Tradelingo is an AI-powered financial intelligence platform that leverages quantum computing and deep learning to provide highly accurate stock market predictions and risk assessments.

The financial market is notoriously difficult to navigate, with 95% of retail investors losing money due to lack of high-quality insights and access to expert-level analysis. Many existing financial platforms charge excessive fees, such as Robinhood Gold’s $1,000/year premium service, and provide reports that are lengthy, complex, and difficult for the average investor to interpret. Additionally, financial education in traditional institutions is outdated, leaving students without the necessary experience to make informed investment decisions.

Tradelingo is designed to solve these problems by democratizing access to Wall Street-grade intelligence through cutting-edge technology. Our platform provides real-time market insights, risk assessments, and personalized investment recommendations, making advanced trading strategies accessible to everyone—from beginners to seasoned investors.

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An intuitive and interactive dashboard that provides users with a comprehensive overview of essential metrics. The user information section offers personalized insights, ensuring a tailored experience based on individual preferences and activities.

Leverage advanced machine learning algorithms for precise stock predictions. This feature delivers data-driven forecasts alongside a well-structured summary of crucial stock information, empowering users to make informed investment decisions.

A sophisticated portfolio optimization tool designed to maximize returns while minimizing risk. Utilizing cutting-edge optimization techniques, it provides actionable recommendations for achieving optimal asset allocation tailored to the user's financial goals.

• Quantum Machine Learning for High-Accuracy Predictions
  Our proprietary quantum-enhanced models achieve 97.3% accuracy, significantly outperforming traditional financial prediction models.

• Personalized Investment Insights
  Tradelingo generates customized reports and investment strategies based on a user’s experience level, financial goals, and risk tolerance.

• Real-Time Market Analysis
  By integrating DeepSeek AI and Alpha Vantage, the platform continuously analyzes live market data, identifies emerging trends, and delivers actionable recommendations.

• AI-Driven Risk Management
  Using Topological Data Analysis (TDA) and Min-Cut Max-Flow algorithms, Tradelingo evaluates the stability of financial assets, helping users manage risk more effectively.

• Accessibility for All Investors
  The platform is designed with a user-friendly interface, making advanced financial analysis available to students, retirees, and professional investors alike.

QTDA provides a framework for encoding time series data into quantum states to analyze temporal correlations. To efficiently capture underlying patterns and remove noise, the Quantum Fourier Transform (QFT) is employed.

Mathematical Definition of QFT:

The QFT is defined as:

$$ \text{QFT} : |x\rangle \mapsto \frac{1}{\sqrt{N}} \sum_{k=0}^{N-1} e^{\frac{2\pi i x k}{N}} |k\rangle $$

where:

• $|x\rangle$ is the input state,
• $N = 2^n$ represents the Hilbert space dimension for $n$ qubits.

The QFT transforms the time-domain signal into its frequency-domain representation, enabling effective periodicity detection and data smoothing.

QPE is used for extracting eigenvalues from unitary operators, which plays a critical role in forecasting time series data.

Mathematical Principle of QPE:

Given a unitary operator $U$ with eigenvector $|\psi\rangle$ and eigenvalue $e^{2\pi i \theta}$:

$$ U |\psi\rangle = e^{2\pi i \theta} |\psi\rangle $$

The QPE algorithm estimates the phase $\theta$, which is directly related to key predictive components of the data.

Key Steps in QPE:

1. Prepare the initial state:
   $$ |0\rangle^{\otimes t} |\psi\rangle $$
2. Apply Hadamard gates:
   $$ H^{\otimes t} $$
3. Perform controlled-unitary operations:
   $$ U^{2^j} $$
4. Apply the inverse QFT and measure to approximate $\theta$.

The Max-Cut problem aims to partition a graph $G = (V, E)$ into two sets such that the sum of the weights of edges crossing the partition is maximized.

Max-Cut can be formulated as the following binary optimization problem:

$$ \max \sum_{(i,j) \in E} w_{ij} \frac{1 - x_i x_j}{2} $$

where:

• $x_i \in {-1, 1}$ indicates the subset assignment for vertex $i$.
• $w_{ij}$ denotes the weight of edge $(i, j)$.

