💹 Trade Whisper — Trend Detection in Financial Markets

Trade Whisper is a compact, logic-driven system for analyzing historical stock data and detecting early signs of bullish or bearish trends. Designed in Jupyter Notebook, it offers a practical foundation for real-world ML trading signal systems and financial forecasting.

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📌 Features

• 📈 Trend Identification: Detects Uptrend, Downtrend, and Sideways movement in stock price data
• 🧠 ML-Ready Labels: Auto-generates labels for training ML models on trend forecasting
• 📊 Visual Insights: Plots trend signals using matplotlib for intuitive analysis
• ⚙️ Extendable: Easily plug into ML workflows for regression, classification, or LSTM modeling

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🧠 How It Works

• Imports price data (CSV)
• Calculates rolling metrics like Moving Averages
• Defines trend direction based on slope & price logic
• Classifies each window as a trend label
• Visualizes results with annotations

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🛠 Tech Stack

• Python 3
• Pandas, NumPy
• Matplotlib
• Scikit-learn (for future enhancement)
• Jupyter Notebook

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💡 Potential Applications

• Building datasets for ML-based stock prediction
• Backtesting trend-detection rules
• Freelance fintech projects
• Educational demos on time series analysis

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🚀 Getting Started

1. Clone the repo
2. Run the notebook: TradeWhisper.ipynb
3. Upload any stock price CSV to start analyzing

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📸 Example Output

🟢 Uptrend detected from 20th July to 30th July
🔴 Downtrend spotted from 3rd August to 9th August

Visual chart is generated with marked trend phases.

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🤝 Ideal For

• Freelancers seeking finance + ML projects
• Recruiters assessing practical ML capabilities
• Clients exploring trend-based trading ideas

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