Great. I will gather a comprehensive overview of how supervised machine learning algorithms like Random Forest, Gaussian Naive Bayes, and others are implemented successfully in crypto trading—regardless of the specific centralized exchange (CEX). The focus will be on real-world implementations or exceptional academic research for intraday or daily trading strategies.

I’ll also compare and sort the algorithms based on performance metrics such as return, Sharpe ratio, Sortino ratio, or profit factor. I’ll share the findings with you shortly.

Overview of Machine Learning Algorithms in Crypto Trading

Algorithmic trading in cryptocurrency markets leverages machine learning (ML) to predict price movements and generate buy/sell signals. Unlike rule-based strategies, ML algorithms can learn complex patterns from historical data – including price history, technical indicators, and even sentiment – to make trading decisions. These algorithms have been applied across various centralized exchanges (Binance, Bitget, Bybit, etc.), in both intraday high-frequency settings and daily swing-trading strategies. Below, we outline key supervised ML algorithms used in crypto trading, how they are implemented, and compare their performance using metrics like returns and risk-adjusted ratios (Sharpe, Sortino, profit factor).

Ensemble Tree-Based Models: Random Forest and Boosting

Random Forest (RF) is a popular ensemble of decision trees known for robust performance on financial data. In crypto trading, RF classifiers/regressors are trained on historical price data (often augmented with technical indicators or other features) to predict the next price movement or return. These predictions are then used to enter long or short positions. Numerous studies report strong results with Random Forest models: for example, Qureshi et al. (2025) found RF to be one of the top-performing models for algorithmic crypto trading. In their evaluation, a Random Forest strategy achieved an annualized return of ~18.2% with a Sharpe ratio ~1.35, outperforming simpler methods like logistic regression (15.5% return, Sharpe ~1.15). Similarly, Jabbar & Jalil (2024) note that Random Forest “exhibit superior performance in terms of profit and risk management” among dozens of models tested. Real-world implementations echo these findings – in one intraday BTC-USD strategy using 1-minute data and technical features, an RF-based trading bot achieved a Sharpe ratio of 4.47 and total return of 367% (over 2 years), dramatically beating buy-and-hold returns. (This strategy’s profit factor was about 1.06, indicating slightly more gross profit than loss, and a modest win rate ~54%. The high Sharpe reflects the consistency of many small gains.) Such results underscore Random Forest’s ability to capture non-linear patterns in crypto markets, making it a powerful tool for both intraday and daily strategies.

Gradient Boosting Machines (GBM) – including implementations like XGBoost, LightGBM, and AdaBoost – are another class of ensemble models frequently and successfully applied to crypto trading. These models build sequential decision trees that focus on correcting errors of previous ones, often yielding high predictive accuracy. In Qureshi et al.’s comparative study, gradient boosting models were top performers: e.g. a Gradient Boosting strategy attained the highest Sharpe ratio (~1.40) and Sortino ratio (~1.55) among tested algorithms, with ~17.9% annual return. XGBoost was similarly strong (Sharpe ~1.30, ~18.0% return), both outperforming classic models like SVM or single decision trees in risk-adjusted returns. These results indicate boosted trees can effectively exploit complex interactions in crypto price data. However, some studies note that in very noisy or limited data scenarios, simpler ensemble methods can rival or beat more complex boosters. For instance, one analysis found Random Forest often outperformed LightGBM, XGBoost, and CatBoost for multi-day crypto price forecasts – possibly because overly complex models overfit noise in the highly volatile crypto market. Nonetheless, overall tree-based ensembles (Random Forest and boosting) are consistently top-tier in performance for crypto trading algorithms.

Other Supervised Algorithms (Linear, SVM, Naive Bayes)

Linear models like Logistic Regression or Stochastic Gradient Descent (SGD) classifiers serve as useful baseline strategies in crypto trading. They predict price direction based on a weighted sum of input features. These models are easy to implement and fast, which is advantageous for high-frequency trading. In practice, linear models can be profitable but usually underperform more flexible non-linear models. For example, logistic regression in one study had a Sharpe ratio ~1.15 and Sortino 1.20, trailing behind ensemble methods. Interestingly, an extensive 2024 evaluation of 41 ML models found that an SGD-based model (a linear classifier optimized via stochastic gradient descent) was among the top performers alongside Random Forest. The authors suggest that with proper tuning, even linear models can adapt quickly to new market data, yielding solid profit and risk metrics. Overall, linear approaches are valued for their simplicity and stability, though they rarely top performance rankings.

Support Vector Machines (SVM) have been applied to predict crypto price direction by finding an optimal boundary (hyperplane) between “up” and “down” movements in the feature space. SVMs can capture some non-linear relations via kernels, albeit at higher computational cost. The success of SVMs in crypto trading has been mixed. In a 2021 study focusing on daily return prediction for Bitcoin, Ethereum, and Ripple, an SVM classifier achieved the highest prediction accuracy among eight models, leading to a trading strategy Sharpe ratio of about 2.8. In that case, SVM outperformed even advanced ensemble methods, which surprisingly had the poorest accuracy for those particular data and features. However, other research has found SVMs to be middling – for instance, Qureshi et al. report an SVM Sharpe ~1.1 (lower than tree ensembles) in their backtests. These differences imply that SVM effectiveness can depend on the feature set and market regime; SVMs may excel when the decision boundary between price-up vs price-down is clear in the given feature space, but can be outshined by ensemble models when relationships are more complex.

