InvestmentResearchLab

InvestmentResearchLab is a Python-based quantitative research project for developing, testing, and evaluating systematic stock-selection and positional-trading strategies.

The project focuses on turning clearly defined trading ideas into repeatable, rule-based screening and ranking engines rather than relying on subjective chart interpretation.

Project Goals

The primary objectives are to:

• Convert trading ideas into explicit, programmable rules.
• Identify stocks that satisfy predefined momentum and trend conditions.
• Separate different strategy definitions so they can be researched independently.
• Rank qualifying stocks based on measurable characteristics.
• Produce clear, structured output for further analysis and decision-making.
• Keep strategy logic transparent and easy to modify.
• Build a foundation for future backtesting and performance analysis.

Strategies

The current research focuses on two related momentum strategies.

Momentum Breakout or Recovery

Momentum Edge

Established Uptrend AND (20-Day Breakout OR Controlled Retracement Recovery)

This strategy looks for stocks already exhibiting an established bullish trend and qualifying through either:

1. A recent 20-day breakout, or
2. A controlled retracement followed by recovery.

Implementation:

src/MomBreakoutOrRecovery/

See the strategy-specific README for the detailed rules, calculations, configuration, and output.

Momentum Breakout Then Recovery

Momentum Continuation

Established Uptrend AND (20-Day Breakout followed by Controlled Retracement Recovery)

This is a stricter continuation setup.

A stock must first establish an uptrend, experience a qualifying 20-day breakout, subsequently undergo a controlled retracement, and then demonstrate recovery.

Implementation:

src/MomBreakoutThenRecovery/

See the strategy-specific README for the detailed rules and implementation.

Project Structure

InvestmentResearchLab/
│
├── README.md
│
├── src/
│   │
│   ├── MomBreakoutOrRecovery/
│   │   ├── README.md
│   │   └── ...
│   │
│   └── MomBreakoutThenRecovery/
│       ├── README.md
│       └── ...
│
└── tests/
    └── ...

Each strategy is intentionally maintained as a separate module so that its rules, calculations, data processing, and outputs can evolve independently.

Design Philosophy

The project follows a few principles:

1. Rules Before Opinions

Trading decisions should be expressed as measurable conditions wherever possible.

Instead of:

"The stock looks strong."

The strategy should define what "strong" means through objective conditions such as trend, price action, breakout behaviour, retracement depth, recovery, volume, and other measurable factors.

2. Simple Before Complex

The goal is not to build the most complicated model.

A simpler strategy with clearly understood behaviour is preferable to a highly optimized system whose results are difficult to explain or reproduce.

3. Separate Strategy Definitions

Similar-looking strategies should not be combined simply because they share common components.

For example:

Momentum Edge
    Uptrend
        AND
    (Breakout OR Recovery)

is intentionally different from:

Momentum Continuation
    Uptrend
        AND
    Breakout
        AND
    Recovery

Keeping them separate allows their results and behaviour to be evaluated independently.

4. Reproducible Research

The same inputs and rules should produce the same screening results.

This makes it possible to compare strategy versions and understand whether changes genuinely improve the strategy.

5. Avoid Unnecessary Optimization

The objective is not to continuously optimize every piece of code or parameter.

Code should first be:

• Correct
• Understandable
• Maintainable
• Reproducible

Optimization should only be introduced when there is a clear reason and measurable benefit.

Technology

The project is currently implemented in:

• Python
• Pandas
• NumPy
• yfinance
• Python virtual environments
• Git / GitHub

Additional libraries may be introduced as the research framework evolves.

Current Status

The project is actively under development.

Current focus:

• Momentum strategy implementation
• Stock screening
• Price and volume analysis
• Strategy-specific ranking
• Structured terminal output
• Research and validation

Future development may include:

• Historical backtesting
• Performance statistics
• Risk/reward analysis
• Portfolio-level analysis
• Strategy comparison
• Trade tracking
• Visualization
• Parameter sensitivity analysis

Important Disclaimer

InvestmentResearchLab is a research and analysis project.

The output of these strategies is not financial advice and should not be treated as a guaranteed prediction of future stock performance.

Historical or simulated results do not guarantee future returns. Any live trading decision should consider risk, liquidity, transaction costs, market conditions, and individual circumstances.

Author

Raviteja

InvestmentResearchLab is developed as an ongoing quantitative trading and investment research project.