Bear's Power is a pivotal indicator designed by Dr. Alexander Elder, used to assess market bearishness. It operates as an oscillator around the zero line, highlighting bearish control. The calculation involves subtracting the exponential moving average from the low of a period. This indicator can offer valuable insights when paired with other trend indicators.

The Bear's Power strategy integrates various techniques, including the Bear's Power Movement, which signals market direction based on current versus previous values. Traders can gain from understanding the indicator through hands-on coding in MQL5, executed in the MetaTrader 5 terminal, to automate and test strategies efficiently. Reliable backtesting is recommended for effectiveness.

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The 'PriceChannel_Signal_v2' custom indicator generates multiple signals for opening and closing positions without employing Stop Loss, Take Profit, or Trailing mechanisms. Two signals each are used for initiating both 'BUY' and 'SELL' positions, with one signal dedicated to closing each type. The EA is configurable for the specified working timeframe, with a single market entry allowed per bar.

Within its operational parameters, the mode settings distinguish between the current bar and new bar signal triggering. An advanced logging feature logs all operational details for comprehensive analysis. By altering 'Money Management' settings, the lot size can be fixed or adjusted to a dynamic percentage risk per trade. The 'Positions: Only one' flag ensures a single active position in the market, maintaining streamlined market engagement. Customization and ...

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Bill Williams’ fractal indicator is key for identifying reversal points. It utilizes a 5-bar pattern, with a bullish formation marked by the lowest low in the middle bar, and a bearish pattern featuring the highest high. Consecutive fractals in the same direction, either bullish or bearish, highlight market support or resistance levels, indicating potential upward or downward movements.

Pattern implementations in MQL5 cover a range from trend continuation to reversals, such as Pattern-0’s bullish formations based on fractal lows and Pattern-3’s price action divergences. Fractal clusters around moving averages (\text{Pattern-7}) and alignment with Fibonacci levels (\text{Pattern-8}) emphasize support and resistance more robustly.

Implementations utilize MQL5's array standard class for precise data management, comparing points and leveraging standard...

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Market cycles significantly impact trading decisions. Cyclic patterns are often observed in response to economic news, time, and seasonal factors, aiding traders in predicting price actions. Various analysis methods such as spectral analysis, moving averages, and dot mapping can be utilized to detect these cycles within financial data, providing a deeper understanding of price movements.

Tools like moving averages can identify potential price cycles amidst noise. However, complexities arise when dealing with a mix of sinusoidal signals, necessitating sophisticated approaches like finite differences and wavelet transformations. Despite these methods, market patterns are unpredictable, emphasizing the need for continuous exploration and adaptation in cycle analysis. Understanding cycles enhances technical analysis, offering insights and innovations in marke...

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The concept involves modifying the iCCI indicator to provide clearer signals through color differentiation. The idea is to utilize the iMA formula to enhance visibility and decision-making. By applying this coloring principle, users can gain a better understanding of market dynamics. It's crucial to align oscillator signals with a trend indicator to increase reliability. This method aims to improve the efficiency of the iCCI indicator, offering a more intuitive approach to interpreting data. Implementing such modifications requires careful consideration of market conditions and thorough testing to ensure accuracy and usability. An effective combination of these elements can assist in refining trading strategies.

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Discover the fundamentals of neural networks and their vast potential in trading algorithm development. This article delves into the architecture of multilayer perceptrons (MLP) and deep neural networks, highlighting their strengths in task complexity and practical applications like stock market predictions. Learn about feedforward, convolutional, and recurrent neural networks, and understand the role of activation functions such as ReLU, Sigmoid, TanH, and SoftMax. By exploring input, hidden, and output layers, this piece provides insights into crafting effective neural network architectures, essential for MetaTrader 5 developers pursuing advancements in algorithmic trading solutions. Ideal for both seasoned developers and those looking to enhance their MQL5 coding skills.

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A utility designed for copying trades between MT4 and MT5 accounts is available, allowing seamless integration regardless of whether accounts are Netting or Hedging. Users can filter by instrument name and magic numbers to select positions for copying. The system handles only market positions directly and processes pending orders when they become active market positions. Configuration requires two terminals on the same server to share a data folder for communication. On the sender account, the Adviser operates in sender mode; on the receiver account, it functions in receiver mode. For copying from MT4 to MT5, deploy this utility as the sender and a compatible MT5 product as the receiver. Conversely, for MT5 to MT4 copies, use this as the receiver. More detailed instructions are provided within the utility documentation.

