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Merge pull request #5 from Akashdesarda/experimental
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segregate helper function to make api more focused
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@Akashdesarda
Akashdesarda authored Sep 27, 2020
2 parents 05f9944 + 7b8d0c4 commit 057ad8b
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155 changes: 19 additions & 136 deletions stock_analysis/indicator.py
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Original file line number Diff line number Diff line change
@@ -1,17 +1,16 @@
import os
import yaml
import datetime
import dateutil
import datetime
import pandas as pd
import yfinance as yf
import multiprocessing
from dataclasses import dataclass
from stock_analysis.utils.logger import logger
from stock_analysis.data_retrive import DataRetrive
from typing import Dict, List, Tuple, Union
from stock_analysis.data_retrive import DataRetrive
from stock_analysis.utils.logger import logger
from stock_analysis.utils.formula_helpers import exponential_moving_avarage,\
percentage_diff_analysis, outcome_analysis

now_strting = datetime.datetime.now().strftime('%d-%m-%Y')
yf.pdr_override()
logger = logger()
pd.options.display.float_format = '{:,.2f}'.format

Expand Down Expand Up @@ -117,13 +116,13 @@ def ema_indicator(self, ema_canditate: Tuple[int, int] = (50, 200),
ema_indicator_df = pd.DataFrame(result)
ema_indicator_df.dropna(inplace=True)
ema_indicator_df['percentage_diff'] = ema_indicator_df.apply(
lambda x: self._percentage_diff_analysis(
lambda x: percentage_diff_analysis(
x[f'ema{str(ema_canditate[0])}'],
x[f'ema{str(ema_canditate[1])}']),
axis=1
)
ema_indicator_df['outcome'] = ema_indicator_df.apply(
lambda x: self._outcome_analysis(x['percentage_diff']),
lambda x: outcome_analysis(x['percentage_diff']),
axis=1
)

Expand Down Expand Up @@ -293,9 +292,11 @@ def ema_crossover_indicator_detail(self,
else:
return ema_quote

def _ema_indicator_n3(self, ema_canditate: Tuple[int, int] = (5, 13, 26),

def _ema_indicator_n3(self,
ema_canditate: Tuple[int, int] = (5, 13, 26),
cutoff_date: Union[str, datetime.datetime] = 'today',
verbosity: int = 1):
verbosity: int = 1)->pd.DataFrame:

with multiprocessing.Pool(multiprocessing.cpu_count() - 1) as pool:
result = pool.starmap(self._parallel_ema_indicator_n3,
Expand All @@ -308,124 +309,6 @@ def _ema_indicator_n3(self, ema_canditate: Tuple[int, int] = (5, 13, 26),
f"Here are sample 5 company\n{ema_indicator_df.head()}")
return ema_indicator_df

def _exponential_moving_avarage(self,
data_df: Union[pd.Series, List],
period: int,
cutoff_date: Union[str, datetime.datetime] = 'today',
smoothing_factor: int = 2,
verbosity: int = 1) -> float:
"""Calculate exponential moving avarage based on given period
Parameters
----------
data : Union[pd.Series,List]
Data to calculate ema
period : int
Period for which ema has to be calculated
smoothing_factor : int, optional
Smoothing factor which will be used to calculate
Multiplying factor, by default 2
Returns
-------
float
ema value
"""
ema_list = []
# Calculating multiplying factor
mf = smoothing_factor/(1 + period)

