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 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}")
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
y_scaler = preprocessing.MinMaxScaler()
scaled_values = y_scaler.fit_transform(
np.array(stock.iloc[:, 4]).reshape(-1, 1))
stock.iloc[:, 4] = scaled_values
scaler = preprocessing.MinMaxScaler()
scaled_values = scaler.fit_transform(stock.iloc[:, 5:])
stock.iloc[:, 5:] = scaled_values
window_size = 50
X = []