def init(self, base_data):
base_data = base_data.sort_values("date")
base_data[f"ma{self.ma_fast_days}"] = SMAIndicator(
base_data["close"], self.ma_fast_days).sma_indicator()
base_data[f"ma{self.ma_slow_days}"] = SMAIndicator(
base_data["close"], self.ma_slow_days).sma_indicator()
base_data["madiff"] = (base_data[f"ma{self.ma_fast_days}"] -
base_data[f"ma{self.ma_slow_days}"])
base_data["bool_signal"] = base_data["madiff"].map(lambda x: 1
if x > 0 else -1)
base_data["bool_signal_shift1"] = (
base_data["bool_signal"].shift(1).fillna(0))
base_data["bool_signal_shift1"] = base_data[
"bool_signal_shift1"].astype(int)
base_data["signal"] = 0
base_data.loc[((base_data["bool_signal"] > 0)
& (base_data["bool_signal_shift1"] < 0)),
"signal", ] = 1
base_data.loc[((base_data["bool_signal"] < 0)
& (base_data["bool_signal_shift1"] > 0)),
"signal", ] = -1
return base_data
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 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 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()
return df
def get_trend_indicators(df, threshold=0.5, plot=False):
df_trend = df.copy()
# add custom trend indicators
df_trend["sma7"] = SMAIndicator(close=df["Close"], window=7, fillna=True).sma_indicator()
df_trend["sma25"] = SMAIndicator(close=df["Close"], window=25, fillna=True).sma_indicator()
df_trend["sma99"] = SMAIndicator(close=df["Close"], window=99, fillna=True).sma_indicator()
df_trend = add_trend_ta(df_trend, high="High", low="Low", close="Close")
return DropCorrelatedFeatures(df_trend, threshold, plot)
def create_trade_sign(self, stock_price: pd.DataFrame) -> pd.DataFrame:
stock_price = stock_price.sort_values("date")
stock_price[f"ma{self.ma_days}"] = SMAIndicator(
stock_price["close"], self.ma_days).sma_indicator()
stock_price["bias"] = (
(stock_price["close"] - stock_price[f"ma{self.ma_days}"]) /
stock_price[f"ma{self.ma_days}"]) * 100
stock_price[f"max_last_k_days{self.last_k_days}"] = (
stock_price["close"].shift(1).rolling(
window=self.last_k_days).max())
stock_price[f"min_last_k_days{self.last_k_days}"] = (
stock_price["close"].shift(1).rolling(
window=self.last_k_days).min())
stock_price["signal"] = 0
stock_price.loc[((stock_price["bias"] < self.bais_lower)
&
(stock_price["close"] >
stock_price[f"max_last_k_days{self.last_k_days}"])),
"signal", ] = 1
stock_price.loc[((stock_price["bias"] > self.bais_upper)
&
(stock_price["close"] <
stock_price[f"min_last_k_days{self.last_k_days}"])),
"signal", ] = -1
return stock_price
def init(self, base_data):
base_data = base_data.sort_values("date")
base_data[f"ma{self.ma_days}"] = SMAIndicator(
base_data["close"], self.ma_days).sma_indicator()
base_data["bias"] = (
(base_data["close"] - base_data[f"ma{self.ma_days}"]) /
base_data[f"ma{self.ma_days}"]) * 100
base_data[f"max_last_k_days{self.last_k_days}"] = (
base_data["close"].shift(1).rolling(window=self.last_k_days).max())
base_data[f"min_last_k_days{self.last_k_days}"] = (
base_data["close"].shift(1).rolling(window=self.last_k_days).min())
base_data["signal"] = 0
base_data.loc[((base_data["bias"] < self.bais_lower)
& (base_data["close"] >
base_data[f"max_last_k_days{self.last_k_days}"])),
"signal", ] = 1
base_data.loc[((base_data["bias"] > self.bais_upper)
& (base_data["close"] <
base_data[f"min_last_k_days{self.last_k_days}"])),
"signal", ] = -1
return base_data
def add_price_moving_features(df, windows=[50, 100, 200]):
for w in windows:
df[f"ma_{w}"] = SMAIndicator(close=df["close"], window=w,
fillna=False).sma_indicator()
df[f"ema_{w}"] = EMAIndicator(close=df["close"],
