Algorithmic trading, or algo-trading, is a revolutionary approach to trading in the financial industry. It uses advanced math and computer algorithms to help traders make quick and precise decisions. Algo-trading relies on powerful technology to analyze huge amounts of data, historical price movements, and market trends. By detecting patterns and trading opportunities, these algorithms can automatically execute trades, removing the need for human decision-making.In simple terms, algo-trading is like having a smart assistant for traders. It uses advanced computer programs to quickly analyze market data, spot trends, and execute trades automatically.
Angle One API is a crucial tool in this automated trading world. It provides an easy-to-use interface and strong features for developers to create and implement their trading strategies. With the help of technologies like Machine Learning, Deep Learning, AI, OpenCV, and platforms like AWS (Amazon Web Services), algo-trading using Angle One API becomes efficient and sophisticated, making trading faster and more precise.
Angle One API acts as the gateway to this high-tech trading world, providing developers with user-friendly tools to create their trading strategies effortlessly. By harnessing the power of Machine Learning, Deep Learning, AI, and platforms like AWS, algo-trading with Angle One API transforms the way traders operate, ensuring they stay ahead in the fast-paced digital finance landscape. It’s all about making trading faster, smarter, and more efficient.
Contents
Certificate
List of Abbreviations
List of Figures
1 Introduction
1.1 Overview
1.2 Motivation
1.3 ProblemDefinitionandObjectives
2 Literature Survey
2.1 Literature Review
3 Software Requirement Specification
3.1 AssumptionsandDependencies
3.1.1 Assumptions
3.1.2 Dependencies
3.2 FunctionalRequirements
3.2.1 System Feature 1: Capturing and storing data
3.2.2 System Feature 2: Historical Data
3.2.3 System Feature 3: Object Detection
3.3 SystemRequirements
3.3.1 HardwareRequirements
3.3.2 Software Requirements
3.4 AnalysisModels:CRISPDMModel
4 System Design
4.1 SystemArchitecture
4.2 Specification of input/output
4.3 Dataflowdiagram
4.4 Class Diagram
4.5 Activity Diagram
4.6 Use case diagram
4.7 AlgorithmSpecification
4.7.1 5EMAStrategy
4.7.2 MeanReversionAlgorithm:
4.7.3 SigmoidActivationFunction:
4.7.4 Cross-Entropy loss
4.7.5 Artificial intelligence (AI) algorithms
5 Project Plan
5.1 Project Task Set
5.2 Risk Management
5.2.1 Risk Identification
5.2.2 Risk Analysis
5.2.3 Risks
5.3 ProjectSchedule
5.4 Team Organization
5.4.1 Team Structure
6 CONCLUSIONS
6.1 Conclusions
REFERENCES
ACKNOWLEDGEMENT
It has really been an exciting and a prolonged experience to work on this pro ject. We are lucky to get invaluable contributions from all the people around us. We express our deepest and sincere gratitude to our project guide Prof. P. P. Yadav for his thorough guidance, constant availability and motivation to work harder. This project would not have been completed without his valuable insights and suggestions.
We are very much thankful to Prof T. D. Khadtare, Head, Department of In-formation Technology, Dr. S. D. Markande, Principal and Prof. S. A. Kulkarni, Vice principal, Sinhgad Institute of Technology and Science, Narhe for their help, support and co-operation during this project work.
We would also like to thank the Sinhgad Technical Educational Society for providing access to the institutional facilities for our project work.
ABSTRACT
Algorithmic trading, or algo-trading, is a revolutionary approach to trading in the financial industry. It uses advanced math and computer algorithms to help traders make quick and precise decisions. Algo-trading relies on powerful tech-nology to analyze huge amounts of data, historical price movements, and market trends. By detecting patterns and trading opportunities, these algorithms can automatically execute trades, removing the need for human decision-making.In simple terms, algo-trading is like having a smart assistant for traders. It uses advanced computer programs to quickly analyze market data, spot trends, and execute trades automatically.
