color='#547980')
plt.text(pd.to_datetime('2017-12-20'),
(portfolio['total asset'] +
np.std(portfolio['total asset'])).loc['2017-12-20'],
'What if we use MACD here?')
plt.axvline('2017/11/15', linestyle=':', label='Exit', c='#ff847c')
plt.legend()
plt.title('Portfolio Performance')
plt.ylabel('Asset Value')
plt.xlabel('Date')
plt.show()
import oil_money_trading_backtest as om
signals = om.signal_generation(dataset, 'brent', 'nok', om.oil_money)
p = om.portfolio(signals, 'nok')
om.plot(signals, 'nok')
om.profit(p, 'nok')
dic = {}
for holdingt in range(5, 20):
for stopp in np.arange(0.3, 1.1, 0.05):
signals=om.signal_generation(dataset,'brent','nok',om.oil_money, \
holding_threshold=holdingt, \
stop=stopp)
p = om.portfolio(signals, 'nok')
dic[holdingt, stopp] = p['asset'].iloc[-1] / p['asset'].iloc[0] - 1
profile = pd.DataFrame({
#best to follow up with a momentum strategy
#maybe this is not a statistical arbitrage after all
#the model is a trend following entry indicator
# In[17]:
#now lets construct a trend following strategy based on the previous strategy
#call it oil money version 2 or whatever
#here i would only import the strategy script as this is a script for analytics and visualization
#the official trading strategy script is in the following link
# https://github.com/je-suis-tm/quant-trading/blob/master/Oil%20Money%20project/Oil%20Money%20Trading%20backtest.py
import oil_money_trading_backtest as om
#generate signals,monitor portfolio performance
#plot positions and total asset
signals=om.signal_generation(dataset,'brent','nok',om.oil_money)
p=om.portfolio(signals,'nok')
om.plot(signals,'nok')
om.profit(p,'nok')
#but thats not enough, we are not happy with the return
#come on, 2 percent return?
#i may as well as deposit the money into the current account
#and get 0.75% risk free interest rate
#therefore, we gotta try different holding period and stop loss/profit point
#the double loop is very slow, i almost wanna do it in julia
#plz go get a coffee or even lunch and dont wait for it
dic={}
for holdingt in range(5,20):
for stopp in np.arange(0.3,1.1,0.05):
signals=om.signal_generation(dataset,'brent','nok',om.oil_money \
#shrink data size for better viz
dataset = df['2016':]
dataset.reset_index(inplace=True)
# In[14]:
#import the strategy script as this is a script for analytics and visualization
#the official trading strategy script is in the following link
# https://github.com/je-suis-tm/quant-trading/blob/master/Oil%20Money%20project/Oil%20Money%20Trading%20backtest.py
import oil_money_trading_backtest as om
#generate signals,monitor portfolio performance
#plot positions and total asset
signals = om.signal_generation(dataset,
'vasconia',
'cop',
om.oil_money,
stop=0.001)
p = om.portfolio(signals, 'cop')
om.plot(signals, 'cop')
om.profit(p, 'cop')
# In[15]:
#try different holding period and stop loss/profit point
dic = {}
for holdingt in range(5, 20):
for stopp in np.arange(0.001, 0.005, 0.0005):
signals = om.signal_generation(dataset,
'vasconia',
'cop',