def stock_process():
start = datetime(2010, 1, 1, 0, 0, 0, 0, pytz.utc)
end = datetime(2011, 1, 1, 0, 0, 0, 0, pytz.utc)
data = load_from_yahoo(stocks=['AAPL'], indexes={}, start=start, end=end)
plt.plot(data[:sample_size])
plt.show()
def main(algofile, start, end, save, define, securities):
"""Runs a strategy in FILE and saves the data in the database"""
frequency = 'daily'
symbols = [a.upper() for a in securities.split(',')]
start_data = start - pd.DateOffset(months=12)
define_parts = [assign.split('=', 2) for assign in define]
define_dict = {a[0]: a[1] for a in define_parts}
define_dict['securities'] = symbols
define_dict['frequency'] = frequency
data = load_from_yahoo(stocks=symbols,
indexes={},
start=start_data,
end=end)
data = data.dropna()
algotext = algofile.read()
algoname = getattr(algofile, 'name', '<algorithm>')
algo = TradingAlgorithm(
script=algotext,
algo_filename=algoname,
sim_params=create_simulation_parameters(start=start,
end=end,
data_frequency=frequency),
**define_dict)
perf = algo.run(data, overwrite_sim_params=False)
if save:
write_to_db(algoname, algotext, perf, define_dict, start, end)
def example():
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=['IBM'], indexes={}, start=start,
end=end)
dma = DualMovingAverage()
results = dma.run(data)
index = [br.date for br in trading.environment.benchmark_returns]
rets = [br.returns for br in trading.environment.benchmark_returns]
bm_returns = pd.Series(rets, index=index).ix[start:end]
results['benchmark_returns'] = (1 + bm_returns).cumprod().values
results['algorithm_returns'] = (1 + results.returns).cumprod()
fig = plt.figure()
ax1 = fig.add_subplot(211, ylabel='cumulative returns')
results[['algorithm_returns', 'benchmark_returns']].plot(ax=ax1,
sharex=True)
ax2 = fig.add_subplot(212)
data['IBM'].plot(ax=ax2, color='r')
results[['short_mavg', 'long_mavg']].plot(ax=ax2)
ax2.plot(results.ix[results.buy].index, results.short_mavg[results.buy],
'^', markersize=10, color='m')
ax2.plot(results.ix[results.sell].index, results.short_mavg[results.sell],
'v', markersize=10, color='k')
plt.legend(loc=0)
def run_algorithm(
security='AAPL',
start_date='20100101',
end_date='20150101',
initial_cash=100000,
rsi_window=15,
low_RSI=30,
high_RSI=70):
logging.debug('run_algorithm begin')
# dates
start = dateutil.parser.parse(start_date)
end = dateutil.parser.parse(end_date)
# get data from yahoo
data = load_from_yahoo(stocks=[security], indexes={}, start=start, end=end)
logging.debug('done loading from yahoo. {} {} {}'.format(
security, start_date, end_date))
# create and run algorithm
algo = TradingAlgorithm(
initialize=initialize,
handle_data=handle_data,
capital_base=initial_cash)
algo.security = security
initialize.low_RSI = low_RSI
initialize.high_RSI = high_RSI
initialize.rsi_window = rsi_window
logging.debug('starting to run algo...')
results = algo.run(data).dropna()
logging.debug('done running algo')
return results
def stock_process():
start = datetime(2010, 1, 1, 0, 0, 0, 0, pytz.utc)
end = datetime(2011, 1, 1, 0, 0, 0, 0, pytz.utc)
data = load_from_yahoo(stocks=['AAPL'], indexes={}, start=start, end=end)
plt.plot(data[:sample_size])
plt.show()
def run_algorithm(security='AAPL',
start_date='20100101',
end_date='20150101',
initial_cash=100000,
rsi_window=15,
low_RSI=30,
high_RSI=70):
logging.debug('run_algorithm begin')
# dates
start = dateutil.parser.parse(start_date)
end = dateutil.parser.parse(end_date)
# get data from yahoo
data = load_from_yahoo(stocks=[security], indexes={}, start=start, end=end)
logging.debug('done loading from yahoo. {} {} {}'.format(
security, start_date, end_date))
# create and run algorithm
algo = TradingAlgorithm(initialize=initialize,
handle_data=handle_data,
capital_base=initial_cash)
algo.security = security
initialize.low_RSI = low_RSI
initialize.high_RSI = high_RSI
initialize.rsi_window = rsi_window
logging.debug('starting to run algo...')
