bitcoin_df.py

def count_drop(numbers):
    return len([x for x in numbers if x < 0])

def count_up(numbers):
    return len([x for x in numbers if x > 0])

df.rename(columns={'Value': 'Value USD'}, inplace=True)
df['Value 2'] = df['Value USD'].shift(-1)
df['Max 7'] = df['Value USD'].rolling(7).max()
df['Min 7'] = df['Value USD'].rolling(7).min()
df['Change'] = df['Value USD'] - df['Value 2']
df['Mean Change 7'] = df['Change'].rolling(7).mean()
df['Drop 7'] = df['Change'].rolling(7).apply(count_drop, raw=True)
df['Up 7'] = df['Change'].rolling(7).apply(count_up, raw=True)

def decider(row):
    if row['Drop 7'] < row['Up 7'] and row['Mean Change 7'] > 0 and row['Change'] > 0:
        return 2
    elif row['Drop 7'] == row['Up 7'] and row['Mean Change 7'] > 0 and row['Change'] >= 0:
        return 1
    elif row['Drop 7'] > row['Up 7'] and row['Mean Change 7'] < 0 and row['Change'] >= 0:
        return 0
    elif row['Drop 7'] < row['Up 7'] and row['Mean Change 7'] > 0 or row['Change'] >= 0:
        return -1
    elif row['Drop 7'] < row['Up 7'] and row['Mean Change 7'] < 0 or row['Change'] < 0:
        return -2
    elif row['Drop 7'] < row['Up 7'] and row['Mean Change 7'] > 0 and row['Change'] < 0:
        return -3
    elif row['Drop 7'] > row['Up 7'] and row['Mean Change 7'] < 0 and row['Change'] < 0:
        return -4
      
df['Predict'] = df.apply(decider, axis=1)