# Obtaining the number of observations
n_obs=len(btc_data)
loops=range(n_obs)
results={}

# Looking at the period over which we want to calculate out the
# volatility. 48*14 = 672
n_period=672
test_vol=[]
error_capture=[]
dates = []

for x in loops:
    # Obtaining the subset over which we will train our
    # GARCH model.
    train=btc_data[x+n_period:(n_period*2)+x]

    # Generating the model. GARCH models don't work well
    # with small values close to 0. So the returns will be
    # multiplied by 1000 to change that. This will be accounted
    # for later.
    am_train = arch_model(train[['pct_change']]*1000,p=1,o=1,q=1,vol='GARCH')

    try:
        # Attempting to fit the model.
        res_train = am_train.fit(0,disp='off',show_warning=0)

        # Sometimes convergence errors occur. When this happens we flag
        # the observation where the error occured to be cleaned later.
        if res_train.convergence_flag != 0:
            error_capture.append(x)
    except:
        error_capture.append(x)

    # Estimating the value of the volatility for the point
    # at the end of the 14 day time period.
    yhat = res_train.forecast(horizon=1)

    # Annualizing the measure, and dividing by 1000 to account
    # for the previous scaling up.
    test_vol.append((yhat.variance.values[-1, :]**0.5)*((48*365)**0.5)/1000)
    dates.append(btc_data.iloc[x]['timestamp'])

results['vol14']=test_vol
results['error14']=error_capture
results['dates']=dates

def error_clean(volatility,error_points):
    cleaned_vol=volatility
    error=set(error_points)
    for x in error_points:
        if ((x+1) in error and (x-1) in error) == False:
            if (x-1) < 0:
                cleaned_vol[x]=volatility[x+1]
            elif (x+1) >=len(volatility):
                cleaned_vol[x]=volatility[x-1]
            else:
                cleaned_vol[x]=(volatility[x+1]+volatility[x-1])/2
        elif ((x+1) in error and (x+2) in error) == True:
            cleaned_vol[x]=volatility[x-1]
    return cleaned_vol

cleaner='vol14'
error='error14'
results[cleaner]=error_clean(results[cleaner],results[error])