Stock Market Indicators

https://github.com/voice32/stock_market_indicators
Stock Market Indicators
A small Python library with most the common stock market indicators.

Requirements
Pandas
Numpy
Installation
Clone or download the indicators.py file into your project directory.

Usage
Import the module: import indicators

The functions in this library accept the data in Pandas DataFrame format. The data should contain OPEN, HIGH, LOW, CLOSE and VOLUME columns. See the comments for each function for the list of required columns. Their default names are hardcoded in functions’ params, however you may supply your own column names, if they are different. Sometimes you would also need to provide periods over which to calculate the indicator values. However, for all of them the default (recommended) values are pre-assigned.

List of implemented techinical indicators
Exponential moving average (EMA)
Moving Average Convergence/Divergence Oscillator (MACD)
Accumulation Distribution (A/D)
On Balance Volume (OBV)
Price-volume trend (PVT)
Average true range (ATR)
Bollinger Bands
Chaikin Oscillator
Typical Price
Ease of Movement
Mass Index
Average directional movement index
Money Flow Index (MFI)
Negative Volume Index (NVI)
Positive Volume Index (PVI)
Momentum
Relative Strenght Index (RSI)
Chaikin Volatility (CV)
William’s Accumulation/Distribution
William’s % R
TRIX
Ultimate Oscillator

"""
Copyright, Rinat Maksutov, 2017.
License: GNU General Public License
"""

import numpy as np
import pandas as pd

"""
Exponential moving average
Source: http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:moving_averages
Params: 
    data: pandas DataFrame
    period: smoothing period
    column: the name of the column with values for calculating EMA in the 'data' DataFrame
    
Returns:
    copy of 'data' DataFrame with 'ema[period]' column added
"""
def ema(data, period=0, column='<CLOSE>'):
    data['ema' + str(period)] = data[column].ewm(ignore_na=False, min_periods=period, com=period, adjust=True).mean()
    
    return data

"""
Moving Average Convergence/Divergence Oscillator (MACD)
Source: http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:moving_average_convergence_divergence_macd
Params: 
    data: pandas DataFrame
    period_long: the longer period EMA (26 days recommended)
    period_short: the shorter period EMA (12 days recommended)
    period_signal: signal line EMA (9 days recommended)
    column: the name of the column with values for calculating MACD in the 'data' DataFrame
    
Returns:
    copy of 'data' DataFrame with 'macd_val' and 'macd_signal_line' columns added
"""
def macd(data, period_long=26, period_short=12, period_signal=9, column='<CLOSE>'):
    remove_cols = []
    if not 'ema' + str(period_long) in data.columns:
        data = ema(data, period_long)
        remove_cols.append('ema' + str(period_long))

    if not 'ema' + str(period_short) in data.columns:
        data = ema(data, period_short)
        remove_cols.append('ema' + str(period_short))

    data['macd_val'] = data['ema' + str(period_short)] - data['ema' + str(period_long)]
    data['macd_signal_line'] = data['macd_val'].ewm(ignore_na=False, min_periods=0, com=period_signal, adjust=True).mean()

    data = data.drop(remove_cols, axis=1)
        
    return data

"""
Accumulation Distribution 
Source: http://stockcharts.com/school/doku.php?st=accumulation+distribution&id=chart_school:technical_indicators:accumulation_distribution_line
Params: 
    data: pandas DataFrame
    trend_periods: the over which to calculate AD
    open_col: the name of the OPEN values column
	high_col: the name of the HIGH values column
	low_col: the name of the LOW values column
	close_col: the name of the CLOSE values column
	vol_col: the name of the VOL values column
    
