From Novice to Expert: Navigating Market Irregularities

Contents

1. Introduction
2. Implementation
3. Testing
4. Conclusion
5. Key Lessons
6. Attachments

Introduction

Most beginner traders quickly become familiar with support and resistance early in their market studies. This is largely because the concept is widely accessible and extensively discussed across trading literature and online resources. While this foundational knowledge is valuable, overconfidence in static support and resistance levels has pushed many traders out of the game as market behavior continues to evolve.

Markets are dynamic by nature. Levels that once appeared reliable can lose effectiveness as volatility increases and price structure becomes irregular. Today, we address this challenge by introducing an MQL5-based algorithmic approach that identifies dynamic support and resistance zones rather than fixed price lines. This innovation enhances traditional support and resistance trading by transforming a manual, static technique into a continuously adaptive, rule-driven system.

By leveraging the computer’s ability to process time-series data, the algorithm recalculates these zones on every tick and refreshes them in real time. The result is a fully dynamic framework that ensures no opportunity is missed, even as market conditions shift rapidly.

Problem Justification

Treating support or resistance as a single horizontal line can be effective under clean market conditions, but it often fails in choppy or transitional market structures. In such environments, traders are frequently stopped out prematurely, orders remain unfilled, or hesitation arises due to uncertainty about whether a setup will hold.

Our solution addresses this limitation by redefining support and resistance as probability zones rather than precise price levels. These zones represent areas where price interaction is statistically more likely, increasing the chances of order execution while reducing emotional hesitation and the risk of long-term drawdowns.

Even without oscillator confirmation—such as RSI—the aggregation logic used to calculate these zones increases their reliability. In the next subsection, we will explore the conceptual foundation behind this approach before moving into the implementation section, where we bring the idea to life using MQL5.

Understanding the Concept of Probability Zones

As with most of my work, this idea began by translating a manual trading concept into an algorithmic solution. My development process often starts on a MetaTrader 5 chart, where visual analysis reveals recurring problems that can be addressed programmatically using MQL5’s rich algorithmic capabilities.

While analyzing price action, I encountered repeated choppy market conditions—far more frequently than clean textbook setups. This raised an important question: How can we reduce the time spent waiting for “perfect” setups when the market rarely provides them?

Even prolonged patience can lead to disappointment, as many setups only become valid after the opportunity has already passed. To address this, we need a solution that provides a range of probability, allowing traders to participate from better positions rather than reacting too late. This approach also enables more precise risk sizing relative to account capital.

Traditionally, support or resistance is identified by averaging multiple price reactions into a single level. However, beyond this average line, price often produces external spikes that cannot be ignored. These spikes contribute significantly to the width of the support or resistance zone and are a primary cause of market choppiness—sometimes attributed to institutional activity or liquidity sweeps.

While the exact behavior of large market participants remains uncertain, the resulting uneven price structure is undeniable and often undermines trader confidence. The solution presented in this article focuses on restoring that confidence by embracing market imperfection rather than fighting it.

A diagram below illustrates this core concept. The external spikes, often dismissed as noise or manipulation, are integral to defining the true zone. Our algorithm does not ignore them; it uses them to calculate the robust boundaries of market interest.

 Fig. 1. Observations from Historical Price Analysis

To explain this concept clearly, I used USDJPY on the H1 timeframe and deliberately scrolled back through historical price data. This was not a real-time trading exercise. My goal at this stage was purely structural analysis—to study how price behaved around support and resistance over time without the pressure of live execution.

By moving through past candles, I could objectively observe repeated reactions and identify patterns that are often overlooked when trading in real time.

1. Marking the Extreme Price Rejections

As I examined the chart history, I first focused on the most extreme price movements.

At the upper boundary, I placed a line at what I call extreme resistance. This level captured the highest upward spikes where price was repeatedly rejected. For example, at point B, price surged into this area and immediately reversed. While price occasionally reached this level, it never stayed there for long. This told me that this region represented the upper limit of market acceptance, not a precise trading entry.

At the lower boundary, I marked extreme support. At point A, price dipped sharply before buyers stepped in aggressively. These deep downward spikes were infrequent, but when they occurred, they defined the lowest boundary of the structure.

2. Identifying the Average Reaction Levels

Next, still working through historical data, I looked for areas where price reacted most often, not just where it reacted most aggressively.

