Algorithmic Trading Strategies Research Articles

Investigating the profit performance of quantitative timing trading strategies in the Shanghai copper futures market, 2020–2022

AbstractIn conducting an extensive examination, we scrutinize the efficacy of algorithmic trading strategies applied to Futures CopperMainContinuous in the Shanghai Futures Exchange, utilizing a comprehensive data set spanning from January 2020 to December 2022. To mitigate the potential risk of data‐snooping bias—the probability that any favorable results may inadvertently arise from random events rather than the inherent value of the strategies employed to generate these results—our study prudently conducts a reality check and advanced assessments. Throughout the evaluated period, the benchmark demarcation between the in‐sample and out‐of‐sample stages is established in February 2022. Regrettably, our meticulous exploration fails to identify any successful or advantageous algorithmic trading strategies within these categories, particularly following the systematic elimination of data snooping bias. These results underscore the intrinsic challenges in accurately identifying and implementing profit‐generating algorithmic trading strategies within the volatile and intricate futures market.

Understanding Investment Decision-making: A Qualitative Inquiry into High-Frequency Trading, Investment Strategies, and Portfolio Performance in the Financial Market

This qualitative study explores investment decision-making in the context of high-frequency trading (HFT), investment strategies, and portfolio performance within the financial market. The research aims to provide insights into the complex dynamics influencing investment decisions and their implications for market participants. Adopting a qualitative research design, the study conducts a comprehensive review and analysis of existing literature from academic journals, books, and conference proceedings. The data collection process involves synthesizing insights from diverse sources to uncover underlying themes, patterns, and contradictions surrounding investment decision-making. Through thematic analysis and constant comparison, the study identifies key findings related to the role of HFT, diversity of investment strategies, and evaluation of portfolio performance. The results highlight the transformative impact of HFT on market liquidity, efficiency, and stability, while also raising concerns about market fragmentation and systemic risks. Additionally, the study explores the evolution of investment strategies, ranging from traditional approaches like fundamental analysis to emerging techniques such as algorithmic trading and quantitative strategies. It underscores the importance of tailoring investment strategies to individual preferences and market conditions for optimizing portfolio performance. Furthermore, the study evaluates alternative frameworks such as post-modern portfolio theory (PMPT) and factor investing, offering promising avenues for enhancing portfolio resilience and risk-adjusted returns. Overall, the research contributes to a deeper understanding of investment decision-making processes and informs stakeholders in the financial industry about effective strategies for navigating the dynamic landscape of the financial market.

Synergizing quantitative finance models and market microstructure analysis for enhanced algorithmic trading strategies

In today’s complex financial markets, “Algorithmic Trading” has become very important. The study delves into the amalgamation of four pivotal indicators - Relative Strength Index (RSI), Exponential Moving Average (EMA), Volume-Weighted Average Price (VWAP), and Moving Average Convergence/Divergence (MACD) Relative Strength Index (RSI), Exponential Moving Average (EMA), Volume-Weighted Average Price (VWAP), and Moving Average Convergence/Divergence (MACD) to create and develop a potent trading strategy. Through intensive backtesting and parameter tuning, our study demonstrates 60.63 % profitable trades on the National Stock Exchange (NSE), India, surpassing the standalone indicators. The Weapon Candle Strategy created using the four indicators presents its efficiency as it was able to achieve a profit factor of 1.882. This suggests that when these four technical indicators combined to make a strategy, it can provide significantly more accurate and reliable trading signals compared to using a combination of two or three indicators. Algorithmic traders should use a multi-indicator approach to achieve a more comprehensive understanding of the market and make informed trading decisions.

