def CCI(df0, n=160):
df = df0
df['cci'] = ema_indicator(cci(df['high'],
df['low'],
df['close'],
n,
0.015,
fillna=False),
4,
fillna=False)
return df
def test_cci(self):
target = 'CCI'
result = cci(high=self._df['High'],
low=self._df['Low'],
close=self._df['Close'],
n=20,
c=0.015,
fillna=False)
pd.testing.assert_series_equal(self._df[target].tail(),
result.tail(),
check_names=False)
def CCI(df, intervals):
"""
Commodity Channel Index: Measures the current price level relative to an average price level over a given period of time.
CCI is high when prices are far above their average and low when prices are below their average
Reference: https://www.investopedia.com/articles/active-trading/031914/how-traders-can-utilize-cci-commodity-channel-index-trade-stock-trends.asp
"""
from ta.trend import cci
from tqdm.auto import tqdm
for interval in tqdm(intervals):
df['cci_' + str(interval)] = cci(df['high'],
df['low'],
df['close'],
interval,
fillna=True)
def comm_channel(datos, start, end = '', window = 10, factorC = 0.015):
'''
ENTRADA
datos: Pandas dataframe que contiene al menos una columna de fechas (DATE) y otra
columna numérica
start, end: strings en formato 'YYYY-MM-DD' representando la fecha de inicio
y la fecha final respectivamente
window: Entero que representa la ventan de tiempo a utilizar
factorC: Real (ver la teoría de este indicador)
SALIDA
resultado: Dataframe datos con una columna extra conteniendo la información
del indicador
'''
#Localiza la fecha de inicio y revisa si hay suficiente información
indiceInicio=datos[datos['Date']==start].index[0]
if window > indiceInicio + 1:
print 'No hay suficiente historia para esta fecha'
return datos
#Último índice
if end=='':
lastIndex=datos.shape[0] - 1
else:
lastIndex=datos[datos['Date']==end].index[0]
#calcula el indicador
indicador = cci(datos['High'], datos['Low'], datos['Adj Close'], window, factorC)
#agrega la nueva columna
resultado = deepcopy(datos)
resName = 'Comm-Chan-' + str(window) + '-' + str(factorC)
resultado[resName] = indicador
#Filtra a partir del índice correspondiente a la fecha start
resultado=resultado.iloc[indiceInicio:lastIndex+1,:]
resultado=resultado.reset_index(drop=True)
#añade metadatos
resultado.tipo = 'comm-chan'
resultado.resName = resName
return resultado
def cci_cb_pressed(stock_analysis_tool: QMainWindow,
cb: QCheckBox) -> None:
"""
:param cb:
:return:
"""
if cb.isChecked():
# Add CCI to Display Graph
stock_analysis_tool.df['cci'] = cci(
close=stock_analysis_tool.df['close'],
low=stock_analysis_tool.df['low'],
high=stock_analysis_tool.df['high'],
n=stock_analysis_tool.cci_n)
stock_analysis_tool.add_column_to_graph(column_name='cci')
else:
# Remove CCI from Display Graph
stock_analysis_tool.remove_column_from_graph(column_name='cci')
stock_analysis_tool.df = stock_analysis_tool.df.drop("cci",
axis=1)
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 engineer_data_over_single_interval(df: pd.DataFrame,
indicators: list,
ticker: str = "",
rsi_n: int = 14,
cmo_n: int = 7,
macd_fast: int = 12,
macd_slow: int = 26,
macd_sign: int = 9,
roc_n: int = 12,
cci_n: int = 20,
dpo_n: int = 20,
cmf_n: int = 20,
adx_n: int = 14,
mass_index_low: int = 9,
mass_index_high: int = 25,
trix_n: int = 15,
stochastic_oscillator_n: int = 14,
stochastic_oscillator_sma_n: int = 3,
ultimate_oscillator_short_n: int = 7,