Angle One API is a crucial tool in this automated trading world. It provides an easy-to-use interface and strong features for developers to create and implement their trading strategies. With the help of technologies like Machine Learning, Deep Learning, AI, OpenCV, and platforms like AWS (Amazon Web Services), algo-trading using Angle One API becomes efficient and sophisticated, making trading faster and more precise.
Angle One API acts as the gateway to this high-tech trading world, providing developers with user-friendly tools to create their trading strategies effortlessly. By harnessing the power of Machine Learning, Deep Learning, AI, and plat-forms like AWS, algo-trading with Angle One API transforms the way traders operate, ensuring they stay ahead in the fast-paced digital finance landscape. It’s all about making trading faster, smarter, and more efficient.
Keywords: Machine Learning, Deep Learning, AI, OpenCV, Algo Trading, Vir-tual Environment, CNN, AWS.
List of Abbreviations
API Application Programming Interface
CNN Convolution Neural Network
AI Artificial Intelligence
GTA V Grand Theft Auto 5
YOLO You Only Look Once
GAN Generative Adversarial Network
GPU Graphical Processing Unit
CUDA Compute Unified Device Architecture
ROI Region of Intrest
ML Machine Learning
NLP Natural Language Processing
List of Figures
3.1 CRISPDMModel
4.1 SystemArchitecture
4.2 DFDdiagram
4.3 Class Diagram .
4.4 Activity Diagram
4.5 UseCaseDiagram
4.6 Linear RegressionArchitecture
4.7 logistic regression
5.1 Activity Sheet
[The figure 3.1 is not included for copyright reasons.]
Chapter 1 Introduction
This chapter discusses the background, relevance and the motivation behind the project.
1.1 Overview
Algorithmic Trading Using Angle One Smart API: Transforming Financial Mar- ketsIn the realm of modern finance, algorithmic trading stands as a beacon of innovation, employing advanced mathematical algorithms and real-time data analysis to revolutionize trading. The integration of these strategies with the Angle One Smart API, a robust financial data and trading platform, has opened new horizons in the financial world.Algorithmic Trading Unveiled:Algorithmic trading is the epitome of efficiency in trading, harnessing the power of com-plex algorithms and historical data to execute trades with remarkable precision and speed. The Angle One Smart API: A Comprehensive Solution:Angle One Smart API emerges as a versatile platform, boasting an array of features includ-ing real-time market data, historical data accessibility, and advanced trading capabilities. Its user-friendly interface makes it the ideal playground for the implementation of algorithmic trading strategies. Diverse Algorithmic Trad-ing Strategies:This integration embarks on a journey through the intricacies of algorithmic trading, exploring strategies such as statistical arbitrage, trend fol-lowing, and machine learning models. These strategies, driven by real-time data provided by the API, aim to maximize profits and optimize trading decisions.
Backtesting and Validation:Understanding the significance of historical data analysis and backtesting, the Angle One Smart API offers a powerful toolset to ensure strategies are rigorously tested and validated
The Remarkable Impact: The integration of algorithmic trading with the An-gle One Smart API empowers traders and financial institutions alike. It equips them with data-driven decision-making capabilities, risk mitigation strategies, and the ability to seize lucrative market opportunities. This transformative union has reshaped the financial landscape, promising optimal financial out-comes in the ever-evolving world of finance.
1.2 Motivation
The motivation behind employing algorithmic trading with Angle One Smart API lies in its ability to harness cutting-edge technology for financial gain. By automating trading decisions using advanced algorithms and real-time market data, traders seek to capitalize on market inefficiencies, optimize strategies, and enhance profitability. The API’s comprehensive features, including real-time data access and powerful analytics tools, drive efficiency and enable precise decision-making. Moreover, the potential for rapid execution and scalability fuels the desire to utilize this technology, allowing traders to navigate volatile markets with agility and confidence, ultimately leading to improved trading outcomes and financial success.
1.3 Problem Definition and Objectives
The problem is that financial markets are complicated and always changing. It’s hard for regular trading methods to keep up with these changes. Traders want smart computer programs to help them trade automatically, make more money, and avoid losing too much. Creating a good trading system using Angle One Smart API is crucial. It helps traders seize opportunities as soon as they appear and manage their trades efficiently and safely.