Real Time-Cutting Algorithmic Trading

a. Brief overview of algorithmic trading. Algorithmic trading, also known as algo trading or automated trading, refers to the use of computer algorithms and mathematical models to execute trading orders in financial markets. The primary goal of algorithmic trading is to achieve efficient and optimized execution of trading strategies, leveraging the speed and precision of computer systems. Here is a brief overview of algorithmic trading: 1. **Automation:** Algorithmic trading involves automating the process of buying or selling financial instruments, such as stocks, bonds, currencies, or commodities. The trading decisions are based on predefined rules and algorithms, eliminating the need for manual intervention. 2. **Speed:** One of the key advantages of algorithmic trading is its ability to execute trades at extremely high speeds. Algorithms can analyze market conditions, make decisions, and execute trades in a fraction of a second, responding to market changes much faster than human traders. 3. **Quantitative Analysis:** Algorithmic trading relies heavily on quantitative analysis and statistical models. Traders develop algorithms that are based on mathematical formulas, historical price data, technical indicators, and other relevant factors to identify trading opportunities. 4. **Market Making:** Some algorithmic trading strategies are designed for market making, where traders provide liquidity to the market by quoting bid and ask prices. Market-making algorithms aim to profit from the spread between the buying and selling prices. 5. **Arbitrage:** Algorithmic trading is often employed for arbitrage strategies, which involve exploiting price discrepancies between different markets or instruments. These discrepancies may arise due to inefficiencies, delays in information dissemination, or other market imperfections. 6. **Risk Management:** Algo trading systems typically incorporate risk management measures to control exposure and mitigate potential losses. These measures may include setting stop-loss orders, position sizing algorithms, and other risk controls. 7. **Backtesting:** Before deploying algorithms in live markets, traders often perform extensive backtesting. This involves running historical market data through the algorithm to simulate how it would have performed in the past. This helps refine and optimize the algorithm for real-world conditions. 8. **Machine Learning:** Some advanced algorithmic trading strategies incorporate machine learning techniques to adapt and learn from changing market conditions. Machine learning algorithms can analyze vast amounts of data to identify patterns and make predictions. 9. **Regulatory Considerations:** Algorithmic trading is subject to various regulations to ensure fair and orderly markets. Regulatory bodies may impose rules regarding algorithmic trading practices, transparency, and risk management to prevent market manipulation and protect investors. Algorithmic trading has become increasingly prevalent in financial markets, with institutional investors, hedge funds, and proprietary trading firms utilizing sophisticated algorithms to execute trades efficiently and capitalize on market opportunities.

Robo-Advisors: Revolutionizing Wealth Management through the Integration of Big Data and Artificial Intelligence in Algorithmic Trading Strategies

Robo-Advisors: Revolutionizing Wealth Management through the Integration of Big Data and Artificial Intelligence in Algorithmic Trading Strategies

Design Algorithmic Trading Strategies with Expert Advisor Using Linear Weighted Moving Average (LWMA) and Stochastic Oscillator Technical Indicators

Earlier this decade, the financial sector saw a paradigm change, with automated trading (AT) systems gaining popularity as essential tools for traders and investors. This research explores deeply the design and implementation of Expert Advisors (EAs) for automated financial built using a combination of the Linear Weighted Moving Average (LWMA) and Stochastic oscillator technical indicators. The EAs are built using the programming language MetaQuotes Language 4 (MLQ4) at Metatrader 4 (MT4) platform, enabling automated trade execution. It implements a machine learning genetic algorithm, trained on historical data to optimize the parameters of the LWMA and Stochastic trading rules. The core strategy relies on the LWMA to identify the overall market trend direction while the Stochastic oscillator provides additional signals for timing entry and exit points based on momentum. The EAs were coded to generate automated buy and sell signals for algorithmic trading (AT) based on a set of defined rules using thresholds for each indicators. Extensive historical backtesting using Gold (XAU/USD) currency pair across multiple timeframes from 5-minute (M5) up to 4-hour (H4) charts was conducted using 5 years of price data from 2019 to 2023 for evaluation. The goal of this study is to assess if the EAs could potentially produce consistent profits over time while minimizing drawdowns in different market conditions. The results demonstrate that the system was able to generate annual returns ranging from 3.04% up to 232.19% depending on the aggression of the timeframe settings. Meanwhile, maximum drawdowns were controlled to reasonable levels between 0.5% to 8.27% which is below 10% of potential loss throughout the backtests. An hourly timeframe configuration provided a balanced blend between strong profitability and drawdown control based on the backtest analysis. All the timeframes used for the test show positive results and the M5 timeframe is the best chosen timeframe to trade using this EAs implementation.

Unleashing the Power of Tweets and News in Stock-Price Prediction Using Machine-Learning Techniques

Price prediction tools play a significant role in small investors’ behavior. As such, this study aims to propose a method to more effectively predict stock prices in North America. Chiefly, the study addresses crucial questions related to the relevance of news and tweets in stock-price prediction and highlights the potential value of considering such parameters in algorithmic trading strategies—particularly during times of market panic. To this end, we develop innovative multi-layer perceptron (MLP) and long short-term memory (LSTM) neural networks to investigate the influence of Twitter count (TC), and news count (NC) variables on stock-price prediction under both normal and market-panic conditions. To capture the impact of these variables, we integrate technical variables with TC and NC and evaluate the prediction accuracy across different model types. We use Bloomberg Twitter count and news publication count variables in North American stock-price prediction and integrate them into MLP and LSTM neural networks to evaluate their impact during the market pandemic. The results showcase improved prediction accuracy, promising significant benefits for traders and investors. This strategic integration reflects a nuanced understanding of the market sentiment derived from public opinion on platforms like Twitter.

