TheCodeyBunch_Buffer_6.0 Loop Buffer 6.0 Project

Importing necessary libraries

import numpy as np import pandas as pd from collections import deque import matplotlib.pyplot as plt from sklearn.cluster import KMeans from scipy.optimize import minimize

Sample Data - To be Replaced with actual CSV or database queries

stock_data = pd.DataFrame({ 'Company': ['TCS', 'Infosys', 'Wipro'], 'CurrentPrice': [3600, 1500, 580], 'High': [3650, 1520, 600], 'Low': [3500, 1450, 560], 'Return': [0.07, 0.05, 0.04], 'Volatility': [0.15, 0.12, 0.11] })

user_holdings = pd.DataFrame({ 'UserID': [1, 1, 1], 'Company': ['TCS', 'Infosys', 'Wipro'], 'Quantity': [10, 20, 30], 'BuyPrice': [3400, 1400, 550] })

----- Data Structures USED -----

Hash Table: Stock lookup, ensures much faster access to for lookup

stock_hash = {row['Company']: row for _, row in stock_data.iterrows()}

BST: Sorting by return (simple simulation using list sort), easily sort out shares

sorted_by_return = stock_data.sort_values(by='Return', ascending=False)

Queue: Rolling window for 2-day moving average simulation for the prices fluctuations

price_history = { 'TCS': deque([3500, 3600], maxlen=2), 'Infosys': deque([1450, 1500], maxlen=2), 'Wipro': deque([560, 580], maxlen=2) }

moving_avg = {company: sum(prices)/len(prices) for company, prices in price_history.items()}

----- Mean-Variance Optimization ----- PARAMETER/CRITERIA FOR INDIVIDUAL PORTFOLIO OPTIMISATION

def mean_variance_optimization(returns, cov_matrix, risk_free_rate=0.01): num_assets = len(returns)

def portfolio_performance(weights):
    ret = np.dot(weights, returns)
    vol = np.sqrt(np.dot(weights.T, np.dot(cov_matrix, weights)))
    sharpe = (ret - risk_free_rate) / vol
    return -sharpe  # negative for minimization

constraints = ({'type': 'eq', 'fun': lambda x: np.sum(x) - 1})
bounds = tuple((0, 1) for _ in range(num_assets))
initial_guess = num_assets * [1. / num_assets]

result = minimize(portfolio_performance, initial_guess, method='SLSQP', bounds=bounds, constraints=constraints)
return result.x

returns = stock_data['Return'].values cov_matrix = np.diag(stock_data['Volatility'].values ** 2) optimal_weights = mean_variance_optimization(returns, cov_matrix)

----- ML Clustering for (Risk Profiles) -----

risk_profiles = stock_data[['Return', 'Volatility']] kmeans = KMeans(n_clusters=2) clusters = kmeans.fit_predict(risk_profiles) stock_data['RiskCluster'] = clusters

----- Portfolio Analysis for User -----

def analyze_user_portfolio(user_id): user_data = user_holdings[user_holdings['UserID'] == user_id] print(f"\nPortfolio for User {user_id}:") total_value = 0 for _, row in user_data.iterrows(): company = row['Company'] quantity = row['Quantity'] current_price = stock_hash[company]['CurrentPrice'] value = quantity * current_price total_value += value print(f"{company}: {quantity} shares @ ₹{current_price} = ₹{value}") print(f"Total Portfolio Value: ₹{total_value}")

----- Recommendation based on MVO -----

def show_optimized_portfolio(): print("\nOptimized Portfolio Allocation:") for i, row in stock_data.iterrows(): print(f"{row['Company']}: {round(optimal_weights[i]*100, 2)}%")

Run analysis and show results

analyze_user_portfolio(1) show_optimized_portfolio()

Show Cluster Plot for Risks

plt.scatter(stock_data['Return'], stock_data['Volatility'], c=stock_data['RiskCluster']) plt.xlabel('Return') plt.ylabel('Volatility') plt.title('Risk Clusters of Stocks') plt.grid(True) plt.show() labels = stock_data['Company'] sizes = optimal_weights * 100 colors = ['#ff9999', '#66b3ff', '#99ff99']

Final Piechart Summarizing all Values

fig, ax = plt.subplots(figsize=(6, 6)) ax.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=140, colors=colors) ax.axis('equal') # Equal aspect ratio ensures pie is drawn as a circle plt.title('Optimized Portfolio Allocation (MVO)') plt.tight_layout() plt.show()