#! /usr/bin/env python3
import data, technicals, plot, render, distribute
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import time
import os
os.chdir('/home/daily_reports/DJIA/')
print('EXECUTING ./DJIA/main.py')
start = time.time()
df = data.DJIA()
components = data.components()
performance = data.performance(df)
fig1 = plot.DowJonesIndustrialAverageWBollingerBands(df)
df_MA = technicals.moving_averages(df)
df_BB = technicals.bollinger_bands(df)
render.render(performance, df_MA, df_BB)
distribute.to_pdf('index.html', 'DailyDJIAReport.pdf')
#distribute.send('/home/daily_reports/DJIA/DJIA Daily Report.pdf')
print(time.time() - start)
#! /usr/bin/env python3
import data, plot, bollinger_bands, render, distribute
import pandas as pd
import matplotlib.pyplot as plt
import time
import os
print("EXECUTING ./FX/main.py")
start = time.time()
os.chdir('/home/daily_reports/FX/')
data.refresh()
df = pd.read_csv('FX.csv', parse_dates=['Date'], dtype=float)
fig1 = plot.plot_developed_currencies(df)
fig2 = plot.plot_emerging_currencies(df)
fig3 = plot.plot_trade_weighted_dollar()
bands = bollinger_bands.generate_bollinger_bands(df)
render.render(df, bands, 'template.html', 'index.html')
distribute.to_pdf('index.html', 'DailyForeignExchangeReport.pdf')
print(time.time() - start)
import matplotlib.pyplot as plt
import time
import os
print("Executing File ./BondYields/main.py")
os.chdir('/home/daily_reports/BondYields/')
start = time.time()
yields = data.yields()
DGS10 = data.DGS10()
T10YIE = data.T10YIE()
T10Y2Y = data.T10Y2Y()
year_ago = data.year_ago(yields)
#refresh_nominal.refresh()
#refresh_real.refresh()
#data = 'data.csv'
#df = pd.read_csv(data, parse_dates=['Date'], dtype=float)
df_MA = moving_averages.moving_averages(DGS10)
#df_real = pd.read_csv('dataR.csv', parse_dates=['DATE'], dtype=float)
fig1 = plot.yield_curve(yields, year_ago)
fig2 = plot.yield_spread(T10Y2Y)
fig3 = plot.moving_averages(df_MA)
#fig4 = plot.exponential_moving_averages(df, df_MA)
fig5 = plot.breakeven_rate(T10YIE)
moving_averages = list(df_MA.iloc[-1])[2:]
render.render(yields, DGS10, T10Y2Y, T10YIE, moving_averages)
distribute.to_pdf('index.html', 'DailyTreasuryYieldReport.pdf')
print(time.time() - start)
#! /usr/bin/env python3
import data, plot, technicals, render, distribute
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import time
import os
os.chdir('/home/daily_reports/SP500/')
print(os.getcwd())
start = time.time()
df = data.SP500()
df_bb = technicals.bollinger_bands(df)
details = data.quote_details()
ratios = data.ratios()
performance = data.performance(df)
active, gainers, losers = data.big_movers()
fig1 = plot.SP500(df)
render.render(df, df_bb, active, gainers, losers, details, ratios, performance)
distribute.to_pdf('index.html', 'DailySP500Report.pdf')
print("DURATION: {}".format(time.time() - start))
#! /usr/bin/env python3
import data, plot, technicals, render, distribute
import time
import pandas as pd
import os
os.chdir('/home/daily_reports/NASDAQ/')
start = time.time()
df = data.NASDAQCOM()
gainers, losers = data.movers()
stats = data.daily_stats()
faang = data.faang()
df_MA = technicals.moving_averages(df)
df_BB = technicals.bollinger_bands(df)
technicals = technicals.technicals(df)
fig1 = plot.NASDAQ(df)
render.render(df, df_BB, df_MA, stats, technicals, gainers, losers, faang)
distribute.to_pdf('index.html')
#distribute.send('DailyNASDAQReport.pdf')
print(time.time() - start)