def unit_quote_retrive(self, company: str) -> pd.DataFrame:
logger.info(f"Retriving Detail Quote data for {company}")
try:
return DataRetrive.single_company_quote(f"{company}.NS")
except (KeyError, IndexError, ValueError):
logger.warning(f"Cannot retrive data for {company}")
return "Invalid"
def _parallel_ema_indicator_n3(
self,
company: str,
ema_canditate: Tuple[int, int] = (5, 13, 26),
cutoff_date: Union[str, datetime.datetime] = 'today',
verbosity: int = 1):
logger.info(f"Retriving data for {company}")
company_df = DataRetrive.single_company_complete(
company_name=f"{company}.NS")
if company_df['Close'].isnull().sum() != 0:
logger.warning(f"{company} have some missing value, fixing it")
company_df.dropna(inplace=True)
try:
EMA_A = exponential_moving_avarage(data_df=company_df,
cutoff_date=cutoff_date,
period=ema_canditate[0],
verbosity=verbosity)
EMA_B = exponential_moving_avarage(data_df=company_df,
cutoff_date=cutoff_date,
period=ema_canditate[1],
verbosity=verbosity)
EMA_C = exponential_moving_avarage(data_df=company_df,
cutoff_date=cutoff_date,
period=ema_canditate[2],
verbosity=verbosity)
percentage_diff_cb = percentage_diff_analysis(EMA_C, EMA_B)
percentage_diff_ca = percentage_diff_analysis(EMA_C, EMA_A)
percentage_diff_ba = percentage_diff_analysis(EMA_B, EMA_A)
if (percentage_diff_cb < 1) and (percentage_diff_ca <
1) and (percentage_diff_ba < 1):
action = 'buy'
else:
action = 'sell'
except (KeyError, IndexError, ValueError):
logger.warning(f"{company} has less record than minimum required")
EMA_A, EMA_B, EMA_C, action = pd.NA, pd.NA, pd.NA, pd.NA
return {
'company':
company,
'ema_date':
now_strting
if cutoff_date == 'today' else cutoff_date.strftime('%d-%m-%Y'),
f'ema{str(ema_canditate[0])}':
EMA_A,
f'ema{str(ema_canditate[1])}':
EMA_B,
f'ema{str(ema_canditate[2])}':
EMA_C,
# 'percentage_diffCB': percentage_diff_cb,
# 'percentage_diffCA': percentage_diff_ca,
# 'percentage_diffBA': percentage_diff_ba,
'action':
action
}
def unit_momentum(self, company: str, start, end, verbosity: int = 1):
logger.info(f"Retriving data for {company}")
try:
company_df = DataRetrive.single_company_specific(
company_name=f"{company}.NS", start_date=start, end_date=end
)
company_df.reset_index(inplace=True)
ar_yearly = annualized_rate_of_return(
end_date=company_df.iloc[-1].Close,
start_date=company_df.iloc[0].Close,
duration=1,
) # (company_df.iloc[-30,0] - company_df.iloc[0,0]).days/365)
ar_monthly = annualized_rate_of_return(
end_date=company_df.iloc[-1].Close,
start_date=get_appropriate_date_momentum(
company_df, company, verbosity=verbosity
)[1],
duration=(company_df.iloc[-1, 0] - company_df.iloc[-30, 0]).days / 30,
)
monthly_start_date = get_appropriate_date_momentum(
company_df, company, verbosity=0
)[0].strftime("%d-%m-%Y")
except (IndexError, KeyError, ValueError):
if verbosity > 0:
logger.debug(f"Data is not available for: {company}")
company_df = pd.DataFrame(
{"Date": [datetime.datetime(1000, 1, 1)] * 30, "Close": [pd.NA] * 30}
)
ar_yearly, ar_monthly, monthly_start_date = pd.NA, pd.NA, pd.NA
return {
"company": company,
"yearly_start_date": company_df.iloc[0].Date.strftime("%d-%m-%Y"),
"yearly_start_date_close": company_df.iloc[0].Close,
"yearly_end_date": company_df.iloc[-1].Date.strftime("%d-%m-%Y"),
"yearly_end_date_close": company_df.iloc[-1].Close,
"return_yearly": ar_yearly,
"monthly_start_date": monthly_start_date,
"monthly_start_date_close": company_df.iloc[-30].Close,
"monthly_end_date": company_df.iloc[-1].Date.strftime("%d-%m-%Y"),
"monthly_end_date_close": company_df.iloc[-1].Close,
"return_monthly": ar_monthly,
}
def unit_vol_indicator_n_days(self, company: str = None, duration: int = 90):
end = datetime.datetime.now()
start = end - dateutil.relativedelta.relativedelta(days=duration)
logger.info(f"Retriving data for {company}")
company_df = DataRetrive.single_company_specific(
company_name=f"{company}.NS", start_date=start, end_date=end
)
buy_stock = company_df.iloc[-1].Volume > company_df["Volume"].mean()
print(f"Problem with {company}, moving on")