Returns:
    copy of 'data' DataFrame with 'acc_dist' and 'acc_dist_ema[trend_periods]' columns added
"""
def acc_dist(data, trend_periods=21, open_col='<OPEN>', high_col='<HIGH>', low_col='<LOW>', close_col='<CLOSE>', vol_col='<VOL>'):
    for index, row in data.iterrows():
        if row[high_col] != row[low_col]:
            ac = ((row[close_col] - row[low_col]) - (row[high_col] - row[close_col])) / (row[high_col] - row[low_col]) * row[vol_col]
        else:
            ac = 0
        data.set_value(index, 'acc_dist', ac)
    data['acc_dist_ema' + str(trend_periods)] = data['acc_dist'].ewm(ignore_na=False, min_periods=0, com=trend_periods, adjust=True).mean()
    
    return data

"""
On Balance Volume (OBV)
Source: http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:on_balance_volume_obv
Params: 
    data: pandas DataFrame
    trend_periods: the over which to calculate OBV
	close_col: the name of the CLOSE values column
	vol_col: the name of the VOL values column
    
Returns:
    copy of 'data' DataFrame with 'obv' and 'obv_ema[trend_periods]' columns added
"""
def on_balance_volume(data, trend_periods=21, close_col='<CLOSE>', vol_col='<VOL>'):
    for index, row in data.iterrows():
        if index > 0:
            last_obv = data.at[index - 1, 'obv']
            if row[close_col] > data.at[index - 1, close_col]:
                current_obv = last_obv + row[vol_col]
            elif row[close_col] < data.at[index - 1, close_col]:
                current_obv = last_obv - row[vol_col]
            else:
                current_obv = last_obv
        else:
            last_obv = 0
            current_obv = row[vol_col]

        data.set_value(index, 'obv', current_obv)

    data['obv_ema' + str(trend_periods)] = data['obv'].ewm(ignore_na=False, min_periods=0, com=trend_periods, adjust=True).mean()
    
    return data

"""
Price-volume trend (PVT) (sometimes volume-price trend)
Source: https://en.wikipedia.org/wiki/Volume%E2%80%93price_trend
Params: 
    data: pandas DataFrame
    trend_periods: the over which to calculate PVT
	close_col: the name of the CLOSE values column
	vol_col: the name of the VOL values column
    
Returns:
    copy of 'data' DataFrame with 'pvt' and 'pvt_ema[trend_periods]' columns added
"""
def price_volume_trend(data, trend_periods=21, close_col='<CLOSE>', vol_col='<VOL>'):
    for index, row in data.iterrows():
        if index > 0:
            last_val = data.at[index - 1, 'pvt']
            last_close = data.at[index - 1, close_col]
            today_close = row[close_col]
            today_vol = row[vol_col]
            current_val = last_val + (today_vol * (today_close - last_close) / last_close)
        else:
            current_val = row[vol_col]

        data.set_value(index, 'pvt', current_val)

    data['pvt_ema' + str(trend_periods)] = data['pvt'].ewm(ignore_na=False, min_periods=0, com=trend_periods, adjust=True).mean()
        
    return data

"""
Average true range (ATR)
Source: https://en.wikipedia.org/wiki/Average_true_range
Params: 
    data: pandas DataFrame
    trend_periods: the over which to calculate ATR
    open_col: the name of the OPEN values column
	high_col: the name of the HIGH values column
	low_col: the name of the LOW values column
	close_col: the name of the CLOSE values column
	vol_col: the name of the VOL values column
	drop_tr: whether to drop the True Range values column from the resulting DataFrame
    
Returns:
    copy of 'data' DataFrame with 'atr' (and 'true_range' if 'drop_tr' == True) column(s) added
"""
def average_true_range(data, trend_periods=14, open_col='<OPEN>', high_col='<HIGH>', low_col='<LOW>', close_col='<CLOSE>', drop_tr = True):
    for index, row in data.iterrows():
        prices = [row[high_col], row[low_col], row[close_col], row[open_col]]
        if index > 0:
            val1 = np.amax(prices) - np.amin(prices)
            val2 = abs(np.amax(prices) - data.at[index - 1, close_col])
            val3 = abs(np.amin(prices) - data.at[index - 1, close_col])
            true_range = np.amax([val1, val2, val3])

        else:
            true_range = np.amax(prices) - np.amin(prices)

        data.set_value(index, 'true_range', true_range)
    data['atr'] = data['true_range'].ewm(ignore_na=False, min_periods=0, com=trend_periods, adjust=True).mean()
    if drop_tr:
        data = data.drop(['true_range'], axis=1)
        
    return data

"""
Bollinger Bands
Source: https://en.wikipedia.org/wiki/Bollinger_Bands
Params: 
    data: pandas DataFrame
    trend_periods: the over which to calculate BB
	close_col: the name of the CLOSE values column
    
