Uncategorized

Financial markets attract a vast number of investors due to their broad accessibility and potential for high profitability. Among all available asset classes, cryptocurrencies stand out for their exceptional volatility, which creates unique opportunities for generating substantial profits over short time periods. An additional advantage is their 24/7 trading cycle, allowing traders to capture market changes at different times. However, this same volatility presents not only opportunities

...

The Opening Range Breakout (ORB) strategy addresses this by first capturing the upper and lower limits of that opening interval (configurable to any duration) and shading the range on the chart as a rectangle. These bounds serve both as visual landmarks and precise trigger levels. Only when price breaks beyond the rectangle, revisits that boundary, and breaks out again does the strategy confirm entry, filtering out noise and isolating moves driven by actual market conviction.

...

From the beginning of this article series, our emphasis has been on aligning our experts with the prevailing sentiment of the daily (D1) candles. The shape of the daily candle has served as the primary guiding feature. However, we needed to scale down to lower timeframes to identify entry levels within the D1 market. For example, at the M1 timeframe, we wanted the market to reach extreme levels on the Relative Strength Index (RSI) to signal potential trades for the Expert Advisor. At this

...

Pattern recognition has always been a valuable tool for traders. Whether it's identifying unique combinations of candlesticks or drawing imaginary lines on a chart, these patterns have become an integral part of technical analysis. Humans have always excelled at finding and recognizing patterns—so much so that it is often said we sometimes see patterns where there are none. Therefore, it would benefit us to apply more objective techniques when identifying potentially profitable patterns in

...

This article continues our look at reinforcement learning by considering another algorithm, namely the Monte-Carlo. This algorithm is very similar and in fact arguably encompasses both Q-Learning and SARSA in that it can be either on-policy or off-policy. What sets it apart though is the emphasis on episodes. These simply are a way of batching the reinforcement learning cycle updates, that we introduced in this article, such that the updating of the Q-Values of the Q-Map happens less frequently.

...