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REVIEW 4 major objections 5 minor 34 references

Can Artificial Intelligence Trade the Stock Market?

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A model-free AI trading agent that never forecasts prices beat buy-and-hold on all five markets it tested, the paper reports.

desk verdict A transparent DRL trading study with impressive but statistically unbacked Sharpe ratios; worth reviewing if the authors add a supervised baseline and significance tests. read the letter →

arxiv 2506.04658 v1 pith:IWDFG4NO submitted 2025-06-05 q-fin.TR cs.LGq-fin.CP

Jędrzej Maskiewicz , Pawe{l} Sakowski This is my paper · ORCID
classification q-fin.TRcs.LGq-fin.CP
keywords deepreinforcementlearningalgorithmictradingProximalPolicyOptimizationDoubleQ-NetworkTransformernetworkwalk-forwardSharperatiomarkettiming
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper is trying to establish that deep reinforcement learning (DRL) can trade financial markets profitably without predicting future prices. The authors train two model-free DRL algorithms, DDQN and PPO, with two network architectures on daily data for three currency pairs, the S&P 500, and Bitcoin over 2019-2023, using walk-forward optimization. Their best configuration, PPO with a Transformer network, beat buy-and-hold on all five assets and even outperformed a perfect-foresight annual strategy on several, with Sharpe ratios of 0.847, 1.752, 2.179, 2.158, and 1.093. The claimed edge comes from learning when to be long, short, or out of the market, using strategic non-engagement to avoid unfavorable conditions. If true, this would show that trading skill can be acquired by interaction rather than forecasting, and would challenge strong forms of the efficient-market hypothesis.

What carries the argument

The central machinery is a model-free DRL policy trained with temporal-difference learning, where the agent's action space is three discrete positions: long, short, or out of the market, and the reward is scaled profit-and-loss. For PPO, the policy is updated with a clipped surrogate objective and Generalized Advantage Estimation, while the DDQN variant uses double Q-learning with separate primary and target networks. The authors pair these algorithms with two function approximators, a fully connected neural network and a Transformer, and evaluate them through a walk-forward procedure that retrains from scratch on rolling windows, validates on one year, and tests on the next year. The load-bearing element is the learned "stay out" action, which the results show is used roughly 30% of the time and is presented as the key to superior risk-adjusted returns.

What would settle it

Run the same walk-forward protocol on shuffled return series (or on synthetic price paths with the same drift and volatility); if the validation-selection procedure still yields out-of-sample Sharpe ratios near 2.18 for USD/JPY, the headline numbers are selection artifacts. The more direct check is to construct a bootstrap or deflated-Sharpe confidence interval for each reported out-of-sample Sharpe ratio; if the interval for the EUR/USD PPO_T result (0.847) includes zero, the claim that DRL beats the market is not supported.

Watch

Extended reading notes

Core claim

The paper's central claim is that DRL, as an advanced form of AI, can effectively conduct financial trading by autonomously identifying and exploiting patterns within complex, high-dimensional data, without being given a model of price dynamics. Empirically, the paper reports that PPO combined with a Transformer network (PPO_T) produced annualized Sharpe ratios of 0.847 on EUR/USD, 1.752 on EUR/JPY, 2.179 on USD/JPY, 2.158 on the S&P 500, and 1.093 on Bitcoin over the 2019-2023 out-of-sample period, beating buy-and-hold in final balance on every asset and beating a hypothetical perfect-foresight annual strategy on EUR/USD, EUR/JPY, USD/JPY, and the S&P 500. The authors attribute the success to DRL's ability to manage risk by choosing not to trade during unfavorable conditions, a mechanism supervised-learning approaches lack, and to the Transformer's attention mechanism capturing sequential dependencies in market data. The paper also reports a consistent ordering across configurations: PPO_T best, followed by DDQN_T, DDQN_NN, and PPO_NN, showing that network architecture matters at least as much as the choice of RL algorithm.

