def get_returns(self, benchmark=''):
returns = {}
if benchmark:
try:
benchmark_data = (
get_benchmark_returns(benchmark,
self.configuration['index'][0],
self.configuration['index'][-1]))
except Exception as e:
raise KeyError(e)
else:
#TODO Automatic detection given exchange market (on command line) ?
raise NotImplementedError()
#NOTE Could be more efficient. But len(benchmark_data.date) !=
# len(self.results.returns.index). Maybe because of different markets
dates = pd.DatetimeIndex([d.date for d in benchmark_data])
returns['benchmark_return'] = pd.Series(
[d.returns for d in benchmark_data], index=dates)
returns['benchmark_c_return'] = (
(returns['benchmark_return'] + 1).cumprod()) - 1
returns['algo_return'] = pd.Series(
self.results.returns.values, index=dates)
returns['algo_c_return'] = pd.Series(
((self.results.returns.values + 1).cumprod()) - 1, index=dates)
df = pd.DataFrame(returns, index=dates)
if benchmark is None:
df = df.drop(['benchmark_return', 'benchmark_c_return'], axis=1)
return df
def get_returns(self, benchmark='', timestamp='one_month', save=False, db_id=None):
returns = dict()
if benchmark:
try:
benchmark_data = get_benchmark_returns(benchmark, self.configuration['start'], self.configuration['end'])
except:
raise KeyError()
else:
#TODO Automatic detection given exchange market (on command line) ?
raise NotImplementedError()
#NOTE Could be more efficient. But len(benchmark_data.date) != len(self.results.returns.index). Maybe because of different markets
dates = pd.DatetimeIndex([d.date for d in benchmark_data])
returns['Benchmark.Returns'] = pd.Series([d.returns for d in benchmark_data], index=dates)
returns['Benchmark.CReturns'] = ((returns['Benchmark.Returns'] + 1).cumprod()) - 1
returns['Returns'] = pd.Series(self.results.returns, index=dates)
returns['CReturns'] = pd.Series(((self.results.returns + 1).cumprod()) - 1, index=dates)
df = pd.DataFrame(returns, index=dates)
if save:
raise NotImplementedError()
self.datafeed.stock_db.saveDFToDB(df, table=db_id)
if benchmark is None:
df = df.drop(['Benchmark.Returns', 'Benchmark.CReturns'], axis=1)
return df
def get_returns(self, benchmark=None, timestamp='one_month', save=False, db_id=None):
returns = dict()
if benchmark:
#TODO Benchmark fields in database for guessing name like for stocks
benchmark_symbol = '^GSPC'
benchmark_data = get_benchmark_returns(benchmark_symbol, self.backtest_cfg['start'], self.backtest_cfg['end'])
else:
raise NotImplementedError()
#benchmark_data = [d for d in benchmark_data if (d.date >= self.backtest_cfg['start']) and (d.date <= self.backtest_cfg['end'])]
dates = pd.DatetimeIndex([d.date for d in benchmark_data])
returns['Benchmark.Returns'] = pd.Series([d.returns for d in benchmark_data], index=dates)
returns['Benchmark.CReturns'] = ((returns['Benchmark.Returns'] + 1).cumprod()) - 1
returns['Returns'] = pd.Series(self.results.returns, index=dates)
returns['CReturns'] = pd.Series(((self.results.returns + 1).cumprod()) - 1, index=dates)
df = pd.DataFrame(returns, index=dates)
if save:
raise NotImplementedError()
self.feeds.stock_db.saveDFToDB(df, table=db_id)
if benchmark is None:
df = df.drop(['Benchmark.Returns', 'Benchmark.CReturns'], axis=1)
return df
def get_returns(self, benchmark='', timestamp='one_month', save=False, db_id=None):
returns = dict()
if benchmark:
try:
benchmark_data = get_benchmark_returns(benchmark, self.configuration['start'], self.configuration['end'])
except:
raise KeyError()
else:
#TODO Automatic detection given exchange market (on command line) ?