results = algo.run(data).dropna()
logging.debug('done running algo')
return results
def setUp(self):
setup_logger(self)
start = pd.datetime(1993, 1, 1, 0, 0, 0, 0, pytz.utc)
end = pd.datetime(1994, 1, 1, 0, 0, 0, 0, pytz.utc)
self.data = factory.load_from_yahoo(stocks=['AAPL'],
indexes={},
start=start, end=end)
def setUp(self):
setup_logger(self)
start = pd.datetime(1993, 1, 1, 0, 0, 0, 0, pytz.utc)
end = pd.datetime(1994, 1, 1, 0, 0, 0, 0, pytz.utc)
self.data = factory.load_from_yahoo(stocks=['AAPL'],
indexes={},
start=start,
end=end)
def __init__(self, movimientos):
self.movimientos = movimientos
fechas = []
emisoras = set()
for movimiento in movimientos:
fechas.append(movimiento.fecha)
emisoras.add(movimiento.emisora)
emisoras = list(emisoras)
fecha_inicio = min(fechas)
self.data = load_from_yahoo(stocks=emisoras, indexes={}, start=fecha_inicio, adjusted=False)
def load_symbols(self, name, symbols=['AAPL','FB','CSCO','INTC'], start=None, end=None):
start = start or date.today() - datetime.timedelta(days=1*365)
end = end or date.today()
name = "%s%s" % (self.path, name)
print "Loading %s from %s to %s into %s" % (",".join(symbols), start, end, name)
if os.path.isfile(name):
self.data = pd.read_pickle(name)
else:
self.data = load_from_yahoo(stocks=symbols, start=start, end=end)
self.data.to_pickle(name)
return self.data
def querydata(symbols, start, end, useDatastore=True):
def utc(dt):
return datetime(dt.year, dt.month, dt.day, 0, 0, 0, 0, pytz.utc)
if not isinstance(start, datetime):
start = utc(datetime.strptime(start, '%Y-%m-%d'))
end = utc(datetime.strptime(end, '%Y-%m-%d'))
if useDatastore:
return load_from_datastore(stocks=symbols, start=start, end=end)
else:
return load_from_yahoo(stocks=symbols, indexes={}, start=start, end=end)
def __init__(self, movimientos):
self.movimientos = movimientos
fechas = []
emisoras = set()
for movimiento in movimientos:
fechas.append(movimiento.fecha)
emisoras.add(movimiento.emisora)
emisoras = list(emisoras)
fecha_inicio = min(fechas)
self.data = load_from_yahoo(stocks=emisoras,
indexes={},
start=fecha_inicio,
adjusted=False)
def test_load_from_yahoo(self):
stocks = ['AAPL', 'GE']
start = pd.datetime(1993, 1, 1, 0, 0, 0, 0, pytz.utc)
end = pd.datetime(2002, 1, 1, 0, 0, 0, 0, pytz.utc)
data = load_from_yahoo(stocks=stocks, start=start, end=end)
assert data.index[0] == pd.Timestamp('1993-01-04 00:00:00+0000')
assert data.index[-1] == pd.Timestamp('2001-12-31 00:00:00+0000')
for stock in stocks:
assert stock in data.columns
np.testing.assert_raises(
AssertionError, load_from_yahoo, stocks=stocks,
start=end, end=start
)
def test_load_from_yahoo(self):
stocks = ['AAPL', 'GE']
start = pd.datetime(1993, 1, 1, 0, 0, 0, 0, pytz.utc)
end = pd.datetime(2002, 1, 1, 0, 0, 0, 0, pytz.utc)
data = load_from_yahoo(stocks=stocks, start=start, end=end)
assert data.index[0] == pd.Timestamp('1993-01-04 00:00:00+0000')
assert data.index[-1] == pd.Timestamp('2001-12-31 00:00:00+0000')
for stock in stocks:
assert stock in data.columns
np.testing.assert_raises(
AssertionError, load_from_yahoo, stocks=stocks,
start=end, end=start
)
def run(pair, start, end, z_threshold, window):
class Pairtrade(TradingAlgorithm):
def initialize(context):
context.in_short = False
context.in_long = False
context.p1 = context.symbol(pair[0])
context.p2 = context.symbol(pair[1])
context.window = window
context.i = 0
def handle_data(context, data):
context.i += 1
if context.i < context.window:
return
s1_history = data.history(context.p1, 'price', context.window,
'1d')
s2_history = data.history(context.p2, 'price', context.window,
'1d')
diff = (s1_history - s2_history)
mean = diff.mean()
s1_price = data.current(context.p1, 'price')
s2_price = data.current(context.p2, 'price')
zscore = ((s1_price - s2_price) - diff.mean()) / diff.std()
context.record(zscore=zscore, s1=s1_price, s2=s2_price)
if zscore > z_threshold and not context.in_short:
context.order_target_percent(context.p1, -0.5)
context.order_target_percent(context.p2, 0.5)
context.in_short = True
elif zscore < -z_threshold and not context.in_long:
context.order_target_percent(context.p1, 0.5)
context.order_target_percent(context.p2, -0.5)
context.in_long = True
elif abs(zscore) < .5:
context.order_target_percent(context.p1, 0)
context.order_target_percent(context.p2, 0)
context.in_short = False
context.in_long = False
data = load_from_yahoo(stocks=pair, indexes={}, start=start, end=end)
pairtrade = Pairtrade()
return pairtrade.run(data)
def fetch_equities_daily(self, symbols, ohlc=False, r_type=False, returns=False, **kwargs):
if len(symbols) == 0:
return pd.DataFrame()
if isinstance(symbols, str):
symbols = symbols.split(",")
if ohlc:
data = load_bars_from_yahoo(stocks=symbols, **kwargs)
# data.items = symbols
else:
data = load_from_yahoo(stocks=symbols, **kwargs)
# data.columns = symbols
# NOTE Would it work with a pandas panel ?
if returns:
data = ((data - data.shift(1)) / data).fillna(method="bfill")
if r_type:
data = convert_to_r_matrix(data)
return data
def run(symbol, startDate, endDate, startingCapital=100000):
class MovingAverage(TradingAlgorithm):
def initialize(self):
self.stock = self.symbol(symbol)
def handle_data(self, data):
current_date = data.current_dt.strftime('%Y-%m-%d')
if math.isnan(emaLong[current_date]):
return
if emaShort[current_date] > emaLong[current_date]:
self.order_target(self.stock, 100)
elif emaShort[current_date] < emaLong[current_date]:
self.order_target(self.stock, 0)
self.record(price=data.current(self.stock, 'price'),
emaShort=emaShort[current_date])
# Fetch Data
data = load_from_yahoo(stocks=[symbol],
indexes={},
start=startDate,
end=endDate)
stockReturns = data.tail(1)[symbol].values[0] - data.head(
1)[symbol].values[0]
# Setup
emaShort = pd.rolling_mean(data[symbol], window=100)
emaLong = pd.rolling_mean(data[symbol], window=200)
# Back test
simpleTrade = MovingAverage()
results = simpleTrade.run(data)
returnAmount = (results[-1:].portfolio_value.values[0] /
startingCapital) - 1
return (stockReturns, returnAmount, results)
def fetch_equities_daily(self, equities, ohlc=False,
r_type=False, returns=False, **kwargs):
if len(equities) == 0:
return pd.DataFrame()
if isinstance(equities, str):
equities = equities.split(',')
symbols = [self.datafeed.guess_name(equity) for equity in equities]
if ohlc:
data = load_bars_from_yahoo(stocks=symbols, **kwargs)
data.items = equities
else:
data = load_from_yahoo(stocks=symbols, **kwargs)
data.columns = equities
#NOTE Would it work with a pandas panel ?
