Enhancing Portfolio Management: Tracking ETF Data — Part 1
Streamline Your Investments with FMP’s ETF API: A Data-Driven Guide to Smarter Portfolio Management
Photo by Markus Winkler
Managing a diverse portfolio can be challenging, especially when it comes to keeping track of ETFs. Exchange-Traded Funds (ETFs) are popular for their ability to offer diversification and cost-efficiency. But tracking their performance manually can quickly become overwhelming.
This is where Financial Modeling Prep’s (FMP) API steps in. It simplifies ETF tracking by providing real-time data, historical trends, and key metrics at your fingertips. Whether you’re a professional portfolio manager or an individual investor, FMP’s API makes it easy to monitor ETFs and make informed decisions.
In this article, we’ll explore how FMP’s API can transform the way you track ETFs. By the end, you’ll know exactly how to integrate this tool into your portfolio management strategy. Let’s dive in!
Exchange-Traded Funds (ETFs) are integral to modern investment strategies, offering diversification, liquidity, and cost-efficiency. However, effectively managing a portfolio with ETFs requires a data-driven approach. Key metrics like historical price trends, sector allocations, and expense ratios can significantly impact performance over time.
Why track ETF data?
ETF prices, like individual stocks, fluctuate based on market conditions. Tracking historical data helps identify trends, volatility patterns, and seasonality. For instance, an ETF heavily weighted in technology sectors might perform differently based on industry cycles or macroeconomic conditions. Monitoring these patterns can help you optimize your portfolio’s allocation.
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Additionally, technical indicators — like moving averages (MA), relative strength index (RSI), and Bollinger Bands — can provide deeper insights into an ETF’s momentum or overbought/oversold conditions. By integrating these into your analysis, you can make data-backed decisions to rebalance your portfolio.
How FMP’s API helps:
FMP’s ETF-related APIs provide comprehensive datasets, including:
ETF List API: Fetch the latest list of available ETFs, their tickers, and key identifiers.Historical Data API: Retrieve daily, weekly, or monthly price histories to analyze performance trends.Technical Indicator API: Calculate technical metrics (e.g., MA, RSI) on ETF prices for precise analysis.
By combining these APIs, you can automate ETF tracking, identify underperforming funds, and uncover opportunities for rebalancing.
Next, we’ll dive deeper into how to use FMP’s API to access and analyze ETF data effectively.
Exploring and Using FMP’s ETF API
FMP’s ETF API is a powerful tool for fetching, analyzing, and automating ETF data. It provides endpoints for retrieving ETF lists, historical data, and even advanced metrics like technical indicators.
Let’s break it down step by step and explore how to harness its potential with real-world applications.
1. Getting Started with the ETF API
To begin, you’ll need an API key from Financial Modeling Prep. Once you have the key, the base URL for accessing the ETF API is:
https://financialmodelingprep.com/api/v3/
Key Endpoints:
ETF List API: /etf/list
• Fetches a list of all available ETFs.Historical Data API: /historical-price-full/
• Provides historical price data for specific ETFs.Technical Indicator API: /technical_indicator/
• Computes technical indicators like Moving Average (MA), Relative Strength Index (RSI), etc.
2. Fetching the ETF List
Start by pulling the list of ETFs and their metadata:
import requests
import pandas as pd
# Define API key and base URL
api_key = “your_api_key”
base_url = “https://financialmodelingprep.com/api/v3/etf/list”
# Fetch ETF list
response = requests.get(f”{base_url}?apikey={api_key}”)
etf_list = response.json()
# Convert to a DataFrame for easier analysis
etf_df = pd.DataFrame(etf_list)
print(etf_df.head())
This request returns details like ETF symbols, names, and other metadata. You can filter ETFs by sector or other criteria for targeted analysis.
