Stable2026

Fully automated trading with occasional manual adjustments during the EA development phase. The algorithmic system independently opens and closes trades based on predefined rules. However, due to the current optimization and testing stage, a small percentage of manual trades may be executed to promptly adapt to non‑standard market conditions.

Trading is carried out using key support and resistance levels: entry points are determined when price zones are tested or broken out. The deposit utilization is high, which allows maximizing trading potential but also significantly increases the overall portfolio volatility. The risk level is considered quite high, corresponding to an aggressive trading approach.

Standard risk management tools are applied: take profit is set, and a formal stop loss is in place. At the same time, the primary risk limitation mechanism is the forced closure of all open positions at the end of the trading day. This prevents overnight holding of trades and protects the account from gaps and unpredictable movements at the next session’s opening. This approach reduces the impact of external factors and makes the risk more manageable despite the aggressive deposit utilization.

The EA statistics are relevant from July 2026 onwards — this is when key performance metrics started being recorded and analysed: profitability, drawdown, number of trades, ratio of profitable to losing trades, and risk indicators relative to the deposit. These data reflect the strategy’s performance under current market conditions and serve as the basis for further optimization of the EA parameters.

Prior to July 2026, trading was also carried out on this account — the account was used continuously, without interruptions or platform changes. I deliberately did not switch to a different account in order to preserve a unified trade history. This allows for a comprehensive view of performance dynamics, tracking the evolution of the strategy, and assessing the impact of implemented changes on actual results.

Having a single continuous history enables comparison between periods before and after the EA updates, helping to identify consistent patterns and to distinguish the effect of algorithm modifications from the influence of market conditions. This record‑keeping approach makes the analysis more objective: it is possible to compare not only formal metrics but also the strategy’s behavioural characteristics across different market regimes — from trending to ranging environments.

It is important to note that trading conditions and EA parameters prior to July 2026 may have differed, so historical data should be interpreted in the context of the settings that were in place at that time.