def create_PMI_index():
start_date = datetime(2003, 11, 1)
end_date = datetime.today()
pmi = read_data(start_date, end_date, 'PMI')
pmi["date"] = pmi.index.copy()
create_PMI_RSI(pmi)
create_PMI_health(pmi)
return pmi.rename(columns={"Index": "PMI"})
def load_data():
def _append_last_return(x):
x["Last Return"] = x["Close"].diff(1) / x["Close"].shift(1)
vix = read_data(start_date, end_date, 'vix')
vix["date"] = vix.index
_append_last_return(vix)
sp500 = read_data(start_date, end_date, 'sp500')
sp500["date"] = sp500.index
_append_last_return(sp500)
price = pd.merge(vix,
sp500,
left_on='date',
right_on='date',
how='inner',
suffixes=["_vix", "_sp500"])
price.index = price.date
return price
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from model.vix import build_vix_sig
from TVIX.data.data_util import read_data
__author__ = 'junyan'
holding_period = 1000
locking_period = 40
stoploss = 0.6
start_date = pd.datetime(2011, 12, 1)
tvix_data = read_data().set_index('Date')
sp500_data = pd.read_csv(
"/Users/junyan/Desktop/tvix/data/sp500.csv",
parse_dates=True).sort_values(['Date']).set_index('Date')
tvix_data.columns = ["tvix_" + i for i in tvix_data.columns]
sp500_data.columns = ["sp500_" + i for i in sp500_data.columns]
sp500_data["sp500_60 return"] = (sp500_data["sp500_Adj Close"].diff(60) /
sp500_data["sp500_Adj Close"])
sp500_data["sp500_120 return"] = (sp500_data["sp500_Adj Close"].diff(120) /
sp500_data["sp500_Adj Close"])
data = pd.concat(
[tvix_data, sp500_data],
axis=1, join='outer')
import pandas as pd
import matplotlib.pyplot as plt
from TVIX.data.data_util import read_data
from TVIX.model.econ import attach_PMI_index
start_date = pd.datetime(2011, 1, 1)
end_date = pd.datetime.today()
tvix = read_data(start_date, end_date, 'tvix')
spy = read_data(start_date, end_date, 'spy')
tvix['return'] = tvix['Close'].diff(1) / tvix['Close'].shift(1)
tvix['date'] = tvix.index
tvix = tvix[tvix['return'] < 2]
spy['return'] = spy['Close'].diff(1) / spy['Close'].shift(1)
spy['date'] = spy.index
comp = pd.merge(tvix[['Close', 'return', 'date']],
spy[['Close', 'return', 'date']],
on='date',
suffixes=('_tvix', '_sp'))
comp.index = comp.date
comp['ratio'] = -comp['return_sp'] / comp['return_tvix']
comp.loc[((comp['return_sp'] < 0) & (comp['return_tvix'] > 0)) |
(comp.ratio < 0.2), 'ratio'] = 0
comp[['Close_sp', 'ratio']].plot(secondary_y='ratio')
plt.show()
import pandas as pd
import matplotlib.pyplot as plt
from TVIX.data.data_util import read_data
from TVIX.model.econ import attach_PMI_index
start_date = pd.datetime(2011, 1, 1)
end_date = pd.datetime.today()
holding_period = 10
tvix = read_data(start_date, end_date, 'tvix')
tvix['date'] = tvix.index
tvix['tvix_last_return'] = tvix['Close'].diff(1)/tvix['Close'].shift(1)
tvix['tvix_return'] = tvix['Close'].diff(1).shift(-1)/tvix['Close']
tvix['vix_sell_sig'] = tvix['Close'].rolling(4).apply(
lambda x: (x[0] < x[1]) and (x[1] > x[2]) and (x[2] > x[3])
# lambda x: (x[0] < x[1]) and (x[1] > x[2])
)
tvix = attach_PMI_index(tvix)
def current_econ_is_health(background):
if background.PMI_Health > 0.1 and background.PMI_RSI > -0.7:
return True
else:
return False
def compute_max_drawn_down(PNL):
current_max = PNL[0]
from TVIX.data.data_util import read_data
from datetime import datetime
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
start_date = pd.datetime(2010, 1, 1)
end_date = datetime.today()
sp500 = read_data(start_date, end_date, 'sp500')
def create_day_down_reverse(price_row):
mid = (price_row['Open'] + price_row['Close'])/2
ret = price_row['Close'] - price_row['Open']
down = mid - price_row['Low']
up = price_row['High'] - mid
return down/up
sp500['down_reverse'] = sp500.apply(create_day_down_reverse, axis=1)
sp500['down_reverse_10'] = pd.rolling_mean(sp500['down_reverse'], 10)
sp500[['Close', 'down_reverse_10']].plot(secondary_y='down_reverse_10')
plt.show()
sp500[sp500['down_reverse'] < 0]