3. Retrieving Historical Price Data
Understanding price trends is critical for evaluating an ETF. The Historical Data API allows you to fetch daily, weekly, or monthly data:
# Define ETF symbol and endpoint
symbol = “SPY”
historical_url = f”https://financialmodelingprep.com/api/v3/historical-price-full/{symbol}”
# Fetch historical data
response = requests.get(f”{historical_url}?apikey={api_key}”)
historical_data = response.json()
# Extract and convert to a DataFrame
prices = historical_data[“historical”]
prices_df = pd.DataFrame(prices)
print(prices_df.head())
# Example: Calculate Daily Returns
prices_df[‘daily_return’] = prices_df[‘close’].pct_change()
print(prices_df[[‘date’, ‘close’, ‘daily_return’]].head())
4. Adding Technical Analysis
Technical indicators can provide actionable insights for ETF analysis. Using the Technical Indicator API, you can calculate metrics like the Moving Average (MA) or Bollinger Bands.
# Endpoint for technical indicators
indicator_url = f”https://financialmodelingprep.com/api/v3/technical_indicator/daily/{symbol}”
# Fetch 50-day Moving Average
params = {“apikey”: api_key, “period”: 50, “type”: “sma”} # SMA: Simple Moving Average
response = requests.get(indicator_url, params=params)
technical_data = response.json()
# Convert to DataFrame
sma_df = pd.DataFrame(technical_data)
print(sma_df.head())
You can overlay this data on a price chart to visualize the ETF’s momentum and decide whether to buy, hold, or sell.
5. Automating ETF Tracking
By combining the APIs, you can build a pipeline that automatically tracks ETF performance. For example:
Fetch the ETF list to identify candidates for tracking.Use the Historical Data API to monitor price trends and calculate returns.Apply Technical Indicator API metrics for buy/sell signals.# List of ETF symbols to track
etfs = [“SPY”, “QQQ”, “IWM”]
# Automate data fetching and analysis
for etf in etfs:
# Fetch historical data
historical_url = f”https://financialmodelingprep.com/api/v3/historical-price-full/{etf}”
response = requests.get(f”{historical_url}?apikey={api_key}”)
prices = response.json()[“historical”]
prices_df = pd.DataFrame(prices)
# Calculate 50-day SMA
prices_df[’50_day_sma’] = prices_df[‘close’].rolling(window=50).mean()
print(f”Analysis for {etf}”)
print(prices_df[[‘date’, ‘close’, ’50_day_sma’]].tail())
6. Mathematical Insights
Mathematical analysis forms the backbone of ETF tracking. Below are a few key formulas you can implement:
Daily Return:
where R_t is the return, P_t is today’s price, and P_{t-1} is yesterday’s price.
Moving Average (MA):
where n is the period, and P_{t-i} is the price on day i .
Bollinger Bands:
where sigma is the standard deviation.
By leveraging these APIs, code, and mathematical techniques, you can transform raw ETF data into actionable insights. In the next section, we’ll look at practical applications for portfolio optimization.
Using ETF Data for Portfolio Optimization
ETF data is more than just numbers — it’s a toolkit for optimizing your portfolio. With FMP’s ETF API, you can leverage historical trends, technical indicators, and key metrics to make smarter investment decisions. Let’s explore some practical ways to use this data.
1. Tracking ETF Performance Across Time
By analyzing historical data, you can monitor an ETF’s performance over different time horizons. For instance:
Short-term Analysis: Evaluate daily or weekly trends to identify momentum or volatility.Long-term Analysis: Assess monthly or yearly data to gauge stability and growth potential.
Python Example: Calculating Annualized Return
Using FMP’s Historical Data API, you can calculate the compound annual growth rate (CAGR):
import numpy as np
# Calculate CAGR
initial_price = prices_df[‘close’].iloc[-1]
final_price = prices_df[‘close’].iloc[0]
num_years = len(prices_df) / 252 # Assuming 252 trading days per year
cagr = (final_price / initial_price) ** (1 / num_years) – 1
print(f”Compound Annual Growth Rate (CAGR): {cagr:.2%}”)
2. Diversifying Across Sectors and Industries
ETF sector allocation data helps balance risk. For instance, an ETF heavily invested in technology might perform differently during economic downturns compared to a healthcare-focused ETF. By analyzing allocations, you can ensure your portfolio remains diversified.
Practical Step: Fetch sector allocation data for ETFs in your portfolio, then visualize the distribution to spot overexposure.
