Quantify [Entry Model] | Fractalyst

2024년 12월 12일

What’s the indicator’s purpose and functionality?

Quantify is a machine learning entry model designed to help traders identify high-probability setups to refine their strategies.

➙ Simply pick your bias, select your entry timeframes, and let Quantify handle the rest for you.



Can the indicator be applied to any market approach/trading strategy?

Absolutely, all trading strategies share one fundamental element: Directional Bias

Once you’ve determined the market bias using your own personal approach, whether it’s through technical analysis or fundamental analysis, select the trend direction in the Quantify user inputs.



The algorithm will then adjust its calculations to provide optimal entry levels aligned with your chosen bias. This involves analyzing historical patterns to identify setups with the highest potential expected values, ensuring your setups are aligned with the selected direction.



Can the indicator be used for different timeframes or trading styles?
Yes, regardless of the timeframe you’d like to take your entries, the indicator adapts to your trading style.



Whether you’re a swing trader, scalper, or even a position trader, the algorithm dynamically evaluates market conditions across your chosen timeframe.

How can this indicator help me to refine my trading strategy?

1. Focus on Positive Expected Value
• The indicator evaluates every setup to ensure it has a positive expected value, helping you focus only on trades that statistically favor long-term profitability.



2. Adapt to Market Conditions
• By analyzing real-time market behavior and historical patterns, the algorithm adjusts its calculations to match current conditions, keeping your strategy relevant and adaptable.



3. Eliminate Emotional Bias
• With clear probabilities, expected values, and data-driven insights, the indicator removes guesswork and helps you avoid emotional decisions that can damage your edge.



4. Optimize Entry Levels
• The indicator identifies optimal entry levels based on your selected bias and timeframes, improving robustness in your trades.



5. Enhance Risk Management
• Using tools like the Kelly Criterion, the indicator suggests optimal position sizes and risk levels, ensuring that your strategy maintains consistency and discipline.



6. Avoid Overtrading
• By highlighting only high-potential setups, the indicator keeps you focused on quality over quantity, helping you refine your strategy and avoid unnecessary losses.



How can I get started to use the indicator for my entries?
1. Set Your Market Bias
• Determine whether the market trend is Bullish or Bearish using your own approach.
• Select the corresponding bias in the indicator’s user inputs to align it with your analysis.



2. Choose Your Entry Timeframes
• Specify the timeframes you want to focus on for trade entries.
• The indicator will dynamically analyze these timeframes to provide optimal setups.



3. Let the Algorithm Analyze
• Quantify evaluates historical data and real-time price action to calculate probabilities and expected values.
• It highlights setups with the highest potential based on your selected bias and timeframes.

4. Refine Your Entries
• Use the insights provided—entry levels, probabilities, and risk calculations—to align your trades with a math-driven edge.
• Avoid overtrading by focusing only on setups with positive expected value.

5. Adapt to Market Conditions
• The indicator continuously adapts to real-time market behavior, ensuring its recommendations stay relevant and precise as conditions change.

How does the indicator calculate the current range?

The indicator calculates the current range by analyzing swing points from the very first bar on your charts to the latest available bar it identifies external liquidity levels, also known as BSLQ (buy-side liquidity levels) and SSLQ (sell-side liquidity levels).

What's the purpose of these levels? What are the underlying calculations?

1. Understanding Swing highs and Swing Lows

Swing High: A Swing High is formed when there is a high with 2 lower highs to the left and right.

Swing Low: A Swing Low is formed when there is a low with 2 higher lows to the left and right.



2. Understanding the purpose and the underlying calculations behind Buyside, Sellside and Pivot levels.



3. Identifying Discount and Premium Zones.



4. Importance of Risk-Reward in Premium and Discount Ranges



How does the script calculate probabilities?

The script calculates the probability of each liquidity level individually. Here's the breakdown:

1. Upon the formation of a new range, the script waits for the price to reach and tap into pivot level level. Status: "■" - Inactive
2. Once pivot level is tapped into, the pivot status becomes activated and it waits for either liquidity side to be hit. Status: "▶" - Active
3. If the buyside liquidity is hit, the script adds to the count of successful buyside liquidity occurrences. Similarly, if the sellside is tapped, it records successful sellside liquidity occurrences.
4. Finally, the number of successful occurrences for each side is divided by the overall count individually to calculate the range probabilities.



Note: The calculations are performed independently for each directional range. A range is considered bearish if the previous breakout was through a sellside liquidity. Conversely, a range is considered bullish if the most recent breakout was through a buyside liquidity.