# Calculating first SMA
sma0 = (sum(data_df['Close'][:period])) / period

# Calculating first EMA
ema0 = (data_df['Close'][period] * mf) + (sma0 * (1 - mf))

# Calculating latest EMA
ema_pre = ema0

for idx in range(1, len(data_df)-50):
ema = (data_df['Close'][idx + 50] * mf) + (ema_pre * (1 - mf))
ema_pre = ema
ema_list.append(ema)
# if cutoff_date is not None:
if idx == (len(data_df) - 50):
break
data_df['ema'] = [pd.NA] * (len(data_df) - len(ema_list)) + ema_list
if cutoff_date == 'today':
date = data_df.index[-1]
else:
date = self._get_appropriate_date(
company_df=data_df,
desired_date=cutoff_date,
verbosity=verbosity
)

return float(data_df[data_df.index == date]['ema'])

def _get_appropriate_date(self,
company_df: pd.DataFrame,
desired_date: datetime.datetime,
verbosity: int = 1) -> Tuple[datetime.datetime, float]:
"""
Return appropriate date which is present in data record.
Parameters
----------
company_df : pd.DataFrame
Company dataframe
duration : datetime.datetime
Desired date cut-off to calculate ema
verbosity : int, optional
Level of detail logging, by default 1
Returns
-------
Tuple[datetime.datetime,float]
Date,Close value on date retrived
Raises
------
ValueError
If desired old is older than first record
"""
if desired_date < company_df.index[0]:
logger.error(
f"Given desired date {desired_date.strftime('%d-%m-%Y')} is older than first recorded date {company_df.index[0].strftime('%d-%m-%Y')}")

if verbosity > 0:
logger.debug(
f"Your desired EMA cut-off date is {desired_date.strftime('%d-%m-%Y')}")

for day_idx in range(1, 100):
if desired_date not in company_df.index:
date = desired_date - \
dateutil.relativedelta.relativedelta(days=day_idx)
else:
date = desired_date
if date in company_df.index:
break
if verbosity > 0 and desired_date != date:
logger.warning(
f"Desired date: {desired_date.strftime('%d-%m-%Y')} not found going for next possible date: {date.strftime('%d-%m-%Y')}")

return date

def _percentage_diff_analysis(self,
ema_a,
ema_b):
"""
Used to calculate Percentage difference
"""
return abs((ema_b - ema_a)/((ema_a + ema_b) / 2) * 100)

def _outcome_analysis(self, percentage_diff):
if 5 < percentage_diff < 5:
outcome = 'close by'
else:
outcome = 'far away'
return outcome

# TODO: Add all parallel executor function here
def _parallel_vol_indicator_n_days(self,
company: str = None,
Expand Down Expand Up @@ -463,13 +346,13 @@ def _parallel_ema_indicator(self,
logger.warning(f"{company} have some missing value, fixing it")
company_df.dropna(inplace=True)
try:
EMA_A = self._exponential_moving_avarage(
EMA_A = exponential_moving_avarage(
data_df=company_df,
cutoff_date=cutoff_date,
period=ema_canditate[0],
verbosity=verbosity
)
EMA_B = self._exponential_moving_avarage(
EMA_B = exponential_moving_avarage(
data_df=company_df,
cutoff_date=cutoff_date,
period=ema_canditate[1],
Expand Down Expand Up @@ -505,28 +388,28 @@ def _parallel_ema_indicator_n3(self, company: str,
logger.warning(f"{company} have some missing value, fixing it")
company_df.dropna(inplace=True)
try:
EMA_A = self._exponential_moving_avarage(
EMA_A = exponential_moving_avarage(
data_df=company_df,
cutoff_date=cutoff_date,
period=ema_canditate[0],
verbosity=verbosity
)
EMA_B = self._exponential_moving_avarage(
EMA_B = exponential_moving_avarage(
data_df=company_df,
cutoff_date=cutoff_date,
period=ema_canditate[1],
verbosity=verbosity
)
EMA_C = self._exponential_moving_avarage(
EMA_C = exponential_moving_avarage(
data_df=company_df,
cutoff_date=cutoff_date,
period=ema_canditate[2],
verbosity=verbosity
)

