from EcoFin.stat.equityVolatility import EquityVolatility
# -------------------------[Set-up]-------------------------
ticker = Ticker('MSFT')
optionManager = OptionManager(ticker, now=None)
i = 0 # <- Time To Maturity curve
exp = optionManager.getExpirations()[i]
optionChain = optionManager.getOptionChain(exp=exp)
# ----------------------------------------------------------
strikeList = optionChain.getStrikeList()
volatility_back = 260
series = ticker.getHistory(
end=optionChain.getChainDate()).tail(volatility_back)
price = optionChain.getSpotPrice()
r = optionChain.getRiskFreeRate()
sigma = EquityVolatility(series).meanSigma()
maturity = optionChain.getTimeToMaturity().days
forwardPrice = optionChain.getForwardPrice()
fig, ax = plt.subplots()
chain = optionChain.getChain()
theoreticalPrices = {'call': [], 'put': []}
for strike in strikeList:
option = BSM(price, strike, r, sigma, maturity)
and is released under the "BSD Open Source License".
"""
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from EcoFin.assetAllocation.markowitzModel import MarkowitzModel
from EcoFin.dataDownload.ticker import Ticker
market = ['AAPL', 'AMZN', 'CSCO', 'MSFT', 'GOOGL']
marketDataFrame = pd.DataFrame(columns=market)
for company in market:
ticker = Ticker(company)
df = ticker.getHistory().tail(260)
marketDataFrame[company] = df['Close']
model = MarkowitzModel(marketDataFrame)
randW = model.randomWeights(10000)
rets = []
stds = []
for r in randW:
performance = model.portfolioPerformance(r)
rets.append(performance.ret)
stds.append(performance.std)
plt.scatter(stds,
rets,
optionManager = OptionManager(ticker, now=1492646400)
exp = optionManager.getExpirationByMaturity(30, method='greater')
optionChain = optionManager.getOptionChain(exp=exp)
# ----------------------------------------------------------
ticker_info = ticker.getInfo()
forwardPrice = optionChain.getForwardPrice()
deepOptionChain = DeepOptionChain(optionChain)
data = deepOptionChain.getDeepOptionChain()
data['weights'] = data.loc[:, ['openInterest_call', 'openInterest_put']].sum(axis=1) / \
np.nansum(data.loc[:, ['openInterest_call', 'openInterest_put']].to_numpy())
history_back = 100
history = ticker.getHistory(end=optionChain.getChainDate()).tail(history_back)
data['S_mean'] = np.mean(data[['S_U', 'S_L']], axis=1)
forecast = np.mean(data[['S_U', 'S_L', 'S_mean']])
fig, axs = plt.subplots(3, figsize=(15, 8))
fig.suptitle('No-arbitrage price bounds ({})'.format(ticker_info.longName),
fontsize=16)
# chart 1
axs[0].set_title('No arbitrage price bounds')
axs[0].plot(data.strike,
data['S_U'],
label='Upper bound ($S^{U})_{now}$)',
color='green')
axs[0].plot(data.strike,
forwardP.index,
linestyle="dashed",
color='white',
alpha=1,
label='Forward Price')
ax.legend()
plt.colorbar(c)
# Plot underlying price
ax = fig.add_subplot(224)
ax.set_title('Underlying prices')
# Get underlying price history
history_back = 30
history = ticker.getHistory(end=now).tail(history_back)
ax.plot(history.Close)
plt.show()
# Save frame
# ----------------------[EXPORT BLOCK]--------------------------------
path = r'../../../Export/{}'.format(ticker.ticker)
if not os.path.exists(path):
os.makedirs(path)
plt.savefig(r'{}/{}_{}.png'.format(path, now, ticker.ticker))
# ----------------------[EXPORT BLOCK]--------------------------------
except:
print('Error: {}'.format(now))
pass
""" tickerHistory.py Created by Luca Camerani at 06/10/2020, University of Milano-Bicocca. ([email protected]) All rights reserved. This file is part of the EcoFin-Library (https://github.com/LucaCamerani/EcoFin-Library), and is released under the "BSD Open Source License". """ import matplotlib.pyplot as plt from EcoFin.dataDownload.ticker import Ticker ticker = Ticker('MSFT') history = ticker.getHistory().tail(100) plt.plot(history.index, history.Close, label='Price ($S_t$)') plt.show() print(ticker.getInfo())