def main():
# -------------------------[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)
# ----------------------------------------------------------
start_time = time.time()
impliedVolatility = ImpliedVolatility(optionChain)
curve = impliedVolatility.getImpliedVolatility()
print('chrono: {}'.format(time.time() - start_time))
fig, ax = plt.subplots(figsize=(8, 5))
ax.set(xlabel='Strike',
ylabel='Volatility',
title='Option implied volatility ({})'.format(
ticker.getInfo().ticker))
ax.plot(curve.calls.strike,
curve.calls.IV,
label='Calls',
color='green',
alpha=.5)
ax.plot(curve.puts.strike,
curve.puts.IV,
label='Puts',
color='red',
alpha=.5)
ax.plot(curve.calls.strike,
curve.calls.naturalIV,
linestyle='dotted',
label='Original Calls',
color='green')
ax.plot(curve.puts.strike,
curve.puts.naturalIV,
linestyle='dotted',
label='Original Puts',
color='red')
plt.legend()
plt.show()
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 os
import pandas as pd
from EcoFin.dataDownload.optionsManager import OptionManager
from EcoFin.dataDownload.ticker import Ticker
from EcoFin.options.deepOptionChain import DeepOptionChain
# -------------------------[Set-up]-------------------------
ticker = Ticker('MSFT')
optionManager = OptionManager(ticker)
exp = optionManager.getExpirationByMaturity(30, method='greater')
optionChain = optionManager.getOptionChain(exp=exp)
# ----------------------------------------------------------
ticker_info = ticker.getInfo()
forwardPrice = optionChain.getForwardPrice()
deepOptionChain = DeepOptionChain(optionChain, computeIV=False)
data = deepOptionChain.getDeepOptionChain()
# ----------------------[EXPORT BLOCK]--------------------------------
path = '../Export/[{}]'.format(ticker.ticker)
if not os.path.exists(path):
os.makedirs(path)
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
import numpy as np
from EcoFin.dataDownload.optionsManager import OptionManager
from EcoFin.dataDownload.ticker import Ticker
from EcoFin.options.blackScholesModel import BSM
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()
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
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,
Created by Luca Camerani at 31/08/2020, 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 time import matplotlib.pyplot as plt from EcoFin.dataDownload.ticker import Ticker from EcoFin.options.optionChain import OptionChain ticker = Ticker('MSFT') i = 0 while time.time() >= ticker.optionsExp[i]: i += 1 exp = ticker.optionsExp[i] optionChain = OptionChain(ticker, expiration=exp) data = getPricesCurve(optionChain) fig, ax = plt.subplots() for type in ['call', 'put']: if type == 'call': ax.plot(data.call['strike'], data.call['prices'], label=type) else:
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tqdm
from mpl_toolkits.mplot3d import axes3d
from EcoFin.dataDownload.optionsManager import OptionManager
from EcoFin.dataDownload.ticker import Ticker
from EcoFin.options.deepOptionSurface import DeepOptionSurface
from EcoFin.options.utils import daysToMaturity
# ---------------------------[Set-Up]--------------------------------
ticker = Ticker('MSFT')
increment = 86400 # 1 day
date1 = 1551657600 # Monday 4 March 2019
date2 = date1 + increment * 1
# -------------------------------------------------------------------
for now in tqdm.tqdm(range(date1, date2, increment),
desc='Generate frames'): # Compute day by day
try:
optionManager = OptionManager(ticker, now=now)
optSurface = optionManager.getOptionSurface()
deepSurface = DeepOptionSurface(optSurface,
computeIV=False,
computeBSM=False,
progressBar=False)
def execute(tick: str):
ticker = Ticker(tick)
optionManager = OptionManager(ticker)
output = pd.DataFrame
for now in range(date1, date2, increment): # Compute day by day
try:
# Exclude bad results and weekends
if optionManager.setNow(now) is not False and datetime.fromtimestamp(now).weekday() not in [5, 6]:
exp = optionManager.getExpirationByMaturity(maturity_min, method='greater')
optionChain = optionManager.getOptionChain(exp=exp)