ultimate_oscillator_medium_n: int = 14,
ultimate_oscillator_long_n: int = 28,
ao_short_n: int = 5,
ao_long_n: int = 34,
kama_n: int = 10,
tsi_high_n: int = 25,
tsi_low_n: int = 13,
eom_n: int = 14,
force_index_n: int = 13,
ichimoku_low_n: int = 9,
ichimoku_medium_n: int = 26):
from ta.momentum import rsi, wr, roc, ao, stoch, uo, kama, tsi
from ta.trend import macd, macd_signal, cci, dpo, adx, mass_index, trix, ichimoku_a
from ta.volume import chaikin_money_flow, acc_dist_index, ease_of_movement, force_index
# Momentum Indicators
if Indicators.RELATIVE_STOCK_INDEX in indicators:
Logger.console_log(message="Calculating " +
Indicators.RELATIVE_STOCK_INDEX.value +
" for stock " + ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.RELATIVE_STOCK_INDEX.value] = rsi(close=df['close'],
n=rsi_n)
if Indicators.WILLIAMS_PERCENT_RANGE in indicators:
Logger.console_log(message="Calculating " +
Indicators.WILLIAMS_PERCENT_RANGE.value +
" for stock " + ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.WILLIAMS_PERCENT_RANGE.value] = wr(
df['high'], df['low'], df['close'])
if Indicators.CHANDE_MOMENTUM_OSCILLATOR in indicators:
Logger.console_log(message="Calculating " +
Indicators.CHANDE_MOMENTUM_OSCILLATOR.value +
" for stock " + ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.CHANDE_MOMENTUM_OSCILLATOR.
value] = chande_momentum_oscillator(close_data=df['close'],
period=cmo_n)
if Indicators.RATE_OF_CHANGE in indicators:
Logger.console_log(message="Calculating " +
Indicators.RATE_OF_CHANGE.value + " for stock " +
ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.RATE_OF_CHANGE.value] = roc(close=df['close'], n=roc_n)
if Indicators.STOCHASTIC_OSCILLATOR in indicators:
Logger.console_log(message="Calculating " +
Indicators.STOCHASTIC_OSCILLATOR.value +
" for stock " + ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.STOCHASTIC_OSCILLATOR.value] = stoch(
high=df['high'],
low=df['low'],
close=df['close'],
n=stochastic_oscillator_n,
d_n=stochastic_oscillator_sma_n)
if Indicators.ULTIMATE_OSCILLATOR in indicators:
Logger.console_log(message="Calculating " +
Indicators.ULTIMATE_OSCILLATOR.value +
" for stock " + ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.ULTIMATE_OSCILLATOR.value] = uo(
high=df['high'],
low=df['low'],
close=df['close'],
s=ultimate_oscillator_short_n,
m=ultimate_oscillator_medium_n,
len=ultimate_oscillator_long_n)
if Indicators.AWESOME_OSCILLATOR in indicators:
Logger.console_log(message="Calculating " +
Indicators.AWESOME_OSCILLATOR.value +
" for stock " + ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.AWESOME_OSCILLATOR.value] = ao(high=df['high'],
low=df['low'],
s=ao_short_n,
len=ao_long_n)
if Indicators.KAUFMAN_ADAPTIVE_MOVING_AVERAGE in indicators:
Logger.console_log(message="Calculating " +
Indicators.KAUFMAN_ADAPTIVE_MOVING_AVERAGE.value +
" for stock " + ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.KAUFMAN_ADAPTIVE_MOVING_AVERAGE.value] = kama(
close=df['close'], n=kama_n)
if Indicators.TRUE_STRENGTH_INDEX in indicators:
Logger.console_log(message="Calculating " +
Indicators.TRUE_STRENGTH_INDEX.value +
" for stock " + ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.TRUE_STRENGTH_INDEX.value] = tsi(close=df['close'],
r=tsi_high_n,
s=tsi_low_n)
# Trend Indicator
if Indicators.MOVING_AVERAGE_CONVERGENCE_DIVERGENCE in indicators:
Logger.console_log(
message="Calculating " +
Indicators.MOVING_AVERAGE_CONVERGENCE_DIVERGENCE.value +
" for stock " + ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.MOVING_AVERAGE_CONVERGENCE_DIVERGENCE.value] = macd(close=df['close'],
n_slow=macd_slow,
n_fast=macd_fast) - \
macd_signal(close=df['close'],
n_slow=macd_slow,
n_fast=macd_fast,
n_sign=macd_sign)
if Indicators.COMMODITY_CHANNEL_INDEX in indicators:
Logger.console_log(message="Calculating " +
Indicators.COMMODITY_CHANNEL_INDEX.value +
" for stock " + ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.COMMODITY_CHANNEL_INDEX.value] = cci(high=df['high'],
low=df['low'],
close=df['close'],
n=cci_n)
if Indicators.DETRENDED_PRICE_OSCILLATOR in indicators:
Logger.console_log(message="Calculating " +
Indicators.DETRENDED_PRICE_OSCILLATOR.value +
" for stock " + ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.DETRENDED_PRICE_OSCILLATOR.value] = dpo(
close=df['close'], n=dpo_n)
if Indicators.AVERAGE_DIRECTIONAL_INDEX in indicators:
Logger.console_log(message="Calculating " +
Indicators.AVERAGE_DIRECTIONAL_INDEX.value +
" for stock " + ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.AVERAGE_DIRECTIONAL_INDEX.value] = adx(high=df['high'],
low=df['low'],
close=df['close'],
n=adx_n)
if Indicators.MASS_INDEX in indicators:
Logger.console_log(message="Calculating " +
Indicators.MASS_INDEX.value + " for stock " +
ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.MASS_INDEX.value] = mass_index(high=df['high'],
low=df['low'],
n=mass_index_low,
n2=mass_index_high)
if Indicators.TRIPLE_EXPONENTIALLY_SMOOTHED_MOVING_AVERAGE in indicators:
Logger.console_log(
message="Calculating " +
Indicators.TRIPLE_EXPONENTIALLY_SMOOTHED_MOVING_AVERAGE.value +
" for stock " + ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.TRIPLE_EXPONENTIALLY_SMOOTHED_MOVING_AVERAGE.
value] = trix(close=df['close'], n=trix_n)
if Indicators.ICHIMOKU_A in indicators:
Logger.console_log(message="Calculating " +
Indicators.ICHIMOKU_A.value + " for stock " +
ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.ICHIMOKU_A.value] = ichimoku_a(high=df['high'],
low=df['low'],
n1=ichimoku_low_n,
n2=ichimoku_medium_n)
# Volume Indicator
if Indicators.CHAIKIN_MONEY_FLOW in indicators:
Logger.console_log(message="Calculating " +
Indicators.CHAIKIN_MONEY_FLOW.value +
" for stock " + ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.CHAIKIN_MONEY_FLOW.value] = chaikin_money_flow(
high=df['high'],
low=df['low'],
close=df['close'],
volume=df['volume'],
n=cmf_n)
if Indicators.ACCUMULATION_DISTRIBUTION_INDEX in indicators:
Logger.console_log(message="Calculating " +
Indicators.ACCUMULATION_DISTRIBUTION_INDEX.value +
" for stock " + ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.ACCUMULATION_DISTRIBUTION_INDEX.value] = acc_dist_index(
high=df['high'],
low=df['low'],
close=df['close'],
volume=df['volume'])
if Indicators.EASE_OF_MOVEMENT in indicators:
Logger.console_log(message="Calculating " +
Indicators.EASE_OF_MOVEMENT.value + " for stock " +
ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.EASE_OF_MOVEMENT.value] = ease_of_movement(
high=df['high'], low=df['low'], volume=df['volume'], n=eom_n)
if Indicators.FORCE_INDEX in indicators:
Logger.console_log(message="Calculating " +
Indicators.FORCE_INDEX.value + " for stock " +
ticker,