What does the multi-timeframe functionality offer?

You can incorporate up to 4 higher timeframe probabilities directly into the table.

This feature allows you to analyze the probabilities of buyside and sellside liquidity across multiple timeframes, without the need to manually switch between them.

By viewing these higher timeframe probabilities in one place, traders can spot larger market trends and refine their entries and exits with a better understanding of the overall market context.

What are the multi-timeframe underlying calculations?

The script uses the same calculations (mentioned above) and uses security function to request the data such as price levels, bar time, probabilities and booleans from the user-input timeframe.

How does the Indicator Identifies Positive Expected Values?

Quantify instantly calculates whether a trade setup has the potential to generate positive expected value (EV).

To determine a positive EV setup, the indicator uses the formula:

EV = ( P(Win) × R(Win) ) − ( P(Loss) × R(Loss))

where:

- P(Win) is the probability of a winning trade.
- R(Win) is the reward or return for a winning trade, determined by the current risk-to-reward ratio (RR).
- P(Loss) is the probability of a losing trade.
- R(Loss) is the loss incurred per losing trade, typically assumed to be -1.

By calculating these values based on historical data and the current trading setup, the indicator helps you understand whether your trade has a positive expected value.



How can I know that the setup I'm going to trade with has a positive EV?

If the indicator detects that the adjusted pivot and buy/sell side probabilities have generated positive expected value (EV) in historical data, the risk-to-reward (RR) label within the range box will be colored blue and red .

If the setup does not produce positive EV, the RR label will appear gray.

This indicates that even the risk-to-reward ratio is greater than 1:1, the setup is not likely to yield a positive EV because, according to historical data, the number of losses outweighs the number of wins relative to the RR gain per winning trade.

What is the confidence level in the indicator, and how is it determined?

The confidence level in the indicator reflects the reliability of the probabilities calculated based on historical data. It is determined by the sample size of the probabilities used in the calculations. A larger sample size generally increases the confidence level, indicating that the probabilities are more reliable and consistent with past performance.

How does the confidence level affect the risk-to-reward (RR) label?

The confidence level (★) is visually represented alongside the probability label. A higher confidence level indicates that the probabilities used to determine the RR label are based on a larger and more reliable sample size.

How can traders use the confidence level to make better trading decisions?

Traders can use the confidence level to gauge the reliability of the probabilities and expected value (EV) calculations provided by the indicator. A confidence level above 95% is considered statistically significant and indicates that the historical data supporting the probabilities is robust. This high confidence level suggests that the probabilities are reliable and that the indicator’s recommendations are more likely to be accurate.

In data science and statistics, a confidence level above 95% generally means that there is less than a 5% chance that the observed results are due to random variation. This threshold is widely accepted in research and industry as a marker of statistical significance. Studies such as those published in the Journal of Statistical Software and the American Statistical Association support this threshold, emphasizing that a confidence level above 95% provides a strong assurance of data reliability and validity.

Conversely, a confidence level below 95% indicates that the sample size may be insufficient and that the data might be less reliable. In such cases, traders should approach the indicator’s recommendations with caution and consider additional factors or further analysis before making trading decisions.

How does the sample size affect the confidence level, and how does it relate to my TradingView plan?

The sample size for calculating the confidence level is directly influenced by the amount of historical data available on your charts. A larger sample size typically leads to more reliable probabilities and higher confidence levels.

Here’s how the TradingView plans affect your data access:

Essential Plan
The Essential Plan provides basic data access with a limited amount of historical data. This can lead to smaller sample sizes and lower confidence levels, which may weaken the robustness of your probability calculations. Suitable for casual traders who do not require extensive historical analysis.

Plus Plan
The Plus Plan offers more historical data than the Essential Plan, allowing for larger sample sizes and more accurate confidence levels. This enhancement improves the reliability of indicator calculations. This plan is ideal for more active traders looking to refine their strategies with better data.

Premium Plan
The Premium Plan grants access to extensive historical data, enabling the largest sample sizes and the highest confidence levels. This plan provides the most reliable data for accurate calculations, with up to 20,000 historical bars available for analysis. It is designed for serious traders who need comprehensive data for in-depth market analysis.

PRO+ Plans
The PRO+ Plans offer the most extensive historical data, allowing for the largest sample sizes and the highest confidence levels. These plans are tailored for professional traders who require advanced features and significant historical data to support their trading strategies effectively.

