Realistic Algorithmic Trading Review Using Python

Abstract

This project presents a comprehensive and realistic simulation framework for algorithmic trading using the Python programming language. A central component of the work is an author-wise review of 50 academic and industry research contributions related to trading strategies, models, and performance evaluation. By utilizing the Faker library for synthetic author and title generation, and pandas for structured data manipulation, the system creates randomized yet credible ETF-based trade records. These synthetic logs include details such as buy/sell dates, quantities, trade outcomes (profit/loss/open), and performance percentages, effectively mimicking real-world financial activity. In parallel, the literature survey compiles diverse insights from existing research, identifying recurring challenges such as model overfitting, unrealistic backtesting assumptions, poor generalization in volatile market conditions, and a lack of interpretability in black-box models. Each issue is paired with its respective proposed solution, creating a structured and comparative view of the domain’s evolution. To enhance accessibility and visualization, all data outputs are displayed within a user-friendly web-based dashboard developed using Streamlit. This includes a dynamically generated author-wise review table, an Excel export option, and a pie chart summarizing the distribution of trading results. By merging practical simulation with a literature-backed evaluation, the project aims to provide a holistic, research-driven foundation for understanding, analyzing, and prototyping algorithmic trading systems.

Introduction

The project focuses on algorithmic trading, which uses computer algorithms to execute financial trades based on predefined rules to reduce human bias and increase efficiency. Recent advances incorporate AI, machine learning, and reinforcement learning to analyze vast data and enable rapid decision-making, making algorithmic trading accessible beyond large institutions.

The project develops a Python-based simulation framework generating synthetic trade data (50 records) linked with hypothetical academic papers, mimicking real-world research on trading strategies. It integrates data manipulation (pandas), visualization (matplotlib), and a Streamlit web interface to present interactive trade reviews.

A comprehensive literature review categorizes 50 research works highlighting common challenges such as overfitting, market volatility, lack of real-world validation, model complexity, hybrid strategies, sentiment data use, risk management, and evaluation transparency. The simulation maps each synthetic trade to these research challenges and solutions, fostering deeper understanding.

Future enhancements include integrating real-time market data, advanced machine learning models for strategy optimization, robust backtesting and risk analysis, portfolio management, cloud deployment, live trading API integration, and expanding the literature review database.

Conclusion

Algorithmic trading, often referred to as algo trading or automated trading, involves the execution of financial transactions using computer algorithms that follow pre-programmed instructions. These instructions are typically based on a combination of time, price, volume, and technical indicators. The core idea behind algorithmic trading is to minimize human intervention, reduce emotional bias, and increase the efficiency and speed of order execution

References

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Copyright

Copyright © 2025 Ishan Kumar Rai. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.