The objectives are discussed below:
1. Automated Trading: Implement an automated trading system using An-gle One Smart API to execute trades swiftly and efficiently without manual intervention.
2. Real-time Market Analysis: Develop algorithms that continuously ana-lyze real-time market data, identifying trends, patterns, and potential trading opportunities.
3. Optimized Trading Strategies: Design and implement trading strate-gies tailored to various market conditions, considering factors such as technical indicators, historical data, and market news. Strive for strategies that offer consistent profits while managing risks effectively.
4. Risk Management: Implement robust risk management techniques, in-cluding stop-loss orders, position sizing, and portfolio diversification, to protect capital and minimize potential losses.
Chapter 2 Literature Survey
2.1 Literature Review
Literature Review: Algorithmic Trading Using Angle One Smart API
In the dynamic landscape of financial markets, Algorithmic Trading has be-come an indispensable tool, reshaping trading practices and investment strate-gies. This literature review explores the integration of Angle One Smart API, a powerful data and trading platform, within the realm of Algorithmic Trad-ing. Several key themes emerge from the existing literature, shedding light on the transformative potential and challenges associated with this integration. Real-Time Data Integration: One of the fundamental aspects explored in the literature is the significance of real-time data in Algorithmic Trading. Angle One Smart API provides a wealth of real-time market data, allowing traders to access live price feeds, order book data, and trade history. Studies emphasize the critical role of real-time data in enabling algorithms to respond swiftly to market fluctuations, identify arbitrage opportunities, and execute trades with precision.[1] Algorithmic Strategies and Optimization: The literature under-scores the diverse range of algorithmic strategies made possible by Angle One Smart API. From statistical arbitrage and trend following to machine learning-based models, researchers have delved into the optimization of these strategies using Angle One Smart API’s historical data. These studies highlight how ad-vanced algorithms, informed by historical market patterns and real-time data, can enhance trading outcomes and maximize profitability.[2] Risk Management and Compliance: Effective risk management and compliance with regulatory standards are paramount in Algorithmic Trading. Literature in this domain discusses the integration of risk management protocols within algorithms uti-lizing Angle One Smart API. This includes techniques such as position sizing, portfolio diversification, and adherence to legal and ethical trading practices. Researchers emphasize the importance of algorithms that not only optimize profits but also manage risks prudently.[3] Machine Learning and Artificial In-telligence: Machine learning and artificial intelligence (AI) have emerged as powerful tools in Algorithmic Trading. Studies explore the integration of ma-chine learning algorithms, leveraging Angle One Smart API’s data streams to create predictive models. These AI-driven algorithms learn from historical data, adapt to changing market conditions, and optimize trading decisions, paving the way for more sophisticated and adaptive trading strategies.[4] challenges and Future Directions: While the literature highlights the transformative potential of Algorithmic Trading Using Angle One Smart API, it also delves into chal-lenges. Issues such as data security, algorithmic biases, and market liquidity are discussed. Moreover, researchers point towards the need for continuous inno-vation, exploring novel algorithmic techniques and addressing ethical consider-ations in algorithmic trading practices.more sophisticated and adaptive trading strategies.[5] In summary, the literature review illustrates the multifaceted na-ture of Algorithmic Trading Using Angle One Smart API. From real-time data integration and algorithmic optimization to risk management and the appli-cation of advanced technologies, researchers have explored diverse dimensions of this integration. While challenges exist, the literature collectively under-scores the tremendous potential this fusion holds, shaping the future landscape of Algorithmic Trading in the global financial markets.[6] Mahinda The study by K. S. M. A.-G. M. A.-M. Ramzi Saifan published in Informatica provides valuable insights into the application of ensemble machine learning methods in algorithmic stock market trading. By leveraging the collective intelligence of multiple algorithms, these methods offer the potential to create sophisti-cated, adaptive, and robust trading strategies. The research underscores the importance of adopting advanced machine learning techniques in the financial sector, paving the way for innovative approaches to stock market trading and investment practices.[7]
Giuseppe Nuti’s article provides a comprehensive overview of algorithmic trading, shedding light on its strategies, impact on market efficiency, regula-tory challenges, and technological advancements. The article underscores the transformative influence of algorithms in the realm of finance, revolutionizing trading practices and shaping the future of financial markets. As algorithmic trading continues to evolve, it remains essential for market participants, reg-ulators, and researchers to stay abreast of these developments, ensuring the stability and integrity of global financial systems[8]
Chapter 3 Software Requirement Specification
In the last chapter, we discussed the previous work that was done regarding video classification, In this chapter we will be discussing the Software Require-ment Specification required for our model.