deepOptionChain = DeepOptionChain(optionChain, computeIV=computeIV, progressBar=False)
summary = {'Date': unixtimestamp_to_date(optionChain.getChainDate()),
'Exp': unixtimestamp_to_date(optionChain.getChainExpiration()),
'Maturity': optionChain.getTimeToMaturity().days,
'ForwardPrice': optionChain.getForwardPrice(),
'SpotPrice': optionChain.getSpotPrice(),
}
# compute signals
chainWeights = ChainWeights(optionChain)
for mode, weights in {'EW': chainWeights.computeEquallyWeights(),
'Beta': chainWeights.computeBetaWeights(),
'ATM': chainWeights.computeATMWeights(),
'OI': chainWeights.computeOpenInterestsWeights(),
'Moneyness': chainWeights.computeMoneynessWeights()}.items():
synopsis = OptionChainSinopsys(deepOptionChain, weights=weights)
OPS = synopsis.computeOptionPriceSpread()
IVS = synopsis.computeImpliedVolatilitySpread()
NAP = synopsis.computeNoArbitragePrice()
OIR = synopsis.computeOpenInterestRatio()
summary['OPS_[{}]'.format(mode)] = OPS.mean
summary['IVS_[{}]'.format(mode)] = IVS.mean
summary['NAP_[{}]'.format(mode)] = NAP.value
summary['NAP_ret_[{}]'.format(mode)] = NAP.ret
summary['OIR_[{}]'.format(mode)] = OIR.mean
PCD = synopsis.computePutCallDelta()
summary['PCD'] = PCD
# Compute volatility metrics
VIX = EquityVIX(deepOptionChain)
summary['VIX_[hist]'] = VIX.getHistoricalVolatility()
summary['VIX_[mean]'] = VIX.getMeanVIX().value
summary['VIX_[beta]'] = VIX.getBetaVIX().value
summary['VIX_[CBOE]'] = VIX.getCBOEVIX()
if output.empty:
output = pd.DataFrame(columns=list(summary.keys()), index=[])
output = output.append(summary, ignore_index=True)
except Exception as e:
logs.append('Error with [{}] - [{}]: {}'.format(ticker.ticker, now, e))
pass
# ----------------------[EXPORT BLOCK]--------------------------------
path = '../Export/BackTest/{}'.format(ticker.ticker)
if not os.path.exists(path):
os.makedirs(path)
try:
with pd.ExcelWriter('{}/backTest_[{}].xlsx'.format(path, maturity_min)) as writer:
output.to_excel(writer, sheet_name='Results', index=False)
except:
pass
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
import numpy as np
from EcoFin.dataDownload.optionsManager import OptionManager
from EcoFin.dataDownload.ticker import Ticker
from EcoFin.options.impliedVolatility import ImpliedVolatility
from EcoFin.options.volatilitySmile import VolatilitySmile
# -------------------------[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)
# ----------------------------------------------------------
price = optionChain.getSpotPrice()
forwardPrice = optionChain.getForwardPrice()
impliedVolatility = ImpliedVolatility(optionChain)
volatilitySmile = VolatilitySmile(impliedVolatility)
smile = volatilitySmile.getVolatilitySmile()
and is released under the "BSD Open Source License".
"""
import os
from datetime import datetime
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from EcoFin.dataDownload.optionsManager import OptionManager
from EcoFin.dataDownload.ticker import Ticker
from EcoFin.options.deepOptionChain import DeepOptionChain
# -------------------------[Set-up]-------------------------
ticker = Ticker('MSFT')
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
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
import numpy as np
from EcoFin.dataDownload.optionsManager import OptionManager
from EcoFin.dataDownload.ticker import Ticker
from EcoFin.options.chainWeights import ChainWeights
from EcoFin.options.impliedVolatility import ImpliedVolatility
from EcoFin.options.volatilitySmile import VolatilitySmile
# -------------------------[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)
# ----------------------------------------------------------
fig, axs = plt.subplots(2, figsize=(15, 8), sharex=True)
fig.suptitle('Weigh implied volatility ({})'.format(ticker.getInfo().ticker),
fontsize=16)
forwardPrice = optionChain.getForwardPrice()
impliedVolatility = ImpliedVolatility(optionChain)
volatilitySmile = VolatilitySmile(impliedVolatility)
""" 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())