Zero-shot Cryptocurrency Trading

• Zero-shot cryptocurrency trading is an approach that deploys algorithmic trading systems without scenario-specific retraining, using generalizable feature engineering and model-less architectures.
• It leverages diverse methodologies including CNN-based portfolio selection, rule-based technical strategies, and dynamic grid algorithms to navigate rapidly evolving crypto market regimes.
• By integrating multi-modal data sources such as on-chain metrics, traditional market data, and zero-shot NLP sentiment signals, these systems aim to achieve robust, risk-adjusted returns.

Zero-shot cryptocurrency trading refers to the deployment of algorithmic trading systems or agents in a cryptocurrency market regime, asset class, or environment that was not encountered during their training or design phase—that is, with no scenario-specific calibration, retraining, or exposure to retraining labels. These systems rely on generalizable pattern-recognition, robust feature engineering, or model-less trading rules that are intended to operate effectively despite the domain shift typical of rapidly evolving crypto assets and market microstructure. Zero-shot approaches leverage transfer learning, modular architectures, continuous adaptation, or rule-based logic to mitigate the need for retraining when encountering novel market conditions, coins, or information environments.

1. Foundations and Definitions

Zero-shot trading in cryptocurrencies is grounded in methodologies that do not require retraining or prior task-specific tuning for new assets or regimes. Several frameworks underpin zero-shot trading:

• Model-less neural architectures: As in the CNN-based portfolio management system (Jiang et al., 2016), where a convolutional network outputs portfolio weights purely from normalized historic prices, the absence of explicit financial priors or handcrafted theory imparts natural flexibility and adaptability for zero-shot deployment.
• Rule-based technical strategies: The comprehensive survey (Fang et al., 2020) catalogs modular trading systems (e.g., plug-in technical triggers, arbitrage bots) and indicator-based decision rules (e.g., moving-average crossovers, pairs trading), which, by definition, are broadly deployable without systematic retraining.
• Pretrained and meta-learning frameworks: Text-based sentiment extraction via zero-shot classifiers (e.g., BART MNLI (Gurgul et al., 2023)) and fine-tuned LLMs (Li et al., 27 Jun 2024, Wahidur et al., 2023) enable agents to interpret sentiment or market events in unseen domains.
• Dynamic grid algorithms: The DGT strategy (Chen et al., 13 Jun 2025) executes trades on minute-level data without history-driven calibration, resetting its grid boundaries in real time to maintain outperformance, embodying true zero-shot adaptation.

Zero-shot trading is distinguished from few-shot or transfer learning approaches, which require adaptation on small labeled samples post-deployment.

2. Architectures and Methodologies

Neural Network Approaches

Several architectures have demonstrated efficacy in zero-shot cryptocurrency trading:

• CNN-based Portfolio Selection: The model in (Jiang et al., 2016) uses normalized historical price matrices as input to a 12×4 convolutional layer (spanning assets and a short time window), followed by a 500-neuron fully connected layer and a softmax output for portfolio weights. The absence of pooling preserves time-and-asset specificity. Training employs deterministic policy gradients optimizing average logarithmic return:

$$R = \frac{1}{n}\sum_{t=1}^{n} \ln(\vec{\omega_t} \cdot \vec{y_t})$$

This architecture allows the agent to be deployed in new markets without retraining.

• Multi-Component Hybrid Systems: The Autoencoder-CNN-GAN framework (Hu et al., 24 Dec 2024) denoises input data with autoencoders, uses one-dimensional CNNs for local feature extraction, and feeds the output into a GAN, whose adversarial training produces feature vectors for price movement prediction. The GAN uses the minimax value function:

$$V(OD, OG) = \mathbb{E}_{x\sim P_{data}}[\log D(x)] + \mathbb{E}_{z\sim P_z}[\log(1 - D(G(z)))]$$

Modular architectures enable adaptation without task-specific finetuning.

• Recurrent Neural Networks for Price Forecasting: Studies (Tumpa et al., 5 Nov 2024, Hu et al., 2021) implement LSTM, GRU, and FastLSTM architectures for time-series error minimization and real-time prediction, with bidirectional processing to capture market context. These architectures are robust to unseen market fluctuations if hyperparameter sets are chosen for broad generalizability.

Modular and Rule-Based Systems

• Technical Indicator Engines: Surveyed platforms (Fang et al., 2020) (e.g., Freqtrade, Catalyst) and pipeline strategies (MACD, RSI, Bollinger Bands, pairs trading) can be instantiated upon new markets or coins via simple parameter specification.
• Dynamic Grid Algorithms: DGT (Chen et al., 13 Jun 2025) adaptively resets grid boundaries upon price excursions, reinvesting profits and capital to maintain exposure and arbitrage opportunities. No training or calibration is required:

Grid levels: $P \cdot (1+k)^i$ for $i \in \mathbb{Z}$

Reset rule:

If price $\notin$ grid, set new $P_{new} = \text{current price}$; reinvest recovered capital + arbitrage profits; reset grid levels.

