def average_directional_index(df, lookback=14):
"""
ADX technical indicator
:param df: Chart data, as pandas dataframe
:param lookback: Optional. Look-back period. Default 14
:return: A pandas series
"""
high_values = df['high']
low_values = df['low']
up_move = (high_values - high_values.shift(1).fillna(0))
dn_move = (low_values.shift(1).fillna(0) - low_values)
up_index = (up_move > dn_move) & (up_move > 0)
dn_index = (dn_move > up_move) & (dn_move > 0)
plus_dm, minus_dm = high_values*0, low_values*0
plus_dm[up_index] = up_move[up_index]
minus_dm[dn_index] = dn_index[dn_index]
this_atr = atr(df, lookback)
plus_di = 100*(ema((plus_dm/this_atr), lookback))
minus_di = 100*(ema((minus_dm/this_atr), lookback))
adx = 100 * (ema(((plus_di - minus_di)/(plus_di + minus_di)).abs(), lookback))
return adx
def moving_average_convergence_divergence(df, column='close', short=12, long=26, medium=9, return_as='delta'):
"""
MACD technical indicator.
Is returned either as
- The delta between main- and signal-line
- The main-line
- The signal-line
:param df: Chart data, as pandas dataframe
:param column: Optional. Column upon which indicator is to be computed. Default 'close'
:param short: Optional. Short look-back period. Default 12
:param long: Optional. Long look-back period. Default 26
:param medium: Optional. Medium look-back period. Default 9
:param return_as: Optional. How to return the indicator. Can be 'delta', 'main' or 'signal'. Default 'delta'
:return: A pandas series
"""
values = df[column]
# The basic EMAs
short_ema = ema(values, short)
long_ema = ema(values, long)
# The lines calculated from the EMAs
main_line = short_ema - long_ema
signal_line = ema(main_line, medium)
if return_as == 'delta':
return signal_line - main_line
elif return_as == 'main':
return main_line
else:
return signal_line
def triple_exponential_moving_average(df, column='close', lookback_trix=14, lookback_signal=9, return_as='delta'):
"""
TRIX technical indicator.
Is returned either as
- The delta between main- and signal-line
- The main-line
- The signal-line
:param df: Chart data, as pandas dataframe
:param column: Optional. Column upon which indicator is to be computed. Default 'close'
:param lookback_trix: Optional. The lookback-period for the main-line. Default 14
:param lookback_signal: Optional. The lookback-period for the signal-line. Default9
:param return_as: Optional. How to return the indicator. Can be 'delta', 'main' or 'signal'. Default 'delta'
:return: A pandas series
"""
values = df[column]
single_ema = ema(values, lookback_trix)
double_ema = ema(single_ema, lookback_trix)
triple_ema = ema(double_ema, lookback_trix)
triple_ema_prev = triple_ema.shift(1).fillna(triple_ema.iloc[0])
trix = 100 * (triple_ema - triple_ema_prev)/(triple_ema_prev)
signal = ema(trix, lookback_signal)
if return_as == 'delta':
return signal - trix
elif return_as == 'main':
return trix
else:
return signal
def relative_strength_index(df, column='close', lookback=14):
"""
RSI technical indicator
:param df: Chart data, as pandas dataframe
:param column: Optional. Column upon which indicator is to be computed. Default 'close'
:param lookback: Optional. Look-back period. Default 14
:return: A pandas series
"""
values = df[column]
deltas = values.diff()
gain_pos, loss_pos = deltas > 0, deltas < 0
gains, losses = deltas * 0, deltas * 0
gains[gain_pos], losses[loss_pos] = deltas[gain_pos], deltas[loss_pos] * -1
avg_gain, avg_loss = ema(gains, lookback=lookback), ema(losses,
lookback=lookback)
return (100 - (100 / (1 + avg_gain / avg_loss))).fillna(0)
def mass_index(df, short=9, long=25):
"""
AO technical indicator
:param df: Chart data, as pandas dataframe
:param short: Optional. Short look-back period. Default 9
:param long: Optional. Long look-back period. Default 25
:return: A pandas series
"""
high_values = df['high']
low_values = df['low']
high_low_delta = high_values - low_values
ema_single = ema(high_low_delta, short)
ema_double = ema(ema_single, short)
ema_div = ema_single/ema_double
midx = ema_div.rolling(window=long, min_periods=0).sum().fillna(0)
return midx
def force_index(df, lookback=14):
"""
FIDX technical indicator
:param df: Chart data, as pandas dataframe
:param lookback: Optional. Look-back period. Default 14
:return: A pandas series
"""
close_values = df['close']
volumes = df['volume']
prev_close_values = close_values.shift(1).fillna(0)
base = (close_values - prev_close_values) * volumes
fidx = ema(base, lookback)
return fidx
def accumulation_distribution_line(df, lookback=20):
"""
ADL technical indicator
:param df: Chart data, as pandas dataframe
:param lookback: Optional. Look-back period. Default 20
:return: A pandas series
"""
high_values = df['high']
low_values = df['low']
close_values = df['close']
volumes = df['volume']
mf_multiplier = ((close_values - low_values) -
(high_values - close_values)) / (high_values - low_values)
mf_volume = mf_multiplier * volumes
adl = ema(mf_volume, lookback)
return adl
def keltner_channel(df, column='close', lookback=14, return_as='total_delta'):
"""
KLTCH technical indicator.
Can be returned as
- Delta between upper and lower band
- Delta between upper and middle or middle and lower band
- Either of the three bands
:param df: Chart data, as pandas dataframe
:param column: Optional. Column upon which indicator is to be computed. Default 'close'
:param lookback: Optional. Look-back period. Default 14
:param return_as: Optional. How to return the indicator. Can be 'total_delta', 'upper_delta', 'lower_delta', 'upper_band', 'middle_band' and 'lower_band'. Default 'total_delta'
:return: A pandas series.
"""
values = df[column]
mband = ema(values, lookback)
double_atr = average_true_range(df, lookback)*2
uband = mband + double_atr
lband = mband - double_atr
if return_as == 'total_delta':
return uband - lband
elif return_as == 'upper_delta':
return uband - mband
elif return_as == 'lower_delta':
return mband - lband
elif return_as == 'upper_band':
return uband
elif return_as == 'lower_band':
return lband
elif return_as == 'middle_band':
return mband
else:
raise ValueError("return_as: expected to be either 'total_delta', 'upper_delta' or 'lower_delta', but was " + str(return_as) + " instead!")
def average_true_range(df, lookback=14):
"""
ATR technical indicator
:param df: Chart data, as pandas dataframe
:param lookback: Optional. Look-back period. Default 14
:return: A pandas series
"""
high_values = df['high']
low_values = df['low']
close_values = df['close']
a = high_values - low_values
b = (high_values - close_values.shift(1).fillna(0)).abs()
c = (low_values - close_values.shift(1).fillna(0)).abs()
true_ranges = pd.concat([a,b,c], axis=1).max(axis=1)
atr = ema(true_ranges, lookback)
return atr