Returns:
    copy of 'data' DataFrame with 'bol_bands_middle', 'bol_bands_upper' and 'bol_bands_lower' columns added
"""
def bollinger_bands(data, trend_periods=20, close_col='<CLOSE>'):

    data['bol_bands_middle'] = data[close_col].ewm(ignore_na=False, min_periods=0, com=trend_periods, adjust=True).mean()
    for index, row in data.iterrows():

        s = data[close_col].iloc[index - trend_periods: index]
        sums = 0
        middle_band = data.at[index, 'bol_bands_middle']
        for e in s:
            sums += np.square(e - middle_band)

        std = np.sqrt(sums / trend_periods)
        d = 2
        upper_band = middle_band + (d * std)
        lower_band = middle_band - (d * std)

        data.set_value(index, 'bol_bands_upper', upper_band)
        data.set_value(index, 'bol_bands_lower', lower_band)

    return data

"""
Chaikin Oscillator
Source: http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:chaikin_oscillator
Params: 
    data: pandas DataFrame
	periods_short: period for the shorter EMA (3 days recommended)
	periods_long: period for the longer EMA (10 days recommended)
	high_col: the name of the HIGH values column
	low_col: the name of the LOW values column
	close_col: the name of the CLOSE values column
	vol_col: the name of the VOL values column
    
Returns:
    copy of 'data' DataFrame with 'ch_osc' column added
"""
def chaikin_oscillator(data, periods_short=3, periods_long=10, high_col='<HIGH>',
                       low_col='<LOW>', close_col='<CLOSE>', vol_col='<VOL>'):
    ac = pd.Series([])
    val_last = 0
	
    for index, row in data.iterrows():
        if row[high_col] != row[low_col]:
            val = val_last + ((row[close_col] - row[low_col]) - (row[high_col] - row[close_col])) / (row[high_col] - row[low_col]) * row[vol_col]
        else:
            val = val_last
        ac.set_value(index, val)
	val_last = val

    ema_long = ac.ewm(ignore_na=False, min_periods=0, com=periods_long, adjust=True).mean()
    ema_short = ac.ewm(ignore_na=False, min_periods=0, com=periods_short, adjust=True).mean()
    data['ch_osc'] = ema_short - ema_long

    return data

"""
Typical Price
Source: https://en.wikipedia.org/wiki/Typical_price
Params: 
    data: pandas DataFrame
	high_col: the name of the HIGH values column
	low_col: the name of the LOW values column
	close_col: the name of the CLOSE values column
    
Returns:
    copy of 'data' DataFrame with 'typical_price' column added
"""
def typical_price(data, high_col = '<HIGH>', low_col = '<LOW>', close_col = '<CLOSE>'):
    
    data['typical_price'] = (data[high_col] + data[low_col] + data[close_col]) / 3

    return data

"""
Ease of Movement
Source: http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:ease_of_movement_emv
Params: 
    data: pandas DataFrame
	period: period for calculating EMV
	high_col: the name of the HIGH values column
	low_col: the name of the LOW values column
	vol_col: the name of the VOL values column
    