3. Data Modalities and Feature Engineering

Zero-shot systems integrate a spectrum of data sources and engineered features:

• On-chain Metrics: Transaction counts, active wallets, gas consumption, and hashrate indices contribute to real-time market assessments (Li et al., 27 Jun 2024, Zhāng, 4 Aug 2025).
• Traditional Market Data: Multi-timeframe OHLCV, tick-level orderbook gaps, and global volumes feed trend and direction networks (Zhāng, 4 Aug 2025).
• Technical Indicators: Moving averages, MACD, ROC, RSI, and Bollinger Bands generate buy/sell pressures and inform agent decision boundaries (Fang et al., 2020, Ghahramani et al., 2022, Hu et al., 2021).
• Off-chain and Sentiment Signals: News, tweets, Reddit posts, and social metrics are processed using zero-shot NLP models such as BART MNLI (Gurgul et al., 2023) and instruction-tuned LLMs (Wahidur et al., 2023), producing continuous bullishness/market impact scores without retraining.

The integration of these modalities via feature selection (e.g., XGBoost scoring (Ghahramani et al., 2022)) and autoencoding (Ghahramani et al., 2022, Hu et al., 24 Dec 2024) allows agents to generalize across changing regimes and asset classes.

4. Trading Strategies, Execution Logic, and Risk Management

• Portfolio Allocation Agents: Softmax outputs ensure that normalized portfolio weights are directly interpretable as allocations; position updates maximize risk-adjusted capital change rate (Jiang et al., 2016).
• Binary Classification for Entry/Exit: Systems set action thresholds on model outputs, e.g., only enter positions on signals above confidence threshold derived from sigmoid outputs and user-defined risk/reward ratios (Ghahramani et al., 2022).
• Position Sizing and Dynamic Management: Bet sizes adapt proportionally to prediction confidence; positions are held for fixed intervals but closed early upon reaching stop-losses (Hu et al., 24 Dec 2024).
• Reflective Agents: Post-hoc analysis of trade outcomes influences future data weighting and model input focus (Li et al., 27 Jun 2024).
• Risk Metrics: Use of Sharpe ratios, maximum drawdown, and profit factor allow for calibrated position sizing and adjustment to volatility (Jiang et al., 2016, Hu et al., 24 Dec 2024, Li et al., 27 Jun 2024).

Zero-shot systems often incorporate built-in risk controls—e.g., stop-losses, VaR models, automated rebalancing—and benefit from technical trading rules that naturally scale across assets and conditions (Fang et al., 2020).

5. Performance Evaluation and Comparative Findings

Performance of zero-shot systems is typically assessed via:

Strategy/Agent	Return/Efficacy	Risk Management
CNN (portfolio) (Jiang et al., 2016)	10-fold return in 1.8mo; competitive Sharpe ratio	Lower drawdown than PAMR
DGT (Dynamic Grid) (Chen et al., 13 Jun 2025)	>60% IRR BTC, lower MDD, robust vs. BH and grid	Automated grid resets reduce risk
Autoencoder-CNN-GANs (Hu et al., 24 Dec 2024)	61.2% prediction accuracy; compounded 120% 5y	Sharpe Ratio 2.5; MDD < 15%
RCURRENCY RNN (Hu et al., 2021)	Stable/increased portfolio vs. buy-hold	RSI and MACD strat high Sharpe ratio
LLM Reflective Agent (Li et al., 27 Jun 2024)	+3% vs. buy-hold, robust across market regimes	Reflection boost; ablation shows impact
Multi-timeframe NN (Zhāng, 4 Aug 2025)	Statistically confident, HF execution	Soft attention adapts to volatility

These findings underscore several themes:

• Systems that combine multiple data modalities and adaptively integrate technical, on-chain, and NLP features consistently achieve superior, risk-adjusted returns without retraining.
• Dynamic adaptation mechanisms—reflective agents, grid resets, and modular architectures—are critical for sustaining performance as market regimes shift.
• Zero-shot approaches remain challenged by the “expiration” of learned market patterns and domain shift when new assets possess novel volatility/liquidity profiles.

6. Implications, Limitations, and Future Directions

Zero-shot cryptocurrency trading presents compelling advantages: rapid deployability, broad asset coverage, and resilience to unseen events or domain drift. However, several limitations remain:

• Overfitting and Expiry Effects: Strategies trained on historical patterns may fail to generalize as market dynamics “expire” (Jiang et al., 2016, Hu et al., 24 Dec 2024).
• Domain Adaptation: Robust feature engineering or meta-learning (Zhāng, 4 Aug 2025, Asgari et al., 2021) is required to mitigate domain shift when deploying models on new assets or information streams.
• Trading Frequency and Transaction Costs: Systems with high-frequency execution may be more sensitive to fee structures and latency (Chen et al., 13 Jun 2025, Zhāng, 4 Aug 2025).
• Interpreting Social Signals: Zero-shot LLM-based sentiment agents (Gurgul et al., 2023, Wahidur et al., 2023) perform well with concise instructions and larger model scale but may experience reduced generalization on complex prompts or noisy data.

Emerging research points to several avenues:

• Fine-tuning and Continuous Online Learning: While pure zero-shot systems forego retraining, ongoing fine-tuning and meta-adaptation can further enhance generalizability (Li et al., 27 Jun 2024).
• Ensemble Models: Combining diverse architectural types—e.g., hybrid LSTM-GAN-CNN—may improve robustness (Hu et al., 24 Dec 2024, Tumpa et al., 5 Nov 2024).
• Expanded Data Modalities: Incorporating more granular, multimodal data (orderbook, cross-exchange) and scaling neural architectures (Zhāng, 4 Aug 2025) promises increased adaptability.

Zero-shot cryptocurrency trading thus represents an overview of model-less neural adaptation, technical trading logic, zero-shot NLP sentiment signals, and dynamic grid-based execution. These approaches are continually refined to address the volatility, novelty, and information complexity that define modern crypto markets.