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)
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,
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