return {
"company": company,
"current date": company_df.index[-1].strftime("%d-%m-%Y"),
"start date": company_df.index[0].strftime("%d-%m-%Y"),
"current volume": company_df.iloc[-1].Volume,
"mean volume": company_df["Volume"].mean(),
"close price": company_df.iloc[-1].Close,
"action": buy_stock,
}
def _parallel_vol_indicator_n_days(self,
company: str = None,
duration: int = 90):
end = datetime.datetime.now()
start = end - dateutil.relativedelta.relativedelta(days=duration)
logger.info(f"Retriving data for {company}")
company_df = DataRetrive.single_company_specific(
company_name=f"{company}.NS", start_date=start, end_date=end)
buy_stock = company_df.iloc[-1].Volume > company_df['Volume'].mean()
print(f"Problem with {company}, moving on")
return {
'company': company,
'current date': company_df.index[-1].strftime('%d-%m-%Y'),
'start date': company_df.index[0].strftime('%d-%m-%Y'),
'current volume': company_df.iloc[-1].Volume,
'mean volume': company_df['Volume'].mean(),
'close price': company_df.iloc[-1].Close,
'action': buy_stock
}
def _parallel_ema_indicator(self,
company: str = None,
ema_canditate: Tuple[int, int] = (50, 200),
cutoff_date: Union[
str, datetime.datetime] = 'today',
verbosity: int = 1) -> Dict:
logger.info(f"Retriving data for {company}")
company_df = DataRetrive.single_company_complete(
company_name=f"{company}.NS")
if company_df['Close'].isnull().sum() != 0:
logger.warning(f"{company} have some missing value, fixing it")
company_df.dropna(inplace=True)
try:
EMA_A = exponential_moving_avarage(data_df=company_df,
cutoff_date=cutoff_date,
period=ema_canditate[0],
verbosity=verbosity)
EMA_B = exponential_moving_avarage(data_df=company_df,
cutoff_date=cutoff_date,
period=ema_canditate[1],
verbosity=verbosity)
if EMA_A > EMA_B:
action = 'buy'
else:
action = 'sell'
except (KeyError, IndexError, ValueError):
logger.warning(f"{company} has less record than minimum rexquired")
EMA_A, EMA_B, action = pd.NA, pd.NA, pd.NA
return {
'company':
company,
'ema_date':
now_strting
if cutoff_date == 'today' else cutoff_date.strftime('%d-%m-%Y'),
f'ema{str(ema_canditate[0])}':
EMA_A,
f'ema{str(ema_canditate[1])}':
EMA_B,
'action':
action
}
def unit_ema_indicator(
self,
company: str = None,
ema_canditate: Tuple[int, int] = (50, 200),
cutoff_date: Union[str, datetime.datetime] = "today",
verbosity: int = 1,
) -> Dict:
logger.info(f"Retriving data for {company}")
company_df = DataRetrive.single_company_complete(company_name=f"{company}.NS")
if company_df["Close"].isnull().sum() != 0:
logger.warning(f"{company} have some missing value, fixing it")
company_df.dropna(inplace=True)
try:
EMA_A = exponential_moving_average(
data_df=company_df,
cutoff_date=cutoff_date,
period=ema_canditate[0],
verbosity=verbosity,
)
EMA_B = exponential_moving_average(
data_df=company_df,
cutoff_date=cutoff_date,
period=ema_canditate[1],
verbosity=verbosity,
)
if EMA_A > EMA_B:
action = "buy"
else:
action = "sell"
except (KeyError, IndexError, ValueError):
logger.warning(f"{company} has less record than minimum rexquired")
EMA_A, EMA_B, action = pd.NA, pd.NA, pd.NA
return {
"company": company,
"ema_date": now_strting
if cutoff_date == "today"
else cutoff_date.strftime("%d-%m-%Y"),
f"ema{str(ema_canditate[0])}": EMA_A,
f"ema{str(ema_canditate[1])}": EMA_B,
"action": action,
}
def _parallel_quote_retrive(self, company: str) -> pd.DataFrame:
logger.info(f"Retriving Detail Quote data for {company}")
return DataRetrive.single_company_quote(f'{company}.NS')
def ema_crossover_indicator_detail(self,
ema_canditate: Tuple[int, int,
int] = (5, 13, 26),
save: bool = True,
export_path: str = '.',
verbosity: int = 1) -> pd.DataFrame:
"""Exponential moving average for crossover triple period technique
Parameters
----------
ema_canditate : Tuple[int, int, int], optional
Three Period (or days) to calculate EMA, by default (5,13,26)
save : bool, optional
Save to hard disk, by default True
export_path : str, optional
Path to save, to be used only if 'save' is true, by default '.'
