Added Aroon Oscillator and change in SMA docstring.

README.md

@@ -10,6 +10,7 @@ Accumulation/Distribution
 Aroon
   -Aroon Up
   -Aroon Down
+  -Aroon Oscillator
 Average Directional Index
 Average True Range
 Average True Range Percent

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 +37,15 @@ 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/force_index.py

@@ -0,0 +1,19 @@
+from __future__ import absolute_import
+from pyti import catch_errors
+import numpy as np
+
+
+def force_index(close_data, volume):
+    """
+    Force Index.
+
+    Formula:
+    """
+    catch_errors.check_for_input_len_diff(
+        close_data, volume
+        )
+
+    pc = np.diff(close_data, 1)
+    fi = pc * volume[1:]
+    fi = np.append(np.nan, fi)
+    return fi

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/true_strength_index.py

@@ -0,0 +1,41 @@
+from __future__ import absolute_import
+import numpy as np
+from pyti import catch_errors
+from pyti.function_helper import fill_for_noncomputable_vals
+from pyti.exponential_moving_average import (
+    exponential_moving_average as ema
+    )
+
+
+def true_strength_index(close_data):
+    """
+    True Strength Index.
+
+    Double Smoothed PC
+    ------------------
+    PC = Current Price minus Prior Price
+    First Smoothing = 25-period EMA of PC
+    Second Smoothing = 13-period EMA of 25-period EMA of PC
+
+    Double Smoothed Absolute PC
+    ---------------------------
+    Absolute Price Change |PC| = Absolute Value of Current Price minus Prior Price
+    First Smoothing = 25-period EMA of |PC|
+    Second Smoothing = 13-period EMA of 25-period EMA of |PC|
+
+    TSI = 100 x (Double Smoothed PC / Double Smoothed Absolute PC)
+    """
+    if len(close_data) < 40:
+        raise RuntimeError("Data must have at least 40 items")
+
+    pc = np.diff(close_data, 1)
+    apc = np.abs(pc)
+
+    num = ema(pc, 25)
+    num = ema(num, 13)
+    den = ema(apc, 25)
+    den = ema(den, 13)
+
+    tsi = 100 * num / den
+    tsi = fill_for_noncomputable_vals(close_data, tsi)
+    return tsi