📊 XGBoost Stock Trading Notebook Documentation

Welcome to the documentation for the Jupyter notebook xgboost.ipynb, which implements, visualizes, and compares both rule-based and machine learning (XGBoost) strategies for predicting next-day movement (up/down) of selected Indian stocks using technical indicators. This notebook is designed to help you explore the interplay between technical signals and modern ML models in stock trading.

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🗂️ Index

1. Overview
2. Data Collection & Initialization
3. Data Visualization
4. Technical Indicator Preprocessing
5. Feature Engineering
6. Model Training & Backtesting
   • ML (XGBoost) Approach
   • Rule-Based Strategy
7. Agreement Analysis
8. Evaluation Metrics
9. Trading Pipeline Flowchart
10. XGBoost Model API (Model Asset)
11. Best Practices & Limitations
12. References

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1. Overview

This notebook demonstrates a quantitative trading pipeline that answers the question:

Can simple technical signals be improved upon by machine learning for next-day price direction prediction?

It does this across three large Indian stocks:

• RELIANCE.NS (Reliance Industries)
• HDFCBANK.NS (HDFC Bank)
• INFY.NS (Infosys)

The notebook fetches historical prices, computes technical indicators, creates trading signals, and compares a rule-based trading approach with an XGBoost classifier, then assesses where they agree (consensus signals).

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2. Data Collection & Initialization

Key Steps:

• Imports: yfinance, pandas, numpy, matplotlib, seaborn, plotly, scikit-learn, ta (technical analysis), xgboost, and utility modules.
• Stock Selection:

  symbols = ["RELIANCE.NS", "HDFCBANK.NS", "INFY.NS"]

• Fetching Metadata:
  • Loops through each symbol, downloads metadata (name, market, sector), and prints it.
  • Uses yfinance Ticker.info.

Example Output:

Symbol: RELIANCE.NS
Name: RELIANCE INDUSTRIES LTD
Market: in_market
Sector: Energy
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3. Data Visualization

• Price & Volume Chart: For each symbol, fetches 5 years of historical OHLCV data and plots Close price and Volume on dual y-axes.
• Tabular Preview: Displays the first and last 5 rows for a 6-month window.

Visualization Example:

• The plot shows the evolution of price and volume, highlighting regime changes, trends, and volatility.

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4. Technical Indicator Preprocessing

For each stock, the notebook computes:

• RSI (14 days)
• MACD and MACD Signal
• SMA 20, SMA 50
• EMA 20
• Volatility: 10-day rolling std of Close
• Buy_Signal: (RSI < 35) and (SMA_20 > SMA_50)
• Target: Next-day close > today’s close (binary classification)

Returns a DataFrame with these features and signals.

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5. Feature Engineering

• Feature Set:
  • RSI, MACD, MACD_Signal, SMA_20, EMA_20, Volatility
• Target:
  • Next-day price movement (binary: 1 = up, 0 = down)
• Rule-based Logic:
  • Buy signal when RSI < 35 and SMA_20 > SMA_50

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6. Model Training & Backtesting

ML (XGBoost) Approach

• Train/Test Split:
  • Train: All data before 1 Feb 2025
  • Test: 1 Feb 2025 – 31 Jul 2025
• Searches best XGBoost hyperparameters via TimeSeriesSplit and precision scoring.
• Metrics: Accuracy, Precision, Recall, F1, Confusion Matrix.

Example Output:

Best Parameters: {'learning_rate': 0.01, 'max_depth': 4, 'n_estimators': 50}
Accuracy: 48.36%
Precision: 48.98%
Recall: 78.69%
F1-Score: 60.38%

• Confusion Matrix: Plotted for each stock.

Rule-Based Strategy

• Simulates trading whenever the logic signal is True.
• Trade_Return: (next day's close – today’s close) / today’s close – 0.1% (transaction cost)
• Performance metrics: total trades, win ratio, average profit/loss, cumulative return.
• Cumulative return plot: Shows compounding of rule-based trades.

