first commit

app.py

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+## -- REQUIRED LIBRARIES -- ##
+import streamlit as st
+
+st.set_page_config(page_title='Models')
+
+## -- -- ##
+
+def welcome_page():
+    st.title("Welcome to Models!")
+    st.write("""Click on the model name to load and run the model""")
+    st.write("""**Beware, the loading and fitting time of some models may take up to 15 minutes**""")
+    if st.button(label='Yield Curve Prediction with XGBoost Model'):
+        with st.spinner('Fetching the data... fitting the model... predicting...'):
+            #import time
+            #time.sleep(5)
+            from predict_page import show_predict_page
+            #show_predict_page()
+            st.balloons()
+        st.success('Done!')
+
+welcome_page()
+
+
+
+
+#from predict_page import show_predict_page

predict_page.py

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+## -- REQUIRED LIBRARIES -- ##
+import streamlit as st
+import pickle
+
+import pandas as pd
+import numpy as np
+from matplotlib.pyplot import cm
+import matplotlib.pyplot as plt
+
+import re
+
+from tqdm import tqdm
+
+import requests
+from bs4 import BeautifulSoup
+import json
+from lxml import objectify
+from lxml import etree
+from lxml import html
+import lxml.html
+import lxml.html.soupparser
+
+import datetime
+from datetime import datetime, date, time
+from datetime import timedelta
+from dateutil.relativedelta import relativedelta
+
+import yfinance as yahooFinance
+
+import sklearn
+import tensorflow as tf
+from tensorflow import keras
+
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.metrics import mean_squared_error
+from sklearn.multioutput import MultiOutputRegressor
+from sklearn.linear_model import ElasticNet
+from sklearn.linear_model import Ridge
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+from sklearn.model_selection import RandomizedSearchCV
+from sklearn.model_selection import GridSearchCV
+
+import xgboost
+from xgboost import XGBRegressor
+
+## -- -- ##
+
+## -- U.S. Treasury Yield Curve Data -- ##
+
+def extractEntries(sopa):
+    """Assumes a bs4 object downloaded from the U.S. Treasury website.
+    Returns a list with sections of the url with the yield curve data"""
+    entries = sopa.find_all('content')
+    tx = str(entries)
+    tx = tx[1:]
+    tx = tx.rstrip(tx[-1])
+    tx = tx.split(",")
+    return tx
+
+def processEntries2(texto):
+    """Assumes a list with sections of the url with the yield curve data.
+    Returns a dict in with each key corresponds to a row"""
+    entries = {}
+    colPos = ["id","new_date","bc_1month","bc_2month","bc_3month","bc_4month","bc_6month",
+            "bc_1year","bc_2year","bc_3year","bc_5year","bc_7year",
+            "bc_10year","bc_20year","bc_30year"]
+    for i in range(len(texto)):
+        currEntry = texto[i]
+        currEntrySplit = currEntry.split("\n")
+        currEntryLen = len(currEntrySplit)
+        subSetEntryList = currEntrySplit[2:(currEntryLen-3)]
+        currRow = [pd.NA]*15
+        for j in range(len(subSetEntryList)):
+            item = re.findall('>(.+?)<', subSetEntryList[j])
+            category = re.findall('d:(.+?)>', subSetEntryList[j])
+            try:
+                dataItem = item[0]
+            except:
+                pass
+            try:
+              extractCat = category[1].lower()
+            except:
+              pass
+            try: 
+              posInRow = colPos.index(extractCat)
+            except:
+              pass
+            try:
+              currRow[posInRow] = dataItem
+            except:
+              pass
+        entries[i] = currRow
+    return entries
+
+def getYieldData2(yrs):
+    """Assumes a list of years.
