New example: Charts, Volume, Closing, Daily return plots.

Author: alexwox

examples/README.md

@@ -6,11 +6,7 @@ This directory contains example scripts demonstrating how to use the Borsdata AP
 
 Before running these examples, make sure you have:
 
-1. Installed the borsdata-client package:
-
-   ```bash
-   pip install borsdata-client
-   ```
+1. Installed the borsdata-client package by cloning the repo.
 
 2. Set up your API key as an environment variable or in a `.env` file:
    ```

examples/portfolio_analysis.py

@@ -66,7 +66,7 @@ def calculate_portfolio_value(
             instrument = get_instrument_by_ticker(client, ticker)
 
             # Get historical prices
-            prices = get_historical_prices(client, instrument.insId)
+            prices = get_historical_prices(client, instrument.ins_id)
 
             if not prices:
                 print(f"No price data available for {ticker}")

examples/stock_visualization.py

@@ -0,0 +1,162 @@
+#!/usr/bin/env python3
+"""
+Stock visualization example using the Borsdata API client.
+
+This example demonstrates how to create beautiful visualizations of stock data
+including price trends, volume, and basic technical indicators.
+"""
+
+import os
+from datetime import datetime, timedelta
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from dotenv import load_dotenv
+from borsdata_client import BorsdataClient
+
+# Set style for prettier plots
+plt.style.use('seaborn-v0_8')
+sns.set_palette("husl")
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Get API key from environment
+api_key = os.getenv("BORSDATA_API_KEY")
+if not api_key:
+    raise ValueError("BORSDATA_API_KEY environment variable not set")
+
+def get_stock_data(client: BorsdataClient, ticker: str, days: int = 365) -> pd.DataFrame:
+    """
+    Fetch stock data and convert it to a pandas DataFrame.
+    
+    Args:
+        client: BorsdataClient instance
+        ticker: Stock ticker symbol
+        days: Number of days of historical data to fetch
+        
+    Returns:
+        DataFrame with stock price data
+    """
+    # Find the instrument
+    instruments = client.get_instruments()
+    instrument = next((i for i in instruments if i.ticker == ticker), None)
+    if not instrument:
+        raise ValueError(f"Could not find instrument with ticker {ticker}")
+    
+    # Get historical prices
+    today = datetime.now()
+    start_date = today - timedelta(days=days)
+    prices = client.get_stock_prices(
+        instrument_id=instrument.ins_id,
+        from_date=start_date,
+        to_date=today
+    )
+    
+    # Convert to DataFrame
+    df = pd.DataFrame([{
+        'Date': datetime.strptime(p.d, '%Y-%m-%d'),
+        'Open': p.o,
+        'High': p.h,
+        'Low': p.l,
+        'Close': p.c,
+        'Volume': p.v
+    } for p in prices])
+    
+    df.set_index('Date', inplace=True)
+    
+    # Add some technical indicators
+    df['SMA_20'] = df['Close'].rolling(window=20).mean()
+    df['SMA_50'] = df['Close'].rolling(window=50).mean()
+    df['Daily_Return'] = df['Close'].pct_change()
+    
+    return df
+
+def plot_stock_analysis(df: pd.DataFrame, ticker: str):
+    """
+    Create a comprehensive visualization of stock data.
+    
+    Args:
+        df: DataFrame with stock price data
+        ticker: Stock ticker symbol for the title
+    """
+    # Create a figure with subplots
+    fig = plt.figure(figsize=(16, 10), constrained_layout=True)
+    
+    # Define grid for subplots
+    gs = fig.add_gridspec(3, 1, height_ratios=[2, 1, 1])
+    
+    # Price and Moving Averages
+    ax1 = fig.add_subplot(gs[0])
+    ax1.plot(df.index, df['Close'], label='Close Price', linewidth=1.5)
+    ax1.plot(df.index, df['SMA_20'], label='20-day SMA', linestyle='--', alpha=0.8)
+    ax1.plot(df.index, df['SMA_50'], label='50-day SMA', linestyle='--', alpha=0.8)
+    ax1.set_title(f'{ticker} Stock Price Analysis', fontsize=14, pad=20)
+    ax1.set_ylabel('Price')
+    ax1.legend(loc='center left', bbox_to_anchor=(1.02, 0.5),
+              fancybox=True, shadow=True, framealpha=1)
+    ax1.grid(True, alpha=0.3)
+    
+    # Volume
+    ax2 = fig.add_subplot(gs[1], sharex=ax1)
+    ax2.bar(df.index, df['Volume'], alpha=0.5, color='darkblue')
+    ax2.set_ylabel('Volume')
+    ax2.grid(True, alpha=0.3)
+    
+    # Daily Returns
+    ax3 = fig.add_subplot(gs[2], sharex=ax1)
+    ax3.plot(df.index, df['Daily_Return'], color='green', alpha=0.7)
+    ax3.axhline(y=0, color='black', linestyle='-', alpha=0.3)
+    ax3.fill_between(df.index, df['Daily_Return'], 0, 
+                     where=(df['Daily_Return'] >= 0), color='green', alpha=0.3)
+    ax3.fill_between(df.index, df['Daily_Return'], 0, 
+                     where=(df['Daily_Return'] < 0), color='red', alpha=0.3)
+    ax3.set_ylabel('Daily Returns')
+    ax3.grid(True, alpha=0.3)
+    
+    # Adjust layout and display
+    plt.xlabel('Date')
+    
+    # Add some statistics as text
+    stats_text = (
+        f"Statistics:\n"
+        f"Current Price: {df['Close'].iloc[-1]:.2f}\n"
+        f"52-week High: {df['High'].max():.2f}\n"
+        f"52-week Low: {df['Low'].min():.2f}\n"
+        f"Daily Vol.: {df['Daily_Return'].std()*100:.2f}%"
+    )
+    
+    # Add text in a better position that won't conflict with tight_layout
+    ax1.text(0.02, 0.98, stats_text,
+             transform=ax1.transAxes,
+             bbox=dict(facecolor='white', alpha=0.8, edgecolor='none'),
+             fontsize=10,
+             verticalalignment='top')
+    
+    return fig
+
+def main():
+    """Run the stock visualization example."""
+    # Example tickers (can be changed to any stock available in Borsdata)
+    tickers = ['ERIC B', 'VOLV B']
+    
+    with BorsdataClient(api_key) as client:
+        for ticker in tickers:
+            try:
+                print(f"\nFetching data for {ticker}...")
+                df = get_stock_data(client, ticker)
+                
+                print("Creating visualization...")
+                fig = plot_stock_analysis(df, ticker)
+                
+                # Save the plot
+                filename = f"{ticker.replace(' ', '_')}_analysis.png"
+                fig.savefig(filename)
+                print(f"Saved visualization to {filename}")
+                plt.close(fig)
+                
+            except Exception as e:
+                print(f"Error processing {ticker}: {e}")
+
+if __name__ == "__main__":
+    main() 

requirements-dev.txt

@@ -1,4 +1,7 @@
 -r requirements.txt
+pandas>=2.0.0
+matplotlib>=3.8.0
+seaborn>=0.13.0
 pytest>=7.4.0
 pytest-cov>=4.1.0
 pytest-mock>=3.11.1