For many traders, the Premium Plan offers a good balance of affordability and sufficient sample size for accurate confidence levels.

What is the HTF probability table and how does it work?

The HTF (Higher Time Frame) probability table is a feature that allows you to view buy and sellside probabilities and their status from timeframes higher than your current chart timeframe.

Here’s how it works:

Data Request: The table requests and retrieves data from user-defined higher timeframes (HTFs) that you select.

Probability Display: It displays the buy and sellside probabilities for each of these HTFs, providing insights into the likelihood of price movements based on higher timeframe data.

Detailed Tooltips: The table includes detailed tooltips for each timeframe, offering additional context and explanations to help you understand the data better.

What do the different colors in the HTF probability table indicate?

The colors in the HTF probability table provide visual cues about the expected value (EV) of trading setups based on higher timeframe probabilities:

Blue: Suggests that entering a long position from the HTF user-defined pivot point, targeting buyside liquidity, is likely to result in a positive expected value (EV) based on historical data and sample size.



Red: Indicates that entering a short position from the HTF user-defined pivot point, targeting sellside liquidity, is likely to result in a positive expected value (EV) based on historical data and sample size.

Gray: Shows that neither long nor short trades from the HTF user-defined pivot point are expected to generate positive EV, suggesting that trading these setups may not be favorable.



What machine learning techniques are used in Quantify?

Quantify offers two main machine learning approaches:
1. Adaptive Learning (Fixed Sample Size): The algorithm learns from the entire dataset without resampling, maintaining a stable model that adapts to the latest market conditions.

2. Bootstrap Resampling: This method creates multiple subsets of the historical data, allowing the model to train on varying sample sizes. This technique enhances the robustness of predictions by ensuring that the model is not overfitting to a single dataset.



How does machine learning affect the expected value calculations in Quantify?

Machine learning plays a key role in improving the accuracy of expected value (EV) calculations. By analyzing historical price action, liquidity hits, and market bias patterns, the model continuously adjusts its understanding of risk and reward, allowing the expected value to reflect the most likely market movements. This results in more precise EV predictions, helping traders focus on setups that maximize profitability.

What is the Kelly Criterion, and how does it work in Quantify?

The Kelly Criterion is a mathematical formula used to determine the optimal position size for each trade, maximizing long-term growth while minimizing the risk of large drawdowns. It calculates the percentage of your portfolio to risk on a trade based on the probability of winning and the expected payoff.



Quantify integrates this with user-defined inputs to dynamically calculate the most effective position size in percentage, aligning with the trader’s risk tolerance and desired exposure.

How does Quantify use the Kelly Criterion in practice?

Quantify uses the Kelly Criterion to optimize position sizing based on the following factors:
1. Confidence Level: The model assesses the confidence level in the trade setup based on historical data and sample size. A higher confidence level increases the suggested position size because the trade has a higher probability of success.

2. Max Allowed Drawdown (User-Defined): Traders can set their preferred maximum allowed drawdown, which dictates how much loss is acceptable before reducing position size or stopping trading. Quantify uses this input to ensure that risk exposure aligns with the trader’s risk tolerance.

3. Probabilities: Quantify calculates the probabilities of success for each trade setup. The higher the probability of a successful trade (based on historical price action and liquidity levels), the larger the position size suggested by the Kelly Criterion.

What is a trailing stoploss, and how does it work in Quantify?

A trailing stoploss is a dynamic risk management tool that moves with the price as the market trend continues in the trader’s favor. Unlike a fixed take profit, which stays at a set level, the trailing stoploss automatically adjusts itself as the market moves, locking in profits as the price advances.



In Quantify, the trailing stoploss is enhanced by incorporating market structure liquidity levels (explain above). This ensures that the stoploss adjusts intelligently based on key price levels, allowing the trader to stay in the trade as long as the trend remains intact, while also protecting profits if the market reverses.

Why would a trader prefer a trailing stoploss based on liquidity levels instead of a fixed take-profit level?

Traders who use trailing stoplosses based on liquidity levels prefer this method because:
1. Market-Driven Flexibility: The stoploss follows the market structure rather than being static at a pre-defined level. This means the stoploss is less likely to be hit by small market fluctuations or false reversals. The stoploss remains adaptive, moving as the market moves.

2. Riding the Trend: Traders can capture more profit during a sustained trend because the trailing stop will adjust only when the trend starts to reverse significantly, based on key liquidity levels. This allows them to hold positions longer without prematurely locking in profits.

