이 글은 몬테카를로 시뮬레이션 기반 주가 예측하는 방법을 보여준다.
다음은 주가를 다운로드하여, 평균, 편차를 계산한 후, 95% 수준에서 시뮬레이션을 수행해, 수익률을 예측하는 코드이다.
import numpy as np
import matplotlib.pyplot as plt
from eodhd import APIClient
import config as cfg
api = APIClient(cfg.API_KEY)
def get_ohlc_data():
df = api.get_historical_data("GSPC.INDX", "d", results=365)
return df
if __name__ == "__main__":
df = get_ohlc_data()
# Calculate daily returns
daily_returns = df["adjusted_close"].pct_change().dropna()
# Simulation parameters
initial_investment = 10000 # Initial investment amount
num_simulations = 1000 # Number of simulations
forecast_days = 365 # Investment horizon in days
desired_return = 0.10 # Desired return (10%)
# Calculate the average daily return
average_daily_return = daily_returns.mean()
# Calculate volatility as the standard deviation of daily returns
volatility = daily_returns.std()
print(f"Average Daily Return: {average_daily_return}")
print(f"Volatility: {volatility}")
# Simulating future returns
simulated_end_returns = np.zeros(num_simulations)
for i in range(num_simulations):
random_returns = np.random.normal(
average_daily_return, volatility, forecast_days
)
cumulative_return = np.prod(1 + random_returns)
simulated_end_returns[i] = initial_investment * cumulative_return
# Calculate the final investment values
final_investment_values = simulated_end_returns
# Calculate Value at Risk (VaR) and Expected Tail Loss (Conditional VaR)
confidence_level = 0.95
sorted_returns = np.sort(final_investment_values)
index_at_var = int((1 - confidence_level) * num_simulations)
var = initial_investment - sorted_returns[index_at_var]
conditional_var = initial_investment - sorted_returns[:index_at_var].mean()
print(f"Value at Risk (95% confidence): £{var:,.2f}")
print(f"Expected Tail Loss (Conditional VaR): £{conditional_var:,.2f}")
num_success = np.sum(
final_investment_values >= initial_investment * (1 + desired_return)
)
probability_of_success = num_success / num_simulations
print(
f"Probability of achieving at least a {desired_return*100}% return: {probability_of_success*100:.2f}%"
)
plt.figure(figsize=(10, 6))
plt.hist(final_investment_values, bins=50, alpha=0.75)
plt.axvline(
initial_investment * (1 + desired_return),
color="r",
linestyle="dashed",
linewidth=2,
)
plt.axvline(initial_investment - var, color="g", linestyle="dashed", linewidth=2)
plt.title("Distribution of Final Investment Values")
plt.xlabel("Final Investment Value")
plt.ylabel("Frequency")
plt.show()
"""
# Plotting the results
plt.figure(figsize=(10, 6))
plt.plot(simulations.T, color="blue", alpha=0.025)
plt.title("Monte Carlo Simulation of Future Prices")
plt.xlabel("Day")
plt.ylabel("Price")
plt.show()
"""
레퍼런스
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