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calculator.py
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calculator.py
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from typing import List, Optional, Tuple
from torch import Tensor
import torch
from alphagen.data.calculator import AlphaCalculator
from alphagen.data.expression import Expression
from alphagen.utils.correlation import batch_pearsonr, batch_spearmanr
from alphagen.utils.pytorch_utils import normalize_by_day
from alphagen_qlib.stock_data import StockData
class QLibStockDataCalculator(AlphaCalculator):
def __init__(self, data: StockData, target: Optional[Expression]):
self.data = data
if target is None: # Combination-only mode
self.target_value = None
else:
self.target_value = normalize_by_day(target.evaluate(self.data))
def _calc_alpha(self, expr: Expression) -> Tensor:
return normalize_by_day(expr.evaluate(self.data))
def _calc_IC(self, value1: Tensor, value2: Tensor) -> float:
return batch_pearsonr(value1, value2).mean().item()
def _calc_rIC(self, value1: Tensor, value2: Tensor) -> float:
# return batch_pearsonr(value1, value2).mean().item()
def chunk_batch_spearmanr(x,y,chunk_size=300):
n_days = len(x)
spearmanr_list= []
cur_fct = 0
for i in range(0,n_days,chunk_size):
spearmanr_list.append(batch_spearmanr(x[i:i+chunk_size],y[i:i+chunk_size]))
spearmanr_list = torch.cat(spearmanr_list,dim=0)
return spearmanr_list
return chunk_batch_spearmanr(value1, value2).mean().item()
def _calc_real_rIC(self, value1: Tensor, value2: Tensor) -> float:
# return batch_pearsonr(value1, value2).mean().item()
return batch_spearmanr(value1, value2).mean().item()
def _calc_real_rICIR(self, value1: Tensor, value2: Tensor) -> float:
# return batch_pearsonr(value1, value2).mean().item()
aaa = batch_spearmanr(value1, value2)
RIC = aaa.mean().item()
_std = aaa.std().item()
return RIC/_std
def make_ensemble_alpha(self, exprs: List[Expression], weights: List[float]) -> Tensor:
n = len(exprs)
factors: List[Tensor] = [self._calc_alpha(exprs[i]) * weights[i] for i in range(n)]
return sum(factors) # type: ignore
def calc_single_IC_ret(self, expr: Expression) -> float:
value = self._calc_alpha(expr)
return self._calc_IC(value, self.target_value)
def calc_single_rIC_ret(self, expr: Expression) -> float:
value = self._calc_alpha(expr)
return self._calc_rIC(value, self.target_value)
def calc_single_all_ret(self, expr: Expression) -> Tuple[float, float]:
value = self._calc_alpha(expr)
return self._calc_IC(value, self.target_value), self._calc_rIC(value, self.target_value)
def calc_mutual_IC(self, expr1: Expression, expr2: Expression) -> float:
value1, value2 = self._calc_alpha(expr1), self._calc_alpha(expr2)
return self._calc_IC(value1, value2)
def calc_pool_IC_ret(self, exprs: List[Expression], weights: List[float]) -> float:
with torch.no_grad():
ensemble_value = self.make_ensemble_alpha(exprs, weights)
return self._calc_IC(ensemble_value, self.target_value)
def calc_pool_rIC_ret(self, exprs: List[Expression], weights: List[float]) -> float:
with torch.no_grad():
ensemble_value = self.make_ensemble_alpha(exprs, weights)
return self._calc_real_rIC(ensemble_value, self.target_value)
def calc_pool_rICIR_ret(self, exprs: List[Expression], weights: List[float]) -> float:
with torch.no_grad():
ensemble_value = self.make_ensemble_alpha(exprs, weights)
return self._calc_real_rICIR(ensemble_value, self.target_value)
def calc_pool_all_ret(self, exprs: List[Expression], weights: List[float]) -> Tuple[float, float]:
with torch.no_grad():
ensemble_value = self.make_ensemble_alpha(exprs, weights)
return self._calc_IC(ensemble_value, self.target_value), self._calc_rIC(ensemble_value, self.target_value)
def calc_pool_all_ret_raw(self, exprs: List[Expression], weights: List[float]) -> Tuple[float, float]:
with torch.no_grad():
ensemble_value = self.make_ensemble_alpha(exprs, weights)
ic = batch_pearsonr(ensemble_value, self.target_value)
ric = batch_spearmanr(ensemble_value, self.target_value)
return ic,ric