Majority Voting for Crypto Trading

• Majority voting for crypto trading is a method that aggregates discrete trading signals from diverse RL agents to improve decision stability and risk-adjusted performance.
• The approach leverages massively parallelized GPU training and ensemble diversity from multiple DQN variants, leading to enhanced sample efficiency and faster convergence in volatile markets.
• Empirical evaluations demonstrate that ensemble methods yield lower drawdowns and higher Sharpe ratios in high-frequency crypto trading compared to individual agents.

Majority voting for crypto trading refers to the aggregation of discrete trading actions generated by an ensemble of reinforcement learning (RL) agents—specifically, diverse Deep Q-Network (DQN) variants—using a simple, unweighted voting protocol. This approach has demonstrated enhanced risk-adjusted performance, robustness to market noise, and improved sample efficiency, particularly in high-frequency cryptocurrency trading environments characterized by volatile dynamics and policy instability. The methodological foundations, computational design, and quantitative results of majority voting in crypto RL stem from large-scale GPU-enabled ensemble evaluations, as exemplified by recent research in the ACM ICAIF FinRL Contest context (Holzer et al., 18 Jan 2025).

1. Ensemble Architecture and Majority-Voting Protocol

In the majority voting paradigm, $N$ independently trained RL agents output discrete actions $a_i(s)\in\mathcal{A}$ for a given state $s$. The action space $\mathcal{A}$ is typically symmetric and integer-valued (e.g., $\{0, \pm1, \pm2, \ldots\}$), but is instantiated as $\{\text{Sell},\,\text{Hold},\,\text{Buy}\}$ in high-frequency crypto trading tasks. The ensemble action at each decision step is selected via majority vote: $$A_{\mathrm{ensemble}}(s) = \arg\max_{a\in\mathcal{A}} \sum_{i=1}^{N} \mathbb{I}(a_i(s)=a)$$ with $\mathbb{I}(\cdot)$ denoting the indicator function. All agents are equally weighted, and ties can be resolved either randomly among the most-voted actions or by selecting the action with the highest average Q-value from the ensemble: $$A_{\mathrm{tie}}(s) = \arg\max_{a\in\mathcal{A}_{\mathrm{tie}}} \frac{1}{N}\sum_{i}Q_{\theta_i}(s,a)$$ with $\mathcal{A}_{\mathrm{tie}}$ the set of tied actions.

The ensemble typically consists of DQN-family variants (DQN, Double-DQN, Dueling-DQN), each trained using individual initializations and architectures to promote behavioral diversity.

2. Massively Parallelized Training via GPU Vectorization

To address the RL sampling bottleneck and maximize throughput, both simulation (environment stepping) and learning (gradient updates) phases are executed fully on a GPU. All agents interact with $a_i(s)\in\mathcal{A}$0 parallel sub-environments ($a_i(s)\in\mathcal{A}$1 for cryptocurrency), yielding tensorized batches of states, actions, and transitions with shapes $a_i(s)\in\mathcal{A}$2. Replay buffers and transitions reside exclusively on the GPU, eliminating CPU–GPU transfer overhead.

Agent updates are asynchronous but leverage full GPU capacity via batched processing. This architecture, implemented with PyTorch JIT vectorization (vmap), achieves a per-GPU sample throughput of $a_i(s)\in\mathcal{A}$3k steps per second, a $a_i(s)\in\mathcal{A}$4 increase relative to a single-environment baseline.

3. RL Algorithms, Objective Functions, and Diversity

Each ensemble agent $a_i(s)\in\mathcal{A}$5 minimizes a mean-squared temporal-difference (TD) loss: $a_i(s)\in\mathcal{A}$6 For Double-DQN, the target maximization is decoupled ("double" update): $a_i(s)\in\mathcal{A}$7 Dueling-DQN decomposes $a_i(s)\in\mathcal{A}$8.

To further encourage ensemble diversity—particularly in equity tasks—a KL-divergence penalty may be introduced: $a_i(s)\in\mathcal{A}$9 In crypto, diversity derives mainly from differing DQN variants and stochastic initialization rather than explicit regularization.

4. Cryptocurrency Trading Experimental Setup

The experimental framework is constructed around second-level limit order book (LOB) data for BTC covering 2021-04-07 to 2021-04-19. Each state $s$0 encodes account state (balance, price, holding) and eight technical indicators extracted by an RNN from a set of 101 formulaic alphas. The discrete action space encompasses $s$1, with reward defined as the change in portfolio value, $s$2.

Training employs in-sample episodes (2021-04-17 to 2021-04-19) with offline test evaluation post-2021-04-19 09:09:22. DQN variants use three 128-unit feed-forward layers, learning rate $s$3, batch size $s$4, and exploration $s$5.

5. Empirical Results and Performance Metrics

In the ACM ICAIF FinRL crypto trading task (Holzer et al., 18 Jan 2025), an ensemble configured as "Ensemble-1" (one DQN, one Double-DQN, one Dueling-DQN, majority vote) demonstrates significant quantitative improvements:

Model	Cumulative Return	Sharpe	Max Drawdown
DQN	0.34%	0.15	–0.93%
Double DQN	0.48%	0.21	–0.98%
Dueling DQN	0.48%	0.21	–0.98%
Ensemble-1	0.66%	0.28	–0.73%

Ensemble-1 reduces maximum drawdown by ~$s$6 percentage points (≈25% relative), and improves the Sharpe ratio by $s$7 (33% relative increase) compared to the best single agent. The win/loss ratio increases slightly (from $s$8 to $s$9), and ablation over ensemble size $\mathcal{A}$0 yields near-identical results, confirming the marginal benefit of larger ensemble size in a narrow discrete action space.

6. Robustness, Stability, and Theoretical Perspective

Majority voting substantially mitigates the policy instability inherent to single DQN-type crypto trading agents. By averaging across independent agent decisions, the ensemble mechanism reduces idiosyncratic or spurious trades, consistent with Condorcet's theorem: as long as the mean agent success probability $\mathcal{A}$1, the ensemble action converges in probability to the optimal decision.

Massive parallelization (2,048 GPU environments) eliminates the simulation bottleneck, decreasing gradient estimate variance and stabilizing overall learning dynamics. Empirically, ensemble return trajectories exhibit less pronounced drawdowns and smoother capital paths amidst high-frequency LOB fluctuations.

The combined effect of per-agent variance reduction (via parallel sampling) and action aggregation noise reduction (via majority voting) yields not only faster convergence in training but also lower tail risk and more robust out-of-sample performance.

7. Practical Implications and Limitations

Majority voting offers a computationally efficient and theoretically grounded mechanism for stabilizing RL-based crypto trading systems. In high-frequency settings where action space cardinality is small and market volatility is pronounced, majority voting outperforms or matches single-agent and traditional baselines in cumulative return, drawdown minimization, and Sharpe ratio consistency.

However, in these environments, additional diversity gains from expanding ensemble size are marginal, as agent policies are highly correlated due to architecture and data constraints. This suggests the efficacy of majority voting is contingent on agent heterogeneity and action space structure. Broader action spaces or less correlated agent architectures may require more sophisticated aggregation or diversity-promoting methods for further improvement (Holzer et al., 18 Jan 2025).