qhjqhj00 sentarl-trading-eval
Intelligent Trading Systems: A Sentiment-Aware Reinforcement Learning Approach — Paiva et al. (2021) (arXiv:2112.02095, 2021)
What this evaluates
Evaluates a sentiment-aware reinforcement learning agent's ability to generate profitable and stable trading strategies across diverse market conditions, transaction cost regimes, and varying levels of financial news coverage. It benchmarks performance against a sentiment-free RL ablation and a buy-and-hold strategy using standard financial return and risk metrics.
Datasets
- 20-Asset Financial Time Series & News Corpus (2018-2020) — total 5267; splits: train (3377), test (374); repo https://github.com/xicocao/its-sentarl
Metrics
Total Return (TR)(primary) — range: percent- Accumulated return over the initial wealth: TR = (sum_{t=1}^T ρ_t^{Trader}) / ψ, where ψ is the initial cash required to buy φ shares at t=1.
Annualized Return (AR)— range: percent- Normalizes TR across different evaluation periods: AR = (1 + TR)^(D/365) - 1, where D is the total number of trading days in the test set.
Sharpe Ratio (SR)— range: other- Measures performance stability across trials/periods: SR = avg(TR_k) / std(TR_k), where k indexes different random initializations and rolling windows.
Input / output format
Input: Hourly price time series, corresponding financial news headlines, and pre-computed sentiment scores aggregated into the agent's state representation.
Output: Discrete trading action (buy/sell/hold) for a fixed position size (φ=1 share) at each hourly timestep.
Scoring recipe
def compute_metrics(returns, initial_wealth, trading_days, all_tr_trs):
TR = sum(returns) / initial_wealth
AR = (1 + TR) ** (trading_days / 365) - 1
SR = np.mean(all_tr_trs) / np.std(all_tr_trs)
return TR, AR, SR
Common pitfalls
- News coverage is highly sparse (~25% average), meaning sentiment features are missing for most hourly price points and must be handled carefully during state construction.
- RL performance is highly sensitive to weight initialization; results must be averaged over multiple random seeds (5 used) to be statistically reliable.
- Transaction costs drastically alter strategy viability; metrics must be evaluated and reported under both zero-cost and high-cost (0.25%) regimes.
Evidence (verbatim from paper)
We report standard metrics for measuring the performance of autonomous trading agents. Initially, we have the TR given by the accumulated return from each instant (Eq. 6) over the initial wealth ψ as formulated below... Then, a metric such as the annualized return (AR) is recommended to compare research with data with different periods and sizes... Ultimately, the TR for all trials of a model k – meaning the different initializations and periods – determine the SR metric, formulated with inspiration from previous work [4, 17] as SR = avg(TR_k) / std(TR_k).
Citation
@misc{paiva2021intelligent,
title={Intelligent Trading Systems: A Sentiment-Aware Reinforcement Learning Approach},
author={Paiva et al. (2021)},
year={2021},
note={arXiv:2112.02095}
}
- arXiv: 2112.02095