percentage_diff_cb = self._percentage_diff_analysis(EMA_C, EMA_B)
percentage_diff_ca = self._percentage_diff_analysis(EMA_C, EMA_A)
percentage_diff_ba = self._percentage_diff_analysis(EMA_B, EMA_A)
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'
Expand Down
98 changes: 10 additions & 88 deletions stock_analysis/unit_strategy.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -4,11 +4,13 @@
import pandas as pd
import yfinance as yf
import multiprocessing
from dataclasses import dataclass
from typing import List, Tuple
from dataclasses import dataclass
from stock_analysis.indicator import Indicator
from stock_analysis.utils.logger import logger
from stock_analysis.data_retrive import DataRetrive
from stock_analysis.utils.logger import logger
from stock_analysis.utils.helpers import get_appropriate_date_momentum
from stock_analysis.utils.formula_helpers import annualized_rate_of_return

yf.pdr_override()
logger = logger()
Expand Down Expand Up @@ -188,88 +190,8 @@ def momentum_with_ema_strategy(self,
else:
return momentum_ema_df

@staticmethod
def _annualized_rate_of_return(end_date: int,
start_date: int,
duration: float) -> float:
"""
Calculate annulized rate of return
Parameters
----------
end_date : int
Close value Current date or most present date.
Consider it as going from bottom to top.
start_date : int
Close value on Start date or first record.
Consider it as going from bottom to top.
duration : float
Total duration wrt to year
Returns
-------
float
Annulized return
"""
return (((end_date / start_date) ** (1/duration)) - 1) * 100

@staticmethod
def _get_appropriate_date(company_df: pd.DataFrame,
company,
duration: Tuple[int, int] = (0, 1),
verbosity: int = 1) -> Tuple[datetime.datetime, float]:
"""
Return appropriate date which is present in data record.
Parameters
----------
company_df : pd.DataFrame
Company dataframe
duration : Tuple[year,month], optional
Desired duration to go back to retrive record, by default (0,1)
verbosity : int, optional
Level of detail logging,1=< Deatil, 0=Less detail , by default 1
Returns
-------
Tuple[datetime.datetime,float]
Date,Close value on date retrived
Raises
------
ValueError
If desired old is older than first record
"""

current_date = company_df.iloc[-1].Date
desired_date = current_date - \
dateutil.relativedelta.relativedelta(
years=duration[0], months=duration[1])
if desired_date < company_df.iloc[0].Date:
logger.error(
f"Given desired date {desired_date.strftime('%d-%m-%Y')} is older than first recorded date {company_df.iloc[0].Date.strftime('%d-%m-%Y')}")
raise ValueError
dd_copy = desired_date

if verbosity > 0:
logger.debug(
f"Your desired date for monthly return for {company} is {desired_date.strftime('%d-%m-%Y')}")

if len(company_df.loc[company_df['Date'] == desired_date]) != 0:
desired_close = company_df.loc[company_df['Date'] == desired_date]
else:
for i in range(1, 100):
if len(company_df.loc[company_df['Date'] == desired_date]) == 0:
desired_date = desired_date - \
dateutil.relativedelta.relativedelta(days=i)
desired_close = company_df.loc[company_df['Date'] == desired_date]
break
if verbosity > 0:
logger.warning(
f"Desired date: {dd_copy.strftime('%d-%m-%Y')} not found going for next possible date: {desired_date.strftime('%d-%m-%Y')}")
return desired_date, desired_close.iloc[-1].Close

def _parallel_momentum(self, company: str,
def _parallel_momentum(self,
company: str,
start,
end,
verbosity: int = 1):
Expand All @@ -280,18 +202,18 @@ def _parallel_momentum(self, company: str,
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 = self._annualized_rate_of_return(
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 = self._annualized_rate_of_return(
ar_monthly = annualized_rate_of_return(
end_date=company_df.iloc[-1].Close,
start_date=self._get_appropriate_date(
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 = self._get_appropriate_date(
monthly_start_date = get_appropriate_date_momentum(
company_df, company, verbosity=0)[0].strftime('%d-%m-%Y')
except (IndexError, KeyError, ValueError):
if verbosity > 0:
Expand Down
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