def chaikin_money_flow(close_data, high_data, low_data, volume, period):
"""
Chaikin Money Flow.
Formula:
CMF = SUM[(((Cn - Ln) - (Hn - Cn)) / (Hn - Ln)) * V] / SUM(Vn)
"""
catch_errors.check_for_input_len_diff(close_data, high_data, low_data,
volume)
catch_errors.check_for_period_error(close_data, period)
close_data = np.array(close_data)
high_data = np.array(high_data)
low_data = np.array(low_data)
volume = np.array(volume)
cmf = [
sum((((close_data[idx + 1 - period:idx + 1] -
low_data[idx + 1 - period:idx + 1]) -
(high_data[idx + 1 - period:idx + 1] -
close_data[idx + 1 - period:idx + 1])) /
(high_data[idx + 1 - period:idx + 1] -
low_data[idx + 1 - period:idx + 1])) *
volume[idx + 1 - period:idx + 1]) /
sum(volume[idx + 1 - period:idx + 1])
for idx in range(period - 1, len(close_data))
]
cmf = fill_for_noncomputable_vals(close_data, cmf)
return cmf
def money_flow_index(close_data, high_data, low_data, volume, period):
"""
Money Flow Index.
Formula:
MFI = 100 - (100 / (1 + PMF / NMF))
"""
catch_errors.check_for_input_len_diff(
close_data, high_data, low_data, volume
)
catch_errors.check_for_period_error(close_data, period)
mf = money_flow(close_data, high_data, low_data, volume)
tp = typical_price(close_data, high_data, low_data)
flow = [tp[idx] > tp[idx-1] for idx in range(1, len(tp))]
pf = [mf[idx] if flow[idx] else 0 for idx in range(0, len(flow))]
nf = [mf[idx] if not flow[idx] else 0 for idx in range(0, len(flow))]
pmf = [sum(pf[idx+1-period:idx+1]) for idx in range(period-1, len(pf))]
nmf = [sum(nf[idx+1-period:idx+1]) for idx in range(period-1, len(nf))]
# Dividing by 0 is not an issue, it turns the value into NaN which we would
# want in that case
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
money_ratio = np.array(pmf) / np.array(nmf)
mfi = 100 - (100 / (1 + money_ratio))
mfi = fill_for_noncomputable_vals(close_data, mfi)
return mfi
def avg_helper(close_data, low_data, period):
catch_errors.check_for_input_len_diff(close_data, low_data)
catch_errors.check_for_period_error(close_data, period)
bp = buying_pressure(close_data, low_data)
tr = true_range(close_data, period)
avg = [
sum(bp[idx + 1 - period:idx + 1]) / sum(tr[idx + 1 - period:idx + 1])
for idx in range(period - 1, len(close_data))
]
avg = fill_for_noncomputable_vals(close_data, avg)
return avg
def conversion_base_line_helper(data, period):
"""
The only real difference between TenkanSen and KijunSen is the period value
"""
catch_errors.check_for_period_error(data, period)
cblh = [(np.max(data[idx + 1 - period:idx + 1]) +
np.min(data[idx + 1 - period:idx + 1])) / 2
for idx in range(period - 1, len(data))]
cblh = fill_for_noncomputable_vals(data, cblh)
return cblh
def momentum(data, period):
"""
Momentum.
Formula:
DATA[i] - DATA[i - period]
"""
catch_errors.check_for_period_error(data, period)
momentum = [data[idx] - data[idx+1-period] for idx in range(period-1, len(data))]
momentum = fill_for_noncomputable_vals(data, momentum)
return momentum
def relative_strength_index(data, period):
"""
Relative Strength Index.
Formula:
RSI = 100 - (100 / 1 + (prevGain/prevLoss))
"""
catch_errors.check_for_period_error(data, period)
period = int(period)
changes = [
data_tup[1] - data_tup[0] for data_tup in zip(data[::1], data[1::1])
]
filtered_gain = [val < 0 for val in changes]
gains = [
0 if filtered_gain[idx] is True else changes[idx]
for idx in range(0, len(filtered_gain))
]
filtered_loss = [val > 0 for val in changes]
losses = [
0 if filtered_loss[idx] is True else abs(changes[idx])
for idx in range(0, len(filtered_loss))
]
avg_gain = np.mean(gains[:period])
avg_loss = np.mean(losses[:period])
rsi = []
if avg_loss == 0:
rsi.append(100)
else:
rs = avg_gain / avg_loss
rsi.append(100 - (100 / (1 + rs)))
for idx in range(1, len(data) - period):
avg_gain = ((avg_gain * (period - 1) + gains[idx + (period - 1)]) /
period)
avg_loss = ((avg_loss * (period - 1) + losses[idx + (period - 1)]) /
period)
if avg_loss == 0:
rsi.append(100)
else:
rs = avg_gain / avg_loss
rsi.append(100 - (100 / (1 + rs)))
rsi = fill_for_noncomputable_vals(data, rsi)
return rsi
def buying_pressure(close_data, low_data):
"""
Buying Pressure.
