We will use Wilder’s Smoothing for most of our following indicators, and below is the function that can be generally used to obtain this Smoothing. The next evaluation includes metrics such as precision, recall, and F1-Score, which are used to provide a more comprehensive picture of the model’s performance. The results of this evaluation are shown in Table 8, which reflects how well each model predicted the data based on these metrics. We see that 9 out of 10 stocks gave a positive and decent returns over the 7 trading day period. Thus, below is the table with the probability of prediction, the actual movement after 7 days and the percentage change that took place in next 7 days.
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Therefore, there is an urgent need for an approach that can integrate multiple sources of information and better handle market complexity 8. Machine learning technology has the advantage of analyzing complex and non-linear patterns and the ability to process large amounts of data 9. Therefore, this technology can be an effective solution to improve accuracy and efficiency in predicting stock prices 10. The findings of this study have a few important implications for theory and practice in stock price prediction 29. First, the developed prediction model shows that combining technical analysis techniques with machine learning methods can produce higher prediction accuracy compared to traditional approaches 30-32. This emphasizes the need for the integration of advanced methods in stock market analysis, paving the way for more complex and adaptive models in dealing with rapidly changing market dynamics 33.
An attention mechanism and residual network based knowledge graph-enhanced recommender system
MAPE is usually used to measure prediction error in percentage, but in this case, we need to handle zero values to calculate it. Thus, we would have purchased the stock at $306.76 and sold it at $333.21 thus making 8.62% profit in 7 days. Random Forest is a commonly used Machine Learning model for Regression and Classification problems. However, given the complexity of the model, it is important to carefully understand the parameters that go into the model to prevent in-sample overfitting or underfitting, a standard bias-variance tradeoff. The clustering resulted in, to a large extent, an industry wide classification of stocks which is in line with our initial thought. Hasan Hasibul is a Principal Architect at Octus leading the DevOps team, with nearly 12 years of experience in building scalable, complex architectures while following software development best practices.
Hybrid Quantum Fuzzy Neural Network Approach- Based SNS Sentimental Analysis for Stock Market Prediction
Supervised learning is a form of machine learning that involves training a model with labeled data, meaning each input has a corresponding output or target. This enables the model to map inputs to outputs and make predictions on new or unseen data. It can be used in technical analysis for various tasks, such as classification and regression. Classification is assigning a label or category to an input, such as whether a price trend is bullish or bearish.
The research has successfully developed and tested a stock price prediction model that uses a combination of technical analysis techniques and machine learning methods. The results show that the proposed model is more accurate than traditional methods, especially in the face of high market volatility. The findings emphasize that the integration between technical data and machine learning algorithms not only improves prediction accuracy, but also provides a deeper understanding of stock market dynamics.
This challenge is particularly acute in credit markets, where the complexity of information and the need for quick, accurate insights directly impacts investment outcomes. Financial institutions need a solution that can not only aggregate and process large volumes of data but also deliver actionable intelligence in a conversational, user-friendly format. Mathematics is the foundation of artificial intelligence and machine learning, providing the tools to create and train these systems. To learn, make predictions, and solve problems, AI relies on math to break down complex data, analyze patterns, and measure probabilities.
Key areas of mathematics utilized in AI and machine learning
- The next evaluation includes metrics such as precision, recall, and F1-Score, which are used to provide a more comprehensive picture of the model’s performance.
- In the following sections, we dive into crucial details within key components in our solution.
- Before moving on to the next indicator, I would like to mention another type of smoothening or moving average that is commonly used with other indicators.
- Math is the language used to develop and program these systems, making it essential for anyone wanting to work in these fast-growing fields.
- Regressor is when we want prediction in a non-linear regression form whereas Classifier is used as a non-linear logistic model.
- This wide range suggests that the dataset covers stocks with varying levels of market activity and volatility, supporting a robust analysis of different market conditions.
- We will use Classifier as we want to assess the probability of an up move (probability of 1) for every stock.
He has 12 years of sales experience and 10 years of experience in cloud services, IT infrastructure, and SaaS. Tim is dedicated to helping customers develop and implement digital innovation strategies. His focus areas include business transformation, financial and operational optimization, and security. He leads teams that build, maintain and support Octus’s customer-facing GenAI applications, including CreditAI, our flagship AI offering.
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Machines can do this almost instantaneously by following structured algorithms that process information and improve accuracy over time. MACD uses two exponentially moving averages and creates a trend analysis based on their convergence or divergence. Although most commonly used MACD slow and fast signals are based on 26 days and 12 days respectively, I have used 15 days and 5 days to be consistent with other indicators.
