Multi-Strategy Portfolio Optimizer [LuxAlgo]The Multi-Strategy Portfolio Optimizer indicator is a comprehensive quantitative tool that evaluates 9 distinct trading setups across trend-following, momentum, and mean-reversion categories to construct an optimized, equally-weighted portfolio.
🔶 USAGE
This script aims to help users identify which trading methodologies are currently performing best on a specific ticker and timeframe, while simultaneously monitoring how well those strategies diversify each other to create a smoother equity curve.
🔹 Strategy Selection & Evaluation
The optimizer evaluates three unique parameter variations for each of the following 9 strategy types:
Supertrend & EMA Crossovers: Captures sustained directional trends.
MACD & CCI: Focuses on momentum shifts and overextended breakouts.
Donchian Channels: Classic breakout logic based on price extremes.
RSI Trend: Uses RSI levels to confirm momentum direction.
RSI Rev, Bollinger Bands & Stochastic: Targets mean-reversion and overbought/oversold exhaustion.
The tool automatically selects the "Best Setting" for each category by comparing the cumulative performance of all three variations across the available chart history. Only the top-performing variation from each category is included in the final portfolio calculation.
🔹 Equity Dashboards
The indicator features two primary visual interfaces to monitor performance and risk:
Floating Curves Box: Displays a real-time equity curve of the total portfolio (thick white line) against the individual active strategies (faded colored lines). This allows users to see the recent performance stability over a user-defined lookback.
Correlation Heatmap: Analyzes the statistical relationship between active strategies. This table uses color-coding to show how similar or different strategy returns are, providing a "Diversification Grade" (e.g., Excellent, Good, Poor) to help users avoid over-exposure to a single market regime.
🔹 Trade Visualization
Users can enable "Show Past & Open Trades" to audit the simulated performance directly on the price action. The script plots entry lines and shaded ATR-based Stop Loss (red) and Take Profit (green) zones for both currently active and historical trades.
🔶 DETAILS
🔹 Best Setting Logic
For every strategy category, the script runs three parallel simulations with different sensitivity settings. The "Best Setting" displayed in the dashboard is the variation that has achieved the highest cumulative percentage return since the beginning of the chart.
🔹 Portfolio Calculation (Equal Weight)
The Portfolio Equity Curve is calculated by averaging the cumulative returns of all active "best" setups on a bar-by-bar basis. This simulates an equally weighted allocation where the capital is distributed evenly across all chosen trading methodologies, aiming to reduce the drawdown typically associated with a single-strategy approach.
🔹 Diversification & Correlation
The Heatmap calculates a Pearson correlation coefficient over a rolling 100-bar window for every pair of active strategies.
Correlation > 0.7 (Red): Strategies are moving in lockstep, offering little diversification.
Correlation near 0 (Yellow): Strategies are independent, providing healthy diversification.
Correlation < -0.2 (Green): Strategies are inversely correlated, which can significantly hedge portfolio volatility.
🔹 Auto-Scaling Polylines
The floating curves dashboard uses a dynamic normalization algorithm. It captures the highest and lowest equity values within the user-defined lookback (Curves Length) and scales them to fit within the box height. This ensures the curves remain visible and proportional regardless of whether the returns are 1% or 100%.
🔶 SETTINGS
🔹 Strategies
Enable : Toggles whether a specific strategy category is evaluated and included in the portfolio math.
🔹 Risk Management
Enable Stop Loss & Take Profit: Toggles the ATR-based exit engine.
ATR Length: The period used for calculating volatility-based exits.
Stop Loss / Take Profit Mult: The multipliers that define the distance of exit targets from the entry price.
Show Past & Open Trades: Visualizes the execution zones on the chart.
🔹 Dashboard
Main Dashboard / Correlation Heatmap: Toggles the visibility of the tables.
Position: Moves the UI elements to different corners of the chart.
Curves Length: Determines the lookback for the floating equity chart.
Curves Vertical Position: Allows you to pin the curves box to the Top, Middle, or Bottom of the price range.
Curves Box Height (%): Adjusts the vertical scale of the equity chart relative to the price action.