Naïve Bayes (NB) classifiers (e.g. Gaussian Naive Bayes) represent a simple probabilistic approach, predicting price direction based on Bayes’ theorem under an independence assumption between features. While fast and easy to implement, Naive Bayes often struggles with the complexity of financial data. Empirical results typically show NB lagging behind other algorithms in both accuracy and trading returns. For example, Qureshi et al. found Gaussian Naive Bayes to perform worst among a wide range of classifiers, with significantly lower precision (e.g. F1-score ~0.28 vs ~0.76 for Random Forest in one configuration). Other comparisons have similarly reported Naive Bayes strategies yielding near-zero or negative returns. The poor showing is attributed to NB’s “naive” assumption – market features (indicators, etc.) are rarely independent in reality – and its inability to capture nonlinear feature interactions. Thus, Naive Bayes is generally not as successful in crypto trading, especially compared to the more powerful models above. It may still serve as a lightweight benchmark or be useful in ensemble blends, but by itself it usually ranks at the bottom in performance.

Neural Network Approaches (MLPs, RNNs)

Although the question focuses on supervised algorithms like the above, it’s worth noting that neural networks (which are also typically trained in a supervised manner for price prediction) have been explored extensively in crypto trading. Multi-layer Perceptrons (feed-forward networks) can learn nonlinear mappings from technical indicators or other features to future price changes. Recurrent Neural Networks (RNNs), especially LSTM and GRU networks, are adept at sequence modeling and have been used to capture temporal dependencies in crypto price series. Research on daily price forecasting finds that deep networks sometimes outperform simpler models for certain assets – for example, one multi-coin study noted that for Bitcoin specifically, RNN/GRU models achieved better prediction accuracy than any ML regression model, including Random Forest, for longer-horizon forecasts. This suggests RNNs can leverage Bitcoin’s larger historical data and detect time-dependent patterns better in some cases. However, the same study also observed that very complex architectures (e.g. Transformers or deep hybrid models) did not outperform simpler models consistently. Due to limited and noisy data in crypto, a straightforward model like a GRU or even Random Forest often proved more robust than an over-parameterized deep network that might overfit noise. In practice, neural networks require careful tuning and lots of data; they have shown promise (especially when incorporating alternative data like social-media sentiment or on-chain metrics), but their real-world trading performance must justify the added complexity. Many winning strategies still rely on the tree ensembles or other simpler algorithms unless a large data edge is present.

Strategy Implementation (Intraday vs Daily)

Implementing these algorithms in a trading strategy involves several steps common to both intraday and daily horizons. First, data preparation is key – practitioners collect historical price data (OHLCV from exchanges) and often engineer features such as technical indicators (moving averages, RSI, MACD, etc.), momentum measures, volatility metrics, or even external signals (news sentiment, Twitter volume, Google Trends). The data is typically labeled in a supervised manner – e.g. next period’s price direction (up/down) for classification, or next period’s return for regression. Given the often imbalanced nature of price direction data (markets slightly drift upward over long periods), techniques like resampling or class weighting may be applied to improve model training. The algorithm (whether Random Forest, XGBoost, SVM, etc.) is then trained on a portion of the historical data, and its hyperparameters are tuned (sometimes via cross-validation or Bayesian optimization) to maximize predictive performance or trading profitability.

For intraday strategies, the models operate on high-frequency data (from 1-minute up to hourly candles). Speed and robustness are crucial here – algorithms like Random Forest or logistic regression are fast to predict and retrain, which helps in rapidly evolving intraday markets. The example RF intraday strategy mentioned earlier used 1-minute bars and generated frequent buy/sell signals. In such fast strategies, one must consider transaction costs and slippage; even if a model predicts well, high turnover can eat into profits (e.g. the RF strategy had a high Sharpe but a relatively low profit factor partly due to many trades). Intraday implementations often utilize rolling retraining (updating the model periodically as new data arrives) to adapt to regime changes. Simpler models (with fewer parameters) can be advantageous to avoid overfitting short-term noise and to execute with low latency.

For daily or longer-horizon strategies, the algorithms may integrate broader features (longer technical trends, macro indicators, network/on-chain data, etc.) and typically trade less frequently. A model might predict the next day’s return or the probability of a price increase and allocate capital accordingly (e.g. go long if tomorrow’s return forecast is positive beyond a threshold, otherwise go short or move to cash). These strategies are easier to backtest over longer histories. Many academic studies operate on daily data – for instance, the SVM strategy with Sharpe 2.8 was based on daily positions. Daily models have more time for computation, so more complex algorithms (like neural networks or ensembles with extensive feature sets) can be deployed if they add value. Backtesting is used to evaluate performance, and robust studies also perform forward-testing on unseen data or even live paper trading to ensure the model’s profitability isn’t a backtest mirage.