Computer-Assisted Analysis of Qualitative News Dissemination

This study delves into the potential of utilizing deep learning (DL) techniques to analyze qualitative news dissemination for trading purposes. DL, renowned for its prowess in handling vast datasets and deciphering intricate patterns, holds promise in aiding investors seeking to enhance their trading strategies. Specifically, Long Short-Term Memory (LSTM) networks, known for their capacity to retain contextual information, are explored in this research. By employing DL models, we aim to forecast market sentiment based on news headlines, focusing on the Dow Jones industrial average from 2008 to 2020. Leveraging 25 daily news headlines, we extend our analysis to develop an algorithmic trading strategy. Through rigorous testing across two distinct cases over five-time steps, our study evaluates the effectiveness of DL-driven approaches in real-world trading scenarios. Furthermore, this research contributes to the growing body of literature on the intersection of deep learning and financial markets. By examining the application of DL in qualitative news analysis for trading purposes, we provide insights into the potential implications for investors and financial analysts. The findings offer valuable guidance for leveraging advanced computational techniques for decision-making in dynamic market environments. Additionally, this study underscores the importance of incorporating qualitative news data into trading strategies, highlighting the role of DL in extracting meaningful signals from unstructured textual information’s Overall, our findings shed light on the opportunities and challenges associated with harnessing DL for trading on news sentiment.

Enhancing Stock Market Forecasts with Double Deep Q-Network in Volatile Stock Market Environments

Stock market prediction is a subject of great interest within the finance industry and beyond. In this context, our research investigates the use of reinforcement learning through implementing the double deep Q-network (DDQN) alongside technical indicators and sentiment analysis, utilizing data from Yahoo Finance and StockTwits to forecast NVIDIA’s short-term stock movements over the dynamic and volatile period from 2 January 2020, to 21 September 2023. By incorporating financial data, the model’s effectiveness is assessed in three stages: initial reliance on closing prices, the introduction of technical indicators, and the integration of sentiment analysis. Early findings showed a dominant buy tendency (63.8%) in a basic model. Subsequent phases used technical indicators for balanced decisions and sentiment analysis to refine strategies and moderate rewards. Comparative analysis underscores a progressive increase in profitability, with average profits ranging from 57.41 to 119.98 with full data integration and greater outcome variability. These results reveal the significant impact of combining diverse data sources on the model’s predictive accuracy and profitability, suggesting that integrating sentiment analysis alongside traditional financial metrics can significantly enhance the sophistication and effectiveness of algorithmic trading strategies in fluctuating market environments.

Turbulence hierarchy in foreign exchange markets.

We present a multiscale stochastic analysis of foreign exchange rates using the H-theory formalism, which provides a hierarchical intermittency model for the information cascade in the currency market. We examine the distributions of returns and volatilities for the three most traded currency pairs: euro-U.S. dollar, U.S. dollar-Japanese yen, and British pound-U.S. dollar. We find that these markets have a hierarchy of timescales, with larger markets exhibiting more hierarchy levels. We provide a theoretical framework for understanding why the number of levels in the information cascade increases with market size, in analogy with similar behavior for the energy cascade in turbulence as a function of Reynolds number. We briefly argue that using turbulence-like models for financial markets can also provide valuable insights for developing efficient algorithmic trading strategies.

Algorithmic Trading Strategies: Leveraging Machine Learning Models for Enhanced Performance in the US Stock Market

Algorithmic Trading Strategies: Leveraging Machine Learning Models for Enhanced Performance in the US Stock Market

Defining and Controlling Information Leakage in US Equities Trading

We present a new framework for defining information leakage in the setting of US equities trading, and construct methods for deriving trading schedules that stay within specified information leakage bounds. Our approach treats the stock market as an interactive protocol performed in the presence of an adversary, and draws inspiration from the related disciplines of differential privacy as well as quantitative information flow. We apply a linear programming solver using examples from historical trade and quote (TAQ) data for US equities and describe how this framework can inform actual algorithmic trading strategies.

