reviews: 
Comments: The work proposed a method for financial timeseries representation learning.
Strengths: The description of model architecture is clear.
Weaknesses: Literature
The work has limited literature research. I give some examples on related works: 

1. Deep timeseries architectures
[1]: Zhou, Haoyi, et al. "Informer: Beyond efficient transformer for long sequence time-series forecasting." Proceedings of the AAAI conference on artificial intelligence. Vol. 35. No. 12. 2021.
[2]: He, Yangdong, and Jiabao Zhao. "Temporal convolutional networks for anomaly detection in time series." Journal of Physics: Conference Series. Vol. 1213. No. 4. IOP Publishing, 2019.
[3]: Wu, Haixu, et al. "Timesnet: Temporal 2d-variation modeling for general time series analysis." arXiv preprint arXiv:2210.02186 (2022).
[4]: Goswami, Mononito, et al. "Moment: A family of open time-series foundation models." arXiv preprint arXiv:2402.03885 (2024).

2. Timeseries representation learning
[1]: Eldele, Emadeldeen, et al. "Time-series representation learning via temporal and contextual contrasting." arXiv preprint arXiv:2106.14112 (2021).
[2]: Yue, Zhihan, et al. "Ts2vec: Towards universal representation of time series." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 36. No. 8. 2022.
[3]: Nie, Yuqi, et al. "A time series is worth 64 words: Long-term forecasting with transformers." arXiv preprint arXiv:2211.14730 (2022).

It is important to compare the proposed methodology with recently proposed representation learning technique to prove the effectiveness. 

Experiment 
1. Baseline comparison against existing representation learning method: In order to claim that the proposed method is effective, the author should also consider comparing the proposed method with some existing methods for timeseries representation learning, see corresponding section in literature above. 

2. Ablation: The author should conduct ablation study on the pipeline to investigate the critical components that influence the performance. Some examples can be 1) varying model architecture for contrastive learning 2) experiment with different methods to create negative & positive samples for the proposed contrasitive learning method

3. Evaluation metric: 
Market friction assumption: Can author provide the market friction assumption used during backtesting (such as transaction cost, assumed deviation from position entry, etc.) In addition, could you clarify whether your calculation of return for sharpe involves transaction cost? I ask this because in this type of trading strategy, despite the win-ratio (% profitable trade) might be higher than chance due to certain gain in predictive power, transaction cost could be very high and potentially eat up all the profit. This should be taken care of because the baseline, buy-and-hold, completes in two transactions. 

Some other financial metrics to consider: max drawdown, sortino ratio. This gives the reader another perspective of the strategy from risk perspective.

Another note is that it seems like a position can be short only. If this is true, It would be helpful to provide margin needed at anytime just to be practical. 

4. Size of dataset: Since the test conducted was only 20% of three currency timeseries. It would be helpful to backtest on more currency timeseries or other asset timeseries. This is to make sure the arrived results are statistically significant. 

Additional Comment
1. Figure 6: It is not convincing that this plot shows the effectiveness of produced embedding. Essentially the unsupervised classes are plotted. Then, it is expected that the boundary from k-mean should be clear. The author should conduct some analysis on each cluster to see if there are unique properties for each of them. 

2. lowest error rate with zero and mean model: It would be helpful for the author to provide more insights here. For example, does this suggest the forecasting method are worse than trivial baseline? If justified using mean-reverting property, can you show us that a forecasting model with mean-reverting behavior-forecast work well?
Recommendation: 3
Reviewer confidence: 4
Soundness/correctness: 1
Clarity: 2