def test_obv(self):
target = 'OBV'
result = on_balance_volume(close=self._df['Close'],
volume=self._df['Volume'],
fillna=False)
pd.testing.assert_series_equal(self._df[target].tail(),
result.tail(),
check_names=False)
def get_technical_indicators(dataset, key='Close'):
# Create 7 and 21 days Moving Average
dataset['ma7'] = dataset[key].rolling(window=7).mean()
dataset['ma21'] = dataset[key].rolling(window=21).mean()
# Create MACD -> Moving Average Convergence/Divergence (trend and momentum indicator)
dataset['26ema'] = dataset[key].ewm(span=26).mean()
dataset['12ema'] = dataset[key].ewm(span=12).mean()
dataset['MACD'] = (dataset['12ema'] - dataset['26ema'])
# Create Bollinger Bands
# Bollinger Bands are volatility bands placed above and below a moving average. Volatility is based on the standard deviation, which changes as volatility
# increases and decreases. The bands automatically widen when volatility increases and narrow when volatility decreases.
dataset['20std'] = dataset[key].rolling(20).std()
dataset['upper_band'] = dataset['ma21'] + (dataset['20std'] * 2)
dataset['lower_band'] = dataset['ma21'] - (dataset['20std'] * 2)
# Create Exponential moving average
dataset['ema'] = dataset[key].ewm(com=0.5).mean()
# Create Momentum
dataset['momentum_1'] = dataset[key].diff(1)
dataset['momentum_10'] = dataset[key].diff(10)
# close price raw return 1, 10 days horizon
# dataset['returnsClosePrevRaw1'] = dataset['Close'].pct_change(1)
# dataset['returnsClosePrevRaw10'] = dataset['Close'].pct_change(10)
# open price raw return 1, 10 days horizon
# dataset['returnsOpenPrevRaw1'] = dataset['Open'].pct_change(1)
# dataset['returnsOpenPrevRaw10'] = dataset['Open'].pct_change(10)
# Create AROON - identifing when trends are likely to change direction, n=20
dataset['aroon_up_20'] = dataset[key].rolling(20, min_periods=0).apply(lambda x: float(np.argmax(x) + 1) / 20 * 100,
raw=True)
dataset['aroon_down_20'] = dataset[key].rolling(20, min_periods=0).apply(
lambda x: float(np.argmin(x) + 1) / 20 * 100, raw=True)
# Create CCI, Commodity Channel Index
pp = (dataset['High'] + dataset['Low'] + dataset[key]) / 3.0
dataset['cci'] = (pp - pp.rolling(20, min_periods=0).mean()) / (0.015 * pp.rolling(20, min_periods=0).std())
# Create STOCH - Stochastic Oscillator
smin = dataset['Low'].rolling(14, min_periods=0).min()
smax = dataset['High'].rolling(14, min_periods=0).max()
dataset['stoch'] = 100 * (dataset[key] - smin) / (smax - smin)
# Create RSI - Relative Strength Index
dataset['rsi'] = rsi(dataset[key])
# Create ADI - Accumulation/Distribution Index
dataset['adi'] = acc_dist_index(dataset['High'], dataset['Low'], dataset[key], dataset['Volume'])
# Create OBV - On-balance volume
dataset['obv'] = on_balance_volume(dataset[key], dataset['Volume'])
return dataset
def __dataframe(self):
"""Create an comprehensive list of from data.
