The paper limits the profitability of technical trading rules and finds they are not better than random trading.
problem The profitability of technical trading rules in stock markets is controversial.
method Proves the upper bound of cumulative return and investigates the profitability of technical trading rules using bootstrap methodology.
result Technical trading rules are not better than random trading and less profitable than the market.
In this paper we use fuzzy systems theory to convert the technical trading rules commonly used by stock practitioners into excess demand functions which are then used to drive the price dynamics. The technical trading rules are recorded in natural languages where fuzzy words and vague expressions abound. In Part I of t…
Technical trading rules have a long history of being used by practitioners in financial markets. Their profitable ability and efficiency of technical trading rules are yet controversial. In this paper, we test the performance of more than seven thousands traditional technical trading rules on the Shanghai Securities Co…
Study on profitability of technical trading rules using high-frequency data of Chinese Index Futures.
problem Investigating the profitability of technical trading rules with high-frequency data of Chinese Index Futures.
method Converted MA, KDJ, and Bollinger bands into stationary processes and used ADF-test and SPA test to verify stationarity and assess trading rules' performance.
result Significant combinations of parameters for each indicator were found, but trading profits were eliminated with transaction costs included.
Study evaluates technical trading rules on various markets, introduces DFRD+/- method.
problem Evaluating the profitability and robustness of technical trading rules across different markets.
method Investigated 21,000 technical trading rules on 12 markets over 12 years, introduced DFRD+/- method.
result DFRD+/- method is adaptive and more powerful, accommodating discrete p-values.
GA-MSSR optimizes forex trading rules for higher returns and reduced risk.
problem Noisy market data affects the consistency and profitability of trading algorithms.
method Optimized trading rules derived from technical indicators using a Genetic Algorithm.
result GA-MSSR achieved superior performance with significant positive returns and reduced risk factors.
Decision trees improve intraday trading strategies for NIFTY50 stocks.
problem Creating optimal trading rules for individual stocks.
method Using decision trees to generate unique trading rules for each stock based on technical indicators.
result Decision tree strategies outperform simple buy-and-hold for many stocks.
Although technical trading rules have been widely used by practitioners in financial markets, their profitability still remains controversial. We here investigate the profitability of moving average (MA) and trading range break (TRB) rules by using the Shanghai Stock Exchange Composite Index (SHCI) from May 21, 1992 th…
We propose a new indicator for technical analysis. The indicator emphasizes maximums and minimums in price series with inherent smoothing and has a potential to be useful in both mechanical trading rules and chart pattern analysis.
Research integrates sentiment analysis with reinforcement learning for better trading strategies.
problem Improving trading performance by integrating sentiment data.
method Developed a sentiment-driven trading system using a large language model and reinforcement learning.
result Sentiment signals from FinGPT improve trading performance when combined with technical indicators.
TINs use neural networks to interpret technical indicators for trading.
problem Lack of interpretable neural architectures for technical indicators in trading.
method Introduced TINs, a neural architecture that reformulates technical indicators into trainable modules.
result Improved risk-adjusted performance compared to traditional indicator-based strategies.
In this dissertation, the main goal is visualisation of financial time series. We expect that visualisation of financial time series will be a useful auxiliary for technical analysis. Firstly, we review the technical analysis methods and test our trading rules, which are built by the essential concepts of technical ana…
The aim of this paper is to compare the performances of the optimal strategy under parameters mis-specification and of a technical analysis trading strategy. The setting we consider is that of a stochastic asset price model where the trend follows an unobservable Ornstein-Uhlenbeck process. For both strategies, we prov…
Trading strategies improved by classifying financial time-series images.
problem Improving financial trading strategies using image classification.
method Created a dataset of financial time-series images, labeled them, and trained machine learning models.
result Machine learning models trained on image data outperformed traditional time-series analysis.
Study improves MACD trading strategy with volume and price adjustments.
problem Signal lag and false signals in traditional MACD trading rules.
method Develops VP-MACD framework with sensitivity calibration.
result Proposed framework outperforms baseline MACD in profitability and risk-adjusted return.
