def capm(symbol, index, start_date=None, end_date=None):
stock = data.get_hist(symbol, start_date, end_date)
bench = data.get_hist(index, start_date, end_date)
y = stock.close
y = ind.returnize(y)
x = stock.ix[bench.index]['close']
x = ind.returnize(x)
regr = OLS(y, x)
return regr
def get_non_trading_days(start, end):
sh = data.get_hist('sh')
# reverse to get non trading_days
day = pd.tseries.offsets.CDay(holidays=sh.index)
return pd.date_range(start=start,
end=end,
freq=day)
def get_trade_days(start_date, end_date):
sh = data.get_hist('sh')
start_i, end_i = trade_index(sh, start_date, end_date)
return sh.index[start_i: end_i]
from zipline.utils.factory import create_simulation_parameters
sim_params = create_simulation_parameters(
start = pd.to_datetime("2014-01-01 09:30:00").tz_localize("Asia/Shanghai").tz_convert("UTC"), #Bug in code doesn't set tz if these are not specified (finance/trading.py:SimulationParameters.calculate_first_open[close])
end = pd.to_datetime("2014-12-31 15:00:00").tz_localize("Asia/Shanghai").tz_convert("UTC"),
data_frequency = "daily",
emission_rate = "daily",
sids = ["600000"])
prefix = '000666'
codes = filter(lambda s: s.startswith(prefix), data.get_basics().index)
start = '2014-01-01'
end = '2015-04-30'
benchmark = data.get_hist('sh')
d = Market.get_stocks(codes, start, end)
# d[code].prices.index = d[code].prices.index.to_datetime().tz_localize('UTC')
# d[code].prices['price'] = d[code].prices['close']
d = pd.Panel(d)
with open('/home/leo/Workspace/stock/algorithms/aberration.py', 'r') as f:
algo_text = f.read()
# d = zipline.data.load_bars_from_yahoo(stocks=['AAPL'], start=start, end=end)
algo = zipline.TradingAlgorithm(script=algo_text,
namespace={},
capital_base=100000,
sim_params=sim_params)
# results = algo.run(d, benchmark_return_source=d[code]['close'].pct_change())
results = algo.run(d)
import pandas as pd
import pytz
from datetime import datetime, timedelta
from dateutil import rrule
from functools import partial
import data
sh = data.get_hist('sh')
# start = sh.index[0]
# end_base = pd.Timestamp('today', tz='UTC')
# # Give an aggressive buffer for logic that needs to use the next trading
# # day or minute.
# end = end_base + timedelta(days=365)
def get_non_trading_days(start, end):
sh = data.get_hist('sh')
# reverse to get non trading_days
day = pd.tseries.offsets.CDay(holidays=sh.index)
return pd.date_range(start=start,
end=end,
freq=day)
non_trading_days = get_non_trading_days(sh.index[0], sh.index[-1])
trading_day = pd.tseries.offsets.CDay(holidays=non_trading_days)
def get_trading_days(start, end, trading_day=trading_day):
return pd.date_range(start=start.date(),
end=end.date(),
freq=trading_day).tz_localize('UTC')