Metadata-Version: 2.4
Name: mmrl
Version: 0.1.1
Summary: Market Making RL - simulation, experiments, and CLI
Author: Aviral Poddar
License-Expression: MIT
Project-URL: Homepage, https://github.com/Aviral1303/Market-Making-RL-Agent
Project-URL: Repository, https://github.com/Aviral1303/Market-Making-RL-Agent
Project-URL: Issues, https://github.com/Aviral1303/Market-Making-RL-Agent/issues
Keywords: market-making,quant,reinforcement-learning,finance,mlflow,fastapi
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Intended Audience :: Science/Research
Requires-Python: >=3.10
Description-Content-Type: text/markdown
Requires-Dist: numpy>=1.23.0
Requires-Dist: pandas>=2.3.0
Requires-Dist: PyYAML>=6.0.1
Requires-Dist: tqdm>=4.66
Requires-Dist: mlflow>=2.14
Requires-Dist: typer>=0.12
Requires-Dist: matplotlib>=3.8
Requires-Dist: seaborn>=0.13
Requires-Dist: pydantic>=2.0
Provides-Extra: api
Requires-Dist: fastapi>=0.115; extra == "api"
Requires-Dist: uvicorn[standard]>=0.30; extra == "api"
Requires-Dist: redis>=5.0; extra == "api"
Requires-Dist: rq>=1.16; extra == "api"
Requires-Dist: prometheus-client>=0.20; extra == "api"
Requires-Dist: duckdb>=1.1; extra == "api"
Provides-Extra: rl
Requires-Dist: gymnasium>=0.28; extra == "rl"
Requires-Dist: stable-baselines3>=2.3; extra == "rl"
Requires-Dist: torch>=2.3; (platform_machine != "arm64" or sys_platform != "darwin") and extra == "rl"

# Market Making RL Agent

![PyPI](https://img.shields.io/pypi/v/mmrl.svg)
![Tests](https://img.shields.io/badge/tests-passing-brightgreen)
![Python](https://img.shields.io/badge/python-3.10%2B-blue)
![License](https://img.shields.io/badge/license-MIT-green)

## Why this is useful
- End-to-end, deployable research stack: config-driven envs, MLflow tracking, CLI, REST API with async jobs, and DuckDB persistence
- Microstructure features that matter: OU price dynamics with regime switching, probabilistic fills, fees/slippage, multi-asset correlation, depth-aware quoting, size decisions
- Baselines and RL: Naive, Inventory-aware, Avellaneda–Stoikov, PPO; hyperparameter search with Optuna

## 60s Quickstart
```
pip install mmrl
mmrl backtest
mmrl evaluate  # Naive vs Rule-Based vs A–S vs PPO
mmrl analyze strategy_comparison.csv --plot  # analyze your returns file
```

From source:
```
pip install -r requirements.txt
```

## Colab Notebooks
- Quickstart
  
  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Aviral1303/Market-Making-RL-Agent/blob/main/notebooks/Quickstart.ipynb)

- Grid Search + Heatmaps
  
  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Aviral1303/Market-Making-RL-Agent/blob/main/notebooks/Grid_Heatmaps.ipynb)

- RL vs Rule-Based
  
  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Aviral1303/Market-Making-RL-Agent/blob/main/notebooks/RL_vs_RuleBased.ipynb)

- Multi-asset & Replay
  
  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Aviral1303/Market-Making-RL-Agent/blob/main/notebooks/MultiAsset_Replay.ipynb)

## Multi-asset
- Configure under `multi_asset` in `configs/inventory.yaml`
- Run:
```
python3 experiments/evaluate_multi_asset.py
python3 analysis/plot_multi_asset.py results/.../multi_asset_history.csv
```

## API
- Start stack:
```
docker compose up -d redis worker api mlflow
curl http://localhost:8000/health
curl http://localhost:8000/config/schema  # config JSON schema
```
- Submit jobs:
```
curl -X POST http://localhost:8000/backtest -H 'Content-Type: application/json' -d '{"steps": 200}'
curl -X POST http://localhost:8000/grid -H 'Content-Type: application/json' -d '{"execution": {"alpha_grid": [1.0, 1.5]}}'
curl http://localhost:8000/trades/<run_id>?limit=100
curl http://localhost:8000/runs/<run_dir_name>/artifacts
curl -L -o run.zip http://localhost:8000/runs/<run_dir_name>/download
```

## Hyperparameter Optimization
```
python3 experiments/hyperopt.py
```

## Notable features
- Multi-asset Gym wrapper with per-asset, per-level actions (offsets + sizes)
- Depth-aware agent placing quotes at multiple levels with regime-conditioned parameters
- DuckDB persistence of runs/metrics/trades and a `/trades/{run_id}` endpoint
- MLflow logging of params, metrics, artifacts with run IDs written per run
- CLI extras: `mmrl fetch-data` (CCXT sample), `mmrl config-validate`, `mmrl config-schema`

## Roadmap
- Postgres-backed storage and public demo deployment
- Size-aware RL policy across multi-asset Gym env
- Notebooks + Colab badges for quick experimentation

## Packaging
- Install from source or build wheel with `python -m build` (requires `build`).
- Optional extras:
  - `mmrl[api]` for FastAPI stack
  - `mmrl[rl]` for Gymnasium/SB3

## Data utilities
- Fetch sample trades to Parquet:
```
python3 -m mmrl.cli fetch-data --exchange binance --symbol BTC/USDT --limit 1000 --out data/btcusdt.parquet
```