window=w,
fillna=False).ema_indicator()
return df
def add_indicators(self,data):
warnings.filterwarnings("ignore",category=RuntimeWarning)
# SMA n =20
data['ma'] = SMAIndicator(data['Close'],20).sma_indicator()
data['rsi'] = RSIIndicator(data['Close'],14).rsi()
data['macd'] = MACD(data['Close'],26,12,9).macd()
data['macd_signal'] = MACD(data['Close'],26,12,9).macd_signal()
data['macd_hist'] = MACD(data['Close'],26,12,9).macd_diff()
data['adx'] = ADXIndicator(data['High'],data['Low'],data['Close'],14).adx()
data['adx_neg'] = ADXIndicator(data['High'],data['Low'],data['Close'],14).adx_neg()
data['adx_pos'] = ADXIndicator(data['High'],data['Low'],data['Close'],14).adx_pos()
return data
def create_trade_sign(self, stock_price: pd.DataFrame) -> pd.DataFrame:
stock_price = stock_price.sort_values("date")
stock_price[f"ma{self.ma_fast_days}"] = SMAIndicator(
stock_price["close"], self.ma_fast_days).sma_indicator()
stock_price[f"ma{self.ma_slow_days}"] = SMAIndicator(
stock_price["close"], self.ma_slow_days).sma_indicator()
stock_price["madiff"] = (stock_price[f"ma{self.ma_fast_days}"] -
stock_price[f"ma{self.ma_slow_days}"])
stock_price["bool_signal"] = stock_price["madiff"].map(
lambda x: 1 if x > 0 else -1)
stock_price["bool_signal_shift1"] = (
stock_price["bool_signal"].shift(1).fillna(0))
stock_price["bool_signal_shift1"] = stock_price[
"bool_signal_shift1"].astype(int)
stock_price["signal"] = 0
stock_price.loc[((stock_price["bool_signal"] > 0)
& (stock_price["bool_signal_shift1"] < 0)),
"signal", ] = 1
stock_price.loc[((stock_price["bool_signal"] < 0)
& (stock_price["bool_signal_shift1"] > 0)),
"signal", ] = -1
return stock_price
def create_trade_sign(self, stock_price: pd.DataFrame) -> pd.DataFrame:
stock_price = stock_price.sort_values("date")
stock_price[f"ma{self.ma_days}"] = SMAIndicator(
stock_price["close"], self.ma_days).sma_indicator()
stock_price["bias"] = (
(stock_price["close"] - stock_price[f"ma{self.ma_days}"]) /
stock_price[f"ma{self.ma_days}"]) * 100
stock_price = stock_price.dropna()
stock_price.index = range(len(stock_price))
stock_price["signal"] = stock_price["bias"].map(
lambda x: 1
if x < self.bias_lower else (-1 if x > self.bias_upper else 0))
stock_price["signal"] = stock_price["signal"].fillna(0)
return stock_price
def init(self, base_data):
base_data = base_data.sort_values("date")
base_data[f"ma{self.ma_days}"] = SMAIndicator(
base_data["close"], self.ma_days).sma_indicator()
base_data["bias"] = (
(base_data["close"] - base_data[f"ma{self.ma_days}"]) /
base_data[f"ma{self.ma_days}"]) * 100
base_data = base_data.dropna()
base_data.index = range(len(base_data))
base_data["signal"] = base_data["bias"].map(
lambda x: 1
if x < self.bias_lower else (-1 if x > self.bias_upper else 0))
base_data["signal"] = base_data["signal"].fillna(0)
return base_data
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 getDailyData(self, symbol, since, debug=False):
try:
print("GET DAILY DATA")
print("using key [%d/%d] = %s " %
(Market.keyindex, len(
Market.apikeys), Market.apikeys[Market.keyindex]))
data, meta_data = self.ts[Market.keyindex].get_daily(
symbol=symbol, outputsize='full')
Market.keyindex = (Market.keyindex + 1) % len(Market.apikeys)
self.data = data
self.meta_data = meta_data
self.data = self.data.sort_index()
# map values to standard OHLC format
self.data['O'] = self.data['1. open']
self.data['H'] = self.data['2. high']
self.data['L'] = self.data['3. low']
self.data['C'] = self.data['4. close']
self.data['V'] = self.data['5. volume']
if debug:
print("===VALUES===")
print(self.data)
