def add_volatility_ta(df: pd.DataFrame,
high: str,
low: str,
close: str,
fillna: bool = False,
colprefix: str = ""):
"""Add volatility 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.
"""
# Average True Range
df[f'{colprefix}volatility_atr'] = AverageTrueRange(
close=df[close], high=df[high], low=df[low], n=10,
fillna=fillna).average_true_range()
# Bollinger Bands
indicator_bb = BollingerBands(close=df[close], n=20, ndev=2, fillna=fillna)
df[f'{colprefix}volatility_bbh'] = indicator_bb.bollinger_hband()
df[f'{colprefix}volatility_bbl'] = indicator_bb.bollinger_lband()
df[f'{colprefix}volatility_bbm'] = indicator_bb.bollinger_mavg()
df[f'{colprefix}volatility_bbhi'] = indicator_bb.bollinger_hband_indicator(
)
df[f'{colprefix}volatility_bbli'] = indicator_bb.bollinger_lband_indicator(
)
# Keltner Channel
indicator_kc = KeltnerChannel(close=df[close],
high=df[high],
low=df[low],
n=10,
fillna=fillna)
df[f'{colprefix}volatility_kcc'] = indicator_kc.keltner_channel_central()
df[f'{colprefix}volatility_kch'] = indicator_kc.keltner_channel_hband()
df[f'{colprefix}volatility_kcl'] = indicator_kc.keltner_channel_lband()
df[f'{colprefix}volatility_kchi'] = indicator_kc.keltner_channel_hband_indicator(
)
df[f'{colprefix}volatility_kcli'] = indicator_kc.keltner_channel_lband_indicator(
)
# Donchian Channel
indicator_dc = DonchianChannel(close=df[close], n=20, fillna=fillna)
df[f'{colprefix}volatility_dcl'] = indicator_dc.donchian_channel_lband()
df[f'{colprefix}volatility_dch'] = indicator_dc.donchian_channel_hband()
df[f'{colprefix}volatility_dchi'] = indicator_dc.donchian_channel_hband_indicator(
)
df[f'{colprefix}volatility_dcli'] = indicator_dc.donchian_channel_lband_indicator(
)
return df
def run(ticker):
signal_is_sent = check_signal_is_sent(ticker)
if (not signal_is_sent):
tf_1hour = get_candles(ticker)
rsi_1hour = RSIIndicator(close=tf_1hour.close).rsi()
bolinger_1hour = BollingerBands(close=tf_1hour.close)
condition_short = rsi_1min[-1] >= 80 and rsi_5min[-1] >= 80
condition_long = rsi_1min[-1] <= 20 and rsi_5min[-1] <= 20
# Trade size depends on STOP_LOSS_THRESH. MT5 limitations.
STOP_LOSS_THRESH = (
open_close_hour_dif_mean[ticker]
)
trade_size = calculate_trade_size(
STOP_LOSS_THRESH, tf_1min.close[-1]
) / ticker_info[ticker]['min_lot']
trade_size = round(trade_size)
cur_time = str(datetime.now().time())
if condition_short:
sl = round(
tf_1min.close[-1] + STOP_LOSS_THRESH * tf_1min.close[-1],
ticker_info[ticker]['price_digits']
)
tp = round(
tf_1min.close[-1] - STOP_LOSS_THRESH * tf_1min.close[-1],
ticker_info[ticker]['price_digits']
)
print('\n', cur_time, ticker, ': SHORT', str(trade_size), tf_1min.close[-1], sl, tp, '\n')
messsage = ' '.join(
[cur_time, ticker, 'SHORT', str(trade_size), str(sl), str(tp)]
)
send_message(messsage)
set_signal_is_sent_flag(ticker)
elif condition_long:
sl = round(
tf_1min.close[-1] - STOP_LOSS_THRESH * tf_1min.close[-1],
ticker_info[ticker]['price_digits']
)
tp = round(
tf_1min.close[-1] + STOP_LOSS_THRESH * tf_1min.close[-1],
ticker_info[ticker]['price_digits']
)
print('\n', cur_time, ticker, ': LONG', str(trade_size), tf_1min.close[-1], sl, tp, '\n')
messsage = ' '.join(
[cur_time, ticker, 'LONG', str(trade_size), str(sl), str(tp)]
)
send_message(messsage)
set_signal_is_sent_flag(ticker)
def get_bollinger_bands(config, crypto):
close_prices = crypto.prices
dataframe = crypto.technical_indicators
window = 20
indicator_bb = BollingerBands(close=close_prices,
window=window,
window_dev=2)
dataframe['Bollinger_Bands_Middle'] = indicator_bb.bollinger_mavg()
dataframe['Bollinger_Bands_Upper'] = indicator_bb.bollinger_hband()
dataframe['Bollinger_Bands_Lower'] = indicator_bb.bollinger_lband()
generate_buy_sell_signals(
lambda x, signal: signal['Close'].values[x] < signal[
'Bollinger_Bands_Lower'].values[x], lambda x, signal: signal[
'Close'].values[x] > signal['Bollinger_Bands_Upper'].values[x],
dataframe, 'Bollinger_Bands')
return dataframe
class TestBollingerBands(unittest.TestCase):
"""
https://school.stockcharts.com/doku.php?id=technical_indicators:bollinger_bands
"""
_filename = 'ta/tests/data/cs-bbands.csv'
def setUp(self):
self._df = pd.read_csv(self._filename, sep=',')
self._indicator = BollingerBands(close=self._df['Close'],
n=20,
ndev=2,
fillna=False)
def tearDown(self):
del (self._df)
def test_mavg(self):
target = 'MiddleBand'
result = self._indicator.bollinger_mavg()
pd.testing.assert_series_equal(self._df[target].tail(),
result.tail(),
check_names=False)
def test_hband(self):
target = 'HighBand'
result = self._indicator.bollinger_hband()
pd.testing.assert_series_equal(self._df[target].tail(),
result.tail(),
check_names=False)
def test_lband(self):
target = 'LowBand'
result = self._indicator.bollinger_lband()
pd.testing.assert_series_equal(self._df[target].tail(),
result.tail(),
check_names=False)
def test_wband(self):
target = 'WidthBand'
result = self._indicator.bollinger_wband()
pd.testing.assert_series_equal(self._df[target].tail(),
result.tail(),
check_names=False)
def test_hband_indicator(self):
target = 'CrossUp'
result = self._indicator.bollinger_hband_indicator()
pd.testing.assert_series_equal(self._df[target].tail(),
result.tail(),
check_names=False)
def test_lband_indicator(self):
target = 'CrossDown'
result = self._indicator.bollinger_lband_indicator()
pd.testing.assert_series_equal(self._df[target].tail(),
result.tail(),
check_names=False)
def get_signal(self, stock, minute_price):
bb_bars = self.wb.get_bars(stock, interval=self.time_period, extendTrading=0, count=20)