Returns:
    copy of 'data' DataFrame with 'emv' and 'emv_ema_[period]' columns added
"""
def ease_of_movement(data, period=14, high_col='<HIGH>', low_col='<LOW>', vol_col='<VOL>'):
    for index, row in data.iterrows():
        if index > 0:
            midpoint_move = (row[high_col] + row[low_col]) / 2 - (data.at[index - 1, high_col] + data.at[index - 1, low_col]) / 2
        else:
            midpoint_move = 0
        
        diff = row[high_col] - row[low_col]
		
        if diff == 0:
			#this is to avoid division by zero below
            diff = 0.000000001			
            
        vol = row[vol_col]
        if vol == 0:
            vol = 1
        box_ratio = (vol / 100000000) / (diff)
        emv = midpoint_move / box_ratio
        
        data.set_value(index, 'emv', emv)
        
    data['emv_ema_'+str(period)] = data['emv'].ewm(ignore_na=False, min_periods=0, com=period, adjust=True).mean()
        
    return data

"""
Mass Index
Source: http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:mass_index
Params: 
    data: pandas DataFrame
	period: period for calculating MI (9 days recommended)
	high_col: the name of the HIGH values column
	low_col: the name of the LOW values column
    
Returns:
    copy of 'data' DataFrame with 'mass_index' column added
"""
def mass_index(data, period=25, ema_period=9, high_col='<HIGH>', low_col='<LOW>'):
    high_low = data[high_col] - data[low_col] + 0.000001	#this is to avoid division by zero below
    ema = high_low.ewm(ignore_na=False, min_periods=0, com=ema_period, adjust=True).mean()
    ema_ema = ema.ewm(ignore_na=False, min_periods=0, com=ema_period, adjust=True).mean()
    div = ema / ema_ema

    for index, row in data.iterrows():
        if index >= period:
            val = div[index-25:index].sum()
        else:
            val = 0
        data.set_value(index, 'mass_index', val)
         
    return data

"""
Average directional movement index
Source: https://en.wikipedia.org/wiki/Average_directional_movement_index
Params: 
    data: pandas DataFrame
	periods: period for calculating ADX (14 days recommended)
	high_col: the name of the HIGH values column
	low_col: the name of the LOW values column
    
Returns:
    copy of 'data' DataFrame with 'adx', 'dxi', 'di_plus', 'di_minus' columns added
"""
def directional_movement_index(data, periods=14, high_col='<HIGH>', low_col='<LOW>'):
    remove_tr_col = False
    if not 'true_range' in data.columns:
        data = average_true_range(data, drop_tr = False)
        remove_tr_col = True

    data['m_plus'] = 0.
    data['m_minus'] = 0.
    
    for i,row in data.iterrows():
        if i>0:
            data.set_value(i, 'm_plus', row[high_col] - data.at[i-1, high_col])
            data.set_value(i, 'm_minus', row[low_col] - data.at[i-1, low_col])
    
    data['dm_plus'] = 0.
    data['dm_minus'] = 0.
    
    for i,row in data.iterrows():
        if row['m_plus'] > row['m_minus'] and row['m_plus'] > 0:
            data.set_value(i, 'dm_plus', row['m_plus'])
            
        if row['m_minus'] > row['m_plus'] and row['m_minus'] > 0:
            data.set_value(i, 'dm_minus', row['m_minus'])
    
    data['di_plus'] = (data['dm_plus'] / data['true_range']).ewm(ignore_na=False, min_periods=0, com=periods, adjust=True).mean()
    data['di_minus'] = (data['dm_minus'] / data['true_range']).ewm(ignore_na=False, min_periods=0, com=periods, adjust=True).mean()
    
    data['dxi'] = np.abs(data['di_plus'] - data['di_minus']) / (data['di_plus'] + data['di_minus'])
    data.set_value(0, 'dxi',1.)
    data['adx'] = data['dxi'].ewm(ignore_na=False, min_periods=0, com=periods, adjust=True).mean()
    data = data.drop(['m_plus', 'm_minus', 'dm_plus', 'dm_minus'], axis=1)
    if remove_tr_col:
        data = data.drop(['true_range'], axis=1)
         
    return data

"""
Money Flow Index (MFI)
Source: http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:money_flow_index_mfi
Params: 
    data: pandas DataFrame
	periods: period for calculating MFI (14 days recommended)
	vol_col: the name of the VOL values column
    
