def test_adl2(self):
target = 'ADLine'
result = AccDistIndexIndicator(high=self._df['High'],
low=self._df['Low'],
close=self._df['Close'],
volume=self._df['Volume'],
fillna=False).acc_dist_index()
pd.testing.assert_series_equal(self._df[target].tail(),
result.tail(),
check_names=False)
def ta_acc_distr_index(df):
"""
Accumulation Distribution Index
:param df: pandas dataframe
:return: pandas dataframe
"""
temp_df = df.copy()
temp = AccDistIndexIndicator(temp_df["High"],
temp_df["Low"],
temp_df["Close"],
temp_df["Volume"],
fillna=False)
temp_df["adi"] = temp.acc_dist_index()
return temp_df
def get_indicators(df):
"""
Add set of technical indicators to the dataframe, return original data frame with new features
"""
feature_df = df.copy()
feature_df['RSI'] = RSIIndicator(close=df[CLOSE]).rsi()
feature_df['Stochastic'] = StochasticOscillator(high=df[HIGH],
low=df[LOW],
close=df[CLOSE]).stoch()
feature_df['Stochastic_signal'] = StochasticOscillator(
high=df[HIGH], low=df[LOW], close=df[CLOSE]).stoch_signal()
feature_df['ADI'] = AccDistIndexIndicator(
high=df[HIGH], low=df[LOW], close=df[CLOSE],
volume=df[VOLUME]).acc_dist_index()
feature_df['OBV'] = OnBalanceVolumeIndicator(
close=df[CLOSE], volume=df[VOLUME]).on_balance_volume()
feature_df['ATR'] = AverageTrueRange(high=df[HIGH],
low=df[LOW],
close=df[CLOSE]).average_true_range()
feature_df['ADX'] = ADXIndicator(high=df[HIGH],
low=df[LOW],
close=df[CLOSE]).adx()
feature_df['ADX_pos'] = ADXIndicator(high=df[HIGH],
low=df[LOW],
close=df[CLOSE]).adx_pos()
feature_df['ADX_neg'] = ADXIndicator(high=df[HIGH],
low=df[LOW],
close=df[CLOSE]).adx_neg()
feature_df['MACD'] = MACD(close=df[CLOSE]).macd()
feature_df['MACD_diff'] = MACD(close=df[CLOSE]).macd_diff()
feature_df['MACD_signal'] = MACD(close=df[CLOSE]).macd_signal()
return feature_df
def setUpClass(cls):
cls._df = pd.read_csv(cls._filename, sep=',')
cls._params = dict(high=cls._df['High'],
low=cls._df['Low'],
close=cls._df['Close'],
volume=cls._df['Volume'],
fillna=False)
cls._indicator = AccDistIndexIndicator(**cls._params)
def add_volume_ta(df: pd.DataFrame, high: str, low: str, close: str, volume: str,
fillna: bool = False, colprefix: str = "") -> pd.DataFrame:
"""Add volume technical analysis features to dataframe.
Args:
df (pandas.core.frame.DataFrame): Dataframe base.
high (str): Name of 'high' column.
low (str): Name of 'low' column.
close (str): Name of 'close' column.
volume (str): Name of 'volume' column.
fillna(bool): if True, fill nan values.
colprefix(str): Prefix column names inserted
Returns:
pandas.core.frame.DataFrame: Dataframe with new features.