bb = BollingerBands(close=bb_bars['close'], fillna=True)
# set series objects to indicators calculations
bb_upper = bb.bollinger_hband()
bb_lower = bb.bollinger_lband()
# get value for latest interval
current_bb_upper = bb_upper[19]
current_bb_lower = bb_lower[19]
difference = current_bb_upper - current_bb_lower
threshold_price = difference * self.threshold
lower_threshold = current_bb_lower + threshold_price
upper_threshold = current_bb_upper - threshold_price
if minute_price <= lower_threshold:
return 1
elif minute_price >= upper_threshold:
return -1
else:
return 0
def AddIndicators(df):
# Add Simple Moving Average (SMA) indicators
df["sma7"] = SMAIndicator(close=df["Close"], window=7, fillna=True).sma_indicator()
df["sma25"] = SMAIndicator(close=df["Close"], window=25, fillna=True).sma_indicator()
df["sma99"] = SMAIndicator(close=df["Close"], window=99, fillna=True).sma_indicator()
# Add Bollinger Bands indicator
indicator_bb = BollingerBands(close=df["Close"], window=20, window_dev=2)
df['bb_bbm'] = indicator_bb.bollinger_mavg()
df['bb_bbh'] = indicator_bb.bollinger_hband()
df['bb_bbl'] = indicator_bb.bollinger_lband()
# Add Parabolic Stop and Reverse (Parabolic SAR) indicator
indicator_psar = PSARIndicator(high=df["High"], low=df["Low"], close=df["Close"], step=0.02, max_step=2, fillna=True)
df['psar'] = indicator_psar.psar()
# Add Moving Average Convergence Divergence (MACD) indicator
# df["MACD"] = macd(close=df["Close"], window_slow=26, window_fast=12, fillna=True) # mazas
# Add Relative Strength Index (RSI) indicator
df["RSI"] = rsi(close=df["Close"], window=14, fillna=True) # mazas
return df
def transform_one(self, entity_id, df: pd.DataFrame) -> pd.DataFrame:
indicator_bb = BollingerBands(close=df["close"],
window=20,
window_dev=2)
# Add Bollinger Bands features
df["bb_bbm"] = indicator_bb.bollinger_mavg()
df["bb_bbh"] = indicator_bb.bollinger_hband()
df["bb_bbl"] = indicator_bb.bollinger_lband()
# Add Bollinger Band high indicator
df["bb_bbhi"] = indicator_bb.bollinger_hband_indicator()
# Add Bollinger Band low indicator
df["bb_bbli"] = indicator_bb.bollinger_lband_indicator()
# Add Width Size Bollinger Bands
df["bb_bbw"] = indicator_bb.bollinger_wband()
# Add Percentage Bollinger Bands
df["bb_bbp"] = indicator_bb.bollinger_pband()
return df
def bollinger_bands(data, days=20, std=2, price="Close"):
df = data
# Initialize Bollinger Bands Indicator
indicator_bb = BollingerBands(close=df[price], window=days, window_dev=std)
# Add Bollinger Bands features
df["bb_ma"] = indicator_bb.bollinger_mavg()
df["bb_high"] = indicator_bb.bollinger_hband()
df["bb_low"] = indicator_bb.bollinger_lband()
# Add Bollinger Band high indicator
df["bb_bbhi"] = indicator_bb.bollinger_hband_indicator()
# Add Bollinger Band low indicator
df["bb_bbli"] = indicator_bb.bollinger_lband_indicator()
return df
def analysis(df, ma_f, ma_s, period):
df["ema_f"] = ta.ema(high=df.high, low=df.low, close=df.close, length=ma_f)
df["ema_s"] = ta.ema(high=df.high, low=df.low, close=df.close, length=ma_s)
df.fillna(0, inplace=True)
df['s'] = (df['ema_s'] - df['ema_s'].shift(1)) >= 0
df['f'] = (df['ema_f'] - df['ema_f'].shift(1)) >= 0
df['buy_ema'] = df['ema_f'] > df['ema_s']
df['sell_ema'] = df['ema_f'] <= df['ema_s']
df['buy_change'] = (df['buy_ema'] != df['buy_ema'].shift(1)) & df['buy_ema']
df['sell_change'] = (df['sell_ema'] != df['sell_ema'].shift(1)) & df['sell_ema']
# df["RSI"] = ta.rsi(high=df.high, low=df.low, close=df.close, length=period)
# df['RSIs'] = (df['RSI'] - df['RSI'].shift(1)) >= 0
# df['RSI_ups'] = df.groupby(
# (df['RSIs'] != df['RSIs'].shift(1)).cumsum()).cumcount() + 1
# df['adx'] = ta.adx(high=df.high, low=df.low, close=df.close, length=period)['ADX_%s' % period]
# df['adx_s'] = (df['adx'] - df['adx'].shift(1)) >= 0
# df['adx_ups'] = df.groupby(
# (df['adx_s'] != df['adx_s'].shift(1)).cumsum()).cumcount() + 1
# df['ATR'] = df.ta.atr()
indicator_bb = BollingerBands(close=df.close, window=10, window_dev=1.8)
df['bb_bbm'] = indicator_bb.bollinger_mavg()
df['b_m'] = (df['bb_bbm'] - df['bb_bbm'].shift(1)) >= 0
df['bb_bbh'] = indicator_bb.bollinger_hband()
df['bb_bbl'] = indicator_bb.bollinger_lband()
df['bb_bbhi'] = indicator_bb.bollinger_hband_indicator()
df['bb_bbli'] = indicator_bb.bollinger_lband_indicator()
df['pvt'] = ta.pvt(close=df.close, volume=df.volume)
df['pvt_t'] = (df['pvt'] - df['pvt'].shift(1)) >= 0
df['close_variation'] = df['close'] - df['close'].shift(1)
#
# df = ema(df, ma_f, ma_s)
df = rsi(df, period)
df = momentum(df)
df['trend'] = df['momentum_s'] & df['RSIs']
return df
def _bollinger_bands(self, df, close):
bb = BollingerBands(close=close)
df['bb_bbm'] = bb.bollinger_mavg()
df['bb_bbh'] = bb.bollinger_hband()
df['bb_bbl'] = bb.bollinger_lband()
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from ta.volatility import BollingerBands
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()
def setUpClass(cls):
cls._df = pd.read_csv(cls._filename, sep=',')
cls._params = dict(close=cls._df['Close'], n=20, ndev=2, fillna=False)
cls._indicator = BollingerBands(**cls._params)
def PlotBB(stock_ticker, close, window, deviation_1, deviation_2):
def GetSignal(data):
buy_signal = [] # buy list
sell_signal = [] # sell list
for i in range(len(data['Close'])):
# If the closing price is above Upper_2 band. [SELL]
if data['Close'][i] > data['Upper_2'][i]:
buy_signal.append(np.nan)
sell_signal.append(data['Close'][i])
# If closing price is below lower_2 band. [BUY]
elif data['Close'][i] < data['Lower_2'][i]:
sell_signal.append(np.nan)
buy_signal.append(data['Close'][i])