Regardless of timeframe, success in real-world implementation requires accounting for practical factors: trading fees, liquidity constraints (especially in lower-cap altcoins), and risk management (stop-loss rules, position sizing). Performance metrics like Sharpe ratio (reward-to-variability), Sortino ratio (reward-to-downside-risk), maximum drawdown, and profit factor give a fuller picture of a strategy’s quality beyond simple return. For example, a strategy might have high returns but also high volatility and deep drawdowns (lower Sharpe/Sortino), which may not suit all investors. The best algorithms tend to deliver balanced performance, yielding solid returns with controlled risk.

Performance Comparison and Rankings

Taking into account both academic research and real-world reports, we can summarize how various algorithms stack up in crypto trading:

1. Gradient Boosting (e.g. XGBoost, LightGBM) – Top-tier performance. Often achieves the highest risk-adjusted returns. In one study, gradient boosting had the highest Sharpe (≈1.40) and Sortino (~1.55) with ~140% cumulative return. These models excel at capturing complex nonlinear interactions, though they require careful tuning and can overfit if data is limited.
2. Random Forest – Top-tier performance. Almost on par with boosting methods in many cases. RF frequently outperforms other algorithms in backtests and was identified as best-in-class for profit and risk management by comprehensive analyses. For instance, an RF model showed Sharpe ~1.35 and Sortino ~1.50 with ~135% return, beating simpler models. Random Forests are praised for their robustness to noise and have proven successful in both intraday and daily strategies (with Sharpe up to 4+ in a short-term setting).
3. Other Tree Ensembles (Bagging, AdaBoost) – Upper-mid performance. Variants like bagging or AdaBoost perform well but typically slightly lag RF/GBM. They still often outperform non-ensemble methods. For example, AdaBoost and bagging had decent results but were not the very top in a 2025 comparison (Sharpe in the 1.1–1.3 range). These can be alternatives when diversity or simplicity is desired over more complex boosting.
4. Support Vector Machines – Mid-tier performance (with exceptions). SVMs sometimes achieve excellent accuracy (as seen with daily return prediction for BTC/ETH/XRP where SVM was best), translating to solid trading performance (Sharpe ~2.8 in that case). However, in other studies SVM ranked behind ensembles in both accuracy and Sharpe. Thus, SVM can be powerful with the right features, but its overall track record in crypto is mixed.
5. Linear Models (Logistic Regression/SGD) – Mid to lower-tier performance. These models provide respectable baseline results. Logistic regression often yields positive but modest returns (Sharpe around 1.0–1.2 in studies). Notably, an SGD classifier was highlighted as a strong performer in at least one analysis, showing that with proper adaptation, linear models can hold their own. Still, they generally underperform nonlinear models in complex market conditions.
6. Neural Networks (MLP/RNN) – Varied performance. When sufficient data and tuning are available, neural nets can shine – e.g. GRU networks topping Random Forest for Bitcoin forecasting in one case. Deep learning models have achieved high accuracy and occasionally very high returns in specific research settings. However, they also risk overfitting; some advanced architectures did worse than simpler models in noisy crypto data. On average, traditional ML ensembles have proven more reliable, but this could change as more data (and better techniques like transformers) are applied to crypto trading.
7. Naive Bayes – Bottom-tier performance. Across the board, Naive Bayes classifiers have the weakest outcomes. They tend to barely break even or incur losses in trading simulations. In comparative evaluations, NB often sits at the bottom in accuracy and Sharpe; for example, one study showed Gaussian NB with only ~0.36 accuracy and F1 ~0.28, far below other models (which were ≥0.60 accuracy). It is generally not chosen for serious crypto trading strategies except as a reference.

In summary, ensemble methods (Random Forest and boosted trees) have demonstrated the most consistent success in crypto trading, delivering higher returns and better Sharpe/Sortino ratios in both academic research and practical projects. Neural network approaches are an active area of development and can outperform in certain scenarios, but they require caution. Simpler algorithms like SVMs or logistic regression can be part of a toolkit for their simplicity and sometimes surprisingly robust performance, though they usually trail ensembles in absolute terms. Finally, evaluating any algorithm’s performance requires a holistic look at returns and risk: the best models not only maximize return but do so with controlled volatility and drawdowns (hence higher Sharpe/Sortino). As the field evolves, combining these algorithms with sound risk management and possibly hybrid models (or ensemble-of-ensembles) is a promising direction for even better profitability in crypto trading.

Sources: The information and performance metrics above are drawn from recent academic studies and industry reports, including a 2025 PeerJ Computer Science article comparing ML models for crypto trading, an ArXiv preprint analyzing 41 models on Bitcoin data, a 2024 Politecnico di Milano thesis on crypto price forecasting, a 2021 quantitative research on daily crypto returns (Falcon & Lyu), and a 2024 QuantInsti EPAT project demonstrating an RF intraday strategy. These sources provide a comprehensive view of how algorithms are successfully implemented in crypto trading and their relative performance.