Maximus-AI: Using Elman Neural Networks for Implementing a SLMR Trading Strategy

Abstract. For a long time moving averages has been used for a financial data smoothing. It is one of the first indicators in technical analysis trading. Many traders debated that one moving average is better than other. As a result a lot of moving averages have been created. In this empirical study we overview 19 most popular moving averages, create a taxonomy and compare them using two most important factors -smoothness and lag. Smoothness indicates how much an indicator change (angle) and lag indicates how much moving average is lagging behind the current price. The aim is to have values as smooth as possible to avoid erroneous trades and with minimal lag -to increase trend detection speed. This large-scale empirical study performed on 1850 real-world time series including stocks, ETF, Forex and futures daily data demonstrate that the best smoothness/lag ratio is achieved by the Exponential Hull Moving Average (with price correction) and Triple Exponential Moving Average (without correction).

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“…where n= (5,10,21,63). At mentioned periods, we measured smoothness of SMA and selected other MA with the same or better smoothness.…”

Section: Methodsmentioning

confidence: 99%

“…In [5] authors use recursive Elman neural network to calculate moving average. The average is later used in proposed stop loss -maximum return (SLMR) trading strategy.…”

Section: Introductionmentioning

confidence: 99%

Moving Averages for Financial Data Smoothing

Raudys

Lenčiauskas

Malčius

2013

Communications in Computer and Information Science

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“…Algorithmic trading has been widely studied in its different subareas, including risk control (Pichler et al, 2021), portfolio optimization (Giudici et al, 2020), and trading strategy (Marques and Gomes, 2010;Vella and Ng, 2015;Chen et al, 2021).…”

Section: Reinforcement Learning In Tradingmentioning

confidence: 99%

QF-TraderNet: Intraday Trading via Deep Reinforcement With Quantum Price Levels Based Profit-And-Loss Control

Qiu

Yuan

et al. 2021

Front. Artif. Intell.

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Reinforcement Learning (RL) based machine trading attracts a rich profusion of interest. However, in the existing research, RL in the day-trade task suffers from the noisy financial movement in the short time scale, difficulty in order settlement, and expensive action search in a continuous-value space. This paper introduced an end-to-end RL intraday trading agent, namely QF-TraderNet, based on the quantum finance theory (QFT) and deep reinforcement learning. We proposed a novel design for the intraday RL trader’s action space, inspired by the Quantum Price Levels (QPLs). Our action space design also brings the model a learnable profit-and-loss control strategy. QF-TraderNet composes two neural networks: 1) A long short term memory networks for the feature learning of financial time series; 2) a policy generator network (PGN) for generating the distribution of actions. The profitability and robustness of QF-TraderNet have been verified in multi-type financial datasets, including FOREX, metals, crude oil, and financial indices. The experimental results demonstrate that QF-TraderNet outperforms other baselines in terms of cumulative price returns and Sharpe Ratio, and the robustness in the acceidential market shift.

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Maximus-AI: Using Elman Neural Networks for Implementing a SLMR Trading Strategy

Cited by 2 publications

References 4 publications

Moving Averages for Financial Data Smoothing

Moving Averages for Financial Data Smoothing

QF-TraderNet: Intraday Trading via Deep Reinforcement With Quantum Price Levels Based Profit-And-Loss Control

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