In the further sub-sections, we would be discussing the System Requirements required for the pro ject and the analysis model to be used for the same.
3.1 Assumptions and Dependencies
3.1.1 Assumptions
1. Virtual environments should be closely analogous to the real world and should represent it precisely otherwise any discrepancies will lead to erro-neous models and would ultimately be inefficient for use in the real world.
2. Fully autonomous cars have not been implemented completely in every situation possible thus partial automation for vehicles is being in develop-ment.
3. While training cars in a virtual environment relies on input from handheld devices such as keyboards, controllers which is challenging to map onto real cars in practice.
4. The machine used for the purpose has all the required specification.
3.1.2 Dependencies
1. Python 3.7.3
2. MatplotLib 3.0.0
3. Tensorflow 2.3.0
4. Keras 2.3.1
5. OpenCV 4.2.0
6. Natural Language Processing 1.0.2189.0
3.2 Functional Requirements
This section describes all the functionalities of the system. There are 5 func-tional requirements described below -
3.2.1 System Feature 1: Capturing and storing data
The environment frames as well as console inputs will be captured and stored for model training purposes.
3.2.2 System Feature 2: Historical Data
Fetching the data from Historical Data.
3.2.3 System Feature 3: Object Detection
Shares and Analysis of Shares (vehicles/ people) on the frame.
3.3 System Requirements
3.3.1 Hardware Requirements
1. Intel core i5 7300HQ or better.
2. RAM: Minimum 8GB, Recommended 16GB.
3. Storage: 150GB.
3.3.2 Software Requirements
1. Windows 10 OS (64 bit)
2. AWS
3. Linux Ser
3.4 Analysis Models: CRISP DM Model
1. Business Understanding: Understand the business problem you want to solve with algorithmic trading. Define your trading objectives, risk tol-erance, and performance metrics. Determine the market conditions and assets you want to trade using the Angle One Smart API.
2. Data Understanding: Gather data from Angle One Smart API, which pro-vides historical and real-time market data. Understand the data’s struc-ture, features, and quality. Explore the data to identify patterns, trends, and potential predictors for trading decisions.
3. Data Preparation: Cleanse and preprocess the data. Handle missing val-ues, outliers, and inconsistencies. Convert data into suitable formats for modeling. Create relevant features and indicators that can be used to make trading decisions. Split the data into training and testing sets for model validation.
4. Modeling: Choose appropriate algorithms for your trading strategy. Com-mon algorithms used in algorithmic trading include machine learning tech-niques like decision trees, random forests, support vector machines, and deep learning models. Train multiple models using the training data and tune hyperparameters for better performance. Experiment with different features and indicators to enhance the model’s predictive power
Illustrations are not included in the reading sample
Figure 3.1: CRISP DM Model
The Iterative Model allows the accessing earlier phases, in which the vari-ations made respectively. The final output of the proposed work renewed at the end of the Software Development Life Cycle (SDLC) process.The following Figure 3.1 shows the iterative model.
Chapter 4 System Design
This chapter will discuss the system design of the project. It would provide an insight into the system architecture and also the various underlying architec-tures that the model would make use of.
4.1 System Architecture
Illustrations are not included in the reading sample
Figure 4.1: System Architecture
Designing the system architecture for Algorithmic Trading Using Angle One Smart API involves structuring a robust and scalable framework that inte-grates data sources, algorithmic modules, execution systems, and risk manage-ment components. Here’s a conceptual overview of the system architecture for Algorithmic Trading Using Angle One Smart API: Fig.4.1.