verbosity : int, optional
Level of detail logging,1=< Deatil, 0=Less detail , by default 1
Returns
-------
pd.DataFrame
"""
logger.info("Performing EMA Indicator Task")
ema_short = self._ema_indicator_n3(ema_canditate=ema_canditate,
verbosity=verbosity)
logger.info("Extarcting detail company quote data")
batch_company_quote = pd.DataFrame()
with multiprocessing.Pool(multiprocessing.cpu_count() - 1) as pool:
company_quote = pool.map(self._parallel_quote_retrive,
ema_short['company'])
for single_company_quote in company_quote:
batch_company_quote = batch_company_quote.append(
single_company_quote)
batch_company_quote = batch_company_quote.reset_index().rename(
columns={'index': 'company'})
batch_company_quote = batch_company_quote[[
'company', 'longName', 'price', 'regularMarketVolume', 'marketCap',
'bookValue', 'priceToBook', 'averageDailyVolume3Month',
'averageDailyVolume10Day', 'fiftyTwoWeekLowChange',
'fiftyTwoWeekLowChangePercent', 'fiftyTwoWeekRange',
'fiftyTwoWeekHighChange', 'fiftyTwoWeekHighChangePercent',
'fiftyTwoWeekLow', 'fiftyTwoWeekHigh'
]]
batch_company_quote['company'] = batch_company_quote[
'company'].str.replace('.NS', '')
ema_quote = ema_short.merge(batch_company_quote,
on='company',
validate='1:1')
if verbosity > 0:
logger.debug(f"Here are sample 5 company\n{ema_quote.head()}")
if save is not False:
ema_quote.to_csv(
f"{export_path}/ema_indicator_detail{str(ema_canditate[0])}-{str(ema_canditate[1])}_{len(self.data['company'])}company_{now_strting}.csv",
index=False)
if verbosity > 0:
logger.debug(
f"Exported at {export_path}/ema_indicator_detail{str(ema_canditate[0])}-{str(ema_canditate[1])}_{len(self.data['company'])}company_{now_strting}.csv"
)
else:
return ema_quote
def relative_momentum_with_ema(
self,
end_date: str = "today",
top_company_count: int = 20,
ema_canditate: Tuple[int, int] = (50, 200),
save: bool = True,
export_path: str = ".",
verbosity: int = 1,
) -> Optional[pd.DataFrame]:
"""The strategy is used to identity stocks with 'good performance'
based on desired 'return' duration and 'exponential moving avg'.
Args:
end_date (str, optional): End date of of stock record to retrive. Must be in format: dd/mm/yyyy. Defaults to 'today'.
top_company_count (int, optional): No of top company to retrieve based on Annualized return. Defaults to 20.
ema_canditate (Tuple[int, int], optional): Period (or days) to calculate EMA. Defaults to (50, 200).
save (bool, optional): Wether to export to disk. Defaults to True.
export_path (str, optional): Path to export csv.To be used only if 'save' is True. Defaults to '.'.
verbosity (int, optional): Level of detail logging,1=< Deatil, 0=Less detail. Defaults to 1.