This paper investigates the effects of the "uptick rule" (a short selling regulation formally known as rule 10a-1) by means of a simple stock market model, based on the ARED (adaptive rational equilibrium dynamics) modeling framework, where heterogeneous and adaptive beliefs on the future prices of a risky asset were f…
Enhances trading signals using image analysis and weighted moving averages.
problem Improving price trend trading strategies in financial markets.
method Image-induced importance weights applied to weighted moving averages of trading signals.
result Significant enhancement of price trend trading signals with improved portfolio selection.
Abstract: A new approach to technical indicators without lag.
problem Defining classical technical indicators as bounded operators for lag-free trading.
method Using linear algebra to redefine technical indicators as bounded operators in l∞(N) space. result Demonstrated the no-lag versions of technical indicators are simpler and more effective.
Neural network predicts stock prices using technical indicators.
problem Predicting stock prices for optimal trading.
method Converted financial data into buy-sell-hold signals, trained MLP ANN model with Apache Spark.
result Neural network model performs comparably to Buy and Hold strategy.
Algorithm learns optimal trading parameters from technical strategies.
problem Optimizing wealth from technical trading strategies.
method Adversarial expert based online learning algorithm for parameter optimization.
result Aggregated trading strategies outperform benchmarks after accounting for costs.
Machine learning models outperform traditional technical analysis in Bitcoin trading.
problem Maximizing profits in the Bitcoin market using trading signals.
method Comparison of machine learning models (LightGBM, LSTM) and technical analysis strategies (EMA, MACD+ADX).
result LSTM model achieved a 65.23% cumulative return over a year, significantly outperforming other strategies.
We present a universal algorithm for online trading in Stock Market which performs asymptotically at least as good as any stationary trading strategy that computes the investment at each step using a fixed function of the side information that belongs to a given RKHS (Reproducing Kernel Hilbert Space). Using a universa…
This paper uses feature preprocessing and RRL to automate profitable financial trading.
problem Automating profitable financial trading strategies.
method Feature preprocessing (PCA, DWT) followed by Recurrent Reinforcement Learning (RRL).
result The proposed strategy is effective, robust, and mitigates RRL's drawbacks.
Study finds traditional technical indicators underperform in high-frequency trading, suggesting risk management over prediction.
problem Inadequately explored effectiveness of technical indicators in high-frequency trading, particularly at minute-level frequency.
method Evaluation of random forest models with traditional technical indicators on minute-level SPY data.
result In-sample performance is superior to out-of-sample, with risk-adjusted metrics not outperforming a simple buy-and-hold strategy.
Online trading platforms manipulate profits and losses, causing 82% of retail traders to lose money.
problem Manipulation of online trading platforms leading to financial losses for retail traders.
method Independent recording of trade details using REST API responses, comparison with broker reviews.
result 82% of retail traders lose money due to platform technical issues.
QTMRL uses RL with multi-indicators to improve trading adaptability.
problem Traditional trading models fail in volatile markets due to rigid assumptions.
method Combines multi-indicators with RL for adaptive portfolio management.
result QTMRL outperforms baselines in profitability and risk control.
High-frequency trading strategy boosts battery storage profits.
problem Maximizing revenue for battery energy storage systems in intraday markets.
method Adapted dynamic programming for continuous intraday markets, considering limit order book dynamics.
result Dynamic programming strategy outperforms standard re-optimization methods, increasing profits by 58% and 14% respectively.
Study improves cryptocurrency price prediction using deep learning with trading and social media indicators.
problem Predicting price movements of cryptocurrencies using deep learning.
method Used deep learning algorithms (MLP, CNN, LSTM, ALSTM) on hourly and daily data of Bitcoin and Ethereum.
result Unrestricted model with trading and social media indicators outperforms restricted model.
In this survey, a short introduction in the recent discovery of log-normally distributed market-technical trend data will be given. The results of the statistical evaluation of typical market-technical trend variables will be presented. It will be shown that the log-normal assumption fits better to empirical trend data…
The paper proposes a method to optimize rule-based models for better accuracy and interpretability.
problem Developing rule-based models for regression and classification with better accuracy and interpretability.
method Column generation to optimize over an exponentially large space of rules, using integer programming or a heuristic.
result The proposed methods achieve better accuracy-complexity trade-offs than existing rule ensemble algorithms.
Paper finds optimal selling rule for pairs trading with stock constraints.
problem Identifying the best time to sell in pairs trading of stocks.
method Optimal pairs-trading selling rule with constraints on trading.
result Closed-form solution for optimal policy determined by a threshold curve.
