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‎.gitignore‎

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# Logs
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*.log
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logs/
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# Local development files
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local_settings.py
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*.local.py
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*_local.py
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# Database files
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*.db
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*.sqlite
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*.sqlite3
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# Data files
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*.csv
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*.xlsx
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*.json
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*.pkl
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*.pickle
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*.h5
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*.hdf5
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# Temporary files
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*.tmp
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*.temp

‎README.md‎

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@@ -11,8 +11,18 @@ A modern Python client for the Borsdata API, featuring:
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## Installation
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### From PyPI (Recommended)
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```bash
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pip install borsdata-client
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```
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### From Source
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```bash
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pip install -r requirements.txt
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git clone https://github.com/yourusername/modern-borsdata-client.git
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cd modern-borsdata-client
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pip install -e .
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```
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## Quick Start
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# Get all instruments
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instruments = client.get_instruments()
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# Get stock prices for an instrument
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# Get stock prices for a specific instrument
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today = datetime.now()
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last_month = today - timedelta(days=30)
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prices = client.get_stock_prices(
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instrument_id=3, # Example ID
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from_date=last_month,
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one_year_ago = today - timedelta(days=365)
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stock_prices = client.get_stock_prices(
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instrument_id=instruments[0].insId,
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from_date=one_year_ago,
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to_date=today
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)
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# Get financial reports
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reports = client.get_reports(
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instrument_id=3,
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report_type="year",
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max_count=5
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)
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# Print the results
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print(f"Found {len(instruments)} instruments")
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print(f"Got {len(stock_prices)} price points for {instruments[0].name}")
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```
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## Using in Your Projects
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### As a Dependency
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Add to your project's requirements.txt:
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```
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borsdata-client>=0.1.0
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```
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Or in your pyproject.toml:
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```toml
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[project]
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dependencies = [
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"borsdata-client>=0.1.0",
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]
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```
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### Environment Variables
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For better security, you can store your API key in an environment variable:
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# The client will automatically close the connection when exiting the context
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```python
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import os
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from borsdata_client import BorsdataClient
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from dotenv import load_dotenv
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# Load environment variables from .env file
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load_dotenv()
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# Get API key from environment
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api_key = os.getenv("BORSDATA_API_KEY")
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# Initialize client
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client = BorsdataClient(api_key)
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```
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## Documentation

‎examples/README.md‎

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# Borsdata Client Examples
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This directory contains example scripts demonstrating how to use the Borsdata API client.
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## Prerequisites
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Before running these examples, make sure you have:
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1. Installed the borsdata-client package:
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```bash
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pip install borsdata-client
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```
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2. Set up your API key as an environment variable or in a `.env` file:
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```
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BORSDATA_API_KEY=your_api_key_here
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```
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## Examples
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### Basic Usage (`basic_usage.py`)
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A simple example showing how to fetch instruments, markets, and stock prices.
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```bash
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python basic_usage.py
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```
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### Portfolio Analysis (`portfolio_analysis.py`)
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A more advanced example demonstrating how to analyze a portfolio of stocks.
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**Additional Requirements:**
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- pandas: `pip install pandas`
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```bash
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python portfolio_analysis.py
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```
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## Customizing the Examples
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Feel free to modify these examples to suit your needs. You can:
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- Change the portfolio stocks in `portfolio_analysis.py`
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- Adjust the time periods for historical data
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- Add additional analysis metrics
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- Integrate with visualization libraries like matplotlib or plotly

‎examples/basic_usage.py‎

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#!/usr/bin/env python3
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"""
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Basic usage example for the Borsdata API client.
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This example demonstrates how to use the client to fetch basic data from the Borsdata API.
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"""
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import os
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from datetime import datetime, timedelta
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from dotenv import load_dotenv
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from borsdata_client import BorsdataClient
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# Load environment variables from .env file
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load_dotenv()
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# Get API key from environment
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api_key = os.getenv("BORSDATA_API_KEY")
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if not api_key:
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raise ValueError("BORSDATA_API_KEY environment variable not set")
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def main():
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"""Run the example."""
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# Use the client as a context manager
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with BorsdataClient(api_key) as client:
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# Get all instruments
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print("Fetching instruments...")
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instruments = client.get_instruments()
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print(f"Found {len(instruments)} instruments")
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# Get markets
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print("\nFetching markets...")
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markets = client.get_markets()
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print(f"Found {len(markets)} markets:")
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for market in markets:
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print(f" - {market.name} (ID: {market.id})")
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# Get stock prices for a specific instrument (using the first one as an example)
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if instruments:
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instrument = instruments[0]
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print(f"\nFetching stock prices for {instrument.name}...")
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today = datetime.now()
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one_month_ago = today - timedelta(days=30)
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prices = client.get_stock_prices(
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instrument_id=instrument.insId,
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from_date=one_month_ago,
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to_date=today
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)
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print(f"Found {len(prices)} price points:")
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for i, price in enumerate(prices[:5]): # Show only first 5 prices
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print(f" - {price.d}: Close: {price.c}, Volume: {price.v}")
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if len(prices) > 5:
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print(f" ... and {len(prices) - 5} more")
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if __name__ == "__main__":
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main()

