def subsample_data(filename_data, filename_symbology, dir_pickle, start_date, end_date, query_attribute, query_criteria, include_avg):
query_criteria_filename = '-'.join(query_criteria[:3])
pickle_name = dir_pickle+'pickle_sentiment_'+start_date+'_'+end_date+'_'+query_attribute+'_'+query_criteria_filename+'_'+str(include_avg)+'.p'
try:
data = pd.read_pickle(pickle_name)
print("Loaded from pre-created pickle")
except:
print("Subsampling data from csv")
# try to read first from pickle
# read csv
data = pd.read_csv(filename_data)
# merge with symbology csv for additional info
data_symbology = pd.read_csv(filename_symbology)
# convert headers to uppercase for ease of use
data_symbology.columns = [x.upper() for x in data_symbology.columns]
data = pd.merge(data, data_symbology, left_on='SYMBOL', right_on='SYMBOL', how = "left")
# perform filter query based on parameters
data = data[data[query_attribute].isin(query_criteria)]
# convert timestamps to datetime objects
data['DATE'] = data['TIMESTAMP_UTC'].apply(lambda x: datetime.strptime(x,'%Y-%m-%dT%H:%M:%SZ'))
data['DATE'] = data['DATE'].apply(lambda x: x.strftime('%x'))
data['DATE'] = data['DATE'].apply(lambda x: pd.to_datetime(x))
# query between start and end date
data = data[(data['DATE'] >= start_date) & (data['DATE'] <= end_date)]
# remove avg
if include_avg == False:
avg_cols = [col for col in data.columns if 'AVG' in col]
data.drop(avg_cols,inplace=True,axis=1)
# save as pickle
data.to_pickle(pickle_name)
# return dataframe
return data
import pandas as pd
from pandas_datareader import data
# Fetch daily data for 4 years
SYMBOL = 'GOOG'
start_date = '2014-01-01'
end_date = '2018-01-01'
SRC_DATA_FILENAME = SYMBOL + '_data.pkl'
try:
data = pd.read_pickle(SRC_DATA_FILENAME)
except FileNotFoundError:
data = data.DataReader(SYMBOL, 'yahoo', start_date, end_date)
data.to_pickle(SRC_DATA_FILENAME)
# Variables/constants for EMA Calculation:
NUM_PERIODS_FAST = 10 # Static time period parameter for the fast EMA
K_FAST = 2 / (NUM_PERIODS_FAST + 1
) # Static smoothing factor parameter for fast EMA
ema_fast = 0
ema_fast_values = [] # we will hold fast EMA values for visualization purposes
NUM_PERIODS_SLOW = 40 # Static time period parameter for slow EMA
K_SLOW = 2 / (NUM_PERIODS_SLOW + 1
) # Static smoothing factor parameter for slow EMA
ema_slow = 0
ema_slow_values = [] # we will hold slow EMA values for visualization purposes
apo_values = [] # track computed absolute price oscillator value signals
# Variables for Trading Strategy trade, position & pnl management:
