Metadata-Version: 2.4
Name: amquant
Version: 0.1.1
Summary: A lightweight library for loading market data and computing engineered features.
Author-email: Your Name <you@example.com>
License: MIT
Keywords: quant,finance,market-data,features,trading
Requires-Python: >=3.10
Description-Content-Type: text/markdown
Requires-Dist: requests>=2.31
Requires-Dist: pandas>=2.0
Provides-Extra: dev
Requires-Dist: pytest>=8.0; extra == "dev"

**activate the virtual env with** :  source venv/bin/activate   

**interactive program** :python3 -i main.py

**DATA ACCESS:**

After `main()` executes in the same Python session:

- `RAW_SERIES: dict[str, Series]` — populated with fully enriched `Series` objects (OHLCV + metadata).
- `FEATURE_SETS: dict[str, FeatureSet]` — contains computed technical features per symbol.

Both dicts are module-level globals, accessible immediately post-run via `RAW_SERIES['BIAT']` or `FEATURE_SETS['MC']`.

Data lives in memory for the duration of the Python process / REPL session. Not persisted to disk unless explicitly saved.

For library use: expose `RAW_SERIES` and `FEATURE_SETS` via a `DataManager` singleton or return them from `load_universe()` for clean dependency injection.

**demo**

list(RAW_SERIES.keys())
RAW_SERIES["BNP"].bars[0]
FEATURE_SETS["BNP"].ret_1d[:5]
FEATURE_SETS["BNP"].sma_20[:5]
FEATURE_SETS["BNP"].__dict__.keys()
len(RAW_SERIES["BNP"].bars)
len(RAW_SERIES)
len(FEATURE_SETS)

# AmQuant



**l**
rm -rf venv
python3 -m venv venv
source venv/bin/activate

python -m pip install --upgrade pip
python -m pip install build twine
python -m build