Args:
None
Returns:
result: dataframe for learning
"""
# Calculate the percentage and real differences between columns
difference = math.Difference(self._ohlcv)
num_difference = difference.actual()
pct_difference = difference.relative()
# Create result to return.
result = pd.DataFrame()
# Add current value columns
result['open'] = self._ohlcv['open']
result['high'] = self._ohlcv['high']
result['low'] = self._ohlcv['low']
result['close'] = self._ohlcv['close']
result['volume'] = self._ohlcv['volume']
# Add columns of differences
result['num_diff_open'] = num_difference['open']
result['num_diff_high'] = num_difference['high']
result['num_diff_low'] = num_difference['low']
result['num_diff_close'] = num_difference['close']
result['pct_diff_open'] = pct_difference['open']
result['pct_diff_high'] = pct_difference['high']
result['pct_diff_low'] = pct_difference['low']
result['pct_diff_close'] = pct_difference['close']
result['pct_diff_volume'] = pct_difference['volume']
# Add date related columns
# result['day'] = self._dates.day
result['weekday'] = self._dates.weekday
# result['week'] = self._dates.week
result['month'] = self._dates.month
result['quarter'] = self._dates.quarter
# result['dayofyear'] = self._dates.dayofyear
# Moving averages
result['ma_open'] = result['open'].rolling(
self._globals['ma_window']).mean()
result['ma_high'] = result['high'].rolling(
self._globals['ma_window']).mean()
result['ma_low'] = result['low'].rolling(
self._globals['ma_window']).mean()
result['ma_close'] = result['close'].rolling(
self._globals['ma_window']).mean()
result['ma_volume'] = result['volume'].rolling(
self._globals['vma_window']).mean()
result['ma_volume_long'] = result['volume'].rolling(
self._globals['vma_window_long']).mean()
result[
'ma_volume_delta'] = result['ma_volume_long'] - result['ma_volume']
# Standard deviation related
result['ma_std_close'] = result['close'].rolling(
self._globals['ma_window']).std()
result['std_pct_diff_close'] = result['pct_diff_close'].rolling(
self._globals['ma_window']).std()
result['bollinger_lband'] = volatility.bollinger_lband(result['close'])
result['bollinger_hband'] = volatility.bollinger_lband(result['close'])
result[
'bollinger_lband_indicator'] = volatility.bollinger_lband_indicator(
result['close'])
result[
'bollinger_hband_indicator'] = volatility.bollinger_hband_indicator(
result['close'])
# Rolling ranges
result['amplitude'] = result['high'] - result['low']
_min = result['low'].rolling(self._globals['week']).min()
_max = result['high'].rolling(self._globals['week']).max()
result['amplitude_medium'] = abs(_min - _max)
_min = result['low'].rolling(2 * self._globals['week']).min()
_max = result['high'].rolling(2 * self._globals['week']).max()
result['amplitude_long'] = abs(_min - _max)
_min = result['volume'].rolling(self._globals['week']).min()
_max = result['volume'].rolling(self._globals['week']).max()
result['vol_amplitude'] = abs(_min - _max)
_min = result['volume'].rolling(2 * self._globals['week']).min()
_max = result['volume'].rolling(2 * self._globals['week']).max()
result['vol_amplitude_long'] = abs(_min - _max)
# Volume metrics
result['force_index'] = volume.force_index(result['close'],
result['volume'])
result['negative_volume_index'] = volume.negative_volume_index(
result['close'], result['volume'])
result['ease_of_movement'] = volume.ease_of_movement(
result['high'], result['low'], result['close'], result['volume'])
result['acc_dist_index'] = volume.acc_dist_index(
result['high'], result['low'], result['close'], result['volume'])
result['on_balance_volume'] = volume.on_balance_volume(
result['close'], result['volume'])
result['on_balance_volume_mean'] = volume.on_balance_volume(
result['close'], result['volume'])
result['volume_price_trend'] = volume.volume_price_trend(
result['close'], result['volume'])
# Calculate the Stochastic values
result['k'] = momentum.stoch(result['high'],
result['low'],
result['close'],
n=self._globals['kwindow'])
result['d'] = momentum.stoch_signal(result['high'],
result['low'],
result['close'],
n=self._globals['kwindow'],
d_n=self._globals['dwindow'])
# Calculate the Miscellaneous values
result['rsi'] = momentum.rsi(result['close'],
n=self._globals['rsiwindow'],
fillna=False)
miscellaneous = math.Misc(self._ohlcv)
result['proc'] = miscellaneous.proc(self._globals['proc_window'])
# Calculate ADX
result['adx'] = trend.adx(result['high'],
result['low'],
result['close'],
n=self._globals['adx_window'])
# Calculate MACD difference
result['macd_diff'] = trend.macd_diff(
result['close'],
n_fast=self._globals['macd_sign'],
n_slow=self._globals['macd_slow'],
n_sign=self._globals['macd_sign'])
# Create series for increasing / decreasing closes (Convert NaNs to 0)
_result = np.nan_to_num(result['pct_diff_close'].values)
_increasing = (_result >= 0).astype(int) * self._buy
_decreasing = (_result < 0).astype(int) * self._sell
result['increasing'] = _increasing + _decreasing
# Stochastic subtraciton
result['k_d'] = pd.Series(result['k'].values - result['d'].values)
# Other indicators
result['k_i'] = self._stochastic_indicator(result['k'], result['high'],
result['low'],
result['ma_close'])
result['d_i'] = self._stochastic_indicator(result['d'], result['high'],
result['low'],
result['ma_close'])
result['stoch_i'] = self._stochastic_indicator_2(
result['k'], result['d'], result['high'], result['low'],
result['ma_close'])
result['rsi_i'] = self._rsi_indicator(result['rsi'], result['high'],
result['low'],
result['ma_close'])
result['adx_i'] = self._adx_indicator(result['adx'])
result['macd_diff_i'] = self._macd_diff_indicator(result['macd_diff'])
result['volume_i'] = self._volume_indicator(result['ma_volume'],
result['ma_volume_long'])
# Create time shifted columns
for step in range(1, self._ignore_row_count + 1):
# result['t-{}'.format(step)] = result['close'].shift(step)
result['tpd-{}'.format(step)] = result['close'].pct_change(
periods=step)
# result['tad-{}'.format(step)] = result[
# 'close'].diff(periods=step)
# Mask increasing with
result['increasing_masked'] = _mask(result['increasing'].to_frame(),
result['stoch_i'],
as_integer=True).values
# Get class values for each vector
classes = pd.DataFrame(columns=self._shift_steps)
for step in self._shift_steps:
# Shift each column by the value of its label
classes[step] = result[self._label2predict].shift(-step)
# Remove all undesirable columns from the dataframe
undesired_columns = ['open', 'close', 'high', 'low', 'volume']
for column in undesired_columns:
result = result.drop(column, axis=1)
# Delete the firsts row of the dataframe as it has NaN values from the
# .diff() and .pct_change() operations
result = result.iloc[self._ignore_row_count:]
classes = classes.iloc[self._ignore_row_count:]
# Convert result to float32 to conserve memory
result = result.astype(np.float32)
# Return
return result, classes
def test_obv(self):
target = "OBV"
result = on_balance_volume(**self._params)
pd.testing.assert_series_equal(self._df[target].tail(),
result.tail(),
check_names=False)
def __dataframe(self):
"""Create an comprehensive list of from data.
Args:
None
Returns:
result: dataframe for learning
"""
# Calculate the percentage and real differences between columns
difference = math.Difference(self._ohlcv)
num_difference = difference.actual()
pct_difference = difference.relative()