Returns:
Record based on monthly and yearly calculation and EMA calculation
Example:
```python
from stock_analysis import MomentumStrategy
sa = MomentumStrategy('./data/company_list.yaml')
mes = sa.relative_momentum_with_ema('01/06/2020', 30)
```
"""
logger.info("Performing Momentum Strategy task")
momentum_df = self.relative_momentum(
end_date=end_date,
top_company_count=top_company_count,
save=False,
verbosity=verbosity,
)
momentum_df.reset_index(drop=True, inplace=True)
ind = Indicator(company_name=momentum_df.loc[:, "company"])
logger.info(
f"Performing EMA task on top {top_company_count} company till {end_date}"
)
if end_date == "today":
cutoff_date = end_date
save_date = datetime.datetime.now().strftime("%d-%m-%Y")
else:
save_date = end_date.replace("/", "-")
cutoff_date = datetime.datetime.strptime(end_date, "%d/%m/%Y")
assert isinstance(cutoff_date,
datetime.datetime), "Incorrect date type"
ema_df = ind.ema_indicator(
ema_canditate=ema_canditate,
cutoff_date=cutoff_date,
save=False,
verbosity=verbosity,
)
momentum_ema_df = momentum_df.merge(ema_df,
on="company",
validate="1:1")
if save is True:
new_folder(export_path)
momentum_ema_df.reset_index(drop=True, inplace=True)
momentum_ema_df.to_csv(
f"{export_path}/momentum_ema{ema_canditate[0]}-{ema_canditate[1]}_{save_date}_top_{top_company_count}.csv",
index=False,
)
logger.debug(
f"Saved at {export_path}/momentum_ema{ema_canditate[0]}-{ema_canditate[1]}_{save_date}_top_{top_company_count}.csv"
)
if verbosity > 0:
logger.debug(f"Sample output:\n{momentum_ema_df.head()}")
else:
return momentum_ema_df
def momentum_with_ema_strategy(self,
end_date: str = 'today',
top_company_count: int = 20,
ema_canditate: Tuple[int, int] = (50, 200),
save: bool = True,
export_path: str = '.',
verbosity: int = 1) -> pd.DataFrame:
"""The strategy is used to identity stocks with 'good performance'
based on desired 'return' duration and 'exponential moving avg'.
Parameters
----------
end_date : str, optional
End date of of stock record to retrive.
Must be in format: dd/mm/yyyy, by default 'today' for current date
top_company_count : int, optional
No of top company to retrieve based on Annualized return,
by default 20
ema_canditate : Tuple[int, int], optional
Period (or days) to calculate EMA, by default (50,200)
save : int, optional
Wether to export to disk, by default True
export_path : str, optional
Path to export csv.To be used only if 'save' is True,by default'.'
verbosity : int, optional
Level of detail logging,1=< Deatil, 0=Less detail , by default 1
Returns
-------
pd.DataFrame
Record based on monthly and yearly calculation and EMA calculation
"""
logger.info("Performing Momentum Strategy task")
momentum_df = self.momentum_strategy(
end_date=end_date,
top_company_count=top_company_count,
save=False,
verbosity=verbosity)
momentum_df.reset_index(drop=True, inplace=True)
ind = Indicator(company_name=momentum_df.loc[:, 'company'])
logger.info(
f"Performing EMA task on top {top_company_count} company till {end_date}"
)
if end_date == 'today':
cutoff_date = end_date
save_date = datetime.datetime.now().strftime('%d-%m-%Y')
else:
save_date = end_date.replace('/', '-')
cutoff_date = datetime.datetime.strptime(end_date, '%d/%m/%Y')
assert isinstance(cutoff_date,
datetime.datetime), 'Incorrect date type'
ema_df = ind.ema_indicator(ema_canditate=ema_canditate,
cutoff_date=cutoff_date,
save=False,
verbosity=verbosity)
momentum_ema_df = momentum_df.merge(ema_df,
on='company',
validate='1:1')
if save is True:
momentum_ema_df.reset_index(drop=True, inplace=True)
momentum_ema_df.to_csv(
f"{export_path}/momentum_ema{ema_canditate[0]}-{ema_canditate[1]}_{save_date}_top_{top_company_count}.csv",
index=False)
logger.debug(
f"Saved at {export_path}/momentum_ema{ema_canditate[0]}-{ema_canditate[1]}_{save_date}_top_{top_company_count}.csv"
)
if verbosity > 0:
logger.debug(f"Sample output:\n{momentum_ema_df.head()}")
else:
return momentum_ema_df
def test_company_name():