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7. Agreement Analysis

• Consensus trades: Days when both the ML model and the rule-based logic say 'buy'.
• Agreement Metrics:
  • Total agreement trades
  • Profitable agreement trades
  • Win ratio
  • Average return
  • Cumulative return plot for agreement-only trades

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8. Evaluation Metrics

Metric	Description
Accuracy	Correct predictions / total predictions
Precision	Correct “up” predictions / all “up” predictions
Recall	Correct “up” predictions / all actual “up” instances
F1-Score	Harmonic mean of precision and recall
Win Ratio	Profitable trades / total trades
Avg Profit/Loss	Average % gain/loss per trade
Cumulative Return	Compound return from following the strategy

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9. Trading Pipeline Flowchart

Below is a diagram showing the pipeline from data preprocessing to trade execution.

flowchart TD
    A[Raw Stock Price Data] --> B[Technical Indicator Calculation]
    B --> C[Feature Engineering]
    C --> D1[ML Model (XGBoost) Prediction]
    C --> D2[Rule-Based Logic Signal]
    D1 --> E[Signal Agreement Analysis]
    D2 --> E
    E --> F{Consensus?}
    F -- Yes --> G[Simulated Trade (Agreement)]
    F -- No --> H[No Trade]
    G --> I[Backtest Performance Metrics]
    H --> I

Loading

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10. XGBoost Model API (Model Asset)

For each stock, the trained XGBoost model is saved as a JSON asset (e.g., INFY_model.json). To use the model, load and call predict() with the 6-feature input.

{
    "title": "Stock Direction Prediction (XGBoost Model)",
    "description": "Predicts next-day price direction (up/down) based on technical indicators.",
    "method": "POST",
    "baseUrl": "http://localhost:8501",
    "endpoint": "/predict",
    "headers": [
        {
            "key": "Content-Type",
            "value": "application/json",
            "required": true
        }
    ],
    "bodyType": "json",
    "requestBody": "{\n  \"RSI\": 31.27,\n  \"MACD\": -5.33,\n  \"MACD_Signal\": -4.21,\n  \"SMA_20\": 1567.32,\n  \"EMA_20\": 1571.88,\n  \"Volatility\": 11.09\n}",
    "responses": {
        "200": {
            "description": "Prediction result",
            "body": "{\n  \"prediction\": 0\n}"
        }
    }
}

Here, 0 = down, 1 = up.

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11. Best Practices & Limitations

Best Practices

• Always use a strictly out-of-sample backtest period.
• Tune ML model hyperparameters using time-series cross-validation.
• Incorporate transaction costs in backtests.
• Compare ML with simple rules to avoid overfitting hype.

Limitations

• No risk management: Position sizing, stop-losses, and slippage not modeled.
• No walk-forward retraining: Models are not retrained during backtest.
• No feature selection or ensembling: Only basic technicals used.
• No capital constraints or realistic brokerage simulation.
• Small sample in backtest window may not generalize.

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12. References

• XGBoost Documentation
• TA-Lib & Technical Analysis Library
• yfinance Documentation
• Scikit-learn Metrics
• Investopedia: RSI, MACD, SMA

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📝 Summary Table

Stock	ML Model Accuracy	Rule-Based Win Ratio	Agreement Trades	Agreement Win Ratio
INFY.NS	48.36%	0.00%	2	0.00%
RELIANCE.NS	50.82%	40.00%	3	33.33%
HDFCBANK.NS	45.90%	0.00%	0	0.00%

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📌 Key Takeaways

• Both ML (XGBoost) and simple logic are very noisy for next-day direction, even when tuned and filtered.
• "Agreement" (consensus) trades are rare and not consistently profitable (in this test window).
• Visualization shows the signals are sparse and gains are often offset by losses, especially with transaction costs.
• Despite the power of ML, overfitting and limited predictability in short-term price movement remain major challenges.

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This notebook provides a full, reproducible pipeline for technical trading ML research, and exposes the reality of predictive edge in liquid stocks. Feel free to use it as a base for deeper studies, adding more features, or more realistic trading simulation!