4.2 Specification of input/output
Authentication: Typically, APIs require authentication credentials (API key, secret key, etc.) to access the trading services.
Market Data: Input parameters might include the symbol/ticker for the asset you want to trade, timeframes for historical data, and specific indicators you want to use for analysis.
Order Details: Parameters related to the type of order (market, limit, stop, etc.), order quantity, price, and any special instructions.
Risk Management: Some APIs allow you to set parameters related to risk management, such as stop-loss and take-profit levels.
Other Parameters: Depending on the service, there might be additional parameters related to trading strategies, backtesting, or simulation.
4.3 Data flow diagram
Illustrations are not included in the reading sample
Figure 4.2: DFD diagram
Creating a data flow diagram for algorithmic trading using the Angle One Smart API involves illustrating how data moves through the system. Here’s a simpli-fied representation of the data flow for an algorithmic trading application using the Angle One Smart API:
Algorithmic Trading using Angle One Smart API - Data Flow Diagram 1. Ex-ternal Data Sources: Market Data: Real-time and historical market data from various exchanges. User Input: Trading preferences, strategies, and instruc-tions provided by the user through the interface. 2. Input Processing: Data Parsing: Market data and user input are parsed and processed to extract rele-vant information. Strategy Implementation: Algorithmic trading strategies are applied to the parsed market data to generate trading signals. 3. Decision Mak-ing: Signal Generation: Based on the trading strategies and market analysis, trading signals (buy, sell, hold) are generated..
4.4 Class Diagram
Illustrations are not included in the reading sample
Figure 4.3: Class Diagram
In the class diagram provided for algorithmic trading using the Angle One Smart API, several key classes and their relationships are depicted. The Algorithmic- Trading class serves as the central component, responsible for interacting with the API. It encapsulates attributes such as APIKey and APISecret for authen-tication and includes methods like initializeAPI to set up the API credentials, executeOrder to execute trading orders, getAccountBalance to retrieve account balance, and getMarketData to fetch market data for a specific symbol.
4.5 Activity Diagram
Illustrations are not included in the reading sample
Figure 4.4: Activity Diagram
Figure 4.8 shows activity diagram of the project. Activity consists of loading the game, making sure neural network model is loaded and then internal working of model such as capturing gamescreen, predicting output and catching erroralong with logs management of car and exiting.
4.6 Use case diagram
Illustrations are not included in the reading sample
Figure 4.5: Use Case Diagram
the context of algorithmic trading using the Angle One Smart API, a use case diagram provides a high-level view of the system’s functionalities from an end-user perspective. Here’s a description of the use case diagram for algorithmic trading with the Angle One Smart API:
The Use Case Diagram for Algorithmic Trading using the Angle One Smart API showcases several primary actors interacting with the trading system. One of the key actors is the Trader, who initiates various actions within the system. The trader can perform essential tasks such as Login to access the trading plat-form, View Market Data to observe real-time and historical data for different assets, and Configure Trading Strategies to set up and customize algorithms based on specific market conditions.
The Trader can also Place Orders, including market, limit, and stop orders, leveraging the capabilities of the Angle One Smart API. Additionally, the Trader has the option to Monitor Portfolio, enabling them to keep track of their holdings, executed trades, and account balance in real-time.
4.7 Algorithm Specification
4.7.1 5 EMA Strategy
5EMA Algorithmic Trading Strategy Specification: 1. Strategy Overview: The 5EMA strategy is a trend-following strategy that utilizes two exponential mov-ing averages: a short-term EMA (5-period) and a long-term EMA (20-period). It generates buy signals when the short-term EMA crosses above the long-term EMA and sell signals when the short-term EMA crosses below the long-term EMA.
2. Algorithm Steps: Initialization:
Set the short-term EMA period to 5 and the long-termEMA period to 20. Fetch historical price data for the selected asset using the Angle One Smart API. Calculate the initial short-term and long-term EMAs.
