Computer Science And Technology - Research Publications

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    OG-CAT: A Novel Algorithmic Trading Alternative to Investment in Crypto Market
    (Springer, 2023-03-28T00:00:00) Khurana, Surinder Singh; Singh, Parvinder; Garg, Naresh Kumar
    Cryptocurrencies have emerged as a good tool for investment/trading in the last decade. The investors have achieved promising gains with the long-term investments made at reasonably good price/time. However, investment in cryptocurrencies is also exposed to extremely high volatility. Due to this, the investment may suffer from a high drawdown as the price may fall. In this work, we proposed optimized Greedy-cost averaging based trading (OG-CAT) a novel trading framework as an alternative to long-term investment in cryptocurrencies. The approach exploits the wavy structure of the price movement of cryptocurrencies, the high volatility of price, and the concept of cost averaging. Furthermore, the parameters of the approach are optimized with the simulated annealing algorithm. The approach is evaluated on the two prominent cryptocurrencies: bitcoin and ethereum. During the evaluation, OG-CAT not only outperformed the buy-and-hold investment approach in terms of profit but also demonstrated a lower drawdown. The profit percentage in the case of trading BTC with OG-CAT is 1.63 times more and the max drawdown is 1.62 times less than compared to the buy-and-hold strategy. � 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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    Revolutionize AI Trading Bots with AutoML-Based Multi-timeframe Bitcoin Price Prediction
    (Springer, 2023-06-27T00:00:00) Khurana, Surinder Singh; Singh, Parvinder; Garg, Naresh Kumar
    Multi-timeframe analysis/prediction provides essential information to traders. It gives a broader perspective of market trends and is used to identify significant levels of support and resistance. This will help traders/trading bots in making trading decisions. The majority of current studies focused on forecasting the closing price of daily candlesticks or high-frequency time frames, such as those of 1�min or 5�min. For artificially intelligent trading bots focusing on swing trading, price prediction related to other time frames is very significant. In this research, we present a study on developing a model to enable artificial intelligent-based trading bots to predict price components (open, high, low, and close prices) of the next 30-min, 1-h, and 4-h candlesticks of Bitcoin price. The study used two Auto-Machine Learning libraries: Tree-Based Pipeline Optimization Tool (TPOT) and AutoSklearn, to find the most suitable model for the task. The models are trained on historical price data of Bitcoin, and technical indicators are computed on these data. The performance of the trained models is evaluated in terms of R2 Score (Coefficient of Determination), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results showed that TPOT outperformed AutoSklearn library for all three time frames. It predicted all price components of 30-min candlestick with R2 Score of 0.999. � 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.