The paper tackles backtest overfitting in cryptocurrency trading using deep reinforcement learning.
problem Backtest overfitting in deep reinforcement learning for cryptocurrency trading.
method Formulated hypothesis test for overfitting detection, trained agents, estimated overfitting probability, and rejected overfitted agents.
result Less overfitted deep reinforcement learning agents outperformed more overfitted agents and market benchmarks.
In-sample overfitting is a drawback of any backtest-based investment strategy. It is thus of paramount importance to have an understanding of why and how the in-sample overfitting occurs. In this article we propose a simple framework that allows one to model and quantify in-sample PnL overfitting. This allows us to com…
Systematic trading strategies are rule-based procedures which choose portfolios and allocate assets. In order to attain certain desired return profiles, quantitative strategists must determine a large array of trading parameters. Backtesting, the attempt to identify the appropriate parameters using historical data avai…
Calibrating a trading rule using a historical simulation (also called backtest) contributes to backtest overfitting, which in turn leads to underperformance. In this paper we propose a procedure for determining the optimal trading rule (OTR) without running alternative model configurations through a backtest engine. We…
AutoQuant addresses cryptocurrency backtesting fragility by modeling execution costs and improving strategy selection.
problem Fragile backtests of cryptocurrency perpetual futures ignoring microstructure frictions and execution costs.
method Execution-centric framework with Bayesian optimization, double screening, and strict T+1 semantics.
result Fee-only and zero-cost backtests overestimate returns, highlighting the importance of modeling execution costs.
Framework mitigates overfitting in quantitative trading strategies.
problem Overfitting during strategy transition from backtest to live trading.
method Three-stage protocol: IS, WFA, OOS; majority pass, purge gaps, cliff veto, etc.
result Demonstrates how to detect overfitting through performance decay and drawdown behavior.
GT-Score reduces overfitting in trading strategies by integrating multiple criteria.
problem Overfitting in data-driven financial models leads to unreliable out-of-sample performance.
method Integrates performance, statistical significance, consistency, and downside risk into a composite objective function.
result Improves generalization ratio by 98% compared to baseline objective functions in walk-forward validation.
AlphaX uses AI to outperform Brazilian stock market benchmarks.
problem AI strategies often overperform in backtests but underperform in real markets due to lookahead bias.
method Controlled simulations to mitigate lookahead bias, using Value Investing principles.
result AlphaX strategy outperforms major benchmarks and technical indicators.
A new method for backtesting ES forecasts in banking.
problem Designing a model-free backtesting procedure for Expected Shortfall forecasts.
method Use e-values and e-processes to introduce backtest e-statistics for VaR and ES.
result The proposed method can be applied to various risk measures and statistical quantities.
New method tests risk measures for various distortions.
problem Testing risk measures for different distortions.
method Stratification and randomization of risk levels.
result Method performs well in numerical case studies.
The study uses machine learning to predict financial market trends.
problem Predicting financial market trends using low-frequency data.
method Modular online machine learning framework using stacked autoencoders and neural networks.
result The approach can predict financial market feature fluctuations effectively.
This paper introduces novel backtests for the risk measure Expected Shortfall (ES) following the testing idea of Mincer and Zarnowitz (1969). Estimating a regression framework for the ES stand-alone is infeasible, and thus, our tests are based on a joint regression for the Value at Risk and the ES, which allows for dif…
Conditional forecasts of risk measures play an important role in internal risk management of financial institutions as well as in regulatory capital calculations. In order to assess forecasting performance of a risk measurement procedure, risk measure forecasts are compared to the realized financial losses over a perio…
We study a class of backtests for forecast distributions in which the test statistic depends on a spectral transformation that weights exceedance events by a function of the modeled probability level. The weighting scheme is specified by a kernel measure which makes explicit the user's priorities for model performance.…
Backtesting framework for CLMMs on Uniswap V3 reduces reward estimation error.
problem Estimating rewards for CLMMs in Uniswap V3 liquidity pools.
method Parametric model for liquidity distribution, historical data analysis.
result Error in reward estimation less than 1% for each pool.
In recent years several trading platforms appeared which provide a backtest engine to calculate historic performance of self designed trading strategies on underlying candle data. The construction of a correct working backtest engine is, however, a subtle task as shown by Maier-Paape and Platen (cf. arXiv:1412.5558 [q-…
Backtests of structured strategies lose much of their predictive power in live trading.
problem Uncertainty in how marketed backtests predict live performance of structured strategies.
method Analysis of 1,726 structured strategies from ten global institutions.
result Raw backtests have limited portability into live trading and deteriorate sharply.
