# DNN-ForwardTesting: A New Trading Strategy Validation using Statistical Timeseries Analysis and Deep Neural Networks

arXiv: https://arxiv.org/abs/2210.11532 · HTML: https://mlsanity.com/paper/2210.11532/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 48 references that are in the map.
- First posted on arXiv 20 Oct 2022.

## Builds on (most cited first, 11 of 11)

- [Volatility Modeling of Stocks from Selected Sectors of the Indian Economy Using GARCH](https://mlsanity.com/paper/2105.13898/) (2021)
- [DeepMAL -- Deep Learning Models for Malware Traffic Detection and Classification](https://mlsanity.com/paper/2003.04079/) (2020)
- [A Logic-based Multi-agent System for Ethical Monitoring and Evaluation of Dialogues](https://mlsanity.com/paper/2109.08294/) (2021)
- [Application of Facebook's Prophet Algorithm for Successful Sales Forecasting Based on Real-world Data](https://mlsanity.com/paper/2005.07575/) (2020)
- [Dataset Optimization Strategies for MalwareTraffic Detection](https://mlsanity.com/paper/2009.11347/) (2020)
- [A Novel Resampling Technique for Imbalanced Dataset Optimization](https://mlsanity.com/paper/2012.15231/) (2020)
- [A Stock Trading System for a Medium Volatile Asset using Multi Layer Perceptron](https://mlsanity.com/paper/2201.12286/) (2022)
- [Exploring the predictability of range-based volatility estimators using RNNs](https://mlsanity.com/paper/1803.07152/) (2018)
- [Stacking with Neural network for Cryptocurrency investment](https://mlsanity.com/paper/1902.07855/) (2019)
- [On the Bound of Cumulative Return in Trading Series and the Verification Using Technical Trading Rules](https://mlsanity.com/paper/2005.13974/) (2020)
- [Optimal Technical Indicator-based Trading Strategies Using NSGA-II](https://mlsanity.com/paper/2111.13364/) (2021)

## Built on by (most cited first, 2 of 2)

- [A Comparative Analysis of Statistical and Machine Learning Models for Outlier Detection in Bitcoin Limit Order Books](https://mlsanity.com/paper/2507.14960/) (2025)
- [A Framework for Predictive Directional Trading Based on Volatility and Causal Inference](https://mlsanity.com/paper/2507.09347/) (2025)

## Abstract (arXiv)

In general, traders test their trading strategies by applying them on the historical market data (backtesting), and then apply to the future trades the strategy that achieved the maximum profit on such past data. In this paper, we propose a new trading strategy, called DNN-forwardtesting, that determines the strategy to apply by testing it on the possible future predicted by a deep neural network that has been designed to perform stock price forecasts and trained with the market historical data. In order to generate such an historical dataset, we first perform an exploratory data analysis on a set of ten securities and, in particular, analize their volatility through a novel k-means-based procedure. Then, we restrict the dataset to a small number of assets with the same volatility coefficient and use such data to train a deep feed-forward neural network that forecasts the prices for the next 30 days of open stocks market. Finally, our trading system calculates the most effective technical indicator by applying it to the DNNs predictions and uses such indicator to guide its trades. The results confirm that neural networks outperform classical statistical techniques when performing such forecasts, and their predictions allow to select a trading strategy that, when applied to the real future, increases Expectancy, Sharpe, Sortino, and Calmar ratios with respect to the strategy selected through traditional backtesting.