+    Returns a pandas dataframe with the yield curve for the years in the list"""
+    colNames = ["Id","Date","1-month","2-month","3-month","4-month","6-month","1-year","2-year","3-year","5-year","7-year","10-year","20-year","30-year"]
+    treasuryYieldCurve = pd.DataFrame(columns=colNames)
+    for i in tqdm(range(len(yrs))):
+        currURL = 'https://home.treasury.gov/resource-center/data-chart-center/interest-rates/pages/xml?data=daily_treasury_yield_curve&field_tdr_date_value={}'.format(yrs[i])
+        try:
+            r = requests.get(currURL)
+        except:
+            print(i,r.status_code)
+        soup = BeautifulSoup(r.text, features="lxml")
+        txt = extractEntries(soup)
+        data = processEntries2(txt)
+        df = pd.DataFrame.from_dict(data, orient='index',columns=colNames)
+        treasuryYieldCurve = pd.concat([treasuryYieldCurve, df], ignore_index=True, axis=0)
+    return treasuryYieldCurve
+
+def tblFormater(yldData):
+    """Assumes a pandas dataframe with the yield curve data for a given number of years.
+    Returns the pandas dataframe with correct data types."""
+    #print("start")
+    yldData["Id"] = yldData["Id"].apply(lambda x: int(x) if pd.notnull(x) else x)
+    yldData["Date"] = yldData["Date"].apply(lambda x: str(x).replace("T"," ") if pd.notnull(x) else x)
+    yldData["Date"] = yldData["Date"].apply(lambda x: datetime.strptime(x,"%Y-%m-%d %H:%M:%S") if pd.notnull(x) else x)
+    yldData["1-month"] = yldData["1-month"].apply(lambda x: float(x) if pd.notnull(x) else x)
+    yldData["2-month"] = yldData["2-month"].apply(lambda x: float(x) if pd.notnull(x) else x)
+    yldData["3-month"] = yldData["3-month"].apply(lambda x: float(x) if pd.notnull(x) else x)
+    yldData["4-month"] = yldData["4-month"].apply(lambda x: float(x) if pd.notnull(x) else x)
+    yldData["6-month"] = yldData["6-month"].apply(lambda x: float(x) if pd.notnull(x) else x)
+    yldData["1-year"] = yldData["1-year"].apply(lambda x: float(x) if pd.notnull(x) else x)
+    yldData["2-year"] = yldData["2-year"].apply(lambda x: float(x) if pd.notnull(x) else x)
+    yldData["3-year"] = yldData["3-year"].apply(lambda x: float(x) if pd.notnull(x) else x)
+    yldData["5-year"] = yldData["5-year"].apply(lambda x: float(x) if pd.notnull(x) else x)
+    yldData["7-year"] = yldData["7-year"].apply(lambda x: float(x) if pd.notnull(x) else x)
+    yldData["10-year"] = yldData["10-year"].apply(lambda x: float(x) if pd.notnull(x) else x)
+    yldData["20-year"] = yldData["20-year"].apply(lambda x: float(x) if pd.notnull(x) else x)
+    yldData["30-year"] = yldData["30-year"].apply(lambda x: float(x) if pd.notnull(x) else x)
+    return yldData
+
+yieldCurveCurrent = getYieldData2([datetime.now().year])
+yieldCurveCurrent2 = tblFormater(yieldCurveCurrent)
+
+## -- Stock Data -- ##
+
+# Top 25 stocks traded in the U.S. plus publickly traded stocks of defense companies
+stocks = ['MSFT','AMZN','TSLA','GOOGL','GOOG','BRK-B','UNH','JNJ','XOM','JPM',
+         'META','V','PG','NVDA','HD','CVX','LLY','MA','ABBV','PFE','MRK','PEP','BAC','KO','LMT','NOC','GD','BA','RTX']
+
+def stckFormater(tbl):
+  histTable = tbl.reset_index()
+  histTable['Date'] = histTable['Date'].apply(lambda x: str(x)[:19] if pd.notnull(x) else x)
+  histTable['Date'] = histTable['Date'].apply(lambda x: datetime.strptime(str(x),"%Y-%m-%d %H:%M:%S") if pd.notnull(x) else x)
+  return histTable
+
+def stckPull(stcks,startDate = datetime(2022, 1, 1),endDate = datetime.now()):
+  """Assumes a list of stock tickers.