# add trend indicator.
self.data = ta.utils.dropna(self.data)
self.data['sma20'] = SMAIndicator(close=self.data['C'],
n=20,
fillna=True).sma_indicator()
self.data['sma50'] = SMAIndicator(close=self.data['C'],
n=50,
fillna=True).sma_indicator()
self.data['sma100'] = SMAIndicator(close=self.data['C'],
n=100,
fillna=True).sma_indicator()
self.data['sma200'] = SMAIndicator(close=self.data['C'],
n=200,
fillna=True).sma_indicator()
self.data['ema9'] = EMAIndicator(close=self.data['C'],
n=9,
fillna=True).ema_indicator()
self.data['ema20'] = EMAIndicator(close=self.data['C'],
n=20,
fillna=True).ema_indicator()
self.data = ta.add_all_ta_features(self.data,
open="O",
high="H",
low="L",
close="C",
volume="V",
fillna=True)
self.data = self.data.loc[self.data.index >= since]
candle_names = talib.get_function_groups()['Pattern Recognition']
if debug:
print(candle_names)
included_items = ('CDLDOJI', 'CDLHAMMER', 'CDLEVENINGSTAR',
'CDLHANGINGMAN', 'CDLSHOOTINGSTAR')
candle_names = [
candle for candle in candle_names if candle in included_items
]
for candle in candle_names:
self.data[candle] = getattr(talib, candle)(self.data['O'],
self.data['H'],
self.data['L'],
self.data['C'])
self.data['action'] = 0
if debug:
print("===VALUES===")
print(self.data)
except:
print("ERROR GETTING MARKET DATA")
time.sleep(60)
self.data = None
# max 5 api calls per minutes
#time.sleep(60 / (2 * len(Market.apikeys)))
#time.sleep(60 / 4)
return self.data
df2 = pd.DataFrame()
SYMBOL = "'BPCL '"
df2 = pd.DataFrame()
for i in df_unique_symbols:
#SYMBOL = "'ALPSINDUS '"
#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))))
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
def add_trend_ta(
df: pd.DataFrame,
high: str,
low: str,
close: str,
fillna: bool = False,
colprefix: str = "",
vectorized: bool = False,
) -> pd.DataFrame:
"""Add trend 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.
"""
# MACD
indicator_macd = MACD(close=df[close],
window_slow=26,
window_fast=12,
window_sign=9,
fillna=fillna)
df[f"{colprefix}trend_macd"] = indicator_macd.macd()
df[f"{colprefix}trend_macd_signal"] = indicator_macd.macd_signal()
df[f"{colprefix}trend_macd_diff"] = indicator_macd.macd_diff()
# SMAs
df[f"{colprefix}trend_sma_fast"] = SMAIndicator(
close=df[close], window=12, fillna=fillna).sma_indicator()
df[f"{colprefix}trend_sma_slow"] = SMAIndicator(
close=df[close], window=26, fillna=fillna).sma_indicator()
# EMAs
df[f"{colprefix}trend_ema_fast"] = EMAIndicator(
close=df[close], window=12, fillna=fillna).ema_indicator()
df[f"{colprefix}trend_ema_slow"] = EMAIndicator(
close=df[close], window=26, fillna=fillna).ema_indicator()
# Vortex Indicator
indicator_vortex = VortexIndicator(high=df[high],
low=df[low],
close=df[close],
window=14,
fillna=fillna)
df[f"{colprefix}trend_vortex_ind_pos"] = indicator_vortex.vortex_indicator_pos(
)
df[f"{colprefix}trend_vortex_ind_neg"] = indicator_vortex.vortex_indicator_neg(
)
df[f"{colprefix}trend_vortex_ind_diff"] = indicator_vortex.vortex_indicator_diff(
)
# TRIX Indicator
df[f"{colprefix}trend_trix"] = TRIXIndicator(close=df[close],
window=15,
fillna=fillna).trix()
# Mass Index
df[f"{colprefix}trend_mass_index"] = MassIndex(high=df[high],
low=df[low],
window_fast=9,
window_slow=25,
fillna=fillna).mass_index()
# DPO Indicator
df[f"{colprefix}trend_dpo"] = DPOIndicator(close=df[close],
