def add_trend_indicators(data: pd.DataFrame) -> pd.DataFrame:
"""Adds the trend indicators.
Parameters
----------
data : pd.DataFrame
A dataframe with daily stock values. Must include: open, high,
low, close and volume. It should also be sorted in a descending
manner.
Returns
-------
pd.DataFrame
The input dataframe with the indicators added.
"""
adx = ADXIndicator(data['high'], data['low'], data['close'])
ema = EMAIndicator(data['close'])
ema_200 = EMAIndicator(data['close'], n=200)
ichimoku = IchimokuIndicator(data['high'], data['low'])
macd = MACD(data['close'])
sma = SMAIndicator(data['close'], n=14)
sma_200 = SMAIndicator(data['close'], n=200)
data.loc[:, 'adx'] = adx.adx()
data.loc[:, 'adx_pos'] = adx.adx_pos()
data.loc[:, 'adx_neg'] = adx.adx_neg()
data.loc[:, 'ema'] = ema.ema_indicator()
data.loc[:, 'ema_200'] = ema_200.ema_indicator()
data.loc[:, 'ichimoku_a'] = ichimoku.ichimoku_a()
data.loc[:, 'ichimoku_b'] = ichimoku.ichimoku_b()
data.loc[:, 'ichimoku_base_line'] = ichimoku.ichimoku_base_line()
data.loc[:, 'ichimoku_conversion_line'] = (
ichimoku.ichimoku_conversion_line())
data.loc[:, 'macd'] = macd.macd()
data.loc[:, 'macd_diff'] = macd.macd_diff()
data.loc[:, 'macd_signal'] = macd.macd_signal()
data.loc[:, 'sma'] = sma.sma_indicator()
data.loc[:, 'sma_200'] = sma_200.sma_indicator()
return data
def add_trend_ta(
df: pd.DataFrame,
high: str,
low: str,
close: str,
fillna: bool = False,
colprefix: str = "",
vectorized: bool = False,
) -> pd.DataFrame:
"""Add trend 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.
fillna(bool): if True, fill nan values.
colprefix(str): Prefix column names inserted
vectorized(bool): if True, use only vectorized functions indicators
Returns:
pandas.core.frame.DataFrame: Dataframe with new features.
"""
# MACD
indicator_macd = MACD(close=df[close],
window_slow=26,
window_fast=12,
window_sign=9,
fillna=fillna)
df[f"{colprefix}trend_macd"] = indicator_macd.macd()
df[f"{colprefix}trend_macd_signal"] = indicator_macd.macd_signal()
df[f"{colprefix}trend_macd_diff"] = indicator_macd.macd_diff()
# SMAs
df[f"{colprefix}trend_sma_fast"] = SMAIndicator(
close=df[close], window=12, fillna=fillna).sma_indicator()
df[f"{colprefix}trend_sma_slow"] = SMAIndicator(
close=df[close], window=26, fillna=fillna).sma_indicator()
# EMAs
df[f"{colprefix}trend_ema_fast"] = EMAIndicator(
close=df[close], window=12, fillna=fillna).ema_indicator()
df[f"{colprefix}trend_ema_slow"] = EMAIndicator(
close=df[close], window=26, fillna=fillna).ema_indicator()
# Vortex Indicator
indicator_vortex = VortexIndicator(high=df[high],
low=df[low],
close=df[close],
window=14,
fillna=fillna)
df[f"{colprefix}trend_vortex_ind_pos"] = indicator_vortex.vortex_indicator_pos(
)
df[f"{colprefix}trend_vortex_ind_neg"] = indicator_vortex.vortex_indicator_neg(
)
df[f"{colprefix}trend_vortex_ind_diff"] = indicator_vortex.vortex_indicator_diff(
)
# TRIX Indicator
df[f"{colprefix}trend_trix"] = TRIXIndicator(close=df[close],
window=15,
fillna=fillna).trix()
# Mass Index
df[f"{colprefix}trend_mass_index"] = MassIndex(high=df[high],