Size: Controls the scale of the text and tables (Tiny to Huge). Indicator
Strategy Sensitivity MatrixThe Strategy Sensitivity Matrix is an institutional-grade backtesting tool designed to evaluate the robustness and parameter sensitivity of trend-following strategies. It enables users to compare the historical performance of a broad range of parameter combinations across multiple metrics to assess the overall stability of the selected strategy. The model displays the complete backtest landscape in a structured, color-coded matrix that allows investors to quickly identify robust parameter regions and evaluate historical performance stability across parameter combinations.
At its core, the matrix systematically evaluates a wide range of parameter combinations, where every individual cell represents the backtest result for one unique parameter configuration. Users can switch between volatility-based strategies and moving-average strategies. In volatility mode, the matrix rows represent volatility lengths and the matrix columns represent volatility factors. In crossover mode, the rows represent fast moving-average lengths and the columns represent slow moving-average lengths. Supported volatility types include the Average True Range (ATR), Standard Deviation (SD), and Mean Absolute Deviation (MAD). Supported moving-average types include the Exponential Moving Average (EMA), Simple Moving Average (SMA), Wilder’s Moving Average (RMA), and Weighted Moving Average (WMA). Supported display metrics include:
CAGR = Compounded Annual Growth Rate.
Sharpe = CAGR per unit of standard deviation.
Sortino = CAGR per unit of downside deviation.
Martin = CAGR relative to the Ulcer Index (UI).
Calmar = CAGR relative to maximum drawdown.
Max DD = Largest peak-to-trough decline in value.
Alpha (α) = Excess annualized risk-adjusted returns.
Expectancy = Average expected return per trade.
Profit Factor = Total gross profit per unit of losses.
Win Rate = Ratio of profitable trades to total trades.
Trades/Year = Average number of trades per year.
The matrix follows an intuitive percentile-based coloring framework that dynamically compares the relative performance and stability of all parameter combinations. Stronger values above or equal to the matrix median are highlighted in green, with bright green representing the top 10% of all parameter combinations. Weaker values below the matrix median are highlighted in orange, while red represents objectively weak performance based on the selected metric. Broad clusters of consistently strong results generally suggest lower parameter sensitivity and potentially greater robustness, while isolated peaks generally suggest elevated parameter sensitivity.
The summary table displayed above the matrix provides a broader distribution-level statistical overview of results across all parameter combinations. This structure allows investors to evaluate whether strong historical performance appears statistically widespread or narrowly concentrated across the parameter landscape. Stable parameter landscapes generally exhibit lower standard deviation, similar median and average values, and smaller performance gaps between the best and top 10% parameter combinations. The summary table includes the following sections:
Start = Start month and year of the selected backtest period.
End = End month and year of the selected backtest period.
Metric = Performance metric currently displayed in the matrix.
B&H = Buy-and-hold performance for the selected metric.
Best = Best-performing parameter combination in the matrix.
Top 10% = Average value of the top 10% parameter combinations.
Median = Median value across all parameter combinations.
Average = Average value across all parameter combinations.
Std Dev = Standard deviation of all parameter combinations.
≥ B&H = Percentage of combinations equal or better than B&H.
In summary, the Strategy Sensitivity Matrix is a powerful robustness analysis tool designed to help investors make data-driven decisions when evaluating parameter combinations across trend-following strategies. By evaluating the full parameter landscape, investors can quickly determine whether strong historical performance appears broadly distributed across stable parameter regions or narrowly concentrated within isolated parameter combinations. While historical robustness can provide valuable insight into past market behavior over the selected backtest period, users should remain mindful that market structures evolve over time and that historically stable parameter regions may not necessarily persist in future market conditions. Indicator
Volatility Stop SelectorThe Volatility Stop Selector is a comprehensive trend-following tool designed to automatically identify the optimal volatility stop strategy. It features adjustable parameters and an integrated backtester that delivers institutional-grade insights into the recommended strategy. The model continuously adapts to new data in real time by evaluating multiple volatility length and factor combinations, determining the best-performing configuration, and presenting the backtest results in a clear, color-coded table that benchmarks performance against the buy-and-hold strategy.
