import matplotlib.pyplot as plt
from datetime import datetime
import pytz
from alephnull.algorithm import TradingAlgorithm
from alephnull.utils.factory import load_from_yahoo
SYMBOL = 'GS'
class BuyStock(TradingAlgorithm): # inherit from TradingAlgorithm
"""This is the simplest possible algorithm that does nothing but
buy 1 share of SYMBOL on each event.
"""
def handle_data(self, data): # overload handle_data() method
self.order(SYMBOL, 1) # order SID (=0) and amount (=1 shares)
if __name__ == '__main__':
start = datetime(2008, 1, 1, 0, 0, 0, 0, pytz.utc)
end = datetime(2013, 1, 1, 0, 0, 0, 0, pytz.utc)
data = load_from_yahoo(stocks=[SYMBOL], indexes={}, start=start, end=end)
simple_algo = BuyStock()
results = simple_algo.run(data)
ax1 = plt.subplot(211)
results.portfolio_value.plot(ax=ax1)
ax2 = plt.subplot(212, sharex=ax1)
stock_data = getattr(data, SYMBOL)
stock_data.plot(ax=ax2)
plt.gcf().set_size_inches(18, 8)
def sell_spread(self):
"""
decrease exposure, regardless of position long/short.
buy for a short position, sell for a long.
"""
ko_amount = self.portfolio.positions['KO'].amount
self.order('KO', -1 * ko_amount)
pep_amount = self.portfolio.positions['PEP'].amount
self.order('PEP', -1 * pep_amount)
if __name__ == '__main__':
start = datetime(2013, 1, 1, 0, 0, 0, 0, pytz.utc)
end = datetime(2014, 1, 1, 0, 0, 0, 0, pytz.utc)
data = load_from_yahoo(stocks=['PEP', 'KO'],
indexes={},
start=start,
end=end)
pairtrade = Pairtrade()
results = pairtrade.run(data)
data['spreads'] = np.nan
ax1 = plt.subplot(211)
data[['PEP', 'KO']].plot(ax=ax1)
plt.ylabel('price')
plt.setp(ax1.get_xticklabels(), visible=False)
ax2 = plt.subplot(212, sharex=ax1)
results.zscores.plot(ax=ax2, color='r')
plt.ylabel('zscored spread')
elif self.short_ema < self.long_ema and self.invested:
self.order('AAPL', -100)
self.invested = False
self.sell = True
self.record(AAPL=data['AAPL'].price,
short_ema=self.short_ema['AAPL'],
long_ema=self.long_ema['AAPL'],
buy=self.buy,
sell=self.sell)
if __name__ == '__main__':
start = datetime(1990, 1, 1, 0, 0, 0, 0, pytz.utc)
end = datetime(1991, 1, 1, 0, 0, 0, 0, pytz.utc)
data = load_from_yahoo(stocks=['AAPL'], indexes={}, start=start, end=end)
dma = DualEMATaLib()
results = dma.run(data).dropna()
fig = plt.figure()
ax1 = fig.add_subplot(211, ylabel='portfolio value')
results.portfolio_value.plot(ax=ax1)
ax2 = fig.add_subplot(212)
results[['AAPL', 'short_ema', 'long_ema']].plot(ax=ax2)
ax2.plot(results.ix[results.buy].index,
results.short_ema[results.buy],
'^',
markersize=10,
else:
self.order(SYMBOL, quantity_to_buy
) # order SID (=0) and amount (=1 shares)
if quantity_to_buy == 0:
TRACK.append(str(timestamp) + " had a 0-sell!")
self.last_price = price
if __name__ == '__main__':
start = datetime(2008, 1, 1, 0, 0, 0, 0, pytz.utc)
end = datetime(2013, 1, 1, 0, 0, 0, 0, pytz.utc)
data = load_from_yahoo(stocks=[SYMBOL],
indexes={},
start=start,
end=end,
adjusted=True)
simple_algo = BuyStock()
results = simple_algo.run(data)
ax1 = plt.subplot(211)
ax2 = plt.subplot(212)
TRACK_STRIPPED = [x for x in TRACK if type(x) == tuple]
futures_indexes = [timestamp for (_, _, timestamp) in TRACK_STRIPPED]
futures_quantity_data = [
quantity_owned for (_, quantity_owned, _) in TRACK_STRIPPED
]
futures_margin_data = [margin for (margin, _, _) in TRACK_STRIPPED]
futures_margin_series = TimeSeries(index=futures_indexes,
self.buy = True
elif self.short_ema < self.long_ema and self.invested:
self.order('AAPL', -100)
self.invested = False
self.sell = True
self.record(AAPL=data['AAPL'].price,
short_ema=self.short_ema['AAPL'],
long_ema=self.long_ema['AAPL'],
buy=self.buy,
sell=self.sell)
if __name__ == '__main__':
start = datetime(1990, 1, 1, 0, 0, 0, 0, pytz.utc)
end = datetime(1991, 1, 1, 0, 0, 0, 0, pytz.utc)
data = load_from_yahoo(stocks=['AAPL'], indexes={}, start=start,
end=end)
dma = DualEMATaLib()
results = dma.run(data).dropna()
fig = plt.figure()
ax1 = fig.add_subplot(211, ylabel='portfolio value')
results.portfolio_value.plot(ax=ax1)
ax2 = fig.add_subplot(212)
results[['AAPL', 'short_ema', 'long_ema']].plot(ax=ax2)
ax2.plot(results.ix[results.buy].index, results.short_ema[results.buy],
'^', markersize=10, color='m')
ax2.plot(results.ix[results.sell].index, results.short_ema[results.sell],
'v', markersize=10, color='k')
Original matlab implementation: John Duchi ([email protected]) Python-port: Copyright 2013 by Thomas Wiecki ([email protected]). """ v = np.asarray(v) p = len(v) # Sort v into u in descending order v = (v > 0) * v u = np.sort(v)[::-1] sv = np.cumsum(u) rho = np.where(u > (sv - b) / np.arange(1, p + 1))[0][-1] theta = np.max([0, (sv[rho] - b) / (rho + 1)]) w = (v - theta) w[w < 0] = 0 return w if __name__ == '__main__': import pylab as pl start = datetime(2004, 1, 1, 0, 0, 0, 0, pytz.utc) end = datetime(2008, 1, 1, 0, 0, 0, 0, pytz.utc) data = load_from_yahoo(stocks=STOCKS, indexes={}, start=start, end=end) data = data.dropna() olmar = OLMAR() results = olmar.run(data) results.portfolio_value.plot() pl.show()
import pytz
from alephnull.algorithm import TradingAlgorithm
from alephnull.utils.factory import load_from_yahoo
SYMBOL = "GS"
class BuyStock(TradingAlgorithm): # inherit from TradingAlgorithm
"""This is the simplest possible algorithm that does nothing but
buy 1 share of SYMBOL on each event.
