def get_Question3():
if path.exists('./lib/AAPL_Daily_Candle.csv'):
df = pd.read_csv('./lib/AAPL_Daily_Candle.csv')
else:
config = configparser.ConfigParser()
config.read('./lib/application.config')
key = config['finnhub']['key']
resolution = config['question2']['resolution']
symbol = config['question2']['symbol']
start_time = config['question2']['start_time']
end_time = config['question2']['end_time']
start = tools.date_to_epoch(start_time)
end = tools.date_to_epoch(end_time)
res = tools.getStockCandles(key, symbol, resolution, start, end)
res.pop('s', None)
df = pd.DataFrame(res)
df.to_csv('./lib/AAPL_Daily_Candle.csv')
# Insert Date column in df
df.insert(0, "Date", "")
# convert epotch time to datetime stamp in Date column
df['Date'] = pd.to_datetime(df['t'], unit='s')
df.insert(7, "daily_return", float, allow_duplicates=True)
df1 = tools.cal_DailyReturn(df)
return df1
def get_Question1():
if path.exists('./lib/AAPL_1Day_Candle.csv'):
df = pd.read_csv('./lib/AAPL_1Day_Candle.csv')
else:
config = configparser.ConfigParser()
config.read('./lib/application.config')
key = config['finnhub']['key']
resolution = config['finnhub']['resolution']
symbol = config['finnhub']['symbol']
start_time = config['finnhub']['start_time']
end_time = config['finnhub']['end_time']
start = tools.date_to_epoch(start_time)
end = tools.date_to_epoch(end_time)
res = tools.getStockCandles(key, symbol, resolution, start, end)
res.pop('s', None)
df = pd.DataFrame(res)
df.to_csv('./lib/AAPL_1Day_Candle.csv')
# Insert Date column in df
df.insert(0, "Date", "")
# convert epotch time to datetime stamp in Date column
df['Date'] = pd.to_datetime(df['t'], unit='s')
df.insert(7, "avg_price", float, allow_duplicates=True)
df["avg_price"] = (df['h'] + df['l'] + df['c']) / 3
df.insert(8, "cumulative_volume", int, allow_duplicates=True)
df.insert(9, "avg_price_volume", float, allow_duplicates=True)
df.insert(10, "cumulative_price_volume", float, allow_duplicates=True)
df.insert(11, "VWAP", float, allow_duplicates=True)
for index in df.index:
df.at[index,
'avg_price_volume'] = df.at[index, 'v'] * df.at[index,
'avg_price']
if index == 0:
df.at[index, 'cumulative_volume'] = df.at[index, 'v']
df.at[index, 'cumulative_price_volume'] = df.at[index,
'avg_price_volume']
else:
df.at[index, 'cumulative_volume'] = df.at[
index - 1, 'cumulative_volume'] + df.at[index, 'v']
df.at[index, 'cumulative_price_volume'] = df.at[
index - 1,
'cumulative_price_volume'] + df.at[index, 'avg_price_volume']
df.at[index, 'VWAP'] = df.at[index, 'cumulative_price_volume'] / df.at[
index, 'cumulative_volume']
return df
#get_Question1()
import pandas as pd
import configparser
import lib.Tools as tools
config = configparser.ConfigParser()
config.read('./lib/application.config')
symbols = config['question4']['symbol'].split(',')
#print(symbols[3])
start_time = config['question2']['start_time']
end_time = config['question2']['end_time']
key = config['finnhub']['key']
resolution = config['question2']['resolution']
start = tools.date_to_epoch(start_time)
end = tools.date_to_epoch(end_time)
symbol_dict = {}
for symbol in symbols:
res = tools.getStockCandles(key, symbol, resolution, start, end)
res.pop('s', None)
df = pd.DataFrame(res)
# Insert Date column in df
df.insert(0, "Date", "")
# convert epotch time to datetime stamp in Date column
df['Date'] = pd.to_datetime(df['t'], unit='s')
df.insert(7, "daily_return", float, allow_duplicates=True)