low=df[low],
window_fast=9,
window_slow=25,
fillna=fillna).mass_index()
# DPO Indicator
df[f"{colprefix}trend_dpo"] = DPOIndicator(close=df[close],
window=20,
fillna=fillna).dpo()
# KST Indicator
indicator_kst = KSTIndicator(
close=df[close],
roc1=10,
roc2=15,
roc3=20,
roc4=30,
window1=10,
window2=10,
window3=10,
window4=15,
nsig=9,
fillna=fillna,
)
df[f"{colprefix}trend_kst"] = indicator_kst.kst()
df[f"{colprefix}trend_kst_sig"] = indicator_kst.kst_sig()
df[f"{colprefix}trend_kst_diff"] = indicator_kst.kst_diff()
# Ichimoku Indicator
indicator_ichi = IchimokuIndicator(
high=df[high],
low=df[low],
window1=9,
window2=26,
window3=52,
visual=False,
fillna=fillna,
)
df[f"{colprefix}trend_ichimoku_conv"] = indicator_ichi.ichimoku_conversion_line(
)
df[f"{colprefix}trend_ichimoku_base"] = indicator_ichi.ichimoku_base_line()
df[f"{colprefix}trend_ichimoku_a"] = indicator_ichi.ichimoku_a()
df[f"{colprefix}trend_ichimoku_b"] = indicator_ichi.ichimoku_b()
# Schaff Trend Cycle (STC)
df[f"{colprefix}trend_stc"] = STCIndicator(
close=df[close],
window_slow=50,
window_fast=23,
cycle=10,
smooth1=3,
smooth2=3,
fillna=fillna,
).stc()
if not vectorized:
# Average Directional Movement Index (ADX)
indicator_adx = ADXIndicator(high=df[high],
low=df[low],
close=df[close],
window=14,
fillna=fillna)
df[f"{colprefix}trend_adx"] = indicator_adx.adx()
df[f"{colprefix}trend_adx_pos"] = indicator_adx.adx_pos()
df[f"{colprefix}trend_adx_neg"] = indicator_adx.adx_neg()
# CCI Indicator
df[f"{colprefix}trend_cci"] = CCIIndicator(
high=df[high],
low=df[low],
close=df[close],
window=20,
constant=0.015,
fillna=fillna,
).cci()
# Ichimoku Visual Indicator
indicator_ichi_visual = IchimokuIndicator(
high=df[high],
low=df[low],
window1=9,
window2=26,
window3=52,
visual=True,
fillna=fillna,
)
df[f"{colprefix}trend_visual_ichimoku_a"] = indicator_ichi_visual.ichimoku_a(
)
df[f"{colprefix}trend_visual_ichimoku_b"] = indicator_ichi_visual.ichimoku_b(
)
# Aroon Indicator
indicator_aroon = AroonIndicator(close=df[close],
window=25,
fillna=fillna)
df[f"{colprefix}trend_aroon_up"] = indicator_aroon.aroon_up()
df[f"{colprefix}trend_aroon_down"] = indicator_aroon.aroon_down()
df[f"{colprefix}trend_aroon_ind"] = indicator_aroon.aroon_indicator()
# PSAR Indicator
indicator_psar = PSARIndicator(
high=df[high],
low=df[low],
close=df[close],
step=0.02,
max_step=0.20,
fillna=fillna,
)
# df[f'{colprefix}trend_psar'] = indicator.psar()
df[f"{colprefix}trend_psar_up"] = indicator_psar.psar_up()
df[f"{colprefix}trend_psar_down"] = indicator_psar.psar_down()
df[f"{colprefix}trend_psar_up_indicator"] = indicator_psar.psar_up_indicator(
)
df[f"{colprefix}trend_psar_down_indicator"] = indicator_psar.psar_down_indicator(
)
return df
def add_trend_ta(df: pd.DataFrame,
high: str,
low: str,
close: str,
fillna: bool = False,
colprefix: str = ""):
"""Add trend 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.
fillna(bool): if True, fill nan values.
colprefix(str): Prefix column names inserted
Returns:
pandas.core.frame.DataFrame: Dataframe with new features.