At its core, the model systematically backtests a wide range of volatility stop combinations to identify the configuration that maximizes the selected optimization metric. Users can choose to optimize for absolute returns or risk-adjusted returns using metrics such as the Sharpe, Sortino, Martin, or Calmar ratios. The Martin ratio is particularly well suited for volatility-based risk management strategies, as it evaluates returns relative to the Ulcer Index, capturing both the depth and duration of drawdowns and therefore favoring smoother equity curves. Alternatively, users can enable manual optimization to test custom volatility length and factor settings and view the corresponding backtest results. The label displays the Compounded Annual Growth Rate (CAGR) of the strategy, with the buy-and-hold CAGR in parentheses for comparison. The table presents the backtest results based on the volatility length and factor displayed at the top:
Sharpe = CAGR per unit of standard deviation.
Sortino = CAGR per unit of downside deviation.
Calmar = CAGR relative to maximum drawdown.
Max DD = Largest peak-to-trough decline in value.
Beta (β) = Return sensitivity relative to buy-and-hold.
Alpha (α) = Excess annualized risk-adjusted returns.
Win Rate = Ratio of profitable trades to total trades.
Profit Factor = Total gross profit per unit of losses.
Expectancy = Average expected return per trade.
Trades/Year = Average number of trades per year.
This indicator is designed with flexibility in mind, enabling users to specify the start date of the backtesting period, the preferred volatility type, and the price source. Supported volatility types include the Average True Range (ATR), Standard Deviation (SD), and Mean Absolute Deviation (MAD). Supported price sources include Close, Heikin Ashi, HL2, HLC3, and OHLC4. To minimize overfitting, users can define constraints such as a minimum and maximum number of trades per year, as well as an optional optimization margin that prioritizes more robust combinations by requiring more reactive combinations to exceed this threshold. The table follows an intuitive color-coded logic that enables quick performance comparison against buy-and-hold (B&H):
Sharpe = Green indicates better than B&H, while red indicates worse.
Sortino = Green indicates better than B&H, while red indicates worse.
Calmar = Green indicates better than B&H, while red indicates worse.
Max DD = Green indicates better than B&H, while red indicates worse.
Beta (β) = Green indicates better than B&H, while red indicates worse.
Alpha (α) = Green indicates above 0%, while red indicates below 0%.
Win Rate = Green indicates above 50%, while red indicates below 50%.
Profit Factor = Green indicates above 2, while red indicates below 1.
Expectancy = Green indicates above 0%, while red indicates below 0%.
In summary, the Volatility Stop Selector is a powerful tool designed to help investors make data-driven decisions when selecting volatility-based trend-following strategies. By optimizing for risk-adjusted returns, investors can identify the best configurations using institutional-grade metrics. While results are based on the selected historical period, users should be mindful of overfitting, as past results may not persist under future market conditions. Since the model continuously recalibrates to incorporate new data, the recommended length and factor may evolve over time. Indicator
Adaptive Trend SelectorThe Adaptive Trend Selector is a comprehensive trend-following tool designed to automatically identify the optimal moving average crossover strategy. It features adjustable parameters and an integrated backtester that delivers institutional-grade insights into the recommended strategy. The model continuously adapts to new data in real time by evaluating multiple moving average combinations, determining the best performing lengths, and presenting the backtest results in a clear, color-coded table that benchmarks performance against the buy-and-hold strategy.
At its core, the model systematically backtests a wide range of moving average combinations to identify the configuration that maximizes the selected optimization metric. Users can choose to optimize for absolute returns or risk-adjusted returns using the Sharpe, Sortino, or Calmar ratios. Alternatively, users can enable manual optimization to test custom fast and slow moving average lengths and view the corresponding backtest results. The label displays the Compounded Annual Growth Rate (CAGR) of the strategy, with the buy-and-hold CAGR in parentheses for comparison. The table presents the backtest results based on the fast and slow lengths displayed at the top:
Sharpe = CAGR per unit of standard deviation.
Sortino = CAGR per unit of downside deviation.
Calmar = CAGR relative to maximum drawdown.
Max DD = Largest peak-to-trough decline in value.
Beta (β) = Return sensitivity relative to buy-and-hold.
Alpha (α) = Excess annualized risk-adjusted returns.
Win Rate = Ratio of profitable trades to total trades.
Profit Factor = Total gross profit per unit of losses.
Expectancy = Average expected return per trade.
Trades/Year = Average number of trades per year.