Python Example: Visualizing Sector Allocationsimport matplotlib.pyplot as plt
# Sample sector allocation data
sector_data = {
“Technology”: 40,
“Healthcare”: 25,
“Finance”: 20,
“Energy”: 15
}
# Plot pie chart
plt.pie(sector_data.values(), labels=sector_data.keys(), autopct=’%1.1f%%’)
plt.title(“Sector Allocation”)
plt.show()
3. Identifying Overperforming and Underperforming ETFs
FMP’s Technical Indicator API can help identify ETFs that deviate from their average performance:
Moving Average Crossover: Compare short-term (e.g., 20-day) and long-term (e.g., 50-day) MAs to identify buy/sell signals.Relative Strength Index (RSI): Detect overbought or oversold conditions, helping to time your trades.Python Example: Moving Average Crossover# Calculate short-term and long-term MAs
prices_df[’20_day_sma’] = prices_df[‘close’].rolling(window=20).mean()
prices_df[’50_day_sma’] = prices_df[‘close’].rolling(window=50).mean()
# Identify crossovers
prices_df[‘signal’] = (prices_df[’20_day_sma’] > prices_df[’50_day_sma’]).astype(int)
print(prices_df[[‘date’, ‘close’, ’20_day_sma’, ’50_day_sma’, ‘signal’]].tail())
4. Risk Management: Monitoring Volatility
Volatility analysis helps you understand an ETF’s risk profile. You can calculate metrics like standard deviation and beta to assess how an ETF behaves relative to the broader market.
Python Example: Calculating Volatility# Calculate daily returns and standard deviation
prices_df[‘daily_return’] = prices_df[‘close’].pct_change()
volatility = prices_df[‘daily_return’].std() * np.sqrt(252) # Annualized volatility
print(f”Annualized Volatility: {volatility:.2%}”)
5. Portfolio Rebalancing with ETF Data
Using the data from FMP’s API, you can automate rebalancing:
Fetch ETF weights in your portfolio.Compare them to your target allocation.Identify underweight or overweight positions.current_weights = {“SPY”: 0.35, “QQQ”: 0.45, “IWM”: 0.20} # Current weights
target_weights = {“SPY”: 0.40, “QQQ”: 0.40, “IWM”: 0.20} # Target weights
# Calculate rebalancing percentages
rebalance = {etf: target_weights[etf] – current_weights[etf] for etf in current_weights}
print(“Rebalancing Needed:”, rebalance)
By combining these applications, you can use ETF data to minimize risk, maximize returns, and maintain a balanced portfolio. In the next section, we’ll bring it all together with a simplified case study that demonstrates these techniques in action.
Case Study: Simplified ETF Portfolio Tracker
To illustrate how Financial Modeling Prep’s ETF API can transform portfolio management, let’s walk through a simplified project: building an ETF Portfolio Tracker. This tracker will:
Monitor ETF performance in real time.Calculate key metrics like moving averages and returns.Provide actionable insights for rebalancing and optimization.
1. Setting Up the Portfolio Tracker
We’ll start by defining a sample portfolio and fetching the required data.
Portfolio Definition:
portfolio = {
“SPY”: 50_000, # Investment in SPY ETF
“QQQ”: 30_000, # Investment in QQQ ETF
“IWM”: 20_000 # Investment in IWM ETF
}
total_investment = sum(portfolio.values())
print(f”Total Investment: ${total_investment}”)
Fetching Data:
Using the ETF List and Historical Data APIs, retrieve the price history for each ETF in the portfolio:
import requests
def fetch_etf_data(symbol, api_key):
url = f”https://financialmodelingprep.com/api/v3/historical-price-full/{symbol}?apikey={api_key}”
response = requests.get(url)
return response.json()[“historical”]
api_key = “your_api_key”
etf_data = {symbol: fetch_etf_data(symbol, api_key) for symbol in portfolio.keys()}
2. Calculating Portfolio Metrics
Using the historical price data, calculate portfolio-level metrics such as daily returns, cumulative returns, and volatility.
