\n \n
import requests\nimport pandas as pd\nimport requests_cache\nimport os\nfrom matplotlib import pyplot as plt\nfrom tqdm import tqdm\nimport warnings\n\napi_token = os.environ.get('EODHD_API_TOKEN')\n\ndef get_sp500_tickers():\n\n INDEX_NAME = 'GSPC.INDX'\n url = f'https:\/\/eodhd.com\/api\/mp\/unicornbay\/spglobal\/comp\/{INDEX_NAME}'\n query = {'api_token': api_token, \"fmt\": \"json\"}\n\n data = requests.get(url, params=query)\n\n if data.status_code != 200:\n tqdm.write(f\"Error: {data.status_code}\")\n tqdm.write(data.text)\n return []\n\n data = data.json()\n df_stocks = pd.DataFrame(data['Components']).T.reset_index()\n\n df_stocks.to_csv('csv\/stocks_universe.csv', index=False)\n tickers = df_stocks['Code'].tolist()\n return tickers\n\n\n# Get S&P 500 tickers\nsp500_tickers = get_sp500_tickers()<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n Now lets get the prices of all the SP500 stocks from 2024 and store them in a dataframe named sp_500_prices_df<\/p>\n\n\n\n
\n
\n \n
start_date = '2024-01-01'\nsp_500_prices_df = pd.DataFrame()\n\nfor ticker in tqdm(sp500_tickers, desc=\"Fetching ticker data\"):\n try:\n # Construct the API URL\n ticker_code = f\"{ticker}.US\"\n url = f'https:\/\/eodhd.com\/api\/eod\/{ticker_code}'\n\n # Set query parameters\n query = {\n 'api_token': api_token,\n 'fmt': 'json',\n 'from': start_date\n }\n\n response = requests.get(url, params=query)\n\n if response.status_code == 200:\n # Parse the JSON response\n data = response.json()\n\n # Convert to DataFrame\n ticker_df = pd.DataFrame(data)\n\n # Extract date and adjusted_close\n if 'adjusted_close' in ticker_df.columns:\n prices = ticker_df[['date', 'adjusted_close']].copy()\n prices.set_index('date', inplace=True)\n\n # Rename the column to the ticker symbol\n prices.rename(columns={'adjusted_close': ticker}, inplace=True)\n\n # Join with the main DataFrame\n if sp_500_prices_df.empty:\n sp_500_prices_df = prices\n else:\n sp_500_prices_df = sp_500_prices_df.join(prices, how='outer')\n\n else:\n tqdm.write(f\"No adjusted close data available for {ticker}\")\n else:\n tqdm.write(f\"Error fetching data for {ticker}: {response.status_code}\")\n tqdm.write(response.text)\n except Exception as e:\n tqdm.write(f\"Exception occurred while processing {ticker}: {str(e)}\")\n\nreturns = sp_500_prices_df.pct_change().dropna()<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n The next step is to get into the fundamentals. We\u2019ll enrich our S&P 500 stocks dataframe by adding columns for Sector, Industry, Market Capitalisation, and P\/E ratio\u2014details that will be essential for our grouping later on. For Market Capitalisation, you\u2019ll see that I categorize it into more intuitive groups, such as Mega, Large, and so on, to make the data easier to interpret.<\/p>\n\n\n\n
\n
\n \n
df = pd.DataFrame()\n\n# Fetch fundamental data for each ticker\nfor ticker in tqdm(sp500_tickers, desc=\"Fetching fundamental data\"):\n try:\n # Construct the API URL\n ticker_code = f\"{ticker}.US\"\n url = f'https:\/\/eodhd.com\/api\/fundamentals\/{ticker_code}'\n\n # Set query parameters\n query = {\n 'api_token': api_token,\n 'fmt': 'json'\n }\n\n # Make the API request\n response = requests.get(url, params=query)\n\n # Check if the request was successful\n if response.status_code == 200:\n # Parse the JSON response\n data = response.json()\n\n # Extract the required metrics\n metrics = {\n 'Ticker': ticker,\n 'Name': data.get('General', {}).get('Name', None),\n 'Sector': data.get('General', {}).get('Sector', None),\n 'Industry': data.get('General', {}).get('Industry', None),\n 'Market Capitalisation': data.get('Highlights', {}).get('MarketCapitalization', None),\n 'P\/E Ratio': data.get('Highlights', {}).get('PERatio', None)\n }\n\n # Add to the dataframe\n df = pd.concat([df, pd.DataFrame([metrics])], ignore_index=True)\n else:\n tqdm.write(f\"Error fetching fundamental data for {ticker}: {response.status_code}\")\n