3. Avoiding Premature Exits: Fixed stoploss levels may exit a trade too early in volatile markets, while liquidity-based trailing stoploss levels respect the natural flow of price action, preventing the trader from exiting too soon during pullbacks or minor retracements.



🎲 Becoming the House: Gaining an Edge Over the Market

In American roulette, the casino has a 5.26% edge due to the presence of the 0 and 00 pockets. On even-money bets, players face a 47.37% chance of winning, while true 50/50 odds would require a 50% chance. This edge—the gap between the payout odds and the true probabilities—ensures that, statistically, the casino will always win over time, even if individual players win occasionally.

From a Trader’s Perspective

In trading, your edge comes from identifying and executing setups with a positive expected value (EV). For example:
• If you identify a setup with a 55.48% chance of winning and a 1:1 risk-to-reward (RR) ratio, your trade has a statistical advantage over a neutral (50/50) probability.

This edge works in your favor when applied consistently across a series of trades, just as the casino’s edge ensures profitability across thousands of spins.

🎰 Applying the Concept to Trading

Like casinos leverage their mathematical edge in games of chance, you can achieve long-term success in trading by focusing on setups with positive EV and managing your trades systematically. Here’s how:
1. Probability Advantage: Prioritize trades where the probability of success (win rate) exceeds the breakeven rate for your chosen risk-to-reward ratio.
• Example: With a 1:1 RR, you need a win rate above 50% to achieve positive EV.
2. Risk-to-Reward Ratio (RR): Even with a win rate below 50%, you can gain an edge by increasing your RR (e.g., a 40% win rate with a 2:1 RR still has positive EV).
3. Consistency and Discipline: Just as casinos profit by sticking to their mathematical advantage over thousands of spins, traders must rely on their edge across many trades, avoiding emotional decisions or overleveraging.

By targeting favorable probabilities and managing trades effectively, you “become the house” in your trading. This approach allows you to leverage statistical advantages to enhance your overall performance and achieve sustainable profitability.

What Makes the Quantify Indicator Original?

1. Data-Driven Edge
Unlike traditional indicators that rely on static formulas, Quantify leverages probability-based analysis and machine learning. It calculates expected value (EV) and confidence levels to help traders identify setups with a true statistical edge.

2. Integration of Market Structure
Quantify uses market structure liquidity levels to dynamically adapt. It identifies key zones like swing highs/lows and liquidity traps, enabling users to align entries and exits with where the market is most likely to react. This bridges the gap between price action analysis and quantitative trading.

3. Sophisticated Risk Management
The Kelly Criterion implementation is unique. Quantify allows traders to input their maximum allowed drawdown, dynamically adjusting risk exposure to maintain optimal position sizing. This ensures risk is scientifically controlled while maximizing potential growth.

4. Multi-Timeframe and Liquidity-Based Trailing Stops
The indicator doesn’t just suggest fixed profit-taking levels. It offers market structure-based trailing stop-loss functionality, letting traders ride trends as long as liquidity and probabilities favor the position, which is rare in most tools.

5. Customizable Bias and Adaptive Learning
• Directional Bias: Traders can set a bullish or bearish bias, and the indicator recalculates probabilities to align with the trader’s market outlook.
• Adaptive Learning: The machine learning model adapts to changes in data (via resampling or bootstrap methods), ensuring that predictions stay relevant in evolving markets.

6. Positive EV Focus
The focus on positive EV setups differentiates it from reactive indicators. It shifts trading from chasing signals to acting on setups that statistically favor profitability, akin to how professional quant funds operate.

7. User Empowerment
Through features like customizable timeframes, real-time probability updates, and visualization tools, Quantify empowers users to make data-informed decisions.

Terms and Conditions | Disclaimer

Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.

Built-in components, features, and functionalities of our charting tools are the intellectual property of Fractalyst use, reproduction, or distribution of these proprietary elements is prohibited.

By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer and agrees to respect our intellectual property rights and comply with all applicable laws and regulations.

1월 24일

릴리즈 노트

- Enhanced the indicator for faster quantitative analysis, resulting in quicker insights and reduced computational load.

3월 25일

릴리즈 노트

Introducing Breakout Model

The Quantify TradingView indicator now features a powerful Breakout Model alongside the existing Orderflow Model. This new addition allows traders to capitalize on trending markets across various asset classes, including futures , cryptocurrencies, stocks, and commodities on any timeframe.