Developed Forex trading heuristics with high profit potential.
problem Reduced trade opportunities due to technical indicator values.
method Machine learning simulation of 10 years of Forex data.
result Optimized trade parameters for 118 pips daily profit.
Improved MACD trading strategies with other indicators for better performance.
problem Evaluating the effectiveness of MACD-based trading strategies in the US stock market.
method Backtested various MACD-based trading strategies on US stock indices using Python.
result Win-rate of MACD strategies improved with other momentum indicators, leading to a new VPVMA indicator.
A novel algorithm for actively trading stocks is presented. While traditional expert advice and "universal" algorithms (as well as standard technical trading heuristics) attempt to predict winners or trends, our approach relies on predictable statistical relations between all pairs of stocks in the market. Our empirica…
Simplifies random forests by breaking down trees into rules for better interpretability.
problem Balancing model complexity and accuracy in random forests.
method Breaking down random forest trees into individual classification rules and selecting a subset.
result A few selected rules can achieve acceptable accuracy similar to the original model, leading to simpler models.
NEAT algorithm optimizes stock trading with reduced risk.
problem Maximizing earnings while minimizing risk in stock trading.
method Applied NEAT algorithm to stock trading with multiple technical indicators, using progressive training data and a multi-objective fitness function.
result NEAT model achieved similar returns to Buy & Hold but with lower risk and stability.
Study examines moving average trading rule's performance on stock indexes.
problem Linking trading rule performance to asset stochastic process.
method Calculated Sharpe ratio as a function of look-back period and asset distribution.
result Performance depends on autocorrelation vs. drift for different look-back periods.
Technical trading represents a class of investment strategies for Financial Markets based on the analysis of trends and recurrent patterns of price time series. According standard economical theories these strategies should not be used because they cannot be profitable. On the contrary it is well-known that technical t…
Explains three neural network types: feedforward, convolutional, and recurrent.
problem None explicitly stated; focuses on technical introduction.
method Detailed explanation of neural network architectures and backpropagation.
result Derivation of forward pass and backpropagation rules for each network.
VGRSI uses price visibility graphs to generate profitable trading signals.
problem Ineffective traditional technical analysis indicators in financial markets.
method Visibility Graphs Relative Strength Index (VGRSI) based on backward visibility relations in price data.
result VGRSI signals generated substantial profits across different asset classes.
Technical trading rules and linear regressive models are often used by practitioners to find trends in financial data. However, these models are unsuited to find non-linearly separable patterns. We propose a decision tree forecasting model that has the flexibility to capture arbitrary patterns. To illustrate, we constr…
Hybrid AI system combines technical, sentiment analysis for adaptive equity trading.
problem Traditional trading strategies fail during high volatility and regime shifts.
method Combines trend-following, mean-reversion, sentiment analysis, machine learning, and market regime filtering.
result Hybrid model achieved 135.49% return on investment over 24 months.
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…
Algorithm maximizes wealth from best pairs rebalancing rule in hindsight.
problem Maximizing wealth from best pairs rebalancing rule in hindsight.
method Extends Ordentlich and Cover's max-min universal portfolio to achieve a percentage of the hindsight-optimized wealth.
result Achieves a compound-annual growth rate arbitrarily close to the best pairs rebalancing rule in hindsight.
Proposes a new framework for predicting stock market movements using sparse neural architectures.
problem Challenging problem of predicting stock market movements using technical indicators.
method Multi-criteria optimization approach to evolve sparse neural architectures.
result Evolved parsimonious networks with better generalization capabilities.
Wavelet denoised-ResNet with LightGBM predicts Forex rate of change.
problem Forecasting Foreign Exchange (Forex) rate of change for trading opportunities.
method Wavelet denoising, ResNet, LightGBM, technical indicators, image features.
result The model outperforms baseline models with low MAE, MSE, and RMSE.
Banks must manage their trading books, not just value them. Pricing includes valuation adjustments collectively known as XVA (at least credit, funding, capital and tax), so management must also include XVA. In trading book management we focus on pricing, hedging, and allocation of prices or hedging costs to desks on an…
This paper studies the problem of determining the optimal cut-off for pairs trading rules. We consider two correlated assets whose spread is modelled by a mean-reverting process with stochastic volatility, and the optimal pair trading rule is formulated as an optimal switching problem between three regimes: flat positi…