Update app.py

app.py

@@ -12,6 +12,22 @@ from langchain.chains import LLMChain, SequentialChain
 from textwrap import dedent
 import google.generativeai as genai
 
+
+import yfinance as yf
+from pypfopt.discrete_allocation import DiscreteAllocation, get_latest_prices
+from pypfopt import EfficientFrontier
+from pypfopt import risk_models
+from pypfopt import expected_returns
+from pypfopt import plotting
+import copy
+import numpy as np
+import pandas as pd
+import plotly.express as px
+import matplotlib.pyplot as plt
+from datetime import datetime
+import datetime
+
+
 # Tool import
 from crewai.tools.gemini_tools import GeminiSearchTools
 from langchain.tools.yahoo_finance_news import YahooFinanceNewsTool
@@ -319,6 +335,87 @@ bot_inputs = [
     chatbot_component
 ]
 
+# Portfolio Analysis +++++++++++++++++++++++++++++++++++
+
+def plot_cum_returns(data, title):    
+    daily_cum_returns = 1 + data.dropna().pct_change()
+    daily_cum_returns = daily_cum_returns.cumprod()*100
+    fig = px.line(daily_cum_returns, title=title)
+    return fig
+
+def plot_efficient_frontier_and_max_sharpe(mu, S): 
+    # Optimize portfolio for max Sharpe ratio and plot it out with efficient frontier curve
+    ef = EfficientFrontier(mu, S)
+    fig, ax = plt.subplots(figsize=(6,4))
+    ef_max_sharpe = copy.deepcopy(ef)
+    plotting.plot_efficient_frontier(ef, ax=ax, show_assets=False)
+    # Find the max sharpe portfolio
+    ef_max_sharpe.max_sharpe(risk_free_rate=0.02)
+    ret_tangent, std_tangent, _ = ef_max_sharpe.portfolio_performance()
+    ax.scatter(std_tangent, ret_tangent, marker="*", s=100, c="r", label="Max Sharpe")
+    # Generate random portfolios with random weights
+    n_samples = 1000
+    w = np.random.dirichlet(np.ones(ef.n_assets), n_samples)
+    rets = w.dot(ef.expected_returns)
+    stds = np.sqrt(np.diag(w @ ef.cov_matrix @ w.T))
+    sharpes = rets / stds
+    ax.scatter(stds, rets, marker=".", c=sharpes, cmap="viridis_r")
+    # Output
+    ax.legend()
+    return fig
+
+def output_results(start_date, end_date, tickers_string):
+    tickers = tickers_string.split(',')
+    
+    # Get Stock Prices
+    stocks_df = yf.download(tickers, start=start_date, end=end_date)['Adj Close']
+    
+    # Plot Individual Stock Prices
+    fig_indiv_prices = px.line(stocks_df, title='Price of Individual Stocks')
+        
+    # Plot Individual Cumulative Returns
+    fig_cum_returns = plot_cum_returns(stocks_df, 'Cumulative Returns of Individual Stocks Starting with $100')
+    
+    # Calculatge and Plot Correlation Matrix between Stocks
+    corr_df = stocks_df.corr().round(2)
+    fig_corr = px.imshow(corr_df, text_auto=True, title = 'Correlation between Stocks')
+
+    # Calculate expected returns and sample covariance matrix for portfolio optimization later
+    mu = expected_returns.mean_historical_return(stocks_df)
+    S = risk_models.sample_cov(stocks_df)
+
+    # Plot efficient frontier curve
+    fig_efficient_frontier = plot_efficient_frontier_and_max_sharpe(mu, S)
+
+    # Get optimized weights
+    ef = EfficientFrontier(mu, S)
+    ef.max_sharpe(risk_free_rate=0.04)
+    weights = ef.clean_weights()
+    expected_annual_return, annual_volatility, sharpe_ratio = ef.portfolio_performance()
+    
+    expected_annual_return, annual_volatility, sharpe_ratio = '{}%'.format((expected_annual_return*100).round(2)), \
+    '{}%'.format((annual_volatility*100).round(2)), \
+    '{}%'.format((sharpe_ratio*100).round(2))
+    
+    weights_df = pd.DataFrame.from_dict(weights, orient = 'index')
+    weights_df = weights_df.reset_index()
+    weights_df.columns = ['Tickers', 'Weights']
+
+    # Calculate returns of portfolio with optimized weights
+    stocks_df['Optimized Portfolio'] = 0
+    for ticker, weight in weights.items():
+        stocks_df['Optimized Portfolio'] += stocks_df[ticker]*weight
+
+    # Plot Cumulative Returns of Optimized Portfolio
+    fig_cum_returns_optimized = plot_cum_returns(stocks_df['Optimized Portfolio'], 'Cumulative Returns of Optimized Portfolio Starting with $100')
+
+    return  fig_cum_returns_optimized, weights_df, fig_efficient_frontier, fig_corr,   \
+            expected_annual_return, annual_volatility, sharpe_ratio, fig_indiv_prices, fig_cum_returns
+
+
+
+
+
 
 # Interface =============================================