+  Returns a pandas dataframe with the daily closing price for each stock."""
+  currStockName = stcks[0]
+  currStock = yahooFinance.Ticker(currStockName)
+  currStockHist = currStock.history(start=startDate, end=endDate)
+  currStockFormated = stckFormater(currStockHist)
+  currStock2 = currStockFormated[['Date','Close']]
+  stocksTable = currStock2.rename(columns={"Close": currStockName})
+  for i in range(1,len(stcks)):
+    currStockName = stcks[i]
+    currStock = yahooFinance.Ticker(currStockName)
+    currStockHist = currStock.history(start=startDate, end=endDate)
+    currStockFormated = stckFormater(currStockHist)
+    currStock2 = currStockFormated[['Date','Close']]
+    currStockTable = currStock2.rename(columns={"Close": currStockName})
+    stocksTable = pd.merge(stocksTable,currStockTable,on='Date',how='outer')
+  return stocksTable.sort_values(by=['Date'])
+
+stocksData = stckPull(stocks)
+
+## -- U.S. Bureau of Labor Statistics Data -- ##
+
+def getBLS(start=str(datetime(2022, 1, 1).year),end=str(datetime.now().year)): 
+    """Assumes a start year and an end year. Both strings.
+    Defaults: year=current year minus ten years, end=current year.
+    System-allowed range is 9 years.
+    Returns the following series from the U.S. Bureau of Labor Statistics:
+    CPI, Import/Export Price Index, National Employment"""
+    #CUUR0000SA0L1E = Consumer Price Index - All Urban Consumers
+    #EIUCOCANMANU = Import/Export Price Indexes
+    #CEU0800000003 = National Employment, Hours, and Earnings
+    #CXUMENBOYSLB0101M = Consumer Expenditure Survey - Annual Publication thus EXCLUDED
+    headers = {'Content-type': 'application/json'}
+    data = json.dumps({"seriesid": ['CUUR0000SA0L1E','EIUCOCANMANU','CEU0800000003'],"startyear":start, "endyear":end})
+    p = requests.post('https://api.bls.gov/publicAPI/v2/timeseries/data/', data=data, headers=headers)
+    json_data = json.loads(p.text)
+
+    msg = json_data['message']
+    for item in msg:
+        print(item)
+    
+    colNames = ["seriesId","year","period","periodName","value"]
+    blsData = pd.DataFrame(columns=colNames)
+    
+    for series in json_data['Results']['series']:
+        seriesId = series['seriesID']
+        for item in series['data']:
+            year = item['year']
+            period = item['period']
+            periodName = item['periodName']
+            value = item['value']
+            row = [[seriesId,year,period,periodName,value]]
+            temp_df = pd.DataFrame(row,columns=colNames)
+            blsData = pd.concat([blsData,temp_df], ignore_index=True, axis=0)
+
+    return blsData
+
+blsData = getBLS()
+
+## -- Federal Reserve Data -- ##
+
+series_id = ['DFF','RRPONTSYD','SP500','DCOILWTICO','SOFR','DJIA','NASDAQCOM']
+
+def getFRED(nombreSerie):
+    """Assumes a series valid with the St. Louis FRED API.
+    Returns a pandas data frame with the series values/observations."""