if returns:
data = ((data - data.shift(1)) / data).fillna(method='bfill')
if r_type:
data = convert_to_r_matrix(data)
return data
import warnings
warnings.filterwarnings('ignore')
import matplotlib.pyplot as plt
from datetime import datetime
import pytz
from zipline.algorithm import TradingAlgorithm
from zipline.utils.factory import load_from_yahoo
start = datetime(2016, 1, 1, 0, 0, 0, 0, pytz.utc)
end = datetime(2016, 12, 1, 0, 0, 0, 0, pytz.utc)
pair = ['AMZN', 'FB'] # 6%
pair = ['RRC', 'HAL'] # 119%
data = load_from_yahoo(stocks=pair, indexes={}, start=start, end=end)
z_threshold = 1
class Pairtrade(TradingAlgorithm):
def initialize(context):
context.in_short = False
context.in_long = False
context.p1 = context.symbol(pair[0])
context.p2 = context.symbol(pair[1])
context.window = 20
context.i = 0
def handle_data(context, data):
context.i += 1
self.sell_spread()
self.invested = False
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__':
data = load_from_yahoo(stocks=['PEP', 'KO'], indexes={})
pairtrade = Pairtrade()
results = pairtrade.run(data)
data['spreads'] = np.nan
data.spreads[pairtrade.window_length:] = pairtrade.spreads
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)
data.spreads.plot(ax=ax2, color='r')
plt.ylabel('spread')
tw) > window_size else tw
# (6) Create CI for n-day window as price target range
# Return tuple with upper and lower bound
def CI(window, sds):
mean = np.mean(window)
sd = np.std(window)
return (mean - sds * sd, mean + sds * sd)
if __name__ == '__main__':
universe = ['FB']
data = load_from_yahoo(stocks=universe,
indexes={},
start=datetime(2016, 4, 3),
end=datetime.today())
olmar = TradingAlgorithm(initialize=initialize,
handle_data=handle_data,
capital_base=10000)
backtest = olmar.run(data)
backtest.to_csv('backtest-50-2012.csv')
print backtest['algorithm_period_return'][-1]
import pyfolio as pf
returns, positions, transactions, gross_lev = pf.utils.extract_rets_pos_txn_from_zipline(
backtest)
pf.create_full_tear_sheet(returns,
positions=positions,
transactions=transactions,
gross_lev=gross_lev,
# sad1_amount = self.portfolio.positions[sid1].amount
# self.order(sid1, -1 * sad1_amount)
for i in range(0, len(securities)):
self.order(securities[i], self.portfolio.positions[securities[i]].amount*-1)
for i in range(0, len(factors)):
self.order(factors[i], self.portfolio.positions[factors[i]].amount*-1)
# sad2_amount = self.portfolio.positions[sid2].amount
# self.order(sid2, -1 * sad2_amount)
if __name__ == '__main__':
###########################################################################################################
start = datetime(2006, 1, 1, 0, 0, 0, 0, pytz.utc)
end = datetime(2008, 1, 1, 0, 0, 0, 0, pytz.utc)
data = load_from_yahoo(stocks= (securities + factors), indexes={},
start=start, end=end)
## heard to save in the csv file and reget it. because the time zone and the timestampe is different
#data.to_csv("PEP_KO_2007-2008.csv",index = True ,cols=('PEP','KO'))
# the cols = () is order sensitive. If we save them as PEP and KO in cols them they will change the order
# If want to load another dataset then denote these lines.
############################################################################################################
#read_data = pd.DataFrame.from_csv('PEP_KO_2007-2008.csv',infer_datetime_format = True)
#read_data.index = pd.DatetimeIndex(read_data["Date"])
pairtrade = Pairtrade()
results = pairtrade.run(data)
#pd.DataFrame.join(data, results.pnl, on = 'index')
result = pd.concat([results.zscores,results.orders,results.positions,results.pnl],axis = 1)
self.order(self.stk.symbol, -1 * stk_amount)
self.initBP = 0 # set buying power to 0
self.invested = False
self.holdingTime = 0
if __name__ == '__main__':
start = datetime(2011, 1, 1, 0, 0, 0, 0, pytz.utc)
end = datetime(2012, 2, 1, 0, 0, 0, 0, pytz.utc)
stksymbol = 'CSCO'
etfsymbol = 'SPY'
data = load_from_yahoo(stocks=[stksymbol, etfsymbol],
indexes={},
start=start,
end=end)
pairtrade = Pairtrade(stksymbol, etfsymbol)
results = pairtrade.run(data)
data['spreads'] = np.nan
#print results.portfolio_value
ax1 = plt.subplot(311)
data[[stksymbol, etfsymbol]].plot(ax=ax1)
plt.ylabel('price')
plt.setp(ax1.get_xticklabels(), visible=False)
ax2 = plt.subplot(312, sharex=ax1)
results.zscores.plot(ax=ax2, color='r')
def __init__(self, trace=False):
BuyApple.trace = trace
super(BuyApple, self).__init__()
def initialize(context):
if BuyApple.trace: print("---> initialize")
if BuyApple.trace: print(context)
if BuyApple.trace: print("<--- initialize")
def handle_data(self, context):
if BuyApple.trace: print("---> handle_data")
if BuyApple.trace: print(context)
self.order("AAPL", 1)
if BuyApple.trace: print("<-- handle_data")
# %% 02 通过zipline 下载网络数据
# zipline has its own method to load data from Yahoo! Finance
data = zpf.load_from_yahoo(stocks=['AAPL'],
indexes={},
start=datetime(1990, 1, 1),
end=datetime(2014, 1, 1),
adjusted=False)
# %% 03 输出数据plot
# data.plot(figsize=(12,8));
# %% 03 回测
result = BuyApple(trace=False).run(data[0:10])
# %% 04 输出回测结果
result.portfolio_value.plot(figsize=(12, 8))
self.amount = 100
self.data = []
print("--->BuEveryDay.initialize() end")
def handle_data(self,data):
print('----BuyEveryDay.handle_data().',data[0]['dt'])
self.data.append(data[0]['dt']) #mid collect all data
self.order(self.sid,self.amount) #mid open 1 long position.
