Initial commit

app.py

@@ -0,0 +1,124 @@
+"""
+Demo is based on https://scikit-learn.org/stable/auto_examples/applications/plot_stock_market.html
+"""
+
+import sys
+import numpy as np
+import pandas as pd
+
+symbol_dict = {
+    "TOT": "Total",
+    "XOM": "Exxon",
+    "CVX": "Chevron",
+    "COP": "ConocoPhillips",
+    "VLO": "Valero Energy",
+    "MSFT": "Microsoft",
+    "IBM": "IBM",
+    "TWX": "Time Warner",
+    "CMCSA": "Comcast",
+    "CVC": "Cablevision",
+    "YHOO": "Yahoo",
+    "DELL": "Dell",
+    "HPQ": "HP",
+    "AMZN": "Amazon",
+    "TM": "Toyota",
+    "CAJ": "Canon",
+    "SNE": "Sony",
+    "F": "Ford",
+    "HMC": "Honda",
+    "NAV": "Navistar",
+    "NOC": "Northrop Grumman",
+    "BA": "Boeing",
+    "KO": "Coca Cola",
+    "MMM": "3M",
+    "MCD": "McDonald's",
+    "PEP": "Pepsi",
+    "K": "Kellogg",
+    "UN": "Unilever",
+    "MAR": "Marriott",
+    "PG": "Procter Gamble",
+    "CL": "Colgate-Palmolive",
+    "GE": "General Electrics",
+    "WFC": "Wells Fargo",
+    "JPM": "JPMorgan Chase",
+    "AIG": "AIG",
+    "AXP": "American express",
+    "BAC": "Bank of America",
+    "GS": "Goldman Sachs",
+    "AAPL": "Apple",
+    "SAP": "SAP",
+    "CSCO": "Cisco",
+    "TXN": "Texas Instruments",
+    "XRX": "Xerox",
+    "WMT": "Wal-Mart",
+    "HD": "Home Depot",
+    "GSK": "GlaxoSmithKline",
+    "PFE": "Pfizer",
+    "SNY": "Sanofi-Aventis",
+    "NVS": "Novartis",
+    "KMB": "Kimberly-Clark",
+    "R": "Ryder",
+    "GD": "General Dynamics",
+    "RTN": "Raytheon",
+    "CVS": "CVS",
+    "CAT": "Caterpillar",
+    "DD": "DuPont de Nemours",
+}
+
+
+symbols, names = np.array(sorted(symbol_dict.items())).T
+
+quotes = []
+
+for symbol in symbols:
+    print("Fetching quote history for %r" % symbol, file=sys.stderr)
+    url = (
+        "https://raw.githubusercontent.com/scikit-learn/examples-data/"
+        "master/financial-data/{}.csv"
+    )
+    quotes.append(pd.read_csv(url.format(symbol)))
+
+close_prices = np.vstack([q["close"] for q in quotes])
+open_prices = np.vstack([q["open"] for q in quotes])
+
+# The daily variations of the quotes are what carry the most information
+variation = close_prices - open_prices
+
+
+from sklearn import covariance
+
+alphas = np.logspace(-1.5, 1, num=10)
+edge_model = covariance.GraphicalLassoCV(alphas=alphas)
+
+# standardize the time series: using correlations rather than covariance
+# former is more efficient for structurerelations rather than covariance
+# former is more efficient for structure recovery
+X = variation.copy().T
+X /= X.std(axis=0)
+edge_model.fit(X)
+
+
+
+from sklearn import cluster
+
+_, labels = cluster.affinity_propagation(edge_model.covariance_, random_state=0)
+n_labels = labels.max()
+
+
+
+import gradio as gr
+
+title = " 📈 Visualizing the stock market structure 📈"
+
+with gr.Blocks(title=title) as demo:
+    gr.Markdown(f"# {title}")
+    gr.Markdown(" Data is of 56 stocks between the period of 2003 - 2008 <br>")
+    gr.Markdown(" Stocks the move in together with each other are grouped together in a cluster <br>")
+
+    gr.Markdown(" **[Demo is based on sklearn docs](https://scikit-learn.org/stable/auto_examples/applications/plot_stock_market.html)**")
+
+    for i in range(n_labels + 1):
+        gr.Markdown( f"Cluster {i + 1}: {', '.join(names[labels == i])}")
+    
+    gr.Markdown( f"## In progress")
+demo.launch()

requirements.txt

@@ -0,0 +1 @@
+scikit-learn==1.2.1