+    apiKey = '9180dde91a32bac5c7699bbf994870bc'
+    file_type = 'json'
+    seriesName = nombreSerie
+
+    urlSeriesObservations = 'https://api.stlouisfed.org/fred/series/observations?series_id={}&api_key={}&file_type={}'.format(nombreSerie,apiKey,file_type)
+    r = requests.get(urlSeriesObservations)
+    json_data = json.loads(r.text)
+    
+    colNames = ['Date',seriesName]
+    df = pd.DataFrame(columns=colNames)
+
+    for item in json_data['observations']:
+        currDate = item['date']
+        currDate = datetime.strptime(currDate,"%Y-%m-%d")
+        currValue = item['value']
+        row = [[currDate,currValue]]
+        temp_df = pd.DataFrame(row,columns=colNames)
+        df = pd.concat([df,temp_df], ignore_index=True, axis=0)
+    
+    return df
+
+def multiSeriesFRED(seriesList):
+    """Assumes a list of series, valid with the St. Louis FRED API.
+    Returns a pandas dataframe with the series merged by date."""
+    df = pd.merge(getFRED(seriesList[0]),getFRED(seriesList[1]),on='Date',how='outer')
+    for i in range(2,len(seriesList)):
+        temp_df = getFRED(seriesList[i])
+        df = pd.merge(df,temp_df,on='Date',how='outer')
+    return df
+
+fredData = multiSeriesFRED(series_id)
+
+## -- Data Processing -- ##
+
+mergedEconData = pd.merge(yieldCurveCurrent2,stocksData,on="Date",how="left")
+mergedEconData = pd.merge(mergedEconData,fredData,on="Date",how="left")
+blsData['month'] = pd.NA
+blsData['seriesName'] = pd.NA
+seriesDict = {'CUUR0000SA0L1E':'CPI','EIUCOCANMANU':'Import_Export_Indx','CEU0800000003':'ntnlEmployment'}
+for i in range(len(blsData)):
+    month = int(re.sub('[a-zA-Z]','',blsData.iloc[i,2]))
+    blsData.iloc[i,5] = month
+    blsData.iloc[i,6] = seriesDict.get(blsData.iloc[i,0])
+mergedEconData['CPI'] = pd.NA
+mergedEconData['Import_Export_Indx'] = pd.NA
+mergedEconData['ntnlEmployment'] = pd.NA
+for i in range(len(mergedEconData)):
+    mergedEconData.iloc[i,1] = mergedEconData.iloc[i,1].date()
+for i in range(len(blsData)):
+    blsData.iloc[i,1] = int(blsData.iloc[i,1])
+colsDict = {'CPI':51,'Import_Export_Indx':52,'ntnlEmployment':53}
+for i in tqdm(range(len(mergedEconData))):
+    obsMonth = mergedEconData.iloc[i,1].month
+    obsYear = mergedEconData.iloc[i,1].year   
+    for j in range(len(blsData)):
+        currYear = blsData.iloc[j,1]
+        currMonth = blsData.iloc[j,5]
+        if (obsMonth==currMonth) and (obsYear==currYear):
+            colPos = colsDict.get(blsData.iloc[j,6])
+            mergedEconData.iloc[i,colPos] = blsData.iloc[j,4]
+yLabels = mergedEconData[["Date","1-month","2-month","3-month","4-month","6-month",
+                 "1-year","2-year","3-year","5-year","7-year",
+                 "10-year","20-year","30-year"]].copy()
+xLabels = mergedEconData[['Date','MSFT', 'AMZN', 'TSLA', 'GOOGL', 'GOOG', 'BRK-B', 'UNH',
+       'JNJ', 'XOM', 'JPM', 'META', 'V', 'PG', 'NVDA', 'HD', 'CVX', 'LLY',