tqdm.write(response.text)\n except Exception as e:\n tqdm.write(f\"Exception occurred while processing fundamental data for {ticker}: {str(e)}\")\n\n# Define capitalization bins and labels\nbins = [0, 1e10, 1e11, 1e12, 1e13]\nlabels = ['Small (<10B)', 'Medium (10B-100B)', 'Large (100B-1T)', 'Mega (>1T)']\n\n# Create a new column for capitalization bins\ndf['Cap_Bin'] = pd.cut(df['Market Capitalisation'], bins=bins, labels=labels)<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n We will also add in the same dataframe the \u00a0<\/span>6-month volatility, which will later be used as a feature for grouping.<\/span><\/p>\n\n\n\n \n
\n \n
sp_500_prices_df.index = pd.to_datetime(sp_500_prices_df.index)\n\n# Sort by date\nsp_500_prices_df = sp_500_prices_df.sort_index()\n\n# Get the last 6 months of data\nsix_months_ago = sp_500_prices_df.index[-1] - pd.DateOffset(months=6)\nsix_month_prices = sp_500_prices_df[sp_500_prices_df.index >= six_months_ago]\n\n# Calculate daily returns\ndaily_returns = six_month_prices.pct_change().dropna()\n\n# Calculate volatility (standard deviation of returns)\nvolatility = daily_returns.std() * (252 ** 0.5) # Annualized volatility\n\n# Add volatility to the fundamentals dataframe\nfor ticker in sp500_tickers:\n if ticker in volatility.index:\n mask = df['Ticker'] == ticker\n df.loc[mask, '6-Month Volatility'] = volatility[ticker]<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n Clustering with fundamentals<\/h2>\n\n\n\n With all the required data in place, we\u2019ll now organise the stocks into 11 clusters. I selected this number because there are 11 sectors in the S&P 500, and I\u2019m interested to see how closely our clusters will match the actual sector classifications.<\/p>\n\n\n\n
In a nutshell:<\/p>\n\n\n\n
\nWe define the features and normalise them<\/li>\n\n\n\n In case there are missing data we impute them<\/li>\n\n\n\n We use Principal Component Analysis (PCA) to reduce the complexity<\/li>\n\n\n\n We use K-Means and group them into 11 clusters<\/li>\n<\/ul>\n\n\n\n \n
\n \n
# Preprocessing\nfeatures = df[['Market Capitalisation','P\/E Ratio','6-Month Volatility']]\n\n# Normalize features\nfrom sklearn.preprocessing import StandardScaler\n\nscaler = StandardScaler()\nscaled_data = scaler.fit_transform(features)\n\n# Impute Missing Data\n# The most common approach is to fill missing values with the mean, median, or another strategy using SimpleImputer from scikit-learn:\n\nfrom sklearn.impute import SimpleImputer\n\nimputer = SimpleImputer(strategy='mean') # or 'median'\nimputed_data = imputer.fit_transform(features)\n\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.decomposition import PCA\n\nscaler = StandardScaler()\nscaled_data = scaler.fit_transform(imputed_data)\n\npca = PCA(n_components=0.95)\nprincipal_components = pca.fit_transform(scaled_data)\n\nfrom sklearn.cluster import KMeans\n\nk = 11 # Typical for sector-based clustering\nmodel = KMeans(n_clusters=k)\nclusters = model.fit_predict(principal_components)\ndf['ClusterFeatures'] = clusters<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n Let\u2019s plot and see how the stocks are distributed per sector:<\/p>\n\n\n\n
\n
\n \n
# Group by Sector and ClusterCorrelation, then count\nsector_cluster_counts = df.groupby(['Sector', 'ClusterFeatures']).size().reset_index(name='Count')\n\n# Pivot for a matrix view\npivot_table = sector_cluster_counts.pivot(index='Sector', columns='ClusterFeatures', values='Count').fillna(0).astype(int)\n\n# Create a stacked bar chart\nplt.figure(figsize=(14, 10))\npivot_table_percentage = pivot_table.div(pivot_table.sum(axis=1), axis=0) * 100\n\n# Plot stacked bar chart\npivot_table_percentage.T.plot(kind='bar', stacked=True, figsize=(14, 10), colormap='tab20')\nplt.title('Percentage Distribution of Sectors across Correlation Features', fontsize=16)\nplt.xlabel('Correlation Features', fontsize=14)\nplt.ylabel('Percentage', fontsize=14)\nplt.legend(title='Sector', bbox_to_anchor=(1.05, 1), loc='upper left')\nplt.tight_layout()\nplt.show()<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n <\/p>\n\n\n\n
Not so insightful. The groups are unbalanced and not related to sector. So lets use some box plots:<\/p>\n\n\n\n