"""
def handle_data(self, data): # overload handle_data() method
self.order(SYMBOL, 1) # order SID (=0) and amount (=1 shares)
if __name__ == "__main__":
start = datetime(2008, 1, 1, 0, 0, 0, 0, pytz.utc)
end = datetime(2013, 1, 1, 0, 0, 0, 0, pytz.utc)
data = load_from_yahoo(stocks=[SYMBOL], indexes={}, start=start, end=end)
simple_algo = BuyStock()
results = simple_algo.run(data)
ax1 = plt.subplot(211)
results.portfolio_value.plot(ax=ax1)
ax2 = plt.subplot(212, sharex=ax1)
stock_data = getattr(data, SYMBOL)
stock_data.plot(ax=ax2)
plt.gcf().set_size_inches(18, 8)
def sell_spread(self):
"""
decrease exposure, regardless of position long/short.
buy for a short position, sell for a long.
"""
ko_amount = self.portfolio.positions['KO'].amount
self.order('KO', -1 * ko_amount)
pep_amount = self.portfolio.positions['PEP'].amount
self.order('PEP', -1 * pep_amount)
if __name__ == '__main__':
start = datetime(2013, 1, 1, 0, 0, 0, 0, pytz.utc)
end = datetime(2014, 1, 1, 0, 0, 0, 0, pytz.utc)
data = load_from_yahoo(stocks=['PEP', 'KO'], indexes={},
start=start, end=end)
pairtrade = Pairtrade()
results = pairtrade.run(data)
data['spreads'] = np.nan
ax1 = plt.subplot(211)
data[['PEP', 'KO']].plot(ax=ax1)
plt.ylabel('price')
plt.setp(ax1.get_xticklabels(), visible=False)
ax2 = plt.subplot(212, sharex=ax1)
results.zscores.plot(ax=ax2, color='r')
plt.ylabel('zscored spread')
plt.show()
# it is just distributed over a larger number of contracts
if quantity_to_buy == 0:
TRACK.append("0 to buy, what a shame")
else:
self.order(SYMBOL, quantity_to_buy) # order SID (=0) and amount (=1 shares)
if quantity_to_buy == 0:
TRACK.append(str(timestamp) + " had a 0-sell!")
self.last_price = price
if __name__ == '__main__':
start = datetime(2008, 1, 1, 0, 0, 0, 0, pytz.utc)
end = datetime(2013, 1, 1, 0, 0, 0, 0, pytz.utc)
data = load_from_yahoo(stocks=[SYMBOL], indexes={}, start=start,
end=end, adjusted=True)
simple_algo = BuyStock()
results = simple_algo.run(data)
ax1 = plt.subplot(211)
ax2 = plt.subplot(212)
TRACK_STRIPPED = [x for x in TRACK if type(x) == tuple]
futures_indexes = [timestamp for (_, _, timestamp) in TRACK_STRIPPED]
futures_quantity_data = [quantity_owned for (_, quantity_owned, _) in TRACK_STRIPPED]
futures_margin_data = [margin for (margin, _, _) in TRACK_STRIPPED]
futures_margin_series = TimeSeries(index=futures_indexes, data=futures_margin_data)
futures_margin_series.plot(ax=ax1)
futures_quantity_series = TimeSeries(index=futures_indexes, data=futures_quantity_data)
futures_quantity_series.plot(ax=ax2)
Original matlab implementation: John Duchi ([email protected]) Python-port: Copyright 2013 by Thomas Wiecki ([email protected]). """ v = np.asarray(v) p = len(v) # Sort v into u in descending order v = (v > 0) * v u = np.sort(v)[::-1] sv = np.cumsum(u) rho = np.where(u > (sv - b) / np.arange(1, p + 1))[0][-1] theta = np.max([0, (sv[rho] - b) / (rho + 1)]) w = (v - theta) w[w < 0] = 0 return w if __name__ == '__main__': import pylab as pl start = datetime(2004, 1, 1, 0, 0, 0, 0, pytz.utc) end = datetime(2008, 1, 1, 0, 0, 0, 0, pytz.utc) data = load_from_yahoo(stocks=STOCKS, indexes={}, start=start, end=end) data = data.dropna() olmar = OLMAR() results = olmar.run(data) results.portfolio_value.plot() pl.show()
