def get_moving_average(i, window: int):
"""
returns the moving average of indicator i
i: pandas series
windows: [20, 50, 200]
"""
sma = SMAIndicator(close=i, window=window)
return sma.sma_indicator()
def add_trend_indicators(data: pd.DataFrame) -> pd.DataFrame:
"""Adds the trend indicators.
Parameters
----------
data : pd.DataFrame
A dataframe with daily stock values. Must include: open, high,
low, close and volume. It should also be sorted in a descending
manner.
Returns
-------
pd.DataFrame
The input dataframe with the indicators added.
"""
adx = ADXIndicator(data['high'], data['low'], data['close'])
ema = EMAIndicator(data['close'])
ema_200 = EMAIndicator(data['close'], n=200)
ichimoku = IchimokuIndicator(data['high'], data['low'])
macd = MACD(data['close'])
sma = SMAIndicator(data['close'], n=14)
sma_200 = SMAIndicator(data['close'], n=200)
data.loc[:, 'adx'] = adx.adx()
data.loc[:, 'adx_pos'] = adx.adx_pos()
data.loc[:, 'adx_neg'] = adx.adx_neg()
data.loc[:, 'ema'] = ema.ema_indicator()
data.loc[:, 'ema_200'] = ema_200.ema_indicator()
data.loc[:, 'ichimoku_a'] = ichimoku.ichimoku_a()
data.loc[:, 'ichimoku_b'] = ichimoku.ichimoku_b()
data.loc[:, 'ichimoku_base_line'] = ichimoku.ichimoku_base_line()
data.loc[:, 'ichimoku_conversion_line'] = (
ichimoku.ichimoku_conversion_line())
data.loc[:, 'macd'] = macd.macd()
data.loc[:, 'macd_diff'] = macd.macd_diff()
data.loc[:, 'macd_signal'] = macd.macd_signal()
data.loc[:, 'sma'] = sma.sma_indicator()
data.loc[:, 'sma_200'] = sma_200.sma_indicator()
return data
def __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
# 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)
df['sma_200'] = indicator_sma_200.sma_indicator()
indicator_sma_100 = SMAIndicator(close=df["adj_close"], window=100)
df['sma_100'] = indicator_sma_100.sma_indicator()
indicator_sma_50 = SMAIndicator(close=df["adj_close"], window=50)
df['sma_50'] = indicator_sma_50.sma_indicator()
# RSI Indicator
indicator_rsi_6 = RSIIndicator(close=df["adj_close"], window=6)
df['rsi_6'] = indicator_rsi_6.rsi()
indicator_rsi_14 = RSIIndicator(close=df["adj_close"], window=14)
df['rsi_14'] = indicator_rsi_14.rsi()
# MACD Indicator
indicator_macd = MACD(close=df["adj_close"])
df['macd'] = indicator_macd.macd()
# df = df.dropna(how='all)
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()
#print(SYMBOL)
SYMBOL = f'\'{i}\''
#print(SYMBOL)
df = pd.read_sql_query(
f'select {low_price} low_price, {open_price} open_price,{macd} MACD,{macd_signal} MACD_SIGNAL,'
f'{close_price} close_price , {date_recorded} date_recorded ,{ema_indicator} ema_indicator,'
f'{high_price} high_price , {volume1} volume , {SYMBOL} symbol'
f' from nifty_500_technical_analysis where nifty_500_technical_analysis.symbol = {SYMBOL}',
conn)
#40 days SMA calculation
indicator = SMAIndicator(close=df["close_price"], n=40, fillna=False)
SMA_INDICATOR = "SMA_indicator"
# print(indicator)
df[SMA_INDICATOR] = indicator.sma_indicator()
try:
df['sma_line_Crossover'] = np.where(
df['close_price'] > df['SMA_indicator'], 1, 0)
print(df)
df['sma_line_Crossover'] = np.where(
df['close_price'] < df['SMA_indicator'], -1,
df['sma_line_Crossover'])
print(df)
df['SMA_buy_sell'] = (2 * (np.sign(df['sma_line_Crossover'] -
df['sma_line_Crossover'].shift(1))))
print(df)
except TypeError as e:
df['sma_line_Crossover'] = 111
df['sma_line_Crossover'] = 111
def get_sma(data, days):
"""
Function to get the EMA of a ticker
"""
sma = SMAIndicator(close=data, n=days)
return sma.sma_indicator()
def SMA(df,n):
sma = SMAIndicator(df['Close'],n)
df['SMA-'+str(n)] = sma.sma_indicator()
return df