I placed an average resistance line by aggregating multiple reaction points such as d, e, and f. Price repeatedly stalled or reversed around this level, even though it sometimes pushed higher toward the extreme resistance.

Similarly, I identified average support by observing repeated bounces at points g, h, i, and j. This level was tested far more frequently than the extreme support and clearly acted as a central area of demand.

This step made something clear to me: the market respects ranges of price, not single exact numbers.

Understanding the Zone Structure

Once all four lines were in place, the structure revealed itself naturally.

Between Average Resistance and Extreme Resistance, price frequently spiked, rejected, and consolidated. The same behavior appeared between Average Support and Extreme Support. These regions formed support and resistance zones, not because of theory, but because of repeated historical behavior.

What many traders describe as “false breakouts” were simply price testing the outer edges of these zones. After identifying the extreme and average levels by scrolling through historical price data, I took the next step and converted those lines into zones. This is where the idea truly became practical. Instead of leaving the structure as four isolated horizontal lines, I marked the space between them using rectangles to visually represent how I want the system to function in practice. See the illustration below, Fig. 2.

Fig. 2. Support and Resistance Probability Zones marked with filled rectangles

As I observed price action within this rectangle, several things stood out clearly. Price did not need to touch the exact extreme resistance to react. In numerous instances, reactions occurred inside the zone, as seen around points d, e, and f. Occasionally, price spiked deeper into the zone—such as at point B—but these spikes were short-lived and quickly rejected.

In the algorithmic version:

1. The system will calculate the average and extreme boundaries.
2. It will draw zones dynamically using rectangles
3. Price interaction will be evaluated inside the zone, not at a single line
4. Trades, alerts, or confirmations can be triggered based on zone interaction rules.

Although this example was built from historical analysis, it clearly defines the logic that can operate in real time once implemented in MQL5. After defining the support and resistance zones and marking them with rectangles, I continued scrolling forward within the same historical section to observe how price eventually interacted with these zones. I did not change the structure or redraw the zones—I simply watched how price behaved once it reached the boundaries again. This step was important because a zone should not only explain rejections; it must also explain breakouts.

Fig. 3: Zone breakout

In Fig. 3, as price approached the resistance zone, I noticed repeated attempts to push higher, but each move initially stalled within the shaded area. This behavior showed clear compression—buyers were gradually absorbing sell-side pressure inside the zone rather than being immediately rejected.

Instead of treating these failed pushes as false signals, I interpreted them as pressure building inside the zone. A zone does not immediately reject price; it contains price until one side is exhausted.

Eventually, price broke cleanly above the resistance zone, as highlighted by the circled area. This was not a minor spike or a wick above a line. It was a decisive expansion, with strong bullish candles closing well above the zone’s upper boundary.

What stood out to me was that the breakout occurred after prolonged interaction within the zone, not on first contact. This confirmed that the zone had already done its job—absorbing orders and revealing intent.

Why the Zone Makes the Breakout Clearer

If I had used a single resistance line, this move might have appeared confusing or even misleading—was it a fake breakout or a real one?

By using a resistance zone, the breakout became unambiguous:

• Price exited the entire area of resistance.
• Momentum expanded beyond historical rejection limits
• Structure clearly shifted from range to continuation

What You Will Learn and Build

In the following sections, we will transition from concept to code. We will:

1. Deconstruct the Algorithm: Break down the logic for identifying swing points, clustering touches, and calculating the dynamic average and extreme levels.
2. Build the MQL5 indicator: Write the code that paints these probability zones directly onto your chart in real-time, with clear visual distinction between support and resistance.
3. Integrate into a Trading System: Demonstrate how to use these algorithmic zones as a foundation for generating high-probability signals, moving from visualization to execution.

Implementation

Having established the conceptual foundation of probability-based support and resistance zones through historical analysis, we now move into the implementation phase. In this section, we systematically translate the manual observations into a structured, repeatable, and real-time MQL5 indicator.

Rather than attempting to predict price, we observe, aggregate, and visualize market behavior. Each implementation step reflects a deliberate design choice that mirrors what we previously did manually on the chart—only now executed objectively and continuously by the platform.

1. Structural Foundation and User Configuration

Every robust MQL5 indicator begins with clear property declarations and configurable parameters. This establishes the indicator's identity and provides the trader with essential controls to adapt the tool to different instruments and timeframes.