\n
\n \n
import matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Set up the figure with 3 subplots side by side\nfig, axes = plt.subplots(1, 3, figsize=(18, 6))\n\n# Boxplot for Market Capitalization\nsns.boxplot(x='ClusterFeatures', y='Market Capitalisation', data=df, ax=axes[0])\naxes[0].set_title('Market Cap by Correlation Cluster')\naxes[0].set_ylabel('Market Cap ($)')\naxes[0].set_xlabel('Correlation Cluster')\n# Use log scale for Market Cap due to wide range\naxes[0].set_yscale('log')\n\n# Boxplot for P\/E Ratio\nsns.boxplot(x='ClusterFeatures', y='P\/E Ratio', data=df, ax=axes[1])\naxes[1].set_title('P\/E Ratio by Correlation Cluster')\naxes[1].set_ylabel('P\/E Ratio')\naxes[1].set_xlabel('Correlation Cluster')\n# Set a reasonable y-limit to handle outliers\naxes[1].set_ylim(0, df['P\/E Ratio'].quantile(0.95))\n\n# Boxplot for 6-Month Volatility\nsns.boxplot(x='ClusterFeatures', y='6-Month Volatility', data=df, ax=axes[2])\naxes[2].set_title('6-Month Volatility by Correlation Cluster')\naxes[2].set_ylabel('Volatility (Annualized)')\naxes[2].set_xlabel('Correlation Cluster')\n\nplt.tight_layout()\nplt.show()<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n <\/p>\n\n\n\n
What I can see from the box plots in a first glance is:<\/p>\n\n\n\n
\nGroup 4 consists of Apple, Nvidia, and Microsoft\u2014clearly representing the largest stocks by market capitalization. Group 5 features the next tier of giants, including Google, Amazon, and Meta, while Group 9 brings together Broadcom, Lilly, Oracle, and Tesla. The accompanying boxplot of P\/E Ratios shows that the algorithm effectively distinguished these mega-cap companies based on their P\/E Ratio.<\/li>\n\n\n\n It\u2019s also noteworthy that groups 0, 1, 2, 7, and 10 are predominantly made up of small- and mid-cap stocks. Group 10 stands out with the highest P\/E ratio and shows moderate to low volatility, while the remaining four groups are mainly distinguished by their varying levels of volatility.<\/li>\n<\/ul>\n\n\n\nI use this method when I see a stock I like and want similar stocks with comparable features. I don\u2019t want to miss an opportunity if I\u2019m right, as other stocks in the same group could be even better options. This may seem like it could be done easily with a good screener. But with more features and groups, this method can quickly generate groups of stocks sharing the same features, expanding your search and providing better insights for decision-making.<\/p>\n\n\n\n
Clustering with price correlation<\/h2>\n\n\n\n Now we move to something more interesting and familiar – the price action! We’ll perform and other clustering process using each stock’s correlation, grouping (again into 11 clusters) stocks based on their price movements.<\/p>\n\n\n\n
In a nutshell:<\/p>\n\n\n\n
\nCalculate the correlation matrix.<\/li>\n\n\n\n Convert the correlations into distances.<\/li>\n\n\n\n Perform hierarchical clustering using the average linkage method.<\/li>\n<\/ul>\n\n\n\n \n
\n \n
import numpy as np\n\ncorr_matrix = returns.corr()\n\ndistance_matrix = np.sqrt(2 * (1 - corr_matrix))\ndistance_matrix\n\nfrom scipy.cluster.hierarchy import linkage, dendrogram\n\n# Average linkage often performs best for financial data[1][5]\nZ = linkage(distance_matrix, method='average')\n\nfrom scipy.cluster.hierarchy import fcluster\n\n# t is the number of clusters\nclusters = fcluster(Z, t=11, criterion='maxclust')\n\ndf.to_csv('csv\/sp500_clustered.csv', index=False)\ndf['ClusterCorrelation'] = clusters<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n Now let’s plot the same bar chart based on the sectors.<\/p>\n\n\n\n
\n
\n \n