# Create result to return.
result = pd.DataFrame()
# Add current value columns
# NOTE Close must be first for correct correlation column dropping
result['close'] = self._ohlcv['close']
result['open'] = self._ohlcv['open']
result['high'] = self._ohlcv['high']
result['low'] = self._ohlcv['low']
result['volume'] = self._ohlcv['volume']
# Add columns of differences
# NOTE Close must be first for correct correlation column dropping
result['num_diff_close'] = num_difference['close']
result['pct_diff_close'] = pct_difference['close']
result['num_diff_open'] = num_difference['open']
result['pct_diff_open'] = pct_difference['open']
result['num_diff_high'] = num_difference['high']
result['pct_diff_high'] = pct_difference['high']
result['num_diff_low'] = num_difference['low']
result['pct_diff_low'] = pct_difference['low']
result['pct_diff_volume'] = pct_difference['volume']
# Add date related columns
# result['day'] = self._dates.day
result['weekday'] = self._dates.weekday
# result['week'] = self._dates.week
result['month'] = self._dates.month
result['quarter'] = self._dates.quarter
# result['dayofyear'] = self._dates.dayofyear
# Moving averages
result['ma_open'] = result['open'].rolling(
self._globals['ma_window']).mean()
result['ma_high'] = result['high'].rolling(
self._globals['ma_window']).mean()
result['ma_low'] = result['low'].rolling(
self._globals['ma_window']).mean()
result['ma_close'] = result['close'].rolling(
self._globals['ma_window']).mean()
result['ma_volume'] = result['volume'].rolling(
self._globals['vma_window']).mean()
result['ma_volume_long'] = result['volume'].rolling(
self._globals['vma_window_long']).mean()
result['ma_volume_delta'] = result[
'ma_volume_long'] - result['ma_volume']
# Standard deviation related
result['ma_std_close'] = result['close'].rolling(
self._globals['ma_window']).std()
result['std_pct_diff_close'] = result['pct_diff_close'].rolling(
self._globals['ma_window']).std()
result['bollinger_lband'] = volatility.bollinger_lband(result['close'])
result['bollinger_hband'] = volatility.bollinger_lband(result['close'])
result['bollinger_lband_indicator'] = volatility.bollinger_lband_indicator(result['close'])
result['bollinger_hband_indicator'] = volatility.bollinger_hband_indicator(result['close'])
# Rolling ranges
result['amplitude'] = result['high'] - result['low']
_min = result['low'].rolling(
self._globals['week']).min()
_max = result['high'].rolling(
self._globals['week']).max()
result['amplitude_medium'] = abs(_min - _max)
_min = result['low'].rolling(
2 * self._globals['week']).min()
_max = result['high'].rolling(
2 * self._globals['week']).max()
result['amplitude_long'] = abs(_min - _max)
_min = result['volume'].rolling(
self._globals['week']).min()
_max = result['volume'].rolling(
self._globals['week']).max()
result['vol_amplitude'] = abs(_min - _max)
_min = result['volume'].rolling(
2 * self._globals['week']).min()
_max = result['volume'].rolling(
2 * self._globals['week']).max()
result['vol_amplitude_long'] = abs(_min - _max)
# Volume metrics
result['force_index'] = volume.force_index(
result['close'], result['volume'])
result['negative_volume_index'] = volume.negative_volume_index(
result['close'], result['volume'])
result['ease_of_movement'] = volume.ease_of_movement(
result['high'], result['low'], result['close'], result['volume'])
result['acc_dist_index'] = volume.acc_dist_index(
result['high'], result['low'], result['close'], result['volume'])
result['on_balance_volume'] = volume.on_balance_volume(
result['close'], result['volume'])
result['on_balance_volume_mean'] = volume.on_balance_volume(
result['close'], result['volume'])
result['volume_price_trend'] = volume.volume_price_trend(
result['close'], result['volume'])
# Calculate the Stochastic values
result['k'] = momentum.stoch(
result['high'],
result['low'],
result['close'],
