algo-stat
Quant analysis in 3 lines. A self-contained Python toolkit for market data, metrics, charts, Monte Carlo, backtesting, ML, macro, options, mortgage, and Alpaca paper trading.
algo-stat = library + CLI + REST API + Streamlit dashboard
Install (local laptop)
cd algo-stat
python -m venv .venv && source .venv/bin/activate
pip install -e ".[all,test,dev]" # all = trading + ml-deep
cp .env.example .env # add FRED_API_KEY (free, optional)
API keys?
yfinanceneeds none. FRED needs a free key (signup: https://fred.stlouisfed.org/docs/api/api_key.html). Alpaca only if you want paper trading.
3-line analyses (Jupyter / Python REPL)
import algo_stat as a; a.use_dark()
a.report(a.fetch("NVDA", period="1y"), ticker="NVDA")
import algo_stat as a
a.compare(["AAPL","MSFT","NVDA","GOOGL"], period="2y").show()
import algo_stat as a
a.MonteCarlo("NVDA").simulate(n=1000, days=252).plot()
import algo_stat as a
bt = a.Backtester("NVDA", period="2y").run(a.SMACrossover(20, 50))
print(bt.stats); bt.plot()
More examples in notebooks/quickstart.py.
CLI
algo-stat quote NVDA
algo-stat analyze NVDA --period 1y --chart
algo-stat compare AAPL,MSFT,NVDA,GOOGL --period 2y --chart
algo-stat montecarlo NVDA --n 1000 --days 252 --chart
algo-stat backtest NVDA --fast 20 --slow 50 --chart
algo-stat mortgage 300000 60000 0.055 --years 25
algo-stat macro cpi --years 10
algo-stat serve # FastAPI on :8000
algo-stat dashboard # Streamlit on :8501
REST API
algo-stat serve
# → open http://127.0.0.1:8000/docs (Swagger UI)
Endpoints:
| Method | Path | Purpose |
|---|---|---|
| GET | /health |
health check |
| GET | /quote/{ticker} |
fundamental snapshot |
| GET | /prices/{ticker} |
OHLCV tail |
| GET | /performance/{ticker} |
CAGR / Sharpe / VaR / MDD |
| GET | /compare?tickers=... |
normalized growth |
| POST | /montecarlo |
GBM simulation summary |
| POST | /backtest/sma |
SMA crossover stats |
| POST | /mortgage |
mortgage summary |
| GET | /macro/{series} |
FRED series |
| POST | /options/black-scholes |
option pricing |
Streamlit dashboard
algo-stat dashboard
7 tabs: Performance · Compare · Monte Carlo · Backtest · Mortgage · Macro · Options.
Package layout
algo_stat/
data/ market.py · macro.py · universe.py
metrics/ returns.py · performance.py · risk.py
charts/ theme.py · price.py · compare.py · correlation.py · fundamentals.py
derivatives/ montecarlo.py · options.py
backtest/ engine.py · strategies.py
ml/ pca.py · regression.py · classifier.py
property/ mortgage.py
trading/ alpaca_client.py
cli.py api.py dashboard.py report.py config.py
Tests
pytest -q
Credits
© 2026 Squid Consultancy Group Ltd — Elastic License 2.0.
Metadata
Release files for algorithm-stat 0.1.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| algorithm_stat-0.1.2.tar.gz | 64.6 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| algorithm_stat-0.1.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 150.7 kB
Release files / algorithm_stat-0.1.2.tar.gz
| Download URL | algorithm_stat-0.1.2.tar.gz |
|---|---|
| Size | 64.6 kB |
| Tags | Source |
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Release files / algorithm_stat-0.1.2-py3-none-any.whl
| Download URL | algorithm_stat-0.1.2-py3-none-any.whl |
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| Size | 86.1 kB |
| Tags | Python 3 |
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