# The first few values for Bollinger Bands will be NaN as the SMA hasn't been calculated for the given time period.
else:
buy_signal.append(np.nan)
sell_signal.append(np.nan)
return buy_signal, sell_signal
# Getting values of Bollinger Bands and SMA from the `ta` module
BBD1 = BollingerBands(close=close,
window=int(window),
window_dev=deviation_1,
fillna=False)
BBD2 = BollingerBands(close=close,
window=int(window),
window_dev=deviation_2,
fillna=False)
SMA = sma_indicator(close=close, window=int(window), fillna=False)
BB_Upper_1 = BBD1.bollinger_hband().dropna()
BB_Lower_1 = BBD1.bollinger_lband().dropna()
BB_Upper_2 = BBD2.bollinger_hband().dropna()
BB_Lower_2 = BBD2.bollinger_lband().dropna()
# Creating DataFrame
df = pd.DataFrame()
# Adding all the required columns to the DataFrame
df['Close'] = close.dropna()
df['Upper_2'] = BB_Upper_2
df['Upper_1'] = BB_Upper_1
df['SMA'] = SMA
df['Lower_1'] = BB_Lower_1
df['Lower_2'] = BB_Lower_2
df['Buy'], df['Sell'] = GetSignal(df)
# Initialising matplotlib
fig = plt.figure(figsize=(7, 6))
ax = fig.add_subplot(1, 1, 1)
x_axis = df.index
# Plotting Bollinger Bands, Closing price and SMA
ax.fill_between(x_axis, df['Upper_2'], df['Lower_2'], color='#ffe59e')
ax.plot(x_axis,
df['Close'],
color='#a8ff26',
lw=3,
label='Close Price')
ax.plot(x_axis,
df['SMA'],
color='#f7b21b',
lw=3,
label=f'SMA: {window} periods')
len_list = df[df['Sell'].notna()].index.tolist()
# Getting SELL data points
for k, j in zip(range(0, len(len_list)), df['Sell'].dropna().tolist()):
date = df[df['Sell'].notna()].index[k]
date = date.to_pydatetime()
x_axis = date
y_axis = j
# Annotating the chart at sell points
ax.annotate(text='S',
xy=(x_axis, y_axis),
arrowprops=dict(facecolor='red', shrink=0.05))
# Getting BUY data points
for k, j in zip(range(0, len(len_list)), df['Buy'].dropna().tolist()):
date = df[df['Buy'].notna()].index[k]
date = date.to_pydatetime()
x_axis = date
y_axis = j
# Annotating the chart at buy points
ax.annotate(text='B',
xy=(x_axis, y_axis),
arrowprops=dict(facecolor='green', shrink=0.05))
plt.ylabel('Price', fontsize=15)
plt.xlabel('Date', fontsize=15)
plt.title(f'Bollinger Bands {stock_ticker}', fontsize=20)
plt.legend()
plt.grid()
try:
plt.savefig(f'image/{stock_ticker}/BB_{stock_ticker}.png',
bbox_inches='tight')
except:
directory = f'image/{stock_ticker}'
for f in os.listdir(stock_ticker):
os.remove(os.path.join(directory, f))
plt.savefig(f'image/{stock_ticker}/BB_{stock_ticker}.png',
bbox_inches='tight')
end_date = st.sidebar.date_input('End date', today)
if start_date < end_date:
st.sidebar.success('Start date: `%s`\n\nEnd date:`%s`' %
(start_date, end_date))
else:
st.sidebar.error('Error: End date must fall after start date.')
##############
# Stock data #
##############
# https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html#momentum-indicators
df = yf.download(option, start=start_date, end=end_date, progress=False)
indicator_bb = BollingerBands(df['Close'])
bb = df
bb['bb_h'] = indicator_bb.bollinger_hband()
bb['bb_l'] = indicator_bb.bollinger_lband()
bb = bb[['Close', 'bb_h', 'bb_l']]
macd = MACD(df['Close']).macd()
rsi = RSIIndicator(df['Close']).rsi()
###################
# Set up main app #
###################
st.write('Stock Bollinger Bands')
def add_volatility_ta(
df: pd.DataFrame,
high: str,
low: str,
close: str,
fillna: bool = False,
colprefix: str = "",
vectorized: bool = False,
) -> pd.DataFrame:
"""Add volatility 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.