Returns:
    copy of 'data' DataFrame with 'money_flow_index' column added
"""
def money_flow_index(data, periods=14, vol_col='<VOL>'):
    remove_tp_col = False
    if not 'typical_price' in data.columns:
        data = typical_price(data)
        remove_tp_col = True
    
    data['money_flow'] = data['typical_price'] * data[vol_col]
    data['money_ratio'] = 0.
    data['money_flow_index'] = 0.
    data['money_flow_positive'] = 0.
    data['money_flow_negative'] = 0.
    
    for index,row in data.iterrows():
        if index > 0:
            if row['typical_price'] < data.at[index-1, 'typical_price']:
                data.set_value(index, 'money_flow_positive', row['money_flow'])
            else:
                data.set_value(index, 'money_flow_negative', row['money_flow'])
    
        if index >= periods:
            period_slice = data['money_flow'][index-periods:index]
            positive_sum = data['money_flow_positive'][index-periods:index].sum()
            negative_sum = data['money_flow_negative'][index-periods:index].sum()

            if negative_sum == 0.:
				#this is to avoid division by zero below
                negative_sum = 0.00001
            m_r = positive_sum / negative_sum

            mfi = 1-(1 / (1 + m_r))

            data.set_value(index, 'money_ratio', m_r)
            data.set_value(index, 'money_flow_index', mfi)
          
    data = data.drop(['money_flow', 'money_ratio', 'money_flow_positive', 'money_flow_negative'], axis=1)
    
    if remove_tp_col:
        data = data.drop(['typical_price'], axis=1)

    return data

"""
Negative Volume Index (NVI)
Source: http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:negative_volume_inde
Params: 
    data: pandas DataFrame
	periods: period for calculating NVI (255 days recommended)
	close_col: the name of the CLOSE values column
	vol_col: the name of the VOL values column
    
Returns:
    copy of 'data' DataFrame with 'nvi' and 'nvi_ema' columns added
"""
def negative_volume_index(data, periods=255, close_col='<CLOSE>', vol_col='<VOL>'):
    data['nvi'] = 0.
    
    for index,row in data.iterrows():
        if index > 0:
            prev_nvi = data.at[index-1, 'nvi']
            prev_close = data.at[index-1, close_col]
            if row[vol_col] < data.at[index-1, vol_col]:
                nvi = prev_nvi + (row[close_col] - prev_close / prev_close * prev_nvi)
            else: 
                nvi = prev_nvi
        else:
            nvi = 1000
        data.set_value(index, 'nvi', nvi)
    data['nvi_ema'] = data['nvi'].ewm(ignore_na=False, min_periods=0, com=periods, adjust=True).mean()
    
    return data

"""
Positive Volume Index (PVI)
Source: https://www.equities.com/news/the-secret-to-the-positive-volume-index
Params: 
    data: pandas DataFrame
	periods: period for calculating PVI (255 days recommended)
	close_col: the name of the CLOSE values column
	vol_col: the name of the VOL values column
    
Returns:
    copy of 'data' DataFrame with 'pvi' and 'pvi_ema' columns added
"""
def positive_volume_index(data, periods=255, close_col='<CLOSE>', vol_col='<VOL>'):
    data['pvi'] = 0.
    
    for index,row in data.iterrows():
        if index > 0:
            prev_pvi = data.at[index-1, 'pvi']
            prev_close = data.at[index-1, close_col]
            if row[vol_col] > data.at[index-1, vol_col]:
                pvi = prev_pvi + (row[close_col] - prev_close / prev_close * prev_pvi)
            else: 
                pvi = prev_pvi
        else:
            pvi = 1000
        data.set_value(index, 'pvi', pvi)
    data['pvi_ema'] = data['pvi'].ewm(ignore_na=False, min_periods=0, com=periods, adjust=True).mean()

    return data

"""
Momentum
Source: https://en.wikipedia.org/wiki/Momentum_(technical_analysis)
Params: 
    data: pandas DataFrame
	periods: period for calculating momentum
	close_col: the name of the CLOSE values column
    
Returns:
    copy of 'data' DataFrame with 'momentum' column added
"""
def momentum(data, periods=14, close_col='<CLOSE>'):
    data['momentum'] = 0.
    