This indicator is designed with flexibility in mind, enabling users to specify the start date of the backtesting period and the preferred moving average strategy. Supported strategies include the Exponential Moving Average (EMA), Simple Moving Average (SMA), Wilder’s Moving Average (RMA), Weighted Moving Average (WMA), and Volume-Weighted Moving Average (VWMA). To minimize overfitting, users can define constraints such as a minimum and maximum number of trades per year, as well as an optional optimization margin that prioritizes longer, more robust combinations by requiring shorter-length strategies to exceed this threshold. The table follows an intuitive color logic that enables quick performance comparison against buy-and-hold (B&H):
Sharpe = Green indicates better than B&H, while red indicates worse.
Sortino = Green indicates better than B&H, while red indicates worse.
Calmar = Green indicates better than B&H, while red indicates worse.
Max DD = Green indicates better than B&H, while red indicates worse.
Beta (β) = Green indicates better than B&H, while red indicates worse.
Alpha (α) = Green indicates above 0%, while red indicates below 0%.
Win Rate = Green indicates above 50%, while red indicates below 50%.
Profit Factor = Green indicates above 2, while red indicates below 1.
Expectancy = Green indicates above 0%, while red indicates below 0%.
In summary, the Adaptive Trend Selector is a powerful tool designed to help investors make data-driven decisions when selecting moving average crossover strategies. By optimizing for risk-adjusted returns, investors can confidently identify the best lengths using institutional-grade metrics. While results are based on the selected historical period, users should be mindful of potential overfitting, as past results may not persist under future market conditions. Since the model recalibrates to incorporate new data, the recommended lengths may evolve over time. Indicator
*Auto Backtest & Optimize EngineFull-featured Engine for Automatic Backtesting and parameter optimization. Allows you to test millions of different combinations of stop-loss and take profit parameters, including on any connected indicators.
⭕️ Key Futures
Quickly identify the optimal parameters for your strategy.
Automatically generate and test thousands of parameter combinations.
A simple Genetic Algorithm for result selection.
Saves time on manual testing of multiple parameters.
Detailed analysis, sorting, filtering and statistics of results.
Detailed control panel with many tooltips.
Display of key metrics: Profit, Win Rate, etc..
Comprehensive Strategy Score calculation.
In-depth analysis of the performance of different types of stop-losses.
Possibility to use to calculate the best Stop-Take parameters for your position.
Ability to test your own functions and signals.
Customizable visualization of results.
Flexible Stop-Loss Settings:
• Auto ━ Allows you to test all types of Stop Losses at once(listed below).
• S.VOLATY ━ Static stop based on volatility (Fixed, ATR, STDEV).
• Trailing ━ Classic trailing stop following the price.
• Fast Trail ━ Accelerated trailing stop that reacts faster to price movements.
• Volatility ━ Dynamic stop based on volatility indicators.
• Chandelier ━ Stop based on price extremes.
• Activator ━ Dynamic stop based on SAR.
• MA ━ Stop based on moving averages (9 different types).
• SAR ━ Parabolic SAR (Stop and Reverse).
Advanced Take-Profit Options:
• R:R: Risk/Reward ━ sets TP based on SL size.
• T.VOLATY ━ Calculation based on volatility indicators (Fixed, ATR, STDEV).
Testing Modes:
• Stops ━ Cyclical stop-loss testing
• Pivot Point Example ━ Example of using pivot points
• External Example ━ Built-in example how test functions with different parameters
• External Signal ━ Using external signals
⭕️ Usage
━ First Steps:
When opening, select any point on the chart. It will not affect anything until you turn on Manual Start mode (more on this below).
The chart will immediately show the best results of the default Auto mode. You can switch Part's to try to find even better results in the table.
Now you can display any result from the table on the chart by entering its ID in the settings.
Repeat steps 3-4 until you determine which type of Stop Loss you like best. Then set it in the settings instead of Auto mode.
* Example: I flipped through 14 parts before I liked the first result and entered its ID so I could visually evaluate it on the chart.
Then select the stop loss type, choose it in place of Auto mode and repeat steps 3-4 or immediately follow the recommendations of the algorithm.
Now the Genetic Algorithm at the bottom right will prompt you to enter the Parameters you need to search for and select even better results.
Parameters must be entered All at once before they are updated. Enter recommendations strictly in fields with the same names.
Repeat steps 5-6 until there are approximately 10 Part's left or as you like. And after that, easily pour through the remaining Parts and select the best parameters.
━ Example of the finished result.