window=20,
fillna=fillna).dpo()
# KST Indicator
indicator_kst = KSTIndicator(
close=df[close],
roc1=10,
roc2=15,
roc3=20,
roc4=30,
window1=10,
window2=10,
window3=10,
window4=15,
nsig=9,
fillna=fillna,
)
df[f"{colprefix}trend_kst"] = indicator_kst.kst()
df[f"{colprefix}trend_kst_sig"] = indicator_kst.kst_sig()
df[f"{colprefix}trend_kst_diff"] = indicator_kst.kst_diff()
# Ichimoku Indicator
indicator_ichi = IchimokuIndicator(
high=df[high],
low=df[low],
window1=9,
window2=26,
window3=52,
visual=False,
fillna=fillna,
)
df[f"{colprefix}trend_ichimoku_conv"] = indicator_ichi.ichimoku_conversion_line(
)
df[f"{colprefix}trend_ichimoku_base"] = indicator_ichi.ichimoku_base_line()
df[f"{colprefix}trend_ichimoku_a"] = indicator_ichi.ichimoku_a()
df[f"{colprefix}trend_ichimoku_b"] = indicator_ichi.ichimoku_b()
# Schaff Trend Cycle (STC)
df[f"{colprefix}trend_stc"] = STCIndicator(
close=df[close],
window_slow=50,
window_fast=23,
cycle=10,
smooth1=3,
smooth2=3,
fillna=fillna,
).stc()
if not vectorized:
# Average Directional Movement Index (ADX)
indicator_adx = ADXIndicator(high=df[high],
low=df[low],
close=df[close],
window=14,
fillna=fillna)
df[f"{colprefix}trend_adx"] = indicator_adx.adx()
df[f"{colprefix}trend_adx_pos"] = indicator_adx.adx_pos()
df[f"{colprefix}trend_adx_neg"] = indicator_adx.adx_neg()
# CCI Indicator
df[f"{colprefix}trend_cci"] = CCIIndicator(
high=df[high],
low=df[low],
close=df[close],
window=20,
constant=0.015,
fillna=fillna,
).cci()
# Ichimoku Visual Indicator
indicator_ichi_visual = IchimokuIndicator(
high=df[high],
low=df[low],
window1=9,
window2=26,
window3=52,
visual=True,
fillna=fillna,
)
df[f"{colprefix}trend_visual_ichimoku_a"] = indicator_ichi_visual.ichimoku_a(
)
df[f"{colprefix}trend_visual_ichimoku_b"] = indicator_ichi_visual.ichimoku_b(
)
# Aroon Indicator
indicator_aroon = AroonIndicator(close=df[close],
window=25,
fillna=fillna)
df[f"{colprefix}trend_aroon_up"] = indicator_aroon.aroon_up()
df[f"{colprefix}trend_aroon_down"] = indicator_aroon.aroon_down()
df[f"{colprefix}trend_aroon_ind"] = indicator_aroon.aroon_indicator()
# PSAR Indicator
indicator_psar = PSARIndicator(
high=df[high],
low=df[low],
close=df[close],
step=0.02,
max_step=0.20,
fillna=fillna,
)
# df[f'{colprefix}trend_psar'] = indicator.psar()
df[f"{colprefix}trend_psar_up"] = indicator_psar.psar_up()
df[f"{colprefix}trend_psar_down"] = indicator_psar.psar_down()
df[f"{colprefix}trend_psar_up_indicator"] = indicator_psar.psar_up_indicator(
)
df[f"{colprefix}trend_psar_down_indicator"] = indicator_psar.psar_down_indicator(
)
return df
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)
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()
def getDailyData(self, symbol, since, debug=False):
#try:
print("GET IBKR INTRADAY DATA")
now = datetime.now()
print("CREATE CONTRACT FOR %s" % symbol)
contract = IBMarket.conn.createStockContract(symbol)
dirname = os.path.dirname(__file__)
filename = "data/daily/" + symbol + "_" + now.strftime(
"%Y_%m_%d") + "_1_day.csv"
if os.path.isfile(filename) == True:
os.remove(filename)
print("REQUEST HISTORICAL DATA FOR %s SINCE %s" % (symbol, since))
IBMarket.market_data_available[symbol] = False
IBMarket.conn.requestHistoricalData(contract,
rth=self.rth,
resolution="1 day",
lookback="12 M",
csv_path='data/daily/')
print("WAIT FOR DATA...")
