For precise price forecasts, sophisticated analytical techniques are necessary due to the severe and erratic volatility of cryptocurrency markets. Algorithmic trading, which blends technical analysis, machine learning, and deep learning, has shown itself to be a successful method for anticipating and making decisions regarding cryptocurrency prices, whereas manual trading depends on subjective evaluations. By offering clear visualizations of price movements that aim at enabling traders to identify trends and reversals, charting techniques and tools are essential in trading since their integration with advanced algorithms enhances the reliability of trading signals in general. In this regard, this scrutiny delves into comparing the effectiveness of the Kagi line charting technique and Japanese candlestick data so as to predict one-step-ahead logarithmic price returns in the Bitcoin/USDT market. By integrating a BiTCN model with Kagi chart patterns derived from OHLC data to capture key market dynamics while filtering out temporal noise, the study aims to improve forecasting accuracy. The performance of both approaches is assessed using various metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), and the coefficient of determination (R2). The findings showed that the Kagi charting technique significantly outperforms Japanese candlestick data with consistently higher R2 values and lower (MSE, MAE) values, thereby highlighting the ability of Kagi lines to predict market movements. While their effectiveness may vary across different timeframes and cryptocurrency markets, this study highlights the potential of Kagi charting techniques as a valuable tool seeking to refine algorithmic trading strategies in volatile markets.