"""
# Accumulation Distribution Index
df[f'{colprefix}volume_adi'] = AccDistIndexIndicator(
high=df[high], low=df[low], close=df[close], volume=df[volume], fillna=fillna).acc_dist_index()
# On Balance Volume
df[f'{colprefix}volume_obv'] = OnBalanceVolumeIndicator(
close=df[close], volume=df[volume], fillna=fillna).on_balance_volume()
# Chaikin Money Flow
df[f'{colprefix}volume_cmf'] = ChaikinMoneyFlowIndicator(
high=df[high], low=df[low], close=df[close], volume=df[volume], fillna=fillna).chaikin_money_flow()
# Force Index
df[f'{colprefix}volume_fi'] = ForceIndexIndicator(
close=df[close], volume=df[volume], n=13, fillna=fillna).force_index()
# Money Flow Indicator
df[f'{colprefix}volume_mfi'] = MFIIndicator(
high=df[high], low=df[low], close=df[close], volume=df[volume], n=14, fillna=fillna).money_flow_index()
# Ease of Movement
indicator = EaseOfMovementIndicator(high=df[high], low=df[low], volume=df[volume], n=14, fillna=fillna)
df[f'{colprefix}volume_em'] = indicator.ease_of_movement()
df[f'{colprefix}volume_sma_em'] = indicator.sma_ease_of_movement()
# Volume Price Trend
df[f'{colprefix}volume_vpt'] = VolumePriceTrendIndicator(
close=df[close], volume=df[volume], fillna=fillna).volume_price_trend()
# Negative Volume Index
df[f'{colprefix}volume_nvi'] = NegativeVolumeIndexIndicator(
close=df[close], volume=df[volume], fillna=fillna).negative_volume_index()
# Volume Weighted Average Price
df[f'{colprefix}volume_vwap'] = VolumeWeightedAveragePrice(
high=df[high], low=df[low], close=df[close], volume=df[volume], n=14, fillna=fillna
).volume_weighted_average_price()
return df
def setUpClass(cls):
cls._df = pd.read_csv(cls._filename, sep=",")
cls._params = dict(
high=cls._df["High"],
low=cls._df["Low"],
close=cls._df["Close"],
volume=cls._df["Volume"],
fillna=False,
)
cls._indicator = AccDistIndexIndicator(**cls._params)
def handle(self, *args, **options):
# import pdb
# pdb.set_trace()
if not options['update']:
NSETechnical.objects.all().delete()
symbols = Symbol.objects.all()
for symbol in symbols:
nse_history_data = NSEHistoricalData.objects.filter(
symbol__symbol_name=symbol).order_by('timestamp')
if not nse_history_data:
continue
nse_technical = pd.DataFrame(
list(
nse_history_data.values('timestamp', 'open', 'high', 'low',
'close', 'total_traded_quantity')))
'''
Moving average convergence divergence
'''
indicator_macd = MACD(close=nse_technical['close'],
window_slow=26,
window_fast=12,
window_sign=9,
fillna=False)
nse_technical["trend_macd"] = indicator_macd.macd()
nse_technical["trend_macd_signal"] = indicator_macd.macd_signal()
nse_technical["trend_macd_diff"] = indicator_macd.macd_diff()
'''
Simple Moving Average
'''
nse_technical["trend_sma_fast"] = SMAIndicator(
close=nse_technical['close'], window=12,
fillna=False).sma_indicator()
nse_technical["trend_sma_slow"] = SMAIndicator(
close=nse_technical['close'], window=26,
fillna=False).sma_indicator()
'''
Exponential Moving Average
'''
nse_technical["trend_ema_fast"] = EMAIndicator(
close=nse_technical['close'], window=12,
fillna=False).ema_indicator()
nse_technical["trend_ema_slow"] = EMAIndicator(
close=nse_technical['close'], window=26,
fillna=False).ema_indicator()
'''
Ichimoku Indicator
'''
indicator_ichi = IchimokuIndicator(
high=nse_technical['high'],
low=nse_technical['low'],
window1=9,
window2=26,
window3=52,
visual=False,
fillna=False,
)
nse_technical[
"trend_ichimoku_conv"] = indicator_ichi.ichimoku_conversion_line(
)
nse_technical[
"trend_ichimoku_base"] = indicator_ichi.ichimoku_base_line()
nse_technical["trend_ichimoku_a"] = indicator_ichi.ichimoku_a()
nse_technical["trend_ichimoku_b"] = indicator_ichi.ichimoku_b()
indicator_ichi_visual = IchimokuIndicator(
high=nse_technical['high'],
low=nse_technical['low'],
window1=9,
window2=26,
window3=52,
visual=True,
fillna=False,
)