Formula:
BP = current close - min()
"""
catch_errors.check_for_input_len_diff(close_data, low_data)
bp = [
close_data[idx] - np.min([low_data[idx], close_data[idx - 1]])
for idx in range(1, len(close_data))
]
bp = fill_for_noncomputable_vals(close_data, bp)
return bp
def rate_of_change(data, period):
"""
Rate of Change.
Formula:
(Close - Close n periods ago) / (Close n periods ago) * 100
"""
catch_errors.check_for_period_error(data, period)
rocs = [((data[idx] - data[idx - (period - 1)]) / data[idx -
(period - 1)]) * 100
for idx in range(period - 1, len(data))]
rocs = fill_for_noncomputable_vals(data, rocs)
return rocs
def aroon_up(data, period):
"""
Aroon Up.
Formula:
AROONUP = (((PERIOD) - (PERIODS since PERIOD high)) / (PERIOD)) * 100
"""
catch_errors.check_for_period_error(data, period)
period = int(period)
a_up = [((period - list(reversed(data[idx + 1 - period:idx + 1])).index(
np.max(data[idx + 1 - period:idx + 1]))) / float(period)) * 100
for idx in range(period - 1, len(data))]
a_up = fill_for_noncomputable_vals(data, a_up)
return a_up
def aroon_down(data, period):
"""
Aroon Down.
Formula:
AROONDWN = (((PERIOD) - (PERIODS SINCE PERIOD LOW)) / (PERIOD)) * 100
"""
catch_errors.check_for_period_error(data, period)
period = int(period)
a_down = [((period - list(reversed(data[idx + 1 - period:idx + 1])).index(
np.min(data[idx + 1 - period:idx + 1]))) / float(period)) * 100
for idx in range(period - 1, len(data))]
a_down = fill_for_noncomputable_vals(data, a_down)
return a_down
def percent_k(data, period):
"""
%K.
Formula:
%k = data(t) - low(n) / (high(n) - low(n))
"""
catch_errors.check_for_period_error(data, period)
percent_k = [((data[idx] - np.min(data[idx + 1 - period:idx + 1])) /
(np.max(data[idx + 1 - period:idx + 1]) -
np.min(data[idx + 1 - period:idx + 1])))
for idx in range(period - 1, len(data))]
percent_k = fill_for_noncomputable_vals(data, percent_k)
return percent_k
def standard_variance(data, period):
"""
Standard Variance.
Formula:
(Ct - AVGt)^2 / N
"""
catch_errors.check_for_period_error(data, period)
sv = [
np.var(data[idx + 1 - period:idx + 1], ddof=1)
for idx in range(period - 1, len(data))
]
sv = fill_for_noncomputable_vals(data, sv)
return sv
def detrended_price_oscillator(data, period):
"""
Detrended Price Oscillator.
Formula:
DPO = DATA[i] - Avg(DATA[period/2 + 1])
"""
catch_errors.check_for_period_error(data, period)
period = int(period)
dop = [
data[idx] - np.mean(data[idx + 1 - (int(period / 2) + 1):idx + 1])
for idx in range(period - 1, len(data))
]
dop = fill_for_noncomputable_vals(data, dop)
return dop
def exponential_moving_average(data, period):
"""
Exponential Moving Average.
Formula:
p0 + (1 - w) * p1 + (1 - w)^2 * p2 + (1 + w)^3 * p3 +...
/ 1 + (1 - w) + (1 - w)^2 + (1 - w)^3 +...
where: w = 2 / (N + 1)
"""
catch_errors.check_for_period_error(data, period)
emas = [exponential_moving_average_helper(
data[idx - period + 1:idx + 1], period) for idx in range(period - 1, len(data))]
emas = fill_for_noncomputable_vals(data, emas)
return emas
def smoothed_moving_average(data, period):
"""
Smoothed Moving Average.
Formula:
smma = avg(data(n)) - avg(data(n)/n) + data(t)/n
"""
catch_errors.check_for_period_error(data, period)
smma = [
((np.mean(data[idx - (period - 1):idx + 1]) -
(np.mean(data[idx - (period - 1):idx + 1]) / period) + data[idx]) /
period) for idx in range(0, len(data))
]
smma = fill_for_noncomputable_vals(data, smma)
return smma
def standard_deviation(data, period):
"""
Standard Deviation.