self.record(AAPL=data[self.sid].price) #mid add one column named 'AAPL' to returns of Algorithm.run()
if __name__ == '__main__':
import zipline.utils.factory as zpf
from datetime import datetime
import matplotlib.pyplot as plt
data = zpf.load_from_yahoo(stocks=['AAPL'],
indexes={},
start=datetime(1997, 1, 1),
end=datetime(1998, 6, 1),
adjusted=True)
algo = BuyEveryDay(instant_fill=True,
capital_base=50000,
env=None,
sim_params = None, # 设置有此参数时，start和end不能再设置，否则，显得多余也会运行assert错误
#start = algo['start'],
#end = algo['end'],
data_frequency = 'daily')
def dumpDict(dictStr):
""""""
import json
jsonDumpsIndentStr = json.dumps(dictStr, indent=4,skipkeys = False,default=str,sort_keys=True)
print (jsonDumpsIndentStr)
algo.dumpDict = dumpDict
plt.setp(ax1.get_xticklabels(), visible=False)
plt.legend(loc=0)
ax2 = fig.add_subplot(312, ylabel='Price')
data[algo.symbol].plot(ax=ax2, color='green')
plt.setp(ax2.get_xticklabels(), visible=False)
plt.legend(loc=0)
ax3 = fig.add_subplot(313, ylabel='Score')
results.Score.plot(ax=ax3, color='red')
results.Ideal.plot(ax=ax3, color='blue')
plt.setp(ax3.get_xticklabels(), visible=False)
plt.legend(loc=0)
plt.gcf().set_size_inches(18, 8)
plt.show()
if __name__ == '__main__':
start = datetime(2012, 1, 1, 0, 0, 0, 0, pytz.utc)
end = datetime(2015, 3, 1, 0, 0, 0, 0, pytz.utc)
algo = TradingAlgorithm(initialize=initialize,
handle_data=handle_data,
capital_base=10000)
data = load_from_yahoo(stocks=[algo.symbol],
indexes={},
start=start,
end=end).dropna()
results = algo.run(data)
show_results(algo, data, results)
'^', markersize=10, color='m')
ax2.plot(sells.index, results.short_mavg.ix[sells.index],
'v', markersize=10, color='k')
plt.legend(loc=0)
else:
msg = 'AAPL, short_mavg & long_mavg data not captured using record().'
ax2.annotate(msg, xy=(0.1, 0.5))
log.info(msg)
plt.show()
if __name__ =='__main__':
from datetime import datetime
import pytz
import matplotlib.pyplot as plt
from zipline import TradingAlgorithm
from zipline.utils.factory import load_from_yahoo
start = datetime(1990, 1, 1, 0, 0, 0, 0, pytz.utc)
end = datetime(2002, 1, 1, 0, 0, 0, 0, pytz.utc)
import pdb
pdb.set_trace()
data = load_from_yahoo(stocks=['AAPL'], indexes={}, start=start,
end=end, adjusted=False)
algo = TradingAlgorithm(initialize=initialize, handle_data=handle_data)
res = algo.run(data).dropna()
analyze(res)
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import matplotlib.pyplot as plt
from zipline.algorithm import TradingAlgorithm
from zipline.utils.factory import load_from_yahoo
class BuyApple(TradingAlgorithm): # inherit from TradingAlgorithm
"""This is the simplest possible algorithm that does nothing but
buy 1 apple share on each event.
"""
def handle_data(self, data): # overload handle_data() method
self.order('SPY', 1) # order SID (=0) and amount (=1 shares)
if __name__ == '__main__':
data = load_from_yahoo(stocks=['SPY'], indexes={}, start="1990-01-01", end="2012-01-01")
simple_algo = BuyApple()
results = simple_algo.run(data)
results.portfolio_value.plot()
plt.show()
context.log.debug(str(notional) + ' - notional start ' + tradeday.strftime('%m/%d/%y'))
context.d = tradeday
#context.record(short_mavg = short[context.stocks[1]],
#long_mavg = long[context.stocks[1]],
#goog_price = context.price)
if __name__ == '__main__':
import sys
sys.path.append('/home/mid/PythonProjects/xpower')
import zipline.utils.factory as zpf
import matplotlib.pyplot as plt
data = zpf.load_from_yahoo(stocks=['AAPL', 'IBM', 'CSCO'],
indexes={},
start=dt.datetime(2016, 1, 1),
end=dt.datetime(2016, 3, 5),
adjusted=True)
algo = DualEmaTalib(instant_fill=True,
capital_base=50000,
env=None,
sim_params = None, # 设置有此参数时，start和end不能再设置，否则，显得多余也会运行assert错误
#start = algo['start'],
#end = algo['end'],
data_frequency = 'daily')
def dumpDict(dictStr):
""""""
import json
jsonDumpsIndentStr = json.dumps(dictStr, indent=4,skipkeys = False,default=str,sort_keys=True)
print (jsonDumpsIndentStr)
def analyze(data, results):
def from_yahoo():
start = datetime(2016, 1, 1, 0, 0, 0, 0, pytz.utc)
end = datetime(2016, 3, 8, 0, 0, 0, 0, pytz.utc)
tickers = ['AAPL', 'GOOGL']
return load_from_yahoo(stocks=tickers, indexes={}, start=start, end=end)
stock.symbol,
context.sector_mappings[context.fundamental_df[stock]['morningstar_sector_code']]))
order_target_percent(stock, weight)
if __name__ == '__main__':
import sys
sys.path.append('/home/mid/PythonProjects/xpower')
import zipline.utils.factory as zpf
from datetime import datetime
import matplotlib.pyplot as plt
data = zpf.load_from_yahoo(stocks=['XLY',
'XLF',
'XLK'],
indexes={},
start=datetime(2015, 1, 1),
end=datetime(2016, 3, 5),
adjusted=True)
algo = DualEmaTalib(instant_fill=False,
capital_base=50000,
env=None,
sim_params = None, # 设置有此参数时，start和end不能再设置，否则，显得多余也会运行assert错误
#start = algo['start'],
#end = algo['end'],
data_frequency = 'daily')
def dumpDict(dictStr):
""""""
import json
jsonDumpsIndentStr = json.dumps(dictStr, indent=4,skipkeys = False,default=str,sort_keys=True)
print (jsonDumpsIndentStr)
# -*- coding: utf-8 -*-
"""
Created on Thu Sep 24 15:48:18 2015
@author: mid
"""
import numpy as np
import zipline as zp
import zipline.utils.factory as zpf
from datetime import datetime
import matplotlib.pyplot as plt
# load data for Coke and Pepsi and visualize
data = zpf.load_from_yahoo(stocks=['PEP', 'KO'],
indexes={},
start=datetime(1997, 1, 1),
end=datetime(1998, 6, 1),
adjusted=True)
data.plot(figsize=(12, 8))
plt.savefig('5104OS_07_20.png', bbox_inches='tight', dpi=300)
# calculate and plot the spread
plt.subplot()
data['Spread'] = data.PEP - data.KO
data['1997':].Spread.plot(figsize=(12, 8))
plt.ylabel('Spread')
plt.axhline(data.Spread.mean())
plt.savefig('5104OS_07_21.png', bbox_inches='tight', dpi=300)
import statsmodels.api as sm
ax2 = plt.subplot(212, sharex=ax1)
results.zscores.plot(ax=ax2, color='r')
plt.ylabel('Z-scored spread')
plt.gcf().set_size_inches(18, 8)
plt.show()
# Note: this if-block should be removed if running
# this algorithm on quantopian.com
if __name__ == '__main__':
logbook.StderrHandler().push_application()
