def BBands(close, window, deviation_1, deviation_2):
# 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['Positions'] = get_signal(df)
return df['Close'], df['Positions']
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()
bollinger_bands2 = BollingerBands(self.df.close, ndev=1)
self.df["bb_h1"] = bollinger_bands2.bollinger_hband()
self.df["bb_l1"] = bollinger_bands2.bollinger_lband()
self.df[f"buy_signal"] = (is_crossover(self.df.close, self.df.bb_h1))
self.df[f"sell_signal"] = (is_crossover(self.df.bb_l1, self.df.close))
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 add_volatility_indicators(data: pd.DataFrame) -> pd.DataFrame:
"""Adds the volatility 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.
"""
bb = BollingerBands(data['close'])
data.loc[:, 'bollinger_hband'] = bb.bollinger_hband()
data.loc[:, 'bollinger_hband_indicator'] = bb.bollinger_hband_indicator()
data.loc[:, 'bollinger_lband'] = bb.bollinger_lband()
data.loc[:, 'bollinger_lband_indicator'] = bb.bollinger_lband_indicator()
data.loc[:, 'bollinger_mavg'] = bb.bollinger_mavg()
data.loc[:, 'bollinger_pband'] = bb.bollinger_pband()
data.loc[:, 'bollinger_wband'] = bb.bollinger_wband()
return data
def get_signal(self, input_df, ticker, run_id):
df = input_df.copy()
indicator_bb = BollingerBands(
close=df["Close"],
window=self.indicator["window"],
window_dev=self.indicator["window_dev"],
fillna=self.indicator["fillna"],
)
# 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()
df["bb_bbhi"] = indicator_bb.bollinger_hband_indicator()
df["bb_bbli"] = indicator_bb.bollinger_lband_indicator()
df["bb_bbp"] = indicator_bb.bollinger_pband()
row = df.iloc[-1]
if row.bb_bbhi.item():
sell_signal = {
"ticker":
ticker,
"datetime":
row.Date,
"indicator":
self.name,
"param":
self.param,
"reason":
"High BollingerBand percentage - currently at {}%".format(
int(row.bb_bbp.item() * 100.0)),
"image":
self.draw_image(df, ticker, run_id),
}
else:
sell_signal = None
if row.bb_bbli.item():
buy_signal = {
"ticker":
ticker,
"datetime":
row.Date,
"indicator":
self.name,
"param":
self.param,
"reason":
"Low BollingerBand percentage - currently at {}%".format(
int(row.bb_bbp.item() * 100.0)),
"image":
self.draw_image(df, ticker, run_id),
}
else:
buy_signal = None
return buy_signal, sell_signal
def add_volatility_ta(df: pd.DataFrame,
high: str,
low: str,
close: str,
fillna: bool = False,
colprefix: str = "") -> 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
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_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_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(self):
# Bollinger Bands
bb_indicator = BollingerBands(close=self._close,
n=self._chan_period,
ndev=self._bol_band_std_dev,
fillna=self._fillna)
self._bb_hband = bb_indicator.bollinger_hband()
self._bb_lband = bb_indicator.bollinger_lband()
# Keltner Channel
kb_indicator = KeltnerChannel(high=self._high,
low=self._low,
close=self._close,
n=self._chan_period,
n_atr=self._kelt_std_dev,
fillna=self._fillna,
ov=False)
self._kb_hband = kb_indicator.keltner_channel_hband()
self._kb_lband = kb_indicator.keltner_channel_lband()
# Momentum Oscillator
smo_indicator = SMOIndicator(high=self._high,
low=self._low,
close=self._close,
n=self._mom_period,
fillna=self._fillna)
self._oscillator = smo_indicator.smo()
# Bar and Signal Colors
self._squeeze = bb_indicator.bollinger_wband(
) - kb_indicator.keltner_channel_wband()
self._squeeze = self._squeeze.ge(0).astype(int)
def AddIndicators(df):
# 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()
return df
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 bollinger_bands(df):
indicator_bb = BollingerBands(close=df["Close"])
df['bb_bbh'] = indicator_bb.bollinger_hband()
df['bb_bbl'] = indicator_bb.bollinger_lband()
df['bb_avg'] = indicator_bb.bollinger_mavg()
df['bb_bbh_ind'] = indicator_bb.bollinger_hband_indicator()
df['bb_bbl_ind'] = indicator_bb.bollinger_lband_indicator()
df['bb_pband'] = indicator_bb.bollinger_pband()
df['bb_wband'] = indicator_bb.bollinger_wband()
def techIndicator(df1):
#OFtrader
# Initialize Bollinger Bands Indicator
from ta.volatility import BollingerBands
indicator_bb = BollingerBands(close=df1["Close"], window=10, window_dev=2)
# Add Bollinger Bands features
df1['bb_bbh'] = indicator_bb.bollinger_hband()
df1['bb_bbl'] = indicator_bb.bollinger_lband()
# Initialize Bollinger Bands Indicator
from ta.trend import PSARIndicator
indicator_SAR = PSARIndicator(high=df1["high"],