- A buy signal occurs when a short-term moving average crosses above a long-term moving average, and a sell signal occurs when it crosses below.
- The closing price plot shows the trend of the stock price over the entire period covered by the dataset.
- He leads teams that build, maintain and support Octus’s customer-facing GenAI applications, including CreditAI, our flagship AI offering.
- Hence, when predicting for a particular company, we will use the model in the corresponding cluster’s pickle file and make our prediction.
- Before implementing machine learning for trading, it’s crucial to understand that it requires a systematic approach, combining both technical expertise and market knowledge.
- Such a chronological division is imperative for time series data to avoid data leakage and to replicate a real-world scenario where future data is unavailable during the training phase of the model.
- In addition, data from that year is generally complete and includes various trends and relevant economic events, thus providing a more representative picture of stock market dynamics in regular situations.
The following screenshot showcases how we use custom Datadog dashboards to provide a live view of the document ingestion pipeline. This visualization offers both a high-level overview and detailed insights into the ingestion process, helping us understand the volume, format, and status of the documents processed. The bottom half of the dashboard presents a time-series view of document processing volumes. The timeline tracks fluctuations in processing rates, identifies peak activity periods, and provides actionable insights to optimize throughput. This detailed monitoring system enables us to maintain efficiency, minimize failures, and provide scalability. Average Directional Index was developed by Wilder to assess the strength of a trend in stock prices.
Prior to Octus, Kishore has 15+ years of experience in engineering leadership roles across large corporations, startups, research labs, and academia. Rohan Acharya is an AI Engineer at Octus, specializing in building and optimizing AI-driven solutions at scale. With expertise in GenAI and NLP, he focuses on designing and deploying intelligent systems that enhance automation and decision-making. His work involves developing robust AI architectures and advancing Octus’s AI initiatives, including the evolution of CreditAI. We made this choice because semantic chunking offered the best balance between implementation simplicity and retrieval performance.
Materials and Methods
Table 5 shows that the moving averages (MA_20 and MA_50) start as for the first 20 and 50 days respectively, as they require that many previous data points to calculate. Where traders once relied on gut instinct and manual chart analysis, sophisticated algorithms now process vast amounts of data in milliseconds. This transformation hasn’t just changed how we trade futures – it’s revolutionized what’s possible in the markets. This guide explores essential machine learning for trading concepts transforming the futures market. This allows machines to recognize patterns, perform complex calculations, and make real-time performance adjustments, enabling these systems to experience enormous growth and improvement. Integrating these models can provide more effective tools for investors and analysts to make timelier and data-driven investment decisions.
This issue could potentially be addressed by modifying the model parameters or employing an alternative optimization technique. In order to find the stocks with highest probability of up move, we sort the prediction column in a descending order and pick and top 10 stocks. In order to trade using this model, we would obtain a probability of an upward movement for each stock.
Regressor is when we want prediction in a non-linear regression form whereas Classifier is used as a non-linear logistic model. We will use Classifier as we want to assess the probability of an up move (probability of 1) for every stock. While scipy offers a TrainTestSplit function, we will not use that here since our data is a time series data and we want to split the Train-Test as a timeline rather than randomly selecting observations as train or test. We first convert our index into a date time index and split the data to before and after 31st December 2018.
The RMSE and MAE provide a sense of the average error magnitude, with lower values indicating better model performance. The next visualization displays the seasonal decomposition of the time series data (Figure 7), which divides the data into the main components of trend, seasonality, and residual (random variation). The trend component reflects long-term price movements, while the seasonal component shows patterns that repeat at certain intervals. The remaining fluctuations are shown as the residual component, which reflects random variation. This analysis helps identify the presence of seasonal patterns or cycles in stock price movements.
Enhancing stock return prediction in the Chinese market: A GAN-based approach
Technical analysis is the study of historical price and volume data to forecast future price movements. Unlike fundamental analysis, which evaluates a company’s financial health and future prospects, technical analysis relies exclusively on chart patterns and technical indicators. Traders use this information to decide when to buy or sell stocks, commodities, or other financial instruments.
Descriptive statistics, including mean, median, and standard machine learning technical analysis deviation of the dataset, are summarized in Table 6. The diagnostics plot (refer to Figure 11) is utilized to evaluate the model’s fit by examining the residuals for normality, autocorrelation, and heteroscedasticity. A convergence warning was issued during the optimization process, indicating that the model might not have achieved the optimal solution.
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