n=self._globals['kwindow'])
result['d'] = momentum.stoch_signal(
result['high'],
result['low'],
result['close'],
n=self._globals['kwindow'],
d_n=self._globals['dwindow'])
# Calculate the Miscellaneous values
result['rsi'] = momentum.rsi(
result['close'],
n=self._globals['rsiwindow'],
fillna=False)
miscellaneous = math.Misc(self._ohlcv)
result['proc'] = miscellaneous.proc(self._globals['proc_window'])
# Calculate ADX
result['adx'] = trend.adx(
result['high'],
result['low'],
result['close'],
n=self._globals['adx_window'])
# Calculate MACD difference
result['macd_diff'] = trend.macd_diff(
result['close'],
n_fast=self._globals['macd_sign'],
n_slow=self._globals['macd_slow'],
n_sign=self._globals['macd_sign'])
# Create series for increasing / decreasing closes (Convert NaNs to 0)
_result = np.nan_to_num(result['pct_diff_close'].values)
_increasing = (_result >= 0).astype(int) * self._buy
_decreasing = (_result < 0).astype(int) * self._sell
result['increasing'] = _increasing + _decreasing
# Stochastic subtraciton
result['k_d'] = pd.Series(result['k'].values - result['d'].values)
# Other indicators
result['k_i'] = self._stochastic_indicator(
result['k'], result['high'], result['low'], result['ma_close'])
result['d_i'] = self._stochastic_indicator(
result['d'], result['high'], result['low'], result['ma_close'])
result['stoch_i'] = self._stochastic_indicator_2(
result['k'], result['d'],
result['high'], result['low'], result['ma_close'])
result['rsi_i'] = self._rsi_indicator(
result['rsi'], result['high'], result['low'], result['ma_close'])
result['adx_i'] = self._adx_indicator(result['adx'])
result['macd_diff_i'] = self._macd_diff_indicator(result['macd_diff'])
result['volume_i'] = self._volume_indicator(
result['ma_volume'], result['ma_volume_long'])
# Create time shifted columns
for step in range(1, self._ignore_row_count + 1):
result['t-{}'.format(step)] = result['close'].shift(step)
result['tpd-{}'.format(step)] = result[
'close'].pct_change(periods=step)
result['tad-{}'.format(step)] = result[
'close'].diff(periods=step)
# Mask increasing with
result['increasing_masked'] = _mask(
result['increasing'].to_frame(),
result['stoch_i'],
as_integer=True).values
# Get class values for each vector
classes = pd.DataFrame(columns=self._shift_steps)
for step in self._shift_steps:
# Shift each column by the value of its label
if self._binary is True:
# Classes need to be 0 or 1 (One hot encoding)
classes[step] = (
result[self._label2predict].shift(-step) > 0).astype(int)
else:
classes[step] = result[self._label2predict].shift(-step)
# classes[step] = result[self._label2predict].shift(-step)
# Delete the firsts row of the dataframe as it has NaN values from the
# .diff() and .pct_change() operations
ignore = max(max(self._shift_steps), self._ignore_row_count)
result = result.iloc[ignore:]
classes = classes.iloc[ignore:]
# Convert result to float32 to conserve memory
result = result.astype(np.float32)
# Return
return result, classes
def OBV(df0):
df = df0
df['obv'] = on_balance_volume(df['close'], df['volume'], fillna=False)
return df
df["ADX_POSVT_indicator"] = indicator.adx_pos()
try:
df["ADX_indicator"] = indicator.adx()
except (IndexError, ValueError) as e:
df["ADX_indicator"] = "0"
print("The ADX error is coming for SYMBOL" + f'{i}')
except (IndexError, ValueError) as e:
df["ADX_indicator"] = "0"
print("The ADX error is coming for SYMBOL" + f'{i}')
# df['ADX'] = indicator.adx()
#print(df)
#print(df)
"""OBV"""
indicator = on_balance_volume(close=df["close_price"],
volume=df["volume"],
fillna=True)
#print(indicator)
df["obv_indicator"] = indicator
#print(df)
"""EMA"""
indicator = ema_indicator(close=df["close_price"], n=12, fillna=True)
#print(indicator)
df["EMA_indicator"] = indicator
#print(df)
df2 = df2.append(df)
df2.to_csv('MACD_price.csv', mode='w', header=False)
table_name1 = 'TECHNICAL_ANALYSIS1'