"""
# Bollinger Bands
indicator_bb = BollingerBands(close=df[close],
window=20,
window_dev=2,
fillna=fillna)
df[f"{colprefix}volatility_bbm"] = indicator_bb.bollinger_mavg()
df[f"{colprefix}volatility_bbh"] = indicator_bb.bollinger_hband()
df[f"{colprefix}volatility_bbl"] = indicator_bb.bollinger_lband()
df[f"{colprefix}volatility_bbw"] = indicator_bb.bollinger_wband()
df[f"{colprefix}volatility_bbp"] = indicator_bb.bollinger_pband()
df[f"{colprefix}volatility_bbhi"] = indicator_bb.bollinger_hband_indicator(
)
df[f"{colprefix}volatility_bbli"] = indicator_bb.bollinger_lband_indicator(
)
# Keltner Channel
indicator_kc = KeltnerChannel(close=df[close],
high=df[high],
low=df[low],
window=10,
fillna=fillna)
df[f"{colprefix}volatility_kcc"] = indicator_kc.keltner_channel_mband()
df[f"{colprefix}volatility_kch"] = indicator_kc.keltner_channel_hband()
df[f"{colprefix}volatility_kcl"] = indicator_kc.keltner_channel_lband()
df[f"{colprefix}volatility_kcw"] = indicator_kc.keltner_channel_wband()
df[f"{colprefix}volatility_kcp"] = indicator_kc.keltner_channel_pband()
df[f"{colprefix}volatility_kchi"] = indicator_kc.keltner_channel_hband_indicator(
)
df[f"{colprefix}volatility_kcli"] = indicator_kc.keltner_channel_lband_indicator(
)
# Donchian Channel
indicator_dc = DonchianChannel(high=df[high],
low=df[low],
close=df[close],
window=20,
offset=0,
fillna=fillna)
df[f"{colprefix}volatility_dcl"] = indicator_dc.donchian_channel_lband()
df[f"{colprefix}volatility_dch"] = indicator_dc.donchian_channel_hband()
df[f"{colprefix}volatility_dcm"] = indicator_dc.donchian_channel_mband()
df[f"{colprefix}volatility_dcw"] = indicator_dc.donchian_channel_wband()
df[f"{colprefix}volatility_dcp"] = indicator_dc.donchian_channel_pband()
if not vectorized:
# Average True Range
df[f"{colprefix}volatility_atr"] = AverageTrueRange(
close=df[close],
high=df[high],
low=df[low],
window=10,
fillna=fillna).average_true_range()
# Ulcer Index
df[f"{colprefix}volatility_ui"] = UlcerIndex(
close=df[close], window=14, fillna=fillna).ulcer_index()
return df
# Calac data
last_entry_df = dao.get_all_stocks_prices_max_entry_date(exchange)
last_entry_dict = dict(zip(last_entry_df['equity_id'].tolist(), last_entry_df['last_entry'].tolist()))
for idx1, row1 in equity_ref_df.iterrows():
equity_id = row1['equity_id']
ticker = row1['local_code']
try:
insert_list = list()
# get all the prices for equity
ticker_eod_df = eod_df[eod_df['equity_id'] == equity_id]
ticker_eod_df = ticker_eod_df.sort_values(by=['trading_date'])
df = dropna(ticker_eod_df)
# Initialize Bollinger Bands Indicator
indicator_bb = BollingerBands(close=df["adj_close"], window=20, window_dev=2)
# Add Bollinger Bands features
df['bb_bbm'] = indicator_bb.bollinger_mavg()
df['bb_bbh'] = indicator_bb.bollinger_hband()
df['bb_bbl'] = indicator_bb.bollinger_lband()
# EMA Indicator
indicator_ema_200 = EMAIndicator(close=df["adj_close"], window=200)
df['ema_200'] = indicator_ema_200.ema_indicator()
indicator_ema_100 = EMAIndicator(close=df["adj_close"], window=100)
df['ema_100'] = indicator_ema_100.ema_indicator()
indicator_ema_50 = EMAIndicator(close=df["adj_close"], window=50)
df['ema_50'] = indicator_ema_50.ema_indicator()
# SMA Indicator
indicator_sma_200 = SMAIndicator(close=df["adj_close"], window=200)
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}")
def __init__(self, symbols):
# data = json.loads(symbols)
# df_stock = pd.json_normalize(symbols)
# df_stock = pd.read_csv(fn,names = ['sym']).drop_duplicates()
df_stock = pd.DataFrame(symbols)
ls_stock = df_stock['sym'].to_list()
df_stock = df_stock.reset_index()
df_stock.columns = ['sort', 'sym']
df_stock.head()
# In[3]:
start = dt.date.today() + relativedelta(days=-150)
end = dt.date.today() + relativedelta(days=-0)
ls_tickers = ls_stock
ls_df = []
for ticker in ls_tickers:
try:
df = web.DataReader(ticker, 'yahoo', start, end)
except Exception as e:
print(str(e))
continue
df['sym'] = ticker
ls_df.append(df.copy())
df_price = pd.concat(ls_df).reset_index()
df_price.columns = [
'dte', 'hgh', 'low', 'opn', 'cls', 'vol', 'cls_adj', 'sym'
]
df_price.sort_values(['sym', 'dte'], inplace=True)
df_price = df_price[['dte', 'sym', 'hgh', 'low', 'cls', 'vol']].copy()
df_price['curr'] = end
df_price['curr'] = pd.to_datetime(df_price['curr'])
df_price['dte'] = pd.to_datetime(df_price['dte'])
df_price['ndays'] = (df_price['curr'] - df_price['dte']).dt.days
df_price['ndays'] = df_price.groupby(['sym'])['ndays'].rank()
df_price[df_price['sym'] == 'SPY'].head()
# In[4]:
ls_df = []
ls_tickers = ls_stock
for ticker in ls_tickers:
#df = dropna(df_price[df_price['sym']==ticker])
df = df_price[df_price['sym'] == ticker].copy()
indicator_bb = BollingerBands(close=df['cls'],
window=20,
window_dev=2)
indicator_macd = MACD(close=df['cls'],
window_fast=12,
window_slow=26,
window_sign=9)
indicator_rsi14 = RSIIndicator(close=df['cls'], window=14)
indicator_cci20 = cci(high=df['hgh'],
low=df['low'],
close=df['cls'],
window=20,
constant=0.015)
indicator_obv = OnBalanceVolumeIndicator(close=df['cls'],
volume=df['vol'],
fillna=True)
indicator_vol_sma20 = SMAIndicator(close=df['vol'], window=20)
indicator_ema03 = EMAIndicator(close=df['cls'], window=3)
indicator_ema05 = EMAIndicator(close=df['cls'], window=5)
indicator_ema08 = EMAIndicator(close=df['cls'], window=8)
indicator_ema10 = EMAIndicator(close=df['cls'], window=10)
