Pythonを使用した取引戦略とポートフォリオ分析

import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
 import yfinance as yf
 import ffn
 %matplotlib inline

def TSMStrategy(returns, period=1, shorts=False):
if shorts:
position = returns.rolling(period).mean().map(
lambda x: -1 if x <= 0 else 1)
else:
position = returns.rolling(period).mean().map(
lambda x: 0 if x <= 0 else 1)
performance = position.shift(1) * returns
return performance
 ticker = 'TCS'
 yftcs = yf.Ticker(ticker)
 data = yftcs.history(start='2005-01-01', end='2021-12-31')
 returns = np.log(data['Close'] / data['Close'].shift(1)).dropna()
 performance = TSMStrategy(returns, period=1, shorts=False).dropna()
 years = (performance.index.max() - performance.index.min()).days / 365
 perf_cum = np.exp(performance.cumsum())
 tot = perf_cum[-1] - 1
 ann = perf_cum[-1] ** (1 / years) - 1
 vol = performance.std() * np.sqrt(252)
 rfr = 0.02
 sharpe = (ann - rfr) / vol
 print(f"1-day TSM Strategy yields:" +
f"nt{tot*100:.2f}% total returns" +
f"nt{ann*100:.2f}% annual returns" +
f"nt{sharpe:.2f} Sharpe Ratio")
 tcs_ret = np.exp(returns.cumsum())
 b_tot = tcs_ret[-1] - 1
 b_ann = tcs_ret[-1] ** (1 / years) - 1
 b_vol = returns.std() * np.sqrt(252)
 b_sharpe = (b_ann - rfr) / b_vol
 print(f"Baseline Buy-and-Hold Strategy yields:" +
f"nt{b_tot*100:.2f}% total returns" +
f"nt{b_ann*100:.2f}% annual returns" +
f"nt{b_sharpe:.2f} Sharpe Ratio")

1-day TSM Strategy yields:
 -45.15% total returns
 -7.10% annual returns
 -0.17 Sharpe Ratio
 Baseline Buy-and-Hold Strategy yields:
 -70.15% total returns
 -13.78% annual returns
 -0.22 Sharpe Ratio

 import matplotlib.gridspec as gridspec
 periods = [3, 5, 15, 30, 90]
 fig = plt.figure(figsize=(12, 10))
 gs = fig.add_gridspec(4, 4)
 ax0 = fig.add_subplot(gs[:2, :4])
 ax1 = fig.add_subplot(gs[2:, :2])
 ax2 = fig.add_subplot(gs[2:, 2:])
 ax0.plot((np.exp(returns.cumsum()) - 1) * 100, label=ticker, linestyle='-')
 perf_dict = {'tot_ret': {'buy_and_hold': (np.exp(returns.sum()) - 1)}}
 perf_dict['ann_ret'] = {'buy_and_hold': b_ann}
 perf_dict['sharpe'] = {'buy_and_hold': b_sharpe}
 for p in periods:
log_perf = TSMStrategy(returns, period=p, shorts=False)
perf = np.exp(log_perf.cumsum())
perf_dict['tot_ret'][p] = (perf[-1] - 1)
ann = (perf[-1] ** (1/years) - 1)
perf_dict['ann_ret'][p] = ann
vol = log_perf.std() * np.sqrt(252)
perf_dict['sharpe'][p] = (ann - rfr) / vol
ax0.plot((perf - 1) * 100, label=f'{p}-Day Mean')
 
 ax0.set_ylabel('Returns (%)')
 ax0.set_xlabel('Date')
 ax0.set_title('Cumulative Returns')
 ax0.grid()
 ax0.legend()
 _ = [ax1.bar(i, v * 100) for i, v in enumerate(perf_dict['ann_ret'].values())]
 ax1.set_xticks([i for i, k in enumerate(perf_dict['ann_ret'])])
 ax1.set_xticklabels([f'{k}-Day Mean'
if type(k) is int else ticker for
k in perf_dict['ann_ret'].keys()],
rotation=45)
 ax1.grid()
 ax1.set_ylabel('Returns (%)')
 ax1.set_xlabel('Strategy')
 ax1.set_title('Annual Returns')
 _ = [ax2.bar(i, v) for i, v in enumerate(perf_dict['sharpe'].values())]
 ax2.set_xticks([i for i, k in enumerate(perf_dict['sharpe'])])
 ax2.set_xticklabels([f'{k}-Day Mean'
if type(k) is int else ticker for
k in perf_dict['sharpe'].keys()],
rotation=45)
 ax2.grid()
 ax2.set_ylabel('Sharpe Ratio')
 ax2.set_xlabel('Strategy')
 ax2.set_title('Sharpe Ratio')
 plt.tight_layout()
 plt.show()

import pandas_datareader.data as web
 stocks = ['SPY','GLD','TLT','HYG']
 
 data = web.DataReader(stocks,data_source='yahoo',start='01/01/2019')['Adj Close']
 data.sort_index(ascending=True,inplace=True)
 perf = data.calc_stats()
 perf.plot()

 returns = data.to_log_returns().dropna()
 print(returns.head())
 
 
 Symbols SPY GLD TLT HYG
 Date
 2019-01-03 -0.024152 0.009025 0.011315 0.000494
 2019-01-04 0.032947 -0.008119 -0.011642 0.016644
 2019-01-07 0.007854 0.003453 -0.002953 0.009663
 2019-01-08 0.009351 -0.002712 -0.002631 0.006470
 2019-01-09 0.004663 0.006398 -0.001566 0.001193

ax = returns.hist(figsize=(20, 10),bins=30)

ffn.to_drawdown_series(data).plot(figsize=(15,10))

returns.calc_mean_var_weights().as_format('.2%')
 #结果
 SPY 46.60%
 GLD 53.40%
 TLT 0.00%
 HYG 0.00%
 dtype: object

returns.plot_corr_heatmap()