docs: legacy, streamlit fixed

Author: ayushkrtiwari

README.md

@@ -0,0 +1,227 @@
+# 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
+
+QuantResearchStarter aims to provide a clean, well-documented starting point for quantitative research and backtesting. It focuses on:
+
+* **Readability**: idiomatic Python, type hints, and small modules you can read and change quickly.
+* **Testability**: deterministic vectorized backtests with unit tests and CI.
+* **Extensibility**: plug-in friendly factor & data adapters so you can try new ideas fast.
+
+---
+
+## Key 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, portfolio constraints, and configurable rebalancing frequencies (daily, weekly, monthly).
+* **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.
+
+---
+
+## Quick start
+
+### Requirements
+
+* Python 3.10+
+* pip
+
+### Install locally
+
+```bash
+# Clone the repository
+git clone https://github.com/username/QuantResearchStarter.git
+cd QuantResearchStarter
+
+# Install package in development mode
+pip install -e .
+
+# Install development dependencies (tests, linters, docs)
+pip install -e ".[dev]"
+
+# Optional UI dependencies
+pip install streamlit plotly
+```
+
+### Quick CLI Usage
+
+After installation, you can use the CLI in two ways:
+
+**Option 1: Direct command (if PATH is configured)**
+```bash
+qrs --help
+# 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
+```
+
+**Option 2: Python module (always works)**
+```bash
+python -m quant_research_starter.cli --help
+python -m quant_research_starter.cli generate-data -o data_sample/sample_prices.csv -s 5 -d 365
+python -m quant_research_starter.cli compute-factors -d data_sample/sample_prices.csv -f momentum -f value
+python -m quant_research_starter.cli backtest -d data_sample/sample_prices.csv -s output/factors.csv -o output/backtest_results.json
+```
+
+### Demo (one-line)
+
+```bash
+make demo
+```
+
+### Step-by-step demo
+
+```bash
+# generate synthetic sample price series
+python -m quant_research_starter.cli generate-data -o data_sample/sample_prices.csv -s 5 -d 365
+
+# compute example factors
+python -m quant_research_starter.cli compute-factors -d data_sample/sample_prices.csv -f momentum -f value -o output/factors.csv
+
+# run a backtest
+python -m quant_research_starter.cli backtest -d data_sample/sample_prices.csv -s output/factors.csv -o output/backtest_results.json
+
+# DISCLAIMER: OLD VERSION
+# optional: start the Streamlit dashboard, if on main stream
+streamlit run src/quant_research_starter/dashboard/streamlit_app.py
+# NEW VERSION: if streamlit is in legacy folder
+streamlit run legacy/streamlit/streamlit_app.py
+```
+
+---
+
+## Example: small strategy (concept)
+
+```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())
+```
+
+### Rebalancing Frequency
+
+The backtester supports different rebalancing frequencies to match your strategy needs:
+
+```python
+from quant_research_starter.backtest import VectorizedBacktest
+# Daily rebalancing (default)
+bt_daily = VectorizedBacktest(prices, signals, rebalance_freq="D")
+
+# Weekly rebalancing (reduces turnover and transaction costs)
+bt_weekly = VectorizedBacktest(prices, signals, rebalance_freq="W")
+
+# Monthly rebalancing (lowest turnover)
+bt_monthly = VectorizedBacktest(prices, signals, rebalance_freq="M")
+
+results = bt_monthly.run()
+```
+
+Supported frequencies:
+- `"D"`: Daily rebalancing (default)
+- `"W"`: Weekly rebalancing (rebalances when the week changes)
+- `"M"`: Monthly rebalancing (rebalances when the month changes)
+
+> The code above is illustrative—see `examples/` for fully working notebooks and scripts.
+
+---
+
+## CLI reference
+
+Run `python -m quant_research_starter.cli --help` or `python -m quant_research_starter.cli <command> --help` for full usage. Main commands include:
+
+* `python -m quant_research_starter.cli generate-data` — create synthetic price series or download data from adapters
+* `python -m quant_research_starter.cli compute-factors` — calculate and export factor scores
+* `python -m quant_research_starter.cli backtest` — run the vectorized backtest and export results
+
+**Note:** If you have the `qrs` command in your PATH, you can use `qrs` instead of `python -m quant_research_starter.cli`.
+
+---
+
+## 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
+
+We include unit tests and a CI workflow (GitHub Actions). Run tests locally with:
+
+```bash
+pytest -q
+```
+
+The CI pipeline runs linting, unit tests, and builds docs on push/PR.
+
+---
+
+## Contributing
+
+Contributions are very welcome. Please follow these steps:
+
+1. Fork the repository
+2. Create a feature branch
+3. Add tests for new behavior
+4. Open a pull request with a clear description and rationale
+
+Please review `CONTRIBUTING.md` and the `CODE_OF_CONDUCT.md` before submitting.
+
+---
+
+## AI policy — short & practical
+
+**Yes — you are allowed to use AI tools** (ChatGPT, Copilot, Codeium, etc.) to help develop, prototype, or document code in this repository.
+
+A few friendly guidelines:
+
+* **Be transparent** when a contribution is substantially generated by an AI assistant — add a short note in the PR or commit message (e.g., "Generated with ChatGPT; reviewed and adapted by <your-name>").
+* **Review and test** all AI-generated code. Treat it as a helpful draft, not final production-quality code.
+* **Follow licensing** and attribution rules for any external snippets the AI suggests. Don’t paste large verbatim copyrighted material.
+* **Security & correctness**: double-check numerical logic, data handling, and anything that affects trading decisions.
+
+This policy is intentionally permissive: we want the community to move fast while keeping quality and safety in mind.
+
+---
+
+## License
+
+This project is licensed under the MIT License — see the `LICENSE` file for details.
+
+---
+
+## Acknowledgements
+
+Built with inspiration from open-source quant libraries and the research community. If you use this project in papers or public work, a short citation or mention is appreciated.