indicator_ema12 = EMAIndicator(close=df['cls'], window=12)
indicator_ema15 = EMAIndicator(close=df['cls'], window=15)
indicator_ema30 = EMAIndicator(close=df['cls'], window=30)
indicator_ema35 = EMAIndicator(close=df['cls'], window=35)
indicator_ema40 = EMAIndicator(close=df['cls'], window=40)
indicator_ema45 = EMAIndicator(close=df['cls'], window=45)
indicator_ema50 = EMAIndicator(close=df['cls'], window=50)
indicator_ema60 = EMAIndicator(close=df['cls'], window=60)
# Add Bollinger Band high indicator
df['bb_bbhi'] = indicator_bb.bollinger_hband_indicator()
# Add Bollinger Band low indicator
df['bb_bbli'] = indicator_bb.bollinger_lband_indicator()
#df['macd'] = indicator_macd.macd()
df['macd'] = indicator_macd.macd_diff()
#df['macd_signal'] = indicator_macd.macd_signal()
df['obv'] = indicator_obv.on_balance_volume()
df['vol_sma20'] = indicator_vol_sma20.sma_indicator()
df['ema03'] = indicator_ema03.ema_indicator()
df['ema05'] = indicator_ema05.ema_indicator()
df['ema08'] = indicator_ema08.ema_indicator()
df['ema10'] = indicator_ema10.ema_indicator()
df['ema12'] = indicator_ema12.ema_indicator()
df['ema15'] = indicator_ema15.ema_indicator()
df['ema30'] = indicator_ema30.ema_indicator()
df['ema35'] = indicator_ema35.ema_indicator()
df['ema40'] = indicator_ema40.ema_indicator()
df['ema45'] = indicator_ema45.ema_indicator()
df['ema50'] = indicator_ema50.ema_indicator()
df['ema60'] = indicator_ema60.ema_indicator()
df['rsi14'] = indicator_rsi14.rsi()
df['cci20'] = indicator_cci20
ls_df.append(df.copy())
df = pd.concat(ls_df)
df['score_vol_sma20'] = df[['vol',
'vol_sma20']].apply(lambda x: x[0] / x[1],
axis=1)
df['emash_min'] = df[[
'ema03', 'ema05', 'ema08', 'ema10', 'ema12', 'ema15'
]].min(axis=1)
df['emash_max'] = df[[
'ema03', 'ema05', 'ema08', 'ema10', 'ema12', 'ema15'
]].max(axis=1)
df['emash_avg'] = df[[
'ema03', 'ema05', 'ema08', 'ema10', 'ema12', 'ema15'
]].mean(axis=1)
#df['score_short'] = df[['cls','emash_min','emash_max','emash_min']].apply(lambda x: 100 * (x[0]-x[1])/(x[2]-x[3]),axis=1)
df['emalg_min'] = df[[
'ema30', 'ema35', 'ema40', 'ema45', 'ema50', 'ema60'
]].min(axis=1)
df['emalg_max'] = df[[
'ema30', 'ema35', 'ema40', 'ema45', 'ema50', 'ema60'
]].max(axis=1)
df['emalg_avg'] = df[[
'ema30', 'ema35', 'ema40', 'ema45', 'ema50', 'ema60'
]].mean(axis=1)
#df['score_long'] = df[['cls','emalg_min','emalg_max','emalg_min']].apply(lambda x: 100 * (x[0]-x[1])/(x[2]-x[3]),axis=1)
df['ema_min'] = df[[
'ema03', 'ema05', 'ema08', 'ema10', 'ema12', 'ema15', 'ema30',
'ema35', 'ema40', 'ema45', 'ema50', 'ema60'
]].min(axis=1)
df['ema_max'] = df[[
'ema03', 'ema05', 'ema08', 'ema10', 'ema12', 'ema15', 'ema30',
'ema35', 'ema40', 'ema45', 'ema50', 'ema60'
]].max(axis=1)
df['score_ovlp_ema'] = df[[
'emash_min', 'emalg_max', 'ema_max', 'ema_min'
]].apply(lambda x: 100 * (x[0] - x[1]) / (x[2] - x[3]), axis=1)
df = pd.merge(df_stock, df, on=['sym'],
how='inner').sort_values(['sort', 'ndays'])
decimals = pd.Series([1, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0],
index=[
'cls', 'ndays', 'vol', 'score_vol_sma20',
'bb_bbhi', 'bb_bbli', 'macd', 'obv', 'rsi14',
'cci20', 'score_ovlp_ema'
])
cols = [
'ndays', 'dte', 'sort', 'sym', 'cls', 'vol', 'score_vol_sma20',
'bb_bbhi', 'bb_bbli', 'macd', 'obv', 'rsi14', 'cci20',
'score_ovlp_ema'
]
df = df[df['ndays'] <= 10][cols].round(decimals).copy()
print(df['score_ovlp_ema'].min(), df['score_ovlp_ema'].max())
df[df['sym'] == 'QQQ'].head(50)
self.df = df
def features_df(stocks,
rsi_vals=False,
macd_vals=False,
ppo=False,
awesome_oscillator_val=False,
daily_log_return=False,
_return=False,
change=False,
volatility=False,
min5=False,
min10=False,
min30=False,
hband_indicator=False,
lband_indicator=False,
corr=False):
"""
Calculates the momentum, RSI, moving average convergence/divergence (MACD), Percentage Price Oscillator,
awesome oscillator indicator, daily log return column, return, change, volatility, 5-10 and 30 mins moving average,
high band indicator, lower band indicator, correlation of the given stock dataframe.
:param stocks: dataframe of stocks
:param rsi_vals: Default value False. If you want the RSI column, set True
:param macd_vals: Default value False. If you want the MACD column, set True
:param ppo: Default value False. If you want the Percentage Price Oscillator column, set True
:param awesome_oscillator_val: Default value False. If you want the awesome oscillator indicator column, set True
:param daily_log_return: Default value False. If you want the daily log return column, set True
:param _return: Default value False. If you want the return column, set True
:param change: Default value False. If you want the change column, set True
:param volatility: Default value False. If you want the volatility column, set True
:param min5: Default value False. If you want the min5 column, set True
:param min10: Default value False. If you want the min10 column, set True
:param min30: Default value False. If you want the min30 column, set True
:param hband_indicator: Default value False. If you want the high band indicator column, set True
:param lband_indicator: Default value False. If you want the lower band indicator column, set True
:param corr: Default value False. If you want the correlation column, set True
:return: a dataframe with the different features of said stock Default value False.
