#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Financial Data Analysis with Spyder Created on Thu May 20 10:17:27 2021 @author: Spyder Team """ # %% Import libraries import numpy as np import pandas as pd import matplotlib as mpl from scipy.optimize import minimize import matplotlib.pyplot as plt import pprint from Historic_Crypto import Cryptocurrencies from Historic_Crypto import HistoricalData import yfinance as yf # %% Detailed stock information netflix = yf.Ticker("NFLX") netflix_info = netflix.info pprint.pprint(netflix_info) # %%% Show major holders print(netflix.major_holders) # %%% Show institutional holders print(netflix.institutional_holders) # %%% Get historical market data hist = netflix.history(period="max") print(hist) # %% Set general style of plotting plt.style.use("fivethirtyeight") mpl.rcParams["savefig.dpi"] = 300 mpl.rcParams["font.family"] = "serif" np.set_printoptions(precision=5, suppress=True, formatter={"float": lambda x: f"{x:6.3f}"}) # ---- PORTFOLIO 1 # %% Portfolio 1 (Google, Apple, Microsoft, Netflix, Amazon) # %%% Download finance data SYMBOLS_1 = ["GOOG", "AAPL", "MSFT", "NFLX", "AMZN"] data_1 = yf.download(" ".join(SYMBOLS_1), start="2012-01-01", end="2021-01-01", group_by="Ticker") # %% Format downloaded data data_1 = data_1.stack(level=1).rename_axis( ["Date", "Ticker"]).reset_index(level=1) close_data_1 = data_1[data_1.Ticker == "Close"].drop( "Ticker", inplace=False, axis=1) # %% Monthly data monthly_data_1 = close_data_1.resample("M").ffill().pct_change() # %%% Mean and stadard deviation print(monthly_data_1.mean()) print(monthly_data_1.std()) # %% Plot daily timeline (close_data_1/ close_data_1.iloc[0]).plot(figsize=(16, 10), title="Portfolio 1 daily stock growth") # %% Plot monthly timeline (monthly_data_1 + 1).cumprod().plot(figsize=(16, 10), title="Portfolio 1 monthly stock growth") # %% Returns per day rets_1 = np.log(close_data_1/close_data_1.shift(1)).dropna() weights_1 = [0.2, 0.2, 0.2, 0.2, 0.2] # %% Portfolio returns def portfolio_return(returns, weights): return np.dot(returns.mean(), weights) * 252 print(portfolio_return(rets_1, weights_1)) # %% Portfolio volatility def portfolio_volatility(returns, weights): return np.dot(weights, np.dot(returns.cov() * 252, weights)) ** 0.5 print(portfolio_volatility(rets_1, weights_1)) # %% Portfolio Sharpe ratio def portfolio_sharpe(returns, weights): return portfolio_return(returns, weights) / portfolio_volatility(returns, weights) print(portfolio_sharpe(rets_1, weights_1)) # %% Sharpe ratio with Monte Carlo simulation def monte_carlo_sharpe(returns, symbols, weights): sim_weights = np.random.random((1000, len(symbols))) sim_weights = (sim_weights.T / sim_weights.sum(axis=1)).T volat_ret = [(portfolio_volatility(returns[symbols], weights), portfolio_return(returns[symbols], weights)) for weights in sim_weights] volat_ret = np.array(volat_ret) sharpe_ratio = volat_ret[:, 1] / volat_ret[:, 0] return volat_ret, sharpe_ratio port_1_vr, port_1_sr = monte_carlo_sharpe(rets_1, SYMBOLS_1, weights_1) # %%% Plot Sharpe scatter plot with color mapping plt.figure(figsize=(16, 10)) fig = plt.scatter(port_1_vr[:, 0], port_1_vr[:, 1], c=port_1_sr, cmap="cool") CB = plt.colorbar(fig) CB.set_label("Sharpe ratio") plt.xlabel("expected volatility") plt.ylabel("expected return") plt.title(" | ".join(SYMBOLS_1)) # %% Optimal weights start_year, end_year = (2012, 2020) def optimal_weights(returns, symbols, actual_weights, start_y, end_y): bounds = len(symbols) * [(0, 1), ] constraints = {"type": "eq", "fun": lambda