+       'MA', 'ABBV', 'PFE', 'MRK', 'PEP', 'BAC', 'KO', 'LMT', 'NOC', 'GD',
+       'BA', 'RTX', 'DFF', 'RRPONTSYD', 'SP500', 'SOFR', 'DJIA',
+       'NASDAQCOM', 'CPI', 'Import_Export_Indx', 'ntnlEmployment']].copy()
+lastBLSDataRow = 0
+lastCpiVal = 0
+lastImpExVal = 0
+lastEmplVal = 0
+blsUpToDate = False
+for i in reversed(range(len(xLabels) + 0)) :
+    if not(pd.isnull(xLabels.iloc[i,38])) and not(pd.isnull(xLabels.iloc[i,37])) and not(pd.isnull(xLabels.iloc[i,36])):
+        lastBLSDataRow = i
+        lastCpiVal = xLabels.iloc[i,36]
+        lastImpExVal = xLabels.iloc[i,37]
+        lastEmplVal = xLabels.iloc[i,38]
+        break
+
+if lastCpiVal == 0:
+    blsUpToDate = True
+
+if blsUpToDate == False:
+    for i in range((lastBLSDataRow+1),len(xLabels)):
+        xLabels.iloc[i,36] = lastCpiVal 
+        xLabels.iloc[i,37] = lastImpExVal 
+        xLabels.iloc[i,38] = lastEmplVal
+
+for i in range(len(xLabels.columns)):
+    if pd.isnull(xLabels.iloc[(len(xLabels)-1),i]):
+        xLabels.iloc[(len(xLabels)-1),i] = xLabels.iloc[(len(xLabels)-2),i]
+
+for i in range(len(xLabels)):
+    if pd.isnull(xLabels.iloc[i,33]):
+        xLabels.iloc[i,33] = 0.0
+    if pd.isnull(xLabels.iloc[i,3]):
+        xLabels.iloc[i,3] = 0.0
+    if pd.isnull(xLabels.iloc[i,12]):
+        xLabels.iloc[i,12] = 0.0
+    if pd.isnull(xLabels.iloc[i,19]):
+        xLabels.iloc[i,19] = 0.0
+    if pd.isnull(xLabels.iloc[i,32]):
+        xLabels.iloc[i,32] = 0.0
+    if pd.isnull(xLabels.iloc[i,34]):
+        xLabels.iloc[i,34] = 0.0
+    if pd.isnull(xLabels.iloc[i,11]):
+        xLabels.iloc[i,11] = 0.0    
+    if xLabels.iloc[i,31]==".":
+        xLabels.iloc[i,31] = 0.0
+
+dte = datetime.now() - relativedelta(years=1)
+dte2 = date(dte.year, dte.month, dte.day)
+
+yLabels2 = yLabels[yLabels['Date']>dte2] 
+# Dropping 2-month and 4-month columns
+yLabels2 = yLabels2[['Date', '1-month', '3-month','6-month', '1-year',
+       '2-year', '3-year', '5-year', '7-year', '10-year', '20-year',
+       '30-year']]
+
+xLabels2 = xLabels[xLabels['Date']>dte2] 
+
+X = xLabels2[['MSFT', 'AMZN', 'TSLA', 'GOOGL', 'GOOG', 'BRK-B', 'UNH', 'JNJ',
+       'XOM', 'JPM', 'META', 'V', 'PG', 'NVDA', 'HD', 'CVX', 'LLY', 'MA',
+       'ABBV', 'PFE', 'MRK', 'PEP', 'BAC', 'KO', 'LMT', 'NOC', 'GD', 'BA',
+       'RTX', 'DFF', 'RRPONTSYD', 'SP500', 'SOFR', 'DJIA', 'NASDAQCOM', 'CPI',
+       'Import_Export_Indx', 'ntnlEmployment']]
+
+Y = yLabels2[['1-month', '3-month', '6-month', '1-year', '2-year', '3-year',
+       '5-year', '7-year', '10-year', '20-year', '30-year']]
+
+todayYvalues = yLabels2.iloc[(len(yLabels2)-1),:]
+todayYvalues = todayYvalues[1:]
+for i in range(len(todayYvalues)):
+    todayYvalues[i] = np.float64(todayYvalues[i])
+
+todayXvalues = xLabels2.iloc[(len(xLabels2)-1),:]
+todayXvalues = todayXvalues[1:]
+for i in range(len(todayXvalues)):
+    todayXvalues[i] = np.float64(todayXvalues[i])
+
+Yseries = Y.iloc[:len(Y)-1,:].copy()
