##FastAI Tabular Model
#Data Cleaning
def order(x):
  if (x['Close']*1.07 < x['followDayClose']):
    return 'buy'
  if (x['Close'] <= x['followDayClose']):
       return 'hold'
  else: return 'sell'

df['followDayClose'] = df['Close'].shift(-1)
df['order'] = df.apply(order, axis=1)

#`Categorify` will transform columns that are in your `cat_names` into that type, 
 # along with label encoding our categorical data:
to = TabularPandas(df, cat, cat_names)
cats = to.procs.categorify

#Normalize
#To properly work with our numerical columns, we need to show a relationship between them all that our model can understand. 
 #This is commonly done through Normalization, where we scale the data between -1 and 1, and compute a z-score
to = TabularPandas(df, norm,cat_names=cat_names, cont_names=cont_names)
norms = to.procs.normalize

#FillMissing
fm = FillMissing(fill_strategy=FillStrategy.median)
to = TabularPandas(df, fm, cont_names=cont_names, cat_names=cat_names)

#Building DataLoader Object and loading
dls = to.dataloaders()

#Running Training
learn = tabular_learner(dls, [10000,1], metrics=accuracy)
learn.lr_find()

#Export Model
learn.export('/content/drive/MyDrive/Code/ethModel.pkl')

#Import Dataset which needs to have YYYY-MM-DD date field titled 'DS' and a dependent varible titled 'Y'
df = pd.read_csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_peyton_manning.csv')

#Instantiate a Prophet Object and call fit on the DataFrame
m = Prophet()
m.fit(df)

#Create a DataFrame with 365 days of future timestamps
future = m.make_future_dataframe(periods=365)

#Take future timestamps and apply model to predict future
forecast = m.predict(future)
fig1 = m.plot(forecast)