low=df1["low"],
close=df1["Close"])
# Add Bollinger Bands features
df1['sar_high'] = indicator_SAR.psar_up()
df1['sar_low'] = indicator_SAR.psar_down()
from ta.trend import EMAIndicator
indicator_EMA = EMAIndicator(close=df1["Close"], window=7)
df1['Media7'] = indicator_EMA.ema_indicator()
df1['sar_low'] = df1['sar_low'].fillna(0)
df1['sar_high'] = df1['sar_high'].fillna(0)
df1['Distancia_M7'] = df1['Close'] / df1['Media7']
df1['Distancia_BBH'] = df1['Close'] / df1['bb_bbh']
df1['Distancia_BBL'] = df1['Close'] / df1['bb_bbl']
df1['Distancia_SAR'] = np.where(df1['sar_high'] > 0,
df1['Close'] / df1['sar_high'],
df1['sar_low'] / df1['Close'])
df1['posicao_sar'] = np.where(df1['sar_high'] > 0, '1', '0')
corte = 3
df1["Distancia_M7"] = pd.qcut(df1["Distancia_M7"], corte, labels=False)
df1["Distancia_BBH"] = pd.qcut(df1["Distancia_BBH"], corte, labels=False)
df1["Distancia_BBL"] = pd.qcut(df1["Distancia_BBL"], 15, labels=False)
df1["Distancia_SAR"] = pd.qcut(df1["Distancia_SAR"], 15, labels=False)
#
# Padrão Bom com M7: 3 - BBH: 3 - BBL: 15 - DSAR: 15
df1 = df1.drop(["sar_high", "sar_low", "bb_bbh", "bb_bbl", "Media7"],
axis=1)
#df1.tail(50)
return df1
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 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
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 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 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)
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
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()
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()
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 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')
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
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')
st.line_chart(bb)
progress_bar = st.progress(0)
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
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
column += 1
# If the column reaches the max, add another row
if column == 3:
row += 1
column = 0
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}")
'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:]
EVALUATION_INTERVAL = int(e.data.shape[0] / BATCH_SIZE) * 2
features = e.data[features_considered]
assert (list(features)[0] == 'Close')
features.index = e.date()
# features.plot(subplots=True)
# plt.show()
dataset = features.values
def app():
ticker_list = pd.read_csv(
'https://raw.githubusercontent.com/kennethmina24/stockapp/master/List%20of%20Names%20(Stocks)'
)
#Heading Info
tickerSymbol = st.sidebar.selectbox('Stock ticker',
ticker_list) # Select ticker symbol
tickerData = yf.Ticker(tickerSymbol) # Get ticker data
#Ticker information
string_logo = '<img src=%s>' % tickerData.info['logo_url']
st.markdown(string_logo, unsafe_allow_html=True)
string_name = tickerData.info['longName']
st.header('**%s**' % string_name)
string_summary = tickerData.info['longBusinessSummary']
st.info(string_summary)
# Function to display values and their name
#########################
#def display_summary(tickerSymbol):
# info = get_info(tickerSymbol)
#info_names = ["Name: ", "Close Price: ", "Open Price: ", "52-Week Range: ", "Dividend Rate & Yield: ", \
#"Market Cap: ", "PE Ratio: ", "EPS: "]
# print out info in sidebar
#for name,infoValue in zip(info_names, info):
# row = \
# f"""<div>
# <span style='float: left;'><b>{name}</b></span>
# <span style='float: right;'> {infoValue}</span>
# </div>
###########
# sidebar #
###########
import datetime
today = datetime.date.today()
before = today - datetime.timedelta(days=1825)
start_date = st.sidebar.date_input('Start date', before)
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.')
###############
#Download Data#
###############
df = yf.download(tickerSymbol,
start=start_date,
end=end_date,
progress=False)
#Indicators#
# Bollinger Bands#
indicator_bb = BollingerBands(df['Low'])
bb = df
bb['bb_h'] = indicator_bb.bollinger_hband()
bb['bb_l'] = indicator_bb.bollinger_lband()
bb = bb[['Low', 'bb_h', 'bb_l']]
bb = bb.tail(5)
# Resistence Strength Indicator
rsi = RSIIndicator(df['Low']).rsi()
# Plot RSI
st.header('Stock RSI ')
st.write(
"Overbought(Selling Pressure) above 70 /// Oversold(Buying Pressure) below 30"
)
st.dataframe(rsi.tail(3))
###################
# Set up main app #
###################
# Data of recent days
# Plot the prices and the bolinger bands
st.write('Recent Data')
st.dataframe(bb)
progress_bar = st.progress(0)