    for index,row in data.iterrows():
        if index >= periods:
            prev_close = data.at[index-periods, close_col]
            val_perc = (row[close_col] - prev_close)/prev_close

            data.set_value(index, 'momentum', val_perc)

    return data

"""
Relative Strenght Index
Source: https://en.wikipedia.org/wiki/Relative_strength_index
Params: 
    data: pandas DataFrame
	periods: period for calculating momentum
	close_col: the name of the CLOSE values column
    
Returns:
    copy of 'data' DataFrame with 'rsi' column added
"""
def rsi(data, periods=14, close_col='<CLOSE>'):
    data['rsi_u'] = 0.
    data['rsi_d'] = 0.
    data['rsi'] = 0.
    
    for index,row in data.iterrows():
        if index >= periods:
            
            prev_close = data.at[index-periods, close_col]
            if prev_close < row[close_col]:
                data.set_value(index, 'rsi_u', row[close_col] - prev_close)
            elif prev_close > row[close_col]:
                data.set_value(index, 'rsi_d', prev_close - row[close_col])
            
    data['rsi'] = data['rsi_u'].ewm(ignore_na=False, min_periods=0, com=periods, adjust=True).mean() / (data['rsi_u'].ewm(ignore_na=False, min_periods=0, com=periods, adjust=True).mean() + data['rsi_d'].ewm(ignore_na=False, min_periods=0, com=periods, adjust=True).mean())
    
    data = data.drop(['rsi_u', 'rsi_d'], axis=1)
        
    return data

"""
Chaikin Volatility (CV)
Source: https://www.marketvolume.com/technicalanalysis/chaikinvolatility.asp
Params: 
    data: pandas DataFrame
	ema_periods: period for smoothing Highest High and Lowest Low difference
	change_periods: the period for calculating the difference between Highest High and Lowest Low
	high_col: the name of the HIGH values column
	low_col: the name of the LOW values column
	close_col: the name of the CLOSE values column
    
Returns:
    copy of 'data' DataFrame with 'chaikin_volatility' column added
"""
def chaikin_volatility(data, ema_periods=10, change_periods=10, high_col='<HIGH>', low_col='<LOW>', close_col='<CLOSE>'):
    data['ch_vol_hl'] = data[high_col] - data[low_col]
    data['ch_vol_ema'] = data['ch_vol_hl'].ewm(ignore_na=False, min_periods=0, com=ema_periods, adjust=True).mean()
    data['chaikin_volatility'] = 0.
    
    for index,row in data.iterrows():
        if index >= change_periods:
            
            prev_value = data.at[index-change_periods, 'ch_vol_ema']
            if prev_value == 0:
                #this is to avoid division by zero below
                prev_value = 0.0001
            data.set_value(index, 'chaikin_volatility', ((row['ch_vol_ema'] - prev_value)/prev_value))
            
    data = data.drop(['ch_vol_hl', 'ch_vol_ema'], axis=1)
        
    return data

"""
William's Accumulation/Distribution
Source: https://www.metastock.com/customer/resources/taaz/?p=125
Params: 
    data: pandas DataFrame
	high_col: the name of the HIGH values column
	low_col: the name of the LOW values column
	close_col: the name of the CLOSE values column
    
Returns:
    copy of 'data' DataFrame with 'williams_ad' column added
"""
def williams_ad(data, high_col='<HIGH>', low_col='<LOW>', close_col='<CLOSE>'):
    data['williams_ad'] = 0.
    
    for index,row in data.iterrows():
        if index > 0:
            prev_value = data.at[index-1, 'williams_ad']
            prev_close = data.at[index-1, close_col]
            if row[close_col] > prev_close:
                ad = row[close_col] - min(prev_close, row[low_col])
            elif row[close_col] < prev_close:
                ad = row[close_col] - max(prev_close, row[high_col])
            else:
                ad = 0.
                                                                                                        
            data.set_value(index, 'williams_ad', (ad+prev_value))
        
    return data

"""
William's % R
Source: https://www.metastock.com/customer/resources/taaz/?p=126
Params: 
    data: pandas DataFrame
	periods: the period over which to calculate the indicator value
	high_col: the name of the HIGH values column
	low_col: the name of the LOW values column
	close_col: the name of the CLOSE values column
    
