chore: update readme

Author: PerHac13

README.md

@@ -1,42 +1,23 @@
 # QuantResearchStarter
 
-[![Python Version](https://img.shields.io/badge/python-3.10%2B-blue)](https://www.python.org/)
-[![License: MIT](https://img.shields.io/badge/license-MIT-green)](LICENSE)
-[![CI](https://github.com/username/QuantResearchStarter/actions/workflows/ci.yml/badge.svg)](https://github.com/username/QuantResearchStarter/actions)
-
-A modular, open-source quantitative research and backtesting framework built for clarity, reproducibility, and extensibility. Ideal for researchers, students, and engineers building and testing systematic strategies.
-
----
-
-## Why this project
+A modular, open-source quantitative research and backtesting framework designed for clarity and extensibility. Perfect for researchers, students, and developers interested in quantitative finance.
 
-QuantResearchStarter provides a clean, well-documented starting point for quantitative research and backtesting. Its priorities are:
-
-* **Readability**: idiomatic Python, type hints, and small modules you can read and change quickly.
-* **Testability**: deterministic vectorized backtests with unit tests and CI.
-* **Extensibility**: plugin-friendly factor & data adapters so you can try new ideas fast.
-
----
+![Python Version](https://img.shields.io/badge/python-3.10%2B-blue)
+![License](https://img.shields.io/badge/license-MIT-green)
+[![CI](https://github.com/username/QuantResearchStarter/actions/workflows/ci.yml/badge.svg)](https://github.com/username/QuantResearchStarter/actions)
 
 ## Features
 
-* **Data management** — download market data or generate synthetic price series for experiments.
-* **Factor library** — example implementations of momentum, value, size, and volatility factors.
-* **Vectorized backtesting engine** — supports transaction costs, slippage, and portfolio constraints.
-* **Risk & performance analytics** — returns, drawdowns, Sharpe, turnover, and other risk metrics.
-* **CLI & scripts** — small tools to generate data, compute factors, and run backtests from the terminal.
-* **Production-ready utilities** — type hints, tests, continuous integration, and documentation scaffolding.
+- **Data Management**: Download real data or generate synthetic data for testing
+- **Factor Library**: Implement momentum, value, size, and volatility factors
+- **Backtesting Engine**: Vectorized backtester with transaction costs and constraints
+- **Risk Metrics**: Comprehensive performance and risk analytics
+- **Modular Design**: Easy to extend with new factors and strategies
+- **Production Ready**: Type hints, tests, CI/CD, and documentation
 
----
+## Quick Start
 
-## Quick start
-
-### Requirements
-
-* Python 3.10+
-* pip
-
-### Install locally
+### Installation
 
 ```bash
 # Clone the repository
@@ -46,125 +27,26 @@ cd QuantResearchStarter
 # Install package in development mode
 pip install -e .
 
-# Install development dependencies (tests, linters, docs)
+# Install development dependencies
 pip install -e ".[dev]"
 
-# Optional UI dependencies
+# Optional UI
 pip install streamlit plotly
 ```
 
-### Demo (one-line)
+### Quick Demo
 
 ```bash
 make demo
 ```
 
-### Step-by-step demo
+Or step-by-step:
 
 ```bash
-# generate synthetic sample price series
 qrs generate-data -o data_sample/sample_prices.csv -s 5 -d 365
-
-# compute example factors
 qrs compute-factors -d data_sample/sample_prices.csv -f momentum -f value -o output/factors.csv
-
-# run a backtest
 qrs backtest -d data_sample/sample_prices.csv -s output/factors.csv -o output/backtest_results.json
 
-# optional: start the Streamlit dashboard
+# Streamlit dashboard (optional)
 streamlit run src/quant_research_starter/dashboard/streamlit_app.py
 ```
-
----
-
-## Minimal example
-
-```python
-from quant_research_starter.backtest import Backtester
-from quant_research_starter.data import load_prices
-from quant_research_starter.factors import Momentum
-
-prices = load_prices("data_sample/sample_prices.csv")
-factor = Momentum(window=63)
-scores = factor.compute(prices)
-
-bt = Backtester(prices, signals=scores, capital=1_000_000)
-results = bt.run()
-print(results.performance.summary())
-```
-
-> See the `examples/` directory for fully working notebooks and scripts.
-
----
-
-## CLI reference
-
-Run `qrs --help` or `qrs <command> --help` for full usage. Main commands include:
-
-* `qrs generate-data` — create synthetic price series or download data from adapters
-* `qrs compute-factors` — calculate and export factor scores
-* `qrs backtest` — run the vectorized backtest and export results
-
----
-
-## Project structure (overview)
-
-```
-QuantResearchStarter/
-├─ src/quant_research_starter/
-│  ├─ data/              # data loaders & adapters
-│  ├─ factors/           # factor implementations
-│  ├─ backtest/          # backtester & portfolio logic
-│  ├─ analytics/         # performance and risk metrics
-│  ├─ cli/               # command line entry points
-│  └─ dashboard/         # optional Streamlit dashboard
-├─ examples/             # runnable notebooks & example strategies
-├─ tests/                # unit + integration tests
-└─ docs/                 # documentation source
-```
-
----
-
-## Tests & CI
-
-Run unit tests locally with:
-
-```bash
-pytest -q
-```
-
-CI runs linting (ruff), formatting checks (black), and unit tests across supported Python versions. The workflow is defined in `.github/workflows/ci.yml`.
-
----
-
-## Contributing
-
-Contributions are welcome. Please follow these steps:
-
-1. Fork the repository
-2. Create a descriptive branch (feature or fix)
-3. Add tests for new behavior
-4. Open a pull request with a clear description and rationale
-
-Before submitting, ensure your tests pass and formatting/linting checks succeed.
-
----
-
-## AI policy (short & practical)
-
-**Yes — you may use AI tools** (ChatGPT, Copilot, etc.) to help write or review code and documentation. Please follow these guidelines:
-
-* **Disclose** substantial AI assistance in the PR or commit message (e.g., "Generated with ChatGPT; reviewed and adapted by @your-username").
-* **Review thoroughly** all AI-generated code for correctness, security, numerical stability, and licensing concerns.
-* **Add tests** for AI-generated logic when applicable.
-* **Respect licenses**: do not paste or rely on large verbatim copyrighted snippets without appropriate permission or attribution.
-
-This policy encourages fast iteration while maintaining quality and transparency.
-
----
-
-## License
-
-This project is available under the MIT License — see the `LICENSE` file for details.
-
----