Week #1 #

Team Formation and Project Proposal #

Team Members #

Value proposition #

Problem: #

The primary problem addressed by our ATS (Automatic Trade System) project is unpredictability and complexity of modern trading exchanges and financial markets. Due to the gigantic amount of information and changes every second, it is difficult for a person to make a quick and effective sell or buy decisions.

From our point of view, a modern trader and investor faces the following challenges:

• Time-Consuming Processes: Manual trading requires constant monitoring of market conditions, which is time-intensive.
• Human Error: Emotional decision-making and fatigue can lead to mistakes and inconsistent trading results.
• Market Volatility: Rapid market changes make it difficult for human traders to react quickly and efficiently.
• Complexity in Strategy Execution: Implementing and managing multiple trading strategies manually is complex and cumbersome.
• Limited Accessibility: Many sophisticated trading strategies are not accessible to retail traders due to their complexity or the need for continuous oversight.

Solution Description: #

Our ATS project automates and speeds up the trading/investment process by integrating machine learning (ML) for decision-making, various trade algorithms, and an API-driven trade bot. Our project enables users to rigorously test their strategies and hypotheses in a sandbox environment before deploying them in the live market. Additionally, the system facilitates real-time trading and market monitoring. Key features include:

• ML-Based Decision Engine: Utilizes machine learning to analyze market data and select the most appropriate trading algorithm based on current conditions.
• Diverse Trade Algorithms: Implements multiple trading strategies to handle different market scenarios, such as trend following, mean reversion, and arbitrage.
• Automated Trade Execution: The trade bot executes trades via API, ensuring timely and precise actions without human intervention.
• Real-Time Data Analysis: Continuously processes and analyzes market data to adapt strategies dynamically.
• Research orientation: Our project aims to structure and summarize the most effective and relevant trading algorithms, providing a comprehensive analysis of their performance for all users. This initiative ensures transparency and facilitates informed decision-making based on empirical data.

Benefits to Users: #

Users of our project will experience numerous benefits:

• Increased Productivity: Automates repetitive tasks, allowing traders to focus on strategy and analysis rather than execution.
• Reduced Human Error: Eliminates emotional trading and fatigue-related mistakes, leading to more consistent results.
• Enhanced Efficiency: Executes trades instantly based on real-time data, capturing opportunities that might be missed manually.
• Accessibility: Makes sophisticated trading strategies accessible to retail traders, leveling the playing field.
• Cost and Time Savings: Reduces the need for continuous market monitoring and manual trading, saving time and potentially lowering costs associated with poor trade execution.

Our product offers a prime opportunity to enhance user’s financial capital while gaining insights into automated trading and the most effective practices in the field.

Differentiation: #

A standout feature of this project is its orientation towards an open-source business model. This approach allows individuals to access public developments and practices while also contributing their own innovations. As a result, the product remains in tune with market dynamics and helps raise awareness about automated trading systems. Users will benefit not only from publicly available earning strategies but also have the option to request personalized implementation and customization of their systems through our services. Additionally, we plan to offer an interactive online system that provides real-time analysis of various trading algorithms’ effectiveness. What sets our ATS project apart from competitors includes advanced ML integration, which dynamically selects the best trading algorithm for greater adaptability and intelligence; a comprehensive strategy suite that addresses various market conditions; real-time adaptability through continuous data analysis and strategy adjustment; and a user-centric design that ensures even non-expert users can leverage advanced trading technologies with ease.

User Impact: #

The user impact of our project extends beyond individual traders to encompass broader societal and industry implications. By adopting an open-source business model, we not only empower individual users to access and contribute to the development of automated trading systems but also foster a collaborative community that drives innovation and knowledge sharing within the industry. This collaborative ethos promotes transparency and accountability, ultimately enhancing trust in automated trading technologies and contributing to the overall integrity of financial markets.

Furthermore, our provision of personalized implementation and customization services enables users to tailor the software to their specific trading needs and preferences. This not only enhances user satisfaction and productivity but also fosters a culture of continuous improvement and adaptation within the industry.

Moreover, our interactive online system for real-time analysis of trading algorithms’ effectiveness serves as a valuable educational resource for both novice and experienced traders, empowering them with the knowledge and insights needed to make informed trading decisions. This democratization of access to analytical tools and expertise not only improves individual trading outcomes but also contributes to the overall efficiency and resilience of financial markets.

User Testimonials/Use Cases: #

1. Use Case: Novice Trader’s Learning Journey:

   • User Profile: A novice trader is eager to delve into automated trading but lacks an extensive experience.
   • Challenge: Limited knowledge and understanding of automated trading systems, seeking a platform to facilitate learning and participation.
   • Solution: Utilized the open-source aspect of the platform to access public developments and contribute ideas, accelerating learning and fostering a sense of community involvement. Leveraged personalized implementation services to tailor the software to individual trading style, resulting in enhanced satisfaction and performance. Utilized real-time analysis feature for informed decision-making.
   • Outcome: Experienced accelerated learning curve, increased confidence, and improved trading performance, becoming an active participant in the trading community.

2. Use Case: Experienced Trader’s Competitive Edge:

   • User Profile: Seasoned trader with years of experience seeking to optimize trading strategies and maintain competitiveness.
   • Challenge: Need for advanced tools and capabilities to refine strategies, adapt to market changes, and stay ahead of competitors.
   • Solution: Engaged with the open-source community to explore innovative developments and contribute expertise, driving continuous improvement and collaboration. Customized the platform to align with specific trading requirements, leveraging real-time analysis for informed decision-making and strategy optimization.
   • Outcome: Achieved enhanced trading performance, maintained competitive edge, and capitalized on market opportunities through agility and data-driven insights.