Key features of the Breakout Model include:

- Dynamic Trailing Stop-Loss: The model employs an adaptive trailing stop-loss method to maximize net profit and expected value. This stop-loss automatically trails as price moves towards your selected bias in user inputs.

Real-Time Alerts: Traders can now receive timely notifications for breakout signals, stop-loss adjustments, and key price levels, enhancing their ability to react to market movements.
Multi-Timeframe Compatibility: The Breakout Model works seamlessly across various chart intervals, from short-term to long-term analysis.

By integrating the Breakout Model with the existing Orderflow Model, traders now have a comprehensive toolkit for analyzing both ranging and trending market conditions.

This update significantly enhances the indicator's versatility and potential for identifying high-probability trade setups across diverse market environments.

4월 4일

릴리즈 노트

Introducing the Fractal Entry Model

- identifies potential market reversals and continuations by confirming swing candles that form structural highs or lows.

- This model is more aggressive compared to other methods, aiming to capture early moves in the market structure.



It is best suited for traders comfortable with higher risk and looking for earlier entry opportunities.

4월 4일

릴리즈 노트

- Feature Update: Implemented barstate.isconfirmed for fractal entries. Entries now wait for the candle to close in real-time before confirming, ensuring accurate signal validation.

- Bug Fix: Fixed issues with alerts to ensure proper triggering and reliability.

4월 4일

릴리즈 노트

- Enhanced Fractal Model Entry Performance: Improved the script algorithm for identifying fractal model entry levels, resulting in more accurate trade execution and better market timing.

- Increased Profit Factor: Optimized entry logic and risk management, leading to a higher overall profit factor.

4월 5일

릴리즈 노트

- Improved the calculation logic for identifying high probability fractal models.

4월 9일

릴리즈 노트

New Features
- Performance Analytics Table: Added detailed performance breakdowns on quarterly, monthly, and weekly timeframes, providing traders with more comprehensive insights into entry models effectiveness

- Fixed Level Take Profit: Implemented predetermined take profit and breakeven levels for traders who prioritize higher win rate strategies
Improvements

- Enhanced ML Algorithm: Improved machine learning calculation logic to intelligently avoid consecutive losing trades, resulting in more consistent performance.

- Profit Factor Implementation: Replaced Expected Value (E(X)) with Profit Factor for more accurate trade setup evaluation (Profit Factor > 1.0 indicates profitable setups)

4월 9일

릴리즈 노트

Enhanced
Improved multi-timeframe table colorization logic that highlights the most profitable timeframes based on profit factor, enabling faster identification of optimal trading setups.

4월 9일

릴리즈 노트

- Streamlined User Interface: Completely redesigned the user flow to remove subjective decision points, resulting in a more consistent experience.

- Automated Machine Learning: The script now automatically adjusts machine learning parameters based on usage patterns, eliminating the need for manual configuration.

- Enhanced Kelly Criterion: Refined the Kelly criterion calculation for more accurate position sizing and improved risk management.

4월 10일

릴리즈 노트

- Integrated k-Nearest Neighbors (kNN) machine learning algorithm to automatically detect market bias.

- Added detailed and visually formatted tooltips for all major input categories.

- Tooltips now include additional notes and tips to improve usability and provide guidance for traders on selecting optimal settings.

4월 15일

릴리즈 노트

New Features
- Auto Level Detection: Added "Auto" option in level detection functionality. The script now intelligently determines the optimal or trailing stoploss method based on market conditions.

- Daily PnL Table: Implemented a new daily table showing Risk-Reward PnL comparison between current and previous week. Helps traders track performance improvements over time.

Improvements
- Enhanced models performance by optimizing calculation method

4월 15일

릴리즈 노트

- Fixed an issue with breakout entries

4월 15일

릴리즈 노트

- Fixed a visual bug with entries

릴리즈 노트

- Enhanced Backtesting Table: Implemented comprehensive quantitative metrics to provide deeper insights into strategy performance:

- Profit Factor: Ratio of gross profits to gross losses, measuring the system's profitability efficiency.

- Expectancy: Represents the average expected profit per trade.

- Win/Loss Statistics: Detailed breakdown of wins, losses, and break-even percentages, along with maximum consecutive wins/losses to evaluate strategy consistency.

- Drawdown Metrics: Added Maximum Drawdown (largest peak-to-trough decline) and Average Drawdown measurements to better assess risk exposure.

- Sharpe Ratio: Risk-adjusted return metric that compares profitability against volatility, with higher values indicating better risk-adjusted performance.

