def triangular_moving_average(data, period):
"""
Triangular Moving Average.
Formula:
TMA = SMA(SMA())
"""
catch_errors.check_for_period_error(data, period)
tma = sma(sma(data, period), period)
return tma
def volume_oscillator(volume, short_period, long_period):
"""
Volume Oscillator.
Formula:
vo = 100 * (SMA(vol, short) - SMA(vol, long) / SMA(vol, long))
"""
catch_errors.check_for_period_error(volume, short_period)
catch_errors.check_for_period_error(volume, long_period)
vo = (100 * ((sma(volume, short_period) - sma(volume, long_period)) /
sma(volume, long_period)))
return vo
def between_day_min_ma(day_csv, min_csv, remote=True):
day_rb = pd.read_csv(day_csv, index_col=0)
day_rb.index = pd.DatetimeIndex(day_rb.index)
min_rb = pd.read_csv(min_csv, index_col=0)
min_rb.index = pd.DatetimeIndex(min_rb.index)
short = sma(min_rb['close'], 5)
long = sma(day_rb['close'], 2)
# plot_kline(day_rb)
_plot_lines((list(range(min_rb.shape[0])), list(range(day_rb.shape[0]))),
(short, long),
('min_short', 'day_long'),
remote,
'between_day_min_ma.html')
def center_band(data, period):
"""
Center Band.
Formula:
SMA(data)
"""
cb = sma(data, period)
return cb
def center_band(close_data, high_data, low_data, period):
"""
Center Band.
Formula:
CB = SMA(TP)
"""
tp = typical_price(close_data, high_data, low_data)
cb = sma(tp, period)
return cb
def percent_d(data, period):
"""
%D.
Formula:
%D = SMA(%K, 3)
"""
p_k = percent_k(data, period)
percent_d = sma(p_k, 3)
return percent_d
def band_width(high_data, low_data, period):
"""
Bandwidth.
Formula:
BW = SMA(H - L)
"""
catch_errors.check_for_input_len_diff(high_data, low_data)
diff = np.array(high_data) - np.array(low_data)
bw = sma(diff, period)
return bw
def commodity_channel_index(close_data, high_data, low_data, period):
"""
Commodity Channel Index.
Formula:
CCI = (TP - SMA(TP)) / (0.015 * Mean Deviation)
"""
catch_errors.check_for_input_len_diff(close_data, high_data, low_data)
catch_errors.check_for_period_error(close_data, period)
tp = typical_price(close_data, high_data, low_data)
cci = ((tp - sma(tp, period)) /
(0.015 * np.mean(np.absolute(tp - np.mean(tp)))))
return cci
def middle_bollinger_band(data, period, std=2.0):
"""
Middle Bollinger Band.
Formula:
m_bb = sma()
"""
catch_errors.check_for_period_error(data, period)
period = int(period)
mid_bb = sma(data, period)
return mid_bb
def between_days_ma(day_csv, remote=True):
day_rb = pd.read_csv(day_csv, index_col=0)
day_rb.index = pd.DatetimeIndex(day_rb.index)
index = list(range(day_rb.shape[0]))
ma_list = [day_rb['close'].values]
names = ['origin_data']
for p in range(2, 20, 2):
ma_list.append(sma(day_rb['close'], p))
names.append('%s_day_period' % p)
_plot_lines((index,) * len(ma_list),
ma_list,
names,
remote,
'between_days_ma.html')
def upper_bollinger_band(data, period, std_mult=2.0):
"""
Upper Bollinger Band.
Formula:
u_bb = SMA(t) + STD(SMA(t-n:t)) * std_mult
"""
catch_errors.check_for_period_error(data, period)
period = int(period)
simple_ma = sma(data, period)[period - 1:]
upper_bb = []
for idx in range(len(data) - period + 1):
std_dev = np.std(data[idx:idx + period])
upper_bb.append(simple_ma[idx] + std_dev * std_mult)
upper_bb = fill_for_noncomputable_vals(data, upper_bb)
return np.array(upper_bb)