mirror of
https://github.com/OpenBB-finance/OpenBB.git
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d3d33a219b
* refactor openbb-technical for v5 * forward refs * linting * no python 3.15 in test matrix * reame * ty * move all params to body --------- Co-authored-by: deeleeramone <>
91 lines
2.8 KiB
Markdown
Vendored
91 lines
2.8 KiB
Markdown
Vendored
# Getting Started
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## Install
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```bash
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pip install openbb-technical
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```
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The extension depends on `openbb-core[pandas]`, `pandas-ta-openbb`, `scikit-learn`, `scipy`, and `statsmodels` — all installed transitively.
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## Three ways to call an endpoint
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Every endpoint is exposed identically through the Python SDK, the REST API, and the CLI. Pick whichever fits your workflow.
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### 1. Python
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```python
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from openbb import obb
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data = obb.equity.price.historical(
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symbol="SPY", start_date="2024-01-01", provider="yfinance"
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).results
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result = obb.technical.rsi(data=data, length=14)
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print(result.results[0]) # first row of the RSI series
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df = result.to_df() # convert results to a DataFrame
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```
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### 2. REST API
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Start the API server (defaults to `http://localhost:8000`):
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```bash
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openbb-api
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```
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POST to the endpoint. The `data` array is the request body; scalar args are query-string parameters:
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```bash
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curl -X POST 'http://localhost:8000/api/v1/technical/rsi?length=14' \
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-H 'Content-Type: application/json' \
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-d '[{"date":"2024-01-02","open":471.5,"high":473.7,"low":471.0,"close":472.6,"volume":78900},
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{"date":"2024-01-03","open":472.5,"high":473.1,"low":468.7,"close":468.8,"volume":111200},
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...]'
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```
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Endpoints that take more than one body argument (`screen`, `multi`, `correlation`, `relative_rotation`, `realized_volatility_compare`, `signals/candlestick_patterns`) wrap the body as `{"data": [...], "<other>": [...]}`.
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### 3. CLI
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```bash
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openbb -i
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> /technical/rsi --data <path_or_json> --length 14
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```
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The CLI auto-generates an argparse interface from each endpoint's signature, so `--help` after any command lists every parameter with its type and default.
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## Working with returns
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Every endpoint returns an `OBBject`. The interesting bits:
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```python
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result = obb.technical.bbands(data=data, length=20)
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result.results # list[BbandsData] — one row per bar
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result.to_df() # pandas DataFrame keyed by date
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result.to_dict() # dict form
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len(result.results) # row count
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result.results[-1] # last row (most recent bar)
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```
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## Discovering what's available
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The `indicators` endpoint introspects the router and returns a structured catalog:
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```python
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catalog = obb.technical.indicators(category="all").results
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for entry in catalog.indicators:
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print(entry.name, entry.category, [p.name for p in entry.params])
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```
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Filter by category — `overlay`, `oscillator`, `volatility`, `volume`, `trend`, `signal`, `structure`, `stats`, `multi`, or `all`.
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## Next steps
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- [Single-symbol indicator workflows](./user-guides/01-indicators.md)
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- [Signal endpoints](./user-guides/02-signals.md)
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- [Multi-symbol analysis](./user-guides/03-multi-symbol.md)
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- [Multi-indicator composition + catalog](./user-guides/04-composition-and-discovery.md)
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- [Full API reference](./api-reference.md)
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