━ Example of use with Takes
You can also test at the same time along with Take Profit. In this example, I simply enabled Risk/Reward mode and immediately specified in the TP field Maximum RR, Minimum RR and Step. So in this example I can test (3-1) / 0.1 = 20 Takes of different sizes. There are additional tips in the settings.
━
* Soon you will start to understand how the system works and things will become much easier.
* If something doesn't work, just reset the engine settings and start over again.
* Use the tips I have left in the settings and on the Panel.
━ Details:
Sort ━ Sorting results by Score, Profit, Trades, etc..
Filter ━ Filtring results by Score, Profit, Trades, etc..
Trade Type ━ Ability to disable Long\Short but only from statistics.
BackWin ━ Backtest Window Number of Candle the script can test.
Manual Start ━ Enabling it will allow you to call a Stop from a selected point. which you selected when you started the engine.
* If you have a real open position then this mode can help to save good Stop\Take for it.
1 - 9 Сheckboxs ━ Allow you to disable any stop from Auto mode.
Ex Source - Allow you to test Stops/Takes from connected indicators.
Connection guide:
//@version=6
indicator("My script")
rsi = ta.rsi(close, 14)
buy = not na(rsi) and ta.crossover (rsi, 40) // OS = 40
sell = not na(rsi) and ta.crossunder(rsi, 60) // OB = 60
Signal = buy ? +1 : sell ? -1 : 0
plot(Signal, "🔌Connector🔌", display = display.none)
* Format the signal for your indicator in a similar style and then select it in Ex Source.
⭕️ How it Works
Hypothesis of Uniform Distribution of Rare Elements After Mixing.
'This hypothesis states that if an array of N elements contains K valid elements, then after mixing, these valid elements will be approximately uniformly distributed.'
'This means that in a random sample of k elements, the proportion of valid elements should closely match their proportion in the original array, with some random variation.'
'According to the central limit theorem, repeated sampling will result in an average count of valid elements following a normal distribution.'
'This supports the assumption that the valid elements are evenly spread across the array.'
'To test this hypothesis, we can conduct an experiment:'
'Create an array of 1,000,000 elements.'
'Select 1,000 random elements (1%) for validation.'
'Shuffle the array and divide it into groups of 1,000 elements.'
'If the hypothesis holds, each group should contain, on average, 1~ valid element, with minor variations.'
* I'd like to attach more details to My hypothesis but it won't be very relevant here. Since this is a whole separate topic, I will leave the minimum part for understanding the engine.
Practical Application
To apply this hypothesis, I needed a way to generate and thoroughly mix numerous possible combinations. Within Pine, generating over 100,000 combinations presents significant challenges, and storing millions of combinations requires excessive resources.
I developed an efficient mechanism that generates combinations in random order to address these limitations. While conventional methods often produce duplicates or require generating a complete list first, my approach guarantees that the first 10% of possible combinations are both unique and well-distributed. Based on my hypothesis, this sampling is sufficient to determine optimal testing parameters.
Most generators and randomizers fail to accommodate both my hypothesis and Pine's constraints. My solution utilizes a simple Linear Congruential Generator (LCG) for pseudo-randomization, enhanced with prime numbers to increase entropy during generation. I pre-generate the entire parameter range and then apply systematic mixing. This approach, combined with a hybrid combinatorial array-filling technique with linear distribution, delivers excellent generation quality.
My engine can efficiently generate and verify 300 unique combinations per batch. Based on the above, to determine optimal values, only 10-20 Parts need to be manually scrolled through to find the appropriate value or range, eliminating the need for exhaustive testing of millions of parameter combinations.
For the Score statistic I applied all the same, generated a range of Weights, distributed them randomly for each type of statistic to avoid manual distribution.
Score ━ based on Trade, Profit, WinRate, Profit Factor, Drawdown, Sharpe & Sortino & Omega & Calmar Ratio.
⭕️ Notes
For attentive users, a little tricks :)
To save time, switch parts every 3 seconds without waiting for it to load. After 10-20 parts, stop and wait for loading. If the pause is correct, you can switch between the rest of the parts without loading, as they will be cached. This used to work without having to wait for a pause, but now it does slower. This will save a lot of time if you are going to do a deeper backtest.
Sometimes you'll get the error “The scripts take too long to execute.”
For a quick fix you just need to switch the TF or Ticker back and forth and most likely everything will load.