Formula:
std = sqrt(avg(abs(x - avg(x))^2))
"""
catch_errors.check_for_period_error(data, period)
stds = [
np.std(data[idx + 1 - period:idx + 1], ddof=1)
for idx in range(period - 1, len(data))
]
stds = fill_for_noncomputable_vals(data, stds)
return stds
def stochrsi(data, period):
"""
StochRSI.
Formula:
SRSI = ((RSIt - RSI LOW) / (RSI HIGH - LOW RSI)) * 100
"""
rsi = relative_strength_index(data, period)[period:]
stochrsi = [
100 * ((rsi[idx] - np.min(rsi[idx + 1 - period:idx + 1])) /
(np.max(rsi[idx + 1 - period:idx + 1]) -
np.min(rsi[idx + 1 - period:idx + 1])))
for idx in range(period - 1, len(rsi))
]
stochrsi = fill_for_noncomputable_vals(data, stochrsi)
return stochrsi
def double_smoothed_stochastic(data, period):
"""
Double Smoothed Stochastic.
Formula:
dss = 100 * EMA(Close - Lowest Low) / EMA(Highest High - Lowest Low)
"""
catch_errors.check_for_period_error(data, period)
lows = [data[idx] - np.min(data[idx+1-period:idx+1]) for idx in range(period-1, len(data))]
sm_lows = ema(ema(lows, period), period)
highs = [np.max(data[idx+1-period:idx+1]) - np.min(data[idx+1-period:idx+1]) for idx in range(period-1, len(data))]
sm_highs = ema(ema(highs, period), period)
dss = (sm_lows / sm_highs) * 100
dss = fill_for_noncomputable_vals(data, dss)
return dss
def linear_weighted_moving_average(data, period):
"""
Linear Weighted Moving Average.
Formula:
LWMA = SUM(DATA[i]) * i / SUM(i)
"""
catch_errors.check_for_period_error(data, period)
idx_period = list(range(1, period + 1))
lwma = [(sum([
i * idx_period[data[idx - (period - 1):idx + 1].index(i)]
for i in data[idx - (period - 1):idx + 1]
])) / sum(range(1,
len(data[idx + 1 - period:idx + 1]) + 1))
for idx in range(period - 1, len(data))]
lwma = fill_for_noncomputable_vals(data, lwma)
return lwma
def simple_moving_average(data, period):
"""
Simple Moving Average.
Formula:
SUM(data / N)
"""
catch_errors.check_for_period_error(data, period)
# Mean of Empty Slice RuntimeWarning doesn't affect output so it is
# supressed
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
sma = [
np.mean(data[idx - (period - 1):idx + 1])
for idx in range(0, len(data))
]
sma = fill_for_noncomputable_vals(data, sma)
return sma
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)
def volume_adjusted_moving_average(close_data, volume, period):
"""
Volume Adjusted Moving Average.
Formula:
VAMA = SUM(CLOSE * VolumeRatio) / period
"""
catch_errors.check_for_input_len_diff(close_data, volume)
catch_errors.check_for_period_error(close_data, period)
avg_vol = np.mean(volume)
vol_incr = avg_vol * 0.67
vol_ratio = [val / vol_incr for val in volume]
close_vol = np.array(close_data) * vol_ratio
vama = [
sum(close_vol[idx + 1 - period:idx + 1]) / period
for idx in range(period - 1, len(close_data))
]
vama = fill_for_noncomputable_vals(close_data, vama)
return vama
def weighted_moving_average(data, period):
"""
Weighted Moving Average.
Formula:
(P1 + 2 P2 + 3 P3 + ... + n Pn) / K
where K = (1+2+...+n) = n(n+1)/2 and Pn is the most recent price
"""
catch_errors.check_for_period_error(data, period)
k = (period * (period + 1)) / 2.0
wmas = []
for idx in range(0, len(data) - period + 1):
product = [
data[idx + period_idx] * (period_idx + 1)
for period_idx in range(0, period)
]
wma = sum(product) / k
wmas.append(wma)
wmas = fill_for_noncomputable_vals(data, wmas)
return wmas
def true_range(close_data, period):
"""
True Range.
Formula:
TRt = MAX(abs(Ht - Lt), abs(Ht - Ct-1), abs(Lt - Ct-1))
"""
catch_errors.check_for_period_error(close_data, period)
tr = [
np.max([
np.max(close_data[idx + 1 - period:idx + 1]) -
np.min(close_data[idx + 1 - period:idx + 1]),
abs(
np.max(close_data[idx + 1 - period:idx + 1]) -
close_data[idx - 1]),
abs(
np.min(close_data[idx + 1 - period:idx + 1]) -
close_data[idx - 1])
]) for idx in range(period - 1, len(close_data))
]
tr = fill_for_noncomputable_vals(close_data, tr)
return tr
