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[GH-139] Add Awesome Oscillator
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The AO indicator is a good indicator for measuring the market dynamics,
it reflects specific changes in the driving force of the market, which
helps to identify the strength of the trend, including the points of
its formation and reversal.

Awesome Oscillator Formula

* MEDIAN PRICE = (HIGH+LOW)/2
* AO = SMA(MEDIAN PRICE, 5)-SMA(MEDIAN PRICE, 34)

Examples:
* `df['ao']` returns the Awesome Oscillator with default windows (5, 34)
* `df['ao_3,10']` returns the Awesome Oscillator with a window of 3 and 10
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jealous committed Jun 14, 2023
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19 changes: 18 additions & 1 deletion README.md
Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@
[![codecov](https://codecov.io/gh/jealous/stockstats/branch/master/graph/badge.svg?token=IFMD1pVJ7T)](https://codecov.io/gh/jealous/stockstats)
[![pypi](https://img.shields.io/pypi/v/stockstats.svg)](https://pypi.python.org/pypi/stockstats)

VERSION: 0.5.3
VERSION: 0.5.4

## Introduction

Expand Down Expand Up @@ -56,6 +56,7 @@ Supported statistics/indicators are:
* Supertrend: with the Upper Band and Lower Band
* Aroon: Aroon Oscillator
* Z: Z-Score
* AO: Awesome Oscillator

## Installation

Expand Down Expand Up @@ -693,6 +694,22 @@ Where:
Examples:
* `df['close_75_z']` returns the Z-Score of close price with a window of 75

#### [Awesome Oscillator](https://www.ifcm.co.uk/ntx-indicators/awesome-oscillator)

The AO indicator is a good indicator for measuring the market dynamics,
it reflects specific changes in the driving force of the market, which
helps to identify the strength of the trend, including the points of
its formation and reversal.

Awesome Oscillator Formula

* MEDIAN PRICE = (HIGH+LOW)/2
* AO = SMA(MEDIAN PRICE, 5)-SMA(MEDIAN PRICE, 34)

Examples:
* `df['ao']` returns the Awesome Oscillator with default windows (5, 34)
* `df['ao_3,10']` returns the Awesome Oscillator with a window of 3 and 10

## Issues

We use [Github Issues](https://github.com/jealous/stockstats/issues) to track
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63 changes: 49 additions & 14 deletions stockstats.py
Original file line number Diff line number Diff line change
Expand Up @@ -105,6 +105,9 @@ class StockDataFrame(pd.DataFrame):
KAMA_SLOW = 34
KAMA_FAST = 5

AO_SLOW = 34
AO_FAST = 5

MULTI_SPLIT_INDICATORS = ("kama",)

# End of options
Expand Down Expand Up @@ -548,32 +551,33 @@ def _get_supertrend(self, window=None):
m_atr = self.SUPERTREND_MUL * self._atr(window)
hl_avg = (high + low) / 2.0
# basic upper band
b_ub = hl_avg + m_atr
b_ub = list(hl_avg + m_atr)
# basic lower band
b_lb = hl_avg - m_atr
b_lb = list(hl_avg - m_atr)

size = len(close)
ub = np.empty(size, dtype=np.float64)
lb = np.empty(size, dtype=np.float64)
st = np.empty(size, dtype=np.float64)
close = list(close)

for i in range(size):
if i == 0:
ub[i] = b_ub.iloc[i]
lb[i] = b_lb.iloc[i]
if close.iloc[i] <= ub[i]:
ub[i] = b_ub[i]
lb[i] = b_lb[i]
if close[i] <= ub[i]:
st[i] = ub[i]
else:
st[i] = lb[i]
continue

last_close = close.iloc[i - 1]
curr_close = close.iloc[i]
last_close = close[i - 1]
curr_close = close[i]
last_ub = ub[i - 1]
last_lb = lb[i - 1]
last_st = st[i - 1]
curr_b_ub = b_ub.iloc[i]
curr_b_lb = b_lb.iloc[i]
curr_b_ub = b_ub[i]
curr_b_lb = b_lb[i]

# calculate current upper band
if curr_b_ub < last_ub or last_close > last_ub:
Expand Down Expand Up @@ -1170,6 +1174,35 @@ def _get_mfi(self, window=None):
mfi.iloc[:window] = 0.5
self[column_name] = mfi

def _get_ao(self, windows=None):
""" get awesome oscillator
The AO indicator is a good indicator for measuring the market dynamics,
it reflects specific changes in the driving force of the market, which
helps to identify the strength of the trend, including the points of
its formation and reversal.
Awesome Oscillator Formula
* MEDIAN PRICE = (HIGH+LOW)/2
* AO = SMA(MEDIAN PRICE, 5)-SMA(MEDIAN PRICE, 34)
https://www.ifcm.co.uk/ntx-indicators/awesome-oscillator
"""
if windows is None:
fast = self.AO_FAST
slow = self.AO_SLOW
column_name = 'ao'
else:
n0, n1 = self.to_ints(windows)
fast = min(n0, n1)
slow = max(n0, n1)
column_name = 'ao_{},{}'.format(fast, slow)

median_price = (self['high'] + self['low']) * 0.5
ao = self._sma(median_price, fast) - self._sma(median_price, slow)
self[column_name] = ao

def _get_kama(self, column, windows, fasts=None, slows=None):
""" get Kaufman's Adaptive Moving Average.
Implemented after
Expand Down Expand Up @@ -1200,17 +1233,18 @@ def _get_kama(self, column, windows, fasts=None, slows=None):
slow_ema_smoothing = 2.0 / (slow + 1)
smoothing_2 = fast_ema_smoothing - slow_ema_smoothing
efficient_smoothing = efficiency_ratio * smoothing_2
smoothing = 2 * (efficient_smoothing + slow_ema_smoothing)
smoothing = list(2 * (efficient_smoothing + slow_ema_smoothing))

# start with simple moving average
kama = self._sma(col, window)
kama = list(self._sma(col, window))
col_list = list(col)
if len(kama) >= window:
last_kama = kama.iloc[window - 1]
last_kama = kama[window - 1]
else:
last_kama = 0.0
for i in range(window, len(kama)):
cur = smoothing.iloc[i] * (col.iloc[i] - last_kama) + last_kama
kama.iloc[i] = cur
cur = smoothing[i] * (col_list[i] - last_kama) + last_kama
kama[i] = cur
last_kama = cur
self[column_name] = kama

Expand Down Expand Up @@ -1347,6 +1381,7 @@ def handler(self):
'supertrend_lb',
'supertrend_ub'): self._get_supertrend,
('aroon',): self._get_aroon,
('ao',): self._get_ao,
}

def __init_not_exist_column(self, key):
Expand Down
10 changes: 10 additions & 0 deletions test.py
Original file line number Diff line number Diff line change
Expand Up @@ -671,6 +671,16 @@ def test_supertrend(self):
assert_that(st_ub[idx], near_to(14.6457))
assert_that(st_lb[idx], near_to(12.9021))

def test_ao(self):
stock = self.get_stock_90day()
ao = stock['ao']
ao1 = stock['ao_5,34']
ao2 = stock['ao_5,10']
idx = 20110302
assert_that(ao[idx], near_to(-0.112))
assert_that(ao1[idx], equal_to(ao[idx]))
assert_that(ao2[idx], near_to(-0.071))

def test_drop_column_inplace(self):
stock = self._supor[:20]
stock.columns.name = 'Luke'
Expand Down

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