The error appears because of problems on the side of the site because the engine is very heavy. It can also appear if you set too long a period for testing in BackWin or use a heavy indicator for testing.
Manual Start - Allow you to Start you Result from any point. Which in turn can help you choose a good stop-stick for your real position.
* It took me half a year from idea to current realization. This seems to be one of the few ways to build something automatic in backtest format and in this particular Pine environment. There are already better projects in other languages, and they are created much easier and faster because there are no limitations except for personal PC. If you see solutions to improve this system I would be glad if you share the code. At the moment I am tired and will continue him not soon.
Also You can use my previosly big Backtest project with more manual settings(updated soon)
Indicator
Risk Reward Optimiser [ChartPrime]█ CONCEPTS
In modern day strategy optimization there are few options when it comes to optimizing a risk reward ratio. Users frequently need to experiment and go through countless permutations in order to tweak, adjust and find optimal in their data.
Therefore we have created the Risk Reward Optimizer.
The Risk Reward Optimizer is a technical tool designed to provide traders with comprehensive insights into their trading strategies.
It offers a range of features and functionalities aimed at enhancing traders' decision-making process.
With a focus on comprehensive data, it is there to help traders quickly and efficiently locate Risk Reward optimums for inbuilt of custom strategies.
█ Internal and external Signals:
The script can optimize risk to reward ratio for any type of signals
You can utilize the following :
🔸Internal signals ➞ We have included a number of common indicators into the optimizer such as:
▫️ Aroon
▫️ AO (Awesome Oscillator)
▫️ RSI (Relative Strength Index)
▫️ MACD (Moving Average Convergence Divergence)
▫️ SuperTrend
▫️ Stochastic RSI
▫️ Stochastic
▫️ Moving averages
All these indicators have 3 conditions to generate signals :
Crossover
High Than
Less Than
🔸External signal
▫️ by incorporating your own indicators into the analysis. This flexibility enables you to tailor your strategy to your preferences.
◽️ How to link your signal with the optimizer:
In order to be able to analysis your signal we need to read it and to do so we would need to PLOT your signal with a defined value
plot( YOUR LONG Condition ? 100 : 0 , display = display.data_window)
█ Customizable Risk to Reward Ratios:
This tool allows you to test seven different customizable risk to reward ratios , helping you determine the most suitable risk-reward balance for your trading strategy. This data-driven approach takes the guesswork out of setting stop-loss and take-profit levels.
█ Comprehensive Data Analysis:
The tool provides a table displaying key metrics, including:
Total trades
Wins
Losses
Profit factor
Win rate
Profit and loss (PNL)
This data is essential for refining your trading strategy.
🔸 It includes a tooltip for each risk to reward ratio which gives data for the:
Most Profitable Trade USD value
Most Profitable Trade % value
Most Profitable Trade Bar Index
Most Profitable Trade Time (When it occurred)
Position and size is adjustable
█ Visual insights with histograms:
Visualize your trading performance with histograms displaying each risk to reward ratio trade space, showing total trades, wins, losses, and the ratio of profitable trades.
This visual representation helps you understand the strengths and weaknesses of your strategy.
It offers tooltips for each RR ratio with the average win and loss percentages for further analysis.
█ Dynamic Highlighting:
A drop-down menu allows you to highlight the maximum values of critical metrics such as:
Profit factor
Win rate
PNL
for quick identification of successful setups.
█ Stop Loss Flexibility:
You can adjust stop-loss levels using three different calculation methods:
ATR
Pivot
VWAP
This allows you to align risk-reward ratios with your preferred risk tolerance.
█ Chart Integration:
Visualize your trades directly on your price chart, with each trade displayed in a distinct color for easy tracking.
When your take-profit (TP) level is reached , the tool labels the corresponding risk-reward ratio for that specific TP, simplifying trade management.
█ Detailed Tooltips:
Tooltips provide deeper insights into your trading performance. They include information about the most profitable trade, such as the time it occurred, the bar index, and the percentage gain. Histogram tooltips also offer average win and loss percentages for further analysis.
█ Settings:
█ Code:
In summary, the Risk Reward Optimizer is a data-driven tool that offers traders the ability to optimize their risk-reward ratios, refine their strategies, and gain a deeper understanding of their trading performance. Whether you're a day trader, swing trader, or investor, this tool can help you make informed decisions and improve your trading outcomes. Indicator
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