Algorithmic Trading and AI: A Review of Strategies and Market Impact

This review explores the dynamic intersection of algorithmic trading and artificial intelligence (AI) within financial markets. It delves into the evolution, strategies, and broader market impact of algorithmic trading fueled by AI technologies. Examining the symbiotic relationship between advanced algorithms and AI, the review navigates through the various strategies employed, shedding light on their implications for market efficiency, liquidity, and overall stability. From high-frequency trading to machine learning-driven predictive analytics, this review unveils the multifaceted landscape of algorithmic trading in the era of AI, presenting both opportunities and challenges for financial markets. The review begins by tracing the historical development of algorithmic trading, emphasizing the paradigm shift with the integration of AI. From traditional programmatic trading to the emergence of sophisticated algorithms driven by machine learning and deep learning, the evolution sets the stage for a comprehensive understanding of the subject. An in-depth analysis of diverse algorithmic trading strategies unfolds, covering areas such as trend following, statistical arbitrage, market making, and sentiment analysis. The incorporation of AI introduces adaptive learning capabilities, enabling algorithms to evolve and optimize strategies based on real-time market conditions. Exploring the impact of algorithmic trading on financial markets, the review examines how AI-driven strategies contribute to market efficiency, liquidity provision, and price discovery. It dissects the implications for traditional market structures, regulatory considerations, and the potential risks associated with algorithmic dominance. Acknowledging the transformative power of algorithmic trading with AI, the review critically assesses the challenges and ethical considerations. From algorithmic bias to systemic risks, the review delves into the darker corners of this technological advancement, prompting a reflection on the need for responsible and transparent practices. The review concludes by peering into the future trajectory of algorithmic trading fueled by AI. Anticipated innovations, regulatory responses, and the evolving landscape of financial markets are discussed, offering insights into the ongoing transformation and potential disruptions in the realm of algorithmic trading. In essence, this review provides a nuanced perspective on the intricate relationship between algorithmic trading and AI, offering a comprehensive understanding of their strategies and the transformative impact on financial markets.

Developing and testing a custom algorithmic trading strategy using exponential moving average, relative strength index, and sentiment analysis

<p>Stock trading is a popular and important profession that requires near-to-perfect data analytical skills, mathematical and statistical knowledge, and a broad understanding of buying and selling stocks. Often, due to the number of factors to consider and the intervention of human bias, traders and investors make wrong decisions that cost them millions of dollars. Therefore, automated algorithmic trading has gained traction in the marketplace due to its ability to process huge amounts of data, perform mathematical calculations and make quick and effective decisions. Most algorithmic trading strategies rely on a single technical indicator; however, it has been found that combining two or more indicators makes a trading strategy profitable. Therefore, this paper proposes a custom algorithmic trading strategy that combines important technical indicators such as the Exponential Moving Average and Relative Strength Index and utilizes sentiment analysis of financial news as well. This combination of technical indicators and sentiment analysis is not prevalent in existing research. The performance of the strategy was tested on fifteen stocks from different sectors of the US market using Python’s VectorBt library. The results showed that most of the stocks produced a higher win rate with the custom strategy as compared to other strategies, with the highest win rate of 88% for the S&P 500 index. To carry out sentiment analysis, a NLP model using BERT was developed which achieved an accuracy of 84%. Finally, to test the strategy on real-time data, paper trading was carried out on the Alpaca API and after six months the portfolio’s ROI is 6.26%.</p>

Optimization of Cryptocurrency Algorithmic Trading Strategies Using the Decomposition Approach

A cryptocurrency is a non-centralized form of money that facilitates financial transactions using cryptographic processes. It can be thought of as a virtual currency or a payment mechanism for sending and receiving money online. Cryptocurrencies have gained wide market acceptance and rapid development during the past few years. Due to the volatile nature of the crypto-market, cryptocurrency trading involves a high level of risk. In this paper, a new normalized decomposition-based, multi-objective particle swarm optimization (N-MOPSO/D) algorithm is presented for cryptocurrency algorithmic trading. The aim of this algorithm is to help traders find the best Litecoin trading strategies that improve their outcomes. The proposed algorithm is used to manage the trade-offs among three objectives: the return on investment, the Sortino ratio, and the number of trades. A hybrid weight assignment mechanism has also been proposed. It was compared against the trading rules with their standard parameters, MOPSO/D, using normalized weighted Tchebycheff scalarization, and MOEA/D. The proposed algorithm could outperform the counterpart algorithms for benchmark and real-world problems. Results showed that the proposed algorithm is very promising and stable under different market conditions. It could maintain the best returns and risk during both training and testing with a moderate number of trades.