"""
# MACD
indicator_macd = MACD(close=df[close],
n_fast=12,
n_slow=26,
n_sign=9,
fillna=fillna)
df[f'{colprefix}trend_macd'] = indicator_macd.macd()
df[f'{colprefix}trend_macd_signal'] = indicator_macd.macd_signal()
df[f'{colprefix}trend_macd_diff'] = indicator_macd.macd_diff()
# EMAs
df[f'{colprefix}trend_ema_fast'] = EMAIndicator(
close=df[close], n=12, fillna=fillna).ema_indicator()
df[f'{colprefix}trend_ema_slow'] = EMAIndicator(
close=df[close], n=26, fillna=fillna).ema_indicator()
# Average Directional Movement Index (ADX)
indicator = ADXIndicator(high=df[high],
low=df[low],
close=df[close],
n=14,
fillna=fillna)
df[f'{colprefix}trend_adx'] = indicator.adx()
df[f'{colprefix}trend_adx_pos'] = indicator.adx_pos()
df[f'{colprefix}trend_adx_neg'] = indicator.adx_neg()
# Vortex Indicator
indicator = VortexIndicator(high=df[high],
low=df[low],
close=df[close],
n=14,
fillna=fillna)
df[f'{colprefix}trend_vortex_ind_pos'] = indicator.vortex_indicator_pos()
df[f'{colprefix}trend_vortex_ind_neg'] = indicator.vortex_indicator_neg()
df[f'{colprefix}trend_vortex_ind_diff'] = indicator.vortex_indicator_diff()
# TRIX Indicator
indicator = TRIXIndicator(close=df[close], n=15, fillna=fillna)
df[f'{colprefix}trend_trix'] = indicator.trix()
# Mass Index
indicator = MassIndex(high=df[high],
low=df[low],
n=9,
n2=25,
fillna=fillna)
df[f'{colprefix}trend_mass_index'] = indicator.mass_index()
# CCI Indicator
indicator = CCIIndicator(high=df[high],
low=df[low],
close=df[close],
n=20,
c=0.015,
fillna=fillna)
df[f'{colprefix}trend_cci'] = indicator.cci()
# DPO Indicator
indicator = DPOIndicator(close=df[close], n=20, fillna=fillna)
df[f'{colprefix}trend_dpo'] = indicator.dpo()
# KST Indicator
indicator = KSTIndicator(close=df[close],
r1=10,
r2=15,
r3=20,
r4=30,
n1=10,
n2=10,
n3=10,
n4=15,
nsig=9,
fillna=fillna)
df[f'{colprefix}trend_kst'] = indicator.kst()
df[f'{colprefix}trend_kst_sig'] = indicator.kst_sig()
df[f'{colprefix}trend_kst_diff'] = indicator.kst_diff()
# Ichimoku Indicator
indicator = IchimokuIndicator(high=df[high],
low=df[low],
n1=9,
n2=26,
n3=52,
visual=False,
fillna=fillna)
df[f'{colprefix}trend_ichimoku_a'] = indicator.ichimoku_a()
df[f'{colprefix}trend_ichimoku_b'] = indicator.ichimoku_b()
indicator = IchimokuIndicator(high=df[high],
low=df[low],
n1=9,
n2=26,
n3=52,
visual=True,
fillna=fillna)
df[f'{colprefix}trend_visual_ichimoku_a'] = indicator.ichimoku_a()
df[f'{colprefix}trend_visual_ichimoku_b'] = indicator.ichimoku_b()
# Aroon Indicator
indicator = AroonIndicator(close=df[close], n=25, fillna=fillna)
df[f'{colprefix}trend_aroon_up'] = indicator.aroon_up()
df[f'{colprefix}trend_aroon_down'] = indicator.aroon_down()
df[f'{colprefix}trend_aroon_ind'] = indicator.aroon_indicator()
# PSAR Indicator
indicator = PSARIndicator(high=df[high],
low=df[low],
close=df[close],
step=0.02,
max_step=0.20,
fillna=fillna)
df[f'{colprefix}trend_psar'] = indicator.psar()
df[f'{colprefix}trend_psar_up'] = indicator.psar_up()
df[f'{colprefix}trend_psar_down'] = indicator.psar_down()
df[f'{colprefix}trend_psar_up_indicator'] = indicator.psar_up_indicator()
df[f'{colprefix}trend_psar_down_indicator'] = indicator.psar_down_indicator(
)
return df
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}")
from ta.trend import MACD
from ta.momentum import RSIIndicator
from keras.models import Sequential
from keras.layers import Conv1D, MaxPool1D, Bidirectional, LSTM, Dropout, TimeDistributed
from keras.layers import Dense, GlobalAveragePooling2D
from ta.trend import IchimokuIndicator
from sklearn.linear_model import LinearRegression
from ta import add_all_ta_features
from ta.utils import dropna
import matplotlib.pyplot as plt
filename = 'AAPL'
stock = pd.read_csv('Data/' + filename + '.csv')
indicator_bb = BollingerBands(close=stock["Close"], n=20, ndev=2)
macd = MACD(close=stock["Close"])
rsi = RSIIndicator(close=stock["Close"])
ichi = IchimokuIndicator(high=stock["High"], low=stock["Low"])
stock['macd'] = macd.macd()
stock['rsi'] = rsi.rsi()
stock['bb_bbm'] = indicator_bb.bollinger_mavg()
stock['bb_bbh'] = indicator_bb.bollinger_hband()
stock['bb_bbl'] = indicator_bb.bollinger_lband()
stock['ichi_a'] = ichi.ichimoku_a()
stock['ichi_b'] = ichi.ichimoku_b()
stock['ichi_base'] = ichi.ichimoku_base_line()
stock['ichi_conv'] = ichi.ichimoku_conversion_line()
stock = stock.fillna(0)
print(stock)
scaler = preprocessing.MinMaxScaler()
scaled_values = scaler.fit_transform(stock.iloc[:, 1:4])
stock.iloc[:, 1:4] = scaled_values