while IBMarket.market_data_available[symbol] == False:
time.sleep(5)
IBMarket.market_data_available[symbol] = False
print("RENAME CSV FILE TO %s" % filename)
os.rename("data/daily/" + symbol + ".csv", filename)
print("READ CSV FILE:%s" % filename)
self.data = pd.read_csv(filename,
index_col='datetime',
parse_dates=True)
self.data = self.data.sort_index()
if debug:
print("===VALUES===")
print(self.data)
print("===CALCULATE ALL TA===")
self.data = ta.utils.dropna(self.data)
self.data['sma20'] = SMAIndicator(close=self.data['C'],
n=20,
fillna=True).sma_indicator()
self.data['sma50'] = SMAIndicator(close=self.data['C'],
n=50,
fillna=True).sma_indicator()
self.data['sma100'] = SMAIndicator(close=self.data['C'],
n=100,
fillna=True).sma_indicator()
self.data['sma200'] = SMAIndicator(close=self.data['C'],
n=200,
fillna=True).sma_indicator()
self.data['ema9'] = EMAIndicator(close=self.data['C'],
n=9,
fillna=True).ema_indicator()
self.data['ema20'] = EMAIndicator(close=self.data['C'],
n=20,
fillna=True).ema_indicator()
self.data = ta.add_all_ta_features(self.data,
open="O",
high="H",
low="L",
close="C",
volume="V",
fillna=True)
self.data = self.data.loc[self.data.index >= since]
print("===CALCULATE DOJI===")
candle_names = talib.get_function_groups()['Pattern Recognition']
included_items = ('CDLDOJI', 'CDLHAMMER', 'CDLEVENINGSTAR',
'CDLHANGINGMAN', 'CDLSHOOTINGSTAR')
candle_names = [
candle for candle in candle_names if candle in included_items
]
for candle in candle_names:
self.data[candle] = getattr(talib,
candle)(self.data['O'], self.data['H'],
self.data['L'], self.data['C'])
if debug:
print("===VALUES===")
print(self.data[[
'C', 'V', 'WAP', 'sma20', 'sma50', 'sma100', 'sma200',
'momentum_rsi', 'trend_cci', 'momentum_stoch_signal',
'trend_adx'
]])
#except:
# print("ERROR GETTING MARKET DATA")
# time.sleep(60)
# self.data = None
#30 request / 10 mins -> need to wait 20 secs
time.sleep(20)
return self.data
def getIntraDayData(self, symbol, since, period, live=False, debug=False):
#try:
print("GET IBKR INTRADAY DATA")
if live == True:
lookback = int(period * 1600 / 60 / 24)
else:
dtime = datetime.now() - datetime.strptime(since, '%Y-%m-%d')
#lookback = dtime.total_seconds() / 60 / 60 / 24
lookback = 7
now = datetime.now()
print("CREATE CONTRACT FOR %s" % symbol)
contract = IBMarket.conn.createStockContract(symbol)
dirname = os.path.dirname(__file__)
filename = "data/intraday/" + symbol + "_" + now.strftime(
"%Y_%m_%d") + "_" + str(period) + "_min.csv"
if os.path.isfile(filename) == True:
os.remove(filename)
print(
"REQUEST HISTORICAL DATA FOR %s, LOOKBACK %d DAYS, SINCE %s, RTH:%d"
% (symbol, lookback, since, self.rth))
IBMarket.market_data_available[symbol] = False
IBMarket.conn.requestHistoricalData(contract,
rth=self.rth,
resolution=str(period) + " mins",
lookback=str(lookback) + " D",
csv_path='data/intraday/')
print("WAIT FOR DATA...")