Expected Shortfall (ES) has been widely accepted as a risk measure that is conceptually superior to Value-at-Risk (VaR). At the same time, however, it has been criticised for issues relating to backtesting. In particular, ES has been found not to be elicitable which means that backtesting for ES is less straightforward…
New method allows backtesting of systemic risk forecasts.
problem Systemic risk measures are not elitable and identifiable, making backtesting impossible.
method Introduces multi-objective elicitability and Diebold--Mariano type tests.
result Proposes a traffic-light approach for backtesting.
A new method tests Expected Shortfall by analyzing both duration and severity of VaR violations.
problem Lack of separate testing for frequency and severity in ES backtesting.
method Uses bivariate orthogonal polynomials to derive moment conditions for durations and severities.
result Proposes a Wald test for identifying mis-specified components in ES models.
The paper examines sizing strategies for algorithmic trading in volatile markets.
problem High volatility creates challenges for algorithmic traders.
method Investigates different sizing models and backtesting techniques for financial trading.
result Sizing models can lower Value at Risk (VaR) during crisis events.
The paper uses machine learning to simulate financial markets and improve trading strategy backtesting.
problem Improving risk management of quantitative investment strategies.
method Simulates financial markets using Boltzmann Machines and Generative Adversarial Networks to preserve asset return distributions and dependencies.
result Developed a framework to estimate backtest statistics more accurately.
In this paper we try to design the necessary calculation needed for backtesting trading systems when only candle chart data are available. We lay particular emphasis on situations which are not or not uniquely decidable and give possible strategies to handle such situations.
We propose a new backtesting framework for Expected Shortfall that could be used by the regulator. Instead of looking at the estimated capital reserve and the realised cash-flow separately, one could bind them into the secured position, for which risk measurement is much easier. Using this simple concept combined with …
This paper investigates bias in resampled backtests for financial portfolios, finding it often negligible.
problem Bias in resampled backtests for financial portfolio evaluation.
method Investigation of bias in rolling-window mean-variance portfolios using resampling techniques.
result The bias in Sharpe Ratio estimates from IID resampling is often a fraction of estimation noise, making it tolerable.
This work presents a theoretical and empirical evaluation of Anderson-Darling test when the sample size is limited. The test can be applied in order to backtest the risk factors dynamics in the context of Counterparty Credit Risk modelling. We show the limits of such test when backtesting the distributions of an intere…
A new stock selection strategy uses combined machine learning with dynamic weighting methods.
problem Improving stock selection accuracy and performance.
method Combined machine learning algorithms with static and dynamic weighting methods.
result IC-based dynamic weighting outperforms static evaluation metrics in backtested returns and predictive performance.
Using non-linear machine learning methods and a proper backtest procedure, we critically examine the claim that Google Trends can predict future price returns. We first review the many potential biases that may influence backtests with this kind of data positively, the choice of keywords being by far the greatest culpr…
In this note, we comment on the relevance of elicitability for backtesting risk measure estimates. In particular, we propose the use of Diebold-Mariano tests, and show how they can be implemented for Expected Shortfall (ES), based on the recent result of Fissler and Ziegel (2015) that ES is jointly elicitable with Valu…
Survey of determinism issues in financial AI systems.
problem Vulnerabilities in reproducibility of financial AI systems.
method Literature review and first-party experiments on public financial datasets.
result Proposed a layered evaluation framework linking modality-specific metrics to audit readiness.
Benchmark detects decision-time leakage in financial backtests.
problem Detecting decision-time leakage in financial machine-learning backtests.
method Toggles one evaluation convention at a time around a clean t+1-open reference, holding other factors fixed. result Inflation is highly selective, affecting specific features and execution methods.
New method corrects risk estimation bias, improving backtesting results.
problem Underestimation of risk by existing methods, especially in small samples.
method Proposes a new algorithm for bias correction using generalized Pareto distributions.
result The new algorithm leads to improved efficiency in estimating risk with heavy tails or heteroscedasticity.
New metrics quantify implementation risk in portfolio backtesting, revealing systematic differences in engine implementations.
problem Systematic divergence in backtested portfolio metrics due to differences in engine implementations.
method Formalized implementation risk, proposed four metrics, executed 15 strategies through five engines, analyzed source-code defects.
result Implementation risk introduces measurable ambiguity in performance attribution, but does not alter investment decisions.
The paper optimizes portfolios using clustering and Sharpe ratio-based optimization.
problem Optimizing portfolio performance in financial modeling.
method Combines K-Means clustering for asset segmentation and Sharpe ratio-based optimization.
result Optimized portfolios outperform traditional equal-weighted benchmarks.