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Explore a precise approach to enhancing MetaTrader 5 trading strategies using cross-market analysis and algorithmic innovations. By incorporating real-time data from XAGUSD, XAGEUR, and EURUSD, a nuanced strategy emerges, allowing traders to preemptively capture market trends. The solution leverages the inherent links between these markets, aided by technical indicators and statistical models, to refine decision-making and minimize noise. The algorithm uses historical data and machine learning models within MQL5 to streamline operations, offering a structured pathway to improve profitability and reduce volatility in trading performance, maintaining a focus on practical application for developers.

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This utility allows for seamless transaction copying from one MT5 or MT4 account to another MT5 account, supporting both Netting and Hedging account types in any configuration. The utility enables filtering by instrument name and magic numbers for positions to be copied. It currently transmits data solely on market positions; pending orders are addressed once they become market orders. In the Sender mode, one terminal dispatches data, while the other terminal operates in Receiver mode to obtain it. The terminals must reside on the same server to access a common shared data folder for information exchange.

In the Sender account's terminal, activate the utility in Sender mode and configure the necessary settings. For the Receiver account, activate the utility in Receiver mode and adjust the settings accordingly. For transferring between MT5 and MT4, a...

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Financial market analysis continually seeks new methods, exploring even unconventional ones like astrology. William Gann popularized this approach, aiming to correlate celestial positions with market trends. Despite ongoing skepticism, some traders still explore financial astrology. Using Python with libraries like Skyfield for astronomical data and MetaTrader 5 for financial data, researchers attempt to combine ancient knowledge with modern technology.

In the process, data collection and synchronization are essential. Despite rigorous statistical analysis, no significant correlations between planetary movements and market prices were found. Machine learning models like CatBoost also failed to produce reliable predictions, confirming astrology's limited value in modern financial markets.

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The comprehensive History Management EX5 library for MetaTrader 5 simplifies interaction with trade histories by enabling easy scanning, retrieval, sorting, and categorization of data like closed positions and pending orders. Efficient integration of this library into MQL5 projects allows developers to implement a unified interface for accessing various trade activities seamlessly. Key benefits include intuitive data retrieval functions, time-saving automation, and error-free handling, which reduce manual coding and enhance workflow efficiency. The library supports practical applications such as custom filtering by symbol or time, detailed trade analysis, scalable project integration, and historical backtesting, aiding in informed decision-making for trading strategies.

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The Intrusion Detector EA automates the monitoring of quarter levels based on Ilian Yotov's Quarters Theory, aiming to enhance market analysis by identifying potential price intrusions. By systematically dividing the market into 1000-pip ranges, with Major Whole Numbers, the EA marks Large (250-pip) and Small Quarters, along with overshoot and undershoot levels.

This expert advisor checks the proximity of current prices to these levels on every tick, providing real-time alerts and a commentary panel to facilitate trader decision-making. The EA reduces manual monitoring efforts, making quarter theory actionable and efficient for forex trading. It ensures traders are promptly informed about key market movements through its systematic approach and tailored alerts.

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The Time To Close MT5 TimeToClose-v1.01 is an indicator providing real-time countdowns until candle close, integrating seamlessly with chart themes. Designed for simplicity, it features dynamic color adaptation, matching text color to the candle’s border or body based on its direction. This ensures visual coherence across themes.

Optimized for strategy testing, the indicator leverages MQL_VISUAL_MODE to disable rendering in non-visual back-tests, saving resources. It supports multiple time frames, adjusting automatically through PeriodSeconds() for precise measurement from 1-minute to monthly.

Customizable display options include text separators, font size, and anchor point for flexibility. Optional DateTime display is available via the ShowTimeDate parameter. With EventSetMillisecondTimer(1000), it delivers precise 1-second updates with minimal C...

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The study of neural networks in trading emphasizes pattern recognition and data selection. For new traders, optimizing inputs like MA indicators significantly influences outcomes. Testing geometric shapes through neural networks provides a unique approach but has limitations. Using filtered data such as Moving Averages helps in model training, while geometric shape models can influence trading results.