‎examples/portfolio_analysis.py‎

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#!/usr/bin/env python3
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"""
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Portfolio analysis example using the Borsdata API client.
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This example demonstrates how to use the client to analyze a portfolio of stocks.
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"""
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import os
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from datetime import datetime, timedelta
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from typing import Dict, List, Tuple
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import pandas as pd
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from dotenv import load_dotenv
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from borsdata_client import BorsdataClient, Instrument, StockPrice
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# Load environment variables from .env file
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load_dotenv()
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# Get API key from environment
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api_key = os.getenv("BORSDATA_API_KEY")
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if not api_key:
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raise ValueError("BORSDATA_API_KEY environment variable not set")
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# Define a sample portfolio (ticker: number of shares)
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PORTFOLIO = {
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"ERIC B": 100, # Ericsson
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"VOLV B": 50, # Volvo
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"SEB A": 75, # SEB
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}
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def get_instrument_by_ticker(client: BorsdataClient, ticker: str) -> Instrument:
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"""Find an instrument by its ticker symbol."""
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instruments = client.get_instruments()
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for instrument in instruments:
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if instrument.ticker == ticker:
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return instrument
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raise ValueError(f"Instrument with ticker {ticker} not found")
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def get_historical_prices(
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client: BorsdataClient,
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instrument_id: int,
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days: int = 365
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) -> List[StockPrice]:
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"""Get historical prices for an instrument."""
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today = datetime.now()
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start_date = today - timedelta(days=days)
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return client.get_stock_prices(
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instrument_id=instrument_id,
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from_date=start_date,
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to_date=today
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)
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def calculate_portfolio_value(
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client: BorsdataClient,
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portfolio: Dict[str, int]
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) -> Tuple[float, Dict[str, float], pd.DataFrame]:
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"""Calculate the current value of a portfolio and its performance."""
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total_value = 0.0
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stock_values = {}
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price_history = {}
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# Get data for each stock in the portfolio
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for ticker, shares in portfolio.items():
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try:
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# Get the instrument
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instrument = get_instrument_by_ticker(client, ticker)
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# Get historical prices
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prices = get_historical_prices(client, instrument.insId)
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if not prices:
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print(f"No price data available for {ticker}")
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continue
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# Get the latest price
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latest_price = prices[-1].c
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stock_value = latest_price * shares
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# Store the results
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total_value += stock_value
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stock_values[ticker] = stock_value
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# Create a time series of prices
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dates = [price.d for price in prices]
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close_prices = [price.c for price in prices]
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price_history[ticker] = pd.Series(close_prices, index=dates)
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print(f"{ticker}: {shares} shares at {latest_price:.2f} = {stock_value:.2f}")
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except ValueError as e:
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print(f"Error processing {ticker}: {e}")
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# Create a DataFrame with all price histories
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df = pd.DataFrame(price_history)
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# Calculate portfolio value over time (weighted by shares)
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portfolio_weights = {}
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for ticker, shares in portfolio.items():
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if ticker in df.columns:
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portfolio_weights[ticker] = shares
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# Calculate weighted sum
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if portfolio_weights:
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df['Portfolio'] = sum(df[ticker] * shares for ticker, shares in portfolio_weights.items())
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return total_value, stock_values, df
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def main():
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"""Run the portfolio analysis example."""
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with BorsdataClient(api_key) as client:
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print("Analyzing portfolio...")
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total_value, stock_values, price_history = calculate_portfolio_value(client, PORTFOLIO)
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print("\nPortfolio Summary:")
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print(f"Total Value: {total_value:.2f}")
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print("\nAllocation:")
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for ticker, value in stock_values.items():
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percentage = (value / total_value) * 100 if total_value > 0 else 0
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print(f" {ticker}: {value:.2f} ({percentage:.2f}%)")
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# Calculate performance metrics if we have data
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if not price_history.empty and 'Portfolio' in price_history.columns:
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portfolio_series = price_history['Portfolio']
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# Calculate returns
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returns = portfolio_series.pct_change().dropna()
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print("\nPerformance Metrics:")
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print(f" Start Value: {portfolio_series.iloc[0]:.2f}")
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print(f" End Value: {portfolio_series.iloc[-1]:.2f}")
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total_return = (portfolio_series.iloc[-1] / portfolio_series.iloc[0] - 1) * 100
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print(f" Total Return: {total_return:.2f}%")
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annualized_return = ((1 + total_return/100) ** (365/len(portfolio_series)) - 1) * 100
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print(f" Annualized Return: {annualized_return:.2f}%")
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volatility = returns.std() * (252 ** 0.5) * 100 # Annualized volatility
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print(f" Volatility (Annualized): {volatility:.2f}%")
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if volatility > 0:
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sharpe = annualized_return / volatility # Simplified Sharpe ratio (no risk-free rate)
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print(f" Sharpe Ratio: {sharpe:.2f}")
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if __name__ == "__main__":
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main()

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