bugfixing & new analysis

pyti/accumulation_distribution.py

@@ -17,11 +17,18 @@ def accumulation_distribution(close_data, high_data, low_data, volume):
 
  ad = np.zeros(len(close_data))
  for idx in range(1, len(close_data)):
- ad[idx] = (
- (((close_data[idx] - low_data[idx]) -
- (high_data[idx] - close_data[idx])) /
- (high_data[idx] - low_data[idx]) *
- volume[idx]) +
- ad[idx-1]
- )
+ candle = high_data[idx] - low_data[idx]
+ if candle == 0:
+ if high_data[idx] != close_data[idx]:
+ raise RuntimeError("High and low are equals but close is not.")
+ else:
+ ad[idx] = ad[idx - 1]
+ else:
+ ad[idx] = (
+ (((close_data[idx] - low_data[idx]) -
+ (high_data[idx] - close_data[idx])) /
+ (high_data[idx] - low_data[idx]) *
+ volume[idx]) +
+ ad[idx-1]
+ )
  return ad

pyti/aroon.py

@@ -37,3 +37,14 @@ def aroon_down(data, period):
  float(period)) * 100 for idx in range(period-1, len(data))]
  a_down = fill_for_noncomputable_vals(data, a_down)
  return a_down
+
+
+def aroon_oscillator(data, period):
+ """
+ Aroon Oscillator.
+ Formula:
+ AO = AROON_UP(PERIOD) - AROON_DOWN(PERIOD)
+ """
+ catch_errors.check_for_period_error(data, period)
+ period = int(period)
+ return aroon_up(data, period) - aroon_down(data, period)

pyti/chaikin_money_flow.py

@@ -1,6 +1,8 @@
 from __future__ import absolute_import
+
 import numpy as np
 from pyti import catch_errors
+from pyti.exponential_moving_average import exponential_moving_average as ema
 from pyti.function_helper import fill_for_noncomputable_vals
 from six.moves import range
 
@@ -20,9 +22,32 @@ def chaikin_money_flow(close_data, high_data, low_data, volume, period):
  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 = [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 chaikin_oscillator(close_data, high_data, low_data, volume, short_period=3, long_period=10):
+ """
+ Chaikin Oscillator (CHO).
+ Chaikin Accumulation Distribution Line (ADL).
+
+ Formula:
+ ADL = M(Period-1) + M(Period)
+ CHO = ADL = 3day EMA(ADL) - 10day EMA(ADL)
+ """
+ ac = []
+ val_last = 0
+ for index in range(0, len(close_data)):
+ if high_data[index] != low_data[index]:
+ val = val_last + ((close_data[index] - low_data[index]) - (high_data[index] - close_data[index])) / (
+ high_data[index] - low_data[index]) * volume[index]
+ else:
+ val = val_last
+ ac.append(val)
+ cho = ema(ac, short_period) - ema(ac, long_period)
+ return cho

pyti/relative_strength_index.py

@@ -18,11 +18,8 @@ def relative_strength_index(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))]
+ gains = [0 if val < 0 else val for val in changes]
+ losses = [0 if val > 0 else abs(val) for val in changes]
 
  avg_gain = np.mean(gains[:period])
  avg_loss = np.mean(losses[:period])

pyti/simple_moving_average.py

@@ -11,7 +11,7 @@ def simple_moving_average(data, period):
  Simple Moving Average.
 
  Formula:
- SUM(data / N)
+ SUM(data) / N
  """
  catch_errors.check_for_period_error(data, period)
  # Mean of Empty Slice RuntimeWarning doesn't affect output so it is

pyti/stochastic.py

@@ -13,7 +13,7 @@ def percent_k(data, period):
  %K.
 
  Formula:
- %k = data(t) - low(n) / (high(n) - low(n))
+ %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])) /