raise NotImplementedError()
#NOTE Could be more efficient. But len(benchmark_data.date) != len(self.results.returns.index). Maybe because of different markets
dates = pd.DatetimeIndex([d.date for d in benchmark_data])
returns['Benchmark.Returns'] = pd.Series([d.returns for d in benchmark_data], index=dates)
returns['Benchmark.CReturns'] = ((returns['Benchmark.Returns'] + 1).cumprod()) - 1
returns['Returns'] = pd.Series(self.results.returns, index=dates)
returns['CReturns'] = pd.Series(((self.results.returns + 1).cumprod()) - 1, index=dates)
df = pd.DataFrame(returns, index=dates)
if save:
raise NotImplementedError()
self.datafeed.stock_db.saveDFToDB(df, table=db_id)
if benchmark is None:
df = df.drop(['Benchmark.Returns', 'Benchmark.CReturns'], axis=1)
return df
def get_returns(self, benchmark=None, timestamp='one_month', save=False, db_id=None):
returns = dict()
if benchmark:
benchmark_symbol = self.feeds.guess_name(benchmark)
if benchmark_symbol:
benchmark_data = get_benchmark_returns(benchmark_symbol, self.backtest_cfg['start'], self.backtest_cfg['end'])
else:
raise KeyError()
else:
#TODO Automatic detection given exchange market (on command line) ? Or s&p500 as only implemented in zipline currently (but not for long !)
raise NotImplementedError()
#NOTE Could be more efficient. But len(benchmark_data.date) != len(self.results.returns.index). Maybe because of different markets
dates = pd.DatetimeIndex([d.date for d in benchmark_data])
returns['Benchmark.Returns'] = pd.Series([d.returns for d in benchmark_data], index=dates)
returns['Benchmark.CReturns'] = ((returns['Benchmark.Returns'] + 1).cumprod()) - 1
returns['Returns'] = pd.Series(self.results.returns, index=dates)
returns['CReturns'] = pd.Series(((self.results.returns + 1).cumprod()) - 1, index=dates)
df = pd.DataFrame(returns, index=dates)
if save:
raise NotImplementedError()
self.feeds.stock_db.saveDFToDB(df, table=db_id)
if benchmark is None:
df = df.drop(['Benchmark.Returns', 'Benchmark.CReturns'], axis=1)
return df
def test(self, sd, ed, live_start_date, benchmark='SPY'):
trading_days = date_range(sd, ed, freq=trading_day)
self.timestep_progress = tqdm(total=len(trading_days) / 21)
results = run_algorithm(initialize=self.initialize_testing_algo,
capital_base=self.capital,
start=sd,
end=ed)
returns, positions, transactions = pf.utils.extract_rets_pos_txn_from_zipline(
results)
benchmark_returns = get_benchmark_returns(benchmark, sd, ed)
benchmark_returns.ix[sd] = 0.0
pf.create_full_tear_sheet(returns,
positions=positions,
transactions=transactions,
benchmark_rets=benchmark_returns,
live_start_date=live_start_date,
round_trips=True)
def get_non_trading_days(start, end):
# 对non_trading_days 进行改写
# 改写主要获取所有的时间日期，然后在剔除交易日期，最后在和交易日期进行
# 从000001 中获取所有的交易日期
#print start,end,'start,end'
tradeing_date = [
canonicalize_datetime(i)
for i in (get_benchmark_returns('000001', start, end)).index
]
#print tradeing_date[-5:]
# 获取在给定是时间内所有的日期
all_date = [canonicalize_datetime(t) for t in pd.date_range(start, end)]
non_trading_rules = []
start = canonicalize_datetime(start)
end = canonicalize_datetime(end)
#this is the rule of saturday and sunday
weekends = rrule.rrule(rrule.YEARLY,
byweekday=(rrule.SA, rrule.SU),
cache=True,
dtstart=start,
until=end)
non_trading_rules.append(weekends)
#first day of the year
new_years = rrule.rrule(rrule.MONTHLY,
byyearday=1,
cache=True,
dtstart=start,
until=end)
non_trading_rules.append(new_years)
# 5.1
may_1st = rrule.rrule(rrule.MONTHLY,
bymonth=5,
bymonthday=1,
cache=True,
dtstart=start,
until=end)
non_trading_rules.append(may_1st)
#10.1,2,3
oct_1st = rrule.rrule(rrule.MONTHLY,
bymonth=10,
bymonthday=1,
cache=True,
dtstart=start,
until=end)
non_trading_rules.append(oct_1st)
oct_2nd = rrule.rrule(rrule.MONTHLY,
bymonth=10,
bymonthday=2,
cache=True,
dtstart=start,
until=end)
non_trading_rules.append(oct_2nd)
oct_3rd = rrule.rrule(rrule.MONTHLY,
bymonth=10,
bymonthday=3,
cache=True,
dtstart=start,
until=end)
non_trading_rules.append(oct_3rd)
non_trading_ruleset = rrule.rruleset()
for rule in non_trading_rules:
non_trading_ruleset.rrule(rule)
non_trading_days = non_trading_ruleset.between(start, end, inc=True)
#bias_day = [p for p in all_date if (p not in tradeing_date and p not in non_trading_days)]
non_trading_days = non_trading_days + [
p for p in all_date
if (p not in tradeing_date and p not in non_trading_days)
]
non_trading_days.sort()
return pd.DatetimeIndex(non_trading_days)
if __name__ == '__main__':