# Set the simulation start and end dates.
start = datetime(2000, 1, 1, 0, 0, 0, 0, pytz.utc)
end = datetime(2002, 1, 1, 0, 0, 0, 0, pytz.utc)
# Load price data from yahoo.
data = load_from_yahoo(stocks=['PEP', 'KO'], indexes={},
start=start, end=end)
# Create and run the algorithm.
pairtrade = Pairtrade()
results = pairtrade.run(data)
# Plot the portfolio data.
analyze(results=results)
def get_data(self, sids, start, end):
return factory.load_from_yahoo(stocks=sids,
indexes={},
start=start,
end=end)
# Note: this function can be removed if running
# this algorithm on quantopian.com
def analyze(context=None, results=None):
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
results.portfolio_value.plot(ax=ax)
ax.set_ylabel('Portfolio value (USD)')
plt.show()
# Note: this if-block should be removed if running
# this algorithm on quantopian.com
if __name__ == '__main__':
# Set the simulation start and end dates.
start = datetime(2004, 1, 1, 0, 0, 0, 0, pytz.utc)
end = datetime(2008, 1, 1, 0, 0, 0, 0, pytz.utc)
# Load price data from yahoo.
data = load_from_yahoo(stocks=STOCKS, indexes={}, start=start, end=end)
data = data.dropna()
# Create and run the algorithm.
olmar = TradingAlgorithm(handle_data=handle_data,
initialize=initialize,
identifiers=STOCKS)
results = olmar.run(data)
# Plot the portfolio data.
analyze(results=results)
(sym_price - etf_price))
else:
action = 'BUY'
self.set_log(day, sym, etf, zscore, action,
(sym_price - etf_price))
else:
action = '---'
self.actions[sym]['ACTION'].append(action)
return
if __name__ == '__main__':
feed = build_feed()
data = load_from_yahoo(stocks=feed,
indexes={},
start=start,
end=end,
adjusted=True)
pairtrade = Pairtrade()
results = pairtrade.run(data)
###########################################################################
# Generate metrics
print 'Generating Risk Report...........'
print 'Using S&P500 as benchmark........'
start = results.first_valid_index().replace(tzinfo=pytz.utc)
end = results.last_valid_index().replace(tzinfo=pytz.utc)
env = trading.SimulationParameters(start, end)
returns_risk = create_returns_from_list(results.returns, env)
""""""
import json
jsonDumpsIndentStr = json.dumps(dictStr, indent=4,skipkeys = False,default=str,sort_keys=True)
print (jsonDumpsIndentStr)
if __name__ == '__main__':
import sys,os
xpower = '/home/mid/PythonProjects/xpower'
xpower = os.path.abspath(os.path.join(os.path.dirname(__file__),os.pardir))
sys.path.append(xpower)
import zipline.utils.factory as zpf
from datetime import datetime
import matplotlib.pyplot as plt
data = zpf.load_from_yahoo(stocks=['CERN', 'DLTR'],
indexes={},
start=datetime(2015, 1, 1),
end=datetime(2016, 3, 5),
adjusted=True)
algo = DualEmaTalib(instant_fill=False,
capital_base=50000,
env=None,
sim_params = None, # 设置有此参数时，start和end不能再设置，否则，显得多余也会运行assert错误
#start = algo['start'],
#end = algo['end'],
data_frequency = 'daily')
def analyze(data, results):
fig = plt.figure()
ax1 = fig.add_subplot(211, ylabel='Price in $')
data.plot(ax=ax1, color='r', lw=2.)
'''
if ranks is None: return
if debug: print "Ranks: ",ranks
if debug: print "Self.secs: ",self.secs
ranked_secs = sorted(self.secs, key=lambda x: ranks[x], reverse=True)
if debug: print "Ranked_secs: ", ranked_secs
top_secs = ranked_secs[0:self.top_k]
if debug: print "top_secs: ", top_secs
wt = dict(((sec,self.weight if sec in top_secs else 0.0) for sec in self.secs))
if debug: print "wt: ",wt
self.reweight(data,wt)
if __name__ == '__main__':
# SPY EFA AGG VNQ GLD
data = load_from_yahoo(stocks=['SPY','XLY','XLP','XLE','XLF','XLB','XLK','XLV','XLI','XLU'], indexes={}, start= pd.datetime(2002, 1, 1, 0, 0, 0, 0, pytz.utc), end= pd.datetime(2013, 1, 1, 0, 0, 0, 0, pytz.utc))
dma = Faber_TacticalAllocation()
results = dma.run(data)
if debug: print results.transactions
if debug: print results.returns
if debug: print results.positions
fig = plt.figure()
ax1 = fig.add_subplot(211)
results.portfolio_value.plot(ax=ax1)
ax2 = fig.add_subplot(212)
data['SPY'].plot(ax=ax2)
# # results[['short_mavg', 'long_mavg']].plot(ax=ax2)
# ax2.plot(results.ix[results.buy].index, results.short_mavg[results.buy],
if __name__ == '__main__':
import matplotlib.pyplot as plt
# run algorithm and get results
results = run_algorithm(security='AAPL',
start_date='20100101',
end_date='20150101',
initial_cash=100000,
rsi_window=15,
low_RSI=30,
high_RSI=70)
# get s&p500 and nasdaq indexes
index_data = load_from_yahoo(stocks=['^gspc', '^ixic'],
indexes={},
start=results.index[0],
end=results.index[-1])
# portfolio value, stock holdings and S&P 500 index
fig = plt.figure(figsize=(12, 6))
ax11 = fig.add_subplot(311)
ax12, ax13 = ax11.twinx(), ax11.twinx()
ax13.spines['right'].set_position(('axes', 1.07))
ax11.set_ylabel('portfolio value', color='blue')
ax12.set_ylabel('holdings', color='green')
ax13.set_ylabel('S&P 500', color='red')
# portfolio value
ax11.plot(results.index, results.portfolio_value, color='blue')
# holdings (number of stocks owned)
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[self.KO].amount
self.order(self.KO, -1 * ko_amount)
pep_amount = self.portfolio.positions[self.PEP].amount
self.order(self.PEP, -1 * pep_amount)
if __name__ == '__main__':
start = datetime(2000, 1, 1, 0, 0, 0, 0, pytz.utc)
end = datetime(2002, 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)
# TODO Bugged - indices are out of bounds
# data[[pairtrade.PEPsid, pairtrade.KOsid]].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')
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 = TradingAlgorithm(handle_data=handle_data, initialize=initialize, identifiers=STOCKS) results = olmar.run(data) results.portfolio_value.plot() pl.show()
self.order(sym, -1 * sym_amount)
if sym_amount > 0:
action = 'SELL'
self.set_log(day, sym, etf, zscore, action, (sym_price-etf_price))
else:
action = 'BUY'
self.set_log(day, sym, etf, zscore, action, (sym_price-etf_price))
else:
action = '---'
self.actions[sym]['ACTION'].append(action)
return
if __name__ == '__main__':
feed = build_feed()
data = load_from_yahoo(stocks=feed, indexes={},
start=start, end=end, adjusted=True)
pairtrade = Pairtrade()
results = pairtrade.run(data)
###########################################################################
# Generate metrics
print 'Generating Risk Report...........'