status=Logger.LogStatus.EMPHASIS)
df[Indicators.FORCE_INDEX.value] = force_index(close=df['close'],
volume=df['volume'],
n=force_index_n)
def test_cci(self):
target = 'CCI'
result = cci(**self._params)
pd.testing.assert_series_equal(self._df[target].tail(),
result.tail(),
check_names=False)
def cci(self, high, low, close):
cci = tat.cci(high=high, low=low, close=close).to_numpy()
return cci
def bar():
history = fx.get_history(instrument=symbol,
timeframe="m1",
quotes_count=300)
# print (history)
if history.size != 0:
df = pd.DataFrame(history)
df = df[[
'Date',
'BidOpen',
'BidHigh',
'BidLow',
'BidClose',
'Volume',
]]
df.rename(columns={
'Date': "datetime",
'BidOpen': "open",
"BidHigh": "high",
"BidLow": "low",
"BidClose": "close",
'Volume': "volume",
},
inplace=True)
df['ema_f_h'] = trend.ema(df.high, periods=34)
df['ema_f_c'] = trend.ema(df.close, periods=34)
df['ema_f_l'] = trend.ema(df.low, periods=34)
df['ema_s_h'] = trend.ema(df.high, periods=144)
df['ema_s_c'] = trend.ema(df.close, periods=144)
df['ema_s_l'] = trend.ema(df.low, periods=144)
df['ema_across'] = np.where(df.ema_f_c > df.ema_s_c, int(1),
int(-1)) # 上穿1，下穿-1
# MACD(df, fast=55, slow=144, n=55)
df["macd"] = trend.MACD(df.close, n_slow=144, n_fast=34,
n_sign=34).macd()
df["macd_signal"] = trend.MACD(df.close,
n_slow=144,
n_fast=34,
n_sign=34).macd_signal()
df['macd_across'] = np.where(df.macd > df.macd_signal, int(1),
int(-1)) # 上穿1，下穿-1
df['rsi'] = momentum.rsi(
df.close,
n=34,
)
df['emarsi'] = trend.ema(df.rsi, periods=34)
df['rsi_postion'] = np.where(df.rsi > df.emarsi, int(1),
int(-1)) # 上穿1，下穿-1
# df['stoch'] = momentum.stoch(high=df["high"],low=df["low"],close=df["close"],n=144)
# df['stoch_signal'] = momentum.stoch_signal(high=df["high"],low=df["low"],close=df["close"],n=144)
# df["stoch_sig"] = np.where(df["stoch"] > df["stoch_signal"], 1, 0)
# df["stoch_sig_shfit"] = df["stoch_sig"].shift(1)
df['cci'] = trend.cci(
df.high,
df.low,
df.close,
n=55,
)
df['emacci'] = trend.ema(df.cci, periods=55)
df['cci_postion'] = np.where(df.cci > df.emacci, int(1),
int(-1)) # 上穿1，下穿-1
# 价格为于快线与慢线之上则为1，快慢线之下则为-1，其它为0
df["close_postion"] = np.where(
(df['close'] > df['ema_f_c']) & (df['close'] > df['ema_s_c']),
int(1),
np.where(
(df['close'] < df['ema_f_c']) & (df['close'] < df['ema_s_c']),
int(-1), int(0)))
return df
df['rsi'] = momentum.rsi(
df.close,
n=34,
)
df['emarsi'] = trend.ema(df.rsi, periods=34)
df['rsi_postion'] = np.where(df.rsi > df.emarsi, int(1),
int(-1)) # 上穿1，下穿-1
# df['stoch'] = momentum.stoch(high=df["high"],low=df["low"],close=df["close"],n=144)
# df['stoch_signal'] = momentum.stoch_signal(high=df["high"],low=df["low"],close=df["close"],n=144)
# df["stoch_sig"] = np.where(df["stoch"] > df["stoch_signal"], 1, 0)
# df["stoch_sig_shfit"] = df["stoch_sig"].shift(1)
df['cci'] = trend.cci(
df.high,
df.low,
df.close,
n=55,
)
df['emacci'] = trend.ema(df.cci, periods=55)
df['cci_postion'] = np.where(df.cci > df.emacci, int(1),
int(-1)) # 上穿1，下穿-1
# 价格为于快线与慢线之上则为1，快慢线之下则为-1，其它为0
df["close_postion"] = np.where(
(df['close'] > df['ema_f_c']) &
(df['close'] > df['ema_s_c']), int(1),
np.where((df['close'] < df['ema_f_c']) &
(df['close'] < df['ema_s_c']), int(-1), int(0)))
# if df.iloc[-1]["macd_across"] != df.iloc[-2]["macd_across"]:
# if df.iloc[-1]["macd_across"] ==1:
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)