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Implement geometric accumulation mode for risk_analysis function (#964) (#1938)

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Co-authored-by: eabjab <buege.ethan@gmail.com>
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Linlang
2025-05-27 14:24:16 +08:00
committed by GitHub
parent 14d54aa2a1
commit c38e799ce7
1 changed files with 27 additions and 11 deletions
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32
qlib/contrib/evaluate.py
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@@ -7,7 +7,7 @@ from __future__ import print_function
import numpy as np
import pandas as pd
import warnings
from typing import Union
from typing import Union, Literal
from ..log import get_module_logger
from ..utils import get_date_range
@@ -24,16 +24,14 @@ from ..data.dataset.utils import get_level_index
logger = get_module_logger("Evaluate")
def risk_analysis(r, N: int = None, freq: str = "day"):
def risk_analysis(r, N: int = None, freq: str = "day", mode: Literal["sum", "product"] = "sum"):
"""Risk Analysis
NOTE:
The calculation of annulaized return is different from the definition of annualized return.
The calculation of annualized return is different from the definition of annualized return.
It is implemented by design.
Qlib tries to cumulated returns by summation instead of production to avoid the cumulated curve being skewed exponentially.
Qlib tries to cumulate returns by summation instead of production to avoid the cumulated curve being skewed exponentially.
All the calculation of annualized returns follows this principle in Qlib.
TODO: add a parameter to enable calculating metrics with production accumulation of return.
Parameters
----------
r : pandas.Series
@@ -42,11 +40,14 @@ def risk_analysis(r, N: int = None, freq: str = "day"):
scaler for annualizing information_ratio (day: 252, week: 50, month: 12), at least one of `N` and `freq` should exist
freq: str
analysis frequency used for calculating the scaler, at least one of `N` and `freq` should exist
mode: Literal["sum", "product"]
the method by which returns are accumulated:
- "sum": Arithmetic accumulation (linear returns).
- "product": Geometric accumulation (compounded returns).
"""
def cal_risk_analysis_scaler(freq):
_count, _freq = Freq.parse(freq)
# len(D.calendar(start_time='2010-01-01', end_time='2019-12-31', freq='day')) = 2384
_freq_scaler = {
Freq.NORM_FREQ_MINUTE: 240 * 238,
Freq.NORM_FREQ_DAY: 238,
@@ -62,11 +63,26 @@ def risk_analysis(r, N: int = None, freq: str = "day"):
if N is None:
N = cal_risk_analysis_scaler(freq)
if mode == "sum":
mean = r.mean()
std = r.std(ddof=1)
annualized_return = mean * N
information_ratio = mean / std * np.sqrt(N)
max_drawdown = (r.cumsum() - r.cumsum().cummax()).min()
elif mode == "product":
cumulative_curve = (1 + r).cumprod()
# geometric mean (compound annual growth rate)
mean = cumulative_curve.iloc[-1] ** (1 / len(r)) - 1
# volatility of log returns
std = np.log(1 + r).std(ddof=1)
cumulative_return = cumulative_curve.iloc[-1] - 1
annualized_return = (1 + cumulative_return) ** (N / len(r)) - 1
# max percentage drawdown from peak cumulative product
max_drawdown = (cumulative_curve / cumulative_curve.cummax() - 1).min()
else:
raise ValueError(f"risk_analysis accumulation mode {mode} is not supported. Expected `sum` or `product`.")
information_ratio = mean / std * np.sqrt(N)
data = {
"mean": mean,
"std": std,