def test_pandl(self):
fx = pd.DataFrame([2.0] * 10, dt_range1)
price = pd.DataFrame(
[100, 103, 105, 106, 110, 105, 104.5, np.nan, 120, np.nan, 142],
dt_range2)
trades = pd.concat([
pd.DataFrame(
dict(trades=[2, 1, -1, np.nan, 1],
fill_price=[102.9, 105.5, 106.5, np.nan, 106.]),
pd.date_range(start=pd.datetime(2015, 1, 2), periods=5)),
pd.DataFrame(
dict(trades=[-1, 1, -1], fill_price=[107, 119, 132]),
pd.date_range(start=pd.datetime(2015, 1, 8), periods=3))
])
ans = pandl(price, trades, marktomarket=True, fx=fx)
np.testing.assert_almost_equal(
ans.pandl_base[1:],
[0.0, 10.4, 6., 14., -16., -9., 15., 48., 78., 40.])
ans2 = pandl(price, trades, marktomarket=False, fx=fx)
np.testing.assert_almost_equal(ans2.pandl_base[1:],
[10.4, 6., 0., -2., 6., 48., 78.])
def test_pandl(self):
fx = pd.DataFrame([2.0] * 10, dt_range1)
price = pd.DataFrame(
[100, 103, 105, 106, 110, 105, 104.5, np.nan, 120, np.nan,
142], dt_range2)
trades = pd.concat([
pd.DataFrame(
dict(
trades=[2, 1, -1, np.nan, 1],
fill_price=[102.9, 105.5, 106.5, np.nan, 106.]),
pd.date_range(start=pd.datetime(2015, 1, 2), periods=5)),
pd.DataFrame(
dict(trades=[-1, 1, -1], fill_price=[107, 119, 132]),
pd.date_range(start=pd.datetime(2015, 1, 8), periods=3))
])
ans = pandl(price, trades, marktomarket=True, fx=fx)
np.testing.assert_almost_equal(ans.pandl_base[1:], [
0.0, 10.4, 6., 14., -16., -9., 15., 48., 78., 40.
])
ans2 = pandl(price, trades, marktomarket=False, fx=fx)
np.testing.assert_almost_equal(ans2.pandl_base[1:],
[10.4, 6., 0., -2., 6., 48., 78.])
def _pandl_for_instrument(system, instrument_code, this_stage,
percentage, delayfill, roundpositions):
this_stage.log.msg("Calculating pandl for instrument for %s" %
instrument_code,
instrument_code=instrument_code)
price = this_stage.get_daily_price(instrument_code)
positions = this_stage.get_notional_position(instrument_code)
fx = this_stage.get_fx_rate(instrument_code)
value_of_price_point = this_stage.get_value_of_price_move(
instrument_code)
if percentage:
capital = this_stage.get_notional_capital()
else:
capital = None
instr_pandl = pandl(price=price,
positions=positions,
delayfill=delayfill,
roundpositions=roundpositions,
fx=fx,
value_of_price_point=value_of_price_point,
capital=capital)
return instr_pandl
def _pandl_for_instrument_forecast(system, instrument_code, rule_variation_name, this_stage, delayfill):
price=this_stage.get_instrument_price(instrument_code)
forecast=this_stage.get_capped_forecast(instrument_code, rule_variation_name)
get_daily_returns_volatility=this_stage.get_daily_returns_volatility(instrument_code)
pandl_fcast=pandl(price=price, delayfill=delayfill,
get_daily_returns_volatility=get_daily_returns_volatility,
forecast=forecast, capital=0.0)
return pandl_fcast
def _pandl_for_instrument(system, instrument_code, this_stage, percentage, delayfill, roundpositions):
price=this_stage.get_instrument_price(instrument_code)
positions=this_stage.get_notional_position(instrument_code)
fx=this_stage.get_fx_rate(instrument_code)
value_of_price_point=this_stage.get_value_of_price_move(instrument_code)
if percentage:
capital=this_stage.get_notional_capital()
else:
capital=None
instr_pandl=pandl(price=price, positions=positions, delayfill=delayfill, roundpositions=roundpositions,
fx=fx, value_of_price_point=value_of_price_point, capital=capital )
return instr_pandl
def _pandl_for_instrument_forecast(
system, instrument_code, rule_variation_name, this_stage, delayfill):
this_stage.log.msg("Calculating pandl for instrument forecast for %s %s" % (instrument_code, rule_variation_name),