EVALUATION OF SUPPORT VECTOR MACHINE BASED STOCK PRICE PREDICTION


 
 
 In recent years with the advent of computational power, Machine Learning has become a popular approach in financial forecasting, particularly for stock price analysis. In this paper, the authors develop a non-recurrent active trading algorithm based on stock price prediction, using Support Vector Machines on high frequency data, and compare its risk adjusted performance to the returns of a statistical portfolio predicted by the Capital Asset Pricing Model. The authors selected the three highest volume securities from a pool of 100 initially selected stock dataset to investigate the algorithmic trading strategy. The abnormal return estimates are significant and positive, and the systematic risk is lower than unity in all cases, suggesting lower risk compared to the market. Moreover, the estimated beta values for all stocks were close to zero, indicating a market independent process. The correlation analysis revealed weak correlations among the processes, supporting the potential for risk reduction and volatility mitigation through portfolio diversification. The authors tested an equally weighted portfolio of the selected three assets and demonstrated a remarkable return of 1348% during the evaluation period from July 1st, 2020, to January 1st, 2023. The results suggest that the weak form of market efficiency can be questioned, as the algorithmic trading strategy, employing a Support Vector Machine binary classification model, has consistently generated statistically significant and substantial abnormal returns using historical market data.
 
 

Graph embedded dynamic mode decomposition for stock price prediction

We present an algorithmic trading strategy based upon a graph version of the dynamic mode decomposition (DMD) model. Unlike the traditional DMD model which tries to characterize a stock’s dynamics based on all other stocks in a universe, the proposed model characterizes a stock’s dynamics based only on stocks that are deemed relevant to the stock in question. The relevance between each pair of stocks in a universe is represented as a directed graph and is updated dynamically. The incorporation of a graph model into DMD effects a model reduction that avoids overfitting of data and improves the quality of the trend predictions. We show that, in a practical setting, the precision and recall rate of the proposed model are significantly better than the traditional DMD and the benchmarks. The proposed model yields portfolios that have more stable returns in most of the universes we backtested.

Algorithmic trading using combinational rule vector and deep reinforcement learning

Algorithmic trading rules are widely used in financial markets as technical analysis tools for security trading. However, traditional trading rules are not sufficient to make a trading decision. In this paper, we propose a new algorithmic trading method called CR-DQN, which incorporates deep Q-learning with two popular trading rules: moving average (MA) and trading range break-out (TRB). The input of deep Q-learning is combinational rule vectors, whose component is a linear combination of 140 rules produced by MA and TRB with different parameters. Due to non-stationary characteristics, we devise a reward driven combination weight updating scheme to generate combinational rule vectors, which can capture intrinsic features of financial data. Since the sparse reward exists in CR-DQN, we design a piecewise reward function which shows great potential in the experiments. Taking combinational rule vectors as input, the LSTM based Deep Q-learning network is used to learn an optimal algorithmic trading strategy. We apply our model to both Chinese and non-Chinese stock markets, and CR-DQN exhibits the best performance on a variety of evaluation criteria compared to many other approaches, demonstrating the effectiveness of the proposed method.

Model-based reinforcement learning with non-Gaussian environment dynamics and its application to portfolio optimization.

The rapid development of quantitative portfolio optimization in financial engineering has produced promising results in AI-based algorithmic trading strategies. However, the complexity of financial markets poses challenges for comprehensive simulation due to various factors, such as abrupt transitions, unpredictable hidden causal factors, and heavy tail properties. This paper aims to address these challenges by employing heavy-tailed preserving normalizing flows to simulate the high-dimensional joint probability of the complex trading environment under a model-based reinforcement learning framework. Through experiments with various stocks from three financial markets (Dow, NASDAQ, and S&P), we demonstrate that Dow outperforms the other two based on multiple evaluation metrics in our testing system. Notably, our proposed method mitigates the impact of unpredictable financial market crises during the COVID-19 pandemic, resulting in a lower maximum drawdown. Additionally, we explore the explanation of our reinforcement learning algorithm, employing the pattern causality method to study interactive relationships among stocks, analyzing dynamics of training for loss functions to ensure convergence, visualizing high-dimensional state transition data with t-SNE to uncover effective patterns for portfolio optimization, and utilizing eigenvalue analysis to study convergence properties of the environment's model.

Financial Technical Indicator and Algorithmic Trading Strategy Based on Machine Learning and Alternative Data

The aim of this paper is to introduce a two-step trading algorithm, named TI-SiSS. In the first step, using some technical analysis indicators and the two NLP-based metrics (namely Sentiment and Popularity) provided by FinScience and based on relevant news spread on social media, we construct a new index, named Trend Indicator. We exploit two well-known supervised machine learning methods for the newly introduced index: Extreme Gradient Boosting and Light Gradient Boosting Machine. The Trend Indicator, computed for each stock in our dataset, is able to distinguish three trend directions (upward/neutral/downward). Combining the Trend Indicator with other technical analysis indexes, we determine automated rules for buy/sell signals. We test our procedure on a dataset composed of 527 stocks belonging to American and European markets adequately discussed in the news.