print 'Using S&P500 as benchmark........'
start = results.first_valid_index().replace(tzinfo=pytz.utc)
end = results.last_valid_index().replace(tzinfo=pytz.utc)
env = trading.SimulationParameters(start, end)
returns_risk = create_returns_from_list(results.returns, env)
algo_returns = RiskMetricsBase(start, end, returns_risk).algorithm_period_returns
elif abs(zscore) < .5 and self.invested:
self.sell_spread()
self.invested = False
def sell_spread(self):
"""
decrease exposure, regardless of position long/short.
buy for a short position, sell for a long.
"""
CVX_amount = self.portfolio.positions['CVX'].amount
self.order('CVX', -1 * CVX_amount)
XOM_amount = self.portfolio.positions['XOM'].amount
self.order('XOM', -1 * XOM_amount)
if __name__ == '__main__':
data = load_from_yahoo(stocks=['XOM', 'CVX'], start=datetime(2008,1,1), end=datetime(2012,1,1), indexes={})
pairtrade = Pairtrade()
results = pairtrade.run(data)
data['spreads'] = np.nan
data.spreads[pairtrade.window_length:] = pairtrade.spreads
ax1 = plt.subplot(211)
data[['XOM', 'CVX']].plot(ax=ax1)
plt.ylabel('price')
plt.setp(ax1.get_xticklabels(), visible=False)
ax2 = plt.subplot(212, sharex=ax1)
data.spreads.plot(ax=ax2, color='r')
plt.ylabel('spread')
# Record our variables to see the algo behavior. You can record up to
# 5 custom variables. To see only a certain variable, deselect the
# variable name in the custom graph in the backtest.
context.record(short_mavg = short[context.stocks[1]],
long_mavg = long[context.stocks[1]],
goog_price = context.price)
if __name__ == '__main__':
import sys
sys.path.append('/home/mid/PythonProjects/xpower')
import zipline.utils.factory as zpf
import matplotlib.pyplot as plt
data = zpf.load_from_yahoo(stocks=['BA','AAPL'],
indexes={},
start=datetime(2015, 1, 1),
end=datetime(2016, 3, 5),
adjusted=True)
algo = DualEmaTalib(instant_fill=True,
capital_base=50000,
env=None,
sim_params = None, # 设置有此参数时，start和end不能再设置，否则，显得多余也会运行assert错误
#start = algo['start'],
#end = algo['end'],
data_frequency = 'daily')
def dumpDict(dictStr):
""""""
import json
jsonDumpsIndentStr = json.dumps(dictStr, indent=4,skipkeys = False,default=str,sort_keys=True)
print (jsonDumpsIndentStr)
def analyze(data, results):
elif abs(zscore) < .5 and self.invested:
self.sell_spread()
self.invested = False
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('KO', -1 * pep_amount)
if __name__ == '__main__':
data = load_from_yahoo(stocks=['PEP', 'KO'], indexes={})
pairtrade = Pairtrade()
results = pairtrade.run(data)
data['spreads'] = np.nan
data.spreads[pairtrade.window_length:] = pairtrade.spreads
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)
data.spreads.plot(ax=ax2, color='r')
plt.ylabel('spread')
#context.record(short_mavg = short[context.stocks[1]],
#long_mavg = long[context.stocks[1]],
#goog_price = context.price)
if __name__ == '__main__':
import sys
sys.path.append('/home/mid/PythonProjects/xpower')
import zipline.utils.factory as zpf
import matplotlib.pyplot as plt
data = zpf.load_from_yahoo(stocks=['AAPL', 'IBM', 'CSCO'],
indexes={},
start=dt.datetime(2016, 1, 1),
end=dt.datetime(2016, 3, 5),
adjusted=True)
algo = DualEmaTalib(
instant_fill=True,
capital_base=50000,
env=None,
sim_params=None, # 设置有此参数时，start和end不能再设置，否则，显得多余也会运行assert错误
#start = algo['start'],
#end = algo['end'],
data_frequency='daily')
def dumpDict(dictStr):
""""""
import json
jsonDumpsIndentStr = json.dumps(dictStr,
self.portfolio.positions[securities[i]].amount * -1)
for i in range(0, len(factors)):
self.order(factors[i],
self.portfolio.positions[factors[i]].amount * -1)
# sad2_amount = self.portfolio.positions[sid2].amount
# self.order(sid2, -1 * sad2_amount)
if __name__ == '__main__':
###########################################################################################################
start = datetime(2006, 1, 1, 0, 0, 0, 0, pytz.utc)
end = datetime(2008, 1, 1, 0, 0, 0, 0, pytz.utc)
data = load_from_yahoo(stocks=(securities + factors),
indexes={},
start=start,
end=end)
## heard to save in the csv file and reget it. because the time zone and the timestampe is different
#data.to_csv("PEP_KO_2007-2008.csv",index = True ,cols=('PEP','KO'))