+Xseries = X.iloc[:len(Y)-1,:].copy()
+
+#Handles missing values codified as "." - Raplaces with 0
+for i in range(len(Xseries)):
+    for j in range(29,len(Xseries.columns)):
+        if Xseries.iloc[i,j] == ".":
+            Xseries.iloc[i,j] = 0
+
+#Handles missing values codified as np.nan or pd.NA - Raplaces with 0
+for i in range(len(Xseries)):
+    for j in range(len(Xseries.columns)):
+        if pd.isnull(Xseries.iloc[i,j]):
+            Xseries.iloc[i,j] = 0
+
+#Transform all observations to np.float64 type 
+Xseries = Xseries.astype(np.float64)
+
+#Handles missing values codified as np.nan or pd.NA - Raplaces with previous observation value
+for i in range(len(Yseries)):
+    for j in range(len(Yseries.columns)):
+        if pd.isnull(Yseries.iloc[i,j]):
+            Yseries.iloc[i,j] = Yseries.iloc[i-1,j]
+
+#Transform all observations to np.float64 type
+Yseries = Yseries.astype(np.float64) 
+
+lastDate = yLabels2.tail(1).iloc[0,0]
+
+yPlotVals = yLabels2.tail(11).head(10)
+
+## -- Plots --##
+
+lastDateX = xLabels2.tail(1).iloc[0,0]
+xPlotVals = xLabels2.tail(91).head(90)
+
+## --- Stocks --- ##
+
+stocksPlot = xPlotVals.iloc[:,0:30]
+color = cm.rainbow(np.linspace(0, 1, len(stocksPlot.columns)))
+selStocks = [1,2,4,6,7,8,10,12,15,16,17,18,19,25,26,27,28]
+
+fig2 = plt.figure()
+
+for i in range(1,len(selStocks)):
+    c = color[i]
+    plt.plot(stocksPlot[stocksPlot.columns[0]],
+             stocksPlot[stocksPlot.columns[selStocks[i]]],
+             linestyle='solid',marker='.',label='{}'.format(stocksPlot.columns[selStocks[i]]),color=c)
+
+plt.legend(loc="upper right", frameon=True,
+          bbox_to_anchor=(1.35, 1.0))
+plt.xticks(rotation = 45)
+plt.title("Last 90 Days of Selected Best-Performing Stocks")
+plt.grid()
+#plt.show()
+
+## --- Bureau of Labor Statistics Data --- ##
+
+blsTable = xPlotVals.iloc[:,36:39]
+blsTbl = blsTable.tail(1)
+
+## --- U.S. Federal Reserve Data --- ##
+
+fedVals = xPlotVals[["Date","DFF","RRPONTSYD","SP500","SOFR","DJIA","NASDAQCOM"]]
+fedPlot = fedVals.tail(31).head(30)  
+
+color = cm.rainbow(np.linspace(0, 1, len(fedPlot.columns)))
+fedLabels = ["notUsedVal","Federal Funds Effective Rate",
+"Overnight Reverse Repurchase Agreements",
+"S&P 500",
+"Secured Overnight Financing Rate",
+"Dow Jones Industrial Average",
+"NASDAQ Composite Index"]
+
+fig3 = plt.figure()
+plt.plot(fedPlot[fedPlot.columns[0]],
+         fedPlot[fedPlot.columns[1]],
+         linestyle='solid',marker='.',label='{}'.format(fedLabels[1]),
+         color=color[0])
+plt.xticks(rotation = 45)
+plt.title("Last 30 Days of {} - Source U.S. Federal Reserve".format(fedLabels[1]))
+plt.grid()
+
+fig4 = plt.figure()
+plt.plot(fedPlot[fedPlot.columns[0]],
+         fedPlot[fedPlot.columns[2]],
+         linestyle='solid',marker='.',label='{}'.format(fedLabels[2]),
+         color=color[1])
+plt.xticks(rotation = 45)
+plt.title("Last 30 Days of {} - Source U.S. Federal Reserve".format(fedLabels[2]))