Trading Logic:
Buy Signal:
Generate a buy signal when the short-term EMA crosses above the long-term EMA. Confirm the buy signal with additional criteria if desired (e.g., volume analysis). Place a market order to buy the asset using the Angle One Smart API.
Sell Signal:
Generate a sell signal when the short-term EMA crosses below the long-term EMA. Confirm the sell signal with additional criteria if desired (e.g., stop-loss or profit-taking thresholds). Place a market order to sell the asset using the Angle One Smart API.
2.1.1 Coordinates and positions of predicted bounding boxes which should con-tain objects,
2.1.2 A probability that each bounding box contains object,
2.1.3 Probabilities that each object inside its bounding box belongs to a specific class.
We will be using YOLOv3-tiny to detect on road objects and drive the car smoothly without collision.
Illustrations are not included in the reading sample
Figure 4.6: Linear RegressionArchitecture
2.7.2 Mean Reversion Algorithm:
Pairs Trading: This strategy involves trading two correlated assets simultane-ously. When the price spread between the assets deviates from its historical average, the algorithm generates trading signals to buy the undervalued as-set and sell the overvalued asset, expecting the spread to revert to its mean. Bollinger Bands: Bollinger Bands consist of a middle band being an N-period simple moving average, an upper band at K times an N-period standard devia-tion above the middle band, and a lower band at K times an N-period standard deviation below the middle band. Mean reversion traders use these bands to identify overbought or oversold conditions. 4.7.
Illustrations are not included in the reading sample
Figure 4.7: logistic regression
2.7.3 Sigmoid Activation Function:
Sigmoid is a linear activation function especially used in models where we have to predict probability as an output. Like probability, the output of sigmoid func-tion ranges from 0.00 to 1.00. Sigmoid Activation Function can be represented as:
2.7.4 Cross-Entropy loss
As our CNN model gives probability scores as output, we’ll use cross-entropy as our loss function. Cross-entropy loss increases as the predicted probability diverges from the actual label. In case of Multilabel classification we calculate loss for each label separately using formula:
Illustrations are not included in the reading sample
Where,
M - number of classes (dog, cat, fish).
log - the natural log.
y - binary indicator (0 or 1) if class label.
c - is the correct classification for observation o.
p - predicted probability observation o is of class c.
2.7.5 Artificial intelligence (AI) algorithms
Decision Tree One of the most common supervised learning algorithms, decision trees get their name because of their tree-like structure (even though the tree is inverted). The “roots” of the tree are the training datasets and they lead to specific nodes which denote a test attribute. Nodes often lead to other nodes, and a node that doesn’t lead onward is called a “leaf”.
Decision trees classify all the data into decision nodes. It uses a selection criteria called Attribute Selection Measures (ASM) which takes into account various measures (some examples would be entropy, gain ratio, information gain, etc). Using the root data and following the ASM, the decision tree can classify the data it is given by following the training data into sub-nodes until it reaches the conclusion.
Chapter 5 Project Plan
This chapter tells the basic plan that have been considered in our project. It tells about various factors of the project including the overview of risk manage-ment and the schedule of the same.
5.1 Pro ject Task Set
1. Requirement gathering: Finalizing all requirements, input output specifica-tions and a rough idea about methods, and analyzing feasibility of project.
2. Literature survey: Finding and studying pro ject related research papers.
3. Project Architecture: Designing suitable project architecture.
4. Pipeline Design: Designing pipeline for project from data gathering to cross validation of model.
5. Train and Test various EMA strategies: Training and testing of various models to determine the better performing model.
5.2 Risk Management
Project risk management is the process of identifying, analyzing and then re-sponding to any risk that arises over the life cycle of a project to help the project remain on track and meet its goal. Risk management isn’t reactive only; it should be part of the planning process to figure out risk that might happen in the pro ject and how to control that risk if it in fact occurs.
5.2.1 Risk Identification
Minimal risks are involved in the project model owing to the learning done and avoidance of the previous models.
