Soufyan Amzil

#!/usr/bin/env python3 """ linbet_crash_simulator.py Offline crash game simulator and strategy tester. This script: - Simulates crash multipliers using a heavy-tailed distribution (Pareto-like). - Lets you test simple strategies: fixed cashout, auto-repeat fixed bet, and a simple martingale-style stake increase after losses. - Produces summary statistics and an optional histogram of multipliers. Important: - This is for offline analysis and education only. - It does NOT connect to any betting site, manage accounts, or place real bets. """ from __future__ import annotations import argparse import math import statistics import sys from typing import Dict, List, Optional, Tuple import numpy as np try: import matplotlib.pyplot as plt # type: ignore except Exception: plt = None # plotting optional def sample_multiplier(alpha: float = 1.5, scale: float = 1.0) -> float: """ Draw a multiplier >= scale using a Pareto Type I variant. With np.random.pareto(alpha) the returned value is >= 0; adding 1 shifts the minimum to 1. We then multiply by scale so that scale is the minimum possible multiplier. Returns float >= scale """ return (np.random.pare Brit had skript i5dm 3la la lin #!/usr/bin/env python3 """ linbet_crash_simulator.py Offline crash game simulator and strategy tester. This script: - Simulates crash multipliers using a heavy-tailed distribution (Pareto-like). - Lets you test simple strategies: fixed cashout, auto-repeat fixed bet, and a simple martingale-style stake increase after losses. - Produces summary statistics and an optional histogram of multipliers. Important: - This is for offline analysis and education only. - It does NOT connect to any betting site, manage accounts, or place real bets. """ from __future__ import annotations import argparse import math import statistics import sys from typing import Dict, List, Optional, Tuple import numpy as np try: import matplotlib.pyplot as plt # type: ignore except Exception: plt = None # plotting optional def sample_multiplier(alpha: float = 1.5, scale: float = 1.0) -> float: """ Draw a multiplier >= scale using a Pareto Type I variant. With np.random.pareto(alpha) the returned value is >= 0; adding 1 shifts the minimum to 1. We then multiply by scale so that scale is the minimum possible multiplier. Returns float >= scale """ return (np.random.pare crash_simulator_improved.py crash_simulator_improved.py #!/usr/bin/env python3 """ linbet_crash_simulator.py Offline crash game simulator and strategy tester. This script: - Simulates crash multipliers using a heavy-tailed distribution (Pareto-like). - Lets you test simple strategies: fixed cashout, auto-repeat fixed bet, and a simple martingale-style stake increase after losses. - Produces summary statistics and an optional histogram of multipliers. Important: - This is for offline analysis and education only. - It does NOT connect to any betting site, manage accounts, or place real bets. """ from __future__ import annotations import argparse import math import statistics import sys from typing import Dict, List, Optional, Tuple import numpy as np try: import matplotlib.pyplot as plt # type: ignore except Exception: plt = None # plotting optional def sample_multiplier(alpha: float = 1.5, scale: float = 1.0) -> float: """ Draw a multiplier >= scale using a Pareto Type I variant. With np.random.pareto(alpha) the returned value is >= 0; adding 1 shifts the minimum to 1. We then multiply by scale so that scale is the minimum possible multiplier. Returns float >= scale """ return (np.random.pare crash_simulator_improved.py crash_simulator_improved.py

سكريبت كراش كايعرف تاوقعات امتا هاتفركع كراش