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Abstract: Quantitative algorithmic trading and machine learning have transformed financial markets by introducing advanced computational techniques for market analysis, decision-making, risk management, and order execution. This paper explores Deep Reinforcement Learning (DRL) for developing day trading strategies within the Ibovespa futures market, the world's most traded stock index futures contract. We chose the Ibovespa market for its high liquidity and volatility, which present unique challenges and opportunities for trading. We propose a novel methodology for the development, evaluation, and deployment of DRL-powered strategies. This methodology involves model generation, approval, selection, and execution, supported by comprehensive metrics for assessing returns, risk, and stability. We introduce the FUT-DRL trading strategy, a new approach that models the trading problem as a Partially Observable Markov Decision Process using DRL agents and the Deep Q-Network algorithm to navigate market complexities. Our empirical study, utilizing a diverse five-year dataset under varied conditions, shows that FUT-DRL outperforms traditional strategies, such as trend-following and mean - reversion, achieving an annualized return over 100 % and a Probabilistic Sharpe Ratio of 0.96. This research advances the integration of machine learning with quantitative trading, offering a robust framework for strategy development, and highlighting DRL's effectiveness in the volatile Ibovespa market, demonstrating the potential of adaptive and intelligent systems to evolve trading strategies in rapidly changing financial markets.