5.2.2 Risk Analysis
Risk analysis is a crucial aspect of algorithmic trading using any API, including the Angle One Smart API. Here are key areas for risk analysis when imple-menting algo trading strategies:
1. Market Risk: Price Volatility: Assess the historical price volatility of the assets being traded. Higher volatility increases the risk of significant price fluc-tuations. Liquidity Risk: Evaluate the liquidity of the assets. Illiquid assets can lead to slippage, where trades are executed at unfavorable prices. Correlation Risk: Understand the correlation between assets in the portfolio. Diversifica-tion might not be effective if assets are highly correlated.
2. Operational Risk: Technology Failures: Analyze the robustness of the trad-ing system. Failures in hardware, software, or network connectivity can lead to missed trades or erroneous executions.
5.2.3 Risks
1. Computer Malfunction: Drivers (Compatibility) Issues, Framework Issues.
2. Computational Uptime: Training and Testing time of EMA.
3. Real world scenarios: Certain real-world scenarios might be difficult to sim-ulate (e.g. general traffic in India).
4. Low model accuracy: Using experimental EMA models may result increase in overall time consumption.
5.3 Pro ject Schedule
The time required for completion and the time coverage for various tasks are covered in Fig. 5.1.
Illustrations are not included in the reading sample
Figure 5.1: Activity Sheet
5.4 Team Organization
According to Savitribai Phule Pune University(SPPU) rules, the project team is supposed to be of 5 students from Final year of IT Engineering. The team was formed in June 2023, comprising of- Arnav Nisal, Sujit Surwase, Ritesh Ghatge, Mahesh Pathak, Kunal Barbhai.
5.4.1 Team Structure
Ms.Priti Yadav- Project Guide
All the members contributed equally in the project and all the segments re-ceived appropriate attention from all the members consisting aspects of pro ject designing,implementation and testing etc.
In this chapter, we looked at the project plan including Identification of risks. In the next chapter, we will conclude the report.
Chapter 6 CONCLUSIONS
In conclusion, algorithmic trading using the Angle One Smart API offers a powerful platform for developing sophisticated trading strategies, automating trades, and gaining a competitive edge in the financial markets. The API pro-vides access to real-time market data, order execution capabilities, and robust security features, making it a valuable tool for algorithmic traders and financial institutions.
6.1 Conclusions
By leveraging the Angle One Smart API, traders can implement a wide range of trading strategies, including trend following, mean reversion, arbitrage, and machine learning-based approaches. The API’s flexibility and reliability enable the creation of complex algorithms that analyze market data, generate trading signals, and execute orders with speed and precision.
Furthermore, the integration of the Angle One Smart API opens up op-portunities for continuous innovation and research in the field of algorithmic trading.
REFERENCES
[1] Ritesh Kumar Dubey “ Algorithmic Trading Efficiency and its Impact on financial market
[2] S. J. Brown, “S. J. Brown, ”The Efficient Market Hypothesis, the Finan-cial Analysts Journal, and the Professional Status of Investment Manage-ment,” Financial Analysts Journal, vol. 76, no. (2), pp. 5-14, 2020..
[3] .Alvaro Cartea “S. J. J. R. .Alvaro Cartea, ’’Algorithmic Trading, Stochas-tic Control, and Mutually Exciting Processes,” SIAM Review, vol. 60, no. (3), pp. 673-703, 2018.
[4] G. D. S. Ritika Chopra “G. D. S. Ritika Chopra, ’Application of Arti-ficial Intelligence in Stock Market Forecasting: A Critique, Review, and Research Agenda,’ Journal of Risk and Financial Management, vol. 14, no. (11), pp. 526-560, 2021.
[5] JAlexander Posth “ ’The Applicability of Self-Play Algorithms to Trading and Forecasting Financial Markets,’ Frontiers in Artifcial Intelligence, vol. 4, no. (1), pp. 1-6, 2021.
[6] Mahinda Mailagaha Kumbure “’Machine learning techniques and data for stock market forecasting: Aliterature review,’ Expert Systems With Appli-cations, vol. 197, no. (2), pp. 1-41, 2022.