Load-bearing premise

The load-bearing premise is that selecting each agent generation on one year of validation data, across 20 configuration-asset combinations and with no multiple-testing correction or significance tests, produces an unbiased estimate of true out-of-sample performance.

Editorial extensions

If this is right

  • PPO with a Transformer network, the best configuration, beat buy-and-hold on all five assets and beat a perfect-foresight annual strategy on four, implying the claimed edge is not just about overall market direction but about timing and risk avoidance.
  • Transformer backbones consistently outperformed fully connected networks for both algorithms, so architecture choice is at least as decisive as the RL algorithm itself.
  • DRL agents added value mainly by staying out of the market during unfavorable conditions, a behavior that has no direct analogue in supervised-learning trading rules.
  • The weaker Bitcoin results, blamed on the shorter training history, imply that data quantity and coverage are limiting factors for this approach.
  • If these results generalize, model-free DRL could serve as an adaptive trading layer that does not rely on price forecasts, with implications for the efficient-market debate.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The reported out-of-sample Sharpe ratios are likely optimistic because the best agent generation was chosen on one year of validation data across many configuration-asset combinations without multiple-testing correction; a bootstrap or deflated-Sharpe adjustment would give a fairer estimate.
  • The paper's own Section 5.1 notes that training on historical prices cannot capture the price impact an agent would have at scale, so the results apply to small traders rather than strategies large enough to move markets.
  • If the "stay out" behavior is the true source of edge, the results may be better characterized as a volatility- or tail-risk-avoidance effect than as evidence of predictable directional patterns, and that distinction is testable by decomposing returns into exposure and timing components.
  • A natural extension, also suggested by the authors, is continuous position sizing or hierarchical multi-agent management, which could outperform the discrete long/short/out action space in risk-adjusted terms.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper tests two deep reinforcement learning algorithms, DDQN and PPO, each paired with either a fully connected neural network or a Transformer architecture, as trading agents on five assets (EUR/USD, EUR/JPY, USD/JPY, S&P 500, Bitcoin) over 2019–2023. The authors use a walk-forward optimization scheme with a validation year preceding each out-of-sample year, and they compare the agents against buy-and-hold and a hypothetical perfect-foresight annual strategy. The central empirical claim, stated in the Conclusions, is that PPO with a Transformer network achieves strong risk-adjusted returns (Sharpe ratios of 0.847, 1.752, 2.179, 2.158, and 1.093 across the five assets), supporting the hypothesis that DRL can effectively trade financial markets.

Significance. If the reported results were statistically validated, the paper would offer a notable demonstration that model-free DRL agents can deliver positive risk-adjusted returns across multiple asset classes without explicit price forecasting. The study has several strengths: it uses a multi-asset walk-forward design, accounts for transaction costs, reports a range of performance and behavioral metrics, and includes an explicit discussion of methodological limitations. However, the central claim is not supported by the evidence as presented: there are no significance tests or confidence intervals for the Sharpe ratios, the best-performing configuration appears to be selected from a large set of tested settings with no multiple-testing correction, and the abstract's claim of an edge over supervised learning is never tested empirically. These shortcomings are load-bearing for the paper's main conclusion.