instrument_code=instrument_code, rule_variation_name=rule_variation_name)
price = this_stage.get_daily_price(instrument_code)
forecast = this_stage.get_capped_forecast(
instrument_code, rule_variation_name)
get_daily_returns_volatility = this_stage.get_daily_returns_volatility(
instrument_code)
pandl_fcast = pandl(price=price, delayfill=delayfill,
get_daily_returns_volatility=get_daily_returns_volatility,
forecast=forecast, capital=0.0)
return pandl_fcast
def _pandl_for_subsystem(
system, instrument_code, this_stage, percentage, delayfill, roundpositions):
this_stage.log.msg("Calculating pandl for subsystem for instrument %s" % instrument_code,
instrument_code=instrument_code)
price = this_stage.get_daily_price(instrument_code)
positions = this_stage.get_subsystem_position(instrument_code)
fx = this_stage.get_fx_rate(instrument_code)
value_of_price_point = this_stage.get_value_of_price_move(
instrument_code)
if percentage:
capital = this_stage.get_notional_capital()
else:
capital = None
instr_pandl = pandl(price=price, positions=positions, delayfill=delayfill, roundpositions=roundpositions,
fx=fx, value_of_price_point=value_of_price_point, capital=capital)
return instr_pandl
def _pandl_for_instrument_forecast(system, instrument_code,
rule_variation_name, this_stage,
delayfill):
this_stage.log.msg(
"Calculating pandl for instrument forecast for %s %s" %
(instrument_code, rule_variation_name),
instrument_code=instrument_code,
rule_variation_name=rule_variation_name)
price = this_stage.get_daily_price(instrument_code)
forecast = this_stage.get_capped_forecast(instrument_code,
rule_variation_name)
get_daily_returns_volatility = this_stage.get_daily_returns_volatility(
instrument_code)
pandl_fcast = pandl(
price=price,
delayfill=delayfill,
get_daily_returns_volatility=get_daily_returns_volatility,
forecast=forecast,
capital=0.0)
return pandl_fcast
fast_ewma = pd.ewma(price, span=Lfast)
slow_ewma = pd.ewma(price, span=Lslow)
raw_ewmac = fast_ewma - slow_ewma
vol = robust_vol_calc(price.diff())
return divide_df_single_column(raw_ewmac, vol)
"""
Try it out
(this isn't properly scaled at this stage of course)
"""
instrument_code = 'EDOLLAR'
price = data.get_instrument_price(instrument_code)
ewmac = calc_ewmac_forecast(price, 32, 128)
print(ewmac.tail(5))
from matplotlib.pyplot import show
ewmac.plot()
show()
"""
Did we make money?
"""
from syscore.accounting import pandl
account = pandl(price, forecast=ewmac)
account.curve().plot()
show()
vol = robust_vol_calc(price.diff())
return divide_df_single_column(raw_ewmac, vol)
"""
Try it out
(this isn't properly scaled at this stage of course)
"""
instrument_code = 'EDOLLAR'
price = data.get_daily_price(instrument_code)
ewmac = calc_ewmac_forecast(price, 32, 128)
ewmac.columns = ['forecast']
print(ewmac.tail(5))
from matplotlib.pyplot import show
ewmac.plot()
show()
"""
Did we make money?
"""
from syscore.accounting import pandl
account = pandl(price, forecast=ewmac, capital=0.0)
account.curve().plot()
show()
print(account.stats())
return divide_df_single_column(raw_ewmac, vol) """ Try it out (this isn't properly scaled at this stage of course) """ instrument_code = "EDOLLAR" price = data.get_daily_price(instrument_code) ewmac = calc_ewmac_forecast(price, 32, 128) ewmac.columns = ["forecast"] print(ewmac.tail(5)) from matplotlib.pyplot import show ewmac.plot() show() """ Did we make money? """ from syscore.accounting import pandl account = pandl(price, forecast=ewmac, capital=0.0) account.curve().plot() show() print(account.stats())