nse_technical[
"trend_visual_ichimoku_a"] = indicator_ichi_visual.ichimoku_a(
)
nse_technical[
"trend_visual_ichimoku_b"] = indicator_ichi_visual.ichimoku_b(
)
'''
Bollinger Band
'''
indicator_bb = BollingerBands(close=nse_technical['close'],
window=20,
window_dev=2,
fillna=False)
nse_technical["volatility_bbm"] = indicator_bb.bollinger_mavg()
nse_technical["volatility_bbh"] = indicator_bb.bollinger_hband()
nse_technical["volatility_bbl"] = indicator_bb.bollinger_lband()
nse_technical["volatility_bbw"] = indicator_bb.bollinger_wband()
nse_technical["volatility_bbp"] = indicator_bb.bollinger_pband()
nse_technical[
"volatility_bbhi"] = indicator_bb.bollinger_hband_indicator()
nse_technical[
"volatility_bbli"] = indicator_bb.bollinger_lband_indicator()
'''
Accumulation Distribution Index
'''
nse_technical["volume_adi"] = AccDistIndexIndicator(
high=nse_technical['high'],
low=nse_technical['low'],
close=nse_technical['close'],
volume=nse_technical['total_traded_quantity'],
fillna=False).acc_dist_index()
'''
Money Flow Index
'''
nse_technical["volume_mfi"] = MFIIndicator(
high=nse_technical['high'],
low=nse_technical['low'],
close=nse_technical['close'],
volume=nse_technical['total_traded_quantity'],
window=14,
fillna=False,
).money_flow_index()
'''
Relative Strength Index (RSI)
'''
nse_technical["momentum_rsi"] = RSIIndicator(
close=nse_technical['close'], window=14, fillna=False).rsi()
'''
Stoch RSI (StochRSI)
'''
indicator_srsi = StochRSIIndicator(close=nse_technical['close'],
window=14,
smooth1=3,
smooth2=3,
fillna=False)
nse_technical["momentum_stoch_rsi"] = indicator_srsi.stochrsi()
nse_technical["momentum_stoch_rsi_k"] = indicator_srsi.stochrsi_k()
nse_technical["momentum_stoch_rsi_d"] = indicator_srsi.stochrsi_d()
nse_technical.replace({np.nan: None}, inplace=True)
nse_technical.replace([np.inf, -np.inf], None, inplace=True)
list_to_create = []
list_to_update = []
for index in range(len(nse_history_data) - 1, -1, -1):
data = nse_history_data[index]
if data.technicals:
break
technical = NSETechnical(
nse_historical_data=data,
trend_macd=nse_technical['trend_macd'][index],
trend_macd_signal=nse_technical['trend_macd_signal']
[index],
trend_macd_diff=nse_technical['trend_macd_diff'][index],
trend_sma_fast=nse_technical['trend_sma_fast'][index],
trend_sma_slow=nse_technical['trend_sma_slow'][index],
trend_ema_fast=nse_technical['trend_ema_fast'][index],
trend_ema_slow=nse_technical['trend_ema_slow'][index],
trend_ichimoku_conv=nse_technical['trend_ichimoku_conv']
[index],
trend_ichimoku_base=nse_technical['trend_ichimoku_base']
[index],
trend_ichimoku_a=nse_technical['trend_ichimoku_a'][index],
trend_ichimoku_b=nse_technical['trend_ichimoku_b'][index],
trend_visual_ichimoku_a=nse_technical[
'trend_visual_ichimoku_a'][index],
trend_visual_ichimoku_b=nse_technical[
'trend_visual_ichimoku_b'][index],
volatility_bbm=nse_technical['volatility_bbm'][index],
volatility_bbh=nse_technical['volatility_bbh'][index],
volatility_bbl=nse_technical['volatility_bbl'][index],
volatility_bbw=nse_technical['volatility_bbw'][index],
volatility_bbp=nse_technical['volatility_bbp'][index],
volatility_bbhi=nse_technical['volatility_bbhi'][index],
volatility_bbli=nse_technical['volatility_bbli'][index],
volume_adi=nse_technical['volume_adi'][index],
volume_mfi=nse_technical['volume_mfi'][index],
momentum_rsi=nse_technical['momentum_rsi'][index],
momentum_stoch_rsi=nse_technical['momentum_stoch_rsi']
[index],
momentum_stoch_rsi_k=nse_technical['momentum_stoch_rsi_k']
[index],
momentum_stoch_rsi_d=nse_technical['momentum_stoch_rsi_d']
[index])
data.technicals = True
list_to_update.append(data)
list_to_create.append(technical)
NSETechnical.objects.bulk_create(list_to_create)
NSEHistoricalData.objects.bulk_update(list_to_update,
['technicals'])
print(f"Technicals updated for {symbol}")