major comments (4)
  1. [Section 4, Tables 1–5; Conclusions] The headline Sharpe ratios for PPO_T (0.847, 1.752, 2.179, 2.158, 1.093) are single point estimates from one five-year backtest, with no confidence intervals, bootstrap resampling, or significance tests reported anywhere. For daily returns, the sampling uncertainty of an annualized Sharpe ratio over five years is substantial (roughly ±0.7 for a Sharpe near 1.6), so the observed differences between PPO_T and buy-and-hold (e.g., 1.093 vs. 0.89 on Bitcoin) are well within noise. The statement in the Conclusions that the findings 'robustly support' the research hypothesis is not justified without formal inference.
  2. [Chapter IV, introductory paragraphs; Section 4.6] The evaluation protocol involves a large implicit multiple-comparison problem. The authors state that they did not perform random or grid search but 'relied on the expert knowledge of the authors to select and test parameters,' and Section 4.6 reports testing gamma values of 0.75, 0.9, and 0.95, plus reward scaling factors of 100 or 1000. Across four model configurations and five assets, this yields roughly 20 configuration-asset cells, and the paper highlights the best-performing one (PPO_T). With no correction for best-of-N selection, the reported out-of-sample Sharpe ratios are likely inflated by selection, and the results do not distinguish skill from luck.
  3. [Abstract; Section 5.2] The abstract claims a 'substantial edge over classical approaches, based on supervised learning in terms of risk-adjusted returns,' but no supervised learning baseline is implemented or evaluated in the paper. Section 5.2 discusses supervised learning only conceptually, arguing that it lacks a natural 'stay out of the market' mechanism. Without an empirical comparison to at least one supervised baseline (e.g., a logistic regression or a recurrent neural network with the same inputs), this claim is unsupported and should be either removed or backed by experiments.
  4. [Section 3.1] The walk-forward agent generation selection is underspecified and non-reproducible. The text says 'we select agent’s generation that have undergone at least half of their training period' and that the authors 'analyse agents from two generations before and after the chosen generation,' but it does not state how many generations were trained, how a generation is defined, or whether the generation ultimately reported was chosen based on validation performance. If validation Sharpe was used to select the generation, this selection step must be accounted for in the inference; otherwise the out-of-sample claim is not credible as stated.
minor comments (5)
  1. [Throughout] The manuscript contains many typos and grammatical errors, including 'Netowork' in table captions, 'Suprisly' in Section 4.5, 'Suppressively' in Section 4.6, 'especialy' in Section 3.1, and 'notatios' in Chapter IV.
  2. [Sections 4.2–4.5] Several cross-references to figures and tables are incorrect: Section 4.2 refers to Figure 4 for EUR/JPY but the correct figure is Figure 5; Section 4.3 refers to 'Table 2' for USD/JPY but should be Table 3; Section 4.4 refers to 'Table 2' and 'Figure 5' for S&P 500 but should be Table 4 and Figure 9; Section 4.5 refers to 'Figure 5' for Bitcoin but should be Figure 11.
  3. [Equation [14]] The CAGR formula is not typeset correctly; the exponent and fraction appear as garbled text. It should be rewritten in standard mathematical notation.
  4. [Bibliography] There are several formatting errors in the references, such as 'Mnih, V olodymyr' with an extraneous space and the duplicated phrase 'Long Short-Term Memory. Long Short-Term Memory.' in the Hochreiter entry.
  5. [Section 3.1] The paper does not state the specific data sources for the OHLC data (e.g., which vendor or API), nor does it indicate whether code is available. This limits reproducibility of the empirical results.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the walk-forward results are genuine out-of-sample simulations compared with external benchmarks, not consequences of the paper's inputs by construction.

full rationale

The paper's central claim is that PPO with a Transformer network achieved positive out-of-sample Sharpe ratios across five assets. The derivation chain is: train on a rolling training window, select an agent generation using one year of validation data, then evaluate on the following out-of-sample year, and compare with buy-and-hold and a perfect-foresight annual benchmark. Nothing in this chain defines the reported performance in terms of the claim being tested. Hyperparameters such as gamma and reward scaling were chosen before testing on the basis of expert judgment, and the paper explicitly states that no random or grid search was used; any selection among configurations is model selection on validation data, which is standard practice and not a circular step. The perfect-foresight benchmark is an external upper bound, not an output of the model. The paper contains no self-citation chain invoked as proof, no uniqueness theorem imported from the authors' own prior work, and no fitted parameter renamed as a prediction. Concerns about multiple-testing, lack of significance tests, and potential overfitting of the walk-forward protocol are statistical validity questions, not circularity. They should be assessed as correctness risk, but they do not make the derivation equivalent to its inputs by construction. Therefore the circularity score is 0.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The central claim rests on standard financial data and RL assumptions: historical price data is representative, execution at midnight is realistic, transaction costs are as assumed, and the walk-forward selection protocol provides unbiased evaluation. Several of these are stated but not justified quantitatively, and the paper itself flags the action-environment mismatch.

