Transforming Algorithmic Trading with AI: Achieving Competitive Market Edge

This research examines the use of an algorithmic trading bot that applies the dollar cost averaging (DCA) strategy. This strategy is coded by Python. The bot installation programme is able to perform market orders, delineate trading positions, and calculate a variety of parameters which the consumers will use to make trade decisions. The bot eliminates the need for trading during the most unfavorable times by automating trade execution and profit calculations, creating accurate profit targets, and at the same time reducing the liability by setting safety orders. DCA bot makes purchases of financial assets from currently existing marketplaces and uses a way that the bot will double the trade volume under the bet price change to recoup from previous entry. Given the test run in a fake trading platform using the historical data from the market, the bot always could achieve the 2% profit suggested for each trade. The employment of real-time orders safety through mitigation of risk induced by unexpected price movements and provision of optimum timing for trade exit enhanced the profitability of trades. Main stats, like average profit per trade, and account drawdown helped in formation of the strategy. Last but not least, the results prove that the CNN-based bot used as the automated trading tool is a reliable one with possible further optimization in a real-time trading environment that perhaps will lead to performance and robustness improvement.