gerador adicionado

src/generator.py

@@ -0,0 +1,48 @@
+
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+
+
+def simulate_prices_alt(mu, sigma, n):
+    # mu, sigma, n = 7.0, 3.0, 1000 # mean and standard deviation
+    s = np.random.normal(mu, sigma, n)
+    return s
+
+def client_simulate_prices_alt():
+    s = simulate_prices_alt(7.0, 3.0, 50) # mean and standard deviation, number of samples
+
+def simulate_prices(n, k, mu, sigma, delta_t, S0):
+    ## https://medium.com/@MachineLearningYearning/how-to-simulate-stock-prices-452042862989
+    r = (mu*delta_t + sigma * np.sqrt(delta_t) * np.random.normal(size=(n, k)))
+    p = np.cumprod(r+1, axis=0) * S0 # compute cumulative returns, and multiply by initial price to calculate prices
+    return r, p
+
+def client_simulate_prices():
+    # # Simulate stock prices using a geometric Brownian motion model
+    # This is a simple model for stock prices, where the price follows a random walk with
+
+    # time parameters
+    T = .5 # simulate half a year 
+    delta_t = 1.0/252.0 # each day is 1/252 of a trading year
+    n = int(T / delta_t) # total number of samples
+    S0 = 10 # initial price of stock
+    mu = 1 # drift
+    sigma = 1 # volatility
+    k = 1 # number of realizations
+
+    r, p = simulate_prices(n, k, mu, sigma, delta_t, S0)
+
+    print(p[:,:100])
+    plt.figure()
+    plt.plot(p[:,:100])
+    plt.title(f'Simulated Stock Prices\n{1} Realizations')
+    plt.show()
+
+def client_test_simulate_prices():
+    df = pd.read_csv("my_data1.csv")
+    print(df.head())
+    print(df.describe())
+
+client_simulate_prices()

src/streamlit_app.py

@@ -1,6 +1,6 @@
 import streamlit as st
 import pandas as pd
-# import numpy as np
+import numpy as np
 
 #data = np.random.randint(1, 100, size=50)
 #df = pd.DataFrame(data, columns=['random_numbers'])