+plt.grid()
+
+fig5 = plt.figure()
+plt.plot(fedPlot[fedPlot.columns[0]],
+         fedPlot[fedPlot.columns[3]],
+         linestyle='solid',marker='.',label='{}'.format(fedLabels[3]),
+         color=color[2])
+plt.xticks(rotation = 45)
+plt.title("Last 30 Days of {} - Source U.S. Federal Reserve".format(fedLabels[3]))
+plt.grid()
+
+fig6 = plt.figure()
+plt.plot(fedPlot[fedPlot.columns[0]],
+         fedPlot[fedPlot.columns[4]],
+         linestyle='solid',marker='.',label='{}'.format(fedLabels[4]),
+         color=color[3])
+plt.xticks(rotation = 45)
+plt.title("Last 30 Days of {} - Source U.S. Federal Reserve".format(fedLabels[4]))
+plt.grid()
+
+fig7 = plt.figure()
+plt.plot(fedPlot[fedPlot.columns[0]],
+         fedPlot[fedPlot.columns[5]],
+         linestyle='solid',marker='.',label='{}'.format(fedLabels[5]),
+         color=color[4])
+plt.xticks(rotation = 45)
+plt.title("Last 30 Days of {} - Source U.S. Federal Reserve".format(fedLabels[5]))
+plt.grid()
+
+fig8 = plt.figure()
+plt.plot(fedPlot[fedPlot.columns[0]],
+         fedPlot[fedPlot.columns[6]],
+         linestyle='solid',marker='.',label='{}'.format(fedLabels[6]),
+         color=color[5])
+plt.xticks(rotation = 45)
+plt.title("Last 30 Days of {} - Source U.S. Federal Reserve".format(fedLabels[6]))
+plt.grid()
+
+#plt.show()
+
+## --- Yield Curve --- ##
+
+color = cm.rainbow(np.linspace(0, 1, len(yPlotVals.columns)))
+
+fig1 = plt.figure()
+
+for i in range(1,len(yPlotVals.columns)):
+    c = color[i]
+    plt.plot(yPlotVals[yPlotVals.columns[0]],
+             yPlotVals[yPlotVals.columns[i]],
+             linestyle='solid',marker='o',label='{}'.format(yPlotVals.columns[i]),color=c)
+plt.legend(loc="upper right", frameon=True,
+          bbox_to_anchor=(1.35, 1.0))
+plt.xticks(rotation = 45)
+plt.title("Last 10 Days of U.S. Treasury Yield Curve")
+plt.grid()
+#plt.show()
+
+## -- -- ##
+
+## -- Loading Model -- ##
+
+###### -- PICKLED MODELS ARE NOT WORKING -- #####
+#def load_model():
+#    with open('xgboostModelYieldCurve4.pkl','rb') as file:
+#        retrievedData = pickle.load(file)
+#    return retrievedData
+
+#modelData = load_model()
+#retrievedModel = modelData['model'] 
+###### -- PICKLED MODELS ARE NOT WORKING -- #####
+
+## -- fitting the model with only one year of data -- ##
+
+Yseries2 = Yseries.copy()
+Xseries2 = Xseries.copy()
+Yseries2 = Yseries2.astype('float32')
+Xseries2 = Xseries2.astype('float32')
+
+bestModel = MultiOutputRegressor(XGBRegressor(subsample = 0.5, n_estimators = 100, max_depth = 3,
+                              learning_rate = 0.3, colsample_bytree = 0.5, colsample_bylevel = 0.8999999999999999,seed = 20))
+
+bestModel.fit(Xseries2,Yseries2)
+
+## -- -- ##
+
+todayXvalues = xLabels2.loc[[xLabels2.index[len(xLabels2.index)-1]]]
+todayXvalues = todayXvalues.iloc[:,1:]
+todayXvalues = todayXvalues.astype(np.float64)
+
+ynew2 = bestModel.predict(todayXvalues)