#property copyright "Copyright 2025, Clemence Benjamin."
#property link      "https://www.mql5.com"
#property version   "1.00"
#property indicator_chart_window
#property indicator_buffers 4
#property indicator_plots   4

//--- Input Parameters (Optimized for M5)
input int    LookBackPeriod   = 150;    // Bars to analyze
input int    SwingSensitivity = 5;      // Swing detection (3-10)
input int    ZonePadding      = 20;     // Zone padding in points
input color  SupportColor     = clrRoyalBlue;   // Support zone color
input color  ResistanceColor  = clrCrimson;     // Resistance zone color
input bool   ShowZoneLabels   = true;   // Show zone labels

//--- Buffers
double SupportHighBuffer[];
double SupportLowBuffer[];
double ResistanceHighBuffer[];
double ResistanceLowBuffer[];

Key Design:

1. Chart Window Placement: The indicator draws directly on the price chart (indicator_chart_window) for immediate visual context.
2. Configurable Sensitivity: Parameters like SwingSensitivity and LookBackPeriod allow adaptation from scalping (M1-M5) to swing trading (H1-H4) timeframes.
3. Four Data Buffers: Each buffer stores one boundary of our two zones, enabling the indicator to plot dashed boundary lines if needed, though our primary visualization uses chart objects.

2. Core Market Structure Detection: Swing Point Algorithm

The accuracy of our zones depends entirely on correctly identifying significant market turning points. We implement a straightforward yet effective swing detection algorithm that compares each bar to its immediate neighbors.

//+------------------------------------------------------------------+
//| Simple swing high detection                                      |
//+------------------------------------------------------------------+
bool IsSwingHighSimple(int index, int period, const double& high[])
{
   if(index < period || index >= ArraySize(high)-period)
      return false;
   
   double currentHigh = high[index];
   
   for(int i=1; i<=period; i++)
   {
      if(currentHigh <= high[index-i] || currentHigh <= high[index+i])
         return false;
   }
   return true;
}

//+------------------------------------------------------------------+
//| Simple swing low detection                                       |
//+------------------------------------------------------------------+
bool IsSwingLowSimple(int index, int period, const double& low[])
{
   if(index < period || index >= ArraySize(low)-period)
      return false;
   
   double currentLow = low[index];
   
   for(int i=1; i<=period; i++)
   {
      if(currentLow >= low[index-i] || currentLow >= low[index+i])
         return false;
   }
   return true;
}

These functions implement a local extremum detection method. A SwingSensitivity of 5 means a bar is considered a swing high only if it's higher than the 5 bars before and after it. This creates a balance between sensitivity (detecting minor turns) and significance (ignoring market noise).

3. Zone Calculation Engine: From Points to Probability Ranges

The heart of our system transforms discrete swing points into continuous probability zones. This process involves collecting valid swing points, determining their range, and applying intelligent padding.

//+------------------------------------------------------------------+
//| Find support and resistance zones                                |
//+------------------------------------------------------------------+
void FindZones(const double& high[], const double& low[], 
               double& supportHigh, double& supportLow,
               double& resistanceHigh, double& resistanceLow)
{
   double supportLevels[], resistanceLevels[];
   ArrayResize(supportLevels, 0);
   ArrayResize(resistanceLevels, 0);
   
   int limit = MathMin(ArraySize(high), LookBackPeriod);
   
   // Find swing points
   for(int i=SwingSensitivity; i<limit-SwingSensitivity; i++)
   {
      if(IsSwingLowSimple(i, SwingSensitivity, low))
      {
         ArrayResize(supportLevels, ArraySize(supportLevels)+1);
         supportLevels[ArraySize(supportLevels)-1] = low[i];
      }
      
      if(IsSwingHighSimple(i, SwingSensitivity, high))
      {
         ArrayResize(resistanceLevels, ArraySize(resistanceLevels)+1);
         resistanceLevels[ArraySize(resistanceLevels)-1] = high[i];
      }
   }
   
   // Calculate support zone
   if(ArraySize(supportLevels) > 0)
   {
      // Find the most recent significant support
      supportHigh = 0;
      supportLow = DBL_MAX;
      
      for(int i=0; i<ArraySize(supportLevels); i++)
      {
         if(supportLevels[i] > supportHigh) supportHigh = supportLevels[i];
         if(supportLevels[i] < supportLow) supportLow = supportLevels[i];
      }
      