# Group by Sector and ClusterCorrelation, then count\nsector_cluster_counts = df.groupby(['Sector', 'ClusterCorrelation']).size().reset_index(name='Count')\n\n# Pivot for a matrix view\npivot_table = sector_cluster_counts.pivot(index='Sector', columns='ClusterCorrelation', values='Count').fillna(0).astype(int)\n\n# Create a stacked bar chart\nplt.figure(figsize=(14, 10))\npivot_table_percentage = pivot_table.div(pivot_table.sum(axis=1), axis=0) * 100\n\n# Plot stacked bar chart\npivot_table_percentage.T.plot(kind='bar', stacked=True, figsize=(14, 10), colormap='tab20')\nplt.title('Percentage Distribution of Sectors across Correlation Clusters', fontsize=16)\nplt.xlabel('Correlation Cluster', fontsize=14)\nplt.ylabel('Percentage', fontsize=14)\nplt.legend(title='Sector', bbox_to_anchor=(1.05, 1), loc='upper left')\nplt.tight_layout()\nplt.show()<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n <\/p>\n\n\n\n
Let\u2019s discuss this bar chart:<\/p>\n\n\n\n
\n\u00a0Energy\u00a0<\/strong>stocks are one cluster, indicating that this is a highly distinctive sector that moves together!<\/li>\n\n\n\nMost Utility <\/strong>stocks in group 2 are from the Regular Electric <\/strong>industry, highlighting its distinct behavior within the sector.<\/li>\n\n\n\nAll the Mega <\/strong>stocks are in Group 7, except Berkshire, which is in Group 9. Nearly all technology stocks are also in Group 7, underscoring tech\u2019s dominant role in the market.demonstrating that technology is the primary driver of the market.<\/li>\n<\/ul>\n\n\n\nWhat to do next<\/h2>\n\n\n\n In the scope of this post, it is not so easy to examine all the possibilities of the clustering method. However, with some minor changes (or a bit more) of the above code, you can experiment and find insightful information that will guide you towards your personal trading edge:<\/p>\n\n\n\n
\nAdd more features: dividend yield, debt\/equity, revenue growth.<\/li>\n\n\n\n Use technical indicators: RSI, MACD, moving averages.<\/li>\n\n\n\n Incorporate alternative data: news sentiment, ESG scores.<\/li>\n\n\n\n Apply clustering to rolling time windows.<\/li>\n\n\n\n Build cluster-based stock screeners.<\/li>\n\n\n\n Set up alerts for cluster composition changes.<\/li>\n\n\n\n Automate clustering pipeline with scheduled updates.<\/li>\n<\/ul>\n\n\n\nThanks for reading!<\/p>\n","protected":false},"excerpt":{"rendered":"
Clustering stocks can substantially enhance your trading strategies and assist in managing diversification with professionalism. The concept of clustering involves the grouping of stocks according to their fundamental characteristics and\/or their price movements. A common way to classify stocks is by sector. If you haven\u2019t already, consider making sector diversification a key part of your […]<\/p>\n","protected":false},"author":30,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[48],"tags":[],"coding-language":[],"ready-to-go-solution":[],"qualification":[],"financial-apis-category":[],"financial-apis-manuals":[],"class_list":["post-6448","post","type-post","status-publish","format-standard","hentry","category-stocks-data-processing-examples"],"yoast_head":"\n
Clustering for Traders: Boost Your Portfolio\u2019s Performance with Data Science | EODHD APIs Academy<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/eodhd.com\/financial-academy\/stocks-data-processing-examples\/clustering-for-traders-boost-your-portfolios-performance-with-data-science\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Clustering for Traders: Boost Your Portfolio\u2019s Performance with Data Science\" \/>\n<meta property=\"og:description\" content=\"Clustering stocks can substantially enhance your trading strategies and assist in managing diversification with professionalism. The concept of clustering involves the grouping of stocks according to their fundamental characteristics and\/or their price movements. 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