If you want the RSI column, set True
"""
if rsi_vals is True:
stocks['rsi_vals'] = RSIIndicator(close=stocks.Close, window=10).rsi()
if macd_vals is True:
stocks['macd_vals'] = macd(stocks.Close,
window_slow=26,
window_fast=12)
if ppo is True:
stocks['Ppo'] = np.array(
PercentagePriceOscillator(stocks.Close,
window_slow=26,
window_fast=12).ppo())
if awesome_oscillator_val is True:
stocks['awesome_oscillator'] = awesome_oscillator(stocks.High,
stocks.Low,
window1=5,
window2=29)
if daily_log_return is True:
stocks['daily_log_return'] = daily_return(close=stocks.Close)
if _return is True:
stocks['Return'] = round(stocks['Close'] / stocks['Open'] - 1, 3)
if change is True:
stocks['Change'] = (stocks.Close - stocks.Close.shift(1)).fillna(0)
if volatility is True:
stocks['Volatility'] = stocks.Close.ewm(21).std()
if min5 is True:
stocks['min5'] = stocks.Close.rolling(5).mean()
if min10 is True:
stocks['min10'] = stocks.Close.rolling(10).mean()
if min30 is True:
stocks['min30'] = stocks.Close.rolling(30).mean()
if hband_indicator is True:
stocks['hband_indicator'] = BollingerBands(
close=stocks.Close).bollinger_hband_indicator()
if lband_indicator is True:
stocks['lband_indicator'] = BollingerBands(
close=stocks.Close).bollinger_lband_indicator()
if corr is True:
stocks['Corr'] = stocks.Close.rolling(window=10).corr(stocks['min10'])
return stocks.iloc[-1, :]
from ta.momentum import RSIIndicator
# from ta.volume import put_call_ratio
from ta.trend import EMAIndicator
from ta.trend import SMAIndicator
from ta.trend import cci
from ta.volume import OnBalanceVolumeIndicator
ls_df = []
ls_tickers = ls_stock
for ticker in ls_tickers:
#df = dropna(df_price[df_price['sym']==ticker])
df = df_price[df_price['sym'] == ticker].copy()
indicator_bb = BollingerBands(close=df['cls'], window=20, window_dev=2)
indicator_macd = MACD(close=df['cls'],
window_fast=12,
window_slow=26,
window_sign=9)
indicator_rsi14 = RSIIndicator(close=df['cls'], window=14)
indicator_cci20 = cci(high=df['hgh'],
low=df['low'],
close=df['cls'],
window=20,
constant=0.015)
indicator_obv = OnBalanceVolumeIndicator(close=df['cls'],
volume=df['vol'],
fillna=True)
indicator_vol_sma20 = SMAIndicator(close=df['vol'], window=20)
def GetPriceDataFromExchangeFinnHub(event, context):
retVal = {}
retVal["data"] = []
# For debugging the input, write the EVENT to CloudWatch logs
print(json.dumps(event))
# Data is sent to Lambda via a HTTPS POST call. We want to get to the payload send by Snowflake
event_body = event["body"]
payload = json.loads(event_body)
for row in payload["data"]:
sflkRowRef = row[
0] # This is how Snowflake keeps track of data as it gets returned
symbol = row[
1] # The data passed in from Snowflake that the input row contains.
fromDate = row[2]
toDate = row[3]
# Will return URL without token to Snowflake for tracking
URL = f'https://finnhub.io/api/v1/stock/candle?symbol={symbol}&resolution=D&from={fromDate}&to={toDate}'
# Add our FinnHubAPI Key to the end of the URL.
# This is in a new variable which will not be returned to Snowflake
URLWithToken = f'{URL}&token={FinnHubAPI.TOKEN}'
# GET data from the API
httpData = requests.get(url=URLWithToken).json()
# Convert to Pandas DataFrame
df = pd.DataFrame(httpData)
# Add the column names
print("Adding column names")
df.columns = [
"Close", "High", "Low", "Open", "Status", "OpenTime", "Volume"
]
# Set DateTime columns to correct type
df['OpenTime'] = pd.to_datetime(df['OpenTime'], unit='ms')
df['Open'] = df['Open'].astype(float)
df['High'] = df['High'].astype(float)
df['Low'] = df['Low'].astype(float)
df['Close'] = df['Close'].astype(float)
df['Volume'] = df['Volume'].astype(float)
# Clean NaN values
print("Cleaning NA values")
df = dropna(df)
# Calculate the Bollinger Bands indicator
indicator_bb = BollingerBands(close=df["Close"], n=20, ndev=2)
df['bb_bbm'] = indicator_bb.bollinger_mavg()
df['bb_bbh'] = indicator_bb.bollinger_hband()
df['bb_bbl'] = indicator_bb.bollinger_lband()
df['bb_bbhi'] = indicator_bb.bollinger_hband_indicator()
df['bb_bbli'] = indicator_bb.bollinger_lband_indicator()
df['bb_bbw'] = indicator_bb.bollinger_wband()
df['bb_bbp'] = indicator_bb.bollinger_pband()
print("converting OHLC pandas to JSON. This does it as a string")
buffer = df.to_json(orient="records")
print("Interpret the JSON string into a dictionary for output")
jsonResponse = json.loads(buffer)
# Prepare the output response
response = {}
response["url"] = URL
response["response"] = jsonResponse
retVal["data"].append([sflkRowRef, response])
# For debugging the output, write the RETurn VALue to CloudWatch logs
# print(json.dumps(retVal))
return retVal
def setUp(self):
self._df = pd.read_csv(self._filename, sep=',')
self._indicator = BollingerBands(close=self._df['Close'],
n=20,
ndev=2,
fillna=False)
def analyze(self):
bollinger_bands = BollingerBands(self.df.close, )
self.df["bb_h2"] = bollinger_bands.bollinger_hband()
self.df["bb_l2"] = bollinger_bands.bollinger_lband()
self.df[f"buy_signal"] = (self.df.bb_h2 - self.df.bb_l2 <= 200)
])
df_bars = dropna(df_bars)
