def forecast(sym, market, df, dt, insts, conf, calc_sharpe=True):
df2 = df.copy()
df2 = df2.ix[df2.index <= dt]
df2.sprice = df2.sprice / conf['price_divider']["%s_%s" % (sym,market)]
carryoffset = insts[(sym,market)]['carryoffset']
res = pd.DataFrame()
funcs = get_funcs(df, carryoffset)
for x in funcs: res[x] = funcs[x]()
res2 = res * pd.Series(conf['forecast_scalars'])
res2[res2 < -20.] = -20
res2[res2 > 20.] = 20
res3 = res2 * pd.Series(conf['forecast_weights'])
res3['forecast'] = res3.sum(axis=1)
res3['sprice'] = df2.sprice
#res[['carry','ewmac64_256','ewmac32_128','ewmac16_64']].to_csv("out1.csv")
#res2[['carry','ewmac64_256','ewmac32_128','ewmac16_64']].to_csv("out2.csv")
#res3[['carry','ewmac64_256','ewmac32_128','ewmac16_64','forecast','sprice']].to_csv("out3.csv")
#res3.to_csv("out3.csv")
sharpe = None; tval = None; pval = None
if calc_sharpe: sharpe,tval,pval = util.sharpe(res3.sprice, res3.forecast)
return res3.forecast, sharpe, tval, pval
def test_returns_sharpe_skew():
import util, zipfile, pandas as pd
with zipfile.ZipFile("../alg/legacycsv.zip", "r") as z:
df = pd.read_csv(z.open("EDOLLAR_price.csv"), index_col=0, parse_dates=True)
fast_ewma = pd.ewma(df.PRICE, span=32)
slow_ewma = pd.ewma(df.PRICE, span=128)
raw_ewmac = fast_ewma - slow_ewma
vol = util.robust_vol_calc(df.PRICE.diff())
forecast = raw_ewmac / vol
sr, tval, pval = util.sharpe(df.PRICE, forecast)
assert sr - 0.50 < 0.01
s = util.skew(df.PRICE, forecast)
assert s - (-0.57) < 0.01
def test_returns_sharpe_skew():
import util, zipfile, pandas as pd
with zipfile.ZipFile('../alg/legacycsv.zip', 'r') as z:
df = pd.read_csv(z.open('EDOLLAR_price.csv'),
index_col=0,
parse_dates=True)
fast_ewma = pd.ewma(df.PRICE, span=32)
slow_ewma = pd.ewma(df.PRICE, span=128)
raw_ewmac = fast_ewma - slow_ewma
vol = util.robust_vol_calc(df.PRICE.diff())
forecast = raw_ewmac / vol
sr, tval, pval = util.sharpe(df.PRICE, forecast)
assert sr - 0.50 < 0.01
s = util.skew(df.PRICE, forecast)
assert s - (-0.57) < 0.01