free parameters (6)
  • Reward scaling factor = 100 or 1000 per asset
    Chosen by authors to scale PnL rewards; stated in Section 3.1: 'we included scaling of those rewards by factor (different for different assets). Mostly 100 or 1000.'
  • Gamma (TD/GAE discount) = 0.75, with 0.9 and 0.95 also tested
    Discount factor for TD and GAE; chosen by expert judgment, no search; Section 4: 'the gamma parameter ... was set at 0.75'.
  • Lookback window length = 20 days
    Length of state input; chosen as a balance between recent and historical data; Section 4.
  • Agent generation selection threshold = at least half of the training period
    Heuristic to pick which trained agent to test; Section 3.1: 'we select agent's generation that have undergone at least half of their training period.'
  • Transaction cost rates = FX 0.01%, S&P 500 0.025%, BTC 0.1%
    Assumed costs per asset class, stated in Section 4.
  • Unreported network hyperparameters = not provided
    Network sizes, learning rates, batch sizes, and training episodes are not listed, despite being required to reproduce the experiments; Section 4 mentions only that parameters were chosen by expert knowledge.
assumptions (5)
  • domain assumption The RL environment can be modeled as a Markov decision process in which agent actions do not affect the data-generating process.
    Central to training model-free agents on historical prices; the paper itself acknowledges in Section 5.1 that 'the agent thinks that his actions are changing prices which is definitely not true.'
  • domain assumption Historical data from 2005 (or 2013 for Bitcoin) is representative of future market conditions in 2019-2023.
    Walk-forward training relies on the assumption that patterns learned on past data transfer to the out-of-sample years; Section 3.1.
  • domain assumption The risk-free rate is zero for the 2019-2023 period.
    The paper states 'we consider the risk-free return as 0 for this research because it was effectively zero until early 2022' (Section 3.2).
  • domain assumption Zero swap points and no leverage approximate real trading conditions.
    Section 4: 'We haven't included any swap points as well as leverage was not included.'
  • ad hoc to paper The validation-based selection of the agent generation yields an unbiased estimate of out-of-sample performance.
    The paper selects the generation after at least half of training, then verifies neighboring generations, but reports no significance testing; Section 3.1.

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Cite this review

Pith. "Pith review of Can Artificial Intelligence Trade the Stock Market?." pith.science (2026). https://pith.science/paper/IWDFG4NO

@misc{pith2026250604658,
  author       = {Pith},
  title        = {Pith review of: Can Artificial Intelligence Trade the Stock Market?},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IWDFG4NO}},
  note         = {Machine review of arXiv:2506.04658}
}
read the original abstract

The paper explores the use of Deep Reinforcement Learning (DRL) in stock market trading, focusing on two algorithms: Double Deep Q-Network (DDQN) and Proximal Policy Optimization (PPO) and compares them with Buy and Hold benchmark. It evaluates these algorithms across three currency pairs, the S&P 500 index and Bitcoin, on the daily data in the period of 2019-2023. The results demonstrate DRL's effectiveness in trading and its ability to manage risk by strategically avoiding trades in unfavorable conditions, providing a substantial edge over classical approaches, based on supervised learning in terms of risk-adjusted returns.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

34 extracted references · 34 canonical work pages

  1. [1]

    clipping,

    Actor-Critic method in Reinforcement Learning: Interaction between Policy, Value Function and Environment. Source: Sutton, R. S., & Barto, A. G. „Actor-Critic method”. Reinforcement Learning: An Introduction. (2018) 22 2.3.5. Proximal Policy Optimisation (PPO) Proximal Policy Optimisation (PPO) is a policy-based model that utilizes the actor-critic method...