      // Adjust for zone
      double zonePadding = ZonePadding * _Point;
      supportHigh += zonePadding * 2;  // Add padding above
      supportLow -= zonePadding;       // Add padding below
   }
   // ... similar logic for resistance zone
}

Implementation Details:

• Dynamic Array Management: We use ArrayResize() to efficiently collect swing points without pre-allocating fixed memory.
• Asymmetric Padding Strategy: Support zones receive more padding above (zonePadding * 2), while resistance zones receive more padding below. This reflects the observed market behavior where prices tend to overshoot before reversing.
• Bounds Checking: The DBL_MAX and zero initialization ensure robust handling of edge cases where no swing points are detected.

4. Visual Presentation System: Clear Zone Rendering

A trading tool must communicate information instantly and clearly. Our drawing system creates distinct visual layers that represent the probability zones with maximum clarity.

//+------------------------------------------------------------------+
//| Draw support and resistance zones                                |
//+------------------------------------------------------------------+
void DrawZones(double supHigh, double supLow, double resHigh, double resLow)
{
   string name;
   datetime startTime = iTime(_Symbol, _Period, LookBackPeriod);
   datetime endTime = TimeCurrent();
   
   // Delete old objects first
   ObjectsDeleteAll(0, "SZone");
   ObjectsDeleteAll(0, "RZone");
   // ... additional cleanup
   
   // Only draw if we have valid values
   if(supHigh > 0 && supLow > 0 && supHigh > supLow)
   {
      // Draw support zone (filled rectangle)
      name = "SZone_Rect";
      ObjectCreate(0, name, OBJ_RECTANGLE, 0, startTime, supHigh, endTime, supLow);
      ObjectSetInteger(0, name, OBJPROP_COLOR, SupportColor);
      ObjectSetInteger(0, name, OBJPROP_BGCOLOR, SupportColor);
      ObjectSetInteger(0, name, OBJPROP_FILL, true);
      ObjectSetInteger(0, name, OBJPROP_BACK, true);
      ObjectSetInteger(0, name, OBJPROP_WIDTH, 0);
      
      // Draw support average line (middle of zone)
      name = "SZone_Avg";
      ObjectCreate(0, name, OBJ_HLINE, 0, 0, (supHigh + supLow) / 2);
      ObjectSetInteger(0, name, OBJPROP_COLOR, clrWhite);
      ObjectSetInteger(0, name, OBJPROP_STYLE, STYLE_SOLID);
      ObjectSetInteger(0, name, OBJPROP_WIDTH, 2);
      
      // ... boundary lines and labels
   }
   // ... similar drawing for resistance zone
}

Visual Hierarchy Design:

• Primary Layer: Filled rectangles with OBJPROP_BACK=true ensure price action remains visible over the zones.
• Secondary Layer: Solid white  lines (2px width) indicate the highest probability reaction point.
• Tertiary Layer: Dashed colored boundaries (1px width) define the zone edges.
• Informational Layer: Optional text labels and a status indicator in the chart corner.

5. Real-Time Processing Engine: The OnCalculate Function

The indicator's OnCalculate() function serves as the main processing loop, executing on every tick to ensure zones remain current with market developments.

//+------------------------------------------------------------------+
//| Indicator iteration function                                     |
//+------------------------------------------------------------------+
int OnCalculate(const int rates_total,
                const int prev_calculated,
                const datetime &time[],
                const double &open[],
                const double &high[],
                const double &low[],
                const double &close[],
                const long &tick_volume[],
                const long &volume[],
                const int &spread[])
{
   // Check if we have enough data
   if(rates_total < LookBackPeriod + SwingSensitivity * 2)
      return(0);
   
   // Set arrays as series
   ArraySetAsSeries(high, true);
   ArraySetAsSeries(low, true);
   
   // Find zones
   double supHigh, supLow, resHigh, resLow;
   FindZones(high, low, supHigh, supLow, resHigh, resLow);
   
   // Fill buffers
   int limit = MathMin(rates_total, LookBackPeriod);
   for(int i=0; i<limit; i++)
   {
      SupportHighBuffer[i] = supHigh;
      SupportLowBuffer[i] = supLow;
      ResistanceHighBuffer[i] = resHigh;
      ResistanceLowBuffer[i] = resLow;
   }
   
