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  1. Using Renko-bar Regression with Error Correction

    by , 10-22-2026 at 05:12 AM
    Renko charts have an important property: the size of each bar is fixed and equal to brick_size. This means that when forecasting the next bar, the target is effectively discrete. It takes three values: +log(1 + brick_size/close) when moving up by one brick; -log(1 - brick_size/close) when moving down; and a rare zero value at reversal points, when the close of the current bar coincides with the boundary. It would seem that this is an ideal task for classification.
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  2. The Kalman Filter for Price Smoothing: An Exact Implementation in MQL5

    by , 10-21-2026 at 06:21 AM
    The Kalman Filter, developed by Rudolf Kalman in 1960 and famously used for spacecraft navigation on the Apollo program, approaches this differently. Rather than a fixed weighting scheme, it maintains an explicit estimate of its own uncertainty at every step, and uses that uncertainty to calculate, mathematically, exactly how much weight a new observation deserves. When the filter is confident in its current estimate, it leans on that. When a new observation is more informative than what
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  3. How to Set Up an AI Hedge Fund in MetaTrader 5

    by , 10-20-2026 at 02:45 AM
    The previous article ended with an honest admission: a system consisting of several analysts is a good start, but it is not the end of the conversation. The council demonstrated that several voices are better than one, but even fifteen voices working together on a single pair still amount to a small room.
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  4. The Hypothesis of Randomness: Searching for Hidden Patterns in Price Series

    by , 10-19-2026 at 12:15 PM
    Testing this hypothesis on the quotes of financial instruments can provide valuable information to analysts who develop trading systems based on detecting statistically significant deviations from the efficient market hypothesis. This article examines how to test the randomness hypothesis using the chi-square method. This is a versatile and well-studied method that can be used to test various statistical hypotheses; it is based on comparing the observed and expected frequencies of events.
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  5. Adaptive Trade Management in MQL5: Building a Broker-Aware, Deterministic, and Explainable Exit Engine

    by , 10-17-2026 at 06:41 AM
    This article presents an architecture where exit modules never modify a position directly. Each module produces a proposal. A policy engine ranks all well-formed proposals in a strict, documented order. A validator checks the candidates in that order against risk invariants and broker constraints, and the first candidate that passes is handed to an execution adapter, the only component that interacts with the trade server. A reconciliation step then compares the broker-side position with
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