arXiv research

The paper analyzes MACD using operator theory.

A new method Expectigrad improves on Adam and RMSProp by reducing divergence and improving performance.

Machine learning models outperform traditional technical analysis in Bitcoin trading.

We study capital process behavior in the fair-coin game and biased-coin games in the framework of the game-theoretic probability of Shafer and Vovk (2001). We show that if Skeptic uses a Bayesian strategy with a beta prior, the capital process is lucidly expressed in terms of the past average of Reality's moves. From t…

Deep CNN model predicts stock prices with high accuracy.

The paper optimizes portfolios using MACD signals derived from price history.

The purpose of this research is to apply technical analysis of Sutte Indicator in stock trading which will assist in the investment decision making process i.e. buying or selling shares. This research takes data of "A" on the Indonesia Stock Exchange(IDX or BEI) 29 November 2006 until 20 September 2016 period. To see t…

We propose a method for pricing American options whose pay-off depends on the moving average of the underlying asset price. The method uses a finite dimensional approximation of the infinite-dimensional dynamics of the moving average process based on a truncated Laguerre series expansion. The resulting problem is a fin…

Improved averaging method for noisy observations converges strongly.

BEMA reduces bias in EMA, leading to faster convergence and better performance.

Adam and RMSProp are two of the most influential adaptive stochastic algorithms for training deep neural networks, which have been pointed out to be divergent even in the convex setting via a few simple counterexamples. Many attempts, such as decreasing an adaptive learning rate, adopting a big batch size, incorporatin…

We show Vector Autoregressive Moving Average models with scalar Moving Average components could be estimated by generalized least square (GLS) for each fixed moving average polynomial. The conditional variance of the GLS model is the concentrated covariant matrix of the moving average process. Under GLS the likelihood …

We examine two different techniques for parameter averaging in GAN training. Moving Average (MA) computes the time-average of parameters, whereas Exponential Moving Average (EMA) computes an exponentially discounted sum. Whilst MA is known to lead to convergence in bilinear settings, we provide the -- to our knowledge …

New moving average adapts weight dynamically based on polynomial and wavefunction.

Several recently proposed stochastic optimization methods that have been successfully used in training deep networks such as RMSProp, Adam, Adadelta, Nadam are based on using gradient updates scaled by square roots of exponential moving averages of squared past gradients. In many applications, e.g. learning with large …

Rank-statistic method approximates $f$-divergences without density-ratio estimation.

PACE optimizes training for averaged language models, improving performance.

This work proposes a model averaging method for SVM that avoids redundant covariates and achieves asymptotic optimality.

In this note we discuss the mathematical tools to define trend indicators which are used to describe market trends. We explain the relation between averages and moving averages on the one hand and the so called exponential moving average (EMA) on the other hand. We present a lot of examples and give the definition of t…

New adaptive methods solve weakly convex stochastic optimization problems.

This study uses moving average cluster entropy to analyze financial market dynamics.

Study improves MACD trading strategy with volume and price adjustments.

Paper analyzes SVGD algorithm for non-asymptotic convergence.

STORM-PG uses momentum for faster policy gradient updates.

A new method normalizes flow mixtures for better inference across different data types.

TINs use neural networks to interpret technical indicators for trading.

GPA improves LLM training speed by 8.71% for Llama-160M models.

Unified analysis of finite weight averaging methods in deep learning.

Improving optimization for iterate-averaged language models

In the paper, we introduce a new measure of correlation between possibly non-stationary series. As the measure is based on the detrending moving-average cross-correlation analysis (DMCA), we label it as the DMCA coefficient $ρ_{DMCA}(λ)$ with a moving average window length $λ$. We analytically show that the coefficient…

Riesz regression connects to density ratio estimation for causal inference.

The Hurst exponent $H$ of long range correlated series can be estimated by means of the Detrending Moving Average (DMA) method. A computational tool defined within the algorithm is the generalized variance $σ_{DMA}^2={1}/{(N-n)}\sum_i [y(i)-\widetilde{y}_n(i)]^2\:$, with $\widetilde{y}_n(i)= {1}/{n}\sum_{k}y(i-k)$ the…

The set of directions from a quadratic differential that diverge on average under Teichmuller geodesic flow has Hausdorff dimension exactly equal to one-half.

A new method for averaging model predictions using minimum divergence.

In Part II of this paper, we concentrate our analysis on the price dynamical model with the moving average rules developed in Part I of this paper. By decomposing the excessive demand function, we reveal that it is the interplay between trend-following and contrarian actions that generates the price chaos, and give par…

The possibility that price dynamics is affected by its distance from a moving average has been recently introduced as new statistical tool. The purpose is to identify the tendency of the price dynamics to be attractive or repulsive with respect to its own moving average. We consider a number of tests for various models…

We introduce a simple algorithm, True Asymptotic Natural Gradient Optimization (TANGO), that converges to a true natural gradient descent in the limit of small learning rates, without explicit Fisher matrix estimation. For quadratic models the algorithm is also an instance of averaged stochastic gradient, where the par…

Paper predicts cryptocurrency bull and bear phases using Bitcoin's moving averages.

The paper tackles learning to control systems with unknown parameters using Brownian noise.

New method estimates velocity fields for minimizing $f$-divergences without overfitting.

New algorithms solve complex multi-level optimization problems with improved efficiency.

We consider the minimization of composite objective functions composed of the expectation of quadratic functions and an arbitrary convex function. We study the stochastic dual averaging algorithm with a constant step-size, showing that it leads to a convergence rate of O(1/n) without strong convexity assumptions. This …

Study improves reinforcement learning for stable long-term performance.

While classic work in convex-concave min-max optimization relies on average-iterate convergence results, the emergence of nonconvex applications such as training Generative Adversarial Networks has led to renewed interest in last-iterate convergence guarantees. Proving last-iterate convergence is challenging because ma…

Optimal algorithms for Riemannian optimization with reduced complexity.

Optimal weight windows are found by projecting the origin onto a convex polytope.

Efficient method for pricing Bermudan moving average options using GPR-GHQ.

Geometric tempering improves sampling from distributions, with exponential convergence rates.