[7] Cheng-Hsiung Hsieh, Dung-Ching Lin, Cheng-Jia Wang, Zong-Ting Chen and Jiun-Jian Liaw “Real-Time Car Detection and Driving Safety Alarm System With Google Tensorflow Object Detection API” In 2019 International Conference on Machine Learning and Cybernetics (ICMLC), Chaoyang University of Technology, Taichung, Taiwan, doi: 10.1109/ICMLC48188.2019.8949265.
[8] Giuseppe Nuti “”Algorithmic Trading,” Computer, vol. 44, no. (11), pp. 61-69, 2011.
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The table of contents provides a structured overview of the document, allowing readers to quickly navigate to specific sections such as the introduction, literature survey, software requirements specification, system design, project plan, conclusions, and references.
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The key themes include algorithmic trading, real-time data integration, algorithmic strategies and optimization, risk management, machine learning, and the application of the Angle One Smart API.
What is the significance of the "Acknowledgement" section?
The "Acknowledgement" section expresses gratitude to individuals and institutions that contributed to the project, including the project guide, department heads, and the Sinhgad Technical Educational Society.
What is the main focus of the "Abstract"?
The "Abstract" provides a concise overview of algorithmic trading and its benefits, highlighting the role of Angle One API in facilitating automated trading strategies using technologies like Machine Learning, Deep Learning, AI, and platforms like AWS.
What are the listed abbreviations and why are they included?
The list of abbreviations defines common terms used throughout the document, such as API, CNN, AI, GTA V, YOLO, GAN, GPU, CUDA, ROI, ML, and NLP, ensuring clarity and comprehension for readers.
What type of information is provided in the "List of Figures" section?
The "List of Figures" section enumerates the figures present in the document, such as CRISP DM Model, System Architecture, DFD diagram, Class Diagram, Activity Diagram, Use Case Diagram, Linear Regression Architecture, logistic regression, and Activity Sheet. (Note some figures are excluded for copyright reasons.)
What is covered in the "Introduction" chapter?
The "Introduction" chapter discusses the background, relevance, and motivation behind the project, providing an overview of algorithmic trading, the Angle One Smart API, and the objectives of integrating these technologies.
What does the "Literature Survey" chapter entail?
The "Literature Survey" chapter reviews existing literature on algorithmic trading, focusing on the integration of Angle One Smart API, real-time data integration, algorithmic strategies, risk management, machine learning, and future directions in the field.
What system requirements are listed in the "Software Requirement Specification" chapter?
The "Software Requirement Specification" chapter outlines the assumptions, dependencies, functional requirements, hardware requirements (e.g., Intel core i5, 8GB RAM), and software requirements (e.g., Windows 10 OS, AWS, Linux Server) necessary for the project.
What is the CRISP DM model described in the text?
The CRISP DM model is an analysis model used to describe the steps of the project. The steps include understanding the business, understanding the data, data preparation, and modelling.
What topics are discussed in the "System Design" chapter?
The "System Design" chapter discusses the system architecture, specification of input/output, data flow diagram (DFD), class diagram, activity diagram, use case diagram, and algorithm specifications for the project, providing a detailed overview of the system's design and functionality.
What are the algorithm specifications mentioned?
The algorithm specifications include the 5EMA strategy, mean reversion algorithm, sigmoid activation function, cross-entropy loss, and the use of artificial intelligence (AI) algorithms in the algorithmic trading system.
What is included in the "Project Plan" chapter?
The "Project Plan" chapter includes information about the project task set, risk management (including risk identification and analysis), the project schedule, and team organization.
What is discussed in the "Conclusions" chapter?
The "Conclusions" chapter summarizes the project's findings and highlights the potential benefits of algorithmic trading using the Angle One Smart API, emphasizing its ability to automate trades and gain a competitive advantage in financial markets.
What types of sources are listed in the "References" section?
The "References" section lists various research papers and articles related to algorithmic trading, financial markets, and machine learning, providing sources for the information presented in the document.
- Quote paper
- Arnav Nisal (Author), 2024, Algorithmic trading using Angle One Smart API, Munich, GRIN Verlag, https://www.grin.com/document/1463526