    work page 2018

  2. [2]

    In our case, we organise the data into five windows, where each validation set encompasses one year of data and the subsequent out-of-sample test also spans one year

    This approach involves dividing the data into multiple sequential windows. In our case, we organise the data into five windows, where each validation set encompasses one year of data and the subsequent out-of-sample test also spans one year. The first validation period is set in 2018 with 2019 designated as the out-of-sample testing year. The following te...

    work page 2018

  3. [3]

    Source: Own study on the 2019-2023 daily data for EUR/USD (1557 observations) with starting capital of 10

    Balance changes for DDQN-NN, DDQN-T, PPO-NN, PPO-T and two benchmarks over 2019-2023 years for the EUR/USD. Source: Own study on the 2019-2023 daily data for EUR/USD (1557 observations) with starting capital of 10

    work page 2019

  4. [4]

    Source: Own study on the 2019-2023 daily data for EUR/USD (1557 observations) with starting capital of 10

    Drawdowns for DDQN-NN, DDQN-T, PPO-NN, PPO-T and two benchmarks over 2019-2023 years for the EUR/USD. Source: Own study on the 2019-2023 daily data for EUR/USD (1557 observations) with starting capital of 10

    work page 2019

  5. [5]

    Walk-forward optimisation process. Iteration\Years 2005-2016 2017 2018 2019 2020 2021 2022 2023 First optimisation train validation test Second optimisation train validation test Third optimisation train validation test Fourth optimisation train validation test Fifth final optimisation train validation test Source: Own study. The table outlines the distri...

    work page 2005

  6. [6]

    #+𝛾 𝑉(𝑠!

    where: 𝑚𝑎𝑥8! 𝑄(𝑠,,𝑎,;𝜃6) - maximum predicted Q-value for 𝑠!"#, considering all possible 𝑎,. This is calculated with the Target network 𝜃6 The target network guides the learning of DQN agent to prioritize actions in a given state that lead to the highest discounted future rewards. Additionally, DQN incorporates experience replay, which stores a batch of ex...

    work page 2016

  7. [7]

    Source: Own study on the 2019-2023 daily data for USD/JPY (1557 observations) with starting capital of 10

    Balance changes for DDQN-NN, DDQN-T, PPO-NN, PPO-T and two benchmarks over 2019-2023 years for the USD/JPY. Source: Own study on the 2019-2023 daily data for USD/JPY (1557 observations) with starting capital of 10

    work page 2019

  8. [8]

    EURUSD Final balance Provision Sum Total trades CAGR Annualised std

    Performance measures for DDQN-NN, DDQN-T, PPO-NN, PPO-T and two benchmarks over 2019-2023 years for the EUR/USD. EURUSD Final balance Provision Sum Total trades CAGR Annualised std. Sharpe ratio Sortino Ratio Maximum Drawdown Maximum Drawdown Duration Average Position Duration Win Rate In Long In Short Out of the market DDQN_NN 14 444.74 -351.9 300 7.63% ...

    work page 2019

Show all 34 references
  1. [9]

    Balance changes for DDQN-NN, DDQN-T, PPO-NN, PPO-T and two benchmarks over 2019-2023 years for the S&P

    2019

  2. [10]

    Drawdowns for DDQN-NN, DDQN-T, PPO-NN, PPO-T and two benchmarks over 2019-2023 years for the S&P

    2019

  3. [11]

    The agents can take position with whole capital between long, short and out of the market

    DDQN is Double Deep Q-Network, PPO is Proximal Policy Optimisation, NN- fully connected Neural Netowork and T – transformer Network. The agents can take position with whole capital between long, short and out of the market. ‘Perfect’ annual strategy benchmark is a hypothetical...

