def __init__(self,
horse_model=None,
mu=DEFAULT_MU,
sigma=DEFAULT_SIGMA,
beta=DEFAULT_BETA,
tau=DEFAULT_TAU,
draw_probability=DEFAULT_DRAW,
risk_aversion=0.1,
min_races=3,
max_exposure=50):
if horse_model is None:
logging.debug(
'Balius created from scratch: mu=%.2f sigma=%.2f beta=%.2f tau=%.2f draw_prob=%.2f '
'risk_aversion=%.2f min_races=%d max_exposure=%.2f' %
(mu, sigma, beta, tau, draw_probability, risk_aversion,
min_races, max_exposure))
self.hm = HorseModel(mu=mu,
sigma=sigma,
beta=beta,
tau=tau,
draw_probability=draw_probability)
else:
logging.debug(
'Balius created from horse model: ts=%s risk_aversion=%.2f min_races=%d max_exposure=%.2f'
%
(str(horse_model._ts), risk_aversion, min_races, max_exposure))
self.hm = horse_model
self.risk_aversion = risk_aversion
self.min_races = min_races
self.max_expsoure = max_exposure
def __init__(self, horse_model=None, mu=DEFAULT_MU, sigma=DEFAULT_SIGMA, beta=DEFAULT_BETA,
tau=DEFAULT_TAU, draw_probability=DEFAULT_DRAW, risk_aversion=0.1, min_races=3, max_exposure=50):
if horse_model is None:
logging.debug('Balius created from scratch: mu=%.2f sigma=%.2f beta=%.2f tau=%.2f draw_prob=%.2f '
'risk_aversion=%.2f min_races=%d max_exposure=%.2f' %
(mu, sigma, beta, tau, draw_probability, risk_aversion, min_races, max_exposure))
self.hm = HorseModel(mu=mu, sigma=sigma, beta=beta, tau=tau, draw_probability=draw_probability)
else:
logging.debug('Balius created from horse model: ts=%s risk_aversion=%.2f min_races=%d max_exposure=%.2f' %
(str(horse_model._ts), risk_aversion, min_races, max_exposure))
self.hm = horse_model
self.risk_aversion = risk_aversion
self.min_races = min_races
self.max_expsoure = max_exposure
class Balius(object):
def __init__(self,
horse_model=None,
mu=DEFAULT_MU,
sigma=DEFAULT_SIGMA,
beta=DEFAULT_BETA,
tau=DEFAULT_TAU,
draw_probability=DEFAULT_DRAW,
risk_aversion=0.1,
min_races=3,
max_exposure=50):
if horse_model is None:
logging.debug(
'Balius created from scratch: mu=%.2f sigma=%.2f beta=%.2f tau=%.2f draw_prob=%.2f '
'risk_aversion=%.2f min_races=%d max_exposure=%.2f' %
(mu, sigma, beta, tau, draw_probability, risk_aversion,
min_races, max_exposure))
self.hm = HorseModel(mu=mu,
sigma=sigma,
beta=beta,
tau=tau,
draw_probability=draw_probability)
else:
logging.debug(
'Balius created from horse model: ts=%s risk_aversion=%.2f min_races=%d max_exposure=%.2f'
%
(str(horse_model._ts), risk_aversion, min_races, max_exposure))
self.hm = horse_model
self.risk_aversion = risk_aversion
self.min_races = min_races
self.max_expsoure = max_exposure
def handle_race(self, ex, race):
if race['event'] == TO_BE_PLACED or race['n_runners'] < 3:
return
runners = race['selection']
if np.all(self.hm.get_runs(runners) >= self.min_races):
prices = ex.get_market_prices(race['market_id'])
prices = dict(map(lambda x: (x['selection'], x), prices))
odds = np.array(
map(lambda r: prices[r]['back_prices'][0]['price'], runners))
implied = get_implied_from_odds(odds)
p = self.hm.pwin_trapz(runners)
rel = p / implied - 1.0
t = 0.05
p[rel < -t] = implied[rel < -t] * 0.95
p[rel > t] = implied[rel > t] * 1.05
w = risk.nwin1_l2reg(p, odds, self.risk_aversion)
returns = risk.nwin1_bet_returns(w, odds)
if np.any(returns <= -self.max_expsoure):
logging.warning('Maximum exposure limit of %.2f reached!' %
self.max_expsoure)
logging.warning(
'Ignoring bets w=%s runners=%s with potential returns=%s' %