df_bars['Open'] = pd.to_numeric(df_bars['Open'],
errors='coerce')
df_bars['Close'] = pd.to_numeric(df_bars['Close'],
errors='coerce')
df_bars['High'] = pd.to_numeric(df_bars['High'],
errors='coerce')
df_bars['Low'] = pd.to_numeric(df_bars['Low'], errors='coerce')
df_bars['Quote asset volume'] = pd.to_numeric(
df_bars['Quote asset volume'], errors='coerce')
# Initialize Bollinger Bands Indicator
indicator_bb = BollingerBands(close=df_bars["Close"],
window=20,
window_dev=2)
# Add Bollinger Bands features
# df_bars['bb_bbm'] = indicator_bb.bollinger_mavg()
# df_bars['bb_bbh'] = indicator_bb.bollinger_hband()
#df_bars['bb_bbl'] = indicator_bb.bollinger_lband()
# Add Bollinger Band high indicator
# df_bars['bb_bbhi'] = indicator_bb.bollinger_hband_indicator()
# Add Bollinger Band low indicator
# df_bars['bb_bbli'] = indicator_bb.bollinger_lband_indicator()
# Add Width Size Bollinger Bands
df_bars['bb_bbw_' + tf] = indicator_bb.bollinger_wband()
end="2020-06-01")['Adj Close']
stock_data_biotech = stock_data_biotech.dropna(axis='columns')
# Initalise the plot and row/column variables
figure, axis = plt.subplots(4, 3)
row = 0
column = 0
# Loop over the tickers
for ticker in stock_data_biotech.columns:
# Initalise the DataFrame
data_plot = pd.DataFrame(stock_data_biotech[ticker])
# Initialize Bollinger Bands Indicator
indicator_bb = BollingerBands(close=stock_data_biotech[ticker],
window=20,
window_dev=2)
# Add Bollinger Bands features
data_plot['bb_bbm'] = indicator_bb.bollinger_mavg()
data_plot['bb_bbh'] = indicator_bb.bollinger_hband()
data_plot['bb_bbl'] = indicator_bb.bollinger_lband()
# Create the plot
axis[row, column].plot(data_plot)
axis[row, column].set_title(health_etfs_in_biotech[ticker]['long_name'],
fontsize=6)
axis[row, column].set_xticks([])
axis[row, column].set_yticks([])
# Count a column further
bot = os.environ["telegram_token_bot"]
df = pd.read_csv('datasets/ETHUSDT-1m-sma-15.csv')
print(df)
df["EMA12"] = ta.ema(high=df.high, low=df.low, close=df.close, length=12)
df["RSI2"] = ta.rsi(high=df.high, low=df.low, close=df.close, length=14)
df['ATR2'] = df.ta.atr()
df['Momentum'] = df.ta.mom()
df['adx'] = ta.adx(high=df.high, low=df.low, close=df.close, length=14)['ADX_14']
print('---------------------')
df['pvt'] = ta.pvt(close=df.close, volume=df.volume)
indicator_bb = BollingerBands(close=df.close, window=14, window_dev=2)
df['bb_bbm'] = indicator_bb.bollinger_mavg()
df['bb_bbh'] = indicator_bb.bollinger_hband()
df['bb_bbl'] = indicator_bb.bollinger_lband()
df['bb_bbhi'] = indicator_bb.bollinger_hband_indicator()
df['bb_bbli'] = indicator_bb.bollinger_lband_indicator()
macd = ta.macd(close=df.close, fast=12, slow=26, signal_indicators=True, signal=9)
df['macd_osc'] = macd['MACD_12_26_9']
df['macd_h'] = macd['MACDh_12_26_9']
df['macd_s'] = macd['MACDs_12_26_9']
df['macd_xa'] = macd['MACDh_12_26_9_XA_0']
df['macd_xb'] = macd['MACDh_12_26_9_XB_0']
df['macd_a'] = macd['MACD_12_26_9_A_0']
BUFFER_SIZE = 10000
EPOCHS = 10
CLASSIFICATION = True
step = 1
history_size = 21
target_distance = 2
features_considered = [
'Close', 'Volume', 'MA_short', 'MA_long', 'Wilders_EMA', 'bb_bbm',
'bb_bbh', 'bb_bbl', 'bb_bbhi', 'bb_bbli'
]
# features_considered = ['Close', 'MA_short', 'MA_long', 'Wilders_EMA']
# Initialize Bollinger Bands Indicator
e = EquityData('data/SPY.csv', 'SPY')
indicator_bb = BollingerBands(close=e.close(), n=20, ndev=2)
e.data['MA_short'] = moving_average(e, window=history_size)
e.data['MA_long'] = moving_average(e, window=5)
e.data['Wilders_EMA'] = e.close().ewm(alpha=1 / history_size,
adjust=False).mean()
# Add Bollinger Bands features
e.data['bb_bbm'] = indicator_bb.bollinger_mavg()
e.data['bb_bbh'] = indicator_bb.bollinger_hband()
e.data['bb_bbl'] = indicator_bb.bollinger_lband()
# Add Bollinger Band high indicator
e.data['bb_bbhi'] = indicator_bb.bollinger_hband_indicator()
# Add Bollinger Band low indicator
e.data['bb_bbli'] = indicator_bb.bollinger_lband_indicator()
e.data = e.data[21:]
def applyIndicator(self, full_company_price):
self.data = full_company_price
high = self.data['high']
low = self.data['low']
close = self.data['close']
volume = self.data['volume']
EMA12 = EMAIndicator(close, 12, fillna=False)
EMA30 = EMAIndicator(close, 20, fillna=False)
EMA60 = EMAIndicator(close, 60, fillna=False)
MACD1226 = MACD(close, 26, 12, 9, fillna=False)
MACD2452 = MACD(close, 52, 24, 18, fillna=False)
ROC12 = ROCIndicator(close, 12, fillna=False)
ROC30 = ROCIndicator(close, 30, fillna=False)
ROC60 = ROCIndicator(close, 60, fillna=False)
RSI14 = RSIIndicator(close, 14, fillna=False)
RSI28 = RSIIndicator(close, 28, fillna=False)
RSI60 = RSIIndicator(close, 60, fillna=False)
AROON25 = AroonIndicator(close, 25, fillna=False)
AROON50 = AroonIndicator(close, 50, fillna=False)
AROON80 = AroonIndicator(close, 80, fillna=False)
MFI14 = MFIIndicator(high, low, close, volume, 14, fillna=False)
MFI28 = MFIIndicator(high, low, close, volume, 28, fillna=False)
MFI80 = MFIIndicator(high, low, close, volume, 80, fillna=False)
CCI20 = CCIIndicator(high, low, close, 20, 0.015, fillna=False)