# the cols = () is order sensitive. If we save them as PEP and KO in cols them they will change the order
# If want to load another dataset then denote these lines.
############################################################################################################
#read_data = pd.DataFrame.from_csv('PEP_KO_2007-2008.csv',infer_datetime_format = True)
#read_data.index = pd.DatetimeIndex(read_data["Date"])
pairtrade = Pairtrade()
results = pairtrade.run(data)
#pd.DataFrame.join(data, results.pnl, on = 'index')
result = pd.concat(
self.short_mavgs = []
self.long_mavgs = []
def handle_data(self, data):
short_mavg = data['AAPL'].short_mavg['price']
long_mavg = data['AAPL'].long_mavg['price']
if short_mavg > long_mavg and not self.invested:
self.order('AAPL', 100)
self.invested = True
elif short_mavg < long_mavg and self.invested:
self.order('AAPL', -100)
self.invested = False
# Save mavgs for later analysis.
self.short_mavgs.append(short_mavg)
self.long_mavgs.append(long_mavg)
if __name__ == '__main__':
data = load_from_yahoo(stocks=['AAPL'], indexes={})
dma = DualMovingAverage()
results = dma.run(data)
results.portfolio_value.plot()
data['short'] = dma.short_mavgs
data['long'] = dma.long_mavgs
data[['AAPL', 'short', 'long']].plot()
plt.legend(loc=0)
plt.show()
def sell_spread(self,sid1,sid2):
"""
decrease exposure, regardless of position long/short.
buy for a short position, sell for a long.
"""
sad1_amount = self.portfolio.positions[sid1].amount
self.order(sid1, -1 * sad1_amount)
sad2_amount = self.portfolio.positions[sid2].amount
self.order(sid2, -1 * sad2_amount)
if __name__ == '__main__':
###########################################################################################################
start = datetime(2006, 1, 1, 0, 0, 0, 0, pytz.utc)
end = datetime(2008, 1, 1, 0, 0, 0, 0, pytz.utc)
data = load_from_yahoo(stocks=['XLF', 'JPM'], indexes={},
start=start, end=end)
## heard to save in the csv file and reget it. because the time zone and the timestampe is different
#data.to_csv("PEP_KO_2007-2008.csv",index = True ,cols=('PEP','KO'))
# the cols = () is order sensitive. If we save them as PEP and KO in cols them they will change the order
# If want to load another dataset then denote these lines.
############################################################################################################
#read_data = pd.DataFrame.from_csv('PEP_KO_2007-2008.csv',infer_datetime_format = True)
#read_data.index = pd.DatetimeIndex(read_data["Date"])
pairtrade = Pairtrade()
results = pairtrade.run(data)
#pd.DataFrame.join(data, results.pnl, on = 'index')
result = pd.concat([results.zscores,results.orders,results.positions,results.pnl],axis = 1)
self.buy = True
elif self.short_mavg < self.long_mavg and self.invested:
self.order('AAPL', -5000)
self.invested = False
self.sell = True
self.record(short_mavg=self.short_mavg,
long_mavg=self.long_mavg,
buy=self.buy,
sell=self.sell)
if __name__ == '__main__':
start = datetime(2002, 1, 1, 0, 0, 0, 0, pytz.utc)
end = datetime(2003, 1, 1, 0, 0, 0, 0, pytz.utc)
data = load_from_yahoo(stocks=['AAPL'], indexes={}, start=start, end=end)
dma = DualMovingAverage()
results = dma.run(data)
br = trading.environment.benchmark_returns
bm_returns = br[(br.index >= start) & (br.index <= end)]
results['benchmark_returns'] = (1 + bm_returns).cumprod().values
results['algorithm_returns'] = (1 + results.returns).cumprod()
fig = plt.figure()
ax1 = fig.add_subplot(211, ylabel='cumulative returns')
results[['algorithm_returns', 'benchmark_returns']].plot(ax=ax1,
sharex=True)
ax2 = fig.add_subplot(212)
self.order('DDD', -100)
self.invested = False
sell = True
# Record state variables. A column for each
# variable will be added to the performance
# DataFrame returned by .run()
self.record(short_mavg=short_mavg,
long_mavg=long_mavg,
buy=buy,
sell=sell)
# Load data
start = datetime(2008, 11, 11, 0, 0, 0, 0, pytz.utc)
end = datetime(2013, 11, 11, 0, 0, 0, 0, pytz.utc)
data = load_from_yahoo(stocks=['DDD'], indexes={}, start=start,
end=end, adjusted=False)
# Run algorithm
dma = DualMovingAverage()
perf = dma.run(data)
# Plot results
fig = plt.figure()
ax1 = fig.add_subplot(211, ylabel='Price in $')
data['DDD'].plot(ax=ax1, color='r', lw=2.)
perf[['short_mavg', 'long_mavg']].plot(ax=ax1, lw=2.)