Key findings include counter-trend strategies and considerations for indicators like TEMA. Optimization challenges involve computing resources and require efficient strategies for substantial passes. Focus on finding patterns that offer a high profit factor ratio. Building an optimized database with weight ratios stored in CSV files can streamline the development of sophisticated trading systems. Continued research and experimentation are essential in develo...

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A zigzag indicator identifies swing points using a step size to determine price movement thresholds. The "scale" input adjusts swing detection sensitivity, dictating how price changes affect the indicator. Unlike traditional Zigzag with a "depth" parameter, this focuses on price movement for detecting swing reversals. It maintains leg continuation until confirmation of a new swing, making it effective for swing analysis. Originally developed by Evgeniy Chumakov as an MT4 indicator, it enhances price analysis by increasing the scale value, resulting in more zigzag points. Default settings vary by market; for example, use a scale of 3000 for XAUUSD and 25000 for BTCUSD. Adjust scale through trial and error for other markets.

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When developing a zigzag indicator, the setup requires precise components: a zigzag plot, two data buffers for storing highs and lows, and input parameters. System variables reset upon recalculation. Arrays like upWaves and dwWaves handle highs and lows respectively, while other variables track wave type, start and end points, and bar distances.

The process begins with initializing the rolling ATR calculation. Before moving forward, bars collected must exceed the ATR period. Initial ATR calculation involves accumulating bar ranges, with subsequent updates clipping and adding new ranges based on ATR/period.

Retracements begin after a valid wave forms. Once ATR equals the period, determine the initial wave from high or low points that reach valid ATR sizes. This avoids starting with a retracement. With established waves, the system updates highs and lo...

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Creating specialized WinForms objects in a library requires structuring categories similar to MS Visual Studio. Standard controls, containers, and other elements should inherit from a shared base object, CWinFormBase, to ensure consistent attributes and methods. Developing a GroupBox within the containers category and a CheckBox in standard controls facilitates identifying shared properties necessitating a common class for each category to streamline code development for future objects.

Incorporating macro substitutions for default properties upon object creation and refining enum lists to reflect categories supports library enhancement. GroupBox, designed to visually group objects, incorporates a frame, while CheckBox, inheriting from the Label object, allows states like checked, unchecked, or undefined using a label. Future implementations will conside...

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A new update now allows automatic export of trade history after running an Expert Advisor in the Strategy Tester. The exported file is stored in either the shared terminal folder (Common/Files) or the terminal folder (MQL4/Files). Users have the option to either let the file name be generated automatically or manually set it using the Export() method. This history file is useful for replicating the trade sequence on another trading server with the Simple History Receiver EA.

Basic usage involves instantiating the object in the global scope and adding the Export() method call to OnTester(). For extended usage, instantiate the object in the global scope, add parameter names and values in OnInit(), and include the Export() method call in OnTester(). The Export() method provides additional options for customization.

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The adaptation of large-scale models to new tasks has been significantly influenced by the use of transformers, particularly due to their Self-Attention mechanism. While this offers robust modeling within a specific context window, it faces limitations in scale and context length. Sequence modeling can benefit from State Space Models (SSMs), which efficiently handle long-range dependencies with linear scaling. The Mamba algorithm introduces a novel class of selective SSMs that selectively process input data, allowing for efficient sequence transformations. The key lies in its unique selection mechanism that adjusts state interactions based on the input, thus enhancing performance in handling large sequences.

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The EA utilizes a custom indicator called 'CHO Smoothed', which integrates the Chaikin Oscillator (CHO) line and a smoothed version using 'Moving Average'. It operates on a specified timeframe, known as the 'Working timeframe'. Key to its operation is the intersection of the two lines, which generates trading signals. When the 'Use ZeroLevel' parameter is set to 'true', only signals that meet specific conditions are permitted—‘BUY’ signals below zero and ‘SELL’ ones above zero.

The EA’s settings allow for optimization on the specified timeframe, limiting one market entry per bar. Different modes determine signal searching, with bar #0 considering each tick and bar #1 focusing on new bar births. Trade directions can be limited to BUY, SELL, or both, based on the ‘Trade mode parameter’.

Time controls manage the intervals during which signals are searched. Th...

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