adding first draft of the func

build/lib/stocksim/__main__.py

@@ -0,0 +1,4 @@
+from .cli import cli
+
+if __name__ == "__main__":
+    cli()

build/lib/stocksim/cli.py

@@ -0,0 +1,50 @@
+import click
+import yfinance as yf
+import numpy as np
+import pandas as pd
+from datetime import datetime, timedelta
+
+@click.command()
+@click.option('--ticker', required=True, help='Stock ticker symbol')
+@click.option('--days', default=30, help='Number of days for simulation')
+@click.option('--target-price', required=True, type=float, help='Target price for probability calculation')
+@click.option('--simulations', default=1000, help='Number of Monte Carlo simulations')
+def monte_carlo_simulation(ticker, days, target_price, simulations):
+    # Download historical data
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=5*365)  # 5 years ago
+    data = yf.download(ticker, start=start_date, end=end_date)
+
+    # Calculate daily returns
+    returns = data['Close'].pct_change().dropna()
+
+    # Calculate mean and variance of daily returns
+    mu = returns.mean()
+    var = returns.var()
+
+    # Simulate future stock prices
+    last_price = data['Close'].iloc[-1]
+    simulation_df = pd.DataFrame()
+
+    for i in range(simulations):
+        prices = [last_price]
+        for _ in range(days):
+            price = prices[-1] * (1 + np.random.normal(mu, var**0.5))
+            prices.append(price)
+        simulation_df[i] = prices
+
+    # Calculate probability of exceeding target price
+    final_prices = simulation_df.iloc[-1]
+    prob_above_target = (final_prices > target_price).mean()
+
+    # Output results
+    click.echo(f"Monte Carlo simulation results for {ticker}:")
+    click.echo(f"Number of simulations: {simulations}")
+    click.echo(f"Number of days: {days}")
+    click.echo(f"Target price: ${target_price:.2f}")
+    click.echo(f"Probability of price above target: {prob_above_target:.2%}")
+    click.echo(f"Current price: ${last_price:.2f}")
+    click.echo(f"Simulated price range: ${final_prices.min():.2f} - ${final_prices.max():.2f}")
+
+if __name__ == '__main__':
+    monte_carlo_simulation()

pyproject.toml

@@ -5,12 +5,15 @@ description = "Simulate stock price over a future period"
 readme = "README.md"
 authors = [{name = "Lyubomir Georgiev"}]
 license = {text = "Apache-2.0"}
-requires-python = ">=3.8"
+requires-python = ">=3.10"
 classifiers = [
     "License :: OSI Approved :: Apache Software License"
 ]
 dependencies = [
-    "click"
+    "click",
+    "pandas",
+    "numpy",
+    "yfinance"
 ]
 
 [project.urls]

stocksim/cli.py

@@ -1,21 +1,59 @@
 import click
-
+import yfinance as yf
+import numpy as np
+import pandas as pd
+from datetime import datetime, timedelta
 
 @click.group()
 @click.version_option()
 def cli():
     "Simulate stock price over a future period"
 
+@click.command()
+@click.option('--ticker', required=True, help='Stock ticker symbol')
+@click.option('--days', default=30, help='Number of days for simulation')
+@click.option('--target-price', required=True, type=float, help='Target price for probability calculation')
+@click.option('--simulations', default=1000, help='Number of Monte Carlo simulations')
+def cli(ticker, days, target_price, simulations):
+    # Download historical data
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=5*365)  # 5 years ago
+    data = yf.download(ticker, start=start_date, end=end_date)
+
+    # Calculate daily returns
+    returns = data['Close'].pct_change().dropna()
+
+    # Calculate mean and variance of daily returns
+    mu = returns.mean()
+    var = returns.var()
+
+    # Simulate future stock prices
+    last_price = data['Close'].iloc[-1]
+
+    # Create a matrix of random returns
+    random_returns = np.random.normal(mu, var**0.5, size=(days, simulations))
+
+    # Calculate price paths
+    price_paths = last_price * np.exp(np.cumsum(random_returns, axis=0))
+
+    # Insert the initial price at the beginning of each path
+    price_paths = np.insert(price_paths, 0, last_price, axis=0)
+
+    # Convert to DataFrame
+    simulation_df = pd.DataFrame(price_paths, columns=[f'Sim_{i}' for i in range(simulations)])
+
+    # Calculate probability of exceeding target price
+    final_prices = simulation_df.iloc[-1]
+    prob_above_target = (final_prices > target_price).mean()
+
+    # Output results
+    click.echo(f"Monte Carlo simulation results for {ticker}:")
+    click.echo(f"Number of simulations: {simulations}")
+    click.echo(f"Number of days: {days}")
+    click.echo(f"Target price: ${target_price:.2f}")
+    click.echo(f"Probability of price above target: {prob_above_target:.2%}")
+    click.echo(f"Current price: ${last_price:.2f}")
+    click.echo(f"Simulated price range: ${final_prices.min():.2f} - ${final_prices.max():.2f}")
 
-@cli.command(name="command")
-@click.argument(
-    "example"
-)
-@click.option(
-    "-o",
-    "--option",
-    help="An example option",
-)
-def first_command(example, option):
-    "Command description goes here"
-    click.echo("Here is some output")
+if __name__ == '__main__':
+    cli()