ax1.plot(perf.ix[perf.buy].index, perf.short_mavg[perf.buy],
'^', markersize=10, color='m')
ax1.plot(perf.ix[perf.sell].index, perf.short_mavg[perf.sell],
'v', markersize=10, color='k')
self.order(self.stockB, -1 * stockB_amount)
#log.info(str(self.get_datetime())+' '+str(stockB_amount))
stockA_amount = self.portfolio.positions[self.stockA].amount
self.order(self.stockA, -1 * stockA_amount)
if __name__ == '__main__':
log = Logger('')
start = datetime(2011, 3, 28, 0, 0, 0, 0, pytz.utc)
end = datetime(2014, 3, 28, 0, 0, 0, 0, pytz.utc)
#stockA='gaz'
#stockB='uco'
#stockA='mzz'
#stockB='xop'
stockA='IVV'
stockB='SPY'
data = load_from_yahoo(stocks=[stockA, stockB], indexes={},
start=start, end=end)
pairtrade = Pairtrade(stockA,stockB)
results = pairtrade.run(data)
#data['spreads'] = np.nan
ax1 = plt.subplot(411)
data[[stockA,stockB]].plot(ax=ax1)
plt.ylabel('price')
plt.setp(ax1.get_xticklabels(), visible=False)
ax2 = plt.subplot(412, sharex=ax1)
results.zscores.plot(ax=ax2, color='r')
plt.ylabel('zscore')
ax3 = plt.subplot(413, sharex=ax1)
pd.TimeSeries(np.array(pairtrade.spreads),results.zscores.index).plot(ax=ax3, color='b')
plt.ylabel('spread')
plt.subplots_adjust(left=0.1, right=0.98, bottom=0.0, top=0.96)
ax1 = fig.add_subplot(311, ylabel='Cumulative Returns')
results[['algorithm_returns', 'benchmark_returns']].plot(ax=ax1, sharex=True)
plt.setp(ax1.get_xticklabels(), visible=False)
plt.legend(loc=0)
ax2 = fig.add_subplot(312, ylabel='Price')
data[algo.symbol].plot(ax=ax2, color='green')
plt.setp(ax2.get_xticklabels(), visible=False)
plt.legend(loc=0)
ax3 = fig.add_subplot(313, ylabel='Score')
results.Score.plot(ax=ax3, color='red')
results.Ideal.plot(ax=ax3, color='blue')
plt.setp(ax3.get_xticklabels(), visible=False)
plt.legend(loc=0)
plt.gcf().set_size_inches(18, 8)
plt.show()
if __name__ == '__main__':
start = datetime(2012, 1, 1, 0, 0, 0, 0, pytz.utc)
end = datetime(2015, 3, 1, 0, 0, 0, 0, pytz.utc)
algo = TradingAlgorithm(initialize=initialize, handle_data=handle_data, capital_base=10000)
data = load_from_yahoo(stocks=[algo.symbol], indexes={},
start=start, end=end).dropna()
results = algo.run(data)
show_results(algo, data, results)
cumulative['cp_sum'] = cumulative['cumsum_c'] + cumulative['cumsum_p']
mpp = cumulative.ix[cumulative['cp_sum'].idxmax()]['strike']
return mpp
def add_to_window(context, window_size, datapoint, ticker):
tw = context.target_window[ticker]
tw.append(datapoint)
context.target_window[ticker] = tw[-window_size:] if len(tw) > window_size else tw
# (6) Create CI for n-day window as price target range
# Return tuple with upper and lower bound
def CI(window, sds):
mean = np.mean(window)
sd = np.std(window)
return (mean - sds * sd, mean + sds * sd)
if __name__ == '__main__':
universe = ['FB']
data = load_from_yahoo(stocks=universe,
indexes={}, start=datetime(2016, 4, 3),
end=datetime.today())
olmar = TradingAlgorithm(initialize=initialize, handle_data=handle_data, capital_base=10000)
backtest = olmar.run(data)
backtest.to_csv('backtest-50-2012.csv')
print backtest['algorithm_period_return'][-1]
import pyfolio as pf
returns, positions, transactions, gross_lev = pf.utils.extract_rets_pos_txn_from_zipline(backtest)
pf.create_full_tear_sheet(returns, positions=positions, transactions=transactions, gross_lev=gross_lev, live_start_date='2004-10-22')
results[["PEP", "KO"]].plot(ax=ax1)
plt.ylabel("Price (USD)")
plt.setp(ax1.get_xticklabels(), visible=False)
ax2 = plt.subplot(212, sharex=ax1)
results.zscores.plot(ax=ax2, color="r")
plt.ylabel("Z-scored spread")
plt.gcf().set_size_inches(18, 8)
plt.show()
# Note: this if-block should be removed if running
# this algorithm on quantopian.com
if __name__ == "__main__":
logbook.StderrHandler().push_application()
# Set the simulation start and end dates.
start = datetime(2000, 1, 1, 0, 0, 0, 0, pytz.utc)
end = datetime(2002, 1, 1, 0, 0, 0, 0, pytz.utc)
# Load price data from yahoo.
data = load_from_yahoo(stocks=["PEP", "KO"], indexes={}, start=start, end=end)
# Create and run the algorithm.
pairtrade = Pairtrade()
results = pairtrade.run(data)
# Plot the portfolio data.
analyze(results=results)
results.portfolio_value.plot(ax=ax1)
ax1.set_ylabel('Portfolio value (USD)')
ax2 = plt.subplot(212, sharex=ax1)
results.AAPL.plot(ax=ax2)
ax2.set_ylabel('AAPL price (USD)')
# Show the plot.
plt.gcf().set_size_inches(18, 8)
plt.show()
# Set the simulation start and end dates
start = datetime(2014, 1, 1, 0, 0, 0, 0, pytz.utc)
end = datetime(2014, 11, 1, 0, 0, 0, 0, pytz.utc)
# Load price data from yahoo.
data = load_from_yahoo(stocks=['AAPL'], indexes={}, start=start,end=end)
# %% 根据字符串数组构造Datetime
strDate = ['2015-01-05', '2015-01-06', '2015-01-07', '2015-01-08', '2015-01-09',
'2015-01-12', '2015-01-13', '2015-01-14', '2015-01-15', '2015-01-16']
date = pd.to_datetime(strDate)
data = pd.DataFrame(np.random.randn(10),index = date)
#%% 人工构建Datetime
longer_ts = pd.Series(np.random.randn(100),
index = pd.date_range('1/1/2000',periods = 100,tz = 'UTC',name = 'Date'),name='AAPL')
data = pd.DataFrame(longer_ts)
# Create and run the algorithm.
algo = TradingAlgorithm(initialize=initialize, handle_data=handle_data,identifiers=['AAPL'])
stk_amount = self.portfolio.positions[self.stk.symbol].amount
self.order(self.stk.symbol, -1 * stk_amount)
self.initBP = 0 # set buying power to 0
self.invested = False
self.holdingTime = 0
if __name__ == '__main__':
start = datetime(2011, 1, 1, 0, 0, 0, 0, pytz.utc)
end = datetime(2012, 2, 1, 0, 0, 0, 0, pytz.utc)
stksymbol = 'CSCO'
etfsymbol = 'SPY'
data = load_from_yahoo(stocks=[stksymbol, etfsymbol], indexes={},
start=start, end=end)
pairtrade = Pairtrade(stksymbol, etfsymbol)
results = pairtrade.run(data)
data['spreads'] = np.nan
#print results.portfolio_value
ax1 = plt.subplot(311)
data[[stksymbol, etfsymbol]].plot(ax=ax1)
plt.ylabel('price')
plt.setp(ax1.get_xticklabels(), visible=False)
ax2 = plt.subplot(312, sharex=ax1)
results.zscores.plot(ax=ax2, color='r')
plt.ylabel('zscored spread')
