Coverage for src / monte_neo / core / generator_utils.py: 100%

1from __future__ import annotations 

2 

3import time 

4from typing import TYPE_CHECKING, Any 

5 

6import pandas as pd 

7 

8if TYPE_CHECKING: 

9 pass 

10 

11# Parameter search spaces for each indicator type 

12PARAM_SPACES = { 

13 "sma": { 

14 "fast_period": (5, 50), 

15 "slow_period": (20, 200), 

16 }, 

17 "rsi": { 

18 "period": (5, 30), 

19 "overbought": (65, 85), 

20 "oversold": (15, 35), 

21 }, 

22 "macd": { 

23 "fast": (8, 20), 

24 "slow": (20, 40), 

25 "signal": (5, 15), 

26 }, 

27 "dynamic": {}, 

28} 

29 

30 

31def estimate_time(generator: Any, data: pd.DataFrame) -> float: 

32 """Estimate generation time in minutes. 

33 

34 Args: 

35 generator: IndicatorGenerator instance (typed as Any to avoid circular import) 

36 data: Sample data. 

37 

38 Returns: 

39 Estimated time in minutes. 

40 """ 

41 # Run small sample 

42 sample_iterations = 10 

43 start = time.time() 

44 

45 for _ in range(sample_iterations): 

46 indicator = generator._generate_random_indicator() 

47 signals = indicator.generate_signals(data) 

48 _ = generator.metrics_calc.calculate_all(data, signals) 

49 

50 elapsed = time.time() - start 

51 time_per_iter = elapsed / sample_iterations 

52 

53 # Account for MC validation (~10x slower) 

54 mc_factor = ( 

55 10 

56 if any( 

57 [ 

58 generator.config.use_mc_shuffling, 

59 generator.config.use_mc_noise, 

60 ] 

61 ) 

62 else 2 

63 ) 

64 

65 total_seconds = time_per_iter * generator.config.max_iterations * mc_factor 

66 return total_seconds / 60