def bollinger(df=None, filename=None, interval=None, n=20, ndev=2):
if filename:
filename_abs_path = f'{os.getcwd()}/{filename}'
try:
df = pd.read_csv(filename_abs_path)
except:
raise FileNotFoundError(f'{filename_abs_path}\n\nDoes not exist.')
# Make all columns lowercase
df.columns = [col.lower() for col in df]
if interval:
df = group_candles(df, interval)
# Make sure we have more rows of data than the size of the rolling average
if len(df) < n:
raise AssertionError(
'Rolling average cannot be larger than number of records.')
df['date'] = df['date'].shift(1)
_bollinger = {}
# Create base simple moving average
_bollinger['sma'] = sma(df['close'], n=n)
rng = ndev * sdev(df['close'], n=n)
# Create upper and lower bands
_bollinger['h_band'] = _bollinger['sma'] + rng
_bollinger['l_band'] = _bollinger['sma'] - rng
return _bollinger
def __init__(self, df=None, filename=None, interval=None):
if filename:
df = pd.read_csv(os.getcwd() + '/' + filename)
# Now that we have our df, make all headers lowercase
df.columns = [col.lower() for col in df]
if interval:
df = group_candles(df, interval)
self.df = df
self.ichimoku = {}
self._validate_data(interval)
def __init__(self, df=None, filename=None, interval=None):
if filename:
filename_abs_path = f"{os.getcwd()}/{filename}"
try:
df = pd.read_csv(filename_abs_path)
except:
raise FileNotFoundError(
f"{filename_abs_path}\n\nDoes not exist.")
# Make all columns lowercase
df.columns = [col.lower() for col in df]
if interval:
df = group_candles(df, interval)
self.df = df
self.bollinger = {}
def __init__(self, df=None, filename=None, interval=None):
if filename:
filename_abs_path = f"{os.getcwd()}/{filename}"
try:
df = pd.read_csv(filename_abs_path)
except:
raise FileNotFoundError(f"{filename_abs_path}\n\nDoes not exist.")
# Now that we have our df, make all headers lowercase
df.columns = [col.lower() for col in df]
if interval:
df = group_candles(df, interval)
self.df = df
self.ichimoku = {}
self._validate_data(interval)