legacy/streamlit/README.md

@@ -12,21 +12,21 @@
     "@tailwindcss/vite": "^4.1.17",
     "chart.js": "^4.5.1",
     "lucide-react": "^0.263.1",
-    "react": "^18.2.0",
+    "react": "^19.2.0",
     "react-chartjs-2": "^5.3.1",
-    "react-dom": "^18.2.0",
-    "react-router-dom": "^6.8.0"
+    "react-dom": "^19.2.0",
+    "react-router-dom": "^6.30.1"
   },
   "devDependencies": {
     "@tailwindcss/cli": "^4.1.17",
     "@tailwindcss/postcss": "^4.1.17",
     "@types/react": "^18.3.26",
     "@types/react-dom": "^18.3.7",
     "@vitejs/plugin-react": "^4.7.0",
-    "autoprefixer": "^10.4.21",
+    "autoprefixer": "^10.4.22",
     "postcss": "^8.5.6",
     "tailwindcss": "^4.1.17",
     "typescript": "^5.9.3",
-    "vite": "^5.0.0"
+    "vite": "^5.4.21"
   }
 }

src/quant_research_starter/frontend/cauweb/package.json

@@ -0,0 +1,16 @@
+[project]
+name = "quant-research-frontend"
+version = "0.1.0"
+description = ""
+authors = [
+    {name = "AYUSH KUMAR TIWARI",email = "[email protected]"}
+]
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+]
+
+
+[build-system]
+requires = ["poetry-core>=2.0.0,<3.0.0"]
+build-backend = "poetry.core.masonry.api"

src/quant_research_starter/frontend/cauweb/pyproject.toml

@@ -1,5 +1,6 @@
 import React from 'react';
 import ReactDOM from 'react-dom/client';
+
 import { App } from './App';
 import './styles/globals.css'; // Make sure this import exists
 import './index.css';