Machine Learning Integration Before machine learning, the early stage of algorithmic trading consisted of pre-programmed rules designed to respond to that market's specific condition. Traders and developers coded instructions based on technical indicators - such as relative strength index, moving averages - to automate long or short orders. A significant pivotal shift in algorithmic trading as machine learning was adopted. Specifically deep reinforcement learning (DRL) which allows systems to dynamically adapt to its current market conditions. Unlike previous models, DRL uses simulations to train algorithms. Enabling them to learn and optimize its algorithm iteratively. A 2022 study by Ansari et al, showed that DRL framework “learns adaptive policies by balancing risks and reward, excelling in volatile conditions where static systems falter”. This self-adapting capability allows algorithms to market shifts, offering a significant edge over traditional algorithmic trading.[12] Complementing DRL, directional change (DC) algorithms represent another advancement on core market events rather than fixed time intervals. A 2023 study by Adegboye, Kampouridis, and Otero explains that “DC algorithms detect subtle trend transitions, improving trade timing and profitability in turbulent markets”. DC algorithms detect subtle trend transitions such as uptrend, reversals, improving trade timing and profitability in volatile markets. This approach specifically captures the natural flow of market movement from higher high to lows.[13] In practice, the DC algorithm works by defining two trends: upwards or downwards, which are triggered when a price moves beyond a certain threshold followed by a confirmation period(overshoot). This algorithm structure allows traders to pinpoint the stabilization of trends with higher accuracy. DC aligns trades with volatile, unstable market rhythms. By aligning trades with basic market rhythms, DC enhances precision, especially in volatile markets where traditional algorithms tend to misjudge their momentum due to fixed-interval data. ``` python import pandas as pd import numpy ```