def add_volume_indicators(data: pd.DataFrame) -> pd.DataFrame:
"""Adds the volume 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.
"""
chaikin = ChaikinMoneyFlowIndicator(data['high'], data['low'],
data['close'], data['volume'])
mfi = MFIIndicator(data['high'], data['low'], data['close'],
data['volume'])
obv = OnBalanceVolumeIndicator(data['close'], data['volume'])
data.loc[:, 'chaikin'] = chaikin.chaikin_money_flow()
data.loc[:, 'mfi'] = mfi.money_flow_index()
data.loc[:, 'obv'] = obv.on_balance_volume()
return data
def OBV(self, df):
'''
data range(0,inf)
'''
df_OBV = df.copy()
obv = OnBalanceVolumeIndicator(df['Close'], df['Volume'])
df['OBV'] = obv.on_balance_volume()
return df
def test_obv2(self):
target = 'OBV'
result = OnBalanceVolumeIndicator(close=self._df['Close'],
volume=self._df['Volume'],
fillna=False).on_balance_volume()
pd.testing.assert_series_equal(self._df[target].tail(),
result.tail(),
check_names=False)
def get_indicators(df):
"""
Add set of technical indicators to the dataframe, return original data frame with new features
"""
feature_df = df.copy()
feature_df['RSI'] = RSIIndicator(close=df[CLOSE]).rsi()
feature_df['Stochastic'] = StochasticOscillator(high=df[HIGH],
low=df[LOW],
close=df[CLOSE]).stoch()
feature_df['Stochastic_signal'] = StochasticOscillator(
high=df[HIGH], low=df[LOW], close=df[CLOSE]).stoch_signal()
feature_df['ADI'] = AccDistIndexIndicator(
high=df[HIGH], low=df[LOW], close=df[CLOSE],
volume=df[VOLUME]).acc_dist_index()
feature_df['OBV'] = OnBalanceVolumeIndicator(
close=df[CLOSE], volume=df[VOLUME]).on_balance_volume()
feature_df['ATR'] = AverageTrueRange(high=df[HIGH],
low=df[LOW],
close=df[CLOSE]).average_true_range()
feature_df['ADX'] = ADXIndicator(high=df[HIGH],
low=df[LOW],
close=df[CLOSE]).adx()
feature_df['ADX_pos'] = ADXIndicator(high=df[HIGH],
low=df[LOW],
close=df[CLOSE]).adx_pos()
feature_df['ADX_neg'] = ADXIndicator(high=df[HIGH],
low=df[LOW],
close=df[CLOSE]).adx_neg()
feature_df['MACD'] = MACD(close=df[CLOSE]).macd()
feature_df['MACD_diff'] = MACD(close=df[CLOSE]).macd_diff()
feature_df['MACD_signal'] = MACD(close=df[CLOSE]).macd_signal()
return feature_df
def add_volume_ta(df: pd.DataFrame, high: str, low: str, close: str, volume: str,
fillna: bool = False, colprefix: str = "") -> pd.DataFrame:
"""Add volume 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.
volume (str): Name of 'volume' column.
fillna(bool): if True, fill nan values.
colprefix(str): Prefix column names inserted
Returns:
pandas.core.frame.DataFrame: Dataframe with new features.
"""
# Accumulation Distribution Index
df[f'{colprefix}volume_adi'] = AccDistIndexIndicator(
high=df[high], low=df[low], close=df[close], volume=df[volume], fillna=fillna).acc_dist_index()
# On Balance Volume
df[f'{colprefix}volume_obv'] = OnBalanceVolumeIndicator(
close=df[close], volume=df[volume], fillna=fillna).on_balance_volume()
# Chaikin Money Flow
df[f'{colprefix}volume_cmf'] = ChaikinMoneyFlowIndicator(
high=df[high], low=df[low], close=df[close], volume=df[volume], fillna=fillna).chaikin_money_flow()
# Force Index
df[f'{colprefix}volume_fi'] = ForceIndexIndicator(
close=df[close], volume=df[volume], n=13, fillna=fillna).force_index()
# Money Flow Indicator
df[f'{colprefix}volume_mfi'] = MFIIndicator(
high=df[high], low=df[low], close=df[close], volume=df[volume], n=14, fillna=fillna).money_flow_index()
# Ease of Movement
indicator = EaseOfMovementIndicator(high=df[high], low=df[low], volume=df[volume], n=14, fillna=fillna)
df[f'{colprefix}volume_em'] = indicator.ease_of_movement()
df[f'{colprefix}volume_sma_em'] = indicator.sma_ease_of_movement()
# Volume Price Trend
df[f'{colprefix}volume_vpt'] = VolumePriceTrendIndicator(
close=df[close], volume=df[volume], fillna=fillna).volume_price_trend()
# Negative Volume Index
df[f'{colprefix}volume_nvi'] = NegativeVolumeIndexIndicator(
close=df[close], volume=df[volume], fillna=fillna).negative_volume_index()
# Volume Weighted Average Price
df[f'{colprefix}volume_vwap'] = VolumeWeightedAveragePrice(
high=df[high], low=df[low], close=df[close], volume=df[volume], n=14, fillna=fillna
).volume_weighted_average_price()
return df
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 setUpClass(cls):
cls._df = pd.read_csv(cls._filename, sep=",")
cls._params = dict(close=cls._df["Close"],
volume=cls._df["Volume"],
fillna=False)
cls._indicator = OnBalanceVolumeIndicator(**cls._params)
#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)