    2019

  4. [12]

    Source: Own study on the 2019-2023 daily data for EUR/JPY (1557 observations) with starting capital of 10

    Performance measures for DDQN-NN, DDQN-T, PPO-NN, PPO-T and two benchmarks over 2019-2023 years for the EUR/JPY. Source: Own study on the 2019-2023 daily data for EUR/JPY (1557 observations) with starting capital of 10

    2019

  5. [13]

    Source: Own study on the 2019-2023 daily data for EUR/ JPY (1557 observations) with starting capital of 10

    Balance changes for DDQN-NN, DDQN-T, PPO-NN, PPO-T and two benchmarks over 2019-2023 years for the EUR/JPY. Source: Own study on the 2019-2023 daily data for EUR/ JPY (1557 observations) with starting capital of 10

    2019

  6. [14]

    Source: Own study on the 2019-2023 daily data for EUR/ JPY (1557 observations) with starting capital of 10

    Drawdowns for DDQN-NN, DDQN-T, PPO-NN, PPO-T and two benchmarks over 2019-2023 years for the EUR/JPY. Source: Own study on the 2019-2023 daily data for EUR/ JPY (1557 observations) with starting capital of 10

    2019

  7. [15]

    The agents can take position with whole capital between long, short and out of the market

    DDQN is Double Deep Q-Network, PPO is Proximal Policy Optimisation, NN- fully connected Neural Netowork and T – transformer Network. The agents can take position with whole capital between long, short and out of the market. ‘Perfect’ annual strategy benchmark is a hypothetical...

    2019

  8. [16]

    Source: Own study on the 2019-2023 daily data for USD/JPY (1557 observations) with starting capital of 10

    Performance measures for DDQN-NN, DDQN-T, PPO-NN, PPO-T and two benchmarks over 2019-2023 years for the USD/JPY. Source: Own study on the 2019-2023 daily data for USD/JPY (1557 observations) with starting capital of 10

    2019

  9. [18]

    Source: Own study on the 2019-2023 daily data for USD/JPY (1557 observations) with starting capital of 10

    Drawdowns for DDQN-NN, DDQN-T, PPO-NN, PPO-T and two benchmarks over 2019-2023 years for the USD/JPY. Source: Own study on the 2019-2023 daily data for USD/JPY (1557 observations) with starting capital of 10

    2019

  10. [19]

    The agents can take position with whole capital between long, short and out of the market

    DDQN is Double Deep Q-Network, PPO is Proximal Policy Optimisation, NN- fully connected Neural Netowork and T – transformer Network. The agents can take position with whole capital between long, short and out of the market. ‘Perfect’ annual strategy benchmark is a hypothetical...

  11. [21]

    Source: Own study on the 2019-2023 daily data for S&P 500 (1253 observations) with starting capital of 10

    Performance measures for DDQN-NN, DDQN-T, PPO-NN, PPO-T and two benchmarks over 2019-2023 years for the S&P 500 index. Source: Own study on the 2019-2023 daily data for S&P 500 (1253 observations) with starting capital of 10

    2019

  12. [25]

    Source: Own study on the 2019-2023 daily data for S&P 500 (1253 observations) with starting capital of 10

    2019

  13. [26]

    The agents can take position with whole capital between long, short and out of the market

    DDQN is Double Deep Q-Network, PPO is Proximal Policy Optimisation, NN- fully connected Neural Netowork and T – transformer Network. The agents can take position with whole capital between long, short and out of the market. ‘Perfect’ annual strategy benchmark is a hypothetical...