CCI40 = CCIIndicator(high, low, close, 40, 0.015, fillna=False)
CCI100 = CCIIndicator(high, low, close, 100, 0.015, fillna=False)
WILLR14 = WilliamsRIndicator(high, low, close, 14, fillna=False)
WILLR28 = WilliamsRIndicator(high, low, close, 28, fillna=False)
WILLR60 = WilliamsRIndicator(high, low, close, 60, fillna=False)
BBANDS20 = BollingerBands(close, 20, 2, fillna=False)
KC20 = KeltnerChannel(high, low, close, 20, 10, fillna=False)
STOCH14 = StochasticOscillator(high, low, close, 14, 3, fillna=False)
STOCH28 = StochasticOscillator(high, low, close, 28, 6, fillna=False)
STOCH60 = StochasticOscillator(high, low, close, 60, 12, fillna=False)
CMI20 = ChaikinMoneyFlowIndicator(high,
low,
close,
volume,
20,
fillna=False)
CMI40 = ChaikinMoneyFlowIndicator(high,
low,
close,
volume,
40,
fillna=False)
CMI100 = ChaikinMoneyFlowIndicator(high,
low,
close,
volume,
100,
fillna=False)
self.data['ema12'] = (close - EMA12.ema_indicator()) / close
self.data['ema30'] = (close - EMA30.ema_indicator()) / close
self.data['ema60'] = (close - EMA60.ema_indicator()) / close
self.data['macd1226'] = MACD1226.macd() - MACD1226.macd_signal()
self.data['macd2452'] = MACD2452.macd() - MACD2452.macd_signal()
self.data['roc12'] = ROC12.roc()
self.data['roc30'] = ROC30.roc()
self.data['roc60'] = ROC60.roc()
self.data['rsi14'] = RSI14.rsi()
self.data['rsi28'] = RSI28.rsi()
self.data['rsi60'] = RSI60.rsi()
self.data['aroon25'] = AROON25.aroon_indicator()
self.data['aroon50'] = AROON50.aroon_indicator()
self.data['aroon80'] = AROON80.aroon_indicator()
self.data['mfi14'] = MFI14.money_flow_index()
self.data['mfi28'] = MFI28.money_flow_index()
self.data['mfi80'] = MFI80.money_flow_index()
self.data['cci20'] = CCI20.cci()
self.data['cci40'] = CCI40.cci()
self.data['cci100'] = CCI100.cci()
self.data['willr14'] = WILLR14.wr()
self.data['willr28'] = WILLR28.wr()
self.data['willr60'] = WILLR60.wr()
self.data['bband20up'] = (BBANDS20.bollinger_hband() - close) / close
self.data['bband20down'] = (close - BBANDS20.bollinger_lband()) / close
self.data['stoch14'] = STOCH14.stoch()
self.data['stoch28'] = STOCH28.stoch()
self.data['stoch60'] = STOCH60.stoch()
self.data['cmi20'] = CMI20.chaikin_money_flow()
self.data['cmi40'] = CMI40.chaikin_money_flow()
self.data['cmi100'] = CMI100.chaikin_money_flow()
self.data['kc20up'] = (KC20.keltner_channel_hband() - close) / close
self.data['kc20down'] = (close - KC20.keltner_channel_lband()) / close
return self.data
ppo_indicator = PercentagePriceOscillator(close = df["Close"], window_slow = 20, window_fast = 10, window_sign = 9, fillna = False)
df['ppo'] = ppo_indicator.ppo()
roc_indicator = ROCIndicator(close = df["Close"], window = 12, fillna = False)
df['roc'] = roc_indicator.roc()
macd_indicator = MACD(close = df["Close"], window_slow = 20, window_fast = 12, window_sign = 9, fillna = False)
df['macd'] = macd_indicator.macd()
rsi_indicator = RSIIndicator(close = df["Close"], window = 14, fillna = False)
df['rsi'] = rsi_indicator.rsi()
aroon_indicator = AroonIndicator(close = df["Close"], window = 20, fillna = False)
df['aroon'] = aroon_indicator.aroon_indicator()
boll_indicator = BollingerBands(close = df["Close"], window = 20, window_dev = 2, fillna = False)
df['boll_mavg'] = boll_indicator.bollinger_mavg()
df.rename(columns = {"Close": "price"}, inplace=True)
prices = df['price'].to_numpy()
df['day_of_month'] = df.index.day
df['day_of_week'] = df.index.dayofweek
df['month'] = df.index.month
df.dropna(inplace=True)
df = df.drop(df.columns[[0, 1, 2, 4, 5]], axis=1)
X_columns = ['price', 'kama', 'ppo', 'roc', 'macd', 'rsi', 'aroon', 'boll_mavg',
'day_of_month', 'day_of_week', 'month']
]
features_considered = [
'Close', 'Volume', 'Wilders_EMA', 'bb_bbm', 'bb_bbh', 'bb_bbl', 'bb_bbhi',
'bb_bbli', 'trend_adx', 'momentum_stoch_signal', 'trend_trix',
'trend_ichimoku_a', 'momentum_kama', 'momentum_tsi', 'volatility_kcli'
]
features_considered = ['close', 'MA_long', 'MA_short']
# features_considered = ['Close', 'MA_short', 'MA_long', 'Wilders_EMA']
# Initialize Bollinger Bands Indicator
e = EquityData('data/SPY.csv', 'SPY')
data = pyfinancialdata.get_multi_year(provider='histdata',
instrument='SPXUSD',
years=[2016, 2017, 2018],
time_group='10min')
e.data = data
indicator_bb = BollingerBands(close=e.data['close'], n=20, ndev=2)
# e.data = add_all_ta_features(e.data, open="open", high="high", low="low", close="close")
e.data['MA_long'] = e.data['close'].rolling(window=52).mean()
e.data['MA_short'] = e.data['close'].rolling(window=7).mean()
# e.data['MA_short'] = moving_average(e, window=5)
# e.data['Wilders_EMA'] = e.close().ewm(alpha=1/history_size, adjust=False).mean()
# # Add Bollinger Bands features
# e.data['bb_bbm'] = indicator_bb.bollinger_mavg()
# e.data['bb_bbh'] = indicator_bb.bollinger_hband()
# e.data['bb_bbl'] = indicator_bb.bollinger_lband()
# # Add Bollinger Band high indicator
# e.data['bb_bbhi'] = indicator_bb.bollinger_hband_indicator()
# # Add Bollinger Band low indicator
# e.data['bb_bbli'] = indicator_bb.bollinger_lband_indicator()