3. Use Case: Institutional Investor’s Efficiency Enhancement:

   • User Profile: Large institutional investor managing diverse portfolio seeking to streamline trading operations.
   • Challenge: Need for efficient and adaptable trading solutions to optimize portfolio performance and risk management.
   • Solution: Implemented the platform to leverage advanced ML integration for dynamic algorithm selection and comprehensive strategy suite for diversified trading approaches. Utilized real-time adaptability to remain agile and responsive to market fluctuations, ensuring optimized performance across asset classes.
   • Outcome: Realized significant cost savings, improved trading performance, and enhanced risk management capabilities, solidifying the platform’s value proposition in institutional trading environments.

Lean Questionnaire #

1. What problem or need does your software project address?

   Our software project addresses the need for accessible and collaborative automated trading solutions in the financial markets. By embracing an open-source business model, we aim to empower users to contribute to the development of the platform, fostering innovation and knowledge sharing within the trading community.

2. Who are your target users or customers?

   Our target users include individual traders, both novice and experienced, institutional investors, and trading professionals seeking to optimize their trading strategies and performance in the financial markets.

3. How will you validate and test your assumptions about the project?

   We will validate our assumptions through user feedback, market research, and iterative testing of new algorithms and ML tools. This will involve conducting user interviews, gathering feedback from early adopters, monitoring usage metrics, and analyzing market trends to ensure alignment with user needs and preferences.

4. What metrics will you use to measure the success of your project?

   Key metrics to measure the success of our project include community engagement metrics such as the number of active contributors, the frequency of contributions, and the growth of the open-source ecosystem around the platform. Additionally, we will track user satisfaction scores, trading performance, and adoption rates to gauge the platform’s impact on the financial markets.

5. How do you plan to iterate and pivot if necessary based on user feedback?

   We plan to iterate and pivot based on user feedback by actively soliciting input from the open-source community, prioritizing features and enhancements based on user needs and preferences. This collaborative approach will enable us to rapidly respond to changing market dynamics and continuously improve the platform to better serve our target users.

Leveraging AI, Open-Source, and Experts #

AI #

Our team plans to leverage AI technologies (ANN for time series anomalies detection) extensively throughout the development and implementation of our project. AI will play a crucial role in several aspects, including market analysis, algorithm selection, and trade execution. We will utilize advanced machine learning algorithms to analyze large volumes of market data, identify patterns, and predict market trends with greater accuracy. Additionally, AI will be employed to dynamically select the most effective trading algorithms based on current market conditions, ensuring optimal performance and adaptability. Furthermore, AI-powered trade execution systems will enable fast and precise transactions, minimizing latency and maximizing efficiency in the trading process. Overall, AI will be a cornerstone of our project, empowering us to deliver sophisticated and competitive automated trading solutions to our users.

Open-Source #

Our team recognizes the immense value of the open-source community and plans to leverage it fully for the development and success of our project. By adopting an open-source approach, we aim to foster collaboration, innovation, and transparency within the trading community. We will make our project accessible to developers and enthusiasts worldwide, inviting them to contribute code, ideas, and insights to improve the platform continuously. This collaborative model will enable us to harness the collective expertise and creativity of the community, accelerating the development cycle and ensuring the platform remains cutting-edge and relevant. Furthermore, by embracing open-source principles, we demonstrate our commitment to inclusivity, accessibility, and democratization of automated trading technologies, ultimately benefiting users and the industry as a whole.

Experts #

Our team recognizes the importance of domain expertise and plans to leverage the insights and guidance of industry experts throughout the development and deployment of our project. We will utilize expert knowledge by extensively researching scientific articles, blogs, and YouTube content. This approach allows us to tap into a wealth of information and insights from experienced professionals in the field of automated trading systems. By carefully studying and synthesizing this diverse range of resources, we aim to gain a deep understanding of industry best practices, emerging trends, and potential pitfalls. This knowledge will inform our decision-making processes and ensure that our platform is built upon a solid foundation of expertise and understanding.

Defining the Vision for Your Project #

Overview #

Our project, an Automated Trade System (ATS), addresses the pressing challenges faced by traders and investors in navigating the complexities of modern financial markets. The primary problem lies in the unpredictability and rapid changes within these markets, making it challenging for individuals to make quick and effective trading decisions. Our solution leverages advanced technologies such as machine learning (ML) and an open-source framework to automate and streamline the trading process, providing users with a comprehensive platform to develop, test, and execute trading strategies with efficiency and precision.

Key features include an ML-based decision engine for dynamic algorithm selection, diverse trade algorithms to handle various market scenarios, and automated trade execution through API-driven bots. Users benefit from increased productivity, reduced human error, enhanced efficiency, and improved accessibility to sophisticated trading strategies. Moreover, our open-source approach fosters collaboration, innovation, and transparency within the trading community, empowering users to contribute to the platform’s development and access cutting-edge technologies.

Overall, our project aims to revolutionize the way traders and investors interact with financial markets, offering a solution that not only addresses their immediate needs but also promotes continuous learning, adaptation, and growth within the industry.

Schematic Drawings #

Tech Stack #

In out project we are planning to use the following tech stack:

1. Programming languages: Python and JavaScript
2. Libraries and frameworks: pandas, numpy, pytorch, tensorflow, scikit-learn, Vue.js …
3. API: Binance Rest API or Tinkoff API
4. Git CI|CD
5. Databases: MongoDB, Postgres

Note: we haven’t finalized the stack yet and it may change in the future.