Preface: In this video series Matt shows how to use the EODHD Financial APIs for the Stock Analysis with Python.
Don’t forget to explore other 2 Parts to learn more:
Part 1 Obtaining Stocks Data for the Analysis in Python
Part 2 Processing Stocks Data in Python for the Analysis
Part 3 Filtering and visualizing the Stocks Data Analysis results in Python
import datetime as dt
from eod import EodHistoricalData
import json
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import requests
DEFAULT_DATE = dt.date.today() - dt.timedelta(396)
TODAY =dt.date.today()
def plot_performance(folder):
"""
returns figure containing relative performance of all securities in folder
"""
files = [file for file in os.listdir(folder) if not file.startswith('0')]
fig, ax = plt.subplots(math.ceil(len(files)/ 4), 4, figsize=(16,16))
count = 0
for row in range(math.ceil(len(files)/ 4)):
for column in range(4):
try:
data = pd.read_csv(f"{folder}/{files[count]}")['close']
data = (data/data[0] -1) * 100
ax[row,column].plot(data, label= files[count][:-4])
ax[row,column].legend()
ax[row,column].yaxis.set_major_formatter(mtick.PercentFormatter())
ax[row,column].axhline(0, c='r', ls='--')
except:
pass
count +=1
plt.show()
def main():
key = open('api_token.txt').read()
plot_performance('energy')
if __name__ == '__main__':
main()import datetime as dt
from eod import EodHistoricalData
import json
import math
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import numpy as np
import os
import pandas as pd
import requests
DEFAULT_DATE = dt.date.today() - dt.timedelta(396)
TODAY =dt.date.today()
def get_earnings(key):
"""
returns list of tickers for companies reporting in the next week
"""
client =EodHistoricalData(key)
eps = pd.DataFrame(client.get_calendar_earnings())
symbols =[]
for row in range(len(eps)):
if eps.earnings.iloc[row]['code'].endswith('US'):
symbols.append(eps.earnings[row]['code'][:-3])
print(f"There are {len(symbols)} companies reporting this week")
return symbols
def get_dividends(key, exchange ='US', date= dt.date.today()):
"""
returns securities with specific ex-date
"""
client = EodHistoricalData(key)
data = pd.DataFrame(client.get_bulk_markets(exchange= exchange,
date= date, type_='dividends'))
return data
def main():
key = open('api_token.txt').read()
print(get_earnings(key))
print(get_dividends(key ))
if __name__ == '__main__':
main()import datetime as dt
from eod import EodHistoricalData
import json
import math
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import numpy as np
import os
import pandas as pd
import requests
DEFAULT_DATE = dt.date.today() - dt.timedelta(396)
TODAY =dt.date.today()
def screen_example(symbols, key):
"""
Returns DataFrame with current price, 52-week high and ratio
of high to current price. Symbols is list-like
"""
high = {}
call = f"https://eodhistoricaldata.com/api/eod-bulk-last-day/US?api_token={key}&fmt=json"
data = pd.DataFrame(requests.get(call).json())
data.reset_index(drop=True)
client = EodHistoricalData(key)
for ticker in symbols:
try:
high[ticker] = client.get_fundamental_equity(f"{ticker}.US")['Technicals']['52WeekHigh']
print(f"fetching {ticker}")
except:
print(f"{ticker} not available skipping")
finally:
print(f"All available data downloaded")
mask = data.code.isin(symbols)
prices = data[['code', 'close']][mask]
high = pd.Series(high, name='high')
prices = prices.merge(high, right_on=high.index, left_on ='code')
prices['ratio'] = prices['close'] / prices['high']
return prices
def main():
key = open('api_token.txt').read()
sp = get_sp()[:10]
print(screen_example(sp,key))
if __name__ == '__main__':
main()from eod import EodHistoricalData
import datetime as dt
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import numpy as np
import os
import pandas as pd
import seaborn as sb
sb.set_theme()
DEFAULT_DATE = dt.date.isoformat(dt.date.today() - dt.timedelta(396))
class Stock:
def __init__(self, symbol, key, date =DEFAULT_DATE, folder=None):
self.symbol = symbol
self.key = key
self.date = date
self.folder = folder
self.data = self.get_data()
def get_data(self):
available_data = [filename[:-4]
for filename in os.listdir(self.folder) if not filename.startswith('0')]
if self.symbol in available_data:
data = pd.read_csv(f"{self.folder}/{self.symbol}.csv",
index_col='date').round(2)
else:
client = EodHistoricalData(self.key)
data = pd.DataFrame(client.get_prices_eod(self.symbol,
from_=self.date)).round(2)
data.index = pd.DatetimeIndex(data.date).date