logger.info("Loading")
with open(f"{data_path}/sample_company.yaml", "r") as f:
data = yaml.load(f, Loader=yaml.FullLoader)
assert isinstance(data["company"], list)
def unit_dma_absolute(
self,
company: str = None,
end_date: Union[str, datetime.datetime] = "today",
period: int = 200,
cutoff: int = 5,
) -> Dict:
logger.info(f"Retriving data for {company}")
if end_date == "today":
cutoff_date = datetime.datetime.today()
else:
cutoff_date = datetime.datetime.strptime(end_date, "%d/%m/%Y").date()
start_date = cutoff_date - dateutil.relativedelta.relativedelta(months=18)
try:
company_df = DataRetrive.single_company_specific(
company_name=f"{company}.NS",
start_date=start_date,
end_date=cutoff_date,
)
if company_df["Close"].isnull().sum() != 0:
logger.warning(f"{company} have some missing value, fixing it")
company_df.dropna(inplace=True)
except (KeyError, ValueError, IndexError):
company_df = pd.DataFrame(
{
"Open": pd.NA,
"High": pd.NA,
"Low": pd.NA,
"Close": pd.NA,
"Adj Close": pd.NA,
"Volume": pd.NA,
},
index=["Date"],
)
action = "Invalid"
try:
sma = simple_moving_average(company_df["Close"][-(period - 1) :], period)
turnover_value = turnover(company_df["Volume"][-period:], sma) / 10000000
buy = sma + (sma * (cutoff / 100))
sell = sma - (sma * (cutoff / 100))
if (buy < company_df["Close"][-1]) and (turnover_value > 1):
action = "buy"
elif (sell > company_df["Close"][-1]) and (turnover_value > 1):
action = "sell"
elif (sell < company_df["Close"][-1] < buy) and (turnover_value < 1):
action = "no action"
long_name = self.unit_quote_retrive(company)["longName"][0]
current_price = company_df["Close"][-1]
except (KeyError, IndexError, ValueError, TypeError, ZeroDivisionError):
logger.warning(f"{company} has less record than minimum rexquired")
long_name, sma, current_price, action, turnover_value, buy, sell = (
f"{company} (Invalid name)",
"Invalid",
"Invalid",
"Invalid",
"Invalid",
"Invalid",
"Invalid",
)
company = f"Problem with {company}"
return {
"company name": long_name,
"nse symbol": company,
"sma_date": now_strting
if cutoff_date == "today"
else cutoff_date.strftime("%d-%m-%Y"),
"current price": current_price,
"sma": sma,
"ideal buy": buy,
"ideal sell": sell,
"turnover in cr.": turnover_value,
"action": action,
}
def unit_ema_indicator_n3(
self,
company: str,
ema_canditate: Tuple[int, int] = (5, 13, 26),
cutoff_date: Union[str, datetime.datetime] = "today",
verbosity: int = 1,
) -> Dict:
logger.info(f"Retriving data for {company}")
company_df = DataRetrive.single_company_complete(company_name=f"{company}.NS")
if company_df["Close"].isnull().sum() != 0:
logger.warning(f"{company} have some missing value, fixing it")
company_df.dropna(inplace=True)
try:
EMA_A = exponential_moving_average(
data_df=company_df,
cutoff_date=cutoff_date,
period=ema_canditate[0],
verbosity=verbosity,
)
EMA_B = exponential_moving_average(
data_df=company_df,
cutoff_date=cutoff_date,
period=ema_canditate[1],
verbosity=verbosity,
)
EMA_C = exponential_moving_average(
data_df=company_df,
cutoff_date=cutoff_date,
period=ema_canditate[2],
verbosity=verbosity,
)
percentage_diff_cb = percentage_diff(EMA_C, EMA_B, return_absolute=True)
percentage_diff_ca = percentage_diff(EMA_C, EMA_A, return_absolute=True)
percentage_diff_ba = percentage_diff(EMA_B, EMA_A, return_absolute=True)
if (
(percentage_diff_cb < 1)
and (percentage_diff_ca < 1)
and (percentage_diff_ba < 1)
):
action = "buy"
else:
action = "sell"
except (KeyError, IndexError, ValueError):
logger.warning(f"{company} has less record than minimum required")
EMA_A, EMA_B, EMA_C, action = pd.NA, pd.NA, pd.NA, pd.NA
return {
"company": company,
"ema_date": now_strting
if cutoff_date == "today"
else cutoff_date.strftime("%d-%m-%Y"),
f"ema{str(ema_canditate[0])}": EMA_A,
f"ema{str(ema_canditate[1])}": EMA_B,
f"ema{str(ema_canditate[2])}": EMA_C,
# 'percentage_diffCB': percentage_diff_cb,
# 'percentage_diffCA': percentage_diff_ca,
# 'percentage_diffBA': percentage_diff_ba,
"action": action,
}
import warnings
from stock_analysis.unit_strategy import UnitStrategy
from stock_analysis.indicator import Indicator
from stock_analysis.utils.logger import logger
logger = logger()
warnings.filterwarnings('ignore')
logger.info("Please give your input to Start Stock Analysis")
task = int(
input("Choice task to Perform \n1.Unit Strategy \n2.Indicator \nChoice: "))
# Tasks for Unit Strategy
if task == 1:
yaml_path = input("Enter Company name yaml file location: ")
sa = UnitStrategy(path=yaml_path)
sub_task = int(
input("Choose sub task to perform: \n1.Momentum Strategy\n2.Momentum with EMA \nChoice: "))
# Sub-task for Momentum Strategy
if sub_task == 1:
sub_task_para = int(input("Choose input parameter nature \n1.Default --> i)Date = today, \
ii)Top company count = 20 iii)Export path = Same folder iv)Verbosity (level of detail logging): Detail\
\n2.Manual \nChoice: "))
if sub_task_para == 1:
sa.momentum_strategy()
elif sub_task_para == 2:
logger.info(
"In Manual mode whereever you see 'default' then presing enter key will take default value")
sub_task_para_date = (
input("Input desired date (default: today)(eg: 01/06/2020): ") or 'today')
def ema_crossover_detail_indicator(
self,
ema_canditate: Tuple[int, int, int] = (5, 13, 26),
save: bool = True,
export_path: str = ".",
verbosity: int = 1,
) -> Optional[pd.DataFrame]:
"""Exponential moving average for crossover triple period technique
Args:
ema_canditate (Tuple[int, int, int], optional): Three Period (or days) to calculate EMA. Defaults to (5, 13, 26).
save (bool, optional): Save to hard disk. Defaults to True.
export_path (str, optional): Path to save, to be used only if 'save' is true. Defaults to ".".
verbosity (int, optional): Level of detail logging,1=< Deatil, 0=Less detail. Defaults to 1.
Returns:
Results is based on crossover ema and detailed metrics
Example:
```python
from stock_analysis.indicator import Indicator
ind = Indicator('./data/company_list.yaml')
ema = ind.ema_crossover_detail_indicator((5,10,020), '01/06/2020')
```
"""
logger.info("Performing EMA Indicator Task")
ema_short = self._ema_indicator_n3(ema_canditate=ema_canditate,
verbosity=verbosity)
logger.info("Extarcting detail company quote data")
batch_company_quote = pd.DataFrame()
with parallel_backend(n_jobs=-1, backend="multiprocessing"):
company_quote = Parallel()(
delayed(self.unit_quote_retrive)(company)
for company in ema_short["company"])
for single_company_quote in company_quote:
if isinstance(single_company_quote, pd.DataFrame):
batch_company_quote = batch_company_quote.append(
single_company_quote)
batch_company_quote = batch_company_quote.reset_index().rename(
columns={"index": "company"})
batch_company_quote = batch_company_quote[[
"company",
"longName",
"price",
"regularMarketVolume",
"marketCap",
"bookValue",
"priceToBook",
"averageDailyVolume3Month",
"averageDailyVolume10Day",
"fiftyTwoWeekLowChange",
"fiftyTwoWeekLowChangePercent",
"fiftyTwoWeekRange",
"fiftyTwoWeekHighChange",
"fiftyTwoWeekHighChangePercent",
"fiftyTwoWeekLow",
"fiftyTwoWeekHigh",
]]
batch_company_quote["company"] = batch_company_quote[
"company"].str.replace(".NS", "")
ema_quote = ema_short.merge(batch_company_quote,
on="company",
validate="1:1")
if verbosity > 0:
logger.debug(f"Here are sample 5 company\n{ema_quote.head()}")
if save is not False:
ema_quote.to_csv(
f"{export_path}/ema_crossover_detail_indicator{str(ema_canditate[0])}-{str(ema_canditate[1])}-{str(ema_canditate[2])}_{len(self.data['company'])}company_{now_strting}.csv",
index=False,
)
if verbosity > 0:
logger.debug(
f"Exported at {export_path}/ema_crossover_detail_indicator{str(ema_canditate[0])}-{str(ema_canditate[1])}-{str(ema_canditate[2])}_{len(self.data['company'])}company_{now_strting}.csv"
)
else:
return ema_quote
def test_yaml():
logger.info("Loading")
with open(f"{data_path}/sample_company.yaml", "r") as f:
data = yaml.load(f, Loader=yaml.FullLoader)
assert isinstance(data, dict), "Incorrect data"