A new risk measure, the lambda value at risk (Lambda VaR), has been recently proposed from a theoretical point of view as a generalization of the value at risk (VaR). The Lambda VaR appears attractive for its potential ability to solve several problems of the VaR. In this paper we propose three nonparametric backtestin…
Robust forecast framework reduces distribution error by 63%.
problem Accurate distribution forecast for planning decisions.
method Backtest-based bootstrap and adaptive residual selection.
result Reduces Absolute Coverage Error by more than 63%.
AlphaEval evaluates alpha mining models efficiently and comprehensively.
problem Lack of systematic evaluation for alpha mining models.
method Unified, parallelizable evaluation framework assessing predictive power, stability, robustness, financial logic, and diversity.
result AlphaEval achieves evaluation consistency comparable to comprehensive backtesting, providing more comprehensive insights and higher efficiency.
This research improves forecasting and testing of risk contributions using Expected Shortfall.
problem Improving risk allocation and testing methods for regulatory standards.
method Developed a comprehensive framework for backtesting and forecasting Expected Shortfall contributions.
result Proposed a novel semiparametric model for forecasting dynamic Expected Shortfall contributions.
FinRL-Meta offers market environments and benchmarks for financial reinforcement learning.
problem Challenges in creating high-quality market environments and benchmarks for financial reinforcement learning.
method DataOps paradigm, automatic pipeline, community-wise competitions, Jupyter/Python demos.
result Openly accessible FinRL-Meta library for data-driven financial reinforcement learning.
CLVSA predicts financial market trends using LSTM and attention mechanisms.
problem Predicting trends in financial markets due to complex interactions.
method Hybrid model combining LSTM, sequence-to-sequence, attention, and convolutional LSTM.
result CLVSA outperforms basic models in predicting financial market trends.
This paper evaluates LLMs for technical market analysis, finding GPT-4 Turbo and FinGPT outperform passive benchmarks.
problem Evaluating LLMs for technical market analysis in financial markets.
method Structured evaluation of five LLMs (GPT-4 Turbo, Claude 3 Opus, Gemini 1.5 Pro, Llama 3 70B, FinGPT) on four tasks: candlestick pattern recognition, directional signal generation, backtesting, and financial report comprehension.
result GPT-4 Turbo and FinGPT outperform passive benchmarks in simulated backtesting, with GPT-4 Turbo achieving the highest annualized return and Sharpe ratio.
A new property fixes look-ahead bias in backtesting and trading pipelines.
problem Fixing look-ahead bias in backtesting and trading pipelines.
method Developed a pipeline calculus separating availability from reference time, and a type-and-effect system for the value-independent fragment.
result The check scales linearly and catches all leaks, including those missed by differential and tiling detectors.
Agent Trading Arena trains LLMs in real-time financial markets to improve numerical reasoning.
problem Limited real-world training for LLMs in financial markets.
method Virtual zero-sum stock market with competitive multi-agent trading.
result LLMs perform better with chart-based visualizations and a reflection module.
Anonymizing company names in financial news improves trading performance, contrary to initial expectations.
problem Look-ahead and distraction biases in sentiment analysis of financial news.
method Investigated trading strategies based on original and anonymized headlines, comparing performance.
result Anonymized headlines outperform original in-sample, suggesting distraction effect is stronger.
Study compares quantum and classical ML in crypto trading, finding hybrid models outperform.
problem Comparing quantum and classical machine learning in crypto trading strategies.
method Backtesting 10 models across multiple crypto assets using classical ML, quantum ML, hybrid models, and transformer models.
result Hybrid quantum models achieve superior performance with 13.99% return and 1.76 Sharpe ratio.
New algorithm corrects risk estimation bias for heavy-tailed data.
problem Underestimation of risk in banking and insurance due to bias in estimation procedures.
method Proposes a new algorithm for bias correction and applies it to generalized Pareto distributions.
result The algorithm leads to more accurate risk estimation, especially in heavy-tailed data.
Neural-SDE model accurately simulates option risks.
problem Estimating accurate risk scenarios for option portfolios.
method Arbitrage-free neural-SDE market model for joint option dynamics.
result Models produce more efficient and accurate VaR evaluations.
Deep RL for portfolio management shows poor robustness.
problem Robustness of Deep RL algorithms in online portfolio management.
method Proposed a training and evaluation process for assessing DRL algorithms.
result Most Deep RL algorithms are not robust, generalizing poorly and degrading quickly.