weights: weights.sum() - 1} opt_weights = {} for year in range(start_y, end_y): _rets = returns[symbols].loc[f"{year}-01-01":f"{year}-12-31"] _opt_w = minimize(lambda weights: -portfolio_sharpe(_rets, weights), actual_weights, bounds=bounds, constraints=constraints)["x"] opt_weights[year] = _opt_w return opt_weights opt_weights_1 = optimal_weights(rets_1, SYMBOLS_1, weights_1, start_year, end_year) port_1_ow = pd.DataFrame.from_dict(opt_weights_1, orient='index') port_1_ow.columns = SYMBOLS_1 # %% Expected and realized returns def exp_real_rets(returns, opt_weights, symbols, start_year, end_year): _rets = {} for year in range(start_year, end_year): prev_year = returns[symbols].loc[f"{year}-01-01":f"{year}-12-31"] current_year = returns[symbols].loc[f"{year + 1}-01-01":f"{year + 1}-12-31"] expected_pr = portfolio_return(prev_year, opt_weights[year]) realized_pr = portfolio_return(current_year, opt_weights[year]) _rets[year + 1] = [expected_pr, realized_pr] return _rets port_1_exp_real = pd.DataFrame.from_dict( exp_real_rets(rets_1, opt_weights_1, SYMBOLS_1, start_year, end_year), orient='index') port_1_exp_real.columns = ["expected", "realized"] # %%% Plot expected and realized returns port_1_exp_real.plot(kind="bar", figsize=(16, 10),title="Expected vs. realized portfolio returns") # %%% Mean and standard deviation print(port_1_exp_real.mean()) print(port_1_exp_real[["expected", "realized"]].corr()) # ---- PORTFOLIO 2 # %% Portfolio 2 (Pfizer, Astra Zeneca, Johnson N Johnson) # %%% Download finance data SYMBOLS_2 = ["PFE", "AZN", "JNJ"] # Pfizer, Astra Zeneca, Johnson N Johnson data_2 = yf.download(" ".join(SYMBOLS_2), start="2012-01-01", end="2021-01-01", group_by="Ticker") # %% Format downloaded data data_2 = data_2.stack(level=1).rename_axis( ["Date", "Ticker"]).reset_index(level=1) close_data_2 = data_2[data_2.Ticker == "Close"].drop( "Ticker", inplace=False, axis=1) # %% Monthly data monthly_data_2 = close_data_2.resample("M").ffill().pct_change() # %%% Mean and standard deviation print("Mean:") print(monthly_data_2.mean()) print("STD:") print(monthly_data_2.std()) # %% Plot daily timeline rets_2 = np.log(close_data_2[SYMBOLS_2] / close_data_2[SYMBOLS_2].shift(1)).dropna() (close_data_2[SYMBOLS_2] / close_data_2[SYMBOLS_2].iloc[0]).plot(figsize=(16, 10), title="Portfolio 2 daily stock growth") # %% Plot monthly timeline (monthly_data_2 + 1).cumprod().plot(figsize=(16, 10), title="Portfolio 2 monthly stock grouth") # %% Returns per day weights_2 = len(close_data_2.columns) * [1 / len(close_data_2.columns)] # %% Portfolio returns print(f"Portfolio 2 returns: {portfolio_return(rets_2, weights_2):.4f}") # %% Portfolio volatility print(f"Portfolio 2 volatility: {portfolio_volatility(rets_2, weights_2):.4f}") # %% Portfolio Sharpe ratio print(f"Portfolio 2 Sharpe: {portfolio_sharpe(rets_2, weights_2):.4f}") # %% Sharpe ratio with Monte Carlo simulation port_2_vr, port_2_sr = monte_carlo_sharpe(rets_2, SYMBOLS_2, weights_2) # %%% Plot scatter plot with color mapping plt.figure(figsize=(16, 10)) fig = plt.scatter(port_2_vr[:, 0], port_2_vr[:, 1], c=port_2_sr, cmap="cool") CB = plt.colorbar(fig) CB.set_label("Sharpe ratio") plt.xlabel("expected volatility") plt.ylabel("expected return") plt.title(" | ".join(SYMBOLS_2)) # %% Optimal weights start_year, end_year = (2012, 2020) opt_weights_2 = optimal_weights( rets_2, SYMBOLS_2, weights_2, start_year, end_year) port_2_ow = pd.DataFrame.from_dict(opt_weights_2, orient='index') port_2_ow.columns = SYMBOLS_2 # %% Expected