from datetime import datetime
import pytz
from zipline.algorithm import TradingAlgorithm
from zipline.utils.factory import load_from_yahoo
# Set the simulation start and end dates.
start = datetime(2005, 11, 14, 0, 0, 0, 0, pytz.utc)
end = datetime(2013, 10, 14, 0, 0, 0, 0, pytz.utc)
# Load price data from yahoo.
data = load_from_yahoo(stocks=['AAPL', 'AXP', 'BA', 'CAT', 'CSCO', 'CVX', 'DD', 'DIS', 'GE', 'GS', 'HD', 'IBM', 'INTC', 'JNJ', 'JPM', 'KO', 'MCD', 'MMM', 'MRK', 'MSFT', 'NKE', 'PFE', 'PG', 'TRV', 'UNH', 'UTX', 'V', 'VZ', 'WMT', 'XOM'], indexes={}, start=start,
end=end)
benchmarks = get_benchmark_returns("^DJI", start, end)
data.bench = benchmarks
end2 = datetime(2013, 10, 14, 0, 0, 0, 0, pytz.utc)
start2 = datetime(2007, 5, 07, 0, 0, 0, 0, pytz.utc)
dw= web.DataReader("^GSPC", "yahoo", start=start2, end = end2)
data.dowJones = dw
# Create and run the algorithm.
algo = TradingAlgorithm(initialize=initialize, handle_data=handle_data,
identifiers=['AAPL', 'AXP', 'BA', 'CAT', 'CSCO', 'CVX', 'DD', 'DIS', 'GE', 'GS', 'HD', 'IBM', 'INTC', 'JNJ', 'JPM', 'KO', 'MCD', 'MMM', 'MRK', 'MSFT', 'NKE', 'PFE', 'PG', 'TRV', 'UNH', 'UTX', 'V', 'VZ', 'WMT', 'XOM'])
results = algo.run(data)
# # plt.show()
# # print('Returns: ', perfData.returns)
# # print('Alpha: ', perfData.alpha)
# # print('Beta: ', perfData.beta)
# # print('Sharpe: ', perfData.sharpe)
# # print('Drawdown: ', perfData.max_drawdown)
# Use pyfolio (by Quantopian) to generate tearsheet
import pyfolio as pf
returns, positions, transactions = pf.utils.extract_rets_pos_txn_from_zipline(perfData)
# print(positions.tail())
# Get benchmark returns and compare it with our algorithm's returns
from zipline.data.benchmarks import get_benchmark_returns
bm_returns = get_benchmark_returns(args['benchmark'], start, end) # requires network connection
bm_returns.name = 'Benchmark (%s)' % args['benchmark']
returns.name = 'Algorithm'
ax = plt.gca()
pf.plot_rolling_returns(returns, factor_returns=bm_returns, logy=False, ax=ax)
# pf.plot_rolling_returns(returns, logy=False, ax=ax)
# pf.create_returns_tear_sheet(returns)
# pf.create_full_tear_sheet(perfData, positions=positions, transactions=transactions,
# live_start_date='2018-08-1', round_trips=False)
import matplotlib.ticker as mtick
ax.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))