Returns:
    copy of 'data' DataFrame with 'williams_r' column added
"""
def williams_r(data, periods=14, high_col='<HIGH>', low_col='<LOW>', close_col='<CLOSE>'):
    data['williams_r'] = 0.
    
    for index,row in data.iterrows():
        if index > periods:
            data.set_value(index, 'williams_r', ((max(data[high_col][index-periods:index]) - row[close_col]) / 
                                                 (max(data[high_col][index-periods:index]) - min(data[low_col][index-periods:index]))))
        
    return data

"""
TRIX
Source: https://www.metastock.com/customer/resources/taaz/?p=114
Params: 
    data: pandas DataFrame
	periods: the period over which to calculate the indicator value
	signal_periods: the period for signal moving average
	close_col: the name of the CLOSE values column
    
Returns:
    copy of 'data' DataFrame with 'trix' and 'trix_signal' columns added
"""
def trix(data, periods=14, signal_periods=9, close_col='<CLOSE>'):
    data['trix'] = data[close_col].ewm(ignore_na=False, min_periods=0, com=periods, adjust=True).mean()
    data['trix'] = data['trix'].ewm(ignore_na=False, min_periods=0, com=periods, adjust=True).mean()
    data['trix'] = data['trix'].ewm(ignore_na=False, min_periods=0, com=periods, adjust=True).mean()
    data['trix_signal'] = data['trix'].ewm(ignore_na=False, min_periods=0, com=signal_periods, adjust=True).mean()
        
    return data

"""
Ultimate Oscillator
Source: http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:ultimate_oscillator
Params: 
    data: pandas DataFrame
	period_1: the period of the first average (7 days recommended)
	period_2: the period of the second average (14 days recommended)
	period_3: the period of the third average (28 days recommended)
	high_col: the name of the HIGH values column
	low_col: the name of the LOW values column
	close_col: the name of the CLOSE values column
    
Returns:
    copy of 'data' DataFrame with 'ultimate_oscillator' column added
"""
def ultimate_oscillator(data, period_1=7,period_2=14, period_3=28, high_col='<HIGH>', low_col='<LOW>', close_col='<CLOSE>'):
    data['ultimate_oscillator'] = 0.
    data['uo_bp'] = 0.
    data['uo_tr'] = 0.
    data['uo_avg_1'] = 0.
    data['uo_avg_2'] = 0.
    data['uo_avg_3'] = 0.

    for index,row in data.iterrows():
        if index > 0:
                           
            bp = row[close_col] - min(row[low_col], data.at[index-1, close_col])
            tr = max(row[high_col], data.at[index-1, close_col]) - min(row[low_col], data.at[index-1, close_col])
            
            data.set_value(index, 'uo_bp', bp)
            data.set_value(index, 'uo_tr', tr)
            if index >= period_1:
                uo_avg_1 = sum(data['uo_bp'][index-period_1:index]) / sum(data['uo_tr'][index-period_1:index])
                data.set_value(index, 'uo_avg_1', uo_avg_1)
            if index >= period_2:
                uo_avg_2 = sum(data['uo_bp'][index-period_2:index]) / sum(data['uo_tr'][index-period_2:index])
                data.set_value(index, 'uo_avg_2', uo_avg_2)
            if index >= period_3:
                uo_avg_3 = sum(data['uo_bp'][index-period_3:index]) / sum(data['uo_tr'][index-period_3:index])
                data.set_value(index, 'uo_avg_3', uo_avg_3)
                uo = (4 * uo_avg_1 + 2 * uo_avg_2 + uo_avg_3) / 7
                data.set_value(index, 'ultimate_oscillator', uo)

    data = data.drop(['uo_bp', 'uo_tr', 'uo_avg_1', 'uo_avg_2', 'uo_avg_3'], axis=1)
        
    return data