and realized returns port_2_exp_real = pd.DataFrame.from_dict( exp_real_rets(rets_2, opt_weights_2, SYMBOLS_2, start_year, end_year), orient='index') port_2_exp_real.columns = ["expected", "realized"] # %%% Plot expected and realized returns port_2_exp_real.plot(kind="bar", figsize=(16, 10),title="Expected vs. realized portfolio returns") # %%% Mean and standard deviation print("Expected and realized means:") print(port_2_exp_real.mean()) print("Expected and realized correlations:") print(port_2_exp_real[["expected", "realized"]].corr()) # ---- PORTFOLIO 3 # %% Portfolio 3 (ETH, BTC, LTC)) crypto_list = Cryptocurrencies( coin_search="", extended_output=True).find_crypto_pairs() print(crypto_list.loc[crypto_list.base_currency == "ETH"]) # %%% Download and format ETC data ETC_HIST = HistoricalData( "ETH-USD", 3600 * 24, "2016-01-01-00-00", "2021-01-01-00-00").retrieve_data() ETC_HIST.rename(columns={"close": "ETC"}, inplace=True) ETC_HIST.drop(["low", "high", "open", "volume"], axis=1, inplace=True) # %%% Download and format BTC data BTC_HIST = HistoricalData( "BTC-USD", 3600 * 24, "2016-01-01-00-00", "2021-01-01-00-00").retrieve_data() BTC_HIST.rename(columns={"close": "BTC"}, inplace=True) BTC_HIST.drop(["low", "high", "open", "volume"], axis=1, inplace=True) # %%% Download and format LTC data LTC_HIST = HistoricalData( "LTC-USD", 3600 * 24, "2016-01-01-00-00", "2021-01-01-00-00").retrieve_data() LTC_HIST.rename(columns={"close": "LTC"}, inplace=True) LTC_HIST.drop(["low", "high", "open", "volume"], axis=1, inplace=True) # %% Merge data form BTC, LTC and ETC crypto_hist = pd.merge(BTC_HIST, ETC_HIST, on=["time"]) crypto_hist = pd.merge(crypto_hist, LTC_HIST, on=["time"]) crypto_hist.set_index("time") # %%% Crypto Ticker Symbols SYMBOLS_3 = ["BTC", "ETC", "LTC"] #%%% Plot monthly timeline monthly_data_3 = crypto_hist.resample("M").ffill().pct_change() (monthly_data_3 + 1).cumprod().plot(figsize=(16, 10), title="Portfolio 3 monthly stock growth") # %% Returns per day rets_3 = np.log(crypto_hist[SYMBOLS_3] / crypto_hist[SYMBOLS_3].shift(1)).dropna() # %% Weights weights_3 = len(crypto_hist.columns) * [1 / len(crypto_hist.columns)] # %% Portfolio retunrs print(portfolio_return(rets_3, weights_3)) # %% Portfolio volatility print((portfolio_volatility(rets_3, weights_3))) # %% Portfolio Sharpe ratio print(portfolio_sharpe(rets_3, weights_3)) # %% Sharpe ratio with Monte Carlo simulation port_3_vr, port_3_sr = monte_carlo_sharpe(rets_3, SYMBOLS_3, weights_3) # %%% Plot scatter plot with color mapping plt.figure(figsize=(16, 10)) fig = plt.scatter(port_3_vr[:, 0], port_3_vr[:, 1], c=port_3_sr, cmap="cool") CB = plt.colorbar(fig) CB.set_label("Sharpe ratio") plt.xlabel("expected volatility") plt.ylabel("expected return") plt.title(" | ".join(SYMBOLS_3)) # %% Optimal weights start_year, end_year = (2016, 2021) opt_weights_3 = optimal_weights( rets_3, SYMBOLS_3, weights_3, start_year, end_year) port_3_ow = pd.DataFrame.from_dict(opt_weights_3, orient='index') port_3_ow.columns = SYMBOLS_3 # %% Expected and realized returns start_year, end_year = (2016, 2020) port_3_exp_real = pd.DataFrame.from_dict( exp_real_rets(rets_3, opt_weights_3, SYMBOLS_3, start_year, end_year), orient='index') port_3_exp_real.columns = ["expected", "realized"] # %%% Plot expected and realized returns port_3_exp_real.plot(kind="bar", figsize=(16, 10),title="Expected vs. realized portfolio returns") # %%% Mean and standard deviation print(port_3_exp_real.mean()) print(port_3_exp_real[["expected", "realized"]].corr())