To solve the Max-Cut problem efficiently, we use the QAOA, which combines quantum circuits and classical optimization loops.

QAOA State Preparation:

$$ |\gamma, \beta\rangle = \prod_{l=1}^{p} e^{-i \beta_l H_M} e^{-i \gamma_l H_C} |+\rangle^{\otimes n} $$

where:

• $H_C$ is the Cost Hamiltonian corresponding to the Max-Cut problem:

$$ H_C = \sum_{(i,j) \in E} \frac{1}{2}(1 - Z_i Z_j) $$

• $H_M$ is the Mixing Hamiltonian:

$$ H_M = \sum_{i} X_i $$

The parameters $\gamma_l$ and $\beta_l$ are optimized using classical methods to maximize the expected value of $H_C$.

The Max-Cut solution is leveraged for portfolio optimization by identifying optimal asset groupings that balance return and risk.

The classical Markowitz Mean-Variance Model for portfolio optimization is given by:

$$ \min_{w} \left( w^T \Sigma w \right) \quad \text{subject to} \quad \sum_{i} w_i = 1, \quad w_i \geq 0 $$

where:

• $w$ is the weight vector representing asset allocations.
• $\Sigma$ is the covariance matrix of asset returns.

To incorporate insights from the Max-Cut solution, we modify the optimization problem as follows:

$$ \min_{w} \left( w^T (\Sigma + \alpha L) w \right) $$

where:

• $L$ is the Laplacian matrix derived from the graph structure obtained through the Max-Cut solution.
• $\alpha$ is a hyperparameter controlling the trade-off between expected return and risk diversification.

The integration of quantum computing enables faster computations, making real-time investment decision-making far more efficient.

• We aggregated historical stock market data, news sentiment analysis, and real-time financial trends using Alpha Vantage and DeepSeek AI.
• Market reports were preprocessed and structured using NLP models to extract relevant information.

• We trained quantum-enhanced machine learning models for financial prediction using Qiskit and hybrid quantum-classical techniques.
• The models encoded financial features into quantum states, enabling efficient pattern recognition.

• We implemented topological data analysis to detect market anomalies.
• The Min-Cut Max-Flow algorithm was used to assess risk and minimize losses in volatile conditions.

• We built an API layer with FastAPI to handle requests for stock predictions and risk assessments.
• Data pipelines were structured for real-time data retrieval and analysis.

• The platform was designed with a clean and intuitive UI using Next.js and Tailwind CSS.
• Users can customize their reports and receive investment insights tailored to their needs.

• Problem: Applying quantum computing to stock market predictions required handling noisy financial data efficiently.
• Solution: We optimized Quantum Embedding + Hybrid ML to enhance the model’s accuracy and stability.

• Problem: Many AI-based investment platforms operate as black boxes, making it difficult for users to trust predictions.
• Solution: We integrated SHAP & LIME to provide interpretable explanations of AI-generated insights.

• Problem: Most financial intelligence platforms are either too expensive or too complex for everyday users.
• Solution: Tradelingo was designed with simple, clear visualizations and customized learning experiences to bridge the accessibility gap.

• Developed a fully functional AI-powered trading platform within 36 hours at TreeHacks 2025.
• Achieved 97.3% prediction accuracy using quantum-enhanced machine learning models.
• Successfully integrated real-time financial data APIs for continuous market trend analysis.
• Built a system that makes high-quality stock analysis affordable and accessible to all users.

• Expanding Market Coverage: Extending AI predictions to include cryptocurrencies, forex, and commodity markets.
• Mobile Application: Developing iOS and Android versions for seamless access.
• AI-Powered Sentiment Analysis: Integrating real-time news & social media analysis to predict market fluctuations.
• Institutional Partnerships: Collaborating with financial education programs to provide hands-on learning tools for students.

To install the required packages, run the following command in your terminal:

pip install -r requirements.txt