strategy applications (Korajczyk & Sadka, 2004).
## References
Aldridge, I. (2013). *High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems*. 2nd ed. Hoboken, NJ: John Wiley & Sons.
Ang, A. (2014). *Asset Management: A Systematic Approach to Factor Investing*. New York: Oxford University Press.
Arms, R. W. (1989). *The Arms Index (TRIN): An Introduction to the Volume Analysis of Stock and Bond Markets*. Homewood, IL: Dow Jones-Irwin.
Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013). Value and momentum everywhere. *Journal of Finance*, 68(3), 929-985.
Barroso, P., & Santa-Clara, P. (2015). Momentum has its moments. *Journal of Financial Economics*, 116(1), 111-120.
Bollinger, J. (1992). *Bollinger on Bollinger Bands*. New York: McGraw-Hill.
Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. *Journal of Finance*, 47(5), 1731-1764.
Calmar, T. (1991). The Calmar ratio: A smoother tool. *Futures*, 20(1), 40.
Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). *The Econometrics of Financial Markets*. Princeton, NJ: Princeton University Press.
Chekhlov, A., Uryasev, S., & Zabarankin, M. (2005). Drawdown measure in portfolio optimization. *International Journal of Theoretical and Applied Finance*, 8(1), 13-58.
Daniel, K., & Moskowitz, T. J. (2016). Momentum crashes. *Journal of Financial Economics*, 122(2), 221-247.
Fama, E. F., & French, K. R. (1996). Multifactor explanations of asset pricing anomalies. *Journal of Finance*, 51(1), 55-84.
Granville, J. E. (1963). *Granville's New Key to Stock Market Profits*. Englewood Cliffs, NJ: Prentice-Hall.
Guidolin, M. (2011). Markov switching models in empirical finance. In D. N. Drukker (Ed.), *Missing Data Methods: Time-Series Methods and Applications* (pp. 1-86). Bingley: Emerald Group Publishing.
Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. *Econometrica*, 57(2), 357-384.
Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. *Journal of Finance*, 48(1), 65-91.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. *Econometrica*, 47(2), 263-291.
Kaufman, P. J. (1995). *Smarter Trading: Improving Performance in Changing Markets*. New York: McGraw-Hill.
Korajczyk, R. A., & Sadka, R. (2004). Are momentum profits robust to trading costs? *Journal of Finance*, 59(3), 1039-1082.
Leland, H. E. (1980). Who should buy portfolio insurance? *Journal of Finance*, 35(2), 581-594.
Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. *Journal of Finance*, 55(4), 1705-1765.
Markowitz, H. (1952). Portfolio selection. *Journal of Finance*, 7(1), 77-91.
Murphy, J. J. (1999). *Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications*. New York: New York Institute of Finance.
Prechter, R. R., & Frost, A. J. (1978). *Elliott Wave Principle: Key to Market Behavior*. Gainesville, GA: New Classics Library.
Pring, M. J. (2002). *Technical Analysis Explained: The Successful Investor's Guide to Spotting Investment Trends and Turning Points*. 4th ed. New York: McGraw-Hill.
Roncalli, T. (2013). *Introduction to Risk Parity and Budgeting*. Boca Raton, FL: CRC Press.
Shefrin, H., & Statman, M. (1985). The disposition to sell winners too early and ride losers too long: Theory and evidence. *Journal of Finance*, 40(3), 777-790.
Taleb, N. N. (2007). *The Black Swan: The Impact of the Highly Improbable*. New York: Random House.
Taylor, M. P., & Allen, H. (1992). The use of technical analysis in the foreign exchange market. *Journal of International Money and Finance*, 11(3), 304-314.
Treadway, A. B. (1969). On rational entrepreneurial behavior and the demand for investment. *Review of Economic Studies*, 36(2), 227-239.
Wilder, J. W. (1978). *New Concepts in Technical Trading Systems*. Greensboro, NC: Trend Research.
Money Risk Management with Trade Tracking
Overview
The Money Risk Management with Trade Tracking indicator is a powerful tool designed for traders on TradingView to simplify trade simulation and risk management. Unlike the TradingView Strategy Tester, which can be complex for beginners, this indicator provides an intuitive, beginner-friendly interface to evaluate trading strategies in a realistic manner, mirroring real-world trading conditions.
Built on the foundation of open-source contributions from LuxAlgo and TCP, this indicator integrates external indicator signals, overlays take-profit (TP) and stop-loss (SL) levels, and provides detailed money management analytics. It empowers traders to visualize potential profits, losses, and risk-reward ratios, making it easier to understand the financial outcomes of their strategies.
Key Features
Signal Integration: Seamlessly integrates with external long and short signals from other indicators, allowing traders to overlay TP/SL levels based on their preferred strategies.
Realistic Trade Simulation: Simulates trades as they would occur in real-world scenarios, accounting for initial capital, risk percentage, leverage, and compounding effects.
Money Management Dashboard: Displays critical metrics such as current capital, unrealized P&L, risk amount, potential profit, risk-reward ratio, and trade status in a customizable, beginner-friendly table.
TP/SL Visualization: Plots TP and SL levels on the chart with customizable styles (solid, dashed, dotted) and colors, along with optional labels for clarity.
Performance Tracking: Tracks total trades, win/loss counts, win rate, and profit factor, providing a clear overview of strategy performance.
Liquidation Risk Alerts: Warns traders if stop-loss levels risk liquidation based on leverage settings, enhancing risk awareness.
Benefits for Traders
Beginner-Friendly: Simplifies the complexities of the TradingView Strategy Tester, offering an intuitive interface for new traders to simulate and evaluate trades without confusion.
Real-World Insights: Helps traders understand the actual profit or loss potential of their strategies by factoring in capital, risk, and leverage, bridging the gap between theoretical backtesting and real-world execution.
Enhanced Decision-Making: Provides clear, real-time analytics on risk-reward ratios, unrealized P&L, and trade performance, enabling informed trading decisions.
Customizable and Flexible: Allows customization of TP/SL settings, table positions, colors, and sizes, catering to individual trader preferences.
Risk Management Focus: Encourages disciplined trading by highlighting risk amounts, potential profits, and liquidation risks, fostering better financial planning.
Why This Indicator Stands Out
Many traders struggle to translate backtested strategy results into real-world outcomes due to the abstract nature of percentage-based profitability metrics. This indicator addresses that challenge by providing a practical, user-friendly tool that simulates trades with real-world parameters like capital, leverage, and compounding. Its open-source nature ensures accessibility, while its integration with other indicators makes it versatile for various trading styles.
How to Use
Add to TradingView: Copy the Pine Script code into TradingView’s Pine Editor and add it to your chart.
Configure Inputs: Set your initial capital, risk percentage, leverage, and TP/SL values in the indicator settings. Select external long/short signal sources if integrating with other indicators.
Monitor Dashboards: Use the Money Management and Target Dashboard tables to track trade performance and risk metrics in real time.
Analyze Results: Review win rates, profit factors, and P&L to refine your trading strategy.
Credits
This indicator builds upon the open-source contributions of LuxAlgo and TCP , whose efforts in sharing their code have made this tool possible. Their dedication to the trading community is deeply appreciated.