(w.tolist(), runners, returns.tolist()))
else:
logging.info(
'Betting on market_id=%s: |exposure|=%.2f collateral=%.2f'
% (race['market_id'], np.sum(np.abs(w)), np.min(returns)))
bets = ({
'selection_id': prices[r]['selection_id'],
'amount': w[i],
'data': {
'p': p[i],
'implied': implied[i]
}
} for i, r in enumerate(runners))
ex.place_exchange_bets(race['market_id'], bets)
if 'ranking' in race:
self.hm.fit_race(race)
def to_dict(self):
return {
'hm': self.hm.to_dict(),
'risk': {
'risk_aversion': self.risk_aversion,
'min_races': self.min_races,
'max_exposure': self.max_expsoure
}
}
@classmethod
def from_dict(cls, strat):
hm = HorseModel.from_dict(strat['hm'])
return cls(horse_model=hm, **strat['risk'])
def from_dict(cls, strat):
hm = HorseModel.from_dict(strat['hm'])
return cls(horse_model=hm, **strat['risk'])
class Balius(object):
def __init__(self, horse_model=None, mu=DEFAULT_MU, sigma=DEFAULT_SIGMA, beta=DEFAULT_BETA,
tau=DEFAULT_TAU, draw_probability=DEFAULT_DRAW, risk_aversion=0.1, min_races=3, max_exposure=50):
if horse_model is None:
logging.debug('Balius created from scratch: mu=%.2f sigma=%.2f beta=%.2f tau=%.2f draw_prob=%.2f '
'risk_aversion=%.2f min_races=%d max_exposure=%.2f' %
(mu, sigma, beta, tau, draw_probability, risk_aversion, min_races, max_exposure))
self.hm = HorseModel(mu=mu, sigma=sigma, beta=beta, tau=tau, draw_probability=draw_probability)
else:
logging.debug('Balius created from horse model: ts=%s risk_aversion=%.2f min_races=%d max_exposure=%.2f' %
(str(horse_model._ts), risk_aversion, min_races, max_exposure))
self.hm = horse_model
self.risk_aversion = risk_aversion
self.min_races = min_races
self.max_expsoure = max_exposure
def handle_race(self, ex, race):
if race['event'] == TO_BE_PLACED or race['n_runners'] < 3:
return
runners = race['selection']
if np.all(self.hm.get_runs(runners) >= self.min_races):
prices = ex.get_market_prices(race['market_id'])
prices = dict(map(lambda x: (x['selection'], x), prices))
odds = np.array(map(lambda r: prices[r]['back_prices'][0]['price'], runners))
implied = get_implied_from_odds(odds)
p = self.hm.pwin_trapz(runners)
rel = p / implied - 1.0
t = 0.05
p[rel < -t] = implied[rel < -t] * 0.95
p[rel > t] = implied[rel > t] * 1.05
w = risk.nwin1_l2reg(p, odds, self.risk_aversion)
returns = risk.nwin1_bet_returns(w, odds)
if np.any(returns <= -self.max_expsoure):
logging.warning('Maximum exposure limit of %.2f reached!' % self.max_expsoure)
logging.warning('Ignoring bets w=%s runners=%s with potential returns=%s'
% (w.tolist(), runners, returns.tolist()))
else:
logging.info('Betting on market_id=%s: |exposure|=%.2f collateral=%.2f' %
(race['market_id'], np.sum(np.abs(w)), np.min(returns)))
bets = ({'selection_id': prices[r]['selection_id'],
'amount': w[i],
'data': {
'p': p[i],
'implied': implied[i]
}
} for i, r in enumerate(runners))
ex.place_exchange_bets(race['market_id'], bets)
if 'ranking' in race:
self.hm.fit_race(race)
def to_dict(self):
return {
'hm': self.hm.to_dict(),
'risk': {
'risk_aversion': self.risk_aversion,
'min_races': self.min_races,
'max_exposure': self.max_expsoure
}
}
@classmethod
def from_dict(cls, strat):
hm = HorseModel.from_dict(strat['hm'])
return cls(horse_model=hm, **strat['risk'])
def from_dict(cls, strat):
hm = HorseModel.from_dict(strat['hm'])
return cls(horse_model=hm, **strat['risk'])