Daily and Cumulative Returns:
import pandas as pd
# Process SPY data as an example
spy_data = pd.DataFrame(etf_data[“SPY”])
spy_data[“daily_return”] = spy_data[“close”].pct_change()
spy_data[“cumulative_return”] = (1 + spy_data[“daily_return”]).cumprod()
print(spy_data[[“date”, “close”, “daily_return”, “cumulative_return”]].tail())
Portfolio Volatility:
Combine returns from all ETFs to calculate overall portfolio volatility:
# Merge data for all ETFs
returns_data = pd.DataFrame({
symbol: pd.DataFrame(data)[“close”].pct_change()
for symbol, data in etf_data.items()
})
# Calculate portfolio-level volatility
portfolio_weights = {symbol: investment / total_investment for symbol, investment in portfolio.items()}
portfolio_volatility = (returns_data.cov() * pd.Series(portfolio_weights)).sum().sum() ** 0.5
print(f”Portfolio Volatility: {portfolio_volatility:.2%}”)
3. Technical Analysis for ETF Signals
Incorporate technical indicators like moving averages to identify potential buy/sell signals.
Example: Moving Average Analysis
# Calculate 50-day and 200-day moving averages for SPY
spy_data[“50_day_sma”] = spy_data[“close”].rolling(window=50).mean()
spy_data[“200_day_sma”] = spy_data[“close”].rolling(window=200).mean()
# Add buy/sell signals
spy_data[“signal”] = (spy_data[“50_day_sma”] > spy_data[“200_day_sma”]).astype(int)
print(spy_data[[“date”, “close”, “50_day_sma”, “200_day_sma”, “signal”]].tail())
4. Visualizing the Tracker
To make the portfolio tracker more actionable, visualize key metrics and insights.
Portfolio Allocation:
import matplotlib.pyplot as plt
# Plot portfolio allocation
allocations = {etf: portfolio[etf] / total_investment * 100 for etf in portfolio.keys()}
plt.bar(allocations.keys(), allocations.values())
plt.title(“Portfolio Allocation (%)”)
plt.ylabel(“Allocation”)
plt.show()
Performance Trends:
Plot the historical performance of ETFs and cumulative returns:
plt.plot(spy_data[“date”], spy_data[“cumulative_return”], label=”SPY Cumulative Return”)
plt.legend()
plt.title(“Cumulative Returns Over Time”)
plt.xlabel(“Date”)
plt.ylabel(“Cumulative Return”)
plt.show()
5. Automating Rebalancing Decisions
Using real-time ETF data, you can automate rebalancing. Compare actual weights to target weights and calculate required trades.
Example Rebalancing Workflow:
target_weights = {“SPY”: 0.50, “QQQ”: 0.30, “IWM”: 0.20}
# Calculate current weights
current_weights = {etf: portfolio[etf] / total_investment for etf in portfolio.keys()}
rebalance = {etf: target_weights[etf] – current_weights[etf] for etf in target_weights.keys()}
print(“Rebalancing Recommendations:”)
for etf, adjustment in rebalance.items():
print(f”{etf}: {‘Buy’ if adjustment > 0 else ‘Sell’} {abs(adjustment) * total_investment:.2f}”)
This simplified tracker demonstrates the power of FMP’s API in automating ETF monitoring and portfolio management. In the final section, we’ll summarize the key takeaways and encourage you to build your own tracker.
Conclusion
Tracking ETFs is essential for maintaining a well-balanced and optimized portfolio. With Financial Modeling Prep’s ETF API, you gain access to comprehensive data and tools to simplify this process. From historical price trends to technical indicators and portfolio-level insights, the API empowers you to make data-driven decisions effortlessly.
In this article, we explored how to use FMP’s ETF API for practical applications like calculating returns, analyzing sector allocations, and automating portfolio rebalancing. We even demonstrated a simplified ETF portfolio tracker to show the API in action.
Whether you’re a professional portfolio manager or an individual investor, FMP’s API provides the foundation to take your portfolio management to the next level. Now, it’s time for you to dive into the API, experiment with your own projects, and unlock the full potential of ETF data.
Enhancing Portfolio Management: Tracking ETF Data — Part 1 was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.