- Ulcer Index: Measures the depth and duration of drawdowns, focusing on downside volatility. Lower values indicate less painful drawdowns and faster recovery periods.

- MAE/MFE: Maximum Adverse Excursion/Maximum Favorable Excursion analysis to show how far trades move against or in favor before closing, helping optimize entry/exit timing.

- Risk of Ruin (RoR): Calculates the probability of losing your entire trading capital or reaching a point where continued trading becomes impossible.

- Duration Analysis: Average trade duration statistics to help traders understand typical holding periods for different market conditions.

- Aggressive Trailing Stop-Loss: Implemented a new trailing stop-loss algorithm specifically optimized for forex markets:
- Dynamically adjusts based on market volatility
- Helps capture larger portions of trends while protecting profits
- Configurable sensitivity settings

Improved
- Performance Optimization: Refactored code logic for significantly faster calculations:
- Reduced memory usage by 30%
- Decreased indicator loading time on large datasets
- Optimized loop structures for better performance on historical data analysis

릴리즈 노트

**Major Feature Addition: Pyramiding Trades**
- Compounding Performance Calculations
- Completely revamped performance metrics to calculate based on compounding effects rather than R-multiples for more realistic performance measurement
- Added support for pyramiding (position stacking) that allows adding positions after the initial trade's stop-loss has moved to breakeven
- Implemented user toggleable pyramiding with "ON/OFF" setting via user interface
- Added maximum pyramid entries control (currently supports 1 additional position)
- Created pyramid entry detection logic that automatically triggers when stop-loss moves to breakeven
- Implemented separate risk calculation for pyramid entries to maintain consistent risk management
- Added visual indicators and alerts for pyramid entries on the chart
- Integrated pyramid position management with existing trailing stop mechanism

**Risk Management for Pyramiding**
- Implemented separate position sizing calculation for pyramid entries based on market conditions
- Incorporated pyramid trade results in performance statistics accounting for compound risk effect
- Enhanced profit calculation to account for multiple entry points in the same trade direction

## Backtesting Table and Metrics Improvements

**Enhanced Performance Metrics**
- Added robust Sharpe Ratio calculation for risk-adjusted return measurement
- Implemented Ulcer Index to measure depth and duration of drawdowns
- Added Risk of Ruin calculation based on Kelly Criterion
- Integrated CAGR (Compound Annual Growth Rate) for annualized performance comparison
- Added MAE/MFE (Maximum Adverse Excursion/Maximum Favorable Excursion) ratio to measure trade efficiency
- Implemented recovery factor and peak equity tracking

**Table UI and Visualization Improvements**
- Reorganized backtesting table for better readability with logical grouping of metrics
- Added comprehensive tooltips explaining each metric with performance benchmarks
- Implemented color-coding system to quickly identify strengths and weaknesses
- Enhanced visibility of key performance indicators with conditional formatting
- Created separate view options for Backtest, Monthly, Weekly, and Daily performance analysis
- Improved confidence level calculation based on sample size

**Statistical Analysis Enhancements**
- Added tracking for maximum consecutive wins and losses
- Implemented separate statistics tracking for bullish and bearish trades
- Added combined total performance statistics across all trade types
- Enhanced drawdown analysis with separate tracking for realized and maximum drawdowns
- Improved trade duration tracking and reporting

snapshot

## Calculation Logic and Performance Optimization

**Algorithmic Improvements**
- Implemented efficient single-pass algorithm for statistical calculations to reduce computational overhead
- Optimized Sharpe ratio and standard deviation calculations for better performance
- Enhanced Ulcer Index calculation with more efficient implementation
- Improved Kelly Criterion calculation with better handling of edge cases
- Optimized bias detection with pre-calculated values

**Code Optimization**
- Reduced redundant calculations by caching frequently used values
- Improved handling of array operations to minimize memory usage
- Implemented grouped assignments to reduce code size and improve readability
- Optimized conditional logic for better performance in trade management
- Pre-calculated array sizes to avoid multiple calls to array.size function

**Trade Management Refinements**
- Improved trailing stop logic for better trade exit timing
- Enhanced breakeven detection and management
- Optimized position sizing based on dynamic risk calculation
- Improved drawdown tracking during active trades
- Refined trade duration calculation for more accurate reporting

릴리즈 노트

- Improved accuracy and fixed all discrepancies between tables and trades on charts
- Improved RoR calculation based on Kelly criterion formula
- Improved color threshold of metrics to be aligned with the detailed tooltips
- Improved script performance