+
+predictVals = pd.DataFrame(data = ynew2,columns=todayYvalues.index.values)
+actualVals = pd.DataFrame(data = todayYvalues.values.reshape(1,11),columns=todayYvalues.index)
+
+yesterdayVals = yPlotVals.iloc[len(yPlotVals)-1,:]
+yesterdayDate = yesterdayVals[0]
+
+fig9 = plt.figure()
+fig9.set_figwidth(15)
+fig9.set_figheight(6)
+
+color = cm.rainbow(np.linspace(0, 1, len(yPlotVals.columns)))
+for i in range(1,len(yPlotVals.columns)):
+    c = color[i]
+    plt.plot(yPlotVals[yPlotVals.columns[0]],
+             yPlotVals[yPlotVals.columns[i]],
+             linestyle='solid',marker='o',label='{}'.format(yPlotVals.columns[i]),color=c)
+
+for i in range(len(ynew2[0])):
+    c = color[i]
+    plt.plot(lastDate, ynew2[0][i], color=c,markeredgecolor="black",markersize=10,marker="*")
+
+for i in range(len(todayYvalues.values)):
+    c = color[i]
+    plt.plot(lastDate,todayYvalues.values[i],color=c,markeredgecolor="black",markersize=10,marker="X")
+
+for i in range(len(todayYvalues.values)):
+    c = color[i]
+    plt.arrow(yesterdayDate, yesterdayVals[i+1], 1, (todayYvalues.values[i]-yesterdayVals[i+1]), 
+              color=c,linestyle="--")
+
+for i in range(len(ynew2[0])):
+    c = color[i]
+    plt.arrow(yesterdayDate, yesterdayVals[i+1], 1, (ynew2[0][i]-yesterdayVals[i+1]), 
+              color='black',linestyle="--")
+
+plt.legend(loc="upper right", frameon=True,
+          bbox_to_anchor=(1.15, 1.0))
+plt.xticks(rotation = 45)
+plt.title("Last 10 Days of U.S. Treasury Yield Curve Overlayed with Predicted Value [Star] and Actual Value [Cross]")
+plt.grid()
+#plt.show()
+
+## -- Page Loading with Streamlit-- ##
+
+def show_predict_page():
+    st.title("U.S. Treasury Yield Curve Prediction with XGBoost Model")
+    
+    st.write("""### U.S. Treasury Yield Curve - Predicted v. Actual Value""")
+    
+    st.write("""#### Table1: Predicted Values""")
+    st.dataframe(data=predictVals)
+    st.write("""#### Table2: Actual Values""")
+    st.dataframe(data=actualVals)
+
+    st.pyplot(fig=fig9)
+
+    st.title("Selected Data Used to Generate the Prediction")
+
+    st.write("""### Selection of Most Important Stocks in the U.S. Economy - 90 days Prior Prediction""")
+
+    st.pyplot(fig=fig2)
+
+    st.write("""### Latest indicators from the U.S. Bureau of Labor Statistics""")
+
+    st.dataframe(data=blsTbl)
+    
+    st.write("""### Selected indicators from the U.S. Federal Reserve""")
+
+    st.pyplot(fig=fig3)
+    st.pyplot(fig=fig4)
+    st.pyplot(fig=fig5)
+    st.pyplot(fig=fig6)
+    st.pyplot(fig=fig7)
+    st.pyplot(fig=fig8)
+
+    st.write("""### U.S. Treasury Yield Curve 10 days Prior Prediction""")
+
+    st.pyplot(fig=fig1)
+
+show_predict_page()

requirements.txt

@@ -0,0 +1,16 @@
+streamlit
+pandas 
+numpy
+matplotlib 
+matplotlib.pyplot 
+re
+tqdm
+requests
+BeautifulSoup4
+json
+lxml
+datetime
+dateutil.relativedelta
+yfinance
+scikit-learn
+xgboost