   // Draw zones on chart
   DrawZones(supHigh, supLow, resHigh, resLow);
   
   // Also publish to global variables for EA access
   if(supHigh > 0 && supLow > 0 && resHigh > 0 && resLow > 0)
   {
      GlobalVariableSet("GLOBAL_SUPPORT_HIGH", supHigh);
      GlobalVariableSet("GLOBAL_SUPPORT_LOW", supLow);
      // ... additional global variables
      GlobalVariableSet("GLOBAL_ZONES_TIME", TimeCurrent());
   }
   
   return(rates_total);
}

Processing Optimizations:

• Efficient Array Handling: ArraySetAsSeries(true) optimizes access patterns by indexing array element 0 as the most recent bar.
• Buffer Population: While chart objects provide the primary visualization, populating indicator buffers enables potential backtesting integration.
• Global Variable Bridge: Storing calculated zones in global variables (GlobalVariableSet()) creates an interface for Expert Advisors to access zone data programmatically, enabling automated trading strategies.

6. System Integration and Cleanup

Professional MQL5 programming requires careful resource management. The initialization and deinitialization functions ensure proper setup and cleanup.

//+------------------------------------------------------------------+
//| Indicator initialization function                                |
//+------------------------------------------------------------------+
int OnInit()
{
   // Set buffer properties
   SetIndexBuffer(0, SupportHighBuffer, INDICATOR_DATA);
   // ... buffer setup for all four buffers
   
   // Set plotting properties
   PlotIndexSetInteger(0, PLOT_DRAW_TYPE, DRAW_LINE);
   PlotIndexSetInteger(0, PLOT_LINE_STYLE, STYLE_DASH);
   PlotIndexSetInteger(0, PLOT_LINE_COLOR, SupportColor);
   // ... similar setup for other plots
   
   IndicatorSetString(INDICATOR_SHORTNAME, "Dynamic S/R Zones");
   IndicatorSetInteger(INDICATOR_DIGITS, _Digits);
   
   return(INIT_SUCCEEDED);
}

//+------------------------------------------------------------------+
//| Indicator deinitialization function                              |
//+------------------------------------------------------------------+
void OnDeinit(const int reason)
{
   // Clean up all objects
   ObjectsDeleteAll(0, "SZone");
   ObjectsDeleteAll(0, "RZone");
   ObjectsDeleteAll(0, "Sup");
   ObjectsDeleteAll(0, "Res");
   ObjectsDeleteAll(0, "ZoneInfo");
   
   // Clean up global variables
   GlobalVariableDel("GLOBAL_SUPPORT_HIGH");
   // ... delete all related global variables
}

Resource Management Strategy:

• OnInit() Configuration: Properly configures all indicator properties in a single, organized function.
• Comprehensive Cleanup: OnDeinit() removes every chart object and global variable created by the indicator, preventing chart clutter and memory leaks when the indicator is removed.

Testing

One interesting outcome of the indicator we developed is its ability to be evaluated directly within the MetaTrader 5 Strategy Tester. This capability allows us to validate the logic not only through visual, discretionary observation on live charts, but also through structured testing in a controlled, historical environment. We ensure that the same calculation logic and drawing routines are executed consistently in both real-time and simulated conditions, which is essential for objective verification.

We tested the indicator in real time to observe how the support and resistance probability zones evolve as new price data becomes available. In parallel, we replayed historical market data using the MetaTrader 5 Strategy Tester to study how price interacted with these zones in the past. This dual-mode testing approach allows us to confirm that the zones are not hindsight-biased and that they adapt dynamically as price progresses candle by candle.

In the historical tests shown below, we can clearly observe price respecting the identified zones as the market unfolds. Price reactions such as pauses, rejections, and short consolidations occur repeatedly around the highlighted areas, reinforcing the validity of the detected support and resistance ranges. This behavior is consistent across different segments of the tested data, which strengthens our confidence in the robustness of the logic.

Fig. 4: Testing the indicator in the Strategy Tester visually.

Overall, the test results are promising and suggest that the indicator provides a reliable foundation for a support and resistance–based trading strategy. While further forward testing and parameter refinement are always recommended, the current outcomes demonstrate that the zones are meaningful, stable, and practical for both analytical and trading purposes.

