docs: readme unchanged

readme unchanged

Author: ayushkrtiwari

README.md

@@ -1,8 +1,8 @@
-# QuantResearch_Opcode
+# QuantResearch
 
 check out the [link](https://qrsopcode.netlify.app/)
 
-> **QuantResearch_Opcode** — research-grade quantitative strategy starter kit with an interactive React/TypeScript frontend (cauweb), Python backtesting core, and legacy Streamlit dashboards archived under `legacy/streamlit/`.
+> **QuantResearch** — research-grade quantitative strategy starter kit with an interactive React/TypeScript frontend (cauweb), Python backtesting core, and legacy Streamlit dashboards archived under `legacy/streamlit/`.
 
 ---
 
@@ -43,13 +43,6 @@ This README explains how to run both components in dev and production, how realt
 
 ## What’s included (high-level)
 
-* **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.
-* **Hyperparameter optimization** — automated tuning with Optuna, pruning, and distributed study support.
-* **CLI & scripts** — small tools to generate data, compute factors, run backtests, and optimize hyperparameters from the terminal.
-* **Production-ready utilities** — type hints, tests, continuous integration, and documentation scaffolding.
 ```
 / (repo root)
 ├─ src/quant_research_starter/
@@ -210,47 +203,11 @@ The frontend expects a stable WS message contract. A suggested minimal schema (e
 
 * `src/quant_research_starter/frontend/cauweb/src/lib/wsClient.ts` — typed, reconnecting WS client (if present) -->
 
-* `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
-* `python -m quant_research_starter.cli autotune` — optimize hyperparameters with Optuna
 <!-- Server responsibilities:
 
 * Translate backend events (backtest updates, order events) into WS messages and publish to Redis pub/sub / message broker.
 * Implement room/topic filtering and auth checks on subscribe.
 
-### Hyperparameter Tuning (Autotune)
-
-The `autotune` command automates hyperparameter search using Optuna with pruning support for efficient optimization.
-
-**Basic usage:**
-```bash
-# Optimize momentum factor hyperparameters
-qrs autotune -f momentum -n 100 -m sharpe_ratio
-
-# Use YAML configuration file
-qrs autotune -c examples/autotune_config.yaml
-```
-
-**Key features:**
-- **Pruning**: Early stopping of bad trials to save computation time
-- **Distributed tuning**: Optional RDB storage (SQLite, PostgreSQL, MySQL) for multi-worker setups
-- **Flexible objectives**: Optimize any metric (Sharpe ratio, total return, CAGR, etc.)
-- **Factor support**: Optimize momentum, volatility, and other factor hyperparameters
-
-**Example YAML configuration:**
-```yaml
-data_file: "data_sample/sample_prices.csv"
-factor_type: "momentum"
-n_trials: 100
-metric: "sharpe_ratio"
-output: "output/tuning_results.json"
-pruner: "median"  # Options: none, median, percentile
-storage: "sqlite:///optuna.db"  # Optional: for distributed runs
-```
-
-See `examples/autotune_config.yaml` for a complete example configuration.
-
 ---
 
 ## APIs & Data flows: to be updated
@@ -271,18 +228,6 @@ See `examples/autotune_config.yaml` for a complete example configuration.
 # from repo root
 pytest -q
 ```
-QuantResearchStarter/
-├─ src/quant_research_starter/
-│  ├─ data/              # data loaders & adapters
-│  ├─ factors/           # factor implementations
-│  ├─ backtest/          # backtester & portfolio logic
-│  ├─ analytics/         # performance and risk metrics
-│  ├─ tuning/            # Optuna hyperparameter optimization
-│  ├─ cli/               # command line entry points
-│  └─ dashboard/         # optional Streamlit dashboard
-├─ examples/             # runnable notebooks & example strategies
-├─ tests/                # unit + integration tests
-└─ docs/                 # documentation source
 
 ### Frontend tests
 
@@ -378,4 +323,4 @@ If you hit an environment-specific issue, add a detailed issue with logs and rep
 
 This project is licensed under the license in `LICENSE` (check root). For questions, open an issue or contact the maintainers listed in `AUTHORS` / `MAINTAINERS` files.
 
----
+---