def getData(self, tickers) :
settings = Settings()
dp = TimeSeries(settings).get_agg_ETF_data(tickers)
dp = dp.fillna(method='pad', axis=0)
dp = dp.fillna(method='bfill', axis=0)
dp = dp.dropna()
dp = dp.reindex_axis(['open', 'high', 'low', 'close', 'volume', 'price'], axis=2)
return dp
'GOOG/NYSEARCA_XLV',
'GOOG/NYSEARCA_XLY'
]
bm_tickers = [
'GOOG/NYSE_SPY',
'GOOG/NYSEARCA_TLT',
'GOOG/NYSEARCA_GLD' #gold
]
settings = Settings()
dp = TimeSeries(settings).get_agg_data(sector_tickers+bm_tickers)
dp = dp.fillna(method='pad', axis=0)
dp = dp.fillna(method='bfill', axis=0)
dp = dp[:,'2000-01-01'::,:]
dp = dp.dropna()
rets = pd.DataFrame()
rets[1] = Momentum_Sector_Rotation(dp).run_trading().portfolio_value
#rets[1] = Portfolio1(dp).run_trading().portfolio_value
#rets[2] = Portfolio2(dp).run_trading().portfolio_value
# rets[3] = Portfolio3(dp).run_trading().portfolio_value
# rets[4] = Portfolio4(dp).run_trading().portfolio_value
# rets[5] = Portfolio5(dp).run_trading().portfolio_value
#
# rets[6] = Portfolio6(dp).run_trading().portfolio_value
# rets[7] = Momentum(dp).run_trading().portfolio_vlaue
rets.plot(figsize=[20,12])
try:
# Get the strongest 5 in momentum
mom = returns_6m.T.sum(axis=1)
selected_indices = mom[mom>0].order().tail(len(mom) /2).index
# selected_indices = mom.index
# selected_indices = mom[mom > 0 ].index
selected_returns = returns_60d[selected_indices]
weights = minimize_vol(selected_returns.T)
# weights = minimize_vol(returns_60d.T)
# Rebalance portfolio accordingly
for stock, weight in zip(selected_returns.columns, weights):
order_target_percent(stock, weight)
except :
# Sometimes this error is thrown
# ValueError: Rank(A) < p or Rank([P; A; G]) < n
pass
# Instantinate algorithm
algo = TradingAlgorithm(initialize=initialize,
handle_data=handle_data)
# Run algorithm
results = algo.run(dp.dropna())
ret_ports = pd.DataFrame()
ret_ports[5] = results.portfolio_value
ret_ports.plot(figsize=[20,10])
print results
'GOOG/NYSEARCA_XLI', 'GOOG/NYSEARCA_XLK', 'GOOG/NYSEARCA_XLP',
'GOOG/NYSEARCA_XLU', 'GOOG/NYSEARCA_XLV', 'GOOG/NYSEARCA_XLY'
]
bm_tickers = [
'GOOG/NYSE_SPY',
'GOOG/NYSEARCA_TLT',
'GOOG/NYSEARCA_GLD' #gold
]
settings = Settings()
dp = TimeSeries(settings).get_agg_data(sector_tickers + bm_tickers)
dp = dp.fillna(method='pad', axis=0)
dp = dp.fillna(method='bfill', axis=0)
dp = dp[:, '2000-01-01'::, :]
dp = dp.dropna()
rets = pd.DataFrame()
rets[1] = Momentum_Sector_Rotation(dp).run_trading().portfolio_value
#rets[1] = Portfolio1(dp).run_trading().portfolio_value
#rets[2] = Portfolio2(dp).run_trading().portfolio_value
# rets[3] = Portfolio3(dp).run_trading().portfolio_value
# rets[4] = Portfolio4(dp).run_trading().portfolio_value
# rets[5] = Portfolio5(dp).run_trading().portfolio_value
#
# rets[6] = Portfolio6(dp).run_trading().portfolio_value
# rets[7] = Momentum(dp).run_trading().portfolio_vlaue
rets.plot(figsize=[20, 12])
print 'done!'