while IBMarket.market_data_available[symbol] == False:
time.sleep(5)
IBMarket.market_data_available[symbol] = False
print("RENAME CSV FILE")
os.rename("data/intraday/" + symbol + ".csv", filename)
print("READ CSV DATA")
self.data = pd.read_csv(filename,
index_col='datetime',
parse_dates=True)
self.data = self.data.sort_index()
self.data = self.data[:-1]
if debug:
print("===VALUES===")
print(self.data)
print("===CALCULATE SMA===")
# add trend indicator.
#self.data['sma20'] = self.data['C'].rolling(20, min_periods=20).mean()
#self.data['sma50'] = self.data['C'].rolling(50, min_periods=50).mean()
#self.data['sma100'] = self.data['C'].rolling(100, min_periods=100).mean()
#self.data['sma200'] = self.data['C'].rolling(200, min_periods=200).mean()
print("===CALCULATE ALL TA===")
self.data = ta.utils.dropna(self.data)
self.data['sma20'] = SMAIndicator(close=self.data['C'],
n=20,
fillna=True).sma_indicator()
self.data['sma50'] = SMAIndicator(close=self.data['C'],
n=50,
fillna=True).sma_indicator()
self.data['sma100'] = SMAIndicator(close=self.data['C'],
n=100,
fillna=True).sma_indicator()
self.data['sma200'] = SMAIndicator(close=self.data['C'],
n=200,
fillna=True).sma_indicator()
self.data['ema9'] = EMAIndicator(close=self.data['C'],
n=9,
fillna=True).ema_indicator()
self.data['ema20'] = EMAIndicator(close=self.data['C'],
n=20,
fillna=True).ema_indicator()
self.data = ta.add_all_ta_features(self.data,
open="O",
high="H",
low="L",
close="C",
volume="V",
fillna=True)
self.data['mvwap'] = self.data['volume_vwap'].rolling(
14, min_periods=14).mean()
self.data = self.data.loc[self.data.index >= since]
print("===CALCULATE DOJI===")
candle_names = talib.get_function_groups()['Pattern Recognition']
if debug:
print(candle_names)
included_items = ('CDLDOJI', 'CDLHAMMER', 'CDLEVENINGSTAR',
'CDLHANGINGMAN', 'CDLSHOOTINGSTAR')
candle_names = [
candle for candle in candle_names if candle in included_items
]
for candle in candle_names:
self.data[candle] = getattr(talib,
candle)(self.data['O'], self.data['H'],
self.data['L'], self.data['C'])
if debug:
print("===VALUES===")
print(self.data)
#except:
# print("ERROR GETTING MARKET DATA")
# time.sleep(60)
# self.data = None
return self.data
def add_trend_ta(df: pd.DataFrame,
high: str,
low: str,
close: str,
fillna: bool = False,
colprefix: str = "") -> pd.DataFrame:
"""Add trend 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.