Conclusion

It is entirely possible to navigate complex market structures by employing probability-based zones within a structured trading framework. Throughout this discussion, we demonstrated how abstract, manually applied support and resistance concepts can be translated into precise, rule-driven algorithms. By doing so, we bridged the gap between discretionary chart analysis and systematic indicator development.

We explored the complete development path—from conceptual market structure logic to a fully functional custom indicator—highlighting how probability zones can be derived, visualized, and validated using historical and real-time data.

The resulting tool enables clear and consistent identification of support and resistance probability zones, reducing subjectivity and improving analytical confidence during decision-making.

Although the current implementation does not yet include full trade automation, it already provides practical value as a decision-support tool. Traders can use these zones to enhance trade location, risk management, and confluence analysis alongside other technical signals.

The Strategy Tester results further reinforce the robustness of the approach, showing that price frequently respects these zones as market structure unfolds.

Looking ahead, we aim to extend this work by automating zone updates, adding probabilistic weighting based on historical reactions, and integrating direct trade-execution logic. These enhancements will transform the indicator from an analytical aid into a complete, rule-based trading component suitable for Expert Advisors.

Below this conclusion, we summarize the key lessons learned and provide the full source code discussed in this article. We welcome questions, feedback, and open discussion in the comments section, as collaborative dialogue remains essential for refining ideas and advancing practical trading solutions.

Key Lessons

Key Lesson	Description:
Support and resistance can be treated as zones.	We treat support and resistance as price ranges derived from repeated swing interactions, not as single horizontal lines. This helps traders think in probabilities and improves decision-making around entries, stops, and targets.
Swing structure drives market context.	By detecting swing highs and lows directly from price data, we reinforce the importance of market structure over lagging indicators. Traders learn how trend behavior and reactions define valid trading areas.
MQL5 indicators follow a strict lifecycle.	We demonstrate how OnInit, OnCalculate, and OnDeinit work together in MQL5. Understanding this lifecycle helps traders and developers build stable indicators that behave correctly in live charts and the Strategy Tester.
Buffers enable strategy integration.	Even when using chart objects for visualization, indicator buffers remain essential. Traders learn how buffers allow Expert Advisors and other tools to consume indicator logic programmatically.
Global variables enable EA communication.	Publishing zone values via global variables shows how indicators can share market context with Expert Advisors, enabling automated trading decisions based on structural analysis.
Asymmetric zone padding reflects market psychology.	Adding more padding above support and below resistance acknowledges that markets tend to overshoot before reversing. This technical adjustment captures real trader behavior and reduces false breakouts.
Chart objects require proper cleanup.	Using ObjectsDeleteAll() with specific prefixes prevents memory leaks and chart clutter. This disciplined approach is crucial for professional indicator development and long-term chart stability.
Parameter optimization requires context awareness.	The optimal SwingSensitivity and LookBackPeriod vary by timeframe and volatility. Successful traders learn to adjust these based on market conditions rather than seeking universal "perfect" settings.
Visual hierarchy enhances trading decisions.	Using solid lines for averages, dashed lines for boundaries, and filled rectangles creates immediate visual understanding. This design approach helps traders process complex information quickly during fast markets.
Probability zones reduce emotional trading.	By providing a defined area rather than a precise price, zones decrease the anxiety of "missing the exact level" and help traders focus on risk/reward ratios within statistically significant areas.
Modular code structure enables easy enhancement.	Separating swing detection, zone calculation, and visualization functions allows for future upgrades like adding volume confirmation or multi-timeframe analysis without rewriting the entire indicator.
Real-time processing demands efficiency.	The OnCalculate function's structure, with early exits for insufficient data and proper array series handling, ensures the indicator remains responsive even on lower timeframes during high volatility.

Attachments

Source Filename	Version	Description:
SRProbabilityZones.mq5	1.0	Core indicator that dynamically calculates and visualizes support and resistance as probability-based zones rather than static lines. It algorithmically identifies swing highs and lows within a user-defined lookback period, clusters these price interactions, and draws distinct colored rectangles (blue for support, red for resistance) on the chart. The zone's boundaries represent the range of probable price reaction, with a solid white line marking the high-probability average within each zone. The indicator updates on every tick and publishes its calculated values to global variables for integration with Expert Advisors. Tip: After downloading the SRProbabilityZones.mq5 source code, you can compile it in MetaEditor and attach it to any MetaTrader 5 chart to immediately visualize dynamic support and resistance probability zones.

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