data.drop(columns=['date'], inplace = True)
return data
def main():
KEY = open('api_token.txt').read()
test = Stock(symbol='AAPL', key=KEY)
print(test.data)
if __name__ == '__main__':
main()from eod import EodHistoricalData
import datetime as dt
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import numpy as np
import os
import pandas as pd
import seaborn as sb
sb.set_theme()
DEFAULT_DATE = dt.date.isoformat(dt.date.today() - dt.timedelta(396))
class Stock:
def __init__(self, symbol, key, date =DEFAULT_DATE, folder=None):
self.symbol = symbol
self.key = key
self.date = date
self.folder = folder
self.data = self.get_data()
def get_data(self):
available_data = [filename[:-4]
for filename in os.listdir(self.folder) if not filename.startswith('0')]
if self.symbol in available_data:
data = pd.read_csv(f"{self.folder}/{self.symbol}.csv",
index_col='date').round(2)
else:
client = EodHistoricalData(self.key)
data = pd.DataFrame(client.get_prices_eod(self.symbol,
from_=self.date)).round(2)
data.index = pd.DatetimeIndex(data.date).date
data.drop(columns=['date'], inplace = True)
self.calc_vol(data)
return data
def calc_vol(self, df):
df['returns'] = np.log(df.close).diff().round(4)
df['volatility'] = df.returns.rolling(21).std().round(4)
df['change'] = df['close'].diff()
df['hi_low_spread'] = ((df['high'] -df['low']) / df['open']).round(2)
df['exp_change'] = (df.volatility * df.close.shift(1)).round(2)
df['magnitude'] = (df.change / df.exp_change).round(2)
df['abs_magnitude'] = np.abs(df.magnitude)
df.dropna(inplace= True)
def plot_return_dist(self):
start = self.data.index[0]
end = self.data.index[-1]
plt.hist(self.data['returns'], bins=20, edgecolor='w')
plt.suptitle(f"Distribution of returns for {self.symbol}", fontsize=14)
plt.title(f"From {start} to {end}", fontsize=12)
plt.show()
def main():
KEY = open('api_token.txt').read()
test = Stock(symbol='AAPL', key=KEY)
print(test.data)
test.plot_return_dist()
if __name__ == '__main__':
main()from eod import EodHistoricalData
import datetime as dt
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import numpy as np
import os
import pandas as pd
import seaborn as sb
sb.set_theme()
DEFAULT_DATE = dt.date.isoformat(dt.date.today() - dt.timedelta(396))
class Stock:
def __init__(self, symbol, key, date =DEFAULT_DATE, folder=None):
self.symbol = symbol
self.key = key
self.date = date
self.folder = folder
self.data = self.get_data()
def get_data(self):
available_data = [filename[:-4]
for filename in os.listdir(self.folder) if not filename.startswith('0')]
if self.symbol in available_data:
data = pd.read_csv(f"{self.folder}/{self.symbol}.csv",
index_col='date').round(2)
else:
client = EodHistoricalData(self.key)
data = pd.DataFrame(client.get_prices_eod(self.symbol,
from_=self.date)).round(2)
data.index = pd.DatetimeIndex(data.date).date
data.drop(columns=['date'], inplace = True)
self.calc_vol(data)
return data
def calc_vol(self, df):
df['returns'] = np.log(df.close).diff().round(4)
df['volatility'] = df.returns.rolling(21).std().round(4)
df['change'] = df['close'].diff()
df['hi_low_spread'] = ((df['high'] -df['low']) / df['open']).round(2)
df['exp_change'] = (df.volatility * df.close.shift(1)).round(2)
df['magnitude'] = (df.change / df.exp_change).round(2)
df['abs_magnitude'] = np.abs(df.magnitude)
df.dropna(inplace= True)
def plot_return_dist(self):
start = self.data.index[0]
end = self.data.index[-1]
plt.hist(self.data['returns'], bins=20, edgecolor='w')
plt.suptitle(f"Distribution of returns for {self.symbol}", fontsize=14)
plt.title(f"From {start} to {end}", fontsize=12)
plt.show()
def plot_volatility(self):
start = self.data.index[0]
end = self.data.index[-1]
plt.scatter(self.data['returns'], self.data['abs_magnitude'])
plt.axhline(0, c='r', ls='--')
plt.axvline(0, c='r', ls='--')
plt.suptitle(f"Volatility of returns for {self.symbol}", fontsize=14)
plt.title(f"From {start} to {end}", fontsize=12)
plt.show()
def plot_performance(self):
start = self.data.index[0]
end = self.data.index[-1]
plt.plot((self.data.close / self.data.close[0] - 1) * 100)
plt.axhline(0, c='r', ls='--')
plt.suptitle(f"Volatility of returns for {self.symbol}", fontsize=14)
plt.title(f"From {start} to {end}", fontsize=12)
plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter())
plt.show()
def main():
KEY = open('api_token.txt').read()
test = Stock(symbol='AAPL', key=KEY)
test.plot_volatility()
test.plot_performance()
if __name__ == '__main__':
main()