"""
# MACD
indicator_macd = MACD(close=df[close],
n_slow=26,
n_fast=12,
n_sign=9,
fillna=fillna)
df[f'{colprefix}trend_macd'] = indicator_macd.macd()
df[f'{colprefix}trend_macd_signal'] = indicator_macd.macd_signal()
df[f'{colprefix}trend_macd_diff'] = indicator_macd.macd_diff()
# SMAs
df[f'{colprefix}trend_sma_fast'] = SMAIndicator(
close=df[close], n=12, fillna=fillna).sma_indicator()
df[f'{colprefix}trend_sma_slow'] = SMAIndicator(
close=df[close], n=26, fillna=fillna).sma_indicator()
# EMAs
df[f'{colprefix}trend_ema_fast'] = EMAIndicator(
close=df[close], n=12, fillna=fillna).ema_indicator()
df[f'{colprefix}trend_ema_slow'] = EMAIndicator(
close=df[close], n=26, fillna=fillna).ema_indicator()
# Average Directional Movement Index (ADX)
indicator = ADXIndicator(high=df[high],
low=df[low],
close=df[close],
n=14,
fillna=fillna)
df[f'{colprefix}trend_adx'] = indicator.adx()
df[f'{colprefix}trend_adx_pos'] = indicator.adx_pos()
df[f'{colprefix}trend_adx_neg'] = indicator.adx_neg()
# Vortex Indicator
indicator = VortexIndicator(high=df[high],
low=df[low],
close=df[close],
n=14,
fillna=fillna)
df[f'{colprefix}trend_vortex_ind_pos'] = indicator.vortex_indicator_pos()
df[f'{colprefix}trend_vortex_ind_neg'] = indicator.vortex_indicator_neg()
df[f'{colprefix}trend_vortex_ind_diff'] = indicator.vortex_indicator_diff()
# TRIX Indicator
indicator = TRIXIndicator(close=df[close], n=15, fillna=fillna)
df[f'{colprefix}trend_trix'] = indicator.trix()
# Mass Index
indicator = MassIndex(high=df[high],
low=df[low],
n=9,
n2=25,
fillna=fillna)
df[f'{colprefix}trend_mass_index'] = indicator.mass_index()
# CCI Indicator
indicator = CCIIndicator(high=df[high],
low=df[low],
close=df[close],
n=20,
c=0.015,
fillna=fillna)
df[f'{colprefix}trend_cci'] = indicator.cci()
# DPO Indicator
indicator = DPOIndicator(close=df[close], n=20, fillna=fillna)
df[f'{colprefix}trend_dpo'] = indicator.dpo()
# KST Indicator
indicator = KSTIndicator(close=df[close],
r1=10,
r2=15,
r3=20,
r4=30,
n1=10,
n2=10,
n3=10,
n4=15,
nsig=9,
fillna=fillna)
df[f'{colprefix}trend_kst'] = indicator.kst()
df[f'{colprefix}trend_kst_sig'] = indicator.kst_sig()
df[f'{colprefix}trend_kst_diff'] = indicator.kst_diff()
# Ichimoku Indicator
indicator = IchimokuIndicator(high=df[high],
low=df[low],
n1=9,
n2=26,
n3=52,
visual=False,
fillna=fillna)
df[f'{colprefix}trend_ichimoku_a'] = indicator.ichimoku_a()
df[f'{colprefix}trend_ichimoku_b'] = indicator.ichimoku_b()
indicator = IchimokuIndicator(high=df[high],
low=df[low],
n1=9,
n2=26,
n3=52,
visual=True,
fillna=fillna)
df[f'{colprefix}trend_visual_ichimoku_a'] = indicator.ichimoku_a()
df[f'{colprefix}trend_visual_ichimoku_b'] = indicator.ichimoku_b()
# Aroon Indicator
indicator = AroonIndicator(close=df[close], n=25, fillna=fillna)
df[f'{colprefix}trend_aroon_up'] = indicator.aroon_up()
df[f'{colprefix}trend_aroon_down'] = indicator.aroon_down()
df[f'{colprefix}trend_aroon_ind'] = indicator.aroon_indicator()
# PSAR Indicator
indicator = PSARIndicator(high=df[high],
low=df[low],
close=df[close],
step=0.02,
max_step=0.20,
fillna=fillna)
df[f'{colprefix}trend_psar'] = indicator.psar()
df[f'{colprefix}trend_psar_up'] = indicator.psar_up()
df[f'{colprefix}trend_psar_down'] = indicator.psar_down()
df[f'{colprefix}trend_psar_up_indicator'] = indicator.psar_up_indicator()
df[f'{colprefix}trend_psar_down_indicator'] = indicator.psar_down_indicator(
)
return df
def get_sma(data, days):
"""
Function to get the EMA of a ticker
"""
sma = SMAIndicator(close=data, n=days)
return sma.sma_indicator()
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 SMA(df,n):
sma = SMAIndicator(df['Close'],n)
df['SMA-'+str(n)] = sma.sma_indicator()
return df
