def main():
out = []
tickers = pd.read_excel('./tickers.xlsx')
#tickers = pd.DataFrame({'Ticker': ['GUSH', 'AAPL']})
print (tickers)
for tick in tickers['Ticker']:
print (tick)
try:
st = Scraper()
df = st.scrape(tick)
#df.to_csv('./output/df.csv')
ind = df['Date'].iloc[-50:]
ind = ind.reset_index()
#ind.to_csv('./output/ind.csv')
del df['Date']
points = []
for record in df['Adj Close']:
points.append(float(record))
print (record)
best_case = 0
#for w in [0.8, 0.9, 1, 1.1, 1.2, 1.5, 2, 3, 4, 5]:
for w in [5]:
segment = Segment(points, 0, len(points), multiplier=w)
segment.get_turning_points()
index = sorted(segment.turning_points)
print(index)
closes = []
for i in index:
closes.append(points[i])
profit = Profit.profit(closes)
#verify logic below
if profit > best_case:
print("weight: ", w)
best_case = profit
print("profit: ", profit)
best_index = index
best_closes = closes
t = Transform()
transform = t.trend(best_index, best_closes)
#pd.DataFrame(transform).to_excel('./output/transformed.xlsx')
df['transformed'] = transform
sra = t.correlation(df)
significant_sra = t.significant(sra)
df_norm = t.normalize(df[significant_sra['Field']])
#df_norm.to_csv('./output/normalizedinputs.csv')
n = NeuralNetwork()
neural, actual = n.network(df_norm, transform)
pd.DataFrame({'Neural': neural}).to_csv('./output/neural.csv')
esf = ExponentialSmoothing()
smoothed = esf.es(neural, actual)
smoothed['Closes'] = points[-50:]
smoothed['Date'] = ind['Date']
smoothed['Ticker'] = tick
if len(out) == 0:
out = smoothed
else:
out = out.append(smoothed)
except:
pass
today = datetime.date.today()
pd.DataFrame(out).to_excel('./output/smoothed %s.xlsx'%format(today))