    2020

  14. [27]

    Source: Own study on the 2019-2023 daily data for BTC (1824 observations) with starting capital of 10

    Performance measures for DDQN-NN, DDQN-T, PPO-NN, PPO-T and two benchmarks over 2019-2023 years for the Bitcoin cryptocurrency notated in USD. Source: Own study on the 2019-2023 daily data for BTC (1824 observations) with starting capital of 10

    2019

  15. [28]

    Source: Own study on the 2019-2023 daily data for BTC (1824 observations) with starting capital of 10

    Balance changes, on the logarithmic for DDQN-NN, DDQN-T, PPO-NN, PPO-T and two benchmarks over 2019-2023 years for the Bitcoin currency notated in USD. Source: Own study on the 2019-2023 daily data for BTC (1824 observations) with starting capital of 10

    2019

  16. [29]

    Source: Own study on the 2019-2023 daily data for BTC (1824 observations) with starting capital of 10

    Drawdowns for DDQN-NN, DDQN-T, PPO-NN, PPO-T and two benchmarks over 2019-2023 years for the Bitcoin currency notated in USD. Source: Own study on the 2019-2023 daily data for BTC (1824 observations) with starting capital of 10

    2019

  17. [30]

    The agents can take position with whole capital between long, short and out of the market

    DDQN is Double Deep Q-Network, PPO is Proximal Policy Optimisation, NN- fully connected Neural Netowork and T – transformer Network. The agents can take position with whole capital between long, short and out of the market. ‘Perfect’ annual strategy benchmark is a hypothetical...

    2022

  18. [32]

    Which, it is worth to note, is an outstanding result

    If an investor had managed to avoid the 10 worst trading days since the year 2003, the overall return of a buy-and-hold strategy would have increased by 2.5 times. Which, it is worth to note, is an outstanding result. This highlights the importance of knowing when to trade and...

    2003

  19. [500]

    equity market and is often considered a proxy for the overall health of the U.S

    The S&P 500 Index, introduced by Standard & Poor's in 1957, is widely regarded as the most significant benchmark of the U.S. equity market and is often considered a proxy for the overall health of the U.S. economy. According to S&P Dow Jones Indices, the S&P 500 accounts for a...

    1957

  20. [1000]

    26 In developing our methodology, we select agent’s generation that have undergone at least half of their training period

    By scaling the PnL reward, we provided clearer incentives for the agents, leading to more effective training outcomes. 26 In developing our methodology, we select agent’s generation that have undergone at least half of their training period. This approach helps ensure that the...

    1996

  21. [1965]

    Fama, Eugene F., and Kenneth R. French. The Cross-Section of Expected Stock Returns. Journal of Finance 47, no. 2, 1992 Frank, Arnold, Sortino, Frank A., and Lee, Neil. Performance Measurement in a Downside Risk Framework. Journal of Investing,

    1992

  22. [1989]

    - Generalised Advantage Estimation a - Action 𝜋

    61 LIST OF APPENDICES List of shorts CAGR - Compound annual growth rate DDQN - Double Deep Q-Network DL - Deep Learning DRL - Deep Reinforcement Learning DRL - Deep Reinforcement Learning DQN - Deep Q-Network EMH - Efficient Market Hypothesis FOREX - Foreign Exchange Market HR...

    2019

  23. [2005]

    With each new window, we initiate training of the agent from scratch

    For Bitcoin, which entered the financial markets later, the data collection starts from 01/01/2013. With each new window, we initiate training of the agent from scratch. This strategy of reinitializing the learning process for each window prevents any carryover of biases or ov...

    2013

  24. [2014]

    2024 Q1 Crypto Report

    CoinGecko. 2024 Q1 Crypto Report. www.coingecko.com,

    2024

  25. [2019]

    Those which starts indicate first agent’s decision to stay out of the market. 4.1. EUR-USD We begin the presentation of our results with the most traded currency pair on the Foreign Exchange Market (FOREX) - EUR/USD. According to Bank for International Settlements (2019), the ...

    2019

  26. [2023]

    The Sharpe ratio of 1.01 indicates favorable risk-adjusted returns and in the risk-adjusted terms DDQN_T outperformed Buy and Hold benchmark

    The DDQN_T strategy demonstrated impressive performance with a final balance of $50,038.35 and an annualized return of 37.99%. The Sharpe ratio of 1.01 indicates favorable risk-adjusted returns and in the risk-adjusted terms DDQN_T outperformed Buy and Hold benchmark. Behavior...

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